我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.ndfromtxt()。
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:])
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)
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)
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)
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)
def test_array(self): # Test outputing 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)
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)
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)
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)
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') test = np.ndfromtxt(data, dtype=None) 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)
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)
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)
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)
def test_autonames_and_usecols(self): # Tests names and usecols data = TextIO('A B C D\n aaaa 121 45 9.1') test = np.ndfromtxt(data, usecols=('A', 'C', 'D'), names=True, dtype=None) control = np.array(('aaaa', 45, 9.1), dtype=[('A', '|S4'), ('C', int), ('D', float)]) assert_equal(test, control)
def test_converters_with_usecols_and_names(self): # Tests names and usecols data = TextIO('A B C D\n aaaa 121 45 9.1') test = np.ndfromtxt(data, usecols=('A', 'C', 'D'), names=True, dtype=None, converters={'C': lambda s: 2 * int(s)}) control = np.array(('aaaa', 90, 9.1), dtype=[('A', '|S4'), ('C', int), ('D', float)]) assert_equal(test, control)
def test_converters_cornercases(self): # Test the conversion to datetime. converter = { 'date': lambda s: strptime(s, '%Y-%m-%d %H:%M:%SZ')} data = TextIO('2009-02-03 12:00:00Z, 72214.0') test = np.ndfromtxt(data, delimiter=',', dtype=None, names=['date', 'stid'], converters=converter) control = np.array((datetime(2009, 2, 3), 72214.), dtype=[('date', np.object_), ('stid', float)]) assert_equal(test, control)
def test_converters_cornercases2(self): # Test the conversion to datetime64. converter = { 'date': lambda s: np.datetime64(strptime(s, '%Y-%m-%d %H:%M:%SZ'))} data = TextIO('2009-02-03 12:00:00Z, 72214.0') test = np.ndfromtxt(data, delimiter=',', dtype=None, names=['date', 'stid'], converters=converter) control = np.array((datetime(2009, 2, 3), 72214.), dtype=[('date', 'datetime64[us]'), ('stid', float)]) assert_equal(test, control)
def test_unused_converter(self): # Test whether unused converters are forgotten data = TextIO("1 21\n 3 42\n") test = np.ndfromtxt(data, usecols=(1,), converters={0: lambda s: int(s, 16)}) assert_equal(test, [21, 42]) # data.seek(0) test = np.ndfromtxt(data, usecols=(1,), converters={1: lambda s: int(s, 16)}) assert_equal(test, [33, 66])
def test_dtype_with_converters(self): dstr = "2009; 23; 46" test = np.ndfromtxt(TextIO(dstr,), delimiter=";", dtype=float, converters={0: bytes}) control = np.array([('2009', 23., 46)], dtype=[('f0', '|S4'), ('f1', float), ('f2', float)]) assert_equal(test, control) test = np.ndfromtxt(TextIO(dstr,), delimiter=";", dtype=float, converters={0: float}) control = np.array([2009., 23., 46],) assert_equal(test, control)
def test_missing(self): data = TextIO('1,2,3,,5\n') test = np.ndfromtxt(data, dtype=int, delimiter=',', converters={3: lambda s: int(s or - 999)}) control = np.array([1, 2, 3, -999, 5], int) assert_equal(test, control)
def test_usecols_with_structured_dtype(self): # Test usecols with an explicit structured dtype data = TextIO("JOE 70.1 25.3\nBOB 60.5 27.9") names = ['stid', 'temp'] dtypes = ['S4', 'f8'] test = np.ndfromtxt( data, usecols=(0, 2), dtype=list(zip(names, dtypes))) assert_equal(test['stid'], [b"JOE", b"BOB"]) assert_equal(test['temp'], [25.3, 27.9])
def test_shaped_dtype(self): c = TextIO("aaaa 1.0 8.0 1 2 3 4 5 6") dt = np.dtype([('name', 'S4'), ('x', float), ('y', float), ('block', int, (2, 3))]) x = np.ndfromtxt(c, dtype=dt) a = np.array([('aaaa', 1.0, 8.0, [[1, 2, 3], [4, 5, 6]])], dtype=dt) assert_array_equal(x, a)
def test_default_field_format(self): # Test default format data = "0, 1, 2.3\n4, 5, 6.7" mtest = np.ndfromtxt(TextIO(data), delimiter=",", dtype=None, defaultfmt="f%02i") ctrl = np.array([(0, 1, 2.3), (4, 5, 6.7)], dtype=[("f00", int), ("f01", int), ("f02", float)]) assert_equal(mtest, ctrl)
def test_single_dtype_wo_names(self): # Test single dtype w/o names data = "0, 1, 2.3\n4, 5, 6.7" mtest = np.ndfromtxt(TextIO(data), delimiter=",", dtype=float, defaultfmt="f%02i") ctrl = np.array([[0., 1., 2.3], [4., 5., 6.7]], dtype=float) assert_equal(mtest, ctrl)
def test_single_dtype_w_explicit_names(self): # Test single dtype w explicit names data = "0, 1, 2.3\n4, 5, 6.7" mtest = np.ndfromtxt(TextIO(data), delimiter=",", dtype=float, names="a, b, c") ctrl = np.array([(0., 1., 2.3), (4., 5., 6.7)], dtype=[(_, float) for _ in "abc"]) assert_equal(mtest, ctrl)
def test_single_dtype_w_implicit_names(self): # Test single dtype w implicit names data = "a, b, c\n0, 1, 2.3\n4, 5, 6.7" mtest = np.ndfromtxt(TextIO(data), delimiter=",", dtype=float, names=True) ctrl = np.array([(0., 1., 2.3), (4., 5., 6.7)], dtype=[(_, float) for _ in "abc"]) assert_equal(mtest, ctrl)
def test_easy_structured_dtype(self): # Test easy structured dtype data = "0, 1, 2.3\n4, 5, 6.7" mtest = np.ndfromtxt(TextIO(data), delimiter=",", dtype=(int, float, float), defaultfmt="f_%02i") ctrl = np.array([(0, 1., 2.3), (4, 5., 6.7)], dtype=[("f_00", int), ("f_01", float), ("f_02", float)]) assert_equal(mtest, ctrl)
def test_incomplete_names(self): # Test w/ incomplete names data = "A,,C\n0,1,2\n3,4,5" kwargs = dict(delimiter=",", names=True) # w/ dtype=None ctrl = np.array([(0, 1, 2), (3, 4, 5)], dtype=[(_, int) for _ in ('A', 'f0', 'C')]) test = np.ndfromtxt(TextIO(data), dtype=None, **kwargs) assert_equal(test, ctrl) # w/ default dtype ctrl = np.array([(0, 1, 2), (3, 4, 5)], dtype=[(_, float) for _ in ('A', 'f0', 'C')]) test = np.ndfromtxt(TextIO(data), **kwargs)
def test_fixed_width_names(self): # Test fix-width w/ names data = " A B C\n 0 1 2.3\n 45 67 9." kwargs = dict(delimiter=(5, 5, 4), names=True, dtype=None) ctrl = np.array([(0, 1, 2.3), (45, 67, 9.)], dtype=[('A', int), ('B', int), ('C', float)]) test = np.ndfromtxt(TextIO(data), **kwargs) assert_equal(test, ctrl) # kwargs = dict(delimiter=5, names=True, dtype=None) ctrl = np.array([(0, 1, 2.3), (45, 67, 9.)], dtype=[('A', int), ('B', int), ('C', float)]) test = np.ndfromtxt(TextIO(data), **kwargs) assert_equal(test, ctrl)
def test_filling_values(self): # Test missing values data = b"1, 2, 3\n1, , 5\n0, 6, \n" kwargs = dict(delimiter=",", dtype=None, filling_values=-999) ctrl = np.array([[1, 2, 3], [1, -999, 5], [0, 6, -999]], dtype=int) test = np.ndfromtxt(TextIO(data), **kwargs) assert_equal(test, ctrl)
