pyjamas-rfglab 2022.5.1

PyJAMAS is Just A More Awesome SIESTA

PyJAMAS

PyJAMAS is Just A More Awesome Siesta.

Documentation

You can find the official PyJAMAS documentation, with detailed installation instructions, here.

Installing PyJAMAS

The easiest way to install PyJAMAS is using PyPi.

Note that for MacOS, there is also an experimental PyJAMAS app that you can download here.

A note on the Python interpreter

PyJAMAS requires that you have Python installed.

PyJAMAS has been extensively tested with Python 3.8 and 3.9.

PyJAMAS does NOT work with Python 2.

MacOS and Linux

Open a terminal. If you had previously installed PyJAMAS, we recommend uninstalling the previous version:

To install PyJAMAS, type:

$ python3 -m pip install --no-cache-dir -U pyjamas-rfglab

To run PyJAMAS, type:

$ pyjamas

at the user prompt.

If the executable fails to run, you can also try to execute PyJAMAS by opening a terminal and typing:

$ python3 -m pyjamas.pjscore

Apple M1

PyJAMAS can take advantage of the new Apple M1 processors and their GPU capabilities. However, this requires a different installation process using miniconda.

First, install miniconda. In contrast to pip, miniconda can install packages running natively on the M1 architecture.

Install Tensorflow:

$ conda install -c apple tensorflow

Then install Jupyter:

$ conda install -c conda-forge jupyter jupyterlab

And finally install the rest of packages necessary for PyJAMAS to run:

$ conda install openblas pyqt joblib lxml pandas scikit-image scikit-learn seaborn shapely opencv

The last step is to install PyJAMAS:

$ python -m pip install --no-deps pyjamas-rfglab

GPU support on the Apple M1

PyJAMAS supports the use of the GPU on the M1 chip. To do this, install tensorflow-metal (version 0.2 if you are on Big Sur - OSX 11, version 0.3 if you are on Monterey - OSX 12). For example, on Big Sur:

$ python -m pip install tensorflow-metal==0.2

Windows

Before installing PyJAMAS, you will need to install Shapely, a package used in PyJAMAS to represent geometric objects such as points or polygons. Under Windows, Shapely fails to install with the PyJAMAS PyPi package. It is recommended to start by manually installing Shapely. To that end, download the appropriate Shapely version from this link. For example, use Shapely‑1.6.4.post2‑cp37‑cp37m‑win_amd64.whl for a 64-bit machine running Python 3.7. Open a command prompt and navigate to the folder that contains the downloaded .whl file using the cd command. Complete the installation of Shapely by typing:

$ python -m pip install Shapely‑1.6.4.post2‑cp37‑cp37m‑win_amd64.whl

substituting the downloaded file name. Note that, depending on your Python installation, the executable for the Python interpreter might be py.

Once Shapely, has been set up, you can proceed with a regular PyPi installation of PyJAMAS. Open a command prompt and type:

$ python -m pip install --no-cache-dir -U pyjamas-rfglab

To run PyJAMAS type:

$ pyjamas

at the user prompt.

If the executable fails to run, you can also try to execute PyJAMAS by opening a command prompt and typing:

$ python -m pyjamas.pjscore

GPU support under Windows

PyJAMAS supports the use of CUDA -based GPUs in Windows. Please, check here for instructions on how to configure your system. Briefly:

1. Download and install the NVIDIA GPU drivers.

2. Download and install the CUDA Toolkit.

3. Download and install the cuDNN SDK (https://developer.nvidia.com/cudnn and https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html).

Known problems: CUDA and cuDNN are picky with the version of each other that they talk to. If PyJAMAS displays an error that cusolver64_10.dll is not found:

1. Go to the folder C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\V11.2\bin (replacing V11.2 by whichever version you installed).

2. Create a copy of the file cusolver64_11.dll.

3. Rename the copy as cusolver64_10.dll.

Citing PyJAMAS

If you use PyJAMAS, please cite:

Fernandez-Gonzalez R, Balaghi N, Wang K, Hawkins R, Rothenberg K, McFaul C, Schimmer C, Ly M, do Carmo A, Scepanovic G, Erdemci-Tandogan G, Castle V. PyJAMAS: open-source, multimodal segmentation and analysis of microscopy images. Bioinformatics. 2021 Aug 13:btab589. doi: 10.1093/bioinformatics/btab589.