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Merged
merged 3 commits into from
Apr 27, 2023
Merged

Workaround patches #931

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a490062
Avoid future numpy deprecation, generator stack inputs
garrettwrong Apr 25, 2023
41be4d8
Use caching to workaround immutable_src+ray memory leak
garrettwrong Apr 25, 2023
e64b81c
Remove a flaky superfluous windows env for now
garrettwrong Apr 25, 2023
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4 changes: 3 additions & 1 deletion .github/workflows/workflow.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -64,14 +64,16 @@ jobs:
shell: bash -el {0}
strategy:
matrix:
os: [ubuntu-latest, ubuntu-20.04, macOS-latest, macOS-11, windows-2019]
os: [ubuntu-latest, ubuntu-20.04, macOS-latest, macOS-11]
backend: [default, openblas]
python-version: ['3.8']
include:
- os: ubuntu-latest
backend: intel
- os: macOS-latest
backend: accelerate
- os: windows-2019
backend: default

steps:
- uses: actions/checkout@v3
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5 changes: 5 additions & 0 deletions gallery/experiments/experimental_abinitio_pipeline_10028.py
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Expand Up @@ -98,6 +98,9 @@
# logger.info("Invert the global density contrast")
# src = src.invert_contrast()

# Caching is used for speeding up large datasets on high memory machines.
src = src.cache()

# %%
# Optional: CWF Denoising
# -----------------------
Expand All @@ -119,6 +122,8 @@
cwf_denoiser = DenoiserCov2D(src)
# Use denoised src for classification
classification_src = cwf_denoiser.denoise()
# Cache for speedup. Avoids recomputing.
classification_src = classification_src.cache()
# Peek, what do the denoised images look like...
if interactive:
classification_src.images[:10].show()
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3 changes: 3 additions & 0 deletions gallery/experiments/experimental_abinitio_pipeline_10081.py
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Expand Up @@ -75,6 +75,9 @@
aiso_noise_estimator = AnisotropicNoiseEstimator(src)
src.whiten(aiso_noise_estimator.filter)

# Caching is used for speeding up large datasets on high memory machines.
src = src.cache()

# %%
# Class Averaging
# ----------------------
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2 changes: 1 addition & 1 deletion src/aspire/source/coordinates.py
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Expand Up @@ -366,7 +366,7 @@ def _extract_ctf(self, data_block):

# get unique ctfs from the data block
# i'th entry of `indices` contains the index of `filter_params` with corresponding CTF params
ctf_data = np.stack(data_block[c] for c in CTF_params).astype(self.dtype).T
ctf_data = np.stack([data_block[c] for c in CTF_params]).astype(self.dtype).T
filter_params, indices = np.unique(
ctf_data,
return_inverse=True,
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2 changes: 1 addition & 1 deletion src/aspire/source/relion.py
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Expand Up @@ -107,7 +107,7 @@ def __init__(
# If these all exist in the STAR file, we may create CTF filters for the source
if set(CTF_params).issubset(metadata.keys()):
# partition particles according to unique CTF parameters
ctf_data = np.stack(metadata[k] for k in CTF_params).T
ctf_data = np.stack([metadata[k] for k in CTF_params]).T
filter_params, filter_indices = np.unique(
ctf_data,
return_inverse=True,
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