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proc_nortek_wpa_adcp.py
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#!/usr/bin/env python
# Last modified: Time-stamp: <2010-12-09 16:13:37 haines>
"""
how to parse data, and assert what data and info goes into
creating and updating monthly netcdf files
RDI/Wavesmon processed adcp current profile data
parser : sample date and time, currents, water temperature, pressure and water_depth
creator : lat, lon, z, time, ens, u, v, w, water_depth, water_temp (at tranducer depth), pressure
updator : time, ens, u, v, w, water_depth, water_temp (at tranducer depth), pressure
Examples
--------
>> (parse, create, update) = load_processors('proc_rdi_logdata_adcp')
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 *
import seawater
now_dt = datetime.utcnow()
now_dt.replace(microsecond=0)
def parser(platform_info, sensor_info, lines):
"""
parse and assign ocean profile current data from Nortek AWAC ADCP Data
Notes
-----
1. This parser requires date/time be parsed from .wap for each to
get sig_wave_ht for determining depth of each bin and surface mask
and check time same as in .wpa file.
2. multiple profiles in one file separated by header w/time,
pitch, roll, heading, ducer pressure, bottom temp, top bin#
bottom bin# (??). The profile data is several lines one for
each bin.
MM DD YYYY HH MM SS ERR STATUS BATT SNDSPD HDG PITCH ROLL PRESS WTEMP ?? ?? TBIN BBIN
07 31 2008 23 54 00 0 48 18.2 1525.8 270.1 -2.4 0.2 10.503 21.64 0 0 3 34
1 0.9 0.071 24.04 0.029 0.065 -0.058 123 126 124
2 1.4 0.089 342.38 -0.027 0.085 -0.057 110 111 113
3 1.9 0.065 310.03 -0.050 0.042 -0.063 102 104 104
4 2.4 0.063 46.93 0.046 0.043 -0.045 93 95 99
5 2.9 0.049 355.33 -0.004 0.049 -0.047 87 89 92
...
NBIN DEPTH SPEED DIR U V W E1? E2? E3?
32 16.4 0.184 331.76 -0.087 0.162 -0.162 26 25 27
33 16.9 0.137 288.70 -0.130 0.044 -0.181 26 24 26
34 17.4 0.070 32.78 0.038 0.059 -0.248 25 25 26
3. not sure if depth column is hab or down from surface?
"""
# get sample datetime from filename
fn = sensor_info['fn']
sample_dt_start = filt_datetime(fn)
nbins = sensor_info['nbins'] # Number of bins in data
nbursts = len(lines)/(nbins+1)
data = {
'dt' : numpy.array(numpy.ones((nbursts,), dtype=object)*numpy.nan),
'time' : numpy.array(numpy.ones((nbursts,), dtype=long)*numpy.nan),
'z' : numpy.array(numpy.ones((nbins,), dtype=float)*numpy.nan),
'u' : numpy.array(numpy.ones((nbursts,nbins), dtype=float)*numpy.nan),
'v' : numpy.array(numpy.ones((nbursts,nbins), dtype=float)*numpy.nan),
'w' : numpy.array(numpy.ones((nbursts,nbins), dtype=float)*numpy.nan),
'e1' : numpy.array(numpy.ones((nbursts,nbins), dtype=int)*numpy.nan),
'e2' : numpy.array(numpy.ones((nbursts,nbins), dtype=int)*numpy.nan),
'e3' : numpy.array(numpy.ones((nbursts,nbins), dtype=int)*numpy.nan),
'wd' : numpy.array(numpy.ones((nbursts), dtype=float)*numpy.nan),
'wl' : numpy.array(numpy.ones((nbursts), dtype=float)*numpy.nan),
'water_temp' : numpy.array(numpy.ones((nbursts), dtype=float)*numpy.nan),
'pressure' : numpy.array(numpy.ones((nbursts), dtype=float)*numpy.nan),
}
# these items can also be teased out of raw adcp but for now get from config file
th = sensor_info['transducer_ht'] # Transducer height above bottom (meters)
bh = sensor_info['blanking_ht'] # Blanking height above Transducer (meters)
bin_size = sensor_info['bin_size'] # Bin Size (meters)
# compute height for each bin above the bottom
bins = numpy.arange(1,nbins+1)
# bin_habs = (bins*bin_size+bin_size/2)+th+bh
bin_habs = (bins*bin_size+bin_size/2)+th+bh
# added by SH -- 15 Oct 2008
# raw2proc:ticket:27 adjust bin_habs along beam to nadir
# Nortek awac beam angle is fixed at 25 deg
# adjustment is cos(25 deg) (which is approx .90*height)
# -------------------
# bin_habs = (bin_habs*numpy.cos(25.*numpy.pi/180))
# -------------------
# commented out by SH -- 18 Aug 2010
# This does not apply to habs provided in .wpa. They
# are adjusted for beam angle in ascii output.
iaboveblank = bin_habs > th+bh+(bin_size)
# current profile count
i = 0
wpa = []
for line in lines:
wpa = []
# split line and parse float and integers
sw = re.split(' ', line)
for s in sw:
m = re.search(REAL_RE_STR, s)
if m:
wpa.append(float(m.groups()[0]))
if len(wpa)==19:
# get sample datetime from data
sample_str = '%02d-%02d-%4d %02d:%02d:%02d' % tuple(wpa[0:6])
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='%m-%d-%Y %H:%M:%S')
# these items can also be teased out of raw adcp but for now get from config file
# th = sensor_info['transducer_ht'] # Transducer height above bottom (meters)
error_code = int(wpa[6])
status_code = int(wpa[7])
battery_voltage = wpa[8] # volts
sound_speed = wpa[9] # m/s
heading = wpa[10] # deg
pitch = wpa[11] # deg
roll = wpa[12] # deg
pressure = wpa[13] # dbar
# pressure (dbar) converted to water depth
wd = th + seawater.depth(pressure, platform_info['lat']) # m
temperature = wpa[14] # deg C
start_bin = int(wpa[17]) # first good bin from transducer (?)
wpa_nbins = int(wpa[18]) # Number of bins
# check this is same as in sensor_info
# initialize for new profile
hab = numpy.ones(nbins)*numpy.nan
spd = numpy.ones(nbins)*numpy.nan
dir = numpy.ones(nbins)*numpy.nan
u = numpy.ones(nbins)*numpy.nan
v = numpy.ones(nbins)*numpy.nan
w = numpy.ones(nbins)*numpy.nan
e1 = numpy.array(numpy.ones((nbins), dtype=int)*numpy.nan)
e2 = numpy.array(numpy.ones((nbins), dtype=int)*numpy.nan)
e3 = numpy.array(numpy.ones((nbins), dtype=int)*numpy.nan)
elif len(wpa)==10:
# current profile data at each bin
bin_number = wpa[0]
j = wpa[0]-1
# print j
hab[j] = wpa[1]
spd[j] = wpa[2] # m/s
dir[j] = wpa[3] # deg N
u[j] = wpa[4] # m/s
v[j] = wpa[5] # m/s
w[j] = wpa[6] # m/s
e1[j] = int(wpa[7]) # echo dB ??
e2[j] = int(wpa[8]) #
e3[j] = int(wpa[9]) #
# ibad = (current_spd==-32768) | (current_dir==-32768)
# current_spd[ibad] = numpy.nan
# current_dir[ibad] = numpy.nan
# if done reading profile, just read data for last bin
if bin_number==nbins:
# compute water mask
# if positive is up, in water is less than zero depth
bin_depths = (bin_habs)-(wd)
iwater = bin_depths+bin_size/2 < 0
iwater = iwater*iaboveblank
# use nominal water depth (MSL) averaged from full pressure record
# this should be checked/recalulated every so often
z = bin_habs+platform_info['mean_water_depth']
data['dt'][i] = sample_dt # sample datetime
data['time'][i] = dt2es(sample_dt) # sample time in epoch seconds
data['z'] = z
data['wd'][i] = -1*wd
data['wl'][i] = platform_info['mean_water_depth'] - (-1*wd)
data['water_temp'][i] = temperature
data['pressure'][i] = pressure
data['u'][i][iwater] = u[iwater]
data['v'][i][iwater] = v[iwater]
data['w'][i][iwater] = w[iwater]
data['e1'][i] = e1
data['e2'][i] = e2
data['e3'][i] = e3
# ready for next burst
i = i+1
# if j+1==nbins
# if len(wpa)==19 elif ==10
# for line
return data
def creator(platform_info, sensor_info, data):
#
#
title_str = sensor_info['description']+' at '+ platform_info['location']
if 'mean_water_depth' in platform_info.keys():
msl_str = platform_info['mean_water_depth']
else:
msl_str = 'None'
if 'mean_water_depth_time_period' in platform_info.keys():
msl_tp_str = platform_info['mean_water_depth_time_period']
else:
msl_tp_str = 'None'
global_atts = {
'title' : title_str,
'institution' : 'University 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://nccoos.unc.edu',
'contact' : 'Sara Haines ([email protected])',
#
'source' : 'fixed-profiler (acoustic doppler) 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-profiler',
'institution_code' : platform_info['institution'],
'platform_code' : platform_info['id'],
'package_code' : sensor_info['id'],
# institution specific
'project' : 'North Carolina Coastal Ocean Observing System (NCCOOS)',
'project_url' : 'http://nccoos.unc.edu',
# 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"),
#
'mean_water_depth' : msl_str,
'mean_water_depth_time_period' : msl_tp_str,
#
'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' : numpy.nan,
}
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 mean-sea-level',
'positive' : 'up',
'units': 'm',
'axis': 'Z',
},
# data variables
'u': {'short_name' : 'u',
'long_name': 'East/West Component of Current',
'standard_name': 'eastward_current',
'units': 'm s-1',
'reference': 'clockwise from True East',
},
'v': {'short_name' : 'v',
'long_name': 'North/South Component of Current',
'standard_name': 'northward_current',
'units': 'm s-1',
'reference': 'clockwise from True North',
},
'w': {'short_name' : 'w',
'long_name': 'Vertical Component of Current',
'standard_name': 'upward_current',
'units': 'm s-1',
'reference': 'clockwise from True North',
},
'e1': {'short_name' : 'e1',
'long_name': 'Echo Beam 1 (??)',
'standard_name': 'beam_echo',
'units': 'dB',
},
'e2': {'short_name' : 'e2',
'long_name': 'Echo Beam 2 (??)',
'standard_name': 'beam_echo',
'units': 'dB',
},
'e3': {'short_name' : 'e3',
'long_name': 'Echo Beam 3 (??)',
'standard_name': 'beam_echo',
'units': 'dB',
},
'wd': {'short_name': 'wd',
'long_name': 'Water Depth',
'standard_name': 'water_depth',
'reference':'zero at surface',
'positive' : 'up',
'units': 'm',
},
'wl': {'short_name': 'wl',
'long_name': 'Water Level',
'standard_name': 'water_level',
'reference':'MSL',
'reference_to_MSL' : 0.,
'reference_MSL_datum' : platform_info['mean_water_depth'],
'reference_MSL_datum_time_period' : platform_info['mean_water_depth_time_period'],
'positive' : 'up',
'z' : 0.,
'units': 'm',
},
'pressure': {'short_name': 'p',
'long_name': 'Pressure',
'standard_name': 'pressure',
'units': 'dbar',
},
'water_temp': {'short_name': 'wtemp',
'long_name': 'Water Temperature at Transducer',
'standard_name': 'water_temperature',
'units': 'deg_C',
},
}
# dimension names use tuple so order of initialization is maintained
dim_inits = (
('ntime', NC.UNLIMITED),
('nlat', 1),
('nlon', 1),
('nz', sensor_info['nbins'])
)
# 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
('u', NC.FLOAT, ('ntime', 'nz')),
('v', NC.FLOAT, ('ntime', 'nz')),
('w', NC.FLOAT, ('ntime', 'nz')),
('e1', NC.INT, ('ntime', 'nz')),
('e2', NC.INT, ('ntime', 'nz')),
('e3', NC.INT, ('ntime', 'nz')),
('wd', NC.FLOAT, ('ntime',)),
('wl', NC.FLOAT, ('ntime',)),
('pressure', NC.FLOAT, ('ntime',)),
('water_temp', 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', data['z']),
#
('time', data['time'][i]),
('u', data['u'][i]),
('v', data['v'][i]),
('w', data['w'][i]),
('e1', data['e1'][i]),
('e2', data['e2'][i]),
('e3', data['e3'][i]),
('wd', data['wd'][i]),
('wl', data['wl'][i]),
('pressure', data['pressure'][i]),
('water_temp', data['water_temp'][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 = {
# 'u': {'max': max(data.u),
# 'min': min(data.v),
# },
# 'v': {'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]),
('u', data['u'][i]),
('v', data['v'][i]),
('w', data['w'][i]),
('e1', data['e1'][i]),
('e2', data['e2'][i]),
('e3', data['e3'][i]),
('wd', data['wd'][i]),
('wl', data['wl'][i]),
('pressure', data['pressure'][i]),
('water_temp', data['water_temp'][i]),
)
return (global_atts, var_atts, var_data)
#