Develop

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.13.2
+current_version = 0.14.0
 commit = True
 tag = True
 

.github/workflows/scheduled_workflow.yml

@@ -0,0 +1,24 @@
+name: ASPIRE Python Scheduled Workflow
+
+on:
+  schedule:
+    - cron: '15 0 * * 0'  # Every Sunday at 00:15 UTC
+
+
+jobs:
+  scheduled-tests:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        ref: develop
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
+    - name: Install Dependencies
+      run: |
+        pip install -e ".[dev]"
+    - name: Scheduled Tests
+      run: pytest -m scheduled

.github/workflows/workflow.yml

@@ -156,15 +156,15 @@ jobs:
         shell: bash -el {0}
     strategy:
       matrix:
-        os: [ubuntu-latest, ubuntu-20.04, macOS-latest, macOS-13]
+        os: [ubuntu-latest, ubuntu-22.04, macOS-latest, macOS-13]
         backend: [default, openblas]
         python-version: ['3.9']
         include:
           - os: ubuntu-latest
             backend: intel
           - os: macOS-latest
             backend: accelerate
-          - os: windows-2019
+          - os: windows-2022
             backend: win64
 
     steps:

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 - v0.13.2
+# ASPIRE - Algorithms for Single Particle Reconstruction - v0.14.0
 
 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: v0.13.2 https://doi.org/10.5281/zenodo.5657281
+ComputationalCryoEM/ASPIRE-Python: v0.14.0 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 = "0.13.2"
+release = version = "0.14.0"
 
 # 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 v0.13.2
+Aspire v0.14.0
 ==============
 
 Algorithms for Single Particle Reconstruction

gallery/experiments/commonline_lud_simulated_data.py

@@ -0,0 +1,113 @@
+"""
+Commonlines Method using LUD
+============================
+
+This tutorial demonstrates using the Least Unsquared Deviations (LUD)
+commonlines method for estimating particle orientations. This tutorial
+reproduces the "Experiments on simulated images" found in the publication:
+
+Orientation Determination of Cryo-EM Images Using Least Unsquared Deviations,
+L. Wang, A. Singer, and  Z. Wen, SIAM J. Imaging Sciences, 6, 2450-2483 (2013).
+"""
+
+# %%
+# Imports
+# -------
+
+import logging
+from fractions import Fraction
+from itertools import product
+
+import numpy as np
+
+from aspire.abinitio import CommonlineIRLS, CommonlineLUD
+from aspire.noise import WhiteNoiseAdder
+from aspire.source import Simulation
+from aspire.utils import mean_aligned_angular_distance
+from aspire.volume import Volume
+
+logger = logging.getLogger(__name__)
+
+
+# %%
+# Parameters
+# ----------
+# Set up some initializing parameters. We will run the LUD algorithm using ADMM
+# and IRLS methods under various spectral norm constraints and levels of noise.
+
+SNR = ["1/8", "1/16", "1/32"]  # Signal-to-noise ratio
+METHOD = ["ADMM", "IRLS"]
+ALPHA = [0.90, 0.75, 0.67]  # Spectral norm constraint
+n_imgs = 500  # Number of images in our source
+dtype = np.float64
+pad_size = 129
+
+# %%
+# Load Volume Map
+# ---------------
+# We will generate simulated noisy images from a low res volume
+# map available in our data folder. This volume map is a 65 x 65 x 65
+# voxel volume which we intend to upsample to 129 x 129 x 129.
+# To do this we use our ``downsample`` method which, when provided a voxel
+# size larger than the input volume, internally zero-pads in Fourier
+# space to increase the overall shape of the volume.
+vol = (
+    Volume.load("../tutorials/data/clean70SRibosome_vol_65p.mrc")
+    .astype(dtype)
+    .downsample(pad_size)
+)
+logger.info("Volume map data" f" shape: {vol.shape} dtype:{vol.dtype}")
+
+# %%
+# Generate Noisy Images and Estimate Rotations
+# --------------------------------------------
+# A ``Simulation`` object is used to generate simulated data at various
+# noise levels. Then rotations are estimated using the ``CommonlineLUD`` and
+# ``CommonlineIRLS`` algorithms. Results are measured by computing the mean
+# aligned angular distance between the ground truth rotations and the globally
+# aligned estimated rotations.
+
+# Build table to dislay results.
+col_width = 21
+table = []
+table.append(
+    f"{'METHOD':<{col_width}} {'SNR':<{col_width}} {'ALPHA':<{col_width}} {'Mean Angular Distance':<{col_width}}"
+)
+table.append("-" * (col_width * 4))
+
+for method, snr, alpha in product(METHOD, SNR, ALPHA):
+    # Generate a white noise adder with specified SNR.
+    noise_adder = WhiteNoiseAdder.from_snr(snr=Fraction(snr))
+
+    # Initialize a Simulation source to generate noisy, centered images.
+    src = Simulation(
+        n=n_imgs,
+        vols=vol,
+        offsets=0,
+        amplitudes=1,
+        noise_adder=noise_adder,
+        dtype=dtype,
+    ).cache()
+
+    # Estimate rotations using the LUD algorithm.
+    if method == "ADMM":
+        orient_est = CommonlineLUD(src, alpha=alpha)
+    else:
+        orient_est = CommonlineIRLS(src, alpha=alpha)
+    est_rotations = orient_est.estimate_rotations()
+
+    # Find the mean aligned angular distance between estimates and ground truth rotations.
+    mean_ang_dist = mean_aligned_angular_distance(est_rotations, src.rotations)
+
+    # Append results to table.
+    table.append(
+        f"{method:<{col_width}} {snr:<{col_width}} {str(alpha):<{col_width}} {mean_ang_dist:<{col_width}}"
+    )
+
+# %%
+# Display Results
+# ---------------
+# Display table of results for both methods using various spectral norm
+# constraints and noise levels.
+
+logger.info("\n" + "\n".join(table))