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torch-ctf-estimation

Contrast transfer function estimation for cryo-EM images in PyTorch.

Overview

torch-ctf-estimation fits defocus, astigmatism, and (optionally) sample thickness from a micrograph power spectrum.

  • 1D defocus on the mean spectrum, then 2D defocus / astigmatism on patches
  • Spatial defocus as a spline grid or a linear tilt model
  • 1D thickness grid search against a thickness-modulated CTF, with optional joint defocus+thickness refinement (1D scalar or 2D field)
  • Laser phase plate (LPP) CTF support via torch-ctf

This package is the algorithm: estimate_ctf (defocus only) and estimate_ctf_and_thickness (defocus + thickness) are self-contained compositions of the lower-level primitives, usable directly from notebooks, tests, or another program. Downstream tools can chain these primitives differently and add optics / metrics file I/O.

Installation

This package is part of the TeamTomo monorepo. See the main repository README for development setup instructions.

Usage

import torch
from torch_ctf_estimation import estimate_ctf
from torch_ctf_estimation.models import CTFFittingParams, OpticalParams

image = torch.randn(1024, 1024)
optical = OpticalParams(
    pixel_spacing_angstroms=1.0,
    voltage_kev=300.0,
    spherical_aberration_mm=2.7,
    amplitude_contrast_fraction=0.07,
)
fitting = CTFFittingParams(
    defocus_grid_resolution=(1, 3, 3),
    frequency_fit_range_angstroms=(30.0, 5.0),
    defocus_range_microns=(0.5, 5.0),
    patch_sidelength=128,
)
mean_ps, result1d, result2d = estimate_ctf(image, optical, fitting)

To also estimate sample thickness, use estimate_ctf_and_thickness with a ThicknessParams:

from torch_ctf_estimation import estimate_ctf_and_thickness
from torch_ctf_estimation.models import ThicknessParams

thickness = ThicknessParams(refine_dim="2d")
result = estimate_ctf_and_thickness(image, optical, fitting, thickness)
result.result2d, result.thickness1d, result.thickness_joint

Project links

Data verified by PyPI on Sep 11, 2026
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  • Released:
Newer release available (0.6.0)

3 maintainers

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Credits

Author: Alister Burt

GitHub Statistics

Data verified by PyPI on Sep 11, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

BSD License (BSD-3-Clause)

Requires

Python >=3.11

Provides Extra

plot

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