Python fits.Column方法代码示例

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_is_fits_image_file_table_img(self, dp_mock):
        with tempfile.TemporaryDirectory() as tmpdir:
            with self.settings(MEDIA_ROOT=tmpdir):
                img_file = os.path.join(tmpdir, 'img.blah')  # file name should be irrelevant
                img = fits.PrimaryHDU(np.arange(100))
                img.header['EXTNAME'] = 'SCI'
                hdul = fits.HDUList([img])
                hdul.writeto(img_file)

                tabimg_file = os.path.join(tmpdir, 'both.fits')
                table = fits.BinTableHDU.from_columns([
                    fits.Column(name='col1', format='I', array=np.array([1, 2, 3])),
                    fits.Column(name='col2', format='I', array=np.array([4, 5, 6]))
                ])
                hdul = fits.HDUList([img, table])
                hdul.writeto(tabimg_file)
                self.data_product.data = tabimg_file
                self.assertTrue(is_fits_image_file(self.data_product.data.file)) 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_byteswap(self):
        p = fits.PrimaryHDU()
        l = fits.HDUList()

        n = np.zeros(3, dtype='i2')
        n[0] = 1
        n[1] = 60000
        n[2] = 2

        c = fits.Column(name='foo', format='i2', bscale=1, bzero=32768,
                        array=n)
        t = fits.BinTableHDU.from_columns([c])

        l.append(p)
        l.append(t)

        l.writeto(self.temp('test.fits'), overwrite=True)

        with fits.open(self.temp('test.fits')) as p:
            assert p[1].data[1]['foo'] == 60000.0 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_binary_table_data(self):
        a1 = np.array(['NGC1001', 'NGC1002', 'NGC1003'])
        a2 = np.array([11.1, 12.3, 15.2])
        col1 = fits.Column(name='target', format='20A', array=a1)
        col2 = fits.Column(name='V_mag', format='E', array=a2)
        cols = fits.ColDefs([col1, col2])
        tbhdu = fits.BinTableHDU.from_columns(cols)
        tbhdu.writeto(self.temp('tmp.fits'), overwrite=True, checksum=True)
        with fits.open(self.temp('tmp.fits'), checksum=True) as hdul:
            assert comparerecords(tbhdu.data, hdul[1].data)
            assert 'CHECKSUM' in hdul[0].header
            assert hdul[0].header['CHECKSUM'] == 'D8iBD6ZAD6fAD6ZA'
            assert 'DATASUM' in hdul[0].header
            assert hdul[0].header['DATASUM'] == '0'
            assert 'CHECKSUM' in hdul[1].header
            assert hdul[1].header['CHECKSUM'] == 'aD1Oa90MaC0Ma90M'
            assert 'DATASUM' in hdul[1].header
            assert hdul[1].header['DATASUM'] == '1062205743' 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_column_endianness(self):
        """
        Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/77
        (Astropy doesn't preserve byte order of non-native order column arrays)
        """

        a = [1., 2., 3., 4.]
        a1 = np.array(a, dtype='<f8')
        a2 = np.array(a, dtype='>f8')

        col1 = fits.Column(name='a', format='D', array=a1)
        col2 = fits.Column(name='b', format='D', array=a2)
        cols = fits.ColDefs([col1, col2])
        tbhdu = fits.BinTableHDU.from_columns(cols)

        assert (tbhdu.data['a'] == a1).all()
        assert (tbhdu.data['b'] == a2).all()

        # Double check that the array is converted to the correct byte-order
        # for FITS (big-endian).
        tbhdu.writeto(self.temp('testendian.fits'), overwrite=True)
        with fits.open(self.temp('testendian.fits')) as hdul:
            assert (hdul[1].data['a'] == a2).all()
            assert (hdul[1].data['b'] == a2).all() 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_fits_record_len(self):
        counts = np.array([312, 334, 308, 317])
        names = np.array(['NGC1', 'NGC2', 'NGC3', 'NCG4'])
        c1 = fits.Column(name='target', format='10A', array=names)
        c2 = fits.Column(name='counts', format='J', unit='DN', array=counts)
        c3 = fits.Column(name='notes', format='A10')
        c4 = fits.Column(name='spectrum', format='5E')
        c5 = fits.Column(name='flag', format='L', array=[1, 0, 1, 1])
        coldefs = fits.ColDefs([c1, c2, c3, c4, c5])
        tbhdu = fits.BinTableHDU.from_columns(coldefs)
        tbhdu.writeto(self.temp('table1.fits'))

        t1 = fits.open(self.temp('table1.fits'))

        assert len(t1[1].data[0]) == 5
        assert len(t1[1].data[0][0:4]) == 4
        assert len(t1[1].data[0][0:5]) == 5
        assert len(t1[1].data[0][0:6]) == 5
        assert len(t1[1].data[0][0:7]) == 5
        assert len(t1[1].data[0][1:4]) == 3
        assert len(t1[1].data[0][1:5]) == 4
        assert len(t1[1].data[0][1:6]) == 4
        assert len(t1[1].data[0][1:7]) == 4

        t1.close() 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_bin_table_with_logical_array(self):
        c1 = fits.Column(name='flag', format='2L',
                         array=[[True, False], [False, True]])
        coldefs = fits.ColDefs([c1])

        tbhdu1 = fits.BinTableHDU.from_columns(coldefs)

        assert (tbhdu1.data.field('flag')[0] ==
                np.array([True, False], dtype=bool)).all()
        assert (tbhdu1.data.field('flag')[1] ==
                np.array([False, True], dtype=bool)).all()

        tbhdu = fits.BinTableHDU.from_columns(tbhdu1.data)

        assert (tbhdu.data.field('flag')[0] ==
                np.array([True, False], dtype=bool)).all()
        assert (tbhdu.data.field('flag')[1] ==
                np.array([False, True], dtype=bool)).all() 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_attribute_field_shadowing(self):
        """
        Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/86

        Numpy recarray objects have a poorly-considered feature of allowing
        field access by attribute lookup.  However, if a field name conincides
        with an existing attribute/method of the array, the existing name takes
        precence (making the attribute-based field lookup completely unreliable
        in general cases).

        This ensures that any FITS_rec attributes still work correctly even
        when there is a field with the same name as that attribute.
        """

        c1 = fits.Column(name='names', format='I', array=[1])
        c2 = fits.Column(name='formats', format='I', array=[2])
        c3 = fits.Column(name='other', format='I', array=[3])

        t = fits.BinTableHDU.from_columns([c1, c2, c3])
        assert t.data.names == ['names', 'formats', 'other']
        assert t.data.formats == ['I'] * 3
        assert (t.data['names'] == [1]).all()
        assert (t.data['formats'] == [2]).all()
        assert (t.data.other == [3]).all() 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_dim_column_byte_order_mismatch(self):
        """
        When creating a table column with non-trivial TDIMn, and
        big-endian array data read from an existing FITS file, the data
        should not be unnecessarily byteswapped.

        Regression test for https://github.com/astropy/astropy/issues/3561
        """

        data = fits.getdata(self.data('random_groups.fits'))['DATA']
        col = fits.Column(name='TEST', array=data, dim='(3,1,128,1,1)',
                          format='1152E')
        thdu = fits.BinTableHDU.from_columns([col])
        thdu.writeto(self.temp('test.fits'))

        with fits.open(self.temp('test.fits')) as hdul:
            assert np.all(hdul[1].data['TEST'] == data) 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_column_with_scaling(self):
        """Check that a scaled column if correctly saved once it is modified.
        Regression test for https://github.com/astropy/astropy/issues/6887
        """
        c1 = fits.Column(name='c1', array=np.array([1], dtype='>i2'),
                         format='1I', bscale=1, bzero=32768)
        S = fits.HDUList([fits.PrimaryHDU(),
                          fits.BinTableHDU.from_columns([c1])])

        # Change value in memory
        S[1].data['c1'][0] = 2
        S.writeto(self.temp("a.fits"))
        assert S[1].data['c1'] == 2

        # Read and change value in memory
        with fits.open(self.temp("a.fits")) as X:
            X[1].data['c1'][0] = 10
            assert X[1].data['c1'][0] == 10

            # Write back to file
            X.writeto(self.temp("b.fits"))

        # Now check the file
        with fits.open(self.temp("b.fits")) as hdul:
            assert hdul[1].data['c1'][0] == 10 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_extend_variable_length_array(self):
        """Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/54"""

        def test(format_code):
            arr = [[1] * 10] * 10
            col1 = fits.Column(name='TESTVLF', format=format_code, array=arr)
            col2 = fits.Column(name='TESTSCA', format='J', array=[1] * 10)
            tb_hdu = fits.BinTableHDU.from_columns([col1, col2], nrows=15)
            # This asserts that the normal 'scalar' column's length was extended
            assert len(tb_hdu.data['TESTSCA']) == 15
            # And this asserts that the VLF column was extended in the same manner
            assert len(tb_hdu.data['TESTVLF']) == 15
            # We can't compare the whole array since the _VLF is an array of
            # objects, but comparing just the edge case rows should suffice
            assert (tb_hdu.data['TESTVLF'][0] == arr[0]).all()
            assert (tb_hdu.data['TESTVLF'][9] == arr[9]).all()
            assert (tb_hdu.data['TESTVLF'][10] == ([0] * 10)).all()
            assert (tb_hdu.data['TESTVLF'][-1] == ([0] * 10)).all()

        for code in ('PJ()', 'QJ()'):
            test(code) 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_variable_length_table_format_pd_from_object_array(self):
        def test(format_code):
            a = np.array([np.array([7.2e-20, 7.3e-20]), np.array([0.0]),
                          np.array([0.0])], 'O')
            acol = fits.Column(name='testa', format=format_code, array=a)
            tbhdu = fits.BinTableHDU.from_columns([acol])
            with ignore_warnings():
                tbhdu.writeto(self.temp('newtable.fits'), overwrite=True)
            with fits.open(self.temp('newtable.fits')) as tbhdu1:
                assert tbhdu1[1].columns[0].format.endswith('D(2)')
                for j in range(3):
                    for i in range(len(a[j])):
                        assert tbhdu1[1].data.field(0)[j][i] == a[j][i]

        for code in ('PD()', 'QD()'):
            test(code) 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_variable_length_table_format_pd_from_list(self):
        def test(format_code):
            a = [np.array([7.2e-20, 7.3e-20]), np.array([0.0]),
                 np.array([0.0])]
            acol = fits.Column(name='testa', format=format_code, array=a)
            tbhdu = fits.BinTableHDU.from_columns([acol])
            with ignore_warnings():
                tbhdu.writeto(self.temp('newtable.fits'), overwrite=True)

            with fits.open(self.temp('newtable.fits')) as tbhdu1:
                assert tbhdu1[1].columns[0].format.endswith('D(2)')
                for j in range(3):
                    for i in range(len(a[j])):
                        assert tbhdu1[1].data.field(0)[j][i] == a[j][i]

        for code in ('PD()', 'QD()'):
            test(code) 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_variable_length_table_format_pa_from_object_array(self):
        def test(format_code):
            a = np.array([np.array(['a', 'b', 'c']), np.array(['d', 'e']),
                          np.array(['f'])], 'O')
            acol = fits.Column(name='testa', format=format_code, array=a)
            tbhdu = fits.BinTableHDU.from_columns([acol])
            with ignore_warnings():
                tbhdu.writeto(self.temp('newtable.fits'), overwrite=True)

            with fits.open(self.temp('newtable.fits')) as hdul:
                assert hdul[1].columns[0].format.endswith('A(3)')
                for j in range(3):
                    for i in range(len(a[j])):
                        assert hdul[1].data.field(0)[j][i] == a[j][i]

        for code in ('PA()', 'QA()'):
            test(code) 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_variable_length_table_format_pa_from_list(self):
        def test(format_code):
            a = ['a', 'ab', 'abc']
            acol = fits.Column(name='testa', format=format_code, array=a)
            tbhdu = fits.BinTableHDU.from_columns([acol])
            with ignore_warnings():
                tbhdu.writeto(self.temp('newtable.fits'), overwrite=True)

            with fits.open(self.temp('newtable.fits')) as hdul:
                assert hdul[1].columns[0].format.endswith('A(3)')
                for j in range(3):
                    for i in range(len(a[j])):
                        assert hdul[1].data.field(0)[j][i] == a[j][i]

        for code in ('PA()', 'QA()'):
            test(code) 

# 需要导入模块: from astropy.io import fits [as 别名]
# 或者: from astropy.io.fits import Column [as 别名]
def test_zero_precision_float_column(self):
        """
        Regression test for https://github.com/astropy/astropy/issues/3422
        """

        c = fits.Column('TEST', 'F5.0', array=[1.1, 2.2, 3.3])
        # The decimal places will be clipped
        t = fits.TableHDU.from_columns([c])
        t.writeto(self.temp('test.fits'))

        with fits.open(self.temp('test.fits')) as hdul:
            assert hdul[1].header['TFORM1'] == 'F5.0'
            assert hdul[1].data['TEST'].dtype == np.dtype('float64')
            assert np.all(hdul[1].data['TEST'] == [1.0, 2.0, 3.0])

            # Check how the raw data looks
            raw = np.rec.recarray.field(hdul[1].data, 'TEST')
            assert raw.tostring() == b'   1.   2.   3.'