Saravanan666/pypi-org huggingface dataset:
Package: c3pred
C3PredSoftware project of the iGEM Team Tübingen 2019
Prediction of cargo transport potential of short peptides.Free software: MIT licenseDocumentation:https://c3pred.readthedocs.io.Installation$gitclonehttps://github.com/steffenlem/c3pred.git$pythonsetup.pyinstall$c3predUsageThe CLI - Command Line InterfaceUsage:c3pred[OPTIONS]Consolescriptforc3pred.Options:-s,--sequenceTEXTIftheinputisaFASTAproteinstring,pleaseusethisflag-u,--uniprotTEXTIftheinputisaUniProtKBaccessionnumber,pleaseusethisflag-g,--igemTEXTIftheinputisaiGEMRegistryID,pleaseusethisflag--helpShowthismessageandexit.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.HistoryiGEMtuebingen (2019-10-16)First release on PyPI.
Package: c3s-magic-wps
c3s magic wpsWeb Processing Service for Climate Data Analysis in the MAGIC project. The software in this WPS powers the processes behind the C3S-MAGIC portal. The processes are run on CP4CDS infrastructure.Free software: Apache Software License 2.0Documentation:https://c3s-magic-wps.readthedocs.io.LinksClimate Data StoreC3S MAGIC PortalCP4CDS Quality ControlCreditsThis package was created withCookiecutterand thebird-house/cookiecutter-birdhouseproject template.CHANGELOG1.0.0rc1 (2019-06-06)First release candidate of stable release of c3s-magic-wps. A Web Processing Service for Climate Data Analysis in the MAGIC project. The software in this WPS powers the processes behind the C3S-MAGIC portal. The processes are run on CP4CDS infrastructure.
Package: c3s-sm
Reading and reshuffling of C3S soil moisture Written in Python.InstallationSetup of a complete environment withcondacan be performed using the following
commands:gitclonegit@github.com:TUW-GEO/c3s_sm.gitc3s_smcdc3s_smcondaenvcreate-fenvironment.ymlsourceactivatec3s_smTutorialsWe provide (general) tutorials on using the C3S Soil Moisture data:Tutorial 1: DataAccess from CDS & Anomaly computationThese tutorials are designed to run onmybinder.orgYou can find the code for all examples inthis repository.Supported ProductsAt the moment this package supports C3S soil moisture data
in netCDF format (reading and time series creation)
with a spatial sampling of 0.25 degrees.ContributeWe are happy if you want to contribute. Please raise an issue explaining what
is missing or if you find a bug. We will also gladly accept pull requests
against our master branch for new features or bug fixes.Development setupFor Development we also recommend acondaenvironment. You can create one
including test dependencies and debugger by runningconda env create-fenvironment.yml. This will create a newc3s_smenvironment which you can activate by usingsource activate c3s_sm.GuidelinesIf you want to contribute please follow these steps:Fork the c3s_sm repository to your accountClone the repository, make sure you usegit clone--recursiveto also get
the test data repository.make a new feature branch from the c3s_sm master branchAdd your featurePlease include tests for your contributions in one of the test directories.
We use py.test so a simple function called test_my_feature is enoughsubmit a pull request to our master branchNoteThis project has been set up using PyScaffold 2.5. For details and usage
information on PyScaffold seehttp://pyscaffold.readthedocs.org/.
Package: c3tools
c3toolsCollections of simple commands.Installpip install c3toolsIncluded packagesBasic Toolsipython==5.8.0                    # python 2 onlyipython                           # python 3 onlyNetwork Toolsudp-testtcpingmtcping                           # python 3 onlyserviceping                       # python 3 onlyecho-server>=0.1.3urltesting                        # python 3 onlySystem Toolsps2uname2python-abspathsshrun                            # python 3 onlyssh-get-sysinfo                   # python 3 onlyService Toolsipa-utilssftpdpython-sendmailkafka-admin-service               # python 3 onlykadminutils                       # python 3 onlydjango-crontab-manager            # python 3 onlydjango-crontab-agent              # python 3 onlyopenldapctrl                      # python 3 onlyFile Toolsfilesplitortailcopytruncateunzip-aes                         # python 3 onlyfilename-transencodinglogio                             # python 3 onlyAutomationbrowserctrl                       # python 3 onlyFile Converting Toolspdf-toolsmakegifgif-framesimagespackconvert-csv-to-xlsx               # python 3 onlygrayscale-image                   # python 3 onlyCodec Toolspwgenpython-json2csvpython-json2yamlhashtoolsqrimg>=0.2.0rot-codectransencodingOffice Toolsxlsx-splitxlsx-xargsDependenciesopenpyxl==2.6.4                   # python 2 onlyopenpyxl                          # python 3 onlynumpy==1.16.5                     # python 2 onlynumpy                             # python 3 onlyReleasesv0.8.0 2022/01/19Upgrade qrimg to 0.2.0.Add urltesting.Add logio.Add kafka-admin-service.Add kadminutils.Add django-crontab-manager and django-crontab-agent.Add openldapctrl.v0.7.0 2020/05/31Add browserctrl.v0.6.0 2020/05/23Add sshrun.Add ssh-get-sysinfo.v0.5.0 2020/04/09Add convert-csv-to-xlsx.Add mtcping.Add grayscale-image.Add python-abspath.v0.4.0 2020/03/27Add filename-transencoding.Add echo-server.v0.3.0 2020/03/11Add unzip-aes tool.v0.2.2 2019/12/02Fix install problem.Add imagespack tool.v0.2.0 2019/11/05Add python2 and python3 distinguish.v0.1.0 2019/08/10First release
Package: c3-toolset
C3 - An integrated tool-set for Control, Calibration and CharacterizationThe C3 package is intended to close the loop between open-loop control optimization, control pulse calibration, and model-matching based on calibration data.Installationpipinstallc3-toolsetIf you want to try out the bleeding edge (possibly buggy) version under development:pipinstallc3-toolset-nightlyUsageC3  provides a simple Python API through which it may integrate with virtually any experimental setup.
Contact us atc3@q-optimize.org.The paper introducing C3 as a concept can be found on thearxiv.Documentation is availablehereon RTD.Examples are available in theexamples/directory and can also be run online using thelaunch|binderbadge above.If you wish to contribute, please check out the issues tab and also theCONTRIBUTING.mdfor useful resources.The source code is available on Github athttps://github.com/q-optimize/c3.CitationIf you usec3-toolsetin your research, please cite it as below:@article{Wittler2021,
   title={Integrated Tool Set for Control, Calibration, and Characterization of Quantum Devices Applied to Superconducting Qubits},
   volume={15},
   DOI={10.1103/physrevapplied.15.034080},
   number={3},
   journal={Physical Review Applied},
   author={Wittler, Nicolas and Roy, Federico and Pack, Kevin and Werninghaus, Max and Saha Roy, Anurag and Egger, Daniel J. and Filipp, Stefan and Wilhelm, Frank K. and Machnes, Shai},
   year={2021},
   month={Mar}
}
Package: c3-toolset-nightly
C3 - An integrated tool-set for Control, Calibration and CharacterizationThe C3 package is intended to close the loop between open-loop control optimization, control pulse calibration, and model-matching based on calibration data.Installationpipinstallc3-toolsetIf you want to try out the bleeding edge (possibly buggy) version under development:pipinstallc3-toolset-nightlyThere is no official support forc3-toolseton Apple Silicon devices, but you can check theCONTRIBUTING.mdfor instructions on setting up an experimental version.UsageC3  provides a simple Python API through which it may integrate with virtually any experimental setup.
Contact us atc3@q-optimize.org.The paper introducing C3 as a scientific concept can be found inWittler2021. Software and implementation details are described inSahaRoy2022.Documentation is availablehereon RTD.ExamplesThe following notebooks are available in theexamples/directory and can also be run online using thelaunch|binderbadge above:Model-based Optimal Control for Single Qubit GateModel-based Optimal Control for Two Qubit Engtangling GateModel-free Calibration on Simulated HardwareSimulated Model Learning with data from Simulated CalibrationFull loop - Control, Calibration & CharacterizationMinimum example for using the Qiskit interfaceMinimum example for optimizing piece-wise constant pulsesUnderstanding the ParameterMapFrequency Dependent CouplingContributingIf you wish to contribute, please check out the issues tab and also theCONTRIBUTING.mdfor useful resources.The source code is available on Github athttps://github.com/q-optimize/c3.CitationIf you usec3-toolsetin your research, please cite it as below:@article{Wittler2021,
   title={Integrated Tool Set for Control, Calibration, and Characterization of Quantum Devices Applied to Superconducting Qubits},
   volume={15},
   DOI={10.1103/physrevapplied.15.034080},
   number={3},
   journal={Physical Review Applied},
   author={Wittler, Nicolas and Roy, Federico and Pack, Kevin and Werninghaus, Max and Saha Roy, Anurag and Egger, Daniel J. and Filipp, Stefan and Wilhelm, Frank K. and Machnes, Shai},
   year={2021},
   month={Mar}
}
Package: c3tt-rpc-client
Python Client Library for C3-Ticket-TrackerClient-Library forC3-Ticket-Tracker.ExampleGet the next Encoding-Ticket, work on it and set it Done:importosimportsocketfromsrc.c3tt_rpc_clientimportC3TTClientcli=C3TTClient(os.environ['CRS_TRACKER'],os.environ['CRS_TOKEN'],socket.gethostname(),os.environ['CRS_SECRET'])ticket=cli.assign_next_unassigned_for_state(ticket_type='encoding',to_state='encoding')ifticketisNone:print('No Ticket for encoding')else:print('Got Ticket for encoding:')print(ticket)print('Set Ticket done')cli.set_ticket_done(ticket)DeployingTo Deploy a new Version to pypi, run./upload_dist.sh. This willClean previous Build-ResultsSetup a venvInstall build/dist Tooling (built, twine, flake8)Lint the CodeCreate whl and tar.gz artifactsUpload them to pypi
Package: c45-decision-tree
C4.5 Decision Tree ClassifierIntroductionWhat is C4.5?C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. C4.5 builds decision trees from a set of training data in the same way as ID3, using the concept of information entropy. The training data is a set S = {s1, s2, s3, ... , sn} of already classified samples. Each sample si consists of a tuple (xi, ci) where xi is a vector of attributes and ci is the class. The algorithm recursively splits the set S into subsets Si using an attribute ai that maximizes the information gain (or minimizes the information entropy) of the resulting subsets. The splitting procedure stops when the algorithm has reached a predefined termination criterion, such as when all samples in the resulting subset Si belong to the same class, or when all attributes in the resulting subset Si have the same values. The resulting tree is then used to classify new unseen samples x by traversing the tree from root to leaf and assigning the class of the leaf to the sample.Top attributesIn this library, for top attribute use gain ratio instead of information gain. Gain ratio is a modification of information gain that reduces its bias for attributes with a large number of distinct values. Gain ratio is defined as:GainRatio(S, A) = Gain(S, A) / SplitInfo(S, A)whereSplitInfo(S, A) = - sum ( |Sv| / |S| ) * log2 ( |Sv| / |S| )and Sv is the subset of S for which attribute A has value v.RequirementsPython 3.6 or abovePandasNumpyImplementationInstallationpip install c45-decision-treeTrain ModelFor training model, you need to prepare data in pandas dataframe format. For train model you need to can callfitmethod with 2 parameters, first is data and second is target. For example:fromC45importC45Classifierimportpandasaspddata=pd.read_csv('data.csv')X=data.drop(['target'],axis=1)y=data['target']model=C45Classifier()model.fit(X,y)PredictFor predict data, you can callpredictmethod with 1 parameter, first is data. For example:data_test=pd.read_csv('data_test.csv')model.predict(data_test)EvaluateFor evaluate model, you can callevaluatemethod with 2 parameters, first is data and second is target. For example:data_test=pd.read_csv('data_test.csv')X_test=data_test.drop(['target'],axis=1)y_test=data_test['target']model.evaluate(X_test,y_test)Summary ModelFor summary model, you can callsummarymethod. For example:model.summary()Save and Load ModelFor save you can use pickle library. For example:importpicklewithopen('model.pkl','wb')asf:pickle.dump(model,f)model=pickle.load(open('model.pkl','rb'))Draw TreeFor draw tree use library graphviz and must be installed in your computer. For example:importgraphvizmodel.generate_tree_diagram(graphviz,"File Name")Example output:Write RulesFor write rules, you can callwrite_rulesmethod. For example:model.write_rules()
Package: c4-achitecture
<!– TABLE OF CONTENTS –>
## Table of Contents[Introduction](#introduction)[Architecture](#architecture)[Requirements](#requirements)[Installation](#installation)[Configuration](#configuration)[How to start](#how-to-start)[Usage](#usage)
* [Running prediction service locally](#running-prediction-service-locally)
* [Training model](#training-model)
* [Other usages](#other-usages)<!– INTRODUCTION –>
## IntroductionLorem ipsum dolor sit amet, consectetur adipiscing elit. :## Architecture![Architecture](c4/images/architecture_graph.png)<!– REQUIREMENTS –>
## RequirementsLorem ipsum dolor sit amet, consectetur adipiscing elit.Praesent suscipit dolor congue justo feugiat, et consectetur nulla varius.Phasellus a libero sagittis, imperdiet felis ac, imperdiet justo.<!– INSTALLATION –>
## InstallationLorem ipsum dolor sit amet, consectetur adipiscing elit.cmdPraesent suscipit dolor congue justo feugiat, et consectetur nulla varius.cmd<!– CONFIGURATION –>
## ConfigurationcmdConfigurable parameters:<!– HOW TO START –>
## How to startTo start the service, use:cmdStopping the service with:cmdIf the service crashes,cmd<!– USAGE EXAMPLES –>
## Usage### Running prediction service locallySed blandit tortor eu eleifend facilisis.cmdProin tristique arcu non rutrum dapibus.cmd### Training modelProin pretium nunc lobortis volutpat sagittis.cmdAenean et nisi sed massa pellentesque tristique.cmd### Other usagesLorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur et facilisis tellus, et pretium nibh. Vivamus condimentum mauris nec lacus gravida, quis feugiat augue consequat. Aenean nec libero et risus facilisis blandit id nec arcu. Donec eget augue quam. Proin ullamcorper sodales lectus et consectetur. Nulla non nunc elit. In condimentum suscipit volutpat. Praesent non ullamcorper lectus. Nulla non varius lorem, elementum pulvinar mi. Duis at mi elementum, ultricies tortor quis, elementum mauris.
Package: c4counter
c4counterThis skript takes fasta files containing human HLA regions and returns the number and types of C4 genes (C4A / C4B, HERV / no HERV). In addition, a simple graphical representation in svg format is generated (name: c4.svg)Dependenciesminimap2 is used for mappingInstallationpippip install c4counterplease make sure you have minimap2 installedbiocondaconda install c4counterconda will install minimap2 for you
Package: c4cs
A sample project that exists as an aid to thePython Packaging User Guide’sTutorial on Packaging and Distributing
Projects.This projects does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.This is the README file for the project.The file should use UTF-8 encoding and be written usingreStructuredText. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.
Package: c4dot5-decision-tree
C4.5 Decision TreeImplementation of the Quinlan's algorithm to train a decision tree and make inference.Installationpip install c4dot5-decision-treeUsageTo train a decision tree classifier, import the class DecisionTreeClassifier and call the .fit() method.
The training dataset must be a pandas DataFrame with a column namedtargetto identify the target classes of the classification.importpandasaspdfromc4dot5.DecisionTreeClassifierimportDecisionTreeClassifiertraining_dataset=pd.read_csv("https://raw.githubusercontent.com/piepor/C4.5-Decision-Trees/main/src/data_example/training_dataset.csv")attributes_map={"Outlook":"categorical","Humidity":"continuous","Windy":"boolean","Temperature":"continuous"}decision_tree=DecisionTreeClassifier(attributes_map)decision_tree.fit(training_dataset)To make predictions, simply use the .predict() methoddata_input=pd.DataFrame.from_dict({"Outlook":["sunny"],"Temperature":[65],"Humidity":[90],"Windy":[False]})prediction=decision_tree.predict(data_input)print(prediction)To visualize the decision tree use method .view(). It will show the tree and save in a folder (default to './figures')decision_tree.view(folder_name='figures',title='Quinlan-Tree')To extract the splitting rules of the decision tree use the method .get_rules()rules=decision_tree.get_rules()
Package: c4m-flexio
Flexible, scalable, IO libraryA IO library is a collection of cells that allows to do the input and output from on-chip signals to outside the chip. Typically these libraries with a fixed set of cells with a fixed layout and functionality and provided by the foundry or a third party.Purpose of c4m-flexio library is to have a library that is easily scalable between different technologies using thePDKMasterframework. It will also be configurable to adapt the cells to one's needs.Release historyv0.4.2:
Several features for IHP SG13G2 support: RCClamp in IOPadVdd, custom layout manipulation
suport, prefix all cell names.v0.4.1:
Implement layout without the bond pad included and pin that connects to the bond located at the
bottom of the cell. This also implements ESD using diodes between pad and iovdd/iovdd net.
This was done during development of IO library for the IHP sg13g2 process.v0.4.0:
started implemented a new layout where no bond pad is present in the IO cell layout. This is for a
new IO library for the IHP SG13g2 process. This new layout is still considered WIP but the old
layout with a bond pad included should still be available.This release was made to allow to release a new Sky130 library with latest state of the IO cells
and updated for the latest version of PDKMaster and c4m-flexcell.v0.3.1: no code change; mark IO cell code compatible with dev v0.4.0 of c4m-flexcellv0.3.0: Update forrelease v0.9.0 of PDKMasterno notes for older releasesStatusThis repository is currently considered experimental code with no backwards compatibility guarantees whatsoever. The library now progresses at the need of tape-outs.
If interested head over togitterfor further discussion.
Package: c4m-flexmem
Flexible, scalable memory macro generatorsIn a circuit design often on-chip memories are needed. These blocks are typically custom generated next to the standard cell libraries as building blocks out of standard cells will take too much area.Purpose of c4m-flexmem module is to have generators that are easily scalable between different technologies using thePDKMasterframework. Currently only SRAM generators are included but in the future it is planned to have other types like embedded DRAM, non-volatile, ROM etc.Release historyv0.1.0: Update forrelease v0.9.0 of PDKMasterno notes for older releasesStatusThis repository is currently considered experimental code with no backwards compatibility guarantees whatsoever. The library now progresses at the need of tape-outs.
If interested head over togitterfor further discussion.
Package: c4m-pdk-freepdk45
PDKMaster based FreePDK45 PDKThis is an example implementation of the non-manufacturableFreePDK45as aPDKMasterbased PDK under theChips4Makersumbrella. One of it's functions is to be a guide for making newPDKMasterPDKs for manufacturable processes.ReleasesThis packages is released in two different formats. First is a release on PyPI with only the python package included. Second is a tarball release on github. Next to the installable python package also contains the generated setup files for other tools and examples of how the PDKMaster FreePDK45 can be used.Current release is v0.0.1:PyPI releasetarball releaseSource repository overviewCurrently this repo lacks documentation. So, as an introduction to the package here an overview is given of the several parts in the repo with a short description:c4m/pdk/freepdk/: The PDKMaster Technology python module, including c4m-flexcell based standard cell library.Makefile,scripts/: generate several release files:copy of python moduleviews of the standard cells using the PDKMaster export functionalities:spice netlistverilog behavioral modelvhdl behavioral modelgds layoutsliberty file for librarymake_in_docker.py: Will run the building of everything from theMakefilein a docker prepared docker container. Currently the the docker image to download is 2.3GB in size.design/:portfolio.ipynb: Python notebook show (part of) the FreePDK45 contentarlet6502/*: Directory with Arlet's 6502 implementation and example flow of how to do synthesis and P&R using the FreePDK45 FlexLib standard cells library. When using it from a git checkout first the coriolis files will need to be built in the top directory. In the release package this will be included and not be needed.make_in_docker.sh: you can also here run the flow inside a docker container of not all tools are installed locally. The same 2.3GB docker image is used for themake_in_docker.shfrom the top directory.inverter/inverter.ipynb: Python notebook showing how the PDKMaster framework can be used to design a balanced inverter. It combines several design aspects in one integrated flow:spice simulation to compute the width of the inverter's NMOS and PMOS transistors to be balanced, e.g  the same on current.generate minimal area layout based on the design rulesVerify functionality using Spice simulation.StatusThis repository is currently usingPDKMasterandc4m-flexcellwhich currently have unstable APIs. Heavy non-backwards compatible changes are still be expected. If interested head over togitterfor further discussion.The PyPi release only contains the PDKMaster Technology definition; the gitlab release also contains all the generated views and example design files.