Cryodrgn

- 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).

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

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)

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+

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