[](https://opensource.org/licenses/GPL-3.0)
# PyJAMAS
[**Py**JAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) is **J**ust **A** **M**ore **A**wesome **S**iesta.
## Documentation
You can find the official [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) documentation, with detailed
installation instructions, [**here**](https://pyjamas.readthedocs.io).
## Installing PyJAMAS
The easiest way to install [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) is using the
[**app**](https://www.quantmorph.ca/software/pyjamas-python) available for MacOS (Intel and M1 processors) and Windows
10.
You can also install [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/)
using [PyPi](https://pypi.org/project/pyjamas-rfglab/).
### A note on the *Python interpreter*
[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) requires that you
have [Python](https://www.python.org/downloads/) installed.
[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) has been extensively tested
with [Python 3.8 and 3.9](https://www.python.org/downloads/).
[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) does **NOT** work with Python 2.
### MacOS and Linux
Open a terminal. If you had previously installed [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/), we
recommend uninstalling the previous version:
To install [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/), type:
$ python3 -m pip install --no-cache-dir -U pyjamas-rfglab
To run [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/), type:
$ pyjamas
at the user prompt.
If the executable fails to run, you can also try to execute [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) by opening a terminal and typing:
$ python3 -m pyjamas.pjscore
### Apple M1
[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) can take advantage of the new Apple M1 processors and their GPU capabilities. However, this requires a different installation process using [miniconda](https://docs.conda.io/en/latest/miniconda.html).
First, install [miniconda](https://docs.conda.io/en/latest/miniconda.html). In contrast to pip, [miniconda](https://docs.conda.io/en/latest/miniconda.html) 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](https://bitbucket.org/rfg_lab/pyjamas/src/master/) to run:
$ conda install openblas pyqt joblib lxml pandas scikit-image scikit-learn seaborn shapely opencv
The last step is to install [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/):
$ python -m pip install --no-deps pyjamas-rfglab
### GPU support on the Apple M1
[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) 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](https://bitbucket.org/rfg_lab/pyjamas/src/master/), you will need to install [Shapely](https://pypi.org/project/Shapely/), a package used in [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) to represent geometric objects such as points or polygons. Under Windows, [Shapely](https://pypi.org/project/Shapely/) fails to install with the [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) [PyPi](https://pypi.org/project/pyjamas-rfglab/) package. It is recommended to start by manually installing [Shapely](https://pypi.org/project/Shapely/). To that end, download the appropriate Shapely version from [this link](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely). 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](https://pypi.org/project/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](https://www.python.org/downloads/) installation, the executable for the [Python](https://www.python.org/downloads/) interpreter might be *py*.
Once [Shapely](https://pypi.org/project/Shapely/), has been set up, you can proceed with a regular [PyPi](https://pypi.org/project/pyjamas-rfglab/) installation of [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/). Open a command prompt and type:
$ python -m pip install --no-cache-dir -U pyjamas-rfglab
To run [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) type:
$ pyjamas
at the user prompt.
If the executable fails to run, you can also try to execute [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) by opening a command prompt and typing:
$ python -m pyjamas.pjscore
#### GPU support under Windows
[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) supports the use of [CUDA](https://developer.nvidia.com/cuda-toolkit-archive/) -based GPUs in Windows. Please, check [here](https://tensorflow/install/gpu) for instructions on how to configure your system. Briefly:
1. Download and install the [NVIDIA GPU drivers](https://www.nvidia.com/drivers).
2. Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).
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](https://bitbucket.org/rfg_lab/pyjamas/src/master/) 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](https://bitbucket.org/rfg_lab/pyjamas/src/master/), 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.
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"description": "\n[](https://opensource.org/licenses/GPL-3.0)\n\n# PyJAMAS\n\n[**Py**JAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) is **J**ust **A** **M**ore **A**wesome **S**iesta.\n\n## Documentation\n\nYou can find the official [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) documentation, with detailed\ninstallation instructions, [**here**](https://pyjamas.readthedocs.io).\n\n## Installing PyJAMAS\n\nThe easiest way to install [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) is using the\n[**app**](https://www.quantmorph.ca/software/pyjamas-python) available for MacOS (Intel and M1 processors) and Windows\n10.\n\nYou can also install [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/)\nusing [PyPi](https://pypi.org/project/pyjamas-rfglab/).\n\n### A note on the *Python interpreter*\n\n[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) requires that you\nhave [Python](https://www.python.org/downloads/) installed.\n\n[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) has been extensively tested\nwith [Python 3.8 and 3.9](https://www.python.org/downloads/).\n\n[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) does **NOT** work with Python 2.\n\n### MacOS and Linux\n\nOpen a terminal. If you had previously installed [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/), we\nrecommend uninstalling the previous version:\n\nTo install [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/), type: \n\n $ python3 -m pip install --no-cache-dir -U pyjamas-rfglab\n\nTo run [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/), type: \n\n $ pyjamas\n\nat the user prompt.\n\nIf the executable fails to run, you can also try to execute [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) by opening a terminal and typing:\n\n $ python3 -m pyjamas.pjscore\n\n### Apple M1\n[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) can take advantage of the new Apple M1 processors and their GPU capabilities. However, this requires a different installation process using [miniconda](https://docs.conda.io/en/latest/miniconda.html).\n\nFirst, install [miniconda](https://docs.conda.io/en/latest/miniconda.html). In contrast to pip, [miniconda](https://docs.conda.io/en/latest/miniconda.html) can install packages running natively on the M1 architecture.\n\nInstall Tensorflow:\n\n $ conda install -c apple tensorflow\n\nThen install Jupyter:\n\n $ conda install -c conda-forge jupyter jupyterlab\n\nAnd finally install the rest of packages necessary for [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) to run:\n\n $ conda install openblas pyqt joblib lxml pandas scikit-image scikit-learn seaborn shapely opencv\n\nThe last step is to install [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/):\n\n $ python -m pip install --no-deps pyjamas-rfglab\n\n### GPU support on the Apple M1\n[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) 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:\n\n $ python -m pip install tensorflow-metal==0.2\n\n### Windows\nBefore installing [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/), you will need to install [Shapely](https://pypi.org/project/Shapely/), a package used in [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) to represent geometric objects such as points or polygons. Under Windows, [Shapely](https://pypi.org/project/Shapely/) fails to install with the [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) [PyPi](https://pypi.org/project/pyjamas-rfglab/) package. It is recommended to start by manually installing [Shapely](https://pypi.org/project/Shapely/). To that end, download the appropriate Shapely version from [this link](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely). For example, use Shapely\u20111.6.4.post2\u2011cp37\u2011cp37m\u2011win_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](https://pypi.org/project/Shapely/) by typing:\n\n $ python -m pip install Shapely\u20111.6.4.post2\u2011cp37\u2011cp37m\u2011win_amd64.whl\n\nsubstituting the downloaded file name. Note that, depending on your [Python](https://www.python.org/downloads/) installation, the executable for the [Python](https://www.python.org/downloads/) interpreter might be *py*. \n\nOnce [Shapely](https://pypi.org/project/Shapely/), has been set up, you can proceed with a regular [PyPi](https://pypi.org/project/pyjamas-rfglab/) installation of [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/). Open a command prompt and type: \n\n $ python -m pip install --no-cache-dir -U pyjamas-rfglab\n\nTo run [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) type: \n\n $ pyjamas\n\nat the user prompt. \n\nIf the executable fails to run, you can also try to execute [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) by opening a command prompt and typing:\n\n $ python -m pyjamas.pjscore\n\n#### GPU support under Windows\n[PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) supports the use of [CUDA](https://developer.nvidia.com/cuda-toolkit-archive/) -based GPUs in Windows. Please, check [here](https://tensorflow/install/gpu) for instructions on how to configure your system. Briefly:\n\n1. Download and install the [NVIDIA GPU drivers](https://www.nvidia.com/drivers).\n \n2. Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).\n\n3. Download and install the cuDNN SDK (https://developer.nvidia.com/cudnn and https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html).\n\n**Known problems**: CUDA and cuDNN are picky with the version of each other that they talk to. If [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/) displays an error that cusolver64_10.dll is not found:\n\n1. Go to the folder C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\V11.2\\bin (replacing V11.2 by whichever version you installed).\n\n2. Create a copy of the file cusolver64_11.dll.\n\n3. Rename the copy as cusolver64_10.dll.\n\n#### Citing PyJAMAS\nIf you use [PyJAMAS](https://bitbucket.org/rfg_lab/pyjamas/src/master/), please cite:\n\nFernandez-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.\n\n",
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