def _to_table(self, vo_table):
'''
Return the current table as a VOT object
'''
table = VOTable(vo_table)
# Add keywords
for key in self.keywords:
if isinstance(self.keywords[key], basestring):
arraysize = '*'
else:
arraysize = None
param = Param(table, name=key, ID=key, value=self.keywords[key], arraysize=arraysize)
table.params.append(param)
# Define some fields
n_rows = len(self)
fields = []
for i, name in enumerate(self.names):
data = self.data[name]
unit = self.columns[name].unit
description = self.columns[name].description
dtype = self.columns[name].dtype
column_type = smart_dtype(dtype)
if data.ndim > 1:
arraysize = str(data.shape[1])
else:
arraysize = None
if column_type in type_dict:
datatype = type_dict[column_type]
elif column_type == np.int8:
warnings.warn("int8 unsupported - converting to int16")
datatype = type_dict[np.int16]
elif column_type == np.uint16:
warnings.warn("uint16 unsupported - converting to int32")
datatype = type_dict[np.int32]
elif column_type == np.uint32:
warnings.warn("uint32 unsupported - converting to int64")
datatype = type_dict[np.int64]
elif column_type == np.uint64:
raise Exception("uint64 unsupported")
else:
raise Exception("cannot use numpy type " + str(column_type))
if column_type == np.float32:
precision = 'E9'
elif column_type == np.float64:
precision = 'E17'
else:
precision = None
if datatype == 'char':
if arraysize is None:
arraysize = '*'
else:
raise ValueError("Cannot write vector string columns to VO files")
field = Field(vo_table, ID=name, name=name, \
datatype=datatype, unit=unit, arraysize=arraysize, \
precision=precision)
field.description = description
fields.append(field)
table.fields.extend(fields)
table.create_arrays(n_rows)
# Character columns are stored as object columns in the vo_table
# instance. Leaving the type as string should work, but causes
# a segmentation fault on MacOS X with Python 2.6 64-bit so
# we force the conversion to object type columns.
for name in self.names:
dtype = self.columns[name].dtype
column_type = smart_dtype(dtype)
# Add data to the table
# At the moment, null values in VO table are dealt with via a
# 'mask' record array
if column_type == np.string_:
table.array[name] = self.data[name].astype(np.object_)
if self._masked:
table.array.mask[name] = self.data[name].mask.astype(np.object_)
else:
if self.data[name].dtype.type == np.bytes_ and type(self.columns[name].null) != bytes:
table.array.mask[name] = (self.data[name] == \
self.columns[name].null.encode('utf-8')).astype(np.object_)
else:
table.array.mask[name] = (self.data[name] == \
self.columns[name].null).astype(np.object_)
else:
table.array[name] = self.data[name]
if self._masked:
table.array.mask[name] = self.data[name].mask
else:
table.array.mask[name] = self.data[name] == \
self.columns[name].null
table.name = self.table_name
return table
fits_pixel_type = ''
if hdr['BITPIX'] == -64:
fits_pixel_type = 'double'
elif hdr['BITPIX'] == -32:
fits_pixel_type = 'float'
else:
raise RuntimeError('Unrecognized BITPIX value for file converstion')
# Create the FIELD elements
f1 = Field(t,
name='wavenumber',
datatype=fits_pixel_type,
ucd='em.wavenumber;em.MIR',
unit='cm-1')
f1.description = 'wavenumber merged over orders'
f2 = Field(t,
name='intensity',
datatype=fits_pixel_type,
ucd='phot.flux.density;em.MIR',
unit='erg.s-1.cm-1.sr-1')
f3 = Field(t,
name='intensity_err',
datatype=fits_pixel_type,
ucd='stat.error;phot.flux.density;em.MIR',
unit='erg.s-1.cm-1.sr-1')
f3.description = 'error (standard deviation)'
f4 = Field(t,
def _to_table(self, vo_table):
'''
Return the current table as a VOT object
'''
table = VOTable(vo_table)
# Add keywords
for key in self.keywords:
if isinstance(self.keywords[key], basestring):
arraysize = '*'
else:
arraysize = None
param = Param(table,
name=key,
ID=key,
value=self.keywords[key],
arraysize=arraysize)
table.params.append(param)
# Define some fields
n_rows = len(self)
fields = []
for i, name in enumerate(self.names):
data = self.data[name]
unit = self.columns[name].unit
description = self.columns[name].description
dtype = self.columns[name].dtype
column_type = smart_dtype(dtype)
if data.ndim > 1:
arraysize = str(data.shape[1])
else:
arraysize = None
if column_type in type_dict:
datatype = type_dict[column_type]
elif column_type == np.int8:
warnings.warn("int8 unsupported - converting to int16")
datatype = type_dict[np.int16]
elif column_type == np.uint16:
warnings.warn("uint16 unsupported - converting to int32")
datatype = type_dict[np.int32]
elif column_type == np.uint32:
warnings.warn("uint32 unsupported - converting to int64")
datatype = type_dict[np.int64]
elif column_type == np.uint64:
raise Exception("uint64 unsupported")
else:
raise Exception("cannot use numpy type " + str(column_type))
if column_type == np.float32:
precision = 'F9'
elif column_type == np.float64:
precision = 'F17'
else:
precision = None
if datatype == 'char':
if arraysize is None:
arraysize = '*'
else:
raise ValueError(
"Cannot write vector string columns to VO files")
field = Field(vo_table, ID=name, name=name, \
datatype=datatype, unit=unit, arraysize=arraysize, \
precision=precision)
field.description = description
fields.append(field)
table.fields.extend(fields)
table.create_arrays(n_rows)
# Character columns are stored as object columns in the vo_table
# instance. Leaving the type as string should work, but causes
# a segmentation fault on MacOS X with Python 2.6 64-bit so
# we force the conversion to object type columns.
for name in self.names:
dtype = self.columns[name].dtype
column_type = smart_dtype(dtype)
# Add data to the table
# At the moment, null values in VO table are dealt with via a
# 'mask' record array
if column_type == np.string_:
table.array[name] = self.data[name].astype(np.object_)
if self._masked:
table.mask[name] = self.data[name].mask.astype(np.object_)
else:
table.mask[name] = (self.data[name] == \
self.columns[name].null).astype(np.object_)
else:
table.array[name] = self.data[name]
if self._masked:
table.mask[name] = self.data[name].mask
else:
table.mask[name] = self.data[name] == \
self.columns[name].null
table.name = self.table_name
return table