Python numpy.find_common_type方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def __init__(self,
                 sites,
                 Ws,
                 bc='finite',
                 IdL=None,
                 IdR=None,
                 max_range=None,
                 explicit_plus_hc=False):
        self.sites = list(sites)
        self.chinfo = self.sites[0].leg.chinfo
        self.dtype = dtype = np.find_common_type([W.dtype for W in Ws], [])
        self._W = [W.astype(dtype, copy=True) for W in Ws]
        self.IdL = self._get_Id(IdL, len(sites))
        self.IdR = self._get_Id(IdR, len(sites))
        self.grouped = 1
        self.bc = bc
        self.max_range = max_range
        self.explicit_plus_hc = explicit_plus_hc
        self.test_sanity() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def __init__(self, sites, Bs, SVs, bc='finite', form='B', norm=1.):
        self.sites = list(sites)
        self.chinfo = self.sites[0].leg.chinfo
        self.dtype = dtype = np.find_common_type([B.dtype for B in Bs], [])
        self.form = self._parse_form(form)
        self.bc = bc  # one of ``'finite', 'periodic', 'segment'``.
        self.norm = norm
        self.grouped = 1

        # make copies of Bs and SVs
        self._B = [B.astype(dtype, copy=True).itranspose(self._B_labels) for B in Bs]
        self._S = [None] * (self.L + 1)
        for i in range(self.L + 1)[self.nontrivial_bonds]:
            self._S[i] = np.array(SVs[i], dtype=np.float)
        if self.bc == 'infinite':
            self._S[-1] = self._S[0]
        elif self.bc == 'finite':
            self._S[0] = self._S[-1] = np.ones([1])
        self._transfermatrix_keep = 1
        self.test_sanity() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def astype(self, dtype, copy=True):
        """Return copy with new dtype, upcasting all blocks in ``_data``.

        Parameters
        ----------
        dtype : convertible to a np.dtype
            The new data type.
            If None, deduce the new dtype as common type of ``self._data``.
        copy : bool
            Whether to make a copy of the blocks even if the type didn't change.

        Returns
        -------
        copy : :class:`Array`
            Deep copy of self with new dtype.
        """
        cp = self.copy(deep=False)  # manual deep copy: don't copy every block twice
        cp._qdata = cp._qdata.copy()
        if dtype is None:
            dtype = np.find_common_type([d.dtype for d in self._data], [])
        cp.dtype = dtype = np.dtype(dtype)
        if copy or dtype != self.dtype:
            cp._data = [d.astype(dtype, copy=copy) for d in self._data]
        return cp 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def __init__(self, *args, **kwargs):
        self._structure = kwargs.get('structure', None)
        for A in args:
            if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
                raise ValueError(
                        'For now, the ProductOperator implementation is '
                        'limited to the product of multiple square matrices.')
        if args:
            n = args[0].shape[0]
            for A in args:
                for d in A.shape:
                    if d != n:
                        raise ValueError(
                                'The square matrices of the ProductOperator '
                                'must all have the same shape.')
            self.shape = (n, n)
            self.ndim = len(self.shape)
        self.dtype = np.find_common_type([x.dtype for x in args], [])
        self._operator_sequence = args 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def _nbool_correspond_all(u, v, w=None):
    if u.dtype == v.dtype == bool and w is None:
        not_u = ~u
        not_v = ~v
        nff = (not_u & not_v).sum()
        nft = (not_u & v).sum()
        ntf = (u & not_v).sum()
        ntt = (u & v).sum()
    else:
        dtype = np.find_common_type([int], [u.dtype, v.dtype])
        u = u.astype(dtype)
        v = v.astype(dtype)
        not_u = 1.0 - u
        not_v = 1.0 - v
        if w is not None:
            not_u = w * not_u
            u = w * u
        nff = (not_u * not_v).sum()
        nft = (not_u * v).sum()
        ntf = (u * not_v).sum()
        ntt = (u * v).sum()
    return (nff, nft, ntf, ntt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def _nbool_correspond_ft_tf(u, v, w=None):
    if u.dtype == v.dtype == bool and w is None:
        not_u = ~u
        not_v = ~v
        nft = (not_u & v).sum()
        ntf = (u & not_v).sum()
    else:
        dtype = np.find_common_type([int], [u.dtype, v.dtype])
        u = u.astype(dtype)
        v = v.astype(dtype)
        not_u = 1.0 - u
        not_v = 1.0 - v
        if w is not None:
            not_u = w * not_u
            u = w * u
        nft = (not_u * v).sum()
        ntf = (u * not_v).sum()
    return (nft, ntf) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def test_where_type(self):
        # Test the type conservation with where
        x = np.arange(4, dtype=np.int32)
        y = np.arange(4, dtype=np.float32) * 2.2
        test = where(x > 1.5, y, x).dtype
        control = np.find_common_type([np.int32, np.float32], [])
        assert_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def test_scalar_loses1(self):
        res = np.find_common_type(['f4', 'f4', 'i2'], ['f8'])
        assert_(res == 'f4') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def test_scalar_loses2(self):
        res = np.find_common_type(['f4', 'f4'], ['i8'])
        assert_(res == 'f4') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def test_scalar_wins(self):
        res = np.find_common_type(['f4', 'f4', 'i2'], ['c8'])
        assert_(res == 'c8') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def test_scalar_wins2(self):
        res = np.find_common_type(['u4', 'i4', 'i4'], ['f4'])
        assert_(res == 'f8') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def test_find_common_type_boolean(self):
        # Ticket #1695
        assert_(np.find_common_type([], ['?', '?']) == '?') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def from_hdf5(cls, hdf5_loader, h5gr, subpath):
        """Load instance from a HDF5 file.

        This method reconstructs a class instance from the data saved with :meth:`save_hdf5`.

        Parameters
        ----------
        hdf5_loader : :class:`~tenpy.tools.hdf5_io.Hdf5Loader`
            Instance of the loading engine.
        h5gr : :class:`Group`
            HDF5 group which is represent the object to be constructed.
        subpath : str
            The `name` of `h5gr` with a ``'/'`` in the end.

        Returns
        -------
        obj : cls
            Newly generated class instance containing the required data.
        """
        obj = cls.__new__(cls)  # create class instance, no __init__() call
        hdf5_loader.memorize_load(h5gr, obj)

        obj.sites = hdf5_loader.load(subpath + "sites")
        obj.chinfo = hdf5_loader.load(subpath + "chinfo")
        obj._W = hdf5_loader.load(subpath + "tensors")
        obj.dtype = np.find_common_type([W.dtype for W in obj._W], [])
        obj.IdL = hdf5_loader.load(subpath + "index_identity_left")
        obj.IdR = hdf5_loader.load(subpath + "index_identity_right")
        obj.grouped = hdf5_loader.get_attr(h5gr, "grouped")
        obj.bc = hdf5_loader.load(subpath + "boundary_condition")
        obj.max_range = hdf5_loader.load(subpath + "max_range")
        obj.explicit_plus_hc = h5gr.attrs.get("explicit_plus_hc", False)
        obj.test_sanity()
        return obj 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def from_hdf5(cls, hdf5_loader, h5gr, subpath):
        """Load instance from a HDF5 file.

        This method reconstructs a class instance from the data saved with :meth:`save_hdf5`.

        Parameters
        ----------
        hdf5_loader : :class:`~tenpy.tools.hdf5_io.Hdf5Loader`
            Instance of the loading engine.
        h5gr : :class:`Group`
            HDF5 group which is represent the object to be constructed.
        subpath : str
            The `name` of `h5gr` with a ``'/'`` in the end.

        Returns
        -------
        obj : cls
            Newly generated class instance containing the required data.
        """
        obj = cls.__new__(cls)  # create class instance, no __init__() call
        hdf5_loader.memorize_load(h5gr, obj)

        obj.sites = hdf5_loader.load(subpath + "sites")
        obj._B = hdf5_loader.load(subpath + "tensors")
        obj._S = hdf5_loader.load(subpath + "singular_values")
        obj.bc = hdf5_loader.load(subpath + "boundary_condition")
        form = hdf5_loader.load(subpath + "canonical_form")
        obj.form = [tuple(f) for f in form]
        obj.norm = hdf5_loader.get_attr(h5gr, "norm")

        obj.grouped = hdf5_loader.get_attr(h5gr, "grouped")
        obj._transfermatrix_keep = hdf5_loader.get_attr(h5gr, "transfermatrix_keep")
        obj.chinfo = hdf5_loader.load(subpath + "chinfo")
        obj.dtype = np.find_common_type([B.dtype for B in obj._B], [])
        obj.test_sanity()
        return obj 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import find_common_type [as 別名]
def set_svd_theta(self, i, theta, trunc_par=None, update_norm=False):
        """SVD a two-site wave function `theta` and save it in `self`.

        Parameters
        ----------
        i : int
            `theta` is the wave function on sites `i`, `i`+1.
        theta : :class:`~tenpy.linalg.np_conserved.Array`
            The two-site wave function with labels combined into ``"(vL.p0)", "(p1.vR)"``,
            ready for svd.
        trunc_par : None | dict
            Parameters for truncation, see :cfg:config:`truncation`.
            If ``None``, no truncation is done.
        update_norm : bool
            If ``True``, multiply the norm of `theta` into :attr:`norm`.
        """
        i0 = self._to_valid_index(i)
        i1 = self._to_valid_index(i0 + 1)
        self.dtype = np.find_common_type([self.dtype, theta.dtype], [])
        qtotal_LR = [self._B[i0].qtotal, None]
        if trunc_par is None:
            U, S, VH = npc.svd(theta, qtotal_LR=qtotal_LR, inner_labels=['vR', 'vL'])
            renorm = np.linalg.norm(S)
            S /= renorm
            err = None
            if update_norm:
                self.norm *= renorm
        else:
            U, S, VH, err, renorm = svd_theta(theta, trunc_par, qtotal_LR)
            if update_norm:
                self.norm *= renorm
        U = U.split_legs().ireplace_label('p0', 'p')
        VH = VH.split_legs().ireplace_label('p1', 'p')
        self._B[i0] = U.itranspose(self._B_labels)
        self.form[i0] = self._valid_forms['A']
        self._B[i1] = VH.itranspose(self._B_labels)
        self.form[i1] = self._valid_forms['B']
        self.set_SR(i, S)
        return err