Python numpy.ScalarType方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def test_truediv(Poly):
    # true division is valid only if the denominator is a Number and
    # not a python bool.
    p1 = Poly([1,2,3])
    p2 = p1 * 5

    for stype in np.ScalarType:
        if not issubclass(stype, Number) or issubclass(stype, bool):
            continue
        s = stype(5)
        assert_poly_almost_equal(op.truediv(p2, s), p1)
        assert_raises(TypeError, op.truediv, s, p2)
    for stype in (int, long, float):
        s = stype(5)
        assert_poly_almost_equal(op.truediv(p2, s), p1)
        assert_raises(TypeError, op.truediv, s, p2)
    for stype in [complex]:
        s = stype(5, 0)
        assert_poly_almost_equal(op.truediv(p2, s), p1)
        assert_raises(TypeError, op.truediv, s, p2)
    for s in [tuple(), list(), dict(), bool(), np.array([1])]:
        assert_raises(TypeError, op.truediv, p2, s)
        assert_raises(TypeError, op.truediv, s, p2)
    for ptype in classes:
        assert_raises(TypeError, op.truediv, p2, ptype(1)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def check_truediv(Poly):
    # true division is valid only if the denominator is a Number and
    # not a python bool.
    p1 = Poly([1,2,3])
    p2 = p1 * 5

    for stype in np.ScalarType:
        if not issubclass(stype, Number) or issubclass(stype, bool):
            continue
        s = stype(5)
        assert_poly_almost_equal(op.truediv(p2, s), p1)
        assert_raises(TypeError, op.truediv, s, p2)
    for stype in (int, long, float):
        s = stype(5)
        assert_poly_almost_equal(op.truediv(p2, s), p1)
        assert_raises(TypeError, op.truediv, s, p2)
    for stype in [complex]:
        s = stype(5, 0)
        assert_poly_almost_equal(op.truediv(p2, s), p1)
        assert_raises(TypeError, op.truediv, s, p2)
    for s in [tuple(), list(), dict(), bool(), np.array([1])]:
        assert_raises(TypeError, op.truediv, p2, s)
        assert_raises(TypeError, op.truediv, s, p2)
    for ptype in classes:
        assert_raises(TypeError, op.truediv, p2, ptype(1)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def _record_tabular(self, data, step):
        if self._x_axis:
            nonexist_axes = []
            for axis in [self._x_axis] + self._additional_x_axes:
                if axis not in data.as_dict:
                    nonexist_axes.append(axis)
            if nonexist_axes:
                self._warn('{} {} exist in the tabular data.'.format(
                    ', '.join(nonexist_axes),
                    'do not' if len(nonexist_axes) > 1 else 'does not'))

        for key, value in data.as_dict.items():
            if isinstance(value,
                          np.ScalarType) and self._x_axis in data.as_dict:
                if self._x_axis is not key:
                    x = data.as_dict[self._x_axis]
                    self._record_kv(key, value, x)

                for axis in self._additional_x_axes:
                    if key is not axis and key in data.as_dict:
                        x = data.as_dict[axis]
                        self._record_kv('{}/{}'.format(key, axis), value, x)
            else:
                self._record_kv(key, value, step)
            data.mark(key) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def test_rvs(self):
        vals = stats.randint.rvs(5,30,size=100)
        assert_(numpy.all(vals < 30) & numpy.all(vals >= 5))
        assert_(len(vals) == 100)
        vals = stats.randint.rvs(5,30,size=(2,50))
        assert_(numpy.shape(vals) == (2,50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.randint.rvs(15,46)
        assert_((val >= 15) & (val < 46))
        assert_(isinstance(val, numpy.ScalarType), msg=repr(type(val)))
        val = stats.randint(15,46).rvs(3)
        assert_(val.dtype.char in typecodes['AllInteger']) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def test_rvs(self):
        vals = stats.randint.rvs(5, 30, size=100)
        assert_(numpy.all(vals < 30) & numpy.all(vals >= 5))
        assert_(len(vals) == 100)
        vals = stats.randint.rvs(5, 30, size=(2, 50))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.randint.rvs(15, 46)
        assert_((val >= 15) & (val < 46))
        assert_(isinstance(val, numpy.ScalarType), msg=repr(type(val)))
        val = stats.randint(15, 46).rvs(3)
        assert_(val.dtype.char in typecodes['AllInteger']) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def numpy_fallback_array_equal(name='xp'):
    """
    Decorator that checks fallback_mode results are equal to NumPy ones.
    Checks ndarrays.

    Args:
        name(str): Argument name whose value is either
        ``numpy`` or ``cupy`` module.
    """
    def decorator(impl):
        @functools.wraps(impl)
        def test_func(self, *args, **kwargs):

            kwargs[name] = fallback_mode.numpy
            fallback_result = impl(self, *args, **kwargs)

            kwargs[name] = numpy
            numpy_result = impl(self, *args, **kwargs)

            if isinstance(numpy_result, numpy.ndarray):
                # if numpy returns ndarray, cupy must return ndarray
                assert isinstance(fallback_result, fallback.ndarray)

                fallback_mode.numpy.testing.assert_array_equal(
                    numpy_result, fallback_result)

                assert fallback_result.dtype == numpy_result.dtype

            elif isinstance(numpy_result, numpy.ScalarType):
                # if numpy returns scalar
                # cupy may return 0-dim array
                assert numpy_result == fallback_result._cupy_array.item() or \
                    (numpy_result == fallback_result._numpy_array).all()

            else:
                assert False

        return test_func
    return decorator 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def _record_kv(self, key, value, step):
        if isinstance(value, np.ScalarType):
            self._writer.add_scalar(key, value, step)
        elif isinstance(value, plt.Figure):
            self._writer.add_figure(key, value, step)
        elif isinstance(value, scipy.stats._distn_infrastructure.rv_frozen):
            shape = (self._histogram_samples,) + value.mean().shape
            self._writer.add_histogram(key, value.rvs(shape), step)
        elif isinstance(value, scipy.stats._multivariate.multi_rv_frozen):
            self._writer.add_histogram(key, value.rvs(self._histogram_samples), step)
        elif isinstance(value, Histogram):
            self._writer.add_histogram(key, value, step) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def _record_kv(self, key, value, step):
        if isinstance(value, np.ScalarType):
            self._writer.add_scalar(key, value, step)
        elif isinstance(value, plt.Figure):
            self._writer.add_figure(key, value, step)
        elif isinstance(value, scipy.stats._distn_infrastructure.rv_frozen):
            shape = (self._histogram_samples, ) + value.mean().shape
            self._writer.add_histogram(key, value.rvs(shape), step)
        elif isinstance(value, scipy.stats._multivariate.multi_rv_frozen):
            self._writer.add_histogram(key, value.rvs(self._histogram_samples),
                                       step)
        elif isinstance(value, Histogram):
            self._writer.add_histogram(key, value, step) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def __call__(self, lhs, rhs):
        if lhs is rhs:
            return 2*lhs
        if isinstance(lhs, np.ScalarType) and lhs == 0:
            return rhs
        if isinstance(rhs, np.ScalarType) and rhs == 0:
            return lhs
        return super(Add, self).__call__(lhs, rhs) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def __getitem__(self, key):
        if isinstance(key, str):
            raise MAError("Unavailable for masked array.")
        if not isinstance(key, tuple):
            key = (key,)
        objs = []
        scalars = []
        final_dtypedescr = None
        for k in range(len(key)):
            scalar = False
            if isinstance(key[k], slice):
                step = key[k].step
                start = key[k].start
                stop = key[k].stop
                if start is None:
                    start = 0
                if step is None:
                    step = 1
                if isinstance(step, complex):
                    size = int(abs(step))
                    newobj = np.linspace(start, stop, num=size)
                else:
                    newobj = np.arange(start, stop, step)
            elif isinstance(key[k], str):
                if (key[k] in 'rc'):
                    self.matrix = True
                    self.col = (key[k] == 'c')
                    continue
                try:
                    self.axis = int(key[k])
                    continue
                except (ValueError, TypeError):
                    raise ValueError("Unknown special directive")
            elif type(key[k]) in np.ScalarType:
                newobj = asarray([key[k]])
                scalars.append(k)
                scalar = True
            else:
                newobj = key[k]
            objs.append(newobj)
            if isinstance(newobj, ndarray) and not scalar:
                if final_dtypedescr is None:
                    final_dtypedescr = newobj.dtype
                elif newobj.dtype > final_dtypedescr:
                    final_dtypedescr = newobj.dtype
        if final_dtypedescr is not None:
            for k in scalars:
                objs[k] = objs[k].astype(final_dtypedescr)
        res = concatenate(tuple(objs), axis=self.axis)
        return self._retval(res) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ScalarType [as 別名]
def __getitem__(self, key):
        trans1d = self.trans1d
        ndmin = self.ndmin
        objs = []
        scalars = []
        arraytypes = []
        scalartypes = []
        if isinstance(key, str):
            raise NotImplementedError
        if not isinstance(key, tuple):
            key = (key,)

        for i, k in enumerate(key):
            scalar = False
            if isinstance(k, slice):
                raise NotImplementedError
            elif isinstance(k, str):
                if i != 0:
                    raise ValueError(
                        'special directives must be the first entry.')
                raise NotImplementedError
            elif type(k) in numpy.ScalarType:
                newobj = from_data.array(k, ndmin=ndmin)
                scalars.append(i)
                scalar = True
                scalartypes.append(newobj.dtype)
            else:
                newobj = from_data.array(k, copy=False, ndmin=ndmin)
                if ndmin > 1:
                    ndim = from_data.array(k, copy=False).ndim
                    if trans1d != -1 and ndim < ndmin:
                        newobj = self._output_obj(newobj, ndim, ndmin, trans1d)

            objs.append(newobj)
            if not scalar and isinstance(newobj, core.ndarray):
                arraytypes.append(newobj.dtype)

        final_dtype = numpy.find_common_type(arraytypes, scalartypes)
        if final_dtype is not None:
            for k in scalars:
                objs[k] = objs[k].astype(final_dtype)

        return join.concatenate(tuple(objs), axis=self.axis)