def _random_tile_coordinates(self, slide: Slide) -> CoordinatePair:
"""Return 0-level Coordinates of a tile picked at random within the box.
Parameters
----------
slide : Slide
Slide from which calculate the coordinates. Needed to calculate the box.
Returns
-------
CoordinatePair
Random tile Coordinates at level 0
"""
box_mask_lvl = self.box_mask_lvl(slide)
tile_w_lvl, tile_h_lvl = self.tile_size
x_ul_lvl = np.random.choice(sparse.where(box_mask_lvl)[1])
y_ul_lvl = np.random.choice(sparse.where(box_mask_lvl)[0])
x_br_lvl = x_ul_lvl + tile_w_lvl
y_br_lvl = y_ul_lvl + tile_h_lvl
tile_wsi_coords = scale_coordinates(
reference_coords=CoordinatePair(x_ul_lvl, y_ul_lvl, x_br_lvl,
y_br_lvl),
reference_size=slide.level_dimensions(level=self.level),
target_size=slide.level_dimensions(level=0),
)
return tile_wsi_coords
def _genTables(self):
"Generate the multiplication tables."
self.bladeTupMap = generate_blade_tup_map(self.bladeTupList)
self.bitmap_to_linear_map = generate_bitmap_to_linear_index_map(self.bladeTupList, self.firstIdx)
self.linear_map_to_bitmap = np.zeros(len(self.bladeTupMap), dtype=int)
for bitmap, linear in enumerate(self.bitmap_to_linear_map):
self.linear_map_to_bitmap[linear] = int(bitmap)
self.gmt, imt_prod_mask, omt_prod_mask, lcmt_prod_mask = construct_tables(
np.array(self.gradeList),
self.linear_map_to_bitmap,
self.bitmap_to_linear_map,
self.sig
)
self.omt = sparse.where(omt_prod_mask, self.gmt, self.gmt.dtype.type(0))
self.imt = sparse.where(imt_prod_mask, self.gmt, self.gmt.dtype.type(0))
self.lcmt = sparse.where(lcmt_prod_mask, self.gmt, self.gmt.dtype.type(0))
# This generates the functions that will perform the various products
self.gmt_func = get_mult_function(self.gmt, self.gradeList)
self.imt_func = get_mult_function(self.imt, self.gradeList)
self.omt_func = get_mult_function(self.omt, self.gradeList)
self.lcmt_func = get_mult_function(self.lcmt, self.gradeList)
self.inv_func = get_leftLaInv(self.gmt, self.gradeList)
# these are probably not useful, but someone might want them
self.imt_prod_mask = imt_prod_mask
self.omt_prod_mask = omt_prod_mask
self.lcmt_prod_mask = lcmt_prod_mask
def get_mult_function(mt: sparse.COO,
gradeList,
grades_a=None,
grades_b=None,
filter_mask=None):
'''
Returns a function that implements the mult_table on two input multivectors
'''
if (filter_mask is None) and (grades_a is not None) and (grades_b
is not None):
# If not specified explicitly, we can specify sparseness by grade
filter_mask = np.zeros(mt.nnz, dtype=bool)
k_list, _, m_list = mt.coords
for i in range(len(filter_mask)):
if gradeList[k_list[i]] in grades_a:
if gradeList[m_list[i]] in grades_b:
filter_mask[i] = 1
filter_mask = sparse.COO(coords=mt.coords,
data=filter_mask,
shape=mt.shape)
if filter_mask is not None:
# We can pass the sparse filter mask directly
mt = sparse.where(filter_mask, mt, mt.dtype.type(0))
return _get_mult_function(mt)
else:
return _get_mult_function_runtime_sparse(mt)
def sparsesort(numbers, coords):
X, Y = coords
matrix = np.zeros((X, Y))
for i in range(numbers):
rx, ry = random.randint(0, X - 1), random.randint(0, Y - 1)
matrix[rx, ry] = random.random()
print()
print(r'Sorting {} random numbers in a sparse ("zero-filled") {} x {} matrix:'.\
format(numbers,X,Y))
print(matrix)
print()
sparse_matrix = sparse.COO(matrix)
where = sparse.where(sparse_matrix)
smd = sparse_matrix.data
matrix_dict = dict(zip(smd, zip(*where)))
matrix_sorted = collections.OrderedDict(sorted(matrix_dict.items()))
mitems = matrix_sorted.items()
print('sorting results:')
k = 1
for i, j in mitems:
print('item #{}: {} @ coordinate {}'.format(k, i, j))
k += 1
print()
def _genTables(self):
"Generate the multiplication tables."
self.gmt, imt_prod_mask, omt_prod_mask, lcmt_prod_mask = construct_tables(
self._basis_blade_order, self.sig)
self.omt = sparse.where(omt_prod_mask, self.gmt,
self.gmt.dtype.type(0))
self.imt = sparse.where(imt_prod_mask, self.gmt,
self.gmt.dtype.type(0))
self.lcmt = sparse.where(lcmt_prod_mask, self.gmt,
self.gmt.dtype.type(0))
# This generates the functions that will perform the various products
self.gmt_func = get_mult_function(self.gmt, self.gradeList)
self.imt_func = get_mult_function(self.imt, self.gradeList)
self.omt_func = get_mult_function(self.omt, self.gradeList)
self.lcmt_func = get_mult_function(self.lcmt, self.gradeList)
# these are probably not useful, but someone might want them
self.imt_prod_mask = imt_prod_mask
self.omt_prod_mask = omt_prod_mask
self.lcmt_prod_mask = lcmt_prod_mask
def _truncate_sparse(arr, abs=None, rel=None):
assert (abs is not None) != (rel is not None)
abs_arr = np.absolute(arr)
cutoff = abs if abs is not None else rel * abs_arr.max()
return sparse.where(abs_arr < cutoff, 0, arr)