Saravanan666/pypi-org huggingface dataset:
Package: orsopy
orsopy is a Python library that implements ORSO functionality, which currently includes thereduced data file format.
The orsopy package is used by a range of data reduction and analysis packages for the writing and reading of reduced reflectometry data.
This data is written following theORSO defined specification, enabling a metadata-rich and flexible file to be created.ORSOis an open organisation aimed at improving the scientific techniques of neutron and X-ray reflectometry.
In the interest of transparency, all minutes from orsopy developer meetings are available in theDocumentsin the sidebar of this page.
If you are interested in getting involved in developing orsopy, please feel free tocontributeor get in touch on theORSO Slack(where there is a channel dedicated to orsopy).FeaturesReading and writing of ORSO specification reduced reflectivity files.
Package: orsvm
ORSVMORSVMis a free software package which provides a SVM classifier with  some novel orthogonal polynomial kernels.
This library provides a complete path of using the SVM classifier from normalization to calculation of SVM equation and the final evaluation.
In order to classify the dataset with ORSVM, there is a need to normalize the dataset whether using normal or fractional kernels.
ORSVM library needs numpy and cvxopt libraries to be installed. Arrays, matrices and linear algebraic functions have been used repeatedly from numpy and
the heart of SVM algorithm which is finding the Support Vectors is done by use of a convex quadratic solver from cvxopt library which is in turn a free python package for
convex optimization.
For a comprehensive introduction to fractional orthogonal kernel function and the use cases in SVM, refer toLearning with Fractional Orthogonal Kernel Classifiers in Support Vector Machinesbook.A suitable guide oncvxoptpackage is available athttp://cvxopt.orgabout installation and how to use.InstallYou can install orsvm using:pip install orsvmDependenciesFollowing dependencies will be installed:cvxoptpandasnumpysklearnConda environment (suggested)conda create --n ORSVM python=3.8 pandas sklearn numpy cvxopt
conda activate ORSVM
pip install orsvmDocumentationThe latest documentation can be found here:http://orsvm.readthedocs.io/Cite
Package: ortec.scientific.benchmarks.loadbuilding
The goal of theORTEC Scientific Benchmark platformis to let people work on more practical combinatorial optimization problems.
In order to allow you to get right to solving them, several Python tools have been developed to view and analyze instances and solutions.
This package is specific for the loadbuilding benchmark instances and provides in short the following functionality:InstanceFile-type Conversion toolData Validity Checking toolFind Applicable Constraints and Objectives toolSolutionFile-type Conversion toolValidation toolSolution Viewer toolScreenshot of the Solution ViewerInstallationORTEC scientific benchmark tools for loadbuilding is published onPyPIand can be installed from there:pip install ortec.scientific.benchmarks.loadbuildingDocumentationThe documentation for these tools as well as the file specifications for the instance and solution files can be downloaded fromhttps://benchmarks.ortec.com/:ToolsInstancesSolutionsRelease HistoryRelease 1.0.1 (2019-03-28)Changed:Improved legend of the solution-viewer, images are now always visibleImproved help text of the commandsImproved handling of pallets and boxesImproved loading YAML files, stopped the use of deprecated functionsImproved the (internal) result structure of the solution validationFixed:Fixed the handling of orientations HLW and WHL, they were switched in the calculationsFixed the center of gravity calculation of single itemsFixed the import of OrderedDict in the strategies to write YAML and JSON solutionsRelease 1.0 (2018-05-22)New Features:Added the the following tools:osbl-instanceosbl-solutionosbl-solution-viewerosbl-find-constraints-objectivesThe MIT License (MIT)Copyright (c) 2018 ORTECScientificBenchmarksPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: ortei
# Package.ONNX-Runtime-Engine-InterfaceONNX Runtime으로 Engine을 만들 때 공통으로 사용할 수 있는 Interface를 제공합니다.## Status[![Basic PR Check](https://github.com/404Vector/Package.ONNX-Runtime-Engine-Interface/actions/workflows/workflow_pr_check_basic.yml/badge.svg?branch=main)](https://github.com/404Vector/Package.ONNX-Runtime-Engine-Interface/actions/workflows/workflow_pr_check_basic.yml)
Package: or-testbed
OR-TestbedOR-testbed is a framework designed to solve combinatorial optimization problems through the use and modeling ofmetaheuristics. Since metaheuristics are based on certain basic assumptions,
they can be adapted for a multitude of different problems.The contribution of OR-Testbed is to provide the implementation of the basic structure of metaheuristics.
In this way, the problems to be solved (and therefore the related structures needed) only need to be modeled once.
Then, once each of the functions and parameters of the metaheuristics to be used have been adapted, the problem
can be solved with all of them from a centralized point.In summary, the objective of the framework is to implement the most relevant metaheuristics in the state of the art,
as well as the techniques commonly used in literature to improve them. This lets developers and researchers to have a
centralized repository of implemented metaheuristics, techniques and potential execution service for all of their problems.At this moment, the list of implemented metaheuristics is:GRASPSimulated AnnealingTabu SearchTo see the development plans at a glance, all relevant information can be found in theroadmap.InstallingSince OR-Testbed is coded in pure Python no requirements are needed, just execute:pip install or-testbedExamples and usageThere is no oneliner example, but there is a tutorial in the docs section. Also, check the availableexampleswhere some problems and algorithms are implemented and the usual workflow is showed.ContributingAll contributions will be greatly appreciated. The main alternatives are to report or correct bugs,
suggest changes or functionalities, open issues and, of course, implement new metaheuristics.TestingRun the unit test collection with:pytestBuilding docsFirst, build the docs using Sphinx:cd docs
make htmlThen open build/html/index.html in your browser to view the docs.Read more aboutSphinx.Other resourcesSince using OR-Testbed requires both coding and optimization related knowledge, this section will be more focused
on the latter:Metaheuristic.Wikipedia IntroductionSurvey on metaheuristics #1.Boussaid et al (2013)Survey on metaheuristics #2.Bianchi et al (2008)GRASP.Resende (1995)Simulated Annealing.Kirkpatrick et al (1983)Tabu Search.Glover (2003)Ant Colony Optimization (ACO) survey.Mohan (2001)
Package: ort-gpu-nightly-featurizer
ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models.
For more information on ONNX Runtime, please seeaka.ms/onnxruntimeor theGithub project.Changes1.3.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.11.3.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.01.2.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.2.01.1.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.1.01.0.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.0.00.5.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.5.00.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.4.0
Package: ort-gpu-nightly-training
ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models.
For more information on ONNX Runtime, please seeaka.ms/onnxruntimeor theGithub project.Changes1.7.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.7.01.6.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.6.01.5.3Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.31.5.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.21.5.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.11.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.4.01.3.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.11.3.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.01.2.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.2.01.1.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.1.01.0.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.0.00.5.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.5.00.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.4.0
Package: orthanc-mock
orthanc-mockMock for the python orthanc plugin intended for development.This library is intended to be used while developing python scripts
with the Python plugin for Orthanc. It simply provides the
functions/classes/enums signatures for the autocomplete.Installationpipinstallorthanc-mockUsageNote that theorthanc-mocklibrary should be installed as adevdependency so that it does not interfere while running inside Orthanc.importorthanc# Functions are accessibleorthanc.RegisterRestCallback()orthanc.RestApiGet()...# Classes are as wellorthanc.DicomInstance.GetInstanceMetadata()orthanc.DicomInstance.HasInstanceMetadata()...# And the enumorthanc.ChangeType.NEW_STUDYorthanc.ChangeType.ORTHANC_STOPPED...CreditsTheorthanc.pyhas been generated from thedata/python-sdk.txtfile,
which is from thePython Orthanc Plugin
Package: orthanc-rest-client
Orthanc REST clientProvides a REST client targeted atOrthancREST API endpoints.Based on the excellentapironlibrary.Installpip install orthanc-rest-clientHow to useImport the pre-defined client and pass the server detailsfrom orthanc_rest_client import Orthanc
orthanc = Orthanc('http://localhost:8042')

# Patient endpoints
orthanc.get_patients()
orthanc.get_patient(id)
...and so on

# Study endpoints
orthanc.get_studies()
orthanc.get_study(id)
...and so on

# Series endpoints
orthanc.get_series()
orthanc.get_one_series(id)
...and so on

# Instance endpoints
orthanc.get_instances()
orthanc.get_instance(id)
...and so on

# Get changes
orthanc.get_changes()

# Find objects by query
query = {'Level': 'Patient',
            'Query': {'PatientName': 'Jon*'},
        }
orthanc.find(query)

# Get previous queries
orthanc.get_queries()There are many other preconfigured endpoints.AuthenticationPass valid auth object:from requests.auth import HTTPBasicAuth
auth = HTTPBasicAuth('orthanc', 'orthanc')
orthanc = Orthanc('https://test.server.com', auth=auth)Then call functions normally (the auth object is passed automatically).Advanced examplesFor example, to save an instance file to local directory:def save_dcm_file(instance_id):
    fileName = '.'.join([instance_id, "dcm"])
    with open(fileName, 'wb') as dcm:
        for chunk in orthanc.get_instance_file(instance_id):
            dcm.write(chunk)To get a zip of DCM files from a series:with open('test.zip', 'wb') as z:
    for chunk in orthanc.get_series_archive(<id>):
        z.write(chunk)Security warning on non-HTTPS endpointsThe rest client will warn when using HTTP endpoints. Strongly consider using HTTPS given the data sensitivity.You can disable the warning using thewarn_insecurekeyword argument:orthanc = Orthanc('http://insecure.endpoint.com', warn_insecure=False)Further helpapironOrthanc documentationOrthanc REST API
Package: orthauth
orthauthA library to separate configuration and authentication from program logicTHIS IS NOT ENCRYPTEDTHIS IS NOT A PASSWORD MANAGERTHIS IS NOT A SECURE SECRET STORAGE SYSTEMYOU CAN SHOOT YOURSELF IN THE FOOT WITH THISThere isNO encryptionfor secrets stored using orthauth.
Orthauth can source credientials from a variety of sources
but it isINTENTIONALLY INSECURE.If you do not understand theuse casefor this as well as the
risks if used outside a secure environment then DO NOT USE IT.
No one can help you if you get pwnd.Use caseThe primary use case fororthauthis to keep api keys from leaking into
source code and winding up in public repositories.orthauthdoes not provide
operational security for any auth store that it uses. It is up to the user to
secure those and the systems they reside on as they see fit.Whileorthauthattemts to prevent secrets from leaking via debug messages
or logging, it doesn't know anything about the secretness of the values it
returns, and once it has returned that value, it is up to the consuming code
to prevent the contents of the value from leaking.orthauthis indented to unify two common ways managing configuration
variables and credentials: setting them environment variables, and including
them in a plain text file with permissions set to0600(and preferably kept
in a folder set to0700).For example running a program in the following wayexport API_KEY=lolplzdonotstealthis; ./my-script-that-needs-the-keyor using a file like~/.pgpassor emacs.authinfo. Note that
pgpass probably shouldn't be a source for most python implementations
because libraries like psycopg2 are able to read it directly. However in
other languages that do not have a library that supports reading from pgpass
directly, then pgpass would be a useful source.By making it possible to provide credentials seemlessley in multiple ways
the hope is to reduce the use of different solutions in different environments
without incuring the massive complexity of maintaining a managed authentication
infrasturcture.ApproachDecoratorsA layer of indirection between names in a code base and config/secrets structure.Be clear about what should be considered public information. Thus prevent anything
stored as a secret from being used as a key to find another secret.Bare minimum to store static configuration information, anything more should
be implemented in the language consuming the config, not in the config.Currently supported config formatsFormatSupportInstalljsonbuiltinpip install orthauthpython dictionary literalsbuiltinpip install orthauthyamlrequires pyyamlpip install orthauth[yaml]Usageimportorthauthasoaauth=oa.AuthConfig('path/to/config.yaml')@auth.tangential_init('api_key','some-service-api-key')classThatNeedsAuth:""" needs authenticated connection to some-service """tna=ThatNeedsAuth()print(tna.api_key)Haven't been scared off yet?
See thedeveloper guidefor more examples.
Package: orthoani
OrthoANIA Python implementation of theOrthoANIalgorithm for nucleotide identity measurement.🗺️ OverviewOrthoANI is a metric proposed byLeeet al.in 2016 to improve computation of Average Nucleotide Identity. It usesBLASTnto find orthologous
blocks in a pair of sequences, and then computes the average identity only
considering alignments of reciprocal orthologs.This project is a reimplementation of the closed-source Java implementation
provided by the authors onezbiocloud.net.
It relies onBiopythonto handle the I/O and the
interaction with the BLAST+ binaries.🔧 InstallingInstalling withpipis the easiest:$pipinstallorthoaniorthoanialso requires the BLAST+ binaries to be installed on your machine
and available somewhere in your$PATH.💡 ExampleUse Biopython to load two FASTA files, and thenorthoani.orthoanito compute
the OrthoANI metric between them:importorthoanifromBio.SeqIOimportreadgenome_1=read("sequence1.fa","fasta")genome_2=read("sequence2.fa","fasta")ani=orthoani.orthoani(genome_1,genome_2)orthoanican also be used from the CLI using a very simple command-line
interface:$orthoani-qsequence1.fa-rsequence2.fa0.5725🐏 Memoryorthoaniuses the machine temporary folder to handle BLAST+ input and output
files, which is configurable throughtempfile.tempdir.
On some systems (like ArchLinux), this filesystem can reside in memory, which means
that your computer could have trouble processing very large files. If this
happens, try changing the value of thetempfile.tempdirto a directory that
is actually located on physical storage.📏 PrecisionValues computed by this package and the original Java implementation may differ
slightly because in Java the authors perform rounding of floating-point values
at the sub-percent level, while this library uses the full values.📜 AboutThis library is provided under the open-sourceMIT license.This project is in no way not affiliated, sponsored, or otherwise endorsed by
theoriginal OrthoANI authors. It was developed byMartin Larraldeduring his PhD project
at theEuropean Molecular Biology Laboratoryin
theZeller team.
Package: orthofinder-tools
OrthoFinder ToolsIdeaCalculate the most common gene name of each orthogroup by majority vote:annotate_orthogroupsCreate plots analogous to roary_plots:orthofinder_plotsSetuppipinstallorthofinder-toolsUsageannotate_orthogroupsPrerequisitesYour FASTA sequences must have some description, e.g.:>gnl|extdb|STRAIN-XY_000001 DNA-directed RNA polymerase subunit beta' [Pediococcus stilesii]
MIDVNKFESMQIGLASPDKIRMWSYGEVKKPETINYRTLKPEKDGLFDERIFGPTKDYECACGKYKRIRY
...From this protein,DNA-directed RNA polymerase subunit beta'will be extracted.Command line usageannotate_orthogroups --help

annotate_orthogroups \
    --orthogroups_tsv /path/to/N0_or_Orthogroups.tsv \
    --hog True \
    --fasta_dir /path/to/fastas \
    --file_endings faa \
    --out outfile.tsv \
    --simple TrueIf--simple=Falseresulting tsv looks like this:HOGBest Gene NameGene Name OccurrencesN0.HOG0000000amino acid ABC transporter{JSON}N0.HOG0000001IS30 family transposase{JSON}N0.HOG0000002IS5/IS1182 family transposase{JSON}The JSON is a dictionary with key='gene name' -> value=occurrence, for example:{
  'Integrase core domain protein': 47,
  'hypothetical protein': 15,
  'IS30 family transposase': 126
}If--simple=Trueresulting tsv looks like this (no header):N0.HOG0000000amino acid ABC transporterN0.HOG0000001IS30 family transposaseN0.HOG0000002IS5/IS1182 family transposaseUsage as python class# load classfromorthofinder_toolsimportOrthogroupToGeneNamePATH_TO_ORTHOFINDER_FASTAS='/path/to/OrthoFinder/fastas'CURRENT_FOLDER='Results_Mon00'otg=OrthogroupToGeneName(fasta_dir=PATH_TO_ORTHOFINDER_FASTAS,file_endings='faa',)otg.load_hog(hog_tsv=F'{PATH_TO_ORTHOFINDER_FASTAS}/OrthoFinder/{CURRENT_FOLDER}/Phylogenetic_Hierarchical_Orthogroups/N0.tsv')otg.majority_dictwill be a python dict with key='orthogroup' -> value='best name', for example:{
  'N0.HOG0000000': 'amino acid ABC transporter',
  'N0.HOG0000001': 'IS30 family transposase',
  'N0.HOG0000002': 'IS5/IS1182 family transposase',
}otg.save_majority_df(outfile='path/to/outfile.tsv)writes the following file:HOG Best Gene Name  Gene Name Occurrences
N0.HOG0000000   amino acid ABC transporter Counter({'amino acid ABC transporter': 43})
...otg.save_orthogroup_to_gene_ids(outfile='path/to/outfile.tsv)writes the following file (no header):N0.HOG0000000   gene_1  gene_2
N0.HOG0000001   gene_3  gene_4  gene_5
...otg.save_orthogroup_to_gene_ids(outfile='path/to/outfile.tsv)writes the following file (no header):N0.HOG0000000	amino acid ABC transporter ATP-binding protein
N0.HOG0000001	ATP-binding cassette domain-containing protein
...orthofinder_plotsDisclaimer:This script is a port ofroary_plotsby
Marco Galardini (marco@ebi.ac.uk).# Command line usage:
orthofinder_plots --help
orthofinder_plots --tree data/SpeciesTree_rooted.txt --orthogroups_tsv data/Orthogroups.tsv --out outputThree files will be created:Usage as python class# load classfromorthofinder_toolsimportcreate_plotscreate_plots(tree='/path/to/SpeciesTree_rooted.txt',orthogroups_tsv='/path/to/Orthogroups.tsv',format='svg',no_labels=False,out='/path/to/output/folder')
Package: orthofisher
Docs·Report Bug·Request FeatureOrthofisher conducts automated and high-throughout identification of a predetermined set of orthologs, which can be used for phylgenomics, gene family copy number determination and more!If you found orthofisher useful, please citeorthofisher: a broadly applicable tool for automated gene identification and retrieval. Steenwyk & Rokas 2021, G3 Genes|Genomes|Genetics. doi:10.1093/g3journal/jkab250.GuideQuick StartPerformance AssessmentFAQQuick StartFor detailed instructions on usage and a tutorial, please see theonline documentation.1) PrerequisiteBefore installing orthofisher, please first installHMMER3and add the HMMER to your .bashrc path. For example, my .bashrc has the following:exportPATH=$PATH:/home/steenwj/SOFTWARE/hmmer-3.1b2-linux-intel-x86_64/binaries2) Install orthofisherIf you are having trouble installing orthofisher, please contact the lead developer, Jacob L. Steenwyk, viaemailortwitterto get help.To install viaanaconda, execute the follwoing command:condainstall-cjlsteenwykorthofisherVisit here for more information:https://anaconda.org/jlsteenwyk/orthofisherTo install viapip, execute the follwoing command:pipinstallorthofisherTo install from source, execute the follwoing command:# downloadgitclonehttps://github.com/JLSteenwyk/orthofisher.git# change dircdorthofisher/# installmakeinstallIf you run into software dependency issues, install orthofisher in a virtual environment. To do so, create your virtual environment with the following command:# create virtual environmentpython-mvenv.venv# activate virtual environmentsource.venv/bin/activateNext, install the software using your preferred method above. Thereafter, you will be able to use orthofisher.To deactivate your virtual environment, use the following command:# deactivate virtual environmentdeactivateNote, the virtual environment must be activated to useorthofisher.Performance AssessmentUsing 1,530 sequence similarity searches across six model eukaryotic proteomes, the performance of orthofisher was compared to results obtained from BUSCO. Examination of precision and recall revealed near perfect performance. More specifically, orthofisher had arecall of 1.0 and precision of 0.99. Precision is less than 1.0 because priors of expected sequence length and sequence similarity scores--which are not implemented in orthofisher--resulted in more missing genes in the BUSCO pipeline than the orthofisher pipeline.FAQI am having trouble installing orthofisher, what should I do?Please install orthofisher using a virtual environment as described in the installation instructions. If you are still running into issues after installing in a virtual environment, please contact Jacob L. Steenwyk viaemailortwitter.orthofisher is developed and maintained byJacob Steenwyk
Package: orthogonal
Orthogonal Drawing AlgorithmThis project is a fork ofthis project.Developer NotesThis project usesbuildlackeyfor day to day development buildsNoteFor all kind of problems, requests, enhancements, bug reports, etc.,
please drop me an e-mail.
Written byHumberto A. Sanchez II(C) 2023I am concerned about GitHub's Copilot projectI urge you to read about theGive up GitHubcampaign fromthe Software Freedom Conservancy.While I do not advocate for all the issues listed there I do not like that
a company like Microsoft may profit from open source projects.I continue to use GitHub because it offers the services I need for free.  But, I continue
to monitor their terms of service.Any use of this project's code by GitHub Copilot, past or present, is done
without my permission.  I do not consent to GitHub's use of this project's
code in Copilot.