def read_cdf(fname):
"""
Read a CDF file that follows the ISTP/IACG guidelines.
Parameters
----------
fname : path-like
Location of single CDF file to read.
Returns
-------
list[GenericTimeSeries]
A list of time series objects, one for each unique time index within
the CDF file.
References
----------
Space Physics Guidelines for CDF https://spdf.gsfc.nasa.gov/sp_use_of_cdf.html
"""
cdf = cdflib.CDF(str(fname))
# Extract the time varying variables
cdf_info = cdf.cdf_info()
meta = cdf.globalattsget()
all_var_keys = cdf_info['rVariables'] + cdf_info['zVariables']
var_attrs = {key: cdf.varattsget(key) for key in all_var_keys}
# Get keys that depend on time
var_keys = [var for var in var_attrs if 'DEPEND_0' in var_attrs[var]]
# Get unique time index keys
time_index_keys = sorted(set([var_attrs[var]['DEPEND_0'] for var in var_keys]))
all_ts = []
# For each time index, construct a GenericTimeSeries
for index_key in time_index_keys:
try:
index = cdf.varget(index_key)
except ValueError:
# Empty index for cdflib >= 0.3.20
continue
if index is None:
# Empty index for cdflib <0.3.20
continue
# TODO: use to_astropy_time() instead here when we drop pandas in timeseries
index = CDFepoch.to_datetime(index)
df = pd.DataFrame(index=pd.DatetimeIndex(name=index_key, data=index))
units = {}
for var_key in sorted(var_keys):
attrs = var_attrs[var_key]
if attrs['DEPEND_0'] != index_key:
continue
# Get data
if cdf.varinq(var_key)['Last_Rec'] == -1:
log.debug(f'Skipping {var_key} in {fname} as it has zero elements')
continue
data = cdf.varget(var_key)
# Get units
if 'UNITS' in attrs:
unit_str = attrs['UNITS']
try:
unit = u.Unit(unit_str)
except ValueError:
if unit_str in _known_units:
unit = _known_units[unit_str]
else:
warn_user(f'astropy did not recognize units of "{unit_str}". '
'Assigning dimensionless units. '
'If you think this unit should not be dimensionless, '
'please raise an issue at https://github.com/sunpy/sunpy/issues')
unit = u.dimensionless_unscaled
else:
warn_user(f'No units provided for variable "{var_key}". '
'Assigning dimensionless units.')
unit = u.dimensionless_unscaled
if data.ndim == 2:
# Multiple columns, give each column a unique label
for i, col in enumerate(data.T):
df[var_key + f'_{i}'] = col
units[var_key + f'_{i}'] = unit
else:
# Single column
df[var_key] = data
units[var_key] = unit
all_ts.append(GenericTimeSeries(data=df, units=units, meta=meta))
if not len(all_ts):
log.debug(f'No data found in file {fname}')
return all_ts
def time_epoch_to_datetime_tt2000(self, to_np):
cdfepoch.to_datetime(self.epochs_tt2000)
def _convert_cdf_time_types(data,
atts,
properties,
to_datetime=False,
to_unixtime=False):
'''
# Converts CDF time types into either datetime objects, unixtime, or nothing
# If nothing, ALL CDF_EPOCH16 types are converted to CDF_EPOCH, because xarray can't handle int64s
'''
if not hasattr(data, '__len__'):
data = [data]
if to_datetime and to_unixtime:
print(
"Cannot convert to both unixtime and datetime. Continuing with conversion to unixtime."
)
to_datetime = False
# Convert all data in the "data" variable to unixtime or datetime if needed
data_type = properties['Data_Type_Description']
if len(data) == 0 or data_type not in ('CDF_EPOCH', 'CDF_EPOCH16',
'CDF_TIME_TT2000'):
new_data = data
else:
if to_datetime:
new_data = cdfepoch.to_datetime(data)
if 'UNITS' in atts:
atts['UNITS']['Data'] = 'Datetime (UTC)'
elif to_unixtime:
new_data = cdfepoch.unixtime(data)
if 'UNITS' in atts:
atts['UNITS']['Data'] = 'seconds'
else:
if data_type == 'CDF_EPOCH16':
new_data = cdfepoch.compute(cdfepoch.breakdown(data)[0:7])
else:
new_data = data
# Convert all the attributes in the "atts" dictionary to unixtime or datetime if needed
new_atts = {}
for att in atts:
data_type = atts[att]['Data_Type']
data = atts[att]['Data']
if not hasattr(data, '__len__'):
data = [data]
if len(data) == 0 or data_type not in ('CDF_EPOCH', 'CDF_EPOCH16',
'CDF_TIME_TT2000'):
new_atts[att] = data
else:
if to_datetime:
new_atts[att] = cdfepoch.to_datetime(data)
elif to_unixtime:
new_atts[att] = cdfepoch.unixtime(data)
else:
if data_type == 'CDF_EPOCH16':
new_atts[att] = cdfepoch.compute(
cdfepoch.breakdown(data)[0:7])
else:
new_atts[att] = data
return new_data, new_atts
def time_epoch_to_datetime(self, to_np):
cdfepoch.to_datetime(self.epochs)
def read_cdf(fname):
"""
Read a CDF file that follows the ISTP/IACG guidelines.
Parameters
----------
fname : path-like
Location of single CDF file to read.
Returns
-------
list[GenericTimeSeries]
A list of time series objects, one for each unique time index within
the CDF file.
References
----------
Space Physics Guidelines for CDF https://spdf.gsfc.nasa.gov/sp_use_of_cdf.html
"""
cdf = cdflib.CDF(str(fname))
# Extract the time varying variables
cdf_info = cdf.cdf_info()
meta = cdf.globalattsget()
all_var_keys = cdf_info['rVariables'] + cdf_info['zVariables']
var_attrs = {key: cdf.varattsget(key) for key in all_var_keys}
# Get keys that depend on time
var_keys = [var for var in var_attrs if 'DEPEND_0' in var_attrs[var]]
# Get unique time index keys
time_index_keys = sorted(
set([var_attrs[var]['DEPEND_0'] for var in var_keys]))
all_ts = []
# For each time index, construct a GenericTimeSeries
for index_key in time_index_keys:
index = cdf.varget(index_key)
# TODO: use to_astropy_time() instead here when we drop pandas in timeseries
index = CDFepoch.to_datetime(index)
df = pd.DataFrame(index=pd.DatetimeIndex(name=index_key, data=index))
units = {}
for var_key in sorted(var_keys):
attrs = var_attrs[var_key]
if attrs['DEPEND_0'] != index_key:
continue
# Get data
data = cdf.varget(var_key)
# Get units
unit = attrs['UNITS']
if unit in ['None', '', 'unitless']:
unit = u.dimensionless_unscaled
else:
unit = u.Unit(unit)
if data.ndim == 2:
# Multiple columns, give each column a unique label
for i, col in enumerate(data.T):
df[var_key + f'_{i}'] = col
units[var_key + f'_{i}'] = unit
else:
# Single column
df[var_key] = data
units[var_key] = unit
all_ts.append(GenericTimeSeries(data=df, units=units, meta=meta))
return all_ts