Version 3.0.1-beta

This is a patch release to address #312 and #310 found in v3.0.0-beta by updating the list of dependencies and making some updates to the analysis Jupyter notebooks produced by experiments.

Version 3.0.0-beta

The official cryoDRGN-ET release for heterogeneous subtomogram analysis.

• [NEW] Heterogeneous reconstruction of subtomograms. See documentation on gitbook
• [NEW] cryodrgn direct_traversal for making movies
• Updated cryodrgn backproject_voxel for voxel-based homogeneous reconstruction
• Major refactor of dataset loading for handling large datasets

Version 2.3.0

• Model configuration files are now saved as human-readable config.yaml files (#235)

• Fix machine stamp in output .mrc files for better compatibility with downstream tools (#260)

• Better documentation of help flags in ab initio reconstruction tools (#258)

• [FIX] By default, window images in cryodrgn abinit_homo (now consistent with other reconstruction tools) (#258)

• [FIX] Reduce memory usage when using --preprocessed and --ind (#272)

• Updated functionality in cryodrgn_utils filter_star

• Upcoming in the next minor version (v2.4):

  • We are working on a major refactor of data loading for handling large datasets. This will entail an API change for the mrc.py library module

Version 2.2.0

• New ab initio reconstruction tools:

  • cryodrgn abinit_homo
  • cryodrgn abinit_het

• New utility script for writing cryoSPARC .cs files:

  • cryodrgn_utils write_cs

• Improved plotting in cryodrgn analyze

• Documentation and tutorial converted to sphinx docs: https://zhonge.github.io/cryodrgn/

• Many codebase improvements with open-source software development practices (e.g. continuous integration tests, black, flake8, pyright, logging, and PyPi packaging).

Version 1.1.2

Minor release with updated documentation (and testing PyPI packing).

Version 1.1.0

Updated default settings to larger model architecture, modified positional encoding, and accelerated training:

• Mixed precision training is now turned on by default (Use --no-amp to revert to single precision training)
• Encoder/decoder architecture is now 1024x3 by default (Use --enc-dim 256 and --dec-dim 256 to revert)
• Gaussian Fourier featurization for faster training and higher resolution density maps (Use --pe-type geom_lowf to revert)

Version 1.0.0

Release for version 1.0.0

NEW: cryodrgn analyze_landscape for automatic classification and energy landscape inference
NEW: Faster training and higher resolution model with Gaussian Fourier featurization (Use --pe-type gaussian)
NEW: cryodrgn_utils <command> -h for standalone utility scripts
NEW: cryodrgn_utils write_star for converting cryoDRGN particle selections to .star files
Add pytorch native mixed precision training and fix support for pytorch 1.9+

Version 0.3.4

Updates to auxiliary scripts

• FIX: Bug in write_starfile.py when provided particle stack is chunked (.txt file)
• Support micrograph coordinates and additional column headers to write_starfile.py
• New helper scripts: analyze_convergence.py (in beta testing) contributed by Barrett Powell (thanks!) and make_train_test.py for splitting up particle stacks for training

Version 0.3.3

cryoDRGN version 0.3.3b

• Faster image preprocessing and smaller memory footprint
• New: cryodrgn preprocess for large datasets (in beta testing - see here for details)
• Known issue with PyTorch version 1.9+

(Version 0.3.3b patches a minor argument naming bug for --lazy)

Version 0.3.2

Minor updates to 0.3.1 software

• New: Additional Jupyter notebook for particles filtering, cryoDRGN_filtering.ipynb
• New: cryodrgn view_config
• Fix: Compatibility and deprecation fixes with pytorch 1.7 (#29), scipy (#39), and seaborn (3111efd)
• Updated: More functionality for converting to starfiles with write_starfile.py (#44)
• Performance improvements to cryodrgn eval_vol and a minor fix for a corner case (#33)
• Note: The logged KL divergence in stdout is no longer scaled by beta, however the default objective is identical