=================
Cryoassess plugin
=================
This plugin provides a wrapper for `Cryoassess <https://github.com/cianfrocco-lab/Automatic-cryoEM-preprocessing>`_ software tools for automatic micrograph and 2D classes assessment.
.. image:: https://img.shields.io/pypi/v/scipion-em-cryoassess.svg
:target: https://pypi.python.org/pypi/scipion-em-cryoassess
:alt: PyPI release
.. image:: https://img.shields.io/pypi/l/scipion-em-cryoassess.svg
:target: https://pypi.python.org/pypi/scipion-em-cryoassess
:alt: License
.. image:: https://img.shields.io/pypi/pyversions/scipion-em-cryoassess.svg
:target: https://pypi.python.org/pypi/scipion-em-cryoassess
:alt: Supported Python versions
.. image:: https://img.shields.io/sonar/quality_gate/scipion-em_scipion-em-cryoassess?server=https%3A%2F%2Fsonarcloud.io
:target: https://sonarcloud.io/dashboard?id=scipion-em_scipion-em-cryoassess
:alt: SonarCloud quality gate
.. image:: https://img.shields.io/pypi/dm/scipion-em-cryoassess
:target: https://pypi.python.org/pypi/scipion-em-cryoassess
:alt: Downloads
Installation
-------------
You will need to use 3.0+ version of Scipion to be able to run these protocols. To install the plugin, you have two options:
a) Stable version
.. code-block::
scipion installp -p scipion-em-cryoassess
b) Developer's version
* download repository
.. code-block::
git clone -b devel https://github.com/scipion-em/scipion-em-cryoassess.git
* install
.. code-block::
scipion installp -p /path/to/scipion-em-cryoassess --devel
Cryoassess software will be installed automatically with the plugin but you can also use an existing installation by providing *CRYOASSESS_ENV_ACTIVATION* (see below).
You also have to download training models separately (see below).
**Important:** you need to have conda (miniconda3 or anaconda3) pre-installed to use this program.
Configuration variables
-----------------------
*CONDA_ACTIVATION_CMD*: If undefined, it will rely on conda command being in the
PATH (not recommended), which can lead to execution problems mixing scipion
python with conda ones. One example of this could can be seen below but
depending on your conda version and shell you will need something different:
CONDA_ACTIVATION_CMD = eval "$(/extra/miniconda3/bin/conda shell.bash hook)"
*CRYOASSESS_ENV_ACTIVATION* (default = conda activate cryoassess-1.0.0):
Command to activate the cryoassess environment.
The deep-learning models can be downloaded from
`authors' website <https://cosmic-cryoem.org/software/cryo-assess/>`_ and the folder with models is set with:
*CRYOASSESS_MODELS* (default = software/em/cryoassess-models)
Verifying
---------
To check the installation, simply run the following Scipion test:
``scipion test cryoassess.tests.test_protocols_cryoassess.TestCryoassess``
Supported versions
------------------
1.0.0
Protocols
----------
* assess micrographs
* assess 2D classes
References
-----------
1. High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines. Yilai Li, Jennifer N.Cash, John J.G. Tesmer, Michael A.Cianfrocco. Structure 2020, Volume 28 (7), Pages 858-869.e3
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"description": "=================\nCryoassess plugin\n=================\n\nThis plugin provides a wrapper for `Cryoassess <https://github.com/cianfrocco-lab/Automatic-cryoEM-preprocessing>`_ software tools for automatic micrograph and 2D classes assessment.\n\n.. image:: https://img.shields.io/pypi/v/scipion-em-cryoassess.svg\n :target: https://pypi.python.org/pypi/scipion-em-cryoassess\n :alt: PyPI release\n\n.. image:: https://img.shields.io/pypi/l/scipion-em-cryoassess.svg\n :target: https://pypi.python.org/pypi/scipion-em-cryoassess\n :alt: License\n\n.. image:: https://img.shields.io/pypi/pyversions/scipion-em-cryoassess.svg\n :target: https://pypi.python.org/pypi/scipion-em-cryoassess\n :alt: Supported Python versions\n\n.. image:: https://img.shields.io/sonar/quality_gate/scipion-em_scipion-em-cryoassess?server=https%3A%2F%2Fsonarcloud.io\n :target: https://sonarcloud.io/dashboard?id=scipion-em_scipion-em-cryoassess\n :alt: SonarCloud quality gate\n\n.. image:: https://img.shields.io/pypi/dm/scipion-em-cryoassess\n :target: https://pypi.python.org/pypi/scipion-em-cryoassess\n :alt: Downloads\n\nInstallation\n-------------\n\nYou will need to use 3.0+ version of Scipion to be able to run these protocols. To install the plugin, you have two options:\n\na) Stable version\n\n.. code-block::\n\n scipion installp -p scipion-em-cryoassess\n\nb) Developer's version\n\n * download repository\n\n .. code-block::\n\n git clone -b devel https://github.com/scipion-em/scipion-em-cryoassess.git\n\n * install\n\n .. code-block::\n\n scipion installp -p /path/to/scipion-em-cryoassess --devel\n\nCryoassess software will be installed automatically with the plugin but you can also use an existing installation by providing *CRYOASSESS_ENV_ACTIVATION* (see below).\nYou also have to download training models separately (see below).\n\n**Important:** you need to have conda (miniconda3 or anaconda3) pre-installed to use this program.\n\nConfiguration variables\n-----------------------\n\n*CONDA_ACTIVATION_CMD*: If undefined, it will rely on conda command being in the\nPATH (not recommended), which can lead to execution problems mixing scipion\npython with conda ones. One example of this could can be seen below but\ndepending on your conda version and shell you will need something different:\nCONDA_ACTIVATION_CMD = eval \"$(/extra/miniconda3/bin/conda shell.bash hook)\"\n\n*CRYOASSESS_ENV_ACTIVATION* (default = conda activate cryoassess-1.0.0):\nCommand to activate the cryoassess environment.\n\nThe deep-learning models can be downloaded from\n`authors' website <https://cosmic-cryoem.org/software/cryo-assess/>`_ and the folder with models is set with:\n\n*CRYOASSESS_MODELS* (default = software/em/cryoassess-models)\n\nVerifying\n---------\n\nTo check the installation, simply run the following Scipion test:\n\n``scipion test cryoassess.tests.test_protocols_cryoassess.TestCryoassess``\n\nSupported versions\n------------------\n\n1.0.0\n\nProtocols\n----------\n\n* assess micrographs\n* assess 2D classes\n\nReferences\n-----------\n\n1. High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines. Yilai Li, Jennifer N.Cash, John J.G. Tesmer, Michael A.Cianfrocco. Structure 2020, Volume 28 (7), Pages 858-869.e3\n\n\n",
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