Develop~~>Main v0.14.1

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.14.0
+current_version = 0.14.1
 commit = True
 tag = True
 

README.md

@@ -5,7 +5,7 @@
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5657281.svg)](https://doi.org/10.5281/zenodo.5657281)
 [![Downloads](https://static.pepy.tech/badge/aspire/month)](https://pepy.tech/project/aspire)
 
-# ASPIRE - Algorithms for Single Particle Reconstruction - v0.14.0
+# ASPIRE - Algorithms for Single Particle Reconstruction - v0.14.1
 
 The ASPIRE-Python project supersedes [Matlab ASPIRE](https://github.com/PrincetonUniversity/aspire).
 
@@ -20,7 +20,7 @@ For more information about the project, algorithms, and related publications ple
 Please cite using the following DOI. This DOI represents all versions, and will always resolve to the latest one.
 
 ```
-ComputationalCryoEM/ASPIRE-Python: v0.14.0 https://doi.org/10.5281/zenodo.5657281
+ComputationalCryoEM/ASPIRE-Python: v0.14.1 https://doi.org/10.5281/zenodo.5657281
 
 ```
 

docs/source/conf.py

@@ -86,7 +86,7 @@
 # built documents.
 #
 # The full version, including alpha/beta/rc tags.
-release = version = "0.14.0"
+release = version = "0.14.1"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/source/index.rst

@@ -1,4 +1,4 @@
-Aspire v0.14.0
+Aspire v0.14.1
 ==============
 
 Algorithms for Single Particle Reconstruction

gallery/experiments/experimental_abinitio_pipeline_10028_jsb.py

@@ -88,12 +88,15 @@
 logger.info("Perform phase flip to input images.")
 src = src.phase_flip().cache()
 
+# Legacy MATLAB cropped the images to an odd resolution.
+src = src.crop_pad(src.L - 1).cache()
+
 # Downsample the images.
 logger.info(f"Set the resolution to {img_size} X {img_size}")
-src = src.downsample(img_size).cache()
+src = src.legacy_downsample(img_size).cache()
 
 # Normalize the background of the images.
-src = src.normalize_background().cache()
+src = src.legacy_normalize_background().cache()
 
 # Estimate the noise and whiten based on the estimated noise.
 src = src.legacy_whiten().cache()

gallery/experiments/experimental_abinitio_pipeline_10073_jsb.py

@@ -29,8 +29,6 @@
 import logging
 from pathlib import Path
 
-import numpy as np
-
 from aspire.abinitio import CLSync3N
 from aspire.denoising import LegacyClassAvgSource
 from aspire.reconstruction import MeanEstimator
@@ -82,12 +80,15 @@
 logger.info("Perform phase flip to input images.")
 src = src.phase_flip().cache()
 
+# Legacy MATLAB cropped the images to an odd resolution.
+src = src.crop_pad(src.L - 1).cache()
+
 # Downsample the images.
 logger.info(f"Set the resolution to {img_size} X {img_size}")
-src = src.downsample(img_size).cache()
+src = src.legacy_downsample(img_size).cache()
 
 # Normalize the background of the images.
-src = src.normalize_background().cache()
+src = src.legacy_normalize_background().cache()
 
 # Estimate the noise and whiten based on the estimated noise.
 src = src.legacy_whiten().cache()
@@ -115,8 +116,8 @@
 
 # We'll continue our pipeline by selecting ``n_classes`` from ``avgs``.
 # To capture a broader range of viewing angles, uniformly select every ``k`` image.
-k = (avgs.n - 1) // n_classes
-avgs = avgs[::k].cache()
+k = 3
+avgs = avgs[: n_classes * k : k].cache()
 
 
 # %%
@@ -133,8 +134,7 @@
 logger.info("Apply custom mask")
 # 10073 benefits from a masking procedure that is more aggressive than the default.
 # Note, since we've manually masked, the default masking is disabled below in `CLSync3N`.
-# This also upcasts to double precision, which is helpful for this reconstruction.
-mask = fuzzy_mask((img_size, img_size), np.float64, r0=0.4 * img_size, risetime=2)
+mask = fuzzy_mask((img_size, img_size), avgs.dtype, r0=0.4 * img_size, risetime=2)
 avgs = ArrayImageSource(avgs.images[:] * mask)
 
 logger.info("Begin Orientation Estimation")

gallery/experiments/legacy_preprocessing.py

@@ -0,0 +1,47 @@
+"""
+Legacy Preprocessing Pipeline
+=============================
+
+This notebook demonstrates reproducing preprocessing results from the
+legacy ASPIRE MATLAB workflow system for the EMPIAR 10028 ribosome
+dataset.
+
+https://www.ebi.ac.uk/empiar/EMPIAR-10028
+"""
+
+# %%
+# Source data and Preprocessing
+# -----------------------------
+#
+# Load the data and apply preprocessing steps corresponding to the MATLAB prompts:
+#
+# >>> Phaseflip projections? (Y/N)? [Y] Y
+# >>> Enter pixel size in Angstrom (-1 to read from STAR file): [-1.000000]
+# >>> Number of projections to read? [105247]
+# >>> Crop? (Y/N)? [Y] Y
+# >>> Crop to size? [360]359
+# >>> Downsample? (Y/N)? [Y] Y
+# >>> Downsample to size? [360]179
+# >>> Normalize background of images to variance 1? (Y/N)? [Y]
+# >>> Prewhiten? (Y/N)? [Y]
+# >>> Split data into groups? (Y/N)? [Y] N
+
+from aspire.source import RelionSource
+
+# Inputs
+# Note the published ``shiny_2sets.star`` requires removal of a stray '9' character on line 5476.
+starfile_in = "10028/data/shiny_2sets_fixed9.star"
+# Caching, while not required, will increase speed in exchange for potentially increased memory usage.
+src = RelionSource(starfile_in).cache()
+
+# Define preprocessing steps.
+src = src.phase_flip().cache()
+src = src.crop_pad(359).cache()
+src = src.legacy_downsample(179).cache()
+src = src.legacy_normalize_background().cache()
+src = src.legacy_whiten().cache()
+src = src.invert_contrast().cache()
+
+# Save the preprocessed images.
+# `save_mode=single` will save a STAR file and single mrcs holding the image stack.
+src.save("10028_legacy_preprocessed_179px.star", save_mode="single", overwrite=True)

gallery/tutorials/aspire_introduction.py

@@ -571,7 +571,7 @@ def noise_function(x, y):
 
 # Generate several CTFs.
 ctf_filters = [
-    RadialCTFFilter(pixel_size=vol_ds.pixel_size, defocus=d)
+    RadialCTFFilter(defocus=d)
     for d in np.linspace(defocus_min, defocus_max, defocus_ct)
 ]
 

gallery/tutorials/pipeline_demo.py

@@ -67,7 +67,7 @@
 defocus_ct = 7
 
 ctf_filters = [
-    RadialCTFFilter(pixel_size=original_vol.pixel_size, defocus=d)
+    RadialCTFFilter(defocus=d)
     for d in np.linspace(defocus_min, defocus_max, defocus_ct)
 ]
 

gallery/tutorials/tutorials/basic_image_array.py

@@ -127,7 +127,7 @@ def noise_function(x, y):
 # The implementation of batching for memory management
 # would be managed behind the scenes for you.
 
-imgs_src = ArrayImageSource(imgs_with_noise)
+imgs_src = ArrayImageSource(imgs_with_noise, pixel_size=1.0)
 
 # We'll copy the orginals for comparison later, before we process them further.
 noisy_imgs_copy = imgs_src.images[:n_imgs].asnumpy()

gallery/tutorials/tutorials/class_averaging.py

@@ -91,7 +91,7 @@
 
 # So now that we have cooked up an example dataset, lets create an
 # ASPIRE source
-src = ArrayImageSource(example_array)
+src = ArrayImageSource(example_array, pixel_size=1.0)
 
 # Let's peek at the images to make sure they're shuffled up nicely
 src.images[:10].show()

gallery/tutorials/tutorials/cov2d_simulation.py

@@ -68,7 +68,7 @@
 print("Initialize simulation object and CTF filters.")
 # Create filters
 ctf_filters = [
-    RadialCTFFilter(pixel_size, voltage, defocus=d, Cs=2.0, alpha=0.1)
+    RadialCTFFilter(voltage, defocus=d, Cs=2.0, alpha=0.1)
     for d in np.linspace(defocus_min, defocus_max, defocus_ct)
 ]
 
@@ -94,6 +94,7 @@
     amplitudes=1.0,
     dtype=dtype,
     noise_adder=noise_adder,
+    pixel_size=pixel_size,
 )
 
 
@@ -108,7 +109,9 @@
 h_idx = sim.filter_indices
 
 # Evaluate CTF in the 8X8 FB basis
-h_ctf_fb = [ffbbasis.filter_to_basis_mat(filt) for filt in ctf_filters]
+h_ctf_fb = [
+    ffbbasis.filter_to_basis_mat(filt, pixel_size=pixel_size) for filt in ctf_filters
+]
 
 # Get clean images from projections of 3D map.
 print("Apply CTF filters to clean images.")

gallery/tutorials/tutorials/cov3d_simulation.py

@@ -36,16 +36,15 @@
     L=img_size,
     C=3,
     dtype=dtype,
+    pixel_size=10,
 ).generate()
 
 # Create a simulation object with specified filters
 sim = Simulation(
     L=img_size,
     n=num_imgs,
     vols=vols,
-    unique_filters=[
-        RadialCTFFilter(pixel_size=10, defocus=d) for d in np.linspace(1.5e4, 2.5e4, 7)
-    ],
+    unique_filters=[RadialCTFFilter(defocus=d) for d in np.linspace(1.5e4, 2.5e4, 7)],
     dtype=dtype,
 )
 
@@ -89,7 +88,7 @@
 eigs_est, lambdas_est = eigs(covar_est, num_eigs)
 
 # Eigs returns column-major, so we transpose and construct a volume.
-eigs_est = Volume(np.transpose(eigs_est, (3, 0, 1, 2)))
+eigs_est = Volume(np.transpose(eigs_est, (3, 0, 1, 2)), pixel_size=vols.pixel_size)
 
 # Truncate the eigendecomposition. Since we know the true rank of the
 # covariance matrix, we enforce it here.