pyjamas-rfglab


Namepyjamas-rfglab JSON
Version 2022.7.1 PyPI version JSON
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home_pagehttps://bitbucket.org/rfg_lab/pyjamas
SummaryPyJAMAS is Just A More Awesome SIESTA
upload_time2022-07-03 01:17:04
maintainer
docs_urlNone
authorRodrigo Fernandez-Gonzalez
requires_python>=3.8
license
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            ![Logo](./docs/images/paperfigure_v6.png)
[![GPLv3 License](https://img.shields.io/badge/License-GPL%20v3-yellow.svg)](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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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. 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