def test_attempts_empty(self):
filename = 'test_attempts_empty.nc'
# From dataframe
TimeSeries.from_dataframe(self.df,
output_directory=self.output_directory,
output_filename=filename,
latitude=self.latitude,
longitude=self.longitude,
station_name=self.station_name,
global_attributes=self.global_attributes,
variable_name=self.vname,
variable_attributes=self.vatts)
def test_attempts_4(self):
filename = 'test_attempts_4.nc'
# From dataframe
with self.assertRaises(ValueError):
TimeSeries.from_dataframe(self.df,
output_directory=self.output_directory,
output_filename=filename,
latitude=self.latitude,
longitude=self.longitude,
station_name=self.station_name,
global_attributes=self.global_attributes,
variable_name=self.vname,
variable_attributes=self.vatts,
attempts=4)
def test_timeseries_profile_from_dataframe(self):
filename = 'test_timeseries_profile_from_dataframe.nc'
times = [0, 1000, 2000, 3000, 4000, 5000]
verticals = [0, 1, 2]
values = [20, 21, 22]
attrs = dict(standard_name='sea_water_temperature')
# From dataframe
df = pd.DataFrame({
'depth':
np.tile(verticals, 6),
'time':
np.repeat([datetime.utcfromtimestamp(x) for x in times], 3),
'value':
np.tile(values, 6)
})
ts = TimeSeries.from_dataframe(
df,
output_directory=self.output_directory,
output_filename=filename,
latitude=self.latitude,
longitude=self.longitude,
station_name=self.station_name,
global_attributes=self.global_attributes,
variable_name='temperature',
variable_attributes=attrs,
attempts=4)
ts.add_instrument_variable('temperature')
nc = netCDF4.Dataset(os.path.join(self.output_directory, filename))
assert nc is not None
# Basic metadata on all timeseries
self.assertEqual(nc.cdm_data_type, 'Station')
self.assertEqual(nc.geospatial_lat_units, 'degrees_north')
self.assertEqual(nc.geospatial_lon_units, 'degrees_east')
self.assertEqual(nc.geospatial_vertical_units, 'meters')
self.assertEqual(nc.geospatial_vertical_positive, 'down')
self.assertEqual(nc.featureType, 'timeSeriesProfile')
self.assertEqual(nc.geospatial_vertical_resolution, '1 1')
self.assertEqual(nc.geospatial_vertical_min, 0)
self.assertEqual(nc.geospatial_vertical_max, 2)
assert nc.variables.get('time').size == len(times)
assert nc.variables.get('time')[:].dtype == np.int32
assert nc.variables.get('z').size == len(verticals)
assert nc.variables.get('z').positive == 'down'
assert nc.variables.get('z')[:].dtype == np.int32
assert nc.variables.get(
'temperature').size == len(times) * len(verticals)
assert (nc.variables.get('temperature')[:] == np.tile(
values, 6).reshape(6, 3)).all()
def parse_type_1(output_format, site_id, contents, output, csv_link):
"""
# ---------------------------------- WARNING ----------------------------------------
# The data you have obtained from this automated U.S. Geological Survey database
# have not received Director's approval and as such are provisional and subject to
# revision. The data are released on the condition that neither the USGS nor the
# United States Government may be held liable for any damages resulting from its use.
# Additional info: http://waterdata.usgs.gov/ga/nwis/help/?provisional
#
# File-format description: http://waterdata.usgs.gov/nwis/?tab_delimited_format_info
# Automated-retrieval info: http://waterdata.usgs.gov/nwis/?automated_retrieval_info
#
# Contact: [email protected]
# retrieved: 2012-11-20 12:05:22 EST (caww01)
#
# Data for the following 1 site(s) are contained in this file
# USGS 395740074482628 South Branch Rancocas Cr at S Main St nr Lumberton
# -----------------------------------------------------------------------------------
#
# Data provided for site 395740074482628
# DD parameter Description
# 03 00035 Wind speed, miles per hour
# 07 00025 Barometric pressure, millimeters of mercury
# 09 00045 Precipitation, total, inches
# 19 63160 Stream water level elevation above NAVD 1988, in feet
#
# Data-value qualification codes included in this output:
# P Provisional data subject to revision.
#
agency_cd site_no datetime tz_cd 03_00035 03_00035_cd 07_00025 07_00025_cd 09_00045 09_00045_cd 19_63160 19_63160_cd
5s 15s 20d 6s 14n 10s 14n 10s 14n 10s 14n 10s
USGS 395740074482628 2012-10-28 13:00 EST 4.2 P 755 P 3.22 P
USGS 395740074482628 2012-10-28 13:15 EST 6.4 P 754 P 0.00 P 3.36 P
USGS 395740074482628 2012-10-28 13:30 EST 3.6 P 754 P 0.00 P 3.50 P
USGS 395740074482628 2012-10-28 13:45 EST 3.2 P 754 P 0.00 P 3.63 P
USGS 395740074482628 2012-10-28 14:00 EST 7.0 P 754 P 0.00 P 3.76 P
USGS 395740074482628 2012-10-28 14:15 EST 4.0 P 754 P 0.00 P 3.87 P
...
"""
# lat/lon point: http://waterservices.usgs.gov/nwis/site/?sites=395740074482628
variable_map = {
'01_00065': {
'long_name': 'Gage height',
'geoid_name': 'NAVD88',
'vertical_datum': 'NAVD88',
'water_surface_reference_datum': 'NAVD88',
'standard_name': 'water_surface_height_above_reference_datum',
'units': 'feet'
},
'03_00035': {
'long_name': 'Wind Speed',
'standard_name': 'wind_speed',
'units': 'mph'
},
'04_00035': {
'long_name': 'Wind Gust',
'standard_name': 'wind_speed_of_gust',
'units': 'mph'
},
'05_00035': {
'long_name': 'Wind Speed',
'standard_name': 'wind_speed',
'units': 'mph'
},
'06_00035': {
'long_name': 'Wind Gust',
'standard_name': 'wind_speed_of_gust',
'units': 'mph'
},
'04_00036': {
'long_name': 'Wind Direction',
'standard_name': 'wind_from_direction',
'units': 'degrees'
},
'02_00036': {
'long_name': 'Wind Direction',
'standard_name': 'wind_from_direction',
'units': 'degrees'
},
'05_00025': {
'long_name': 'Air Pressure',
'standard_name': 'air_pressure',
'units': 'mm of mercury'
},
'07_00025': {
'long_name': 'Air Pressure',
'standard_name': 'air_pressure',
'units': 'mm of mercury'
},
'09_00025': {
'long_name': 'Air Pressure',
'standard_name': 'air_pressure',
'units': 'mm of mercury'
},
'03_00045': {
'long_name': 'Total Precipitation',
'standard_name': 'lwe_thickness_of_precipitation_amount',
'units': 'inches'
},
'08_00045': {
'long_name': 'Total Precipitation',
'standard_name': 'lwe_thickness_of_precipitation_amount',
'units': 'inches'
},
'09_00045': {
'long_name': 'Total Precipitation',
'standard_name': 'lwe_thickness_of_precipitation_amount',
'units': 'inches'
},
'06_00052': {
'long_name': 'Relative Humidity',
'standard_name': 'relative_humidity',
'units': 'percent'
},
'07_00052': {
'long_name': 'Relative Humidity',
'standard_name': 'relative_humidity',
'units': 'percent'
},
'08_00052': {
'long_name': 'Relative Humidity',
'standard_name': 'relative_humidity',
'units': 'percent'
},
'05_00020': {
'long_name': 'Air Temperature',
'standard_name': 'air_temperature',
'units': 'degrees_Celsius'
},
'06_00020': {
'long_name': 'Air Temperature',
'standard_name': 'air_temperature',
'units': 'degrees_Celsius'
},
'07_00020': {
'long_name': 'Air Temperature',
'standard_name': 'air_temperature',
'units': 'degrees_Celsius'
},
'19_63160': {
'long_name': 'Water Surface Height Above Reference Datum (NAVD88)',
'geoid_name': 'NAVD88',
'vertical_datum': 'NAVD88',
'water_surface_reference_datum': 'NAVD88',
'standard_name': 'water_surface_height_above_reference_datum',
'units': 'feet'
},
'01_63160': {
'long_name': 'Water Surface Height Above Reference Datum (NAVD88)',
'geoid_name': 'NAVD88',
'vertical_datum': 'NAVD88',
'water_surface_reference_datum': 'NAVD88',
'standard_name': 'water_surface_height_above_reference_datum',
'units': 'feet'
},
}
# Get metadata from a seperate endpoint.
d = requests.get(
"http://waterservices.usgs.gov/nwis/site/?sites={!s}".format(site_id))
try:
d.raise_for_status()
except requests.exceptions.HTTPError:
logger.error(
"Could not find lat/lon endpoint for station {!s}, skipping. Status code: {!s}"
.format(site_id, d.status_code))
return
_, hz, dz = split_file(d.text, "agency_cd")
# Strip off the one line after the headers
dz = dz[1:]
dfz = pd.DataFrame(dz, columns=hz)
lat = float(dfz["dec_lat_va"][0])
lon = float(dfz["dec_long_va"][0])
sensor_vertical_datum = dfz["alt_datum_cd"][0] or "NAVD88"
try:
z = float(dfz["alt_va"][0])
except ValueError:
z = 0.
loc = "POINT({!s} {!s} {!s})".format(lon, lat, z)
name = dfz["station_nm"][0]
comments, headers, data = split_file(contents, "agency_cd")
df = pd.DataFrame(data, columns=headers)
fillvalue = -9999.9
# Combine date columns
dates = df["datetime"]
tz = df["tz_cd"]
new_dates = list()
for i in range(len(dates)):
try:
new_dates.append(
parse(dates[i] + " " + tz[i]).astimezone(pytz.utc))
except BaseException:
# Remove row. Bad date.
df.drop(i, axis=0, inplace=True)
continue
df['time'] = new_dates
df['depth'] = [z for x in range(len(df['time']))]
# Strip out "_cd" columns (quality checks for USGS)
for h in headers:
if "_cd" in h:
df.drop(h, axis=1, inplace=True)
# Add global attributes to appear in the resulting NetCDF file
global_attributes = dict(
title=name,
summary=
'USGS Hurricane Sandy Rapid Response Stations. Data acquired from "http://ga.water.usgs.gov/flood/hurricane/sandy/datafiles/.',
keywords=
"usgs, waterdata, elevation, water, waterlevel, sandy, hurricane, rapid, response, %s"
% site_id,
keywords_vocaublary="None",
naming_authority='gov.usgs',
id=site_id,
cdm_data_type="Station",
history="NetCDF file generated from {!s}".format(csv_link),
creator="USGS",
creator_url="http://waterdata.usgs.gov",
creator_institution="USGS",
creator_urn="gov.usgs",
publisher="Axiom Data Science",
publisher_uri="http://axiomdatascience.com",
processing_level="None",
acknowledgement="None",
geospatial_bounds=loc,
geospatial_lat_min=lat,
geospatial_lat_max=lat,
geospatial_lon_min=lon,
geospatial_lon_max=lon,
license="Freely Distributed",
date_created=datetime.utcnow().replace(second=0,
microsecond=0).isoformat())
def to_floats(x):
try:
return float(x)
except ValueError:
return fillvalue
min_time = df['time'].min()
max_time = df['time'].max()
full_station_urn = "urn:ioos:station:{!s}:{!s}".format(
global_attributes["naming_authority"], site_id)
if output_format == 'cf16':
output_filename = '{}_{}-{}.nc'.format(
site_id, min_time.strftime('%Y%m%dT%H%M%S'),
max_time.strftime('%Y%m%dT%H%M%S'))
times = [calendar.timegm(x.timetuple()) for x in df["time"]]
verticals = df['depth'].values
ts = TimeSeries(output,
latitude=lat,
longitude=lon,
station_name=full_station_urn,
global_attributes=global_attributes,
output_filename=output_filename,
times=times,
verticals=verticals,
vertical_axis_name='height',
vertical_positive='down')
for var in df.columns:
if var in [
'datetime', 'time', 'depth', 'tz_cd', 'site_no', 'agency_cd'
]:
continue
try:
var_meta = variable_map[var]
except KeyError:
logger.error(
"Variable {!s} was not found in variable map!".format(var))
continue
# Convert to floats
df[var] = df[var].map(to_floats)
# Change feet to meters
if var_meta["units"] in ["feet", "ft"]:
df[var] = np.asarray(
[v * 0.3048 if v != fillvalue else v for v in df[var]])
var_meta["units"] = "meters"
if output_format == 'axiom':
full_sensor_urn = "urn:ioos:sensor:{!s}:{!s}:{!s}".format(
global_attributes["naming_authority"], site_id,
var_meta["standard_name"])
output_directory = os.path.join(output, full_sensor_urn)
output_filename = '{}_{}-{}.nc'.format(
var, min_time.strftime('%Y%m%dT%H%M%S'),
max_time.strftime('%Y%m%dT%H%M%S'))
ts = TimeSeries.from_dataframe(
df,
output_directory,
output_filename,
lat,
lon,
full_station_urn,
global_attributes,
var_meta["standard_name"],
var_meta,
sensor_vertical_datum=sensor_vertical_datum,
fillvalue=fillvalue,
data_column=var,
vertical_axis_name='height',
vertical_positive='down')
ts.add_instrument_metadata(urn=full_sensor_urn)
elif output_format == 'cf16':
# Variable names shouldn't start with a number
try:
int(var[0])
variable_name = 'v_{}'.format(var)
except:
variable_name = var
ts.add_variable(variable_name,
values=df[var].values,
attributes=var_meta,
fillvalue=fillvalue,
sensor_vertical_datum=sensor_vertical_datum)
def parse_type_2(output_format, site_id, contents, output, csv_link):
"""
# These data are provisional and subject to revision.
# Data processed as of 12/05/2012 11:54:29.
# Data collected as part of Hurricane Sandy (2012) Storm Tide project.
# Data are archived at http://water.usgs.gov/floods/events/2012/isaac/index.php
# Elevation determined from GPS surveys (NAVD 88).
# Time datum is GMT (Greenwich Mean Time).
# Water density estimated on basis of sensor location
# where saltwater = 63.989 lb/ft3 (Saltwater = dissolved solids concentration greater than 20000 milligrams per liter)
# where brackish water = 63.052 lb/ft3 (Brackish water = dissolved solids concentration between 1000 and 20000 milligrams per liter)
# where freshwater = 62.428 lb/ft3 (Freshwater = dissolved solids concentration less than 1000 milligrams per liter)
# The equation used to compute elevation from recorded pressure is
# (((sp-bp)*144)/d)+e
# Where sp = surge pressure in psi; bp = barometric pressure in psi;
# d = water density in lb/ft3; and e = elevation of sensor in ft above NAVD 88.
# Barometric data from nearest pressure sensor. Location for the barometric sensor is listed below.
# Elevation is computer-rounded to two decimal places.
# Sensor information
# Site id = SSS-NY-WES-001WL
# Site type = water level
# Horizontal datum used is NAD 83
# Sensor location latitude 40.942755
# Sensor location longitude -73.719828
# Sensor elevation above NAVD 88 = -3.97 ft
# Lowest recordable water elevation is -3.90 ft
# Water density value used = 63.989 lb/ft3
# Barometric sensor site (source of bp) = SSS-NY-WES-002BP
# Barometric sensor location latitude 40.90754368
# Barometric sensor location longitude -73.8692184
date_time_GMT elevation nearest_barometric_sensor_psi
10-28-2012 06:00:00 0.88 14.5145
10-28-2012 06:00:30 0.86 14.5145
10-28-2012 06:01:00 0.85 14.5170
10-28-2012 06:01:30 0.85 14.5145
10-28-2012 06:02:00 0.84 14.5170
10-28-2012 06:02:30 0.81 14.5145
10-28-2012 06:03:00 0.76 14.5145
...
"""
variable_map = {
'elevation': {
'long_name':
'Water Level Elevation above Reference Datum (NAVD88)',
'geoid_name': 'NAVD88',
'vertical_datum': 'NAVD88',
'water_surface_reference_datum': 'NAVD88',
'standard_name': 'water_surface_height_above_reference_datum',
'units': 'feet'
},
}
def to_floats(x):
try:
return float(x)
except ValueError:
return fillvalue
comments, headers, data = split_file(contents, "date_time_GMT")
df = pd.DataFrame(data, columns=headers)
fillvalue = -9999.9
lat = None
lon = None
z = 0
name = site_id
sensor_vertical_datum = "NAVD88"
for c in comments:
if "Sensor location latitude" in c:
lat = float(
list(filter(None, map(lambda x: x.strip(), c.split(" "))))[-1])
elif "Sensor location longitude" in c:
lon = float(
list(filter(None, map(lambda x: x.strip(), c.split(" "))))[-1])
elif "Site id" in c:
site_id = list(filter(None, map(lambda x: x.strip(),
c.split(" "))))[-1]
name = site_id
elif "Sensor elevation" in c:
sensor_vertical_datum = "".join(c.split("=")[0].split(" ")[4:6])
l = list(filter(None, map(lambda x: x.strip(), c.split(" "))))
z = float(l[-2])
if l[-1] in ["feet", "ft"]:
z *= 0.3048
loc = "POINT({!s} {!s} {!s})".format(lon, lat, z)
df['time'] = df["date_time_GMT"].map(lambda x: parse(x + " UTC"))
df['depth'] = [z for x in range(len(df['time']))]
# Add global attributes to appear in the resulting NetCDF file
global_attributes = dict(
title=name,
summary=
'USGS Hurricane Sandy Rapid Response Stations. Data acquired from http://ga.water.usgs.gov/flood/hurricane/sandy/datafiles/.',
keywords=
"usgs, waterdata, elevation, water, waterlevel, sandy, hurricane, rapid, response, %s"
% site_id,
keywords_vocaublary="None",
naming_authority='gov.usgs',
id=site_id,
cdm_data_type="Station",
history="NetCDF file generated from {!s}".format(csv_link),
creator="USGS",
creator_url="http://waterdata.usgs.gov",
creator_institution="USGS",
creator_urn="gov.usgs",
publisher="Axiom Data Science",
publisher_uri="http://axiomdatascience.com",
processing_level="None",
acknowledgement="None",
geospatial_bounds=loc,
geospatial_lat_min=lat,
geospatial_lat_max=lat,
geospatial_lon_min=lon,
geospatial_lon_max=lon,
license="Freely Distributed",
date_created=datetime.utcnow().replace(second=0,
microsecond=0).isoformat())
full_station_urn = "urn:ioos:station:{!s}:{!s}".format(
global_attributes["naming_authority"], site_id)
min_time = df["time"].min()
max_time = df["time"].max()
if output_format == 'cf16':
times = [calendar.timegm(x.timetuple()) for x in df['time']]
verticals = df['depth'].values
output_filename = '{}_{}-{}.nc'.format(
site_id, min_time.strftime('%Y%m%dT%H%M%S'),
max_time.strftime('%Y%m%dT%H%M%S'))
ts = TimeSeries(output,
latitude=lat,
longitude=lon,
station_name=full_station_urn,
global_attributes=global_attributes,
output_filename=output_filename,
times=times,
verticals=verticals)
for var in df.columns:
if var in ['date_time_GMT', 'time', 'depth']:
continue
try:
int(var[0])
variable_name = 'v_{}'.format(var)
except:
variable_name = var
try:
var_meta = variable_map[var]
except KeyError:
logger.error(
"Variable {!s} was not found in variable map!".format(var))
continue
# Convert to floats
df[var] = df[var].map(to_floats)
if var_meta["units"] in ["feet", "ft"]:
df[var] = [v * 0.3048 if v != fillvalue else v for v in df[var]]
var_meta["units"] = "meters"
if output_format == 'axiom':
full_sensor_urn = "urn:ioos:sensor:{!s}:{!s}:{!s}".format(
global_attributes["naming_authority"], site_id,
var_meta["standard_name"])
output_directory = os.path.join(output, full_sensor_urn)
output_filename = '{}_{}-{}.nc'.format(
var, min_time.strftime('%Y%m%dT%H%M%S'),
max_time.strftime('%Y%m%dT%H%M%S'))
ts = TimeSeries.from_dataframe(
df,
output_directory,
output_filename,
lat,
lon,
full_station_urn,
global_attributes,
var_meta["standard_name"],
var_meta,
sensor_vertical_datum=sensor_vertical_datum,
fillvalue=fillvalue,
data_column=var)
ts.add_instrument_metadata(urn=full_sensor_urn)
elif output_format == 'cf16':
ts.add_variable(variable_name,
values=df[var].values,
attributes=var_meta,
fillvalue=fillvalue,
sensor_vertical_datum=sensor_vertical_datum)
def parse_type_1(output_format, site_id, contents, output, csv_link):
"""
# ---------------------------------- WARNING ----------------------------------------
# The data you have obtained from this automated U.S. Geological Survey database
# have not received Director's approval and as such are provisional and subject to
# revision. The data are released on the condition that neither the USGS nor the
# United States Government may be held liable for any damages resulting from its use.
# Additional info: http://waterdata.usgs.gov/ga/nwis/help/?provisional
#
# File-format description: http://waterdata.usgs.gov/nwis/?tab_delimited_format_info
# Automated-retrieval info: http://waterdata.usgs.gov/nwis/?automated_retrieval_info
#
# Contact: [email protected]
# retrieved: 2012-11-20 12:05:22 EST (caww01)
#
# Data for the following 1 site(s) are contained in this file
# USGS 395740074482628 South Branch Rancocas Cr at S Main St nr Lumberton
# -----------------------------------------------------------------------------------
#
# Data provided for site 395740074482628
# DD parameter Description
# 03 00035 Wind speed, miles per hour
# 07 00025 Barometric pressure, millimeters of mercury
# 09 00045 Precipitation, total, inches
# 19 63160 Stream water level elevation above NAVD 1988, in feet
#
# Data-value qualification codes included in this output:
# P Provisional data subject to revision.
#
agency_cd site_no datetime tz_cd 03_00035 03_00035_cd 07_00025 07_00025_cd 09_00045 09_00045_cd 19_63160 19_63160_cd
5s 15s 20d 6s 14n 10s 14n 10s 14n 10s 14n 10s
USGS 395740074482628 2012-10-28 13:00 EST 4.2 P 755 P 3.22 P
USGS 395740074482628 2012-10-28 13:15 EST 6.4 P 754 P 0.00 P 3.36 P
USGS 395740074482628 2012-10-28 13:30 EST 3.6 P 754 P 0.00 P 3.50 P
USGS 395740074482628 2012-10-28 13:45 EST 3.2 P 754 P 0.00 P 3.63 P
USGS 395740074482628 2012-10-28 14:00 EST 7.0 P 754 P 0.00 P 3.76 P
USGS 395740074482628 2012-10-28 14:15 EST 4.0 P 754 P 0.00 P 3.87 P
...
"""
# lat/lon point: http://waterservices.usgs.gov/nwis/site/?sites=395740074482628
variable_map = {
'01_00065' : {'long_name' : 'Gage height', 'geoid_name' : 'NAVD88', 'vertical_datum' : 'NAVD88', 'water_surface_reference_datum' : 'NAVD88', 'standard_name' : 'water_surface_height_above_reference_datum', 'units': 'feet'},
'03_00035' : {'long_name' : 'Wind Speed', 'standard_name' : 'wind_speed', 'units': 'mph'},
'04_00035' : {'long_name' : 'Wind Gust', 'standard_name' : 'wind_speed_of_gust', 'units': 'mph'},
'05_00035' : {'long_name' : 'Wind Speed', 'standard_name' : 'wind_speed', 'units': 'mph'},
'06_00035' : {'long_name' : 'Wind Gust', 'standard_name' : 'wind_speed_of_gust', 'units': 'mph'},
'04_00036' : {'long_name' : 'Wind Direction', 'standard_name' : 'wind_from_direction', 'units': 'degrees'},
'02_00036' : {'long_name' : 'Wind Direction', 'standard_name' : 'wind_from_direction', 'units': 'degrees'},
'05_00025' : {'long_name' : 'Air Pressure', 'standard_name' : 'air_pressure', 'units': 'mm of mercury'},
'07_00025' : {'long_name' : 'Air Pressure', 'standard_name' : 'air_pressure', 'units': 'mm of mercury'},
'09_00025' : {'long_name' : 'Air Pressure', 'standard_name' : 'air_pressure', 'units': 'mm of mercury'},
'03_00045' : {'long_name' : 'Total Precipitation', 'standard_name' : 'lwe_thickness_of_precipitation_amount', 'units': 'inches'},
'08_00045' : {'long_name' : 'Total Precipitation', 'standard_name' : 'lwe_thickness_of_precipitation_amount', 'units': 'inches'},
'09_00045' : {'long_name' : 'Total Precipitation', 'standard_name' : 'lwe_thickness_of_precipitation_amount', 'units': 'inches'},
'06_00052' : {'long_name' : 'Relative Humidity', 'standard_name' : 'relative_humidity', 'units': 'percent'},
'07_00052' : {'long_name' : 'Relative Humidity', 'standard_name' : 'relative_humidity', 'units': 'percent'},
'08_00052' : {'long_name' : 'Relative Humidity', 'standard_name' : 'relative_humidity', 'units': 'percent'},
'05_00020' : {'long_name' : 'Air Temperature', 'standard_name' : 'air_temperature', 'units': 'degrees_Celsius'},
'06_00020' : {'long_name' : 'Air Temperature', 'standard_name' : 'air_temperature', 'units': 'degrees_Celsius'},
'07_00020' : {'long_name' : 'Air Temperature', 'standard_name' : 'air_temperature', 'units': 'degrees_Celsius'},
'19_63160' : {'long_name' : 'Water Surface Height Above Reference Datum (NAVD88)', 'geoid_name' : 'NAVD88', 'vertical_datum' : 'NAVD88', 'water_surface_reference_datum' : 'NAVD88', 'standard_name' : 'water_surface_height_above_reference_datum', 'units': 'feet'},
'01_63160' : {'long_name' : 'Water Surface Height Above Reference Datum (NAVD88)', 'geoid_name' : 'NAVD88', 'vertical_datum' : 'NAVD88', 'water_surface_reference_datum' : 'NAVD88', 'standard_name' : 'water_surface_height_above_reference_datum', 'units': 'feet'},
}
# Get metadata from a seperate endpoint.
d = requests.get("http://waterservices.usgs.gov/nwis/site/?sites={!s}".format(site_id))
try:
d.raise_for_status()
except requests.exceptions.HTTPError:
logger.error("Could not find lat/lon endpoint for station {!s}, skipping. Status code: {!s}".format(site_id, d.status_code))
return
_, hz, dz = split_file(d.text, "agency_cd")
# Strip off the one line after the headers
dz = dz[1:]
dfz = pd.DataFrame(dz, columns=hz)
lat = float(dfz["dec_lat_va"][0])
lon = float(dfz["dec_long_va"][0])
sensor_vertical_datum = dfz["alt_datum_cd"][0] or "NAVD88"
try:
z = float(dfz["alt_va"][0])
except ValueError:
z = 0.
loc = "POINT({!s} {!s} {!s})".format(lon, lat, z)
name = dfz["station_nm"][0]
comments, headers, data = split_file(contents, "agency_cd")
df = pd.DataFrame(data, columns=headers)
fillvalue = -9999.9
# Combine date columns
dates = df["datetime"]
tz = df["tz_cd"]
new_dates = list()
for i in range(len(dates)):
try:
new_dates.append(parse(dates[i] + " " + tz[i]).astimezone(pytz.utc))
except BaseException:
# Remove row. Bad date.
df.drop(i, axis=0, inplace=True)
continue
df['time'] = new_dates
df['depth'] = [ z for x in range(len(df['time'])) ]
# Strip out "_cd" columns (quality checks for USGS)
for h in headers:
if "_cd" in h:
df.drop(h, axis=1, inplace=True)
# Add global attributes to appear in the resulting NetCDF file
global_attributes = dict(
title=name,
summary='USGS Hurricane Sandy Rapid Response Stations. Data acquired from "http://ga.water.usgs.gov/flood/hurricane/sandy/datafiles/.',
keywords="usgs, waterdata, elevation, water, waterlevel, sandy, hurricane, rapid, response, %s" % site_id,
keywords_vocaublary="None",
naming_authority='gov.usgs',
id=site_id,
cdm_data_type="Station",
history="NetCDF file generated from {!s}".format(csv_link),
creator="USGS",
creator_url="http://waterdata.usgs.gov",
creator_institution="USGS",
creator_urn="gov.usgs",
publisher="Axiom Data Science",
publisher_uri="http://axiomdatascience.com",
processing_level="None",
acknowledgement="None",
geospatial_bounds=loc,
geospatial_lat_min=lat,
geospatial_lat_max=lat,
geospatial_lon_min=lon,
geospatial_lon_max=lon,
license="Freely Distributed",
date_created=datetime.utcnow().replace(second=0, microsecond=0).isoformat()
)
def to_floats(x):
try:
return float(x)
except ValueError:
return fillvalue
min_time = df['time'].min()
max_time = df['time'].max()
full_station_urn = "urn:ioos:station:{!s}:{!s}".format(global_attributes["naming_authority"], site_id)
if output_format == 'cf16':
output_filename = '{}_{}-{}.nc'.format(site_id, min_time.strftime('%Y%m%dT%H%M%S'), max_time.strftime('%Y%m%dT%H%M%S'))
times = [ calendar.timegm(x.timetuple()) for x in df["time"] ]
verticals = df['depth'].values
ts = TimeSeries(output, latitude=lat, longitude=lon, station_name=full_station_urn, global_attributes=global_attributes, output_filename=output_filename, times=times, verticals=verticals, vertical_axis_name='z')
for var in df.columns:
if var in ['datetime', 'time', 'depth', 'tz_cd', 'site_no', 'agency_cd']:
continue
try:
var_meta = variable_map[var]
except KeyError:
logger.error("Variable {!s} was not found in variable map!".format(var))
continue
# Convert to floats
df[var] = df[var].map(to_floats)
# Change feet to meters
if var_meta["units"] in ["feet", "ft"]:
df[var] = np.asarray([ v * 0.3048 if v != fillvalue else v for v in df[var] ])
var_meta["units"] = "meters"
if output_format == 'axiom':
full_sensor_urn = "urn:ioos:sensor:{!s}:{!s}:{!s}".format(global_attributes["naming_authority"], site_id, var_meta["standard_name"])
output_directory = os.path.join(output, full_sensor_urn)
output_filename = '{}_{}-{}.nc'.format(var, min_time.strftime('%Y%m%dT%H%M%S'), max_time.strftime('%Y%m%dT%H%M%S'))
ts = TimeSeries.from_dataframe(df, output_directory, output_filename, lat, lon, full_station_urn, global_attributes, var_meta["standard_name"], var_meta, sensor_vertical_datum=sensor_vertical_datum, fillvalue=fillvalue, data_column=var, vertical_axis_name='height')
ts.add_instrument_metadata(urn=full_sensor_urn)
ts.close()
elif output_format == 'cf16':
# Variable names shouldn't start with a number
try:
int(var[0])
variable_name = 'v_{}'.format(var)
except:
variable_name = var
ts.add_variable(variable_name, values=df[var].values, attributes=var_meta, fillvalue=fillvalue, sensor_vertical_datum=sensor_vertical_datum)
if output_format == 'cf16':
ts.close()
def parse_type_2(output_format, site_id, contents, output, csv_link):
"""
# These data are provisional and subject to revision.
# Data processed as of 12/05/2012 11:54:29.
# Data collected as part of Hurricane Sandy (2012) Storm Tide project.
# Data are archived at http://water.usgs.gov/floods/events/2012/isaac/index.php
# Elevation determined from GPS surveys (NAVD 88).
# Time datum is GMT (Greenwich Mean Time).
# Water density estimated on basis of sensor location
# where saltwater = 63.989 lb/ft3 (Saltwater = dissolved solids concentration greater than 20000 milligrams per liter)
# where brackish water = 63.052 lb/ft3 (Brackish water = dissolved solids concentration between 1000 and 20000 milligrams per liter)
# where freshwater = 62.428 lb/ft3 (Freshwater = dissolved solids concentration less than 1000 milligrams per liter)
# The equation used to compute elevation from recorded pressure is
# (((sp-bp)*144)/d)+e
# Where sp = surge pressure in psi; bp = barometric pressure in psi;
# d = water density in lb/ft3; and e = elevation of sensor in ft above NAVD 88.
# Barometric data from nearest pressure sensor. Location for the barometric sensor is listed below.
# Elevation is computer-rounded to two decimal places.
# Sensor information
# Site id = SSS-NY-WES-001WL
# Site type = water level
# Horizontal datum used is NAD 83
# Sensor location latitude 40.942755
# Sensor location longitude -73.719828
# Sensor elevation above NAVD 88 = -3.97 ft
# Lowest recordable water elevation is -3.90 ft
# Water density value used = 63.989 lb/ft3
# Barometric sensor site (source of bp) = SSS-NY-WES-002BP
# Barometric sensor location latitude 40.90754368
# Barometric sensor location longitude -73.8692184
date_time_GMT elevation nearest_barometric_sensor_psi
10-28-2012 06:00:00 0.88 14.5145
10-28-2012 06:00:30 0.86 14.5145
10-28-2012 06:01:00 0.85 14.5170
10-28-2012 06:01:30 0.85 14.5145
10-28-2012 06:02:00 0.84 14.5170
10-28-2012 06:02:30 0.81 14.5145
10-28-2012 06:03:00 0.76 14.5145
...
"""
variable_map = {
'elevation' : {'long_name' : 'Water Level Elevation above Reference Datum (NAVD88)', 'geoid_name' : 'NAVD88', 'vertical_datum' : 'NAVD88', 'water_surface_reference_datum' : 'NAVD88', 'standard_name' : 'water_surface_height_above_reference_datum', 'units': 'feet'},
}
def to_floats(x):
try:
return float(x)
except ValueError:
return fillvalue
comments, headers, data = split_file(contents, "date_time_GMT")
df = pd.DataFrame(data, columns=headers)
fillvalue = -9999.9
lat = None
lon = None
z = 0
name = site_id
sensor_vertical_datum = "NAVD88"
for c in comments:
if "Sensor location latitude" in c:
lat = float(filter(None, map(lambda x: x.strip(), c.split(" ")))[-1])
elif "Sensor location longitude" in c:
lon = float(filter(None, map(lambda x: x.strip(), c.split(" ")))[-1])
elif "Site id" in c:
site_id = filter(None, map(lambda x: x.strip(), c.split(" ")))[-1]
name = site_id
elif "Sensor elevation" in c:
sensor_vertical_datum = "".join(c.split("=")[0].split(" ")[4:6])
l = filter(None, map(lambda x: x.strip(), c.split(" ")))
z = float(l[-2])
if l[-1] in ["feet", "ft"]:
z *= 0.3048
loc = "POINT({!s} {!s} {!s})".format(lon, lat, z)
df['time'] = df["date_time_GMT"].map(lambda x: parse(x + " UTC"))
df['depth'] = [ z for x in range(len(df['time'])) ]
# Add global attributes to appear in the resulting NetCDF file
global_attributes = dict(
title=name,
summary='USGS Hurricane Sandy Rapid Response Stations. Data acquired from http://ga.water.usgs.gov/flood/hurricane/sandy/datafiles/.',
keywords="usgs, waterdata, elevation, water, waterlevel, sandy, hurricane, rapid, response, %s" % site_id,
keywords_vocaublary="None",
naming_authority='gov.usgs',
id=site_id,
cdm_data_type="Station",
history="NetCDF file generated from {!s}".format(csv_link),
creator="USGS",
creator_url="http://waterdata.usgs.gov",
creator_institution="USGS",
creator_urn="gov.usgs",
publisher="Axiom Data Science",
publisher_uri="http://axiomdatascience.com",
processing_level="None",
acknowledgement="None",
geospatial_bounds=loc,
geospatial_lat_min=lat,
geospatial_lat_max=lat,
geospatial_lon_min=lon,
geospatial_lon_max=lon,
license="Freely Distributed",
date_created=datetime.utcnow().replace(second=0, microsecond=0).isoformat()
)
full_station_urn = "urn:ioos:station:{!s}:{!s}".format(global_attributes["naming_authority"], site_id)
min_time = df["time"].min()
max_time = df["time"].max()
if output_format == 'cf16':
times = [ calendar.timegm(x.timetuple()) for x in df['time'] ]
verticals = df['depth'].values
output_filename = '{}_{}-{}.nc'.format(site_id, min_time.strftime('%Y%m%dT%H%M%S'), max_time.strftime('%Y%m%dT%H%M%S'))
ts = TimeSeries(output, latitude=lat, longitude=lon, station_name=full_station_urn, global_attributes=global_attributes, output_filename=output_filename, times=times, verticals=verticals)
for var in df.columns:
if var in ['date_time_GMT', 'time', 'depth']:
continue
try:
int(var[0])
variable_name = 'v_{}'.format(var)
except:
variable_name = var
try:
var_meta = variable_map[var]
except KeyError:
logger.error("Variable {!s} was not found in variable map!".format(var))
continue
# Convert to floats
df[var] = df[var].map(to_floats)
if var_meta["units"] in ["feet", "ft"]:
df[var] = [ v * 0.3048 if v != fillvalue else v for v in df[var] ]
var_meta["units"] = "meters"
if output_format == 'axiom':
full_sensor_urn = "urn:ioos:sensor:{!s}:{!s}:{!s}".format(global_attributes["naming_authority"], site_id, var_meta["standard_name"])
output_directory = os.path.join(output, full_sensor_urn)
output_filename = '{}_{}-{}.nc'.format(var, min_time.strftime('%Y%m%dT%H%M%S'), max_time.strftime('%Y%m%dT%H%M%S'))
ts = TimeSeries.from_dataframe(df, output_directory, output_filename, lat, lon, full_station_urn, global_attributes, var_meta["standard_name"], var_meta, sensor_vertical_datum=sensor_vertical_datum, fillvalue=fillvalue, data_column=var)
ts.add_instrument_metadata(urn=full_sensor_urn)
ts.close()
elif output_format == 'cf16':
ts.add_variable(variable_name, values=df[var].values, attributes=var_meta, fillvalue=fillvalue, sensor_vertical_datum=sensor_vertical_datum)
if output_format == 'cf16':
ts.close()
def main(output, station, datatype):
if datatype == 'met':
headers = met_header
mapping = met_mapping
elif datatype == 'waves':
headers = waves_header
mapping = waves_mapping
elif datatype == 'currents':
headers = currents_header
mapping = currents_mapping
df = None
def dp(*args):
datestr = "".join([str(x) for x in args])
try:
return datetime.strptime(datestr, '%Y %m %d %H %M %S')
except ValueError:
return np.nan
datapath = os.path.abspath(
os.path.join(os.path.dirname(__file__), 'data', datatype))
for csv_file in sorted(os.listdir(datapath)):
f = os.path.join(datapath, csv_file)
cf = pd.read_csv(
f,
header=None,
names=headers,
parse_dates={
'time': ['year', 'month', 'day', 'hour', 'minute', 'second']
},
date_parser=dp)
cf.dropna(subset=['time'], inplace=True)
if df is None:
df = cf
else:
df = df.append(cf)
fillvalue = -9999.9
# Station metadata
stat_meta = stations[station]
station_urn = IoosUrn(asset_type='station',
authority=global_attributes['naming_authority'],
label=stat_meta['title'])
for var in df.columns:
try:
var_meta = mapping[var]
except KeyError:
logger.error(
"Variable {!s} was not found in variable map!".format(var))
continue
sensor_urn = urnify(station_urn.authority, station_urn.label, var_meta)
gas = copy(global_attributes)
gas['keywords'] = var_meta['keywords']
gas['title'] = stat_meta['title']
gas['description'] = stat_meta['description']
skip_variable_attributes = [
'keywords', 'height_above_site', 'depth_below_surface',
'add_offset'
]
vas = {
k: v
for k, v in var_meta.items() if k not in skip_variable_attributes
}
if var_meta.get('height_above_site') and stat_meta.get('site_height'):
# Convert to positive down
df['depth'] = -1 * (stat_meta['site_height'] +
var_meta['height_above_site'])
else:
df['depth'] = var_meta.get('depth_below_surface', np.nan)
if 'add_offset' in var_meta:
df[var] = df[var] + var_meta['add_offset']
output_filename = '{}_{}_{}.nc'.format(station_urn.label, datatype,
var_meta['standard_name'])
ts = TimeSeries.from_dataframe(
df,
output,
output_filename,
stat_meta['latitude'],
stat_meta['longitude'],
station_urn.urn,
gas,
var_meta["standard_name"],
vas,
sensor_vertical_datum=var_meta.get('vertical_datum'),
fillvalue=fillvalue,
data_column=var,
vertical_axis_name='height',
vertical_positive='down')
ts.add_instrument_metadata(urn=sensor_urn)
def main(output_format, output, do_download, download_folder, filesubset=None):
if do_download is True:
try:
os.makedirs(download_folder)
except OSError:
pass
waf = 'http://ga.water.usgs.gov/flood/hurricane/sandy/datafiles/'
r = requests.get(waf)
soup = BeautifulSoup(r.text, "lxml")
for link in soup.find_all('a'):
# Skip non .txt files
site_id, ext = os.path.splitext(link['href'])
if ext != ".txt":
continue
if filesubset and site_id.lower() not in filesubset:
# Skip this file!
continue
csv_link = waf + link['href']
logger.info("Downloading '{}'".format(csv_link))
d = requests.get(csv_link)
try:
d.raise_for_status()
except requests.exceptions.HTTPError:
logger.error("Could not download: {!s}, skipping. Status code: {!s}".format(csv_link, d.status_code))
continue
with open(os.path.join(download_folder, os.path.basename(csv_link)), 'wt') as f:
f.write(d.text)
# Yes, this uses lots of RAM, but we need to match up lon/lat positions later on.
results = []
for datafile in os.listdir(download_folder):
site_id = os.path.splitext(os.path.basename(datafile))[0]
if filesubset and site_id.lower() not in filesubset:
# Skip this file!
continue
with open(os.path.join(download_folder, datafile)) as d:
contents = d.read()
r = None
for line in contents.split("\n"):
if "agency_cd" in line:
r = parse_type_1(output_format, site_id, contents, output)
break
elif "date_time_GMT" in line:
r = parse_type_2(output_format, site_id, contents, output)
break
else:
continue
if r is None:
logger.error('Could not process file: {}'.format(datafile))
else:
logger.info("Processed {}".format(datafile))
results.append(r)
results = sorted(results, key=attrgetter('lon', 'lat'))
gresults = groupby(results, attrgetter('lon', 'lat'))
for (glon, glat), group in gresults:
groups = [ x for x in list(group) if x ]
# Strip off the variable type if need be
gsite = groups[0].site
if gsite[-2:] in ['WV', 'BP', 'WL']:
gsite = gsite[:-2]
for result in groups:
gas = get_globals(glat, glon, result.z, result.name, gsite)
station_urn = IoosUrn(asset_type='station',
authority=gas['naming_authority'],
label=gsite)
if output_format == 'cf16':
# If CF, a file for each result dataframe
times = [ calendar.timegm(x.timetuple()) for x in result.df['time'] ]
verticals = result.df['depth'].values
output_filename = '{}.nc'.format(result.site)
ts = TimeSeries(output, latitude=glat, longitude=glon, station_name=gsite, global_attributes=gas, output_filename=output_filename, times=times, verticals=verticals)
for var in result.df.columns:
if var in ['date_time_GMT', 'datetime', 'time', 'depth', 'tz_cd', 'site_no', 'agency_cd']:
continue
try:
var_meta = copy(variable_map[var])
except KeyError:
logger.error("Variable {!s} was not found in variable map!".format(var))
continue
# Convert to floats
result.df[var] = result.df[var].map(to_floats)
if var_meta["units"].lower() in ["feet", "ft"]:
result.df[var] = result.df[var].apply(lambda x: None if pd.isnull(x) else x * 0.3048)
var_meta["units"] = "meters"
elif var_meta["units"].lower() in ["psi"]:
result.df[var] = result.df[var].apply(lambda x: None if pd.isnull(x) else x * 68.9476)
var_meta["units"] = "mbar"
elif var_meta["units"].lower() in ['millimeters of mercury']:
result.df[var] = result.df[var].apply(lambda x: None if pd.isnull(x) else x * 1.33322)
var_meta["units"] = "mbar"
# Now put the fillvalue we want to be interpreted
result.df.fillna(fillvalue, inplace=True)
if output_format == 'axiom':
# If Axiom, a file for each variable
output_directory = os.path.join(output, gsite)
output_filename = '{}_{}.nc'.format(result.site, var_meta['standard_name'])
ts = TimeSeries.from_dataframe(result.df, output_directory, output_filename, glat, glon, station_urn.urn, gas, var_meta["standard_name"], var_meta, sensor_vertical_datum='NAVD88', fillvalue=fillvalue, data_column=var, vertical_axis_name='height')
sensor_urn = urnify(station_urn.authority, station_urn.label, var_meta)
ts.add_instrument_metadata(urn=sensor_urn)
elif output_format == 'cf16':
# If CF, add variable to existing TimeSeries
try:
int(var[0])
variable_name = 'v_{}'.format(var)
except BaseException:
variable_name = var
ts.add_variable(variable_name, values=result.df[var].values, attributes=var_meta, fillvalue=fillvalue, sensor_vertical_datum='NAVD88')
def main(output_format, output, do_download, download_folder, filesubset=None):
if do_download is True:
try:
os.makedirs(download_folder)
except OSError:
pass
waf = 'http://ga.water.usgs.gov/flood/hurricane/sandy/datafiles/'
r = requests.get(waf)
soup = BeautifulSoup(r.text, "lxml")
for link in soup.find_all('a'):
# Skip non .txt files
site_id, ext = os.path.splitext(link['href'])
if ext != ".txt":
continue
if filesubset and site_id.lower() not in filesubset:
# Skip this file!
continue
csv_link = waf + link['href']
logger.info("Downloading '{}'".format(csv_link))
d = requests.get(csv_link)
try:
d.raise_for_status()
except requests.exceptions.HTTPError:
logger.error(
"Could not download: {!s}, skipping. Status code: {!s}".
format(csv_link, d.status_code))
continue
with open(
os.path.join(download_folder, os.path.basename(csv_link)),
'wt') as f:
f.write(d.text)
# Yes, this uses lots of RAM, but we need to match up lon/lat positions later on.
results = []
for datafile in os.listdir(download_folder):
site_id = os.path.splitext(os.path.basename(datafile))[0]
if filesubset and site_id.lower() not in filesubset:
# Skip this file!
continue
with open(os.path.join(download_folder, datafile)) as d:
contents = d.read()
r = None
for line in contents.split("\n"):
if "agency_cd" in line:
r = parse_type_1(output_format, site_id, contents, output)
break
elif "date_time_GMT" in line:
r = parse_type_2(output_format, site_id, contents, output)
break
else:
continue
if r is None:
logger.error('Could not process file: {}'.format(datafile))
else:
logger.info("Processed {}".format(datafile))
results.append(r)
results = sorted(results, key=attrgetter('lon', 'lat'))
gresults = groupby(results, attrgetter('lon', 'lat'))
for (glon, glat), group in gresults:
groups = [x for x in list(group) if x]
# Strip off the variable type if need be
gsite = groups[0].site
if gsite[-2:] in ['WV', 'BP', 'WL']:
gsite = gsite[:-2]
for result in groups:
gas = get_globals(glat, glon, result.z, result.name, gsite)
station_urn = IoosUrn(asset_type='station',
authority=gas['naming_authority'],
label=gsite)
if output_format == 'cf16':
# If CF, a file for each result dataframe
times = [
calendar.timegm(x.timetuple()) for x in result.df['time']
]
verticals = result.df['depth'].values
output_filename = '{}.nc'.format(result.site)
ts = TimeSeries(output,
latitude=glat,
longitude=glon,
station_name=gsite,
global_attributes=gas,
output_filename=output_filename,
times=times,
verticals=verticals)
for var in result.df.columns:
if var in [
'date_time_GMT', 'datetime', 'time', 'depth', 'tz_cd',
'site_no', 'agency_cd'
]:
continue
try:
var_meta = copy(variable_map[var])
except KeyError:
logger.error(
"Variable {!s} was not found in variable map!".format(
var))
continue
# Convert to floats
result.df[var] = result.df[var].map(to_floats)
if var_meta["units"].lower() in ["feet", "ft"]:
result.df[var] = result.df[var].apply(
lambda x: None if pd.isnull(x) else x * 0.3048)
var_meta["units"] = "meters"
elif var_meta["units"].lower() in ["psi"]:
result.df[var] = result.df[var].apply(
lambda x: None if pd.isnull(x) else x * 68.9476)
var_meta["units"] = "mbar"
elif var_meta["units"].lower() in ['millimeters of mercury']:
result.df[var] = result.df[var].apply(
lambda x: None if pd.isnull(x) else x * 1.33322)
var_meta["units"] = "mbar"
# Now put the fillvalue we want to be interpreted
result.df.fillna(fillvalue, inplace=True)
if output_format == 'axiom':
# If Axiom, a file for each variable
output_directory = os.path.join(output, gsite)
output_filename = '{}_{}.nc'.format(
result.site, var_meta['standard_name'])
ts = TimeSeries.from_dataframe(
result.df,
output_directory,
output_filename,
glat,
glon,
station_urn.urn,
gas,
var_meta["standard_name"],
var_meta,
sensor_vertical_datum='NAVD88',
fillvalue=fillvalue,
data_column=var,
vertical_axis_name='height')
sensor_urn = urnify(station_urn.authority,
station_urn.label, var_meta)
ts.add_instrument_metadata(urn=sensor_urn)
elif output_format == 'cf16':
# If CF, add variable to existing TimeSeries
try:
int(var[0])
variable_name = 'v_{}'.format(var)
except BaseException:
variable_name = var
ts.add_variable(variable_name,
values=result.df[var].values,
attributes=var_meta,
fillvalue=fillvalue,
sensor_vertical_datum='NAVD88')
