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#!/usr/bin/env python |
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""" |
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Parse sodar data and assert what data and info goes into |
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creating and updating monthly netcdf files. |
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|
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>> (parse, create, update) = load_processors('proc_rdi_rawdata_sodar') |
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>> data = parse(lines) |
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>> create(platform_info, sensor_info, data) |
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>> update(platform_info, sensor_info, data) |
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""" |
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|
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from sodar import rawData |
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import math |
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import numpy as n |
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from datetime import timedelta |
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from procutil import scanf_datetime, dt2es |
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|
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INVALID = '-9999' |
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BLOCKNUMBER = 'BL#' |
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MONTH = 'MONTH' |
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DAY = 'DAY' |
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YEAR = 'YEAR' |
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HOUR = 'HOUR' |
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MINUTE = 'MIN' |
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VALIDATIONS1 = 'VAL1' |
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VALIDATIONS2 = 'VAL2' |
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VALIDATIONS3 = 'VAL3' |
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VALIDATIONS4 = 'VAL4' |
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PROBABILITY1 = 'SPU1' |
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PROBABILITY2 = 'SPU2' |
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PROBABILITY3 = 'SPU3' |
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PROBABILITY4 = 'SPU4' |
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AMBIENTNOISE1 = 'NOIS1' |
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AMBIENTNOISE2 = 'NOIS2' |
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AMBIENTNOISE3 = 'NOIS3' |
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AMBIENTNOISE4 = 'NOIS4' |
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CLUTTER = 'FEMAX' |
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SOFTWARE = 'SOFTW' |
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FREQUENCIES11 = 'FE11' |
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FREQUENCIES12 = 'FE12' |
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FREQUENCIES21 = 'FE21' |
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FREQUENCIES22 = 'FE22' |
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SIGNALNOISE1 = 'SNR1' |
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SIGNALNOISE2 = 'SNR2' |
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SIGNALNOISE3 = 'SNR3' |
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SIGNALNOISE4 = 'SNR4' |
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REFERENCE = 'CHECK' |
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JAM = 'JAM' |
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ALTITUDE = 'ALT' |
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ECHO = 'CT' |
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RADIAL = 'SPEED' |
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THETA = 'DIR' |
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VERTICAL = 'W' |
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|
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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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|
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rawDataObject = rawdata.RawData('\n'.join(lines)) |
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|
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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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|
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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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|
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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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'es' : 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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|
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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[BLOCKNUM]) |
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|
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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[MINUTE]), |
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} |
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dt = '%(month)02d-%(day)02d-%(year)04d %(hour)02d:%02d(min)' % dt |
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dt = scanf_datetime(dt, fmt='%m-%d-%Y %H:%M') |
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if sensor_info['utc_offset']: |
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dt = dt + timedelta(hours=sensor_info['utc_offset']) |
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data['dt'][sampleIndex] = dt |
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|
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data['es'][sampleIndex] = dt2es(dt) |
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data['val1'][sampleIndex] = int(sample[VALIDATIONS1]) |
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data['val2'][sampleIndex] = int(sample[VALIDATIONS2]) |
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data['val3'][sampleIndex] = int(sample[VALIDATIONS3]) |
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data['val4'][sampleIndex] = int(sample[VALIDATIONS4]) |
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data['spu1'][sampleIndex] = int(sample[PROBABILITY1]) |
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data['spu2'][sampleIndex] = int(sample[PROBABILITY2]) |
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data['spu3'][sampleIndex] = int(sample[PROBABILITY]) |
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data['spu4'][sampleIndex] = int(sample[PROBABILITY4]) |
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|
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data['nois1'][sampleIndex] = int(sample[AMBIENTNOISE1]) |
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data['nois2'][sampleIndex] = int(sample[AMBIENTNOISE2]) |
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data['nois3'][sampleIndex] = int(sample[AMBIENTNOISE3]) |
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data['nois4'][sampleIndex] = int(sample[AMBIENTNOISE4]) |
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data['femax'][sampleIndex] = int(sample[CLUTTER]) |
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data['softw'][sampleIndex] = int(sample[SOFTWARE]) |
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data['fe11'][sampleIndex] = int(sample[FREQUENCIES11]) |
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data['fe12'][sampleIndex] = int(sample[FREQUENCIES12]) |
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data['fe21'][sampleIndex] = int(sample[FREQUENCIES21]) |
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data['fe22'][sampleIndex] = int(sample[FREQUENCIES22]) |
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data['snr1'][sampleIndex] = int(sample[SIGNALNOISE1]) |
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data['snr2'][sampleIndex] = int(sample[SIGNALNOISE2]) |
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data['snr3'][sampleIndex] = int(sample[SIGNALNOISE3]) |
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data['snr4'][sampleIndex] = int(sample[SIGNALNOISE4]) |
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data['check'][sampleIndex] = int(sample[REFERENCE]) |
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data['jam'][sampleIndex] = int(sample[JAM]) |
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for altitude,alttitudeIndex in zip(altitudes, range(len(altitudes))): |
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echo = sample[altitude][ECHO] |
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radial = sample[altitude][RADIAL] |
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theta = sample[altitude][THETA] |
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vertical = sample[altitude][VERTICAL] |
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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][altitideIndex] = 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' : 'Sara Haines (haines@email.unc.edu)', |
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# |
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'source' : 'fixed-profiler (acoustic doppler) observation', |
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'history' : 'Data processed by NCCOOS', |
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'comment' : 'File created using pycdf'+pycdfVersion()+' and numpy '+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['instituion'], |
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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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'start_date' : data['sample_dt'].strftime("%Y-%m-%d %H:%M:%S"), |
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'end_date' : data['sample_dt'].strftime("%Y-%m-%d %H:%M:%S"), |
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'release_date' : now.strftime("%Y-%m-%d %H:%M:%S"), |
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# |
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'creation_date' : now.strftime("%Y-%m-%d %H:%M:%S"), |
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'modification_date' : now.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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|
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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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'units': 'm', |
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'axis': 'Z', |
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}, |
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# data variables |
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'u': {'long_name': 'East/West Component of Current', |
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'standard_name': 'eastward_current', |
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'units': 'm s-1', |
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'reference': 'clockwise from True East', |
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}, |
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'v': {'long_name': 'North/South Component of Current', |
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'standard_name': 'northward_current', |
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'units': 'm s-1', |
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'reference': 'clockwise from True North', |
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}, |
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'w': {'long_name': 'Upward/Downward Component of Current', |
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'standard_name': 'upward_current', |
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'units': 'm s-1', |
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'positive': 'up', |
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}, |
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'back_scatter':{'long_name': 'Backscatter', |
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'standard_name': 'back_scatter', |
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'units': 'decibels', |
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}, |
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'wtemp': {'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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} |
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|
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|
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# integer values |
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ntime=NC.UNLIMITED |
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nlat=1 |
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nlon=1 |
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nz=sensor_info['nbins'] |
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|
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# dimension names use tuple so order of initialization is maintained |
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dimensions = ('ntime', 'nlat', 'nlon', 'nz') |
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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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('u', NC.FLOAT, ('ntime', 'nz')), |
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('v', NC.FLOAT, ('ntime', 'nz')), |
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('w', NC.FLOAT, ('ntime', 'nz')), |
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('back_scatter', NC.FLOAT, ('ntime', 'nz')), |
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('wtemp', NC.FLOAT, ('ntime',)), |
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) |
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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', []), |
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('u', []), |
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| 312 |
('v', []), |
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('w', []), |
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('back_scatter', []), |
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('wtemp', []), |
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) |
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| 317 |
|
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return (global_atts, dimensions, var_inits, var_data) |
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| 319 |
|
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def updater(platform_info, sensor_info, data): |
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| 321 |
# |
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| 322 |
global_atts = { |
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# timeframe of data contained in file yyyy-mm-dd HH:MM:SS |
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| 324 |
'end_date' : data['sample_dt'].strftime("%Y-%m-%d %H:%M:%S"), |
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'release_date' : now.strftime("%Y-%m-%d %H:%M:%S"), |
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# |
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'creation_date' : now.strftime("%Y-%m-%d %H:%M:%S"), |
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'modification_date' : now.strftime("%Y-%m-%d %H:%M:%S"), |
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} |
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| 330 |
# var data |
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| 331 |
var_data = ( |
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('u', data['u']), |
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('v', data['v']), |
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('w', data['w']), |
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('back_scatter', data['back_scatter']), |
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('wtemp', data['wtemp']), |
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) |
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return (global_atts, var_data) |
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| 339 |
# |
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| 340 |
|
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