def todok(self, copy=False):
from .dok import dok_matrix
self.sum_duplicates()
dok = dok_matrix((self.shape), dtype=self.dtype)
dok.update(izip(izip(self.row,self.col),self.data))
return dok
def _set_arrayXarray(self, row, col, x):
row = list(map(int, row.ravel()))
col = list(map(int, col.ravel()))
x = x.ravel()
dict.update(self, izip(izip(row, col), x))
for i in np.nonzero(x == 0)[0]:
key = (row[i], col[i])
if dict.__getitem__(self, key) == 0:
# may have been superseded by later update
del self[key]
def _set_arrayXarray(self, row, col, x):
row = list(map(int, row.ravel()))
col = list(map(int, col.ravel()))
x = x.ravel()
dict.update(self, izip(izip(row, col), x))
for i in np.nonzero(x == 0)[0]:
key = (row[i], col[i])
if dict.__getitem__(self, key) == 0:
# may have been superseded by later update
del self[key]
def __setitem__(self, index, x):
if isinstance(index, tuple) and len(index) == 2:
# Integer index fast path
i, j = index
if (isintlike(i) and isintlike(j) and 0 <= i < self.shape[0]
and 0 <= j < self.shape[1]):
v = np.asarray(x, dtype=self.dtype)
if v.ndim == 0 and v != 0:
dict.__setitem__(self, (int(i), int(j)), v[()])
return
i, j = self._unpack_index(index)
i, j = self._index_to_arrays(i, j)
if isspmatrix(x):
x = x.toarray()
# Make x and i into the same shape
x = np.asarray(x, dtype=self.dtype)
x, _ = np.broadcast_arrays(x, i)
if x.shape != i.shape:
raise ValueError("Shape mismatch in assignment.")
if np.size(x) == 0:
return
min_i = i.min()
if min_i < -self.shape[0] or i.max() >= self.shape[0]:
raise IndexError('Index (%d) out of range -%d to %d.' %
(i.min(), self.shape[0], self.shape[0]-1))
if min_i < 0:
i = i.copy()
i[i < 0] += self.shape[0]
min_j = j.min()
if min_j < -self.shape[1] or j.max() >= self.shape[1]:
raise IndexError('Index (%d) out of range -%d to %d.' %
(j.min(), self.shape[1], self.shape[1]-1))
if min_j < 0:
j = j.copy()
j[j < 0] += self.shape[1]
dict.update(self, izip(izip(i.flat, j.flat), x.flat))
if 0 in x:
zeroes = x == 0
for key in izip(i[zeroes].flat, j[zeroes].flat):
if dict.__getitem__(self, key) == 0:
# may have been superseded by later update
del self[key]
def __setitem__(self, index, x):
if isinstance(index, tuple) and len(index) == 2:
# Integer index fast path
i, j = index
if (isintlike(i) and isintlike(j) and 0 <= i < self.shape[0]
and 0 <= j < self.shape[1]):
v = np.asarray(x, dtype=self.dtype)
if v.ndim == 0 and v != 0:
dict.__setitem__(self, (int(i), int(j)), v[()])
return
i, j = self._unpack_index(index)
i, j = self._index_to_arrays(i, j)
if isspmatrix(x):
x = x.toarray()
# Make x and i into the same shape
x = np.asarray(x, dtype=self.dtype)
x, _ = np.broadcast_arrays(x, i)
if x.shape != i.shape:
raise ValueError("shape mismatch in assignment")
if np.size(x) == 0:
return
min_i = i.min()
if min_i < -self.shape[0] or i.max() >= self.shape[0]:
raise IndexError('index (%d) out of range -%d to %d)' %
(i.min(), self.shape[0], self.shape[0] - 1))
if min_i < 0:
i = i.copy()
i[i < 0] += self.shape[0]
min_j = j.min()
if min_j < -self.shape[1] or j.max() >= self.shape[1]:
raise IndexError('index (%d) out of range -%d to %d)' %
(j.min(), self.shape[1], self.shape[1] - 1))
if min_j < 0:
j = j.copy()
j[j < 0] += self.shape[1]
dict.update(self, izip(izip(i.flat, j.flat), x.flat))
if 0 in x:
zeroes = x == 0
for key in izip(i[zeroes].flat, j[zeroes].flat):
if dict.__getitem__(self, key) == 0:
# may have been superseded by later update
del self[key]
def _insert_many(self, i, j, x):
"""Inserts new nonzero at each (i, j) with value x
Here (i,j) index major and minor respectively.
i, j and x must be non-empty, 1d arrays.
Inserts each major group (e.g. all entries per row) at a time.
Maintains has_sorted_indices property.
Modifies i, j, x in place.
"""
order = np.argsort(i, kind='mergesort') # stable for duplicates
i = i.take(order, mode='clip')
j = j.take(order, mode='clip')
x = x.take(order, mode='clip')
do_sort = self.has_sorted_indices
# Update index data type
idx_dtype = get_index_dtype((self.indices, self.indptr),
maxval=(self.indptr[-1] + x.size))
self.indptr = np.asarray(self.indptr, dtype=idx_dtype)
self.indices = np.asarray(self.indices, dtype=idx_dtype)
i = np.asarray(i, dtype=idx_dtype)
j = np.asarray(j, dtype=idx_dtype)
# Collate old and new in chunks by major index
indices_parts = []
data_parts = []
ui, ui_indptr = _compat_unique(i, return_index=True)
ui_indptr = np.append(ui_indptr, len(j))
new_nnzs = np.diff(ui_indptr)
prev = 0
for c, (ii, js, je) in enumerate(izip(ui, ui_indptr, ui_indptr[1:])):
# old entries
start = self.indptr[prev]
stop = self.indptr[ii]
indices_parts.append(self.indices[start:stop])
data_parts.append(self.data[start:stop])
# handle duplicate j: keep last setting
uj, uj_indptr = _compat_unique(j[js:je][::-1], return_index=True)
if len(uj) == je - js:
indices_parts.append(j[js:je])
data_parts.append(x[js:je])
else:
indices_parts.append(j[js:je][::-1][uj_indptr])
data_parts.append(x[js:je][::-1][uj_indptr])
new_nnzs[c] = len(uj)
prev = ii
# remaining old entries
start = self.indptr[ii]
indices_parts.append(self.indices[start:])
data_parts.append(self.data[start:])
# update attributes
self.indices = np.concatenate(indices_parts)
self.data = np.concatenate(data_parts)
nnzs = np.asarray(np.ediff1d(self.indptr, to_begin=0), dtype=idx_dtype)
nnzs[1:][ui] += new_nnzs
self.indptr = np.cumsum(nnzs, out=nnzs)
if do_sort:
# TODO: only sort where necessary
self.has_sorted_indices = False
self.sort_indices()
self.check_format(full_check=False)
def _insert_many(self, i, j, x):
"""Inserts new nonzero at each (i, j) with value x
Here (i,j) index major and minor respectively.
i, j and x must be non-empty, 1d arrays.
Inserts each major group (e.g. all entries per row) at a time.
Maintains has_sorted_indices property.
Modifies i, j, x in place.
"""
order = np.argsort(i, kind='mergesort') # stable for duplicates
i = i.take(order, mode='clip')
j = j.take(order, mode='clip')
x = x.take(order, mode='clip')
do_sort = self.has_sorted_indices
# Update index data type
idx_dtype = get_index_dtype((self.indices, self.indptr),
maxval=(self.indptr[-1] + x.size))
self.indptr = np.asarray(self.indptr, dtype=idx_dtype)
self.indices = np.asarray(self.indices, dtype=idx_dtype)
i = np.asarray(i, dtype=idx_dtype)
j = np.asarray(j, dtype=idx_dtype)
# Collate old and new in chunks by major index
indices_parts = []
data_parts = []
ui, ui_indptr = np.unique(i, return_index=True)
ui_indptr = np.append(ui_indptr, len(j))
new_nnzs = np.diff(ui_indptr)
prev = 0
for c, (ii, js, je) in enumerate(izip(ui, ui_indptr, ui_indptr[1:])):
# old entries
start = self.indptr[prev]
stop = self.indptr[ii]
indices_parts.append(self.indices[start:stop])
data_parts.append(self.data[start:stop])
# handle duplicate j: keep last setting
uj, uj_indptr = np.unique(j[js:je][::-1], return_index=True)
if len(uj) == je - js:
indices_parts.append(j[js:je])
data_parts.append(x[js:je])
else:
indices_parts.append(j[js:je][::-1][uj_indptr])
data_parts.append(x[js:je][::-1][uj_indptr])
new_nnzs[c] = len(uj)
prev = ii
# remaining old entries
start = self.indptr[ii]
indices_parts.append(self.indices[start:])
data_parts.append(self.data[start:])
# update attributes
self.indices = np.concatenate(indices_parts)
self.data = np.concatenate(data_parts)
nnzs = np.asarray(np.ediff1d(self.indptr, to_begin=0), dtype=idx_dtype)
nnzs[1:][ui] += new_nnzs
self.indptr = np.cumsum(nnzs, out=nnzs)
if do_sort:
# TODO: only sort where necessary
self.has_sorted_indices = False
self.sort_indices()
self.check_format(full_check=False)
