def get_env(group):
# type: (List[ReflData]) -> Columns
"""
Extract sample environment from all fields in group into separate fields.
"""
head = group[0]
# Gather environment variables such as temperature and field.
# Make sure they are all in the same units.
columns = dict((e.name, []) for e in head.sample.environment)
converter = dict(
(e.name, unit.Converter(e.units)) for e in head.sample.environment)
for data in group:
for env_name, env_list in columns.items():
env = data.sample.environment.get(env_name, None)
if env is not None:
values = converter[env_name](env.average, units=env.units)
else:
values = None
env_list.append(values)
# Drop environment variables that are not defined in every file
columns = dict((env_name, env_list)
for env_name, env_list in columns.items()
if not any(v is None for v in env_list))
return columns
def data_as(field, units):
"""
Return value of field in the desired units.
"""
if field.name.split('/')[-1] == 'sourceAperture':
return process_sourceAperture(field, units)
else:
converter = unit.Converter(field.attrs.get('units', ''))
value = converter(field.value, units)
return value
def data_as(field, units):
"""
Return value of field in the desired units.
"""
if field.name.split('/')[-1] == 'sourceAperture':
return process_sourceAperture(field, units)
else:
units_in = field.attrs.get('units', '')
if type(units_in) == bytes:
units_in = units_in.decode('utf-8')
converter = unit.Converter(units_in)
value = converter(field.value, units)
return value
def process_sourceAperture(field, units):
import numpy as np
def handler(v):
return np.float(v.split()[0])
handle_values = np.vectorize(handler)
value = handle_values(field.value)
units_from = ""
v0 = field.value[0].split()
if len(v0) > 1:
units_from = v0[1]
converter = unit.Converter(units_from)
return converter(value, units)
def data_as(group, fieldname, units, rep=1):
"""
Return value of field in the desired units.
"""
if fieldname not in group:
return np.NaN
field = group[fieldname]
converter = unit.Converter(field.attrs.get('units', ''))
value = converter(field.value, units)
if rep != 1:
if value.shape[0] == 1:
return np.repeat(value, rep, axis=0)
elif value.shape[0] != rep:
raise ValueError("field %r does not match counts in %r" %
(field.name, field.file.filename))
else:
return value
else:
return value
def process_sourceAperture(field, units):
import numpy as np
def handler(v):
if _s(v) == 'OUT':
return v
else:
return np.float(v.split()[0])
handle_values = np.vectorize(handler)
value = handle_values(field.value)
units_from = ""
v0 = field.value[0].split()
if _s(value[0]) == 'OUT':
return value
if len(v0) > 1:
units_from = v0[1]
if type(units_from) == bytes:
units_from = units_from.decode('utf-8')
converter = unit.Converter(units_from)
return converter(value, units)
def data_as(group, fieldname, units, rep=None, NA=None, dtype=None):
"""
Return value of field in the desired units.
"""
if fieldname not in group:
return NA
field = group[fieldname]
units_in = _s(field.attrs.get('units', ''))
converter = unit.Converter(units_in)
value = converter(field[()], units)
if dtype is not None:
value = np.asarray(value, dtype=dtype)
if rep is not None:
if np.isscalar(value) or len(value) == 1:
return np.repeat(value, rep, axis=0)
elif len(value) == rep:
return value
else:
raise ValueError("field %r does not match counts in %r" %
(field.name, field.file.filename))
else:
return value