Developer installation

Clone pyHiM repository

  1. Create a folder where you want to install pyHiM and go inside to clone the repository. Standard location to do it is: $HOME/Repositories/pyHiM

mkdir $HOME/Repositories
cd $HOME/Repositories

  1. Choose your clone method between HTTPS or SSH key:

    • HTTPS (For latest version user)

      git clone https://github.com/pyHi-M/pyHiM.git

    • SSH (ONLY for developer)

      git clone git@github.com:pyHi-M/pyHiM.git

  2. Open your $HOME/.bashrc using nano

nano $HOME/.bashrc

  1. Add the following line to the end

export PATH="$PATH:$HOME/Repositories/pyHiM/src"
export PATH="$PATH:$HOME/Repositories/pyHiM/src/toolbox/file_handling"
export PATH="$PATH:$HOME/Repositories/pyHiM/src/postProcessing"
export PYTHONPATH="$PYTHONPATH:$HOME/Repositories/pyHiM/src"
export MPLBACKEND=agg

Note

Make sure you change .../Repositories/... with your directory name (step 1.) if this is not where you put pyHiM !

Installation using uv

This is much faster than conda or mamba and is now the preferred installation method.

To install using uv just go to the Repository folder and run the bash script:

cd $HOME/Repositories/pyHiM
bash install_pyhim_uv.sh

Make sure you added the environmental variables in `.bashrc’.

Everytime you run pyHiM you need to activate the environment doing:

source $HOME/Repositories/pyHiM/.venv/bin/activate

Install using conda/mamba

Follow the Miniconda instructions: Installing miniconda

Or follow these instructions to install mamba:

curl -Ls https://micro.mamba.pm/api/micromamba/$(uname)-$(uname -m)/latest | tar -xvj bin/micromamba

Automatically configure pyHiM

Run this command in your terminal:

cd $HOME/Repositories/pyHiM
conda env create -f environment.yml

or

cd $HOME/Repositories/pyHiM
mamba env create -f environment.yml

Verify GPU recognition

After activating the conda environment, you can verify that TensorFlow detects your GPU by starting Python and running:

import tensorflow as tf
print(tf.__version__)
print("Built with CUDA:", tf.test.is_built_with_cuda())
print("Built with GPU support:", tf.test.is_built_with_gpu_support())
print("Visible GPUs:", tf.config.list_physical_devices("GPU"))

Selecting a GPU device

If your machine has multiple GPUs, you can select which one pyHiM uses by setting CUDA_VISIBLE_DEVICES before launching the application. For example, to use device 2 on a machine with three GPUs, run the following in your shell prior to starting pyHiM:

export CUDA_VISIBLE_DEVICES=2

This environment variable limits visibility to the specified device index, letting you choose a different GPU than the default.

Note

If you get this error: ImportError: Dask\'s distributed scheduler is not installed.

You solve by running pip install dask[complete] distributed --upgrade.

Install traceratops

If you installed pyHiM using uv you can ignore this section as the bash script already installs traceratops.

Otherwise run one of the following:

Latest master version [stable]

conda activate pyhim39
cd $HOME/Repositories
git clone https://github.com/pyHi-M/traceratops.git
cd $HOME/Repositories/traceratops
pip install -e .

Latest dev version [stable]

conda activate pyhim39
cd $HOME/Repositories
git clone git@github.com:pyHi-M/traceratops.git
cd $HOME/Repositories/traceratops
pip install -e ".[dev]"

Install apifish

If you installed pyHiM using uv you can ignore this section as the bash script already installs traceratops.

Otherwise run one of the following:

  1. Navigate where you want install apifish

cd $HOME/Repositories

  1. Choose your clone method between HTTPS or SSH key:

    • HTTPS

      git clone https://github.com/apiFISH/apiFISH.git

    • SSH

      git clone git@github.com:apiFISH/apiFISH.git

  2. Switch on development branch

cd apiFISH && git checkout development

  1. Update PYTHONPATH env variable by adding the following line to your local ~/.bashrc

export PYTHONPATH="$PYTHONPATH:$HOME/Repositories/apiFISH"

Test pyHiM

  • The tests use the pytest module.

  • The test resources are inside pyhim-small-dataset. It’s a sub-module of pyHiM, so to get the dataset you need to run:

    • git submodule update --init --recursive OR

    • git clone --recurse-submodules <HTTPS/SSH>

  • To run the tests:

Using uv

If you used uv to install pyHiM run:

cd $HOME/Repositories/pyHiM
source .venv/bin/activate
pytest tests/ -vv

Using conda

If you used conda to install pyHiM run:

cd ~Repositories/pyHiM/
conda activate pyhim39
pytest tests/ -vv

Using mamba

If you used mamba to install pyHiM run:

cd ~Repositories/pyHiM/
mamba activate pyhim39
pytest tests/ -vv

Additional installation to generate documentation

conda install sphinx
conda install -c conda-forge myst-parser
conda install -c conda-forge sphinxcontrib-mermaid
conda install -c conda-forge sphinx-panels
conda install -c conda-forge sphinx_rtd_theme

Update PYTHONPATH env variable, for fileProcessing scripts documentation, by adding the following line to your local ~/.bashrc

export PYTHONPATH="$PYTHONPATH:$HOME/Repositories/pyHiM/src/fileProcessing"

Build documentation locally

Install in your conda env:

pip install nbsphinx ipython sphinx-book-theme
conda install pandoc

Generate documentation:

cd docs/
make html

A build/html/ folder has been created with a index.html file inside, open it with your favorite browser.

Script installation for super-computer centers (e.g. Meso-LR)

To access the private repository of pyHiM, please first create an SSH key and put it in your keyring. Follow the steps described here.

Then run the following automatic script:

#!/bin/bash

# load conda
module load  python/Anaconda/3-5.1.0

# create environment and install packages
conda create --name pyHiM python=3.7.2 dask numpy matplotlib astropy scikit-learn pandas
conda activate pyHiM
conda install photutils -c astropy
pip install mrc roipoly tqdm stardist csbdeep pympler
pip install --upgrade tensorflow

# api-fish
cd $HOME/Repositories
git clone git@github.com:apiFISH/apiFISH.git
cd apifish && git checkout development
echo 'export PYTHONPATH="$PYTHONPATH:$HOME/Repositories/apiFISH"'  >> ~/.bashrc

# clone pyHiM
cd $HOME/Repositories
git clone git@github.com:pyHi-M/pyHiM.git
git checkout development

# settings
ln -s $HOME/Repositories/pyHiM/src/toolbox/file_handling/cleanHiM_run.py $HOME/bin/cleanHiM

Step to setup pre-commit in local

  • environment installation pip install pre-commit

  • check if it’s well installed pre-commit --version

  • install command of the file “.pre-commit-config.yaml” inside “.git/hooks/pre-commit” pre-commit install

  • fix strange issue or warning pre-commit autoupdate --repo https://github.com/pre-commit/pre-commit-hooks

  • test pre-commit without any commit pre-commit run --all-files

  • Update pre-commit file

    • pre-commit clean

    • pre-commit autoupdate

    • pre-commit install