def test_from_dict(self):
d = dict(standard_name='lwe_thickness_of_precipitation_amount')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount'
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
vertical_datum='NAVD88')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#vertical_datum=navd88'
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
vertical_datum='NAVD88',
discriminant='2')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount-2#vertical_datum=navd88'
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: sum (interval: PT24H) time: mean')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:sum;interval=pt24h'
# Interval as a dict key (not inline with cell_methods)
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: sum time: mean',
interval='pt24h')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:sum;interval=pt24h'
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: minimum within years time: mean over years')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean_over_years,time:minimum_within_years'
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: variance (interval: PT1H comment: sampled instantaneously)')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:variance;interval=pt1h'
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: variance time: mean (interval: PT1H comment: sampled instantaneously)')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:variance;interval=pt1h'
# Interval specified twice
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: variance time: mean (interval: PT1H comment: sampled instantaneously)',
interval='PT1H')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:variance;interval=pt1h'
# Interval specified twice
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: variance time: mean (interval: PT1H comment: sampled instantaneously)',
interval='PT1H',
discriminant='2')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount-2#cell_methods=time:mean,time:variance;interval=pt1h'
def test_from_variable(self):
filename = 'test_urn_from_variable.nc'
times = [0, 1000, 2000, 3000, 4000, 5000]
verticals = None
ts = TimeSeries(output_directory=self.output_directory,
latitude=self.latitude,
longitude=self.longitude,
station_name=self.station_name,
global_attributes=self.global_attributes,
output_filename=filename,
times=times,
verticals=verticals)
values = [20, 21, 22, 23, 24, 25]
attrs = dict(standard_name='lwe_thickness_of_precipitation_amount',
vertical_datum='NAVD88')
ts.add_variable('temperature', values=values, attributes=attrs)
ts.ncd.sync()
urn = urnify('axiom', 'foo', ts.ncd.variables['temperature'])
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#vertical_datum=navd88'
values = [20, 21, 22, 23, 24, 25]
attrs = dict(
standard_name='lwe_thickness_of_precipitation_amount',
cell_methods=
'time: variance (interval: PT1H comment: sampled instantaneously)')
ts.add_variable('temperature2', values=values, attributes=attrs)
ts.ncd.sync()
urn = urnify('axiom', 'foo', ts.ncd.variables['temperature2'])
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:variance;interval=pt1h'
values = [20, 21, 22, 23, 24, 25]
attrs = dict(
standard_name='lwe_thickness_of_precipitation_amount',
cell_methods=
'time: variance time: mean (interval: PT1H comment: sampled instantaneously)'
)
ts.add_variable('temperature3', values=values, attributes=attrs)
ts.ncd.sync()
urn = urnify('axiom', 'foo', ts.ncd.variables['temperature3'])
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:variance;interval=pt1h'
values = [20, 21, 22, 23, 24, 25]
attrs = dict(
standard_name='lwe_thickness_of_precipitation_amount',
cell_methods=
'time: variance time: mean (interval: PT1H comment: sampled instantaneously)',
discriminant='2')
ts.add_variable('temperature4', values=values, attributes=attrs)
ts.ncd.sync()
urn = urnify('axiom', 'foo', ts.ncd.variables['temperature4'])
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount-2#cell_methods=time:mean,time:variance;interval=pt1h'
def test_from_variable(self):
filename = 'test_urn_from_variable.nc'
times = [0, 1000, 2000, 3000, 4000, 5000]
verticals = None
ts = TimeSeries(output_directory=self.output_directory,
latitude=self.latitude,
longitude=self.longitude,
station_name=self.station_name,
global_attributes=self.global_attributes,
output_filename=filename,
times=times,
verticals=verticals)
values = [20, 21, 22, 23, 24, 25]
attrs = dict(standard_name='lwe_thickness_of_precipitation_amount',
vertical_datum='NAVD88')
ts.add_variable('temperature', values=values, attributes=attrs)
ts.ncd.sync()
urn = urnify('axiom', 'foo', ts.ncd.variables['temperature'])
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#vertical_datum=navd88'
values = [20, 21, 22, 23, 24, 25]
attrs = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: variance (interval: PT1H comment: sampled instantaneously)')
ts.add_variable('temperature2', values=values, attributes=attrs)
ts.ncd.sync()
urn = urnify('axiom', 'foo', ts.ncd.variables['temperature2'])
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:variance;interval=pt1h'
values = [20, 21, 22, 23, 24, 25]
attrs = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: variance time: mean (interval: PT1H comment: sampled instantaneously)')
ts.add_variable('temperature3', values=values, attributes=attrs)
ts.ncd.sync()
urn = urnify('axiom', 'foo', ts.ncd.variables['temperature3'])
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:variance;interval=pt1h'
values = [20, 21, 22, 23, 24, 25]
attrs = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: variance time: mean (interval: PT1H comment: sampled instantaneously)',
discriminant='2')
ts.add_variable('temperature4', values=values, attributes=attrs)
ts.ncd.sync()
urn = urnify('axiom', 'foo', ts.ncd.variables['temperature4'])
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount-2#cell_methods=time:mean,time:variance;interval=pt1h'
ts.close()
def test_instrumnet_metadata_variable(self):
filename = 'test_timeseries.nc'
times = [0, 1000, 2000, 3000, 4000, 5000]
verticals = None
gats = copy(self.global_attributes)
gats['naming_authority'] = 'pyaxiom'
gats['geospatial_bounds_vertical_crs'] = 'NAVD88'
ts = TimeSeries(output_directory=self.output_directory,
latitude=self.latitude,
longitude=self.longitude,
station_name=self.station_name,
global_attributes=gats,
output_filename=filename,
times=times,
verticals=verticals)
values = [20, 21, 22, 23, 24, 25]
attrs = dict(standard_name='sea_water_temperature')
ts.add_variable('temperature', values=values, attributes=attrs, create_instrument_variable=True, sensor_vertical_datum='bar')
nc = netCDF4.Dataset(os.path.join(self.output_directory, filename))
assert nc is not None
assert nc.geospatial_bounds_vertical_crs == 'NAVD88' # First one set
datavar = nc.variables.get('temperature')
instrument_var_name = datavar.instrument
instvar = nc.variables[instrument_var_name]
assert instvar.short_name == 'sea_water_temperature'
assert instvar.ioos_code == urnify(gats['naming_authority'], gats['id'], attrs)
def add_instrument_variable(self, variable_name):
if variable_name not in self._nc.variables:
logger.error("Variable {} not found in file, cannot create instrument metadata variable")
return
elif 'id' not in self._nc.ncattrs() or 'naming_authority' not in self._nc.ncattrs():
logger.error("Global attributes 'id' and 'naming_authority' are required to create an instrument variable")
return
instr_var_name = "{}_instrument".format(variable_name)
instrument = self._nc.createVariable(instr_var_name, "i4")
datavar = self._nc.variables[variable_name]
vats = { k: getattr(datavar, k) for k in datavar.ncattrs() }
instrument_urn = urnify(self._nc.naming_authority, self._nc.id, vats)
inst_urn = IoosUrn.from_string(instrument_urn)
instrument.long_name = 'Instrument measuring {} from {}'.format(inst_urn.component, inst_urn.label)
instrument.ioos_code = instrument_urn
instrument.short_name = inst_urn.component
instrument.definition = "http://mmisw.org/ont/ioos/definition/sensorID"
datavar.instrument = instr_var_name
# Append the instrument to the ancilary variables
av = getattr(datavar, 'ancillary_variables', '')
av += ' {}'.format(instr_var_name)
datavar.ancillary_variables = av.strip()
self._nc.sync()
def test_from_dict(self):
d = dict(standard_name='lwe_thickness_of_precipitation_amount')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount'
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
vertical_datum='NAVD88')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#vertical_datum=navd88'
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
vertical_datum='NAVD88',
discriminant='2')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount-2#vertical_datum=navd88'
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: sum (interval: PT24H) time: mean')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:sum;interval=pt24h'
# Interval as a dict key (not inline with cell_methods)
d = dict(standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: sum time: mean',
interval='pt24h')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:sum;interval=pt24h'
d = dict(
standard_name='lwe_thickness_of_precipitation_amount',
cell_methods='time: minimum within years time: mean over years')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean_over_years,time:minimum_within_years'
d = dict(
standard_name='lwe_thickness_of_precipitation_amount',
cell_methods=
'time: variance (interval: PT1H comment: sampled instantaneously)')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:variance;interval=pt1h'
d = dict(
standard_name='lwe_thickness_of_precipitation_amount',
cell_methods=
'time: variance time: mean (interval: PT1H comment: sampled instantaneously)'
)
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:variance;interval=pt1h'
# Interval specified twice
d = dict(
standard_name='lwe_thickness_of_precipitation_amount',
cell_methods=
'time: variance time: mean (interval: PT1H comment: sampled instantaneously)',
interval='PT1H')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:variance;interval=pt1h'
# Interval specified twice
d = dict(
standard_name='lwe_thickness_of_precipitation_amount',
cell_methods=
'time: variance time: mean (interval: PT1H comment: sampled instantaneously)',
interval='PT1H',
discriminant='2')
urn = urnify('axiom', 'foo', d)
assert urn == 'urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount-2#cell_methods=time:mean,time:variance;interval=pt1h'
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')
