Saravanan666/pypi-org huggingface dataset:
Package: gene-loci-comparison
Gene Loci ComparisonCreate fancy (bokeh) gene locus plots from GenBank files!This repository extends the functionality of the excellentDnaFeaturesViewerSetuppipinstallgit+https://github.com/MrTomRod/gene_loci_comparison.gitExamplesBokehThis library can also create interactive html/javascript-based plots. Open these files in your browser:single locusmultiple loci with synchronized panningSingle locus, specify colorsfromgene_loci_comparisonimportLocus,Lociimportmatplotlibimportmatplotlib.pyplotaspltmatplotlib.rcParams['font.family']="PT Sans Narrow"locus=Locus(gbk_file='/path/to/file.gbk',locus_tag='FAM3257_001019')locus_to_color=dict(FAM3257_001014='#1271c3',FAM3257_001015='#3171c3',FAM3257_001016='#5d71c3',FAM3257_001017='#9371c3',FAM3257_001018='#b171c3',FAM3257_001019='#cb71c3',FAM3257_001020='#ea71c3',FAM3257_001021='#fd71c3',# FAM3257_001021='#fd71c3'  # last gene: white (default color))locus.colorize(locus_to_color)ax,_=locus.plot(figure_width=12)plt.show()Multiple locifromgene_loci_comparisonimportLociimportmatplotlibmatplotlib.rcParams['font.family']="PT Sans Narrow"# Each locus is made from a gbk-file, a gene identifier and a title (optional)loci_of_interest=[dict(gbk='/path/to/file1.gbk',gene='FAM3257_00934',title='title1'),dict(gbk='/path/to/file2.gbk',gene='FAM3257_000019',title='title2'),dict(gbk='/path/to/file3.gbk',gene='FAM3257_001020',title='title3'),]# Highlight selected geneslocus_to_color_dict={locus['gene']:'#1984ff'forlocusinloci_of_interest}# Generate loci objectloci=Loci.generate(loci_of_interest,locus_to_color_dict=locus_to_color_dict)plot=loci.plot(auto_reverse=False)plot.show()Multiple loci, autoreversedTo automatically reverse loci based on the direction of the selected genes. Simply setauto_reversetoTrue.Multiple loci, with GC contentChange plotting method fromplot_multipletoplot_multiple_gc.plot=loci.plot_gc(auto_reverse=True,window_bp=200)
Package: gene-map
Tool for converting between various gene ids.Installation$pipinstallgene_mapUsage$gene_map--helpUsage:gene_map[OPTIONS]Mapgeneidsbetweenvariousformats.Options:-i,--inputTEXTIfitexists,treatedasfilewithwhitespace-separatedgeneids.Otherwisetreatedasageneiditself.[required]--fromTEXTSourceIDtype.[required]--toTEXTTargetIDtype.[required]-o,--outputFILENAMECSV-filetosaveresultto.--organism[ARATH_3702|CAEEL_6239|CHICK_9031|DANRE_7955|DICDI_44689|DROME_7227|ECOLI_83333|HUMAN_9606|MOUSE_10090|RAT_10116|SCHPO_284812|YEAST_559292]OrganismtoconvertIDsin.--cache-dirDIRECTORYFoldertostoreID-databasesin.-q,--quietSuppressloggingofmapping-statistics.--force-downloadForcedownloadofmapping-database.--helpShowthismessageandexit.Getting startedCommandline usageInputs can be either gene ids or files containing whitespace-separated
gene ids:$catmygenes.txtP63244P08246P68871$gene_map\-iP35222-iInvalidID-imygenes.txt-iP04637\--fromACC--toGene_Name\-ogene_mapping.csvMapped5/6genes.$catgene_mapping.csvID_from,ID_toP04637,TP53P08246,ELANEP35222,CTNNB1P63244,RACK1P68871,HBBIt is also possible to simply try to convert all given inputs without
knowing their ID type, by using--fromauto:$gene_map\-iP35222\-iTP53\-i'9606.ENSP00000306407'\--fromauto\--toGeneIDMapped3/3genes.ID_from,ID_to9606.ENSP00000306407,79007P35222,1499TP53,7157Attention: if an ID is valid for multiple types, unintended side-effects
may occur. Furthermore, all IDs are treated as strings.API usage>>>fromgene_mapimportGeneMapper>>>stringdb_ids=['9606.ENSP00000306407','9606.ENSP00000337461']>>>gm=GeneMapper()# defaults to HUMAN_9606>>>gm.query(stringdb_ids,source_id_type='STRING',target_id_type='GeneID')#                ID_from  ID_to#0  9606.ENSP00000306407  79007#1  9606.ENSP00000337461  90529
Package: genenga
This utility is generate PDF for printing address “Happy new year card”,
a.k.a. “Nengajo” in Japanese.RequirementDebian Packagestexlive-binaries (>= 2018.20181218.449446)texlive-lang-japaneselibc-binpython3.5 overfonts-takao-gothicfonts-takao-minchomakepython3-pystacheformat of address.csvaddress list is CSV. It is syntax is below:status,PersonLastName,PersonFirstName1,PersonFirstName2,address,address2,address3,PostalCode1,PostalCode2,PostalCode3,PostalCode4,PostalCode5,PostalCode6,PostalCode7Example1,子猫,にゃんこ,ねこちゃん,東京都中央区ねこまた町０ー０,,,0,0,0,0,0,0,0
1,猫山,にゃんごろ,,東京都太田区ねこむら町０ー０,キャットマンション１０１,,0,0,0,0,0,0,0
1,猫村,にゃん太,にゃんこ,東京都新宿区ねこ町０ー０,,,0,0,0,0,0,0,0
0,猫太,ねこのすけ,,東京都三鷹市こねこ町０ー０,,,0,0,0,0,0,0,0
1,猫野,ねこ太,ねこ助,神奈川県横浜市こねこ町０ー０,,,0,0,0,0,0,0,0WarningThe csv format has changed in v0.5.0.address3is inserted beforePostalCode1.Flag of address first fieldtarget this year.next year target but not sent this year.sent the before last.remove.UsageUpdate address.csvReset last year status.Update latest status.convert photo, extractbb photoimageedit nenga-yoko.texmake, generate nenga-yoko.pdf, print this pdfgenerate atena.pdf, print this:$ genenga -t path/to/yourtemplate.mustache address.csvHISTORY0.7.0 (2017-04-07)Supports Python 3.7.Drops support for Python 2.7.Fixes pylint, pydocstyle violations.0.6.0 (2017-01-06)Supports Python 3.5 over.Changes LaTeX template layout.Changes default fonts to Takao Gothic and Takao Mincho.Fixes pylint violations.0.5.1 (2016-12-23)Fixes address.mustache template for TeX Live 2016.0.5.0 (2015-12-31)Changes CSV formats.Supports Python 3.5.0.4.1 (2015-04-26)Fixes pep8 violations.Fixes pep257 violations.0.4.0 (2014-07-25)Applied pychecker and pylint, and fixed those violations.Increase code coverage to > 90%.0.3 (2013-12-23)Change template layoutChange “–template-path” option to be requriredFix a bug: always used a default template instead of a loaded templateThanks to Kojima Takanori <takanori.kojima@gmail.com> for patch.0.2 (2012-09-21)suport pystache 0.5.xRefacotroing0.1 (2011-12-01)First release
Package: genens
genensgenens is an AutoML system for pipeline optimization based on developmental genetic programming.InstallationClone the repository.git clone https://github.com/gabrielasuchopar/genens.git
pip install genensUsing genensAs for now, the GenensClassifier is ready to be used. It has an interface similar to other scikit-learn estimators. Whenfit()is called, the evolutionary optimization is run. After it finishes,predict()produces a prediction with the best of optimized pipelines. Alternatively, you can callget_best_pipelines()to get pipelines from the pareto front.from genens import GenensClassifier
from sklearn.datasets import load_iris()

iris = load_iris()
train_X, test_X, train_y, test_y = train_test_split(iris.data, iris.target, test_size=0.25)

clf = GenensClassifier()
clf.fit(train_X, train_y)
... # process of evolution

pred = clf.predict(test_X)TestsDirectory ./genens/tests contains scripts for running dataset tests and produce data about evolution process along with pickle files of best optimized pipelines.
Sample config files are included in./genens/tests/config.Run genEns on a dataset specified in the config file.python ./genens/tests/run_datasets.py --out OUT_DIR config CONFIGRuns genEns on theOpenML-CC18 benchmarking suitepython ./genens/tests/run_openml.py --out OUT_DIR --config CONFIGMore tests are to be included in later releases.
Package: genenv
GENENVThis command line utility tool allows you to create python virtual environements with already installed dependencies
you want easily!FeaturesYou can create virtual environments with pre-installed packages that you specify in command lineInstead of providing package name on command line, you can add atemplate.txtfile containing
names of packages you want intalledInstall packages in already existing virtual environments without having to activate themyou can create virtual environments with no packages installed.How it worksEssentially, the process of virtual environments creation and installation of packages is done in the bash script.createnv.sh.
The bash script is called as a subprocessin the python file.Clickis used to provide a CLI interface and collect arguments from the command line to pass into bash script when
its being called.UsageInstall with pippipinstallgenenvNOTE:if you specify package names on command line and template.txt file exits ,template.txt file will be ignored.if no template.txt file or package name(s) are provided as on command line as arguments, you'll recieve prompt if you still
want to proceed to create virtual environment with no preinstalled dependencies.To use contents oftemplate.txtfile , do not provide any package names after you specify environment nameCreating a virtual environment with namemeseekswith django and flask installedspecifying dependecies on command linegenv-nmeseeksflaskdjangoCreating a virtual environment with namemeseekswith dependencies specified intemplate.txtfilecreate  a file with nametemplate.txtin same directory you will run commandAdd package names to itgenv-nmeseeksIt detects and reads contentstemplate.txtfile
then installs package names specified inside.
Iftemplate.txtfile is available and empty, environemnt is created with no installed dependencies.Installing dependecies/packages in already existing virtual environmentspecify the name option flag as the name of the environment  you want to install package inif you want to install numpy in an existing virtual environment calledplutomake sure youre in directory that venv is located then rungenv-nplutonumpyrungenenv --helpto bring up help menuUsage: genenv [OPTIONS] [DEPENDENCIES]...

  specify package names after name flag to install them,read documentation for
  how that works

Options:
  -n, --name TEXT  provide name to be used for your virtual environment
                   [required]
  --help           Show this message and exit.ContributingFork this repository to your GitHub account.Clone the forked repository to your local machine.Create a new branch for the feature you want to work on.Make your contributions.Push your local branch to your remote repository.Open a pull request to the develop branch of this repository.Setting up the tool for local developmentClone this repository to your local machine.Create a virtual environment for your project and activate it. Install all dependencies from  requirements.txt file.In the root directory of the project, develop the project locally from the setup configuration.python3setup.pydevelopyou can now run commands  usinggenv
Package: genepass
genepassgenerating passward randomly!
（create password randomly）Installpip install genepassUsageimportgenepass#複数のパスワードをランダムで作成し提案してくれるgenepass.suggest_passwords()#Defaultgenepass.suggest_passwords(suggest_num=10,security_level='strong',length=8,character_sets=None)Optionsuggest_num : intNumber of suggestionallowed : 1-100(default:10)security_level : stringstrong : DIGITS & CAPITAL_LETTER & SMALL_LETTER（default）god    : DIGITS & CAPITAL_LETTER & SMALL_LETTER & SYMBOLScustom : Under constructionlength : intNumber of characters in the generated passwordallowed : 4-32（default:8）character_sets : list or Nonecharacters type you want needDIGITS         : 数字CAPITAL_LETTER : 大文字SMALL_LETTER   : 小文字SYMBOLS        : 記号
Package: genepattern-python
GenePattern Python LibraryThis is a Python library for working with GenePattern programmatically. Behind the scenes, calls from this library execute the GenePattern REST API.Supported Python VersionsThis library requires Python 3.6+. The bundled data submodulegp.dataalso requirespandas, although the rest of the module does not.Python 2 Support:Support for Python 2 was removed in version 1.4.0. Python 2 users should use version 1.3.1.InstallingIt is recommended to install this library from PIP. Simply execute the command below:pip install genepattern-pythonUpgradingTo upgrade to the latest version of the library, execute the command below:pip install genepattern-python --upgradeTutorialA tutorial on how to use the GenePattern Python Library isavailable here."Connection Reset by Peer" ErrorConnecting to the GenePattern public server now requires TLS 1.2+. Older versions of SSL and TLS will no longer work. If you're attempting to connect and receiving a "Connection Reset by Peer" error, you will need to update the OpenSSL library associated with your Python installation.
Package: genepidgin
Genepidgin is a suite of tools that assist in evaluation and assignment gene product names. There are three primary components:genepidgin *cleaner*standardizes gene names per UNIPROT naming guidelinesgenepidgin *compare*compares two or more sets of gene namesgenepidgin *select*selects the most appropriate product name from a vareity of homology evidenceFor more information, including current development status, please visitgenepidgin’s documentation.
Package: geneplexus
PyGenePlexusA Python package of the GenePlexus analysis pipeline.TheGenePlexus paperTherepositoryfor reproducing the experimentsThewebserverDocumentationDataQuick startInstallationInstall theGenePlexuspackage viapip.pipinstallgeneplexusRun GenePlexus piplineExample scriptSeeexample/example_run.pyfor example usage of the API.Command-line interfacegeneplexus--input_fileexample/input_genes.txt--output_direxample_resultFull CLI options (check out withgeneplexus --help)Run the GenePlexus pipline on a input gene list.

optional arguments:
  -h, --help            show this help message and exit
  -i , --input_file     Input gene list (.txt) file (one gene per line). (default: None)
  -d , --gene_list_delimiter
                        Delimiter used in the gene list. Use 'newline' if the genes are separated
                        by new line, and use 'tab' if the genes are seperate by tabs. Other
                        generic separator are also supported, e.g. ', '. (default: newline)
  -n , --network        Network to use. {format_choices(config.ALL_NETWORKS)} (default: STRING)
  -f , --feature        Types of feature to use. The choices are: {Adjacency, Embedding,
                        Influence} (default: Embedding)
  -g , --gsc            Geneset collection used to generate negatives and the modelsimilarities.
                        The choices are: {GO, DisGeNet} (default: GO)
  -s , --small_edgelist_num_nodes
                        Number of nodes in the small edgelist. (default: 50)
  -dd , --data_dir      Directory in which the data are stored, if set to None, then use the
                        default data directory ~/.data/geneplexus (default: None)
  -od , --output_dir    Output directory with respect to the repo root directory. (default:
                        result/)
  -l , --log_level      Logging level. The choices are: {CRITICAL, ERROR, WARNING, INFO, DEBUG}
                        (default: INFO)
  -q, --quiet           Suppress log messages (same as setting log_level to CRITICAL). (default:
                        False)
  -z, --zip-output      If set, then compress the output directory into a Zip file. (default:
                        False)
  --clear-data          Clear data directory and exit. (default: False)
  --overwrite           Overwrite existing result directory if set. (default: False)
  --skip-mdl-sim        Skip model similarity computation. This computation is not yet available
                        when using custom networks due to the lack of pretrained models for
                        comparison. (default: False)DevInstallationInstall the PyGenePlexus package in editable mode with dev dependenciespipinstall-e."[dev]"TestingRun the default test suitepytesttest/By default, test data will be cached. Thus, after the first test run, data redownload will not be tested. To force redownload, specify the--cache-clearoptionpytesttest/--cache-clearBuilding DocumentationInstall doc dependenciespip install -r docs/requirements.txtBuildcddocs
makehtmlOpen docopen build/html/index.html
Package: geneplot
geneplot libraryA Python library for plotting single nucleotide polymorhpysm (SNP) data and
protein domain information on the exon-intron structure of a gene. it takes as
input standard files and formats generated from  popular tools, including
InterproScan, the General Feature Format v3 (GFF3) and the Variant Call
Format (VCF). For more details see:Code availabilityhttps://github.com/gonzalezibeas/geneplotInstallationpip install geneplot
´´´
## Documentation

https://geneplot.readthedocs.io/en/latest/#

## Get started
Plot geneID (from the GFF3 file) and proteinID domains (from IPR file)
and SNPs listed on sample_ID from the VCF files directory.

```Python
import geneplot as gp

#input data
filegff = '/path-to-data/file.gff3'
iprfile = '/path-to-data/file.ipr'
vcffiles = '/path-to-data/'

# class instantiation (genome object)
genome_1 = gp.genome(filegff, iprfile=iprfile, vcffiles=vcffiles)

# class instantiation (gene object)
gene_1 = genome_1.gene(mRNAid='transcriptID', proteinid='proteinID')

# plot
gene_1.plot('Pfam', sp='sampleID', onlycoding=True)
Package: genepy
# Evolutionary Algorithm \- Genetic Programming[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/amstuta/genetic.py/blob/master/LICENSE.md)This package is a high-level library implementing agenetic programming algorithm using a tree representation for individuals. Itis compatible with Python 2.7 and all the following versions and uses only thestandard library (no dependencies).It can be used to solve linear and nonlinear regression problems and isusable through a simple interface:- A fit function that takes as inputs the training examples, target values and the number of iterations- A predict function that takes as parameter an input for predictionAn example of usage can be found in the example.py script. This example requires tohave the libraries numpy and matplotlib installed.### InstallationThe package can be installed simply using Pypi:```shpip install genetic```### UsageTo use it, you need to give the algorithm:- A fitness function that takes as parameters:- An instance of the class Tree- The training examples: array-like object of shape [n_samples, n_features]- The target values: array-like object of shape [n_samples]- The function set that can be used in the treeBasic example:```pythonfrom operator import add, sub, mulfrom genetic.core import EvolutionaryAlgorithm# Load your datasettrain_features, train_targets = ...test_features, test_targets   = ...# Define a fitness functiondef compute_fitness(tree, features, targets):...return fitness# Define the parameters of the algorithmparameters = {'min_depth':        2,'max_depth':        5,'nb_trees':         50,'max_const':        100,'func_ratio':       0.5,'var_ratio':        0.5,'crossover_prob':   0.8,'mutation_prob':    0.2,'iterations':       80,'functions':        [add,sub,mul],'fitness_function': compute_fitness}# Create object and train algorithmea = EvolutionaryAlgorithm(**parameters)ea.fit(train_features, train_targets)# Make a predictionpredicted = ea.predict(test_features[0])```
Package: genepy3d
GENEPY3DGeNePy3D is an open-source python library for quantitative geometry, in particular in the context of quantitative microscopy.
It provides an easy interface to perform complex data science workflow on geometric data,
manipulating point cloud, surfaces, curves and trees, converting between them,
and performing analysis tasks to extract information and knowledge out of raw geometrical data.GeNePy3D can be used as a 'middleware' library, that is linking out to recognised and established specialised library when needed but providing easy to use interfaces.
The aim is to democratise access to methods from a wide range of mathematical sub-discipline to biological data scientist and computational biologist.Further information can be found in the main documentationhttps://genepy3d.gitlab.io/.Reference documentation is on readthedocshttps://genepy3d.readthedocs.io/
Package: genepy-cli
Genepy CLIThis is the HackInScience/Genepy command line client.$genepylistexercisesExercises
┏━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃Done┃Title┃
┡━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
││Djangoview│
││HelloWorld│
││Print42│
││Numberofsecondsinayear│
││Usingoperators│
└──────┴─────────────────────────────────────────┘This is currently usefull mostly for teachers to download/upload exercises:$genepypull--pagensi
$treensi
nsi
├──distance
│├──check.py
│├──initial_solution.py
│├──meta
│├──wording_en.md
│└──wording_fr.md
└──print-42├──check.py├──initial_solution.py├──meta├──wording_en.md└──wording_fr.md3directories,10files
$genepy-vpush
INFO:genepy:[fr]Uploading'Print 42'INFO:genepy:[en]Uploading'Print 42'INFO:genepy:[fr]Uploading'Distance'INFO:genepy:[en]Uploading'Distance'
Package: general
common utils for python====================this package include some useful general libs in python, include:- frequently used decorators- monkey patch for class- reflection, smartlog and yml_config and so on- cli interface tools- git workflow tools- project management tools, contains a scaffold, a update command toinstall=========pip install generalhow to use===========some are python packages you can import directly.``gy`` is a shell command you can run ``gy --help`` to see the usage. The most useful 3 workflow is  ``init``, ``sync``, ``push/merge``. And the default dev, release branch.``ly`` is a project manage and you can run ``ly --help`` to see the usage. The most useful command is ``new``, ``project-update`` to new and update a project.``gy self-update`` to update gy locally, is a command when you develop the package and want to refresh the package in system.
Package: generalbrowser
generalbrowserMissing description.This package and 6 other make upManderaGeneral.InformationPackageVerLatest ReleasePythonPlatformLvlTodoTestsgeneralbrowser0.0.92022-06-21 09:11 CEST3.8,3.9Windows, Ubuntu310.0 %Contentsgeneralbrowser├─Information├─Contents├─Installation├─Attributes└─TodoInstallationCommandgenerallibrarygeneralfilegeneralguidjangodjangorestframeworkdillrequestspip install generalbrowserYesYesYesYesYesYesYesAttributesModule: generalbrowser(Untested)├─Class: GeneralClient(Untested)│  ├─Method: create_page(Untested)│  ├─Method: deserialize(Untested)│  ├─Method: get(Untested)│  ├─Method: post(Untested)│  ├─Method: render_response(Untested)│  ├─Method: response_to_file(Untested)│  ├─Method: store_session(Untested)│  └─Method: url(Untested)├─Class: GeneralClientModel(Untested)│  └─Method: create_page(Untested)├─Class: GeneralClientPage(Untested)└─Class: GeneralModelPage(Untested)TodoModuleMessageserver.pySend client models in header instead? That way we can serialize inside success method instead.Generated 2022-06-21 09:11 CEST for commit0d00cd3.
Package: general-conversors
Multi-purposes conversors for PythonInstallationUse the package managerpipto install general-conversors.pip3installgeneral-conversorsUsageTo 1-Level dictionaryfromconversor.dictimportToOneLevelsource={'error':False,'result':{'executed':True,'payload':'Hello world','code':200,'source_code_url':'http://google.com'}}## replace_underscore as Trueconversor=ToOneLevel(source=source,replace_underscore=True)result=conversor.convert()print('Result replacing underscore:',result)# Result replacing underscore: {'error': False, 'result.executed': True, 'result.payload': 'Hello world', 'result.code': 200, 'result.source.code.url': 'http://google.com'}## replace_underscore as False (Default)conversor=ToOneLevel(source=source)result=conversor.convert()print('Result:',result)# Result: {'error': False, 'result.executed': True, 'result.payload': 'Hello world', 'result.code': 200, 'result.source_code_url': 'http://google.com'}To N-level level dictionaryfromconversor.dictimportToMultiLevelsource={'error':False,'result.executed':True,'result.payload':'Hello world','result.code':200}conversor=ToMultiLevel(source=source)result=conversor.convert()print('Result:',result)# Result: {'error': False, 'result': {'executed': True, 'payload': 'Hello world', 'code': 200}}ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT