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#!/usr/bin/env python |
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# Last modified: Time-stamp: <2012-06-28 14:47:42 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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"TOA5","CR1000_B1","CR1000","37541","CR1000.Std.21","CPU:NCWIND_12_Buoy_All.CR1","58723","CTD1_6Min" |
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"TIMESTAMP","RECORD","ID","Temp","Cond","Depth","SampleDate","SampleTime","SampleNum" |
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"TS","RN","","","","","","","" |
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"","","Smp","Smp","Smp","Smp","Smp","Smp","Smp" |
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"2011-10-05 21:08:06",43,4085,24.5027,5.18209,3.347 |
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"2011-10-05 21:14:06",44,4085,24.5078,5.18305,3.454 |
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"2011-10-05 21:56:07",45,4085,24.5247,5.19257,3.423 |
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"2011-10-05 22:02:06",46,4085,24.5105,5.18714,3.526 |
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"2011-10-05 22:08:07",47,4085,24.519,5.19096,3.547 |
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"2011-10-05 22:14:06",48,4085,24.5207,5.19172,3.508 |
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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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'wtemp' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'cond' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'press' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'salin' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'density' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'depth' : 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 -- %s' % (i,line) |
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continue |
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# replace "NAN" |
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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[1:6]: |
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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 len(sw)>=6: |
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dstr = re.sub('"', '', sw[0]) |
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# print dstr |
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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 sensor_info['utc_offset']: |
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sample_dt = scanf_datetime(dstr, 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(dstr, fmt='%Y-%m-%d %H:%M:%S') |
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# ***** TO DO: need to adjust any drift of offset in CTD sample time to CR1000 clock |
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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)==5: |
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# |
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# (pg 31 SBE IMP Microcat User Manual) |
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# "#iiFORMAT=1 (default) Output converted to data |
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# date format dd mmm yyyy, |
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# conductivity = S/m, |
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# temperature precedes conductivity" |
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sn = csi[1] # ctd serial number == check against platform configuration |
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data['wtemp'][i] = csi[2] # water temperature (C) |
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data['cond'][i] = csi[3] # specific conductivity (S/m) |
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data['press'][i] = csi[4] # pressure decibars |
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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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# Quality Control steps for temp, depth, and cond |
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# (1) within range |
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# (2) if not pumped |
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good = (5<data['wtemp']) & (data['wtemp']<30) |
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bad = ~good |
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data['wtemp'][bad] = numpy.nan |
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good = (2<data['cond']) & (data['cond']<7) |
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bad = ~good |
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data['cond'][bad] = numpy.nan |
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# calculate depth, salinity and density |
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import seawater.csiro |
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import seawater.constants |
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# seawater.constants.C3515 is units of mS/cm |
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# data['cond'] is units of S/m |
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# You have: mS cm-1 |
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# You want: S m-1 |
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# <S m-1> = <mS cm-1>*0.1 |
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# <S m-1> = <mS cm-1>/10 |
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data['depth'] = -1*seawater.csiro.depth(data['press'], platform_info['lat']) # meters |
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data['salin'] = seawater.csiro.salt(10*data['cond']/seawater.constants.C3515, data['wtemp'], data['press']) # psu |
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data['density'] = seawater.csiro.dens(data['salin'], data['wtemp'], data['press']) # kg/m^3 |
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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': 'Depth', |
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'standard_name': 'depth', |
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'reference':'zero at sea-surface', |
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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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'wtemp': {'short_name': 'wtemp', |
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'long_name': 'Water Temperature', |
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'standard_name': 'water_temperature', |
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'units': 'degrees_Celsius', |
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}, |
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'cond': {'short_name': 'cond', |
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'long_name': 'Conductivity', |
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'standard_name': 'conductivity', |
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'units': 'S m-1', |
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}, |
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'press': {'short_name': 'press', |
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'long_name': 'Pressure', |
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'standard_name': 'water_pressure', |
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'units': 'decibar', |
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}, |
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'depth': {'short_name': 'depth', |
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'long_name': 'Depth', |
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'standard_name': 'depth', |
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'reference':'zero at sea-surface', |
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'positive' : 'up', |
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'units': 'm', |
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'comment': 'Derived using seawater.csiro.depth(press,lat)', |
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}, |
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'salin': {'short_name': 'salin', |
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'long_name': 'Salinity', |
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'standard_name': 'salinity', |
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'units': 'psu', |
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'comment': 'Derived using seawater.csiro.salt(cond/C3515,wtemp,press)', |
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}, |
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'density': {'short_name': 'density', |
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'long_name': 'Density', |
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'standard_name': 'density', |
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'units': 'kg m-3', |
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'comment': 'Derived using seawater.csiro.dens0(salin,wtemp,press)', |
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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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# 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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('wtemp', NC.FLOAT, ('ntime',)), |
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('cond', NC.FLOAT, ('ntime',)), |
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('press', NC.FLOAT, ('ntime',)), |
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# derived variables |
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('depth', NC.FLOAT, ('ntime',)), |
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('salin', NC.FLOAT, ('ntime',)), |
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('density', NC.FLOAT, ('ntime',)), |
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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', sensor_info['nominal_depth']), |
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# |
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('time', data['time'][i]), |
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# |
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('wtemp', data['wtemp'][i]), |
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('cond', data['cond'][i]), |
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('press', data['press'][i]), |
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# derived variables |
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('depth', data['depth'][i]), |
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('salin', data['salin'][i]), |
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('density', data['density'][i]), |
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) |
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return (global_atts, var_atts, dim_inits, var_inits, var_data) |
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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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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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('wtemp', data['wtemp'][i]), |
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('cond', data['cond'][i]), |
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('press', data['press'][i]), |
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# derived variables |
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('depth', data['depth'][i]), |
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('salin', data['salin'][i]), |
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('density', data['density'][i]), |
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) |
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return (global_atts, var_atts, var_data) |
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# |
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