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proc_cr1000_gps.py
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#!/usr/bin/env python
# Last modified: Time-stamp: <2014-08-27 17:22:15 haines>
"""
how to parse data, and assert what data and info goes into
creating and updating monthly netcdf files
parse gps data collected on Campbell Scientific DataLogger (loggernet) (csi)
parser : sample date and time,
creator : lat, lon, z, time,
updator : time,
Examples
--------
>> (parse, create, update) = load_processors('proc_csi_adcp_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):
"""
Example gps data
"TOA5","CR1000_B1","CR1000","55356","CR1000.Std.25","CPU:UNC CHill_20_Buoy1_Revision2013.CR1","59421","GPS_1Hr"
"TIMESTAMP","RECORD","GPSParseStr(1)","GPSParseStr(2)","GPSParseStr(3)","GPSParseStr(4)","GPSParseStr(5)","GPSParseStr(6)","GPSParseStr(7)","GPSParseStr(8)","GPSParseStr(9)","GPSParseStr(10)","GPSParseStr(11)","GPSParseStr(12)"
"TS","RN","","","","","","","","","","","",""
"","","Smp","Smp","Smp","Smp","Smp","Smp","Smp","Smp","Smp","Smp","Smp","Smp"
"2014-02-01 00:52:01",77,"$GPRMC","005155","A","3443.3939","N","07645.1690","W","000.0","000.0","010214","010.0","W"
"2014-02-01 01:52:01",78,"$GPRMC","015155","A","3443.3926","N","07645.1688","W","000.0","000.0","010214","010.0","W"
"2014-02-01 02:52:01",79,"$GPRMC","025155","A","3443.3927","N","07645.1686","W","000.2","000.0","010214","010.0","W"
"2014-02-01 03:52:01",80,"$GPRMC","035155","A","3443.3931","N","07645.1685","W","000.0","000.0","010214","010.0","W"
"2014-02-01 04:52:01",81,"$GPRMC","045155","A","3443.3943","N","07645.1687","W","000.0","000.0","010214","010.0","W"
"2014-02-01 05:52:01",82,"$GPRMC","055155","A","3443.3934","N","07645.1695","W","000.0","000.0","010214","010.0","W"
"2014-02-01 06:52:01",83,"$GPRMC","065155","A","3443.3930","N","07645.1680","W","000.0","000.0","010214","010.0","W"
RMC - NMEA has its own version of essential gps pvt (position, velocity, time) data. It is called RMC, The Recommended Minimum, which will look similar to:
$GPRMC,123519,A,4807.038,N,01131.000,E,022.4,084.4,230394,003.1,W*6A
Where:
RMC Recommended Minimum sentence C
123519 Fix taken at 12:35:19 UTC
A Status A=active or V=Void.
4807.038,N Latitude 48 deg 07.038' N
01131.000,E Longitude 11 deg 31.000' E
022.4 Speed over the ground in knots
084.4 Track angle in degrees True
230394 Date - 23rd of March 1994
003.1,W Magnetic Variation
*6A The checksum data, always begins with *
"""
import numpy
from datetime import datetime
from time import strptime
# get sample datetime from filename
fn = sensor_info['fn']
sample_dt_start = filt_datetime(fn)
# how many samples (don't count header 4 lines)
nsamp = len(lines[4:])
N = nsamp
data = {
'dt' : numpy.array(numpy.ones((N,), dtype=object)*numpy.nan),
'time' : numpy.array(numpy.ones((N,), dtype=long)*numpy.nan),
'gps_dt' : numpy.array(numpy.ones((N,), dtype=object)*numpy.nan), # gps fix datetime object
'gps_time' : numpy.array(numpy.ones((N,), dtype=long)*numpy.nan), # gps fix time (epoch secs)
'gps_active' : numpy.array(numpy.ones((N,), dtype=int)*numpy.nan), # quality of fix (1 == Active, 0 == Void)
'gps_lat' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), # lat of fix
'gps_lon' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), # lon of fix
'gps_mvar' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), # magnetic variation at fix
'gps_sog' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), # speed over ground (kts)
'gps_cog' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan), # course over ground or track deg True
}
# sample count
i = 0
for line in lines[4:]:
csi = []
# split line
sw = re.split(',', line)
if len(sw)<=0:
print ' ... skipping line %d ' % (i,)
continue
# replace any "NAN" text with a number
for index, s in enumerate(sw):
m = re.search(NAN_RE_STR, s)
if m:
sw[index] = '-99999'
# parse date-time, and all other float and integers
# for s in sw[1:]:
# m = re.search(REAL_RE_STR, s)
# if m:
# csi.append(float(m.groups()[0]))
if sensor_info['utc_offset']:
sample_dt = scanf_datetime(sw[0], fmt='"%Y-%m-%d %H:%M:%S"') + \
timedelta(hours=sensor_info['utc_offset'])
else:
sample_dt = scanf_datetime(sw[0], fmt='"%Y-%m-%d %H:%M:%S"')
data['dt'][i] = sample_dt # sample datetime
data['time'][i] = dt2es(sample_dt) # sample time in epoch seconds
if len(sw)==14:
# remove all the double quotes (")
for index, str in enumerate(sw):
sw[index] = re.sub('\"', '', str)
# >>> sw
# ['2014-02-01 00:52:01', '77', '$GPRMC', '005155', 'A',
# '3443.3939', 'N', '07645.1690', 'W', '000.0', '000.0', '010214', '010.0', 'W']
# quality of fix
if sw[4] != 'A':
data['gps_active'][i] = 0
print '... Invalid GPS Fix -- skipping line %d' % (i,)
continue
gps_dt_str = sw[11] + ' ' + sw[3] # ddmmyy HHMMSS or ddmmyy HHMMSS.f
gps_dt = scanf_datetime(gps_dt_str, fmt='%d%m%y %H%M%S')
if gps_dt == None:
gps_dt = scanf_datetime(gps_dt_str, fmt='%d%m%y %H%M%S.%f')
gps_dt.replace(microsecond=0)
data['gps_dt'][i] = gps_dt # gps fix datetime object
data['gps_time'][i] = dt2es(gps_dt) # gps fix time (epoch secs)
# lat of fix
m = re.search('^(\d{2})(\d+\.\d+)', sw[5])
if m:
latdeg = float(m.groups()[0])
latmin = float(m.groups()[1])
if sw[6]=='N':
data['gps_lat'][i]=latdeg + latmin/60
else:
data['gps_lat'][i]=-1*(latdeg + latmin/60)
# lon of fix
m = re.search('^(\d{3})(\d+\.\d+)', sw[7])
if m:
londeg = float(m.groups()[0])
lonmin = float(m.groups()[1])
if sw[8]=='E':
data['gps_lon'][i]=londeg + lonmin/60
else:
data['gps_lon'][i]=-1*(londeg + lonmin/60)
# magnetic variation at fix
if sw[13]=='E':
data['gps_mvar'][i] = float(sw[12])
else:
data['gps_mvar'][i] = -1*float(sw[12])
# speed over ground (kts)
data['gps_sog'][i] = float(sw[9])
# course over ground or track (deg True N)
data['gps_cog'][i] = float(sw[10])
i=i+1
else:
print ' ... skipping line %d -- %s ' % (i,line)
continue
# if re.search
# for line
# return the -99999 back into Nan's
# for vn in ['gps_lat', 'gps_lon', 'gps_mvar', 'gps_cog', 'gps_sog']:
# bad = data[vn]==-99999
# data[vn][bad] = numpy.nan
# or if GPS has no fix and resets to (0,0) coordinates
for vn in ['gps_lat', 'gps_lon']:
bad = data[vn]==0
data[vn][bad] = numpy.nan
# 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):
#
#
# subset data only to month being processed (see raw2proc.process())
i = data['in']
title_str = sensor_info['description']+' at '+ platform_info['location']
global_atts = {
'title' : title_str,
'institution' : platform_info['institution'],
'institution_url' : platform_info['institution_url'],
'institution_dods_url' : platform_info['institution_dods_url'],
'metadata_url' : platform_info['metadata_url'],
'references' : platform_info['references'],
'contact' : platform_info['contact'],
#
'source' : platform_info['source']+' '+sensor_info['source'],
'history' : 'raw2proc using ' + sensor_info['process_module'],
'comment' : 'File created using pycdf'+pycdfVersion()+' and numpy '+pycdfArrayPkg(),
# conventions
'Conventions' : platform_info['conventions'],
# SEACOOS CDL codes
'format_category_code' : platform_info['format_category_code'],
'institution_code' : platform_info['institution_code'],
'platform_code' : platform_info['id'],
'package_code' : sensor_info['id'],
# institution specific
'project' : platform_info['project'],
'project_url' : platform_info['project_url'],
# timeframe of data contained in file yyyy-mm-dd HH:MM:SS
# first date in monthly file
'start_date' : data['dt'][i][0].strftime("%Y-%m-%d %H:%M:%S"),
# last date in monthly file
'end_date' : data['dt'][i][-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': 'Altitude',
'standard_name': 'altitude',
'reference':'zero at mean sea level',
'positive' : 'up',
'units': 'm',
'axis': 'Z',
},
# data variables
'gps_time' : {'short_name': 'gps_time',
'long_name': 'GPS Time',
'standard_name': 'time',
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC
},
'gps_lat' : {'short_name': 'gps_lat',
'long_name': 'GPS Latitude',
'standard_name': 'latitude',
'reference':'geographic coordinates',
'units': 'degrees_north',
'valid_range':(-90.,90.),
},
'gps_lon' : {'short_name': 'gps_lon',
'long_name': 'GPS Longitude',
'standard_name': 'longitude',
'reference':'geographic coordinates',
'units': 'degrees_east',
'valid_range':(-180.,180.),
},
'gps_mvar' : {'short_name': 'gps_mvar',
'long_name': 'GPS Magnetic Variation',
'standard_name': 'magnetic_variation',
'units': 'degrees',
},
'gps_active' : {'short_name': 'gps_active',
'long_name': 'GPS Quality of Fix (active=1 or void=0)',
'standard_name': '',
'units': '',
},
'gps_sog' : {'short_name': 'gps_sog',
'long_name': 'GPS Speed Over Ground',
'standard_name': 'speed',
'units': 'knots',
},
'gps_cog' : {'short_name': 'gps_cog',
'long_name': 'GPS course Over Ground',
'reference' : 'clockwise from True North',
'standard_name': 'direction',
'units': 'degrees',
},
}
# 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
('gps_time', NC.INT, ('ntime',)),
('gps_lat', NC.FLOAT, ('ntime',)),
('gps_lon', NC.FLOAT, ('ntime',)),
('gps_mvar', NC.FLOAT, ('ntime',)),
('gps_active', NC.FLOAT, ('ntime',)),
('gps_sog', NC.FLOAT, ('ntime',)),
('gps_cog', 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]),
#
('gps_time', data['gps_time'][i]),
('gps_lat', data['gps_lat'][i]),
('gps_lon', data['gps_lon'][i]),
('gps_active', data['gps_active'][i]),
('gps_sog', data['gps_sog'][i]),
('gps_cog', data['gps_cog'][i]),
)
return (global_atts, var_atts, dim_inits, var_inits, var_data)
def updater(platform_info, sensor_info, data):
#
# subset data only to month being processed (see raw2proc.process())
i = data['in']
global_atts = {
# update times of data contained in file (yyyy-mm-dd HH:MM:SS)
# last date in monthly file
'end_date' : data['dt'][i][-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),
# },
# }
# data
var_data = (
('time', data['time'][i]),
#
('gps_time', data['gps_time'][i]),
('gps_lat', data['gps_lat'][i]),
('gps_lon', data['gps_lon'][i]),
('gps_active', data['gps_active'][i]),
('gps_sog', data['gps_sog'][i]),
('gps_cog', data['gps_cog'][i]),
)
return (global_atts, var_atts, var_data)
#