Python numpy.issubclass_方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubclass_ [as 別名]
def __init__(self, theta, phi, r, tt, tp, w=None, eps=1E-16):
        if np.issubclass_(w, float):
            w = ones(len(theta)) * w
        nt_, np_ = 8 + len(tt), 8 + len(tp)
        tt_, tp_ = zeros((nt_,), float), zeros((np_,), float)
        tt_[4:-4], tp_[4:-4] = tt, tp
        tt_[-4:], tp_[-4:] = np.pi, 2. * np.pi
        tt_, tp_, c, fp, ier = dfitpack.spherfit_lsq(theta, phi, r, tt_, tp_,
                                                     w=w, eps=eps)
        if ier < -2:
            deficiency = 6 + (nt_ - 8) * (np_ - 7) + ier
            message = _spherefit_messages.get(-3) % (deficiency, -ier)
            warnings.warn(message)
        elif ier not in [0, -1, -2]:
            message = _spherefit_messages.get(ier, 'ier=%s' % (ier))
            raise ValueError(message)

        self.fp = fp
        self.tck = tt_, tp_, c
        self.degrees = (3, 3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubclass_ [as 別名]
def __init__(self, theta, phi, r, tt, tp, w=None, eps=1E-16):
        if np.issubclass_(w, float):
            w = ones(len(theta)) * w
        nt_, np_ = 8 + len(tt), 8 + len(tp)
        tt_, tp_ = zeros((nt_,), float), zeros((np_,), float)
        tt_[4:-4], tp_[4:-4] = tt, tp
        tt_[-4:], tp_[-4:] = np.pi, 2. * np.pi
        tt_, tp_, c, fp, ier = dfitpack.spherfit_lsq(theta, phi, r, tt_, tp_,
                                                     w=w, eps=eps)
        if ier < -2:
            deficiency = 6 + (nt_ - 8) * (np_ - 7) + ier
            message = _spherefit_messages.get(-3) % (deficiency, -ier)
            warnings.warn(message)
        elif not ier in [0, -1, -2]:
            message = _spherefit_messages.get(ier, 'ier=%s' % (ier))
            raise ValueError(message)
        self.fp = fp
        self.tck = tt_, tp_, c
        self.degrees = (3, 3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubclass_ [as 別名]
def __call__(self, instance, attribute, value):
        if isinstance(value, np.ndarray):
            value_type = value.dtype
        else:
            value_type = type(value)

        if self.dtype is not None and not np.issubdtype(value_type, self.dtype):
            # TODO: add better error message
            raise TypeError(
                f"Attribute '{attribute.name}' of {instance.__class__} "
                + f"must have dtype {self.dtype}"
            )

        if self.subclass is not None and not np.issubclass_(
            type(value), self.subclass
        ):
            # TODO: add better error message
            raise TypeError(
                f"Attribute '{attribute.name}' of {instance.__class__} "
                + f"must be of subclass {self.subclass}"
            ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubclass_ [as 別名]
def type_to_builtin_type(type):
    # Infer from numpy type if it is one
    if type.__module__ == np.__name__:
        return numpy_type_to_builtin_type(type)

    # Otherwise, try to infer from a few generic python types
    if np.issubclass_(type, bool):
        return types_bool
    elif np.issubclass_(type, six.integer_types):
        return types_int32
    elif np.issubclass_(type, six.string_types):
        return types_str
    elif np.issubclass_(type, float):
        return types_fp32
    else:
        raise TypeError("Could not determine builtin type for " + str(type)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubclass_ [as 別名]
def find_estimator(est):
    """Return estimator class.

    Return an estimator class. If input is a class, check if it implements
    methods 'estimate' and 'is_parallel' necessary for network analysis
    (see abstract class 'Estimator' for documentation). If input is a string,
    search for class with that name in IDTxl and return it.

    Args:
        est : str | Class
            name of an estimator class implemented in IDTxl or custom estimator
            class

    Returns
        Class
            Estimator class
    """
    if inspect.isclass(est):
        # Test if provided class implements the Estimator class. This
        # constraint may be relaxed in the future.
        if not np.issubclass_(est, Estimator):
            raise RuntimeError('Provided class should implement abstract class'
                               ' Estimator.')
        return est
    elif type(est) is str:
        module_list = _package_contents()
        estimator = None
        for m in module_list:
            try:
                module = importlib.import_module('.' + m, __package__)
                return getattr(module, est)
            except AttributeError:
                pass
        if not estimator:
            raise RuntimeError('Estimator {0} not found.'.format(est))
    else:
        raise TypeError('Please provide an estimator class or the name of an '
                        'estimator as string.') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubclass_ [as 別名]
def numpy_scalar_to_python(scalar):
    """
    Converts a NumPy scalar to a regular python type.
    """
    scalar_type = type(scalar)
    if np.issubclass_(scalar_type, np.float_):
        return float(scalar)
    elif np.issubclass_(scalar_type, np.int_):
        return int(scalar)
    return scalar 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubclass_ [as 別名]
def numpy_type_to_builtin_type(nptype):
    if type(nptype) == np.dtype:
        nptype = nptype.type

    if np.issubclass_(nptype, np.bool) or np.issubclass_(nptype, np.bool_):
        # numpy as 2 bool types it looks like. what is the difference?
        return types_bool
    elif np.issubclass_(nptype, np.int8):
        return types_int8
    elif np.issubclass_(nptype, np.int16):
        return types_int16
    elif np.issubclass_(nptype, np.int32):
        return types_int32
    elif np.issubclass_(nptype, np.int64):
        return types_int64
    elif np.issubclass_(nptype, np.uint8):
        return types_int8
    elif np.issubclass_(nptype, np.uint16):
        return types_int16
    elif np.issubclass_(nptype, np.uint32):
        return types_int32
    elif np.issubclass_(nptype, np.uint64):
        return types_int64
    elif np.issubclass_(nptype, np.int):
        # Catch all int
        return types_int32
    elif np.issubclass_(nptype, np.object_):
        # symbolic shape is considered int32
        return types_int32
    elif np.issubclass_(nptype, np.float16):
        return types_fp16
    elif np.issubclass_(nptype, np.float32) or np.issubclass_(nptype, np.single):
        return types_fp32
    elif np.issubclass_(nptype, np.float64) or np.issubclass_(nptype, np.double):
        return types_fp64
    elif (
        np.issubclass_(nptype, six.string_types)
        or np.issubclass_(nptype, np.string_)
        or np.issubclass_(nptype, np.str_)
    ):
        return types_str
    else:
        raise TypeError("Unsupported numpy type: %s" % (nptype))


# Tries to get the equivalent builtin type of a
# numpy or python type.