Metadata-Version: 2.1
Name: PyBerries
Version: 0.2.8.post1
Summary: Processing of Bacmman measurement tables
Home-page: https://gitlab.com/MEKlab/pyberries
Author: Daniel Thedie
Author-email: daniel.thedie@ed.ac.uk
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy (>=1.24.3)
Requires-Dist: scipy (>=1.10)
Requires-Dist: pandas (>=2.0)
Requires-Dist: matplotlib (>=3.7)
Requires-Dist: tifffile (>=2023.4.12)
Requires-Dist: h5py (>=3.8.0)
Requires-Dist: seaborn (>=0.12.2)
Requires-Dist: pybacmman (>=0.5.1)
Requires-Dist: IPython (>=8.13.2)

# PyBerries

PyBerries is a Python package that can be used to import, manipulate and plot data from Bacmman measurement tables.

It relies mainly on Pandas for data handling and Seaborn/Matplotlib for plotting.

[[_TOC_]]

----

## Installation

### Anaconda (recommended)

  Anaconda will install both Python and Jupyter-lab (used to run Python notebooks) easily. Note however that it requires ~5 Gb free disk space.
  For a lighter installation procedure, see the next section "Command line install".

  - Download Anaconda from the [official website](https://www.anaconda.com/)
  - Run the installer (leave all options as default)
  - Start "Anaconda Navigator"
  - In Anaconda, launch the "Jupyter Lab" module

### Command line install (advanced users)

  - Open a terminal (macOS/Linux) or Powershell (Windows)
  - Install Python
      - Enter the command `python --version`
      - If an error or a version < 3.9 is shown, download and install Python from the [official website](https://www.python.org/downloads/)
  - After installing, restart your terminal/powershell; both of the above commands should display a version number
  - Install Jupyter Lab
      - In a terminal/powershell, run the command `python -m pip install jupyterlab`
      - After the installation completes, Jupyter Lab can be started using the command `jupyter-lab`


## Getting started

Try downloading and running the [tutorial notebook](./Tutorial/Tutorial.ipynb) to get acquainted with data import and plotting in PyBerries.

For further details, see the [main functionalities](./doc/PyBerries_main_functionalities.md) documentation, as well as the [DatasetPool](./doc/DatasetPool.md) documentation.

For info and examples on plots, see the [preset plots gallery](./doc/Plot_preset_gallery.md) and the [Seaborn documentation](https://seaborn.pydata.org/index.html)
