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#!/usr/bin/env python |
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""" |
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Parse data and assert what data creates and updates monthly NetCDF files. |
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Remtech PA0 processed sodar wind profile data. |
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""" |
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import math |
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import numpy as n |
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import pycdf |
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import datetime |
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import procutil |
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from sodar.remtech import rawData |
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INVALID = '-9999' |
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nowDt = datetime.datetime.utcnow().replace(microsecond=0) |
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def parser(platform_info, sensor_info, lines): |
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""" |
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Parse and assign wind profile data from raw Sodar file. |
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""" |
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rawDataObject = rawData.RawData(''.join(lines)) |
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numSamples = len(rawDataObject) |
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minAltitude = sensor_info['min_altitude'] |
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altitudeInterval = sensor_info['altitude_interval'] |
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numAltitudes = sensor_info['num_altitudes'] |
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sensorElevation = sensor_info['sensor_elevation'] |
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altitudes = [(altitudeNum * altitudeInterval) + minAltitude |
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for altitudeNum in range(numAltitudes)] |
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elevations = [altitude + sensorElevation for altitude in altitudes] |
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altitudes = [str(altitude) for altitude in altitudes] |
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data = { |
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'block' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'dt' : n.array(n.ones((numSamples,), dtype=object) * n.nan), |
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'time' : n.array(n.ones((numSamples,), dtype=long) * n.nan), |
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'val1' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'val2' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'val3' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'val4' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'spu1' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'spu2' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'spu3' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'spu4' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'nois1' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'nois2' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'nois3' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'nois4' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'femax' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'softw' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'fe11' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'fe12' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'fe21' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'fe22' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'snr1' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'snr2' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'snr3' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'snr4' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'check' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'jam' : n.array(n.ones((numSamples,), dtype=int) * n.nan), |
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'z' : n.array(elevations, dtype=float), |
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'u' : n.array(n.ones((numSamples, |
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numAltitudes), dtype=float) * n.nan), |
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'v' : n.array(n.ones((numSamples, |
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numAltitudes), dtype=float) * n.nan), |
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'w' : n.array(n.ones((numSamples, |
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numAltitudes), dtype=float) * n.nan), |
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'echo' : n.array(n.ones((numSamples, |
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numAltitudes), dtype = int) * n.nan), |
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} |
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for sample in rawDataObject: |
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sampleIndex = rawDataObject.index(sample) |
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data['block'][sampleIndex] = int(sample['BL#']) |
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dt = {'month' : int(sample['MONTH']), |
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'day' : int(sample['DAY']), |
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'year' : int(sample['YEAR']), |
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'hour' : int(sample['HOUR']), |
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'min' : int(sample['MIN']), |
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} |
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dt = '%(month)02d-%(day)02d-%(year)04d %(hour)02d:%(min)02d' % dt |
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dt = procutil.scanf_datetime(dt, fmt='%m-%d-%Y %H:%M') |
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if sensor_info['utc_offset']: |
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dt = dt + datetime.timedelta(hours=sensor_info['utc_offset']) |
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data['dt'][sampleIndex] = dt |
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data['time'][sampleIndex] = procutil.dt2es(dt) |
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data['val1'][sampleIndex] = int(sample['VAL1']) |
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data['val2'][sampleIndex] = int(sample['VAL2']) |
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data['val3'][sampleIndex] = int(sample['VAL3']) |
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data['val4'][sampleIndex] = int(sample['VAL4']) |
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data['spu1'][sampleIndex] = int(sample['SPU1']) |
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data['spu2'][sampleIndex] = int(sample['SPU2']) |
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data['spu3'][sampleIndex] = int(sample['SPU3']) |
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data['spu4'][sampleIndex] = int(sample['SPU4']) |
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data['nois1'][sampleIndex] = int(sample['NOIS1']) |
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data['nois2'][sampleIndex] = int(sample['NOIS2']) |
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data['nois3'][sampleIndex] = int(sample['NOIS3']) |
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data['nois4'][sampleIndex] = int(sample['NOIS4']) |
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data['femax'][sampleIndex] = int(sample['FEMAX']) |
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data['softw'][sampleIndex] = int(sample['SOFTW']) |
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data['fe11'][sampleIndex] = int(sample['FE11']) |
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data['fe12'][sampleIndex] = int(sample['FE12']) |
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data['fe21'][sampleIndex] = int(sample['FE21']) |
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data['fe22'][sampleIndex] = int(sample['FE22']) |
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data['snr1'][sampleIndex] = int(sample['SNR1']) |
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data['snr2'][sampleIndex] = int(sample['SNR2']) |
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data['snr3'][sampleIndex] = int(sample['SNR3']) |
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data['snr4'][sampleIndex] = int(sample['SNR4']) |
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data['check'][sampleIndex] = int(sample['CHECK']) |
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data['jam'][sampleIndex] = int(sample['JAM']) |
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for altitude,altitudeIndex in zip(altitudes, range(len(altitudes))): |
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echo = sample[altitude]['CT'] |
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radial = sample[altitude]['SPEED'] |
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theta = sample[altitude]['DIR'] |
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vertical = sample[altitude]['W'] |
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if radial != INVALID and theta != INVALID: |
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theta = math.pi * float(theta) / 180.0 |
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radial = float(radial) |
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data['u'][sampleIndex][altitudeIndex] = radial * math.sin(theta) |
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data['v'][sampleIndex][altitudeIndex] = radial * math.cos(theta) |
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if echo != INVALID: |
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data['echo'][sampleIndex][altitudeIndex] = echo |
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if vertical != INVALID: |
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data['w'][sampleIndex][altitudeIndex] = vertical |
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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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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' : 'Unversity of North Carolina at Chapel Hill (UNC-CH)', |
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'institution_url' : 'http://nccoos.unc.edu', |
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'institution_dods_url' : 'http://nccoos.unc.edu', |
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'metadata_url' : 'http://nccoos.unc.edu', |
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'references' : 'http://nccoos.unc.edu', |
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'contact' : 'cbc (cbc@unc.edu)', |
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# |
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'source' : 'fixed-profiler (acoustic doppler) observation', |
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'history' : 'raw2proc using ' + sensor_info['process_module'], |
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'comment' : 'File created using pycdf'+pycdf.pycdfVersion()+' and numpy '+pycdf.pycdfArrayPkg(), |
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# conventions |
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'Conventions' : 'CF-1.0; SEACOOS-CDL-v2.0', |
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# SEACOOS CDL codes |
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'format_category_code' : 'fixed-profiler', |
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'institution_code' : platform_info['institution'], |
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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' : 'North Carolina Coastal Ocean Observing System (NCCOOS)', |
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'project_url' : 'http://nccoos.unc.edu', |
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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'][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'][-1].strftime("%Y-%m-%d %H:%M:%S"), |
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'release_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"), |
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# |
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'creation_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"), |
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'modification_date' : nowDt.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': 'Longtitude', |
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'standard_name': 'longtitude', |
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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': 'Height', |
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'standard_name': 'height', |
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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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'u': {'short_name' : 'u', |
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'long_name': 'East/West Component of Wind', |
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'standard_name': 'eastward_wind', |
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'units': 'cm s-1', |
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}, |
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'v': {'short_name' : 'v', |
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'long_name': 'North/South Component of Wind', |
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'standard_name': 'northward_wind', |
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'units': 'cm s-1', |
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}, |
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'w': {'short_name' : 'w', |
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'long_name': 'Vertical Component of Wind', |
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'standard_name': 'upward_wind', |
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'units': 'cm s-1', |
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}, |
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'echo': {'short_name' : 'echo', |
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'long_name': 'Echo Stength', |
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'standard_name': 'echo_strenth', |
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}, |
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'block' : {'short_name': 'block', |
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'long_name': 'Block Number', |
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'standard_name': 'block_number' |
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}, |
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'val1': {'short_name' : 'val1', |
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'long_name': 'Number of Beam Validations 1', |
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'standard_name': 'validations_1', |
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}, |
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'val2': {'short_name' : 'val2', |
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'long_name': 'Number of Beam Validations 2', |
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'standard_name': 'validations_2', |
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}, |
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'val3': {'short_name' : 'val3', |
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'long_name': 'Number of Beam Validations 3', |
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'standard_name': 'validations_3', |
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}, |
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'val4': {'short_name' : 'val4', |
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'long_name': 'Number of Beam Validations 4', |
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'standard_name': 'validations_4', |
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}, |
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'spu1': {'short_name' : 'spu1', |
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'long_name': 'Normalized Probability of False Signal 1', |
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'standard_name': 'probability_1', |
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}, |
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'spu2': {'short_name' : 'spu2', |
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'long_name': 'Normalized Probability of False Signal 1', |
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'standard_name': 'probability_2', |
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}, |
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'spu3': {'short_name' : 'spu3', |
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'long_name': 'Normalized Probability of False Signal 3', |
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'standard_name': 'probability_3', |
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}, |
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'spu4': {'short_name' : 'spu4', |
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'long_name': 'Normalized Probability of False Signal 4', |
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'standard_name': 'probability_4', |
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}, |
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'nois1': {'short_name' : 'nois1', |
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'long_name': 'Environmental Noise 1', |
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'standard_name': 'ambient_1', |
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'units': 'dB', |
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}, |
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'nois2': {'short_name' : 'nois2', |
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'long_name': 'Environmental Noise 2', |
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'standard_name': 'ambient_2', |
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'units': 'dB', |
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}, |
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'nois3': {'short_name' : 'nois3', |
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'long_name': 'Environmental Noise 3', |
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'standard_name': 'ambient_3', |
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'units': 'dB', |
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}, |
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'nois4': {'short_name' : 'nois4', |
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'long_name': 'Environmental Noise 4', |
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'standard_name': 'ambient_4', |
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'units': 'dB', |
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}, |
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'femax': {'short_name': 'femax', |
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'long_name': 'Maximum Ground Clutter', |
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'standard_name': 'max_clutter', |
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}, |
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'softw': {'short_name': 'softw', |
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'long_name': 'Software Version', |
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'standard_name': 'software', |
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}, |
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'fe11': {'short_name': 'fe11', |
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'long_name': 'Number of Frequencies Emitted 11', |
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'standard_name': 'frequencies_11', |
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}, |
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'fe12': {'short_name': 'fe12', |
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'long_name': 'Number of Frequencies Emitted 12', |
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'standard_name': 'frequencies_12', |
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}, |
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'fe21': {'short_name': 'fe21', |
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'long_name': 'Number of Frequencies Emitted 21', |
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'standard_name': 'frequencies_21', |
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}, |
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'fe22': {'short_name': 'fe22', |
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'long_name': 'Number of Frequencies Emitted 22', |
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'standard_name': 'frequencies_22', |
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}, |
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'snr1': {'short_name' : 'snr1', |
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'long_name': 'Average Signal To Noise Ratio 1', |
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'standard_name': 'signal_to_noise_1', |
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'units': 'dB', |
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}, |
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'snr2': {'short_name' : 'snr2', |
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'long_name': 'Average Signal To Noise Ratio 2', |
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'standard_name': 'signal_to_noise_2', |
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'units': 'dB', |
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}, |
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'snr3': {'short_name' : 'snr3', |
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330 |
'long_name': 'Average Signal To Noise Ratio 3', |
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331 |
'standard_name': 'signal_to_noise_3', |
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'units': 'dB', |
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}, |
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'snr4': {'short_name' : 'snr4', |
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'long_name': 'Average Signal To Noise Ratio 4', |
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'standard_name': 'signal_to_noise_4', |
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'units': 'dB', |
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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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342 |
dim_inits = ( |
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343 |
('ntime', pycdf.NC.UNLIMITED), |
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344 |
('nlat', 1), |
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('nlon', 1), |
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346 |
('nz', sensor_info['num_altitudes']) |
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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', pycdf.NC.INT, ('ntime',)), |
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356 |
('lat', pycdf.NC.FLOAT, ('nlat',)), |
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357 |
('lon', pycdf.NC.FLOAT, ('nlon',)), |
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358 |
('z', pycdf.NC.FLOAT, ('nz',)), |
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# data variables |
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360 |
('u', pycdf.NC.FLOAT, ('ntime', 'nz')), |
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('v', pycdf.NC.FLOAT, ('ntime', 'nz')), |
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('w', pycdf.NC.FLOAT, ('ntime', 'nz')), |
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363 |
('echo', pycdf.NC.FLOAT, ('ntime', 'nz')), |
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('block', pycdf.NC.INT, ('ntime',)), |
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('val1', pycdf.NC.INT, ('ntime',)), |
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366 |
('val2', pycdf.NC.INT, ('ntime',)), |
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367 |
('val3', pycdf.NC.INT, ('ntime',)), |
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368 |
('val4', pycdf.NC.INT, ('ntime',)), |
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('spu1', pycdf.NC.INT, ('ntime',)), |
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('spu2', pycdf.NC.INT, ('ntime',)), |
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('spu3', pycdf.NC.INT, ('ntime',)), |
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('spu4', pycdf.NC.INT, ('ntime',)), |
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('nois1', pycdf.NC.INT, ('ntime',)), |
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374 |
('nois2', pycdf.NC.INT, ('ntime',)), |
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('nois3', pycdf.NC.INT, ('ntime',)), |
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376 |
('nois4', pycdf.NC.INT, ('ntime',)), |
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377 |
('femax', pycdf.NC.INT, ('ntime',)), |
---|
378 |
('softw', pycdf.NC.INT, ('ntime',)), |
---|
379 |
('fe11', pycdf.NC.INT, ('ntime',)), |
---|
380 |
('fe12', pycdf.NC.INT, ('ntime',)), |
---|
381 |
('fe21', pycdf.NC.INT, ('ntime',)), |
---|
382 |
('fe22', pycdf.NC.INT, ('ntime',)), |
---|
383 |
('snr1', pycdf.NC.INT, ('ntime',)), |
---|
384 |
('snr2', pycdf.NC.INT, ('ntime',)), |
---|
385 |
('snr3', pycdf.NC.INT, ('ntime',)), |
---|
386 |
('snr4', pycdf.NC.INT, ('ntime',)), |
---|
387 |
) |
---|
388 |
|
---|
389 |
# subset data only to month being processed (see raw2proc.process()) |
---|
390 |
i = data['in'] |
---|
391 |
|
---|
392 |
# var data |
---|
393 |
var_data = ( |
---|
394 |
('time', data['time'][i]), |
---|
395 |
('lat', platform_info['lat']), |
---|
396 |
('lon', platform_info['lon']), |
---|
397 |
('z', data['z']), |
---|
398 |
('u', data['u'][i]), |
---|
399 |
('v', data['v'][i]), |
---|
400 |
('w', data['w'][i]), |
---|
401 |
('echo', data['echo'][i]), |
---|
402 |
('block', data['block'][i]), |
---|
403 |
('val1', data['val1'][i]), |
---|
404 |
('val2', data['val1'][i]), |
---|
405 |
('val3', data['val1'][i]), |
---|
406 |
('val4', data['val1'][i]), |
---|
407 |
('spu1', data['spu1'][i]), |
---|
408 |
('spu2', data['spu2'][i]), |
---|
409 |
('spu3', data['spu3'][i]), |
---|
410 |
('spu4', data['spu4'][i]), |
---|
411 |
('nois1', data['nois1'][i]), |
---|
412 |
('nois2', data['nois2'][i]), |
---|
413 |
('nois3', data['nois3'][i]), |
---|
414 |
('nois4', data['nois4'][i]), |
---|
415 |
('femax', data['femax'][i]), |
---|
416 |
('softw', data['softw'][i]), |
---|
417 |
('fe11', data['fe11'][i]), |
---|
418 |
('fe12', data['fe12'][i]), |
---|
419 |
('fe21', data['fe21'][i]), |
---|
420 |
('fe22', data['fe22'][i]), |
---|
421 |
('snr1', data['snr1'][i]), |
---|
422 |
('snr2', data['snr2'][i]), |
---|
423 |
('snr3', data['snr3'][i]), |
---|
424 |
('snr4', data['snr4'][i]), |
---|
425 |
) |
---|
426 |
|
---|
427 |
return (global_atts, var_atts, dim_inits, var_inits, var_data) |
---|
428 |
|
---|
429 |
def updater(platform_info, sensor_info, data): |
---|
430 |
# |
---|
431 |
global_atts = { |
---|
432 |
# update times of data contained in file (yyyy-mm-dd HH:MM:SS) |
---|
433 |
# last date in monthly file |
---|
434 |
'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"), |
---|
435 |
'release_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"), |
---|
436 |
# |
---|
437 |
'modification_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"), |
---|
438 |
} |
---|
439 |
|
---|
440 |
# data variables |
---|
441 |
# update any variable attributes like range, min, max |
---|
442 |
var_atts = {} |
---|
443 |
|
---|
444 |
# subset data only to month being processed (see raw2proc.process()) |
---|
445 |
i = data['in'] |
---|
446 |
|
---|
447 |
# data |
---|
448 |
var_data = ( |
---|
449 |
('time', data['time'][i]), |
---|
450 |
('u', data['u'][i]), |
---|
451 |
('v', data['v'][i]), |
---|
452 |
('w', data['w'][i]), |
---|
453 |
('echo', data['echo'][i]), |
---|
454 |
('block', data['block'][i]), |
---|
455 |
('val1', data['val1'][i]), |
---|
456 |
('val2', data['val1'][i]), |
---|
457 |
('val3', data['val1'][i]), |
---|
458 |
('val4', data['val1'][i]), |
---|
459 |
('spu1', data['spu1'][i]), |
---|
460 |
('spu2', data['spu2'][i]), |
---|
461 |
('spu3', data['spu3'][i]), |
---|
462 |
('spu4', data['spu4'][i]), |
---|
463 |
('nois1', data['nois1'][i]), |
---|
464 |
('nois2', data['nois2'][i]), |
---|
465 |
('nois3', data['nois3'][i]), |
---|
466 |
('nois4', data['nois4'][i]), |
---|
467 |
('femax', data['femax'][i]), |
---|
468 |
('softw', data['softw'][i]), |
---|
469 |
('fe11', data['fe11'][i]), |
---|
470 |
('fe12', data['fe12'][i]), |
---|
471 |
('fe21', data['fe21'][i]), |
---|
472 |
('fe22', data['fe22'][i]), |
---|
473 |
('snr1', data['snr1'][i]), |
---|
474 |
('snr2', data['snr2'][i]), |
---|
475 |
('snr3', data['snr3'][i]), |
---|
476 |
('snr4', data['snr4'][i]), |
---|
477 |
) |
---|
478 |
|
---|
479 |
return (global_atts, var_atts, var_data) |
---|