calab 0.2.2

CaLab: calcium imaging analysis tools — deconvolution and data preparation

CaLab Python

Calcium imaging analysis tools — deconvolution and data preparation. Python companion package for the CaLab tools.

The calab package runs the same Rust FISTA solver used by the CaTune web app (compiled to a native Python extension via PyO3), and provides utilities for loading data from common pipelines, interactive parameter tuning in the browser, and batch deconvolution from scripts.

Installation

pip install calab

# Optional: CaImAn HDF5 and Minian Zarr loaders
pip install calab[loaders]

Note: Pre-built wheels include the compiled Rust solver for Linux, macOS, and Windows. No Rust toolchain is needed for installation.

Quick Start

import numpy as np
import calab

# Load your calcium traces (n_cells x n_timepoints)
traces = np.load("my_traces.npy")

# Interactive tuning: opens CaTune in the browser, returns exported params
params = calab.tune(traces, fs=30.0)

# Batch deconvolution with tuned parameters
activity = calab.run_deconvolution(
    traces, fs=30.0,
    tau_r=params["parameters"]["tau_rise_s"],
    tau_d=params["parameters"]["tau_decay_s"],
    lam=params["parameters"]["lambda"],
)

Loading Data

Direct loaders (CaImAn, Minian)

# CaImAn HDF5 — reads traces and sampling rate directly
traces, meta = calab.load_caiman("caiman_results.hdf5")

# Minian Zarr — reads traces, fs must be provided
traces, meta = calab.load_minian("minian_output/", fs=30.0)

# Both return (ndarray, dict) with shape (n_cells, n_timepoints)
print(meta)
# {'source': 'caiman', 'sampling_rate_hz': 30.0, 'num_cells': 256, 'num_timepoints': 9000}

Requires optional dependencies: pip install calab[loaders]

Saving for CaTune

calab.save_for_tuning(traces, fs=30.0, path="my_recording")
# Creates my_recording.npy + my_recording_metadata.json

Interactive Tuning (Bridge)

calab.tune() starts a local HTTP server, opens CaTune in the browser with your data pre-loaded, and waits for you to export parameters:

params = calab.tune(traces, fs=30.0)
# Browser opens → tune parameters → click Export
# params contains the CaTune export JSON

The bridge serves traces via http://127.0.0.1:<port> and the web app communicates back via the ?bridge= URL parameter.

Deconvolution

Run FISTA deconvolution using the native Rust solver:

# Basic: returns non-negative activity array
activity = calab.run_deconvolution(traces, fs=30.0, tau_r=0.02, tau_d=0.4, lam=0.01)

# Full: returns activity, baseline, reconvolution, iterations, converged
result = calab.run_deconvolution_full(traces, fs=30.0, tau_r=0.02, tau_d=0.4, lam=0.01)
print(f"Baseline: {result.baseline}, Converged: {result.converged}")

Note: The deconvolved output represents scaled neural activity, not discrete spikes or firing rates. The signal is scaled by an unknown constant (indicator expression level, optical path, etc.), so absolute values should not be interpreted as spike counts.

Bandpass Filter

Apply the same FFT bandpass filter used in the CaTune web app:

filtered = calab.bandpass_filter(trace, tau_rise=0.02, tau_decay=0.4, fs=100.0)

Using CaTune Export JSON

Load parameters from a CaTune export and run deconvolution:

params = calab.load_export_params("catune-params-2025-01-15.json")
# {'tau_rise': 0.02, 'tau_decay': 0.4, 'lambda_': 0.01, 'fs': 30.0, 'filter_enabled': False}

# One-step pipeline: loads params, optionally filters, deconvolves
activity = calab.deconvolve_from_export(traces, "catune-params-2025-01-15.json")

Kernel Math

kernel = calab.build_kernel(tau_rise=0.02, tau_decay=0.4, fs=30.0)
g1, g2, d, r = calab.tau_to_ar2(tau_rise=0.02, tau_decay=0.4, fs=30.0)
L = calab.compute_lipschitz(kernel)

CLI

The calab command-line tool is installed with the package:

# Interactive tuning
calab tune my_traces.npy --fs 30.0

# Batch deconvolution with exported params
calab deconvolve my_traces.npy --params catune-params.json -o activity.npy

# Convert from CaImAn/Minian to CaLab format
calab convert caiman_results.hdf5 --format caiman -o my_recording

# Show file info
calab info my_traces.npy

API Reference

Function	Description
tune(traces, fs, ...)	Open CaTune in browser for interactive tuning
load_caiman(path, ...)	Load traces from CaImAn HDF5 file
load_minian(path, ...)	Load traces from Minian Zarr directory
build_kernel(tau_rise, tau_decay, fs)	Build double-exponential calcium kernel
tau_to_ar2(tau_rise, tau_decay, fs)	Derive AR(2) coefficients from tau values
compute_lipschitz(kernel)	Lipschitz constant for FISTA step size
run_deconvolution(traces, fs, tau_r, tau_d, lam)	FISTA deconvolution, returns activity
run_deconvolution_full(traces, fs, tau_r, tau_d, lam)	Full result with baseline, reconvolution
bandpass_filter(trace, tau_rise, tau_decay, fs)	FFT bandpass filter from kernel params
save_for_tuning(traces, fs, path)	Save traces for CaTune browser tool
load_tuning_data(path)	Load traces saved by save_for_tuning
load_export_params(path)	Load params from CaTune export JSON
deconvolve_from_export(traces, params_path)	Full pipeline: load params + deconvolve