merge with dev

Author: wolfidan

ci/environment-ci.yml

@@ -13,8 +13,8 @@ dependencies:
   - cartopy
   - cvxopt
   - wradlib
-  - xarray<2024.10.0
-  - boto3
+  - xarray
+  - boto3<1.37
   - xradar
   - metpy
   - pytest-cov

config/pyart/mch_config.py

@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -56,7 +56,7 @@
     "T0 = datetime.datetime(2024,5,10,10,10)\n",
     "T1 = datetime.datetime(2024,5,10,10,20)\n",
     "\n",
-    "files = retrieve_mch_prod(OUTPUT_DIRECTORY ,T0, T1,  product_name='MLA')\n",
+    "files = retrieve_mch_prod(T0, T1,  product_name='MLA', folder_out = OUTPUT_DIRECTORY)\n",
     "\n",
     "print(len(files))\n",
     "print(files[0])\n",
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +92,7 @@
     }
    ],
    "source": [
-    "files = retrieve_mch_prod(OUTPUT_DIRECTORY ,T0, T1, product_name='MLA', sweeps = [1,2,3,4])\n",
+    "files = retrieve_mch_prod(T0, T1, product_name='MLA', folder_out = OUTPUT_DIRECTORY, sweeps = [1,2,3,4])\n",
     "print(len(files))\n",
     "print(files[0:4])"
    ]
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -119,7 +119,7 @@
     }
    ],
    "source": [
-    "files = retrieve_mch_prod(OUTPUT_DIRECTORY ,T0, T1,  product_name='RZC')\n",
+    "files = retrieve_mch_prod(T0, T1,  product_name='RZC', folder_out = OUTPUT_DIRECTORY)\n",
     "print(len(files))\n",
     "print(files)"
    ]
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -146,7 +146,7 @@
     }
    ],
    "source": [
-    "files = retrieve_mch_prod(OUTPUT_DIRECTORY ,T0, T1,  product_name='CPC', pattern='*00060*')\n",
+    "files = retrieve_mch_prod(T0, T1,  product_name='CPC', folder_out = OUTPUT_DIRECTORY, pattern='*00060*')\n",
     "print(len(files))\n",
     "print(files)"
    ]
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +191,7 @@
    "source": [
     "T0 = datetime.datetime(2024,5,10,8,10)\n",
     "T1 = datetime.datetime(2024,5,10,10,20)\n",
-    "files = retrieve_mch_prod(OUTPUT_DIRECTORY ,T0, T1,  product_name='CPC', pattern='^(?!.*00005\\.801\\.gif$).*\\.gif$', pattern_type='regex')\n",
+    "files = retrieve_mch_prod(T0, T1,  product_name='CPC', folder_out = OUTPUT_DIRECTORY, pattern='^(?!.*00005\\.801\\.gif$).*\\.gif$', pattern_type='regex')\n",
     "\n",
     "print(files)"
    ]

config/pyart/mf_config.py

@@ -122,7 +122,7 @@ def plot_surface(
     max_lat = prdcfg["gridMapImageConfig"].get("latmax", 49.5)
     embellish = prdcfg["gridMapImageConfig"].get("embellish", True)
     colorbar_flag = prdcfg["gridMapImageConfig"].get("colorbar_flag", True)
-    exact_limits = prdcfg["gridMapImageConfig"].get("exact_limits", 0)
+    exact_limits = prdcfg["gridMapImageConfig"].get("exact_limits", True)
 
     if exact_limits:
         lon_lines = np.arange(min_lon, max_lon + lonstep, lonstep)

doc/source/notebooks/retrieve_meteoswiss_data_from_cscs.ipynb

@@ -370,14 +370,19 @@ def plot_ppi_map(
     max_lon = prdcfg["ppiMapImageConfig"].get("lonmax", 12.5)
     min_lat = prdcfg["ppiMapImageConfig"].get("latmin", 43.5)
     max_lat = prdcfg["ppiMapImageConfig"].get("latmax", 49.5)
+    exact_limits = prdcfg["ppiMapImageConfig"].get("exact_limits", True)
     resolution = prdcfg["ppiMapImageConfig"].get("mapres", "110m")
     if resolution not in ("110m", "50m", "10m"):
         warn("Unknown map resolution: " + resolution)
         resolution = "110m"
     background_zoom = prdcfg["ppiMapImageConfig"].get("background_zoom", 8)
 
-    lon_lines = np.arange(np.floor(min_lon), np.ceil(max_lon) + 1, lonstep)
-    lat_lines = np.arange(np.floor(min_lat), np.ceil(max_lat) + 1, latstep)
+    if exact_limits:
+        lon_lines = np.arange(min_lon, max_lon + lonstep, lonstep)
+        lat_lines = np.arange(min_lat, max_lat + latstep, latstep)
+    else:
+        lon_lines = np.arange(np.floor(min_lon), np.ceil(max_lon) + 1, lonstep)
+        lat_lines = np.arange(np.floor(min_lat), np.ceil(max_lat) + 1, latstep)
 
     fig = plt.figure(figsize=[xsize, ysize], dpi=dpi)
 

src/pyart

@@ -234,6 +234,7 @@
 from .read_data_other import read_intercomp_scores_ts, read_quantiles  # noqa
 from .read_data_other import read_selfconsistency, read_colocated_gates  # noqa
 from .read_data_other import read_colocated_data, read_antenna_pattern  # noqa
+from .read_data_other import read_colocated_data_time_avg  # noqa
 from .read_data_other import read_last_state, read_rhi_profile, read_centroids  # noqa
 from .read_data_other import read_excess_gates, read_histogram  # noqa
 from .read_data_other import read_profile_ts, read_histogram_ts  # noqa
@@ -253,7 +254,7 @@
 from .read_data_sensor import read_trt_thundertracking_traj_data  # noqa
 from .read_data_sensor import read_coord_sensors, read_disdro_parsivel  # noqa
 from .read_data_sensor import read_knmi  # noqa
-from .read_data_sensor import (# noqa
+from .read_data_sensor import (  # noqa
     read_radiosounding_wyoming,
     read_radiosounding_igra,
 )

src/pyrad_proc/pyrad/graph/plots_grid.py

@@ -216,6 +216,7 @@
     "elevation_angle": "elevation_angle",
     "min_vis_elevation": "min_vis_elevation",
     "min_vis_altitude": "min_vis_altitude",
+    "min_vis_altitude_above_ground": "min_vis_altitude_above_ground",
     "incident_angle": "incident_angle",
     "sigma_0": "sigma_0",
     "effective_area": "effective_area",

src/pyrad_proc/pyrad/graph/plots_vol.py

@@ -18,7 +18,6 @@
 import pathlib
 from warnings import warn
 import numpy as np
-import pandas as pd
 from scipy.interpolate import RegularGridInterpolator
 
 # check existence of gdal
@@ -343,39 +342,33 @@ def read_ascii_data(fname, fill_value=None):
 
     # read the data
     try:
-        asciidata = pd.read_csv(fname, header=None)
-
-        metadata = {}
-        metadata["columns"] = int(asciidata.iloc[0][0].split(" ")[1])
-        metadata["rows"] = int(asciidata.iloc[1][0].split(" ")[1])
-        metadata["min. X"] = float(asciidata.iloc[2][0].split(" ")[1])
-        metadata["min. Y"] = float(asciidata.iloc[3][0].split(" ")[1])
-        metadata["resolution"] = float(asciidata.iloc[4][0].split(" ")[1])
-        metadata["flag value"] = float(asciidata.iloc[5][0].split(" ")[1])
-        metadata["max. X"] = (
-            metadata["min. X"] + metadata["resolution"] * metadata["columns"]
-        )
-        metadata["max. Y"] = (
-            metadata["min. Y"] + metadata["resolution"] * metadata["rows"]
-        )
-        metadata["value units"] = "m"
+        with open(fname, "r") as file:
+            lines = [file.readline().strip().split() for _ in range(6)]
+
+        # Parse metadata
+        metadata = {
+            "columns": int(lines[0][1]),
+            "rows": int(lines[1][1]),
+            "min. X": float(lines[2][1]),
+            "min. Y": float(lines[3][1]),
+            "resolution": float(lines[4][1]),
+            "flag value": float(lines[5][1]),
+            "max. X": float(lines[2][1]) + float(lines[4][1]) * int(lines[0][1]),
+            "max. Y": float(lines[3][1]) + float(lines[4][1]) * int(lines[1][1]),
+            "value units": "m",
+        }
 
-        if not fill_value:
+        # Set fill value if not provided
+        if fill_value is None:
             fill_value = metadata["flag value"]
 
-        raster_array = pd.read_csv(fname, skiprows=6, header=None, sep=" ")
-        raster_array = np.array(raster_array)
-        raster_array = raster_array[np.isfinite(raster_array)]
-        raster_array = np.reshape(raster_array, (metadata["rows"], metadata["columns"]))
-        raster_array = np.ma.masked_equal(raster_array, fill_value)
+        # Read raster data, skipping first 6 lines
+        raster_array = np.loadtxt(fname, skiprows=6)
 
-        rasterarray = pd.read_csv(fname, skiprows=6, header=None, sep=" ")
-        rasterarray = np.array(rasterarray)
-        rasterarray = rasterarray[np.isfinite(rasterarray)]
-        rasterarray = np.reshape(rasterarray, (metadata["rows"], metadata["columns"]))
-        rasterarray = np.ma.masked_equal(rasterarray, fill_value)
+        # Mask NODATA values
+        raster_array = np.ma.masked_equal(raster_array, fill_value)
 
-        dem = {"metadata": metadata, "data": rasterarray}
+        dem = {"metadata": metadata, "data": raster_array}
 
         return dem
 

src/pyrad_proc/pyrad/io/__init__.py

@@ -66,7 +66,7 @@
 
 from pyart.config import get_fillvalue, get_field_colormap, get_field_limits
 from pyart.core import antenna_to_cartesian, cartesian_to_geographic_aeqd
-from pyart.graph.cm import cmap_d
+from cmweather.cm import cmap_d
 
 from .io_aux import generate_field_name_str
 from .flock_utils import lock_file, unlock_file

src/pyrad_proc/pyrad/io/io_aux.py

@@ -34,6 +34,7 @@
 
     process_raw_grid
     process_grid
+    process_grid_stats
     process_grid_point
     process_grid_multiple_points
     process_grid_time_stats
@@ -273,7 +274,11 @@
 from .process_aux import process_moving_azimuthal_average  # noqa
 
 from .process_grid import process_grid, process_raw_grid, process_grid_point  # noqa
-from .process_grid import process_grid_time_stats, process_grid_time_stats2  # noqa
+from .process_grid import (  # noqa
+    process_grid_time_stats,
+    process_grid_time_stats2,
+    process_grid_stats,
+)
 from .process_grid import process_grid_fields_diff, process_grid_mask  # noqa
 from .process_grid import process_normalize_luminosity, process_pixel_filter  # noqa
 from .process_grid import process_grid_texture, process_grid_multiple_points  # noqa
@@ -353,12 +358,12 @@
 from .process_monitoring import process_monitoring  # noqa
 
 from .process_retrieve import process_signal_power, process_snr, process_ccor  # noqa
-from .process_retrieve import ( # noqa
+from .process_retrieve import (  # noqa
     process_l,
     process_cdr,
     process_bird_density,
     process_refl_from_zdr,
-)  
+)
 from .process_retrieve import process_rainrate, process_vol_refl, process_rcs  # noqa
 from .process_retrieve import process_rcs_pr, process_rainfall_accumulation  # noqa
 from .process_retrieve import process_radial_noise_hs, process_vpr  # noqa

src/pyrad_proc/pyrad/io/read_data_dem.py

@@ -298,6 +298,9 @@ def get_process_func(dataset_type, dsname):
     elif dataset_type == "GRID_MASK":
         func_name = "process_grid_mask"
         dsformat = "GRID"
+    elif dataset_type == "GRID_STATS":
+        func_name = "process_grid_stats"
+        dsformat = "GRID"
     elif dataset_type == "GRID_TEXTURE":
         func_name = "process_grid_texture"
         dsformat = "GRID"