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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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6 |
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parse data from YSI 6600 V2-2 on an automated veritical profiler (avp) |
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parser : date and time, water_depth for each profile |
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sample time, sample depth, as cast measures water |
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temperature, conductivity, salinity, pH, dissolved oxygen, |
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turbidity, and chlorophyll |
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creator : lat, lon, z, time, water_depth, water_temp, cond, |
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salin, ph, turb, chl, do |
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18 |
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updator : z, time, water_depth, water_temp, cond, salin, ph, |
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turb, chl, do |
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using moving point CDL |
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Examples |
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-------- |
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27 |
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>> (parse, create, update) = load_processors('proc_avp_ysi_6600_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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32 |
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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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|
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def parser(platform_info, sensor_info, lines): |
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""" |
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parse Automated Vertical Profile Station (AVP) Water Quality Data |
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|
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month, day, year, hour, min, sec, temp (deg. C), conductivity |
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(mS/cm), salinity (ppt or PSU), depth (meters), pH, turbidity (NTU), |
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54 |
chlorophyll (micrograms per liter), DO (micrograms per liter) |
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55 |
|
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Notes |
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----- |
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1. Column Format |
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59 |
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temp, cond, salin, depth, pH, turb, chl, DO |
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(C), (mS/cm), (ppt), (m), pH, (NTU), (ug/l), (ug/l) |
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Profile Time: 00:30:00 |
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Profile Date: 08/18/2008 |
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Profile Depth: 255.0 cm |
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Profile Location: Stones Bay Serial No: 00016B79, ID: AVP1_SERDP |
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08/18/08 00:30:06 26.94 41.87 26.81 0.134 8.00 3.4 4.5 6.60 |
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08/18/08 00:30:07 26.94 41.87 26.81 0.143 8.00 3.4 4.8 6.59 |
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69 |
08/18/08 00:30:08 26.94 41.87 26.81 0.160 8.00 3.4 4.8 6.62 |
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08/18/08 00:30:09 26.94 41.87 26.81 0.183 8.00 3.4 4.8 6.66 |
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71 |
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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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fn = sensor_info['fn'] |
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sample_dt_start = filt_datetime(fn) |
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81 |
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nsamp = 0 |
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for line in lines: |
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if re.search(r"[\x1a]", line): |
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continue |
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m=re.search("^\d{2}\/\d{2}\/\d{2}", line) |
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if m: |
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nsamp=nsamp+1 |
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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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'z' : numpy.array(numpy.ones((N,), dtype=long)*numpy.nan), |
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'wd' : numpy.array(numpy.ones((N,), dtype=long)*numpy.nan), |
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'wl' : numpy.array(numpy.ones((N,), dtype=long)*numpy.nan), |
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'batt' : numpy.array(numpy.ones((N,), dtype=float)*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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'salin' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'turb' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'ph' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'chl' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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'do' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), |
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} |
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110 |
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111 |
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for i in range(N): |
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data['dt'][i] = datetime(1970,1,1) |
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114 |
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115 |
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i = 0 |
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117 |
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for line in lines: |
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if re.search(r"[\x1a]", line): |
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continue |
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123 |
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ysi = [] |
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125 |
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sw = re.split('[\s/\:]*', line) |
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for s in sw: |
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m = re.search(REAL_RE_STR, s) |
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if m: |
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ysi.append(float(m.groups()[0])) |
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131 |
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if re.search("Profile Depth:", line) and i<N: |
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sw = re.match("Profile Depth: " + REAL_RE_STR + "(\\w+)", line) |
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if (ysi[0] is not None) and (sw is not None): |
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unit_str = sw.groups()[-1] |
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if unit_str is not None: |
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(wd, unit_str) = udconvert(ysi[0], unit_str, 'm') |
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else: |
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wd = numpy.nan |
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else: |
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wd = numpy.nan |
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wl = platform_info['mean_water_depth'] - (-1*wd) |
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data['wl'][i] = wl |
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data['wd'][i] = -1*wd |
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146 |
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if re.search("Voltage", line) and i<N: |
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batt = ysi[0] |
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data['batt'][i] = batt |
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150 |
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if re.search("Profile Location:", line): |
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sw = re.findall(r'\w+:\s(\w+)*', line) |
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154 |
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155 |
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156 |
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if re.search("^\d{2}\/\d{2}\/\d{2}", line) and len(ysi)==14 and i<N: |
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158 |
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sample_str = '%02d-%02d-%02d %02d:%02d:%02d' % tuple(ysi[0:6]) |
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160 |
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161 |
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try: |
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sample_dt = scanf_datetime(sample_str, fmt='%m-%d-%y %H:%M:%S') |
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except ValueError: |
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165 |
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try: |
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sample_dt = scanf_datetime(sample_str, fmt='%d-%m-%y %H:%M:%S') |
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except: |
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sample_dt = datetime(1970,1,1) |
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170 |
|
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if sample_dt is not None: |
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wtemp = ysi[6] |
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cond = ysi[7] |
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salin = ysi[8] |
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depth = ysi[9] |
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176 |
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ph = ysi[10] |
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turb = ysi[11] |
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chl = ysi[12] |
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do = ysi[13] |
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181 |
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data['dt'][i] = sample_dt |
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data['time'][i] = dt2es(sample_dt) |
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184 |
|
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185 |
data['wtemp'][i] = wtemp |
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data['cond'][i] = cond |
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187 |
data['salin'][i] = salin |
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data['z'][i] = -1*depth |
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189 |
|
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data['turb'][i] = turb |
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data['ph'][i] = ph |
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data['chl'][i] = chl |
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data['do'][i] = do |
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i=i+1 |
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195 |
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else: |
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print 'skipping line, ill-formed date ... ' + str(line) |
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198 |
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elif (len(ysi)>=6 and len(ysi)<14): |
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print 'skipping bad data line ... ' + str(line) |
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201 |
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203 |
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204 |
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return data |
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206 |
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def creator(platform_info, sensor_info, data): |
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210 |
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i = data['in'] |
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dt = data['dt'][i] |
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213 |
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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-automated-profiler observation', |
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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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227 |
|
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'Conventions' : 'CF-1.0; SEACOOS-CDL-v2.0', |
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229 |
|
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'format_category_code' : 'fixed-profiler-ragged', |
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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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234 |
|
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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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237 |
|
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238 |
|
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'start_date' : dt[0].strftime("%Y-%m-%d %H:%M:%S"), |
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240 |
|
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'end_date' : dt[-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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243 |
|
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'creation_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
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245 |
'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
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246 |
'process_level' : 'level1', |
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247 |
|
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248 |
|
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'_FillValue' : numpy.nan, |
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250 |
} |
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251 |
|
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var_atts = { |
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253 |
|
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254 |
'time' : {'short_name': 'time', |
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255 |
'long_name': 'Time of Profile', |
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256 |
'standard_name': 'time', |
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257 |
'units': 'seconds since 1970-1-1 00:00:00 -0', |
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258 |
'axis': 'T', |
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259 |
}, |
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260 |
'lat' : {'short_name': 'lat', |
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261 |
'long_name': 'Latitude', |
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262 |
'standard_name': 'latitude', |
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263 |
'reference':'geographic coordinates', |
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264 |
'units': 'degrees_north', |
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265 |
'valid_range':(-90.,90.), |
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266 |
'axis': 'Y', |
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267 |
}, |
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268 |
'lon' : {'short_name': 'lon', |
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269 |
'long_name': 'Longitude', |
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270 |
'standard_name': 'longitude', |
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271 |
'reference':'geographic coordinates', |
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272 |
'units': 'degrees_east', |
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273 |
'valid_range':(-180.,180.), |
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274 |
'axis': 'Y', |
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275 |
}, |
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276 |
'z' : {'short_name': 'z', |
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277 |
'long_name': 'z', |
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278 |
'standard_name': 'z', |
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279 |
'reference':'zero is surface', |
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280 |
'positive' : 'up', |
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281 |
'units': 'm', |
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282 |
'axis': 'Z', |
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283 |
}, |
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284 |
|
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285 |
'batt': {'short_name': 'batt', |
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286 |
'long_name': 'Battery', |
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287 |
'standard_name': 'battery_voltage', |
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288 |
'units': 'volts', |
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289 |
}, |
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290 |
'wd': {'short_name': 'wd', |
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291 |
'long_name': 'Water Depth', |
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292 |
'standard_name': 'water_depth', |
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293 |
'reference' : 'zero at sea-surface', |
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294 |
'positive' : 'up', |
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295 |
'units': 'm', |
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296 |
}, |
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297 |
'wl': {'short_name': 'wl', |
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298 |
'long_name': 'Water Level', |
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299 |
'standard_name': 'water_level', |
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300 |
'reference':'MSL', |
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301 |
'reference_to_MSL' : 0., |
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302 |
'reference_MSL_datum' : platform_info['mean_water_depth'], |
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303 |
'reference_MSL_datum_time_period' : platform_info['mean_water_depth_time_period'], |
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304 |
'positive' : 'up', |
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305 |
'z' : 0., |
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306 |
'units': 'm', |
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307 |
}, |
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308 |
'wtemp': {'short_name': 'wtemp', |
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309 |
'long_name': 'Water Temperature', |
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310 |
'standard_name': 'water_temperature', |
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311 |
'units': 'degrees_Celsius', |
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312 |
}, |
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313 |
'cond': {'short_name': 'cond', |
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314 |
'long_name': 'Conductivity', |
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315 |
'standard_name': 'conductivity', |
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316 |
'units': 'mS cm-1', |
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317 |
}, |
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318 |
'salin': {'short_name': 'salin', |
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319 |
'long_name': 'Salinity', |
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320 |
'standard_name': 'salinity', |
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321 |
'units': 'PSU', |
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322 |
}, |
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323 |
'turb': {'short_name': 'turb', |
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324 |
'long_name': 'Turbidity', |
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325 |
'standard_name': 'turbidity', |
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326 |
'units': 'NTU', |
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327 |
}, |
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328 |
'ph': {'short_name': 'ph', |
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329 |
'long_name': 'pH', |
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330 |
'standard_name': 'ph', |
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331 |
'units': '', |
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332 |
}, |
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333 |
'chl': {'short_name': 'chl', |
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334 |
'long_name': 'Chlorophyll', |
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335 |
'standard_name': 'chlorophyll', |
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336 |
'units': 'ug l-1', |
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337 |
}, |
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338 |
'do': {'short_name': 'do', |
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339 |
'long_name': 'Dissolved Oxygen', |
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340 |
'standard_name': 'dissolved_oxygen', |
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341 |
'units': 'mg l-1', |
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342 |
}, |
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343 |
} |
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344 |
|
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345 |
|
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346 |
dim_inits = ( |
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347 |
('ntime', NC.UNLIMITED), |
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348 |
('nlat', 1), |
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349 |
('nlon', 1), |
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350 |
) |
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351 |
|
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352 |
|
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353 |
|
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354 |
|
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355 |
|
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356 |
var_inits = ( |
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357 |
|
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358 |
('time', NC.INT, ('ntime',)), |
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359 |
('lat', NC.FLOAT, ('nlat',)), |
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360 |
('lon', NC.FLOAT, ('nlon',)), |
---|
361 |
('z', NC.FLOAT, ('ntime',)), |
---|
362 |
|
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363 |
('batt', NC.FLOAT, ('ntime',)), |
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364 |
('wd', NC.FLOAT, ('ntime',)), |
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365 |
('wl', NC.FLOAT, ('ntime',)), |
---|
366 |
|
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367 |
('wtemp', NC.FLOAT, ('ntime',)), |
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368 |
('cond', NC.FLOAT, ('ntime',)), |
---|
369 |
('salin', NC.FLOAT, ('ntime',)), |
---|
370 |
('turb', NC.FLOAT, ('ntime',)), |
---|
371 |
('ph', NC.FLOAT, ('ntime',)), |
---|
372 |
('chl', NC.FLOAT, ('ntime',)), |
---|
373 |
('do', NC.FLOAT, ('ntime',)), |
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374 |
) |
---|
375 |
|
---|
376 |
|
---|
377 |
var_data = ( |
---|
378 |
('lat', platform_info['lat']), |
---|
379 |
('lon', platform_info['lon']), |
---|
380 |
('time', data['time'][i]), |
---|
381 |
('z', data['z'][i]), |
---|
382 |
|
---|
383 |
('batt', data['batt'][i]), |
---|
384 |
('wd', data['wd'][i]), |
---|
385 |
('wl', data['wl'][i]), |
---|
386 |
|
---|
387 |
('wtemp', data['wtemp'][i]), |
---|
388 |
('cond', data['cond'][i]), |
---|
389 |
('salin', data['salin'][i]), |
---|
390 |
('turb', data['turb'][i]), |
---|
391 |
('ph', data['ph'][i]), |
---|
392 |
('chl', data['chl'][i]), |
---|
393 |
('do', data['do'][i]), |
---|
394 |
) |
---|
395 |
|
---|
396 |
return (global_atts, var_atts, dim_inits, var_inits, var_data) |
---|
397 |
|
---|
398 |
def updater(platform_info, sensor_info, data): |
---|
399 |
|
---|
400 |
|
---|
401 |
i = data['in'] |
---|
402 |
dt = data['dt'][i] |
---|
403 |
|
---|
404 |
global_atts = { |
---|
405 |
|
---|
406 |
|
---|
407 |
'end_date' : dt[-1].strftime("%Y-%m-%d %H:%M:%S"), |
---|
408 |
'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
---|
409 |
|
---|
410 |
'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
---|
411 |
} |
---|
412 |
|
---|
413 |
|
---|
414 |
|
---|
415 |
var_atts = {} |
---|
416 |
|
---|
417 |
|
---|
418 |
|
---|
419 |
|
---|
420 |
|
---|
421 |
|
---|
422 |
|
---|
423 |
|
---|
424 |
|
---|
425 |
|
---|
426 |
var_data = ( |
---|
427 |
('time', data['time'][i]), |
---|
428 |
('z', data['z'][i]), |
---|
429 |
|
---|
430 |
('batt', data['batt'][i]), |
---|
431 |
('wd', data['wd'][i]), |
---|
432 |
('wl', data['wl'][i]), |
---|
433 |
|
---|
434 |
('wtemp', data['wtemp'][i]), |
---|
435 |
('cond', data['cond'][i]), |
---|
436 |
('salin', data['salin'][i]), |
---|
437 |
('turb', data['turb'][i]), |
---|
438 |
('ph', data['ph'][i]), |
---|
439 |
('chl', data['chl'][i]), |
---|
440 |
('do', data['do'][i]), |
---|
441 |
) |
---|
442 |
|
---|
443 |
return (global_atts, var_atts, var_data) |
---|
444 |
|
---|
445 |
|
---|