Python numpy.complex64() Examples
The following are 30
code examples of numpy.complex64().
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Example #1
| Source File: test_function_base.py From lambda-packs with MIT License | 6 votes |
|
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
np.uint32, np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
assert_array_equal(np.cumsum(a, axis=0), tgt)
tgt = np.array(
[[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
assert_array_equal(np.cumsum(a2, axis=0), tgt)
tgt = np.array(
[[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
assert_array_equal(np.cumsum(a2, axis=1), tgt)
Example #2
| Source File: test_function_base.py From recruit with Apache License 2.0 | 6 votes |
|
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
assert_raises(ArithmeticError, np.prod, a)
assert_raises(ArithmeticError, np.prod, a2, 1)
else:
assert_equal(a.prod(axis=0), 26400)
assert_array_equal(a2.prod(axis=0),
np.array([50, 36, 84, 180], ctype))
assert_array_equal(a2.prod(axis=-1),
np.array([24, 1890, 600], ctype))
Example #3
| Source File: test_function_base.py From recruit with Apache License 2.0 | 6 votes |
|
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
np.uint32, np.float32, np.float64, np.complex64,
np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
assert_array_equal(np.cumsum(a, axis=0), tgt)
tgt = np.array(
[[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
assert_array_equal(np.cumsum(a2, axis=0), tgt)
tgt = np.array(
[[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
assert_array_equal(np.cumsum(a2, axis=1), tgt)
Example #4
| Source File: test_function_base.py From recruit with Apache License 2.0 | 6 votes |
|
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
assert_raises(ArithmeticError, np.cumprod, a)
assert_raises(ArithmeticError, np.cumprod, a2, 1)
assert_raises(ArithmeticError, np.cumprod, a)
else:
assert_array_equal(np.cumprod(a, axis=-1),
np.array([1, 2, 20, 220,
1320, 6600, 26400], ctype))
assert_array_equal(np.cumprod(a2, axis=0),
np.array([[1, 2, 3, 4],
[5, 12, 21, 36],
[50, 36, 84, 180]], ctype))
assert_array_equal(np.cumprod(a2, axis=-1),
np.array([[1, 2, 6, 24],
[5, 30, 210, 1890],
[10, 30, 120, 600]], ctype))
Example #5
| Source File: misc.py From tenpy with GNU General Public License v3.0 | 6 votes |
|
def zero_if_close(a, tol=1.e-15):
"""set real and/or imaginary part to 0 if their absolute value is smaller than `tol`.
Parameters
----------
a : ndarray
numpy array to be rounded
tol : float
the threashold which values to consider as '0'.
"""
if a.dtype == np.complex128 or a.dtype == np.complex64:
ar = np.choose(np.abs(a.real) < tol, [a.real, np.zeros(a.shape)])
ai = np.choose(np.abs(a.imag) < tol, [a.imag, np.zeros(a.shape)])
return ar + 1j * ai
else:
return np.choose(np.abs(a) < tol, [a, np.zeros_like(a)])
Example #6
| Source File: fourier.py From lambda-packs with MIT License | 6 votes |
|
def _get_output_fourier(output, input):
if output is None:
if input.dtype.type in [numpy.complex64, numpy.complex128,
numpy.float32]:
output = numpy.zeros(input.shape, dtype=input.dtype)
else:
output = numpy.zeros(input.shape, dtype=numpy.float64)
return_value = output
elif type(output) is type:
if output not in [numpy.complex64, numpy.complex128,
numpy.float32, numpy.float64]:
raise RuntimeError("output type not supported")
output = numpy.zeros(input.shape, dtype=output)
return_value = output
else:
if output.shape != input.shape:
raise RuntimeError("output shape not correct")
return_value = None
return output, return_value
Example #7
| Source File: _testutils.py From lambda-packs with MIT License | 6 votes |
|
def assert_no_overwrite(call, shapes, dtypes=None):
"""
Test that a call does not overwrite its input arguments
"""
if dtypes is None:
dtypes = [np.float32, np.float64, np.complex64, np.complex128]
for dtype in dtypes:
for order in ["C", "F"]:
for faker in [_id, _FakeMatrix, _FakeMatrix2]:
orig_inputs = [_get_array(s, dtype) for s in shapes]
inputs = [faker(x.copy(order)) for x in orig_inputs]
call(*inputs)
msg = "call modified inputs [%r, %r]" % (dtype, faker)
for a, b in zip(inputs, orig_inputs):
np.testing.assert_equal(a, b, err_msg=msg)
Example #8
| Source File: test_linalg.py From recruit with Apache License 2.0 | 6 votes |
|
def test_0_size(self):
# Check that all kinds of 0-sized arrays work
class ArraySubclass(np.ndarray):
pass
a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
res, res_v = linalg.eigh(a)
assert_(res_v.dtype.type is np.float64)
assert_(res.dtype.type is np.float64)
assert_equal(a.shape, res_v.shape)
assert_equal((0, 1), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(a, np.ndarray))
a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
res, res_v = linalg.eigh(a)
assert_(res_v.dtype.type is np.complex64)
assert_(res.dtype.type is np.float32)
assert_equal(a.shape, res_v.shape)
assert_equal((0,), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(a, np.ndarray))
Example #9
| Source File: test_linalg.py From recruit with Apache License 2.0 | 6 votes |
|
def test_0_size(self):
# Check that all kinds of 0-sized arrays work
class ArraySubclass(np.ndarray):
pass
a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
res = linalg.eigvalsh(a)
assert_(res.dtype.type is np.float64)
assert_equal((0, 1), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(res, np.ndarray))
a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
res = linalg.eigvalsh(a)
assert_(res.dtype.type is np.float32)
assert_equal((0,), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(res, np.ndarray))
Example #10
| Source File: dtypes.py From lambda-packs with MIT License | 6 votes |
|
def min(self):
"""Returns the minimum representable value in this data type.
Raises:
TypeError: if this is a non-numeric, unordered, or quantized type.
"""
if (self.is_quantized or self.base_dtype in
(bool, string, complex64, complex128)):
raise TypeError("Cannot find minimum value of %s." % self)
# there is no simple way to get the min value of a dtype, we have to check
# float and int types separately
try:
return np.finfo(self.as_numpy_dtype()).min
except: # bare except as possible raises by finfo not documented
try:
return np.iinfo(self.as_numpy_dtype()).min
except:
raise TypeError("Cannot find minimum value of %s." % self)
Example #11
| Source File: dtypes.py From lambda-packs with MIT License | 6 votes |
|
def max(self):
"""Returns the maximum representable value in this data type.
Raises:
TypeError: if this is a non-numeric, unordered, or quantized type.
"""
if (self.is_quantized or self.base_dtype in
(bool, string, complex64, complex128)):
raise TypeError("Cannot find maximum value of %s." % self)
# there is no simple way to get the max value of a dtype, we have to check
# float and int types separately
try:
return np.finfo(self.as_numpy_dtype()).max
except: # bare except as possible raises by finfo not documented
try:
return np.iinfo(self.as_numpy_dtype()).max
except:
raise TypeError("Cannot find maximum value of %s." % self)
Example #12
| Source File: test_function_base.py From lambda-packs with MIT License | 6 votes |
|
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, np.prod, a)
self.assertRaises(ArithmeticError, np.prod, a2, 1)
else:
assert_equal(a.prod(axis=0), 26400)
assert_array_equal(a2.prod(axis=0),
np.array([50, 36, 84, 180], ctype))
assert_array_equal(a2.prod(axis=-1),
np.array([24, 1890, 600], ctype))
Example #13
| Source File: test_function_base.py From lambda-packs with MIT License | 6 votes |
|
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, np.cumprod, a)
self.assertRaises(ArithmeticError, np.cumprod, a2, 1)
self.assertRaises(ArithmeticError, np.cumprod, a)
else:
assert_array_equal(np.cumprod(a, axis=-1),
np.array([1, 2, 20, 220,
1320, 6600, 26400], ctype))
assert_array_equal(np.cumprod(a2, axis=0),
np.array([[1, 2, 3, 4],
[5, 12, 21, 36],
[50, 36, 84, 180]], ctype))
assert_array_equal(np.cumprod(a2, axis=-1),
np.array([[1, 2, 6, 24],
[5, 30, 210, 1890],
[10, 30, 120, 600]], ctype))
Example #14
| Source File: test_0020_scipy_gmres.py From pyscf with Apache License 2.0 | 6 votes |
|
def test_scipy_gmres_linop_parameter(self):
""" This is a test on gmres method with a parameter-dependent linear operator """
for omega in np.linspace(-10.0, 10.0, 10):
for eps in np.linspace(-10.0, 10.0, 10):
linop_param = linalg.aslinearoperator(vext2veff_c(omega, eps, n))
Aparam = np.zeros((n,n), np.complex64)
for i in range(n):
uv = np.zeros(n, np.complex64); uv[i] = 1.0
Aparam[:,i] = linop_param.matvec(uv)
x_ref = np.dot(inv(Aparam), b)
x_itr,info = linalg.lgmres(linop_param, b)
derr = abs(x_ref-x_itr).sum()/x_ref.size
self.assertLess(derr, 1e-6)
Example #15
| Source File: test_scalarmath.py From recruit with Apache License 2.0 | 6 votes |
|
def test_signed_zeros(self):
with np.errstate(all="ignore"):
for t in [np.complex64, np.complex128]:
# tupled (numerator, denominator, expected)
# for testing as expected == numerator/denominator
data = (
(( 0.0,-1.0), ( 0.0, 1.0), (-1.0,-0.0)),
(( 0.0,-1.0), ( 0.0,-1.0), ( 1.0,-0.0)),
(( 0.0,-1.0), (-0.0,-1.0), ( 1.0, 0.0)),
(( 0.0,-1.0), (-0.0, 1.0), (-1.0, 0.0)),
(( 0.0, 1.0), ( 0.0,-1.0), (-1.0, 0.0)),
(( 0.0,-1.0), ( 0.0,-1.0), ( 1.0,-0.0)),
((-0.0,-1.0), ( 0.0,-1.0), ( 1.0,-0.0)),
((-0.0, 1.0), ( 0.0,-1.0), (-1.0,-0.0))
)
for cases in data:
n = cases[0]
d = cases[1]
ex = cases[2]
result = t(complex(n[0], n[1])) / t(complex(d[0], d[1]))
# check real and imag parts separately to avoid comparison
# in array context, which does not account for signed zeros
assert_equal(result.real, ex[0])
assert_equal(result.imag, ex[1])
Example #16
| Source File: test_scalarmath.py From recruit with Apache License 2.0 | 6 votes |
|
def test_zero_division(self):
with np.errstate(all="ignore"):
for t in [np.complex64, np.complex128]:
a = t(0.0)
b = t(1.0)
assert_(np.isinf(b/a))
b = t(complex(np.inf, np.inf))
assert_(np.isinf(b/a))
b = t(complex(np.inf, np.nan))
assert_(np.isinf(b/a))
b = t(complex(np.nan, np.inf))
assert_(np.isinf(b/a))
b = t(complex(np.nan, np.nan))
assert_(np.isnan(b/a))
b = t(0.)
assert_(np.isnan(b/a))
Example #17
| Source File: test_function_base.py From auto-alt-text-lambda-api with MIT License | 6 votes |
|
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
np.uint32, np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
assert_array_equal(np.cumsum(a, axis=0), tgt)
tgt = np.array(
[[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
assert_array_equal(np.cumsum(a2, axis=0), tgt)
tgt = np.array(
[[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
assert_array_equal(np.cumsum(a2, axis=1), tgt)
Example #18
| Source File: test_function_base.py From auto-alt-text-lambda-api with MIT License | 6 votes |
|
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, np.prod, a)
self.assertRaises(ArithmeticError, np.prod, a2, 1)
else:
assert_equal(a.prod(axis=0), 26400)
assert_array_equal(a2.prod(axis=0),
np.array([50, 36, 84, 180], ctype))
assert_array_equal(a2.prod(axis=-1),
np.array([24, 1890, 600], ctype))
Example #19
| Source File: test_function_base.py From auto-alt-text-lambda-api with MIT License | 6 votes |
|
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, np.cumprod, a)
self.assertRaises(ArithmeticError, np.cumprod, a2, 1)
self.assertRaises(ArithmeticError, np.cumprod, a)
else:
assert_array_equal(np.cumprod(a, axis=-1),
np.array([1, 2, 20, 220,
1320, 6600, 26400], ctype))
assert_array_equal(np.cumprod(a2, axis=0),
np.array([[1, 2, 3, 4],
[5, 12, 21, 36],
[50, 36, 84, 180]], ctype))
assert_array_equal(np.cumprod(a2, axis=-1),
np.array([[1, 2, 6, 24],
[5, 30, 210, 1890],
[10, 30, 120, 600]], ctype))
Example #20
| Source File: test_random.py From auto-alt-text-lambda-api with MIT License | 6 votes |
|
def test_shuffle(self):
# Test lists, arrays (of various dtypes), and multidimensional versions
# of both, c-contiguous or not:
for conv in [lambda x: np.array([]),
lambda x: x,
lambda x: np.asarray(x).astype(np.int8),
lambda x: np.asarray(x).astype(np.float32),
lambda x: np.asarray(x).astype(np.complex64),
lambda x: np.asarray(x).astype(object),
lambda x: [(i, i) for i in x],
lambda x: np.asarray([[i, i] for i in x]),
lambda x: np.vstack([x, x]).T,
# gh-4270
lambda x: np.asarray([(i, i) for i in x],
[("a", object, 1),
("b", np.int32, 1)])]:
np.random.seed(self.seed)
alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
np.random.shuffle(alist)
actual = alist
desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
np.testing.assert_array_equal(actual, desired)
Example #21
| Source File: test_umath.py From recruit with Apache License 2.0 | 6 votes |
|
def test_branch_cuts_complex64(self):
# check branch cuts and continuity on them
_check_branch_cut(np.log, -0.5, 1j, 1, -1, True, np.complex64)
_check_branch_cut(np.log2, -0.5, 1j, 1, -1, True, np.complex64)
_check_branch_cut(np.log10, -0.5, 1j, 1, -1, True, np.complex64)
_check_branch_cut(np.log1p, -1.5, 1j, 1, -1, True, np.complex64)
_check_branch_cut(np.sqrt, -0.5, 1j, 1, -1, True, np.complex64)
_check_branch_cut(np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64)
_check_branch_cut(np.arccos, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64)
_check_branch_cut(np.arctan, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64)
_check_branch_cut(np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64)
_check_branch_cut(np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True, np.complex64)
_check_branch_cut(np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64)
# check against bogus branch cuts: assert continuity between quadrants
_check_branch_cut(np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64)
_check_branch_cut(np.arccos, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64)
_check_branch_cut(np.arctan, [ -2, 2], [1j, 1j], 1, 1, False, np.complex64)
_check_branch_cut(np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1, False, np.complex64)
_check_branch_cut(np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1, False, np.complex64)
_check_branch_cut(np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1, False, np.complex64)
Example #22
| Source File: spectral.py From audiomate with MIT License | 5 votes |
|
def stft_from_frames(frames, window='hann', dtype=np.complex64):
"""
Variation of the librosa.core.stft function,
that computes the short-time-fourier-transfrom from frames instead from the signal.
See http://librosa.github.io/librosa/_modules/librosa/core/spectrum.html#stft
"""
win_length = frames.shape[0]
n_fft = win_length
fft_window = filters.get_window(window, win_length, fftbins=True)
# Reshape so that the window can be broadcast
fft_window = fft_window.reshape((-1, 1))
# Pre-allocate the STFT matrix
stft_matrix = np.empty((int(1 + n_fft // 2), frames.shape[1]),
dtype=dtype,
order='F')
# how many columns can we fit within MAX_MEM_BLOCK?
n_columns = int(util.MAX_MEM_BLOCK / (stft_matrix.shape[0] *
stft_matrix.itemsize))
for bl_s in range(0, stft_matrix.shape[1], n_columns):
bl_t = min(bl_s + n_columns, stft_matrix.shape[1])
# RFFT and Conjugate here to match phase from DPWE code
stft_matrix[:, bl_s:bl_t] = fft.fft(fft_window *
frames[:, bl_s:bl_t],
axis=0)[:stft_matrix.shape[0]].conj()
return stft_matrix
Example #23
| Source File: svd_robust.py From tenpy with GNU General Public License v3.0 | 5 votes |
|
def _set_CLAPACK_callsignatures(lapack_lib):
"""define the call signature of the CLAPACK functions which we need.
See http://www.netlib.org/lapack/explore-html/d8/d70/group__lapack.html
for the (fortran) signature.
In the C version, all arguments must be given as pointers of the corresponding C types.
"""
# Shorthand data type for the arrays.
# s/d/c/z = fortran single/double/complex_single/complex_double
s_arr = np.ctypeslib.ndpointer(dtype=np.float32, ndim=1)
d_arr = np.ctypeslib.ndpointer(dtype=np.float64, ndim=1)
c_arr = np.ctypeslib.ndpointer(dtype=np.complex64, ndim=1)
z_arr = np.ctypeslib.ndpointer(dtype=np.complex128, ndim=1)
s_2arr = np.ctypeslib.ndpointer(dtype=np.float32, ndim=2)
d_2arr = np.ctypeslib.ndpointer(dtype=np.float64, ndim=2)
c_2arr = np.ctypeslib.ndpointer(dtype=np.complex64, ndim=2)
z_2arr = np.ctypeslib.ndpointer(dtype=np.complex128, ndim=2)
lapack_lib.sgesvd_.argtypes = \
[POINTER(c_char), POINTER(c_char), POINTER(c_int), POINTER(c_int),
s_2arr, POINTER(c_int), s_arr, s_2arr, POINTER(c_int), s_2arr,
POINTER(c_int), s_arr, POINTER(c_int), POINTER(c_int)]
lapack_lib.dgesvd_.argtypes = \
[POINTER(c_char), POINTER(c_char), POINTER(c_int), POINTER(c_int),
d_2arr, POINTER(c_int), d_arr, d_2arr, POINTER(c_int), d_2arr,
POINTER(c_int), d_arr, POINTER(c_int), POINTER(c_int)]
lapack_lib.cgesvd_.argtypes = \
[POINTER(c_char), POINTER(c_char), POINTER(c_int), POINTER(c_int),
c_2arr, POINTER(c_int), s_arr, c_2arr, POINTER(c_int),
c_2arr, POINTER(c_int), c_arr, POINTER(c_int), s_arr,
POINTER(c_int)]
lapack_lib.zgesvd_.argtypes = \
[POINTER(c_char), POINTER(c_char), POINTER(c_int), POINTER(c_int),
z_2arr, POINTER(c_int), d_arr, z_2arr, POINTER(c_int),
z_2arr, POINTER(c_int), z_arr, POINTER(c_int), d_arr,
POINTER(c_int)]
Example #24
| Source File: basic.py From lambda-packs with MIT License | 5 votes |
|
def _raw_fftn_dispatch(x, shape, axes, overwrite_x, direction):
tmp = _asfarray(x)
try:
work_function = _DTYPE_TO_FFTN[tmp.dtype]
except KeyError:
raise ValueError("type %s is not supported" % tmp.dtype)
if not (istype(tmp, numpy.complex64) or istype(tmp, numpy.complex128)):
overwrite_x = 1
overwrite_x = overwrite_x or _datacopied(tmp, x)
return _raw_fftnd(tmp,shape,axes,direction,overwrite_x,work_function)
Example #25
| Source File: test_DataFrameModel.py From pandas-qt with MIT License | 5 votes |
|
def test_unhandledDtype(self, model, index):
dataFrame = pandas.DataFrame([92.289+151.96j], columns=['A'])
dataFrame['A'] = dataFrame['A'].astype(numpy.complex64)
model.setDataFrame(dataFrame)
assert not model.dataFrame().empty
assert model.dataFrame() is dataFrame
assert index.isValid()
model.enableEditing(True)
with pytest.raises(TypeError) as excinfo:
model.setData(index, numpy.complex64(92+151j))
assert "unhandled data type" in unicode(excinfo.value)
Example #26
| Source File: test_DataFrameModel.py From pandas-qt with MIT License | 5 votes |
|
def test_unhandledDtype(self, model, index):
dataFrame = pandas.DataFrame([92.289+151.96j], columns=['A'])
dataFrame['A'] = dataFrame['A'].astype(numpy.complex64)
model.setDataFrame(dataFrame)
assert not model.dataFrame().empty
assert model.dataFrame() is dataFrame
assert index.isValid()
assert model.data(index) == None
# with pytest.raises(TypeError) as excinfo:
# model.data(index)
# assert "unhandled data type" in unicode(excinfo.value)
Example #27
| Source File: sar_data.py From SAR-change-detection with MIT License | 5 votes |
|
def load(self, path, code, shape, header):
"Load SARData object for a given month code"
self.shape = shape
self.size = shape[0]*shape[1]
extension = ".emi" if header else ""
self.hhhh = read_sar_file(path + '/{}/{}hhhh{}'.format(code, code, extension), np.float32, header)
self.hhhv = read_sar_file(path + '/{}/{}hhhv{}'.format(code, code, extension), np.complex64, header)
self.hvhv = read_sar_file(path + '/{}/{}hvhv{}'.format(code, code, extension), np.float32, header)
self.hhvv = read_sar_file(path + '/{}/{}hhvv{}'.format(code, code, extension), np.complex64, header)
self.hvvv = read_sar_file(path + '/{}/{}hvvv{}'.format(code, code, extension), np.complex64, header)
self.vvvv = read_sar_file(path + '/{}/{}vvvv{}'.format(code, code, extension), np.float32, header)
return self
Example #28
| Source File: test_constructors.py From recruit with Apache License 2.0 | 5 votes |
|
def test_constructor_complex_dtypes(self):
# GH10952
a = np.random.rand(10).astype(np.complex64)
b = np.random.rand(10).astype(np.complex128)
df = DataFrame({'a': a, 'b': b})
assert a.dtype == df.a.dtype
assert b.dtype == df.b.dtype
Example #29
| Source File: test_scalarmath.py From recruit with Apache License 2.0 | 5 votes |
|
def test_branches(self):
with np.errstate(all="ignore"):
for t in [np.complex64, np.complex128]:
# tupled (numerator, denominator, expected)
# for testing as expected == numerator/denominator
data = list()
# trigger branch: real(fabs(denom)) > imag(fabs(denom))
# followed by else condition as neither are == 0
data.append((( 2.0, 1.0), ( 2.0, 1.0), (1.0, 0.0)))
# trigger branch: real(fabs(denom)) > imag(fabs(denom))
# followed by if condition as both are == 0
# is performed in test_zero_division(), so this is skipped
# trigger else if branch: real(fabs(denom)) < imag(fabs(denom))
data.append((( 1.0, 2.0), ( 1.0, 2.0), (1.0, 0.0)))
for cases in data:
n = cases[0]
d = cases[1]
ex = cases[2]
result = t(complex(n[0], n[1])) / t(complex(d[0], d[1]))
# check real and imag parts separately to avoid comparison
# in array context, which does not account for signed zeros
assert_equal(result.real, ex[0])
assert_equal(result.imag, ex[1])
Example #30
| Source File: test_scalarmath.py From recruit with Apache License 2.0 | 5 votes |
|
def test_modular_power(self):
# modular power is not implemented, so ensure it errors
a = 5
b = 4
c = 10
expected = pow(a, b, c) # noqa: F841
for t in (np.int32, np.float32, np.complex64):
# note that 3-operand power only dispatches on the first argument
assert_raises(TypeError, operator.pow, t(a), b, c)
assert_raises(TypeError, operator.pow, np.array(t(a)), b, c)
