PyBerries 0.2.19

Processing of Bacmman measurement tables

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.

Installation

Optional: install Jupyter-lab (to run Jupyter Notebooks)

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
• Run the installer (leave all options as default)
• Start "Anaconda Navigator"
• In Anaconda, launch the "Jupyter Lab" module (you might need to click on "Install" first)

Command line install

• 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
• After installing, restart your terminal/powershell; the python --version command should display a version number > 3.9
• 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

Installing the package

To install the package, use the following command in a terminal:

python -m pip install PyBerries

You can also install a specific version number (useful e.g. to make sure you code won't be broken by a future update):

python -m pip install PyBerries==0.2.8

In a jupyter notebook, use the command:

%pip install PyBerries, or %pip install PyBerries==0.2.8 for a specific version.

Getting started

Try downloading and running the tutorial notebook to get acquainted with data import and plotting in PyBerries.

For further details, see the main functionalities documentation, as well as the DatasetPool documentation.

For more info and examples on plots, see the plot_preset documentation and the Seaborn documentation