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Avoid using csr_sample_values #821

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Merged
merged 2 commits into from
Jul 7, 2025
Merged

Avoid using csr_sample_values #821

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87a53f3
Use csr_array instead of csr_matrix
psavery Jun 28, 2025
e830e1e
Avoid using csr_sample_values
psavery Jun 28, 2025
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42 changes: 8 additions & 34 deletions hexrd/imageseries/load/framecache.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,10 +7,7 @@
import h5py
import numpy as np
import yaml
from scipy.sparse import csr_matrix

# FIXME: figure out if there is a public way to import this function
from scipy.sparse._compressed import csr_sample_values
from scipy.sparse import csr_array

from . import ImageSeriesAdapter, RegionType
from ..imageseriesiter import ImageSeriesIterator
Expand Down Expand Up @@ -220,7 +217,9 @@ def __getitem__(self, key):
if len(key) == 3:
# This is definitely used frequently and needs to
# be performant.
return _extract_sparse_values(mat, key[1], key[2])
ind1 = key[1]
ind2 = key[2]
return mat[ind1.ravel(), ind2.ravel()].reshape(ind1.shape)
elif len(key) == 2:
# Not sure if this will actually be used.
return mat[key[1]].toarray()
Expand Down Expand Up @@ -264,15 +263,15 @@ def _load_framecache_npz(
num_frames: int,
shape: tuple[int, int],
dtype: np.dtype,
) -> list[csr_matrix]:
) -> list[csr_array]:

framelist = []
arrs = np.load(filepath)
for i in range(num_frames):
row = arrs[f"{i}_row"]
col = arrs[f"{i}_col"]
data = arrs[f"{i}_data"]
frame = csr_matrix((data, (row, col)),
frame = csr_array((data, (row, col)),
shape=shape,
dtype=dtype)

Expand All @@ -294,7 +293,7 @@ def _load_framecache_fch5(
shape: tuple[int, int],
dtype: np.dtype,
max_workers: int,
) -> list[csr_matrix]:
) -> list[csr_array]:

framelist = [None] * num_frames

Expand All @@ -309,7 +308,7 @@ def read_list_arrays_method_thread(i):
row = frame_indices[:, 0]
col = frame_indices[:, 1]
mat_data = frame_data[:, 0]
frame = csr_matrix((mat_data, (row, col)),
frame = csr_array((mat_data, (row, col)),
shape=shape,
dtype=dtype)

Expand All @@ -329,28 +328,3 @@ def read_list_arrays_method_thread(i):
range(num_frames)))

return framelist


def _extract_sparse_values(
mat: csr_matrix,
row: np.ndarray,
col: np.ndarray,
) -> np.ndarray:
# This was first copied from here: https://github.com/scipy/scipy/blob/a465e2ce014c1b20b0e4b949e46361e5c2fb727e/scipy/sparse/_compressed.py#L556-L569
# And then subsequently modified to return the internal `val` array.

# It uses the `csr_sample_values()` function to extract values. This is
# excellent because it skips the creation of a new sparse array (and
# subsequent conversion to a numpy array *again*). It provides a nearly
# 10% performance boost for `pull_spots()`.
idx_dtype = mat.indices.dtype
M, N = mat._swap(mat.shape)
major, minor = mat._swap((row, col))
major = np.asarray(major, dtype=idx_dtype)
minor = np.asarray(minor, dtype=idx_dtype)

val = np.empty(major.size, dtype=mat.dtype)
csr_sample_values(M, N, mat.indptr, mat.indices, mat.data,
major.size, major.ravel(), minor.ravel(), val)

return val.reshape(major.shape)
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