def test1_read_with_comment():
filename = _get_full_name('test1_with_comments.csv')
al = np.loadtxt(filename, delimiter=',')
a = read(filename, comment='#')
assert_array_equal(a, al)
with open(filename, 'r') as f:
a = read(f, comment='#')
assert_array_equal(a, al)
def test1_read_usecols(basename, delim):
filename = path.join(path.dirname(__file__), 'data', basename)
al = np.loadtxt(filename, delimiter=delim, usecols=[0, 2])
a = read(filename, usecols=[0, 2], delimiter=delim)
assert_array_equal(a, al)
with open(filename, 'r') as f:
a = read(f, usecols=[0, 2], delimiter=delim)
assert_array_equal(a, al)
def test1_read1(basename, delim):
filename = _get_full_name(basename)
al = np.loadtxt(filename, delimiter=delim)
a = read(filename, delimiter=delim)
assert_array_equal(a, al)
with open(filename, 'r') as f:
a = read(f, delimiter=delim)
assert_array_equal(a, al)
def test1_read_sci(basename, sci):
filename = path.join(path.dirname(__file__), 'data', basename)
filename_e = path.join(path.dirname(__file__), 'data', 'test1e.csv')
al = np.loadtxt(filename_e, delimiter=',')
a = read(filename, sci=sci)
assert_array_equal(a, al)
with open(filename, 'r') as f:
a = read(f, sci=sci)
assert_array_equal(a, al)
def test_read_from_generator_1():
def gen():
for i in range(4):
yield f'{i},{2*i},{i**2}'
data = read(gen(), dtype=int)
assert_equal(data, [[0, 0, 0], [1, 2, 1], [2, 4, 4], [3, 6, 9]])
def test_decimal_is_comma():
filename = path.join(path.dirname(__file__), 'data',
'decimal_is_comma.txt')
a = read(filename, decimal=',', delimiter=' ')
expected = np.array([[1.5, 1.75, 2.0], [2.5, 2.75, 3.0], [3.5, 3.75, 4.0],
[4.5, 4.75, 5.0]])
assert_array_equal(a, expected)
def test_ndmin_single_row_or_col(fn, shape):
filename = _get_full_name(fn)
data = [1, 2, 3, 4, 5]
arr2d = np.array(data).reshape(shape)
a = read(filename, delimiter=' ')
assert_array_equal(a, arr2d)
a = read(filename, delimiter=' ', ndmin=0)
assert_array_equal(a, data)
a = read(filename, delimiter=' ', ndmin=1)
assert_array_equal(a, data)
a = read(filename, delimiter=' ', ndmin=2)
assert_array_equal(a, arr2d)
def test_complex_analyze():
s = '3,2.0,199\n4,1.0+2.0j,225\n2,9.5-3.0j,432'
a = read(StringIO(s))
dt = np.dtype([('f0', np.uint8), ('f1', np.complex128), ('f2', np.uint16)])
assert a.dtype == dt
assert_equal(a['f0'], [3, 4, 2])
assert_equal(a['f1'], [2.0, 1 + 2j, 9.5 - 3j])
assert_equal(a['f2'], [199, 225, 432])
def test_read_from_generator_2():
def gen():
for i in range(3):
yield f'{i} {i/4}'
data = read(gen(), dtype='i,d', delimiter=' ')
expected = np.array([(0, 0.0), (1, 0.25), (2, 0.5)], dtype='i,d')
assert_equal(data, expected)
def test_converter_with_structured_dtype():
txt = StringIO('1.5,2.5,Abc\n3.0,4.0,dEf\n5.5,6.0,ghI\n')
dt = np.dtype([('m', np.int32), ('r', np.float32), ('code', 'U8')])
conv = {0: lambda s: int(10 * float(s)), -1: lambda s: s.upper()}
a = read(txt, dtype=dt, converters=conv)
expected = np.array([(15, 2.5, 'ABC'), (30, 4.0, 'DEF'), (55, 6.0, 'GHI')],
dtype=dt)
assert_equal(a, expected)
def test_complex(dt, imaginary_unit, with_parens):
s = '(1.0-2.5j),3.75,(7+-5.0j)\n(4),(-19e2j),(0)'.replace(
'j', imaginary_unit)
if not with_parens:
s = s.replace('(', '').replace(')', '')
a = read(StringIO(s), dtype=dt, imaginary_unit=imaginary_unit)
expected = np.array([[1.0 - 2.5j, 3.75, 7 - 5j], [4.0, -1900j, 0]],
dtype=dt)
assert_equal(a, expected)
def test_quoted_field():
filename = _get_full_name('quoted_field.csv')
a = read(filename)
expected_dtype = np.dtype([('f0', 'S8'), ('f1', np.float64)])
assert a.dtype == expected_dtype
expected = np.array([('alpha, x', 2.5), ('beta, x', 4.5),
('gamma, x', 5.0)],
dtype=expected_dtype)
assert_array_equal(a, expected)
def test_string_no_length_given(dtype, actual_dtype):
# The given dtype is just 'S' or 'U', with no length. In these
# cases, the length of the resulting dtype is determined by the
# longest string found in the file.
txt = StringIO('AAA,5-1\nBBBBB,0-3\nC,4-9\n')
a = read(txt, dtype=dtype)
expected = np.array([['AAA', '5-1'], ['BBBBB', '0-3'], ['C', '4-9']],
dtype=actual_dtype)
assert_equal(a.dtype, expected.dtype)
assert_equal(a, expected)
def test_cast_float_to_int(dt):
# Currently the parser_config flag 'allow_float_for_int' is hardcoded
# to be true. This means that if the parsing of an integer value
# fails, the code will attempt to parse it as a float, and then
# cast the float value to an integer. This flag is only used when
# an explicit dtype is given.
txt = StringIO('1.0,2.1,3.7\n4,5,6')
a = read(txt, dtype=dt)
expected = np.array([[1, 2, 3], [4, 5, 6]], dtype=dt)
assert_equal(a, expected)
def test_float_conversion():
# Some tests that the conversion to float64 works as accurately
# as the Python built-in `float` function. In a naive version of
# the float parser, these strings resulted in values that were off
# by a ULP or two.
strings = [
'0.9999999999999999', '9876543210.123456', '5.43215432154321e+300',
'0.901', '0.333'
]
txt = StringIO('\n'.join(strings))
a = read(txt)
expected = np.array([float(s) for s in strings]).reshape((len(strings), 1))
assert_equal(a, expected)
def test_unpack_structured():
filename = _get_full_name('mixed_types1.dat')
expected_dtype = np.dtype([('f0', np.uint16), ('f1', np.float64),
('f2', 'S7'), ('f3', np.int8)])
expected = np.array([(1000, 2.4, "alpha", -34), (2000, 3.1, "beta", 29),
(3500, 9.9, "gamma", 120), (4090, 8.1, "delta", 0),
(5001, 4.4, "epsilon", -99),
(6543, 7.8, "omega", -1)],
dtype=expected_dtype)
a, b, c, d = read(filename, delimiter=';', quote="'", unpack=True)
assert_array_equal(a, expected['f0'])
assert_array_equal(b, expected['f1'])
assert_array_equal(c, expected['f2'])
assert_array_equal(d, expected['f3'])
def test_dtype_and_skiprows(explicit_dtype: bool, skiprows: int):
filename = _get_full_name('mixed_types1.dat')
expected_dtype = np.dtype([('f0', np.uint16), ('f1', np.float64),
('f2', 'S7'), ('f3', np.int8)])
expected = np.array([(1000, 2.4, "alpha", -34), (2000, 3.1, "beta", 29),
(3500, 9.9, "gamma", 120), (4090, 8.1, "delta", 0),
(5001, 4.4, "epsilon", -99),
(6543, 7.8, "omega", -1)],
dtype=expected_dtype)
dt = expected_dtype if explicit_dtype else None
a = read(filename, quote="'", delimiter=';', skiprows=skiprows, dtype=dt)
assert_array_equal(a, expected[skiprows:])
def test_structured_dtype_1():
dt = [("a", 'u1', 2), ("b", 'u1', 2)]
a = read(StringIO("0,1,2,3\n6,7,8,9\n"), dtype=dt)
expected = np.array([((0, 1), (2, 3)), ((6, 7), (8, 9))], dtype=dt)
assert_array_equal(a, expected)
def test_bad_ndmin(badval):
with pytest.raises(ValueError):
read('foo.bar', ndmin=badval)
def test_bad_nonneg_int(param, badval, exc):
with pytest.raises(exc):
read('foo.bar', **{param: badval})
def test_unpack_array():
filename = _get_full_name('test1.csv')
a, b, c = read(filename, delimiter=',', unpack=True)
assert_array_equal(a, np.array([1.0, 4.0, 7.0, 0.0]))
assert_array_equal(b, np.array([2.0, 5.0, 8.0, 1.0]))
assert_array_equal(c, np.array([3.0, 6.0, 9.0, 2.0]))
def test_comment_two_chars(comment):
content = '# IGNORE\n1.5,2.5# ABC\n3.0,4.0# XXX\n5.5,6.0\n'
txt = StringIO(content.replace('#', comment))
a = read(txt, dtype=np.float64, comment=comment)
assert_equal(a, [[1.5, 2.5], [3.0, 4.0], [5.5, 6.0]])
def test_max_rows():
txt = StringIO('1.5,2.5\n3.0,4.0\n5.5,6.0')
a = read(txt, dtype=np.float64, max_rows=2)
assert_equal(a.dtype, np.float64)
assert_equal(a, np.array([[1.5, 2.5], [3.0, 4.0]]))
def test_blank_lines():
txt = StringIO('1 2 30\n\n4 5 60\n \n7 8 90')
a = read(txt, delimiter=' ')
assert_equal(a.dtype, np.uint8)
assert_equal(
a, np.array([[1, 2, 30], [4, 5, 60], [7, 8, 90]], dtype=np.uint8))
def test_bad_values(dtype):
txt = StringIO('1.5,2.5\n3.0,XXX\n5.5,6.0')
with pytest.raises(RuntimeError, match=f'bad {dtype.name} value'):
read(txt, dtype=dtype)
def test_converters():
txt = StringIO('1.5,2.5\n3.0,XXX\n5.5,6.0')
conv = {-1: lambda s: np.nan if s == 'XXX' else float(s)}
a = read(txt, dtype=np.float64, converters=conv)
assert_equal(a, [[1.5, 2.5], [3.0, np.nan], [5.5, 6.0]])
def test_converters_and_usecols():
txt = StringIO('1.5,2.5,3.5\n3.0,4.0,XXX\n5.5,6.0,7.5\n')
conv = {-1: lambda s: np.nan if s == 'XXX' else float(s)}
a = read(txt, dtype=np.float64, converters=conv, usecols=[0, 2])
assert_equal(a, [[1.5, 3.5], [3.0, np.nan], [5.5, 7.5]])
def test_unicode_with_converter():
txt = StringIO('cat,dog\nαβγ,δεζ\nabc,def\n')
conv = {0: lambda s: s.upper()}
a = read(txt, dtype=np.dtype('U12'), converters=conv)
assert_equal(a, [['CAT', 'dog'], ['ΑΒΓ', 'δεζ'], ['ABC', 'def']])
def test_structured_dtype_2():
dt = [("a", 'u1', (2, 2))]
a = read(StringIO("0 1 2 3"), delimiter=' ', dtype=dt)
assert_array_equal(a, np.array([(((0, 1), (2, 3)), )], dtype=dt))
