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#!/usr/bin/env python |
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# Last modified: Time-stamp: <2012-04-23 14:12:38 haines> |
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""" |
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how to parse data, and assert what data and info goes into |
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creating and updating monthly netcdf files |
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parse data met data collected on Campbell Scientific DataLogger (loggernet) (csi) |
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parser : sample date and time, |
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creator : lat, lon, z, time, |
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updator : time, |
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Examples |
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-------- |
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>> (parse, create, update) = load_processors('proc_csi_adcp_v2') |
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or |
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>> si = get_config(cn+'.sensor_info') |
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>> (parse, create, update) = load_processors(si['adcp']['proc_module']) |
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>> lines = load_data(filename) |
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>> data = parse(platform_info, sensor_info, lines) |
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>> create(platform_info, sensor_info, data) or |
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>> update(platform_info, sensor_info, data) |
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""" |
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from raw2proc import * |
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from procutil import * |
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from ncutil import * |
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now_dt = datetime.utcnow() |
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now_dt.replace(microsecond=0) |
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def parser(platform_info, sensor_info, lines): |
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""" |
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Example met data |
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"TOA5","CR1000_B1","CR1000","37541","CR1000.Std.21","CPU:NCWIND_12_Buoy_All.CR1","58723","AMet_6Min" |
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"TIMESTAMP","RECORD","Baro_mbar_Avg","RHumidity_Avg","RHumidity_Std","AirTempC_Avg","AirTempC_Std","Rain","Psp_Avg","Psp_Std","Pir_Wm2_Avg","Pir_Wm2_Std" |
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"TS","RN","","","","","","","","","","" |
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"","","Avg","Avg","Std","Avg","Std","Smp","Avg","Std","Avg","Std" |
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"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 |
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"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 |
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"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 |
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"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 |
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""" |
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import numpy |
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from datetime import datetime |
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from time import strptime |
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# get sample datetime from filename |
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fn = sensor_info['fn'] |
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sample_dt_start = filt_datetime(fn) |
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# how many samples (don't count header 4 lines) |
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nsamp = len(lines[4:]) |
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N = nsamp |
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data = { |
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'dt' : numpy.array(numpy.ones((N,), dtype=object)*numpy.nan), |
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'time' : numpy.array(numpy.ones((N,), dtype=long)*numpy.nan), |
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'air_press' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'rh' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'rh_std' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'air_temp' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'air_temp_std' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'rain' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'psp' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'psp_std' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'pir' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'pir_std' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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} |
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# sample count |
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i = 0 |
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for line in lines[4:]: |
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csi = [] |
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# split line |
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sw = re.split(',', line) |
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if len(sw)<=0: |
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print ' ... skipping line %d ' % (i,) |
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continue |
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# replace any "NAN" text with a number |
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for index, s in enumerate(sw): |
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m = re.search(NAN_RE_STR, s) |
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if m: |
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sw[index] = '-99999' |
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# parse date-time, and all other float and integers |
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for s in sw[1:]: |
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m = re.search(REAL_RE_STR, s) |
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if m: |
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csi.append(float(m.groups()[0])) |
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if sensor_info['utc_offset']: |
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sample_dt = scanf_datetime(sw[0], fmt='"%Y-%m-%d %H:%M:%S"') + \ |
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timedelta(hours=sensor_info['utc_offset']) |
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else: |
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sample_dt = scanf_datetime(sw[0], fmt='"%Y-%m-%d %H:%M:%S"') |
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data['dt'][i] = sample_dt # sample datetime |
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data['time'][i] = dt2es(sample_dt) # sample time in epoch seconds |
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if len(csi)==11: |
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# |
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# data['samplenum'][i] = csi[0] # sample number assigned by datalogger in table |
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data['air_press'][i] = csi[1] # Heise Barometer (mbar) |
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data['rh'][i] = csi[2] # relative humidity avg (60 samples for 1 min) |
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data['rh_std'][i] = csi[3] # relative humidity std |
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data['air_temp'][i] = csi[4] # air temperature avg (deg C) |
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data['air_temp_std'][i] = csi[5] # air temperature std (deg C) |
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data['rain'][i] = csi[6] # precip gauge cummulative |
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data['psp'][i] = csi[7] # PSP avg |
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data['psp_std'][i] = csi[8] # PSP std |
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data['pir'][i] = csi[9] # PIR avg (W m-2) |
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data['pir_std'][i] = csi[10] # PIR std (W m-2) |
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i=i+1 |
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else: |
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print ' ... skipping line %d -- %s ' % (i,line) |
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continue |
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# if re.search |
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# for line |
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# check that no data[dt] is set to Nan or anything but datetime |
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# keep only data that has a resolved datetime |
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keep = numpy.array([type(datetime(1970,1,1)) == type(dt) for dt in data['dt'][:]]) |
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if keep.any(): |
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for param in data.keys(): |
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data[param] = data[param][keep] |
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return data |
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def creator(platform_info, sensor_info, data): |
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# |
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# |
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# subset data only to month being processed (see raw2proc.process()) |
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i = data['in'] |
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title_str = sensor_info['description']+' at '+ platform_info['location'] |
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global_atts = { |
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'title' : title_str, |
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'institution' : platform_info['institution'], |
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'institution_url' : platform_info['institution_url'], |
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'institution_dods_url' : platform_info['institution_dods_url'], |
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'metadata_url' : platform_info['metadata_url'], |
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'references' : platform_info['references'], |
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'contact' : platform_info['contact'], |
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# |
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'source' : platform_info['source']+' '+sensor_info['source'], |
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'history' : 'raw2proc using ' + sensor_info['process_module'], |
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'comment' : 'File created using pycdf'+pycdfVersion()+' and numpy '+pycdfArrayPkg(), |
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# conventions |
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'Conventions' : platform_info['conventions'], |
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# SEACOOS CDL codes |
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'format_category_code' : platform_info['format_category_code'], |
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'institution_code' : platform_info['institution_code'], |
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'platform_code' : platform_info['id'], |
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'package_code' : sensor_info['id'], |
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# institution specific |
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'project' : platform_info['project'], |
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'project_url' : platform_info['project_url'], |
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# timeframe of data contained in file yyyy-mm-dd HH:MM:SS |
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# first date in monthly file |
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'start_date' : data['dt'][i][0].strftime("%Y-%m-%d %H:%M:%S"), |
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# last date in monthly file |
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'end_date' : data['dt'][i][-1].strftime("%Y-%m-%d %H:%M:%S"), |
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'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
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# |
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'creation_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
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'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
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'process_level' : 'level1', |
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# |
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# must type match to data (e.g. fillvalue is real if data is real) |
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'_FillValue' : -99999., |
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} |
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var_atts = { |
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# coordinate variables |
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'time' : {'short_name': 'time', |
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'long_name': 'Time', |
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'standard_name': 'time', |
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'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC |
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'axis': 'T', |
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}, |
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'lat' : {'short_name': 'lat', |
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'long_name': 'Latitude', |
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'standard_name': 'latitude', |
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'reference':'geographic coordinates', |
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'units': 'degrees_north', |
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'valid_range':(-90.,90.), |
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'axis': 'Y', |
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}, |
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'lon' : {'short_name': 'lon', |
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'long_name': 'Longitude', |
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'standard_name': 'longitude', |
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'reference':'geographic coordinates', |
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'units': 'degrees_east', |
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'valid_range':(-180.,180.), |
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'axis': 'Y', |
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}, |
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'z' : {'short_name': 'z', |
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'long_name': 'Altitude', |
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'standard_name': 'altitude', |
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'reference':'zero at mean sea level', |
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'positive' : 'up', |
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'units': 'm', |
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'axis': 'Z', |
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}, |
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# data variables |
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'air_press': {'short_name': 'air_press', |
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'long_name': 'Air Pressure', |
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'standard_name': 'air_pressure', |
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'units': 'mbar', |
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'z': sensor_info['barometer_height'], |
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'z_units' : 'meter', |
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}, |
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'air_temp': {'short_name': 'air_temp', |
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'long_name': 'Air Temperature', |
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'standard_name': 'air_temperature', |
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'units': 'degC', |
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'z': sensor_info['temperature_height'], |
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'z_units' : 'meter', |
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}, |
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'air_temp_std': {'short_name': 'air_temp_std', |
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'long_name': 'Standard Deviation of Air Temperature', |
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'standard_name': 'air_temperature', |
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'units': 'degC', |
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}, |
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'rh': {'short_name': 'rh', |
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'long_name': 'Relative Humidity', |
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'standard_name': 'relative_humidity', |
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'units': '%', |
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'z': sensor_info['temperature_height'], |
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'z_units' : 'meter', |
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}, |
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'rh_std': {'short_name': 'rh_std', |
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'long_name': 'Standard Deviation of Relative Humidity', |
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'standard_name': 'relative_humidity', |
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'units': '%', |
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}, |
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'rain': {'short_name': 'rain', |
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'long_name': '6-Minute Rain', |
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'standard_name': 'rain', |
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'units': 'inches', |
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}, |
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'psp': {'short_name': 'psp', |
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'long_name': 'Short-wave Radiation', |
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'standard_name': 'downwelling_shortwave_irradiance', |
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'units': 'W m-2', |
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}, |
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'psp_std': {'short_name': 'psp_std', |
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'long_name': 'Standard Deviation of Short-wave Radiation', |
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'standard_name': 'shortwave_radiation', |
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'units': 'W m-2', |
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}, |
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'pir': {'short_name': 'pir', |
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'long_name': 'Long-wave Radiation', |
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'standard_name': 'longwave_radiation', |
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'units': 'W m-2', |
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}, |
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'pir_std': {'short_name': 'pir_std', |
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'long_name': 'Standard Deviation of Long-wave Radiation', |
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'standard_name': 'longwave_radiation', |
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'units': 'W m-2', |
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}, |
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} |
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|
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# dimension names use tuple so order of initialization is maintained |
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dim_inits = ( |
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('ntime', NC.UNLIMITED), |
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('nlat', 1), |
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('nlon', 1), |
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('nz', 1), |
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) |
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|
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# using tuple of tuples so order of initialization is maintained |
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# using dict for attributes order of init not important |
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# use dimension names not values |
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# (varName, varType, (dimName1, [dimName2], ...)) |
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var_inits = ( |
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# coordinate variables |
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('time', NC.INT, ('ntime',)), |
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('lat', NC.FLOAT, ('nlat',)), |
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('lon', NC.FLOAT, ('nlon',)), |
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('z', NC.FLOAT, ('nz',)), |
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# data variables |
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('air_press', NC.FLOAT, ('ntime',)), |
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('rh', NC.FLOAT, ('ntime',)), |
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('rh_std', NC.FLOAT, ('ntime',)), |
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('air_temp', NC.FLOAT, ('ntime',)), |
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('air_temp_std', NC.FLOAT, ('ntime',)), |
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('rain', NC.FLOAT, ('ntime',)), |
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('psp', NC.FLOAT, ('ntime',)), |
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('psp_std', NC.FLOAT, ('ntime',)), |
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('pir', NC.FLOAT, ('ntime',)), |
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('pir_std', NC.FLOAT, ('ntime',)), |
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) |
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|
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# subset data only to month being processed (see raw2proc.process()) |
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i = data['in'] |
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|
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# var data |
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var_data = ( |
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('lat', platform_info['lat']), |
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('lon', platform_info['lon']), |
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('z', platform_info['altitude']), |
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# |
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('time', data['time'][i]), |
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# |
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('air_press', data['air_press'][i]), |
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('rh', data['rh'][i]), |
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('rh_std', data['rh_std'][i]), |
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('air_temp', data['air_temp'][i]), |
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('air_temp_std', data['air_temp_std'][i]), |
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('rain', data['rain'][i]), |
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('psp', data['psp'][i]), |
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('psp_std', data['psp_std'][i]), |
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('pir', data['pir'][i]), |
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('pir_std', data['pir_std'][i]), |
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) |
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|
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return (global_atts, var_atts, dim_inits, var_inits, var_data) |
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|
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def updater(platform_info, sensor_info, data): |
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# |
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# subset data only to month being processed (see raw2proc.process()) |
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i = data['in'] |
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|
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global_atts = { |
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# update times of data contained in file (yyyy-mm-dd HH:MM:SS) |
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# last date in monthly file |
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'end_date' : data['dt'][i][-1].strftime("%Y-%m-%d %H:%M:%S"), |
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'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
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# |
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'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
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} |
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|
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# data variables |
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# update any variable attributes like range, min, max |
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var_atts = {} |
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# var_atts = { |
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# 'wtemp': {'max': max(data.u), |
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# 'min': min(data.v), |
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# }, |
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# 'cond': {'max': max(data.u), |
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# 'min': min(data.v), |
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# }, |
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# } |
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|
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# data |
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var_data = ( |
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('time', data['time'][i]), |
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| 364 |
# |
|---|
| 365 |
('air_press', data['air_press'][i]), |
|---|
| 366 |
('rh', data['rh'][i]), |
|---|
| 367 |
('rh_std', data['rh_std'][i]), |
|---|
| 368 |
('air_temp', data['air_temp'][i]), |
|---|
| 369 |
('air_temp_std', data['air_temp_std'][i]), |
|---|
| 370 |
('rain', data['rain'][i]), |
|---|
| 371 |
('psp', data['psp'][i]), |
|---|
| 372 |
('psp_std', data['psp_std'][i]), |
|---|
| 373 |
('pir', data['pir'][i]), |
|---|
| 374 |
('pir_std', data['pir_std'][i]), |
|---|
| 375 |
) |
|---|
| 376 |
|
|---|
| 377 |
return (global_atts, var_atts, var_data) |
|---|
| 378 |
# |
|---|
