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File Content: `dia.py`

"""Sparse DIAgonal format"""

from __future__ import division, print_function, absolute_import

__docformat__ = "restructuredtext en"

__all__ = ['dia_matrix', 'isspmatrix_dia']

import numpy as np

from .base import isspmatrix, _formats, spmatrix
from .data import _data_matrix
from .sputils import (isshape, upcast_char, getdtype, get_index_dtype,
                      get_sum_dtype, validateaxis)
from ._sparsetools import dia_matvec


class dia_matrix(_data_matrix):
    """Sparse matrix with DIAgonal storage

    This can be instantiated in several ways:
        dia_matrix(D)
            with a dense matrix

        dia_matrix(S)
            with another sparse matrix S (equivalent to S.todia())

        dia_matrix((M, N), [dtype])
            to construct an empty matrix with shape (M, N),
            dtype is optional, defaulting to dtype='d'.

        dia_matrix((data, offsets), shape=(M, N))
            where the ``data[k,:]`` stores the diagonal entries for
            diagonal ``offsets[k]`` (See example below)

    Attributes
    ----------
    dtype : dtype
        Data type of the matrix
    shape : 2-tuple
        Shape of the matrix
    ndim : int
        Number of dimensions (this is always 2)
    nnz
        Number of nonzero elements
    data
        DIA format data array of the matrix
    offsets
        DIA format offset array of the matrix

    Notes
    -----

    Sparse matrices can be used in arithmetic operations: they support
    addition, subtraction, multiplication, division, and matrix power.

    Examples
    --------

    >>> import numpy as np
    >>> from scipy.sparse import dia_matrix
    >>> dia_matrix((3, 4), dtype=np.int8).toarray()
    array([[0, 0, 0, 0],
           [0, 0, 0, 0],
           [0, 0, 0, 0]], dtype=int8)

    >>> data = np.array([[1, 2, 3, 4]]).repeat(3, axis=0)
    >>> offsets = np.array([0, -1, 2])
    >>> dia_matrix((data, offsets), shape=(4, 4)).toarray()
    array([[1, 0, 3, 0],
           [1, 2, 0, 4],
           [0, 2, 3, 0],
           [0, 0, 3, 4]])

    """
    format = 'dia'

    def __init__(self, arg1, shape=None, dtype=None, copy=False):
        _data_matrix.__init__(self)

        if isspmatrix_dia(arg1):
            if copy:
                arg1 = arg1.copy()
            self.data = arg1.data
            self.offsets = arg1.offsets
            self.shape = arg1.shape
        elif isspmatrix(arg1):
            if isspmatrix_dia(arg1) and copy:
                A = arg1.copy()
            else:
                A = arg1.todia()
            self.data = A.data
            self.offsets = A.offsets
            self.shape = A.shape
        elif isinstance(arg1, tuple):
            if isshape(arg1):
                # It's a tuple of matrix dimensions (M, N)
                # create empty matrix
                self.shape = arg1   # spmatrix checks for errors here
                self.data = np.zeros((0,0), getdtype(dtype, default=float))
                idx_dtype = get_index_dtype(maxval=max(self.shape))
                self.offsets = np.zeros((0), dtype=idx_dtype)
            else:
                try:
                    # Try interpreting it as (data, offsets)
                    data, offsets = arg1
                except:
                    raise ValueError('unrecognized form for dia_matrix constructor')
                else:
                    if shape is None:
                        raise ValueError('expected a shape argument')
                    self.data = np.atleast_2d(np.array(arg1[0], dtype=dtype, copy=copy))
                    self.offsets = np.atleast_1d(np.array(arg1[1],
                                                          dtype=get_index_dtype(maxval=max(shape)),
                                                          copy=copy))
                    self.shape = shape
        else:
            #must be dense, convert to COO first, then to DIA
            try:
                arg1 = np.asarray(arg1)
            except:
                raise ValueError("unrecognized form for"
                        " %s_matrix constructor" % self.format)
            from .coo import coo_matrix
            A = coo_matrix(arg1, dtype=dtype, shape=shape).todia()
            self.data = A.data
            self.offsets = A.offsets
            self.shape = A.shape

        if dtype is not None:
            self.data = self.data.astype(dtype)

        #check format
        if self.offsets.ndim != 1:
            raise ValueError('offsets array must have rank 1')

        if self.data.ndim != 2:
            raise ValueError('data array must have rank 2')

        if self.data.shape[0] != len(self.offsets):
            raise ValueError('number of diagonals (%d) '
                    'does not match the number of offsets (%d)'
                    % (self.data.shape[0], len(self.offsets)))

        if len(np.unique(self.offsets)) != len(self.offsets):
            raise ValueError('offset array contains duplicate values')

    def __repr__(self):
        format = _formats[self.getformat()][1]
        return "<%dx%d sparse matrix of type '%s'\n" \
               "\twith %d stored elements (%d diagonals) in %s format>" % \
               (self.shape + (self.dtype.type, self.nnz, self.data.shape[0],
                              format))

    def _data_mask(self):
        """Returns a mask of the same shape as self.data, where
        mask[i,j] is True when data[i,j] corresponds to a stored element."""
        num_rows, num_cols = self.shape
        offset_inds = np.arange(self.data.shape[1])
        row = offset_inds - self.offsets[:,None]
        mask = (row >= 0)
        mask &= (row < num_rows)
        mask &= (offset_inds < num_cols)
        return mask

    def count_nonzero(self):
        mask = self._data_mask()
        return np.count_nonzero(self.data[mask])

    def getnnz(self, axis=None):
        if axis is not None:
            raise NotImplementedError("getnnz over an axis is not implemented "
                                      "for DIA format")
        M,N = self.shape
        nnz = 0
        for k in self.offsets:
            if k > 0:
                nnz += min(M,N-k)
            else:
                nnz += min(M+k,N)
        return int(nnz)

    getnnz.__doc__ = spmatrix.getnnz.__doc__
    count_nonzero.__doc__ = spmatrix.count_nonzero.__doc__

    def sum(self, axis=None, dtype=None, out=None):
        validateaxis(axis)

        if axis is not None and axis < 0:
            axis += 2

        res_dtype = get_sum_dtype(self.dtype)
        num_rows, num_cols = self.shape
        ret = None

        if axis == 0:
            mask = self._data_mask()
            x = (self.data * mask).sum(axis=0)
            if x.shape[0] == num_cols:
                res = x
            else:
                res = np.zeros(num_cols, dtype=x.dtype)
                res[:x.shape[0]] = x
            ret = np.matrix(res, dtype=res_dtype)

        else:
            row_sums = np.zeros(num_rows, dtype=res_dtype)
            one = np.ones(num_cols, dtype=res_dtype)
            dia_matvec(num_rows, num_cols, len(self.offsets),
                       self.data.shape[1], self.offsets, self.data, one, row_sums)

            row_sums = np.matrix(row_sums)

            if axis is None:
                return row_sums.sum(dtype=dtype, out=out)

            if axis is not None:
                row_sums = row_sums.T

            ret = np.matrix(row_sums.sum(axis=axis))

        if out is not None and out.shape != ret.shape:
            raise ValueError("dimensions do not match")

        return ret.sum(axis=(), dtype=dtype, out=out)

    sum.__doc__ = spmatrix.sum.__doc__

    def _mul_vector(self, other):
        x = other

        y = np.zeros(self.shape[0], dtype=upcast_char(self.dtype.char,
                                                       x.dtype.char))

        L = self.data.shape[1]

        M,N = self.shape

        dia_matvec(M,N, len(self.offsets), L, self.offsets, self.data, x.ravel(), y.ravel())

        return y

    def _mul_multimatrix(self, other):
        return np.hstack([self._mul_vector(col).reshape(-1,1) for col in other.T])

    def _setdiag(self, values, k=0):
        M, N = self.shape

        if values.ndim == 0:
            # broadcast
            values_n = np.inf
        else:
            values_n = len(values)

        if k < 0:
            n = min(M + k, N, values_n)
            min_index = 0
            max_index = n
        else:
            n = min(M, N - k, values_n)
            min_index = k
            max_index = k + n

        if values.ndim != 0:
            # allow also longer sequences
            values = values[:n]

        if k in self.offsets:
            self.data[self.offsets == k, min_index:max_index] = values
        else:
            self.offsets = np.append(self.offsets, self.offsets.dtype.type(k))
            m = max(max_index, self.data.shape[1])
            data = np.zeros((self.data.shape[0]+1, m), dtype=self.data.dtype)
            data[:-1,:self.data.shape[1]] = self.data
            data[-1, min_index:max_index] = values
            self.data = data

    def todia(self, copy=False):
        if copy:
            return self.copy()
        else:
            return self

    todia.__doc__ = spmatrix.todia.__doc__

    def transpose(self, axes=None, copy=False):
        if axes is not None:
            raise ValueError(("Sparse matrices do not support "
                              "an 'axes' parameter because swapping "
                              "dimensions is the only logical permutation."))

        num_rows, num_cols = self.shape
        max_dim = max(self.shape)

        # flip diagonal offsets
        offsets = -self.offsets

        # re-align the data matrix
        r = np.arange(len(offsets), dtype=np.intc)[:, None]
        c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]
        pad_amount = max(0, max_dim-self.data.shape[1])
        data = np.hstack((self.data, np.zeros((self.data.shape[0], pad_amount),
                                              dtype=self.data.dtype)))
        data = data[r, c]
        return dia_matrix((data, offsets), shape=(
            num_cols, num_rows), copy=copy)

    transpose.__doc__ = spmatrix.transpose.__doc__

    def diagonal(self):
        idx, = np.where(self.offsets == 0)
        n = min(self.shape)
        if idx.size == 0:
            return np.zeros(n, dtype=self.data.dtype)
        return self.data[idx[0],:n]

    diagonal.__doc__ = spmatrix.diagonal.__doc__

    def tocsc(self, copy=False):
        from .csc import csc_matrix
        if self.nnz == 0:
            return csc_matrix(self.shape, dtype=self.dtype)

        num_rows, num_cols = self.shape
        num_offsets, offset_len = self.data.shape
        offset_inds = np.arange(offset_len)

        row = offset_inds - self.offsets[:,None]
        mask = (row >= 0)
        mask &= (row < num_rows)
        mask &= (offset_inds < num_cols)
        mask &= (self.data != 0)

        idx_dtype = get_index_dtype(maxval=max(self.shape))
        indptr = np.zeros(num_cols + 1, dtype=idx_dtype)
        indptr[1:offset_len+1] = np.cumsum(mask.sum(axis=0))
        indptr[offset_len+1:] = indptr[offset_len]
        indices = row.T[mask.T].astype(idx_dtype, copy=False)
        data = self.data.T[mask.T]
        return csc_matrix((data, indices, indptr), shape=self.shape,
                          dtype=self.dtype)

    tocsc.__doc__ = spmatrix.tocsc.__doc__

    def tocoo(self, copy=False):
        num_rows, num_cols = self.shape
        num_offsets, offset_len = self.data.shape
        offset_inds = np.arange(offset_len)

        row = offset_inds - self.offsets[:,None]
        mask = (row >= 0)
        mask &= (row < num_rows)
        mask &= (offset_inds < num_cols)
        mask &= (self.data != 0)
        row = row[mask]
        col = np.tile(offset_inds, num_offsets)[mask.ravel()]
        data = self.data[mask]

        from .coo import coo_matrix
        A = coo_matrix((data,(row,col)), shape=self.shape, dtype=self.dtype)
        A.has_canonical_format = True
        return A

    tocoo.__doc__ = spmatrix.tocoo.__doc__

    # needed by _data_matrix
    def _with_data(self, data, copy=True):
        """Returns a matrix with the same sparsity structure as self,
        but with different data.  By default the structure arrays are copied.
        """
        if copy:
            return dia_matrix((data, self.offsets.copy()), shape=self.shape)
        else:
            return dia_matrix((data,self.offsets), shape=self.shape)


def isspmatrix_dia(x):
    return isinstance(x, dia_matrix)
Name	Size	Permission
__pycache__	---	0755
csgraph	---	0755
linalg	---	0755
tests	---	0755
__init__.py	6560 bytes	0644
_csparsetools.cpython-35m-x86_64-linux-gnu.so	387552 bytes	0755
_sparsetools.cpython-35m-x86_64-linux-gnu.so	3222936 bytes	0755
base.py	36385 bytes	0644
bsr.py	23088 bytes	0644
compressed.py	43146 bytes	0644
construct.py	23928 bytes	0644
coo.py	18187 bytes	0644
csc.py	6947 bytes	0644
csr.py	15482 bytes	0644
data.py	7513 bytes	0644
dia.py	12642 bytes	0644
dok.py	17654 bytes	0644
extract.py	4711 bytes	0644
generate_sparsetools.py	12511 bytes	0644
lil.py	17551 bytes	0644
setup.py	2206 bytes	0644
sparsetools.py	715 bytes	0644
spfuncs.py	2823 bytes	0644
sputils.py	12255 bytes	0644
File Content: dia.py

File Content: `dia.py`
