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proc_sbe37_ctd.py
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#!/usr/bin/env python
# Last modified: Time-stamp: <2014-01-09 15:05:20 haines>
"""
how to parse data, and assert what data and info goes into
creating and updating monthly netcdf files
parse data ctd data collected on Seabird CTD -- SBE37
derive salinity, depth, and density using seawater.csiro toolbox
parser : sample date and time, wtemp, cond, press, (derive) depth, salin, dens
creator : lat, lon, z, time, wtemp, cond, press, depth, salin, dens
updator : time, wtemp, cond, press, depth, salin, dens
Examples
--------
>>> sensor_info = sensor_info['ctd1']
>>> (parse, create, update) = import_processors(sensor_info['process_module'])
>>> data = parse(platform_info, sensor_info, lines)
>>> create(platform_info, sensor_info, data) or
>>> update(platform_info, sensor_info, data)
Testing
-------
from raw2proc import *
cn = 'b1_config_20111112'
sensor_info = get_config(cn+'.sensor_info')
sensor_info = sensor_info['ctd1']
platform_info = get_config(cn+'.platform_info')
(parse, create, update) = import_processors(sensor_info['process_module'])
filename = '/seacoos/data/nccoos/level0/b1/ctd1/store/B1_CTD1_2011_11_12.asc'
lines = load_data(filename)
sensor_info['fn'] = filename
data = parse(platform_info, sensor_info, lines)
create(platform_info, sensor_info, data)
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):
"""
Header comments start with '*'
Last line of comments *END*
* Sea-Bird SBE37 Data File:
* FileName = C:\Documents and Settings\haines\Desktop\nc-wind 2012 CTD Recovery\B1_CTD1_3085_2012-04-07.asc
...
* S>
*END*
start time = 12 Nov 2011 15:28:43
sample interval = 360 seconds
start sample number = 1
17.4036, 4.35264, 3.521, 12 Nov 2011, 15:28:43
17.4289, 4.35624, 3.593, 12 Nov 2011, 15:34:44
17.4110, 4.35376, 3.600, 12 Nov 2011, 15:40:44
17.4106, 4.35395, 3.618, 12 Nov 2011, 15:46:44
17.3798, 4.34961, 3.515, 12 Nov 2011, 15:52:44
17.3861, 4.35033, 3.708, 12 Nov 2011, 15:58:44
17.4136, 4.35348, 3.488, 12 Nov 2011, 16:04:44
17.4269, 4.35530, 3.616, 12 Nov 2011, 16:10:44
17.4421, 4.35679, 3.612, 12 Nov 2011, 16:16:44
17.4417, 4.35679, 3.537, 12 Nov 2011, 16:22:44
... EOF
"""
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)
# tease out the header info and where it ends
# **** may want more info extracted later for data attributes (e.g. sensor coeff)
serial_number, end_idx, sample_interval_str = (None, None, None)
for idx, k in enumerate(lines[0:100]):
# serial number
m = re.search(r'^\*.*(SERIAL NO\.)\s+(\d*)', k)
if m: serial_number = m.group(2)
m = re.search(r'^\*\w+(sample interval)\s*=\s*(.*)', k)
if m: sample_interval_str = m.group(2)
m = re.search(r'^(\*END\*).*', k)
if m: end_idx = idx
# check that serial_info serial_number, sample_interval matches
# split data from header info and get how many samples (start count 3 lines past *END*)
if end_idx: lines = lines[end_idx+3:]
nsamp = len(lines)
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),
'press' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
'salin' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
'density' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
'depth' : numpy.array(numpy.ones((N,), dtype=float)*numpy.nan),
}
# sample count
i = 0
for line in lines:
# if line has weird ascii chars -- skip it and iterate to next line
csi = []
# split line
sw = re.split(',', line)
if len(sw)<=0:
print ' ... skipping line %d -- %s' % (i,line)
continue
# replace "NAN"
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[0:3]:
m = re.search(REAL_RE_STR, s)
if m:
csi.append(float(m.groups()[0]))
if len(sw)>=5:
dstr = sw[3]+' '+sw[4]
# print dstr
m = re.search('\s*(\d{2})\s*(\w{2,3})\s*(\d{4})\s*(\d{2}):(\d{2}):(\d{2}).*', dstr)
else:
print ' ... skipping line %d -- %s ' % (i,line)
continue
if m:
dstr = '%s %s %s %s:%s:%s' % m.groups()
else:
print ' ... skipping line %d -- %s ' % (i,line)
continue
if sensor_info['utc_offset']:
sample_dt = scanf_datetime(dstr, fmt='%d %b %Y %H:%M:%S') + \
timedelta(hours=sensor_info['utc_offset'])
else:
sample_dt = scanf_datetime(dstr, fmt='%d %b %Y %H:%M:%S')
# ***** TO DO: may need to adjust any offset in CTD sample time to UTC clock
# This requires knowing what UTC time is at a CTD sample
data['dt'][i] = sample_dt # sample datetime
data['time'][i] = dt2es(sample_dt) # sample time in epoch seconds
if len(csi)==3:
#
# (pg 31 SBE IMP Microcat User Manual)
# "#iiFORMAT=1 (default) Output converted to data
# date format dd mmm yyyy,
# conductivity = S/m,
# temperature precedes conductivity"
data['wtemp'][i] = csi[0] # water temperature (C)
data['cond'][i] = csi[1] # specific conductivity (S/m)
data['press'][i] = csi[2] # pressure decibars
i=i+1
else:
print ' ... skipping line %d -- %s ' % (i,line)
continue
# if re.search
# 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]
# Quality Control steps for temp, depth, and cond
# (1) within range
# (2) if not pumped
good = (-5<data['wtemp']) & (data['wtemp']<30)
bad = ~good
data['wtemp'][bad] = numpy.nan
good = (0<data['cond']) & (data['cond']<7)
bad = ~good
data['cond'][bad] = numpy.nan
# press range depends on deployment depth and instrument transducer rating
# calculate depth, salinity and density
import seawater.csiro
import seawater.constants
# seawater.constants.C3515 is units of mS/cm
# data['cond'] is units of S/m
# You have: mS cm-1
# You want: S m-1
# <S m-1> = <mS cm-1>*0.1
# <S m-1> = <mS cm-1>/10
data['depth'] = -1*seawater.csiro.depth(data['press'], platform_info['lat']) # meters
data['salin'] = seawater.csiro.salt(10*data['cond']/seawater.constants.C3515, data['wtemp'], data['press']) # psu
data['density'] = seawater.csiro.dens(data['salin'], data['wtemp'], data['press']) # kg/m^3
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': 'Depth',
'standard_name': 'depth',
'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': 'S m-1',
},
'press': {'short_name': 'press',
'long_name': 'Pressure',
'standard_name': 'water_pressure',
'units': 'decibar',
},
'depth': {'short_name': 'depth',
'long_name': 'Depth',
'standard_name': 'depth',
'reference':'zero at sea-surface',
'positive' : 'up',
'units': 'm',
'comment': 'Derived using seawater.csiro.depth(press,lat)',
},
'salin': {'short_name': 'salin',
'long_name': 'Salinity',
'standard_name': 'salinity',
'units': 'psu',
'comment': 'Derived using seawater.csiro.salt(cond/C3515,wtemp,press)',
},
'density': {'short_name': 'density',
'long_name': 'Density',
'standard_name': 'density',
'units': 'kg m-3',
'comment': 'Derived using seawater.csiro.dens0(salin,wtemp,press)',
},
}
# 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',)),
('press', NC.FLOAT, ('ntime',)),
# derived variables
('depth', NC.FLOAT, ('ntime',)),
('salin', NC.FLOAT, ('ntime',)),
('density', NC.FLOAT, ('ntime',)),
)
# var data
var_data = (
('lat', platform_info['lat']),
('lon', platform_info['lon']),
('z', sensor_info['nominal_depth']),
#
('time', data['time'][i]),
#
('wtemp', data['wtemp'][i]),
('cond', data['cond'][i]),
('press', data['press'][i]),
# derived variables
('depth', data['depth'][i]),
('salin', data['salin'][i]),
('density', data['density'][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]),
('wtemp', data['wtemp'][i]),
('cond', data['cond'][i]),
('press', data['press'][i]),
# derived variables
('depth', data['depth'][i]),
('salin', data['salin'][i]),
('density', data['density'][i]),
)
return (global_atts, var_atts, var_data)
#