/usr/lib/python3/dist-packages/pyfits/tests/test_image.py is in python3-pyfits 1:3.4-4.
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import math
import os
import re
import time
import warnings
import numpy as np
import pyfits as fits
from ..util import PyfitsPendingDeprecationWarning
from ..hdu.compressed import SUBTRACTIVE_DITHER_1, DITHER_SEED_CHECKSUM
from . import PyfitsTestCase
from .test_table import comparerecords
from .util import ignore_warnings, CaptureStdio
from nose.tools import assert_raises
from warnings import catch_warnings
class TestImageFunctions(PyfitsTestCase):
def test_constructor_name_arg(self):
"""Like the test of the same name in test_table.py"""
hdu = fits.ImageHDU()
assert hdu.name == ''
assert 'EXTNAME' not in hdu.header
hdu.name = 'FOO'
assert hdu.name == 'FOO'
assert hdu.header['EXTNAME'] == 'FOO'
# Passing name to constructor
hdu = fits.ImageHDU(name='FOO')
assert hdu.name == 'FOO'
assert hdu.header['EXTNAME'] == 'FOO'
# And overriding a header with a different extname
hdr = fits.Header()
hdr['EXTNAME'] = 'EVENTS'
hdu = fits.ImageHDU(header=hdr, name='FOO')
assert hdu.name == 'FOO'
assert hdu.header['EXTNAME'] == 'FOO'
def test_constructor_copies_header(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/153
Ensure that a header from one HDU is copied when used to initialize new
HDU.
"""
ifd = fits.HDUList(fits.PrimaryHDU())
phdr = ifd[0].header
phdr['FILENAME'] = 'labq01i3q_rawtag.fits'
primary_hdu = fits.PrimaryHDU(header=phdr)
ofd = fits.HDUList(primary_hdu)
ofd[0].header['FILENAME'] = 'labq01i3q_flt.fits'
# Original header should be unchanged
assert phdr['FILENAME'] == 'labq01i3q_rawtag.fits'
def test_open(self):
# The function "open" reads a FITS file into an HDUList object. There
# are three modes to open: "readonly" (the default), "append", and
# "update".
# Open a file read-only (the default mode), the content of the FITS
# file are read into memory.
r = fits.open(self.data('test0.fits')) # readonly
# data parts are latent instantiation, so if we close the HDUList
# without touching data, data can not be accessed.
r.close()
assert_raises(Exception, lambda x: x[1].data[:2, :2], r)
def test_open_2(self):
r = fits.open(self.data('test0.fits'))
info = ([(0, 'PRIMARY', 'PrimaryHDU', 138, (), '', '')] +
[(x, 'SCI', 'ImageHDU', 61, (40, 40), 'int16', '')
for x in range(1, 5)])
try:
assert r.info(output=False) == info
finally:
r.close()
def test_primary_with_extname(self):
"""Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/151
Tests that the EXTNAME keyword works with Primary HDUs as well, and
interacts properly with the .name attribute. For convenience
hdulist['PRIMARY'] will still refer to the first HDU even if it has an
EXTNAME not equal to 'PRIMARY'.
"""
prihdr = fits.Header([('EXTNAME', 'XPRIMARY'), ('EXTVER', 1)])
hdul = fits.HDUList([fits.PrimaryHDU(header=prihdr)])
assert 'EXTNAME' in hdul[0].header
assert hdul[0].name == 'XPRIMARY'
assert hdul[0].name == hdul[0].header['EXTNAME']
info = [(0, 'XPRIMARY', 'PrimaryHDU', 5, (), '', '')]
assert hdul.info(output=False) == info
assert hdul['PRIMARY'] is hdul['XPRIMARY']
assert hdul['PRIMARY'] is hdul[('XPRIMARY', 1)]
hdul[0].name = 'XPRIMARY2'
assert hdul[0].header['EXTNAME'] == 'XPRIMARY2'
hdul.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as hdul:
assert hdul[0].name == 'XPRIMARY2'
def test_io_manipulation(self):
# This legacy test also tests numerous deprecated interfaces for
# backwards compatibility
# Get a keyword value. An extension can be referred by name or by
# number. Both extension and keyword names are case insensitive.
with fits.open(self.data('test0.fits')) as r:
assert r['primary'].header['naxis'] == 0
assert r[0].header['naxis'] == 0
# If there are more than one extension with the same EXTNAME value,
# the EXTVER can be used (as the second argument) to distinguish
# the extension.
assert r['sci', 1].header['detector'] == 1
# append a new card
r[0].header['xxx'] = 1.234e56
assert (repr(r[0].header[-3:]) ==
"EXPFLAG = 'NORMAL ' / Exposure interruption indicator \n"
"FILENAME= 'vtest3.fits' / File name \n"
"XXX = 1.234E+56 ")
# rename a keyword
r[0].header.rename_keyword('filename', 'fname')
assert_raises(ValueError, r[0].header.rename_keyword, 'fname',
'history')
assert_raises(ValueError, r[0].header.rename_keyword, 'fname',
'simple')
r[0].header.rename_keyword('fname', 'filename')
# get a subsection of data
assert np.array_equal(r[2].data[:3, :3],
np.array([[349, 349, 348],
[349, 349, 347],
[347, 350, 349]], dtype=np.int16))
# We can create a new FITS file by opening a new file with "append"
# mode.
with fits.open(self.temp('test_new.fits'), mode='append') as n:
# Append the primary header and the 2nd extension to the new
# file.
n.append(r[0])
n.append(r[2])
# The flush method will write the current HDUList object back
# to the newly created file on disk. The HDUList is still open
# and can be further operated.
n.flush()
assert n[1].data[1, 1] == 349
# modify a data point
n[1].data[1, 1] = 99
# When the file is closed, the most recent additions of
# extension(s) since last flush() will be appended, but any HDU
# already existed at the last flush will not be modified
del n
# If an existing file is opened with "append" mode, like the
# readonly mode, the HDU's will be read into the HDUList which can
# be modified in memory but can not be written back to the original
# file. A file opened with append mode can only add new HDU's.
os.rename(self.temp('test_new.fits'),
self.temp('test_append.fits'))
with fits.open(self.temp('test_append.fits'),
mode='append') as a:
# The above change did not take effect since this was made
# after the flush().
assert a[1].data[1, 1] == 349
a.append(r[1])
del a
# When changes are made to an HDUList which was opened with
# "update" mode, they will be written back to the original file
# when a flush/close is called.
os.rename(self.temp('test_append.fits'),
self.temp('test_update.fits'))
with fits.open(self.temp('test_update.fits'),
mode='update') as u:
# When the changes do not alter the size structures of the
# original (or since last flush) HDUList, the changes are
# written back "in place".
assert u[0].header['rootname'] == 'U2EQ0201T'
u[0].header['rootname'] = 'abc'
assert u[1].data[1, 1] == 349
u[1].data[1, 1] = 99
u.flush()
# If the changes affect the size structure, e.g. adding or
# deleting HDU(s), header was expanded or reduced beyond
# existing number of blocks (2880 bytes in each block), or
# change the data size, the HDUList is written to a temporary
# file, the original file is deleted, and the temporary file is
# renamed to the original file name and reopened in the update
# mode. To a user, these two kinds of updating writeback seem
# to be the same, unless the optional argument in flush or
# close is set to 1.
del u[2]
u.flush()
# the write method in HDUList class writes the current HDUList,
# with all changes made up to now, to a new file. This method
# works the same disregard the mode the HDUList was opened
# with.
u.append(r[3])
u.writeto(self.temp('test_new.fits'))
del u
# Another useful new HDUList method is readall. It will "touch" the
# data parts in all HDUs, so even if the HDUList is closed, we can
# still operate on the data.
with fits.open(self.data('test0.fits')) as r:
r.readall()
assert r[1].data[1, 1] == 315
# create an HDU with data only
data = np.ones((3, 5), dtype=np.float32)
hdu = fits.ImageHDU(data=data, name='SCI')
assert np.array_equal(hdu.data,
np.array([[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]],
dtype=np.float32))
# create an HDU with header and data
# notice that the header has the right NAXIS's since it is constructed
# with ImageHDU
hdu2 = fits.ImageHDU(header=r[1].header, data=np.array([1, 2],
dtype='int32'))
assert (repr(hdu2.header[1:5]) ==
"BITPIX = 32 / array data type \n"
"NAXIS = 1 / number of array dimensions \n"
"NAXIS1 = 2 \n"
"PCOUNT = 0 / number of parameters ")
def test_memory_mapping(self):
# memory mapping
f1 = fits.open(self.data('test0.fits'), memmap=1)
f1.close()
def test_verification_on_output(self):
# verification on output
# make a defect HDUList first
err_text = "HDUList's 0th element is not a primary HDU."
with catch_warnings(record=True) as w:
x = fits.ImageHDU()
# HDUList can take a list or one single HDU
hdu = fits.HDUList(x)
with CaptureStdio():
hdu.verify()
assert len(w) == 3
assert err_text in str(w[1].message)
fix_text = err_text + " Fixed by inserting one as 0th HDU."
with catch_warnings(record=True) as w:
with CaptureStdio():
hdu.writeto(self.temp('test_new2.fits'), 'fix')
assert len(w) == 3
assert fix_text in str(w[1].message)
def test_section(self):
# section testing
fs = fits.open(self.data('arange.fits'))
assert np.array_equal(fs[0].section[3, 2, 5], 357)
assert np.array_equal(
fs[0].section[3, 2, :],
np.array([352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362]))
assert np.array_equal(fs[0].section[3, 2, 4:],
np.array([356, 357, 358, 359, 360, 361, 362]))
assert np.array_equal(fs[0].section[3, 2, :8],
np.array([352, 353, 354, 355, 356, 357, 358, 359]))
assert np.array_equal(fs[0].section[3, 2, -8:8],
np.array([355, 356, 357, 358, 359]))
assert np.array_equal(
fs[0].section[3, 2:5, :],
np.array([[352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362],
[363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373],
[374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384]]))
assert np.array_equal(fs[0].section[3, :, :][:3, :3],
np.array([[330, 331, 332],
[341, 342, 343],
[352, 353, 354]]))
dat = fs[0].data
assert np.array_equal(fs[0].section[3, 2:5, :8], dat[3, 2:5, :8])
assert np.array_equal(fs[0].section[3, 2:5, 3], dat[3, 2:5, 3])
assert np.array_equal(fs[0].section[3:6, :, :][:3, :3, :3],
np.array([[[330, 331, 332],
[341, 342, 343],
[352, 353, 354]],
[[440, 441, 442],
[451, 452, 453],
[462, 463, 464]],
[[550, 551, 552],
[561, 562, 563],
[572, 573, 574]]]))
assert np.array_equal(fs[0].section[:, :, :][:3, :2, :2],
np.array([[[0, 1],
[11, 12]],
[[110, 111],
[121, 122]],
[[220, 221],
[231, 232]]]))
assert np.array_equal(fs[0].section[:, 2, :], dat[:, 2, :])
assert np.array_equal(fs[0].section[:, 2:5, :], dat[:, 2:5, :])
assert np.array_equal(fs[0].section[3:6, 3, :], dat[3:6, 3, :])
assert np.array_equal(fs[0].section[3:6, 3:7, :], dat[3:6, 3:7, :])
assert np.array_equal(fs[0].section[:, ::2], dat[:, ::2])
assert np.array_equal(fs[0].section[:, [1, 2, 4], 3],
dat[:, [1, 2, 4], 3])
assert np.array_equal(
fs[0].section[:, np.array([True, False, True]), :],
dat[:, np.array([True, False, True]), :])
assert np.array_equal(
fs[0].section[3:6, 3, :, ...], dat[3:6, 3, :, ...])
assert np.array_equal(fs[0].section[..., ::2], dat[..., ::2])
assert np.array_equal(fs[0].section[..., [1, 2, 4], 3],
dat[..., [1, 2, 4], 3])
def test_section_data_single(self):
a = np.array([1])
hdu = fits.PrimaryHDU(a)
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
sec = hdul[0].section
dat = hdul[0].data
assert np.array_equal(sec[0], dat[0])
assert np.array_equal(sec[...], dat[...])
assert np.array_equal(sec[..., 0], dat[..., 0])
assert np.array_equal(sec[0, ...], dat[0, ...])
def test_section_data_square(self):
a = np.arange(4).reshape((2, 2))
hdu = fits.PrimaryHDU(a)
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
d = hdul[0]
dat = hdul[0].data
assert (d.section[:, :] == dat[:, :]).all()
assert (d.section[0, :] == dat[0, :]).all()
assert (d.section[1, :] == dat[1, :]).all()
assert (d.section[:, 0] == dat[:, 0]).all()
assert (d.section[:, 1] == dat[:, 1]).all()
assert (d.section[0, 0] == dat[0, 0]).all()
assert (d.section[0, 1] == dat[0, 1]).all()
assert (d.section[1, 0] == dat[1, 0]).all()
assert (d.section[1, 1] == dat[1, 1]).all()
assert (d.section[0:1, 0:1] == dat[0:1, 0:1]).all()
assert (d.section[0:2, 0:1] == dat[0:2, 0:1]).all()
assert (d.section[0:1, 0:2] == dat[0:1, 0:2]).all()
assert (d.section[0:2, 0:2] == dat[0:2, 0:2]).all()
def test_section_data_cube(self):
a = np.arange(18).reshape((2, 3, 3))
hdu = fits.PrimaryHDU(a)
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
d = hdul[0]
dat = hdul[0].data
assert (d.section[:, :, :] == dat[:, :, :]).all()
assert (d.section[:, :] == dat[:, :]).all()
assert (d.section[:] == dat[:]).all()
assert (d.section[0, :, :] == dat[0, :, :]).all()
assert (d.section[1, :, :] == dat[1, :, :]).all()
assert (d.section[0, 0, :] == dat[0, 0, :]).all()
assert (d.section[0, 1, :] == dat[0, 1, :]).all()
assert (d.section[0, 2, :] == dat[0, 2, :]).all()
assert (d.section[1, 0, :] == dat[1, 0, :]).all()
assert (d.section[1, 1, :] == dat[1, 1, :]).all()
assert (d.section[1, 2, :] == dat[1, 2, :]).all()
assert (d.section[0, 0, 0] == dat[0, 0, 0]).all()
assert (d.section[0, 0, 1] == dat[0, 0, 1]).all()
assert (d.section[0, 0, 2] == dat[0, 0, 2]).all()
assert (d.section[0, 1, 0] == dat[0, 1, 0]).all()
assert (d.section[0, 1, 1] == dat[0, 1, 1]).all()
assert (d.section[0, 1, 2] == dat[0, 1, 2]).all()
assert (d.section[0, 2, 0] == dat[0, 2, 0]).all()
assert (d.section[0, 2, 1] == dat[0, 2, 1]).all()
assert (d.section[0, 2, 2] == dat[0, 2, 2]).all()
assert (d.section[1, 0, 0] == dat[1, 0, 0]).all()
assert (d.section[1, 0, 1] == dat[1, 0, 1]).all()
assert (d.section[1, 0, 2] == dat[1, 0, 2]).all()
assert (d.section[1, 1, 0] == dat[1, 1, 0]).all()
assert (d.section[1, 1, 1] == dat[1, 1, 1]).all()
assert (d.section[1, 1, 2] == dat[1, 1, 2]).all()
assert (d.section[1, 2, 0] == dat[1, 2, 0]).all()
assert (d.section[1, 2, 1] == dat[1, 2, 1]).all()
assert (d.section[1, 2, 2] == dat[1, 2, 2]).all()
assert (d.section[:, 0, 0] == dat[:, 0, 0]).all()
assert (d.section[:, 0, 1] == dat[:, 0, 1]).all()
assert (d.section[:, 0, 2] == dat[:, 0, 2]).all()
assert (d.section[:, 1, 0] == dat[:, 1, 0]).all()
assert (d.section[:, 1, 1] == dat[:, 1, 1]).all()
assert (d.section[:, 1, 2] == dat[:, 1, 2]).all()
assert (d.section[:, 2, 0] == dat[:, 2, 0]).all()
assert (d.section[:, 2, 1] == dat[:, 2, 1]).all()
assert (d.section[:, 2, 2] == dat[:, 2, 2]).all()
assert (d.section[0, :, 0] == dat[0, :, 0]).all()
assert (d.section[0, :, 1] == dat[0, :, 1]).all()
assert (d.section[0, :, 2] == dat[0, :, 2]).all()
assert (d.section[1, :, 0] == dat[1, :, 0]).all()
assert (d.section[1, :, 1] == dat[1, :, 1]).all()
assert (d.section[1, :, 2] == dat[1, :, 2]).all()
assert (d.section[:, :, 0] == dat[:, :, 0]).all()
assert (d.section[:, :, 1] == dat[:, :, 1]).all()
assert (d.section[:, :, 2] == dat[:, :, 2]).all()
assert (d.section[:, 0, :] == dat[:, 0, :]).all()
assert (d.section[:, 1, :] == dat[:, 1, :]).all()
assert (d.section[:, 2, :] == dat[:, 2, :]).all()
assert (d.section[:, :, 0:1] == dat[:, :, 0:1]).all()
assert (d.section[:, :, 0:2] == dat[:, :, 0:2]).all()
assert (d.section[:, :, 0:3] == dat[:, :, 0:3]).all()
assert (d.section[:, :, 1:2] == dat[:, :, 1:2]).all()
assert (d.section[:, :, 1:3] == dat[:, :, 1:3]).all()
assert (d.section[:, :, 2:3] == dat[:, :, 2:3]).all()
assert (d.section[0:1, 0:1, 0:1] == dat[0:1, 0:1, 0:1]).all()
assert (d.section[0:1, 0:1, 0:2] == dat[0:1, 0:1, 0:2]).all()
assert (d.section[0:1, 0:1, 0:3] == dat[0:1, 0:1, 0:3]).all()
assert (d.section[0:1, 0:1, 1:2] == dat[0:1, 0:1, 1:2]).all()
assert (d.section[0:1, 0:1, 1:3] == dat[0:1, 0:1, 1:3]).all()
assert (d.section[0:1, 0:1, 2:3] == dat[0:1, 0:1, 2:3]).all()
assert (d.section[0:1, 0:2, 0:1] == dat[0:1, 0:2, 0:1]).all()
assert (d.section[0:1, 0:2, 0:2] == dat[0:1, 0:2, 0:2]).all()
assert (d.section[0:1, 0:2, 0:3] == dat[0:1, 0:2, 0:3]).all()
assert (d.section[0:1, 0:2, 1:2] == dat[0:1, 0:2, 1:2]).all()
assert (d.section[0:1, 0:2, 1:3] == dat[0:1, 0:2, 1:3]).all()
assert (d.section[0:1, 0:2, 2:3] == dat[0:1, 0:2, 2:3]).all()
assert (d.section[0:1, 0:3, 0:1] == dat[0:1, 0:3, 0:1]).all()
assert (d.section[0:1, 0:3, 0:2] == dat[0:1, 0:3, 0:2]).all()
assert (d.section[0:1, 0:3, 0:3] == dat[0:1, 0:3, 0:3]).all()
assert (d.section[0:1, 0:3, 1:2] == dat[0:1, 0:3, 1:2]).all()
assert (d.section[0:1, 0:3, 1:3] == dat[0:1, 0:3, 1:3]).all()
assert (d.section[0:1, 0:3, 2:3] == dat[0:1, 0:3, 2:3]).all()
assert (d.section[0:1, 1:2, 0:1] == dat[0:1, 1:2, 0:1]).all()
assert (d.section[0:1, 1:2, 0:2] == dat[0:1, 1:2, 0:2]).all()
assert (d.section[0:1, 1:2, 0:3] == dat[0:1, 1:2, 0:3]).all()
assert (d.section[0:1, 1:2, 1:2] == dat[0:1, 1:2, 1:2]).all()
assert (d.section[0:1, 1:2, 1:3] == dat[0:1, 1:2, 1:3]).all()
assert (d.section[0:1, 1:2, 2:3] == dat[0:1, 1:2, 2:3]).all()
assert (d.section[0:1, 1:3, 0:1] == dat[0:1, 1:3, 0:1]).all()
assert (d.section[0:1, 1:3, 0:2] == dat[0:1, 1:3, 0:2]).all()
assert (d.section[0:1, 1:3, 0:3] == dat[0:1, 1:3, 0:3]).all()
assert (d.section[0:1, 1:3, 1:2] == dat[0:1, 1:3, 1:2]).all()
assert (d.section[0:1, 1:3, 1:3] == dat[0:1, 1:3, 1:3]).all()
assert (d.section[0:1, 1:3, 2:3] == dat[0:1, 1:3, 2:3]).all()
assert (d.section[1:2, 0:1, 0:1] == dat[1:2, 0:1, 0:1]).all()
assert (d.section[1:2, 0:1, 0:2] == dat[1:2, 0:1, 0:2]).all()
assert (d.section[1:2, 0:1, 0:3] == dat[1:2, 0:1, 0:3]).all()
assert (d.section[1:2, 0:1, 1:2] == dat[1:2, 0:1, 1:2]).all()
assert (d.section[1:2, 0:1, 1:3] == dat[1:2, 0:1, 1:3]).all()
assert (d.section[1:2, 0:1, 2:3] == dat[1:2, 0:1, 2:3]).all()
assert (d.section[1:2, 0:2, 0:1] == dat[1:2, 0:2, 0:1]).all()
assert (d.section[1:2, 0:2, 0:2] == dat[1:2, 0:2, 0:2]).all()
assert (d.section[1:2, 0:2, 0:3] == dat[1:2, 0:2, 0:3]).all()
assert (d.section[1:2, 0:2, 1:2] == dat[1:2, 0:2, 1:2]).all()
assert (d.section[1:2, 0:2, 1:3] == dat[1:2, 0:2, 1:3]).all()
assert (d.section[1:2, 0:2, 2:3] == dat[1:2, 0:2, 2:3]).all()
assert (d.section[1:2, 0:3, 0:1] == dat[1:2, 0:3, 0:1]).all()
assert (d.section[1:2, 0:3, 0:2] == dat[1:2, 0:3, 0:2]).all()
assert (d.section[1:2, 0:3, 0:3] == dat[1:2, 0:3, 0:3]).all()
assert (d.section[1:2, 0:3, 1:2] == dat[1:2, 0:3, 1:2]).all()
assert (d.section[1:2, 0:3, 1:3] == dat[1:2, 0:3, 1:3]).all()
assert (d.section[1:2, 0:3, 2:3] == dat[1:2, 0:3, 2:3]).all()
assert (d.section[1:2, 1:2, 0:1] == dat[1:2, 1:2, 0:1]).all()
assert (d.section[1:2, 1:2, 0:2] == dat[1:2, 1:2, 0:2]).all()
assert (d.section[1:2, 1:2, 0:3] == dat[1:2, 1:2, 0:3]).all()
assert (d.section[1:2, 1:2, 1:2] == dat[1:2, 1:2, 1:2]).all()
assert (d.section[1:2, 1:2, 1:3] == dat[1:2, 1:2, 1:3]).all()
assert (d.section[1:2, 1:2, 2:3] == dat[1:2, 1:2, 2:3]).all()
assert (d.section[1:2, 1:3, 0:1] == dat[1:2, 1:3, 0:1]).all()
assert (d.section[1:2, 1:3, 0:2] == dat[1:2, 1:3, 0:2]).all()
assert (d.section[1:2, 1:3, 0:3] == dat[1:2, 1:3, 0:3]).all()
assert (d.section[1:2, 1:3, 1:2] == dat[1:2, 1:3, 1:2]).all()
assert (d.section[1:2, 1:3, 1:3] == dat[1:2, 1:3, 1:3]).all()
assert (d.section[1:2, 1:3, 2:3] == dat[1:2, 1:3, 2:3]).all()
def test_section_data_four(self):
a = np.arange(256).reshape((4, 4, 4, 4))
hdu = fits.PrimaryHDU(a)
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
d = hdul[0]
dat = hdul[0].data
assert (d.section[:, :, :, :] == dat[:, :, :, :]).all()
assert (d.section[:, :, :] == dat[:, :, :]).all()
assert (d.section[:, :] == dat[:, :]).all()
assert (d.section[:] == dat[:]).all()
assert (d.section[0, :, :, :] == dat[0, :, :, :]).all()
assert (d.section[0, :, 0, :] == dat[0, :, 0, :]).all()
assert (d.section[:, :, 0, :] == dat[:, :, 0, :]).all()
assert (d.section[:, 1, 0, :] == dat[:, 1, 0, :]).all()
assert (d.section[:, :, :, 1] == dat[:, :, :, 1]).all()
def test_section_data_scaled(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/143
This is like test_section_data_square but uses a file containing scaled
image data, to test that sections can work correctly with scaled data.
"""
hdul = fits.open(self.data('scale.fits'))
d = hdul[0]
dat = hdul[0].data
assert (d.section[:, :] == dat[:, :]).all()
assert (d.section[0, :] == dat[0, :]).all()
assert (d.section[1, :] == dat[1, :]).all()
assert (d.section[:, 0] == dat[:, 0]).all()
assert (d.section[:, 1] == dat[:, 1]).all()
assert (d.section[0, 0] == dat[0, 0]).all()
assert (d.section[0, 1] == dat[0, 1]).all()
assert (d.section[1, 0] == dat[1, 0]).all()
assert (d.section[1, 1] == dat[1, 1]).all()
assert (d.section[0:1, 0:1] == dat[0:1, 0:1]).all()
assert (d.section[0:2, 0:1] == dat[0:2, 0:1]).all()
assert (d.section[0:1, 0:2] == dat[0:1, 0:2]).all()
assert (d.section[0:2, 0:2] == dat[0:2, 0:2]).all()
# Test without having accessed the full data first
hdul = fits.open(self.data('scale.fits'))
d = hdul[0]
assert (d.section[:, :] == dat[:, :]).all()
assert (d.section[0, :] == dat[0, :]).all()
assert (d.section[1, :] == dat[1, :]).all()
assert (d.section[:, 0] == dat[:, 0]).all()
assert (d.section[:, 1] == dat[:, 1]).all()
assert (d.section[0, 0] == dat[0, 0]).all()
assert (d.section[0, 1] == dat[0, 1]).all()
assert (d.section[1, 0] == dat[1, 0]).all()
assert (d.section[1, 1] == dat[1, 1]).all()
assert (d.section[0:1, 0:1] == dat[0:1, 0:1]).all()
assert (d.section[0:2, 0:1] == dat[0:2, 0:1]).all()
assert (d.section[0:1, 0:2] == dat[0:1, 0:2]).all()
assert (d.section[0:2, 0:2] == dat[0:2, 0:2]).all()
assert not d._data_loaded
def test_do_not_scale_image_data(self):
hdul = fits.open(self.data('scale.fits'), do_not_scale_image_data=True)
assert hdul[0].data.dtype == np.dtype('>i2')
hdul = fits.open(self.data('scale.fits'))
assert hdul[0].data.dtype == np.dtype('float32')
def test_append_uint_data(self):
"""Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/56
(BZERO and BSCALE added in the wrong location when appending scaled
data)
"""
fits.writeto(self.temp('test_new.fits'), data=np.array([],
dtype='uint8'))
d = np.zeros([100, 100]).astype('uint16')
fits.append(self.temp('test_new.fits'), data=d)
f = fits.open(self.temp('test_new.fits'), uint=True)
assert f[1].data.dtype == 'uint16'
def test_uint_header_consistency(self):
"""
Regression test for https://github.com/astropy/astropy/issues/2305
This ensures that an HDU containing unsigned integer data always has
the apppriate BZERO value in its header.
"""
for int_size in (16, 32, 64):
# Just make an array of some unsigned ints that wouldn't fit in a
# signed int array of the same bit width
max_uint = (2 ** int_size) - 1
if int_size == 64:
# Otherwise may get an overflow error, at least on Python 2
max_uint = np.uint64(int_size)
dtype = 'uint%d' % int_size
arr = np.empty(100, dtype=dtype)
arr.fill(max_uint)
arr -= np.arange(100, dtype=dtype)
uint_hdu = fits.PrimaryHDU(data=arr)
assert np.all(uint_hdu.data == arr)
assert uint_hdu.data.dtype.name == 'uint%d' % int_size
assert 'BZERO' in uint_hdu.header
assert uint_hdu.header['BZERO'] == (2 ** (int_size - 1))
filename = 'uint%d.fits' % int_size
uint_hdu.writeto(self.temp(filename))
with fits.open(self.temp(filename), uint=True) as hdul:
new_uint_hdu = hdul[0]
assert np.all(new_uint_hdu.data == arr)
assert new_uint_hdu.data.dtype.name == 'uint%d' % int_size
assert 'BZERO' in new_uint_hdu.header
assert new_uint_hdu.header['BZERO'] == (2 ** (int_size - 1))
def test_blanks(self):
"""Test image data with blank spots in it (which should show up as
NaNs in the data array.
"""
arr = np.zeros((10, 10), dtype=np.int32)
# One row will be blanks
arr[1] = 999
hdu = fits.ImageHDU(data=arr)
hdu.header['BLANK'] = 999
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
assert np.isnan(hdul[1].data[1]).all()
def test_invalid_blanks(self):
"""
Test that invalid use of the BLANK keyword leads to an appropriate
warning, and that the BLANK keyword is ignored when returning the
HDU data.
Regression test for https://github.com/astropy/astropy/issues/3865
"""
arr = np.arange(5, dtype=np.float64)
hdu = fits.PrimaryHDU(data=arr)
hdu.header['BLANK'] = 2
with catch_warnings(record=True) as w:
hdu.writeto(self.temp('test_new.fits'))
# Allow the HDU to be written, but there should be a warning
# when writing a header with BLANK when then data is not
# int
assert len(w) == 1
assert "Invalid 'BLANK' keyword in header" in str(w[0].message)
# Should also get a warning when opening the file, and the BLANK
# value should not be applied
with catch_warnings(record=True) as w:
with fits.open(self.temp('test_new.fits')) as h:
assert len(w) == 1
assert "Invalid 'BLANK' keyword in header" in str(w[0].message)
assert np.all(arr == h[0].data)
def test_scale_back_with_blanks(self):
"""
Test that when auto-rescaling integer data with "blank" values (where
the blanks are replaced by NaN in the float data), that the "BLANK"
keyword is removed from the header.
Further, test that when using the ``scale_back=True`` option the blank
values are restored properly.
Regression test for https://github.com/astropy/astropy/issues/3865
"""
# Make the sample file
arr = np.arange(5, dtype=np.int32)
hdu = fits.PrimaryHDU(data=arr)
hdu.scale('int16', bscale=1.23)
# Creating data that uses BLANK is currently kludgy--a separate issue
# TODO: Rewrite this test when scaling with blank support is better
# supported
# Let's just add a value to the data that should be converted to NaN
# when it is read back in:
hdu.data[0] = 9999
hdu.header['BLANK'] = 9999
hdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as hdul:
data = hdul[0].data
assert np.isnan(data[0])
hdul.writeto(self.temp('test2.fits'))
# Now reopen the newly written file. It should not have a 'BLANK'
# keyword
with catch_warnings(record=True) as w:
with fits.open(self.temp('test2.fits')) as hdul2:
assert len(w) == 0
assert 'BLANK' not in hdul2[0].header
data = hdul2[0].data
assert np.isnan(data[0])
# Finally, test that scale_back keeps the BLANKs correctly
with fits.open(self.temp('test.fits'), scale_back=True,
mode='update') as hdul3:
data = hdul3[0].data
assert np.isnan(data[0])
with fits.open(self.temp('test.fits'),
do_not_scale_image_data=True) as hdul4:
assert hdul4[0].header['BLANK'] == 9999
assert hdul4[0].header['BSCALE'] == 1.23
assert hdul4[0].data[0] == 9999
def test_bzero_with_floats(self):
"""Test use of the BZERO keyword in an image HDU containing float
data.
"""
arr = np.zeros((10, 10)) - 1
hdu = fits.ImageHDU(data=arr)
hdu.header['BZERO'] = 1.0
hdu.writeto(self.temp('test_new.fits'))
hdul = fits.open(self.temp('test_new.fits'))
arr += 1
assert (hdul[1].data == arr).all()
def test_rewriting_large_scaled_image(self):
"""Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/84 and
https://aeon.stsci.edu/ssb/trac/pyfits/ticket/101
"""
hdul = fits.open(self.data('fixed-1890.fits'))
orig_data = hdul[0].data
with ignore_warnings():
hdul.writeto(self.temp('test_new.fits'), clobber=True)
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
assert (hdul[0].data == orig_data).all()
hdul.close()
# Just as before, but this time don't touch hdul[0].data before writing
# back out--this is the case that failed in
# https://aeon.stsci.edu/ssb/trac/pyfits/ticket/84
hdul = fits.open(self.data('fixed-1890.fits'))
with ignore_warnings():
hdul.writeto(self.temp('test_new.fits'), clobber=True)
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
assert (hdul[0].data == orig_data).all()
hdul.close()
# Test opening/closing/reopening a scaled file in update mode
hdul = fits.open(self.data('fixed-1890.fits'),
do_not_scale_image_data=True)
hdul.writeto(self.temp('test_new.fits'), clobber=True,
output_verify='silentfix')
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
orig_data = hdul[0].data
hdul.close()
hdul = fits.open(self.temp('test_new.fits'), mode='update')
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
assert (hdul[0].data == orig_data).all()
hdul = fits.open(self.temp('test_new.fits'))
hdul.close()
def test_image_update_header(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/105
Replacing the original header to an image HDU and saving should update
the NAXISn keywords appropriately and save the image data correctly.
"""
# Copy the original file before saving to it
self.copy_file('test0.fits')
with fits.open(self.temp('test0.fits'), mode='update') as hdul:
orig_data = hdul[1].data.copy()
hdr_copy = hdul[1].header.copy()
del hdr_copy['NAXIS*']
hdul[1].header = hdr_copy
with fits.open(self.temp('test0.fits')) as hdul:
assert (orig_data == hdul[1].data).all()
def test_open_scaled_in_update_mode(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/119
(Don't update scaled image data if the data is not read)
This ensures that merely opening and closing a file containing scaled
image data does not cause any change to the data (or the header).
Changes should only occur if the data is accessed.
"""
# Copy the original file before making any possible changes to it
self.copy_file('scale.fits')
mtime = os.stat(self.temp('scale.fits')).st_mtime
time.sleep(1)
fits.open(self.temp('scale.fits'), mode='update').close()
# Ensure that no changes were made to the file merely by immediately
# opening and closing it.
assert mtime == os.stat(self.temp('scale.fits')).st_mtime
# Insert a slight delay to ensure the mtime does change when the file
# is changed
time.sleep(1)
hdul = fits.open(self.temp('scale.fits'), 'update')
orig_data = hdul[0].data
hdul.close()
# Now the file should be updated with the rescaled data
assert mtime != os.stat(self.temp('scale.fits')).st_mtime
hdul = fits.open(self.temp('scale.fits'), mode='update')
assert hdul[0].data.dtype == np.dtype('>f4')
assert hdul[0].header['BITPIX'] == -32
assert 'BZERO' not in hdul[0].header
assert 'BSCALE' not in hdul[0].header
assert (orig_data == hdul[0].data).all()
# Try reshaping the data, then closing and reopening the file; let's
# see if all the changes are preseved properly
hdul[0].data.shape = (42, 10)
hdul.close()
hdul = fits.open(self.temp('scale.fits'))
assert hdul[0].shape == (42, 10)
assert hdul[0].data.dtype == np.dtype('>f4')
assert hdul[0].header['BITPIX'] == -32
assert 'BZERO' not in hdul[0].header
assert 'BSCALE' not in hdul[0].header
def test_scale_back(self):
"""A simple test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/120
The scale_back feature for image HDUs.
"""
self.copy_file('scale.fits')
with fits.open(self.temp('scale.fits'), mode='update',
scale_back=True) as hdul:
orig_bitpix = hdul[0].header['BITPIX']
orig_bzero = hdul[0].header['BZERO']
orig_bscale = hdul[0].header['BSCALE']
orig_data = hdul[0].data.copy()
hdul[0].data[0] = 0
with fits.open(self.temp('scale.fits'),
do_not_scale_image_data=True) as hdul:
assert hdul[0].header['BITPIX'] == orig_bitpix
assert hdul[0].header['BZERO'] == orig_bzero
assert hdul[0].header['BSCALE'] == orig_bscale
zero_point = int(math.floor(-orig_bzero / orig_bscale))
assert (hdul[0].data[0] == zero_point).all()
with fits.open(self.temp('scale.fits')) as hdul:
assert (hdul[0].data[1:] == orig_data[1:]).all()
def test_image_none(self):
"""
Regression test for https://github.com/spacetelescope/PyFITS/issues/27
"""
with fits.open(self.data('test0.fits')) as h:
h[1].data
h[1].data = None
h[1].writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as h:
assert h[1].data is None
assert h[1].header['NAXIS'] == 0
assert 'NAXIS1' not in h[1].header
assert 'NAXIS2' not in h[1].header
def test_invalid_blank(self):
"""
Regression test for https://github.com/astropy/astropy/issues/2711
If the BLANK keyword contains an invalid value it should be ignored for
any calculations (though a warning should be issued).
"""
data = np.arange(100, dtype=np.float64)
hdu = fits.PrimaryHDU(data)
hdu.header['BLANK'] = 'nan'
hdu.writeto(self.temp('test.fits'))
with catch_warnings(record=True) as w:
with fits.open(self.temp('test.fits')) as hdul:
assert np.all(hdul[0].data == data)
assert len(w) == 2
msg = "Invalid value for 'BLANK' keyword in header"
assert msg in str(w[0].message)
msg = "Invalid 'BLANK' keyword"
assert msg in str(w[1].message)
def test_scaled_image_fromfile(self):
"""
Regression test for https://github.com/astropy/astropy/issues/2710
"""
# Make some sample data
a = np.arange(100, dtype=np.float32)
hdu = fits.PrimaryHDU(data=a.copy())
hdu.scale(bscale=1.1)
hdu.writeto(self.temp('test.fits'))
with open(self.temp('test.fits'), 'rb') as f:
file_data = f.read()
hdul = fits.HDUList.fromstring(file_data)
assert np.allclose(hdul[0].data, a)
class TestCompressedImage(PyfitsTestCase):
def test_empty(self):
"""
Regression test for https://github.com/astropy/astropy/issues/2595
"""
hdu = fits.CompImageHDU()
assert hdu.data is None
hdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits'), mode='update') as hdul:
assert len(hdul) == 2
assert isinstance(hdul[1], fits.CompImageHDU)
assert hdul[1].data is None
# Now test replacing the empty data with an array and see what
# happens
hdul[1].data = np.arange(100, dtype=np.int32)
with fits.open(self.temp('test.fits')) as hdul:
assert len(hdul) == 2
assert isinstance(hdul[1], fits.CompImageHDU)
assert np.all(hdul[1].data == np.arange(100, dtype=np.int32))
def test_comp_image(self):
argslist = [
(np.zeros((2, 10, 10), dtype=np.float32), 'RICE_1', 16),
(np.zeros((2, 10, 10), dtype=np.float32), 'GZIP_1', -0.01),
(np.zeros((100, 100)) + 1, 'HCOMPRESS_1', 16)
]
for byte_order in ('<', '>'):
for args in argslist:
yield (self._test_comp_image,) + args + (byte_order,)
@ignore_warnings(PyfitsPendingDeprecationWarning)
def _test_comp_image(self, data, compression_type, quantize_level,
byte_order):
data = data.newbyteorder(byte_order)
primary_hdu = fits.PrimaryHDU()
ofd = fits.HDUList(primary_hdu)
chdu = fits.CompImageHDU(data, name='SCI',
compressionType=compression_type,
quantizeLevel=quantize_level)
ofd.append(chdu)
ofd.writeto(self.temp('test_new.fits'), clobber=True)
ofd.close()
with fits.open(self.temp('test_new.fits')) as fd:
assert (fd[1].data == data).all()
assert fd[1].header['NAXIS'] == chdu.header['NAXIS']
assert fd[1].header['NAXIS1'] == chdu.header['NAXIS1']
assert fd[1].header['NAXIS2'] == chdu.header['NAXIS2']
assert fd[1].header['BITPIX'] == chdu.header['BITPIX']
@ignore_warnings(PyfitsPendingDeprecationWarning)
def test_comp_image_hcompression_1_invalid_data(self):
"""
Tests compression with the HCOMPRESS_1 algorithm with data that is
not 2D and has a non-2D tile size.
"""
assert_raises(ValueError, fits.CompImageHDU,
np.zeros((2, 10, 10), dtype=np.float32), name='SCI',
compressionType='HCOMPRESS_1', quantizeLevel=16,
tileSize=[2, 10, 10])
@ignore_warnings(PyfitsPendingDeprecationWarning)
def test_comp_image_hcompress_image_stack(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/171
Tests that data containing more than two dimensions can be
compressed with HCOMPRESS_1 so long as the user-supplied tile size can
be flattened to two dimensions.
"""
cube = np.arange(300, dtype=np.float32).reshape((3, 10, 10))
hdu = fits.CompImageHDU(data=cube, name='SCI',
compressionType='HCOMPRESS_1',
quantizeLevel=16, tileSize=[5, 5, 1])
hdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as hdul:
assert np.abs(hdul['SCI'].data - cube).max() < 1./15.
def test_subtractive_dither_seed(self):
"""
Regression test for https://github.com/spacetelescope/PyFITS/issues/32
Ensure that when floating point data is compressed with the
SUBTRACTIVE_DITHER_1 quantization method that the correct ZDITHER0 seed
is added to the header, and that the data can be correctly
decompressed.
"""
array = np.arange(100.0).reshape(10, 10)
csum = (array[0].view('uint8').sum() % 10000) + 1
hdu = fits.CompImageHDU(data=array,
quantize_method=SUBTRACTIVE_DITHER_1,
dither_seed=DITHER_SEED_CHECKSUM)
hdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as hdul:
assert isinstance(hdul[1], fits.CompImageHDU)
assert 'ZQUANTIZ' in hdul[1]._header
assert hdul[1]._header['ZQUANTIZ'] == 'SUBTRACTIVE_DITHER_1'
assert 'ZDITHER0' in hdul[1]._header
assert hdul[1]._header['ZDITHER0'] == csum
assert np.all(hdul[1].data == array)
def test_disable_image_compression(self):
with catch_warnings():
# No warnings should be displayed in this case
warnings.simplefilter('error')
with fits.open(self.data('comp.fits'),
disable_image_compression=True) as hdul:
# The compressed image HDU should show up as a BinTableHDU, but
# *not* a CompImageHDU
assert isinstance(hdul[1], fits.BinTableHDU)
assert not isinstance(hdul[1], fits.CompImageHDU)
with fits.open(self.data('comp.fits')) as hdul:
assert isinstance(hdul[1], fits.CompImageHDU)
def test_open_comp_image_in_update_mode(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/167
Similar to test_open_scaled_in_update_mode(), but specifically for
compressed images.
"""
# Copy the original file before making any possible changes to it
self.copy_file('comp.fits')
mtime = os.stat(self.temp('comp.fits')).st_mtime
time.sleep(1)
fits.open(self.temp('comp.fits'), mode='update').close()
# Ensure that no changes were made to the file merely by immediately
# opening and closing it.
assert mtime == os.stat(self.temp('comp.fits')).st_mtime
def test_open_scaled_in_update_mode_compressed(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/88 2
Identical to test_open_scaled_in_update_mode() but with a compressed
version of the scaled image.
"""
# Copy+compress the original file before making any possible changes to
# it
with fits.open(self.data('scale.fits'),
do_not_scale_image_data=True) as hdul:
chdu = fits.CompImageHDU(data=hdul[0].data,
header=hdul[0].header)
chdu.writeto(self.temp('scale.fits'))
mtime = os.stat(self.temp('scale.fits')).st_mtime
time.sleep(1)
fits.open(self.temp('scale.fits'), mode='update').close()
# Ensure that no changes were made to the file merely by immediately
# opening and closing it.
assert mtime == os.stat(self.temp('scale.fits')).st_mtime
# Insert a slight delay to ensure the mtime does change when the file
# is changed
time.sleep(1)
hdul = fits.open(self.temp('scale.fits'), 'update')
hdul[1].data
hdul.close()
# Now the file should be updated with the rescaled data
assert mtime != os.stat(self.temp('scale.fits')).st_mtime
hdul = fits.open(self.temp('scale.fits'), mode='update')
assert hdul[1].data.dtype == np.dtype('float32')
assert hdul[1].header['BITPIX'] == -32
assert 'BZERO' not in hdul[1].header
assert 'BSCALE' not in hdul[1].header
# Try reshaping the data, then closing and reopening the file; let's
# see if all the changes are preseved properly
hdul[1].data.shape = (42, 10)
hdul.close()
hdul = fits.open(self.temp('scale.fits'))
assert hdul[1].shape == (42, 10)
assert hdul[1].data.dtype == np.dtype('float32')
assert hdul[1].header['BITPIX'] == -32
assert 'BZERO' not in hdul[1].header
assert 'BSCALE' not in hdul[1].header
def test_write_comp_hdu_direct_from_existing(self):
with fits.open(self.data('comp.fits')) as hdul:
hdul[1].writeto(self.temp('test.fits'))
with fits.open(self.data('comp.fits')) as hdul1:
with fits.open(self.temp('test.fits')) as hdul2:
assert np.all(hdul1[1].data == hdul2[1].data)
assert comparerecords(hdul1[1].compressed_data,
hdul2[1].compressed_data)
def test_rewriting_large_scaled_image_compressed(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/88 1
Identical to test_rewriting_large_scaled_image() but with a compressed
image.
"""
with fits.open(self.data('fixed-1890.fits'),
do_not_scale_image_data=True) as hdul:
chdu = fits.CompImageHDU(data=hdul[0].data,
header=hdul[0].header)
chdu.writeto(self.temp('fixed-1890-z.fits'))
hdul = fits.open(self.temp('fixed-1890-z.fits'))
orig_data = hdul[1].data
with ignore_warnings():
hdul.writeto(self.temp('test_new.fits'), clobber=True)
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
assert (hdul[1].data == orig_data).all()
hdul.close()
# Just as before, but this time don't touch hdul[0].data before writing
# back out--this is the case that failed in
# https://aeon.stsci.edu/ssb/trac/pyfits/ticket/84
hdul = fits.open(self.temp('fixed-1890-z.fits'))
with ignore_warnings():
hdul.writeto(self.temp('test_new.fits'), clobber=True)
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
assert (hdul[1].data == orig_data).all()
hdul.close()
# Test opening/closing/reopening a scaled file in update mode
hdul = fits.open(self.temp('fixed-1890-z.fits'),
do_not_scale_image_data=True)
hdul.writeto(self.temp('test_new.fits'), clobber=True,
output_verify='silentfix')
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
orig_data = hdul[1].data
hdul.close()
hdul = fits.open(self.temp('test_new.fits'), mode='update')
hdul.close()
hdul = fits.open(self.temp('test_new.fits'))
assert (hdul[1].data == orig_data).all()
hdul = fits.open(self.temp('test_new.fits'))
hdul.close()
def test_scale_back_compressed(self):
"""
Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/88 3
Identical to test_scale_back() but uses a compressed image.
"""
# Create a compressed version of the scaled image
with fits.open(self.data('scale.fits'),
do_not_scale_image_data=True) as hdul:
chdu = fits.CompImageHDU(data=hdul[0].data,
header=hdul[0].header)
chdu.writeto(self.temp('scale.fits'))
with fits.open(self.temp('scale.fits'), mode='update',
scale_back=True) as hdul:
orig_bitpix = hdul[1].header['BITPIX']
orig_bzero = hdul[1].header['BZERO']
orig_bscale = hdul[1].header['BSCALE']
orig_data = hdul[1].data.copy()
hdul[1].data[0] = 0
with fits.open(self.temp('scale.fits'),
do_not_scale_image_data=True) as hdul:
assert hdul[1].header['BITPIX'] == orig_bitpix
assert hdul[1].header['BZERO'] == orig_bzero
assert hdul[1].header['BSCALE'] == orig_bscale
zero_point = int(math.floor(-orig_bzero / orig_bscale))
assert (hdul[1].data[0] == zero_point).all()
with fits.open(self.temp('scale.fits')) as hdul:
assert (hdul[1].data[1:] == orig_data[1:]).all()
# Extra test to ensure that after everything the data is still the
# same as in the original uncompressed version of the image
with fits.open(self.data('scale.fits')) as hdul2:
# Recall we made the same modification to the data in hdul
# above
hdul2[0].data[0] = 0
assert (hdul[1].data == hdul2[0].data).all()
def test_lossless_gzip_compression(self):
"""Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/198"""
noise = np.random.normal(size=(1000, 1000))
chdu1 = fits.CompImageHDU(data=noise, compressionType='GZIP_1')
# First make a test image with lossy compression and make sure it
# wasn't compressed perfectly. This shouldn't happen ever, but just to
# make sure the test non-trivial.
chdu1.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as h:
assert np.abs(noise - h[1].data).max() > 0.0
del h
chdu2 = fits.CompImageHDU(data=noise, compressionType='GZIP_1',
quantizeLevel=0.0) # No quantization
with ignore_warnings():
chdu2.writeto(self.temp('test.fits'), clobber=True)
with fits.open(self.temp('test.fits')) as h:
assert (noise == h[1].data).all()
def test_compression_column_tforms(self):
"""Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/199"""
# Some interestingly tiled data so that some of it is quantized and
# some of it ends up just getting gzip-compressed
data2 = ((np.arange(1, 8, dtype=np.float32) * 10)[:, np.newaxis] +
np.arange(1, 7))
np.random.seed(1337)
data1 = np.random.uniform(size=(6 * 4, 7 * 4))
data1[:data2.shape[0], :data2.shape[1]] = data2
chdu = fits.CompImageHDU(data1, compressionType='RICE_1',
tileSize=(6, 7))
chdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits'),
disable_image_compression=True) as h:
assert re.match(r'^1PB\(\d+\)$', h[1].header['TFORM1'])
assert re.match(r'^1PB\(\d+\)$', h[1].header['TFORM2'])
def test_compression_update_header(self):
"""Regression test for
https://github.com/spacetelescope/PyFITS/issues/23
"""
self.copy_file('comp.fits')
with fits.open(self.temp('comp.fits'), mode='update') as hdul:
assert isinstance(hdul[1], fits.CompImageHDU)
hdul[1].header['test1'] = 'test'
hdul[1]._header['test2'] = 'test2'
with fits.open(self.temp('comp.fits')) as hdul:
assert 'test1' in hdul[1].header
assert hdul[1].header['test1'] == 'test'
assert 'test2' in hdul[1].header
assert hdul[1].header['test2'] == 'test2'
# Test update via index now:
with fits.open(self.temp('comp.fits'), mode='update') as hdul:
hdr = hdul[1].header
hdr[hdr.index('TEST1')] = 'foo'
with fits.open(self.temp('comp.fits')) as hdul:
assert hdul[1].header['TEST1'] == 'foo'
# Test slice updates
with fits.open(self.temp('comp.fits'), mode='update') as hdul:
hdul[1].header['TEST*'] = 'qux'
with fits.open(self.temp('comp.fits')) as hdul:
assert list(hdul[1].header['TEST*'].values()) == ['qux', 'qux']
with fits.open(self.temp('comp.fits'), mode='update') as hdul:
hdr = hdul[1].header
idx = hdr.index('TEST1')
hdr[idx:idx + 2] = 'bar'
with fits.open(self.temp('comp.fits')) as hdul:
assert list(hdul[1].header['TEST*'].values()) == ['bar', 'bar']
# Test updating a specific COMMENT card duplicate
with fits.open(self.temp('comp.fits'), mode='update') as hdul:
hdul[1].header[('COMMENT', 1)] = 'I am fire. I am death!'
with fits.open(self.temp('comp.fits')) as hdul:
assert hdul[1].header['COMMENT'][1] == 'I am fire. I am death!'
assert hdul[1]._header['COMMENT'][1] == 'I am fire. I am death!'
# Test deleting by keyword and by slice
with fits.open(self.temp('comp.fits'), mode='update') as hdul:
hdr = hdul[1].header
del hdr['COMMENT']
idx = hdr.index('TEST1')
del hdr[idx:idx + 2]
with fits.open(self.temp('comp.fits')) as hdul:
assert 'COMMENT' not in hdul[1].header
assert 'COMMENT' not in hdul[1]._header
assert 'TEST1' not in hdul[1].header
assert 'TEST1' not in hdul[1]._header
assert 'TEST2' not in hdul[1].header
assert 'TEST2' not in hdul[1]._header
def test_compression_update_header_with_reserved(self):
"""
Ensure that setting reserved keywords related to the table data
structure on CompImageHDU image headers fails.
"""
def test_set_keyword(hdr, keyword, value):
with catch_warnings(record=True) as w:
hdr[keyword] = value
assert len(w) == 1
assert str(w[0].message).startswith(
"Keyword %r is reserved" % keyword)
assert keyword not in hdr
with fits.open(self.data('comp.fits')) as hdul:
hdr = hdul[1].header
test_set_keyword(hdr, 'TFIELDS', 8)
test_set_keyword(hdr, 'TTYPE1', 'Foo')
test_set_keyword(hdr, 'ZCMPTYPE', 'ASDF')
test_set_keyword(hdr, 'ZVAL1', 'Foo')
def test_compression_header_append(self):
with fits.open(self.data('comp.fits')) as hdul:
imghdr = hdul[1].header
tblhdr = hdul[1]._header
with catch_warnings(record=True) as w:
imghdr.append('TFIELDS')
assert len(w) == 1
assert 'TFIELDS' not in imghdr
imghdr.append(('FOO', 'bar', 'qux'), end=True)
assert 'FOO' in imghdr
assert imghdr[-1] == 'bar'
assert 'FOO' in tblhdr
assert tblhdr[-1] == 'bar'
imghdr.append(('CHECKSUM', 'abcd1234'))
assert 'CHECKSUM' in imghdr
assert imghdr['CHECKSUM'] == 'abcd1234'
assert 'CHECKSUM' not in tblhdr
assert 'ZHECKSUM' in tblhdr
assert tblhdr['ZHECKSUM'] == 'abcd1234'
def test_compression_header_insert(self):
with fits.open(self.data('comp.fits')) as hdul:
imghdr = hdul[1].header
tblhdr = hdul[1]._header
# First try inserting a restricted keyword
with catch_warnings(record=True) as w:
imghdr.insert(1000, 'TFIELDS')
assert len(w) == 1
assert 'TFIELDS' not in imghdr
assert tblhdr.count('TFIELDS') == 1
# First try keyword-relative insert
imghdr.insert('TELESCOP', ('OBSERVER', 'Phil Plait'))
assert 'OBSERVER' in imghdr
assert imghdr.index('OBSERVER') == imghdr.index('TELESCOP') - 1
assert 'OBSERVER' in tblhdr
assert tblhdr.index('OBSERVER') == tblhdr.index('TELESCOP') - 1
# Next let's see if an index-relative insert winds up being
# sensible
idx = imghdr.index('OBSERVER')
imghdr.insert('OBSERVER', ('FOO',))
assert 'FOO' in imghdr
assert imghdr.index('FOO') == idx
assert 'FOO' in tblhdr
assert tblhdr.index('FOO') == tblhdr.index('OBSERVER') - 1
def test_compression_header_set_before_after(self):
with fits.open(self.data('comp.fits')) as hdul:
imghdr = hdul[1].header
tblhdr = hdul[1]._header
with catch_warnings(record=True) as w:
imghdr.set('ZBITPIX', 77, 'asdf', after='XTENSION')
assert len(w) == 1
assert 'ZBITPIX' not in imghdr
assert tblhdr.count('ZBITPIX') == 1
assert tblhdr['ZBITPIX'] != 77
# Move GCOUNT before PCOUNT (not that there's any reason you'd
# *want* to do that, but it's just a test...)
imghdr.set('GCOUNT', 99, before='PCOUNT')
assert imghdr.index('GCOUNT') == imghdr.index('PCOUNT') - 1
assert imghdr['GCOUNT'] == 99
assert tblhdr.index('ZGCOUNT') == tblhdr.index('ZPCOUNT') - 1
assert tblhdr['ZGCOUNT'] == 99
assert tblhdr.index('PCOUNT') == 5
assert tblhdr.index('GCOUNT') == 6
assert tblhdr['GCOUNT'] == 1
imghdr.set('GCOUNT', 2, after='PCOUNT')
assert imghdr.index('GCOUNT') == imghdr.index('PCOUNT') + 1
assert imghdr['GCOUNT'] == 2
assert tblhdr.index('ZGCOUNT') == tblhdr.index('ZPCOUNT') + 1
assert tblhdr['ZGCOUNT'] == 2
assert tblhdr.index('PCOUNT') == 5
assert tblhdr.index('GCOUNT') == 6
assert tblhdr['GCOUNT'] == 1
def test_compression_header_append_commentary(self):
"""
Regression test for https://github.com/astropy/astropy/issues/2363
"""
hdu = fits.CompImageHDU(np.array([0], dtype=np.int32))
hdu.header['COMMENT'] = 'hello world'
assert hdu.header['COMMENT'] == ['hello world']
hdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as hdul:
assert hdul[1].header['COMMENT'] == ['hello world']
def test_compression_with_gzip_column(self):
"""
Regression test for https://github.com/spacetelescope/PyFITS/issues/71
"""
arr = np.zeros((2, 7000), dtype='float32')
# The first row (which will be the first compressed tile) has a very
# wide range of values that will be difficult to quantize, and should
# result in use of a GZIP_COMPRESSED_DATA column
arr[0] = np.linspace(0, 1, 7000)
arr[1] = np.random.normal(size=7000)
hdu = fits.CompImageHDU(data=arr)
hdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as hdul:
comp_hdu = hdul[1]
# GZIP-compressed tile should compare exactly
assert np.all(comp_hdu.data[0] == arr[0])
# The second tile uses lossy compression and may be somewhat off,
# so we don't bother comparing it exactly
def test_duplicate_compression_header_keywords(self):
"""
Regression test for https://github.com/astropy/astropy/issues/2750
Tests that the fake header (for the compressed image) can still be read
even if the real header contained a duplicate ZTENSION keyword (the
issue applies to any keyword specific to the compression convention,
however).
"""
arr = np.arange(100, dtype=np.int32)
hdu = fits.CompImageHDU(data=arr)
header = hdu._header
# append the duplicate keyword
hdu._header.append(('ZTENSION', 'IMAGE'))
hdu.writeto(self.temp('test.fits'))
with fits.open(self.temp('test.fits')) as hdul:
assert header == hdul[1]._header
# There's no good reason to have a duplicate keyword, but
# technically it isn't invalid either :/
assert hdul[1]._header.count('ZTENSION') == 2
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