def testDeconvertConstantArray(self):
"""When a constant value array's data is overridden with non-
constant data, test that when saving the file it removes
all constant value array keywords and is treated as a normal image
HDU.
"""
data = np.ones((100, 100))
hdu = stpyfits.PrimaryHDU(data=data)
hdu.header['PIXVALUE'] = 1
hdu.writeto(self.temp('test.fits'))
with stpyfits.open(self.temp('test.fits'), mode='update') as h:
assert_equal(h[0].header['PIXVALUE'], 1)
h[0].data[20:80, 20:80] = 2
with fits.open(self.temp('test.fits')) as h:
assert_true('PIXVALUE' not in h[0].header)
assert_true('NPIX1' not in h[0].header)
assert_true('NPIX2' not in h[0].header)
assert_equal(h[0].header.count('NAXIS'), 1)
assert_equal(h[0].header['NAXIS'], 2)
assert_equal(h[0].header['NAXIS1'], 100)
assert_equal(h[0].header['NAXIS2'], 100)
assert_equal(h[0].data.max(), 2)
assert_equal(h[0].data.min(), 1)
def __init__(self, image):
self.hdulist = fits.open(image)
self.ampstring = self.hdulist[0].header['CCDAMP']
self.flatcorr = self.hdulist[0].header['FLATCORR']
self.flshcorr = self.hdulist[0].header['FLSHCORR']
self.darkcorr = self.hdulist[0].header['DARKCORR']
self.configure_arrays()
def testOpenConvienceFunction(self):
"""Test the open convience function in both the fits and stpyfits
namespace."""
hdul = stpyfits.open(self.data('cdva2.fits'))
hdul1 = fits.open(self.data('cdva2.fits'))
assert_equal(hdul[0].header['NAXIS'], 2)
assert_equal(hdul1[0].header['NAXIS'], 0)
assert_equal(hdul[0].header['NAXIS1'], 10)
assert_equal(hdul[0].header['NAXIS2'], 10)
assert_raises(KeyError, lambda: hdul1[0].header['NAXIS1'])
assert_raises(KeyError, lambda: hdul1[0].header['NAXIS2'])
assert_raises(KeyError, lambda: hdul[0].header['NPIX1'])
assert_raises(KeyError, lambda: hdul[0].header['NPIX2'])
assert_equal(hdul1[0].header['NPIX1'], 10)
assert_equal(hdul1[0].header['NPIX2'], 10)
assert_true((hdul[0].data == np.ones((10, 10), dtype=np.int32)).all())
assert_equal(hdul1[0].data, None)
hdul.close()
hdul1.close()
def testOpenConvienceFunction(self):
"""Test the open convience function in both the fits and stpyfits
namespace."""
hdul = stpyfits.open(self.data('cdva2.fits'))
hdul1 = fits.open(self.data('cdva2.fits'))
assert_equal(hdul[0].header['NAXIS'], 2)
assert_equal(hdul1[0].header['NAXIS'], 0)
assert_equal(hdul[0].header['NAXIS1'], 10)
assert_equal(hdul[0].header['NAXIS2'], 10)
assert_raises(KeyError, lambda: hdul1[0].header['NAXIS1'])
assert_raises(KeyError, lambda: hdul1[0].header['NAXIS2'])
assert_raises(KeyError, lambda: hdul[0].header['NPIX1'])
assert_raises(KeyError, lambda: hdul[0].header['NPIX2'])
assert_equal(hdul1[0].header['NPIX1'], 10)
assert_equal(hdul1[0].header['NPIX2'], 10)
assert_true((hdul[0].data == np.ones((10, 10), dtype=np.int32)).all())
assert_equal(hdul1[0].data, None)
hdul.close()
hdul1.close()
def testwritetoConvienceFunction(self):
"""Test the writeto convience function in both the fits and stpyfits
namespace."""
hdul = stpyfits.open(self.data('cdva2.fits'))
hdul1 = fits.open(self.data('cdva2.fits'))
header = hdul[0].header.copy()
header['NAXIS'] = 0
stpyfits.writeto(self.temp('new.fits'), hdul[0].data, header,
clobber=True)
fits.writeto(self.temp('new1.fits'), hdul1[0].data,hdul1[0].header,
clobber=True)
hdul.close()
hdul1.close()
info1 = fits.info(self.temp('new.fits'), output=False)
info2 = stpyfits.info(self.temp('new.fits'), output=False)
info3 = fits.info(self.temp('new1.fits'), output=False)
info4 = stpyfits.info(self.temp('new1.fits'), output=False)
assert_equal(info1, [(0, 'PRIMARY', 'PrimaryHDU', 6, (), '', '')])
assert_equal(info2,
[(0, 'PRIMARY', 'PrimaryHDU', 6, (10, 10), 'int32', '')])
assert_equal(info3, [(0, 'PRIMARY', 'PrimaryHDU', 6, (), '', '')])
assert_equal(info4,
[(0, 'PRIMARY', 'PrimaryHDU', 6, (10, 10), 'uint8', '')])
def testwritetoConvienceFunction(self):
"""Test the writeto convience function in both the fits and stpyfits
namespace."""
hdul = stpyfits.open(self.data('cdva2.fits'))
hdul1 = fits.open(self.data('cdva2.fits'))
header = hdul[0].header.copy()
header['NAXIS'] = 0
stpyfits.writeto(self.temp('new.fits'),
hdul[0].data,
header,
clobber=True)
fits.writeto(self.temp('new1.fits'),
hdul1[0].data,
hdul1[0].header,
clobber=True)
hdul.close()
hdul1.close()
info1 = fits.info(self.temp('new.fits'), output=False)
info2 = stpyfits.info(self.temp('new.fits'), output=False)
info3 = fits.info(self.temp('new1.fits'), output=False)
info4 = stpyfits.info(self.temp('new1.fits'), output=False)
assert_equal(info1, [(0, 'PRIMARY', 'PrimaryHDU', 6, (), '', '')])
assert_equal(info2,
[(0, 'PRIMARY', 'PrimaryHDU', 6, (10, 10), 'int32', '')])
assert_equal(info3, [(0, 'PRIMARY', 'PrimaryHDU', 6, (), '', '')])
assert_equal(info4,
[(0, 'PRIMARY', 'PrimaryHDU', 6, (10, 10), 'uint8', '')])
def testImageBaseHDUWriteToMethod(self):
"""Test the writeto method of _ConstantValueImageBaseHDU in the
stpyfits namespace."""
n = np.zeros(10)
n = n + 1
hdu = stpyfits.PrimaryHDU(n)
hdu.header.set('PIXVALUE', 1., 'constant pixel value', after='EXTEND')
hdu.writeto(self.temp('new.fits'), clobber=True)
hdul = stpyfits.open(self.temp('new.fits'))
hdul1 = fits.open(self.temp('new.fits'))
assert_equal(hdul[0].header['NAXIS'], 1)
assert_equal(hdul[0].header['NAXIS1'], 10)
assert_equal(hdul[0].header['PIXVALUE'], 1.0)
assert_raises(KeyError, lambda: hdul[0].header['NPIX1'])
assert_true((hdul[0].data == np.ones(10, dtype=np.float32)).all())
assert_equal(hdul1[0].header['NAXIS'], 0)
assert_equal(hdul1[0].header['NPIX1'], 10)
assert_equal(hdul1[0].header['PIXVALUE'], 1.0)
assert_raises(KeyError, lambda: hdul1[0].header['NAXIS1'])
assert_equal(hdul1[0].data, None)
hdul.close()
hdul1.close()
def testDeconvertConstantArray(self):
"""When a constant value array's data is overridden with non-
constant data, test that when saving the file it removes
all constant value array keywords and is treated as a normal image
HDU.
"""
data = np.ones((100, 100))
hdu = stpyfits.PrimaryHDU(data=data)
hdu.header['PIXVALUE'] = 1
hdu.writeto(self.temp('test.fits'))
with stpyfits.open(self.temp('test.fits'), mode='update') as h:
assert_equal(h[0].header['PIXVALUE'], 1)
h[0].data[20:80, 20:80] = 2
with fits.open(self.temp('test.fits')) as h:
assert_true('PIXVALUE' not in h[0].header)
assert_true('NPIX1' not in h[0].header)
assert_true('NPIX2' not in h[0].header)
assert_equal(h[0].header.count('NAXIS'), 1)
assert_equal(h[0].header['NAXIS'], 2)
assert_equal(h[0].header['NAXIS1'], 100)
assert_equal(h[0].header['NAXIS2'], 100)
assert_equal(h[0].data.max(), 2)
assert_equal(h[0].data.min(), 1)
def __init__(self, image):
self.hdulist = fits.open(image)
self.ampstring = self.hdulist[0].header['CCDAMP']
self.flatcorr = self.hdulist[0].header['FLATCORR']
self.flshcorr = self.hdulist[0].header['FLSHCORR']
self.darkcorr = self.hdulist[0].header['DARKCORR']
self.configure_arrays()
def testImageBaseHDUWriteToMethod(self):
"""Test the writeto method of _ConstantValueImageBaseHDU in the
stpyfits namespace."""
n = np.zeros(10)
n = n + 1
hdu = stpyfits.PrimaryHDU(n)
hdu.header.set('PIXVALUE', 1., 'constant pixel value', after='EXTEND')
hdu.writeto(self.temp('new.fits'), clobber=True)
hdul = stpyfits.open(self.temp('new.fits'))
hdul1 = fits.open(self.temp('new.fits'))
assert_equal(hdul[0].header['NAXIS'], 1)
assert_equal(hdul[0].header['NAXIS1'], 10)
assert_equal(hdul[0].header['PIXVALUE'], 1.0)
assert_raises(KeyError, lambda: hdul[0].header['NPIX1'])
assert_true((hdul[0].data == np.ones(10, dtype=np.float32)).all())
assert_equal(hdul1[0].header['NAXIS'], 0)
assert_equal(hdul1[0].header['NPIX1'], 10)
assert_equal(hdul1[0].header['PIXVALUE'], 1.0)
assert_raises(KeyError, lambda: hdul1[0].header['NAXIS1'])
assert_equal(hdul1[0].data, None)
hdul.close()
hdul1.close()
def _read_DQ_arrays(flt_name):
with fits.open(flt_name) as h:
ampstring = h[0].header['CCDAMP']
dq1 = h['dq', 1].data
if (ampstring == 'ABCD'):
dq2 = h['dq', 2].data
else:
dq2 = None
return dq1, dq2
def _read_DQ_arrays(flt_name):
with fits.open(flt_name) as h:
ampstring = h[0].header['CCDAMP']
dq1 = h['dq', 1].data
if (ampstring == 'ABCD'):
dq2 = h['dq', 2].data
else:
dq2 = None
return dq1, dq2
def hdr_vals_for_overscan(root):
"""Retrieve header keyword values from RAW and SPT
FITS files to pass on to :func:`check_oscntab` and
:func:`check_overscan`.
Parameters
----------
root : str
Rootname of the observation. Can be relative path
to the file excluding the type of FITS file and
extension, e.g., '/my/path/jxxxxxxxq'.
Returns
-------
ccdamp : str
Amplifiers used to read out the CCDs.
xstart : int
Starting column of the readout in detector
coordinates.
ystart : int
Starting row of the readout in detector
coordinates.
xsize : int
Number of columns in the readout.
ysize : int
Number of rows in the readout.
"""
with fits.open(root + '_spt.fits') as hdu:
spthdr = hdu[0].header
with fits.open(root + '_raw.fits') as hdu:
prihdr = hdu[0].header
xstart = spthdr['SS_A1CRN']
ystart = spthdr['SS_A2CRN']
xsize = spthdr['SS_A1SZE']
ysize = spthdr['SS_A2SZE']
ccdamp = prihdr['CCDAMP']
return ccdamp, xstart, ystart, xsize, ysize
def hdr_vals_for_overscan(root):
"""Retrieve header keyword values from RAW and SPT
FITS files to pass on to :func:`check_oscntab` and
:func:`check_overscan`.
Parameters
----------
root : str
Rootname of the observation. Can be relative path
to the file excluding the type of FITS file and
extension, e.g., '/my/path/jxxxxxxxq'.
Returns
-------
ccdamp : str
Amplifiers used to read out the CCDs.
xstart : int
Starting column of the readout in detector
coordinates.
ystart : int
Starting row of the readout in detector
coordinates.
xsize : int
Number of columns in the readout.
ysize : int
Number of rows in the readout.
"""
with fits.open(root + '_spt.fits') as hdu:
spthdr = hdu[0].header
with fits.open(root + '_raw.fits') as hdu:
prihdr = hdu[0].header
xstart = spthdr['SS_A1CRN']
ystart = spthdr['SS_A2CRN']
xsize = spthdr['SS_A1SZE']
ysize = spthdr['SS_A2SZE']
ccdamp = prihdr['CCDAMP']
return ccdamp, xstart, ystart, xsize, ysize
def extract_dark(prihdr, scihdu):
"""Extract superdark data from ``DARKFILE`` or ``DRKCFILE``.
Parameters
----------
prihdr : obj
FITS primary header HDU.
scihdu : obj
Extension HDU of the science image.
This is only used to extract subarray data.
Returns
-------
dark : ndarray or `None`
Superdark, if any. Subtract this to apply ``DARKCORR``.
"""
if prihdr.get('PCTECORR', 'OMIT') == 'COMPLETE':
darkfile = prihdr.get('DRKCFILE', 'N/A')
else:
darkfile = prihdr.get('DARKFILE', 'N/A')
if darkfile == 'N/A':
return None
darkfile = from_irafpath(darkfile)
ampstring = prihdr['CCDAMP']
# Calculate DARKTIME
exptime = prihdr.get('EXPTIME', 0.0)
flashdur = prihdr.get('FLASHDUR', 0.0)
darktime = exptime + flashdur
if exptime > 0: # Not BIAS
darktime += 3.0
with fits.open(darkfile) as hdudark:
if ampstring == 'ABCD':
dark = np.concatenate(
(hdudark['sci', 1].data, hdudark['sci', 2].data[::-1, :]),
axis=1)
elif ampstring in ('A', 'B', 'AB'):
dark = extract_ref(scihdu, hdudark['sci', 2])
else:
dark = extract_ref(scihdu, hdudark['sci', 1])
dark = dark * darktime
return dark
def extract_dark(prihdr, scihdu):
"""Extract superdark data from ``DARKFILE`` or ``DRKCFILE``.
Parameters
----------
prihdr : obj
FITS primary header HDU.
scihdu : obj
Extension HDU of the science image.
This is only used to extract subarray data.
Returns
-------
dark : ndarray or `None`
Superdark, if any. Subtract this to apply ``DARKCORR``.
"""
if prihdr.get('PCTECORR', 'OMIT') == 'COMPLETE':
darkfile = prihdr.get('DRKCFILE', 'N/A')
else:
darkfile = prihdr.get('DARKFILE', 'N/A')
if darkfile == 'N/A':
return None
darkfile = from_irafpath(darkfile)
ampstring = prihdr['CCDAMP']
# Calculate DARKTIME
exptime = prihdr.get('EXPTIME', 0.0)
flashdur = prihdr.get('FLASHDUR', 0.0)
darktime = exptime + flashdur
if exptime > 0: # Not BIAS
darktime += 3.0
with fits.open(darkfile) as hdudark:
if ampstring == 'ABCD':
dark = np.concatenate(
(hdudark['sci', 1].data,
hdudark['sci', 2].data[::-1, :]), axis=1)
elif ampstring in ('A', 'B', 'AB'):
dark = extract_ref(scihdu, hdudark['sci', 2])
else:
dark = extract_ref(scihdu, hdudark['sci', 1])
dark = dark * darktime
return dark
def testImageBaseHDU__getattr__Method(self):
"""Test the __getattr__ method of ImageBaseHDU in both the fits
and stpyfits namespace."""
hdul = stpyfits.open(self.data('cdva2.fits'))
hdul1 = fits.open(self.data('cdva2.fits'))
hdu = hdul[0]
hdu1 = hdul1[0]
assert_true((hdu.data == np.ones((10, 10), dtype=np.int32)).all())
assert_equal(hdu1.data, None)
hdul.close()
hdul1.close()
def testImageBaseHDU__getattr__Method(self):
"""Test the __getattr__ method of ImageBaseHDU in both the fits
and stpyfits namespace."""
hdul = stpyfits.open(self.data('cdva2.fits'))
hdul1 = fits.open(self.data('cdva2.fits'))
hdu = hdul[0]
hdu1 = hdul1[0]
assert_true((hdu.data == np.ones((10, 10), dtype=np.int32)).all())
assert_equal(hdu1.data, None)
hdul.close()
hdul1.close()
def extract_flash(prihdr, scihdu):
"""Extract postflash data from ``FLSHFILE``.
Parameters
----------
prihdr : obj
FITS primary header HDU.
scihdu : obj
Extension HDU of the science image.
This is only used to extract subarray data.
Returns
-------
flash : ndarray or `None`
Postflash, if any. Subtract this to apply ``FLSHCORR``.
"""
flshfile = prihdr.get('FLSHFILE', 'N/A')
flashsta = prihdr.get('FLASHSTA', 'N/A')
flashdur = prihdr.get('FLASHDUR', 0.0)
if flshfile == 'N/A' or flashdur <= 0:
return None
if flashsta != 'SUCCESSFUL':
warnings.warn('Flash status is {0}'.format(flashsta),
AstropyUserWarning)
flshfile = from_irafpath(flshfile)
ampstring = prihdr['CCDAMP']
with fits.open(flshfile) as hduflash:
if ampstring == 'ABCD':
flash = np.concatenate(
(hduflash['sci', 1].data,
hduflash['sci', 2].data[::-1, :]), axis=1)
elif ampstring in ('A', 'B', 'AB'):
flash = extract_ref(scihdu, hduflash['sci', 2])
else:
flash = extract_ref(scihdu, hduflash['sci', 1])
flash = flash * flashdur
return flash
def extract_flash(prihdr, scihdu):
"""Extract postflash data from ``FLSHFILE``.
Parameters
----------
prihdr : obj
FITS primary header HDU.
scihdu : obj
Extension HDU of the science image.
This is only used to extract subarray data.
Returns
-------
flash : ndarray or `None`
Postflash, if any. Subtract this to apply ``FLSHCORR``.
"""
flshfile = prihdr.get('FLSHFILE', 'N/A')
flashsta = prihdr.get('FLASHSTA', 'N/A')
flashdur = prihdr.get('FLASHDUR', 0.0)
if flshfile == 'N/A' or flashdur <= 0:
return None
if flashsta != 'SUCCESSFUL':
warnings.warn('Flash status is {0}'.format(flashsta),
AstropyUserWarning)
flshfile = from_irafpath(flshfile)
ampstring = prihdr['CCDAMP']
with fits.open(flshfile) as hduflash:
if ampstring == 'ABCD':
flash = np.concatenate(
(hduflash['sci', 1].data,
hduflash['sci', 2].data[::-1, :]), axis=1)
elif ampstring in ('A', 'B', 'AB'):
flash = extract_ref(scihdu, hduflash['sci', 2])
else:
flash = extract_ref(scihdu, hduflash['sci', 1])
flash = flash * flashdur
return flash
def testDimensionlessConstantValueArray(self):
"""Tests a case that was reported where an HDU can be a constant
value HDU (it has a PIXVALUE and NAXIS=0) but NPIX1 = NPIX2 = 0 as
well.
"""
hdu = stpyfits.PrimaryHDU()
hdu.header['NAXIS'] = 0
hdu.header['BITPIX'] = 16
hdu.header['NPIX1'] = 0
hdu.header['NPIX2'] = 0
hdu.header['PIXVALUE'] = 0
hdu.writeto(self.temp('test.fits'))
with stpyfits.open(self.temp('test.fits')) as h:
assert_true(h[0].data is None)
h.writeto(self.temp('test2.fits'))
def extract_flatfield(prihdr, scihdu):
"""Extract flatfield data from ``PFLTFILE``.
Parameters
----------
prihdr : obj
FITS primary header HDU.
scihdu : obj
Extension HDU of the science image.
This is only used to extract subarray data.
Returns
-------
invflat : ndarray or `None`
Inverse flatfield, if any. Multiply this to apply ``FLATCORR``.
"""
for ff in ['DFLTFILE', 'LFLTFILE']:
vv = prihdr.get(ff, 'N/A')
if vv != 'N/A':
warnings.warn(f'{ff}={vv} is not accounted for',
AstropyUserWarning)
flatfile = prihdr.get('PFLTFILE', 'N/A')
if flatfile == 'N/A':
return None
flatfile = from_irafpath(flatfile)
ampstring = prihdr['CCDAMP']
with fits.open(flatfile) as hduflat:
if ampstring == 'ABCD':
invflat = np.concatenate((1 / hduflat['sci', 1].data,
1 / hduflat['sci', 2].data[::-1, :]),
axis=1)
elif ampstring in ('A', 'B', 'AB'):
invflat = 1 / extract_ref(scihdu, hduflat['sci', 2])
else:
invflat = 1 / extract_ref(scihdu, hduflat['sci', 1])
return invflat
def extract_flatfield(prihdr, scihdu):
"""Extract flatfield data from ``PFLTFILE``.
Parameters
----------
prihdr : obj
FITS primary header HDU.
scihdu : obj
Extension HDU of the science image.
This is only used to extract subarray data.
Returns
-------
invflat : ndarray or `None`
Inverse flatfield, if any. Multiply this to apply ``FLATCORR``.
"""
for ff in ['DFLTFILE', 'LFLTFILE']:
vv = prihdr.get(ff, 'N/A')
if vv != 'N/A':
warnings.warn('{0}={1} is not accounted for'.format(ff, vv),
AstropyUserWarning)
flatfile = prihdr.get('PFLTFILE', 'N/A')
if flatfile == 'N/A':
return None
flatfile = from_irafpath(flatfile)
ampstring = prihdr['CCDAMP']
with fits.open(flatfile) as hduflat:
if ampstring == 'ABCD':
invflat = np.concatenate(
(1 / hduflat['sci', 1].data,
1 / hduflat['sci', 2].data[::-1, :]), axis=1)
elif ampstring in ('A', 'B', 'AB'):
invflat = 1 / extract_ref(scihdu, hduflat['sci', 2])
else:
invflat = 1 / extract_ref(scihdu, hduflat['sci', 1])
return invflat
def testDimensionlessConstantValueArray(self):
"""Tests a case that was reported where an HDU can be a constant
value HDU (it has a PIXVALUE and NAXIS=0) but NPIX1 = NPIX2 = 0 as
well.
"""
hdu = stpyfits.PrimaryHDU()
hdu.header['NAXIS'] = 0
hdu.header['BITPIX'] = 16
hdu.header['NPIX1'] = 0
hdu.header['NPIX2'] = 0
hdu.header['PIXVALUE'] = 0
hdu.writeto(self.temp('test.fits'))
with stpyfits.open(self.temp('test.fits')) as h:
assert_true(h[0].data is None)
h.writeto(self.temp('test2.fits'))
def testPrimaryHDUConstructor(self):
"""Test the PrimaryHDU constructor in both the fits and stpyfits
namespace. Although stpyfits does not reimplement the
constructor, it does add _ConstantValueImageBaseHDU to the
inheritance hierarchy of fits.PrimaryHDU when accessed through the
stpyfits namespace. This method tests that that inheritance is
working"""
n = np.zeros(10)
n = n + 1
hdu = stpyfits.PrimaryHDU(n)
hdu.header.set('PIXVALUE', 1.0, 'Constant pixel value', after='EXTEND')
hdu.header.set('NAXIS', 0)
stpyfits.writeto(self.temp('new.fits'),
hdu.data,
hdu.header,
clobber=True)
hdul = stpyfits.open(self.temp('new.fits'))
hdul1 = fits.open(self.temp('new.fits'))
assert_equal(hdul[0].header['NAXIS'], 1)
assert_equal(hdul[0].header['NAXIS1'], 10)
assert_equal(hdul[0].header['PIXVALUE'], 1.0)
assert_raises(KeyError, lambda: hdul[0].header['NPIX1'])
assert_true((hdul[0].data == np.ones(10, dtype=np.float32)).all())
assert_equal(hdul1[0].header['NAXIS'], 0)
assert_equal(hdul1[0].header['NPIX1'], 10)
assert_equal(hdul1[0].header['PIXVALUE'], 1.0)
assert_raises(KeyError, lambda: hdul1[0].header['NAXIS1'])
assert_equal(hdul1[0].data, None)
hdul.close()
hdul1.close()
def testStrayPixvalue(self):
"""Regression test for #885
(https://svn.stsci.edu/trac/ssb/stsci_python/ticket/885)
Tests that HDUs containing a non-zero NAXIS as well as a PIXVALUE
keyword in their header are not treated as constant value HDUs.
"""
data = np.arange(100).reshape((10, 10))
phdu = fits.PrimaryHDU(data=data)
hdu = fits.ImageHDU(data=data)
phdu.header['PIXVALUE'] = 10
hdu.header['PIXVALUE'] = 10
hdul = fits.HDUList([phdu, hdu])
hdul.writeto(self.temp('test.fits'))
with stpyfits.open(self.temp('test.fits')) as h:
assert_false(isinstance(h[0], stpyfits.ConstantValuePrimaryHDU))
assert_false(isinstance(h[1], stpyfits.ConstantValueImageHDU))
assert_true((h[0].data == data).all())
assert_true((h[1].data == data).all())
def testStrayPixvalue(self):
"""Regression test for #885
(https://svn.stsci.edu/trac/ssb/stsci_python/ticket/885)
Tests that HDUs containing a non-zero NAXIS as well as a PIXVALUE
keyword in their header are not treated as constant value HDUs.
"""
data = np.arange(100).reshape((10, 10))
phdu = fits.PrimaryHDU(data=data)
hdu = fits.ImageHDU(data=data)
phdu.header['PIXVALUE'] = 10
hdu.header['PIXVALUE'] = 10
hdul = fits.HDUList([phdu, hdu])
hdul.writeto(self.temp('test.fits'))
with stpyfits.open(self.temp('test.fits')) as h:
assert_false(isinstance(h[0], stpyfits.ConstantValuePrimaryHDU))
assert_false(isinstance(h[1], stpyfits.ConstantValueImageHDU))
assert_true((h[0].data == data).all())
assert_true((h[1].data == data).all())
def testPrimaryHDUConstructor(self):
"""Test the PrimaryHDU constructor in both the fits and stpyfits
namespace. Although stpyfits does not reimplement the
constructor, it does add _ConstantValueImageBaseHDU to the
inheritance hierarchy of fits.PrimaryHDU when accessed through the
stpyfits namespace. This method tests that that inheritance is
working"""
n = np.zeros(10)
n = n + 1
hdu = stpyfits.PrimaryHDU(n)
hdu.header.set('PIXVALUE', 1.0, 'Constant pixel value', after='EXTEND')
hdu.header.set('NAXIS', 0)
stpyfits.writeto(self.temp('new.fits'), hdu.data, hdu.header,
clobber=True)
hdul = stpyfits.open(self.temp('new.fits'))
hdul1 = fits.open(self.temp('new.fits'))
assert_equal(hdul[0].header['NAXIS'], 1)
assert_equal(hdul[0].header['NAXIS1'], 10)
assert_equal(hdul[0].header['PIXVALUE'], 1.0)
assert_raises(KeyError, lambda: hdul[0].header['NPIX1'])
assert_true((hdul[0].data == np.ones(10, dtype=np.float32)).all())
assert_equal(hdul1[0].header['NAXIS'], 0)
assert_equal(hdul1[0].header['NPIX1'], 10)
assert_equal(hdul1[0].header['PIXVALUE'], 1.0)
assert_raises(KeyError, lambda: hdul1[0].header['NAXIS1'])
assert_equal(hdul1[0].data, None)
hdul.close()
hdul1.close()
def testHDUListFlushMethod(self):
"""Test the flush method of HDUList in both the fits and stpyfits
namespace."""
hdu = stpyfits.PrimaryHDU()
hdu1 = stpyfits.ImageHDU()
hdu.data = np.zeros((10, 10), dtype=np.int32)
hdu1.data = hdu.data + 2
hdu.header.set('BITPIX', 32)
hdu1.header.set('BITPIX', 32)
hdu.header.set('NAXIS', 2)
hdu.header.set('NAXIS1',
10,
'length of constant array axis 1',
after='NAXIS')
hdu.header.set('NAXIS2',
10,
'length of constant array axis 2',
after='NAXIS1')
hdu.header.set('PIXVALUE', 0, 'Constant pixel value')
hdu1.header.set('PIXVALUE', 2, 'Constant pixel value', after='GCOUNT')
hdu1.header.set('NAXIS', 2)
hdu1.header.set('NAXIS1',
10,
'length of constant array axis 1',
after='NAXIS')
hdu1.header.set('NAXIS2',
10,
'length of constant array axis 2',
after='NAXIS1')
hdul = stpyfits.HDUList([hdu, hdu1])
hdul.writeto(self.temp('new.fits'), clobber=True)
hdul = stpyfits.open(self.temp('new.fits'), 'update')
d = np.arange(10, dtype=np.int32)
d = d * 0
d = d + 3
hdul[0].data = d
hdul.flush()
hdul.close()
assert_equal(stpyfits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 6, (10, ), 'int32', ''),
(1, '', 'ImageHDU', 8, (10, 10), 'int32', '')])
assert_equal(fits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 6, (), '', ''),
(1, '', 'ImageHDU', 8, (), '', '')])
hdul1 = stpyfits.open(self.temp('new.fits'))
hdul2 = fits.open(self.temp('new.fits'))
assert_equal(hdul1[0].header['NAXIS'], 1)
assert_equal(hdul1[0].header['NAXIS1'], 10)
assert_equal(hdul1[0].header['PIXVALUE'], 3)
assert_raises(KeyError, lambda: hdul1[0].header['NPIX1'])
assert_true(
(hdul1[0].data == (np.zeros(10, dtype=np.int32) + 3)).all())
assert_equal(hdul2[0].header['NAXIS'], 0)
assert_equal(hdul2[0].header['NPIX1'], 10)
assert_equal(hdul2[0].header['PIXVALUE'], 3)
assert_raises(KeyError, lambda: hdul2[0].header['NAXIS1'])
assert_equal(hdul2[0].data, None)
hdul1.close()
hdul2.close()
hdul3 = stpyfits.open(self.temp('new.fits'), 'update')
d = np.arange(15, dtype=np.int32)
d = d * 0
d = d + 4
hdul3[0].data = d
hdul3.close() # Note that close calls flush
assert_equal(stpyfits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 6, (15, ), 'int32', ''),
(1, '', 'ImageHDU', 8, (10, 10), 'int32', '')])
assert_equal(fits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 6, (), '', ''),
(1, '', 'ImageHDU', 8, (), '', '')])
hdul1 = stpyfits.open(self.temp('new.fits'))
hdul2 = fits.open(self.temp('new.fits'))
assert_equal(hdul1[0].header['NAXIS'], 1)
assert_equal(hdul1[0].header['NAXIS1'], 15)
assert_equal(hdul1[0].header['PIXVALUE'], 4)
assert_raises(KeyError, lambda: hdul1[0].header['NPIX1'])
assert_true(
(hdul1[0].data == (np.zeros(15, dtype=np.int32) + 4)).all())
assert_equal(hdul2[0].header['NAXIS'], 0)
assert_equal(hdul2[0].header['NPIX1'], 15)
assert_equal(hdul2[0].header['PIXVALUE'], 4)
assert_raises(KeyError, lambda: hdul2[0].header['NAXIS1'])
assert_equal(hdul2[0].data, None)
hdul1.close()
hdul2.close()
def testHDUListWritetoMethod(self):
"""Test the writeto method of HDUList in both the fits and stpyfits
namespace."""
hdu = stpyfits.PrimaryHDU()
hdu1 = stpyfits.ImageHDU()
hdu.data = np.zeros((10, 10), dtype=np.int32)
hdu1.data = hdu.data + 2
hdu.header.set('BITPIX', 32)
hdu1.header.set('BITPIX', 32)
hdu.header.set('NAXIS', 2)
hdu.header.set('NAXIS1',
10,
'length of constant array axis 1',
after='NAXIS')
hdu.header.set('NAXIS2',
10,
'length of constant array axis 2',
after='NAXIS1')
hdu.header.set('PIXVALUE', 0, 'Constant pixel value')
hdu1.header.set('PIXVALUE', 2, 'Constant pixel value', after='GCOUNT')
hdu1.header.set('NAXIS', 2)
hdu1.header.set('NAXIS1',
10,
'length of constant array axis 1',
after='NAXIS')
hdu1.header.set('NAXIS2',
10,
'length of constant array axis 2',
after='NAXIS1')
hdul = stpyfits.HDUList([hdu, hdu1])
hdul.writeto(self.temp('new.fits'), clobber=True)
assert_equal(stpyfits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 7, (10, 10), 'int32', ''),
(1, '', 'ImageHDU', 8, (10, 10), 'int32', '')])
assert_equal(fits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 7, (), '', ''),
(1, '', 'ImageHDU', 8, (), '', '')])
hdul1 = stpyfits.open(self.temp('new.fits'))
hdul2 = fits.open(self.temp('new.fits'))
assert_equal(hdul1[0].header['NAXIS'], 2)
assert_equal(hdul1[0].header['NAXIS1'], 10)
assert_equal(hdul1[0].header['NAXIS2'], 10)
assert_equal(hdul1[0].header['PIXVALUE'], 0)
assert_raises(KeyError, lambda: hdul1[0].header['NPIX1'])
assert_raises(KeyError, lambda: hdul1[0].header['NPIX2'])
assert_true((hdul1[0].data == np.zeros((10, 10),
dtype=np.int32)).all())
assert_equal(hdul1[1].header['NAXIS'], 2)
assert_equal(hdul1[1].header['NAXIS1'], 10)
assert_equal(hdul1[1].header['NAXIS2'], 10)
assert_equal(hdul1[1].header['PIXVALUE'], 2)
assert_raises(KeyError, lambda: hdul1[1].header['NPIX1'])
assert_raises(KeyError, lambda: hdul1[1].header['NPIX2'])
assert_true((hdul1[1].data == (np.zeros(
(10, 10), dtype=np.int32) + 2)).all())
assert_equal(hdul2[0].header['NAXIS'], 0)
assert_equal(hdul2[0].header['NPIX1'], 10)
assert_equal(hdul2[0].header['NPIX2'], 10)
assert_equal(hdul2[0].header['PIXVALUE'], 0)
assert_raises(KeyError, lambda: hdul2[0].header['NAXIS1'])
assert_raises(KeyError, lambda: hdul2[0].header['NAXIS2'])
assert_equal(hdul2[0].data, None)
assert_equal(hdul2[1].header['NAXIS'], 0)
assert_equal(hdul2[1].header['NPIX1'], 10)
assert_equal(hdul2[1].header['NPIX2'], 10)
assert_equal(hdul2[1].header['PIXVALUE'], 2)
assert_raises(KeyError, lambda: hdul2[1].header['NAXIS1'])
assert_raises(KeyError, lambda: hdul2[1].header['NAXIS2'])
hdul1.close()
hdul2.close()
def testupdateConvienceFunction(self):
"""Test the update convience function in both the fits and stpyfits
namespace."""
hdul = stpyfits.open(self.data('cdva2.fits'))
hdul1 = fits.open(self.data('cdva2.fits'))
header = hdul[0].header.copy()
header['NAXIS'] = 0
stpyfits.writeto(self.temp('new.fits'), hdul[0].data, header,
clobber=True)
hdu = stpyfits.ImageHDU()
hdu1 = fits.ImageHDU()
hdu.data = hdul[0].data
hdu1.data = hdul1[0].data
hdu.header.set('BITPIX', 32)
hdu1.header.set('BITPIX', 32)
hdu.header.set('NAXIS', 0)
hdu.header.set('PIXVALUE', 1, 'Constant pixel value', after='GCOUNT')
hdu1.header.set('PIXVALUE', 1, 'Constant pixel value', after='GCOUNT')
hdu.header.set('NPIX1', 10, 'length of constant array axis 1',
after='GCOUNT')
hdu.header.set('NPIX2', 10, 'length of constant array axis 2',
after='NPIX1')
stpyfits.append(self.temp('new.fits'), hdu.data, hdu.header)
d = hdu.data * 0
stpyfits.update(self.temp('new.fits'), d, hdu.header, 1)
assert_equal(fits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 7, (), '', ''),
(1, '', 'ImageHDU', 8, (), '', '')])
assert_equal(stpyfits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 7, (10, 10), 'int32', ''),
(1, '', 'ImageHDU', 8, (10, 10), 'int32', '')])
hdul7 = stpyfits.open(self.temp('new.fits'))
assert_equal(hdul7[1].header['NAXIS'], 2)
assert_equal(hdul7[1].header['NAXIS1'], 10)
assert_equal(hdul7[1].header['NAXIS2'], 10)
assert_equal(hdul7[1].header['PIXVALUE'], 0)
assert_raises(KeyError, lambda: hdul7[1].header['NPIX1'])
assert_raises(KeyError, lambda: hdul7[1].header['NPIX2'])
assert_true((hdul7[1].data ==
np.zeros((10, 10), dtype=np.int32)).all())
hdul8 = fits.open(self.temp('new.fits'))
assert_equal(hdul8[1].header['NAXIS'], 0)
assert_equal(hdul8[1].header['NPIX1'], 10)
assert_equal(hdul8[1].header['NPIX2'], 10)
assert_equal(hdul8[1].header['PIXVALUE'], 0)
assert_raises(KeyError, lambda: hdul8[1].header['NAXIS1'])
assert_raises(KeyError, lambda: hdul8[1].header['NAXIS2'])
assert_equal(hdul8[1].data, None)
hdul7.close()
hdul8.close()
hdul.close()
hdul1.close()
def reset_dq_bits(input,bits,extver=None,extname='dq'):
""" This function resets bits in the integer array(s) of a FITS file.
Parameters
----------
filename : str
full filename with path
bits : str
sum or list of integers corresponding to all the bits to be reset
extver : int, optional
List of version numbers of the DQ arrays
to be corrected [Default Value: None, will do all]
extname : str, optional
EXTNAME of the DQ arrays in the FITS file
[Default Value: 'dq']
Notes
-----
The default value of None for the 'extver' parameter specifies that all
extensions with EXTNAME matching 'dq' (as specified by the 'extname'
parameter) will have their bits reset.
Examples
--------
1. The following command will reset the 4096 bits in all
the DQ arrays of the file input_file_flt.fits::
reset_dq_bits("input_file_flt.fits", 4096)
2. To reset the 2,32,64 and 4096 bits in the second DQ array,
specified as 'dq,2', in the file input_file_flt.fits::
reset_dq_bits("input_file_flt.fits", "2,32,64,4096", extver=2)
"""
# Interpret bits value
bits = bitmask.interpret_bits_value(bits)
flist, fcol = parseinput.parseinput(input)
for filename in flist:
# open input file in write mode to allow updating the DQ array in-place
p = fits.open(filename,mode='update')
# Identify the DQ array to be updated
# If no extver is specified, build a list of all DQ arrays in the file
if extver is None:
extver = []
for hdu in p:
# find only those extensions which match the input extname
# using case-insensitive name comparisons for 'extname'
if 'extver' in hdu.header and \
hdu.header['extname'].lower() == extname.lower():
extver.append(int(hdu.header['extver']))
else:
# Otherwise, insure that input extver values are a list
if not isinstance(extver, list): extver = [extver]
# for each DQ array identified in the file...
for extn in extver:
dqarr = p[extname,extn].data
dqdtype = dqarr.dtype
# reset the desired bits
p[extname,extn].data = (dqarr & ~bits).astype(dqdtype) # preserve original dtype
log.info('Reset bit values of %s to a value of 0 in %s[%s,%s]' %
(bits, filename, extname, extn))
# close the file with the updated DQ array(s)
p.close()
def reset_dq_bits(input,bits,extver=None,extname='dq'):
""" This function resets bits in the integer array(s) of a FITS file.
Parameters
----------
filename : str
full filename with path
bits : str
sum or list of integers corresponding to all the bits to be reset
extver : int, optional
List of version numbers of the DQ arrays
to be corrected [Default Value: None, will do all]
extname : str, optional
EXTNAME of the DQ arrays in the FITS file
[Default Value: 'dq']
Notes
-----
The default value of None for the 'extver' parameter specifies that all
extensions with EXTNAME matching 'dq' (as specified by the 'extname'
parameter) will have their bits reset.
Examples
--------
1. The following command will reset the 4096 bits in all
the DQ arrays of the file input_file_flt.fits::
reset_dq_bits("input_file_flt.fits", 4096)
2. To reset the 2,32,64 and 4096 bits in the second DQ array,
specified as 'dq,2', in the file input_file_flt.fits::
reset_dq_bits("input_file_flt.fits", "2,32,64,4096", extver=2)
"""
# Interpret bits value
bits = interpret_bit_flags(bits)
flist, fcol = parseinput.parseinput(input)
for filename in flist:
# open input file in write mode to allow updating the DQ array in-place
p = fits.open(filename, mode='update', memmap=False)
# Identify the DQ array to be updated
# If no extver is specified, build a list of all DQ arrays in the file
if extver is None:
extver = []
for hdu in p:
# find only those extensions which match the input extname
# using case-insensitive name comparisons for 'extname'
if 'extver' in hdu.header and \
hdu.header['extname'].lower() == extname.lower():
extver.append(int(hdu.header['extver']))
else:
# Otherwise, insure that input extver values are a list
if not isinstance(extver, list): extver = [extver]
# for each DQ array identified in the file...
for extn in extver:
dqarr = p[extname,extn].data
dqdtype = dqarr.dtype
# reset the desired bits
p[extname,extn].data = (dqarr & ~bits).astype(dqdtype) # preserve original dtype
log.info('Reset bit values of %s to a value of 0 in %s[%s,%s]' %
(bits, filename, extname, extn))
# close the file with the updated DQ array(s)
p.close()
def crrej_plus(filelist, outroot, keep_intermediate_files=False, verbose=True):
r"""Perform CRREJ and ACS2D on given BLV_TMP or BLC_TMP files.
The purpose of this is primarily for combining destriped
products from :func:`destripe_plus`.
.. note::
If you want to run this step, it is important to keep
intermediate files when running :func:`destripe_plus`.
Parameters
----------
filelist : str or list of str
List of BLV_TMP or BLC_TMP files to be combined and processed.
Do not mix BLV and BLC in the same list, but rather run this
once for BLV and once for BLC separately.
Input filenames in one of these formats:
* a Python list of filenames
* a partial filename with wildcards ('\*blx_tmp.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
outroot : str
Rootname for combined product, which will be named
``<outroot>_crj.fits`` (for BLV inputs) or
``<outroot>_crc.fits`` (for BLC inputs).
keep_intermediate_files : bool
Keep de-striped CRJ_TMP or CRC_TMP around after CRREJ.
verbose : bool
Print informational messages.
Raises
------
ValueError
Ambiguous input list.
Examples
--------
>>> from acstools.acs_destripe_plus import destripe_plus, crrej_plus
First, run :func:`destripe_plus`. Remember to keep its intermediate files:
>>> destripe_plus(..., keep_intermediate_files=True)
Now, run this function, once on BLV only, and once again on BLC only:
>>> rootname = 'j12345678'
>>> crrej_plus('*_blv_tmp.fits', rootname)
>>> crrej_plus('*_blc_tmp.fits', rootname)
"""
# Optional package dependencies
from stsci.tools import parseinput
filelist = parseinput.parseinput(filelist)[0]
is_blv = np.all(['blv_tmp.fits' in f for f in filelist])
is_blc = np.all(['blc_tmp.fits' in f for f in filelist])
if is_blv is is_blc:
raise ValueError('Inputs must be all BLV_TMP or all BLC_TMP files')
if is_blv:
sfx = 'crj'
else: # is_blc
sfx = 'crc'
tmpname = f'{outroot}_{sfx}_tmp.fits'
acsrej.acsrej(filelist, tmpname, verbose=verbose)
acs2d.acs2d(tmpname, verbose=verbose) # crx_tmp -> crx
# Work around a bug in ACS2D that names crx_tmp -> crx_tmp_flt, see
# https://github.com/spacetelescope/hstcal/pull/470
wrongname = f'{outroot}_{sfx}_tmp_flt.fits'
rightname = f'{outroot}_{sfx}.fits'
if os.path.isfile(wrongname) and not os.path.exists(rightname):
os.rename(wrongname, rightname)
# Brute-force fixing of final output header.
with fits.open(rightname, 'update') as pf:
pf[0].header['FILENAME'] = rightname
pf[0].header['ROOTNAME'] = outroot
for hdu in pf[1:]:
hdu.header['EXPNAME'] = outroot
hdu.header['ROOTNAME'] = outroot
# Delete intermediate file
if not keep_intermediate_files and os.path.isfile(tmpname):
os.remove(tmpname)
# Delete extraneous trailer file generated by ACSREJ. Its contents are
# copied to the trailer file generated by ACS2D after anyway.
final_tra = f'{outroot}_{sfx}_tmp.tra'
extra_tra = f'{outroot}.tra'
if os.path.isfile(extra_tra) and os.path.isfile(final_tra):
os.remove(extra_tra)
# If fixing the HSTCAL bug changes TRA naming too, this needs to be
# adjusted accordingly.
os.rename(final_tra, f'{outroot}_{sfx}.tra')
def testupdateConvienceFunction(self):
"""Test the update convience function in both the fits and stpyfits
namespace."""
hdul = stpyfits.open(self.data('cdva2.fits'))
hdul1 = fits.open(self.data('cdva2.fits'))
header = hdul[0].header.copy()
header['NAXIS'] = 0
stpyfits.writeto(self.temp('new.fits'),
hdul[0].data,
header,
clobber=True)
hdu = stpyfits.ImageHDU()
hdu1 = fits.ImageHDU()
hdu.data = hdul[0].data
hdu1.data = hdul1[0].data
hdu.header.set('BITPIX', 32)
hdu1.header.set('BITPIX', 32)
hdu.header.set('NAXIS', 0)
hdu.header.set('PIXVALUE', 1, 'Constant pixel value', after='GCOUNT')
hdu1.header.set('PIXVALUE', 1, 'Constant pixel value', after='GCOUNT')
hdu.header.set('NPIX1',
10,
'length of constant array axis 1',
after='GCOUNT')
hdu.header.set('NPIX2',
10,
'length of constant array axis 2',
after='NPIX1')
stpyfits.append(self.temp('new.fits'), hdu.data, hdu.header)
d = hdu.data * 0
stpyfits.update(self.temp('new.fits'), d, hdu.header, 1)
assert_equal(fits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 7, (), '', ''),
(1, '', 'ImageHDU', 8, (), '', '')])
assert_equal(stpyfits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 7, (10, 10), 'int32', ''),
(1, '', 'ImageHDU', 8, (10, 10), 'int32', '')])
hdul7 = stpyfits.open(self.temp('new.fits'))
assert_equal(hdul7[1].header['NAXIS'], 2)
assert_equal(hdul7[1].header['NAXIS1'], 10)
assert_equal(hdul7[1].header['NAXIS2'], 10)
assert_equal(hdul7[1].header['PIXVALUE'], 0)
assert_raises(KeyError, lambda: hdul7[1].header['NPIX1'])
assert_raises(KeyError, lambda: hdul7[1].header['NPIX2'])
assert_true((hdul7[1].data == np.zeros((10, 10),
dtype=np.int32)).all())
hdul8 = fits.open(self.temp('new.fits'))
assert_equal(hdul8[1].header['NAXIS'], 0)
assert_equal(hdul8[1].header['NPIX1'], 10)
assert_equal(hdul8[1].header['NPIX2'], 10)
assert_equal(hdul8[1].header['PIXVALUE'], 0)
assert_raises(KeyError, lambda: hdul8[1].header['NAXIS1'])
assert_raises(KeyError, lambda: hdul8[1].header['NAXIS2'])
assert_equal(hdul8[1].data, None)
hdul7.close()
hdul8.close()
hdul.close()
hdul1.close()
def testHDUListWritetoMethod(self):
"""Test the writeto method of HDUList in both the fits and stpyfits
namespace."""
hdu = stpyfits.PrimaryHDU()
hdu1 = stpyfits.ImageHDU()
hdu.data = np.zeros((10, 10), dtype=np.int32)
hdu1.data = hdu.data + 2
hdu.header.set('BITPIX', 32)
hdu1.header.set('BITPIX', 32)
hdu.header.set('NAXIS', 2)
hdu.header.set('NAXIS1', 10, 'length of constant array axis 1',
after='NAXIS')
hdu.header.set('NAXIS2', 10, 'length of constant array axis 2',
after='NAXIS1')
hdu.header.set('PIXVALUE', 0, 'Constant pixel value')
hdu1.header.set('PIXVALUE', 2, 'Constant pixel value', after='GCOUNT')
hdu1.header.set('NAXIS', 2)
hdu1.header.set('NAXIS1', 10, 'length of constant array axis 1',
after='NAXIS')
hdu1.header.set('NAXIS2', 10, 'length of constant array axis 2',
after='NAXIS1')
hdul = stpyfits.HDUList([hdu,hdu1])
hdul.writeto(self.temp('new.fits'), clobber=True)
assert_equal(stpyfits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 7, (10, 10), 'int32', ''),
(1, '', 'ImageHDU', 8, (10, 10), 'int32', '')])
assert_equal(fits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 7, (), '', ''),
(1, '', 'ImageHDU', 8, (), '', '')])
hdul1 = stpyfits.open(self.temp('new.fits'))
hdul2 = fits.open(self.temp('new.fits'))
assert_equal(hdul1[0].header['NAXIS'], 2)
assert_equal(hdul1[0].header['NAXIS1'], 10)
assert_equal(hdul1[0].header['NAXIS2'], 10)
assert_equal(hdul1[0].header['PIXVALUE'], 0)
assert_raises(KeyError, lambda: hdul1[0].header['NPIX1'])
assert_raises(KeyError, lambda: hdul1[0].header['NPIX2'])
assert_true((hdul1[0].data ==
np.zeros((10, 10), dtype=np.int32)).all())
assert_equal(hdul1[1].header['NAXIS'], 2)
assert_equal(hdul1[1].header['NAXIS1'], 10)
assert_equal(hdul1[1].header['NAXIS2'], 10)
assert_equal(hdul1[1].header['PIXVALUE'], 2)
assert_raises(KeyError, lambda: hdul1[1].header['NPIX1'])
assert_raises(KeyError, lambda: hdul1[1].header['NPIX2'])
assert_true((hdul1[1].data ==
(np.zeros((10, 10), dtype=np.int32) + 2)).all())
assert_equal(hdul2[0].header['NAXIS'], 0)
assert_equal(hdul2[0].header['NPIX1'], 10)
assert_equal(hdul2[0].header['NPIX2'], 10)
assert_equal(hdul2[0].header['PIXVALUE'], 0)
assert_raises(KeyError, lambda: hdul2[0].header['NAXIS1'])
assert_raises(KeyError, lambda: hdul2[0].header['NAXIS2'])
assert_equal(hdul2[0].data, None)
assert_equal(hdul2[1].header['NAXIS'], 0)
assert_equal(hdul2[1].header['NPIX1'], 10)
assert_equal(hdul2[1].header['NPIX2'], 10)
assert_equal(hdul2[1].header['PIXVALUE'], 2)
assert_raises(KeyError, lambda: hdul2[1].header['NAXIS1'])
assert_raises(KeyError, lambda: hdul2[1].header['NAXIS2'])
hdul1.close()
hdul2.close()
def testHDUListFlushMethod(self):
"""Test the flush method of HDUList in both the fits and stpyfits
namespace."""
hdu = stpyfits.PrimaryHDU()
hdu1 = stpyfits.ImageHDU()
hdu.data = np.zeros((10, 10), dtype=np.int32)
hdu1.data = hdu.data + 2
hdu.header.set('BITPIX', 32)
hdu1.header.set('BITPIX', 32)
hdu.header.set('NAXIS', 2)
hdu.header.set('NAXIS1', 10, 'length of constant array axis 1',
after='NAXIS')
hdu.header.set('NAXIS2', 10, 'length of constant array axis 2',
after='NAXIS1')
hdu.header.set('PIXVALUE', 0, 'Constant pixel value')
hdu1.header.set('PIXVALUE', 2, 'Constant pixel value', after='GCOUNT')
hdu1.header.set('NAXIS', 2)
hdu1.header.set('NAXIS1', 10, 'length of constant array axis 1',
after='NAXIS')
hdu1.header.set('NAXIS2', 10, 'length of constant array axis 2',
after='NAXIS1')
hdul = stpyfits.HDUList([hdu, hdu1])
hdul.writeto(self.temp('new.fits'), clobber=True)
hdul = stpyfits.open(self.temp('new.fits'), 'update')
d = np.arange(10, dtype=np.int32)
d = d * 0
d = d + 3
hdul[0].data = d
hdul.flush()
hdul.close()
assert_equal(stpyfits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 6, (10,), 'int32', ''),
(1, '', 'ImageHDU', 8, (10, 10), 'int32', '')])
assert_equal(fits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 6, (), '', ''),
(1, '', 'ImageHDU', 8, (), '', '')])
hdul1 = stpyfits.open(self.temp('new.fits'))
hdul2 = fits.open(self.temp('new.fits'))
assert_equal(hdul1[0].header['NAXIS'], 1)
assert_equal(hdul1[0].header['NAXIS1'], 10)
assert_equal(hdul1[0].header['PIXVALUE'], 3)
assert_raises(KeyError, lambda: hdul1[0].header['NPIX1'])
assert_true((hdul1[0].data ==
(np.zeros(10, dtype=np.int32) + 3)).all())
assert_equal(hdul2[0].header['NAXIS'], 0)
assert_equal(hdul2[0].header['NPIX1'], 10)
assert_equal(hdul2[0].header['PIXVALUE'], 3)
assert_raises(KeyError, lambda: hdul2[0].header['NAXIS1'])
assert_equal(hdul2[0].data, None)
hdul1.close()
hdul2.close()
hdul3 = stpyfits.open(self.temp('new.fits'), 'update')
d = np.arange(15, dtype=np.int32)
d = d * 0
d = d + 4
hdul3[0].data = d
hdul3.close() # Note that close calls flush
assert_equal(stpyfits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 6, (15,), 'int32', ''),
(1, '', 'ImageHDU', 8, (10, 10), 'int32', '')])
assert_equal(fits.info(self.temp('new.fits'), output=False),
[(0, 'PRIMARY', 'PrimaryHDU', 6, (), '', ''),
(1, '', 'ImageHDU', 8, (), '', '')])
hdul1 = stpyfits.open(self.temp('new.fits'))
hdul2 = fits.open(self.temp('new.fits'))
assert_equal(hdul1[0].header['NAXIS'], 1)
assert_equal(hdul1[0].header['NAXIS1'], 15)
assert_equal(hdul1[0].header['PIXVALUE'], 4)
assert_raises(KeyError, lambda: hdul1[0].header['NPIX1'])
assert_true((hdul1[0].data ==
(np.zeros(15, dtype=np.int32) + 4)).all())
assert_equal(hdul2[0].header['NAXIS'], 0)
assert_equal(hdul2[0].header['NPIX1'], 15)
assert_equal(hdul2[0].header['PIXVALUE'], 4)
assert_raises(KeyError, lambda: hdul2[0].header['NAXIS1'])
assert_equal(hdul2[0].data, None)
hdul1.close()
hdul2.close()