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proc_rdi_logdata_dw.py
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#!/usr/bin/env python
# Last modified: Time-stamp: <2008-10-01 12:47:50 haines>
"""
how to parse data, and assert what data and info goes into
creating and updating monthly netcdf files
RDI/Wavesmon processed adcp current profile data
parser : sample date and time, ensemble number, wave summary output from WavesMon software
creator : lat, lon, z, time, sig_wave_ht, peak_wave_period, peak_wave_dir,
max_wave_ht, max_wave_period, water_depth
updater : time, sig_wave_ht, peak_wave_period, peak_wave_dir,
max_wave_ht, max_wave_period, water_depth
Examples
--------
>> (parse, create, update) = load_processors('proc_rdi_logdata_adcp')
or
>> si = get_config(cn+'.sensor_info')
>> (parse, create, update) = load_processors(si['adcp']['proc_module'])
>> lines = load_data(filename)
>> data = parse(platform_info, sensor_info, lines)
>> create(platform_info, sensor_info, data) or
>> update(platform_info, sensor_info, data)
"""
from raw2proc import *
from procutil import *
from ncutil import *
now_dt = datetime.utcnow()
now_dt.replace(microsecond=0)
def parser(platform_info, sensor_info, lines):
"""
parse and assign currents data from RDI ADCP Log Data
"""
i = 0
for line in lines:
# split line and parse float and integers
rdi = []
sw = re.split(',', line)
for s in sw:
m = re.search(REAL_RE_STR, s)
if m:
rdi.append(float(m.groups()[0]))
# assign specific fields
n = len(rdi)
burst_num = int(rdi[0]) # Ensemble Number
# get sample datetime from data
sample_str = '%02d-%02d-%02d %02d:%02d:%02d' % tuple(rdi[1:7])
if sensor_info['utc_offset']:
sample_dt = scanf_datetime(sample_str, fmt='%y-%m-%d %H:%M:%S') + \
timedelta(hours=sensor_info['utc_offset'])
else:
sample_dt = scanf_datetime(sample_str, fmt='%y-%m-%d %H:%M:%S')
# datetime(*strptime(sample_str, "%y-%m-%d %H:%M:%S")[0:6])
# get sample datetime from filename
# compare with datetime from filename
sig_wave_ht = rdi[8] # Significant Wave Height (Hs, meters)
peak_wave_period = rdi[9] # Peak Wave Period (Tp, sec)
peak_wave_dir = rdi[10] # Peak Wave Direction (deg N)
max_wave_ht = rdi[12] # Maximum Wave Height (Hmax, meters)
mean_wave_period = rdi[13] # Maximum Wave Period (Tmean, sec)
water_depth = rdi[11]/1000 # Water Depth (meters) (based on ADCP backscatter or input config??)
nbins = int(rdi[14]) # Number of bins
# set up dict of data if first line
if i==0:
data = {
'en' : numpy.array(numpy.ones((len(lines),), dtype=int)*numpy.nan),
'dt' : numpy.array(numpy.ones((len(lines),), dtype=object)*numpy.nan),
'time' : numpy.array(numpy.ones((len(lines),), dtype=long)*numpy.nan),
'sig_wave_ht' : numpy.array(numpy.ones((len(lines)), dtype=float)*numpy.nan),
'peak_wave_period' : numpy.array(numpy.ones((len(lines)), dtype=float)*numpy.nan),
'peak_wave_dir' : numpy.array(numpy.ones((len(lines)), dtype=float)*numpy.nan),
'max_wave_ht' : numpy.array(numpy.ones((len(lines)), dtype=float)*numpy.nan),
'mean_wave_period' : numpy.array(numpy.ones((len(lines)), dtype=float)*numpy.nan),
'water_depth' : numpy.array(numpy.ones((len(lines)), dtype=float)*numpy.nan),
}
data['en'][i] = burst_num
data['dt'][i] = sample_dt # sample datetime
data['time'][i] = dt2es(sample_dt) # sample time in epoch seconds
data['sig_wave_ht'][i] = sig_wave_ht
data['peak_wave_period'][i] = peak_wave_period
data['peak_wave_dir'][i] = peak_wave_dir
data['max_wave_ht'][i] = max_wave_ht
data['mean_wave_period'][i] = mean_wave_period
data['water_depth'][i] = water_depth
i = i+1
return data
def creator(platform_info, sensor_info, data):
#
#
title_str = sensor_info['description']+' at '+ platform_info['location']
global_atts = {
'title' : title_str,
'institution' : 'University of North Carolina at Chapel Hill (UNC-CH)',
'institution_url' : 'http://nccoos.unc.edu',
'institution_dods_url' : 'http://nccoos.unc.edu',
'metadata_url' : 'http://nccoos.unc.edu',
'references' : 'http://nccoos.unc.edu',
'contact' : 'Sara Haines ([email protected])',
#
'source' : 'directional wave (acoustic doppler) observation',
'history' : 'raw2proc using ' + sensor_info['process_module'],
'comment' : 'File created using pycdf'+pycdfVersion()+' and numpy '+pycdfArrayPkg(),
# conventions
'Conventions' : 'CF-1.0; SEACOOS-CDL-v2.0',
# SEACOOS CDL codes
'format_category_code' : 'directional waves',
'institution_code' : platform_info['institution'],
'platform_code' : platform_info['id'],
'package_code' : sensor_info['id'],
# institution specific
'project' : 'North Carolina Coastal Ocean Observing System (NCCOOS)',
'project_url' : 'http://nccoos.unc.edu',
# timeframe of data contained in file yyyy-mm-dd HH:MM:SS
'start_date' : data['dt'][0].strftime("%Y-%m-%d %H:%M:%S"),
'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"),
'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
#
'creation_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
'process_level' : 'level1',
#
# must type match to data (e.g. fillvalue is real if data is real)
'_FillValue' : -99999.,
}
var_atts = {
# coordinate variables
'time' : {'short_name': 'time',
'long_name': 'Time',
'standard_name': 'time',
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC
'axis': 'T',
},
'lat' : {'short_name': 'lat',
'long_name': 'Latitude',
'standard_name': 'latitude',
'reference':'geographic coordinates',
'units': 'degrees_north',
'valid_range':(-90.,90.),
'axis': 'Y',
},
'lon' : {'short_name': 'lon',
'long_name': 'Longitude',
'standard_name': 'longitude',
'reference':'geographic coordinates',
'units': 'degrees_east',
'valid_range':(-180.,180.),
'axis': 'Y',
},
'z' : {'short_name': 'z',
'long_name': 'Height',
'standard_name': 'height',
'reference':'zero at sea-surface',
'units': 'm',
'axis': 'Z',
},
# data variables
'en' : {'short_name': 'en',
'long_name': 'Ensemble Number',
'standard_name': 'ensemble_number',
'units': 'None',
},
'sig_wave_ht' : {'short_name': 'Hs',
'long_name': 'Significant Wave Height',
'definition': 'Four times the square root of the first moment of the wave spectrum (4*sqrt(m0))',
'standard_name': 'significant_wave_height',
'units': 'm',
},
'peak_wave_period' : {'short_name': 'Tp',
'long_name': 'Peak Wave Period',
'definition': 'Period of strongest wave (wave energy maximum)',
'standard_name': 'peak_wave_period',
'units': 'sec',
},
'peak_wave_dir' : {'short_name': 'Dp',
'long_name': 'Peak Wave Direction',
'definition': 'Direction from which strongest waves (wave energy max) are coming',
'standard_name': 'peak_wave_from_direction',
'units': 'deg from N',
'reference': 'clockwise from True North',
},
'max_wave_ht' : {'short_name': 'Hmax',
'long_name': 'Maximum Wave Height',
'standard_name': 'max_wave_height',
'units': 'm',
},
'mean_wave_period' : {'short_name': 'Tmean',
'long_name': 'Mean Wave Period',
'definition': 'Zero-moment of the non-directional spectrum divided by the first-moment (m0/m1)',
'standard_name': 'mean_wave_period',
'units': 'sec',
},
'water_depth': {'short_name': '',
'long_name': 'Water Depth',
'standard_name': 'water_depth',
'units': 'm',
},
}
# integer values
ntime=NC.UNLIMITED
nlat=1
nlon=1
nz=1
# dimension names use tuple so order of initialization is maintained
dim_inits = (
('ntime', NC.UNLIMITED),
('nlat', 1),
('nlon', 1),
('nz', 1)
)
# using tuple of tuples so order of initialization is maintained
# using dict for attributes order of init not important
# use dimension names not values
# (varName, varType, (dimName1, [dimName2], ...))
var_inits = (
# coordinate variables
('time', NC.INT, ('ntime',)),
('lat', NC.FLOAT, ('nlat',)),
('lon', NC.FLOAT, ('nlon',)),
('z', NC.FLOAT, ('nz',)),
# data variables
('en', NC.INT, ('ntime', )),
('sig_wave_ht', NC.FLOAT, ('ntime',)),
('peak_wave_period', NC.FLOAT, ('ntime',)),
('peak_wave_dir', NC.FLOAT, ('ntime',)),
('max_wave_ht', NC.FLOAT, ('ntime',)),
('mean_wave_period', NC.FLOAT, ('ntime',)),
('water_depth', NC.FLOAT, ('ntime',)),
)
# subset data only to month being processed (see raw2proc.process())
i = data['in']
# var data
var_data = (
('lat', platform_info['lat']),
('lon', platform_info['lon']),
('z', 0),
#
('time', data['time'][i]),
('en', data['en'][i]),
('sig_wave_ht', data['sig_wave_ht'][i]),
('peak_wave_period', data['peak_wave_period'][i]),
('peak_wave_dir', data['peak_wave_dir'][i]),
('max_wave_ht', data['max_wave_ht'][i]),
('mean_wave_period', data['mean_wave_period'][i]),
('water_depth', data['water_depth'][i]),
)
return (global_atts, var_atts, dim_inits, var_inits, var_data)
def updater(platform_info, sensor_info, data):
#
global_atts = {
# update times of data contained in file (yyyy-mm-dd HH:MM:SS)
# last date in monthly file
'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"),
'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
#
'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
}
# data variables
# update any variable attributes like range, min, max
var_atts = {}
# var_atts = {
# 'u': {'max': max(data.u),
# 'min': min(data.v),
# },
# 'v': {'max': max(data.u),
# 'min': min(data.v),
# },
# }
# subset data only to month being processed (see raw2proc.process())
i = data['in']
# data
var_data = (
('time', data['time'][i]),
('en', data['en'][i]),
('sig_wave_ht', data['sig_wave_ht'][i]),
('peak_wave_period', data['peak_wave_period'][i]),
('peak_wave_dir', data['peak_wave_dir'][i]),
('max_wave_ht', data['max_wave_ht'][i]),
('mean_wave_period', data['mean_wave_period'][i]),
('water_depth', data['water_depth'][i]),
)
return (global_atts, var_atts, var_data)
#