def test_from_bad_string(self):
u = IoosUrn.from_string('urn:ioos:sensor:whatami')
assert u.urn is None
u = IoosUrn.from_string('urn:ioos:nothinghere')
assert u.urn is None
u = IoosUrn.from_string('urn:totesbroken')
assert u.urn is None
def test_from_bad_string(self):
u = IoosUrn.from_string('urn:ioos:sensor:whatami')
assert u.urn is None
u = IoosUrn.from_string('urn:ioos:nothinghere')
assert u.urn is None
u = IoosUrn.from_string('urn:totesbroken')
assert u.urn is None
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 create_file(output, ncfile, varname, df):
with EnhancedDataset(ncfile) as ncd:
var = ncd[varname]
latitude = ncd.get_variables_by_attributes(
standard_name='latitude')[0][:]
longitude = ncd.get_variables_by_attributes(
standard_name='longitude')[0][:]
project = ncd.original_folder
feature_name = '{}_{}'.format(project, ncd.MOORING).lower()
station_urn = IoosUrn(authority=ncd.naming_authority,
label=feature_name,
asset_type='station').urn
discriminant = ncd.id.replace('-', '_')
output_filename = '{0}_{1}-{2}_{3}_TO_{4}.nc'.format(
feature_name, var.name, discriminant,
df['time'].min().strftime("%Y%m%dT%H%M%SZ"),
df['time'].max().strftime("%Y%m%dT%H%M%SZ"))
output_directory = os.path.join(output, feature_name)
if not os.path.isdir(output_directory):
os.makedirs(output_directory)
file_global_attributes = {k: getattr(ncd, k) for k in ncd.ncattrs()}
# original_folder is the project name
file_global_attributes.update(
dict(title='{} - {}'.format(project, ncd.MOORING),
id=feature_name))
variable_attributes = {k: getattr(var, k) for k in var.ncattrs()}
# Add the specific sensor as a discriminant
variable_attributes.update(dict(discriminant=discriminant))
fillvalue = -9999.9
if hasattr(var, "_FillValue"):
fillvalue = var._FillValue
vertical_datum = None
if 'crs' in ncd.variables and hasattr(ncd.variables['crs'],
'vertical_datum'):
vertical_datum = ncd.variables['crs'].vertical_datum
ts = TimeSeries.from_dataframe(df,
output_directory,
output_filename,
latitude,
longitude,
station_urn,
file_global_attributes,
var.standard_name,
variable_attributes,
sensor_vertical_datum=vertical_datum,
fillvalue=fillvalue,
vertical_axis_name='height',
vertical_positive='down')
ts.add_instrument_variable(variable_name=var.standard_name)
del ts
def test_cdiac_urn(self):
u = IoosUrn.from_string(
'urn:ioos:sensor:gov.ornl.cdiac:cheeca_80w_25n:sea_water_temperature'
)
assert u.asset_type == 'sensor'
assert u.authority == 'gov.ornl.cdiac'
assert u.label == 'cheeca_80w_25n'
assert u.component == 'sea_water_temperature'
def test_messy_urn(self):
u = IoosUrn.from_string(
'urn:ioos:sensor:myauthority:mylabel:standard_name#key=key1:value1,key2:value2;some_other_key=some_other_value'
)
assert u.asset_type == 'sensor'
assert u.authority == 'myauthority'
assert u.label == 'mylabel'
assert u.component == 'standard_name#key=key1:value1,key2:value2;some_other_key=some_other_value'
def test_from_string(self):
u = IoosUrn.from_string('urn:ioos:sensor:myauthority:mylabel')
assert u.asset_type == 'sensor'
assert u.authority == 'myauthority'
assert u.label == 'mylabel'
u = IoosUrn.from_string('urn:ioos:sensor:myauthority:mylabel:mycomponent')
assert u.asset_type == 'sensor'
assert u.authority == 'myauthority'
assert u.label == 'mylabel'
assert u.component == 'mycomponent'
u = IoosUrn.from_string('urn:ioos:sensor:myauthority:mylabel:mycomponent:myversion')
assert u.asset_type == 'sensor'
assert u.authority == 'myauthority'
assert u.label == 'mylabel'
assert u.component == 'mycomponent'
assert u.version == 'myversion'
def test_change_sensor_to_station(self):
u = IoosUrn.from_string('urn:ioos:sensor:myauthority:mylabel:mycomponent')
assert u.asset_type == 'sensor'
assert u.authority == 'myauthority'
assert u.label == 'mylabel'
assert u.component == 'mycomponent'
u.asset_type = 'station'
u.component = None
assert u.urn == 'urn:ioos:station:myauthority:mylabel'
def test_change_sensor_to_station(self):
u = IoosUrn.from_string(
'urn:ioos:sensor:myauthority:mylabel:mycomponent')
assert u.asset_type == 'sensor'
assert u.authority == 'myauthority'
assert u.label == 'mylabel'
assert u.component == 'mycomponent'
u.asset_type = 'station'
u.component = None
assert u.urn == 'urn:ioos:station:myauthority:mylabel'
def test_from_string(self):
u = IoosUrn.from_string('urn:ioos:sensor:myauthority:mylabel')
assert u.asset_type == 'sensor'
assert u.authority == 'myauthority'
assert u.label == 'mylabel'
u = IoosUrn.from_string(
'urn:ioos:sensor:myauthority:mylabel:mycomponent')
assert u.asset_type == 'sensor'
assert u.authority == 'myauthority'
assert u.label == 'mylabel'
assert u.component == 'mycomponent'
u = IoosUrn.from_string(
'urn:ioos:sensor:myauthority:mylabel:mycomponent:myversion')
assert u.asset_type == 'sensor'
assert u.authority == 'myauthority'
assert u.label == 'mylabel'
assert u.component == 'mycomponent'
assert u.version == 'myversion'
def test_setattr(self):
u = IoosUrn()
u.asset_type = 'sensor'
u.authority = 'me'
u.label = 'mysupersensor'
assert u.urn == 'urn:ioos:sensor:me:mysupersensor'
u.version = 'abc'
assert u.urn == 'urn:ioos:sensor:me:mysupersensor:abc'
u.component = 'temp'
assert u.urn == 'urn:ioos:sensor:me:mysupersensor:temp:abc'
def test_setattr(self):
u = IoosUrn()
u.asset_type = 'sensor'
u.authority = 'me'
u.label = 'mysupersensor'
assert u.urn == 'urn:ioos:sensor:me:mysupersensor'
u.version = 'abc'
assert u.urn == 'urn:ioos:sensor:me:mysupersensor:abc'
u.component = 'temp'
assert u.urn == 'urn:ioos:sensor:me:mysupersensor:temp:abc'
def test_from_long_string(self):
u = IoosUrn.from_string(
'urn:ioos:sensor:whatami:wow:i:have:lots:of:things')
assert u.urn == 'urn:ioos:sensor:whatami:wow:i:have'
def __init__(self, output_directory, latitude, longitude, station_name, global_attributes, times=None, verticals=None, vertical_fill=None, output_filename=None, vertical_axis_name=None, vertical_positive=None):
if output_filename is None:
output_filename = '{}_{}.nc'.format(station_name, int(random.random() * 100000))
logger.info("No output filename specified, saving as {}".format(output_filename))
self.vertical_positive = vertical_positive or 'down'
self.vertical_axis_name = vertical_axis_name or 'z'
self.time_axis_name = 'time'
# Make directory
if not os.path.exists(output_directory):
os.makedirs(output_directory)
self.time = None
self.out_file = os.path.abspath(os.path.join(output_directory, output_filename))
if os.path.isfile(self.out_file):
os.remove(self.out_file)
with EnhancedDataset(self.out_file, 'w') as nc:
# Global attributes
# These are set by this script, we don't someone to be able to set them manually
global_skips = ["time_coverage_start", "time_coverage_end", "time_coverage_duration", "time_coverage_resolution",
"featureType", "geospatial_vertical_positive", "geospatial_vertical_min", "geospatial_vertical_max",
"geospatial_lat_min", "geospatial_lon_min", "geospatial_lat_max", "geospatial_lon_max", "geospatial_bounds"
"geospatial_vertical_resolution", "geospatial_lat_resolution", "geospatial_lon_resolution",
"Conventions", "date_created", "date_modified", "date_issued"]
for k, v in global_attributes.items():
if v is None:
v = "None"
if k not in global_skips:
nc.setncattr(k, v)
now_date = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:00Z")
nc.setncattr("Conventions", "CF-1.6,ACDD-1.3")
nc.setncattr("date_created", now_date)
nc.setncattr("date_modified", now_date)
nc.setncattr("date_issued", now_date)
if not hasattr(nc, "date_metadata_modified"):
nc.setncattr("date_metadata_modified", now_date)
# Allow the customization of this attribute
if 'cdm_data_type' not in global_attributes:
nc.setncattr('cdm_data_type', 'Station')
old_history = getattr(nc, 'history', '')
new_history = '{} - {} - {}'.format(now_date, 'pyaxiom', 'File created using pyaxiom')
if old_history:
nc.setncattr('history', '{}\n{}'.format(old_history, new_history))
else:
nc.setncattr('history', new_history)
# Station name
nc.createDimension("feature_type_instance", len(station_name))
name = nc.createVariable("feature_type_instance", "S1", ("feature_type_instance",))
name.cf_role = "timeseries_id"
name.long_name = "Identifier for each feature type instance"
name[:] = list(station_name)
# Location
lat = nc.createVariable("latitude", get_type(latitude))
lat.units = "degrees_north"
lat.standard_name = "latitude"
lat.long_name = "sensor latitude"
lat.axis = "Y"
lat.valid_min = latitude
lat.valid_max = latitude
lat[:] = latitude
nc.setncattr("geospatial_lat_min", latitude)
nc.setncattr("geospatial_lat_max", latitude)
nc.setncattr("geospatial_lat_resolution", 0)
nc.setncattr("geospatial_lat_units", "degrees_north")
lon = nc.createVariable("longitude", get_type(longitude))
lon.units = "degrees_east"
lon.standard_name = "longitude"
lon.long_name = "sensor longitude"
lon.axis = "X"
lon.valid_min = longitude
lon.valid_max = longitude
lon[:] = longitude
nc.setncattr("geospatial_lon_min", longitude)
nc.setncattr("geospatial_lon_max", longitude)
nc.setncattr("geospatial_lon_resolution", 0)
nc.setncattr("geospatial_lon_units", "degrees_east")
nc.setncattr("geospatial_bounds", "POINT({} {})".format(longitude, latitude))
if not hasattr(nc, "geospatial_bounds_crs"):
nc.setncattr("geospatial_bounds_crs", "EPSG:4326")
# Metadata variables
self.crs = nc.createVariable("crs", "i4")
self.crs.long_name = "http://www.opengis.net/def/crs/EPSG/0/4326"
self.crs.grid_mapping_name = "latitude_longitude"
self.crs.epsg_code = "EPSG:4326"
self.crs.semi_major_axis = float(6378137.0)
self.crs.inverse_flattening = float(298.257223563)
platform = nc.createVariable("platform", "i4")
platform.definition = "http://mmisw.org/ont/ioos/definition/stationID"
urn = IoosUrn.from_string(station_name)
if urn.valid() is True:
platform.short_name = global_attributes.get("title", urn.label)
platform.long_name = global_attributes.get('summary', 'Station {}'.format(urn.label))
platform.ioos_code = urn.urn
else:
platform.short_name = global_attributes.get("title", station_name)
platform.long_name = global_attributes.get("summary", station_name)
platform.ioos_code = station_name
if vertical_fill is None:
vertical_fill = -9999.9
self.vertical_fill = vertical_fill
self._nc = EnhancedDataset(self.out_file, 'a')
self.setup_times_and_verticals(times, verticals)
logger.info("Created file at '{}'".format(self.out_file))
def dictify_urn(urn, combine_interval=True):
"""
By default, this will put the `interval` as part of the `cell_methods`
attribute (NetCDF CF style). To return `interval` as its own key, use
the `combine_interval=False` parameter.
"""
ioos_urn = IoosUrn.from_string(urn)
if ioos_urn.valid() is False:
return dict()
if ioos_urn.asset_type != 'sensor':
logger.error("This function only works on 'sensor' URNs.")
return dict()
if '#' in ioos_urn.component:
standard_name, extras = ioos_urn.component.split('#')
else:
standard_name = ioos_urn.component
extras = ''
d = dict(standard_name=standard_name)
# Discriminant
if '-' in ioos_urn.component:
d['discriminant'] = standard_name.split('-')[-1]
d['standard_name'] = standard_name.split('-')[0]
intervals = []
cell_methods = []
if extras:
for section in extras.split(';'):
key, values = section.split('=')
if key == 'interval':
# special case, intervals should be appended to the cell_methods
for v in values.split(','):
intervals.append(v)
else:
if key == 'cell_methods':
value = [ x.replace('_', ' ').replace(':', ': ') for x in values.split(',') ]
cell_methods = value
else:
value = ' '.join([x.replace('_', ' ').replace(':', ': ') for x in values.split(',')])
d[key] = value
if combine_interval is True:
if cell_methods and intervals:
if len(cell_methods) == len(intervals):
d['cell_methods'] = ' '.join([ '{} (interval: {})'.format(x[0], x[1].upper()) for x in zip(cell_methods, intervals) ])
else:
d['cell_methods'] = ' '.join(cell_methods)
for i in intervals:
d['cell_methods'] += ' (interval: {})'.format(i.upper())
elif cell_methods:
d['cell_methods'] = ' '.join(cell_methods)
for i in intervals:
d['cell_methods'] += ' (interval: {})'.format(i.upper())
elif intervals:
raise ValueError("An interval without a cell_method is not allowed! Not possible!")
else:
d['cell_methods'] = ' '.join(cell_methods)
d['interval'] = ','.join(intervals).upper()
if 'vertical_datum' in d:
d['vertical_datum'] = d['vertical_datum'].upper()
return d
def urnify_from_dict(naming_authority, station_identifier, data_dict):
def clean_value(v):
return v.replace('(', '').replace(')', '').strip().replace(' ', '_')
extras = []
intervals = [] # Because it can be part of cell_methods and its own dict key
if 'cell_methods' in data_dict and data_dict['cell_methods']:
cm = data_dict['cell_methods']
keys = []
values = []
sofar = ''
for i, c in enumerate(cm):
if c == ":":
if len(keys) == len(values):
keys.append(clean_value(sofar))
else:
for j in reversed(range(0, i)):
if cm[j] == " ":
key = clean_value(cm[j+1:i])
values.append(clean_value(sofar.replace(key, '')))
keys.append(key)
break
sofar = ''
else:
sofar += c
# The last value needs appending
values.append(clean_value(sofar))
pairs = zip(keys, values)
mems = []
cell_intervals = []
pairs = sorted(pairs)
for group, members in itertools.groupby(pairs, lambda x: x[0]):
if group == 'interval':
cell_intervals = [m[1] for m in members]
elif group in ['time', 'area']: # Ignore 'comments'. May need to add more things here...
member_strings = []
for m in members:
member_strings.append('{}:{}'.format(group, m[1]))
mems.append(','.join(member_strings))
if mems:
extras.append('cell_methods={}'.format(','.join(mems)))
if cell_intervals:
intervals += cell_intervals
if 'bounds' in data_dict and data_dict['bounds']:
extras.append('bounds={0}'.format(data_dict['bounds']))
if 'vertical_datum' in data_dict and data_dict['vertical_datum']:
extras.append('vertical_datum={0}'.format(data_dict['vertical_datum']))
if 'interval' in data_dict and data_dict['interval']:
if isinstance(data_dict['interval'], (list, tuple,)):
intervals += data_dict['interval']
elif isinstance(data_dict['interval'], str):
intervals += [data_dict['interval']]
if 'standard_name' in data_dict and data_dict['standard_name']:
variable_name = data_dict['standard_name']
elif 'name' in data_dict and data_dict['name']:
variable_name = data_dict['name']
else:
variable_name = ''.join(random.choice(string.ascii_uppercase) for _ in range(8)).lower()
logger.warning("Had to randomly generate a variable name: {0}".format(variable_name))
if 'discriminant' in data_dict and data_dict['discriminant']:
variable_name = '{}-{}'.format(variable_name, data_dict['discriminant'])
if intervals:
intervals = list(set(intervals)) # Unique them
extras.append('interval={}'.format(','.join(intervals)))
if extras:
variable_name = '{0}#{1}'.format(variable_name, ';'.join(extras))
u = IoosUrn(asset_type='sensor',
authority=naming_authority,
label=station_identifier,
component=variable_name,
version=None)
return u.urn
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 test_args(self):
u = IoosUrn(asset_type='sensor', authority='me', label='mysupersensor')
assert u.urn == 'urn:ioos:sensor:me:mysupersensor'
def dictify_urn(urn, combine_interval=True):
"""
By default, this will put the `interval` as part of the `cell_methods`
attribute (NetCDF CF style). To return `interval` as its own key, use
the `combine_interval=False` parameter.
"""
ioos_urn = IoosUrn.from_string(urn)
if ioos_urn.valid() is False:
return dict()
if ioos_urn.asset_type != 'sensor':
logger.error("This function only works on 'sensor' URNs.")
return dict()
if '#' in ioos_urn.component:
standard_name, extras = ioos_urn.component.split('#')
else:
standard_name = ioos_urn.component
extras = ''
d = dict(standard_name=standard_name)
# Discriminant
if '-' in ioos_urn.component:
d['discriminant'] = ioos_urn.component.split('-')[-1]
d['standard_name'] = ioos_urn.component.split('-')[0]
intervals = []
cell_methods = []
if extras:
for section in extras.split(';'):
key, values = section.split('=')
if key == 'interval':
# special case, intervals should be appended to the cell_methods
for v in values.split(','):
intervals.append(v)
else:
if key == 'cell_methods':
value = [ x.replace('_', ' ').replace(':', ': ') for x in values.split(',') ]
cell_methods = value
else:
value = ' '.join([x.replace('_', ' ').replace(':', ': ') for x in values.split(',')])
d[key] = value
if combine_interval is True:
if cell_methods and intervals:
if len(cell_methods) == len(intervals):
d['cell_methods'] = ' '.join([ '{} (interval: {})'.format(x[0], x[1].upper()) for x in zip(cell_methods, intervals) ])
else:
d['cell_methods'] = ' '.join(cell_methods)
for i in intervals:
d['cell_methods'] += ' (interval: {})'.format(i.upper())
elif cell_methods:
d['cell_methods'] = ' '.join(cell_methods)
for i in intervals:
d['cell_methods'] += ' (interval: {})'.format(i.upper())
elif intervals:
raise ValueError("An interval without a cell_method is not allowed! Not possible!")
else:
d['cell_methods'] = ' '.join(cell_methods)
d['interval'] = ','.join(intervals).upper()
if 'vertical_datum' in d:
d['vertical_datum'] = d['vertical_datum'].upper()
return d
def test_cdiac_urn(self):
u = IoosUrn.from_string('urn:ioos:sensor:gov.ornl.cdiac:cheeca_80w_25n:sea_water_temperature')
assert u.asset_type == 'sensor'
assert u.authority == 'gov.ornl.cdiac'
assert u.label == 'cheeca_80w_25n'
assert u.component == 'sea_water_temperature'
def test_constructor_no_data(self):
u = IoosUrn()
assert u.urn is None
def test_constructor_with_bad_data(self):
u = IoosUrn(notanattribute='foo')
assert u.urn is None
def test_station_cant_have_component(self):
u = IoosUrn(asset_type='station', component='something')
assert u.urn is None
def test_from_long_string(self):
u = IoosUrn.from_string('urn:ioos:sensor:whatami:wow:i:have:lots:of:things')
assert u.urn == 'urn:ioos:sensor:whatami:wow:i:have'
def test_no_label(self):
u = IoosUrn(asset_type='station', authority='me')
assert u.urn is None
def test_messy_urn(self):
u = IoosUrn.from_string('urn:ioos:sensor:myauthority:mylabel:standard_name#key=key1:value1,key2:value2;some_other_key=some_other_value')
assert u.asset_type == 'sensor'
assert u.authority == 'myauthority'
assert u.label == 'mylabel'
assert u.component == 'standard_name#key=key1:value1,key2:value2;some_other_key=some_other_value'
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')
