Python numpy.ndfromtxt方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_usecols(self):
        # Test the selection of columns
        # Select 1 column
        control = np.array([[1, 2], [3, 4]], float)
        data = TextIO()
        np.savetxt(data, control)
        data.seek(0)
        test = np.ndfromtxt(data, dtype=float, usecols=(1,))
        assert_equal(test, control[:, 1])
        #
        control = np.array([[1, 2, 3], [3, 4, 5]], float)
        data = TextIO()
        np.savetxt(data, control)
        data.seek(0)
        test = np.ndfromtxt(data, dtype=float, usecols=(1, 2))
        assert_equal(test, control[:, 1:])
        # Testing with arrays instead of tuples.
        data.seek(0)
        test = np.ndfromtxt(data, dtype=float, usecols=np.array([1, 2]))
        assert_equal(test, control[:, 1:]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_invalid_raise(self):
        # Test invalid raise
        data = ["1, 1, 1, 1, 1"] * 50
        for i in range(5):
            data[10 * i] = "2, 2, 2, 2 2"
        data.insert(0, "a, b, c, d, e")
        mdata = TextIO("\n".join(data))
        #
        kwargs = dict(delimiter=",", dtype=None, names=True)
        # XXX: is there a better way to get the return value of the
        # callable in assert_warns ?
        ret = {}

        def f(_ret={}):
            _ret['mtest'] = np.ndfromtxt(mdata, invalid_raise=False, **kwargs)
        assert_warns(ConversionWarning, f, _ret=ret)
        mtest = ret['mtest']
        assert_equal(len(mtest), 45)
        assert_equal(mtest, np.ones(45, dtype=[(_, int) for _ in 'abcde']))
        #
        mdata.seek(0)
        assert_raises(ValueError, np.ndfromtxt, mdata,
                      delimiter=",", names=True) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_auto_dtype_largeint(self):
        # Regression test for numpy/numpy#5635 whereby large integers could
        # cause OverflowErrors.

        # Test the automatic definition of the output dtype
        #
        # 2**66 = 73786976294838206464 => should convert to float
        # 2**34 = 17179869184 => should convert to int64
        # 2**10 = 1024 => should convert to int (int32 on 32-bit systems,
        #                 int64 on 64-bit systems)

        data = TextIO('73786976294838206464 17179869184 1024')

        test = np.ndfromtxt(data, dtype=None)

        assert_equal(test.dtype.names, ['f0', 'f1', 'f2'])

        assert_(test.dtype['f0'] == float)
        assert_(test.dtype['f1'] == np.int64)
        assert_(test.dtype['f2'] == np.integer)

        assert_allclose(test['f0'], 73786976294838206464.)
        assert_equal(test['f1'], 17179869184)
        assert_equal(test['f2'], 1024) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_auto_dtype_largeint(self):
        # Regression test for numpy/numpy#5635 whereby large integers could
        # cause OverflowErrors.

        # Test the automatic definition of the output dtype
        #
        # 2**66 = 73786976294838206464 => should convert to float
        # 2**34 = 17179869184 => should convert to int64
        # 2**10 = 1024 => should convert to int (int32 on 32-bit systems,
        #                 int64 on 64-bit systems)

        data = TextIO('73786976294838206464 17179869184 1024')

        test = np.ndfromtxt(data, dtype=None)

        assert_equal(test.dtype.names, ['f0', 'f1', 'f2'])

        assert_(test.dtype['f0'] == np.float)
        assert_(test.dtype['f1'] == np.int64)
        assert_(test.dtype['f2'] == np.integer)

        assert_allclose(test['f0'], 73786976294838206464.)
        assert_equal(test['f1'], 17179869184)
        assert_equal(test['f2'], 1024) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_record(self):
        # Test w/ explicit dtype
        data = TextIO('1 2\n3 4')
        test = np.ndfromtxt(data, dtype=[('x', np.int32), ('y', np.int32)])
        control = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
        assert_equal(test, control)
        #
        data = TextIO('M 64.0 75.0\nF 25.0 60.0')
        descriptor = {'names': ('gender', 'age', 'weight'),
                      'formats': ('S1', 'i4', 'f4')}
        control = np.array([('M', 64.0, 75.0), ('F', 25.0, 60.0)],
                           dtype=descriptor)
        test = np.ndfromtxt(data, dtype=descriptor)
        assert_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_array(self):
        # Test outputting a standard ndarray
        data = TextIO('1 2\n3 4')
        control = np.array([[1, 2], [3, 4]], dtype=int)
        test = np.ndfromtxt(data, dtype=int)
        assert_array_equal(test, control)
        #
        data.seek(0)
        control = np.array([[1, 2], [3, 4]], dtype=float)
        test = np.loadtxt(data, dtype=float)
        assert_array_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_1D(self):
        # Test squeezing to 1D
        control = np.array([1, 2, 3, 4], int)
        #
        data = TextIO('1\n2\n3\n4\n')
        test = np.ndfromtxt(data, dtype=int)
        assert_array_equal(test, control)
        #
        data = TextIO('1,2,3,4\n')
        test = np.ndfromtxt(data, dtype=int, delimiter=',')
        assert_array_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_comments(self):
        # Test the stripping of comments
        control = np.array([1, 2, 3, 5], int)
        # Comment on its own line
        data = TextIO('# comment\n1,2,3,5\n')
        test = np.ndfromtxt(data, dtype=int, delimiter=',', comments='#')
        assert_equal(test, control)
        # Comment at the end of a line
        data = TextIO('1,2,3,5# comment\n')
        test = np.ndfromtxt(data, dtype=int, delimiter=',', comments='#')
        assert_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_skiprows(self):
        # Test row skipping
        control = np.array([1, 2, 3, 5], int)
        kwargs = dict(dtype=int, delimiter=',')
        #
        data = TextIO('comment\n1,2,3,5\n')
        test = np.ndfromtxt(data, skip_header=1, **kwargs)
        assert_equal(test, control)
        #
        data = TextIO('# comment\n1,2,3,5\n')
        test = np.loadtxt(data, skiprows=1, **kwargs)
        assert_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_auto_dtype(self):
        # Test the automatic definition of the output dtype
        data = TextIO('A 64 75.0 3+4j True\nBCD 25 60.0 5+6j False')
        with warnings.catch_warnings(record=True) as w:
            warnings.filterwarnings('always', '', np.VisibleDeprecationWarning)
            test = np.ndfromtxt(data, dtype=None)
            assert_(w[0].category is np.VisibleDeprecationWarning)
        control = [np.array([b'A', b'BCD']),
                   np.array([64, 25]),
                   np.array([75.0, 60.0]),
                   np.array([3 + 4j, 5 + 6j]),
                   np.array([True, False]), ]
        assert_equal(test.dtype.names, ['f0', 'f1', 'f2', 'f3', 'f4'])
        for (i, ctrl) in enumerate(control):
            assert_equal(test['f%i' % i], ctrl) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_auto_dtype_uniform(self):
        # Tests whether the output dtype can be uniformized
        data = TextIO('1 2 3 4\n5 6 7 8\n')
        test = np.ndfromtxt(data, dtype=None)
        control = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
        assert_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_fancy_dtype(self):
        # Check that a nested dtype isn't MIA
        data = TextIO('1,2,3.0\n4,5,6.0\n')
        fancydtype = np.dtype([('x', int), ('y', [('t', int), ('s', float)])])
        test = np.ndfromtxt(data, dtype=fancydtype, delimiter=',')
        control = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=fancydtype)
        assert_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_names_overwrite(self):
        # Test overwriting the names of the dtype
        descriptor = {'names': ('g', 'a', 'w'),
                      'formats': ('S1', 'i4', 'f4')}
        data = TextIO(b'M 64.0 75.0\nF 25.0 60.0')
        names = ('gender', 'age', 'weight')
        test = np.ndfromtxt(data, dtype=descriptor, names=names)
        descriptor['names'] = names
        control = np.array([('M', 64.0, 75.0),
                            ('F', 25.0, 60.0)], dtype=descriptor)
        assert_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_autonames_and_usecols(self):
        # Tests names and usecols
        data = TextIO('A B C D\n aaaa 121 45 9.1')
        with warnings.catch_warnings(record=True) as w:
            warnings.filterwarnings('always', '', np.VisibleDeprecationWarning)
            test = np.ndfromtxt(data, usecols=('A', 'C', 'D'),
                                names=True, dtype=None)
            assert_(w[0].category is np.VisibleDeprecationWarning)
        control = np.array(('aaaa', 45, 9.1),
                           dtype=[('A', '|S4'), ('C', int), ('D', float)])
        assert_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ndfromtxt [as 別名]
def test_converters_with_usecols_and_names(self):
        # Tests names and usecols
        data = TextIO('A B C D\n aaaa 121 45 9.1')
        with warnings.catch_warnings(record=True) as w:
            warnings.filterwarnings('always', '', np.VisibleDeprecationWarning)
            test = np.ndfromtxt(data, usecols=('A', 'C', 'D'), names=True,
                                dtype=None,
                                converters={'C': lambda s: 2 * int(s)})
            assert_(w[0].category is np.VisibleDeprecationWarning)
        control = np.array(('aaaa', 90, 9.1),
                           dtype=[('A', '|S4'), ('C', int), ('D', float)])
        assert_equal(test, control)