pyGraFT

Python port of GraFT (Graph-Filtered Temporal dictionary learning), originally implemented in MATLAB at GraFT-analysis.

GraFT decomposes a pixel × time movie into a shared temporal dictionary and per-pixel sparse spatial maps, using a re-weighted L1 sparse coding step where the re-weighting is driven by a spatial/graph kernel that couples nearby or similar pixels. It's built for calcium-imaging data, but the algorithm itself is domain-agnostic.

Status

Core algorithm (graft.graft), Gaussian noise model, both the fixed- convolution and graph-embedding spatial kernels.
Patch-based processing + component merging (graft.patch_graft) for movies too large to fit comfortably in memory, including optional memory-mapped input (only the patch currently being processed is read from disk).
Preprocessing: denoising (wavelet shrinkage), centering/normalization, triangle-threshold masking (graft.preprocessing).
Motion correction: rigid and patch-based (piecewise-rigid) registration, built on scikit-image's subpixel phase correlation (graft.motion_correction).
A PySide6 GUI (graftapp, install the gui extra) covering the full workflow: load a movie → preprocess (motion-correct/denoise/ normalize/crop/mask) → set parameters → run → browse/ visualize/play results → save.
An optional compiled C++ extension (graft._native) accelerates the per-pixel sparse-inference solve, the dominant cost of a GraFT run; graft transparently falls back to an equivalent pure-Python solver if no C++17 compiler is available at install time.

Not yet ported: the Poisson noise model (likely_form='poisson' raises NotImplementedError) and post-hoc coefficient refinement (reCalcCoefSparse/WithLS.m).

Install

pip install pygraft-gui          # library only
pip install "pygraft-gui[gui]"   # library + the graftapp GUI

From source:

pip install -e ".[test,gui]"

Usage

Library

from graft import graft, patch_graft
from graft.simulate import sim_spatial_data

data, profiles, temporal = sim_spatial_data(dims=(30, 30, 100), n_dict=4)

# Single field-of-view
D, S, extras = graft(data, dict_init=None, corr_kern=None,
                      params={"n_dict": 4, "lambda": 0.6})

# Patch-based (splits into overlapping patches, then merges results).
# `data` can be a memory-mapped array (e.g. numpy.load(path, mmap_mode="r"))
# -- only each patch's own slice is read from disk.
D, S, extras = patch_graft(data, n_dict=4, patches=None, corr_kern=None,
                            params={"patchSize": (20, 20)})

GUI

graftapp
# or: python -m graftapp

Development

pip install -e ".[test,gui]"
pytest
python -m pyflakes src/ tests/

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pygraft-gui 0.2.0

pyGraFT

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