Saravanan666/pypi-org huggingface dataset:
Package: cgatshowcase
# cgat-showcasecgat-showcase is a repository containing an example pipeline constructed to demonstrate how the [cgat-core](https://github.com/cgat-developers/cgat-core) workflow management system can be used to create common workflows required in bioinformatics analysis.Within this repository is an example [pipeline](https://github.com/cgat-developers/cgat-showcase/blob/master/cgatshowcase/pipeline_transdiffexprs.py)pipeline_transdiffexprs.pythat performs pseudoalignment of fastq files
with [kallisto](https://pachterlab.github.io/kallisto/about.html) and differential expression using [DESeq2](https://www.bioconductor.org/packages/release/bioc/html/DESeq2.html). It can be run locally on your own machine or distributed across a cluster depending on your requirements.Documentation on how to run this pipeline can be found [here](https://cgat-showcase.readthedocs.io/en/latest/) and documentation on how
to build your own custom workflow from scratch can be found [here](https://cgat-core.readthedocs.io/en/latest/defining_workflow/Tutorial.html).InstallationThe following sections describe how to install the cgat-showcase pipeline.We recommend installing using conda and the steps are described below:`conda install -c cgat cgatshowcase`Alternatively, the pipeline can be installed using pip:`pip install cgatshowcase`However, you will require certain software to run the pipeline. More detail on installation can be found on the [Installation](https://cgat-showcase.readthedocs.io/en/latest/getting_started/Installation.html) documentation.
Package: cg-atv2-python-insert
cg_atv2_python_insertThis package provides functionality to parse a Python script, identify specific
'magic comments', and substitute these comments with code from other files.
It's designed to be used in environments where code templates and supplementary
files are involved, such as automated grading or code templating systems.The script supports command-line interaction, allowing users to specify the
file to be processed. It then reads through the specified Python script, looks
for magic comments in the format# CG_INSERT filename, and replaces these
comments with the contents of the referenced file, executing it as part of the
script.The file to be processed cannot contain any call tosys.exit(),quit(),exit(),os._exit()or any method that raises theSystemExiterror.
This will raise aValueErrorif the filled in template is then run.Usage:Run the script from the command line with the filename as an argument../example.pyfoo=4# CG_INSERT other.pyprint(foo+bar)./other.pybar=3Runningpython -m cg_atv2_python_insert example.pyresults in the following file being generated./filled_example.pyfoo = 4

try:
    exec(compile(open('other.py').read(), 'other.py', 'exec'), globals())
except SystemExit as e:
    raise ValueError("Call not allowed") from e

print (foo+bar)If the filled template is then run, the result printed on screen will be7Module contains:CLI To generate a python file where each magic comment is substituted by the correct exec call.Limitation:This module is not intended to handle complex substitutions or manage
dependencies between inserted scripts. The magic comment needs to be placed at the top level,
outside of any function or class.
Package: cgc-cli
CGC-CliWorks on Linux and OS X.Requirements Linux:
Docker==1.7.* (need to support api version 1.19)Install:
- install docker and add your user to docker group
- pip install cgc-cliRequirements OS X:
docker-machine (need to support api version 1.19)Install:
- install boot2docker
- boot2docker start
- pip install cgc-cliRunning:
- make sure that boot2docker is running before using cgc-cli (boot2docker start)
Package: cgcgnet
CGCGNetCoarse-grained crystal graph neural networksInstallationWe strongly recommend theAnacondadistribution of Python (3.10 release is required). You can create and activate a new conda environment with commandsconda create --name cgcgnet python=3.10
conda activate cgcgnet
conda install ipykernel openbabelCGCGNet can be installed via the following commandpip install cgcgnet
Package: cgcloud-mesos
The CGCloud plugin for Mesos lets you setup a fully configured Apache Mesos
cluster in EC2 in just minutes, regardless of the number of nodes.PrerequisitesThecgcloud-mesospackage requires that thecgcloud-corepackage and
itsprerequisitesare present.InstallationRead the entire section before pasting any commands and ensure that all
prerequisites are installed. It is recommended to install this plugin into the
virtualenv you created for CGCloud:source ~/cgcloud/bin/activate
pip install cgcloud-mesosIf you getDistributionNotFound: No distributions matching the version forcgcloud-mesos, try runningpip install--precgcloud-mesos.Be sure toconfigurecgcloud-corebefore proceeding.ConfigurationModify your.profileor.bash_profileby adding the following line:export CGCLOUD_PLUGINS="cgcloud.mesos:$CGCLOUD_PLUGINS"Login and out (or, on OS X, start a new Terminal tab/window).Verify the installation by running:cgcloud list-rolesThe output should include themesos-boxrole.UsageCreate a singlet2.microbox to serve as the template for the cluster
nodes:cgcloud create -IT mesos-boxTheIoption stops the box once it is fully set up and takes an image (AMI)
of it. TheToption terminates the box after that.Now create a cluster by booting a master and the slaves from that AMI:cgcloud create-cluster mesos -s 2 -t m3.largeThis will launch a master and two slaves using them3.largeinstance type.SSH into the master:cgcloud ssh mesos-master… or the first slave:cgcloud ssh -o 0 mesos-slave… or the second slave:cgcloud ssh -o 1 mesos-slave
Package: cgcloud-spark
The CGCloud plugin for Spark lets you setup a fully configured Apache
Spark cluster in EC2 in just minutes, regardless of the number of nodes. While
Apache Spark already comes with a script calledspark-ec2that lets you
build a cluster in EC2, CGCloud Spark differs fromspark-ec2in the
following ways:Tachyon or Yarn are not includedSetup time does not scale linearly with the number of nodes. Setting up a 100
node cluster takes just as long as setting up a 10 node cluster (2-3 min, as
opposed to 45min withspark-ec2). This is made possible by baking all
required software into a single AMI. All slave nodes boot up concurrently and
join the cluster autonomously in just a few minutes.Unlike withspark-ec2, the cluster can be stopped and started via the EC2
API or the EC2 console, without involvement of cgcloud.The Spark services (master and worker) run as an unprivileged user, not root
as with spark-ec2. Ditto for the HDFS services (namenode, datanode and
secondarynamenode).The Spark and Hadoop services are started automatically as the instance boots
up, via a regular init script.Nodes can be added easily, simply by booting up new instances from the AMI.
They will join the cluster automatically. HDFS may have to be rebalanced
after that.You can customize the AMI that cluster nodes boot from by subclassing the
SparkMaster and SparkSlave classes.CGCloud Spark uses the CGCLoud Agent which takes care of maintaining a list
of authorized keypairs on each node.CGCloud Spark is based on the official Ubuntu Trusty 14.04 LTS, not the
Amazon Linux AMI.PrerequisitesThecgcloud-sparkpackage requires that thecgcloud-corepackage and
itsprerequisitesare present.InstallationRead the entire section before pasting any commands and ensure that all
prerequisites are installed. It is recommended to install this plugin into the
virtualenv you created for CGCloud:source ~/cgcloud/bin/activate
pip install cgcloud-sparkIf you getDistributionNotFound: No distributions matching the version forcgcloud-spark, try runningpip install--precgcloud-spark.Be sure toconfigurecgcloud-corebefore proceeding.ConfigurationModify your.profileor.bash_profileby adding the following line:export CGCLOUD_PLUGINS="cgcloud.spark:$CGCLOUD_PLUGINS"Login and out (or, on OS X, start a new Terminal tab/window).Verify the installation by running:cgcloud list-rolesThe output should include thespark-boxrole.UsageCreate a singlet2.microbox to serve as the template for the cluster
nodes:cgcloud create -IT spark-boxTheIoption stops the box once it is fully set up and takes an image (AMI)
of it. TheToption terminates the box after that.Now create a cluster by booting a master and the slaves from that AMI:cgcloud create-cluster spark -s 2 -t m3.largeThis will launch a master and two slaves using them3.largeinstance type.SSH into the master:cgcloud ssh spark-master… or the first slave:cgcloud ssh -o 0 spark-slave… or the second slave:cgcloud ssh -o 1 spark-slave
Package: cgcloud-toil
The CGCloud plugin for Toil lets you setup a fully configured Toil/Mesos
cluster in EC2 in just minutes, regardless of the number of nodes.PrerequisitesThecgcloud-toilpackage requires that thecgcloud-corepackage and
itsprerequisitesare present.InstallationRead the entire section before pasting any commands and ensure that all
prerequisites are installed. It is recommended to install this plugin into the
virtualenv you created for CGCloud:source ~/cgcloud/bin/activate
pip install cgcloud-toilIf you getDistributionNotFound: No distributions matching the version forcgcloud-toil, try runningpip install--precgcloud-toil.Be sure toconfigurecgcloud-corebefore proceeding.ConfigurationModify your.profileor.bash_profileby adding the following line:export CGCLOUD_PLUGINS="cgcloud.toil:$CGCLOUD_PLUGINS"Login and out (or, on OS X, start a new Terminal tab/window).Verify the installation by running:cgcloud list-rolesThe output should include thetoil-boxrole.UsageCreate a singlet2.microbox to serve as the template for the cluster
nodes:cgcloud create -IT toil-boxTheIoption stops the box once it is fully set up and takes an image (AMI)
of it. TheToption terminates the box after that.Substitutetoil-latest-boxfortoil-boxif you want to use the latest
unstable release of Toil.Now create a cluster by booting a leader and the workers from that AMI:cgcloud create-cluster toil -s 2 -t m3.largeThis will launch a leader and two workers using them3.largeinstance type.SSH into the leader:cgcloud ssh toil-leader… or the first worker:cgcloud ssh -o 0 toil-worker… or the second worker:cgcloud ssh -o 1 toil-worker
Package: cgcomponents
CG ComponentsGetting StartedDependenciesYou need Python 3.7 or later to usecgcomponents.pip install setuptools
pip install wheel
pip install twineInstallationClone this repo to your local machine using:git clone https://github.com/ramirooliveiracg/cgcomponents.gitFeaturesFile structure for PyPI packagesSetup with package informationsLicense example
Package: cgdat
Conditional Game Data Analyse Tool (CGAT)This package contains a simple csv data analyse GUI which I created for a friend of mine to help him with his data analysis.InstallationInstallationInstall using the python package index (PyPi)OS X & Linuxpip install cgdatWarningThis package was only tested on the windows operation system.Windowspip install cgdatDevelopment setupIf you want to manually install the python package please fork fromgithuband run the following commands:python setup.py build
python setup.py develop
python setup.py installA overview of the CGDAT GUI.UsageThis package can both be imported as a python package or run as a stand alone gui (see fig 1). To import the python package useimport cgdat. To use as a stand alone package run thecgdat-guicmd.Tool interfaceFig 1: A overview of the CGDAT GUI.FunctionsThis repository can be used to perform a simple csv game data filtering. The tool can filter a input data file based on:Conditions:Example:Speed > 10 & Acceleration < 5A time section file:A file containing aStart TimeandEnd Timecolumn specifying the sections where you want to apply the condition filter.Player name:In the gui you can specify for which players you want to conduct the analysis. For this option to work theinput_data.csvfile needs to contain aNamecolumn.Further you can add also add a safety padding to the data filtering. Meaning that the program will also include a number of samples before and after the specified conditions are met.Console commandsThis package also installs some additional console commands:cgdat-gui- This will launch the CGDAT gui.cgdat-shortcut- This will create a shortcut to launch the GUI on your desktop folder.Further documentationAdditional documentation can be loaded in the GUI by clicking thedocumentationoption in the help menu or pressing theF2shortcut.Additional informationLicenceThis tool is licensed under aMIT license.MetaRick Staa -github pageContributingFork it (<https://github.com/rickstaa/CGDAT>)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am ‘Add some fooBar’)Push to the branch (git push origin feature/fooBar)Create a new Pull RequestContributorsCreated by Rick StaaMaintained by Wesley Bosman(wesleybosmann@gmail.com)CreditsCDAT icon created by FreePis fromwww.flaticon.com.
Package: cgecore
# cge_core_moduleCore module for the Center for Genomic EpidemiologyThis module contains classes and functions needed to run the service wrappers and pipeline scriptsThe pypi project can be found here:https://pypi.org/project/cgecore/# How to update:
1. Make changes to the modules
2. Bump the version number accordingly in cgecore/__init__.py
3. Install package locally
4. Test the changes locally (for both python2 and python3)
5. Distribute to Pypi# Install package locally
python2 setup.py installpython3 setup.py install# Distribute to PyPi
python3 setup.py sdist bdist_wheeltwine upload dist/*deprecated:~~python setup.py sdist upload -r pypi~~
Package: cgen
cgen offers a simple abstract syntax tree for C and related languages
(C++/CUDA/OpenCL) to allow structured code generation from Python.
To represent mathematical expressions, cgen can be used withpymbolic.Places on the web related to cgen:Python package index(download releases)Documentation(read how things work)Github(get latest source code, file bugs)cgen is licensed under the liberalMIT licenseand free for commercial, academic,
and private use. All of cgen’s dependencies can be automatically installed from
the package index after using:pip install cgen
Package: cgenerator
C container generator.ContentsRequirementsInstallationUsageLicenseRequirementspython >= 2.6InstallationC container generatorshould be installed using pip:pip install cgeneratorUsagecgenerator <type name> <output folder> <is custom>
cgenerator uint ../src/ False
cgenerator 'struct matrix' ./source/ TrueLicenseLicensed under aGPL license.Online documentationDocs.
Package: cgeo
CGEOThis project is a collection of several algorithms and applications of computational geometry.It is built for quick and easy use of well-known performance-oriented algorithms and important data structures in the field of computational geometry in any Python program.This is an ongoing project, some features are done and ready, and some are under construction.FeaturesYou can find the followong algorithms available:Convex HullGift-WrappingConvex Hull by Jarvis marchGrahm-ScanConvex Hull by Ronald GrahamDevide & Conquer Convex HullSegmentation IntersectionPolygon TriangulationShapes2D Basic ShapesPointLineSegmentPolygonBalanced Binary Segment TreeSmallest Enclosing DiscAll the algorithms are implemented using c++.
But a user can use this source code as a C++ extension for a Python program.The source code is wrapperd withpybind11,
and you can find multiple code-examples of python programs with the wrapped package via the followinglink.How To RunFor some running examples you can checkout theexamplesdirectory which has some cool examples for each class and algorithm. And you can also run each one of it by yourself.I suggest reading the relevant examples before trying to use the functionality by yourself.You can use this project in one of the following options:As a submodule within your repository with the following steps:Link the repository to your own repo withgit submodule add https://github.com/tomsabala/CGEO.Enter theCGEOdirectory.2.1) Update the pybind11 extension withgit submodule update --init.2.2) Build the package with./run.sh.Clone this repo, to your local machine:Clone repo:git clone git@github.com:tomsabala/CGEO.git.Enter theCGEOdirectory.2.1) Update the pybind11 extension withgit submodule update --init.2.2) Build the package with./run.sh.You can import the library in any*.pyfile withfrom CGEO.build import libGeo_2D.
In order to run yourfile.pyprogram:Set the path for user defined modules withexport PYTHONPATH=..Execute programpython file.py.::notice that therun.shshell script includes some linux os commands. If you are under windows OS, build the library using the suitable commands.After scanning dependencies and build are finish, you can use it as an extension in your own python environment.
Package: cgePy
cge 0.7.3A lightweight terminal graphics engine.For a list of changes in 0.7.3, visitcgepy.github.io/changelogLooking for a simple, lightweight package? Easily managable features?cgepy will handle that, with a sturdy system to build interfaces on top of.Current features:A flexible grid system where you can swap between multiple canvases.Custom coloring that is applicable to all textHigher resolution display capabilities vs. colored unicode full blocks.A basic sprite system for easier manipulation of the canvas.Upcoming features:The sprite system will be revamped entirely to allow systematic loading and manipulation of many points at once.A stroke function to eliminate repeated write operations, save time, and serve as a replacement for the old map system.Text will be added back as a more integrated feature that will come built into the existing grid framework.Note: cgepy has not been tested on windows, and  coloring may not appear due to how powershell handles ansi. This can be fixed via the colorama library.
Package: cgettext
This is a drop-in C replacement for Python’s gettext.GNUTranslations.  For our
fairly large set of languages and strings, it makes parsing 10x faster.Just import GNUTranslations from cgettext instead of gettext.You can safely install this module on PyPy and other implementations where a C
extension is undesirable, and it will simply re-export the built-in class.The repository includes a small module, test_cgettext, that runs the built-in
Python gettext tests with GNUTranslations monkeypatched.
Package: cg-feedback-helpers
cg_feedback_helpersThis package provides functionality to provide feedback messages. It
mainly provides the classAsserter. TheAsserterhas a number of
methods that are documented onthe official
docs, which allow
to run assertions. Each assertion can provide either positive or negative
feedback. A few helpers are also provided to aid with input and output
coupled when running assertions. At the end of a run, callAsserter::emit_successto guarantee the user receives some feedback
if everything was correct.The package outputs feedback in the following format:{"tag":"feedback","contents":[{"value":<yourfeedbackmessage>,"sentiment":<"positive"|"negative">},]}Usage:The following example shows how the asserter can be used to check that
the functiongreet_userresponds with the correct output to a user
input.fromcg_feedback_helpersimportasserter,helpersdefgreet_user():name=input()print(f"Hi{name}")withhelpers.capture_output()asbuffer,helpers.as_stdin("John"):greet_user()output=helpers.get_lines_from_buffer(buffer)asserter.has_length(output,1)asserter.equals(output[0],"John")asserter.emit_success()The output of which will be:{"tag":"feedback","contents":[{"value":"Got expected length 1","sentiment":"positive"}]}
{"tag":"feedback","contents":[{"value":"Got expected value Hi John","sentiment":"positive"}]}
{"tag":"feedback","contents":[{"value":"Everything was correct! Good job!","sentiment":"positive"}]}Module contains:Asserterclass, of which the default messages produced can be
configured, as well as its failure behavior (either using exceptions
orsys.exit);helpersto make it easier to work with input/output tests.Limitation:The module currently does not support markdown feedback, nor theneutralsentiment.
Package: cg-flatland
FlatlandEasier development of polymer structure prediction toolsThe development and debugging of modern neural network approaches for protein structure prediction has the potential to be a somewhat complicated task. Here we provide a framework, Flatland, for simulating training datasets of the form used by current state-of-the-art systems. The central task performed is to evolve a population of polymer sequences, predict their folded structure, produce distance matrix and bond angle tensors, and calculate spectra of per-polymer compound or other polymer interaction likelihoods (emulating protein-protein and compound-protein interaction datasets). This framework intentionally performs such simulations in a highly simplified manner that does not accurately model the real biological processes. Relatedly, it provides the means to simplify the problem to an arbitrary extent - including permitting the reduction from three to two dimensions, reducing the amino acid alphabet from twenty-one to three, and simplifying characteristics of the evolutionary optimization. We provide the framework together with a collection of starting-point notebooks for training neural network models to learn from such data by way of an open source repository to kick-start the development efforts of those interested in contributing to solving this vitally important problem.InstallationYou can install the last released version of Flatland via pippipinstallcg-flatlandOr install the most recent development version of Flatland using gitgitclonehttps://github.com/cayley-group/flatland
pipinstall-eflatlandDemonstrationsWe're putting together various demonstration notebooks which you'll be able to access via the following:Data GenerationStructure Solver Example