Python numpy.issubsctype方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubsctype [as 別名]
def test_reconstruction(projector):
    """Test RayTransform for reconstruction."""
    if (
        isinstance(projector.geometry, odl.tomo.ConeBeamGeometry)
        and projector.geometry.pitch != 0
    ):
        pytest.skip('reconstruction with CG is hopeless with so few angles')

    # Create Shepp-Logan phantom
    vol = odl.phantom.shepp_logan(projector.domain, modified=True)

    # Project data
    projections = projector(vol)

    # Reconstruct using ODL
    recon = projector.domain.zero()
    odl.solvers.conjugate_gradient_normal(projector, recon, projections,
                                          niter=20)

    # Make sure the result is somewhat close to the actual result
    maxerr = vol.norm() * 0.5
    if np.issubsctype(projector.domain.dtype, np.complexfloating):
        # Error has double the amount of components practically
        maxerr *= np.sqrt(2)
    assert recon.dist(vol) < maxerr 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubsctype [as 別名]
def _copy_array_if_base_present(a):
    """
    Copy the array if its base points to a parent array.
    """
    if a.base is not None:
        return a.copy()
    elif np.issubsctype(a, np.float32):
        return np.array(a, dtype=np.double)
    else:
        return a 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubsctype [as 別名]
def _copy_array_if_base_present(a):
    """
    Copies the array if its base points to a parent array.
    """
    if a.base is not None:
        return a.copy()
    elif np.issubsctype(a, np.float32):
        return np.array(a, dtype=np.double)
    else:
        return a 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubsctype [as 別名]
def is_numeric_dtype(dtype):
    """Return ``True`` if ``dtype`` is a numeric type."""
    dtype = np.dtype(dtype)
    return np.issubsctype(getattr(dtype, 'base', None), np.number) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubsctype [as 別名]
def is_int_dtype(dtype):
    """Return ``True`` if ``dtype`` is an integer type."""
    dtype = np.dtype(dtype)
    return np.issubsctype(getattr(dtype, 'base', None), np.integer) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubsctype [as 別名]
def is_real_floating_dtype(dtype):
    """Return ``True`` if ``dtype`` is a real floating point type."""
    dtype = np.dtype(dtype)
    return np.issubsctype(getattr(dtype, 'base', None), np.floating) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubsctype [as 別名]
def is_complex_floating_dtype(dtype):
    """Return ``True`` if ``dtype`` is a complex floating point type."""
    dtype = np.dtype(dtype)
    return np.issubsctype(getattr(dtype, 'base', None), np.complexfloating) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubsctype [as 別名]
def slice(self, columns_specifier):
        """Locate a subset of design matrix columns, specified symbolically.

        A patsy design matrix has two levels of structure: the individual
        columns (which are named), and the :ref:`terms <formulas>` in
        the formula that generated those columns. This is a one-to-many
        relationship: a single term may span several columns. This method
        provides a user-friendly API for locating those columns.

        (While we talk about columns here, this is probably most useful for
        indexing into other arrays that are derived from the design matrix,
        such as regression coefficients or covariance matrices.)

        The `columns_specifier` argument can take a number of forms:

        * A term name
        * A column name
        * A :class:`Term` object
        * An integer giving a raw index
        * A raw slice object

        In all cases, a Python :func:`slice` object is returned, which can be
        used directly for indexing.

        Example::

          y, X = dmatrices("y ~ a", demo_data("y", "a", nlevels=3))
          betas = np.linalg.lstsq(X, y)[0]
          a_betas = betas[X.design_info.slice("a")]

        (If you want to look up a single individual column by name, use
        ``design_info.column_name_indexes[name]``.)
        """
        if isinstance(columns_specifier, slice):
            return columns_specifier
        if np.issubsctype(type(columns_specifier), np.integer):
            return slice(columns_specifier, columns_specifier + 1)
        if (self.term_slices is not None
            and columns_specifier in self.term_slices):
            return self.term_slices[columns_specifier]
        if columns_specifier in self.term_name_slices:
            return self.term_name_slices[columns_specifier]
        if columns_specifier in self.column_name_indexes:
            idx = self.column_name_indexes[columns_specifier]
            return slice(idx, idx + 1)
        raise PatsyError("unknown column specified '%s'"
                            % (columns_specifier,))