EVOLUTION-MANAGER

Edit File: 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, check_shape, matrix)
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 stored values, including explicit zeros
    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 = check_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 = check_shape(A.shape)
        elif isinstance(arg1, tuple):
            if isshape(arg1):
                # It's a tuple of matrix dimensions (M, N)
                # create empty matrix
                self._shape = check_shape(arg1)
                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 Exception:
                    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 = check_shape(shape)
        else:
            #must be dense, convert to COO first, then to DIA
            try:
                arg1 = np.asarray(arg1)
            except Exception:
                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 = check_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 = 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 = 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 = 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, k=0):
        rows, cols = self.shape
        if k <= -rows or k >= cols:
            raise ValueError("k exceeds matrix dimensions")
        idx, = np.nonzero(self.offsets == k)
        first_col, last_col = max(0, k), min(rows + k, cols)
        if idx.size == 0:
            return np.zeros(last_col - first_col, dtype=self.data.dtype)
        return self.data[idx[0], first_col:last_col]

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 resize(self, *shape):
        shape = check_shape(shape)
        M, N = shape
        # we do not need to handle the case of expanding N
        self.data = self.data[:, :N]

if (M > self.shape[0] and
                np.any(self.offsets + self.shape[0] < self.data.shape[1])):
            # explicitly clear values that were previously hidden
            mask = (self.offsets[:, None] + self.shape[0] <=
                    np.arange(self.data.shape[1]))
            self.data[mask] = 0

self._shape = shape

resize.__doc__ = spmatrix.resize.__doc__

def isspmatrix_dia(x):
    """Is x of dia_matrix type?

Parameters
    ----------
    x
        object to check for being a dia matrix

Returns
    -------
    bool
        True if x is a dia matrix, False otherwise

Examples
    --------
    >>> from scipy.sparse import dia_matrix, isspmatrix_dia
    >>> isspmatrix_dia(dia_matrix([[5]]))
    True

>>> from scipy.sparse import dia_matrix, csr_matrix, isspmatrix_dia
    >>> isspmatrix_dia(csr_matrix([[5]]))
    False
    """
    return isinstance(x, dia_matrix)