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_csparsetools.pyx.in
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# -*- cython -*-
#
# Tempita-templated Cython file
#
"""
Fast snippets for LIL matrices.
"""
{{py:
IDX_TYPES = {
"int32": "cnp.npy_int32",
"int64": "cnp.npy_int64",
}
VALUE_TYPES = {
"bool_": "cnp.npy_bool",
"int8": "cnp.npy_int8",
"uint8": "cnp.npy_uint8",
"int16": "cnp.npy_int16",
"uint16": "cnp.npy_uint16",
"int32": "cnp.npy_int32",
"uint32": "cnp.npy_uint32",
"int64": "cnp.npy_int64",
"uint64": "cnp.npy_uint64",
"float32": "cnp.npy_float32",
"float64": "cnp.npy_float64",
"longdouble": "long double",
"complex64": "float complex",
"complex128": "double complex",
"clongdouble": "long double complex",
}
def get_dispatch(types):
for pyname, cyname in types.items():
yield pyname, cyname
def get_dispatch2(types, types2):
for pyname, cyname in types.items():
for pyname2, cyname2 in types2.items():
yield pyname, pyname2, cyname, cyname2
def define_dispatch_map(map_name, prefix, types):
result = ["cdef dict %s = {\n" % map_name]
for pyname, cyname in types.items():
a = "np.dtype(np.%s)" % (pyname,)
b = prefix + "_" + pyname
result.append('%s: %s,' % (a, b))
result.append("}\n\n")
return "\n".join(result)
def define_dispatch_map2(map_name, prefix, types, types2):
result = ["cdef dict %s = {\n" % map_name]
for pyname, cyname in types.items():
for pyname2, cyname2 in types2.items():
a = "(np.dtype(np.%s), np.dtype(np.%s))" % (pyname, pyname2)
b = prefix + "_" + pyname + "_" + pyname2
result.append('%s: %s,' % (a, b))
result.append("}\n\n")
return "\n".join(result)
}}
cimport cython
cimport numpy as cnp
import numpy as np
cnp.import_array()
@cython.wraparound(False)
cpdef lil_get1(cnp.npy_intp M, cnp.npy_intp N, object[:] rows, object[:] datas,
cnp.npy_intp i, cnp.npy_intp j):
"""
Get a single item from LIL matrix.
Doesn't do output type conversion. Checks for bounds errors.
Parameters
----------
M, N, rows, datas
Shape and data arrays for a LIL matrix
i, j : int
Indices at which to get
Returns
-------
x
Value at indices.
"""
cdef list row, data
if i < -M or i >= M:
raise IndexError('row index (%d) out of bounds' % (i,))
if i < 0:
i += M
if j < -N or j >= N:
raise IndexError('column index (%d) out of bounds' % (j,))
if j < 0:
j += N
row = rows[i]
data = datas[i]
cdef cnp.npy_intp pos = bisect_left(row, j)
if pos != len(data) and row[pos] == j:
return data[pos]
else:
return 0
@cython.wraparound(False)
cpdef int lil_insert(cnp.npy_intp M, cnp.npy_intp N, object[:] rows,
object[:] datas, cnp.npy_intp i, cnp.npy_intp j,
object x) except -1:
"""
Insert a single item to LIL matrix.
Checks for bounds errors and deletes item if x is zero.
Parameters
----------
M, N, rows, datas
Shape and data arrays for a LIL matrix
i, j : int
Indices at which to get
x
Value to insert.
"""
cdef list row, data
if i < -M or i >= M:
raise IndexError('row index (%d) out of bounds' % (i,))
if i < 0:
i += M
if j < -N or j >= N:
raise IndexError('column index (%d) out of bounds' % (j,))
if j < 0:
j += N
row = rows[i]
data = datas[i]
cdef cnp.npy_intp pos = bisect_left(row, j)
if x == 0:
if pos < len(row) and row[pos] == j:
del row[pos]
del data[pos]
else:
if pos == len(row):
row.append(j)
data.append(x)
elif row[pos] != j:
row.insert(pos, j)
data.insert(pos, x)
else:
data[pos] = x
def lil_get_lengths(object[:] input,
cnp.ndarray output):
return _LIL_GET_LENGTHS_DISPATCH[output.dtype](input, output)
{{for NAME, T in get_dispatch(IDX_TYPES)}}
@cython.boundscheck(False)
@cython.wraparound(False)
def _lil_get_lengths_{{NAME}}(object[:] input,
cnp.ndarray[{{T}}] output):
for i in range(len(input)):
output[i] = len(input[i])
{{endfor}}
{{define_dispatch_map('_LIL_GET_LENGTHS_DISPATCH', '_lil_get_lengths', IDX_TYPES)}}
def lil_flatten_to_array(object[:] input,
cnp.ndarray output):
return _LIL_FLATTEN_TO_ARRAY_DISPATCH[output.dtype](input, output)
{{for NAME, T in get_dispatch(VALUE_TYPES)}}
@cython.boundscheck(False)
@cython.wraparound(False)
def _lil_flatten_to_array_{{NAME}}(object[:] input not None, cnp.ndarray[{{T}}] output not None):
cdef list row
cdef size_t pos = 0
for i in range(len(input)):
row = input[i]
for j in range(len(row)):
output[pos] = row[j]
pos += 1
{{endfor}}
{{define_dispatch_map('_LIL_FLATTEN_TO_ARRAY_DISPATCH', '_lil_flatten_to_array', VALUE_TYPES)}}
def lil_fancy_get(cnp.npy_intp M, cnp.npy_intp N,
object[:] rows,
object[:] datas,
object[:] new_rows,
object[:] new_datas,
cnp.ndarray i_idx,
cnp.ndarray j_idx):
"""
Get multiple items at given indices in LIL matrix and store to
another LIL.
Parameters
----------
M, N, rows, data
LIL matrix data, initially empty
new_rows, new_idx
Data for LIL matrix to insert to.
Must be preallocated to shape `i_idx.shape`!
i_idx, j_idx
Indices of elements to insert to the new LIL matrix.
"""
return _LIL_FANCY_GET_DISPATCH[i_idx.dtype](M, N, rows, datas, new_rows, new_datas, i_idx, j_idx)
{{for NAME, IDX_T in get_dispatch(IDX_TYPES)}}
def _lil_fancy_get_{{NAME}}(cnp.npy_intp M, cnp.npy_intp N,
object[:] rows,
object[:] datas,
object[:] new_rows,
object[:] new_datas,
{{IDX_T}}[:,:] i_idx,
{{IDX_T}}[:,:] j_idx):
cdef cnp.npy_intp x, y
cdef cnp.npy_intp i, j
cdef object value
cdef list new_row
cdef list new_data
for x in range(i_idx.shape[0]):
new_row = []
new_data = []
for y in range(i_idx.shape[1]):
i = i_idx[x,y]
j = j_idx[x,y]
value = lil_get1(M, N, rows, datas, i, j)
if value is not 0:
# Object identity as shortcut
new_row.append(y)
new_data.append(value)
new_rows[x] = new_row
new_datas[x] = new_data
{{endfor}}
{{define_dispatch_map('_LIL_FANCY_GET_DISPATCH', '_lil_fancy_get', IDX_TYPES)}}
def lil_fancy_set(cnp.npy_intp M, cnp.npy_intp N,
object[:] rows,
object[:] data,
cnp.ndarray i_idx,
cnp.ndarray j_idx,
cnp.ndarray values):
"""
Set multiple items to a LIL matrix.
Checks for zero elements and deletes them.
Parameters
----------
M, N, rows, data
LIL matrix data
i_idx, j_idx
Indices of elements to insert to the new LIL matrix.
values
Values of items to set.
"""
if values.dtype == np.bool_:
# Cython doesn't support np.bool_ as a memoryview type
values = values.view(dtype=np.uint8)
assert i_idx.shape[0] == j_idx.shape[0] and i_idx.shape[1] == j_idx.shape[1]
return _LIL_FANCY_SET_DISPATCH[i_idx.dtype, values.dtype](M, N, rows, data, i_idx, j_idx, values)
{{for PYIDX, PYVALUE, IDX_T, VALUE_T in get_dispatch2(IDX_TYPES, VALUE_TYPES)}}
@cython.boundscheck(False)
@cython.wraparound(False)
def _lil_fancy_set_{{PYIDX}}_{{PYVALUE}}(cnp.npy_intp M, cnp.npy_intp N,
object[:] rows,
object[:] data,
{{IDX_T}}[:,:] i_idx,
{{IDX_T}}[:,:] j_idx,
{{VALUE_T}}[:,:] values):
cdef cnp.npy_intp x, y
cdef cnp.npy_intp i, j
for x in range(i_idx.shape[0]):
for y in range(i_idx.shape[1]):
i = i_idx[x,y]
j = j_idx[x,y]
lil_insert(M, N, rows, data, i, j, values[x, y])
{{endfor}}
{{define_dispatch_map2('_LIL_FANCY_SET_DISPATCH', '_lil_fancy_set', IDX_TYPES, VALUE_TYPES)}}
def lil_get_row_ranges(cnp.npy_intp M, cnp.npy_intp N,
object[:] rows, object[:] datas,
object[:] new_rows, object[:] new_datas,
object irows,
cnp.npy_intp j_start,
cnp.npy_intp j_stop,
cnp.npy_intp j_stride,
cnp.npy_intp nj):
"""
Column-slicing fast path for LIL matrices.
Extracts values from rows/datas and inserts in to
new_rows/new_datas.
Parameters
----------
M, N
Shape of input array
rows, datas
LIL data for input array, shape (M, N)
new_rows, new_datas
LIL data for output array, shape (len(irows), nj)
irows : iterator
Iterator yielding row indices
j_start, j_stop, j_stride
Column range(j_start, j_stop, j_stride) to get
nj : int
Number of columns corresponding to j_* variables.
"""
cdef cnp.npy_intp nk, k, j, a, b, m, r, p
cdef list cur_row, cur_data, new_row, new_data
if j_stride == 0:
raise ValueError("cannot index with zero stride")
for nk, k in enumerate(irows):
if k >= M or k < -M:
raise ValueError("row index %d out of bounds" % (k,))
if k < 0:
k += M
if j_stride == 1 and nj == N:
# full row slice
new_rows[nk] = list(rows[k])
new_datas[nk] = list(datas[k])
else:
# partial row slice
cur_row = rows[k]
cur_data = datas[k]
new_row = new_rows[nk]
new_data = new_datas[nk]
if j_stride > 0:
a = bisect_left(cur_row, j_start)
for m in range(a, len(cur_row)):
j = cur_row[m]
if j >= j_stop:
break
r = (j - j_start) % j_stride
if r != 0:
continue
p = (j - j_start) // j_stride
new_row.append(p)
new_data.append(cur_data[m])
else:
a = bisect_right(cur_row, j_stop)
for m in range(a, len(cur_row)):
j = cur_row[m]
if j > j_start:
break
r = (j - j_start) % j_stride
if r != 0:
continue
p = (j - j_start) // j_stride
new_row.insert(0, p)
new_data.insert(0, cur_data[m])
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
cdef inline cnp.npy_intp bisect_left(list a, cnp.npy_intp x) except -1:
"""
Bisection search in a sorted list.
List is assumed to contain objects castable to integers.
Parameters
----------
a
List to search in
x
Value to search for
Returns
-------
j : int
Index at value (if present), or at the point to which
it can be inserted maintaining order.
"""
cdef Py_ssize_t hi = len(a)
cdef Py_ssize_t lo = 0
cdef Py_ssize_t mid, v
while lo < hi:
mid = lo + (hi - lo) // 2
v = a[mid]
if v < x:
lo = mid + 1
else:
hi = mid
return lo
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
cdef inline cnp.npy_intp bisect_right(list a, cnp.npy_intp x) except -1:
"""
Bisection search in a sorted list.
List is assumed to contain objects castable to integers.
Parameters
----------
a
List to search in
x
Value to search for
Returns
-------
j : int
Index immediately at the right of the value (if present), or at
the point to which it can be inserted maintaining order.
"""
cdef cnp.npy_intp hi = len(a)
cdef cnp.npy_intp lo = 0
cdef cnp.npy_intp mid, v
while lo < hi:
mid = (lo + hi) // 2
v = a[mid]
if x < v:
hi = mid
else:
lo = mid + 1
return lo
cdef _fill_dtype_map(map, chars):
"""
Fill in Numpy dtype chars for problematic types, working around
Numpy < 1.6 bugs.
"""
for c in chars:
if c in "SUVO":
continue
dt = np.dtype(c)
if dt not in map:
for k, v in map.items():
if k.kind == dt.kind and k.itemsize == dt.itemsize:
map[dt] = v
break
cdef _fill_dtype_map2(map):
"""
Fill in Numpy dtype chars for problematic types, working around
Numpy < 1.6 bugs.
"""
for c1 in np.typecodes['Integer']:
for c2 in np.typecodes['All']:
if c2 in "SUVO":
continue
dt1 = np.dtype(c1)
dt2 = np.dtype(c2)
if (dt1, dt2) not in map:
for k, v in map.items():
if (k[0].kind == dt1.kind and k[0].itemsize == dt1.itemsize and
k[1].kind == dt2.kind and k[1].itemsize == dt2.itemsize):
map[(dt1, dt2)] = v
break
_fill_dtype_map(_LIL_FANCY_GET_DISPATCH, np.typecodes['Integer'])
_fill_dtype_map2(_LIL_FANCY_SET_DISPATCH)