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cryoJAX
Distributions: noisy images and likelihoods
mjo22/cryojax
cryoJAX
mjo22/cryojax
Welcome to cryoJAX!
Examples
Examples
Introductory
Introductory
Compute a scattering potential
Simulate an image
Read a particle stack
Intermediate
Intermediate
Simulate a superposition of images with vmap
Write a STAR file of simulated data
Run a cross-correlation search
API
API
cryojax.simulator
cryojax.simulator
Representing volumes
Representing volumes
Scattering potential representations
Representing poses
Structural ensembles
Image formation
Image formation
Scattering potential integration methods
Contrast transfer functions
Scattering approximations
Putting it all together
Putting it all together
Configuring an image
Rendering an image
cryojax.inference
cryojax.inference
cryojax.inference.distributions
cryojax.inference.distributions
Distributions: noisy images and likelihoods
Grid search interface
cryojax.data
cryojax.data
RELION compatibility
cryojax.io
cryojax.io
Reading and writing MRC files
Reading atomic structures
cryojax.image
cryojax.image
Utility functions for image and volume arrays
cryojax.image.operators
cryojax.image.operators
Filters and masks
cryojax.coordinates
cryojax.coordinates
Creating coordinate systems
Transforming coordinate systems
cryojax.rotations
cryojax.rotations
Representing rotations
Helpers for converting angular parameterizations
cryojax.constants
cryojax.constants
Working with physical units
Scattering factor parameters
cryojax.utils
cryojax.utils
Helper functions for Equinox filtering
Reparameterizing pytrees
Useful JAX functions
Distributions: noisy images and likelihoods
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