def read_rec(fileobj, **keys):
import numpy
header=keys.get('header',False)
view=keys.get('view',numpy.ndarray)
rows=keys.get('rows',None)
columns=keys.get('columns',None)
fields=keys.get('fields',None)
ensure_native = keys.get('ensure_native',False)
if header == 'only':
return sfile.read_header(fileobj)
# if dtype is sent, we assume there is no header at all
dtype = keys.get('dtype',None)
if dtype is not None:
data = read_rec_plain(fileobj, **keys)
header=False
else:
if header:
data,hdr = sfile.read(fileobj, header=header, view=view,
rows=rows, fields=fields, columns=columns)
else:
data = sfile.read(fileobj, header=header, view=view,
rows=rows, fields=fields, columns=columns)
if ensure_native:
numpy_util.to_native(data, inplace=True)
if header:
return data,hdr
else:
return data
def read_fits(fname, **keywords):
"""
Name:
read_fits
Purpose:
Read data from a single fits file.
Calling Sequence:
data=read_fits(fname, **keywords)
Inputs:
fname: The file name
Keywords:
ext: Which extension, or HDU, to read. Default first with data.
view: What view of the data to return. Default is numpy.ndarray
header: Return the data,header tuple? Default False.
rows: Subset of the rows to return if reading a binary table extension.
columns: Subset of the columns to return if reading a binary table.
fields: synonymous with columns
lower: Force the field names to be lower case.
upper: Force the field names to be upper case.
ensure_native: FITS always stores big-endian byte order. Sending
ensure_native=True forces the byte ordering to be machine native.
Example:
import esutil
data=esutil.io.read('test.fits', ext=1, )
"""
import numpy
if fname[-4:] == '.bz2':
return read_fits_bz2(fname, **keywords)
if fits_package is None:
raise ImportError("Could not import fitsio or pyfits")
if fits_package == 'fitsio':
result = read_fits_fitsio(fname, **keywords)
elif fits_package == 'pyfits':
result = read_fits_pyfits(fname, **keywords)
else:
raise ValueError("expected fitsio or pyfits")
h=None
if isinstance(result,tuple):
d,h = result
elif keywords.get('header') == 'only':
return result
else:
d = result
lower= keywords.get('lower',False)
upper= keywords.get('upper',False)
if lower:
d.dtype.names = [n.lower() for n in d.dtype.names]
elif upper:
d.dtype.names = [n.upper() for n in d.dtype.names]
view = keywords.get('view',numpy.ndarray)
if view is not None:
d=d.view(view)
ensure_native = keywords.get('ensure_native',False)
if ensure_native:
numpy_util.to_native(d, inplace=True)
if h is not None:
return d,h
else:
return d
def read_fits(fileobj, **keywords):
"""
Name:
read_fits
Purpose:
Read data from a single fits file.
Calling Sequence:
data=read_fits(fileobj, **keywords)
Inputs:
fileobj: The file name/file object for a fits file.
Keywords:
ext: Which extension, or HDU, to read. Default first with data.
view: What view of the data to return. Default is numpy.ndarray
header: Return the data,header tuple? Default False.
rows: Subset of the rows to return if reading a binary table extension.
columns: Subset of the columns to return if reading a binary table.
fields: synonymous with columns
lower: Force the field names to be lower case.
upper: Force the field names to be upper case.
ensure_native: FITS always stores big-endian byte order. Sending
ensure_native=True forces the byte ordering to be machine native.
Example:
import esutil
data=esutil.io.read('test.fits', ext=1, )
"""
import numpy
if isinstance(fileobj,basestring) and 'bz2' in fileobj:
return read_fits_bz2(fileobj, **keywords)
if fits_package is None:
raise ImportError("Could not import fitsio or pyfits")
if fits_package == 'fitsio':
result = read_fits_fitsio(fileobj, **keywords)
elif fits_package == 'pyfits':
result = read_fits_pyfits(fileobj, **keywords)
else:
raise ValueError("expected fitsio or pyfits")
h=None
if isinstance(result,tuple):
d,h = result
elif keywords.get('header') == 'only':
return result
else:
d = result
lower= keywords.get('lower',False)
upper= keywords.get('upper',False)
if lower:
d.dtype.names = [n.lower() for n in d.dtype.names]
elif upper:
d.dtype.names = [n.upper() for n in d.dtype.names]
view = keywords.get('view',numpy.ndarray)
if view is not None:
d=d.view(view)
ensure_native = keywords.get('ensure_native',False)
if ensure_native:
numpy_util.to_native(d, inplace=True)
if h is not None:
return d,h
else:
return d