diff --git a/0_control_files/control_Africa.txt b/0_control_files/control_Africa.txt deleted file mode 100644 index 155f444..0000000 --- a/0_control_files/control_Africa.txt +++ /dev/null @@ -1,152 +0,0 @@ -# SUMMA workflow setting file. -# Characters '|' and '#' are used as separators to find the actual setting values. Any text behind '|' is assumed to be part of the setting value, unless preceded by '#'. - -# Note on path specification -# If deviating from default paths, a full path must be specified. E.g. '/home/user/non-default/path' - - -# Modeling domain settings -root_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data # Root folder where data will be stored. -domain_name | Africa # Used as part of the root folder name for the prepared data. - - -# Shapefile settings - SUMMA catchment file -catchment_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment'. -catchment_shp_name | Africa_gruhru.shp # Name of the catchment shapefile. Requires extension '.shp'. -catchment_shp_gruid | GRU_ID # Name of the GRU ID column (can be any numeric value, HRU's within a single GRU have the same GRU ID). -catchment_shp_hruid | HRU_ID # Name of the HRU ID column (consecutive from 1 to total number of HRUs, must be unique). -catchment_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -catchment_shp_lat | center_lat # Name of the latitude column. Should be a value representative for the HRU. Typically the centroid. -catchment_shp_lon | center_lon # Name of the longitude column. Should be a value representative for the HRU. Typically the centroid. - - -# Shapefile settings - mizuRoute river network file -river_network_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/river_network'. -river_network_shp_name | Africa_river_rawMERIT.shp # Name of the river network shapefile. Requires extension '.shp'. -river_network_shp_segid | COMID # Name of the segment ID column. -river_network_shp_downsegid | NextDownID # Name of the downstream segment ID column. -river_network_shp_slope | slope # Name of the slope column. Slope must be in in units [length/length]. -river_network_shp_length | length # Name of the segment length column. Length must be in units [m]. - - -# Shapefile settings - mizuRoute catchment file -river_basin_shp_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_Africa/shapefiles/catchment/ # Same file as the SUMMA catchments -river_basin_shp_name | Africa_gruhru.shp # Name of the routing subbasins shapefile needed for remapping. Requires extension '.shp'. -river_basin_shp_rm_hruid | GRU_ID # Name of the routing basin ID column. -river_basin_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -river_basin_shp_hru_to_seg | hru_to_seg # Name of the column that shows which river segment each HRU connects to. HRUs and river segments have the same COMID so this works. - - -# Shapefile settings - SUMMA-to-mizuRoute -river_basin_needs_remap | no # 'no' if routing basins map 1:1 onto model GRUs. 'yes' if river segments span multiple GRUs or if multiple segments are inside a single GRU. - - -# Install settings -github_summa | https://github.com/CH-Earth/summa # Replace this with the path to your own fork if you forked the repo. -github_mizuroute | https://github.com/ncar/mizuroute # Replace this with the path to your own fork if you forked the repo. -install_path_summa | default # If 'default', clones source code into 'root_path/installs/summa'. -install_path_mizuroute | default # If 'default', clones source code into 'root_path/installs/mizuRoute'. -exe_name_summa | summa.exe # Name of the compiled executable. -exe_name_mizuroute | mizuRoute.exe # Name of the compiled executable. - - -# Forcing settings -forcing_raw_time | 1979,1979 # Years to download: Jan-[from],Dec-[to]. -forcing_raw_space | 37.34/-17.95/-34.8/54.47 # Bounding box of the shapefile: lat_max/lon_min/lat_min/lon_max. Will be converted to ERA5 download coordinates in script. Order and use of '/' to separate values is mandatory. -forcing_time_step_size | 3600 # Size of the forcing time step in [s]. Must be constant. -forcing_measurement_height | 3 # Reference height for forcing measurements [m]. -forcing_shape_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/forcing'. -forcing_shape_name | era5_grid.shp # Name of the forcing shapefile. Requires extension '.shp'. -forcing_shape_lat_name | lat # Name of the latitude field that contains the latitude of ERA5 data points. -forcing_shape_lon_name | lon # Name of the longitude field that contains the latitude of ERA5 data points. -forcing_geo_path | default # If 'default', uses 'root_path/domain_[name]/forcing/0_geopotential'. -forcing_raw_path | default # If 'default', uses 'root_path/domain_[name]/forcing/1_raw_data'. -forcing_merged_path | default # If 'default', uses 'root_path/domain_[name]/forcing/2_merged_data'. -forcing_easymore_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_temp_easymore'. -forcing_basin_avg_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_basin_averaged_data'. -forcing_summa_path | default # If 'default', uses 'root_path/domain_[name]/forcing/4_SUMMA_input'. - - -# Parameter settings - DEM -parameter_dem_main_url | http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/distribute/v1.0.1/ # Primary download URL for MERIT Hydro adjusted elevation data. Needs to be appended with filenames. -parameter_dem_file_template | elv_{}{}.tar # Template for download file names. -parameter_dem_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/1_MERIT_hydro_raw_data'. -parameter_dem_unpack_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/2_MERIT_hydro_unpacked_data'. -parameter_dem_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/3_vrt'. -parameter_dem_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/4_domain_vrt'. -parameter_dem_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/5_elevation'. -parameter_dem_tif_name | elevation.tif # Name of the final DEM for the domain. Must be in .tif format. - - -# Parameter settings - soil -parameter_soil_hydro_ID | 1361509511e44adfba814f6950c6e742 # ID of the Hydroshare resource to download. -parameter_soil_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/1_soil_classes_global'. -parameter_soil_domain_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/2_soil_classes_domain'. -parameter_soil_tif_name | soil_classes.tif # Name of the final soil class overview for the domain. Must be in .tif format. - - -# Parameter settings - land -parameter_land_list_path | default # If 'default', uses 'summaWorkflow_public/3b_parameters/MODIS_MCD12Q1_V6/1_download/'. Location of file with data download links. -parameter_land_list_name | daac_mcd12q1_data_links.txt # Name of file that contains list of MODIS download urls. -parameter_land_raw_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_BowAtBanff/parameters/landclass/1_MODIS_raw_data # If 'default', uses 'root_path/domain_[name]/parameters/landclass/1_MODIS_raw_data'. -parameter_land_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/2_vrt_native_crs'. Virtual dataset composed of .hdf files. -parameter_land_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/3_vrt_epsg_4326'. Virtual dataset projected in EPSG:4326. -parameter_land_vrt3_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/4_domain_vrt_epsg_4326'. Virtual dataset cropped to model domain. -parameter_land_vrt4_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/5_multiband_domain_vrt_epsg_4326'. Multiband cropped virtual dataset. -parameter_land_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/6_tif_multiband'. -parameter_land_mode_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/7_mode_land_class'. -parameter_land_tif_name | land_classes.tif # Name of the final landclass overview for the domain. Must be in .tif format. - - -# Intersection settings -intersect_dem_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_dem'. -intersect_dem_name | catchment_with_merit_dem.shp # Name of the shapefile with intersection between catchment and MERIT Hydro DEM, stored in column 'elev_mean'. -intersect_soil_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_soilgrids'. -intersect_soil_name | catchment_with_soilgrids.shp # Name of the shapefile with intersection between catchment and SOILGRIDS-derived USDA soil classes, stored in columns 'USDA_{1,...n}' -intersect_land_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_modis'. -intersect_land_name | catchment_with_modis.shp # Name of the shapefile with intersection between catchment and MODIS-derived IGBP land classes, stored in columns 'IGBP_{1,...n}' -intersect_forcing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_forcing'. -intersect_routing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_routing'. -intersect_routing_name | catchment_with_routing_basins.shp # Name of the shapefile with intersection between hydrologic model catchments and routing model catchments. - - -# Experiment settings - general -experiment_id | run1 # Descriptor of the modelling experiment; used as output folder name. -experiment_time_start | default # Simulation start. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-01-01 00:00'. -experiment_time_end | default # Simulation end. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-12-31 23:00'. -experiment_output_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA'. -experiment_output_mizuRoute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute'. -experiment_log_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA/SUMMA_logs'. -experiment_log_mizuroute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute/mizuRoute_logs'. -experiment_backup_settings | yes # Flag to (not) create a copy of the model settings in the output folder; "no" or "yes". Copying settings may be undesirable if files are large. - - -# Experiment settings - SUMMA -settings_summa_path | default # If 'default', uses 'root_path/domain_[name]/settings/SUMMA'. -settings_summa_filemanager | fileManager.txt # Name of the file with the SUMMA inputs. -settings_summa_coldstate | coldState.nc # Name of the file with intial states. -settings_summa_trialParams | trialParams.nc # Name of the file that can contain trial parameter values (note, can be empty of any actual parameter values but must be provided and must contain an 'hruId' variable). -settings_summa_forcing_list | forcingFileList.txt # Name of the file that has the list of forcing files. -settings_summa_attributes | attributes.nc # Name of the attributes file. -settings_summa_connect_HRUs | no # Attribute setting: "no" or "yes". Tricky concept, see README in ./5_model_input/SUMMA/3f_attributes. If no; all HRUs modeled as independent columns (downHRUindex = 0). If yes; HRUs within each GRU are connected based on relative HRU elevation (highest = upstream, lowest = outlet). -settings_summa_trialParam_n | 1 # Number of trial parameter specifications. Specify 0 if none are wanted (they can still be included in this file but won't be read). -settings_summa_trialParam_1 | maxstep,900 # Name of trial parameter and value to assign. Value assumed to be float. - - -# Experiment settings - mizuRoute -settings_mizu_path | default # If 'default', uses 'root_path/domain_[name]/settings/mizuRoute'. -settings_mizu_parameters | param.nml.default # Name of the routing parameters file. -settings_mizu_topology | topology.nc # Name of the river network topology file. -settings_mizu_remap | routing_remap.nc # Name of the optional catchment remapping file, for cases when SUMMA uses different catchments than mizuRoute. -settings_mizu_control_file | mizuroute.control # Name of the control file. -settings_mizu_routing_var | averageRoutedRunoff # Name of SUMMA output variable to use for routing. -settings_mizu_routing_units | m/s # Units of the variable to be routed. -settings_mizu_routing_dt | 3600 # Size of the routing time step [s]. -settings_mizu_output_freq | annual # Frequency with which mizuRoute generates new output files. Must be one of 'single', 'day', 'month', 'annual'. -settings_mizu_output_vars | 0 # Routing output. '0' for both KWT and IRF; '1' IRF only; '2' KWT only. -settings_mizu_within_basin | 0 # '0' (no) or '1' (IRF routing). Flag to enable within-basin routing by mizuRoute. Should be set to 0 if SUMMA is run with "subRouting" decision "timeDlay". -settings_mizu_make_outlet | n/a # Segment ID or IDs that should be set as network outlet. Specify multiple IDs separated by commas: X,Y,Z. Specify no IDs as: n/a. Note that this can also be done in the network shapefile. - - -# Postprocessing settings -visualization_folder | default # If 'default', uses 'root_path/domain_[name]/visualization'. diff --git a/0_control_files/control_Bow_at_Banff.txt b/0_control_files/control_CAN_05BB001.txt similarity index 68% rename from 0_control_files/control_Bow_at_Banff.txt rename to 0_control_files/control_CAN_05BB001.txt index 10a869d..2197748 100644 --- a/0_control_files/control_Bow_at_Banff.txt +++ b/0_control_files/control_CAN_05BB001.txt @@ -6,15 +6,19 @@ # Modeling domain settings -root_path | /project/gwf/gwf_cmt/wknoben/CWARHM_data # Root folder where data will be stored. -domain_name | BowAtBanff # Used as part of the root folder name for the prepared data. +root_path | /path/to/output/folder # Root folder where data will be stored. +domain_name | CAN_05BB001 # Used as part of the root folder name for the prepared data, must be the same name as in CAMELS-spat database + + +# database settings - CAMELS-spat folder +camels_spath | /path/to/camels-spat/dataset/ # Path de the CAMELS-spat dataset, must be "/path/to/camels-spat/dataset/[name]". # Shapefile settings - SUMMA catchment file catchment_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment'. -catchment_shp_name | bow_distributed_elevation_zone.shp # Name of the catchment shapefile. Requires extension '.shp'. +catchment_shp_name | CAN_05BB001_distributed_basin.shp # Name of the catchment shapefile. Requires extension '.shp'. catchment_shp_gruid | GRU_ID # Name of the GRU ID column (can be any numeric value, HRU's within a single GRU have the same GRU ID). -catchment_shp_hruid | HRU_ID # Name of the HRU ID column (consecutive from 1 to total number of HRUs, must be unique). +catchment_shp_hruid | HRU_ID # Name of the HRU ID column (consecutive from 1 to total number of HRUs, must be unique). Note: With CAMELS-spat there is only 1 HRU per GRU. catchment_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] catchment_shp_lat | center_lat # Name of the latitude column. Should be a value representative for the HRU. Typically the centroid. catchment_shp_lon | center_lon # Name of the longitude column. Should be a value representative for the HRU. Typically the centroid. @@ -22,7 +26,7 @@ catchment_shp_lon | center_lon # Name # Shapefile settings - mizuRoute river network file river_network_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/river_network'. -river_network_shp_name | bow_river_network_from_merit_hydro.shp # Name of the river network shapefile. Requires extension '.shp'. +river_network_shp_name | CAN_05BB001_distributed_river.shp # Name of the river network shapefile. Requires extension '.shp'. river_network_shp_segid | COMID # Name of the segment ID column. river_network_shp_downsegid | NextDownID # Name of the downstream segment ID column. river_network_shp_slope | slope # Name of the slope column. Slope must be in in units [length/length]. @@ -31,16 +35,12 @@ river_network_shp_length | length # Name # Shapefile settings - mizuRoute catchment file river_basin_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/river_basins'. -river_basin_shp_name | bow_distributed.shp # Name of the routing subbasins shapefile needed for remapping. Requires extension '.shp'. +river_basin_shp_name | CAN_05BB001_distributed_basin.shp # Name of the routing subbasins shapefile needed for remapping. Requires extension '.shp'. river_basin_shp_rm_hruid | COMID # Name of the routing basin ID column. river_basin_shp_area | area # Name of the catchment area column. Area must be in units [m^2] river_basin_shp_hru_to_seg | hru_to_seg # Name of the column that shows which river segment each HRU connects to. -# Shapefile settings - SUMMA-to-mizuRoute -river_basin_needs_remap | no # 'no' if routing basins map 1:1 onto model GRUs. 'yes' if river segments span multiple GRUs or if multiple segments are inside a single GRU. - - # Install settings github_summa | https://github.com/CH-Earth/summa # Replace this with the path to your own fork if you forked the repo. github_mizuroute | https://github.com/ncar/mizuroute # Replace this with the path to your own fork if you forked the repo. @@ -51,49 +51,24 @@ exe_name_mizuroute | mizuroute.exe # Name # Forcing settings -forcing_raw_time | 2008,2013 # Years to download: Jan-[from],Dec-[to]. -forcing_raw_space | 51.74/-116.55/50.95/-115.52 # Bounding box of the shapefile: lat_max/lon_min/lat_min/lon_max. Will be converted to ERA5 download coordinates in script. Order and use of '/' to separate values is mandatory. +forcing_raw_time | 2000,2020 # Years to download: Jan-[from],Dec-[to]. +forcing_data_name | era5 # era5, rdrs or em-earth. forcing_time_step_size | 3600 # Size of the forcing time step in [s]. Must be constant. forcing_measurement_height | 3 # Reference height for forcing measurements [m]. -forcing_shape_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/forcing'. -forcing_shape_name | era5_grid.shp # Name of the forcing shapefile. Requires extension '.shp'. -forcing_shape_lat_name | lat # Name of the latitude field that contains the latitude of ERA5 data points. -forcing_shape_lon_name | lon # Name of the longitude field that contains the latitude of ERA5 data points. -forcing_geo_path | default # If 'default', uses 'root_path/domain_[name]/forcing/0_geopotential'. -forcing_raw_path | default # If 'default', uses 'root_path/domain_[name]/forcing/1_raw_data'. -forcing_merged_path | default # If 'default', uses 'root_path/domain_[name]/forcing/2_merged_data'. -forcing_easymore_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_temp_easymore'. -forcing_basin_avg_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_basin_averaged_data'. forcing_summa_path | default # If 'default', uses 'root_path/domain_[name]/forcing/4_SUMMA_input'. # Parameter settings - DEM -parameter_dem_main_url | http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/distribute/v1.0.1/ # Primary download URL for MERIT Hydro adjusted elevation data. Needs to be appended with filenames. -parameter_dem_file_template | elv_{}{}.tar # Template for download file names. -parameter_dem_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/1_MERIT_hydro_raw_data'. -parameter_dem_unpack_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/2_MERIT_hydro_unpacked_data'. -parameter_dem_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/3_vrt'. -parameter_dem_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/4_domain_vrt'. parameter_dem_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/5_elevation'. parameter_dem_tif_name | elevation.tif # Name of the final DEM for the domain. Must be in .tif format. # Parameter settings - soil -parameter_soil_hydro_ID | 1361509511e44adfba814f6950c6e742 # ID of the Hydroshare resource to download. -parameter_soil_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/1_soil_classes_global'. parameter_soil_domain_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/2_soil_classes_domain'. parameter_soil_tif_name | soil_classes.tif # Name of the final soil class overview for the domain. Must be in .tif format. # Parameter settings - land -parameter_land_list_path | default # If 'default', uses 'summaWorkflow_public/3b_parameters/MODIS_MCD12Q1_V6/1_download/'. Location of file with data download links. -parameter_land_list_name | daac_mcd12q1_data_links.txt # Name of file that contains list of MODIS download urls. -parameter_land_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/1_MODIS_raw_data'. -parameter_land_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/2_vrt_native_crs'. Virtual dataset composed of .hdf files. -parameter_land_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/3_vrt_epsg_4326'. Virtual dataset projected in EPSG:4326. -parameter_land_vrt3_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/4_domain_vrt_epsg_4326'. Virtual dataset cropped to model domain. -parameter_land_vrt4_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/5_multiband_domain_vrt_epsg_4326'. Multiband cropped virtual dataset. -parameter_land_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/6_tif_multiband'. parameter_land_mode_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/7_mode_land_class'. parameter_land_tif_name | land_classes.tif # Name of the final landclass overview for the domain. Must be in .tif format. @@ -105,9 +80,6 @@ intersect_soil_path | default # If ' intersect_soil_name | catchment_with_soilgrids.shp # Name of the shapefile with intersection between catchment and SOILGRIDS-derived USDA soil classes, stored in columns 'USDA_{1,...n}' intersect_land_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_modis'. intersect_land_name | catchment_with_modis.shp # Name of the shapefile with intersection between catchment and MODIS-derived IGBP land classes, stored in columns 'IGBP_{1,...n}' -intersect_forcing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_forcing'. -intersect_routing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_routing'. -intersect_routing_name | catchment_with_routing_basins.shp # Name of the shapefile with intersection between hydrologic model catchments and routing model catchments. # Experiment settings - general @@ -142,10 +114,10 @@ settings_mizu_control_file | mizuroute.control # Name settings_mizu_routing_var | averageRoutedRunoff # Name of SUMMA output variable to use for routing. settings_mizu_routing_units | m/s # Units of the variable to be routed. settings_mizu_routing_dt | 3600 # Size of the routing time step [s]. -settings_mizu_output_freq | annual # Frequency with which mizuRoute generates new output files. Must be one of 'single', 'day', 'month', 'annual'. -settings_mizu_output_vars | 0 # Routing output. '0' for both KWT and IRF; '1' IRF only; '2' KWT only. +settings_mizu_output_freq | yearly # Frequency with which mizuRoute generates new output files. Must be one of 'single', 'daily', 'monthly', 'yearly'. +settings_mizu_output_vars | 1 # Option for routing schemes. 0: Sum; 1: IRF; 2: KWT; 3: KW: 4: MC; 5: DW. Saves no data if not specified settings_mizu_within_basin | 0 # '0' (no) or '1' (IRF routing). Flag to enable within-basin routing by mizuRoute. Should be set to 0 if SUMMA is run with "subRouting" decision "timeDlay". -settings_mizu_make_outlet | 71028585 # Segment ID or IDs that should be set as network outlet. Specify multiple IDs separated by commas: X,Y,Z. Specify no IDs as: n/a. Note that this can also be done in the network shapefile. +settings_mizu_make_outlet | default # Segment ID or IDs that should be set as network outlet. Specify multiple IDs separated by commas: X,Y,Z. Specify no IDs as: n/a. Note that this can also be done in the network shapefile. If default, The HRU with the largest upstream surface is selected. # Postprocessing settings diff --git a/0_control_files/control_Europe.txt b/0_control_files/control_Europe.txt deleted file mode 100644 index 177a81c..0000000 --- a/0_control_files/control_Europe.txt +++ /dev/null @@ -1,152 +0,0 @@ -# SUMMA workflow setting file. -# Characters '|' and '#' are used as separators to find the actual setting values. Any text behind '|' is assumed to be part of the setting value, unless preceded by '#'. - -# Note on path specification -# If deviating from default paths, a full path must be specified. E.g. '/home/user/non-default/path' - - -# Modeling domain settings -root_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data # Root folder where data will be stored. -domain_name | Europe # Used as part of the root folder name for the prepared data. - - -# Shapefile settings - SUMMA catchment file -catchment_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment'. -catchment_shp_name | Europe_gruhru.shp # Name of the catchment shapefile. Requires extension '.shp'. -catchment_shp_gruid | GRU_ID # Name of the GRU ID column (can be any numeric value, HRU's within a single GRU have the same GRU ID). -catchment_shp_hruid | HRU_ID # Name of the HRU ID column (consecutive from 1 to total number of HRUs, must be unique). -catchment_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -catchment_shp_lat | center_lat # Name of the latitude column. Should be a value representative for the HRU. Typically the centroid. -catchment_shp_lon | center_lon # Name of the longitude column. Should be a value representative for the HRU. Typically the centroid. - - -# Shapefile settings - mizuRoute river network file -river_network_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/river_network'. -river_network_shp_name | Europe_river_rawMERIT.shp # Name of the river network shapefile. Requires extension '.shp'. -river_network_shp_segid | COMID # Name of the segment ID column. -river_network_shp_downsegid | NextDownID # Name of the downstream segment ID column. -river_network_shp_slope | slope # Name of the slope column. Slope must be in in units [length/length]. -river_network_shp_length | length # Name of the segment length column. Length must be in units [m]. - - -# Shapefile settings - mizuRoute catchment file -river_basin_shp_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_Europe/shapefiles/catchment/ # Same file as the SUMMA catchments -river_basin_shp_name | Europe_gruhru.shp # Name of the routing subbasins shapefile needed for remapping. Requires extension '.shp'. -river_basin_shp_rm_hruid | GRU_ID # Name of the routing basin ID column. -river_basin_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -river_basin_shp_hru_to_seg | hru_to_seg # Name of the column that shows which river segment each HRU connects to. HRUs and river segments have the same COMID so this works. - - -# Shapefile settings - SUMMA-to-mizuRoute -river_basin_needs_remap | no # 'no' if routing basins map 1:1 onto model GRUs. 'yes' if river segments span multiple GRUs or if multiple segments are inside a single GRU. - - -# Install settings -github_summa | https://github.com/CH-Earth/summa # Replace this with the path to your own fork if you forked the repo. -github_mizuroute | https://github.com/ncar/mizuroute # Replace this with the path to your own fork if you forked the repo. -install_path_summa | default # If 'default', clones source code into 'root_path/installs/summa'. -install_path_mizuroute | default # If 'default', clones source code into 'root_path/installs/mizuRoute'. -exe_name_summa | summa.exe # Name of the compiled executable. -exe_name_mizuroute | mizuRoute.exe # Name of the compiled executable. - - -# Forcing settings -forcing_raw_time | 1979,1979 # Years to download: Jan-[from],Dec-[to]. -forcing_raw_space | 81.81/-24.37/12.59/69.56 # Bounding box of the shapefile: lat_max/lon_min/lat_min/lon_max. Will be converted to ERA5 download coordinates in script. Order and use of '/' to separate values is mandatory. -forcing_time_step_size | 3600 # Size of the forcing time step in [s]. Must be constant. -forcing_measurement_height | 3 # Reference height for forcing measurements [m]. -forcing_shape_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/forcing'. -forcing_shape_name | era5_grid.shp # Name of the forcing shapefile. Requires extension '.shp'. -forcing_shape_lat_name | lat # Name of the latitude field that contains the latitude of ERA5 data points. -forcing_shape_lon_name | lon # Name of the longitude field that contains the latitude of ERA5 data points. -forcing_geo_path | default # If 'default', uses 'root_path/domain_[name]/forcing/0_geopotential'. -forcing_raw_path | default # If 'default', uses 'root_path/domain_[name]/forcing/1_raw_data'. -forcing_merged_path | default # If 'default', uses 'root_path/domain_[name]/forcing/2_merged_data'. -forcing_easymore_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_temp_easymore'. -forcing_basin_avg_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_basin_averaged_data'. -forcing_summa_path | default # If 'default', uses 'root_path/domain_[name]/forcing/4_SUMMA_input'. - - -# Parameter settings - DEM -parameter_dem_main_url | http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/distribute/v1.0.1/ # Primary download URL for MERIT Hydro adjusted elevation data. Needs to be appended with filenames. -parameter_dem_file_template | elv_{}{}.tar # Template for download file names. -parameter_dem_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/1_MERIT_hydro_raw_data'. -parameter_dem_unpack_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/2_MERIT_hydro_unpacked_data'. -parameter_dem_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/3_vrt'. -parameter_dem_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/4_domain_vrt'. -parameter_dem_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/5_elevation'. -parameter_dem_tif_name | elevation.tif # Name of the final DEM for the domain. Must be in .tif format. - - -# Parameter settings - soil -parameter_soil_hydro_ID | 1361509511e44adfba814f6950c6e742 # ID of the Hydroshare resource to download. -parameter_soil_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/1_soil_classes_global'. -parameter_soil_domain_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/2_soil_classes_domain'. -parameter_soil_tif_name | soil_classes.tif # Name of the final soil class overview for the domain. Must be in .tif format. - - -# Parameter settings - land -parameter_land_list_path | default # If 'default', uses 'summaWorkflow_public/3b_parameters/MODIS_MCD12Q1_V6/1_download/'. Location of file with data download links. -parameter_land_list_name | daac_mcd12q1_data_links.txt # Name of file that contains list of MODIS download urls. -parameter_land_raw_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_BowAtBanff/parameters/landclass/1_MODIS_raw_data # If 'default', uses 'root_path/domain_[name]/parameters/landclass/1_MODIS_raw_data'. -parameter_land_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/2_vrt_native_crs'. Virtual dataset composed of .hdf files. -parameter_land_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/3_vrt_epsg_4326'. Virtual dataset projected in EPSG:4326. -parameter_land_vrt3_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/4_domain_vrt_epsg_4326'. Virtual dataset cropped to model domain. -parameter_land_vrt4_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/5_multiband_domain_vrt_epsg_4326'. Multiband cropped virtual dataset. -parameter_land_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/6_tif_multiband'. -parameter_land_mode_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/7_mode_land_class'. -parameter_land_tif_name | land_classes.tif # Name of the final landclass overview for the domain. Must be in .tif format. - - -# Intersection settings -intersect_dem_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_dem'. -intersect_dem_name | catchment_with_merit_dem.shp # Name of the shapefile with intersection between catchment and MERIT Hydro DEM, stored in column 'elev_mean'. -intersect_soil_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_soilgrids'. -intersect_soil_name | catchment_with_soilgrids.shp # Name of the shapefile with intersection between catchment and SOILGRIDS-derived USDA soil classes, stored in columns 'USDA_{1,...n}' -intersect_land_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_modis'. -intersect_land_name | catchment_with_modis.shp # Name of the shapefile with intersection between catchment and MODIS-derived IGBP land classes, stored in columns 'IGBP_{1,...n}' -intersect_forcing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_forcing'. -intersect_routing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_routing'. -intersect_routing_name | catchment_with_routing_basins.shp # Name of the shapefile with intersection between hydrologic model catchments and routing model catchments. - - -# Experiment settings - general -experiment_id | run1 # Descriptor of the modelling experiment; used as output folder name. -experiment_time_start | default # Simulation start. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-01-01 00:00'. -experiment_time_end | default # Simulation end. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-12-31 23:00'. -experiment_output_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA'. -experiment_output_mizuRoute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute'. -experiment_log_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA/SUMMA_logs'. -experiment_log_mizuroute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute/mizuRoute_logs'. -experiment_backup_settings | yes # Flag to (not) create a copy of the model settings in the output folder; "no" or "yes". Copying settings may be undesirable if files are large. - - -# Experiment settings - SUMMA -settings_summa_path | default # If 'default', uses 'root_path/domain_[name]/settings/SUMMA'. -settings_summa_filemanager | fileManager.txt # Name of the file with the SUMMA inputs. -settings_summa_coldstate | coldState.nc # Name of the file with intial states. -settings_summa_trialParams | trialParams.nc # Name of the file that can contain trial parameter values (note, can be empty of any actual parameter values but must be provided and must contain an 'hruId' variable). -settings_summa_forcing_list | forcingFileList.txt # Name of the file that has the list of forcing files. -settings_summa_attributes | attributes.nc # Name of the attributes file. -settings_summa_connect_HRUs | no # Attribute setting: "no" or "yes". Tricky concept, see README in ./5_model_input/SUMMA/3f_attributes. If no; all HRUs modeled as independent columns (downHRUindex = 0). If yes; HRUs within each GRU are connected based on relative HRU elevation (highest = upstream, lowest = outlet). -settings_summa_trialParam_n | 1 # Number of trial parameter specifications. Specify 0 if none are wanted (they can still be included in this file but won't be read). -settings_summa_trialParam_1 | maxstep,900 # Name of trial parameter and value to assign. Value assumed to be float. - - -# Experiment settings - mizuRoute -settings_mizu_path | default # If 'default', uses 'root_path/domain_[name]/settings/mizuRoute'. -settings_mizu_parameters | param.nml.default # Name of the routing parameters file. -settings_mizu_topology | topology.nc # Name of the river network topology file. -settings_mizu_remap | routing_remap.nc # Name of the optional catchment remapping file, for cases when SUMMA uses different catchments than mizuRoute. -settings_mizu_control_file | mizuroute.control # Name of the control file. -settings_mizu_routing_var | averageRoutedRunoff # Name of SUMMA output variable to use for routing. -settings_mizu_routing_units | m/s # Units of the variable to be routed. -settings_mizu_routing_dt | 3600 # Size of the routing time step [s]. -settings_mizu_output_freq | annual # Frequency with which mizuRoute generates new output files. Must be one of 'single', 'day', 'month', 'annual'. -settings_mizu_output_vars | 0 # Routing output. '0' for both KWT and IRF; '1' IRF only; '2' KWT only. -settings_mizu_within_basin | 0 # '0' (no) or '1' (IRF routing). Flag to enable within-basin routing by mizuRoute. Should be set to 0 if SUMMA is run with "subRouting" decision "timeDlay". -settings_mizu_make_outlet | n/a # Segment ID or IDs that should be set as network outlet. Specify multiple IDs separated by commas: X,Y,Z. Specify no IDs as: n/a. Note that this can also be done in the network shapefile. - - -# Postprocessing settings -visualization_folder | default # If 'default', uses 'root_path/domain_[name]/visualization'. diff --git a/0_control_files/control_NorthAmerica.txt b/0_control_files/control_NorthAmerica.txt deleted file mode 100644 index c2c53e3..0000000 --- a/0_control_files/control_NorthAmerica.txt +++ /dev/null @@ -1,230 +0,0 @@ -# SUMMA workflow setting file. -# Characters '|' and '#' are used as separators to find the actual setting values. Any text behind '|' is assumed to be part of the setting value, unless preceded by '#'. - -# Note on path specification -# If deviating from default paths, a full path must be specified. E.g. '/home/user/non-default/path' - - -# Modeling domain settings -root_path | /project/gwf/gwf_cmt/wknoben/summaWorkflow_data # Root folder where data will be stored. -domain_name | NorthAmerica # Used as part of the root folder name for the prepared data. - - -# Shapefile settings - SUMMA catchment file -catchment_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment'. -catchment_shp_name | merit_hydro_basins_and_coastal_hillslopes_merged_NA.shp # Name of the catchment shapefile. Requires extension '.shp'. -catchment_shp_gruid | COMID # Name of the GRU ID column (can be any numeric value, HRU's within a single GRU have the same GRU ID). -catchment_shp_hruid | COMID # Name of the HRU ID column (consecutive from 1 to total number of HRUs, must be unique). -catchment_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -catchment_shp_lat | center_lat # Name of the latitude column. Should be a value representative for the HRU. Typically the centroid. -catchment_shp_lon | center_lon # Name of the longitude column. Should be a value representative for the HRU. Typically the centroid. - - -# Shapefile settings - mizuRoute river network file -river_network_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/river_network'. -river_network_shp_name | merit_river_na_full.shp # Name of the river network shapefile. Requires extension '.shp'. -river_network_shp_segid | COMID # Name of the segment ID column. -river_network_shp_downsegid | NextDownID # Name of the downstream segment ID column. -river_network_shp_slope | slope # Name of the slope column. Slope must be in in units [length/length]. -river_network_shp_length | length # Name of the segment length column. Length must be in units [m]. - - -# Shapefile settings - mizuRoute catchment file -river_basin_shp_path | /project/gwf/gwf_cmt/wknoben/summaWorkflow_data/domain_NorthAmerica/shapefiles/catchment/ # Same file as the SUMMA catchments -river_basin_shp_name | merit_hydro_basins_and_coastal_hillslopes_merged_NA.shp # Name of the routing subbasins shapefile needed for remapping. Requires extension '.shp'. -river_basin_shp_rm_hruid | COMID # Name of the routing basin ID column. -river_basin_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -river_basin_shp_hru_to_seg | COMID # Name of the column that shows which river segment each HRU connects to. HRUs and river segments have the same COMID so this works. - - -# Shapefile settings - SUMMA-to-mizuRoute -river_basin_needs_remap | no # 'no' if routing basins map 1:1 onto model GRUs. 'yes' if river segments span multiple GRUs or if multiple segments are inside a single GRU. - - -# Install settings -github_summa | https://github.com/ncar/summa # Replace this with the path to your own fork if you forked the repo. -github_mizuroute | https://github.com/ncar/mizuroute # Replace this with the path to your own fork if you forked the repo. -install_path_summa | default # If 'default', clones source code into 'root_path/installs/summa'. -install_path_mizuroute | default # If 'default', clones source code into 'root_path/installs/mizuRoute'. -exe_name_summa | summa.exe # Name of the compiled executable. -exe_name_mizuroute | mizuroute.exe # Name of the compiled executable. - - -# Forcing settings -forcing_raw_time | 1979,2019 # Years to download: Jan-[from],Dec-[to]. -forcing_raw_space | 85/-179.5/5/-50 # Bounding box of the shapefile: lat_max/lon_min/lat_min/lon_max. Will be converted to ERA5 download coordinates in script. Order and use of '/' to separate values is mandatory. -forcing_time_step_size | 3600 # Size of the forcing time step in [s]. Must be constant. -forcing_measurement_height | 3 # Reference height for forcing measurements [m]. -forcing_shape_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/forcing'. -forcing_shape_name | era5_grid.shp # Name of the forcing shapefile. Requires extension '.shp'. -forcing_shape_lat_name | lat # Name of the latitude field that contains the latitude of ERA5 data points. -forcing_shape_lon_name | lon # Name of the longitude field that contains the latitude of ERA5 data points. -forcing_geo_path | default # If 'default', uses 'root_path/domain_[name]/forcing/0_geopotential'. -forcing_raw_path | default # If 'default', uses 'root_path/domain_[name]/forcing/1_raw_data'. -forcing_merged_path | default # If 'default', uses 'root_path/domain_[name]/forcing/2_merged_data'. -forcing_easymore_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_temp_easymore'. -forcing_basin_avg_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_basin_averaged_data'. -forcing_summa_path | default # If 'default', uses 'root_path/domain_[name]/forcing/4_SUMMA_input'. - - -# Parameter settings - DEM -parameter_dem_main_url | http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/distribute/v1.0.1/ # Primary download URL for MERIT Hydro adjusted elevation data. Needs to be appended with filenames. -parameter_dem_file_template | elv_{}{}.tar # Template for download file names. -parameter_dem_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/1_MERIT_hydro_raw_data'. -parameter_dem_unpack_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/2_MERIT_hydro_unpacked_data'. -parameter_dem_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/3_vrt'. -parameter_dem_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/4_domain_vrt'. -parameter_dem_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/5_elevation'. -parameter_dem_tif_name | elevation.tif # Name of the final DEM for the domain. Must be in .tif format. - - -# Parameter settings - soil -parameter_soil_hydro_ID | 1361509511e44adfba814f6950c6e742 # ID of the Hydroshare resource to download. -parameter_soil_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/1_soil_classes_global'. -parameter_soil_domain_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/2_soil_classes_domain'. -parameter_soil_tif_name | soil_classes.tif # Name of the final soil class overview for the domain. Must be in .tif format. - - -# Parameter settings - land -parameter_land_list_path | default # If 'default', uses 'summaWorkflow_public/3b_parameters/MODIS_MCD12Q1_V6/1_download/'. Location of file with data download links. -parameter_land_list_name | daac_mcd12q1_data_links.txt # Name of file that contains list of MODIS download urls. -parameter_land_raw_path | /project/gwf/gwf_cmt/wknoben/summaWorkflow_data/domain_BowAtBanff/parameters/landclass/1_MODIS_raw_data # If 'default', uses 'root_path/domain_[name]/parameters/landclass/1_MODIS_raw_data'. -parameter_land_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/2_vrt_native_crs'. Virtual dataset composed of .hdf files. -parameter_land_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/3_vrt_epsg_4326'. Virtual dataset projected in EPSG:4326. -parameter_land_vrt3_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/4_domain_vrt_epsg_4326'. Virtual dataset cropped to model domain. -parameter_land_vrt4_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/5_multiband_domain_vrt_epsg_4326'. Multiband cropped virtual dataset. -parameter_land_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/6_tif_multiband'. -parameter_land_mode_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/7_mode_land_class'. -parameter_land_tif_name | land_classes.tif # Name of the final landclass overview for the domain. Must be in .tif format. - - -# Intersection settings -intersect_dem_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_dem'. -intersect_dem_name | catchment_with_merit_dem.shp # Name of the shapefile with intersection between catchment and MERIT Hydro DEM, stored in column 'elev_mean'. -intersect_soil_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_soilgrids'. -intersect_soil_name | catchment_with_soilgrids.shp # Name of the shapefile with intersection between catchment and SOILGRIDS-derived USDA soil classes, stored in columns 'USDA_{1,...n}' -intersect_land_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_modis'. -intersect_land_name | catchment_with_modis.shp # Name of the shapefile with intersection between catchment and MODIS-derived IGBP land classes, stored in columns 'IGBP_{1,...n}' -intersect_forcing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_forcing'. -intersect_routing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_routing'. -intersect_routing_name | catchment_with_routing_basins.shp # Name of the shapefile with intersection between hydrologic model catchments and routing model catchments. - - -# Experiment settings - general -experiment_id | run1 # Descriptor of the modelling experiment; used as output folder name. -experiment_time_start | default # Simulation start. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-01-01 00:00'. -experiment_time_end | default # Simulation end. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-12-31 23:00'. -experiment_output_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA'. -experiment_output_mizuRoute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute'. -experiment_log_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA/SUMMA_logs'. -experiment_log_mizuroute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute/mizuRoute_logs'. -experiment_backup_settings | yes # Flag to (not) create a copy of the model settings in the output folder; "no" or "yes". Copying settings may be undesirable if files are large. - - -# Experiment settings - SUMMA -settings_summa_path | default # If 'default', uses 'root_path/domain_[name]/settings/SUMMA'. -settings_summa_filemanager | fileManager.txt # Name of the file with the SUMMA inputs. -settings_summa_coldstate | coldState.nc # Name of the file with intial states. -settings_summa_trialParams | trialParams.nc # Name of the file that can contain trial parameter values (note, can be empty of any actual parameter values but must be provided and must contain an 'hruId' variable). -settings_summa_forcing_list | forcingFileList.txt # Name of the file that has the list of forcing files. -settings_summa_attributes | attributes.nc # Name of the attributes file. -settings_summa_connect_HRUs | no # Attribute setting: "no" or "yes". Tricky concept, see README in ./5_model_input/SUMMA/3f_attributes. If no; all HRUs modeled as independent columns (downHRUindex = 0). If yes; HRUs within each GRU are connected based on relative HRU elevation (highest = upstream, lowest = outlet). -settings_summa_trialParam_n | 1 # Number of trial parameter specifications. Specify 0 if none are wanted (they can still be included in this file but won't be read). -settings_summa_trialParam_1 | maxstep,900 # Name of trial parameter and value to assign. Value assumed to be float. - - -# Experiment settings - mizuRoute -settings_mizu_path | default # If 'default', uses 'root_path/domain_[name]/settings/mizuRoute'. -settings_mizu_parameters | param.nml.default # Name of the routing parameters file. -settings_mizu_topology | topology.nc # Name of the river network topology file. -settings_mizu_remap | routing_remap.nc # Name of the optional catchment remapping file, for cases when SUMMA uses different catchments than mizuRoute. -settings_mizu_control_file | mizuroute.control # Name of the control file. -settings_mizu_routing_var | averageRoutedRunoff # Name of SUMMA output variable to use for routing. -settings_mizu_routing_units | m/s # Units of the variable to be routed. -settings_mizu_routing_dt | 3600 # Size of the routing time step [s]. -settings_mizu_output_freq | annual # Frequency with which mizuRoute generates new output files. Must be one of 'single', 'day', 'month', 'annual'. -settings_mizu_output_vars | 0 # Routing output. '0' for both KWT and IRF; '1' IRF only; '2' KWT only. -settings_mizu_within_basin | 0 # '0' (no) or '1' (IRF routing). Flag to enable within-basin routing by mizuRoute. Should be set to 0 if SUMMA is run with "subRouting" decision "timeDlay". -settings_mizu_make_outlet | n/a # Segment ID or IDs that should be set as network outlet. Specify multiple IDs separated by commas: X,Y,Z. Specify no IDs as: n/a. Note that this can also be done in the network shapefile. - - -# Postprocessing settings -visualization_folder | default # If 'default', uses 'root_path/domain_[name]/visualization'. - - -# Default folder structure -# Example of the resulting folder structure in "root_path". -# New domains will go into their own folder. - -- summWorkflow_data - | - |_ domain_BowAtBanff - | | - | |_ forcing - | | |_ 0_geopotential - | | |_ 1_raw_data - | | |_ 2_merged_data - | | |_ 3_basin_averaged_data - | | |_ 4_SUMMA_input - | | - | |_ parameters - | | |_ soilclass - | | | |_ 1_soil_classes_global - | | | |_ 2_soil_classes_domain - | | | - | | |_ landclass - | | | |_ 1_MODIS_raw_data - | | | |_ 2_vrt_native_crs - | | | |_ 3_vrt_epsg_4326 - | | | |_ 4_domain_vrt_epsg_4326 - | | | |_ 5_multiband_domain_vrt_epsg_4326 - | | | |_ 6_tif_multiband - | | | |_ 7_mode_land_class - | | | - | | |_ dem - | | |_ 1_MERIT_hydro_raw_data - | | |_ 2_MERIT_hydro_unpacked_data - | | |_ 3_vrt - | | |_ 4_domain_vrt - | | |_ 5_elevation - | | - | |_ settings - | | |_ mizuRoute - | | |_ SUMMA - | | - | |_ shapefiles - | | |_ catchment - | | |_ catchment_intersection - | | | |_ with_dem - | | | |_ with_forcing - | | | |_ with_soil - | | | |_ with_veg - | | |_ forcing - | | |_ river_basins - | | |_ river_network - | | - | |_ simulations - | | |_run1 - | | | |_ 0_settings_backup - | | | | |_ summa - | | | | |_ mizuRoute - | | | |_ summa - | | | | |_run_settings - | | | | |_SUMMA_logs - | | | |_ mizuRoute - | | | | |_run_settings - | | | | |_mizuRoute_logs - | | |_run2 - | | |_ ... - | | - | |_ visualization - | - |_ domain_global - | |_ ... - | - |_ domain_northAmerica - | |_ ... - | - |_ installs - |_ mizuRoute - |_ SUMMA \ No newline at end of file diff --git a/0_control_files/control_NorthAsia.txt b/0_control_files/control_NorthAsia.txt deleted file mode 100644 index d7048e3..0000000 --- a/0_control_files/control_NorthAsia.txt +++ /dev/null @@ -1,152 +0,0 @@ -# SUMMA workflow setting file. -# Characters '|' and '#' are used as separators to find the actual setting values. Any text behind '|' is assumed to be part of the setting value, unless preceded by '#'. - -# Note on path specification -# If deviating from default paths, a full path must be specified. E.g. '/home/user/non-default/path' - - -# Modeling domain settings -root_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data # Root folder where data will be stored. -domain_name | NorthAsia # Used as part of the root folder name for the prepared data. - - -# Shapefile settings - SUMMA catchment file -catchment_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment'. -catchment_shp_name | NorthAsia_gruhru.shp # Name of the catchment shapefile. Requires extension '.shp'. -catchment_shp_gruid | GRU_ID # Name of the GRU ID column (can be any numeric value, HRU's within a single GRU have the same GRU ID). -catchment_shp_hruid | HRU_ID # Name of the HRU ID column (consecutive from 1 to total number of HRUs, must be unique). -catchment_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -catchment_shp_lat | center_lat # Name of the latitude column. Should be a value representative for the HRU. Typically the centroid. -catchment_shp_lon | center_lon # Name of the longitude column. Should be a value representative for the HRU. Typically the centroid. - - -# Shapefile settings - mizuRoute river network file -river_network_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/river_network'. -river_network_shp_name | NorthAsia_river_rawMERIT.shp # Name of the river network shapefile. Requires extension '.shp'. -river_network_shp_segid | COMID # Name of the segment ID column. -river_network_shp_downsegid | NextDownID # Name of the downstream segment ID column. -river_network_shp_slope | slope # Name of the slope column. Slope must be in in units [length/length]. -river_network_shp_length | length # Name of the segment length column. Length must be in units [m]. - - -# Shapefile settings - mizuRoute catchment file -river_basin_shp_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_NorthAsia/shapefiles/catchment/ # Same file as the SUMMA catchments -river_basin_shp_name | NorthAsia_gruhru.shp # Name of the routing subbasins shapefile needed for remapping. Requires extension '.shp'. -river_basin_shp_rm_hruid | GRU_ID # Name of the routing basin ID column. -river_basin_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -river_basin_shp_hru_to_seg | hru_to_seg # Name of the column that shows which river segment each HRU connects to. HRUs and river segments have the same COMID so this works. - - -# Shapefile settings - SUMMA-to-mizuRoute -river_basin_needs_remap | no # 'no' if routing basins map 1:1 onto model GRUs. 'yes' if river segments span multiple GRUs or if multiple segments are inside a single GRU. - - -# Install settings -github_summa | https://github.com/CH-Earth/summa # Replace this with the path to your own fork if you forked the repo. -github_mizuroute | https://github.com/ncar/mizuroute # Replace this with the path to your own fork if you forked the repo. -install_path_summa | default # If 'default', clones source code into 'root_path/installs/summa'. -install_path_mizuroute | default # If 'default', clones source code into 'root_path/installs/mizuRoute'. -exe_name_summa | summa.exe # Name of the compiled executable. -exe_name_mizuroute | mizuRoute.exe # Name of the compiled executable. - - -# Forcing settings -forcing_raw_time | 1979,1979 # Years to download: Jan-[from],Dec-[to]. -forcing_raw_space | 81.26/-180.0/45.56/180.0 # Bounding box of the shapefile: lat_max/lon_min/lat_min/lon_max. Will be converted to ERA5 download coordinates in script. Order and use of '/' to separate values is mandatory. -forcing_time_step_size | 3600 # Size of the forcing time step in [s]. Must be constant. -forcing_measurement_height | 3 # Reference height for forcing measurements [m]. -forcing_shape_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/forcing'. -forcing_shape_name | era5_grid.shp # Name of the forcing shapefile. Requires extension '.shp'. -forcing_shape_lat_name | lat # Name of the latitude field that contains the latitude of ERA5 data points. -forcing_shape_lon_name | lon # Name of the longitude field that contains the latitude of ERA5 data points. -forcing_geo_path | default # If 'default', uses 'root_path/domain_[name]/forcing/0_geopotential'. -forcing_raw_path | default # If 'default', uses 'root_path/domain_[name]/forcing/1_raw_data'. -forcing_merged_path | default # If 'default', uses 'root_path/domain_[name]/forcing/2_merged_data'. -forcing_easymore_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_temp_easymore'. -forcing_basin_avg_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_basin_averaged_data'. -forcing_summa_path | default # If 'default', uses 'root_path/domain_[name]/forcing/4_SUMMA_input'. - - -# Parameter settings - DEM -parameter_dem_main_url | http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/distribute/v1.0.1/ # Primary download URL for MERIT Hydro adjusted elevation data. Needs to be appended with filenames. -parameter_dem_file_template | elv_{}{}.tar # Template for download file names. -parameter_dem_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/1_MERIT_hydro_raw_data'. -parameter_dem_unpack_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/2_MERIT_hydro_unpacked_data'. -parameter_dem_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/3_vrt'. -parameter_dem_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/4_domain_vrt'. -parameter_dem_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/5_elevation'. -parameter_dem_tif_name | elevation.tif # Name of the final DEM for the domain. Must be in .tif format. - - -# Parameter settings - soil -parameter_soil_hydro_ID | 1361509511e44adfba814f6950c6e742 # ID of the Hydroshare resource to download. -parameter_soil_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/1_soil_classes_global'. -parameter_soil_domain_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/2_soil_classes_domain'. -parameter_soil_tif_name | soil_classes.tif # Name of the final soil class overview for the domain. Must be in .tif format. - - -# Parameter settings - land -parameter_land_list_path | default # If 'default', uses 'summaWorkflow_public/3b_parameters/MODIS_MCD12Q1_V6/1_download/'. Location of file with data download links. -parameter_land_list_name | daac_mcd12q1_data_links.txt # Name of file that contains list of MODIS download urls. -parameter_land_raw_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_BowAtBanff/parameters/landclass/1_MODIS_raw_data # If 'default', uses 'root_path/domain_[name]/parameters/landclass/1_MODIS_raw_data'. -parameter_land_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/2_vrt_native_crs'. Virtual dataset composed of .hdf files. -parameter_land_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/3_vrt_epsg_4326'. Virtual dataset projected in EPSG:4326. -parameter_land_vrt3_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/4_domain_vrt_epsg_4326'. Virtual dataset cropped to model domain. -parameter_land_vrt4_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/5_multiband_domain_vrt_epsg_4326'. Multiband cropped virtual dataset. -parameter_land_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/6_tif_multiband'. -parameter_land_mode_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/7_mode_land_class'. -parameter_land_tif_name | land_classes.tif # Name of the final landclass overview for the domain. Must be in .tif format. - - -# Intersection settings -intersect_dem_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_dem'. -intersect_dem_name | catchment_with_merit_dem.shp # Name of the shapefile with intersection between catchment and MERIT Hydro DEM, stored in column 'elev_mean'. -intersect_soil_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_soilgrids'. -intersect_soil_name | catchment_with_soilgrids.shp # Name of the shapefile with intersection between catchment and SOILGRIDS-derived USDA soil classes, stored in columns 'USDA_{1,...n}' -intersect_land_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_modis'. -intersect_land_name | catchment_with_modis.shp # Name of the shapefile with intersection between catchment and MODIS-derived IGBP land classes, stored in columns 'IGBP_{1,...n}' -intersect_forcing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_forcing'. -intersect_routing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_routing'. -intersect_routing_name | catchment_with_routing_basins.shp # Name of the shapefile with intersection between hydrologic model catchments and routing model catchments. - - -# Experiment settings - general -experiment_id | run1 # Descriptor of the modelling experiment; used as output folder name. -experiment_time_start | default # Simulation start. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-01-01 00:00'. -experiment_time_end | default # Simulation end. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-12-31 23:00'. -experiment_output_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA'. -experiment_output_mizuRoute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute'. -experiment_log_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA/SUMMA_logs'. -experiment_log_mizuroute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute/mizuRoute_logs'. -experiment_backup_settings | yes # Flag to (not) create a copy of the model settings in the output folder; "no" or "yes". Copying settings may be undesirable if files are large. - - -# Experiment settings - SUMMA -settings_summa_path | default # If 'default', uses 'root_path/domain_[name]/settings/SUMMA'. -settings_summa_filemanager | fileManager.txt # Name of the file with the SUMMA inputs. -settings_summa_coldstate | coldState.nc # Name of the file with intial states. -settings_summa_trialParams | trialParams.nc # Name of the file that can contain trial parameter values (note, can be empty of any actual parameter values but must be provided and must contain an 'hruId' variable). -settings_summa_forcing_list | forcingFileList.txt # Name of the file that has the list of forcing files. -settings_summa_attributes | attributes.nc # Name of the attributes file. -settings_summa_connect_HRUs | no # Attribute setting: "no" or "yes". Tricky concept, see README in ./5_model_input/SUMMA/3f_attributes. If no; all HRUs modeled as independent columns (downHRUindex = 0). If yes; HRUs within each GRU are connected based on relative HRU elevation (highest = upstream, lowest = outlet). -settings_summa_trialParam_n | 1 # Number of trial parameter specifications. Specify 0 if none are wanted (they can still be included in this file but won't be read). -settings_summa_trialParam_1 | maxstep,900 # Name of trial parameter and value to assign. Value assumed to be float. - - -# Experiment settings - mizuRoute -settings_mizu_path | default # If 'default', uses 'root_path/domain_[name]/settings/mizuRoute'. -settings_mizu_parameters | param.nml.default # Name of the routing parameters file. -settings_mizu_topology | topology.nc # Name of the river network topology file. -settings_mizu_remap | routing_remap.nc # Name of the optional catchment remapping file, for cases when SUMMA uses different catchments than mizuRoute. -settings_mizu_control_file | mizuroute.control # Name of the control file. -settings_mizu_routing_var | averageRoutedRunoff # Name of SUMMA output variable to use for routing. -settings_mizu_routing_units | m/s # Units of the variable to be routed. -settings_mizu_routing_dt | 3600 # Size of the routing time step [s]. -settings_mizu_output_freq | annual # Frequency with which mizuRoute generates new output files. Must be one of 'single', 'day', 'month', 'annual'. -settings_mizu_output_vars | 0 # Routing output. '0' for both KWT and IRF; '1' IRF only; '2' KWT only. -settings_mizu_within_basin | 0 # '0' (no) or '1' (IRF routing). Flag to enable within-basin routing by mizuRoute. Should be set to 0 if SUMMA is run with "subRouting" decision "timeDlay". -settings_mizu_make_outlet | n/a # Segment ID or IDs that should be set as network outlet. Specify multiple IDs separated by commas: X,Y,Z. Specify no IDs as: n/a. Note that this can also be done in the network shapefile. - - -# Postprocessing settings -visualization_folder | default # If 'default', uses 'root_path/domain_[name]/visualization'. diff --git a/0_control_files/control_Oceania.txt b/0_control_files/control_Oceania.txt deleted file mode 100644 index 8331517..0000000 --- a/0_control_files/control_Oceania.txt +++ /dev/null @@ -1,152 +0,0 @@ -# SUMMA workflow setting file. -# Characters '|' and '#' are used as separators to find the actual setting values. Any text behind '|' is assumed to be part of the setting value, unless preceded by '#'. - -# Note on path specification -# If deviating from default paths, a full path must be specified. E.g. '/home/user/non-default/path' - - -# Modeling domain settings -root_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data # Root folder where data will be stored. -domain_name | Oceania # Used as part of the root folder name for the prepared data. - - -# Shapefile settings - SUMMA catchment file -catchment_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment'. -catchment_shp_name | Oceania_gruhru.shp # Name of the catchment shapefile. Requires extension '.shp'. -catchment_shp_gruid | GRU_ID # Name of the GRU ID column (can be any numeric value, HRU's within a single GRU have the same GRU ID). -catchment_shp_hruid | HRU_ID # Name of the HRU ID column (consecutive from 1 to total number of HRUs, must be unique). -catchment_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -catchment_shp_lat | center_lat # Name of the latitude column. Should be a value representative for the HRU. Typically the centroid. -catchment_shp_lon | center_lon # Name of the longitude column. Should be a value representative for the HRU. Typically the centroid. - - -# Shapefile settings - mizuRoute river network file -river_network_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/river_network'. -river_network_shp_name | Oceania_river_rawMERIT.shp # Name of the river network shapefile. Requires extension '.shp'. -river_network_shp_segid | COMID # Name of the segment ID column. -river_network_shp_downsegid | NextDownID # Name of the downstream segment ID column. -river_network_shp_slope | slope # Name of the slope column. Slope must be in in units [length/length]. -river_network_shp_length | length # Name of the segment length column. Length must be in units [m]. - - -# Shapefile settings - mizuRoute catchment file -river_basin_shp_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_Oceania/shapefiles/catchment/ # Same file as the SUMMA catchments -river_basin_shp_name | Oceania_gruhru.shp # Name of the routing subbasins shapefile needed for remapping. Requires extension '.shp'. -river_basin_shp_rm_hruid | GRU_ID # Name of the routing basin ID column. -river_basin_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -river_basin_shp_hru_to_seg | hru_to_seg # Name of the column that shows which river segment each HRU connects to. HRUs and river segments have the same COMID so this works. - - -# Shapefile settings - SUMMA-to-mizuRoute -river_basin_needs_remap | no # 'no' if routing basins map 1:1 onto model GRUs. 'yes' if river segments span multiple GRUs or if multiple segments are inside a single GRU. - - -# Install settings -github_summa | https://github.com/CH-Earth/summa # Replace this with the path to your own fork if you forked the repo. -github_mizuroute | https://github.com/ncar/mizuroute # Replace this with the path to your own fork if you forked the repo. -install_path_summa | default # If 'default', clones source code into 'root_path/installs/summa'. -install_path_mizuroute | default # If 'default', clones source code into 'root_path/installs/mizuRoute'. -exe_name_summa | summa.exe # Name of the compiled executable. -exe_name_mizuroute | mizuRoute.exe # Name of the compiled executable. - - -# Forcing settings -forcing_raw_time | 1979,1979 # Years to download: Jan-[from],Dec-[to]. -forcing_raw_space | 18.63/95.21/-50.81/179.9 # Bounding box of the shapefile: lat_max/lon_min/lat_min/lon_max. Will be converted to ERA5 download coordinates in script. Order and use of '/' to separate values is mandatory. -forcing_time_step_size | 3600 # Size of the forcing time step in [s]. Must be constant. -forcing_measurement_height | 3 # Reference height for forcing measurements [m]. -forcing_shape_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/forcing'. -forcing_shape_name | era5_grid.shp # Name of the forcing shapefile. Requires extension '.shp'. -forcing_shape_lat_name | lat # Name of the latitude field that contains the latitude of ERA5 data points. -forcing_shape_lon_name | lon # Name of the longitude field that contains the latitude of ERA5 data points. -forcing_geo_path | default # If 'default', uses 'root_path/domain_[name]/forcing/0_geopotential'. -forcing_raw_path | default # If 'default', uses 'root_path/domain_[name]/forcing/1_raw_data'. -forcing_merged_path | default # If 'default', uses 'root_path/domain_[name]/forcing/2_merged_data'. -forcing_easymore_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_temp_easymore'. -forcing_basin_avg_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_basin_averaged_data'. -forcing_summa_path | default # If 'default', uses 'root_path/domain_[name]/forcing/4_SUMMA_input'. - - -# Parameter settings - DEM -parameter_dem_main_url | http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/distribute/v1.0.1/ # Primary download URL for MERIT Hydro adjusted elevation data. Needs to be appended with filenames. -parameter_dem_file_template | elv_{}{}.tar # Template for download file names. -parameter_dem_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/1_MERIT_hydro_raw_data'. -parameter_dem_unpack_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/2_MERIT_hydro_unpacked_data'. -parameter_dem_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/3_vrt'. -parameter_dem_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/4_domain_vrt'. -parameter_dem_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/5_elevation'. -parameter_dem_tif_name | elevation.tif # Name of the final DEM for the domain. Must be in .tif format. - - -# Parameter settings - soil -parameter_soil_hydro_ID | 1361509511e44adfba814f6950c6e742 # ID of the Hydroshare resource to download. -parameter_soil_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/1_soil_classes_global'. -parameter_soil_domain_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/2_soil_classes_domain'. -parameter_soil_tif_name | soil_classes.tif # Name of the final soil class overview for the domain. Must be in .tif format. - - -# Parameter settings - land -parameter_land_list_path | default # If 'default', uses 'summaWorkflow_public/3b_parameters/MODIS_MCD12Q1_V6/1_download/'. Location of file with data download links. -parameter_land_list_name | daac_mcd12q1_data_links.txt # Name of file that contains list of MODIS download urls. -parameter_land_raw_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_BowAtBanff/parameters/landclass/1_MODIS_raw_data # If 'default', uses 'root_path/domain_[name]/parameters/landclass/1_MODIS_raw_data'. -parameter_land_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/2_vrt_native_crs'. Virtual dataset composed of .hdf files. -parameter_land_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/3_vrt_epsg_4326'. Virtual dataset projected in EPSG:4326. -parameter_land_vrt3_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/4_domain_vrt_epsg_4326'. Virtual dataset cropped to model domain. -parameter_land_vrt4_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/5_multiband_domain_vrt_epsg_4326'. Multiband cropped virtual dataset. -parameter_land_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/6_tif_multiband'. -parameter_land_mode_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/7_mode_land_class'. -parameter_land_tif_name | land_classes.tif # Name of the final landclass overview for the domain. Must be in .tif format. - - -# Intersection settings -intersect_dem_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_dem'. -intersect_dem_name | catchment_with_merit_dem.shp # Name of the shapefile with intersection between catchment and MERIT Hydro DEM, stored in column 'elev_mean'. -intersect_soil_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_soilgrids'. -intersect_soil_name | catchment_with_soilgrids.shp # Name of the shapefile with intersection between catchment and SOILGRIDS-derived USDA soil classes, stored in columns 'USDA_{1,...n}' -intersect_land_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_modis'. -intersect_land_name | catchment_with_modis.shp # Name of the shapefile with intersection between catchment and MODIS-derived IGBP land classes, stored in columns 'IGBP_{1,...n}' -intersect_forcing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_forcing'. -intersect_routing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_routing'. -intersect_routing_name | catchment_with_routing_basins.shp # Name of the shapefile with intersection between hydrologic model catchments and routing model catchments. - - -# Experiment settings - general -experiment_id | run1 # Descriptor of the modelling experiment; used as output folder name. -experiment_time_start | default # Simulation start. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-01-01 00:00'. -experiment_time_end | default # Simulation end. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-12-31 23:00'. -experiment_output_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA'. -experiment_output_mizuRoute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute'. -experiment_log_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA/SUMMA_logs'. -experiment_log_mizuroute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute/mizuRoute_logs'. -experiment_backup_settings | yes # Flag to (not) create a copy of the model settings in the output folder; "no" or "yes". Copying settings may be undesirable if files are large. - - -# Experiment settings - SUMMA -settings_summa_path | default # If 'default', uses 'root_path/domain_[name]/settings/SUMMA'. -settings_summa_filemanager | fileManager.txt # Name of the file with the SUMMA inputs. -settings_summa_coldstate | coldState.nc # Name of the file with intial states. -settings_summa_trialParams | trialParams.nc # Name of the file that can contain trial parameter values (note, can be empty of any actual parameter values but must be provided and must contain an 'hruId' variable). -settings_summa_forcing_list | forcingFileList.txt # Name of the file that has the list of forcing files. -settings_summa_attributes | attributes.nc # Name of the attributes file. -settings_summa_connect_HRUs | no # Attribute setting: "no" or "yes". Tricky concept, see README in ./5_model_input/SUMMA/3f_attributes. If no; all HRUs modeled as independent columns (downHRUindex = 0). If yes; HRUs within each GRU are connected based on relative HRU elevation (highest = upstream, lowest = outlet). -settings_summa_trialParam_n | 1 # Number of trial parameter specifications. Specify 0 if none are wanted (they can still be included in this file but won't be read). -settings_summa_trialParam_1 | maxstep,900 # Name of trial parameter and value to assign. Value assumed to be float. - - -# Experiment settings - mizuRoute -settings_mizu_path | default # If 'default', uses 'root_path/domain_[name]/settings/mizuRoute'. -settings_mizu_parameters | param.nml.default # Name of the routing parameters file. -settings_mizu_topology | topology.nc # Name of the river network topology file. -settings_mizu_remap | routing_remap.nc # Name of the optional catchment remapping file, for cases when SUMMA uses different catchments than mizuRoute. -settings_mizu_control_file | mizuroute.control # Name of the control file. -settings_mizu_routing_var | averageRoutedRunoff # Name of SUMMA output variable to use for routing. -settings_mizu_routing_units | m/s # Units of the variable to be routed. -settings_mizu_routing_dt | 3600 # Size of the routing time step [s]. -settings_mizu_output_freq | annual # Frequency with which mizuRoute generates new output files. Must be one of 'single', 'day', 'month', 'annual'. -settings_mizu_output_vars | 0 # Routing output. '0' for both KWT and IRF; '1' IRF only; '2' KWT only. -settings_mizu_within_basin | 0 # '0' (no) or '1' (IRF routing). Flag to enable within-basin routing by mizuRoute. Should be set to 0 if SUMMA is run with "subRouting" decision "timeDlay". -settings_mizu_make_outlet | n/a # Segment ID or IDs that should be set as network outlet. Specify multiple IDs separated by commas: X,Y,Z. Specify no IDs as: n/a. Note that this can also be done in the network shapefile. - - -# Postprocessing settings -visualization_folder | default # If 'default', uses 'root_path/domain_[name]/visualization'. diff --git a/0_control_files/control_SouthAmerica.txt b/0_control_files/control_SouthAmerica.txt deleted file mode 100644 index 22fe2d5..0000000 --- a/0_control_files/control_SouthAmerica.txt +++ /dev/null @@ -1,152 +0,0 @@ -# SUMMA workflow setting file. -# Characters '|' and '#' are used as separators to find the actual setting values. Any text behind '|' is assumed to be part of the setting value, unless preceded by '#'. - -# Note on path specification -# If deviating from default paths, a full path must be specified. E.g. '/home/user/non-default/path' - - -# Modeling domain settings -root_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data # Root folder where data will be stored. -domain_name | SouthAmerica # Used as part of the root folder name for the prepared data. - - -# Shapefile settings - SUMMA catchment file -catchment_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment'. -catchment_shp_name | SouthAmericac_gruhru.shp # Name of the catchment shapefile. Requires extension '.shp'. -catchment_shp_gruid | GRU_ID # Name of the GRU ID column (can be any numeric value, HRU's within a single GRU have the same GRU ID). -catchment_shp_hruid | HRU_ID # Name of the HRU ID column (consecutive from 1 to total number of HRUs, must be unique). -catchment_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -catchment_shp_lat | center_lat # Name of the latitude column. Should be a value representative for the HRU. Typically the centroid. -catchment_shp_lon | center_lon # Name of the longitude column. Should be a value representative for the HRU. Typically the centroid. - - -# Shapefile settings - mizuRoute river network file -river_network_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/river_network'. -river_network_shp_name | SouthAmerica_river_rawMERIT.shp # Name of the river network shapefile. Requires extension '.shp'. -river_network_shp_segid | COMID # Name of the segment ID column. -river_network_shp_downsegid | NextDownID # Name of the downstream segment ID column. -river_network_shp_slope | slope # Name of the slope column. Slope must be in in units [length/length]. -river_network_shp_length | length # Name of the segment length column. Length must be in units [m]. - - -# Shapefile settings - mizuRoute catchment file -river_basin_shp_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_SouthAmerica/shapefiles/catchment/ # Same file as the SUMMA catchments -river_basin_shp_name | SouthAmerica_gruhru.shp # Name of the routing subbasins shapefile needed for remapping. Requires extension '.shp'. -river_basin_shp_rm_hruid | GRU_ID # Name of the routing basin ID column. -river_basin_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -river_basin_shp_hru_to_seg | hru_to_seg # Name of the column that shows which river segment each HRU connects to. HRUs and river segments have the same COMID so this works. - - -# Shapefile settings - SUMMA-to-mizuRoute -river_basin_needs_remap | no # 'no' if routing basins map 1:1 onto model GRUs. 'yes' if river segments span multiple GRUs or if multiple segments are inside a single GRU. - - -# Install settings -github_summa | https://github.com/CH-Earth/summa # Replace this with the path to your own fork if you forked the repo. -github_mizuroute | https://github.com/ncar/mizuroute # Replace this with the path to your own fork if you forked the repo. -install_path_summa | default # If 'default', clones source code into 'root_path/installs/summa'. -install_path_mizuroute | default # If 'default', clones source code into 'root_path/installs/mizuRoute'. -exe_name_summa | summa.exe # Name of the compiled executable. -exe_name_mizuroute | mizuRoute.exe # Name of the compiled executable. - - -# Forcing settings -forcing_raw_time | 1979,1979 # Years to download: Jan-[from],Dec-[to]. -forcing_raw_space | 14.84/-91.58/-55.57/-34.8 # Bounding box of the shapefile: lat_max/lon_min/lat_min/lon_max. Will be converted to ERA5 download coordinates in script. Order and use of '/' to separate values is mandatory. -forcing_time_step_size | 3600 # Size of the forcing time step in [s]. Must be constant. -forcing_measurement_height | 3 # Reference height for forcing measurements [m]. -forcing_shape_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/forcing'. -forcing_shape_name | era5_grid.shp # Name of the forcing shapefile. Requires extension '.shp'. -forcing_shape_lat_name | lat # Name of the latitude field that contains the latitude of ERA5 data points. -forcing_shape_lon_name | lon # Name of the longitude field that contains the latitude of ERA5 data points. -forcing_geo_path | default # If 'default', uses 'root_path/domain_[name]/forcing/0_geopotential'. -forcing_raw_path | default # If 'default', uses 'root_path/domain_[name]/forcing/1_raw_data'. -forcing_merged_path | default # If 'default', uses 'root_path/domain_[name]/forcing/2_merged_data'. -forcing_easymore_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_temp_easymore'. -forcing_basin_avg_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_basin_averaged_data'. -forcing_summa_path | default # If 'default', uses 'root_path/domain_[name]/forcing/4_SUMMA_input'. - - -# Parameter settings - DEM -parameter_dem_main_url | http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/distribute/v1.0.1/ # Primary download URL for MERIT Hydro adjusted elevation data. Needs to be appended with filenames. -parameter_dem_file_template | elv_{}{}.tar # Template for download file names. -parameter_dem_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/1_MERIT_hydro_raw_data'. -parameter_dem_unpack_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/2_MERIT_hydro_unpacked_data'. -parameter_dem_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/3_vrt'. -parameter_dem_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/4_domain_vrt'. -parameter_dem_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/5_elevation'. -parameter_dem_tif_name | elevation.tif # Name of the final DEM for the domain. Must be in .tif format. - - -# Parameter settings - soil -parameter_soil_hydro_ID | 1361509511e44adfba814f6950c6e742 # ID of the Hydroshare resource to download. -parameter_soil_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/1_soil_classes_global'. -parameter_soil_domain_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/2_soil_classes_domain'. -parameter_soil_tif_name | soil_classes.tif # Name of the final soil class overview for the domain. Must be in .tif format. - - -# Parameter settings - land -parameter_land_list_path | default # If 'default', uses 'summaWorkflow_public/3b_parameters/MODIS_MCD12Q1_V6/1_download/'. Location of file with data download links. -parameter_land_list_name | daac_mcd12q1_data_links.txt # Name of file that contains list of MODIS download urls. -parameter_land_raw_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_BowAtBanff/parameters/landclass/1_MODIS_raw_data # If 'default', uses 'root_path/domain_[name]/parameters/landclass/1_MODIS_raw_data'. -parameter_land_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/2_vrt_native_crs'. Virtual dataset composed of .hdf files. -parameter_land_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/3_vrt_epsg_4326'. Virtual dataset projected in EPSG:4326. -parameter_land_vrt3_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/4_domain_vrt_epsg_4326'. Virtual dataset cropped to model domain. -parameter_land_vrt4_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/5_multiband_domain_vrt_epsg_4326'. Multiband cropped virtual dataset. -parameter_land_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/6_tif_multiband'. -parameter_land_mode_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/7_mode_land_class'. -parameter_land_tif_name | land_classes.tif # Name of the final landclass overview for the domain. Must be in .tif format. - - -# Intersection settings -intersect_dem_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_dem'. -intersect_dem_name | catchment_with_merit_dem.shp # Name of the shapefile with intersection between catchment and MERIT Hydro DEM, stored in column 'elev_mean'. -intersect_soil_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_soilgrids'. -intersect_soil_name | catchment_with_soilgrids.shp # Name of the shapefile with intersection between catchment and SOILGRIDS-derived USDA soil classes, stored in columns 'USDA_{1,...n}' -intersect_land_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_modis'. -intersect_land_name | catchment_with_modis.shp # Name of the shapefile with intersection between catchment and MODIS-derived IGBP land classes, stored in columns 'IGBP_{1,...n}' -intersect_forcing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_forcing'. -intersect_routing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_routing'. -intersect_routing_name | catchment_with_routing_basins.shp # Name of the shapefile with intersection between hydrologic model catchments and routing model catchments. - - -# Experiment settings - general -experiment_id | run1 # Descriptor of the modelling experiment; used as output folder name. -experiment_time_start | default # Simulation start. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-01-01 00:00'. -experiment_time_end | default # Simulation end. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-12-31 23:00'. -experiment_output_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA'. -experiment_output_mizuRoute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute'. -experiment_log_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA/SUMMA_logs'. -experiment_log_mizuroute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute/mizuRoute_logs'. -experiment_backup_settings | yes # Flag to (not) create a copy of the model settings in the output folder; "no" or "yes". Copying settings may be undesirable if files are large. - - -# Experiment settings - SUMMA -settings_summa_path | default # If 'default', uses 'root_path/domain_[name]/settings/SUMMA'. -settings_summa_filemanager | fileManager.txt # Name of the file with the SUMMA inputs. -settings_summa_coldstate | coldState.nc # Name of the file with intial states. -settings_summa_trialParams | trialParams.nc # Name of the file that can contain trial parameter values (note, can be empty of any actual parameter values but must be provided and must contain an 'hruId' variable). -settings_summa_forcing_list | forcingFileList.txt # Name of the file that has the list of forcing files. -settings_summa_attributes | attributes.nc # Name of the attributes file. -settings_summa_connect_HRUs | no # Attribute setting: "no" or "yes". Tricky concept, see README in ./5_model_input/SUMMA/3f_attributes. If no; all HRUs modeled as independent columns (downHRUindex = 0). If yes; HRUs within each GRU are connected based on relative HRU elevation (highest = upstream, lowest = outlet). -settings_summa_trialParam_n | 1 # Number of trial parameter specifications. Specify 0 if none are wanted (they can still be included in this file but won't be read). -settings_summa_trialParam_1 | maxstep,900 # Name of trial parameter and value to assign. Value assumed to be float. - - -# Experiment settings - mizuRoute -settings_mizu_path | default # If 'default', uses 'root_path/domain_[name]/settings/mizuRoute'. -settings_mizu_parameters | param.nml.default # Name of the routing parameters file. -settings_mizu_topology | topology.nc # Name of the river network topology file. -settings_mizu_remap | routing_remap.nc # Name of the optional catchment remapping file, for cases when SUMMA uses different catchments than mizuRoute. -settings_mizu_control_file | mizuroute.control # Name of the control file. -settings_mizu_routing_var | averageRoutedRunoff # Name of SUMMA output variable to use for routing. -settings_mizu_routing_units | m/s # Units of the variable to be routed. -settings_mizu_routing_dt | 3600 # Size of the routing time step [s]. -settings_mizu_output_freq | annual # Frequency with which mizuRoute generates new output files. Must be one of 'single', 'day', 'month', 'annual'. -settings_mizu_output_vars | 0 # Routing output. '0' for both KWT and IRF; '1' IRF only; '2' KWT only. -settings_mizu_within_basin | 0 # '0' (no) or '1' (IRF routing). Flag to enable within-basin routing by mizuRoute. Should be set to 0 if SUMMA is run with "subRouting" decision "timeDlay". -settings_mizu_make_outlet | n/a # Segment ID or IDs that should be set as network outlet. Specify multiple IDs separated by commas: X,Y,Z. Specify no IDs as: n/a. Note that this can also be done in the network shapefile. - - -# Postprocessing settings -visualization_folder | default # If 'default', uses 'root_path/domain_[name]/visualization'. diff --git a/0_control_files/control_SouthAsia.txt b/0_control_files/control_SouthAsia.txt deleted file mode 100644 index c68252d..0000000 --- a/0_control_files/control_SouthAsia.txt +++ /dev/null @@ -1,152 +0,0 @@ -# SUMMA workflow setting file. -# Characters '|' and '#' are used as separators to find the actual setting values. Any text behind '|' is assumed to be part of the setting value, unless preceded by '#'. - -# Note on path specification -# If deviating from default paths, a full path must be specified. E.g. '/home/user/non-default/path' - - -# Modeling domain settings -root_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data # Root folder where data will be stored. -domain_name | SouthAsia # Used as part of the root folder name for the prepared data. - - -# Shapefile settings - SUMMA catchment file -catchment_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment'. -catchment_shp_name | SouthAsia_gruhru.shp # Name of the catchment shapefile. Requires extension '.shp'. -catchment_shp_gruid | GRU_ID # Name of the GRU ID column (can be any numeric value, HRU's within a single GRU have the same GRU ID). -catchment_shp_hruid | HRU_ID # Name of the HRU ID column (consecutive from 1 to total number of HRUs, must be unique). -catchment_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -catchment_shp_lat | center_lat # Name of the latitude column. Should be a value representative for the HRU. Typically the centroid. -catchment_shp_lon | center_lon # Name of the longitude column. Should be a value representative for the HRU. Typically the centroid. - - -# Shapefile settings - mizuRoute river network file -river_network_shp_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/river_network'. -river_network_shp_name | SouthAsia_river_rawMERIT.shp # Name of the river network shapefile. Requires extension '.shp'. -river_network_shp_segid | COMID # Name of the segment ID column. -river_network_shp_downsegid | NextDownID # Name of the downstream segment ID column. -river_network_shp_slope | slope # Name of the slope column. Slope must be in in units [length/length]. -river_network_shp_length | length # Name of the segment length column. Length must be in units [m]. - - -# Shapefile settings - mizuRoute catchment file -river_basin_shp_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_SouthAsia/shapefiles/catchment/ # Same file as the SUMMA catchments -river_basin_shp_name | SouthAsia_gruhru.shp # Name of the routing subbasins shapefile needed for remapping. Requires extension '.shp'. -river_basin_shp_rm_hruid | GRU_ID # Name of the routing basin ID column. -river_basin_shp_area | HRU_area # Name of the catchment area column. Area must be in units [m^2] -river_basin_shp_hru_to_seg | hru_to_seg # Name of the column that shows which river segment each HRU connects to. HRUs and river segments have the same COMID so this works. - - -# Shapefile settings - SUMMA-to-mizuRoute -river_basin_needs_remap | no # 'no' if routing basins map 1:1 onto model GRUs. 'yes' if river segments span multiple GRUs or if multiple segments are inside a single GRU. - - -# Install settings -github_summa | https://github.com/CH-Earth/summa # Replace this with the path to your own fork if you forked the repo. -github_mizuroute | https://github.com/ncar/mizuroute # Replace this with the path to your own fork if you forked the repo. -install_path_summa | default # If 'default', clones source code into 'root_path/installs/summa'. -install_path_mizuroute | default # If 'default', clones source code into 'root_path/installs/mizuRoute'. -exe_name_summa | summa.exe # Name of the compiled executable. -exe_name_mizuroute | mizuRoute.exe # Name of the compiled executable. - - -# Forcing settings -forcing_raw_time | 1979,1979 # Years to download: Jan-[from],Dec-[to]. -forcing_raw_space | 55.94/57.6/1.27/150.38 # Bounding box of the shapefile: lat_max/lon_min/lat_min/lon_max. Will be converted to ERA5 download coordinates in script. Order and use of '/' to separate values is mandatory. -forcing_time_step_size | 3600 # Size of the forcing time step in [s]. Must be constant. -forcing_measurement_height | 3 # Reference height for forcing measurements [m]. -forcing_shape_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/forcing'. -forcing_shape_name | era5_grid.shp # Name of the forcing shapefile. Requires extension '.shp'. -forcing_shape_lat_name | lat # Name of the latitude field that contains the latitude of ERA5 data points. -forcing_shape_lon_name | lon # Name of the longitude field that contains the latitude of ERA5 data points. -forcing_geo_path | default # If 'default', uses 'root_path/domain_[name]/forcing/0_geopotential'. -forcing_raw_path | default # If 'default', uses 'root_path/domain_[name]/forcing/1_raw_data'. -forcing_merged_path | default # If 'default', uses 'root_path/domain_[name]/forcing/2_merged_data'. -forcing_easymore_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_temp_easymore'. -forcing_basin_avg_path | default # If 'default', uses 'root_path/domain_[name]/forcing/3_basin_averaged_data'. -forcing_summa_path | default # If 'default', uses 'root_path/domain_[name]/forcing/4_SUMMA_input'. - - -# Parameter settings - DEM -parameter_dem_main_url | http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/distribute/v1.0.1/ # Primary download URL for MERIT Hydro adjusted elevation data. Needs to be appended with filenames. -parameter_dem_file_template | elv_{}{}.tar # Template for download file names. -parameter_dem_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/1_MERIT_hydro_raw_data'. -parameter_dem_unpack_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/2_MERIT_hydro_unpacked_data'. -parameter_dem_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/3_vrt'. -parameter_dem_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/4_domain_vrt'. -parameter_dem_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/dem/5_elevation'. -parameter_dem_tif_name | elevation.tif # Name of the final DEM for the domain. Must be in .tif format. - - -# Parameter settings - soil -parameter_soil_hydro_ID | 1361509511e44adfba814f6950c6e742 # ID of the Hydroshare resource to download. -parameter_soil_raw_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/1_soil_classes_global'. -parameter_soil_domain_path | default # If 'default', uses 'root_path/domain_[name]/parameters/soilclass/2_soil_classes_domain'. -parameter_soil_tif_name | soil_classes.tif # Name of the final soil class overview for the domain. Must be in .tif format. - - -# Parameter settings - land -parameter_land_list_path | default # If 'default', uses 'summaWorkflow_public/3b_parameters/MODIS_MCD12Q1_V6/1_download/'. Location of file with data download links. -parameter_land_list_name | daac_mcd12q1_data_links.txt # Name of file that contains list of MODIS download urls. -parameter_land_raw_path | /gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_BowAtBanff/parameters/landclass/1_MODIS_raw_data # If 'default', uses 'root_path/domain_[name]/parameters/landclass/1_MODIS_raw_data'. -parameter_land_vrt1_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/2_vrt_native_crs'. Virtual dataset composed of .hdf files. -parameter_land_vrt2_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/3_vrt_epsg_4326'. Virtual dataset projected in EPSG:4326. -parameter_land_vrt3_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/4_domain_vrt_epsg_4326'. Virtual dataset cropped to model domain. -parameter_land_vrt4_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/5_multiband_domain_vrt_epsg_4326'. Multiband cropped virtual dataset. -parameter_land_tif_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/6_tif_multiband'. -parameter_land_mode_path | default # If 'default', uses 'root_path/domain_[name]/parameters/landclass/7_mode_land_class'. -parameter_land_tif_name | land_classes.tif # Name of the final landclass overview for the domain. Must be in .tif format. - - -# Intersection settings -intersect_dem_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_dem'. -intersect_dem_name | catchment_with_merit_dem.shp # Name of the shapefile with intersection between catchment and MERIT Hydro DEM, stored in column 'elev_mean'. -intersect_soil_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_soilgrids'. -intersect_soil_name | catchment_with_soilgrids.shp # Name of the shapefile with intersection between catchment and SOILGRIDS-derived USDA soil classes, stored in columns 'USDA_{1,...n}' -intersect_land_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_modis'. -intersect_land_name | catchment_with_modis.shp # Name of the shapefile with intersection between catchment and MODIS-derived IGBP land classes, stored in columns 'IGBP_{1,...n}' -intersect_forcing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_forcing'. -intersect_routing_path | default # If 'default', uses 'root_path/domain_[name]/shapefiles/catchment_intersection/with_routing'. -intersect_routing_name | catchment_with_routing_basins.shp # Name of the shapefile with intersection between hydrologic model catchments and routing model catchments. - - -# Experiment settings - general -experiment_id | run1 # Descriptor of the modelling experiment; used as output folder name. -experiment_time_start | default # Simulation start. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-01-01 00:00'. -experiment_time_end | default # Simulation end. If 'default', constructs this from 'forcing_raw_time' setting and uses all downloaded forcing data; e.g. '1979-12-31 23:00'. -experiment_output_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA'. -experiment_output_mizuRoute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute'. -experiment_log_summa | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/SUMMA/SUMMA_logs'. -experiment_log_mizuroute | default # If 'default', uses 'root_path/domain_[name]/simulations/[experiment_id]/mizuRoute/mizuRoute_logs'. -experiment_backup_settings | yes # Flag to (not) create a copy of the model settings in the output folder; "no" or "yes". Copying settings may be undesirable if files are large. - - -# Experiment settings - SUMMA -settings_summa_path | default # If 'default', uses 'root_path/domain_[name]/settings/SUMMA'. -settings_summa_filemanager | fileManager.txt # Name of the file with the SUMMA inputs. -settings_summa_coldstate | coldState.nc # Name of the file with intial states. -settings_summa_trialParams | trialParams.nc # Name of the file that can contain trial parameter values (note, can be empty of any actual parameter values but must be provided and must contain an 'hruId' variable). -settings_summa_forcing_list | forcingFileList.txt # Name of the file that has the list of forcing files. -settings_summa_attributes | attributes.nc # Name of the attributes file. -settings_summa_connect_HRUs | no # Attribute setting: "no" or "yes". Tricky concept, see README in ./5_model_input/SUMMA/3f_attributes. If no; all HRUs modeled as independent columns (downHRUindex = 0). If yes; HRUs within each GRU are connected based on relative HRU elevation (highest = upstream, lowest = outlet). -settings_summa_trialParam_n | 1 # Number of trial parameter specifications. Specify 0 if none are wanted (they can still be included in this file but won't be read). -settings_summa_trialParam_1 | maxstep,900 # Name of trial parameter and value to assign. Value assumed to be float. - - -# Experiment settings - mizuRoute -settings_mizu_path | default # If 'default', uses 'root_path/domain_[name]/settings/mizuRoute'. -settings_mizu_parameters | param.nml.default # Name of the routing parameters file. -settings_mizu_topology | topology.nc # Name of the river network topology file. -settings_mizu_remap | routing_remap.nc # Name of the optional catchment remapping file, for cases when SUMMA uses different catchments than mizuRoute. -settings_mizu_control_file | mizuroute.control # Name of the control file. -settings_mizu_routing_var | averageRoutedRunoff # Name of SUMMA output variable to use for routing. -settings_mizu_routing_units | m/s # Units of the variable to be routed. -settings_mizu_routing_dt | 3600 # Size of the routing time step [s]. -settings_mizu_output_freq | annual # Frequency with which mizuRoute generates new output files. Must be one of 'single', 'day', 'month', 'annual'. -settings_mizu_output_vars | 0 # Routing output. '0' for both KWT and IRF; '1' IRF only; '2' KWT only. -settings_mizu_within_basin | 0 # '0' (no) or '1' (IRF routing). Flag to enable within-basin routing by mizuRoute. Should be set to 0 if SUMMA is run with "subRouting" decision "timeDlay". -settings_mizu_make_outlet | n/a # Segment ID or IDs that should be set as network outlet. Specify multiple IDs separated by commas: X,Y,Z. Specify no IDs as: n/a. Note that this can also be done in the network shapefile. - - -# Postprocessing settings -visualization_folder | default # If 'default', uses 'root_path/domain_[name]/visualization'. diff --git a/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.cpg b/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.cpg new file mode 100644 index 0000000..3ad133c --- /dev/null +++ b/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.cpg @@ -0,0 +1 @@ +UTF-8 \ No newline at end of file diff --git a/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.dbf b/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.dbf new file mode 100644 index 0000000..b44c4de Binary files /dev/null and b/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.dbf differ diff --git a/0_example/shapefiles/catchment/bow_distributed_elevation_zone.prj b/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.prj similarity index 100% rename from 0_example/shapefiles/catchment/bow_distributed_elevation_zone.prj rename to 0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.prj diff --git a/0_example/shapefiles/river_basins/bow_distributed.shp b/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.shp similarity index 67% rename from 0_example/shapefiles/river_basins/bow_distributed.shp rename to 0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.shp index 2c85a15..16ba1a3 100644 Binary files a/0_example/shapefiles/river_basins/bow_distributed.shp and b/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.shp differ diff --git a/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.shx b/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.shx new file mode 100644 index 0000000..9058ed0 Binary files /dev/null and b/0_example/shapefiles/catchment/CAN_05BB001_distributed_basin.shx differ diff --git a/0_example/shapefiles/catchment/_workflow_log/1_sort_catchment_shape.py b/0_example/shapefiles/catchment/_workflow_log/1_sort_catchment_shape.py deleted file mode 100644 index 9f6f0de..0000000 --- a/0_example/shapefiles/catchment/_workflow_log/1_sort_catchment_shape.py +++ /dev/null @@ -1,105 +0,0 @@ -# Sort catchment shape -# Sorts the catchment shape by `gruId` first and `hruId` second, to ensure HRU order follows SUMMA conventions. Not strictly necessary for cases where each GRU contains one HRU, but essential for cases where the GRUs contain multiple HRUs. - -# modules -import geopandas as gpd -from pathlib import Path -from shutil import copyfile -from datetime import datetime - - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line: - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find location of catchment shapefile -# Catchment shapefile path & name -catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path') -catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name') - -# Specify default path if needed -if catchment_path == 'default': - catchment_path = make_default_path('shapefiles/catchment') # outputs a Path() -else: - catchment_path = Path(catchment_path) # make sure a user-specified path is a Path() - -# Find GRU and HRU variables -gru_id = read_from_control(controlFolder/controlFile,'catchment_shp_gruid') -hru_id = read_from_control(controlFolder/controlFile,'catchment_shp_hruid') - - -# --- Open shape and sort -# Open the shape -shp = gpd.read_file(catchment_path/catchment_name) - -# Sort -shp = shp.sort_values(by=[gru_id,hru_id]) - -# Save -shp.to_file(catchment_path/catchment_name) - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = catchment_path -log_suffix = '_sort_shape.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = '1_sort_catchment_shape.py' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Sorted catchment shape by GRU ID first and HRU ID second to follow SUMMA conventions.'] - for txt in lines: - file.write(txt) \ No newline at end of file diff --git a/0_example/shapefiles/catchment/_workflow_log/20210410_sort_shape.txt b/0_example/shapefiles/catchment/_workflow_log/20210410_sort_shape.txt deleted file mode 100644 index 4566683..0000000 --- a/0_example/shapefiles/catchment/_workflow_log/20210410_sort_shape.txt +++ /dev/null @@ -1,2 +0,0 @@ -Log generated by 1_sort_catchment_shape.py on 2021/04/10 14:58:52 -Sorted catchment shape by GRU ID first and HRU ID second to follow SUMMA conventions. \ No newline at end of file diff --git a/0_example/shapefiles/catchment/bow_distributed_elevation_zone.cpg b/0_example/shapefiles/catchment/bow_distributed_elevation_zone.cpg deleted file mode 100644 index cd89cb9..0000000 --- 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files a/0_example/shapefiles/river_network/bow_river_network_from_merit_hydro.shp and /dev/null differ diff --git a/0_example/shapefiles/river_network/bow_river_network_from_merit_hydro.shx b/0_example/shapefiles/river_network/bow_river_network_from_merit_hydro.shx deleted file mode 100644 index eb6eaf9..0000000 Binary files a/0_example/shapefiles/river_network/bow_river_network_from_merit_hydro.shx and /dev/null differ diff --git a/0_tools/ERA5_check_merged_forcing_values.ipynb b/0_tools/ERA5_check_merged_forcing_values.ipynb deleted file mode 100644 index 01ea8be..0000000 --- a/0_tools/ERA5_check_merged_forcing_values.ipynb +++ /dev/null @@ -1,327 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ERA5 sanity check\n", - "Checks if forcing data in merged ERA5 files are all:\n", - "- Within user-specified ranges;\n", - "- Not missing;\n", - "- Not NaN.\n", - "\n", - "Also checks the time dimension in each file to find if timesteps are:\n", - "- Not NaN;\n", - "- Consecutive;\n", - "- Equidistant.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Modules\n", - "from pathlib import Path\n", - "from datetime import datetime\n", - "import netCDF4 as nc4\n", - "import numpy as np\n", - "import pandas as pd\n", - "import os\n", - "import sys" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### User settings" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Location of merged files\n", - "path_to_data = Path( 'C:/Globus endpoint/summaWorkflow_data/domain_BowAtBanff/forcing/2_merged_data' )" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Location and name of logfile\n", - "log_folder = path_to_data / 'sanity_checks'\n", - "log_file = 'log_sanity_checks.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# File pattern\n", - "file_base = 'ERA5_NA_'\n", - "file_end = '.nc'" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Years to check (Jan-years[0] to Dec-years[1])\n", - "years = [2008,2013]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Feasible variable ranges\n", - "ranges = {\n", - " 'pptrate': [0,0.05],\n", - " 'airpres': [25000, 175000],\n", - " 'airtemp': [173,373],\n", - " 'spechum': [0,1],\n", - " 'SWRadAtm': [0,2750],\n", - " 'LWRadAtm': [0,1000],\n", - " 'windspd': [0,150],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# number of standard deviations to check\n", - "n = 8" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Standard settings" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the variables we want to check\n", - "var_names = {'time','pptrate','airpres','airtemp','spechum','SWRadAtm','LWRadAtm','windspd'}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Do the checks" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the output folder if doesn't exist\n", - "log_folder.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare a dictionary to store results in\n", - "report = {\n", - " 'file name': [],\n", - " 'data type': [],\n", - " 'data unit': [],\n", - " 'num NaNs': [],\n", - " 'num missing': [],\n", - " 'num < min': [],\n", - " 'num > max': []\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the log file\n", - "logFile = open(log_folder / log_file,'w')\n", - "\n", - "# log start\n", - "logFile.write('Opened for writing on ' + str(datetime.now()) + '\\n');\n", - "\n", - "# Loop over variables first, so that reports aggregated per variable for easy comparison\n", - "for var in var_names:\n", - " \n", - " # Print where we are\n", - " logFile.write('\\n')\n", - " logFile.write('Now checking variable // ' + var + ' // \\n')\n", - " if var == 'time':\n", - " spacing = \"{:20} {:10} {:35} {:10} {:15} {:15}\\n\"\n", - " logFile.write(spacing.format('file_name',\n", - " 'data_type','data_unit',\n", - " 'num NaN',\n", - " 'consecutive?','equidistant?'))\n", - " else:\n", - " # Create the log headers\n", - " head_min = 'num < {}'.format(ranges[var][0])\n", - " head_max = 'num > {}'.format(ranges[var][1])\n", - " head_low_out = 'num < {}*stdev'.format(n)\n", - " head_upp_out = 'num > {}*stdev'.format(n)\n", - " \n", - " spacing = \"{:20} {:10} {:35} {:10} {:18} {:12} {:12} {:15} {:15}\\n\"\n", - " logFile.write(spacing.format('file_name',\n", - " 'data_type', 'data_unit',\n", - " 'num NaN', 'num missing_value',\n", - " head_min,head_max,\n", - " head_low_out,head_upp_out))\n", - " \n", - " # Loop over all files (year & month)\n", - " for year in range(years[0],years[1]+1):\n", - " for month in range(1,13):\n", - " \n", - " # Specify the file name\n", - " file_name = (file_base + str(year) + str(month).zfill(2) + file_end)\n", - " file_full = path_to_data / file_name\n", - "\n", - " # Check if this file exists\n", - " if not os.path.isfile(file_full):\n", - " continue\n", - " \n", - " # Open netcdf file for specific year and month\n", - " with nc4.Dataset(file_full) as src:\n", - " \n", - " # Extract the variable into a numpy array\n", - " dat = np.array(src[var][:])\n", - " \n", - " # Get basic information\n", - " chk_size = dat.shape\n", - " chk_isnan = np.isnan(dat).sum()\n", - " \n", - " # Get the information that depends on attributes\n", - " try: chk_type = src[var].dtype\n", - " except: chk_type = 'n/a'\n", - " \n", - " try: chk_units = src[var].units\n", - " except: chk_units = 'n/a'\n", - " \n", - " try:\n", - " chk_missv = src[var].missing_value\n", - " chk_missn = (dat == chk_missv).sum()\n", - " except:\n", - " chk_missv = chk_missn = 'n/a'\n", - " \n", - " # Get the standard deviation and mean\n", - " stdv = np.std(dat)\n", - " mean = np.mean(dat)\n", - " \n", - " # Count how often the data goes beyond the defined 'sane' ranges and if we have outliers\n", - " if var in ranges:\n", - " chk_min = ranges[var][0]\n", - " chk_max = ranges[var][1] \n", - " chk_under = (dat < chk_min).sum()\n", - " chk_over = (dat > chk_max).sum()\n", - " \n", - " # outliers\n", - " chk_neg_out = (dat < mean-n*stdv).sum()\n", - " chk_pos_out = (dat > mean+n*stdv).sum()\n", - " else:\n", - " chk_under = 'n/a'\n", - " chk_over = 'n/a'\n", - " \n", - " # Check if time values are consecutive and equidistant\n", - " if var == 'time':\n", - " if all(np.sort(dat) == dat): chk_cons = True \n", - " else: chk_cons = False\n", - " if all(np.diff(dat) == 1): chk_equid = True \n", - " else: chk_equid = False \n", - " \n", - " # update the dictionary\n", - " report['file name'].append(file_name)\n", - " report['data type'].append(chk_type)\n", - " report['data unit'].append(chk_units)\n", - " report['num NaNs'].append(chk_isnan)\n", - " report['num missing'].append(chk_missn)\n", - " report['num < min'].append(chk_under)\n", - " report['num > max'].append(chk_over)\n", - " \n", - " # print to file\n", - " if var == 'time':\n", - " logFile.write(spacing.format(str(file_name),\n", - " str(chk_type),\n", - " str(chk_units),\n", - " str(chk_isnan),\n", - " str(chk_cons),\n", - " str(chk_equid)))\n", - " else:\n", - " logFile.write(spacing.format(str(file_name),\n", - " str(chk_type),\n", - " str(chk_units),\n", - " str(chk_isnan),\n", - " str(chk_missn),\n", - " str(chk_under),\n", - " str(chk_over),\n", - " str(chk_neg_out),\n", - " str(chk_pos_out)))\n", - " \n", - "# log end\n", - "logFile.write('\\n')\n", - "logFile.write('Finished on ' + str(datetime.now()) + '\\n');\n", - "\n", - "# File handling\n", - "logFile.close()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/0_tools/ERA5_check_merged_forcing_values.py b/0_tools/ERA5_check_merged_forcing_values.py deleted file mode 100644 index f6fcfdd..0000000 --- a/0_tools/ERA5_check_merged_forcing_values.py +++ /dev/null @@ -1,194 +0,0 @@ -# ERA5 sanity check -# Checks if forcing data in merged ERA5 files are all: -# - Within user-specified ranges; -# - Not missing; -# - Not NaN. -# -# Also checks the time dimension in each file to find if timesteps are: -# - Not NaN; -# - Consecutive; -# - Equidistant. - -# Modules -from pathlib import Path -from datetime import datetime -import netCDF4 as nc4 -import numpy as np -import pandas as pd -import os -import sys - -# --- User settings -# Location of merged files -path_to_data = Path('/project/gwf/gwf_cmt/wknoben/summaWorkflow_data/domain_NorthAmerica/forcing/2_merged_data') - -# Location and name of logfile -log_folder = path_to_data / 'sanity_checks' -log_file = 'log_sanity_checks.txt' - -# Make the output folder if doesn't exist -log_folder.mkdir(parents=True, exist_ok=True) - -# File pattern -file_base = 'ERA5_merged_' -file_end = '.nc' - -# Years to check (Jan-years[0] to Dec-years[1]) -years = [1979,2019] - -# "Feasible" variable ranges -ranges = { - 'pptrate': [0,0.05], # 50 mm/h - 'airpres': [25000, 175000], - 'airtemp': [173,373], # +/- 100 degrees C - 'spechum': [0,1], - 'SWRadAtm': [0,2750], # 2 times solar constant - 'LWRadAtm': [0,1000], - 'windspd': [0,150], # 100 km/h above maximum gust known -} - -# number of standard deviations to check -n = 8 - -# --- Standard settings -# Define the variables we want to check -var_names = {'time','pptrate','airpres','airtemp','spechum','SWRadAtm','LWRadAtm','windspd'} - -# --- Checks -# Prepare a dictionary to store results in -report = { - 'file name': [], - 'data type': [], - 'data unit': [], - 'num NaNs': [], - 'num missing': [], - 'num < min': [], - 'num > max': [] -} - -# Open the log file -logFile = open(log_folder / log_file,'w') - -# log start -logFile.write('Opened for writing on ' + str(datetime.now()) + '\n'); - -# Loop over variables first, so that reports aggregated per variable for easy comparison -for var in var_names: - - # Print where we are - logFile.write('\n') - logFile.write('Now checking variable // ' + var + ' // \n') - if var == 'time': - spacing = "{:20} {:10} {:35} {:10} {:15} {:15}\n" - logFile.write(spacing.format('file_name', - 'data_type','data_unit', - 'num NaN', - 'consecutive?','equidistant?')) - else: - # Create the log headers - head_min = 'num < {}'.format(ranges[var][0]) - head_max = 'num > {}'.format(ranges[var][1]) - head_low_out = 'num < {}*stdev'.format(n) - head_upp_out = 'num > {}*stdev'.format(n) - - spacing = "{:20} {:10} {:35} {:10} {:18} {:12} {:12} {:15} {:15}\n" - logFile.write(spacing.format('file_name', - 'data_type', 'data_unit', - 'num NaN', 'num missing_value', - head_min,head_max, - head_low_out,head_upp_out)) - - # Loop over all files (year & month) - for year in range(years[0],years[1]+1): - for month in range(1,13): - - # Specify the file name - file_name = (file_base + str(year) + str(month).zfill(2) + file_end) - file_full = path_to_data / file_name - - # Check if this file exists - if not os.path.isfile(file_full): - continue - - # Open netcdf file for specific year and month - with nc4.Dataset(file_full) as src: - - # Extract the variable into a numpy array - dat = np.array(src[var][:]) - - # Get basic information - chk_size = dat.shape - chk_isnan = np.isnan(dat).sum() - - # Get the information that depends on attributes - try: chk_type = src[var].dtype - except: chk_type = 'n/a' - - try: chk_units = src[var].units - except: chk_units = 'n/a' - - try: - chk_missv = src[var].missing_value - chk_missn = (dat == chk_missv).sum() - except: - chk_missv = chk_missn = 'n/a' - - # Get the standard deviation and mean - stdv = np.std(dat) - mean = np.mean(dat) - - # Count how often the data goes beyond the defined 'sane' ranges - if var in ranges: - chk_min = ranges[var][0] - chk_max = ranges[var][1] - chk_under = (dat < chk_min).sum() - chk_over = (dat > chk_max).sum() - - # outliers - chk_neg_out = (dat < mean-n*stdv).sum() - chk_pos_out = (dat > mean+n*stdv).sum() - else: - chk_under = 'n/a' - chk_over = 'n/a' - - # Check if time values are consecutive and equidistant - if var == 'time': - if all(np.sort(dat) == dat): chk_cons = True - else: chk_cons = False - if all(np.diff(dat) == 1): chk_equid = True - else: chk_equid = False - - # update the dictionary - report['file name'].append(file_name) - report['data type'].append(chk_type) - report['data unit'].append(chk_units) - report['num NaNs'].append(chk_isnan) - report['num missing'].append(chk_missn) - report['num < min'].append(chk_under) - report['num > max'].append(chk_over) - - # print to file - if var == 'time': - logFile.write(spacing.format(str(file_name), - str(chk_type), - str(chk_units), - str(chk_isnan), - str(chk_cons), - str(chk_equid))) - else: - logFile.write(spacing.format(str(file_name), - str(chk_type), - str(chk_units), - str(chk_isnan), - str(chk_missn), - str(chk_under), - str(chk_over), - str(chk_neg_out), - str(chk_pos_out))) - -# log end -logFile.write('\n') -logFile.write('Finished on ' + str(datetime.now()) + '\n'); - -# File handling -logFile.close() \ No newline at end of file diff --git a/0_tools/ERA5_find_download_coordinates_from_shapefile.ipynb b/0_tools/ERA5_find_download_coordinates_from_shapefile.ipynb deleted file mode 100644 index ba09da6..0000000 --- a/0_tools/ERA5_find_download_coordinates_from_shapefile.ipynb +++ /dev/null @@ -1,230 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Find bounding box coordinates\n", - "Script to automatically find the bounding box for a given shapefile. Slightly rounds coordinates outward so that any cutouts based on these coordinates fully cover the modelling domain." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import geopandas as gpd\n", - "from pathlib import Path" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " for line in open(file):\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line:\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find spatial domain as bounding box of shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# function to round coordinates of a bounding box to ERA5s 0.25 degree resolution\n", - "def round_bounding_box(coords):\n", - " \n", - " '''Assumes coodinates are an array: [lon_min,lat_min,lon_max,lat_max].\n", - " Returns separate lat and lon vectors.'''\n", - " \n", - " # Extract values\n", - " lon = [coords[0],coords[2]]\n", - " lat = [coords[1],coords[3]]\n", - " \n", - " # Round to two decimals\n", - " rounded_lon = [math.floor(lon[0]*100)/100, math.ceil(lon[1]*100)/100]\n", - " rounded_lat = [math.floor(lat[0]*100)/100, math.ceil(lat[1]*100)/100]\n", - " \n", - " # Store as control file string\n", - " control_string = '{}/{}/{}/{}'.format(rounded_lat[1],rounded_lon[0],rounded_lat[0],rounded_lon[1]) \n", - " \n", - " return control_string, rounded_lat, rounded_lon" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Find name and location of catchment shapefile\n", - "shp_path = read_from_control(controlFolder/controlFile, 'catchment_shp_path')\n", - "shp_name = read_from_control(controlFolder/controlFile, 'catchment_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if shp_path == 'default':\n", - " shp_path = make_default_path('shapefiles/catchment')\n", - "else:\n", - " shp_path = Path(shp_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the shapefile\n", - "shp = gpd.read_file(shp_path/shp_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the latitude and longitude of the bounding box\n", - "bounding_box = shp.total_bounds" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the rounded bounding box\n", - "coordinates,lat,lon = round_bounding_box(bounding_box)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Specify coordinates as 51.74/-116.55/50.95/-115.52 in control file.\n" - ] - } - ], - "source": [ - "# Print in ERA5 format\n", - "print('Specify coordinates as {}/{}/{}/{} in control file.'.format(lat[1],lon[0],lat[0],lon[1]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:geospatialTools]", - "language": "python", - "name": "conda-env-geospatialTools-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/0_tools/ERA5_find_download_coordinates_from_shapefile.py b/0_tools/ERA5_find_download_coordinates_from_shapefile.py deleted file mode 100644 index ca37c98..0000000 --- a/0_tools/ERA5_find_download_coordinates_from_shapefile.py +++ /dev/null @@ -1,89 +0,0 @@ -# Script to automatically find the bounding box for a given shapefile. -# Slightly rounds coordinates outward so that any cutouts based on these coordinates fully cover the modelling domain. - -# Modules -import math -import geopandas as gpd -from pathlib import Path - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - for line in open(file): - - # ... find the line with the requested setting - if setting in line: - break - - # Extract the setting's value\n", - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - -# --- Find spatial domain as bounding box of shapefile -# function to round coordinates of a bounding box to ERA5s 0.25 degree resolution -def round_bounding_box(coords): - - '''Assumes coodinates are an array: [lon_min,lat_min,lon_max,lat_max]. - Returns separate lat and lon vectors.''' - - # Extract values - lon = [coords[0],coords[2]] - lat = [coords[1],coords[3]] - - # Round to two decimals - rounded_lon = [math.floor(lon[0]*100)/100, math.ceil(lon[1]*100)/100] - rounded_lat = [math.floor(lat[0]*100)/100, math.ceil(lat[1]*100)/100] - - # Store as control file string - control_string = '{}/{}/{}/{}'.format(rounded_lat[1],rounded_lon[0],rounded_lat[0],rounded_lon[1]) - - return control_string, rounded_lat, rounded_lon - -# Find name and location of catchment shapefile -shp_path = read_from_control(controlFolder/controlFile, 'catchment_shp_path') -shp_name = read_from_control(controlFolder/controlFile, 'catchment_shp_name') - -# Specify default path if needed -if shp_path == 'default': - shp_path = make_default_path('shapefiles/catchment') -else: - shp_path = Path(shp_path) - -# Open the shapefile -shp = gpd.read_file(shp_path/shp_name) - -# Get the latitude and longitude of the bounding box -bounding_box = shp.total_bounds - -# Find the rounded bounding box -coordinates,lat,lon = round_bounding_box(bounding_box) - -# Print in ERA5 format -print('Specify coordinates as {}/{}/{}/{} in control file.'.format(lat[1],lon[0],lat[0],lon[1])) diff --git a/0_tools/ERA5_subset_forcing_file_by_lat_lon.py b/0_tools/ERA5_subset_forcing_file_by_lat_lon.py deleted file mode 100644 index 4e4775a..0000000 --- a/0_tools/ERA5_subset_forcing_file_by_lat_lon.py +++ /dev/null @@ -1,138 +0,0 @@ -# Script to subset a smaller lat/lon region from a larger one. -# Reads the region of interest from control_active.txt -# Assumes we're after raw ERA5 data and reads the output location from control_active.txt - -from pathlib import Path -import math -import xarray as xr - -# Specify the input file(s) -in_base = '/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/forcing/' -inout_paths = ['1_ERA5_raw_data', '1_ERA5_raw_data', '0_geopotential'] -in_files = ['ERA5_pressureLevel137_197901.nc', 'ERA5_surface_197901.nc', 'ERA5_geopotential.nc'] -control_names = ['forcing_raw_path', 'forcing_raw_path', 'forcing_geo_path'] - - -# --- Control file handling - -# Easy access to control file folder -controlFolder = Path('../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Define coordinates of interest - -# Find the spatial extent the data needs to cover -bounding_box = read_from_control(controlFolder/controlFile,'forcing_raw_space') -bounding_box = bounding_box.split('/') # split string -bounding_box = [float(value) for value in bounding_box] # string to array - -# function to round coordinates of a bounding box to ERA5s 0.25 degree resolution -def round_coords_to_ERA5(coords): - - '''Assumes coodinates are an array: [lat_max,lon_min,lat_min,lon_max] (top-left, bottom-right). - Returns separate lat and lon vectors.''' - - # Extract values - lon = [coords[1],coords[3]] - lat = [coords[2],coords[0]] - - # Round to ERA5 0.25 degree resolution - rounded_lon = [math.floor(lon[0]*4)/4, math.ceil(lon[1]*4)/4] - rounded_lat = [math.floor(lat[0]*4)/4, math.ceil(lat[1]*4)/4] - - # Find if we are still in the representative area of a different ERA5 grid cell - if lat[0] > rounded_lat[0]+0.125: - rounded_lat[0] += 0.25 - if lon[0] > rounded_lon[0]+0.125: - rounded_lon[0] += 0.25 - if lat[1] < rounded_lat[1]-0.125: - rounded_lat[1] -= 0.25 - if lon[1] < rounded_lon[1]-0.125: - rounded_lon[1] -= 0.25 - - # Make outputs - lon_min = rounded_lon[0] - lon_max = rounded_lon[1] - lat_min = rounded_lat[0] - lat_max = rounded_lat[1] - - return lon_min, lon_max, lat_min, lat_max - -# Find the rounded bounding box -lon_min, lon_max, lat_min, lat_max = round_coords_to_ERA5(bounding_box) -print('Rounded {} to {},{},{},{}'.format(bounding_box,lat_max,lon_min,lat_min,lon_max)) - -# --- Subset data - -# Function to subset data -def extract_ERA5_subset(infile, outfile, latmin, latmax, lonmin, lonmax): - with xr.open_dataset(infile) as ds: - if lonmax > 180: - # convert ds longitude form -180/180 to 0/360 - lon = ds['longitude'].values - lon[lon < 0] = lon[lon < 0] + 360 - ds['longitude'] = lon - ds = ds.sortby('longitude') - ds_sub = ds.sel(latitude = slice(latmax, latmin), longitude = slice(lonmin, lonmax)) - ds_sub.to_netcdf(outfile) - -# Loop -for ix,in_file in enumerate(in_files): - - # Find where the data needs to go and make that folder - outPath = read_from_control(controlFolder/controlFile, control_names[ix]) - - # Specify the default paths if required - if outPath == 'default': - pathString = ''.join(['forcing/',inout_paths[ix]]) - outPath = make_default_path(pathString) # returns Path() object - else: - outPath = Path(outPath) # ensure Path() object - - # Make the folder if it doesn't exist - outPath.mkdir(parents=True, exist_ok=True) - - # Define in and out as strings - inFull = str(Path(in_base)/inout_paths[ix]/in_file) - outFull = str(outPath/in_file) - - # Subset the data - print('Subsetting from {} into {}'.format(inFull,outFull)) - extract_ERA5_subset(inFull, outFull, lat_min, lat_max, lon_min, lon_max) diff --git a/0_tools/MERIT_fix_circular_downstream_ids.py b/0_tools/MERIT_fix_circular_downstream_ids.py deleted file mode 100644 index c32dbd3..0000000 --- a/0_tools/MERIT_fix_circular_downstream_ids.py +++ /dev/null @@ -1,89 +0,0 @@ -# Loop over a river network shapefile and replace downstream IDs with -# zero values if the reach ID is identical to the downstream ID. -# W. Knoben, 2022 - -# Assumes that if we have a circular connection (reach ID == downstream -# ID) that the reach is not supposed to be connected and should be -# treated as its own outlet. - -# !!! Replaces the source file - use with caution !!! - -# modules -import os -import geopandas as gpd -from pathlib import Path - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find location of river network shapefile -# River network shapefile path & name -river_network_path = read_from_control(controlFolder/controlFile,'river_network_shp_path') -river_network_name = read_from_control(controlFolder/controlFile,'river_network_shp_name') - -# Specify default path if needed -if river_network_path == 'default': - river_network_path = make_default_path('shapefiles/river_network') # outputs a Path() -else: - river_network_path = Path(river_network_path) # make sure a user-specified path is a Path() - -# Find the field names we're after -river_seg_id = read_from_control(controlFolder/controlFile,'river_network_shp_segid') -river_down_seg_id = read_from_control(controlFolder/controlFile,'river_network_shp_downsegid') - - -# --- Open the shape and process -# Load the data -shp = gpd.read_file(river_network_path/river_network_name) -if (shp[river_seg_id] == shp[river_down_seg_id]).sum() > 0: - - # Print a log - print('About to fix circular river segment connections in {}/{}.'.format(river_network_path,river_network_name)) - - # Apply the correction - shp[river_down_seg_id][shp[river_seg_id] == shp[river_down_seg_id]] = 0 - - # Save into original location - shp.to_file(river_network_path/river_network_name) - -else: - print('No circular circular river segment connections found in {}/{}.'.format(river_network_path,river_network_name)) \ No newline at end of file diff --git a/1_folder_prep/make_folder_structure.ipynb b/1_folder_prep/make_folder_structure.ipynb deleted file mode 100644 index a9f229e..0000000 --- a/1_folder_prep/make_folder_structure.ipynb +++ /dev/null @@ -1,289 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SUMMA workflow: make folder structure\n", - "Makes the initial folder structure for a given control file. All other files in the workflow will look for the file `control_active.txt` during their execution. This script:\n", - "\n", - "1. Copies the specified control file into `control_active.txt`;\n", - "2. Prepares a folder structure using the settings in `control_active.txt`.\n", - "3. Creates a copy of itself to be stored in the new folder structure.\n", - "\n", - "The destination folders are referred to as \"domain folders\"." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the control file to use\n", - "sourceFile = 'control_Bow_at_Banff.txt'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do not change below this line." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Modules\n", - "import os\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Copy the control file into `control_active.txt`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "copyfile( controlFolder/sourceFile, controlFolder/controlFile );" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the main domain folder" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the domain folders need to go\n", - "# Immediately store as a 'Path' to avoid issues with '/' and '\\' on different operating systems\n", - "rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the domain name\n", - "domainName = read_from_control(controlFolder/controlFile,'domain_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the domain folder inside 'root'\n", - "domainFolder = 'domain_' + domainName\n", - "Path( rootPath / domainFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make the shapefile folders" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the catchment shapefile folder in 'control_active'\n", - "catchmentShapeFolder = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "networkShapeFolder = read_from_control(controlFolder/controlFile,'river_network_shp_path')\n", - "riverBasinFolder = read_from_control(controlFolder/controlFile,'river_basin_shp_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required\n", - "if catchmentShapeFolder == 'default':\n", - " catchmentShapeFolder = 'shapefiles/catchment'\n", - "if networkShapeFolder == 'default':\n", - " networkShapeFolder = 'shapefiles/river_network'\n", - "if riverBasinFolder == 'default':\n", - " riverBasinFolder = 'shapefiles/river_basins'" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# Try to make the shapefile folders; does nothing if the folder already exists\n", - "Path( rootPath / domainFolder / catchmentShapeFolder ).mkdir(parents=True, exist_ok=True)\n", - "Path( rootPath / domainFolder / networkShapeFolder ).mkdir(parents=True, exist_ok=True)\n", - "Path( rootPath / domainFolder / riverBasinFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( rootPath / domainFolder / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy the control file\n", - "copyfile(controlFolder / sourceFile, rootPath / domainFolder / logFolder / sourceFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'make_folder_structure.ipynb'\n", - "copyfile(thisFile, rootPath / domainFolder / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + '_log.txt'\n", - "with open(rootPath / domainFolder / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Generated folder structure using ' + sourceFile]\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/1_folder_prep/make_folder_structure.py b/1_folder_prep/make_folder_structure.py index e3a11af..d5e135b 100644 --- a/1_folder_prep/make_folder_structure.py +++ b/1_folder_prep/make_folder_structure.py @@ -12,17 +12,25 @@ The destination folders are referred to as "domain folders". ''' -# Specify the control file to use -sourceFile = 'control_Bow_at_Banff.txt' +# Specify the control file to use. Use + # --- Do not change below this line. # Modules -import os +import sys from pathlib import Path from shutil import copyfile from datetime import datetime + +# Access to the source file +if len(sys.argv) > 2: + print("Usage: %s " % sys.argv[0]) + sys.exit(0) +# Otherwise continue +sourceFile = sys.argv[1] + # --- Copy the control file into `control_active.txt` # Easy access to control file folder controlFolder = Path('../0_control_files') diff --git a/3a_forcing/1_make_forcing.py b/3a_forcing/1_make_forcing.py new file mode 100644 index 0000000..309d4e9 --- /dev/null +++ b/3a_forcing/1_make_forcing.py @@ -0,0 +1,452 @@ +# Copy base settings +# Copies the base settings into the mizuRoute settings folder. + +# modules +import pandas as pd +import netCDF4 as nc4 +import xarray as xr +import numpy as np +from pathlib import Path +from shutil import copyfile +from datetime import datetime + +# --- Control file handling +# Easy access to control file folder +controlFolder = Path('../0_control_files') + +# Store the name of the 'active' file in a variable +controlFile = 'control_active.txt' + +# Function to extract a given setting from the control file +def read_from_control( file, setting ): + + # Open 'control_active.txt' and ... + with open(file) as contents: + for line in contents: + + # ... find the line with the requested setting + if setting in line and not line.startswith('#'): + break + + # Extract the setting's value + substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) + substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found + substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines + + # Return this value + return substring + +# Function to specify a default path +def make_default_path(suffix): + + # Get the root path + rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) + + # Get the domain folder + domainName = read_from_control(controlFolder/controlFile,'domain_name') + domainFolder = 'domain_' + domainName + + # Specify the forcing path + defaultPath = rootPath / domainFolder / suffix + + return defaultPath + + +# --- Find where the forcing data are + +rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) +domainName = read_from_control(controlFolder/controlFile,'domain_name') + +# CAMELS-spath +CAMELS_spath = read_from_control(controlFolder/controlFile,'camels_spath') + +# Specify default path if needed +if CAMELS_spath == 'default': + CAMELS_spath = rootPath / 'CAMELS-spat' +else: + CAMELS_spath = Path(CAMELS_spath) # make sure a user-specified path is a Path() + + +# Metadata +metadata_path = CAMELS_spath +metadata_name = "camels-spat-metadata.csv" + +df_metadata = pd.read_csv(metadata_path / metadata_name) + + +country, station_id = domainName.split("_") + +# Get categories +category_value = df_metadata.loc[(df_metadata['Country'] == country) & (df_metadata['Station_id'] == station_id), 'subset_category'] + +# Ensure category_value is a string +if not category_value.empty: + category_value = category_value.iloc[0] # Convert Series to string +else: + raise ValueError("No matching subset category found.") # Handle missing value + + +# Get timezone +tz = df_metadata.loc[(df_metadata['Country'] == country) & (df_metadata['Station_id'] == station_id), 'dv_flow_obs_timezone'] + +# Ensure tz is a string +if not tz.empty: + tz = tz.iloc[0] # Convert Series to string +else: + raise ValueError("No matching time zone found.") # Handle missing value + +# Spatialisation +spa = 'distributed' + +# Forcing data product +forcing_name = read_from_control(controlFolder/controlFile,'forcing_data_name') + +# Forcing path +forcing_path = CAMELS_spath / 'forcing' / category_value / forcing_name / (forcing_name + "-" + spa) + +# Simulation times +sim_start = read_from_control(controlFolder/controlFile,'experiment_time_start') +sim_end = read_from_control(controlFolder/controlFile,'experiment_time_end') + + +if sim_start == 'default': + raw_time = read_from_control(controlFolder/controlFile,'forcing_raw_time') # downloaded forcing (years) + year_start,_ = raw_time.split(',') # split into separate variables + sim_start = year_start + '-01-01 00:00' # construct the filemanager field + + +if sim_end == 'default': + raw_time = read_from_control(controlFolder/controlFile,'forcing_raw_time') # downloaded forcing (years) + _,year_end = raw_time.split(',') # split into separate variables + sim_end = year_end + '-12-31 23:00' # construct the filemanager field + + +forcing_file = domainName + "_" + forcing_name + "_" + spa +".nc" +if forcing_name == "em-earth": + forcing_file = forcing_file.replace(forcing_name, "em_earth") + + + +# --- Find where the forcing data will be + +forcing_summa_path = read_from_control(controlFolder/controlFile,'forcing_summa_path') + +# Specify default path if needed +if forcing_summa_path == 'default': + forcing_summa_path = make_default_path('forcing/4_SUMMA_input') # outputs a Path() +else: + forcing_summa_path = Path(forcing_summa_path) # make sure a user-specified path is a Path() + +# Make the folder if it doesn't exist +forcing_summa_path.mkdir(parents=True, exist_ok=True) + + +# --- Find the time step size of the forcing data +# Value in control file +data_step = read_from_control(controlFolder/controlFile,'forcing_time_step_size') + +# Convert to int +data_step = int(data_step) + + +# Creation of nc files for SUMMA + +# Function to create new nc variables +def create_and_fill_nc_var(ncid, var_name, var_type, dim, fill_val, fill_data, long_name, units): + + # Make the variable + ncvar = ncid.createVariable(var_name, var_type, dim, fill_value = fill_val) + + # Add the data + ncvar[:] = fill_data + + # Add meta data + ncvar.long_name = long_name + ncvar.units = units + + return + +def to_datetime(dates): + date_list = list() + for date in dates: + + timestamp = ((date - np.datetime64('1970-01-01T00:00:00')) + / np.timedelta64(1, 's')) + date_list.append(datetime.utcfromtimestamp(timestamp)) + return(date_list) + + + +# open forcing .nc file available in CAMELS-spat +nc_forcing = xr.open_dataset(forcing_path / forcing_file) + + + +# create a backup to fill variables other than P & T with era5 data +if forcing_name == "em-earth": + backup_forcing_path = Path(str(forcing_path).replace(forcing_name, "era5")) + backup_forcing_file = forcing_file.replace("em_earth", "era5") + backup_nc_forcing = xr.open_dataset(backup_forcing_path / backup_forcing_file) + + # Find the common timestamps + common_times = np.intersect1d(nc_forcing['time'].values, backup_nc_forcing['time'].values) + + # Subset both datasets to the common times + nc_forcing = nc_forcing.sel(time=common_times) + backup_nc_forcing = backup_nc_forcing.sel(time=common_times) + + + +# name of the output forcing .nc file for SUMMA +forcing_summa_name = forcing_file + + +variable_mapping = { + "air_pressure": { + "era5": "sp", + "rdrs": "RDRS_v2.1_P_P0_SFC", + }, + "longwave_radiation": { + "era5": "msdwlwrf", + "rdrs": "RDRS_v2.1_P_FI_SFC" + + }, + "shortwave_radiation": { + "era5": "msdwswrf", + "rdrs": "RDRS_v2.1_P_FB_SFC" + }, + "precipitation": { + "era5": "mtpr", + "rdrs": "RDRS_v2.1_A_PR0_SFC", + "em-earth": "prcp", + }, + "air_temperature": { + "era5": "t", + "rdrs": "RDRS_v2.1_P_TT_1.5m", + "em-earth": "tmean", + }, + "windspeed": { + "era5": "w", + "rdrs": "RDRS_v2.1_P_UVC_10m", + }, + "specific_humidity": { + "era5": "q", + "rdrs": "RDRS_v2.1_P_HU_1.5m", + }, +} + + + +# Make the netcdf file +with nc4.Dataset(forcing_summa_path/forcing_summa_name, 'w', format='NETCDF4') as ncid: + + # Set general attributes + now = datetime.now() + ncid.setncattr('Author', "Created by SUMMA workflow scripts using CAMELS-spat database") + ncid.setncattr('History','Created ' + now.strftime('%Y/%m/%d %H:%M:%S')) + ncid.setncattr('Purpose','Create forcing .nc files for SUMMA') + ncid.setncattr('Conventions', nc_forcing.attrs['Conventions']) + ncid.setncattr('Source', nc_forcing.attrs['Source']) + + # Define the seg and hru dimensions + ncid.createDimension('hru', len(nc_forcing['hru'])) + ncid.createDimension('time', len(nc_forcing['time'])) + + # Time Variable + timevalue = to_datetime(nc_forcing['time'].values) + + time_varid = ncid.createVariable('time', nc_forcing['time'].encoding['dtype'], ('time', )) + + time_varid[:] = nc4.date2num(timevalue, \ + units = nc_forcing['time'].encoding['units'], \ + calendar = nc_forcing['time'].encoding['calendar']); + + time_varid.long_name = nc_forcing['time'].attrs['long_name'] + time_varid.units = nc_forcing['time'].encoding['units'] + time_varid.calendar = nc_forcing['time'].encoding['calendar'] + time_varid.standard_name = nc_forcing['time'].attrs['standard_name'] + time_varid.axis = nc_forcing['time'].attrs['axis'] + + + # Other Variables + create_and_fill_nc_var(ncid, 'latitude', \ + nc_forcing['latitude'].encoding['dtype'], \ + ('hru',), \ + False, \ + nc_forcing['latitude'].values[0, :].astype(float), \ + nc_forcing['latitude'].attrs['long_name'], \ + nc_forcing['latitude'].attrs['units']) + + create_and_fill_nc_var(ncid, 'longitude', \ + nc_forcing['longitude'].encoding['dtype'], \ + ('hru',), \ + False, \ + nc_forcing['longitude'].values[0, :].astype(float), \ + nc_forcing['longitude'].attrs['long_name'], \ + nc_forcing['longitude'].attrs['units']) + + create_and_fill_nc_var(ncid, 'hruId', \ + nc_forcing['hruId'].encoding['dtype'], \ + ('hru',), \ + False, \ + (10 * nc_forcing['hruId'].values[0, :]).astype(int), \ + nc_forcing['hruId'].attrs['long_name'], \ + nc_forcing['hruId'].attrs['units']) + + # Air pressure + nc_forcing_tmp = nc_forcing + airpres_var = variable_mapping["air_pressure"].get(forcing_name) + if airpres_var is None: + print(f"Warning: Air pressure data not available for {forcing_name}. Using era5 instead.") + nc_forcing_tmp = backup_nc_forcing + airpres_var = variable_mapping["air_pressure"].get("era5") + + create_and_fill_nc_var(ncid, 'airpres', \ + nc_forcing_tmp[airpres_var].encoding['dtype'], \ + ('time','hru',), \ + nc_forcing_tmp[airpres_var].encoding['_FillValue'], \ + nc_forcing_tmp[airpres_var].values.astype(float), \ + nc_forcing_tmp[airpres_var].attrs['long_name'], \ + nc_forcing_tmp[airpres_var].attrs['units']) + + + # Long-wave radiation + nc_forcing_tmp = nc_forcing + longwave_radiation = variable_mapping["longwave_radiation"].get(forcing_name) + if longwave_radiation is None: + print(f"Warning: Long-wave radiation data not available for {forcing_name}. Using era5 instead.") + nc_forcing_tmp = backup_nc_forcing + longwave_radiation = variable_mapping["longwave_radiation"].get("era5") + + create_and_fill_nc_var(ncid, 'LWRadAtm', \ + nc_forcing_tmp[longwave_radiation].encoding['dtype'], \ + ('time','hru',), \ + nc_forcing_tmp[longwave_radiation].encoding['_FillValue'], \ + nc_forcing_tmp[longwave_radiation].values.astype(float), \ + nc_forcing_tmp[longwave_radiation].attrs['long_name'], \ + nc_forcing_tmp[longwave_radiation].attrs['units']) + + + # Short-wave radiation + nc_forcing_tmp = nc_forcing + shortwave_radiation = variable_mapping["shortwave_radiation"].get(forcing_name) + if shortwave_radiation is None: + print(f"Warning: Short-wave radiation data not available for {forcing_name}. Using era5 instead.") + nc_forcing_tmp = backup_nc_forcing + shortwave_radiation = variable_mapping["shortwave_radiation"].get("era5") + + create_and_fill_nc_var(ncid, 'SWRadAtm', \ + nc_forcing_tmp[shortwave_radiation].encoding['dtype'], \ + ('time','hru',), \ + nc_forcing_tmp[shortwave_radiation].encoding['_FillValue'], \ + nc_forcing_tmp[shortwave_radiation].values.astype(float), \ + nc_forcing_tmp[shortwave_radiation].attrs['long_name'], \ + nc_forcing_tmp[shortwave_radiation].attrs['units']) + + + # Precipitation + nc_forcing_tmp = nc_forcing + precipitation = variable_mapping["precipitation"].get(forcing_name) + if precipitation is None: + print(f"Warning: Precipitation data not available for {forcing_name}. Using era5 instead.") + nc_forcing_tmp = backup_nc_forcing + precipitation = variable_mapping["precipitation"].get("era5") + + create_and_fill_nc_var(ncid, 'pptrate', \ + nc_forcing_tmp[precipitation].encoding['dtype'], \ + ('time','hru',), \ + nc_forcing_tmp[precipitation].encoding['_FillValue'], \ + nc_forcing_tmp[precipitation].values.astype(float), \ + nc_forcing_tmp[precipitation].attrs['long_name'], \ + nc_forcing_tmp[precipitation].attrs['units']) + + + # Air temperature + nc_forcing_tmp = nc_forcing + air_temperature = variable_mapping["air_temperature"].get(forcing_name) + if air_temperature is None: + print(f"Warning: Air temperature data not available for {forcing_name}. Using era5 instead.") + nc_forcing_tmp = backup_nc_forcing + air_temperature = variable_mapping["air_temperature"].get("era5") + + create_and_fill_nc_var(ncid, 'airtemp', \ + nc_forcing_tmp[air_temperature].encoding['dtype'], \ + ('time','hru',), \ + nc_forcing_tmp[air_temperature].encoding['_FillValue'], \ + nc_forcing_tmp[air_temperature].values.astype(float), \ + nc_forcing_tmp[air_temperature].attrs['long_name'], \ + nc_forcing_tmp[air_temperature].attrs['units']) + + + # Specific humidity + nc_forcing_tmp = nc_forcing + specific_humidity = variable_mapping["specific_humidity"].get(forcing_name) + if specific_humidity is None: + print(f"Warning: Specific humidity data not available for {forcing_name}. Using era5 instead.") + nc_forcing_tmp = backup_nc_forcing + specific_humidity = variable_mapping["specific_humidity"].get("era5") + + + create_and_fill_nc_var(ncid, 'spechum', \ + nc_forcing_tmp[specific_humidity].encoding['dtype'], \ + ('time','hru',), \ + nc_forcing_tmp[specific_humidity].encoding['_FillValue'], \ + nc_forcing_tmp[specific_humidity].values.astype(float), \ + nc_forcing_tmp[specific_humidity].attrs['long_name'], \ + nc_forcing_tmp[specific_humidity].attrs['units']) + + + # Wind speed + nc_forcing_tmp = nc_forcing + windspeed = variable_mapping["windspeed"].get(forcing_name) + if windspeed is None: + print(f"Warning: Wind speed data not available for {forcing_name}. Using era5 instead.") + nc_forcing_tmp = backup_nc_forcing + windspeed = variable_mapping["windspeed"].get("era5") + + + create_and_fill_nc_var(ncid, 'windspd', \ + nc_forcing_tmp[windspeed].encoding['dtype'], \ + ('time','hru',), \ + nc_forcing_tmp[windspeed].encoding['_FillValue'], \ + nc_forcing_tmp[windspeed].values.astype(float), \ + nc_forcing_tmp[windspeed].attrs['long_name'], \ + nc_forcing_tmp[windspeed].attrs['units']) + + + # Data step Variable + datastep_varid = ncid.createVariable('data_step', 'int32') + + datastep_varid[:] = data_step + + datastep_varid.long_name = 'data step length in seconds' + datastep_varid.units = 's' + + + +# --- Code provenance +# Generates a basic log file in the domain folder and copies the control file and itself there. + +# Set the log path and file name +logPath = forcing_summa_path +log_suffix = '_make_forcing.txt' + +# Create a log folder +logFolder = '_workflow_log' +Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) + +# Copy this script +thisFile = '1_make_forcing.py' +copyfile(thisFile, logPath / logFolder / thisFile); + +# Get current date and time +now = datetime.now() + +# Create a log file +logFile = now.strftime('%Y%m%d') + log_suffix +with open( logPath / logFolder / logFile, 'w') as file: + + lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', + 'Adapt CAMELS-spat forcing data for SUMMA and mizuRoute.'] + for txt in lines: + file.write(txt) \ No newline at end of file diff --git a/3a_forcing/1a_download_forcing/README.md b/3a_forcing/1a_download_forcing/README.md deleted file mode 100644 index d844b30..0000000 --- a/3a_forcing/1a_download_forcing/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# Forcing data download -Downloads ERA5 forcing data (surface and pressure level variables) for the domain specified in the control file. Note that downloads of surface level and pressure level data are retrieved from two different types of data storage on the ECMWF side. Different restrictions may apply to both storage types at any given time and downloads of surface and pressure level data are typically not equally fast. - - -## Notebooks vs Python and shell scripts -Notebooks are set up for serial downloads, Python and shell scripts together run downloads in parallel. Notebooks read the control file to find download path, download period and spatial domain. Downloads data and makes log file. Shell scripts read the control file to find download path, download period and spatial domain. They then call the relevant Python script with path, download year and spatial domain as input arguments using the `parallel` command line utility. The code in the python scripts downloads the data, after which the shell scripts write simple log files. Note that ECMWF sometimes restricts data access to the ERA5 data (e.g. only 1 connection per user may be allowed). In such cases parallelization on the user's side will not speed up the downloads. - - -## Download setup instructions -Downloading ERA5 data requires: -- Registration: https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome -- Setup of the `cdsapi`: https://cds.climate.copernicus.eu/api-how-to - - -## Download run instructions -Execute the download script and keep the terminal or notebook open until the downloads fully complete. No manual interaction with the https://cds.climate.copernicus.eu/ website is required. - - -## Download workflow -Data is provided in the GRIB2 format by ECMWF. A subset of the data can be found on the Climate Data Store (https://cds.climate.copernicus.eu/#!/home), which is interpolated to a lat/lon grid and available in netcdf format. - -Steps to downloading ERA5 through CDS can be found here: https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5 -1. Make a CDS account -2. Create a .cdsapi file in $HOME/ -3. Install the cdsapi (pip install --user cdsapi) -4. Run the scripts/notebooks to download data - - -## Assumptions not specified in `control_active.txt` -- Downloads are of hourly data in monthly chunks. Requires changes to download scripts to adjust; -- Maximum number of parallel download jobs is set to 5. Requires minor changes to `run_download_[data]_annual.sh` to adjust. - - -## Suggested data citation -Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), 2020-03-26. https://cds.climate.copernicus.eu/cdsapp#!/home \ No newline at end of file diff --git a/3a_forcing/1a_download_forcing/download_ERA5_pressureLevel_annual.ipynb b/3a_forcing/1a_download_forcing/download_ERA5_pressureLevel_annual.ipynb deleted file mode 100644 index 0320b49..0000000 --- a/3a_forcing/1a_download_forcing/download_ERA5_pressureLevel_annual.ipynb +++ /dev/null @@ -1,440 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download ERA5 pressure level data\n", - "Requires use of the Copernicus Data Store API\n", - "CDS registration: https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome \n", - "CDS api setup: https://cds.climate.copernicus.eu/api-how-to" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import cdsapi # copernicus connection\n", - "import calendar # to find days per month\n", - "import os # to check if file already exists\n", - "import math\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where to save the data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the raw forcing needs to go\n", - "forcingPath = read_from_control(controlFolder/controlFile,'forcing_raw_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required\n", - "if forcingPath == 'default':\n", - " forcingPath = make_default_path('forcing/1_ERA5_raw_data')\n", - "else: \n", - " forcingPath = Path(forcingPath) # ensure Path() object " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "forcingPath.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find temporal and spatial domain from control file" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Find which years to download\n", - "years = read_from_control(controlFolder/controlFile,'forcing_raw_time')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Split the string into 2 integers\n", - "years = years.split(',')\n", - "years = [int(year) for year in years]" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the spatial extent the data needs to cover\n", - "bounding_box = read_from_control(controlFolder/controlFile,'forcing_raw_space') " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Convert bounding box coordinates to the forcing spatial grid" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# function to round coordinates of a bounding box to ERA5s 0.25 degree resolution\n", - "def round_coords_to_ERA5(coords):\n", - " \n", - " '''Assumes coodinates are an array: [lat_max,lon_min,lat_min,lon_max] (top-left, bottom-right).\n", - " Returns separate lat and lon vectors.'''\n", - " \n", - " # Extract values\n", - " lon = [coords[1],coords[3]]\n", - " lat = [coords[2],coords[0]]\n", - " \n", - " # Round to ERA5 0.25 degree resolution\n", - " rounded_lon = [math.floor(lon[0]*4)/4, math.ceil(lon[1]*4)/4]\n", - " rounded_lat = [math.floor(lat[0]*4)/4, math.ceil(lat[1]*4)/4]\n", - " \n", - " # Find if we are still in the representative area of a different ERA5 grid cell\n", - " if lat[0] > rounded_lat[0]+0.125:\n", - " rounded_lat[0] += 0.25\n", - " if lon[0] > rounded_lon[0]+0.125:\n", - " rounded_lon[0] += 0.25\n", - " if lat[1] < rounded_lat[1]-0.125:\n", - " rounded_lat[1] -= 0.25\n", - " if lon[1] < rounded_lon[1]-0.125:\n", - " rounded_lon[1] -= 0.25\n", - " \n", - " # Make a download string\n", - " dl_string = '{}/{}/{}/{}'.format(rounded_lat[1],rounded_lon[0],rounded_lat[0],rounded_lon[1])\n", - " \n", - " return dl_string, rounded_lat, rounded_lon" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert string to array\n", - "bounding_box = bounding_box.split('/')\n", - "bounding_box = [float(value) for value in bounding_box]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the rounded bounding box\n", - "coordinates,_,_ = round_coords_to_ERA5(bounding_box)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting download of [51.75/-116.5/51.0/-115.5] for years 2008-2013.\n" - ] - } - ], - "source": [ - "# Check what we selected\n", - "print('Starting download of [{}] for years {}-{}.'.format(coordinates,years[0],years[1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download the data in monthly chunks" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Trying to download 1979-01-01/to/1979-01-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197901.nc\n", - "Trying to download 1979-02-01/to/1979-02-28 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197902.nc\n", - "Trying to download 1979-03-01/to/1979-03-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197903.nc\n", - "Trying to download 1979-04-01/to/1979-04-30 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197904.nc\n", - "Trying to download 1979-05-01/to/1979-05-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197905.nc\n", - "Trying to download 1979-06-01/to/1979-06-30 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197906.nc\n", - "Trying to download 1979-07-01/to/1979-07-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197907.nc\n", - "Trying to download 1979-08-01/to/1979-08-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197908.nc\n", - "Trying to download 1979-09-01/to/1979-09-30 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197909.nc\n", - "Trying to download 1979-10-01/to/1979-10-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197910.nc\n", - "Trying to download 1979-11-01/to/1979-11-30 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197911.nc\n", - "Trying to download 1979-12-01/to/1979-12-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_pressureLevel137_197912.nc\n" - ] - } - ], - "source": [ - "# Start the year loop\n", - "for year in range(years[0],years[1]+1): \n", - " \n", - " # Start the month loop\n", - " for month in range (1,13): # this loops through numbers 1 to 12\n", - " \n", - " # find the number of days in this month\n", - " daysInMonth = calendar.monthrange(year,month) \n", - " \n", - " # compile the date string in the required format. Append 0's to the month number if needed (zfill(2))\n", - " date = str(year) + '-' + str(month).zfill(2) + '-01/to/' + \\\n", - " str(year) + '-' + str(month).zfill(2) + '-' + str(daysInMonth[1]).zfill(2) \n", - " \n", - " # compile the file name string\n", - " file = forcingPath / ('ERA5_pressureLevel137_' + str(year) + str(month).zfill(2) + '.nc')\n", - "\n", - " # track progress\n", - " print('Trying to download ' + date + ' into ' + str(file))\n", - "\n", - " # if file doesn't yet exist, download the data\n", - " if not os.path.isfile(file):\n", - "\n", - " # Make sure the connection is re-tried if it fails\n", - " retries_max = 10\n", - " retries_cur = 1\n", - " while retries_cur <= retries_max:\n", - " try:\n", - "\n", - " # connect to Copernicus (requires .cdsapirc file in $HOME)\n", - " c = cdsapi.Client()\n", - "\n", - " # specify and retrieve data\n", - " c.retrieve('reanalysis-era5-complete', { # do not change this!\n", - " 'class': 'ea',\n", - " 'expver': '1',\n", - " 'stream': 'oper',\n", - " 'type': 'an',\n", - " 'levtype': 'ml',\n", - " 'levelist': '137',\n", - " 'param': '130/131/132/133',\n", - " 'date': date,\n", - " 'time': '00/to/23/by/1',\n", - " 'area': coordinates,\n", - " 'grid': '0.25/0.25', # Latitude/longitude grid: east-west (longitude) and north-south resolution (latitude).\n", - " 'format' : 'netcdf',\n", - " }, file)\n", - " \n", - " # track progress\n", - " print('Successfully downloaded ' + str(file))\n", - "\n", - " except Exception as e:\n", - " print('Error downloading ' + str(file) + ' on try ' + str(retries_cur))\n", - " print(str(e))\n", - " retries_cur += 1\n", - " continue\n", - " else:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( forcingPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'download_ERA5_pressureLevel_annual.ipynb'\n", - "copyfile(thisFile, forcingPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + '_pressure_level_log.txt'\n", - "with open( forcingPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Downloaded ERA5 pressure level data for space (lat_max, lon_min, lat_min, lon_max) [{}] for time Jan-{} / Dec-{}.'.format(coordinates,years[0],years[1])]\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/3a_forcing/1a_download_forcing/download_ERA5_pressureLevel_annual.py b/3a_forcing/1a_download_forcing/download_ERA5_pressureLevel_annual.py deleted file mode 100644 index baf5685..0000000 --- a/3a_forcing/1a_download_forcing/download_ERA5_pressureLevel_annual.py +++ /dev/null @@ -1,113 +0,0 @@ -# modules -import cdsapi # copernicus connection -import calendar # to find days per month -import os # to check if file already exists -import sys # to handle command line arguments (sys.argv[0] = name of this file, sys.argv[1] = arg1, ...) -import math -from pathlib import Path -from shutil import copyfile -from datetime import datetime - -''' -Downloads 1 year of ERA5 data as monthly chunks. -Usage: python download_ERA5_pressureLevel_annual.py -''' - -# Get the year we're downloading from command line argument -year = int(sys.argv[1]) # arguments are string by default; string to integer - -# Get the spatial coordinates as the second command line argument -bounding_box = sys.argv[2] # string -bounding_box = bounding_box.split('/') # split string -bounding_box = [float(value) for value in bounding_box] # string to array - -# Get the path as the second command line argument -forcingPath = Path(sys.argv[3]) # string to Path() - -# --- Convert the bounding box to download coordinates -# function to round coordinates of a bounding box to ERA5s 0.25 degree resolution -def round_coords_to_ERA5(coords): - - '''Assumes coodinates are an array: [lat_max,lon_min,lat_min,lon_max] (top-left, bottom-right). - Returns separate lat and lon vectors.''' - - # Extract values - lon = [coords[1],coords[3]] - lat = [coords[2],coords[0]] - - # Round to ERA5 0.25 degree resolution - rounded_lon = [math.floor(lon[0]*4)/4, math.ceil(lon[1]*4)/4] - rounded_lat = [math.floor(lat[0]*4)/4, math.ceil(lat[1]*4)/4] - - # Find if we are still in the representative area of a different ERA5 grid cell - if lat[0] > rounded_lat[0]+0.125: - rounded_lat[0] += 0.25 - if lon[0] > rounded_lon[0]+0.125: - rounded_lon[0] += 0.25 - if lat[1] < rounded_lat[1]-0.125: - rounded_lat[1] -= 0.25 - if lon[1] < rounded_lon[1]-0.125: - rounded_lon[1] -= 0.25 - - # Make a download string - dl_string = '{}/{}/{}/{}'.format(rounded_lat[1],rounded_lon[0],rounded_lat[0],rounded_lon[1]) - - return dl_string, rounded_lat, rounded_lon - -# Find the rounded bounding box -coordinates,_,_ = round_coords_to_ERA5(bounding_box) - -# --- Start the month loop -for month in range (1,13): # this loops through numbers 1 to 12 - - # find the number of days in this month - daysInMonth = calendar.monthrange(year,month) - - # compile the date string in the required format. Append 0's to the month number if needed (zfill(2)) - date = str(year) + '-' + str(month).zfill(2) + '-01/to/' + \ - str(year) + '-' + str(month).zfill(2) + '-' + str(daysInMonth[1]).zfill(2) - - # compile the file name string - file = forcingPath / ('ERA5_pressureLevel137_' + str(year) + str(month).zfill(2) + '.nc') - - # track progress - print('Trying to download ' + date + ' into ' + str(file)) - - # if file doesn't yet exist, download the data - if not os.path.isfile(file): - - # Make sure the connection is re-tried if it fails - retries_max = 10 - retries_cur = 1 - while retries_cur <= retries_max: - try: - - # connect to Copernicus (requires .cdsapirc file in $HOME) - c = cdsapi.Client() - - # specify and retrieve data - c.retrieve('reanalysis-era5-complete', { # do not change this! - 'class': 'ea', - 'expver': '1', - 'stream': 'oper', - 'type': 'an', - 'levtype': 'ml', - 'levelist': '137', - 'param': '130/131/132/133', - 'date': date, - 'time': '00/to/23/by/1', - 'area': coordinates, - 'grid': '0.25/0.25', # Latitude/longitude grid: east-west (longitude) and north-south resolution (latitude). - 'format' : 'netcdf', - }, file) - - # track progress - print('Successfully downloaded ' + str(file)) - - except Exception as e: - print('Error downloading ' + str(file) + ' on try ' + str(retries_cur)) - print(str(e)) - retries_cur += 1 - continue - else: - break \ No newline at end of file diff --git a/3a_forcing/1a_download_forcing/download_ERA5_surfaceLevel_annual.ipynb b/3a_forcing/1a_download_forcing/download_ERA5_surfaceLevel_annual.ipynb deleted file mode 100644 index b86b7d9..0000000 --- a/3a_forcing/1a_download_forcing/download_ERA5_surfaceLevel_annual.ipynb +++ /dev/null @@ -1,662 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download ERA5 surface level data", - "Requires use of the Copernicus Data Store API\n", - "CDS registration: https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome \n", - "CDS api setup: https://cds.climate.copernicus.eu/api-how-to" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import cdsapi # copernicus connection\n", - "import calendar # to find days per month\n", - "import os # to check if file already exists\n", - "import math\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where to save the data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the raw forcing needs to go\n", - "# Immediately store as a 'Path' to avoid issues with '/' and '\\' on different operating systems\n", - "forcingPath = read_from_control(controlFolder/controlFile,'forcing_raw_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required\n", - "if forcingPath == 'default':\n", - " forcingPath = make_default_path('forcing/1_ERA5_raw_data')\n", - "else: \n", - " forcingPath = Path(forcingPath) # ensure Path() object " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "forcingPath.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find temporal and spatial domain from control file" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Find which years to download\n", - "years = read_from_control(controlFolder/controlFile,'forcing_raw_time')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Split the string into 2 integers\n", - "years = years.split(',')\n", - "years = [int(year) for year in years]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the spatial extent the data needs to cover\n", - "bounding_box = read_from_control(controlFolder/controlFile,'forcing_raw_space') " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Convert bounding box coordinates to the forcing spatial grid" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# function to round coordinates of a bounding box to ERA5s 0.25 degree resolution\n", - "def round_coords_to_ERA5(coords):\n", - " \n", - " '''Assumes coodinates are an array: [lat_max,lon_min,lat_min,lon_max] (top-left, bottom-right).\n", - " Returns separate lat and lon vectors.'''\n", - " \n", - " # Extract values\n", - " lon = [coords[1],coords[3]]\n", - " lat = [coords[2],coords[0]]\n", - " \n", - " # Round to ERA5 0.25 degree resolution\n", - " rounded_lon = [math.floor(lon[0]*4)/4, math.ceil(lon[1]*4)/4]\n", - " rounded_lat = [math.floor(lat[0]*4)/4, math.ceil(lat[1]*4)/4]\n", - " \n", - " # Find if we are still in the representative area of a different ERA5 grid cell\n", - " if lat[0] > rounded_lat[0]+0.125:\n", - " rounded_lat[0] += 0.25\n", - " if lon[0] > rounded_lon[0]+0.125:\n", - " rounded_lon[0] += 0.25\n", - " if lat[1] < rounded_lat[1]-0.125:\n", - " rounded_lat[1] -= 0.25\n", - " if lon[1] < rounded_lon[1]-0.125:\n", - " rounded_lon[1] -= 0.25\n", - " \n", - " # Make a download string\n", - " dl_string = '{}/{}/{}/{}'.format(rounded_lat[1],rounded_lon[0],rounded_lat[0],rounded_lon[1])\n", - " \n", - " return dl_string, rounded_lat, rounded_lon" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert string to array\n", - "bounding_box = bounding_box.split('/')\n", - "bounding_box = [float(value) for value in bounding_box]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the rounded bounding box\n", - "coordinates,_,_ = round_coords_to_ERA5(bounding_box)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting download of [51.75/-116.5/51.0/-115.5] for years 2008-2013.\n" - ] - } - ], - "source": [ - "# Check what we selected\n", - "print('Starting download of [{}] for years {}-{}.'.format(coordinates,years[0],years[1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download the data in monthly chunks" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Trying to download 1979-01-01/1979-01-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197901.nc\n", - "Trying to download 1979-02-01/1979-02-28 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197902.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-02-08 20:30:03,470 INFO Welcome to the CDS\n", - "2021-02-08 20:30:03,470 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels\n", - "2021-02-08 20:30:03,655 INFO Request is queued\n", - "2021-02-08 20:30:04,824 INFO Request is running\n", - "2021-02-08 20:34:24,749 INFO Request is completed\n", - "2021-02-08 20:34:24,750 INFO Downloading https://download-0004.copernicus-climate.eu/cache-compute-0004/cache/data5/adaptor.mars.internal-1612841403.7407765-19743-7-ff673673-bdec-4749-a35d-0a83ad617707.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197902.nc (162K)\n", - "2021-02-08 20:34:26,107 INFO Download rate 119.4K/s \n", - "2021-02-08 20:34:26,448 INFO Welcome to the CDS\n", - "2021-02-08 20:34:26,449 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully downloaded C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197902.nc\n", - "Trying to download 1979-03-01/1979-03-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197903.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-02-08 20:34:26,630 INFO Request is queued\n", - "2021-02-08 20:34:27,793 INFO Request is running\n", - "2021-02-08 20:38:46,632 INFO Request is completed\n", - "2021-02-08 20:38:46,633 INFO Downloading https://download-0007.copernicus-climate.eu/cache-compute-0007/cache/data6/adaptor.mars.internal-1612841666.7446716-1491-31-30a15bc1-2f83-4f23-9d68-813166b3a6b4.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197903.nc (179.2K)\n", - "2021-02-08 20:38:47,932 INFO Download rate 138K/s \n", - "2021-02-08 20:38:48,272 INFO Welcome to the CDS\n", - "2021-02-08 20:38:48,272 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully downloaded C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197903.nc\n", - "Trying to download 1979-04-01/1979-04-30 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197904.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-02-08 20:38:48,515 INFO Request is queued\n", - "2021-02-08 20:38:49,709 INFO Request is running\n", - "2021-02-08 20:43:08,561 INFO Request is completed\n", - "2021-02-08 20:43:08,562 INFO Downloading https://download-0002.copernicus-climate.eu/cache-compute-0002/cache/data2/adaptor.mars.internal-1612841928.7930326-31274-33-505b3ae2-65ba-4037-bc13-3990e61a5e65.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197904.nc (173.5K)\n", - "2021-02-08 20:43:11,546 INFO Download rate 58.2K/s \n", - "2021-02-08 20:43:11,942 INFO Welcome to the CDS\n", - "2021-02-08 20:43:11,943 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully downloaded C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197904.nc\n", - "Trying to download 1979-05-01/1979-05-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197905.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-02-08 20:43:12,130 INFO Request is queued\n", - "2021-02-08 20:43:13,294 INFO Request is running\n", - "2021-02-08 20:47:32,153 INFO Request is completed\n", - "2021-02-08 20:47:32,154 INFO Downloading https://download-0002.copernicus-climate.eu/cache-compute-0002/cache/data0/adaptor.mars.internal-1612842192.3640003-7815-19-db30b55c-b696-4db8-a4da-ff553c58fe78.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197905.nc (179.2K)\n", - 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"2021-02-08 21:13:47,295 INFO Download rate 133.7K/s \n", - "2021-02-08 21:13:47,646 INFO Welcome to the CDS\n", - "2021-02-08 21:13:47,647 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully downloaded C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197911.nc\n", - "Trying to download 1979-12-01/1979-12-31 into C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197912.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-02-08 21:13:47,850 INFO Request is queued\n", - "2021-02-08 21:13:49,010 INFO Request is running\n", - "2021-02-08 21:18:07,789 INFO Request is completed\n", - "2021-02-08 21:18:07,789 INFO Downloading https://download-0000.copernicus-climate.eu/cache-compute-0000/cache/data1/adaptor.mars.internal-1612844028.1393278-6028-23-7b8bd343-b89d-4d9e-bc63-4df6ea0585a6.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197912.nc (179.2K)\n", - "2021-02-08 21:18:09,125 INFO Download rate 134.2K/s \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully downloaded C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\1_ERA5_raw_data\\ERA5_surface_197912.nc\n" - ] - } - ], - "source": [ - "# Start the year loop\n", - "for year in range(years[0],years[1]+1): \n", - " \n", - " # Start the month loop\n", - " for month in range (1,13): # this loops through numbers 1 to 12\n", - " \n", - " # find the number of days in this month\n", - " daysInMonth = calendar.monthrange(year,month) \n", - " \n", - " # compile the date string in the required format. Append 0's to the month number if needed (zfill(2))\n", - " date = str(year) + '-' + str(month).zfill(2) + '-01/' + \\\n", - " str(year) + '-' + str(month).zfill(2) + '-' + str(daysInMonth[1]).zfill(2) \n", - " \n", - " # compile the file name string\n", - " file = forcingPath / ('ERA5_surface_' + str(year) + str(month).zfill(2) + '.nc')\n", - "\n", - " # track progress\n", - " print('Trying to download ' + date + ' into ' + str(file))\n", - "\n", - " # if file doesn't yet exist, download the data\n", - " if not os.path.isfile(file):\n", - "\n", - " # Make sure the connection is re-tried if it fails\n", - " retries_max = 10\n", - " retries_cur = 1\n", - " while retries_cur <= retries_max:\n", - " try:\n", - "\n", - " # connect to Copernicus (requires .cdsapirc file in $HOME)\n", - " c = cdsapi.Client()\n", - "\n", - " # specify and retrieve data\n", - " c.retrieve('reanalysis-era5-single-levels', { # do not change this!\n", - " 'product_type': 'reanalysis',\n", - " 'format': 'netcdf',\n", - " 'variable': [\n", - " 'mean_surface_downward_long_wave_radiation_flux', \n", - " 'mean_surface_downward_short_wave_radiation_flux',\n", - " 'mean_total_precipitation_rate', \n", - " 'surface_pressure',\n", - " ],\n", - " 'date': date,\n", - " 'time': '00/to/23/by/1',\n", - " 'area': coordinates, # North, West, South, East. Default: global\n", - " 'grid': '0.25/0.25', # Latitude/longitude grid: east-west (longitude) and north-south resolution (latitude).\n", - " },\n", - " file) # file path and name\n", - " \n", - " # track progress\n", - " print('Successfully downloaded ' + str(file))\n", - " \n", - " except Exception as e:\n", - " print('Error downloading ' + str(file) + ' on try ' + str(retries_cur))\n", - " print(str(e))\n", - " retries_cur += 1\n", - " continue\n", - " else:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( forcingPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'download_ERA5_surfaceLevel_annual.ipynb'\n", - "copyfile(thisFile, forcingPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + '_surface_level_log.txt'\n", - "with open( forcingPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Downloaded ERA5 surface level data for space (lat_max, lon_min, lat_min, lon_max) [{}] for time Jan-{} / Dec-{}.'.format(coordinates,years[0],years[1])]\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/3a_forcing/1a_download_forcing/download_ERA5_surfaceLevel_annual.py b/3a_forcing/1a_download_forcing/download_ERA5_surfaceLevel_annual.py deleted file mode 100644 index f0f09d6..0000000 --- a/3a_forcing/1a_download_forcing/download_ERA5_surfaceLevel_annual.py +++ /dev/null @@ -1,119 +0,0 @@ -# modules -import cdsapi # copernicus connection -import calendar # to find days per month -import os # to check if file already exists -import sys # to handle command line arguments (sys.argv[0] = name of this file, sys.argv[1] = arg1, ...) -import math -from pathlib import Path -from shutil import copyfile -from datetime import datetime - -# CDS registration: https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome -# CDS api setup: https://cds.climate.copernicus.eu/api-how-to - -''' -Downloads 1 year of ERA5 data as monthly chunks. -Usage: python download_ERA5_surfaceLevel_annual.py -''' - -# Get the year we're downloading from command line argument -year = int(sys.argv[1]) # arguments are string by default; string to integer - -# Get the spatial coordinates as the second command line argument -bounding_box = sys.argv[2] # string -bounding_box = bounding_box.split('/') # split string -bounding_box = [float(value) for value in bounding_box] # string to array - -# Get the path as the second command line argument -forcingPath = Path(sys.argv[3]) # string to Path() - -# --- Convert the bounding box to download coordinates -# function to round coordinates of a bounding box to ERA5s 0.25 degree resolution -def round_coords_to_ERA5(coords): - - '''Assumes coodinates are an array: [lat_max,lon_min,lat_min,lon_max] (top-left, bottom-right). - Returns separate lat and lon vectors.''' - - # Extract values - lon = [coords[1],coords[3]] - lat = [coords[2],coords[0]] - - # Round to ERA5 0.25 degree resolution - rounded_lon = [math.floor(lon[0]*4)/4, math.ceil(lon[1]*4)/4] - rounded_lat = [math.floor(lat[0]*4)/4, math.ceil(lat[1]*4)/4] - - # Find if we are still in the representative area of a different ERA5 grid cell - if lat[0] > rounded_lat[0]+0.125: - rounded_lat[0] += 0.25 - if lon[0] > rounded_lon[0]+0.125: - rounded_lon[0] += 0.25 - if lat[1] < rounded_lat[1]-0.125: - rounded_lat[1] -= 0.25 - if lon[1] < rounded_lon[1]-0.125: - rounded_lon[1] -= 0.25 - - # Make a download string - dl_string = '{}/{}/{}/{}'.format(rounded_lat[1],rounded_lon[0],rounded_lat[0],rounded_lon[1]) - - return dl_string, rounded_lat, rounded_lon - -# Find the rounded bounding box -coordinates,_,_ = round_coords_to_ERA5(bounding_box) - -# --- Start the month loop -for month in range (1,13): # this loops through numbers 1 to 12 - - # find the number of days in this month - daysInMonth = calendar.monthrange(year,month) - - # compile the date string in the required format. Append 0's to the month number if needed (zfill(2)) - date = str(year) + '-' + str(month).zfill(2) + '-01/' + \ - str(year) + '-' + str(month).zfill(2) + '-' + str(daysInMonth[1]).zfill(2) - - # compile the file name string - file = forcingPath / ('ERA5_surface_' + str(year) + str(month).zfill(2) + '.nc') - - # track progress - print('Trying to download ' + date + ' into ' + str(file)) - - # if file doesn't yet exist, download the data - if not os.path.isfile(file): - - # Make sure the connection is re-tried if it fails - retries_max = 10 - retries_cur = 1 - while retries_cur <= retries_max: - try: - - # connect to Copernicus (requires .cdsapirc file in $HOME) - c = cdsapi.Client() - - # specify and retrieve data - c.retrieve( - 'reanalysis-era5-single-levels', - { - 'product_type': 'reanalysis', - 'format': 'netcdf', - 'variable': [ - 'mean_surface_downward_long_wave_radiation_flux', - 'mean_surface_downward_short_wave_radiation_flux', - 'mean_total_precipitation_rate', - 'surface_pressure', - ], - 'date': date, - 'time': '00/to/23/by/1', - 'area': coordinates, # North, West, South, East. Default: global - 'grid': '0.25/0.25', # Latitude/longitude grid: east-west (longitude) and north-south - }, - file) # file path and name - - # track progress - print('Successfully downloaded ' + str(file)) - - except Exception as e: - print('Error downloading ' + str(file) + ' on try ' + str(retries_cur)) - print(str(e)) - retries_cur += 1 - continue - else: - break \ No newline at end of file diff --git a/3a_forcing/1a_download_forcing/run_download_ERA5_pressureLevel.sh b/3a_forcing/1a_download_forcing/run_download_ERA5_pressureLevel.sh deleted file mode 100755 index 2114978..0000000 --- a/3a_forcing/1a_download_forcing/run_download_ERA5_pressureLevel.sh +++ /dev/null @@ -1,83 +0,0 @@ -#!/bin/bash - -# Script to download ERA5 pressure level data -# Reads download path, years to download and spatial extent from 'summaWorkflow_public/0_control_files/control_active.txt'. - -# Requires use of the Copernicus Data Store API -# CDS registration: https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome -# CDS api setup: https://cds.climate.copernicus.eu/api-how-to - -module load python - -# --- Settings -# -- Find where to save data -# Find the line with the forcing path -setting_line=$(grep -m 1 "^forcing_raw_path" ../../0_control_files/control_active.txt) # -m 1 ensures we only return the top-most result. This is needed because variable names are sometimes used in comments in later lines - -# Extract the path -forcing_path=$(echo ${setting_line##*|}) # remove the part that ends at "|" -forcing_path=$(echo ${forcing_path%%#*}) # remove the part starting at '#'; does nothing if no '#' is present - -# Specify the default path if needed -if [ "$forcing_path" = "default" ]; then - - # Get the root path - root_line=$(grep -m 1 "^root_path" ../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # Get the domain path - domain_line=$(grep -m 1 "^domain_name" ../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - forcing_path="${root_path}/domain_${domain_name}/forcing/1_ERA5_raw_data/" -fi - -# Make the folder if it doesn't exist -mkdir -p $forcing_path - -# -- Find temporal and spatial domain -# - time -setting_line=$(grep -m 1 "^forcing_raw_time" ../../0_control_files/control_active.txt) -years=$(echo ${setting_line##*|}) -years=$(echo ${years%%#*}) -arrayYears=(${years//,/ }) # split string into array for later use, based on delimiter ',' - -# - space -setting_line=$(grep -m 1 "^forcing_raw_space" ../../0_control_files/control_active.txt) -coordinates=$(echo ${setting_line##*|}) -coordinates=$(echo ${coordinates%%#*}) - - -# --- Parallel runs -# Build the target variable (we need this because we can't use brace expansion based on 'arrayYears' -years=""; -for (( year=$(( arrayYears[0] )); year<=$(( arrayYears[1] )); year++ )); do - years="$years $year"; -done - -# Run the ERA5 downloads with the parallel command, and move them to background -parallel --jobs=5 python download_ERA5_pressureLevel_annual.py ::: $years ::: $coordinates ::: $forcing_path - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Make a log directory if it doesn't exist -log_path="${forcing_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_pressure_level_log.txt" - -# Copy this script -this_file='run_download_ERA5_pressureLevel.sh' -that_file='download_ERA5_pressureLevel_annual.py' -cp $this_file $log_path -cp $that_file $log_path - -# Create a log file -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo "Downloaded ERA5 pressure level data for space (lat_max, lon_min, lat_min, lon_max) [${coordinates}] for time Jan-${arrayYears[0]} / Dec-${arrayYears[1]}" >> $log_path/$log_file # 2nd line, append to existing file diff --git a/3a_forcing/1a_download_forcing/run_download_ERA5_surfaceLevel.sh b/3a_forcing/1a_download_forcing/run_download_ERA5_surfaceLevel.sh deleted file mode 100755 index cf02d14..0000000 --- a/3a_forcing/1a_download_forcing/run_download_ERA5_surfaceLevel.sh +++ /dev/null @@ -1,82 +0,0 @@ -#!/bin/bash - -# Script to download ERA5 surface level data -# Reads download path, years to download and spatial extent from 'summaWorkflow_public/0_control_files/control_active.txt'. - -# Requires use of the Copernicus Data Store API -# CDS registration: https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome -# CDS api setup: https://cds.climate.copernicus.eu/api-how-to - -module load python - -# --- Settings -# -- Find where to save data -# Find the line with the forcing path -setting_line=$(grep -m 1 "^forcing_raw_path" ../../0_control_files/control_active.txt) # -m 1 ensures we only return the top-most result. This is needed because variable names are sometimes used in comments in later lines - -# Extract the path -forcing_path=$(echo ${setting_line##*|}) # remove the part that ends at "|" -forcing_path=$(echo ${forcing_path%%#*}) # remove the part starting at '#'; does nothing if no '#' is present - -# Specify the default path if needed -if [ "$forcing_path" = "default" ]; then - - # Get the root path - root_line=$(grep -m 1 "^root_path" ../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # Get the domain path - domain_line=$(grep -m 1 "^domain_name" ../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - forcing_path="${root_path}/domain_${domain_name}/forcing/1_ERA5_raw_data/" -fi - -# Make the folder if it doesn't exist -mkdir -p $forcing_path - -# -- Find temporal and spatial domain -# - time -setting_line=$(grep -m 1 "^forcing_raw_time" ../../0_control_files/control_active.txt) -years=$(echo ${setting_line##*|}) -years=$(echo ${years%%#*}) -arrayYears=(${years//,/ }) # split string into array for later use, based on delimiter ',' - -# - space -setting_line=$(grep -m 1 "^forcing_raw_space" ../../0_control_files/control_active.txt) -coordinates=$(echo ${setting_line##*|}) -coordinates=$(echo ${coordinates%%#*}) - - -# --- Parallel runs -# Build the target variable (we need this because we can't use brace expansion based on 'arrayYears' -years=""; -for (( year=$(( arrayYears[0] )); year<=$(( arrayYears[1] )); year++ )); do - years="$years $year"; -done - -# Run the ERA5 downloads with the parallel command, and move them to background -parallel --jobs=5 python download_ERA5_surfaceLevel_annual.py ::: $years ::: $coordinates ::: $forcing_path - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Make a log directory if it doesn't exist -log_path="${forcing_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_surface_level_log.txt" - -# Copy this script -this_file='run_download_ERA5_surfaceLevel.sh' -that_file='download_ERA5_surfaceLevel_annual.py' -cp $this_file $log_path -cp $that_file $log_path - -# Create a log file -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo "Downloaded ERA5 surface level data for space (lat_max, lon_min, lat_min, lon_max) [${coordinates}] for time Jan-${arrayYears[0]} / Dec-${arrayYears[1]}" >> $log_path/$log_file # 2nd line, append to existing file \ No newline at end of file diff --git a/3a_forcing/1b_download_geopotential/README.md b/3a_forcing/1b_download_geopotential/README.md deleted file mode 100644 index 2e0fdf1..0000000 --- a/3a_forcing/1b_download_geopotential/README.md +++ /dev/null @@ -1,26 +0,0 @@ -# ERA5 invariant data -Downloads various auxiliary data, such as land mask and geopotential for ERA5 data. We currently only use geopotential (parameter download ID 129) to estimate ERA5 elevation and use this to define temperature lapse rates. - -Source: https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation#ERA5:datadocumentation-Parameterlistings - - -## Download setup instructions -Downloading ERA5 data requires: -- Registration: https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome -- Setup of the `cdsapi`: https://cds.climate.copernicus.eu/api-how-to - -## Download run instructions -Execute the download script and keep the terminal or notebook open until the downloads fully complete. No manual interaction with the https://cds.climate.copernicus.eu/ website is required. - -## Levels -These variables are available to surface and pressure levels. We need them to set up SUMMA so we'll use the surface level variables, because those correspond to ground surface. - -## Time -These parameters are invariant in time, so we can select any time we need for them (see section "Parameter listings", Table 1 on: https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation). - -## Geopotential to elevation -We use the geopotential given for each ERA5 data point to estimate the elevation at this data point: "at the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography." To do so, we need to divide geopotential by gravitational acceleration (https://apps.ecmwf.int/codes/grib/param-db?id=129): `g = 9.80665 m s-2'. This step is performed during the preparation of SUMMA inputs. - -## Assumptions not specified in `control_active.txt` -- Name of the download file hard-coded as `ERA5_geopotential.nc`. - diff --git a/3a_forcing/1b_download_geopotential/download_ERA5_geopotential.ipynb b/3a_forcing/1b_download_geopotential/download_ERA5_geopotential.ipynb deleted file mode 100644 index 634534a..0000000 --- a/3a_forcing/1b_download_geopotential/download_ERA5_geopotential.ipynb +++ /dev/null @@ -1,426 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download ERA5 geopotential data\n", - "Geopotential data can be converted into elevation, which is needed for temperature lapsing.", - "Requires use of the Copernicus Data Store API\n", - "CDS registration: https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome \n", - "CDS api setup: https://cds.climate.copernicus.eu/api-how-to" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import cdsapi # copernicus connection\n", - "import calendar # to find days per month\n", - "import os # to check if file already exists\n", - "import math\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where to save the data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the raw forcing needs to go\n", - "geoPath = read_from_control(controlFolder/controlFile,'forcing_geo_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required\n", - "if geoPath == 'default':\n", - " geoPath = make_default_path('forcing/0_geopotential')\n", - "else: \n", - " geoPath = Path(geoPath) # ensure Path() object " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "geoPath.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find spatial domain from control file" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the spatial extent the data needs to cover\n", - "bounding_box = read_from_control(controlFolder/controlFile,'forcing_raw_space') " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# function to round coordinates of a bounding box to ERA5s 0.25 degree resolution\n", - "def round_coords_to_ERA5(coords):\n", - " \n", - " '''Assumes coodinates are an array: [lon_min,lat_min,lon_max,lat_max].\n", - " Returns separate lat and lon vectors.'''\n", - " \n", - " # Extract values\n", - " lon = [coords[1],coords[3]]\n", - " lat = [coords[2],coords[0]]\n", - " \n", - " # Round to ERA5 0.25 degree resolution\n", - " rounded_lon = [math.floor(lon[0]*4)/4, math.ceil(lon[1]*4)/4]\n", - " rounded_lat = [math.floor(lat[0]*4)/4, math.ceil(lat[1]*4)/4]\n", - " \n", - " # Find if we are still in the representative area of a different ERA5 grid cell\n", - " if lat[0] > rounded_lat[0]+0.125:\n", - " rounded_lat[0] += 0.25\n", - " if lon[0] > rounded_lon[0]+0.125:\n", - " rounded_lon[0] += 0.25\n", - " if lat[1] < rounded_lat[1]-0.125:\n", - " rounded_lat[1] -= 0.25\n", - " if lon[1] < rounded_lon[1]-0.125:\n", - " rounded_lon[1] -= 0.25\n", - " \n", - " # Make a download string\n", - " dl_string = '{}/{}/{}/{}'.format(rounded_lat[1],rounded_lon[0],rounded_lat[0],rounded_lon[1])\n", - " \n", - " return dl_string, rounded_lat, rounded_lon" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert string to array\n", - "bounding_box = bounding_box.split('/')\n", - "bounding_box = [float(value) for value in bounding_box]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the rounded bounding box\n", - "coordinates,_,_ = round_coords_to_ERA5(bounding_box)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting download of [51.75/-116.5/51.0/-115.5].\n" - ] - } - ], - "source": [ - "# Check what we selected\n", - "print('Starting download of [{}].'.format(coordinates))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Specify a date to download\n", - "Geopotential is part of the ERA5 \"invariant\" data, which are constant through time." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify an arbitrary date to download\n", - "date = '2019-01-01'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download the data" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify a filename\n", - "file = geoPath / 'ERA5_geopotential.nc'" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-02-24 11:09:59,066 INFO Welcome to the CDS\n", - "2021-02-24 11:09:59,066 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-complete\n", - "2021-02-24 11:09:59,238 INFO Request is queued\n", - "2021-02-24 19:55:54,160 INFO Request is completed\n", - "2021-02-24 19:55:54,188 INFO Downloading https://download-0014.copernicus-climate.eu/cache-compute-0014/cache/data5/adaptor.mars.external-1614221671.0050914-29905-32-4b846a4e-52af-41c1-8b24-534cf0efcb8c.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\0_geopotential\\ERA5_geopotential.nc (5.1K)\n", - "2021-02-24 19:55:55,246 INFO Download rate 4.9K/s \n" - ] - }, - { - "ename": "TypeError", - "evalue": "can only concatenate str (not \"WindowsPath\") to str", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;31m# track progress\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Successfully downloaded '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"WindowsPath\") to str", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Error downloading '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfile\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' on try '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mretries_cur\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 30\u001b[0m \u001b[0mretries_cur\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"WindowsPath\") to str" - ] - } - ], - "source": [ - "# if file doesn't yet exist, download the data\n", - "if not os.path.isfile(file):\n", - "\n", - " # Make sure the connection is re-tried if it fails\n", - " retries_max = 10\n", - " retries_cur = 1\n", - " while retries_cur <= retries_max:\n", - " try:\n", - " \n", - " # connect to Copernicus (requires .cdsapirc file in $HOME)\n", - " c = cdsapi.Client()\n", - "\n", - " # specify and retrieve data\n", - " c.retrieve('reanalysis-era5-complete', { # do not change this!\n", - " 'stream': 'oper',\n", - " 'levtype': 'sf',\n", - " 'param': '26/228007/27/28/29/30/43/74/129/160/161/162/163/172',\n", - " 'date': date,\n", - " 'time': '00',#/to/23/by/1',\n", - " 'area': coordinates,\n", - " 'grid': '0.25/0.25', # Latitude/longitude grid: east-west (longitude) and north-south resolution (latitude).\n", - " 'format' : 'netcdf',\n", - " }, file)\n", - " \n", - " # track progress\n", - " print('Successfully downloaded ' + str(file))\n", - "\n", - " except:\n", - " print('Error downloading ' + str(file) + ' on try ' + str(retries_cur))\n", - " retries_cur += 1\n", - " continue\n", - " else:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_era5_invariants_log'\n", - "Path( geoPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'download_ERA5_geopotential.ipynb'\n", - "copyfile(thisFile, geoPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + '_pressure_level_log.txt'\n", - "with open( geoPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Downloaded ERA5 geopotential data for space (lat_max, lon_min, lat_min, lon_max) [{}].'.format(coordinates)]\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/3a_forcing/1b_download_geopotential/download_ERA5_geopotential.py b/3a_forcing/1b_download_geopotential/download_ERA5_geopotential.py deleted file mode 100644 index a79d5da..0000000 --- a/3a_forcing/1b_download_geopotential/download_ERA5_geopotential.py +++ /dev/null @@ -1,184 +0,0 @@ -# Script to download ERA5 geopotential data. -# Geopotential data can be converted into elevation, which is needed for temperature lapsing. - -# Requires use of the Copernicus Data Store API -# CDS registration: https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome -# CDS api setup: https://cds.climate.copernicus.eu/api-how-to - -# modules -import cdsapi # copernicus connection -import calendar # to find days per month -import os # to check if file already exists -import math -from pathlib import Path -from shutil import copyfile -from datetime import datetime - - -# --- Control file handling - -# Easy access to control file folder -controlFolder = Path('../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find where to save the data - -# Find the path where the raw forcing needs to go -geoPath = read_from_control(controlFolder/controlFile,'forcing_geo_path') - -# Specify the default paths if required -if geoPath == 'default': - geoPath = make_default_path('forcing/0_geopotential') -else: - geoPath = Path(geoPath) # ensure Path() object - -# Make the folder if it doesn't exist -geoPath.mkdir(parents=True, exist_ok=True) - - -# --- Find spatial domain from control file - -# Find the spatial extent the data needs to cover -bounding_box = read_from_control(controlFolder/controlFile,'forcing_raw_space') -bounding_box = bounding_box.split('/') # split string -bounding_box = [float(value) for value in bounding_box] # string to array - -# function to round coordinates of a bounding box to ERA5s 0.25 degree resolution -def round_coords_to_ERA5(coords): - - '''Assumes coodinates are an array: [lat_max,lon_min,lat_min,lon_max] (top-left, bottom-right). - Returns separate lat and lon vectors.''' - - # Extract values - lon = [coords[1],coords[3]] - lat = [coords[2],coords[0]] - - # Round to ERA5 0.25 degree resolution - rounded_lon = [math.floor(lon[0]*4)/4, math.ceil(lon[1]*4)/4] - rounded_lat = [math.floor(lat[0]*4)/4, math.ceil(lat[1]*4)/4] - - # Find if we are still in the representative area of a different ERA5 grid cell - if lat[0] > rounded_lat[0]+0.125: - rounded_lat[0] += 0.25 - if lon[0] > rounded_lon[0]+0.125: - rounded_lon[0] += 0.25 - if lat[1] < rounded_lat[1]-0.125: - rounded_lat[1] -= 0.25 - if lon[1] < rounded_lon[1]-0.125: - rounded_lon[1] -= 0.25 - - # Make a download string - dl_string = '{}/{}/{}/{}'.format(rounded_lat[1],rounded_lon[0],rounded_lat[0],rounded_lon[1]) - - return dl_string, rounded_lat, rounded_lon - -# Find the rounded bounding box -coordinates,_,_ = round_coords_to_ERA5(bounding_box) - - -# --- Specify date to download - -# Geopotential is part of the ERA5 "invariant" data, which are constant through time. -# Therefore, specify an arbitrary date to download -date = '2019-01-01' - - -# --- Download the data - -# Specify a filename -file = geoPath / 'ERA5_geopotential.nc' - -# if file doesn't yet exist, download the data -if not os.path.isfile(file): - - # Make sure the connection is re-tried if it fails - retries_max = 10 - retries_cur = 1 - while retries_cur <= retries_max: - try: - - # connect to Copernicus (requires .cdsapirc file in $HOME) - c = cdsapi.Client() - - # specify and retrieve data - c.retrieve('reanalysis-era5-complete', { # do not change this! - 'stream': 'oper', - 'levtype': 'sf', - 'param': '26/228007/27/28/29/30/43/74/129/160/161/162/163/172', - 'date': date, - 'time': '00',#/to/23/by/1', - 'area': coordinates, - 'grid': '0.25/0.25', # Latitude/longitude grid: east-west (longitude) and north-south resolution (latitude). - 'format' : 'netcdf', - }, file) - - # track progress - print('Successfully downloaded ' + str(file)) - - except: - print('Error downloading ' + str(file) + ' on try ' + str(retries_cur)) - retries_cur += 1 - continue - else: - break - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Create a log folder -logFolder = '_era5_invariants_log' -Path( geoPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = 'download_ERA5_geopotential.py' -copyfile(thisFile, geoPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + '_pressure_level_log.txt' -with open( geoPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Downloaded ERA5 geopotential data for space (lat_max, lon_min, lat_min, lon_max) [{}].'.format(coordinates)] - for txt in lines: - file.write(txt) \ No newline at end of file diff --git a/3a_forcing/2_merge_forcing/ERA5_surface_and_pressure_level_combiner.ipynb b/3a_forcing/2_merge_forcing/ERA5_surface_and_pressure_level_combiner.ipynb deleted file mode 100644 index def832a..0000000 --- a/3a_forcing/2_merge_forcing/ERA5_surface_and_pressure_level_combiner.ipynb +++ /dev/null @@ -1,512 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Combine separate surface and pressure level downloads\n", - "Creates a single monthly `.nc` file with SUMMA-ready variables for further processing. Combines ERA5's `u` and `v` wind components into a single directionless wind vector." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "from datetime import datetime\n", - "from shutil import copyfile\n", - "from pathlib import Path\n", - "import netCDF4 as nc4\n", - "import numpy as np\n", - "import time\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find source and destination paths" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the raw forcing is\n", - "# Immediately store as a 'Path' to avoid issues with '/' and '\\' on different operating systems\n", - "forcingPath = read_from_control(controlFolder/controlFile,'forcing_raw_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the merged forcing needs to go\n", - "mergePath = read_from_control(controlFolder/controlFile,'forcing_merged_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required\n", - "if forcingPath == 'default':\n", - " forcingPath = make_default_path('forcing/1_ERA5_raw_data')\n", - "else: \n", - " forcingPath = Path(forcingPath) # ensure Path() object " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "if mergePath == 'default':\n", - " mergePath = make_default_path('forcing/2_merged_data')\n", - "else: \n", - " mergePath = Path(mergePath) # ensure Path() object " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the merge folder if it doesn't exist\n", - "mergePath.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find years to merge" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Find which years were downloaded\n", - "years = read_from_control(controlFolder/controlFile,'forcing_raw_time')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Split the string into 2 integers\n", - "years = years.split(',')\n", - "years = [int(year) for year in years]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Merge the files" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finished merging ERA5_surface_197901.nc and ERA5_pressureLevel137_197901.nc into ERA5_merged_197901.nc\n", - "Finished merging ERA5_surface_197902.nc and ERA5_pressureLevel137_197902.nc into ERA5_merged_197902.nc\n", - "Finished merging ERA5_surface_197903.nc and ERA5_pressureLevel137_197903.nc into ERA5_merged_197903.nc\n", - "Finished merging ERA5_surface_197904.nc and ERA5_pressureLevel137_197904.nc into ERA5_merged_197904.nc\n", - "Finished merging ERA5_surface_197905.nc and ERA5_pressureLevel137_197905.nc into ERA5_merged_197905.nc\n", - "Finished merging ERA5_surface_197906.nc and ERA5_pressureLevel137_197906.nc into ERA5_merged_197906.nc\n", - "Finished merging ERA5_surface_197907.nc and ERA5_pressureLevel137_197907.nc into ERA5_merged_197907.nc\n", - "Finished merging ERA5_surface_197908.nc and ERA5_pressureLevel137_197908.nc into ERA5_merged_197908.nc\n", - "Finished merging ERA5_surface_197909.nc and ERA5_pressureLevel137_197909.nc into ERA5_merged_197909.nc\n", - "Finished merging ERA5_surface_197910.nc and ERA5_pressureLevel137_197910.nc into ERA5_merged_197910.nc\n", - "Finished merging ERA5_surface_197911.nc and ERA5_pressureLevel137_197911.nc into ERA5_merged_197911.nc\n", - "Finished merging ERA5_surface_197912.nc and ERA5_pressureLevel137_197912.nc into ERA5_merged_197912.nc\n" - ] - } - ], - "source": [ - "# Loop through all years and months\n", - "for year in range(years[0],years[1]+1):\n", - " for month in range (1,13):\n", - "\n", - " # Define file names \n", - " data_pres = 'ERA5_pressureLevel137_' + str(year) + str(month).zfill(2) + '.nc'\n", - " data_surf = 'ERA5_surface_' + str(year) + str(month).zfill(2) + '.nc'\n", - " data_dest = 'ERA5_merged_' + str(year) + str(month).zfill(2) + '.nc'\n", - "\n", - " # Step 1: convert lat/lon in the pressure level file to range [-180,180], [-90,90]\n", - " # Extract the variables we need for the similarity check in a way that closes the files implicitly\n", - " with nc4.Dataset(forcingPath / data_pres) as src1, nc4.Dataset(forcingPath / data_surf) as src2:\n", - " pres_lat = src1.variables['latitude'][:]\n", - " pres_lon = src1.variables['longitude'][:]\n", - " pres_time = src1.variables['time'][:]\n", - " surf_lat = src2.variables['latitude'][:]\n", - " surf_lon = src2.variables['longitude'][:]\n", - " surf_time = src2.variables['time'][:]\n", - "\n", - " # Update the pressure level coordinates\n", - " pres_lat[pres_lat > 90] = pres_lat[pres_lat > 90] - 180\n", - " pres_lon[pres_lon > 180] = pres_lon[pres_lon > 180] - 360\n", - "\n", - " # Step 2: check that coordinates and time are the same between the both files\n", - " # Compare dimensions (lat, long, time)\n", - " flag_loc_and_time_same = [all(pres_lat == surf_lat), all(pres_lon == surf_lon), all(pres_time == surf_time)]\n", - "\n", - " # Check that they are all the same\n", - " if not all(flag_loc_and_time_same):\n", - " err_txt = 'Dimension mismatch while merging ' + data_pres + ' and ' + data_surf + '. Check latitude, longitude and time dimensions in both files. Continuing with next files.'\n", - " print(err_txt)\n", - " continue\n", - "\n", - " # Step 3: combine everything into a single .nc file\n", - " # Order of writing things:\n", - " # - Meta attributes from both source files\n", - " # - Dimensions (lat, lon, time)\n", - " # - Variables: long, lat and time\n", - " # - Variables: forcing at surface\n", - " # - Variables: forcing at pressure level 137\n", - "\n", - " # Define the variables we want to transfer\n", - " variables_surf_transfer = ['longitude','latitude','time']\n", - " variables_surf_convert = ['sp','mtpr','msdwswrf','msdwlwrf']\n", - " variables_pres_convert = ['t','q']\n", - " attr_names_expected = ['scale_factor','add_offset','_FillValue','missing_value','units','long_name','standard_name'] # these are the attributes we think each .nc variable has \n", - " loop_attr_copy_these = ['units','long_name','standard_name'] # we will define new values for _FillValue and missing_value when writing the .nc variables' attributes\n", - "\n", - " # Open the destination file and transfer information\n", - " with nc4.Dataset(forcingPath / data_pres) as src1, nc4.Dataset(forcingPath / data_surf) as src2, \\\n", - " nc4.Dataset(mergePath / data_dest, \"w\") as dest: \n", - " \n", - " # === Some general attributes\n", - " dest.setncattr('History','Created ' + time.ctime(time.time()))\n", - " dest.setncattr('Language','Written using Python')\n", - " dest.setncattr('Reason','(1) ERA5 surface and pressure files need to be combined into a single file (2) Wind speed U and V components need to be combined into a single vector (3) Forcing variables need to be given to SUMMA without scale and offset')\n", - " \n", - " # === Meta attributes from both sources\n", - " for name in src1.ncattrs():\n", - " dest.setncattr(name + ' (pressure level (10m) data)', src1.getncattr(name))\n", - " for name in src2.ncattrs():\n", - " dest.setncattr(name + ' (surface level data)', src1.getncattr(name))\n", - " \n", - " # === Dimensions: latitude, longitude, time\n", - " # NOTE: we can use the lat/lon from the surface file (src2), because those are already in proper units. If there is a mismatch between surface and pressure we shouldn't have reached this point at all due to the check above\n", - " for name, dimension in src2.dimensions.items():\n", - " if dimension.isunlimited():\n", - " dest.createDimension( name, None)\n", - " else:\n", - " dest.createDimension( name, len(dimension))\n", - " \n", - " # === Get the surface level generic variables (lat, lon, time)\n", - " for name, variable in src2.variables.items():\n", - " \n", - " # Transfer lat, long and time variables because these don't have scaling factors\n", - " if name in variables_surf_transfer:\n", - " dest.createVariable(name, variable.datatype, variable.dimensions, fill_value = -999)\n", - " dest[name].setncatts(src1[name].__dict__)\n", - " dest.variables[name][:] = src2.variables[name][:]\n", - " \n", - " # === For the forcing variables, we need to:\n", - " # 1. Extract them (this automatically applies scaling and offset with nc4) and apply non-negativity constraints\n", - " # 2. Create a .nc variable with the right SUMMA name and file type\n", - " # 3. Put all data into the new .nc file\n", - " \n", - " # === Transfer the surface level data first, for no particular reason\n", - " # This should contain surface pressure (sp), downward longwave (msdwlwrf), downward shortwave (msdwswrf) and precipitation (mtpr)\n", - " for name, variable in src2.variables.items():\n", - " \n", - " # Check that we are only using the names we expect, and thus the names for which we have the required code ready\n", - " if name in variables_surf_convert:\n", - " \n", - " # 0. Reset the dictionary that we keep attribute values in\n", - " loop_attr_source_values = {name: 'n/a' for name in attr_names_expected}\n", - " \n", - " # 1a. Get the values of this variable from the source (this automatically applies scaling and offset)\n", - " loop_val = variable[:]\n", - "\n", - " # 1b. Apply non-negativity constraint. This is intended to remove very small negative data values that sometimes occur\n", - " loop_val[loop_val < 0] = 0\n", - " \n", - " # 1c. Get the attributes for this variable from source\n", - " for attrname in variable.ncattrs():\n", - " loop_attr_source_values[attrname] = variable.getncattr(attrname)\n", - " \n", - " # 2a. Find what this ERA5 variable should be called in SUMMA\n", - " if name == 'sp':\n", - " name_summa = 'airpres'\n", - " elif name == 'msdwlwrf':\n", - " name_summa = 'LWRadAtm'\n", - " elif name == 'msdwswrf':\n", - " name_summa = 'SWRadAtm'\n", - " elif name == 'mtpr':\n", - " name_summa = 'pptrate' \n", - " else:\n", - " name_summa = 'n/a/' # no name so we don't start overwriting data if a new name is not defined for some reason\n", - " \n", - " # 2b. Create the .nc variable with the proper SUMMA name\n", - " # Inputs: variable name as needed by SUMMA; data type: 'float'; dimensions; no need for fill value, because thevariable gets populated in this same script\n", - " dest.createVariable(name_summa, 'f4', ('time','latitude','longitude'), fill_value = False)\n", - " \n", - " # 3a. Select the attributes we want to copy for this variable, based on the dictionary defined before the loop starts\n", - " loop_attr_copy_values = {use_this: loop_attr_source_values[use_this] for use_this in loop_attr_copy_these}\n", - " \n", - " # 3b. Copy the attributes FIRST, so we don't run into any scaling/offset issues\n", - " dest[name_summa].setncattr('missing_value',-999)\n", - " dest[name_summa].setncatts(loop_attr_copy_values)\n", - " \n", - " # 3c. Copy the data SECOND\n", - " dest[name_summa][:] = loop_val\n", - " \n", - " # === Transfer the pressure level variables next, using the same procedure as above\n", - " for name, variable in src1.variables.items():\n", - " if name in variables_pres_convert:\n", - " \n", - " # 0. Reset the dictionary that we keep attribute values in\n", - " loop_attr_source_values = {name: 'n/a' for name in attr_names_expected}\n", - " \n", - " # 1a. Get the values of this variable from the source (this automatically applies scaling and offset)\n", - " loop_val = variable[:] \n", - " \n", - " # 1b. Get the attributes for this variable from source\n", - " for attrname in variable.ncattrs():\n", - " loop_attr_source_values[attrname] = variable.getncattr(attrname)\n", - " \n", - " # 2a. Find what this ERA5 variable should be called in SUMMA\n", - " if name == 't':\n", - " name_summa = 'airtemp'\n", - " elif name == 'q':\n", - " name_summa = 'spechum'\n", - " elif name == 'u':\n", - " name_summa = 'n/a/' # we shouldn't reach this part of the code, because 'u' is not specified in 'variables_pres_convert'\n", - " elif name == 'v':\n", - " name_summa = 'n/a' # as with 'u', because both are needed to calculate total wind speed first\n", - " else:\n", - " name_summa = 'n/a/' # no name so we don't start overwriting data if a new name is not defined for some reason\n", - " \n", - " # 2b. Create the .nc variable with the proper SUMMA name\n", - " # Inputs: variable name as needed by SUMMA; data type: 'float'; dimensions; no need for fill value, because thevariable gets populated in this same script\n", - " dest.createVariable(name_summa, 'f4', ('time','latitude','longitude'), fill_value = False)\n", - " \n", - " # 3a. Select the attributes we want to copy for this variable, based on the dictionary defined before the loop starts\n", - " loop_attr_copy_values = {use_this: loop_attr_source_values[use_this] for use_this in loop_attr_copy_these}\n", - " \n", - " # 3b. Copy the attributes FIRST, so we don't run into any scaling/offset issues\n", - " dest[name_summa].setncattr('missing_value',-999)\n", - " dest[name_summa].setncatts(loop_attr_copy_values)\n", - " \n", - " # 3c. Copy the data SECOND\n", - " dest[name_summa][:] = loop_val\n", - " \n", - " # === Calculate combined wind speed and store\n", - " # 1a. Get the values of this variable from the source (this automatically applies scaling and offset)\n", - " pres_u = src1.variables['u'][:]\n", - " pres_v = src1.variables['v'][:]\n", - " \n", - " # 1b. Create the variable attribute 'units' from the source data. This lets us check if the source units match (they should match)\n", - " unit_u = src1.variables['u'].getncattr('units')\n", - " unit_v = src1.variables['v'].getncattr('units')\n", - " unit_w = '(({})**2 + ({})**2)**0.5'.format(unit_u,unit_v) \n", - " \n", - " # 2a. Set the summa_name\n", - " name_summa = 'windspd'\n", - " \n", - " # 2b. Create the .nc variable with the proper SUMMA name\n", - " # Inputs: variable name as needed by SUMMA; data type: 'float'; dimensions; no need for fill value, because thevariable gets populated in this same script\n", - " dest.createVariable(name_summa,'f4',('time','latitude','longitude'),fill_value = False)\n", - " \n", - " # 3a. Set the attributes FIRST, so we don't run into any scaling/offset issues\n", - " dest[name_summa].setncattr('missing_value',-999)\n", - " dest[name_summa].setncattr('units',unit_w)\n", - " dest[name_summa].setncattr('long_name','wind speed at the measurement height, computed from ERA5 U and V-components')\n", - " dest[name_summa].setncattr('standard_name','wind_speed')\n", - " \n", - " # 3b. Copy the data SECOND\n", - " # Creating a new variable first and writing to .nc later seems faster than directly writing to .nc\n", - " pres_w = ((pres_u**2)+(pres_v**2))**0.5\n", - " dest[name_summa][:] = pres_w\n", - " \n", - " print('Finished merging {} and {} into {}'.format(data_surf,data_pres,data_dest))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( mergePath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'ERA5_surface_and_pressure_level_combiner.ipynb'\n", - "copyfile(thisFile, mergePath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + '_merge_forcing_log.txt'\n", - "with open( mergePath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Merged ERA5 pressure and surface level data into single files.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/3a_forcing/2_merge_forcing/ERA5_surface_and_pressure_level_combiner.py b/3a_forcing/2_merge_forcing/ERA5_surface_and_pressure_level_combiner.py deleted file mode 100644 index 245da04..0000000 --- a/3a_forcing/2_merge_forcing/ERA5_surface_and_pressure_level_combiner.py +++ /dev/null @@ -1,313 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# Combine separate surface and pressure level downloads -# Creates a single monthly `.nc` file with SUMMA-ready variables for further processing. # Combines ERA5's `u` and `v` wind components into a single directionless wind vector. - -# modules -from datetime import datetime -from shutil import copyfile -from pathlib import Path -import netCDF4 as nc4 -import numpy as np -import time -import sys -import os - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find source and destination paths -# Find the path where the raw forcing is -# Immediately store as a 'Path' to avoid issues with '/' and '\' on different operating systems -forcingPath = read_from_control(controlFolder/controlFile,'forcing_raw_path') - -# Find the path where the merged forcing needs to go -mergePath = read_from_control(controlFolder/controlFile,'forcing_merged_path') - -# Specify the default paths if required -if forcingPath == 'default': - forcingPath = make_default_path('forcing/1_ERA5_raw_data') -else: - forcingPath = Path(forcingPath) # ensure Path() object - -if mergePath == 'default': - mergePath = make_default_path('forcing/2_merged_data') -else: - mergePath = Path(mergePath) # ensure Path() object - -# Make the merge folder if it doesn't exist -mergePath.mkdir(parents=True, exist_ok=True) - - -# --- Find the years to merge -# Find which years were downloaded -years = read_from_control(controlFolder/controlFile,'forcing_raw_time') - -# Split the string into 2 integers -years = years.split(',') -years = [int(year) for year in years] - - -# --- Merge the files -# Loop through all years and months -for year in range(years[0],years[1]+1): - for month in range (1,13): - - # Define file names - data_pres = 'ERA5_pressureLevel137_' + str(year) + str(month).zfill(2) + '.nc' - data_surf = 'ERA5_surface_' + str(year) + str(month).zfill(2) + '.nc' - data_dest = 'ERA5_merged_' + str(year) + str(month).zfill(2) + '.nc' - - # Step 1: convert lat/lon in the pressure level file to range [-180,180], [-90,90] - # Extract the variables we need for the similarity check in a way that closes the files implicitly - with nc4.Dataset(forcingPath / data_pres) as src1, nc4.Dataset(forcingPath / data_surf) as src2: - pres_lat = src1.variables['latitude'][:] - pres_lon = src1.variables['longitude'][:] - pres_time = src1.variables['time'][:] - surf_lat = src2.variables['latitude'][:] - surf_lon = src2.variables['longitude'][:] - surf_time = src2.variables['time'][:] - - # Update the pressure level coordinates - pres_lat[pres_lat > 90] = pres_lat[pres_lat > 90] - 180 - pres_lon[pres_lon > 180] = pres_lon[pres_lon > 180] - 360 - - # Step 2: check that coordinates and time are the same between the both files - # Compare dimensions (lat, long, time) - flag_loc_and_time_same = [all(pres_lat == surf_lat), all(pres_lon == surf_lon), all(pres_time == surf_time)] - - # Check that they are all the same - if not all(flag_loc_and_time_same): - err_txt = 'Dimension mismatch while merging ' + data_pres + ' and ' + data_surf + '. Check latitude, longitude and time dimensions in both files. Continuing with next files.' - print(err_txt) - continue - - # Step 3: combine everything into a single .nc file - # Order of writing things: - # - Meta attributes from both source files - # - Dimensions (lat, lon, time) - # - Variables: long, lat and time - # - Variables: forcing at surface - # - Variables: forcing at pressure level 137 - - # Define the variables we want to transfer - variables_surf_transfer = ['longitude','latitude','time'] - variables_surf_convert = ['sp','mtpr','msdwswrf','msdwlwrf'] - variables_pres_convert = ['t','q'] - attr_names_expected = ['scale_factor','add_offset','_FillValue','missing_value','units','long_name','standard_name'] # these are the attributes we think each .nc variable has - loop_attr_copy_these = ['units','long_name','standard_name'] # we will define new values for _FillValue and missing_value when writing the .nc variables' attributes - - # Open the destination file and transfer information - with nc4.Dataset(forcingPath / data_pres) as src1, nc4.Dataset(forcingPath / data_surf) as src2, nc4.Dataset(mergePath / data_dest, "w") as dest: - - # === Some general attributes - dest.setncattr('History','Created ' + time.ctime(time.time())) - dest.setncattr('Language','Written using Python') - dest.setncattr('Reason','(1) ERA5 surface and pressure files need to be combined into a single file (2) Wind speed U and V components need to be combined into a single vector (3) Forcing variables need to be given to SUMMA without scale and offset') - - # === Meta attributes from both sources - for name in src1.ncattrs(): - dest.setncattr(name + ' (pressure level (10m) data)', src1.getncattr(name)) - for name in src2.ncattrs(): - dest.setncattr(name + ' (surface level data)', src1.getncattr(name)) - - # === Dimensions: latitude, longitude, time - # NOTE: we can use the lat/lon from the surface file (src2), because those are already in proper units. If there is a mismatch between surface and pressure we shouldn't have reached this point at all due to the check above - for name, dimension in src2.dimensions.items(): - if dimension.isunlimited(): - dest.createDimension( name, None) - else: - dest.createDimension( name, len(dimension)) - - # === Get the surface level generic variables (lat, lon, time) - for name, variable in src2.variables.items(): - - # Transfer lat, long and time variables because these don't have scaling factors - if name in variables_surf_transfer: - dest.createVariable(name, variable.datatype, variable.dimensions, fill_value = -999) - dest[name].setncatts(src1[name].__dict__) - dest.variables[name][:] = src2.variables[name][:] - - # === For the forcing variables, we need to: - # 1. Extract them (this automatically applies scaling and offset with nc4) and apply non-negativity constraints - # 2. Create a .nc variable with the right SUMMA name and file type - # 3. Put all data into the new .nc file - - # === Transfer the surface level data first, for no particular reason - # This should contain surface pressure (sp), downward longwave (msdwlwrf), downward shortwave (msdwswrf) and precipitation (mtpr) - for name, variable in src2.variables.items(): - - # Check that we are only using the names we expect, and thus the names for which we have the required code ready - if name in variables_surf_convert: - - # 0. Reset the dictionary that we keep attribute values in - loop_attr_source_values = {name: 'n/a' for name in attr_names_expected} - - # 1a. Get the values of this variable from the source (this automatically applies scaling and offset) - loop_val = variable[:] - - # 1b. Apply non-negativity constraint. This is intended to remove very small negative data values that sometimes occur - loop_val[loop_val < 0] = 0 - - # 1c. Get the attributes for this variable from source - for attrname in variable.ncattrs(): - loop_attr_source_values[attrname] = variable.getncattr(attrname) - - # 2a. Find what this ERA5 variable should be called in SUMMA - if name == 'sp': - name_summa = 'airpres' - elif name == 'msdwlwrf': - name_summa = 'LWRadAtm' - elif name == 'msdwswrf': - name_summa = 'SWRadAtm' - elif name == 'mtpr': - name_summa = 'pptrate' - else: - name_summa = 'n/a/' # no name so we don't start overwriting data if a new name is not defined for some reason - - # 2b. Create the .nc variable with the proper SUMMA name - # Inputs: variable name as needed by SUMMA; data type: 'float'; dimensions; no need for fill value, because thevariable gets populated in this same script - dest.createVariable(name_summa, 'f4', ('time','latitude','longitude'), fill_value = False) - - # 3a. Select the attributes we want to copy for this variable, based on the dictionary defined before the loop starts - loop_attr_copy_values = {use_this: loop_attr_source_values[use_this] for use_this in loop_attr_copy_these} - - # 3b. Copy the attributes FIRST, so we don't run into any scaling/offset issues - dest[name_summa].setncattr('missing_value',-999) - dest[name_summa].setncatts(loop_attr_copy_values) - - # 3c. Copy the data SECOND - dest[name_summa][:] = loop_val - - # === Transfer the pressure level variables next, using the same procedure as above - for name, variable in src1.variables.items(): - if name in variables_pres_convert: - - # 0. Reset the dictionary that we keep attribute values in - loop_attr_source_values = {name: 'n/a' for name in attr_names_expected} - - # 1a. Get the values of this variable from the source (this automatically applies scaling and offset) - loop_val = variable[:] - - # 1b. Get the attributes for this variable from source - for attrname in variable.ncattrs(): - loop_attr_source_values[attrname] = variable.getncattr(attrname) - - # 2a. Find what this ERA5 variable should be called in SUMMA - if name == 't': - name_summa = 'airtemp' - elif name == 'q': - name_summa = 'spechum' - elif name == 'u': - name_summa = 'n/a/' # we shouldn't reach this part of the code, because 'u' is not specified in 'variables_pres_convert' - elif name == 'v': - name_summa = 'n/a' # as with 'u', because both are needed to calculate total wind speed first - else: - name_summa = 'n/a/' # no name so we don't start overwriting data if a new name is not defined for some reason - - # 2b. Create the .nc variable with the proper SUMMA name - # Inputs: variable name as needed by SUMMA; data type: 'float'; dimensions; no need for fill value, because thevariable gets populated in this same script - dest.createVariable(name_summa, 'f4', ('time','latitude','longitude'), fill_value = False) - - # 3a. Select the attributes we want to copy for this variable, based on the dictionary defined before the loop starts - loop_attr_copy_values = {use_this: loop_attr_source_values[use_this] for use_this in loop_attr_copy_these} - - # 3b. Copy the attributes FIRST, so we don't run into any scaling/offset issues - dest[name_summa].setncattr('missing_value',-999) - dest[name_summa].setncatts(loop_attr_copy_values) - - # 3c. Copy the data SECOND - dest[name_summa][:] = loop_val - - # === Calculate combined wind speed and store - # 1a. Get the values of this variable from the source (this automatically applies scaling and offset) - pres_u = src1.variables['u'][:] - pres_v = src1.variables['v'][:] - - # 1b. Create the variable attribute 'units' from the source data. This lets us check if the source units match (they should match) - unit_u = src1.variables['u'].getncattr('units') - unit_v = src1.variables['v'].getncattr('units') - unit_w = '(({})**2 + ({})**2)**0.5'.format(unit_u,unit_v) - - # 2a. Set the summa_name - name_summa = 'windspd' - - # 2b. Create the .nc variable with the proper SUMMA name - # Inputs: variable name as needed by SUMMA; data type: 'float'; dimensions; no need for fill value, because thevariable gets populated in this same script - dest.createVariable(name_summa,'f4',('time','latitude','longitude'),fill_value = False) - - # 3a. Set the attributes FIRST, so we don't run into any scaling/offset issues - dest[name_summa].setncattr('missing_value',-999) - dest[name_summa].setncattr('units',unit_w) - dest[name_summa].setncattr('long_name','wind speed at the measurement height, computed from ERA5 U and V-components') - dest[name_summa].setncattr('standard_name','wind_speed') - - # 3b. Copy the data SECOND - # Creating a new variable first and writing to .nc later seems faster than directly writing to .nc - pres_w = ((pres_u**2)+(pres_v**2))**0.5 - dest[name_summa][:] = pres_w - - print('Finished merging {} and {} into {}'.format(data_surf,data_pres,data_dest)) - - -# --- Code provenance -# Create a log folder -logFolder = '_workflow_log' -Path( mergePath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = 'ERA5_surface_and_pressure_level_combiner.py' -copyfile(thisFile, mergePath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + '_pressure_level_log.txt' -with open( mergePath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Merged ERA5 pressure and surface level data into single files.'] - for txt in lines: - file.write(txt) - diff --git a/3a_forcing/2_merge_forcing/README.md b/3a_forcing/2_merge_forcing/README.md deleted file mode 100644 index 58d69e1..0000000 --- a/3a_forcing/2_merge_forcing/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# ERA5 data merger -Creates a single monthly `.nc` file with SUMMA-ready variables for further processing. Combines ERA5's `u` and `v` wind components into a single directionless wind vector. - -## Description -We downloaded separate ERA5 data at pressure level 137 and the surface. We want all the forcing aggregated into a single file and to be in the right units for SUMMA (ERA5 units are already OK for use in SUMMA): -- Precipitation as `pptrate` in [kg m\*\*-2 s**-1] -- Air pressure at measurement height (assumed to be equivalent to air pressure at the surface) as `airpres` in [Pa] -- Downward shortwave radiation as `SWRadAtm` in [W m**-2] -- Downward longwave radiation as `LWRadAtm` in [W m**-2] -- Temperature at measurement height as `airtemp` in [K] -- Specific humidity at measurement height as `spechum` in [g g**-1] -- Wind speed at measurement height as 'windspd' in [m s**-1] - -The ERA5 data are split out over two files, that each contain hourly data for a given combination of year and month. Files named `ERA5_surface\_[yyyymm].nc` contain variables at the surface: -- precipitation (`mtpr`), -- surface pressure (`sp`), -- mean surface downward shortwave radiation (`msdwswrf`) -- mean surface downward longwave radiation(`msdwlwrf`) - -Files named `ERA5_pressureLevel137\_[yyymm].nc` contain variables at at the lowest pressure level of the atmospheric model (10m): -- temperature (`t`), -- specific humidity (`q`) -- u-component of wind speed (`u`) -- v-component of wind speed (`v`) - -This script goes through the following steps: -1. Convert longitude coordinates in pressureLevel file to range [-180,180] -2. Checks -- are lat/lon the same for both data sets? -- are times the same for both datasets? -3. Aggregate data into a single file `ERA5_NA_[yyyymm].nc`, keeping the relevant metadata in place - -## Assumptions not included in `control_active.txt` -Code assumes it operates on the same years that were downloaded, contained in field `forcing_raw_time` in the control file. To merge only a subset of these files, change the specification of the `years` variable. \ No newline at end of file diff --git a/3a_forcing/3_create_shapefile/README.md b/3a_forcing/3_create_shapefile/README.md deleted file mode 100644 index e01f43f..0000000 --- a/3a_forcing/3_create_shapefile/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Create ERA5 shapefile -The shapefile for the forcing data needs to represent the regular latitude/longitude grid of the ERA5 data. We need this for later intersection with the catchment shape(s) so we can create appropriately weighted forcing for each model element. - -Notebook/script reads location of merged forcing data and the spatial extent of the data from the control file. - -## Assumptions not included in `control_active.txt` -- Code assumes that the merged forcing contains dimension variables with the names "latitude" and "longitude". This is the case for ERA5. \ No newline at end of file diff --git a/3a_forcing/3_create_shapefile/create_ERA5_shapefile.ipynb b/3a_forcing/3_create_shapefile/create_ERA5_shapefile.ipynb deleted file mode 100644 index 90a0707..0000000 --- a/3a_forcing/3_create_shapefile/create_ERA5_shapefile.ipynb +++ /dev/null @@ -1,818 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Make ERA5 shapefile\n", - "The shapefile for the forcing data needs to represent the regular latitude/longitude grid of the ERA5 data. We need this for later intersection with the catchment shape(s) so we can create appropriately weighted forcing for each model element. \n", - "\n", - "Note that ERA5 data should be thought of as being provided for a specific point in space. These points are the lat/lon coordinates found as netcdf dimensions in the data files. Here, these coordinates will act as center points for a grid. Each point is assumed to be representative for an equal-sized area around itself. See: https://confluence.ecmwf.int/display/CKB/ERA5%3A+What+is+the+spatial+reference\n", - "\n", - "We'll also add the elevation of each ERA5 grid cell for later temperature lapse rates. As per ERA5 docs, geopotential `[m^2 s^-2]` can be divided by `g=9.80665` `[m s^-2]` to obtain ERA5 elevation in `[m]`. Source: https://confluence.ecmwf.int/display/CKB/ERA5%3A+surface+elevation+and+orography#ERA5:surfaceelevationandorography-Howtocalculatesurfacegeopotentialheight\n", - "\n", - "#### Acknowledgements\n", - "The code to generate the shapefile is inspired by a function from the EASYMORE toolbox: https://github.com/ShervanGharari/EASYMORE/. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import shapefile # this is installed as \"PyShp\" library\n", - "import numpy as np\n", - "import xarray as xr\n", - "import netCDF4 as nc4\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of merged forcing data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the merged forcing is\n", - "mergePath = read_from_control(controlFolder/controlFile,'forcing_merged_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default path if required\n", - "if mergePath == 'default':\n", - " mergePath = make_default_path('forcing/2_merged_data')\n", - "else: \n", - " mergePath = Path(mergePath) # ensure Path() object " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find spatial extent of domain" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Find which locations to download\n", - "coordinates = read_from_control(controlFolder/controlFile,'forcing_raw_space')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of geopotential data file" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Find file path\n", - "geoPath = read_from_control(controlFolder/controlFile,'forcing_geo_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default path if required\n", - "if geoPath == 'default':\n", - " geoPath = make_default_path('forcing/0_geopotential')\n", - "else: \n", - " geoPath = Path(geoPath) # ensure Path() object " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the filename\n", - "geoName = 'ERA5_geopotential.nc'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the shapefile needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the new shapefile needs to go\n", - "shapePath = read_from_control(controlFolder/controlFile,'forcing_shape_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default path if required\n", - "if shapePath == 'default':\n", - " shapePath = make_default_path('shapefiles/forcing')\n", - "else: \n", - " shapePath = Path(shapePath) # ensure Path() object " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Find name of the new shapefil\n", - "shapeName = read_from_control(controlFolder/controlFile,'forcing_shape_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the names of the latitude and longitude fields\n", - "field_lat = read_from_control(controlFolder/controlFile,'forcing_shape_lat_name')\n", - "field_lon = read_from_control(controlFolder/controlFile,'forcing_shape_lon_name')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Read the source file to find the grid spacing" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Find an .nc file in the forcing path\n", - "for file in os.listdir(mergePath):\n", - " if file.endswith('.nc'):\n", - " forcing_file = file\n", - " break" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the dimension variable names\n", - "source_name_lat = \"latitude\"\n", - "source_name_lon = \"longitude\"" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the file and get the dimensions\n", - "with nc4.Dataset(mergePath / forcing_file) as src:\n", - " lat = src.variables[source_name_lat][:]\n", - " lon = src.variables[source_name_lon][:]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the spacing\n", - "half_dlat = abs(lat[1] - lat[0])/2\n", - "half_dlon = abs(lon[1] - lon[0])/2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the new shape" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# create a new shapefile object\n", - "with shapefile.Writer(str(shapePath / shapeName)) as w:\n", - " w.autoBalance = 1 # turn on function that keeps file stable if number of shapes and records don't line up\n", - " w.field(\"ID\",'N') # create (N)umerical attribute fields, integer\n", - " w.field(field_lat,'F',decimal=4) # float with 4 decimals\n", - " w.field(field_lon,'F',decimal=4)\n", - " ID = 0 # start ID counter of empty\n", - " \n", - " for i in range(0,len(lon)):\n", - " for j in range(0,len(lat)):\n", - " ID += 1\n", - " center_lon = lon[i]\n", - " center_lat = lat[j]\n", - " vertices = []\n", - " parts = []\n", - " vertices.append([center_lon-half_dlon, center_lat])\n", - " vertices.append([center_lon-half_dlon, center_lat+half_dlat])\n", - " vertices.append([center_lon , center_lat+half_dlat])\n", - " vertices.append([center_lon+half_dlon, center_lat+half_dlat])\n", - " vertices.append([center_lon+half_dlon, center_lat])\n", - " vertices.append([center_lon+half_dlon, center_lat-half_dlat])\n", - " vertices.append([center_lon , center_lat-half_dlat])\n", - " vertices.append([center_lon-half_dlon, center_lat-half_dlat])\n", - " vertices.append([center_lon-half_dlon, center_lat])\n", - " parts.append(vertices)\n", - " w.poly(parts)\n", - " w.record(ID, center_lat, center_lon)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the shapefile to see if this matches the bounds\n", - "shp = gpd.read_file( shapePath / shapeName )\n", - "shp.plot(figsize=(10,10), facecolor='none', edgecolor='b');\n", - "plt.title('Grid should cover catchment bounding box [{}]'.format(coordinates));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Add the geopotential data to the shape" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the geopotential data file\n", - "geo = xr.open_dataset( geoPath / geoName ).isel(time=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the geopotential data to check\n", - "geo['z'].plot();" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Open shapefile (again)\n", - "shp = gpd.read_file( shapePath / shapeName )" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the constant\n", - "g = 9.80665" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Add new column to shapefile\n", - "shp = shp.assign(elev_m = -999) # insert a placeholder value" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# For each row in the shapefile, match its ERA5 lat/lon coordinates \n", - "# with those in the 'geo' file and extract the appropriate geopotential\n", - "for index, row in shp.iterrows():\n", - " \n", - " # Find elevation\n", - " elev = geo['z'].sel(latitude = row['lat'], longitude=row['lon']).values.flatten() / g\n", - " \n", - " # Add elevation into shapefile\n", - " shp.at[index,'elev_m'] = elev[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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IDlatlongeometryelev_m
01.051.75-116.50POLYGON ((-116.62500 51.75000, -116.62500 51.8...2243
12.051.50-116.50POLYGON ((-116.62500 51.50000, -116.62500 51.6...2093
23.051.25-116.50POLYGON ((-116.62500 51.25000, -116.62500 51.3...1810
34.051.00-116.50POLYGON ((-116.62500 51.00000, -116.62500 51.1...1637
45.051.75-116.25POLYGON ((-116.37500 51.75000, -116.37500 51.8...2284
56.051.50-116.25POLYGON ((-116.37500 51.50000, -116.37500 51.6...2258
67.051.25-116.25POLYGON ((-116.37500 51.25000, -116.37500 51.3...1999
78.051.00-116.25POLYGON ((-116.37500 51.00000, -116.37500 51.1...1641
89.051.75-116.00POLYGON ((-116.12500 51.75000, -116.12500 51.8...2223
910.051.50-116.00POLYGON ((-116.12500 51.50000, -116.12500 51.6...2259
1011.051.25-116.00POLYGON ((-116.12500 51.25000, -116.12500 51.3...2088
1112.051.00-116.00POLYGON ((-116.12500 51.00000, -116.12500 51.1...1848
1213.051.75-115.75POLYGON ((-115.87500 51.75000, -115.87500 51.8...2115
1314.051.50-115.75POLYGON ((-115.87500 51.50000, -115.87500 51.6...2150
1415.051.25-115.75POLYGON ((-115.87500 51.25000, -115.87500 51.3...2169
1516.051.00-115.75POLYGON ((-115.87500 51.00000, -115.87500 51.1...2003
1617.051.75-115.50POLYGON ((-115.62500 51.75000, -115.62500 51.8...1871
1718.051.50-115.50POLYGON ((-115.62500 51.50000, -115.62500 51.6...1953
1819.051.25-115.50POLYGON ((-115.62500 51.25000, -115.62500 51.3...2020
1920.051.00-115.50POLYGON ((-115.62500 51.00000, -115.62500 51.1...2041
\n", - "
" - ], - "text/plain": [ - " ID lat lon geometry \\\n", - "0 1.0 51.75 -116.50 POLYGON ((-116.62500 51.75000, -116.62500 51.8... \n", - "1 2.0 51.50 -116.50 POLYGON ((-116.62500 51.50000, -116.62500 51.6... \n", - "2 3.0 51.25 -116.50 POLYGON ((-116.62500 51.25000, -116.62500 51.3... \n", - "3 4.0 51.00 -116.50 POLYGON ((-116.62500 51.00000, -116.62500 51.1... \n", - "4 5.0 51.75 -116.25 POLYGON ((-116.37500 51.75000, -116.37500 51.8... \n", - "5 6.0 51.50 -116.25 POLYGON ((-116.37500 51.50000, -116.37500 51.6... \n", - "6 7.0 51.25 -116.25 POLYGON ((-116.37500 51.25000, -116.37500 51.3... \n", - "7 8.0 51.00 -116.25 POLYGON ((-116.37500 51.00000, -116.37500 51.1... \n", - "8 9.0 51.75 -116.00 POLYGON ((-116.12500 51.75000, -116.12500 51.8... \n", - "9 10.0 51.50 -116.00 POLYGON ((-116.12500 51.50000, -116.12500 51.6... \n", - "10 11.0 51.25 -116.00 POLYGON ((-116.12500 51.25000, -116.12500 51.3... \n", - "11 12.0 51.00 -116.00 POLYGON ((-116.12500 51.00000, -116.12500 51.1... \n", - "12 13.0 51.75 -115.75 POLYGON ((-115.87500 51.75000, -115.87500 51.8... \n", - "13 14.0 51.50 -115.75 POLYGON ((-115.87500 51.50000, -115.87500 51.6... \n", - "14 15.0 51.25 -115.75 POLYGON ((-115.87500 51.25000, -115.87500 51.3... \n", - "15 16.0 51.00 -115.75 POLYGON ((-115.87500 51.00000, -115.87500 51.1... \n", - "16 17.0 51.75 -115.50 POLYGON ((-115.62500 51.75000, -115.62500 51.8... \n", - "17 18.0 51.50 -115.50 POLYGON ((-115.62500 51.50000, -115.62500 51.6... \n", - "18 19.0 51.25 -115.50 POLYGON ((-115.62500 51.25000, -115.62500 51.3... \n", - "19 20.0 51.00 -115.50 POLYGON ((-115.62500 51.00000, -115.62500 51.1... \n", - "\n", - " elev_m \n", - "0 2243 \n", - "1 2093 \n", - "2 1810 \n", - "3 1637 \n", - "4 2284 \n", - "5 2258 \n", - "6 1999 \n", - "7 1641 \n", - "8 2223 \n", - "9 2259 \n", - "10 2088 \n", - "11 1848 \n", - "12 2115 \n", - "13 2150 \n", - "14 2169 \n", - "15 2003 \n", - "16 1871 \n", - "17 1953 \n", - "18 2020 \n", - "19 2041 " - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check\n", - "shp" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Overwrite the existing shapefile\n", - "shp.to_file( shapePath / shapeName )" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# close the files\n", - "geo.close()\n", - "shp = []" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( shapePath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'create_ERA5_shapefile.ipynb'\n", - "copyfile(thisFile, shapePath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + '_forcing_shapefile_log.txt'\n", - "with open( shapePath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Created ERA5 regular latitude/longitude grid.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/3a_forcing/3_create_shapefile/create_ERA5_shapefile.py b/3a_forcing/3_create_shapefile/create_ERA5_shapefile.py deleted file mode 100644 index 8ac6ce0..0000000 --- a/3a_forcing/3_create_shapefile/create_ERA5_shapefile.py +++ /dev/null @@ -1,212 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# Make ERA5 shapefile -# The shapefile for the forcing data needs to represent the regular latitude/longitude grid of the ERA5 data. We need this for later intersection with the catchment shape(s) so we can create appropriately weighted forcing for each model element. - -# Note that ERA5 data should be thought of as being provided for a specific point in space. These points are the lat/lon coordinates found as netcdf dimensions in the data files. Here, these coordinates will act as center points for a grid. Each point is assumed to be representative for an equal-sized area around itself. See: https://confluence.ecmwf.int/display/CKB/ERA5%3A+What+is+the+spatial+reference - -# We'll also add the elevation of each ERA5 grid cell for later temperature lapse rates. As per ERA5 docs, geopotential `[m^2 s^-2]` can be divided by `g=9.80665` `[m s^-2]` to obtain ERA5 elevation in `[m]`. Source: https://confluence.ecmwf.int/display/CKB/ERA5%3A+surface+elevation+and+orography#ERA5:surfaceelevationandorography-Howtocalculatesurfacegeopotentialheight - -# Acknowledgements -#The code to generate the shapefile is inspired by a function from the CANDEX toolbox: https://github.com/ShervanGharari/candex. - -# modules -import os -import shapefile # listed for install as "PyShp" library -import numpy as np -import xarray as xr -import netCDF4 as nc4 -import geopandas as gpd -from pathlib import Path -from shutil import copyfile -from datetime import datetime - -# ---- Control file handling -# Easy access to control file folder -controlFolder = Path('../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find location of merged forcing data -# Find the path where the merged forcing is -mergePath = read_from_control(controlFolder/controlFile,'forcing_merged_path') - -# Specify the default path if required -if mergePath == 'default': - mergePath = make_default_path('forcing/2_merged_data') -else: - mergePath = Path(mergePath) # ensure Path() object - - -# --- Find location of geopotential data file -# Find file path -geoPath = read_from_control(controlFolder/controlFile,'forcing_geo_path') - -# Specify the default path if required -if geoPath == 'default': - geoPath = make_default_path('forcing/0_geopotential') -else: - geoPath = Path(geoPath) # ensure Path() object - -# Specify the filename -geoName = 'ERA5_geopotential.nc' - - -# --- Find where the shapefile needs to go -# Find the path where the new shapefile needs to go -shapePath = read_from_control(controlFolder/controlFile,'forcing_shape_path') - -# Specify the default path if required -if shapePath == 'default': - shapePath = make_default_path('shapefiles/forcing') -else: - shapePath = Path(shapePath) # ensure Path() object - -# Find name of the new shapefile -shapeName = read_from_control(controlFolder/controlFile,'forcing_shape_name') - -# Find the names of the latitude and longitude fields -field_lat = read_from_control(controlFolder/controlFile,'forcing_shape_lat_name') -field_lon = read_from_control(controlFolder/controlFile,'forcing_shape_lon_name') - - -# --- Read the source file to find the grid spacing -# Find an .nc file in the forcing path -for file in os.listdir(mergePath): - if file.endswith('.nc'): - forcing_file = file - break - -# Set the dimension variable names -source_name_lat = "latitude" -source_name_lon = "longitude" - -# Open the file and get the dimensions and thus the spatial extent of the domain -with nc4.Dataset(mergePath / forcing_file) as src: - lat = src.variables[source_name_lat][:] - lon = src.variables[source_name_lon][:] - -# Find the spacing -half_dlat = abs(lat[1] - lat[0])/2 -half_dlon = abs(lon[1] - lon[0])/2 - - -# --- Create the new shape -with shapefile.Writer(str(shapePath / shapeName)) as w: - w.autoBalance = 1 # turn on function that keeps file stable if number of shapes and records don't line up - w.field("ID",'N') # create (N)umerical attribute fields, integer - w.field(field_lat,'F',decimal=4) # float with 4 decimals - w.field(field_lon,'F',decimal=4) - ID = 0 # start ID counter of empty - - for i in range(0,len(lon)): - for j in range(0,len(lat)): - ID += 1 - center_lon = lon[i] - center_lat = lat[j] - vertices = [] - parts = [] - vertices.append([center_lon-half_dlon, center_lat]) - vertices.append([center_lon-half_dlon, center_lat+half_dlat]) - vertices.append([center_lon , center_lat+half_dlat]) - vertices.append([center_lon+half_dlon, center_lat+half_dlat]) - vertices.append([center_lon+half_dlon, center_lat]) - vertices.append([center_lon+half_dlon, center_lat-half_dlat]) - vertices.append([center_lon , center_lat-half_dlat]) - vertices.append([center_lon-half_dlon, center_lat-half_dlat]) - vertices.append([center_lon-half_dlon, center_lat]) - parts.append(vertices) - w.poly(parts) - w.record(ID, center_lat, center_lon) - - -# --- Add the geopotential data to the shape -# Open the geopotential data file -geo = xr.open_dataset( geoPath / geoName ).isel(time=0) - -# Open shapefile -shp = gpd.read_file( shapePath / shapeName ) - -# Define the constant -g = 9.80665 - -# Add new column to shapefile -shp = shp.assign(elev_m = -999) # insert a placeholder value - -# For each row in the shapefile, match its ERA5 lat/lon coordinates -# with those in the 'geo' file and extract the appropriate geopotential -for index, row in shp.iterrows(): - - # Find elevation - elev = geo['z'].sel(latitude = row['lat'], longitude=row['lon']).values.flatten() / g - - # Add elevation into shapefile - shp.at[index,'elev_m'] = elev[0] - -# Overwrite the existing shapefile -shp.to_file( shapePath / shapeName ) - -# close the files -geo.close() -shp = [] - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Create a log folder -logFolder = '_workflow_log' -Path( shapePath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = 'create_ERA5_shapefile.py' -copyfile(thisFile, shapePath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + '_pressure_level_log.txt' -with open( shapePath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Created ERA5 regular latitude/longitude grid.'] - for txt in lines: - file.write(txt) diff --git a/3a_forcing/README.md b/3a_forcing/README.md deleted file mode 100644 index a1808a1..0000000 --- a/3a_forcing/README.md +++ /dev/null @@ -1,84 +0,0 @@ -# Forcing data: ERA5 -The ERA5 product is a reanalysis data set. See documentation (link below) for details about which observations are used and how they are combined in the reanalysis. ERA5's forcing variables (called parameters in the ERA5 documentation) can be either instantaneous or time-step averages (see the data documentation). They are provided at various heights: surface and single level for every of the 137 air layers that ERA5 uses. - -## Download setup instructions -Downloading ERA5 data requires: -- Registration: https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome -- Setup of the `cdsapi`: https://cds.climate.copernicus.eu/api-how-to - - -## Forcing needed to run SUMMA -SUMMA needs the following forcing variables (https://summa.readthedocs.io/en/master/input_output/SUMMA_input/#meteorological-forcing-files): -- Precipitation rate [kg m-2 s-1] -- Downward shortwave radiation at the upper boundary [W m-2] -- Downward longwave radiation at the upper boundary [W m-2] -- Air pressure at the the measurement height [Pa] -- Air temperature at the measurement height [K] -- Wind speed at the measurement height [m s-1] -- Specific humidity at the measurement height [g g-1] - -The upper boundary refers to the upper boundary of the SUMMA domain, so this would be at some height above the canopy or ground (in case there is no canopy). The measurement height is the height (above bare ground) where the meteorological variables are specified. This value is specified as `mHeight` in the local attributes file. SUMMA forcing time stamps are **period-ending** and the forcing information should reflect the **average conditions over the time interval** of length `data_step` preceding the time stamp. - - -## SUMMA needs vs ERA5 availability -Generally speaking, ERA5 data are available as hourly data for the period 1979 to current minus 5 days. This will be extended to cover the period 1950-1970. Spatial resolution of the data is a 31km grid over the Earth's surface. - -ERA5 data preparation includes interactions between an atmospheric model and a land surface model. This model setup includes different layers and ERA5 data is available at 137 different pressure levels (i.e. some height above the surface), as well as at the surface. The lowest atmospheric level is L137, at geopotential and geometric altitude 10 m (https://www.ecmwf.int/en/forecasts/documentation-and-support/137-model-levels) and data here relies only on the atmospheric model. Any variables at a height lower than L137 (i.e. at the surface) are the result of interpolation between atmospheric model and land model. We want to use only the outcomes from the ECMWF atmospheric model, because in our setup SUMMA takes on the role of a land surface model. Therefore we obtain (1) air temperature, (2) wind speed and (3) specific humidity at the lowest pressure level (L137). (4) Precipitation, (5) downward shortwave radiation, (6) downward longwave radiation and (7) air pressure are unaffected by the land model coupling and can be downloaded at the surface level. This is beneficial because surface-level downloads are substantially faster than pressure-level downloads. For more information see: - -- ERA5 hourly data on single levels from 1970 to present (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview) -- ERA5 hourly data on pressure levels from 1970 to present (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview) - - -## Workflow description -- 1_download: contains notebooks/scripts to download the necessary variables from ERA5's surface level and pressure level data sets; -- 2_merge: contains notebooks/scripts to merge the pressure and surface level data into a single file, and convert `u` and `v` wind components into a single vector; -- 3_create_shapefile: contains notebooks/scripts to make a shapefile for ERA5 data for later use. - - -### Surface-level variable downloads -We need the following ERA5 variables at the surface: -- Mean total precipitation rate [kg m-2 s-1] -- Mean surface downward short-wave radiation flux [W m-2] -- Mean surface downward long-wave radiation flux [W m-2] -- Surface pressure [Pa] - -These variables can be downloaded straight through the CDS API (see template_ERA5_singleLevel.py; https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form). - - -### Pressure-level variable downloads -We need the following variables at the lowest pressure level (L137) of the atmospheric model to run SUMMA: -- Temperature [K] -- Wind speed - - U-component and V-component of wind speed [m s-1] - - These will be converted into a directionless wind speed vector -- Specific humidity [kg kg-1] - -This is not directly available through the CDS API and must come from MARS instead (see template_ERA5_pressureLevel.py; https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5#). - -## Suggested citation -**General**: - -Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), 2020-03-26. https://cds.climate.copernicus.eu/cdsapp#!/home - -**Pressure level data**: - -Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, P., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thépaut, J.-N., 2017. Complete ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate - -**Single level data**: - -Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J.-N., 2018. ERA5 hourly data on single levels from 1979 to present. https://doi.org/10.24381/cds.adbb2d47 - -## Reference pages -- General description (last access 8-01-2020): https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 -- Difference with ERA_interim (last access 8-01-2020): https://confluence.ecmwf.int//pages/viewpage.action?pageId=74764925 -- Product main page (last access 8-01-2020): http://climate.copernicus.eu/climate-reanalysis -- Documentation (last access 8-01-2020): https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation -- Download guide (last access 8-01-2020): https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5 - -## Control file settings -This section lists all the settings in `control_active.txt` that the code in this folder uses. -- **forcing_raw_time**: years for which to download ERA5 data -- **forcing_raw_space**: catchment bounding box, i.e. the area for which forcing is required -- **forcing_shape_name**: name for the newly generated forcing grid shapefile -- **forcing_shape_lat_name, forcing_shape_lon_name**: names for the latitude and longitude fields in the newly generated forcing grid shapefile -- **forcing_raw_path, forcing_geo_path, forcing_merged_path, forcing_shape_path**: file paths diff --git a/4a_sort_shape/1_sort_catchment_shape.py b/3b_parameters/1_make_demtiff.py similarity index 50% rename from 4a_sort_shape/1_sort_catchment_shape.py rename to 3b_parameters/1_make_demtiff.py index d602573..de6f41b 100644 --- a/4a_sort_shape/1_sort_catchment_shape.py +++ b/3b_parameters/1_make_demtiff.py @@ -1,13 +1,12 @@ -# Sort catchment shape -# Sorts the catchment shape by `gruId` first and `hruId` second, to ensure HRU order follows SUMMA conventions. Not strictly necessary for cases where each GRU contains one HRU, but essential for cases where the GRUs contain multiple HRUs. +# Copy base settings +# Copies the base settings into the mizuRoute settings folder. # modules -import geopandas as gpd +import pandas as pd from pathlib import Path from shutil import copyfile from datetime import datetime - # --- Control file handling # Easy access to control file folder controlFolder = Path('../0_control_files') @@ -50,46 +49,79 @@ def make_default_path(suffix): return defaultPath -# --- Find location of catchment shapefile -# Catchment shapefile path & name -catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path') -catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name') +# --- Find where the dem is + +rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) +domainName = read_from_control(controlFolder/controlFile,'domain_name') + +# CAMELS-spath +CAMELS_spath = read_from_control(controlFolder/controlFile,'camels_spath') # Specify default path if needed -if catchment_path == 'default': - catchment_path = make_default_path('shapefiles/catchment') # outputs a Path() +if CAMELS_spath == 'default': + CAMELS_spath = rootPath / 'CAMELS-spat' else: - catchment_path = Path(catchment_path) # make sure a user-specified path is a Path() + CAMELS_spath = Path(CAMELS_spath) # make sure a user-specified path is a Path() + + +# Metadata +metadata_path = CAMELS_spath +metadata_name = "camels-spat-metadata.csv" + +df_metadata = pd.read_csv(metadata_path / metadata_name) + + +country, station_id = domainName.split("_") + +# Get categories +category_value = df_metadata.loc[(df_metadata['Country'] == country) & (df_metadata['Station_id'] == station_id), 'subset_category'] + +# Ensure category_value is a string +if not category_value.empty: + category_value = category_value.iloc[0] # Convert Series to string +else: + raise ValueError("No matching subset category found.") # Handle missing value + -# Find GRU and HRU variables -gru_id = read_from_control(controlFolder/controlFile,'catchment_shp_gruid') -hru_id = read_from_control(controlFolder/controlFile,'catchment_shp_hruid') +#DEM +dem_file_path = CAMELS_spath / 'geospatial' / category_value / 'merit' / domainName +dem_file_name = domainName + '_merit_hydro_elv.tif' + +# --- Find where the dem will be -# --- Open shape and sort -# Open the shape -shp = gpd.read_file(catchment_path/catchment_name) +parameter_dem_tif_path = read_from_control(controlFolder/controlFile,'parameter_dem_tif_path') +parameter_dem_tif_name = read_from_control(controlFolder/controlFile,'parameter_dem_tif_name') -# Sort -shp = shp.sort_values(by=[gru_id,hru_id]) +# Specify default path if needed +if parameter_dem_tif_path == 'default': + parameter_dem_tif_path = make_default_path('parameters/dem/5_elevation') # outputs a Path() +else: + parameter_dem_tif_path = Path(parameter_dem_tif_path) # make sure a user-specified path is a Path() -# Save -shp.to_file(catchment_path/catchment_name) +# Make the folder if it doesn't exist +parameter_dem_tif_path.mkdir(parents=True, exist_ok=True) +# --- Copy the dem from CAMELS-spat database to parameters folder + + +copyfile(dem_file_path / dem_file_name, parameter_dem_tif_path / parameter_dem_tif_name); + + # --- Code provenance # Generates a basic log file in the domain folder and copies the control file and itself there. # Set the log path and file name -logPath = catchment_path -log_suffix = '_sort_shape.txt' +logPath = parameter_dem_tif_path +log_suffix = '_make_demtiff.txt' # Create a log folder logFolder = '_workflow_log' Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) # Copy this script -thisFile = '1_sort_catchment_shape.py' +thisFile = '1_make_demtiff.py' copyfile(thisFile, logPath / logFolder / thisFile); # Get current date and time @@ -100,6 +132,6 @@ def make_default_path(suffix): with open( logPath / logFolder / logFile, 'w') as file: lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Sorted catchment shape by GRU ID first and HRU ID second to follow SUMMA conventions.'] + 'Copy CAMELS-spat dem to current workflow.'] for txt in lines: - file.write(txt) \ No newline at end of file + file.write(txt) \ No newline at end of file diff --git a/3b_parameters/2_make_landclasstiff.py b/3b_parameters/2_make_landclasstiff.py new file mode 100644 index 0000000..cfac73b --- /dev/null +++ b/3b_parameters/2_make_landclasstiff.py @@ -0,0 +1,135 @@ +# Copy base settings +# Copies the base settings into the mizuRoute settings folder. + +# modules +import pandas as pd +from pathlib import Path +from shutil import copyfile +from datetime import datetime + +# --- Control file handling +# Easy access to control file folder +controlFolder = Path('../0_control_files') + +# Store the name of the 'active' file in a variable +controlFile = 'control_active.txt' + +# Function to extract a given setting from the control file +def read_from_control( file, setting ): + + # Open 'control_active.txt' and ... + with open(file) as contents: + for line in contents: + + # ... find the line with the requested setting + if setting in line and not line.startswith('#'): + break + + # Extract the setting's value + substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) + substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found + substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines + + # Return this value + return substring + +# Function to specify a default path +def make_default_path(suffix): + + # Get the root path + rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) + + # Get the domain folder + domainName = read_from_control(controlFolder/controlFile,'domain_name') + domainFolder = 'domain_' + domainName + + # Specify the forcing path + defaultPath = rootPath / domainFolder / suffix + + return defaultPath + + +# --- Find where the landclass is + +rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) +domainName = read_from_control(controlFolder/controlFile,'domain_name') + +# CAMELS-spath +CAMELS_spath = read_from_control(controlFolder/controlFile,'camels_spath') + +# Specify default path if needed +if CAMELS_spath == 'default': + CAMELS_spath = rootPath / 'CAMELS-spat' +else: + CAMELS_spath = Path(CAMELS_spath) # make sure a user-specified path is a Path() + +# Metadata +metadata_path = CAMELS_spath +metadata_name = "camels-spat-metadata.csv" + +df_metadata = pd.read_csv(metadata_path / metadata_name) + + +country, station_id = domainName.split("_") + +# Get categories +category_value = df_metadata.loc[(df_metadata['Country'] == country) & (df_metadata['Station_id'] == station_id), 'subset_category'] + +# Ensure category_value is a string +if not category_value.empty: + category_value = category_value.iloc[0] # Convert Series to string +else: + raise ValueError("No matching subset category found.") # Handle missing value + +# Landclass +landclass_file_path = CAMELS_spath / 'geospatial' / category_value / 'modis-land' / domainName +landclass_file_name = domainName + '_2001_2022_mode_MCD12Q1_LC_Type1.tif' + + +# --- Find where the landclass will be + +parameter_landclass_mode_path = read_from_control(controlFolder/controlFile,'parameter_land_mode_path') +parameter_landclass_tif_name = read_from_control(controlFolder/controlFile,'parameter_land_tif_name') + +# Specify default path if needed +if parameter_landclass_mode_path == 'default': + parameter_landclass_mode_path = make_default_path('parameters/landclass/7_mode_land_class') # outputs a Path() +else: + parameter_landclass_mode_path = Path(parameter_landclass_mode_path) # make sure a user-specified path is a Path() + +# Make the folder if it doesn't exist +parameter_landclass_mode_path.mkdir(parents=True, exist_ok=True) + + +# --- Copy the landclass from CAMELS-spat database to parameters folder + + +copyfile(landclass_file_path / landclass_file_name, parameter_landclass_mode_path / parameter_landclass_tif_name); + + +# --- Code provenance +# Generates a basic log file in the domain folder and copies the control file and itself there. + +# Set the log path and file name +logPath = parameter_landclass_mode_path +log_suffix = '_make_landclasstiff.txt' + +# Create a log folder +logFolder = '_workflow_log' +Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) + +# Copy this script +thisFile = '2_make_landclasstiff.py' +copyfile(thisFile, logPath / logFolder / thisFile); + +# Get current date and time +now = datetime.now() + +# Create a log file +logFile = now.strftime('%Y%m%d') + log_suffix +with open( logPath / logFolder / logFile, 'w') as file: + + lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', + 'Copy CAMELS-spat landclass to current workflow.'] + for txt in lines: + file.write(txt) \ No newline at end of file diff --git a/3b_parameters/3a_convert_sandSiltClay_to_soilclass.py b/3b_parameters/3a_convert_sandSiltClay_to_soilclass.py new file mode 100644 index 0000000..9844bd3 --- /dev/null +++ b/3b_parameters/3a_convert_sandSiltClay_to_soilclass.py @@ -0,0 +1,298 @@ +# SOILGRIDS +# Downloaded sand/silt/clay content at the 7 soil depths provided by SOILGRIDS (Hengl et al., 2017). Use these fractions to determine the USDA soil class per depth (Benham et al., 2009). +# +# Workflow +# +# Combine separate sand/silt/clay percentages into a single soil class: +# 1. Define modules etc +# 2. Define file locations +# - Fixed location +# - Handle command line argument that specifies the soil depth +# 3. For each SOILGRIDS soil depth (sl1 to sl7) ... +# - Define functions used in the following steps +# - Open the CLYPPT_M_[sl]_250m, SLTPPT_M_[sl]_250m, SNDPPT_[sl]_250m .tif files and ... +# - Extract the geotif bands that contain sand/silt/clay % +# - Compare sand/silt/clay % to USDA soil triangle and assign soil class +# - Save resulting soil class file as a new .tif +# References +# Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotic A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748. https://doi.org/10.1371/journal.pone.0169748 +# Benham, E., Ahrens, R. J., & Nettleton, W. D. (2009). Clarification of Soil Texture Class Boundaries. United States Department of Agriculture. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/ks/soils/?cid=nrcs142p2_033171 + +import pandas as pd +import numpy as np +from osgeo import gdal +from pathlib import Path +from shutil import copyfile +from datetime import datetime + +# --- Control file handling +# Easy access to control file folder +controlFolder = Path('../0_control_files') + +# Store the name of the 'active' file in a variable +controlFile = 'control_active.txt' + +# Function to extract a given setting from the control file +def read_from_control( file, setting ): + + # Open 'control_active.txt' and ... + with open(file) as contents: + for line in contents: + + # ... find the line with the requested setting + if setting in line and not line.startswith('#'): + break + + # Extract the setting's value + substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) + substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found + substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines + + # Return this value + return substring + +# Function to specify a default path +def make_default_path(suffix): + + # Get the root path + rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) + + # Get the domain folder + domainName = read_from_control(controlFolder/controlFile,'domain_name') + domainFolder = 'domain_' + domainName + + # Specify the forcing path + defaultPath = rootPath / domainFolder / suffix + + return defaultPath + + +# --- Find where the soil is + +rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) +domainName = read_from_control(controlFolder/controlFile,'domain_name') + +# CAMELS-spath +CAMELS_spath = read_from_control(controlFolder/controlFile,'camels_spath') + +# Specify default path if needed +if CAMELS_spath == 'default': + CAMELS_spath = rootPath / 'CAMELS-spat' +else: + CAMELS_spath = Path(CAMELS_spath) # make sure a user-specified path is a Path() + +# Metadata +metadata_path = CAMELS_spath +metadata_name = "camels-spat-metadata.csv" + +df_metadata = pd.read_csv(metadata_path / metadata_name) + + +country, station_id = domainName.split("_") + +# Get categories +category_value = df_metadata.loc[(df_metadata['Country'] == country) & (df_metadata['Station_id'] == station_id), 'subset_category'] + +# Ensure category_value is a string +if not category_value.empty: + category_value = category_value.iloc[0] # Convert Series to string +else: + raise ValueError("No matching subset category found.") # Handle missing value + +# Soil +soil_file_path = CAMELS_spath / 'geospatial' / category_value / 'soilgrids' / domainName + +file_clay = domainName + '_clay_' +file_sand = domainName + '_sand_' +file_silt = domainName + '_silt_' +file_end = '_mean.tif' + +# --- Find where the soil will be + +parameter_soil_domain_path = read_from_control(controlFolder/controlFile,'parameter_soil_domain_path') + +# Specify default path if needed +if parameter_soil_domain_path == 'default': + parameter_soil_domain_path = make_default_path('parameters/soilclass/2_soil_classes_domain') # outputs a Path() +else: + parameter_soil_domain_path = Path(parameter_soil_domain_path) # make sure a user-specified path is a Path() + +# Make the folder if it doesn't exist +parameter_soil_domain_path.mkdir(parents=True, exist_ok=True) +file_des = 'soil_classes_' +sl_list = ["0-5cm", "5-15cm", "15-30cm", "30-60cm", "60-100cm", "100-200cm"] + + +# --- Open SOILGRIDS and convert into soil classes +# define a few functions + +# Start of function definition -------------------------------------------------------------------------- +# Opens geotif file, extracts data from a single band and computes corner & center coordinates in lat/lon +def open_soilgrids_geotif(file): + + print('Opening ' + str(file)) + ds = gdal.Open(str(file)) # open the file; enforce string for gdal + band = ds.GetRasterBand(1) # get the data band; we know there is only a single band per SOILGRIDS file + data = band.ReadAsArray() # convert to numpy array for further manipulation + width = ds.RasterXSize # pixel width + height = ds.RasterYSize # pixel height + gt = ds.GetGeoTransform() # geolocation + coords = np.zeros((5,2)) # coordinates of bounding box + coords[0,0] = coords[1,0] = gt[0] + coords[0,1] = coords[2,1] = gt[3] + coords[2,0] = coords[3,0] = gt[0] + width*gt[1] + coords[1,1] = coords[3,1] = gt[3] + height*gt[5] + coords[4,0] = gt[0] + (width/2)*gt[1] + coords[4,1] = gt[3] + (height/2)*gt[5] + + return data, coords + +# ------------------------------------------------------------------------------------------------------- +# Takes sand/silt/clay percentage and returns USDA soil class +def find_usda_soilclass(sand,silt,clay,no_data_value): + + # Based on Benham et al., 2009 and matching the following soil class table: + # SUMMA-ROSETTA-STAS-RUC soil parameter table + # 1 'CLAY' + # 2 'CLAY LOAM' + # 3 'LOAM' + # 4 'LOAMY SAND' + # 5 'SAND' + # 6 'SANDY CLAY' + # 7 'SANDY CLAY LOAM' + # 8 'SANDY LOAM' + # 9 'SILT' + # 10 'SILTY CLAY' + # 11 'SILTY CLAY LOAM' + # 12 'SILT LOAM' + + # Initialize a results array + soilclass = np.zeros(sand.shape) + + # Legend + soiltype = ['clay','clay loam','loam','loamy sand','sand','sandy clay','sandy clay loam','sandy loam','silt','silty clay','silty clay loam','silt loam'] + + # Classify + soilclass[(clay >= 400) & (sand <= 450) & (silt < 400)] = 1 + soilclass[(clay >= 270) & (clay < 400) & (sand > 200) & (sand <= 450)] = 2 + soilclass[(clay >= 70) & (clay < 270) & (silt >= 280) & (silt < 500) & (sand < 520)] = 3 + soilclass[((silt + 15 * clay) >= 150) & ((silt + 2* clay) < 300)] = 4 + soilclass[((silt + 15 * clay) < 150)] = 5 + soilclass[(clay >= 350 )& (sand > 450)] = 6 + soilclass[(clay >= 200) & (clay < 350) & (silt < 280) & (sand > 450)] = 7 + soilclass[((clay >= 70) & (clay < 200) & (sand > 520) & ((silt+2*clay) >= 300)) | ((clay < 70) & (silt < 500) & ((silt+2*clay >= 300)))] = 8 + soilclass[(silt >= 800) & (clay < 120)] = 9 + soilclass[(clay >= 400) & (silt >= 400)] = 10 + soilclass[(clay >= 270) & (clay < 400) & (sand <= 200)] = 11 + soilclass[((silt >= 500) & (clay >= 120) & (clay < 270)) | ((silt >= 500) & (silt < 800) & (clay < 120))] = 12 + soilclass[(sand == no_data_value) | (silt == no_data_value) | (clay == no_data_value)] = 0 + + return soilclass, soiltype + +# ------------------------------------------------------------------------------------------------------- +# Creates a new geotif file from a template file and data that needs to be saved +def create_new_tif(template_file, data, new_file_name): + + # Code based on: https://gis.stackexchange.com/questions/164853/reading-modifying-and-writing-a-geotiff-with-gdal-in-python + + # get stuff from the template + ds = gdal.Open(str(template_file)) # open the file + + # get shape from the data + [cols, rows] = data.shape # get the shape of the geotiff + + # create the new file + driver = gdal.GetDriverByName("GTiff") # specify driver + outdata = driver.Create(str(new_file_name), rows, cols, 1, gdal.GDT_UInt16, options = [ 'COMPRESS=DEFLATE' ]) # open file + outdata.SetGeoTransform(ds.GetGeoTransform())# sets same geotransform as input + outdata.SetProjection(ds.GetProjection())# sets same projection as input + outdata.GetRasterBand(1).WriteArray(data) # pass the manipulated values + outdata.GetRasterBand(1).SetNoDataValue(-1)# if you want these values transparent (0) will be nan I guess + outdata.FlushCache() # saves to disk! + + return + +# End of function definition ---------------------------------------------------------------------------- + + +for sl in sl_list: + + # For the specified soil depth ... + # Explicitly create the path + clay_path = soil_file_path / (file_clay + sl + file_end) + sand_path = soil_file_path / (file_sand + sl + file_end) + silt_path = soil_file_path / (file_silt + sl + file_end) + + # Get the geotif bands that contain sand/silt/clay and their coordinates + sand, sand_coord = open_soilgrids_geotif( sand_path ) + silt, silt_coord = open_soilgrids_geotif( silt_path ) + clay, clay_coord = open_soilgrids_geotif( clay_path ) + + + # Break if coordinates do not match + coords_all_match = np.logical_and( (clay_coord == sand_coord).all(), (clay_coord == silt_coord).all()) + if not coords_all_match: + print('Coordinates do not match at soil level ' + sl + '. Aborting.') + + # Specify the 'no data value' + # Note: we know this is 255 from running 'gdalinfo [file] -mm' from the command line, there is no easy way to get this programmatically + # See: https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-1-a3253eb96082 + noData = -32768 + + # Compare sand/silt/clay % to USDA soil triangle and assign soil class + soilclass,_ = find_usda_soilclass(sand,silt,clay,noData) + + # Save resulting soil class file as a new .tif, using an existing one as template + src = soil_file_path / (file_clay + sl + file_end) + des = parameter_soil_domain_path / (file_des + sl + file_end) + create_new_tif(src,soilclass,des) + + + +# --- Code provenance +# Generates a basic log file in the domain folder and copies the control file and itself there. + +# Set the log path and file name +logPath = parameter_soil_domain_path +log_suffix = '_convert_sandSiltClay_to_soilclass.txt' + +# Create a log folder +logFolder = '_workflow_log' +Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) + +# Copy this script +thisFile = '3a_convert_sandSiltClay_to_soilclass.py' +copyfile(thisFile, logPath / logFolder / thisFile); + +# Get current date and time +now = datetime.now() + +# Create a log file +logFile = now.strftime('%Y%m%d') + log_suffix +with open( logPath / logFolder / logFile, 'w') as file: + + lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', + 'Copy CAMELS-spat soil information to current workflow and convert it to soilclass'] + for txt in lines: + file.write(txt) + + + + + + + + + + + + + + + + + + + + + diff --git a/3b_parameters/MODIS_MCD12Q1_V6/7_find_mode_land_class/find_mode_landclass.py b/3b_parameters/3b_make_soilclasstiff.py similarity index 55% rename from 3b_parameters/MODIS_MCD12Q1_V6/7_find_mode_land_class/find_mode_landclass.py rename to 3b_parameters/3b_make_soilclasstiff.py index 45b900e..083f4a7 100644 --- a/3b_parameters/MODIS_MCD12Q1_V6/7_find_mode_land_class/find_mode_landclass.py +++ b/3b_parameters/3b_make_soilclasstiff.py @@ -1,197 +1,165 @@ -# Find mode land class - -# Modules -import os -import numpy as np -from pathlib import Path -import scipy.stats as sc -from shutil import copyfile -from datetime import datetime -from osgeo import gdal, ogr, osr - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find source and destination locations -# Find where the soil classes are -landClassPath = read_from_control(controlFolder/controlFile,'parameter_land_tif_path') - -# Specify the default paths if required -if landClassPath == 'default': - landClassPath = make_default_path('parameters/landclass/6_tif_multiband') # outputs a Path() -else: - landClassPath = Path(landClassPath) # make sure a user-specified path is a Path() - -# Find where the mode soil class needs to go -modeLandClassPath = read_from_control(controlFolder/controlFile,'parameter_land_mode_path') - -# Specify the default paths if required -if modeLandClassPath == 'default': - modeLandClassPath = make_default_path('parameters/landclass/7_mode_land_class') # outputs a Path() -else: - modeLandClassPath = Path(modeLandClassPath) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -modeLandClassPath.mkdir(parents=True, exist_ok=True) - -# --- Filenames -# Find the name of the source file -for file in os.listdir(landClassPath): - if file.endswith(".tif"): - source_file = file - -# New file -dest_file = read_from_control(controlFolder/controlFile,'parameter_land_tif_name') - - -# --- Function definition -# Opens geotif file, extracts data from a single band and computes corner & center coordinates in lat/lon -def open_geotif(file,band): - - # Do the things - ds = gdal.Open(file) # open the file - band = ds.GetRasterBand(band) # get the data band; there should be 18 for each of the 18 years - data = band.ReadAsArray() # convert to numpy array for further manipulation - width = ds.RasterXSize # pixel width - height = ds.RasterYSize # pixel height - rasterSize = [width,height] - geoTransform = ds.GetGeoTransform() # geolocation - boundingBox = np.zeros((5,2)) # coordinates of bounding box - boundingBox[0,0] = boundingBox[1,0] = geoTransform[0] - boundingBox[0,1] = boundingBox[2,1] = geoTransform[3] - boundingBox[2,0] = boundingBox[3,0] = geoTransform[0] + width*geoTransform[1] - boundingBox[1,1] = boundingBox[3,1] = geoTransform[3] + height*geoTransform[5] - boundingBox[4,0] = geoTransform[0] + (width/2)*geoTransform[1] - boundingBox[4,1] = geoTransform[3] + (height/2)*geoTransform[5] - - return data, geoTransform, rasterSize, boundingBox - -# Writes data into a new geotif file -# Source: https://gis.stackexchange.com/questions/199477/gdal-python-cut-geotiff-image/199565 -def write_geotif_sameDomain(src_file,des_file,des_data): - - # load the source file to get the appropriate attributes - src_ds = gdal.Open(src_file) - - # get the geotransform - des_transform = src_ds.GetGeoTransform() - - # get the data dimensions - ncols = des_data.shape[1] - nrows = des_data.shape[0] - - # make the file - driver = gdal.GetDriverByName("GTiff") - dst_ds = driver.Create(des_file,ncols,nrows,1,gdal.GDT_Float32, options = [ 'COMPRESS=DEFLATE' ]) - dst_ds.GetRasterBand(1).WriteArray( des_data ) - dst_ds.SetGeoTransform(des_transform) - wkt = src_ds.GetProjection() - srs = osr.SpatialReference() - srs.ImportFromWkt(wkt) - dst_ds.SetProjection( srs.ExportToWkt() ) - - # close files - src_ds = None - des_ds = None - - return - - -# ------------------------------------------------------------- - -# --- Find mode land class -# Get land use classes for each year -land_use_classes = np.dstack((open_geotif( str(landClassPath/source_file) ,1)[0], \ - open_geotif( str(landClassPath/source_file) ,2)[0], \ - open_geotif( str(landClassPath/source_file) ,3)[0], \ - open_geotif( str(landClassPath/source_file) ,4)[0], \ - open_geotif( str(landClassPath/source_file) ,5)[0], \ - open_geotif( str(landClassPath/source_file) ,6)[0], \ - open_geotif( str(landClassPath/source_file) ,7)[0], \ - open_geotif( str(landClassPath/source_file) ,8)[0], \ - open_geotif( str(landClassPath/source_file) ,9)[0], \ - open_geotif( str(landClassPath/source_file) ,10)[0], \ - open_geotif( str(landClassPath/source_file) ,11)[0], \ - open_geotif( str(landClassPath/source_file) ,12)[0], \ - open_geotif( str(landClassPath/source_file) ,13)[0], \ - open_geotif( str(landClassPath/source_file) ,14)[0], \ - open_geotif( str(landClassPath/source_file) ,15)[0], \ - open_geotif( str(landClassPath/source_file) ,16)[0], \ - open_geotif( str(landClassPath/source_file) ,17)[0], \ - open_geotif( str(landClassPath/source_file) ,18)[0])) - -# Extract mode -mode = sc.mode(land_use_classes,axis=2)[0].squeeze() - -# Store this in a new geotif file -src_file = str(landClassPath/source_file) -des_file = str(modeLandClassPath/dest_file) -write_geotif_sameDomain(src_file,des_file,mode) - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = modeLandClassPath -log_suffix = '_mode_over_years_log.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = 'find_mode_landclass.py' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Found mode landclass over years'] - for txt in lines: - file.write(txt) - +# Modules +import numpy as np +import scipy.stats as sc +from pathlib import Path +from osgeo import gdal, osr +from shutil import copyfile +from datetime import datetime + +# --- Control file handling +# Easy access to control file folder +controlFolder = Path('../0_control_files') + +# Store the name of the 'active' file in a variable +controlFile = 'control_active.txt' + +# Function to extract a given setting from the control file +def read_from_control( file, setting ): + + # Open 'control_active.txt' and ... + with open(file) as contents: + for line in contents: + + # ... find the line with the requested setting + if setting in line and not line.startswith('#'): + break + + # Extract the setting's value + substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) + substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found + substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines + + # Return this value + return substring + +# Function to specify a default path +def make_default_path(suffix): + + # Get the root path + rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) + + # Get the domain folder + domainName = read_from_control(controlFolder/controlFile,'domain_name') + domainFolder = 'domain_' + domainName + + # Specify the forcing path + defaultPath = rootPath / domainFolder / suffix + + return defaultPath + + + +# --- Find where the soil will be + +parameter_soil_domain_path = read_from_control(controlFolder/controlFile,'parameter_soil_domain_path') + +# Specify default path if needed +if parameter_soil_domain_path == 'default': + parameter_soil_domain_path = make_default_path('parameters/soilclass/2_soil_classes_domain') # outputs a Path() +else: + parameter_soil_domain_path = Path(parameter_soil_domain_path) # make sure a user-specified path is a Path() + + +file_base = 'soil_classes_' +file_end = '_mean.tif' +file_dest = 'soil_classes' + +# auxiliary functions +# ------------------------------------------------------------- +# Opens geotif file, extracts data from a single band and computes corner & center coordinates in lat/lon +def open_soilgrids_geotif(file): + + # Do the things + ds = gdal.Open(file) # open the file + band = ds.GetRasterBand(1) # get the data band; we know there is only a single band per SOILGRIDS file + data = band.ReadAsArray() # convert to numpy array for further manipulation + width = ds.RasterXSize # pixel width + height = ds.RasterYSize # pixel height + rasterSize = [width,height] + geoTransform = ds.GetGeoTransform() # geolocation + boundingBox = np.zeros((5,2)) # coordinates of bounding box + boundingBox[0,0] = boundingBox[1,0] = geoTransform[0] + boundingBox[0,1] = boundingBox[2,1] = geoTransform[3] + boundingBox[2,0] = boundingBox[3,0] = geoTransform[0] + width*geoTransform[1] + boundingBox[1,1] = boundingBox[3,1] = geoTransform[3] + height*geoTransform[5] + boundingBox[4,0] = geoTransform[0] + (width/2)*geoTransform[1] + boundingBox[4,1] = geoTransform[3] + (height/2)*geoTransform[5] + + return data, geoTransform, rasterSize, boundingBox + +# ------------------------------------------------------------- +# Writes data into a new geotif file +# Source: https://gis.stackexchange.com/questions/199477/gdal-python-cut-geotiff-image/199565 +def write_geotif_sameDomain(src_file,des_file,des_data): + + # load the source file to get the appropriate attributes + src_ds = gdal.Open(src_file) + + # get the geotransform + des_transform = src_ds.GetGeoTransform() + + # get the data dimensions + ncols = des_data.shape[1] + nrows = des_data.shape[0] + + # make the file + driver = gdal.GetDriverByName("GTiff") + dst_ds = driver.Create(des_file,ncols,nrows,1,gdal.GDT_Float32, options = [ 'COMPRESS=DEFLATE' ]) + dst_ds.GetRasterBand(1).WriteArray( des_data ) + dst_ds.SetGeoTransform(des_transform) + wkt = src_ds.GetProjection() + srs = osr.SpatialReference() + srs.ImportFromWkt(wkt) + dst_ds.SetProjection( srs.ExportToWkt() ) + + # close files + src_ds = None + + return + +# ------------------------------------------------------------- + +# Get soil classes for all soil levels +soilclasses = np.dstack((open_soilgrids_geotif(str(parameter_soil_domain_path / (file_base + '0-5cm' + file_end)))[0], \ + open_soilgrids_geotif(str(parameter_soil_domain_path / (file_base + '5-15cm' + file_end)))[0], \ + open_soilgrids_geotif(str(parameter_soil_domain_path / (file_base + '15-30cm' + file_end)))[0], \ + open_soilgrids_geotif(str(parameter_soil_domain_path / (file_base + '30-60cm' + file_end)))[0], \ + open_soilgrids_geotif(str(parameter_soil_domain_path / (file_base + '60-100cm' + file_end)))[0], \ + open_soilgrids_geotif(str(parameter_soil_domain_path / (file_base + '100-200cm' + file_end)))[0])) + +# Extract mode +mode = sc.mode(soilclasses,axis=2)[0].squeeze() + +# Store this in a new geotif file +src_file = str(parameter_soil_domain_path / (file_base + '0-5cm' + file_end)) +des_file = str(parameter_soil_domain_path / (file_dest + '.tif')) +write_geotif_sameDomain(src_file,des_file,mode) + + + +# --- Code provenance +# Generates a basic log file in the domain folder and copies the control file and itself there. + +# Set the log path and file name +logPath = parameter_soil_domain_path +log_suffix = '_make_soilclasstiff.txt' + +# Create a log folder +logFolder = '_workflow_log' +Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) + +# Copy this script +thisFile = '3b_make_soilclasstiff.py' +copyfile(thisFile, logPath / logFolder / thisFile); + +# Get current date and time +now = datetime.now() + +# Create a log file +logFile = now.strftime('%Y%m%d') + log_suffix +with open( logPath / logFolder / logFile, 'w') as file: + + lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', + 'Aggregate the information to create soil class tiff'] + for txt in lines: + file.write(txt) \ No newline at end of file diff --git a/3b_parameters/MERIT_Hydro_DEM/1_download/README.md b/3b_parameters/MERIT_Hydro_DEM/1_download/README.md deleted file mode 100644 index 463a20c..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/1_download/README.md +++ /dev/null @@ -1,16 +0,0 @@ -# MERIT-Hydro adjusted elevation download -Downloads MERIT-Hydro adjusted elevation data. Data is available in blocks of 30x30 degrees in a regular lat/lon projection. The download code downloads the blocks required to cover the domain. Subsetting to the exact domain happens in follow-up steps. - -## Download setup instructions -MERIT Hydro downloads require registration through a Google webform. See: http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/ - -Store the obtained user details (username and password) in a new file `$HOME/.merit` (Unix/Linux) or `C:\Users\[user]\.merit` (Windows) as follows (replace `[user]` and `[pass]` with your own credentials): - -``` -user: [user] -pass: [pass] - -``` - -## Download run instructions -Execute the download script and keep the terminal or notebook open until the downloads fully complete. No manual interaction with the http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/ website is required. \ No newline at end of file diff --git a/3b_parameters/MERIT_Hydro_DEM/1_download/download_merit_hydro_adjusted_elevation.ipynb b/3b_parameters/MERIT_Hydro_DEM/1_download/download_merit_hydro_adjusted_elevation.ipynb deleted file mode 100644 index 068a6c6..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/1_download/download_merit_hydro_adjusted_elevation.ipynb +++ /dev/null @@ -1,432 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download MERIT Hydro adjusted elevation\n", - "Data source: http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/index.html. Download requires the user to be registered, which can be done through the website.\n", - "\n", - "Workflow:\n", - "- Find the data locations;\n", - "- Determine the files that need to be downloaded to cover the modelling domain;\n", - "- Download data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "from shutil import copyfile\n", - "from pathlib import Path\n", - "import numpy as np\n", - "import requests\n", - "import shutil\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where to save the files" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the raw files need to go\n", - "merit_path = read_from_control(controlFolder/controlFile,'parameter_dem_raw_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required \n", - "if merit_path == 'default':\n", - " merit_path = make_default_path('parameters/dem/1_MERIT_raw_data') # outputs a Path()\n", - "else:\n", - " merit_path = Path(merit_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "merit_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find the download area and which MERIT packages cover this area" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the download url info\n", - "merit_url = read_from_control(controlFolder/controlFile,'parameter_dem_main_url')\n", - "merit_template = read_from_control(controlFolder/controlFile,'parameter_dem_file_template')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Find which locations to download\n", - "coordinates = read_from_control(controlFolder/controlFile,'forcing_raw_space')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Split coordinates into the format the download interface needs\n", - "coordinates = coordinates.split('/')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Store coordinates as floats in individual variables\n", - "domain_min_lon = np.array(float(coordinates[1]))\n", - "domain_max_lon = np.array(float(coordinates[3]))\n", - "domain_min_lat = np.array(float(coordinates[2]))\n", - "domain_max_lat = np.array(float(coordinates[0]))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the edges of the download areas\n", - "lon_right_edge = np.array([-150,-120, -90,-60,-30, 0,30,60,90,120,150,180])\n", - "lon_left_edge = np.array([-180,-150,-120,-90,-60,-30, 0,30,60, 90,120,150])\n", - "lat_bottom_edge = np.array([-60,-30,0, 30,60]) # NOTE: latitudes -90 to -60 are NOT part of the MERIT domain\n", - "lat_top_edge = np.array([-30, 0,30,60,90]) " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the download variables\n", - "dl_lon_all = np.array(['w180','w150','w120','w090','w060','w030','e000','e030','e060','e090','e120','e150'])\n", - "dl_lat_all = np.array(['s60','s30','n00','n30','n60'])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the lower-left corners of each download square\n", - "dl_lons = dl_lon_all[(domain_min_lon < lon_right_edge) & (domain_max_lon > lon_left_edge)]\n", - "dl_lats = dl_lat_all[(domain_min_lat < lat_top_edge) & (domain_max_lat > lat_bottom_edge)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get authentication info" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the login details file and store as a dictionary\n", - "merit_login = {}\n", - "with open(os.path.expanduser(\"~/.merit\")) as file:\n", - " for line in file:\n", - " (key, val) = line.split(':')\n", - " merit_login[key] = val.strip() # remove whitespace, newlines" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the authentication details\n", - "usr = merit_login['user']\n", - "pwd = merit_login['pass']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Do the downloads" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Retry settings\n", - "retries_max = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully downloaded http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/distribute/v1.0.1/elv_n30w120.tar\n" - ] - } - ], - "source": [ - "# Loop over the download files\n", - "for dl_lon in dl_lons:\n", - " for dl_lat in dl_lats:\n", - " \n", - " # Skip those combinations for which no MERIT data exists\n", - " if (dl_lat == 'n00' and dl_lon == 'w150') or \\\n", - " (dl_lat == 's60' and dl_lon == 'w150') or \\\n", - " (dl_lat == 's60' and dl_lon == 'w120'):\n", - " continue\n", - " \n", - " # Make the download URL\n", - " file_url = (merit_url + merit_template).format(dl_lat,dl_lon)\n", - " \n", - " # Extract the filename from the URL\n", - " file_name = file_url.split('/')[-1].strip() # Get the last part of the url, strip whitespace and characters\n", - " \n", - " # If file already exists in destination, move to next file\n", - " if os.path.isfile(merit_path / file_name):\n", - " continue\n", - " \n", - " # Make sure the connection is re-tried if it fails\n", - " retries_cur = 1\n", - " while retries_cur <= retries_max:\n", - " try: \n", - "\n", - " # Send a HTTP request to the server and save the HTTP response in a response object called resp\n", - " # 'stream = True' ensures that only response headers are downloaded initially (and not all file contents too, which are 2GB+)\n", - " with requests.get(file_url.strip(), auth=(usr, pwd), stream=True) as response:\n", - " \n", - " # Decode the response\n", - " response.raw.decode_content = True\n", - " content = response.raw\n", - " \n", - " # Write to file\n", - " with open(merit_path / file_name, 'wb') as data:\n", - " shutil.copyfileobj(content, data)\n", - "\n", - " # print a completion message\n", - " print('Successfully downloaded ' + str(merit_path) + '/' + file_url)\n", - "\n", - " except Exception as e:\n", - " print('Error downloading ' + file_url + ' on try ' + str(retries_cur) + ' with error: ' + str(e))\n", - " retries_cur += 1\n", - " continue\n", - " else:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = merit_path\n", - "log_suffix = '_merit_dem_download_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'download_merit_hydro_adjusted_elevation.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Downloaded MERIT Hydro adjusted elevation for area (lat_max, lon_min, lat_min, lon_max) [{}].'.format(coordinates)]\n", - " for txt in lines:\n", - " file.write(txt) " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/3b_parameters/MERIT_Hydro_DEM/1_download/download_merit_hydro_adjusted_elevation.py b/3b_parameters/MERIT_Hydro_DEM/1_download/download_merit_hydro_adjusted_elevation.py deleted file mode 100644 index 293770d..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/1_download/download_merit_hydro_adjusted_elevation.py +++ /dev/null @@ -1,198 +0,0 @@ -# Download MERIT Hydro adjusted elevation data -# -# Data source: http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/index.html. Download requires the user to be registered, which can be done through the website -# -# Workflow: -# - Find data locations; -# - Determine the files that need to be downloaded to cover the modelling domain; -# - Download data. - -# Modules -from datetime import datetime -from shutil import copyfile -from pathlib import Path -import numpy as np -import requests -import shutil -import os - - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find where to save the files -# Find the path where the raw files need to go -merit_path = read_from_control(controlFolder/controlFile,'parameter_dem_raw_path') - -# Specify the default paths if required -if merit_path == 'default': - merit_path = make_default_path('parameters/dem/1_MERIT_raw_data') # outputs a Path() -else: - merit_path = Path(merit_path) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -merit_path.mkdir(parents=True, exist_ok=True) - - -# --- Find the download area and which MERIT packages cover this area -# Get the download url info -merit_url = read_from_control(controlFolder/controlFile,'parameter_dem_main_url') -merit_template = read_from_control(controlFolder/controlFile,'parameter_dem_file_template') - -# Find which locations to download -coordinates = read_from_control(controlFolder/controlFile,'forcing_raw_space') - -# Split coordinates into the format the download interface needs -coordinates = coordinates.split('/') - -# Store coordinates as floats in individual variables -domain_min_lon = np.array(float(coordinates[1])) -domain_max_lon = np.array(float(coordinates[3])) -domain_min_lat = np.array(float(coordinates[2])) -domain_max_lat = np.array(float(coordinates[0])) - -# Define the edges of the download areas -lon_right_edge = np.array([-150,-120, -90,-60,-30, 0,30,60,90,120,150,180]) -lon_left_edge = np.array([-180,-150,-120,-90,-60,-30, 0,30,60, 90,120,150]) -lat_bottom_edge = np.array([-60,-30,0, 30,60]) # NOTE: latitudes -90 to -60 are NOT part of the MERIT domain -lat_top_edge = np.array([-30, 0,30,60,90]) - -# Define the download variables -dl_lon_all = np.array(['w180','w150','w120','w090','w060','w030','e000','e030','e060','e090','e120','e150']) -dl_lat_all = np.array(['s60','s30','n00','n30','n60']) - -# Find the lower-left corners of each download square -dl_lons = dl_lon_all[(domain_min_lon < lon_right_edge) & (domain_max_lon > lon_left_edge)] -dl_lats = dl_lat_all[(domain_min_lat < lat_top_edge) & (domain_max_lat > lat_bottom_edge)] - - -# --- Get authentication info -# Open the login details file and store as a dictionary -merit_login = {} -with open(os.path.expanduser("~/.merit")) as file: - for line in file: - (key, val) = line.split(':') - merit_login[key] = val.strip() # remove whitespace, newlines - -# Get the authentication details -usr = merit_login['user'] -pwd = merit_login['pass'] - - -# --- Do the downloads -# Retry settings -retries_max = 10 - -# Loop over the download files -for dl_lon in dl_lons: - for dl_lat in dl_lats: - - # Skip those combinations for which no MERIT data exists - if (dl_lat == 'n00' and dl_lon == 'w150') or \ - (dl_lat == 's60' and dl_lon == 'w150') or \ - (dl_lat == 's60' and dl_lon == 'w120'): - continue - - # Make the download URL - file_url = (merit_url + merit_template).format(dl_lat,dl_lon) - - # Extract the filename from the URL - file_name = file_url.split('/')[-1].strip() # Get the last part of the url, strip whitespace and characters - - # If file already exists in destination, move to next file - if os.path.isfile(merit_path / file_name): - continue - - # Make sure the connection is re-tried if it fails - retries_cur = 1 - while retries_cur <= retries_max: - try: - - # Send a HTTP request to the server and save the HTTP response in a response object called resp - # 'stream = True' ensures that only response headers are downloaded initially (and not all file contents too, which are 2GB+) - with requests.get(file_url.strip(), auth=(usr, pwd), stream=True) as response: - - # Decode the response - response.raw.decode_content = True - content = response.raw - - # Write to file - with open(merit_path / file_name, 'wb') as data: - shutil.copyfileobj(content, data) - - # print a completion message - print('Successfully downloaded ' + str(merit_path) + '/' + file_url) - - except Exception as e: - print('Error downloading ' + file_url + ' on try ' + str(retries_cur) + ' with error: ' + str(e)) - retries_cur += 1 - continue - else: - break - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = merit_path -log_suffix = '_merit_dem_download_log.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = 'download_merit_hydro_adjusted_elevation.py' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Downloaded MERIT Hydro adjusted elevation for area (lat_max, lon_min, lat_min, lon_max) [{}].'.format(coordinates)] - for txt in lines: - file.write(txt) \ No newline at end of file diff --git a/3b_parameters/MERIT_Hydro_DEM/2_unpack/unpack_merit_hydro_dem.sh b/3b_parameters/MERIT_Hydro_DEM/2_unpack/unpack_merit_hydro_dem.sh deleted file mode 100755 index 5c2dd94..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/2_unpack/unpack_merit_hydro_dem.sh +++ /dev/null @@ -1,86 +0,0 @@ -#!/bin/bash - -# Script to unpack downloaded MERIT data. - - -#--------------------------------- -# Specify settings -#--------------------------------- - -# --- Location of raw data -dest_line=$(grep -m 1 "^parameter_dem_raw_path" ../../../0_control_files/control_active.txt) # full settings line -source_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -source_path=$(echo ${source_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$source_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # source path - source_path="${root_path}/domain_${domain_name}/parameters/dem/1_MERIT_raw_data" - -fi - -# --- Location where converted data needs to go -dest_line=$(grep -m 1 "^parameter_dem_unpack_path" ../../../0_control_files/control_active.txt) # full settings line -dest_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -dest_path=$(echo ${dest_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$dest_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # destination path - dest_path="${root_path}/domain_${domain_name}/parameters/dem/2_MERIT_hydro_unpacked_data" -fi - -# Make destination directory -mkdir -p $dest_path - - -#--------------------------------- -# Unpack the data -#--------------------------------- - -for file in $source_path/*.tar; do - tar xf "$file" -C $dest_path -done - - -#--------------------------------- -# Code provenance -#--------------------------------- -# Generates a basic log file in the domain folder and copies the control file and itself there. -# Make a log directory if it doesn't exist -log_path="${dest_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_compile_log.txt" - -# Make the log -this_file='unpack_merit_hydro_dem.sh' -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo 'Unpacked raw MERIT Hydro data.' >> $log_path/$log_file # 2nd line, append to existing file - -# Copy this file to log directory -cp $this_file $log_path diff --git a/3b_parameters/MERIT_Hydro_DEM/3_create_vrt/README.md b/3b_parameters/MERIT_Hydro_DEM/3_create_vrt/README.md deleted file mode 100644 index c3688e6..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/3_create_vrt/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# Combine .geotiff tiles into .vrt -The MERIT Hydro DEM is composed of smaller .tif files that contain part of the global map. We need to combine these into a single map for further analysis. - -## Virtual Dataset (VRT) -Gdal VRT's are "a mosaic of the list of input GDAL datasets" (https://gdal.org/programs/gdalbuildvrt.html). Essentially this stiches together the individual .tif files and allows further GDAL operations on the data. - -## Scripts -### make_merit_dem_vrt.sh -Loops over the individual MERIT Hydro DEM folders (each folder represents a larger square of the Earth's surface, each file in a folder is a smaller square of data inside this larger square) and stiches those together into a single virtual data set. It only extracts band 1 which contains the surface elevation in [m] (see MERIT Hydro docs or use 'gdalinfo '). - -## Input required -- Downloaded and extracted .tif files -- Location of the folder that contains these files -- Location and names for list of files output and vrt output - -## Output generated -- GDAL .vrt files that contain the MERIT Hydro elevation data, stiched together into a single map. - -## MERIT Hydro reference page -http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/ - -## Notes -- Python can read .vrt files (source: https://jgomezdans.github.io/stitching-together-modis-data.html) - -``` -import numpy as np -import matplotlib.pyplot as plt -from osgeo import gdal - -g = gdal.Open ( "mosaic_sinu.vrt" ) # Open file -data = g.ReadAsArray() # Read contents -mdata = np.ma.array ( data, mask=data>30000 ) # Mask data -cmap = plt.cm.jet # Set colormap -cmap.set_bad ( 'k' ) # Set masked values to black -# Next line scales the GPP data by 0.0001 to get the right units -# and plots it. -plt.imshow ( mdata*0.0001, interpolation='nearest', vmax=0.007, cmap=cmap) -plt.colorbar ( orientation='horizontal' ) -``` diff --git a/3b_parameters/MERIT_Hydro_DEM/3_create_vrt/make_merit_dem_vrt.sh b/3b_parameters/MERIT_Hydro_DEM/3_create_vrt/make_merit_dem_vrt.sh deleted file mode 100755 index ebea8d7..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/3_create_vrt/make_merit_dem_vrt.sh +++ /dev/null @@ -1,95 +0,0 @@ -#!/bin/bash - -# Make a virtual dataset (VRT) for tile of MERIT data, for easier data handling. -# First creates a .txt file that contain the names of all .tif files, then creates the VRT. - -# load gdal -module load gdal/3.0.4 - -#--------------------------------- -# Specify settings -#--------------------------------- - -# --- Location of raw data -dest_line=$(grep -m 1 "^parameter_dem_unpack_path" ../../../0_control_files/control_active.txt) # full settings line -source_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -source_path=$(echo ${source_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$source_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # source path - source_path="${root_path}/domain_${domain_name}/parameters/dem/2_MERIT_hydro_unpacked_data" - -fi - -# --- Location where converted data needs to go -dest_line=$(grep -m 1 "^parameter_dem_vrt1_path" ../../../0_control_files/control_active.txt) # full settings line -dest_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -dest_path=$(echo ${dest_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$dest_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # destination path - dest_path="${root_path}/domain_${domain_name}/parameters/dem/3_vrt" -fi - -# Make destination directory -mkdir -p "${dest_path}/filelists" - - -#--------------------------------- -# Make the VRTs -#--------------------------------- - -# Specify filenames for the filelist and the vrt -OUTTXT="${dest_path}/filelists/MERIT_Hydro_dem_filelist.txt" -OUTVRT="${dest_path}/MERIT_Hydro_dem.vrt" - -# Find the .tif files and store in a .txt file we can use for gdalbuildvrt -find $source_path -name "*.tif" >> $OUTTXT - -# Make the vrt -gdalbuildvrt $OUTVRT -input_file_list $OUTTXT -sd 1 -resolution highest - - -#--------------------------------- -# Code provenance -#--------------------------------- -# Generates a basic log file in the domain folder and copies the control file and itself there. -# Make a log directory if it doesn't exist -log_path="${dest_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_compile_log.txt" - -# Make the log -this_file='make_merit_dem_vrt.sh' -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo 'Created Virtual Dataset from MERIT .tif data.' >> $log_path/$log_file # 2nd line, append to existing file - -# Copy this file to log directory -cp $this_file $log_path \ No newline at end of file diff --git a/3b_parameters/MERIT_Hydro_DEM/4_specify_subdomain/README.md b/3b_parameters/MERIT_Hydro_DEM/4_specify_subdomain/README.md deleted file mode 100644 index 75bb2c5..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/4_specify_subdomain/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Specify subdomain -Reads the spatial extent of the modelling domain and subsets the existing `.vrt` file to that domain. diff --git a/3b_parameters/MERIT_Hydro_DEM/4_specify_subdomain/specify_subdomain.sh b/3b_parameters/MERIT_Hydro_DEM/4_specify_subdomain/specify_subdomain.sh deleted file mode 100755 index f25ca26..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/4_specify_subdomain/specify_subdomain.sh +++ /dev/null @@ -1,122 +0,0 @@ -# GDAL doc: https://gdal.org/programs/gdalwarp.html -# MERIT doc: http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/ - -# Extract the modeling domain out of the larger-domain VRT. - -# load gdal -module load gdal/3.0.4 - - -#--------------------------------- -# Specify settings -#--------------------------------- - -# --- Location of source VRT data -dest_line=$(grep -m 1 "^parameter_dem_vrt1_path" ../../../0_control_files/control_active.txt) # full settings line -source_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -source_path=$(echo ${source_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$source_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # source path - source_path="${root_path}/domain_${domain_name}/parameters/dem/3_vrt" - -fi - -# --- Location where cropped VRT needs to go -dest_line=$(grep -m 1 "^parameter_dem_vrt2_path" ../../../0_control_files/control_active.txt) # full settings line -dest_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -dest_path=$(echo ${dest_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$dest_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # destination path - dest_path="${root_path}/domain_${domain_name}/parameters/dem/4_domain_vrt" -fi - -# Make destination directory -mkdir -p $dest_path - - -# --- Find dimensions of modeling domain -domain_line=$(grep -m 1 "^forcing_raw_space" ../../../0_control_files/control_active.txt) # full settings line -domain_full=$(echo ${domain_line##*|}) # removing the leading text up to '|' -domain_full=$(echo ${domain_full%%#*}) # removing the trailing comments, if any are present - -# Separate the values into an array -while IFS='/' read -ra domain_array; do - LAT_MAX=${domain_array[0]} - LON_MIN=${domain_array[1]} - LAT_MIN=${domain_array[2]} - LON_MAX=${domain_array[3]} -done <<< "$domain_full" - - -#--------------------------------- -# Crop the domain -#--------------------------------- - -# Find the source file -source_file=$(find $source_path/*.vrt) - -# Extract the filename -filename=$(basename -- $source_file) - -# Make the output filename -dest_file=$dest_path/"domain_"$filename - -# Do the cut out -gdal_translate -of VRT -projwin $LON_MIN $LAT_MAX $LON_MAX $LAT_MIN $source_file $dest_file - - -#--------------------------------- -# Code provenance -#--------------------------------- -# Generates a basic log file in the domain folder and copies the control file and itself there. -# Make a log directory if it doesn't exist -log_path="${dest_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_specify_subdomain_log.txt" - -# Make the log -this_file='specify_subdomain.sh' -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo 'Cropped VRTs to modeling domain.' >> $log_path/$log_file # 2nd line, append to existing file - -# Copy this file to log directory -cp $this_file $log_path - - - - - - - - - - diff --git a/3b_parameters/MERIT_Hydro_DEM/5_convert_to_tif/README.md b/3b_parameters/MERIT_Hydro_DEM/5_convert_to_tif/README.md deleted file mode 100644 index deeee56..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/5_convert_to_tif/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Convert to .tif -Takes the existing .vrt file that covers the modelling domain and creates a new `.tif` file with the DEM for this domain. \ No newline at end of file diff --git a/3b_parameters/MERIT_Hydro_DEM/5_convert_to_tif/convert_vrt_to_tif.sh b/3b_parameters/MERIT_Hydro_DEM/5_convert_to_tif/convert_vrt_to_tif.sh deleted file mode 100755 index 1f180f7..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/5_convert_to_tif/convert_vrt_to_tif.sh +++ /dev/null @@ -1,129 +0,0 @@ -# Convert the MERIT .vrt to .tif - -# modules -module load gdal/3.0.4 - - -#--------------------------------- -# Specify settings -#--------------------------------- - -# --- Location of source data -dest_line=$(grep -m 1 "^parameter_dem_unpack_path" ../../../0_control_files/control_active.txt) # full settings line -data_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -data_path=$(echo ${data_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$data_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # source path - data_path="${root_path}/domain_${domain_name}/parameters/dem/2_MERIT_hydro_unpacked_data" - -fi - -# --- Location of source VRT -dest_line=$(grep -m 1 "^parameter_dem_vrt2_path" ../../../0_control_files/control_active.txt) # full settings line -source_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -source_path=$(echo ${source_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$source_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # source path - source_path="${root_path}/domain_${domain_name}/parameters/dem/4_domain_vrt" - -fi - -# --- Location where converted data needs to go -dest_line=$(grep -m 1 "^parameter_dem_tif_path" ../../../0_control_files/control_active.txt) # full settings line -dest_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -dest_path=$(echo ${dest_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$dest_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # destination path - dest_path="${root_path}/domain_${domain_name}/parameters/dem/5_elevation" -fi - -# Make destination directory -mkdir -p $dest_path - -# --- Filenames -# Source file -vrt_file=$(ls $source_path/*.vrt) - -# Find the name of the output file from control file -name_line=$(grep -m 1 "^parameter_dem_tif_name" ../../../0_control_files/control_active.txt) # full settings line -dest_name=$(echo ${name_line##*|}) # removing the leading text up to '|' -dest_name=$(echo ${dest_name%%#*}) # removing the trailing comments, if any are present - -# Make the destination path+name -tif_file="${dest_path}/${dest_name}" - -#-------------------------------------------------------------------------- -# Check if we need BIGTIFF (IF_NEEDED doesn't work with compression, sadly) -#-------------------------------------------------------------------------- -fold_size=($(du -s $data_path)) -if (($fold_size > 4000000)); then - bigtiff_flag='YES' -else - bigtiff_flag='NO' -fi - - -#--------------------------------- -# Create .tif file -#--------------------------------- -gdal_translate -co COMPRESS="DEFLATE" -co BIGTIFF=$bigtiff_flag $vrt_file $tif_file - - -#--------------------------------- -# Code provenance -#--------------------------------- -# Generates a basic log file in the domain folder and copies the control file and itself there. -# Make a log directory if it doesn't exist -log_path="${dest_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_compile_log.txt" - -# Make the log -this_file='convert_vrt_to_tif.sh' -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo 'Converted MERIT .vrt into .tif.' >> $log_path/$log_file # 2nd line, append to existing file - -# Copy this file to log directory -cp $this_file $log_path diff --git a/3b_parameters/MERIT_Hydro_DEM/README.md b/3b_parameters/MERIT_Hydro_DEM/README.md deleted file mode 100644 index 0a5df70..0000000 --- a/3b_parameters/MERIT_Hydro_DEM/README.md +++ /dev/null @@ -1,67 +0,0 @@ -# MERIT-Hydro -Note that the MERIT DEM (http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/) has been superceded by MERIT Hydro DEM (http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/). - -## Download setup instructions -MERIT Hydro downloads require registration through a Google webform. See: http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/ - -Store the obtained user details (username and password) in a new file `$HOME/.merit` (Unix/Linux) or `C:\Users\[user]\.merit` (Windows) as follows (replace `[user]` and `[pass]` with your own credentials): - -``` -user: [user] -pass: [pass] -``` - -## Description -Adjusted elevation is reprepared in 4-byte float (float32). The elevations are adjusted to satisfy the condition 'downstream is not higher than its upstream' while minimizing the required modifications from the original DEM. The elevation above EGM96 geoid is represented in meter, and the vertical increment is set to 10cm. The undefined pixels (oceans) are represented by the value -9999 (MERIT webpage, accessed 2020-07-05). - -Using gdalinfo on one of the source files shows: -``` -[wknoben@gra-login1 ~]$ gdalinfo /project/6008034/Model_Output/ClimateForcingData/MERIT_Hydro_adjusted_elevation/elv_n00e000/n00e005_elv.tif -Driver: GTiff/GeoTIFF -Files: /project/6008034/Model_Output/ClimateForcingData/MERIT_Hydro_adjusted_elevation/elv_n00e000/n00e005_elv.tif -Size is 6000, 6000 -Coordinate System is: -GEOGCS["WGS 84", - DATUM["WGS_1984", - SPHEROID["WGS 84",6378137,298.257223563, - AUTHORITY["EPSG","7030"]], - AUTHORITY["EPSG","6326"]], - PRIMEM["Greenwich",0], - UNIT["degree",0.0174532925199433], - AUTHORITY["EPSG","4326"]] -Origin = (4.999583333333334,4.999583333333334) -Pixel Size = (0.000833333333333,-0.000833333333333) -Metadata: - AREA_OR_POINT=Area -Image Structure Metadata: - COMPRESSION=DEFLATE - INTERLEAVE=BAND -Corner Coordinates: -Upper Left ( 4.9995833, 4.9995833) ( 4d59'58.50"E, 4d59'58.50"N) -Lower Left ( 4.9995833, -0.0004167) ( 4d59'58.50"E, 0d 0' 1.50"S) -Upper Right ( 9.9995833, 4.9995833) ( 9d59'58.50"E, 4d59'58.50"N) -Lower Right ( 9.9995833, -0.0004167) ( 9d59'58.50"E, 0d 0' 1.50"S) -Center ( 7.4995833, 2.4995833) ( 7d29'58.50"E, 2d29'58.50"N) -Band 1 Block=6000x1 Type=Float32, ColorInterp=Gray - NoData Value=-9999 -``` - -Summary -- Data is in [m] above EGM96 spheroid -- Ocean tiles are -9999 -- Data is stored in band 1 in each .tif -- Data is in regular lat/long (EPSG:4326) - -## Source -http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/ - -## Suggested citation -Yamazaki D., D. Ikeshima, J. Sosa, P.D. Bates, G.H. Allen, T.M. Pavelsky. MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets. Water Resources Research, vol.55, pp.5053-5073, 2019, doi: 10.1029/2019WR024873 - -## Control file settings -This section lists all the settings in `control_active.txt` that the code in this folder uses. -- **parameter_dem_main_url**: main download URL -- **parameter_dem_file_template**: Merit Hydro file name template, will be populated with correct spatial extent and appended to main URL to form complete download URL -- **forcing_raw_space**: bounding box of the modelling domain, used to find the correct Merit files to download and later to subset to the exact extent of the modelling domain -- **parameter_dem_raw_path, parameter_dem_unpack_path, parameter_dem_vrt1_path, parameter_dem_vrt2_path, parameter_dem_tif_path**: file paths -- **parameter_dem_tif_name**: name for the final .tif file that contains the domain DEM diff --git a/3b_parameters/MODIS_MCD12Q1_V6/1_download/README.md b/3b_parameters/MODIS_MCD12Q1_V6/1_download/README.md deleted file mode 100644 index 0a02799..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/1_download/README.md +++ /dev/null @@ -1,19 +0,0 @@ -# MODIS download -MODIS downloads are performed directly through the URLs provided in the file `daac_mcd12q1_data_links.txt`. - -The downloads require registration through NASA's EarthData website. See: https://urs.earthdata.nasa.gov/ - -Authentication is handled through Python's `requests` package. Store user details (username and password) in a new file `$HOME/.netrc` (Unix/Linux) or `C:\Users\[user]\.netrc` (Windows) as follows (replace `[name]` and `[pass]` with your own credentials): - -``` -machine urs.earthdata.nasa.gov -login [name] -password [pass] - -``` -For details, see: https://lpdaac.usgs.gov/resources/e-learning/how-access-lp-daac-data-command-line/ - -**_Note: given that these passwords are stored as plain text, it is strongly recommended to use a unique password that is different from any other passwords you currently have in use._** - -## Download run instructions -Execute the download script and keep the terminal or notebook open until the downloads fully complete. No manual interaction with the https://urs.earthdata.nasa.gov/ website is required. \ No newline at end of file diff --git a/3b_parameters/MODIS_MCD12Q1_V6/1_download/daac_mcd12q1_data_links.txt b/3b_parameters/MODIS_MCD12Q1_V6/1_download/daac_mcd12q1_data_links.txt deleted file mode 100644 index 48c08d0..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/1_download/daac_mcd12q1_data_links.txt +++ /dev/null @@ -1,5669 +0,0 @@ -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2007.01.01/MCD12Q1.A2007001.h22v02.006.2018145224134.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2007.01.01/MCD12Q1.A2007001.h17v02.006.2018145222617.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2007.01.01/MCD12Q1.A2007001.h23v16.006.2018145224405.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2007.01.01/MCD12Q1.A2007001.h22v06.006.2018145224153.hdf 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-https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h29v10.006.2018149132510.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h31v07.006.2018149132918.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h27v11.006.2018149131712.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h22v03.006.2018149130454.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h27v04.006.2018149131311.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h29v05.006.2018149132255.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h21v05.006.2018149130340.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h22v02.006.2018149130455.hdf 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-https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h20v09.006.2018149130223.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h29v08.006.2018149132407.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h24v04.006.2018149130724.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h23v03.006.2018149130611.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h20v02.006.2018149130143.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h29v07.006.2018149132317.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h21v03.006.2018149130323.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h23v07.006.2018149130631.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h21v11.006.2018149130413.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h28v05.006.2018149132008.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h24v06.006.2018149130734.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h23v08.006.2018149130641.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h27v12.006.2018149131737.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h22v09.006.2018149130531.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h24v07.006.2018149130846.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h20v16.006.2018149130303.hdf -https://e4ftl01.cr.usgs.gov//MODV6_Cmp_C/MOTA/MCD12Q1.006/2016.01.01/MCD12Q1.A2016001.h26v05.006.2018149131157.hdf \ No newline at end of file diff --git a/3b_parameters/MODIS_MCD12Q1_V6/1_download/download_modis_mcd12q1_v6.ipynb b/3b_parameters/MODIS_MCD12Q1_V6/1_download/download_modis_mcd12q1_v6.ipynb deleted file mode 100644 index eb8abde..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/1_download/download_modis_mcd12q1_v6.ipynb +++ /dev/null @@ -1,402 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download MODIS MCD12Q1_V6\n", - "Script based on example provided on: https://git.earthdata.nasa.gov/projects/LPDUR/repos/daac_data_download_python/browse\n", - "\n", - "Requires a `.netrc` file in user's home directory with login credentials for `urs.earthdata.nasa.gov`. See: https://lpdaac.usgs.gov/resources/e-learning/how-access-lp-daac-data-command-line/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import time\n", - "import shutil\n", - "import requests\n", - "from netrc import netrc\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get the download settings" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Path and name of file with download links\n", - "links_path = read_from_control(controlFolder/controlFile,'parameter_land_list_path')\n", - "links_file = read_from_control(controlFolder/controlFile,'parameter_land_list_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required \n", - "if links_path == 'default':\n", - " links_path = Path('./') # outputs a Path()\n", - "else:\n", - " links_path = Path(links_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Find where the data needs to go\n", - "modis_path = read_from_control(controlFolder/controlFile,'parameter_land_raw_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required \n", - "if modis_path == 'default':\n", - " modis_path = make_default_path('parameters/landclass/1_MODIS_raw_data') # outputs a Path()\n", - "else:\n", - " modis_path = Path(modis_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Make output dir\n", - "modis_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get the authentication info\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# authentication url\n", - "url = 'urs.earthdata.nasa.gov'" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# make the netrc directory\n", - "netrc_folder = os.path.expanduser(\"~/.netrc\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Get user name and password - not great, but these are stored as plain text on the user's machine regardless..\n", - "usr = netrc(netrc_folder).authenticators(url)[0]\n", - "pwd = netrc(netrc_folder).authenticators(url)[2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Do the downloads" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the download links from file\n", - "file_list = open(links_file, 'r').readlines()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Retry settings: connection can be unstable, so specify a number of retries\n", - "retries_max = 100 " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully downloaded: MCD12Q1.A2001001.h02v10.006.2018142183006.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h03v09.006.2018142183026.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h01v10.006.2018142182931.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h03v10.006.2018142183036.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h00v08.006.2018142182903.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h03v07.006.2018142183028.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h04v11.006.2018142183101.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h00v09.006.2018142182901.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h02v11.006.2018142183012.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h01v08.006.2018142182920.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h03v11.006.2018142183043.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h04v09.006.2018142183050.hdf\n", - "Error downloading MCD12Q1.A2001001.h01v09.006.2018142182927.hdf on try 1\n", - "Successfully downloaded: MCD12Q1.A2001001.h01v09.006.2018142182927.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h02v08.006.2018142182955.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h02v06.006.2018142182941.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h01v11.006.2018142182942.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h03v06.006.2018142183016.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h00v10.006.2018142182916.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h02v09.006.2018142182957.hdf\n", - "Successfully downloaded: MCD12Q1.A2001001.h04v10.006.2018142183058.hdf\n" - ] - } - ], - "source": [ - "# Loop over the download files\n", - "for file_url in file_list:\n", - " \n", - " # Make the file name\n", - " file_name = file_url.split('/')[-1].strip() # Get the last part of the url, strip whitespace and characters\n", - " \n", - " # Check if file already exists (i.e. interupted earlier download) and move to next file if so\n", - " if (modis_path / file_name).is_file():\n", - " continue \n", - " \n", - " # Make sure the connection is re-tried if it fails\n", - " retries_cur = 1\n", - " while retries_cur <= retries_max:\n", - " try:\n", - " # Send a HTTP request to the server and save the HTTP response in a response object called resp\n", - " # 'stream = True' ensures that only response headers are downloaded initially (and not all file contents too, which are 2GB+)\n", - " with requests.get(file_url.strip(), verify=True, stream=True, auth=(usr,pwd)) as response:\n", - " \n", - " # Decode the response\n", - " response.raw.decode_content = True\n", - " content = response.raw \n", - " \n", - " # Write to file\n", - " with open(modis_path / file_name, 'wb') as data:\n", - " shutil.copyfileobj(content, data)\n", - " \n", - " # Progress\n", - " print('Successfully downloaded: {}'.format(file_name))\n", - " time.sleep(3) # sleep for a bit so we don't overwhelm the server\n", - " \n", - " except:\n", - " print('Error downloading ' + file_name + ' on try ' + str(retries_cur))\n", - " retries_cur += 1\n", - " continue\n", - " else:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = modis_path\n", - "log_suffix = '_modis_download_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'download_modis_mcd12q1_v6.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Downloaded MODIS MCD12Q1_V6 data with global coverage.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/3b_parameters/MODIS_MCD12Q1_V6/1_download/download_modis_mcd12q1_v6.py b/3b_parameters/MODIS_MCD12Q1_V6/1_download/download_modis_mcd12q1_v6.py deleted file mode 100644 index 17d12b3..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/1_download/download_modis_mcd12q1_v6.py +++ /dev/null @@ -1,164 +0,0 @@ -# Download MODIS MCD12Q1_V6 -# Script based on example provided on: https://git.earthdata.nasa.gov/projects/LPDUR/repos/daac_data_download_python/browse -# Requires a `.netrc` file in user's home directory with login credentials for `urs.earthdata.nasa.gov`. See: https://lpdaac.usgs.gov/resources/e-learning/how-access-lp-daac-data-command-line/ - -# modules -import os -import time -import shutil -import requests -from netrc import netrc -from pathlib import Path -from shutil import copyfile -from datetime import datetime - - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Get the download settings -# Path and name of file with download links -links_path = read_from_control(controlFolder/controlFile,'parameter_land_list_path') -links_file = read_from_control(controlFolder/controlFile,'parameter_land_list_name') - -# Specify the default paths if required -if links_path == 'default': - links_path = Path('./') # outputs a Path() -else: - links_path = Path(links_path) # make sure a user-specified path is a Path() - -# Find where the data needs to go -modis_path = read_from_control(controlFolder/controlFile,'parameter_land_raw_path') - -# Specify the default paths if required -if modis_path == 'default': - modis_path = make_default_path('parameters/landclass/1_MODIS_raw_data') # outputs a Path() -else: - modis_path = Path(modis_path) # make sure a user-specified path is a Path() - -# Make output dir -modis_path.mkdir(parents=True, exist_ok=True) - - -# --- Get the authentication info -# authentication url -url = 'urs.earthdata.nasa.gov' - -# make the netrc directory -netrc_folder = os.path.expanduser("~/.netrc") - -# Get user name and password - not great, but these are stored as plain text on the user's machine regardless.. -usr = netrc(netrc_folder).authenticators(url)[0] -pwd = netrc(netrc_folder).authenticators(url)[2] - - -# --- Do the downloads -# Get the download links from file -file_list = open(links_file, 'r').readlines() - -# Retry settings: connection can be unstable, so specify a number of retries -retries_max = 100 - -# Loop over the download files -for file_url in file_list: - - # Make the file name - file_name = file_url.split('/')[-1].strip() # Get the last part of the url, strip whitespace and characters - - # Check if file already exists (i.e. interupted earlier download) and move to next file if so - if (modis_path / file_name).is_file(): - continue - - # Make sure the connection is re-tried if it fails - retries_cur = 1 - while retries_cur <= retries_max: - try: - # Send a HTTP request to the server and save the HTTP response in a response object called resp - # 'stream = True' ensures that only response headers are downloaded initially (and not all file contents too, which are 2GB+) - with requests.get(file_url.strip(), verify=True, stream=True, auth=(usr,pwd)) as response: - - # Decode the response - response.raw.decode_content = True - content = response.raw - - # Write to file - with open(modis_path / file_name, 'wb') as data: - shutil.copyfileobj(content, data) - - # Progress - print('Successfully downloaded: {}'.format(file_name)) - time.sleep(3) # sleep for a bit so we don't overwhelm the server - - except: - print('Error downloading ' + file_name + ' on try ' + str(retries_cur)) - retries_cur += 1 - continue - else: - break - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = modis_path -log_suffix = '_modis_download_log.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = 'download_modis_mcd12q1_v6.py' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Downloaded MODIS MCD12Q1_V6 data with global coverage.'] - for txt in lines: - file.write(txt) \ No newline at end of file diff --git a/3b_parameters/MODIS_MCD12Q1_V6/2_create_vrt/README.md b/3b_parameters/MODIS_MCD12Q1_V6/2_create_vrt/README.md deleted file mode 100644 index 8f835b6..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/2_create_vrt/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# Combine .hdf into .vrt -The satellite data is composed of small .hdf files that contain part of the global map. We need to combine these into a single map (per year of data) for further analysis. - -## Virtual Dataset (VRT) -Gdal VRT's are "a mosaic of the list of input GDAL datasets" (https://gdal.org/programs/gdalbuildvrt.html). Essentially this stiches together the individual .hdf files and allows further GDAL operations on the data. - -## Scripts -### make_vrt_per_year.sh -Loops over years 2001 to 2018, creates a list of MODIS tiles for the given year and stiches those together into a single virtual data set for each year. It extracts only sub dataset 1, i.e. the IBGP classification sub dataset (see MODIS docs or use 'gdalinfo '). - -## Input required -- Downloaded .hdf files -- Location of the folder that contains these files -- Location and names for list of files output and vrt output - -## Output generated -- GDAL .vrt files that contain the MODIS data for year X, stiched together into a single map. - -## Notes -- Python can read .vrt files (source: https://jgomezdans.github.io/stitching-together-modis-data.html) - -``` -import numpy as np -import matplotlib.pyplot as plt -from osgeo import gdal - -g = gdal.Open ( "mosaic_sinu.vrt" ) # Open file -data = g.ReadAsArray() # Read contents -mdata = np.ma.array ( data, mask=data>30000 ) # Mask data -cmap = plt.cm.jet # Set colormap -cmap.set_bad ( 'k' ) # Set masked values to black -# Next line scales the GPP data by 0.0001 to get the right units -# and plots it. -plt.imshow ( mdata*0.0001, interpolation='nearest', vmax=0.007, cmap=cmap) -plt.colorbar ( orientation='horizontal' ) -``` diff --git a/3b_parameters/MODIS_MCD12Q1_V6/2_create_vrt/make_vrt_per_year.sh b/3b_parameters/MODIS_MCD12Q1_V6/2_create_vrt/make_vrt_per_year.sh deleted file mode 100755 index 0f99e23..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/2_create_vrt/make_vrt_per_year.sh +++ /dev/null @@ -1,113 +0,0 @@ -#!/bin/bash - -# Make a virtual dataset (VRT) for each year of MODIS data, for easier data handling. -# First creates .txt files that contain the names of .h5 files per year, then creates separate VRTs for each year. - -# load gdal -module load gdal/3.0.4 - - -#--------------------------------- -# Specify settings -#--------------------------------- - -# --- Location of raw data -dest_line=$(grep -m 1 "^parameter_land_raw_path" ../../../0_control_files/control_active.txt) # full settings line -source_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -source_path=$(echo ${source_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$source_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # source path - source_path="${root_path}/domain_${domain_name}/parameters/landclass/1_MODIS_raw_data" - -fi - - -# --- Location where converted data needs to go -dest_line=$(grep -m 1 "^parameter_land_vrt1_path" ../../../0_control_files/control_active.txt) # full settings line -dest_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -dest_path=$(echo ${dest_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$dest_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # destination path - dest_path="${root_path}/domain_${domain_name}/parameters/landclass/2_vrt_native_crs" -fi - -# Make destination directory -mkdir -p "${dest_path}/filelists" - -# --------------------------- -# Check if GDAL supports HDF4 -# --------------------------- -hdf4_check=$(gdalinfo --formats | grep 'HDF4') -if [[ $hdf4_check != *'HDF4'* ]]; then - echo No HDF4 support found in GDAL module. Check GDAL distribution. - exit 1 -fi - -#--------------------------------- -# Make the VRTs -#--------------------------------- - -# Loop over the years and create a .txt file that we can use as input for gdalbuildvrt -# Note that our variable of interest (MCD12Q1:LC_Type1) is stored in sub dataset 1 (-sd 1) -for YEAR in {2001..2018} -do - # specify the output name for the file list - OUTTXT="${dest_path}/filelists/MCD12Q1_filelist_"$YEAR".txt" - - # store the hdf files for that year in a temporary file - ls $source_path/MCD12Q1.A$YEAR* >> $OUTTXT - - # Specify the output name for the VRT - OUTVRT="${dest_path}/MCD12Q1_"$YEAR".vrt" - - # Make the vrt - gdalbuildvrt $OUTVRT -input_file_list $OUTTXT -sd 1 -resolution highest - -done - - -#--------------------------------- -# Code provenance -#--------------------------------- -# Generates a basic log file in the domain folder and copies the control file and itself there. -# Make a log directory if it doesn't exist -log_path="${dest_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_compile_log.txt" - -# Make the log -this_file='make_vrt_per_year.sh' -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo 'Created Virtual Datasets from MODIS .hdf data for each year of record.' >> $log_path/$log_file # 2nd line, append to existing file - -# Copy this file to log directory -cp $this_file $log_path \ No newline at end of file diff --git a/3b_parameters/MODIS_MCD12Q1_V6/3_reproject_vrt/README.md b/3b_parameters/MODIS_MCD12Q1_V6/3_reproject_vrt/README.md deleted file mode 100644 index 27ab3f6..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/3_reproject_vrt/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Reproject .vrt -Changes the geographical projection of the `.vrt` to EPSG:4326 to allow intersection with the catchment shapefiles. diff --git a/3b_parameters/MODIS_MCD12Q1_V6/3_reproject_vrt/reproject_vrt.sh b/3b_parameters/MODIS_MCD12Q1_V6/3_reproject_vrt/reproject_vrt.sh deleted file mode 100755 index 53018c1..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/3_reproject_vrt/reproject_vrt.sh +++ /dev/null @@ -1,104 +0,0 @@ -# GDAL doc: https://gdal.org/programs/gdalwarp.html -# MODIS doc: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf - -# Reprojects the VRTs with MODIS data into EPSG:4326, which the rest of the workflow is based on. - -# load gdal -module load gdal/3.0.4 - - -#--------------------------------- -# Specify settings -#--------------------------------- - -# --- Location of raw data -dest_line=$(grep -m 1 "^parameter_land_vrt1_path" ../../../0_control_files/control_active.txt) # full settings line -source_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -source_path=$(echo ${source_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$source_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # source path - source_path="${root_path}/domain_${domain_name}/parameters/landclass/2_vrt_native_crs" - -fi - -# --- Location where converted data needs to go -dest_line=$(grep -m 1 "^parameter_land_vrt2_path" ../../../0_control_files/control_active.txt) # full settings line -dest_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -dest_path=$(echo ${dest_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$dest_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # destination path - dest_path="${root_path}/domain_${domain_name}/parameters/landclass/3_vrt_epsg_4326" -fi - -# Make destination directory -mkdir -p $dest_path - - -# --- Define the projection to use -PROJ="EPSG:4326" - -#--------------------------------- -# Reproject -#--------------------------------- - -# Loop over all vrt files in PATH_SRC -for FILE_SRC in $source_path/MCD*.vrt -do - - # extract the file name - FILENAME=$(basename -- $FILE_SRC) - - # construct the destination file - FILE_DES=$dest_path/$FILENAME - - # do the warping - gdalwarp -of VRT -t_srs $PROJ $FILE_SRC $FILE_DES - -done - - -#--------------------------------- -# Code provenance -#--------------------------------- -# Generates a basic log file in the domain folder and copies the control file and itself there. -# Make a log directory if it doesn't exist -log_path="${dest_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_compile_log.txt" - -# Make the log -this_file='reproject_vrt.sh' -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo 'Reprojected VRTs into EPSG:4326.' >> $log_path/$log_file # 2nd line, append to existing file - -# Copy this file to log directory -cp $this_file $log_path \ No newline at end of file diff --git a/3b_parameters/MODIS_MCD12Q1_V6/4_specify_subdomain/README.md b/3b_parameters/MODIS_MCD12Q1_V6/4_specify_subdomain/README.md deleted file mode 100644 index 880fb44..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/4_specify_subdomain/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Specify subdomain -Subsets the `.vrt` files to the modeling domain. diff --git a/3b_parameters/MODIS_MCD12Q1_V6/4_specify_subdomain/specify_subdomain.sh b/3b_parameters/MODIS_MCD12Q1_V6/4_specify_subdomain/specify_subdomain.sh deleted file mode 100755 index 93d13e5..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/4_specify_subdomain/specify_subdomain.sh +++ /dev/null @@ -1,120 +0,0 @@ -# GDAL doc: https://gdal.org/programs/gdalwarp.html -# MODIS doc: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf - -# Extract the modeling domain out of the global-cover VRTs. - -# load the module -module load gdal/3.0.4 - -#--------------------------------- -# Specify settings -#--------------------------------- - -# --- Location of source VRT data -dest_line=$(grep -m 1 "^parameter_land_vrt2_path" ../../../0_control_files/control_active.txt) # full settings line -source_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -source_path=$(echo ${source_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$source_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # source path - source_path="${root_path}/domain_${domain_name}/parameters/landclass/3_vrt_epsg_4326" - -fi - -# --- Location where cropped VRT needs to go -dest_line=$(grep -m 1 "^parameter_land_vrt3_path" ../../../0_control_files/control_active.txt) # full settings line -dest_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -dest_path=$(echo ${dest_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$dest_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # destination path - dest_path="${root_path}/domain_${domain_name}/parameters/landclass/4_domain_vrt_epsg_4326" -fi - -# Make destination directory -mkdir -p $dest_path - -# --- Find dimensions of modeling domain -domain_line=$(grep -m 1 "^forcing_raw_space" ../../../0_control_files/control_active.txt) # full settings line -domain_full=$(echo ${domain_line##*|}) # removing the leading text up to '|' -domain_full=$(echo ${domain_full%%#*}) # removing the trailing comments, if any are present - -# Separate the values into an array -while IFS='/' read -ra domain_array; do - LAT_MAX=${domain_array[0]} - LON_MIN=${domain_array[1]} - LAT_MIN=${domain_array[2]} - LON_MAX=${domain_array[3]} -done <<< "$domain_full" - - -#--------------------------------- -# Crop the domain -#--------------------------------- - -# Loop over all files -for FILE_SRC in $source_path/MCD*.vrt -do - - # Extract the filename - FILENAME=$(basename -- $FILE_SRC) - - # construct the destination file - FILE_DES=$dest_path/"domain_"$FILENAME - - # Do the cut out - gdal_translate -of VRT -projwin $LON_MIN $LAT_MAX $LON_MAX $LAT_MIN $FILE_SRC $FILE_DES - -done - - -#--------------------------------- -# Code provenance -#--------------------------------- -# Generates a basic log file in the domain folder and copies the control file and itself there. -# Make a log directory if it doesn't exist -log_path="${dest_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_specify_subdomain_log.txt" - -# Make the log -this_file='specify_subdomain.sh' -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo 'Cropped VRTs to modeling domain.' >> $log_path/$log_file # 2nd line, append to existing file - -# Copy this file to log directory -cp $this_file $log_path - - - - - - - diff --git a/3b_parameters/MODIS_MCD12Q1_V6/5_multiband_vrt/README.md b/3b_parameters/MODIS_MCD12Q1_V6/5_multiband_vrt/README.md deleted file mode 100644 index 53bb2dd..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/5_multiband_vrt/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Create multiband .vrt -So far, we have used separate `.vrt` files for each year. In this step these are combined to a single multiband .vrt. diff --git a/3b_parameters/MODIS_MCD12Q1_V6/5_multiband_vrt/create_multiband_vrt.sh b/3b_parameters/MODIS_MCD12Q1_V6/5_multiband_vrt/create_multiband_vrt.sh deleted file mode 100755 index 7ccfc39..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/5_multiband_vrt/create_multiband_vrt.sh +++ /dev/null @@ -1,94 +0,0 @@ -# Create a new multiband VRT with each data year being one band - -# load the module -module load gdal/3.0.4 - -#--------------------------------- -# Specify settings -#--------------------------------- - -# --- Location of source data -dest_line=$(grep -m 1 "^parameter_land_vrt3_path" ../../../0_control_files/control_active.txt) # full settings line -source_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -source_path=$(echo ${source_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$source_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # source path - source_path="${root_path}/domain_${domain_name}/parameters/landclass/4_domain_vrt_epsg_4326" - -fi - -# --- Location where converted data needs to go -dest_line=$(grep -m 1 "^parameter_land_vrt4_path" ../../../0_control_files/control_active.txt) # full settings line -dest_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -dest_path=$(echo ${dest_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$dest_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # destination path - dest_path="${root_path}/domain_${domain_name}/parameters/landclass/5_multiband_domain_vrt_epsg_4326" -fi - -# Make destination directory -mkdir -p $dest_path - -# --- Specify output filename -FILENAME="domain_MCD12Q1_2001_2018.vrt" - - -#--------------------------------- -# Create multiband VRT -#--------------------------------- - -# Find VRT filenames and store in temporary file -ls ${source_path}/*.vrt > filelist.txt - -# Create the multiband VRT -gdalbuildvrt -separate -input_file_list filelist.txt ${dest_path}/${FILENAME} -resolution highest - -# Remove the temporary file -rm -f filelist.txt - - -#--------------------------------- -# Code provenance -#--------------------------------- -# Generates a basic log file in the domain folder and copies the control file and itself there. -# Make a log directory if it doesn't exist -log_path="${dest_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_compile_log.txt" - -# Make the log -this_file='create_multiband_vrt.sh' -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo 'Combined annual VRTs into a single multiband VRT.' >> $log_path/$log_file # 2nd line, append to existing file - -# Copy this file to log directory -cp $this_file $log_path \ No newline at end of file diff --git a/3b_parameters/MODIS_MCD12Q1_V6/6_convert_to_tif/README.md b/3b_parameters/MODIS_MCD12Q1_V6/6_convert_to_tif/README.md deleted file mode 100644 index 684dcef..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/6_convert_to_tif/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Convert to .tif -Creates a multiband `.tif` file out of the multiband `.vrt`. diff --git a/3b_parameters/MODIS_MCD12Q1_V6/6_convert_to_tif/convert_vrt_to_tif.sh b/3b_parameters/MODIS_MCD12Q1_V6/6_convert_to_tif/convert_vrt_to_tif.sh deleted file mode 100755 index 5c2b30d..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/6_convert_to_tif/convert_vrt_to_tif.sh +++ /dev/null @@ -1,86 +0,0 @@ -# Convert the multiband VRT to .tif - -# modules -module load gdal/3.0.4 - -#--------------------------------- -# Specify settings -#--------------------------------- - -# --- Location of source data -dest_line=$(grep -m 1 "^parameter_land_vrt4_path" ../../../0_control_files/control_active.txt) # full settings line -source_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -source_path=$(echo ${source_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$source_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # source path - source_path="${root_path}/domain_${domain_name}/parameters/landclass/5_multiband_domain_vrt_epsg_4326" - -fi - -# --- Location where converted data needs to go -dest_line=$(grep -m 1 "^parameter_land_tif_path" ../../../0_control_files/control_active.txt) # full settings line -dest_path=$(echo ${dest_line##*|}) # removing the leading text up to '|' -dest_path=$(echo ${dest_path%%#*}) # removing the trailing comments, if any are present - -# Specify the default path if needed -if [ "$dest_path" = "default" ]; then - - # Get the root path and append the appropriate install directories - root_line=$(grep -m 1 "^root_path" ../../../0_control_files/control_active.txt) - root_path=$(echo ${root_line##*|}) - root_path=$(echo ${root_path%%#*}) - - # domain name - domain_line==$(grep -m 1 "^domain_name" ../../../0_control_files/control_active.txt) - domain_name=$(echo ${domain_line##*|}) - domain_name=$(echo ${domain_name%%#*}) - - # destination path - dest_path="${root_path}/domain_${domain_name}/parameters/landclass/6_tif_multiband" -fi - -# Make destination directory -mkdir -p $dest_path - -# --- Filenames -vrt_file=$(ls $source_path/*.vrt) -tif_file="${dest_path}/$(basename "$vrt_file" .vrt).tif" - -#--------------------------------- -# Create .tif file -#--------------------------------- -gdal_translate -co "COMPRESS=DEFLATE" $vrt_file $tif_file - - -#--------------------------------- -# Code provenance -#--------------------------------- -# Generates a basic log file in the domain folder and copies the control file and itself there. -# Make a log directory if it doesn't exist -log_path="${dest_path}/_workflow_log" -mkdir -p $log_path - -# Log filename -today=`date '+%F'` -log_file="${today}_compile_log.txt" - -# Make the log -this_file='convert_vrt_to_tif.sh' -echo "Log generated by ${this_file} on `date '+%F %H:%M:%S'`" > $log_path/$log_file # 1st line, store in new file -echo 'Converted multiband VRT into .tif.' >> $log_path/$log_file # 2nd line, append to existing file - -# Copy this file to log directory -cp $this_file $log_path diff --git a/3b_parameters/MODIS_MCD12Q1_V6/7_find_mode_land_class/README.md b/3b_parameters/MODIS_MCD12Q1_V6/7_find_mode_land_class/README.md deleted file mode 100644 index e6490d1..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/7_find_mode_land_class/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Find mode land class -Takes the multiband `.tif` for the modeling domain and finds the mode vegetation class for each pixel. Every band contains data for a given year and this approach is in line with the recommendation in the MODIS docs to not use the data from any individual year due to the uncertainties involved. Using the mode likely gives us a more representative vegetation class per pixel. \ No newline at end of file diff --git a/3b_parameters/MODIS_MCD12Q1_V6/7_find_mode_land_class/find_mode_landclass.ipynb b/3b_parameters/MODIS_MCD12Q1_V6/7_find_mode_land_class/find_mode_landclass.ipynb deleted file mode 100644 index 2f407c6..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/7_find_mode_land_class/find_mode_landclass.ipynb +++ /dev/null @@ -1,435 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Find mode land class" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "from pathlib import Path\n", - "import scipy.stats as sc\n", - "from shutil import copyfile\n", - "from datetime import datetime\n", - "from osgeo import gdal, ogr, osr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find source and destination locations" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find where the soil classes are\n", - "landClassPath = read_from_control(controlFolder/controlFile,'parameter_land_tif_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required \n", - "if landClassPath == 'default':\n", - " landClassPath = make_default_path('parameters/landclass/6_tif_multiband') # outputs a Path()\n", - "else:\n", - " landClassPath = Path(landClassPath) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Find where the mode soil class needs to go\n", - "modeLandClassPath = read_from_control(controlFolder/controlFile,'parameter_land_mode_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required \n", - "if modeLandClassPath == 'default':\n", - " modeLandClassPath = make_default_path('parameters/landclass/7_mode_land_class') # outputs a Path()\n", - "else:\n", - " modeLandClassPath = Path(modeLandClassPath) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "modeLandClassPath.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Filenames" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the name of the source file\n", - "for file in os.listdir(landClassPath):\n", - " if file.endswith(\".tif\"):\n", - " source_file = file" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# New file\n", - "dest_file = read_from_control(controlFolder/controlFile,'parameter_land_tif_name')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Function definition" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Opens geotif file, extracts data from a single band and computes corner & center coordinates in lat/lon\n", - "def open_geotif(file,band):\n", - " \n", - " # Do the things\n", - " ds = gdal.Open(file) # open the file\n", - " band = ds.GetRasterBand(band) # get the data band; there should be 18 for each of the 18 years\n", - " data = band.ReadAsArray() # convert to numpy array for further manipulation\n", - " width = ds.RasterXSize # pixel width\n", - " height = ds.RasterYSize # pixel height\n", - " rasterSize = [width,height]\n", - " geoTransform = ds.GetGeoTransform() # geolocation\n", - " boundingBox = np.zeros((5,2)) # coordinates of bounding box\n", - " boundingBox[0,0] = boundingBox[1,0] = geoTransform[0]\n", - " boundingBox[0,1] = boundingBox[2,1] = geoTransform[3]\n", - " boundingBox[2,0] = boundingBox[3,0] = geoTransform[0] + width*geoTransform[1]\n", - " boundingBox[1,1] = boundingBox[3,1] = geoTransform[3] + height*geoTransform[5]\n", - " boundingBox[4,0] = geoTransform[0] + (width/2)*geoTransform[1]\n", - " boundingBox[4,1] = geoTransform[3] + (height/2)*geoTransform[5]\n", - " \n", - " return data, geoTransform, rasterSize, boundingBox" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Writes data into a new geotif file\n", - "# Source: https://gis.stackexchange.com/questions/199477/gdal-python-cut-geotiff-image/199565\n", - "def write_geotif_sameDomain(src_file,des_file,des_data):\n", - " \n", - " # load the source file to get the appropriate attributes\n", - " src_ds = gdal.Open(src_file)\n", - " \n", - " # get the geotransform\n", - " des_transform = src_ds.GetGeoTransform()\n", - " \n", - " # get the data dimensions\n", - " ncols = des_data.shape[1]\n", - " nrows = des_data.shape[0]\n", - " \n", - " # make the file\n", - " driver = gdal.GetDriverByName(\"GTiff\")\n", - " dst_ds = driver.Create(des_file,ncols,nrows,1,gdal.GDT_Float32, options = [ 'COMPRESS=DEFLATE' ])\n", - " dst_ds.GetRasterBand(1).WriteArray( des_data ) \n", - " dst_ds.SetGeoTransform(des_transform)\n", - " wkt = src_ds.GetProjection()\n", - " srs = osr.SpatialReference()\n", - " srs.ImportFromWkt(wkt)\n", - " dst_ds.SetProjection( srs.ExportToWkt() )\n", - " \n", - " # close files\n", - " src_ds = None\n", - " des_ds = None\n", - "\n", - " return" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find mode land class " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Get land use classes for each year\n", - "land_use_classes = np.dstack((open_geotif( str(landClassPath/source_file) ,1)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,2)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,3)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,4)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,5)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,6)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,7)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,8)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,9)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,10)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,11)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,12)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,13)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,14)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,15)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,16)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,17)[0], \\\n", - " open_geotif( str(landClassPath/source_file) ,18)[0]))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Extract mode\n", - "mode = sc.mode(land_use_classes,axis=2)[0].squeeze()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Store this in a new geotif file\n", - "src_file = str(landClassPath/source_file)\n", - "des_file = str(modeLandClassPath/dest_file)\n", - "write_geotif_sameDomain(src_file,des_file,mode)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = modeLandClassPath\n", - "log_suffix = '_mode_over_years_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'find_mode_landclass.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Found mode landclass over years']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "WindowsPath('C:/Globus endpoint/summaWorkflow_data/domain_BowAtBanff/parameters/landclass/7_mode_land_class')" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logPath" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/3b_parameters/MODIS_MCD12Q1_V6/README.md b/3b_parameters/MODIS_MCD12Q1_V6/README.md deleted file mode 100644 index 6027afb..0000000 --- a/3b_parameters/MODIS_MCD12Q1_V6/README.md +++ /dev/null @@ -1,126 +0,0 @@ -# SUMMA vegetation parameters - -## Noah-MP in SUMMA -SUMMA's approach to vegetation parameters is based on that of the Noah-MP land model and relies on look-up tables. Briefly, the user defines for each model element a representative vegetation type as a numerical value (e.g. "evergreen needleleaf forest" might be encoded as "1", "evergreen broadleaf forests" as "2", etc). SUMMA then navigates the look-up table to extract typical values of vegetation properties for the specified vegetation type and uses these values for further computations. - -SUMMA currently has several different look-up tables available in the file `TBL_VEGPARM.TBL` (found in the folder `5_model_input/SUMMA/0_base_settings`). This workflow assumes that the MODIFIED_IGBP_MODIS_NOAH table is used to define vegetation type index values (specified below). Vegetation classes are stored as SUMMA parameters in a variable called "vegTypeIndex", kept in "attributes.nc" (https://summa.readthedocs.io/en/latest/input_output/SUMMA_input/#attribute-and-parameter-files). - -Further details can be found in the Noah-MP documentation, see e.g.: https://ral.ucar.edu/solutions/products/noah-multiparameterization-land-surface-model-noah-mp-lsm - - -## Download setup instructions -The downloads require registration through NASA's EarthData website. See: https://urs.earthdata.nasa.gov/ - -Authentication is handled through Python's `requests` package. Store user details (username and password) in a new file `$HOME/.netrc` (Unix/Linux) or `C:\Users\[user]\.netrc` (Windows) as follows (replace `[name]` and `[pass]` with your own credentials): - -``` -machine urs.earthdata.nasa.gov -login -password -``` -For details, see: https://lpdaac.usgs.gov/resources/e-learning/how-access-lp-daac-data-command-line/ - -**_Note: given that these passwords are stored as plain text, it is strongly recommended to use a unique password that is different from any other passwords you currently have in use._** - - -## GDAL requirements -MODIS data is provided in the HDF4 format. This format is not supported by certain GDAL distributions. Ensure your local GDAL install can work with HDF4. See e.g.: -- linux: https://gis.stackexchange.com/questions/135867/gdal-hdf4-driver-on-linux-system -- mac: https://stackoverflow.com/questions/45598772/binding-gdal-with-r-on-osx-mac-cant-open-explore-h4-files-in-r - - - -## MODIS Land Cover Type Product (MCD12Q1) -MODIS MCD12Q1 data (Friedl et al., 2019) are satellite data of global land cover classes at 500m resolution and are available for years 2001 to 2018. Raw satellite data has already been processed into various vegetation classifications. - -Main information page: https://lpdaac.usgs.gov/products/mcd12q1v006/ - -Land cover classes doc: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf - -Current validation status: Stage 2. Product accuracy has been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts. (https://lpdaac.usgs.gov/products/mcd12q1v006/, 2020-05-19. Validation definitions: https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) - -#### Data use -Documentation strongly recommends *against* comparing land cover classes between years, due to uncertainty in the land cover determination procedure. We therefore use the most common land cover class across years 2001 to 2018 as the representative class for each grid cell. - -#### Known issues -From the documentation: - -- The "units" field is missing in the metadata, however, this information can be found in the table above or on page 5 of the User Guide. -- Areas of permanent sea ice are mapped as water if they are identifed as water according to the C6 Land/Water mask (Carroll et al., 2009). Some land areas, for example glaciers within permanent topographic shadows, were mapped as water according to this mask, which introduces isolated errors in the product. -- Wetlands are under-represented. -- In areas of the tropics where cropland field sizes tend to be much smaller than a MODIS pixel, agriculture is sometimes underrepresented (i.e., labeled as natural vegetation). -- Areas of temperate evergreen needleleaf forests are misclassified as broadleaf evergreen forests in Japan, the Pacific Northwest of North America, and Chile. Similarly, areas of evergreen broadleaf forests are misclassified as evergreen needleleaf forests in Australia and parts of South America. -- Some grassland areas are classified as savannas (sparse forest). -- There is a glacier in Chile that is screened as if it were permanently cloud covered and is partially classified as grassland. - - -## MODIS to SUMMA -The MCD12Q1 IGBP land cover classification matches existing SUMMA tables for 17/20 classes. The 3 different tundra classes are missing. See tables below. The workflow code downloads the MODIS data (global domain), subsets these to the modeling domain and finds a representative vegetation class for each model element. The vegetation class for each model element is saved in "attributes.nc", which is part of the SUMMA input files. - - -## IGBP vegetation classification table -This section shows the classification table as used for the MODIS data (https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf) and SUMMA's implementation of the table. Because both tables use the same order of classes, no remapping from one to the other is needed. Because classes are only identified numerically in SUMMA's input files, switching to different look-up tables or input data should be done with care. - - -### IGBP Table as used in MODIS -| Name | Value | Description | -|---------------------------------------|-------|----------------------------------------------------------------------------------------------------------| -| Evergreen Needleleaf Forests | 1 | Dominated by evergreen conifer trees (canopy>2m). Tree cover>60%. | -| Evergreen Broadleaf Forests | 2 | Dominated by evergreen broadleaf and palmatetrees (canopy>2m). Tree cover>60%. | -| Deciduous Needleleaf Forests | 3 | Dominated by deciduous needleleaf (larch) trees(canopy>2m). Tree cover>60%. | -| Deciduous Broadleaf Forests | 4 | Dominated by deciduous broadleaf trees (canopy>2m). Tree cover>60% | -| Mixed Forests | 5 | Dominated by neither deciduous nor evergreen(40-60% of each) tree type (canopy>2m). Tree cover>60% | -| Closed Shrublands | 6 | Dominated by woody perennials (1-2m height)>60% cover. | -| Open Shrublands | 7 | Dominated by woody perennials (1-2m height)10-60% cover. | -| Woody Savannas | 8 | Tree cover 30-60% (canopy>2m) | -| Savannas | 9 | Tree cover 10-30% (canopy>2m). | -| Grasslands | 10 | Dominated by herbaceous annuals (<2m). | -| Permanent Wetlands | 11 | Permanently inundated lands with 30-60% watercover and>10% vegetated cover. | -| Croplands | 12 | At least 60% of area is cultivated cropland. | -| Urban and Built-up Lands | 13 | At least 30% impervious surface area includingbuilding materials, asphalt, and vehicles. | -| Cropland/Natural Vegetation Mosaics | 14 | Mosaics of small-scale cultivation 40-60% withnatural tree, shrub, or herbaceous vegetation | -| Permanent Snow and Ice | 15 | At least 60% of area is covered by snow and icefor at least 10 months of the year. | -| Barren | 16 | At least 60% of area is non-vegetated barren(sand, rock, soil) areas with less than 10% veg-etation. | -| Water Bodies | 17 | At least 60% of area is covered by permanent wa-ter bodies | -| Unclassified | 255 | Has not received a map label because of missinginputs | - -## IGBP Table as currently implemented in SUMMA -Table name: "MODIFIED_IGBP_MODIS_NAOH" - -| Value | Name | -|-------|------------------------------------| -| 1 | Evergreen Needleleaf Forest | -| 2 | Evergreen Broadleaf Forest | -| 3 | Deciduous Needleleaf Forest | -| 4 | Deciduous Broadleaf Forest | -| 5 | Mixed Forests | -| 6 | Closed Shrublands | -| 7 | Open Shrublands | -| 8 | Woody Savannas | -| 9 | Savannas' | -| 10 | Grasslands' | -| 11 | Permanent wetlands | -| 12 | Croplands' | -| 13 | Urban and Built-Up | -| 14 | cropland/natural vegetation mosaic | -| 15 | Snow and Ice | -| 16 | Barren or Sparsely Vegetate | -| 17 | Water | -| 18 | Wooded Tundra | -| 19 | Mixed Tundra | -| 20 | Barren Tundra | - -## References -Friedl, M., Sulla-Menashe, D. (2019). MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2020-05-20 from https://doi.org/10.5067/MODIS/MCD12Q1.006 - -## Control file settings -This section lists all the settings in `control_active.txt` that the code in this folder uses. -- **parameter_land_list_path, parameter_land_list_name**: specify the location and name of the file that contains all MODIS download URLs -- **forcing_raw_space**: bounding box of the modelling domain, used to subset global data to the exact extent of the modelling domain -- **parameter_land_raw_path, parameter_land_vrt1_path, parameter_land_vrt2_path, parameter_land_vrt3_path, parameter_land_vrt4_path, parameter_land_tif_path, parameter_land_mode_path**: file paths -- **parameter_land_tif_name**: name of the .tif file that contains the land classes for the domain - - - - - diff --git a/3b_parameters/README.md b/3b_parameters/README.md deleted file mode 100644 index 923ad4a..0000000 --- a/3b_parameters/README.md +++ /dev/null @@ -1,14 +0,0 @@ -# Parameters -SUMMA requires certain inputs that need to be derived from geospatial data fields. Here we obtain and pre-process: -- Merit-Hydro digital elevation model (DEM) data; -- SOILGRIDS soil properties data; -- MODIS vegetation properties data. - -## Elevation -Elevation data are needed to compare the elevation of each model element to that elevation of the forcing grid that we use as model input. The difference between both is used to apply lapse rates to air temperature data, to approximate temperature gradients based on altitude. - -## Soil properties -SUMMA uses a lookup table approach to find values for certain soil parameters. Soils are divided into different categories. A representative soil category per model elements is derived from SOILGRIDS maps that show sand, silt and clay percentages. - -## Vegetation properties -SUMMA uses a lookup table approach to find values for certain vegetation parameters. Vegetation cover is divided into different categories. MODIS data provides vegetation classes as per the IGBP classification, which lets us find a representative vegetation type per model element. \ No newline at end of file diff --git a/3b_parameters/SOILGRIDS/1_download/README.md b/3b_parameters/SOILGRIDS/1_download/README.md deleted file mode 100644 index 7f7428d..0000000 --- a/3b_parameters/SOILGRIDS/1_download/README.md +++ /dev/null @@ -1,20 +0,0 @@ -# Soil download through Hydroshare -A global map of USGS soil classes has been prepared and is available on Hydroshare. This avoids lengthy preprocessing and provides a consistent download location for this data set. - -Hydroshare resource: https://www.hydroshare.org/resource/1361509511e44adfba814f6950c6e742/ - -## Download registration -Hydroshare downloads require registration through the Hydroshare website. See: https://www.hydroshare.org/sign-up/?next= - -Hydroshare downloads use the Python package `hs_restclient`. Downloads require authentication through the client. Store user details (username and password) in a new file `$HOME/.hydroshare` (Unix/Linux) or `C:\Users\[user]\.hydroshare` (Windows) as follows (replace `[name]` and `[pass]` with your own credentials): - -``` -name: [name] -pass: [pass] - -``` - -**_Note: given that these passwords are stored as plain text, it is strongly recommended to use a unique password that is different from any other passwords you currently have in use._** - -## Download run instructions -Execute the download script and keep the terminal or notebook open until the downloads fully complete. No manual interaction with the https://www.hydroshare.org/ website is required. \ No newline at end of file diff --git a/3b_parameters/SOILGRIDS/1_download/download_soilclass_global_map.ipynb b/3b_parameters/SOILGRIDS/1_download/download_soilclass_global_map.ipynb deleted file mode 100644 index 5802133..0000000 --- a/3b_parameters/SOILGRIDS/1_download/download_soilclass_global_map.ipynb +++ /dev/null @@ -1,342 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download SOILGRIDS-derived soil texture class map\n", - "SOILGRIDS data (Hengl et al., 2017) showing global sand, silt and clay percentages have been downloaded and processed into a global map of USDA soil texture classes. See: https://www.hydroshare.org/resource/1361509511e44adfba814f6950c6e742/\n", - "\n", - "This script downloads and unpacks this global map.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime\n", - "from hs_restclient import HydroShare, HydroShareAuthBasic" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find the Hydroshare download ID" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the ID in the control file\n", - "download_ID = read_from_control(controlFolder/controlFile,'parameter_soil_hydro_ID')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where to save the data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the raw soil maps need to go\n", - "soil_path = read_from_control(controlFolder/controlFile,'parameter_soil_raw_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required \n", - "if soil_path == 'default':\n", - " soil_path = make_default_path('parameters/soilclass/1_soil_classes_global') # outputs a Path()\n", - "else:\n", - " soil_path = Path(soil_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "soil_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get authentication info" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the login details file and store as a dictionary\n", - "hydroshare_login = {}\n", - "with open(os.path.expanduser(\"~/.hydroshare\")) as file:\n", - " for line in file:\n", - " (key, val) = line.split(':')\n", - " hydroshare_login[key] = val.strip() # remove whitespace, newlines" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the authentication details\n", - "usr = hydroshare_login['name']\n", - "pwd = hydroshare_login['pass']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download the data" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Authenticate the user\n", - "auth = HydroShareAuthBasic(username = usr, password = pwd)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Make a hydroshare object - note: needs authentication\n", - "hs = HydroShare(auth=auth)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the resource ID and download the resource data\n", - "#out = hs.getResource(download_ID, destination=soil_path)\n", - "out = hs.getResourceFile(download_ID, \"usda_mode_soilclass_250m_ll.tif\", destination = soil_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\parameters\\soilclass\\1_soil_classes_global\\usda_mode_soilclass_250m_ll.tif\n" - ] - } - ], - "source": [ - "# Check for output messages\n", - "print(out)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = soil_path\n", - "log_suffix = '_soilclass_download_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'download_soilclass_global_map.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Downloaded SOILGRIDS-derived soil texture classes (global coverage).']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/3b_parameters/SOILGRIDS/1_download/download_soilclass_global_map.py b/3b_parameters/SOILGRIDS/1_download/download_soilclass_global_map.py deleted file mode 100644 index 8145c35..0000000 --- a/3b_parameters/SOILGRIDS/1_download/download_soilclass_global_map.py +++ /dev/null @@ -1,129 +0,0 @@ -# Download SOILGRIDS-derived soil texture class map -# SOILGRIDS data (Hengl et al., 2017) showing global sand, silt and clay percentages have been downloaded and -# processed into a global map of USDA soil texture classes. -# See: https://www.hydroshare.org/resource/1361509511e44adfba814f6950c6e742/ -# -# This script downloads and unpacks this global map. - -# modules -import os -from pathlib import Path -from shutil import copyfile -from datetime import datetime -from hs_restclient import HydroShare, HydroShareAuthBasic - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find the Hydroshare download ID -# Find the ID in the control file -download_ID = read_from_control(controlFolder/controlFile,'parameter_soil_hydro_ID') - - -# --- Find where to save the data -# Find the path where the raw soil maps need to go -soil_path = read_from_control(controlFolder/controlFile,'parameter_soil_raw_path') - -# Specify the default paths if required -if soil_path == 'default': - soil_path = make_default_path('parameters/soilclass/1_soil_classes_global') # outputs a Path() -else: - soil_path = Path(soil_path) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -soil_path.mkdir(parents=True, exist_ok=True) - - -# --- Get authentication info -# Open the login details file and store as a dictionary -hydroshare_login = {} -with open(os.path.expanduser("~/.hydroshare")) as file: - for line in file: - (key, val) = line.split(':') - hydroshare_login[key] = val.strip() # remove whitespace, newlines - -# Get the authentication details -usr = hydroshare_login['name'] -pwd = hydroshare_login['pass'] - - -# --- Download the data -# Authenticate the user -auth = HydroShareAuthBasic(username = usr, password = pwd) - -# Make a hydroshare object - note: needs authentication -hs = HydroShare(auth=auth) - -# Specify the resource ID and download the resource data -#out = hs.getResource(download_ID, destination=soil_path) -out = hs.getResourceFile(download_ID, "usda_mode_soilclass_250m_ll.tif", destination = soil_path) - -# Check for output messages -print(out) - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = soil_path -log_suffix = '_soilclass_download_log.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = 'download_soilclass_global_map.ipynb' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Downloaded SOILGRIDS-derived soil texture classes (global coverage).'] - for txt in lines: - file.write(txt) diff --git a/3b_parameters/SOILGRIDS/2_extract_domain/README.md b/3b_parameters/SOILGRIDS/2_extract_domain/README.md deleted file mode 100644 index c76a5b8..0000000 --- a/3b_parameters/SOILGRIDS/2_extract_domain/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Extract domain -Subsets the global map of soil classes to cover only the domain specified in the control file. \ No newline at end of file diff --git a/3b_parameters/SOILGRIDS/2_extract_domain/extract_domain.ipynb b/3b_parameters/SOILGRIDS/2_extract_domain/extract_domain.ipynb deleted file mode 100644 index 586f104..0000000 --- a/3b_parameters/SOILGRIDS/2_extract_domain/extract_domain.ipynb +++ /dev/null @@ -1,354 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Extract modelling domain\n", - "The downloaded soil texture class map covers most of the globe. This script subsets the map to the domain of interest." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from osgeo import gdal\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line:\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the data is" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the raw soil maps need to go\n", - "soil_raw_path = read_from_control(controlFolder/controlFile,'parameter_soil_raw_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required \n", - "if soil_raw_path == 'default':\n", - " soil_raw_path = make_default_path('parameters/soilclass/1_soil_classes_global') # outputs a Path()\n", - "else:\n", - " soil_raw_path = Path(soil_raw_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the new map needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the path where the subset soil map need to go\n", - "soil_domain_path = read_from_control(controlFolder/controlFile,'parameter_soil_domain_path')\n", - "soil_domain_name = read_from_control(controlFolder/controlFile,'parameter_soil_tif_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default paths if required \n", - "if soil_domain_path == 'default':\n", - " soil_domain_path = make_default_path('parameters/soilclass/2_soil_classes_domain') # outputs a Path()\n", - "else:\n", - " soil_domain_path = Path(soil_domain_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "soil_domain_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find the extent of the domain" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Find which locations to download\n", - "coordinates = read_from_control(controlFolder/controlFile,'forcing_raw_space')" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Split coordinates into the format the download interface needs\n", - "coordinates = coordinates.split('/')" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "# Transcribe into bounding box\n", - "bbox = (float(coordinates[1]),float(coordinates[0]),float(coordinates[3]),float(coordinates[2]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Open the map and do the subsetting\n", - "Source: https://gis.stackexchange.com/questions/244667/cropping-area-of-interest-from-raster-file-using-python" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the name of the source file\n", - "for file in os.listdir(soil_raw_path):\n", - " if file.endswith('.tif'):\n", - " soil_raw_name = file" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert paths to strings\n", - "old_tif = str(soil_raw_path/soil_raw_name)\n", - "new_tif = str(soil_domain_path/soil_domain_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "# Crop to domain\n", - "ds = gdal.Translate(new_tif, old_tif, projWin=bbox)" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "# Close the data set\n", - "ds = None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = soil_domain_path\n", - "log_suffix = '_soilclass_cropping_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = 'extract_domain.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Cropped SOILGRIDS-derived soil texture class map to local domain.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/3b_parameters/SOILGRIDS/2_extract_domain/extract_domain.py b/3b_parameters/SOILGRIDS/2_extract_domain/extract_domain.py deleted file mode 100644 index dba90cb..0000000 --- a/3b_parameters/SOILGRIDS/2_extract_domain/extract_domain.py +++ /dev/null @@ -1,136 +0,0 @@ -# Extract modelling domain -# The downloaded soil texture class map covers most of the globe. This script subsets the map to the domain of interest. - -# module -import os -from osgeo import gdal -from pathlib import Path -from shutil import copyfile -from datetime import datetime - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line: - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find where the data is -# Find the path where the raw soil maps need to go -soil_raw_path = read_from_control(controlFolder/controlFile,'parameter_soil_raw_path') - -# Specify the default paths if required -if soil_raw_path == 'default': - soil_raw_path = make_default_path('parameters/soilclass/1_soil_classes_global') # outputs a Path() -else: - soil_raw_path = Path(soil_raw_path) # make sure a user-specified path is a Path() - - -# --- Find where the new map needs to go -# Find the path where the subset soil map need to go -soil_domain_path = read_from_control(controlFolder/controlFile,'parameter_soil_domain_path') -soil_domain_name = read_from_control(controlFolder/controlFile,'parameter_soil_tif_name') - -# Specify the default paths if required -if soil_domain_path == 'default': - soil_domain_path = make_default_path('parameters/soilclass/2_soil_classes_domain') # outputs a Path() -else: - soil_domain_path = Path(soil_domain_path) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -soil_domain_path.mkdir(parents=True, exist_ok=True) - - -# --- Find the extent of the domain -# Find which locations to download -coordinates = read_from_control(controlFolder/controlFile,'forcing_raw_space') - -# Split coordinates into the format the download interface needs -coordinates = coordinates.split('/') - -# Transcribe into bounding box -bbox = (float(coordinates[1]),float(coordinates[0]),float(coordinates[3]),float(coordinates[2])) - - -# --- Open the map and do the subsetting -# Source: https://gis.stackexchange.com/questions/244667/cropping-area-of-interest-from-raster-file-using-python - -# Find the name of the source file -for file in os.listdir(soil_raw_path): - if file.endswith('.tif'): - soil_raw_name = file - -# Convert paths to strings -old_tif = str(soil_raw_path/soil_raw_name) -new_tif = str(soil_domain_path/soil_domain_name) - -# Crop to domain -#ds = gdal.Translate(new_tif, old_tif, projWin=bbox) -translate_options = gdal.TranslateOptions(format = 'GTiff', projWin = bbox, creationOptions = ['COMPRESS=DEFLATE']) -ds = gdal.Translate(new_tif, old_tif, options=translate_options) - -# Close the data set -ds = None - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = soil_domain_path -log_suffix = '_soilclass_cropping_log.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = 'extract_domain.py' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Cropped SOILGRIDS-derived soil texture class map to local domain.'] - for txt in lines: - file.write(txt) \ No newline at end of file diff --git a/3b_parameters/SOILGRIDS/README.md b/3b_parameters/SOILGRIDS/README.md deleted file mode 100644 index d42d7c9..0000000 --- a/3b_parameters/SOILGRIDS/README.md +++ /dev/null @@ -1,62 +0,0 @@ -# SUMMA soil parameters -## Noah-MP in SUMMA -SUMMA's approach to soil parameters is based on that of the Noah-MP land model and relies on look-up tables. Briefly, the user defines for each model element a representative soil class as a numerical value (e.g. "sand" might be encoded as "1", "silty clay" as "11", etc). SUMMA then navigates the look-up table to extract typical values of hydraulic properties for the specified soil class and uses these values for further computations. - -SUMMA currently has several different look-up tables available in the file `TBL_SOILPARM.TBL` (found in the folder `5_model_input/SUMMA/0_base_settings`). This workflow assumes that the ROSETTA table is used to define soil class index values (specified below). Soil classes are stored as SUMMA parameters in a variable called "soilTypeIndex", kept in "attributes.nc" (https://summa.readthedocs.io/en/latest/input_output/SUMMA_input/#attribute-and-parameter-files). - -Further details can be found in the Noah-MP documentation, see e.g.: https://ral.ucar.edu/solutions/products/noah-multiparameterization-land-surface-model-noah-mp-lsm - - -## Download registration -Hydroshare downloads require registration through the Hydroshare website. See: https://www.hydroshare.org/sign-up/?next= - -Hydroshare downloads use the Python package `hs_restclient`. Downloads require authentication through the client. Store user details (username and password) in a new file `$HOME/.hydroshare` (Unix/Linux) or `C:\Users\[user]\.hydroshare` (Windows) as follows (replace `[name]` and `[pass]` with your own credentials): - -``` -name: [name] -pass: [pass] -``` - -**_Note: given that these passwords are stored as plain text, it is strongly recommended to use a unique password that is different from any other passwords you currently have in use._** - - -## SOILGRIDS data -The SOILGRIDS 250m data set (Hengl et al, 2017) provides values for various soil properties on a global scale. The data set uses machine learning and various local soil products to provide these soil property estimates. The SOILGRIDS native resolution is 250m x 250m, and soil properties are provided at 7 different depths for any given grid cell. - - -## SOILGRIDS to SUMMA -SOILGRIDS does not provide soil classes directly. Instead we rely on the SOILGRIDS data fields that specify each location's sand, silt and clay percentage in the soil. We relate these percentages to soil classes through the USDA soil classification triangle (Benham et al, 2009). Then we define the most commonly occurring soil class for the 7 depth and choose that class as representative of the soil column at a given pixel. These preprocessing steps have already been completed and stored as a global map of soil classes on the Hydroshare repository (Knoben, 2021). The workflow code downloads this map and finds a representative soil class for each model element. The soil class for each model element is saved in "attributes.nc", which is part of the SUMMA input files. - - -## ROSETTA soil parameter table -This section records the ROSETTA soil parameter table that was used to define soil classes. Note that soil class numbering is arbitrary and the assigned soil classes thus only make sense in the context of how the classes were defined initially. In other words, this workflow assumes soil classes follow the numbering as given in this table. Other tables of soil properties might call "sand" class 12 instead of 1, and thus switching between soil tables needs to be done with care. This table is part of the parameter file `TBL_VEGPARM.TBL`. - -| index | 'theta_res | theta_sat | vGn_alpha | vGn_n | k_soil | BB | DRYSMC | HC | MAXSMC | REFSMC | SATPSI | SATDK | SATDW | WLTSMC | QTZ | class | -|-------|------------|-----------|-----------|-------|----------|------|--------|------|--------|--------|--------|----------|----------|--------|------|-----------------| -| 1 | 0.098 | 0.459 | -1.496 | 1.253 | 1.71E-06 | 1.4 | 0.068 | 1.09 | 0.482 | 0.412 | 0.405 | 1.28E-06 | 1.12E-05 | 0.286 | 0.25 | CLAY | -| 2 | 0.079 | 0.442 | -1.581 | 1.416 | 9.47E-07 | 8.52 | 0.095 | 1.23 | 0.476 | 0.382 | 0.63 | 2.45E-06 | 1.13E-05 | 0.25 | 0.35 | CLAY LOAM | -| 3 | 0.061 | 0.399 | -1.112 | 1.472 | 1.39E-06 | 5.39 | 0.078 | 1.21 | 0.451 | 0.329 | 0.478 | 6.95E-06 | 1.43E-05 | 0.155 | 0.4 | LOAM | -| 4 | 0.049 | 0.39 | -3.475 | 1.746 | 1.22E-05 | 4.38 | 0.057 | 1.41 | 0.41 | 0.383 | 0.09 | 1.56E-04 | 5.14E-06 | 0.075 | 0.82 | LOAMY SAND | -| 5 | 0.053 | 0.375 | -3.524 | 3.177 | 7.44E-05 | 4.05 | 0.045 | 1.47 | 0.395 | 0.236 | 0.121 | 1.76E-04 | 6.08E-07 | 0.068 | 0.92 | SAND | -| 6 | 0.117 | 0.385 | -3.342 | 1.208 | 1.31E-06 | 0.4 | 0.1 | 1.18 | 0.426 | 0.338 | 0.153 | 2.17E-06 | 1.87E-05 | 0.219 | 0.52 | SANDY CLAY | -| 7 | 0.063 | 0.384 | -2.109 | 1.33 | 1.53E-06 | 7.12 | 0.1 | 1.18 | 0.42 | 0.314 | 0.299 | 6.30E-06 | 9.90E-06 | 0.175 | 0.6 | SANDY CLAY LOAM | -| 8 | 0.039 | 0.387 | -2.667 | 1.449 | 4.43E-06 | 4.9 | 0.065 | 1.34 | 0.435 | 0.383 | 0.218 | 3.47E-05 | 8.05E-06 | 0.114 | 0.6 | SANDY LOAM | -| 9 | 0.05 | 0.489 | -0.658 | 1.679 | 5.06E-06 | 5.3 | 0.034 | 1.27 | 0.485 | 0.383 | 0.786 | 7.20E-06 | 2.39E-05 | 0.179 | 0.1 | SILT | -| 10 | 0.111 | 0.481 | -1.622 | 1.321 | 1.11E-06 | 0.4 | 0.07 | 1.15 | 0.492 | 0.404 | 0.49 | 1.03E-06 | 9.64E-06 | 0.283 | 0.1 | SILTY CLAY | -| 11 | 0.09 | 0.482 | -0.839 | 1.521 | 1.29E-06 | 7.75 | 0.089 | 1.32 | 0.477 | 0.387 | 0.356 | 1.70E-06 | 2.37E-05 | 0.218 | 0.1 | SILTY CLAY LOAM | -| 12 | 0.065 | 0.439 | -0.506 | 1.663 | 2.11E-06 | 5.3 | 0.067 | 1.27 | 0.485 | 0.36 | 0.786 | 7.20E-06 | 2.39E-05 | 0.179 | 0.25 | SILT LOAM | - -## References -Benham, E., Ahrens, R. J., & Nettleton, W. D. (2009). Clarification of Soil Texture Class Boundaries. United States Department of Agriculture. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/ks/soils/?cid=nrcs142p2_033171 - -Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748. doi:10.1371/journal.pone.0169748 - -Knoben, W. J. M. (2021). Global USDA-NRCS soil texture class map, HydroShare, https://doi.org/10.4211/hs.1361509511e44adfba814f6950c6e742 - - -## Control file settings -This section lists all the settings in `control_active.txt` that the code in this folder uses. -- **parameter_soil_hydro_ID**: ID of the Hydroshare resource that contains the global soil texture class map needed by the download API -- **forcing_raw_space**: bounding box of the modelling domain, used to subset global data to the exact extent of the modelling domain -- **parameter_soil_raw_path, parameter_soil_domain_path**: file paths -- **parameter_soil_tif_name**: name of the .tif that contains the soil classes for the domain \ No newline at end of file diff --git a/3c_shapefiles/1_make_shapefiles.py b/3c_shapefiles/1_make_shapefiles.py new file mode 100644 index 0000000..e220252 --- /dev/null +++ b/3c_shapefiles/1_make_shapefiles.py @@ -0,0 +1,279 @@ +# Copy base settings +# Copies the base settings into the mizuRoute settings folder. + +# modules +from pathlib import Path +from shutil import copyfile +from geopandas import gpd +import pandas as pd +import xarray as xr +from datetime import datetime +from shapely.geometry import Point, LineString, MultiLineString +import numpy as np +from rasterio import open as rio_open + + +# --- Control file handling +# Easy access to control file folder +controlFolder = Path('../0_control_files') + +# Store the name of the 'active' file in a variable +controlFile = 'control_active.txt' + +# Function to extract a given setting from the control file +def read_from_control( file, setting ): + + # Open 'control_active.txt' and ... + with open(file) as contents: + for line in contents: + + # ... find the line with the requested setting + if setting in line and not line.startswith('#'): + break + + # Extract the setting's value + substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) + substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found + substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines + + # Return this value + return substring + +# Function to specify a default path +def make_default_path(suffix): + + # Get the root path + rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) + + # Get the domain folder + domainName = read_from_control(controlFolder/controlFile,'domain_name') + domainFolder = 'domain_' + domainName + + # Specify the forcing path + defaultPath = rootPath / domainFolder / suffix + + return defaultPath + + +# --- Find where the shapefiles are + +rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) +domainName = read_from_control(controlFolder/controlFile,'domain_name') + +# CAMELS-spath +CAMELS_spath = read_from_control(controlFolder/controlFile,'camels_spath') + +# Specify default path if needed +if CAMELS_spath == 'default': + CAMELS_spath = rootPath / 'CAMELS-spat' +else: + CAMELS_spath = Path(CAMELS_spath) # make sure a user-specified path is a Path() + +# Metadata +metadata_path = CAMELS_spath +metadata_name = "camels-spat-metadata.csv" + +df_metadata = pd.read_csv(metadata_path / metadata_name) + + +country, station_id = domainName.split("_") + +# Get categories +category_value = df_metadata.loc[(df_metadata['Country'] == country) & (df_metadata['Station_id'] == station_id), 'subset_category'] + +# Ensure category_value is a string +if not category_value.empty: + category_value = category_value.iloc[0] # Convert Series to string +else: + raise ValueError("No matching subset category found.") # Handle missing value + + +#shapefiles +spa = 'distributed' + +shapefiles_path = CAMELS_spath / 'shapefiles' / category_value / ('shapes-' + spa) / domainName + +river_file_name = domainName + '_'+ spa + '_river.shp' +catchment_file_name = domainName + '_'+ spa + '_basin.shp' + + +# --- Modify the river basin shapefile for MizuRoute + + +shp_river_basin = gpd.read_file( shapefiles_path / catchment_file_name ) + +shp_river_basin['COMID'] = (10*shp_river_basin['COMID']).astype(int) +shp_river_basin = shp_river_basin.assign(area = shp_river_basin['unitarea']*1000000) +shp_river_basin = shp_river_basin.assign(hru_to_seg = shp_river_basin['COMID']) + + + +# --- Modify the the river network shapefile for Mizuroute + +shp_river_network_ini = gpd.read_file( shapefiles_path / river_file_name ) + +is_empty = shp_river_network_ini.isnull().all(axis=1).all() + +def fill_empty_rivershp(river, basin): + + river['COMID'] = basin.iloc[0]['COMID'] + river['lengthdir'] = None + river['sinuosity'] = None + river['slope'] = None + river['uparea'] = basin.iloc[0]['area'] + river['order'] = None + river['strmDrop_t'] = None + river['slope_taud'] = None + river['NextDownID'] = 0 + river['maxup'] = 0 + river['up1'] = 0 + river['up2'] = 0 + river['up3'] = 0 + river['up4'] = 0 + river['new_len_km'] = None + + return river + + +if is_empty: + + shp_river_network = fill_empty_rivershp(shp_river_network_ini, shp_river_basin) + +else: + + shp_river_network = shp_river_network_ini + + # Multiply COMID by 10 and convert to integer to ensure splitted catchments are taken into account + shp_river_network['COMID'] = (10*shp_river_network['COMID']).astype(int) + shp_river_network['NextDownID'] = (10*shp_river_network['NextDownID']).astype(int) + for col in ['up1', 'up2', 'up3', 'up4']: + shp_river_network[col] = ( + shp_river_network[col] + .where(shp_river_network[col] == 0, (10 * shp_river_network[col]).astype(int)) + .astype(int) # Ensures integer type even with NaNs + ) + + +shp_river_network = shp_river_network.assign(length = shp_river_network['new_len_km']*1000) + +# Set the NextDownID of the downstream GRU to 0 +next_down_ids = set(shp_river_network["NextDownID"]) +comid_values = set(shp_river_network["COMID"]) + +search_value = next_down_ids - comid_values +search_value = next(iter(search_value)) + +matching_row = shp_river_network[shp_river_network["NextDownID"] == search_value] + + +shp_river_network.loc[matching_row.index, 'NextDownID'] = 0 + +shp_river_network = shp_river_network.set_index('COMID') +shp_river_network = shp_river_network.reindex(shp_river_basin['COMID']) +shp_river_network = shp_river_network.reset_index() + + + +# --- Modify the the catchment shapefile for SUMMA + +shp_catchment = gpd.read_file( shapefiles_path / catchment_file_name ) + +shp_catchment['COMID'] = (10*shp_catchment['COMID']).astype(int) +shp_catchment = shp_catchment.assign(HRU_area = shp_catchment['unitarea']*1000000) +shp_catchment = shp_catchment.assign(HRU_ID = shp_catchment['COMID']) +shp_catchment = shp_catchment.assign(GRU_ID = shp_catchment['COMID']) + + + +# Use forcing to get the lat and lon values +forcing_name = read_from_control(controlFolder/controlFile,'forcing_data_name') +forcing_path = CAMELS_spath / 'forcing' / category_value / forcing_name / (forcing_name + '-' + spa) + +forcing_file = domainName + "_" + forcing_name + "_" + spa +".nc" +if forcing_name == "em-earth": + forcing_file = forcing_file.replace(forcing_name, "em_earth") + +nc_forcing = xr.open_dataset(forcing_path / forcing_file) + +lat = nc_forcing['latitude'].isel(time=0).values +lon = nc_forcing['longitude'].isel(time=0).values + +shp_catchment = shp_catchment.assign(center_lat = lat) +shp_catchment = shp_catchment.assign(center_lon = lon) + + + +# --- Find river network output folder +# River network shapefile path & name +river_network_path = read_from_control(controlFolder/controlFile,'river_network_shp_path') +river_network_name = read_from_control(controlFolder/controlFile,'river_network_shp_name') + +# Specify default path if needed +if river_network_path == 'default': + river_network_path = make_default_path('shapefiles/river_network') # outputs a Path() +else: + river_network_path = Path(river_network_path) # make sure a user-specified path is a Path() + + + +# --- Find river basin shapefile (routing catchments) output folder + +pd.options.mode.chained_assignment = None # Avoid warning: value is trying to be set on a copy of a slice from a DataFrame + +# River network shapefile path & name +river_basin_path = read_from_control(controlFolder/controlFile,'river_basin_shp_path') +river_basin_name = read_from_control(controlFolder/controlFile,'river_basin_shp_name') + +# Specify default path if needed +if river_basin_path == 'default': + river_basin_path = make_default_path('shapefiles/river_basins') # outputs a Path() +else: + river_basin_path = Path(river_basin_path) # make sure a user-specified path is a Path() + + + +# --- Find catchment shapefile output folder +# Catchment shapefile path & name +catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path') +catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name') + +# Specify default path if needed +if catchment_path == 'default': + catchment_path = make_default_path('shapefiles/catchment') # outputs a Path() +else: + catchment_path = Path(catchment_path) # make sure a user-specified path is a Path() + + +# --- Save shapefiles + +shp_river_basin.to_file(river_basin_path / river_basin_name) +shp_river_network.to_file(river_network_path / river_network_name) +shp_catchment.to_file(catchment_path / catchment_name) + + +# --- Code provenance +# Generates a basic log file in the domain folder and copies the control file and itself there. + +# Set the log path and file name +logPath = river_network_path / '..' +log_suffix = '_make_shapefiles.txt' + +# Create a log folder +logFolder = '_workflow_log' +Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) + +# Copy this script +thisFile = '1_make_shapefiles.py' +copyfile(thisFile, logPath / logFolder / thisFile); + +# Get current date and time +now = datetime.now() + +# Create a log file +logFile = now.strftime('%Y%m%d') + log_suffix +with open( logPath / logFolder / logFile, 'w') as file: + + lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', + 'Adapt CAMELS-spat shapefiles for SUMMA and mizuRoute.'] + for txt in lines: + file.write(txt) \ No newline at end of file diff --git a/4b_remapping/1_topo/1_find_HRU_elevation.py b/4_remapping/1_topo/1_find_HRU_elevation.py similarity index 94% rename from 4b_remapping/1_topo/1_find_HRU_elevation.py rename to 4_remapping/1_topo/1_find_HRU_elevation.py index 26a6fd1..689b818 100644 --- a/4b_remapping/1_topo/1_find_HRU_elevation.py +++ b/4_remapping/1_topo/1_find_HRU_elevation.py @@ -9,11 +9,10 @@ # modules import os import geopandas as gpd -from rasterstats import zonal_stats from pathlib import Path from shutil import copyfile from datetime import datetime - +from exactextract import exact_extract # --- Control file handling # Easy access to control file folder @@ -113,16 +112,14 @@ def make_default_path(suffix): # --- Rasterstats analysis # Load the shapefile -gdf = gpd.read_file(str(intersect_path/intersect_name)) +gdf = gpd.read_file(intersect_path / intersect_name) +raster_path = dem_path / dem_name # Calculate zonal statistics -stats = zonal_stats(gdf, - str(dem_path/dem_name), - stats=['mean'], - all_touched=True) +elev_means = exact_extract(str(raster_path), gdf, 'mean', output='pandas') # Add the mean elevation to the GeoDataFrame -gdf['elev_mean'] = [stat['mean'] for stat in stats] +gdf['elev_mean'] = elev_means # Save the updated GeoDataFrame gdf.to_file(str(intersect_path/intersect_name)) diff --git a/4b_remapping/1_topo/2_find_HRU_soil_classes.py b/4_remapping/1_topo/2_find_HRU_soil_classes.py similarity index 81% rename from 4b_remapping/1_topo/2_find_HRU_soil_classes.py rename to 4_remapping/1_topo/2_find_HRU_soil_classes.py index 2705a71..bc842b3 100644 --- a/4b_remapping/1_topo/2_find_HRU_soil_classes.py +++ b/4_remapping/1_topo/2_find_HRU_soil_classes.py @@ -7,10 +7,9 @@ from shutil import copyfile from datetime import datetime import geopandas as gpd -import rasterio -from rasterstats import zonal_stats import pandas as pd import numpy as np +from exactextract import exact_extract # --- Control file handling @@ -95,29 +94,40 @@ def make_default_path(suffix): # --- Rasterstats analysis # Load the shapefile gdf = gpd.read_file(catchment_path / catchment_name) +raster_path = soil_path / soil_name -# Open the raster file -with rasterio.open(soil_path / soil_name) as src: - affine = src.transform - array = src.read(1) # Read the first band +# Perform exactextract with fractional coverage per class +stats = exact_extract( + str(raster_path), + gdf, + ["unique", "frac"], + output='pandas' +) -# Get unique values in the raster -unique_values = np.unique(array).astype(int) -unique_values = unique_values[unique_values != src.nodata] # Remove nodata value if present +# Build a list of row-wise dicts: {soil_class: fraction} +rows = [] +for i, (classes, fractions) in stats.iterrows(): + row_dict = {k: v for k, v in zip(classes, fractions)} + rows.append(row_dict) -# Perform zonal statistics for each unique value -results = [] -for value in unique_values: - category_stats = zonal_stats(gdf, array, affine=affine, stats=['count'], categorical=True, - category_map={value: 1}) - counts = [stat.get(1, 0) for stat in category_stats] # Get count for category 1, default to 0 if not present - results.append(pd.DataFrame(counts, columns=[f'USGS_{value}'])) +# Convert to DataFrame +df_stats = pd.DataFrame(rows) -# Combine all results -hist_df = pd.concat(results, axis=1).fillna(0).astype(int) +# Replace NaN with 0, ensure float type +df_stats = df_stats.fillna(0).astype(float) -# Combine the original GeoDataFrame with the histogram results -result = gdf.join(hist_df) +# Sort columns numerically +sorted_columns = sorted(df_stats.columns, key=lambda x: int(x)) +df_stats = df_stats[sorted_columns] + +# Round to 4 digit +df_stats = df_stats.round(4) + +# Rename columns to USGS soil class format +df_stats.columns = [f'USGS_{int(col)}' for col in df_stats.columns] + +# Merge with original GeoDataFrame +result = gdf.join(df_stats) # Save the result result.to_file(intersect_path / intersect_name) diff --git a/4b_remapping/1_topo/3_find_HRU_land_classes.py b/4_remapping/1_topo/3_find_HRU_land_classes.py similarity index 89% rename from 4b_remapping/1_topo/3_find_HRU_land_classes.py rename to 4_remapping/1_topo/3_find_HRU_land_classes.py index 94e0220..80d54f2 100644 --- a/4b_remapping/1_topo/3_find_HRU_land_classes.py +++ b/4_remapping/1_topo/3_find_HRU_land_classes.py @@ -7,9 +7,8 @@ from shutil import copyfile from datetime import datetime import geopandas as gpd -import rasterio -from rasterstats import zonal_stats import pandas as pd +from exactextract import exact_extract # --- Control file handling @@ -94,18 +93,27 @@ def make_default_path(suffix): # --- Rasterstats analysis # Load the shapefile gdf = gpd.read_file(catchment_path / catchment_name) +raster_path = land_path / land_name -# Open the raster file -with rasterio.open(land_path / land_name) as src: - affine = src.transform - array = src.read(1) - nodata = src.nodata # Perform zonal statistics -stats = zonal_stats(gdf, array, affine=affine, nodata=nodata, categorical=True) +stats = exact_extract( + str(raster_path), + gdf, + ["unique", "frac"], + output='pandas' +) -# Convert stats to DataFrame -df_stats = pd.DataFrame(stats) + +# Initialize list to store per-row dicts +rows = [] + +for i, (classes, fractions) in stats.iterrows(): + row_dict = {k: v for k, v in zip(classes, fractions)} + rows.append(row_dict) + +# Convert list of dicts to DataFrame +df_stats = pd.DataFrame(rows) # Convert column names to integers and sort them sorted_columns = sorted(df_stats.columns, key=lambda x: int(x)) @@ -113,8 +121,11 @@ def make_default_path(suffix): # Reorder DataFrame based on sorted column names df_stats = df_stats[sorted_columns] -# Replace NaN with 0 and convert to integers -df_stats = df_stats.fillna(0).astype(int) +# Replace NaN with 0 and convert to float +df_stats = df_stats.fillna(0).astype(float) + +# Round to 4 digit +df_stats = df_stats.round(4) # Rename columns df_stats.columns = [f'IGBP_{int(col)}' for col in df_stats.columns] diff --git a/4a_sort_shape/1_sort_catchment_shape.ipynb b/4a_sort_shape/1_sort_catchment_shape.ipynb deleted file mode 100644 index 59cf9c5..0000000 --- a/4a_sort_shape/1_sort_catchment_shape.ipynb +++ /dev/null @@ -1,274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sort catchment shape\n", - "Sorts the catchment shape by `gruId` first and `hruId` second, to ensure HRU order follows SUMMA conventions. Not strictly necessary for cases where each GRU contains one HRU, but essential for cases where the GRUs contain multiple HRUs." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of catchment shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Catchment shapefile path & name\n", - "catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if catchment_path == 'default':\n", - " catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " catchment_path = Path(catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Find GRU and HRU variables\n", - "gru_id = read_from_control(controlFolder/controlFile,'catchment_shp_gruid')\n", - "hru_id = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Open shape and sort" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the shape\n", - "shp = gpd.read_file(catchment_path/catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort\n", - "shp = shp.sort_values(by=[gru_id,hru_id])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Save\n", - "shp.to_file(catchment_path/catchment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = catchment_path\n", - "log_suffix = '_sort_shape.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_sort_catchment_shape.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Sorted catchment shape by GRU ID first and HRU ID second to follow SUMMA conventions.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/4a_sort_shape/README.md b/4a_sort_shape/README.md deleted file mode 100644 index f03736b..0000000 --- a/4a_sort_shape/README.md +++ /dev/null @@ -1,62 +0,0 @@ -# Sort catchment shape -SUMMA makes certain assumptions about GRU and HRU order in its input files: -1. It expects the gruId and hruId variables to have identical orders in the forcing, attributes, coldState and trialParameter `.nc` files. In cases where each GRU contains exactly one HRU, no action is needed beyond ensuring that these files use the same order of IDs. -2. In cases where a GRU contains multiple HRUs, additional action is needed. In a multiple-HRU-per-GRU case, the catchment shapefile needs to be sorted by GRU ID first and HRU ID second, so that the order of HRUs in the shapefile matches SUMMA's expectations. - -An example of correct and incorrect GRU & HRU sorting is provided below. - -## Example -Imagine a case where 2 GRUs are each divided into 2 HRUs. The GRU IDs are 1 and 2, and the HRU IDs are 1-4. - -### Incorrect sorting -This example is sorted by HRU IDs first, and GRU IDs second. This does not match SUMMA's input requirements. - -- GRU 1, HRU 1 -- GRU 2, HRU 2 -- GRU 1, HRU 3 -- GRU 2, HRU 4 - -### Correct sorting -This example is sorted by GRU IDs first, and HRU IDs second. This matches SUMMA's input requirements. - -- GRU 1, HRU 1 -- GRU 1, HRU 3 -- GRU 2, HRU 2 -- GRU 2, HRU 4 - - -## Explanation -SUMMA checks its input files for consistency when the model is first initialized. It first builds a data structure of GRU and HRU IDs as follows: -- GRU 1 - * HRU 1 - * HRU 3 -- GRU 2 - * HRU 2 - * HRU 4 - -Next, SUMMA checks the order of HRU IDs in all other netcdf files, to ensure these orders are the same. Critically, it reads these IDs in a nested for-loop (pseudo code): - -``` -idx = 1 -for gru in all_GRUs: - for hru in HRUs_in_this_GRU: - check if hruId_in_nc_file(idx) == hru - idx += 1 - end -end -``` - -Therefore, it assumes the HRUs can be found at the following indices in each `.nc` file: -- GRU 1 - * HRU 1: expected at index 1 - * HRU 3: expected at index 2 -- GRU 2 - * HRU 2: expected at index 3 - * HRU 4: expected at index 4 - -The sorting ensures that this expectation is met. - -## Control file settings -This section lists all the settings in `control_active.txt` that the code in this folder uses. -- **catchment_shp_path, catchment_shp_name**: location of the catchment shapefile. -- **catchment_shp_gruid, catchment_shp_hruid**: names of the GRU and HRU ID columns in the shapefile diff --git a/4b_remapping/1_topo/1_find_HRU_elevation.ipynb b/4b_remapping/1_topo/1_find_HRU_elevation.ipynb deleted file mode 100644 index def957c..0000000 --- a/4b_remapping/1_topo/1_find_HRU_elevation.ipynb +++ /dev/null @@ -1,385 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Intersect catchment with MERIT DEM\n", - "Finds the mean elevation of each HRU in the model setup with rasterstats.\n", - "\n", - "### Note\n", - "The workflow is thus:\n", - "1. Find the source catchment shapefile;\n", - "2. Copy the source catchment shapefile to the destintion location;\n", - "3. Run the zonal statistics algorithm on the copy." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import geopandas as gpd\n", - "from rasterstats import zonal_stats\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of shapefile and DEM" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Catchment shapefile path & name\n", - "catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if catchment_path == 'default':\n", - " catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " catchment_path = Path(catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# DEM path & name\n", - "dem_path = read_from_control(controlFolder/controlFile,'parameter_dem_tif_path')\n", - "dem_name = read_from_control(controlFolder/controlFile,'parameter_dem_tif_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if dem_path == 'default':\n", - " dem_path = make_default_path('parameters/dem/5_elevation') # outputs a Path()\n", - "else:\n", - " dem_path = Path(dem_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the intersection needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersected shapefile path and name\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_dem_path')\n", - "intersect_name = read_from_control(controlFolder/controlFile,'intersect_dem_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_dem') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "intersect_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Copy the source catchment shapefile into the destination location" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the name without extension\n", - "catchment_base = catchment_name.replace('.shp','')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Loop over directory contents and copy files that match the filename of the shape\n", - "for file in os.listdir(catchment_path):\n", - " if catchment_base in file: # copy only the relevant files in case there are more than 1 .shp files\n", - " \n", - " # make the output file name\n", - " _,ext = os.path.splitext(file) # extension of current file\n", - " basefile,_ = os.path.splitext(intersect_name) # name of the intersection file w/o extension\n", - " newfile = basefile + ext # new name + old extension\n", - " \n", - " # copy\n", - " copyfile(catchment_path/file, intersect_path/newfile);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Rasterstats analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the shapefile\n", - "gdf = gpd.read_file(str(intersect_path/intersect_name))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate zonal statistics\n", - "stats = zonal_stats(gdf, \n", - " str(dem_path/dem_name), \n", - " stats=['mean'], \n", - " all_touched=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Add the mean elevation to the GeoDataFrame\n", - "gdf['elev_mean'] = [stat['mean'] for stat in stats]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the updated GeoDataFrame\n", - "gdf.to_file(str(intersect_path/intersect_name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = intersect_path\n", - "log_suffix = '_catchment_dem_intersect_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_find_HRU_elevation.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Found mean HRU elevation from MERIT Hydro adjusted elevation DEM.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/4b_remapping/1_topo/2_find_HRU_soil_classes.ipynb b/4b_remapping/1_topo/2_find_HRU_soil_classes.ipynb deleted file mode 100644 index 8de6fd1..0000000 --- a/4b_remapping/1_topo/2_find_HRU_soil_classes.ipynb +++ /dev/null @@ -1,390 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Intersect catchment with SOILGRIDS soil classes\n", - "Counts the occurence of each soil class in each HRU in the model setup with rasterstats." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime\n", - "import geopandas as gpd\n", - "import rasterio\n", - "from rasterstats import zonal_stats\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of shapefile and soil class .tif" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Catchment shapefile path & name\n", - "catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if catchment_path == 'default':\n", - " catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " catchment_path = Path(catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing shapefile path & name\n", - "soil_path = read_from_control(controlFolder/controlFile,'parameter_soil_domain_path')\n", - "soil_name = read_from_control(controlFolder/controlFile,'parameter_soil_tif_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if soil_path == 'default':\n", - " soil_path = make_default_path('parameters/soilclass/2_soil_classes_domain') # outputs a Path()\n", - "else:\n", - " soil_path = Path(soil_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the intersection needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersected shapefile path and name\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_soil_path')\n", - "intersect_name = read_from_control(controlFolder/controlFile,'intersect_soil_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_soilgrids') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "intersect_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Rasterstats analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the shapefile\n", - "gdf = gpd.read_file(catchment_path / catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the raster file\n", - "with rasterio.open(soil_path / soil_name) as src:\n", - " affine = src.transform\n", - " array = src.read(1) # Read the first band" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Get unique values in the raster\n", - "unique_values = np.unique(array).astype(int)\n", - "unique_values = unique_values[unique_values != src.nodata] # Remove nodata value if present" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/cyrilthebault/Postdoc_Ucal/02_DATA/01_RAW/CWARHM-main/cwarhm-env/lib/python3.11/site-packages/rasterstats/io.py:328: NodataWarning: Setting nodata to -999; specify nodata explicitly\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# Perform zonal statistics for each unique value\n", - "results = []\n", - "for value in unique_values:\n", - " category_stats = zonal_stats(gdf, array, affine=affine, stats=['count'], categorical=True, \n", - " category_map={value: 1})\n", - " counts = [stat.get(1, 0) for stat in category_stats] # Get count for category 1, default to 0 if not present\n", - " results.append(pd.DataFrame(counts, columns=[f'USGS_{value}']))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Combine all results\n", - "hist_df = pd.concat(results, axis=1).fillna(0).astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Combine the original GeoDataFrame with the histogram results\n", - "result = gdf.join(hist_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the result\n", - "result.to_file(intersect_path / intersect_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = intersect_path\n", - "log_suffix = '_catchment_soilgrids_intersect_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '2_find_HRU_soil_classes.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Counted the occurrence of soil classes within each HRU.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/4b_remapping/1_topo/3_find_HRU_land_classes.ipynb b/4b_remapping/1_topo/3_find_HRU_land_classes.ipynb deleted file mode 100644 index a73be67..0000000 --- a/4b_remapping/1_topo/3_find_HRU_land_classes.ipynb +++ /dev/null @@ -1,414 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Intersect catchment with MODIS-derived IGBP land classes\n", - "Counts the occurence of each land class in each HRU in the model setup with rasterstats." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime\n", - "import geopandas as gpd\n", - "import rasterio\n", - "from rasterstats import zonal_stats\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of shapefile and land class .tif" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Catchment shapefile path & name\n", - "catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if catchment_path == 'default':\n", - " catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " catchment_path = Path(catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing shapefile path & name\n", - "land_path = read_from_control(controlFolder/controlFile,'parameter_land_mode_path')\n", - "land_name = read_from_control(controlFolder/controlFile,'parameter_land_tif_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if land_path == 'default':\n", - " land_path = make_default_path('parameters/landclass/7_mode_land_class') # outputs a Path()\n", - "else:\n", - " land_path = Path(land_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the intersection needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersected shapefile path and name\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_land_path')\n", - "intersect_name = read_from_control(controlFolder/controlFile,'intersect_land_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_modis') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "intersect_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Rasterstats analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the shapefile\n", - "gdf = gpd.read_file(catchment_path / catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the raster file\n", - "with rasterio.open(land_path / land_name) as src:\n", - " affine = src.transform\n", - " array = src.read(1)\n", - " nodata = src.nodata" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/cyrilthebault/Postdoc_Ucal/02_DATA/01_RAW/CWARHM-main/cwarhm-env/lib/python3.11/site-packages/rasterstats/io.py:328: NodataWarning: Setting nodata to -999; specify nodata explicitly\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# Perform zonal statistics\n", - "stats = zonal_stats(gdf, array, affine=affine, nodata=nodata, categorical=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert stats to DataFrame\n", - "df_stats = pd.DataFrame(stats)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert column names to integers and sort them\n", - "sorted_columns = sorted(df_stats.columns, key=lambda x: int(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Reorder DataFrame based on sorted column names\n", - "df_stats = df_stats[sorted_columns]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Replace NaN with 0 and convert to integers\n", - "df_stats = df_stats.fillna(0).astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Rename columns\n", - "df_stats.columns = [f'IGBP_{int(col)}' for col in df_stats.columns]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Merge stats with original GeoDataFrame\n", - "gdf_result = gdf.join(df_stats)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the result\n", - "gdf_result.to_file(intersect_path / intersect_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = intersect_path\n", - "log_suffix = '_catchment_modis_intersect_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '3_find_HRU_land_classes.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Counted the occurrence of IGBP land classes within each HRU.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/4b_remapping/1_topo/README.md b/4b_remapping/1_topo/README.md deleted file mode 100644 index a10ae5e..0000000 --- a/4b_remapping/1_topo/README.md +++ /dev/null @@ -1,54 +0,0 @@ -# Topographic analysis - -## Geospatial remapping -Pre-processing steps have prepared maps of domain-wide elevation, soil classes and vegetation types in `.tif` format. Here this data is mapped onto the Hydrologic Response Units SUMMA wil use. -1. Script 1 maps the MERIT Hydro DEM to HRUs through a zonal mean, resulting in the mean elevation of each HRU. -2. Script 2 maps the SOILGRIDS-derived USGS soil classes to HRUs through a zonal histogram, resulting in an occurrence count of each soil class in each HRU. -3. Script 3 maps the MODIS IGBP vegetation types to HRUs through a zonal histogram, resulting in an occurrence count of each vegetation type in each HRU. - -These scripts result in new intersection files between the catchment and each of the three data sets. This information is needed to populate certain fields in SUMMA's attribute `.nc` file. - - -## QGIS analysis -This part of the workflow requires functions from the QGIS library. At the time of writing, there are multiple ways to achieve this: -1. Install the `qgis` package available on conda-forge and import QGIS functionality in a Python script as you would any other package. The Jupyter notebooks in this folder use this approach. Conda-forge: https://anaconda.org/conda-forge/qgis -2. Install a stand-alone QGIS application on the system and interact with it through a Python script. The Python scripts in this folder use a version of this approach, modified to work in an HPC environment. See: https://docs.qgis.org/3.16/en/docs/pyqgis_developer_cookbook/intro.html - - -### Differences -Key differences between both approaches are as follows: -1. When QGIS is installed as a (Conda) package, all imports can occur at the top of the script. If Python interacts with a standalone QGIS install, importing the `processing` tools needs to happen after the QGIS path is initialized (see scripts `2` and `3`). -2. When QGIS is installed as a (Conda) package, there is no need to specify the plugin path where `processing` can be found. If Python interacts with a standalone QGIS install, it may be/is necessary to specify the path to the plugin folder. See e.g.: https://gis.stackexchange.com/questions/279874/using-qgis3-processing-algorithms-from-standalone-pyqgis-scripts-outside-of-gui - - -### Code development -The notebooks were developed on a local machine with the QGIS package installed via Conda. The scripts were developed in an HPC environment were QGIS is available as a module (Copernicus cluster, Global Water Futures, Universisty of Saskatchewan, Canada). Below is an example of how this works: - - -``` -# Load the required HPC modules -module load gcc qgis - -# Run DEM intersection (does not need `processing` tools) -python 1_find_HRU_elevation.py - -# Set the path to the QGIS plugin folder -export PYTHONPATH="$EBROOTQGIS/share/qgis/python/plugins:$PYTHONPATH" - -# Run the soil and land intersection -python 2_find_HRU_soil_classes.py -python 3_find_HRU_land_classes.py -``` - -The scripts can easily be adapted to interact with a local QGIS install by specifying the plugin path before the `import processing` line (as shown in the link above). - - -## Assumptions not included in `control_active.txt` -Code assumes we're after a zonal histogram (soil and land classes) or a zonal mean (DEM). Changes to the code are needed to change these functions to something else if desired. - -## Control file settings -This section lists all the settings in `control_active.txt` that the code in this folder uses. -- **catchment_shp_path, catchment_shp_name**: location and file name of the shapefile that contains the delineation of model elements. -- **parameter_dem_tif_path, parameter_dem_tif_name, parameter_soil_domain_path, parameter_soil_domain_name, parameter_land_mode_path, parameter_land_mode_name**: locations of the geospatial parameter fields. -- **intersect_dem_path, intersect_dem_name, intersect_soil_path, intersect_soil_name, intersect_land_path, intersect_land_name**: location where the files that contain the intersections between model elements and data need to be saved. - diff --git a/4b_remapping/2_forcing/1_make_one_weighted_forcing_file.ipynb b/4b_remapping/2_forcing/1_make_one_weighted_forcing_file.ipynb deleted file mode 100644 index 47e5594..0000000 --- a/4b_remapping/2_forcing/1_make_one_weighted_forcing_file.ipynb +++ /dev/null @@ -1,851 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create one (1) area-weighted forcing file\n", - "We need to find how the ERA5 gridded forcing maps onto the catchment to create area-weighted forcing as SUMMA input. This involves two steps:\n", - "1. Intersect the ERA5 shape with the user's catchment shape to find the overlap between a given (sub) catchment and the forcing grid;\n", - "2. Create an area-weighted, catchment-averaged forcing time series.\n", - "\n", - "The EASYMORE package (https://github.com/ShervanGharari/EASYMORE) provides the necessary functionality to do this. EASYMORE performs the GIS step (1, shapefile intersection) and the area-weighting step (2, create new forcing `.nc` files) as part of a single `nc_remapper()` call. To allow for parallelization, EASYMORE can save the output from the GIS step into a restart `.csv` file which can be used to skip the GIS step. This allows (manual) parallelization of area-weighted forcing file generation after the GIS procedures have been run once. The full workflow here is thus:\n", - "1. [This script] Call `nc_remapper()` with ERA5 and user's shapefile, and one ERA5 forcing `.nc` file;\n", - " - EASYMORE performs intersection of both shapefiles;\n", - " - EASYMORE saves the outcomes of this intersection to a `.csv` file;\n", - " - EASYMORE creates an area-weighted forcing file from a single provided ERA5 source `.nc` file\n", - "2. [Follow-up script] Call `nc_remapper()` with intersection `.csv` file and all other forcing `.nc` files.\n", - "3. [Follow-up script] Apply lapse rates to temperature variable.\n", - "\n", - "Parallelization of step 2 (2nd `nc_remapper()` call) requires an external loop that sends (batches of) the remaining ERA5 raw forcing files to individual processors. As with other steps that may be parallelized, creating code that does this is left to the user." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import glob\n", - "import easymore\n", - "from pathlib import Path\n", - "from shutil import rmtree\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of shapefiles" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Catchment shapefile path & name\n", - "catchment_path = read_from_control(controlFolder/controlFile,'intersect_dem_path')\n", - "catchment_name = read_from_control(controlFolder/controlFile,'intersect_dem_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if catchment_path == 'default':\n", - " catchment_path = make_default_path('shapefiles/catchment_intersection/with_dem') # outputs a Path()\n", - "else:\n", - " catchment_path = Path(catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing shapefile path & name\n", - "forcing_shape_path = read_from_control(controlFolder/controlFile,'forcing_shape_path')\n", - "forcing_shape_name = read_from_control(controlFolder/controlFile,'forcing_shape_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_shape_path == 'default':\n", - " forcing_shape_path = make_default_path('shapefiles/forcing') # outputs a Path()\n", - "else:\n", - " forcing_shape_path = Path(forcing_shape_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the intersection needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersected shapefile path. Name is set by EASYMORE as [prefix]_intersected_shapefile.shp\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_forcing_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_forcing') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "intersect_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find the forcing files (merged ERA5 data)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Location of merged ERA5 files\n", - "forcing_merged_path = read_from_control(controlFolder/controlFile,'forcing_merged_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_merged_path == 'default':\n", - " forcing_merged_path = make_default_path('forcing/2_merged_data') # outputs a Path()\n", - "else:\n", - " forcing_merged_path = Path(forcing_merged_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Find files in folder\n", - "forcing_files = [forcing_merged_path/file for file in os.listdir(forcing_merged_path) if os.path.isfile(forcing_merged_path/file) and file.endswith('.nc')]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort the files\n", - "forcing_files.sort()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the temporary EASYMORE files need to go" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Location for EASYMORE temporary storage\n", - "forcing_easymore_path = read_from_control(controlFolder/controlFile,'forcing_easymore_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_easymore_path == 'default':\n", - " forcing_easymore_path = make_default_path('forcing/3_temp_easymore') # outputs a Path()\n", - "else:\n", - " forcing_easymore_path = Path(forcing_easymore_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "forcing_easymore_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the area-weighted forcing needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Location for EASYMORE forcing output\n", - "forcing_basin_path = read_from_control(controlFolder/controlFile,'forcing_basin_avg_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_basin_path == 'default':\n", - " forcing_basin_path = make_default_path('forcing/3_basin_averaged_data') # outputs a Path()\n", - "else:\n", - " forcing_basin_path = Path(forcing_basin_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "forcing_basin_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### EASYMORE" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize an EASYMORE object\n", - "esmr = easymore.easymore()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# Author name\n", - "esmr.author_name = 'SUMMA public workflow scripts'" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Data license\n", - "esmr.license = 'Copernicus data use license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf'" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Case name, used in EASYMORE-generated file naes\n", - "esmr.case_name = read_from_control(controlFolder/controlFile,'domain_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# ERA5 shapefile and variable names\n", - "# Variable names can be hardcoded because we set them when we generate this shapefile as part of the workflow\n", - "esmr.source_shp = forcing_shape_path/forcing_shape_name # shapefile\n", - "esmr.source_shp_lat = read_from_control(controlFolder/controlFile,'forcing_shape_lat_name') # name of the latitude field\n", - "esmr.source_shp_lon = read_from_control(controlFolder/controlFile,'forcing_shape_lon_name') # name of the longitude field" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Catchment shapefile and variable names\n", - "esmr.target_shp = catchment_path/catchment_name\n", - "esmr.target_shp_ID = read_from_control(controlFolder/controlFile,'catchment_shp_hruid') # name of the HRU ID field\n", - "esmr.target_shp_lat = read_from_control(controlFolder/controlFile,'catchment_shp_lat') # name of the latitude field\n", - "esmr.target_shp_lon = read_from_control(controlFolder/controlFile,'catchment_shp_lon') # name of the longitude field" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# ERA5 netcdf file and variable names\n", - "esmr.source_nc = str(forcing_files[0]) # first file in the list; Path() to string\n", - "esmr.var_names = ['airpres',\n", - " 'LWRadAtm',\n", - " 'SWRadAtm',\n", - " 'pptrate',\n", - " 'airtemp',\n", - " 'spechum',\n", - " 'windspd'] # variable names of forcing data - hardcoded because we prescribe them during ERA5 merging\n", - "esmr.var_lat = 'latitude' # name of the latitude dimensions\n", - "esmr.var_lon = 'longitude' # name of the longitude dimension\n", - "esmr.var_time = 'time' # name of the time dimension" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Temporary folder where the EASYMORE-generated GIS files and remapping file will be saved\n", - "esmr.temp_dir = str(forcing_easymore_path) + '/' # Path() to string; ensure the trailing '/' EASYMORE wants" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Output folder where the catchment-averaged forcing will be saved\n", - "esmr.output_dir = str(forcing_basin_path) + '/' # Path() to string; ensure the trailing '/' EASYMORE wants" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# Netcdf settings\n", - "esmr.remapped_dim_id = 'hru' # name of the non-time dimension; prescribed by SUMMA\n", - "esmr.remapped_var_id = 'hruId' # name of the variable associated with the non-time dimension\n", - "esmr.format_list = ['f4'] # variable type to save forcing as. Single entry here will be used for all variables\n", - "esmr.fill_value_list = ['-9999'] # fill value" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# Flag that we do not want the data stored in .csv in addition to .nc\n", - "esmr.save_csv = False" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Flag that we currently have no remapping file\n", - "esmr.remap_csv = '' " - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# Enforce that we want our HRUs returned in the order we put them in\n", - "esmr.sort_ID = False" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "EASYMORE is given multiple varibales to be remapped but only on format and fill valueEASYMORE repeat the format and fill value for all the variables in output files\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE detects that target shapefile is in WGS84 (epsg:4326)\n", - "EASYMORE detects that the field for ID is provided in sink/target shapefile\n", - "EASYMORE detects that the field latitude is provided in sink/target shapefile\n", - "EASYMORE detects that the field longitude is provided in sink/target shapefile\n", - "it seems everything is OK with the sink/target shapefile; added to EASYMORE object target_shp_gpd\n", - "EASYMORE will save standard shapefile for EASYMORE claculation as:\n", - "C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_temp_easymore/BowAtBanff_target_shapefile.shp\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "EASYMORE detects case 1 - regular lat/lon\n", - "EASYMORE detect the shapefile is provided and will resave it here:\n", - "C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_temp_easymore/BowAtBanff_source_shapefile.shp\n", - "-116.625 50.875 -115.375 51.875\n", - "EASYMORE decides the netCDF file has longtitude values of -180 to 180; creating the extended\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\wmk934\\Anaconda3\\envs\\summa-env\\lib\\site-packages\\easymore\\easymore.py:145: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n", - " shp_int.to_file(self.temp_dir+self.case_name+'_intersected_shapefile.shp') # save the intersected files\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_merged_200801.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-01-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 12:59:45.764749\n", - "Ended at date and time 2021-05-06 13:00:06.342181\n", - "------\n" - ] - } - ], - "source": [ - "# Run EASYMORE\n", - "# Note on centroid warnings: in this case we use a regular lat/lon grid to represent ERA5 forcing and ...\n", - "# centroid estimates without reprojecting are therefore acceptable.\n", - "# Note on deprecation warnings: this is a EASYMORE issue that cannot be resolved here. Does not affect current use.\n", - "esmr.nc_remapper()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Confirm that HRU order in shape and forcing file is the same" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import geopandas as gpd" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# Open the shapefile\n", - "shp = gpd.read_file(catchment_path/catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# open the forcing\n", - "forcing_file = [file for file in os.listdir(forcing_basin_path) if file.endswith('.nc')]\n", - "EASYMORE_forcing = xr.open_dataset(forcing_basin_path/forcing_file[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 0.0\n", - "1 0.0\n", - "2 0.0\n", - "3 0.0\n", - "4 0.0\n", - " ... \n", - "113 0.0\n", - "114 0.0\n", - "115 0.0\n", - "116 0.0\n", - "117 0.0\n", - "Name: HRU_ID, Length: 118, dtype: float64" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shp[esmr.target_shp_ID] - EASYMORE_forcing['hruId']" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# close\n", - "shp = []\n", - "EASYMORE_forcing.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Move files to prescribed locations" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "# Remapping file \n", - "remap_file = esmr.case_name + '_remapping.csv'\n", - "copyfile( esmr.temp_dir + remap_file, intersect_path / remap_file);" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersected shapefile\n", - "for file in glob.glob(esmr.temp_dir + esmr.case_name + '_intersected_shapefile.*'):\n", - " copyfile( file, intersect_path / os.path.basename(file));" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# Remove the temporary EASYMORE directory to save space\n", - "try:\n", - " rmtree(esmr.temp_dir)\n", - "except OSError as e:\n", - " print (\"Error: %s - %s.\" % (e.filename, e.strerror)) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance - intersection shapefile\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = intersect_path\n", - "log_suffix = '_catchment_forcing_intersect_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_make_one_weighted_forcing_file.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Intersect shapefiles of catchment and ERA5.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance - weighted forcing file\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = forcing_basin_path\n", - "log_suffix = '_create_one_weighted_forcing_file_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_make_one_weighted_forcing_file.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Made a weighted forcing file based on intersect shapefiles of catchment and ERA5.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/4b_remapping/2_forcing/1_make_one_weighted_forcing_file.py b/4b_remapping/2_forcing/1_make_one_weighted_forcing_file.py deleted file mode 100644 index 0442c1d..0000000 --- a/4b_remapping/2_forcing/1_make_one_weighted_forcing_file.py +++ /dev/null @@ -1,284 +0,0 @@ -# Create one (1) area-weighted forcing file -# We need to find how the ERA5 gridded forcing maps onto the catchment to create area-weighted forcing as SUMMA input. This involves two steps: -# 1. Intersect the ERA5 shape with the user's catchment shape to find the overlap between a given (sub) catchment and the forcing grid; -# 2. Create an area-weighted, catchment-averaged forcing time series. -# -# The EASYMORE package (https://github.com/ShervanGharari/EASYMORE) provides the necessary functionality to do this. EASYMORE performs the GIS step (1, shapefile intersection) and the area-weighting step (2, create new forcing `.nc` files) as part of a single `nc_remapper()` call. To allow for parallelization, EASYMORE can save the output from the GIS step into a restart `.csv` file which can be used to skip the GIS step. This allows (manual) parallelization of area-weighted forcing file generation after the GIS procedures have been run once. The full workflow here is thus: -# 1. [This script] Call `nc_remapper()` with ERA5 and user's shapefile, and one ERA5 forcing `.nc` file; -# - EASYMORE performs intersection of both shapefiles; -# - EASYMORE saves the outcomes of this intersection to a `.csv` file; -# - EASYMORE creates an area-weighted forcing file from a single provided ERA5 source `.nc` file -# 2. [Follow-up script] Call `nc_remapper()` with intersection `.csv` file and all other forcing `.nc` files. -# 3. [Follow-up script] Apply lapse rates to temperature variable. -# -# Parallelization of step 2 (2nd `nc_remapper()` call) requires an external loop that sends (batches of) the remaining ERA5 raw forcing files to individual processors. As with other steps that may be parallelized, creating code that does this is left to the user. - -# modules -import os -import glob -import easymore -from pathlib import Path -from shutil import rmtree -from shutil import copyfile -from datetime import datetime - - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find location of shapefiles -# Catchment shapefile path & name -catchment_path = read_from_control(controlFolder/controlFile,'intersect_dem_path') -catchment_name = read_from_control(controlFolder/controlFile,'intersect_dem_name') - -# Specify default path if needed -if catchment_path == 'default': - catchment_path = make_default_path('shapefiles/catchment_intersection/with_dem') # outputs a Path() -else: - catchment_path = Path(catchment_path) # make sure a user-specified path is a Path() - -# Forcing shapefile path & name -forcing_shape_path = read_from_control(controlFolder/controlFile,'forcing_shape_path') -forcing_shape_name = read_from_control(controlFolder/controlFile,'forcing_shape_name') - -# Specify default path if needed -if forcing_shape_path == 'default': - forcing_shape_path = make_default_path('shapefiles/forcing') # outputs a Path() -else: - forcing_shape_path = Path(forcing_shape_path) # make sure a user-specified path is a Path() - - -# --- Find where the intersection needs to go -# Intersected shapefile path. Name is set by EASYMORE as [prefix]_intersected_shapefile.shp -intersect_path = read_from_control(controlFolder/controlFile,'intersect_forcing_path') - -# Specify default path if needed -if intersect_path == 'default': - intersect_path = make_default_path('shapefiles/catchment_intersection/with_forcing') # outputs a Path() -else: - intersect_path = Path(intersect_path) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -intersect_path.mkdir(parents=True, exist_ok=True) - - -# --- Find the forcing files (merged ERA5 data) -# Location of merged ERA5 files -forcing_merged_path = read_from_control(controlFolder/controlFile,'forcing_merged_path') - -# Specify default path if needed -if forcing_merged_path == 'default': - forcing_merged_path = make_default_path('forcing/2_merged_data') # outputs a Path() -else: - forcing_merged_path = Path(forcing_merged_path) # make sure a user-specified path is a Path() - -# Find files in folder -forcing_files = [forcing_merged_path/file for file in os.listdir(forcing_merged_path) if os.path.isfile(forcing_merged_path/file) and file.endswith('.nc')] - -# Sort the files -forcing_files.sort() - - -# --- Find where the temporary EASYMORE files need to go -# Location for EASYMORE temporary storage -forcing_easymore_path = read_from_control(controlFolder/controlFile,'forcing_easymore_path') - -# Specify default path if needed -if forcing_easymore_path == 'default': - forcing_easymore_path = make_default_path('forcing/3_temp_easymore') # outputs a Path() -else: - forcing_easymore_path = Path(forcing_easymore_path) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -forcing_easymore_path.mkdir(parents=True, exist_ok=True) - - -# --- Find where the area-weighted forcing needs to go -# Location for EASYMORE forcing output -forcing_basin_path = read_from_control(controlFolder/controlFile,'forcing_basin_avg_path') - -# Specify default path if needed -if forcing_basin_path == 'default': - forcing_basin_path = make_default_path('forcing/3_basin_averaged_data') # outputs a Path() -else: - forcing_basin_path = Path(forcing_basin_path) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -forcing_basin_path.mkdir(parents=True, exist_ok=True) - - -# --- EASYMORE -# Initialize an EASYMORE object -esmr = easymore.easymore() - -# Author name -esmr.author_name = 'SUMMA public workflow scripts' - -# Data license -esmr.license = 'Copernicus data use license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf' - -# Case name, used in EASYMORE-generated file naes -esmr.case_name = read_from_control(controlFolder/controlFile,'domain_name') - -# ERA5 shapefile and variable names -# Variable names can be hardcoded because we set them when we generate this shapefile as part of the workflow -esmr.source_shp = forcing_shape_path/forcing_shape_name # shapefile -esmr.source_shp_lat = read_from_control(controlFolder/controlFile,'forcing_shape_lat_name') # name of the latitude field -esmr.source_shp_lon = read_from_control(controlFolder/controlFile,'forcing_shape_lon_name') # name of the longitude field - -# Catchment shapefile and variable names -esmr.target_shp = catchment_path/catchment_name -esmr.target_shp_ID = read_from_control(controlFolder/controlFile,'catchment_shp_hruid') # name of the HRU ID field -esmr.target_shp_lat = read_from_control(controlFolder/controlFile,'catchment_shp_lat') # name of the latitude field -esmr.target_shp_lon = read_from_control(controlFolder/controlFile,'catchment_shp_lon') # name of the longitude field - -# ERA5 netcdf file and variable names -esmr.source_nc = str(forcing_files[0]) # first file in the list; Path() to string -esmr.var_names = ['airpres', - 'LWRadAtm', - 'SWRadAtm', - 'pptrate', - 'airtemp', - 'spechum', - 'windspd'] # variable names of forcing data - hardcoded because we prescribe them during ERA5 merging -esmr.var_lat = 'latitude' # name of the latitude dimensions -esmr.var_lon = 'longitude' # name of the longitude dimension -esmr.var_time = 'time' # name of the time dimension - -# Temporary folder where the EASYMORE-generated GIS files and remapping file will be saved -esmr.temp_dir = str(forcing_easymore_path) + '/' # Path() to string; ensure the trailing '/' EASYMORE wants - -# Output folder where the catchment-averaged forcing will be saved -esmr.output_dir = str(forcing_basin_path) + '/' # Path() to string; ensure the trailing '/' EASYMORE wants - -# Netcdf settings -esmr.remapped_dim_id = 'hru' # name of the non-time dimension; prescribed by SUMMA -esmr.remapped_var_id = 'hruId' # name of the variable associated with the non-time dimension -esmr.format_list = ['f4'] # variable type to save forcing as. Single entry here will be used for all variables -esmr.fill_value_list = ['-9999'] # fill value - -# Flag that we do not want the data stored in .csv in addition to .nc -esmr.save_csv = False - -# Flag that we currently have no remapping file -esmr.remap_csv = '' - -# Enforce that we want our HRUs returned in the order we put them in -esmr.sort_ID = False - -# Run EASYMORE -# Note on centroid warnings: in this case we use a regular lat/lon grid to represent ERA5 forcing and ... -# centroid estimates without reprojecting are therefore acceptable. -# Note on deprecation warnings: this is a EASYMORE issue that cannot be resolved here. Does not affect current use. -esmr.nc_remapper() - - -# --- Move files to prescribed locations -# Remapping file -remap_file = esmr.case_name + '_remapping.csv' -copyfile( esmr.temp_dir + remap_file, intersect_path / remap_file); - -# Intersected shapefile -for file in glob.glob(esmr.temp_dir + esmr.case_name + '_intersected_shapefile.*'): - copyfile( file, intersect_path / os.path.basename(file)); - -# Remove the temporary EASYMORE directory to save space -try: - rmtree(esmr.temp_dir) -except OSError as e: - print ("Error: %s - %s." % (e.filename, e.strerror)) - - -# --- Code provenance - intersection shapefile -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = intersect_path -log_suffix = '_catchment_forcing_intersect_log.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = '1_make_one_weighted_forcing_file.py' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Intersect shapefiles of catchment and ERA5.'] - for txt in lines: - file.write(txt) - - -# --- Code provenance - weighted forcing file -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = forcing_basin_path -log_suffix = '_create_one_weighted_forcing_file_log.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = '1_make_one_weighted_forcing_file.py' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Made a weighted forcing file based on intersect shapefiles of catchment and ERA5.'] - for txt in lines: - file.write(txt) \ No newline at end of file diff --git a/4b_remapping/2_forcing/2_make_all_weighted_forcing_files.ipynb b/4b_remapping/2_forcing/2_make_all_weighted_forcing_files.ipynb deleted file mode 100644 index bdb53a6..0000000 --- a/4b_remapping/2_forcing/2_make_all_weighted_forcing_files.ipynb +++ /dev/null @@ -1,2171 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create all area-weighted forcing files\n", - "We need to find how the ERA5 gridded forcing maps onto the catchment to create area-weighted forcing as SUMMA input. This involves two steps:\n", - "1. Intersect the ERA5 shape with the user's catchment shape to find the overlap between a given (sub) catchment and the forcing grid;\n", - "2. Create an area-weighted, catchment-averaged forcing time series.\n", - "\n", - "The EASYMORE package (https://github.com/ShervanGharari/EASYMORE) provides the necessary functionality to do this. EASYMORE performs the GIS step (1, shapefile intersection) and the area-weighting step (2, create new forcing `.nc` files) as part of a single `nc_remapper()` call. To allow for parallelization, EASYMORE can save the output from the GIS step into a restart `.csv` file which can be used to skip the GIS step. This allows (manual) parallelization of area-weighted forcing file generation after the GIS procedures have been run once. The full workflow is thus:\n", - "1. [Previous script] Call `nc_remapper()` with ERA5 and user's shapefile, and one ERA5 forcing `.nc` file;\n", - " - EASYMORE performs intersection of both shapefiles;\n", - " - EASYMORE saves the outcomes of this intersection to a `.csv` file;\n", - " - EASYMORE creates an area-weighted forcing file from a single provided ERA5 source `.nc` file\n", - "2. [This script] Call `nc_remapper()` with intersection `.csv` file and all other forcing `.nc` files.\n", - "3. [Follow-up script] Apply lapse rates to temperature variable.\n", - "\n", - "Parallelization of step 2 (2nd `nc_remapper()` call) requires an external loop that sends (batches of) the remaining ERA5 raw forcing files to individual processors. As with other steps that may be parallelized, creating code that does this is left to the user." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import easymore\n", - "from pathlib import Path\n", - "from shutil import rmtree\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the EASYMORE restart file is" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersected shapefile path. Name is set by EASYMORE as [prefix]_intersected_shapefile.shp\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_forcing_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_forcing') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Remapping filename\n", - "domain = read_from_control(controlFolder/controlFile,'domain_name')\n", - "remap_file = domain + '_remapping.csv'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find the forcing files (merged ERA5 data)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Location of merged ERA5 files\n", - "forcing_merged_path = read_from_control(controlFolder/controlFile,'forcing_merged_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_merged_path == 'default':\n", - " forcing_merged_path = make_default_path('forcing/2_merged_data') # outputs a Path()\n", - "else:\n", - " forcing_merged_path = Path(forcing_merged_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Find files in folder\n", - "forcing_files = [forcing_merged_path/file for file in os.listdir(forcing_merged_path) if os.path.isfile(forcing_merged_path/file) and file.endswith('.nc')]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort the files\n", - "forcing_files.sort()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the area-weighted forcing needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Location for SUMMA-ready files\n", - "forcing_basin_path = read_from_control(controlFolder/controlFile,'forcing_basin_avg_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_basin_path == 'default':\n", - " forcing_basin_path = make_default_path('forcing/3_basin_averaged_data') # outputs a Path()\n", - "else:\n", - " forcing_basin_path = Path(forcing_basin_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "forcing_basin_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### EASYMORE" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize a EASYMORE object\n", - "esmr = easymore.easymore()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Author name\n", - "esmr.author_name = 'SUMMA public workflow scripts'" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Data license\n", - "esmr.license = 'Copernicus data use license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf'" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Case name, used in EASYMORE-generated file naes\n", - "esmr.case_name = read_from_control(controlFolder/controlFile,'domain_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# ERA5 netcdf variable names\n", - "esmr.var_names = ['airpres',\n", - " 'LWRadAtm',\n", - " 'SWRadAtm',\n", - " 'pptrate',\n", - " 'airtemp',\n", - " 'spechum',\n", - " 'windspd'] # variable names of forcing data - hardcoded because we prescribe them during ERA5 merging\n", - "esmr.var_lat = 'latitude' # name of the latitude dimensions\n", - "esmr.var_lon = 'longitude' # name of the longitude dimension\n", - "esmr.var_time = 'time' # name of the time dimension" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Temporary folder where the EASYMORE-generated GIS files and remapping file will be saved\n", - "esmr.temp_dir = '' # Force this to be empty" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Output folder where the catchment-averaged forcing will be saved\n", - "esmr.output_dir = str(forcing_basin_path) + '/' # Path() to string; ensure the trailing '/' EASYMORE wants" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Netcdf settings\n", - "esmr.remapped_dim_id = 'hru' # name of the non-time dimension; prescribed by SUMMA\n", - "esmr.remapped_var_id = 'hruId' # name of the variable associated with the non-time dimension\n", - "esmr.format_list = ['f4'] # variable type to save forcing as. Single entry here will be used for all variables\n", - "esmr.fill_value_list = ['-9999'] # fill value" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Flag that we do not want the data stored in .csv in addition to .nc\n", - "esmr.save_csv = False" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Flag that we currently have no remapping file\n", - "esmr.remap_csv = str(intersect_path / remap_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Enforce that we want our HRUs returned in the order we put them in\n", - "esmr.sort_ID = False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Run EASYMORE - this can should be parallelized for speed ups" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "EASYMORE is given multiple varibales to be remapped but only on format and fill valueEASYMORE repeat the format and fill value for all the variables in output files\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200801.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-01-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:02:07.907220\n", - "Ended at date and time 2021-05-06 13:02:28.883397\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200802.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-02-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:02:28.943292\n", - "Ended at date and time 2021-05-06 13:02:48.094927\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200803.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-03-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:02:48.163813\n", - "Ended at date and time 2021-05-06 13:03:09.552575\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200804.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-04-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:03:09.621766\n", - "Ended at date and time 2021-05-06 13:03:30.230493\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200805.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-05-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:03:30.301412\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:03:51.091247\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200806.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-06-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:03:51.140680\n", - "Ended at date and time 2021-05-06 13:04:12.355413\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200807.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-07-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:04:12.420665\n", - "Ended at date and time 2021-05-06 13:04:34.455578\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200808.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-08-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:04:34.520478\n", - "Ended at date and time 2021-05-06 13:04:56.335523\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200809.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-09-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:04:56.400618\n", - "Ended at date and time 2021-05-06 13:05:17.870766\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200810.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-10-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:05:17.935975\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:05:41.296232\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200811.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-11-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:05:41.350824\n", - "Ended at date and time 2021-05-06 13:06:06.256061\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200812.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2008-12-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:06:06.335986\n", - "Ended at date and time 2021-05-06 13:06:33.686088\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200901.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-01-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:06:33.746037\n", - "Ended at date and time 2021-05-06 13:07:00.666408\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200902.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-02-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:07:00.736212\n", - "Ended at date and time 2021-05-06 13:07:19.891794\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200903.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-03-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:07:19.951474\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:07:39.730924\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200904.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-04-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:07:39.791511\n", - "Ended at date and time 2021-05-06 13:07:59.089038\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200905.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-05-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:07:59.148619\n", - "Ended at date and time 2021-05-06 13:08:18.816197\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200906.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-06-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:08:18.875672\n", - "Ended at date and time 2021-05-06 13:08:37.961162\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200907.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-07-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:08:38.012555\n", - "Ended at date and time 2021-05-06 13:08:57.856990\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200908.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-08-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:08:57.916781\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:09:17.613832\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200909.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-09-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:09:17.665321\n", - "Ended at date and time 2021-05-06 13:09:36.657978\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200910.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-10-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:09:36.718506\n", - "Ended at date and time 2021-05-06 13:09:56.428842\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200911.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-11-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:09:56.472693\n", - "Ended at date and time 2021-05-06 13:10:16.048553\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_200912.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2009-12-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:10:16.098440\n", - "Ended at date and time 2021-05-06 13:10:37.551300\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201001.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-01-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:10:37.602558\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:10:57.401980\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201002.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-02-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:10:57.473464\n", - "Ended at date and time 2021-05-06 13:11:15.397560\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201003.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-03-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:11:15.447287\n", - "Ended at date and time 2021-05-06 13:11:35.172700\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201004.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-04-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:11:35.224056\n", - "Ended at date and time 2021-05-06 13:11:54.420831\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201005.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-05-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:11:54.470428\n", - "Ended at date and time 2021-05-06 13:12:14.134176\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201006.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-06-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:12:14.183820\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:12:33.244661\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201007.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-07-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:12:33.295899\n", - "Ended at date and time 2021-05-06 13:12:53.924057\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201008.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-08-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:12:53.975509\n", - "Ended at date and time 2021-05-06 13:13:14.018188\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201009.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-09-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:13:14.067733\n", - "Ended at date and time 2021-05-06 13:13:34.112029\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201010.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-10-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:13:34.182827\n", - "Ended at date and time 2021-05-06 13:13:55.929668\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201011.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-11-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:13:55.989309\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:14:15.594321\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201012.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2010-12-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:14:15.642502\n", - "Ended at date and time 2021-05-06 13:14:36.071268\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201101.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-01-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:14:36.122663\n", - "Ended at date and time 2021-05-06 13:14:59.162393\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201102.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-02-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:14:59.284659\n", - "Ended at date and time 2021-05-06 13:15:19.070981\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201103.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-03-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:15:19.139700\n", - "Ended at date and time 2021-05-06 13:15:43.275249\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201104.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-04-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:15:43.334885\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:16:02.546570\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201105.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-05-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:16:02.598110\n", - "Ended at date and time 2021-05-06 13:16:22.518323\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201106.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-06-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:16:22.578728\n", - "Ended at date and time 2021-05-06 13:16:41.679378\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201107.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-07-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:16:41.728529\n", - "Ended at date and time 2021-05-06 13:17:01.577756\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201108.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-08-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:17:01.637043\n", - "Ended at date and time 2021-05-06 13:17:21.494743\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201109.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-09-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:17:21.546277\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:17:40.615133\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201110.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-10-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:17:40.675237\n", - "Ended at date and time 2021-05-06 13:18:00.425034\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201111.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-11-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:18:00.476636\n", - "Ended at date and time 2021-05-06 13:18:19.581427\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201112.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2011-12-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:18:19.633023\n", - "Ended at date and time 2021-05-06 13:18:39.865786\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201201.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-01-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:18:39.930951\n", - "Ended at date and time 2021-05-06 13:19:03.939374\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201202.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-02-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:19:03.999336\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:19:26.249072\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201203.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-03-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:19:26.314066\n", - "Ended at date and time 2021-05-06 13:19:49.979051\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201204.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-04-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:19:50.059475\n", - "Ended at date and time 2021-05-06 13:20:12.879670\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201205.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-05-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:20:12.939453\n", - "Ended at date and time 2021-05-06 13:20:36.459299\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201206.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-06-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:20:36.519427\n", - "Ended at date and time 2021-05-06 13:20:59.289488\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201207.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-07-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:20:59.349459\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:21:23.009535\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201208.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-08-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:21:23.079852\n", - "Ended at date and time 2021-05-06 13:21:46.950047\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201209.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-09-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:21:47.009793\n", - "Ended at date and time 2021-05-06 13:22:09.810262\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201210.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-10-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:22:09.869720\n", - "Ended at date and time 2021-05-06 13:22:33.559850\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201211.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-11-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:22:33.620429\n", - "Ended at date and time 2021-05-06 13:23:01.179944\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201212.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2012-12-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:23:01.250001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:23:26.810446\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201301.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-01-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:23:26.880533\n", - "Ended at date and time 2021-05-06 13:23:51.773239\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201302.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-02-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:23:51.840652\n", - "Ended at date and time 2021-05-06 13:24:14.600510\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201303.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-03-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:24:14.670394\n", - "Ended at date and time 2021-05-06 13:24:39.660313\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201304.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-04-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:24:39.720670\n", - "Ended at date and time 2021-05-06 13:25:04.250397\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201305.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-05-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:25:04.310576\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:25:29.550526\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201306.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-06-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:25:29.630978\n", - "Ended at date and time 2021-05-06 13:25:54.100748\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201307.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-07-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:25:54.161093\n", - "Ended at date and time 2021-05-06 13:26:19.390752\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201308.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-08-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:26:19.461281\n", - "Ended at date and time 2021-05-06 13:26:44.781025\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201309.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-09-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:26:44.860953\n", - "Ended at date and time 2021-05-06 13:27:09.311100\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201310.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-10-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:27:09.382076\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ended at date and time 2021-05-06 13:27:34.531154\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201311.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-11-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:27:34.591110\n", - "Ended at date and time 2021-05-06 13:27:59.151694\n", - "------\n", - "No temporary folder is provided for EASYMORE; this will result in EASYMORE saving the files in the same directory as python script\n", - "remap file is provided; EASYMORE will use this file and skip calculation of remapping\n", - "EASYMORE will remap variable airpres from source file to variable airpres in remapped NeCDF file\n", - "EASYMORE will remap variable LWRadAtm from source file to variable LWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable SWRadAtm from source file to variable SWRadAtm in remapped NeCDF file\n", - "EASYMORE will remap variable pptrate from source file to variable pptrate in remapped NeCDF file\n", - "EASYMORE will remap variable airtemp from source file to variable airtemp in remapped NeCDF file\n", - "EASYMORE will remap variable spechum from source file to variable spechum in remapped NeCDF file\n", - "EASYMORE will remap variable windspd from source file to variable windspd in remapped NeCDF file\n", - "EASYMORE case exists in the remap file\n", - "EASYMORE detects that the varibales from the netCDF files are identicalin dimensions of the varibales and latitude and longitude\n", - "EASYMORE detects that all the varibales have dimensions of:\n", - "['time', 'latitude', 'longitude']\n", - "EASYMORE detects that the longitude varibales has dimensions of:\n", - "['longitude']\n", - "EASYMORE detects that the latitude varibales has dimensions of:\n", - "['latitude']\n", - "------REMAPPING------\n", - "Remapping C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\2_merged_data\\ERA5_NA_201312.nc to C:\\Globus endpoint\\summaWorkflow_data\\domain_BowAtBanff\\forcing\\3_basin_averaged_data/BowAtBanff_remapped_2013-12-01-00-00-00.nc\n", - "Started at date and time 2021-05-06 13:27:59.221365\n", - "Ended at date and time 2021-05-06 13:28:24.541311\n", - "------\n" - ] - } - ], - "source": [ - "# Loop over the remaining forcing files\n", - "for file in forcing_files[1:]: # skip the first one, as we completed that in the previous script\n", - " \n", - " # ERA5 forcing files to use\n", - " esmr.source_nc = str(file) # Path() to string\n", - " \n", - " # Note on centroid warnings: in this case we use a regular lat/lon grid to represent ERA5 forcing and ...\n", - " # centroid estimates without reprojecting are therefore acceptable.\n", - " # Note on deprecation warnings: this is a EASYMORE issue that cannot be resolved here. Does not affect current use.\n", - " esmr.nc_remapper()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = forcing_basin_path\n", - "log_suffix = '_create_all_weighted_forcing_file_log.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '2_make_all_weighted_forcing_files.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Made all remaining weighted forcing files based on restart file from intersected shapefiles of catchment and ERA5.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/4b_remapping/2_forcing/2_make_all_weighted_forcing_files.py b/4b_remapping/2_forcing/2_make_all_weighted_forcing_files.py deleted file mode 100644 index de116e5..0000000 --- a/4b_remapping/2_forcing/2_make_all_weighted_forcing_files.py +++ /dev/null @@ -1,200 +0,0 @@ -# Create all area-weighted forcing files -# We need to find how the ERA5 gridded forcing maps onto the catchment to create area-weighted forcing as SUMMA input. This involves two steps: -# 1. Intersect the ERA5 shape with the user's catchment shape to find the overlap between a given (sub) catchment and the forcing grid; -# 2. Create an area-weighted, catchment-averaged forcing time series. -# -# The EASYMORE package (https://github.com/ShervanGharari/candex_newgen) provides the necessary functionality to do this. EASYMORE performs the GIS step (1, shapefile intersection) and the area-weighting step (2, create new forcing `.nc` files) as part of a single `nc_remapper()` call. To allow for parallelization, EASYMORE can save the output from the GIS step into a restart `.csv` file which can be used to skip the GIS step. This allows (manual) parallelization of area-weighted forcing file generation after the GIS procedures have been run once. The full workflow is thus: -# 1. [Previous script] Call `nc_remapper()` with ERA5 and user's shapefile, and one ERA5 forcing `.nc` file; -# - EASYMORE performs intersection of both shapefiles; -# - EASYMORE saves the outcomes of this intersection to a `.csv` file; -# - EASYMORE creates an area-weighted forcing file from a single provided ERA5 source `.nc` file -# 2. [This script] Call `nc_remapper()` with intersection `.csv` file and all other forcing `.nc` files. -# 3. [Follow-up script] Apply lapse rates to temperature variable. -# -# Parallelization of step 2 (2nd `nc_remapper()` call) requires an external loop that sends (batches of) the remaining ERA5 raw forcing files to individual processors. As with other steps that may be parallelized, creating code that does this is left to the user. - -# modules -import os -import easymore -from pathlib import Path -from shutil import rmtree -from shutil import copyfile -from datetime import datetime - - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find where the EASYMORE restart file is -# Intersected shapefile path. Name is set by EASYMORE as [prefix]_intersected_shapefile.shp -intersect_path = read_from_control(controlFolder/controlFile,'intersect_forcing_path') - -# Specify default path if needed -if intersect_path == 'default': - intersect_path = make_default_path('shapefiles/catchment_intersection/with_forcing') # outputs a Path() -else: - intersect_path = Path(intersect_path) # make sure a user-specified path is a Path() - -# Remapping filename -domain = read_from_control(controlFolder/controlFile,'domain_name') -remap_file = domain + '_remapping.csv' - - -# --- Find the forcing files (merged ERA5 data) -# Location of merged ERA5 files -forcing_merged_path = read_from_control(controlFolder/controlFile,'forcing_merged_path') - -# Specify default path if needed -if forcing_merged_path == 'default': - forcing_merged_path = make_default_path('forcing/2_merged_data') # outputs a Path() -else: - forcing_merged_path = Path(forcing_merged_path) # make sure a user-specified path is a Path() - -# Find files in folder -forcing_files = [forcing_merged_path/file for file in os.listdir(forcing_merged_path) if os.path.isfile(forcing_merged_path/file) and file.endswith('.nc')] - -# Sort the files -forcing_files.sort() - - -# --- Find where the area-weighted forcing needs to go -# Location for SUMMA-ready files -forcing_basin_path = read_from_control(controlFolder/controlFile,'forcing_basin_avg_path') - -# Specify default path if needed -if forcing_basin_path == 'default': - forcing_basin_path = make_default_path('forcing/3_basin_averaged_data') # outputs a Path() -else: - forcing_basin_path = Path(forcing_basin_path) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -forcing_basin_path.mkdir(parents=True, exist_ok=True) - - -# --- EASYMORE -# Initialize an EASYMORE object -esmr = easymore.easymore() - -# Author name -esmr.author_name = 'SUMMA public workflow scripts' - -# Data license -esmr.license = 'Copernicus data use license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf' - -# Case name, used in EASYMORE-generated file naes -esmr.case_name = read_from_control(controlFolder/controlFile,'domain_name') - -# ERA5 netcdf variable names -esmr.var_names = ['airpres', - 'LWRadAtm', - 'SWRadAtm', - 'pptrate', - 'airtemp', - 'spechum', - 'windspd'] # variable names of forcing data - hardcoded because we prescribe them during ERA5 merging -esmr.var_lat = 'latitude' # name of the latitude dimensions -esmr.var_lon = 'longitude' # name of the longitude dimension -esmr.var_time = 'time' # name of the time dimension - -# Temporary folder where the EASYMORE-generated GIS files and remapping file will be saved -esmr.temp_dir = '' # Force this to be empty - -# Output folder where the catchment-averaged forcing will be saved -esmr.output_dir = str(forcing_basin_path) + '/' # Path() to string; ensure the trailing '/' EASYMORE wants - -# Netcdf settings -esmr.remapped_dim_id = 'hru' # name of the non-time dimension; prescribed by SUMMA -esmr.remapped_var_id = 'hruId' # name of the variable associated with the non-time dimension -esmr.format_list = ['f4'] # variable type to save forcing as. Single entry here will be used for all variables -esmr.fill_value_list = ['-9999'] # fill value - -# Flag that we do not want the data stored in .csv in addition to .nc -esmr.save_csv = False - -# Flag that we currently have no remapping file -esmr.remap_csv = str(intersect_path / remap_file) - -# Enforce that we want our HRUs returned in the order we put them in -esmr.sort_ID = False - -# Flag that we want to skip existing remap files -esmr.overwrite_existing_remap = False - -# --- Run EASYMORE - this can be parallelized for speed ups -# Loop over the remaining forcing files -for file in forcing_files[1:]: # skip the first one, as we completed that in the previous script - - # ERA5 forcing files to use - esmr.source_nc = str(file) # Path() to string - - # Note on centroid warnings: in this case we use a regular lat/lon grid to represent ERA5 forcing and ... - # centroid estimates without reprojecting are therefore acceptable. - # Note on deprecation warnings: this is an EASYMORE issue that cannot be resolved here. Does not affect current use. - esmr.nc_remapper() - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = forcing_basin_path -log_suffix = '_create_all_weighted_forcing_file_log.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = '2_make_all_weighted_forcing_files.py' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Made all remaining weighted forcing files based on restart file from intersected shapefiles of catchment and ERA5.'] - for txt in lines: - file.write(txt) \ No newline at end of file diff --git a/4b_remapping/2_forcing/3_temperature_lapsing_and_datastep.ipynb b/4b_remapping/2_forcing/3_temperature_lapsing_and_datastep.ipynb deleted file mode 100644 index 25ffd3f..0000000 --- a/4b_remapping/2_forcing/3_temperature_lapsing_and_datastep.ipynb +++ /dev/null @@ -1,1608 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Temperature lapsing and data_step\n", - "The size discrepancy between MERIT basins and the typical coverage of ERA5 grid cells makes it appropriate to apply a temperature lapse rate. This script loops over existing basin-averaged forcing files and applies a lapse rate to the `airtemp` variable. Lapse arte is determined based on the average elevation difference between the basin shape and the ERA5 grid cell(s) that cover the basin.\n", - "\n", - "In addition, this script adds the `data_step` variable to each forcing file, which SUMMA needs to know the time resolution of the forcing inputs.\n", - "\n", - "#### Environmental Lapse Rate\n", - "The temperature lapse rate is assumed to have a constant value of `0.0065` `[K m-1]` (Wallace & Hobbs, 2006, p. 421).\n", - "\n", - "_Wallace, J., and P. Hobbs (2006), Atmospheric Science: An Introductory Survey, 483 pp., Academic Press, Burlington, Mass_" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import numpy as np\n", - "import xarray as xr\n", - "import pandas as pd\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of intersection file" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersected shapefile path. Name is set by EASYMORE as [prefix]_intersected_shapefile.shp\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_forcing_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_forcing') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the file name\n", - "domain = read_from_control(controlFolder/controlFile,'domain_name')\n", - "intersect_name = domain + '_intersected_shapefile.csv' # can also be .shp, but using the .csv is easier on memory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the EASYMORE-prepared forcing files are" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing files as produced by EASYMORE\n", - "forcing_easymore_path = read_from_control(controlFolder/controlFile,'forcing_basin_avg_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_easymore_path == 'default':\n", - " forcing_easymore_path = make_default_path('forcing/3_basin_averaged_data') # outputs a Path()\n", - "else:\n", - " forcing_easymore_path = Path(forcing_easymore_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the files\n", - "_,_,forcing_files = next(os.walk(forcing_easymore_path))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find the time step size of the forcing data" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Value in control file\n", - "data_step = read_from_control(controlFolder/controlFile,'forcing_time_step_size')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert to int\n", - "data_step = int(data_step)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the final forcing needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Location for SUMMA-ready files\n", - "forcing_summa_path = read_from_control(controlFolder/controlFile,'forcing_summa_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_summa_path == 'default':\n", - " forcing_summa_path = make_default_path('forcing/4_SUMMA_input') # outputs a Path()\n", - "else:\n", - " forcing_summa_path = Path(forcing_summa_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "forcing_summa_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find the area-weighted lapse value for each basin" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the intersection file\n", - "topo_data = pd.read_csv(intersect_path/intersect_name) " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Find hruId name in user's shapefile\n", - "hru_ID_name = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')\n", - "gru_ID_name = read_from_control(controlFolder/controlFile,'catchment_shp_gruid')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the column names\n", - "# Note that column names are truncated at 10 characters in the ESRI shapefile, but NOT in the .csv we use here\n", - "gru_ID = 'S_1_' + gru_ID_name # EASYMORE prefix + user's hruId name\n", - "hru_ID = 'S_1_' + hru_ID_name # EASYMORE prefix + user's hruId name\n", - "forcing_ID = 'S_2_ID' # fixed name assigned by EASYMORE\n", - "catchment_elev = 'S_1_elev_mean' # EASYMORE prefix + name used in catchment+DEM intersection step\n", - "forcing_elev = 'S_2_elev_m' # EASYMORE prefix + name used in ERA5 shapefile generation\n", - "weights = 'weight' # EASYMORE feature" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the lapse rate\n", - "lapse_rate = 0.0065 # [K m-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate weighted lapse values for each HRU \n", - "# Note that these lapse values need to be ADDED to ERA5 temperature data\n", - "topo_data['lapse_values'] = topo_data[weights] * lapse_rate * \\\n", - " (topo_data[forcing_elev] - topo_data[catchment_elev]) # [K]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the total lapse value per basin; i.e. sum the individual contributions of each HRU+ERA5-grid overlapping part\n", - "lapse_values = topo_data.groupby([gru_ID,hru_ID]).lapse_values.sum().reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " S_1_GRU_ID lapse_values\n", - "S_1_HRU_ID \n", - "1 71028597 -0.530803\n", - "2 71028609 -0.413910\n", - "3 71028676 -0.863720\n", - "4 71028700 -0.666452\n", - "5 71028740 -1.204602\n", - "... ... ...\n", - "114 71030774 4.228418\n", - "115 71031120 4.034569\n", - "116 71031359 3.808350\n", - "117 71031506 4.732154\n", - "118 71028585 3.967619\n", - "\n", - "[118 rows x 2 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Sort and set hruID as the index variable\n", - "lapse_values = lapse_values.sort_values(hru_ID).set_index(hru_ID)\n", - "lapse_values" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.53080339, -0.4139101 , -0.86372035, -0.66645241, -1.20460167,\n", - " -1.5746951 , -1.32778159, -0.6674116 , -0.04476636, 0.98314857,\n", - " -0.31718611, -1.05653687, -0.15626734, -1.50455369, -1.5866296 ,\n", - " -0.34931969, -1.88584627, -1.1627504 , -0.78166432, -0.32949239,\n", - " -1.10062264, -2.07139943, -2.15017622, -1.96050107, -1.65279644,\n", - " -1.44900648, -1.76136919, -2.06491089, -1.2496712 , -1.58894415,\n", - " -1.50327352, -0.98183415, -2.69426726, -2.29230857, -1.32682617,\n", - " -1.81413165, -0.63797317, -1.89566932, -0.91513997, -1.06523235,\n", - " -0.68307467, -2.40340754, -0.9828666 , -2.03269987, -1.00217416,\n", - " -0.80221279, -1.01821046, -0.60629124, 2.20874312, 2.55817335,\n", - " 2.75609708, 3.27298514, 2.39364004, 2.62071082, 2.87247782,\n", - " 3.5091499 , 3.5641254 , 3.94130768, 4.15876513, 3.21665357,\n", - " 2.70481439, 3.31225587, 1.90095689, 2.07705035, 3.00127523,\n", - " 1.67074649, 2.91552636, 2.38271938, 2.84854313, 1.7375961 ,\n", - " 1.16705255, 0.91088268, 1.1286033 , 1.68684366, 1.80288825,\n", - " 0.81209337, 1.22142872, 1.81148479, 1.21110668, 2.06745644,\n", - " 2.038747 , 0.91835683, 1.99259831, 2.13396367, 2.99524386,\n", - " 3.52642541, 2.4691832 , 2.27109729, 3.24622727, 2.28119157,\n", - " 2.36868726, 2.1719001 , 2.1117102 , 2.17147878, 2.27150965,\n", - " 2.09775288, 2.11790855, 2.91637562, 4.6519249 , 4.20096641,\n", - " 4.87172038, 4.85498352, 4.73973229, 4.4134994 , 4.05478277,\n", - " 3.85912812, 4.38400163, 4.76547606, 3.92671465, 4.42763742,\n", - " 4.54177357, 3.87359052, 4.44774456, 4.22841764, 4.03456912,\n", - " 3.80835016, 4.73215396, 3.96761858])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lapse_values['lapse_values'].values" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Close the main file\n", - "del topo_data # hopefully this saves some RAM but this is apparently not so straightforward in Python.. Can't hurt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Check what we're doing" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.lines import Line2D" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Find location of DEM intersection for shapes and elevation\n", - "elev_catchment_path = read_from_control(controlFolder/controlFile,'intersect_dem_path')\n", - "elev_catchment_name = read_from_control(controlFolder/controlFile,'intersect_dem_name')\n", - "\n", - "# Specify default path if needed\n", - "if elev_catchment_path == 'default':\n", - " elev_catchment_path = make_default_path('shapefiles/catchment_intersection/with_dem') # outputs a Path()\n", - "else:\n", - " elev_catchment_path = Path(elev_catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the GRU and HRU identifiers\n", - "hm_gruid = read_from_control(controlFolder/controlFile,'catchment_shp_gruid')\n", - "hm_hruid = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# catchment with DEM\n", - "catchment = gpd.read_file(elev_catchment_path/elev_catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a shapefile with only GRU boundaries for overlay\n", - "hm_grus_only = catchment[[hm_gruid,'geometry']] # keep only the gruId and geometry\n", - "hm_grus_only = hm_grus_only.dissolve(by=hm_gruid) # Dissolve HRU delineation" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# ensure that HRU_ID is the index\n", - "catchment = catchment.set_index(hm_hruid)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# merge with the lapse rate dataframe\n", - "catchment['lapse_rate'] = lapse_values['lapse_values']" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot\n", - "cmap_elev = 'terrain'\n", - "cmap_lapse = 'coolwarm'\n", - "\n", - "# figure\n", - "fig, axs = plt.subplots(1,2,figsize=(20,10))\n", - "plt.tight_layout()\n", - "plt.rcParams.update({'font.size': 14})\n", - "\n", - "# --- elevation\n", - "axId = 0\n", - "\n", - "# Data\n", - "catchment.plot(ax=axs[axId], column='elev_mean',edgecolor='k', cmap = cmap_elev, legend=False)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "\n", - "# Custom colorbar\n", - "cax = fig.add_axes([0.46, 0.1, 0.02, 0.5])\n", - "vmin,vmax = catchment['elev_mean'].min(),catchment['elev_mean'].max()\n", - "sm = plt.cm.ScalarMappable(cmap=cmap_elev, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "\n", - "\n", - "# Custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color='k', lw=1)]\n", - "label = ['SUMMA GRUs',\n", - " 'SUMMA HRUs']\n", - "axs[axId].legend(lines,label,loc='lower left');\n", - "\n", - "# Chart junk\n", - "axs[axId].set_title('(a) HRU elevation derived from MERIT DEM [m.a.s.l]');\n", - "axs[axId].set_frame_on(False)\n", - "\n", - "\n", - "# --- lapse rates\n", - "axId = 1\n", - "\n", - "# Data\n", - "catchment.plot(ax=axs[axId], column='lapse_rate',edgecolor='k', cmap = cmap_lapse, legend=False, vmin=-5, vmax=5)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "\n", - "# Custom colorbar\n", - "cax = fig.add_axes([0.96, 0.1, 0.02, 0.5])\n", - "vmin,vmax = catchment['lapse_rate'].min(),catchment['lapse_rate'].max()\n", - "sm = plt.cm.ScalarMappable(cmap=cmap_lapse, norm=plt.Normalize(vmin=-5, vmax=5))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "\n", - "\n", - "# Custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color='k', lw=1)]\n", - "label = ['SUMMA GRUs',\n", - " 'SUMMA HRUs']\n", - "axs[axId].legend(lines,label,loc='lower left');\n", - "\n", - "# Chart junk\n", - "axs[axId].set_title('(b) Temperature lapse rates [K]');\n", - "axs[axId].set_frame_on(False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Loop over forcing files; apply lapse rates and add data_step variable" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting on BowAtBanff_remapped_2008-01-01-00-00-00-GT.nc\n", - "Starting on BowAtBanff_remapped_2008-01-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-02-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-03-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-04-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-05-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-06-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-07-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-08-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-09-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-10-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-11-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2008-12-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-01-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-02-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-03-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-04-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-05-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-06-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-07-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-08-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-09-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-10-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-11-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2009-12-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-01-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-02-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-03-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-04-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-05-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-06-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-07-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-08-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-09-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-10-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-11-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2010-12-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-01-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-02-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-03-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-04-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-05-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-06-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-07-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-08-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-09-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-10-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-11-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2011-12-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-01-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-02-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-03-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-04-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-05-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-06-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-07-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-08-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-09-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-10-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-11-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2012-12-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-01-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-02-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-03-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-04-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-05-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-06-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-07-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-08-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-09-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-10-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-11-01-00-00-00.nc\n", - "Starting on BowAtBanff_remapped_2013-12-01-00-00-00.nc\n" - ] - } - ], - "source": [ - "# Initiate the loop\n", - "for file in forcing_files:\n", - " \n", - " # Progress\n", - " print('Starting on ' + file)\n", - " \n", - " # load the data\n", - " with xr.open_dataset(forcing_easymore_path / file) as dat:\n", - " \n", - " # --- Temperature lapse rates\n", - " # Find the lapse rates by matching the HRU order in the forcing file with that in 'lapse_values'\n", - " lapse_values_sorted = lapse_values['lapse_values'].loc[dat['hruId'].values]\n", - " \n", - " # Make a data array of size (nTime,nHru) \n", - " addThis = xr.DataArray(np.tile(lapse_values_sorted.values, (len(dat['time']),1)), dims=('time','hru')) \n", - " \n", - " # Get the air temperature variables\n", - " tmp_longname = dat['airtemp'].long_name\n", - " tmp_units = dat['airtemp'].units\n", - " \n", - " # Subtract lapse values from existing temperature data - this creates a new variable without meta-data\n", - " dat['airtemp'] = dat['airtemp'] + addThis\n", - " \n", - " # Add the attributes back in\n", - " dat.airtemp.attrs['long_name'] = tmp_longname\n", - " dat.airtemp.attrs['units'] = tmp_units\n", - " \n", - " # --- Time step specification \n", - " dat['data_step'] = data_step\n", - " dat.data_step.attrs['long_name'] = 'data step length in seconds'\n", - " dat.data_step.attrs['units'] = 's'\n", - " \n", - " # --- Save to file in new location\n", - " dat.to_netcdf(forcing_summa_path/file) " - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "dat = xr.open_dataset(forcing_summa_path/file)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:    (hru: 118, time: 744)\n",
-       "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2013-12-01 ... 2013-12-31T23:00:00\n",
-       "Dimensions without coordinates: hru\n",
-       "Data variables:\n",
-       "    latitude   (hru) float64 51.15 51.16 51.17 51.19 ... 51.67 51.65 51.67 51.66\n",
-       "    longitude  (hru) float64 51.15 51.16 51.17 51.19 ... 51.67 51.65 51.67 51.66\n",
-       "    hruId      (hru) float64 48.0 97.0 118.0 1.0 49.0 ... 46.0 95.0 47.0 96.0\n",
-       "    airpres    (time, hru) float32 7.936e+04 7.936e+04 ... 7.689e+04 7.676e+04\n",
-       "    LWRadAtm   (time, hru) float32 229.0 229.0 229.0 236.1 ... 234.1 238.2 237.4\n",
-       "    SWRadAtm   (time, hru) float32 8.874 8.874 8.874 8.258 ... 75.06 72.24 72.78\n",
-       "    pptrate    (time, hru) float32 9.535e-07 9.535e-07 ... 8.192e-06 7.459e-06\n",
-       "    airtemp    (time, hru) float64 267.0 269.7 271.6 266.7 ... 267.1 263.9 267.0\n",
-       "    spechum    (time, hru) float32 0.002819 0.002819 ... 0.002433 0.002399\n",
-       "    windspd    (time, hru) float32 1.702 1.702 1.702 1.522 ... 1.578 1.267 1.34\n",
-       "    data_step  int32 3600\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    Author:       The data were written by SUMMA public workflow scripts\n",
-       "    License:      Copernicus data use license: https://cds.climate.copernicus...\n",
-       "    History:      Created Thu May  6 13:27:59 2021\n",
-       "    Source:       Case: BowAtBanff; remapped by script from library of Sherva...
" - ], - "text/plain": [ - "\n", - "Dimensions: (hru: 118, time: 744)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2013-12-01 ... 2013-12-31T23:00:00\n", - "Dimensions without coordinates: hru\n", - "Data variables:\n", - " latitude (hru) float64 ...\n", - " longitude (hru) float64 ...\n", - " hruId (hru) float64 ...\n", - " airpres (time, hru) float32 ...\n", - " LWRadAtm (time, hru) float32 ...\n", - " SWRadAtm (time, hru) float32 ...\n", - " pptrate (time, hru) float32 ...\n", - " airtemp (time, hru) float64 ...\n", - " spechum (time, hru) float32 ...\n", - " windspd (time, hru) float32 ...\n", - " data_step int32 ...\n", - "Attributes:\n", - " Conventions: CF-1.6\n", - " Author: The data were written by SUMMA public workflow scripts\n", - " License: Copernicus data use license: https://cds.climate.copernicus...\n", - " History: Created Thu May 6 13:27:59 2021\n", - " Source: Case: BowAtBanff; remapped by script from library of Sherva..." - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dat" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "dat.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Check what we're doing" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# open old and new file\n", - "forcing_old = xr.open_dataset(forcing_easymore_path / file)\n", - "forcing_new = xr.open_dataset(forcing_summa_path / file)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare dataframes with temperature for a given time\n", - "time = 0\n", - "old_temp = {'airtemp': forcing_old['airtemp'].isel(time=time).values, 'hruId': forcing_old['hruId']}\n", - "new_temp = {'airtemp': forcing_new['airtemp'].isel(time=time).values, 'hruId': forcing_old['hruId']}" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Make dataframes\n", - "df_old_temp = pd.DataFrame(old_temp)\n", - "df_new_temp = pd.DataFrame(new_temp)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Set indices\n", - "df_old_temp = df_old_temp.set_index('hruId')\n", - "df_new_temp = df_new_temp.set_index('hruId')" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Merge with catchment shapefile\n", - "catchment['old_T'] = df_old_temp['airtemp']\n", - "catchment['new_T'] = df_new_temp['airtemp']" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# figure\n", - "fig, axs = plt.subplots(1,2,figsize=(20,10))\n", - "plt.tight_layout()\n", - "plt.rcParams.update({'font.size': 14})\n", - "\n", - "vmin = min(df_new_temp.min().values,df_old_temp.min().values)\n", - "vmax = max(df_new_temp.max().values,df_old_temp.max().values)\n", - "\n", - "# --- elevation\n", - "axId = 0\n", - "\n", - "# Data\n", - "catchment.plot(ax=axs[axId], column='old_T',edgecolor='k', legend=False, vmin=vmin, vmax=vmax)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "\n", - "# Custom colorbar\n", - "cax = fig.add_axes([0.46, 0.1, 0.02, 0.5])\n", - "sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "\n", - "\n", - "# Custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color='k', lw=1)]\n", - "label = ['SUMMA GRUs',\n", - " 'SUMMA HRUs']\n", - "axs[axId].legend(lines,label,loc='lower left');\n", - "\n", - "# Chart junk\n", - "axs[axId].set_title('(a) Old forcing [K]');\n", - "axs[axId].set_frame_on(False)\n", - "\n", - "\n", - "# --- lapse rates\n", - "axId = 1\n", - "\n", - "# Data\n", - "catchment.plot(ax=axs[axId], column='new_T',edgecolor='k', legend=False, vmin=vmin, vmax=vmax)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "\n", - "# Custom colorbar\n", - "cax = fig.add_axes([0.96, 0.1, 0.02, 0.5])\n", - "sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "\n", - "\n", - "# Custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color='k', lw=1)]\n", - "label = ['SUMMA GRUs',\n", - " 'SUMMA HRUs']\n", - "axs[axId].legend(lines,label,loc='lower left');\n", - "\n", - "# Chart junk\n", - "axs[axId].set_title('(b) Lapsed forcing [K]');\n", - "axs[axId].set_frame_on(False)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# close files\n", - "forcing_old.close()\n", - "forcing_new.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = forcing_summa_path\n", - "log_suffix = '_temperature_lapse_and_datastep.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '3_temperature_lapsing_and_datastep.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Applied temperature lapse rate to forcing data and added data_step variable.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/4b_remapping/2_forcing/3_temperature_lapsing_and_datastep.py b/4b_remapping/2_forcing/3_temperature_lapsing_and_datastep.py deleted file mode 100644 index 8b1903e..0000000 --- a/4b_remapping/2_forcing/3_temperature_lapsing_and_datastep.py +++ /dev/null @@ -1,215 +0,0 @@ -# Temperature lapsing and data_step -# The size discrepancy between MERIT basins and the typical coverage of ERA5 grid cells makes it appropriate to apply a temperature lapse rate. This script loops over existing basin-averaged forcing files and applies a lapse rate to the `airtemp` variable. Lapse arte is determined based on the average elevation difference between the basin shape and the ERA5 grid cell(s) that cover the basin. -# -# In addition, this script adds the `data_step` variable to each forcing file, which SUMMA needs to know the time resolution of the forcing inputs. -# -# Environmental Lapse Rate -# The temperature lapse rate is assumed to have a constant value of `0.0065` `[K m-1]` (Wallace & Hobbs, 2006, p. 421). -# -# Wallace, J., and P. Hobbs (2006), Atmospheric Science: An Introductory Survey, 483 pp., Academic Press, Burlington, Mass - -# modules -import os -import numpy as np -import xarray as xr -import pandas as pd -from pathlib import Path -from shutil import copyfile -from datetime import datetime - - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Find location of intersection file -# Intersected shapefile path. Name is set by EASYMORE as [prefix]_intersected_shapefile.shp -intersect_path = read_from_control(controlFolder/controlFile,'intersect_forcing_path') - -# Specify default path if needed -if intersect_path == 'default': - intersect_path = make_default_path('shapefiles/catchment_intersection/with_forcing') # outputs a Path() -else: - intersect_path = Path(intersect_path) # make sure a user-specified path is a Path() - -# Make the file name -domain = read_from_control(controlFolder/controlFile,'domain_name') -intersect_name = domain + '_intersected_shapefile.csv' # can also be .shp, but using the .csv is easier on memory - - -# --- Find where the EASYMORE-prepared forcing files are -# Forcing files as produced by EASYMORE -forcing_easymore_path = read_from_control(controlFolder/controlFile,'forcing_basin_avg_path') - -# Specify default path if needed -if forcing_easymore_path == 'default': - forcing_easymore_path = make_default_path('forcing/3_basin_averaged_data') # outputs a Path() -else: - forcing_easymore_path = Path(forcing_easymore_path) # make sure a user-specified path is a Path() - -# Find the files -_,_,forcing_files = next(os.walk(forcing_easymore_path)) -forcing_files.sort() # technically doesn't matter but w/e - -# --- Find the time step size of the forcing data -# Value in control file -data_step = read_from_control(controlFolder/controlFile,'forcing_time_step_size') - -# Convert to int -data_step = int(data_step) - - -# --- Find where the final forcing needs to go -# Location for SUMMA-ready files -forcing_summa_path = read_from_control(controlFolder/controlFile,'forcing_summa_path') - -# Specify default path if needed -if forcing_summa_path == 'default': - forcing_summa_path = make_default_path('forcing/4_SUMMA_input') # outputs a Path() -else: - forcing_summa_path = Path(forcing_summa_path) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -forcing_summa_path.mkdir(parents=True, exist_ok=True) - - -# --- Find the area-weighted lapse value for each basin -# Load the intersection file -topo_data = pd.read_csv(intersect_path/intersect_name) - -# Find hruId name in user's shapefile -hru_ID_name = read_from_control(controlFolder/controlFile,'catchment_shp_hruid') -gru_ID_name = read_from_control(controlFolder/controlFile,'catchment_shp_gruid') - -# Specify the column names -# Note that column names are truncated at 10 characters in the ESRI shapefile, but NOT in the .csv we use here -gru_ID = 'S_1_' + gru_ID_name # EASYMORE prefix + user's hruId name -hru_ID = 'S_1_' + hru_ID_name # EASYMORE prefix + user's hruId name -forcing_ID = 'S_2_ID' # fixed name assigned by EASYMORE -catchment_elev = 'S_1_elev_mean' # EASYMORE prefix + name used in catchment+DEM intersection step -forcing_elev = 'S_2_elev_m' # EASYMORE prefix + name used in ERA5 shapefile generation -weights = 'weight' # EASYMORE feature - -# Define the lapse rate -lapse_rate = 0.0065 # [K m-1] - -# Calculate weighted lapse values for each HRU -# Note that these lapse values need to be ADDED to ERA5 temperature data -topo_data['lapse_values'] = topo_data[weights] * lapse_rate * (topo_data[forcing_elev] - topo_data[catchment_elev]) # [K] - -# Find the total lapse value per basin; i.e. sum the individual contributions of each HRU+ERA5-grid overlapping part -# Account for the special case where gru_ID and hru_ID share the same column and thus name -if gru_ID == hru_ID: - lapse_values = topo_data.groupby([hru_ID]).lapse_values.sum().reset_index() # Sort by HRU -else: - lapse_values = topo_data.groupby([gru_ID,hru_ID]).lapse_values.sum().reset_index() # sort by GRU first and HRU second - -# Sort and set hruID as the index variable -lapse_values = lapse_values.sort_values(hru_ID).set_index(hru_ID) - -# Close the main file -del topo_data # hopefully this saves some RAM but this is apparently not so straightforward in Python.. Can't hurt - - -# --- Loop over forcing files; apply lapse rates and add data-step variable -# Initiate the loop -for file in forcing_files: - - # Progress - print('Starting on ' + file) - - # load the data - with xr.open_dataset(forcing_easymore_path / file) as dat: - - # --- Temperature lapse rates - # Find the lapse rates by matching the HRU order in the forcing file with that in 'lapse_values' - lapse_values_sorted = lapse_values['lapse_values'].loc[dat['hruId'].values] - - # Make a data array of size (nTime,nHru) - addThis = xr.DataArray(np.tile(lapse_values_sorted.values, (len(dat['time']),1)), dims=('time','hru')) - - # Get the air temperature variables - tmp_longname = dat['airtemp'].long_name - tmp_units = dat['airtemp'].units - - # Subtract lapse values from existing temperature data - dat['airtemp'] = dat['airtemp'] + addThis - - # Add the attributes back in - dat.airtemp.attrs['long_name'] = tmp_longname - dat.airtemp.attrs['units'] = tmp_units - - # --- Time step specification - dat['data_step'] = data_step - dat.data_step.attrs['long_name'] = 'data step length in seconds' - dat.data_step.attrs['units'] = 's' - - # --- Save to file in new location - dat.to_netcdf(forcing_summa_path/file) - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = forcing_summa_path -log_suffix = '_temperature_lapse_and_datastep.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = '3_temperature_lapsing_and_datastep.py' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Applied temperature lapse rate to forcing data and added data_step variable.'] - for txt in lines: - file.write(txt) - diff --git a/4b_remapping/2_forcing/README.md b/4b_remapping/2_forcing/README.md deleted file mode 100644 index 797480d..0000000 --- a/4b_remapping/2_forcing/README.md +++ /dev/null @@ -1,44 +0,0 @@ -# Create SUMMA-ready forcing files - -The scripts in this folder accomplish several goals: -- SUMMA expects its forcing inputs with dimensions `(hru,time)` meaning that we need to convert the downloaded ERA5 data from it's source dimensions `(lat,lon,time)` into SUMMA's required format. -- The forcing data is given at grid points, which we initially assume are representative values for a grid cell that extends 0.125 degrees in the cardinal directions around the point. We have created a shapefile that outlines this grid as part of the forcing pre-processing. In this setup, SUMMA is run with catchments (and not over a grid), so we need to map the gridded forcing data to the catchments. -- In our setup, the ERA5 grid cells are fairly large compared to the spatial extent of our catchment delineation. It is therefore unlikely that the raw ERA5 data is equally representative for all catchments. To somewhat account for the impact of topography on meteorological conditions, we apply a lapse rate of `0.0065` `[K m-1]` (Wallace and Hobbs, 2006) to the temperature data. -Some further details are provided below. - - -## Remapping of forcing data -We need to find how the ERA5 gridded forcing maps onto the catchment to create area-weighted forcing as SUMMA input. This involves two steps: -1. Intersect the ERA5 shape with the user's catchment shape to find the overlap between a given (sub) catchment and the forcing grid; -2. Create an area-weighted, catchment-averaged forcing time series. - -The EASYMORE package (https://github.com/ShervanGharari/EASYMORE) provides the necessary functionality to do this. EASYMORE performs the GIS step (1, shapefile intersection) and the area-weighting step (2, create new forcing `.nc` files) as part of a single `nc_remapper()` call. To allow for parallelization, EASYMORE can save the output from the GIS step into a restart `.csv` file which can be used to skip the GIS step. This allows (manual) parallelization of area-weighted forcing file generation after the GIS procedures have been run once. The workflow here is thus: -1. [Script 1] Call `nc_remapper()` with ERA5 and user's shapefile, and one ERA5 forcing `.nc` file; - - EASYMORE performs intersection of both shapefiles; - - EASYMORE saves the outcomes of this intersection to a `.csv` file; - - EASYMORE creates an area-weighted forcing file from a single provided ERA5 source `.nc` file -2. [Script 2] Call `nc_remapper()` with intersection `.csv` file and all other forcing `.nc` files. - -Parallelization of step 2 (2nd `nc_remapper()` call) requires an external loop that sends (batches of) the remaining ERA5 raw forcing files to individual processors. As with other steps that may be parallelized, creating code that does this is left to the user. - - -## Temperature lapse rate -The size discrepancy between MERIT basins and the typical coverage of ERA5 grid cells makes it appropriate to apply a temperature lapse rate. Script 3 loops over existing basin-averaged forcing files and applies a lapse rate to the `airtemp` variable. Lapse rate is determined based on the average elevation difference between the basin shape and the ERA5 grid cell(s) that cover the basin. The lapse rate is set to `0.0065` `[K m-1]` (Wallace and Hobbs, 2006) as a global average value. - - -## Assumptions not included in `control_actve.txt` -The applied lapse rate is hard-coded in script 3. This is a globally average value that the script applies based on elevation differences only. Both the choice of value and methodology can be improved for local regions. We refer the user to the discussion in Wallace and Hobbs (2006). - - -## References -Wallace, J., and P. Hobbs (2006), Atmospheric Science: An Introductory Survey, 483 pp., Academic Press, Burlington, Mass - - -## Control file settings -This section lists all the settings in `control_active.txt` that the code in this folder uses. -- **intersect_dem_path, intersect_dem_name**: location and name of the file that contains the intersection between catchment shape and DEM. -- **forcing_shape_path, forcing_shape_name**: location and name of the file that contains the forcing shapefile. -- **intersect_forcing_path**: file path where the intersection between catchment and forcing shapefiles needs to go and can be found. -- **forcing_merged_path, forcing_easymore_path, forcing_basin_avg_path, forcing_summa_path**: file paths where the merged forcing can be found and where the temporary EASYMORE files, the HRU-averaged forcing files, and the final SUMMA-ready input files need to go. -- **forcing_time_step_size**: time step size of forcing data in [s]. -- **catchment_shp_hruid, catchment_shp_gruid**: names of columns in the catchment shapefiles. diff --git a/4b_remapping/README.md b/4b_remapping/README.md deleted file mode 100644 index 7f65987..0000000 --- a/4b_remapping/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# Mapping of preprocessed data onto model elements -Code to map preprocessed geospatial data and forcing data onto model elements. Remapping of forcing also includes the application of temperature lapse rates. This requires information on the forcing data source elevation (geopotential data, prepared during forcing preprocessing) and information on the elevation of each model element. Model element elevation is obtained from mapping preprocessed DEM data onto model elements. Mapping of the geospatial data thus needs to happen before the forcing is remapped onto model elements. - -- `1_topo` includes scripts to map the prepared geospatial data (DEM, soil classes, vegetation types) to Hydrologic Response Units (HRUs); -- `2_forcing` includes scripts to map ERA5 gridded forcing to HRUs, apply temperature lapse rates and finalize the forcing `.nc` files for use by SUMMA. Preparing the forcing files for use by SUMMA is a minor step (it involves the addition of a single scalar variable to each forcing file that contains the time step size of the forcing data) and included here for efficiency. - -## Control file settings -Specified in each sub-folder. diff --git a/5_model_input/SUMMA/1a_copy_base_settings/1_copy_base_settings.ipynb b/5_model_input/SUMMA/1a_copy_base_settings/1_copy_base_settings.ipynb deleted file mode 100644 index eb38b16..0000000 --- a/5_model_input/SUMMA/1a_copy_base_settings/1_copy_base_settings.ipynb +++ /dev/null @@ -1,274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Copy base settings\n", - "Copies the base settings into the SUMMA settings folder." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define where the base settings are" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Base settings\n", - "base_settings_path = Path('../0_base_settings')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the settings need to go" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Settings path \n", - "settings_path = read_from_control(controlFolder/controlFile,'settings_summa_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if settings_path == 'default':\n", - " settings_path = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " settings_path = Path(settings_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "settings_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Copy the settings" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Loop over all files and copy\n", - "for file in os.listdir(base_settings_path): \n", - " \n", - " # skip readme \n", - " # some users have reported that a notebook checkpoint folder mysteriously appears - skip that too\n", - " if 'readme.md' not in file.lower() and '.ipynb_checkpoints' not in file.lower():\n", - " copyfile(base_settings_path/file, settings_path/file);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = settings_path\n", - "log_suffix = '_copy_base_settings.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_copy_base_settings.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Copied the SUMMA base settings.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/SUMMA/1b_file_manager/1_create_file_manager.ipynb b/5_model_input/SUMMA/1b_file_manager/1_create_file_manager.ipynb deleted file mode 100644 index cc5dde8..0000000 --- a/5_model_input/SUMMA/1b_file_manager/1_create_file_manager.ipynb +++ /dev/null @@ -1,388 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create file manager\n", - "Populates a text file with the required inputs for a SUMMA run." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the file manager needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing file list path & name\n", - "filemanager_path = read_from_control(controlFolder/controlFile,'settings_summa_path')\n", - "filemanager_name = read_from_control(controlFolder/controlFile,'settings_summa_filemanager')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if filemanager_path == 'default':\n", - " filemanager_path = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " filemanager_path = Path(filemanager_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "filemanager_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Read the required information from control file" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the experiment ID\n", - "experiment_id = read_from_control(controlFolder/controlFile,'experiment_id')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Simulation times\n", - "sim_start = read_from_control(controlFolder/controlFile,'experiment_time_start')\n", - "sim_end = read_from_control(controlFolder/controlFile,'experiment_time_end')\n", - "\n", - "# Define default times if needed\n", - "if sim_start == 'default':\n", - " raw_time = read_from_control(controlFolder/controlFile,'forcing_raw_time') # downloaded forcing (years)\n", - " year_start,_ = raw_time.split(',') # split into separate variables\n", - " sim_start = year_start + '-01-01 00:00' # construct the filemanager field\n", - "\n", - "if sim_end == 'default':\n", - " raw_time = read_from_control(controlFolder/controlFile,'forcing_raw_time') # downloaded forcing (years)\n", - " _,year_end = raw_time.split(',') # split into separate variables\n", - " sim_end = year_end + '-12-31 23:00' # construct the filemanager field" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Paths - settings folder\n", - "path_to_settings = read_from_control(controlFolder/controlFile,'settings_summa_path')\n", - "\n", - "# Specify default path if needed\n", - "if path_to_settings == 'default':\n", - " path_to_settings = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " path_to_settings = Path(path_to_settings) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# Paths - forcing folder\n", - "path_to_forcing = read_from_control(controlFolder/controlFile,'forcing_summa_path')\n", - "\n", - "# Specify default path if needed\n", - "if path_to_forcing == 'default':\n", - " path_to_forcing = make_default_path('forcing/4_SUMMA_input') # outputs a Path()\n", - "else:\n", - " path_to_forcing = Path(path_to_forcing) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# Paths - output folder\n", - "path_to_output = read_from_control(controlFolder/controlFile,'experiment_output_summa')\n", - "\n", - "# Specify default path if needed\n", - "if path_to_output == 'default': \n", - " path_to_output = make_default_path('simulations/' + experiment_id + '/SUMMA') # outputs a Path()\n", - "else:\n", - " path_to_output = Path(path_to_output) # make sure a user-specified path is a Path()\n", - " \n", - "# Make the folder if it doesn't exist\n", - "path_to_output.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# File names of setting files\n", - "initial_conditions_nc = read_from_control(controlFolder/controlFile,'settings_summa_coldstate')\n", - "attributes_nc = read_from_control(controlFolder/controlFile,'settings_summa_attributes')\n", - "trial_parameters_nc = read_from_control(controlFolder/controlFile,'settings_summa_trialParams')\n", - "forcing_file_list_txt = read_from_control(controlFolder/controlFile,'settings_summa_forcing_list')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make the file" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the file list\n", - "with open(filemanager_path / filemanager_name, 'w') as fm:\n", - " \n", - " # Header\n", - " fm.write(\"controlVersion 'SUMMA_FILE_MANAGER_V3.0.0' ! file manager version \\n\")\n", - " \n", - " # Simulation times\n", - " fm.write(\"simStartTime '{}' ! \\n\".format(sim_start))\n", - " fm.write(\"simEndTime '{}' ! \\n\".format(sim_end))\n", - " fm.write(\"tmZoneInfo 'utcTime' ! \\n\")\n", - " \n", - " # Prefix for SUMMA outputs\n", - " fm.write(\"outFilePrefix '{}' ! \\n\".format(experiment_id))\n", - " \n", - " # Paths\n", - " fm.write(\"settingsPath '{}/' ! \\n\".format(path_to_settings))\n", - " fm.write(\"forcingPath '{}/' ! \\n\".format(path_to_forcing))\n", - " fm.write(\"outputPath '{}/' ! \\n\".format(path_to_output))\n", - " \n", - " # Input file names\n", - " fm.write(\"initConditionFile '{}' ! Relative to settingsPath \\n\".format(initial_conditions_nc))\n", - " fm.write(\"attributeFile '{}' ! Relative to settingsPath \\n\".format(attributes_nc))\n", - " fm.write(\"trialParamFile '{}' ! Relative to settingsPath \\n\".format(trial_parameters_nc))\n", - " fm.write(\"forcingListFile '{}' ! Relative to settingsPath \\n\".format(forcing_file_list_txt))\n", - " \n", - " # Base files (not domain-dependent)\n", - " fm.write(\"decisionsFile 'modelDecisions.txt' ! Relative to settingsPath \\n\")\n", - " fm.write(\"outputControlFile 'outputControl.txt' ! Relative to settingsPath \\n\")\n", - " fm.write(\"globalHruParamFile 'localParamInfo.txt' ! Relative to settingsPath \\n\")\n", - " fm.write(\"globalGruParamFile 'basinParamInfo.txt' ! Relative to settingsPatho \\n\")\n", - " fm.write(\"vegTableFile 'TBL_VEGPARM.TBL' ! Relative to settingsPath \\n\")\n", - " fm.write(\"soilTableFile 'TBL_SOILPARM.TBL' ! Relative to settingsPath \\n\")\n", - " fm.write(\"generalTableFile 'TBL_GENPARM.TBL' ! Relative to settingsPath \\n\")\n", - " fm.write(\"noahmpTableFile 'TBL_MPTABLE.TBL' ! Relative to settingsPath \\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = filemanager_path\n", - "log_suffix = '_make_file_manager.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_create_file_manager.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Generated file manager.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/SUMMA/1b_file_manager/1_create_file_manager.py b/5_model_input/SUMMA/1b_file_manager/1_create_file_manager.py index e959943..7cea1b5 100644 --- a/5_model_input/SUMMA/1b_file_manager/1_create_file_manager.py +++ b/5_model_input/SUMMA/1b_file_manager/1_create_file_manager.py @@ -137,7 +137,7 @@ def make_default_path(suffix): # Simulation times fm.write("simStartTime '{}' ! \n".format(sim_start)) fm.write("simEndTime '{}' ! \n".format(sim_end)) - fm.write("tmZoneInfo 'utcTime' ! \n") + fm.write("tmZoneInfo 'localTime' ! \n") # Prefix for SUMMA outputs fm.write("outFilePrefix '{}' ! \n".format(experiment_id)) diff --git a/5_model_input/SUMMA/1c_forcing_file_list/1_create_forcing_file_list.ipynb b/5_model_input/SUMMA/1c_forcing_file_list/1_create_forcing_file_list.ipynb deleted file mode 100644 index dd7523b..0000000 --- a/5_model_input/SUMMA/1c_forcing_file_list/1_create_forcing_file_list.ipynb +++ /dev/null @@ -1,305 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create forcing file list\n", - "Populates a text file with the names of the forcing files used as SUMMA input." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find forcing location" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing path\n", - "forcing_path = read_from_control(controlFolder/controlFile,'forcing_summa_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_path == 'default':\n", - " forcing_path = make_default_path('forcing/4_SUMMA_input') # outputs a Path()\n", - "else:\n", - " forcing_path = Path(forcing_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where forcing file list needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing file list path & name\n", - "file_list_path = read_from_control(controlFolder/controlFile,'settings_summa_path')\n", - "file_list_name = read_from_control(controlFolder/controlFile,'settings_summa_forcing_list')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if file_list_path == 'default':\n", - " file_list_path = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " file_list_path = Path(file_list_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "file_list_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make the file" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Find a list of forcing files\n", - "_,_,forcing_files = next(os.walk(forcing_path))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort this list\n", - "forcing_files.sort()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the file list\n", - "with open(file_list_path / file_list_name, 'w') as f:\n", - " for file in forcing_files:\n", - " f.write(str(file) + \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = file_list_path\n", - "log_suffix = '_make_forcing_file_list.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_create_forcing_file_list.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Generated forcing file list.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/SUMMA/1c_forcing_file_list/1_create_forcing_file_list.py b/5_model_input/SUMMA/1c_forcing_file_list/1_create_forcing_file_list.py index 30e3b2b..3289a8c 100644 --- a/5_model_input/SUMMA/1c_forcing_file_list/1_create_forcing_file_list.py +++ b/5_model_input/SUMMA/1c_forcing_file_list/1_create_forcing_file_list.py @@ -77,16 +77,22 @@ def make_default_path(suffix): # --- Make the file -# Find a list of forcing files -_,_,forcing_files = next(os.walk(forcing_path)) +domainName = read_from_control(controlFolder/controlFile,'domain_name') -# Sort this list -forcing_files.sort() +# Spatialisation +spa = 'distributed' + +# Forcing data product +forcing_name = read_from_control(controlFolder/controlFile,'forcing_data_name') + +# Forcing file +forcing_file = domainName + "_" + forcing_name + "_" + spa +".nc" +if forcing_name == "em-earth": + forcing_file = forcing_file.replace(forcing_name, "em_earth") # Create the file list with open(file_list_path / file_list_name, 'w') as f: - for file in forcing_files: - f.write(str(file) + "\n") + f.write(forcing_file) # --- Code provenance diff --git a/5_model_input/SUMMA/1d_initial_conditions/1_create_coldState.ipynb b/5_model_input/SUMMA/1d_initial_conditions/1_create_coldState.ipynb deleted file mode 100644 index 60f79c9..0000000 --- a/5_model_input/SUMMA/1d_initial_conditions/1_create_coldState.ipynb +++ /dev/null @@ -1,481 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create coldstate.nc\n", - "Creates empty `coldstate.nc` file for initial SUMMA runs. This can be replaced by a more elegant initial conditions file, such as generated by SUMMA's `-r y` (create a restart file, yearly intervals) command line option. \n", - "\n", - "## Note on HRU order\n", - "HRU order must be the same in forcing, attributes, initial conditions and trial parameter files. Order will be taken from forcing files to ensure consistency.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import numpy as np\n", - "import xarray as xr\n", - "import netCDF4 as nc4\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find forcing location and an example file" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing path\n", - "forcing_path = read_from_control(controlFolder/controlFile,'forcing_summa_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_path == 'default':\n", - " forcing_path = make_default_path('forcing/4_SUMMA_input') # outputs a Path()\n", - "else:\n", - " forcing_path = Path(forcing_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Find a list of forcing files\n", - "_,_,forcing_files = next(os.walk(forcing_path))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Select a random file as a template for hruId order\n", - "forcing_name = forcing_files[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the cold state file needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Cold state path & name\n", - "coldstate_path = read_from_control(controlFolder/controlFile,'settings_summa_path')\n", - "coldstate_name = read_from_control(controlFolder/controlFile,'settings_summa_coldstate')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if coldstate_path == 'default':\n", - " coldstate_path = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " coldstate_path = Path(coldstate_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "coldstate_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find order and number of HRUs in forcing file" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the forcing file\n", - "forc = xr.open_dataset(forcing_path/forcing_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the sorting order from the forcing file\n", - "forcing_hruIds = forc['hruId'].values.astype(int) # 'hruId' is prescribed by SUMMA so this variable must exist" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Number of HRUs\n", - "num_hru = len(forcing_hruIds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define the dimensions and fill values" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the dimensions\n", - "nSoil = 8 # number of soil layers\n", - "nSnow = 0 # assume no snow layers currently exist\n", - "midSoil = 8 # midpoint of soil layer\n", - "midToto = 8 # total number of midpoints for snow+soil layers\n", - "ifcToto = midToto+1 # total number of layer boundaries\n", - "scalarv = 1 # auxiliary dimension variable" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "# Time step size\n", - "dt_init = read_from_control(controlFolder/controlFile,'forcing_time_step_size') " - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "# Layer variables\n", - "mLayerDepth = np.asarray([0.025, 0.075, 0.15, 0.25, 0.5, 0.5, 1, 1.5])\n", - "iLayerHeight = np.asarray([0, 0.025, 0.1, 0.25, 0.5, 1, 1.5, 2.5, 4])" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [], - "source": [ - "# States\n", - "scalarCanopyIce = 0 # Current ice storage in the canopy\n", - "scalarCanopyLiq = 0 # Current liquid water storage in the canopy\n", - "scalarSnowDepth = 0 # Current snow depth\n", - "scalarSWE = 0 # Current snow water equivalent\n", - "scalarSfcMeltPond = 0 # Current ponded melt water\n", - "scalarAquiferStorage = 1.0 # Current aquifer storage\n", - "scalarSnowAlbedo = 0 # Snow albedo\n", - "scalarCanairTemp = 283.16 # Current temperature in the canopy airspace\n", - "scalarCanopyTemp = 283.16 # Current temperature of the canopy \n", - "mLayerTemp = 283.16 # Current temperature of each layer; assumed that all layers are identical\n", - "mLayerVolFracIce = 0 # Current ice storage in each layer; assumed that all layers are identical\n", - "mLayerVolFracLiq = 0.2 # Current liquid water storage in each layer; assumed that all layers are identical\n", - "mLayerMatricHead = -1.0 # Current matric head in each layer; assumed that all layers are identical" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make the initial conditions file" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ - "# auxiliary function used by the block that creates the .nc file\n", - "def create_and_fill_nc_var(nc, newVarName, newVarVal, fillDim1, fillDim2, newVarDim, newVarType, fillVal):\n", - " \n", - " # Make the fill value\n", - " if newVarName == 'iLayerHeight' or newVarName == 'mLayerDepth':\n", - " fillWithThis = np.full((fillDim1,fillDim2), newVarVal).transpose()\n", - " else:\n", - " fillWithThis = np.full((fillDim1,fillDim2), newVarVal)\n", - " \n", - " # Make the variable in the file\n", - " ncvar = nc.createVariable(newVarName, newVarType, (newVarDim, 'hru',),fill_value=fillVal) \n", - " \n", - " # Fill the variable\n", - " ncvar[:] = fillWithThis\n", - " \n", - " return " - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the empty trial params file\n", - "with nc4.Dataset(coldstate_path/coldstate_name, \"w\", format=\"NETCDF4\") as cs:\n", - " \n", - " # === Some general attributes\n", - " now = datetime.now()\n", - " cs.setncattr('Author', \"Created by SUMMA workflow scripts\")\n", - " cs.setncattr('History','Created ' + now.strftime('%Y/%m/%d %H:%M:%S'))\n", - " cs.setncattr('Purpose','Create a cold state .nc file for initial SUMMA runs')\n", - " \n", - " # === Define the dimensions \n", - " cs.createDimension('hru',num_hru)\n", - " cs.createDimension('midSoil',midSoil)\n", - " cs.createDimension('midToto',midToto)\n", - " cs.createDimension('ifcToto',ifcToto)\n", - " cs.createDimension('scalarv',scalarv)\n", - " \n", - " # === Variables ===\n", - " var = 'hruId'\n", - " cs.createVariable(var, 'i4', 'hru', fill_value = False)\n", - " cs[var].setncattr('units', '-')\n", - " cs[var].setncattr('long_name', 'Index of hydrological response unit (HRU)')\n", - " cs[var][:] = forcing_hruIds\n", - " \n", - " # time step size\n", - " create_and_fill_nc_var(cs, 'dt_init', dt_init, 1, num_hru, 'scalarv', 'f8', False)\n", - " \n", - " # Number of layers\n", - " create_and_fill_nc_var(cs, 'nSoil', nSoil, 1, num_hru, 'scalarv', 'i4', False)\n", - " create_and_fill_nc_var(cs, 'nSnow', nSnow, 1, num_hru, 'scalarv', 'i4', False)\n", - " \n", - " # States\n", - " create_and_fill_nc_var(cs, 'scalarCanopyIce', scalarCanopyIce, 1, num_hru, 'scalarv', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'scalarCanopyLiq', scalarCanopyLiq, 1, num_hru, 'scalarv', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'scalarSnowDepth', scalarSnowDepth, 1, num_hru, 'scalarv', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'scalarSWE', scalarSWE, 1, num_hru, 'scalarv', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'scalarSfcMeltPond', scalarSfcMeltPond, 1, num_hru, 'scalarv', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'scalarAquiferStorage', scalarAquiferStorage, 1, num_hru, 'scalarv', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'scalarSnowAlbedo', scalarSnowAlbedo, 1, num_hru, 'scalarv', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'scalarCanairTemp', scalarCanairTemp, 1, num_hru, 'scalarv', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'scalarCanopyTemp', scalarCanopyTemp, 1, num_hru, 'scalarv', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'mLayerTemp', mLayerTemp, midToto, num_hru, 'midToto', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'mLayerVolFracIce', mLayerVolFracIce, midToto, num_hru, 'midToto', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'mLayerVolFracLiq', mLayerVolFracLiq, midToto, num_hru, 'midToto', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'mLayerMatricHead', mLayerMatricHead, midSoil, num_hru, 'midSoil', 'f8', False)\n", - " \n", - " # layer dimensions\n", - " create_and_fill_nc_var(cs, 'iLayerHeight', iLayerHeight, num_hru, ifcToto, 'ifcToto', 'f8', False)\n", - " create_and_fill_nc_var(cs, 'mLayerDepth', mLayerDepth, num_hru, midToto, 'midToto', 'f8', False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = coldstate_path\n", - "log_suffix = '_make_initial_conditions_file.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_create_coldState.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Generated initial condition .nc file.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/SUMMA/1d_initial_conditions/1_create_coldState.py b/5_model_input/SUMMA/1d_initial_conditions/1_create_coldState.py index 87ffe3b..d3691bf 100644 --- a/5_model_input/SUMMA/1d_initial_conditions/1_create_coldState.py +++ b/5_model_input/SUMMA/1d_initial_conditions/1_create_coldState.py @@ -57,6 +57,14 @@ def make_default_path(suffix): # --- Find forcing location and an example file +domainName = read_from_control(controlFolder/controlFile,'domain_name') + +# Spatialisation +spa = 'distributed' + +# Forcing data product +forcing_name = read_from_control(controlFolder/controlFile,'forcing_data_name') + # Forcing path forcing_path = read_from_control(controlFolder/controlFile,'forcing_summa_path') @@ -66,11 +74,12 @@ def make_default_path(suffix): else: forcing_path = Path(forcing_path) # make sure a user-specified path is a Path() -# Find a list of forcing files -_,_,forcing_files = next(os.walk(forcing_path)) -# Select a random file as a template for hruId order -forcing_name = forcing_files[0] +# Forcing file +forcing_file = domainName + "_" + forcing_name + "_" + spa +".nc" +if forcing_name == "em-earth": + forcing_file = forcing_file.replace(forcing_name, "em_earth") + # --- Find where the cold state file needs to go @@ -90,7 +99,7 @@ def make_default_path(suffix): # --- Find order and number of HRUs in forcing file # Open the forcing file -forc = xr.open_dataset(forcing_path/forcing_name) +forc = xr.open_dataset(forcing_path/forcing_file) # Get the sorting order from the forcing file forcing_hruIds = forc['hruId'].values.astype(int) # 'hruId' is prescribed by SUMMA so this variable must exist diff --git a/5_model_input/SUMMA/1e_trial_parameters/1_create_trialParams.ipynb b/5_model_input/SUMMA/1e_trial_parameters/1_create_trialParams.ipynb deleted file mode 100644 index 3132c78..0000000 --- a/5_model_input/SUMMA/1e_trial_parameters/1_create_trialParams.ipynb +++ /dev/null @@ -1,411 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create trialParams.nc\n", - "Creates empty `trialParams.nc` file for SUMMA runs. This file will initially be empty, but can later be populated with parameter values the user whishes to test.\n", - "\n", - "## Note on HRU order\n", - "HRU order must be the same in forcing, attributes, initial conditions and trial parameter files. Order will be taken from forcing files to ensure consistency." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import numpy as np\n", - "import xarray as xr\n", - "import netCDF4 as nc4\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find forcing location and an example file" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing path\n", - "forcing_path = read_from_control(controlFolder/controlFile,'forcing_summa_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_path == 'default':\n", - " forcing_path = make_default_path('forcing/4_SUMMA_input') # outputs a Path()\n", - "else:\n", - " forcing_path = Path(forcing_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Find a list of forcing files\n", - "_,_,forcing_files = next(os.walk(forcing_path))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Select a random file as a template for hruId order\n", - "forcing_name = forcing_files[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the trial parameter file needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Trial parameter path & name\n", - "parameter_path = read_from_control(controlFolder/controlFile,'settings_summa_path')\n", - "parameter_name = read_from_control(controlFolder/controlFile,'settings_summa_trialParams')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if parameter_path == 'default':\n", - " parameter_path = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " parameter_path = Path(parameter_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "parameter_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find order and number of HRUs in forcing file" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the forcing file\n", - "forc = xr.open_dataset(forcing_path/forcing_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the sorting order from the forcing file\n", - "forcing_hruIds = forc['hruId'].values.astype(int) # 'hruId' is prescribed by SUMMA so this variable must exist" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Number of HRUs\n", - "num_hru = len(forcing_hruIds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Read any other trial parameters that need to be specified" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "num_tp = int( read_from_control(controlFolder/controlFile,'settings_summa_trialParam_n') )" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "# read the names and values of trial parameters to specify\n", - "all_tp = {}\n", - "for ii in range(0,num_tp):\n", - " \n", - " # Get the values\n", - " par_and_val = read_from_control(controlFolder/controlFile,f'settings_summa_trialParam_{ii+1}')\n", - " \n", - " # Split into parameter and value\n", - " arr = par_and_val.split(',')\n", - " \n", - " # Convert value(s) into float\n", - " if len(arr) > 2:\n", - " # Store all values as an array of floats \n", - " val = np.array(arr[1:], dtype=np.float32)\n", - " else: \n", - " # Convert the single value to a float\n", - " val = float( arr[1] )\n", - " \n", - " # Store in dictionary\n", - " all_tp[arr[0]] = val" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make the trial parameter file" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the empty trial params file\n", - "with nc4.Dataset(parameter_path/parameter_name, \"w\", format=\"NETCDF4\") as tp:\n", - " \n", - " # === Some general attributes\n", - " now = datetime.now()\n", - " tp.setncattr('Author', \"Created by SUMMA workflow scripts\")\n", - " tp.setncattr('History','Created ' + now.strftime('%Y/%m/%d %H:%M:%S'))\n", - " tp.setncattr('Purpose','Create a trial parameter .nc file for initial SUMMA runs')\n", - " \n", - " # === Define the dimensions \n", - " tp.createDimension('hru',num_hru)\n", - " \n", - " # === Variables ===\n", - " var = 'hruId'\n", - " tp.createVariable(var, 'i4', 'hru', fill_value = False)\n", - " tp[var].setncattr('units', '-')\n", - " tp[var].setncattr('long_name', 'Index of hydrological response unit (HRU)')\n", - " tp[var][:] = forcing_hruIds\n", - " \n", - " # Loop over any specified trial parameters and store in file\n", - " for var,val in all_tp.items():\n", - " tp.createVariable(var, 'f8', 'hru', fill_value = False)\n", - " tp[var][:] = val " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = parameter_path\n", - "log_suffix = '_make_trial_parameter_file.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_create_trialParams.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Generated trial parameter .nc file.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/SUMMA/1e_trial_parameters/1_create_trialParams.py b/5_model_input/SUMMA/1e_trial_parameters/1_create_trialParams.py index 2a17b62..90dcf4d 100644 --- a/5_model_input/SUMMA/1e_trial_parameters/1_create_trialParams.py +++ b/5_model_input/SUMMA/1e_trial_parameters/1_create_trialParams.py @@ -57,6 +57,14 @@ def make_default_path(suffix): # --- Find forcing location and an example file +domainName = read_from_control(controlFolder/controlFile,'domain_name') + +# Spatialisation +spa = 'distributed' + +# Forcing data product +forcing_name = read_from_control(controlFolder/controlFile,'forcing_data_name') + # Forcing path forcing_path = read_from_control(controlFolder/controlFile,'forcing_summa_path') @@ -66,12 +74,11 @@ def make_default_path(suffix): else: forcing_path = Path(forcing_path) # make sure a user-specified path is a Path() -# Find a list of forcing files -_,_,forcing_files = next(os.walk(forcing_path)) - -# Select a random file as a template for hruId order -forcing_name = forcing_files[0] +# Forcing file +forcing_file = domainName + "_" + forcing_name + "_" + spa +".nc" +if forcing_name == "em-earth": + forcing_file = forcing_file.replace(forcing_name, "em_earth") # --- Find where the trial parameter file needs to go # Trial parameter path & name @@ -90,7 +97,7 @@ def make_default_path(suffix): # --- Find order and number of HRUs in forcing file # Open the forcing file -forc = xr.open_dataset(forcing_path/forcing_name) +forc = xr.open_dataset(forcing_path/forcing_file) # Get the sorting order from the forcing file forcing_hruIds = forc['hruId'].values.astype(int) # 'hruId' is prescribed by SUMMA so this variable must exist diff --git a/5_model_input/SUMMA/1f_attributes/1_initialize_attributes_nc.ipynb b/5_model_input/SUMMA/1f_attributes/1_initialize_attributes_nc.ipynb deleted file mode 100644 index 6e05d25..0000000 --- a/5_model_input/SUMMA/1f_attributes/1_initialize_attributes_nc.ipynb +++ /dev/null @@ -1,748 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Initialize attributes.nc\n", - "Create attributes.nc file. This needs (https://summa.readthedocs.io/en/master/input_output/SUMMA_input/):\n", - "\n", - "| Variable | dimension | type | units | long name | notes |\n", - "|:----------------|:-----------|:--------|:----------------------|:-------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n", - "| hruId | hru | int | - | Index of hydrological response unit (HRU) | Unique numeric ID for each HRU|\n", - "| gruId | gru | int | - | Index of grouped response unit (GRU) | Unique numeric ID for each GRU |\n", - "| hru2gruId | hru | int | - | Index of GRU to which the HRU belongs | gruId of the GRU to which the HRU belongs |\n", - "| downHRUindex | hru | int | - | Index of downslope HRU (0 = basin outlet) | Downslope HRU must be within the same GRU. If the value is 0, then there is no exchange to a neighboring HRU. Setting this value to 0 for all HRUs emulates a series of independent columns |\n", - "| longitude | hru | double | Decimal degree east | Longitude of HRU's centroid | West is negative or greater than 180 |\n", - "| latitude | hru | double | Decimal degree north | Latitude of HRU's centroid | South is negative |\n", - "| elevation | hru | double | m | Elevation of HRU's centroid | |\n", - "| HRUarea | hru | double | m^2 | Area of HRU | |\n", - "| tan_slope | hru | double | m m-1 | Average tangent slope of HRU | |\n", - "| contourLength | hru | double | m | Contour length of HRU | Width of a hillslope (m) parallel to a stream. Used in groundwatr.f90 |\n", - "| slopeTypeIndex | hru | int | - | Index defining slope | |\n", - "| soilTypeIndex | hru | int | - | Index defining soil type | |\n", - "| vegTypeIndex | hru | int | - | Index defining vegetation type | |\n", - "| mHeight | hru | double | m | Measurement height above bare ground | |\n", - "\n", - "## Note on HRU order\n", - "HRU order must be the same in forcing, attributes, initial conditions and trial parameter files. Order will be taken from forcing files to ensure consistency.\n", - "\n", - "## Fill values\n", - "| Variable | Value |\n", - "|:---------------|:------|\n", - "| hruId | taken from the shapefile index values |\n", - "| gruId | same as hruId, because in this setup HRU and GRU map 1:1 |\n", - "| hru2gruId | same as hruId |\n", - "| downHRUindex | 0, each HRU is independent column |\n", - "| longitude | taken from the shapefile geometry |\n", - "| latitude | taken from the shapefile geometry |\n", - "| elevation | placeholder value -999, fill from the MERIT Hydro DEM |\n", - "| HRUarea | taken from the shapefile attributes |\n", - "| tan_slope | unused in current set up, fixed at 0.1 [-] |\n", - "| contourLength | unused in current set up, fixed at 30 [m] |\n", - "| slopeTypeIndex | unused in current set up, fixed at 1 [-] |\n", - "| soilTypeIndex | placeholder value -999, fill from SOILGRIDS |\n", - "| vegTypeIndex | placeholder value -999, fill from MODIS veg |\n", - "| mHeight | temporarily set at 3 [m] |\n", - "\n", - "## Assumed modeling decisions\n", - "Note that options:\n", - "- tan_slope\n", - "- contourLength\n", - "- slopeTypeIndex \n", - "\n", - "are not set to correct values. `slopeTypeIndex` is a legacy variable that is no longer used. `tan_slope` and `contourLength` are needed for the `qbaseTopmodel` modeling option. These require significant preprocessing of geospatial data and are not yet implemented as part of this workflow.\n", - "\n", - "`downHRUindex` is set to 0, indicating that each HRU will be modeled as an independent column. This can optionally be changed by setting the flag `settings_summa_connect_HRUs` to `yes` in the control file. The notebook that populates the attributes `.nc` file with elevation will in that case also use the relative elevations of HRUs in each GRU to define downslope HRU IDs." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import netCDF4 as nc4\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find shapefile location and name" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Catchment shapefile path & name\n", - "catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if catchment_path == 'default':\n", - " catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " catchment_path = Path(catchment_path) # make sure a user-specified path is a Path()\"" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Variable names used in shapefile\n", - "catchment_hruId_var = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')\n", - "catchment_gruId_var = read_from_control(controlFolder/controlFile,'catchment_shp_gruid')\n", - "catchment_area_var = read_from_control(controlFolder/controlFile,'catchment_shp_area')\n", - "catchment_lat_var = read_from_control(controlFolder/controlFile,'catchment_shp_lat')\n", - "catchment_lon_var = read_from_control(controlFolder/controlFile,'catchment_shp_lon')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find forcing location and an example file" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing path\n", - "forcing_path = read_from_control(controlFolder/controlFile,'forcing_summa_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_path == 'default':\n", - " forcing_path = make_default_path('forcing/4_SUMMA_input') # outputs a Path()\n", - "else:\n", - " forcing_path = Path(forcing_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Find a list of forcing files\n", - "_,_,forcing_files = next(os.walk(forcing_path))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Select a random file as a template for hruId order\n", - "forcing_name = forcing_files[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the forcing measurement height\n", - "forcing_measurement_height = float(read_from_control(controlFolder/controlFile,'forcing_measurement_height'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the attributes need to go" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Attribute path & name\n", - "attribute_path = read_from_control(controlFolder/controlFile,'settings_summa_path')\n", - "attribute_name = read_from_control(controlFolder/controlFile,'settings_summa_attributes')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if attribute_path == 'default':\n", - " attribute_path = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " attribute_path = Path(attribute_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "attribute_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Load the catchment shapefile and sort it based on HRU order in the forcing file" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the catchment shapefile\n", - "shp = gpd.read_file(catchment_path/catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the forcing file\n", - "forc = xr.open_dataset(forcing_path/forcing_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the sorting order from the forcing file\n", - "forcing_hruIds = forc['hruId'].values.astype(int) # 'hruId' is prescribed by SUMMA so this variable must exist" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the hruId variable in the shapefile the index\n", - "shp = shp.set_index(catchment_hruId_var)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# Enforce index as integers\n", - "shp.index = shp.index.astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort the shape based on the forcing HRU order\n", - "shp = shp.loc[forcing_hruIds]" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "# Reset the index so that we reference each row properly in later code\n", - "shp = shp.reset_index()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find number of GRUs and HRUs" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Extract HRU IDs and count unique occurence (should be equal to length of shapefile)\n", - "hru_ids = pd.unique(shp[catchment_hruId_var].values)\n", - "num_hru = len(hru_ids)\n", - "\n", - "gru_ids = pd.unique(shp[catchment_gruId_var].values)\n", - "num_gru = len(gru_ids)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the new attributes file" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 out of 118 HRUs completed.\n", - "2 out of 118 HRUs completed.\n", - "3 out of 118 HRUs completed.\n", - "4 out of 118 HRUs completed.\n", - "5 out of 118 HRUs completed.\n", - "6 out of 118 HRUs completed.\n", - "7 out of 118 HRUs completed.\n", - "8 out of 118 HRUs completed.\n", - "9 out of 118 HRUs completed.\n", - "10 out of 118 HRUs completed.\n", - "11 out of 118 HRUs completed.\n", - "12 out of 118 HRUs completed.\n", - "13 out of 118 HRUs completed.\n", - "14 out of 118 HRUs completed.\n", - "15 out of 118 HRUs completed.\n", - "16 out of 118 HRUs completed.\n", - "17 out of 118 HRUs completed.\n", - "18 out of 118 HRUs completed.\n", - "19 out of 118 HRUs completed.\n", - "20 out of 118 HRUs completed.\n", - "21 out of 118 HRUs completed.\n", - "22 out of 118 HRUs completed.\n", - "23 out of 118 HRUs completed.\n", - "24 out of 118 HRUs completed.\n", - "25 out of 118 HRUs completed.\n", - "26 out of 118 HRUs completed.\n", - "27 out of 118 HRUs completed.\n", - "28 out of 118 HRUs completed.\n", - "29 out of 118 HRUs completed.\n", - "30 out of 118 HRUs completed.\n", - "31 out of 118 HRUs completed.\n", - "32 out of 118 HRUs completed.\n", - "33 out of 118 HRUs completed.\n", - "34 out of 118 HRUs completed.\n", - "35 out of 118 HRUs completed.\n", - "36 out of 118 HRUs completed.\n", - "37 out of 118 HRUs completed.\n", - "38 out of 118 HRUs completed.\n", - "39 out of 118 HRUs completed.\n", - "40 out of 118 HRUs completed.\n", - "41 out of 118 HRUs completed.\n", - "42 out of 118 HRUs completed.\n", - "43 out of 118 HRUs completed.\n", - "44 out of 118 HRUs completed.\n", - "45 out of 118 HRUs completed.\n", - "46 out of 118 HRUs completed.\n", - "47 out of 118 HRUs completed.\n", - "48 out of 118 HRUs completed.\n", - "49 out of 118 HRUs completed.\n", - "50 out of 118 HRUs completed.\n", - "51 out of 118 HRUs completed.\n", - "52 out of 118 HRUs completed.\n", - "53 out of 118 HRUs completed.\n", - "54 out of 118 HRUs completed.\n", - "55 out of 118 HRUs completed.\n", - "56 out of 118 HRUs completed.\n", - "57 out of 118 HRUs completed.\n", - "58 out of 118 HRUs completed.\n", - "59 out of 118 HRUs completed.\n", - "60 out of 118 HRUs completed.\n", - "61 out of 118 HRUs completed.\n", - "62 out of 118 HRUs completed.\n", - "63 out of 118 HRUs completed.\n", - "64 out of 118 HRUs completed.\n", - "65 out of 118 HRUs completed.\n", - "66 out of 118 HRUs completed.\n", - "67 out of 118 HRUs completed.\n", - "68 out of 118 HRUs completed.\n", - "69 out of 118 HRUs completed.\n", - "70 out of 118 HRUs completed.\n", - "71 out of 118 HRUs completed.\n", - "72 out of 118 HRUs completed.\n", - "73 out of 118 HRUs completed.\n", - "74 out of 118 HRUs completed.\n", - "75 out of 118 HRUs completed.\n", - "76 out of 118 HRUs completed.\n", - "77 out of 118 HRUs completed.\n", - "78 out of 118 HRUs completed.\n", - "79 out of 118 HRUs completed.\n", - "80 out of 118 HRUs completed.\n", - "81 out of 118 HRUs completed.\n", - "82 out of 118 HRUs completed.\n", - "83 out of 118 HRUs completed.\n", - "84 out of 118 HRUs completed.\n", - "85 out of 118 HRUs completed.\n", - "86 out of 118 HRUs completed.\n", - "87 out of 118 HRUs completed.\n", - "88 out of 118 HRUs completed.\n", - "89 out of 118 HRUs completed.\n", - "90 out of 118 HRUs completed.\n", - "91 out of 118 HRUs completed.\n", - "92 out of 118 HRUs completed.\n", - "93 out of 118 HRUs completed.\n", - "94 out of 118 HRUs completed.\n", - "95 out of 118 HRUs completed.\n", - "96 out of 118 HRUs completed.\n", - "97 out of 118 HRUs completed.\n", - "98 out of 118 HRUs completed.\n", - "99 out of 118 HRUs completed.\n", - "100 out of 118 HRUs completed.\n", - "101 out of 118 HRUs completed.\n", - "102 out of 118 HRUs completed.\n", - "103 out of 118 HRUs completed.\n", - "104 out of 118 HRUs completed.\n", - "105 out of 118 HRUs completed.\n", - "106 out of 118 HRUs completed.\n", - "107 out of 118 HRUs completed.\n", - "108 out of 118 HRUs completed.\n", - "109 out of 118 HRUs completed.\n", - "110 out of 118 HRUs completed.\n", - "111 out of 118 HRUs completed.\n", - "112 out of 118 HRUs completed.\n", - "113 out of 118 HRUs completed.\n", - "114 out of 118 HRUs completed.\n", - "115 out of 118 HRUs completed.\n", - "116 out of 118 HRUs completed.\n", - "117 out of 118 HRUs completed.\n", - "118 out of 118 HRUs completed.\n" - ] - } - ], - "source": [ - "# Create the new .nc file\n", - "with nc4.Dataset(attribute_path/attribute_name, \"w\", format=\"NETCDF4\") as att:\n", - " \n", - " # General attributes\n", - " now = datetime.now()\n", - " att.setncattr('Author', \"Created by SUMMA workflow scripts\")\n", - " att.setncattr('History','Created ' + now.strftime('%Y/%m/%d %H:%M:%S'))\n", - "\n", - " # Define the dimensions \n", - " att.createDimension('hru',num_hru)\n", - " att.createDimension('gru',num_gru)\n", - " \n", - " # Define the variables\n", - " var = 'hruId'\n", - " att.createVariable(var, 'i4', 'hru', fill_value = False)\n", - " att[var].setncattr('units', '-')\n", - " att[var].setncattr('long_name', 'Index of hydrological response unit (HRU)')\n", - " \n", - " var = 'gruId'\n", - " att.createVariable(var, 'i4', 'gru', fill_value = False)\n", - " att[var].setncattr('units', '-')\n", - " att[var].setncattr('long_name', 'Index of grouped response unit (GRU)')\n", - " \n", - " var = 'hru2gruId'\n", - " att.createVariable(var, 'i4', 'hru', fill_value = False)\n", - " att[var].setncattr('units', '-')\n", - " att[var].setncattr('long_name', 'Index of GRU to which the HRU belongs')\n", - " \n", - " var = 'downHRUindex'\n", - " att.createVariable(var, 'i4', 'hru', fill_value = False)\n", - " att[var].setncattr('units', '-')\n", - " att[var].setncattr('long_name', 'Index of downslope HRU (0 = basin outlet)')\n", - " \n", - " var = 'longitude'\n", - " att.createVariable(var, 'f8', 'hru', fill_value = False)\n", - " att[var].setncattr('units', 'Decimal degree east')\n", - " att[var].setncattr('long_name', 'Longitude of HRU''s centroid')\n", - " \n", - " var = 'latitude'\n", - " att.createVariable(var, 'f8', 'hru', fill_value = False)\n", - " att[var].setncattr('units', 'Decimal degree north')\n", - " att[var].setncattr('long_name', 'Latitude of HRU''s centroid')\n", - " \n", - " var = 'elevation'\n", - " att.createVariable(var, 'f8', 'hru', fill_value = False)\n", - " att[var].setncattr('units', 'm')\n", - " att[var].setncattr('long_name', 'Mean HRU elevation')\n", - " \n", - " var = 'HRUarea'\n", - " att.createVariable(var, 'f8', 'hru', fill_value = False)\n", - " att[var].setncattr('units', 'm^2')\n", - " att[var].setncattr('long_name', 'Area of HRU')\n", - " \n", - " var = 'tan_slope'\n", - " att.createVariable(var, 'f8', 'hru', fill_value = False)\n", - " att[var].setncattr('units', 'm m-1')\n", - " att[var].setncattr('long_name', 'Average tangent slope of HRU')\n", - " \n", - " var = 'contourLength'\n", - " att.createVariable(var, 'f8', 'hru', fill_value = False)\n", - " att[var].setncattr('units', 'm')\n", - " att[var].setncattr('long_name', 'Contour length of HRU')\n", - " \n", - " var = 'slopeTypeIndex'\n", - " att.createVariable(var, 'i4', 'hru', fill_value = False)\n", - " att[var].setncattr('units', '-')\n", - " att[var].setncattr('long_name', 'Index defining slope')\n", - " \n", - " var = 'soilTypeIndex'\n", - " att.createVariable(var, 'i4', 'hru', fill_value = False)\n", - " att[var].setncattr('units', '-')\n", - " att[var].setncattr('long_name', 'Index defining soil type')\n", - " \n", - " var = 'vegTypeIndex'\n", - " att.createVariable(var, 'i4', 'hru', fill_value = False)\n", - " att[var].setncattr('units', '-')\n", - " att[var].setncattr('long_name', 'Index defining vegetation type')\n", - " \n", - " var = 'mHeight'\n", - " att.createVariable(var, 'f8', 'hru', fill_value = False)\n", - " att[var].setncattr('units', 'm')\n", - " att[var].setncattr('long_name', 'Measurement height above bare ground')\n", - " \n", - " # Progress\n", - " progress = 0\n", - " \n", - " # GRU variable\n", - " for idx in range(0,num_gru):\n", - " att['gruId'][idx] = gru_ids[idx]\n", - " \n", - " # HRU variables; due to pre-sorting, these are already in the same order as the forcing files\n", - " for idx in range(0,num_hru):\n", - " \n", - " # Fill values from shapefile\n", - " att['hruId'][idx] = shp.iloc[idx][catchment_hruId_var]\n", - " att['HRUarea'][idx] = shp.iloc[idx][catchment_area_var]\n", - " att['latitude'][idx] = shp.iloc[idx][catchment_lat_var]\n", - " att['longitude'][idx] = shp.iloc[idx][catchment_lon_var]\n", - " att['hru2gruId'][idx] = shp.iloc[idx][catchment_gruId_var]\n", - " \n", - " # Constants\n", - " att['tan_slope'][idx] = 0.1 # Only used in qbaseTopmodel modelling decision\n", - " att['contourLength'][idx] = 30 # Only used in qbaseTopmodel modelling decision\n", - " att['slopeTypeIndex'][idx] = 1 # Needs to be set but not used\n", - " att['mHeight'][idx] = forcing_measurement_height # Forcing data height; used in some scaling equations \n", - " att['downHRUindex'][idx] = 0 # All HRUs modeled as independent columns; optionally changed when elevation is added to attributes.nc\n", - " \n", - " # Placeholders to be filled later\n", - " att['elevation'][idx] = -999\n", - " att['soilTypeIndex'][idx] = -999\n", - " att['vegTypeIndex'][idx] = -999\n", - " \n", - " # Show a progress report\n", - " print(str(progress+1) + ' out of ' + str(num_hru) + ' HRUs completed.')\n", - " \n", - " # Increment the counter\n", - " progress += 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = attribute_path\n", - "log_suffix = '_initialize_attributes.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_initialize_attributes_nc.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Initialized the attributes .nc file.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/SUMMA/1f_attributes/1_initialize_attributes_nc.py b/5_model_input/SUMMA/1f_attributes/1_initialize_attributes_nc.py index 5bf8b6d..3c313d6 100644 --- a/5_model_input/SUMMA/1f_attributes/1_initialize_attributes_nc.py +++ b/5_model_input/SUMMA/1f_attributes/1_initialize_attributes_nc.py @@ -104,6 +104,14 @@ def make_default_path(suffix): # --- Find forcing location and an example file +domainName = read_from_control(controlFolder/controlFile,'domain_name') + +# Spatialisation +spa = 'distributed' + +# Forcing data product +forcing_name = read_from_control(controlFolder/controlFile,'forcing_data_name') + # Forcing path forcing_path = read_from_control(controlFolder/controlFile,'forcing_summa_path') @@ -113,11 +121,11 @@ def make_default_path(suffix): else: forcing_path = Path(forcing_path) # make sure a user-specified path is a Path() -# Find a list of forcing files -_,_,forcing_files = next(os.walk(forcing_path)) -# Select a random file as a template for hruId order -forcing_name = forcing_files[0] +# Forcing file +forcing_file = domainName + "_" + forcing_name + "_" + spa +".nc" +if forcing_name == "em-earth": + forcing_file = forcing_file.replace(forcing_name, "em_earth") # Find the forcing measurement height forcing_measurement_height = float(read_from_control(controlFolder/controlFile,'forcing_measurement_height')) @@ -143,21 +151,14 @@ def make_default_path(suffix): shp = gpd.read_file(catchment_path/catchment_name) # Open the forcing file -forc = xr.open_dataset(forcing_path/forcing_name) +forc = xr.open_dataset(forcing_path/forcing_file) # Get the sorting order from the forcing file forcing_hruIds = forc['hruId'].values.astype(int) # 'hruId' is prescribed by SUMMA so this variable must exist # Make the hruId variable in the shapefile the index shp = shp.set_index(catchment_hruId_var) - -# Enforce index as integers -shp.index = shp.index.astype(int) - -# Sort the shape based on the forcing HRU order -shp = shp.loc[forcing_hruIds] - -# Reset the index so that we reference each row properly in later code +shp = shp.reindex(forcing_hruIds) shp = shp.reset_index() diff --git a/5_model_input/SUMMA/1f_attributes/2a_insert_soilclass_from_hist_into_attributes.ipynb b/5_model_input/SUMMA/1f_attributes/2a_insert_soilclass_from_hist_into_attributes.ipynb deleted file mode 100644 index c915e3f..0000000 --- a/5_model_input/SUMMA/1f_attributes/2a_insert_soilclass_from_hist_into_attributes.ipynb +++ /dev/null @@ -1,553 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Insert SOILGRIDS-derived soil class in SUMMA set up\n", - "Inserts mode soil class of each HRU into the attributes `.nc` file. The intersection code stores a histogram of soil classes in fields `USGS_{0,1,...,12}`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import numpy as np\n", - "import netCDF4 as nc4\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find shapefile location and name" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Path to and name of shapefile with intersection between catchment and soil classes\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_soil_path')\n", - "intersect_name = read_from_control(controlFolder/controlFile,'intersect_soil_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_soilgrids') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Variable names used in shapefile\n", - "intersect_hruId_var = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the attributes file is" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Attribute path & name\n", - "attribute_path = read_from_control(controlFolder/controlFile,'settings_summa_path')\n", - "attribute_name = read_from_control(controlFolder/controlFile,'settings_summa_attributes')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if attribute_path == 'default':\n", - " attribute_path = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " attribute_path = Path(attribute_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Open the files and fill the placeholder values in the attributes file" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Open files\n", - "shp = gpd.read_file(intersect_path/intersect_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Replacing soil class 3 with 3 at HRU 1\n", - "Replacing soil class 3 with 3 at HRU 2\n", - "Replacing soil class 3 with 3 at HRU 3\n", - "Replacing soil class 3 with 3 at HRU 4\n", - "Replacing soil class 3 with 3 at HRU 5\n", - "Replacing soil class 3 with 3 at HRU 6\n", - "Replacing soil class 3 with 3 at HRU 7\n", - "Replacing soil class 3 with 3 at HRU 8\n", - "Replacing soil class 3 with 3 at HRU 9\n", - "Replacing soil class 3 with 3 at HRU 10\n", - "Replacing soil class 3 with 3 at HRU 11\n", - "Replacing soil class 3 with 3 at HRU 12\n", - "Replacing soil class 3 with 3 at HRU 13\n", - "Replacing soil class 3 with 3 at HRU 14\n", - "Replacing soil class 3 with 3 at HRU 15\n", - "Replacing soil class 3 with 3 at HRU 16\n", - "Replacing soil class 3 with 3 at HRU 17\n", - "Replacing soil class 3 with 3 at HRU 18\n", - "Replacing soil class 3 with 3 at HRU 19\n", - "Replacing soil class 3 with 3 at HRU 20\n", - "Replacing soil class 3 with 3 at HRU 21\n", - "Replacing soil class 3 with 3 at HRU 22\n", - "Replacing soil class 3 with 3 at HRU 23\n", - "Replacing soil class 3 with 3 at HRU 24\n", - "Replacing soil class 3 with 3 at HRU 25\n", - "Replacing soil class 3 with 3 at HRU 26\n", - "Replacing soil class 3 with 3 at HRU 27\n", - "Replacing soil class 3 with 3 at HRU 28\n", - "Replacing soil class 3 with 3 at HRU 29\n", - "Replacing soil class 3 with 3 at HRU 30\n", - "Replacing soil class 3 with 3 at HRU 31\n", - "Replacing soil class 3 with 3 at HRU 32\n", - "Replacing soil class 3 with 3 at HRU 33\n", - "Replacing soil class 3 with 3 at HRU 34\n", - "Replacing soil class 3 with 3 at HRU 35\n", - "Replacing soil class 3 with 3 at HRU 36\n", - "Replacing soil class 3 with 3 at HRU 37\n", - "Replacing soil class 3 with 3 at HRU 38\n", - "Replacing soil class 3 with 3 at HRU 39\n", - "Replacing soil class 3 with 3 at HRU 40\n", - "Replacing soil class 3 with 3 at HRU 41\n", - "Replacing soil class 3 with 3 at HRU 42\n", - "Replacing soil class 3 with 3 at HRU 43\n", - "Replacing soil class 3 with 3 at HRU 44\n", - "Replacing soil class 3 with 3 at HRU 45\n", - "Replacing soil class 3 with 3 at HRU 46\n", - "Replacing soil class 3 with 3 at HRU 47\n", - "Replacing soil class 3 with 3 at HRU 48\n", - "Replacing soil class 3 with 3 at HRU 49\n", - "Replacing soil class 3 with 3 at HRU 50\n", - "Replacing soil class 3 with 3 at HRU 51\n", - "Replacing soil class 3 with 3 at HRU 52\n", - "Replacing soil class 3 with 3 at HRU 53\n", - "Replacing soil class 3 with 3 at HRU 54\n", - "Replacing soil class 3 with 3 at HRU 55\n", - "Replacing soil class 3 with 3 at HRU 56\n", - "Replacing soil class 3 with 3 at HRU 57\n", - "Replacing soil class 3 with 3 at HRU 58\n", - "Replacing soil class 3 with 3 at HRU 59\n", - "Replacing soil class 3 with 3 at HRU 60\n", - "Replacing soil class 3 with 3 at HRU 61\n", - "Replacing soil class 3 with 3 at HRU 62\n", - "Replacing soil class 3 with 3 at HRU 63\n", - "Replacing soil class 3 with 3 at HRU 64\n", - "Replacing soil class 3 with 3 at HRU 65\n", - "Replacing soil class 3 with 3 at HRU 66\n", - "Replacing soil class 3 with 3 at HRU 67\n", - "Replacing soil class 3 with 3 at HRU 68\n", - "Replacing soil class 3 with 3 at HRU 69\n", - "Replacing soil class 3 with 3 at HRU 70\n", - "Replacing soil class 3 with 3 at HRU 71\n", - "Replacing soil class 3 with 3 at HRU 72\n", - "Replacing soil class 3 with 3 at HRU 73\n", - "Replacing soil class 3 with 3 at HRU 74\n", - "Replacing soil class 3 with 3 at HRU 75\n", - "Replacing soil class 3 with 3 at HRU 76\n", - "Replacing soil class 3 with 3 at HRU 77\n", - "Replacing soil class 3 with 3 at HRU 78\n", - "Replacing soil class 3 with 3 at HRU 79\n", - "Replacing soil class 3 with 3 at HRU 80\n", - "Replacing soil class 3 with 3 at HRU 81\n", - "Replacing soil class 3 with 3 at HRU 82\n", - "Replacing soil class 3 with 3 at HRU 83\n", - "Replacing soil class 3 with 3 at HRU 84\n", - "Replacing soil class 3 with 3 at HRU 85\n", - "Replacing soil class 3 with 3 at HRU 86\n", - "Replacing soil class 3 with 3 at HRU 87\n", - "Replacing soil class 3 with 3 at HRU 88\n", - "Replacing soil class 3 with 3 at HRU 89\n", - "Replacing soil class 3 with 3 at HRU 90\n", - "Replacing soil class 3 with 3 at HRU 91\n", - "Replacing soil class 3 with 3 at HRU 92\n", - "Replacing soil class 8 with 8 at HRU 93\n", - "Replacing soil class 3 with 3 at HRU 94\n", - "Replacing soil class 8 with 8 at HRU 95\n", - "Replacing soil class 3 with 3 at HRU 96\n", - "Replacing soil class 3 with 3 at HRU 97\n", - "Replacing soil class 3 with 3 at HRU 98\n", - "Replacing soil class 3 with 3 at HRU 99\n", - "Replacing soil class 3 with 3 at HRU 100\n", - "Replacing soil class 3 with 3 at HRU 101\n", - "Replacing soil class 3 with 3 at HRU 102\n", - "Replacing soil class 3 with 3 at HRU 103\n", - "Replacing soil class 3 with 3 at HRU 104\n", - "Replacing soil class 3 with 3 at HRU 105\n", - "Replacing soil class 3 with 3 at HRU 106\n", - "Replacing soil class 3 with 3 at HRU 107\n", - "Replacing soil class 3 with 3 at HRU 108\n", - "Replacing soil class 3 with 3 at HRU 109\n", - "Replacing soil class 3 with 3 at HRU 110\n", - "Replacing soil class 3 with 3 at HRU 111\n", - "Replacing soil class 3 with 3 at HRU 112\n", - "Replacing soil class 3 with 3 at HRU 113\n", - "Replacing soil class 3 with 3 at HRU 114\n", - "Replacing soil class 3 with 3 at HRU 115\n", - "Replacing soil class 3 with 3 at HRU 116\n", - "Replacing soil class 3 with 3 at HRU 117\n", - "Replacing soil class 3 with 3 at HRU 118\n" - ] - } - ], - "source": [ - "# Open the netcdf file for reading+writing\n", - "with nc4.Dataset(attribute_path/attribute_name, \"r+\") as att:\n", - " \n", - " # Loop over the HRUs in the attributes\n", - " for idx in range(0,len(att['hruId'])):\n", - " \n", - " # Find the HRU ID (attributes file) at this index\n", - " attribute_hru = att['hruId'][idx]\n", - " \n", - " # Find the row in the shapefile that contains info for this HRU\n", - " shp_mask = (shp[intersect_hruId_var].astype(int) == attribute_hru)\n", - " \n", - " # Extract the histogram values\n", - " tmp_hist = []\n", - " for j in range (0,13):\n", - " if 'USGS_' + str(j) in shp.columns:\n", - " tmp_hist.append(shp['USGS_' + str(j)][shp_mask].values[0])\n", - " else:\n", - " tmp_hist.append(0)\n", - " \n", - " # Set the '0' class to having -1 occurences -> that must make some other class the most occuring one. \n", - " # Using -1 also accounts for cases where SOILGRIDS has no sand/silt/clay data (oceans, glaciers, open water)\n", - " # and returns soil class =0. In such cases we default to the soilclass with the second most occurences. If \n", - " # tied, we use the first in the list. We should never return soilclass = 0 in this way.\n", - " tmp_hist[0] = -1\n", - " \n", - " # Find the index with the most occurences\n", - " # Note: this assumes that we have USGS_0 to USGS_12 and thus that index == soilclass. \n", - " tmp_sc = np.argmax(np.asarray(tmp_hist))\n", - " \n", - " # Check the assumption that index == soilclass\n", - " if shp['USGS_' + str(tmp_sc)][shp_mask].values != tmp_hist[tmp_sc]:\n", - " print('Index and mode soil class do not match at hru_id ' + \\\n", - " str(shp[intersect_hruId_var][shp_mask].values[0]))\n", - " tmp_sc = -999\n", - " \n", - " # Replace the value\n", - " print('Replacing soil class {} with {} at HRU {}'.format(att['soilTypeIndex'][idx],tmp_sc,attribute_hru))\n", - " att['soilTypeIndex'][idx] = tmp_sc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the files \n", - "shp = gpd.read_file(intersect_path/intersect_name)\n", - "att = xr.open_dataset(attribute_path/attribute_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Set HRU IDs as index in the shape\n", - "shp = shp.set_index(intersect_hruId_var)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort the shape to match the order of the attributes\n", - "shp = shp.loc[att['hruId'].values]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# temporarily store the soiltype in the shape for plotting\n", - "shp['soilTypeIndex'] = att['soilTypeIndex'][:]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Make a bplot to see what we did\n", - "shp.plot(column='soilTypeIndex',figsize=(16,8), legend=True,edgecolor='k')\n", - "ax = plt.gca()\n", - "ax.set_title('SOILGRIDS-derived USDA soilclass');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Close the netCDF file\n", - "att.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = attribute_path\n", - "log_suffix = '_add_soil_to_attributes.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '2a_insert_soilclass_from_hist_into_attributes.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Added soil classes to attributes .nc file.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/SUMMA/1f_attributes/2a_insert_soilclass_from_hist_into_attributes.py b/5_model_input/SUMMA/1f_attributes/2a_insert_soilclass_from_hist_into_attributes.py index 4dd9671..f5dff10 100644 --- a/5_model_input/SUMMA/1f_attributes/2a_insert_soilclass_from_hist_into_attributes.py +++ b/5_model_input/SUMMA/1f_attributes/2a_insert_soilclass_from_hist_into_attributes.py @@ -103,6 +103,7 @@ def make_default_path(suffix): else: tmp_hist.append(0) + # Set the '0' class to having -1 occurences -> that must make some other class the most occuring one. # Using -1 also accounts for cases where SOILGRIDS has no sand/silt/clay data (oceans, glaciers, open water) # and returns soil class =0. In such cases we default to the soilclass with the second most occurences. If @@ -113,16 +114,48 @@ def make_default_path(suffix): # Note: this assumes that we have USGS_0 to USGS_12 and thus that index == soilclass. tmp_sc = np.argmax(np.asarray(tmp_hist)) - # Check the assumption that index == soilclass - if shp['USGS_' + str(tmp_sc)][shp_mask].values != tmp_hist[tmp_sc]: + # Check NA soil class + if max(tmp_hist) == 0: + print('NA soil class defined at HRU ' + \ + str(shp[intersect_hruId_var][shp_mask].values[0])) + + # Check missmatch + elif shp['USGS_' + str(tmp_sc)][shp_mask].values != tmp_hist[tmp_sc]: print('Index and mode soil class do not match at hru_id ' + \ str(shp[intersect_hruId_var][shp_mask].values[0])) - tmp_sc = -999 - # Replace the value - print('Replacing soil class {} with {} at HRU {}'.format(att['soilTypeIndex'][idx],tmp_sc,attribute_hru)) - att['soilTypeIndex'][idx] = tmp_sc - + # Fill the soilclass + else: + # Replace the value + print('Replacing soil class {} with {} at HRU {}'.format(att['soilTypeIndex'][idx],tmp_sc,attribute_hru)) + att['soilTypeIndex'][idx] = tmp_sc + + + # Indices of -999 values + na_indices = [i for i, val in enumerate(att['soilTypeIndex'][:]) if val == -999] + + if(len(na_indices)==len(att['soilTypeIndex'][:])): + print("\nOnly NA were found in this catchment") + elif len(na_indices)>0: + print("\nReplacing NA values with neighbor HRU value") + for idx in na_indices: + found = False + for offset in range(1, len(att['soilTypeIndex'][:])): + left = idx - offset + right = idx + offset + + if left >= 0 and att['soilTypeIndex'][left] != -999: + att['soilTypeIndex'][idx] = att['soilTypeIndex'][left] + print(f"Replacing soil class -999 with {att['soilTypeIndex'][left]} at HRU {att['hruId'][idx]} (based on neighbor HRU {att['hruId'][left]})") + found = True + break + elif right < len(att['soilTypeIndex'][:]) and att['soilTypeIndex'][right] != -999: + att['soilTypeIndex'][idx] = att['soilTypeIndex'][right] + print(f"Replacing soil class -999 with {att['soilTypeIndex'][right]} at HRU {att['hruId'][idx]} (based on neighbor HRU {att['hruId'][right]})") + found = True + break + + # --- Code provenance # Generates a basic log file in the domain folder and copies the control file and itself there. diff --git a/5_model_input/SUMMA/1f_attributes/2b_insert_landclass_from_hist_into_attributes.ipynb b/5_model_input/SUMMA/1f_attributes/2b_insert_landclass_from_hist_into_attributes.ipynb deleted file mode 100644 index 39ecd94..0000000 --- a/5_model_input/SUMMA/1f_attributes/2b_insert_landclass_from_hist_into_attributes.ipynb +++ /dev/null @@ -1,564 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Insert MODIS-derived land class in SUMMA set up\n", - "Inserts mode land class of each HRU into the attributes `.nc` file. The intersection code stores a histogram of land classes in fields `IGBP_{1,...,17}`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import numpy as np\n", - "import netCDF4 as nc4\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find shapefile location and name" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Path to and name of shapefile with intersection between catchment and soil classes\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_land_path')\n", - "intersect_name = read_from_control(controlFolder/controlFile,'intersect_land_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_modis') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Variable names used in shapefile\n", - "intersect_hruId_var = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the attributes file is" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Attribute path & name\n", - "attribute_path = read_from_control(controlFolder/controlFile,'settings_summa_path')\n", - "attribute_name = read_from_control(controlFolder/controlFile,'settings_summa_attributes')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if attribute_path == 'default':\n", - " attribute_path = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " attribute_path = Path(attribute_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Open the files and fill the placeholder values in the attributes file" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Open files\n", - "shp = gpd.read_file(intersect_path/intersect_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Replacing land class 10 with 10 at HRU 1\n", - "Replacing land class 10 with 10 at HRU 2\n", - "Replacing land class 10 with 10 at HRU 3\n", - "Replacing land class 10 with 10 at HRU 4\n", - "Replacing land class 10 with 10 at HRU 5\n", - "Replacing land class 10 with 10 at HRU 6\n", - "Replacing land class 1 with 1 at HRU 7\n", - "Replacing land class 10 with 10 at HRU 8\n", - "Replacing land class 10 with 10 at HRU 9\n", - "Replacing land class 1 with 1 at HRU 10\n", - "Replacing land class 10 with 10 at HRU 11\n", - "Replacing land class 16 with 16 at HRU 12\n", - "Replacing land class 10 with 10 at HRU 13\n", - "Replacing land class 10 with 10 at HRU 14\n", - "Replacing land class 10 with 10 at HRU 15\n", - "Replacing land class 10 with 10 at HRU 16\n", - "Replacing land class 10 with 10 at HRU 17\n", - "Replacing land class 10 with 10 at HRU 18\n", - "Replacing land class 10 with 10 at HRU 19\n", - "Replacing land class 10 with 10 at HRU 20\n", - "Replacing land class 10 with 10 at HRU 21\n", - "Replacing land class 10 with 10 at HRU 22\n", - "Replacing land class 10 with 10 at HRU 23\n", - "Replacing land class 10 with 10 at HRU 24\n", - "Replacing land class 10 with 10 at HRU 25\n", - "Replacing land class 10 with 10 at HRU 26\n", - "Replacing land class 8 with 8 at HRU 27\n", - "Replacing land class 16 with 16 at HRU 28\n", - "Replacing land class 10 with 10 at HRU 29\n", - "Replacing land class 10 with 10 at HRU 30\n", - "Replacing land class 10 with 10 at HRU 31\n", - "Replacing land class 10 with 10 at HRU 32\n", - "Replacing land class 10 with 10 at HRU 33\n", - "Replacing land class 16 with 16 at HRU 34\n", - "Replacing land class 10 with 10 at HRU 35\n", - "Replacing land class 16 with 16 at HRU 36\n", - "Replacing land class 10 with 10 at HRU 37\n", - "Replacing land class 10 with 10 at HRU 38\n", - "Replacing land class 10 with 10 at HRU 39\n", - "Replacing land class 16 with 16 at HRU 40\n", - "Replacing land class 10 with 10 at HRU 41\n", - "Replacing land class 16 with 16 at HRU 42\n", - "Replacing land class 10 with 10 at HRU 43\n", - "Replacing land class 16 with 16 at HRU 44\n", - "Replacing land class 10 with 10 at HRU 45\n", - "Replacing land class 10 with 10 at HRU 46\n", - "Replacing land class 10 with 10 at HRU 47\n", - "Replacing land class 1 with 1 at HRU 48\n", - "Replacing land class 1 with 1 at HRU 49\n", - "Replacing land class 8 with 8 at HRU 50\n", - "Replacing land class 1 with 1 at HRU 51\n", - "Replacing land class 1 with 1 at HRU 52\n", - "Replacing land class 1 with 1 at HRU 53\n", - "Replacing land class 1 with 1 at HRU 54\n", - "Replacing land class 1 with 1 at HRU 55\n", - "Replacing land class 1 with 1 at HRU 56\n", - "Replacing land class 1 with 1 at HRU 57\n", - "Replacing land class 1 with 1 at HRU 58\n", - "Replacing land class 1 with 1 at HRU 59\n", - "Replacing land class 1 with 1 at HRU 60\n", - "Replacing land class 8 with 8 at HRU 61\n", - "Replacing land class 1 with 1 at HRU 62\n", - "Replacing land class 8 with 8 at HRU 63\n", - "Replacing land class 1 with 1 at HRU 64\n", - "Replacing land class 1 with 1 at HRU 65\n", - "Replacing land class 8 with 8 at HRU 66\n", - "Replacing land class 1 with 1 at HRU 67\n", - "Replacing land class 8 with 8 at HRU 68\n", - "Replacing land class 1 with 1 at HRU 69\n", - "Replacing land class 8 with 8 at HRU 70\n", - "Replacing land class 8 with 8 at HRU 71\n", - "Replacing land class 8 with 8 at HRU 72\n", - "Replacing land class 8 with 8 at HRU 73\n", - "Replacing land class 8 with 8 at HRU 74\n", - "Replacing land class 1 with 1 at HRU 75\n", - "Replacing land class 1 with 1 at HRU 76\n", - "Replacing land class 1 with 1 at HRU 77\n", - "Replacing land class 8 with 8 at HRU 78\n", - "Replacing land class 1 with 1 at HRU 79\n", - "Replacing land class 1 with 1 at HRU 80\n", - "Replacing land class 1 with 1 at HRU 81\n", - "Replacing land class 1 with 1 at HRU 82\n", - "Replacing land class 1 with 1 at HRU 83\n", - "Replacing land class 8 with 8 at HRU 84\n", - "Replacing land class 1 with 1 at HRU 85\n", - "Replacing land class 1 with 1 at HRU 86\n", - "Replacing land class 8 with 8 at HRU 87\n", - "Replacing land class 8 with 8 at HRU 88\n", - "Replacing land class 1 with 1 at HRU 89\n", - "Replacing land class 1 with 1 at HRU 90\n", - "Replacing land class 17 with 10 at HRU 91\n", - "Replacing land class 10 with 10 at HRU 92\n", - "Replacing land class 10 with 10 at HRU 93\n", - "Replacing land class 9 with 9 at HRU 94\n", - "Replacing land class 8 with 8 at HRU 95\n", - "Replacing land class 8 with 8 at HRU 96\n", - "Replacing land class 1 with 1 at HRU 97\n", - "Replacing land class 8 with 8 at HRU 98\n", - "Replacing land class 8 with 8 at HRU 99\n", - "Replacing land class 8 with 8 at HRU 100\n", - "Replacing land class 8 with 8 at HRU 101\n", - "Replacing land class 8 with 8 at HRU 102\n", - "Replacing land class 1 with 1 at HRU 103\n", - "Replacing land class 1 with 1 at HRU 104\n", - "Replacing land class 1 with 1 at HRU 105\n", - "Replacing land class 8 with 8 at HRU 106\n", - "Replacing land class 8 with 8 at HRU 107\n", - "Replacing land class 1 with 1 at HRU 108\n", - "Replacing land class 8 with 8 at HRU 109\n", - "Replacing land class 1 with 1 at HRU 110\n", - "Replacing land class 1 with 1 at HRU 111\n", - "Replacing land class 8 with 8 at HRU 112\n", - "Replacing land class 1 with 1 at HRU 113\n", - "Replacing land class 1 with 1 at HRU 114\n", - "Replacing land class 1 with 1 at HRU 115\n", - "Replacing land class 8 with 8 at HRU 116\n", - "Replacing land class 1 with 1 at HRU 117\n", - "Replacing land class 8 with 8 at HRU 118\n", - "HRU identified as mostly water in 0 cases. Note that SUMMA skips hydrologic calculations for such HRUs.\n" - ] - } - ], - "source": [ - "# Open the netcdf file for reading+writing\n", - "with nc4.Dataset(attribute_path/attribute_name, \"r+\") as att:\n", - " \n", - " # Keep track of number of water (class 17) occurrences\n", - " is_water = 0\n", - " \n", - " # Loop over the HRUs in the attributes\n", - " for idx in range(0,len(att['hruId'])):\n", - " \n", - " # Find the HRU ID (attributes file) at this index\n", - " attribute_hru = att['hruId'][idx]\n", - " \n", - " # Find the row in the shapefile that contains info for this HRU\n", - " shp_mask = (shp[intersect_hruId_var].astype(int) == attribute_hru)\n", - " \n", - " # Extract the histogram values\n", - " tmp_hist = []\n", - " for j in range (1,18):\n", - " if 'IGBP_' + str(j) in shp.columns:\n", - " tmp_hist.append(shp['IGBP_' + str(j)][shp_mask].values[0])\n", - " else:\n", - " tmp_hist.append(0)\n", - " \n", - " # Find the index with the most occurences\n", - " # Note: this assumes index == class, but at index 0 we find class 1. \n", - " # Hence we need to increase this value with +1 \n", - " tmp_lc = np.argmax(np.asarray(tmp_hist)) + 1\n", - " \n", - " # Check the assumption that index == soilclass\n", - " if shp['IGBP_' + str(tmp_lc)][shp_mask].values != tmp_hist[tmp_lc - 1]:\n", - " print('Index and mode land class do not match at hru_id ' + \\\n", - " str(shp[intersect_hruId_var][shp_mask].values[0]))\n", - " tmp_lc = -999\n", - " \n", - " # Handle the case where we have water (IGBP = 17)\n", - " if tmp_lc == 17:\n", - " if any(val > 0 for val in tmp_hist[0:-1]): # HRU is mostly water but other land classes are present\n", - " tmp_lc = np.argmax(np.asarray(tmp_hist[0:-1])) + 1 # select 2nd-most common class\n", - " print('HRU {} is predominantly water but some land is detected. Entering land class {} instead of open water.'.format(attribute_hru,tmp_lc))\n", - " else:\n", - " is_water += 1 # HRU is exclusively water\n", - " \n", - " # Replace the value\n", - " print('Replacing land class {} with {} at HRU {}'.format(att['vegTypeIndex'][idx],tmp_lc,attribute_hru))\n", - " att['vegTypeIndex'][idx] = tmp_lc\n", - " \n", - " # Print water counts\n", - " print('{} HRUs were identified as containing only open water. Note that SUMMA skips hydrologic calculations for such HRUs.'.format(is_water))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check\n", - "Plot the identified land classes." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the files \n", - "shp = gpd.read_file(intersect_path/intersect_name)\n", - "att = xr.open_dataset(attribute_path/attribute_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Set HRU IDs as index in the shape\n", - "shp = shp.set_index(intersect_hruId_var)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort the shape to match the order of the attributes\n", - "shp = shp.loc[att['hruId'].values]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# temporarily store the soiltype in the shape for plotting\n", - "shp['vegTypeIndex'] = att['vegTypeIndex'][:]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Make a plot to see what we did\n", - "shp.plot(column='vegTypeIndex',figsize=(16,8), legend=True,edgecolor='k')\n", - "ax = plt.gca()\n", - "ax.set_title('MODIS-derived IGBP landclass');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Close the netCDF file\n", - "att.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = attribute_path\n", - "log_suffix = '_add_veg_to_attributes.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '2b_insert_landclass_from_hist_into_attributes.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Added land classes to attributes .nc file.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/SUMMA/1f_attributes/2c_insert_elevation_into_attributes.ipynb b/5_model_input/SUMMA/1f_attributes/2c_insert_elevation_into_attributes.ipynb deleted file mode 100644 index 51e60ee..0000000 --- a/5_model_input/SUMMA/1f_attributes/2c_insert_elevation_into_attributes.ipynb +++ /dev/null @@ -1,863 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Insert MERIT Hydro elevation in SUMMA set up\n", - "Inserts elevation of each HRU into the attributes `.nc` file. The intersection code stores this value in field `elev_mean`. \n", - "\n", - "If the field `settings_summa_connect_HRUs` is set to `yes` in the control file, this script also finds the downslope HRU (attribute `downHRUindex`) for the HRUs within each GRU. The most downstream HRU (i.e. the GRU outlet) is set to `0` to follow SUMMA conventions. If `settings_summa_connect_HRUs` is set to `no`, all HRUs are modelled as indepdendent columns and outflow from all HRUs inside each GRU is combined into basin-average outflow. No further action is needed, as `downHRUindex` for each HRU has already been set to `0`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import numpy as np\n", - "import netCDF4 as nc4\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find shapefile location and name" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Path to and name of shapefile with intersection between catchment and soil classes\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_dem_path')\n", - "intersect_name = read_from_control(controlFolder/controlFile,'intersect_dem_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_dem') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Variable names used in shapefile\n", - "intersect_hruId_var = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')\n", - "intersect_gruId_var = read_from_control(controlFolder/controlFile,'catchment_shp_gruid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the attributes file is" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Attribute path & name\n", - "attribute_path = read_from_control(controlFolder/controlFile,'settings_summa_path')\n", - "attribute_name = read_from_control(controlFolder/controlFile,'settings_summa_attributes')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if attribute_path == 'default':\n", - " attribute_path = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " attribute_path = Path(attribute_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Open the shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Open files\n", - "shp = gpd.read_file(intersect_path/intersect_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define `downHRUindex` values if requested" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a field with downHRUindex = 0, that we wil potentially overwrite\n", - "shp['downHRUindex'] = 0\n", - "\n", - "# Find if this is requested by the user\n", - "do_downHRUindex = read_from_control(controlFolder/controlFile,'settings_summa_connect_HRUs')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filling downHRUindex of HRU 48 with HRU 97\n", - "Filling downHRUindex of HRU 97 with HRU 118\n", - "Filling downHRUindex of HRU 118 with HRU 0\n", - "Filling downHRUindex of HRU 1 with HRU 49\n", - "Filling downHRUindex of HRU 49 with HRU 100\n", - "Filling downHRUindex of HRU 100 with HRU 0\n", - "Filling downHRUindex of HRU 2 with HRU 50\n", - "Filling downHRUindex of HRU 50 with HRU 101\n", - "Filling downHRUindex of HRU 101 with HRU 0\n", - "Filling downHRUindex of HRU 3 with HRU 51\n", - "Filling downHRUindex of HRU 51 with HRU 102\n", - "Filling downHRUindex of HRU 102 with HRU 0\n", - "Filling downHRUindex of HRU 4 with HRU 52\n", - "Filling downHRUindex of HRU 52 with HRU 103\n", - "Filling downHRUindex of HRU 103 with HRU 0\n", - "Filling downHRUindex of HRU 5 with HRU 53\n", - "Filling downHRUindex of HRU 53 with HRU 104\n", - "Filling downHRUindex of HRU 104 with HRU 0\n", - "Filling downHRUindex of HRU 6 with HRU 54\n", - "Filling downHRUindex of HRU 54 with HRU 105\n", - "Filling downHRUindex of HRU 105 with HRU 0\n", - "Filling downHRUindex of HRU 7 with HRU 55\n", - "Filling downHRUindex of HRU 55 with HRU 106\n", - "Filling downHRUindex of HRU 106 with HRU 0\n", - "Filling downHRUindex of HRU 8 with HRU 56\n", - "Filling downHRUindex of HRU 56 with HRU 107\n", - "Filling downHRUindex of HRU 107 with HRU 0\n", - "Filling downHRUindex of HRU 9 with HRU 57\n", - "Filling downHRUindex of HRU 57 with HRU 0\n", - "Filling downHRUindex of HRU 10 with HRU 58\n", - "Filling downHRUindex of HRU 58 with HRU 0\n", - "Filling downHRUindex of HRU 0 with HRU 99\n", - "Filling downHRUindex of HRU 99 with HRU 0\n", - "Filling downHRUindex of HRU 0 with HRU 59\n", - "Filling downHRUindex of HRU 59 with HRU 0\n", - "Filling downHRUindex of HRU 11 with HRU 60\n", - "Filling downHRUindex of HRU 60 with HRU 0\n", - "Filling downHRUindex of HRU 12 with HRU 61\n", - "Filling downHRUindex of HRU 61 with HRU 0\n", - "Filling downHRUindex of HRU 13 with HRU 62\n", - "Filling downHRUindex of HRU 62 with HRU 108\n", - "Filling downHRUindex of HRU 108 with HRU 0\n", - "Filling downHRUindex of HRU 14 with HRU 63\n", - "Filling downHRUindex of HRU 63 with HRU 109\n", - "Filling downHRUindex of HRU 109 with HRU 0\n", - "Filling downHRUindex of HRU 15 with HRU 64\n", - "Filling downHRUindex of HRU 64 with HRU 110\n", - "Filling downHRUindex of HRU 110 with HRU 0\n", - "Filling downHRUindex of HRU 16 with HRU 65\n", - "Filling downHRUindex of HRU 65 with HRU 0\n", - "Filling downHRUindex of HRU 0 with HRU 98\n", - "Filling downHRUindex of HRU 98 with HRU 0\n", - "Filling downHRUindex of HRU 17 with HRU 66\n", - "Filling downHRUindex of HRU 66 with HRU 0\n", - "Filling downHRUindex of HRU 18 with HRU 67\n", - "Filling downHRUindex of HRU 67 with HRU 111\n", - "Filling downHRUindex of HRU 111 with HRU 0\n", - "Filling downHRUindex of HRU 19 with HRU 68\n", - "Filling downHRUindex of HRU 68 with HRU 0\n", - "Filling downHRUindex of HRU 20 with HRU 69\n", - "Filling downHRUindex of HRU 69 with HRU 112\n", - "Filling downHRUindex of HRU 112 with HRU 0\n", - "Filling downHRUindex of HRU 21 with HRU 70\n", - "Filling downHRUindex of HRU 70 with HRU 0\n", - "Filling downHRUindex of HRU 22 with HRU 71\n", - "Filling downHRUindex of HRU 71 with HRU 0\n", - "Filling downHRUindex of HRU 23 with HRU 72\n", - "Filling downHRUindex of HRU 72 with HRU 0\n", - "Filling downHRUindex of HRU 24 with HRU 73\n", - "Filling downHRUindex of HRU 73 with HRU 0\n", - "Filling downHRUindex of HRU 25 with HRU 74\n", - "Filling downHRUindex of HRU 74 with HRU 113\n", - "Filling downHRUindex of HRU 113 with HRU 0\n", - "Filling downHRUindex of HRU 26 with HRU 75\n", - "Filling downHRUindex of HRU 75 with HRU 114\n", - "Filling downHRUindex of HRU 114 with HRU 0\n", - "Filling downHRUindex of HRU 27 with HRU 76\n", - "Filling downHRUindex of HRU 76 with HRU 0\n", - "Filling downHRUindex of HRU 28 with HRU 77\n", - "Filling downHRUindex of HRU 77 with HRU 0\n", - "Filling downHRUindex of HRU 29 with HRU 78\n", - "Filling downHRUindex of HRU 78 with HRU 0\n", - "Filling downHRUindex of HRU 30 with HRU 79\n", - "Filling downHRUindex of HRU 79 with HRU 0\n", - "Filling downHRUindex of HRU 31 with HRU 80\n", - "Filling downHRUindex of HRU 80 with HRU 115\n", - "Filling downHRUindex of HRU 115 with HRU 0\n", - "Filling downHRUindex of HRU 32 with HRU 81\n", - "Filling downHRUindex of HRU 81 with HRU 0\n", - "Filling downHRUindex of HRU 33 with HRU 82\n", - "Filling downHRUindex of HRU 82 with HRU 116\n", - "Filling downHRUindex of HRU 116 with HRU 0\n", - "Filling downHRUindex of HRU 34 with HRU 83\n", - "Filling downHRUindex of HRU 83 with HRU 0\n", - "Filling downHRUindex of HRU 35 with HRU 84\n", - "Filling downHRUindex of HRU 84 with HRU 117\n", - "Filling downHRUindex of HRU 117 with HRU 0\n", - "Filling downHRUindex of HRU 36 with HRU 85\n", - "Filling downHRUindex of HRU 85 with HRU 0\n", - "Filling downHRUindex of HRU 37 with HRU 86\n", - "Filling downHRUindex of HRU 86 with HRU 0\n", - "Filling downHRUindex of HRU 38 with HRU 87\n", - "Filling downHRUindex of HRU 87 with HRU 0\n", - "Filling downHRUindex of HRU 39 with HRU 88\n", - "Filling downHRUindex of HRU 88 with HRU 0\n", - "Filling downHRUindex of HRU 40 with HRU 89\n", - "Filling downHRUindex of HRU 89 with HRU 0\n", - "Filling downHRUindex of HRU 41 with HRU 90\n", - "Filling downHRUindex of HRU 90 with HRU 0\n", - "Filling downHRUindex of HRU 42 with HRU 91\n", - "Filling downHRUindex of HRU 91 with HRU 0\n", - "Filling downHRUindex of HRU 43 with HRU 92\n", - "Filling downHRUindex of HRU 92 with HRU 0\n", - "Filling downHRUindex of HRU 44 with HRU 93\n", - "Filling downHRUindex of HRU 93 with HRU 0\n", - "Filling downHRUindex of HRU 45 with HRU 94\n", - "Filling downHRUindex of HRU 94 with HRU 0\n", - "Filling downHRUindex of HRU 46 with HRU 95\n", - "Filling downHRUindex of HRU 95 with HRU 0\n", - "Filling downHRUindex of HRU 47 with HRU 96\n", - "Filling downHRUindex of HRU 96 with HRU 0\n" - ] - } - ], - "source": [ - "# Find the downHRUindex value if requested\n", - "if do_downHRUindex.lower() == 'yes':\n", - " \n", - " # Find the unique GRU IDs\n", - " gru_ids = shp[intersect_gruId_var].unique()\n", - " \n", - " # Create a temporary field we'll fill\n", - " shp['downHRUindex'] = 0\n", - " \n", - " # Make hruId the index\n", - " shp.set_index(intersect_hruId_var, inplace=True)\n", - " \n", - " # Loop over the GRUs\n", - " for gru_id in gru_ids:\n", - " \n", - " # Select only the GRU we're currently working on\n", - " gru_mask = (shp[intersect_gruId_var] == gru_id)\n", - " \n", - " # Find the soring order of HRUs in this GRU based on their elevations\n", - " tmp_sort = shp[gru_mask]['elev_mean'].argsort()\n", - " \n", - " # Loop over the HRUs in this GRU and set their downHRUindex in the shapefile\n", - " HRUs_seen = 0\n", - " last_HRU = 0\n", - " for HRU,order in tmp_sort.iteritems():\n", - " if order == 0: \n", - " # most downstream HRU\n", - " print('Filling downHRUindex of HRU {} with HRU {}'.format(last_HRU,HRU)) \n", - " print('Filling downHRUindex of HRU {} with HRU {}'.format(HRU,0)) \n", - " if last_HRU != 0: # If there are more HRUs in this GRU ...\n", - " shp.at[last_HRU, 'downHRUindex'] = int(HRU) # fill the second-to last and also ...\n", - " shp.at[HRU, 'downHRUindex'] = 0 # fill the last (possibly only) HRU\n", - " elif HRUs_seen > 0:\n", - " # not the first iteration\n", - " print('Filling downHRUindex of HRU {} with HRU {}'.format(last_HRU,HRU)) \n", - " shp.at[last_HRU, 'downHRUindex'] = int(HRU)\n", - " HRUs_seen += 1\n", - " last_HRU = HRU\n", - " \n", - " # Reset the index\n", - " shp.reset_index(inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Open the attributes file and fill the placeholder values in the attributes file" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Replacing elevation 2181.1250443522135 [m] with 2181.1250443522135 [m] at HRU 1\n", - "Replacing downHRUindex 49 [m] with 49 [m] at HRU 1\n", - "Replacing elevation 2232.4051443401136 [m] with 2232.4051443401136 [m] at HRU 2\n", - "Replacing downHRUindex 50 [m] with 50 [m] at HRU 2\n", - "Replacing elevation 2301.8800536404706 [m] with 2301.8800536404706 [m] at HRU 3\n", - "Replacing downHRUindex 51 [m] with 51 [m] at HRU 3\n", - "Replacing elevation 2240.0869338322423 [m] with 2240.0869338322423 [m] at HRU 4\n", - "Replacing downHRUindex 52 [m] with 52 [m] at HRU 4\n", - "Replacing elevation 2288.068843562057 [m] with 2288.068843562057 [m] at HRU 5\n", - "Replacing downHRUindex 53 [m] with 53 [m] at HRU 5\n", - "Replacing elevation 2331.4548456084212 [m] with 2331.4548456084212 [m] at HRU 6\n", - "Replacing downHRUindex 54 [m] with 54 [m] at HRU 6\n", - "Replacing elevation 2253.423467422212 [m] with 2253.423467422212 [m] at HRU 7\n", - "Replacing downHRUindex 55 [m] with 55 [m] at HRU 7\n", - "Replacing elevation 2263.222648965515 [m] with 2263.222648965515 [m] at HRU 8\n", - "Replacing downHRUindex 56 [m] with 56 [m] at HRU 8\n", - "Replacing elevation 2256.672683114219 [m] with 2256.672683114219 [m] at HRU 9\n", - "Replacing downHRUindex 57 [m] with 57 [m] at HRU 9\n", - "Replacing elevation 2106.7463736185214 [m] with 2106.7463736185214 [m] at HRU 10\n", - "Replacing downHRUindex 58 [m] with 58 [m] at HRU 10\n", - "Replacing elevation 2306.797862832942 [m] with 2306.797862832942 [m] at HRU 11\n", - "Replacing downHRUindex 60 [m] with 60 [m] at HRU 11\n", - "Replacing elevation 2429.18112792269 [m] with 2429.18112792269 [m] at HRU 12\n", - "Replacing downHRUindex 61 [m] with 61 [m] at HRU 12\n", - "Replacing elevation 2193.0411298393024 [m] with 2193.0411298393024 [m] at HRU 13\n", - "Replacing downHRUindex 62 [m] with 62 [m] at HRU 13\n", - "Replacing elevation 2251.4697985453454 [m] with 2251.4697985453454 [m] at HRU 14\n", - "Replacing downHRUindex 63 [m] with 63 [m] at HRU 14\n", - "Replacing elevation 2262.1000473788886 [m] with 2262.1000473788886 [m] at HRU 15\n", - "Replacing downHRUindex 64 [m] with 64 [m] at HRU 15\n", - "Replacing elevation 2311.798003889686 [m] with 2311.798003889686 [m] at HRU 16\n", - "Replacing downHRUindex 65 [m] with 65 [m] at HRU 16\n", - "Replacing elevation 2293.1301961330055 [m] with 2293.1301961330055 [m] at HRU 17\n", - "Replacing downHRUindex 66 [m] with 66 [m] at HRU 17\n", - "Replacing elevation 2341.9224303943306 [m] with 2341.9224303943306 [m] at HRU 18\n", - "Replacing downHRUindex 67 [m] with 67 [m] at HRU 18\n", - "Replacing elevation 2380.264395329075 [m] with 2380.264395329075 [m] at HRU 19\n", - "Replacing downHRUindex 68 [m] with 68 [m] at HRU 19\n", - "Replacing elevation 2301.928243692535 [m] with 2301.928243692535 [m] at HRU 20\n", - "Replacing downHRUindex 69 [m] with 69 [m] at HRU 20\n", - "Replacing elevation 2257.3265592997454 [m] with 2257.3265592997454 [m] at HRU 21\n", - "Replacing downHRUindex 70 [m] with 70 [m] at HRU 21\n", - "Replacing elevation 2352.439437695002 [m] with 2352.439437695002 [m] at HRU 22\n", - "Replacing downHRUindex 71 [m] with 71 [m] at HRU 22\n", - "Replacing elevation 2333.8318323918893 [m] with 2333.8318323918893 [m] at HRU 23\n", - "Replacing downHRUindex 72 [m] with 72 [m] at HRU 23\n", - "Replacing elevation 2304.6155485563413 [m] with 2304.6155485563413 [m] at HRU 24\n", - "Replacing downHRUindex 73 [m] with 73 [m] at HRU 24\n", - "Replacing elevation 2282.8794914959362 [m] with 2282.8794914959362 [m] at HRU 25\n", - "Replacing downHRUindex 74 [m] with 74 [m] at HRU 25\n", - "Replacing elevation 2265.0246623817884 [m] with 2265.0246623817884 [m] at HRU 26\n", - "Replacing downHRUindex 75 [m] with 75 [m] at HRU 26\n", - "Replacing elevation 2234.6343787747865 [m] with 2234.6343787747865 [m] at HRU 27\n", - "Replacing downHRUindex 76 [m] with 76 [m] at HRU 27\n", - "Replacing elevation 2362.721576433616 [m] with 2362.721576433616 [m] at HRU 28\n", - "Replacing downHRUindex 77 [m] with 77 [m] at HRU 28\n", - "Replacing elevation 2327.208907950236 [m] with 2327.208907950236 [m] at HRU 29\n", - "Replacing downHRUindex 78 [m] with 78 [m] at HRU 29\n", - "Replacing elevation 2329.113305513756 [m] with 2329.113305513756 [m] at HRU 30\n", - "Replacing downHRUindex 79 [m] with 79 [m] at HRU 30\n", - "Replacing elevation 2305.7710661256215 [m] with 2305.7710661256215 [m] at HRU 31\n", - "Replacing downHRUindex 80 [m] with 80 [m] at HRU 31\n", - "Replacing elevation 2319.7143563098407 [m] with 2319.7143563098407 [m] at HRU 32\n", - "Replacing downHRUindex 81 [m] with 81 [m] at HRU 32\n", - "Replacing elevation 2415.539345052886 [m] with 2415.539345052886 [m] at HRU 33\n", - "Replacing downHRUindex 82 [m] with 82 [m] at HRU 33\n", - "Replacing elevation 2413.054304645063 [m] with 2413.054304645063 [m] at HRU 34\n", - "Replacing downHRUindex 83 [m] with 83 [m] at HRU 34\n", - "Replacing elevation 2371.327424044896 [m] with 2371.327424044896 [m] at HRU 35\n", - "Replacing downHRUindex 84 [m] with 84 [m] at HRU 35\n", - "Replacing elevation 2492.927133332092 [m] with 2492.927133332092 [m] at HRU 36\n", - "Replacing downHRUindex 85 [m] with 85 [m] at HRU 36\n", - "Replacing elevation 2357.0340437189216 [m] with 2357.0340437189216 [m] at HRU 37\n", - "Replacing downHRUindex 86 [m] with 86 [m] at HRU 37\n", - "Replacing elevation 2516.085360689106 [m] with 2516.085360689106 [m] at HRU 38\n", - "Replacing downHRUindex 87 [m] with 87 [m] at HRU 38\n", - "Replacing elevation 2399.790764952759 [m] with 2399.790764952759 [m] at HRU 39\n", - "Replacing downHRUindex 88 [m] with 88 [m] at HRU 39\n", - "Replacing elevation 2382.656989395468 [m] with 2382.656989395468 [m] at HRU 40\n", - "Replacing downHRUindex 89 [m] with 89 [m] at HRU 40\n", - "Replacing elevation 2364.009798590133 [m] with 2364.009798590133 [m] at HRU 41\n", - "Replacing downHRUindex 90 [m] with 90 [m] at HRU 41\n", - "Replacing elevation 2485.7857243688713 [m] with 2485.7857243688713 [m] at HRU 42\n", - "Replacing downHRUindex 91 [m] with 91 [m] at HRU 42\n", - "Replacing elevation 2413.538904729149 [m] with 2413.538904729149 [m] at HRU 43\n", - "Replacing downHRUindex 92 [m] with 92 [m] at HRU 43\n", - "Replacing elevation 2552.319670473011 [m] with 2552.319670473011 [m] at HRU 44\n", - "Replacing downHRUindex 93 [m] with 93 [m] at HRU 44\n", - "Replacing elevation 2430.2113419923253 [m] with 2430.2113419923253 [m] at HRU 45\n", - "Replacing downHRUindex 94 [m] with 94 [m] at HRU 45\n", - "Replacing elevation 2407.4173527545177 [m] with 2407.4173527545177 [m] at HRU 46\n", - "Replacing downHRUindex 95 [m] with 95 [m] at HRU 46\n", - "Replacing elevation 2398.2599948145635 [m] with 2398.2599948145635 [m] at HRU 47\n", - "Replacing downHRUindex 96 [m] with 96 [m] at HRU 47\n", - "Replacing elevation 2113.275575029089 [m] with 2113.275575029089 [m] at HRU 48\n", - "Replacing downHRUindex 97 [m] with 97 [m] at HRU 48\n", - "Replacing elevation 1704.8815968540737 [m] with 1704.8815968540737 [m] at HRU 49\n", - "Replacing downHRUindex 100 [m] with 100 [m] at HRU 49\n", - "Replacing elevation 1775.0189928296886 [m] with 1775.0189928296886 [m] at HRU 50\n", - "Replacing downHRUindex 101 [m] with 101 [m] at HRU 50\n", - "Replacing elevation 1744.985064444409 [m] with 1744.985064444409 [m] at HRU 51\n", - "Replacing downHRUindex 102 [m] with 102 [m] at HRU 51\n", - "Replacing elevation 1665.4638240180705 [m] with 1665.4638240180705 [m] at HRU 52\n", - "Replacing downHRUindex 103 [m] with 103 [m] at HRU 52\n", - "Replacing elevation 1738.7191887190781 [m] with 1738.7191887190781 [m] at HRU 53\n", - "Replacing downHRUindex 104 [m] with 104 [m] at HRU 53\n", - "Replacing elevation 1684.8137203694423 [m] with 1684.8137203694423 [m] at HRU 54\n", - "Replacing downHRUindex 105 [m] with 105 [m] at HRU 54\n", - "Replacing elevation 1678.4244692442846 [m] with 1678.4244692442846 [m] at HRU 55\n", - "Replacing downHRUindex 106 [m] with 106 [m] at HRU 55\n", - "Replacing elevation 1679.4948833739265 [m] with 1679.4948833739265 [m] at HRU 56\n", - "Replacing downHRUindex 107 [m] with 107 [m] at HRU 56\n", - "Replacing elevation 1709.6730152445498 [m] with 1709.6730152445498 [m] at HRU 57\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 57\n", - "Replacing elevation 1651.6449719923164 [m] with 1651.6449719923164 [m] at HRU 58\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 58\n", - "Replacing elevation 1618.189980397106 [m] with 1618.189980397106 [m] at HRU 59\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 59\n", - "Replacing elevation 1763.1302202019258 [m] with 1763.1302202019258 [m] at HRU 60\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 60\n", - "Replacing elevation 1845.3284817415126 [m] with 1845.3284817415126 [m] at HRU 61\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 61\n", - "Replacing elevation 1659.4221740126222 [m] with 1659.4221740126222 [m] at HRU 62\n", - "Replacing downHRUindex 108 [m] with 108 [m] at HRU 62\n", - "Replacing elevation 1727.5450937962864 [m] with 1727.5450937962864 [m] at HRU 63\n", - "Replacing downHRUindex 109 [m] with 109 [m] at HRU 63\n", - "Replacing elevation 1706.2268354907878 [m] with 1706.2268354907878 [m] at HRU 64\n", - "Replacing downHRUindex 110 [m] with 110 [m] at HRU 64\n", - "Replacing elevation 1796.2653492576833 [m] with 1796.2653492576833 [m] at HRU 65\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 65\n", - "Replacing elevation 1745.9620791015625 [m] with 1745.9620791015625 [m] at HRU 66\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 66\n", - "Replacing elevation 1718.4629421245725 [m] with 1718.4629421245725 [m] at HRU 67\n", - "Replacing downHRUindex 111 [m] with 111 [m] at HRU 67\n", - "Replacing elevation 1891.5113396812012 [m] with 1891.5113396812012 [m] at HRU 68\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 68\n", - "Replacing elevation 1774.5584678019272 [m] with 1774.5584678019272 [m] at HRU 69\n", - "Replacing downHRUindex 112 [m] with 112 [m] at HRU 69\n", - "Replacing elevation 1820.6775236419758 [m] with 1820.6775236419758 [m] at HRU 70\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 70\n", - "Replacing elevation 1851.7455949884697 [m] with 1851.7455949884697 [m] at HRU 71\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 71\n", - "Replacing elevation 1862.8642035457174 [m] with 1862.8642035457174 [m] at HRU 72\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 72\n", - "Replacing elevation 1829.3687234984898 [m] with 1829.3687234984898 [m] at HRU 73\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 73\n", - "Replacing elevation 1793.0929272320675 [m] with 1793.0929272320675 [m] at HRU 74\n", - "Replacing downHRUindex 113 [m] with 113 [m] at HRU 74\n", - "Replacing elevation 1753.8234884600572 [m] with 1753.8234884600572 [m] at HRU 75\n", - "Replacing downHRUindex 114 [m] with 114 [m] at HRU 75\n", - "Replacing elevation 1933.919295247396 [m] with 1933.919295247396 [m] at HRU 76\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 76\n", - "Replacing elevation 1900.0878893197387 [m] with 1900.0878893197387 [m] at HRU 77\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 77\n", - "Replacing elevation 1874.36350994975 [m] with 1874.36350994975 [m] at HRU 78\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 78\n", - "Replacing elevation 1861.6985952637085 [m] with 1861.6985952637085 [m] at HRU 79\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 79\n", - "Replacing elevation 1769.9297779075466 [m] with 1769.9297779075466 [m] at HRU 80\n", - "Replacing downHRUindex 115 [m] with 115 [m] at HRU 80\n", - "Replacing elevation 1845.7322665075606 [m] with 1845.7322665075606 [m] at HRU 81\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 81\n", - "Replacing elevation 1865.9531506917563 [m] with 1865.9531506917563 [m] at HRU 82\n", - "Replacing downHRUindex 116 [m] with 116 [m] at HRU 82\n", - "Replacing elevation 1874.7576618478367 [m] with 1874.7576618478367 [m] at HRU 83\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 83\n", - "Replacing elevation 1828.3330862389837 [m] with 1828.3330862389837 [m] at HRU 84\n", - "Replacing downHRUindex 117 [m] with 117 [m] at HRU 84\n", - "Replacing elevation 1797.1932520877829 [m] with 1797.1932520877829 [m] at HRU 85\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 85\n", - "Replacing elevation 1715.6011860771387 [m] with 1715.6011860771387 [m] at HRU 86\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 86\n", - "Replacing elevation 1879.1256612340999 [m] with 1879.1256612340999 [m] at HRU 87\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 87\n", - "Replacing elevation 1909.6004163864877 [m] with 1909.6004163864877 [m] at HRU 88\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 88\n", - "Replacing elevation 1758.5804202763911 [m] with 1758.5804202763911 [m] at HRU 89\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 89\n", - "Replacing elevation 1907.6234737221712 [m] with 1907.6234737221712 [m] at HRU 90\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 90\n", - "Replacing elevation 1815.5280036661727 [m] with 1815.5280036661727 [m] at HRU 91\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 91\n", - "Replacing elevation 1923.8615226081024 [m] with 1923.8615226081024 [m] at HRU 92\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 92\n", - "Replacing elevation 1929.2600221445862 [m] with 1929.2600221445862 [m] at HRU 93\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 93\n", - "Replacing elevation 1946.3547414463771 [m] with 1946.3547414463771 [m] at HRU 94\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 94\n", - "Replacing elevation 1934.536977016752 [m] with 1934.536977016752 [m] at HRU 95\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 95\n", - "Replacing elevation 1931.3799124358497 [m] with 1931.3799124358497 [m] at HRU 96\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 96\n", - "Replacing elevation 1694.167915267689 [m] with 1694.167915267689 [m] at HRU 97\n", - "Replacing downHRUindex 118 [m] with 118 [m] at HRU 97\n", - "Replacing elevation 1809.3268275310083 [m] with 1809.3268275310083 [m] at HRU 98\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 98\n", - "Replacing elevation 1542.3192467322717 [m] with 1542.3192467322717 [m] at HRU 99\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 99\n", - "Replacing elevation 1402.249187127386 [m] with 1402.249187127386 [m] at HRU 100\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 100\n", - "Replacing elevation 1419.5045562444948 [m] with 1419.5045562444948 [m] at HRU 101\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 101\n", - "Replacing elevation 1422.0794577998156 [m] with 1422.0794577998156 [m] at HRU 102\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 102\n", - "Replacing elevation 1439.8104171266095 [m] with 1439.8104171266095 [m] at HRU 103\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 103\n", - "Replacing elevation 1443.0692795596449 [m] with 1443.0692795596449 [m] at HRU 104\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 104\n", - "Replacing elevation 1464.18726551272 [m] with 1464.18726551272 [m] at HRU 105\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 105\n", - "Replacing elevation 1494.287981862811 [m] with 1494.287981862811 [m] at HRU 106\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 106\n", - "Replacing elevation 1499.0364102450285 [m] with 1499.0364102450285 [m] at HRU 107\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 107\n", - "Replacing elevation 1435.8498362714915 [m] with 1435.8498362714915 [m] at HRU 108\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 108\n", - "Replacing elevation 1415.8900537939314 [m] with 1415.8900537939314 [m] at HRU 109\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 109\n", - "Replacing elevation 1487.8250122070312 [m] with 1487.8250122070312 [m] at HRU 110\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 110\n", - "Replacing elevation 1470.2656053112398 [m] with 1470.2656053112398 [m] at HRU 111\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 111\n", - "Replacing elevation 1492.0629973234954 [m] with 1492.0629973234954 [m] at HRU 112\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 112\n", - "Replacing elevation 1484.7316059313323 [m] with 1484.7316059313323 [m] at HRU 113\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 113\n", - "Replacing elevation 1442.9007405891693 [m] with 1442.9007405891693 [m] at HRU 114\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 114\n", - "Replacing elevation 1467.2970581054688 [m] with 1467.2970581054688 [m] at HRU 115\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 115\n", - "Replacing elevation 1502.0999755859375 [m] with 1502.0999755859375 [m] at HRU 116\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 116\n", - "Replacing elevation 1440.976313954776 [m] with 1440.976313954776 [m] at HRU 117\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 117\n", - "Replacing elevation 1409.597142016794 [m] with 1409.597142016794 [m] at HRU 118\n", - "Replacing downHRUindex 0 [m] with 0 [m] at HRU 118\n" - ] - } - ], - "source": [ - "# Open the netcdf file for reading+writing\n", - "with nc4.Dataset(attribute_path/attribute_name, \"r+\") as att:\n", - " \n", - " # Loop over the HRUs in the attributes\n", - " for idx in range(0,len(att['hruId'])):\n", - " \n", - " # Find the HRU ID (attributes file) at this index\n", - " attribute_hru = att['hruId'][idx]\n", - " \n", - " # Find the row in the shapefile that contains info for this HRU\n", - " shp_mask = (shp[intersect_hruId_var].astype(int) == attribute_hru)\n", - " \n", - " # Find the elevation & downHRUindex\n", - " tmp_elev = shp['elev_mean'][shp_mask].values[0]\n", - " tmp_down = shp['downHRUindex'][shp_mask].values[0]\n", - " \n", - " # Replace the value\n", - " print('Replacing elevation {} [m] with {} [m] at HRU {}'.format(att['elevation'][idx],tmp_elev,attribute_hru))\n", - " att['elevation'][idx] = tmp_elev\n", - " \n", - " if do_downHRUindex.lower() == 'yes':\n", - " print('Replacing downHRUindex {} with {} at HRU {}'.format(att['downHRUindex'][idx],tmp_down,attribute_hru))\n", - " att['downHRUindex'][idx] = tmp_down" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check\n", - "Plot the elevations." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the files \n", - "shp = gpd.read_file(intersect_path/intersect_name)\n", - "att = xr.open_dataset(attribute_path/attribute_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Set HRU IDs as index in the shape\n", - "shp = shp.set_index(intersect_hruId_var)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort the shape to match the order of the attributes\n", - "shp = shp.loc[att['hruId'].values]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# temporarily store the soiltype in the shape for plotting\n", - "shp['elevation'] = att['elevation'][:]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Make a plot to see what we did\n", - "shp.plot(column='elevation',figsize=(16,8), legend=True,edgecolor='k')\n", - "ax = plt.gca()\n", - "ax.set_title('MERIT-derived elevation');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Close the netCDF file\n", - "att.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = attribute_path\n", - "log_suffix = '_add_elevation_to_attributes.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '2c_insert_elevation_into_attributes.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Added elevation to attributes .nc file.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/mizuRoute/1a_copy_base_settings/1_copy_base_settings.ipynb b/5_model_input/mizuRoute/1a_copy_base_settings/1_copy_base_settings.ipynb deleted file mode 100644 index 02d1c46..0000000 --- a/5_model_input/mizuRoute/1a_copy_base_settings/1_copy_base_settings.ipynb +++ /dev/null @@ -1,274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Copy base settings\n", - "Copies the base settings into the mizuRoute settings folder." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define where the base settings are" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Base settings\n", - "base_settings_path = Path('../0_base_settings')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the settings need to go" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Settings path \n", - "settings_path = read_from_control(controlFolder/controlFile,'settings_mizu_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if settings_path == 'default':\n", - " settings_path = make_default_path('settings/mizuRoute') # outputs a Path()\n", - "else:\n", - " settings_path = Path(settings_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "settings_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Copy the settings" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Loop over all files and copy\n", - "for file in os.listdir(base_settings_path): \n", - " \n", - " # skip readme \n", - " # some users have reported that a notebook checkpoint folder mysteriously appears - skip that too\n", - " if 'readme.md' not in file.lower() and '.ipynb_checkpoints' not in file.lower():\n", - " copyfile(base_settings_path/file, settings_path/file);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = settings_path\n", - "log_suffix = '_copy_base_settings.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_copy_base_settings.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Copied the mizuRoute base settings.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/mizuRoute/1b_network_topology_file/1_create_network_topology_file.ipynb b/5_model_input/mizuRoute/1b_network_topology_file/1_create_network_topology_file.ipynb deleted file mode 100644 index 173ca62..0000000 --- a/5_model_input/mizuRoute/1b_network_topology_file/1_create_network_topology_file.ipynb +++ /dev/null @@ -1,473 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create network topology .nc file\n", - "Core assumption: routing is only performed between GRUs. It is recommended to route the runoff from HRUs inside a given GRU with SUMMA instead. This allows for lateral flows between HRUs. Routing HRU runoff with mizuRoute means all HRUs within a given GRU are effectively disconnected. \n", - "\n", - "**_The code here does not generalize to HRU-routing with mizuRoute without changes._**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import pandas as pd\n", - "import netCDF4 as nc4\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of river network shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# River network shapefile path & name\n", - "river_network_path = read_from_control(controlFolder/controlFile,'river_network_shp_path')\n", - "river_network_name = read_from_control(controlFolder/controlFile,'river_network_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if river_network_path == 'default':\n", - " river_network_path = make_default_path('shapefiles/river_network') # outputs a Path()\n", - "else:\n", - " river_network_path = Path(river_network_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the field names we're after\n", - "river_seg_id = read_from_control(controlFolder/controlFile,'river_network_shp_segid')\n", - "river_down_seg_id = read_from_control(controlFolder/controlFile,'river_network_shp_downsegid')\n", - "river_slope = read_from_control(controlFolder/controlFile,'river_network_shp_slope')\n", - "river_length = read_from_control(controlFolder/controlFile,'river_network_shp_length')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of river basin shapefile (routing catchments)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# River network shapefile path & name\n", - "river_basin_path = read_from_control(controlFolder/controlFile,'river_basin_shp_path')\n", - "river_basin_name = read_from_control(controlFolder/controlFile,'river_basin_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if river_basin_path == 'default':\n", - " river_basin_path = make_default_path('shapefiles/river_basins') # outputs a Path()\n", - "else:\n", - " river_basin_path = Path(river_basin_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the field names we're after\n", - "basin_hru_id = read_from_control(controlFolder/controlFile,'river_basin_shp_rm_hruid')\n", - "basin_hru_area = read_from_control(controlFolder/controlFile,'river_basin_shp_area')\n", - "basin_hru_to_seg = read_from_control(controlFolder/controlFile,'river_basin_shp_hru_to_seg')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the topology file needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Topology .nc path and name\n", - "topology_path = read_from_control(controlFolder/controlFile,'settings_mizu_path')\n", - "topology_name = read_from_control(controlFolder/controlFile,'settings_mizu_topology')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if topology_path == 'default':\n", - " topology_path = make_default_path('settings/mizuRoute') # outputs a Path()\n", - "else:\n", - " topology_path = Path(topology_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "topology_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find if we need to enforce any segments as outlet(s)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the setting in the control file\n", - "river_outlet_ids = read_from_control(controlFolder/controlFile,'settings_mizu_make_outlet')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set flag and convert variable type if needed\n", - "if 'n/a' in river_outlet_ids:\n", - " enforce_outlets = False\n", - "else:\n", - " enforce_outlets = True\n", - " river_outlet_ids = river_outlet_ids.split(',') # does nothing if string contains no comma\n", - " river_outlet_ids = [int(outlet_id) for outlet_id in river_outlet_ids] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make the river network topology file " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the shapefile\n", - "shp_river = gpd.read_file(river_network_path/river_network_name)\n", - "shp_basin = gpd.read_file(river_basin_path/river_basin_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the number of segments and mizuRoute-HRUs (SUMMA-GRUs)\n", - "num_seg = len(shp_river)\n", - "num_hru = len(shp_basin)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Ensure that any segments specified in the control file are identified to mizuRoute as outlets, by setting the downstream segment to 0\n", - "# This indicates to mizuRoute that this segment has no downstream segment attached to it; i.e. is an outlet\n", - "if enforce_outlets:\n", - " for outlet_id in river_outlet_ids:\n", - " if any(shp_river[river_seg_id] == outlet_id):\n", - " shp_river.loc[shp_river[river_seg_id] == outlet_id, river_down_seg_id] = 0\n", - " else:\n", - " print('outlet_id {} not found in {}'.format(outlet_id,river_seg_id))\n", - " \n", - "# Ensure that any segment with length 0 is set to 1m to avoid tripping mizuRoute\n", - "shp_river.loc[shp_river[river_length] == 0, river_length] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to create new nc variables\n", - "def create_and_fill_nc_var(ncid, var_name, var_type, dim, fill_val, fill_data, long_name, units):\n", - " \n", - " # Make the variable\n", - " ncvar = ncid.createVariable(var_name, var_type, (dim,), fill_val)\n", - " \n", - " # Add the data\n", - " ncvar[:] = fill_data \n", - " \n", - " # Add meta data\n", - " ncvar.long_name = long_name \n", - " ncvar.unit = units\n", - " \n", - " return " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the netcdf file\n", - "with nc4.Dataset(topology_path/topology_name, 'w', format='NETCDF4') as ncid:\n", - " \n", - " # Set general attributes\n", - " now = datetime.now()\n", - " ncid.setncattr('Author', \"Created by SUMMA workflow scripts\")\n", - " ncid.setncattr('History','Created ' + now.strftime('%Y/%m/%d %H:%M:%S'))\n", - " ncid.setncattr('Purpose','Create a river network .nc file for mizuRoute routing')\n", - " \n", - " # Define the seg and hru dimensions\n", - " ncid.createDimension('seg', num_seg)\n", - " ncid.createDimension('hru', num_hru)\n", - " \n", - " # --- Variables\n", - " create_and_fill_nc_var(ncid, 'segId', 'int', 'seg', False, \\\n", - " shp_river[river_seg_id].values.astype(int), \\\n", - " 'Unique ID of each stream segment', '-')\n", - " create_and_fill_nc_var(ncid, 'downSegId', 'int', 'seg', False, \\\n", - " shp_river[river_down_seg_id].values.astype(int), \\\n", - " 'ID of the downstream segment', '-')\n", - " create_and_fill_nc_var(ncid, 'slope', 'f8', 'seg', False, \\\n", - " shp_river[river_slope].values.astype(float), \\\n", - " 'Segment slope', '-')\n", - " create_and_fill_nc_var(ncid, 'length', 'f8', 'seg', False, \\\n", - " shp_river[river_length].values.astype(float), \\\n", - " 'Segment length', 'm')\n", - " create_and_fill_nc_var(ncid, 'hruId', 'int', 'hru', False, \\\n", - " shp_basin[basin_hru_id].values.astype(int), \\\n", - " 'Unique hru ID', '-') \n", - " create_and_fill_nc_var(ncid, 'hruToSegId', 'int', 'hru', False, \\\n", - " shp_basin[basin_hru_to_seg].values.astype(int), \\\n", - " 'ID of the stream segment to which the HRU discharges', '-')\n", - " create_and_fill_nc_var(ncid, 'area', 'f8', 'hru', False, \\\n", - " shp_basin[basin_hru_area].values.astype(float), \\\n", - " 'HRU area', 'm^2')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = topology_path\n", - "log_suffix = '_make_river_network_topology.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_create_network_topology_file.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Generated network topology .nc file.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/mizuRoute/1b_network_topology_file/1_create_network_topology_file.py b/5_model_input/mizuRoute/1b_network_topology_file/1_create_network_topology_file.py index 318d042..5a6382a 100644 --- a/5_model_input/mizuRoute/1b_network_topology_file/1_create_network_topology_file.py +++ b/5_model_input/mizuRoute/1b_network_topology_file/1_create_network_topology_file.py @@ -104,22 +104,29 @@ def make_default_path(suffix): # Make the folder if it doesn't exist topology_path.mkdir(parents=True, exist_ok=True) -# --- Find if we need to enforce any segments as outlet(s) + + +# --- Make the river network topology file +# Open the shapefile +shp_river = gpd.read_file(river_network_path/river_network_name) +shp_basin = gpd.read_file(river_basin_path/river_basin_name) + +# Find if we need to enforce any segments as outlet(s) river_outlet_ids = read_from_control(controlFolder/controlFile,'settings_mizu_make_outlet') # Set flag and convert variable type if needed if 'n/a' in river_outlet_ids: enforce_outlets = False +elif 'default' in river_outlet_ids: + enforce_outlets = True + river_outlet_ids = shp_river[shp_river['NextDownID'] == 0]['COMID'].tolist() + else: enforce_outlets = True river_outlet_ids = river_outlet_ids.split(',') # does nothing if string contains no comma river_outlet_ids = [int(outlet_id) for outlet_id in river_outlet_ids] -# --- Make the river network topology file -# Open the shapefile -shp_river = gpd.read_file(river_network_path/river_network_name) -shp_basin = gpd.read_file(river_basin_path/river_basin_name) # Find the number of segments and mizuRoute-HRUs (SUMMA-GRUs) num_seg = len(shp_river) @@ -166,11 +173,11 @@ def create_and_fill_nc_var(ncid, var_name, var_type, dim, fill_val, fill_data, l ncid.createDimension('hru', num_hru) # --- Variables - create_and_fill_nc_var(ncid, 'segId', 'int', 'seg', False, \ - shp_river[river_seg_id].values.astype(int), \ + create_and_fill_nc_var(ncid, 'segId', 'i4', 'seg', False, \ + shp_river[river_seg_id].values.astype(float), \ 'Unique ID of each stream segment', '-') - create_and_fill_nc_var(ncid, 'downSegId', 'int', 'seg', False, \ - shp_river[river_down_seg_id].values.astype(int), \ + create_and_fill_nc_var(ncid, 'downSegId', 'i4', 'seg', False, \ + shp_river[river_down_seg_id].values.astype(float), \ 'ID of the downstream segment', '-') create_and_fill_nc_var(ncid, 'slope', 'f8', 'seg', False, \ shp_river[river_slope].values.astype(float), \ @@ -178,11 +185,11 @@ def create_and_fill_nc_var(ncid, var_name, var_type, dim, fill_val, fill_data, l create_and_fill_nc_var(ncid, 'length', 'f8', 'seg', False, \ shp_river[river_length].values.astype(float), \ 'Segment length', 'm') - create_and_fill_nc_var(ncid, 'hruId', 'int', 'hru', False, \ - shp_basin[basin_hru_id].values.astype(int), \ + create_and_fill_nc_var(ncid, 'hruId', 'i4', 'hru', False, \ + shp_basin[basin_hru_id].values.astype(float), \ 'Unique hru ID', '-') - create_and_fill_nc_var(ncid, 'hruToSegId', 'int', 'hru', False, \ - shp_basin[basin_hru_to_seg].values.astype(int), \ + create_and_fill_nc_var(ncid, 'hruToSegId', 'i4', 'hru', False, \ + shp_basin[basin_hru_to_seg].values.astype(float), \ 'ID of the stream segment to which the HRU discharges', '-') create_and_fill_nc_var(ncid, 'area', 'f8', 'hru', False, \ shp_basin[basin_hru_area].values.astype(float), \ diff --git a/5_model_input/mizuRoute/1b_network_topology_file/README.md b/5_model_input/mizuRoute/1b_network_topology_file/README.md deleted file mode 100644 index 1ef8bf1..0000000 --- a/5_model_input/mizuRoute/1b_network_topology_file/README.md +++ /dev/null @@ -1,11 +0,0 @@ -# Network topology -The network topology contains information about the stream network and the routing basins the network is in. These include: -1. Unique indices of the stream segment; -2. Unique indices of the routing basins (HRUs; equivalent to SUMMA GRUs in this setup); -3. ID of the stream segment each individual segment connects to (should be 0 or negative number to indicate that segment is an outlet); -4. ID of the stream segment a basin drains into; -5. Basin area; -6. Segment slope; -7. Segment length. - -Values for these settings are taken from the user's shapefiles. See: https://mizuroute.readthedocs.io/en/master/Input_data.html \ No newline at end of file diff --git a/5_model_input/mizuRoute/1c_optional_remapping_file/1_remap_summa_catchments_to_routing.ipynb b/5_model_input/mizuRoute/1c_optional_remapping_file/1_remap_summa_catchments_to_routing.ipynb deleted file mode 100644 index d9ef88f..0000000 --- a/5_model_input/mizuRoute/1c_optional_remapping_file/1_remap_summa_catchments_to_routing.ipynb +++ /dev/null @@ -1,624 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create SUMMA-to-mizuRoute catchment remapping file\n", - "Creates a remap `.nc` file for cases where the routing catchments are different from the catchments used to run SUMMA. While mizuRoute is able to perform routing from grid-based outputs, SUMMA does not produce these and the code here is not setup to work with gridded model outputs.\n", - "\n", - "**_This code assumes routing occurs at the GRU level of SUMMA outputs. SUMMA-HRU-level routing is not supported._**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import itertools\n", - "import pandas as pd\n", - "import netCDF4 as nc4\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "import easymore.easymore as esmr\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Check if remapping is needed" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the remap flag\n", - "do_remap = read_from_control(controlFolder/controlFile,'river_basin_needs_remap')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Check\n", - "if do_remap.lower() != 'yes':\n", - " print('Active control file indicates remapping is not needed. Aborting.')\n", - " exit()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of hydrologic model (HM) catchment shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# HM catchment shapefile path & name\n", - "hm_catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "hm_catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if hm_catchment_path == 'default':\n", - " hm_catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " hm_catchment_path = Path(hm_catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the fields we're interested in\n", - "hm_shp_gru_id = read_from_control(controlFolder/controlFile,'catchment_shp_gruid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of routing model (RM) catchment shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Routing model catchment shapefile path & name\n", - "rm_catchment_path = read_from_control(controlFolder/controlFile,'river_basin_shp_path')\n", - "rm_catchment_name = read_from_control(controlFolder/controlFile,'river_basin_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if rm_catchment_path == 'default':\n", - " rm_catchment_path = make_default_path('shapefiles/river_basins') # outputs a Path()\n", - "else:\n", - " rm_catchment_path = Path(rm_catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the fields we're interested in\n", - "rm_shp_hru_id = read_from_control(controlFolder/controlFile,'river_basin_shp_rm_hruid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the intersection needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersected shapefile path. Name is set by EASYMORE as [prefix]_intersected_shapefile.shp\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_routing_path')\n", - "intersect_name = read_from_control(controlFolder/controlFile,'intersect_routing_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_routing') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "intersect_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the remapping file needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Remap .nc path and name\n", - "remap_path = read_from_control(controlFolder/controlFile,'settings_mizu_path')\n", - "remap_name = read_from_control(controlFolder/controlFile,'settings_mizu_remap')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if remap_path == 'default':\n", - " remap_path = make_default_path('settings/mizuRoute') # outputs a Path()\n", - "else:\n", - " remap_path = Path(remap_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "remap_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Call EASYMORE to do the intersection " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Load both shapefiles\n", - "hm_shape = gpd.read_file(hm_catchment_path/hm_catchment_name)\n", - "rm_shape = gpd.read_file(rm_catchment_path/rm_catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a EASYMORE object\n", - "esmr_caller = esmr()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Project both shapes to equal area\n", - "hm_shape = hm_shape.to_crs('EPSG:6933')\n", - "rm_shape = rm_shape.to_crs('EPSG:6933')" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\wmk934\\Anaconda3\\envs\\geospatialTools_qgis_candex_hs\\lib\\site-packages\\candex\\candex.py:893: FutureWarning: Assigning CRS to a GeoDataFrame without a geometry column is now deprecated and will not be supported in the future.\n", - " pairs = gpd.GeoDataFrame(nei, columns=['idx1','idx2'], crs=df1.crs)\n", - "C:\\Users\\wmk934\\Anaconda3\\envs\\geospatialTools_qgis_candex_hs\\lib\\site-packages\\candex\\candex.py:897: FutureWarning: Assigning CRS to a GeoDataFrame without a geometry column is now deprecated and will not be supported in the future.\n", - " pairs = gpd.GeoDataFrame(pairs, columns=pairs.columns, crs=df1.crs)\n" - ] - } - ], - "source": [ - "# Run the intersection\n", - "intersected_shape = esmr.intersection_shp(esmr_caller,rm_shape,hm_shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Reproject the intersection to WSG84\n", - "intersected_shape = intersected_shape.to_crs('EPSG:4326')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\wmk934\\Anaconda3\\envs\\geospatialTools_qgis_candex_hs\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n", - " \n" - ] - } - ], - "source": [ - "# Save the intersection to file\n", - "intersected_shape.to_file(intersect_path/intersect_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Pre-process the variables" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a few shorthand variables\n", - "int_rm_id = 'S_1_' + rm_shp_hru_id\n", - "int_hm_id = 'S_2_' + hm_shp_gru_id\n", - "int_weight = 'AP1N'" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort the intersected shape by RM ID first, and HM ID second. This means all info per RM ID is in consecutive rows\n", - "intersected_shape = intersected_shape.sort_values(by=[int_rm_id,int_hm_id])" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Routing Network HRU ID\n", - "nc_rnhruid = intersected_shape.groupby(int_rm_id).agg({int_rm_id: pd.unique}).values.astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Number of Hydrologic Model elements (GRUs in SUMMA's case) per Routing Network catchment\n", - "nc_noverlaps = intersected_shape.groupby(int_rm_id).agg({int_hm_id: 'count'}).values.astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Hydrologic Model GRU IDs that are associated with each part of the overlap\n", - "multi_nested_list = intersected_shape.groupby(int_rm_id).agg({int_hm_id: list}).values.tolist() # Get the data\n", - "nc_hmgruid = list(itertools.chain.from_iterable(itertools.chain.from_iterable(multi_nested_list))) # Combine 3 nested list into 1" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# Areal weight of each HM GRU per part of the overlaps\n", - "multi_nested_list = intersected_shape.groupby(int_rm_id).agg({int_weight: list}).values.tolist() \n", - "nc_weight = list(itertools.chain.from_iterable(itertools.chain.from_iterable(multi_nested_list))) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make the `.nc` file" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the dimension sizes\n", - "num_hru = len(rm_shape)\n", - "num_data = len(intersected_shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to create new nc variables\n", - "def create_and_fill_nc_var(ncid, var_name, var_type, dim, fill_val, fill_data, long_name, units):\n", - " \n", - " # Make the variable\n", - " ncvar = ncid.createVariable(var_name, var_type, (dim,), fill_val)\n", - " \n", - " # Add the data\n", - " ncvar[:] = fill_data \n", - " \n", - " # Add meta data\n", - " ncvar.long_name = long_name \n", - " ncvar.unit = units\n", - " \n", - " return " - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the netcdf file\n", - "with nc4.Dataset(remap_path/remap_name, 'w', format='NETCDF4') as ncid:\n", - " \n", - " # Set general attributes\n", - " now = datetime.now()\n", - " ncid.setncattr('Author', \"Created by SUMMA workflow scripts\")\n", - " ncid.setncattr('History','Created ' + now.strftime('%Y/%m/%d %H:%M:%S'))\n", - " ncid.setncattr('Purpose','Create a remapping .nc file for mizuRoute routing')\n", - " \n", - " # Define the seg and hru dimensions\n", - " ncid.createDimension('hru', num_hru)\n", - " ncid.createDimension('data', num_data)\n", - " \n", - " # --- Variables\n", - " create_and_fill_nc_var(ncid, 'RN_hruId', 'int', 'hru', False, nc_rnhruid, \\\n", - " 'River network HRU ID', '-')\n", - " create_and_fill_nc_var(ncid, 'nOverlaps', 'int', 'hru', False, nc_noverlaps, \\\n", - " 'Number of overlapping HM_HRUs for each RN_HRU', '-')\n", - " create_and_fill_nc_var(ncid, 'HM_hruId', 'int', 'data', False, nc_hmgruid, \\\n", - " 'ID of overlapping HM_HRUs. Note that SUMMA calls these GRUs', '-')\n", - " create_and_fill_nc_var(ncid, 'weight', 'f8', 'data', False, nc_weight, \\\n", - " 'Areal weight of overlapping HM_HRUs. Note that SUMMA calls these GRUs', '-')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = remap_path\n", - "log_suffix = '_make_remapping_file.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_remap_summa_catchments_to_routing.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Generated remapping .nc file for Hydro model catchments to routing model catchments.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/mizuRoute/1c_optional_remapping_file/1_remap_summa_catchments_to_routing.py b/5_model_input/mizuRoute/1c_optional_remapping_file/1_remap_summa_catchments_to_routing.py deleted file mode 100644 index 964b748..0000000 --- a/5_model_input/mizuRoute/1c_optional_remapping_file/1_remap_summa_catchments_to_routing.py +++ /dev/null @@ -1,245 +0,0 @@ -# Create SUMMA-to-mizuRoute catchment remapping file -# Creates a remap `.nc` file for cases where the routing catchments are different from the catchments used to run SUMMA. While mizuRoute is able to perform routing from grid-based outputs, SUMMA does not produce these and the code here is not setup to work with gridded model outputs. -# -# **_This code assumes routing occurs at the GRU level of SUMMA outputs. SUMMA-HRU-level routing is not supported._** - -# modules -import itertools -import pandas as pd -import netCDF4 as nc4 -import geopandas as gpd -from pathlib import Path -from shutil import copyfile -import easymore.easymore as esmr -from datetime import datetime - - -# --- Control file handling -# Easy access to control file folder -controlFolder = Path('../../../0_control_files') - -# Store the name of the 'active' file in a variable -controlFile = 'control_active.txt' - -# Function to extract a given setting from the control file -def read_from_control( file, setting ): - - # Open 'control_active.txt' and ... - with open(file) as contents: - for line in contents: - - # ... find the line with the requested setting - if setting in line and not line.startswith('#'): - break - - # Extract the setting's value - substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) - substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found - substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines - - # Return this value - return substring - -# Function to specify a default path -def make_default_path(suffix): - - # Get the root path - rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) - - # Get the domain folder - domainName = read_from_control(controlFolder/controlFile,'domain_name') - domainFolder = 'domain_' + domainName - - # Specify the forcing path - defaultPath = rootPath / domainFolder / suffix - - return defaultPath - - -# --- Check if remapping is needed -# Get the remap flag -do_remap = read_from_control(controlFolder/controlFile,'river_basin_needs_remap') - -# Check -if do_remap.lower() != 'yes': - print('Active control file indicates remapping is not needed. Aborting.') - exit() - - -# --- Find location of hydrologic model (HM) catchment shapefile -# HM catchment shapefile path & name -hm_catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path') -hm_catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name') - -# Specify default path if needed -if hm_catchment_path == 'default': - hm_catchment_path = make_default_path('shapefiles/catchment') # outputs a Path() -else: - hm_catchment_path = Path(hm_catchment_path) # make sure a user-specified path is a Path() - -# Find the fields we're interested in -hm_shp_gru_id = read_from_control(controlFolder/controlFile,'catchment_shp_gruid') - - -# --- Find location of routing model (RM) catchment shapefile -# Routing model catchment shapefile path & name -rm_catchment_path = read_from_control(controlFolder/controlFile,'river_basin_shp_path') -rm_catchment_name = read_from_control(controlFolder/controlFile,'river_basin_shp_name') - -# Specify default path if needed -if rm_catchment_path == 'default': - rm_catchment_path = make_default_path('shapefiles/river_basins') # outputs a Path() -else: - rm_catchment_path = Path(rm_catchment_path) # make sure a user-specified path is a Path() - -# Find the fields we're interested in -rm_shp_hru_id = read_from_control(controlFolder/controlFile,'river_basin_shp_rm_hruid') - - -# --- Find where the intersection needs to go -# Intersected shapefile path. Name is set by EASYMORE as [prefix]_intersected_shapefile.shp -intersect_path = read_from_control(controlFolder/controlFile,'intersect_routing_path') -intersect_name = read_from_control(controlFolder/controlFile,'intersect_routing_name') - -# Specify default path if needed -if intersect_path == 'default': - intersect_path = make_default_path('shapefiles/catchment_intersection/with_routing') # outputs a Path() -else: - intersect_path = Path(intersect_path) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -intersect_path.mkdir(parents=True, exist_ok=True) - - -# --- Find where the remapping file needs to go -# Remap .nc path and name -remap_path = read_from_control(controlFolder/controlFile,'settings_mizu_path') -remap_name = read_from_control(controlFolder/controlFile,'settings_mizu_remap') - -# Specify default path if needed -if remap_path == 'default': - remap_path = make_default_path('settings/mizuRoute') # outputs a Path() -else: - remap_path = Path(remap_path) # make sure a user-specified path is a Path() - -# Make the folder if it doesn't exist -remap_path.mkdir(parents=True, exist_ok=True) - - -# --- Call EASYMORE to do the intersection -# Load both shapefiles -hm_shape = gpd.read_file(hm_catchment_path/hm_catchment_name) -rm_shape = gpd.read_file(rm_catchment_path/rm_catchment_name) - -# Create a EASYMORE object -esmr_caller = esmr() - -# Project both shapes to equal area -hm_shape = hm_shape.to_crs('EPSG:6933') -rm_shape = rm_shape.to_crs('EPSG:6933') - -# Run the intersection -intersected_shape = esmr.intersection_shp(esmr_caller,rm_shape,hm_shape) - -# Reproject the intersection to WSG84 -intersected_shape = intersected_shape.to_crs('EPSG:4326') - -# Save the intersection to file -intersected_shape.to_file(intersect_path/intersect_name) - - -# --- Pre-process the variables -# Define a few shorthand variables -int_rm_id = 'S_1_' + rm_shp_hru_id -int_hm_id = 'S_2_' + hm_shp_gru_id -int_weight = 'AP1N' - -# Sort the intersected shape by RM ID first, and HM ID second. This means all info per RM ID is in consecutive rows -intersected_shape = intersected_shape.sort_values(by=[int_rm_id,int_hm_id]) - -# Routing Network HRU ID -nc_rnhruid = intersected_shape.groupby(int_rm_id).agg({int_rm_id: pd.unique}).values.astype(int) - -# Number of Hydrologic Model elements (GRUs in SUMMA's case) per Routing Network catchment -nc_noverlaps = intersected_shape.groupby(int_rm_id).agg({int_hm_id: 'count'}).values.astype(int) - -# Hydrologic Model GRU IDs that are associated with each part of the overlap -multi_nested_list = intersected_shape.groupby(int_rm_id).agg({int_hm_id: list}).values.tolist() # Get the data -nc_hmgruid = list(itertools.chain.from_iterable(itertools.chain.from_iterable(multi_nested_list))) # Combine 3 nested list into 1 - -# Areal weight of each HM GRU per part of the overlaps -multi_nested_list = intersected_shape.groupby(int_rm_id).agg({int_weight: list}).values.tolist() -nc_weight = list(itertools.chain.from_iterable(itertools.chain.from_iterable(multi_nested_list))) - - -# --- Make the `.nc` file -# Find the dimension sizes -num_hru = len(rm_shape) -num_data = len(intersected_shape) - -# Function to create new nc variables -def create_and_fill_nc_var(ncid, var_name, var_type, dim, fill_val, fill_data, long_name, units): - - # Make the variable - ncvar = ncid.createVariable(var_name, var_type, (dim,), fill_val) - - # Add the data - ncvar[:] = fill_data - - # Add meta data - ncvar.long_name = long_name - ncvar.unit = units - - return - -# Make the netcdf file -with nc4.Dataset(remap_path/remap_name, 'w', format='NETCDF4') as ncid: - - # Set general attributes - now = datetime.now() - ncid.setncattr('Author', "Created by SUMMA workflow scripts") - ncid.setncattr('History','Created ' + now.strftime('%Y/%m/%d %H:%M:%S')) - ncid.setncattr('Purpose','Create a remapping .nc file for mizuRoute routing') - - # Define the seg and hru dimensions - ncid.createDimension('hru', num_hru) - ncid.createDimension('data', num_data) - - # --- Variables - create_and_fill_nc_var(ncid, 'RN_hruId', 'int', 'hru', False, nc_rnhruid, \ - 'River network HRU ID', '-') - create_and_fill_nc_var(ncid, 'nOverlaps', 'int', 'hru', False, nc_noverlaps, \ - 'Number of overlapping HM_HRUs for each RN_HRU', '-') - create_and_fill_nc_var(ncid, 'HM_hruId', 'int', 'data', False, nc_hmgruid, \ - 'ID of overlapping HM_HRUs. Note that SUMMA calls these GRUs', '-') - create_and_fill_nc_var(ncid, 'weight', 'f8', 'data', False, nc_weight, \ - 'Areal weight of overlapping HM_HRUs. Note that SUMMA calls these GRUs', '-') - - -# --- Code provenance -# Generates a basic log file in the domain folder and copies the control file and itself there. - -# Set the log path and file name -logPath = remap_path -log_suffix = '_make_remapping_file.txt' - -# Create a log folder -logFolder = '_workflow_log' -Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True) - -# Copy this script -thisFile = '1_remap_summa_catchments_to_routing.py' -copyfile(thisFile, logPath / logFolder / thisFile); - -# Get current date and time -now = datetime.now() - -# Create a log file -logFile = now.strftime('%Y%m%d') + log_suffix -with open( logPath / logFolder / logFile, 'w') as file: - - lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\n', - 'Generated remapping .nc file for Hydro model catchments to routing model catchments.'] - for txt in lines: - file.write(txt) - diff --git a/5_model_input/mizuRoute/1c_optional_remapping_file/README.md b/5_model_input/mizuRoute/1c_optional_remapping_file/README.md deleted file mode 100644 index cc4ca31..0000000 --- a/5_model_input/mizuRoute/1c_optional_remapping_file/README.md +++ /dev/null @@ -1,10 +0,0 @@ -# SUMMA to mizuRoute remapping -Note that this file is **_only_** needed if the defined SUMMA GRUs **_do not_** map 1:1 onto the routing basins as defined for mizuRoute. It is typically easiest to ensure this direct mapping. In cases where the routing basins are different from the GRUs used by SUMMA, this script generates the required mizuRoute input file to do so. - -The optional remap file contains information about how the model elements of the Hydrologic Model (HM; i.e. SUMMA in this setup) map onto the routing basins used by the Routing Model (RM; i.e. mizuRoute). This information includes: -1. Unique RM HRU IDs of the routing basins; -2. Unique HM HRU IDs of the modeled basins (note that in this case what mizuRoute calls a "HM HRU" is equivalent to what SUMMA calls a GRU); -3. The number of HM HRUs each RM HRU is overlapped by; -4. The weights (relative area) each HM HRU contributes to each RM HRU. - -IDs are taken from the user's shapefiles whereas overlap and weight are calculated based on an intersection of both shapefiles. See: https://mizuroute.readthedocs.io/en/master/Input_data.html \ No newline at end of file diff --git a/5_model_input/mizuRoute/1d_control_file/1_create_control_file.ipynb b/5_model_input/mizuRoute/1d_control_file/1_create_control_file.ipynb deleted file mode 100644 index f3b12cb..0000000 --- a/5_model_input/mizuRoute/1d_control_file/1_create_control_file.ipynb +++ /dev/null @@ -1,508 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create control file\n", - "Populates a text file with the required inputs for a mizuRoute run." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "from pathlib import Path\n", - "from shutil import copyfile\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../../../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the control file needs to go" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing file list path & name\n", - "control_path = read_from_control(controlFolder/controlFile,'settings_mizu_path')\n", - "control_name = read_from_control(controlFolder/controlFile,'settings_mizu_control_file')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if control_path == 'default':\n", - " control_path = make_default_path('settings/mizuRoute') # outputs a Path()\n", - "else:\n", - " control_path = Path(control_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the folder if it doesn't exist\n", - "control_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Read the required information from control_active.txt" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the experiment ID\n", - "experiment_id = read_from_control(controlFolder/controlFile,'experiment_id')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Paths - settings folder\n", - "path_to_settings = read_from_control(controlFolder/controlFile,'settings_mizu_path')\n", - "\n", - "# Specify default path if needed\n", - "if path_to_settings == 'default':\n", - " path_to_settings = make_default_path('settings/mizuRoute') # outputs a Path()\n", - "else:\n", - " path_to_settings = Path(path_to_settings) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Paths - SUMMA output/mizuRoute input folder\n", - "path_to_input = read_from_control(controlFolder/controlFile,'experiment_output_summa')\n", - "\n", - "# Specify default path if needed\n", - "if path_to_input == 'default': \n", - " path_to_input = make_default_path('simulations/' + experiment_id + '/SUMMA') # outputs a Path()\n", - "else:\n", - " path_to_input = Path(path_to_input) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Paths - mizuRoute output folder\n", - "path_to_output = read_from_control(controlFolder/controlFile,'experiment_output_mizuRoute')\n", - "\n", - "# Specify default path if needed\n", - "if path_to_output == 'default': \n", - " path_to_output = make_default_path('simulations/' + experiment_id + '/mizuRoute') # outputs a Path()\n", - "else:\n", - " path_to_output = Path(path_to_output) # make sure a user-specified path is a Path()\n", - " \n", - "# Make the folder if it doesn't exist\n", - "path_to_output.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "# Parameter file\n", - "par_file = read_from_control(controlFolder/controlFile,'settings_mizu_parameters')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Simulation times\n", - "sim_start = read_from_control(controlFolder/controlFile,'experiment_time_start')\n", - "sim_end = read_from_control(controlFolder/controlFile,'experiment_time_end')\n", - "\n", - "# Define default times if needed\n", - "if sim_start == 'default':\n", - " raw_time = read_from_control(controlFolder/controlFile,'forcing_raw_time') # downloaded forcing (years)\n", - " year_start,_ = raw_time.split(',') # split into separate variables\n", - " sim_start = year_start + '-01-01 00:00' # construct the filemanager field\n", - "\n", - "if sim_end == 'default':\n", - " raw_time = read_from_control(controlFolder/controlFile,'forcing_raw_time') # downloaded forcing (years)\n", - " _,year_end = raw_time.split(',') # split into separate variables\n", - " sim_end = year_end + '-12-31 23:00' # construct the filemanager field" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "# Topology settings\n", - "topology_nc = read_from_control(controlFolder/controlFile,'settings_mizu_topology')\n", - "\n", - "# Variables below are hard-coded in 1_create_network_topology.py to be consistent with mizuRoute docs\n", - "topology_seg = 'seg' \n", - "topology_hru = 'hru' \n", - "topology_outlet = '-9999' # Indicates to mizuRoute that it needs to route the full network and not use a subset\n", - "\n", - "# Variable names below are hard-coded in 1_create_network_topology.py to be consistent with mizuRoute docs\n", - "topology_var_area = 'area'\n", - "topology_var_length = 'length'\n", - "topology_var_slope = 'slope'\n", - "topology_var_hruId = 'hruId'\n", - "topology_var_hruToSegId = 'hruToSegId'\n", - "topology_var_segId = 'segId'\n", - "topology_var_downSegId = 'downSegId'" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Remap settings\n", - "remap_flag = read_from_control(controlFolder/controlFile,'river_basin_needs_remap')\n", - "if remap_flag.lower() == 'yes':\n", - " do_remap = 'T'\n", - " remap_nc = read_from_control(controlFolder/controlFile,'settings_mizu_remap')\n", - " \n", - " # Variables below are hard-coded in 1_remap_summa_catchments_to_routing.py to be consistent with mizuRoute docs\n", - " remap_var_rn_hru = 'RN_hruId' \n", - " remap_var_weight = 'weight' \n", - " remap_var_hm_gru = 'HM_hruId'\n", - " remap_var_overlap = 'nOverlaps'\n", - " remap_dim_hm_gru = 'hru'\n", - " remap_dim_data = 'data'\n", - "else:\n", - " do_remap = 'F'" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# SUMMA output settings\n", - "routing_nc = experiment_id + '_timestep.nc'\n", - "routing_var_flow = read_from_control(controlFolder/controlFile,'settings_mizu_routing_var')\n", - "routing_var_flow_units = read_from_control(controlFolder/controlFile,'settings_mizu_routing_units')\n", - "routing_dt = read_from_control(controlFolder/controlFile, 'settings_mizu_routing_dt')\n", - "\n", - "# Variables below are hard-coded in SUMMA\n", - "routing_dim_time = 'time' \n", - "routing_var_time = 'time' \n", - "routing_dim_id = 'gru' \n", - "routing_var_id = 'gruId'\n", - "\n", - "# Calendar setting\n", - "routing_nc_calendar = 'standard'" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Misc settings\n", - "output_vars = read_from_control(controlFolder/controlFile,'settings_mizu_output_vars')\n", - "output_freq = read_from_control(controlFolder/controlFile,'settings_mizu_output_freq')\n", - "do_basin_route = read_from_control(controlFolder/controlFile,'settings_mizu_within_basin')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make the file" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# Add some extra whitespace so (most of) the comments line up - easier to read that way\n", - "pad_to = 20 # should be slightly higher than length of longest setting value for maximum neatness" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the file list\n", - "with open(control_path / control_name, 'w') as cf:\n", - " \n", - " # Header\n", - " cf.write(\"! mizuRoute control file generated by SUMMA public workflow scripts \\n\")\n", - " \n", - " # Folders\n", - " cf.write(\"!\\n! --- DEFINE DIRECTORIES \\n\")\n", - " cf.write(\" {:{}}/ ! Folder that contains ancillary data (river network, remapping netCDF) \\n\".format(path_to_settings.__str__(), pad_to))\n", - " cf.write(\" {:{}}/ ! Folder that contains runoff data from SUMMA \\n\".format(path_to_input.__str__(), pad_to))\n", - " cf.write(\" {:{}}/ ! Folder that will contain mizuRoute simulations \\n\".format(path_to_output.__str__(), pad_to))\n", - " \n", - " # Base parameters\n", - " cf.write(\"!\\n! --- NAMELIST FILENAME \\n\")\n", - " cf.write(\" {:{}} ! Spatially constant parameter namelist (should be stored in ) \\n\".format(par_file, pad_to))\n", - " \n", - " # Simulation settings\n", - " cf.write(\"!\\n! --- DEFINE SIMULATION CONTROLS \\n\")\n", - " cf.write(\" {:{}} ! Simulation case name. This used for output netCDF, and restart netCDF name \\n\".format(experiment_id, pad_to))\n", - " cf.write(\" {:{}} ! Time of simulation start. format: yyyy-mm-dd or yyyy-mm-dd hh:mm:ss \\n\".format(sim_start, pad_to))\n", - " cf.write(\" {:{}} ! Time of simulation end. format: yyyy-mm-dd or yyyy-mm-dd hh:mm:ss \\n\".format(sim_end, pad_to))\n", - " cf.write(\" {:{}} ! Option for routing schemes. 0: both; 1: IRF; 2: KWT. Saves no data if not specified \\n\".format(output_vars, pad_to))\n", - " cf.write(\" {:{}} ! Frequency for new output files (single, day, month, or annual) \\n\".format(output_freq, pad_to))\n", - " \n", - " # Topology file\n", - " cf.write(\"!\\n! --- DEFINE TOPOLOGY FILE \\n\")\n", - " cf.write(\" {:{}} ! Name of input netCDF for River Network \\n\".format(topology_nc, pad_to))\n", - " cf.write(\" {:{}} ! Dimension name for reach in river network netCDF \\n\".format(topology_seg, pad_to))\n", - " cf.write(\" {:{}} ! Dimension name for RN_HRU in river network netCDF \\n\".format(topology_hru, pad_to))\n", - " cf.write(\" {:{}} ! Outlet reach ID at which to stop routing (i.e. use subset of full network). -9999 to use full network \\n\".format(topology_outlet, pad_to)) \n", - " cf.write(\" {:{}} ! Name of variable holding hru area \\n\".format(topology_var_area, pad_to))\n", - " cf.write(\" {:{}} ! Name of variable holding segment length \\n\".format(topology_var_length, pad_to))\n", - " cf.write(\" {:{}} ! Name of variable holding segment slope \\n\".format(topology_var_slope, pad_to))\n", - " cf.write(\" {:{}} ! Name of variable holding HRU id \\n\".format(topology_var_hruId, pad_to))\n", - " cf.write(\" {:{}} ! Name of variable holding the stream segment below each HRU \\n\".format(topology_var_hruToSegId, pad_to))\n", - " cf.write(\" {:{}} ! Name of variable holding the ID of each stream segment \\n\".format(topology_var_segId, pad_to))\n", - " cf.write(\" {:{}} ! Name of variable holding the ID of the next downstream segment \\n\".format(topology_var_downSegId, pad_to))\n", - "\n", - " # SUMMA output\n", - " cf.write(\"!\\n! --- DEFINE RUNOFF FILE \\n\")\n", - " cf.write(\" {:{}} ! netCDF name for HM_HRU runoff \\n\".format(routing_nc, pad_to))\n", - " cf.write(\" {:{}} ! Variable name for HM_HRU runoff \\n\".format(routing_var_flow, pad_to))\n", - " cf.write(\" {:{}} ! Units of input runoff. e.g., mm/s \\n\".format(routing_var_flow_units, pad_to)) \n", - " cf.write(\" {:{}} ! Time interval of input runoff in seconds, e.g., 86400 sec for daily step \\n\".format(routing_dt, pad_to)) \n", - " cf.write(\" {:{}} ! Dimension name for time \\n\".format(routing_dim_time, pad_to))\n", - " cf.write(\" {:{}} ! Variable name for time \\n\".format(routing_var_time, pad_to))\n", - " cf.write(\" {:{}} ! Dimension name for HM_HRU ID \\n\".format(routing_dim_id, pad_to)) \n", - " cf.write(\" {:{}} ! Variable name for HM_HRU ID \\n\".format(routing_var_id, pad_to))\n", - " cf.write(\" {:{}} ! Calendar of the nc file if not provided in the time variable of the nc file \\n\".format(routing_nc_calendar, pad_to))\n", - " \n", - " # Remapping\n", - " cf.write(\"!\\n! --- DEFINE RUNOFF MAPPING FILE \\n\")\n", - " cf.write(\" {:{}} ! Logical to indicate runoff needs to be remapped to RN_HRU. T or F \\n\".format(do_remap, pad_to))\n", - " \n", - " if remap_flag.lower() == 'yes':\n", - " cf.write(\" {:{}} ! netCDF name of runoff remapping \\n\".format(remap_nc, pad_to))\n", - " cf.write(\" {:{}} ! Variable name for RN_HRUs \\n\".format(remap_var_rn_hru, pad_to))\n", - " cf.write(\" {:{}} ! Variable name for areal weights of overlapping HM_HRUs \\n\".format(remap_var_weight, pad_to))\n", - " cf.write(\" {:{}} ! Variable name for HM_HRU ID \\n\".format(remap_var_hm_gru, pad_to))\n", - " cf.write(\" {:{}} ! Variable name for a numbers of overlapping HM_HRUs with RN_HRUs \\n\".format(remap_var_overlap, pad_to))\n", - " cf.write(\" {:{}} ! Dimension name for HM_HRU \\n\".format(remap_dim_hm_gru, pad_to))\n", - " cf.write(\" {:{}} ! Dimension name for data \\n\".format(remap_dim_data, pad_to))\n", - " \n", - " # Misc settings\n", - " cf.write(\"!\\n! --- MISCELLANEOUS \\n\")\n", - " cf.write(\" {:{}} ! Hillslope routing options. 0 -> no (already routed by SUMMA), 1 -> use IRF\".format(do_basin_route, pad_to)) # only for routing option 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Code provenance\n", - "Generates a basic log file in the domain folder and copies the control file and itself there." - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the log path and file name\n", - "logPath = control_path\n", - "log_suffix = '_make_control_file.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log folder\n", - "logFolder = '_workflow_log'\n", - "Path( logPath / logFolder ).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy this script\n", - "thisFile = '1_create_control_file.ipynb'\n", - "copyfile(thisFile, logPath / logFolder / thisFile);" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [], - "source": [ - "# Get current date and time\n", - "now = datetime.now()" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a log file \n", - "logFile = now.strftime('%Y%m%d') + log_suffix\n", - "with open( logPath / logFolder / logFile, 'w') as file:\n", - " \n", - " lines = ['Log generated by ' + thisFile + ' on ' + now.strftime('%Y/%m/%d %H:%M:%S') + '\\n',\n", - " 'Generated control file.']\n", - " for txt in lines:\n", - " file.write(txt) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "summa-env", - "language": "python", - "name": "summa-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_model_input/mizuRoute/1d_control_file/1_create_control_file.py b/5_model_input/mizuRoute/1d_control_file/1_create_control_file.py index 8d4d899..cbc400e 100644 --- a/5_model_input/mizuRoute/1d_control_file/1_create_control_file.py +++ b/5_model_input/mizuRoute/1d_control_file/1_create_control_file.py @@ -206,8 +206,8 @@ def make_default_path(suffix): cf.write(" {:{}} ! Simulation case name. This used for output netCDF, and restart netCDF name \n".format(experiment_id, pad_to)) cf.write(" {:{}} ! Time of simulation start. format: yyyy-mm-dd or yyyy-mm-dd hh:mm:ss \n".format(sim_start, pad_to)) cf.write(" {:{}} ! Time of simulation end. format: yyyy-mm-dd or yyyy-mm-dd hh:mm:ss \n".format(sim_end, pad_to)) - cf.write(" {:{}} ! Option for routing schemes. 0: both; 1: IRF; 2: KWT. Saves no data if not specified \n".format(output_vars, pad_to)) - cf.write(" {:{}} ! Frequency for new output files (single, day, month, or annual) \n".format(output_freq, pad_to)) + cf.write(" {:{}} ! Option for routing schemes. 0: Sum; 1: IRF; 2: KWT; 3: KW: 4: MC; 5: DW. Saves no data if not specified \n".format(output_vars, pad_to)) + cf.write(" {:{}} ! Frequency for new output options (case-insensitive): daily, monthly, yearly, or single \n".format(output_freq, pad_to)) # Topology file cf.write("!\n! --- DEFINE TOPOLOGY FILE \n") diff --git a/5_model_input/mizuRoute/1d_control_file/README.md b/5_model_input/mizuRoute/1d_control_file/README.md deleted file mode 100644 index 5137feb..0000000 --- a/5_model_input/mizuRoute/1d_control_file/README.md +++ /dev/null @@ -1,12 +0,0 @@ -# mizuRoute control file -Makes a `.control` file with the settings specified in the (workflow) control file. See: https://mizuroute.readthedocs.io/en/master/Control_file.html - -#### route_opt -This setting determines whether mizuRoute performs routing with an Impulse Response Function (IRF; 1), Kinematic Wave Tracking (KWT; 2) or both (0). Note that if this variable is not specified in the control file, no outputs will be written to disk. The workflow code specifies this vale as `0` by default. - -#### doesBasinRoute -In the default setup in this workflow, the SUMMA modeling decision `subRouting` is set to `timeDlay` which means that within-GRU routing is active inside SUMMA. This is functionally identical to mizuRoute's within-basin routing controlled by the option `doesBasinRoute` and these two should not be used at the same time. Therefore the workflow sets `doesBasinRoute` to `0` (inactive) by default when it generates the mizuRoute control file. - -## Assumptions not specified in `control_active.txt` -- Many of the variable names used in mizuRoute's control file are hard-coded here, because they are hard-coded in the other mizuRoute setup files as well. This is a conscious decision to make the variable names used by the scripts in the workflow correspond closely to the names used in the mizuRoute online documentation. -- It is assumed that routing should be done for the entire provided river network. This can be changed by changing the value of variable `topology_outlet` to the segment ID that should be treated as the de facto outlet of the network. \ No newline at end of file diff --git a/6_model_runs/2_run_mizuRoute.sh b/6_model_runs/2_run_mizuRoute.sh index 83a3f47..1d966a4 100755 --- a/6_model_runs/2_run_mizuRoute.sh +++ b/6_model_runs/2_run_mizuRoute.sh @@ -1,3 +1,4 @@ + # Script to run mizuRoute # Reads all the required info from 'summaWorkflow_public/0_control_files/control_active.txt' diff --git a/7_visualization/1_mizuRoute_and_summa_shapefiles.ipynb b/7_visualization/1_mizuRoute_and_summa_shapefiles.ipynb deleted file mode 100644 index 2748b36..0000000 --- a/7_visualization/1_mizuRoute_and_summa_shapefiles.ipynb +++ /dev/null @@ -1,398 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Catchment and river network delineation\n", - "Plots the shapefiles provided for use in SUMMA and mizuRoute setup side by side." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pyproj\n", - "import numpy as np\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.lines import Line2D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define where to save the figure" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Path and filename\n", - "fig_path = read_from_control(controlFolder/controlFile,'visualization_folder')\n", - "fig_name = 'spatial_delineation_mizuRoute_summa_v2.png'\n", - "\n", - "# Specify default path if needed\n", - "if fig_path == 'default':\n", - " fig_path = make_default_path('visualization') # outputs a Path()\n", - "else:\n", - " fig_path = Path(fig_path) # make sure a user-specified path is a Path()\n", - " \n", - "# Make the folder if it doesn't exist\n", - "fig_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get shapefile location from control file" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# HM catchment shapefile path & name\n", - "hm_catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "hm_catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')\n", - "\n", - "# Specify default path if needed\n", - "if hm_catchment_path == 'default':\n", - " hm_catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " hm_catchment_path = Path(hm_catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Routing model catchment shapefile path & name\n", - "rm_catchment_path = read_from_control(controlFolder/controlFile,'river_basin_shp_path')\n", - "rm_catchment_name = read_from_control(controlFolder/controlFile,'river_basin_shp_name')\n", - "\n", - "# Specify default path if needed\n", - "if rm_catchment_path == 'default':\n", - " rm_catchment_path = make_default_path('shapefiles/river_basins') # outputs a Path()\n", - "else:\n", - " rm_catchment_path = Path(rm_catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# River network shapefile path & name\n", - "rm_river_path = read_from_control(controlFolder/controlFile,'river_network_shp_path')\n", - "rm_river_name = read_from_control(controlFolder/controlFile,'river_network_shp_name')\n", - "\n", - "# Specify default path if needed\n", - "if rm_river_path == 'default':\n", - " rm_river_path = make_default_path('shapefiles/river_network') # outputs a Path()\n", - "else:\n", - " rm_river_path = Path(rm_river_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Load the data" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "hm_catchment = gpd.read_file(hm_catchment_path/hm_catchment_name)\n", - "rm_catchment = gpd.read_file(rm_catchment_path/rm_catchment_name)\n", - "rm_river = gpd.read_file(rm_river_path/rm_river_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reproject to equal area if desired\n", - "Note: not great for Bow at Banff because it stretches things in an awkward way. Results are easier to see in regular lat/lon." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the target CRS (North America Albers Equal Area Conic)\n", - "crs = pyproj.CRS.from_proj4(\"+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m no_defs\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# remap\n", - "hm_catchment = hm_catchment.to_crs(crs)\n", - "rm_catchment = rm_catchment.to_crs(crs)\n", - "rm_river = rm_river.to_crs(crs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a shapefile with GRU boundaries only (SUMMA)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the GRU identifier\n", - "hm_gruid = read_from_control(controlFolder/controlFile,'catchment_shp_gruid')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Select the columns we're after\n", - "hm_grus_only = hm_catchment[[hm_gruid,'geometry']]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Dissolve HRU delineation\n", - "hm_grus_only = hm_grus_only.dissolve(by=hm_gruid)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Figure" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a distinct colormap for river segments\n", - "def get_cmap(n, name='tab20'):\n", - " '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct \n", - " RGB color; the keyword argument name must be a standard mpl colormap name.'''\n", - " return plt.cm.get_cmap(name, n)\n", - "\n", - "n_river = len(rm_river) # number of reaches\n", - "cm_river = get_cmap(n_river) # get the colormap\n", - "col_river = cm_river(range(0,n_river)) # convert to array\n", - "np.random.shuffle(col_river)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 12})" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1,2,figsize=(20,10))\n", - "plt.tight_layout()\n", - "plt.rcParams.update({'font.size': 12})\n", - "\n", - "# --- mizuRoute\n", - "axId = 1\n", - "rm_catchment.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2)\n", - "rm_river.plot(ax=axs[axId],color=col_river,linewidth=4)\n", - "\n", - "# custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color=col_river[1], lw=2)]\n", - "label = ['mizuRoute HRUs',\n", - " 'Stream segments (all non-black lines)']\n", - "axs[axId].legend(lines,label);\n", - "\n", - "# title\n", - "axs[axId].set_title('(b) mizuRoute catchments and stream network');\n", - "axs[axId].set_frame_on(False)\n", - "axs[axId].set_xlabel('Longitude [degrees East]');\n", - "axs[axId].set_ylabel('Latitude [degrees North]');\n", - "\n", - "# --- summa\n", - "axId = 0\n", - "#hruColor = (105/256,105/256,105/256,1)\n", - "hruColor = (229/256,148/256,0/256,1)\n", - "hm_catchment.plot(ax=axs[axId],facecolor='none',edgecolor=hruColor)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "\n", - "# custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color=hruColor, lw=2)]\n", - "label = ['SUMMA GRUs',\n", - " 'SUMMA HRUs']\n", - "axs[axId].legend(lines,label);\n", - "\n", - "# title\n", - "axs[axId].set_title('(a) SUMMA catchments and subcatchments');\n", - "axs[axId].set_frame_on(False)\n", - "axs[axId].set_xlabel('Longitude [degrees East]');\n", - "axs[axId].set_ylabel('Latitude [degrees North]');\n", - "\n", - "# save\n", - "plt.savefig(fig_path/fig_name, bbox_inches='tight', dpi=300)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cwarhm-env", - "language": "python", - "name": "cwarhm-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/7_visualization/1_mizuRoute_and_summa_shapefiles.py b/7_visualization/1_mizuRoute_and_summa_shapefiles.py new file mode 100644 index 0000000..534b2a6 --- /dev/null +++ b/7_visualization/1_mizuRoute_and_summa_shapefiles.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Catchment and river network delineation +# Plots the shapefiles provided for use in SUMMA and mizuRoute setup side by side. + +# In[1]: + + +import pyproj +import numpy as np +import geopandas as gpd +from pathlib import Path +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D + + +# #### Control file handling + +# In[2]: + + +# Easy access to control file folder +controlFolder = Path('../0_control_files') + + +# In[3]: + + +# Store the name of the 'active' file in a variable +controlFile = 'control_active.txt' + + +# In[4]: + + +# Function to extract a given setting from the control file +def read_from_control( file, setting ): + + # Open 'control_active.txt' and ... + with open(file) as contents: + for line in contents: + + # ... find the line with the requested setting + if setting in line and not line.startswith('#'): + break + + # Extract the setting's value + substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) + substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found + substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines + + # Return this value + return substring + + +# In[5]: + + +# Function to specify a default path +def make_default_path(suffix): + + # Get the root path + rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) + + # Get the domain folder + domainName = read_from_control(controlFolder/controlFile,'domain_name') + domainFolder = 'domain_' + domainName + + # Specify the forcing path + defaultPath = rootPath / domainFolder / suffix + + return defaultPath + + +# #### Define where to save the figure + +# In[25]: + + +# Path and filename +fig_path = read_from_control(controlFolder/controlFile,'visualization_folder') +fig_name = 'spatial_delineation_mizuRoute_summa_v2.png' + +# Specify default path if needed +if fig_path == 'default': + fig_path = make_default_path('visualization') # outputs a Path() +else: + fig_path = Path(fig_path) # make sure a user-specified path is a Path() + +# Make the folder if it doesn't exist +fig_path.mkdir(parents=True, exist_ok=True) + + +# #### Get shapefile location from control file + +# In[13]: + + +# HM catchment shapefile path & name +hm_catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path') +hm_catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name') + +# Specify default path if needed +if hm_catchment_path == 'default': + hm_catchment_path = make_default_path('shapefiles/catchment') # outputs a Path() +else: + hm_catchment_path = Path(hm_catchment_path) # make sure a user-specified path is a Path() + + +# In[14]: + + +# Routing model catchment shapefile path & name +rm_catchment_path = read_from_control(controlFolder/controlFile,'river_basin_shp_path') +rm_catchment_name = read_from_control(controlFolder/controlFile,'river_basin_shp_name') + +# Specify default path if needed +if rm_catchment_path == 'default': + rm_catchment_path = make_default_path('shapefiles/river_basins') # outputs a Path() +else: + rm_catchment_path = Path(rm_catchment_path) # make sure a user-specified path is a Path() + + +# In[15]: + + +# River network shapefile path & name +rm_river_path = read_from_control(controlFolder/controlFile,'river_network_shp_path') +rm_river_name = read_from_control(controlFolder/controlFile,'river_network_shp_name') + +# Specify default path if needed +if rm_river_path == 'default': + rm_river_path = make_default_path('shapefiles/river_network') # outputs a Path() +else: + rm_river_path = Path(rm_river_path) # make sure a user-specified path is a Path() + + +# #### Load the data + +# In[16]: + + +hm_catchment = gpd.read_file(hm_catchment_path/hm_catchment_name) +rm_catchment = gpd.read_file(rm_catchment_path/rm_catchment_name) +rm_river = gpd.read_file(rm_river_path/rm_river_name) + + +# #### Reproject to equal area if desired +# Note: not great for Bow at Banff because it stretches things in an awkward way. Results are easier to see in regular lat/lon. + +# In[18]: + + +# Define the target CRS (North America Albers Equal Area Conic) +#crs = pyproj.CRS.from_proj4("+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m no_defs") + + +# In[15]: + + +# remap +# hm_catchment = hm_catchment.to_crs(crs) +# rm_catchment = rm_catchment.to_crs(crs) +# rm_river = rm_river.to_crs(crs) + + +# #### Create a shapefile with GRU boundaries only (SUMMA) + +# In[18]: + + +# Find the GRU identifier +hm_gruid = read_from_control(controlFolder/controlFile,'catchment_shp_gruid') + + +# In[19]: + + +# Select the columns we're after +hm_grus_only = hm_catchment[[hm_gruid,'geometry']] + + +# In[20]: + + +# Dissolve HRU delineation +hm_grus_only = hm_grus_only.dissolve(by=hm_gruid) + + +# #### Figure + +# In[21]: + + +# Create a distinct colormap for river segments +def get_cmap(n, name='tab20'): + '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct + RGB color; the keyword argument name must be a standard mpl colormap name.''' + return plt.cm.get_cmap(name, n) + +n_river = len(rm_river) # number of reaches +cm_river = get_cmap(n_river) # get the colormap +col_river = cm_river(range(0,n_river)) # convert to array +np.random.shuffle(col_river) + + +# In[27]: + + +plt.rcParams.update({'font.size': 12}) + + +# In[29]: + + +fig, axs = plt.subplots(1,2,figsize=(20,10)) +plt.tight_layout() +plt.rcParams.update({'font.size': 12}) + +# --- mizuRoute +axId = 1 +rm_catchment.plot(ax=axs[axId], facecolor='none', edgecolor='k', linewidth=2) + +# Prepare legend elements +lines = [Line2D([0], [0], color='k', lw=2)] +labels = ['mizuRoute HRUs'] + +# Plot river if valid +if not rm_river.empty and rm_river.geometry.notna().any(): + rm_river_valid = rm_river[rm_river.geometry.notna() & rm_river.is_valid] + if not rm_river_valid.empty: + rm_river_valid.plot(ax=axs[axId], color=col_river, linewidth=4) + lines.append(Line2D([0], [0], color=col_river[1], lw=2)) + labels.append('Stream segments (all non-black lines)') + else: + print("rm_river has no valid geometries — skipped.") +else: + print("rm_river is empty or contains no geometry — skipped.") + +# Add legend and labels +axs[axId].legend(lines, labels) +axs[axId].set_title('(b) mizuRoute catchments and stream network') +axs[axId].set_frame_on(False) +axs[axId].set_xlabel('Longitude [degrees East]') +axs[axId].set_ylabel('Latitude [degrees North]') + + +# --- summa +axId = 0 +#hruColor = (105/256,105/256,105/256,1) +hruColor = (229/256,148/256,0/256,1) +hm_catchment.plot(ax=axs[axId],facecolor='none',edgecolor=hruColor) +hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) + +# custom legend +lines = [Line2D([0], [0], color='k', lw=2), + Line2D([0], [0], color=hruColor, lw=2)] +label = ['SUMMA GRUs', + 'SUMMA HRUs'] +axs[axId].legend(lines,label); + +# title +axs[axId].set_title('(a) SUMMA catchments and subcatchments'); +axs[axId].set_frame_on(False) +axs[axId].set_xlabel('Longitude [degrees East]'); +axs[axId].set_ylabel('Latitude [degrees North]'); + +# save +plt.savefig(fig_path/fig_name, bbox_inches='tight', dpi=300) + + +# In[ ]: + + + + diff --git a/7_visualization/2_ERA5_download_coordinates_and_catchment_shapefile.ipynb b/7_visualization/2_ERA5_download_coordinates_and_catchment_shapefile.ipynb deleted file mode 100644 index b6fe4eb..0000000 --- a/7_visualization/2_ERA5_download_coordinates_and_catchment_shapefile.ipynb +++ /dev/null @@ -1,400 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Check ERA5 download domain code\n", - "ERA5 data is available at discrete grid points. The workflow assumes that the representative area covered by a given ERA5 coordinate (i.e. a grid point) extends 0.125 degrees latitude/longitude in each cardinal direction. The ERA5 download scripts therefore modify the spatial coordinates specify in the control file to account for the representative area of each ERA5 grid point. This script visualizes the ERA5 download coordinates, the area they are representative of and the actual extent of the catchment shapefile.\n", - "\n", - "See: https://confluence.ecmwf.int/display/CKB/ERA5%3A+What+is+the+spatial+reference#ERA5:Whatisthespatialreference-Visualisationofregularlat/londata" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.ticker as plticker\n", - "from shapely.geometry import Polygon" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " for line in open(file):\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define where to save the figure" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Path and filename\n", - "fig_path = read_from_control(controlFolder/controlFile,'visualization_folder')\n", - "fig_name = 'era5_download_coordinates_check_v2.png'\n", - "\n", - "# Specify default path if needed\n", - "if fig_path == 'default':\n", - " fig_path = make_default_path('visualization') # outputs a Path()\n", - "else:\n", - " fig_path = Path(fig_path) # make sure a user-specified path is a Path()\n", - " \n", - "# Make the folder if it doesn't exist\n", - "fig_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get the catchment shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Find name and location of catchment shapefile\n", - "shp_path = read_from_control(controlFolder/controlFile, 'catchment_shp_path')\n", - "shp_name = read_from_control(controlFolder/controlFile, 'catchment_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if shp_path == 'default':\n", - " shp_path = make_default_path('shapefiles/catchment')\n", - "else:\n", - " shp_path = Path(shp_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the shapefile\n", - "shp = gpd.read_file(shp_path/shp_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reproduce the ERA5 download code" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# function to round coordinates of a bounding box to ERA5s 0.25 degree resolution\n", - "def round_coords_to_ERA5(coords):\n", - " \n", - " '''Assumes coodinates are an array: [lon_min,lat_min,lon_max,lat_max].\n", - " Returns separate lat and lon vectors.'''\n", - " \n", - " # Extract values\n", - " lon = [coords[1],coords[3]]\n", - " lat = [coords[2],coords[0]]\n", - " \n", - " # Round to ERA5 0.25 degree resolution\n", - " rounded_lon = [math.floor(lon[0]*4)/4, math.ceil(lon[1]*4)/4]\n", - " rounded_lat = [math.floor(lat[0]*4)/4, math.ceil(lat[1]*4)/4]\n", - " \n", - " # Find if we are still in the representative area of a different ERA5 grid cell\n", - " if lat[0] > rounded_lat[0]+0.125:\n", - " rounded_lat[0] += 0.25\n", - " if lon[0] > rounded_lon[0]+0.125:\n", - " rounded_lon[0] += 0.25\n", - " if lat[1] < rounded_lat[1]-0.125:\n", - " rounded_lat[1] -= 0.25\n", - " if lon[1] < rounded_lon[1]-0.125:\n", - " rounded_lon[1] -= 0.25\n", - " \n", - " # Make a download string\n", - " dl_string = '{}/{}/{}/{}'.format(rounded_lat[1],rounded_lon[0],rounded_lat[0],rounded_lon[1])\n", - " \n", - " return dl_string, rounded_lat, rounded_lon" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the spatial extent the data needs to cover\n", - "bounding_box = read_from_control(controlFolder/controlFile,'forcing_raw_space') " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert string to array\n", - "bounding_box = bounding_box.split('/')\n", - "bounding_box = [float(value) for value in bounding_box]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the rounded bounding box\n", - "coordinates, lat, lon = round_coords_to_ERA5(bounding_box)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERA5 coordinates defined as [51.75/-116.5/51.0/-115.5].\n" - ] - } - ], - "source": [ - "# Print in ERA5 format\n", - "print('ERA5 coordinates defined as [{}].'.format(coordinates))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Visualize" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Bounding box as specified in control file\n", - "bb_control_lat = [bounding_box[0],bounding_box[0],bounding_box[2],bounding_box[2],bounding_box[0]]\n", - "bb_control_lon = [bounding_box[1],bounding_box[3],bounding_box[3],bounding_box[1],bounding_box[1]]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# ERA5 grid points from coordinates\n", - "spacing = 0.25\n", - "points_lat = np.arange(lat[0],lat[1]+spacing,spacing).tolist()\n", - "points_lon = np.arange(lon[0],lon[1]+spacing,spacing).tolist()\n", - "\n", - "# Define a plotting grid\n", - "grid_lon_era5_points, grid_lat_era5_points = np.meshgrid(points_lon,points_lat)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Grid cells we assume each ERA5 point is representative of\n", - "cells_lat = np.arange(lat[0]-spacing/2,lat[1]+spacing+spacing/2,spacing).tolist()\n", - "cells_lon = np.arange(lon[0]-spacing/2,lon[1]+spacing+spacing/2,spacing).tolist()\n", - "\n", - "# Define a plotting grid\n", - "grid_lon_era5_cells, grid_lat_era5_cells = np.meshgrid(cells_lon,cells_lat)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 12})" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Make a figure\n", - "fig = plt.figure(figsize=(10,10))\n", - "ax = fig.gca()\n", - "\n", - "# data\n", - "shp.plot(ax=ax,facecolor='None',edgecolor='k'); # catchment\n", - "plt.plot(bb_control_lon,bb_control_lat, \\\n", - " color='b', label='Bounding box of catchment shape as specified in control file') # bounding box\n", - "plt.plot(grid_lon_era5_points, grid_lat_era5_points, \\\n", - " marker='o', color='r', linestyle='none', label='ERA5 data grid') # ERA5 grid points\n", - "plt.plot(grid_lon_era5_cells, grid_lat_era5_cells, \\\n", - " color='r', linestyle='--', label='Grid cell each ERA5 point is assumed to be representative of') # ERA5 grid cells (vertical)\n", - "plt.plot(np.transpose(grid_lon_era5_cells), np.transpose(grid_lat_era5_cells), \\\n", - " color='r', linestyle='--') # ERA5 grid cells (horizontal)\n", - "\n", - "# grid spacing indicating ERA5 grid points\n", - "ax.xaxis.set_major_locator(plticker.MultipleLocator(base=0.25))\n", - "ax.yaxis.set_major_locator(plticker.MultipleLocator(base=0.25))\n", - "ax.grid(which='major', axis='both', linestyle='-', color=[0.9,0.9,0.9])\n", - "ax.axis('equal')\n", - "\n", - "# fix up a legend without duplicates\n", - "handles, labels = plt.gca().get_legend_handles_labels()\n", - "temp = {k:v for k,v in zip(labels, handles)}\n", - "plt.legend(temp.values(), temp.keys(), loc='best')\n", - "\n", - "# labels\n", - "ax.set_xlabel('Longitude [degrees East]');\n", - "ax.set_ylabel('Latitude [degrees North]');\n", - "\n", - "# save\n", - "plt.savefig(fig_path/fig_name, bbox_inches='tight', dpi=300)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cwarhm-env", - "language": "python", - "name": "cwarhm-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/7_visualization/3_forcing_grid_vs_catchment_averaged.ipynb b/7_visualization/3_forcing_grid_vs_catchment_averaged.ipynb deleted file mode 100644 index 074a231..0000000 --- a/7_visualization/3_forcing_grid_vs_catchment_averaged.ipynb +++ /dev/null @@ -1,593 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualize forcing before and after remapping\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import math\n", - "import xarray as xr\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define where to save the figure" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Path and filename\n", - "fig_path = read_from_control(controlFolder/controlFile,'visualization_folder')\n", - "fig_name = 'forcing_remapping_v2.png'\n", - "\n", - "# Specify default path if needed\n", - "if fig_path == 'default':\n", - " fig_path = make_default_path('visualization') # outputs a Path()\n", - "else:\n", - " fig_path = Path(fig_path) # make sure a user-specified path is a Path()\n", - " \n", - "# Make the folder if it doesn't exist\n", - "fig_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of shapefiles" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Catchment shapefile path & name\n", - "catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if catchment_path == 'default':\n", - " catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " catchment_path = Path(catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# HRU fieldname\n", - "shp_hru = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find the forcing files" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Location of merged ERA5 files\n", - "forcing_merged_path = read_from_control(controlFolder/controlFile,'forcing_merged_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_merged_path == 'default':\n", - " forcing_merged_path = make_default_path('forcing/2_merged_data') # outputs a Path()\n", - "else:\n", - " forcing_merged_path = Path(forcing_merged_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Find files in folder\n", - "forcing_source_files = [forcing_merged_path/file for file in os.listdir(forcing_merged_path) if os.path.isfile(forcing_merged_path/file) and file.endswith('.nc')]\n", - "forcing_source_files.sort()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Location for EASYMORE forcing output\n", - "forcing_basin_path = read_from_control(controlFolder/controlFile,'forcing_basin_avg_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_basin_path == 'default':\n", - " forcing_basin_path = make_default_path('forcing/3_basin_averaged_data') # outputs a Path()\n", - "else:\n", - " forcing_basin_path = Path(forcing_basin_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Find files in folder\n", - "forcing_basin_files = [forcing_basin_path/file for file in os.listdir(forcing_basin_path) if os.path.isfile(forcing_basin_path/file) and file.endswith('.nc')]\n", - "forcing_basin_files.sort()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Load the shapefiles and the data" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# shapefile\n", - "catchment = gpd.read_file(catchment_path/catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# netcdfs\n", - "forcing_source = xr.open_dataset(forcing_source_files[0])\n", - "forcing_basin = xr.open_dataset(forcing_basin_files[0]) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot settings and plotting data prep" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# What to plot?\n", - "var = 'airtemp'\n", - "time = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# color settings\n", - "vmin = math.floor(min(forcing_source[var].isel(time=time).min().values,forcing_basin[var].isel(time=time).min()))\n", - "vmax = math.ceil(max(forcing_source[var].isel(time=time).max().values,forcing_basin[var].isel(time=time).max()))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# map basin-averaged netcdf onto shapefile\n", - "if (forcing_basin['hruId'] == catchment[shp_hru]).all(): # check that they are in the same order (they should be)\n", - " catchment['plot_var'] = forcing_basin[var].isel(time=time)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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254.816757 \n", - "4 MULTIPOLYGON (((-115.58458 51.16625, -115.5845... 255.087540 \n", - ".. ... ... \n", - "113 MULTIPOLYGON (((-116.17708 51.66042, -116.1754... 252.932175 \n", - "114 POLYGON ((-116.34125 51.71542, -116.34042 51.7... 252.779205 \n", - "115 POLYGON ((-116.31875 51.63208, -116.31875 51.6... 252.779205 \n", - "116 POLYGON ((-116.47042 51.73542, -116.46708 51.7... 253.381821 \n", - "117 MULTIPOLYGON (((-116.43875 51.64458, -116.4387... 253.204086 \n", - "\n", - "[118 rows x 7 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "catchment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the figure" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 12})" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1,2,figsize=(20,10))\n", - "\n", - "# source data + catchment\n", - "axId = 0\n", - "forcing_source[var].isel(time=time).plot(ax=axs[axId],vmin=vmin,vmax=vmax,cbar_kwargs={'shrink': 0.95});\n", - "catchment.plot(color='None', edgecolor='k', ax=axs[axId]);\n", - "axs[axId].set_ylabel('')\n", - "axs[axId].set_xlabel('')\n", - "time_str = forcing_source['time'].isel(time=time).dt.strftime('%Y-%m-%d %H:%M').values\n", - "axs[axId].set_title('(a) Gridded \\'{}\\' data on {}'.format(var,time_str))\n", - "axs[axId].set_xlabel('Longitude [degrees East]');\n", - "axs[axId].set_ylabel('Latitude [degrees North]');\n", - "\n", - "# remapped data + catchment\n", - "axId = 1\n", - "forcing_source[var].isel(time=time).plot(ax=axs[axId],vmin=vmin,vmax=vmax,cbar_kwargs={'shrink': 0.95});\n", - "catchment.plot(column='plot_var', edgecolor='k', ax=axs[axId],vmin=vmin,vmax=vmax);\n", - "axs[axId].set_ylabel('')\n", - "axs[axId].set_xlabel('')\n", - "time_str = forcing_source['time'].isel(time=time).dt.strftime('%Y-%m-%d %H:%M').values\n", - "axs[axId].set_title('(b) HRU-averaged weighted \\'{}\\' data on {}'.format(var,time_str))\n", - "axs[axId].set_xlabel('Longitude [degrees East]');\n", - "axs[axId].set_ylabel('Latitude [degrees North]');\n", - "\n", - "# save\n", - "plt.savefig(fig_path/fig_name, bbox_inches='tight', dpi=300)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cwarhm-env", - "language": "python", - "name": "cwarhm-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/7_visualization/4_temperature_lapse_rates.ipynb b/7_visualization/4_temperature_lapse_rates.ipynb deleted file mode 100644 index 05292bb..0000000 --- a/7_visualization/4_temperature_lapse_rates.ipynb +++ /dev/null @@ -1,801 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Temperature lapse rates" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import xarray as xr\n", - "import pandas as pd\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.lines import Line2D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define where to save the figure" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Path and filename\n", - "fig_path = read_from_control(controlFolder/controlFile,'visualization_folder')\n", - "fig_name = 'forcing_lapse_rates_v2.png'\n", - "\n", - "# Specify default path if needed\n", - "if fig_path == 'default':\n", - " fig_path = make_default_path('visualization') # outputs a Path()\n", - "else:\n", - " fig_path = Path(fig_path) # make sure a user-specified path is a Path()\n", - " \n", - "# Make the folder if it doesn't exist\n", - "fig_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find catchment-average and temperature-lapsed forcing files" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Forcing files as produced by EASYMORE\n", - "forcing_easymore_path = read_from_control(controlFolder/controlFile,'forcing_basin_avg_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_easymore_path == 'default':\n", - " forcing_easymore_path = make_default_path('forcing/3_basin_averaged_data') # outputs a Path()\n", - "else:\n", - " forcing_easymore_path = Path(forcing_easymore_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the files\n", - "_,_,forcing_files_esmr = next(os.walk(forcing_easymore_path))\n", - "forcing_files_esmr.sort()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Location for SUMMA-ready files\n", - "forcing_summa_path = read_from_control(controlFolder/controlFile,'forcing_summa_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if forcing_summa_path == 'default':\n", - " forcing_summa_path = make_default_path('forcing/4_SUMMA_input') # outputs a Path()\n", - "else:\n", - " forcing_summa_path = Path(forcing_summa_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the files\n", - "_,_,forcing_files_summa = next(os.walk(forcing_summa_path))\n", - "forcing_files_summa.sort()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the catchment/forcing intersection is" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersected shapefile path. Name is set by EASYMORE as [prefix]_intersected_shapefile.shp\n", - "intersect_path = read_from_control(controlFolder/controlFile,'intersect_forcing_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if intersect_path == 'default':\n", - " intersect_path = make_default_path('shapefiles/catchment_intersection/with_forcing') # outputs a Path()\n", - "else:\n", - " intersect_path = Path(intersect_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Make the file name\n", - "domain = read_from_control(controlFolder/controlFile,'domain_name')\n", - "intersect_name = domain + '_intersected_shapefile.csv' # can also be .shp, but using the .csv is easier on memory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the catchment/DEM intersection is" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Find location of DEM intersection for shapes and elevation\n", - "elev_catchment_path = read_from_control(controlFolder/controlFile,'intersect_dem_path')\n", - "elev_catchment_name = read_from_control(controlFolder/controlFile,'intersect_dem_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if elev_catchment_path == 'default':\n", - " elev_catchment_path = make_default_path('shapefiles/catchment_intersection/with_dem') # outputs a Path()\n", - "else:\n", - " elev_catchment_path = Path(elev_catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find where the ERA5 geopotential data is" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Find file path\n", - "geo_path = read_from_control(controlFolder/controlFile,'forcing_geo_path')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the default path if required\n", - "if geo_path == 'default':\n", - " geo_path = make_default_path('forcing/0_geopotential')\n", - "else: \n", - " geo_path = Path(geo_path) # ensure Path() object " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the filename\n", - "geo_name = 'ERA5_geopotential.nc'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reproduce the lapse code" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the intersection file\n", - "topo_data = pd.read_csv(intersect_path/intersect_name) " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Find hruId name in user's shapefile\n", - "hru_ID_name = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')\n", - "gru_ID_name = read_from_control(controlFolder/controlFile,'catchment_shp_gruid')" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the column names\n", - "# Note that column names are truncated at 10 characters in the ESRI shapefile, but NOT in the .csv we use here\n", - "gru_ID = 'S_1_' + gru_ID_name # EASYMORE prefix + user's hruId name\n", - "hru_ID = 'S_1_' + hru_ID_name # EASYMORE prefix + user's hruId name\n", - "forcing_ID = 'S_2_ID' # fixed name assigned by EASYMORE\n", - "catchment_elev = 'S_1_elev_mean' # EASYMORE prefix + name used in catchment+DEM intersection step\n", - "forcing_elev = 'S_2_elev_m' # EASYMORE prefix + name used in ERA5 shapefile generation\n", - "weights = 'weight' # EASYMORE feature" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the lapse rate\n", - "lapse_rate = 0.0065 # [K m-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate weighted lapse values for each HRU \n", - "# Note that these lapse values need to be ADDED to ERA5 temperature data\n", - "topo_data['lapse_values'] = topo_data[weights] * lapse_rate * \\\n", - " (topo_data[forcing_elev] - topo_data[catchment_elev]) # [K]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the total lapse value per basin; i.e. sum the individual contributions of each HRU+ERA5-grid overlapping part\n", - "lapse_values = topo_data.groupby([gru_ID,hru_ID]).lapse_values.sum().reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort and set hruID as the index variable\n", - "lapse_values = lapse_values.sort_values(hru_ID).set_index(hru_ID)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Close the main file\n", - "del topo_data # hopefully this saves some RAM but this is apparently not so straightforward in Python.. Can't hurt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Prepare plotting data (elevation, lapse rates)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# load catchment with DEM\n", - "catchment = gpd.read_file(elev_catchment_path/elev_catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a shapefile with only GRU boundaries for overlay\n", - "hm_grus_only = catchment[[gru_ID_name,'geometry']] # keep only the gruId and geometry\n", - "hm_grus_only = hm_grus_only.dissolve(by=gru_ID_name) # Dissolve HRU delineation" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# Add lapse rates to catchment shapefile\n", - "catchment = catchment.set_index(hru_ID_name) # ensure that HRU_ID is the index\n", - "catchment['lapse_rate'] = lapse_values['lapse_values'] # merge with the lapse rate dataframe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Prepare plotting data (geopotential -> ERA5 elevation)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Open the geopotential data file\n", - "geo = xr.open_dataset( geo_path / geo_name ).isel(time=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the gravitational acceleration constant\n", - "g = 9.80665 # [m s-2]" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Make a new variable\n", - "geo['elev'] = geo['z'] / g" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Prep plotting data (old and new forcing)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# select the files\n", - "idx = 0\n", - "old_file = forcing_files_esmr[idx]\n", - "new_file = forcing_files_summa[idx]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# open old and new file\n", - "forcing_old = xr.open_dataset(forcing_easymore_path / old_file)\n", - "forcing_new = xr.open_dataset(forcing_summa_path / new_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare dataframes with temperature for a given time\n", - "time = 0\n", - "old_temp = {'airtemp': forcing_old['airtemp'].isel(time=time).values, 'hruId': forcing_old['hruId']}\n", - "new_temp = {'airtemp': forcing_new['airtemp'].isel(time=time).values, 'hruId': forcing_old['hruId']}" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Make dataframes\n", - "df_old_temp = pd.DataFrame(old_temp)\n", - "df_new_temp = pd.DataFrame(new_temp)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# Set indices\n", - "df_old_temp = df_old_temp.set_index('hruId')\n", - "df_new_temp = df_new_temp.set_index('hruId')" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# Merge with catchment shapefile\n", - "catchment['old_T'] = df_old_temp['airtemp']\n", - "catchment['new_T'] = df_new_temp['airtemp']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Figure" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# plot settings\n", - "cmap_elev = 'terrain'\n", - "cmap_lapse = 'coolwarm'" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# scaling\n", - "vmin_elev,vmax_elev = catchment['elev_mean'].min(),catchment['elev_mean'].max()\n", - "vmin_rate,vmax_rate = -5,5\n", - "vmin_temp = min(df_new_temp.min().values,df_old_temp.min().values)\n", - "vmax_temp = max(df_new_temp.max().values,df_old_temp.max().values)\n", - "#vmin_temp,vmax_temp = 252,262 # these ranges match those in the grid-to-hru figure" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the forcing time\n", - "time_str_old = forcing_old['time'].isel(time=time).dt.strftime('%Y-%m-%d %H:%M').values\n", - "time_str_new = forcing_new['time'].isel(time=time).dt.strftime('%Y-%m-%d %H:%M').values" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\wmk934\\AppData\\Local\\Temp\\1\\ipykernel_16108\\3857747540.py:130: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " plt.tight_layout()\n" - ] - }, - { - "data": { - "image/png": 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T2twtRERxwYIupZsfAGSIyOFKqd8N6xug7m3/VyNwZ+FR0LKt4kYptVRE5gG4QUTGe8d7DbTbsQdZ7DIcQH8RyVdKmS8gAQCizbjbDRa3FZl8AeBaEemsxy544wIuju7dANBuP/sNWhHTeBv/xQjv35JIv0ahfA7tPZ6ixy54fxsfqMhqp/9Ae9/lSqkfgmxn93uOmwDfr4A2Ad7dInKo0ibxA+C7Ra6X9/Vg/gPte7uRUuqjCIa0CVphvJ1SakIY288G8Lxos0GfCuBa0+vhfi30DqYc1P1Bzjy+7dAKxfpEahCRrtB+EJocxpiJiIgSIdD1s858bXcptI7Dz03bHeX9M6praKVUhYisgNb9+Y3NxVsrhSKSq0cCeAuIp0CbDBfQJuIqg5ZrOyvKczSAdqeSr6AoImdCi1T6xbCd8frCaLv3tfam9b0jGMN/oF37bFBKVYbaOIQG3j/NxekLA2wf6jP28d6d9LG3C/g9AEcppb6M0/eE1fvIgJYPbbYfdb8ukV6HWr4/RP/LDyKikFjQpXSj52h1hlZg1P0HQD8RmQJtIql/ALgdFr8Z9xY2TwbwfJjnbC4ip1is/9ObExXMaGgXQMNFZCa027ceUkotsxhXNrQMrEsAzBRtBl/9YnoHtNyq+6D9FvvREOd9BVoRba6IjPTufwNquzMivlhSSnlE5GFos9zOBPA6gGO8YyoN4xBhf43CNAvarWBzReR+aJ2kQ6BlocXbv6EVDD/yfp3WQuusOBraxGr9vJEcdr/nePN9vwIY6l03FdovIFaKyKMANkO7je4uaJ/1I8EOqJRaJiKvAXhbRJ6AFkvhgTZpRiGA4UqpHy32UyJyK4D3vJ1Cb0K7KG4JoCu0jOgnDLu8CW3W5znQbgk150WH+7XQs7HvEpFFAGoMnczG8dWIyGgA00Rkjve8h0G7lW8ztE5hIiKiVBDo+lnXznBt93do/5ctt/hFbBcAW2PM2/+ndzwfiMhL0O64ag7gRGiTmo2I4dhmlQAWi8hj0OICHoZ2zToFAJRSpSJyD4BnvZFTi6BNknYYtFzXZUqpV0Oc4z8A7gDwivcz/Du0+ABzd7N+fXGriMyCVmj8Tim1X0TeAHC9iPwIrZDYG9qkaOEaDe366hMReQZaIbIJtCLx35RS10VwrJXQPqNnReRBALkARkG7BmtksX3Qz1hEhkDLmy2C1hjSHNrPDn8AWO89Rjy+J76H1kQzTkRqoH3edwbYdiOA3iLyH2hxHn8opf4I5zo0zPdHRBQXzNCltOItoK6GNoGT0YvQLj4vB7AA2oVQH2gXZWZdod0y9nqYpz0PWlHVvNwdxnjXQ8vtvAHATdB+e/9KgM0XQ7sQGOh9vgHAaQCmQYtheAjAZwC6eCdSC3be/dCyer8D8AK04udv0G5nAqw/l5CUUi9Bu2g9E9pvnq+F1qFozjKzEsnXKJyx7AdwDrTJCJ6D9h5/gZaTFVfeW+rOg/aeBkO7iPs3tK/dStTehmXre4434/eriBziXVcMretjKbTvwcUAnoQ2kdqpSimrWxTNrvLuewm075u3oeWibUaQzGOlVBG0i+RcADOgTe4xCVqkweembfdC+4wPgzYjsrm7NtyvxfvQvp9u8Z7jyyDjmw6ty7eD931NgvZ39XSlVHmg/YiIiBIpyPWzbhi0SK83oDUNvI/aXHqj3gj/+jnQWL6B1lixC9ovYhdD++VxB9QWnu3yL2jzVjwD7TpxB4CzlFK+XHul1DRov4xvA+1un0XQipIuaNeYQSmlPoD2C+Ju0D6366DdkfeTabu10K6F+kCbD+NL1EZaDQMw1/v6G9DyXYciTEqpLdDiBdZC+/p9CK1x5XRocVphU0rtANAf2h1MbwMYD+0abE6AXUJ9xmuhXceNh/a1fgba9fqZejdxPL4nvD8n9IM2r8G/oP0M9AksOoehXZNWQLs+/BLatX2416Eh3x8RUbxI8OhDotQjIoOg/Sd/iMXEZOHs/zyA9kqpQJmeBy0ReR/AcUqpo5M9FiIiIiJKDBuun7tA+6X1cVZ31qQaEVEAximlRiV7LAcrfsZERMnFDl1KR7Oh3cZ0S6Q7ishfoHVRjrR7UKlGRP4pIoNF5AwR6Ssis6F1VjwWal8iIiIiOqhEff3sNQLArHQo5hIREdUHzNCltOPNrrwOWq5SpP4K4C6llN23dKWiamhZUa2g3Ta1CcAN3tgEIiIiIqonYrl+9s7z8C20+CIiIiJKAYxcICIiIiIiIiIiIkoTjFwgIiIiIiIiIiIiShOMXLAwcciVbFtOMdqdXnSwyMmtSfYQyCadehwaeiNKG790a5jsIaSca5rdJ8keAxERERERkRE7dImIiIiIiIiIiIjSBAu6RERERERERERERGmCBV0iIiIiIiIiIiKiNMGCLhEREREREREREVGaYEGXiIiIiIiIiIiIKE2woEtERERERERERESUJljQJSIiIiIiIiIiIkoTLOgSERERERERERERpQkWdImIiIiIiIiIiIjSBAu6RERERERERERERGmCBV0iIiIiIiIiIiKiNMGCLhEREREREREREVGaYEGXiIiIiIiIiIiIKE2woEtERERERERERESUJljQJSIiIiIiIiIiIkoTLOgSERERERERERERpQkWdImIiIiIiIiIiIjSBAu6RERERERERERERGmCBV0iIiIiIiIiIiKiNMGCLhEREVEIIjJIRJS+JHs8dhOR/4nIKyG2Oc34GYjIMQkaHhERERHVYyLyiuEadJl33V+9z28wbdtcRL4Vkd0icrJ33RLD/nOS8BZs50r2AIiIiIjSyEUAtiV7EEmyFsCpAHoDGJXksRARERFR/fIngP4ASgNtICItAXwEoCWAM5VSa7wvDQXQCMC8OI8xYVjQJSIiIgrft0qp/yV7EMmglCoD8IWIHJvssRARERFRvVOtlPoi0Isichi0Ym4jAKcrpTbqrymlvvduUx33USYIIxeIiIiIYuC9dWusiNwlIr+KSIWILBSRAu/ypoiUiMhvIjI8jONli8gUEVkvIuUi8qeILAinkCoieSLytIhsEZFqEdnuvcWMRVgiIiIiOiiJSCsAywHkwlTMPVixQ5eIiIgodlcDWA/gFmi3eD0J4F8A8gEsAjAdwKUAJojIOqVUUZBjZXn3Gwst3qGp97hfiMixSqk/g+w7BUBfAPcD2AygGYBuABpH+8aIiIiIiFLY3wB8AkAB6KGU+iXJ40kIFnSJiIiIYlcN4EKllBsARKQ9gDsBPKCUGutdtwxa7telAAIWdJVSJQB8kzuIiBPABwC2A7gCWtE2kFMB/Fsp9ZJh3UGTFUZEREREZHIfgBoA7epLMRdg5AIRERGRHT7Ui7leP3j//EBf4X39JwBHhDqYiFwmIqtEZC8AN4AKAHkA2oTY9UsAg0TkfhE5yVsMJiIiIiI6WP0HgACYKiJZyR5MorCgS0RERBS7Pabn+4Oszw52IBHpA+ANAN8DuBJAFwAnA9gRal9oM/hOA3AdtOJusTePt0GoN0BERERElIbegXbtey6At0QkI8njSQhGLtRDMxctx6YtfwAA2hxxCK4t7JncAREREZHRAAA/KaUG6Su8F6ZNQ+2olCqHdtvZfSJyJIBLAEyAVkgOOSEbUSKIyEIAhd6nHymlzk7meIiIiCi9KaVmebtzpwF4VUQGKKVqkj2ueGKHbj1jLOYCwKbftiVxNERERGShAbSYBaOrAUQUn6CU+lUpNRnAOgDtbRobkR0KDY/PStooiIiI6KChlJoOYBi0hoZ/ichBXfM8qN8c1aUXc4895ugkj4SIiIhE5EgRcYvIaMPq/wA41huVcJaI3AtgDIC9ofYVkc9F5D4RuUBEeorIgwA6Alhs2OYjEfkpinERxUxEFiV7DERERHRwUko9BeBeaLFlM0REkjykuGFBtx55uWiZ7/HTYx9O3kCIiIhIJ9A6b43XZC8CGAfgcgALAPQG0AdASRj7fgLgMgD/BrAQWofCnUqpqYZtnAgdu2V1bCI7nA8AJ516pm+FiKxI2miIiIjooKKUegzAAwCuBfBskocTN8zQrUd+9MYrnHR8hySPhIiIKG05RcSllPJFIiil6vzmXyn1CoBXLNb3ND3/H7TiqXGdB8Ao72L01zD2HY4QWbnmMXjXhTw2AIiICyzyUpRE5GP98WPT38eIW/pj1YoPAOC05I2KiIiI0oX3WlQppWoCXa9C22AsgLGG/Rw4yK5hD6o3Q4G98N4S3+Px9/v/nPfkW0WJHg4REVG6+gnAgWQPIhlE5DRo7/2lZI+F0o93IrQzAKBL9/MAABOem2d+nYiIiCiQI6Fdi34Uxb6LvfseaeuIkogduvXAzKJl+N+fOwAAnU/o6Fvf+YSOWP3tWvy5uwQvL1wKEcGm37bhmMNa4oYLzgx0OCIiovpoAYCTkz2IJFsD/8/gtySNg9JTIQB0OrmHXyG348ndsfbLFb7XRWQxgHMAFCmleidhnERERJR6HgLwjPdxWRT73wKgoffxLjsGlGyilEr2GFLOxCFXHlQfyohprwEAOrY9Do+PHun32n3jJ+Grtd/V2efow1rixhQq6opkJ3sIZKOc3JpkD4Fs0qnHockeAtnol24NQ29Uz1zT7L6DdiIFokQxFGmxdN2+Oq+f0aGB5X5WcSZERERExMiFg96M931RZXWKuQAw/r570a7N3+us/3nrdrxo2JeIiIiIKFLeKIVzAKDzaedabtO52zmB9t0Ut4ERERERpTFGLhzEZi5ajp+2bgcAdO7UMeB2Tz482u+53rX789bteHHBx7ixT+p06hIREaWb888/X+3cuTOh5/z6668/UEqdn9CTElkrBIC2Hbtg4vPvWm4w8YX3/J4bJkv7u4hjrVKewBeyREREdND5hyNXlarE3tn7E6rT6vqZBd2D2KYtfwAAjj36aIwbcU/Y+42/717c88ijWLNhI37+YztGTHsNRx3SAjf1PTteQyUiIjpo7dy5E1999VVCzykizRN6QiILIrJIf/zsnKVh7zfhuXkYfnM/rP50MQB1vIgoAN8qpU6MwzCJiIgoxZSqGjzpSuz8ZRe4f0yr62dGLhykXi5a5nv89LiHI97/sQfuR4fjjvU9/2XbDoyY9hpmLAz/YpyIiIg0npqahC5EKeJ8ADjp1Mjv9pr4/Lvo0v0846oTRESJyHqbxkZERESpSgDJkIQu6YYF3YPUj79tAwCcdHyHqI/xxIOj8OHrc3BSx+N96376/c+Yx0ZEREREBzcR8U3G8Nj096M6xoTn5mHpun3mwm67GIdGRERElPYYuXCQmD7/I/x3W3Gd9ePvHx7zscffdy8A4JwBVwHQun+vK+wZ83GJiIiI6OAgIkUAepnXm4qxUZnw3DwAwBkdGujn+lop9Y+YD0xERESUpljQTXMvFy3zdeOadT7B3vkj/tGhPb5etz7g+YiIiKgupRQ8Hk+yh0EUFyKyEN6Jz8y6dD/PV4y1Q5fu5+mTpZ0oIguVUr1tOzgRERGlDBGBw5V+MQiJxIJumjMWV0/s0B4TR46I27kmjBzh69KdvuAjDO5zVtzOZafhL8wEAEwccm2SR0JERER00PEVc1sd1Qaz5n8btxNNeG4e7r2pL75cucTvvKnOUPQuYhGaiIiI7MCCbhp7eu4Hvscfvj4nIefs3KkjVq9Zi//+UYwR017zrT/60Ja4sU/kE17E28yiD32Ph78wE8e2OhzXFp6TxBEREVF9xInK6GDkjVkAACxdty8h55w0bb4xekGZXk65gqmpg7nQO+aUGycREVFKEUAyOO1XMPx00tTMomXYumM3APujFYIZN+Iev0nSdD//sR0zFy1P2DjCMbPoQ/yw5Xe/dT9s+R3DX5jpV+glIiIiosh4C5W9AOCkUxP7S/0gubyp2LVrNaZC7+dHREREFBV26KapTd6ohX8c3x7jht+T0HPrk6Tpho+bgG/WrcemLX8kdBzBGIu5TocDS956DSPGTcCqb7TbAH/Y8jtmFn3Ibl0iIoo/peDxsEOXDjqFgBaz8Nj09xN6YqtcXkPXbpFSKiUKu8ai7cqNJQCAu4Zcis8/WQywW5eIiCgwATN0Q2BBNw1Nm7/E93jC/fHLzA3XREO2biqwKuYCWgYwAF9hN5WKulbdxAAYEUFEREQpx1iojGdmbpR6JXsA5oniHE6nAiAAMPmFt4xFXSBFuoqDTW4HFp2JiIhSDiMX0tAv23YA0PJsqS6rYq7RhJEj0OXEE/y2TbZA49CLzkRElL4UAE+NJ6ELUZwVAsDxJ3ZL9jisFIXeJO78irmfrtvt12I0+YW3fB27gH+BPImCFZZTouhMRET1iACSIQld0g0Lumls3IjERi2kG6tirk7v1gWQUgXTVYsX+ZaunU8GwNxfIiIiSk1TZ6XktUlWsgegW7mxBOZirtGpPc7VH6ZMpu6y9RV+i05EVKqMkYiIiBJc0BWRnt6LAfOy17BNvog8LiLLRKTU+3rPMI8/KMDx9eUvcXprKavG40Hxzl3YW1qakPMlc2K0mUUfYvgLM8Pe3tilm4oF0yljx/iKukDqdBMTERGlM16PJl7Jnp3YsX0r3AcOxO0chonSzorbScIQSdFz8gtvpWRR1+gU/wnoUnKMRERE9VGyMnRvB/Cl4bnb8LgZgOsAfAPgQwAXRXDchQBONa0TAAsA/Fcp9WfkQ01vHy5fgSdfegk1bg+ys7Ow4JWX4nKe4487Ft99/0PAidGmvFWE7btL0Prwv+D63mfEZQzGgqfTEfp3FRNGjqgzUZpeEE5kdm2wQvKUsWMAAF3OTXocHBERRYuToqUqXo8mgMfjwSU9/woAcHs8mLvsf2jSrMD280x4bp5vYjQrIrICwGkAvlBKmb8+dioE/HNzgzFl6uoTpemSnl074fm5AIARN1+EL1Z8ADB+gYiIEkBEOClaCMkq6H6vlPoiwGu/KqWaAoCInI0ILqCVUjsA7DCuE5Hu0C7KH4xyrCll6luLItp+/4H9qHFrWXpVVdV4ZMpTeODO220f1+QHR9WZGO3lomX48bdtfus2//4nRkyrjUKIR4F36TtvhL2teaI0nV7cjXdh128CN6czrO05SRoREZEteD0aBRGJ6GJUKQW3x4P87CxVVlUtl535N7zx0c9o2rxlvIYIEVmklOrlfawXcnWnmIqmnyilTrd7DMGiFswsJkrTFYrIwngXdcPpup3w/Fz0bJ8bz2EQERFRBFIuQ1cppUJvFZGBAPYDeN3m4ybFtt17I9q+1WGHAQA2VE5FXsNsfLJqNS66YXAcRlZr2vyPMHPR8jrFXCubf/8TLy74OK7jCceEkSOw9J03sPSdN3xRDEBtYVdf7I5lMBZzVy56P+B2xjzdVIuGICKi4JRS8NTUJHSh2PB6NKjzI9nY4XDA5XQAgKp84zHkZGbgql5t4zKwzqf54gvOF5GFIrIE/sVcKz3syoeN5Rj6RGn6YopiMMd22BZ74D2Wsas43H2IiIjih5OihZSsgu6/RaRGRHaJyKsi0ioeJxGRHACXAnhfKbUrHudItILGDSPavsOxbeB0OdClYDhWbZ+IDZVTUVa+Ly5j69ypIwDgl23FvuiFww89BB++Psdy6dhWu5j/+Y/tSc3eNdOLu8bCri5eBdVgxVzAP0+XWbpERES24PVodOq0kQYjIuhxzsUHyqqqHQBQPGssKqsqUVVp//XoxOffxVGt2+lPC+HN0+182rlYum5fnaWLKR/WhiFEVBgNxpSvW+c8NhZVfWP+eG1p0J9mDXm6zNIlIiJKskQXdEsATAZwA4AzATwC4GwAn4uI/WFaQD8ADQHMCrWhiAwWka9E5KtVG3+Kw1Ds8c/LI7vjyul04v/690d5WVWcRlRr3Ih70Kmdf8fFzCceC7j946Pvx/HHHQsA2LTlD8wsWhb1ueNRZJ0wcoSvqOt0OGydRC3SCdyA2jxdACk5iRsREQXm8XgSulBQKXk9arwWFZH43k4VA6XUeaG38jf0vsczXA4HCgaO8gBAXnYWLj3jr7aPDQBenvul3/Pj/3EaJj7/ruW2E56bh6XragvLIrLUjjFEErcQjLGo63A6lUXnbtRFVW8Hs6/wHKqYC2ixCyzqEhFRQgjgcElCl3ST0IKuUupbpdTdSqkFSqnlSqknod221RLaxBR2Gwgtw6wojLFNV0qdpJQ6qUvbY+IwlOS5sv+Fvi5d3Zy358blXI89cD/+cXx7ALUdu8FMfnAU2hz9NwDApjAiGgLxRReEMRlaJPRu3SVvveZX4DWeMxp+E7iFkZ2r07t09WOkQ1FXL16zCE1ERKkgVa9HjdeiSqnpcRiH7e4ZfEFY2zVu2gKDhj4IvUsXAA4c2I/ff41PE4XeefuPU8/E1FdCNxSfdOqZ+sOe0Z4zXsVNPYpBLxKbOndj6Sr27RtJR3E6FnX14rXdcRVERETJlPQMXaXUNwB+BHByqG0jISKHQOu2+LdSyh1q+3Q0dGR482o4HQ5cZejSzc3Pwpx3343buCbcPwIfvj4H40bcE9b2z4yr7Tx9aWFsjRFL3not9EYx0Au8sTAWNVctXhQybsFoytgxWLV4kV/8QioUSq2Ktvo6Y/E6XYrQRER2YoZu6uP1aMT+AwBffR7+PAi9+l3jeyyAp3r/ftx02an2jwy1nbePTw/vGusxw3Yisi7K09oWtxDK5Bfeiml/Y1Fz2fqKsLpzjSyKuilRKDUUbhcan8O/8J0WRWgiovpOAIhTErqkm6QXdL0EgN0XP1cBcCKMuIV006bVoQCAH37+GXc+NCbE1nWtLp6EGndq3Y55csfjAWiTpKWTaIqTxonQomXM1DUeM1nMRVtzIdfpdPoVoW+f/C+8MPejhI+TiIgoCF6Phkkp1Ut/fPUFx0d8UVk8a6wjLzsLLqfL3oHFwJCn215EFkV7HE9NTUJ/IoyymBpz8dlU1PUdM1mMk7vBW2SGqQs5FYvQRERE0Up6QVdETgLwdwCrbD70NQC+U0qtsfm4SXdtr9N9j9f/8GNY+6z7YVOddTt2pc68HI/ed2/U+yaj49OYpxvu+c25uZF05lrRu3WTzfj+jUVmQCvk6l3I5iL0xl+2JmyMREREwfB6NCpFAPD7rz+F9fPEll/qXrPu3x//OR7CNeG5eejc7Rz96fnJHEs4TBOmhd11atwu0s5cswnPz8Wy9RWWx04Cy4Kyw+lUeheyVRGaRV0iIkpXCS3oisi/RWSsiFwkImeKyF3QbtnaCuBpw3a9ROQSAN29q04XkUtEpJfpeG4RecniPCcCaI+DrBvCinkSskDWrN/g9zw3PwtXD7sTW/9Mr45YK/HKzw3GmKerd6QGiz6YWfRh1Lm54UpWlIGx41gvMuuLuWhtLkKzS5eI6gtGLqQOXo8mx48bv/V7vmPWWFRVV+PFqaOTNKK6Jr7wXlT7GYuCKzeW2DaeYPRsXdMkaUG7To1drHZGQyQ7U9ccIWFczEVrvQhtHHMix0pERGESwOGUhC7pJtEduusB9AUwE8AHAO4AMBdAF6XUTsN2zwN4C4B+hfeQ9/nzpuM5vYvZQABuAP+2adwpZWbRMt/jxx64P6x9Xnx8ot/z1cWTkJ2Tgevvvhd/Fu+wc3gxm7loefjbGoqY8c7PNTNPkgbUzbQ158gaO1btkip5upG8J33M7NIlIqIk4PVojIyFwS7+HY8BXXTlzcjMcKFg4ChfITE/O8vz5stPYNG8f6XtbyHiVSQNl2mSNKC2sKu8GbK+CcGM44y1O9coBTJ1I/78JzwfnwmiiYiIEiWhBV2l1Hil1PFKqUZKqQyl1BFKqcFKqW2m7f6qlBKL5a+m7UQpNcjiPMO8x98e33eUeDPe/xibftM+rjZH/y3s/Y449BC4XA50LhjuW7e6eBKyszMw6J93pUT8wnGtjwEAbNryR9j7JKM710ifJG3pO2/4FXf1KAZzV66dhVydVZ5uKkyUFsyUsZFnPxMRpS2l4PF4ErpQYLwetUUhADRu0lxNeG5eWDu4MjIw5J5JqNx/QAoGjnIDWpZuTmaGZ9LoIc4LurSI43DjyldM/HTd7qS091h06+oKYepAtbuYq0uFOIPOXc+O6n0xdoGIKBUJxJHYJd0kPUOXIvPTVu1ngs6dOuKZceEXxUQEI267FRVl/lllq3doRd3r7oo+w9YuTz3ykO+xsQs5kGR251rRi7vGKAad3V25ZnqUgVVh17ikYpGXsQtERETpQ0RW6I/nfbIlop9++l8xBG6PB2VV1b7Z0IpnjXVUvvEYKvZVBNs14UTk00i2T1Yx10gv7FrFPgSKILCTVZyBoVtYxbtz94sVH0S0PWMXiIgonbGgm6bGjbgn4n0EAqfLgVNajvBbv3rHJFRVVeObdevtGl7UOrY9DgB8XcjBJLs7NxCrKIZEsSrsGkUyiVs4YjkWYxeIqL5QYIYuHVROi9eB//XC+KR/8xoiJLqFKj6mcmenRbduwlh06xrZ2rlrPFakkRfG2IVU/loSEdVLAojTkdAl3aTfiAkAcP/4SRHvo6AgsP6lfG5eFh547PFYhxWzx0eP9D0OlqWbat25Znq3ru68Swck9PzmycmMRV67irrGSIloJnkzxi6wS5eIiChtLInHQfOzs9Ts58fZP2tshCY8Nw8ndumpPw3VuZm07NxQ9G5dXb/uRyb0/Hq3rnGxe/I0c35xNN3H7NIlIqJ0xYJumvpy7XcR7+MQgdtdgy+2T6jz2uodk7B/vxtKJf96tH2bvwMInqWbqt25Zo0bNgQA7C0pQZdze/mWRBd4Af+s3ViKulYTvUUbJ8EuXSKqF5SCx1OT0IUojvbH46DFs8aK2+PBd19/Fo/DR2TyjKKItk+FuIVAGjdtDgDYu2cnerbP9S2JLvAClpOnRVXU1Sd6gw2TvJm6dJP/gxAREQEABIDDKQld0k1qV8PIz8uGXNnOJ3SMeP+TO3W0jFzQ5eZl4YKB18Ltdkc7RFtMeXh06I28UrE712jezBd9RV0jvcCbjM7dWIu6dk70xsnRiIiI0k4hAJzY5Yyodu582rkBf+OQn52l7rruPPy0KfLGBbJW9OnPvqKukV7gTUbnrg1FXV83rZ2TvCml0u+neSIiqrdcoTehVPDigo/x8x/ahGgntG+HccMjz9DNzspChsOFstJKy9dX75iEzi3uRb/rB+P9WS/HNN54SsWJvYKZN/NFv+f9r70Re0tLAdQWdnWNGzXCB2+9HtfxTBk7BneOGo2Vq7/0TZwGAMe2OhzXFp5juY8xXkG3avGiuI6TiIiIUoeIFAHwXbRMnhHdHfNH/LW1Z/Wniy2jFYpnjZWCgaNw82VdUfTVbmRkZEY3WBuJyEKlVG/zOqTRLfpFn/7s97zwtKOxd/dOALWFXaPGTZrj3RW/xm08E56fixE3X6RPYlZo6IwtMn/WOqvPfNn6CgAB8uSIiIgOcizoprgZ73+Mn7Zu9z0/qtURmDTqvqiPFypSYfWOSWiXMwzbd+xEyxZ1f5ufaDMXLce1vU6vfW7MbU3xuIVA9AKvsbCrMxZ441ncNRZ1dcbibijRZOYSEdVXCooTlVHasiqkdQk84VVIHo8n6OvFs8aixcBR6HtqSyz6ak/U54nVCV164ttVywDTezd/Ht783LQqKuoFXmNh18hY5I1XcddU1NUVhht7EI/P3ap4T0RESSKAONLqv9eEY0E3hZmLuSd2aI+JI63jEkL5s3gHXnnzLbhVDfIb5gTdNjc/C4PuvAtFs1+BSHL+AnU49lis++EHvxxdczE31eMWQgnWuQtoxd3zLh0Q16KuzlzctRJrvEIoL8z9CEMuOituxyciIqKo+IqXR/y1Nf61YG1UB1FKYcn7r2Peq89nhNp2h7eo2+fUlljw+fZQm8fFEzOKcEaHBlYv+XJbvdm5afvTprlzF6hb5N27p27B1y7G/FqL4m4dpngF2z73U7qf5+sWtuuYRERE8caCboqauWi5r5jb/ti/Y8pD4efKmn2zbj2Gj5sAl8uBnAaZlpOiGa0u1qIXLrzuBsyf+VLU543FEw+NwjkDrgIAzFi4FDde0OugKuZaMRZ49eLu3pKSIHvYx6q4G+8Crq5r55OxcvWXnBiNiA5eKnRXIlEqEpEv9MdL1+2L+jgV5aW4d8iF+39c92UmAOTnZLkR4ueQHbPGIufye6CUSlqDgc7bNeo3U1oqT4QWC2ORt2vbRgCAft2PjGsEA+Bf3AXg6xD2xiro4vKZT3h+ru987NIlIkoV6TlRWSKl5z3r9YDemXrsMUdHXcxd9/0PGDnhMdw/cRIAIKdBFlZtnxjWvqt3TEJlZTXKKypCbxwnJ3ZoDwD46fc/8dDMf/vWH4zFXDNjcTcZE6etWrwoIcVc/Xy6F+Z+lJBzEhERUVi6ANFHLFTuq8C0J0bWXHDqX/Djui8zczIzairfeAzFr4wNq6kkLzsLvbskLwLM9L4LYejOTcqAEkyfTC2eXbqBLFtfYS7mxpVxoraEnZSIiCgGLOimuKfHPhz1vq+/twBfr1+HGrcHGyqnhuzMNcvNy8LlN98a9fljNXHkCF9Rt7J6P4D0zc2NRuOGDQEgYV26qYBdukR0cNIydBO5ENlpwnPzotpv984/8fasqU4AyMnKcBfPGhtRCP+OWWNRWVmJ8rLkXAtNeG5enc5kQ9TCQc8qkuFgZe4QJiKi5BIBxCkJXdJN/amO1TNKKazduBE17uhv8Vy9YxL273fjX2+9Y+PIImPODK4P3bk6c8YuERERUTpZ/uG7yu2NGwm3K9csLzsL/bsfjtKS3baOLRLGTt2GjZqk3098REREdNBhQfcgVOPx4OEnnoTbU4P8htnYUDk16mPlNczG7Hfm4cLrbrBxhJH58PU5vsf9r70xaeOg+Ona+eRkD4GIKG6UAjt0Ke2IyMJY9v9m1TK8+OQDAgCVbzwW9XF2zBqL7AwXLj3jqFiGE5MJz81Dbr6WJ2ucMIwOTrF+7xMRkT3E4Ujokm7Sb8T1wIsLPo5p/5mvv4kvvv3WOwFaeJm5gazaPhEbKqdi374q7Ny9J6ZjxSI/T5uoYG9padLGQPHDHF0iIqLUISJF8GaJntjljIj337VjG+66oRAup8PtnQAtJjtmjcX+Awew8J2ZsR4qau+v3OZ7XNjt6KSNI1n6dT8y2UOIO2OOLou6RESU6ljQTSEzFy3HiGmv4ec/tgOonRQsEjt378Eb899HjdsT9gRo4cjNy8JVtw9DTZK6fubOmOZ73G9Q4rqFz7j48oSdK5hET4yWDHqX7sZftuL2yf/C7ZP/xeIuERFRgonICgC9AOCo1u0weUbkda0H7rjC43I6kJOZEXXUgll+Tpbn8YduxQVdWthxuOjG0KgJAG2SsMLTElPU7dGhCXp0aJKQc1kxTox2sBd1Jzw/11zUVd6FxV0iIko5LOimkE1b/vA9PrFD+zr5seG4/YEH4XQ5kN8wJ+h2nVvci3Y5w9Cl5fCwjrt6xyRkZ2eg/w2DIx6TXRrl5wMASsrKElLUPefyK+N+jlCME6Md7EXdKWPH1Ile2PjLVhZ1ieggoODxeBK6EMXgNABo1+kUvDz3y4h3XrLwDWxe/5UjJzOjJlgxt8XAUci5/B4UDBylwjlu8StjHZVvPIaKfRURj8ku8z/diqws7Ro7UdEL7hoP3DXJ+ztd9OnPfkXdg52pqKsrTMZYiIjqNQHEIQld0g0Luimoc6eOURVzK6uqsLtkLxo0yMQX2yfUeb1dzjAAQJeC4RA4cVmf3igvrQr7+Kt3TEJlZTWUCuu623Zvv/g8GjWsLeqecfHlcc3UdbuTn0E4b+aLfkXdLuf2Qpdzex20xd0pY8dg1eJFWLV4kV/HLou6REREifXM7OgiwBa+M7PK7fGg+JWxTvNrLQaOQouBo1AwaBSUw4Vn5yxD5f4D0mLgqIjO8ctPG6Mamx3+89Uu3+OubRuhV7e/xe1cPTo0QV52ZtyOH66iT3/2Pe7ZPhc92+ce1N26E56fi2XrK7Bsfe0vD9ipS0REqYYF3RRy9KEFAIDVa9ZGtf+SFZ+hxu2xzM3VO3Hb5QyDwIl5M6bjxv+7Arm5OWiXMwydW9wb1jly87LQZ9B1UY3PDm9Pfx4N8/J8z/eWltpe1D3n8itxxsWXIzc/C7n5WTjj4stxxsWXJ61j11jU1dW3jl0WdYkonXFSNKovampqsP7rT7Pzs7PqtJQWDBoFuDKhnBmAKwvvr9qBth0749XFm+BWCLtbNz87C4Mv6YytW34OtWncNGrS3Pe4ZM8unH/qX20/R48OTZCd4ULx9AeQl52Jrm0b+ZZk0Lt0dfUhggGAuVuXnbpERAkjcDgTu6QbFnRTyI19zvI9/ufDYyPev2Pb4yzXd25xL+Bx4MPX5+DD1+dg7ou1ebTvvvQiFv7rZRzYXxNWUXf1jkmorj6AG+8ejl17kjNJ2jszXsCHr89BbgPvLW+lpTjj4svR71p7Yhjc7hpsqpyKb4on4ZviSdhUORWbKqcmtWN33swXsfSdN7D0nTfqbQwDi7pERESJc0aHBhhxS/+I9nE4HAFvWSyrrMaCz7fj/S+KMX/ln771LVoehkVf7cGjz76DsqrqkD9NFc8ai+yMDAzq0xGffjQ/ovHZ5d1PtmDpun1o2LgpAKC0ZA+6tm1kW2HXWMwFgOLpD2Dfvx4BAKzcWGLLOSJV9OnPWLmxBCs3ltS7XF1Tpy67dImIKCWwoJui1n3/Q8T7HH7IX5CR4apTmK0or8a8GdMD7peZmYn3Z82Ep0br4O1cEDxXN79hNv73+1b839Bh+OGnn7F///6Ix2qHd19+Efm5ub7nJaWxZ+uec/mVyM3PqrP+xIJ74XLVuXMwKcwxDAc7FnWJKP0peDw1CV2IYlCkP1i14oOIdhQR/POh52sq9x9wFAwc5evSbTFwFBrmB+8sPbVHL+Tl5SPn8nsQKoJhx6yxyMnKUA//80q8OuNxd3lZcq6H3lvxu1+3rl7YjWXCNHMxV1cw+BG4nKnxo5s5V/dgL+oCsJosjYVdIqI4EmbohpQaVwVUx1+PODzifRwOB959aTqqqg6gi7co27nFvcjPaxByXxHB/JkvYdKo+1BVud+3v5Uvtk/EhsqpyM7JwNBRD6LvdTfgP8uWRzxeO8x9aRo+fH2Or7BbUlaGPtdEFwlxzuVXIivHhW+KJ/nWnVhwL9rkDEN1pRsfvvGqLWO2w7yZL/oe29ml263wAnQrvMC249mFRV0iIqLE61J3cqiQzr/wKmfvS29AWVW17+eM8qpqvLdyW8h9F3y+HfOW/4rqA+6QRd3iV8ZKTmaG58Wpo119uh6C809qgh1//h7xeGOld+saC7t7d++MqqgbrJhbdcCNT9Yl5+44K/GaLO3sjg3Rs30uzu7YMPTGCWQxWRrjF4iIKKlY0E0hM4uW+R6/+FjdSc3CkZmZiYGXXILyMm2ys4ryaswN0p1rdkL7dhh5+1BUVu5H54LgEQyriydhQ+VU5ORk4skZL2HgsH/ikhuHYMHiJdj442b8+vvWqN5DNOa+NA2N8rUJ08orKnDhwOsj2t+qmAsAFWXVWPrOGylVzNXFo0vX7a5Bdo6LRV0iIjspwFPjSehCFINCAOh0cg9MeG5eVAe49rbagmSLgaOQnx9+ca5x0xb49wc/oNodRlF31lhH5RuPofKNx5ApwJXnHYu+3Q6rGjXs8gMb1qzChjWrUF1VGdV7iJRe2G2Qq12P7t29E4XdIivqums8aVHM1ZknS7NLfnYW3CmYBc74BSKixBKHI6FLunElewBUa9NvWudCoCzccF1ceD5ef28+2uUMC6s716x755PxzNhHcNuoB9C5YDhWF9edZM1oVfFEnNJyOP7YXgwAeOrlVwAATpcDZ3frBo9SOOn4DjjztG5Bj7N9x07MfvsdeJTCaSefjD+2b8d/t2wBAPQ552wc1/qYoPu//eLzuGTwzSgpLUNpeTn6DboB774yI+T7DdSZW1FWnTIxC4nicjlRXlYNAOhybi/fus+K3k/msHymjB2DO0eNxsrVX2LjL4n7hQEREVF9M+Xl/0S9b6PGzZDvjU9okNMACw2ZueFo0fIwzF+5HX27/gUtBo7Cjlmh55YonjUWBYNG1ZSV7sn+7OMF+OzjBQCADJcTZ/S6fF92Tq5r6IjHM10ZGUGPM3vahANb/vfjgUaNmrnO6XNF5jtznq1UUOqoY9plXHn9XcF3BrDwi+3o1bkFqiorwu5c7dGhCdw1HuRlZ/rWFQx+BOVV++FyOlKymBsvS9aW+rpz9SKxy+nEkrWlyRyWn1O6n4cvtDiSQhFZqJTqnewxERFR/SNKhZxMtt6ZOOTKpHwoI6a9BgD48PU5yTh9HWs3bMTdjzyKDZVTo9q/S8vhKC/VOoWdLgc8NXU/VqUUMjJcOHDADUArHrrdNXC6HKhxe5CV6UL1fjecLgdG3HILenY9JeR5L7nxZpSUlQEAlr7zRtBtAxVzUy1iwcoZF1/ue7xq8SJbj93l3F7Yuv8ZAECbZnehqtKdMkVdoLbY/NRd1yR5JNSpx6HJHgLZ6JduqXWLayq4ptl9MQdqtT/uOPXmrJl2DCds7bqc+rVS6qSEnpTSnoisAtAZAJau25fk0QB7dhXj8rOPQZbLGVZR10rBwFEePf7B5XCgxuKHH6WUiMMB5dG6211OR7W7xpPl0rp1PG6Px+FyONCmw8nuZ+YsDash5owOWlNFo8ZNsWjlLwG3CxSz0OCaB5I2AVq4Ck87Gnt3a0VrY+eqHXq2z0Xlq9odiwXXPYjKA+6UKuoaO5KVUukXvEhElOLaNcpXr3X7R0LP2XHR8rS6fk6/nuKD1LPzIpt0IhE6tmuLvLwGaJczLGimbiCrvFm7GyqnokGDLCil6iwAfMVcAMhpkOnbHgAyszO8eb2ZmPDcc/j4s5UI9UuIt1983ve4zzXXBt3W7a6xjFlI5WJu/2tvjGsx12zTrsnIznGhy7m9Ui6KgbELRJQOFCdFozTgvX28MxBddm48NGlWgA++KcH+Gk/I+IVAjLEMOVkZbqWUmBcAvmIuAORkZjj17XOyMjzexzUb1q5ynf+PxmHFODT2ZuqW7N0dcZ5uKk2AFoixmBsPLqcTBdc9CAAofvlh5GS40LN9bkrm6xIRURxwUrSQGLmQAl4uWobfincDADqf0DHJo/E3b8Z0vP7eArzy1lthxS8E8sX2yDKBzduvLp6ILgXDMf7p5/DE9BfxypTJaN60acD9GzXMR0lpGcor9qHftTfiXcMkYsGcWHBvSscs9L/2RuwtjX93gsvlxGGZtyEvPwubdk3Gpl2TAWjdut0KL6jTrasXehPVxdu188l+sQsvzP3I97jtUYdhyEVnJWQcREREB5FCADjp1DOjzs6NBxHBa4t/xOVnHR12/EIgxa+MDfdnH5d5++JXxjoBLRO4z6kFePCJ19DtjMC/6J73yRZccOohqCgvibjwWV61P6W7c83FXLu7c4Ha6IWcK0cgPzsLxS8/7Hut4LoHcXbHhnU6dnu2z43LWKwYYhd8vL8UKQRQxBgGIiKKt9T+1W898aM3O/fEDu0xbvg9SR5NXQMu7INHh9+Lqsr96Nwi+ERp8bSqWOv4dWU6cNXtd+CcAVfh/vGTULGv7i2Bb09/Hg3z8wAAJREUQFO5O9dczF21eFHcunM/K3ofqxYv8uXp6vRuXXOnrttdA7fb/o6w8y4dgC7n9vJbzrt0AKaMHePb5vbJ//LL0+WEaUSUchTgqalJ6EIUCRFZqj9+bHrqRCzpmjZvibeW/oJqd03Unbp22TFrLLJdLvXQHQPQ+5SWlWd0aICfNn1nue37n2/zPS7s9jfLbawmQktliSjm6pasLYXLWbfRovjlh+tMmhbPrt1+3Y/0dQfryw/rv/a9LiJKRBS8vxSBN1s3bgMiIqoXBA5nYpd0w4Jukk2bX1t4mjhyRBJHEtyJHdpj7ovT4HarpBZ1AWB18SRfJMOXa7/DJYNvxsgJk7D5F/98sndefMH3+MKB19c5zjmXX4nc/Czf83C6c/W4A33pf+2NsbyVkIznMxdzE0Hv1G3T7C7fOmNRt1vhBehybi/k5WchLz/LtkgGvZC7t6Rud8rekhJfhq7/WF0oLNSuozlhGhERUXi8haeeQOpELVhp3LQFFn25G8rhQs7l96Bg4KikTQRSPGus5GRluPdVlOUAwM2XdcXdN15Q9uqMxw+Yo8EaNW4GANi7Z1dY0QvhxC0UnnY0urZthK5tG0Uc5xAN4/kSVcw1Kquq9sUv6PKzs/wKrDkZLuRnZ9lW2DUWca0mtwtjwrvCUBsQEVH6E5H/eH+xF/0tRFFiQTeJZhYtwy/bigEAnTulVtSClQY5OXj/lZdRVXUAnQuSW9T9YvsEXz5vdoMMrF7zHW657wH0GXSdX8duo4b5AIDS8nK/4qvVZGjhdOea4w72lpbGtahrFa+QqGIuUNupW1XptizqZue4sHX/M75YBqvu3UgEKuTqmcstWrSw3E8phQMHDmDhQjZDEFHq0TJ0PQldiCJQCADHn9gtpaIWrDhdLry/ageuvfUBVO4/IAUDR7lD7xUfxa+MdfmyeTMzar7+4uP8F6eOzjj/H42wce1q33bvrvgNOQ20CbTM0Qs9OjRBXnam73nB4EdQdcCNT9btCXhec4fs3t0741rUDZSVm6hi7pK1pVi2vgKVB9x+Rd3ilx/2TZpW+eoEFL/8cJ2s3Wj1636kZcH2pY8UXvpIIb9x3etR/bWXPqot6LNLl4goepIGGboicgWApBXzWNBNok3eqIWObdti3IjUi1qwkoo/JBonX3O6BBcPHoK3FxZBKYW3pz+P/DzvRbShOGo1GVowVhORNW7UyHfceHTrWh0vkcVco8+K3reMX9CzdY3roo1eOO/SAQELubri4uI66wJNksfYBSIiouBE5D/646mzPkzmUCLy383rDwAAUuTuyOJZY516cTfL6cSwa87Endedv7+sRCvMFq3a4du2sFtt8TXSuAVzcVWfeG3v7p1x69Y1F3MbN2mesGKu0ZK1pXWKugB8RV2dnrUbzRj1rlxjMdeqUPvkO8V+zx0Op9/FaIcuvuZcRi8QER2kRKQxgCkA/pmsMbCgmyQzFvqiyvD46PuTOJLIOBwOZLhcqCirxikthyd7OHWs3jEJOTmZmDb7VfQZdB0AYO6Mab7X+w26wXK/UHELVp2yH7z1uq+oG2ibWCRi8rNIuFxOvy5du1h15ZqLtlYCbWOMXWBRl4hSAjN0KXWdBwCdTzs32eOIyDU33ZfhLeam3M8yxbPGIicro2bNl59kXnR6K2z97b8AgPxGTQDU3qpv7s4Nh7G4+vF3+zB3+RZfUVd/3c6irvFYvuJx6KiBuAlU1LWDVVeusWhrRS/0vvih2+9XC3c8utCvqGvnOImI6hNxOBK6RGgSgA1Kqdfi8NbDEu5Mr2Szn37/EwBwcsfjkzySyJSUlqHvOefizQXvo6y0KtnDsbSqeCIAoF3OMFw19A5MeegBNMrPR0lZGUrKyiz3qSirxtJ33gh9bFOH7AdvvQ4AvkzXMy6+HI0bNsS8mS+GPV7zZGdmK+bPQ/e+/cM+Xrx8VvQ+uhVegDbN7qrTmRsNq45cIHDHbbgWLlyI3r17o6ioCBt/2YrbJ/8LAND2qMMw5KKzYjo2ERHRwUJEVumPJz7/bhJHErntf/4Gd40HZZXVKVfQBYDiV8Y6AaBg4CgMuuB4jHp8DuZ/uhVndGgAAOjV9SitO3emf3duedV+rNxY99oI8C+ufvxdbbzY3OVbAAAXnd4Ke/fs9BV1iz79OezxBopVMHp3xa8xxRjYZcnaUtvGESxaIVZ3PLoQ15+l1Xm9E6YBQJFSqnfMBycioqQSkdMAXIMkxi0AKfhb7frg2XmLfY8fvS+5WbSR2H/gAIaNfgjvLCoCAOTmZ/tea5czDADQuWA42uUMQ7ucYRFNnta54F7bc3nzG+Zg+46duHrYneh99pm+9X2vuc7W8wCo06lrjGcIJlQx1+y8SwdEPDY7WUUvRCOceIVYLFy40Nepq+NEaURERBrvbeCdgdSeCM3KJ0vexf23XgyX0+HOz8nyZYG1GDgKLQaOQsGgUci5/B7fUjBoVFjHLRik7W8nb7euGnv3Vbjp8tOq9S7dkr27Iz5WqIKrsVs3kk7dcIq5yYhYCMbldMbcpRvPYq7O0KWrYwQDEVFqay4iXxmWweYNRCQDwDQAjyulNiV+iLXYoZtgLxctw2/FuwAAnU9I/YnQjP711tv4c+cO1Lg92FA5FZ1b3Osr5AJaUTc/Pxcfvj4HlVVVGHDLbX6v5+ZlYfWO2tzazi3uRUW5VhzMy2uAyqoqtMsZhryG2Vi1fWLM4/1iu5ap1aVgOF6d955vfVlFBXLzs8I+TjjZuOZOXatj7C0t9eveNRdzV8wPPBFJ40YNsbek1LKjNdH06IVAXbp5+VnoVngBPit6P+AxzPEK8WCcIE0kRUL2iKieUoxBoFRTCAD/OPXMlJ8IzejPrb/ikbuuAgC4azyu4lfGosXAUSivqkaDBg0gIhCHE0u9k4pt/n4Nbr/6TORcXjtXRV52FnbM0iai1vcFgPz8hjhQXYWcy+9BfnYWimfZM1l18StjpWDQqJofN34T9OKzYPAjcDlD99oYu3PN5i7fgjOP17qArYq0XdtqDQjGLmDjduEWbvt1PxLvrvg1rG3jIVSXbn52Fs7u2BBL1gZumjDn5MbDHY/WXos+eX9vrFtVBDCCgYgoPN5J0RJsp1LqpBDbDAeQA2BcAsYTFAu6CfajdyK0Ezu0x7jh6TERmu70U0/Be4uXADjgK8Z++Pocy21zsrPx3sszfM/3HziA/tff6FfgzctrUGf/f897D6+88Zat49YjGIDaTuKs7Iyw9o20g1Znjm/Qj7G3tNRXIA63mAsAC2bP8sUunHfpAF8BORk+K3o/YOEa0CZGOyzztrCOFa9ibiAvzP2IsQtERFSviYhvIofHpwf+5WsqatriL8jIzIJLeVBVXY2CQaMgGVlY+uUey+1bH9cJi76q7Yb9fHkRRt9+ma/Am9sg11f81f28aR2GXH4qWgwc5Sv8xkqPYADgV1w2CjduIVrGY+iRDMZ14RRzGzdprsU6JDFHV6d36eqToBkVv/wwcq4cEdZx8hu3sHtolowRDERElJ5EpBWAkQBuAJAlIsZf1mZ5J0orU0olpJODkQsJNGJabVbyxJEjcPsDD+GcAVfhnAFX4b7xk4LsmRpaH3UUFrzyEubPfAkCJ/Lzw8+vyszIwPuzZgIAPnx9Dj58fQ7mzZheZ7v/638hGjTI1iIbbI5gAICmLfIAALt3lIe1vbHoas7PDcbY1Wvu8N1bWhpRMVfXuFFDbf+SEnQ5t1dS4xfsmiCtoKDAhtGExonSiCiZlAI8Hk9CF6IgegJa1MKIW/rjjA4NcEaHBhgy4LQkDyu0zMwsFH25C4u+2oP8vIYQVzbmr/wz7P1PPb0Q81b8DgBYum4f3l+1o842R7fpgAWfF6PaXWN7BAMAVL7xmOX6vOxM9OjQxPK1UJEIRsZJ0nTmWIW9u3eia9tGER0XgF9Xbs/2uejZPhf9uh8Z0THssmRtKcqqAseA6V26geifU9neut8D8cbYBSKicAjEkdglDH8DkA1gDoA9hgUA7vY+7hCPT8MKC7pJMmT4/fh+80++51+t/S6Jo4lMZkYG5s2YjrkvTotoPxEJ2NFr9N7LMzDryclQNVKbx2tTcXfFFuuu+Nz8LJxz+ZUB9wu3mKtn6QbqxNWLsr7xhFnMBbQuXeP+e0tKklbUDZWlq8cuBKJ/Tjt2JOYi2pipyyxdIiIiYNWKD7BqxQe+55s2fIN7BvdJ4ogiM//zP/Heym0R75fXsDGWrgscWwAA2TkNsHD1TiiHy5fFG4/ibqtbJ/geF09/AO6aur+MCTQZWiD6JGnGfY2FW6uCbyQZueb9k9mtGyxLt/jlh+EOEnWTjMgIQ6YuYxeIiNLTGgBnWCyAVuQ9A8BPlnvGAQu6CTThpit8j3/+VbvYan34X3zrbn/goUQPKWUd+peWeO/lGfjw9Tl48uHRqKo8ENEka+E4tdVI3+NviifB7a696Ot/7Y1hT2xm9MFbr1sWdQGteGsuykZqwexZWDF/nl+3bqxF3fMuHRBVx2+wLt1Nuyb7fZ6pwJipyy5dIkosLUM3kQtREJ+Znq8CsAgAvvr8I4y4pX/iR5SCMjIy8f6qHVi6bh8++Go39tfY17HboqF2l9vOssCF1MLTjo6qixaA3+RoRsvWV+DdFb/6FWUjnfDs3RW/Ytn6Cr/97OjS1Tt+IxGqSzeYZHQWGzN12aVLRBRaqnXoKqX2KqWWmRfvy796n4d3O7gNWNBNsDatDvV7fH3vM3zrvt/8E0ZOsL4Nqz5r1+bveO2Zp1FVZU9RN1TsQrS5uTpjvq3VcfSibCTduVbHMBZ1o3XepQN8+0daHI6lSzdZk7uxS5eIiOo7pdRpAJZ4n/5HKXWKUqoQwJcA/Lp2SZOZlY2Fq3bBrbQM3IKBo2KaBGDLiw/5Hhu7dI3MxdhwunN1xi5dK1ZF2Wj4Csd7dkZdIO3X/Ui/Qq6dhdZgsQvJ6iw2dumyqEtERLFgQTfBru11OibcdAUm3HQFru11um/dMYe1BACsXrMW96dBnm6iOV1O1Lg9qCivtr1T18ycmxtJdm4iLZg9y/c4mi5dYzFXZ2eMQ6AuXePxEz0pmrFL9/bJ/2KnLhElhFIKHk9NQheiYJRS5yilRCnVy7Cus/745iu6J2dgKcyVkQFXZjZcDgfKqqptm93KqkvXHLMQSTHXrGvbRlHvG4qx2zfaAql5v0iLw7HELgDASx8l9lr0jkcXsqhLRBQGEUAcjoQu0fJeU9mfzRSCK9EnJGs3XHAmXnz/Y/y8dTu+TJE83X7X3YiKfZXIy22AeS/VncAskcZNfQZOlwM1bg8ccKJdzjDk5mVh9Q7r4nf3ViPrdOA2bZGHFVvGBZ0QzRiREEsht3GjRgnpQm3cqCH2lpRGdS7jPgtenoY+191UZ30oeuzCpl2TozpvMhQWFqKoqAgAO3WJiIhMigAU/rD+a4y4pT8mPBf93UR26N+jFfbu2Yms7Bz858tdSR3Ljxu/RXmZdg3jcjqRc/k9ALQu0OJZY+ts3+rGh7CjtG6xVo9bsJKXnWlbAbZxk+YJ6UJ9d8WvEUcl6IyF25UbS3zvPZJxL1lbGvT8epfukrXR331ntzseXYgn7++NdauKAObpEhFRlNihm0JuvODMZA/Bp//1g1GxrxIAUF4RfVeAXRrl5fkez50xHYtfm40at0LnFveiXc4wv22tirmAFrHQ3ZCbu6lyqt/ruflZMUUtGH3w1utYtXiRL083XoxdulEf4+XIJrcziiV2IdHduTrjBGlERERUSynVG8BiIPnRC/28xVwAqK6qRL8erZI6nn0VZb7H7poaLF23D3OK1qNy/wFfcVcXqJgLADtKK/xe2/evR3yPi6c/4LdtLJ25c5dvibm7N1KRxiXYWXDOuXKE5fpwunSTwZinS0REFA0WdFPUTffen5TzXnzjEJwz4Ko6RdwLrrnOcvtLBt+McwZc5VvO/7+BcRnX/cNuw81XX+17LiJ46fFJ2F/tf4FmLubmNmvg93qw7tyTutXe3paqMQvBdDm3V9iTmwXbJtL3Hu3kaCK23a0YMb1D1xnDbRVERJHw1HgSuhBFSyl1nv44GROk9e/RCmd0aIASU7GvZM/OgEXdwi4FOKNDA9/Sp9uhltvFotPJPfDM7KUAgKXrtOvkw474G264U+vO1SdMMxdzS5/8J5rn5Vge01jMBfzzdO0qxF50evwL4cbYhZ7tc8Mq7Jq7c40izfZ1OZ3Iz84K+HqwLF0iIkpdDqckdEk3rGakGH2CtP9u2YL7EpilqxdmS8v8C57NmjYFAFTv349zBlyFSwbf7Pd6SWmZ3/OamhrL7ULpd93gkPtdeN45+PD1Ob7nBc2bo2j2K2jQIBvtcoahXc4wv4LtxG0PY/T64Zi47WFM3PZwneKurk3OMLTJGYbl/9kY0ZhDscqojQd9cjRdsBzc8y4dgC7n9rJ1XJF06dqVz2uXKXdelewhEBERpaJPgMR36fY3dOXqnpzvQW5+MwDWRd1+3Y9A5T7/69fy0r0Rd/T28xaSz+jQAP0D7NuuUxdfMVd3+aA7MP/Trag+4EbO5ffUKeYCwH/H3hy0sNvq1glocM0Dlnm6sUpU7IJe1NXPGayw26/7kbaOa8naUlQecIedpWvnxGvRevL+3skeAhERpTkWdFPMtb1OR+vD/wIA+CqBWbrmwuxvmzfht82bsGbV576irnm7i28c4re9ebsLr70hrHNfcuPNqNi3z7ffRTfcFNHY33t5Bu65ebDfuonbHq6z3ej1w/2Kuqe2Gok2prgGAMjKy/R1u+pL18LIL7qMRdMV8+OXQbdg9iysmD8PK+bP8xV3AxVszetjiVswCrdL13j+ZEUuEBElHCdFozSjlDpdf3zP4D4JOeeF3Q/3K/I9Od+DJ+dr3ebj/r0DOXlNAHiLut2P8G1XsneX3z5ZOfm+7c7o0AAXdj88rPMbO4IjLTbmN2qC+Z9v91unF3ON/jv2Zr+ibqBCbl5WJs48voHfcu4JeebDRSTSrtdIvbviVyxbX1GnsGtmLuaau3OjtWRtKcqqAjcY6F26dheTo+XNzwUg7qQOhIgoVYlAHIld0o2wqFLXxCFXJv1DGTHtNb/nHY47Fk88GJ9J8y66/iaUVWgXeb9t3hRwuyNatwEA5DZoAJfL6VfcNe7X7h8nodRUIDZr1DAfb09/HpfceDNKyqy3bZSfj7dffD6s99DvusG+orBVMddo+CHWv70Ptu/wQx6MOIqgy7naxNHxLOZa6d5Xuz1Sz++1Ku5aFXL1SdGijZvocm4vbN3/jOVrbZrdhapKt6+wm+x/d/S4h7ZHHYYhF52V1LGku0497L+tlZLnl268JdXsmmb3xXx1d+wxR6vpkxN31w0AnN7vkq+VUicFel1ELgFwBYCTABQA2AJgLoBHlVKW/zGLyDQAgwH8Wyl1lem1bACPALgKQGMAawAMV0p9YtrOAWA4gJsA/AXAJgBjlFLvRP4uKZ5EpAhAL/35Ya2OVnMWrovLTzvmzly9kGt23xVNUVmxFwDQqElzuN0HUOGdqMy4z8irClBRGrxol9+oCeZ/qk2OekHXQ3zHMWrYuCneW/F7WO+hX49WvqKwVTHX77h3PBHwtUD7NrzjiYhjGC46vfZzjXdB10yfqGzZ+oqARVRzMVefFC3asfZsn4vKVycEfN2cs/vSR8m7Hr3+LN9fpSJvdjURERl0aN5YvXfBaQk959GzFga9fk41rmQPgKy1aXUoNm35w/d83fc/2H6OYMVUK82aNsWu3bt9hVOduQi84euv0KnLqdi1e3fAY5WUltU5/2+bN6Fj51Owe88ebZuyMpwz4Co0yMnBezNfDPw+Bt/sN6bRrcdhzOaRAbfPbdYAFbv834NlR2/rcagu3w8AcLoia2ZPZrRA40YNsbekNCFxD0Z6l+6mXZPrvLZp12QclnlbnfUZmS64D/h3krkynDiwP77NCoWFhSgqKsLGX7bG9TxERACgAHhSb1Keu6EVce8H8DuAEwA8BOAMEemqlPKrqIlIVwD/ByDQ7KEvAegN4B4A/wVwK4APRORUpdQaw3aPeM89EsDXAAYAeEtELlBKFYFShlKqUEQ+BHA2AGzd8rPcce15eHKmvTEMvU9p6TfhWKBiLgCMf203RlzeGFWVpXUydo3GzSkGELywW1ayx/L8uQ1boKJ0BwCgdO9unNFBu7urUZPmePeTLQHPaRzPocOfwR8T61736Jrn5WBneaXfOqtC7qHDn0F5tXYt6nJGfmNlKnSi6oVdM7s6c41cTicKrnsQxS9bN2jkZ2f5unj1Yu5N57osJ01zOZ2Ytjh+16MduhTqXbqcqZeIKADhnDdBsaCboq7t5bvTzdete8PdwzHj8YlRHc/YwRpIsO5cAFiz6vM6hdpA+6xZ9TkAoMPJnbF3b4nf9nqnr7mYCwBrV38BAGj/j5NRUqr9zLivshLnDLgK+bm5mPtS3c5SY6fwqsWLfJ2xgYxePzzo67rq8v1Rd6smuphqtGD2LF+Xrk7vEjavt9NnRe+H/Ox1eiE3Nz8La0v9u1Q6F9yLjExXXIu6CxcuTOqkbEREKaCPUmqH4flyEdkNYBaAngA+1l8QkQwA0wGMg9ZZ60dEOgK4EsB1SqmZ3nXLAWwAMAZAX++6AmjF3AlKqce9uy8VkWMATADAgm6KUUqdAwAishBA4dqvVmDELf0x4bnI7z66sPvhKN0b+Jf9QPBirm7CG3txR1//H/AC7TduTjFGXtXSV6DVt9X314u1VscxF4P1CIfGTZpjnqmwe+FptbEOH3+3D+eekBe0qPvfseHNNVFevT/qydGMk6Elujs3kGXrK3wF3sLTjkbRpz9bbtev+5F4d8WvER9/ydpSnN2xYcCibnamyy+W4aZzXcjJcKH434/W2TZnQHg/L0TrjkcXGrt0iYiIIsaCroWzLz8m2UPw023NMfhs1U/49fetGPHoBEy4f0TonbwuGXxznXxcIwGwJUQh12jNqs/Rscup2L17N8K5BFn35eo66/RO32DnX//1lwDgV0Auq6jAOQOu8otiMObt6sVXp8sRsks3nozduYmOWzALdP6rht2FOVPrdtICtVERjRs1wgdvvR7R+YJ16RplZbvqFHJ1q4snJaSoq9u7syru5ziY7d1RGXojShvnNzoi9EYUOaXg8YQuVCWSqZir+9L752Gm9fcAcAKYDIuCLrSC7QEAbxiO7xaR1wGMEJEspVQ1gPMAZAKYY9p/DoCXReQopdQvEb8ZijulVG8R+QjAmdFMlNavRytbirm6vEYtUF6yI6z9xs3ZXmedcf9A59e7fAH/4u7ePTvR97TDfHEN/Xu0QmmJ/3tb/G05zjzeejLeREl2d27jJs0t4x709Xt31x1f46bNsXf3Tt+kavr2kRR3l6wtDdgVbJyw7vqzBPnZWSh+ZYzltvnZWbj+LIl7py4REVkTQVrm2iYSC7ppYOr4K3D7iFexcvXP+Pq79QG3C6d426RpU+w2dNhGUszVrfV230ZrTQT769uaoxh0ZeV1Ow5WFi1E18LeSSnqnnfpgKR25+oCFXL1OIaSsvI6rzXKz/Nbv7ekBOddOiDsou55lw6A212D8rIaHH/YCHy31TrDbEPl1JDH0ou6IpKQCAYiIgIA6LcHfa+vEJGjAYwC0FsptT/A3Q3tAPyilDK3Em6AVsA9xvu4HYBqAD9ZbAcAbQGwoJuilFJniYgCgLtvvACPv/i+5XZ9uh6Ccos8Wp2xQ9a4LhJjZ9ct0sZzf724q2f46nENgH/h1NhN63I6QkYvhJKXlYlzT8jD4m/rXrcFkwrduYGKsO+u+DVgwbXo059ReNrRfsXeSAvT/bofCQBoNeQRbHnhActtKl8PfcejXugtGDQaN53rYlGXiIhSDgMp0sRTE670Pb71/tF1Xu9//U1Bi7m/bd6ELZs3Ye2qz/Hb5k2+JV2sXf2F33gvufFm9B10g++5ORphZdFCX/5tIhmLucnuzo3UnKmT0SjffwblvSUl6HJuL3Q5t1fQXGBzIXvXjnIcf1htJ7nxcTCdC+5F54J7AWhF3Q2VU+tk7BIRUViai8hXhmVwsI1F5DBo8QhLlFJfGV56AcBcpdTSILs3BbDHYv1uw+v6n3tV3ZkxzdtR6loEAF9/8TFG3OIf49S/Ryuc0aFByGKu/qdxSRfjX6ttiuh9Skv061F7V4M5GmHxt+W+/Nto/THxNrhrIv98kt2dGwurGIae7XPRs32ur1gbiHHytR2lFWg15JGIz18waDQKBtX+rFX8yhjkZLhw07nsgyIiotTC/5nSSLcuWvTCj//9L0ZOeAzjRtzje628ou5v39OpYBuupk2aYPeePRFN5hatrLxMdC3sjZVFCyPeN1WLuXtLAs1lo5kzdTL6XKfdTRtJx65VV/KuHdq+xx82wvfYSueCe1FRpuWZuTKcAIB2OcOQm5+F1cWTkJufxU5dIkprSZoUbWe4s/SKSB6A9wC4AVxrWH8VgJMBHBvqENDeptX6aLajFOWdKO0zAF3N0QtWRcR0KtaGS49r2FdRBoRofrWjSzdSqdCdGys9esFMj2KwimEwFnN1xqKuMW7BrGDQaF+2rsupXYvmDBjui2QofmVMXDt1RWShUqq37QcmIkpzjFwIjh26aWTq+CvQ5aS/AQBWr1lruU3TJk3Srvs2EmtXf4FGjRr5rQs0cZmepRutMZtHosYd/g8iwTpYU82Cl+tOLmd8bcHL0zBn6mTfY71zVy/q6s67dIDfRGjm45qLuU1b+HcAdy64F9VVbiiloJTCgf1uHNivPa+ucqNdzjAAWkxDVrYLGZn8HRQRkZ1EJBvAfAB/A3CeUup37/o8AE8AmAigSkQai0hjaNeOGd7nGd7D7IZ1d20Tw+v6n02kbm6DeTtKYUqpbvrje4dcWOf1nLwmadd5G4mxs7cjt2Fzv3WBJi5b/G05qtxuHDr8mUQMDUB6d+fqij79GSs3lvgtjZvWfubG99iv+5Ho2T63zvtunq9lGO8orQhZzK084MZLHym89JHCtMVuTFusPa884EbOgOEoGDQ6Lp26HboU6g8Lg21HRERkhQXdNPPspP/zPf7nw2N9j3MbaBctu/fsQccupyR8XIm0/qvVaNa09ufGQIXURMcupEJ2brwY4xj09xksL7hhcy0bzdyZu9vwXC/mBuq6tSrsxqOoO3vJCluPR0RUh1Lw1NQkdAmHtyD7DoDOAAqVUusMLzcH0ALAo9DiFPTlCACXeR/rHWUbABwlIuZZoNoC2I/azNwNALIAHG2xHQBsDGvglAoWA8CXn33oW9GocTMAQGX5HowY0Dgpg0qUcXOK/QrWxq5YMzuiF8KVDt25oWITAtGLvMbjWHXl6tv8+tS9YR23rKo6YNetubALwNai7h2P1t4FKCKR3xJIRHRQE4jDkdAl3aTfiAnduhwDAFj3/Q++de++PB35eVoRbfduqxi7g8uaVZ/7irrBCqmxdumGy1hUTtW4hT5XD4xp/zlTJ/seWxVzjd25szeP9hV1A6koqw4rQsFY2NWjGexQWKg1Q2zeuo1FXSKqd0TEAeDfAM4CcKFS6gvTJn8COMNi2Q5giffxp95t5wPIAHCp4fguAJcDWKyU0v/x/g+0Am/tb6c1VwFYr5TihGhpQil1nnnduyt+Q6MmWhdl1b7gEU8Hi7xGLQDEtytWnxgtHOnQnRvrGPVO3b17doY8lt6lG0jBoNG+iIVgjIVdPZrBLuzSJSKiaPH+5TRUUlppud6Rhr9RiMWaVZ/jiNZtgm6zsmihXyRApMLN0TUWN7v37Y/GjRpiwexZUZ83HkLl54ZDz9U1vt9A8Q2zN9dOKHFhk+Exnzue+bmbt26L27GJiADA40m528+fhVaAHQegQkSMt/f87o1eWGbeSUSqAGxXSvleU0qtEZE3ADzp7fr9BcDNAI6CoXirlCoWkSkA7hORMgDfQCv6ngmg7r37lLJEpMhq/bufbMEZHYIX0Q4mY2dvxx1943v9/cfE29Dwjici3q9f9yPr5MymEmO3bSSKPv0ZXdv6x68FOpa5Szf32of8npdVVeOlj6xiva3ZnZ/75P29sW6V5V8lIiISZuiGUr8qgAeJ9d9vBQB0PqGjb90lg29GSak2UVidZDqKWrg5uo1Nub57S0rRvW9/dO/bP+bOWLs0btQw5mMYu3QjYezW7d5qpN9rBQUFEBEUFBTENLZQevfuDRHxLUVFtRfQznr2yxAiIgD6bztHAvjctNwQxfGuBTATwFgAC6FFM5yvlPrGtN1I7zbDAHwAoBuAy5RSC6I4JyVPLwA4sUtPv5UXdD0kGWNJefrkaNEKt0u3cRP/nNme7XPRs31u1DEHqcqYp2vF5XSg5c2PBny91eAxfs/vuLgA158luP4swR0Xx/d69Mn7e/vO5V/MFc78S0REEWEVI42NG36P77FezAWALT8enBOiRSvW2AW9SzeYD956HasWL8KqxYsCFneTXdi1q2NYz9IFgk+uZmSMYNi9oxzH590JV4YTBQUF2LFjBwBgx44dcS3qGgu4OqfDgTEDL8ODV18St/MSEakUzNBVSv1VKSUBlodC7HeVxfpKpdQ/lVJ/UUplK6W6GLt4DdvVKKXGKqWOVEplKaWOV0q9HeFHSili8oza/1v79WiFirKDdz6BUOKZo/vHxNvgrgndYDB3+RZ8/N0+v8IucPAVd4s+/dn3OJJOX+NEaXomLgCU7d1h+dhu1h254tb+3fVkWO5ERFRvMUM3FEYupJmhw18N+vpvm1nMNYs1dmHM5pEYfsiDYW//wVuv+x4bs2b3lpSiz9UDUy6KIVLRdunO3jwaV7ceg9KdFaip8QA18BVzdebndundu7YgP2bgZXE5BxERUX1WYsgzNU4YdrDLa9QC5SU7Uiq/du7yLb7HF53eym9sqTLOwtOO9ivMRipYIddd48H2GffXWf/rU/fiyNsnYWfZPt+6689K3K2NtcVccbOAS0REsUq/EnQ99/mXtRc+5wy4yrfUd13O7eU3MVmq0Dt39a5dvaibbFcNuysux+1z3U1BXw80WZqx09fuLt3evXv7unMZrUBERBQbEfGFjp7RoYFv0dWnYi6g5ejqgnXpJip2wUzv2v34u9oiZjK7dPXu4b27k1NY/vWpe1Ex86Gg29gdu6DHLOhYzCUiIjuwupFmmjSuWwyrz5o1bep7vLekJGBhNxGxC8F88NbrfkXdZNFzdEvKyuNyfKfLgZy8rKDb6JOlLXh5mm8BaqMc7IxeMBdzGa1AREmhFDyemoQuRJQ4eY1aANC6X888voFlYTdRsQvB+IqpSezSTZWJ2vSi7sqNJb5Fz+Yt27vDtqJu3ZgFZuUSEYVNJLFLmkloQVdEeoqIslj2GrbJF5HHRWSZiJR6X+8Z4XkOE5GXReRPEakWkV9EZLzNbycpPpz7T3z18QN+S7cux/heH3jD4CSOLvHWrPocv23eVKeway7qrixaiOry6C+iw50cLRhjFEOyBComXzXsLvS57qaYO3dr3B68/tuY0BtamDN1sl9RN1Ys5hIRkRVej8bGKncZgK9iNX3MBUkcXXKMnb3dV9QFghd2YxFtl67OGMWQilm6Xds2Qte2jUJvGCdFn/5sa1HXv5jLrFwiIrJXsjp0bwdwqmE52/BaMwDXAXAD+DDSA4vIXwGsBvB373nOBfCQ93gHpanjr/AVdT9evjzJo0kOc2HXqqgbq6y8THQ5t1dMnbqpSu/YLSkrj1scQziM+byxdumymEtEqUIBKTcpGgHg9ahtlFK94S3qbvyqqN4WdZ+c76lT2DWKNXbhj4m3IdvlwpnHN4i6sJsKXbq6wtOODvk40cxF3VgwM5eIKHoigDgkoUu6SVZB93ul1BeG5SvDa78qpZoqpc4G8HwUx34BwFYAZyil3lRKLVdKzVJKPWDLyFPU1PFX+B4f0bpNWnTqHnnccTjyuONsPeaaVZ/7FXWNYo1dGLN5JCZuezjmTt1kWjF/nu/xVcPu8nXmGsVS1HW6HBhwxOiQ2+XkZaHf4JstXzN26YpInSXSQi+LuUREFACvR22UrkXdO/ra++OQXti1EmvsAqAVdfOyMmM6RrKZc3QLTzvaL1N37+6dSS/q6q4/S+oskXbusphLRETxkHIZukopFXorayJyNIDzADytlDpg36jSgzF6IVU7dY887jgc0boNjmjdBs5sF5zZLt/zI1q3saXAu2bV577Hxi7dWGMXYhVLx/AZF12C7n37o3vf/jjjotgKlMYcXXOWrvG1aIq6705/HpXl1SG3e/23MQEL48boBSt2ZuwSESWEUvB4PAldKDa8Ho1OuhR17+7vwh19Hb5irv5YX+7u77LtXHbHLgBAefV+LP42uvkQYunMPbtjQ/Rsn4ue7XNxdseGUR/HmKPbtW0jv2KusdjbtW2jpBV29S5dK5HGMYjIQjvGRERU34jDkdAl3dh3tRKZf4tIcwB7AXwAYIRSakvwXcLSzftnpYh8CKAHgH0AFgC4Uym1y4ZzpCy9S/ekMx8BoOXpzpoxPZlD8jnyuOPgcXuQkZeJWzfeG3C7F9pOwhGt2wAAHC4Hfv3+e1+R99fvvw/7fM2aNsWu3bvrdOkmUyxjcbtrsHbfkwCAri2Ho3vf/r7XXC4nls59O+xjLZg9C32uHuiXp2vs3NVf04u6xhiERAl0zquG3YWSsnJfUbe4uDjBIyMiooMIr0fjQCnV21vAKtz4VVHI7RPp7v4uuGs8yMvOxPYZ9wfcruXNj/qKvS5n7Q94j88LPzEjr1ELlJfswN49O3HR6a388muTxVhcXra+IuL93TU1qHx1AgCg4LoH0bN97WTNLqcTS9aGP/Fv4ybN6xSX9TH1636k7zVjsTeRjF26Rno3sV7UffKdsK5FC20dHBERERLfoVsCYDKAGwCcCeARaHlln4uIHS13h3r/fBnAjwB6ARgOoDeAD0Qk4PsVkcEi8pWIfDX3/W9sGEryGPN0UyV+weP2YOiWURgSpJgLAEM23ouhW0Zh6JZRvu5dZ7YLHotuTr3wayVQl26sRrceB6cr8r82xjEYi6fhcrmc6NpyOABg5faJWLvvSd+SleOKuHN3wexZQV+LtlO33+CbkZOXFfb2kTJPnMZOXSJKF8zQTSkpeT1qvBYVkeRfvMXA26kLQOt+TYVO3bv7u5Cd4ULFzIew/fnAxVwA2P78/aiY+RAqZj6E7AyXbzF37gaLazBOlJYKebUXnd7K1nEUv/wwKl+d4FtyMlwRde2+u+JXXzcu4F9gfnfFr37PI+nS7dGhCfKy4xdJEd3EaXJQZmcTEcWVJDY/lxm6ISilvlVK3a2UWuDNEnsSwPkAWkKbMCJW+vtZppS6VSn1sVJqOoBbAPwD2u1vgcY2XSl1klLqpIsuONGGoSSPcZI0IPnxC0cedxwy8iK/sNKLu0M23ouM3Ey/aAa9mBssoiFQlm4sqsv3Y2VRZHdNnXfpgJjHsHTu26gos44y0Au8kRZ29aKtlWiKun2uuwk1bg9e/21MWOePlrmoa6V374Nv4joiIrJHql6PGq9FvdunO197brLjF/RibqhCrpXtz9/vW7IzXH7RDPqxAxk7e3vUY7aTuZgbTXduKMUvP4ycDFdEcQzG6AUrxviFZGbqmoVT1H3y/tprUWboEhFRPCQrcsFHKfWNiPwI4GQbDqffwmaejXix988TACyy4TwpL1nxC8Z4BGPMQqjO3FCGfG+9/wvHTfLr1NVjGgCtS9f4WtfC3siKorAcK2MxN5ruXJ3epbty+0TL1/X1eiRDqCiGYF26+uuRxC8seHlanQnW4mXO1Mm+c5mjF3r37o2iIu1nSGca5uAQEVHi8XrUfnqXbjLiF4xFVl/EQhTFXDOrYxjjGQAtoiFQNMO5J+TFPKHZocOf8YuBCIddxVyX04mcK0cgPzsLxS8/XOd1fZ0exxBODEOw8by74ldf/IKeqdu4afOAcQgA8Mm6PejatlGY70jL8F25MfLGi6JPf/aLXzBbt0r/fmd3LhERxUeqVDsEQNSTTxhs8P4Z6Fj1bpYQY/xCIqIXPG6PX1RCODELsRjyfW1Ew9Ato+DMcll27XYt7A1XtgtjNo+M21isxBq1YLR07tuornSjY4M7fPELVvSOXbc79ltuzZ26fa67CX2uuylgx67T5cCAI0bHfN5wWHXpmou5D14d2wRyRER2UEoxciE98Ho0DozxC4nq0nXXeHwxCeFELMTCGM+gRzRYTax20emt4K7x4I+Jt8V0vlgmRIu1M3fJ2lIsW1+BygNuFFz3YMDt9DiGSGMYrJijGfTCrl0TpkVTzNUZC8t6l+6T9/fG9WfV3rbL7lwiouhxUrTgkj5iETkJwN8BrLLhcF8A+BPabXNG+vMvbThHWjHGL8Q7ekGPVjBGJSTakO/vhTNLKygbC7s1bk/Ci7mAvXEPgFbUXTF/XliF3dz8rIizda0Yi7q6QDEM705/HpXl1tEQdjN2CxcUFLCYS0REUeP1aNwVAVr0Qjzd3V8rpuodufEs5AaiF3j1wq7OrvzavKxMnHtCni3HitaStaWoPOBGzpUjQhZ2I41hsKJn6hoLu0DsE6blZWeiR4cmMR3DGL3w5P29DZ25ALtziYgonhJa0BWRf4vIWBG5SETOFJG7APwHwFYATxu26yUilwDo7l11uohcIiK9TMdzi8hL+nOllBvACAC9ReQFETlXRG4B8ByAZQA+jusbTFF6/EI8HXnccXBmu5JSxDXTu3adWf6dEWPaW0cVJEKs3blmxsJuoKKuMVvXjqLuivnzsGL+vKgnTIunHTt2sJhLRCnN4/EkdKHAeD2aeMYu3Xhy13ji3pEbLr2wa5ygy2nDhCt/TLwN7prw/45fdHqrmM9pJdJuXbcNdw7ohd1oJ0yLB2OXrjFmQSkl7M4lIooNJ0ULLtEZuusBXAFgKIAG0LoX5gJ4UCll/BXr8wCONDx/yPvnrwD+aljv9C4+SqlZIuKBNpvwtQB2A5gD4D6llB230ZFJKhVzjYZ8fy9eOG4SDlTsBwBU7NqXkPPaMQlauJbOfRtnXHRJyGzdcHN1w2HO1k2kq4bdFfScLOYSEVEYeD2aRNPHXIDBo9+39Zh6J6yxeJoqtj9/P3KvfQgAUONJ3JfePBFavCxZW4qzOzYMmq0LAPnZWWHn6oajcZPmvmzdwtOODpqrazc9O9eauFnIJSKiREhoQVcpNR7A+DC2+2uYx7MsoSulZgOYHdHgKCrhFHNfaDsJB8r3+57bMUlauPTJ1J5uNdaW42XlZaJrYW+sLFoYcJtEFXN14RZ1gdoJ03TRFngXzJ7lO06oydLswmIuEaUrPUOXUgOvR5OmCHGaHM3ldCA7wxWwM7flzY+ivKr2WtSuSdLCVTHzIV9RNxGsirmx5ucGoxdoz+7YEAXXPRjWhGlG0RR5zROmRcNYbA9H8EIuwGIuEZF9RJCWXbOJlPQMXUosOydGC7cz90D5fvy2eZNvqaly44W2k2wbRyRijV0Ys3kkatyBb3MzToKmsztuwYo+YVqwTF2gNoZBX7JyXOjet39UWbvG6IV4umrYXehz3U1+53nnual457mpvudjBl7GYi4REVEKi0fsgp6ZG6yYCwDlVfvx5HyPb6k64EbLmx+1ezhh+duo5+N6/EQXc430bN1wIhiMS7Q5u++u+NX3ON7RC1bF3NU/7DE+LWIxl4iIEokF3XrC7onRwi3mvtB2Ehwu/2+zX7//HjVVbjzdamzCCrs5zRoAiD12YXTrcXC6Av+1SXR3rpFe1A01WZqRscBrLO6GU+BdMHuWHcMOyVwwNhZydbOXrEjIWIiIouXx1CR0ITrY3d3fhewMV8jM3JY3PwqX0//a7fF5blQdcCP32ocSVthtnq9di+4sr4zreaxiFvp1P9Jiy/gId8I0I73Iqxd29SWSAm80XbpW3xtWzMXc1T/s8RVzu51+jm+ziAdARERBCOBwJHZJM+k3Ykq6SIq5NVVu/Pr993Ve+/X77xParXvDt//0PY7H5GjnXToAXc6tnSPl6Xtu9z3uc/VA288XSDiTpQUSqHs3UGHX6n31G3wzcvKyohq7FeOka+auXAA4sV1bAMDmrdtsOycRERGlPneNJ6zYhPKq/Xh8nrvO+sfnuRParfvrU7XXzfHo0r3o9FY48/gGvufrN/+BJk2bAbAu8saTPmFaWVV1RPuZu3ej7dwNV3nVfnyybk/QbayKuUZTpr0Zl7ERERGFkuhJ0ShJPlv1ky3HOaJ1m7AycIMVc41+/f57HHnccb6M23jm6+Y0a4DKXftQsWsfxrSfiNHrwyt4jmk/0dfZm9usAaq9ecChJj/Ly8lBeWUl9pbEPvFDpPRc3Y4N7kBuflbAbN1grHJ3jZm75vfVb/DNyMzOwOu/jYlx9JpQmbkAMPLWm3DxLcMAaF26V5/dPej2RERElHyxTIx2R18HXE5HWBOghdOB+fg8N+7u7/LLUo1Xxm7z/AbYWbYPO8sr8bdRz+O/Y28Oa7+/jXre19nbPC/Htz7UxGcrVq1D+9aHxjboGLicTuRcOQIAgk6YFohV7m6gvN2VG0vQo0MTuGs8YU+Ol5ediR4dmgQs6oYq5pqJyMJ4RIsQERFZYUG3nujW5ZiYi7pHHnccANhWzNUZt9OLu/Eo7N7w7T8x44QnfEXdYIxFXCPjOqtirrEzd/xtN2LoY0/FMOLY6IVXvbCri7TAa9zWPKkaACx4eVrci7lWMQu6E9u1xTcbNmLz1m0s6hJRalIKnprA+etE9YgtE6OFyszVJ0FzOR2W3blm5m3u7u9Cy5sftb2o++tT9+LI2yf5irrBGIu4RsZ1VsXc9Zv/iH2gNjEWXs/u2NBX3AUiK/AatzMXd43cNR5UzHwohhH7C7eY2+30c/DZ8g8BoJBFXSIi+4hwUrRgGLlQT0wdf4XvcbQTo3ncHgzdMiroNpEWc83iHcVgjF4IJFAxN6NpbUeEOV5BXwJJZOyCmR7DoC96zq6+RBLNYIxlMKpxe5JSzAW0Ll1j9MLoWW8yU5eIiCgF2VXoClXMrTqgxSmEU8y1omfsxiOKIZzohUDF3GYNamOtzPEK+hJIInN0regxDPqi5+zqS6R5u3okg65HhyZhd+bqAkUuFJ52NLq2beR7Hqozd8q0N/2ydEVEicjCiAZDREQUIXbo1iN6l24sE6O90HaSX+fsC20n4YA3ggAAHC5H1MXcVDD8EP+LyTM23eX3fGmbyREdzxi70L1vfzRu1DBhk4kFonfu6owdvNHEM/S57ibbcnMjLebqRt56E8Y9Ow3fbNgIgJm6RJRaFMCJyohscEffur0oejeuLtyu3FAen+e2PJ8djNELZg3veMLv+fb7/ZsCWj4a2XVkk6bNsGf3LuzdsxM92+eicZPmeHfFr5EP2mbm2ARjB2+43bvZmS5fTm+oru1IRDPB2pRpb+LOmy7TO3UBTpJGRBQbASQNJypLJBZ065Gp46/ASWc+EvX+v23e5BeJAAA1VW78tnmTXUP08bg9uDVOWbqBmCdLMxdzAcCZmwlPtRtP3XlbWMccf9uNuO+ZF1FeqV2wJyNPNxRjgTeaeIb39oRXAL669RiU7qzwW9fnupvQKD/PMis33GKubuStNwGAL1OXiIiIDi5PzvfUybt1OR14cr79kSZ393dF3PFpN3MxV1939OOvospdgzWbfg95jBWr1qF7lw7Ys3sXAC2moV/3I1OiqGsUKJ4hWHF3R6l2XSkI3rVtpEde6PQMXXNeLhC6M9dMnyCt87FNItqPiIgoGizoUkT07ls9Tzedu3GNzDELVsVcAOjxzdCIu3TH33YjAPjydFOlU9eKVfdu15bDo5pUTWdVyDWyo5hLRJTSlIKnhh26REbRToxm7L69o6893bhW3DUebJ9h/8RoAPwKikaBIhhitWLVOgDwFXZTrVvXzFzcLbjuwaAdu+UhcnPNRVwjd43HL16BiIhShUAczNANhv3LFJVfv/++3hVzY5WXU5vBu7ekNKm5uuFaOvdtVFe66+Ts9mwVPEsZ0Aq5FzYZXqeYu7wseEGcxVwiIqKDWhEAxDoxGoC4dObqXE5HXDJ0jUqf1OZ2+Nuo59Hwjif8IhisunN15fsPhNWda7Zi1To0adrM99xqUrVUs2RtKSoPuMPO2DULVMxtnp8bdL9Iu3OJiIgSjR269dTAGwZj1ozpyR5GxF44bhIOVOxHRm4mhnwfeSTD063G+j1PVDEXqO3U1SMYUjF+wcrSuW+je9/+fuv27KzbVWsWrJDbuHku9npft7OAO+7ZabYdi4jILlqGbvwKT0TpRCnVW0RUsscRSqAMXT2zNy87M6rM1iNv95/012oCtGDF3Fjp3brtWx8KACkZv2C2ZG0perb3L8C2GhI6Rs5czN33L/99GlzzAAB7i7d33nSZbcciIqrXBAAzdINiQbeesWNitERwuBx4oa12wWuedM0vyzeCwq65mBtqArR4GX/bjb74hXRk7M61ys81RyxYdeS+98sYnJ5/l+3duPqkaA7+w09EREQ2sJp0zZjlG0lh11xgDDUBWiKkQ5cuALicTr/oBT0/N5BQxVwAyMvORNUBeyM7fJOiicQnC4SIiMiLBd16JtaJ0RLl1++/9+X0Wk26ZszyjbSwayVRxdyDQbDu3AubDA/4mlmDvCzfBGZOlwNvPjUl5rHpHrr6EtuORURERPWTy+lA1QG3ZbSDnt0bSWE3UI4rkJxibroydudWmPJzjRPm6ayKuQBQPP0BFAx+BJ2PbQKX04GVG3bZNkbl8WTYdjAiIiILbGOrxwbeMDjZQwgqnJzeX7//Hr9t3oQDFfuDbjfjhNoOCIfLgYymtXm2LOaGz9id27B57a1vFzYZXqeYu7xsctC83EXbHvVtk5Wdgctuv9O2cc5essK2YxERxcw7KVoiF6J0MH3MBckeQlCPz3OHnHTt8XlawbfqgDuizN1mDbJ8jyMt5uZlZqBTm8Mj2ieQZeuDd7qmCndNjWV3rh5hceTtkyIq5uqKpz+Aff96BNkZLnRt1yzotpEQkYW2HYyIqJ4ShyR0STfs0K2HjLEL6Zqla+ZwOYJ26lYacnJP3xBb4dCZm+mLTHj6nttjOlaq6HP1QMtM38aNGqK8ogK5+doPHcbu3NKdFbi69RjL44Wa+Mxs0bZH0euQ+3HZ7XfG1Kl7Yru2+GbDRmzeug2jZ70JAGh92CG4+uzuUR+TiIiIbFcEoHDjV0WYPuYCDB79frLHE7PH57lxd38XWt78qGWnrjE7N9Zu3J/vvhJHP/4qOrU5PKrJ0VJRv+5HWsY/NG7SHOWle5CfnWWxV23Xs1X3c6hirpFd3brdTj9Hj10oNGVFFymlekd1UCIiIgvs0K2Hpo6/At26HAMAKZ+lG65wO3Xt6Mbt8c1QAOldzO3et7/fRGeBJmjbW1IKt7sGK7drWblNmuf5vV66s6JOXm6kxVzdom2PIis7AxffMizqbt2Rt96EE9u19Vu3ees2duwSUVIpsEOXyMhb2CoCgI1fFSV5NPZ5fJ47YKdusLiFaPx895Vwp/Fki/26H4me7XPRr/uRAAJn+e7ds9OvO9eKuTN3378eiaiYq7OjW3fKtDfR7fRzrF4qZNcuEVH4BAIRR0KXdJN+IyZbTB1/RbKHELFOXU7FEa3boFOXUwNu43A58MJx/rMHG+MW7OLMzcTtU56J+Th9rk58XprxnObCbl5OjtUuPsu2jA36eqz0GIZYIhhG3noT3nluKt55bqqvuKt37LKwS0RElBrStVvxjr4O3NE38I9Qj89z+02iBtjbnWtkR/SCXlBNNL2Au3fPTvRsXxvjtX7zHyH3bdEwN+Q2sSie/kDMRd3VP+zxLYYCL4u6RERkGxZ0KW3s2r3b708rv37/vV+X7owTnvCLW7BLj2+GQrmj74rQC6eBOmPjKdg5J94+BM8OvxPPDr/Tr7jbs9UodGxwBzo2uCMBI6zt1o01V9fcsctuXSJKCgV4PJ6ELkRkv1FXt7R8bOZyOvy6dO3uzrVDk6ZasTJQZ2w8hSoir9/8B9Zv/sM3Rl2rIY8g58oRAAIXdaPpzLWiF3U7H9sk5mxdU9cui7pEROEQAA5J7JJmWNAlHNG6TUpNkBZOJ67xNX17fR9jl66xmJtKk5+Nv+1G3+NEdukaz6UXbo2L0cTbh/geG7NzA4k2aiEQO4u6Vt267NglIiJKDXf0daTUBGmjrm6JO/o66hRty0t2+D3WX9e315fsvGa+Lt14decCQPn+A1Fn6K5Ytc73ONFdusYisl681Rcj4xhzrhzhmwzNOClaPNk5YZpFUVd5FxZ3iYgoKpwUrR7TJ0cDtCzdI1q3AQCcefrpSZsorVOXU/06cTt1ORVrVn1ep7i7a/du33jN6wHA497vF7WQSsVcXV5ODsorK+PepRtowjO7nZ5/Fxo3z8V7v1hPlKbrdcj92Fde7XveIC8Li7ZZzwpt12RpgFbYHffsNHyzYaNv3eat22I6JhFReBRzbYmsFQEoBLQsXT3KoO1JhUmbKG3U1S19hdvykh24o68DeY1a+G3TpGkz7Nm9y/e6mb6/OdvVbnrkQrRFXf19xLtLN9CEZ+HEKwRiLOo2z83BzopKAECrWydgy7MjQu5fMPgRX9E9LzsTxdMfCLitPmFa13bNop4sDdCKunfedJk+aZquUEQWpmsECRFRPImDPajB8NOpx6aOvwJfffyAb4I03cfLlyetY9ccp6AXbo3rA+W8mtfHI2rBKNYcXWOXbrwEKuaau3GDyWrawPKxlb07K3B6/l248KjRlq/3OuR+VFcd8GXcvvPcVFRXHcDp+Xeh1yF1Z4QG7OvUBfzzdXXs0iUiIkoOpVRvpZTAO0GabuNXRUnr2DV24RrXGdevWLWuThwAAMuYAJ3d3blA7BOjGTtg4ynWgnHz3Gzf491jh9R5XS/mAsDOsgo0uOYBtLp1guWxCgY/ggbXPICqA25fxm3VATcaXKMVbQOJNVdXZ8zXNXbsxnRQIiKql9ihS34TpA277zV8tuonv45dIP5du8bOXABY+MoMXDn0TpSUlfltl5eTE7IQet8zL6K8svbCLl7duT2+GYqlbeyNGQhEL8w2btQQC2bPCms7wD8vN5IiLgAMnTwVGbmZuOzrYX7r3/zHVFTvDl4s37uzAhceNdqvW1cv5po7bfXnl91+J3odcr9lt66dnbpm7NIlIiJKLmN3ovcW9EJjxy4Q/65dY2cuAHz8nXatc9HprfwKknrBNlAxVF/fvvWhvnXxKOYmmrHLtnGT5nh3xa9hbWcUbUfuj/cN8nvePDcbOyuqgu6zs6zCslu3vGo/Vv+wx2+d3nXbtV0zFAx+JGC3bvH0B9DgmsCdvJGaMu1NdD62iW3HIyKi+oUduuRn6vgr6nTsAlrXbryYi7m6V5+egkb5+X7rjIVaK7dPeSYhxVxdrF26gfS5eiC69+3vW/TC7N6SUr/15sW4XSzFXADwuD0YsL7u52cu8Bpd/ct9vi7evTv98832lVcHLca++dSUoN26dnbqGjl4GwcRxZlSgMdTk9CFKF15i7tF5vXx7No1F3ON5i7f4leY3LN7F7p36RD0eJ3aHG7r+ILRYxfs1q/7kejZPte3GIvae/fs9Hst2HaxdOd2anM48jIz6qz/8b5Bfl27RrvHDvG9trPM/1q0YPAjcDkDX/et3LArZLduXnamLROlAcCdN10W8zGIiA5m4pCELulGlFLJHkPK+XrpaH4oBiedaX1BY1fXrrETeOErMwJu13vQDQCCd+nePuUZKLcHztxM9PhmaMxjC8cnJz4NT7UbT915W8T7Dn3sKQDAivnzfOvszryNppgLALdOnIKrf7kvqn1nHzUeQO1EaYG6cwO57PY7kZWdYdmtG+mxArn4Fq0wPWYgL6Zj0an7IckeAtmoy8C/J3sIKafAdUvMV3et/tJS3XvNADuGE7ahjz31tVLqpISelChO9K5d83q7unaNncB6Z64VY7dusG7TTm0OR7bLiZ/vvjLmsYXj6MdfRZW7JqosXb2T2Nh1GyjzFqjN3Q3FvF003bntWx9qGbEQjqajXgAANM/PxZZnR6Bg8COoOuAOOwO3a7tmyM5wBezWbXDNA3U6fSPl684VcSuPp27lmoioHjvh8AK1bNjlCT1n43ufSavrZ0YuUEjGydOM7O7aDVbMBYBG+fkoKStDeWUlhj72lGVhVy+q3j7lGXxy4tMJKeraEb3QvW9/y/XRFmNjpcctxEqPXdhXXu2XWxvKm09N8RVczRZtexSn58fWeT3u2Wkx7U9EFBkFT030OZdE9Z1SqrdVUVfv2rUriiFYMRfQunXPPF67E0kvhDZp2qxO/MKaTb+jU5vDcfTjryakqPvz3Vei5aOBI7mCMU6M1rN9bp3XY5m4LJn0WAa9S9cqaiGYlRt2JSwOgcVcIiILIoDwbtpg+OlQSPrkacZFZ5w8beANg3FE6zYRTajWqcupYW9rjmAor6zEfc+8aLntU3feBk+1G5+c+HTYx49FtNELgSZ4A5JXzAUCxy2Eyxi70OuQ++F0Rf5PjdPlCDhRWoO8rJiiF77ZsBEA4xaIiIjShT6Bmr7AG8uw8avadIbpYy7AHX0dcZ1QzZwLG6hbdc2m31HlrkHLR2fh6Mdfjdt4dNFGLwSa4A1IbjE3UNxCuIy5u4EmSAvF5XSEjF6ItujLuAUiIooVqxkUFT1nV5887YjWbXwdu/q6cAq7Vtm5wbz69BQsfGWGr7CbKkXdHt8MhXJH3n01/rYb8fQ9t/sWXTKLuXYw5uyGys4N5M2npmBfebXla4u2PYqaKD5vwL8796GrL4nqGEREkWCGLpH9jBOp3dHXgTv6OnzFXX1CtXgUd+cu34KPv9vn183bvvWhlrm6azb9jvWb/0D5/gO2jsHKz3dfiWyXM+qi7vrNf/iWVOD2eLBl9PUxHSNQlm649Exdq6Ju8fQHkJedGTSTN5jPln+oPRBxR3UAIqJ6gBm6wbGgS1EJNHmaUSSFXfPkZ6EYu3VDFXVrKvZHdOxo2TlB2vCnXrDlONGwK25B79IFtLzai28ZhmvvHRnRMezu0h337DR25xIRER086kyeZhbPydTMk6UF4nI4EtKl+/PdV8LtYbyLzjx5mt5Re17X1mEfY+WGXSivsv5Zonj6A3BHEadj7M5l3AIREUWLFQ2KmlUUg74Yi70fL18esKjbrGlTAEBJWVnE5zcXdQMRlyNhXbqOLFdMRV09giHY+4m3WOMWdMYuXV1peXlERd03n5qC6qoDlkXdaLp0jcVcducSUeIoeGpqEroQ1QfmGAbzAkMsQzyKunq3rs6qSxfQOnUT0aULRB+9kEpijVswqjpQ99/DPbutJ3wLJFT0Qtd21pEVgbA7l4goTA5HYpc0k34jprSgF3vN0Qzmwu6aVZ/HdJ5Xn669lT8VunSjjV7QmSd5SzS7unOt6MXq0vLyiPYLFr0QbZYui7lEREQHP28sg6+oG698Xb1Td8/uXQGLuons0o02eiFV2BG3oCvff8AyIzjSLt1g0QuRdOmyO5eIiOzCgi7FlTmaIVi3brTCiV5IVJcuYG/0QqLZ1Z0LAK+3nwyHy4Fnh9+JZ4ffiYm3D/G9Zkf0Qq9D7g9Y6LVizM4lIiKi+sFY1AXiU9idu3yLX1HXSiK7dO0q6gYqTseTnd25OmNGsF7c3bN7Z8zRCwWDH0GDax6IKEeX3blERGQXFnQp7gJ16+pLrMKJXkh0l26s0QsAcOvEKXHL0r114hTLxc7u3AMV++ExdSsbu3RjjV7YV16Nd56bGvaEa8zOJaKkUYDH40noQkS19GgGWBR27+hbe11w0emtoj7H3OVbfI+T3aULxJanW1v0DNxxbIdObQ5H+9aH+i3ZLqdt3bmtxrwEl+m6b8WqdX5F3UhYRS+4nA6s3BA4PzkQducSEQUnIglf0g0rG5QwoSZS6z3ohqiPbYxeCCSRXbqxRC/oRU9AK1DHo6j77HAtpuDqX+7zW+zqzjWfRzfx9iG2RS8wboGIiIgiYVXYNdq7J7ICn1kqdekC0efp+hc941fUdXs82D12iN9iVzEX0OIW1mz6vc76FavW+R7H0qUbS9wCERFRrFjQpYQyT6QWrMBrt0R26QLRRy+Mv+1GPH3P7XGfIM3hcmD2UePxevvJth9bj1uwEkv0glEkk6IxboGIkklxUjSilGKcTA0BirvRMHbppoJYohfMRd14cDkcaDrqBbQa81Jcjh9MtF260brzpssYt0BEFClOihZU+o2YDipTx1/he3zS8fb89j9Qjm6ixRq9YJwgLR5duk/fNQzPDr8TB+JQ5D5QsR9P3zUs4OvRRi9EY9yz0xi3QERERJYMXbsJkcjYBSC26AVjJ2s8unTXbPod6zf/gSp3je1FXau4BaNou3StYhfCYSzmMm6BiIjswOoGJZ3epfvVd+tCbBlcqk2OBsRe1DV26cYrT9fhctjapRusO1cXS/RCpIzFXMYtEFFSKLBDlyhN3Hdrf1uOE6gAumbT76hy1yS0qBtt9AKQmDxd/TOxq6jbasxLqHLXWMYtGEUzQZrV5GihGKMWWMwlIgqfOCShS7phQZeSztilG4twJ0fzVLsTXtSNNk93/G03xj164em7hqGm2m1LUff19pNRU+0O2p2riyV6odch9+P0/LvgDFE4NmIxl4iIiIIoAoBVKz6I6SChcnSBxGfpxtqlG+/oBcDezyRQdq5ZLBOkAUDB4Efgcoa+FvV15xIREdmIBV1KKQ8+MTWm/Y2TowXq0k10lm46ePquYbZEL4SKWjCLtEvX6XKg1yH3Y195Nd55birefCr0ZHi62UtWhL0tEZGdFBQ8Hk9CFyKKjFKqtx3HMeboButoTXT0QixdusZ4goNNtNELBYMfQdUBN1ZuCF3k7nb6OVGNjYioXhMBxJHYJc2k34jpoGRX7AKAkF26QOKjF5y5mRj62FMRRy/c98yLcevMNbp1YviF0UDCiVowM3bpXnzLMN8SqGP3zaemoLoqsu6NE9u1BQBs3rotov2IiIiofoo1diFVu3SjnSAtXlEL8RAqO9eKsUu387FN0PnYJkGLuy6nA+VV+8Mq5gLAlGlvRjQeIiKicLCgSynBGLtgZ5duIImOXujxzVCcsemuiPN0jcXcZ4ffGY+hAdBydDNyM2M6RqTduTq9S9coWMduJF25ADDy1psiHhMRERHVS7bELhi7dINJxgRpkRZ1u3fpENeoBTuFm51rZoxe0AXL1Q23kEtERBRPLOhSyrCzS1cXKHYBSE70QrR5uvEs5gKAx+3BgPV3xfUcgUy8fYjl+wuWq/vOc+EX/cc9Oy2qcRER2YaTohGlBbtiF4zatz406ARpiezSBSLP0zUWc9dv/iMeQ7JNuNm5VozRC+Hk6q7+YU9U5yEiogg4JLFLmmFBl1KGXZOjAf6xC8GKusngzM2MOHqhPnh2+J1+hd1wc3VD+WbDRgCAI8Lb74iIiKj+sit2AYjvZGLRiCZPN97F3E5tDkdeZkZczxHK+s1/YP3mP2zPDL7zpstsPR4RERHAgi6lKDtiF1K1qBtNl+7wp16I02jsEU1+bqI9dPUlyR4CEdVbCh5PTUIXIoqNHbELH3+3z/c8lXJoI+3SBeI/frfHgy2jr4/rOcJl93v9bPmHth6PiKi+EHEkdEk36TdiOigNu+81nHTmI77ndsQuhFPUTfTkaJHS82XjOTHa0MlTk5afa2YsXEcSqxCO0bM4IQUREREFJiIL7T6mcYI0q0JhonN0deF26dbGD1iP3w6p0J1rZHdHdbfTz7H1eERERAALupQiPlv1k99zp9Oeb01zUdcs0ZOj6Zy5mRj62FMhoxfG33aj73G8unSTmZ9rFs/CNRFRsigFKI8noQsRRcZbzC20+7hzl2/xK+qardn0O6rcNWj56KyUnCDNOGFYvKIj7OjObTXmJbhsiNcyFq3tysmdMo1NBUREERMwQzcEFnQppSx8ZQYWvjID81+aHvG+vQfdYLn+1aen+B5bdekma3I0Z5hdscYuXbuLunZ059olnt25ADBmIPPLiIiIKKBCAHA4nerj7/b5xSWE69xOeTi3U16d9XOXb/E9tupyXbPpd6zf/EfKTpBmzJRNpegIo1gmRNN179Ih5fKOiYiIAnElewBUvw2777U63bmxuOCGwXh/Rt1icKP8fJSUlfmiF4ydr0Bt9EKPb4baNha7jL/tRtz3zIsor6z0FXUn3j7ElmN73B783+bYunOjyc8d/tQLKK+sTEikBBFRsokADpvuPCEi+xmjFpZ8WxZTi06gAmnjJs2xd89O7Nm9C+1bH4omTZvZPvlWNPTohVDF0CZNm2HP7l2+6IVUGHus9AKuVQeyXd25ACdFIyKKjkA4sXlQ/HQoqYzF3FhjFsTlgHJ70HvQDbjghsF+r4XK001Wl64jyxUydgHQirp2Fz/t6s6NJD93+FMv4NaJU3zvQS9SG8WjO5eIiIjIijFqweF0qliOtXhNOQDgzOMb1OnUNUYvANbxBcnI042kSzceebp25OdGErfQvUsHtG99KNq3PtT3NdAL1To7i7mAYVI0EbetByYionqNBV1KmmH3veZ7HG3MgpHemdv9h3/CkeWKuKibjAnSenwzFModXtah3Xm6dmTnRtKdq3flBvLOc1NtL+aOe3aarccjIiKig46vmBtrd65u+4irke1yWBZ1jVEO7Vsf6lcYXbPp94THLgDhT5AWjzxdO/Jzw41bCCdSwe5irrE7V3k8qTPzGxERpT0WdClp9O5cuyZAM+r69W1wZLnqdOsGK+omc4K0cLp0AaRcREEk3bnGMevFW+MSD99s2AgAcPBWDSJKModTEroQUWTsKua6HA4c/cRr+PmfVyDb5bDs1g3WqZusLt1wJkgDYGvUQqK7c81duFaL3XzduUREFDmRxC5phlUOSroT2rWz7VjicmDlP7TiaNevb0P3H/5ZpwPWXNQ1SofoBTskejK0eE94FsrRh7RM+DmJiIio/lm8phzl+7U763/+5xXYPuLqOpEG5k5dc5dulbsmZaMXjGKJXejU5nBku5wJ6841ikfhNpBup5+TsHMREVH9woIuJU23LscAAL76zr7f9L8/Yzo81W5fURfQOmCtOnV16Ra9EKuhk6fCmeWKOW4hXKGiFhJh89ZtST0/EdVvIoDDIQldiCh5XA4HWk6YjaOf0OLF8jJdQTt1zZm0qR69EGvsgl3F3EjYlfkbqSnT3kzKeYmI0p4AcDgSu6SZ9BsxHTSmjr8iLsfVi7orjn0CK//xjK9T15yra+zSHfrYU77CbjK6dIHIoheAyHJ0h06eilsnTsGtE6fYVswNNz/XHLVAREREdDBbvKYcH3+3D1Vujy9+wSpX1zhRmrmom+oTpEWqU5vDfZOR2VXMDTduIZzsXCIionTDgi4ljXFSNLu9P2M6Fr4yw68w2/Xr2/y6YI3RC4B/4TGVu3SNObrhFHX1jtyrf7kPV/9yn22ducHyc4c/9YKvgKxLRjH3xHZtE35OIqK6EpufywxdotSweE25r6gLwJeray7q6oxFx2R16UYq3M5Xt8eD3WOHYPfYIbZ15gaLW+jepYOvgGzOzk0WEVmYtJMTEaWdBOfnMkOXKHzxnBRNZ8zUBbQuWGOXbiDJmiAtHONvuzHk5GhDJ0/1LfGIVwjWnZsKEQu6kbfe5Hs8e8mKJI6EiIiIUk0iCmzGTF3AuqgbSDK6dO2KXejU5nDfceyY/CyQ9q0PtVxvNa5kFXMNObqFSRkAEREdlFjQpaSb/9L0uB37/RnTg3bplpSV+R4/fc/tfvsmK3ohHKEmR/O4PXBmueJWzK2pdlt255qLue88N9W3JIvepcscXSJKGmboEqWqQgBwOJ0qkSf9+Z9XWEYbrN/8h9/zZHTp2hm74PZ4bI1YMNLjFsyfGeDfNbz6hz2+JVmMObrs0iUiCp84HAld0k36jZgoDvSOV7NERy+Em6NrnsjNaOjkqcjIzcSA9XfFZeKzYFELqZiXa+zSHT3rTd/Cjl0iIiICgCXflsX9tyAtJ8wOuY1VfEGqdumGE7WQl5lha8SCrtWYl1DlrgkYt5CKebnGLl0RUd6FxV0iIooaC7p0ULvghsFw5mb6nq/8xzMQQ1SAcWI0qyJport0e3wzFI4sV8iibrBIA4/bE5dC7uvtJ2P2UePDmggtVYq5Oqss3c1bt7GoS0RERHF1bqc85GW6sH3E1QCAo594DS0nzPabzMs4MZrZmk2/o8pdk9CibjhdusGKpp3aHB63rtymo14IWsw1SmZXrtmUaW8ai7q6QhZ1iYgoWq5kD4AoXi64YTAcWS50/fo237qaiv1Y+MoM3/NXn56C3oNuABC4SKp36fb4Zmh8B+zV45uhWNpmcsDXjYXnZ4ff6fea3p0bDwcq9tc5X7owdukCwLhnp+GbDRsZw0BECSEAJyojqqfcHg9+/uf/+Z6X73fj4+/2+W0zd/kWnHl8g4DHWLPp94BZsfGid+mGKpyaIw/iVcwFtEnQrCIW0oUxeuHOmy7DZ8s/BJirS0RkTQAIe1CD4adDBy3l9vgVc6OVjCzdYNELwSZCi0dmLhB8EjQr19470vYx2Mlc4CUiIqL6675b+8fluHp3bigXnd4qLuePxc93X4lslzOsCdKM3B5PXIq5BxtjcZeIiCgaLOgSeZknRUumcKIXjN2y8SzmAsFzc430LOLS8vK4jIOIKC1xUjSiVFUEAKtWfBCXg2vduVeE3G7vnp1xOX+swp0gTdepzeHIy8yIy1j0SdCIiKi+EMCR4CXNJPR/RRHpaQiBNy57Ddvki8jjIrJMREq9r/eM4Bz/C3COfva/I7LDg0/Yn7dqzs4NR7CJxpKhxzdDodzhXUTHKzc3UhNvH5LsIRAREQXF61HSKaV664/j1aUbDj1DN5hkTI4WiXhGLUTrvK6tkz0EIiKiuEnWrzlvB3CqYTnb8FozANcBcAP4MMrjf2A6/qkAlkc7WIqPbl2OAQB89d06W4u6Vtm5QN0J0XTGidEC0XN0Ey1Y9IIunrm5kUyEZpbqsQvhmr1kBUbPehOjZ73JSdSIKCp6hm4iFwoLr0cJiFOXrlXcwtFPvGbZZTp3+ZaQx1uz6XeU7z9g2/jCpWfphhLPqIVWY16K6L03adoMALBnd2p2PpsFmxhNRBZ6fxm00PCLIU6kRkQHPQEg4kjokm6SNeLvlVJfGJavDK/9qpRqqpQ6G8DzUR5/p+n4XyilUmeaUwIATB1/hV9R1y6BsnNrKvbj/RnT66x/9ekpvsdDH3vK8pjJyNEFwuvSjWd3rj4RWjhxCzpj7EI6FHX1Yq25YKsXco0Tp23eus1vexZ5iYjSGq9HKS5duud2ykO2y1EnbqF8vxuL1wSPperepYMtY7BLOLEL8Y5aqHLXYP3mP0JO0KZbsar254o06dItDFKsLTT9ad6eBV4ionoq5UrQSimV7DFQ4kwdX3uha0eXbqCohUDduZFIVpduMPHqzo2lM3fi7UNSPks3UKF589ZtmL1kBWYvWeFXyHU6HOhyQqeg+xARBSSJzc9lhm7seD1a79jWpRuomBuuPbt3BXwtWbEL5i5dY9E5XlELrca8hKajXkCVuybsQq6RsUs3FYu6QcZUaOjI9SvUOpxOdUqP8wLtw6IuER1cBMzQDSFZBd1/i0iNiOwSkVdFxO6pXfuIyD4RqRaRL5hXltrs6tINFLUABO7OBYArh9ZOLhZsYrRkdekGi12wuztXL+TWVLsj7sw1MmbppmKXrrHQ/NFbr+Gjt17zFWw3b93mK+Y6HQ589NZrWPzGv/Ho/cPx0Vuv+R3HuA87domI0g6vRwmAf5durAJNhBYobiESazb9jip3TcKLuj/ffSWyXU5fUddYdLY7asFYyI2kK9dsxap1KR29YBzTig3lWLGhHKZibaF3gcPpVCs2lGP5dyXy2PPvmLfzbc+OXSKi+iXRBd0SAJMB3ADgTACPQMsr+1xECmw6xwIAQwGcB+D/2Tvz8Caqto3fJ0mbrixlU3FD2UREBBRBKwi8IAgooAgqgoIisoPIaoGKLGJZi4iKgLyKgoACgiCrvKIoIioqCsrngiiUtnRf0pzvj8lMJ5OZZJLMJGl5ftd1rs5y5pyTUtrJnXvu5xEARQA2M8Ye9XYRY+wpxtgRxtiRTduOGrQUQg9yl64eug95Ct2HPOVxXCtqwRcXc3N19w2HS/euoyNhsdt8ZukaQSARC1pEYvTC489NRZ9nyl+bXKCdPWWihwt313tve4whCsB7NqzD7CkTPc6rxTKQ0EsQBBFRROT9qPxelDHmeaNDhAQ9sQudmyegc/ME1ePK3FwRPXELAHD85N9ez4crS1ctesHXWgMhr6Q0KCFXTqRGL8jXcvCH8p+J+cs3uu0Dgph74LuLbrYxsZ+GEAx4RjKQ0EsQBFEJUb/jMAnO+TcAvpEdOsAY+xTAlxAKU0wzYI6R8n3G2GYAXwCYA+C/Xq57DcBrAPD1vhR6zC5MTF+wGDPHaYuJogsXAO4dNMTtnFrUgh6qJibqFnWXjB2hmbNrJncdHYl9jdJMnePdpmkBRSxoMW/U05i45FXkFRYiJy/PTUgFgCoJwhshtViGKgkJWPXSi4at5fHnpuqKf5g9ZSKmzJ6Hw98cg1Wni6b1Lc2l/q1ubobD3xxT7SdGMwzolOzP0gmCqCRQobLIIVLvR+X3okRY2A6gm6/YBTFSAQA6NItzO5cQbQs4aoEwh+pJNZCVeQFZmRm4rXF1xbmaALQdvNWTamLnoZOGraVL2wa63MK339UFX3y6U1XMVWP+8o1IvjHB7VoNujHGOIDtRrrSCYIgzIEBFbBQWSgJqaCrBuf8KGPsFwC3mjR+GWNsA4B5jLHLOednfV5EhJw7WtfHZ4dP+Yxd4A4n2h7334XrjXeWLpTE4cnpr2POiCcNHd8sjM7PFd25RiIXdZV4E1hFAThQYVcUj6skJKjOo4xOkKPmuvWGnv6iSEyiLkEQRGRC96ME5/xel9iFycN7Yc6yzar9hEiFR/wePyHahs7NE3y6dJs2uALVk2q4uUuViFm6vz77sN/rCAZllq7RXJ26MuhYCiUHD3+P5NY3qWYT+xJXxfzdQERdPeKt0o0rMn/5RnFT9yeBWmOJTBjWRy70dvPWlyAIglCHMfYAgP4AWgGoDeAPAJsAzOac63/02yDCLui6YADMdMWKfwzJeRuhLJ7TH606vABA26WrVfBMD9b4aHQf8pRmjq7o0s0rLMTI+UuQEBsbccKuMkvX6XDikZPG5Oca7c6VI8/TFRk+b6Hb/nuLX5a2n5w6HTl5+QDg5uzVK+7K4x2UYq43IddM5M5fEnUJ4tKDMXLoVhDofpTQ5dINhF/H9UeduWs1z1erXhPZWYIAmJV5wauwe+znv9C0wRWGr9EXvz77MOrMXiPty4ujGYEYt2A0at9Dpcj71YlMt/Od2zZEVmaGh7NXj2vXl5jrS3w1A1EkFp285NQlCKJCwCLu/vlZCCLuFAB/AbgFwAwAdzPG2nLOnV6uNZyw+5cZY60ANARw2KTxbQAeBPAH5/wfM+YgjEFPcbSy/BIcaul/lmzbr0eAO7T/b72zdCGqJiZK+3mFhZic/rpq33Dk6AJC7IK31xAMpfklhuTm6kXM1wXcxVwAeP3FmXhv8cuokhDvdlxvFq+WIzdcYq6IPKNXFHUJgiCIyIDuRwlAX3E0m8WC6xcEdk8hunTV2HTgD+z9rgDVqteUjqm5SiOB8mJjF5AQHRXm1QSGNwc0AOw69IsUySBHT2SCWh955m04URZeo1xdgiAIv+jBOe/LOX+bc36Ac74IQlxXawDtQ72YkDp0GWNvAzgN4CiAbAhq9mQAZwAslfXrCiAegPixbzvGWE0A+ZzzHbJ+DgBrOOeDXfv9AdwH4dP1PwHUATAcQEsItmgigpG7dNUQ3bXK7Fy9+HLpvrNUcI0+PHKs5NZVi2AIV45uZWLeqKc9XLpKXn9xprQtunbVsni1CLeAq4bSqZuyZj0AoEHdy8mxSxCVHIslshwGeh4ZY4x1BPA4gDYArgDwN4BdAKZzzs8pxouBUFzsUQDVABwDMJFz/qminwXARABDAVwG4GcAqZzzjQgRdD9K6EErdmHXsTx0bp6A6xes8zsv15dLFxCEXaA8nze59U0+xcdQkhAdhdzsLGn/j5TBYVxNcIj5ulrsOvSL2/6tjZMAwCOLV4twi7dqiE5dWQQD5eoSBBG5GBzDEyyc8/Mqh79yfa0byrUAoXfoHgfQE8AqADsBjIHw5qE151z+UeZyABsApLj2Z7j2lyvGs7qayGkIb0rmQ3jDsQJAMYB7OOfvGvg6CJOZvmCx5jlms+Bg4wV+O3V9uXRF5G5dMYJBbFqu3VARaOREJPPk1Ok++7z+4kwPx25FRe7UFSHHLkEQYeBZAGUQHhm7B8I91jAAn7hEVwB4GkANALNcfeZAuI/7gjGmtBmuBPAkhHu37gDOAtjJGGuu6PcChPu6dABdIRQK28AYC2WmI92PEt7YDgCHD+7E5OG9VDvsOpaHIoczIKeuN5euHNGpK8YviE2MORBzdEPNr88+DIczpE+Umoa/QrmaY7eiMn/5RnLrEgRBGEM719efQj1xSB26nPM5EN4M+Op3rc7xmGL/CwAdAlocERHIi6NpZemKDtvuQ57CwcYLAAhCZ9uvfRdLs8ZH495BQ8BsFk2nLiCIuqJTV45Y3EuMXbjr6Ei1y03jrqMjsa9RWkjnNBsxL9cXomP3odHPSsci0YWrB3khNbljlyCIykmEZuj2ULgMDjDGMgGsgfDI2F4Az6j0+QXAAQB9AbwJAIyxmwE8DOAJzvkq17EDAH4AkApBPAVjrDYEIXku51zM29nHGKsPYC5cQprZ0P0o4Q1XcbSP4CNLV3Tqio7bhGibLsfur+P64/oF63wWSNt04A/0bne1lKsrkpV5Acmtbwpbji4guHTzSkrDMnc4ER27olMXiEwXrl403LofkVOXIIiIgDGAhdyhW5MxdkS2/xrnXFM4YozVhXCvu5tzfkSrn1lEln+ZuORZPKe/W5auN6futjdew0er38BHq99AWX6JrvHbfj0CySfG6XbqiuN/tPoN6fjk9NexZOwI3XMSnkxc8mrA18ozd/sMHmrEcsKKXNwlly5BEKFCzyNjfjxW1hNAKYD3ZOM7ALwLoAtjzO463AVANID/Ksb8L4CbGGP1/HkNBGEWckFLy6ULCKLu3u8KsPe7Ar8cu7+O66/L5Srm6opN7toNJ78++3BY5zeKQIu6yQuo9Ui+1qDVhA+FWzeUT0sQBEFEGhmc81ay5k3MTQDwIQAHhIiykEOCLhFxKEXdewcN8SrsAuUxDHqjGESn7r2DhqD7kKd0rUsewxDu6IWKjuh0BjyLoulBjF/IzskxbE3hRF4sjSAIIozoeWRMrc+NAE5zzgsUfX+AIODWl/UrBnBKpR8ANPFrtQRhLlL0QodmcV6FXaA8hqHO3LW6hN2EaBs6NItDh2ZxuiIYgPJ8XUAQI8MVuyCn4ZzVhoxzdepK2EKYlZjc+qaghHExfiE7M6PSiLoEQRCEPly1I7YAuA5AF875X+FYR0gjFwhCL4vn9Mfoyevw2WHhPd+R77xnXMnjE7oPeQqHWqZ7jWCQnzvUMl2z0Jo8mkEewyAXJENNVFIsSjOF+de3XIy+X+srEhaJBCLmAkL8ghi90GfwUGxcucLIZYWc2VMmouODVCeHICovDJYIK+qgRM8jY4yxRACLIIi5H8hOJQHIUrkkU3Ze/JrNOec++hFE2JFHLwCCsOsLMUJBjGLwFsMgP379gnVSETQlNovFLZqhWvWayM7KQFbmBZ9FvcykRpwdFwqKkZFfFJb5g0X+fZM7bvWy69Av6Ny2IbIyM5CdmeH7AoIgCMJ/IqyoMAAwxqIAbARwG4BOnPOwVS6N7HcXxCXN4jn9cWTv89K+HqcuIIi7ZfkluoumiTEMas1it7m5eN9ZutDt2k9bLFUb0lTu/PwZRCXFAgCKM5VmqMgnmLgFNSqLS1eEYhcIgjCImoyxI7Km+TiKnkfGGGM2AOsgRC30c0UqSKcBKEVa8bhyX08/gogIOOf3yjOS9Th1AUHYtVksyCtx+OwLCOLuv5MGqLYYm8XNxbvpwB8REb3w45h+ho73R8rgkBVbk0ctBCLmioiZupUNKo5GEAShjqtw8NsAOgK4z1U3IWyQoEtEPGL8AuDbqSvHWezQLepqIYq9cmFXjF4AELYc3Ts/fyYs8xpBON3NkQzFLhBEJcZVFC2UDTozwPQ8Mua6eV0DoBOA+znn3ym6ZELdXVtddl78Wp0xphRwlf0IItKQCvYdPrhTl6gLADYL052rq4Uo9orCbodmcci7GFn/VYyKXQgV4c4gjlQoR5cgiIiDWULbfLMMwIMAXgaQzxi7XdauNPV7oQIJukTEs3hOf6QO7OvXNR+tfgPb3njNEFEXcBd2cwrz3c7ta5SG/7V5Jeg5AmV9S9+u5UhB7s4NNG5ByZ4Nwb1RihSoOBpBEKFG8chYNy+PjL0K4CEIztw9Kud/AFCPMaZ8ZrwJgBKUZ+b+AMAO4HqVfgDwo3+vgCBCg9Kpqzd+YdexfL+KpXlD7uKNsbm/haszew2aLHo36Dn8pUacUO+wIsUuGOXOrYzIc3TJpUsQBKFKV9fXqQA+VzT1HE8TIUGXqNSI8QtG0fbrEbDYPaOnxUzbUCKPXQhW1I2Kj8bINOOF4YlLXsXweQulZoY7t8/goYaPGS7IpUsQlRMGwGJhIW0+16TzkTHGWBqEG9THOecfaAy3BUAUBMeCeJ0Nggi8i3Ne7Dr8MQSB9xHF9Y8COM45P+1z4QRRwRCLpRkh6oqo5fJeKCgOuagrj12IVJducuub0LTBFVIjd653yKVLEEREwVhomw8459dyzplGm2H+N8QdEnSJCoeeHF05zGYxxKUrIrp1rfHRsMZHS8dD7dI1Mku33/HxsNptbuJrsALvxCWvagq4RrhzqyTEA6hcGbpyly5BEITJ+HxkjDE2EcA4AKsAnFT0kVy2nPNjAN4DsIgxNoQx1hHAuwDqAZgu63cOwEIAkxlj4xhj7RljywF0ADAlFC+aIIJku+8unpgh6v47aQASom1IiLZJTtkLBcU+rjIeI126CdFRbuJr80bBPb2a3PomTQGX3LnqkEuXIAii4kCCLlFhaFD3cgBCjq4/oq6R0Qty2n49Am2/HgGbS1QtzSwMi6hrFP2Oj8eA05Mx4PRkRMmE6kBQirnvLX7ZrRnB6y/OlLYrk0tXhGIXCKISEZ4MXV/oeWRM7POESp/yqqUCj0MQfmcB+AjAVQDu4ZwfVfSb6uozGsBOAHcA6Ms536pn0QQRTjjn94rbenN0RXYdy9NdJE0vv47rj1/H9cePo8qjyUIdv2CkS/ePlMHInPW01IItkiYXc786kenWjKZH8rWGjxkuyKVLEEREwBhgsYS2VTAq3oqJS5YBnZIjTtQFgDaHhoVV1BUxMku3NL8ES8ePDvh6pZhrNuF06XZ80POxx2Cg2AWCIEKBnkfGOOftvfQZpBivkHM+jnN+Gec8hnPemnO+X2XeMs75LM75NZxzO+e8Gef8/ZC8aIIwhu2Af8XRRGwWC+rMXWuoU1dEdMoCoXfqyl26kRK9EKqs3OpJNQEA2ZkZps3hi7ubVcHdzaoYNh65dAmCICoGmoIuY+yJAJtalWOCMASlqOsPoqh7sPECqRkl8CpF3VBiVOyC25gGZeqGQswNJ537CzGQHR/sL20HCxVHIwiCIIjIxeXSlURdf9h1LA97vytAkcOJOnPXGiru/jiqL/6dNMCQsfyee0y/iCuQFqqs3F2HfgnJPN5wlDkRE2UzVNQlly5BEETk482h+waA111f9bbXAVxr3nIJQhB1A2XbG6/ho9VvSE3u2j3UMj0ogbfNoWEBXxsM8tgFo1y6/Y6Ph9Oh/pibvNDZxCWvqp4PFeEWjMscThzITcOB3DSUaXy/AoFcugRRuWAstHELOiMXCIIIkGCiF4ByYVcUd0VR9/oF60xx74YCMwqkJURHqeboygudyZ24alT2rNy7m1VBQkw0zr32PBxlxt2LkkuXIIiIIMKKokUaviIXekMoaKGnNYBQyJkgQsa9g4bg3kFDfHfUQO7adRY7TItlMBu5S3dtvTlYW29O0OKulktXHqeQV1joIeBqFUKrzHS9fAqsNuMSbMilSxAEQRARj+TS7dAsLiBhFygvmFZn7loUOZyGFU8LZY6uiNylmzTtVUMyddVydOXu26zMC7qE3VAQjhxdR5kT515TRpobA7l0CYIgIhtvCkQZgL8557/raQBOu67hIVk5cUkjxi6IiMKuP7m6IqJrd9sbr2HbG6+hLL8k6PWFoziaKOqKBBvBoObSlYu3cXbhpj2vsFDVrRtu92woKcgrxq51bxs6ptylS6IuQVR8LBYW0kYQhLnIXbpAcMKu6NjddSwv6OJpoqh6oaA45KKu3KULGJOpq3TpyoXbpKTypL+szAvSuXCJu+HM0TUDcukSBBF2mCW0rYKhuWLOeRTn/Cu9A3HOna5rvjFmaQShjTxLV86R774PStwFAGazBJyvK8/R3dcoLaTCrjx6QcRIl+7EJa+6uW8n9btPEnWBcmE3lDw5dXpI5wsls6dMdBN1U9asJ2GXIAiCICKL7coDorArtkAE3mCKp/04qm9YRV0lwWbqKl26cnfu4S+P4OSp3yRhV3Trhio/91JA7tJljHESdgmCICKHiidBE4SLAZ2SkTqwr9SUAu+R774PyrEbSPyCvDgaEPoCaRJVqgEwxqVrtdswMm2xm5ibOrAvAEHUTR3Y103YDSU5eflhmTdUyEVdgDJ1CaKiwhgoQ5cgKiGc83s550xs0BB4/RV15cXTjBB1Q8m/UwZ6HDPKpSt33p489Zu0ffjLI25uXZFQ5+ce/CEvpPMBgM1qQe2nXjBt/PnLN8pFXYDiFwiCCBWMARZLaFsFw+8VM8ZqM8auVjYzFkcQ/iAXeEVxV3TsBirsBirqKvlfm1dC69hdVf7hebAuXWX0gijmyhGFXfm5ULhnqyTEmz5HuJk9ZSL2bKiYBVIIgiAI4lJCS+ANNI5BzNcNVNSV02TRu6gze43pjl35+NWjowAEH70gunS9OW9Ft65c6L21cRI6t20Y8LwVgX3f5SCvKPi4OG/MX74xLGI1QRAE4R1dgi5jrApjbBVjrADAWQh5ucpGEBGDMpJBHsWgJfBOX7DYo0+goq7cpbuvUZrk1C3NLDRN1PUY1yCXrr+CsOjWzcnLN13Uff3FmaaO7wurzYKul09BXIIdnfs/Eta1EAQR2VCGLkFcerhydiXXrjyOQU3cnTy8l1tcQ4dmcWh5x3+kommBFkurM3uN5NQ1M4ahyaJ33RzB+zu2dRN1jUIu2qrhnq1buXJtCYIgLikYC22rYOh16C4D8BCAlQCGAXhCpRFERCE6dn1l7YrtyHffe/SRi7r+5OoqoxfkmBHD8L82r3iOu8qYiCulIJzy3/e99pdn65odiRApGbo7zs5GmcOzCjNBEARBEJc2omsXijgGZdZuh2ZxOHxwp8f1hw/uRMs7/iNFMPgj7IqxC0rMimGQj/tt13aGjSt398ZH2XBDw/pe+4tuXZHK7tIlCIIgLk1sOvt1ATCBc77MzMUQhBkM6JTstr9290HNLFKLxYIZAx6Q+ojCb6tmN2HmuNG4d9AQ3fOqRS8cbLwAgCDAqhUxCxQ3MXfjZ4aNK3fnVv9GeD1Ztyz3ed2kfvchZc16AMBDo59FlYR4U9y04c7QLXM4sePs7LCugSAIgiCIyMfl1gUAuApLaWSRMgfnzih5P1H8bZ3cBXOWbUaHZnG65lTGLgBAnblrAQhu2h/H9PPzVWgjd/3KxdysktKgxm04Z7Wbu/enIT1xwxtbcEPD+vjpl1Ner01KSkJmZiayMjPQuW1D7Dr0S1BrUYPEYoIgCCJc6HXoMgA/m7kQgggVSueuxWKR8l9nDHhA6qOMbBBjGkRRNhBE165pxdIMFHMB9bgGFhfl06ULwK1QmhnCq9ydSxmzBEFENFQUjSAIGfKsXUjOXeYQjglirtgPisiGYJEXSzMyekHL9StGLgSKXMz9Y1hvAIKoa7dadDl1xfgFUdQ1mnBHOtzdrAoSYqLDugaCIAjTYJbQtgqG3hW/C6CHmQshiFAjCruiiKt1Xl5gzRofjeQT4wKeU+7aNSpLV884gRRGU3PnAkC1z4aA2a26ohfMLJIWbnduuFi7+2C4l0AQBEEQhEGUi7tOVeVTFtkAAOjYLA4J0XofsvTkx1F9TRN1Ac+ohf0d2wY8ljxqQRRzRX4a0hMOp++4q1CIugAuqaJhLuc4QRAEEWY07wYYYx1ku7sALGKMJUL4lDhT2Z9zvtf45RFE+BnQKVmKD6jSqm7Q49mSYuHILERpZiH2NUpDlA/XblRSrNd4Bq9u3yrVgJxsFGcWYG29ObAnxaHv16N9rnF9y8Vei6lV+2yIrugFQHDqFhQXS0XSjI5eCLc7t13ieBzITZMKo+1a97Yp87S+pTkOf3MMJ8+cxdrdBz2iRAiCiFwYqFAZQRBBsx1ANw7g9qvqBDXQj6P6osmS9bhQUCyJuj+O6edR1EykRpzdazyDXlE4adqrqBkfg18mD9LV31chNTFP11f0wuEvj6D1ba0Mj1+IhLiFfd/lIPnGBABAQkw07m5WBfu+yzFlrtvv6oIvPt0JAN0YYx/JY0QIgiCMp2IWKgsl3hy6uwF84vr6AYB6AAYBWO86Jj//iZmLJIhwI7p0sw6cxvGhm4MaS1kwrdQl7mrh7ZybO1ctbmHVR4Ko66I4s0CXW1cu5srduXL0Ri8oi6RFSiEzoziQmwbA/MJos6dMROtbmgOAZgY0QRAEQRCVE3n8wu5fzwQ9nppTVys2wVcRNV/n5bELGflFbs5bPSjduSJ6oxcAT6euEYQ7bgFwj1w499rzcJSZdy86f/lG3H5XF3FXIwOaIAiCCBXeBN27AXRQfFU2+XmCqLTIM3WzDpwOerw2h4apRjcknxjn1kT2NUpTjVbQlcW76iNB7HUJu96ct/5Q7bMhgM6bRqWoWxno3P8RxCWoV482i9lTJkrbFL1AEBUIytAlCMIA5I7IRzYE/3CkvGiaXJT9d9IAqYnUmb3GpxNXGbcgsr9jW3zbtZ0k7AYi6mqhN3oBEERdESPdtdWSaho2lr84ypw499rzIZtv/vKN0jZFLxAEYSoMgMUS2lbB0Fwx5/yAPy2UiyaIcCB/xD1Yl66I3Knr67wY0aCamStz4Wqyyvh7Lr0uXUAQdSsLnfs/AntMFHacnR3yucmlSxAEQRCXNJJL1whRV3TpishFXOX5CwXFuoRdLfZ3bOsm6oYDI1261V1CbnaYnLrhKogmd+mSqEsQBBE+dEnQjLHfGGM3a5xryhj7zdhlEURkYmT0AlDu1FU6cpXnlcKuKOqK+bvIyfZrXl+xCwNOT9Y1jt4CaWbSZ/DQkM9Z5nCGRcwFyKVLEBURwWDAQtoIgqicmBG9oObIVZ5XCrtqom77PYd8zicvkmaUS1fM0g01RuTwBsrdzaogJsrm4c4Vc3TNhKIXCIIIBRwAZyykraKh11N8LQCtZ4tjAFxjyGoIIsKRu3SNiF7Qi1LYFd26bjyuoy6BLHZBT5YuAGR3WOX1vN7ohbnvfqhrPr1USYgHAGTnmFP4QQtvUQtxCXZ0fLA/Ovd/xNQ1kEuXIAiCIIhQohR25W5d8VhWSamusfyNXrhl1Tav5/3J0s3M9KjtbQg9kq81ZVwttKIWzr32PGKibCERdQmCIIjw4k9IBNc43gpAdvBLIYiKBbOGPmNFraCaXy5dWZE0X6KuPSkOAMCzfD8Sx+KikLJmvVenbkGx94IZ/vL6izOl7VC6dL25c3ecnY0DuWmwx0SZKuzKXboEQRAEQVxydAMAK2Na789MQ15MDfBdEE0NZfSClqhbMz5GmKOoxOeYekTd1re1kra/OmGMsBuO2AVfUQuiqJt8Y4Lpwi5BEAQRPjQVKcbYWMbYH4yxPyCIuVvFfVk7D2AZgI9DtWCCiBTu/GFMWObVKqimG52ibt+vR+sestpnQ1D9m2Ga8Qtyd+57i1/2b71ekLt0fYm6fQYPRccH+7s1f4VgvYXQRGG3zGFepWGCICoQDLBYLSFtBEFUfv6e+GhYng9Vc+uK6IldAPTl6f4yeZBf6/Il6prhzpXHLvhy6fZIvhbJNya4tUCcvXoKoZ177XkUvPVCSNy6LAwfLBAEcSnAAGYJbatgeFvxbwD2uBoDcES2L7aNAMYCeNLcZRJE5HGw8QIcbLzAsAJp/iKPX5DQE7sA+OXUBXzHLohImboyt+7cdz/06s59aPxzeGj8c/rWreD1F2f6jF4QhVy182pCcOf+j2g6a725c7tePgXtEse7NQCmRzBQji5BEARBXJrUmbsWdeauNaQ4WiAo3bqA/tgFwL88XV+xCyKiqNug/nWSsNv6tlZoUP86qY+aO7dNk5po06SmrjmUyF26agKtKOSquXjVrrm7WRVNEdabO7f2Uy8g7rHn3RoAcusSBEFUUmxaJzjnHwL4EACYEA6cyjkPXWgoQUQoDepe7pZdGsosXTltDg3D522XwyEXdP0pjrbqI0EAzsmWRF2lK9eeFIfizAJdsQsi1T4bAgDIvuMNQdSV5esq3bkPjX8OLNoKXqD/5l/J6y/OxEOjn/U43mfwUFURd/G4EQCAqctXIq+w0KNPmcMpZeEq0XLndr18CoqLSrFnwzq34537P4IyhxNlDic6938Eu9a9rft1+aL1Lc1x+JtjOHnmLFLWrAcApA7sa9j4BEEYCYPFWvEKLRAEEbFsh6wY1e5fz+CRDXvx9oMdQr6QH0f1RZ25awO+vnp0FLJKSqXoBaUrt2Z8DDLyi3TFLoj8NKQnAOCGN7bghob14XBqPzHVpklN6bzNEpg7a9ehX9C5bUNkZWZ4iLY9kq/1OHb85N8AgOTWNyEr84Ik6m49+H8ABAduQkw0km9M8JgrISZa1Z1b+6kXUFTqwMEf8tyO392sijRenh/fQz3cflcXfPHpTgBuLt3trsJ9BEEQwVMBXbOhhHHu/QkJxlg0gH8ADOKcbwnJqsLM1/tS6LGRCGP7mp/DvQRVRCGtert6aLqiV1jWcLDxAvcDGz/zbwCXqAsIAq5S1F1bbw4AgFWPQbW9j/s1dNYty6VtLTEXAHhJGd5Le8m/dcvHcgm61aoIzgNvQq6c0QvSpes2rlwBQHDUHshN8+irhSjmehNrRWHXarMYKupOmT0Ph785Ju03qHu5W+G+UNA8+fKQzkeYS+uBDcO9hIijtu2ZoJXYBvUu40tSzS2WqKTbYwu+5py38t2TIIiKDGPsI7jE3X8nDQjLGpSCbvXoKDf3rS/a7zkkOXtrxsd4iLpJ014FANSIicY3j3f3a21XL98kbas5c29tnIQ/hz+Aq5a9H3Su7q2Nk7yeF4VcOaKoC0ASY5NvTEDBWy/4NXfcY897iLlyRGHXZrVg33fGFRSeMKyPJOqKcM7pE0yCIIKmRaPr+GcrXgzpnHF3P1yh7p99yt2c8xIADgD6LXoEcYnQoK4gZoXLpauK3tgFEUX8ghJ5cTS90QuA95gGI8VcwD1LVynmLh43QlXMBYCE2FjpukALqxXkFfsUaXetextWm/GfLs6eMhGtb2ku7cud4wRBRA6MARYLC2kjCOLSQO6GDFf0ghJ/YhcA33m68uJoeqMXAO8xDW2a1JQE2KuWvR+wO1cvamIuABw8/L20HUieLiC4c20+stP3fZcDm9UCR5lT1fkbKPOXb/QqJBMEQQQDZyykraKh9y/XBwAeMHEdBFEhkbshw52lK+FP7ILIqo80T/X9erSbqKsXed/3Fr8sZOWOfhYPjX7WUDEXEGIXlHgTckVeHDZY2s7OyUHHB/vrKnom0vXyKbqF2l3r3jbUnSsye8pEj6gHgiAIgiAuKbYDQvRCOFDm6AaCN0fvL5MHBSTqymMaRMFWFHLtVgv+HC68vf3qRCY+/9Ez3zZYjp/8W2reqJ5UA4CQp5t8Y4JmRq4WeUUluly3+77LwcEf8kwRYEnUJQiCCD16Bd0dALoyxt5njD3KGOvIGOsgb2YukiAimXC7dNscGuYp6vrr0vWBMobBF3J3LouLkkTcWseeQa1jz6DmoSfBC0oNEXMBuGXo6hFyRcYseQXW+PKb5mo14zWLninRE7VAEAQhYrGykDaCIC4dwu3S/XFUX/w7aYCbsNt+z6GAx1MrkKYUdX0hF33/HP4A7FaLm5B74qn70fi1D0xx5uoRceXkZme57atl5Gqhx51LEARRIWFMyNANZatg6F3xRgB1AfQG8BaATwDsVnwliEuSUGeWquEh6gbi0nWxvuXioNcjd+fWPPSkJOKKZLR9HTAhgkAvY5a8gtEL0mGx29D52GhEu7532Rn5uq4nMZcgCIIgiAgjrC5dQBB2RfyNXQDgNXYBgEe2rjeUou+Jp+6XhFyR/FKHKc5cPTRvdCWaNrgCTRtcgZgoK7Lnj5LOXT18rq4xxEJoRmbiEgRBEBUHm85+d5u6CoIggqbNoWEAVIqk6aVKNSAnG8WZBVjfcrGmKze7wyqvxdHk7txax55R7cMLSj2KpAVDlYR45OTpE2MBgDuc6HpygrTf8fAI7GgwHwBwX70UANribrWa8SjIK8aeDevQZ/BQ1QJs8iJrBEEQBEEQlwr/ThogFUlrv+eQX8XR9ndsi5t3HAAguHT9EXDlKN25oaBzW/+LiibYo/DXrGHSfs34WGTkFyIjNx9XD5+LjFz1e9GaifEoKi2VxNweydciO9NdmK6WVBNbD/6f32siCIIgKg66LHKc8wO+mtkLJQjCN5+3XR74xbIcXV/F0bTI7rDKr5xdI3hy6nS/xFwt5C5db05d8VzHB/urirlAcEXWCIKofFBRNIIgQkA3ALAyxsO9EBEzXLoiVy/fpHr8llXbdEUyGB23kJXpn9P32M9/Ia/Y/ftzaobsaTYNMVc8l1dUIhU4U4q5gJDHG2iRNYIgiIiBsdC2CoZehy4AgDGWBKANgCQAFwB8wTnPNGNhBFFRWLv7oNfzh1qmoyy//MbSGh+Ntl/ry3j1F0dmoSnj6kUu5mq5c41GLubqyc5V5uaKyF26Iu9luj/y1r/WFDjLnB7XZp0/J203aHwDMi5c0BR7CYIgCIIgzOLviY96vCO9fsE65JU4pP2EaBt+HdfflPmbLFkf1PVyl24gyMVcb+7c/FIHvjphzNtYuTtXT3Zu80ZXwuF0IsEe5bNv3mtT3fb7LHkXO4//qtr3rQOClj/8vtrIzT4vibrk1CUIgqic6BZ0GWOzAIwHEA1AvFEoZoy9zDnXn9xOEJWMk2fOehyTi7jMZsFHq9+QznUf8hQONl5guLDr5s7d+Jlh4wJCrq6aa1eLUIm5T06dLm3rLYTGHU50/sl7kTelkCuy7vxsDLp6OkqKHMg46/nvDgAnT/yE6rVq61pLoIhRD9WqVAEAN/E4ZY3wRuq6y2tjUOf2pq6DIAj9UKEygiDMgjEmPWZ1/YJ1klgrCrk2iwV7vyu/j+vcPAF15q41Rdi9UFAsbX/btZ2hY6sVS9OiRqzddycD6Ny2od/uXIfT6ZaZq4ZSyBXZOKofACDhqRclAVfJsg/PuYm6ZiCPeaiWVNPtHHN3iW+XF+0jCILwCxMKV1YmdH13GGNjAEwB8F8AHQDcACFX978ApjDGvP9FIohKipo791DLdDiLHfho9Rv4aPUb2PbGa27nt73xGj5a/QacxQ4cbLwAh1qmG7IWM925SjFXnpOrRsbdb5q2FpGHRj/rd9SCljvXH1b/MRNljrKgxggGeW5vdk6OphP4t7PnsHb3p6FcGkEQBEEQ4aEbAFisVi46ca9fsA5FDif2fleAXcfy3DrvOpaHvd8VoMjhRJ25a3H9gnWGLyhQMbf9nkOqxxvOWe0zhgEAasQI93kXCou99ouPsqFNk5pe+/hCKebqceeGimUfnvPdKQjkQnF2ZoY34bibqQshCIK4hNHr0H0awGLO+VjZsZ8BHGCM5QF4BsASoxdHEJHM2t0HJXeu1WJBmVN4FL8sv8TNkauFKPTeO2iIJOoa4titUi2w6x4v//B8wOnJAFScuVUTgYu5PnNyfZ1ncVF4aPxzeC/tJa/9Hhr9LADfRc+MdOcaSZ/BQw0tjqZVhA0ANq8o/2BgxqKl+Pann3HyzD+SY1eEnLsEEQYY5doSBGE8LmeuJJhZOGdx0cLbu7wSh5srVw1R6O3QLE4Sdc2KYtBD+z2HVHN3tcTcW1ZtwzePd3c79s3j3TXzdeWceOp+XLXsfZ/99Dpw9Yq5zRtdqStq4dpxC/F/C8Zqnk+IicYTHax4c29oTQbKAmxxCVVRkHdR2GEM278uF9K7tYh2HfbIdf6Nc3692WslCKKiw8ArYK5tKNHrX74WwEca5z5ynSeISwalmOu0IGDnJ7NZ4Cx2wFnsMMatm5Pt/zWP3+t23fqWi7G23hzPmIX3F0ibvly63qh56EnA4Z5F+9DoZyUBF3CPUzCi6Jk/7twnG77g9Xxsgh01L79c83zNGjUAwLAc3T6Dh3oUYdu8It2tyZkxZiSuv/oq1bHIuUsQBEEQFR8PMddq5Q6nE7+O64/rF6zzq+CXzWJBkcOJIofTFLeuHpRibs34GCRNexVJ0151E3NP9O2MJJcgqqf4mT/c2jgJtzZOkvYDiVPwhcPpxF+zhmmerxkvFOnNyCvAteMWavb7Z8kExETZ8EQHq6Hr06JH8rWeBdgYw/ufnsf2oyVC+9rdFX1L645aw13HGNtr2mIJgiAuEfQ6dC8AaApgt8q5G13nCeKSQS7mbnnzNXQf8hQAIW6B2fzLeZFHMnQf8hQOtUw3rWiaJjIx154U5ynkfvJ6+bZOl64/yMVbuairRK8TVw097tzopFiUZBYiJyMfg66ejtV/zFTtt/qPmRh09XTUvPxy1Sxdo3N0lcKwUsBV4+WpEz2OTX4pDSd+PY2TZ/4xbG0EQfiGgTJ0CYIwHClmYfc3uQwAE522RQ6nR8yCN+R9zczX9YZczM2c9TSSpr3q0edE384AgEP33Y3G63cBUHfp6kWMXfj8R3fR9tbGSaieVFNVzDU7VuHUjCdRf8bryMgvREaed4f1P0smIOGpF01dj4h7pALD9qPeIy0A4MXlOzyOpYzsiSOffQwI8Y0EQRDaMACMMnS9ofe7sxnAC4yxAYyxKABgjNkYY/0BpALYaNYCCSKS2fKmIMZue+M1lOWXwFns8MjM9Ydtb7wWvFNXLXLh8XuBPne4xSqoMeD0ZHcx95PX3cVcwDCXrhi78OTU6bocuAmxsQHPpZeOh8sF48K8Ygy6erpm33Bk6aq5cf3BatVdB5MgCIIgiAqAS8yVyCtx+CXmKhHzdcUsXqNov+cQbt5xADfvOKCZkwsIzlw5J/p2lpocuUv3llXbpOPybV+ceOp+2K0WtGlSE53bNnQ7JxdzvzyRJW0nt75J9/iBcmrGk9L2ZaPmBz1ej+Rrgx5DpEq1GrrEXC1Sl24xbC0EQRCXOnoF3ckAjgFYA6CAMfYvgEIAbwP4FkLBNIK45AlGzJWPEUjBNFuSS/BURi7I4xRysr0Ku2vrzSnfUQq5cqomAtDOyq117Bmf66156EmwaKubmDvj0QcQZ7er7ucVFmL0gnRMXb7S59hGUVLk8Crq+opeAIS4BKMYON7TdesPP/xyEgDQoK73NRMEQRAEUTGYPLyXtG2zWPyKWvCGzWIxrGCaMk4hq6TUQ9StHi0ItBn5RaruXCWH7is3eMqjF/yNYTjx1P3gnLsJuNWTPIuliceyMi8ELOrqzc+9ctpyabuo1OFV1BWzdNVIrFYLgNJdGxjVXK8/Jzu4B3Onj7ov6LUQBEEQArr+4nPOcwHcBaAngAUAtri+dgfQjnMe+MfABFGBmb5gsbStpxCaXra98Ro+Wv2GX27dNoc08rjUMnUDydk1A3v5DeiMRx8AADz3YA/MePQBt325yJtXWGjacnY1X+wWmZFx9qxXUdebS9fIHN1qVaoAMCZLGCiPDCEIIkQwwGJhIW0EQVR6tgPA4YM7pQO7juUF5c6VIzp1jcjVVWbjisfkou7+jm2DmkPJn8Mf0N23jJfX7PryRBZ2HjqJL09kSQ0Adh46GbSo6ys/FxDE3KLS8nvLN/eWeRV1/1kyAY4yp+q5ZR+e83uNWmw9+H+GjPPV/8pjGBhjnxgyKEEQlRbOLCFtFQ3dK+YC2zjnEznnT7q+buecK6tWEkSlR3Q4Hvnue8PGfHjkWNw7aAgeHlle0VaMctDD523LP82XIhb63CEdGnB6MgacnlzeRzzvI4ZBlYu5/l+jgt4cXlHkFTHLpVuWX4JFo9zdxRlnz6Iwz/9Hy06e+EnaNtKlaxQpa9ZjzScHwr0MgiAIgiACgHMewA2cd3q3uxodmsWhd7urpWO7juXpjl9osmS9274YsyCSOetp/DJ5kJuoK0YwyPuJKGMW5LT9cJ+0/cew3rrWp0bzN7dK2/JoBTWUoq4Z5BWX4tjPf7kd8yXq6sHI2AUD6UTF0QiCIAKn4knQBBEBDOiUbOh4D48ci4u5gkh6MTfXTdRlNovP+IXP2y6HI1PmXPXiwLUnxbkf8Met+8A44D/luV7Vv/HuMvBGxt1vlu/ERWPGuk0+r5HHL/gj6o5Z8gqs8dF+r1HEarPioaRJXuMX1DDKpWuEyxcA4mLdc+l+/ftfpKxZj5U76F6aIMxELIoWykYQBOEPvdtdjews4dH87KwMN1FXb/zChQL3D8Dlzlw5clHXWz812n64D43X70Jmsf5rvHGhsHzNbZvU8Nl/56GT0rY/Ll29cQtaeBN19cYuRIKoe/V1NygP3c0Y4xaL5Xg41kMQRCTDABbiVsHQrJDDGDsNQK/7lnPOrzdmSQRx6SAXcuVczM3FvYOGuB0ryy/BwcYLPPp6sPEzyZnr5sh10ffr0dK2W2auNx4Y59OV60+BtIy733R35+4ZC3RciBnrNmFGf22XxXMP9sBLG7aioLhYytRNiI3Fi8MGa14zZskrsNht6HxstGYfn+s9K0QUVK9V26/rTp74Sbqmz+Ch2LhyRUDzV6tSxRBR9+1FL0vb09IWS5m6v5/LwOLNOzC6V9eg5yAIgiAIIrRMHt4Lc5ZtDvh6uZgrkp2VgQ7N3E0AeSUO1Jm71ud433Zt5xankDnraY8+v0weBABoOGc1MvL1PbHV9sN9HkKuUe7cP0c8iMYrNqNtkxo49KN39231pJrIysxAVuYFNG1wBaon1cDBw96f2hPiFkZ47aPG8PtqS9EJb+4tw2PtPAWHf5ZMQMJTL6pev+zDcxh+X23kZp83JEs3WF59/1tpO2VkTxz57GMAAOf8RovFUuR0OmO0riUIgiDc8ebQ/UxH+x3Ata5GEJck8hxdNdSiFESUYu6Z306hRlJScAuSxSz4wsOtCwjF0JQF0VTEXLk7N7vDKt3xCYAiauFzV6GvPWMBjQwwOWqZumZFMDRo7O4isNqsYXHpBioEe2PW+NHYvCIdLZo2AQBcyDEmRoMgCBUYOXQJgjAFKUdXXhxNiRiloIxTEM/Jxdy3DzpRxeXqDJSbdxzQ7boVhV0lyrgFNTFXyS2rtumaExDEXLk7FwBODO0Fh9P3vag8egGAJOwGWixNjepJwv1jbvZ5t+M2q8XDpXvZqPmwWbXf1huZpWskqUu3YPvREtx6p2Ao4JzbGWMfhXlZBEFEEJSh6x1Nhy7n/FGtc4yxmgCmAHgAQA6EAmkEcUnRoO7lOHnmLI589z2mL1iMmeM8HaDKKAWl61bkzG+npO3vjnzpcf6qhg3hdDgRFR+NfsfHu51b33IxijMLAnoNoltX7xhaEQtygbbWsWdU+4i4RS2IYq6IK3rBm0sXEERdAG5uXSNJiI1FXmEhMi64OzQyzp4NyqUbKGZm8D4/8hn0Gio4Rlbt3I/Hu7Q3bS6CIAiCIIyDc36vSwDrJi+OpkQu2Ko5b0XePiiImcu3/qt6fmA7G2JsVvyR4v5klDeXrZo7V6tP0rRXtfvIxFw1V+4tq7bhQpG+uhOAe9TCnyMelLbjo2y4rXF12CwWr05dMXqhS9sGyHI5X43M1T14+Hs0bXCFx/E395bhiQ5WXDZqPv5ZMgEAkFdUgrcOVNyyNjOXfIiUkT1w5LOdANAt3OshCIKoKPglQTPGEhljqQB+BTAUwFIA13HOU81YHEFEMgM6JfssjqYWpxAITocTA05P9hBzAUGUFQueyR23anELWugVhNViFeTH9Ii5Xp28e8YCdpuuPF2gXNj1Bnc4/Y5b8BbhEA6MytDVQnTpnv7nHFLWrMdbn3xq6nwEcenBYLGEthEEcWngqzia3JHrzXkrirlaaIm5gOCyzZz1tNRE9Ii5Ig3nrJa2vRVDA9SduHIx98/hD3icl6OMWpBzYmgv/DniQV1OXcDTrWsGw+9zNwa8ubcMeX6I10Zgdv5u6tLyfxPGGKdiaQRBAKAMXR9oOnTlMMZiAIwC8ByARAArAbzAOT9r4toIIuIZ0CkZKWvW++wnd+D6S93r6uvuK8/H1cv6lrLICGXUgkjVROBiLnhWEbJuWS45dQ2JWlCiM083VDRofANOnvhJ2hdjF1b/MdPvsTo+2B/VqlTRHaHQZ/BQNzF38wrtwnjB8PzIZzBqxiz8efYfAMCpv//B2t2fYkCnu0yZjyAIgiAI41HL0pW7c7Wct74Y2M4GgKuKuWr4I+QC+nJ0k+xRkktXLt4qnbl6xFxl1IIa8VE2XXm6ACSHruacARZEq55UA1mZFzxiF4Ih+cYEVEuqia0H/09X/x7J17pn75ooeNzUqh2+P3JA3L2bMbaDc04FHgiCIDTw6tBljNkYY8MB/AZgNoScpsac82dIzCUId3xl6QZDVHy0oeOtb7kYa+vNwdp6c/S5c99XT1UxLGpBiejU/e/7ut26aoxZ8gqsOr93Oxp4Vg0GoBq7UJjn+42AHDFHF9DvtlWKuWazZMY0bF6RjisvvwwAcPLMP1i5g8wRBEEQBFEBkLJ0tfDlwPUOh8Np/CP9DeesRtK0V93EXC137qH77lY950/MAqAdtaDkxNBesFstuK1xdbRtUkOznx6EgmjqsWXe8FVozR8SZe7s7MwM3Y5b90JqDNu/9u8e2B/mvfYJth8tQYvbO4mH7mGMfWPahARBEBUcTUGXMTYIwEkASwB8DqAZ5/wxzvnpEK2NICoEvmIXguGqhg1Vc3ODRVXE1XLnilRN1DzlS8wF4JeTF4Ag6n4+0WsEw0sbtqoel+b0I27BGh8NZvMvCH3Q1dNhtVl99jt54idknS8vSOErE1fNmWuWO1fJ0hnT0ODaawAAv5/LwNrdFL9AEMHCqCgaQRAm4it2IVgcTu6369YXaq5cX1ELctRiF4KJWlBDjF+wWy2aom6Xtg28zxmgO9cXasXRvLHsw3N46wCXhF09oq78/PajJdh+1DwxV86sV7bLRd3mjLEjIZmYIIjIgjGAWULbKhjeVvwmgKsB7ALwA4B+jLFUjeb/s8cEUUkY0CnZtLGdDqfhYq5HxILYfCFz6WZ3WKWap6sLX+5cJV5ydQuKtW8s/XHnekMuxCopzCtGmaNM91iiU9eX8zYUMQveeGnyBDRvcgMAwalLEARBEETFYPLwXoaON7CdDQnRxguSSleuXjE3ySWO+uvMBaArakEN0a2rJurK4xaOn/zb7VzzRlciJsoakDvXF2KO7mWj5sNm1S9CLPvwnJuoq4Vb1EIYciVnvbIdt94ppS20DPkCCIIgKgC+fvszAF0ATNPRCOKSRyt2oVmr20K8EnXWt1ysuwCaN3hWkW7Hbcbdb+J881eCm3DPWKBM+1HBhNhYj2OBFEPzB9Gd603wVSLP4tVy6cqPh0PMFZk+enjY5iaIyojFYglpIwjiksNr7MKwHnUCGtThdOrOztWLPwXQlBy6725pW+7SNdqdq8SbqAsIebdKAo1a8Ie8ohK8uVe/uQAQRF0RNZeuUsw1M2bBGzOXfBiWeQmCiAw4AM5YSFtFQ/OOn3Nu8aP5fuaYICoxvmIXLmRm+j2mGLdgFB5irh5Xrg604hZEIdfvqAUt4qI9MnXj7HYAQF5hIaYuXykdH73AfyG0LL8Ei0Z5vpYGjW/wOGa1WVFS5EDGWf+jxOUuXTVRN5S5uQRBEARBVA7ksQu9213tcT7HwMJawaCnAJpe9Lh0m7+5FVctez9gd64cNVG3elJNAEBW5gUkt74pqPGvnLYcNj8+kLNZLX65c+V4c+nKj4VLzCUIgiB8Ywv3AgiisnHvoCGomqidN+uLqxo2hNMhuFGdDvUb1XebpqE0XzinN2PXSDGXVY/xKdR6nPc3akHJnrHC144LMWPdJszo3xvPPdgDL23YioLiYuQVFkpdF48bEZCoKychNhZ5hYUeRdEABCTkipw88RMaNL4BGRcueBVvw+nOJQjCWMQMXYIgCLNgjH0kbmdnZaBDs7igxhvYzgaH0+k1buHq1JXIKykFACRER/l08irFXH/duYGgFHIDcefKOTG0Fxqv2IzbGleHzWLBoR8voEvbBsjKzEBWpuc9oz/kFZd6xDZ4w19nrpxlH57DY+2Ev0s9kq/F1oP/F/BYBEEQplEBc21DSUi/O4yx9owxrtKyZX0SGWMvM8b2M8ZyXOfbBzhff9f1fxn0EgjCjbW7DyJlzXqcPOMu8F3MzcXF3Fxp/8xvp3SNd1XDhrDabRhwerLU1CjNL8GZ307hzG+nUFbswNp6c/Bu0zTNcT1ycwPFVRjNL9ft5xODF3PlKDJ1n3uwh0cXI/JzXxxW/qZEzaUbDPLohY4P9pecur6KpYWSWUuXh3sJBEEQpkD3o0RlwyXmdvPW5+2D2tFVSga2syHGZkXmrKc1RdqrU1eiyFGGtw868fZBJ4ocZbg6daVqXxGzxNwasXbV48qYhWDFXBGxWJrDKXxPdx46KZ0bNmRAwOMm2KPQvNGVqueG31c74HG1UCuQ5hbBUAEfPyYIgriUCJfcPQpAG1nrJDtXA8ATABwAPgl0AsZYNQALAVBFH8IU1u4+6Cbk2qxWnPntFGokJbn181fM9ea2fbdpGtbWmwOLrfy/7p+//OJV2DUqNxeAW2E0kYy739R1zFA0CqWJrtxA8nOt8dEYs8Q961fM5lVz6QaLGL0ACDELHR/sH1FxC18f/wEAcHXtmmFeCUFUBhgsltA2Qhd0P0pUaBhjHzHGOFxirsVi5aLAWsUl1gH6xdyB7Wx4JNmCGJvVq5CbNO1VFDnKsOaAQzq+5oADRY4yJE17VVXYDSY31xcXCovdxFv5cTOJj7LhtsbV3SIYDh7Yg2FDBqB5oyuRYPevmNxfs4ZJIrGImM2bm33ecFFXWSAt+caEiMjOFZk+6r6wzk8QRPjhYCFtFY1wRS78xDn/QuPc75zzJABgjHUC0DvAOV4C8C2As3C/QSeIoJGLuTarFb+f/Fk6992RL/0aS4xY8BWd8G7TNJQVOzQF4j9/+UUab229OdJ4ZuTmAuWxCzyrCBl3v4ma+56QzhmWm+uNPWOl+AU5oxek++3O3dV8McryS8Bs7p9xvThscNDRDVqILl0xfkFOJMUtDOnaIdxLIAiCMAu6HyUqLEpXrsVi5WsPlErvRpdv/Vf3WPJ4hT9SntLsJ3flqiEKvAPb2ZA07VUphsHI3FwlNWKicaGoxEO8DbYImh5ODO0FALgqfQOqJ9VElksMPXhgDxLsUX4VRLty2nLkFZd6ZOgePPw9klvfhKzMC8g1IQd52YfnMPy+2oqxwy/mAsBX/9shbvr35oogCOISIeIydDnnPNgxGGN3AHgUQDMA04JeFEEo0BJzA8HpcGpGK4iIYq4o2npDKexKGCHm/udJabPa3seR3WGVJOqeb/6KZ38joxbUcIm6KHYAZeVvLvx155bll2DxuBFe+zRofINbVIJRnDzxE6rXKndcRIKY+8JSlX9LgiCISwi6HyUqAJIr1yXkBmwtcjidyJz1tM9+eSWluty+cmFXnrMLBO/ObfvhPmn7j2HC5yxXLxc+3L9q2fsAhAgGs925cuKjbJKYK+KPmAt4z889ePh7NG1wBQAhemHZh+cCW6gG4nhipu72o+EXc+VwzluHew0EQRCRSLgiF95mjJUxxi4wxt5hjHmWYg0QxlgUgNcAzOec63vWnSACJBgx96qGDVH3uvqI8uEm9UfMleNv/0CotvdxsOoxps/jlT1j3cRco5HHLhidpaukSkKCqePr5ejxHwEA19Sp5aMnQRC6cBVFC2UjdEH3o0SFR+7KDYSB7WxeC58B5TELSveoL9YccBgq5gJAZnGpx7EaMe730nIx1yx3rhwxU9dM5NELlwJjH7sz3EsgCCLsMHBmCWmraOhy6DLGLAAsnHOH7FgXAE0B7OWcf6NzvosA0gAcAJAD4BYAUwB8zhi7hXNuxMeNEwHYAczx1VEOY+wpAE8BwJRx96J39xYGLIUgtAnUmdus1W24kJnp1q9GUpJH1MOAJ4aU7xjszpVTbe/j0nbWLZWvkNaLwwZj6vKVyCssNCVLFxDydDMuXEBOXp4p4+tl4tyX8cvp/5P2B99zd/gWQxAEYR4ReT8qvxcF8Brn/DUD1kFUcuZP6I4J87cFdK1Y/EwrLxfQjlkY1qMOcmTiYpVqtfyKeQgEuTtXzjePd5e2RbduqGm+covbfv0Zr+PUDPV750Aw26UbKUwe2gXffuX27/xxuNZCEAQR6eiNXFgHoBjAYwDAGHsagPhMbilj7F7O+W5fg7iEX7n4e4Ax9imEXJxRCPJxNMZYfQBTAfTinPsV1OS6aX4NAL7elxL0Y3YE4Y2rGjb06sx9t2kaSvNLYLFZfIq5ADyOefTrORLYsjTwBWuIuRFB1yWmTyHP0jUjekEZuxBKpr68ED+e/NXjeIO6l4dhNQRROWEuhy4RGUTq/aj8XpQg9HLsi+0BXedLzBWjEmwWi1vxM6WQK5KTfR7DetRxE3WH9ajj1qflpj34unfHgNbb9sN9qu5cJWKmbihpvnKLR8RDRn6h4fNUT6ohZemaKer271gX6/acMWVsNZ4f0QNfH9qpdmo75/zekC2EIIjIowK6ZkOJ3u/O7QDkdwsTALwBoCqATRBuWgOCc34UwC8Abg10DBlLAOwF8AVjrJqrsnA0AObajzVgDuISZ+3ug0Fdf1XDhrDabaoF0N5tmoa19eZIxc+8ibkHt2zGwS2b3c6LyPsd2j4FKCkVRNmeI/1f8APjdHd1i18wOz8XEMTcbONvmNUIZfRCqHh29kseYm6DupchdWBfDOiUHKZVEQRBhB66HyUqKhaLNSAjisPp9Crmiq5cuZgLwEPMTd/OkVC1luo5+X7OVztQ7HSi5aY9gSxXl5irJBRxC4B7xEP2S95rMgTDwcPfmxq9kFhN+He8mBW6WIfJQ7uoibmfcM4ZibkEQRDe0evQrQ3gDCC5DuoBSOec5zLGVgF4J8h1MABGuGKbALgGQJbKuSwAiwGMMWAe4hJl7e6DbgXRAsHpcOKRk55iLgCU5pfgzG+eUXtqYq5ItapVkH0xBxcyM9Gs1W0e0Qttu80u3yks8nTbVk0E3l9Qvv/AOOBirur6qtSMR05GPgBIBdHCilzM/Xwi0GaeaVOFInoBAB4bNxFvLTDvdYgMmzYD/5wXCng0qHsZBnS6y/Q5CeJSxmIhh24FgO5HiQoBY+wjcTuQDF1vubmimKsUcpXO3PTt5f9V5q47hxHdmNRv+dZ/Pdy5VW7tCgBwOMvQeP0uj3mT7FE4dJ8Q9aTXjXvLqm0hd+MqkUctmCnmisijF4xm2YfnpMJooWDSU//Bd0cOiLv7OeeU9UUQRDkM4Izun72h16GbA6CGa7s9gAzO+Xeu/TIAAVdFYoy1AtAQwOFAx5DRD8DdirYTQIZrO/zl44kKjVzMDaQgmreohXebpsFiK/8v2azVbah7XX3Uva6+ppgLAFvXrkG1qlUACM7cutfV929RF3MFkVdsGmLu2xfmSGIuAO9ibqjcuRrz7Wltzn/1F4dpZ8wFS80aLsdFfj4Gjp9k2jwiJOYSBEGUQ/ejREXBJeZ2AwJz53qLWlATc4f1qINHki2qMQty5C5dPf2VZBaXovH6XWi8fpdXMbdWlXhp25uYGwp3rlrUQmWhW4to9O9Y19Q5ZGLuxyTmEgRB+I9eh+4hAJMYYw4IjgJ5/EJ9AH/pGYQx9jaA0wCOAsiGUIRiMgT371JZv64A4gHc5DrUjjFWE0A+53yHrJ8DwBrO+WAA4Jx/oTLnIADFnPP9etZIEFrIoxYCFXPVoha08nLVsnKVYq7I1rVr0GPAQGRfzFHt3753H5Q5nIhJsGPl7zOk88MazXITaeW8fcG9jsuwRrPUX1goxFs11KIWqsUC2YUoyQxNDIORnDzxExo0vkEqjjZw/CSsSZtr+rwk5hKE+VCGbmRB96NERUUp5vrrztUSc/3Jy5U7c+XMXXcOk/rXRt5F9f5jetjgKCtDYqwd59a632Ne/cTzOJ/jeT9aKH+CzEWsShRYqKIVlBgZtZBgj0LzRlfi2M+63labRmK1WlKcQ6iiFzjnXUMyEUEQFQoOBk4Zul7RK+g+B+AjAFsA/AZghuzcQwA+1znOcQD9AYwEEAfgHwgZvNM55xmyfsshPKomIs73O4BrZcetrkYQphJs1II3MVfMy5Ujz8PVEnGVbF27RvNcmcPpIdACwPKfy+u+yMVdNTFXVfgNl5ir5c7dMcrU2AWzUYq6ZjFpXpppYxMEQVQA6H6UqHAEK+YCYm7uUx7H80pK8fZBp9sxbxELWsxdp12ky1FWpirQAsAfb74gbYuCrVrfq5943ucaQoXRUQt/zRqGahPML/TrC7HQmtnRCykje5o6PkEQxKWALkGXc34SQEPGWA3OuTI8cjSEG2E948wB4Kkqefa7Vud4Pv/ScM4H6RmLILRQirmBuHOVublarlzAMy83GOTOXF/IxV0lWi7esBDCQmjh4OSJn1C9Vm3Txp86fyF+/u00AKBB3ctNm4cgCCJSoftRooISlJirlpsrd+Yq8VfM9caYHjYkxvq+FwXUhVxA28UbCe7ccDH8vtqSAFuRSBnZE0c++1jcDaxKHkEQBKHboQsA4JxfYIwlQMjT/ZtzXso5/96cpRFE+DFCzFXLzdUqfgbAa16uv2g5c/1BHrXAqseUZ+dGQtRCuNZQQXnm+Zk4e054g3bd5bUxoFNymFdEEJcKjIqiEQQRMEYUQVNGLYh5uUpnrhIjxNzYaJtHzIK/RJKYq9edW3/G6zg140nN84FSPakGsjIvSNEIFYmJT/0H35dn537BOe8UzvUQBBHhUFE0r+gWdBlj3QGkArgZQgXg2wAcZYy9AWAv5/wdc5ZIEOHBCDFXLFCmdOfKi59pEYiYKzpyra7x9ThzvaGMWvBaCC0UeCmEpmRP63R0PGxeteEGjW/AyRM/mTa+GYhiLhVCIwiCIIiKgUoRtKCjFtSKnxnJmB7lbzGNEHMjLWrBlzu3ZnwsMvILkZFvzhNlBw9/j6YNrjBlbLORibngnLcJ41IIgiAqPLoShhlj9wP4EEJ13omK604DGGj4yggizAQr5gJwc+G+2zQNa+vNQVmxwyNmwUhiEuyIiolCVEyUWwG0QNCMWgiHM1Zv1EK1WAAwrTBaQqwwfsYFZfpMxYHz4Nw2BEH4iasoWigbQRAVHyNyc+VcnboSSdNe9SnmDutRJ+A5RnRjcJSVITbaGGduJEctaLlzT00vd0LXn/G6rnGvnLZcNfrCF8PvMy+mCwD6dTA2nqvVHfcYOh5BEJUbziwhbRUNvSueDmAV57wzgEWKc8cBNDVyUQQRbtbuPihtByrmilhsFknIPfPbKVPF3P2bNqIorxgrf58RtJirjFqQCLWY23WJUOhMb9TCjlHS5p7W6YYv58Vhg313MogBY58zdLzmTRoDAE79/S/e+uRTQ8cmCIIgCMJwDBFzbRaLJOS+fdDp05mbE8Sj/OnbuVRAOFgxF1CPWggHzVduwVXpG6R9X4XQasa7DAA6Xbp5xaU49vNfutdTPakGAJgWu5BYrRYAICfbWAND6tLyuArG2F5DBycIgrjE0Bu5cAMAUVlQWruyIGTqEkSlQXTnGoGZAq4Wj9SYrJmdO/iaGSjK83xULCbB7iYCR0TUgporV4+gXC0WyC7U7dK1xkdj9IJ0MJsFi0Y9o3t5ZsUu1KxRAxkXLiCvoAC9ho5AlYQErEmbG/S400ePwPgX5+K3P/7Cqb//wVuffIrH/kPRCwRhNgygDF2CIAImWGeuWdEKWiza6sCIbt6XXHvAZOQqogsSY+1uIrBW1EKo3bl6YhaUnJo+GNWe028sSLBHoWmDK2CzWHQJu2bHLiz78Bweayf8G3ZrEY2q1Wth3Z4zhozdsm1nfH1oFwDczRj7nKIXCIJQh4H7nzJ0SaHXoZsDoKbGuWsBVLxEdoLwQoO6xj5eFGq8ibmlRaU4uGWzRystKsXga2YAcHfnuhFqd67Slat3fplLVw+dj42GVVG4Tg9mxS6cPPETatYo/5wsJy/PsLHTpk7CDfWvBwCc+vsfw8YlCIIgCILwRe0BkxH7wDgUljiQvp27tcISB2oPmCz1jYSoBaWYm/3SCJ/u3ED4a9YwZM8fBYfTe5E6NcyKXRBdugBwMcu4t/svpG9Dizb/EXdvN2xggiCISwy9gu4nACYzxqrJjnHGmB3ACAA7jF4YQYSTAZ2Sy7efGGL6fM1a3Ya619WXiqiZRVFeMfZv2qh6bv+mjSgtKsUjNSarZ+eGI2ohXHP7QYPGN5gy7skTPyHr/DlTxp49Yawp4xIEoQFl6BIEEQTzJ3QPyTzDetTBI8nlbw/TtxufuZ9bWIz07RyLtnq6hhdtdUiibqQUQtOTmRsOklvfJG3nZp83RdRd9uE5vHXAnLoLs5Z9ZMq4BEFULihD1zt6VzwVwGUAfgbwBoTYhUkAjgG4EsAME9ZGEGFFdOnu3b/f9LkuZGaaPsfga2bAavP+X15L7A0LegqgGcSu5otRll+iq+/U5Svd9itycTSCIAiCICKa7QBw7IvtIZksmOxco1i01YHcwuKIceeKhELM9acwWlam+/2nWVm6oYAx9km410AQBFER0fUXg3P+fwBaANgG4D8AygDcBeALAK0553+btUCCCBehdukCcItAMBpv7lyRHgMGqp8Ip0M2kLnbzPOre1l+CRaPG6ErPzev0FNorl6rtmlOXbN5dSvdQxMEQRBEJMI5v1fcNtulO6xHHWlbjEAIlDE9bEiMtXscrz1gslQwzV9CLeYC8Ds3N1j0FkaTu3M3Hi6Tts2KXjCLVnfcI252YoyRZZcgCMJPdHuKOed/cc4Hc86v5JxHc84v55w/zjn/08wFEkQ4CaVLN9z0GDAQ2RdzwrsIP4VYo7DGR2PMkld89pO7c1emTkNiXJy0X9Gcui2b3ggA+DszC2t3fxrm1RBE5UYsihbKRhBEpUFy6Zop6hrpznWUlbkVNxPJLSxWjVqQM6l/eEXJq9I3eByrGR8b1Jj1Z7we1PVyklvf5OHOreLKujUresEsUpduQZPmd4i73cK5FoIgIhAGgLHQtgqGXyERjDELY6wpY6wdYyzerEURRKQQDpduMLTv3QcxCZ6OCD1xC17F3I4Lg12aftrMA+54ObgxZK7ePa19VxjufGw0uEO7CMXU5SsxekG6hzt30aRxWJk6LfB1hpFpI4eheRPBVXzyzD9465MDYV4RQRAEQRBKXC7dkEUvBJubG4w7d1L/2si76Cks2ywWNF5h/NNrWlyVvgH1lr0v7WfkBxYDJgrBeq9PsEeheaMrNc8rxVzRnbtq5z9uom5F4uU390nbjLGDYVwKQRBEhcOmtyNjbDiA6QBqQsjQvRXAUcbYBwD2cs6XeLm8QrHyev+rixLmkpZ+V9jm/uzsD9i564eId+m2790HUTFRWPn7DI9zRXnFmjEOSmdunN2OgmLhEbMZjz4gfF23SRB195hcTOvzicAdaYDTu3tDF9VigexClGQWYk/rdHQ87D37zOZy6SpjF6YuX+kh5GqJuA0a34CTJ34Kbt0hZPro4Zg492X8cvr/cOrvf9H9cf9jI059l2HCyohwYc3QlyV9SXGZAWMwKlRGEETgcM7vZYyZU53KYAR37nyP42IxNC1GdNP+HXnoxwto26QGGq/YjBNDexmyTi3+HPEg6i17Hw4e/Lf71PTBqPacYCyoP+N1nJrxpNf+f80ahmoT1N9Sa4m5Iqt2/oM+rQXBfPh9tbHsQ+ML6/brcAXe3Wt80mKL2/+Do198AgB3MsZ2cM67Gj4JQRAVEAbunwf1kkPXd4cx9iSAxQA+ANAXgvlZ5CCAPoavjCAihM0bh0nbkerS9Sbm+nLnKp25opgrZ0b/3kCByUJP1yWCO9cIMRcAdowSRF0AJZm+nRH/0XDpysXclanTVMVcMXrBzNiFXkPNKcYxb9Kz0vaTo1ebMgdBEARBEMbwSLLF9DzdQNFy5/rCW8zClyeyAAiibn6pQfeIGjRfuQVXpW/wEHODKYgmd+kGGr3gS8wVMculm+gaNyfbnA/xZ73yEVq27Szu3kN5ugRBEPrQK3ePA5DGOX8KgNLmdwJAI0NXRRARRpfOQt5opLl02/fug+SevTTFXMB7MTTNImgod+dKWC3mRC+0mSe0bA3RNZj4hR2j/OpuU2TpKjNztVg0aZy0XRGLo7Vo2gQA8PW3v2PkxLfDvBqCqJxYrJaQNoIgKh1S3kIoohf8YUwPG0Z0Y4iNtqlm5/qKW1CLWQDKxVwRs6IXrkrfgKvSN2gWQRNdtoFwavpgv6IX1GIX9Ii5gODSrai8kL4N9RpIxd4oT5cgCHAAnLGQtoqG3siFegB2apzLB1DNkNUQRISyeeMwxCUKn84PeGII1r75hiHjNmt1Gy5kZgZ8fZnDibcveN44i3hz5yqjFjwEXAUz+vc2Lnqh6xJtAVeJU/vG1Wj+c2w0djSYrxqz4IvEuDjkFhQY6tKVi8ObVwT+ZsIXz498BikLluD7n3/BZ4dPmTYPQRAEQRCB4crShdHRC8N61AmqINqYHjaXkOsZsyDiLW5B7s5VCrhKxOiFq9I3ID7KFnT8QvOVWzRFXCU1Jy5DxrzhQc2nh79mDcOV05ajeaMrkVitum4xV4mRsQvljl9zxY5l732Nbi2ihZkY+0j8mScIgiDU0WvhyABwrca5RgDOGLIagohgjHbpBiPmis5ctQJocry5c/0Rc6V+/XsDxQ7BURuMW1dFzF15/kWsPP8iEmvI6i1arACzBl8kzU/0ZubKkbt0q9eqbYhT18wIByWp48rdzC3azySnLkEYCGOAxcJC2giCqNwYFbtgjJirbS7whZY7V4tDP17AlyeyUFzmDMqtqyXmXvx6Fy5+vQs1q1d1Ox5Mpm4gRdUcTqebmKsXeezC8Pu0oyz0Ih9j+1F94ncw3Hxre3GzG2OMKvYSxCUOZ5aQtoqGXofuVgApjLH9AH53HeOMsZoAxkLI1iWISo3cpVv3uvoAgA7t2/vt1g3WlSvm5b6lEbHgL3rE3Jc2bPXM1i0oEYRdQMiq1Rtv0NWz2MPK8y9K27kX8stPfPasMAfzXhXZLPQIuXJEly4giLHBFEkLlTtXTosbm+DoDz8CAD47fAojJ76NpfMeCcncBEEQBEHoYjuAbse+2I5Hki1ofns3TJi/ze9BjHPmBi7myvHlzu3StgGyMt0zXB1OJ65K3yDt14i149jgnrrmU4q5F7/eJW1f3+lBZGRd1DWOP2TP13evnFdc6rbvjzN31c5/8HiXy5CTfV4SdYNx6obKnSsyZ8UujH3sDvx8/CsAuIsx9jnnvE1IJicIgqhg6BV0pwHoAOA4gMMQ4iyWAGgM4ByAVFNWRxARRpfON2Lnrh+k/b3796PudfU9hF1/RNuDW/S7C3wVPyvKK0ZMgh0rf5/hsxiaL1RFXC2yC8vFXUBd4FWJWZALuQAwpvHs8p3PJ/qet808ff10sqe1unA6OGWWXy7dMXMXBBW/0KDxDYa7cwsKCxEdHe01w+75Uc8IX9MW4/gvJyl+gSAIgiAiDM75va6iUd0AIU/3kWThfk8u7vor2GpFIqjhTcytPWAyAEjnfOXnekNNxPXGhcJirwKvmitXLuQCnmJu9ksjpAzdas+lexRIUztmJKKY+1DbKADAe4dKvXUH4CnqBorR7tzSkmI4HKWIjUvw2m/hW58hZWQPHPlsJwDcTvELBEEQ6ugSdDnnFxhjrQCMAdAFwK+ua9MBLOSc53i5nCAqDZs3DpO2e/VZLom7e/fvd8vW1SPm+iPkAtpirijkWm0WHNyyGck9e+GRGpNhtVk04xbkvLRhK557sIfbvpaQK665fe8+KHM4EZNgR5Td5u6qBQThtusSd1FXh5jrMY6Is6xcMLZY9efqqriBvVGS6b7GwSmzyrdnzsbK6VN0jbNo0ji3a/1FLuYa4c7NzM7Gk1NSAABThg1Fy5tu9Nr/hfGj0Wuo8Obk6XFv4dUFjwW9BoK41LFYKQaBIAhjkOXpSsIu4F4sTa+Y66+Q6ygrQ2Ks3U3MrT1gMnJdQqnNaoWjrEw6ZrNasWirQ/ccIlpi7uz3hLFSHrHD4RDuBxPiYhFjj/Zw1SrFW195uWpirki8zYriMidqTlwGDqBMFsFgZL5u/Rmvu+33aS2I4Qn2aOQVl+geZ9XOf6RrA3HpDr+vdrkYbFChoJljehV89+XeOFtUNNbt+xf2mFjNvqlLt2LKsHtw7PBegIqkEcQlCw/R0wEVFZ+CLmMsGsA8AO9wzl8A8ILpqyKICoAo7orCrujWleOvaKuFNzG3tKjUYx5/5pWLt2pirnIscS2vnU5RHU8SZpWuXRdKIdftGhE11+3nE4XxXPfPlz19FP+82kLrZflXeA0Kd+5hlaJvrQPLDA4mdsGoqAWnk8NZ5kR0fDRmpS/HWwvmITE+3us1LZo2wdHjP+LLo6fRov1M3NaiXkiE3ZlLP8APJ9Vj2W++4WpMHdZD9RxBEARBXGrIXYtisTTRrSvij2DrC0dZGQrfX+B2rPaAySgscbjNM6aHzeOYPyjFXFHEFUl5xI6Y6GicOfyh5hhVW3YGADfHrojSlSuiJeYCwDd9O6PhOztgY0wSc395uCsAoOE7OzTXUX/mSs1zqmuQ5e3mpk9wO5c4QrvwnBpVqtUKOHpBHrWw/WtjsnMzM/6xOpxOOIqL8EinK/D+/7zHbMxe/rG8SJr4w7TdbLcuY2wHgHs0Tu/lnHc0c36CIAi9+Hwem3NeAmAoAO2P0AjiEmbzxmFSwTSjEYufKcXcwdfMwCM1JqO0qNTDhRuoiKwUcw9u2awp5i7TEHMBYNGJKe6FzXTgS8xlUXFA25eFLF0uuDEkMVerYJpMzO16coJ6HxlKd64bdy8DrP7FVyTGxQHwr7BZg8Y3oHqt4AtYKKmZVB1R9iiU5AvOjscn+nYaPz/yGdzcpLG0/+XR06YVSpu24H30HbUMfUct0xRzAeDbn/7A1LT3TVkDQZgJY6EtiEZF0QjikmS77y6BMaaHDSO6MSTGlhfjrT1gMmIfGIfCEoeHA3fRVs9jakzq73nP40vMBQCHowxnDmqLuQA8ipr54vpOD0rb3iIU5MXRGr6zA7esVxeHRfwpiCZ35yrF3EBYtfMftyJpehh+X2081q78b4iRhdCGjHvJbrMI99MFBfk488dJn9e0usNDVzXFrcsY284Y4y7hWEvMBYAOFovF98IJgjAARkXRfKA3Q/cbADcB+NTEtRBEhWXzxmFuEQxA8O5creJnWq7cQImz2zHjv+4imdbYZQ6npjNXzqIT+qIJdDlzAdQZ/D/8s+JWgJcJ4i4AXloAFhUHXlrgdQ49Yq5Pd25Bid8F0uSxC95cumbk5aqxfslCPPviXPz6x18oKy3Dlt170bNTB6/XzBgtvKGZsWgpvv3pZ3x2+BRGPPc20l8yrlBayuJN+OX//nU7dsuNNyBllPuji1PmL8RPp37Fyd//xYuvbMHUZ/QVPSEIgiCISwFZDIOkOBrhzi3Pyy13h6q5cgMh72K5yKgWsaAm5qY8YkdCnG+f0a+7PZ25WvhbBC3eZpUcu/kO7RgwuTtXT0E0f8RfveiNXnCLWJDRr8MVeHfv34aspcXtnTD2hVWYP3UgEmPsGNbnJnxwuAAWi7aIkrp0i7Qtc+t+xTm/1ZBFQd2R2/SWO/HSyr1u/aYN746jn+8C57w+Y+w457ypUWsgCIIIBL0S9HgAzzLGujNmUIgOQVQyNm8choJcYx6RF8VcQBBwxa9arlx/6TFgoLTtK2JBvqaYBLvqOT2MaTwbg2tNlZrXzFw5FqvgxnWJuXUG/w91Bv8PALTF3CCzcz2Ii8bgmbO991FB7tJt0PgGj/O+xNwB457ze05vJN/WCgBgT7RjzaYPcF5n4b4ZY0bi5hsaAQAOfWlcobRJ89fjxK9nAQC3NLkBm1ekY/OKdA8xFwBmTxiLW25sAgD49sSfmPKy/jdqkcKcV7eh76hlmPOq/9XIiYqPxcpC2giCuDThnEu/AJZPD/zJdNGV6ygrQ25hMWoPmOzVlesvSneuXjE3JjrapztXi+s7PYiqLTt7NG9RCwBgY0yKVRDFXHE73qZd8C2c7lw5cpeuvNAZUO7I1XLw5mTrL0qnh1Ztu8Dl0uUxUVF44M5q4FzfBwMyt24rV3500DDGtsMl5jZqeiu2Hy3B9qMlHmIuAMxatg233tlV3L3RarX9YcQaQglj7CPRiWzUYjuy0QABAABJREFU95AgzIQzFtJW0dAr6G4AUAPAhwCKGGN/Msb+kLXfzVsiQVyaFOUVo7SoFKVFpZKQe3DL5qDFXADIvuhZx1AtYkFET9SCFqKQqxRvPZy5Gu5cfPascM7ifsN82dNHcdnTRwXHrjJ2wcjsXADYNxyw2/wWdRdNGqcp6irF3JWp06QmXpOX79197C/dO3aAxWYB5xy2WBuenjYDOXl5uq6dMWaktD107Jqg1/LiK1vw25/Cm4dbbrwBKaN9FxNJGfUMbmrUEABw6g//inuEmzmvbsM3Pwp/Kr/58XcSdQmCIAgz2Q4AP3wVfAqDzWpF+naOwhKH5MoNVswF3N25cma/5zBczBWFXF8uXK2ohYx5w5H90gjYXG/2G76zAw3f2YFv+nbGN307I95mRbXn0lFz4jL1cQ1y5ybYo/FQ2yif/ZR4i15Q7m88XCY1M0ismoQnxr6E3KJidn51KmzgeOCOarquTV26BS1u7yTuGhW90BUAmrVqh4Vvfeaz88wlH6Jl2y4AAKez7CqD1hBKumlsEwRRAdEr6O4BsBHAWwDeAbDbdUxsnh9hEQQRMPs3bZTEW/l2MPQYMBDJPXshuWcvj3Pe4huCEXMBeLpwP58IVAsgkvuzZ1UduXUG/w/MancXdf0Y36c7V2TfcKDMqXtcETVRV03MVV5jBlaLBQumTEJJXgnGn5gEW4wNT03V/+/aoqngkP3qm/8LOE/3hfQP0XfUMnx74k8AQPMmnvEK3kgd5/tNUaQhF3NFvvnxd3LrXkIwhojL0GWMPcAY28gY+50xVsgY+5kxNocxlqjoV50x9gZjLIMxls8Y280Yu0llvBjG2HzG2FnXeJ8zxu5S6WdhjE1mjP0fY6yIMfYtY6xPUN9ggiAMZ9FWd/FWby6uLyb1r40R3dR/R6kJuSJ6cnO1UAq52S+NQPZLI1AzPtbtmM9x5g1HvqMMvzzcVSqIBggF0355uKtbvq4cufvW13lv7tyzaaPhCOBeFBBEXRHRpSt363oTcft3rBvQnFrc9/BI2KxW1B6Uws+vTkVhkX4jxqxXyj+kYIx9Esj8cpeqeGzua/qHeiF9q3ys/YGsIRyoOXLJrUtEMhwABwtpq2joEnQ554M45497a2YvlCAqAr36LA/3EjRRc+XqoczhDFjMHdNY5miVu3B3jCrf13LmqmGx4t+Vd3oc9hB1d+gT/XS5cw1AKep6E3OVDBw/ydC1XFP3CtiibVjQeB7G/zwJxUUlcDr1vTl4fuQzuLruFQCAzw6fwsjnfIu6YsyA2L7/5S/pXINrr8F0Hc5cLVLTA3tjF0rkYq7FYsGKac+haf3rpPMk7BJh5FkAZQCmQHjcdDmAYQA+YUyoCuGK2driOj8SQB8AUQD2McauVIy3EsCTAFIAdAdwFsBOxlhzRb8XAMwAkA7BGfUFgA2MMXIKEYRxdAMAi8UafIiugUzqX9urM1cLvbm5aiiLncmF21PTB3sc84WNMc1CaPE2q6pL15f71ozsXDXkLl1vMQvK/hez9BVU0wtjDM8v3ITcomIGAAkxdjx4Z3Xd1zdr1U7c7KRXjJQJl9uhcKbKYhR0I7umnbd+kYAoYEP8vWC18tuTuyi7dSNRlyAqHhWvjBtBRDBiUTSbVTtPKxzIM3PFaAV50yKY3FzdGbn+8Nmz4GXF+kRdHeh25xqAXNQV8Sbmim9c9EYi+LWWqZNRWliKBTfMQ3R8NPqNGa/72sUpU1CrRhIA4LMvT2Hm0g9U+816ZQv6jlrm4UwFgJsaN8TmFel4aXJgGXGiU/i4TByORJRi7vIpzwIARvZ7QFXYJYgQ04Nz3pdz/jbn/ADnfBGAUQBaA2jv6tMTwJ0ABnDO13HOP3YdswCQQr4ZYzcDeBjAWM7565zzPQD6AvgDQKqsX20IQvJczvnLnPN9nPOhAPYBmGvuyyWISwO5KLNkmyOi7EZyMVeMVtCKWJBjpDs3WDLmDUdxmVNV1P2mb2fYrRav8QtK/M3ODTR2AXCPXpCj5cyVu3qNplmr9rBHRaPWoBScX52K/IJ8FOt06s597RM0bZksP6T5gSBj7IjciQtXxEL9G1pIebkzl/j/sxXINeHA9ftA+v5YrFa+99scNnf5Juw/no/9x/MhE3fpg1WCqGDY9HRijD3m5bQTwEUA33DOI/vdNUGYiNydu2/z+2FciSDgBurIlVPmcOI1I6IW/HHh+uKzZ8HbzFM9VWfw//DvyjvBFaLujgbz3fajk2LdxVy97lxXcbSV06f4tWQRf6IUynS6ZgOh7mV1cF+nDti8czcm/5WCOVem4p0t2/Bwz+66rn9tdiqeT1uM47+cxA8nz6DvKH1vWq6oUxvLUgP7eZLz/Mhn0GuofjdNqFFGLMjFXDkj+z2Ape++j+Onfgvl8ogwEWmFyjjnaparr1xfxedrewL4m3O+T3bdRcbYVgD3QRCAxX6lAN6T9XMwxt4FMIkxZuecFwPoAiAawH8V8/4XwJuMsXqc89NBvjSCuNSRu3PD+osnkGgFJUa6c40iY95wVHtOvRCyWDBNLKIGCBm69We87ubEFeMe/HXnnk0bjcQR83131MBMkdYf7DGxWPnRKTza+WoALpducnVsPJSDqKhon9e/9PoeAEC3FkJfhWjrlZta3oV5r+8OZNmqMMY+55y3MWxAg5CLuaKQC5XfCXOXb0L7pvGhXh5B+IYxcEYeVG/o/e6sBrDK1VbL2ioAayAUS/s/xtjbjDHfv4EJohISSe5cLTHXmxtXSbDuXAkjxVwRixX/vNpC06kLZ7nTwC1WwUXAztwAc3QDobCoGABQJSHBlPHvvr21tB2TaMfGj3dh/xdf6r7+hfGj0bxJY699br6hETavSJeaEWJuRUCPmCsiirlWC92sEBGB+OjoT66vNwI4rtLvBwBXM8YSZP1Oc86VQec/QBBw68v6FQM4pdIPAJoEuG6CIBBZ7txJ/Wv77qSDSHLnyrExhobv7PAav+C2FoVwm5Ff6HZMjztXJBiXbiSRVPMyRNmsqD0oxXl+dSpioqLwwB3VUFJcpHuMVnfc47PPrXd2ldy424+WGCbmymIXbjdkQAPREHNVmTSsd8jWRRCEsehy6AK4A8DbALYCeB/AvwDqQHicrjuAZwA0BTATwO8Q8tgI4pIhUty5/hY884Zh7lwz+EwQyPgdL+PflXcKIq4GXsVbE3NzjWJNmjlPIV9xWR1YrBakNZ6L8ScmYcEN87D0rf/i3IULuLx2LSTf2srnGNNHh98lO3rW21g87ZGQzSe6b29pcg0mP+3paJZn4a6Y9pzHeTlL3y3/XbFu0TDjFklEHq6iaCGmJmPsiGz/Nc75a1qdGWN1IcQj7Oaci9clAfg/le6Zrq/VAeS5+mV56Zck+5rNuUflIGU/giD8xE3ACaM7Vysn1x9XrkgkunNFMuYJNQBqTlyGW9bvkpy5It/07Sy5dH0VRfNHzAWCd+lGEg8PnY41y1IsAHB+dSpqDUpB37tqYMDwF9Dh3kdQvUYdr9enLt0SknWqMXPJh3KH8Eec83tDNbfoSOaca/0/1yXmAsAXB3eKowZf9ZAgDKYiFioLJXotQc8CeJdzPppzfpBz/ovr60gA6wA8xTmfDyANQD+zFksQkUivPssjwp0rz8kVCVTMDdSdO7xeKgbXmlp+wAx3rhxXpq6WW9eNarI3BIfHBi7mumIXKjpWiwULp02Go9CBBY3nYdxPExEdF4V1Wz7CgjdWo9+Y8fDUXCKHlk1vBACcPZeNF0JUHE0epfDNj797FDJTZuZ6Qx61QO5cwiQyOOetZM2bmJsA4WkrBwB5oVsGociwxyUq+0b2IwjCfyQxN1zuXDUxV09GrhaBuHPrJt+Hqi07m+rOlSNm6npz66rFKuSmT5BapPJYpxqmz/HQ4EmwRwtZuoAg6tqtFqxZMgWP/Ocq3bm64eLm2zqImyHLn5U78dUKmcmP+RJz5e5czp0V3/ZNEJcYet9F/gfAHo1zewF0dG1/ivLcNYK4JJCLueFy56pl5gYq5gKCO3eZH+5cUcgtyit2P9FmHtBxodDEbaP57Fng84ngpconfRXklAQn5IrsGw7YbZVC1L36issxZ8I4lBaWYmGTeRj700RM/kv4d+cWjn6j9Wf+hpppI4ehxY3C09nfh6g4mrJwmda+r5gFwD1qgdy5lR8GBos1tE332hiLAbAFwHUAuijqIWRC3TUrliPP0tkvU/a1OmNMuUBlP4IgAiScUQtqYm6g+OvOFYXcopIS2VEGm8WCK59fgSufX4Fqz6XjyudXBLwmLTLmDUf2SyM0i6XJMUrEDUXsQn5uNgCganXPQmpGsvFQDopKHZKoe251KmKjo5wA0Dc5CefO/mHq/MEw59WPQzqfssgZgG4qAq/kzvU21qRhvcmdS0Q8nFlC2ioaeldcAqClxrmWrvPieCY/a00QkUk4oxbkYu7BLZuDEnP9ZXi9VJQWlXrMOeuJhwCrBSgoAYodwn5BicYoBmCxenfpOtUr+AZEJRJ1G9a7FldffjmKcsrF+Ml/pWD8z5NQUlyKyS+lIScvL4wr1Ob5Uc+EZV55lILo0pW7dX2JuXJIzCXCCWMsCsBGALcB6MY5/17R5QcIubdKmgD4g3OeJ+tXjzEWp9KvBOWZuT8AsAO4XqUfAPzo94sgCCLiCMaVK+KPO7du8n0oKinB7PccsMeVf7a0/WgxthwpQl5xKfKKS7H9aAmKSstMEXWBcreuL1HXCM6mjUaMzYY+ra0VPk/XYrFg7ht7kVdUfi96bnWqpfDdeYiJsmFIj4Y49uU+LyNcUkhi7e3JXdyOKc/7E7VA7lyCqJjoFXQ3AJjJGBvPGLuGMRbr+vosgBkor2rcHMDPxi+TIAgt5FELRgi5/sQtiGLu/k0b3dYx64mHyjtZLZg18EFp20ynrhi/oOS6vrvBbHFAmyVAa4PmFkXdlFlCM0ncHTh+UsDXljocKCwq8trOnj+P//vrjOr1sVVicOLX03h84hScy7gQ8DoqI03rXwdAcOX2HbVMd9SCEmVsA1FJYYg4hy5jzAKhPkJHAPdxzr9Q6bYFQF3GWDvZdVUA9HCdk/eLAvCgrJ8NwEMAdnHOxXfpH0MQeJXB148COM45P+1z4QRBeKD22HWoMaoImr+IYm7q28V48cnLkZ/jmd9rs1hgc/193nKkCEWlZaa6dfMdZVJ+rsgP93dAvM2KxBHzkThiPqqPfDnouc6mjUZu+gTThN34ROHhiYtZnt9TvXDOUZCf67OtXDRJ1XnhcuvylGe6YvvG18lF6mLvtzls7vJN0j5j7CN/ohbc4eZ/AkEQAcLBQtoqGnqLoo0DkAjgJVeT8w6A8a7t4wA+N2ZpBEHoQRm1ECx6i6HJxVytdUhCrmJ/2poNgqi7x8CiZF2XqLpwr+srVLKt13sLTm/qCe70Ec3gD/uGl2/fvQyDZ87GyunG1IRMiItFXkFhwO7Y/V98icWr3oLFaoGzzOmzf2zVGI9jY34UnKhpjediWMpM9OnSGQ9064LoKPoQf2S/B9xycAF9UQsiTetfh+OnfvOIbSCIELIMggD7IoB8xpi8SvdfruiFLRDu6/7LGJsAIWJhMoTMW+l+kHN+jDH2HoBFLtfvaQDDANSDTLzlnJ9jjC0EMJkxlgvgKATRtwOA+0x7pQRR+QlrMTStQmiBojduQS7mAnATc7cfLX8qbMuRIrfrxP2erWJw5fMr8NcLQ41YNgCg2nPpHsd+uF/IWf2yu/DZ2I0f7DVsPkAQdgHg8vGL0ae1FTarBe8dKg163Ld2Z6BP68Drg/z2y7dYMX88fvrmf3A4fd6LWhNj7GUAPCY8tzqV1R6U4kx/cbjtjZfHYf7qg7i+UfOA11WZuD25i+i0lVy6rqgFn78H1K4lCKJioUvQ5ZwXAniUMZYK4HYAlwE4C+Aw5/wXWb+wfzpMEJcCRmfmivjjzi3KK1ad082dq8GsgQ8aK+p2XQJkuxdNEIVcOZKo22YJ8Pmo4OeVs2+4ce5fAIsnjcfglFl+X3fuQiYmv/QyMrNzEB0TBavdijE/POf7Qi+MPzEJaY3mYsP2j7Fl3z4snzkd1atWCWrMioS82Jkcuajrj5grXjt0lvLzUYIIKV1dX6e6mpyZAGZwzp2Mse4AXgbwCoAYCALv3ZzzPxXXPA5BHJ4FoBqAbwHcwzk/qug3FUAegNEQ7id/BtCXc77ViBdFEJcyoc7P1SqCFiwORxnOHPYdt5BXUKg6n1zM9caWI0WGirr1Z65024+3WSURV3m8WMeH7f4iCruJI+YbPrY/cM4xb/KjJZ/u2hBts1p4bHSU89zqVD3KsGafc6tTLQAQ228iRva/Dcve+xrX1m8Kz0j2yolKdi4AYO7yTW5ZuHqiFuTXtm8aL43POb/XwCUTRKWFMXYVgIUQao0xALsBjOGchzzw269nQznnv3DO3+Kcv8Q5XysXcwmCCB1Gu3JF9BZDG14vFVZbcKHhswY+CBQ7jIlfyPasgPvb+k44vamnx/F6vbeAWexC/ILRxEWHPVd3x/4DyM4VXL1GiLki438Woh+4lWPI5Gl4bd27yLwYmgrS4UQp5iojFUb2ewArpj3nl5irNgdRuWEALBYW0uYLzvm1nHOm0WbI+mVyzp/gnCdxzuM45x0559+qjFfIOR/HOb+Mcx7DOW/NOd+v0q+Mcz6Lc34N59zOOW/GOQ9fCD1BEAFhlpjrjzvXZgvcPSoiRjAEG79Qf+ZKZOS7348Wlzlx27YDHn2/7N4OdqvFkNiFSOTC+b9xaPfGaACIjYoq0ynm6iIxxu4EgOEPtUTvNon4bM9m45XxAGGMaRWRD3ZcNzFXWexs7vJN2H88H/uP5/sZtSC4dF2QS5eIODhYxBVFc9WL2AugMYCBAAYAaABgH2Ms3tRviAp6IxfgWtxgAHdBqGQ8lHN+kjHWD8AxzvkJk9ZIEATMc+UGgpY7118Mcep2lQmzh93H4BqO2Xq9t+C39Z0Cm88bBrt0A+E/d96BLXv2IToh2qeYu6DxPBTnFSM6Phol+SWY/Jd3MV88v/CGedix/3/YefCQFOfw34UvIT5OWQ8pdLz4yhZMfcZTwA8GuZjrrwNXD/LYhTmvbsPkp7sbOj5BEARBGIlWtIIRQq5IsO7cQNhypAjdWkQHNYZczM1NnyBta7llv+zeDrdtO4DqI19G1lJj7y8S7NF4qG2UIbELIv07XoF1e/7W1bd6jctgtUUhxgKcW53qVW+oNSgFeUXFSIgpf0Lw/OpUzf6iUxcAag9K4fMm9reIcQ6Dx8xFn8fG6VqjkbS64x4c+exjQIgPMhS5mCtz3xpmSyaXLkH4zZMArgPQiHN+CgAYY98BOAlgKIAFoVyMLoudy1L8HYD5ENTndhAydQHgbgDG/hUiCMINNTE3XKi5c+UF0fwlaKeuijtXwmLVdOoyW5wgvhrt1A2zS/eKOrWRNmUieIkTc65M9dps3IJNry5FlOsJt7RGczHnSu2baJGxP03E5L9SEBVXnqU76LnJcJSp1rMwlVtubAIA+PaE8unv4DBbzAUEd6+8uBo5dSsxEVgUjSCIysfy6eZpMaEQc8OJzWIJ2KUrj1qQi7kAYGMMN36wN6RO3bNpoxFjsxlSJC2xag0AwMWsDN3XWK1WbP4iFw4uRCR4aywqBtuPloBFxYBFxaCopBSx/Sai9iDfTwyeW53KYqOjpJvPNUum4N+/Q1+bIHVpeY1QIwsUaoi5hiN36UZCgUWCkBOBRdF6AvhCFHMBwFXU9zOEoR4E45z77sTYegBNAdwD4G8IFYpbcc6PMsYehpCz1tDUlYaQZ/6Y5vubQoSUtKTLw70ETeISRwAAbm/VEvNTppkyR3LPXtK2Wa7c9r37IComymfkwuBaUz3WIF+fngxdNaat2QDYbf45deXZuYe9XNd6oWqmLiBEM3i9NhDuXgYUuPLbrJaAC6WJGbqbV3gW2DCaUocDAydMQmFBeeEQe4Id405M1HV9WqO5cJY6sWHpIpNWqE2vocL/wVuaXI3JT/cIerxQiLly5MXV1i8Z7qO3uXR4sH5Y549Ealw2Jug3UK1aXcO/+sqYgol6sVie/ppz3iqkkxIEERbkwk/6dnPexozoVv6r0CwRN+URO2Kio3HmoG+HbtWWnT3WMeUhwQyqN0NXSc9WMYiJsvqdpysvhKYUdEUSR8yXiqMpuW3bARSXOQ136l4+fjHyioXvRTCF0sTCaIF+X/3h/04dxzN9WyAuNg4FhQVIiLF7dezKqTUoBUUlpVj7yZ+oWr2WySt159nH2+PHbw8BADjnhgivjDEOmCvmishzeI1aP0EEy0033cQ//OCDkM55ff36Xu+fGWP/APiQcz5UcfwVAA9yzkP6y0dvCOZ/AEx3hfwq7xLOAKhr6KoIogLRpfONAIAvjnyNCan+F7HyBzMjFvTm53ojUDEXcDl1C/y8UfTmztUJs8UZ79LdN1wQiQ+PBey2sOfq6iHKZsM7C1/G5hXp2LwiHYtSpqA4r1j39eN/ngRHiQMvLH0F+YXB/7v4Q7PGjQAA3/xoTA59KMVcQHDqipBLt/LCGQtpIwji0kH+mLSZLl3AXEeuw1GmS8w1Kj9XSSB5ut7cuXr5sns7OHSYrPzlbNpo5KZPQG76BMMcu2Zzbf2m2H60BO9/lo2Pvi5GscOBWjrcuoAQ1RAbHYUB/7kKv574xuSVuvPyqv3SNmNse7DjyZ2yZou5gBC9oDY3QYSbMNw/12SMHZG1pxRLSgKQpbLUTADVzf5+KNEr6EYDyNU4VxWAcQE9BFHB2LxxmJuoWxFp37sPYhLsvjv6YM47HwQ3gNUCtJmnL37BS3auB16iF+r13gI4TYwK2DccCLKSca+hI1TbwPGTDFqkJ9fUvQKxcTGYc2Uq0hrN1XVNTKIdR4//iEHPTTZtXWrMHDtS2k5d+oHf1895dRv6jlomNZFQiLkiFL1AEARBBMl2APjhq+2mi7qVGX9EXbVCaFp4i14AgHib1dQiaXnFJXAEcD/6eJfLpO1uLaJVW/+O5ni7GGPYcDALpU7uTwwDYqOjMPbRNsi68K8p69KieWvJgd3VX1GUMfYRY4yLDbKoBYOXqQlFLxAEACCDc95K1l5T6aP2/zIsbgq9gu53APponOsKoGKqWARhEJs3DpO2zXbpGo3eqAVvVKtaBQCQX6Tf0anGrIEPCi5fPZm6/rhzPx8FHB4L7iwObZ5uEIyZ6ztPPScvDwPGeS98FgzvLHwZG5cvgcXJdAm7YrauJcqCh8eGNlq9hStL9/jJM+g7ahlmL9+q+1rRkSvHYtH759EYlHm6ROWCA3BwHtJGEMSlhculK4m6FY2UR+xIiIvV1ffMwQ/hcJj3YbxeUVerEJoaWUufRW76BBSXOVWFXbPydPWuT4ucbM/cZCUXs3z3CZRoeww2f5GHZe8eQXGZUxR2vf6RO7c6FTHRUXji3tDGSM1e/jGuvEZKoezGGFNX79XppjwQiqgFOXOXb3ITdUM1L0F4g3MW0qaDLAguXSXVoe7cNRW9Gbq9AbwPYCWAdwDsAfAYhAJpkwH05Jx/bOI6Qwpl6EYekZyhK9Krz3Ls3PUDAO083WCLm5kRuZDcsxdWnn9Rd//h9VJRWlSK/Zs2eowDAPExdkx++P6g1zVtzQbB2RoX7Zmrqzc7V402S8AsdsGZq8CUPF1AyNQtdviVpSvm5wLA/OFDAADTXluD4lLhgYg4ux0FxeUCemJ8PN5aMM+gBXuSX1CAJyZNRUlxKaLjozH+Z+/u4DlXpmLV/NmoVqWKaWtS8sLSV3D0+I/Svt48WtGVu2KaeeK4XobOegkAcEuTazD56e4hn58ydD0xIkO3Zatr+OchztC1U4YuQVySiLmbAHDjrd0wbKan0U6rwJkezIhc8Cc7V8Rbhm7V6rWwbs+ZoNfVs1UMHE4nEuxRHrm6cnduIGKpVqbujR/sDVh81TPnxsP6hfDHu1wmCbq7jgk1Fvq0uwK5FzOlY52bx7hdY9T3XouPN79Z9urckdZoq8Vntm5sv4kY9fyr6HL/42AhjCIa/WhbnPzxiLi7g3PuUxwV/9/uP55v5tJ00b5pvHx3uzzShSBCyU033cQ3f+D5nt1MGtS/zleG7l4A0ZzzOxXH90PQV9uZvEQ3dFmQOOebADwD4EEAYmWhtwCMATCiMom5BBEoyugFpVM3WDHXDAKJWlh2OgVlDs9Htoxy6YpounXlYm4gfD7Kt1NX3oxw7QYRuyCKuQAw66mBmD98COYPH4KZQwYgPqb83y43Px+9ho4wzbEbHxeH95YsxJq0uXAUObCgsXfxODo+GoMnTcPPv51GQWEhVr+/CfkFBaasTeT5kc+4FZCb+NJ6U+czA3LpEgRBEEEi2XOVTt1J/WtjRDcWsJhrFnqzc30RX0WoRXMx67whMQBbjhRh+9ESVbeu3qgFLWyMqcYvxNusSBwx362ZGcXgDTV37sYDf2PXsSJJ4FUWH7uYdR59kmuYtqZ7ej1h3XgoBw7OfGbrJsbY+ZIXnkaftlUBADs/WFX29aFdpq1NZPF/D+GW2zuJu139ydSdNKy3OYvyA5lLFyCnLkEo2QLgdsbYdeIBxti1AO5wnQspuhy6UmfG4gG0AVAbwAUAhzjnWtm6FRZy6EYeFcGhKyJ36mphZnEzf/DXnSsyuNZU1dcgunTlGOHYnfbme8DnE4WdNjIhMRg3beuFuK7vbp/dDHPt+unSFR268bExmPHEo177zlj5X+QXFXkct0dH492lvqMb/OW7Ez9j+sKlmPyX9xvpBY3nobSwFM4yJyxW4fPD50cMQ/MmNxi+Jjlyp27tGlWQPn2A1/6iQ7dp/evcCpSFi3C6dMmh64kRDt0Wra7h//sytNnS8dZh5NAliEsYuVNXCzMLnOklEHcuIBRGKyopQerb7kaCF5+8HPk57kKkEa7Rnq1iEBNlxV8vDA3anSui5dJVctu2AygucyJraXBxVpePX4wihwPvHdJX/qZPa6HwXNXqtbBh358++/e6sw7y8y56HK9arSbW7f3bv8X6wOl04v7W8YiNsjnPrU71alKL7TcRVluUo8xRagOA5q07Fs1eviPG2zVGMH7QXfjpuy/E3Y855121+roya7sBkeHSBcqdulznc+gEYTQ33dSMb/pAf4yeETSsf60vh248gG8BFAKYBiFZ7QUAiQCacc7zQrJQF36FBHLO8znnuznn73DOd1ZGMZcggkXu1FUjEsTc5J69kNyzl0937uBaUzG41lQMr+f+SFNMgh3te3vGaosuXTn5RcXGFEvruNC/QmjecLlu1Vy6SpgtLiwu3cS4OABAfqGnUKtkxuBHMX/4EMTHuN+bFpeU4LFxE/1bpw4aXVcP1iirz0zdcScmwh4fDQCIjouCs8yJmYuXeb1Gi3MZFzBp3svIzfd9k/v8yGfQsqnwf/DchRyMm71O1xzHT/0W0NrMgly6BEEQRBBougJnv+eoMGJu1ZadUTf5PumriFaO7tTXz0pOXREjHLtbjhQhr7jUr0JoevBWKE3EqHzds2mjEWOz4aG2Ubr6V6lW7nh+8O6rfPbf/L9/PRy7AHAxO8O/herAYrHg6YmLHYUlpRZfmbqJMXZJzAWAn745GLCYu/TF4UUfb16py/yVtvpT3NK6o7h7T0UqNBYJTmGCiEQ45/kAOgD4BcBaAG8DOA2gQ6jFXMCLQ5cxdpc/A3HOPzVkRREAOXQjj4rk0BWJSxwBIDIEXDnte/dBmcOJmAS7ZiG04fVSUZQnOB4ObtmsWjhNK0tXjjxmYtYTDwW17mlvvud+wABBF4Bmnq6c05t6gjsKAItVKLAWKCa6dJXIXbtivm6vocLPpDyWIFCKS0owYPxzKC1xICbRjrE/+RaO51yZitfnvoCa1av7Pd/xn3/B9MXpsFgYNqQv1nWN3Kl7Y4O6mD7yfs81vbpNEk4tFguWTwltMTc1RIeu1WLBukXDfPQ2FnLoemKEQ/eWVtfwA4e9504bTVXbM+TQJQhCcupGgoArx5eYWzf5PuQVCMKpzWZF6tvFHtdouXTlyB2724+WBLVmMVNXJNis28QR85GbPgHVR74Mu9WCL7t7j140yqnrT5auWo6uP4gZu3EJVfD+pxno1+EK5GRnoEq1mnjXANfuFwe2Yvb4BxEbHYVzPjJ1AcGtCwT+s9CthWBUeGf3X6iWVFvXNc892RHHvz4IQN3tKnfnhroQmhblObrMwblT3ycABGEwTW9qxjd9sC2kczaqf02Fun/25tDdD2Cfq8m3tRpBECoos3TDTZnDiZXnX9QUcwGgKK8YB7dslsTo/Zs2SgKviFaWrpyta9cEv2A1go1A+HyU1LTydOXU671FiGdwBllRed9wwG7D4JmzdXX3x6WrZMbgRxFrFxzYYr6uyMDxwQtL9uhorF+6CO1a34qiXH25ydEJ0Rg+44WA5rPZbHCWOeEoLcOX336n65rnRz6DFk2bAAB+OHkGfUct82iRJuYuffd9aTvUYi5BEARBhApfubl5BYWSk1gUbFPfLpZEXkDbpStn6utnpe1gXbq3tC3PFjWicJk4RtbSZ5Hv43UAglPX4UdcohYJ9mjdLt1VO/+RtvW4dJWIjt2CvBx0axGNHJdbN8cg1+7t7Xrglfe/Ra7OGh6F7wrRbf7ETsqJiormADDwnmt1X/PS63ukbcYYVzZEmJgrd+eSmEsQkY03QfduCFbiDgB6ATgDYCeAxyH80nkcwC7X8ftNXSVBVEDkBdIiBT1F0IbXS4XV5vmrwWqz6I5eUGPam+8FHL3gdl1cdEBjaKJD1D29qaeQpWuxBj+fH9ELiyaNk7ZnvPlfv6dKHTLAI4YBAHLy8gwRdQFg5KABiIq2+YxfAIDxJyahpKhEcs36Q6Pr6sHi+rmct+INFJfoc1Y8P/IZ3OIjs9doMXfY3JcxbG5gj0WKsQ9Wi1+JSESEwzng4DykjSAIIhJJecSOKQ/ZkBAXG9D1NpvVLXohIS4WKY94v7c1olha/4518dX/dkj7l4/X97SQXrSKpInctu2Az2gGvZgdvSBnw74/VWMYAKBv+zp+jaXFldc2Qnx8AmL7TUTtQSk+b7ITYuzo3SYRTqf/BYt7PSo8tWO3WfHAndV1j9Hqjnu8njdazO10cxV0utkzCk8PXxzc6dpikWXrJy5JOFhIW0VD8x0j5/yA2CAIurs4590452+58nPfcgV7fwKAQlYIQsHmjZHjrmvfuw+Se/byiE1Q4i1GYf+mjSgtKnUTdfW4dOW5uvk6Pz1X4nZdsUOILjASnU7doOIW5MRFY3DKLF1OXblLNxBRV8zXFVtMtCCIGyXqWi0WrE17CY4iBxY2eclnf3uCHS8sfQX9Ro/z2VcOYwy1qlUDANhibOg3chweHqtPhE0ZPRybV6RLTSQ+NhYrpj1nuDPX6XDC6eP/hS/InUsQBEFUNsTIhItf7/IatVC1ZWfYbOofoqu5dGOio72KuvJc3YtZ5zX7eUN+3cbDZcgrDi6+QUnW0mdRXOb0KtrmO8pgY8YIDv6Iuqt2/uMm6vrLhn1/YtexIqlVqVYDAJCXk4WH2l/m93iqcxzMxJ2deiO3qNjnJ+LnV6ciysLQ6/YEv5268YlVpTEsTgceuKOqrutSl27B9qMlbk1k//F8mOHMdZSVBSzqCvBdhi2GIAhT0GsBug/Aexrn3nOdJwgiQtETswAIUQveMnHVohd8uXS3rl3jliPsr0tX3n9l6jQhf7bA2JtoAF5F3Xq9txhXHA0QXLqHx+py6i6aNC6o6AUlLzz5GOzRws17Tl4eeg0dEbSwa4+ORs1q1VCU43t9405MxOS/UlBcVIJnX5yDWemv6J4nO0eowzn+50mIjg/cqV0lIQEAkF9oXFETOaKTOFCXLlE5cYS4EQRByFkzt0dY59dTAA0oj1rwlourxN/ohW4tov1y6sr76s2eDQRvou6X3dshXkPkDhR/RV2jeH//GVSpmgQAyM3JRLcW0ejX4Yqgx61/Qwvdfc+vToXdZsX9rePwVO+bcn84dkjXdXIB+PzqVBQVB2ZWkdPzTv+jLHyx+1uhhomjzP+f19uTpWiRbsatiCACgxy63tEr6FoAaFVKaQDA2L8uBFHJCGeOrp6YBUA7akGJMnpBj0tXTn5RsRS/MOedD7xGMcx55wN1V6/VYrxLFxBEXUeB6ql6vbeAWexA64XGCbs6nbry6AUjmPXkQMTZy38mcvKCL8j5wrjRsMdEY86VqUhr7Dt+IaZKDH794wy++emE7jnubnN7+Q4DHI7AZKs1aeXrG5+2NKAxvCG6c6NibAGLunNeDW0BAIIgCKLSsh0Afv5mh69+pqFXzK2bfJ+mM1eOMnZBL6JLFyiPX+jfsa5PgVfNlWqzWgyPXQDK83Rv/GCvx7kvu7eD3WpB4oj5qD7SmA+NRVG3T2ur7giGQLJ0lbx/4G/JqQsYk6nbs99wKXqh1iDvJhZAEGRjbLayv/7v58Tpw/Vpl+3v6RfsMiXEKIqc7EzDxlSSGGP326U7d/kmk1ZDEITR6BV0PwIwhzH2IGPMCgCMMStjrC+AWQDonSdBqBDuHN32vfv4jFkQ8eXOFVFz6QZCflGxJNZqRTHIj69MnVa+bZZLt80SwYmrgVgcjVnsgqgrtkARnbp+FEoLJHZBjZlDBmD+8CHSvuDUnRjweLVr1sC7ixdg4fOT4Sh0YEHjeV77j/3xOUz+KwVOhxMr3nkPF7Kyfc5x123lBUfHn5iE0pLAfYhVEoTqvXmFhYaLuiumPQcASD051e/ohab1rwMAfPPj7+g7ahkJuwRBEERQcM7vDfcafBVAE8krKNTlzE19uxhFJSV+Z+kquZh1XhJrtfJ1tdy57x0qRZHDYYqoCwA/3N9B9fiX3dvhh/s7uAm7iSPmBzXX2bTRyE2f4NOtG0yWrhrv7z+DXcfKn+7q1iI6qFzdmNh4bDiYiQ+/yEVpGdcl6p5bnWotfHceCgoLcPzo/3z2r3XZVbBZrag9KEX6YTj86UcBrXfdnjPSdtfbjMkTVnLurRcDcumKuAq3BfYCCYIwHb2C7igA30OIVyhkjP0LoBDAu67jBgVLEkTlIpw5uv6Iuf6idOn6UxxtZeo0KUJAzrQ333Nz6yqjFjwXYYJL11mGer23+OwmuXWdBj12t2+4IOp6cesGm6WrhbxoWk5ePh4eHVye7LVX1sXM0SNQrFP0j0m04+MDBzF02nSfOWZn/vk3qLXJWZM2z03UHTrrJYxfYIywO2zuy7AnBBYJMbLfA5KoC5CwW1ngCG1BNCqKRhCEGuGIXUh5xB5wATRvqGXp+opdyM8pd9qK4qSci1nn3dy6/TvW9ZoZ+96hUsOzdKuPfFlXtIJc2DUKXxEMwWbpaiEvmpaXk+V3JIaSqGg7Nn6eg6LSUtTWIeoCQGKMnU95qhNO//Kd134lxUVwlJUht6jYCgCJsXY+f/IjAa9VfO2FBXlo3zQe7ZvG4/7kawIeT6TTzVWQGCN8wBGIS1cWuwAA3UjYJcJDaOMWKm3kAuc8g3OeDKALgBcAbHR97cw5v4tzfsHENRJEpSDUsQtlDqcpYi7gWSDN39iFRZPGCXm4KuKuGMngq4DayulTjC+QZrHit/Wd8Nv6Tj4LpEnCr1GF0ny4dZVZukaJumLRNDGCobAo+Jzepo0aSvELC2/w4dT9ScjUtdqteGjUWE1RN7+gAMvWvhP02uSsSZuHhPjyn7+8gkKMSws+TsPpcCL15FQAgD0h2u/YhZH9HsCKac95CLsEQRAEESBhiV3QG7Ug4q/LVhm9oPf6jYfLsGrnP9h4uExqcoFXdOsqC6GprsGE6AUxcsFbgTSRL7u3M3RuPaKuiBEuXaC8aFrV6jWlY8EKxhaLBW9s+QWFJaWoPSjF56ec51anstjoKIx++Db89fsvmv3mTnoYNosFiTF2BwCcW5XKCgrV49r0sG7PGVSpVtPtWHZW8PETjrIynHvrRQCBuXTnLt+E/cfzPYTdoBdGEISh+PWRHuf8E875C5zzZ1xfd5u1MIKoLMhjF0Ip6ipdtEajjF7wx6Urx5u4C2i4c8VzRkcvfD5KEFUPj9UskCaH2eKMzdQFyt26OkRdI5k5ZICh4727eAGuvbIuinL1OXXH/zwJLMqCfqPHocxZ/uGA0+nEjydP4dGxz8FisyC2SoyXUfxn7YKXsHlFuiTs5hcWYcx8496YBRK7ICIKuyLk0q24cIAcugRBhA157EK4i6MZSSAuXQB4vMtlHsdEgVfNgeqtEJrRLt2spc8iN30CctMnoLjMqUvYjbdZDcvUBXyLukZHL4hs2PeXWwRDsNS+/GrMfm03couKddnuzq1ORUx0FJ7p0wyZGe5F4M7++StmP9e/7PCnH8HhdOLc6lSbUet8d+/f2H60BNuPlv8ctW8aj/sMcOoGiyjsipBLlwg1nLOQtoqGcc9oEAShyuaNw8KepeuN4fVSMbjWVF0F0ZTIRWO9Lt0xcxdonpMLloB3Mbd8ESYWSHMWe3Xr1uu9xWvmbsDsGw6UqX8v5QXSJix7w9D4BREhT3dS0OO0aNrEr/7jf54E2BgeGVse+/D3uXN4fqEgmNvjojHmx+e0Lg+KtQtekiIYCouLDY1gMApy6RIEQRBBILl0I03UrZt8H6q27IyikhJdGbpy/HHpikXRcrK13Z/yWAHdazCxQJoeYffL7u0M/yDPm6irjF4wUtSV07/jFUGPcVW9RgCA2H766kScd4m6j3et53Z86jPdSr7Yu1mMWZCKONQalILExKpBr1NEHj9xMSsjIkRdwC2CgVy6BBFBaH6yxBgrA9CGc/6lnoFcxdJKANzKOT9q0PoIolKweeMwxCWOAAAk9+yF21u1xPwUHWJlEJQ5nIgCMLjWVK/9rDYLDm7ZbMicoktXrbhatapVkH0xB7kF3h9LkguWelg5fYrgZr17mSCEGokrToG3XmjsuHqIi8bgmbMFF7KCxLg46ftopFM3PiYG+a7IhZy8PAwcPxFr0rxHJnijZvXqsNgsmHNlKmIS7Rj7k++b6fE/T8KcK1NxMTcXVRMTUSspCQAQkxjjJuamNZqLuHj3TL6sizn48tvvkHXxIqokJMBqLc+ga938ZlSrkuh17jVp8zBw/CTk5OUBgJvrhyCCQXToEgRBhAvO+b0ud123n7/ZgSkP2dDolq4YOGmraXM6HGU4c/hD1E2+z+vfVJvNitnvBVboNPXtYkx5qPwt7ZmDH6Jqy86qfae+flbq+3iXy9ziA+RoHdfivUOleKhtFC4fvxhn00b7da0espY+a6gDVy9n00bj8vGL8VDbKLx3qNTt3Kqd/+DxLpchJ/u8oXm6AJBQpRrycrJxMSsDfdvXwfr9gddQsNmiYbNa4ChzovagFH5udapPC9751amoNSgFvdtWwaZDOQCA6jUvi/7nzGkUvjsPcGkotQalwGmx4f0D7q//4CfvI+diJjLP/40atcqzgK+q1xg3tUz2OrdYKK1v+zrIy8nCxQDjF2xWK2o/NlWKXRBzdHd/mxPQeHOXb0L7pvEBXUsQgcKBCplrG0q8PSrAAFzGGLvaj7Hou00QGnTpfCN27voBgODUNVvYtdosKC0qNUys1cOy0ymaAvLWtWuQ3LMXAGBwyiwkxsX5Ld5qsXL6FAxOMSnOos0Sry7cer234Lf1nYyfd99w4O5lqqKu+H0TX/OMN/+LGU88GvSUMwYLYzz/+lsoKilBTl4+Bo6fhDVpc9FrqPCBxOYV6brH69r+LnRtfxcWvbkGBw5/pfu66IRoDJ48De+nL8bWPfsAAEzx16UkvwTvrUhH5sWL+O6nn7Fxx8f4+9/zgJWpRhys2rQZ7y5K8zn3mrS5bqJuIFhsFqQ0eFHK0Q2WpvWvw/FTvxkyFkEQBHHpIhd1AcGta6awa7NZUbVl56AEW6OJr1IL+TnnkZN93quo6y/vHSpFn9a+i5kFioNzfOslL1eMXchaGlxxWyVn00YjccR81XOrdv4jvebOzWNQtXotbNj3Z9Bzbvr0HzzQvi5ysi8gLycL/Tpcjnf3npVyjatWryUJn76IT6yKLV8V4djhvUgZ3o35I+rG9puIjH//QlR0DH769nOPPnlFxdh+NBclxUX4+89TWPLCsOKcixeK//7jVBWbxQKH0/1+NMpmw4df6svbXb//X3RrEViBXQDY/W2OmwB77q0XEdvX2J8NgiDCj69nrDcDOK2znYQgohMEocLmjcNQkJsuxS8A5cKuGdm6+zdtVHXKGj2HPEfXF9WqlldY9eXUjQjaLAGz2MsLoGnAbHHG5uiKiHm6KbNUM3XNKJIGAC88+RjsUcLnfTl5eZKYCwADx+t7ZE3O6Mcfgy3ahrRGc3X1H39iEqzRVvQdOQYffrIHzjKnatTCe9s+wuDnpmLp2v/ir3/Owck5ouOiMfkvz2KAVov+SJFgxFwAWD7pWRTnleegBVIYTY4o5vrzGojIgjJ0CYKIFDjn93IhKHC7eMysGIbUt4sx+z2H3zEK/uJP7MLU18/qil4IaB0mRS9UH/ky4m3exWIzYhdEEuzR6NPaqhq/oCwmZxTv7z+DKtVqAABysi+gW4toafxA5mneugNGz3gdhSWlTE+RNABIiLHjie4NsPiFoVxeCE3O0S92Y2ifZnxUv1Y48f1h+99/nKoCALHRUR7uAntMrPKQJg/d7ZnzHE4mDesd7iUQlygcLKStouHt3eHjAJ4IoJ3WGpAx1p4xxlVatqxPImPsZcbYfsZYjut8ez0vxnXtesbYKcZYPmMsmzF2mDEWvHWNIAwi1MKu2fhTfG3r2jVujmFvebr+L8SELF1nmU8xFxBcunD6Vz1WN/uGC4XaVAqlKYukGZmpO+upQYiN9nQG5OTlq/T2DmMM65cshKPYgbTGOkXdnyehtMSBguIixGgUQlu/fScAIVt38l8pmPxXCsb++BzSGs1FbFwMNq9Il9rbC9TdJUoGjCsXjuUFyYIh9eRURMXYghJ1AWDdomGGrIcgLnXofpQgPIVd0a0bafm6elAWR/PF1NfPSttqBdICxegCaYAg5tqtFnzpxZ1rNmfTRiM3fYJqpq5YTE6kc/MYwzJ15aKukn4dLvd7vI7dB+CZKct0F0k7vzoVsVE2HPn0I6ZVCG3aM91w4Z8/nLHRUWWF786D2M6tTrUAkIqdbT9agvWfXtC1zv4d6yL3YqZfr00PYuxCIHxxcKdri0WG1Z4gCABeBF3O+ZoAW5aOeUcBaCNr8ueVa0AQhh0APvHz9US7rpsDoCeAhwGcALCWMTbWz7EIwlS0hN2Kxv5NG1FaVKpb1AXKnbq5BQWGiborp08BCgy8ifYzN9fDpdtmiTCGUc5d0a3rRdQFjHXrpj75GOYPHyI1EXnRtIHjJ6HX0BE+C6kxxrBsRgochQ4svEFfLm9slRjY46IxVsWda0+wIzpeEHJF9+6cK1Ox4IZ5sMGKdxb6L572HT4GefkVwDlOEIRR0P0occnDOb8XIXDrmo3cpZtXUOjTFSx36Rop6hrt0nVwrlvMjbdZkThivpS3W33ky0gcMV9qweKtUJrSqWuUsPv+/jPYdaxIagmJ1QAIrt3+HYV82v4d66Jbi2h0axHtU+i9p/dgRNlsqDXI82kuNc6tTkVsdJRDXghNJDHWzgEgNjqKn1udagWEXN3YfhMR229iQMXS+iTXcHMg7z/uv5FCRMzRFTn31otwlAVrQOG7ghyAIAgDCdfzmz9xzr+QtSOyc79zzpM4550ALPdnUM75Bc75w5zzlZzzPZzz7ZzzgQC+gHBTThARhyjsilREl64o6g6uNRVWm+9fK1vXrnETdSOSw+XvuX9b3wmnN/X02r1e7y1gFrsg4rZeCGax47q+u4117noRdVemTjMtgkEkPrbcKStGMcjjCdSiCkSxFwAuq1UTne+8A0W5+h69HPPjc6pRCwAw7sREN6FXjHOwOS263bhKSh3l9+pGuXONZM6r28K9BCIIHCFuhC7ofpQgoO7WrWikvl2MopISKbPXF2ZFLxjt0rUxhtu2HcCNH+zFjR/sxW3bDmj2/bJ7O/xwfwfYrRYkjpgPu9WCH+7vgHibFTZlMYIA0RJ1RaeuUtg1yq0rsungP4hLqCKNL49iAAShV0m/uy9HtxbRkgD82gc/oqikFLUHpXgWXVDh3OpU27lVnu7cc6tSmcuNKxVJyysS7nG3Hy3Bewf8/7kqzM+VtoMRc43m9uQu4ma3cK6DuPSgyAXveCuKFhY4NyX85wIA9SAlgogQxKJpYvyCFkYUUpuQOgtfHPna0KJs/ub1youkRSwWqyTkcmcxflvfCcwWpxnDoCeeIWj2Ddd0Dy+aNA5j5i5AbkGBFMEACEKsIUXTXGNMe201ikvVJaPHxk0EB/dwuorF1QY/9AB2ffYZFt4wD2N/8j+PV420RnMBh/Cn478BirmPjJkgbRsh5ioLo6WenIqJl08PaCyxKNo3P/6OOa9uw+Snuwe9PoIgvEP3o8SliKtoGgcguXTVxF0jiqitmdtDGtuoomz+ZvVOff0spjwUcW+H3cha+qzkrs1Nn4DqI1/GbdsOeHXtKs992b0dbvxgr2Fr8lUoDRCiLHKyz0tuXaMKpgHAB/87h953XYa8nGzV81rFxETht84V1+Leh4bjw3VLDTW3ycXcYIkkMRcA5i7fJBVZc/2O2O5y9xMEEUbC5dB9mzFWxhi7wBh7hzF2tZGDMwEbY6wGY+wpAF0ALDJyDoIwms0bh7nFL2jxxZGvA3LxTkidheSevZDcs5cU7RDoWEbQY8DAsMzrF5+PAncUCHEKAHB4LLiz2KdbV44pBdPiolWLpAGeEQyAOfm68iiG+cOHIC5G0Chy8/NVYwtE967NakXjetfqdun6Iq3RXFi5Be8tWYjNK9J9X6BBQaH+7D09KAujBcPIfg+gaf3rAEASdYmKBedUFC1CoftRgvBEculqOXUDzdtdM7cHpjxkw5SHbG5jV0RHsBYPtY1Cgl1dUAwU0V0riqjFZU6fbl2zSbBHq0YviKza+Y9pMQwAsOnTf9yiGHYdK0LV6rV8X+iiaYs7DVkHUB6zkJhY1RAx1yh2f5uD3CL3++1gcnRlLl2AnLpESGDgPLStohFqQfcigDQAQwB0APAChLyyzxljtQ2cZziAUgAZANIBjOacv+XtAsbYU4yxI4yxIz++c9TApRCEfuSirs1mQUFuulsTz/krxIqOXDXCldubfTHH0PEGp5gkTFus4I4CITqh9ULAWQbuKMBv6zv5vhaeUQyG5OqK0Qsps1SFXTGCQR7DAAjCrlnMHDxAEnVFlNm7IrfdfDMsVgsWNnkp4PnSGs3FnCtTA87L1cLMqAV7QnTAhdFG9ntA2v7mx9+NWhJBXKpE5P2o/F7UJQATRMhRZuoCzME5Z2KDIm/XHzz7V74CS44yJ86mjTZ0zKylz8LGGOJtVtitFjg4h40x5Dv0x3qJ+bryJmbtBoIYvdCntVVT2A1VDIPIhn1/YtexIsQnlAuWotirpPltHWCzWFB7UEpQn4DWGpQCbo0KOGJBTiCF3nyhlqMbG2ULSNSdu3xTxDmHCeJSJ6SCLuf8G875s5zzrZzzA5zzRQDuAVAHQmEKo3gPwK0AugJ4A8BSxthQH2t7jXPeinPeqsnDLQxcCkH4h5ipm5PlKfrJBV+9oq5czFWKxPI+4lfRxSu2CamzpONmuHmVbtKAsVqAOGMdEQCAz0dJebrX9d0t5OK6tvUiRjFI1xuRq7tvuLAuL8IuUC7uipiRrSsyc/AA1SJqSu77T0ckVa2KopzABeaS/BJsXpEecF5uOEg9ORVRMbaARV0RqyVcD9cQgcIRWncuOXS9E6n3o/J7Uc75awaugyD8QszUFZozSu2cuK/XpSvvpza2/LzcySt3Aov7kYoZ7lyRrKXPorhMiHwVc3LjdWQFi4gxDD/c30Fqwf6tOJs2GrnpE/wWdkW3rlls/t85TSH3obuFAnjxiVUxYtqryC0qDtiSV2tQCpjNjg0HMwNfrAy1/N9gUXPpBlMcbdKw3kYsiyB0wQE4wULaKhp+vStkjFkYY00ZY+0YY/FGLIBzfhTALxBueA2Bc36ec36Ec/4x5/wZAGsBvMwY034uhCAqCEpR1xdyMVcpEsvHkUcxKK83I6Ih4ouiKbFY8dv6TlKOrr/IBWDVGAaNXFyfKIVdDXFXXjAtXAwcX56ZO2rQgLCtI5wYIequWzTMwBURBAHQ/ShBBIAUzeBL1JXn5aq4cqVx1OIY5Ofk412KiKKumIfrLUdXjR/u7+C2H2+zurl0q498OSDXrlLYFZta4bRwIcYx5F4sF1//c99AwcE6KCVgp4WT66qr5hdGumA73VwFiTGe0e2JMXa0bxrvt1P3i4M7XVuVz11PEBUR3YIuY2w4gH8AfAtgL4BGruMfMMaCdTMwCAK8WRwBkADBeUEQFZ7NG8sFHbnAquawFfHl+BWRu3jVMn2NimjYunaNIeOIrJw+BSgwMbdKdOq6cnRFcdefPF0RKYZBFHXFr8FEMojCrg7XbqiJjxFcGDl55TeoNar9P3tnHt9Enf//1yRp0/SggIDwxXN3Ue5LORRQFBEVXUUQEVHXRUXkajmkCHIJUuVoKbeCroqKIOqu4oUoKoioiCCX4k8U11UBKdAzbZLP74/pZ/KZyUwyk0zSFt7Px2MeJJO5gIrTZ1/zeteN+bjRpgsSwfDceXCn66d0Zh6cjIDP2jcAi9a8asdlEdUEA3Xo1hLofpQgTCJWM4hSV5uuVQtayaeX+IWq4gHCtuqKB05N7dx95bNKlPt8aDJuYdzOUbhoPIoWT1DELl+i6dP94sYrVf+/8DEGt9MRdR0DF7t8SXG5VFL33j6Nldd66dl4Ig5ku+0KWe5KkoS6ZzWuLCr3mo86Cxz910yUlBTbc4Fxwuf348jzs0PWH3l+NsrWzov6Xlr73zFBxAsGKaFLbcOU0JUk6X4ACwG8AeB2QPU7/RRA/2gvQJKkSwFcBGB7tMcwwZUAigEcieM5CCKhaNO1RglbQJa0Rmh7e0Xxy+sftBUNdpOVu8CeAzkdshC9aok9xzNCR+5aFbtit67kcCt1DH8Z+IFa9kaDTmrXbBL64RXPYMKSlZiwZCUeXvFM9NcgMH3oEOX1PeNyAAD/d3YjJLuTMP/i3KiOmZyWjLvHT4y8oQn4NdnF8Nx5SEpxYebByYbbuNOTMWzWk6aTunt++NGuywthzvK3MHD0Ehq2RpzR0P0oQVhHK3X10rXq7fUlkFrq8s5eeVt1/YP1qgeziLIxFhIhdYGg2OWL1x+ISuqK3bppLie+uPFKpdLBzp7d/l2cOHXCXMfsDZek4tr2Kbi2fQpuuMSmajYEU7olxSeVdRPnrE6qSunaH7WtRq5pVwc9W6fppnNFYhmSZjeSJG2QJIlJkrShuq+FIGoLZhO6YwHMZ4w9AOB1zWcHUJXWjYQkSS9KkjRLkqRbJUm6WpKkcQDeBfArgEXCdtdLkjQAQI+qVVdKkjRAkqTrNcfzSZK0Sng/TJKkZyVJurOqFuJWSZLWABgAYBZjrOaMnSSIGNFL1wLBhK2RpDU6llFvbzyxu3Zh1bRH5L5Yry/+UpdTJXeZz/rv4cJb/4O/DPxA6dgV1yuD1OwQuxrCiVrmC6DFntFosWc0JLfTNrkbTOkGkwwv5c2Dr9yHBS2esHy8cd/loKy0HDv37ovpurTXFAvDc+dh2KwnI8pcQE7pPvHbDNNJ3dZ/+4sdl6gLH7K2c9/PGDSEajuJ0x+6HyUI+9BP2AYHqUEQtZGOo9fZq4MikO0grY4s+czKRjNwqZsxcm7cxS6H1zFYlbpc4O695WpVhYModmOVukWLJ6jWpackRxS16SnJKFk1FSlJLkXuxip4xZTuHVf/HwCgVYduuOK6Qb6icm/UAwreeLEAlZWx/bPOr8cOrmlXB54kF8rWztNN54rEMiTNTqok7g1Vb28Ity1BEEEkZuKxPEmSygHcwBj7UJIkJ+SJvZcyxr6WJKkngHcZYxGbzSVJmgTgDgDnA0iFXOHwDoBpjLHfhO1+qtpGy8+MsQuE7RiA5xhj/6h6fzmAKQA6AKgPearwfgB5jDHTP+l56PAUelaxhjG/vv1TPwnrpGaMBAB0vfQSzJ06Jey2N911D06cPAVAFrdGFQu8FkIc3GUHQ2c8DrhdstRMBJcVQHK4Q+RsrBx67e9gAa8sjqOlqp+3xR75GN91WYZASSUklwNPDvunatOHVzwDye3ExdvVPa3fdVkG5vWHbG+FCUtWAgA8KW68tHA+AGDthnfw8n82YNJ/p1o+3oLmT6CyrBIdW7XA5BHmemXvHjsRRSX63WQrpjxs+RpEhs16Ek/8NsPSPlObzUZluQ/LcsabOj4AdGh5PiY9eKOl88xZ/hZ27vs5ZF++XuSaXi2wZvUDlo5/OnNW46yYn79q3vFc9swnoT9ciSfdMsbtYIxdmtCT1hJqy/0oQRD6VP03h4s7XI97ct403G72/U1QciooatPqNMTkp38L2Y73867fbn+VU/8uzhChGU8yRs4N6cqNlc5vfQyvP4DCRZHvVYzIGCkPsS1ZJd/vnT0iF8XlsgR1OR14e4c6GHFt+xRlW5G0oTNjqmsY0LOpMnzs7a/l85868SeGXNMUnuQk/5F/zbRUv9DoH1MDReVeh8vpxPzntqJZy8jD1e/o1RQnC41/gBBLhy6XuZFErhbPwPGmztuztTJG6e2qH+aYRpC2qn01Mjfq4xOnHy1bt2erX9uU0HNecnGDWnX/bPYnUccAXGDw2cWQEw0RYYzNYYy1ZYxlMsaSGGPnMsYeEG+eq7a7QHy0Rlgu0Gwn8ZvnqvefMcZuYIw1YYy5GWNNGWPX0M0zQdiDWPMQaTgal7n8dY+/98NNd92j2kb73k5WTXsk4UndaFK6kbAtrStw8fbhIQlcvjCDxOjF24frbm8luctTumXCtN3mf40+eTr2wES4PC58vXe/6X2MZG6shOvMDYeVPl2e0tUK2EiI0la7L3/vdDjQptnfAAAfbDL/50kQtRG6HyWIWo+plK4oc/n72fcbh0Tsql0QcVVVFyQqqeuSpKiqF8LxxY1X2lLBIPLHkhyUrJqqm8K9tr18v5g2dGbIfukpySHbWkntvro5VFvUqXsWMuo1LI6mS/fIv2Y6ytY8gZQkFyY90MvUPjVN5gLmqxe69ujDX0aTojVK4N4AAA6Hk3W8/HrttgRBhMEVeRMAwJsApkqStBkA/26QSZLUAEA25G5dgiBOc15fPxz9+i/De+/vVaSumNTladtP/xNsZkn3eFBcVgYgKHb1GDp1FjJSU5GfM9a261017RE5qXvVksQldeMAT/0eeu3vYJcVWEvr9llu+JE2hRsJve33tzYvmacPHaKkdO1CgoTkZOtzGXgal6deY0nnmunMDYc7PRnDc+dFTOmOGjRAuV6z6CVw5yx/C5MevFHVmfvU9EcAyP8dEvGBxkETBEHYA2OsL0/pPpd7kyqly9O2j78S/Ff39S98uKdPE5wqPIqSU0eVbbScOnEU9/ZpjGff+922a33ls0oAclI3ERQuGq+kYe2E1zF0futj1Bs1z1Ja98Kc8HM4/lhifpaB3rZnj8jFDZekhqR8I3HblY2w7uPYK80b3TsVxeVeZGSE76vVklGnLt787Ffc3P08nKxKDUdLLDIXqKpeGBj57zR32WtiStc02l5cSZI2aBO467ZVSEDwvxW9bYgzj9o4qCyRmE3oTgHgBbAHwAeQJwAXQH58zA8g9MdnBEGclojdveJANlHUiq/njLwfiyaMRrrHE/HYRaWl9g1IqyLhSd04Yjmt22c5cKIsrtfkSEuKql93SLb8+OE5jc+GM8kZVY8uAHiLvXg5f77l/cbOl//8Vkx5OGqZG64zd2qz2ZjYZBqmNot8Y20lpWsVvQTuzn0/q0Sv0xF6K0BdugRBEEQNRzWMjS8crbR97r3fUKdqKFY4Tp04iv5dnLandV1OR8JSuvHEalr3wpzFOFYc33vRP5bkICXJZTqpqwxHKzqBO3o1BQBc3/9+azZWoKjMiw07vHjl42OWzl906gQA4N9bDmPznpKo07lGMrfR3ZPhGTgenoHj0eju6EIHdmBQqXBD1RC0kCdXKKVLEOYxJXQZY38CuBTAHABJAP4f5HTvYgCXMcZOhtmdIIjTjNfXW0t2AkGxu2hCMF3K34vCt1ZLXYcTP669RlkOvfZ3ZbELPkjNlNgVZC7vz7UbXsVgVuqmeeTH6EpKy3DPuBzUr1sX9902AN4ib4Q97aFOerp8/rLo+9eAYCr3id9m6MrcynIfVkx5GJXlPtNiN548Nf0RZN01SCV1xc84VLsQHxgAH2MJXQiCIE5nqpJ7liaZPffeb3j9Cx9e/8Knkrt66+wckgYkblAaT+e2euNDtHrjQ6V+odUbH9p2DnFgWiSxK8pcvU5cu/hjSQ58fnM/HF/30S9ITZPrBXj9wYB7xiW5HA40+sdUy0XK6Slu3H5lA9Pbv7wpWPtwU7dzrJ5O4Zp2ddCzdZqhzC2r9CmiuKzSF1Hqmq1diAKlUmH9dr9W2Cqf8ZWT84JPkOkJX+JMQgJjiV1qG6anOTLGihhjjzHGujPGLmKMXcYYm8EYOxV5b4IgTldEKau3hNtHZM7I+2u/1N02GtierSzMVyovAa+tUhcIil0EDO47qwahAfGTuRwrUnf6P4cgNUUOQZwqLgYAdG7XJqbznzhVZHrb5+bnKq/HzV8UZsvwBHwB3YoF7ZCzZTnjsWLKw/AW2zvUXqxLsLJN1l2DVO+16Vzxc0rpEgRBEDWchoAsg7iU1Vv0EOWudh3H7pTuK59VYv12P4q99t4TiBQtnqBavP6AInPj0a3LxW6ktG48ZS4nPSXZdEr3ja3BqoU7rv4/pHjS0KJD95PR9Oge/ddMFBebvxcFgIzM+gCAopOFVk+n4PP7UbZ2nqHM/WBXUNN8sOtURKl75PnZ8PnN+2xJklgk4Sp+zisVJue9JUpdOBxOxj/jiNKXpC5BGGNa6BIEQXD69V8Wl+MmROqWxu8mOgSHU17iNDANACRXqq0D06Ll4u3DDQeqaZkx9C7bzpucnowHH50W1b6829kKvGZBOwCNVyyIMlfE4XKETenyHt1IiIPRBo5eolrue+QZ1XujSgWewnU6HKp0rvbzDzbtJ6lrE4xRQpcgCCJerP/ca2usiid141W/kEh41y2Xu3ZLXcBcDcP5WfYMUwuHlZSuyMkT5qoS7OSVj2Lrab6mXR1kpIS2ROjJXM4Hu06hqDz2J+KEwWiALFyZsBwR30MngQuoU7hamcs/p+oFgkHu0E3kUtswFLqSJH1oYdmUyIsmCKJ6ee/9vQD0uzdjJd5SF05H4vp0t41WDTDjNQx2oqpgEKXu9mzl5fc9nrb1nEY40pIwYclKTFiyUknrPrzimYjJ3f/9Ef1AinEHcuAtT5yk5123Mw9OViQuF7krpjxsONhsWc74sCldsz26owYNUKSullM6HXV60jbrrkFYNXOKrszln1P1AkEQBFGTiWdqT9u3a3f9QqL7dIsWT1C9F6sY7MIordsgXb6nP1YUn2CDlvSUZFzbPgXXtk9R0ro3XJIaNrmbWbcBAoEA/t93u9KjP68bt3WvF+3ulvH5/UoyV+zKNZK5HJfTGXOfbu6y17B5T4lW7HJCiqr1ErgAsH67H+u3GyeCqXqBICITzsY4AEjC0hxATwAXAPBU/doTwMVVnxMEcQYgpnPzx42Myzm0UtdOqm1ImiO+042VgWmi1K0r/xn6C+M7jIJz8fbhaLFnNFrsGQ3mC+DhFc9ETO32GzYSe7//IeZzH/rlv5b3iWYYmsPlUNK53uIK5RhGIle7rx1duqMGDVCGuekNdVs1c4qyGEnbSFD1AkEQBFHD0U3+2YW2fsFOeJ9udQ1JS3PF7570ixuvVD0lcig3+L1C2tD4z1H/Y0kOSlZNRcmqqcqgNJ8/EDa56/WWoaT4JCpKi5wZKW7LHbqAXLtQUhrdULNY4IlcANi8pySszAXsS+kCQbHLF1Hwclm7frtfN4FrFqpeIIjwGApdxlhPxthVjLGrACwEUAmgK2PsL1X9uX8BcFnV+to/spMgCFPEM50rMmfk/XE7tkrq8iXebBsNOJxgvlIlpXvotb+rBqjFiq7UrSYcaUmQ3PI3DE8O+2fYba/s2jmmcyWnJyNn7nzL+2XPs/7ntCxnvDLozOGS/xswK4YjpXRrGlS9YB8Mia1boMoFgiBOZ0SxY3fdghHx6NPlUrfJuIVxH5YGyPULXn8AJb6gs+z81schA9RiJc3ljDgsLRH8sSQHKUkupKckh92uvKwEGXXqQbLhexu/Lz4/BNDicjpVidzNe+yRybEMRstd9pot1yBC1QsEDUULj9l/tR4D8Chj7AtxJWNsO4DpAGbZfF0EQdRw4pXO1cP22gUIUpcviZK627OVIWnMV6oaoGYHKql7IjHJXD0u3j4cF28frlvDIPL6isXKcLRoGXcgBxXeSjyx/GlUVFZG3L5OuvxEXWl5eVTn44POzKRyE0V6qsf2Y1L1AkEQBFFDiWs6V0Ts042X1C33+eI+LI1TuGi8alhaic+vGqBmh9QV6xcyRs5V1idiMJqWP5bk4I8lOUoVg171Qp26DcAYQ2pGvcpohqJx0lPc6Nc1HYd/tHbPdHOP8y2fi0vcSIlcPcLVLlgdjJYIqHqBIIwxK3SbATAqDzoC4G/2XA5BEDWZeA1DM0KsXRg6dZbuEovsXTXtEWVJaA3DttFgAa+6hsHhtK1f98Jb/wMEgjdjLfaMDrN1fOE1DI60JN3Py71evPvxpzGfx53uxuc7d+EfD0+KuO1z83OV12PnV3+amWN2MJoe88eOsvlqZKh6wR4YaCgaQRCE3SQinSv26fIhadolFtH7ymeVeOUz+YfRiezW5WJXu05M78aKO85P81mBVzHoVS9IkoSD+3ag5OSfSdFWLgBy7UJKchLGDDb35FmdumcBAE4WJnYom521C4mCUrpnLoEEL7UNs//KHgIwzOCzYQB+suVqCIKo0SSqboEjdukaoSd7o5G81SF1xYFpXPLaPTQNAPa3LpCXtgnuDdbAe3VF7hr7MD7+8kukZIRO6rXC2AMTkZyeDIdk7ns7ntItKYsupRsPzA5GSzSU0iUIgiDOZLRD0rToid5oJG91dOtqpa5LkmyrXjheEXxqKtXpQNrQmci87zFbjh0t6SnJSko3vY48xOxk4VGMv6c7PMlJOPKvmTEXDCclha944Kz58Dfl9d+7nxvraU9rKKVLEPqYtTIzANwkSdIeSZKmS5I0vOrXPQD6Qq5dIAjiDOHiC85L2LnmjLwfiyaM1l2MZG9RaWntkLpato22rXqBc3HffyuLw5FcbVKXJ3Ult1MldX0+PwK+ALL3T4z5HBXFFZg4zFz3spjSHfNkfsznPp0RU7oNmmRTUjdKfAleCIIgCPvgQ9K0i5Ho5ZLXqtit7oFpdqZ06ycHn87a3LUTvujWBckOqVqlLu/VveGSVDidQXfrCwRw5F/2DG2rqDAfFshU0t/HcUsU1QvxIJYe3XiiGZDGSOyeGVCHbnhMCV3G2BoAfQCcBDAJwJKqX08A6MMYeyVeF0gQRM2hz7WtAAD7fvypei+kCm2CV5S8tVbqxpFmfdbIUpcndqshtXvx9uFgmhRqSp0UW47tTndjesFi09vzlG55RQWGzXpSWcYtWGTL9WgZnjsP7vTwqQ13erJyHYmCJ9sfmPm44TY8pQvISV0SuwRBEER1Myv7puq+BEX0crSS99QJo9ZCY6pb6tqV0v30hh4h6zZ37YRkh6SkdTPveyzhyV0udcWqg4wUty2PSB3910wkOx24rUd9U9u/vOlXJSl8ovAYerZOQ8/WaXGVu9e0q4OMFOMn4448PxueJFdMUrd/F+tB59svT0b/Lk7cfrn+vbJmQBogi12SusQZjcQs9qxJkuQA0ADAMcZYzXs21AYeOjyFyudqGPPrN6nuSyCqSM2Qh6G1/MsFGN7f/noAO5i0+GkUl+kPBMtITUV+ztiIxxg643HA7QI+GmH35YXnsgIg4IfkSpW7cKPkx7XXAACcyXXwt94vhHz+3YabcXHff+O7DTcnvGN3f2t1b+2k/9o3JGPOOTOx6snZqJ+ZaWr7e8ZNxKli/cnA6akeW7tph816Ek/8NiPidhObTMOKKQ+bPu64BYtQXBr8el81c4ql63pg5uNISpFTNJXllXhq6iOG2+a/sAbfHvxBeX/stzxL56qNnNU4K+Yf1/+1wzlszofx6Tk24vb6OTsYY5cm9KQEQRAJoEri3ABAJVNrGv06uww/q1O3IZ597/ew+99+eRJ8/gDS3cn4bf4Yuy8vLPVGzYPb6cAXN14Z03FavfEhAOCLbl1CPuv5+ZfK64oAw8mVj8Z0LiucnzUPx4qCT8WVrXnC1uN7Bk3E21+bH3J3Q0d9iVm3XgO88enPdl0WAKBn6zSUrQ0/s6HR3ZNRVukzPXTtlh7n44SmC3j9dvNJby6Ai7euR+PeQ1DurcArnxn/+c3OvhFff/YOf/s2Y6yv6ZMRtYbmrTuyZ9bZUwFjlm4t69Sq+2fLRZiMsQBj7MjpKnMJggiPmNJdtj564RhPwnXvhhuwJi4Z7pTqSepuGw1sz465T9fhloWmv0L/Ruzivv+Wt3N5qq2GYdJ/p9oqcwEgOT0ZD0yeCrM/rHxufvAGfuHYkaqvm+LSsrgmdu1AK3Ojwe8LYOGhqVh4aCqSUpLCJnWz7hqkEsZ6Kd1BQ56iBC9BEAQRN0R5UxNSukZY7d3VLqkZZ2H9dn+1pHULF42H1x+wrU9Xj81dO2Fz105xO3444ilzOf87/EPkjTRs2VuEuvUbKO/tTu1GSudyisq9pmUugJhkrnb73zeuhs8ffn9NWjdkUJokSRuqahmomoE4rTH+saGAJEmRvuNmjLHqbTgnCCIhvL5+OPr1X4b33t9rW/XC6LzFIY/hSy4HCrJHRn3MOSPvVyV1F00YHTa5q6WotOpGr7RClrqJTupuGw12WYGStAVgKbV7wc3rVfsa0azPGhx8bxD2t12CFruj+z3ub7MEYH5AcqLFtwn+c9Iw7kAO5l+ci1sfDKYhM9LS8PwC/Zv1wWPGq97PHj4UADB52Srla6W4tAzjFiyKOa3rcDkwsck0uNOTMfPg5JiOxYklmavHwkNTMaxh5Gtr0+xv+PbgD0r9gh4fbNqPQUOewprVD8R8XQRBEASh4W0AN+zY+k7EDc1w22XuEInkcjqxbps36mM+95489OqePk1wqlCuXnj9C5/qfTi49K1TtyFKi/5Ek3ELE5rULVw0HvVGzVNStgCQ5nLGnNrVsrlrJ/T8/Etk3vdY1CndzPseg48xuCTJ0jEa1kmL6nyRSE9xY+TtHcAkJ7zlpUhNq4NXPz2mu+3tV6l7lt/69BAA4MYeF+LE8eA+WmkaLUXlXngGjkdGihtHnp+tuw3v0TUjdUXRbFXkcm6/PBnpqcFQRXqqB7dfnhw2pTs57y0l2StJEk9yvF31qyh5Q4QvQZwumBK6CD/0jP/HQ0KXIM4QXl8/XKlesAPmC6D392qx9mGHAoyaKz+aH63cnTPy/rDvjQgRv6UVQJc8YLu+uIob29RVCKyL+cfbf/vU+LF5Lc36rMF3G242vX0IzI+L+/5bFsNVctdMjcPCdvMwZtf4iNuF27/0T1m8p56VqhzLleJCRUnwBrCopAR3j50YInXvzBqPsnL9wRVascvTupxo6hiW5cjXNzx3HqY2m20odd3pyRieO0/Z3gx2yFxOSrobD8x8PGz1QtZdg0LqF/T4YNN+266rNsMY4LNYcUUQBEEYwxjryyXOrOybMCXvzZiO5/P7Qx5Db3T3ZFVtQrSCl4tdo/d6iNKX9/AW+80/wm8XhYvU9yIZI+fG5Tybu3ZC563bo97fxxi+6NYFPT//EmlD5eFm4eRug4xUHCsqxdFT+rVbVjjvgZk4eqoEDeuk4fBTcg7u6L9mwjMoOPC3tOQUbruyEdZ9fES17+1XNUHRyeO6x+ViFwC6t8oAINclcKKpYxAFrXgsLUeenw3PQHP3oXaIZp/fj983rlbe/75xNdK79Y+4X8fLrxerFwBB3jocThYI+CVATuxSLUPtpDYOKkskpoQuYyykmkGSpHoAbgIwDsAt9l4WQRC1BS5dOXZ16169MygERbmrJdYkrxaeFpZcDqQludViVxSqdT3Aew/adl5TOJxK6tYorfvTv/sj4D1p/dBV1QtWU7r72yyBwyX/RJ2nfQO+Muxvs8Qwreus54G/sAylf5ZGLXVFmQtAdSxxvcftRpnXi6KSEvQbZvx1snCs/mezhw9VpXU5ouC1KneX5YwPO/Rs5sHJmNhkmunj2Y3ZlG7WXYOU11zuOh0OPDVdFsFDp84CAErpEgRBEPHiKICGO7a+oxKvl3S7PmbBCyAkvagVvJxYk7xaeFrY5XQitU59VZpXK1QbpHtwKNe+++BIuCRJSeyGS+v2ePtTHK+otHTsVKcjqpRu5n2PIdUp6wpe4dB563YkOyTD4/2cP14Rv+c9MFMRsVbhMhcAjp4qgWfQRMPUb0nRCcOuXECuWzCibv0GqrQuEKxjAOLTtWslpQtEn841wmxKlyP26jocTrZuW4UkrKOULnFaYnkoWsgBJCkbQG/G2GnzHwkNRat50FC0mkekhK4VsTtqbkFIQtcsH3YogL8k9H/00Yje0XmL4XC7cPXO0YbHDaE6xC4AXFYAyeEOkbp6NQu8LzcSXMaqcDiBQDBxu791QfB12yVwOJLRrM8a42MZ1DB83+Np+Avlc0XTozvnnJnKa1d9D3zHQ6s0uKSdtHQlSg2SuOJ2ZtETvFaGmAGRB6RNbTYbleW+iCldLoajTejygWgLD6n/DsZcODPigLRIiAne2jw8zY6haBe2P4fN2JTYOpJ7GjxSq4Y6EARBWEV4zFoXK2L3tsvc8CS5DB9BD0ejuyejqFxf6FqVveJ1hDuuSKKlLqfeqHnwMRYidvVkrt5QND16fv4lSv3qGjaXJMHHGEpWyfcqmffJDwafXPkoMu97DMkOybCLt+fnXxoOXBMHo0XToyvK3HBs2VuEG7tfGDbJGk7m6qGtYwCsS91r2tWJ+DXvGTgem/eE/z1yqWxm0J8et1+ejBR3siqhyzEzIC0SvJaBUdSz1tG8dUe2cu2nCT1nj1bpter+2Q6hezWA/zDG0u25pOqHhG7Ng4RuzYML3dKixco63q3LMSt1eSrWmZasSubGAheyZsSu1fNv7roElaJAtFPq9lkOnDDR81vXA5wow18GfgAgNJl7waAP8dOaqwGYF7p6GEneFrtHYH/rgojHPvjeIAT8FSFS16rQnX9xLiDJHbliOrfDviwAwLfdV4RIXcnlQP7ohyIeOxbGLAh+/VtJ6g7PnYekFFfYLt2JTaZFFMVc6GakpiI/Z6ypc4sMnToLK47q38jbIXV5SpeELgldgiAIu+FC1+l0Ffl8lXWq1m2AkMZLlNQ1otHdk1FW6YsodXkqN1yvqZbz7pumEop2id0LcxbjWHHke1F+voyRc7H3lqvDpnLNCl09tJKXp3ErAkypWoi0v5HU5SldM0K34T+morjcK/8daSoVTubJtWyZ2aH3O1ZlrVVEuWtV6vZsnRZSNSJiRuje0uN8RVZHk9INJ3QBIL1b/5jSv1zoAnibahdqFyR0I2OH0F0AoB9j7EJ7Lqn6IaFb8yChWzNJzRipErocUexaSeqKCVm7MJO0jVYkh4hdTrSC16zMNYHDXRcB7wkAsQldPQ6+NwiBQNWfacAPh8ujm9JVba+RuvtbBys0IgldnsZ1uBxyP25x8O+TC11ALXU77MvCzpb5ltO3VtGmda1I3UgpXTNCd9yCRcpgtGhSukYJXU6sUpcL3Wt6tai1tQt2CN0L2jdlUz9IrNAd2nByrbohJQiCiAZJkphe8i5asRuNWI2EmaRttOfTSl1OtHLXrMw1w95brlbqGWIRukZ03rpdSe+mOh2GKV1AX+paTeh6Bk3E219X4O+dPPAkuZS/Uy5zAeCvU5bjWIlwX+hOQrnPj827rdehWSFaqWuH0OXHAaKvXejfxYniret1P4s1pStWMYCkbq2ieauO7KkEC90rW5+GQleSpGd0VicDaA2gDYBpjLFZNl9btUFCt+ZBQrf2EYvUZT71o1Z2JnftxlDqGmEkezUyV6+CYmPzPCAQ/kbpgkHyjTNP5wL2C11AndyNJHSV7QWpy4Wu2bqF+c1zUVFcgeT0ZLjcrpCErh67Oi0B8/rjntIF1GLXrNS1Q+jy4wDR1y6ES+kCwLCGk6M+tli7UFulLgldgiCI2olW6r7+hc/0vlzscuwUvPHASOzqEU72ih29eqIv4/YJEYd87r0leA8aT6ErJncjCV2+vSh1eToXMCd0G/1jqiJxM1LcukJXj6Y5i2us1I0kdHnCPFKPrh1CF0BCpC5VL9QeSOhGxtRQNABXA9D+y10O4GcA+QCes/GaCII4DXh9/XBF6u778SfTw9P06hFG5y3Ghx0KaqTU7fm5WtJEFLwnytTD1XQw7BMO+BVh+/OrN4L5SlUf889EnMl1wp4rWiIJXL3tv9twc9TnG3cgR/Ve7NA1ot2XI7Cr0xJkFSyNu9QVh6fxxGw4hufOgzvdeDAGALjTkzE8d17EHt14k5LuxgMzH48qpZt11yBF6n6waf8ZOyCNATCvEQiCIAg74Ek8Xs2gHWgWLrWrrUi47TI3Gt09ucZK3cMrgz8gjiR3jxWXhQxX02Ik+XyMKcK281sfo8QXFHguSYLbGTJLPW5EErh62/f8/MuQQWlGQ8y0HPmX+t5TrFwIx6+5I3WrGOzmrU8PKVL3ROEx3NLj/JgHpR15fjY8A83fh97bp3FUPbqAscwFgN83rkZ6t/5RHReQh6dxaSxJ0gZK6dYOGAAG8u/hMCV0GWMXxPk6CII4DRGlrpZ9P/5k+jgF2SMxOm8xNl6kvrmsicldreAVMZPm5TLXKI3Lk7eSKxWSK1WRupIrVbUdr1zwV5ibTJsIHC4P9rdZoqpeWNhuHsbsip+wbPflCOxsmR+344vMHj5U1akbjoAvELY/FwBmHpyMqc1mV7vUXXhoKoY1nIyhU2dFldTVSl2CIAiCSDBvQ2fK/Y6t72BW9k2mqhjWbfPKHbs6cqumpXdFuavFSpLXKI3LU7dpLifSXE5F6rqdDtVwtJrI5q6d0HnrdgBAg4xUHCsqNf3nEQvp7iT0bJsZ95SuVuqG45p2dZCR4o54zIwUN3q2ToPL6TRM6tat1wAnCo/h1ImjUV23y+lE495DDHt0AfkJuP5dnHA5nVEldTtefj1P6d5AUpc4XTAldCVJuhvABsbYnzqf1QdwI2PsebsvjiCI2s/r64eHrOMD1Xhq10wlg1FyV5S8NVHwihjJXl3RG/CrhC0cTvQ+EHykK1z9gl56tyYgpnSd9TzwF5Yp1Qlzzplpqn5BHIhmFkdaUkJSuiLjFiwy3aUbjpkHJ2Nik2mmts3KXRDVYDQzpKS7UV7sjSmpy/t0z9SULkEQBFE96IkbXsewY+s7SnI3Us+u0WAznt4FgCI+NKsGCV4RI9kril6ezvUxphK2LklC0SvBZG+4+gVterfn519aTtTGi1SnQ0npirULnkETTVUvnPfATMsSOFEpXUCWut1bZUTczuf348jLkX+/R56frVQvGPHGpz8rtQvR8MpnFeLwMl14SjfFnYzbL0+2LHUn570lVi+E/ICHIGojZisXngVwGYAQoQvgwqrPSegSBGGK7l2bYcvnB5X3YiWDlb5dLnlHzS3AogmjVYK3pstdESPRy3ylhvULotwFZMGr9OY61DdEYtWBmb7beCKmdHmPbu655r7p0ZO533ZfgTZbhoXdL5Ep3XSPx3TtwsQm0+BOTw6b1DUjc9NTPSguLUNRafwkPh+aNubCmVFL3TbN/nZGVy9E6hwkCIIgEgdjrK+2Y9eK3BXh6V1A7ukVk7w1We6KGIneEp/fsH5BlLuALHhbvfGhIoHL1s5T/hwqAkxJxprpu40nYvWCVfRk7l+nLMf/mxV5GHKiUroikWoX+A8iqvtr9PbLk5Ge6om4Ha9liLZ+gaoXah9UeRwes0PRAgC6Msa+0PnsCgAbGWOR8/q1BBqKVvOgoWinFwd2BB/HGZ3zkkrucqyIXT20w9Vqi+BVJW81qVxT+180D72/H69KLrfYE/x972+7RDm+KHfFIWfaz+xGey4OT+hycZt6VqqqjkHVm1s3DTgh31CHG4zGSeSANF67EG442rBZT2LFlIcjDkYDzA1H44PRMlJTLad0Iw1F0zLmwpmoLK+MSuqKQ9KO/ZaYpEqs2DEU7fz2TdnEjYlLiAPAiEZTatVQB4IgiOpEK3cBa1LXCHG4Wm2Ru2LyVpvKNbs/ANTLSFXkZ8mq4FNYmfc9pvohJxe84pAzcX28EM/nhAQ/mCqhyzty9dYBwFmpbvxZKqe2G6R5TEndzOw8bNlbZMv1h0MckLZ5j36a+Jp2wTkbniRXxK9Nz8DxhscCgoPR6tRtaLlHt38XZ9gOXS2xDEmjAWm1h4tbdWQrXtma0HNe1Sa1Vt0/GyZ0JUlqD6CjsOomSZJaazbzABgEINTGEARBmKAgd7DyWpS7eoPUwqEVwNqKBp7erfFiN+CPSuRyen8/Hpu7LlHeizIXAFrsDqaBeUL24HuDEAhUhIjfg+8NiovUbdZnDX7YeJfS7+twehDwl4UkcMXXC9sJ6ZCPq26srzQ3jAKoeSldLmgdLgemNpsdsU834jkTkNLl8E7daDhTqxcYKKFLEARRkxGTenp1DGbQE8BiTQNP7tZ0scurFrz+gGWZCwSTu0bDtMSBZDwl2/PzL1ERYCHiN55VDW7JgVLIQtcvzH/XpnDPe2AmDj81Fec9EAwW/JFzFwCgZcFa/FnqxbGSyE9mAYnt0o1UuyD24cZSl8ARe3RjGY5mhliGpFFKt3ZBt8/hCTeG8mYAK6sWBmCy8J4viwA0B2A9pkMQBKGhIHcwvt48Dd27NrO8LxfAo+YWYNn6/4QeO3skFk0YjYDXh40XzcPGi+bhww7mhXEi4KnaaGUuJ9LgNQWHE99tuFmWubvVtQ8tdo9AIFCBg+8NiulaAOCHjXfhuw0344eNdynvxWFtF/V+EQB0u3HnnDMTc86ZGbY399vuK0xdB+/SjYTZwWZGzB4+1PS2y3LGw1scPl3gTk/G8Fz9xx05YhI4K3eB6fNzuWqWMRfOxLCGk+F0xT7FmgakEQRBEDWRKsHzttX9uADu19mFWdk3hXy+bpsXr3/hQ1mlD56B4+EZOF555L2mEa3M1UOUtHqU+gOoCDCV6AVk8VsRYOj5+ZcxX0Of7TvQeet29Nm+Q1lX6At2wnok+b7GM2hiSKXC0VMluuuj4dfckfAJKWQjerbNRM+2mTGfzywupzPmr8U3Pv0Zdes1AICoh6OZpXHvIXA5w3fuhqPj5dfzl9SlS9Rqwv3IMR/AvwBIAH4EcCuAnZptvAD+YGZ6GwiCIEwipnbNoK1tCNfJKyZ3rSSAE4FRX65Vkup7TEldrcTV+9yOpC6Xt9pfAaD5da/h4If/UO/w8RPAzTOVSoWQzwD58yp8JgW2lZQul7pSlbgU6zs4ksthW4XDxCbTDKsXzA5Giyalu2rmFMtS1+lyRFW3oMVlgxSuLTBGCV2CIIjahNXUnpU+XjG1ayUBnCiMOnPjgVbi6n1uR1KXy1v+qyh2N53fBgDQ6+dvVfv8kXOXksDVrudoPzODmZSuzx9AujsJ3VtlwOV0YPPuk+jZNjNEBvPPYuWDXacipnQzUty4pl0dVbJXizgcLZ4p3eLSMqzfrj8cmjidkBAAtWKEw/C7KcbYScbYz4yxnyAPPnu76r24/E4ylyCI6oYne/XSvft+/Kl6LqoaMRqyFg0tdo/Q7bs1A0/miojvm1/3GgC14FWE7b+nyq/rpoV+BujLXhOYTel22JeFDvuyILmdkNxO5T1fHGlJUZ3fiEg9umaIJqX7wMzHkZJuvgJ/4aGp8OvI7WjoeeXFthyHIAiCIKobxlhfxphU1cmpSvfu2PqObmL3TOH8rNgE8cmVj6q6da0iylsA6Lx1uyqdCwCPHPlJef1Hzl2KtN03eiDOSnWrPuO0LFgb1fWYTen+mjsSJ/OykeJyonurDKS4nDiZl61azBzHLo48P1vpgw6H1ZSu2YFodsI7dAmitmMqHlMlb603ThMEQSQYUe6GY3TeYjjTkhN0VbUchzOq6gWVqDVAlc6VdB6d4mJXlLlCOpcLVrO0+3IEJLfTlNTl27f7Ui3IIw1Ym7xslenrAYI9uuEwU7sAQLkhLiottVS9kEjaNPsbgDOrcoF36CZyIQiCIKoHjdwFIEtdLbdd5kZGymkzV9yQY0Wx9/u7JAmdt26Pqn5BK29FeDp3e5nxoLJ9oweqJC9HTOeezLNWl8ZTumbgYvfX3OBThk1zFiMzOw8uZ8172umNT39WXt/bp3FczpGe6sHtl0f/fZxQucDT9UQNhAFgTEroUtsw/BdAkiS/JEmdq14Hqt4bLcb/ShIEQVQzep26zBeo2cPRahDR9uk6k4PTc5tf91rIcvDDf6ilLzP56FSU6VxOuy9H6FYomCGSzAWA4jJriWYzPbozD05GUorLVJeuKHWHTp0Vsoii96mpj6C82Nrjginpbjww83FL+4hk3RX8Oho05Kmoj0MQBEEQtRmf31+jh6PVJE6ufBQlq6bG1Klb1+HEpvPbqJb+v+wLqVowg5jOtSpzAVnSpricUffkFnsrsWVvkWHdwo09LozquJHISHGjZ+s0XNOuTtjtxJRu/y7OkEUUva98VoFybwUa9x5i+jp+37gaKe7kqKXu5Ly3qEeXOC0I9yOdmQD+K7wOtzwWx2skCIKICl6/oK1doHSudbjU/W7DzabF7t96v6C81vbkamVu8+teg8PpAXpG6GbVpHOjxZGWhDELFptO6nICJZVhZa6Yzl0x5WHz12MipTvz4GQEfAEMm/Vk2O1EqasHF71c7DpdDoy5cKbh9loWHpqKpJQkDJ06K2qxeyamdAmCIIgzEqV+QaxdOFPSuQDQICPVtmPxQWlW0rr1XHJP8YmAOjjQ/5d9Ies8kgP/l/sCwqHXqxsN0UrdpjmLwyZzb+xxIU4cP2b5eswMRjvy/GyUrZ0XsXpBHJCmhyh67+3TGK98VoHiUmuBCDukLodSukRtxbCFnTE2Q3g9PSFXQxAEYSMFuYPRsaf8T9my9f/B8P5/x+i8xXC4XZTOjQI+RG1/2yWqLlyHyxNxaJq/4pQidbVVDLxL96LeL+L7jXcicOVEuX5hs0YWGg1KiwJeo6AdkpZVsNSwH3dXpyXKkDQ9Ji9bZTmdy1mWMz6iqOWYEcVin67IuAWLlBtmXsvw1NRH8MDMxzHmwplYeCj8JGrOwkNTMebCmagsrzS1vZasuwZZHsZWm2EA6FEmgiCIMw/GWF8+MI3XLtx2mRueJNdpn85tWCcNR0+V2FK5IMIHqWXe9xg6b90OAEh1OgyHpr3X5RJlu/6/7AMQKnd59QIA3Hh4L87OfQFOAP/T1CwAsVUtaPk1dyQys/NU63q2zUS6W/9etGnOYpT7/GEHoYkyd/Me8/fNZgajAYBnoLkhzmL1gsgtPc7HicLgNfKuXZfTica9h+D3jatNHd8OOl5+Pe/TpZRuDYVaxMJjqnRFkqRnJEnSze1LknS+JEnP2HtZBEEQ9iCmdEnm2kOL3SPQYs9oZQn4jesYxNoFf8UpQ5nLuaj3i3Ja15EcmtYVZG4s6VwjsgqWQnI7QzpzAetVC1bSuYlk/thRWDHl4ZBahtTkFMvVC+XFXjw1NUKimiAIgiDOcBhjffnrWdk3nTFVC4dXxj7wNRy8hoFXMZhJ7Z4I+MPKXAB467xW2HR+GyTrpHVjrVrQQ9un6/MHVH25IsXeyrAyV6xasCJzrVC2NrYhd298+jM27ylRXV//Lk74/H7LKd3i0jK88ln0o57ElC5B1EbMtmj/A0BDg88aALjHlqshCIKwmYLcwcprxtgZJ3O/7/F03M/R4tsRCPj16xjE2gUR3qNrxEW9X5Sl7pUTg4vAzpb5+KbVopiv3ZGWhKyCpWFlLhC5akEknjLX7HC0SGhrGYpK7U3PWOHM6NFN7EA0GopGEARR43gbkIejOR01b5BVbSeajl3eo2vEW+e1QrLkwNm5LyiLmM7NzM5D/ez8WC9dVb0QLp1rhmiqFqoTvVoGK126sQ5HE6HahZoJg5TQpbZh5f8mRt8dNAYQ3TOmBEEQCYCndOFn2Hn/+uq9mASRVF+Wdf7CxPzz3OLbEUpaVxS7P2wMPqomDkQzA0/ravdpe8VqtL1iNSRHEna2zFeWaARvuy9HQHI7w8pcICh+zTBugfXrGJ47D+70yDekZoejmWH+2FFwhKmQiESsw9HEHt0zQ+oSBEEQZypVKd23AcAfiG4wKxGZSB274kA0M/C0rnafd+u3xLv1WyIZEjKz85QlWsHLpW6Ky2mYzjVD3fpBQXpLj/Mt7XtNuzqme53NDkeLBE/rupxOZZ2VlO7vG1dH7PONBA1HI2ozht/JSZLUT5Kk5yVJer5q1Qz+XljWAVgFYEdCrpYgCCIKCnIHK1L32MeHqvlqEkPPz43lZDxRxK6vDD9svCukYsFOWndbqcjdtlesBmPR3dC1+3JEWJnLt2G+8N+ApXtkiV5cWmZZ6gZ8Acw8GH4QBcdOqRvwBfDEb9E9Ernw0FT4I/yZhCPrrkFnzHA0BlBClyAI4gxHrF7oN2dldV5Kwjk/K/Z7FrNo07p9ttuvKm4v/A4A8Hr95orcfbd+S/gNM3CR+TV3ZEwyFwDe+vSQInXFnlozWKkCMTsczcq5G9aJ3N8bD2g4Wg2GAYEEL7WNcNGc8wD0qFoYgPbCe760AfAZgAfiepUEQRAxIlYvnCkp3WpFcqpkrtlUbiw4nCm21DBo2dVpCXa2zA87EA0AZg8fqry2ki4wm84VmXlwMgIxyFSj845rbj5xO+bCmXDGkPAFZKlLEARBEGcQbwPAuzsPVPd1JAQu6ewejGaGkysfRak/gEKffWNJ6zrkJOlJgxCBBw7bqhhEmuYsRmZ2HlzOyPddb31qPbxiJZ1rN/zc0XQup3frb8s1UEqXqK0Y/ovAGFvIGLuQMXYhgMMArufvhaU5Y+xWxth3ibtkgiCI6BBTumeS1P2uy7KEn9NZNygKHU5PmC2ts/uTIdi7bXjI+tbdVkJyJBlK3W9aLYpK+AZKKrFw7EjTHbpWGJ47D0kpLsw8OBlTm83GxCbTMLWZuXRErH26Yio47axUAEDxnyVhpe645o9jWMPJGNZwMsqLvfD7AsjKXRD1NeS/sCbqfWsTlNAlCIIggDMvpStKurNH5FbjlYQOP4uG9ee2VF7zlK4IT+wmQzKUupnZeZbPW+ytxJa9RWEHosUCT+c2unsyPAPHo9Hd5p4ay0hxx1S7cE27OvAkuUKSwRf0vddwnwv63ov0bv1VMvfePo2jvgai5sIAMCYldKltmIrWVMnbXfG+GIIgiHhSk6sXPuxQgI0XzQtZPuxQEPWxOIGSSuxvXZAwsft9j6dV3b2BQAW+33hnzMd1JgdvGP2VRbpil0tdvW5dxvxhha8euzotiZjM1UMcOBYJb3EFJjaZhspyH1ZMeRiV5T5TUtfO6oWpeyaqpK4e45o/rvtZUWlp1FL324M/AABcMSZ9CYIgCKIWoaR0a5rU5UJPXMzKPaPjcMorfQmVumLNgxNAr5+/xY2H98Z8XDGle93xfYZil/fr1s/OVy2AfQPV7MLldMIzcDzKKn3YvKcEZZU+U3/vR56fDU+SK+o+XW3Ng5LoPnHKUOoeOxFa53bqxNGope7s7Bvx9WfvVL2T7ItzE0QCcFnZWJKkegCaAUjRfsYY+8SuiyIIgogXBbmD0bGnnBbYef96dHjankd19PiwQwH8JRWmtpVcDiyaMDpk/ai55oUuPx8/1qTFT6O4TBarc0fch4dXPIPvuizDxdtD061m+K7LMgRKKlXrHGlJIcdTDWL7+AkAQKDnI/h+4524qPeLUZ0bAJpd/S8AwMEP/6HUOfgri7B323C0uiwoq1t3C35ztPuTIYrgdThT0LrbSuzZeh++abUI7feOCnu+XZ2WgHn9lpK56R4PisvKTFcuLMsZr7tu2KwnTe3Pk718e4fLoXtMM0zdMxETm0wDIMvb+QceUX0uytwLvx2FQ20WIS3FjZJyL4pKY3uU8vdf5se0P0EQBEHUFhhjfau6Om+Id/VCo7sno6jca3p7l9OJ179QO61+nS0pAzS6ezLKKn3Kcfj+aXXOQsmpP3H2iFz8sSTH0jE5Z4/IRXG5+t46PSU55HjnZ81T1Tz8L0ce0vt/uS/gxsN78dZ5raI6PyCndPv/sg8nAnLtwknmx+2F3+GVehertnu9fnMAQL/jBzTrWwAArju+z9T5muYsNlW1EAsf7DoV8r5na3OdtlzINrp7Mnq2ToPL6Qw5nhEupxON7p6sHOPwyhk4775pOHqqRFfcipK37I1F8NwyCnXqNcSpwqOK1H32vd9NnZsjylzGAkmWdiaIasbUv86SJKUAeAbAQABGOWSnwXqCIIgaRfeuzbDl84NxTel+2KEAAa9PV9LafR4AuHrnaPhLKlTnmzPyfpUQfnLYPzFhiX4ShMtaraAVJa7kcmDuiPtU+2mP932Pp4NvqmQuAGDz47ZIXSBU7Poriwy3bXvF6pB1ZqUur1oQySpYCuYLQHI5dEXv7OFDMWbBYpO/E3sQh6lxIasHT/Iuyxlv2NubdlYqSv4sDUniijUMF34b/DObNPgWTHnmFQDA0KmzkJGaivycsdH9Rk5jGAPVIBAEQRAKVVI37v9jKCr3hgjaeCCmOcsqfVi3LSiRL+l2PXZsfQcnC4/i/W/KcW37kGyYAk/wagUtF7kupwPvf1Ou+uyGS1JDJLEoc/+okrmALHbtkroAFLFr1KkLBMWuFg8cqJ+dj+N5WWHPxesWRHq2zQSAuFUwAKGyNRJ8OzGVrUfP1mnYvEf/aTBAlrp6x7ig7726kve5937DPX2axCR1AYBkbs2Ebp/DY/bHbY8C6AngHgAvABgBoBzAPwA0ATAmDtdGEAQRF8SUrlhN0ODKCyMmds2mbiWXAwXZsU2qHZ23GM60UOkmXgOvA9h40byw1QDTn1mN6f8corzXpm25rH14xTPY37oAjjT5noZ5/SESV0RyObC/tckUcZXUPfDurXA4PVGLXTGhGy1mpK4jLUmRs/zPVnI70X73aOzqtARjFiw2FLsAMG7BIswfGz4FbITD5cDUZrNVsjZWAr6A0rsr9ueKiCldjlHVgpSWhEefX6ekdIFg/UIkqZuVuyDmVC9BEARBnA6I8uq6Ds3x+iTj+y6O2eStyxm/zJV4DeJ5RJkLAFPy3lSlfF1OhyJgtYlbnkRNGzoT6SnyPbCRyOW8vaMUN1ySirShM0M+E2Uuh0vdXj9/C4/kiFrs9vr526j247xevzn6HT8QUeqmu5PQvVVGyDoAynqX06Erd3u2TkPdeg3wxqc/W74+Kyldq/DjZqS4wwrjC/rei582PAtAXbVQ9oZcn5bhceO2y9xYt81rWere26cxTp04GutvhSCqFbNCtz+AmQDWQBa62xljXwN4VpKkdQCuA/BOmP0JgiBqFDylK8KHpYlSVytwjaoR7GZ03mI43C5cvTP0XNokbjh4BUBJmXwTzAWsXtoWkFO8gJy8lVwO5b0R/PPpz6xWzgFAnc4V2SwnPbnYBWBZ7sYqczmRpG67L0cor3d1WqJax3/d1WkJsgqWqqSuWLsQrdTlKVqrUlcUtiumPKys54lcXtFgBl67oK1a4Fzw+YM41GYRJt19CwBgzktvmKpf0MpcqfbNH7AMA0ClbARBEISGtwHcIK7gvbqi1NWTt3rVCPHgtsvcyEhxh6zX1ipY4e0dpbi2fQrShs4MK2p5ktfoc+0xAeC2q87FycLIkk6sYOBi1orc7f+LubqESHCpm5mdByckXbH7a24wIJKZnYeTedkh22gHrdWt3wAnjh8DAJwoPBb19fFu3UjiVYQPSvP5/aokLl/Hj+UZON7wmA3rpKlqF7RVC5wjL89DozvGW5a6JHNrDwHDggACMC90zwOwlzHmlySpEoD4o5pnADwLSukSBFGLKMgdrHo/OuclpYZBTO0CQMu/XIDh/f+eyMsD8wVw9T61tBU7cqOF1y5EErV8WzOEyFwzbA4+wm+liuHgh/8IWadXq2CW1t1WYvcnQyJuJ8pdEW2nMCDXLkxetspSl64e0UhdLmy9xcEfQgzPnYekFJdyjEjHEmsXjKoWODyl+9jdt4XUL5iFMWDQkKewZvUDpvchCIIgiNoOY6yv+F7s1dU+cn5Jt+sxJe/NRF4egKqhVS+rf0jPZa42iWvEPX2a6K43I2qtoJa5Ev7IiXx/9z8hwXt27gumz8X7cznv1m9pel8tvJLBTKeunsxtmhNa9fXWp3KtnDbZaxXehWslqXvk+dnyDyH86j8jn9+PsrXB77HE11rE2gWjqgXlfC/Pg+cW+R5VW7/Qv4v5hLokSRu0/00SRE3HrBX4E0B61etfALQTPmsAwPw4b4IgiBpIQe5gdO/aTPezfT/+lNiLMYAnc81WOYhD0UTCVShYRVfmGqVzjdj8OAKBChx491YcePdWfL/xTsNNxXRu2ytWxyRzOQ5nCr5ptSjyhgK7Oi1RUrt6gn328KExXxcgS93Kcp/pVC0gC1t3ejKGzXoSw2Y9qZK5Zpi6Z6LyWq9qQeSCzx8E3E48+vw6AECaTorHiFn/vB0XnSN/k/fBpv2m96ut+BhL6EIQBEHULqpk0tt6n+3Y+g5mZd+U0OvRS+dalbkAcEonMWtG5rqcDtPDwEKTudb/P+iEXKPQ6+dvcePhvab2ebd+y5hkbiw0zVmMzOw8FHtDwwV2w/t0zXLk+dnISHGjZ+s0ZdFLeoejYR1ZIutVLWjh1QuALHXr1Gto6hyvf+HDJd2u529vqPqhClGDYCyxS23DbEL3cwAdINcqrAfwmCRJGZCfIBwHYEt8Lo8gCCJxaFO7AJSu3WXr/wMgVO4mKr37YYcCy8lcUeZalbhmtxdl7pPDB+PhZS8B108FSr1Aqht4J7TPTBcxsXvlRN1NxHSuMym2xIGI2SFpnF2dloB55dSBUYfu5GWrbLu+ZTnjMWzWk5b2ibV7l6d0OXrpXA6vXgDkIWlWuPvaK5RUL6V0CYIgiDMdvYQgT+7u2PqOqouWE6/0rl461+qQNTGdazWRy2sUIqGVuV8cKETn5vWUxG16sgv/b+wdEY9jNq1rV92CFrND0jh8WBofkBZPounTNVvRYITRcDTdcwkpXUCWumaZkvcmZmXfhB1b3wE0FSgEUdMxK3SfgFy7AACzAPwNcqeuE7Ls1Z8IQxAEUcvhXbtGKd19P/6EUXPloWDxkrsfdihAwOuzNGRt0uKnldd2JnJFpj8TTMc+ObxKhjsdgLdSlrtPrQG4nI0kdwWJ63AGH/owGoDmrywKWRcLZqUul7lGg9A4esnoWIjHkLRwTN0zEc8OWY0Dmw5G3jhGLjqnCb7/72/4YNN+NGgS+ijhNb1a1HrRy0CpWYIgCCI6GGN9udTV+1wreuMheHmPr5Uha/zR93iiJ3O1ry9veRbOzn3BlNjlItcjqUMU/X/ZF1KzYDdmh6Q1zVmMYm+lkl7WG4YWD3hKN1ZRawXepWsGcUCaVcTBfZIk6d2wvU11DImHQQJj1KEbDlNxL8bYV4yx16peFzHG+kOuYKjLGLucMWZ9bCJBEEQtQFvF4HQ68PXmafh687SQiga7qhlG5y2GM02e7BuNzAXsF4pajHpzn3xgEJ58YFDw9fDBsuwtjXBz9fETgCR/kxDwlyn1C3YNQDND624rITmSLNcv6JHusbeJaFnOeFUvbiK4d3Xk7jkO79KNhruvvUKpXtDjTKhjqC4kSTpHkqRFkiRtkySpVJIkJknSBTrbtZIk6TVJkv4nSVKJJEl7JUkaJ0mSS7NdiiRJcyVJ+k2SpLKq416hczyHJEmTJEn6SZKkckmSdkmS1F+7HUEQBCGjrmOQfIwxicmmI6SioSppGBNi3YI4AC3aqgW7+3IBczIXAD7b9ye+OFCIcl8Af13wcthj/lGV0i1jAaV+odfP38Zd5nJer98cyZBQPzs/Ieezwge7ToUM54s3h1fOCNu1K3Lk5XnwJLuU6gWrCNULelAdA1EjkViMiRFJkq4BsIAx1taeS6p+Hjo8hWI0NYz59Y2/2SdqHwd2nH5TRXk1gx0p3VFzC9D7+/FRy1x+DMC+dG64wWdKOreKGc+uR4nNN3zn3/kxAOCXV29GwHsCQGzD0MKxZ+t9YIFKw6Tuzpb5WDg28t/JmAWhQyoAID3Vg/ljR2HcgkXK0DS+zgg+2IyTiKTuzNZPKLUL4SoXOD91XQ54/Xjs7tuiOt/z73+C7/8bfEROcjnAfAEAwLHf8ox2iztnNc6KORrQqG0TNuAte3qVzbLs/Nk7GGOXhttGkqSeAF4BsAPyU1fXAriQMfaTsM3/AdgF4FcAjwM4BqAXgEkA5jLGJgrbvgigL4AJAH4EMALA9QAuY4x9I2w3G8B4AJOrzj0IwP0AbmSM6fZHEgRBEJHh6UIrtQh69OvsUkSaZ+D4qI7HE492ytzQrlwZUeACQJ/Lm6Hw+DHbzsvZ0r4jAKD7N18DiG0YWjj6HT+ACrCwSd3M7Dxs2Rv+iTWjoWh16zXAG5/+jFt6nI8ThcdU68IhduEmKql73n3TlJSuUYeuSKM7xqOswtoPH0SE+gU4HE4WCPiV+0BGcdGE8reWl7IFz29P6Dlv7uSKeP9ck4h+VHqQTACtbDgOQRBErYWndWNN6fJ0biwyNx5Uh8yVXKk4/86PdWVuPImU1HWkJSGrYGnE4xildItLyzBs1pOKzOXrxi0If5PqLa5AZbkPleU+TGwyzdKgtGgQO3TNwAekTXnmlajSundfewVm/fN2zPrn7XJftDv4WOegIU9ZPl5NgqHGDkX7hDF2NmPsBgBGf2k3Qh6AO5AxtpYx9iFjbDKAtQDu5htJktQOwGAA2YyxpxljmwAMBHAYck0X364RZJmbyxibxxj7iDE2DMBHAHKt/LkSBEEQ9sPTuY3ungzPwPGWahbiSaJlbqrDoSxb2ndUZO6Ne3bHfOxI2JXUrVu/ge76E4XH0LN1miJz+bpbepwf9ngupxNllT6UVfrgGTje0qC0aBBlrll4UrdfZ5du53QkpuS9ide/kBPpDgkkcKsTBgQSvNQ2rH+FEwRBECEU5A5WUrqx4i+pgORy1BiZK6IVuCJamfvCE7OU13dNngrJ7UTjzyJ3of7WYTkAgAW8+PnFKwHIcpf5goIxXulcTrhO3XZfjsCuTkuQVbA0bJfu7OGhiczJy1aF1GGkezwoLitTCV4ty3JCh0JYHZQWLWbSuZwLPn9QSepGYsozr2DWP2/X/Yz5Arhw5wj8/tB/UPbpz1S7ECcYYwETmyVX/artPzkBdTDg7wAqISd++fF9kiStAZAjSZKbMeYF0KfqmNr/iFcDeEaSpAsZY4fM/y4IgiAILbOyb4qpR5f35caa9LUTo3oFEa3M3bgrGEi4vkMqUpKc+GVG5HvRhpOWojQQgEuSfR5P5KY6HCgNmPlfZ+xE6tRNdyehZ9vMsB26b30a+r/TG3tciBMa4V23fgOcOH5MJXj1+GCX+lbA6qA0q4gy10w6l8OHpJn5+u3X2aW73W2XueFJduHIy/nKwDVJkjZQly5RkyChSxAEYTOj5hZg0YTRUe1bEyWuGeySuQDQZOeDIeu45AXiL3M5rbutxO5P9Htk2305Ajtb5ls+pp7kBYzrGaqLma2fiLyRDTz6/LqQioZHn18HKS0JANB46d9xqI18Az9oyFO1djgaT+jWUtYBmAZgsSRJEwD8Cbly4S4A4k+xWgE4xBjTRrv3Qha4f6t63QqAF8APOtsBQEsAJHQJgiCi420AN8TSoxvto+qJIhqZCwC+QAC/zBhu6hxH5+j/wL5ezhLldbzqFkTCSd1fc0eiac7iiFJXi57kBYL1DLf0OD9i9UKisSJzOWaGpPG+Xb3tfH4/jrycDwC47pKWeHfHPsBgMCERP2rv7XNisKNygSAIgjiDsVPmGuHITFZe7/5kCPZstacbOOJ5nSkxVy9YIVLtQjRMbTYbE5tMs1TTYLVuQURbvcAXXaq2E2samC8gH6MKT9Xjf5TStUwDSZK+Epao/iNkjP0B4DIALSD34p4EsB7AE4wxMSZeH4Ded9nHhc/5rydY6BAH7XYEQRAEYYpIydze7VKQ7k6y9ZzXHd+HfscP2HpMPcLVL/yaOxI+vz2JYV7PYKZ6wSq8voMvZqoazrtvWkzn1A5Ju+0yd8jANJ/fj7I3FikVDeK2GZ7gtq8/GvxBAA1HI2oShgldSZL+YvIYjW26FoIgiFrL6JyXlNfRpnNrG3p9ufGQuX/0fAaBwmAdQYd9WVGlY6MhUvWCXdch1i6MW7Ao7IA0LRObTMMTvxnXfXiLK7BiysMA5OFqU5vNDjtUTUznWqlbEOFC9qeuwWS1KGwBQEpLUolbnsTVIqZ0GzTJxjW9WtS6pC4DUA0PrR6zY6iDJEkNAbwGoATAAMgJ3asBTJEkycsY418wEuTfasghdN6b2Y4gCIKwQJVougGQhzmhhvy7ettV52LdR7/E7fhmkrmFuSNiPk+zx55RXp/MywYgDyZLBK/Xb47rju/T/cxM9YIZ3vr0kFLHEKl6QQsXtEaD0orKvdi8J1ifYKaqwWp3rh5HXp6HRneMR7/OLkXQir26fN2Rl+UBgI3uGI/bLnOr0rk63EDVC4mD1Yx/xmos4SoXfoD+DbcWoxtzgiCIM4LROS9hy+cHAQBOR8168GHCkpWq92meFEz/p36NgBkeXvaS4WfxkLkAQmQuAEiSE7s/GQKHMwWtu6002NMewkldntLlXbpZBUvBfAFILkfYfl0ts4cPVfp1w3Xpqs7tcmBiEzm9EEnSjluwCMWlZUhP9aC03KvsBwDu9GTVvrGkc7WIwpbL3Qt36n9TJaUlYcozryh1CwDwU9ulYMwP+YEiWQRTUjfhPAzgAgDnM8Z4AnezJElOAI9JkrSKMXYMcsL2PJ3961X9elz4tZ4kSZImpavdjiAIgrCGInPXf+6tdgtSp15DnCo8ipOFR3Ft+xRlfWa9hjEJ3s7N6xl+pk3m+gIBW5K5zR57BsdKQu/PnJBw3fF98MCB1+s3j/k84fDAYVi9oBXLPdtmAoBlyfvWp4eU6oWerdNUElYPl9OpDM7z+SPPT+Ail+/HyUhxG8rgaOoWRLis5XhuGWV4TC6AtZoso99opLmcuLRhPXz82zGAqheIGkI4oXtvwq6CIAiiFsNlLgDkj6v+DtxJi582/KykrNzws2gRRS5gr8wV4TIXgCJWv2m1SOm5jafcNZK62gFpzBdAh31Z2NVpidKLu3DsSKWaIdIQNStduuKgtOG58zCxybQQOcvhkri4tExJ63KMhqtFm841QpS7Zj9nzI923Z5T3u/aeo+t15QwWK3u0G0D4AdB5nK+AJAEuRv3GOQO3H6SJKVqenRbAqhAsDN3LwA3gL9C3aPLywj1I0gEQRCEKWqCzL2nTxOcEoaYiZw0WB8r8UrmAlDJXJ7OBaDI1frZ+XEXu+H6dMWUbs+2mUhxOQEEe3FdTgc27z6J7q0ysGVvkW3XJA5Ju6ZdHUXSagVtRopblcpN9HA1kUiCWCuAM/qNhtvpwI5bewEAmq99HwANSEsEDECg1t4+JwZDocsYe87oM4IgCCJI967NFKm7bP1/MLz/36vtWiYtfhrFZcGbzpn3DFReT31uLQBg+jOrVWI3Ump3+jOhQ8i0EleFL4DGO8wNnYjEHz2fCfu5KFe/abUIe7beF1epqzckTZS64joA2NkyH2MWLIYjLQmBksq4XBcQlLtGclZk2KwnkZ7qsVTrUNOozQPSaiG/A7hckqR6GqnbperXX6t+/Q/kIWm3AXgOACRJcgG4HcD7jDHez/IuZMF7J9RD1YYA2MMYo4FoBEEQMTAr+yZMyXuz2s6vJ3M/2C3fm17T1gMAqsQuEDm1e9tV54as0wpckes7pNremQuoZa6IKHb7HT8QV6mrV70gDkjz+QP4dd4Y5bPM7DykuJzB5K3JeoZI6VwtXNJe065OyGcpyS4UCTVtPVunoW69BsrwNZfTiUZ3T1YksJjerW58jGFPlcwFgCubNKCULlFjCJfQJQiCIExQkDtYqV3Y9+NP1XotRjJXRJvSLSkrV6oZtHJXK3/DilxUpXPT7LuBFusWItF+76i4S10+JE2vT3dXpyWQ3E7VejFVLKZ2rVYyRMuzQ0JlPABVrYNY3SDyU9flEVO1iSajXjsUFe7CB5v2k9S1CUmSBlS9vKTq1+slSToK4Chj7GMAyyHL1/clSZoLuUO3J4DxAF5njP0CAIyxbyRJegVAviRJSQAOARgO4MKq/VG13RFJkvIATJIkqQjA15Cl79UAbo7rb5YgCOL05m0AN+zY+k61XoQoc7nI5WTWa6ib0BVrGbRy97arzlXtk1mvIV7dHL6ywRcI4JcZ9oQLxO7cSBzPy4p7Wjdc9ULTnMWAS30vqpXQTXMWo3urDCW1G2/Ou2+abh+u2NP7wa5TqoRvTWZFj46U0iVqDAkVupIk9QTwkc5HJxljdau2yQAwDcClADoCyABwFWNss4njXwRgBICrAPwFQBGALwE8yhjbFfNvgCAIwoCC3MHo2NN4MFU80KZxRfRkbqrbjVKvV7VN7pp/q9aJclePh2bOwdKpk3Q/s7tqQUznimI0HPGWukYpXSCYyjVC/DyaYWq8B1dEL2mrJ2cBYMFo+brHFqglr1jdoEr4ev041GZRyPAyKwQ7cGUkyYkLdocX2eI+Dqc6wfOXlmPx474FtU7qMtToyoV1mvc8av4xgJ6Msc8lSeoBYCqAhQDqAPgJwEwA8zX73gtgNoBZAOoC2AXgOsbY15rtJgMoBjAG8nDd7wAMZIwlJFJG96MEQZyOMMb6SpKU0P/ZhKtW0MpcAFj/8WH0v/I8RdB+sLtM9R5ASOeulkiVDXanc/W6c8PBRWu8BqaFq174NTdy9RvfJtrru6XH+SoZKyZtASC9Tj2cKDwWImclALsO/g4AaNussXIsvi9P+IrHdzmd8NwyChked0gVglky+o1W3YO5JAlFrxeY3idNI8gBdUqXpG58qbm3zzWD6krojoZ8Y8sRhz+fBeCfkBMbGwHcauG410K+eX6uav+6kId5bJckqRtjbEcM10wQBFFjCCdzjcgZFBp+E9dp5a4Il8FFJSW6UtcOmfv75U+BlVRCSkuClOyylM4V4VI3XkPTjFK6kdjVaYlSuyC5Ig/P0xO4WopLyzBuwSLMHzsK4xZYGxrB9xPfc2b983bl9ZRnXrF03HAwJktijjxXCyrp63CmoG1X4zSMVuoSscEYi9i1yBj7HCYeLWSMlQEYW7WE284PWfqGj/zHH7ofJQjitCQRtQvhZG441n982PC9Vu6KfLC7TKls6N0uJSSpKw5B+2VGdPej5057CsXeSuUYYjrXqG7BiHgOTDOqXohE05zFKPbK96Iup7lBzlqBq+VE4TFFzIbbNs2djI7N/w9fH/gf6tU/C4XH/1TtKx6Ps26b/H1Jv872aSsfY/Dcor6Hd0mS8hkgS1yxZkHLih4dMezTr6l6gah2qkvo7q/65kCPnxlj9QFAkqRrYO0Geg2AJeLUZEmSPoScJBkD4O7oLpcgCKLmYCRzjSoWzKInfEW48C0qUT82ZZvM9frxwhOzcNfkqWBC36zZdK6IODTN7rRuuJSuEbs6LQHz+rFwrPmheUYylydtH316HUrKvIrUFbe//uAE1T7vt1+IcUtfwvyHBiPN4zbcTw/J5Yi6fkGbxuXpW4czBW26rsC3nw8DgLACV4+/tBxbqwakMbCanNA9k6H7UYIgTjcSVrugJ3P1UrlW0MpeLWJlgyh+r++QipQkZ0w1C+dOewrllX5s3FWO6zukol7OkqiPBcR/YJpR9YIRTXMWo9znNz0U7ZYe5wOAoaDdffB3XNmllUrMitumu5PxW77657sZw3MBAB9v3xt2XwB4/Yvgz1hdTica3TE+qpSuNo2rTd/uuLUXLnltEwCElbhaqHohMdDtc3jM/VgmgYg3v1Hse0y7P2PsJIDvATSN9doIgiDMsGz9f+J2bL2hZ3yJN3rCN1aZ+1s7+WaZlVTihdkzAQAZbo/yeTQyV6T93lFggUrs2XpfTMfRwlO6ZgmUVFrqzBWrDxaMHqJaOI/dfxvSPG4AavkruRx4v/1Cw2M/dv9tymsjmTvnpTeC2999m0qwx8IFux/Chd/Kfyfffj4MbbquQJuuK2I65qAhT9lybQQhQvejBEHURhIlle7p00R5/cHuMmWJN0bCV+7MjT6Vy5O57+wsBQDlV47VdK7I8bwsnMzLRhkCUR9Dj9frN4cf9tuuuvUbAJBFrihZdx/8XbUAspitV/8sZXtx22JvRdjzfLx9r/JaTxqLX2PrtnlRVKb/FKFVil4vQNkbi1D2xiKU+OQnxHbc2gs7LMhczpVNGvCXN0iStMGWCyQIC1SX0H1RkiS/JEl/SpL0kiRJ58XrRJIk1QfQGgA9l0kQRFzp3rUZAMR1MJqZoWcJxReIWub+frks4n5rtwSoqiB4aOackARwrMRD6rbuthKSIwk7W+Ybit1dnZZgZ8t87GyZb6piAQDSPZ7IGwmIUpcz/6HB8Jeob6L9JRWY/9Bg5b12n9VzH8TquQ8iI03urCsp96qkrt1csPshBPzG06nNkFGvHQDUitoF3qGbyIUwBd2PEgRx2jIr+6a4HDfaqoV4EWtnbrG3EuWVfrgcwXu1AT3PVV7b1cfLKxj6HT9gy/EAOaWbmZ2H+tn5hts0zVmMzOw8pWohEm99eihkHRe4eohSN1a+/v6ocqxThUfRr7NLJXbtxiVJSjo3Glb06KiSurZcFKHAGBBgUkKX2kaiKxdOQh6g8TGAUwA6AHgEwDZJkjowxo7E4ZyLIHdw54fbSJKkBwA8AABXzrkeLQd3jMOlEARxOpPIwWjVIXOnv/iq8vruyVPBfAFIadHf5LKSSrzwhLpCU5S5ksuhGh7mSEuKOHjMiHj06vLqBbH/VSRQUmmpYgEAZg8fijELFgOAKo0bDjFxa4QzLVmpXOD78OFoq+cGqxSWTf8Hhk//F4pKyhWpO2nwLTHVLsSL2la7QNQoauT9qHgvCuApxhjFzwmCiIa41i6IMjcRqVyR6zqkq94HqxaiT+e6HA5VIndAz3NVdQ7lPr8yQCzdnWRq8JgeYgVDv+MHbKlf4Mcw6tPl1222ZkFLvfpnqZK0RpjZJhxffx/88970+QH06tochcf/BBD8eouldiGeiNULBJFoEprQZYztZIyNZ4y9yRj7mDGWD+A6AGdDHkxhK5IkTQIwGMBIxtgPEa7tKcbYpYyxS0nmEgRR05i0+GmMmht+Imu8CfiCj4oxXwBNdo2ILZ0bJrW6cOxI5I9+CAvHjlQW5vVjV6clymKV9ntHocO+LFvTum2vWG34mSMtCVkFS6M+9qNPr4t6Xy3XfjMGzGfuUb9l0/+hSuoC9tYuxIOaXrvAIE/bSuRCGFNT70fFe1GSuQRBREu8ahfu6dNENZwq0TIXAHx+PzI8Kcr7WGQuAFXNAkeUuVv2FmHz7pPYsrcIW/YWodznR9Mc+QfvXJZa5XheFirAbE/q6qV0o62K4LULhcf/xJVdWsVyaabo1VUttzd9fkAleQF7axc4PsaiqlogEgdjiV1qG9XeocsY+xpyp1gnO48rSdKDAB4HMIUxZm3aCkEQRIzY2aNrNAQtkUx/8VUkpydX6zXkj34IzOsH8/oRiEEu2l3B4HCm6FYvtPtyhGmJKsJrF0rKvGGl7rilL2FswWqMW/pSyHpnmvm/q+HT/6W8HjJhOQBZ6oo8+vy6mNLY8aY21C4QNRu6HyUIgghPddcsXNchHRmeFBx5NTgnIMUVvweO9VKtm3efVCV2udy1it1S9/X6zZEMSVfqpruT0LNtpqXjvfXpIZXUNaJj8/9D22aN0bZZY3Rs/n+WzgFAqVcQz9G5xdno3OJsy8ciiDORahe6VUiAfY3ekiTdBWApgPmMsdl2HZcgCCIS9eqmApB7dO2SunpD0BJNwBfAmP0TMeGXR5V1f/SMzk38fvlTEdOek5et0l2fP/ohS8PFjGi/d1TMHa6c1t1Wou0Vq3WrF6JJ6c4ePlQldccWrNYVu8wXwPUHJ8DhdqmkLvMFcO03Y0yfr6hE/nMYMukp1a+cOS+9AeYL2Fq38FPbpXA4UyJvGIYf9y1QXjudNeV2hqjl0P0oQRC1HnE4Uzx6dBM1AE2Lz+9XZG7DTLl64VhJGZo9Zv1+9NxpT6FezhJVb66W7q0ycGOPC0PW88SuK8Z7j3hIXb0hab/mjoTPbz1gIHbptm3WWDep6/MHULQsB0XLclTn6Nj8/5DuDg0XpLuTVeJXr6rB5w+EXG+8u3SjZdinX1PdAlGtVPt3QJIkXQrgIgDbbTpePwDPAljJGBtvxzEJgiDMMDrnJRSeCD62te/HnzBp8dO2Hd9OkTv9xVdVnbjhtpv63FpVOtdzliytA4VllqXu75c/Beb144UnZuGF2TNDPs9ISwMgS2wjqWsXkuTE7k+G2JrUjZTSzSpYakrwilIXCJ/WvfabMSFSVw/eo2uIL4CMncMBtxNDJj0VUrtwqI3+8Der/NR2KSRHEtp0XWFpvx/3LcCurfcoS1HhLgCyzP3jv/NtubZ4QUPRaj50P0oQxGmEMpxpx9Z3aqwMu65DekgnrtF217T1qKoWDr80XyV1rXDutKdQXunHxl3lIXULAJBZr6Hy+sTxY4bH8fkDUffpcuyWukbVC9qUbs+2maZSuzylC8gpWq3YdTkdaJK1oOocyUpS1+cP4Lf8sSHH+y1/bFi53LnF2UhPcSM9xY3OLc5WDVvjCfHz7p4U8brNkNFvNNJcTkv7cIHLl49/C359SNR4FReociE8CR2KJknSiwAOAfgawAnIQygmAfgV8rAIvt31ANIAtKladaUkSQ0AlDDG3hG28wF4jjE2tOr9FQBeBrAbwL8kSeoqnN7LGNsZp98aQRAEtnx+EIAsmNI8HpwqLkFxWRlGzS1AuseDOSPvj+n4uWv+jZxBN+uuL/UGO6VS3W7d7UQCvgCS05NlsXvnAADqoWe8Lzc5PRlj9k9U7Tvym3FY3H4+yv4sRaDQ/E30b+3k3lvtIDSRpVMn4aGZc1BUUmK5ZmJXpyVKFYOZAWrt947Czpb5CPjLsWfrfTEPSuND0rTwlG7+6IfAfAHV+3DMHj4UgJxWLi4rU6SuOATtnWZzcf3BCbj2mzF4v/3CsMJW3Gb+Q4OR5nGjRKeLLGPLfSjqvhJF3gpkpKUoCV4A+PmKlTj/E2MB/lPbpUpSWZKcuGD3Q6p1gCy+o5G5XOCK1AaZS9Q86H6UIIgzgYy6DVF0QpZgpwqP4p4+TfDce7/F7Xz9rzxP1T+bWa8h1n982HB73od7XYd0vLuzGEBw6Nm7O4txXYd0ZZsjr4b+MPzwS/Ph6TvM8nUWeyuxcZfxU1qvbv4FANC7XfgniVxOB5rmLFZJ3aY5i1Hsle9FzQ5QO56XZdugtNfrN9cdkPZr7kg0zVmsSNwUl1O5znDwlO6NPS5U5LZYj/D1gf+hbbPGAKAI3IzhuWGPyVO6Xx/4X8hnPn8AR56fBgBoNHQGik4W4uvvj6qGpB09VRz2+Bn9Ris/2HZJEopeL1DWuSRJPg9jSHM5LffnigKXIwG+AGM1t5eMOK1JdEJ3D4C/Q04svAcgC8BrALowxsT/OpYBWAdgatX76VXvl2mO56xaOFcDcEO+Md8KYJuwvG7fb4MgCELN6JygSFuTPw+r5jyGOulpyjoudq0mdsXtS71eTH1ubcgiyly+Xe6af0c89pj9E+FKcSlpXVeKCwFfAAFfABN+eRQTfnk0ROZyRn4zTnltNqXbZFd4wcpZOjXyT94llyNkMFqgpDJkgFokOuzLQod9WbbVL5hJ6bb7cgQktxNjFiy2nNYVBazkcqi6cnlSN1x/rjgcTRTDWjK23Af4Alg2/R9YPTdYtRAoLMPPV4SK75/aLsWhNosgOZLQrttzaNftOUiOJNU6AGjX7bmYZK7T6cCx3/KUpdbIXEYJ3RoG3Y8SBHFaItYt5K8/glWbGDLqyonTU4VHo07rivtc09aju5zUdOyeLDyK/leeF/a4R15dCE9ykiJyfX4/PMlJuKatB57kJJRtWKHqzDUimtqFWNm8+2SIFC32VuoOUIvE8bwslMF6LYIeRildXr3Ak8Xp7iR0b5VhKqn71qeHdDuF9Uh3J+vWLXB+yx+LFJcrYufukVXTkJKUhM4tzg4Zkua5ZZRuUjej32i4nQ4cGHgtDgy8Fm6nA55bRinr3E6H8tqKzNVWKzDGJL6QzI0vAZbYpbaRUKHLGJvDGGvLGMtkjCUxxs5ljD3AGPtNs90F4n8kwnKBZjuJMfYP4f10g/1C9iUIgoiW0TkvoWPPGapFTOdyVs15DOsWLQgRu2axOgxNPFc4qSsOOONS15Xiwpj9E5Gcnmxq+Nni9kGRZqV6QUpLwl2Tp0beMAL5ox8KOxiND1AzI3UBOU1qR/VC624rITmSQqSuIy0JYxYshqNqsFi7L0egw74s02KXp3UBKNUL8x8aDH9JhWq7a78ZE7E/l1cvhBu4BgBIS8KQCcvlQWlpSUA9Oamilbq8QkEra9t0XaFax6WuWXjFQm2qViBqB3Q/ShDE6YAkSRskSWLigqq6BYfDqaiJ/PVHFKkLWBtudk+fJujX2WVpH1EiayWvHlzq8loFLnAjidzzBgfDBVa6dNPdSbi+Q6qpbQHo9uiagQ9QMyt1nZBsqV6INCAt3S3fi/6aOxIn87KR4nKie6sM03IXgG6fLue3/LG6dQvabfSqF1xOBxoNnaG851K340UN0fGihqptj54qVkldLnNFUbvj1l4qebvj1l6WU7nDPv2aqhWIGktCKxcIgiBOB7i81eJ0OrAmf17I+lVzHgMA3DYq/M2NiFbmrlu0AEMnPYpTxSXKez1WzXlM2U6b3AWgJHHF5K3R63CU/anuHDOqXuAD0KS0JDT+7AFTx46GXZ2WQHKpf0aZP/ohZBUsxa5OS0zVL3zTalHcqheMzs/X72yZb/r4PKU7bulLYdO4RvDqhZKS0K8PkYwtoYK76OpngcJyReqe/8l9YMyPtl3tT8aIFQu1XebyDl2CIAiCsIOqJO4Nep85HE729EafJK7LX38EADC0l7zabP2CVuSu2sSUY6zaZPz/tfz1R5Tt+l95Xkj1wnUd0lWduFp5W7Yh8tM8R0+qH73nUvfgo/9UrT932lMo9lYi3Z2EX2aYvxfNrNcQJwuPhu3R1atdENm8+yR6ts1EZnZexAoGu6sX+h0/gPrZ+Tiel6Ws1zs/X9c0ZzHKfaEDfkXq1m+AE8ePqWoXooV37op8sf+PEHF7ZNW0kH3PGz4bx06VKPULnltGAQD2WJS1ZuAyl6oVEg8DwJgUcbszmWofikYQBFFb4MlczrpFC1SLnsyNBj2ZCwQTv0Yyl8MFMgBVSldP5kaDmM598c85ymttSlccgMa8fvzWbon8XmcYmhFmB6MFSip1O2mtJHXb7x0FFqi0JamrV71gN8wXiJjGNULcT6xUiETGh/eqkrqJoFZVKxAEQRBEHOGpXAhJ3FWbGMRFK3NFxPqFcPBkLocfW/s6HEYp3es6pMOTnGSqSsEMZW8uDTsgjXfmllf6FbmrNwxNC+/SDYde7YLeNlv2FpnqrLVzSNrr9ZvDD/M/TC72VmLz7pNht+GdunYQKcUbjsPLJiuvz7t7ElySZHnAmRmGffq18ppkLlETIaFLEARhEjGZK1YrWCVSj66ezLWKWL0w/cVXMfW5tbbIXCA0nVungXwuUfApMrdK3mp/jURGmnzMcJUTksuBnS3zsbNlfkg6VyRSPYMIl7q7PxkSk9g1ql7QsqvTkojXn1WwFGMWmHtcLxqUWoVUc/epGR/eq7w+1GYRHM7wQ0M4u7beg11b7zG17Y/7ovu6r6kwJLY/l9LABEEQpy1KKlcviRsJntSNhJWKBTPnErt27ZC5Yt0CABxe/aTymlcvnDvtKdTLWQKXQ77HemdnKcor/cp7u3A5HcjMzkNmdh5cMXx/wOFS97rj+2IWu0Z9uiJNcxZHvPaebTN1Kxk6Nv+/sH25ZnE5HUqtQnqK29K+R08Vh1QtGNF87fuqLtxwiFULVLNQTTCAJXiJB5IkXSRJ0kJJknZLklQsSdJvkiT9R5KkdrEemyoXCIIgLBKLZD1VXILisjJMWvw05oy8P2QbUfZGex5ATunyigc+5MxueDp32XdTcOdZcofV8ZFvoeLr/+kmcV94YpbpYy+dOgl3TZwSdhu9RK4eWQVLle5aM7TfKz+2ZaUGQQ+96gUtfJBbOJgvgA77smK+HpFNXYKCOGPncEv7Fnd8SnntdGWgdZfwsnnP9pHw+4pU7432EQegAbH94IQgCIIgTlfadLkBWY9viOlZZKPaBXEAmpkkbjgy6jZE0QlZDjfMTMfhl+x54kZbt8CPf/RkMY6VlKHBI0shQcLGXeqht2aSuVaJlGqNBl6TEGsFw+v1m+O64/vCbsMHuYXD5w/gZF42MrPzlHVXdmkFnz+A3xZHl7S9cEKB8jolKQlHng+tVjCizpDJqvdmZO7l//5I9fqzm68y3FYrcymdS8TItQCuAvAcgK8B1AXwMIDtkiR1Y4ztiPbAJHQJgiAShNhvW1xWhlFzCyLvZBOL28/HyG/GRd4wSuo0SMOpYyXwfvqzso4L2Yy0NCydGjqJNhwPzZwTeaM4I0lO7P5kCBzOlKh7dXn1ApfEseBIS1KSxmMLVgMA3mk2F8n1Pei1PbwUFtnUZTEqjluvSyju+BQY88PhSIHT6YHPV6wStUCovNXD7ysylLqnU28uQRAEQcSLb7e/HfW+XLKeKjyqqlWIB2KXrp6EjZWyN4NDZQ+vfhLnDXkYR08Wwx9gABh6tws+RZRZr6GpGoWahB29ujylK3bpiqS7k9CzbaYpMZ3uTlKqI3iPbsbwXDRIT8WhuaMtXdex4qBc1+vJNaLOkMlwOxzIcEgorNQPzl7+749wPEzFxXFvpaHUJZlLxIE1AJYwFswAS5L0IYCfAIwBcHe0B6boC0EQRAJZNecxpQ4hHNGmc7WD1zKr6hC0NQnRIvbniiz7bopSvaClqKTEsqAtKimxfG1GWKlcEGm/dxQ67MuKqVe3dbeVYMx4wIQjLQljFixGVsFSw204RoPVrMpZcXujdG5xx6dCFklKwiWXrECHDgvRrp3+10E4mdux80q4XBnKdru23oM9gogWaxZOt95cBvlZvUQuBEEQxOmLw+GMOjqbv/6I0m8bjmjTucOudWHYtS7ldXqqR/lMW5VgN2L1ghZtl29tIdZe3Uhdur/mjkSKyxlSp2C07cm87JD1x4pLVYnbSIjblr74uO42dYZMRuqdjyD1zkeURC6XuVuvuRwfXX2Z4fHDydz67iRlG17BIKZ3SebWLAIssUs8YIwdE2Vu1bqTAL4H0DSWY5PQJQiCSDDicDO9IWfRytxB2eMByFL3tlFj4Ul3Y+3/m658biRjzbKwxRNhxfCy7+RE7tPTJiEjNVX1WbSCNlIdgRnEyoVdnZaYGpAm0n7vKAT85ZE3jIJ2X46QpbEvoPu5ti6iw74suOp7QrbbFKH2gPNOs7nK63AyV5KSQpYOHcz33XXsvBIdO69UvQeAth3zVNvxtO63nw9T0rlUs0AQBEEQ4bHanaslf/0RaIepicQic1PcyUhxJ2NoLwkp7mT8vnE1GtStA0BO6cYidRsNGANP32Fht+Gp3fe+8SKzXmRxXR00zVmMpjnm5yPEKnUjden+mjsSPr/+vageRlLXDBnDcyNuy8XtN316AADcDgdS73xEkblaRCErsn9Ab0Xg8vdbb+qpWgcEBXCrdcF+XZK5RDyRJKk+gNYA9sdyHKpcIAiCMMHonJfienzerxsLfl8A752cG7I+s0EaTh4rQdmfpZarFxa2eAIVxRUAAEeYwV2clHQ3Hpz9BJZPDg5fu3+GnM59aOYcy9ULk5etwuzhQy3to4X5Ami/ezR2dVoC5pXTsjtb5sORlmSYetUiSU7s2XpfVNULDmeKPPhMcoIxPzrsywrdJi0JWQVLVb3AWQVLIbmdIdfYZsswRUo73C74jpeh4ngZNnVZHLZ64d2L1D8oELtwxRSxw5FiSd6G68QVpS7H5cqAT0jyalO9p1Myl8MAGlRGEARBxIQkSRvieXyx7zZafH4/ft+4OmT9TxuexQV978WxE6eiql5oNGAMisrK4XI61esHZuPI2jyDvYC1H/0XANCnvTxoa0DPc6OqXbixx4V469NDlvfTo2nOYpT75PuuzOw8pLuT8Gtu5ABDLPULr9dvjn7HD6ikrraCQa96oWfbTPj8AaS7Q93mybxsRUrzGoYLJxRYql5wSRJS73wELkn++QS/V0pzOhVxy6WuHvWSXCis9KkSuVq5u/WmniH7bb2pJ7q9uVm1nzgsjYag1QwY4jeoLAwNJEn6Snj/FGPsKcOto2cRAAlAfiwHIaFLEARhgi2fHwQQv/TgqjmPRb3voOzx8PsC8KTrT4Vd+/+mY+BfpytSNxJaiTvznoHKZ1OfWwsAcLocGHr+dKz6ebpq31U/T1cGpHEyUlNRVFpqKaWbkZaGohK5a9gOqQuEDiDLKliKXZ2WmJK67feOwjetFkUldfn2e7beB+b363bqtvtyhDL0LKtgKZgvEFY4i+u/7b5Ckbpa3r1ogSJrHc4UtO36DHZtvQcAIEnBm/OOHSNXPmhxudKVHt09Fjp8xZTu7q+zVXKXIAiCIAhDbgCUuoWYErp65K8/EvW+w651wef3q+oVtPy04Vmkd+tv+phc4gKAy+nEB7vl+5xr2srneO8bryJqtWR43Oh7SSo27JDvezPrNcTJwqOWaxf4fieOH7O0n5aebTOR7k5SZK4oTbu3yjB9nON5WarBZFbgUhcAyhCaxv01d6RybFHk/jpvjOExuYj+65TlOFZSplQviFK37vAnVJUPHjiU87sdDnx1zeXo9sFnAICvdNK34fjo6svQ/r1PAQRFbri6BREuerViFwAYebIzmWOMsUvDbSBJ0jUANpo41seMsZ46+08CMBjAUMbYD1FdZRX0XCNBEEQExHTumvx51XgloQzKHo/klCS8d3Iu3vh1lu42XOaaYWGLJ+Ar92HmPQMx856BmH7nAN3tlk+eiPJir+5nPKXLWTDB+EbQiKVTJyEjTe7k5VI3FnZ1WgJJkzC22q0ba/VC624r0faK1ZAcSfim1SLdbXgqt8O+LNPp4TZbgo8evtNsrpLEffeiBZAcSWjX7Tm06/Yc2nRdoRKvHTosVJZoaNduPlyudAByyjbSMDQ92nbMg8OZAklyRt64luNjLKELQRAEcXoSa92C3fCaheKt63XTuQBwQd97TctcXqtQVlGJD3aX4YPdZXh3Z2iqd+BV58DldKLRwNDH/4+szYPPH3z6iCd1ATmlaxYxzdu9VQZu7HGh6X1FeJ2BVuYCgMvpsFS/4IQUU5/u6/WbG1YwpLuT0L1VBlJcTpzMyzaVHAaA/zfrQTRIk0X7seJSZAzPVT7zg+Hd+i2VJVkKfvnyFO7Way7XrVIwQ70k2b0e91aalrkiW2/qif0DegMAytY8EWFrItEwltjFJJ8BaGFiCRl2JknSgwAeBzCFMfZMrH8+JHQJgiDCMDrnpbinc2PB7wsYilyOWZkLABXFFYYSN3fNv1XveUpXy6qfp8Nv0AlrZTiaXVJXcjnAvH5VnUG08OqFWGjdbaWu1HWkJelWLJhB7NWVHEl4p9lcSI4ktOm6QrVdNNI1HKLUjZb2lyyG5KCaMoIgCIIwIt51C7FgVLMgcuzEKdPHKyorN5S4/a88T3l9svAoNuwoRVGZfsBAC+/TPVl41JLUFXt4o03qupwOXZkLAJt3n1QqC8wQa58uIIvdZEghUpcLXLMiV+T/zXpQ9T5jeC4yhufCo1FOJ8MMC46Gj66+TJG6sZDmciJ10MTIGxJnPIyxUsbYARPLYXE/SZLuArAUwHzG2Gw7rqXm2QmCIIgaglbm1rR0rhkG/nW66W0XtngibE9uqVd9w8xTuneeNUlX7IrwIWlFJSUJl7r5ox8ylLmSy2FpSFr7vaPAApVxkbrtvhwRlcz9ptUi+IS6BUlKUhK5RsQqYUXatZuPSy5ZgUsuCZ5v99ehaZlwpKc3s+16aiKMUUKXIAiCsI4kSRskSWJQ1y3UeqIdjGZUmdBoYLZuUldk7Uf/VUlds7y6+Rds3BV8QiualO7m3Sd1ZW60HM/L0q1NsAKXupnZeSqxqzfwLBL1s/NDqiAyJSferd/SsO/XDgnL+ejqy/BNnx74pk8P1XG7vbnZ9DEuaVBPKYagDt2aQ4AldokXkiT1A/AsgJWMsfF2HZeELkEQhA41WeYOyh6P20aNxW2jxhr25nL00rmL21sfOiWmc5+epu7IfXrapJD6Bb3aBbukrp3kj34IzOvHzpb52Nky35TcjbV6gcOl7s6W+YYVDCK8Y/ebVouU693ZMh+SIwltr1gNZ5LcwRau09bpkrfx+awPJDEDF8U+X5FpqfvDdwtx6uS3VddHtyUEQRAEASip3Bv4e4fDyWpK3cKwa10Y2kvC0F5S2N5cLQ3q1QWAqAajaXnvG/ne0+V0oqzCh7IKn0rq8h5dkWirF4BgUjfWPl09XE4HMrPzkJmdZ7p+IZbqBc7r9ZvLNQg6aV096mfnKwu/3szsPCRDUioVMqtqtIySuPzzwsr4OFMxsWulguGT34N/rwHG6NExwjYkSboCwMsAdgP4lyRJXYWlQyzHprJngiAIDTVV5orDzyLVLGj5rmwhLjtvMo4fLTYcjDZm/0TMPVd/OJs2ncvhctfpcuDOsyYhJd2NVT/Lw9KGnj8dD85+Assny48vLZgwBmPnLrQ8IA2Qpe5dE6dY2scsYnp3zAJzN9G8esHqgDQt4sA0LnUZ8yudskxzM7yzZT4czhS06fZcyLFaXbYMe7cNh7/SuM+2dZfFylC0eNCu3Xzs2jUOPl+x6WFnisx1OvDHL9Z/2EAQBEEQpxuizBVEbrXLXHH4WaSaBc4Ffe9VXh/88HXU63AVADmle/il2P+/zwefAUCf9m54bnoIGR43jqzNQ6OB2arhaIB6QNqAnueqenLD8ermX9C7XUrM16tHNEPSYhmQpoUPTKufnY/jeVmon50PPxicVV9yfKiZWJ/wev0Wusd6pd7FuO74PgDA7YXf4ZV6Fxt+Hi/EYWlmGLZlp/KaMVbt/50Rpx1XA3AD6ABgq+aznwFcEO2BKQpDEAShoabK3EjDz7Ro6xa2HQ5W9cw99zHMPfcxy2ldbTqXw6VtZXmlUr+g16UrDkizktK1sm0isCuly+FpXZ62FV9rl3ASudVly5TXRildzo4dw7Bjx7Cw20RDu3bRfXP2x39PX5nLQJULBEEQhCWUZG5NSuVGGn6mh7Y/V0zpevoOM12/cE3bYBKYp3P1eO8br9Krqx2OBkRfvZAorA5JswuxgoEnbpMhqdK3fKiaUY0CJ1JKl9P+vU/R/r1PcdWH22z7fWgxU7sgpnOJGkSCB6LF6/aZMTadMSYZLBfEcmwSugRBEAZUt8wVqxWSU5Isp3L16hbqN1R3p5b9WarIXZ7O1Q4/Exk7d6HhZ09Pm6T06obDavXCQzPnWE70RovZTt1vWi2Cw2lvSqN1t5WKrBVfR4tRSpfXLnB27Yqux44gCIIgiLjxdnVfAIfXK6S4ky2JXECdzi3c+REAOaXLpS4QFLt8AdTy1gpiDQOvX7CjemFAz3Pjls7Vsnn3SZT7/BGlbv3s/JCBY7HCKxi4sDUjb/XQpnK1cOHLiVf9AmCuduGKxg3idn6CiCckdAmCIAx4fNnTth3rtlFjLW0vJnKtpHL1+K4sKGG3HZ6N78oW4ruyhSFyl1Pq9YZI3VS33NVbVKpf1yDidDmUlK62SxcI7dONhLjNwrHWp+6axWzdQk1m77bhEbdp3WWxSur6fMW2Sl0SxKEwyNM1ErkQBEEQtRfGWF+7jzm0l7WgryhyraZyOdp0Lufgh6+jcOdHKrGrpf+V56nef7Db/ByFDTtKlU7dI2vz4El2hUhdKyndRCV5e7bNRPdWGfD5Yxt4Vp3cXvhdxEqFV+pdjHfrt1Sti2dKNxIrusdUY0rECQYgEEjsUtuQGD2WF8JDh6fQH0oNY379JtV9CYSNHNhR8x5vEhE7dDu0bIFHht8f9bF47y3H6XJgTV745C+XubFIXADokzkBgJzKFesW9OD9unpwmct7dI1qF0QenP0EklKSlC7dyvJKpZaBc/+MYDo3Iy0NS6eGHldM58ZT5mYVLAXzBeBIS0K7L0eY2uebVosgOZJiTtLaxe5Phqjet9Pp2RUx6tJ1udJN1ybs3Bms0AgEQisoOnaO/Gfz9Rf3AQCO/WZPD5zdnNU4K+ZHXd2tzmZNX7ndjssxzaE2i3Ywxi5N6EkJgiAI25AkiQFAmy43IOvxDVEfh/feclxOJ1a8H/lHf0N7SSjeuj7q8wJyQpdL3Qb16uLgh6+H3Z7362rJrNcQ6z8+rCR3w1UuiPRp70bZm0sBAJ6bHgrZr0979XDhzHoNdTt1eTp3y15z8wGioWfbTKS4nCj2VuJknrnBsvWz85EMKaoUbbzQylytuA23LadekgsfXX2ZqfN1++AzAMDWay5Htw8+Q4mmYmP/gN4RjzFsy06ldoE6dGsO5190KXtk8VcJPeeDfaRadf9MCV2CIAgNBbmD0b1rMwDAzn37Yz6eJ92tJG2TU5Jw26ixGJQ9PmQ7XrFgh8wFgMwGaQCA40eLcbFnDDo2ethwW295JVwuB+aNvDPks1Kv13AomhHLJ09U+nT1unSBYPUCIKdw75o4JaSCIZ5VC1kFSzFmwWKMWbAYktuJDvuyTMtcQO7RZYFK7Nl6X9yu0SzaZG4kmSviTK6jeu/zhYp9bd/uzp1jsGPHMDBWqSwZO9XXYKaS4ofvjCs8TicYEtufSx26BEEQpwVvA8C322NvX0hP9aB463oUb12PFHcyhvaSMLSXhGHXqmek81Tu0F4S0lOjqz0Q+WnDs2hQV77POFZ4Aud2ixw8fmlLAHXqNlStizYhK1Yv6MFTuuJ5zFQw2AVP5HZvlYEUlxO/5o40LXMBeTBaBRj6HT8Qx6s0hzaZy3t3w6GtXuAUVvpCEru8b1ek2wefwRsIwBsIoP17n8IbCKD0xcctX7vQoUsPOdUwTocO3XjiirwJQRDEmUdB7mB07DkDQLAuIZq07pq8eaq6BS5qb2k6JaSGwZPutkXkcirK1fckJUXGUrakyKvI3DSPGyVl+tuaSedylk+eqErhauED0sbOXahUOXCxq8XudG5WwVJIbifa7x4d03Ha7x2FnS3z7bmoCOzdNhz+yiI4kzJUw88AwF8ZfWLkb71fUF5/t+FmAHJtgpjSdThSEAiUK1JXkpwhArfo6meV19JXD4B1WoWvv7gPDmcK2l+iX2dx6uS3AOQBhARBEARBBGGM9eUpXbEuwWpid8X7Pgy71oXGvYfg942rVdUJjXsPUR07PdUTVbWCEY17D0FxabAqobyiAud264tftoa//uVv/YEHbzwbp04ERa62gsEMG3aUKilc3qW7YUewPkzs0h141Tk4WXgUJwuPJqQvlydyf503JvLGYTiel4XM7MQ85XR74Xc4yfzIlJwhPbnaAWi3F34XsUv3lXoXKxJ4Z+/uAICrN3+OwkpfSK/uN316oNsHn6mkrkuScGq1+inE84bPVn3e4tWNSHM58dUtV+tew7AtO5XXjLGksBdMEDUMEroEQRAGdO/aTKleAOS07uPLnrYsdZ0uB25pOkUla+0Ut3rc0nQKyjTDyVwuBzo2ehhfH3lSd5/xi1/EvJF3YsbQAcq6aateNZS7VuBdutraBUBf7GqZvGwVZg8fGvN1RFOtEAlJcmL3J0PgcKbEtX6BS1t/ZRF2fzIEzqQM1XqRPdtHonUXc53AP2y8S5G6zuQ68Fecgs9XrErkAgDqpSDjw3t1jlBFoaZy4cuhkABF7AJQyV0xnfvHf81VPNRmKDVLEARBRMHbAG4QV3y7/W3kP9LXstTV69C1U95qadx7CMq9Fcio2xBFVWL2+c0VGNzd3A9xl7/1BwAoYjfWlO6RtXnw3PSQ4XZrP/qvInXjSc+2mfD5A0h3J+HXXHsCC05IuO74PnjgiGv9Ape2J5kf1x3fp4jd2wu/M9zWKh/27IoOG7cAQEgiFwAa1EnD4WWTdfc9b/hsHDsVfLqv6OVcAEDGHTlo8epGAFDJXbFqAZTOrZHQ7XN4SOgSBEEYUJA7WHnNe3WjkbralG68uaXpFFSUqye68vTt+MUvGu6nV7cwY+iAmKSuOCAtElzsinDJW1xmfhCGEXalcrW03zsKgNypmwixy9ETuc6kOvBXnoLfFzmx63RlwO8rgr8iOLDkb71fwA8b71KtUygsR1GHZRHFrvTVA+oVVWIXUMtd5ToonUsQBEEQumiHo0mStAHADXbUMMQTLnONRLIV9NK6VhBTupEQE7sifP8be1yItz49FNV1APalcrUcz8sCIHfqJkLscrjYFdl0fhv0+ll+AstMSlePekmukIQu59ipEqTe+Yiu2BVlbtma4FBmLnYBtdwV8FE6l6iN0HdRBEEQJrC7VzeelBV7VYPX0jzmbmKNEBO7Vlk+eSLKi70oL/bqpnMjoSd5o4HLXJ7KjUdNQvu9o9BhX5atvbp7tw3H7k+GqAaetbz2VTiT1L23La99FS2vfRUXX/WMsm7P9vCpD6ME7996v4CL+/4bF/f9NwDgsv3ZcNUXevSqxG5Rh2WqmgUO6/288Um/HKoSvk6n44xI5xIEQRCEHWgFrxVcTica9x4SeUMbKC4tCxm89tIWeZ6Cy+XS7dI9t1tfuFz6eTOe1gXMD0QTidSlGwnetXvi+LEIW4bH5w8oqdymOYvRNMfc01RmOZ6XhZN52bb36l53fJ9K3G46vw3qOkL7bzed3wYAlM+spHR5KheQU7o7e3dXahjK3lyKsjeXomFmurINF7tixUKDOmnK6/MemKl7nqKXc1G25glc1yEovEnmErUVEroEQRAmERO7t40aqyyPL3s64r68diGe3NJ0CvpkToDTpf6n3Y7KhFjQXk+iGbNgMZgvgEBJJXa2zLcsc3d1WoKdLfOxq9MSU9vbOSzNqBv34queCZG6HL7e7ysKK3UjCV+RTlsfDBW7QFDuXv0sUC9FWRdO6rIx7yivzxSZywAaikYQBEHYCh9eNrSXhPxHInveFe/7UO6tiKvUbdx7CNK79YfLqT/sCpBrF4p1KraKS0vx/OaKiOcYeNU5lq9rw45SFMVwP2yU3LVCz7aZAIDM7DxkZueh3OdHsbcywl4yTXMWIzM7z7QA5sPSrju+L2axq1enAADrz21pKHbXnxschma0v1lSnU5k/F0OZBxe/WRYsXt42WRF6h49ZTxYud8Tz+LdnfKfi0RVCzUWxoBAgpfaBgldgiAIC/CUroiZxO6avHmoKK+0XepyidsncwIqyiuxbtECVTpXC+/RjZaxcxdG3khDNMlcPSYvWxXVfnyg2sKxI5UlElzi7myZD+b1Y+HYkWBev2Wpu/uTIbpid8/W+5TkrZH43btteMi6lte+qry++KpnlGSuiCh7w1UvmKllAIBtLYKDNrjY1Uvtqjp0C8vBrghN7wIAtv4CoPpFP0EQBEHUUkL6FnivbiRWvO9TDSmzCy5yy70VWLWJhaRz7aBOXTklG0uXbjjRnAhcTge27C3Clr3yPVi62zgYyiUul79b9hah3Oe3JHXDpXX7HT+gJG+NxC8fgibCU7gcLna168WUrpHUFdfzNK6WrVdfFvKD6nBiV6xd8AyaiEb/mBpyTFHmBiidS9RiqEOXIAjCAmJKFwA69pwBAKZ6dXmXbp/MCfCkyzUIsQ5HKyv2Yt2iBWG3Ebtxcx+8I2yPrhFpHjdKyryGQ8si8fS0SVHtBwAZqalKj260w9FEiZtVsBSONON7t12dligSVyR/9EMYs8D8o3G8W3dny/yQbt2Avxwd9mUB0O/e3bttuCqdq5W2kbj4qmew731zVRnOZP2kLwA4XB6wgH5aptPWBwEAX3ZbDt9xnW8OK/xglz4FpCZB+kTu3FWlc385M9K5gJwwoNQsQRAEYQfhenXNDkvTpnRjGY7G+3JXbQr9/1xW/0ZRH1fL8rf+MD1QTY8NO6K7h9USbY/u5t0nlddKl26YoWjF3kpF/IrH6Nk2E01zFpseqHY8LwuZ2XmKsOXdumUI4GResIZC27+rlblaYRuJ9ee2VLp0jaoXoh2cxjm8Wh70fN6Qh3H0ZHHI51999ye6tmyIRv+YiiP/kisY+j0RDByQzK35MLp/DgsJXYIgiBjo3rWZMizNDE6XA35fQBla1idzgupzT7rblOS9pekUlBV7DVOOQyc9aup6zDJj6ICoRLAdLJgwxrbhaNouXatILodS2eBISzJ9nA77shRxCwCSFEyIaIeqabEqc82iNxSNc/C9QQj4yqqErh/bWuThsv363XOdtj6oSvFyPlkvJ8Gv6P+knNbt0JjSuQRBEARhM4yxvpIkMUBO6kbC5XSi3Kv+YW16t/7B16keU4K3ce8hKC4tg8vpNEzkFkU5yKwmklmvIU4WHo25R9eMzA0Hl7qZ2XlIdyeZOo4TEiogizHeheuEeliddrCaiFWZa5ZMyWkodbt9uA2lfj9SnU6ldqHoP/pPynGx67npIdX63pddjM/3HVVJXapaIE4nSOgSBEHEQEHuYCWla4ZwdQiA3M1rhkjJ3FPFxr1RsTJ27kLbhpWZZcGEMbh/xhwAiDqlG6vMBeSULsdsWpcncbm41ePb7ivAdG5ow8ncA5vuQsAfveBu3WUxdm29BwDww8a78LfeLygiV3I4cdn+bHzeqgCSw4mue0eHPdZl+7OxrUUeUt1ulHrljrq/31WA/7wwGp+sfxhX3JyrkrlnUjoXqOrQre6LIAiCIE57HA4nAzSmTkOkOoShvcLurlBcWqabyuWI6Vw+EM0uBl51ji29tlZY+9F/0ae9/IRd91YZqFu/geWkbqwyl8PTvt1bZZjansvacPx1ynIcKwm9rwwnc288vBdlLPLf7bv1W+quf6XexYo8vnrz5/iwZ1dF5LokCWVvLkXG30fAxxhcUuSvy7I3l8Jz00OoU68hThUeRWGVfP9831FcevFZlM6thVBANzwkdAmCIGoZtzSdEjbhKKZzxbqFWBFrF6pD6sZSvRBO5u7qtASBEjkx7UhLQqCk0lTPbrRpXS3fdl+hX1lggCJyJSfwUWgyFv2mACfUQv/bz4cBANp0XQEA2LX1HrTr9pwqpfvdhpsVkcuJJHJFnGnJKPNWKlL3xCn9RxvPNJlLEARBEIni6Y0+czY2Rhr3HhK2jzarf6O4pHPr1G2IUyeO4mThUfRp70ZmvYYJFbs8pQvAclI3ksxtmrMYxd5K06lbQO7kzcyW7wWt7KfFSOYawUWuE8DvD4c+XdZq0Tr8qRlC1+/4AZQhoFQ68AoIntItrPShw8YtisjlGKVyjcjwuFF66rjyvvdlF2PjNrmrl9K5xOkGPfNIEARRi7il6RRUlFcaJn2HTno0buncGUMHIM0jJxO41E0kCyaMQUZqKgBYql6IJHN5X+7CsSMVsWuG/NEPRbWfSIjM/ShPXiQnHE6PatsDm+7CvvcHIBCokLf50CDtLchcSXJi19Z7wAKVYIFK7Np6j5LK3bX1npDBaFYErpbOX42Aw+1CmT/4Z/H3uwrw8Mx1yvsztWqBQe7QTeRCEARBEPGAd+aGS/qKMtfOdO7yt/5QhqMB8oC0gVedY9vxI7H2o//ivW+8kTfUwecPhJW54uCzzOw8uJyR75k27z6pDFmzMjBNiyhzf394iLLocePhvahgAfz+8BD8arCNKHOdkHDd8X2oAEPRshxUgCnvK8BCKhesClwtR9bmwef3o049+eukUCPeJVA6tzYRCCR2qW1QQpcgCKKGMCh7vDIsTY9IMhdQVy3Ymc7lzBg6ANNWvaokde+fMQcZqakJS+uK1Qt2oE3jSi4HmM/6/80llwO7Oi2xlNLVlbmcD+chcFUwKXtg011BkRuOflOUl9cfnBBmQzXvNJsLQB5wxoedRUPnr0aoqhdOnCrF5zv+HwDA5XLgd0rnEgRBEEStJlLVgojdVQuALHUB4MEbz1bSutVRwWAVl9OhO8yMy1xeo7B590l0b5WhGqJmBr6fFbTJXK3EdUIWuG+d1wpAUOYaiVxATudyipblhHx+YtlE3f0yhucCkAec8U7caNGmdC+9+CzldYCxhKTYCSIRnJlRGYIgiBqI3xcIOxCtrNgbsYOXEw+ZyxGTuoCc1k0EY+cujErm5o9+KCRBu6vTEuxsmQ9JkxgVO3JjPUc4wspcALh6vCqhG/CXGSdyRU5El85Ori+fy0r1Qzgm3n4zUt3qH06QzCUIgiCIms2wa11IT/UYfh6pagFQd+daIT01FXf3TDa9vZjW5TUI8WbgVecoPbpW2bz7JIq96ntFrcxNNFZqFgCgLILMBRBStWCWBunyU3hHTxbjvCEPR3UMjjalK0CPMBGnFZTQJQiCqAFESueaQezOjTczhg4AAIxf/GJCzjd27sKYxDFP0AJyKldyOQx7cs3058ZKRJnrSEbzXi+o+3LjSK/tI5WUbqw405Ix/aVXMX3wADzxyr+VIWmDhjyFNasfsOUctQ+qQSAIgiBqPj6/H79vXG34uZlBaNF25/6ydQPqdbjK0j7L3/oDg7snJqM28KpzbBPHvC/X5XToytwte4t09rKXv05ZrrzWJnObPrkafgAeSf6z7fXztwDkxG4kzvK4o5K6h+aOVlK6R08WW95fC0/p/vtLH27upGgvSufWIhijoWiRIKFLEARRA4iUzg03CC2evbk1Aa3MjUa45o9+CGMWLA4rcu1CO2RNrGEISebWTQs9APMHZa6ZmgUb2NQlus41PTp/NQJfXLpEkbqrN32Kg7/+hg827bftHARBEARB2EukdG4ktDI3XN2Cy+XCud364petG6I+X6IRZW4swpX348ZT2nJhDIQOSzMzAM0PYNP5bQDIMteoT9dOLpxQYOvxjqzNg+cm+ck7jdQliNMGqlwgCIKoZsykc8uKvfAbdLtqZW6kugWXy4GOjdSPMqVluJGz/GUTV5t4YpW5ItFWKphlZ8t81ZA15vUryWBAp9IgTEWC6ZoFHaz05wJAhU1VCxw+IG36S69iSK8eth67NkJD0QiCIIiazLBrXUhxJ4dN50bCyiC05zdXoFjnySurtQvVQawidsveorhWLIgD1vSGpenJXLH3VuTGw3tNpXJFoq1cOFYc/Hooe3NpVMfQkuFx4+ZOLvTvGttTkET1EWCJXWobJHQJgiBs4vFlT0e1X6R0LmfdogVhP0/zuE115+Y+eAdKitQ3W18feRK+KIaBJYKM1FRbjqOVwZOXrcKYBYtDlsnLVkV9jnSPB8wXUI6VluRGoKQS33ZfgZ0t85Xt7vhRGBIhDDKLmr45gDBE7Z1mc/F++4WmdhXTuZftzw6zpTU6fzUiZMDcoCFP2XZ8giAIgiDU5D/SN6r9IlUtWCGWQWi/bN0An89ny3XURLQy+MYeF6J7q4yQ5cYeF0Z1fJfTgWJvJXz+gHIsnz+gpHXFmoWyd55Gw0x5iNqfZV5dqWumM1fkb/lrVO+bZIX/3oUjpnPtkrmAnNIte3MpfH6/sk6SpNoTCyeICJDQJQiCiJHuXZsBAHbu229J6g7KHo/bRo2NuTu3Trr82H5JmRfjF7+IaatejbiP3SndsXPNycNoWDBhjO3HnLxsFYrL9FOpxWVlUUnedI8n5Jj8vd6wMXf9KlF9okQtdSUnDmy6y+TvBLLM9VbimcfuV60OeH1hxe777RfinWZzbU/nivA+3WZNmwDAGVy7IIGxpIQuBEEQxBnF2wDw7fa3LUtds1ULZgaiAcDg7g48eOPZEbfjtQt2MfCqc2w7Vjiila16nDh+zHB9NJI3PbO+4WeZ2Xkh6dzDaxboSl0n5HSuR3Kg6ZORRf/f8teg8ZOrUe4LoH79s5T15T4fMobnGordJlkLkDE8V5XOjReXdLuev7yBpG7tgffoJmqpbZDQJQiCiJGC3MEqqWsFT7o7Yjo3XH8uAKya85gidQFZ7EbCKKXr9iRZkrppHllGF5WWxlXq2o0oXp948F5lSUtJMdw+nNTVCmKjY93xY46Szr31q9H6UvfDeXLdgllKvXhm+tCQ1fMfGowFo4fAX1KhWs9FbsCrTsA40+x/xJGndMXaBUrpEgRBEIS9MMb6QpC6ZhnaSzKdzi0uLcOK943Tsxl1GyqvT5kYjPb85gqUV1SESN301FQM7u4wXb1Qp+q8JwuPxlXqZtaTz2MkYa0iCtq12/zKUkf4cxQ5cfxYRKkrXhs/XofLrw/ZruydYABFK3UB4NeHh6CCyUlrf8jeoRRX+HDgh8PY8/1P+OyLncr6rw/8D7sP/o5yn08ldbnILRfS2BLkioR4kOFxY9fn75PUJU47qBmaIAiimliTNw+DssfjlqZTTFUuhGPVnMcAALeNGhvTcb4+8iQu9phPxM4YOgDTVr2KkjKvquu2JiOK2ScevFf12dR/3BGy/cx/vYyS8nIluRsJfswynyxSXWnJuO1b/b+XW78ajdcuLYD3eGlQ6p4sh8PpsSZ1wyC5HHi//UJc+80YvN9+IQJeHxaMHoJHn16HEp984z7j7oGY9vxaW86nRUzp8uFog4Y8hTWrH4jL+WokTAICNbsTkCAIgqjdMMb6SpJkKWO2ahPDsGtdaNx7SMyVC/nrjwCQJbFZnt9cgcHd1aEFPiitXoerTB1j+Vt/4MEbz8apE0dVg8tqOkZieOU7v4esu+/6xjh14qiS3I3E2m1BDfvt9veR4UnBkdcWGW5/eM0CeK6Xn/RqtWgd9o66LeI5rPD1gf+hbbPGyvtibwV2H/wdV3ZphcLjfwIA3ojj4DI+IG1q/puYmXUTdmx9B6iSulU/DCFqKKw2FtsmEEroEgRB2MCWzw9Gtd+avHkoKw6fqH3j11lITknCoOzxUZ3DCL3aBcB69cKMoQPsvKwQxORvLP22HKOqBSOm/uMOw+SuFlEQB3wB3PFjjqHM5YQkdZkfzXu9ENzgqmxVP65V5j80WEnp+ksqMP+hwbLMjXJohVXElC5VLxAEQRBEzWLF+z6UeyvQuHf4rtT0VA+GXWteuJmpXQDsqV5Y/tYfMe0fiYFXnRM3WSzKVyNWvvO7YXI3HAMvc8Ln94eVuRxt9QK/Kidkydv4ydWGw9PM4HI60CRrAZpkLYDLKWsoLnMTQYbHjf5d3Zia/6YqqZuwCyCIOEAJXYIgiBrILU2noKzYq1QyvPHrLPTJnGB6/2mrXo0oWnMfvAM5y19Gx0YP4+sjT8Z6ybYydu5C3cSvKGO1NQfpHg9mDw+tHjBCm84NB0/uTlz+rOV9zXDrV6Px8l9ylfffffRP+YUocvtNAV4Pn+TOSEtBUUm57mfvt18Iqaq6Q5S5M+4eiOkvvRqXygUOT+lOHzxASQKfcSldgiAIgqihrHjfp5us5ZL3942r8fvG1Ujv1j/isTLqNkTRiaOmahcA/ZRuTSCcxL2xx4V469NDymsxbVu3fgPlMzvhyV2e1gUiy2CX0wlPsrle/cNrFuC8QWNx9GSRUr1QVlW7wN9z2RtNgldM6fJ0LuffX8Z/EB5P6QLA1Pw345YGJohEUvP+5SQIgqjl3DZqrKXhaHqUFXuxbtECVJRX4pamUyLvUIU4IM0Mel26VjEzhM0sY+cuxP0z5oStb+CDyvQGkEVK8Maa8OVdu5GYtPI5uCwKUiWlC8BfeSp0A17J0G+KLHr7hX5dLMwxHqYmpnM5M+4eaOkao6XzVyPgcLvO4AFpDrlyIZELQRAEccYytJeEob0k0wPS9AajNe49BOXeClPpXRFevQCYT+nWJAZedQ76tHfrylyekhWHlmmrE8x07MYyWG3lO78r/bjhGNw9GZ7kJFPpXM7hNfrDy0S41I02sVsd6VwOT+mKUJduzYUxIJDgpbZBP5YgCIKwge5dm6lqF3bu24/Hlz2NR4bfH3Ffp8sRkr7lQ9DEnl0zrJrzWMJ7dO18dF8rckV5yrtstTzx4L2qnlsRbYo3nkxa+RwCPjnJEK431wjvidA/x4ydwwEARVc/CxSWy1KXI76OgCQM1dP7+5o+eACmv/QqtrXIAyAnajt/NcL08c3Q+asR2NYiD0N69aCULkEQBEHEh7chPEZuZUBacWmZKoHrcjqVIWi8ZxeQZfGqTeHNh9WULq9d4P25VrFTHGtFrlaciglZ7XYDL3MCUCd4+Xu7hqlFYnD3ZPj8/oi9uXpk3BB6T+aSJBStkZ/k89wuf78iJnat8vWB/6nei+lcl9OppGgzPG4cWZtn+fjhEFO6l3S7nrp0iVoPCV2CIAgbKMgdrLwenfMStnx+EDv3mUsgrsmbF/HzQdnjFclrFjO1C0CwSzea2gUxnfv0tEmW9xcRu3L1UrB6A8vEz3gdgpXBZXYxaeVzcLhduP37GGR6wDhpkfHhvSjqsCz0g6uygaqkw5jcFwzrFuY/NDhknTadO31w8GslXgPSOOKAtAZNsnFNrxant9iloWgEQRBEAhClFB+Qlv9IX2Q9Hl6UcnEb7vNh17pUkjcc+euPKBUOD954dsSO21hrF8yKYysYJWD1hpZx6tRtGPXwslhRi9zlUR3Dx9SiPtXhQGkgoLxvWCcNR0+pAwWNn1QP07u8cwfD4+8+KP/ZiXULIus/DwrieFUi8JTu+s+9NCCtFsBqYWo2kZDQJQiCsJmC3MHo2HMGAJhO6UYikvQVqZOehlPFJSgp85ru0h2/+MWorsuudK5RZ64V0lJSdBO8QGwCV0zeGhFNIlfk5b/NVa+olwIUlqPokqeQseMB1bqQ1/4A/vloaMXHo0+vw2P3RzelWHI58MWlS2xP6Ypduqs3fYqDv/4G4EyrXyAIgiCIhPA2gBu+3f62KakbCTMiV8RqSjdaxHTue99Ef19qx+Czle/8bpjgBaIXuFzWhiMWkWuEKHMB4PDT03He/dNDpC4ANP/beSHrruzSCh9v3xuy3kzdAk/r2p3UPbI2D40GZpPUJU4LSOgSBEHEAV7BsHPfftw2aiw6tGxhi9g1w6o5j2HopEcVqRsv7ErnamVutPI1XII3FgK+AO74MScux1bhcMJd1w3v8dKgrBVSuxkfqv9cdBO7AFLdbpR6vTH93fPhZdta5OGy/dmRdzBJ569G4ItLlyhSF8AZUr9ACV2CIAgisTDG+lb1g9omda0gpnTNEG3tgh3C2A6ZywmX4I2FaCoUrOKS5L+venXScfRkke42h5+errxudM8UFJUb329GErfhhqHxtG48krraAWmi1LX9ZERMBGpjsW0CIaFLEAQRBwpyByvVCwAUsQsAHVq2UOoY1i2KPHwgGqx26UZTu2CHLLZL5tZ27vgh2KH88l9yTe83fcgAPLnuTZR6vcp7AJi+WpbtYwtWG+4bCcnlgMNt/20C79LliPULBEEQBEHYh1bq6gnWSH24iSKa2gW70rmizLWzBsEuXtpSofTzxpOit59SXnuujxxEOfLcLHhun4D12/24t08wmbx+ux/9u8jX27ZZ4/hcbIyI1QtT899UxDGldInaRPRFNQRBEERYCnIH4+vN09C9azPVerPduokk98E7UFIUvBFOy3AjZ/nLpvaNJZ1LMjcMDqe8GJGahOkvv4aHb7sJ04cMUGQuIKd0Y2X64AEIeH344tIlMR9LC69eAIAhvXrYfvyaR1VCN5ELQRAEQUDp1TWcjpb/SM1xVzylCwDpqam4u2f4/5/Zlc7l1ESZC8iVCxmelISd77xB5kMhGSlu3H55Mp5973es3+7H+u3yn2Gdug0j7nvXtU0ibsOrFxoNtO+JMaAqpZvsQv+u8j3zJd2u5x9RSreGwCB36CZyqW1QQleHZXs6VvclEBqWSRXVfQmEjczveLy6LyGh9HvjLvSrer3yzhewf9NB5TMxRZvIWgY9xJTu10eexMWeMQk7d02VuZNWPgdXWuLk2GuXFiivle5cAzK23oei7isxfc1rmD7oVtVnD992k+q9mOLl8KqDVLcbE2+/Wfcc0wcPwPSXXrW9T1eb0uUMHLQC/3q6Zn0tnFUzgyUEQRAEYRpt4pCndgFATO626XJDQmsZtIgp3V+2bkC9DleZ2i+zXmR5aIRdVQvxxOf3296Pa8R5g8aq6hbKXpkbZutgSlfLs++pqyfEBC8fHneq8Chu7uRCnXoN8cL7v+keP5HVC5TSJWoblNAlCIJIIPe9eBfm/z4TLXo1C/mM1zI8vix0wFUi0KZ0zTJ27sI4XE31M2nlc3C4XTENO7PCa5cWyP25FsjYch/gdmL6mtcAQJa7q19V3nPEFK82vasVvVrildQVU7rNmsoJjQ8/PoDzLpqIf9z/rK3nIgiCIAgiCGOsL2NMgia5y+VuTUrthoOnQGORsrHI4HgzuHsyBl7mTGg614rM5fCULgDcfnky+ndxon8Xp7IOgCrB++x7v6sSvKdM/P25nE7bU7paxJRu1Q89CKJGQ0KXIAiiGuBily+i4K2JlQx6pHlkMSjWJtRWJq18DhOXP6taEilzAahkbsbO4ab3U6Tu6lcBt1PeV5C8WvQqGiIxffAA+EvsfVKi81cj4HC7MP2lVzGkVw9F6gKy2D2tpC6jygWCIAii5sHFrlbu8iFqdiF23UZCrF2IxPK3/oj2kmocXN6Kiyc5CWXvPB33YWgcsWrBrMwFqlK6SS707+KEJ8mFslfmouyVufD5jSssuOA1y/rPvSir8NkudXmXLiCndEnq1iASXLdAlQsEQRBEVNz34l0AgHGNpwIAHl/2tG31C+MXv4g0jxszhpoXeGaYMXQAxi9+EYCc0l0wwVpFQ01J9vIk7u3fJ07ealFVLViQuco+W+4LeV/UYVnM1xVvxOoF3qW7etOnOPjrb/jw4wPVeWkEQRAEcUbBHzEXh6jFSkbdhig6cRSnThzF4O4O1KnbMG4SduBV52DtR/+1vF9NqVzw+f0oe6d6ntIDQqsWrHLkuVkh63hy95XP7AkFrP/ca3v1gli7AMhSd2bWTdix9R2gSupS/QJRUyGhSxAEUQOxI6VbJz0Np4pLAAAlZdaqFMz256Z53Cgp86KotNSy1I1XsnfSyucQ8AUAAA6XA3PuuyfsNq605Lglcde1WQBfVbKVd/H6SipU54ymasEUaUm6/bo1DV69MH2w/AOHIb16KP2+/7j/2RrXqRsdDkrNEgRBELUCxlhfSZJsyarlrz+CrP6NUFTVnWrHEDMtvI/1ZOFRy1I3XgPRBndPVtKpLqcTL23RF5ridvGqVWh06ygUlZXrfpbhSVHSv9FULUSC9+v27+K0lMY1on9XNzI8sQ/+1cJTuryvVyt1bT8hYRKGQG2MzSYQqlwgCIKooTy+7OmYOnVXzXkM6xYtsLQPH4z2XZm59OyMoQNU1QvRpG7jMRDNlZaMO37MgcPtUioUJq18DkAwkXvHjzm448ecuMjcdW0W4OW/5CLg9eGJB+/FEw/ei4DXp7wPeH1Y10b+u4m2asGIou4r5XRuSWXMx0oEYvUCR+zUJQiCIAiiesh/pG/Mnbr5649g1SbzUub5zRUotvBD/+Vv/aHq0hUlbSTimc7N8KSg7J2n4UlOUioUBncP/nB3cPdkpVIhHrUKjW4dBc/196OsohJrt/mVBYDymoveaKsWDM99zxR4bp8Az+0T4HLaI3OBquFwa0OH6sbKkbV58CS7lOoFQJa6HKpeIGoqlNAlCIKoQbTo1Qz7Nx0EEEzp7ty335YKhmmrXo1Yu5D74B0Yv/hFdGz0MFwucz/zmzF0AKatelVJ6lY3c+67BxOXy/2roqxd12YBJi5/Nq6JXH4eLm6116W9xlirFrQUdV8JeP0h/bjTV8uy1EpvbiLp/NUIbGudryRzTzt4hy5BEARB1CJ47QIfltamyw3Ierzmua3lb/2BB288O+qkrjigyw5e2lKBgZc5AUAlahvdOkpZL6Zj40FRWblu6lhc53I6wyZ4oz53uTdE4vbvIv++7ZK7dnNkbR4aDcw2qnSglG41wQLVfQU1G0roEgRB1CDue/Eu1YA0TiwVDHXS0wCYq13IWf4y0jKsP8okiuKa0o2r5bZvx8YtkSviK6nQrXnQ4nA57K9aKKkEfAE8uS6YKjDz2ixfXLoE21rkYVuLPHxx6ZLYrlWLPzS943LSbQpBEARBVAO6Bbp29OpaZXB3c/cC2qSuFeJRBQEAnuvVYYwjry2KWyJXpNGto+ByOiNu99KWCpXMtSudCwQFrpb+XZy4t0/jqI/vuekhZbF7QFq3Vn8z+ETy2XoigrAJSugSBEHUMPiANE6sg9JWzXkMt42yJjG/PvKk6R5djtinG4l4SV/ejcv7auMJ78e1mvid+a+XUVKuTkLYks69+lnldanXq6RyRUq9Xjy57k08fNtNKPVa61WWXA4EvD7MuHsgAGD6S6/ii0uXoPNXI2K7cAD7h72hvD78/RMxH6/mQQldgiAIovagNwTKrl5dq7y0xXxEb/lbfygCOFJK10o1g1UGd0+OewIXCPbjas9llM4Vue/6xnER2UXlwftLI6l76sTRqKQul9S867Z/VzcaDcy2pYah34wlePervQAACfAFGEuK+aBETDAAjDp0w0JClyAIoobDaxiiTekOnfSo6W155UI0zBg6wPS+8ahm4N24t38f3zoFPuTM4XLgiQfvVeod+OcOoapCT97GjcLw50nzpKCkrDxE9nJBGwk+tEx8b0dFwv5hb+DEJ4cAAE6TNR8EQRAEQVQP+Y/0jXvtgsvlwrndouvtFYekhUP83K6BaHzIWTxlrliR4HI6sXabX6lxMCKyvJWQkRL7D57Pu396xG0y6jZE0YmjUclkLnLF9wYVCZYhmUvURkjoEgRB1HDue/GumFK6p4pLLG3PB6PFwti5C7FgQuSEb6wD0XgiF0Bcu3G5yOUS1whfSYXq83Ayl/fZ6iVprSKmc+ePesBwu6krn0eJjT1pkssRc0pXlLmH9s2x69IIgiAIgrCXtwHcEE3tQlb/Rpa2f35zhemqBS1iSjdRqEXu8ricg4tcLnFFeBeukUQOJ0/Xb/ejfxenKlkbLUdPBb/neP5j42TliJsboUi4pn9/GX2jgcvphOemh5DhcUed1O03I1gjRjKXqE2Q0CUIgqgFRJvSFdO580beaWofntI1OxRNRKxduH9GUM5lpKYqgteuugU7E7nr2iwAEDpETZvGjZaww8hcDhR1X4mMLfdFdeyiq59VpXPHL1uJecP1jzXzvruV1+MWPRXV+USmDx4QU/WCWLVwWstcJgF+qlwgCIIgai+Msb68dsFKSjerv1reJZI+7YNzITLrNdStYIg1nTu4ezI8yUm2iNz/z96dhzdRrX8A/75tylrWUjYXEFQUkVVUFJVNVhEFkUVlUVBxQUEBFVBErlxQURFFBAGRHXGDosgiXPWKV39e9bogigKiIDu00IW05/fHmWmn00kySdM0ab+f55mn7eTMzMkkTU7evPOemr3vB1BwIjVfgVyTdRI2e/3cYd3yShv4mpDMDOoWhpvsXNPL7x0AAAy6Rgp1TCAvazfUTF17qYVCd4jCRwE5nBTNL17bSEQUA6x1dZ+ePdfVNnc8OjHo7Fyrf949IOhtrJOjWaWeOoXRz7yI0c+8GJZyC2YwNxwZuasunoGcTC9yMr1Y1uCfuUtOphfT7h6KaXcP9TvJWZwnLjcgHIpJ/XvrycxCZQnmtvjxQUhZDx6ePQ+ADu6avxeVSQNvQk6mN+hJ0lhqgYiIKOasA9xPjmYP5gZTD9fj8cDjCV/+2fGjB3Pr5oajfu7AtmVwc5t4I5hb+PIKNXvfj/Ss00jPOo3y3YbnLulZp7Hy82ws/TTL1X5S0zPytXVb2sATH587oVmwzh4+KV92bqXy5XB7B/3Y3d7Bk/u7L7d1rhPSca088fEhTZLGUgsUyyKaoSsi7QB87HDTcaVUVaNNJQBPALgEQEsAlQC0V0ptcXmM0QDaG9vXBvCkUmpS4XpORBQ93GTp2oO5brNzQ23vy7S7h+bWkbUHckPNeA0UzLVm1jqxlmYwg7n+AraBTB02GONeXeBYP9e1ELN0raUWWvz4IACg2Zf34NvWr+Rm4EoEgqWh1NMtXaUW4gDFDN1owfEoEVForFm6boQazAV02YVwWPl5dm4N2eNHD+bL2g1VoKxca51bJ/Yau2Yw123Q1hd7dq6Vr+xc04p/Z4WcpWsN5qZ/qBMJat54HwZdI6hUvpzPc5FbTzdAvWM3Vm/LLNQkaQzmRidOiuZfcZVcGAngS8vf1tT2JAC3A/gawAYAvYPc93AAJwC8C+Du0LtIRBR7nLJywxWcDZYZsH18yIB8E4dZbwuWm2CumVnri7Uv9pq3hRFM/Vy7Sf17h1ZL18dEaM2+vAcA8N/GL/gsv1CcSk2pBYp2HI8SEYWZU4mFYIO54WKWKJj3wX7HicFCKbeQF8z1nZWbmp7hd9/2ScwCtXcrXPsJhrXUghnMBYAD78wCoAO7voLML793ICxlF0zBTpJmrZ1LFIuKK6D7k1Jqm4/bdiulqgOAiHRC8APoi5RSOSLiAQfQRFSCmHV0/YmWYK5dxXLlggpw+pLjzfFZMzcc2bbhFK777ItTdq7Vt61fiUh2LgBMWvoW4iu6z0AtfaUWBMhhhm4U4niUiCjMiqtebiD2oG6ogU9vdnbAzFxfAcyi5ImPhzfb930a2qU2FqzfH/bjWrNz7WreeB/Ss05j/uaiL03b5/KyxsR07jOwWTs3uikAOUzQ9SvqJkVThcypVkqxbDIRlUjDltyGh2o/7qpttARyTY8PCb4er92j896AxyFoaJZYiPPEFVswN84hMOmUmexXEGUXUlvM9nv7t61fgcr0usrOnbbiPYzr18t1N+0mLX0LcWU9ridFY3YuxQKOR4mI/As0MVpxZeX6M++DwgU0B7Ytg0rlyxVY72bisqJmnRjNiZtaup74eJTvNwaVypXFgTem+G1rr5trzc41paZnYNHWyETkvNnZSF/ziuv21uxcllugWFVcqTFLRCRbRA6LyFIRObuY+kFEFJOcJka749GJxdCT/J54PYSyAS74KrVgLbHgNphb2EnMnEwdNrjQweRJ/XsDmdlIbet7ErPUDgsKBHOdsnPdqGh8IDmVmYlpK94LaR/BBnOB0pidC0AZGbqRXMgNjkeJiIIX1MRoJYWvUgtm/dtgJi7zxMejZu/7w97HwgaTV/w7C6u/yEZqRqbfdvZgbjgUZmK0PpeXDSoz98YnX2Z2bixQgMpREV1iTaQzdI8DeA7AVui6Yi0APAbgcxFpoZQ6EOH+5BKROwHcCQC47y6gW+fi6goRkU9m2QWnidHs5RaKw8l0/wPAUDmVWgi1xII5iVk0cqqlm9phgc9aub40+/Ie/LfxC37bTB42KHfitFOZwT9uZjAXAD6/sODkE/EVyxQI9DI7l6JEVI5H841FgdeUUq8VRz+IiPyxTowWKEu3ONx9Xa0i2a9TqYVQJzNb+mkWBrYtg5q97/dbizecAk2K5k+gAK5Tdi6gJ3+7vYPHb8mFwk6M1ufysihfRo9Hy/e8x3b8so4TpFmDuczOpVgW0YCuUuq/AP5rWbVVRP4F4D/QE1NMiGR/rIxB82sAIOvejr3QPBGVCtayC33vd64le0G9upHsUi5rdm64JhoDfJdaCOeEZtEqUDDXX3ZuXMUyeHj2PJ9lFx6ft6iw3UP2ySyIJw5PDrq5wG1PLFqZ7++f7nq3dGbnUtSJ1vGodSxKRBTl1gHo/r8v1uGOjuGb1Coc3JQWCJfCTEJmlkio2fv+iNTcLUwN3VCzcQ+8Mwvluxb95Lyp6ZnwxMfjvS/zB477XF4WNW8elS+oy1ILVJIUew1dpdTXIrIDQOvi7gsRUSwINDna9t1/RbA3eYoiO9ep1IK1Zm6o4jxxWNbgn4XaRzDMrNsKZctibN+e+W6bvmoNTmVm4ry6tXFLh7a5tXRRJt4xmFshqQJOHT4V8Jj+snQfn7cIJ9Pz9u0UkA1k0sCb/N4unjh8fuHzuZm61mBu6cvO5aRo0Y7jUSIi94ws3RQA3X21mT7mOox9Zm0Ee5VfuGrZDmxbxphsK3/t3HAEYj3x8SFl+AajctVknDh2ECeOHUSfy+JRuWpygcDu0C55k8W1vKKbLgkxeAIOvDEFZw+fVGCfqdveR6XLr3d1fH9Zuvf2qlnoSfRWb/P9+WP1tsx8QV2WWog9hZvRoOQr9oCuQaAnsSMiogCGLbnN521uJ00rSuHImn103hvI8ebAU7FMbjDXGsgt7DEiNXlaxXLlcDIjL3B6KjMT01etwdi+PXMDuaZf/tqfV27BmwOcPJ1728Q/n8j9/akznnR9fKcsXXswt6iYAd8nFq1kqQWKFRyPEhG5pJTq4bTeDPR+83nka+yGu9xCXt3cvFIL1gnQChuILcpArmneB/sxrFtewNYe2LUGcwHg639/AABIzc5G+X5j8u0rddv7AIBzuuZ9FvFVbsF04J1ZqHnjffmCuk6BXHt2bbhYg7qpRvIJSy1QSVHsAV0RuQTA+QBWBmpLRETuzFvzMYb1bO+6rZnVe0G9uq63K2r2YK45+VkseXzIAADIV7P3VGZmgTq5/liDuVZuJkNr9uU9+Lb1K3h49jxUSCgTkUAuACze9Al++XNf7t8stcAM3WjH8SgRUXhYa+wGY/qY6/DN5+vQvE33kDN7w11uwVo31xrIDVf2b6TM+2A/bm6TP5vYDOxamfVsnZjBXAA4dOx4UMc3Sy+EIyPXrckP9sT/ffZB7t+p6XmPGYO5sSMnBicqi6SIBnRFZAmA3wF8DeAY9CQUjwL4E8BLlnbdAFQEcLGx6hoRqQHgpFLqA0s7L4A3lFJ3WNZdAqA+APNTY2MRMa8LXaeUCnydKhFRjDLLMQRTdsHatrjKNViZ2blxxmS1oU5+Fo0Sy5dHWnp6vnUvjr4v39+jZr4CTzkPstLCk7WRc1Lv56Q3fzB32YxpGDB6XFiOYbIHcq1KZ6kFikYcjxIRRRczmAsA33y+rtDlGsIRcB3YtkxumQVz8rNYC+Q6WfCxwsgbCwZWF3ycFzgbfq0H5cqUQdopPWa1BnMBoEbVKkEHdQEUOObcjQrDO4W3BrM9kGvHUgtUkkQ6Q/d7AAMA3A+gAoD9AN4G8IRS6pCl3WwA9Sx/TzJ+7oYeHJvijcXqPgDWT/19jQUAzgGwK9TOExFFO+ukaaEKlN1rzegFwp/Vm+PNwYDfHsGqi2fk1rktCcFcAPjHiDv83m4Gc8f9/GjYjtnixwfz1dJdNmNagTbTVryHcf16hXwMp0Bu5Url8P3/uS8PUaKpOGboRheOR4mIIsBtYNZensFNuQZrELgwWb1O8kotvJQbzI1EeYRImfnOAb+3e73Z2PfpiiI7/tyNBbMue7X2oHK1ZLz5kXNigBv2YG58vCfV6z1dOeQdUrFSSkGxiK5fEQ3oKqWmAgiYnqOUqu9yfwW+zlFKDQEwJMiuERGVOA/PWpL7u6+g67w1H+f+bs3u9RXUtQdzAZ3Vaz1WYVknLIu1EgtOJi9c5qpdUQRzAeB/befk/m4P5lauWBEnTp7EqczMoIO6vrJxPfFx+O0nZuJS9OJ4lIioyK2DUUd3YFt9oYKboKtSSsxyDb6CwdZArsl6nHBJTc9A+W7Dw1IrN5YMv9aDxArl/bYJNjv37H6jfN5WqUoyUo8b9X2PBleOwXc2rniVymFZBSrxSmsxOyKiEuvCjucVWOerlIJ1/bAlt+VuawZpH561JDfoaw3mxnni8Nz+yejSuXG4u4+pwwZj2t1DS0RW7uSFy/JNihZIVloWpjUKXzD0f23nwHskr8TDrWPzB4vnPPU4KlesCAD5JmgLxCmY64mPw54d0xjMdWTU0I3kQkREVEyMCdPyRV2DmCRtndl++pjrMLBtHKaPuc7HfsRrP044LP00Cys/z8bKz7NjPpg7rFvtAvVzwyW5SiVX7Q4eT8393ROfvy8zVh/Il7F7W+c6ro/PYG7Jp3Iiu8QaBnSJiEqYYUtuw3P7JxdYb83GNV1Qr26Bbe3M4K41mPvM3kkAgNWrRiDt+Ev52i9YtyHUrvs17tUF+SYXi0aTFy7L7ee4VxfkC+baa+XaPT/yHrw4+r6w1c4FkC+Y++LfTyHbW3CkMuepvBIdTyxaiWkr3vO5v8WbPsETi1bmBnPNIC4DuURERGRlBHXzsQZm3Wxnras7sG1cvixcpZQolZOglOqhf8+7WmLqQ4GPE4qpD12Hm9vEF9n+w8EM4JpLsBPFzd3gza2dG4g1UOuLNTs39fP3UK5sGYzoUvBC8UpVkgHoLN1AQd3JD/ZEr9Z5+zAff/M54arzRCVApGvoEhFRhFlLKTw8a0m+8gvDerYvUC7BbB/niUOjaxrip02/5N5mDeZadencGOs/+hEAsH3P3rD1fcG6DQX2t2DdBgztfm3YjhGqYLJvAwVzTaNmvoIyib6zK59r+oyr/QD5Sy28+PdTftuapRcAnan7xKKVAffP0gqxT0TOBDAOwCUAmgEoD+AcpdQuh7aXQ9eQvRxAAoDfAPxDKbXc0qYcgKcA3AqgKoBvAIxTSv3Ltq8447h3AagN4GcAk5VSq8N5/4iIqNitA9Dd/MMMzLoov5Bvu4LE18RW6wB0/++/fU+KFQp7hmu4918Yw7rVdhW0tU565o+bkgtund1vVIGg775Ny1GpTcESXzNWH8DoPjWRevwgThw9mC9g65/P5wJRiceALhFRCWXN0p13y5u5gVlrzdtn77ulwHZOWbqBrF41AgCQWOX+ULrqk1NwOJwB48LwF8x1G8C1clND99ThU672ZS+1YCqbWBa3jn0Ui6fnD8SaWbp3TZycG9j1hYHcECgBsqOyDMK5AG4G8H8APgHQ2amRiPQA8A6ApQAGAsgC0BhAOVvT1wH0ADAGOuB7L4D1ItJGKfWNpd1TAB4GMN44dn8Aq0TkOqVU2C+dJSKi4mHNthWRFBhBWrOcwjefr0PzNnlxWxFJMTJuC2T3uj2eWYM3XKI9G9dXMNdtANdq+LUelCtTBvs2u5sQLf3DeX5vtwZzUz/PuwIssUJ5jOjiwez1+WOx1qBuYCytUBrkcFI0v4SzxhUk697mSYk2Etu1iyi/51r+ErgRFYmHaj+e7+8L6tXNLaXgVKYhkLvKV8v3txnQveDsM/Nl0ZqZtvb1dk4Zuaa04y/53H+kWbNzQwne2rmdEO2pM54EALT48UHH250Cufbs3LENp8CbcbpAUNefAaPHAQD27JgWoGXJc9Z5YwtMeBUsadBE4R9vhaM77g288P+UUpf4ayIicUrpimEiMgzAXNgydEWkEoCdAJYqpR70s69m0Bm5tyulFhjrPAB+APCzUup6Y11NAH8A+KdS6gnL9psAJCulmgZ/Z4mIKFYECrg6TTQZyv5bXNENjz6XlwU89aHrcjNr7bdZWds57N0LKE+gfUSCPZgbSgDXbmh7Qeq29/22OafrbbmTojkFdJ2ycq3BXFOlNr3y1c4NZHgn/bQo7PODYkftei3VrY98GtFjPndPxYDj52jCGrpERKWIfcI0X5OlhcqcJM0elDX/9pdd6yuY6/HE5dbpte6/qGr1+mKtjxvMRGf+jJr5Ch6YMctVMDcQN8FcAJi+cwI85RIKTJBGRUkAVSayiwtmMDeAvgCSATwXoN31AE4DyE3rUUp5ASwH0EVEyhqruwAoA2CxbfvFAC4WkXNc9ImIiGKXnysxwnL5/DqgYFkE69/+SiY43yZeS33W3P0XR/auWSM3XMHc4dd6MLS9YGh7cVVqwQzm+uKmri6Ql6UbLCPTm0oJpVREl1jDkgtERKWItZyCtQxDuKxeNSI3i9bXBGbjXl3gmGFrBnM9njgcO/yiz/336Tsb6z/6MeKlF5yCuIXNzs3x5mDin08EbojA9XOdSiz4Mn3nBDxQa6Lr9lSqtQVwBDrYug7AhQD2AZgHYIpSKttodxGA35VS9rogP0AHcM81fr8IQCaAXx3aAbqUw+/hvhNERBQd7OUUrBm74biE3lp2wV771urmNvGOWbyW/Thmghr7T4FRq3fqQ9dFLFPXqcRCYTNzvd7sgFm5Tnxl59o5ZecCupZunY79HUsvOLn40u7433/WAX5rKxOVLszQJSIqpYYtuS03YzfOE763AzOL1h97hq31d1/BXJNZrzeSJi9clvt7YvnwTBTh1rRGU/HUGU/mq5/7betXcn//X9s5+G/jF3L/Tkyq6Gq/Zj3dYAwZ7hykp0AEyCkT2QWoISJfWZY7Q+x8XQAVoOvnLgTQCcAbACYCeNbSrjqAow7bH7Hcbv48pgqmQdjbERFR6WBk7IZ1cisfWcDitd5mzcbNX2rBf1+sQelITpDmZvKzolKnQz9Uuvz63L9r3pg/qcGp1EIg+zYthzc7O3BDACOfZmJuaaMUkJOjIrrEGmboEhGVYqFMgBaINYvWKdvWmmFrz+L1hDGwHC7WerkA8I8RdxR6n6NmvoIcbw7KJPq/NH5ao6nwZnjx4uj78MCMWQCA5+6/Ew/PnodvW7+CZl/eUyAz97ntj+Gu5PEB+zB95wSMbTjFcZI0u+YXXoBvftqOzVu3B9wvRY1DYaoBFgc9+dl4pdQMY90WEUkCcK+ITFJKHQcgAJxGwvYMJ7ftiIioFAh1AjQ3+8ybiK3gBFq+s3ijc7KtYd1q5/4ejnq5w6/1wOvNDlhmoU6Hfkg7lQ6PJx4LPlYY2l6/XadnnUbNG+/DgXdmFQjmHv+/j1ClleM8qwX4miCNiAKLvk/OREQU81avGoG04y85ZtuuXjXCMYvXX6kFX4q6jq49mN74hKUAAQAASURBVFuYEgtmvVyzZu7EP58IWDc3Ky0Lz4+8J9+6h156Dcqbg5yTWfkyc+cc/AfmHPxHUH1yW0933PChub8zSzcEKq44MnTD5bDx0/7P9hGABOgSCoDOsHXKrq1mud38WU1E7AFcezsiIqJCUUr1sNS+tXPM4g0imJu7fVHX0nUqtRAqs2ZuuTJlkLrtfezbvMJv+7RT6VjwscLcDV6MvLFm7npvdjZS0zNQvuuwAsHcYOzbtBzlypYJqp4u6+iWHkpFdok1zNAlIqKIs5ZNMGvuBhPM7dK5cVjr6PqakM0q1GCuNRs3mInPpjWamlsKw8zOLQpmpu6A0eMAAPGeOMeMXWbpllpmbVv7MNcMyOZY2t0oIhVsdXQbA8hCXs3cHwCUBdAQ+evomt/y/BiOThMREfljzQy2ZvIGs721lm445C/74CyU7FwzGxfQGbH7PvUfxHUy8saaSA0QVHYK5p7T7Tb8/sGbfrcz6+kO75T/u965G/PfV9bRJcqPGbpERFSs0o6/hLTjLwW1jb86ugvWbcC4VxcElb1blMFct9m4pmmNpmJao6mO2bmmBVMez/d3sJm5dtN3TsCLfz+FsollfbaxZulSqfKu8bOrbX0XABkAvjf+fh86Y7ev2UBEPAD6AfhIKZVprP4QOsB7i21/twL4XinFCdGIiCiiAmTy+t3O121TH7oON7eJd529W5TBXDMb101GLpBXL7dOh3656+zB3FfX677UqFbFcR/m+kPHTrjq575Ny5H6+Xu5k6jZg7kA6+gS2TFDl4iIYtqCdRswtPu1BbJszRq9F5x9JoZ2v9bv9iYz8xcoXHkFIC+YG0xWLqDLLPiapC7cgVy7zLRMLJsxLaz7JABKgOywlkEIGxG5yfi1lfGzm4gcBHBQKbVVKfW9iCwEMFlE4gB8DT0x2jAATyml0gBAKfWNiKwA8IKIJAD4HcAIAOfAErxVSh0QkecBPCoiqcb++gHoAKBXEd9dIiKiImUPzP733x9g6kPX4dHn1rrdRsG4CiYuLl69vslbqBrzXm920Bm5ZpkFs16ulRnINe3cuMpxHzs3rnJdRzcUIpJSFPWXKbqoGJyoLJIY0CUiophkLbtgn1zNKlBg1wwCezxxucHc+LjCXcASbDDXzMgFgDhPHJ4feU9umYXxs18vVF+CUTaxrKtJ0qhEsX8Se8X4uRVAO+P3uwD8CeB+ALUA7AIwWillr5MyFMA/AEwBUBXAtwC6KqW+trUbDyANwAMAagP4GcDNSqk1hbsrRERExaPg5Gp5/vvvD3Jvb3FFtwLB3bxgrngB5QEKH8x1O+mZyZz8DAA8nvh8P33xeOJxxlW98Ocn74XazaCx7AJRHpZcICKimOSr7IJZwsFexmH7nr35snHN0gymjh0uyP19xoPOpQ7cyvHmBBXM9WZ48eLo+/Di6Ptyyyy8OPo+jJ/9OtLS00Pux/jG/wyqvdtJ0jgxWrAkaidF05eYOi7tLG2ylFITlFJnKaXKKKXOdwjmQimVrpQarZSqrZQqp5S6TCm1xaFdtlJqilKqnlKqrFKqqVLqrRBPLhERUZQRr/l+Ctvka9bsXbMsQx6VW4Q2HJm5wZRYyMjKwoKPVe4EaAAwd4O3wGRoVrNSvLlB4HBKrFAewzuJ40Rp1rILnBytZFNKISfCS6xhhi4REcUsa4kEQGfZ+rvdXzZvOLJzrROgBWJm5ZoZuXb2YK693II/iUkVkXb4JNIOn3S9jcmcJM0pU9c6MdrZ549Dh2suwMK5rK1LREREpdY65MsWFa+1Fq8xeVq+SJGPbF5l7icuLj637EKwQsnMzcjKyg3iWgWaCO2+Hh7XxwnGvk3LAQCV2jhXYrJm6bL0ApVmDOgSEVHM8jc5mv32Pn1n5wvuAjoA7PXm5P4dHxcXdHauGcQFgDKJZQJm5loDuf7q9IYazAWA57Y/hruSxwMAxjacguk7JwS1va+g7rjhQzFt7gJ889N2AMDmrduD2m/pJYCKzhq6REREFDo3wUQjU9fMKLWVChCvkZmbG8wNJTvXGsgNVDPXXl7BKZgL+J4IzeT1ZuPPLwKXW6jTsX9ukDYYiRXKY0QXD2avz9+/kU+nYOZjPVh6oRRgDV3/GNAlIqJSwR78tQd4CzMJmptALpC/vII/1rq5wQZzAeCBcybn/u7NOB1yUPeBWhMLrB83XGfkDhg9Luh+EREREZVWTsFfa5C3MHVzzRILgVjLK/hjLbNgD+QGKyMzK6Sg7r5Ny1GnY3+fQd3hnQpVlYIo5rGGLhERlUrWYG5hyiw8P/Ke3AnNfJnWaCqeOuNJeDO8juUV7HzVzR02aQqGTpiMoRMmo1xiWZ/bZ6Rl5v6+ePpUZFr+DoY5SRoVkhIgOz6yCxEREUW1cAVzAZ1pW6dDP79t/JVXsAtUauHuLuK33MIZV+WVS5i93psb1A3Wvk3LUa5sGcd6uibW0i25VI6K6BJrGNAlIqJS7cXR9xV6EjR/rFm5gYK542e/jgdmzMr9256dm+3NwZyD/8Ccg//Ai787Z+4+cM5kxNtqCcd74jC24ZSg++5mkjROkEZEREQUkrAEcwE9gZm/CcrcBnNH3lgTQ9vndcUpO9frzcbx//sIf37iu9yCtS/DOwlmrw99ArV9m5bDm51dYP3Fl+ZWW+jOoC6VRgzoEhFRqdOn7+yIHMcM5roN5PrKzDXFe+JwV/L4fCUV7DLSMjFvUv7yCounT80tvRAsX0Hd5hdeAAC5E6QxsEtERETkjjUAWdhgbiBugrlmINdfZq7J44nPl4Hrlic+HpXa9AopU9esp2s18ukUBnWpVGNAl4iIShVr7dzClFowjZr5CsokOk94lZWWFVKJhQVTHnesnTtv0gQsmPJ4vpIKbhU2qJttmTwO0LV0zaAuwMBuIHE5ORFdiIiIKKrlZueGY2fDr/U4lkBwm5nrNAGar9q5s1J0tm2VVp0dbz/jql7wePLKP1WqkgxAl16Yu1GFlKnrq/SCQ1BXGQuDu7FOATkRXmINA7pERFSqWIO54Si1kOPNcTUhmhu+ArmFcdfEvGzewtbTHTB6XL5M3XHDh2LZjGkFArsM6hIREREFFq7sXK83G/s2ryiwPu1UuquauSZ/gVwrjyfeZw3dtFPpmJXiRaIRyE09fhCj++RNsuaJjw9rPV1bUNdUYAVRScOALhERlUpFWTcX0OUW4jyRf5stl1gWwyZNQaWKFQAAJ06eDMt+p++cgBf/fqpApi5QMLDLoK6NUpDs7IguREREVDr4y861ZspGSmKF8rivhwfPrjyQL6hrKop6uiOfTsHcjQpzN+YFo5mlG9sUOClaIAzoEhFRqTTnnTVFtm+3tXOL0sxHH3ZcH++JwwO1JuKBWhNDKr/gj7UMw+at28O6byIiIiIqyCk7122phaL27MoDPm8LZz1dK9bVpdKCAV0iIipVunRuDAD48ffdRbL/YIK55mRo4eQ0KZrV4ulTAQDLZkwLqfxC2cSyBSZIsxo3fGjQ+yz5mKFLREREkeO21II5GVq4jz0rJfCxC1NP1ylL12Svqxv0AShKKCgV2SXWMKBLRESlyupVI8K2L/uEaMEGc+2TobkxbNIUlEssG/R2VstmTMv9fWzDKUFl6k7fOQGecgl+g7omll0gIiIiipw6Hfqh0uXXuyq1MPLGmgUmQ3PL683Gn5+8F9K2APKVRqjTsX/Q2bqBsnRHPp2XmMssXSqpGNAlIqJSq7BlF6wTohUmmBvMZGjZ3hy8+HtwE6dZJ0az82achjfjdNBBXadauibW0s1PFBCXkxPRhYiIiKLf84/0CNu+zDILCz5WAbNz7cFct5OhAcB9PZxr9oYq7VR60Nm6gbJ0AZZeiHkKyMlREV1iDQO6RERU6oSr7EKcJw7TGk0tdDDXraETfAdmnVgnRvMV1F08fSoWT58adPkFf6UX7LV0GdQlIiIiymcdAHz3xbqw7dBtmQUABYK5wQiUnWtOimY3uk/NAus88fHwxIc2cZubLF0GdakkY0CXiIhKnXCVXXh+5D3ISssKagK0UIO5Zvt4TxzuSh6PB84JHNyd+ejD+YK6dmbphVvHPoqyQZZxCJSly6AuERERkTOlVPhSc6Gzc92UWQB0dq4p2GAuAHg88ajSqjOqtOqMM67qVeD2Pz95D15vXvZsYpVkAEDq8YLlHWav92L2+tAmbtu3aTnKlS0TVFA3pAMRRSkGdImIiArJbTB3/OzXc38PNphrmjdpAhZMeRwZPjJqyyWWxbBJeeUTZj76cMB9moHZB2pNxAO1Jrouv+BmgjRrULf04qRoREREVHQysrJcT4IWat1c06wUb26JBjdlEp5decD1viu16RVUPV23QV0Ts3RjCydF848BXSIiKnX69J1dLMcNZRK0YL34++N+M2d98WacxrIZ07BsxjTXNXXdTJA2bvjQoPtCRERERO5FotRCODiVXQCQG5Cdu1Fh7kaFjMws14HdYOvpBtNfomjGgC4REZU66z/6EQAQH1f4t8EXR98X9DahZucWpcXTp+b7Pdig7oDR4/wGdgGU3rILCszQJSIioiKx4OPgA7ORDub6K7tgstbSnb3eG9REaYkVymN4J/GZqWvN0qXYoACoHBXRJdYwoEtERKXWjAfdlUqIJQ+cMxnxnuDe3s1aulbBBnVf/Pspn5nBLLtAREREVHoFKrvgq5auJz7edZZu6ufvBczUJSpJGNAlIqJSa847a4q7C2GXkZaJeZMmhGVfwQR1/SntZRcECnE5ORFdiIiIiIrDGVf1cj1BWyCz13uRkZnluqZuYoXyfuvpUgxRzNANhAFdIiIqdbp0bgwA+PH33SUyqBtOi6dPRaaPCdjsAk2SRkRERET5Pf9Ij+LuQlilnUrHrBR3NX3dmL3e67r0gpt6upwYjUoKBnSJiKjUWb1qRL6g7gMzZsVcYDfeE4cHzpkc1DZ3TQyuvfVYbksvhDIhW4nHGrpERERU0DoA+O6LdRjaXjC0vUQsuPvwzc6Tk0Urt6UXAN9ZupwYLdYo5KjILrGGAV0iIiqVrEFdIPaydbO9OchwmTlbqWIFAMCJkydDOlYwpReYpUtEREQUmFKqB4ygrum7L9b5aB1eaX4mJwuGxxOPM67qFZZ9+RNM6QVfWbqcGI1KGgZ0iYio1Fq9agTSjr8UkyUYFkx53HWW7sxHHy708dyWXgiUpTtk+IJC94WIiIioJFBK9VBKiVJKzHWxVIJhVor7cgiFFUzpBdbSpdKAAV0iIir17CUYYiWoO2/ShAJZuuUSy2LYpMJNYlZYTlm6zS+8AACweev24uhS8VKKJReIiIgokHwlGGIpsGuVWKE87u4iuK+Hc0B1dJ/ClXuo1CZwRnCgWrqsoxsbOCmafwzoEhERIXBQd847a8JWa3fk1GcLvQ9fXvz9cb8ZsqHW0QUKV0t33PChub8zS5eIiIgoP3sJBnv5hecf6RETgd4/P3kPAOD15g+oJlZJBgCkFqLcgyc+HgBc19O1Yx1dKkkY0CUiIjI4TZZmLj/+vjt3fahB3cTy5QEAqSdPFUlQ967k8T5vs9bRDTWoG0wtXSfWLN3SFtSNy8mJ6EJERESxxyzBYP5tTpY2tL3kBni/+2JdyEHdBR/nZSGGa2I0ax3dKq06AwCO/99HBdo9u/JA7u+hZunOXu/F3I3KVekFp7IL1jq6IqKYqRu9FAClVESXWMOALhERkYV9sjQnoQZ1/zHijiIL6pq1dM2grr3swsxHHy705GiA+1q6TmUXxg0fWrpLLxARERG543d2tMIEdStV1ZmyaccPhi2oCyBfUNcM7NqFI0sX0Jm6gbJ0921ajnJlyxQI6lqydAFm6lIMY0CXiIjIxj5ZmscTh7TjLxWYQC0URRXUzUjLxIIpj+dOluYkHJOjAe5KL0zfOQGecgmOQd3SRlhDl4iIiILgNFma5e/cWruhBHVnvnMgX1D37i4SlsBu2ql0vLpe5S5OrFm6heF2gjSnWrojn07B3I2xl41Z6iggJ0dFdIkEERlgZIfvLey+GNAlIiLywQzsHjv8Yr51pgdmzAppv/ag7tAJkzF0wmQ8v2hpSPt74JzJ+YK48yZNwLxJE/xuU5haum6zdJ1q6RIRERGRew6B3dworr3OrlvWoC6QF9i9u4tg1sTw1Oj1FdQ1De8khZ4gjSiWiEhVAM8D2B+O/TGgS0REVAz+MeKOAuu+2/FrSEHdjLTMgAFcUzhq6QbDqfRC6cMMXSIiIgq/uLj4kNMKZ75zAE0vK1hx4Pv/hBYkdsssuwAUvvSCq+M51NKl2KByVESXCJgO4FsA68OxMwZ0iYiIQvTi6PtC3tZag9dayuG7Hb8Wul/+hKuWrlv+snTPPn9cqZscjYiIiChcXt/klcCtnD3/SA9Lhq94rVnAwZiV4q78genZlQfyZe8WdZaur1q6Jk6MRpEgIlcCuBXAveHaJwO6REREQUiscn+h9zHnnTW5NXg9PurdFiVrLd3iyNI1J0YD9ORoDOwSERERBS/UidEA2IK5OQmF7YvHEx9U+3BNkOaGUy1dy+Ro3Y2apgzsUpEQkQQArwF4RikVtuwdBnSJiIhCZM2yDYY1mGvW513/0Y8AgPj44N6a4z1xPidB86cwWbq3jn0UZRPLum5vnyBt3PChWDZjWoHAbomlgLicnIguREREVKLlTowWCmsgOBzBXEBn6gYjXBOkuWUvvTDy6RRrUBcACtafoGKkoFRkFwA1ROQry3JnmO7MOABlAUwN0/4AMKBLREQUFGt5hB9/3x10UNfa3jrZmmnek+5q4ea2dzEBmpPCZOlme3MwfWdwx3QqvTBu+NCg9kFERERE+SdGG9pegsrUtZdaMNeLSMhFRANNgBZIsGUXRnTxILFC+aC2ccrSHfl0CuZujEjtVIoNh5RSl1iW1+wNRKSTkdEdaNlitD8XwHgA9ymlMsLZWQZ0iYiIgrR61Yh8Qd0HZsxyHdgtzlILdqFk6Tpl545tOAVjG04JuK3TBGnWLN2Si5OiERERUdjlpud+98U610HdcJdaKIxQyy54s7Oxb9Py3L/rdOyPSm16oU7H/v6PxwnSYoZSgMrJieji0r8BXOhiGWS0nwlgM4BtIlJVRKoCKANAjL+D+2bCovg/TRIREcUga1AXcM7WnfPOGjwwY1a+xeSUnRtpoWTpOmXnZqZlwptxOmBQl1m6REREROGhlOphTGSWW37BKVv3+Ud6YGh7yV3yti/eYC6Qv+xCYSZHSzuVjrkbVcDJ2QJNkEYUiFLqlFJqu4tlj7FJY+hyHkctywAAdY3fQy7DwIAuERFRiFavGlGgBIM1eGtm49r5y859ftHSIumrL4WppQvo7Nx4TxwWT5+KzLTMgO2dsnRNJXViNFFghi4REREVCaP8Qr5sXWsA17nOrgRX8LYIWbN03QR17eUW6nTsD0+8npDNEx8fMEvXqfSCiROjRZecHBXRpYj0B9DetqwHcMj4fZbvTf1jQJeIiKiQ7Nm6Vh5PHNKOv5RvccrONbf/bkfYJj51JZgsXXu5hbENp8CbcRqLp+svluM9cSFl6ZplF0r0xGhERERERcSerVuQeJVSkrcUzM41to+4Z1cecF16YUQXD8qVLZNbbqFOx/7IyMzC7PU6Pj17vRcZmVlBl16wTI7WnUFdCiel1Dal1BbrAmA/gEzj75A//DGgS0REFAZmtq6b4K2v7U3RmKV769hH4SmXkFtuwR7MBRBylq617MLZ548rgZm6CnE5ORFdiIiIqPQxA7sFl+DKK8ya6H6StXBwW3rBXjs37VR6bjDXNHu911XpBWuW7sinUxjUjUJKqYgusYYBXSIioihhzdKNZFDXmqXri7V2rlMw11TYLF1AZ+qWvKAuERERUdRbBwDf/2ddxIO6Jl9Zuv5KLdi5Kb1gz9JlUJciRSk1RCl1ZmH3w4AuERFRlLCWbohk6YWRU5/1e7u11IK/YC6gs3TdTJBmN274UCybMY3lF4iIiIiKibUe7/f/8VG9oRgEKrVgF0qWLlAwqFvYfhMVJQZ0iYiIokhxlF5IPXnK7+3W7NzMtEyfwVyT29ILTqzlF0oMTopGREREMcII6gKIXOmFh2/OK7Mwd2PBS9+tpRYCBXNNbrJ0nYx8mom5UUEpqJzILrGGAV0iIqIoE8ksXWt27rIZ0/y2HdtwCuI97oYOgUov2OvoEhEREVHUiFiW7sM310RagMnQTG6DuYC7LF2iWOYJ3ISIiIgiafWqEUiscj8AnaU7atDAsO175NRnA2bkWpnlFgKVWrBbPH0qBowe5/P26Tsn4IFaE133I7YpZs0SERFRzFBK9RARBegs3fueCl/Wqr8ArlN2rrV2btqpdMc2vphZutaJ1KzMOrq+AsQikmLNWKbIUUBMZs1GEjN0iYiIopA1S3fohMlhKb/gL5jrlJ1769hH4SmXgOk7J7gqtWDnZoI0fzgxGhEREVGxyc3SvbuLhKX8QijBXLN2rr9J0HwJlKXrVEcXAOvoUkyIaIauiLQD8LHDTceVUlWNNpUAPAHgEgAtAVQC0F4ptcXlMeIAjANwF4DaAH4GMFkptbpwvSciIoqc1atGoE/f2Vj/0Y8ACl9+wR7MDVReAdC1c2fsnBBUqQWrQFm6vjS/8AJ889P2EjMxmiggLienuLtBBo5HiYiIAjOydFNgBDXDUX7BGsx1k2lr1s4NptRCOIx8OgXDO0lEjkW+5SiOn/0prgzdkQDaWJZOltuSANwOwAtgQwj7fgrAJACzAHQDsA3AKhHhNytERBRTVq8agbTjL4VlX8EGc03BlloIB+vEaMzSpSLE8SgREZEfSqkeSqncyGZhsnQDTXzmT9qp9IgFc+1ERBmBbaKoUlwB3Z+UUtssy1eW23YrpaorpToBmB3MTkWkJoCHAfxTKfWsUupjpdRd0FkY/wxf94mIiCIv1LILwUx8ZjInLCtMMNesvxuK5hdeAADYvHV7CQjq6hq6kVzIFY5HiYiI3CnUJGnBTHxmV6lNr6BLLYSDpewCwNILkad0Dd1ILrEm6mroKqUKcxa7ACgDYLFt/WIAF4vIOYXYNxERUbGw1tO1B3WfX7TUb43dYCdBA3QgNtubg3hPXKEyc7O9OZi+c0JI244bPjRfUJcokjgeJSIiymOdGMwpS3fWxB5+6+wGW2oB0PVzzfbFkZ078umUoDOJiSIpojV0LZaISA0AxwCsB/CIUmpPGPZ7EYBMAPZCgz8YPxsD+D0MxyEiIooYaz1dc5I0O/v6puefi9///CvkUguFDeaGw7jhQ0OqwUvkEsejRERE7q0D0N2cJM2J9bYml3bHrp+/DCmYaypsZu6ILh4kVihfqH0QRatIB3SPA3gOwFYAJwC0APAYgM9FpIVS6kAh918dwDGHrIojltsdicidAO4EANx3F9CtcyG7QkREFD72SdICsU+iFkwwNxyBXDflFsomlsWtYx8t9sBxkVNgGYToEpXj0XxjUeA1pdRrhewHERFR2NgnSQuksJOohSMr15xUjWKPQmyWQYikiAZ0lVL/BfBfy6qtIvIvAP+BnpgitOsy8wgAp0c84PSExqD5NQCQdW/zWUNERFHHGtT1eOJw7PCLju0Sq9yf7+9ggrnhcOvYR+EplxCw3ML0nRPwQK2JEeoVkRat41HrWJSIiCgamaUX8gK74lUqJ8Hezh74LY7SBeHOzhWRFGvpCaLiVuw1dJVSXwPYAaB1GHZ3BEA1EbEPmKtZbiciIopZq1eNQNrxl3wGc4G8mrvFJdubg8y0zALrxzacgrENp+RbZ2bplmQChbicnIguFByOR4mIiNxTSvVQSolTMNe8HcZEasXFKTu3Tsf+qNSmF+p07J+7LrFCeQzvJBjeyfl7V8vkaJwYLcKUUhFdYk1x1dC185XJEKwfAJQF0BD565aZn2zdXafqwuVlyuGeqjWQFBdf/FHxUiH2/rkiIQfA4RwvXjl+ANuygpv0iIhKrtWrRgAomKkbSS/+/VS+v8c2nAJvxmkAwAO1JqJsYllM3zkB03dOwNiGUwKWXhgyfAEWzh1apH2mUi+mxqNff/11F4/H84RSqjaiIEmDSq0cETmmlPrW6/U+3apVq1+Ku0NEFB2MEg3F8kHeV3Zu2ql0zN2oMKKLB3U69se+Tcuxb9Ny1OnYHxmZWY77Gvl0is9gL1FxKvaArohcAuB8ACvDsLsPAWQBuAXAk5b1twL4XikVlgkoLi9TDo/XqIPkunWAMmWAAgkYFH4M6DpSCjWzTuPxv8pg8uG9DOoSkaMBo8eh+YUXYNzwog+I+qqdm5mWma/0g3WyM3+lF5pfeAG++Wk7Nm/dHv7ORgpr6Ea9WBuPfv31113Kli07q379+lnly5c/GhcXx4ESFQulFE6fPu05ceJE2/3796f83//938hWrVp9WNz9IqLoMryT4OJLu2Pk0ykROZ6v7FxzkrXZ670FgrqV2vSKSN/IJQXk8KozvyL6bb6ILBGRKSLSW0Q6iMhD0IPePwG8ZGnXTURuAnCVseoaEblJRLrZ9ucVkdfNv41JLJ4H8KiIjBaRdiIyG0AH6MkuwuKeqjV0MLdsWQZzqXiJAGXLILluXdxTpWZx94aIooy19MI3P0UmIJrtzSlQO3dswymI9+QfcsR74vBArYm5JRh8lV6IRBCaSpeSMB71eDxP1K9fP6tixYrpDOZScRIRlClTxlujRo3j9evX9yYkJIwt7j4RUVTJLbvwv0JOklZYaafS8020Nnu9FxmZWbnlFxIrlMeILsWe80jkWqSfrd8DGADgfgAVAOwH8DaAJ5RShyztZgOoZ/l7kvFzN4D6lvXxxmI1HkAagAcA1AbwM4CblVJrwnIPACTFxevMXKJoUSYBSXF88yGi/KKh9AJQMDsXQG55BWumbomlFDN0o0vMj0eVUrXLly9/NBz7IgqXihUrnlJKnVPc/SCi6GGZRE0BwMzHehR5lq5TuQVrdq7V7PXe3HIKbrJ0OTFaZKkcfmftT0QzdJVSU5VSTZVSVZRSCUqps5RSdyql9tna1dcFtgss9W3tRCk1xLYuWyk1RSlVTylV1jjeW+G8H3EAM3MpuoiweB4RBTRt7oIi3b9TuQWn7Fw7s76uvxq6gK6jS1RYJWQ8GsfMXIo2xjyA/JBERE7WAUWfpTuiiwflypYpUG7Bnp1r5YmPR6U2vVCpTS/HoC+Qf2I0EYlM3QiiABgDIiIiKuHM0gtFXXbBqdxCZlqm30BtvCcuYDC3+YUXAEBs19ElIiIiKqUildUaqHauEzPQO3ej8hn0Hfl0Sr6gbjj6SlRYDOgSERGVcGbphaIUanbu4ulTA2bmloQ6unE5ORFdiIiIiMh/dq5p7sbAF71EakI30hQUlMqJ6BJrGNAlAMCWtSm4s1sPdD73fLStXRc9mzTFwwNvxb83bsxt89rUf6J11erwegu+GP61ew9aV62Odxctyl03acS9aF21Onpc1MRxdkJzf/Z93tWjJ1pXrY7bO3dx7KvebxJ6NG7iePv+vX/isurJaJNcG8cOH3Z9DgDAe/o03np9PoZ364EO9Rrg8hq10LVRY4zqNwDrlq/I1881S5aiddWk3KVNcm3c0LwVXp78FDIzMmz3dRpaV03yc+6S8O6iN4PqKxFRKIqq7EIo2bmhYNkFopLpzTffrHrJJZc0ql69erNy5cq1rFu37sWdOnVq+NZbb1U224wePbquiLQ6ffp0ge1//vnnMiLSasaMGTXMdX369KkvIq1q167dNNuhjrW5P/s+L7300kYi0qpFixYXOPXV3G+tWrWaOt3+66+/JsTHx7dKSEhouW/fvqAmOcjMzJRp06Ylt2rVqlHlypWbezyelsnJyU3bt29/7ssvv1zd2s+ZM2cmmf0XkVYJCQktzzrrrCb33XffGadOncpX+iDYc0dEVFSGdxLMfCz8CbvB1M4lKgkY0CUsf3UOxtx6G85q2AATXpqJ51csx+1jHgIAfPWvTwq173IVKuDQvv346pOC+/lg5SpUrJTouF3FSon433++xB+//ZZvfcapU9iydq3P7QBg3fLlyMnJgff0aax/a7Xrvp5MTcXdPXvhhfET0ejii/HE7Jfxynvv4MEpk1G+YkVMvvd+bH6/4Fwm/3xjAeZv+BAvrFyGyzu2x8IZL2DmxCdcH5eIKBKsZRfCHdQNNTs3GLFcdkGMSdEiuRDFkilTptQcNGhQwwYNGmTMmjVr18qVK3956KGH9gHAxo0bKwfa3p9y5crlHDx4MGHt2rWV7Le99dZb1StWrOiYklOxYsWcb775puL333+f78UtNTU1bv369dV8bQcAc+fOTcrJyYHX65X58+dXd9vXo0ePxl1xxRXnP/HEE2ddfPHFp2bPnv37mjVrdjz11FN7K1asmPPAAw+cs3Dhwmr27ebPn//bxo0bt69cufLXa6655sTLL79ce8SIEWe6PS4RUYTkFtAtilq69nILdTr2R0ZmVsDsXIpSSk+KFskl1gT1jTGVTItnzUK7Hj0wcdZLuetaX3M1bhw82DGzNhiVq1ZBvfNaY93ylbj0mmty13/z+Tb8uWsXuvfvj5Rlywpsd+5FF+HIgYP4YMVK3PnoI7nrN69ZC6UU2nTsiP99+aXjMVOWr0DDxhfi5IlUpCxbjn533emqr8+MfQQ//fcbzEl5H00uuSTfbV373oSfv/0OGbbMWwA4/+ImOKtBAwDAZe3b44+dv+G9xUvw0LSpiIvjdyZEFB1WrxqBPn1nY/1HP+YGdcNVyiDbm4MZDtm5y2ZMC8v+AV12YcDocWHbHxFFj5dffrlWp06djq1cuXK3ZXXqQw89dMgpszYYlStXzm7YsOHJRYsWJfXq1SvVXL9+/frEvXv3lr3xxhsPv/3220n27Ro1anTq0KFDCfPnz0+aMWPGX+b6N998s6pSCldfffXxr7/+2jHDYMWKFTXOO++89LS0tPhly5YljR8//oCbvg4bNuzs77//vuIHH3zwc4cOHU5ab7v77ruPfPbZZ+VPnTpVYHDZunXrU02aNMkEgBtvvPHEb7/9VnblypU15s+f/0c8M9OIKEqYdXRFRAHAzMd6FGkZg7RT6a5KKRDFKkabCCeOHkNSrZqOt4UjINmjfz98vGYNMk6dyl2Xsnw5mrdpg7pnn+Vzu279bsYHK1flW7du+Qq0v+46lKtQwXGb7/7zJfb8uhM9+vdD9/4346dvvsVOF5MAHfjrL3y4chVuHDK4QDDX1KhZUzS77NKA+7qgWVNkpqcHXe7B6vNNm3FHl25od3Z9XH3G2ehzyaWYO+2ZkPdHRATooK41U3fA6HEYMHpc2DN2w52dG/uYoUvkz/Hjxz01a9YsWAsAQDgCkgMHDjz84YcfVktNTc19YVq4cGFSq1at0urVq5fla7u+ffsefuutt/Jl2C5ZsiSpS5cuRytUqOCY9bBx48aKu3fvLtuvX7/DN9100+EffvihwldffVUuUB9///33hPfeey9p4MCBB+3BXNOVV16Zfu211zreZtWsWbNTGRkZcfv37w85eWf16tWVW7ZseUGlSpWaV6hQoUX9+vWbPPzww3VC3R8RkcU6QGfpDu8kYSnBYC+3UNSlFkSEBXUjgBm6/vHTFuGili2xdtlyvDlzJnb/+mvY99/h+p5QSmFLir6sIiszE5vefQ89+vfzu133fv3w565d+PaLLwAAB/ftw5db/4XuA3xvl7JsOeLi4tC1703obux/3fLlPtub/u/Tz5CdnY2runZ2e7d8+mvPH0isXBlVqru+wi6fvbt24aEBt6DO2Wfj6fmv47mli3HLvfcg41TA8TsRUUDWoK7pGxdffLk1tuEUeDNOh712rhXr6BKVLE2bNj359ttvJ02cOLHWd999VzbwFsEZNGjQUaUUlixZUhUA0tPTJSUlpdrAgQP9fvs+bNiww3v37i27YcOGigCwa9euhG3btlUePHiwz+3mz5+fFBcXhzvuuOPIsGHDDgPA66+/XiAD2O7DDz+slJ2djeuvv/54UHfOwZ49e8omJiZm16pVK6TrjH/88ccyAwYMOPfMM8/MXLBgwW/Lly//9d57791/8uRJfnYkokIzMnXz1VwobAkGe7kFNxOhheLiS7ubv3ZnUJeKG0suhEnrqqEF78Lty2NHgt7mkeefwyODhmDm45Mw8/FJqFK9Oi5r3w49bxmIyzt0KHSfylesiPbXXYeU5cvRte9N2LpuHbKystDxhl5Y+vIrPrc7o349NL/8cqxbvgLNLrsUH6xchRp1auOSq67CuuUrC7TPyszEhrffwaXtrkFyHZ1AcHHrS/DBylW494nH/WYbH/hLX0lX68z85caUUrBe6hcXF1dgP9nZ2fB6vTiVloYta1Lw8ftrMHrqP0LOKPn52+9wOisLjzz3DBIr67Jxra+5OqR9ERE5Wb1qBBKr3F8k+w53qQWr5hdegG9+2o7NW7djyPAFWDg3PCUjipwC4gpZwogoEBFpVdx9AACl1P8Fu81rr722+6abbmo4ZcqUM6dMmXJm1apVvW3btj0xdOjQw7179z5R2D5Vrlw5p0uXLkeXLFmSdPfddx9ZsmRJ1dOnT8vgwYOPTJkypbav7S644IKsli1bpi1YsCDp2muvPTlv3rzqycnJp6+77rrURYsWFQjSpqeny9q1a6u3adPmRP369U8DQLNmzU6uXr06adasWX/6Gxvu3bu3DAA0aNAg07o+JyenwFjUvp/s7GycPn0ax44di1+yZEnV9evXV5s8efIejye0j3pffPFFxdOnT8vChQt3V69e3XzxSvW7ERFREJRSPYyAaG6EtKhLMITDyKdTMPOxHmYAunug9lQYCjmK42d/+C0rod6552LxJ1sxJ2Utbn/4IZx/8cXYsjYF9/e+Ca8/82xYjtF9QD98uWUrDv39N9YtX4FrunfLDVYG2m7jO+8iKzMT61asRLeb+/oMzG5d9wFSjx/PzcwFgB4D+uPgvv34z5atfo+jlHN6/RsvvIg2NWrlLk/cPaJAm76tL0ebGrXQsX5DPHX/SNw4dDBuvnN4wPvmy/kXN4EnIQHj7xiOTe+9jyMHD4a8LyIiX9KOv4S04y8FbhiA04RoRWXc8KH5Jkdjpi5RydC0adPMH3/88cd169b9fP/99++78MIL0z/66KNqffr0OW/s2LFhucx/8ODBhz///PPKe/bs8SxZsiSpU6dOxyzBSp8GDhx4OCUlpXp6erqsXLkyqU+fPod9BWaXLFlSNTU1Nf6WW245bN3+4MGDCe+9957fga+vseiECRNqlylTppW59O7d+xx7m+bNmzcpU6ZMq5o1azYfNWpU/QEDBhx87LHHQh5Atm7d+pTH41E33nhjgwULFlT7888/mQRERGGnlOqhlBJYSjDEAmvQmVm6VJz45hwmoWTGRpP4+Hi0vPIKtLzyCgC6vMHIPn0xd9p09B0+DJWrVkW88S1/TnY2YPvGPycn29iP81Oq9dVXI6l2LSx7ZTY+37QZzy1b4qpfnW64Ac+OexTzpj+DnT/+hKkLXvfZNmXZcpSrUAGXXNUWqcf01WqXd+gAT0IC1i5bjss7tPe5ba0zzgAA/L13L+qfd17u+usGDsCl7fRkbg8NuMVx22cWL0LNM+ri6KHDWPryK3hr3nw0adUKPQb0z20T79EDf//nTrc5q0EDvLR6Fd54cSaeuGsEsjIz0bhlC9z/5CS0anulz/tARFQYoU6S5jQhWlEaN3wops1dkJupS0RaKJmx0cTj8aBbt25p3bp1SwN0eYPOnTufN2PGjDpjxow5kJycnO3xeBQAeL1eSUhIyBcB9Xq9YuzHMTLas2fP1Bo1apyeOnVqrU8++aTy0qVLXdUZGzJkyJHx48efNXbs2Lq//PJL+eXLl//mq+3ixYuTypUrl9OtW7fUQ4cOxQPA9ddff3z8+PFq4cKFSf6yjc8666wsAPjtt9/KNmvWLDdL9+677z7UtWvXEwBw0003neu07aJFi3bWq1cva//+/Z4XXnih1uLFi5Mvu+yyk/fdd19uYDmYc9ekSZPMt99++5fp06fXHjFixDlZWVnSpEmTk1OnTt3bo0ePtMBnjYjIPSNbN+QCpvb6uZFw8aXdmaVLxY4ZuuQouU4d9Bp0G7K9XvyxcycAoFpyDQDAwX37C7Q311Wvmey4P13Xti8WvzQLVZOSXJdySKxcGVd364o3nn8RF7ZojnMaNXJsd/jAAWzb/DEyTp1C9wuboEP9BuhQvwFuaN4S3tOnsWVtCk6m+r5SrFXbKxEXF4dPPvwo3/oatWqhcYsWaNyiBRISyjhu27DxhWjcogWuvLYTnl+xDGef2xAzH5+E9JN5NW+rJevz4v/c5U1Md8nVV+Gl1auwefdvePndt+FJSMCofgMKNdEaEZET6yRpoYj3xGFswynh7FJAoQSeixcnRSMKVv369U8PGjToUHZ2tnz//fdlAcCcOG337t0J9vZ//PFHAgDUrl3b5+RqvXv3Pvzaa6/VrlatmtdtKYfq1avndOzY8fgrr7xS+6KLLjrVsmXLDKd2f/zxh+fTTz+tnJGREXfOOec0TU5Obp6cnNz8wgsvvNjr9cqGDRuqHj161Odnr65du6bGxcXh/fffr2Jdf/bZZ3uvvvrqU1dfffUpeyDW1KJFi/Srr7761M0333xi06ZNv9arVy/ziSeeOPPEiRO5xwv23PXs2TP1k08++eXo0aP/fffdd3ckJCSovn37nrdv3z4mBBFRVCvqCdEARH1piJJAKU6KFggDuoT9e/c6rt/1yy8AgKSatQAArdq2BQBsXvN+gbab338fZcuVw8WtW/s8zvW33oKrunbF7Q8/FFR92ZuHD8dVXbtg0AMjfbb5YMUqZHu9eGTGs3h1zXv5ltFTn0Zmejo2vVuw36aadeuia9+b8M7CN/D9V1+57ptdmbJlMXLykzhy8CDeen1+7nozs3bzmjUFttn8/hrj3F3iuL/W11yN20bej/STJ/Hn7t0h942IyMnqVQVLyQRj8fSp8GacxtiGUzC24RTEezi0IKLg/PrrrwWCjACwffv2cgBw5plnegGgc+fOqQCwdOnSava2K1eurFa2bFnVvn17n7PI3n333Yfat29/bPTo0fuCqS97//33HzC2K/jNvGHevHlJ2dnZMm3atD1r1qzZYV0mT578R0ZGRtwbb7xRoN+mc8455/T1119/eOnSpcmbN2+u6LpzNuXLl1f/+Mc//jhy5IjnmWeeyc20CPXclS9fXl1//fWpo0eP3p+enh63Y8cO5wwHIqJiMnu9FxmZWajTUV8hW1QTohFFG37DSuh/xZW4pO1VaHddD9StVw8nU1Px2YYNeHv+AnS68QbUPktPFFbv3HPRa9BtePnJp3D04CG0uqotsr3Z+PeGDXh7wUIMHzcWlatW9Xmceueei2eXLg66f83bXI7mbS7z22bd8hWoW68eeg8dAhHJv/0VbfDmzJlYu2w5rr/NuWwCAIx5Zhr++O033N3zBtww6DZc2u4aVK5aFcePHsV///05Dh84gIqJiQH7e033bmjcsgUWv/Qy+g4fhnLly/s4d17j3L2R79ytnr8A//3357ji2k6odcYZOH74MBY+/wKS69RGwwsvDHzCiIgibPH0qbh17KO5v/tjlktofuEFMZhpGxpRYNYskR8tWrS46LLLLkvt1avXsXPPPTfz2LFj8SkpKVWWLl2a3L1796PnnXdeFqBr7fbr1+/Q008/fcaBAwcSOnTocMLr9UpKSkqVZcuWJY8aNeqv5ORkn/9sTZs2zdy4cePOYPvXpUuXtC5duvgtNbB8+fKkM844I+vhhx8+aJ/voWvXrqkvvfRS7SVLltR48MEHfV5u9frrr+/p0KFDuR49epzfv3//Q9dee+2JpKSk7EOHDsVv3bq10qFDhxISExMDvpjccsstx//5z3+eeuWVV2qPGzfuQGJiogrm3E2fPj35k08+SezWrdvxevXqZR08eNDz7LPP1klOTj7dqlWrdJenjYgoYmav92JEFw8qtekVMDvXnNTs4ku7M9M2yilOKuwXA7qE+ydNwmcfbcCcp6fiyMGDiIuPx9kNG+K+SU9gwIi787V97IXnUf+887B22TKsmPMaJC4ODRo1wvgXX0CvQbcVS/9//vY7/PLDD7jrsUcLBHMBfYnddQMHYOGMF/Dnrt04o349x/0kVq6MOSlr8M4bi7D+rdVIWbYc6adOoWpSEi5s3gwTXnoRnfv0dtWnERPG4/7eN+Ht+Qsx8F6d/fbYCzMczt35xrm7NXfb85o0wb83bMTLk3Xwt3K1amh++WWYPHcOypWPbG0gIiK3AgVyTWZph1BLPBBRyTNx4sQ/P/zwwypTp06te/jw4YS4uDhVv379zMcee2zvhAkTDljbLlmyZHejRo0yli1blrRgwYKacXFxqmHDhhnPPffc7lGjRh0qjv5/9tln5Xfs2FH+4Ycf/stp8l6Px4N+/fodfvnll2tv3769zAUXXJDltJ/q1avnbNu27efnn3++xqpVq5LeeuutpPT09Lhq1ap5mzRpcuqFF17YNXz4cFcTdzz55JN/9unT57znnnsu+YknnjgAuD93LVu2PLV+/frKkydPPvPIkSOeKlWqeC+55JK0JUuW/JaYmBh716QSUangNivXnHztf/9Zh5mP9WBQl2KW+JpRtTSTdW/7PSkf1K6HGg0bRKo7BADg8zSQQzt/Q7e/Xc3vUeyea/lLcXeBwuSu8j6vHqUYkljlfgDAshnTivQ4ZnauqTBZugNGjwMA7NlRtH0+67yxBb8pDFL5Kg1Vg8unh6M7rv340U3/p5QqWMuHSoRvv/12V7NmzYoleEnkz7ffflujWbNm9Yu7H0QUW8xJ0Yo6a9bMzrUK9ZjDO+kholKq0GNFKqhy9cbq0i6LInrMTctbx9T4mYXuiIiICIAOuBbFPgeMHocBo8cVyMr95qftRXJMIiIiIoop64C8rNlwmvlYDwzvJBjeSSzBXPEW5TGJIoEBXSIiolKuS+fGAMIfYLVn5AKAJz4Oe3ZMQ4drLsg95oDR4/Id1xoELhEBX6OGbiQXIiIiolihlOoBS4A1XJwycgHxKpWTYD+mNahrDQIz2FtcFJTKiegSaxjQJSIiKuVWrxoR9qCuNZhrBnH37JiG337StXYXzh2aG9Q1j+uUyesU8CUiIiKiksUIsAJAWIKo+YO54lVKiV5yEmzHzA3qFszkzVvPwC5FGwZ0iYiICKtXjciXNVuYAKo9mGsGce0Wzh2aL1vXyhMf5xjwZWCXiIiIqMQKSxmEgsHcvCCunTWom19eWQazT8zapWjiKe4OEBERUXRYOHcohgxfgM1bt+cGdUOZtMxNMNd+XH/MPpn7NidEixlKsQwCERERUQBKqR4ikgKguxnUDWXCMrfBXOtx/d1u9qng/qmoKAA5Oaq4uxHVmKFLREREuaylEOz1b92wZtC6Cea67ZOvTF4iIiIiKjn81bYNfl+Bg7lu+2SWbIBjNi9R5DGgS0RERPlYM2aDLXEQShDYLXtg1xMfG8MYARCXkxPRhYiIiChWFdUkaeFgBneLux8lngJUTk5El1jDkgtERERUQIdrLsgtveBU4qD5hRc4lmNofuEFRRrUBQKXaCAiIiKi2GaUX1AAMLyTc/z04ku7h1SSIRwY1KXiFhupLURERBRR1tILTuyTlE2buwADRo8r8mBuTDJq6EZyISIiIioB/KbnWksyzHysR+6kZVQSKKicyC6xhhm6RERE5MhXJqybScpipRwCEREREUUnf5OVWSdPcw7iirfoekZU/PhpiwAAW9am4M5uPdD53PPRtnZd9GzSFA8PvBX/3rgxt81rU/+J1lWrw+st+Lr41+49aF21Ot5dtCh33aQR96J11erocVET5DjUIzH3Z9/nXT16onXV6ri9cxfHvur9JqFH4yaOt+/f+ycuq56MNsm1cezwYdfn4K4e12NY1+6Ot7276E20rpqEv3bvKdAPc2lb50zcenV7vP/mkqD2vWbJUrSumoQ/fvvNdV+JiIqTr0nKPPFx2LNjGvbsmBa2CdFKBmboEgXy5ptvVr3kkksaVa9evVm5cuVa1q1b9+JOnTo1fOuttyqbbUaPHl1XRFqdPn26wPY///xzGRFpNWPGjBrmuj59+tQXkVa1a9dumu3wf2Huz77PSy+9tJGItGrRooXjZQrmfmvVqtXU6fZff/01IT4+vlVCQkLLffv2uU6gufTSSxu1atWqkdNtM2bMqCEirX7++ecy9n6YS/ny5Vs0btz4whdeeCEpmH3PnDkzSURaff/992Xd9pWIqLhZ6+zmEa85eVm4JkSj4qNUTkSXWMOALmH5q3Mw5tbbcFbDBpjw0kw8v2I5bh/zEADgq399Uqh9l6tQAYf27cdXnxTczwcrV6FipUTH7SpWSsT//vNlgSBnxqlT2LJ2rc/tAGDd8uXIycmB9/RprH9rdaH6H0i1GjUwf8OHmL/hQ/xj3muoVKUynrp/JDa9936RHpeIKBqYgV0GcYmoMKZMmVJz0KBBDRs0aJAxa9asXStXrvzloYce2gcAGzdurBxoe3/KlSuXc/DgwYS1a9dWst/21ltvVa9YsaLjJ7iKFSvmfPPNNxXtQc7U1NS49evXV/O1HQDMnTs3KScnB16vV+bPn1+9MP0PpFq1at6NGzdu37hx4/a5c+f+Xrly5exRo0bVX7BgQbWiPC4RUTQwJyhjEJdKIwZ0CYtnzUK7Hj0wcdZLuLpbV7S+5mrcOHgwnl26GPdNeqJQ+65ctQpaXXUV1i1fmW/9N59vw5+7dqHddT0dtzv3ootwVoMG+GBF/u02r1kLpRQu79DB5zFTlq9Aw8YXovaZZyJl2fJC9T+QhDIJuLh1a1zcujWu6dEdzy1bgkpVquDdRW8W6XGJiIiISoqXX365VqdOnY6tXLly98CBA49ff/31qQ899NChjRs37nz55Zf3FmbflStXzr7ssstSFy1alC9rdf369Yl79+4t26VLl6NO2zVq1OjU2WefnTl//vx827355ptVlVK46qqrjvs65ooVK2qcd9556XXq1MlatmxZgWzZcEpISFAdO3Y82bFjx5O33nrrsfXr1/9aqVKl7Pnz59cIvDURERHFKgZ0CSeOHkNSrZqOt8XFFf4p0qN/P3y8Zg0yTp3KXZeyfDmat2mDumef5XO7bv1uxgcrV+Vbt275CrS/7jqUq1DBcZvv/vMl9vy6Ez3690P3/jfjp2++xc4ITtBTITERZ5/bEH/vLdRnDyybPQd9L70cbWufgQ71GmBQuw74eM3aMPWSiIgiSRQQl5MT0YUolhw/ftxTs2bNgnUUAMTHxxd6/wMHDjz84YcfVktNTc0d2C5cuDCpVatWafXq1cvytV3fvn0Pv/XWW/kybJcsWZLUpUuXoxUqVHD8R9u4cWPF3bt3l+3Xr9/hm2666fAPP/xQ4auvvipX6DvhUpUqVXLq16+f8ddff5UJ3Nq3p556qmaDBg0uKleuXMvKlSs3b9KkyYWLFi2qGqZuEhER+afASdECYECXcFHLlli7bDnenDkTu3/9Nez773B9TyilsCVFl7fJyszEpnffQ4/+/fxu171fP/y5axe+/eILAMDBffvw5dZ/ofsA39ulLFuOuLg4dO17E7ob+1+3PLgsXa/XW2BxqgHsJDs7G3//+SfOqH9OUMe0+mDlKrw4YSI69+mDF1Yux1Nz56BDr+tx4uixkPdJREREFK2aNm168u23306aOHFire+++y7sdVwHDRp0VCmFJUuWVAWA9PR0SUlJqTZw4EC/ky0MGzbs8N69e8tu2LChIgDs2rUrYdu2bZUHDx7sc7v58+cnxcXF4Y477jgybNiwwwDw+uuvB5Wle/r06QKL27Go1+vF/v37y9SrVy8zmGNazZ49u/qTTz55Vu/evY+sWrXql7lz5/5+/fXXHz18+DAn1CYiIooSDOgSHnn+OZx1zjmY+fgk3HTJpejU4FyMv2MYtm3eHJb9l69YEe2vuw4pRmB167p1yMrKQscbevnd7oz69dD88suxbvkKADrQWaNObVxy1VWO7bMyM7Hh7XdwabtrkFynDuqdey4ubn0JPli5yvUg+NttX6BNjVoFlqkPjva5jRn0PbhvH5575FGknUjFHWN8tw/kf19+iXMvugjDx43BJVdfhSs7X4shox5Er0G3hrxPIiIqTpwUjcif1157bfdZZ52VOWXKlDObNWvWpFq1as169ux5zttvv12o+rmmypUr53Tp0uXokiVLkgBgyZIlVU+fPi2DBw8+4m+7Cy64IKtly5ZpCxYsSAKAefPmVU9OTj593XXXpTq1T09Pl7Vr11Zv06bNifr1659u2rRpZrNmzU6uXr06yWlSNidff/11YpkyZVrZlzFjxtTztY0Z9N21a1fC7bfffnZqamr8hAkT9rk6oIPPP/888fzzzz/17LPP7uvZs2dqv379jj/99NP7R40adSjUfRIREQVDQUHl5ER0iTX8ljWMWlct0jkPXPnymN9xqaN6556LxZ9sxbfbvsAXH3+M/335FbasTcFHq9/G3eMfwx1jHi50v7oP6IcH+vTFob//xrrlK3BN925IrBx4jN59QD+89PgkPPTPp7FuxUp0u7mvzzIQW9d9gNTjx3MzcwGgx4D++Ofoh/GfLVtxeYf2AY93XpMmmPDSCwX3nfIB5j/7XIH1B/7ahzY1auX+LSKY8vpcNLnkkoDH8qVxixZ4a958PDNmHK7u0Q3NLr3UZ4kJIiIiIpOItCruPiil/i/YbZo2bZr5448//rhhw4bEDz74oPJXX32V+NFHH1Vbu3Zt9TFjxvw1ffr0kIOTpsGDBx++6aabzt+zZ49nyZIlSZ06dTpWvXr1gJ/eBg4cePjJJ588Mz09/Y+VK1cm9enT57CvMhBLliypmpqaGn/LLbcctm4/bty4s997773KvXv3PhHoeI0aNUp/7bXXdtnXr169uurMmTPr2NcfOHAgoUyZMrmPu4hgzpw5v3Xo0OFkoGP50rp165OLFy9OHjx48Fk33njjsY4dO56sVKlS7H3SJSIiKsEY0A2jUIKp0SI+Ph4tr7wCLa+8AoAubzCyT1/MnTYdfYcPQ+WqVRHv0U+XnOxswJP/qZOTk23sx/kp1frqq5FUuxaWvTIbn2/ajOeWLXHVr0433IBnxz2KedOfwc4ff8LUBa/7bJuybDnKVaiAS65qi9Rjep6Kyzt0gCchAWuXLXcV0K2QWBGNW7QosH7H/753bF89ORnPr1wGlZODvb/vwuwpT+Op+0bi/CYXof755+e2i/fE43SW85VvOdl6fGyeux4D+iMrMxPvvbkYb70+H56EBFxxbSeM+scU1K13dsD7QERE0eXUqT3r/+//7or0BEXMpCuFQgmmRguPx4Nu3bqldevWLQ3Q5Q06d+583owZM+qMGTPmQHJycrbH41EA4PV6JSEhIV+xO6/XK8Z+HIvg9ezZM7VGjRqnp06dWuuTTz6pvHTpUld1xoYMGXJk/PjxZ40dO7buL7/8Un758uW/+Wq7ePHipHLlyuV069Yt9dChQ/EAcP311x8fP368WrhwYZKbgG7FihWzr7766lP29V999ZXjt/vVq1f3vvPOO79kZ2djx44d5aZMmVJ35MiR9S+55JL0Fi1aZJjtPB6PysrKcsyKyM7OFkBPsAYA99577+GMjAxZtGhR8uLFi2t6PB51zTXXHH/ppZf+aNSokc+aw0REROFy8viO9Z+tbcfxsx8M6JKj5Dp10GvQbXjukUfxx86duKhVK1RL1v9LB/ftxxn181/1dXDffgBA9ZrJjvvTdW37YvHMl1AtORmXd+jgqh+JlSvj6m5d8cbzL+LCFs1xTqNGju0OHziAbZs/RrbXi+4XNilw+5a1KTiZmoqKlSq5Oq5bngRPbgD4olat0KhZUwy88mq8MOFxvLAyr3Zv9eRkfLvtC8d9HNy/H3FxcaiapDO8RQS9hw5B76FDcOLYMWzb/DFenDARj91+BxZu2hDW/hMRUdFTSnUt7j4QxZr69eufHjRo0KGJEyee9f3335dt3779KXPitN27dydccMEF+QKLf/zxRwIA1K5d2+fkar179z48Z86c2tWrVz/tJrgKANWrV8/p2LHj8VdeeaX2RRdddKply5YZTu3++OMPz6efflo5OztbzjnnnKb22zds2FD16NGjcdWqVQtrpqvH41FmALh9+/anLr/88pOtW7e+aNSoUWdu2bIlN2idlJR0eteuXYlO+/jrr78S4uLiULt2bS+gx+1jxow5NGbMmEMHDx6Mf/fddytPmDDhrL59+zb47rvvIjfbMBERlVocPwfGGrqE/Xv3Oq7f9csvAICkmrqkQKu2bQEAm9e8X6Dt5vffR9ly5XBx69Y+j3P9rbfgqq5dcfvDDwU1Y/HNw4fjqq5dMOiBkT7bfLBiFbK9Xjwy41m8uua9fMvoqU8jMz0dm94t2O9wq3/eeeg77A589tEG/PD117nrW13VFvv37sWP//1vvvZKKWxZm4LGLVugQmLBMXblqlXRufeN6HTDDdj5E8fPREREVPL8+uuvCU7rt2/fXg4AzjzzTC8AdO7cORUAli5dWs3eduXKldXKli2r2rdv77PUwN13332offv2x0aPHr3P43Gf13L//fcfMLbb76vNvHnzkrKzs2XatGl71qxZs8O6TJ48+Y+MjIy4N954o0C/w61Zs2aZgwYNOrB169YqW7duzc3qbdeuXeq+ffvK/Otf/8qX6ZuTk4OUlJSqTZo0OVmlSpUCwebk5OTs4cOHH+3Zs+eRX375pXxR95+IiIjcYYYuof8VV+KStleh3XU9ULdePZxMTcVnGzbg7fkL0OnGG1D7rDMB6Fq7vQbdhpeffApHDx5Cq6vaItubjX9v2IC3FyzE8HFjUblqVZ/HqXfuuXh26eKg+9e8zeVo3uYyv23WLV+BuvXqoffQIRCR/Ntf0QZvzpyJtcuW4/rbbgn6+MEaPOoBvPPGIsyb9iyeX7EUANDt5r5YOWcuHripH4Y+NBrnXnQhjh0+gncWvoFff/gBM99elbv9Px4YhQqJiWh6aWtUq1EDe3buxLoVK3F5+3ZF3nciIiKiSGvRosVFl112WWqvXr2OnXvuuZnHjh2LT0lJqbJ06dLk7t27Hz3vvPOyAF1rt1+/foeefvrpMw4cOJDQoUOHE16vV1JSUqosW7YsedSoUX8lJyf7nH2sadOmmRs3btwZbP+6dOmS1qVLlzR/bZYvX550xhlnZD388MMH7fM9dO3aNfWll16qvWTJkhoPPvjgYR+7CJsnn3xy/9KlS5OffPLJups3b/4VAO66667Dc+bMqdmrV6/zRo0ata9Zs2bpBw4c8MyfPz95x44dFVavXr3D3H7AgAH1EhMTs6+44oqTtWvXPv3TTz+VW716dVLbtm1dZTUTERFR0WNAl3D/pEn47KMNmPP0VBw5eBBx8fE4u2FD3DfpCQwYcXe+to+98Dzqn3ce1i5bhhVzXoPExaFBo0YY/+IL6DXotmLp/8/ffodffvgBdz32aIFgLqAvsbtu4AAsnPEC/ty1u0C5iHCrnpyMfncNx8IZL+Dnb79Do2ZNUa58ebya8j7mTp2G5bNfxYF9+1C+QgU0uaQVZq95Dy2uaJO7fbPLLsWaJcvwwYqVSDtxAsm1a6PbzX1x56PjirTfRERERMVh4sSJf3744YdVpk6dWvfw4cMJcXFxqn79+pmPPfbY3gkTJhywtl2yZMnuRo0aZSxbtixpwYIFNePi4lTDhg0znnvuud2jRo0qltp3n332WfkdO3aUf/jhh/9ymrzX4/GgX79+h19++eXa27dvL2MvFxFuZ5xxhvf2228/8PLLL9f+7LPPyl955ZXpiYmJ6pNPPvl53LhxdefMmVPr77//TihfvnxOs2bNTq5du/bnrl275gasr7jiirQ333yzxurVq5PS0tLik5OTT/fu3fvIM88882dR9puIiIjcE6Uc5w0o1WTd235Pyge166FGwwaR6g4BAPg8DeTQzt/Q7W9X83sUu+da/lLcXaAwuat8kV89ShF05O8C8/CUemedN7bgN4VExezbb7/d1axZs5iauINKh2+//bZGs2bN6hd3P4iIiEo61tAlIiIiIiIiIiIiihEM6BIRERERERERERHFCAZ0iYiIiIiIiIiIiGIEA7pEREREREREREREMYIBXSIiIiIiIiIiIqIYwYBuCHIAQKni7gZRHqX085KIiIhKg5ycnBwp7k4QWSn9+YgfkoiIiCKAAd0QHM7JBrKyirsbRHmyTuNwjre4e0FEREQRICL709PTyxV3P4isTp48WUFEfi/ufhAREZUGDOiG4JVjh3Dwr31AZiYzdal4KQVkZuHgX3/hleMHirs3REREFAFer/fJXbt2lTl58mR5ZupScVJKISsry3Po0KGqu3bt8pw+fXp6cfeJiIioNPAUdwdi0basDEw+tA/3eE8jKS6eUfGIYODcSQ6AwzlevHL8ALZlnSru7hAREVEEtGzZcv3XX399386dO59QStUGkzSo+OSIyFGl1L+8Xu/UVq1a/VLcHSIiIioNGNAN0basDGw7sLe4u1F6CEtcEBEREZlatmy5HsD64u4HEREREUUev80nIiIiIiIiIiIiihEM6BIRERERERERERHFCAZ0iYiIiIiIiIiIiGIEA7pEREREREREREREMYIBXSIiIiIiIiIiIqIYwYAuERERERERERERUYwQpVRx94FKKRG5Uyn1WnH3o7Tg+Y4snu/I4zmPLJ5vIop1fB2LLJ7vyOM5jyye78jjOafSjBm6VJzuLO4OlDI835HF8x15POeRxfNNRLGOr2ORxfMdeTznkcXzHXk851RqMaBLREREREREREREFCMY0CUiIiIiIiIiIiKKEQzoUnFirZvI4vmOLJ7vyOM5jyyebyKKdXwdiyye78jjOY8snu/I4zmnUouTohERERERERERERHFCGboEhEREREREREREcUIBnSJiIiIiIiIiIiIYgQDulQoIjJaRNaIyD4RUSIyyUe7wSKyWkR2G+0W+tlneRGZJCK/iEimiPwtImtFpEwQ/UoQkf8ZxxoW/D2LTtF0vkVkuIisE5E/ReSkiHwvImOCeZxiQTSdc2Pbi0TkIxFJE5HDIrJARKqHfg+jS7jPt4gsNG63Ly+47M91IvKpiBwRkaMi8pmI9Ar5DkaZaDvfxj4GiciXInLKOOefisjFId1BIirxou192rKPEjkWBaLrnJeG8Wg0nW9jW45FwbFoOEXbOTf2wfEoRT0GdKmwhgOoCeDdAO1uBdAQwAYAJ3w1EpEEAB8AGArgOQDXArgHwF4A8UH062EANYJoHyui6Xw/DmA/gAcAXAdgBYCnACwJsF2siZpzLiJ1AWwBUB7ATQDuBdAJwFoRKSmv52E934aDANrYlucDdUREugJ4H/p5fguAgQD+BvCOiPQItH2MiJrzDQAi8jSAVwGkAOhhHHcTgAputieiUilq3qdtSupYFIiuc14axqNRc745Fs2HY9HwiZpzDnA8SrHDU9wdoJh3kVIqR0Q8AO72066LUioHyH1j8uUhAC2N/f5hWb/abYdEpAGACQDuBLDY7XYxIprOd0ul1EHL3x+LiAB4UkQaKKV+c7GPWBBN53wMgAQAPZVSx4xj/QVgK4AbALztYh/RLtznGwCylFLbQujLIAB/AuinlMo2jvURgN3QA7uUEPYZbaLmfItIGwCPAOitlHrXclNJOM9EVHSi6X0axv5L8lgUiK5zXhrGo9F0vjkWzcOxaPhEzTnneJRiSUn5Fo2KifmCGq520N8Or7INLoI1G8ByAJ8VYh9RKZrOt23wbPrS+HlGsPuLVtF0zgFcDyDFHEAbx/0XgD0ASsSlV0VwvgujDICT5gDaOG42gDSUkPfPKDvfIwD8bhs8ExH5FWXv06YSOxYFouucl4bxaDSdb3AsGnS7QirxY1Eg6s45x6MUM0rMiwDFPhE5G8BZAH4TkbkickJEMkRkk4g0d7mPWwBcAmBcEXa1RAjH+XZwDYAcADvC1c+SpDDnXETKAzgHwPcON/8AoHHYO1xy1BSRQyLiFZEdIjJORNxcNvsagHNFZLyI1BCRZBF5HEB9ALOKtMexLdTz3RbAtyIyVnQtRK/oWoh9i7rDREQAx6LFgePRyOJYtNhwLBp5HI9SiceALkWTusbPcQAaAOgPYACAZABbjAGITyJSDcAMAOOUUoeKsqMlRKHOt52INIWuXzZfKfV3ODtaghTmnFcDIACOOtx2BECJmYwizL6BvrTwZuiskq0ApgKYE2hDpdRHxjYPQ9fhOmD83lsp9UkR9TfWfYMQzzf0/0cn6MyIMQC6AfgJwEopYZN/EFHU4lg08jgejSyORSPvG3AsGmnfgONRKgVYQ5dyiUgn6ALjgWxVSrUrgi6YXzCcgq7LdMro11cAfoUuuu8v2+EZADsBvF4EfQu7EnC+c4lIHQDvQZ//0eHvanjE+DkX46fyc1tUiYLzDaXUC7ZV60QkDcCDIjJNKfWLr21F5HLo2ofrALwJfe6HAFglItcppT4uij6HKtbPN/T/RyUA7ZRSXwOAiGwC8B2Ax6BfY4ioBIuC17FSNRYFSsQ5zxUL49EYP98ci4agNI1Fgdg/5+B4lGIIA7pk9W8AF7pod6qIjn/Y+PmZObgAAKXUHyKyHUALXxuKyGXQb24dAVQREQCobNxcXkSqAjiulHIagBSXmD3fViKSBP2mLdCF6lPD3tPwieVzfhR6EOeU/VANOjMi2hT3+fZlGYAHoS+J9TegewnAD0qpWyzr1ovIp9AZWK7+RyIo1s/3YQBlzMEzoGulGYNofxNkEFHJUdyvY6VtLArE8Dm3iqHxaCyfb45Fw6ekjkWB2D/nHI9SzGBAl3IZb+rbi7ELvwFIh+9vff0VQb8QQDyALQ63zTSWagCOFaqHYRTj51s3EqkMYD2AJABXKaX+DGsPwyyWz7lS6pSI7AJwkcPNjaEvJYoqUXC+ffGXYWJ1MYBXHNZ/CX0ZVlQpAef7BwDNfWwfbQEQIioCUfA6VqrGokDMn3PdKIbGo7F8vjkWDasSORYFSsQ553iUYgZr6FLUUEqdBpAC4CoRqWiuN2o5NULejLVOPgTQ3rYMMG571vg7rQi6HbMKeb4hIhWM7c8B0Fkp9WsRdrdEKOw5B/A+gB4iUsWybVsA9YzbyJ2B0AOyQOd7P4DWDusvBRC1HxajkNvz/Q6AJBG5xFwhInHQdcwCbUtEVGgci0Yex6ORxbFo1OBYNPI4HqUShxm6VCjGC1195H050FhEbjJ+X2epy9QYeTOflgdQz9Juq1LqoPH7EwD+AyBFRJ4DUM5YdwyWmTyN2T0fB9BQKbVbKbUf+g3P2rf6xq8/K6W2FPrORoFoOd/G6tUAroSeeKKiUePJtNNyjJgWZef8GQC3AnhfRKYCqAJgurG/d8J1n4tTOM+3iNSDrje2HLouXFkAN0JfEjtHKbXTclyn8/0SgGdFZCl0/TIAGATgCujnfcyLsvP9OnTtvtUiMgHAIQB3Qn/A7BzO+01EJUe0vE+XlrEoED3n3Fhd4sejUXa+ORblWDTsouycczxKsUMpxYVLyAuAhdDfdDkt9S3tJvlp1862z0sBfAxdV+c4gHcBnGtrM8l+DIe+1TfaDCvu81QSz7ef/SsAQ4r7XJXEc26svxi6RtxJ6FpmCwEkFfd5isbzDV3j7V0AuwFkQF9i+DWA+wDEuTzftwD4wjjXR43fBxT3eSrB57sO9AeWI8Y+PofOuCr2c8WFC5foXKLtfdrWpj5K2Fg02s65n/2XmPFoNJ1vYz3Hoi7PNzgWjdVzzvEol5hYRCkFIiIiIiIiIiIiIop+rKFLREREREREREREFCMY0CUiIiIiIiIiIiKKEQzoEhEREREREREREcUIBnSJiIiIiIiIiIiIYgQDukREREREREREREQxggFdIiIiIiIiIiIiohjBgC5RFBGRSSKiirsfACAiW0Rki+Xv5kb/qhfBsZSITArTvhYa+1PW/vtpHzXnvLQRkXbG+Y+zrT/T8hgqEelUXH0kIiIiIiIiijYM6BKRL/cYi6k5gCcAhD2gWwT2A2iD/P2n6NMO+jllfy86AP343RvpDhERERERERFFO09xd4CIopNS6sfi7kMhZCqlthV3J3wREQGQoJTKKu6+RCPjvGwTkXLF3RciIiIiIiKiaMMMXaIoJyKVRWSWiPwlIpki8rOIjDKCgmabdsal6dcbbQ+JyEERWSwiVW37SxaRZSJyQkSOisgCYzslIu0s7XJLLojIEAALjJt+sVwKX99YlNHGepx2DvuMF5EpIrJPRE4Zx7jIx/1uJiLvG31MF5HPROSq0M8kICItROQTEckQkT9FZCIAcWjnEZFHRWS7cc7/EpHn7AFGEWkgIuuM+3LAaHOneW4s7XYZj8XtIrIdQBaAHsHcTxG5RkQ2iUiqiJwUkfUi0sTWpoux/XERSTOeK48HeY7OEZElxvMnU0S+EZEbbW3OFZE3ReR3o8+/ichsEalma9daRDaIyGHjHP0mIq8Yt02Czs4FgNPmcyqYvhIRERERERGVRszQJYpiRm3RFAAtATwO4H/QgcAZAJIBPGbb5EUAawEMBNAIwHQA2QAGW9q8DeBiAI8C+BVAHwAvBehKCoApACYA6Atgr7F+H4A6QdylSUafZwD4CMAlAN63NxKRlgA+AfBfAMMBnAJwN4CNInKFUur/gjimuc8aADZDl2MYDCATwBgAZzs0XwygJ4BpAP4N4EIATwGoD32+ICJlAGwAUA66tMMBAMMA3OSjC+2hy1Y8abTd5fZ+ikgPAO9BPw63GvsbB+ATEWmqlPpDRBpAn8u3jL5mATgPQIMgztFZAL4w+jcKwEEA/QCsFpEblFLmY1UX+jnwIICjxjEeA7AOulQCRCQRwHoA/wEwBECqcf6uMPYxD8CZAO4A0Bb6eUpEREREREREATCgSxTdukMHu4YqpRYa6z4SkYoAHhKRGUqpQ5b2/1JK3W9p1wjAMBEZopRSItLZ2F8/pdRKo916EXkfzoFNAIBS6qCI7DT+/EYp9at5myVR2C8je3MUgNeUUg9b+pgN4J+25s8A2AOgg1mWQETWA/gewEQAN7g6aH6jAFQE0EUptcfY5wYAu239vAo6iDlYKbXIWL1RRI4AWCwizZVS30AHKRsAuEwp9R9j2w8AfAPnc1kNQCul1H7LsTa5vJ8vAtiqlOpl2fZjAL8BeAg6sNoSQBkAI5RSJ4xmm12fHW0SdMbyNUqpw8a69UagdzKM4LtS6l8A/mXpy7+hvxz4RERaKKX+C+AC4z6PVUp9ZznGQmMfe0XE/GLgC6WUN8i+EhEREREREZVKLLlAFN2uBpADYJlt/WLo4F0b2/oU29//A1AWQC3j78uhMyHfsbV7q9A9Dexi6IDqStv65dY/RKQ8gGsArAKQY5Q/8EAHGjdCn5NQtAGwzQzmAoBS6iSANbZ2XaGzW1ebxzaO/5Fxu3n8ywHsMYO5xv4UgNU+jr/NFsx1dT9F5DwADQEssfXnFIDPLf35BsBpAMtF5CYRqen2xNju+zoAx23HWg+gmYhUNvpURkQeM0pSpBvH/cTYRyPj5y8AjgGYIyK3GkFhIiIiIiIiIiokBnSJolt1AEeUUpm29fstt1sdsf1tbmfWfq0D4KhS6rSt3d+F6qU7ZmkG+7Hsf1cHEA+doXrattwHoJpRiiKU4zvdT/u6mtDB8jTbsQ8YtydZ9ncABfk6l/tsf7u9n2Zg9nWHdteZ/TGyprtAv66/CWC/iHwhItf46I+TmgAGORznGeN2875Phc7mXQxdAuRSAL2N28oZ/TkOXWbiLwCvANgjIt+LSJ8g+kNERERERERENiy5QBTdjgCoLiJlzEvyDbWNn4cdtvFnH3SgMMEW1K3lawMXMoyfZWzrk2x/mwHNWgB+8HPsY9BZyS8DWAQHSqmcoHupj+90P+3rDkPfJ18TsP1l2V9jF/sz2Sf8OgYX91NEzMf4UejMXbssS/uPAXwsImUBXAldJiFFROrbSnP4chg603aaj9vN+94fwCKl1BTzBqNmrr3/3wDoY2T5XmLch5Ui0kwp9b2L/hARERERERGRDQO6RNFtK/TEXX0BLLGsvwU6kLctyP1tg84KvRH5Sx/0dbGtme1b3rb+b+O2Jrb1PWx/fwfgJICbkb+2a39rI6XUSRH5BEAzAF+HGLx18jmAMSJyllLqDwAwahH3tLX7EHrCsSpKqU1+9rcNwFARudRSQ1dgTJoWSBD382cAuwBcpJSy1xr2te9MAJuNIOt7AM4B4Cag+yF0aYoflFLpftpVgM7ctRrqpz9eANtEZCKA66Enmfse+Z9TqS76R0RERERERFTqMaBLFN0+APApgFdFJBk6s7U7gGEAprrMusyllPpIRD4F8JqI1ICeyOom6KAioDNGffnR+HmviLwBHdD7TimVJSIrANwhIjugA5A9ALSzHfuYiDwPYLyIpELXpG0N4A6HY42GnnRrvYi8Dp0NWwN64q94pdQjwdxvw/MA7oGeiG0SdDBxDIB8gUul1BYRWQbgLRGZAeA/0OelPvS5H6eU2gE9udc4AG+LyHgAB6Efl2rGrtwEogPeT2Myu3sBvCciZaAD8YegM4GvgK7jO0NE7oaup7sOwB/Gfh6Fzqp1mw37uHF//yUis6ADydWgg/UNlFK3G+0+BDBYRP4H/RzqbfQll4hcB+BOAO8C+B26fvJI6MDt50Yz8zn1kDGhXLZS6iuXfSUiIiIiIiIqlVhDlyiKGVmbPQC8AR08TDH+Hg1gfIi77Q0dkJsGHRwsB13HFQCO++nLt9B1U3tCB5m/BFDXuPkBAG8bt68w9nm/w24mAXgawG0A3gfQGQUzZKGU+ho62HsYwEzo4O+L0BOr/cvVvSy4z0MAOkIHQ9+ALnXwIYD5Ds1vNfp6E3SG61vQdW1/gVEj1yiB0Rk68/hVY59/GPsF/JxLS59c3U+l1DroYG1FAPOgJymbDl16wwyOfmvcPtXYzyzoQGqHANm21v7sgS6N8C3047QBwGzoydusWdX3Qz9+/4B+vCsBGGDb3S/QwfKJ0F9MLADgBXCtUmqv0WYtdH3de4z78aWbfhIRERERERGVZqInZSei0kxEXgYwBEB1hwnYYoqILITODj4XgFJKZUf4+GsBXKiUahjJ45Y0Rt3da6DrBl+rlHKqH0xERERERERU6rDkAlEpIyJDAFSBLt9QBkBXAHcDeCbWg7kW9aBLQmyFrfRDOInIaABp0NmolaBrEfcAMKKojlkaiMiZ0NnORERERERERGTDgC5R6XMSwIMAGgIoC31Z/mMAninGPoXTJOhyA0DRT7SVCWAUgLOhJ5v7GcAwpdTrRXzcoBkZr/5kq+i5ZONv6FIUpp+LqyNERERERERE0YYlF4iISjgRqQ8duPenvVJqS9H3hoiIiIiIiIgKgwFdIqISTkTKAGgaoNnPSqmizmgmIiIiIiIiokJiQJeIiIiIiIiIiIgoRsQVdweIiIiIiIiIiIiIyB0GdImIiIiIiIiIiIhiBAO6RERERERERERERDGCAV0iIiIiIiIiIiKiGMGALhEREREREREREVGMYECXiIiIiIiIiIiIKEYwoEtEREREREREREQUIxjQJSIiIiIiIiIiIooRDOgSERERERERERERxQgGdImIiIiIiIiIiIhiBAO6RERERERERERERDGCAV0iIiIiIiIiIiKiGMGALhEREREREREREVGMYECXiIiIiIiIiIiIKEYwoEtEREREREREREQUIxjQLcFE5CURWRPkNqNE5DsRcfXcEBElIlN83LZYRHbZ1u0ytjGXYyKyQUTaBrnvhSKy100fyTcR2SIiWwK0qS8ik0SkQYS6FfVEZIiI3F7c/SgOIjJcRNaJyJ8iclJEvheRMSJSxqHtWSLylogcF5ETIvK2iJzt0K6aiMwTkUPGPjeKyMUO7c4WkTdEZI+InBKRHSIyRUQquuy72+M8LSIfichh43VoiMvTE+njDBeR7SKSKSI/i8jdPtrdICL/FZEMEdktIhNEJD7M96WciDwjIvtEJF1EPheRq4O5P0Qlhdvxl/Heqix/lzf+h/q6PM4k47XD43DbufbXFeO9yzoGyxKRncZrUbkg9l3fuG2Ym36SMxFpZ5zHdgHaldoxh5OSNC61/C8NsaxzfLwt/7/nhngsJSKTQu9tbDPGQqOLux/hJiKVReRxEfm3MZ48Zvx+g4/2bY3b00Vkv4jMEJHyDu0uMsaoacZ+F4hIdYd2VxrtDoge638dzOuVm+OIyJnG++rnosf/SkTquz1GpI7jdiwsInEi8qjouEiGiHwrIn3CeV+Mdq4+h1FsY0C3hBKRhgDuAvBkkJu+CqAmgMFh71Se9QDaALgSwAMAGgJYF+wLM0VMfQBPAIj5gXMYDQFQWj9cPQ5gP/T/7nUAVgB4CsASayMRqQBgM4ALoF9PbgNwHoCPxRKAFREB8D6ArgDuB9AHQILR7kxLu4oANgK4GsBEAD0AzAPwEID5gTrt9jiG+wGUB7A20H6L8TjDAcwBsNo41ioAr4jICFu7LkabLwF0A/AigAkAng7zfXkdwHDo58d1APYBWC8izYO9b0SxrBDjLyil0gFMBzBVRBLC3TeLvtDjsB7QY7JHATxThMejwhmC0jvmcFIfJWdcug/6fzHFsm4I+HgXhRsAlLiALoCzAdwDYCuAWwH0A7ADwDsicq+1oYg0BbABwAHosdoEAEMBLLS1qwtgC/QY9SYA9wLoBGCtWJK+jP1thB4bDoceJ34J4HX7eNSJ2+MAOBfAzQCOAvgk0H6L6zhwPxZ+CsAkALOgx+bbAKwSke7hui9uP4dRCaCU4lICFwAvAfgyxG2nA/jBZVsFYIqP2xYD2GVbtwvAYtu6K439PBLEvhcC2Fvc5zmIc1q2uPvgo19bAGwJ0Kad8Vh0Ku7+RsvjY5y3T8PchwQAUtznwkU/kx3WPW48RxpY1j0AIBvAuZZ15wDwAhhtWdfL2La9ZV0VAEcAzLSs62y062w79j+NfVYI0G9XxzHWxxk/zzW2GRLE+Sny4wDwQA/G37Ctnw/gEIAEy7r/Atjq8HhlAagdjvsCoJnRbqitjz8DeL+4n7NcuERyQRDjL+gPdMq2rhqATAA3u90egMfhtgKvK9CBImV9XTbWbwBwynxNcrHv+sZtw4r7fAfxuETdOAx546t2AdptQZjHHNG2BPP4oIjGpdHyHPH1ePv6/w1ivwrApOK+f2E8T0GNmxFjnx2DuF8V4TAGBrAJwB7buncA/GIbJw4ynhstLeueB3AMQFXLuquNdr0t6542xpOJtuNsA/C5i767PY71vWmYcXv9IM5RkR8HLsfC0IlzmQCedHi8vgvjfXH1OYxL7C/M0C2BRKQs9Dd0Sx1uSxaRV0TkD9GX6f4hIm8a25iWA2gsIldEqMtfGz/DfgmA6EsO3xSR341LH34TkdkiUs3SZqzoyw6THLb/UUTetfxdQUSmGfvLMn6Ot30jZl5C11tE5orIQQB/u+2PZT8PWC7F+I+IXGH8vdDW7hwRWSIiB43H9BsRudFhf/0l7/LsH5zaOGzTDsDHxp8bJO8yzXaWNsONS0UyRF+W/br9sg9jmyki8pDoS75PikiKiNQ0lpXG5SB/iMg427bmJWZXi8i7knd5yctiu0QoEo+P6BIV1wC40nI+thi35bt81rLNQrGUH5G8S+zuEZHpIvIX9Jt7VeP23iKyTfTlPsdEZJW4uERGtFGiL7/PEn3JzywRqezj8Rhp3NdUEdkqIhcFOoZS6qDD6i+Nn2dY1l0PYJtS6lfLtr8D+Aw6UGht95dS6mNLu+MA1tjamSUdTtiOfQz6ahMJ0HW3x4FSKifAvor7OG0AJEN/aWb1JoAkAG0BfakVgOY+2iVAZwX44/a+XA/gNHS2ttnOC/1e0kXyv78QlVjif/zVQkQ+Md4r/xSRiXB43VJKHYXOmo1kOYOvobN9aoR7xyJyn+jLTo8Y72fbRKSH5fayxm3POWzbz3i/am5Zd42IbDLet06KyHoRaWLbbouIfCoiPUWXm8mEzmAL2B/LPhqILi90SvSlxM+JyJ3icAmuuBsHJYvIUtGXvR4TkUUw3vMDnL8t8DHmMG4/RwKMASWvfMYFxvk6Kbp00VDj9ttEjw/TRORj0Vnm1u13iS6jNlxEfjXu59ci0t6hv0X6+EiAcak4lBUQ57IGC0Vkr4i0EePyc+iEFohIDdFjvz+Nc7pdRO4M8DjFG/2dYFl3sXHcT21t94qIeax8fQv0eBtqGI/5CRH5S0Rmiq1kihvi8jOJ5VxdISJfGo//LhG539autuiyWH8Z522fiKwVkZqWNgHH6T766nPcbPxvzRFdhuuU6M8SS0XkDMv2C6GzFM+wnNddltsDPuZu7p+PvlcWPRY3t/tZ9FhdLG3MzybXG20Pif6fXiwiVf3tXyl1Uil1yuGmrwDUtRwjAfqKq5VKqdOWdiuhg7L2cV2KUuqY5Tj/ArAHBcfmpwGk2459DO6uBHd1nEKOyyN1HLdj4S7Q580+Nl8M4GIROcfFcdw8Nm4/h1GMY0C3ZLocepCY71IB4w3639CXYswA0B3AWOgP9tb6l99AB026ujyeiIjHviBwgMVU3/i502X7YNQFsBfAg9AvoJMBdASwztJmMYB46POSS0RaAbgQOvgB4z6ZH7JehA6GzIO+/NvpUsWXoM/BbdDfqrvtD0TXpXsB+jKWXtDfKi+FbfAvOmDzBfS3gqOgX7y/BrBaRK63tOtkbP8LgN5Gf18E0Mih31ZfQ1/KAQAjoQNJbYz1EJF/AnjF6Of1AMZAP28+kII1Om8D0AF60H4/gKsALIL+tvg76Mt01gH4pzhfcrIYwK9G/5+HvqRltuU+RurxuQc66/E7y/m4x2H/bowHcD6AOwHcCCBDdB3U1QB+hL6U5i4ATQBsFZFKAfb3D+j/7Q0AekJ/OBkCIMVhsHwr9OW2D0BfbnU2gPfEoV6iC9cAyIG+xMt0EYDvHdr+AKCxy3Zni0ii8fdG6OfvNBFpLCKJItLB6P+rSqmTAfro9jiFFYnjmIF3+3F+MH429tfOGNCdsrSzfnHSznYcN/flIgC/O3yg+AH6vSWken9EMcjX+KsG9KWPNaCDCvdCv1f6uqz6XwCuCSJIE+8wBnNVJ9tQH8BxAIeD2CaYfc+DLvPQDzrIsFZEugGAUioTOqAw0GHccCuA75VS3wCA6MDeJgBpxm0DAVQC8IkxHrI6H8BM6Pf6LsZ2AftjHKcM9PtoM+j39yHQmU3j7XcuiHHQ29CX4D5mHNdr9C0Qn2MOt2NAi1XQl/bfAOD/AMwXkacBjADwCPRYoBEcvpCAfp8fbZyD/tDBtA9EJHccGaHHx++4NEhVoIMty6DHjEtFfwH+GfT4aJLxcw2A2fYAppVSKhv6/7aDZXUH6EDXpWJc4mycrzOQF5S2czPGfBP6M1Nv6HHwvdBlU4Ll6jOJoTJ0oOoN6OfPFgAzJX/t/zeN/o4BcC3047MXQAUgpHG6kwLjZgDVjZ+PQv/vjYG+tPwzy2voU8b9Ooi883qj0S+3j7nf++fEGHunQP9vPQc9Nv8Qeqz+D4dNXoTOtBwI/Xj0MdaF4moA2y1/NwRQDgXHhBnQz6fGRp/LQ7/euRnDLzR+zhSRuiJSVXRJsI7Qn9V8CvI4IYvUceB+LHwR9Ovnrw7tgPxj84Viq7MP9/fF7ecwinXFnSLMJfwLgHHQwZUytvWToVPvW7jYxycAPnLRTgVYdtna74KutemBfnFrDF3zZweAag77DmvJBeO4bY19t7Cs3wDbpSHQAdUjMC7Bgg78KQBX29qNh/5ms6bxdzuj3Tuh9Af6i5Y/AKyzte1ttFtoWfc69OAkydZ2A4BvLH9/Bh0gtF5Kcpmxvy0B+mjen0629fWN59PjtvVmCY0bbI/lDlgu3YQezCgAE2zn4wCABZZ1Q4x2rzqc92wA50fy8THWb4Hz5XCTYLt81vJ83WX5u76xz69huVwMQCL0h+r5Duc6C8CDfvpqDmgX2tbfahzretvjYb/k6iZj/RVB/k81hf7AMte2PgvAPx3aTwHgtfy9A8Byh3bmpU5nWdbVhH5tsr7GzLU+r/300/VxLLeFUnKhyI8DHRBQAMo5PF8VgInG3wONvy9w2MdeAK9b/h4EHWC4Jtj7AuAj6CwAe7tORrurgnlOceESqwt8j7/+Ybwmnm1ZVxG6RIpy2E9HN6/HyCuL4G8ZYmk/xFjXyHi9qAYdVPYCuM/HvsNWcgF6fOMxXjPes6w3xw1dLOuSobOdxlrW/Qpgk22flY3z+IJl3RbjcWgeYn/uNPpzqWWdAPgWlktw4XIcBB38UQD629p9gEKUXID7MaD5WA6yrKtmPO6HAVS2rB9ptK1nWbfL4flbCXqM/GYxPD7t4KPkAhzKClier9b/hYXGul62thOhx1Ln2dbPNe5Hgf8HS5tR0OMh83PDu9AB1zQYz20Ad0M/rxP99M3X4z3EaGu/VHstgB0u/v8KnBvb7b7GvOa5sj9/NwDYDWMca9zPkX7272qc7mNb8zzlGzf7aBsP4Cyj/Y22+1Hgs6PbxzzQ/fPRl+vsj6+xfh50UK+G7TltL6U1y+hbUCXZkPcadotl3RXGuq4O7T+F8b8LHehXAO52aLcYwE7butbQY0rzPScLwB0u+hjUcSy3BVsKIVLHcTUWBvAagP0O7czPArdZ1r2O/J+ZXN8XuPwcxiX2F2bolkx1AZxQSmXZ1neGruv2Xxf7OAjLZRoBzId+MbcvH/hoPxB6MJMJ/S1REwA9lb7MMCQOGcJirC8jIo8Zl86kG8c1M2es2alvArhcRM4z9wedgbBS6ewRQH/ruxvAv21ZMB9BZzlfbuvWOw79dNOfM41llW3z96AH31Zdob9xPm7r03oAzYzLfOKhH4+3lOVSEqXUF9CD9FBdCz3YXmI79hfQGd72WT03KH3picn81ni9pU9e6A8E9kwOQGfwWC03jn+p8XekHp9welcZ766GNtAffuzndC/0+SowU6rF5QDKouAlPMthBOps6zeo/Jdc/c/46br0iYjUgX5e7oTzRBPKYZ09c1/ctDOyLFZAB3Vvg74/Y6AzeF62tLNnqollf27640qkjuPr8MZPp+O4bZevP0qpRUopj1Jqq61N2B5DolLA1/irDfQHvT3mCqWvKljjYz9maRu347DLUXAM5q+s0nbo97cj0B8Y5yilZrk8VgGiZ+y2vh7GW25rJfqy5L+h34tOQ48fct9TlVKfQb+P3GbZbX8YYwxjP+dBZ5jZ3x9PAfgcBd8fdykjs9fW14D9gT6fe5RS/7H0UUFfPWPldhzUBjrwa99+ub1/QQo4BrS1zx2XG2PuA9DPS2spI3NsZh+H2Z+/qdCZh22AiD8+4eJFwUlJu0I/fr87nNMk+M9s+xg6A/IKIzPzGmO7z5CXudsB+rNYWiH6nWL7+38IoWxdkGNeX8/fs5FXbutLAGNEl4y72DIuMgU7TndiHzeb92WE6LInadCPq/lcdfO8cfuYB7p/Tq6G/vJimW39YujEpja29U6PbVkAtVwcC0BuWZKZ0F+2WCcsdjsmdD12NP7vV0N/nu8JHbx8FcCrInKLpZ39CpKgjuNWpI7j6/Auj+F6zKyUukMp5XFo4/a+cGxeCjCgWzKVgw6W2iVBB4bcSIeup+bGPqXUV/YF+oOCkw+gP2xcAX2ZT3kAb0vBSwuz4fuSwXjkD26eti1m8GoqdGbCYuhLaC6FznQF9HkyrQZwEjqbEdDB71owyi0YagKo53Asc8Bvr8G7z6HfbvpTx/h5wLqh0pdzHbLtryZ0Zp29T+alS0nQl3gmwKgTa+O0zi2zZtSvDsevjILnwx6wz/Kz3ukyU3tfzb/NgWSkHp9wsvfBPKcbUfB+XIyC98HKrNeXb59GkPyw5XaT/f/TfM1wdT9F15zeAD0w6GJ8uLM66nBMQGcFWR/zI37awdL2DugMhu5KqcVKqX8ppZ4F8BCAu0WkmdFuJ/Kft8FBHsetSB3HifnY2Y9T3Xa7r3aAvizc12u09Thu7kugdoGOQ1RS+Bp/1UFw78FmPUK347D/cxiDOV1qaboRehzWHfr95h4RGWRrY46xnMZh8bY2jyP/6+EmILckwCbo14f7ocd9raEvOba/1ywGcIPklXK5DcBmpdSfxt/m++PrKPj+eB1cvMcH0Z86sI3BDPbHy+04qA6Ao7YvUZ32Fyw3Y0Arp/GWr7GZ/fHx9fy1jsGAyDw+4XLAGFtb1YQOwtnvg5lk4W8c9i30eKs9gBbQz4Gt0IHe9kYAsB18l1twy2n8Fkqt+mDGvP6ev+ZzoB+A96HL+X0H4E8ReVzySn4FO0534vS8uR95ZU96G/fDDA67ed64fcwD3T8n1QEcsSQHmfZbbrcq7Ni8tdHHzdDjZqd9+xqvmbcfhQ4EBmoH6EnRTgO4Tim1Vim1SSk1EjoJ50Xjy776sJ1bY10wxwkoUsfxw+1Y+AiAag5fCLgZMwdzX9x+DqMYF0qtRIp+h5H3omB1CPknLfKnOgoGD8PliPFhAwA+F5HjABZAD96sNZQOwHd2Sl3kH1y2tt3+s/GzP4BFSqkp5g3iUMNSKXVSRN4BcAuAJ6ADu78ZWSOmwwB+B3Czjz7tsu/WoY2b/piDlXxF9o2MF/uEJYehv02f5qNPfyEvw8Hp291a0N+Wh8KstdcZzm8M4a7FVwt59YXMvwHA/LAXqcfHnwxjmzK2DC1fg1R7H8xzNgT576vJHjS1Mt/Ea1u3Nb6hTkIYHw8j68fMXLjK8oHb6gfk1XC1agxd/sParrOPdnssWSwXQ3+gsNfaNj8IXAj9Yaon8n+w+T3I47gVqeM4MR/fi5D/w42ZRfKjQ7vPzUbGALcC8j8Ovo7j5r78AOBGEamg8tcOawwdHLDXCSMqqXyNv/bB93uwE/NDWFGNw75XxkQpIrIZOjjxjIisVnn1yM2AZl3kvb7Bsg7IG4e9hvyZjuZ7VVfoOqU3K6VyEwpExKnm5JvQ468bReQL6HHdYMvt5nvYo9CBGzt7VrTTe7zb/uyDcyam/fFyOw7aB/0BPsEWFHOddeeDmzFguPh6/lrHYEBkHh9/MpF/XhDA/RgM0PfjAHR9fic/+1gPpZQSka3QWbip0GUvjhr/Y1OgS3Eko/AB3XAJZszr7/n7JwAopQ5A1/O9V3St4MEAnoS+4mA2gh+nO/E1dt+klHrIcj8CTSxl5eoxd3H/nBwBUN3hc0Fty7HDQkQuhh6bfwOgj0MAfif0/8dFtu3KAWgAI4CtlDolesI4X2N465VcFwP41uFY/4G+Ircm9OuQ/XP6X0qprCCO40akjuOL27HwD9CfHxoi//jYPoYvIMjHxu3nMIpxzNAtmbYDSBCRM23rP4IuzN/MYRu7c+Bn0BJmb0DXRBpjG7R9DD0rZEVrYxGpDf2tfe6AyCFD2PwwUQE6mGk11Ec/3gTQUES6QE9E9qbt9g+hL0FLc8pIVkq5+eDlpj97jaWvbf0NKPglzIfQNUx/8NGnTCP74EsAN1m/RRaRy5A3IZ0/5rfD9kyhDdCXEZ3t49j2D4CFZR8A9jeObwb0IvX4APqcOGVOmcHx3BmdRc9Oe4WLYwN60sJUAOf6uA/+/ie3Gf3qb1vfD/p5E5YBi/E/mgL9GtFZWWZPtXkfuoxJA8u29aE/0Lxva3eGiFxjaVcZOmBqbbcf+gOFfYKty4yf5geK/9nOmTlYdnscVyJ1HB8+hw703GJbfyv0h4fPjD7ugQ5yO7U7Dd9lcUxu78v70FcB9LW080A/9z5yyEwhKql8jb8+h349zL2M3Rjb9PSxHzMYUeTjMOP/cwz0B2/r5EtbjJ99HDbrA/0F5jZjH3/5eK8yx3S576sicj70+4C9Hzuhz9NtxnISeiIx08/QAZ+LfLw/fufi7rrtzzboiR8vtbQTFDwXbsdBn0NnNdu3t79f++JrzBFwDOhy/27Yn7+VoLM6zS8LI/n4+BqXAnoc1sS2roeLY5s+BHAB9JeWTvfD3xfrgP5scil0VvJmY93/QT+fJ0EHdj5z3DKPr8c73IL5jOTr+bsHeUH9XEqpn5VSj0F/0WE+HuEYpxfmfvj7PwrqMfdx/5xshY632D/T3QL9XNjmZ1vXjNIHGwD8Bp0tm25vYwSUPwRws6UcAaDn0CiLguO6HiJSxXKMttAZ1vaxeXPRE0laXQb9HnFEKZXlcE7N4Lbb4wQUqeP44XYs/CH0Y+80Nv/exednt/fF7ecwinUqCgr5cgnvgrzC8b1t66tCT4R0EPpbyA7QQbIlACrZ2uXAxWQXxnF8TVy2GM6Toi12aNvD2NdDlnUXQAe3voW+pKw9dIHy36CzHWq76N8y6Ppd90BnULwK/W2YQsEC9XHQgxKzsPu5ttsToN+Y/4SuF9oReobW+6CD5RWMdu3ge7IGV/1BXiH2edAzz46AHqQeg2XCLOjaVfuhA7aDoUtN3ABggq1dJ+Mxfd8410OM/e1D4EnRkqAHSu9AvwlcYj5foC+1SQcw3dhvR2PfSwC09/c8Qd7kDvbzvAWWySAs7fZAZ3Bfi7wJFBYU0+PzPPTAsJ9xPhpZ/neOQQ/er4Me/H5hnOtdlu3rw8eEMgDugs6qfhX6i4V20G/6rwEYGOCxetrY7wtG/x+A/h/6BPknxHN6PMw+DQlwjA+M59L90Je0WZdkS7uKxrn7n3E/rof+X/4NxmQglv+7f0NPBNgf+vm+BToweZatfyegJ+oaDP16MMZY9xUCTIzm9jhG22ugB7j3GedklvH3TS5ecyJ1nLuNx2GK8RyZbPx9r61dd2P9HKPdKOhB9jO2dk6TogVzX5ZDf7AZBv2/95ZxnJaB7gsXLiVlge/xVw3j/+Mn6PeNG6CDOn/AeVK05+Fi4lf4n7iswGSL8PG+a9z2H+iM2/KWdXOh3/+nQ7+f9oTORMsB8ISL/l1kbL8e+j1pMPQ48DfYxodG+7uhy23tg2WyLcvt3Y39rYB+f70Gehz7AoDRlnZb4DyplKv+QGd47oQeDw42jvs29DhEIf/kYG7HQZ9AT3p6n/FaOt98/BF4UjRfYw63Y0DH5wkcxuRwGCMZ7f5A/ufv50afzi+Gx8ffuPRJ6OfQeOOxmAQdbLb/LyyE8wRZVYz7+TP087E99HjuYVgmZvPzWDVG3uRQ3S3r1xjrtvp4zbD2zdfjPQTO4+ZJcHgdceibgmVSNLgf8y6EHmvtQd7zd6HRbrDlvH0JXUqvq3HuZ8Iy8RxcjtN99N08T07j5qnQr0mPQX/eeRp6rGi/vw8Y60ZAZ3Je7PYxd3P/fPQ7Dvp/P9XY9lrj8VUAnvb3f2d7zOv7OUZN6P+TI9CvQfaxeVlL2+bQr1dvG/fhDmO7VbZ9ngGdOLDVuL/9oD/LfIH8nyfMCZXXQ4/1O0OPZRWAGS6ek66OYznWTdDvQebjeBMs49YoOI6rsTCAfxrrRxuPvfm+2tPWLt+kaEE+Nq4+h3GJ/aXYO8CliB5Y/U+9wGF9TejA0D7ogNgf0Bmy1hf7W4wXmSQXx1EIQ0DXuO0zo1/WDxMXGS+GB6EHb/uM/tZzeR5qWF5cj0IPsFvDR+AKOmCoAPzbx/7KQQ+ctkMPto5Av8FPQt4sqO3gO2Douj/Qb/y7jcfiK+iZZ48CeN7W7kzowO+fxmO6D/pb2ltt7QZAD1bMyehuhB5Ub3FxHu8y3gC8sH34gM6k2QadfZAGPSiaBeBMf88TBB/QvRp6Aq4047y/bH2uRPLxgb5Uah30AE1Zz6HxOH0JPUjeAf2N60K4DOgat3eHzvI4AT3w+hX6w1/jAI+TQAfsfrY8F16GZQZrP4+H2acC/xcO2/pa7M/hs6HrU58wztW7cBiUQl9ePN94vE5B19Br5tCuMXRdrj+M87IDwLMAqrl8PXB7nC2+7mOUHecu4xxkQn9Zd4+Pdr2hB3GZ0B/IHgcQ7+P/sV2I96U8gBnQwYUM6Pegdm7uBxcuJWmB7/FXS+gP9hnQ79cToYNPyqHtDgDPujjWJIQvoNvZuG2UZV0cdJ3y/xn9PgU9Hgk4g7llHzdDvydnQI89+sP2nmhpW814nVLQV4A47a8NdHmHo8Y+d0G/b7extNkCh4BhMP2Bvhx2HfR7zUEALwIYZ/Stiq2tm3FQMnQALRX6i99F0B+yC7zuOvTZ35gj4BjQ1/MEwQV0F0MHKczLtv8LoEMxPj6O41LoceCLxnlIhQ4uXwqXAV3L8/B56PIAWdCX438C4EGXz/n90J9ZrMkyo2ALMBrr6zv0zfHxRvgDum7HvAuhv9y4Anp8mwH9+WSkpU1Z6C+Of4D+HzhhtB1o60PAcbqPvpvnySmgWx46IHbQOGdroa9ysN/fitD/g0eN23ZZbvP7mLu9fz76Xhn69cD87L3DeD6Iv/8722Ne38/+zW19LfVt7a+G/kImA/pLvBfgEEyHLqewAfp17ajxPCgQG4AOym+xnP9voL8kiA90boI8jq/7tyVajgOXY2HojPcJ0P9HmdBljwokcxh9LPC/HcR9cfU5jEtsL2I82FTCiMgQ6AFNHZW/joubbT8AcEgpdVvAxhQxRqH7/wAYpJSyl4MosYzn8gIA5ynfl/cTEREVu8KMv4ztL4POjL9QKbUjzN2jQhCRtdCPS8Pi7kskGTUbP1VK3RqoLZU8IrIQOtBoLyVDRETFjDV0S643ob+tvydQQysRaQ59qcmTRdAncklEzhGRZ0Wkl4i0F5F7oC8t+x36mzYiIiKKPiGNvyweAfAGg7nFS0RGi8idxhjsehF5E/py5mcCbUtEREQUCfYJlqiEUEpli8jt0Jf4BaM2gKHMhCx26dBF9gdBXwZ0FHrW4EdCyfghIiKi/2fvzuOcKu/9gX+eLGRgXHCrtV4Fd1RwOiCLBWQRLILUrXot0mIpLr39VVsWKfsO1QFae6/1ura0bler0lKQCs6AgLIIFJe6g2httS7FZYCZLM/vj+Q58+TkJDlJzsk5mfm8+8qLTHJycma07cNnvvk87ith/aV2G9+JZHcteasJyY9Fn4jkx2NfR/Lj3vd5elVEREREKaxcICIiIiIiIiIiIqoQrFwgIiIiIiIiIiIiqhAMdImIiIiIiIiIiIgqBANdIiIiIiIiIiIiogrBQJeIiIiIiIiIiIioQjDQJSIiIiIiIiIiIqoQDHSJiIiIiIiIiIiIKgQDXSIiIiIiIiIiIqIKwUCXiIiIiIiIiIiIqEIw0CUiIiIiIiIiIiKqEAx0iYiIiIiIiIiIiCoEA10iIiIiIiIiIiKiCsFAl4iIiIiIiIiIiKhCMNAlIiIiIiIiIiIiqhAMdImIiIiIiIiIiIgqBANdIiIiIiIiIiIiogrBQJeIiIiIiIiIiIioQjDQJSIiIiIiIiIiIqoQDHSJiIiIiIiIiIiIKgQDXSIiIiIiIiIiIqIKwUCXiIiIiIiIiIiIqEIw0CUiIiIiIiIiIiKqEAx0iYiIiIiIiIiIiCoEA10iIiIiIiIiIiKiCsFAl4iIiIiIiIiIiKhCMNAlIiIiIiIiIiIiqhAMdImIiIiIiIiIiIgqBANdIiIiIiIiIiIiogrBQJeIiIiIiIiIiIioQjDQJSIiIiIiIiIiIqoQDHTbICHESiGETN1Wen09REREbYkQ4rfa/w+vSz3WOfX1ONOxRwshdgohPhVC9Ew9tlZ7/QMefAtEJTGtRZ/x+nqIiIjI37h+zhTy+gKovFIB7nDtoeHZjiUiIiLXfADgMgCfZztACHEsgGcAHAtgsJTyr6mnfgzgcABPunyNRG7R15+DPbsKIiIiqiRcP2sY6LY9wwHgkKM64ctP9np9LURERG1Vk5Ryc7YnhRDHI7kYPRzAACnl39RzUspXU8c0uX6VRA4TQjzl9TUQERFRReL6WcPKhTZEX0B3u+DHXl4KERERZSGEOBHAegDVMC1GiVqBYQBw5KEnGw8IITZ4djVERERU8dri+pmBbhuRqloYBgAdv3qG1XNERETkvZMBPAsgCOB8KeUbHl8PkWOEEA3q/rmnjcbRh52qvuznzRURERFRK9Am188MdNuO4QDQ4fDjcOb51wEADj/29LTniIiIyHNTAPwHgGFSyj1eXwyRU1IDBAMBGEFu91NHmZ8nIiIiKlSbXD8z0G0D9AVyzTcnGI+fNeB6/ZjV2o7D3G2YiIjIG6sBCAC3CyEiXl8MkYOGA8CRh3ROC3KPOKRz2vPaLtQMeImIiMiONrl+ZqDbNgwHgMO+cmrGEx2/2kXd/SZaJnUHCyHWluXKiIiISPc4gLEALgTwmBAi7PH1EJVMCPG0un/u6d9Le66n9rUQQgK4IPUlP0FGREREdrTJ9TMD3VZOCLFG3T974I0Zz595/jgcevRJVi+9gJO6RERE5SelXAbgRgAjATwkhAh6fElERUtN2g4FgKMOO8XymGyPCyHecu3CiIiIqNVoi+vnkNcXQO5JLaCHAEDH47pkPa7r4B+lff3qs/di3wevAclJ3WeklBdYvpCIiIhcIaW8WwhRBeB2AL8TQnxXSpnw+rqIijAcADpWn4Aep15jeYD58R1vPYSPP38LAE4RQrwspezq9kUSERFRZWtr62dO6LZuwwHgkCM74cz+42y/6Mzzx+n1DINTPWbPunB9RERElIWU8lcAbgEwCsC9Qgjh8SURFUQI8ZS63+uM79t+XfdTRxkbpwE4O7UW/auzV0dEREStTVtaPzPQbaX0BXS3IT8u+PVnD7wRhx2T9vG3/qnF9JpsryEiIiJnSSnrAMwA8H0Ad3h8OUSFGgYARx56csEvNIW6AFCTWov+zaFrIyIiolaorayfhZTS62sgh6QqFtI2kOj41TNw5vnXlXRerYIBACClbLW/4SAiInKbEOK3AAYCOBWAlFLGC3x9AMlfyr8FYKOUcrTT10hUDKu1KABc2H1mSefVKhiUVVLKESWdlIiIiCoG18+ZOKHbSggh1sO0gO5w+HElh7lAsoLhvKsW6+/1VI7DiYiIKL9OAKIAitmA9OnUazs5ekVEJRBCrIJFmGuasi1K91NH4cLuM3HkIZ3VQxnvQ0RERK0e188aTuhWuFS4Okx9ffixp+GsATe48l5/W383PvvwDQCc0iUiIiqWEKIzgKNTX34hpXy9wNefDuCw1JefSCn3OHh5RAUxT+Ue1uE49OlS+kBBNk/vmKvuPiWlZLBLRETUBnD9nImBboUTQhj/AA8/9nScNeB6V9/v+UcnqrvrpJSDXH0zh6ifEUNoIiIiImfpa9HqqmPQ96wfuvp+2996EJ98/jaAylnbaaE3qyKIiIjIEaxcqGBCiO3q/nlXLXY9zAWAjsd1UXcHpjamULd619+8CKkFtLov9a+JiIiIqHh6DdeF3We6HuYCQI9Tr9HfX5puvlvnmSaYh/v1OomIiKiycEK3QqV6yi4CkiHrmf3Hle29zZukaXw1dZBtY44UX10rERG1Xt8cVC0/+bSgfRtKtv3Fpr9IKYflP5KoOPo666hDT0aP08q3t4jFJmkGv03t6hPMJlyLEhERZcH1c34MdCuUWhyWo2YhHz9266aHuQE5eNB8sWvXMnzyaVrNChfSRETkunNrquTWv5xY1vcMHvfmdinluWV9U2pT1Fq0HDULdmjduqullBd5eS2Kvh4ddkqytmz7Px/HR/vTavu4HiUiIjLh+jk/Vi5UICHEBnXf6zDXL9egswpzAaCmZgwGD1qIo448Qx063C8feRNCrLT42CA/kkdE1ApIAIky/4fITfr6xA9hronnkzVqXQdjPdoypdvjuCtwTIeT9MN9sbFbjrUo16NERFR2XD/nx0C3MvUDgI5f7ZLvuLYqI8zV1dSM8WOom20x75frIyIiIlKGA8ARh3Ty+jr8SlvXCTnslAlp69Eex11hTOwC6QG5h3IFy1yPEhER+UzI6wug4p15fvl6cyuRVZir1NSMgVbB4IvJCAAYNGSRcX/Xzt/i00+S15ea8uBH8oiIiMg3ep4+xutLMHSsPhH7Gt/1+jLSpELbrOvRYzqcpOoXhgshVvphnTfshJvTvt7+0R/x0cF3AB9dIxEREZV5QlcIMTDLx3j2acccKoRYLIRYJ4T4PPX8QJvnvzbHR4WkEOKrLn1rvpVIxHHg83/hYOOnZXk/L397r328zZaampa/hPjx42Q1tdfiyKPO0B/yTfBMRESFkIjLRFlvlB3Xo+V3sPlzNB78GPFE1LX36HXGta6duxCFrCdN9Qu+nILtccwlOKaqs/rSl9dIREStEdfP+Xg1oXsTgG3a1zHt/lEAxgLYAWANgMsLOO9KAOeZHhMAVgDYLaX8oPBLrWwfvfMCdm9/HJAJBILt0PuKha68z2HHnIzPP9oNZAkdhRCvADgLwNNSym+6chFp7x2QyDERoRx15Bn6RmnDtUC4bNOwuRbGNbXXAgAa1k4px6UQERG1JVyPloGUCTz7t18lv0gkMKDbTxEJH1r26xBCbATQF8BWKWVvF99K783NuxbtcdwV+kZpw03DCb74dFaPYy5Jm9T1+HKIiIgI3nXoviql3KzdXtCe2yulPFJKOQTAnYWcVEr5kem8m5EMrY8CsMzB6/eMEOLFQo6XiRiQ+k1DIt6Mv62/25XRiLMH/VfGY0KIVWoaBckwFwAuNE2pPO30tQwetDBn3YLOYqM0ZXg5pnbNG7jZPJ6IiCpIclMHWdYb2cL1aBGEEKsLflEiAREOSwBY/8rtONj8uePXpRNCPKXd35Rai/ZNPdTLtBbdYH2W0ph7c3Ox2ChNKctErJ336HHMJW5fBhERkYHr5/x8tymalNLpn+IYAM0AHnH4vF7pVsjBHQ4/DgDQ97I6BEOR+GcfvhHe+uQMVy5MEUKsTy0ML7Jx+FAhRL2rF2SDCnYtwt3hpkW/04tqI8wdNGRB1oW/Vr3Aj7oRERG5jOvRnAr8pJUAAgEAkJ1/tRgiHMbGV3/txnXhqMNOUXeHpaq4ngHwjTwv6+fUGq+Uc6iN0tTNVMVgru1wbC2YPlxgr7qMa1EiIiLveVW58KAQ4mgA+wD8BcDPpJSO72IghGgP4EoAf5ZSfuL0+T3yMoCudg8+9KhOgAhg84rpiT4j5wcBYNOTk1y5sI5f7YJ9H7wGAOerx9of9hV8fdgtlse/su5/8fm/3gKAQX7aZEF162qbpulc2RAiV5gLJKsX9E3SnHxvIiJyXwKV18vVBnA9Wpy/oIBQVwiB4zp2jf7z0xfDANCpbgHeuWkiYvFmhILtHL2wHqdeg+dfvQtfHPgQ0NZLRx92KrqfOirj+B1vPYSPP39LfenE+qqguoVcTFUMGe/j4HrUuOZhJ9yU85qPqerMDdKIiKhsuH7OrdwTup8BWAJgHIDBAOYBGALgeSHEV1x4v0sBHAYbH28TQlwvhHghdbvehWtxhJSyoAldEQjixK7DEI81uf7P+szzx+Gwr5ya9li2MBcAzh54Iw47xpikKGny1I1JgZqaMdq0bkBqk7sl1zEUuoEb0NKnm3q97zZxIyIiqhC+XI9W0Fp0WKGvOeM/LgwjEMDeSdMkAIhIBOtf+YXzFwfgvDNvSPv6iEM6WYa5AND91FG4sPtM42shxHonrqGQuoVc0qsYhLSY3HVsLZovzAW4QRoREZGflDXQlVLulFJOlFKukFKul1L+EsAwAMciuTGF08YA+AjAKhvXdreU8tzU7W4XrsUzX+syECI1pase27vrz81uvNfZA2/E4ccmg8+OX+2S//hBP8QhR3VSX5YyGWG7h7YQLVUM80V6wKu/Z1HMG7jZolUvABWykFZ/YWAITURtlYREXJb3Rtn5dT1aiWvRF978va3j2oU64IyvDYFsajJCw4SM4csDH7lyXUcflhwwOOrQk9Hz9DF5jz/qUGPA4Pxcx+Xi1hqnpYohGRKb+nYdWovaHzKoxFCXa1EiosrD9XN+nnfoSil3AHgDQE8nzyuEOA7JaYsHpZSxfMdXohfX/NLWcUIE8B9nDTWmdIOhiPzHG886+xk3zVkDrsN5Vy3GmeePs3V8twt+bNwXQvyllPe2uxlasVTAWwp9ITloyKK8dQtp7197LQYNWWTu1PV8cWq1UNYmP/S/bFTEwp+IiNoWrkcL9hQAfPqFZR2Apa8ekdYYlpDxOLa8eb/Dl5WkJm97nDba1vE9TrvGuC+EeKXIty2oh7YUPY67oqTX62uxYSfcbGs6N+39M0Ndz9eiQNp6lGtRIiJq9TwPdFME4PiWcqMBBNEKdhO2sAoAGv/9d7z8zP8U/OI+I+cLSH91kWjTvBd6eR2FKnJBWPI0cU3ttRnTusWeyyHmhbJp8RyQfguhiYiITLgetUlKafx//IaX/7vgRWWnugUBEYlACFd/D18QNdUL4CwhxFPFn0mW9Zsqck1VcvhsCnW1c3ojfXM3q7WokH4MoYmIiIrleaArhDgXwOkAtjh86u8BeFFK+VeHz+s5fQOCLz55x9ZrPvvXWxmPNTV+6tg1lcruNK8VLxZjpj5dW+9v7iorZDLXiprW9Zr+/ZtCZgABqaaQfRhCExGVTQKyrDcqDNejRVkFAAea/23r7xNfHvhXxmOJRNzhSype91NH4ajDjOqFgnuCy02rXQAKXI+q+4VO5pr1OOYSDDvhZstzeyDLulJINYVsFUIz1CUi8i+un3MLlfPNhBAPAtgDYAeSOwrXApgC4H0A/60ddxGAagBqA7ABqV2IG6WUT2nHxQAsk1L+wPQ+3QF0BTDBtW/GJ8ybkGXz+cd70nbbDYYi2LHq56i5cDw6HP5Vty6vXPSJ17JMRdTUjMGuXcvwyaevAy1TAACwymrHX9PUANy4Vg93GzZ+/jW115q/p7Sv1cZuDWunJJ/kDslERFRmXI9648uD6YFup7oFeOemiXjtvdWyywnDfDGq2+PUa/D0jrkFvy4tJD1loqPXlI2qXdj+z8fx0f49QMt61HItCpjXo8KxtegxVZ3x0cF31DWUfW1nrpAwP61/0eOYSwAA2z/6o3HN7l4dERGRO8o9ofsygG8B+A2AvwD4CYAnAPSWUn6sHXcngMcAqG1nZ6e+vtN0vmDqZjYGQAzAgw5dt6/oHwM7e+CNtl5Te9HktMVMn5HzEQyGsevppTjwhTsbUhSrkN+U68e63Z9rZrFJGmD6CFdmd1fLxKpT1+GXKoNCvif9mt25GiIi/5AA4pBlvVFOXI+WSA8GtaqCnE48pjdEMIi9k6YZ/4KKSCTx7icviL3/2lKx/cIWIWlZmTZJA1rWgzK1DjX2OdCvs9Tp3LRr8L5Tt+Cfvwp2iYjIn7h+zq+sga6UcpGU8hwp5eFSyrCU8gQp5fVSyn+ajusspRQWt86m44SU8lqL97k5df4P3f2Oyk8IsQapj4EdcuSJtl8XqT4SQgSwecV0499SFer+dXUdDvqgfuGQozqpu4WEfCX30ZZCbZI2eNBCc7g7XFhM5ToZ5BrXYFFl4PdeMDWpS0REVG5cjzpiOAC0Cx0iu586ytYLhBA48/jhkNGo2DtpWgxIdemGw4nX//6X0NoXf+7i5bqqJSQ9ZYInk8Y9jrsCw06ZaA52geS1mdbVzoa5xjX4oM7gmKpORX1ffl4zExERZeN5hy4VbAiQ3ESs25CbbL9ICIHT+lyDeKzJelJ39WKHL7Nw3S74sXHfzsLKy+lcKyrc1ft11R2np3Iz3jvVp5sl2NVvvluw+vGaiIicxg4wai2EEBvV/YHnjC9obfMfR9cCiQRkU5NR+9apbkGg868WIxFrdvIySyaEeK6Q470Kc3Uq2LWqfUj2yN5ccm9uzvdPdepaTOuWZS2aqlCwTb9Ohy+FiIgcwPVzbgx0K1TRm4glp3TTPtbWZ+R8JOLN+Pc/X/X832CtE9jOwsrT6dxsslQxlOe9rYNdnaPTEqWci7ULRETeEUJ8WwjxuBBirxDigBDidSHEIiHEodoxnS3CGHXraDrfSUKIPwgh9gkhGoUQDamNxqj16evWid94f23UrXPbpVVInJdvnePnX0pbTOuWjcW0rs7FtWhhlRd67YKf/1kSEZE/+G39zEC3Qr367D1FvU7A+tcOwVAEr29a5vlkgd4JnGth5bfpXDM1ratsXD+/vO+fCnb1m6lrt+RFa3qlROGhul67wEU0EbVmEkBcyrLebJgIIA5gKpJVTncC+CGANUII8/pwEYDzTLcv1JNCiKMAbERyA7AbAFydeqpBCHFmsT838q01bpxURCLynY82h904dyG6nzoKRx5qhKH5funsWXduPmpaV6l//+7yvn9qWle/mSZ3HV6LFlclwSldIiJ/4vo5v1D+Q8iP9n3weuEvEgFImRB9Rs7P+OfeZ+R8bHpyEqRMIPPfw/I69OiT8MXHyd16cxymB4m+C3SVcLga0WgjotFGNKydkvZ4vwHTy3otNbXXYtfO3+LTT14HStiF2Mlu4COPOsO4nmJeT0RERRsppdR3RV0vhPgUwDIAAwHUa8/tllJuznGuHwI4FsAAKeVbACCEqAewG8AcAFc5eeHkOVemaDvVLRDv3DQRH3/+No4+7BQ33sK2c0/7Lp7eMdf28X6oW8imXaA9mhMH0Jw4gNXv3Z72+ODjry/rtfQ45hJs/+iPqhrBwbVo8b3APY65xPi5CCGklNK3/yyJiMhzvlo/c0K3ggghVqn7HY/rUvDrjziui2XlghIMRbDl8alIxL3daLjr4B/ZPtaP07m6/v2mIRyuznhcBbxeTO46MKnr2EZv3ByNiMgbpsWosi315/EFnq4PgDfVYjR1/kYAGwBcLITgAEHrMhyAPsVakGMOPz3rQlNEInLH7ofxWeM/irw0Mht80o/QLtA+43EV8HoxuevApK4jYa4Zw1wiIsrFb+tnLrArRCqpHwQAhx97Gs7sX3iHbiAYhhCBaDzWZPlxtj4j52PziunYtnwGel+xqLQLdlGlfTy/f79paV9v2LgA0WgjAHgyuWsxqas+W7Aq25RE5iREcqM3+Hg6mojITxJeX4A9A1J/vmp6fJEQ4n8BNAJYD2CalPIl7fk4AKsdrZoAtAdwCoAiPlpEfpIaLLhIfX3uad8t6jzt23XM+pnGTnULxN5J07DlzftxwTk/QzDg/V9VrKZIrdZFfjb4pPRhifo9d6A5cQAAMiZ3Afendy0mdYtaiw474WaAa1EiolaL6+fc62dO6PqcEGJNapEzCAA6dPwazhpwQ9HnkzKR8595coO0KA58YfWLh/Izh7el9rb6Qf9+0zB40MKck7tuT++aJnUVq52IZerfP9NfWirzZ09ERNaEEMcDmAtgrZTyhdTDTQDuQrLXaxCSvWHdADxn6vZ6HcBpqS4wdb4AgF6pL490+fLJRUKIlam1gBHmahuHFSwhc//1rFPdAohwGPUv3Vb0ezghW4+u1cf9y3ZRDhl80o8w7JSJlpO7QEvI6+YEb5aN0wpYizr/c6+0oREiIvKW1+tnIe0V/5IHhBBrAAxRXx9+7Ok4a0Bxvy0/8MXH2Pvin/Hvf/wNwWA4ZtWjq2xeMR2JeBS9v30rhPDml96vNPwan3+0G0DLx5/MYa7f6xYKpU/uKuXq2tUmdnMorV4hG21COetUBhFRJTunJixXrjq6rO954n98sBfAx9pDd0spLZMZIcQhANYB+BqAXlLKv2c7rxDiBACvAPiTlHJ06rGTAfwNwDMAbgKwH8A0ADcCCALoI6XcUur3RN7QpidRXXU0+p71X0WdR0qJ9z/Zgb+9m8zMOv9qcc7j906ahkAcuOCcyUW9nxNUj67+UfyWn4eQfu7OLZY+vaukJmFdpU3s5uBcvUK292btAhGRP3D9nH/9zEDXp/Tw8tCjTyqoV9bs0/dfweubfgOIgAwGw4k+I+cH871m84rpkFKi9xULi37fUj3/qLEz71op5dCWBXTrC3PN9HA3VW1QNi3hrjsBbvb34yKaiFonjxak26WU5+Y7TghRBWAVgK8juSnDS7lfYXz0/hQp5RnaY1cAuAPJzR0AYAeSG0NMBNBJSvluwd8EeU4IsQ3AuQBwYfeZRZ+nObYf295YFm1s/iSMRAIiEol1qluQt0/hnZsmYmjtDM8GDEwbo6m9LIYDwLBTJmYc39qsfjsZunuxgZqqgShHmKy/HzhgQETkC1w/518/s3LBv4YDwCFHdSo6zP3swzfx0jP/HX/9uWUAgGAwHLcT5gKqeqEZ0ab9Rb23Ew4/9nR1d0hq50AA/t8IzQl6764XG6cNGrII5Qhz1fsp/KgbEbVKEoiX+WaHECIM4HEkP9o13M5iVL00+V1p36KUjyO5GcRZAE6VUvYAcAiA9xjmVrRzgeIrFmLxJvzt3ZXRdS8uRmPzJ2ERDsc7/2ox7IS5ACAiETzz4q1FvbcTTN/3cBifFKu8moViqEoG88RuOQw74eayhbkA0jZqK9ubEhFRdlw/510/M9D1uW4X/Ljo177/WkPiy0/fC0Im0PeyOuSqWbASDEWwfcWcot+/VGcNuF4PdY9I/tF2ultVx665hqGV4yKaiKgMUh1dDwK4AMAlUsrNNl93IoC+ADI+AialjEspX5VSvi2E+BqA/wRwp4OXTR7pfuqool7XHG3E3z/dGQYAEQ7HOtUtsDVYoHSqW5AcMIiVP1AEkt935mRy66xasGLeTK0163HMJV5fAhER+Zzf1s8MdFspKSU++/DNAPJsPJFLn5HzIRNxvPPXP1ntvFcW5s7gtjCdq+hTukREVLkkkrv0lvNmwx0ArgSwGECjEKKPdvsPABBCLBFC/EIIcZUQYpAQ4kYAG1JvYXQyCSHCqeMuFUIMFkL8GMALSHaFLSn4B0atxj8+fVEikfw30u5UrpmIRNDw8hI0Rb909NoKoU/qtgtUtZm1KBERkVe4fs6PgW4rJBNxvLrhXkAIBEORWN/L6oo+VzAUSfzzjWfbbX3Cu3DxvKtaNs3YsHGBZ9dB7jnyqDPyH0RERE66KPXnNADPm27jUs+9AqAfkjv1rgEwG8AmAL2llPpOmhLAaanjngLwEwD3A/imlNKzXwpTaVJdb0X76LM3sfuDZwWQfwO0XDrVLYAIh/Hs327Pf7BLup86CqFABIA39QNUXqwAIyKiLHy1fmag60NCiPpSXv/OrhX47MM3VWduUdMQSp+R8wN9L6tDPNaEg42f5n+BS0LtOgBoc/UDbQZ7dImIyktK2VlKKbLcZqeOuV9K2VNKeYSUMiSl/KqUcpRpMQopZUxKebGU8lgpZURKeYqUcrqU0rsifipJKsy9CACOPPSkgl9/oPkz7Hz7YSAQiIlIJFbq9XSqWwAZj2Pvv2x9stEVg78+2bj/zJ47PLsOr9S/b7nJd6ui9+hyPUpERGZ+Wz8z0PURIcRKIYQEMAhI2xTMtqbGT/HBmxsBmYDdDdDsCIYi2Lnq55CJuFOnLEjPS1t2GX52Q/k2CatvmFq298ql3BujeUGb0h0uhJCpGxfTRNQKCMTLfCMqlhBiE1Jh7qHtj8W5p3234HP89e1HEggEIMLhoqsWMq4rEkm8/venPd0kLRRMbhIWTRxAfZlC3dV7lmL1nqVleS8r+sZorT3U7XHMJeZQl+tRIiLPcP2cDwNdfzE2hDr82NMz+mPteHHtr6QQAQRDuachNq+Yjk1PTsLmFdNtVYX0GTkfwWAYW5+cUfA1OSUUSW4SFovtL0uo27BuuuvvkY++MVprD3Vraq+1ql7ghAQREVF5fQMAOlafgPPOvKHgF7//yV/xRdNHAREOx3OFuXsnTcM7N03E3kn2ar061S0IdP7VYsRjTQVfk1MG10xCQCS/pbJVLyQSUD3EXhh80o/SQt3WzhTqKty0l4iIfIeBrg91/GqXosLcWPMBxJr3i0CWqoVNT04CAGxeMQMQAfzHWUPj8ViT7X8H+oycj0S8GbKEjdZK0fOSOWmhbn3DVFc7db36PnX9+01LC3Ub1k5Bw9oprTbcram9FoOGLMKgIYvME7sMdYmoYkkACVneG1Gpep3x/aJe9/7HO5uQSKBT3YKMT4rtnTTNuAXiQJ8u4yCjUduhrvL5/n8WdW1OGFLb8umt1W8vdrV+YfWepRCRiGvnt2vwST8y7q9+73asfu/2Vj2t2+OYSzDshJsx7ISbjcc4qUtEVF5cP+fHQNdf6gFg3wevFfXij9/dmbVqYfOK5GTtpicnAUKg12XzcELXbwaD4So1qWvrPYKhCLY+7l0NQc9L5hh9ukAy5HQ61G1YNx31DVMRDEYQDEZQ3zAV9Q1TPZvY1UNdpQ1O7DLUJSIi8jkpE9h34P2IiEQyfiueDHEFAnEgmBC44JzJOKzD1zDw7J9CxBJqWjfvX6dEJILNb9yHxoMfu/NN2BAOaWvRxAGs3fM/jr/H6j1LIcJhnDxvIUQkgtVvLzZuXlBTukpbqGAAYJ7W5aQuERH5hpCyAmPoVizVoYtDjz4JXQf/KN/haQ588TH++tTP0feyurTHN6+YDogAel02z/J1iXgMW5+cjkAgiD4j84eEm56chHbtD493veDHwUiHjgVdo5O2Pjkd8ehB4+twqAP69y89dK1vmIrBgxbafrzcNmxcYGwOFw5Xo98A76sh3LRr52/x6SdGf/gqKeUIL6+HiKhQXc9pJx9deUxZ3/PsE/+xXUp5blnflFoFtRYFgKMPOxXdTx1V0Ouf/usCiHAo0aluQdrgyDs3TcSF3Wdmfd0nn+/G9rceQOdf5Q8s906aBhmNotuJl+C4I7sVdH1OathVh2i8pYYgFIhgyEk/Lvm8epire/uWCRh2ysSSz1+q+j13GPUL7QLtMfj4wj9ZWGlWv3e7usu1KBFRGXD9nB8ndH3qi4/3FPyaqkOOhAgEM6Zt47GmrGEuAASCIfS+YhEkpJrWzZnyB0ORePOBz4I7Vi7EZx++iZgWqpZTr8vmp0/rOtCt27BuOoLBzI+2rX92DoTwx39dzDUMrR0ndYmIiMpqlbrz8edvFfzibideEpfRaGDvpGnGlO7eSdMQCrfP9TIcddjJCIaqbPXqdqpbABEOy5fe/SPefP+ZaFP0C3gxpDKoZlLatG4s0VRyDUO2MHf3jKlAwB9rUXOvbhub1B3O+gUiIvIDTuj6jJqK6HD4caj55oSCX5+Ix7DliakIBsOJPiPnB/JN55p98fE7eLnh1wgGw7LPyPk5t/nbvGK6jMeaBEQAJ339Enz1tL4FX69Tti2fiVjzfgBAKFSF8/tnnwDJpmHddAQCYQw4f5bx2Ppn5yAeb4IQAQwa6K+Kg/qGZPWFk1O6655J/gVq4AXudRMXi5O6RFSpzj6nnXxk5VfK+p7nnPh+RU0YkH+kgqrhQHETugDw+t+fxt5/bTambfNN5+qisQNoeHkpRDiETnW51yN7J01LyKbkfhAiGEK/Lv+F9pGOBV+vExpeXIxobL/xdTjQHhecVNin7XKFuTIaxbCTxjtyrU7RJ3X1vtlSrH7/v5ObwAUCGHZ86dPOTtr+0R/x0cF3jK+llJW3JToRUYXg+jk/f/yalwAYC2gAKCrMBZLTtid2HQa12Vm+6VyzQ4/ujC79vo94PCry9er2GTlf9L2sDsFgOLHnr3/EtuWzGjf/YXLi/dca8NmHb+LLT/9e1PdQjJ6XztU2TDuIZzfY/54B6zAXAOLxJgwetNB3YS4AV6Z0pUwgEAgbwa6fcFKXiIioLIYDwJGHdC4qzAWAk7/a37i/d9I0BENVtl8bDrXHgLNvhozG7EzqBjr/anEyOA4FseHV/8HanQtjL7z5++gnn+/GJ5/vRizeXNT3UKhB5yRD61Ag+UmvaOJA4ZO6iUTFhLlA5mZpThGRSDLU9Rm1WZrCtSgREXmJE7o+oqZzD/vKqTh74I1FnyeRiOOF5TMRjzUhGG5fUKCrHPjiI/x1dZ2tSV0A2LxieiweawqlPSgCOPJrZ30hpRRHHNel6pjOPUNWrxWBAGQigYONn+C9l57aL6VMHHXCOR0OfvlJrPHf7zcDwHGn9z/k8K+cmve6t/1xFmJNyYAzFOqA82106lbaZK6id+kOGrLIkXOue2YapExfQAsR8NXErj6py8kIIqoEnDCgSqFP59qdqM3mmRdvQzx2EIFQOww552cFvz6RiOOZF38O2JjUVfZOmhaTTab1aCCArxx2+hcBEWp31okjIgGRsXcwIAIQSP5i+61/NDQ3Nn3SHA5Whb921Ncje/+1pRGQ8pCqr7Q79WuD2gmRf+nxzF9/jngiGSTb6bxdvWcpkEhARCJGoLt7xlTIpqbkpKoPw1wgfUIXcGFKV/HZtK5pUpefGiMicgHXz/kx0PURFeied5U3u9eaffHJXrxcfweCwbCtzdLMNq+YHo/HmixWzSaqm1bm/k18IBRB78vzL+j1UDffJmbZwtxEIurbIFdRlQuAc4Gu0rB2CgZ88+cAgI1rZxnhtl+C3Ya1UwAw0CWiynD2Oe3kQ38+tqzv+fVOf6+oBSn5g1qLHnFIJ/Q8fYzXl4No7ADWvbIUMh6HiETineoW5F9Xmui1DDmpftock6EiEMTQr9v7FNPTO+YCAMKBKlxw0v/Letzqt5Pr/lNuW5L2uF82QMtFXTvgXJhrnPu923FyXfJnsmd6KtwGfBPu6hPJXI8SETmP6+f8LCcmqfyEEFu9vgazQ4/qhHNHzsALK+Zh84rptiZ1dX1Gzre16E5twib7jJyfc7G96clJ+PDt5/GVk/sg13REz0vm4PlHkwvg9c/OxYDzs0+YSJnIWrPgV/pkLuB8mGvWb8gcAMlgd90z03wT6gLJSSJORRAREZVOCGFshuaHMBdI1i8MrZmGbW8sk//+cm/BYS6QrGWwc9zeSdMkgHinugVZ/360d9I0rNm1EIO6TkDIYhNdXTjUAdHYfkQTB1G/5460egLdsFMmpgWjgL82QMumXquTcDrMBQAEAtgzfSpOmr8QJ81vWZfvmT4Vq9//b1+EukRERF7y90qhjRBCPAWgJwB0PK6Lx1eTLlx1KDp//VuqU9eVce4+I+eLfGEuAARDEbl7++PY8vgUNDX+O+exqk83Hj+IDRvtB5Drn50DIfz7XwtzmOsWIQJY/5efYePalrC735A5Wft11z0zray9u3qXLpAMdlM7DnPXYSLyHQkgDlHWG1ERLgKAow492evryFBz8pUCgUDeTt1SdKpbIHKFualjgFAQ9S/V4YN/v5Lzo2WDzplo9OnqtQRmq/csTXbGamRTk29rFoDMqgU3DDv+x5DRKHZPmoA901s+lXbS/IUQ4XCylsHEyR7ffI6p6pzxmLYe5VqUiKhEXD/n59/kqm0ZBgCHH3s6zuw/zutryXDcaf1w9sAbbW2U5ia1CVsgEMSOVYvw/KMT8fIz/xOPNu3POLbnJXMQalf4pmHxeJNvqxasJnPdms4deMECDBqyCPF4U9rj2UJdKRMZ3btO2Lh+PhrWTkm7bVw/HzW11xrHpD4eOlx7GTdMIyIiKoAQYr263+O00V5eiqV2oQ4Y1HUCEMu/UZrbOtUtgAiH5Yt7nwys3bmg+ekdc7Gv0Xoj4MFfn2zcz7pBmsVGaH7mVm+ulWHH/9hyUvmk+QszqjGsAl6n1L9/N1a/d3va7bPmD43n1VABWtajXIsSEZHr2KHrsdQC+nzAP9252STiUWxbPhNCBIrq1HWSvgmbEAFUH/EfjZ1qLq4+7Jj0qRJVvRAKtcf5/WekPWfuz7XTnWsOVcPhavTv595fLLJN5Lpds6CoTdKCwYhRvQAk6xcSiSgAGM8DQCIRdaSSYeP6+YVPIouAPOIrp4t/f/ha6rrYZ0ZE/nDmORH5uz8fV9b37NVpb0V1gJF39I3Qjj7sVHQ/dZTHV5SdlBL1L92GeKwJIhKRneoWePb/9WkbsAUCOLzquM+PPLRz+1O/Niisf9qrYddiROPJ4YN2gfYZ1Qur316c1p+7e8ZUyGg054SuHqpandNp2SZy3QxzFbVBmohEMqoXjF5dwJhyltGoI3UM9e/fXfIUMteiRETF4/o5Pwa6HtIX0B2/2gVnnu+/6VwzKSU2/2Fy0RuluUHffC0QDKPHyJkItWsPIH2DND18tdoMrb5hat7uXH0jMsXNUNfq/coV5urWPTMNgUA4I9QFYBn0FhvqZgty+15WBwDYsnI2Ys3ZnweSXcsAF9FE5B9ckJKf+W0jNDt2/3MD3vpwPUQ4XNRGaU7bO2laXDYl16IiEESPU0bjyEM7Gc8/89efI55oBoC0jc5W71kKEQ4bE7qFhrmKm6Gul2GubvX7/w0RDqeFugCwe9IEY/M0ID3oLfYas4W5Q8S3AQDr5QpE0WT5HACslX9Qd1dxrwciouJw/ZwfKxe8NRwADvvKqRUR5ib57xcAfUbOD/a9rA59L6uDEAFs++MsvPfK05Aykape6AAgvXrBajO0XDZsXJAWrvYavcTo6Y1GG1HfMLWgrl6772nmRZgLJCsYrOoX9DBXPVZs9YJVmKv+uSq9R8zOeEy/r+NH3YjITxJSlPVGZIcQ4i/qfqWEuQDw+YEPoqm7vliYdqpbEOz8q8Xo/KvFQDiEF97+PZ5/9Z7mptS65oKv/8w4Nq16ocC6BXO4Gg4l17jNiQNY/fbitI3KnGIVHpc7zAVaOnX1Pl0AaWEuACPwLeYaVbWC/j0PEd82bsoAMTLta0Ck/Xt4FL6q7rJ6gYioBFw/58ZA1yNCiKfV/bMH3ujlpRREiAACgRDisSZsXjE97vX1mPUZOR/BYDjx91eexpbHp0JKiZ6XzjWef3aD9VRxvs3QrKZGu1851wh11THFhrr1DVONwFiFx+XY/KwQQgTSNklzgt6Rq3+/5tDWSrZjjjjW2FhwODdKIyIispb6/8YLgWTVQiU57WuDwqm7vvu7TKpjN/7FgX+2W//KL/DlgY8AAKFg8tNj0VRYaLUZWi7mMHdo7zkY2GOyEeoCyfC12FC3fs8daaGw+loJh6uN9/BKtlC3VFZBLgBTaJupJey9Ii0FqBX9zKEu16JEROQ43y2C2pChQLJqoZI0H/gcx5x0LpAMP30xFWHWZ+T8gNo8bcsfJuNg46dG8BqLZW6gBtjfDK3X6CXoNbplEqD7lXMzpnULDXX1462C3B5jl5hf4omBFyxAIhF1LNTNV69QrLO+8QM91FU4IUFERJQu+UmxDl/zdW+ulS8O/AtIJCCbmnz5dxk1sSvCYTz36p1Y++LPMbhmkvH8M3vusJzOlU1NWesWzGGuMrDHZAztPSdtWrfQUFcPi9W0rznc7N/X283olGHH/zitO7cUuYLcfGFuPqZQF+BalIiIHBby+gLaIiHEVnW/cqoWgHisCS8+vVRGm/eLYDCc6DNyvvHvz6YnJ6HvZXXYvGI64rHkIisYitju2d28YjoAONrL22fkfGxeMR07V/0cXz31G/EP3twYBIBnN8xz7D2U7lfOxY7HZiLW1GhUMOTr4wWyb3oGWAe5G9fPR78B00u+3mINvGABGtZOKfk85jC31BDX7Kxv/MC4/7fn7kNqo7ThWV9AROQiCSCOyvsYF7VeQohV6n6fLpWzFpVS4h+f7sIre/8EBAJREQ4HkRpQ2TupJXA0b5bVqS7/L9vV6+0ca5c61zs3TcSGV/67ORSsaheLHzSmdIuhh7m6gT0mY932WxGN7TdCXTu9utk6cpXBA+1XQpRNIIA906dm9OkWwqont9QQ16xW9AMA7JQb8Qk+AFKhLnt1iYjy4/o5Pwa6ZSaEeApATwDoeFxlTef+/ZU18Wjz/iBkAn1Gzg/o4S2QDHWD4fY476rFSMSjeOGPc4wNqoDMgDct/A23RyLWlDxHKJLoM3K+IxMXqVA3ocJcAIjFDiAYtP8RN7sTt92vnIutD0zIeo5otDFtAzVzmJtrEjdUVY3YwUZf1DCo6gVzf64SDEaw7plpOTdGczPMNTvrGz9I+/eQiIiIcBEAHHXoyV5fR0EONO/DK+/9OflFIhHuVLcAeydNg2xqQjCk1nYCF3SfCQD48sBH2PzGvXjnppaNyPSAV70WAELhKsQTMbxz00TbIbBdIhKJH2j6d7tcx+yeMRUIlLb8HdhjMtZsSX6SyiqkVRUK+sZs+nF2w9v69+/G4OOvL+VSSzLs+B9j9Xu3Z31eRCJY/f5/Y9jxP856jLkn1021ol9aqOvqmxERUZvBQLf8hgHA4ceejjP7V85EBAAc3alH8IO3noNMxIww9ryrFlseGwiG0evylvA2EY9h65PT0wPeVPir+8fr67F31wpHPz6nh8Pq/QMBe//q55qgzcU8navOoVcy2A1zAaBm1Fxsvz8ZFvt9SrffkDlY/5efZX1e53aYa8apCCLyhkBc+vKT4dQGCSHWq/s9Thvt5aUUrCp8GAIiBAQlEvEo9k6ahkC8JcA1O6T9MRhS07Jm+eTzt7F998NGwBsMRTJe++WBj/Dc63dh76RpjoW6neoWGIMFerisk01NaUGrzonNzvRzqOld/TE7YW44XI1otNHTHl1Djindk+YvxO5J1kMWZmHYH/IoRa3oh7XyD2V5LyKi1oHr53z40ykjIVp2QD1rwPV4ce3teP7RiXj+0Yn42/q7vbw0W6o7HofeVyxE7ysWASKAYLi97dcGgqHk6wCcd9VinHfVYvS6LLP64GtnDEAwVIVNT04yahicFGrX0nVrh36c3p2bjz7Va57wjUYbCwpzlVBVy7U3rJ2Cjeudq6colFMbpG1ZObv0i7HBtFEa+8uIiKgtOx9IboS2462H8PSOuXh6x1w8/6r/16KBQBBDan6GITVTEAxVIZgQuOCcW2y//qjDTsEF50wGAFzYfaZxX3dI+2Mw5JwpQCyWVuPglM6/sh6GEJEIVu9ZavlcIQGqvkmaYq5VyNaTm4/eo7v6vdux+r3bUf++N//e5OvSVVO62bQLpDapgzN9vIXgWpSIiJzAQNcjf/3LYjR++p7x9WcfvuHh1RRGBILoddk8y0A25+uEyDrRq+t1+Xx0H5FcMG56cpKj4W7vEbMtHw8GI2hYl/097Ia5Vpuj6eGtCmWVQjY8qxk1N+310WijZ6HuwAsWIB7PvgBWtQvZqJ2SY83lqZAwbZTGj7oRUVlJAAkEynojyufjz9/Cx5+/ZXz9xYEP8MKbD3h4RYW54JxbMLib/TBXCQbCuDDLRK8SCARxwTlTEIgnJ2rfuWmiK+Hunjkt13HyvIVAIpFxjD5Fm60/VzewR0tIrV6rB7dqDaYrpCfX/HpPp3VTU7pWTppv/fNUvKiM0DZJ41qUiCgPrp/zq7wrrmBSSqPR+cBnH6i7f1F3Xlr7q3Jfkm9Fqo9Ar8sX4LyrFqPbkJsQj0cdn9jVJ2cHnD8LUibSnqtvsF4g5tL9yrmWoS6QDG/NoWyhakbNRY+xS9KmdUsNdTeun1/UxG+uKd1+Q+ak/Tz9QN8ojZMRRETUhm00ff08gKcA4NMvdmPHWw+V/4p8KCACuOCcybiw+0wM+fo0Ryd2A4ck13GJxuy/2K7fc0dRU7RAy5Su+bWDBy5E/77T0kLZQjc96993GgYPXJj2OiemdNXEbyHyTenm4sVksdokDeBalIiISsdAt/xW6fellMPUY19++i5effZeb67Kxw458kT0vGS2Y6FuvtqFYntzle5XzjXuW51HhbKFTOdanUMPdYu1cf38tH7fQkLdYqd09fcsN1YvEJFX4hBlvRFlI6XsD+Dp1JdPSSm/IaUcDmAzkJzcZaibLhAIYsg5UyBiCTWtK/O/KrsTF7ZM2r59S2bXq7kiAbA3navoU7pqIzSdVShbDBUMNycOFB2Q1r9/d1qQ62TQmqt2wavJYn1KVwghuR4lIsqO6+fcGOiWmZRyhJRSpG4j1GMA1gDAvg9ew6vP3uPpNfqRTCQAmUA81qTXMMTdeC9zb24h3bnlVDOqJTguZkrXKlgtNNQtZkpXf89yb4pmrl7gIpqIiNoiKeU3U2vR4dpj5wH4F4C0KgZKEiKQXI8CkE1NwolgN+M9Uj26etg4tPecgsJcxapL12n6tG+xAan5dQWHwyXULgDAEPFt++/lgFrRTw91AdYvEBFRkUJeXwAlSSkvFEI8DWDovg9e9/pyAABbn5iGeKwJwXD7gvtynfb6c8tiEIEQZALnXbUYu7c/Hvvw7eez/vu7ZeXsjH7WULtq9B6R+bhOr0goJcgNRaoRa3J/CjVUVY3YwcaiJl711/QcswTblk3IeDyfgRcsQMPaKfkPtFDuMFc56xs/wN+euw///vA1gItoIiIig5TyWLWJ7463HkL3U0d5ej0NLy5GNLYfARHCkNrCq7Cc9Fnj+5AyOUswoNt4NB78BC+8/Xuxd9I0dKpbkHH8u1NnIfFl5ppK1S0op9zWst48ed7CtIndYoJcZWCPyVi3/VZEY/uLPocd/ftOQ/264v7Z6MHtwKE/x7o1PwNQWDg87Pgf56xqUFO6w47/cVHX6AZVvbBW/sHjKyEiokrGCV0fkVJe6PU1KFufnIF4LPlx+njUw80OUqoOOSqmf31S98tDgWA7bF4xHZuenJR2rFWYCyQ34Nqycrbx9eBB6R8zCwYjjlUBdL9yLnqNXmL06bpFn9ItVs8x7k0g59scjYioLZBSIC4DZb0RleAvgPdTuirMBYCEjKHhxfwb67opIVs+GLb+paU48tBOOP+sH0NGo3jnpolpx2YLcwEg8WVj1uecNrDH5JJC4UIVWpfgZO3B7kmZ1RWAvSldIiLyH66f86u8K24j/rram0XrtuUz8fyjEzNC3C1/+Jn18X+checfndhye+wWRz96ppza6+qqU85t+UiUEAK1wycjkUhvXTCHueYNyHJN53Y8vLNx3681C7k0rJ1ie3OzXMcMGrKooPctdnM0cxBfTqnpXACI5TqOiIiorUnt7wAAnnTpNry4GE/vmJsxWRqN7c8a6j7z10V4esdc41a/6zbHr+uIQ05Eny7jAAAXdp8JAKhqdzjOPD7541IbppnD3HNWTEfwcOv6A306FwD2zJlp3HcqiF23/VZHzpOLXruw+r3bbQW75ulc3bATbi7sAgIBiEgk69O5unSJiIgqFQNd/1kFAAc+/wB/W1++3VdVMBtrTl88B6pTu/AmYnj+0YnY9sf04C6jVkAmhNVx+Wx9Yrrl+XVfOakXzruqZSHfrv3h6PPtnyMYihi9unpg2+MHS1BzzVz0+MES9PjBkoxwV6lvmIr6hqn45FNnqy52PDazbLULulw9uBvXz0fD2imObkpWyOZoxXT9uklKGfb6GoiobUhAlPVGVKJ1QPmndPWpXGXwwIVGH6xVqNuwqw7xRDTtsVj8YMETvSpIfnrH3KyvPazD14wwVznhmHMxuOZnkNEY3rlpYkaYCwBnPzA+Z7C7Z85MvH3LBCQanV83ul25AKR36QL5g9369+92dDp32PHJSWm7XbpObrxWrJ1yo9eXQETke1w/58ZA12dSG6StBoDPPnyjbO9rDh5PXrwEJy9egs5z5hqhrvm4bctnph1vPm7LE/Y+ar9t+SzEYweN121bPqOga+91+QKc1ueatMd6/CBzwrbmmrlp4eeGjQtQ35C58AuEI9j6wIT024OFT5PqP6seY92b+K0ZNRc9xi5Bj7EtoXW2wNb8uFN1C3andL3cEI2IiIjskVIOUvdfePOBsrxnw666tPBx8MCFGDwwWY/Vv990hELtAaRC3V0ta4ho/EDaa4LBiHHc0zvmph2bi/7ehYagoWA7XHDO5LTHVJirO/uB8WmhbrYgN9C+HdZsmZV+e6G0iV31s3RL/77TkuG7Kdg1M4e55uncYg07/seQTdkHDNSUrtNhcrE+wQfqLj8tRkRERRFSuvIJeSqR2pBCOfTok9B18I9cea+tT84wKhZOXpw94Ns9MdlNFQxVQQSDaYGl/rp3ZkxH4kDuhVIoUo2el8zBtuWzstYghNpVo+el9havW5+cjng0GQpbhbm67fdZd2zleu32+yYUXMOw9YHk+7gZ5lrZfn/yfdWC2irctQpy1aZohVYuKA1rp2DAN60X5RvXzkIiETWCXa/DXK3uYVXqlyhERK45rVt7efufTinre444+ZXtUspzy/qm1KoIIVYBuEh9XdWuozy/602ujK+YJ3OzhY/PbpyLWGoIIBzqAJmII5ZoynjNho3z84ayoWAVBtfcAgCo/+utxnnSj2mPwTX2fqmvfw9WYa7uxZHZP7GU7bUvjpxfcA2Dvima24GumdoobdgJN2cNUc1hrtoUreDKhZTV792Ok+ty/F3G1LM7RHw7y5Hu0zZE41qUiMgC18/5hby+AMpqFYDh6osvPt7j+Bts++OsgioBAtXVSDQ2Jqdptd8lm0PgzvPm451ZM3N+bCw5iZse5p68eEna62LNjXj+0YkIhCLofXnm7sH696HCXADY+bupqP1e9kVrqKoasYPp12YV5O783VQkoqnFvShsmH3HYzPzH+QS9f05Watgh5rS7Tck8y8b/YbMwfq/ZPYwP7d8ckbHrhABfONSd/vejji2i+rRHZ7vWCIiorZISjlcCPEXABcCwMHmfWLbG79Fz9OvdfR9nvnrzxFPNBtf5woez+83E+s3zEE83pQzsO3fLxmK5gp2Y/GDlu8fDlcba6hY/ACe3pHcgDYc6oBB50zMPFGK/j4vX3Ubuj56S9Zjg4d3QPyz9OuyCnJfvuo2JA6kri1Y+Acry1G3kM/q9263fNypydw0gQD2TJ+arFiwICIRY4pXhblrxRPWm6YFAhgiL3f+GlOOwlfVlC7XokREVBRO6FYANa1bdegxqL1ocr7DLekTrNnkms5VzEFtvte8M3MGEvtbFpMnL15iTPrmem+rKd9guD16XTYv47XPP9qyuO41egm2PjAh75SuHcVM5SpqOhco/4Qu0DKla74G9bgbE7pA7ildPdAVIgApEwiEIuh5VXpYv+3RaZDxqOuhrprSlVJWXlkOEVWU07p1kEv/eGpZ3/Nbp7xUURMG5G9CiJVIBU9HH3Yqup86quBzNOyqS6tHsGJ3ilRNf9p53YZNC9J+yT144MKM11udJ1sYbBXs1u+qQyz1vQ3tPQdrXpiDQLtQzlDXjmKmchUvp3OBzH9G6jrU4+FwNfoOTK9ZUxO67QLtMfj464t639Xv/zdEOGwZ6r4zu+XvMUPEt7FWPAHRLowTf5X594u919/i+gSvmtLlWpSIKBPXz/lxQrcyrAIw/OAXH+Fv6+/CWQNusP1CO1O4doJcpfOcuXmnb9OOn5u5QFKTvrnev/O85EfR9PeKRw/g+UcnplUx6H27RvgqAnmndN2kT+d6Eebqsr3/zkdmovbquZbPNaydAiC50O43IPdHBs1yTemmHRcMo9dV1lPXPa9agG2PTsNzyye7HuoSERFRflLKEUKItQAuKGajtIZdix0Lc4H0Kdp8r+vfN3NPB/312d5fTfkC6eFuNLYf9btuM+oaGl5cbIS5ytBzk723XvJ6OjfbPyP1uNUnydRzalM1oPBwd9jxP846Faz//WOt/ANEJGIZ5gKAqIpg7cE/uD6pS0REVCwGuhUgtYheBeCizz58M+txdsJbO2FqPp3nWAeBbrxeHWuuYlBizZl/Oeh1TR22PjjJk1B3x2MzC6qxcEu2IFfVMVhdYyhSnfZ4NNqIjevn2w51N66fDykTiMeb8Fz9PHxjsPXmdr1H5d91WoW6m56cVJYKBiIiIspNSjlEfWrshTd/j3NP+67lcdn6aBWrCdlCJ0itQlo3X6/C3Wc3zkMsdsCoawDSg9O0adpgIG/1Qj6B9u2w5oU5GHpuYeHwuu0t6yYvpnOB7D/j/n2nZZ2Q7jtwBjatm5cW9ha6gVn9+3cDSE7jdp5t/XeOTnfflvc8Kuh996YZWNv8BENdIiLyHQa6FSLVYyYB4MU1v8Q5Q3+S9ry+sZmVYoJbP1HBrqpr2LY8ucmWYq5G6HVNXVrtQbnogajX07mFqr16LnY+MjMj1LUzsbtx/fy0xXc02pgW6j5Xbz39YLbt0eTiv+dVC4wqhi0PZe+rIyKqBBJAAoX3XxL50CoAwz/9Yg92vPVQWvWCeWMzKypc9CpkLNX5/WYYYeQzf/05AqLlr1LmagQnpnS7PnpLzg3UsvF6OrcUfQfOMKoXFLvTuvrma4nGxpyhbjbv3pRcu6pA98RfzWOoS0TkAa6f82OgW1lWARje+O+/49Vn78WZ548znrAKcys9xLWiJoz1KV3X3iscwdYHJ6HXNXUFv9avYa55Mziz2qvnGl26hUzsWn1sTj32XP28nBu0bXt0GhKx5CSPSG0+t+WhiUa/biAU4aQuERGRD6Q+NbYBQD9z9YJViFipwW0uqhYgnmhGHM25D3ZgSrdQfpjOLZVVJQYAo4rBKtjVw1xFhbrqfjbv3jQD8mBqqjyQXIvuvf4WiKpkJYPboa4QYqWUcoTjJyYiolaNgW4FSS2iVwP45r4PXrM8JlBdXXIlgp91njM3Y8O0rBuXldilW/u9hdh+n/0pX7071++sNkXL9Zya3DWHuubJ3J5jlhiBMJAZ5oYi1WnnVRug9b0sMzR/bvlkI9jtPWoxe3WJqKLFuecNtRJSyv7qU2Pb33oQPU69Ju35UKg9zu9nXbvUGvTvOy1jw7RsG5cNPXcW1rwwp6yhbiVP5yrmzdIApFUx6MGtVZALAMHDOiD++f68+368e9MMyOZoywZoar9wAaxtfiIt2HU61D0KX8Un+ABIbThIRETpuH7OjfPLFUZKOUzdf7n+DuPxYLgKQOo30bMqJ1gsRud58xGobgkGswWpva6pQyKavcPNaX7oznVL7dVzjTBWLabNYa4uVJV+rKL/jFSYmy2g/calt6LvZXWQ8ahRuyCCYTy3fHJp34xJauduIiIisu8vAPDJ528bD4SDHQAAsdgBrN+Qe3PUSte/3/S06Vd9KtZs6LmzkDiQZ5LXIZUwnbthk/WmuPn0HTgDA4f+3Pi6/v27LcNcdcwpv7G3XpQHm7IGtEPk5Rgivg3ZHMXe65OBvGgXxlrxRDHfQoZa0c+4z/UoEREVioFuZVoFAF98vMd4oNdl8xFql1xI5/tNdGvQec5cI9TNGaSmpnTdpofKfq1b2PVQaUF/7dUtk99WYa4+2dvte/OMUDebRKzJ1rStHuyqagYnHHFsF3V3OBfRROQmCYE4AmW9EblJHzBQBtVMRDiUXIvG4+X7hbqXwuHUL7BdnIpVG6PZUQnTublquOxQP/PmxIG8G6YFD+uQ8/l3b5phVCzkoge7RjWDQ47CV9VdTukSEWm4fs6PlQuV6WjLR0XbGkfvPGeusUlaNqVujma3R1cPlbffPwGhqmrUjPJX9UW+/lw7VK+uuWbBSrfvtWyEtvPu8SW/t8tVC1xEExER2SSEWGX1+KBzJuLpHf5a/7ipf99pxiZpbil2Y7QNmxagf99pLlyRM/Rp20JYbZqW7VzmKd03rkjfpE4ebGqpWrBhiLwcEGipZSjRTrlRVS4QEREVjIFuZeoFAB2PMyYMse2Ps1r1R/69YrdH17yBWOxgI7bfn9pczCfhbqiquuRQV980rdj33v74LPS4omXSZMvK2Yg1NyLUrhq9R8wu6fpy+dtz9+HfH1p3TwOIufbGREQAErLyfutPlMNFAHDkoSelPVj/V/bcWypxczQ1pTv03Fk5jwuHOhhTutFooxE2h8PVvg53C5Vt0zRDMIC3Ri/AqQ9Yf8/vjZ+DE5a2/CzXyxWIIjl5G0YEA8RIR69XlyPE5VqUiMiE6+fc+NOpYGf2H2fc18PEkxf78yP/nimxdkFN6ebS/cq56DV6CXqNXpKx8ZcKd0utPCiVU6Gy/v3l2lxNp1cwxJoaseXhWyBEwAhzASDW3IgtK2c7co1WsoS5MSmlkFKGXXtjIiKiVurc075r3G94cTFiibZRtWDFzR7dro/eAsQTeY8b2GMyhvaeY1RfKCrcrV83tegOWz/RN00rZNJXVTAkvmw0OnEBGGGu+b7TsoS5XIsSEVFRGOhWGCHE6lzPM8zNVOrmaLXfWwjI/ItoJVu4GzvY6Hmo64Taq+ei55gltsNcJa1XVyYgZcIIcxXz107523P3GfdTi2bBxTMREZFz9P5Wv27I5YZy9OgWSgW72cJdP9i0bl7+g3IYOPTn2cPceMJyOveU30zO6NVdK/9Q0nUUQgtzY1yLEhFRqRjoVp5vqjvPPzrRuLV1Wx+YkLYxmV+ocFcFu34JdXc+4s415KtjyLZZmh4OOz2la6pa4MfZiKjsJMBNHajVEEIYDaJP75hr3JS2FOYCSKsyyDWlq2oXilXI5mg6PdxVvJzSNQJwj4LlU34zGac/nvvnuF6ucPQ9d8qNacExQ1wiovy4fs6v8q64zRP/8voK/CRQrU3ANjVmD3bLULuQS/cr56aFul7Raw9cEQggEI7kPERtlqamfFWYa/x8HKxeMIe5XEATERGR0/Qp3TVbZlkGu+WqXchFTet6OaXrly5fFeqqSd+BQ3/e8s8RTY6FuhY1CxwuICIiR5Q10BVCDBRCSIvbPu2YQ4UQi4UQ64QQn6eeH1jg+xwvhLhfCPGBEKJJCLFHCLHI4W/HE1ImjjV9ZFwAMHYa/ue993p4deXXec5cnLx4SUawaw51y127YKX7ld5vjJYtTN75yExsWzah9MndRAI13y/uv2q1V89NC3VLxTCXiPxCQiAuy3uj7LgeLY15HWpei+56cZmHV+eN/n2nGWEgkDvYLUWxU7rKwB6Tjft+7NJdt+ZnWLfmZ569f9+BMxwNdU1hLrtyiYgKwPVzfl5N6N4E4DztNkR77igAY5H87eWaQk8shOgMYCuA01PvcyGA2WjFvw2VUo5AaiF94LVXPb4ab5iDXatQt1SBcARbH5hQ0qSuX6mJ3VhTo2t1DHbUXt0Sepc6pcswl4iI8uB61CH6WvSTT19vs6Hu4IELM4LdNCXWLnR99BYE2oWwZsusooNdP0zpKnqPbrb75WYOdUthCnO5FiUiIkeFPHrfV6WUm7M8t1dKeSQACCGGALi8wHP/L4D3AQySUkZTj60v7jIrh5RyhOo02z1xAtp3ORPHjRvn9WXltPuWZDB68m11jp2z85y5eGfWTCQaGzNrBVK1C7XfK67bTb1u+325e2L9rMfYJdh+f/L6VXBr/jmpUFcPV20LBLDrN1PyTukGwhFs+/0k9Pxu5j/7UKQasaZGxJobsenJzPA81K4avUfMtn1JXEATkR8k2HLlR1yPOii1Fl0JYLgKdWvOGeP1ZeVVv26qo72/qlKgfl1m1dfQc2dhzZZZJZ2/66O34OWrbkOiuXJ/NxAOVyMabTRC5U3r5qUFzNFoIzatm4e+A2d4cn19B84wJoWtNk0LI4IBYqTt83EtSkRUHK6fc/Mq0M1KSinzH2VNCHEKkpuGfU9bPLclqwAMB/w7qbv7lklAIlldEGiX7FrdPVELSAOBkgPeznPmGufc8dhMo+qg1zV12PqAd2FsKRPD23/b8nNDIIAe1xb/MwpVVSN20CLwNj1XTKjb87t1eTdGA4Ca7y/CzrvHWz5Xe/Vc7HxkZtaeX9WxW0ioS0REVAiuR4tTKaFuw4bpkFofrTl8FcEABvWf78h7rdt+a1rVgRMSB5rTNjkrRMbUcAHqN0xv6fENBjC4yJ9R/77TjJ+5uWJBD3vXrfkZwuFqT4JddR1WVB2D3VBXCLEyNcVORETkGK8C3QeFEEcD2AfgLwB+JqV814Hz9k39eUAIsQbA+QD2A1gB4KdSyk8ceA/fUgsFNan7z3vv9c2UrgpyA+0iOP2W7NObb9w2pSXgTYW7xUzyBqqrrad0PVTStSQSqL1hKQBg1/1TjClbAAUHvDWj5mLXQzPT+nR7jF1i3FfPlTSpW6Js76mCXoa6RFRJpATikhMGPsT1qAvMoa6fqCA32CGMoU/9v6zHrbnoTiNwFMGW/+4WEvIawWRsvyuhbjH0Tt+ippLjCZz8SPJnsOfauelBeIEBr1Vgqq5pw6YFxnNeVUNkC5HVNHGBoe5wRy+OiKgN4Po5v3L/dD4DsATAOACDAcxDsq/seSHEVxw4/9dSf94P4A0AFwGYDGAEgL8IIbJ+v0KI64UQL6Ru1ztwLV4y+nR3T5zgj43SEgl0mb40Z5gLAKffsghdpi9Fl+lLEQiFsXviBARC4ZbpVE3aZK9J5zktgaCTXbo7fzcVyP6vUVb6NejhqW2BAHbdPwUAUDN2EWpvWGrcAsEwtt8/ITnFa1PNqOwhbc2ouQhVtXQRF9Kpu+33kxAIR2wfXyjzxmml9uwSEVGb5Mv1aGtai+rTiPXrpvqiU7dhw3QEIkEMW38Thj71w5zHDn3qhxi2/iYMW38TApGgcWvYMD3tOKtaBUXfKK2UqVinrNt+q6PXcdJvZ+LkR+YbNxEOJSd4bTJvJKcHzKqPWCmkU3dd/VQE2rezfXyhitw4rXL7MYiIyLfKGuhKKXdKKSdKKVdIKddLKX8JYBiAY5HcMKJU6vtZJ6X8kZSyXkp5N4D/AtADyY+/Zbu2u6WU56ZudztwLZ7RN6YAvK9f2H3LJKNeoRAq3D39lkUItItg98QJaTd17mz0DdKckog2odc1hdUd7Hgse32AXT2urUMiar0xgwp4Cw12VWhrec4iQt1tyyYAiUTe/txSmUNdK3977j5Xr4GIiCqXX9ejrWktmmKsRb3eKE2FufmCXCtDn/qhcQtEgqhfN9W4qXNno/p0vWYOc53sDFZO+u3MZKi7bqrtYDffz8cITlOdun5hJ9TdKTca99mhS0REbvC8Q1dKuUMI8QaAng6cTn2Ezbwb8dOpP2sBPOXA+/ieV/ULej2C3ZoFO7K9Pq2iAUjr4NW7dAFg64PuTo9mo4e5RU3nKqkp3Zqx1j8L9bhRyZCniiHXlK56vpD6hZ5jltjqz3VC7dVzjfcyVy/87bn78O8PX1NfciKCiHxAIAHh9UVQDlyPOk9bi5a9fkEPWVsqFgoPc82szqHXMwC5+3fXvDCn5OnRl6+6DQgWNpPjWJgbDGD31dMhqtrhpN9m/rJfPWbUMdioYch1Pf37TjPqF+x26g4cvDCjlzeXdWt+hoFDf277eKXvwBlp9Qtmn+ADdZdrUSKionD9nI9fCikEgKI3n9C8kvoz27kyP7ff+hn1C2WpXkgk0qoS7NQslEKvaDBqGiymdrc+OAmBUBi133N+IiGXkqsWND2urUMiHsXOu8Yb9QtW1MSuVU1FocyTutuWTcC2ZROyT+wGAtj1m+zX5iSrKV1zmMuJCCIiKgDXoy7Q6xfKNaUr4wmjJsFOxUIp9HoGVdFQv25qxuTuuu23AvEEuj56S0nvlzjQjKHnzirqtaVO5g7uPx+DBy6EjMaw59rsv+RXdQyF1jBYMVczqGB33ZqfOTK1W0yYq+jBsprS3Sk3Yq38g/E416JEROQWzwNdIcS5AE4HsMWB020G8AGSH5vTqa+3OfAeFUWvX3C7ekFVK+hVCeV2+i2LjEA5LdiVibKHuYCzdQ9AMtTtMXaJrWA3EI4U3K1rRQ91lWw1DD2/m70awmn6tPCWlbMZ5hKRb0kkN3Uo540Kw/Wo61YByeoFNzVsmI76dVONiVw3g9xsVMCrgl3Fqf7aQPt2WPPCHEfOVazB/edDRmPYffX0vMFuoTUMVlSnrh7sAqVvmBZo3w7r6rP3INuhVy/slBv1yVyA07lEREXj+jm/sl6xEOJBIcR8IcTlQojBQogJAFYDeB/Af2vHXSSE+DaA/qmHBgghvi2EuMh0vpgQwijLlFLGAPwMwAghxP8KIS4UQvwXgF8DWAeg3tVv0Kf0yQi37L4lOQHrRYhrpgLlQCg9z9v1oHOboxWq1OncjPNpwW62UDetW9eBULfH2CXoMXZJ0RumuSnW3Mgwl4iIbOF6tPzKsRYFkpO5bk/k2qWC3WAHbUkSKP2jo10fvQWI2x/yXrf91pLf00qh07qFXHM2KtgtdsM0N+hTunrNgpRScD1KRERuKneH7ssAvgPgxwA6IDm98ASAWVLKj7Xj7gTQSft6durPvQA6a48HUzeDlHKZECKB5G7C3wfwKYAHAEyRUjrxMToy8VOYqzv9lkV447YpSDQnJ0ZjB52dls3GiU3Q7OpxbR22/3ZS3m5du726dpi7dctp5yN5f7YMc4nIl+LefygqTSqo/A6AcwF8BcC7SK7JFkopv0gd0xnAniynOEJKuU8734kA5gEYBOBoAH8H8CiARVLK8v6fRX5cj3po14vLUHPOGEfPqSZh08JTnxj61A+xesCvkl8kyveP3rwRmlsG95+P+g3Tc3brAoCoame7V9eOcLja6NbdtG5ezl5dp6nu3Cy4FiUicgjXz7mVNdCVUi4CkDf1k1J2tnk+y19zSyl/D+D3BV0cFcVOmKuHqgAc2STNLvU+r80f78j5AuEItj44Cb2uyR6KljvktBvqAtqGaUqRAW/NqLnGefJtluYUhrlERI6aiOQidCqSi8daJAPLQUKIb0gp9XG6RQD+ZHr9F+qOEKIawFoAYQAzUuftCWAOgNMA/Kc730JxuB71zCq4tDmaCAYQiASzTuauuehOxPdHja+d2iTNrmHrb2oJdcvAKswttT83FxXQ1m9IVjDY2jBNV0TIa94wrRinPjANb1xhv484T5ALcC1KRNTa+Wr9XO4JXfLYP++9F8eNG+fIuexO5iaam3Dy4pbKgd23TMIbt03xZKJ314MzUXNN8eFj7fcWYvt9E7I+r2+Cpjhdt2DFTqgLIOO5tIC3wHA3VFVdlildqyC3z3cWAwA2PzwRQPa/TBMR+YGEQMJ//zM1Ukr5kfb1eiHEpwCWARiI9FqA3VLKzTnO1RfJhec3pZRPpx5rEEIcCWCiEKKDlNL9UUHyNSnlCCGEoyOqDRumQ8YTeQPa+P5oWqDZsGE61lx0pyfVDK+MXoqzH3Bm0MBKucPctPdJTetmC3UBWD5uhLwFBrv9+04zwmG3p3StwtzBgxaivsEIp1eVq1qEiKgt4Po5//qZgW4b49TGaHbD3DdumwIE0sfkT76tDrtvmYTX5o8v27RusEM14vsbS65d2Pm7qYCwHvsvZ9WCFRXq7rxrPALhSM5gV9GPcWp610m5wlydEGIlF9FERPaZFqOK2qzr+AJP1y715+emx/chuV+D71bjVPkaNkxPTeX+v5zHrbnoTohg+tptUP/5aNgwHasH/Kps07rtOrZH874DiH+237VQN1vNwoZNC9C/7zTH38+K3QoGXdbp3QIC3mKmdN8avQAI5v44b7YgVznqyDPUZn/DC74AIiKqKH5bPzPQbWsCAYkS/2JVSJibiEVx8m2ZoaB6TAW7gLtVDKeNn2e8T6lTulbMYW7tDUux867U+z00EzWj3K8kAGAEsIUGu0Ce6V0gI+A1h+Pbfj8JSCQQCEeKvPpM+s/UKsjt+LUu2PeP1wAuoomInDAg9af5t7+LhBD/C6ARwHoA06SUL2nPrwXwJoBbhRA/RPIjY70A3Azgf33YoUutgIwn8oa5QOZ0rjIoFRSqYBdwt4ph8B+vQ/0l9xihrtPMYW6v7y7BjkeT69NiKwmKpVcwFBPsKsUEvOvqpyLQvl3OY5TEgWYMHPrznMfkCnMBoKZmjD6lS0REbY9n62cGum1M+9PPKC3MnTjBVvCaK8zV6c+7XcWgT+kWEuruenCmEV6GqqqRiCb7gLNN5NbesNQ4Nnaw9KngYujBbr4ahmwsA97fTsqY2u05JlUpkUig9vqlxV2whZ2PtCzqrcJcAOgyYJxRu8ApXSLyMw82dThaCPGC9vXdUsq7sx0shDgewFwAa6WU6nVNAO4C8DSAjwB0QbIz7DkhRC8p5asAIKU8KIToB+BxAK9op70XQP7EjdqcUjZGq183FSIYsLUBmtV0rtkgLSB0e2p38B+vM8LjQqZ0Xxm91AiBg4d3MB7PNpHb67vJtVn3q+Zi6++zV4W5zU63bi6WAe+G6Zah7sChP0+Gue1COPUBe9PIgfbtsK5+KgYOtq6k2LRunnHfHORa4VqUiMhZXD/nxkC37VgFYHgplQu7b5kEAI6FuWZuVzGcNn4e3lw6w1b1gh7i6vTHcoW5ANBtzDxjStcreg2DUsjUrq5m7KLMqd2Ubb+f5Ohkro0N0Az6lC4X0kREho+llOfaOVAIcQiAPwKIAfi+elxK+U8AN2qHbhBCrEZy0TkNwOjU66sA/B+Su/1+Fy0TBjNT5yx/USn5lSMbo+XaAA1o2QRNBANpgW0++tSuWx27evVCLnqIq9MfyxXm+olew6DYndrVnfTbmelTu+awPp6wHebaYXeyWa9d4FqUiKiiVdT6WUjp6N4E5GNqI4r2Xc4samO03RMnoMv03BOYxYa5Ge9ls9ahGKp6occPrBe82cJcNXFrpoe4ZipIDVVVl612IZ/tv01WIyjFBrzqe+s5Zgm2LZtQ8HTuzrvHt0z36o+bwtxs07m619bfq0JdhRtTEJFvnND1MDn+sT5lfc/xZ63ZbmdBmlpMrgLwdQADTB8Fy/aaVQBOkVKekfr6RwD+B8CpUsq3teOuA3A3gK9LKXcV9Y1Qq6PWo8Vu1FW/biqGrb8p6/NrLroTiaZ4QUGulZaOXudDXaPi4fAOllO62cLcdh2r0LzvYMbjuUJcNaEbDleXrUfXjvoN04F4y3q0mIB3z7VzIQ82J78IBgqazgWAN66YZVm5YO7NtTOdu2vXMph+UcG1KBFRCbh+zr9+5oRu21LylK65EuGN26Yg0dzUckAgUHKY66Xt96VPn5rD2kInbvXahe33T/BFsGuuTNAneIsJd7ctm+DYdG4xYS6QrF4whbrs1CUiykMIEUbyY169AAyxsxhVLwWgTwR0A/BvfTGasjX155kAGOhSydL6VFPUNK5S6FRuNoP6z7d8PyfkmtJ9cWT6tV+68b/Svl7e79cFvVcoUm306Navm+qbYNdcm6BP8NoNd0W7sBHoFhrm5lJM53BNzRhzqMu1KBFRK+Sn9TMD3TZESjlCTUUU4+TFS9IqEQAkp3EXu/DRrkTCtS7dbHY9mL5wtJq8DYQjSMSjqL3OXtDYbcw8vLRshjHZ60Wfbj56wFtMPYPdydyXfjcjcyO1ZROMv2iY2Q1zlS4DklPnqlOXiMg/BOKl7UfqOCFEAMCDAC4AMEJKudnm604E0BfAk9rDHwA4QghxqpTyLe3x3qk/33fgkokweODCtE3MgGSAW+y0by4NG6bb6ul1kznMVY/9+cJ7EG+Ko+eo/EMU3a+aa2yOBiTDyg2bFvgi1NXpAa/dcDfxecv60W6Y+/b3b0X885YgXXXomqdyAXuTubqammQvNDdJIyJyAtfP+c7LQJcKoqZvVZ9uJU/j6sw1C9lqFGrGLip4SrfbmOSGCup1fpnUtWI1vVvspmqKVZCrcyLMJSKigt0B4EoACwA0CiH0z7T9XUr5dyHEEgABAM8juanDGQCmAEgA0JOO3wIYD2CVEGIBkh1g5wKYAWA7gE3ufitUiYrdGE2fvq1fN9WRaVwrMp7A0Kfc2dOved8By8dfGe3c5rK67lcl15wq2PXbtK6ZOdzNt6Ha6Y/PyXk+c4ibJp7AujU/K+o6iYiozfHV+rnsW8aRP/zz3ntLev3Jt9VVZJj75tIZaV/venAmtt83wVaYW6pQVbVxP3awEbseKqwnzAs9rq1DIh7FrvunpD3+0rIZWV6hHfO7Gdh59/iMMNeqN1dXSpj72vrS/r0mInKDBJCQgbLebLgo9ec0JBec+k0V7b8CoB+SO/WuATAbycVlbyml8bliKeU7APoA+CuA+UhWPKn+r6FSypaiTKLkvx/mvtGiuDGZq4hgAGsuutO18wPAOSum45XRS/HiyPl4ceT8tAoGq+lcJbY/ams616z7VXMRirSsR9W0rp8N7j8fMhrDnmsLH4R4+/u34o0rZlmGuYFDqy1eob1vgdO5RETkLK6f86+fOaHb9pTco+sVc19voF2k4FqG+P70cNEcNroV5gItk7qqgsGP9QtWelxbh+33p3cL27n2XEGuXrPg5DSu1qEbc+ykREStkJSys41j7gdwv83z/Q3AVSVeFrUBpVaAlYtVh665rzfYIVzwpmn1l9yT9rVVj26uMLdUVtO6fje4//yMSd13rs//dwBzkNvpd+mv2fu95MCCk+Htrl3LHDsXERH5i9/Wzwx025hKWUQjEMAbtyUXWUaIGwik9fXuvmVSxiZtubw2P70qQd8Azc0g16zbmHkF1zb4iT6da9Wfa65YsJrIrb16LrYtm+BatYKU0tvSOyIiImoV9BDX3NfbsGE61lx0p+1Qt/6Se9LqFvQN0NwMcVuDwaaAXe/PtfL292817puDXEVURSCbo5bPFUubPOdwARERuYqBLvnSybfVtfT0Ztl0TR2jNmkr9yZqbVWu6dydd9sPqgOhiLGBmRAB9L76tpKvjYjIr/y2qQMR5SeCASSa4lmrHQb1n29s0mZnWjdbd64XrPYw8LP6DdMhqtoBSJ/ONffnvnHFLNvnPPHu2Xj3+tnJTcwCAQwe4FwfM4cLiIhKx/VzbuzQbcNK7dF1m52e3pNvq8PJi5ekVTFYSevOFYG0PttyTudWOn06V/8Z7rx7fEaY23PMkpx9uT2uWWgcI4JhbHnkFseuUwix0rGTERERkSt2vejvj6cP6j8/76Zrg/rPx+CBC5FoihfUuduuY5Vxv9Dp3FCHMLY9NKmg12TjZg+xo+IJo25Bn85Vk7iqL9cs23SucuLds9Hpd4sg2oVRv366Y5fLtSgREbmNE7ptk9Gj+89778Vx48blfYHvBQI5J3X17tza60v7mH8gHDEqE1pLGLzroZmWk7ehqmrEmg8gEI4ASJ/OjR1sxEu/s94cLd/GZ2Y9rlmI7Q9OxZZHbilpUrfj17qoHt3hWrXIKinliKJPSkRUIimF3Y0WiNqKVQCGf/Lp69j14jLUnDPG6+spmZrWzVbBoHfnllqvcPHT1+HPF96DbQ9NKmpzND/asGmBZZ9vOFyNaOKAMZ1rpnpyrTY+yxfm6pya1j3qyDNU7cJwU80d16NERAXg+jk//nTaoNRiYhUAVOLmaFbsTuo6EcDWjF3k2Lm8sv3+CWkbnWWrUYgdbAQSCeN71qdy1fPmvtxCw1ylxzULIYJhbH54YtHTul0GjEPHr3UxPzycUxJERET+oa9Ftc7Rijeo//ysk7pO1y1c/PR1kPG8G2D71oZNC1C/bio2bFoAAFk3Z4tGG9Omc62YJ3M7/W5RQWGu4sS0bk3NGBx15BlWT3E9SkREjmKg20ZV4m+I35k1E7snTsA7s7Iv6PTN1JS0ugWHBMIR7LxnYsnn2fVQju/FJfp7moNdc2Br1m3MPNeuC2ipYSilgqHLgHHo853F6POdxXq4O1wIIbmQJiKvxGWgrDciv6vEtSgA1K+bmrY5l9mg/vONTdSM1zg4natzonpBBarlpgLcaLQx7efZa3T+wYDAYbnXq6U68e7ZJYe6gwctNG5awMtQl4ioAFw/51Z5V0yO83uXrpJobEz7U7d74gTsnjgBJ99Wlzal++bSGWl1C06pGbsISBQ/FaGC01wbjLkhW7WC0m3MPNTesBS1NyxNC3dfWjYDO+8ab1RNuE1N65baq2sxsauCXYa7REREPlG/bqrv+3SB9PDTHITqE6ciGDCmdOsvucdXm6EpoUhynReNNpY91M33fr1GL0Gv0UuMawSSG6G9c/0i7L46GbJmC3WLmcy1YoS6DVNL7tY1Te3qa1GuR4mIqGgMdNs2o3bBT6GunUlc/bmM+9qUblp3ro8qEvRJ13JO6ephrgpu9Vu2a7QTPBdatbD9wanY/mD2CRcnKhiAloldVjEQkVckgAREWW9EFWKVuuOn6gVzHYCi1wLoQaje/xqNNiIUaG9M6ephrpPTuaXqftXctFC3nPT3U+GtuqVd45VzjfuJzxuNzdD0TdFK8e71yd7cbJzcMC1XFUNJJyYiaqW4fs5PSCnzH0WtVirQSltItO9ypmcbpb0za2baBG6guhqd58zNeNyOYIdqI9B1I8zdedf4ks770rIZRlDaY2xxvbN2WE3l2r3uQidyQ5Fq1F49N+cx2x+cikSsCUIkf58kgmH0uCb3Dsvblk1An++Utpmd7rX196rN0yClrLz/5SaiivK1szvK6/9vQFnfc063P22XUp5b1jclKpJp8ygcdeQZnm2UZrU5VzicHnyGItWINRW2LnUrzF3e79fo9d3i15Fbf5+s3ho8MPdarBTZNjyzU6+w9YEJeY8JH94e0c+SwXng0GqccEf+8PXd62dDNidDd9EujBPvnm3r+GI3S7NS32AMNnDDNCIiE66f8+OEbhunb0qheDmxaw5tE42N2D1xQtrj2XpezY+7UbWgK7VH1+0+WiB/xYId+s81X8durKkR25ZNwM5HrKeOtz84FTIeRZ/vLEbvq29D76tvg4xHsW3ZhJzTuoFQpOT6BV2XAS2/sOCULhERkefS1qKffPq6ZxUMVsFjNNqY9rg+3arr9d0llo+3daVOAIc7tjfuD2n4Seb5P2uZgk580Yi935uC935kHby+e/1s7P3eFCOcHTxgPmRzFHu/NyXvtK4Tk7o6vYbBsZMSEVGbEfL6Ash7+m+E1cTugddexe6JLb8Rd3tq1zyB2/WnS/Hq/85A/ED6AjBUVZ03CNUnXwH3qhZqxi4qW6esCmZDVdWoGZV9AlY/DsisWCjEznsmIhCOZPy8zT9fK7GmRux8ZGbatK4Kc3tffVvaserrLY/cgu0PTrWc1u1xzUJsf3AqtjxyS8brHcBFNBG5TFTkRgtE5WK1Fv3k09fTNstye2rXPEXa79I6AMDmVbMRa9YGC1KBbferrNdj6nE1+Qr4q2qhWPrPJxyuRv++02wdp7MzkWtlwJM3pH0d7tge0Ty9xIkvGvHej+ZnTOvKg00YPCh9rammbuvXT8e718/OOq174t3JMNgpNTVj9CldIiJKw/VzPgx0KY2UcoRVDcOB11517T2z1SmceeO8jFA3X5C4856JaZuVud2bq6Z0a69zrg4AyD5ZGzvYiO335//oWbEVC2kSCdRcl7mxRLcx87IG2bU3LDUCX/NHEROxppy1Cb2vvg1bHrkF25ZNQCAUyQh2XQx1Y06ejIiIiIqXbS2qpnbdCHWzVQIAQJ/hs9NC3VhTI3Y8OjNroAsA2x6a5Pg1ZhPqEMa2hyah56g6R8+b62cSjTamhe3ZlDqZu+3hSQh2CGc8PuDJG7D+srssQ90hDT8xnkt8kf7+714/GwhkDwcGD5iP+vXTsfd7UyCqIpbBrqiKJEPYQKDk+oVdu/y/ESAREfkXO3QpL3OvmeLU1K4+Cdz1p9mDx5d/kQwRc03pqkA3EI6gZqwzu9zms+v+KUjEo0WFuioY1Tt0nahJ0BUbapfSEay+L7VRWrbp3Gy2PHJL1m7dQs+VzeaHk3UZ7NAlIrcdd/YRcuzDg8r6ngtrnqyoDjCiXKwCXsC5qV09nFSTuVb0YDdXb+22hyYhGAni4qevK/na7Pjzhfcg3hQvKtRVk8T61G2uMNduf7D5uGKmc7c+MMGyYsGOtYN+CaClU7fQDtz69dNzduvu/d6UjEnfQmnTuTEpZWZyTUTUhnH9nB8ndMmOVbBYRDs9tZsrzAWAYPtqxA80InawETvvGm8Z7KpQdec9E7Hr/illCXWdqF7INnXr9oRxNqpuoeTzpGoX8k3nmvW++jYjcDXrcc1CbFuWf0o5l9fWe9MRTURERIUr19RurjAXSE7rblyenL5VQWgoUp0xrdtzVB22PTQJf77wnrKEuhc/fR2W9/t1Ua9VwWu2qdtiaxK8pmoZ1JSuVdVCLoMHzC9bHQLDXCIiKgYLKSgvKeUIKaXQb+o5ffO0f957L3ZPnFDQhmrvzLLePMvKmTfOQ7B9SxdX7GAjXlo2w/LY2usWIxGPYtf9zvVc5VLsBmm5NhnzKswFkKxbKCEMNzp8mxqx/cGpEKLw/6kRIpB1o7RSN0nb94/X1F3WLRBRWcQRKOuNqLUxr0eR2kjtk09fN47Z9eIy1K+b6uqGaqF26Wu3bNOqPUfVId4Ux/J+v8afL7zHtesxritVvVCobBu8Ad6GudnqFuzSe3ezbZCWVyCQdaM0Vb1QbOjLugUiovy4fs6t8q6Y/GIVkJzS3T1xAnZPnGBM7KrH7AS7Vt25uZx54zx0/elSI9j1S6hbM3ZRWnevXd3GzEPtDUuNm+JpmOsAfXI6EWsqqh6h99W3IRFrsnyuxzULIWXhP28gfTqXExFERESVSd9IrX7dVNSvm2qEu2pDNTfC3T7DZ6PfpXVp07xbfz8BOx7NHFLoOaoOvb67BLH9UUevwcrFT1+HYCRYdKjb67tLjJsfyHgCg1b+qKRzhDu2B4CMLl27Bg+YD9kctQx1T7x7NkRVJGcnby7aLyI4XEBEREVhoEtFSS2iV+U6ppBgV5+8tUOf1s0b6katQ0GnFTulayXb91MOTtUt6NPHmx+eiM0PT8QLT8wq6BxOT+m+tv5eTucSUdlJCCRkeW9EbUTOtSjQUsvgBn1aN1evrAgGyjKle/HT10HGi/uFd2s04MkbjFAXgDFRu2HjAtvnGDxgPuRB679LnHj37KIGOvTpXA4XEBFZ4/o5Pwa6VDSrKgbzx+CAZLCbLdQNVCcXwvEDhf/m3BzqZhUIlG1KNxAMlxTqGlUFDm6KVrAS6xYUq43rYk2NBYW6va++DTIetQx1i5nS1cNcLqCJiIgqW661qLmWwY1QV03rKlZTukByUrccU7pA8dULflJq3YIu0Zz5+/tsG75lla96Yf30gk7H6VwiInICA11yhVpgw1TNYA52O8+Za/Vy2868sSU09MOUbrHVC4pVCFpOTk3nWtF7dQuRq3qh2C5dhrlEREStn/6JMlXD4EawqyZ1Y02NWUPdck7pFlu94BdO1C0o8f1Ry47ggqd0c1QvFLL253QuERE5hYEuucpczZBrWrdYdqoXyjWlCzhbvVB2Dk3nAkj+vAMBox9YD6udqF7Y/uDUrEGvFb07l4io3BIIlPVGREnmtagbwW6f4bPTQl0r5ZzSdSrU3fGY/c2LneLkdK7S/cq56DV6CXqNXmKEu9FoY8nVC+9ePxt7vzeloB5dTucSEdnH9XNulXfFVHGyTeuqW6nsVC+Ue0q31OoFANh513jXunR33jXe8ubkdG4i2pQxsaBP6ZZavZCINaHPdxbb3nCN3blERERtk3ktCqRvnKZsXjW76PfoM7zltV5P6QKl9emq0DPW1OhqqLvt4UnY+sCEtFsgEnRsOrdhxB0QwfS/7na/cm5aqFsQq+qFQACDB8wv+No4nUtERKVioEtlk28jtZd/Mb7oc+vVC1mVcUq3lOoFfTOxnFPHJai9Yanxp35zajrX/D5KtzHzHKteYN0CEVUKKYG4FGW9EVEmq2BXF2subQ8DP03pAsX36Xa/am5ZQl0ZT2BIw0/Sbk6FuUCybqHnd+oyHu9+ZUvlWylTuqXULRARUW5cP+fHQJfKyrx5BWzsTuyUck7pAsVXL3QbMw+1Nyx1f4O0QAA77xrvSsit6haslFK9oCtkUzTWLRAREZGir0fh4FpUn9L1g1KqF8yhrhtEMIC1g36JhhF3uHL+XIqe0i3Srl3LWLdARESOYqBLnkpN7QIADjnpTEfO6VZNQaFKrV7Qg09XpnSvW4zaG5a6EnInok2ovW5x1ueLrV4oxmvr72XdAhF5LiFFWW9EZI82tVsW5axdAEqrXuh+VcskqxtTuj2/U4deo5cg0RR3PNS1qlvQFTula1m7YIMe5vLTYkRE9nD9nBsDXfKDVQDw5Z5XSzqJ3zZHA0oPdfUpXdeCaod/Jrmmc5VSqhcKpYe5XEATERFRLq88f58j58nWo9tzVB3iTfGyhrrFVi8A5enT7fmdOkdD3YYRdyDRFLesW9AVs0Ga1eZo+ehVC1yLEhGRUxjokuf0Kd1S2N4cLR4te6hbbJ9uWvDpUvWCkz+TXfdPQSIezTmdq5RSvbD9wanYtmwChLD/P2FcQBORVyQEEjJQ1hsRFWwVAPz7w9fyHZdTvh5doPxduqVO6bpdvQAkQ924Qz+TbN25ZiVtkAYkJ3XzDDEAadO5RERkE9fP+VXeFVOr9s7y0qYV9M3Rsk20lrtLtxI49TPJV7VgVuiUrhABbH9wKhKxJvT5zmL0vvo22+8lhFhp+2AiIiJqU5waMNB7dLNN6QLlr14oZUpXr15obYqtXnj3+tmQzVEMHjA/77FHHXlGUddGRESUCwNd8gtHahcA5J3SBVD26oVAOIKdd40vuHrhpWUz3NsUTbPzrvEln8NO1YKZPqW7+eGJxi3bxG7vq2+DjBc2vdHxa13U3eEFvZCIiIjapFJrF/w6pVvsBmm5gmm/ydeda0Wf0q1vmIr6hqm5w91AAPJgk60wFwBqasYUdD1ERER2MNAlX9CnIpyc0s2m3NULNWMXofaGpQX36ephbu0NS924tKRAAIFwpKRTFDqdq6gpXV2uvwAVMpULAF0GjCv4moiInBaHKOuNiIriSO2CPqWbixcbpBUa6u54dKbr+x04xW53rplevaDk6tW1G+QSEVFpuH7OjYEu+YljU7pKro3EvKheKLZP19UwFwASieS1eaDbmHmW31+uXt0+37EfHL+2/t6irouIiIjaFqdqF3Rbfz8h5wZp5ZzSBQrv09XD3F6jl7hxSY6x251rRa9esNOrO3jQwqLeh4iIyCkMdMk3nFxE67ULuUJdLwTCkYKrF9qC2huWpgW7Tk2D7PuHMWUTc+SEREQFkgASUpT1RkSlcap2AXB3M7FiFNOn63aYu+3hSQh28Hb/2l6jl6DX6CVp4a4Tdu1a5uj5iIjaAq6f82OgS77kRO2CX0PdYqZ0/XT9Vorpzy03KaW3f0sgIiKiiuFE7UK/S1umRf3UQ1volC4A7HjM3euX8QQGrfyRq+9hl9Pf6yefvu7o+YiIiAAGuuQTQoiVQgipvnaidsFWqFvmzdEKpfpl3dwYbec9Ez3rzzXT/xkVUqtgh/7vFxFReQkkZKCsNyIqnBBipdPn1DdIswp1y92jq9id0lX1A7GmRtdCXT9M5+qcnqg+6sgzHD0fEVHbwPVzPpV3xdRaDU/7SgQcCd/Moa5ZuTdHUwLhCHbeNT5v9UK3MS0bvLk2pethf66Zm8E1ERERUTapMHd43gML1Gf47LRQ16znqDrEm+JY3u/XvtwgrftVc9NCXTc4MZ3bMOIOiGDpf7XVQ2unenJrasY4ch4iIiIdA13yla4/XZq8/WRxwQUmL/9ivOXjZ96YOxT1anM0u1Ox+pSu06GuE9O5TnFzOhcApKzAUhwiajUSEGW9EVHBkmGuCMh+l9al1SXYtWnFZGxaMTnj8T7DZxv3raZ0e46qQ6/vLvHtBmndr2rplHW7eqFYpWyIpux4bKbv+o6JiNoyrp9zY6BLnjJXLZTq5dutJ14ruXqh25h57oW6DkznFtOf+9KyGdh513i8tGyGcZ/TuUREROQFvWqh3yW3lvQ3umwBqT6lu/X3E3zTqeun6oVy2/HYTGx9YAJ2PDYzI8x1ajoX4KZoRETkDga65LWWj7aVWrMQCACJBF7+xfiMYDdfn65nU7rBcN7aBSAz1HWCU9O5hfTnmsPb2MHGjO/HjelcIiIiIitpVQslrkX7jrwVALBx+aSMSV29egGwri/wok+3kCldN0JdJ/pzC6lbUCHu1gcmGP8MYk2NroW5QNqmaDFHT0xERG0aA13yjD4NUWzNgq7rzckgsPaGpQgEwwWHul5M6daMXQQk7O0y7Hifbpmnc19aNiNnGN3nO4sdD3NfW3+vo+cjIiqGlEBcirLeiMi2lqqFEqdzlcs3/RDBSNAy1NWrHMyTuj1H1ZW9dgGwP6XrRp+uE/25dusW7FQqOB3m6tO5Ukr/7PxGRORzXD/nx0CXvOTINIQVNf1qntbNFep6ukGajSldAI5P6ZaqkOlc/ZpVeKvf3LDvH68Zb+/KGxAREVGr4FSYK4IB/GnovfjWmnEIRoKW07q5JnW9mtK1s0EakN6nW6pyT+eap3Ctbk7TpnOJiIgcxUCXPHdI5zOc+1WINmVbM3YRam9YmjEBaw51dZVQveCEcm+G5vaGZzY87cWbEhEpCRko642IvNF35K3GlO231ozD5Zt+mFFpYJ7UNU/pxpvivq1e0JVSu7Dt4UkIRIJlm87VuRHcZnPUkWeU7b2IiFobrp9zq7wrptZkFQB8uedVx07Y9ebMKdtAOGI5qatUWvVCqXbeMxGBYLjkugW78lUtlMnw/IcQERERlU4EA3ii753409Bk9VOoQzjnpG6sqbGiqhdKrV1wKswthFcbudXUjPHkfYmIqPVjoEuekVKOcOO8KtTdedd47Lp/ijGpa+7V1ad0d9413gh2vZjSBQqrXgAK69Hdec9E7LxrPHbeNd6xMNduf665aoGIqK2REEjI8t6IyDt9R96KfpcmJ21V/YJVr66+UZo51PX7BmmF2vbwJGMzMqfCXLt1C3a6c4mIyF+4fs6PgS55Rt8UzWldb16Mrj9dmhbMmqdg9eoFwFS/4OMpXb1H106oqyZya29Yitobljo2mZurP/elZTOMAFnxIszt+LUuZX9PIiIiIiAZ7KpQF4DRq2sOdRU9dPRqSrdQdidfZTyBIQ0/wZCGnzg2mZurbmHHYzONANncnesVN//uQ0REbQ8DXfKSa5uiGUzBbCAcSZvSzcarDdLs6DZmXt7N0XbeM9G4uVGvkGs61ycVCwCALgPGGfe5iCYiLyUgynojovzKsTbQO3UB61A3Gy+mdJ2qXdj28CRse3iScb/Uzc+y2frAhIzHsk3kehXmaj26rAAjIioA18+5MdAlz3X9yWLX/pvT9ebFOad04wdaFnu1NyxNe60X1Qt2axfybo6WSCAQDLsW5ibiUcvpXHOY2+c7i42bV7QpXS6iiYiISOf+cAGSwaya0gWSoa5VtUGv7y5J+9qLDdKcql2Q8QRkPOFoxYLSMOIOrB30S4hgAL1GL8l43jyRq25e0Xt0OWBAREROYaBLhJYagwxlrl6wW7uQq2ph5z0TEQhHUDN2kSsbn+WqWvBjX65pSldqNy6oiYiICP0uudUXYzl6h67iRfWCnSldq2s1c7piQYnvj6LX6CVZ6xYUL0NcM31Kl2tRIiJyAgNdatVevj0ZbirmqgB9YzSrkNSvU7o5Kw0SCVeC3F33T0l24trYCM0vYa6SpUt3OBfSRFQOEuCmDkRt1KYVkxGMBPGtNclfMP9p6L14ou+daZt56RujWSl39YKdKd1cm4y5VbGgT+ZWmpqaMXqoq3AtSkSUBdfP+YW8vgAit7x8e2Z/bCLahK4/balWOPPGeXiuHhX6AAEAAElEQVT5F8mNu7KGpKkpXTdCUis1YxelbSZmpgfP5poINZ3rhkS0KeP9KoU+pQsAr62/F/v+8RrAGgYiIiJykYwn8K01Nxhfx/ZH0e/S9MnSPsNnY+Py7BOxPUfVYevvM7ti3aSmdHuOyj0Fa6482PbwJMcrFhQ1mVup9OqFXbuW4ZNPXwe4FiUioiJV3q83iexyaFLVb1O6uTZCc6MzF8i9CZqVF56Y5fg1OMkc8BIRuS0hA2W9EZF9rzx/nyvn3bRiMkI2JlU3r5rtyvuX4uKnr0MwErS1QZpOxhOuhLmtjR7uEhGRNa6fc6u8KyZyiZ+mT2vGLkIgGM5ZvaBfr5thLpC7N1enuohzfQyPiIiIyCdWAcC/P3zNlZMnp3Pz/xI51uzPdZPdDdIUt6oWgGTdQiVWLRAREbmlrP+vKIQYaNqUSN32acccKoRYLIRYJ4T4PPX8wALe450s73Gp898ROeGd5c53gpm7c+3ItdGYF+xukAbAtd7cQnUbM8/rSyAi8p8y939VYgdYOXE9SoqUcoS679aUrh2qQzeXcvfoFsrNqoVibdi4wOtLICKiYnH9nJdXv+a8CcB52m2I9txRAMYCiAFYU+T5/2I6/3kA1hd7seSaVQDw5Z5XHQ11rbpzgey1Afk2RgNg9OiWm50N0tzszS1kIzQzv9cuKPl2Gbb4yzg3ryAiah24HiXApSldq7qFPw2913LKtM/w2cb9HY/OtDxfz1F1iO2POnqNdqgu3XzcrFpoGHEH4gV876FIcm0fjfpz8tks1/pSCLGSa1EiIrLiVaD7qpRys3Z7QXtur5TySCnlEAB3Fnn+j03n3yyl/LcD100OSk1FGKGuE7KFuUBqQ7SbM2sDzryxZao0Wz+tFz26QP4pXbd7cxPxKGpvWGqrbkHRaxcqJdRFcpfhtAWyWkDnOJaLaiKyRQJIQJT1RrZwPUqOT+luWjEZG5dPQjASzKhbiO2Pou/IWy1fp6Z0c9VWeTGla6d2wa2qhYYRd2DtoF8i0RRHr9FL0PM7uTdoU7pfOde4X0FTuhlrUfV4jmO5FiWiVovr5/x8V0QkpbQKUKiV0hfRjkzpZqkeKHRTL0seTenm5FLVghHmFhDkKt3GzPN9l64eNHc8rov+1PBUkLsSagEtArLP1YvR5+rF5mPTXuPi5RIRUZlxPdrmODalK+MJXL7ph7a6cwvllyldfYrYraqFhhF3FBzk6vQpXT+Guvo1Hd3xNP0pI9jV15dDe8/B0N5zzMfqr+FalIiojfEq0H1QCBEXQnwihHhICHGiw+cfKYTYL4RoEkJsZl+Z7zkypZurNzfbdC4AvPq/LTULuTZG82pK107tglNUxUKxYa6id+n6cUpXD5q7DBhnDmuHQw9z//M2oR+r01/DKQkioorD9SgBSB8wcEu2ugXF7sZoXk3pBiNBI9TV11FOVy3oU7nFBLlK9yvn+rp6Qb+m2jNGW4W1LetRtHxirPaM0blCXa5FiYjakHIHup8BWAJgHIDBAOYh2Vf2vBDiKw69xwoAPwbwTQDXADgI4EkhxOhcLxJCXC+EeCF1u96hayEbCl1Ev3z7RLx8u0XAWeS0avxAAYs8D6Z0a8YuQiAYLkuom4g2FVyxkI0fqxdeeGIWNj/c8nPsc3XL99llwLiMCVw9zNVfo27mgDfF/DE4Lq6JiJs6+Isv16Nci/qDndqFTSsmY9OKyZaPm3tzlVx1C7pe312S83mvpnStqhd6jc59rcWI748WPZVr5tfqBf1ahvaeY9xXwW46IYf2np32P+rquBxTu1yLElGrwPVzbqFyvpmUcieAndpD64UQzwLYiuTGFNMdeI8f618LIZ4EsBnAIgAP5Hjd3QDuLvX9qTTvLL8HnS+9LuvzqiMXAF7+xfi054rdGCzYvtp2qFt73eLkJmFlVjN2kevv60gthabbmHl4adkMxA42ItbUmBakAi0fhbOqZQhFqnHu5eYFbfFeeGKWrfqHLgPG4bX192LfP18DREAC+Yt0Oh7XxTi+41dPF/v+mfXjmsOFECvLMQVERETZ+XU9yrWo51YBGJ6vdmHTiskIRoIAgI3L0zcLC3UIu1K1QMULRaoRa2pENNqI+oapac+Fw7kneMPhavTvN82xa9mwcYGtaeGjO56Gj/e9Casw10rtGaOxZsss02stDU/tD7GK61EiospX1kDXipRyhxDiDQA9XTp/XAjxGIBbhRDHSSn/6cb7UMlWARiet3YhkUDNdc52xp554zwjHH5p2Yy0ugA/23lP9oqJYqjpXCfpoa5ZroBVBcDFBrsqPFaLeDN9MtdMm7q19Ss6O8cbITFDXaI2SQIV+Vv/toTrUZJSjlCbob7y/H04+7wfWB8XT+Bba24o+PyhDmFsWjE575Tu1t9PQChSje5Xzc16jKpduPjp7EMQbjB36TqtYcQdOWspitH9yrnY8dhMy/VgvnBV9e8WE+raCW8zp3GTas8wBvlt/x9HtnMpO19/QA96rTZaIyLyFa6f8/M80E0RSP7zcvP8cPk9qAT6IjrblG6ujtx8AuEIXr59YtYeXTWlGzvYiJ13jUeoqtp3wW5Gl66D4bbT07k6q5+jedq496iWfy7bH2+ZptUne+2Gu3q9g3nxnivIdVPa5C9DXSLyASHEtwF8B8C5AL4C4F0ATwBYKKX8InVMZwB7spziCCnlvtRxswFk69ZpklJWOXbh7uJ6lGxN6RbjW2vG4Ym+d2Z9PtSu2ujRjTU15gx2e46qw9bfT3D8GvO5+OnrsLzfr42vdzw2M8fRhVN1C07TqxcUc8g7aEj6mnrj+vmIRjMne+1M7eYLc/OFr25QIbGa5OWkLhFR4fy2fvY80BVCnAvgdACPunT+EIArAbwrpfzAjfcgx+Sd0k1Em7Dr/ikFd+Xmqyw488Z5ePV/ZxjVC7GDjdmndVM9usX09ZbCzdoFN6ZzcwlVVRtTu3qYCwA9rkgucvVgF0if2lVf2+VVkKuzCHXVX+i5mCZqA3w4YTARyUXoVAB/B1ALYDaAQUKIb0gp9bLMRQD+ZHr9F9r9ewGsNj1fnXrM/Dpf4nqUgPQBg2xEMIA/Db23qGqFXFO6fYbPBgBsXjU7Ldjd8ejMnNO6XlCfgIo1NSKYpTPY77pfORdbH8geivcbMN0IdXVqalfdt8uLENeKqZKB61Ei8jWun3Mra6ArhHgQyaR6B4B9SH7zUwC8D+C/teMuQvIb6ZZ6aIAQ4mgAjVLKp7TjYgCWSSl/kPr6OwAuQTIYfA/AsQB+BKAHkik6+Vi+RbSarn359olFBar5pnTPvDEZ3qpgN1uo61WPLpD8HhLRJk/e20ndxszL+zNUwS6QObVrhx9CXDNTqKtwYpeIvDBSSvmR9vV6IcSnAJYBGAigXntut5Ryc7YTSSn/juSi1iCE+C6S68xljl2xQ7geJTuy1S70HXkrNq2YXFSom29KF8gMdrOte7ysXYg3HTC+HrTyR2V9fydlq+ZS+g1Ir9PWp3bt8EuIq1OTuqYKBoA1DEREdvhq/VzuCd2XkVzI/hhABwAfIDmePEtK+bF23J0AOmlfz079uRdAZ+3xYOqm7EFy7LkOwJEA9gPYBmCYlPIvTn0T5L5cm6N1vXlxUaGu3QlXfVpXVTAoXlcxlGNztHLb/vistPDWSrapXT+GtvlonbusYSBqIyT8t3OuaTGqbEv9ebwDbzEGwIcA/Lj+4nqUcjFqF9wIde126fYZPtvYdE2vV1A1DH6pXahk+aZ0zaymdv0Y2tqh9fTqNQxcixKRb3D9nJ87pZlZSCkXSSnPkVIeLqUMSylPkFJeb94YQkrZWUopLG6dTccJKeW12tebpZSDpZTHps5/uJRyCBfPFWUVAHy551W8s/yerAd1vXkxEvEodt41HjvvGp/sgLUhEI7g5V+Mx8u3T8x53Jk3zkOwfXXG48bmXqnaBSpdIdUJ+YLfStNlwDh0PK6L+pKTEUTktQGpP83dR4uEEDEhxGdCiD8JIbqZX6gTQvwHgEEAHpRSxty40FJwPUq5pAKtVQCQq0u378hbEW+K44m+d+KJvnfiT0PvtXX+b60Zh2AkiE0rJuc9NtTOYi2aqmHwUqhCaxacYJ7abQ2O7niaujtcCLHSy2shIqpAnq2fyxroEuWjL6LthLpdf7oUXX+61HYNQc3YRcmu2EQi77Fn3jjPOH/Xn7b0y760bAZqr1vcKqoPvPLSshlFv1bv3H3hyWwd4pVDn9jlIpqIHHS0EOIF7XZ9roOFEMcDmAtgrZTyhdTDTQDuAnADkgvMiUjWDzwnhDgzx+m+i+Qa03d1C0R26FOKrzx/X9bj+o68Ff0urUO/S+sQb4oXFOrKeP61aJ/hs43z97u0zgh4C/lluBvKXfPglmI3ddM3UFu3PfekdSWoPWN0Wqjr5bUQEXmsotbPDHTJd8yh7su/GJ8z2AUABAIFTesak7o2pnUVNbGrunWpeMakMzI3RbOjmI3R/IxTukStXwKirDcAH0spz9Vud2e7NiHEIQD+CCAG4PvqcSnlP6WUN0opn5BSbpBS3gPgfAASQK5t3r8HYKeU8kUnfnZEHjGmdDcun5Qz2AXSJ3btBLuhDmFsXD4JG5dPsjWtC7T06wLAjkdnGj26Xlp/2V2OnKdhxB0QwfL91XTHYzNLWkeGw8m1aDS2v9WEukREfsP1c27l7tAlsiW1QdpKpAKuL/eYp9fT6Rud2enX1Z/bdf8UvPyLLL20gYBxbnO3rldCVdXG+1tt2lZJiglzgWT1wpaHkkH8C0/OwrmXVXYVQ5cB47D5EXu/WCAicpIQogrJnXRPBjAgtUFDVlLK94QQGwH0zHK+XgC6APiJw5dKVFbmtWiu+gVF9eJuWjEZT/S9E6EO4awdu/rjfxp6r9GXayaCgbS+3VC7amOztFCkGrH93qxJ23WsQvO+g4juO5D/YB/Sw1x94tYuvU83Gtvv5KUREZHP+WX9zAld8i0p5QgpW1qwbU3qItWvG22y3XGrahisboFgOG2K98wb08NTL3p0u42Zh1BVy7RwpXF6urm1TOkqrF0gaoUkkJCirDc7hBBhAI8D6AVguJTyJZvfkUh+V5bGIDmp8JDNcxH5lnktamdSF0gGuyIYQGx/1Nb7fGvNOFy+6YeWt2AkmDbF22f4bF9ULwz/81hHzzdo5Y9s1VA4Qa9aKCbMVVpjny7AtSgR+QTXz3kx0KVKsErdyTepq0vEoyUHrirs1YNdfbM0r3p0K3Uq96VlMyoyhC4H1i4QUTkJIQIAHgRwAYBLpJSbbb7uRAB9AWyxeK4dgKsBrMqyCzBRpTLWoqqCwQ4RFLZ7dbNRYa8Kdjcun4R4PH0qdtXF95f0HqVyqnahXFrbMIBTuDkaEVFufls/M9Al3zNPR9jR9adLk5O6DoS6QHqwGz+Qvgjcedd4Tzt1nXjvQDiCnfe4/5H/UrtzrfS52pnzeI2boxG1XhK+nDC4A8CVABYDaBRC9NFu/wEAQoglQohfCCGuEkIMEkLcCGADgASAhRbnvBjAkeBmaNTKFLMW7TvyVvQdeVtBvbq56FO8wUgw7bnmfQc9CXXbdawCgIqqXXBqOrc14uZoROQnXD/nx0CXWjVVv+AUI9gNR9Ie92LqVK9dKDXUrRm7CEg4/zG3l5bNMDar23lXS0+xU2EukOzQbS04pUtEZXRR6s9pAJ433dRvmF4B0A/JnXrXAJgNYBOA3lLK1y3OOQbApwD+7NpVE/nAEcd2yX9QSt+Rt6LfpXW26xfsUOGuzotQV69d8OuU7o7HZmLrAxOMG6dzc+PmaEREOflq/cxAlyqOnR7dNIGA4123NWMXIRCOpAW75Z7SdbpLNxCOpIWvpU7sul2vEIp43x/nNH1Kl4haF79NGEgpO0spRZbb7NQx90spe0opj5BShqSUX5VSjsqyGIWU8hIp5VFSymZnf3pEvrEKsLdBmpkIBkqe0jULdQgj1CFsTMo27zvo6PntcHJKN9ghnBa+bnvYXrVFNjsem5l1ncjp3Pz4iTEi8hrXz7kJKbN18hL5i77T8CEnnYnOl15n+7Uv3z4RgWA4OYnqMD24DFVVl73fVk2+1t6w1LFz7rp/ChLxKGqvK26S1hzmOjmRq9vyUDJ0DkWqce5lc1x5j3Lb/IgRpK+SUo7w8lqIyBmHnXGs7Pm/15T1PesH/2K7lPLcsr4pURsghJBAckr37PN+UNBrNy6flDFZ65Qn+t5p3G/XscrxTctyWd7v1wCAcMf2GPDkDY6dd+2gX6LX6CVFv37rAxOM+24FuA1rk0Mj4VAHDOwx2ZX3KLedrz+Aj/e9CQAotGqEiMgpXD/nxwldqhipcGsVkNwcrZBJXSf7dM3Mk7Je9ek6+b6JaFPRYS7gTlduzvfzcEpXC2AdwdoFIiIiXzOmdF95/r6CXiiCAUf6dK2oSVmg/JO6+pSuX6oXytWVGw4n/w4Qje137T3yWbNtDtZsc26wQa9d4JQuEZF/hbI9IYQo9te6y6WUnxb5WqKcpJQj1KTul3teLei1XW9ejJdvn5jW5RoIRxyZ2u02Zp4xlVruPt1QVbXj76s2SSsl1AXKE+Z6acv/3QIgGeoKEUDv/7yt5HN2GTDOCImFECs5pUtU+SRsb7RARD6nr0ULrV7oO/JWAMCmFZONidpQhzC+tab0yqWLV34fQPqkbrkM//NYrLr4fjTvO+ibDdLK9cv+fgOmG1O6nkkkIKoiWLNtDob2dGZviaM7nqamdDlgQESe4Po5v1wTuvcCuCf1p93bPQA6u3e5RMakblG63rwYXX+61LjpU7u77p9S0gRvuasWrN7XqSndXJuk6RudWb1fOSeUvQ6MpUygz9WL0efqxZDSuU3lOKVLRETkX/patNApXaBlo7R+l9Yh3hQ3Jnb/NPReV6Z3y8GNDdKCHcKWPbr6Rmf6JK6V1t6Vu2bbHIiqCE74n7mObnDMKV0iIv/LV7lwOYCTbN5OA8D4nMrq5V+Mx8u/GJ//wCxUFcPOu8YjEY+6VsvgNr3yIVfYWgg1pWumTwJbVUyUe0LZD7b+YRqEcK7BRt8cjYtootZBSlHWGxG5zqhe2Lh8UlHBLpAMd+NNcTzR907Em+JpAW9JF3fx/SWfo1B69cLaQb8sOdgdtPJHkPHMkFKfvo01NdoKdsth3fZby/+miUQyzHXB0R1PU3c5YEBEnuD6ObdcCUQcwD+klHvt3ADsSb2Gu6xROazSv1DBbiG9uooxtXvz4mTAG20q+eLK3aOr9/gqpQarVlO6+vcVbJ8eIpu/Z6+nZ8spEWtypG5Bp0/pMtQlIiLyF/MnxkoJdtXEbt+Rt6LvyFsR2x8t+rpUqNq872DZQ13zRmxOdOqap3T14FZf+8aaGo3nvAp3vezRdQOndImI/C1roCulDEspt9k9kZQykXrNTmcujSg7fYM03Zd7Xi0p3AUABALYedf4oiZ1zZOy5Qx2rSofnJzSVR3BStex84xQF2j5nstp++PO9IT5UZcB48yhruRimqhyJSDKeiOisshYi6pgV92KCXhL2Tzt4pXf9zTUNSu1U9c8patP53b9/jx8/YdLW9bfqWldLzfLbW30KV2uRYmo3Lh+zs25zwgTlZmUcoSUUqgbTIvqL/e8WtLEbjH1C+ZJWa/qB/RguRQ1YxchEAxj5z0T085V86OlAJKhbs2PlqYFu+XU2hfsplAX4EfeiIiIfCPfWhRIBryFhrpqYrfY+gVzqFtOl278r4zHnJrS1Sdvv/7Dpcb9rt/P/KQaUP7+3KG955T1/QAAgQDe+3/uTSTXnjFaD3UBrkWJiHyj4EBXCPEVIcSJ5psbF0dUCH1RjdSCWk3sFhvsFhvqmqmNxMo1sevkRmnm6gUV5upUsKs/V47p2VDEmyC5nLoMGIc+V7ed+gqi1khKICFFWW9EVH7ZAt5i6xhUv26xoa5u1cX3Y3m/X7s+sauf/5AjwwBKr15QU7q5fpGvpnX1oLdh7RRsXD+/6PetBEN7zoI8WHpdXC61Z4z2JqwmojaN6+f8QnYOEkIcBuB2AP8JIJLlsKBTF0VUKinliNRHgoYDLcGucshJZ6Lzpdelvead5ffgyz2vph3T9ebFePn2idh1/5RksGlTqKramGjVawjUJmJWoW+pzMGtuoZSp3QLDYSD7asRP9CIWFMjtj8+Cz2ucG8B2OOKOdjyUObGbeUiRABb/zANgVAEW/7vFsd7dImIiKhymdejKtgFgCOO7YKzz/tB2vGvPH8f/v3ha2mPHXFsF+z7+A080fdOhDqE8a0141Co5f1+bdxXNQzmvlsnrLr4/rSJ4Fuf64vJ39iELz+Nlly9oNNDWyv6Ojwabd2f5iIiorbL7oTuHUiGufcB+CGAsRY3Il9RUxLI07WrbnqYq455Z/k9xqRuIb26VpuUKW7UMJj7bdU1OMF83l135g5Q9W5dtysR/NKh2+vbCyBl5i7MRERE1LZlW4+au3Y3Lp+UEeaq4zoefbpRwVBIt66qXTBzq4ZBP+8drw107Lz6dG+4Qwi77p6Q83g1rau09ildIiJqm2xN6AL4JoBJUso73LwYIjeYdyHWJyUsxKSUYXWMCn7VRK8+5ZuPVaCqpnWdntLVQ9faG3JPLRRCn85V57Wz8VnXsfOw647kcVsemohQpNqVSV2vO3SlTKD3txd4eg1EVBlkBX6Mi4ico69H7axF9eNU+KumetWUbz7m2gUAeKLvnQDg+JSuXrWgh7lffhot6bzrL7srbbp37LNX4/7zH8Guuyeg5volOV+rJnWj0UZsXD8f/QZML+larDAsJiJyD9fPudmd0BUAXnfzQojKxWJSIqb1nYXVMdrzaRus2Qk0s3Fqs7JsnAxzAevrDIQjead0AaRtlOZG8KpP57JjloiIiCqF1b4PaFmPhvXjtOctJ3gLpW+W5mSfbrapX9WjWyw9zL3hhdEAkqFuKBK0Namr1t4q1HWa15UOa7bNgajK1ohIREStmd1A9xEAI928EKJy0xbTlitNc/D75Z5XEQhHSgpNndysrJDzFPNeVtO5QHKTtEAwbKt6wc1N0ryezvVKalqHiCpKeTd0qMRNHYjaqgLWowCAjcsnIdSh+JD04pXfdy3UBTKrFm59rm/R59KrFlSYq4x99mrIuMx7jnKEugDa1KZhXIsSUXlw/ZxP1soFIcRg7cunAfxSCHEokuHWp+bjpZT1zl8ekfdSG1pIADjkuJNLPp++WdnOu8bnndoNVVXnrGfINe1r9V52qh6sOnl1NWMX2Z5UdnuTNK+nczc/MhF9rl7s+sZoHY/rgn3/fA0AhgshVpqrRIiIiKhVW4VUTcNRNceVdKKLV34ffx7xGzTvO5i2SZp5UzOlXceqnPUMdkPhtYN+iXDH9hjw5A22js+3kZrq081XvdD1+/Pw8m9mOF6/4Ie6haE9Z2HNluTQhKiKYM22ORja0509Jo7ueBo+3vcmwLUoEZEv5JrQXQtgTerP5QBOAnAtgEdTj+nPr3HzIol8YBUAfP7uq3j7qXtKOpF5wzQVuGaT67lsU7S53svOtK6dTl671QvmTdL8spGZU1Sg7PbGaF0GjEPH47qoL7P17hGRT0kpynojotZFr1/48Pl3Sz6f1aRuttqEfJuo5Xter12I7juQNnlrh3k6V7FbvQBkTuo6weu6BSC9cuGE/5kLJNxbi9aeMRpHdzxNfcm1KBG5juvn3HIFuoMADDb9ab7pzxO1Wvoi+vN3Xy35fN3GzLMMSmtvWJp2U3beNd4yiLXTxavey+n+3pqxi2wvGs2hbmuw5f9uQSBU3s6yLgPGGff5cTciIqK2RZ+I3DSx9GWAvmmaHspevumHxk1Z3u/XeSdxzXULyq3P9cUdrw00gt1iQt1s7FYvAMlQV3FyujYc6uDYuQqWSCSD3DKpPaMlXOdalIjIW1kDXSnl+kJu5bxoIi/oi+hSp3QVfXo23/OqNsEq2M13HgC2qhYKZXdKF0iGuq3Flv+7BSIYRq9vLyj7e3NKl6jySIAdYETkFGNK14lQV03pKnqIa36+ed9BW8FuNrc+1zct1PWCk1O64XDqXLH9JZ+rGF5tiKZP6TLUJSK3cP2cn61N0YQQu4UQNVme6yqE2O3sZRH5lmPVC0DL9Kx5Itf8fLbahGKnbvPVLtjd+M3uBmlueuHJ8lc4SJnwJMwFOKVLRETUlrlRvWA1kWt+3hzsWoW6k7+xKe/76ZukOTWlq7p0y82JHt5irdk2B6JdOGM6V/XouonVC0RE/mAr0AXQGUC2X/9VAejkyNUQ+Zw+petE9YJdVrUJ5k3J7HTj6q+3c7yd89qtXnj5fnvvZ1co4k2FQ66qhUAogs2PTMSW/7vF1WvglC4RERGVkznY1ad11WNffhq1da5CqxeWDX0s5/OFdOk6VT1mtm77ra6cN6ssVQsn/M9ciHbhsoS6RETkLbuBLpCceLZyLoB9pV8KUYURAXuFXQ6y2uSskCld/fX5Qt1CzhsIR7DrjvE5J3XjB5xdQPe4omWhWs4p3VzTub2+vQB9rl4MEQy7GuzqU7pEVAEkIMt8I6JWbTgAiIAo+3/b9c3UgPwbolkxVy9kC3XDHdsDAA7+uynvOe2Eui//pmXdO2jIokIuOSsvahfyVS0Yoe6WWa4Hu0REruH6Oa+sga4Q4qdCiHeFEO8iGeauUF9rt48A3AFgdbkumMgvaq9f7EnJSrYN1Qp5vZ1Qt5DO3Zqxi1B7w9Ks9Qv6dG7vUYsLvOLs9CndfKHuC0/OwuZHJqbdCg2C7W6EpoJdKd3baZiIiIjatss23OjJWtRqWlexU7sA2OvTHfDkDQVdV75Q143pXL12Id+U7rrttyZDVu1W1GSvjY3QTvifuTjx3lvLMq0rRPl/sUBERICQWWJoIcQlAC5NfTkGya6mj0yHNQH4G4B7pZTetMETlZl50XLYiWfilIuuK/t1vLRsRsbCNFRVbTuI1V+f7XWq1qGQ8+66fwoS0SYgEEDNDxfj5ftnpE3nmgPdLQ8np1h7f+c2W+c32/74LKN2oc/VmWHxC0/OylnLEIpU49zLWha6aqq2939mXs/mRyZavgcAbP3DNCRi1hMkQgQsz1eKzY8YwfkqvQqEiPyn+rTjZJdfjS3re+4YvnC7lPLcsr4pEZWFeS167Hknou9ib5YCfx7xm4wp3TteG2j79T/qsg5AchrXKsBdO+iXAICqIyIYs+ZKW+e8//xHEN0fgwgK1Fy/BC//Jn3NbDWdu65hGgBg4KDC90jYuH6+sclaONQBA3tMTj/39ltzTvCaX6MC2KE9MwcPsnXnAsB7/28m5MH0taia5JUHk2tzq3MWa82WlnNJWYG7CRGRr3H9nF8o2xNSyj8C+CMACCEAYK6Uck+ZrovIz1ZB6y4tZ5eurtuYeRmhbiGTB/rr1aSuObQNVVUbz9tVMza5SN51/5TktK7Wr2sV5gZC4WQAXKQeV8zBlocyp4KzBbm9r1kCANj+h5mINTVmHCNlwujCNcs2nbv1D9Mg49GMsHfL/90CKROQMoEt/3eLo6Fux+O6YN8/XwOSOwzL5LVzMU1ERNQGpK1FP3z+XWyauNKTUPfild/HE33vLPr1hxwZxpefRo3qBXOoG+7YHtF9B2zVLihjn70aQDLY3XX3BMh49gHSdQ3TIOPJtaoIFtJG2KLfgOlGqGsObq3C3H6XJNeDm5+ag1hz8jXrtt/aEuomEsnNzbZkhq+iKpI9zG2OYmjv9GncNdvmGOczh72lOrrjafh435vJ62r5JQMHDYiIyiTrhK5xgBDtAHwA4Fop5Z/KclVEFUItXrya0gWQsTlaoXUM+SZ1i5nStbq2bGEuACRi0aIndAEYgW6ujdJUkJv2ugcnGK9TU7q5pnCtqDA3V1irgl2nJ3VfW3+vCnUVLqKJfKjDacfJLrf/oKzvuXPEgoqaMCCi4gghViIV7l6+6YeeXIM50D3kyDBufa6v7ddP/sYmY0M1q0ndYqZ0lbvOfcC4bzWZ27B2Cq7dei1+2+u3JffqNqydkvN5FeTqVKgLwAhj12yZhRPvLayK4d1xkzPCXJ0Kdp2e0t35+gNGqKtwwICInMD1c355fw0ppWwGEANQeOM9Ueu3CvBuStdKro3OrJg7dc3sbqJWyHU4GeYC6UGuOcztfc0SyzDX/LpiN1ZLxJryhrS9//M2CFHc1EcuXQaMQ8fjuugPDc92LBEREbU++i9yN01c6eWlGFQ4a1e+Pl19c7RlQx+zfd5cx65rmGYEsL/t9duip3PtsgpzAaDPRS3rz6L6dJGczkUg9/UP7TkreUwiYTn5W6zaM0bnDJKJiMg9WSsXTJYD+DaAp927FKLKI6UcoaZ0337qHk+mdFUtglLMhg/dxszLmPTVn9OrGezSj+09anGyKze1SVggnKwucCLMBaxrF7KFuGmv+/ZcY0o31tSIzY9MtLXpmbL1D9NsB7VOd+gqXQaMAwDLiggi8guBBAeWiMg9qwAM//D5dz1583YdqzJ6dAt163N9jT5dswFP3oD1l91lVC8sG/qYrUldvaZBBbaqYiHcIYxr1n3PkcncbLKFuGahdtVG9cKaLbOM3lu75MEmW6Gqk5O5GefuPcfRoJiIiOvn/Oz+KvIpABcJIf4ghBgthLhACDFYv7l5kUQ+5+mUrj5hqxQ6pWvnPQqhv38gHMGWhyYiEAqj+3VL0f26pfj6tYuQiDY5EuYCSAtzc03kZrzu4UlGuAwkJ3Z7fdveZhh2qhaIiIiI3Ob1lO7FK7+Pyzf9EO06VhmPTf7GpqLPt/6yuzIeG/DkDWmTuvno07nXbr0WoUgQDWunIBQJ4tqt1+KaddfgwYEPujKZ2++S22yHuQAQj6dPJVt15GZjZzqXiIhaJ7sTuo+n/rw8dVMkAJH6M+jgdRFVDH1K1yvmDdKKmdJVrDZHK5T+/l+/NnPq4a+/nQK4UEFg15aHJwGJBALhCGp+sAgv/TY1gWzRvWuFYS4RERH5jKdTukD6BmmF1i4A6RukWRnw5A1Gn24+5tD3mnXXZBwT3R91bTo3n01//pmxGVuwfTv0+dPN2DS0DgDw95/OxX/8Ymbecxgbobk4eUtERP5lN9Ad5OpVEFHJVAibrTohH1XdoLpys4W6+QJffTq3+3XWG7Qlok0Zm6SVIhSpth3GJi8ggdobW66t27XzsPN/kz831aWb7XyhSDUSsSb0uXoxXnhyluVx+iZrREQAkGcPWiKiVuHyTT80Qt3J39hU0OZoeu3C+svuytgczS7zdG45bFw/v+DXqCBXCR3eHrHPDiDxRSP+/tO5SHxhvRYNHFoNGY0ZYe667bciGtufdkw41AEDe0wu+JqIiPyE6+fcbI3ISSnX57u5faFElF8pVQt6SJtvc7Rc71/KdHAxtj9uHaoWyvj+LDZW06nnNj8yMetxpWyyRkRERFSE4QAgAt5+akxX7JQuYL05mu6ucx+wfHzZ0MdsVTI4XbcQjRa2Fu178c8RP9Cc9ljvP/w/4362MFc9Jw82GRucmcNcAIjG9he9yRoREVUGuxO6AAAhxJEAzgNwJIBPAGyWUn7qxoURVQohRM6ysl33T0Ei2rKwDIQjqBnrzse7yh2m5nr/bNO5jr+nFqra6c419+Yq+pSu0ufq9Cnizf/XsrGbrvu4lu/1xQcKq28gorZBclMHIiqDyzbcmPE/Nn8aei9i+1vC1VCHML61Zpwr7//nEb8p6fW5NkezQw9zc03nOlm3oE/n2unOVXULwfbt8h7b6Xfp1/ivJb/FgV2vWx47rNNPAAD1792F5sQBI9TlpC4RVSqun3OzHegKIeYDmACgHZK9uQDQJIRYLKV0dgcmosoy3PxAWogbCKDrT1sCv5dvn4idd413PNjVp3Nrb3A2TC108rZcYe72x1umYO1uhIZEAjXX5/65m4Nc4/H/vM3oz639gfUx54yehx33Fld7YZeqeghFWqaKFa3P+Rkp5RBXL4SIiIg8pw8X/GnovUZYq4JcEQyg36V1xvGbVkzGE33vdCXYbd530Lh/x2sDHT231WZp2VQdUZX/IAdsXD+/4OlcGU+g75pJOY8xB7nKVyZcCwDY+70pRoBrNviEG9JCXTfoNQ/hUIe050x7i6zSN+0jIiLn2Ap0hRA/ATAVwH0AHgDwAYCvAhgNYKoQ4iMp5a/cukgiP0otnq3D3Hg0LcTVdb05GQQ6HeyWOp2bra7BKszN16O7457xjoe6TlQrZJvOLUSvby/A5kcmlnSOUui9vXl+Hhdk2ayPC2uiMpOSEwZE5I609agIyNj+qACSYW68KZ4W5Cp9RyY/iu9msFtsmDv5G5syHlt/2V15KxiUqiMiOPjvJhz898Gcx4U7hLGuYRoGDlpQ0PXlCnDtTOfa9d6P5uOE/8/emcdHUd////WZ3ewmBCrU1noUuU8R8fgaUWk9qkWsR9Varf6sVvCqVpqEQIBwJEAEkohHPYpHa8XSFqtoRaoVrVQgVOWQK4TTo1otgkLItTuf3x+zn8ns7Nw7s9mQ9/PxmEd2Zz7zmU+WkLz3ta95vX8z1fP5F/S8Hcv3LvBtPVr0mb02ovEYg3qUalGCIGyh+tkepw7dOwA8wDn/tWZfHYB/MsYOAbgLAAm6RGejTcxlEgeXGaA0/DITc7X4LeyKpmZe0Z4rHL56MVfbOC0dpJwoav9YgoLrrQvf2ucU4dSu6Zmf7lwnSOEo1j1ZbOrSFbz7wnRfm6OZNWEDgKGT2n7m9i5+DA17tptNk/IhBEEQBEEQHQ+9mMsksFA0RxVzhXBrhl7YBYKNY7Bj4tnvGObumom5v7/oL/j56z9J2vfz139imq+r5Ya3bsDvzvyd7TinDlynYu47f5tkGbWgbYxmBcuNYvlHD2J0z185uq5f6MXcUDgX8VibeD7q8rbXYeVLJWbTUC1KEAThA04F3d4AzHJCXwFwpy+rIYgOgvbWtmG/rsGmB4qZFPbm/NQKuxueKg0sX9cKfVyDWcTCyT+vwLrHC9VzzFy6osGYGSNursT7C5NjCYR4W/Az5fXQxim4zck1wo07106ItXPpqsK3Tzm6RkKuVsDV0+u6O7Dr9/ej6dOPksZuuU95zRljr5AzgiAyi0wOA4IgfEQv5p57xVz2rxcn4PLXb8dfz3nU0Jlrhlb4/deL1lEAQaEXc3O65+Ef5y9IGTdr06WoHPU6Gva3OGp+5oY3/1EKAGq2bhBuXB6XcdZL95oeL1hyN965SPm3s3LpnvjbGfjwthkZE3X1Qi6QLN4a0f3bA3Dgi3oAwFFH98Xwc+5QRV7G2Fuc8/MCWSxBEEcMVD9b47S15z4Aw0yOnZQ4ThCdCbWA1u7c8FQpIHnrmDvs3irI8VZlDg/45c41EnNPvb1Gde0Ksdbqem7XohVva58rRu1zxYZiqFcxF4Dizr3VWixXv7fmBqxdMsVyrHDpGjH8RvM4Ci+4EXMFfX/+awydVJM0Nu+7fcRDckYQBEEQRMcmScwVO1+66AmwkLdaFABYSMJfz3kUL130RPordIFWzP3Bm+MNXbmzNimfRZeuvEjd9/uL/uL5miJ2Qc+b/yg1FXPPvWKer9EKRoSPygMAW5fuib+dAcipzXqDwK2YCwAnjxyHUZfPw6jL52H4OXcAAHocM0gc/r7PSyQIguh0OP1r/wKACsbY/2OM5QAAYyzMGLseQDmA54NaIEFkM8PGVzEgIca2NivZufda34ZvOV+aoi5g7I794PdlWPd4oWlOruDU22tSohf0Dda0rly7+awQsQtOs3FFA7AgOfnmtu9NjjVbirpnXjMbnGemiBboBVq3MOb9DR5BEARBENmHVswFgNjhVtuoBSvOuWwuzr1yPmKHU6MP0mHi2e/gl4Pfwi8Hv2WYkyvI6Z6X9HzWpkvVTUt+DyW2oGl/c5Ko60bgveGtGxCOhvDWm1Pwr3/OSjqmFXOH39tWe6151b8oLTMKltytPv7wthlpz/fWe95/HvSEI10ciblmDDvrVt/WQhAE0dlx+u6+FMB6AL8HcJgx9l8AjQAWAdgApWEaQXR60hFztXPI8Vase7zQlbBr5pzVOm5jTQ2Wwq6IUwCQIuQ6uZbASUO0ETdXQgrnJIm5A8trEOqSb/g81tyA2kVFeG/JNNu5/YLHWy1FXSuXruDdF6ZbHndD3QPeBXQAOPzRTvFwWdqLIQjCFUpjh8xtBEF0DjavflJ9zEJSWu5cLX46dfVxCoe+bE0Rdbt+MweAkpdrFLWgR+vS1UYvuI1huOGtGwDOkwTcUF6qiUDsi7U0eBZ17fJzBWsuf0B9zFtaLUVdkaVrRERSxHGbpmWOyAl3AQDEWtKba9Oap9JeC0EQnQeqn61x9Befc34QwPcAXA6gBsBLia8/AvB9zvmhwFZIEFnMnhcXqo+dNEJzyrB7qzDs1zWu3LpmebZGomu6Tc38Qgq1xXgPLFdev36TKjCwvCbpuVbk9SuX1ogNTyZHZpx6a5WlqGvl0tXGN6SLcCfHG3373ilygSAIgiA6NssAYP9/t6k7zrlsblruXC3CqRtvjqct6uqzccU+rag7d9U5aV1Dz81rb3Y8lstt7+KH31uDk26rwPB7a9QNAE66rSJtUdcuPxdQxFy5JaY+H93zV5airlXswgU9b3e9RjPOO32iL/Ps/7zt55Ux9g9fJiUIguikOP4Ilyv8jXM+kXM+LvF1GecdUccmiLRZBgCHdm/1bcKtj5Vh0/2F2PpYmwtTRDk4Qeu6FRELeset1nUrjnuJTfBLEHY6jxB5BUG5dOXWZhRcn9xE5NRbqyDH3Dfd0Obo+unS9QvGGGeM/b2910EQnQXOWUY3giCObIJobrpm2Qz868UJWLNshrrvnMvmOo5f+NulTyc9FzELggtW/Bqj/npHkqgrIhi04wT6mAUtlaNeVx/f/u6NjtZnxOIfLlYfa6MVjNCLukEQb2zBOT+6L2mfnajrBD9jF3zkQsbYP9t7EQRBZC9UP1tDgYoE4QE/i2gh5Ar3ZbyxQRV1xdd1jxdi3W/Nb+3XNzKzEkr1GbtORVUzkdgrG//QJiRLOVFsn2EdXQDAc/xC7R8nQMqJelsolOzZNYuLbRul6fHLpeuXK1nK7aK3cFycEHZ5ols2QRAEQRAdDG3sglfWLJuhipSxlgZV2BXi7l/PeRR/PedRvFNsXi60HGhSH+tjFrRoRV0ApuP0VI56HVOHvYKpw15Bw/4WR+dYsfiHi9G0v23Nmx4usj3npNvaPrD/19ISx05dp3ELelZ89DgAa1HXaexCNoi6Xbp9R28G+x7VogRBEN4Imx1gjO0G4NR9yznn/fxZEkF0HrY+VmZ4G328sQGb7i9M3snlJDHVjFNvr1HHGQmu+qZmTgRdJ+PcOH03/iF5vmF3VmLTo6XYPqMYA2eY5xD3m1SBnfeVIX448YajuQHvLZmG068pNz2ndpFSnJ9yW6Xj9ek59VZlTe8/Yf/6axl+Y4V6zrsvTMcZP/aWuRaO5vsi6g4eP0v9EG/Pc4/g8Ic7tIcphoEgAoKjY37qTxBE1rMMwBht7IIXtGKuFqN9/139If56zqOW8/1m23kpzlw9o/56BwBg5VWPofVAo6N1Gom46bhztWJu0fpr8dDZf8Wmh4sw7O5qy/NCeflq/S7iF866xPpuLCdxC1rCkXzEWhrQIre9NqN7/grLP3oQH942Q4lacMAFPW/Hio8eR4vc6EuWbrqcfn6R+sdw05onsf/zOu1hqkUJglCh+tkeK4fuOw62vQB6JzaC6JRoc3SNMIpSEGjF3KGTajB0UnJTMIHRPjOciL4Co9xdfTQDkOziFce1Y5wKw0bzDf+VMs+wOytNM8C0iPgFrVvXyqlbcIN1UW7FxmeT/80Yk9rFpetVCLai98/uUn/mCIIgCILoeGjvGLNy6YooBX2cgjimFW4vHDULF46ahZxwcu1ptM8MOzFXi96tK9DHLegjFsSm5fcX/cXR+oDkqIWi9dcCAO5ZdRXkuL2fSWTsauMX3Lh1nWAmEI/u+SvwpuQoMN7UjNE9f2U6l59Zun4y7KxbMeryeRh1+Tx1H2OMGvcSBEE4xNShyzk3/biTMfYtAJMBXAPgaygN0giis7EMwJhDu7diz4sL0fvKcSkDtA5cQ9dtAq2oNuhXqSLrlnlKHIGUE8Upv0h2mroVU/WcenuN4znMIha05542zvrXgTZqQYi5AhG9YOXSFWjdulZiqZe4BeGI1b8mp95alZZL1yuZyuBljK3gnF+QkYsRRCeDGg4QBBEQti5drWAr4hSMuHDULPXx90amNuWN8UaE8nLw/VfuTtrvxmVrhHDrrrjgftMxdhELv7/oL2ja77zngdadqyXSJYyNDxRCCjFbp+5Jt1Vg82/Lkty6GUGSXLl0OwI9jhkk3LqXtPdaCILIHqh+tsZVhi5jrBtjrBzATgC3A3gIQF/Oufn9zgRxhJJwRVg2RzOKU/CELOPU22tSxFxAcdkKx6w2H9dNvq2bHF2rfU7EXKtrDbuzElIox1GeLqCIurbIMk651V3cglWEQ3vgV4auGfl9h4iH5ycyzF4N9IIEQRAEQfiCXV8HrSPXymGrFXONWLFqGqRIKEXMBRRB9oIVv1Y3gZ07V8vKqx5TH1s1QwOMnbhaMffmtTdbnm/kzhXcs+oqFK2/1pFTF0hulBYUIkdXYOTSDZqg83eHnXWr+jhRi74Z6AUJgiCOAEwduloYY7kAfgWgBEA3AE8CqOCcfxrg2ggi6+GcX8oYs6340rmtfct96UUo2KEVZM1E4HBuPmJNimN13eOF6jg/ohb0OM3TzRQbny3D8BvbXlcRu3DmNbNdz7VmcTHC0XzHEQrvvjA9ScwNKh6h17XjsPPJebz5i89ESNFoxtgrQXTQJgiCIAgiGDavfhInjbw1aZ/WNWrkunXCilXTAHBDMdcIN0Iu4Mzhm98jorp0teKt3pnrRMw1c+dqiXQJO8rTBewNHF4bohnl6KbL67XTkRPugvNOn+ho/Fvvzc1Y9u5RR/fDV/t2iqfnMcaWc85HZ+TiBEEQHRBLhy5jLMwY+yWAXQDmQHEjDuac30ViLkEkY5elmw5uIwPs+OD3ZVj3eCHWPV7oSJA1E4r9ilrQozp1pxU6dusa4SZuYd1jxsK5UeyCHHPnitA6p526bfVibtD0u7WEDZ1Ug+i3jhW7xjDGVmZsAQRxJMMBzllGN4IgOhXLAMAqdsHOgWsHd+hYdcPKqx7DigvuTxJzzdy5pSsvMjzmJmZBGZ/cCM2Me1ZdhXA0hI0PFGLTw0WurqHHbUM0gV2jNTdEpLac4tbYYceOW72Yq8279Zvh59yOUZfPQ/dvDxS7fsgY2xjYBQmCyG6ofrbFVNBljN0MoB7AgwBWAxjOOb+Jc747Q2sjiI6CZexCOmyZV2yYm5suRiKuXUSDVpTUYyfmml3TimF3VmL4r2osIxh23pcaAZGEi7gFKScKSK5SaLB2yRQwZn/O8BsrcNrYttfILhPXyJmbqeZl/caWIO/4XuLpuRS/QBAEQRDZTdB31PC47Np1a4eRK9cuakGLUexCOlELRoj4hXA0ZCrqbv6tdS3q1Z1rSyJH1ykX9Lwdo3uNV4VdJ6Ku9ri+eVmQnDxyrFbUPZkxti4jFyYIguhgWCkRTwE4EcBrADYDuI4xVm6y+d+CnSA6CIEW0bLsu5irj1gQmx1al+4Hvy8zzNM1w407V49Vrm78sL/N0IzQCrF65FgzOJcdzyVE8Vhzg6mom6mYBSv63HQv8vsMFk/pVjeC8AOe4Y0giE7J5tVPGu5/e7W3enLFqmkI5eWks6QU9GLurE2XOhZz83so4mjT/mZDUdcMp1ELRgi3rpGoq41bOPeKZMHznb9NghQJe3Ln2iFydD+8bYYrQ8IFPW9PEnXNyGTUghEnjxyLHseoteiIdlsIQRDtC9XPltj99mcAfghgqoONIDo9ZrELdQ86Fz8Fwp3rJ24zb80QebpOsGuE5gS7ZmnhaLJ7uPaPEyCFclw3Q3MKYxLWLC4GY5Kl4Ktn+I0VSaKuEe0t5gp6/fS2drs2QRAEQRCuMYxdCEeUuqM15q0W43HZcXauU5xELJhRuvIi9bGb7FynUQtmWIm6QNvrrMVt1MKayx8AC7m4W0ySwJuaMbrnr5yfA0XUFRi5dPVibqacuXqGnfWLdrkuQRBER8H0LwbnXHKxhTK5aILIQixjF6ycpKb47M7Vi7lOXLlOMItb2PiHMry/sNBRIzQnGIm6oS5t4uh7S6Z5nhsA5NZmFFw/P2X/xmdTxfhTb61K+uoGbYM1q+iF9hRzCYLwH8oAIwgiSLR3jK1ZNqMdV2LNyqseUx+7FXO9sPiHi/G7M3+nPvci5gqMRN1QXqIWbWnAmlfTu2k13tiCc350n+PxboVcLVYu3WwQcwmCIACqn+0It/cCCOJIY9P9hWpx54Ut84oBWbmNf93jhYbC64anSiG3Kq4Epxm7foq54dx8W9et/ng6Yq5g2J2V2PRoKbbPKMbAGVXoN6kCO+8rQ/xwQ5KzteD6+ahdlF4Di3A0H7FmcyeyG2duytyJ1y+TTc8IgiAIgjhyYYy9Ih7HWhrwrxcnpDXfilXTwOOyZdzCPy99GPHGVgBAKC/H1slrlJsbNF5jFsy4Z9VVeOjsv2LjA4WQQgzD7q7G5t+WId7YgFhL5uu60b3Gezrvgp63Y/neBQAUR+55p0/0b1EEQRBERnDXAShNGGPnMca4wXZAM6YbY6yKMfYWY+zrxPHzPF7v+sT5H/v0LRBEEoyxVxhjHMAY7f54Y0NSppZTt+WWecWQQjm22bZya7PaKEuOt2Ld44XY8FSp6bz63FyvqHEBLiIUhv+qxhcxV6B36vabVJEyxo/83NOvKVcfG7l000Hr0l2zuFh16to1S8skH/7FOD6EIAhvcJ7ZjTCH6lHiSCMh5o6xGnPhqFmO51uxahqkSAgXrPi1qUj7z0sfhtwSx4WjZuHCUbMgt8Txz0sftpw3nagFK3J75Bru1zdBS8edq0U0S5Pjyi/bk25rq+s2r3nK87yhvAje+dskw2MrPnrc87xmGDVIs2uURhAEkUmofrYmo4Kuhl8BGKnZfqA5djSAXwCIAXjd6wUYY90B3A/gM8+rJAgLUopnJvGhk2rUGACBWzHXym274alSrHu8MKn5wdCSKkth16/cXCC5MZpA2/DMap+fmGXqqq5cWXadnyvlRFH7x2Q3i8jm9ev1S5o7t+3nJNbcgDWLi7PKsXtopxof8nZ7roMgCCJAqB4lOjSpxgKJC4E1J9xWZzgVc1esmoY3Vk6FFAlZCrkrLrgfckscF5zd9uH3BWeXQ26JY8UF9xsKu0FGLTTtb0oSb7X7gyTSJYyNDxQmRTDs/+82bF7zFN752ySE8iKu5jvrpXvB48nNdkU2b4vc6Luoq2+Q9nrt9KyKW9iUhjhOEATRGWivyIWtnPM1Jsf2cs6/CQCMsR8AuMrjNeYB2ADgUyQX6ASRNkliLpP40IlVDEoTQQz6VaroaYWIWLCLTtjwVCnkeKupQDy0pEqdb93jhep8QeTmAprYgKYGbPxDGYb/v7bvOwgBVI82fkFL7aIi1+7cDU8mIix0XYJPv6Y87egGM4RLd+OzqYJ7NuXncs6/395rIAiCCAiqR4kOS6orV+IXjipXAwC/N9L8zi092niF779i3sRL68o1Qgi8K1ZNw4oL7ldjGIKMWsjtEUXT/uYU8Vbvzg2Ce1YpvxaqR/wZobx89e68/f/dhlBexHVDtHhjS0pTtLMumY41r85ErKUBLbL/r+EFPW/Hio8eT5m7vcVcANj/udrgr7Y910EQBJGtZF2GLufpG50ZY+cAuBHAcABT014UQaSiF3O9I8u2Qqsq5pbYN+HSC7sCP8Rc7Xwn/7xCdf/Gmhrw/sLClPF+Ri0YIURdGa1q7jAA1+5cubUZBTdUW47Z+GxZUlSCXwy/sQLvP9H22mWDmLv3z79t7yUQxBEFBzpko4XODNWjRAdA48otV40FXuBxGRes+LXtuHhjqyO3r1bY1ebsAum7cytHtRnmb3/3RgDA42c8CwBq87PcHrmBu3O1RLqE0aJrgOxGzAWUhmjnXmEsop51yXT8a2kJACV64YKet3tbqAliPpGpmw1irhbO+VntvQaCIDIP1c/2tFfkwiLGWJwxto8x9hxj7ES/JmaM5QD4LYD5nPMdfs1LEEakI+ZumVeMLfcV2rpJ3Yi5SWtzOd4LJ/+8Iik6oD0YdmdlkpjrN9rYBb+zdPXo4zrai4ZdqiPizfZcB0EQwcEYu4Yx9jxjbC9jrJExVscYq2SMddOM6W2SNcsTUQL6OYcwxv7CGPufZk53qkZmoXqU6PBoXbleWLFqmmXjM6AtZkHvHrXjgrPLfRVzAaBhf0vKvtweybW0VswNyp2rRWTqBok2eqEzsO7tB9t7CQRBEClkW/3syKHLGJMASJzzmGbfDwEMA7CCc77OyTwAvgJQDeCfAL4GcCqAyQBWM8ZO5Zx/7nAeKyYCiAJwZdFjjN0G4LbE099yzsmiRgSLR2du3YNliOtcAKEu+SlRD9qmVn67c7Voc3XNxnRkTr+mHO8tmYZYc0NgURIivkL/75ppdv1+AZo+/VB9zjm/oB2XQxBHDhxA9jkMigF8CKUO+xhKTTYDwPmMsbM559pPyioBvKQ7/6D2CWPsDAArALwFYCyUmm8AgK4BrD1dsrIepVqU8ML6Tc9gxLCbPJ0rmp+Z5eUC5jELb6+uRGusrW7JCee7innwgtadq+Xnr/9EfSzcupnmkfOXJj2vveZhFCwxf13dErRLN1vY+M7j+GrfTu2u5e21FoIg2hmqn21xGrnwRwDNAG5KXPQOAI8kjrUyxi7lnP/DbpKE8KsVf//JGHsbwFoojSnSuh2NMdYfwBQAP+acu7rPJlE0U+FMZIQt84otnbkbnmrLdLUTcwGk7NOP2/BUqWU+rx3ZLNRuXhisaxZIztINInpBH7uQSXY/+xAaP95tdGhZptdCEERGuYxz/oXm+T8ZY18C+D2A86AUl4JdFlmz4oP/3wN4g3P+Y82hrHT5Z2s9SrUo4YV9+7d7Os9OzBVRCSwkJTU/0wu5gtZYA95eXZkk6r69Orn2LC9Yjmm1oz2tt3LU64buXD0iUzeTPHL+UjTqrhn7yn8nbTiSr2bpBinqrl4+EyNHTw9kbiM+WP0EDnxh+HO8jHPubwc9giCI9Miq+tnpfTNnIfnN/QQATwA4CsBfoRStnuCcvw9gO4D/8zqHhgehvIBrGGPdE3bmCACWeJ7nwzWITk6iCYVntswrhhTKMRRYNzxVinWPF6rNz6zE3KEvTsfQF6cnHRdox516ew3keCvWPV6IDU+5d0588Hvngqk2fiHo/FxAEXNFA4qgyWT0QqbY9XS1kZi7jHPOqIAmCH/hPLOb/XqSilHBvxNfT3D57Z0HYCiA9g8B9wjVo0THRfKU98zjsqWYK1y5WjEXQIqYO/yeGoTy8g2PaZ8PmFWDWLOM8gJvhksnYq6eTMQtAEgSc/2IlTDjrEumBxq9EJGUX02xlszdMbbxnceNxNzXqBYlCAKg+tkOpw7dYwB8Aqiugz4AHuacH2SMPQ3gOa8LSMCgGKrTZSiAXgD2GxzbD+ABAON9uA7RSUnqKMwkDi8NKGQZp4wzdsvKrc2GTbGMxFxB6BtdEP/6MOKHG1D3YFlK9ILWXSu3Nqe4bcO5+UmxCaLRmRFSfj7khgbbcZlCK+b2r6zBjtLgXK6ZiF4AgG0PTMXge+0bjqRL/aOz0PrVl+IpOSAIggCA7ye+btXtr2SMPQagAUpMwRTO+Qea4+cmvuYyxtYAOB1K3bUYwETOeUcJfaR6lOgQaM0FXjJ0rXJzhZirF3L1ztzh97TVqyeNrcDGhwrVcd8bWZrizq2fqhxvicUxdViqNyK/RwSlKy8C4NyN+/uL/pJxN64ebdRCkGKuQBu94DcX9LxdbYyWCTa88xi+3rdLPH2Tor4IguigtFv97FTQ/RrA0YnH5wH4H+d8Y+J5HECuw3lSSGRGDATwZ69zaLjOYC2ToLwwP4GScUEQ6aCKuV4aollFLWx4qhSQ2kzzZvEKWjEXAAY9MwF1N81XRd0t97kTNWNNDY4iFQZU1KC+rG2claiZKXeuoH9l8vU++F0ZTr7Z31gEIDl6wW9Ejq7ceBh1D5Rh0L3+r18LibkEkWH8kAnd8S3G2Lua55aZrIyxEwCUA/gH51yc1wzgcQCvAfgCwGAomWGrGGNncs5F4Xp84uufADwMpfY6IzFfTwDa28iyEqpHiY5CkrkA7s0FVlELRmKuWcSCnlBePuKNDWiNNeCNle5TSxr2txgKvXpyuueh9YDyHtdKzM2EO9coauFIYeVLJQhH8gONXtCIua9yzscEdiGCIDouVD9b4lTQXQVgEmMsBsVRoI1f6A+HhSljbBGA3QDeB3AASoBwKRT370OacZcAyAdwcmLX9xlj3wLQwDl/VTMuBuD3nPNbAcAon4IxdjOAZs75W07WSBBmaN0QnsVcg6gFs7xcJ2KuQCvqGo3fclUFIMtgkSj6l7Vdf+d9ZarjVs+AimSRdOd9xjEDmRBvjTCKWhAO4vZ2Dnth+I0V2Pis4nqONzZkRNQFABJzCeKI5X+c8zOcDGSMdQWwFEAMwC1iP+f8UwB3aIauZIwtB7AZStzWjYn94tPIZznn0xKP32KMhQDcxxgbyjnf4v1b8ReqR4mOil7MdevONRNz3eTlap25Wk4aW4HNT6RGYYnxHzxSDB6XEcqLYNTfkq//ztWPqiKtlvPeSDUcvHVh6vUzFa2gx8+ohVBeBO/8bRLO+dF96S4rLSJSnhrnkKnoBRJzCYLIIjpU/exU0C0B8AqUDm27oHRxE/wUwGqH82wCcD2AewB0AfAZlAze6Zzz/2nGPQrlVjWBuN5eAL01+0OJjSACJd2oBUsxN5GXq0Wbh2sm4uoZ9MwE84OynCLQAkC/SW2CoVbcNRJzjYTf9hJzzdy5fadWBBq7EDR6UTcodv1+QWBzEwTRsWCM5UKp7/oC+D7n3PJDes75R4yxfyE5a3Zf4qu+Bf1rAO4DMAJA1gi6oHqU6ICkK+YCIjf33pT98cZWXDgqOe7JKmLBjJPGmn8QzeOyoUALAOc8f6f6WAi2RmPfufpR2zVkCr+jFs566V68c9H8tOdJF9FoLejohU1rngx0foIgiCDJlvrZkaDLOa8HMJAxdjTnfJ/u8L1QCmEn81QCMA4PTR7X2+F8toUM5/xmJ3MRhBl6MdeLO1efm2vmygXMoxa8oHXm2qEVd/WYuXjbg0w2QmsPht9YgfefCE6U3v3sQ2j69EPxdJnVWIIg/ILBQcmScRhjOQCeB3AmgB/ocr0sT0XyTXCbE1/1N8aJb1r2vMgAoHqU6KCkJeYa5eZqnbl63Iq5VnzwSDFCeRFHY81EXzMXbza4c9uLFR89rgqwHYlNa57E/s/rxNN/tOdaCILIZqh+tpvQqUNXuQrn+xK24qMB/Idz3upi8QTR4fBDzDXKzTVrfgbAtPmZJ0ycuW7QRi2InFcgO6IW9Nm5hDX1j83mrQf2iZ/h1ylugSA6L4wxCcAiABcCuNQoJsDkvBMBnAPgBc3uV6HkhY0G8DfN/h8mvmqzyAiCcIkfTdD0UQsiL1fvzNXjh5grRcIpMQtuySYx16k7t/aah1GwJL3v24hwJB+xlgY1GqEjsfGdx/BVW3buKs75Re25HoIgCDdkW/3sWNBljP0ISjjvKVAU5DMBvM8YewLACs75c07nIoiOgC9ibqJBmd6dq21+ZoYXMVc4csX8Tpy5VuijFto7m9aqEZqeoBqjCTY+W4bhNwafcesnGjGXGqERRKbJfFMHO34DpUHXbAANjLGzNMc+5px/zBirhpLvtRpKU4dBULJmZQBzxODEB/6VAMoYY18DWAGlqcM0KNmyOzLxDRHEkUi6TdCA1KgFo+ZnfvLBI8XqYz/E3GyLWrBz5+b3iKBhfwtiXwUjuJ51yXT8a2lJIHMHjUbMBef8nHZcCkEQHQGqny2xV5UAMMauhBL2+z8AE3Xn7QbwcyfzEEQHI72YBSDJhbvhqVKse7xQyczVxSz4CYtEwcI5YOGcpAZoXjCLWmgPd67TqAUpPx9AcOJzOBrs/BmCsh4Jgrgk8XUKlIJTu41NHNsM4FwonXpfh5Ih+w6AAs55HZIph9Jz4VoocS53ApgPYFxg3wFBHOH4kZur5Z+XPowVF9xvK+a+vdp7/bjxoULwuAwpEvZNzM0md66TRmilK9tMp7XXPOxo3jWXP2AYfWHHio8ed32OG1Yvn+HrfD2OGeTrfARBEBkmq+pnpw7d6QCe5pyPZYyFAczTHNsE4C6H8xBEh0B7a5tXMVdFkrDu8UIlL9ckZsEvhv61DFuunJl2zAKQPVELRkKulTtX2xgtCJfu6deUo3ZRka9zmrFtwRQMHj/bt/ny+wxCw+46APghY+xVzvklducQBOEDHFmXAeYkH5Zz/hSApxzOxwHUJDaCIPzBFzGXhSSsuOB+sJBkG7EAJOfnumX4PTWqQzddMRcwjlpoD/TOXLtGaG5duvHGFpx7xTz7gQmCjl2ISHlokRsRazns67zDzroVK19S3MWMsbc45+f5egGCII4cqH62xamgOwSKagykmp73Q8nUJYgjiTF+TRSkG9eM+rJCU1F3R0UpeEvqrWIsEk1y9GZD1IJbMVcg5edDbmhwvG4pJ6qItJKEguuddxgOKnZBCOhyUyO23FeIUJd8DPpV+tfp9dPbset396Pps48AYDSJugRBEASR/aTrzA0qWsGMk++qwsaHrBu8rvzRw4g3tiTtC+VFkkRgs6iFTLtzncQs6CldeRGmDnvFfmCCUF4E/1paAhaScM6P7rMdH3TswgU9b8fyvQsAACtfKkE4ko+Ro9Ps7ZGgxzGDRFO07zPG1nLOz/RlYoIgiE6GU0H3awDfMjnWG0ouBEEcSSyDj6JuprEUc2Othvm8W66qwI6KUvQvq0xy52rJtDvXawM0rUvXCafcWokNT5ZCjre6Wl9QQvfwGyuw8dkydX5to7x06Xvzr7Fn0cM4/NEuQAlgJwgiE2RfBhhBEETGEUIuC0kpDdc+eKQYK3/0sCrqZkPUgltnrlfOeknJOH7nIufGAsGKjx7HBT1v93tJqksXAGIt/tWiw866FR+sfgIHvtgOAP/n28QEQRx5UP1sidOgntcBlDLGumv2ccZYFMDdULqzEcQRg7Zh1Id/WRj49eoeLMOW+wrVJmpBwVuaMfSvxmLt0L+WgcdaUV9WaJid2x5RCwI3Ym6m2fis8euZLsNvrMBpY4P5vnvf4H/HZYIgCIIggmH9pmcycp23V1fijZVT1ed6wdUP4o0tGH5PDU6+K/UOtpPvqoLcEsPKHz2cNY3QMiHmemHNqzPVxy1yYyBZuhf0vB2je433fV4AOHnkWPtBBEEQhCVOBd0pAI4FUAfgCSg6+SQA6wF8F0rIL0EcaSwDgEM7twZ+IT8dmGbsqCgFJOv/8mZib3vgpAGaX2x4shRyq7Nb6d5bMi3peQdvjkYQBEEQRPayDAD27d+ekYulk53rFyffVYV4Y0vWuHMFmRBz3TRG0ztmg8rSzQSMsX+09xoIgiA6Io7+YnDO9wA4DcDfAFwEIA7gewDWQOnU9p+gFkgQ7UWmXboAMPTF6ermN1buXEHdTca3eWXanavFizvXTdwCAMitzSi4odpRfm6sOfXNzvtPFAbm1A0axtj77b0GgugcsAxvBEF0dLS1aNAu3bdXt/VRGH5PTVru3A8eKUYoL5Kyf+WPHnYsWOrJtJgLwHVubrrEG1sc5edq3bln3FKtPg7CpRskPY4ZJB5eqG1ITRAE0QbVz1Y4/ovKOf+Yc34r5/y7nPMI5/w4zvktnPOPglwgQbQzGXPptjd1N81H/Gt/O9m6ZeODwUZOmCHlRFH7xwm247Tu3D4LqiF1zVefdzSnbtd+Q8TDUxljy9pzLQRBEARBmKK6dIMUdf105/K4nNTcTBBvbDGMWtCy+Yn2/YC8esSfU/bl90gVp91Qe83DaZ2vZc2rM1PcueFcpR4NKnohKIaddSu+8c0+4mmH7V1CEATRXrj6iJQxJjHGhjHGvs8Yy7c/gyA6Nu3h0k2HLVdVgEWiKfudxC1YibmbHi1Ne21O2fhgITY+XJzWHFpX7we/s39jcMqtlYAsmx5/b8k01C4qSnHn9ppVjj4Lqk3Oym5O/Mk45PdRnRGXMMaWt+d6COKIh2d4IwjiiCBRi2YseiHd3Nx03LmbnygzjNySQgwPnf3XtNblhuoRf8aCM9qE3Yb9LZ7mEUJw7CtncQihvAje+dsk0+N6MVe4c0dcX54k6nYkTjn3TvUxY+yddlwKQRDZCNXPljgWdBljvwTwGYCNAFYAGJTY/yJj7FfBLI8gsoIO4dLdclUFWDgH/csqU45ZxS3U3TQfW65su3UrlNf2Wc3A8hoMLK+BHG/NiKg7/Fc1ivBsIa46RcpXvo9YU4MjUdfMpfvekmkpQq6ZiNvRYhd6/fR25J3QWzz9YTsuhSAIgiAIE7QGg2zHqzt340OFpv0Tht1djVhzPCOibtH6axEKA/FY+nOVrrxIfezEpXvWS/eCx41rYDMxVzDi+nL1cVAu3dXLZ9oP8kD3bw8UD89mjFGzdYIgCIc4EnQZY+MAPADgRQDXIjlcYiWAq31fGUFkCR3BpWsl5tq5c/XOXKNieuCMKsdNw7yyeWGZErngg5gLAH2nViSJunaYuXS1Ym6fBdWGYq6IXggydmHLfcHEUfT5f22fxzHG/hnIRQiCIIcBQRC+8MbKqYHn6XrFzJ1rh1XMwvB7FcfwsLur0XLYB5XVgkfOX4rqEX9OEXPTaYimdel6jV6wE3MFQbl0I1IegNRGbH5x8six2jzd0ZSnSxCECtXPljh16BYCqOac3wbgBd2xbUi4dQniCCYrXbpbrqrAlitnmoq5gL0714yB5bpb7iQpEJfuxgcLsfFBc1fGjine4xf6Tq1wNV7v0tVn5prRa1abK6KjuXSBpDzd71ERTRAEQRBZiZp3n4noBTd88EgxNj5UCCkSNnTn2sUtmNWAQswVBBW9UD3iz6ge8WfTJmhTh3kvjUpXXuQqesEodsGJmAsku3Q7GsPOuhX53zhePKU8XYIgCAeEHY7rA+DvJscaAHT3ZTUEkaVwzi9ljHFAceme+JNxvsxb92AZ4ofT+LRbljGgwjzrzMqdq2+CliLg6hg4owrbZxRj06OlGHansXjslM0LjTPSDPHJseuEU26txLrHCg1jFuyQuuZDPtTgq0tXKw4PnZRepp0VJ/5kHPb88VEc3lsPUBFNEP7DAfCO1zmXIIjsQdwxJupRv3h7dWVaDdE+eKTYVMgVxBtbTLN5te5cvYCrZ9jd1dj0cBGqR/wZkS5h3LPqKm+LTvDI+UtNRVw9M0a8ghnrg0++OOule7Hm8gfwzt8mIRTKcyzm6lnx0eO4oOftvqwpU7m8p503HitfKgEAMMZe6UhRIwRBBADVz7Y4dej+D0Bvk2ODAHziy2oIIrvx1aWbjpirOnMNGqBpsXLnuhFz1XEzqiDHW7HxwcK03LpGYu7Fb92Li9+6Fznd89p2ShIgSWm5dL3gNDNXi9al+/4Thb44dYOMcNDT+/qkphScnLoEQRAEkb34FbsQtJhrh+MP+BMMu7saw++tSTtT10zMnbP5EszZfAnyv5kcHRFLI+3BS1M1Hpc9RRxoYxf8yNLVzjHq8nlpz2dH92/1Fw/HMMbeDvyCBEEQHRinDt2XAUxjjL0FYG9iH2eMfQvAr6Fk6xLEEY3WpSvyTLv2G+LarZuuK9cqL9cLTsTcnfelrllubVYyb6E0UjtpnLN4g80LU4XOi9+6V33ceqDNBdB/dhV2lBZaZgAHiRMhV4tw6QKKGLvx2TIMv9Fd7IMgU+5cLfl9B6Nh1zbxdAxjbBnnnBy7BEEQBJE9LAMwZt/+7Xhj5VQc3WMgRgy7yfUkmXDmusHOnbv5t6l3d7UcjqF6xJ/V53k9orjrzSscXU8v5s7ZfIn6ePaoN9DwpXsR1o5zXk9tvmtEvDH52m6cuSOuL8f6P05DrKlBFXXTcepmyp0rOPns27B+5cM4uP9DABjFGKvlnBdkdBEEQRAdBKeC7lQAFwDYBKAWivn5QQCDAXwOoOMG9hCEO5ZBc0v6oZ1bseW+whRh141oO/TF6Y4vbtf8jLc0g0Wi6F9WadsMzQ4jEdeMeGODKu4CxgKvUcyCVsgFgDev/K36uH+lvYi5o7TQ0TinfPA7Y1ft7vFFrly6e6dOSyt+YeOzZb67c2MNByFFcyGFc0zH9Lr2NgDQxi9cYjqYIAhX8A7YaIEgiOwjYTB4BYl6VAi7AJLEXbeCrVkkghFWYu7KHymNv8Qxu/xcK4xEXCsa9zdbCrxGrlytkAukirmzNl2qZuhOHfZKSoM0o31+IsTc959R7lY77aYq23P0oq5X/HbnxmMtkOOtyInmW44bMepubFrzJPZ/XgcAZ1L8AkF0Xqh+tsaRoMs538cYOwPAeAA/BLAzce7DAO7nnH8d2AoJIovQFhPaYvrQzq1J2bpOhFA3Qi5gLuYKIReShKEvTseWK2eivkxxtZrFLWjZeV8Z+k2qSHputv7LV/4SAPDyeY+Cx2WEuuRAioSTXLWAIvBuXliWJOo6EXP186jIsuLUBRSR2mGu7q5Z7mIP9CLq7vFFbY8LJ6BPjXkTOS29ZpUnnesW7Tr8cOe2HjyA+kdnAQC+e8VN+Mag4Zbje19/p+pCZ4z9g3P+g7QXQRAEQRCEL2jydNVaFEhuluZUzHUr5PK4jFBeJEnMXfmjh1VXKQtJ4HFZ3cdCEk6+y16E1GMm5g4tUda7raYIckx5px/JDyEnGkpx1erFW7u8XCMxV9C1K0NTE8eMEa8AHIjF287zM1+39pqHk56/+7RST0bzQ2huiBudYsiI68vVc724dFd89Ljv7twta393+MD+XV0kJuGsi2cgFI6Yjh121q34YPVCHPiC+jsQBEGYYSvoMsYiAOYCeI5zXgHA2/3DBHGEoS+mhVtXi1vR1gxLMTfWmnIdN9fVirdGYq4QcQUvn/copGgIFy67x3A+IczqXbsCvZCrPUdg5LrtX1nTJupCiUOwEk13zSqD3ODc1aF15xq5cb0KtOnELvgatSDLkCJRfPzC7zDwnpkI53ezHJ7fdwgadm0FgAsTUSMrOOcX+rcgYxLRPt83Obycc06uYaJjQg4DgiB8Rmc04ABUt67AjWBre724jPPeSK7tVv7oYcgtsaTrfPBIcco+N+jFXCHiCrbVFCEclTBjw8Wmc0w+6VUASHLsCvSuXIGZmAsAH9WdgB4nfIxwuE3M3f/JdwEAPU742HQdlaNeNz1mROyrtnp47ubRSccmnrTc1Vzh3HzP0QtaMdev7NyW5oMhxGXIkFH7ejnOvmSW5fiTR47TNkkTf0WXBe3WZYwth2JiM+Itzvn5QV6fIAgNVD9bYivocs5bGGO3A3ghA+shiA6H/tY3P9lyVQUgy2qMgiDJlatz4XoRkZ0IuYBWzL3LdK7zX7zN2m2rw4mYy6JRpTGaxp0rBNYdU4rRf3aq80Mr5p56h/0bCquIgz0TJ7uOrxB5uk6iE4KIWNCS0607WDgHcoviTNn+m3IMLbF2G/e6dhz2/ulxNOyuE7suCCpTlzG2BoCTfLTRlKVGEARBEIYkxYL5idaZK7By4Dp15G58qE0ctnPkapFj3FLMBYD8b0Yc5+BaOXP1aJuj9TjhY3Ttat2B3U1DNK07Vy/mesFN9IKZI9fPRmj9h18V3bjmcSAuI97agoaD/0V+t+9YntPjmMHY//k27a4xQUQwMMaWwVnU2HkJcTlwYZkgCMIOpxm66wCcDIA6TRKEAUaibjruXCMhVxVxATVewQ9CXfJ9E3MF5794m6NrOxFzAaD33DnYXThBeU2iUQAAb7a+bU7gRMy1c+fy5mbXDdK0sQtWLt2gxVzBkOK52PW7mtamzz7OkcJhbK2ehCFF91me0+unipNj7+LH0LBnOwBcwhh71U+XLGNsJXRibn7fwWqer2DPoodx+KNdgJKl9nfOuZlzgiCyE279pp8gCCIdNHeOqX6mdNy5QsQFkBSxoBVy/XT/OhVzt9UUIZIfsp1vykrnNxU5FX67dmU4dIija1eGj+pOQM9Bn+DQIXP7mNad66Qhmtad6xdOoxfMBN/Vy2di5Gh/3nMcdXQfDD71Z9j27rMI5UXw/tv349wxc8CYuWli2Fm/UB+v+XsFWpsPAj5/cGHkyP3G0X1xyjl3JI3btPoJ7P9CjTWhGAiCyARUP1viVNAtAvBHxtheAK9wTtHEBKHHqJD2jCxjQEUNdlSUYkdFKfqXVYK3NPsm4tbd1ObO1Iq5RkKugMdl05gFJ+jF25zueY7EXEgSdo8vAotG0XvuHHX3nomTTUXddLNz9bBo1FWGrkDr0jUSdfVirhCNhRC8bcFUDB5vfTuaG44adka46bOPMbCkEttmFaJ53+eIHn2M7Xm9rrtDK+qmbxlJwBhbB2AEAOT3GYxePzX/IKD3DXdj759/i4Zd2wDgYsbYWs75mX6tJRNoPvQhVwdBEAQRCJxzJmrR3S8tRJ/Lx9mdYjxPIl5BCLii2Vk6UQpaNj9hXKsZibgCNWphrbU71wy9E1ePmTM3HG5z44qYBaAthsGM9nTnarGKXtA7c0UTtvXPTUOsuQGxFn9NBz2+PQBQmuTxUCTMVi0vw9mjKyxFXcFZPyzTRjD4ctcYY+xVJMTcbj1OxIhRqY3+BMNGjgWAtjVI0idclk9Idw2ZRGdAonqUIDo4Tu8h/guAowEsBdDEGPuIMfahZtsb3BIJohMiSagvKwSPtYLHWtUmZ34R//pwyj4rMffl8x5FqEuOp2u9eeVv8dp5D6REMDgScwH0n12F/pU14K2tSvRBgt5z56DPgmqwnBwljkGDn9m54losJ0dxCbug16xySF2VTr5C1BWYibkAIOUr58hNqf9O6fDN089lkELYPq8UUiSKnU/OQ6zhoKNze13X5lJgjK1Idy0JN8QIIOHItRBz1TVcexvyew0QT/8v3TVkEl0BPSbxnOhEMJ7ZjSCITs0yADi4d6vnCVhIwlsX1qgCrtwSg9wS89TgzAgjR64V6Yi5s0e9gcknverYiatnxvpLMWvTpWhq4ug56BP0OOFjVcjd/8l30bUrw9RhrygN0wzwy50bzQ/h/WeKbcfpGXF9OcK5Sm2pd+KaOXNH/Kzc9XWcEM7JQ/+TrkS8sYUVLB0PFmZYtdy5EaP7tweKh37dLTYaALp/q7+lmKulxzGDlAecH+/TGjLJGJPHBJGVUP1sjVOF6A0AzwN4BsBzAP6R2Ce2tN/cEwTRxtC/lmHoi9OVr5rH6VB303xsuXImtlw5M+WYnZjrNGrBCL2Q27+yRhUs3dB/dpWhI1eIrVpR1838TuMOes+do+b3usFI1LUScwGg1+xgimjGJPS7dQLklmYMLKmEFM5B/WOzHZ+f33eIeHi+V0GSMfaPhHPohwCQ32dQSryCFb2uv9PLZdsVk4ztMYwxTsIuQRAEkY2cfFcVht9Towq4J99V5YuYu/mJsqTsXC1W7lw5xj07c/VC7qxNikCb3yOStM+OGesvxaFDHPs/+W6KU3f/J99NytfVonXf2h23cueWr70Ictyb4jDi+rbacsVHjyd9BRRnrnDn6lm9PPW9Qzoc26sALCSh9ooFKFg6HnJrq+NzT064ZAGlpvRyfcbYK4kaTH0xTz7beS067KxbtXP908sa2gOjmlO8DlSPEkTHxJGgyzm/mXN+i9UW9EIJoiOQzX8MjVy5TlCiFryJuW9e+Vv1cf/KGtWJ23dqhfrcyp2bgiQluXQFelG371TjvFo9Tty5fqAXda3EXD11D6Qn5OuJHn0MWCiM7fNKMbCkEry1BdyhUN3r2nGIfvs4UfyOSdymZom2aE4UzmqoXd7xvdScXi94LeQzSZKYyyQ+pLQG+f2GaIeQsEsQBEH4jfp3p53XkcTmJ4wbnwH2UQtOcnONmD3qDfWxEHIFpSsvStlnRzgM9Bz0ieGxrl2ZoUvXzn0bRHauEVqX7vK9C2wbpYnxfscuMMZw0v/9AvFGRWgP5UVcuXSP+lY/8fBCpzWUphZ9FboP2XscM9jxtQ3O+Z7rkzOMqMWh+b3Q/fgh+mF09xhBdED8u4ebIAhA/KGUssuwr83MvXzlL1M2M9KNWtC7c9Ol/+yqlOgFgZFT145MNCMTaEVdgZWYK1zGbm9JdEK/cRMhx1qxfV6pLEWi2FZT6vzcWyewnO5Hi6ejzeIXGGOvJRWPGvJ7D8DQSTXoc9O9XpaP/L5qEe2840k7kCLmTqpiAHDiteNgJOxmfoVExuDtsBEE0SnRijLD767Kqm4y2ppmaElN0maFn+7cdJmxvi16Qc9HdScgN9c6fkGP2+xcr7ELQHL0ghYzZ67W1es3Rx3dFywcVl268ZZmxGPO/q2Gn307jjq6n3aXaQ3FGFun628yGgC6du+JUZfPw6jL5yU1XnOKl3Pag5S7xJjEC342nw06fywKbqhGwQ3V0Ii7VIsS2QXVz7Y4aorGGLvJ4rAM4CsA6zjn5qnwBHGEoy2gh/51WrsW0HU3zffsyNWSTiM0pxm5buk/uwo7So1v1es9dw72TJycIuqueyx5vGgOIXDqzvXaHE3Qa5aLwjge93QNJ0S6H42jzzwP+9askAZPrcG2WYX47I0X5WMvvNLRh3wD7piCPc89gsMf7gCU+AVHf/4iR38H/cdNTGfpAJQs3S33Gf8MZANGxbMQc7WceO04fPjnhWjY6T3jkCAIgiB0aN257VqPeolW0OOnO9cvZqy/FFOHGQu2H9UpPbK0zdLOeX0Caq95OMmJGz4qD4B7d2752osw8aTlbpesEqRI6wYpFMaZF5Si9jXlrrpQXgSr/j4NZ4+uQChkbyYZfo5yh5emSZpjKeaoo/up5/sBY2wN5/ws3yb0Cb2xoOBn8xkMficMOn8sahcVZXh1BEH4gSNBF8Dv0KZXa38JaPfJjLE/AbiFc+7vR6EEkeUk/cFU3LntWkCbiblWblw96bpzBX6KuSqShN3ji8CiUSXbVkPvuXOwe3xbUaKNVRB4debq5w4SuakJABDq4j5v2Andh5+JfWsUc60UicpfvvcvKfrN78R6nDrS0d+F3j+7C3v/9DgadteZjsnvMyitSAUnMMZeycIOvbZirkAj5pok7xFHBgzgWWWUIwjiCCSb3Lmbn/AnMkqOcczY4K0Rmt/uXC3hsCLadu3KVBFXS9euDIcOtWmMeuFW/9yJO1cgXLqn3eRPg7r2IpLbDSwUQu0VC+SCpeOl2isWYPXyaRg5utyRqAso0Qf7P99mOyYIR63m2gW+T54mJmKuIXVvPpGpZRGES6h+tsOpoHsOgEUAXgawBMB/AXwHwLUAfgTgLgDDAMwEsBdA6v3QBHFko4q57enOddvwzAy/G6H5Tf/ZSgG7Y0qxqbArsBJvveTmpuvSdcugXznLA3ZLpMe3AEkSWbrS9nml8qd//0v4sxUv4tgf/Bg9TrE3GgQt1lqR33cwGnZtAzJ8e5imQF5mJCRr30wPKa0BLD7c+fDPC9XHnHNvn54QBEEQBFIFHLSTucAsJ9eNK1eQjjtXK+b66c4VzFivzDljxCuGwu5HdSeoLt13LrKuGd2IuUD6Lt1sovfgS7B72zKp9ooF8YKl40NC1P1uv/NwXK+zEM07yvL89ow+GHbWL7QO4WWc84zVpMKRzLmp2uVIzAWAA/8hcwFBdFScCrrFABZzzrVC7XYAKxljBwHcxjn/MWPsGwBuAAm6RCciW6IWtDm5Ai9iLuA9auGNMY8gfritU20g7lwNWmF3z8TJKaJu3tAhaNyiFCl+NT3LpEs3SBiT0G9sCXY+MU8VdQFg+7xSfPrqn/HZP17E4MJKMJadn4pqYxcYY//gnP8g6GvqohTG6N3Bbt5M66IWqIDuDHTAXC6CIDoU6t+f9nLnGom5XoRcgRd37owzX0NLQ1tslVdB2PH1NMJuz0GfGLp1tbgVb9uTdYumBn6N7/b7Hg4d+Dj+xSfrQwBQsHQ8aq9YgI92vomPtr+BkWPKEQ7nBr4Or3T/Vn8c+N8OALgkU9fUvvc0ulNNe9xOzNW6c8lcQGQlVD9b4rQp2kUA3jA5tgJtjWneBmD9V4wgjjzavRGaUWauVzHXC2+MeQSvnfdAkpgLADtKC7FzRil2zihVHwdB/9lV4M3NKfuFmAsAuwsn+HY94dLt6ES/eQz6/vzXaoM0ABhYUglA6UC8rXpSu67PjnZojqZ3Xhg/t4lZAJKjFqiAJgiCIPyiPaMW/BRz3bpzZ5z5Giaf9CpizXLS/lizjPKC5SgvWI6pw15BeUEwzlarZmlBkE5zNKfEW5Q77sKRYOK/BINOuy7EQhJqr1gAQBF1Q5FwHADW/H0Gmhq+DPT66XDy2bdl9HopfRoSBgPD44q5wJS6N58gdy5BdHCcCrotAE43OXZ64riYL3Nt4wkii2hPd65WzL185S8zLubKzfGUa/ZdPAuQJPDmZvDWVvRdPMtQdPUNScKeiRY3B8iy+TGX9J47Bywn54gQdXO/cwKiR38Hckuz+vdg8NQaDCypBI+1YsfCubHYoa/bc4mm9Lo2s0W0IBGlAKDNBZEUtWAj5mohMbcTQV16CYLoJAwtqUlLzAUS7ty1zty5M858DbFmGUNLasByuqj7B86sQf+yarQ0xNHSEMfAmTWqwBsEmRR1y9dehJxcCe8+XRS4sBs0jEkYPvJOxBvbYjIKlo4Pnf1aCaRIGP9+ax72fbalHVeYVahibffjhyTv0x13E7VA9SiRtVD9bIlTQfcvAGYyxooYY70YY3mJr8UAZgD4U2LcCADmHXIIgvAdbdSCH0Kum2ZoQsy97K07sfyyp9T9fRfPahskSej7XLn6OCinbv/ZVeCtrYZxCH0WVKuuWr/iElRRd3yRsgUk7tY94L2piNzSjFjDQcut6b+foPmLTw3PlyLReMu+/4a3P1KO5i8/97yOI5H8fm1FdCLHzJEbQo9WCCYIgiAIL2TD3xK/mqC5RYi5gwurUfeQcX6vFGaQwoq21b+sGrFmOTC37oz1l+LQIa7m5wrinw5At64ME09ajoknLceUU9K/dvnaizB38+jAhN1QJA8AEGvx7tfiXEZL00HbbcfGF+JG5yfcunzLe8/g451vk4s0QcHP5rNB549VnzPGXnETtaDjNT/XRhBE5nCaoVsIoBuAeYlNy3MAhEKyCcBqf5ZGEIQT9FEL6eI0P1cr5gJAi0EjNFXI1T3f9bNp2DmjFP1mVPqwYoVds8oMXbgiO7f33DnYM3Gyry5hbWbvnomTfW2WJuXnQ25oMHxj4oT961fj0+V/ASTJkTtZikRj0P1NGFhSGQKUXN2dT8zDN087B8d8/1JIORFPazqSOPHacfocXEdRC4L8fkPEuRlt6kYQBEEckbRrMzSzRmhecRq3oBVzgeTIh4Ez2xzC/cuS+yiI5zsqilBesBzTav3LtZ06LFVbj386AABwoL4/ACB0XL1v1wMUYRcApp35Ot59ughSiOG0m6rSnvfUG2bh3ae9GyEO7v8IOz94EQcPfgLEbWvRUCgvEgeQ8g9fsHQ8q71igbx789/Ce+uWY/jIO9GtR0/P6zqS6H78EOG0basnHf4eMDyXIIgOhSNBl3PeCOBGxlg5gLMAHAvgUwC1nPPtmnHt/ukwQXQGgsrMdePOjR9uNbxmkjvXhL7Plfsq6u6aVQa5IfmNhFETNCHq+im8auf2s1lar9nlnuZr2f8Fdj45X+axmARJapHCOdLAkkonv+tNxwwsqcT2eaX48t2V2L9hLfqPLUHOUT1cr62jYpBXBkAn6roQc8W5WysLfV0nkeV0wNu4CILoWGQ6P9fvJmgCp83QWhrihtfTirlW9C+r9lXUrRz1etLzbl2ZKuLq9zc2+f9HQQi7E08KJlLCKZxzbK59umX/59siCElyKBLmBUvHOwlENh1TsHS8BACrLp6H9Ssfwimj7ka37j2ztnmv35jVooPOH5uchesgakF7bu2iInV+fXM1gsgKqH62xGnkAgCAc76dc/4M53we5/wPWjGXIIhgqbtpPrZcORNbrpzpuytXoLhz77Id98aYR8BCrn59pND3uXLw1lZf4hf0Yi4A7B5fZJipG2T+bTY0S9u/fg3nsiwBgAsx15a2ZmlA/eOz8fFLzza1fn0AnMvg/Mj8S5u4fa0tTgFIiVQ48dpxGFJa4yo31+A6PBtulyUIgiAIOzY/UYaNDxVi40OFgYi5bty5IkYhHUQEQ7rxC5WjXkfD/pakfY1NHN0H7EgZe6C+P/JymS+xC9lIS9PX2L9vewQAQpGw7FDMdUQoLyIDwIaVD+OdVyfj84/fj3POwbl/vTK8whhbEdC8yWKurhYddP5YFNxQrWzuohagzeGlWpQgOh6O3+gzxvIB3ArgewC+CeB2znk9Y+w6AOs559sCWiNBEDCOVshk8zMtZu5ct/jh1N01qy2zTe/K3V04AXsmTk6KRgCCc+oG6QB2So9Tz2b7/v1PSJEoBpZUhrfPK4Xc4ixmInGO6fHBU5U3atvnlcpfb3k/9+st7ydOlDDonpkI5QXbBdkKxtjfOec/9HnapOI5Idr6ZgXRxC4kX4s48uAAeOdwEREEceRiFq3gh5ArSNed6wU/nLpaMXfu5rY5ppyyHN0H7Ehx6h6o74/uA3ZgyinLMXuDf5EPABDND+H9Z4p9iV0QrF4+EyNHT3c0NpLbDRKTIEMGEgay2isWJDU9syKUF5GFI1ePdn/tFQt43fuLQ3Ub/gwAkKQQzrlktqNr+EmPYwZj/+fbAOB8v+dOEnPb3Le+FRQ6hy/VokR2QfWzLY4EXcZYTwBvAfgugG0AhkHJ1AWUX1w/ADDW8GSCINLG78Zn6WDkztU2RHNLuqKukTtX0KdmfruIun5GL7gl0v1o9Lt1AnY9XYNtswrBciIYOsn+Dc+hPdvx4eLHsG1WoSrcmjGwpFL9Adg2qxBSOAfbH56BQeNnZzxfN7/vYDTs2gYAzlphO0TrUhhSWgMEkEl44rXjAECNXqDb3QiCIIh02P3SQvS5fFwgc+vFXD9F3Gygf1k1tk/3FoWkjVrQirkAMHvD6IyLuuVrL8K0M1/3RdQNR7sg1nzYVWM0xiScM2YOdm9Z1vqfPf/KWXXxPIQiuRh1ub4VTyorXypBvLFFqr1iAQqWjrccW7B0PAMUYRcA4o0t7PChz9Gl6zGO1+oHw876BVa+VALA31rORMz1HYpeIIiOi9N7pqsBNAMYAOB0JL+x/ScU1y5BdGo+rHgusLmDiljQ4jQ/N364VW2EJjBqiOYGr/ELVu5cdX/NfNNGaL3nznHUMMwtLBrF7vFFytYOEQzRo7+DIcVzMXRSDYYU3efonPwT+0GK5gJQRNptswqxfZ79v8fgqTUYWFIJFgqjbsHUtNbthV7X3qY+9utWMX0B7cecVuT3a7vdLehrEe0H45ndCILoVCwDgIN7t9qN80wmxFyncQtBIYWZp+gFfdSCntkbRuPgIeNfzEHFL5SvvQg5uRLefboI7z5dhPefKfY0z4ifVXheQ5+hY3LOGTMHoy6fh7NHl9ufAOCMCxRhFDElL7f2igW25xQsHc8Klo5nobwI3vtnDVqaDnpes1e+8c3e4qGftVzgYq5AG70Q5HUIwi1UP1vjVNC9CMB0zvmHSI0l/gTACb6uiiA6FssA4NB79YGKukCw7lyn+blWOGmIZnruc+WmwqsZVu5cpwSRe9t77hz0WVCNPguqA8vr9RsmhTD413MwdFINhk6qQb9xEx1HNQBKxi6Px7D72Ydifna6dkJ+7wHioV9FqFpAp5ON6xTh1AX8E6UJgiCIzoPWUbf7pYWBXitIZ64c45ix1v6GG7/yc/V4ydO1cuc65UB9f8Rink61pHztRZi7eTTmbh6NnFzJs6ibSfK6fksRgC+ZhXMvmwu5Je5I1AWAgqXjEYqEUfvGbHz95d5gF6rjlHPb3kMxxpalO5+2HgxazAUUl67RtQmCyG6cCroRAGYfdR0FoNWf5RBExyNRRKuibkfEqTvXjj23ecvBVZEk7CgtdOTUdeLO1c5r1SQtCJeun/Nvua/QcKt7oMz+ZI9Ej/4OpGiuY6cuAEiRaLzx493h7Q/PDGxdRvS6rs0xzhh70+35ovmZ2MT+TIi5Aq1LlwrpIxSe4Y0giM6G6tINWtQ9knEj6ho1QjMjHAZCx9UbNkkDgG5dg22S1twQhxx3/8dh/R+nqY9XvlRiuK1eHkzdxxjD2ZfMAm+V3bh1EYqE+YbVj6K58atA1mVG92+rBoNL3NZyBrVoxu4UE1CDNCIrofrZEqeC7kYAV5scuwTAe/4shyA6JlpnRNAuXb95+bxHIUVDablzI93zAADy1+k5M/s+V46+i2c5il9w487tUzMffRZUg7e2Goq6akRCFjlp906dZjsm3tiAbQHGHAz+9RwMmaiI5U6E3YEllaHBU2vAQiFsuz/1dQ6S/L6DxcPzEsWwG3dEqrM3gwU0oLh0KXqBIAiC8IrWYBBk9EJQuIlbmLH2Ysix4P5MOxV1zRqhGTF7g+KUbWzihsJuUNELTtdnRqzJvt52k6/rFkkK4Zwxc3D6+cWQ24Rdy3/8gqXjWSgSxrtvzg1sXUacPHIc8trye8cwxt52cbphLZoJd65g0PljKXqBIDoYjHP7P4aMsasALAHwJIDnALwB4CYombqlAC7nnAf3kSJBdAC0uZtdTx+AE8t+ljKm7qb5aeXhBhG58NKo3+Dit+51PP6NMY9Abo6n5Oi+NOo3AADpG/no/Vt3WbhG7PrZNECWwaLRlGZpu2aVqYKurTtXx+7CCWA5OSlN0gBg9/gi1/M5Yc/EyeCtra6ar2kbq/WtUta0e8pkNZZC6tIF8uG2nyUptwsGj/ceeWFHvLkJ2x+eDt7aCikSxcASazf2tlmFGHBXGXK+0SOwNenZ++eFaNjV9iaWc2dtUYUrN9H8rF0RDdIALKOmFEcO0RN78uMnjM/oNff8qvg9zvkZGb0oQRDtjvZOk269hhg2SdM3OHNDEJEL22qKEI5KjuIWBJNPejVlLVvmKX9DQ13y0W+i9+xXwY6KIsgxjkh+CNNqkwVRrTvXi1g68aTliH86IGV/6Lh6z+Krk2uecYvzOnf9H6epgu6pdyiv9canpyLefFjdt+6x5EZy4Ug+Ro6e7tOKU/n84/f59o1/YVJOyLZp2qqL56HvSZfh+L7ngrGM6aJY//ZDOHjgI/H0Vc65rTgq/t8W3OD/+xC3iAZpCageJdoNqp/tceTQ5Zz/FcBdAH4C4B+J3c8AGA/gbhJzCSI1ekHv1E1XzA0CL1ELFy67CzyeGiHgl0tXYObW1Yq5XuhTM9/eqavdfHDtphO7IMRcAOgzew76VlWjb1U1epdXQMrPV4/JTYex5b7CwBy7oWguhhTNxaBfz4Eca7V160qRKOofm42Gj3Yh3tSI/7z658CzdXtdOw5DJ7W9uWOMrQv0ggFALl2CIAgiTdQ7VPRO3c1PlGHjQ4WexdygcJqda0coT6mL4ocbsHNu+rFU/cuqMXBmjaFb12nUghnhMAzjF7p1ZZh40vKkLcgoBiuM3LnDb5mFU++oUQXecG5+8jktDVj1SnB3jx3z3dPYOZfMAY9x2wiGUF5E3rX5Zax6dQoA4D+73ont+2xL4HdgjfjePej+7YHi6SVu7hqre/OJYBblAo1LF6B6lCCyGkcOXXUwY/kARgI4BsA+AKs455lvI0kQWYzWqWtGkM3N3ODWnSt47bwHDL8H4dLV4odjd9d1U9G/Uikcd5S2OQHScdM6deP65dp169IVDl0pPx+9Z1p3Bt4zfZqhyM3CORhS7P/tZg0f7sTe536DwVOtXTrb55VyOdbKIMuApHx+2PPqX6Bbv6G+r0mLzqm7l3Pe22q8cEXk9xuS1KCsvSCX7pEHOQwIgsg0WqeuGUE2OHOKF3cuoDRGizXLGFyYXKPVPZTqPvbDsbujQlnntNrRabtzBWYuXT3dB+xAYxPH7A3pOXennfk6WptknHZTlaPx7z6t1KLh3HycfLP967fhqcmQW5pS9gfh2uVcxjvLJkOKhOWCpeMtTWqrLp6X9Lxbj15NI0b9MtfXBRmw4V+P4Osv94inyznnl5iN1b5/zAaXLtDm1HV6xxtB+A3Vz/Y4zdAFAHDOGzjn/+CcP8c5/zuJuQSRitapa0Q2iLkvjfoNXhr1G1t37mvnPYDXznsAb4x5JGl/qEsOXj7v0ZTxwqWrRf66wZdmaTtnlLprhGaBcN0auXT1sGi0XVy6UlfF8eDEjdx7Zjn6VlUnOXYBgMdaA3Hs5h1/Ilgo5CRTl0nhnBgASOGcOGQZH/3Fm/Og5cA+7Hh8TizWYP9np9e149C1zenaizFW5+QaDTuzLnOQXBFHEIxndiMIotNjWosOLanpMGLu5JNexYwzX1O/CsxydAfdU6E6dQV+OHb7l1WjpSHuqhGaE6wapQn8ytctX3sRcnIlvP9MsaPxwn0ba2rAB7+zf/1O+cWcFMcuEEzGLmMSBpx8TTzeEpPsMnVDeZGY9nnDwU88i7nb3vtj08c733b0V/aUc+/SOnVHu+zv0K5kg1OYIACqn+0wdegyxr7nZiLOuZvQb4I44hHOiGwQcLW8fN6j4HEZoS45po3Q3hjzCOKHWwEo6zdqnGaWpatl+WVPoeVAIwCg7+L08l13XZcsTPoh6AIwzdPVsmdiIrtWklzl4BrOE5BLN+VaGteuyNfdcp/i/NTGEnhFjrWibsFU8FgrpEhUHlhSafsB4bZZheh/51REjvqm6+s17N2BvX96DIwxDJng7PXTOXXf5JxfoB+T5KhnEh8yqardXQgah26Mc+4uE4XISqIn9uQnFI/P6DV339uxHAYEQQSDqEezQcDVYifmzjjzNbQ0xAEAUphhcGF1yjlmLl0tWsfuwJnpvQYiU1eQbtbtxJOWY+7m0ZhyynLk5TIcqO9vOd4vp66bLF2jHF03iIzdUDiKs8dUYPXyGYi1HEY40gUjR89wPZ+e/V/UY9PaJxGKhG0zdYE2t+6oy+fZjDRm5UslAIAzL56CaO5Rjs7Z+M7j+GrfTgDGbld9LZrJRmhmaHJ0qRYl2g2qn+2xegP+FoA3E5v2sdlGEIQBayb8rb2XkASPy7j4rXtNxVwAiB9uxeUrf6mK0Ze9dacq8ArMsnS1jH75F+kv2IB0IxD61MxXN7M8XS29585RrukxB1c7D8vJcez4dePSTbnWzHJIXboo5yfydQV1D6SfKycl4hy6Dz8Tckuzo7s9pEgUO5/wVkBLOTmALIPH4/hq63pH5+icuuczxrh+Q5aJuR/+eaH6mAroIwzOMrsRBEFkMXa5uS0NcdVJLATbwYXVqsgLmLt0tQy6py0qIF2Xbl6fwepjPxqXiTlmbxiNg4fsrWEH6vsjFrMdZks0P+TYpTvi+jZDgROXrh7h2I3HmrHypRLEWpR+IuJruvT49gCccd4ExBuduabPfk0RZN3ETmphLMQB4N9vOL/7cPg5t2vOt65Fs0HM1bpzqRYl2h2qny2xehN+PoALEtuPAXwC4O8AboHyS+cWAK8l9l8Z6CoJomOyDAA+X7O3vdeh4qQJ2htjHgELpf5qYCHJcfSCEbuum+o5ekF7HotGPc1hhhNRd8/EyYpbVnKVUmOIm+iFXrPaiug906e5v5aucZog3tiAugfTF3UB4LhLfgoWzrGNXwCAgSWV4K0t+HrbetfXyTu+l/r6f/Lys44bupz4k3HI7zvYepDPYu7WucXYOtfZGyU9mtgHH96yEQRBEER2sa2mCFvmFSKSH/J0vhRmSdELkfwQttUUWZzhT7O0nXPL0FDfFs007czXPc1jhlmTNEH3ATtsoxmcEnT0gpaTb64wjGEAgNWv+pOrm5d/NEKRXKy6eB5qr1hgW2SH8iJ459UpkOW43dAUeg44nwGAFAlj1atTwR3O0eMY+1rUTzG3dvEE1C72Fhl34D9UixJER8FUneCc/1NsUATd1zjnYzjnzyTyc59JBHu/DuCqTC2YIDoK2dTM6OXzHsVLo36TEpugxypG4bK37oTcHE8SdZ24dLW5uvLX3jK0tOc5cdS6xalTN524BS0sGsXu8UWOnLpal64nUTeRrys2KU/594gfbvDFqcsYw6DxsyDHWrF9Xqlt4SdFovzjF5/B1upJrq8Vys0/BCju4LoHyrDtfmc/B72uvQ1DJ9Wom7qWvC4YUloD3525spy2m5scEUcYvB02giCILENEJszZfIll1MLkk16FFDb+02zk0g1HJUtRV5urGz/srRbVnnfGLdVobnAvBloxe8NoNDZxS9H24CGOcNif67kRdUdcX54k6rrl5JsrcOodNeoWEnO1Nvom6p49uhzfPmEE4o0ttu6LgqXjIeVIWPXqFNdOXSmUo86BMLBqubNaethZv8Coy+clbep6bqhGIM7cuOxZ1E3wmv0QgggQqp9tcWo3uwLAn0yO/SlxnCCILMVJzAKgRC1YZeIaRS/YuXRHv/yLpBxhty5d7fg+C6oV8bW52dUcTrASdXvPneNbczQxn9MIh16zytOKXki5dsUssFylF0S8sQFb7itM260rhXMQ7tKtSW5ptn2bMbCkkg2eWgPe2oLtvyn/evcfHnR8z53cfLhLYg5IkSjg8XY58aZObvTndr8UEk5iry5dgiAIgvCbD5cstB8UIE4aoAFtUQtWubh63EYvbJ9e6Mqpqx3rNHvWC1ai7oH6/ujW1V/Nz62o6xfDb65AKKrEgsVaG7HypRKsXj4z7Xnzv3Gc47EFS8dDioTxzrJSrH19zqGv9u1yfb2CpeMh+5CB8d5f/LlrTkvBdQkTio3xxojux6txZdSclyCyHKeCrgTALKV9AABv98wQRCehPXN0ncQsAOZRC3r00QtOXLpa5K8b1PiFPbdVWkYx7Lmt0tjVK0m+u3QBWIrFav6tQ2etE5w6dbXRC37QZ9ZsNV8X8O5WSZrz5l/nSpEots0qdBS/IEWi8djBA99o/PTDLraDE/QYcXbSDyj36IIddK/mTd0C/4toIdRL4RzPom6iQQZBEARBpMsyADjU1iQ04zgVc2ec+ZqpM1eLPnbBKeIDXaAtfmHn3DJbgdeoTpJCzPfYBaAtTzd0XH3KsQP1/ZGXyzDxpOWYcspyX64nRN13ny5yHMHgJUtXz/BbZqlOXQCItaRfi57Q91xt9ILt+ISoG29uPNB189qnHF3jmO+eluYq2whHEk5ln/KEjZDyIq5duoPOHxvQagiC8Bungu4rACoZYz9hjIUAgDEWYoxdC2AWgOzq+kQQ2UO75ui+fN6jtjELAjt3rsDIpesF+esGVaw1i2LQ7tc2QgvKpbu7cIJlRq9w1orGZmLzin4+J3iJXTC8dnkF+la1vaZb7itMK4Ihp+s3MLiwEv3GThTxC5bjB5ZUhgZPrQFkGZ+8/BxavvrS9hrfOKmtiB5YUgke8/5zqN562djgu6g7pFSJdRhUXOk6eiG/rYnbmESzDBJ2jxToljGCINqBbIgAs2uAJmhpiDty5g4urEasWXadpasnfrhBFWvN8nXN3Lmn3VSF1iY5EFEXAOKfDjDcf6C+P+KfDkgSdieelJ64W772IszdPNrWrZtOlq4RwxNRDIKVL5WkFcEghXJw9uhynPOjOZBbZaeibujs10oQb23B/s+3246P5H4DLCSh9ooFaubGvk83e1rvyNFt3+u//2RvhvDCiOeLPbl0BVSLEu0O1c+WOBV0fwXgAyjxCo2Msf8CaASwOLH/V8EsjyA6Nu1ZRLsRc92id+m6aY7WZ0G1GiGgZdd1U5PcuvqohRSCcOnKstK0zAbh1k03J1U/n5VbN90sXTO0TdPijQ3YVpNeQRn91nfQ+4a7Ibc4E9ylSFT+avO72PH4HNscs6ZPP0xrbVoG3VuRJOpurSz0TdjdOrdYiYTwwInXjtOKugAJuwRBEIRPtEfswraaIs8N0KwwytK1i13QNlQ1atQVP9yQ5NbdObfM8i6m026q8j1Ld8opyx1FK2iFXb+wi2BIN0vXDO2/hR8RDJIUxjmXzIbcEnMk6gJAKC/CN/37KRzcb11ryvEW8LiMeGNLSJy3bf0fPa9VuHTlWAtqFxWhdlER3luSfp1fu3gCpLwIAG8uXU3sAkC1KEFkLY4EXc75/zjnowD8EEAFgOcTXy/mnH+Pc74vwDUSxBFBpmMXeFwORMwFUhukuY1d6DWrXMnDNRB3RSSDXQM1p43MXCFJiqg6vsh2XiH8+tUozc6tq8/S9c2pm2iaJiIYnAqxVnQ5oTc08QuW73QGllRKg6fWQArnYFv1RHBu/HMUb2zAZ6+/kPbatAy6twJSriZ6orEBdfdPTX9iWVbcuQCkSNR17MKJ147DkNKaFGE3/YUR7Qnjmd0IgiA0tEvsgtOoBYFbl60+esHp+WfcUo0R15fjjFuq1U0rKgq3rr4RmuEaAoheEJELVg3SBAfqzVIRveFE1BX44dIF2pqmhX2MYGCM4cwLJyPuUNQtWDqehSJhrF/1CBoO/td03NZ//wEISQjlRWLiPLm1xfM6R46eroq6glizD2J5XFbcufDm0h10/lgU3FCdIuymvzCCcAfVz9Y4degCADjnr3POKzjndyW+/iOohRHEEYQau5BJUVfvovUbffSCG5euFitxFzBx54pjPkcv9KmZr67FiVjsNAPXDapb14Go6ye9yyvsB7lgcGElot8+DnJLsyNbzsCSSjAphG3VpeBymwbMZRmHdm1T4iAkCVIkmn73Ce06x8/C0Ek1qrArNx1GXY1/HxJ4iV0QCGFXQM4IgiAIwgvaO8bauzman3hx6QLA+j+mfiguBF4hKjoRcwH/XbqzN4zG3M3K1tjkTNjt1pX5lqkL2Iu6fkcvCE7WRTCkSyT3Gzhl5J2INzoTXAuWjkcoEsb7b9+P5sYDSccaDv4Xm9Y8Gd//xXYgLqNg6XjbRsBOGTl6OkZdPg+jLp+n7vPLqZsuQtgVUC1KENmFK0GXIAj3JIrods3SteKNMY/gtfMecNQQTY9WNHbq0t071bw40QqWgLWYqxJkg7TWVku3bu+5cywzd73Se+4cUxFQ2yBtV3GRr/ELgnTzdAXdBpzkarwi6krYdv8UdV/Ll1/gwyVPAACkcE5sYEmlbwW0lsHjZ6kRDHJzkxLB4MNr4DPkjOjIUAYYQRDti+rSzTZRd8aZr2HySa8i1iw7ytDV4salK/7OW8UFaGMFHK8hwAZpToTdA/X9EfP1425rUVcfveCnqKslndgFQW7+0QCAVRfPsxmpIETdf6+4LykKbPOap1r276sXMQvqq117xQKEo3lpr1OgdevGmhuyQtQFkiIYqBYlMgvVz5aYKjiMsThj7EynEyWapMUZY/61fiSIIwStM+KlUb/JiFNXiKuvnfeA5SY3x3H5yl86aohmh5VLN9JdKXbkQ9bOUq1j1wmBRC9o5u6zoDqQBmx2sGjUNk8X8Nepq8/TTVfUzflGD0CSHEUvCAaWVIK3tiB26GtljqN6KGuLRONaMXf7vFJIuckFdOvBA9i39p/47I2l+N+aFdi39p/q1pqYzwptri5g3NWaIAiCIDoiWoPBoV1bsWVeYeDCrmiGJgRbsy3WLGNoSY1rMRdw59IddE/b3UhGLl2BPo7BjqAbpM3eMBrhQD7OtsaNqOsnoYRAGmtpSKtJGqDk6QrTSu0VCxzJNQVLx0OKhLFqeVsMVzT3GxHEZZz9Wonqzq29YgEQZxj5w2Th+b8fvouPd76NnR8sxSc731Y3J03XhFs3nJN4DbzGL4QkrL+6Sn3qJUdXy6Dzx3o+lyCI4LD608AAHMsYO9HFXP6lshPEkccyJD7V/HzNXrw06jc45qxeOGv+jwK5GAtJqlibKS5cdhdeO+8Bw2OjX/4FXhr1GwDA7vFFkLrmJ7lN06FPzXzsHu+us7FTdhdOsHTh9p47J5Br9547B3smTsbuwgkpOb3idRPX3TN9GnrPTP+1FHPsmToFclOTKuoOurcCW+4rBAAMneT8VrgeI0aix4iR+M+rf5YPbFjjuCOKFIli+6MVGFI8D/tq35ShfPiYVITLLc0YOqkGLQe+xKHd2/DFyuXN8cOHopAkQ3fzF/9ajsGFlSn79Qy6twJ1D5QlNU5xjSShrqpUzdFNl/x+Q9CwM7O5h0QAdMBP/QmCOLLgnF+auGV6DNAm7HbtOwQnXjPO9+tJYYbJJ70KKcwwtMS/W+nTIZSXj3hjA2JNDVj/x2lJmbDpcNpNVXj36WBqUQCIxazzckXswuwNo329bvnaizDxJOM4hxHXl6vf87rHChHOzcfJN6cf4TX8ltnY+LsyxJsaEGttxOpXZ2DkJTOwevlMxFoaEI7kY+RoZ0JvOCcX5156H77atxsb1zzOaq9YwAuWjrfVLAqWjseqi+ehqeFLSOEIvt6fepdlvLEFoy6fh3isBYcP/hd16/7UEo81tbQ0fd0VIYkjLiddh4VCOPdSZ7XhyEtmYuVLJY7GGq7/uvmoXdT28zji+WK8P8a+8TNBZB1UP1tid4/1CwB2O9zqQS83QZjCOb+Uc86QcEcAbcJuEI7dy9660xfXrd01tDm6dgiXLmDv1M0GdhdOAMvJURugmWHlpk0HNU/XJKc3iCZpANB71mxVxI43NqhiLgBPrt3jRv9EYuEwts8rdTR+YEklpFAY26on4n+1bzLIMoyiFv674qXWHY/Nwmev/xXxw4eiACCFc+KDpxq8aZScR4qkJeYCGDKxKqm5nJfGaFo0Yq7PN1QSBEEQnQ2jejSoGIbBhdWenbducBO7MOieCkfRC57WEVD0wpRTlqNbV2sNMojYBUE0P4R3ny4ydOomNTLz8fUcfnMFQsIB3HoYK18qURuleWmYdtTRfTD41J8h3hJjTp26obwI3n1rHuree07WNkLT8uV/t/J/vzFXXr/qN2g89HmkpenrrgAQioRTriGFchyvd/WrMxyPzQR1bz7R3ksgCMIAq3e4twD4hYdtt9mEjLHzGGPcYDugGdONMVbFGHuLMfZ14vh5Tr6ZxLl/ZoztYIw1MMYOMMZqGWM3OjmfIDJBpoXdoHHTfG30y79Icgxb5em6JogsXVm2FXMB68zbdOk9dw76LKg2bJSmb5LmZ6Zun9lzIOWlZoJ5ETsZYxhSPA9yLOZK1OWxGHg8ZtoIbd+7b+cASrbu4Kk1GDy1BgNLKkPb55VCiuZi6KQadRs8fraj625b0HZ7nbYhWToMKq6EFM5JS9QFAM6583cCBEGYQvUoQaTWo5mKYQgCfeyCHU6jF9zid4M0QBFz83KZpTs3aMrXXoS5m0cbxi+IeArBuscKfcvU1Yq6elYvn+F6vm8fPxwDh/8E8cYWR3cVi+iFA/t3SWaN0DbXPs1aYw1yKBKOn/1aCcRWsHS8BEBtdjbq8nk4e7QzN/jq5TMRaz3s6ntzQjqxCwf+Q+YCgshGTAVdzvnvPW77HVz3VwBGarYfaI4dDUUYjgFw+xFnJHFeJYDLAfwMwDYAf2CM/drlXAQRKGbCbkfjsrfuhNwcdyzqAsl5un6Jun1q5vuades2RkHv0t1dOMHUWesF1a1rIeoC/rp1e1fMQt+qanUTbLmvEHUPKsV63QNl2HJfYdI+MwbcMRlyrBXb55U6Ur+lSDQmhXPiRu5cKRLlQsgVx7fNKsT2+ZMBScLgX7u/rWzLvAlcbvK/gCYIxjO/2a6JsWsYY88zxvYyxhoZY3WMsUrGWDfNmN4mwidnjHXXzWc2boTfr6ePUD1KdHq02bpAdjZNc4LWpdvSELd1BWtdun6Kun67dO2iFrR068ow8aTlmHKKEpEw5ZTlmHhS25YuVpm6eqeuX8Lu8JsrcOodNerWlq97WG2atnq5Ek+w8qUSW6H3Oz1PBwuHlPxbBySapMWM3LmhvAgHgFAkjIKl40OAkqu76uJ5WHXxPE/N0t55ZWqSA7nghjTc7boc3RHPFwMOGljb8Jr9EILwB6qf7XHf1t4ftnLO12i2dzXH9nLOv8k5/wEA4+5KJnDO93HOf8Y5f5Jz/gbnfBnn/OcA1kApygki69AIuwDQIV26QtR97bwH1MYDVox++ReOm6S1F9qmbLvHF9m6f5PiEcYXgeXkKHP46Ny1EnX7LKgOLIJBkNQ07bASxaB17Bo1ERNiL6A0Sfvm6aMgtzQ7+tszsKQybCTmJo4xfZM0AABjjt24Kchx9f+hX+5cP0nkHhKEXxQDiAOYDGA0lJrrTgCvM8b0/0crkSx8jgRw0GDO3xmMs+8C035QPUoQMHbrdjQGF1Yj1iyrmb12BBW94LdLNxwGug/YgdBx9QgdV4/uA3aYjj1Q3x/xTwcgL1cRdvNyGeKfDkC3rsy3pmpmoq5w6uqFXb/cuoLht8yGFMlV5m9pSIpiUPalfjC/+tUZCbFXEYD/7/xJiLfEUHvFAkdFesHS8WEjd27B0vFM3yQt3tgCQHHm6pulOUGOt7TNn46Y6zPdjx8iHo5pz3UQRBaQVfVzO/TLtIZzHkQO7z4A5l2NCCI7WAZgjIhfMMOPRmprJvwNn6/Z62tTNrd5vdomaVmLJsaBt7YqQm00ahrD4CSeIV2smrD1mlWOvVOnQT7UoEYwAIoQ62fTtN1TJpu6oZXIAg65qTFpv2iu9p3zf4Qv31uJ7fNK4wNLKh03SrNi+7xSiD8dXsXcbfe3Cfa+iLm6xmiDiiuxdU6hzUnGaJqijWGMvZJwUxEdDZ51fWMv45x/oXn+T8bYlwB+D+A8ACs0x3Zxztc4mPMTh+OyHqpHic5IomkaB6C6dI3EXT+aqH24ZKE6t19N2dxm9Q66pwJb5nn725wpZm8Yrbpr524ejSmnLEf3ATssXbv6Ywfq+yN0XL1va7JrlAYoURaxpgbVretXwzQAOOUXc7Dx6SmINzcaHjdrJiaE32jeUTih97n4ZOfbvprbtGJuumSTmAsAg84fqzZZS/yOWEb1KJERqH62pL0cuosYY3HG2D7G2HOMsRP9nJwphBljRzPGbgPwQwAL/LwGQfiN/pY3Mz5fs9eTi3fNhL/hpVG/wUujfqNGO3idyw+WX/ZUu1zXDSLGQTQI67OgGry11VVWbxAN06zm1EcwAMHk62qjGPpWVavuXbnpcIqYC7Rl7zIphNxjTmiQW5p9E3PBJAwpug9DJ3kXYuXmJj+Wo6JvjJYOJ147Dvn92pwR5NQl/EBXjAr+nfh6QibX0o5QPUoQqaguXTOnrte83Q+XLMSWeYXYMq8wae6O6Ag24/1nihHN96XEURHuWiGiNjZxW7du0ETzQ4bRC4IR15cHFsMAKE5dbRTDqXfUJF3Pjm98s7cv6wDaYhbC0TxfxFy/KLhuPuTGlqR96eToaly6ADl1iU5KttXPmRZ0vwJQDWAsgAsAVEDJK1vNGDvGx+v8EkArgP8BeBjAvZzzZ6xOYIzdxhh7N7Hd5uNaCMIxOlE3xjln2k0ccyvECkeuEe2V29tywPhTda+4zbx1jCQpblRZVq4hy+DNzY6vp49i8CNXN2lOg7lEBIM2hgFQhN2g6D2zPCmSAUBK9q6g+/Az8yFJ2D6v1PM9idvnlWLbrMJEXq7HiAUDgoxakCJRz43RTrw2yblERXRHhGd488b3E1/16kolYyzGGPuKMfYSY+xkk/PvZIw1M8YOM8ZWMMZGeV5JsGRlPUq1KJENGBgMYka1KOBeiDUYf8Q1WJLjHOVrL/J1ztkbRiMcVjJy83IZYjFF5D14yPkve5Gvq91E1q4XRPTCu08XmQq7mYphEJycyNsVkQwAVLFXT/dv9wdCEmqvWJDW3Ri1VywA4sxzxIKW1a/OSOt8QwxydKVI2JOoO+j8sVnnHCY6AVQ/W5LRyAXO+ToA6zS7/skYexvAWiiNKaYanuieP0HJKfsWlGYUDzHG4pzzxy3W9lsAv/Xp+gThGavbVxK3wr2CRDTDmgl/s41M0Im5Mc55jngibqsT8xgJv8ec1QsAfI9oEOjdpN4nksBycuzHuaRPzXwAimAscnW1j50gYhK056eLiHfYM3GyMp8kqWvV0mtWedI190yf5kv8guGaHM77zdPOwb7aN+XWr770bGGRW5rTcuS2B4OKK1FXVYqtc4sxZGKV/QnmHHFvgIlA+BZjTJsJ+9tErWMIY+wEAOUA/qHJkm0G8DiUJihfABgMJTNsFWPsTM65tnB9FsDfAPwHQC8AEwCsYIxdxDl/y6fvyReytR6lWpTIFuxqUaCthvxwyUJHcQlaN6+2d4TRPNo4BqAtkkHEIwwtyc6//0G4cwWzNyhxCyIX1607V8QuxD8doO5LN4ZBCNfTznwd7z5dBCnEcNpNqfWNWQyDkdDqB6f8wjwCbfWrMzDykhkIh3MxcPhPsH3dnzzfz62IuRLOHu1PXR1r9b8xb8F189WoBMGI54vx/hhvMXF1bz7hx7IIIpvpUPWzK4cuY0xijA1jjH2fMeaLCsM5fx9K4O//+TFfYs4vOOfvcs6Xc87vAvAHAFWMMf/VHoLIMFrnhBN3rZmYm0CdRxvFoD8/iIiGbG+KloIktTU8i7qPQNQKwEaRCV5F3t5z56DPgupkF7CBa1fbMK29qHugzZFxwo9+1l6RP+3KoOJKSOEcz05dADD4f0x0ANqhS+//OOdnaDarYrQrgKVQPiy4ReznnH/KOb+Dc/5XzvlKzvlCAN+D4mGYop2Dc/7/OOd/Sox7FsC5UIrTWX6/lkFA9ShBuEaNZrCLXtAJtPoPJdV5jOIYtMe083VGZm8YrcYtAKlZuXZoxVxAce1qXbpTTvHm2i1fexHmbh6tOnbFZtQ4rb0QLmGtaHrMd08DU1y6aXSx8z9u3U8XbO3iCZDyIin7pbwIahcVuXbqHviP6f9jgggEqp+tcfyGmjH2SwCfAdgAJeh3UGL/i4yxXzmdx2x6BPHbsI13AXQF8J0Ar0EQGUPrnNAKrNqcXLFpzkl5A2mS26veWmdwzLeIhtEv+9voW+TdBkWfmvlqjIFokLZ7fJGrPF2BGpmQEF7Vr2lEMghhN0nc9Tm71ysiikHk6AJAuOs30p5XjmVvLbl1bjGkiLHwP6i4EpAdNVZW+fDPnfPNKxE8jLFcAC8B6Avgh5zzj63Gc84/AvAv2AifnPODAF6xG5dlUD1KEA7R1pBaUVebk2sg0KaYCyx6SMTMatFszdw97aYqtDbJmHbm64FdY/aG0Zi7uU3YFZuXPN0D9f2hLaViMSAvl3mOYxDCrthycqUkUXf9H9t6OQTlzjVD25Bt1TJlHYwx5OR0i8UbWzzZqguWjkfcp34JgRGXMeL5VBPBiOeLcdqyyUDcXT0qIHMB0dnJlvrZkaDLGBsH4AEALwL4KZSCV7ASwNVO5jGZ+wwAAwHUep3DAd8HcAjA5wFegyAyTZK71sxhm8BU+TLI7c3RHtPlpgXC3qn+NOsSLlovIqsbjMRdt9fU5uCynBx1PlWQTUOMNXLtOnVC7yqZgF3FRcpW4o8grI1iEC7dSI9vgeVElKZmHpAiUWx/0J+fm7oH/c1y2zq3GFI4RxFuTZAiUWytLHTs1G3YGdybV8bYK4wxTs3WOh8Jp+jzAM4EMIZz/oHTU+FM+AxaIPUNqkcJwj16UdfIXasbbygCmfSQyBHHjGpRv126WrExHTIh6gJtwq7YGpu4J1FXm63brSvDgfr+iH86IC1hV6DN2X336SLEmpzVousXFmHdY4VY91gh1i/0r0eGcOnGY22NcIeeeVM44dL1pmxmKbWLJ6B2UZGhO1dLOk3S/IbqUaKjkE31s1OHbiGAas75bQBe0B3bhoRb13ZVjC1ijM1ijF3FGLuAMVYEYDmATwA8pBl3CWPsGgAiDPj7jLFrGGOX6OaLMcae1Dy/nTH2NGPshkQsxFWMscUArgEwi3Oe3OaRIDowLhwNRlELKXNpi+dM4XfsghBaeWtr4KJuyjU9uIOF8CrycLX7/XDZivn1WAq1soy+86vRd74iCPsl7hq5dAf/eg7kWCu2zyt1LfgMLKmE3NKEg/Wb0loXAMQP+/Pzt3VuMbZWFtqKuYDi0h0yucaxUze/3xD7Qd4RTdbGUBEdIFnW1IExJgFYBOBCAFdwztc4+TYYYycCOAc2widj7BsALrUb1x5QPUoQ/uHibi/L22pc1KKqgOwHobzErfgOxUYnCFF34knLAxd2BSKOwUu2bvzTAYh/OiApwkEr7KYr6s7dPDppX06XsK1Qm9MljDvfuwHhaEgVd9MVeLUu3dXLlQZm3br3xDHHnxaLN7Z4jgL7cPsKyPFWz+vSrscPahdPgBQJ47Rlkw3duVrSaZLmJ6JHTOIpNf8l2qD62Xpezu1XzRhrgqI8r2CMhaB07D2Dc/4+Y+w8AMs557lWcyTmKQVwPZSw3y5QIhxeBTCdc/6pZtyexBg9eznnvTXjOIDfc85vTjw/G0oji1MBfBNKV+GtAO7nnNObVIJIE9G4wklztOWXPYWWA40AFOHWLGJBxEK4aTLmhN2FE8ByclLE0qAI6np7Jk4Gb201bHjmFJHP23d+oinb1MmKAC1J6Dsved5dJcr30WdW8vexe6qyDv14N+wqVtYhRaIYXKgInv9b/Q98/s9lGDzV/a132+eVcjnWyvKO73W4z433dHFyTt0DUxFvNG46MaQ0vdv/tlYWKiKtC+qqSiHHWh01SdtaqeYHLrNqWGOEplBOOldXQHuen7Am97s9ec9fFtoP9JEdkwvf45yfYXacMfYogDsAzIbSjEHLx5zzjxlj1VA+/F8NpanDIAClAI4CUMA5r0vMVZw49ibamjqIfRdyzlf6+b2lC9WjBNGxEfWoaJZmRt1DZUkfJIfy8jHonoqUcSKf94xb/K1FAeDdp4tSBM0gmXjS8pSs3HTpPmAHGps4Zm/w/n1MPEkRhe987wYAwBOj/oTWw4rGz0IMI8Ylv/brHitUx2p59PRFacU1bPxdGeIJ8X7U5fMAALHWRqx+bSZCkXC8YOl4V/ELtVcskOONLRILSTjl7LvQrceJtuesXj4TsRbzDxDSydAVYq6dkKvn/TFzHF1X02CNalEicKh+tq+fnQq6HwOYxjl/ykDQvR1ACee8n+1EBEF0aLR/cO1EXW1+rxatuKsVfaWu+eg1y99mCRkXdccX+S5MAwlRNyHAuhV2906dpjqghaArUIVdHSwaTRF0TccbiMJm7Jk+TW3KNnSSUow37KnH3sWPehJ0AWD7PEUQHVpiL4gCwJb7zIuCdARdJzELpufOKXR07Q//vFCNXnAagWJUJHOD7uKQJN6l/yB2eLu7+Qln5J7Qk5+Y4YK0foptQboHxmIlAMzknM9gjP0CwJ0A+gPoBkWYXJE4XqeZ6zIAk6AUoEcB+BrAO1DcqGt9+HYIgiBUtH/bhpaY//3UNlLTI8Rdvegbzs33tXnX+88UQ45zRPNDKF97kW/zmjHllOXIy2WuG6bZ0X3ADhw8xBEOw5Owqxd0tWjFXT368UZjjQRhK9Y9pvxchCP5GDl6OgBgzd8rDrU2H+x69msljufRUnvFAiDOcPbo1A8M9Kx8yfwa7SHmAsD6q6sgt8RQcJ11TV/35hNqYzQ3taJBPaoKtmotyiTe/TsD2YHPtqWMITonVD/bE3YyCMDLAKYxxt4CIEI6OWPsWwB+DSVblyCIIxzO+aXiD/Lna/ZizYS/JYm6QsS9fOUv1X1S1/ykSIWWA42GYq98qAF7p07zVdTtUzMfuwsnKDm10WjGhF2/EeveM3Gy4rZ1IexaxVkYibZWmIm8u0omOBJ1e88sV126vsEYpLB1PpgRQkAVrlevYu7WucWALEOKRD2JuUAiT3dusa1L98Rrx2lduk5JuW2NMfaK5v8yAGBARRUDgPopmS2aiPZD6zC1GPMUgKccjHsZSq1IEAQROIm/YRxQsnS1Ll0h4mqF3lPursGmJ5OF23hjg6Hg62f0AqDELwCKsDvtzNcDF3Vnbxitiqd+IgTi7gN2qPM7FXfLR62wPD525U8dr8No7BOj/oT1C4sci7qh3HzEmxosXbJuqL1iAeKNLQhFbW9YBgCEc7og1qrcLVZwQzXeWzINseb01pKOmAso0Qvvj7F/XzDo/LFal64b9PVoSn169tXzGABs+dcTSIi6FL1AZB3ZVj87zYqZCqAZwCYA/4CSLvEglNvH4gD8tdURBJG1aLPStA3ZtCKt9nHPqhno9dh89HpsPqSu+YZziv1C1PWTdDJus42kZmcOsnW1r6XenesXfWbNcZwDq2Xb/VMAANFvHwsWCnvK0QUAubkJgwvdC/V1908FoAi56Yi5UjgHQybXpIi5dVWl2DqnEHVV9k3fBhVXenoN3TBgdg26DFRzeMckOSUkKeW1pyzdAMiyDDCCIIgOTlIzNrEJ9GLtsFsrcMrdNTjl7ho1N1fLKXe31QLvPl3kW5M0wWk3VaG5Ie7rnO2BNnM3ZpmKrFA+agUavgw2Nnzsyp8iHA05ztcdbpCle0Lfc/K8Xj/e2IJzL5uLs3/oTBIZecmMpOenX1OOghuqPbtzzcTc9VdX4f0xc/D+mDlYf7WzO9mCQFtTnn1NVdJ+o3pz6LljM7QyokNA9bMljgRdzvk+AGcAqASQA2AnFHfvwwBGcs6/CmyFBEFkHV5vfxHirkAIvT2rZgQq6gIAJCn4RmmSpDQyS2x7Jk5WNz9x2jTNr2ZzdrBo1HHTNNEcTW5uRN0DZQjnd8OxF18FuaU5I7f4izdxcpNxjq5TrCIW1Fzc0hrIsVbHwm7QnPDzcUmirngg3LkADI8TBEEQRLaRqEW/8HLusFsrkkRdIeZq9/nt1AUAKcQCb5Qm3LOh4+oROq5ebZIWOq7e92t166p8P1ZN07RirlHcgl+MXflT8LhzNUaKKG5a4dI9vs+5IYQk1F6xwLXqHsqLYPXfp7s9DQDw7l+mejoPUITc2kVFpmKu3BJThWK5JWYr6kp5kaCaoyk1JVMMBN2PHazdn3RMDxkMCMIax90cOecHOecVnPNzOecDOecjOeczOedfB7lAgiCyGyHKmm1W52gJWtTtUzMfvLU1UFFXuIHFxpublS2A6wq3rpmzUzRCA4Jz5wr6zFIEZieibu+Z5ZC6KP3LxK2PXfsOsTrFlljDQcdjB93b5srYvqDM+0Vl2VrMTcQnDJlYpQi7Lf46xJ0UuEZjTvi5rnmMzp2rPU5FtM+Qw4AgCMJvvg0AYBIX7lujzQitY1e/TxCES/eMW6oDderO3Tw6aWts4qqYK8RdvxBu3bxcZinqAsGKuYKcLmHHLt1TftF2d9fq5TMRCkdwVPfeX8UbW1w1RQOAgqXjEW9pcnVOOCdRC7c0ur1cG3EZpy2bbC7mavJwC66bbyvqjni+GIg7v2OMMcbtasUkd24iUmHouWO1oi7AJC6OCbSiL9WjnRyqny1xLOgSBEEIgvrDmhFRN5PRC5Kk5t0GdV0Wjdo6dTOBm+iF3uX2DSOcIkWiqH9stqdztVl6Ttk6txhbKwshRaJJ+0XEglbMTV6oZOnSFTm6duT3S4pN4Lrtc+1zmEQqqC5cSeJad27KcSqiCYIgiA7AKb9M/VuWDsKpG2tqCCR+IZOIjFsh7vot6gKKsJuXa+3WffoHS3y/rh63Ll2BX1m6TprNC/SxC26pXTwBUl5qDwmr5mYF182H3Jh+/EX345OMGPp61LgW1TlwtbEKejFXHNc5eQmCMMBU0GWMrXCxvZHJRRME0e6YZm+mS+DxC5mIXkjQp2Z+UvMyEcPgJ2a5un0WtLly98zIzBsRFo1iV3GRsiXcurtKJtg6d5v3/dfzNQeWVIK3BpvNlkRCtB5UXKmKuKqQW1pj2thsyMQqS5eu0xzdE68dpxV19Xw7ZY+BaHvCz8dhwOwaGIm54jhFLxAEQRDZTJAfOOojGfyOX5BCLNDYBT1zNyc3LtNGMfiFmVs3/5uK6Ni0PzOGipwuYax7rBDrHitU3brrFxZZOnfDkXxwLuPQgY+7er2uEruQQeE/LqvOXG1WrpmYqxKS0s7THXT+WBTcUK0XdgWptaiBAxdQ8nS1mbp6tKIvGQwIwhgrh64EgGm2wQDOA9AbQF7i63kABiWOEwTRCdD+Qe31yNxA/u/rRV0/yUT0giFSsDdEqLm6GlFXfQ0bMpOl22fWHPSdX61EPMiyIuTaiJRb7itE48e7075242cfuz7HUzM0SVLduXJLszqHmZCrP9ePLN0Trx2nNnMzauo2YHZN22Yi2tpB0Qv+w3hmN4IgiCMcy+zNdNHHL/jJaTdVobVJzqioq6Vb1+Deuh+o75/ULG3aygvUx4+eviiw6wrGrvwp7nzvBtz53g1qozQe55bOXTneilhrE+KIhUJ5EU95GErsQuYbMAtHLgAlL9dKzIV/Ll2gTdgVm1bgFWLt2ddUGTpwnULRCwTVz9aYKgyc8/M45+dzzs8H8ACAVgBncc77JvJz+wIYmdj/QGaWSxBEFhCYO1dLz6oZ6uMg83SDaFpmdk1IEnhzs3q9PRMnJzVQSxcjUbe9YNEoWE4OAKDvPOvi8qiT/y+ta0mRKPY8+5CrW90AoO7+Ka6vNWRildroTIj0ToVhO5dutkHRCwRBEEQ2ov2b5HfcghlB5OkKUXfama8H3iwNUOIXGps4Dh5qq5e6D9iR0kAtXbTN0spHrfBlTi+MXflThKMh5HQJW46T4y3IiXQB88GjJsdj9oP8ICQlOXILbvCnX0Y6jdEGnT/WfpBLKHqBIKxxahmrAFDGOV+r3ck5rwUwA8Asn9dFEESWE5Q7V0tQLl2gTdQVW6ZE3T4LqsFbW7F7fBF4a2tSAzU/UEXd8UWBvG5O6TNrjtIsTRvDUJx6u9vQSTWIHfwqrWsNLKkEkyRsq5oI2UH8QqhL4ueqyVsjCtHozJEr1wVOc3SNEN+Tn1D0AkEQBJGlBOrO1aLN0w1K1G1tknHGLdUZce3O3pDcLK2xiSc1UPND1NXGLzR82VaXZaIxmp6xK3+KsSt/qkYxGEUvKJELHCEpGvPSFE0QyovgneVTceir/7g6770l7n+uhIhr58g1xCJ2wW1jtExA0QsEYY5TQXcAgC9Mjn0OoL8/yyEIIpvJ9B9RrUtXOFn1WzruXZFxm+kYBiHsavN1/cz27T13jiqGA1AiENoJEcMAAH2rUtcRb2rEl++tTPs6A0sqwUIhbH94hu3YQb9qa8pWd//UtK/tF05zdI0YeK9/jea0UPQCQRAEka1kwp2rzdMVTdL0WzpC72k3VeG0m6rUx80Nnu74d40QdkXTNLFP695Nl9xo2z9P2NokGzgiisEseuGrfbsQk5vCobyIZ4ttwdLxCEXC2PDOw47GhyNdAACx5swaMPyMXcgU5NIlCGOcCrq7Adxucux2AHt8WQ1BENlORuIWtGiFSSPkQw2+iLztlq0b0PW17txdE4qUbWL7RTGoTl1dg7S6B6fh67qNkCJRf+wAzNl7O/HmTG467Mtlj2TIpesTPMMbQRAE4Qv6Jml6jIReryJvphum6QmH4Vv0whf72kq7vFyGR09fhIUFwefoWpHTJay6dEPRhKDa0oAPahciFAnzgqXj05aemeRsipGjZ6iP3/tLWbqXPaIhl24nhupnS5wKujMBXMYY28QYm8EYuzPxdROAS6HELhAE0UnIGzowY40Qe1bNQK/H5htuZmKvfKih44q6PkUvCPrPqVE3lpPTbqKucOqynJxkUVeOA7KMgSWVaXeNk1ua8d3Lb3Q0dpDG0VpXnX6jsiMZnUuXUyFNEARBdDZEkzT9Zib0CpHXrbDb3g3T/HTpfvvottJOG8PQnqKuyNVdv7AITGsCiMsoWDrel/c3stzqeGw4knB/txz2FL0QBOnk6AaJrkEa1aMEAYeCLud8MYAfAvgKQCmA3yS+HgDwQ875n4JaIEEQWcUyAGjctK291wFAEXu1oq5W5O2oom6Q9JteqQiqwrHbDq7dPrPmpMQKSJGoL/cXSpEo/3DJk47Hq1m6Lc3YWlmobtsfCMYlsXVuMaRI1HKMFImq68gU9VMKla3MPL9X49IFqJB2T4Y79HbELr0EQRBu2fXywvZegir0CvQib6zJ/e307S3q+uXS/WxTv5R9B+r7J7l1FxYsyrhzV4i62n+bUF7ElzvFCpaOh5QTwqrlzmrJkaOnI5yTB0CJXqhdVITaRUWBiru1iydAyouYHh/xfDGkSDgtUXfVEvc9IVa9UIJVS4qx6oUSw+O6BmkANe098qH62Rbmtis4Y0wC8C0A/+OcZ1diNkEQgcOY8qsub9hgHHP3re29HEM+Kp5h2hBM6pqPXrPKbefYXTgBLCcHvefO8Xt5tteFLINFo2lde/d45XYyKT8ffaek5qvumFyI/nNqsGNyYcYzdndNSG5IMXhqjclI92ybVYgBd01Dzje6Oxpf90AZ4o3GPyuhLvm+ZtNurSzEkMn23+vWOYUYUur8Ndn+QBnih9u+hwGz3b2e9WXFYDk5AADe2ooBFebN3j75/UIc3r5Vfc45z5hbvyOTe3xP3vv2zIn0AFA3o/A9zvkZGb0oQRBEBkiIOGMAJImp2caGh81/74dz8zHieut69P1niiHHOaL5IZSvvcjv5Vky5ZTlyMtlOFCfXquc0HH1AID4pwNSjmlF48YmjnG1mWua9vQPlqBpf9tdcWe/ZiwiemXVxfMw6vJ5jsevfMn4+uFoPk6/xv59ixtqFxXhtGXWxpX1V1dBbok5brr23pJpKVnAZ1/jvHmwEIAHPT8T9TfOhtwSw9k/Nn/9tvzrCRz4TDUYLeOcX+r4YkSHgepne1zf4so5lznnn5OYSxCdFtWl+/nDzt2QmUTv3NVilLlrtEld8trFqSsapqV7bdWp3GAsVvafo7wBYtFou8UwDJ5a46uYCygO1/rHZsPph5Xa6IUhpTWqaxcA4ocbAnXs+oFezPWELKNvxRz0rZgDlpNj6dQ94efjkgRjI2cEY+wVcvASBEEQQaEVb7LBpWuG29xd/SbldMEZt1S3i1t39obRaGzivuXpGnGgvn/agrFXghRzBYcP/tf1Of93UzXCUY3D22fXrp07VyA3tjgWc4HUxm5uxFz9+AHPTgHi1lKTzq2b0uNBU4tSPUoc0Thy6DLG7H57cM55MC22CYLIOrTOiF6POf9jb8beuyam3IYPSUKvR+amNa/WqdvrsfmWzl07+izIrIsVaHPrCty6doVLV4i3ZuycWQre2oq+c739W+6amFinJDmaQzh0Q13yMaAwvT8d22Ypn9pqheHt80oht7QV6qG8Lhh076yUc+seTBZDta5YO6HUi3t369xiQJYhRaIYVFxpPs6FQ1cbzeDWmSuon1KIfvPafr53lhTZzqV36lpArgkkHAa3ZdhhMLNjOQwIgiDcoK1Fu/Uagr6XjbM5w5qNjxaD60QkFpIw/E53wpQRm55suxvolLtrkp67ob2curFY2/NuXd25dq0culq6D9iRlkt3YcEixGJKXISTOR49XYl4CB+VhzP/co+nawrW/uQhxL5qTJqr9ooFiDe2JI0LR/IxcvT0lPNXvzoDsValSe//3dRWj637U6rrNWk+D+7d2sUTVLFUyotgxPPGH+K7cehq3bluhVzBqhdKIEXCipgLOHLpAq6iHage7YBQ/WyP0y6OMyyOCUWYBF2C6CRwzi8V0Qu+IMspgumeiZOx9w4D56gLobdn1QzL52YYCb+7xxdlXNTtU5NcRO0unIA9Eyc7EnU/++0Tjq/Tb3olds4sxa6JE7yJurKM/nNqlDlEnIKFuCvl50NuaEjbWVpfU5b0WIjDLBwGNIJuvPEw6h6YmiLqWl1fiLVmwq6XtQ+ZqBS5W+cWo66q1FTUlSJRbJ1brI53glcx1wgWjaK+rNgyeuGEn49zKuqmuCYIgiAIIl0StegrAMYc3OvoA0br+eIybqr9RdK+587/g2Fsgluhd9itFZbPjTASfZsb4ph25usZFXVnbxid9HzKKcsROq7etbBrx4H6/ug+YAcWFizyJOrGYopo3H3Ajjax1kLcze0RRdP+ZsS+akxr3QDUObRzFSwdj1UXJ4uRsZbU2lEr5uo59adtYq2RuGsl9pqhFWhrFxWZjhvxfDHeH+PMROJlHSnEZVXMBRSXbt3VqeK3nu7HDtZGLyTDJN79OwNZ4jjVo8QRiesMXfVExnoAuAxAEYArOee7/VwYQRDZjZmg6yVbd+8dExyLpXsmTgZvbhPr/HDyJq1FuIUT837+8JOGTeCcZvH6jV3G7t6p01LEaDuHrsBLnu6uiUrWcL/pyeKk3VzpunTra1KFVjGXcO0CgJSbB7nJulh36oY1E3c9uXVt8nSdunSFQzcdQVfv0AWcuXSdzJug07sico/vyXuPy7DDoLxjOQwIgiDconXpavHi2N3wcGGKoGvGc+f/AbHDrW3r8MnJKxBuYTHvrpcXwky0zv9mBNNWXuDbtZ1il7F77LCd+GJfm+PZzqErCB1XjzvfcyfoLixYZLgWO9evHy5d4c7VEj4q0eQssf//bqrGv58xF08FWneuGWauXU9uXZssXacuXSEMe3XnAorTdtDzM5P2OXXpOpkboL4PHRGqn+1x6tBNgXO+H8AzjLGjAfwG9KkHQRBQsnX33jEhsKZpehHTDyevYO9dE9VGaKbzJhBZvJkWdoVr18yt6zVSAkjk6eoalkGSlIzVhDi7a0JR22MTMTdpLhOnbrouXe15oS75iB9W5tKKuUIQrVswFXKjsfvBDWau3bQzbA3w4tL1Qn1ZMVg0mrLfiUvXji4DhwgHL9UHBEEQRBAY/n05uHcrNjxc6ErYZSEJz53/B/zszf9nO1Y/xszJK+Z1I/ZufLQYoWgIP3vzZst5BQ1ftqB81IqMi7qzN4w2devqxVw3dOvKVKFVEA4rDlwh9C4sUI6Pq73BVMwF7F2/6bp0jcRcAIb7wtF8SyerEzEXaHPt6oXdWHMD3lsyzZ2oG5Kw/uoq09gFNy5dAFj78nScedlM+4E6Vr1QYpjrO+DZKai/cTZWvVCStqhLEEcqnh266gSMXQDgJc55V3+WRBBER0A4dLWfduqdEk5FXeGKdZsRa4Xq5HUg7Lq9vt4F66eoa+SwNULqmg/5UIPqbNaf12dBteMMXSt2zixNdkQDqki7a0KR53zePTOmqQ3bnDRG2z6vFAAwsKQyyZ0rRFtD96wkBS6IajNs3Th1t84thhTOSTtLV1xfys9Hv8kenM4G7lzBrrLJ4K2taYm6wqXb2V0Recf35L3HZtZhsK2iYzkMCIIg3KLeLSZJB3k8/o3EvqRa1I2o2yam2ou6Tnnu/D8g3hy3FXWFKzfcJcfx9f/0w+fQfKBJfe6XW7d81Ao0fNliO05cb+JJyxH/dIClkOvUoWtE9wE7cPBQm2bRratSUjQ2cTVqwe58M6euEI+dNEYTubihvEhKpMJ1q5WfscUjU5v0ORVrvaIVd906de1cuu+PmYOCG6zXn26Grj4/V0/d1dPTdv8m6PR3jHU0qH62xw9BtwbAjznnffxZEkEQHQXGGDcSarTFtBunrtYh6xcpEQ0GeBWSzcRXrwKvUzHXCULwBdITdI0QIi0AVQg3cunqx2tFXa0T2E7QVV23kgQpnJPU8MyskdmQ0hol1sBhnIJX9EKyK1HXh9gF7fW9RCTUlxWD5eSgb4Xxz3+6oi7FLihQQUoQBBEMTmpRwLmw60VYtUMf0WCE1+vpRV2BV3HXqZjrhNZP+yHnuJ0A0hN0zQgdV6+6d+0yfY1E3ad/sARN+5Wa0omgu+rieRh1+Tz865VJkCJhtemZEHMB4IVL/pD07xHukoN4cxxn3JB+E2krvIq6fgi6Yh4gjaZoBpELgnSjF7b86wltzm6nrkc7GlQ/2+NI0GWMPWWwOwJgGICTAUznnKe2ECcIotOSjqgLWddl2Efnrt+4FWHNxF4jh60ekZ9rhThPuHMB/wVdINm5ayfoquM1oq4QdJ24cwHFoSu3NEOKRMHC4RSHrhF11aWQY62Bu3SBZGHVqaibLTm6Vi5dIL08XV3jtE5bROcd35P3vjXDBemsjlWQEgRB+I1e1D3lbud/y4SwK/BT4A0CM2HXCCuxd+JJy9XHRpnCz53zFGIx6/lbP+2nPg5S0NU6d500adOLutpoBzcOXQAI5UUMBV0jllz4u6wVdbMlR1e4aDMh6nb2u8Y6ElQ/2+M0Q/cCAHrltwnAXgALAPzexzURBHEEoO0+LHJ1tZiJvEbxCHvvmmiYF5sN6MVZO4FXZO9aYdogTpbVY0bOY6PzpPx8y2t5xU7ANRq/Y7L3P8gDS5Kvp83KNWNQUSXqqkszkkU78N4KVdR1kqm7dW4xpEhqdq2WTOXo2pFOnu4JPx+nFXXHMMZe6ayiLkEQBJFZxN8bEc2gz6O1cu3qIxI2PlrsOGO3Pfjp33+mPrYTdxu+bEkSbo0waxAXi7UJtt8csCspCiEcBvJyM6eV2Qm4RuONMnVFEzM7CpaOT3qujVyw4po3bjaMYvCbU39aroq6njJ1DchUji5gLuYCSp5u3dXTPc0LAEPPHauKxlSLEkcSjgRdznnvgNdBEMQRiFbU1R/Tirx2Dt5ej8zF3rsmpgih2ejctYpacOLmVR22Jm5c8RqwaBQsGk1yyRohcmqzARaNYtfECUnRC/U1ZRhQ6D771SmDiiqTcm6DZOC9Feq1tj9QZu3SlWXL/FwAGFRciboq54L0zjllnnJ07ehbMQc7S4pQP6XQk1OXRF2CIAiinVkGg1pUNE8D7CMZht9ZhY2PFuOZgtQbV7PNvasVd/W4cfKauXGF67ZbV4ZuXZkq6ublMnxZ39f9gjPIgfr+CB1XDyD9pmhuCHfJwbuLJgTu0tWLulbULp5g2IxMj5QXURy4IcnUqSuavtld05SQhPobZ5vm6Ip1rFpSDIQkT07d7scOFi5dqkWJIwZHgi5j7CYAr3DO9xkc+yaAH3HOn/F7cQRBdHyM/ljqRd7GTdv0Q1Iwc+4auV2zUegFzMVeQ6E3kU2runAlCX1q2oooM8HXyLm7Y3Kho1iEoOk3vRI7Z5Zi18QJkPLzITe0uVm3zSp0FL+gbYjmlEw6XUNd8h27dP2+ZpDivfhZrJ9SCEiSa7euXtQNZJHZDAdYei0LCIIgCI84qUUP7t2KXS8vtBV1jTASerNN5BWYib1aoVe4c0U2rRBsw2HgZ++0OXet4hf07t3QcfWOYhEyQbeuTHXpamMXVl08z1H0wtqfPORaBM6USxdQRN1/P2N9NyAAIC5jxPPFtsNGPF+sRi+Ycfo15WrsghfO/vE8bfMyQ4RLV4qEseqFEtei7tBzx2qjFzpfLdoRofrZFqcZunEAIznnaw2OnQ5gLec8FMD6CII4wlE7FCdwk7drRhDN1TKNEKpN4xf04zUCb58F1UlCd9/51dg1cYKjBmaZYMfkQmVNojGaJAGybCvoGom5rvJqA26Qpr0WYJ3vK8ZIkailU7euylkOcDo5unYZunrSydTtrE3S8o7ryftkOANs6+yOlQFGEATRXhjdTea0iZoZQTRXyzRCpDaLX9AjBF4hAt9U+wt1jjvfuwELCxY5amCWCUSerhCkz36txJGgayTmRrvn4sev2v8bZypLF4Aq6Fpl6dYuKlIdunbCrpMs3XRydFe9UAIpErZ06Gqpu3p62nm96GS1aEeE6md7JIfjrMJw8gHYRKMTBEGYskz7REQxfP7wk54n7PXIXPDWVuweX6RueyaaB/5nE7sLJ7SJsZLTX9FAn5r5luJv37nz0Xd+NXhrK3ZMLsSOyYXYObNUPb5zZqm6X3/Mb1g02ibmAilO4/qaMmybVYj6mrKk/VoxN9QlP2WfFcKlm0m2P1BmeXxIaQ3klmbLMYOKK20b4WnZOcf6mn4gMnW90GXgEPGw8zkjeIY3giAIwhEJUSepHhWOXa8Mv7MKp9xdg3hzHM8UPIVnCp7Cc+f/Id2lZoTnznlKFWLDTjvuQHHv3lT7CzQ2cYTDiutXy7jaG3DnezegsYkjdFy9unUfsAOAIrIa7Q+CA/X9k/J+145JdXuuunheSk6uVszt0kMRQ5sPNOGFS+z/ba954+akRntBEo4qdbJlBEJIgtwSg9wSw/qrrcXREc8XAw7XvvZlD1m3cdmxmAsk4hdesHdTG9H92MHiYeerRTsiVD9bYvormjE2AsBpml2XMcaG6YblAbgOQL3/SyMIojOg/WRU65AwaqRmhd7Zq49oEPEM2RrHoCLLKfEKbuizoBp7p05Tn/ednyzyavNrhai6c2YpeGtr0thdEydg58zSQNy8/aZXYtfsMjUiQIpEIbc0pzhwtY+14q5wvrrJxs1klq6T2AXVvStJqKsqtc3TdXrNTGQmi0xdL5zw83GqS5fyywiCIIhswage1WbsOsHI1auNaRDRDNnu2hUu2sYmnhSx4BRxjlHeMICkhmQLC5TIA+GYvfO95GPdB+wIzM2bG22Lk9BGR+hduGt/8hDO/Ms9WPuTh9R9UzdeCQCo+f4yHN7f4jiXOJNZunaxC1q3bTpxCQJtjm46zdGckE6TNGqQRhxJWNm/rgDwRGLjAKZonovtIQCDAXQM6xtBEFkN5/xSzjmDziXhBCEAm7l7ez0yF70em5/k3M02164as+BRzBXYNV5TkSTsmFyoiLlzk6/Zd67yWvnh1N01uww7Jhdi1+wy9blWeBxUpIiZRgLotlmF2Dar0FIctXPCCpy6dNMVfp1EQAiGTKyydek6Wbf2mm5cupoIBEfsKpusiLku3OMWdC5nBDkMCIIgOgRGjl0nCAF4w8OFhu7ejuTa9SrmGqEVaY04eIijsYknCb2AIvw2NnFfnLrHDtuJ0HH1OHbYTnXfF/vaHKdduypu3VUXz0uJVIh91Wi43wtOXbrvLpqAdxc5N7akTUiydenacfo15c6cwT5Qf+NsIOS9FiWXbgeC6mdLrG6iWADgd1DiFnYBuArAOt2YZgD/5U6CeAmCIBzi9pNSoyZrwt1r5dx14wDOBE7zcu2QuuY7EnX1Iq7RcT+cukK81X8FFKeqXpAV+4xEXOFs1Z7jNHbBjUtXHSeES6PYA0nyrdHa1jmFGDLZOJd2UHElts6xX7cXl+6A2TWuRV0vTdFMoLgmgiAIIitJtxbVunv1zl2ta9eNAzhTOM3M9QO9iGt03A+nrhBvxVetsHvoPycCALoe/2HSOVM3Xqk6cPX7BfpjTnDi0hX5y/9+pggsJOGMG+bj3UUTUsRgcSxdCq6bb+vSlfIiqF08wTJHV9scLUiXrtzY4jlDlyCOJEw/1uCcf8U538s53wOgD5TQ6L267TMScwmCaG+Es9fI3du4aVs7rar96DXLuPmBF/rOnQ/ebO0gNUM4c7VonwtxVivIin0D763AkNIaNStXe0x/jhucunSHlNZgSGkNpHAOpHCO+lzdH4l6ur7p9UzEXDd4cenWlxWDRZ1/L30r5rjK9LXhNb8m6ggwntmNIAiCyBxWtWi6ebwdnad/sCSt88fV3qBGI3hBK94CQOi4+iR3LgBc/f8+Vx9P3XilKtoW/nOMmpUrjglqvu/axA3AuUv3mjduxnWrxyEUDeHfzxQhFA3hutXjkrZMZfICznN03bp0V71QojZnyxQHPut87w87KlQ/W+PIp54Qb91//EQQBJFhdAW1KXvvmuhKyOrUSJKn6AUnTtEkd67BbfxC2NWKudpz9MfsGFRUCSmc47hB2qCiSjUSQlBXXQo51mrqznUaAaGSyNG1HOJQiBYCuNzQkJEGaV7o1I3RCIIgiCMeo1r04N6tKeM2PlqMcJecjK6tPWja780YoCUchudGaXrxVotw5/79DfMM3MJ/jkkSeQVad+51q8fBDcKl6wQh7F7zxs3qviUX/g6LRy4ESyN2IChOv6bNWOKpQZoD0mmKBiRFLgh3PUF0SEx/AzDG4oyxMxOP5cRzs41umyQIImsxytSFLGd3c7QswmuerpSf7K7VbymxCg6dn17duYJBRZWeXaZ2Yi7gfn1OcnQHFTsTogfeW5Ek6tZPKUzZtELvgIoq1w5sFo2ivsyZIG7ECT9ve9NDRTRBEATRWeFxOaubo2UT42pvwJ3vpZep+62jJRz6z4lJW++TP06JWnCC1p3rVswFFJE2FA15zsmNHW7F/91UbRq3sO5P0wz3p4uUF0HtoiLULrZet9alu2pJccqmFXrP/vE8yC0xJRfXIQOenQIpEvYs6g49dyzl6BJHBFYf6ZQD+Fjz2Gpz3gGGIAgicywDUmMXyJ3rHiHq7phc6FjY7Tul7U+D3rWqF3NFjIGdYKl353pFikSxtbLQsVNXILc0W4q5ntfnwKU7qFgRou1ygLWirhFC6FWFXUnCrjLnDQL7VswBy8lRBGKPwm6ndOlSUweCIIjOiKr8aWMXOos7FwBye/hXc4tGaW7cut8+WpE8/qdz6vY++eOUfV27Mtx32ouW8xnl6nrBq6i75MLfWTpz1/1pmremZA4ao414vhinLZtsG72gbZBmhFboXfvydEXUbXT3mvoh6grIYJDFUP1siWlTNM75TM3jGRlZDUEQhI9wzi9lTEnD+fzhJ3HM3bcqYm5ODrlzPSCaqO2aOCEpC5dFo7ZN0+KHG1SxU+9gFcLnoKJK1FWXKoKlQcMxs0ZpXhAxCnpxdOvcYtN83LrqUsNYCD/WN2RileOGbU6EYm2erhbtGkUsw4CKKtSXFWNX2WQlI9cBfSvmYFfZZPDWVkfj9Zzw83Hum7ERBEEQRAcjUYu+AmCMiF3Y+GgxQtHQEe/OjXbPRfOBJl8iF7SIRmoLCxYhdFw9AKBbV2baNO2zTf3Ucb1PVvxqeiFXRC8AwLEDP8Ks4S8iHAYmvX9lynzpRC3oueaNm7F4ZHK+8ruLJpiK/Usu/B3izXHLRmhaMbfgBucNl500RgOA98c4qxW10Qta3luSLDirj0MS6m+cjQHPTnE0vx90P3awyNPtPAYD4ojCUegKY+wpxlgfk2O9GGNP+bssgiAI31BduiTm+kPfufPRd361ulnFMWhjF+KHG0zFXMGgokq1GZnePWvUPM1Pts4thhTOScnMBdxHLQSxPj/QN5sTbl0pL8919AJvbsaACuow7IhMuws6oMOAIAjiSIVzfql4vOvlhZ0mauGnf/9ZoPOLGAYRxeDEtfu/fbKlmAsAn23viUP/ORG5ualu3XSjFozQ5+nyuJyUl6sldrjVUszVRi24EXPdcNoy53d1GXH6NeUouKE6aX2rligN19y6dOXGFpz943me16J16RJZCNXPtjhN0b4ZwLdNjn0LwM99WQ1BEITPaItoAJ1OzN0zI5gMLS1WcQza2AUtds3M1OZllYXqpsVLXIIRIubBSswF7KMWtAQp5jptjmaHPpbBSQO7oKDb3AiCIIhOwDJAaY7GJMu+vYQHvGTsihxdMz7b3hO5uQyzhr+oblp37uKRC7Fk1ELT852ijV6wcuc6wVPUQjtiFMvgJks33eZoWqgeJToibtoimunVxwJo9GEtBEEQQaF8nC7L+Oy3T7TzUjKD1LXNgZkJhGtXL+zump2cKWsn5GoRbl39OeK5XvD1InQK4dhKzAXcCan6vGAnWEU9aHHaHM0JA++tsIyQsCPd5mjaHN3OUEQzntmNIAiCyB4SBoNlAMBl+iUdFHYZu9qGaE4Qbl39OfFPByD+6QDk5TIsHrlQ3bwKvELUDUVDpu5cJ2gF0veWuDN11C6eACkv4mis0+Zodgi3LjSZwG5cugOenWKb52sHNUfLbqh+tsb0nRxj7MeMsWcYY88kds0UzzXbXwA8CeC9jKyWIAjCA9oiunHL1nZeTWboNcs4typoVGG3uRm7ZpcFKiinCL6yt4JuUFGlpZgrxtjNLxyv2rxgx8iy0vTMAX6KupBlDJnszVHct2KO59ccUHJ0O2VzNIIgCKJTor1r7I1fv9aeS8k4T/9gScaupXfrHjtsp+/XEHMeqO+virvxTwcgFvM+5zVv3JyWmAsAp/60rRmZa7duXMaI553Vlk6bo7m5dvioLv7M5RJqjkZ0ZKysOScCGJXYOIARmudiOxnAKgC3BbpKgiCINNEW0Z3FpduuSFKSmJuJTFm/4gj0aBu1WaFtROamOZpTd66WQcX2ArOX6+6e6dzNsatscloOX0ARdTsNlAFGEARBJAwGn6z6uL3XkRGi3XMBwPfGaE4YV3sDDh7i+GKfT6IjgG8drdQ9ZnN268p8i2LQsuTC32HxyIVgIfu669Sfujd1uHHn+o249vA/jnd9bt3V031ZA7l0sxiqny0x/Y3AOX+Ac96Hc94HwIcALhHPNdtgzvlVnPO6zC2ZIAjCM6pLtzOJurunpte8wAtSXl7bY5dipR1bKwsNHbBq7q6JqCuyct0itzQrLmCHGbpuULN7iytRV1WKrXMKUVdl3GBOT9oCtsYVrG2SZiXq7p45DTtLirCzpEhpoibL2DnHfcSE4JPf+/uGhyAIgiCymc7m0tU2Rnti1J/acSWpzc+8sOeD76qPjZy/wrGbl8tMRd3FI93XPrHDrfi/m6otG6KlRcKdu/7qKrw/Zg7WX+2s5pXyImnFLtQungApEk5xBu+4Za7pOTtumYu6q6cniblrX/ZH2CWIjoYja01CvN0Q9GIIgiCCJJujF/ZMnIzd44tStj0T3YuxYi4Bb27GrglFGRN298yYluTOlWOtqKt2JlJaoW3iFT/cYCjsGjVTU0VPWXYdVVBXXerJhapdqx1ySzO2zimEHGvFkNIa5fVyIOr6mqc7viJJ1DVi98xphsfkhgbPou7h7er/wzRuUiQIgiCIDoXq0s02Ufe58/+AZwqeStqeO/8Pac0jiDXHMyrqamMewmGg6/Ef4tiBH6U9r9alGzqu3lTYFfm6S0Ylb4B/DdV8IyTh/TFzILfEUHBDNeSWmCNRd8TzxZAiYe95urqYBxG7EP/6sKmoG//6cMq+WHODZ1F3y7+ewIHPtqlTeZqEINqJsJvBjLEeAAYAyNUf45y/7deiCIIggoJzfiljSuT5Z799AsfeNtbuFM/smThZcTE6QZLQ67HUT9333uG8OFKvl5jro+IZkA8pAlzfqmrsKpmA3VMno8+sOY7n1LJ7aur3w6LRlPmMoha2zi1GXXWpbVatFSLSYPsDZWqkgcir1cYdaK+xtbJQFXilSBSDiiqVCIW5xbaO27rqUkVkdeHMDXXJR/xwg+PIBaO5h0ysUiIeHKA6e8V4SfLsJB44vgJb5yjz7J45DX2mJ9+yp/137XN/NXb/ughSfj7khoa0s5I5595bOncQOmKjBYIgCMJ/ErXoKwDGBB298Nz5f0DscKvj8Swk4ZS7k2OyNjzsrCbRXjPeHFfnEeeHcrog1nwYT4z6E8au/KmrOQVPjPoTWg8na245XcIp8z39gyVJMQ+T3r8SAHDfaS/i2IEf4bPtPT1dH1Bcur1P/hj/S8QufLFPxrHDduKzTf2Sxh2o7w8AKc3ZxP7QcfWOrrfkwt85ilpIh4Lr5qc8r11UZDI6GSHIrr+6SjknJKXMZ0pIwvqrq9Q5hv9xPDZevwCxrw4bCrdakffKf92FF899BOHcfMSaGlRR98zLZjq7dgKtmNsZ6tGOBtXP1jgSdBljuQCeAnAtAGYyLOTXogiCIAJmGYAxQbp090ycDN7aaijS+n0dAOg9dw54c3PS9XpWzUgShPvOm49dxcbFmRBr9QJtkogrSehbVZ10nn6+PTPabtfX5uYOmVjli6gLpAq7VuKpUXavU1FXRC1o2Tq3WMmuNRFOB95b4ViM9QttMzUhyBohnLzi38IoCkMI0nqBVhvD0Of+tp+BXrPKsfvXys9A/ZRCSPn56De5AgRBEARBmKM1GARJ7HBrikAbBFoXb7w5juF3ttVI3XoNwcG9WxFrasCpd9Rg3WPmtYpw8OoFWiHkshDDqXckfz/rFxaliMRaMXfqxivVx5Pev9I3UReAKuxa5fQKAVdPt65KLMM1K637CYi4BS3vLlLq+8AiGIAUsdUOMe79MdbGkdpFRSi4odr0+PA/jjecY8ctcw1F3hHXl2P9H6elJeoCncNcQBx5OHXolgE4D8DPAfwBwC8BNAH4/+zdeXxU1d0/8M+ZmeyIKEJxgYAIGAUsWy1tlbpQxY1WUShWkApFW1xIgiyyCEEIkkSLKIUgIAhFRFtaRBRr62P7qD8Bq0QimxBwe1QUhewzc35/zJybc+/cdebOZCb5vl+veZHM3LlzE9J6+OQ7n3MngLMB3B+HayOEkLiQF9FyNUHWRXmWE7u2p249HuQ+Zdz/ZEfV76eCZUSGbqprCNcBHH6gwLQa4Mic2eg6t2niMmLaNhzWfvzgFHw8pUB5Xd7YGBHiqng8+HiKvd/giyBRnpaNhjyhGy07oa4nPUM1+Qog1HcrnhveKM3o+drJYUc8Huwrma4Ka2MWDDb17kr9uTJ5SlcwqlpgGRk4XDBFmdIFmuoXrELdQwtmxTzVm3JowoAQQogOuZbg3J+ch6se+4Xlc+xO3sZzslO+Bvl15DAXAM6/cYJqypd5mRLAaidumTc0O7ZswHqkZYeiCqMgV/jhhFL8t7wAywasj3hMDnMFEeq2Oeco2rRhUQe7bc45GtXzhBMHLkC7HgctQ11fdhreXVsQcR8A5X7m9eiGu++sL4AvIwcDRjjfKM3JlK5T4ryerHTTwPjguEW4YPVUAOqqhV/++/cAQt+HXWunYMCYxY5D3f/39znw17eytWgqovWzKca59XeIMfYRgMcBlANoBDCQc747/NjzAD7jnFOoSwhJGeKtbtr7taFuRIDrQlBrR9Xvp4KlpaHrosjfUB9+oMD25K9e7YKYMD3/UeNzfFxYYHmM7Mic2bpVC0aUSVfAcbirNwFr9XpG7NYqiA5g7XXqPV8OnL3ZOVGHuvKGaXbtKwldD4JB1fdEu/kaAMPzikDXk5ODbnPm4dCDTYt5eToXAA5PLlDuq5rZ9DPQ4xHjvw+dMJdzzuP7XsJmltWpM7/g9sRObleU5e/inA9M6IsSQgixzWgtqg119cJb5vVEBKfx8MGyQngzvBj9zztU94taBbvXIAJdEcqKKV3mZfjhBP3BAXGMUZCrZ8+aWfDXNa0x9AJdWXH/v8IfzpOdhLty5YIQ+LyH7euUtetxECdPcfh8sJzW3Ti4HKPeijxm4+By1RTve8/NVoWVZhOxZt7ZOAUIBC2DV9l/bylBsMEPBIKq19Wea/d1C9B/m/7+HqJ2AQB6vTBXNZ0rwlxh6y/KEagPqEJdAPBl5BiGunphLufc6J3opJnQ+tma3QndLgA+5JwHGGONAOTdXlYBWA2a0iWEpBB5l2GgaVFdu7dSNbULAFm9L0THSXcl8vKAYDAizJU7cqMlahfsBLXRhrl2yAGokyoG7SZoQPRhLhAKaO1UJBhdW7Ahclq75/1FtuogrCg1FQ4mdUVgK1+XNhi2Opdcu2BUtSCIKd1upYsj6hccYIyxl7T/m2xROGjCgBBCiIrRWvTT//1ENbULhGoLzr/RPOyLBx4IYvQ/71Td5zTMrXhaf+NUJ0GtHU7DXKCpWxcA5vf9q+3XcivMBZx16uqFuZuvWhNxX7+RoYlc7WSvU6IL18mk7g9fKAyFurUN6gcCQVWAaxTmAuraBaOqBeGGVyfgrz97KvTamvqF/91sf+PgFr8WTUW0frZkNxU4DqBN+ONjAC6RHjsLQJabF0UIIYkW/g/4Nr3Hais+0rs74URHrt0JYXk6V2ZaoeCQXpjrNGDNm1qCoL8RlQvzUbkwX5mG1SMHpHnTy2IKcwWlhsCBfaXTm65TJ2CPumpBQ3xvxFStHb0KFyp1EWJTOCdTvj0faLp2q6C+a/ECsLQ0HC4Idbl5cnJMj5d1e6wUWXl54tOICSVCCCGkNTFbi56sqsTHfy9P6PV8sKxQeWu/4DTMBYBAbeRawk6Yy7xMqWCwog1zo+HzhWoU2pxzFJ16HrP1nMDnPWIKc2Ox+ao12Di43NGmd1EL9+na9cMXCuHJSsc76wuUmycr3dFL+k7PBqBftRBxbLh6AQiFur5Me+vRgeNKcfp5TWvR8C9VCEkZdid03wbQD8DLAF4AUMQYOw2AH0ABgH/H5/IIISRx9H4rK7p2v1z6NIDIcDdR07tHps5wPJkrh7lOQ1y7x8uBX895Zdg/OxTIBhvqHVUpqCZ2DSZm5elcb7b94NCK3U3SBFGzAMCwQ1dvkjhaeVNLHG+0Fmv3rpjSFfSmc4WuxQuUydzc+c462jr9brzy3JY8GcFgvKMsIYQQIhisRV8CcN3JqkpVF60Qr+ldvelcp5usydO5TidyjaoYtLRhbt/7yvDBknxl4jY9x4cH37rB8jx2p3W79vnE1nU5ZXeTNEFsliY2SIunaPp07VY0GDHaHE2PPKULhEJdu3oMHY8DO1biu08qARowSCq0frZmN9BdhFDtAgDMB3ABgHkAvAiFvfq/KiGEkNS3DcB1RlO6tRUfoeru0EIqXuHukakzwBsbHXX3Hit8WPnYzYlc2ZE5TW/H7zkvvEj3eBD0N4bC3YcLlSDSKtyVA0tPetNGcEYboMW6KZqW3VDXbueu29cXl03STPR8oAhHnytH9aHKuL9WVl4eaitDi2iDHb+3tdSglxBCCLES3sxXt28XCE3vykFvPAJe0ePrZJO1iqdn6U7nukkvzNV+XPFkAeb3/autYFcEuW3aqGMkvc5ct9ndJG3zVWtUfxd6m6HFRXhKN9ag1gnf6dlKl67lsdIGaU71GDoeO1crAwa0FiUpw9b/I3POd3LOXwx/fJJzfgtCFQztOOc/4ZxXxfMiCSGkuei8/c3POWfh4nzV2+Lcqmao+v1UsIxQqBlNmAtAt2rBTUa9uT0fLkHPh0uaPp5Xhp7zynS7ZmV508uUCeRgQ71SF+B6MGqiV8FCeHxpjusX9Lg5QQyEqxcsvodu6zLS/j8GRZduNDr9brxcvaCHpiUIIYS0apr1qOFaFAgFvLGS6xZEzcIlk8qirlpwuy8XsBfmAkDvP5Si731l8NcF8OjgrabnFL27p05xpX6hzTlH4x7mCicOXICszNCkbrK5dNTiyF7cOOv75wdMu3ZlN7w6Ad4Mr1K94JRUvaCH6hhIUmKcx9YyzBi7GkAZ57yvO5dECCGpR/w2140p3aq7p6Db46VRh7niHIB707lmG58p07lhh4r1p2pjIbpy5YldN/pz9VhN4VYuzLf12kY1Cd7sHNXGafJ9hucKb2wmJGJSd//jTddnVrkgHJkW+nntVhrdpMgXK1aKSd0QjwcIhv4Bleo7D2f/oDO/YHRid+nd83hq7dJLCCEkdmI96qQWQc/7S/Mx5p3fAgDWXroqqvOJqWE3w1yjrlw5wAWAD8vjMx386SdnAwDOPe9zALFthmamXY+DqK3jppO6GweXY9AY8/WZ0aZovowcDBgxD7s2z4a/vlp1nxm5CzdRk7of/PpxZUrXqENXtvUX5QjUB6Ka1AUg1y8AzMPBg8oaNNXXo6mG1s/Wot8qvcnpAC524TyEEJLKtgGxT+mK6dxYwtx4aI4w15Oeodr4zKh+wW1Wk7p2N1EzmtIN1FRHTB8Haqote3eDDfUI+htDG8gtyHe0UVo0nH6vlQ3SJhdENa3b6Xfj0e2x0lB47PGApTUF2DQVQQghhCSGmM7dcMU6rL10laOahXhKdJjbpg1Tbp9+crYS5va95P9iPrcVtyZ1fRn6a1F/fTXeWV+ghLnivl2bZ+ser/B6EGzwI9jgx+7rFjjaKC0acphrl5jU3bm6QKlRcKLH0PEYOK4UA8eVgnmowpUkN7sduoQQQkyE+81ie8uDOFd9PeDxJE2YK9MGuDJtmJs3o+nYyuLQhKmdTdJEWBr0N6o6eOXKgXhN5wpmnbp2+3b1Jm71QmmxAZlZgKr3Ok43SouWnelcoWvxAmVS18rhyQXG5w4G0bV4gTy1m/K1C+78vwMhhBBi7eO/l8fUoys6WmOd9HWTUb2CTBvmDr6taf309gsPwpfhwZ1vjLZ8rdWD1+LUKQ5fOC0RE7lt2jCcOpWY/6Bbder6stOwc/0U0w7dfiMjJ27fe262KsgFQsGvv7464n6tS0epX8vpRmlOyWGunelcQWySNnCc9Rp25+oC3eN2rZ0Cb4YXN7x6t7LhWkvewDdZ0frZHAW6hBDisqq7pyD3T9G9zScZQ1w73ApzAYPwUpqIjXeYK/QqWGgYmpo9ZsaoViFR4axd+x83nxZ2y+GCKREVDYcLpigd0p1+Nx6HJyubVNAimhBCCDG3DcB1sfToOunJbQ7RhLkAwANB3PnGb2y9xri3xujeXz5orfJxvOoWZGah7oh/3InNV62xDHW19EJeoKmeYdfm2ZbVC4nmJMwV7GySJvp29Y7jgSBuePVuAMAPBnfB/711FGgBAwakZUmO908QQghJWW6GuUa8mVlN51yYj32l8a0bEMzqFexWLzhhVbsQjX0l01G5IN9RTUMs1Rba6gVx06McJ9c0hKdzBWnDtNReRPME3wghhJBWwmoy961NhUjLdneWzXv2AbTrcdDVc+oxq18Y8Y87wQPubNgm6hlsVS849N9bSrD7ugXKzU5Vwwe/fjym19RukrZr7ZSIDdN4IIhf/vv3SkWDfKzYFBAABi++QfmYasASjNbPpgwDXcbY+XZuADol8HoJISQpyf9xj3Y6N9UcKp6F/bPz4x7mamsK8qaXqeoX4smsT7dXwUJl065Yib5dO126WpULzKd7gw31Shdx0N9oGerK07lO6hZkXYsXoNtjpWBpacpNCXfDAS7LyFCO6/ZYqeH3stPvxisfM8Y4LaQJIYSQSOH/PoZ++ck8SRNN7FkT33f92JnMnfDuGFtVC2bW/eI55eNRb03AqLcm4GQC6xf8fv3HRPVCrPqNnKcKdZ347y0lpiFtsLYBl95eqtyCtQ2W53TanatH7tP1ZnhV3bo7Vxcooe0Nr05Qgt1da6eEp3MNK0uuo7UoSRZmv6Y6CHsZNbN5HCGEtEiqBbTHw4HkKdD/uFDdbeXJyUHXudG/jWr/bOPwMB5hLoCIMBcA4PGgcmE+POkZrryGGbPOXDGlK+6vXFQYCiY9HtN+Xa2e9xcpwbXt6ViPRwlz95VMR69C4++DOLc3OweBulpVCOxJz1A9182N5+RJ2yPTZkTcJ2MZGUrQKxwumKJ8P6XANzUndWmlRAghJL6UMPeSP5Q0+1rUm5WDQG01/HXVeO9PTesOX2YO+typX0FlxwdLjNei2slcHgi6Mpm77hfPoe7byGECny80qXtaG4YTBy6I+XXMnNaGGVYvbBysnt4VAa+TKgYgFOqK6oV31hfg0tstfrHv9WD3dQsArwewMSmsdO6K54V5stLxwxf03/UWTd2CTBvM/vVnTxme84ZXJ2DrL8oBeFX3b/35U2jThmHwjzKw4/U6IFXXoqmI1s+mzP7fbVzCroIQQlKb8h/13KcWNfsC+ljhw4aPBavdC+sEOcgF3A1zVa8jdefKAaq8cVq8wl2jUDfi/mAQedPLQveFrytvepky4Wu1iZqTLl35XJWLClG5ID8inBVESBuoqY7oIDZ6zWinc40YBbmmjweDqusQfbokdoyxEQB+DWAggI4AjgJ4EcACzvnJ8DFdARw2OMUZnPMTBueeDmABgP9wzn/m7pUTQggxkwxhbsXT6qlZmby5mZuMJnPdIIe5o95qCghFuLr5svK4B7tmfbryBmk714c28wKaenGZ14OBty/Gu2sLMGiMe+s7eZO0dzZOUUJabUDryUpXbaCW6M3VZFYBsTYA3vrzp5CZyXBs37kAgDPO/QQA7e3QWiXb+tkw0OWcP2PnBIQQQkIbUADAl0ufRsdJdzXbhRwrfBjBU00L5W6PS2HYA6HF0pE5s1XBrtXU7pE5kT1a2hBXJRh0LVi1qh/Qhpr7SqfHNdTVCz/lUFe+DwiFpWKSOJ41EUrAbSMQrlyYD292juEGbamAFtGuKERoEToDwCcA+gF4GMAVjLGfcM7lUZuFAP6mef5JvZOG67geAvCl2xdMCCHE2sd/L8f5Nxq+XTzu9MLcPg+E1o17Hg+tU+SJXcB6alevtkEb4MrefuFB1ztzAXWYK5OD3XY9DsY11PWefSDy9aUN0nggiBH/aPq3yMbB5fBmeJVw1+4mapbTudrjwyHtOxsj6x886T5VzcI76wvgy8hp2nzN68F/bylRQmB5ere5+f1QwlwAGHplJk3ptm5JtX52///lCCGkleGcXy9qF2orPmq26zALc2XaKd1gdXVENQMAnF9SGhH+erNz0PMB4wV3ZXEhPOkZho875eTt/3lTS+Ie6morFgQR6kZckzQNK0/tOq1kEOQ+YbNQdv/js0y/d6rHpOoG2ZFpMyynahMtKy8PtZWVQLi/LGVCXQ6w5HvL2I2c86+kz99gjH0D4BkAPwfwuvTYx5zzt22edxmA9QB6gdaZhBCSSNsAXHeyqrJZL0IOc0WQK4gaBi1tLYPgy8xRHlfuy8jBoOFzTa+BB4K4843fOLpuI3J3rpURb06I+7SuWfXC5qvWQFsXoA2hN1+1Bu+uLVCmdqOxa/Ns+Our1aGsjg9+/bhhF67YfG3AiHm4dNRi1YRvMtu07iya0k0UWj9bSuhCmzH2cwD/1HnoO855u/AxpwGYg9AIc38ApwG4gnP+Lxvn7wngDwCuAHA+Qun3uwBmcc7fj/kLIIQQA+FQN6H/ydEGuDK9MNfTJici8K2aOdvwHHohr1lI6HbVgjydq60JMCJC3Xj165r16Vq9lnhutJO62s3hxAZq2lDXaJM08T3UTvGqppylx3hjo2rzsmiYViR4POhWqv6HhNKZGyb36QKhDdK+WLEyNUPdJKNZjArvhv88V+cxS4yx0Qit3X6N0NvPkhKtRwkhLVFzrEXNqhW0YS4AXDSxCHuXNz2nzwNlqs+19OoZzDbscrM3V9DrzjUT7xoGs+qFEf+40/r6wsFvoD4Q1euLMBdQh7KCz5cFf221bjjb997Qz8SHK0N/5/LfpTLhK9UvMK8Hf/3ZU/Blp5ltUmbqrz97yvRxnw+44V9NNQxbf/6UagO6Nm0iG0zkKV1ai7YuybZ+bq7JifvQ9EUDgLxnY3sAvwWwG8AOADc7OO8vEFo8PxN+fjsADwJ4hzH2U875rhiumRBCkobTMBcAcudH/gZd7z5RzaA9p7h//+OzIqZ03QhzRdjpSc8A8/mi3pxL7teNx7SuUfWClX2l0xH0N9oOp7UBrsybnaNsoCZCXb16CqMQV5xfDoPl57vVWyvOI4JaORw+Mm1G6H5ACXHthMfaUDfqi0u05Jsw0DMk/Kd2vGshY+xPAKoBvAHgIc75HvkAxtgZAB4D8CDn/BvGmr3C0Q5ajxJCWqRE1C6YhblmLppYZPo50FTNIOvzQJly/1ubCiMmdd9+4UH4MjwxTeauGbIBjTV+pGX7cOcbo1XTuUZ1C0ZE2KrdsMwNRtULVjZftQb+mkZH07lygKtHDnWNjr3hfybh5WuXY89Thejz+xJcPL4IHzyRr5xfb8p34LimtejO1dGvReXOXG0fLgB07vUptv48FPr6/aEAV35cz6Z1Z+G2O76m6oVEoPWzqeYKdCtNRo+rOOdnAgBj7Go4W0BvBPAk51z5a2eMvQ7gCID7AbjTik4IIc3IKMw1CnKdMjqPmPDVhoyuhbn+RuTNKENlcSEgTbHaDUC1ElHBYJfy9TmoWTAKc8X3QwS+ItSVj9d+z+SqCG0YbBmcezwx1y+ISdzDBVNUU79Hps0AYL1hmlan342nDdJcxhg7F8A8AK9xzneG764HsBzAqwC+AnAhQp1h/8sY+xHnXF64LgawH8CahF107Gg9SghpaRJWu6AX5upN5UbD6DxyZYMcHDaFuaOjfs01QzbAXx/E4NtK8PYLD6J80NqozyXz+RCXSV2j6gUjYirX7qZouzaH9tAwCnP73lummrTVhrne7DQM2z5R+XzY9onYevnSpsfDf5d6z9ViXg+2/qI86ild4YZ//R5bf/6UUpkgwtvOvT4FAMsgV0bVCwRo/vWzJ8rrjht58RvFc7/WPp9z/h1C36Coxp8JIcSpL5c+Hbdz6/Xkilu86U3zxhrmisnRYEM98qaFwk5vZpbyeLRhrvL8qSUI+ht1+21jIQJSu4IN9Y7CXHmiNm96meom9Ly/CN7sULecthPX7OuVp3KNwtyqmU0b4XUrXQxe786Gbt1KF6PbY6XgjY1KSBxrT2+4vzrpMZ7YG4CzGGM7pdvvDK+NsTYAtiA0oTpO3M85/5xzfjfn/EXO+Zuc83IAlyM0L/GQ9PzLEAop74llHZdMaD1KCElFiQqVKp5ueldPnwfKlFu86U3zAqIzN7owd82QDcpk7o9veRQAlD8Fp9O5shFvTsCotybg5Cl3//N44sAFqmoAt/gywr3F9epKhL73lqluAHDx+CJ4s5qOl48N1DSavs7F45v+LvXC3P/+uWktOmDMYvgtzmfXDf/6PX7579DtVPjv5Ni+cx2FucLQKzPFh9elyno01dD62VxzBbrrGWMBxthxxtgGxliXeL0QY+xMAL0ROf5MCCFu2wYA8dwYzc6mZwkVDEYd5orQsXJhPuAJ/efIajOvaMQj1O1VsBAeXxoqF+YbBrtiEzT567MiAlq75FBXyJtaEtHTqw2Utc/pOa8MPeeVKfcHq6tVoa7b3AiJs/LyxIf0Vjd9X3POB0q3FXoHMcYyEdqB93wA13DOPzE7Kef8GIB/Axgk3b0cwNMAPmGMtWOMtUPoXWDe8Ofu7ZToLlqPEkJarI//7v5b/YHoqxbi5e0XHoypM7exxg9/fRDM27RWe3fLHOVjX3ZaTNennCc8qduux0FXzgeEpnQ3Di7H5suM/643X7UGGweX2w5E+42MHOAQAa4eOdSNVe+CsqaAuK4aO1cXqIJdt/l8UKZzo7Fp3VmqUNeViyLNLaXWz4muXPgOQClC/RHfA+iH0OjxW4yxfpzzL+Pwmk8AYAAeNzsonLyL9H2F0V8cIYQYSeRmFM0R5h7On6J8XFlcCASD8KRHn9MEG+qRN0O9QNROmsqTqrFschaPzdKULl1pEy9ZsKHe8YRxz/uLlK/Z7nO1m6LpkSsXtK/Tc17T63SfVoRDxaFQXYS6ufPnuVK74LaUq11IwrlVxlgagBcA/AjA1dpeL7OnQv0V5YVvd+sc+y2AybBYhyVYUq5HaS1KCHFJXGsX5DA3EVO5soon1L9Ej7VqYc2QDWBej2oi990tc1QTo4H6gNKD68tOs7XxmB55s7R2PQ66Ur8gzmHUpyuu227NgpY3K0c1SWvEzjFmehc0/Rzl/b4IlU81/dJAbIznVu2C2+TqBRIHtH5+3OyECZ3Q5Zy/xzkv5Jz/nXP+Buf8cQDXAvgBQhtTuIoxNh3AaACTOOemvwrjnK+wSuEJIaS5HCt8GFV3T7E+MJ7k4DIYRN70stimc02mVvOmlyFvaomqakBM2YqbU+J8bk7rmoWuTmsZtPQ2OYtWr4KFhsGzVvdpRapJXcDd2oV4oLe5OccY8wBYD+AqAMNNumS1z+sC4KcA3pHuvkLn9j6AivDHm9278tgl63qU1qKEEDfEq3ah4ulZeH9p0y/aEx3mAqFqBa80MRtrb65csyDIYe6gMaUYePtiDBpTikFjShGoD2DzVWsARL/Z2Yg3J6C2jrs+qas3pRttVYSoXQjUVuPDle6tR41UPqV+jbzfF6lCXsDd2gXB73fWm0tIsq2fm71Dl3O+G6FOsUFWxzrBGLsbwAIAMznnq9w8NyGEWKm6e4prXbpGm6Al0uH8KWAZ6mlcNwNHLb1zi+qEoL8xolLACbcrGDzpGbrVC05CVJnci1u5MN/w+ywmjrWvW7mo0NHk9KHipvPvnx36h1r3aepJi8MFkX//ze2LFSvlT+ltbs49CeBWACUAqhljP5Zu5wEAY6yUMfYYY+w2xtgV4bXVmwCCCK2xAACc839pbwBOAPgu/HnSj67QepQQ0hK5WbvQ3DULFU8Uwpudhmtebhpm82Z44/Z6ItSUDbx9sWpiV4S7Trkd6p44cAGyMvVDXV92GnaudzYU0m/kPFuh7p6nCvHBE/n44Il87HnK+RCDqFeQf7Y+fLwQHz6uPlc8axdicdsdX9N0buuTVOvnZg90w7Sjx7GdjLE7ADwFoJRz/ohb5yWEECfc6tLV2wQtkQ7nTwFLS0PXRQvQ7fFSeNrobMRlk+iVNQpkdTf5kuRNLXG0uZgRN0PdXgULQ5O6OuGtdkq3clGh5dSuthc3UFOtH+qGp6QjunwNeo2NJobF93r/w4WqP4V4dOm6ERDXVqreShqHbUHc1QybOlgZFv7zIQBvaW7jw499COBnCHV87QDwMID/ALiUc77PtW9O8qD1KCEk5cnvWolH7UKiNkDT4oGgEuamtwttoFv3bT3W/eI5x+daM2QDygetVfXmCvKmYO89F7kGEhO7es91IlGh7oh/3AkeUK9R311rXVllN9T1Zqfhhv+ZBE+GVwl19zxVqJqklo+Vg1+9qgYeCMKb4cWHjxeq+nTdDnW3/vwptGnDYjrHjtfrlI8ZS/61aCqi9bO5Zg90GWMDAfSEevQ4lvP9CsBqACs559G/15UQQhzSe9v3scKHXTu/m0Hu4fwpqk5cs+NEmCvkzm/aLMHplK7ozc2bUYa8aZHBbM8HmhZ28ZwABuI0qWs1pRsM2pra7Xl/EfKml6kCbqPvh1mgbHotWsEgzl9cCpaWhv0PF6prF4JB1yoXDheEf6Yc9vF+sWIlDk8uUG4C55xxzt3ZsaQV4Zx3DX/v9G4Ph49ZxTkfxDk/g3Pu45x34pyPtrMY5Zz/nHP+s7h/IS6h9SghpAVRvWvl/aX5qHg6/m+bd6riicKITlyj4/Y8nq8KCK/eMl4V6jqxZsgG+OuDGHxbSUTdAgAMGj5XFeoa4YFg1H26QjxCXb9OrKg3pWtnalcb6n7wRL4q2O3z+xIEwjUIw7ZPVEJdHghi2PaJEecbtn1iRLgs+/DxQviy0zDq9THggSDyfl+kCnUBYNsN7rzZJZq6BTGRK24C55wFg7QWbQ2Sbf2c0E3RGGPrARwGsBuhUeJ+AKYD+BShzSLEccMA5ADoE75rCGPsLADVnPOXpeP8AJ7hnN8V/vxyAH8G8AGANYyxH0svX885fy9OXxohhABiAe3xcE9OFguerEbwVDWq7p4CT5scdC55OKaTKxtU6dwvT/F62uToHqcSDIJlZOBw/hR0K1sMQL3pmQj+WEaGKsyVXyN4qtrRlK68wZkZb3YOAjXOzg2Epn/F5K/tjc+CQQQb6rGvdHrMG6Upm6RpaDck035upuf9Rdj/x1nK92P/H2epNkGrXJiv9Pja6eyVX1t8n7W6zV+AwzNnIFBXG3GM0c+gcLhgSlNo7PGgW+li9X0I/0xFEeZqJnKF1JiG4EjKTR1aK1qPEkJaA19mjhKCBWqrUfH0LPS+K7bNq8zsXT5L9dZ5b1YOLppo/HqiD7fiiUL0vje0JhIBb+97S1DxRKFyzDUv/yHi+VdvGY9tQ56IuN9KY40fg28zX4MNGj4Xb20yX1Mxrwebr1qjCnU3X7VG6Xm1u4HaiDcnuLpRmu5r/ONObL5qjRLi+rLTbPfR9hs5D+89N1sJt82qN4Ztn4itly81PZ+Y0u3z+8i/Ax4IYtTr45Rr/PDxQlz8QIlqk7SGE3URz5Nt/flTSqjt8wE3/Ov3yn2+cPrl9yOq6Vx5Ilegydw4ovWzpURP6FYAuAmhiYVXADwA4EWERo+/lo5bBuB5AGKu/uHw58s05/OGb8KVADIQWpj/B+rx57+492UQQoiaPJ2bu/oR1nnpTHhOa3rbvAh2nU7syscHT1Xj8AMFETdtv27wVLWtt8l3XbQALC1NmdZlaWnKBKmodtALc4HopnTNNhCTyVO6hjyeiMnaYEN9xAZqdq4pb3pZTJ28qsuyMaXbq2BhZE2CCbmCQRXAejyqrlxxXrP+XPla5GBYq9v8BUAwiO7TitBzXtPfW7Ba/2frcMEUHJ5cAJaWhm6PlaLbY6FJX/k+AOj2WGmsYa5f85twmoYg0aD1KCGkRZLXo31/U4T+48vgy2yasIx2Wld+zp7H83Vv2qAvUFuNvcvNX+ual++GJ8OrBLk8EAxNeT6eD0+GF9e9ca+qM9dINLULsRp4e+QmXf6aRt0N1KyMeHMCTp5yJ7ky2iBNVC+IyWJfdhreXVtge1J30Bh77xT0Zqfp1i0I8iSvmVGvj1GqF7SbpP31Z0/pTupu/flTyMxk+PbT8/Dtp+chM5Phrz9rui8zkykfO5nO1XblymtRmswlzSmhgS7nfCHnvC/n/HTOeRrnvDPn/Hec8881xxmNMXfVHMc453dKnz9sMv6sei4hhESLMfYSY4zLN0jTueK4zktnIveZhRHBrl1ON0OTX8ss1JU3OBOhrqhVYBkZtrpN5XOb1QFoedIzUFkc+7uP86aWmIawjusUdALiaChhrSaoFRunibBV1CTYDXbl8FV8r/W+B70KFlpOGovQ2ervjGVkYP/sfOyfnQ+WkQFPTlMFg/z3LyoUtGFt1+IFqvtEqGuXqFjQhLmpu2jmCb4RQ7QeJYS0BKbrUda0Hu37myIl1AWcbW5W8fQsvL80Mqw1ow2RrYhQV9QqiADXKsh9bXjT5qhOunTTsn14+4UHbR0LQLdH1w6xgZrdUNfngyvVC1YbpPnCYeuIf9yJUW9NgDfDi3fXFtgOdwEY9ukCocBWr25Be4xe9QLzerDxyrXK5yLUrSjNR0Wp+l1wDSfqVKGuCHPloPbYvnNV4e2xfedGVbNAXbnNiNbPphJauUAIIS3Edbr3ejw8d/UjEe/f6bx0JgCgaqz9wFAb5uY+sxDHJs1H8GS18rmezktnKsfphcF6nbhGH5vRntuoHkHUINiuQIjSvtLpgEf9O8q8qSWoXFRoq07BybFW9KoXjM4p7rdbRwE0fa8rFxWaTuOaXd++0umWlRbd5kf+LBx5eDaC1dVKqJs7fx4QDDqeurVDu+lZSoe5hBBCiIvCk7j661Hm4f3vKlGtR/v+JvSL4d0rQ+sNu/UL2kC2//gy5Rz9xxu/86rvb4qU4/YunxVRvVDxhHrTLG14e90b91peW8OJWtXnItS949WRqvvXDNmAxho/0rJ9uPON0ZbnFXwZOfDXV5v26OrVLsgG3r4YO9dPwcbB5ZYVDG5WL5w4cAHa9TiIzZeVY8SbE5peQ+f1xX2br1qDQH3A9Lzie+Ik4DfizU7DB0+o178XP1ASEdyOen1MxHOfv3Y96k/UKfULf/3ZUwCcd+LaIcJcxuCnaVySbJp9UzRCCEkVYhJCfJ77zEL1TSfMjYZemAs0TfwahbmCCJAB9SStXpgbDfmcF85qWsxrJz73lU5H0N+IvBmhCoTKhfmhz3U2QzNid/I32FCv20nrZFLXzU3S7PTZxiwYjDp8lp8nVypY6frwPNWkbiK0hGoFhqTcpZcQQkiKkdaiyiRu//FlUN00Ya7M7uSsmMwVxLm1H5sxeq2KJwrhyfDaqlKwo8/fZsF7ejYA/Q3SRGeuvz6ohLt6m6FpDRo+1/IYvdoFvWMGjSm11Vnr5iZpRhukGfHXNGLg7YtNj+k30mKPDgespnjN3Lr9duXjbTesgs8XXSeuldvuaGphojA38Wj9bI0CXUIIsa9pEkKqVnDKrEfXKMx1Sq5eEF27boS54pwyud9VBLBKmBsOb7V/WtHtjNXyeEJ1BQvzI6ZzZVb1DBHHhsPnWIJdo+oFrX2l0y2vv3JRoaMJXjvkoFyuVbCj68NNi/nDkwtsV3Qcnlxgq9tZVC0QQgghJELTWlRnEteKmNQFYNql68YEpvxacteuG2GuXLcAABeta1o3iOqFNUM2oHzQWjBvaI3141sehb8+qHzuFub1YOPgcmwcXO7KuUWo6z37QMzBrlGfrmzzVWssr33n+im6lQx7nio07cu1i3k9Sq2Cz+H5Gk7URVQt6OnR9zOcce4n6NH3M8tzis5ceTrX0UURkiBUuUAIIQ6FQ1bHvwb2nJajVCEcK3wYnUsejjjGjTAXUFcvAEC3x531l9ohpnN7FBThQOksBGqqEaipVgJKbXibN8P+JGjPB4pQucA8xNSbyNXjNAwV5421gkGvekFLbORmflAQedPLlPoKcW3R1C0IclB+/mL7PxsfT52i2uDNk5Oj2iBPT9XM2co0r6qqQYdmAzQAtIAmhBBCJNsQDnWdhrmCLzMH/jrjt83LQa+dSVw7rwUA6e2ycPWW8TGdT9DWLQCA9/RsBL6rQd239UqQO/g29VrRzmSuU1ZTrdEQNQmxVjAYVS9oMa/H9OvggSBGvTUBm69aA39NaB344cpZ4IEghm3/fVTX9upNTysfezO8GPX6OFvP2/izpyMmj+1ULXx9PKj82aPvZzjwwTmGx2o7c2k6lyQrmtAlhJAE6bx0pnrTsrunRNyEWMJcIVjdtNi1MxkZix4Fmh62YBCVC/JRuSAf+x93vptyNM8xYhmYGj3PhQoGN6sXVMFyMAjm86FyYb7tWgpBPt5pmMvS0tD90VLD2gUxiSvftMdoN1WTaTZAS/mqBRXa1IEQQkgSkCdn31+aH3FzYzpX77X0QthY9flb05rmonUFSvUCEAoh39pUqNze3TLH9dePNzcqGKyqF0b8407dDcqMjhXEz8nWy5eqwlm75J8HvZ5cI34/UPdZN9R91s3wGDGNK99kItTVI9cscM4ZhbnNjNbPpijQJYSQBJJD3XhQbbwWDMLTpilAduX8JsGwqEnQCtRUOw5orTbsSpRYQ91eBQtVE61anvSMUG1EFKGv+B45/V7ZOf7jqVMibiwtDecXhSo7us3Rn7A169Xt9pg6CNZWMHyxountky0qyCWEEELco3TnxnIS0W9rJtrp3N2rC7F7daHysScrXXlMW5XgNrl6Qctsc7Nk5mavrhFfdlpEnYKRUW9FTvo2nKh1FOrKx/7m7bt0j9n4s6fx7I9Dt40/e1q57zQbXbliGlfPWe09yjEi7JXDXapZIKmEAl1CCEkweXMzvU3Oop3OrRr3UOjPsdNRNXY6WEaG6q3tsU7pHpk6wzQYFlO6PeaXKcGdEG1AG+10rUyuJ9hXOt1xOOukg9epXgULQ1+jQeirrVbIm16mG5zbndKVKyCMpnNFeKu9iTDXjm6PlaLbY6WqzwFEVC2Iad0j02aopnNtv1AKYZwn9EYIIaTlirZuQej7myJoh7caKgAAmDtJREFUN1NTnT+GMNeT7oMn3YfdK/PhSfehz/MPKpOzDSdqYwp1Xxn2J2wb8oTpMWJqd9CYUvgy4jdEEYvNV63B5qvW2D4+1lDXqkvXyZQuYBzq2rH18qWWx2782dPIymTKFG5WJsOzPw7d99X+rhHHG03bfvdpZyXAFZ8f+uBc1X1AUwB8VpemSV6azE0OtH42Rx26hBBiA2PspXieX/TrxiQY1O3K9bTJQfBUqLvXrL9Uz5GpM8Drw2GmycZdymulZ+DA7EL0mNfUWXZgZihE3P/4LPR8oMjoqbr2/3EWet7v7DkRgkH0KliobNQGhIJNT3qG/X5cjyfqPl0xhQuPR+nD1T1mUaGqF7hyUSE8vrSI1+x5f5ESSjOfT+kutvpeaaeAP54qTWJIgTLLyHAU3pr9TMmhruDJyVFN8mqnemk6lxBCCIkU77Wo3HcbtUAQfZ5/MOLu3uvzUXF7GQLf1URVvfDKsD8hUNMYsXHXhyMX4eLnpho+r9/I0Prk3bWhyd13t8zBoOFzHb/+e8/NVs4Vq81XrUGgPgAA2Di4HL7sNFWVgZERb06IulNX7tKVzycTU7pyl+7O9VPAA0HdjcpEpy4A+GtC6+tXb3oav/ib/sStHp8PePbHT8MXTqVENcRpbZqCW7NqhQ7tPfjqeFA1kasNdw99ENmve+iDc9G976eq52lqGVrkcAFpeSjQJYQQe0JvcfN4OKLYEM1K56Uzo35u1biHQp2qGfqbZOXOnxfamCoc6lrRhrhySHz4gQLl/v2LpqPnVE3YOHUhPipSbwQmAjwnU7re7BzbQaVd2g3InGx6lje1JOpN0sTxYlMzbXArjhHTs5WLCkN1GSaBs3z//j82bUinJc4FQDmfeB2W1rQ4dxLgCuLv1awTV492atyspqHFSNFeLkIIIUlFrltwfS0q9906tXt1IRAIquoVtHqvz8f7N8y3fU4R4gKhTbv6PBBaw+15PLSOGTSmVAlqtTxZ6Xh3/RQMCoeTvowc+OurHdcuRPs8rZ3rp8CXnaaEuXJoavQ16Bnx5gRsHGw8aWtGhLoAcPJU5KJkxD/uVM4tB7kj/mEc0Iog+i/D1qH+RJ1SvSCHutuvXKrq8D2tDVNeX0zcduh5BAB0p2/NHNuTi8xzDgNoCnLN6hZkIujVBrthlJMlA1o/W6LKBUIIsSBPROSufsT1BXQsqsY9BJaWhm6Pl6LrIv1QToS5dhyZOgO8sRHdHi8N3cr0d7ztMc+4hkBM6Qrdpzv/B0LPB4qUagER6sZiX+n0iAljp1UKsVYviHoFjy/NsDNXTOXmTS+zHRzLYbfcxyufS5xPtSFa0QLlFo1uc+apOnGjCWZz588L/SLCxvQ3IYQQQmKvW3CbqFm4ZOtM3elcAKi4vcx2mCtqFYL1AfR5oAx9HihD73tLIo5777nZgNeDD0cuinjs4uemAlKFgDxd62RzNHma9921BaHXjIKoM9CGuUAorHZSv+DzIerqhRMHLsCJAxcYVjD4stPw7toCeDO8GPXWBFuTwwDwq5fvQEa7TACh6oWtly9VHvP7gcDnPZRbZkbTj68IcL/a39VxmCt0kDpx7Ya5skMfnIvvPu0MABjzzm+jugZCmgv9C4oQQkyEw1x5Oje5BIOGQa5yiIMN0Xh9vWGIGzGFGZ7S1eo51XgjMCebo7kW6no8CPobI6Zioz1XtBukCb0KFuqGup70DN2KBTvkXl2PLy1UKaFzLrc3m5ND3Wh1LV6gmhZuqRhP7I0QQkjLEe+6hZgY1CyoDvmuxv7pahoNQ9y9y5vWgv76agy6fTGCtQ22ziv6dP311Y5CXbmHN9pJXeb16Ia5ADDw9sVKZYEdbmySduLABcjKjAx1RYBrN8iV/erlO1Sfb718KbZevjRiE7OvoghdzRzbk6uEurFo04Zh3Y9XuXBFxE20fjZHgS4hhBjQhrnJNp1rh5O3wh+ZOsN0UlIbDIsp3Y+K8nWDXZkI/QI11QkPdfOmlhiHuQ4D2rypJQj6G+MS6vYqWBhVmFu5qFAV1DKfz3LCN9YQVtZtzjx0f7QU3R9tquZwugFf5vnnu3Y9hBBCSEvBGHuJMcahrltIedFujBao1Q9UPxy5SHdSV9Zv5DxVqGvXoOFzMfi2pnVkNFO6A29frBvmRmvEmxN0axOcEKHuxsHlqmBXb8MzK5svK4+ogujQ3oPA5z0M+37dCGGFY3tyUfdZN9R91k113u59P7V9jp/8KAPSnljUoUtSAgW6hBCiI5nD3KpxD6Fq7HRUjZ1u2Jsr6E3nOg3btM/pMV+9qVeP+WURVQR6tQtuhbpuEgFt5cJ8VC7MtxXUxlq9ICihrlSTYEbu2BXXKyZx86aX2Qq+xTHx6q2VKxjs/px9sWIlaisrxae0gCaEEEKgWYsCAPPwZKlb2L26ELtX5mP3ynzT3lwt7+nZABDVxmhag8aEf5Hs9SDY4Eewwa8KdUWPriza6gUAUYXBdjGvBxsHh0JRu/ULsVQvCCcOXIDA5z10p3X1bL6sXLmJ6904uBxZmUypVBCBqtEkrtXjsZIndp1UMLz6ep3yMW3QS1IFlT0TQohGsoa58uZnVjULWl1WLsInk+cheNJ4Y7SuixY0bXqmYfQcJdz1ePBRUT486RnoOXUhek5diP2LpuPA7EL0mBeaaug+vQiHFs5yvEEaEAp1KxfkWx8YBXl6V4SmlsKTvdFM1MrkDdOUUDcYbJqU1lRXVC7MN9wsref9RaYbpIljbH+NUeg2Zx4Oz53tqFNXDnNb9AK6RcxUEUIISQTVWrQpyG329ai8+ZlVzYJQcXvTIIC8Mdprw1fi6i3jY76mQZoNxvbcVARPVjoufm4qPhy5SLU5GqDe6OzdLXNUPbmmrzN8Lt7aZP0L+GhEs0laLBukaYkN0zZfVo4Rb07A5svK4feHQmMAyqZmcn2C0eTtFxXd4T37AACgU+9D+KKiu+Hj8SJvlmbHrXd8pXzMOW/2/50RCa2fTdGELiGERErKMNdq87OI52gmJM97rOnzww8U4PADBY6ndbXTucr94dA26G9U6hf0unTlDdKcTOk6OTYR3JrSFcS0rpi2lT/W3sxCZHmDNKt6ikMPFuDQg/Z3Vrar25x51gfpaNFhLiGEEOKMMpmbTFO5Vpuf6dH258pTutuGPGG7fmHP402/kFamc3UMGlOq9OpqN0cDoq9eSBSnm6S5Ra5gEBO3WZlMNX0rNlUzCnMFu1O4meccRuY5h9G5T5VrX4eWndoFeTqXkFRCgS4hhBho7jBXVa2QluZ4KldvqtZzWk7EMSLcFdO5ZiHvoYXGIWGP+WVKr64Zp9UL+x+f5XrVgiGbnbqViwrhSTevu3BK7tCNtk9XZvQ9kzdQA4DDc6PbsZk4Q5s6EEIIcWBbc1+AIOoVPOk+R0EuoJ7OvWTrTAChKV0R6gJNwa64Aerw1gm5hkHUL7hRvfDuljlxm87VGnj7YgTqA5ah7ubLyiM2HIuVqGAQga2d8FaPdipXS9ufG6/6BcBe7cIvrsyM2+uT2ND62RwFuoQQYuDLsjWunatqrLNNtOSJXCdTuXq6rGzqEzvvsdnosnIRuqxcFBHuCsFTkf2nnjYOulc9HmVKV9ulC0T26VqRj8mbrj8h7IZ4VhEkip1N43reX6QKdYPV1a6GuhQQE0IIIbHhnF/v9jl3r3S2zpGDXKdTuYJ2OlfovT4fl2ydqQp2tfYuV69p+jxgfw046PbFSqfuxc9NhSfdFxHqOpnSTdQk7871U/Du2gLwQPwCznjr1PuQZaXCFxXdEfi8h+q+eE7pWnl+XYdme21CYsE4T8EYmhBC4kjuLcu6pBc65t8Z9blE763C40Hu6kcsnxPNRK6WmLj1nJajqlvQI/p19Shhbnji16h2QXZgdiE8vjSlSzfob1RqGZRjZjb9w8KbnYOeDxRpT6Oazo1rmLuoEAgGDftpjZ7j8aXFPEnrFm0gbfX9MgqwPTk5tmsTPp41Q/mY10dOZnd7zPgtkcLhyaGf05bcWZZzVmd+8fWTE/qa764t2MU5H5jQFyWEEOIaxkLzYm075+GCayZEfR7Re6vwetB/XInxE8TzVuYrU7XRqri9TAl1vadno/d681BZ9OtqebNycNHEImVy16xyQfbu2gL0+VsoGN5zU1HE87R9tb6MHN1OXTGda/d1o7Fz/RR4M7zw1zRi1Fv2/r43XxaqR4hmijZetGGuNrg1O1bo0N6DY3tybb1eh55HAABf7e+KDj2P4OQpdb713aedLc9x6x1fKbULLXk9mmpo/WyNJnQJIUQjPBWxDQBq398X8/lYRoYyacvS0kI1CuMeijhOVCy4EeYCUhB7shpHx0/FsUnGoS5v9ANeD659476Ix4KnjDdSM9JjXonSp6vXpQs0VS8AoSncygX5ERUM8axaqFxUiMqF+aGNxsJ9tU7C2bypoa/RTkVDvGknc52E3/LfA6A/ha3t2/141gwcerAAvLFRuZ2/WP2PHJZhXUnxxQp7vXmEEEJIK7QNAL4/Vml1nCVPVjou2ToTl2ydCU+6D7tX5oduq9XvohJTubtX5sOTlR7z68r1CoHvarDn1kctn9PtsdKItUmgNsr1oFS9oEdM6Qpio7REERO5764tgDfDixH/uNN2mAuENkarreNo1+NgHK/SHu1krujdNaOtXhC+Oh6MmNgVfbuq5/c8gto6jto6jsxzDqO2juM3b9/l+NpFmMsY/I6fTEgzogldQggxICYjhGindavGTke3x9Vh15GpMyImGllGhitBrtlryPULsqPjpyph7uvDy9Fwolb3ODvTubIDM/Nx4awyfFSUb/jcQwtnWVY5uD2d6+Z0beXC/LhODwv7/xiaVvZm56g2PxPXILNzPeI5PYqajj0wK3Sfdkr341manyWPB+cvatqRGQCOPDxb+Xvs9lgpDhdMAYLB0M91sf7PtZjOBeBvyZui5bTvzHsneMLg/61LrQkDQgghkbRrUSC6iV2xoZm2NmHPrY8qG4gBoeA3mmoFI9rzw+sx7eJ9/4b5yrt7qmbOVq0PvVk5SrDrZFJWTOl+OHIRgg1+DLp9se5x7z0327Jawc0JXTGRO+Ifd8Z8ro2Dyy3DUzd06n0IXx0PokN7T0RPrnbaVu8YPeJ5X356DgAgr+8XOB7uva37rJv6nJoJXJ8PGPVvdYD7/LXrUX+iTnnc7wfatGH4dN95uq9P07nJi9bP1nzNfQGEEJLEtkHaZbj2/X34smyN81DX48GRqTNUYa2bwa0evTAXXg+OTZqNzkv1306/fcgSXPvGfbhyS9M/EszCXSdEl662dgEIdeoC5sHu/j/OiggxoxFNtYIljyc05evmOXWIaeVATTUqF+YrPbh6U8xOvl+Himeh+7TQsZ6cHASrqxGsrlZN5IrHuj5sXMWg/bvrVhr6B9PhgilKcCuHu/J0bksOcwkhhJAYqNaiQGhi9+Ar5Y5C3f7jSnQ7dN0Mb7X23Poogg1++DJz4K8L/8K3ZLH8y1xTufNDaw4R7MY6pXvxc1Ox5ybjtVG/kfNshbqx2rl+CnggCF92mithLhAKLr1nH8BpbeJbvyA2L/vqeBDesw8ooW2n3ocMj3Wq8oNO6HjuZwAQMZELABntMnHr9tt1nyuHuQAw+j+/BQBs+OkqnH7uMQDqcFcOc2k6l6QiCnQJIcSAvCGF6NWNJtTNXf2I403RYnFk6gzwxkbVfWL6dvuQJYbP06tbuHLLhNhCXWmDNCsi2JWJkNeN6oV4dd7mTS1Rzp+IYFfQ+554s3MQqLH3/RLHykFs92lFOFSsH6wHq6vx8ZQCy2BX25srgl1AHe5KaAFNCCGE6NBujibWo27UMMSTCHONgmQncufPi5jWdWLQ7YsjunKN9Bupv74Rz3/vudmGx9jRNJXrvBbAzIg3Q+H+5svKExLsCiLYldV81hXZ5xwBEJrotTOlq9W+vUeZ0tWqP1GHZ3/8tG6wK4e5Y975rfKxCHYBdbgrMAZ/MEjDBST1UIcuIYTY4Havbjzx+nrVxmvp7bJiOp88setUj3klCDbUI9hQrzuda0Uv5I2GNsw12hAsFnlTS5A3vczVXt39f5yl9PwqrzO9TJnOle/Lm16mmsrV9upqGU3wdp9WhB5FZUoVQ9cnSpQ+ZqAp2P14SgGOPBzZy1w107iruVvpYm3g26KrFlR4gm+EEEJaHG3A64jXY6vD1g3B2oaIjdeU//4bXMeeWx8FvPrxhJjWBaKsPbDo0rUiunZjnd7lgaAylbv5qjXYfNWamM6nNeLNCRj1lvu9ut6zD6iC25rPuuIsnf7bms+6AoDymJMpXTGVC4SmdL/89BylhmHEf+7GiP/cjYx2mcoxIth9/tr1yn3y489ds0H3dUb/57cY885vce5PmioYKMxNYrR+NkWBLiGE2CQvoqvGTlduX5atsX5yuHYhno5MnYHDDxQAHvX/tbtRmRATT/P+p6ZyYT4QDCLYUB8Rjtqxr3Q6Khfm2w5p3dwszWjStuf9RRGhriBXMZiFulaBr6zLwrkRwS7QFO4eeXi2soFJsLraNNSlqgVCCCEkdsrGZivzcfCVcsvj+48rQbDBH9dQd8+tj+L9G+YbBrNAqHZB1asbFqxtQLcS/X5b2XvPGa8xjAy6Xf817YplKlfYuX4KgFDf7cbB5QjUB+CvabR4Vsjmq9Zg4+By2wGw2CzNe/aBmINdvToFADi6p4thsHt0TxfL59vVpg3DX4f8CQBw40t3mga7t26/XXlMntbV+sfkV/Hp/34iPqV3ipGURYEuIYQ4s017h52J3dzVj4A3Nroe6ooQ9/ADBeCNjch9ZqFqOjdCuEc3WocW2g8BhWgmc/U4CSBlYoMwMcVqZ8MwEeJWLsxH0N/oePJWhLpGQbB8fqNz6n298rX3vL9I9+uRw16z6gW7NRZH7m3aAVsEu3pTu/JbIYPV1TgyTf9nvbZSeZtoq1lAMwCMJ/ZGCCGkxYpYi4peXSv9x5XEFGwaEUFusMGP/uPLIqZz3SB+cRz1lKzXYxo0JwLzejBoTKkyZezLNv69tghxRfg7aEwpAvUBR6Gu2bRuux4Hlclbo+BXbIImE1O4ggh2tffLU7pGoa58v5jG1fp439nwa1aMZsGuHOSuvXQVNlyxLuKccphLwwXJi9bP1qhDlxBCHNDpMuMAbPXqii7dww8UgGVkAIh9czReX4/cZ8z7WuVu3Gtfn2Tao2skvV0WGk7URt1f1mO+dYhqRGzUJSZOo9kcTQ49KxcVwpOeYXjsvtLpSoirOsfUEkfTvUq3bji4lbt1gw31yvn1unf3/3GWKnC1E0LLet5fZPtaxT+Q9LD0DHC//vRIl4VzAQBHp89B8FTkzwVvbMThyQW0ERohhBDiIrNeXbubpWmndGPZHE3pyx0fuVb54NnofhmvJ3f+PNsbqukZdLv19K8d0fboDpRev6lL907D4/01jRH1EgNvX4yd66dg81VrbG+oNuLNCdg4uFwJbEW37slTHKPeavpZ0fbvasNcbWBr5eieLkqXrlH1QrQbpwk3vnQnAODv16/Rnci9cFYZ9i0oxIYr1mH0P+8AEJrOFWgtSlIdBbqEEBKbbQhvlmaLxwMEg8qmZYcfUC9MWUaGrZD3yNQZ4PX1hnUGxybNt3c9Nl25ZUJUQbAbuk8vcm1ztJg3RvN4lKDUyeZnedPLlOBWnEd5TLOpmt5z40FvUzThYNF08IZ6sPQMIBjEkXsL0fUJ/WmbLgvnqqZ4BbEZ2uGCKTgybQYyzz+/VU7nKngK/tqfEEJI0uOcXy8GDGxtlub1INig/s/w+zc0rRs9Wem2At49tz4amvb1egwncv11sW9qmyx8GTnw11fH3KNrJ8w1I0LdjYPL4ctOs3Uenw+orQutQ0QXrk+TBGk3VpM5DXPt6tDeYxjqnt/rc5w6xdGmDVNqF375xt26x4pgd/NP/6S6/0DpLPSaUaIKdalqIcXQ+tkUBbqEEBIDeRFth2kdAkLdvLZe12IyN3gyfgvoQwtnubZZmV3dpxfhwMxQ2BntlG7MYS6awlfA/sZqSuXDVOO3H+7/4ywgGLmgNQtz95VOR7Ch3tY16JGneA8Vz0L3aUVKkAuPB12fKMGR+x8MffxH8769rk+U4Mi9hfC0yVGmdatmzkbu/HnoVroYhycXqMJcmogghBBC4oB5OELvVDZkVYewe6W99U2wtkF3KleQp3M1G6LGLNop2Vj0GzkP764NDWK8u7YAvowcx9cQa5griGlfcT1WRFhr5i/D1ulOuZqFuR17VuHUKet/BgU+76F7/xcV3ZXwOK/vF6j8oJMS5Pp8oc3Q/jrkT/D7IwNoPSP+czc2//RP8GblIFDbNAjSa0YJPirKp+lc0uJQoEsIISnmyNQZphuNydO5ct1CrOTaheYIdWOpXjALc+Vg1JOeoapDML+g6KZ1tbT1ClaU6/V4dK9T73yip1dcY+XCfORNL1NN6R6Yla8EuYJVkCtjGRkI1tQqoa5RPUdrXUCnYi8XIYSQ1NL/rhLTMNcte2591LSP9oNnZ8VlOlesBf311VGHqrEQU7qA8y5fqzB381Vr4K9ptD11C4Q6eTcODnUnO3mellGYa0QEuT4fMHfPjRGPL7r8FdR8q+5qbtfjIE6e4kqlg6iAEFO6x48H0fHcz5QgVzCayjXiy05DoL5pQ+gDpbPQoyD0bwaazk09tH42R5uiEUJICjkydUZo8zODSd9jk+bHbTr3yi0TkN4uCwCUUDeRuk8vUvpenQSglmFuuC83b3qZo4nXvKklUT1PpteVmze9DPB4Inp+xUZqyvUaTPyqvjfh0Dnob1Q2aRMhdOXC/Ijvo5MAVyu35BGwtDQEa5oW0VUzZ6t6c0ELaEIIISSlKZ25JpO+cpjr5nRu7vx5qu5/f3013nsu+s1+neo3cl5Er61dPBA0DXPljc82Di4Hs7GB28DbFyubrDnZME1LDnPn7rlRuenp2LMKdXUcc/fciFnv6R8jh7k+X6jmobaO44b/mYTaOq58XlvHIyoXnAa4Wr/ccRd4IAhvlvG/GVrrcAFpeWhClxBCkkTVuIeUzdL0WIW5gLpqwc3pXOHKLRPw+vByZVL3wMx8eHJyEjatK1cvuCFiGjfcceyYx4N9pdMdTemabXym3YDNaKM2vXPqnc+KeK2j0+com51FI7fkEVX1QrC6mqoWCCGEkBbEqmpB5nbVAhAKdYHQL43FtG5zVDA4xbwe3c3MRJgrahQG3r4Y764tUG2iZod4nhPayVxtiOvzhQLcL/fnAmgKc42CXCA0nSvc8D+TIh6/9vXI+wBg6+VLAYQ2OBOduNHSTul+VNS0puacJ2SKnZBEoAldQghJFsGg6YZovL7esoNXiEeYK8iTugAM31rvtkMLZ0UV5uZNLYmYoBXTrtrqCrOeW6evYcYszAXCU8XShG6wod7WtUW7aZw3OzTFIPpvY9W5dA48bXJU97XqMJc3w40QQghxaPfqQniy0g0ft6paANTduU54stJxuHCK7ePlad1YNyqz673nZjsOTYWBty+Gv6ZRdZ82zE00JzULAHDqlHmYCyCiasEu8W+L+hN1+Pv1a6I6h6Cd0pXQCimV0PrZEk3oEkJIErCazrVD7s6Ntyu3hDZX2D5kSUJe79DCWbEFx+EJWgCm/bOAs8nWaFmGueGKCLkvN57kDdJixTIyUHXPNOQuK8axgrlKSMwYe4lzfr0rL0IIIYQQ9wWC6PP8g4YP29kILdru3D7PP4j3b3C2ls2dPw+HJ0cXsDr13nOzXQuORV8u83p0w9xoax2c+MuwdcrH2sncon5/h98PtGkTGmbNPucIAHsbk2WfkR5VqPuLv92lTOk6DZr1iCndvveX4YM/Kmtcms4lLQoFuoQQkgwspnPNNkKLZ29uMtCGudEErkqFgUmQ6xbtJmtyDYN2MldMxqoEg01hro2aBTfIVQ2xyi15BFWFDymh7pdPrEJtxUcAcJ1rL5KCWBRNHoQQQkiiWE3nWtGGuaZ1C14P9tz6qGl4nGzkMDeWwFX048YztBWBMRC5WZqdDdD8fqDms64AQmGuUZ+um1696WlXz/fLHXdh80//BADaUJekEFo/m6PKBUIIaWZ2pnN5fb1ht6s2zLWsW/B6cGySegMJlpmB7Vcutb7YZhBrmCuLtlLBLtWmZdPLEPQ3KpPBQGQlgllFgt2aBT1Ov0/RVjUYERukVd0zDR3v/a2r5yaEEEKIu3avLoQn3RdTwOpkI7RuJYsRrI2c4nRau9AcYg1iB40pjWvFgrzBmt5maXphrtx7K+vYs8rWVK4s2sqFhhNNnbcj/hPbxmiCLzsNH/wxH3uWFrpyPkKSDU3oEkKIS74sW4OO+Xc6f6LFdK6Q+4z5hlvp7bKUKgQz174+KaIqofPSeTg6fqrlc5uDJyfHlZ5ebcipnZYVvNk56Hl/dJu8ebNzEKipVuoLxOfa18p9phhVY6cp1xHt6wnyVDAQCpa108FG5Oncrk+4F3iLDdJkrbp2IQV7uQghhKSWg6+U44JrrNeCESyqFpyIZSO0aGoXUok2DDaqcfBl5ES1yRvzepTJXLnr118TGgqRaxYm7RqNp69+AbXf1qPm2wYsuvwVTP2fa1TnO3WKO5rOfeTHL6s+f/na5Ri2faLl8+TpXLfCXCA0pQtAmdQFWvlaNBXR+tkUTegSQkjstgFA7fv78GXZGttPqhr3EKrGTo+5O9dzWuht+w0narF9yBK8Przc+kkuT+keWujeW/a1uk+PLezUYxTmAlACWflmp5JAhLfac8l/ysTfmwh8mx7wqKZ6rRhVMwT9jahcmG94LrExnNvTuTLRp5vV+0JxV6uuXSCEEELiZBsAfH+sEgdfsbEOlNitWrCzIRoAHJ5cgKqZsy2PE7ULbnnvORuvmWSvY9TJ66+vxrtrC1Q3O6/r9WUZPrZxcHnEdO5dr92CrDNC/w4RoS4Q6srt2LMKbdowFPX7u+XrPvLjlzGnz9/hrw+oNiIL1gew9fKlePna5brPe/na5dh6+VLVdG68nNY1T3x4HWPspbi/ICEJQIEuIYTEKPxbXiXUdYJlZFhO55r15wJA56UzlXAQgK1F0bWvTwKvq1fd13npPLC0NEehrtiRNlhdHddQ121yiNn9uSLl5mmr02kLndBVQxsQG50r95li5D5TDADovHSWbqibN7VENW1rxaiaIW9qSaj2QXMuEeQG/eqdlmP9xYKe3JJHgGBQVbvQWhfRjCf2RgghpPWQ16LfH6u0/bzdK/NtT+cGaxvQf5zxO3l8mVKQZ+OdVd1KFiPY4I8IdT1Z6Tg8ucB29YInJ/S6/vrquIa6voym13GDfK0XTStTbrr7K8De1ydfmzhfm+55EcdN2jVa+Vgb6gLArPduRF1daDHh91t/LQ3VflxcWIa8B0px4R+aBjH6/L4Efe8tQ7A+oAp1RZAbrA+ozuPLTrN+sSj4stNw6tg+CnVTEK2fzVHlAiGENJPc1Y+gatxDODJ1hq3KBTOdl84EAFSNtT/ZqX8eZ9ULV26ZgNeHl6PhRK0rtQiJIAez3Z9TT/92K58WcfzhCcUIfl+tqlIwI84ZrA4F6ywzA12Wz9U9tvPSWTg2qQjBk9VN1Qx1tfCkZzgKdU2FJ361G63JIXTu8kdRNTE+G5PIU7piczR6uxshhBDiLs759Yw5iyT6jy/D7tWFrmxQ1vc3ofXP7pX2N5/qVrIYhycXqO4T12G3eiF3/jxUzZyNYHW1a2FrIhhda6/7It+Ztm9JaM0mJnetXDSt6V1b1Uc+Qlq2DxPfvM3w+LteuwVLB2wAAN3qhVj1+X0JPnii6eciUNOIvveW4cOVsxCoDX0f4rlxmdggrdvwCTi8pRwnj1QCtB4lLQBN6BJCiDuieit57upHQhuemei6aEFog6lxD9k+b7S1C4Dz6gU7vb3ROrRwFg7MbFrcyWHs/j/OclyLADjfAKxb+TTDyV0tVUAcCCL3mWLDMFfQTuoiGFS6b+Wv0e7XpyVP/IppXrPKCbfJU7py9QJNRhBCCCHNr/+4Et1JWS1PVjp2r7a/uZSt2gXAleqF3PnO+2bteu+52Xh3bYFhACset1uLoCWHr0Z63VdkOLlrZm9xPniAm4a5grZ6QUzm+nyhkHdOn78bbp5mB/N68PK1y/HytcvBvB5VmJsIvuw07FlaiG7DJ6gmdRN2AYTEAU3oEkJIEjoydYYS9IpahsMPWP9G3nNaDoInq23XLmy/cimOTZqNzkvjtxCOxqGFs3QnfuUQ0qiv1i7tdK4ZMbl7aOQsW889NHoOWKb9CgN5UheAMgls9vXq0evxFfaVTleqO1QbtC1/FFX3TItL5YIgpnRzlxXjyydWKZO6cXvBZMMB8BR8HxchhJBWof+4Et3J2j23PopgbQM8Wem2NyzzZebAX1dt+51belO6ycBowzLxmNi0TD5G1CJEs6GZFTG5K6Z1AfMwuHJx6Hualm0v8rnrtVtUm6QBoU3RAMAf/txo8zQ7xJQu83oiJnb73m8dasfqlzvuwl+HPo09SwvRZ1JJ3KaBiYto/WyJJnQJIcRlVWOnO9ocTQ+vr0fuMwuR+8xCywlemahesEuvS9cpW9PANomJXO0/AuQAVUys6j1mNcUa7ZSr/Fq2guBA0HIyV6vzUutr2//HWcrUrt7X0vN+42uTp3OF3OXubUZiJrfkkdCU+T3TVH26hBBCCHHf7pX52L0y3/YGaXobo+259VEEG/zoP74MwdoG268tqhcAB1O6ScRoIlfut5U3LRPkx+y8RrR63Vek9OOa4QGOSbtG25rOFe567RbLY0SoG+3Ebp/fq3uYExHmCr/ccRd4IKi6j94xRlIZTegSQog7tkGaOKx9fx++LFuDjvl3Wj/T44mcvpU3QfN4QhujJYjTHl03d6Y1C3I9bXMQ/F5/kSwe006nJrJa4NDoOUB4kehkOleoGhf5d3zBptDX//H44oivz9HXJf086T0vd1kxqu6ZhiP3ht5KyTIyQlUJLsoteUQ5v9CaustScaMFQgghKUe1HnWyQVqwtkE9gev1NG2CJtUi7F6Zj/7jzUM4p1O64vzR9vi6GRzrBblCr/uKVBOysl73FWFvcWjgQDulazbt67bKxQXgAW57Mlf2p0s3RNzn8wGr9l4KABjT8x0ATZuniT/dwrwebP7pn0Kvm52GX+64y9Xzy07rmkdduimA1s/mKNAlhBAXyIuA8G96r6t9f5+t5+auNg/OxOZpqpDXhteHl9vrtw136UZTuyBP5/aYH9tv2A8tNN6sDNDfsEx+TNQhONm4zC2HRs8BS0tDl1XOpnJVgkHDh85fOQ0Hb4ucyK1cmK/8XJiF13lTI3el1k7n5i4rVj6O1wZpgmaDNA5gGy2kCSGEkNho1qMcAA6+Uo4LrjFfDyrBrcnju1cXqkNeE31/U6RUOFTNnG3ZcRtr7UI8NuY1moDV27RMENVX0WxeFis5yHUylSsTvblCTg5DdXVTonbamT6c/EZ90Jw+f1d9/tGTxu8463tv6Ov9cKX+MX0mNf1sxasSQXTp9plUQhukkZRHgS4hhLhM3mXY9pSuBavQVyb36NoJda99fRK2D1kS1XW5NZ1r1JnrhNkEbywBrjx5a4RlZjiuWJBpp3PF13Jw1BxcsHGu6j7txwgGdUPs/X+cZVrBYMrjQVXhQ65P6Rp06QKtoU+XJgwIIYQk1jYA131/rNJWqGvFTpArczylGyV5OnfQmNKoz+PGFK3ZBC8QfYArwlozsQS5RuQwFwCefHsA/vDjXRGhLgB8WBK5Fv1w5SxcPD5yLWpnMzQxrev2pK62S5dC3SRH62dTFOgSQkh8bEN4Srdq7HRkXdLLlWDXjs5LZ+LYpPm2N0eLllvTudowN9rw1WyCNyaBIHKfKbY+LlYeDzw5WQierFaFtcL5K9Vfn97ELgB422Yj8H1NTFUTucuKUTXxQRy5txBdn3D2DzjT85Y8gqrCh5RQF2iaBqZFNCGEEOKe8IDBS3Ax1HVCntK1JcraBTcCYzcrEcwmeGMRj8BWyxdOh9JOy0Dtt/p7bDz59gDl49/1exd11cZDD1bBrVl/rpjWjcek7i933KVUO3QbPkEV6rr+YoTEEQW6hBASB/IiGgh16laNnQ4AyLqkF0QdQ+4zC+Py+p2XzlRez5YoahfcCIvdCnNTXe7qBcrHVWPtB9M9X5iLQ+MWIfB9jfI5AOy/ZQ4Ae/UThjwesLS06J9vQNulK9cvuP5iSYKBOsAIIYQknjbU1QtYrfpwEyWa2gW3pnPlMNfNGgS35E0pVfp54+nud0YrHy8dENmnq7XivUEY0/Md9JpdhoMlTZPJvWaXYd+80PV+8ET8rzsacvVCt+ETlOCYBgySB62frTkrZCSEEGIb5/x6zjlDaFpXIXfrVo2dji/L1iT4yiJd+/ok8Lqm38SzzAxsv3KprefGMp1LYa7asUnS98DjMe1NZpnp2H/bXHRfPRU9X5irhLlAaErX8rUKzCsicpcVgzc2oqrwIesLd0hULwBAx3t/23Q/7TRMCCGEuCocTm0zenz3ynwcfKXc6OHEkjZf82Sl43DhFNPD3ZrOFZIxzAVClQvRbHIWraevfsH2sZk5HhxYUIALCovQa3YZes0OfQ+92TmWz/3gj/n4cIVx5y7QVL3w16FP274mO3654y54M7zYszQ0ZHBa1zzx0HW0HiWpggJdQgiJMxHs6oW7QNP0brMHu+EpXQChSV2L3lg3JWOYe2j0HBwaOQssMyNhrxk82fQPkws2zlX6c/V0XzsLLM2H/bepj9l/21wEquuUkLfnC3NVAa/4OHiqGlUTHzQNdkWoe+TeQleD3dySR1R1Elm9LxQfttgpXUIIIaS5yGtRvfWomN5t7mC3W8liBGsbACBUvWBzLerLsA4PjbhVtRAvlYsL4Mvwxr1uQXj66hdUdQtr919qevyK9wYh6FePUR5YUADeUKMEvL1ml6kCXvnjQG21aajbZ1IJ+t5fhkB9IC6hLg//jHUbPkEV6rr6QoTECQW6hBCSQGYL6uYOdrVTunYdWmj+m/WUFe7OjWWzMyfk6dwLNtkLuLWh7v7b5oKl+SL+ASRP8XZfPVUV8AZPmf9DJndZMXKXPwpe7/xnw4zJlC5vcZMRnCf+RgghhBgwGjZIlmDXLk9OKBiMJZSNJQyOt0SHuQAchbmCmNIFQmFuegZDegZT7gOgmuAVH4tg184maX0mlcBf0+jkS3Gs2/CmjukWtxZNRbR+tkSBLiGENCO9Wga5kiGZpbfLAuDO292am5jGlW+JnMwF1NO5Tiih7i1zwNJ8oc/DdQyGzwkHvM0pt+QRsLQ0JdSVpnQBersbIYQQkhB6a1GxiZpb5K5bS1LtgpXc+fb3fkh2lYsLsLc4X3VLdJgrVy3YDXOB0JRuegbDvnn5SM9gWPHeIN3JXa0LCp29Q495Pa5P6YouXYGqF0gqoU3RCCEkCYjyfcZC1e9flq1Bx/w7XTn39iFLkN4uC1ducXdn4yu3TMD2IUsAhKZ0u093tihLlsneQ6PngKWlocuq5gs4o5nOlXVfOyvi84O3Jcf314y8QZqY0v3yiVUtcpM02tSBEEJIMpPWosomarHyZebAX1eNYHU1Dk8ugCcnJ24h7HvPzUa/kc7PnSyVCzzAMWnXaOsD40RbteDUivcGRdwnJnd7zIh+wzpZn0klyuZlbvnljruw+ad/Uj7vNnwCDm8px8kjlUA41KVN0poPrZ/NUaBLCCFJyI0pXc9pOcrUZ8OJWkfPPTp+qq3j0ttloeFELYLV1Y5D3XhN9h4aPaepcsDrQfcNkUGtfAzLzIhbrcLRiXOUGgsx8cvr6lWveWxSUdTTuWbElG7PTXNcP7ebRPVC7rJiAKFgt2rig6HHaBFNCCGEJBTn/HoxYBCrvr8pwgfPzoK/LrTOicfaz5OTg2B1Nfz11Y5D3XhtiFa5uAA8EPoWMi9D3hT9QFM+Ll6bni2/bBMaa/y6j6Vl+5QJ4GiqFqyseG8QxvR8B/vm5SubpcViz9JC+LLTXLgyNTGl22dSCYDIUNf1FyTEJVS5QAghSerLsjUxdep2XjoTuc8sdPak8MZoXVYusnX4lVsmqKoXopm6jceGaCwzA7nPFIOlpTXVKIwOBZtiIjf3meK4deQenTgHVWOngTc2ovtzRej+XBF4Y6PyOW9sxNGJoetRbYQWxXSu1qExRTh42yzwuoaYz5UI2uoFoIVuksYTfCOEEEJidPCV8pg7dfv+pgj9x9sP8+SN0ezInT9P1aUrh7RW4jmdm5btw6Rdo+HL8CoVCpWLmzplRT/upF2jMWnXaNerFZZftglLB2yAvz6Ai6aVKTcAysci6I22asHI7/q9izE938GYnu/A42OuhLkAwANB/HLHXa6cS/bLHXfBm+FVVS9Qn26SoPWzKZrQJYSQ5LIN4RBLTOnWvr/PlQqG14eXW9YuXPv6JGwfsgTHJs0GvPZ+53fllgl4fXi5Mqnb3LpvmItDI0PBshzWHp04R+nGjedGZ0cnzlGCW+11aa8x1qoFrUNjisAb/RH9uPtvCYXHzd2bayS35BEcuf9BZTKXEEIIIc1P1C6IzdLads7DBde4W+Hlhtz581A1c3bUk7picy635E0pxd7iUDWAHNQuv2yTcr88HRsPjTV+3alj+T7mZaYTvNGqqw5GhLj75oW+brfCXbf9csdd+OvQp40qHVrOgAFpUSjQJYSQJBJ+m9tL0CwcYqlgENULdmoXtl+5NKrNwGLt002EeIa4Ml5Xb3vq2O2qBTGVKwJcrf23zIG3bTa6r56KQ+PsTWHLqgofAq8PV0hkZCC35JHoL1byf8tWAsGg3kPu/gujGSVbBxhjbASAXwMYCKAjgKMAXgSwgHN+MnxMVwCHDU5xBuf8RPi4XABLAPwwfK5qABUAFnHOX47bF0EIISRelAEDmRu9uk4dnlyAbo9Zd7BqQ10nAjXxGUhYOmCDqhc3URucLb9sE5iXWR7HA1wV5roxnTum5zsAmgJcrX3z8uHNzsEFhUU4WOL8nX1y360vO83Vid2zLjkbX7x1VO+hFrMeTTW0fjZHgS4hhCQZbWdorBuldV46E1Vjpzt8zjzbPbqC3KdrJR4boml7ceNJ243rJCw+PKEYwe/V3yM3pnM/Hl9s67jA9zWqP23zeMAbG5G7PLTzdNU901BV+JAroW7t3o/Eh37OufvlaERPIUKL0BkAPgHQD8DDAK5gjP2Ecy4n7AsB/E3z/JPSx20AfA1gZvhcbQFMALCNMXYL5/zFuHwFhBBC4kKvv96tXl1bvB7suTW03rAT5gq58+fh8ORQrYHVlK6TagYnRC9uvCdw5cla7WsZTefK9i1pvs1zAzXVOFgyy3GQzsLvHhRdt3uWFuKvQ592JdT9d+E2Ocyl9SgxklTrZwp0CSEk+W0DcF20U7rHJs23fayoXIiGPKVrJS7VDIEgcp+xF2pGSwlyvR5lClfUO4jH5aoKvfA2XuTX6b1F/x8pFcND/7CRJ3hFQGtFbFomf+5GRcL/LVupfEyL54S6kXP+lfT5G4yxbwA8A+DnAF6XHvuYc/620Yk45x8CUP1rKvxOg8MAxiE0uUAIIaQFOPhKedxrF7qVLFaCWafkTdLMyI+7tSGa6MVNRJDLvEy5blHjYGTfEufhaTT+8ONdysd5M/S/p/sfD12LfD1977f3/RdBrvy5QUWCYxTmEpuSav1Mm6IRQkiSk6ckotkgzfHb+sMbo8XC7gRurBuiHRo9R9n0LJ5TuRGbnG3Qn8jldfWqx8zC3J4vzHWt01aezjUKcwHA2zbblddTeDyoKnwoplPI07kxX0+y4gCCPLE3q0tSL0aFd8N/nhvzl8y5H8B3ABpjPRchhJCksA2Irnbhg2cTNw2aO99+d65bKhcXYG9xflzDXO0mZ3lTmiaXRReuEbMw181O25PfWC/lej5Q5GpnMfN6sPmnf8Jfhz4d9Tn+XbhN+ZjC3CRC62dLNKFLCCGpIaopXXk699o37rP1HGVK1+amaDK5duHAzKbfmHtycpReXbfqFg6NngOWloYuq2IPRY9ODE2sajdRE7UK8kRuNEyDW68Hh8YUofva6L4vH49XTwFX3DwfvV+cqXts3rqm3XvFtG4scpcVx1S9QNO5SWdI+E/tv9YXMsb+hFC31xsAHuKc79E+mTHmQWhY4CyE3jLWE8D98btcQgghiRLe54EDzqZ0P3h2Fvx1zbNp7rtrm6Z8fRk5uhUMsU7nujmVK0JZ7UZq2olcLXkTNm1/rlytYBTe9ppdZth5a5c8nWul5wOhNXXlgtina8XUbrSTutqqhZgviLRGzbZ+pgldQghJAdFM6R6bND+mTbeufX2S4+dcuUV/cR+srsahhbNwaOEsV+oWlDDXhY3Ojk6cA97YCN7YiKqx05SbMo1rMpELAPB6lEA4Gj03zVE2M4uGtmrBk+5Dxc2hIL/i5vnKx/GSu6w49L1zOKn7f8tWto7pXIEn+AacxRjbKd1+Z3Z5jLFzAcwD8BrnfGf47noAywFMBHAFQr1hfQD8L2MsT+c0jyI0UfA5gAcBjOKc/8Put4gQQkjSczSlqw1znfThwuuJarjAiL++WunNdaM/1+2p3OWXbYK/PgB/fQBLB2xQbnoTuWYaa/yqY+1WLXh8DL/r9671gTr+8ONdquncjBwv9j0aCtP3PVqgfGzkwxWxD3swryeqKV2qWkhytH42ldAJXcbYzwH8U+eh7zjn7cLHnAZgDkK7xvUHcBqAKzjn/7L5GvkIfdMGAugEYC7n/OHYrpwQQpKHnSldbZhrdzo32uONdH+uSOmR1Qa50U68WoW5qslaHfImZiLMNQ1sLXTfMBeHRs7S7c+1LcopXb2qhYs2TsPeUcVNE7gu/mPISDR9urQRWtx9zTkfaOdAxlgbAFsQCtbHifs5558DuFs69E3G2HYAHwJ4CMBvNKd6HMBGhNZfYwBsYIyN4JxvjfaLiAdajxJCSHTkKV07og5zEerRdcNF08qUDll/fbVqajdaVlO58oZlerSbmIkw125oa0Q7nSuzqlboMaM06ildOcx98qOfAwDyB7yJygX5yMjxor46oPs8b3ZOqE+3NvZhjz6TSmLaJI3WoiQspdbPzVW5cB+aeiYA9WROewC/BbAbwA4ANzs89wQA3wP4K9TfREIIafH0pnLdCmedEoFtt/Jpqo3D5MecshXmhidrDc8hXQuvq4+5x9foXE42Q+u5aY5qozK7jF7joo3TAIRqFYzqF5pTa6xaSODe4I4wxjIR2oH3fABDOOefmB3POT/GGPs3gEE6j32C0C69ALCVMfYvACUAkirQldB6lBBCXKZXseA0zHWLqCjodV+R7sZg0dQt2KlYaKzxm55bu4mZ1fF2uXUeJ+SqBRHmAkDZrssAhIJdj08/ZO75QJErtQuC003S5O5ckpxo/WyuuQLdSpPd3qo452cCAGPsajhfQF/MOQ8yxnygBTQhpGXZBuA6swOSJczV8rTNcRRwGgoEDTtz3Zi2dZNrX7MBq43Q9o4qTsh0LgBU3TMNLMP+pnStqmohiTHG0gC8AOBHAK7W6/UyeirEG9PM7QTwQHRXlxC0HiWEEJc1V1+uFW2oG23wyQPccjLXaEo2npiXgQeM/9N8sGQWLih0Z4hBZrYRWv6AN9FYH0SvB+Mf6O9ZWggeCMKXbX9OgLpzSTSSaf2cdJuicc5jyuA550G3roUQQpKJk7e5JUuQK3QrnxbzOQ6NngOWGRkaKhULXk/zhbk6waneZLLVOezWLhy8zfyYvaOKEWzw25rOPVYwF51Lo+8ArrpnGlhamu1N0VrjdC4AILbljevCGzCsB3AVgOtNgk3t87oA+CmAv9g4/88AHIrxUpsFrUcJIcSc1cZozTWVa6bXfbEFmpWLC5CWHRmh2Nm4LN7kjdH02OnS9fgYxvR8B5k5Hqx4L2KQUEXbmytP5wr11QHkzUjM94MHghjxH/u/P5Wnc1vVejTV0PrZVHNtiraeMRZgjB1njG0If3GEEEJs0tsY7dik+G5+Zcfrw8vjcl6jqgW5YsF2mBvjJmZ6um+YG3OY3HPTHPBGPw6NMf7HxsfjiyPCXL3pXDu8bbMBAMFT1ThWEN21Ow1zAZrOTSJPArgVobd0VTPGfizdzgMAxlgpY+wxxthtjLErGGN3A3gTQBDAAnEixtjDjLEljLGRjLEhjLGRALYjNLng7v/Y3EXrUUIIcc7RxmgthVHVgui/dbJxGfMyLL9sk+vXGGuY3GNGKXrNLkNdtfnvJLVhrhti2Rhtz9JCR5O5/y7cRtO5JFpJtX5O9ITudwBKAbyBUK9YPwAzALzFGOvHOf8ywdejCO9eJ3awW8E5X9Fc10IIISa2AbhOb2M0bd1Cc2g4URufE+tULURbsSA2MUtGel26H48vdlzdcNHGaU2bohnIW1eoHBM85fxnR4S5AHDk3sKIx1lGRkTQ22qnc5PTsPCfD4VvsrkAHkZo84Z7ANyJ0KZgXwN4HaENvuT/E9qN0FvDRgE4HcAXAN4HcBnn/D9xufrYJOV6lNaihJBUIL9jzGpKtzlUzYzuF91W9KoWot3MLG9KKSoXF2D5ZZtMu3jdZLUpmhmrAFdvOhcAMnK82PdogWnlQqwbo+1ZWghvhhcAsPmnf1I95stO090gTQ5zaT1KHEqq9XNCA13O+XsA3pPueoMx9j8A/h9CG1M0284t4UUzLZwJIUlNXkRXjZ2ue0yHH3dN5CUp5OlctzYaA4yrFtzc0CxZWYW5ZtO5nqx0VNw837B2ofKOkpivj9fXAx4Pcpc/GvFY1cQHVZ//37KVrXo6N9k2deCcd7VxzCoAq2wc9zeENoZICcm6HqW1KCEkhWwDcN33xyqxe6V7m1q5IViduAGHWDYhExUJyy/blJDO3Vg6dKOdxi3bdRn+cOG/onquE/6aRjCvB33vV/9d7FlaiL8OfVoV6lLVQmqh9bO5Zu/Q5ZzvZozth85ub4QQQnSZbo721dtHEnclknhM5+pVLciduVHzelA1dlrCNg0TU7fettnovnqq6rFD4xYh8H0Ncvr3wLkP/Ubp0mXpabphrrdtNgLf11i+ptmUbuUdJapz6AWyVnKXFZsf4PHgyL2FyqSuHObSApokG1qPEkKIfeEBg5dgsh79YsVKdPrd+ARelZpbXbaViwvAAzyiO9eNIJZ5WVQTvk4oE7A11dg3Lx/e7JyIYPdgSdNmcTkX5MHjY/hdv3ex4r1B+MOPd0Wcc9Ku0Vg6YIOt1zeb0t3/+Cxb3b5m+kwyHlDoM6lEFepS1QJpaZo90A2zu9sbIYS0epzz640es7tpWjy5MTV7aPQcIBAEy8xQwlzV5mcxvkaiNk/ztM1RhbKB72twaNwidF89VQlyherdB5TglwcawOsalMfkSVyrKgXV6+tM6WrD3HgRgW/VxAepaoGDVjmpgdajhBBik9F6VAS9tZWJ79h1u25BrzdX3gAt1iA2nkGu0Ou+Iuxb0hScaoNdOcwFgOqDob+3Oj/HmJ7vqM41addoAMDTV7+g3GdUtyCU7boM+QPeVIW6ekGudrrWLXKo669pFHfTcEEqoPWzpWYPdBljAwH0BOB+KzghhLRSu6b+DQMW3WT7WDHV2+HHXW0/L94iwtzw5meppFv5NABQdfYGvq+J6Mk1Y1SrYGcztIs2TsPeUcWouHk+vDmZCQlyAeDLJ1ahtkKZyG3VVQskNdB6lBBC3CHXgznxxYqVqK2sRFZeXtSTvW7XLci9uXKQ69b0b6L0uq8Ie4vV1Rgi2JWJaV49IswFgNpv6x29vqhecGMi167DW8px8kjTLxX8NU0bvVGYS1qKhAa6jLH1AA4jVP57AqFNKKYD+BTAE9JxwwDkAOgTvmsIY+wsANWc85el4/wAnuGc3yXdNxBAVwDifbQXMcZGhD/exjlPzL9mCSGkeWwDcJ2T2gX52Oaqa5CJ6VyEl1rRbn6WjPTqErTBbMXN8+FJ9yFY2wA3iPNoXzd3dTGqxk1z5TUEbZCr0WqnIRgAxmnEIFnQepQQQpKLCHMBoLayMua6BjcC18rFBUrNgtj8LNWCXD0XTStTTezK9wtiMrmxJvR7eDnMBYCsMzIch7oAIl5z4LhS7Fxd4Pg8ZrRBrg4aLkgRtH62lugJ3QoAvwZwL4BshHZxexHAHM7519JxywDkSp8/HP6zCqHFseAN32STAIyVPr81fAOAbgCORHvxhBCS7KKdipBZTffKE71AHKZ6A0HkPlOMoxPnKD23LSHMBYC8dYWmj4sw96KN7gWtvbfMVtU05K6O7L49VjAXnUvtTw1r6Qa5Hs9JHgi0jfqkhMQPrUcJISQB7Aaz2noGO3UNcggcy1SvHrlqQYS5iahHSJRe95m/402eTI6HgeMiv5cf/DEf3qwcXPy76N+NFxHm0lqUtHAJDXQ55wsBLLRxXFeb54toIeec3wngToeXRgghLc72IUuUj41C111TVRtrKtO9RqGuNswFQlO98mvFTNqwLNUqFvQcnmCxeVhYPMJcINSZK2jDXM9pOQierEbwVLXjUNdkGrfVTuKaClofQhKD1qOEEBJ32xDu0T08OTSBaSd05ZwzMZhgFAbLQa4gv45bGmv8WDpggytdualEnkw24nQ6d+pP/mP4mC8zB/66cL9vrbM6BpNpXFqLthS0fjbV7B26hBBCXLcNml2HjaoU5PvlHYvlkFaEwZow1885T7Pa4TgaLWUaFwiFufKmaFaCtQ3YO6rYtVBXuwFa1fgZyF25QPm885JZOHZfkRLq2mUQ5tLimRBCCCGqNaW4z8EmaUoYrNerqzmPH8CrcHkt2pICXL16Bbe0OdPesu/UN8pmZGBej+qxH/469C4yUb3w4YpZtqd0KcwlrZ3H+hBCCCGphHN+vd7EmGYaF0AorNU+V3uMCHe1Ya7Ra31evC7aSzd1aOQs1eZiyejwhGLlOg+NnKUKc602Mev94kz03jLbte5cQN2bm/tMcaibWKPzkqbvadXEB3GswDhQ//KJVaia+KAc5vo55yx8o8UzIYQQQgDorym/WLHS0fPkXt3DkwtUU7hi7SHWovJ69Ojz5TFevb6jz5djb3F+3M7vhn1LZmFvcb5ycxrm5k0pVbpzrchBrRF5OvfaN+6DJ8OLXWunRBzny8wBEJrS/XCF+Xr/8JZyfPDHpg3dpLUorUdJq0ITuoQQ0vIpVQrbhyxR1S8MWHSTXl2CmPDVm3ow+q23MhVc895+1y788+J1Eef7vHgdzp52h2uvES0n07dWYa5QcfN8eLLSDR+X6xOsqKoWnjGvfRDVCwAQPFWNqokP2nkJmoBwgDZ1IIQQ0gqp3jUmglkb9QsR7zbTMEoctwG47tQh29PAtuwtzld97vb5Y2F3Atfuhm52KhfsmvqT/0SEvkNfvke3qu2Hv56H//55Nvx11QjUVqsCWwu0yVkLRutncxToEkJICyVPKshve5PrFK594z6950VMVNh4revDr+Pqf3X1wmE3A+NYmIW5dgNcmZ0OXXni1oy2akFgmRkRtQtA05SuqF+wQEEuIYQQQizJa0p5LaqtU5CPCU/cOl6Litdzey2a7NO4RmGu3QBXJm8GZ8eTH/3c9HE5zJX/zeHNTsOutVMwYMxi1fFyqGsDrUdJq0eBLiGEtAJGgateDYMbtFO0YtI2u19P0+lavYlcQd4oo7mndOWNzqIJb7Wcbohm9Jp6Qa48ndtl+VwcnThHN9QF1PULsqpxoevSq/IgNvDwjRBCCGmltGtRuU5B4loX7tHny9Hl1gmqz8VkbZvuearHtM8zmcD1I5yhaM+faNowN5oAV4sHuGWY+/TVL5g+rjeVqx0gMZrSBZo6dbVExy6tRVsRWj9bog5dQghpXbbJnxhtlhbr+bWhrPjcbLrWJMz1S4s35fzx6uo1IvfjOtnozEzFzfNRMXyeozDXiFWYK3RZPhcsLQ1V42fE9HqEEEIIIVHYZvKYG2+f3wZE1iLIn5tVJhg85pf6WZXzN8f0rujIdSvMrVxcoPTt2qlaqP223vRxO726QNOUrlPhSW9CCGhClxBCWhWjt765eX4xeWG0gdmhkbN0J3WlMNfwLVTyrsmJrl7QC3Fjns4NBG2fw6o/124dAxAKdavGxhYgEyc4QB1ghBBCSES1l/zuMTfeQi+vRbXdt7K9xfkRk7pyQGs0CSqvRUWom6hJXb2KhVgnc3mAY9Ku0Y6fp1e3IG+AJujVuwGhKd0dw5bpVi/oOf28PHz3SSXg8r9dSDKj9bMVmtAlhJBWKrygFlMSbm4oYDZ5ASBywlb+2GoxH22vWizkigVv2+yEvvbeUcWoGD5PFdjuHdV0PZV3lKBieNPb0+xen+jTdYKmIgghhBDiskSuRf3yY/I0rqZqwfRa5LVoIjdIs7P5Wbwsv2wTlg7YoHyeP+BN1eN6VQtWhr58D3ggaOvYHkNNN9EjpFWiQJcQQlqx8MYTzM1NBbRBcfj8yg1SbYKoMJCnc926DrccnlCsms7NW1eI3ltmxzSdq1QtZKWbHrd3VDGCDX7Va/XeMhvBBr8S6monc/PWFdq6BifVC1l9e4kPaSoiSown9kYIIYSkgnitReU1J6TKBOkxAFDqBuQwNxk329on7XNw0bQy5RYtUbVgVbMgglx/fUD1eo31QSXU1Ya5Y975re3riLZ6gbQOtH42R4EuIYQQ15ktzjWBr8zxAjrePbraMNeNEFd05vbeMtuyNzdY24DeL85Un2f4PCAQRLC2QTWZG03IbDfU7Th5nPIxTekSQgghJBVYhMW6U7wO1qLK8+PdpatXtRAtEeT6MryYtGu05SZojTV+XDStDHlTSlWhctDPUV8dwB8u/FfUYS4QmtL1ZHgdhbq0FiUkhDp0CSGEJJymy5eH73MS5m6Diz26JhuyKaINcytung8EgvBkpTva+GzvqGLAG/q9qxzcuq3L8rk4OnEOqsaFr83rQe7KBRHHZfXthdoP9gE0pUsIIYSQFGewr4Ttd4ppu3TdoKl90BXNVG7l4gLwQGj8MC3bZxni6rETKuuFua8PL8eVW8w7hkWf7s7VBar7B44rVX1OPbqEqFGgSwghpFkZbTph8Zzr5U00ZCKc1dt4zUg8w1xPus95kIvwdK7B6/beMjtiOjcWXZbPBQAcnTgHvFG//6zj5HFNoS9xjjZ1IIQQQpJStPszmK1HRTir3XjNSDzDXF+G11GIu/yyTWis8avqGLRhbu/8MlSU5SOjXSbqT9RFnEPc33Ci1tZrDn35HuXj7UOWRIS5QKhHVxv6khaO1s+mKNAlhBCS0j4vXoezp90RMWUrOnqtgl1NbcM2hH/rH2tIGk2YC4SCXDGZq6W9plivUYvX1SN3dbH1gYQQQgghJII2mD11qBJHny83DXU1z+EAQsMOzMMvmlriePBBxgPc8USuqFnYW5wf8VjvfHWoPPKV0brnGPnKaKy9dJWj13WCMfZSc2yUTEgyoUCXEEJIqlJqFw6NnGV4kFWwq9mQLfQWLg9rWkxHwWmYu3dUcSjIBQCvB71fnKlM4FbeURLtZTjGMjNQNX6GbuUCiQEHmL1NnAkhhBCSgvTCT+HUoUrlcb2JXXlDNoiMJsYwV9QsWG16JoipXABgXqb60wjzerDhinUY/U9774hzA9UutCK0frZEgS4hhJCUZPQ2N7nCQX685r39yjQvoNub+yrEdO5fZsU0DYFA0FmY2+DXnb6tvKMEge9ror6MY5OK0HmpcditpfTpWoS6NBVBCCGEEGJI2ehX6ucFoApv9WoWlLWoG5O5k3bpT89qLb9sE/z1gYhKh7wpodoDeTM02cX3l6CizDjIjpY3Ow07VxeAeT0YMGax6jG5doHWo6S1o0CXEEJIKlMqEsK0m1moHreY5o15OlfeAM2KMpUbnsjV0oa5TuoVvG2zEfi+BsGTzndENgt15Y3RwmH5NlpI20QdYIQQQkhLFLEWlTf61RtAMJjm5cp5mCfqtWg0k7n++oAS3sqsNkL78I+F8GU72dPYHtGnu33IEt3H5SldCnVbOFo/m6JAlxBCSMqyWsAZ7GAsa3prGwB4GHc6nStCXADwZKVbTuaqglyTkDbaMBcA8tYVKpUNRyfOUTY9s8so1O04eRy+fGy1CHUBersbIYQQQloxO2GiePeYyVpUmcyNtmpBDnKtOnO19Qp6YS6gvxGajAeCGP3POy2vbcewZapNz+zyZqdh19opulO6B3aspOoF0upRoEsIIaRV0C64tYvqcGga1TSEnSAXMK5X0JJ7c6PZ+GzvqKaNzXhjY9ShbtXYyK+p4+RxAICqcc42e2v1aMCAEEIIadX0wl/VejSG3ly7FQtG9Qpacs2CNsh1KlgfiCrUHfryPdgxbJlhqCuqF0gLRutnU/rbaBNCCCEtX9Nv9D2RXbx29X5xZtOGZgb2jipGxfB5oTBXp15BZtabW3HzfFQMn4eK4fNMax3k68lduQC8rt70NY2ITdJMjwn9Q4QQQgghhDjgVpgLhCZtl1+2yfQYs3oFmZ2qhYqyfNO6hQ1XrFM+9qZlKaGuU0NfvgeeDC92rZ1ieAytRUlrRYEuIYSQVq33ltmxb4JmQp7KdRrmRkznBoKh82yZbTgRvHdUMeDV/Ofd60HV2Gk4OnGOo2vvsnwuWFqabqib1beX+JDe6kYIIYQQ4pwrYS4Q2sBM1ChoLb9sE5YO2BBVmKs3ncsDQYx557cY/c87DM/jr2ls+riuGgPGLI4p1OXhejPZ6efliQ+vo1CXtEYU6BJCCGl1ErXoU8JcG0FuxfB51r25Xg8qhs9TVSpoBWsbIl4vd+UC5K4ujmpS1yjUFdULAMAY47SQNsc4T+iNEEIIIclLXjfFGuZaaazx46JpZaZh7r4ls7C3ON8yzAUA5vWoJnDtiiXUFX26sh5Dx1Oo28LR+tkcBbqEEEJaFdXb22KoWhAqbp5vWH+gF67q0VYsGPXm9n5xJnpvmW1Z8WDI63E8pQuEQl3oTEZIU7pAaCFNwS4hhBBCiDVlOteNk1UuLkBaduQWScsv2wTmtc6LrTZAk118fwn8NY1Ye+kq3cc3XLEOTHq3mC8zR/l4wJjFCEjTu3YZVS/ohLqc1qOktaBAlxBCSGujhLmuVC0EgrY2RLND1Cm46dh9RcrHMffpjpummtTtOHkcclcX6wW7tIjW4jyxN0IIIYQkPbemc3mAY+Kbt0Xc31jjt6xZkPXOL7O1CRrzegw7dP01jbj4/hJ4s0JBrr+uGv/982zVc93s09WEugJVgrUEtH42RYEuIYSQVimevbmAQZdtAniy0lFx83x422YDAIInjTe1cKLL8rnIfaZYd1JXJ9ilUJcQQgghJAFinc51my87DR/+sRB59xSpQl0h2ildwLhPt8fQ8Rg4rhQDxzWF17QWJS0dBbqEEEJapSPzNsTt3Ha7c+Mpb12h/gPhDdKi2STNSsfJ42izND0cQDDBN0IIIYS0CnrTucsv22RrE7R4y7unyPCx7UOWuNanK6Ne3RaC1s+WKNAlhBDS2mwDgFO7Dsbl5E7CXLEZmpusentzVy4I/RnlJmksMyNigzSZvFkaIYQQQghJPLtVC2IzNDeJygUrA8eVRt2nqzelK2h7dR2/ACEpggJdQgghrQrn/Hq3zqXdEM1umCuCXO1maNG8ph1yjy4QCnOFoxPnOJrU7bJ8LlhammmoK9CmFCEMid2hNxV36SWEEEJao6PPl7t6vuWXbcLSARtsVS3sWzIrYjM0u3ggiNH/vCOq5wJQVSPsGLbM8bSu1ZRuj6HjlY9pLZqaaP1sjQJdQgghrZabtQtOJnO1Qa7dzdAqbp4PT7rP9iZslj26Xg94YyN4Y6PjUFevS1fQbpJm+8SEEEIIIa1D6B1jhypdO6GoWbhoWpnldK42zLW7GRoAfPjHQsMN0YCmDl07mNeDYH3A8bSu0QZpMqpeIC0dBbqEEEJaI1dqF3q/OBPB2gbHNQvK820GudGSe3S1U7pAqH5B3JzWL5hVL4hN0pRjaRFNCCGEEKJw8x1jgt2aBQARYa4TVtO5o/95h24lwn//HLnmHTBmMQaMWezo9QWn1Qu0HiUtDQW6hBBCWh23F9FONkCLpmZB6P3iTAQb/KgYPg97RxVbPwE2pnQBVI2fAZaZ4eharKZ0AdWkLi2iOU/sjRBCCCGtxvLLNtmqWQBC07mC0zAXCE3Vrr10FdZeugobrlhnebw3KwcA4K+Lrt7B9Nw2qheoTzeF0frZFAW6hBBCSIzshrna6dxoX6v3ltkI1jboPu7JSkfFzfOVz+UpXUPhYLZq7DRUjZ1mu37BzgZpcqhr66SEEEIIIcQRf33A9iZo0fbmChffX6JUNPht1CTk3RP5LjEj24cscdSna6d6gfp0SUtFgS4hhJBWp7kWc7FM59p10cZplpOzenhjI3JXFyN3dbHtTl07G6R1nDzO8bW0SDRhQAghhJA4SUTVghv0ahcAKIHswHGlGDiuFMH6gO1g16p6AQBN6aYqWj+bokCXEEJIaxRazHlYzP/ljmbSNp69udHKXblA9bHjUHfcNNNgF6CpCEIIIYQQt100zXkwm+gw107tAvM2xVMDxix2tFGaNzsNO1cXGE7qylO6hLQUFOgSQghptXr/ZZa9srEUsndUMeB19p93eQMz5T6HoW7uM8WGk8GtvnaBAwgm+EYIIYQQkiSsaheMNkdjXo/tKd1r37jPclKXpBBaP1uiQJcQQkirdWTehua+BNcFaxtsd/pacRLqmqHaBUIIIYSQ1mHDFetU07axGDBmMYL1AdudulabpBHSklCgSwghpDXaBgCndh1M3VDX6wlN48aZk1DXapO01oxxntAbIYQQQlLD0efLm/sSXOWvacTF95dYH2iTk1DXTp8uVYClDlo/m6NAlxBCSKvDOb8eUqhbMXxeygW7vV+ciWBtg6PnHLvP/i7DstyVC8Dr6i2P67J8blQbshFCCCGEtEKhteihSuwtzsfe4vyEhbuVy2Yl5HXcMmDMYkd9unpTurQxGmlpKNAlhBDSKsmhLpB607oVw+fZPtbbNhsAEDxpvBGF9Uk8NKVLCCGEEOIS7VoUCIW7iRCojWFNKGFeDzZcsc6Vc9l5rVimdGljNNLSUKBLCCGk1eKcX885Z0jBCobeW2bbrl3IW1cY8+u5NaXbat/mxnlib4QQQghJemItGl6PAkitCoaL7y+B3+bkbKzcmNIlKYbWz6Yo0CWEENLqaSsYUibU1ald8GSlo+Lm+c10RSF6U7pZfXuJD+ltboQQQgghkVQVDKkU7Mp82WmoKMvHh3/UHyj4759nx3T+7UOWWB5j1aXbagcMSItCgS4hhBAC61D3yLwNrnXtVt7h3kYRWhdtnGY6IRttjy4A27ULelO6HSePUz5ufYvoBE8XpOCEASGEENLaRdSBaeoXjj5fnhJB7+h/3gEAEYGqNysHAOCvi77ugXlDEZad6gU91KObSmj9bIUCXUIIISRMb7M0cTu16yDE/dGGuqLLNvB9TVxCXbNeXblHN6bN0RobbYW6euQp3dYX6hJCCCGEmJPqwABA2Sxtb3G+EvCeOlQZdah70bQy5WO3NkaTe3TXXroKADDmnd9GHJd3T9P6M9op3QFjFmPguFJb1Qt6tQtyjy5jjNN6lKQyCnQJIYQQid4GFVrRhrp56wrjFuqKLl0R6mprF+TXjmVzNLtdunq1Cx0nj2ud1QscNGFACCGEECfM16IxhLre7NCkbKC2GhVl+a4Fu3KoK4LdiNd2YUoXsLdB2tCX74EnwxsR6kpTukBrWo+mGlo/W6JAlxBCCNHQbpYGwC9tWKFM8EYjHqHu3lHFCDb40XvLbGWzNKPXdoWN6oUuy+eCpaXphrqEEEIIIcSY3mZpEWvRKEPdXvcVKaEuEAp23Qh1/TWN6J1fptz0yFO6sbC7QZpel26PoeMxcFypK9dBSHOiQJcQQggxIC2m0+T7xMdmFQdmtKGuqHWItsohWNuA3i/OVD7v/eJM1ed6YunStTulq9el22oFE3wjhBBCSIugE+wqa1Ftz65dve4rwkXTyiKmdSvK8nHkL+509BqFusLO1QUxb5BGWjhaP5uiQJcQQghpBnrTsrH089rlRpeuE3rVC4QQQgghxAXME9P7xLPO7hJx36nD0YXEdonaBSD26gVbr6fTpUtIS0CBLiGEEBKl3luinyqQg1s3qhzscqtL1y6zKV3ajIIQQgghJHoXTS1h1kfpO/p8uTzh65engJ24+P4S+G3UHwh59xSppnfjPaVr1KUr0FqUpCoKdAkhhBAHGGMxN+YfmbdBDm79sZ7PKXk6uDmmdKWN0QDgutYQ7DLOE3ojhBBCSMsX7cZoALRhbprZsXYwgz0cjLi1QZodel260uZorWItmopo/WyOAl1CCCEkStHWI8hhrrSADu2y63EYGHs9hpugmT4thindqvEzwDIzbB+v3SCt4+RxyF1dHBHsOr4QQgghhJDWSdkYLRpyEOxGmAuEJnWdcGuDNLu01Qs9ho6XQ12A1qIkxVCgSwghhDigrUdwGupqqhYiFtC9/zLL0dvd7GyApiemKd1AMFSl4IBe9ULHyeOcvW4q4zyxN0IIIYS0WPLGaHuL8x1N6mqrFsQHsbwLzWoDNCtOaxd2rZ0Cb7azHFpvSrfH0PEYOK7U0XlIAtH62RQFuoQQQohD4UW0EupWDJ9nO9htzqoFrWimdPWmc49OnIOjE+dYPldvgzTNlC4hhBBCCLFnm/jg1KFK26Gu21ULsYi2doEHghj68j3K5zuGLcP2IUuwY9gy89ejDdJIC0KBLiGEEBIFOdQF9Kd1j8zbgIrh81Q36fnNuoAGopzS1ZnO5XX14I2NlqFuq53S5QCCPLE3QgghhLRonPPrVe8cO1SpO6179Ply7C3OV27S85t/LSrVLsSyOVqgphEDx5UiYLE5m9UGaSSJ0PrZEgW6hBBCSJQiFtLhaV1xk6ZxtQync6Pt5Y1WLF26QGg6F14PclcuAK+rtzxeb0pXeYw2o0gYxtgIxtgLjLEqxlgtY2wfY2whY+w06Ziu4U1C9G7tpOMGMsZWMMY+YozVMMaOMsbWM8a6NcsXRwghhLQiEUMG4WBX3Ax6dpv9nWKCPKVrJ9TV1i3sGLZM2ZCNeT2WU7p61QsCrUWJmWRbP1OgSwghhMRIu5DW8HPOmeamNxGhhMKJ5GRKV1u3cHTiHPDGRuSuXBC6w+uJakpXql1ooZtRJLj/y14HWCGAAIAZAK4FsAzAPQB2MMa068OFAAZrbielx0cBuBjAEgDDAEwD0B/ATsZY56i/bYQQQgixRTtkoEO7Ho1Yi4afn3B59xTZrl7YtXYKPBlepW5hx7BlCNYHMGDMYgDAgDGLEawPOK5ekDZHu45C3WRB62crFOgSQgghLhALaZvhre7zxcfJOKVbNX4GWFqaUrcQEeYCUU/pyrUL4d9e00I6/m7knN/GOV/POX+Dc/44gPsAXArg55pjP+acv625BaTHF3HOf8o5fyp8rg0ILXLPADAhEV8MIYQQQmJfjwpH/mJ/kzU32K1e0HbnBmoalTBXGDBmsa3qBXlKt8fQ8RTqEjuSav1MgS4hhBCSPJQp3USGuvKUriGpO1cvzFXEPqUL0EI67jjnX+nc/W74z3NjPRfnvArAV07PRQghhJBmFVqLHq5MeKgrGE3pmlUtaNmpXtBO6VKoS6wk2/qZAl1CCCEkScjVDYmsXqi8o8T0cblqwTTMRXhK18YGaVodJ49D7urillu/kHxvGdMzJPyntmxvIWPMzxj7jjH2N8ZYH6sTMcbyAHTUORchhBBCkpRqLXo4ef4TblW1oBXNlC4QGerGet0kRrR+NkWBLiGEEJJEmqN6IfB9jcUBTdO5vK7eMMwV7FYv6JHrF0hMzmKM7ZRuvzM7mDF2LoB5AF7jnO8M310PYDmAiQCuQKg3rA+A/w0vOI3O5QPwJ4QmDJ6O/UshhBBCSKKo1qIJmtKtXDZL+XjguNLIa5KqFqzCXMHOlK6eHkPHO34OaTFSav1MgS4hhBCSfBI2pStP5+auLjY99ujEOYDBW9siWFQvaHt0W7zETxh8zTkfKN1WGF0aY6wNgC0I7XitJOqc888553dzzl/knL/JOS8HcDkADuAhk692KYCfAPgN5/xbF757hBBCCEmshE3pVi6bhUCt+WZogt0wF7A3pUuSHK2fTfnsHEQIIYSQxOGcX88Y40BoSrfr7NGunbvyjhLriVyJqFuwqlrQyl25AFXjphk+3mX5XFSNNX6cJAZjLBPA3wCcD2AI5/wTs+M558cYY/8GMMjgfAsB/A7AWM75q25fLyGEEELiT7UW/Us5uv7KvT1OzQJcvelcuTs3UNOoe4wRMaUrb6QmEz26RgExY+wleWKZECB51s80oUsIIYQkJ2VKt2L4PFfqF8zCXL3p3KrxM8DS0tBl+VxbVQsRbGyQZqbFbEbBAQR5Ym82MMbSALwA4EcAruOc77H5FbHwV6U930MApgG4n3O+zua5CCGEEJKclCndirJ8V+oXoglzRXeu2SZoRqymdPV6dAFQj24yoPWzpYRO6DLGfg7gnzoPfcc5bxc+5jQAcwAMBNAfwGkAruCc/8vma3gATEWor6ITgH0A5nHOX4jt6gkhhJDECU9GvITwQjLW+gVtmGtVrwAg1J27aq6zqgWJ1ZSukay+vVD7wT6AFtFxE14vrQdwFYDrOedv23xeFwA/BfAXzf33AZgP4CHO+RMuX66raD1KCCGEWItYi7pQvyCHuXYmbUPduZMcVS24ocfQ8di5uiAhr0VSR7Ktn5urcuE+AO9Kn/ulj9sD+C2A3QB2ALjZ4bmLECodfgjALgCjADzPGLuBc74t6ismhBBCEky8xUu85S0WjsPcMKdVC27oOHmcEgS3jLe6cYBHTn80sycB3ArgEQDVjLEfS499wjn/hDFWitC7ud5CaIOGXgCmAwgCUH4gGGOjADwOYDuA1zXn+p5zvjeeX0gMaD1KCCGEmNCuRWOpX7Da+MyM06oFN4W/9m2pvx5NNbR+trqY5gp0K02S7CrO+ZkAwBi7Gg4W0Iyxjggtnos552KXl38yxi4AUIzwWwYIIYSQVBRtn66Tjc8EsWFZLGGu6N+Nhjyl2zJC3aQzLPznQ4jcoGEugIcBfAjgHgB3IjSh+jWA1wHM5Zzvk46/FqG3kV0bvsneAPBz9y7bVbQeJYQQQuzZBuC6aKd0nWx8prV9yBLHVQtuOP28PHz3ifL10rvGCJBk6+ek2xSNcx7LFNI1ANIBPKu5/1kAqxhj3Tjnh2M4PyGEENIcQovoXQcjQt0j8zbg1K6DaDPgAt2w1+kmaEA4zA0EAa8ntsnccGVDNDpOHocvH1tN1QtxwjnvauOYVQBW2TjuToQWrS0GrUcJIYSQJlabpB35SzlOHa5Em255uhO8TqsWgFB/rpPj3dZj6HgAoOoFoki29XNzBbrrGWNnATgB4BUA0zjnR10478UA6gFoiwY/DP95EQBaQBNCCEkpcoeZ2CRNS3t/mwEXoPbAZ1FXLcQc5rpArl5IeTHlgyROaD1KCCGE2KdM6VaU5eseID/Wplsear84GlWYK8Q6mbtr7RR4s9NiOgdpRrR+NpXoQPc7AKUIjQ9/D6AfgBkA3mKM9eOcfxnj+c8EcEJnquIb6XFdjLHfAfhd+NMVnPMVMV4LIYQQ4hrtxhRWtJuoOQlz3Qhy7dQtsMwMVI2f0ezBMWl1knI9SmtRQgghyczxWjTGTdTc2ABNbKpGSEuU0ECXc/4egPeku95gjP0PgP+H0MYUM2N8CQZAL8JnNq5tBQBaOBNCCElamoW0n3OuO3Kg3UTN0WSuC6rGzwBLS0OX5eZ1C12Wz0XV2BYygWuGAwjShEGySNb1KK1FCSGEJDtpkzTT9ag2+G2O2gS3p3NpT4cEo/WzpcQ3S2twzncD2A9gkAun+wbAGYwx7YL5DOlxQgghJGVxzq/nnDOjMDeseTddCgTB6+oj7j46cQ6OTpyjuk9M6RLSnGg9SgghhNhntR4NB5/Nuh4NTefeo7pvx7Bl2D5kCXYMW6bc581Ow87VBYZduaeflyc+pD0dSFJJlk3RjCYZnPoQQAaA7lD3ll0U/nOvC68BANi9e/c1Pp9vDue8E5IgGCetVpAx9oXf75/bv3//V5r7YgghyUGanmi2X2vnPqOeCj46cQ54YyMAoGrsNLDMDHRZPhddls/F0YlzLKsXUn4qgjrAUkFKrUdpLUqSRJAxdoJz/r7f718wYMCAA819QYSQ5CBvpJZoRtO5gZpGDBxXil1rp2DHsGUY+vI9GPryPdgxbBmC9QHdc/UYOp42RmsutH421eyBLmNsIICeADa5cLrtABoA3A5Afp/nbwBUuLWj8O7du6/JyMhY2rVr14asrKxvPR4P/ZSRZhEMBlltbe3pR44cWbp79+5JFOoSQvRUjZuGrL690HHyuPi/lkF3Lq+rV1U/yJudmVUvZPXthdoP9gE0FUHiKNXWo7QWJcmCc47Gxkbf999//7MvvvjipV27dt03YMCA7c19XYSQ5LJzdQFOPy8PPYaOT8jr6XXn7hi2TNlkbcCYxRGh7vYhSxJybYS4JaG/zWeMrWeMzWeM3cwYu5IxVoDQovdTAE9Ixw1jjI0AcFn4riGMsRGMsWGa8/kZY0+Lz8ObWDwGYDpjLJ8x9nPG2DIAVyK02YUrfD7fnK5duzbk5OTU0gKaNCePx8NzcnJqu3bt2uDz+eZYP4MQ0soob3ULh6LxFwhGdOcenTgH0O5S7PWgauw0pYLBqHohESF0QnCe2Bsx1BLWo7QWJcmCMYb09HT/WWed9V3Xrl39aWlpDzb3NRFCkoqyFv3uk9g2SYtVoKZRtdHagDGLEawPKPUL3uw07Fo7pbkuj+ih9bOpRE/oVgD4NYB7AWQD+ALAiwDmcM6/lo5bBiBX+vzh8J9VALpK93vDN9lDAE4BuB9AJwD7ANzGOf+7K18BAM55p6ysrG/dOh8hscrKyqoLv+WSEEIUia5esDudC0CpV5AndQlJkJRfj9JalCSjnJycGs55t+a+DkJI8tCuRQ/sWBn3KV29ugV5OlcmT+rakfIVYKRFSeiELud8Iee8L+f8dM55Gue8M+f8d5zzzzXHdQ0XbGtvXTXHMc75nZr7Apzz+ZzzXM55Rvj1Nrv8pXhoGoIkk/DPI/XnEUJMffnY6vi+gN3pXA3Rr2vWoQsoOyYTEpMWsh6ltShJOuF9ALWbARJCCBCe1I33lO6utVPgyfBGbIamnc6VDRizGIGaRgTrA4bHyBuj0XqUJAsKgAghhJCWbxuQwNoFCa+rNw9qvR7LMDerby/xYYr26Cb47WIp+JYxQgghhLRciZpqDXXnqsNco+lcLaMwFwhtjCaHujFcIrGN1s9WKNAlhBBCWrhELKL16hbsTOfmrlxgOZnbYnp0CSGEEEJIQplN5woDx5VanidRG7oRYhcFugQAsG7dunYDBw7sdeaZZ16SmZnZ/5xzzulz9dVXd9+8eXNbcUx+fv45jLEBjY2NEc/ft29fOmNsQFlZ2VnivltuuaUrY2xAp06d+gYCgYjniPNpz/mjH/2oF2NsQL9+/S7Uu1Zx3h/84Ad99R4/ePBgmtfrHZCWltb/888/d9QTXV9fzxYtWtRhwIABvdq2bftDn8/Xv0OHDn2vuOKKC5588skz5etcsmRJe3H9jLEBaWlp/Tt37tx70qRJ59bU1Kjebub0e0cIIfESt9oFnboFy+ncKKTk29w4gGAwsTdCUgytRUNoLUoIael2ri7AgR0rXT+vk+5ckgJo/WyJfrIJ5s+f33HMmDHdzz///LqlS5ce2bRp04GCgoLPAeC1115ra/V8M5mZmcGvvvoqbevWradpH9u8efOZOTk5uv+rycnJCf73v//NqaioUI17nTx50vPKK6+cYfQ8ACgvL28fDAbh9/vZqlWrzrR7rd9++63nJz/5Sc85c+Z07tOnT82yZcsO//3vf99fVFT0SU5OTvD+++/vtmbNmjO0z1u1atXHr7322kebNm06OGTIkO+ffPLJTvfcc895dl+XEEISRKldcDvUjXY614nUr10ghBihtWgIrUUJIS3cNvFBPLp0tXULO4YtM+3FJSTVOfqNMWmZnnzyyR9cffXVJzZt2lQl3X2yoKDga71pBifatm0b6N69e/XatWvbDx8+/KS4/5VXXmnzySefZPzqV786/uKLL7bXPq9Xr141X3/9ddqqVaval5WVfSbuX7duXTvOOS6//PLvdu/e3UbvNZ977rmzevToUXvq1Cnvn//85/YPPfTQl3audfz48V0qKipyXn755X1XXnlltfzY3Xff/c1//vOfrJqamoh0YtCgQTW9e/euB4Bf/epX33/88ccZmzZtOmvVqlXHvF7tpteEENI8OOfXh6dbrxOhrmtVBoEguqzSmc5dXezO+RGqXagaN8218yVcCvZyEZIotBYNobUoIaQlExVgjDEOAAd2rIxrjUGgptFWlQJJYrR+NkUTugTfffedr2PHjpHvvwLgxiJw9OjRx7dv337GyZMnlZ+3NWvWtB8wYMCp3NzcBqPn3Xrrrcc3b96smmpYv359+2uuuebb7Oxs3amI1157Laeqqipj5MiRx0eMGHH8ww8/zN65c2em1TUePnw4bcuWLe1Hjx79lXYBLfz0pz+tHTp0qO5jsksuuaSmrq7O88UXX0T9C5MXXnihbf/+/S887bTTfpidnd2va9euvQsLC8+O9nyEEAIoC2llUrdq3DRUjZvm+sSu29O5WilZu0AIMURrUVqLEkJalW1AaEp35+oCVyoYtHUL8a5aoLUoSQYU6BL07du3+sUXX2w/a9asH3zwwQcZ1s9wZsyYMd9yzrF+/fp2AFBbW8teeumlM0aPHn3c7Hnjx48//sknn2Ts2LEjBwCOHDmS9vbbb7cdO3as4fNWrVrV3uPx4K677vpm/PjxxwHg6aefjpi60Nq+fftpgUAAN91003eOvjgdR48ezWjTpk3gBz/4gT+a5+/duzf917/+9QXnnXde/erVqz/euHHjwT/84Q9fVFdX0/9eCSExk0NdofaDfa6d/+jEOeCNja535wIpXrtAu/QSYojWorQWJYS0Hnpr0VgrGLR1C3Y2QovG6efliQ+vo1A3AWj9bIoqF1zCGBvQ3NcAAJzzXU6fs2LFiqoRI0Z0nz9//nnz588/r127dv6f/exn348bN+74zTff/H2s19S2bdvgNddc8+369evb33333d+sX7++XWNjIxs7duw38+fP72T0vAsvvLChf//+p1avXt1+6NCh1StXrjyzQ4cOjTfccMPJtWvXRiyMa2tr2datW88cPHjw9127dm0EgEsuuaT6hRdeaL906dJPzSY8Pvnkk3QAOP/88+vl+4PBIOS3+nk8nohJkUAggMbGRpw4ccK7fv36dq+88soZ8+bNO+rzRfc/r3feeSensbGRrVmzpurMM88U0x8nTZ9ECCEOiLe8AU1ve3Pt3C5XLcjk2gXG2Evy10FIa0drUWO0FnWG1qKEkHjTW4vGu4LBDT2GjseBHStFAJ16AwakRaHfshL07du3fu/evXu3bdu279577/08Ly+v9tVXXz3jlltu6fHggw+68taqsWPHHn/rrbfaHj161Ld+/fr2V1999QlpgWho9OjRx1966aUza2tr2aZNm9rfcsstx40Ww+vXr2938uRJ7+23335cfv5XX32VtmXLFtMNNbjBb2NmzpzZKT09fYC43Xzzzd20x/zwhz/snZ6ePqBjx44/nDx5ctdf//rXX82YMeMrq6/NyKBBg2p8Ph//1a9+df7q1avP+PTTT+kXL4SQpKW3IVo8yVO6NBlBSMtAa1FaixJCWjWlgiEVyKEzrUVJc6JA1yWc813JcIv2+n0+H4YNG3ZqyZIln/3v//7v/n379u3p0aNHbVlZ2dlfffWVN3wMBwC/38+0zxf3iWO0brzxxpNnnXVW48KFC3/w5ptvtr3jjjtM3+Im3Hnnnd80NDSwBx988JwDBw5k3XXXXYbPe/bZZ9tnZmYGhw0bdvLrr7/2fv31196bbrrpO5/Px9esWWP6VrfOnTs3AMDHH3+sSiXuvvvur994443KN954o7JDhw663W5r16499MYbb1Q+99xzBwYPHvz9s88+22Hp0qWq13Pyvevdu3f9iy++eCAYDLJ77rmnW+fOnS/p27fvhS+99JLuxhuEEOKGqHt0A0F0WT7X+jiXdJw8LgWrFzgQTPCNtDrNvQaltSitRQkhJBqxvuNK25+bCHL1QkJfuFWh9bMVCnSJrq5duzaOGTPm60AgwCoqKjIAQGxWUVVVFfH/lseOHUsDgE6dOhluaHHzzTcfX7FiRaczzjjDb/ftc2eeeWbwqquu+u6pp57qdPHFF9f079+/Tu+4Y8eO+f7973+3raur83Tr1q1vhw4dftihQ4cf5uXl9fH7/WzHjh3tvv32W8Of92uvvfakx+PB3/72t9Pl+7t06eK//PLLay6//PKatLQ03f+F9+vXr/byyy+vue22277/xz/+cTA3N7d+zpw5533//ffK6zn93t14440n33zzzQPffvvte3/961/3p6Wl8VtvvbXH559/ThMShBC3KZukRcXrCW2ClkAdJ49L6OsRQhKP1qIhtBYlhBBn4r0hGoCkr4YgrQMFugQHDx7U/XXWRx99lAkA5513nh8AfvGLX5wEgA0bNpyhPXbTpk1nZGRk8CuuuMJw592777776yuuuOJEfn7+5046ve69994vw8/7wuiYlStXtg8EAmzRokVH//73v++Xb/PmzTtWV1fneeaZZyKuW+jWrVvjTTfddHzDhg0dXn/99RzbF6eRlZXFH3nkkWPffPONb/HixR3E/dF+77KysvhNN910Mj8//4va2lrP/v3706O9NkII0RPrVETuygXgjY04OnFOKNiN8wI6JXGA82BCb4SkElqL0lqUEEKiNWDMYgTrA9gxbBmA+G2IRhKM1s+W6DesBP369bv40ksvPTl8+PATF1xwQf2JEye8L7300ukbNmzocN11133bo0ePBiDUbzZy5MivFyxYcO6XX36ZduWVV37v9/vZSy+9dPqf//znDpMnT/6sQ4cOAaPX6du3b/1rr712yOn1XXPNNaeuueaaU2bHbNy4sf25557bUFhY+JXHow4Trr322pNPPPFEp/Xr15/1wAMPGL5N7umnnz565ZVXZl5//fU9R40a9fXQoUO/b9++feDrr7/2vvHGG6d9/fXXaW3atDH8+oTbb7/9u+Li4pqnnnqq09SpU79s06YNd/K9e/TRRzu8+eabbYYNG/Zdbm5uw1dffeUrKSk5u0OHDo0DBgyotfltI4SQhMlduQBV42coH5v58rHVqP1gH7L69qJJW0IIAFqLCrQWJYSQ6AwYsxi71k7B9iFLLKdzxaZmp5+XR5O2JKVRoEswa9asT7dv3376woULzzl+/Hiax+PhXbt2rZ8xY8YnM2fO/FI+dv369VW9evWq+/Of/9x+9erVHT0eD+/evXtdaWlp1eTJk79ujuv/z3/+k7V///6swsLCz7QLaCDUyTZy5MjjTz75ZKePPvoo/cILL2zQO8+ZZ54ZfPvtt/c99thjZz3//PPtN2/e3L62ttZzxhln+Hv37l3z+OOPH5kwYcI3dq5p7ty5n95yyy09SktLO8yZM+dLwP73rn///jWvvPJK23nz5p33zTff+E4//XT/wIEDT61fv/7jNm3apF6xCyGkVbAKcoGmMBcIVTx8+djq1hPqpmAvFyGJQmvREFqLEkJauwM7VkYdstqdyhWbr333SWVMr0cSgNbPppjRjqrE2Pvvv3/kkksuaZYFIyFG3n///bMuueSSrs19HYSQ1MMY4wCQu7o4rq9TNW5axH3RvqY4F+c8YoOfZHO6rwMf3PaXCX3NV75duYtzPjChL0oShtaiJFnRepQQEg3G2EsIbzA2cFxp3F5HTOfKop3U3bm6AEBqrEVTEa2frdGELiGEEEIAIC4Ts/JUrsA5ZyJEbjVTuvQLdEIIIYQQXZzz68Xa0O2pWb0QF4AfwKsArqNJ3SRG62dTtHMJIYQQQrYBTTUIbtELcxFaQKtes2rcNNXrfvnYalSNmxZxPyGEEEIIabG2AdALX6NmFOZyztPCGwMrr3lgx0rV83auLsDO1QWq+wlJJhToEkIIIa2cvKB1K9TVhLl+zjkL39K0ryleV4S4cgisF/gSQgghhJCWJbw2BABXQlRNmBuxFpVeUwl1RYgrh8Difgp2SbKhQJcQQgghroa6OmFumt5xnPPrw71j23Qe9kMn8E3JYJdzIBhM7I0QQgghJPXoTsw6pRPm6q5FgcghA4lqLSoHvhTuJgCtny1RoEsIIYQQAO6FunbCXO3rSlMTyvSEXuArgl1CCCGEENKymNUgOGE3zJVf1+5aVHN+QpoNBbqEEEIIUWhDXafkENjOAtruNZlM8qYGzhN7I4QQQghJQW6FuuFzuboWTfn1aKqh9bMpX3NfACGEEEKSi7zTsNE0bFbfXug4eVzE/dGEwE6uCwAYYy8BuA5NG6wRQgghhJAWIrwWfQnAdaLqQOv08/LQY+j4Zrk2ABBrZUKaC03oEkIIIUSP6fSBUXCb1bdXXC5GJk1JuDJ1kQg8GEzojRBCCCEklZl02wKIfXo3VtLELokTWj+bowldQgghhESQdxrWkqd3xaSuZiM0QgghhBBCYmK0HpWndw/sWIkeQ8drN0IjpMWjQJcQQgghTm1DqPLAbJMyqkNQpGYvFyGEEEJIMrJTyQBai6Y4Wj9bocoFAgBYt25du4EDB/Y688wzL8nMzOx/zjnn9Ln66qu7b968ua04Jj8//xzG2IDGxsaI5+/bty+dMTagrKzsLHHfLbfc0pUxNqBTp059A4FAxHPE+bTn/NGPftSLMTagX79+F+pdqzjvD37wg756jx88eDDN6/UOSEtL6//555/b/qXFj370o14DBgzQfa9wWVnZWYyxAfv27UvXXoe4ZWVl9bvooovyHn/88fZOzr1kyZL2jLEBFRUVGXavlRBCmpPJJmV+eWfg5rg2QkhqorUorUUJIcQJg0oGWouSVoMCXYL58+d3HDNmTPfzzz+/bunSpUc2bdp0oKCg4HMAeO2119paPd9MZmZm8KuvvkrbunXradrHNm/efGZOTo5uUUlOTk7wv//9b452YXny5EnPK6+8cobR8wCgvLy8fTAYhN/vZ6tWrTozluu3csYZZ/hfe+21j1577bWPysvLD7dt2zYwefLkrqtXrz4jnq9LCCHJQN7xlxbOhJBo0Vo0erQWJYS0ZrQWJa0ZBboETz755A+uvvrqE5s2baoaPXr0dzfddNPJgoKCr1977bVDTz755CexnLtt27aBSy+99OTatWtVkwKvvPJKm08++STjmmuu+Vbveb169arp0qVL/apVq1TPW7duXTvOOS677LLvjF7zueeeO6tHjx61Z599dsOf//zniAkFN6WlpfGrrrqq+qqrrqr+zW9+c+KVV145eNpppwVWrVp1lvWzCSGEtAocQJAn9kZICqG1aPRoLUoIIaRFovWzJQp0Cb777jtfx44dI9+7BsDr9cZ8/tGjRx/fvn37GSdPnlR+3tasWdN+wIABp3JzcxuMnnfrrbce37x5s2qqYf369e2vueaab7Ozs3WnIl577bWcqqqqjJEjRx4fMWLE8Q8//DB7586dmTF/ETadfvrpwa5du9Z99tln6dZHGysqKup4/vnnX5yZmdm/bdu2P+zdu3fe2rVr27l0mYQQQgghSYPWou6htSghhBDSOlCgS9C3b9/qF198sf2sWbN+8MEHH7jenTVmzJhvOedYv359OwCora1lL7300hmjR48+bva88ePHH//kk08yduzYkQMAR44cSXv77bfbjh071vB5q1atau/xeHDXXXd9M378+OMA8PTTTzuajGhsbIy4BYOG76pT8fv9+OKLL9Jzc3PrnbymbNmyZWfOnTu388033/zN888/f6C8vPzwTTfd9O3x48dpE0NCCElVPJjYGyEphNaiarQWJYQQQkDrZwsU6BKsWLGiqnPnzvXz588/75JLLul9xhlnXHLjjTd2e/HFF2PqLBPatm0bvOaaa75dv359ewBYv359u8bGRjZ27NhvzJ534YUXNvTv3//U6tWr2wPAypUrz+zQoUPjDTfccFLv+NraWrZ169YzBw8e/H3Xrl0b+/btW3/JJZdUv/DCC+31NsLQs3v37jbp6ekDtLcpU6bkGj1HLLSPHDmS9tvf/rbLyZMnvTNnzvzc1gvqeOutt9r07NmzpqSk5PMbb7zx5MiRI79bsGDBF5MnT/462nMSQgghhCQrWos2obUoIYQQQuyg37K6iDE2oLmvgXO+y+lz+vbtW7937969O3bsaPPyyy+33blzZ5tXX331jK1bt545ZcqUzx599NGoF4TC2LFjj48YMaLn0aNHfevXr29/9dVXnzjzzDMtfwUyevTo43Pnzj2vtrb22KZNm9rfcsstx43eerd+/fp2J0+e9N5+++3H5edPnTq1y5YtW9refPPN31u9Xq9evWpXrFhxRHv/Cy+80G7JkiVna+//8ssv09LT05W/d8YYli9f/vGVV15ZbfVaRgYNGlT97LPPdhg7dmznX/3qVyeuuuqq6tNOOy31fl1ECCEEQKgCjKdgLxdJPbQWNUZrUftoLUoIIaS50frZGgW6LopmAZssfD4fhg0bdmrYsGGngNBbyn7xi1/0KCsrO3vKlClfdujQIeDz+TgA+P1+lpaWpvpflt/vZ+Hz6P4v7sYbbzx51llnNS5cuPAHb775ZtsNGzYctHNdd9555zcPPfRQ5wcffPCcAwcOZG3cuPFjo2OfffbZ9pmZmcFhw4ad/Prrr70AcNNNN3330EMP8TVr1rS3s4jOyckJXH755TXa+3fu3Jmtd/yZZ57p/8tf/nIgEAhg//79mfPnzz/nvvvu6zpw4MDafv361YnjfD4fb2ho0J2IDwQCDAhtagEAf/jDH47X1dWxtWvXdnj22Wc7+nw+PmTIkO+eeOKJY7169TLseSOEEEJI60ZrUVqL0lqUEEIIaR2ocoHo6tq1a+OYMWO+DgQCrKKiIgMAxGYVVVVVadrjjx07lgYAnTp1MtzQ4uabbz6+YsWKTmeccYbfzoIWAM4888zgVVdd9d1TTz3V6eKLL67p379/nd5xx44d8/373/9uW1dX5+nWrVvfDh06/LBDhw4/zMvL6+P3+9mOHTvaffvtt67/vPt8Pn755ZfXXHHFFTUTJ078ZuvWrQcCgQCbPHnyefJx7du3b/zyyy8jvm8A8Nlnn6V5PB506tTJDwAejwdTpkz5es+ePZVffPHFf5cuXXr4/fffz7n11lvPd/v6CSGEJADn1AFGiEO0FrWH1qKEEEJaJFo/W6JAl+DgwYO6i7uPPvooEwDOO+88PwD84he/OAkAGzZsOEN77KZNm87IyMjgV1xxheHbu+6+++6vr7jiihP5+fn/v717D5OjqtM4/n2HJDKIBgyDkaiMIQoKMoooEGCNiJIVEUUwIiybRRH3goIGUeSSqAsiyypidKNcghqCaIQFwnJTCCReFoxEYIFwm4iYEMgFkskFJnP2j3M66TQ9Mz0zPd1VPe/nec7T6apTdU79ptP96+o6p5YOG1b5xeGnnHLK8rTdsu7qXHrppaM2bdqkCy644C833HDD4uLy9a9//akNGzY0XXnllS/rd7W1tbVtPOGEE5bPmzdv5Lx58zZfSTFhwoQ1S5cuHXHXXXdtdXVFV1cXc+fO3WGvvfbqGDly5MveQVpaWjaddNJJq4444oiVjz76aPNg99/MzMys1pyLVo9zUTMzs6HBUy4Y73znO/fcb7/91hx55JGrx40bt3H16tXbzJ07d+RVV13V8qEPfWjVm9/85hchzm82adKk584777wxy5cvH37IIYe80NnZqblz546cPXt2y2mnnfa3lpaWbu/4sPfee2+8/fbbH+9r/w477LC1hx122Nqe6lx99dWjxowZ8+KUKVOebWra+neKiRMnrrnkkktGz5o1a6dTTz21x7sZV8O0adOWXXXVVS3Tpk3b5Te/+c1jACeffPKKGTNm7HzkkUe++bTTTlva1ta2fvny5cMuv/zylsWLF283Z86cxYXtjz322F233377TePHj+8YPXr0Sw899NC2c+bMGXXQQQdVdCWJmZmZWZ44F60u56JmZmaNzyd0jbPPPvvpm2++eeT555+/y4oVK4Y3NTWF1tbWjWeeeeZfzzrrrOXFdWfNmrVk99133zB79uxRV1xxxc5NTU1ht91223DRRRctqdedbxcsWNC8ePHi5ilTpvytNIGGOCfbpEmTVkyfPn30ww8/PGKPPfYY1Lm/xowZ03niiScunz59+ugFCxY0H3jggeu33377cPfddz9yxhln7DJjxozXPvPMM8Obm5u72traOm688cZHJk6cuPlLwvjx49f+9Kc/3WnOnDmj1q5du01LS8tLRx111MoLL7zw6cHst5mZDR7f1MGse85Fq8u5qJmZNQLnzz1TCA5QXy1atKi9ra2tLgmjWXcWLVq0U1tbW2u9+2FmZlt7tV4T9mv6QE3bvL3rmj+GEPataaNWM85FLaucj5qZWTU4f+6dr9A1MzMzG2w5vNGCmZmZmVndOH/ukW+KZmZmZmZmZmZmZpYTvkLXzMzMbBCtYdUtt4df7lTjZj0c38zMzMxyyflz73xC18zMzGwQhRAm1rsPZmZmZmZ54fy5d55ywczMzMzMzMzMzCwnfEK3f7q6urpU706YFaTXo2cMNzMzGxqci1rmhBAAQr37YWZmNhT4hG4/SFq2fv36bevdD7OC9evXbytpWb37YWZmZoPPuahlUUdHx3aSnqx3P8zMzIYCn9Dth87Ozmnt7e0jOjo6mn11hNVTV1eXOjo6mtvb20d0dnZOq3d/zMzMbPA5F7WsCCHw4osvDnvuued2aG9vH/bSSy99u959MjMzGwqUhsZYHy1cuPCwYcOGnRtCGI1PjFv9dEla1tnZOW2fffa5pd6dMTMzs9pwLmoZ0SVpVQhhUWdn5/nvete7Hq13h8zMzIYCn9A1MzMzMzMzMzMzywn/mm9mZmZmZmZmZmaWEz6ha2ZmZmZmZmZmZpYTPqFrZmZmZmZmZmZmlhM+oWtmZmZmZmZmZmaWEz6ha2ZmZmZmZmZmZpYTPqFrZmZmZmZmZmZmlhM+oWt1I+mz9e7DUOJ415bjXXuOeW053maWd34fqy3Hu/Yc89pyvGvPMbehzCd0rZ785ltbjndtOd6155jXluNtZnnn97HacrxrzzGvLce79hxzG7J8QtfMzMzMzMzMzMwsJ3xC18zMzMzMzMzMzCwnfELX6ulH9e7AEON415bjXXuOeW053maWd34fqy3Hu/Yc89pyvGvPMbchSyGEevfBzMzMzMzMzMzMzCrgK3TNzMzMzMzMzMzMcsIndM3MzMzMzMzMzMxywid0bUAkfVHSDZKWSgqSpnZT7x8lzZG0JNWb2cM+myVNlfSopI2SnpF0o6QRfejXcEn3p7Y+0/cjy6YsxVvSSZJukvS0pA5JD0g6vS9/pzzIUszTtntKulXSWkkrJF0h6TX9P8JsqXa8Jc1M60vLdyvsz4clzZe0UtIqSQskHdnvA8yYrMU77eMESfdIWpdiPl/S2/t1gGbW8LL2OV20j4bMRSFbMR8K+WiW4p22dS6Kc9FqylrM0z6cj1rm+YSuDdRJwM7Adb3UOx7YDbgNeKG7SpKGA/8D/BNwEfAB4F+AvwLb9KFfU4Cd+lA/L7IU73OAZcAXgA8DPwe+AczqZbu8yUzMJe0C3Ak0A0cD/wocCtwoqVHez6sa7+RZ4ICS8p3eOiJpInA98XV+HPAp4BngWkmH97Z9TmQm3gCSzgP+C5gLHJ7a/TWwXSXbm9mQlJnP6RKNmotCtmI+FPLRzMTbuehWnItWT2ZiDs5HLT+G1bsDlnt7hhC6JA0DPtdDvcNCCF2w+YOpO18C9kn7fapo+ZxKOyRpLHAW8FngZ5VulxNZivc+IYRni57fIUnANEljQwhPVLCPPMhSzE8HhgNHhBBWp7b+BswDPgr8qoJ9ZF214w3wYgjh9/3oywnA08CkEMKm1NatwBJiYje3H/vMmszEW9IBwFeAo0II1xWtaoQ4m9ngydLnNGn/jZyLQrZiPhTy0SzF27noFs5FqyczMXc+annSKL+iWZ0U3lCrVY/46/AvSpKLvvohcDWwYAD7yKQsxbskeS64Jz2O6ev+sipLMQc+AswtJNCp3buAvwANMfRqEOI9ECOAjkICndrdBKylQT4/MxbvfwaeLEmezcx6lLHP6YKGzUUhWzEfCvloluKNc9E+1xughs9FIXMxdz5qudEwbwKWf5LeCLwBeELSjyW9IGmDpF9LekeF+zgO2Bc4YxC72hCqEe8y3gt0AYur1c9GMpCYS2oG3gQ8UGb1g8Dbqt7hxrGzpOckdUpaLOkMSZUMm/0RME7S1yTtJKlF0jlAK/D9Qe1xvvU33gcBiyR9WXEuxE7FuRCPGewOm5mBc9F6cD5aW85F68a5aO05H7WG5xO6liW7pMczgLHAJ4FjgRbgzpSAdEvSjsB/AmeEEJ4bzI42iAHFu5SkvYnzl10eQnimmh1tIAOJ+Y6AgFVl1q0EGuZmFFV2H3Fo4SeIV5XMA84HZvS2YQjh1rTNFOI8XMvTv48KIdw9SP3Nu/voZ7yJ/z8OJV4ZcTrw98BDwDVqsJt/mFlmORetPeejteVctPbuw7lord2H81EbAjyHrm0m6VDiBOO9mRdCmDAIXSj8wLCOOC/TutSve4HHiJPu93S1w4XA48Blg9C3qmuAeG8m6XXAfxPj/8Xqd7U6ch5zpcfQw7pMyUC8CSF8t2TRTZLWAqdKuiCE8Gh320ranzj34U3AT4mxnwz8QtKHQwh3DEaf+yvv8Sb+/3gVMCGEsBBA0q+BPwNnEt9jzKyBZeB9bEjlotAQMd8sD/lozuPtXLQfhlIuCvmPOc5HLUd8QteK/RZ4awX11g1S+yvS44JCcgEQQnhK0sPAO7vbUNJ+xA+39wMjJQG8Oq1ulrQD8HwIoVwCUi+5jXcxSaOIH9oiTlS/puo9rZ48x3wVMYkrd/XDjsQrI7Km3vHuzmzgVOKQ2J4SukuAB0MIxxUtu0XSfOIVWBX9H6mhvMd7BTCikDxDnCstJdE93SDDzBpHvd/HhlouCjmOebEc5aN5jrdz0epp1FwU8h9z56OWGz6ha5ulD/WH69iFJ4D1dP+rb0+ToL8V2Aa4s8y676WyI7B6QD2sopzHO1aSXg3cAowCDg4hPF3VHlZZnmMeQlgnqR3Ys8zqtxGHEmVKBuLdnZ6uMCn2duAHZZbfQxyGlSkNEO8HgXd0s33WToCY2SDIwPvYkMpFIfcxj5VylI/mOd7ORauqIXNRaIiYOx+13PAcupYZIYSXgLnAwZJeWVie5nLanS13rC3nZuB9JeXYtO4/0vO1g9Dt3BpgvJG0Xdr+TcAHQwiPDWJ3G8JAYw5cDxwuaWTRtgcBu6Z1VplPEROy3uK9DHh3meXvATL7ZTGDKo33tcAoSfsWFkhqIs5j1tu2ZmYD5ly09pyP1pZz0cxwLlp7zket4fgKXRuQ9EbXypYfB94m6ej075uK5mV6G1vufNoM7FpUb14I4dn073OB/wXmSroI2DYtW03RnTzT3T3PAXYLISwJISwjfuAV9601/fOREMKdAz7YDMhKvNPiOcCBxBtPvDLN8VTweFEbuZaxmF8IHA9cL+l8YCTw7bS/a6t1zPVUzXhL2pU439jVxHnhXgF8jDgkdkYI4fGidsvF+xLgPyRdRZy/DOAEYDzxdZ97GYv3ZcS5++ZIOgt4Dvgs8QvmB6t53GbWOLLyOT1UclHITszT4obPRzMWb+eizkWrLmMxdz5q+RFCcHHpdwFmEn/pKldai+pN7aHehJJ9vge4gzivzvPAdcC4kjpTS9so07fWVOcz9Y5TI8a7h/0HYHK9Y9WIMU/L306cI66DOJfZTGBUveOUxXgT53i7DlgCbCAOMVwI/BvQVGG8jwP+kGK9Kv372HrHqYHj/TriF5aVaR+/I15xVfdYubi4ZLNk7XO6pE4rDZaLZi3mPey/YfLRLMU7LXcuWmG8cS6a15g7H3XJRVEIATMzMzMzMzMzMzPLPs+ha2ZmZmZmZmZmZpYTPqFrZmZmZmZmZmZmlhM+oWtmZmZmZmZmZmaWEz6ha2ZmZmZmZmZmZpYTPqFrZmZmZmZmZmZmlhM+oWtmZmZmZmZmZmaWEz6ha5YRkiZLCpLG1bsvpSTNlNRe9LxV0lRJYwehrXZJM6u0r6kppqG4/z3UL/wNWqvRft6kv2voobyjyu3tkP5G+5RZd3tRuz+rZrtmZmZWnvPRzft2PlonzkfNzCozrN4dMLNc+AZwcdHzVuBcYD7wRD061EcHABvr3YkcOR+4vszyxVVuZwfi6+ivwMKSdacAI4Frq9ymmZmZ5ZPz0aHF+aiZWQ98QtfMehVCeLzefRiIEMLv692Hnkh6RQghSwn+E/WOWQjhIQBJWYqLmZmZ1Ynz0cHlfPTlnI+aWZZ5ygWzHJE0XNI30zCwF9PjNyUNL6pTGKZ0sqSvS1oqabWkGyS9vmR/20n6oaQVktZIulbS+LT95KJ6m4e4SZoA3JFW3VY0DGlCWh8kTS1pp7V0n2n5F9IxbJB0r6SDuznuN0maJelZSRsl3SfpY/2JYdE+x0qaK2ld2u/FwCu6qXuSpEWpn89JukzSa0rqtEiaLekFSaskXSHpI8WxSfXulDRf0hGS/pQSxH/py3FKapN0fWpnvaQFpbGT9G5Jt6W/7TpJT0j6wUBiVrL/aZIWSno+xeQ3kvYvqbO9pEsk/SUdzzNp6NoeaRjhk6nqj4teR5Or1UczMzOrPuejzkdTPeejZmZ15Ct0zfLlSuATwHnE4WUHAGcBY4FPldT9KvBb4ERgZ+AiYBbw3qI6PwKOAaYC9wLvT3V6shD4V2A68HngnrT8//pyIJI+DXwXmAn8HBgHzAZeVVLvDcAfgOXAacCzwCRgjqSPhhDKDcXqre0RwG1AczqW5cDJwFFl6n4L+BLwPeB0YAzwTWAvSeNDCJtS1V8BbyfG/THg48Al3XThLWl/3yAOEVxZ6XEqzu91N/An4CRgHfA54PbUnz9K2h64BfhfYDKwhjgscXyFIWqSVPr5EIqOlRSH7xCHp70SOB64S9K+IYQ/pzrfAT4CnAk8CowCDiQObfsTMd6/Yushdbm++sbMzGwIcD7qfNT5qJlZvYUQXFxcMlCIiU4AxnWzfq+0fmrJ8rPS8r3T89b0fF5JvSlp+S7p+e5AF/DlknrfS/UmFy2bCbQXPZ+Q6hxapp/l+thavE/i6ICngJtL6k1K9WYWLbuMmEyOKql7G3BfLzGdGt/mXrb8pNTO/kXLmoAH0/LWon5vAs4p2f7AVO+j6fkH0/NPlNS7Pi2fULTszhT3d5TUreg4gV8DDwEjipZtk5Zdl57vW/ya6MNrsPB3KlfW9rDdNsQfCB8BLi5a/gDwnxW095ke6rQDP6vV/0MXFxcXF5ehXHA+Cs5HnY++vE47zkddXFwyVjzlgll+/F16LL3DauH5e0uWzy15fn96fGN63A8Q8IuSer/sbwf74PWpXFOyfA7QWbJsInAT8LykYYVC/MW/TdKr+9H+AcBToWherhBCV5n+fICYWM8qafsPwAts+ZvsT0y0S2+Y0F0s20MI95Us6/U4JTUT/86/ALqK6gi4vag/jwKrgRmSjk9XW/TFN4F3l5TSIXSHSrpD0gri3+wl4pUeuxdVuweYLOlMSftK2qaP/TAzM7NscT7qfNT5qJlZBnjKBbP8KMyRtbRk+bKS9QUrS54XJvPfNj2+Lj0uL6n3TL961zeFtrdqK4TQmRKyYjsDJ6RSzihiMtvX9ssdZ+myndPjYz20XdjfqhDCS73sr6D0b1hoq7fjfJF49cHZqbyMpKYQwvOS3pfq/AB4laQHgXNDCHO62X+xJSGEe7tbmYbZ3URM7j+djmcTcClbXl8Q7wy8jDjM8t+JQ/l+AnwthLCugn6YmZlZtjgfLc/5aBHno2Zmg88ndM3yo5AQj2breZ1Gp8fSxLM3hSRuZ7bcDADgtX3v2lY2AiNKlo0qeV5oe6u20q/7pXVXEOfouqCb9v7Wjz4uBfYss7z02Asx/SCwqkz9wvqlwI6Shpck0d3FMnSzr96OcxhxeNx04Cdldxyv7CBdcfHxFNN9iXOpXSOpLYTwQDdtVOrjxKsgjio+Xkk7Eq/EKPRlbWr3q5J2BY4GvkX8InDGAPtgZmZmted8tDzno8U7dj5qZjbofELXLD/mpcdPEn9dLjguPd7Vx/39gZjIHQN8u2j5MRVsW7i6ornMuiXE+dWKHV7y/K/EOcs+AVxetPzjvPx96WbikLQHQwjrK+hbJX4H/JOk/QvD3CQ1pf4Uu42YsL4xhHBbD/v7PfFKhY+x9TC5SmJZUMlxbpR0N9AGLCwkyz0JIXQCv5d0NvGGEG8lziU2ENsRr4DY/EVA0iHE4ZNPltsghLAEuEjScWx5ffT0OjIzM7PscT7qfNT5qJlZBviErln2TJS0rGTZ8yGE2yTNBqamX7l/S0y4zgZmhy13cq1ICOERSVcB30jJ4x+BQ4AjUpWekrPFxF/ET5S0kpgIPRJCWANcDZwl6WvExPJg4NiStrskTQMulXRF2mYc8dfz0uFq5xDvjnuXpO8Tb0qwIzEJGxtCOLEvx51cCXwF+JWkM4nD/D4HbDX/WQjhcUkXAN+XtDvxS8wG4A3E+cwuDSHcEUK4VdJ84EeSdiIOiTuamOhCz7Hs63F+kfhl6RZJlxGvxtgJ2AfYJoTwFUkfBj4LXEdMaF9JvAP0GuKXh96MlbR/meWLQwgricn+qcDM9Pd7C/F1+HRxZUm/I96I435gLXG+tTZi/CEOAVwBfFLSn4EO4MkQQl+v7jEzM7Pqcj66NeejzkfNzLKl3ndlc3FxiYUtdxUuVx5IdYYTbxCwhDjp/5L0fHjRflopc6dWttwJeELRsu2AHxKHz60lJjuHp3pHFtWbSdFdhdOyk4EniIn05v0S56y6mJjYrQF+DryHkjsVp7pfSMewAbgXOIiYOM4sqfd64nxYTxOHRy0lXq1wfC8xnUqZuwqndWOJ826tI97N9+J0TJvvKlxU9x+IXwY6UpweAr4PvL6oTgvxi8Aa4jCvnwD/mPbXVlTvTmB+N32q6DiJVzVcTUz8NxKvMLke+FBav3uK+5Mpts+mY92vl3gVXjvdlaOL6p6S9r+eeLOJQ9Ox3VlU5wLgT8DzKXb3A58vafOjwP8RX8/lXiPt+K7CLi4uLi4uNSk4H3U+6nzU+aiLi0suikIoN3WOmQ1Vkk4nJj6tIYS/1Ls/AyFpKnAu8YtHCCFsqnH704lfjF4TQtjYS3Urkq7SaSJeXTI/hHB8nbtkZmZmNeJ8tKrtOx/tJ+ejZpZlnnLBbAhLQ6H2Au4jDsM6GJgCXJP35LlE4eqR1sFqQNJkYCTwIPEmHBOJw+YudPLcL7cC70//nl/PjpiZmdngcT5aPc5Hq875qJlllq/QNRvCJL2XePXDHsR5rZ4mDo06N4SwoZ59qwZJuwC7pKcbQwj3D2JbxwBfA3YDXkEc/nU5MYGuZM4yKyLpLWyZQ25FCKHszS3MzMws35yPVrUt56NV5HzUzLLMJ3TNzMzMzMzMzMzMcqKp3h0wMzMzMzMzMzMzs8r4hK6ZmZmZmZmZmZlZTviErpmZmZmZmZmZmVlO+ISumZmZmZmZmZmZWU74hK6ZmZmZmZmZmZlZTvw/1Ed6BMG7Q54AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# figure\n", - "fig, axs = plt.subplots(2,2,figsize=(20,20))\n", - "axs = axs.flatten()\n", - "plt.tight_layout()\n", - "plt.rcParams.update({'font.size': 16})\n", - "plt.rcParams.update({'axes.titlesize': 16}) \n", - "\n", - "# --- elevation\n", - "axId = 0\n", - "\n", - "# Data\n", - "geo['elev'].plot(ax=axs[axId], cmap = cmap_elev, add_colorbar=False, vmin=vmin_elev, vmax=vmax_elev)\n", - "catchment.plot(ax=axs[axId], column='elev_mean',edgecolor='k', cmap = cmap_elev, legend=False, vmin=vmin_elev, vmax=vmax_elev)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "\n", - "# Custom colorbar\n", - "cax = fig.add_axes([0.46, 0.55, 0.02, 0.4])\n", - "sm = plt.cm.ScalarMappable(cmap=cmap_elev, norm=plt.Normalize(vmin=vmin_elev, vmax=vmax_elev))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "cbr.ax.set_title(' [m.a.s.l.]')\n", - "\n", - "\n", - "# Custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color='k', lw=1)]\n", - "label = ['SUMMA GRUs',\n", - " 'SUMMA HRUs']\n", - "axs[axId].legend(lines,label,loc='lower left');\n", - "\n", - "# Chart junk\n", - "axs[axId].set_title('(a) ERA5 grid and mean HRU elevation');\n", - "axs[axId].set_frame_on(False)\n", - "axs[axId].set_ylabel('Latitude [degrees North]');\n", - "\n", - "\n", - "# --- lapse rates\n", - "axId = 1\n", - "\n", - "# Data\n", - "catchment.plot(ax=axs[axId], column='lapse_rate',edgecolor='k', cmap = cmap_lapse, legend=False, vmin=-5, vmax=5)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "\n", - "# Custom colorbar\n", - "cax = fig.add_axes([0.96, 0.55, 0.02, 0.4])\n", - "sm = plt.cm.ScalarMappable(cmap=cmap_lapse, norm=plt.Normalize(vmin=vmin_rate, vmax=vmax_rate))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "cbr.ax.set_title('[K]')\n", - "\n", - "\n", - "# Custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color='k', lw=1)]\n", - "label = ['SUMMA GRUs',\n", - " 'SUMMA HRUs']\n", - "axs[axId].legend(lines,label,loc='lower left');\n", - "\n", - "# Chart junk\n", - "axs[axId].set_title('(b) Temperature lapse rates');\n", - "axs[axId].set_frame_on(False)\n", - "\n", - "\n", - "# --- reset axes on first plot\n", - "xlim = axs[axId].get_xlim()\n", - "ylim = axs[axId].get_ylim()\n", - "axId = 0\n", - "axs[axId].set_xlim(xlim)\n", - "axs[axId].set_ylim(ylim)\n", - "\n", - "# ---------------------------------------------------------------------------------------------------------------\n", - "# --- old forcing\n", - "axId = 2\n", - "\n", - "# Data\n", - "catchment.plot(ax=axs[axId], column='old_T',edgecolor='k', legend=False, vmin=vmin_temp, vmax=vmax_temp)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "\n", - "# Custom colorbar\n", - "cax = fig.add_axes([0.46, 0.05, 0.02, 0.4])\n", - "vmin = min(df_new_temp.min().values,df_old_temp.min().values)\n", - "vmax = max(df_new_temp.max().values,df_old_temp.max().values)\n", - "sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin_temp, vmax=vmax_temp))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "cbr.ax.set_title('[K]')\n", - "\n", - "# Custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color='k', lw=1)]\n", - "label = ['SUMMA GRUs',\n", - " 'SUMMA HRUs']\n", - "axs[axId].legend(lines,label,loc='lower left');\n", - "\n", - "# Chart junk\n", - "axs[axId].set_title('(c) HRU-averaged temperature on {}'.format(time_str_old))\n", - "axs[axId].set_frame_on(False)\n", - "axs[axId].set_xlabel('Longitude [degrees East]');\n", - "axs[axId].set_ylabel('Latitude [degrees North]');\n", - "\n", - "\n", - "# --- new forcing\n", - "axId = 3\n", - "\n", - "# Data\n", - "catchment.plot(ax=axs[axId], column='new_T',edgecolor='k', legend=False, vmin=vmin_temp, vmax=vmax_temp)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "\n", - "# Custom colorbar\n", - "cax = fig.add_axes([0.96, 0.05, 0.02, 0.4])\n", - "sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin_temp, vmax=vmax_temp))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "cbr.ax.set_title('[K]')\n", - "\n", - "# Custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color='k', lw=1)]\n", - "label = ['SUMMA GRUs',\n", - " 'SUMMA HRUs']\n", - "axs[axId].legend(lines,label,loc='lower left');\n", - "\n", - "# Chart junk\n", - "axs[axId].set_title('(d) HRU-averaged temperature with lapse rates on {}'.format(time_str_new))\n", - "axs[axId].set_frame_on(False)\n", - "axs[axId].set_xlabel('Longitude [degrees East]');\n", - "\n", - "\n", - "# save\n", - "plt.tight_layout()\n", - "plt.savefig(fig_path/fig_name, bbox_inches='tight', dpi=300)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cwarhm-env", - "language": "python", - "name": "cwarhm-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/7_visualization/5_geospatial_parameters_to_model_elements.ipynb b/7_visualization/5_geospatial_parameters_to_model_elements.ipynb deleted file mode 100644 index e80923b..0000000 --- a/7_visualization/5_geospatial_parameters_to_model_elements.ipynb +++ /dev/null @@ -1,529 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualize geospatial parameters before and after remapping\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import xarray as xr\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define where to save the figure" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Path and filename\n", - "fig_path = read_from_control(controlFolder/controlFile,'visualization_folder')\n", - "fig_name = 'geospatial_fields_remapping_v2.png'\n", - "\n", - "# Specify default path if needed\n", - "if fig_path == 'default':\n", - " fig_path = make_default_path('visualization') # outputs a Path()\n", - "else:\n", - " fig_path = Path(fig_path) # make sure a user-specified path is a Path()\n", - " \n", - "# Make the folder if it doesn't exist\n", - "fig_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location the catchment shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# HRU fieldname - this is the same for all three shapefiles\n", - "shp_hru = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Path & name\n", - "catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if catchment_path == 'default':\n", - " catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " catchment_path = Path(catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of source .tifs" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# DEM path & name\n", - "dem_path = read_from_control(controlFolder/controlFile,'parameter_dem_tif_path')\n", - "dem_name = read_from_control(controlFolder/controlFile,'parameter_dem_tif_name')\n", - "\n", - "# Specify default path if needed\n", - "if dem_path == 'default':\n", - " dem_path = make_default_path('parameters/dem/5_elevation') # outputs a Path()\n", - "else:\n", - " dem_path = Path(dem_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Soil path & name\n", - "soil_path = read_from_control(controlFolder/controlFile,'parameter_soil_domain_path')\n", - "soil_name = read_from_control(controlFolder/controlFile,'parameter_soil_tif_name')\n", - "\n", - "# Specify default path if needed\n", - "if soil_path == 'default':\n", - " soil_path = make_default_path('parameters/soilclass/2_soil_classes_domain') # outputs a Path()\n", - "else:\n", - " soil_path = Path(soil_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Land path & name\n", - "land_path = read_from_control(controlFolder/controlFile,'parameter_land_mode_path')\n", - "land_name = read_from_control(controlFolder/controlFile,'parameter_land_tif_name')\n", - "\n", - "# Specify default path if needed\n", - "if land_path == 'default':\n", - " land_path = make_default_path('parameters/landclass/7_mode_land_class') # outputs a Path()\n", - "else:\n", - " land_path = Path(land_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find location of the attributes file" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# attributes path & name\n", - "att_path = read_from_control(controlFolder/controlFile,'settings_summa_path')\n", - "att_name = read_from_control(controlFolder/controlFile,'settings_summa_attributes')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify default path if needed\n", - "if att_path == 'default':\n", - " att_path = make_default_path('settings/SUMMA') # outputs a Path()\n", - "else:\n", - " att_path = Path(att_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Load the shapefiles and the data" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# shapefile\n", - "catchment = gpd.read_file(catchment_path/catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\wmk934\\AppData\\Local\\Temp\\1\\ipykernel_17348\\2777467033.py:2: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n", - " dem = xr.open_rasterio(dem_path/dem_name)\n", - "C:\\Users\\wmk934\\AppData\\Local\\Temp\\1\\ipykernel_17348\\2777467033.py:3: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n", - " soil = xr.open_rasterio(soil_path/soil_name)\n", - "C:\\Users\\wmk934\\AppData\\Local\\Temp\\1\\ipykernel_17348\\2777467033.py:4: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n", - " land = xr.open_rasterio(land_path/land_name)\n" - ] - } - ], - "source": [ - "# tifs\n", - "dem = xr.open_rasterio(dem_path/dem_name)\n", - "soil = xr.open_rasterio(soil_path/soil_name)\n", - "land = xr.open_rasterio(land_path/land_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# attributes\n", - "att = xr.open_dataset(att_path/att_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Map attributes data to catchment HRUs" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Elevation\n", - "if (att['hruId'] == catchment[shp_hru]).all():\n", - " catchment['plot_elev'] = att['elevation']\n", - " catchment['plot_soil'] = att['soilTypeIndex']\n", - " catchment['plot_land'] = att['vegTypeIndex']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot settings and plotting data prep" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Number of classes for land and soil\n", - "n_land = 17\n", - "n_soil = 12" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Colors\n", - "cmap_dem = 'Greys_r'\n", - "#cmap_soil = plt.get_cmap('YlOrBr',n_soil)\n", - "cmap_soil = plt.get_cmap('tab20c',n_soil)\n", - "cmap_soil.set_under('w')\n", - "#cmap_land = plt.get_cmap('Greens',n_land)\n", - "cmap_land = plt.get_cmap('tab20c',n_land)\n", - "cmap_land.set_under('w')" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# color limits\n", - "vmin_dem,vmax_dem = dem.min(),dem.max()\n", - "vmin_soil,vmax_soil = 1-0.5,n_soil+0.5 # slightly hacky way to get the labels for discrete classes in the right locations\n", - "vmin_land,vmax_land = 1-0.5,n_land+0.5 " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# colorbar settings\n", - "ticks_soil = range(n_soil+1)\n", - "ticks_land = range(n_land+1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the figure" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 16})" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(3,2,figsize=(20,30))\n", - "axs = axs.flatten()\n", - "cbar_scale = 0.97\n", - "cbar_font = 14\n", - "\n", - "# --- DEM\n", - "# source data + catchment\n", - "axId = 0\n", - "h = dem.sel(band=1).plot(cmap=cmap_dem, ax=axs[axId], vmin=vmin_dem, vmax=vmax_dem, cbar_kwargs={'shrink': cbar_scale})\n", - "catchment.plot(color='None', edgecolor='k', ax=axs[axId]);\n", - "axs[axId].set_ylabel('Latitude [degrees North]')\n", - "axs[axId].set_xlabel('')\n", - "axs[axId].set_title('(a) Merit Hydro adjusted elevations')\n", - "h.colorbar.ax.set_title('[m.a.s.l.]', fontdict={'fontsize': cbar_font})\n", - "\n", - "# source data + HRU mean\n", - "axId = 1\n", - "h = dem.sel(band=1).plot(cmap=cmap_dem, ax=axs[axId], vmin=vmin_dem, vmax=vmax_dem, cbar_kwargs={'shrink': cbar_scale})\n", - "catchment.plot(column='plot_elev', edgecolor='k', ax=axs[axId], cmap=cmap_dem, vmin=vmin_dem, vmax=vmax_dem);\n", - "axs[axId].set_ylabel('')\n", - "axs[axId].set_xlabel('')\n", - "axs[axId].set_title('(b) HRU-averaged elevation')\n", - "h.colorbar.ax.set_title('[m.a.s.l.]', fontdict={'fontsize': cbar_font})\n", - "\n", - "# --- Soil\n", - "# source data + catchment\n", - "axId = 2\n", - "h = soil.sel(band=1).plot(cmap=cmap_soil, ax=axs[axId], vmin=vmin_soil, vmax=vmax_soil, cbar_kwargs={'shrink': cbar_scale})\n", - "catchment.plot(color='None', edgecolor='k', ax=axs[axId]);\n", - "axs[axId].set_ylabel('Latitude [degrees North]')\n", - "axs[axId].set_xlabel('')\n", - "axs[axId].set_title('(c) Soil texture classes')\n", - "h.colorbar.set_ticks(ticks_soil)\n", - "h.colorbar.ax.set_title('[-]', fontdict={'fontsize': cbar_font})\n", - "\n", - "# add 'no data' to tick labels\n", - "tick_txt = []\n", - "tick_txt = [str(val) for val in ticks_soil]\n", - "tick_txt[0] = 'No data'\n", - "h.colorbar.ax.set_yticklabels(tick_txt)\n", - "\n", - "# source data + HRU mean\n", - "axId = 3\n", - "h = soil.sel(band=1).plot(cmap=cmap_soil, ax=axs[axId], vmin=vmin_soil, vmax=vmax_soil, cbar_kwargs={'shrink': cbar_scale})\n", - "catchment.plot(column='plot_soil', edgecolor='k', ax=axs[axId], cmap=cmap_soil, vmin=vmin_soil, vmax=vmax_soil);\n", - "axs[axId].set_ylabel('')\n", - "axs[axId].set_xlabel('')\n", - "axs[axId].set_title('(d) Mode soil texture class per HRU')\n", - "h.colorbar.set_ticks(ticks_soil)\n", - "h.colorbar.ax.set_yticklabels(tick_txt)\n", - "h.colorbar.ax.set_title('[-]', fontdict={'fontsize': cbar_font})\n", - "\n", - "# --- land\n", - "# source data + catchment\n", - "axId = 4\n", - "h = land.sel(band=1).plot(cmap=cmap_land, ax=axs[axId], vmin=vmin_land, vmax=vmax_land, cbar_kwargs={'shrink': cbar_scale})\n", - "catchment.plot(color='None', edgecolor='k', ax=axs[axId]);\n", - "axs[axId].set_ylabel('Latitude [degrees North]')\n", - "axs[axId].set_xlabel('Longitude [degrees East]')\n", - "axs[axId].set_title('(e) IGBP land classes')\n", - "h.colorbar.set_ticks(ticks_land)\n", - "h.colorbar.ax.set_title('[-]', fontdict={'fontsize': cbar_font})\n", - "\n", - "# source data + HRU mean\n", - "axId = 5\n", - "h = land.sel(band=1).plot(cmap=cmap_land, ax=axs[axId], vmin=vmin_land, vmax=vmax_land, cbar_kwargs={'shrink': cbar_scale})\n", - "catchment.plot(column='plot_land', edgecolor='k', ax=axs[axId], cmap=cmap_land, vmin=vmin_land, vmax=vmax_land);\n", - "axs[axId].set_ylabel('')\n", - "axs[axId].set_xlabel('Longitude [degrees East]')\n", - "axs[axId].set_title('(f) Mode IGBP land class per HRU')\n", - "h.colorbar.set_ticks(ticks_land)\n", - "h.colorbar.ax.set_title('[-]', fontdict={'fontsize': cbar_font})\n", - "\n", - "# save\n", - "plt.tight_layout()\n", - "plt.savefig(fig_path/fig_name, bbox_inches='tight', dpi=300)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cwarhm-env", - "language": "python", - "name": "cwarhm-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/7_visualization/5_geospatial_parameters_to_model_elements.py b/7_visualization/5_geospatial_parameters_to_model_elements.py new file mode 100644 index 0000000..a0fafdc --- /dev/null +++ b/7_visualization/5_geospatial_parameters_to_model_elements.py @@ -0,0 +1,363 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Visualize geospatial parameters before and after remapping +# + +# In[46]: + + +# modules +import os +import xarray as xr +import geopandas as gpd +from pathlib import Path +import matplotlib.pyplot as plt + + +# #### Control file handling + +# In[47]: + + +# Easy access to control file folder +controlFolder = Path('../0_control_files') + + +# In[48]: + + +# Store the name of the 'active' file in a variable +controlFile = 'control_active.txt' + + +# In[49]: + + +# Function to extract a given setting from the control file +def read_from_control( file, setting ): + + # Open 'control_active.txt' and ... + with open(file) as contents: + for line in contents: + + # ... find the line with the requested setting + if setting in line and not line.startswith('#'): + break + + # Extract the setting's value + substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) + substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found + substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines + + # Return this value + return substring + + +# In[50]: + + +# Function to specify a default path +def make_default_path(suffix): + + # Get the root path + rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) + + # Get the domain folder + domainName = read_from_control(controlFolder/controlFile,'domain_name') + domainFolder = 'domain_' + domainName + + # Specify the forcing path + defaultPath = rootPath / domainFolder / suffix + + return defaultPath + + +# #### Define where to save the figure + +# In[51]: + + +# Path and filename +fig_path = read_from_control(controlFolder/controlFile,'visualization_folder') +fig_name = 'geospatial_fields_remapping_v2.png' + +# Specify default path if needed +if fig_path == 'default': + fig_path = make_default_path('visualization') # outputs a Path() +else: + fig_path = Path(fig_path) # make sure a user-specified path is a Path() + +# Make the folder if it doesn't exist +fig_path.mkdir(parents=True, exist_ok=True) + + +# #### Find location the catchment shapefile + +# In[52]: + + +# HRU fieldname - this is the same for all three shapefiles +shp_hru = read_from_control(controlFolder/controlFile,'catchment_shp_hruid') + + +# In[53]: + + +# Path & name +catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path') +catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name') + + +# In[54]: + + +# Specify default path if needed +if catchment_path == 'default': + catchment_path = make_default_path('shapefiles/catchment') # outputs a Path() +else: + catchment_path = Path(catchment_path) # make sure a user-specified path is a Path() + + +# #### Find location of source .tifs + +# In[55]: + + +# DEM path & name +dem_path = read_from_control(controlFolder/controlFile,'parameter_dem_tif_path') +dem_name = read_from_control(controlFolder/controlFile,'parameter_dem_tif_name') + +# Specify default path if needed +if dem_path == 'default': + dem_path = make_default_path('parameters/dem/5_elevation') # outputs a Path() +else: + dem_path = Path(dem_path) # make sure a user-specified path is a Path() + + +# In[56]: + + +# Soil path & name +soil_path = read_from_control(controlFolder/controlFile,'parameter_soil_domain_path') +soil_name = read_from_control(controlFolder/controlFile,'parameter_soil_tif_name') + +# Specify default path if needed +if soil_path == 'default': + soil_path = make_default_path('parameters/soilclass/2_soil_classes_domain') # outputs a Path() +else: + soil_path = Path(soil_path) # make sure a user-specified path is a Path() + + +# In[57]: + + +# Land path & name +land_path = read_from_control(controlFolder/controlFile,'parameter_land_mode_path') +land_name = read_from_control(controlFolder/controlFile,'parameter_land_tif_name') + +# Specify default path if needed +if land_path == 'default': + land_path = make_default_path('parameters/landclass/7_mode_land_class') # outputs a Path() +else: + land_path = Path(land_path) # make sure a user-specified path is a Path() + + +# #### Find location of the attributes file + +# In[58]: + + +# attributes path & name +att_path = read_from_control(controlFolder/controlFile,'settings_summa_path') +att_name = read_from_control(controlFolder/controlFile,'settings_summa_attributes') + + +# In[59]: + + +# Specify default path if needed +if att_path == 'default': + att_path = make_default_path('settings/SUMMA') # outputs a Path() +else: + att_path = Path(att_path) # make sure a user-specified path is a Path() + + +# #### Load the shapefiles and the data + +# In[60]: + + +# shapefile +catchment = gpd.read_file(catchment_path/catchment_name) + + +# In[61]: + + +# tifs +import rioxarray +dem = rioxarray.open_rasterio(dem_path/dem_name) +soil = rioxarray.open_rasterio(soil_path/soil_name) +land = rioxarray.open_rasterio(land_path/land_name) + + +# In[62]: + + +# attributes +att = xr.open_dataset(att_path/att_name) + + +# #### Map attributes data to catchment HRUs + +# In[63]: + + +# Elevation +if (att['hruId'] == catchment[shp_hru]).all(): + catchment['plot_elev'] = att['elevation'] + catchment['plot_soil'] = att['soilTypeIndex'] + catchment['plot_land'] = att['vegTypeIndex'] + + +# #### Plot settings and plotting data prep + +# In[64]: + + +# Number of classes for land and soil +n_land = 17 +n_soil = 12 + + +# In[65]: + + +# Colors +cmap_dem = 'Greys_r' +#cmap_soil = plt.get_cmap('YlOrBr',n_soil) +cmap_soil = plt.get_cmap('tab20c',n_soil) +cmap_soil.set_under('w') +#cmap_land = plt.get_cmap('Greens',n_land) +cmap_land = plt.get_cmap('tab20c',n_land) +cmap_land.set_under('w') + + +# In[71]: + + +# color limits + +import numpy as np + +valid = dem != -9999 +dem = dem.fillna(np.nan).where(valid) + +vmin_dem,vmax_dem = dem.min(),dem.max() +vmin_soil,vmax_soil = 1-0.5,n_soil+0.5 # slightly hacky way to get the labels for discrete classes in the right locations +vmin_land,vmax_land = 1-0.5,n_land+0.5 + + +# In[72]: + + +# colorbar settings +ticks_soil = range(n_soil+1) +ticks_land = range(n_land+1) + + +# #### Create the figure + +# In[73]: + + +plt.rcParams.update({'font.size': 16}) + + +# In[74]: + + +fig, axs = plt.subplots(3,2,figsize=(20,30)) +axs = axs.flatten() +cbar_scale = 0.97 +cbar_font = 14 + +# --- DEM +# source data + catchment +axId = 0 +h = dem.sel(band=1).plot(cmap=cmap_dem, ax=axs[axId], vmin=vmin_dem, vmax=vmax_dem, cbar_kwargs={'shrink': cbar_scale}) +catchment.plot(color='None', edgecolor='k', ax=axs[axId]); +axs[axId].set_ylabel('Latitude [degrees North]') +axs[axId].set_xlabel('') +axs[axId].set_title('(a) Merit Hydro adjusted elevations') +h.colorbar.ax.set_title('[m.a.s.l.]', fontdict={'fontsize': cbar_font}) + +# source data + HRU mean +axId = 1 +h = dem.sel(band=1).plot(cmap=cmap_dem, ax=axs[axId], vmin=vmin_dem, vmax=vmax_dem, cbar_kwargs={'shrink': cbar_scale}) +catchment.plot(column='plot_elev', edgecolor='k', ax=axs[axId], cmap=cmap_dem, vmin=vmin_dem, vmax=vmax_dem); +axs[axId].set_ylabel('') +axs[axId].set_xlabel('') +axs[axId].set_title('(b) HRU-averaged elevation') +h.colorbar.ax.set_title('[m.a.s.l.]', fontdict={'fontsize': cbar_font}) + +# --- Soil +# source data + catchment +axId = 2 +h = soil.sel(band=1).plot(cmap=cmap_soil, ax=axs[axId], vmin=vmin_soil, vmax=vmax_soil, cbar_kwargs={'shrink': cbar_scale}) +catchment.plot(color='None', edgecolor='k', ax=axs[axId]); +axs[axId].set_ylabel('Latitude [degrees North]') +axs[axId].set_xlabel('') +axs[axId].set_title('(c) Soil texture classes') +h.colorbar.set_ticks(ticks_soil) +h.colorbar.ax.set_title('[-]', fontdict={'fontsize': cbar_font}) + +# add 'no data' to tick labels +tick_txt = [] +tick_txt = [str(val) for val in ticks_soil] +tick_txt[0] = 'No data' +h.colorbar.ax.set_yticklabels(tick_txt) + +# source data + HRU mean +axId = 3 +h = soil.sel(band=1).plot(cmap=cmap_soil, ax=axs[axId], vmin=vmin_soil, vmax=vmax_soil, cbar_kwargs={'shrink': cbar_scale}) +catchment.plot(column='plot_soil', edgecolor='k', ax=axs[axId], cmap=cmap_soil, vmin=vmin_soil, vmax=vmax_soil); +axs[axId].set_ylabel('') +axs[axId].set_xlabel('') +axs[axId].set_title('(d) Mode soil texture class per HRU') +h.colorbar.set_ticks(ticks_soil) +h.colorbar.ax.set_yticklabels(tick_txt) +h.colorbar.ax.set_title('[-]', fontdict={'fontsize': cbar_font}) + +# --- land +# source data + catchment +axId = 4 +h = land.sel(band=1).plot(cmap=cmap_land, ax=axs[axId], vmin=vmin_land, vmax=vmax_land, cbar_kwargs={'shrink': cbar_scale}) +catchment.plot(color='None', edgecolor='k', ax=axs[axId]); +axs[axId].set_ylabel('Latitude [degrees North]') +axs[axId].set_xlabel('Longitude [degrees East]') +axs[axId].set_title('(e) IGBP land classes') +h.colorbar.set_ticks(ticks_land) +h.colorbar.ax.set_title('[-]', fontdict={'fontsize': cbar_font}) + +# source data + HRU mean +axId = 5 +h = land.sel(band=1).plot(cmap=cmap_land, ax=axs[axId], vmin=vmin_land, vmax=vmax_land, cbar_kwargs={'shrink': cbar_scale}) +catchment.plot(column='plot_land', edgecolor='k', ax=axs[axId], cmap=cmap_land, vmin=vmin_land, vmax=vmax_land); +axs[axId].set_ylabel('') +axs[axId].set_xlabel('Longitude [degrees East]') +axs[axId].set_title('(f) Mode IGBP land class per HRU') +h.colorbar.set_ticks(ticks_land) +h.colorbar.ax.set_title('[-]', fontdict={'fontsize': cbar_font}) + +# save +plt.tight_layout() +plt.savefig(fig_path/fig_name, bbox_inches='tight', dpi=300) + + +# In[ ]: + + + + diff --git a/7_visualization/6_SWE_SM_ET_Q_per_GRU.ipynb b/7_visualization/6_SWE_SM_ET_Q_per_GRU.ipynb deleted file mode 100644 index fc43fa1..0000000 --- a/7_visualization/6_SWE_SM_ET_Q_per_GRU.ipynb +++ /dev/null @@ -1,656 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualize maximum SWE, mean soil water, mean ET and mean runoff per GRU\n", - "Needs: \n", - "- Catchment shapefile with GRU delineation\n", - "- River shapefile with network delineation\n", - "- SUMMA output `scalarSWE`, `scalarTotalSoilWat`, `scalarTotalET`\n", - "- mizuRoute output `IRFroutedRunoff`\n", - "\n", - "#### Special note\n", - "SUMMA and mizuRoute simulations have been preprocessed into single value statistics per model element, using auxiliary scripts in `~/summaWorkflow_public/0_tools/`. Paths to the `.nc` files that contain these statistics are not read from the `control_active.txt` file but hard-coded the sections \"Simulation statistics file locations\" below.\n", - "\n", - "To improve visualization of large lakes, HydroLAKES lake delineations are plotted on top of the catchment GRUs and river segments. Dealing with HydroLAKES inputs is not considered within scope of the workflow and therefore requires some manual downloading and preprocessing of this data for those who wish to reproduce this step. The relevant code is easily disabled by switching the `plot_lakes = True` flag to `False`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import pyproj\n", - "import matplotlib\n", - "import numpy as np\n", - "import xarray as xr\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.gridspec as gridspec" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Simulation statistics file locations" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# SUMMA simulations\n", - "summa_output_path = Path('C:/Globus endpoint/summaWorkflow_data/domain_NorthAmerica/simulations/run1/statistics')\n", - "summa_output_name = 'run1_summa_day_stats_scalarTotalSoilWat,scalarSWE,scalarTotalET_mean,max,mean.nc'" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# mizuRoute simulations\n", - "mizu_output_path = Path('C:/Globus endpoint/summaWorkflow_data/domain_NorthAmerica/simulations/run1/statistics')\n", - "mizu_output_name = 'run1_mizuRoute_mean_KWT_IRF.nc'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot lakes?" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "plot_lakes = True" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# lakes shapefile\n", - "lake_path = Path('C:/Globus endpoint/HydroLAKES/HydroLAKES_polys_v10_shp')\n", - "lake_name = 'HydroLAKES_polys_v10_subset_NA.shp'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define where to save the figure" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Path and filename\n", - "fig_path = read_from_control(controlFolder/controlFile,'visualization_folder')\n", - "fig_name = 'max_swe_AND_mean_sm_AND_mean_et_AND_mean_q_FOR_NorthAmerica_v4_compressed.png'\n", - "\n", - "# Specify default path if needed\n", - "if fig_path == 'default':\n", - " fig_path = make_default_path('visualization') # outputs a Path()\n", - "else:\n", - " fig_path = Path(fig_path) # make sure a user-specified path is a Path()\n", - " \n", - "# Make the folder if it doesn't exist\n", - "fig_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Catchment shapefile location and variable names" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# HM catchment shapefile path & name\n", - "hm_catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "hm_catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')\n", - "\n", - "# Specify default path if needed\n", - "if hm_catchment_path == 'default':\n", - " hm_catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " hm_catchment_path = Path(hm_catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the GRU and HRU identifiers\n", - "hm_hruid = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### River network shapefile location and variable names" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# River network path & name\n", - "river_network_path = read_from_control(controlFolder/controlFile,'river_network_shp_path')\n", - "river_network_name = read_from_control(controlFolder/controlFile,'river_network_shp_name')\n", - "\n", - "# Specify default path if needed\n", - "if river_network_path == 'default':\n", - " river_network_path = make_default_path('shapefiles/river_network') # outputs a Path()\n", - "else:\n", - " river_network_path = Path(river_network_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the segment ID\n", - "seg_id = read_from_control(controlFolder/controlFile,'river_network_shp_segid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Load all shapefiles and project to Albers Conformal Conic" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# catchment shapefile\n", - "bas = gpd.read_file(hm_catchment_path/hm_catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# river network shapefile\n", - "riv = gpd.read_file(river_network_path/river_network_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# lakes shapefile\n", - "if plot_lakes:\n", - " lakes = gpd.read_file(lake_path/lake_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the target CRS\n", - "acc = 'ESRI:102008'" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Reproject\n", - "bas_albers = bas.to_crs(acc)\n", - "riv_albers = riv.to_crs(acc)\n", - "lak_albers = lakes.to_crs(acc)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "median area = 33.06877343600296 m^2\n", - "mean area = 40.19396140285971 m^2\n" - ] - } - ], - "source": [ - "# Print the median basin size for curiousity\n", - "print('median area = {} m^2'.format(bas['HRU_area'].median() / 10**6))\n", - "print('mean area = {} m^2'.format(bas['HRU_area'].mean() / 10**6))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the albers shapes\n", - "main = Path('C:/Globus endpoint/summaWorkflow_data/domain_NorthAmerica/shapefiles/albers_projection')\n", - "lak_albers = gpd.read_file(main/'lakes.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "riv_albers = gpd.read_file(main/'river.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "bas_albers = gpd.read_file(main/'basin.shp')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pre-processing\n", - "#### Map SUMMA sims to catchment shapes" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the aggregated statistics of SUMMA simulations\n", - "summa = xr.open_dataset(summa_output_path/summa_output_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify variables of interest\n", - "plot_vars = ['scalarSWE','scalarTotalET','scalarTotalSoilWat']" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Match the accummulated values to the correct HRU IDs in the shapefile\n", - "hru_ids_shp = bas_albers[hm_hruid].astype(int) # hru order in shapefile\n", - "for plot_var in plot_vars:\n", - " bas_albers[plot_var] = summa[plot_var].sel(hru=hru_ids_shp.values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Map mizuRoute sims to river shapes" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the aggregated statistics of mizuRoute simulations\n", - "mizu = xr.open_dataset(mizu_output_path/mizu_output_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify variables of interest\n", - "mizu_vars = ['IRFroutedRunoff','KWTroutedRunoff']" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# polygon order in the simulation file\n", - "seg_ids_sim = mizu['reachID'].values" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# ensure that the shapefile is in the same order as the sims\n", - "riv_albers = riv_albers.set_index(riv_albers[seg_id].astype('int'))\n", - "riv_albers = riv_albers.reindex(seg_ids_sim)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Match the accummulated values to the correct HRU IDs in the shapefile\n", - "seg_ids_riv = riv_albers.index.values # hru order in shapefile\n", - "for mizu_var in mizu_vars:\n", - " riv_albers[mizu_var] = mizu[mizu_var].sel(seg=np.where(mizu['reachID'].values == seg_ids_riv)[0]).values[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Add a linewidth column for the river network\n", - "riv_albers['lineWidth'] = np.maximum(np.log10(riv_albers['IRFroutedRunoff'] ),0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Select lakes of a certain size for plotting" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "if plot_lakes:\n", - " minSize = 1000 # km2\n", - " in_domain = (lak_albers['Country'] == 'Canada') | \\\n", - " (lak_albers['Country'] == 'United States of America') | \\\n", - " (lak_albers['Country'] == 'Mexico')\n", - " out_domain = (lak_albers['Pour_long'] > -80) & (lak_albers['Pour_lat'] > 65) # Exclude Baffin Island\n", - " large_lakes_albers = lak_albers.loc[(lak_albers['Lake_area'] > minSize) & in_domain & (~out_domain) ]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the lake color\n", - "if plot_lakes:\n", - " lake_col = (8/255,81/255,156/255)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Figure" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the font size: we need this to be huge so we can also make our plotting area huge, to avoid a gnarly plotting bug\n", - "if 'compressed' in fig_name:\n", - " plt.rcParams.update({'font.size': 25})\n", - "else:\n", - " plt.rcParams.update({'font.size': 100})" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Flip the evaporation values so that they become positive\n", - "bas_albers['plot_ET'] = bas_albers['scalarTotalET'] * -1\n", - "bas_albers['plot_ET'] = bas_albers['plot_ET'].where(bas_albers['scalarTotalET'] != -9999, np.nan)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\wmk934\\AppData\\Local\\Temp\\1\\ipykernel_9736\\2855447990.py:12: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " plt.tight_layout()\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "if 'compressed' in fig_name:\n", - " fig,axs = plt.subplots(2,2,figsize=(35,33))\n", - "else:\n", - " fig,axs = plt.subplots(2,2,figsize=(140,133))\n", - "\n", - "# colorbar axes\n", - "cax1 = fig.add_axes([0.473,0.60,0.02,0.3])\n", - "cax2 = fig.add_axes([0.97 ,0.60,0.02,0.3])\n", - "cax3 = fig.add_axes([0.473,0.10,0.02,0.3])\n", - "cax4 = fig.add_axes([0.97 ,0.10,0.02,0.3])\n", - "\n", - "plt.tight_layout()\n", - "\n", - "# add maps\n", - "bas_albers.plot(ax=axs[0,0], column='scalarSWE', edgecolor='none', legend=True, vmin=0, vmax=1000, \\\n", - " cmap='Greys_r', cax=cax1, zorder=0)\n", - "axs[0,0].set_title('(a) Maximum simulated Snow Water Equivalent')\n", - "axs[0,0].axis('off')\n", - "cax1.set_ylabel('scalarSWE $[kg~m^{-2}]$',labelpad=-600)\n", - "\n", - "# SM\n", - "bas_albers.plot(ax=axs[0,1], column='scalarTotalSoilWat', edgecolor='none', legend=True, vmin=0, \\\n", - " cmap='cividis_r', cax=cax2, zorder=0)\n", - "axs[0,1].set_title('(b) Mean simulated total soil water content')\n", - "axs[0,1].axis('off')\n", - "cax2.set_ylabel('scalarTotalSoilWat $[kg~m^{-2}]$',labelpad=-600)\n", - "\n", - "# ET\n", - "bas_albers.plot(ax=axs[1,0], column='plot_ET', edgecolor='none', legend=True,\\\n", - " cmap='viridis', cax=cax3, zorder=0)\n", - "axs[1,0].set_title('(c) Mean simulated total evapotranspiration')\n", - "axs[1,0].axis('off')\n", - "cax3.set_ylabel('scalarTotalET $[kg~m^{-2}~s^{-1}]$',labelpad=-450)\n", - "\n", - "# Flow\n", - "var = 'IRFroutedRunoff'\n", - "norm = matplotlib.colors.LogNorm(vmin=riv_albers[var].min(), vmax=riv_albers[var].max())\n", - "riv_albers.plot(ax=axs[1,1],column=var, linewidth=riv_albers['lineWidth'], \\\n", - " cmap='Blues', legend=True, cax=cax4, norm=norm, zorder=0)\n", - "axs[1,1].set_title('(d) Mean simulated streamflow')\n", - "axs[1,1].axis('off')\n", - "cax4.set_ylabel('IRFroutedRunoff $[m^3~s^{-1}]$',labelpad=-600)\n", - "\n", - "# lakes\n", - "if plot_lakes:\n", - " large_lakes_albers.plot(ax=axs[0,0], color=lake_col, zorder=1)\n", - " large_lakes_albers.plot(ax=axs[0,1], color=lake_col, zorder=1)\n", - " large_lakes_albers.plot(ax=axs[1,0], color=lake_col, zorder=1)\n", - " large_lakes_albers.plot(ax=axs[1,1], color=lake_col, zorder=1)\n", - "\n", - "# Save \n", - "plt.savefig(fig_path/fig_name, bbox_inches='tight', transparent=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cwarhm-env", - "language": "python", - "name": "cwarhm-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/7_visualization/7_SWE_and_streamflow_per_HRU.ipynb b/7_visualization/7_SWE_and_streamflow_per_HRU.ipynb deleted file mode 100644 index 0f0f59d..0000000 --- a/7_visualization/7_SWE_and_streamflow_per_HRU.ipynb +++ /dev/null @@ -1,686 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SWE and streamflow\n", - "Plots maximum annual SWE per HRU and mean annual streamflow per stream segment. Needs:\n", - "- Catchment shapefile with HRU delineation\n", - "- River network shapefile with stream segments\n", - "- SUMMA output `scalarSWE`\n", - "- mizuRoute output `KWTroutedRunoff`" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# modules\n", - "import os\n", - "import numpy as np\n", - "import xarray as xr\n", - "import geopandas as gpd\n", - "from pathlib import Path\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.lines import Line2D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Control file handling" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Easy access to control file folder\n", - "controlFolder = Path('../0_control_files')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the name of the 'active' file in a variable\n", - "controlFile = 'control_active.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to extract a given setting from the control file\n", - "def read_from_control( file, setting ):\n", - " \n", - " # Open 'control_active.txt' and ...\n", - " with open(file) as contents:\n", - " for line in contents:\n", - " \n", - " # ... find the line with the requested setting\n", - " if setting in line and not line.startswith('#'):\n", - " break\n", - " \n", - " # Extract the setting's value\n", - " substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part)\n", - " substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found\n", - " substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines\n", - " \n", - " # Return this value \n", - " return substring" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to specify a default path\n", - "def make_default_path(suffix):\n", - " \n", - " # Get the root path\n", - " rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') )\n", - " \n", - " # Get the domain folder\n", - " domainName = read_from_control(controlFolder/controlFile,'domain_name')\n", - " domainFolder = 'domain_' + domainName\n", - " \n", - " # Specify the forcing path\n", - " defaultPath = rootPath / domainFolder / suffix\n", - " \n", - " return defaultPath" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define where to save the figure" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Path and filename\n", - "fig_path = read_from_control(controlFolder/controlFile,'visualization_folder')\n", - "fig_name = 'mean_elevation_per_hru_AND_mean_annual_streamflow_per_segment_AND_mean_max_swe_per_hru_FOR_bowAtBanff_v2.png'\n", - "\n", - "# Specify default path if needed\n", - "if fig_path == 'default':\n", - " fig_path = make_default_path('visualization') # outputs a Path()\n", - "else:\n", - " fig_path = Path(fig_path) # make sure a user-specified path is a Path()\n", - " \n", - "# Make the folder if it doesn't exist\n", - "fig_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get shapefile location from control file" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# HM catchment shapefile path & name\n", - "hm_catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path')\n", - "hm_catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name')\n", - "\n", - "# Specify default path if needed\n", - "if hm_catchment_path == 'default':\n", - " hm_catchment_path = make_default_path('shapefiles/catchment') # outputs a Path()\n", - "else:\n", - " hm_catchment_path = Path(hm_catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the GRU and HRU identifiers\n", - "hm_gruid = read_from_control(controlFolder/controlFile,'catchment_shp_gruid')\n", - "hm_hruid = read_from_control(controlFolder/controlFile,'catchment_shp_hruid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get the shapefile with elevation for comparison purposes" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersection of catchment shapefile and DEM; path & name\n", - "elev_catchment_path = read_from_control(controlFolder/controlFile,'intersect_dem_path')\n", - "elev_catchment_name = read_from_control(controlFolder/controlFile,'intersect_dem_name')\n", - "\n", - "# Specify default path if needed\n", - "if elev_catchment_path == 'default':\n", - " elev_catchment_path = make_default_path('shapefiles/catchment_intersection/with_dem') # outputs a Path()\n", - "else:\n", - " elev_catchment_path = Path(elev_catchment_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get the shapefile with the river network" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# River network path & name\n", - "river_network_path = read_from_control(controlFolder/controlFile,'river_network_shp_path')\n", - "river_network_name = read_from_control(controlFolder/controlFile,'river_network_shp_name')\n", - "\n", - "# Specify default path if needed\n", - "if river_network_path == 'default':\n", - " river_network_path = make_default_path('shapefiles/river_network') # outputs a Path()\n", - "else:\n", - " river_network_path = Path(river_network_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the segment ID\n", - "seg_id = read_from_control(controlFolder/controlFile,'river_network_shp_segid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find the location of the simulations" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# SUMMA simulation path\n", - "summa_output_path = read_from_control(controlFolder/controlFile,'experiment_output_summa')\n", - "experiment_id = read_from_control(controlFolder/controlFile,'experiment_id')\n", - "\n", - "# Specify default path if needed\n", - "if summa_output_path == 'default':\n", - " summa_output_path = make_default_path('simulations/' + experiment_id + '/SUMMA') # outputs a Path()\n", - "else:\n", - " summa_output_path = Path(summa_output_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the output file name and variable of interest\n", - "summa_output_name = experiment_id + '_day.nc'\n", - "summa_plot_var = 'scalarSWE'" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# mizuRoute simulation path\n", - "mizu_output_path = read_from_control(controlFolder/controlFile,'experiment_output_mizuRoute')\n", - "experiment_id = read_from_control(controlFolder/controlFile,'experiment_id')\n", - "\n", - "# Specify default path if needed\n", - "if mizu_output_path == 'default':\n", - " mizu_output_path = make_default_path('simulations/' + experiment_id + '/mizuRoute') # outputs a Path()\n", - "else:\n", - " mizu_output_path = Path(mizu_output_path) # make sure a user-specified path is a Path()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify the variable of interest\n", - "mizu_output_name = 'run1*.nc'\n", - "mizu_plot_var = 'KWTroutedRunoff'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Load the shape and data" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# catchment shapefile\n", - "shp_catchment = gpd.read_file(hm_catchment_path/hm_catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# river shapefile\n", - "shp_river = gpd.read_file(river_network_path/river_network_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# catchment with DEM\n", - "shp_elev = gpd.read_file(elev_catchment_path/elev_catchment_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# SUMMA simulations\n", - "sim_summa = xr.open_dataset(summa_output_path/summa_output_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# mizuRoute simulations\n", - "mizu_files = [mizu_output_path/file for file in os.listdir(mizu_output_path) if file.endswith('.nc')] # find all files\n", - "sim_mizu = xr.merge( xr.open_dataset(file) for file in mizu_files) # open all files into a single dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Add a water year definition" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Define in which month the water year starts\n", - "water_year_start = 'Oct' # Assumed to be on the 1st of the month" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert month the number \n", - "months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n", - "start_month = months.index(water_year_start) + 1 # add 1 to account for 0-based indexing in Python" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Add a water year variable for grouping\n", - "sim_summa['water_year'] = sim_summa['time'].dt.year # set initial year\n", - "sim_mizu['water_year'] = sim_mizu['time'].dt.year # set initial year" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Overwrite the year for months that are part of the water year that started last calendar year\n", - "sim_summa['water_year'].loc[sim_summa['time'].dt.month < start_month] -= 1\n", - "sim_mizu['water_year'].loc[sim_mizu['time'].dt.month < start_month] -= 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Select only complete water years\n", - "complete_water_years_summa = (sim_summa['water_year'] > min(sim_summa['water_year'])) & (sim_summa['water_year'] < max(sim_summa['water_year']))\n", - "complete_water_years_mizu = (sim_mizu['water_year'] > min(sim_mizu['water_year'])) & (sim_mizu['water_year'] < max(sim_mizu['water_year']))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the mean maximum water-year SWE per HRU\n", - "plot_dat_summa = sim_summa[summa_plot_var].sel(time=complete_water_years_summa).groupby(sim_summa['water_year'].sel(time=complete_water_years_summa)).max(dim='time').mean(dim='water_year')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the mean water-year streamflow per HRU\n", - "plot_dat_mizu = sim_mizu[mizu_plot_var].sel(time=complete_water_years_mizu).groupby(sim_mizu['water_year'].sel(time=complete_water_years_mizu)).mean(dim='time').mean(dim='water_year')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Match the accummulated values to the correct HRU IDs in the SUMMA shapefile\n", - "hru_ids_shp = shp_catchment[hm_hruid] # hru order in shapefile\n", - "shp_catchment['plot_var'] = plot_dat_summa.sel(hru=hru_ids_shp.values)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Match the accummulated values to the correct stream IDs in the shapefile\n", - "seg_ids_shp = shp_river[seg_id] # stream segment order in shapefile\n", - "shp_river['plot_var'] = plot_dat_mizu.sel(seg=np.where(sim_mizu['reachID'].values == seg_ids_shp.values.astype('int'))[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the units of our plotting variable\n", - "units_summa = sim_summa[summa_plot_var].units\n", - "units_mizu = sim_mizu[mizu_plot_var].units" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "kg m-2\n", - "m3/s\n" - ] - } - ], - "source": [ - "# Check what the units are\n", - "print(units_summa)\n", - "print(units_mizu)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Format the units into something nicer\n", - "units_summa = '$kg~m^{-2}$' # LaTeX syntax: $ for math mode, ~ for space, ^ for superscript, _ for subscript, {} to group\n", - "units_mizu = '$m^3~s^{-1}$'" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a shapefile with only GRU boundaries for overlay\n", - "hm_grus_only = shp_catchment[[hm_gruid,'geometry']] # keep only the gruId and geometry\n", - "hm_grus_only = hm_grus_only.dissolve(by=hm_gruid) # Dissolve HRU delineation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define where the outlet is" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "outlet_lat,outlet_lon = 51.167,-115.555" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "def add_outlet(ax,lat,lon):\n", - " ax.plot(lon,lat,linestyle='None',marker='o',color='r',markersize=10,label='outlet')\n", - " return" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Figure" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Set a colormap\n", - "cmap_elev = 'terrain'\n", - "cmap_q = 'Blues'\n", - "cmap_swe = 'pink'" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "size = 16\n", - "plt.rcParams.update({'font.size': size, \n", - " 'axes.labelsize': size, \n", - " 'xtick.labelsize': size, \n", - " 'ytick.labelsize': size})" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1,2,figsize=(20,10))\n", - "plt.tight_layout()\n", - "\n", - "# --- elevation\n", - "axId = 0\n", - "\n", - "# Data\n", - "shp_elev.plot(ax=axs[axId], column='elev_mean',edgecolor='k', cmap = cmap_elev, legend=False)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "add_outlet(axs[axId],outlet_lat,outlet_lon)\n", - "\n", - "# Custom colorbar\n", - "cax = fig.add_axes([0.46, 0.1, 0.02, 0.5])\n", - "vmin,vmax = shp_elev['elev_mean'].min(),shp_elev['elev_mean'].max()\n", - "sm = plt.cm.ScalarMappable(cmap=cmap_elev, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "cbr.ax.set_title('$[m.a.s.l.]$')\n", - "\n", - "# Custom legend\n", - "lines = [Line2D([0], [0], color='k', lw=2),\n", - " Line2D([0], [0], color='k', lw=1),\n", - " Line2D([0], [0], color='r', linestyle='None', marker='.', markersize=10, lw=2)]\n", - "label = ['SUMMA GRUs',\n", - " 'SUMMA HRUs',\n", - " 'Outlet']\n", - "axs[axId].legend(lines,label,loc='lower left');\n", - "\n", - "# Chart junk\n", - "axs[axId].set_title('(a) Mean HRU elevation derived from MERIT DEM');\n", - "axs[axId].set_frame_on(False)\n", - "axs[axId].set_xlabel('Longitude [degrees East]')\n", - "axs[axId].set_ylabel('Latitude [degrees North]')\n", - "\n", - "\n", - "# --- mean flow\n", - "axId = 1\n", - "\n", - "# Data\n", - "shp_catchment.plot(ax=axs[axId], column='plot_var',edgecolor='k', cmap = cmap_swe, legend=False)\n", - "hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) \n", - "shp_river.plot(ax=axs[axId], column='plot_var', cmap=cmap_q,linewidth=5)\n", - "add_outlet(axs[axId],outlet_lat,outlet_lon)\n", - "\n", - "# Custom colorbars\n", - "cax = fig.add_axes([0.96, 0.1, 0.02, 0.5])\n", - "vmin,vmax = shp_catchment['plot_var'].min(),shp_catchment['plot_var'].max()\n", - "sm = plt.cm.ScalarMappable(cmap=cmap_swe, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "cbr.ax.set_title('[{}]'.format(units_summa))\n", - "\n", - "cax = fig.add_axes([1.02, 0.1, 0.02, 0.5])\n", - "vmin,vmax = shp_river['plot_var'].min(),shp_river['plot_var'].max()\n", - "sm = plt.cm.ScalarMappable(cmap=cmap_q, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n", - "sm._A = []\n", - "cbr = fig.colorbar(sm, cax=cax)\n", - "cbr.ax.set_title('[{}]'.format(units_mizu))\n", - "\n", - "# Chart junk\n", - "axs[axId].set_title('(b) Mean annual max SWE and mean annual Q');\n", - "axs[axId].set_frame_on(False)\n", - "axs[axId].set_xlabel('Longitude [degrees East]')\n", - "\n", - "\n", - "# Save \n", - "plt.savefig(fig_path/fig_name, bbox_inches='tight', transparent=True, dpi=300)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cwarhm-env", - "language": "python", - "name": "cwarhm-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/7_visualization/7_SWE_and_streamflow_per_HRU.py b/7_visualization/7_SWE_and_streamflow_per_HRU.py new file mode 100644 index 0000000..dc326ae --- /dev/null +++ b/7_visualization/7_SWE_and_streamflow_per_HRU.py @@ -0,0 +1,471 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # SWE and streamflow +# Plots maximum annual SWE per HRU and mean annual streamflow per stream segment. Needs: +# - Catchment shapefile with HRU delineation +# - River network shapefile with stream segments +# - SUMMA output `scalarSWE` +# - mizuRoute output `IRFroutedRunoff` + +# In[1]: + + +# modules +import os +import numpy as np +import xarray as xr +import geopandas as gpd +from pathlib import Path +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D + + +# #### Control file handling + +# In[2]: + + +# Easy access to control file folder +controlFolder = Path('../0_control_files') + + +# In[3]: + + +# Store the name of the 'active' file in a variable +controlFile = 'control_active.txt' + + +# In[4]: + + +# Function to extract a given setting from the control file +def read_from_control( file, setting ): + + # Open 'control_active.txt' and ... + with open(file) as contents: + for line in contents: + + # ... find the line with the requested setting + if setting in line and not line.startswith('#'): + break + + # Extract the setting's value + substring = line.split('|',1)[1] # Remove the setting's name (split into 2 based on '|', keep only 2nd part) + substring = substring.split('#',1)[0] # Remove comments, does nothing if no '#' is found + substring = substring.strip() # Remove leading and trailing whitespace, tabs, newlines + + # Return this value + return substring + + +# In[5]: + + +# Function to specify a default path +def make_default_path(suffix): + + # Get the root path + rootPath = Path( read_from_control(controlFolder/controlFile,'root_path') ) + + # Get the domain folder + domainName = read_from_control(controlFolder/controlFile,'domain_name') + domainFolder = 'domain_' + domainName + + # Specify the forcing path + defaultPath = rootPath / domainFolder / suffix + + return defaultPath + + +# #### Define where to save the figure + +# In[6]: + + +# Path and filename +fig_path = read_from_control(controlFolder/controlFile,'visualization_folder') +fig_name = 'mean_elevation_per_hru_AND_mean_annual_streamflow_per_segment_AND_mean_max_swe_per_hru_FOR_bowAtBanff_v2.png' + +# Specify default path if needed +if fig_path == 'default': + fig_path = make_default_path('visualization') # outputs a Path() +else: + fig_path = Path(fig_path) # make sure a user-specified path is a Path() + +# Make the folder if it doesn't exist +fig_path.mkdir(parents=True, exist_ok=True) + + +# #### Get shapefile location from control file + +# In[7]: + + +# HM catchment shapefile path & name +hm_catchment_path = read_from_control(controlFolder/controlFile,'catchment_shp_path') +hm_catchment_name = read_from_control(controlFolder/controlFile,'catchment_shp_name') + +# Specify default path if needed +if hm_catchment_path == 'default': + hm_catchment_path = make_default_path('shapefiles/catchment') # outputs a Path() +else: + hm_catchment_path = Path(hm_catchment_path) # make sure a user-specified path is a Path() + + +# In[8]: + + +# Find the GRU and HRU identifiers +hm_gruid = read_from_control(controlFolder/controlFile,'catchment_shp_gruid') +hm_hruid = read_from_control(controlFolder/controlFile,'catchment_shp_hruid') + + +# #### Get the shapefile with elevation for comparison purposes + +# In[9]: + + +# Intersection of catchment shapefile and DEM; path & name +elev_catchment_path = read_from_control(controlFolder/controlFile,'intersect_dem_path') +elev_catchment_name = read_from_control(controlFolder/controlFile,'intersect_dem_name') + +# Specify default path if needed +if elev_catchment_path == 'default': + elev_catchment_path = make_default_path('shapefiles/catchment_intersection/with_dem') # outputs a Path() +else: + elev_catchment_path = Path(elev_catchment_path) # make sure a user-specified path is a Path() + + +# #### Get the shapefile with the river network + +# In[10]: + + +# River network path & name +river_network_path = read_from_control(controlFolder/controlFile,'river_network_shp_path') +river_network_name = read_from_control(controlFolder/controlFile,'river_network_shp_name') + +# Specify default path if needed +if river_network_path == 'default': + river_network_path = make_default_path('shapefiles/river_network') # outputs a Path() +else: + river_network_path = Path(river_network_path) # make sure a user-specified path is a Path() + + +# In[11]: + + +# Find the segment ID +seg_id = read_from_control(controlFolder/controlFile,'river_network_shp_segid') + + +# #### Find the location of the simulations + +# In[12]: + + +# SUMMA simulation path +summa_output_path = read_from_control(controlFolder/controlFile,'experiment_output_summa') +experiment_id = read_from_control(controlFolder/controlFile,'experiment_id') + +# Specify default path if needed +if summa_output_path == 'default': + summa_output_path = make_default_path('simulations/' + experiment_id + '/SUMMA') # outputs a Path() +else: + summa_output_path = Path(summa_output_path) # make sure a user-specified path is a Path() + + +# In[13]: + + +# Specify the output file name and variable of interest +summa_output_name = experiment_id + '_day.nc' +summa_plot_var = 'scalarSWE' + + +# In[14]: + + +# mizuRoute simulation path +mizu_output_path = read_from_control(controlFolder/controlFile,'experiment_output_mizuRoute') +experiment_id = read_from_control(controlFolder/controlFile,'experiment_id') + +# Specify default path if needed +if mizu_output_path == 'default': + mizu_output_path = make_default_path('simulations/' + experiment_id + '/mizuRoute') # outputs a Path() +else: + mizu_output_path = Path(mizu_output_path) # make sure a user-specified path is a Path() + + +# In[15]: + + +# Specify the variable of interest +mizu_output_name = 'run1*.nc' +mizu_plot_var = 'IRFroutedRunoff' + + +# #### Load the shape and data + +# In[16]: + + +# catchment shapefile +shp_catchment = gpd.read_file(hm_catchment_path/hm_catchment_name) + + +# In[17]: + + +# river shapefile +shp_river = gpd.read_file(river_network_path/river_network_name) + + +# In[18]: + + +# catchment with DEM +shp_elev = gpd.read_file(elev_catchment_path/elev_catchment_name) + + +# In[19]: + + +# SUMMA simulations +sim_summa = xr.open_dataset(summa_output_path/summa_output_name) + + +# In[20]: + + +# mizuRoute simulations +mizu_files = [mizu_output_path/file for file in os.listdir(mizu_output_path) if file.endswith('.nc')] # find all files +sim_mizu = xr.merge( xr.open_dataset(file) for file in mizu_files) # open all files into a single dataset + + +# #### Add a water year definition + +# In[21]: + + +# Define in which month the water year starts +water_year_start = 'Oct' # Assumed to be on the 1st of the month + + +# In[22]: + + +# Convert month the number +months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'] +start_month = months.index(water_year_start) + 1 # add 1 to account for 0-based indexing in Python + + +# In[23]: + + +# Add a water year variable for grouping +sim_summa['water_year'] = sim_summa['time'].dt.year # set initial year +sim_mizu['water_year'] = sim_mizu['time'].dt.year # set initial year + + +# In[24]: + + +# Overwrite the year for months that are part of the water year that started last calendar year +# sim_summa['water_year'].loc[sim_summa['time'].dt.month < start_month] -= 1 +# sim_mizu['water_year'].loc[sim_mizu['time'].dt.month < start_month] -= 1 + + +# #### Preprocessing + +# In[25]: + + +# Select only complete water years +complete_water_years_summa = (sim_summa['water_year'] > min(sim_summa['water_year'])) & (sim_summa['water_year'] < max(sim_summa['water_year'])) +complete_water_years_mizu = (sim_mizu['water_year'] > min(sim_mizu['water_year'])) & (sim_mizu['water_year'] < max(sim_mizu['water_year'])) + + +# In[26]: + + +# Find the mean maximum water-year SWE per HRU +plot_dat_summa = sim_summa[summa_plot_var].sel(time=complete_water_years_summa).groupby(sim_summa['water_year'].sel(time=complete_water_years_summa)).max(dim='time').mean(dim='water_year') + + +# In[27]: + + +# Find the mean water-year streamflow per HRU +plot_dat_mizu = sim_mizu[mizu_plot_var].sel(time=complete_water_years_mizu).groupby(sim_mizu['water_year'].sel(time=complete_water_years_mizu)).mean(dim='time').mean(dim='water_year') + + +# In[28]: + + +# Match the accummulated values to the correct HRU IDs in the SUMMA shapefile +hru_ids_shp = shp_catchment[hm_hruid] # hru order in shapefile +shp_catchment['plot_var'] = plot_dat_summa.sel(hru=hru_ids_shp.values) + + +# In[29]: + + +# Match the accummulated values to the correct stream IDs in the shapefile +seg_ids_shp = shp_river[seg_id] # stream segment order in shapefile +shp_river['plot_var'] = plot_dat_mizu.sel(seg=np.where(sim_mizu['reachID'].values == seg_ids_shp.values.astype('int'))[0]) + + +# In[30]: + + +# Get the units of our plotting variable +units_summa = sim_summa[summa_plot_var].units +units_mizu = sim_mizu[mizu_plot_var].units + + + +# In[41]: + + +# Format the units into something nicer +units_summa = '$kg~m^{-2}$' # LaTeX syntax: $ for math mode, ~ for space, ^ for superscript, _ for subscript, {} to group +units_mizu = '$m^3~s^{-1}$' + + +# In[38]: + + +# Create a shapefile with only GRU boundaries for overlay +hm_grus_only = shp_catchment[[hm_gruid,'geometry']] # keep only the gruId and geometry +hm_grus_only = hm_grus_only.dissolve(by=hm_gruid) # Dissolve HRU delineation + + +# #### Define where the outlet is + +# In[32]: + + +outlet_lat,outlet_lon = 51.167,-115.555 + + +# In[33]: + + +def add_outlet(ax,lat,lon): + ax.plot(lon,lat,linestyle='None',marker='o',color='r',markersize=10,label='outlet') + return + + +# #### Figure + +# In[34]: + + +# Set a colormap +cmap_elev = 'terrain' +cmap_q = 'Blues' +cmap_swe = 'pink' + + +# In[35]: + + +size = 16 +plt.rcParams.update({'font.size': size, + 'axes.labelsize': size, + 'xtick.labelsize': size, + 'ytick.labelsize': size}) + + +# In[43]: + + +fig, axs = plt.subplots(1,2,figsize=(20,10)) +plt.tight_layout() + +# --- elevation +axId = 0 + +# Data +shp_elev.plot(ax=axs[axId], column='elev_mean',edgecolor='k', cmap = cmap_elev, legend=False) +hm_grus_only.plot(ax=axs[axId],facecolor='none',edgecolor='k',linewidth=2) +# add_outlet(axs[axId],outlet_lat,outlet_lon) + +# Custom colorbar +cax = fig.add_axes([0.46, 0.1, 0.02, 0.5]) +vmin,vmax = shp_elev['elev_mean'].min(),shp_elev['elev_mean'].max() +sm = plt.cm.ScalarMappable(cmap=cmap_elev, norm=plt.Normalize(vmin=vmin, vmax=vmax)) +sm._A = [] +cbr = fig.colorbar(sm, cax=cax) +cbr.ax.set_title('$[m.a.s.l.]$') + +# Custom legend +lines = [Line2D([0], [0], color='k', lw=2), + Line2D([0], [0], color='k', lw=1)#, + #Line2D([0], [0], color='r', linestyle='None', marker='.', markersize=10, lw=2) + ] + +label = ['SUMMA GRUs', + 'SUMMA HRUs'#, + #'Outlet' + ] +axs[axId].legend(lines,label,loc='lower left'); + +# Chart junk +axs[axId].set_title('(a) Mean HRU elevation derived from MERIT DEM'); +axs[axId].set_frame_on(False) +axs[axId].set_xlabel('Longitude [degrees East]') +axs[axId].set_ylabel('Latitude [degrees North]') + + +# --- mean flow +axId = 1 + +# Plot catchments +shp_catchment.plot(ax=axs[axId], column='plot_var', edgecolor='k', cmap=cmap_swe, legend=False) +hm_grus_only.plot(ax=axs[axId], facecolor='none', edgecolor='k', linewidth=2) + +# Plot rivers only if they have valid geometry +if not shp_river.empty and shp_river.geometry.notna().any(): + shp_river_valid = shp_river[shp_river.geometry.notna() & shp_river.is_valid] + if not shp_river_valid.empty: + shp_river_valid.plot(ax=axs[axId], column='plot_var', cmap=cmap_q, linewidth=5) + + # Add river colorbar + cax = fig.add_axes([1.02, 0.1, 0.02, 0.5]) + vmin, vmax = shp_river_valid['plot_var'].min(), shp_river_valid['plot_var'].max() + sm = plt.cm.ScalarMappable(cmap=cmap_q, norm=plt.Normalize(vmin=vmin, vmax=vmax)) + sm._A = [] + cbr = fig.colorbar(sm, cax=cax) + cbr.ax.set_title('[{}]'.format(units_mizu)) + else: + print("shp_river has no valid geometries — skipping river plot and colorbar.") +else: + print("shp_river is empty or contains no geometry — skipping river plot and colorbar.") + +# Catchment SWE colorbar +cax = fig.add_axes([0.96, 0.1, 0.02, 0.5]) +vmin, vmax = shp_catchment['plot_var'].min(), shp_catchment['plot_var'].max() +sm = plt.cm.ScalarMappable(cmap=cmap_swe, norm=plt.Normalize(vmin=vmin, vmax=vmax)) +sm._A = [] +cbr = fig.colorbar(sm, cax=cax) +cbr.ax.set_title('[{}]'.format(units_summa)) + +# Chart styling +axs[axId].set_title('(b) Mean annual max SWE and mean annual Q') +axs[axId].set_frame_on(False) +axs[axId].set_xlabel('Longitude [degrees East]') + + +# Save +plt.savefig(fig_path/fig_name, bbox_inches='tight', transparent=True, dpi=300) + + +# In[ ]: + + + + diff --git a/7_visualization/8a_global_sims_to_stats.sh b/7_visualization/8a_global_sims_to_stats.sh deleted file mode 100755 index f453a6c..0000000 --- a/7_visualization/8a_global_sims_to_stats.sh +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash -module load StdEnv/2020 intel/2020.1.217 openmpi/4.0.3 cdo/1.9.8 - -# Define the simulation paths -root_path="/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_" -declare -a domain_name=("Africa" "Europe" "NorthAmerica" "NorthAsia" "Oceania" "SouthAmerica" "SouthAsia") -declare -a sim_folders=("simulations/run1" "simulations/run1" "simulations/run3" "simulations/run1" "simulations/run1" "simulations/run1" "simulations/run1") -declare -a summa_files=("SUMMA/run1_day.nc" "SUMMA/run1_day.nc" "SUMMA/run3_day.nc" "SUMMA/run1_day.nc" "SUMMA/run1_day.nc" "SUMMA/run1_day.nc" "SUMMA/run1_day.nc") -declare -a mizur_files=("mizuRoute/run1.h.1979-01-01-00000.nc" "mizuRoute/run1.h.1979-01-01-00000.nc" "mizuRoute/run3.h.1979-01-01-00000.nc" "mizuRoute/run1.h.1979-01-01-00000.nc" "mizuRoute/run1.h.1979-01-01-00000.nc" "mizuRoute/run1.h.1979-01-01-00000.nc" "mizuRoute/run1.h.1979-01-01-00000.nc") -arraylength=${#domain_name[@]} - -# Define a location where we can temporarily store the simulation files so we don't need to swap back and forth -dest_path="/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/simulation_stats" -mkdir -p $dest_path - -# Loop over all simulations and find the mean values we're after -for (( i=0; i<${arraylength}; i++ )); -do - summa_in="$root_path${domain_name[$i]}/${sim_folders[$i]}/${summa_files[$i]}" - mizur_in="$root_path${domain_name[$i]}/${sim_folders[$i]}/${mizur_files[$i]}" - summa_out="${dest_path}/SUMMA_stat_197901_${domain_name[$i]}.nc" - mizur_out="${dest_path}/mizuRoute_stat_197901_${domain_name[$i]}.nc" - - #echo "index: $i, SUMMA in : $summa_in" - #echo "index: $i, SUMMA out: $summa_out" - #echo "index: $i, mizuR in : $mizur_in" - #echo "index: $i, SUMMA out: $mizur_out" - - echo "Working on ${domain_name[$i]}" - cdo timmean -select,name=scalarTotalET,scalarTotalRunoff,gruId,hruId ${summa_in} ${summa_out} - cdo timmean -select,name=IRFroutedRunoff,reachID ${mizur_in} ${mizur_out} -done diff --git a/7_visualization/8b_reproject_shapefiles.py b/7_visualization/8b_reproject_shapefiles.py deleted file mode 100644 index 12d8994..0000000 --- a/7_visualization/8b_reproject_shapefiles.py +++ /dev/null @@ -1,190 +0,0 @@ -# Goal: project shapefiles (catchments and rivers) into a Winkel Tripel CRS. - -# Modules -import os -import shapely -from shapely.geometry import shape, LineString, MultiLineString -import geopandas as gpd - -# Specify desired projections -dest_proj = 'ESRI:54030' # See: https://epsg.io/54030 -proj_name = 'robinson' -#dest_proj = '+proj=wintri' -#proj_name = 'world_winkel_tripel' - -# Specify file locations -root_path = "/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_" -domains = ["Africa","Europe","NorthAmerica","NorthAsia","Oceania","SouthAmerica","SouthAsia"] -dest_path = '/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/shapefiles' - -# Check output location -if not os.path.exists(dest_path): - os.makedirs(dest_path) - -# --------------------------------------- -# FUNCTIONS - -def split_geometry_along_vertical_splitter(row,splitter): - - '''Treats a specific edge case that shapely.ops.splitter() does not handle: - splits a LineString that partly overlaps with a vertical splitter LineString. - - In: - row - shapefile row containing a "geometry" field. Geometry is assumed to be a LineString - splitter - shapely LineString containing a vertical line defined by two points - - Out: - new_geom - shapely GeometryCollection containing two LineStrings, split along the splitter line''' - - # Assumption 1: we're dealing with LineString geometries - assert type(row['geometry']) == LineString, "Edge case splitter: input geometry must be Linestring." - assert type(splitter) == LineString, "Edge case splitter: splitter geometry must be Linestring." - - # Get actual coordinates - line_coords = list(row['geometry'].coords) - splt_coords = list(splitter.coords) - - # Assumption 2: the splitter is a vertical line - assert len(splt_coords) == 2, "Edge case splitter: splitter geometry must have 2 coordinates only." - assert splt_coords[0][0] == splt_coords[1][0], "Edge case splitter: splitter geometry must be vertical line." - - # Loop over the original coordinates until we find the straight vertical section - splt_here = splt_coords[0][0] # longitude on which to split - splt_indx = [] - for ix,(lon,lat) in enumerate(line_coords): - if lon == splt_here: - splt_indx.append(ix) - - # Assumption 3: there's only a single straight section - assert (splt_indx[-1] - splt_indx[0]) == (len(splt_indx) - 1), "Edge case splitter: coordinate indices of straight section are not consecutive." - - # Split the line into two based on the indices we found - line1 = line_coords[:splt_indx[0]+1] - line2 = line_coords[splt_indx[-1]:] - - # Store as a geometry so that it can nicely feed into the rest of the code - new_geom = shape({'type': 'GeometryCollection', - 'geometries' : [{'type': 'LineString', 'coordinates': line1}, - {'type': 'LineString', 'coordinates': line2}]}) - - return new_geom - -def remove_split_coordinates_from_geometries(geoms,remove_this_coord): - - '''Removes coordinates from split geometries that are exactly on the line used for splitting. - - In: - geoms - Shapely GeometryCollection containing multiple LineStrings - remove_this_coord - Coordinate to be removed. Currently does not distinguish between latitude and longitude - - Out: - fixed_geom - Shapely GeometryCollection containing original LineStrings with identified coordinates removed''' - - fixed_geoms = [] - # Look at the geometries we created through splitting the original and remove any antimeridian coordinates - for geom in geoms: - - # Assumption 1: working with LineStrings - assert type(geom) == LineString, "remove_split_coordinates_from_geometries: input geometries must be Linestring." - - # Prep coordinate lists - old_coords = list(geom.coords) # Coordinate tuples that make up this geometry - new_coords = [] # New list that we will populate with the coordinate tuples we want to keep - - # Copy over the coordinates that we do not want to remove - for jj, xy in enumerate(old_coords): - if remove_this_coord not in xy: - new_coords.append(xy) - - # Convert the coordinate list back into a LineString - fixed_geoms.append(LineString(new_coords)) - - return fixed_geoms - -def fix_northAsia_rivers(shp, - splitter = LineString([(180,25), (180,90)]), # antimeridian - remove_this_coord = 180): # degrees longitude - - '''We first split the river on the antimeridian, but this gives us two river segments that end/start on the antimeridian: - [somewhere,180] & [180,somewhere]. These river segments are stored as LineStrings, which consist of multiple tuples - (lon,lat) that describe where the river is. The second setting is used to remove any line-segment coordinates that - have (in this case) 180 in them, effectively cutting out the part of the river that crosses the antimeridian. Once that - is done the rivers can be safely reprojected.''' - - # Prep output - new_shp = shp.copy() - - # From a shapefile row, takes the geometry field and attempts to split this by a Shapely geometry - for ii, row in shp.iterrows(): - - # Attempt to split the geometry. If it can't be split, we simply get the original geometry back - # Ingoing data type: shapely.geometry.linestring.LineString - # Outgoing data type: shapely.geometry.collection.GeometryCollection[LineString,..]. Could contain multiple lines - try: - new_geom = shapely.ops.split(row['geometry'],splitter) - except ValueError as e: # Above breaks in certain edge cases - if len(e.args) > 0 and e.args[0] == 'Input geometry segment overlaps with the splitter.': - new_geom = split_geometry_along_vertical_splitter(row,splitter) # Specific edge case we can now deal with - else: - raise e # Original error - - # Proceed based on previous outcome - if len(new_geom) == 1: - print('Row {}. No split performed. Keeping original entry.'.format(ii)) - elif len(new_geom) > 1: - print('Row {}. Split performed. Proceeding to remove coordinates directly on the splitting line.'.format(ii)) - - # Further processing - fixed_geoms = remove_split_coordinates_from_geometries(new_geom,remove_this_coord) - new_geom = MultiLineString(fixed_geoms) # Convert to MultiLineString - - # Replace geometry in row - row['geometry'] = new_geom - new_shp.loc[ii] = row - - return new_shp - -# --------------------------------------- - - -# Loop over all domains and reproject -for domain in domains: - - # Construct the file names - if domain == 'NorthAmerica': - src_basin_shp = root_path + domain + '/shapefiles/catchment/merit_hydro_basins_and_coastal_hillslopes_merged_NA.shp' - src_river_shp = root_path + domain + '/shapefiles/river_network/merit_river_na_full.shp' - else: - src_basin_shp = root_path + domain + '/shapefiles/catchment/' + domain + '_gruhru.shp' - src_river_shp = root_path + domain + '/shapefiles/river_network/' + domain + '_river_rawMERIT.shp' - des_basin_shp = dest_path + '/basins_' + domain + '_' + proj_name + '.shp' - des_river_shp = dest_path + '/rivers_' + domain + '_' + proj_name + '.shp' - - # Progress check - if os.path.exists(des_basin_shp) and os.path.exists(des_river_shp): - print('Reprojected files already exist for ' + domain + '. Skipping.') - continue - else: - print('Reprojecting shapefiles for ' + domain) - - # Load the shapes - basins = gpd.read_file(src_basin_shp) - rivers = gpd.read_file(src_river_shp) - - # Ensure the CRS is set to EPSG:4326 - if basins.crs != 'EPSG:4326': - basins.set_crs('EPSG:4326') - if rivers.crs != 'EPSG:4326': - rivers.set_crs('EPSG:4326') - - # Ensure we don't end up with criss-crossing nonsense across the antimeridian - if domain == 'NorthAsia': - rivers = fix_northAsia_rivers(rivers) - - # Reproject - basins_proj = basins.to_crs(dest_proj) - rivers_proj = rivers.to_crs(dest_proj) - - # Save in new location - basins_proj.to_file(des_basin_shp) - rivers_proj.to_file(des_river_shp) \ No newline at end of file diff --git a/7_visualization/8c_global_summa_mean_ET.py b/7_visualization/8c_global_summa_mean_ET.py deleted file mode 100644 index 6c970e3..0000000 --- a/7_visualization/8c_global_summa_mean_ET.py +++ /dev/null @@ -1,127 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # Global SUMMA evapotranspiration -# Plots mean January evapotranspiration per basin. Needs: -# - Catchment shapefiles -# - SUMMA output `scalarTotalET` - -import glob -import matplotlib -import numpy as np -import xarray as xr -import pandas as pd -import geopandas as gpd -from pathlib import Path -import matplotlib.pyplot as plt - - -# #### In/out file locations -# Define the domains we're working with -domains = ['Africa','Europe','NorthAmerica','NorthAsia','Oceania','SouthAmerica','SouthAsia'] - -# Define the river segment shapefile location and names -basin_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/shapefiles/robinson') -basin_name = 'basins_{}_robinson.shp' -basin_basId = 'GRU_ID' - -# Define the lakes shapefile -lakes_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/shapefiles/robinson') -lakes_name = 'HydroLAKES_polys_v10_robinson.shp' - -# Define the continent outline shapefile -continents_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/shapefiles/robinson') -continents_name = 'World_region_Robinson.shp' - -# Define the simulation results -stats_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/simulation_stats') -stats_name = 'SUMMA_stat_197901_{}.nc' -stats_basId = 'gruId' -stats_varId = 'scalarTotalET' - -# Define where to save the figure -fig_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/visualization') -fig_name = 'mean_january_1979_evap_FOR_global_v12.jpg' - -# Make the folder if it doesn't exist -fig_path.mkdir(parents=True, exist_ok=True) - - -# #### Load shapefiles and data -shp_list = [] -for domain in domains: - shp_list.append(gpd.read_file(basin_path/basin_name.format(domain))) -shp = pd.concat(shp_list) - -lakes = gpd.read_file(lakes_path / lakes_name) -continents = gpd.read_file(continents_path/continents_name) - -# Merge the North AMerica 'COMID's with the 'GRU_ID's the other shapes use -shp[basin_basId].fillna(shp['COMID'], inplace=True) - -sims = xr.Dataset() -for domain in domains: - sim = xr.open_dataset(stats_path/stats_name.format(domain)) - sim = sim.assign_coords(hru=sim[stats_basId].values) # Vars have an 'hru' dim, which has values 1..n. Set to gruIDs instead - sim = sim.isel(time=0).drop_vars(['time','time_bnds','hruId','gruId']) # Keep only rlevant info (vars, basin IDs) - if 'gru' in sim.dims: - sim = sim.drop_dims('gru') - sims = xr.merge([sims,sim]) - - -# #### Add simulation values to the shape -# Polygon order in the simulation file -seg_ids_sim = sims['hru'].values.astype('int') - -# Ensure that the shapefile is in the same order as the sims -shp = shp.set_index(shp[basin_basId].astype('int')) # set the index -shp = shp.reindex(seg_ids_sim) # reindex to be in same order as sims file - -# Add the simulations to the shape -shp[stats_varId] = sims[stats_varId].values * -1 # flip the sign so that evap has a positive value - -# Sort the river shapefile so that rivers are sorted from small to large flows -shp = shp.sort_values(by=stats_varId) - - -# #### Prepare plotting settings -# Basin settings -et_ttl = '(a) Mean simulated total evapotranspiration' -et_lbl = '$[kg~m^{-2}~s^{-1}]$' - -# Make a global ET colormap -#colors_low = plt.cm.YlGnBu_r(np.linspace(0,1,256)) -#colors_high = plt.cm.YlOrRd(np.linspace(0,1,256)) -#all_colors = np.vstack((colors_low, colors_high)) -#et_col = matplotlib.colors.LinearSegmentedColormap.from_list('global_et',all_colors) -et_col = 'Spectral_r' -et_min = 0 -et_ctr = 1e-5 -et_max = 1.5e-4 -et_nrm = matplotlib.colors.TwoSlopeNorm(vmin=et_min, vcenter=et_ctr, vmax=et_max) - -# Lake settings -lake_col = (8/255,81/255,156/255) # blue -lake_min = 100 # km^2 - -# Lake selection -lake_plt = lakes.loc[lakes['Lake_area'] > lake_min] - - -# #### Figure -fig, ax = plt.subplots(1, 1, figsize=[120, 90]) -plt.rcParams.update({'font.size': 60}) -plt.rcParams['patch.antialiased'] = False # Prevents an issue with plotting distortion along the 0 degree latitude and longitude lines - -shp.plot(ax=ax, column=stats_varId, - cmap=et_col, vmin=et_min, vmax=et_max, norm=et_nrm, - legend=True, legend_kwds={'label': et_lbl, 'extend': 'both', 'shrink':0.25, 'pad': 0}, - zorder=-1) -lake_plt.plot(ax=ax, color=lake_col, zorder=1) -continents.boundary.plot(ax=ax, color='k', zorder=2) - -ax.set_title(et_ttl) -ax.axis('off') -plt.savefig(fig_path/fig_name, dpi=100, facecolor='w', bbox_inches='tight') - - diff --git a/7_visualization/8d_global_summa_mean_Q.py b/7_visualization/8d_global_summa_mean_Q.py deleted file mode 100644 index 3b7d6b8..0000000 --- a/7_visualization/8d_global_summa_mean_Q.py +++ /dev/null @@ -1,116 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # Global SUMMA runoff -# Plots mean January runoff per basin. Needs: -# - Catchment shapefiles -# - SUMMA output `scalarTotalRunoff` - -import glob -import matplotlib -import numpy as np -import xarray as xr -import pandas as pd -import geopandas as gpd -from pathlib import Path -import matplotlib.pyplot as plt - - -# #### In/out file locations -# Define the domains we're working with -domains = ['Africa','Europe','NorthAmerica','NorthAsia','Oceania','SouthAmerica','SouthAsia'] - -# Define the river segment shapefile location and names -basin_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/shapefiles/robinson') -basin_name = 'basins_{}_robinson.shp' -basin_basId = 'GRU_ID' - -# Define the lakes shapefile -lakes_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/shapefiles/robinson') -lakes_name = 'HydroLAKES_polys_v10_robinson.shp' - -# Define the continent outline shapefile -continents_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/shapefiles/robinson') -continents_name = 'World_region_Robinson.shp' - -# Define the simulation results -stats_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/simulation_stats') -stats_name = 'SUMMA_stat_197901_{}.nc' -stats_basId = 'gruId' -stats_varId = 'scalarTotalRunoff' - -# Define where to save the figure -fig_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/visualization') -fig_name = 'mean_january_1979_runoff_FOR_global_v12.png' - -# Make the folder if it doesn't exist -fig_path.mkdir(parents=True, exist_ok=True) - - -# #### Load shapefiles and data -shp_list = [] -for domain in domains: - shp_list.append(gpd.read_file(basin_path/basin_name.format(domain))) -shp = pd.concat(shp_list) - -lakes = gpd.read_file(lakes_path / lakes_name) -continents = gpd.read_file(continents_path/continents_name) - -# Merge the North AMerica 'COMID's with the 'GRU_ID's the other shapes use -shp[basin_basId].fillna(shp['COMID'], inplace=True) - -sims = xr.Dataset() -for domain in domains: - sim = xr.open_dataset(stats_path/stats_name.format(domain)) - sim = sim.assign_coords(hru=sim[stats_basId].values) # Vars have an 'hru' dim, which has values 1..n. Set to gruIDs instead - sim = sim.isel(time=0).drop_vars(['time','time_bnds','hruId','gruId']) # Keep only rlevant info (vars, basin IDs) - if 'gru' in sim.dims: - sim = sim.drop_dims('gru') - sims = xr.merge([sims,sim]) - - -# #### Add simulation values to the shape -# Polygon order in the simulation file -seg_ids_sim = sims['hru'].values.astype('int') - -# Ensure that the shapefile is in the same order as the sims -shp = shp.set_index(shp[basin_basId].astype('int')) # set the index -shp = shp.reindex(seg_ids_sim) # reindex to be in same order as sims file - -# Add the simulations to the shape -shp[stats_varId] = sims[stats_varId].values - -# Sort the river shapefile so that rivers are sorted from small to large flows -shp = shp.sort_values(by=stats_varId) - -# #### Prepare plotting settings -# Basin settings -q_ttl = '(b) Mean simulated total runoff' -q_lbl = '$[m~s^{-1}]$' -q_col = 'Blues' -q_min = 1e-9 -q_max = 1e-7 -q_nrm = matplotlib.colors.LogNorm(vmin=q_min, vmax=q_max) - -# Lake settings -lake_col = (8/255,81/255,156/255) # blue -lake_min = 100 # km^2 - -# Lake selection -lake_plt = lakes.loc[lakes['Lake_area'] > lake_min] - -# #### Figure -fig, ax = plt.subplots(1, 1, figsize=[120, 90]) -plt.rcParams.update({'font.size': 60}) -plt.rcParams['patch.antialiased'] = False # Prevents an issue with plotting distortion along the 0 degree latitude and longitude lines - -shp.plot(ax=ax, column=stats_varId, - cmap=q_col, vmin=q_min, vmax=q_max, norm=q_nrm, - legend=True, legend_kwds={'label': q_lbl, 'extend': 'both', 'shrink':0.25, 'pad': 0.0}, - zorder=0) -lake_plt.plot(ax=ax, color=lake_col, zorder=1) -continents.boundary.plot(ax=ax, color='k', zorder=2) - -ax.set_title(q_ttl) -ax.axis('off') -plt.savefig(fig_path/fig_name, dpi=100, facecolor='w', bbox_inches='tight') diff --git a/7_visualization/8e_global_mizuRoute_mean_IRF.py b/7_visualization/8e_global_mizuRoute_mean_IRF.py deleted file mode 100644 index 08f6827..0000000 --- a/7_visualization/8e_global_mizuRoute_mean_IRF.py +++ /dev/null @@ -1,115 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # Global streamflow -# Plots mean January streamflow per stream segment. Needs: -# - River network shapefiles with stream segments -# - mizuRoute output `IRFroutedRUnoff` -# -# Possibly relevant: -# - Geopandas dataframes are plotted from the first entry to the last. If we put the most import entries last, they will appear on top of the rest. It could be helpful to sort the river network by flow magnitude, so that the smaller segments form the background and we actually see the large-domain patterns. - -import glob -import matplotlib -import numpy as np -import xarray as xr -import pandas as pd -import geopandas as gpd -from pathlib import Path -import matplotlib.pyplot as plt - -# #### In/out file locations -# Define the domains we're working with -domains = ['Africa','Europe','NorthAmerica','NorthAsia','Oceania','SouthAmerica','SouthAsia'] - -# Define the river segment shapefile location and names -river_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/shapefiles/robinson') -river_name = 'rivers_{}_robinson.shp' -river_segId = 'COMID' - -# Define the lakes shapefile -lakes_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/shapefiles/robinson') -lakes_name = 'HydroLAKES_polys_v10_robinson.shp' - -# Define the continent outline shapefile -continents_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/shapefiles/robinson') -continents_name = 'World_region_Robinson.shp' - -# Define the simulation results -stats_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/simulation_stats') -stats_name = 'mizuRoute_stat_197901_{}.nc' -stats_segId = 'reachID' -stats_varId = 'IRFroutedRunoff' - -# Define where to save the figure -fig_path = Path('/gpfs/tp/gwf/gwf_cmt/wknoben/CWARHM_data/domain_global/visualization') -fig_name = 'mean_january_1979_streamflow_per_segment_FOR_global_v12.png' - -# Make the folder if it doesn't exist -fig_path.mkdir(parents=True, exist_ok=True) - - -# #### Load shapefiles and data -shp_list = [] -for domain in domains: - shp_list.append(gpd.read_file(river_path/river_name.format(domain))) -shp = pd.concat(shp_list) - -lakes = gpd.read_file(lakes_path / lakes_name) -continents = gpd.read_file(continents_path/continents_name) - -sims = xr.Dataset() -for domain in domains: - sim = xr.open_dataset(stats_path/stats_name.format(domain)) - sim['seg'] = sim[stats_segId] - sim = sim.isel(time=0).drop_vars(['time','time_bnds']) # Remove all time info because we know these files contain only a monthly mean value - sims = xr.merge([sims,sim]) - -# #### Perform a check to see if we have data for each river segment -# Polygon order in the simulation file -seg_ids_sim = sims[stats_segId].values.astype('int') - -# Ensure that the shapefile is in the same order as the sims -shp = shp.set_index(shp[river_segId].astype('int')) # set the index -shp = shp.reindex(seg_ids_sim) # reindex to be in same order as sims file - -# Add the simulations to the shape -shp[stats_varId] = sims[stats_varId].values - -# Sort the river shapefile so that rivers are sorted from small to large flows -shp = shp.sort_values(by='IRFroutedRunoff') - -# #### Prepare plotting settings -# River settings -linewidth = np.maximum(np.log10(shp[stats_varId])-2,0) -river_col = 'Blues' -river_ttl = '(c) Mean simulated streamflow' -river_lbl = '$[m^3~s^{-1}]$' -river_min = 1e-1 -river_max = 1e5 -river_nrm = matplotlib.colors.LogNorm(vmin=river_min, vmax=river_max) - -# Lake settings -lake_col = (8/255,81/255,156/255) # blue -lake_min = 100 # km^2 - -# Lake selection -lake_plt = lakes.loc[lakes['Lake_area'] > lake_min] - - -# #### Figure -fig, ax = plt.subplots(1, 1, figsize=[120, 90]) -plt.rcParams.update({'font.size': 60}) - -shp.plot(ax=ax, column=stats_varId, - linewidth=linewidth, - cmap=river_col, vmin=river_min, vmax=river_max, norm=river_nrm, - legend=True, legend_kwds={'label': river_lbl, 'extend': 'both', 'shrink':0.25, 'pad': 0.0}, - zorder=0) -lake_plt.plot(ax=ax, color=lake_col, zorder=1) -continents.boundary.plot(ax=ax, color='k', zorder=2) - -ax.set_title(river_ttl) -ax.axis('off') -plt.savefig(fig_path/fig_name, dpi=100, facecolor='w', bbox_inches='tight') - diff --git a/requirements.txt b/requirements.txt index 71923f7..f20c576 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,6 @@ cdsapi==0.2.4 easymore==0.0.3 +exactextract==0.2.2 GDAL==3.0.4 geopandas==0.10.2 hs_restclient==1.3.7