/usr/lib/python2.7/dist-packages/healpy/fitsfunc.py is in python-healpy 1.10.3-2.
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The actual contents of the file can be viewed below.
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# This file is part of Healpy.
#
# Healpy is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# Healpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Healpy; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
#
# For more information about Healpy, see http://code.google.com/p/healpy
#
"""Provides input and output functions for Healpix maps, alm, and cl.
"""
from __future__ import with_statement
from __future__ import division
import six
import gzip
import tempfile
import shutil
import os
import warnings
import astropy.io.fits as pf
import numpy as np
from . import pixelfunc
from .sphtfunc import Alm
from .pixelfunc import UNSEEN
from . import cookbook as cb
standard_column_names = {
1 : "I_STOKES",
3 : ["%s_STOKES" % comp for comp in "IQU"],
6 : ["II", "IQ", "IU", "QQ", "QU", "UU"]
}
class HealpixFitsWarning(Warning):
pass
def writeto(tbhdu, filename):
# FIXME: Pyfits versions earlier than 3.1.2 had no support or flaky support
# for writing to .gz files or GzipFile objects. Drop this code when
# we decide to drop support for older versions of Pyfits or if we decide
# to support only Astropy.
if isinstance(filename, six.string_types) and filename.endswith('.gz'):
basefilename, ext = os.path.splitext(filename)
with tempfile.NamedTemporaryFile(suffix='.fits') as tmpfile:
tbhdu.writeto(tmpfile.name, clobber=True)
gzfile = gzip.open(filename, 'wb')
try:
try:
shutil.copyfileobj(tmpfile, gzfile)
finally:
gzfile.close()
except:
os.unlink(gzfile.name)
raise
else:
tbhdu.writeto(filename, clobber=True)
def read_cl(filename, dtype=np.float64, h=False):
"""Reads Cl from an healpix file, as IDL fits2cl.
Parameters
----------
filename : str or HDUList or HDU
the fits file name
dtype : data type, optional
the data type of the returned array
Returns
-------
cl : array
the cl array
"""
fits_hdu = _get_hdu(filename, hdu=1)
cl = [fits_hdu.data.field(n) for n in range(len(fits_hdu.columns))]
if len(cl) == 1:
return cl[0]
else:
return cl
def write_cl(filename, cl, dtype=np.float64):
"""Writes Cl into an healpix file, as IDL cl2fits.
Parameters
----------
filename : str
the fits file name
cl : array
the cl array to write to file, currently TT only
"""
# check the dtype and convert it
fitsformat = getformat(dtype)
column_names = ['TEMPERATURE','GRADIENT','CURL','G-T','C-T','C-G']
if isinstance(cl, list):
cols = [pf.Column(name=column_name,
format='%s'%fitsformat,
array=column_cl) for column_name, column_cl in zip(column_names[:len(cl)], cl)]
else: # we write only one TT
cols = [pf.Column(name='TEMPERATURE',
format='%s'%fitsformat,
array=cl)]
tbhdu = pf.BinTableHDU.from_columns(cols)
# add needed keywords
tbhdu.header['CREATOR'] = 'healpy'
writeto(tbhdu, filename)
def write_map(filename,m,nest=False,dtype=np.float32,fits_IDL=True,coord=None,partial=False,column_names=None,column_units=None,extra_header=()):
"""Writes an healpix map into an healpix file.
Parameters
----------
filename : str
the fits file name
m : array or sequence of 3 arrays
the map to write. Possibly a sequence of 3 maps of same size.
They will be considered as I, Q, U maps.
Supports masked maps, see the `ma` function.
nest : bool, optional
If True, ordering scheme is assumed to be NESTED, otherwise, RING. Default: RING.
The map ordering is not modified by this function, the input map array
should already be in the desired ordering (run `ud_grade` beforehand).
fits_IDL : bool, optional
If True, reshapes columns in rows of 1024, otherwise all the data will
go in one column. Default: True
coord : str
The coordinate system, typically 'E' for Ecliptic, 'G' for Galactic or 'C' for
Celestial (equatorial)
partial : bool, optional
If True, fits file is written as a partial-sky file with explicit indexing.
Otherwise, implicit indexing is used. Default: False.
column_names : str or list
Column name or list of column names, if None we use:
I_STOKES for 1 component,
I/Q/U_STOKES for 3 components,
II, IQ, IU, QQ, QU, UU for 6 components,
COLUMN_0, COLUMN_1... otherwise
column_units : str or list
Units for each column, or same units for all columns.
extra_header : list
Extra records to add to FITS header.
dtype: np.dtype or list of np.dtypes, optional
The datatype in which the columns will be stored. Will be converted
internally from the numpy datatype to the fits convention. If a list,
the length must correspond to the number of map arrays.
Default: np.float32.
"""
if not hasattr(m, '__len__'):
raise TypeError('The map must be a sequence')
m = pixelfunc.ma_to_array(m)
if pixelfunc.maptype(m) == 0: # a single map is converted to a list
m = [m]
# check the dtype and convert it
try:
fitsformat = []
for curr_dtype in dtype:
fitsformat.append(getformat(curr_dtype))
except TypeError:
#dtype is not iterable
fitsformat = [getformat(dtype)] * len(m)
if column_names is None:
column_names = standard_column_names.get(len(m), ["COLUMN_%d" % n for n in range(len(m))])
else:
assert len(column_names) == len(m), "Length column_names != number of maps"
if column_units is None or isinstance(column_units, six.string_types):
column_units = [column_units] * len(m)
# maps must have same length
assert len(set(map(len, m))) == 1, "Maps must have same length"
nside = pixelfunc.npix2nside(len(m[0]))
if nside < 0:
raise ValueError('Invalid healpix map : wrong number of pixel')
cols=[]
if partial:
fits_IDL = False
mask = pixelfunc.mask_good(m[0])
pix = np.where(mask)[0]
if len(pix) == 0:
raise ValueError('Invalid healpix map : empty partial map')
m = [mm[mask] for mm in m]
ff = getformat(np.min_scalar_type(-pix.max()))
if ff is None:
ff = 'I'
cols.append(pf.Column(name='PIXEL',
format=ff,
array=pix,
unit=None))
for cn, cu, mm, curr_fitsformat in zip(column_names, column_units, m,
fitsformat):
if len(mm) > 1024 and fits_IDL:
# I need an ndarray, for reshape:
mm2 = np.asarray(mm)
cols.append(pf.Column(name=cn,
format='1024%s' % curr_fitsformat,
array=mm2.reshape(mm2.size//1024,1024),
unit=cu))
else:
cols.append(pf.Column(name=cn,
format='%s' % curr_fitsformat,
array=mm,
unit=cu))
tbhdu = pf.BinTableHDU.from_columns(cols)
# add needed keywords
tbhdu.header['PIXTYPE'] = ('HEALPIX', 'HEALPIX pixelisation')
if nest: ordering = 'NESTED'
else: ordering = 'RING'
tbhdu.header['ORDERING'] = (ordering,
'Pixel ordering scheme, either RING or NESTED')
if coord:
tbhdu.header['COORDSYS'] = (coord,
'Ecliptic, Galactic or Celestial (equatorial)')
tbhdu.header['EXTNAME'] = ('xtension',
'name of this binary table extension')
tbhdu.header['NSIDE'] = (nside,'Resolution parameter of HEALPIX')
if not partial:
tbhdu.header['FIRSTPIX'] = (0, 'First pixel # (0 based)')
tbhdu.header['LASTPIX'] = (pixelfunc.nside2npix(nside)-1,
'Last pixel # (0 based)')
tbhdu.header['INDXSCHM'] = ('EXPLICIT' if partial else 'IMPLICIT',
'Indexing: IMPLICIT or EXPLICIT')
tbhdu.header['OBJECT'] = ('PARTIAL' if partial else 'FULLSKY',
'Sky coverage, either FULLSKY or PARTIAL')
# FIXME: In modern versions of Pyfits, header.update() understands a
# header as an argument, and headers can be concatenated with the `+'
# operator.
for args in extra_header:
tbhdu.header[args[0]] = args[1:]
writeto(tbhdu, filename)
def read_map(filename,field=0,dtype=np.float64,nest=False,partial=False,hdu=1,h=False,
verbose=True,memmap=False):
"""Read an healpix map from a fits file. Partial-sky files,
if properly identified, are expanded to full size and filled with UNSEEN.
Parameters
----------
filename : str or HDU or HDUList
the fits file name
field : int or tuple of int, or None, optional
The column to read. Default: 0.
By convention 0 is temperature, 1 is Q, 2 is U.
Field can be a tuple to read multiple columns (0,1,2)
If the fits file is a partial-sky file, field=0 corresponds to the
first column after the pixel index column.
If None, all columns are read in.
dtype : data type or list of data types, optional
Force the conversion to some type. Passing a list allows different
types for each field. In that case, the length of the list must
correspond to the length of the field parameter. Default: np.float64
nest : bool, optional
If True return the map in NEST ordering, otherwise in RING ordering;
use fits keyword ORDERING to decide whether conversion is needed or not
If None, no conversion is performed.
partial : bool, optional
If True, fits file is assumed to be a partial-sky file with explicit indexing,
if the indexing scheme cannot be determined from the header.
If False, implicit indexing is assumed. Default: False.
A partial sky file is one in which OBJECT=PARTIAL and INDXSCHM=EXPLICIT,
and the first column is then assumed to contain pixel indices.
A full sky file is one in which OBJECT=FULLSKY and INDXSCHM=IMPLICIT.
At least one of these keywords must be set for the indexing
scheme to be properly identified.
hdu : int, optional
the header number to look at (start at 0)
h : bool, optional
If True, return also the header. Default: False.
verbose : bool, optional
If True, print a number of diagnostic messages
memmap : bool, optional
Argument passed to astropy.io.fits.open, if True, the map is not read into memory,
but only the required pixels are read when needed. Default: False.
Returns
-------
m | (m0, m1, ...) [, header] : array or a tuple of arrays, optionally with header appended
The map(s) read from the file, and the header if *h* is True.
"""
fits_hdu = _get_hdu(filename, hdu=hdu, memmap=memmap)
nside = fits_hdu.header.get('NSIDE')
if nside is None:
warnings.warn("No NSIDE in the header file : will use length of array", HealpixFitsWarning)
else:
nside = int(nside)
if verbose: print('NSIDE = {0:d}'.format(nside))
if not pixelfunc.isnsideok(nside):
raise ValueError('Wrong nside parameter.')
ordering = fits_hdu.header.get('ORDERING','UNDEF').strip()
if ordering == 'UNDEF':
ordering = (nest and 'NESTED' or 'RING')
warnings.warn("No ORDERING keyword in header file : "
"assume %s"%ordering)
if verbose: print('ORDERING = {0:s} in fits file'.format(ordering))
sz=pixelfunc.nside2npix(nside)
ret = []
# partial sky: check OBJECT, then INDXSCHM
obj = fits_hdu.header.get('OBJECT', 'UNDEF').strip()
if obj != 'UNDEF':
if obj == 'PARTIAL':
partial = True
elif obj == 'FULLSKY':
partial = False
schm = fits_hdu.header.get('INDXSCHM', 'UNDEF').strip()
if schm != 'UNDEF':
if schm == 'EXPLICIT':
if obj == 'FULLSKY':
raise ValueError('Incompatible INDXSCHM keyword')
partial = True
elif schm == 'IMPLICIT':
if obj == 'PARTIAL':
raise ValueError('Incompatible INDXSCHM keyword')
partial = False
if schm == 'UNDEF':
schm = (partial and 'EXPLICIT' or 'IMPLICIT')
warnings.warn("No INDXSCHM keyword in header file : "
"assume {}".format(schm))
if verbose:
print('INDXSCHM = {0:s}'.format(schm))
if field is None:
field = range(len(fits_hdu.data.columns) - 1*partial)
if not (hasattr(field, '__len__') or isinstance(field, str)):
field = (field,)
if partial:
# increment field counters
field = tuple(f if isinstance(f, str) else f+1 for f in field)
try:
pix = fits_hdu.data.field(0).astype(int).ravel()
except pf.VerifyError as e:
print(e)
print("Trying to fix a badly formatted header")
fits_hdu.verify("fix")
pix = fits_hdu.data.field(0).astype(int).ravel()
try:
assert len(dtype) == len(field), "The number of dtypes are not equal to the number of fields"
except TypeError:
dtype = [dtype] * len(field)
for ff, curr_dtype in zip(field, dtype):
try:
m=fits_hdu.data.field(ff).astype(curr_dtype).ravel()
except pf.VerifyError as e:
print(e)
print("Trying to fix a badly formatted header")
m=fits_hdu.verify("fix")
m=fits_hdu.data.field(ff).astype(curr_dtype).ravel()
if partial:
mnew = UNSEEN * np.ones(sz, dtype=curr_dtype)
mnew[pix] = m
m = mnew
if (not pixelfunc.isnpixok(m.size) or (sz>0 and sz != m.size)) and verbose:
print('nside={0:d}, sz={1:d}, m.size={2:d}'.format(nside,sz,m.size))
raise ValueError('Wrong nside parameter.')
if not nest is None: # no conversion with None
if nest and ordering == 'RING':
idx = pixelfunc.nest2ring(nside,np.arange(m.size,dtype=np.int32))
m = m[idx]
if verbose: print('Ordering converted to NEST')
elif (not nest) and ordering == 'NESTED':
idx = pixelfunc.ring2nest(nside,np.arange(m.size,dtype=np.int32))
m = m[idx]
if verbose: print('Ordering converted to RING')
try:
m[pixelfunc.mask_bad(m)] = UNSEEN
except OverflowError:
pass
ret.append(m)
if len(ret) == 1:
if h:
return ret[0],fits_hdu.header.items()
else:
return ret[0]
else:
if h:
ret.append(fits_hdu.header.items())
return tuple(ret)
else:
return tuple(ret)
def write_alm(filename,alms,out_dtype=None,lmax=-1,mmax=-1,mmax_in=-1):
"""Write alms to a fits file.
In the fits file the alms are written
with explicit index scheme, index = l*l + l + m +1, possibly out of order.
By default write_alm makes a table with the same precision as the alms.
If specified, the lmax and mmax parameters truncate the input data to
include only alms for which l <= lmax and m <= mmax.
Parameters
----------
filename : str
The filename of the output fits file
alms : array, complex or list of arrays
A complex ndarray holding the alms, index = m*(2*lmax+1-m)/2+l, see Alm.getidx
lmax : int, optional
The maximum l in the output file
mmax : int, optional
The maximum m in the output file
out_dtype : data type, optional
data type in the output file (must be a numpy dtype). Default: *alms*.real.dtype
mmax_in : int, optional
maximum m in the input array
"""
if not cb.is_seq_of_seq(alms):
alms = [alms]
l2max = Alm.getlmax(len(alms[0]),mmax=mmax_in)
if (lmax != -1 and lmax > l2max):
raise ValueError("Too big lmax in parameter")
elif lmax == -1:
lmax = l2max
if mmax_in == -1:
mmax_in = l2max
if mmax == -1:
mmax = lmax
if mmax > mmax_in:
mmax = mmax_in
if (out_dtype == None):
out_dtype = alms[0].real.dtype
l,m = Alm.getlm(lmax)
idx = np.where((l <= lmax)*(m <= mmax))
l = l[idx]
m = m[idx]
idx_in_original = Alm.getidx(l2max, l=l, m=m)
index = l**2 + l + m + 1
hdulist = pf.HDUList()
for alm in alms:
out_data = np.empty(len(index),
dtype=[('index','i'),
('real',out_dtype),
('imag',out_dtype)])
out_data['index'] = index
out_data['real'] = alm.real[idx_in_original]
out_data['imag'] = alm.imag[idx_in_original]
cindex = pf.Column(name="index", format=getformat(np.int32), unit="l*l+l+m+1", array=out_data['index'])
creal = pf.Column(name="real", format=getformat(out_dtype), unit="unknown", array=out_data['real'])
cimag = pf.Column(name="imag", format=getformat(out_dtype), unit="unknown", array=out_data['imag'])
tbhdu = pf.BinTableHDU.from_columns([cindex,creal,cimag])
hdulist.append(tbhdu)
writeto(hdulist, filename)
def read_alm(filename,hdu=1,return_mmax=False):
"""Read alm from a fits file.
In the fits file, the alm are written
with explicit index scheme, index = l**2+l+m+1, while healpix cxx
uses index = m*(2*lmax+1-m)/2+l. The conversion is done in this
function.
Parameters
----------
filename : str or HDUList or HDU
The name of the fits file to read
hdu : int, optional
The header to read. Start at 0. Default: hdu=1
return_mmax : bool, optional
If true, both the alms and mmax is returned in a tuple. Default: return_mmax=False
Returns
-------
alms[, mmax] : complex array or tuple of a complex array and an int
The alms read from the file and optionally mmax read from the file
"""
idx, almr, almi = mrdfits(filename,hdu=hdu)
l = np.floor(np.sqrt(idx-1)).astype(np.long)
m = idx - l**2 - l - 1
if (m<0).any():
raise ValueError('Negative m value encountered !')
lmax = l.max()
mmax = m.max()
alm = almr*(0+0j)
i = Alm.getidx(lmax,l,m)
alm.real[i] = almr
alm.imag[i] = almi
if return_mmax:
return alm, mmax
else:
return alm
## Generic functions to read and write column of data in fits file
def _get_hdu(input_data, hdu=None, memmap=None):
"""
Return an HDU from a FITS file
Parameters
----------
input_data : str or HDUList or HDU instance
The input FITS file, either as a filename, HDU list, or HDU instance.
Returns
-------
fits_hdu : HDU
The extracted HDU
"""
if isinstance(input_data, six.string_types):
hdulist = pf.open(input_data, memmap=memmap)
return _get_hdu(hdulist, hdu=hdu)
if isinstance(input_data, pf.HDUList):
if isinstance(hdu, int) and hdu >= len(input_data):
raise ValueError('Available hdu in [0-%d]' % len(input_data))
else:
fits_hdu = input_data[hdu]
elif isinstance(input_data, (pf.PrimaryHDU, pf.ImageHDU, pf.BinTableHDU, pf.TableHDU, pf.GroupsHDU)):
fits_hdu = input_data
else:
raise TypeError("First argument should be a input_data, HDUList instance, or HDU instance")
return fits_hdu
def mrdfits(filename, hdu=1):
"""
Read a table in a fits file.
Parameters
----------
filename : str or HDUList or HDU
The name of the fits file to read, or an HDUList or HDU instance.
hdu : int, optional
The header to read. Start at 0. Default: hdu=1
Returns
-------
cols : a list of arrays
A list of column data in the given header
"""
fits_hdu = _get_hdu(filename, hdu=hdu)
val=[]
for i in range(len(fits_hdu.columns)):
val.append(fits_hdu.data.field(i))
return val
def mwrfits(filename,data,hdu=1,colnames=None,keys=None):
"""Write columns to a fits file in a table extension.
Parameters
----------
filename : str
The fits file name
data : list of 1D arrays
A list of 1D arrays to write in the table
hdu : int, optional
The header where to write the data. Default: 1
colnames : list of str
The column names
keys : dict-like
A dictionary with keywords to write in the header
"""
# Check the inputs
if colnames is not None:
if len(colnames) != len(data):
raise ValueError("colnames and data must the same length")
else:
colnames = ['']*len(data)
cols=[]
for line in six.moves.xrange(len(data)):
cols.append(pf.Column(name=colnames[line],
format=getformat(data[line]),
array=data[line]))
tbhdu = pf.BinTableHDU.from_columns(cols)
if type(keys) is dict:
for k,v in keys.items():
tbhdu.header[k] = v
# write the file
writeto(tbhdu, filename)
def getformat(t):
"""Get the FITS convention format string of data type t.
Parameters
----------
t : data type
The data type for which the FITS type is requested
Returns
-------
fits_type : str or None
The FITS string code describing the data type, or None if unknown type.
"""
conv = {
np.dtype(np.bool): 'L',
np.dtype(np.uint8): 'B',
np.dtype(np.int16): 'I',
np.dtype(np.int32): 'J',
np.dtype(np.int64): 'K',
np.dtype(np.float32): 'E',
np.dtype(np.float64): 'D',
np.dtype(np.complex64): 'C',
np.dtype(np.complex128): 'M'
}
try:
if t in conv:
return conv[t]
except:
pass
try:
if np.dtype(t) in conv:
return conv[np.dtype(t)]
except:
pass
try:
if np.dtype(type(t)) in conv:
return conv[np.dtype(type(t))]
except:
pass
try:
if np.dtype(type(t[0])) in conv:
return conv[np.dtype(type(t[0]))]
except:
pass
try:
if t is str:
return 'A'
except:
pass
try:
if type(t) is str:
return 'A%d'%(len(t))
except:
pass
try:
if type(t[0]) is str:
l=max(len(s) for s in t)
return 'A%d'%(l)
except:
pass
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