diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/random/_generator.pyx | 71 | ||||
| -rw-r--r-- | numpy/random/mtrand.pyx | 38 | ||||
| -rw-r--r-- | numpy/random/tests/test_randomstate_regression.py | 3 |
3 files changed, 53 insertions, 59 deletions
diff --git a/numpy/random/_generator.pyx b/numpy/random/_generator.pyx index 32eda25f7..c7ba4c534 100644 --- a/numpy/random/_generator.pyx +++ b/numpy/random/_generator.pyx @@ -17,7 +17,6 @@ from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t, from ._bounded_integers cimport (_rand_bool, _rand_int32, _rand_int64, _rand_int16, _rand_int8, _rand_uint64, _rand_uint32, _rand_uint16, _rand_uint8, _gen_mask) -from ._bounded_integers import _integers_types from ._pcg64 import PCG64 from numpy.random cimport bitgen_t from ._common cimport (POISSON_LAM_MAX, CONS_POSITIVE, CONS_NONE, @@ -312,13 +311,13 @@ cdef class Generator: """ cdef double temp - key = np.dtype(dtype).name - if key == 'float64': + _dtype = np.dtype(dtype) + if _dtype == np.float64: return double_fill(&random_standard_uniform_fill, &self._bitgen, size, self.lock, out) - elif key == 'float32': + elif _dtype == np.float32: return float_fill(&random_standard_uniform_fill_f, &self._bitgen, size, self.lock, out) else: - raise TypeError('Unsupported dtype "%s" for random' % key) + raise TypeError('Unsupported dtype %r for random' % _dtype) def beta(self, a, b, size=None): """ @@ -454,20 +453,20 @@ cdef class Generator: >>> n = np.random.default_rng().standard_exponential((3, 8000)) """ - key = np.dtype(dtype).name - if key == 'float64': + _dtype = np.dtype(dtype) + if _dtype == np.float64: if method == u'zig': return double_fill(&random_standard_exponential_fill, &self._bitgen, size, self.lock, out) else: return double_fill(&random_standard_exponential_inv_fill, &self._bitgen, size, self.lock, out) - elif key == 'float32': + elif _dtype == np.float32: if method == u'zig': return float_fill(&random_standard_exponential_fill_f, &self._bitgen, size, self.lock, out) else: return float_fill(&random_standard_exponential_inv_fill_f, &self._bitgen, size, self.lock, out) else: - raise TypeError('Unsupported dtype "%s" for standard_exponential' - % key) + raise TypeError('Unsupported dtype %r for standard_exponential' + % _dtype) def integers(self, low, high=None, size=None, dtype=np.int64, endpoint=False): """ @@ -559,39 +558,39 @@ cdef class Generator: high = low low = 0 - dt = np.dtype(dtype) - key = dt.name - if key not in _integers_types: - raise TypeError('Unsupported dtype "%s" for integers' % key) - if not dt.isnative: - raise ValueError('Providing a dtype with a non-native byteorder ' - 'is not supported. If you require ' - 'platform-independent byteorder, call byteswap ' - 'when required.') + _dtype = np.dtype(dtype) # Implementation detail: the old API used a masked method to generate # bounded uniform integers. Lemire's method is preferable since it is # faster. randomgen allows a choice, we will always use the faster one. cdef bint _masked = False - if key == 'int32': + if _dtype == np.int32: ret = _rand_int32(low, high, size, _masked, endpoint, &self._bitgen, self.lock) - elif key == 'int64': + elif _dtype == np.int64: ret = _rand_int64(low, high, size, _masked, endpoint, &self._bitgen, self.lock) - elif key == 'int16': + elif _dtype == np.int16: ret = _rand_int16(low, high, size, _masked, endpoint, &self._bitgen, self.lock) - elif key == 'int8': + elif _dtype == np.int8: ret = _rand_int8(low, high, size, _masked, endpoint, &self._bitgen, self.lock) - elif key == 'uint64': + elif _dtype == np.uint64: ret = _rand_uint64(low, high, size, _masked, endpoint, &self._bitgen, self.lock) - elif key == 'uint32': + elif _dtype == np.uint32: ret = _rand_uint32(low, high, size, _masked, endpoint, &self._bitgen, self.lock) - elif key == 'uint16': + elif _dtype == np.uint16: ret = _rand_uint16(low, high, size, _masked, endpoint, &self._bitgen, self.lock) - elif key == 'uint8': + elif _dtype == np.uint8: ret = _rand_uint8(low, high, size, _masked, endpoint, &self._bitgen, self.lock) - elif key == 'bool': + elif _dtype == np.bool_: ret = _rand_bool(low, high, size, _masked, endpoint, &self._bitgen, self.lock) + elif not _dtype.isnative: + raise ValueError('Providing a dtype with a non-native byteorder ' + 'is not supported. If you require ' + 'platform-independent byteorder, call byteswap ' + 'when required.') + else: + raise TypeError('Unsupported dtype %r for integers' % _dtype) + if size is None and dtype in (bool, int, np.compat.long): if np.array(ret).shape == (): @@ -1041,14 +1040,14 @@ cdef class Generator: [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random """ - key = np.dtype(dtype).name - if key == 'float64': + _dtype = np.dtype(dtype) + if _dtype == np.float64: return double_fill(&random_standard_normal_fill, &self._bitgen, size, self.lock, out) - elif key == 'float32': + elif _dtype == np.float32: return float_fill(&random_standard_normal_fill_f, &self._bitgen, size, self.lock, out) else: - raise TypeError('Unsupported dtype "%s" for standard_normal' % key) + raise TypeError('Unsupported dtype %r for standard_normal' % _dtype) def normal(self, loc=0.0, scale=1.0, size=None): """ @@ -1230,19 +1229,19 @@ cdef class Generator: """ cdef void *func - key = np.dtype(dtype).name - if key == 'float64': + _dtype = np.dtype(dtype) + if _dtype == np.float64: return cont(&random_standard_gamma, &self._bitgen, size, self.lock, 1, shape, 'shape', CONS_NON_NEGATIVE, 0.0, '', CONS_NONE, 0.0, '', CONS_NONE, out) - if key == 'float32': + if _dtype == np.float32: return cont_f(&random_standard_gamma_f, &self._bitgen, size, self.lock, shape, 'shape', CONS_NON_NEGATIVE, out) else: - raise TypeError('Unsupported dtype "%s" for standard_gamma' % key) + raise TypeError('Unsupported dtype %r for standard_gamma' % _dtype) def gamma(self, shape, scale=1.0, size=None): """ diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx index a04026c0d..b38e8bbbe 100644 --- a/numpy/random/mtrand.pyx +++ b/numpy/random/mtrand.pyx @@ -15,7 +15,6 @@ from libc.stdint cimport int64_t, uint64_t from ._bounded_integers cimport (_rand_bool, _rand_int32, _rand_int64, _rand_int16, _rand_int8, _rand_uint64, _rand_uint32, _rand_uint16, _rand_uint8,) -from ._bounded_integers import _integers_types from ._mt19937 import MT19937 as _MT19937 from numpy.random cimport bitgen_t from ._common cimport (POISSON_LAM_MAX, CONS_POSITIVE, CONS_NONE, @@ -724,17 +723,7 @@ cdef class RandomState: high = low low = 0 - dt = np.dtype(dtype) - key = dt.name - if key not in _integers_types: - raise TypeError('Unsupported dtype "%s" for randint' % key) - if not dt.isnative: - # numpy 1.17.0, 2019-05-28 - warnings.warn('Providing a dtype with a non-native byteorder is ' - 'not supported. If you require platform-independent ' - 'byteorder, call byteswap when required.\nIn future ' - 'version, providing byteorder will raise a ' - 'ValueError', DeprecationWarning) + _dtype = np.dtype(dtype) # Implementation detail: the use a masked method to generate # bounded uniform integers. Lemire's method is preferable since it is @@ -743,24 +732,31 @@ cdef class RandomState: cdef bint _masked = True cdef bint _endpoint = False - if key == 'int32': + if _dtype == np.int32: ret = _rand_int32(low, high, size, _masked, _endpoint, &self._bitgen, self.lock) - elif key == 'int64': + elif _dtype == np.int64: ret = _rand_int64(low, high, size, _masked, _endpoint, &self._bitgen, self.lock) - elif key == 'int16': + elif _dtype == np.int16: ret = _rand_int16(low, high, size, _masked, _endpoint, &self._bitgen, self.lock) - elif key == 'int8': + elif _dtype == np.int8: ret = _rand_int8(low, high, size, _masked, _endpoint, &self._bitgen, self.lock) - elif key == 'uint64': + elif _dtype == np.uint64: ret = _rand_uint64(low, high, size, _masked, _endpoint, &self._bitgen, self.lock) - elif key == 'uint32': + elif _dtype == np.uint32: ret = _rand_uint32(low, high, size, _masked, _endpoint, &self._bitgen, self.lock) - elif key == 'uint16': + elif _dtype == np.uint16: ret = _rand_uint16(low, high, size, _masked, _endpoint, &self._bitgen, self.lock) - elif key == 'uint8': + elif _dtype == np.uint8: ret = _rand_uint8(low, high, size, _masked, _endpoint, &self._bitgen, self.lock) - elif key == 'bool': + elif _dtype == np.bool_: ret = _rand_bool(low, high, size, _masked, _endpoint, &self._bitgen, self.lock) + elif not _dtype.isnative: + raise ValueError('Providing a dtype with a non-native byteorder ' + 'is not supported. If you require ' + 'platform-independent byteorder, call byteswap ' + 'when required.') + else: + raise TypeError('Unsupported dtype %r for randint' % _dtype) if size is None and dtype in (bool, int, np.compat.long): if np.array(ret).shape == (): diff --git a/numpy/random/tests/test_randomstate_regression.py b/numpy/random/tests/test_randomstate_regression.py index 1d8a0ed5a..9f9728df2 100644 --- a/numpy/random/tests/test_randomstate_regression.py +++ b/numpy/random/tests/test_randomstate_regression.py @@ -163,8 +163,7 @@ class TestRegression: def test_warns_byteorder(self): # GH 13159 other_byteord_dt = '<i4' if sys.byteorder == 'big' else '>i4' - with pytest.deprecated_call(match='non-native byteorder is not'): - random.randint(0, 200, size=10, dtype=other_byteord_dt) + assert_raises(ValueError, random.randint, 0, 200, size=10, dtype=other_byteord_dt) def test_named_argument_initialization(self): # GH 13669 |