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 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 testGetvalExtensionHDU(self):
"""Regression test for an issue that came up with the fact that
ImageHDU has a different argument signature from PrimaryHDU.
"""
data = np.ones((100, 100))
hdu = stpyfits.ImageHDU(data=data)
hdu.header['PIXVALUE'] = 1
hdu.header['FOO'] = 'test'
hdul = stpyfits.HDUList([stpyfits.PrimaryHDU(), hdu])
hdul.writeto(self.temp('test.fits'))
assert_equal(stpyfits.getval(self.temp('test.fits'), 'FOO', ext=1),
'test')
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 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()