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proc_cr10x_wq_v1.py
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#!/usr/bin/env python
# Last modified: Time-stamp: <2010-12-09 16:14:19 haines>
"""
how to parse data, and assert what data and info goes into
creating and updating monthly netcdf files
parse data water quality data from ysi 6600 V2
collected on Campbell Scientific DataLogger (loggernet) (csi)
parser : sample date and time,
water temperature, conductivity, pH, dissolved oxygen, turbidity, and system battery
creator : lat, lon, z, time, wtemp, cond, ph, turb, do_sat, do_mg, battvolts
updator : time, wtemp, cond, ph, turb, do_sat, do_mg, battvolts
Examples
--------
>> (parse, create, update) = load_processors('proc_csi_wq_v2')
or
>> si = get_config(cn+'.sensor_info')
>> (parse, create, update) = load_processors(si['adcp']['proc_module'])
>> lines = load_data(filename)
>> data = parse(platform_info, sensor_info, lines)
>> create(platform_info, sensor_info, data) or
>> update(platform_info, sensor_info, data)
"""
from raw2proc import *
from procutil import *
from ncutil import *
now_dt = datetime.utcnow()
now_dt.replace(microsecond=0)
def parser(platform_info, sensor_info, lines):
"""
From FSL (CSI datalogger program files):
"""
import numpy
from datetime import datetime
from time import strptime
import math
# get sample datetime from filename
fn = sensor_info['fn']
sample_dt_start = filt_datetime(fn)
# how many samples
nsamp = 0
for line in lines:
m=re.search("^1,", line)
if m:
nsamp=nsamp+1
N = nsamp
data = {
'dt' : numpy.array(numpy.ones((N,), dtype=object)*numpy.nan),
'time' : numpy.array(numpy.ones((N,), dtype=long)*numpy.nan),
'wtemp' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
'cond' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
'do_sat' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
'do_mg' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
'ph' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
'turb' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
'battvolts' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
}
# sample count
i = 0
for line in lines:
csi = []
# split line and parse float and integers
m=re.search("^1,", line)
if m:
sw = re.split(',', line)
else:
continue
for s in sw:
m = re.search(REAL_RE_STR, s)
if m:
csi.append(float(m.groups()[0]))
if len(csi)==14:
# get sample datetime from data
yyyy = csi[1]
yday = csi[2]
(MM, HH) = math.modf(csi[3]/100.)
MM = math.ceil(MM*100.)
if (HH == 24):
yday=yday+1
HH = 0.
sample_str = '%04d-%03d %02d:%02d' % (yyyy, yday, HH, MM)
# if sensor_info['utc_offset']:
# sample_dt = scanf_datetime(sample_str, fmt='%m-%d-%Y %H:%M:%S') + \
# timedelta(hours=sensor_info['utc_offset'])
# else:
sample_dt = scanf_datetime(sample_str, fmt='%Y-%j %H:%M')
data['dt'][i] = sample_dt # sample datetime
data['time'][i] = dt2es(sample_dt) # sample time in epoch seconds
#
data['wtemp'][i] = csi[4] # water temperature (C)
data['cond'][i] = csi[5] # specific conductivity (mS/cm)
data['do_sat'][i] = csi[6] # saturated dissolved oxygen (% air sat)
data['do_mg'][i] = csi[7] # dissolved oxygen (mg/l)
data['ph'][i] = csi[8] # ph
data['turb'][i] = csi[9] # turbidity (NTU)
# no adcp's prior to March 2005
# data['sontek_wl'][i] = csi[5] # sontek water level (ft)
# data['sontek_flow'][i] = csi[6] # sontek flow (cfs)
# data['press_wl'][i] = csi[10] # pressure water level (ft ?? or inches)
# data['rain'][i] = csi[11] # 15 sec rain count ??
# data['press_flow'][i] = csi[12] # flow flow (cfs)
data['battvolts'][i] = csi[13] # battery (volts)
i=i+1
# if-elif
# for line
# check that no data[dt] is set to Nan or anything but datetime
# keep only data that has a resolved datetime
keep = numpy.array([type(datetime(1970,1,1)) == type(dt) for dt in data['dt'][:]])
if keep.any():
for param in data.keys():
data[param] = data[param][keep]
return data
def creator(platform_info, sensor_info, data):
#
#
title_str = sensor_info['description']+' at '+ platform_info['location']
global_atts = {
'title' : title_str,
'institution' : 'Unversity of North Carolina at Chapel Hill (UNC-CH)',
'institution_url' : 'http://nccoos.unc.edu',
'institution_dods_url' : 'http://nccoos.unc.edu',
'metadata_url' : 'http://nccoos.unc.edu',
'references' : 'http://ehs.unc.edu',
'contact' : 'Sara Haines ([email protected])',
'station_owner' : 'Environment, Health, and Safety Office',
'station_contact' : 'Sharon Myers ([email protected])',
#
'source' : 'fixed-point observation',
'history' : 'raw2proc using ' + sensor_info['process_module'],
'comment' : 'File created using pycdf'+pycdfVersion()+' and numpy '+pycdfArrayPkg(),
# conventions
'Conventions' : 'CF-1.0; SEACOOS-CDL-v2.0',
# SEACOOS CDL codes
'format_category_code' : 'fixed-point',
'institution_code' : platform_info['institution'],
'platform_code' : platform_info['id'],
'package_code' : sensor_info['id'],
# institution specific
'project' : 'Environment, Health, and Safety (EHS)',
'project_url' : 'http://ehs.unc.edu/environment/water_quality',
# timeframe of data contained in file yyyy-mm-dd HH:MM:SS
# first date in monthly file
'start_date' : data['dt'][0].strftime("%Y-%m-%d %H:%M:%S"),
# last date in monthly file
'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"),
'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
#
'creation_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
'process_level' : 'level1',
#
# must type match to data (e.g. fillvalue is real if data is real)
'_FillValue' : -99999.,
}
var_atts = {
# coordinate variables
'time' : {'short_name': 'time',
'long_name': 'Time',
'standard_name': 'time',
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC
'axis': 'T',
},
'lat' : {'short_name': 'lat',
'long_name': 'Latitude',
'standard_name': 'latitude',
'reference':'geographic coordinates',
'units': 'degrees_north',
'valid_range':(-90.,90.),
'axis': 'Y',
},
'lon' : {'short_name': 'lon',
'long_name': 'Longitude',
'standard_name': 'longitude',
'reference':'geographic coordinates',
'units': 'degrees_east',
'valid_range':(-180.,180.),
'axis': 'Y',
},
'z' : {'short_name': 'z',
'long_name': 'Height',
'standard_name': 'height',
'reference':'zero at sea-surface',
'positive' : 'up',
'units': 'm',
'axis': 'Z',
},
# data variables
'wtemp': {'short_name': 'wtemp',
'long_name': 'Water Temperature',
'standard_name': 'water_temperature',
'units': 'degrees_Celsius',
},
'cond': {'short_name': 'cond',
'long_name': 'Conductivity',
'standard_name': 'conductivity',
'units': 'mS cm-1',
},
'turb': {'short_name': 'turb',
'long_name': 'Turbidity',
'standard_name': 'turbidity',
'units': 'NTU',
},
'ph': {'short_name': 'ph',
'long_name': 'pH',
'standard_name': 'ph',
'units': '',
},
'do_mg': {'short_name': 'do_mg',
'long_name': 'ROX Optical Dissolved Oxygen, Derived Concentration',
'standard_name': 'dissolved_oxygen_concentration',
'units': 'mg l-1',
},
'do_sat': {'short_name': 'do_sat',
'long_name': 'ROX Optical Dissolved Oxygen, Percent of Air Saturation',
'standard_name': 'dissolved_oxygen_relative_to_air_saturation',
'units': '%',
},
'battvolts': {'short_name': 'battery',
'long_name': 'Battery Voltage of the Station',
'standard_name': 'battery_voltage',
'units': 'volts',
},
}
# dimension names use tuple so order of initialization is maintained
dim_inits = (
('ntime', NC.UNLIMITED),
('nlat', 1),
('nlon', 1),
('nz', 1),
)
# using tuple of tuples so order of initialization is maintained
# using dict for attributes order of init not important
# use dimension names not values
# (varName, varType, (dimName1, [dimName2], ...))
var_inits = (
# coordinate variables
('time', NC.INT, ('ntime',)),
('lat', NC.FLOAT, ('nlat',)),
('lon', NC.FLOAT, ('nlon',)),
('z', NC.FLOAT, ('nz',)),
# data variables
('wtemp', NC.FLOAT, ('ntime',)),
('cond', NC.FLOAT, ('ntime',)),
('turb', NC.FLOAT, ('ntime',)),
('ph', NC.FLOAT, ('ntime',)),
('do_mg', NC.FLOAT, ('ntime',)),
('do_sat', NC.FLOAT, ('ntime',)),
('battvolts', NC.FLOAT, ('ntime',)),
)
# subset data only to month being processed (see raw2proc.process())
i = data['in']
# var data
var_data = (
('lat', platform_info['lat']),
('lon', platform_info['lon']),
('z', platform_info['altitude']),
#
('time', data['time'][i]),
#
('wtemp', data['wtemp'][i]),
('cond', data['cond'][i]),
('turb', data['turb'][i]),
('ph', data['ph'][i]),
('do_mg', data['do_mg'][i]),
('do_sat', data['do_sat'][i]),
('battvolts', data['battvolts'][i]),
)
return (global_atts, var_atts, dim_inits, var_inits, var_data)
def updater(platform_info, sensor_info, data):
#
global_atts = {
# update times of data contained in file (yyyy-mm-dd HH:MM:SS)
# last date in monthly file
'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"),
'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
#
'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
}
# data variables
# update any variable attributes like range, min, max
var_atts = {}
# var_atts = {
# 'wtemp': {'max': max(data.u),
# 'min': min(data.v),
# },
# 'cond': {'max': max(data.u),
# 'min': min(data.v),
# },
# }
# subset data only to month being processed (see raw2proc.process())
i = data['in']
# data
var_data = (
('time', data['time'][i]),
('wtemp', data['wtemp'][i]),
('cond', data['cond'][i]),
('turb', data['turb'][i]),
('ph', data['ph'][i]),
('do_mg', data['do_mg'][i]),
('do_sat', data['do_sat'][i]),
('battvolts', data['battvolts'][i]),
)
return (global_atts, var_atts, var_data)
#