| 1 |
#!/usr/bin/env python |
|---|
| 2 |
# Last modified: Time-stamp: <2012-05-15 15:44:51 haines> |
|---|
| 3 |
""" |
|---|
| 4 |
how to parse data, and assert what data and info goes into |
|---|
| 5 |
creating and updating monthly netcdf files |
|---|
| 6 |
|
|---|
| 7 |
parse data met data collected on Campbell Scientific DataLogger (loggernet) (csi) |
|---|
| 8 |
|
|---|
| 9 |
parser : sample date and time, |
|---|
| 10 |
|
|---|
| 11 |
creator : lat, lon, z, time, |
|---|
| 12 |
updator : time, |
|---|
| 13 |
|
|---|
| 14 |
|
|---|
| 15 |
Examples |
|---|
| 16 |
-------- |
|---|
| 17 |
|
|---|
| 18 |
>> (parse, create, update) = load_processors('proc_csi_adcp_v2') |
|---|
| 19 |
or |
|---|
| 20 |
>> si = get_config(cn+'.sensor_info') |
|---|
| 21 |
>> (parse, create, update) = load_processors(si['adcp']['proc_module']) |
|---|
| 22 |
|
|---|
| 23 |
>> lines = load_data(filename) |
|---|
| 24 |
>> data = parse(platform_info, sensor_info, lines) |
|---|
| 25 |
>> create(platform_info, sensor_info, data) or |
|---|
| 26 |
>> update(platform_info, sensor_info, data) |
|---|
| 27 |
|
|---|
| 28 |
""" |
|---|
| 29 |
|
|---|
| 30 |
|
|---|
| 31 |
from raw2proc import * |
|---|
| 32 |
from procutil import * |
|---|
| 33 |
from ncutil import * |
|---|
| 34 |
|
|---|
| 35 |
now_dt = datetime.utcnow() |
|---|
| 36 |
now_dt.replace(microsecond=0) |
|---|
| 37 |
|
|---|
| 38 |
def parser(platform_info, sensor_info, lines): |
|---|
| 39 |
""" |
|---|
| 40 |
Example met data |
|---|
| 41 |
|
|---|
| 42 |
"TOA5","CR1000_B1","CR1000","37541","CR1000.Std.21","CPU:NCWIND_12_Buoy_All.CR1","58723","AMet_6Min" |
|---|
| 43 |
"TIMESTAMP","RECORD","Baro_mbar_Avg","RHumidity_Avg","RHumidity_Std","AirTempC_Avg","AirTempC_Std","Rain","Psp_Avg","Psp_Std","Pir_Wm2_Avg","Pir_Wm2_Std" |
|---|
| 44 |
"TS","RN","","","","","","","","","","" |
|---|
| 45 |
"","","Avg","Avg","Std","Avg","Std","Smp","Avg","Std","Avg","Std" |
|---|
| 46 |
"2011-11-01 00:00:59",4590,14.3792,75.59,0.579,15.67,0.05,-22.35,1197.037,45.58967,371.5126,0.9030571 |
|---|
| 47 |
"2011-11-01 00:06:59",4591,14.37995,74.96,0.912,16.61,0.048,-21,-1071.813,129.5147,381.2539,0.2076943 |
|---|
| 48 |
"2011-11-01 00:12:59",4592,14.3792,72.71,2.677,17.29,0.032,-15.58,-2056.658,0,381.1828,0.1402813 |
|---|
| 49 |
"2011-11-01 00:18:59",4593,14.3791,72.63,0.928,17.67,0.041,-19.64,-1895.86,9.866026,381.0333,0.2442325 |
|---|
| 50 |
|
|---|
| 51 |
""" |
|---|
| 52 |
|
|---|
| 53 |
import numpy |
|---|
| 54 |
from datetime import datetime |
|---|
| 55 |
from time import strptime |
|---|
| 56 |
|
|---|
| 57 |
# get sample datetime from filename |
|---|
| 58 |
fn = sensor_info['fn'] |
|---|
| 59 |
sample_dt_start = filt_datetime(fn) |
|---|
| 60 |
|
|---|
| 61 |
# how many samples (don't count header 4 lines) |
|---|
| 62 |
nsamp = len(lines[4:]) |
|---|
| 63 |
|
|---|
| 64 |
N = nsamp |
|---|
| 65 |
data = { |
|---|
| 66 |
'dt' : numpy.array(numpy.ones((N,), dtype=object)*numpy.nan), |
|---|
| 67 |
'time' : numpy.array(numpy.ones((N,), dtype=long)*numpy.nan), |
|---|
| 68 |
'air_press' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
|---|
| 69 |
'rh' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
|---|
| 70 |
'rh_std' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
|---|
| 71 |
'air_temp' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
|---|
| 72 |
'air_temp_std' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
|---|
| 73 |
'rain' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
|---|
| 74 |
'psp' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
|---|
| 75 |
'psp_std' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
|---|
| 76 |
'pir' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
|---|
| 77 |
'pir_std' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
|---|
| 78 |
} |
|---|
| 79 |
|
|---|
| 80 |
# sample count |
|---|
| 81 |
i = 0 |
|---|
| 82 |
|
|---|
| 83 |
for line in lines[4:]: |
|---|
| 84 |
csi = [] |
|---|
| 85 |
# split line |
|---|
| 86 |
sw = re.split(',', line) |
|---|
| 87 |
if len(sw)<=0: |
|---|
| 88 |
print ' ... skipping line %d ' % (i,) |
|---|
| 89 |
continue |
|---|
| 90 |
|
|---|
| 91 |
# replace any "NAN" text with a number |
|---|
| 92 |
for index, s in enumerate(sw): |
|---|
| 93 |
m = re.search(NAN_RE_STR, s) |
|---|
| 94 |
if m: |
|---|
| 95 |
sw[index] = '-99999' |
|---|
| 96 |
|
|---|
| 97 |
# parse date-time, and all other float and integers |
|---|
| 98 |
for s in sw[1:]: |
|---|
| 99 |
m = re.search(REAL_RE_STR, s) |
|---|
| 100 |
if m: |
|---|
| 101 |
csi.append(float(m.groups()[0])) |
|---|
| 102 |
|
|---|
| 103 |
if sensor_info['utc_offset']: |
|---|
| 104 |
sample_dt = scanf_datetime(sw[0], fmt='"%Y-%m-%d %H:%M:%S"') + \ |
|---|
| 105 |
timedelta(hours=sensor_info['utc_offset']) |
|---|
| 106 |
else: |
|---|
| 107 |
sample_dt = scanf_datetime(sw[0], fmt='"%Y-%m-%d %H:%M:%S"') |
|---|
| 108 |
|
|---|
| 109 |
data['dt'][i] = sample_dt # sample datetime |
|---|
| 110 |
data['time'][i] = dt2es(sample_dt) # sample time in epoch seconds |
|---|
| 111 |
|
|---|
| 112 |
if len(csi)==11: |
|---|
| 113 |
# |
|---|
| 114 |
# data['samplenum'][i] = csi[0] # sample number assigned by datalogger in table |
|---|
| 115 |
data['air_press'][i] = csi[1] # Heise Barometer (psi) to mbar |
|---|
| 116 |
data['rh'][i] = csi[2] # relative humidity avg (60 samples for 1 min) |
|---|
| 117 |
data['rh_std'][i] = csi[3] # relative humidity std |
|---|
| 118 |
data['air_temp'][i] = csi[4] # air temperature avg (deg C) |
|---|
| 119 |
data['air_temp_std'][i] = csi[5] # air temperature std (deg C) |
|---|
| 120 |
data['rain'][i] = csi[6]/100. # precip gauge cummulative (mm) |
|---|
| 121 |
data['psp'][i] = csi[7] # PSP avg |
|---|
| 122 |
data['psp_std'][i] = csi[8] # PSP std |
|---|
| 123 |
data['pir'][i] = csi[9] # PIR avg (W m-2) |
|---|
| 124 |
data['pir_std'][i] = csi[10] # PIR std (W m-2) |
|---|
| 125 |
i=i+1 |
|---|
| 126 |
else: |
|---|
| 127 |
print ' ... skipping line %d -- %s ' % (i,line) |
|---|
| 128 |
continue |
|---|
| 129 |
|
|---|
| 130 |
# if re.search |
|---|
| 131 |
# for line |
|---|
| 132 |
|
|---|
| 133 |
data['air_press'] = udconvert(data['air_press'], 'psi', 'mbar')[0] |
|---|
| 134 |
# cannot figure out how to combine the two operations |
|---|
| 135 |
# for some reason, this one liner does not work |
|---|
| 136 |
# good = -40<at & at<60 |
|---|
| 137 |
above_tol=-40<data['air_temp'] |
|---|
| 138 |
below_tol=data['air_temp']<60 |
|---|
| 139 |
good = above_tol & below_tol |
|---|
| 140 |
bad = ~good |
|---|
| 141 |
data['air_temp'][bad] = numpy.nan |
|---|
| 142 |
|
|---|
| 143 |
# check that no data[dt] is set to Nan or anything but datetime |
|---|
| 144 |
# keep only data that has a resolved datetime |
|---|
| 145 |
keep = numpy.array([type(datetime(1970,1,1)) == type(dt) for dt in data['dt'][:]]) |
|---|
| 146 |
if keep.any(): |
|---|
| 147 |
for param in data.keys(): |
|---|
| 148 |
data[param] = data[param][keep] |
|---|
| 149 |
|
|---|
| 150 |
return data |
|---|
| 151 |
|
|---|
| 152 |
|
|---|
| 153 |
def creator(platform_info, sensor_info, data): |
|---|
| 154 |
# |
|---|
| 155 |
# |
|---|
| 156 |
# subset data only to month being processed (see raw2proc.process()) |
|---|
| 157 |
i = data['in'] |
|---|
| 158 |
|
|---|
| 159 |
title_str = sensor_info['description']+' at '+ platform_info['location'] |
|---|
| 160 |
global_atts = { |
|---|
| 161 |
'title' : title_str, |
|---|
| 162 |
'institution' : platform_info['institution'], |
|---|
| 163 |
'institution_url' : platform_info['institution_url'], |
|---|
| 164 |
'institution_dods_url' : platform_info['institution_dods_url'], |
|---|
| 165 |
'metadata_url' : platform_info['metadata_url'], |
|---|
| 166 |
'references' : platform_info['references'], |
|---|
| 167 |
'contact' : platform_info['contact'], |
|---|
| 168 |
# |
|---|
| 169 |
'source' : platform_info['source']+' '+sensor_info['source'], |
|---|
| 170 |
'history' : 'raw2proc using ' + sensor_info['process_module'], |
|---|
| 171 |
'comment' : 'File created using pycdf'+pycdfVersion()+' and numpy '+pycdfArrayPkg(), |
|---|
| 172 |
# conventions |
|---|
| 173 |
'Conventions' : platform_info['conventions'], |
|---|
| 174 |
# SEACOOS CDL codes |
|---|
| 175 |
'format_category_code' : platform_info['format_category_code'], |
|---|
| 176 |
'institution_code' : platform_info['institution_code'], |
|---|
| 177 |
'platform_code' : platform_info['id'], |
|---|
| 178 |
'package_code' : sensor_info['id'], |
|---|
| 179 |
# institution specific |
|---|
| 180 |
'project' : platform_info['project'], |
|---|
| 181 |
'project_url' : platform_info['project_url'], |
|---|
| 182 |
# timeframe of data contained in file yyyy-mm-dd HH:MM:SS |
|---|
| 183 |
# first date in monthly file |
|---|
| 184 |
'start_date' : data['dt'][i][0].strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 185 |
# last date in monthly file |
|---|
| 186 |
'end_date' : data['dt'][i][-1].strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 187 |
'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 188 |
# |
|---|
| 189 |
'creation_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 190 |
'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 191 |
'process_level' : 'level1', |
|---|
| 192 |
# |
|---|
| 193 |
# must type match to data (e.g. fillvalue is real if data is real) |
|---|
| 194 |
'_FillValue' : -99999., |
|---|
| 195 |
} |
|---|
| 196 |
|
|---|
| 197 |
var_atts = { |
|---|
| 198 |
# coordinate variables |
|---|
| 199 |
'time' : {'short_name': 'time', |
|---|
| 200 |
'long_name': 'Time', |
|---|
| 201 |
'standard_name': 'time', |
|---|
| 202 |
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC |
|---|
| 203 |
'axis': 'T', |
|---|
| 204 |
}, |
|---|
| 205 |
'lat' : {'short_name': 'lat', |
|---|
| 206 |
'long_name': 'Latitude', |
|---|
| 207 |
'standard_name': 'latitude', |
|---|
| 208 |
'reference':'geographic coordinates', |
|---|
| 209 |
'units': 'degrees_north', |
|---|
| 210 |
'valid_range':(-90.,90.), |
|---|
| 211 |
'axis': 'Y', |
|---|
| 212 |
}, |
|---|
| 213 |
'lon' : {'short_name': 'lon', |
|---|
| 214 |
'long_name': 'Longitude', |
|---|
| 215 |
'standard_name': 'longitude', |
|---|
| 216 |
'reference':'geographic coordinates', |
|---|
| 217 |
'units': 'degrees_east', |
|---|
| 218 |
'valid_range':(-180.,180.), |
|---|
| 219 |
'axis': 'Y', |
|---|
| 220 |
}, |
|---|
| 221 |
'z' : {'short_name': 'z', |
|---|
| 222 |
'long_name': 'Altitude', |
|---|
| 223 |
'standard_name': 'altitude', |
|---|
| 224 |
'reference':'zero at mean sea level', |
|---|
| 225 |
'positive' : 'up', |
|---|
| 226 |
'units': 'm', |
|---|
| 227 |
'axis': 'Z', |
|---|
| 228 |
}, |
|---|
| 229 |
# data variables |
|---|
| 230 |
'air_press': {'short_name': 'air_press', |
|---|
| 231 |
'long_name': 'Air Pressure', |
|---|
| 232 |
'standard_name': 'air_pressure', |
|---|
| 233 |
'units': 'mbar', |
|---|
| 234 |
'z': sensor_info['barometer_height'], |
|---|
| 235 |
'z_units' : 'meter', |
|---|
| 236 |
}, |
|---|
| 237 |
'air_temp': {'short_name': 'air_temp', |
|---|
| 238 |
'long_name': 'Air Temperature', |
|---|
| 239 |
'standard_name': 'air_temperature', |
|---|
| 240 |
'units': 'degC', |
|---|
| 241 |
'z': sensor_info['temperature_height'], |
|---|
| 242 |
'z_units' : 'meter', |
|---|
| 243 |
}, |
|---|
| 244 |
'air_temp_std': {'short_name': 'air_temp_std', |
|---|
| 245 |
'long_name': 'Standard Deviation of Air Temperature', |
|---|
| 246 |
'standard_name': 'air_temperature', |
|---|
| 247 |
'units': 'degC', |
|---|
| 248 |
}, |
|---|
| 249 |
'rh': {'short_name': 'rh', |
|---|
| 250 |
'long_name': 'Relative Humidity', |
|---|
| 251 |
'standard_name': 'relative_humidity', |
|---|
| 252 |
'units': '%', |
|---|
| 253 |
'z': sensor_info['temperature_height'], |
|---|
| 254 |
'z_units' : 'meter', |
|---|
| 255 |
}, |
|---|
| 256 |
'rh_std': {'short_name': 'rh_std', |
|---|
| 257 |
'long_name': 'Standard Deviation of Relative Humidity', |
|---|
| 258 |
'standard_name': 'relative_humidity', |
|---|
| 259 |
'units': '%', |
|---|
| 260 |
}, |
|---|
| 261 |
'rain': {'short_name': 'rain', |
|---|
| 262 |
'long_name': '6-Minute Rain', |
|---|
| 263 |
'standard_name': 'rain', |
|---|
| 264 |
'units': 'inches', |
|---|
| 265 |
}, |
|---|
| 266 |
'psp': {'short_name': 'psp', |
|---|
| 267 |
'long_name': 'Short-wave Radiation', |
|---|
| 268 |
'standard_name': 'downwelling_shortwave_irradiance', |
|---|
| 269 |
'units': 'W m-2', |
|---|
| 270 |
}, |
|---|
| 271 |
'psp_std': {'short_name': 'psp_std', |
|---|
| 272 |
'long_name': 'Standard Deviation of Short-wave Radiation', |
|---|
| 273 |
'standard_name': 'shortwave_radiation', |
|---|
| 274 |
'units': 'W m-2', |
|---|
| 275 |
}, |
|---|
| 276 |
'pir': {'short_name': 'pir', |
|---|
| 277 |
'long_name': 'Long-wave Radiation', |
|---|
| 278 |
'standard_name': 'longwave_radiation', |
|---|
| 279 |
'units': 'W m-2', |
|---|
| 280 |
}, |
|---|
| 281 |
'pir_std': {'short_name': 'pir_std', |
|---|
| 282 |
'long_name': 'Standard Deviation of Long-wave Radiation', |
|---|
| 283 |
'standard_name': 'longwave_radiation', |
|---|
| 284 |
'units': 'W m-2', |
|---|
| 285 |
}, |
|---|
| 286 |
} |
|---|
| 287 |
|
|---|
| 288 |
# dimension names use tuple so order of initialization is maintained |
|---|
| 289 |
dim_inits = ( |
|---|
| 290 |
('ntime', NC.UNLIMITED), |
|---|
| 291 |
('nlat', 1), |
|---|
| 292 |
('nlon', 1), |
|---|
| 293 |
('nz', 1), |
|---|
| 294 |
) |
|---|
| 295 |
|
|---|
| 296 |
# using tuple of tuples so order of initialization is maintained |
|---|
| 297 |
# using dict for attributes order of init not important |
|---|
| 298 |
# use dimension names not values |
|---|
| 299 |
# (varName, varType, (dimName1, [dimName2], ...)) |
|---|
| 300 |
var_inits = ( |
|---|
| 301 |
# coordinate variables |
|---|
| 302 |
('time', NC.INT, ('ntime',)), |
|---|
| 303 |
('lat', NC.FLOAT, ('nlat',)), |
|---|
| 304 |
('lon', NC.FLOAT, ('nlon',)), |
|---|
| 305 |
('z', NC.FLOAT, ('nz',)), |
|---|
| 306 |
# data variables |
|---|
| 307 |
('air_press', NC.FLOAT, ('ntime',)), |
|---|
| 308 |
('rh', NC.FLOAT, ('ntime',)), |
|---|
| 309 |
('rh_std', NC.FLOAT, ('ntime',)), |
|---|
| 310 |
('air_temp', NC.FLOAT, ('ntime',)), |
|---|
| 311 |
('air_temp_std', NC.FLOAT, ('ntime',)), |
|---|
| 312 |
('rain', NC.FLOAT, ('ntime',)), |
|---|
| 313 |
('psp', NC.FLOAT, ('ntime',)), |
|---|
| 314 |
('psp_std', NC.FLOAT, ('ntime',)), |
|---|
| 315 |
('pir', NC.FLOAT, ('ntime',)), |
|---|
| 316 |
('pir_std', NC.FLOAT, ('ntime',)), |
|---|
| 317 |
) |
|---|
| 318 |
|
|---|
| 319 |
# subset data only to month being processed (see raw2proc.process()) |
|---|
| 320 |
i = data['in'] |
|---|
| 321 |
|
|---|
| 322 |
# var data |
|---|
| 323 |
var_data = ( |
|---|
| 324 |
('lat', platform_info['lat']), |
|---|
| 325 |
('lon', platform_info['lon']), |
|---|
| 326 |
('z', platform_info['altitude']), |
|---|
| 327 |
# |
|---|
| 328 |
('time', data['time'][i]), |
|---|
| 329 |
# |
|---|
| 330 |
('air_press', data['air_press'][i]), |
|---|
| 331 |
('rh', data['rh'][i]), |
|---|
| 332 |
('rh_std', data['rh_std'][i]), |
|---|
| 333 |
('air_temp', data['air_temp'][i]), |
|---|
| 334 |
('air_temp_std', data['air_temp_std'][i]), |
|---|
| 335 |
('rain', data['rain'][i]), |
|---|
| 336 |
('psp', data['psp'][i]), |
|---|
| 337 |
('psp_std', data['psp_std'][i]), |
|---|
| 338 |
('pir', data['pir'][i]), |
|---|
| 339 |
('pir_std', data['pir_std'][i]), |
|---|
| 340 |
) |
|---|
| 341 |
|
|---|
| 342 |
return (global_atts, var_atts, dim_inits, var_inits, var_data) |
|---|
| 343 |
|
|---|
| 344 |
def updater(platform_info, sensor_info, data): |
|---|
| 345 |
# |
|---|
| 346 |
# subset data only to month being processed (see raw2proc.process()) |
|---|
| 347 |
i = data['in'] |
|---|
| 348 |
|
|---|
| 349 |
global_atts = { |
|---|
| 350 |
# update times of data contained in file (yyyy-mm-dd HH:MM:SS) |
|---|
| 351 |
# last date in monthly file |
|---|
| 352 |
'end_date' : data['dt'][i][-1].strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 353 |
'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 354 |
# |
|---|
| 355 |
'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
|---|
| 356 |
} |
|---|
| 357 |
|
|---|
| 358 |
# data variables |
|---|
| 359 |
# update any variable attributes like range, min, max |
|---|
| 360 |
var_atts = {} |
|---|
| 361 |
# var_atts = { |
|---|
| 362 |
# 'wtemp': {'max': max(data.u), |
|---|
| 363 |
# 'min': min(data.v), |
|---|
| 364 |
# }, |
|---|
| 365 |
# 'cond': {'max': max(data.u), |
|---|
| 366 |
# 'min': min(data.v), |
|---|
| 367 |
# }, |
|---|
| 368 |
# } |
|---|
| 369 |
|
|---|
| 370 |
# data |
|---|
| 371 |
var_data = ( |
|---|
| 372 |
('time', data['time'][i]), |
|---|
| 373 |
# |
|---|
| 374 |
('air_press', data['air_press'][i]), |
|---|
| 375 |
('rh', data['rh'][i]), |
|---|
| 376 |
('rh_std', data['rh_std'][i]), |
|---|
| 377 |
('air_temp', data['air_temp'][i]), |
|---|
| 378 |
('air_temp_std', data['air_temp_std'][i]), |
|---|
| 379 |
('rain', data['rain'][i]), |
|---|
| 380 |
('psp', data['psp'][i]), |
|---|
| 381 |
('psp_std', data['psp_std'][i]), |
|---|
| 382 |
('pir', data['pir'][i]), |
|---|
| 383 |
('pir_std', data['pir_std'][i]), |
|---|
| 384 |
) |
|---|
| 385 |
|
|---|
| 386 |
return (global_atts, var_atts, var_data) |
|---|
| 387 |
# |
|---|
