/
proc_mini_andi_current.py
579 lines (541 loc) · 25.8 KB
/
proc_mini_andi_current.py
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"""
Parse data and assert what data creates and updates monthly NetCDF files.
Spongenet mini_andi current parameters sponge data.
"""
import math
import numpy as n
import pycdf
import datetime
import procutil
from spongenet.parse import Data
nowDt = datetime.datetime.utcnow().replace(microsecond=0)
def parser(platform_info, sensor_info, lines):
"""
Parse and assign sponge data from XML file.
"""
_data = Data(''.join(lines))
# Each Device tag represents a time sample.
num_samples = len(_data.devices)
data = {
'dt' : n.array(n.ones((num_samples,)) * n.nan, dtype=object),
'time' : n.array(n.ones((num_samples,)) * n.nan, dtype=long),
'pdt' : n.array(n.ones((num_samples,)) * n.nan, dtype=object),
'ptime' : n.array(n.ones((num_samples,)) * n.nan, dtype=long),
'ds' : n.array(n.ones((num_samples,)) * n.nan, dtype=object),
'session' : n.array(n.ones((num_samples,)) * n.nan, dtype=long),
'pds' : n.array(n.ones((num_samples,)) * n.nan, dtype=object),
'psession' : n.array(n.ones((num_samples,)) * n.nan, dtype=long),
'record' : n.array(n.ones((num_samples,)) * n.nan, dtype=int),
'status' : n.array(n.ones((num_samples,)) * n.nan, dtype=int),
'pstatus' : n.array(n.ones((num_samples,)) * n.nan, dtype=int),
'abs_speed' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'direction' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'v' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'u' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'heading' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'tiltx' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'tilty' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'std_speed' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'strength' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'pings' : n.array(n.ones((num_samples,)) * n.nan, dtype=int),
}
for (sample_index, sample) in enumerate(_data.devices):
# sample time at the platform
dt = {'month' : int(sample['time'][5:7]),
'day' : int(sample['time'][8:10]),
'year' : int(sample['time'][0:4]),
'hour' : int(sample['time'][11:13]),
'min' : int(sample['time'][14:16]),
'sec' : int(sample['time'][17:19]),
}
dt = '%(month)02d-%(day)02d-%(year)04d %(hour)02d:%(min)02d:%(sec)02d' \
% dt
dt = procutil.scanf_datetime(dt, fmt='%m-%d-%Y %H:%M:%S')
if sensor_info['utc_offset']:
dt = dt + datetime.timedelta(hours=sensor_info['utc_offset'])
data['dt'][sample_index] = dt
data['time'][sample_index] = procutil.dt2es(dt)
# sample time at the package
package_dt = {'month' : int(sample['data_time'][5:7]),
'day' : int(sample['data_time'][8:10]),
'year' : int(sample['data_time'][0:4]),
'hour' : int(sample['data_time'][11:13]),
'min' : int(sample['data_time'][14:16]),
'sec' : int(sample['data_time'][17:19]),
}
package_dt = ('%(month)02d-%(day)02d-%(year)04d ' +
'%(hour)02d:%(min)02d:%(sec)02d') \
% package_dt
package_dt = procutil.scanf_datetime(package_dt, fmt='%m-%d-%Y %H:%M:%S')
if sensor_info['utc_offset']:
package_dt = package_dt + \
datetime.timedelta(hours=sensor_info['utc_offset'])
data['pdt'][sample_index] = package_dt
data['ptime'][sample_index] = procutil.dt2es(package_dt)
# platform session time
ds = {'month' : int(sample['sessionid'][14:16]),
'day' : int(sample['sessionid'][17:19]),
'year' : int(sample['sessionid'][9:13]),
'hour' : int(sample['sessionid'][20:22]),
'min' : int(sample['sessionid'][23:25]),
'sec' : int(sample['sessionid'][26:28]),
}
ds = '%(month)02d-%(day)02d-%(year)04d %(hour)02d:%(min)02d:%(sec)02d' \
% ds
ds = procutil.scanf_datetime(ds, fmt='%m-%d-%Y %H:%M:%S')
if sensor_info['utc_offset']:
ds = ds + datetime.timedelta(hours=sensor_info['utc_offset'])
data['ds'][sample_index] = ds
data['session'][sample_index] = procutil.dt2es(ds)
# package session time
package_ds = {'month' : int(sample['data_sessionid'][5:7]),
'day' : int(sample['data_sessionid'][8:10]),
'year' : int(sample['data_sessionid'][0:4]),
'hour' : int(sample['data_sessionid'][11:13]),
'min' : int(sample['data_sessionid'][14:16]),
'sec' : int(sample['data_sessionid'][17:19]),
}
package_ds = ('%(month)02d-%(day)02d-%(year)04d ' +
'%(hour)02d:%(min)02d:%(sec)02d') \
% package_ds
package_ds = procutil.scanf_datetime(package_ds, fmt='%m-%d-%Y %H:%M:%S')
if sensor_info['utc_offset']:
package_ds = package_ds + \
datetime.timedelta(hours=sensor_info['utc_offset'])
data['pds'][sample_index] = package_ds
data['psession'][sample_index] = procutil.dt2es(package_ds)
# platform variables
try:
data['record'][sample_index] = int(sample["recordnumber"])
except KeyError:
pass
try:
data['status'][sample_index] = int(sample["status"].
partition(":")[0])
except (KeyError, AttributeError, ):
pass
# package variables
try:
data['pstatus'][sample_index] = int(sample.sensors
[sensor_info["id_number"]]
["status"].
partition(":")[0])
except (KeyError, AttributeError, ):
pass
try:
data['abs_speed'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["abs_speed_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['direction'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["direction_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['v'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["v_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['u'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["u_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['heading'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["heading_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['tiltx'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["tiltx_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['tilty'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["tilty_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['std_speed'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["std_speed_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['strength'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["strength_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['pings'][sample_index] = int(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["pings_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
return data
def creator(platform_info, sensor_info, data):
#
#
title_str = sensor_info['description']+' at '+ sensor_info['location']
global_atts = {
# Required
'title' : title_str,
'institution' : platform_info['institution'],
'institution_url' : platform_info['institution_url'],
'institution_dods_url' : platform_info['institution_dods_url'],
'contact' : platform_info['contact'],
'Conventions' : platform_info['conventions'],
# Required by Scout
'format_category_code' : platform_info['format_category_code'],
'institution_code' : platform_info['institution_code'],
'platform_code' : platform_info['id'],
'package_code' : sensor_info['id'],
# Required by Version tracking
'format' : platform_info['format'],
'seacoos_rt_version' : platform_info['seacoos_rt_version'],
# Recommended
'_FillValue' : n.nan,
'missing_value' : n.nan,
'source' : platform_info['source'],
'references' : platform_info['references'],
'metadata_url' : platform_info['metadata_url'],
'history' : 'raw2proc using ' + sensor_info['process_module'],
'comment' : 'File created using pycdf ' + \
pycdf.pycdfVersion() + ' and ' + \
pycdf.pycdfArrayPkg() + ' ' + \
n.__version__,
'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'][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' : nowDt.strftime("%Y-%m-%d %H:%M:%S"),
'creation_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"),
'modification_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"),
'process_level' : 'level1',
# Custom
'id_number' : platform_info['id_number'],
'description' : platform_info['description'],
'serial_number' : platform_info['serial_number'],
'product_number' : platform_info['product_number'],
'product_name' : platform_info['product_name'],
'type' : platform_info['type'],
'protocol_version' : platform_info['protocol_version'],
'xmlns' : platform_info['xmlns'],
'location' : platform_info['location'],
'vertical_position': platform_info['vertical_position'],
'owner' : platform_info['owner'],
'package_id_number' : sensor_info['id_number'],
'package_description' : sensor_info['description'],
'package_serial_number' : sensor_info['serial_number'],
'package_product_number' : sensor_info['product_number'],
'package_product_name' : sensor_info['product_name'],
'package_adr' : sensor_info['adr'],
'package_protocol_version' : sensor_info['protocol_version'],
'package_vertical_position' : sensor_info['vertical_position'],
}
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': 'Longtitude',
'standard_name': 'longtitude',
'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': 'meters',
'axis': 'Z',
},
# data variables
'ptime' : {'short_name': 'ptime',
'long_name': 'Package Time',
'standard_name': 'none',
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC
},
'session' : {'short_name': 'session',
'long_name': 'Session ID',
'standard_name': 'none',
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC
},
'psession' : {'short_name': 'ptime',
'long_name': 'Package Session ID',
'standard_name': 'none',
'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC
},
'record' : {'short_name': 'record',
'long_name': 'Record Number',
'standard_name': 'none',
'units' : 'none',
},
'status': {'short_name' : 'status',
'long_name': 'Platform Status Code',
'standard_name': 'none',
'units' : 'none',
'value_map' : platform_info['status_map'],
},
'pstatus': {'short_name' : 'pstatus',
'long_name': 'Package Status Code',
'standard_name': 'none',
'units' : 'none',
'value_map' : sensor_info['status_map'],
},
'abs_speed' : {'short_name' : 'c_spd',
'long_name': sensor_info['abs_speed_description'],
'standard_name': 'current_speed',
'units': 'cm s-1',
'id_number' : sensor_info['abs_speed_id'],
'type' : sensor_info['abs_speed_type'],
'format' : sensor_info['abs_speed_format'],
'non_standard_units' : sensor_info['abs_speed_units'],
'range_min' : sensor_info['abs_speed_range_min'],
'range_max' : sensor_info['abs_speed_range_max'],
},
'direction' : {'short_name' : 'c_dir',
'long_name': sensor_info['direction_description'],
'standard_name': 'current_to_direction',
'units': 'degrees_true',
'id_number' : sensor_info['direction_id'],
'type' : sensor_info['direction_type'],
'format' : sensor_info['direction_format'],
'non_standard_units' : sensor_info['direction_units'],
'range_min' : sensor_info['direction_range_min'],
'range_max' : sensor_info['direction_range_max'],
},
'v' : {'short_name' : 'water_v',
'long_name': sensor_info['v_description'],
'standard_name': 'northward_current',
'units': 'cm s-1',
'id_number' : sensor_info['v_id'],
'type' : sensor_info['v_type'],
'format' : sensor_info['v_format'],
'non_standard_units' : sensor_info['v_units'],
'range_min' : sensor_info['v_range_min'],
'range_max' : sensor_info['v_range_max'],
},
'u' : {'short_name' : 'water_u',
'long_name': sensor_info['u_description'],
'standard_name': 'eastward_current',
'units': 'cm s-1',
'id_number' : sensor_info['u_id'],
'type' : sensor_info['u_type'],
'format' : sensor_info['u_format'],
'non_standard_units' : sensor_info['u_units'],
'range_min' : sensor_info['u_range_min'],
'range_max' : sensor_info['u_range_max'],
},
'heading' : {'short_name' : 'heading',
'long_name': sensor_info['heading_description'],
'standard_name': 'none',
'units': 'none',
'id_number' : sensor_info['heading_id'],
'type' : sensor_info['heading_type'],
'format' : sensor_info['heading_format'],
'non_standard_units' : sensor_info['heading_units'],
'range_min' : sensor_info['heading_range_min'],
'range_max' : sensor_info['heading_range_max'],
},
'tiltx' : {'short_name' : 'tiltx',
'long_name': sensor_info['tiltx_description'],
'standard_name': 'none',
'units': 'none',
'id_number' : sensor_info['tiltx_id'],
'type' : sensor_info['tiltx_type'],
'format' : sensor_info['tiltx_format'],
'non_standard_units' : sensor_info['tiltx_units'],
'range_min' : sensor_info['tiltx_range_min'],
'range_max' : sensor_info['tiltx_range_max'],
},
'tilty' : {'short_name' : 'tilty',
'long_name': sensor_info['tilty_description'],
'standard_name': 'none',
'units': 'none',
'id_number' : sensor_info['tilty_id'],
'type' : sensor_info['tilty_type'],
'format' : sensor_info['tilty_format'],
'non_standard_units' : sensor_info['tilty_units'],
'range_min' : sensor_info['tilty_range_min'],
'range_max' : sensor_info['tilty_range_max'],
},
'std_speed' : {'short_name' : 'beam_vel',
'long_name': sensor_info['std_speed_description'],
'standard_name': 'beam_velocity',
'units': 'cm s-1',
'id_number' : sensor_info['std_speed_id'],
'type' : sensor_info['std_speed_type'],
'format' : sensor_info['std_speed_format'],
'non_standard_units' : sensor_info['std_speed_units'],
'range_min' : sensor_info['std_speed_range_min'],
'range_max' : sensor_info['std_speed_range_max'],
},
'strength' : {'short_name' : 'beam_echo',
'long_name': sensor_info['strength_description'],
'standard_name': 'beam_echo_intensity',
'units': 'db',
'id_number' : sensor_info['strength_id'],
'type' : sensor_info['strength_type'],
'format' : sensor_info['strength_format'],
'non_standard_units' : sensor_info['strength_units'],
'range_min' : sensor_info['strength_range_min'],
'range_max' : sensor_info['strength_range_max'],
},
'pings' : {'short_name' : 'pings',
'long_name': sensor_info['pings_description'],
'standard_name': 'none',
'units': 'none',
'id_number' : sensor_info['pings_id'],
'type' : sensor_info['pings_type'],
'format' : sensor_info['pings_format'],
'non_standard_units' : sensor_info['pings_units'],
'range_min' : sensor_info['pings_range_min'],
'range_max' : sensor_info['pings_range_max'],
},
}
# dimension names use tuple so order of initialization is maintained
dim_inits = (
('ntime', pycdf.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', pycdf.NC.INT, ('ntime',)),
('lat', pycdf.NC.FLOAT, ('nlat',)),
('lon', pycdf.NC.FLOAT, ('nlon',)),
('z', pycdf.NC.FLOAT, ('nz',)),
# data variables
('ptime', pycdf.NC.INT, ('ntime',)),
('session', pycdf.NC.INT, ('ntime',)),
('psession', pycdf.NC.INT, ('ntime',)),
('record', pycdf.NC.INT, ('ntime',)),
('status', pycdf.NC.INT, ('ntime',)),
('pstatus', pycdf.NC.INT, ('ntime',)),
('abs_speed', pycdf.NC.FLOAT, ('ntime',)),
('direction', pycdf.NC.FLOAT, ('ntime',)),
('v', pycdf.NC.FLOAT, ('ntime',)),
('u', pycdf.NC.FLOAT, ('ntime',)),
('heading', pycdf.NC.FLOAT, ('ntime',)),
('tiltx', pycdf.NC.FLOAT, ('ntime',)),
('tilty', pycdf.NC.FLOAT, ('ntime',)),
('std_speed', pycdf.NC.FLOAT, ('ntime',)),
('strength', pycdf.NC.FLOAT, ('ntime',)),
('pings', pycdf.NC.INT, ('ntime',)),
)
# subset data only to month being processed (see raw2proc.process())
i = data['in']
# var data
var_data = (
('time', data['time'][i]),
('lat', sensor_info['lat']),
('lon', sensor_info['lat']),
('z', sensor_info['elevation']),
('ptime', data['ptime'][i]),
('session', data['session'][i]),
('psession', data['psession'][i]),
('record', data['record'][i]),
('status', data['status'][i]),
('pstatus', data['pstatus'][i]),
('abs_speed', data['abs_speed'][i]),
('direction', data['direction'][i]),
('v', data['v'][i]),
('u', data['u'][i]),
('heading', data['heading'][i]),
('tiltx', data['tiltx'][i]),
('tilty', data['tilty'][i]),
('std_speed', data['std_speed'][i]),
('strength', data['strength'][i]),
('pings', data['pings'][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' : nowDt.strftime("%Y-%m-%d %H:%M:%S"),
#
'modification_date' : nowDt.strftime("%Y-%m-%d %H:%M:%S"),
}
# data variables
# update any variable attributes like range, min, max
var_atts = {}
# subset data only to month being processed (see raw2proc.process())
i = data['in']
# data
var_data = (
('time', data['time'][i]),
('ptime', data['ptime'][i]),
('session', data['session'][i]),
('psession', data['psession'][i]),
('record', data['record'][i]),
('status', data['status'][i]),
('pstatus', data['pstatus'][i]),
('abs_speed', data['abs_speed'][i]),
('direction', data['direction'][i]),
('v', data['v'][i]),
('u', data['u'][i]),
('heading', data['heading'][i]),
('tiltx', data['tiltx'][i]),
('tilty', data['tilty'][i]),
('std_speed', data['std_speed'][i]),
('strength', data['strength'][i]),
('pings', data['pings'][i]),
)
return (global_atts, var_atts, var_data)