Metadata-Version: 2.4
Name: brighteyes-flism
Version: 0.11.2
Summary: A toolbox for analysing FLISM images
Home-page: https://github.com/VicidominiLab/brighteyes-flism
Author: Mattia Donato
Author-email: mattia.donato@iit.it
License: GPL-3.0-or-later
Project-URL: Bug Tracker, https://github.com/VicidominiLab/brighteyes-flism/issues
Project-URL: Documentation, https://brighteyes-flism.readthedocs.io
Project-URL: Source Code, https://github.com/VicidominiLab/brighteyes-flism
Project-URL: User Support, https://github.com/VicidominiLab/brighteyes-flism/issues
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: brighteyes-mcs-file>=0.2.5
Requires-Dist: h5py
Requires-Dist: joblib
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-image
Requires-Dist: tqdm
Provides-Extra: ism
Requires-Dist: brighteyes-ism>=1.4.3; extra == "ism"
Dynamic: license-file

BrightEyes-FLISM
---------------

## Installation

Install `brighteyes-flism` from PyPI:

    pip install brighteyes-flism

You can also install the latest development version directly from GitHub:

    pip install git+https://github.com/VicidominiLab/BrightEyes-Flism

In case of local development:
    
    git clone https://github.com/VicidominiLab/BrightEyes-Flism.git
    cd BrightEyes-Flism
    pip install -e .

`brighteyes_flism` re-exports the fitting and calibration helpers from
`brighteyes_mcs_file`, including the optional `model_fn`, `p0`, `bounds`, and
`parameter_names` arguments for custom multi-parameter fit models. This includes
`perform_fit_data`, `fit_data_with_ref_or_irf`, `calibrate_h5_file`, and
`generate_fit_maps`.

The HDF5 helpers understand the BrightEyes MCS 0.0.6 layout, including
`/raw/spad`, `/raw/aux`, `/calibration/results/<product>/...`, and grouped
virtual channels under `/output/virtual_channels/<kind>/channel_<index>`.
Use `load_raw(...)` for current raw payloads and `load_virtual_channel(...)`
for grouped virtual channels. They keep reader support for older raw
acquisition files with root-level `data` and `data_channels_extra` datasets.

For plotting calibrated files, use `load_calibration_summary(...)` and
`load_calibration_fit_traces(...)` instead of hard-coding calibration paths.
