Saravanan666/pypi-org huggingface dataset:
Package: olek
Olek: Python Web Framework built for learning purposesOlek is a simple python web framework built for learning purposes.It's a WSGI framework and can be used with any WSGI application server such as Gunicorn.InstallationpipinstallolekHow to use itBasic usage:fromolek.apiimportOlekAPIapp=OlekAPI()@app.route("/home")defhome(request,response):response.text="Hello from the HOME page"@app.route("/hello/{name}")defgreeting(request,response,name):response.text=f"Hello,{name}"@app.route("/book")classBooksResource:defget(self,req,resp):resp.text="Books Page"defpost(self,req,resp):resp.text="Endpoint to create a book"@app.route("/template")deftemplate_handler(req,resp):resp.body=app.template("index.html",context={"name":"Olek","title":"Best Framework"}).encode()Unit TestsThe recommended way of writing unit tests is withpytest. There are two built in fixtures
that you may want to use when writing unit tests with Olek. The first one isappwhich is an instance of the mainOlekAPIclass:deftest_route_overlap_throws_exception(app):@app.route("/")defhome(req,resp):resp.text="Welcome Home."withpytest.raises(AssertionError):@app.route("/")defhome2(req,resp):resp.text="Welcome Home2."The other one isclientthat you can use to send HTTP requests to your handlers. It is based on the famousrequestsand it should feel very familiar:deftest_parameterized_route(app,client):@app.route("/{name}")defhello(req,resp,name):resp.text=f"hey{name}"assertclient.get("http://testserver/matthew").text=="hey matthew"TemplatesThe default folder for templates istemplates. You can change it when initializing the mainOlekAPI()class:app=OlekAPI(templates_dir="templates_dir_name")Then you can use HTML files in that folder like so in a handler:@app.route("/show/template")defhandler_with_template(req,resp):resp.html=app.template("example.html",context={"title":"Awesome Framework","body":"welcome to the future!"})Static FilesJust like templates, the default folder for static files isstaticand you can override it:app=OlekAPI(static_dir="static_dir_name")Then you can use the files inside this folder in HTML files:<!DOCTYPE html><htmllang="en"><head><metacharset="UTF-8"><title>{{title}}</title><linkhref="/static/main.css"rel="stylesheet"type="text/css"></head><body><h1>{{body}}</h1><p>This is a paragraph</p></body></html>MiddlewareYou can create custom middleware classes by inheriting from theolek.middleware.Middlewareclass and overriding its two methods
that are called before and after each request:fromolek.apiimportOlekAPIfromolek.middlewareimportMiddlewareapp=OlekAPI()classS1mpleCustomMiddleware(Middleware):defprocess_request(self,req):print("Before dispatch",req.url)defprocess_response(self,req,res):print("After dispatch",req.url)app.add_middleware(S1mpleCustomMiddleware)
Package: oleo
oleoInteractive netlist visualisation tools compatible with GDSFactoryFree software: MIT licenseDocumentation:https://oleo.readthedocs.ioInstallationTo install use pip:$ pip install oleoFor a development installation (requiresNode.jsandYarn version 1),$ git clone https://github.com/daquintero/oleo.git
$ cd oleo
$ pip install -e .
$ jupyter nbextension install --py --symlink --overwrite --sys-prefix oleo
$ jupyter nbextension enable --py --sys-prefix oleoWhen actively developing your extension for JupyterLab, run the command:$ jupyter labextension develop --overwrite oleoThen you need to rebuild the JS when you make a code change:$ cd js
$ yarn run buildYou then need to refresh the JupyterLab page when your javascript changes.
Package: ole-py
ole-pyMicrosoft OLE file parser in pure Python.
You can read more about OLE file athere.Installationpip install ole-pyExampleimportolewithole.open('examples\\testfile.hwp')asf:print(f.list_streams())print('='*40)print(f.get_stream('PrvText').read().decode('utf-16le'))Result:('FileHeader', 'DocInfo', '\x05HwpSummaryInformation', 'PrvImage', 'PrvText', 'Scripts/JScriptVersion', 'Scripts/DefaultJScript', 'DocOptions/_LinkDoc', 'BodyText/Section0')
========================================
2018년 육군 전문특기병(어학병 포함) 모집 일정
<특기명><모집인원><접수기간><1차발표><면접일시><면접장소><최종발표><입영시기>
<영어어학병><55><2017-11-03><~><2017-11-14><2017-11-14><2017-12-07   2017-12-07><09:00
14:00><합동군사대학교   국방어학원><2017-12-22><18년 1~3월><66><2018-01-25><~><2018-02-08><2018-02-08><2018-03-08   2018-03-08><09:00  14:00><2018-03-23><18년 4~6월><64><2018-04-25><~><2018-05-09><2018-05-09><2018-06-07   2018-06-07><09:00  14:00><2018-06-22><18년 7~9월><65><2018-07-25><~><2018-08-09><2018-08-09><2018-09-06   2018-09-06><09:00  14:00><2018-09-21><18년 10~12월>
<프랑스어어학병><1><2018-05-24><~><2018-06-07><없음><2018-07-04><13:00><2018-07-27><18년 8월><1><2018-09-27><~><2018-10-11><없음><2018-11-06><13:00><2018-11-23><18년 12월>
<스페인어어학병><1><2018-04-25><~><2018-05-09><없음><2018-06-12><13:00><2018-06-22><18년 7월><1><2018-09-27><~><2018-10-11><없음><2018-11-07><13:00><2018-11-23><18년 12월>
<독일어어학병><1><2017-11-03><~><2017-11-14><없음><2017-12-04><13:00><2017-12-22><18년 1월><2><2018-05-24><~><2018-06-07><없음><2018-07-05><13:00><2018-07-27><18년 8월>
<일본어어학병><2><2017-11-03><~><>LicenseMIT
Package: olha
olhaA package to generate TCR/BCR sequences fast, based onolga. Use the same syntax as olga but is up to 20x faster and can optionally generate non-functional sequences and include point-mutation "sequencing" errors. It also allows for selection of specific V/J pairs for generation.Written in C++, interface with python3 via pybind11.InstallationpipinstallolhaExampleimportolgaimportolga.sequence_generationimportolga.load_modelimportolha## olga model loadingparams_file_name=f'{olga.__path__[0]}/default_models/human_T_beta/model_params.txt'marginals_file_name=f'{olga.__path__[0]}/default_models/human_T_beta/model_marginals.txt'V_anchor_pos_file=f'{olga.__path__[0]}/default_models/human_T_beta/V_gene_CDR3_anchors.csv'J_anchor_pos_file=f'{olga.__path__[0]}/default_models/human_T_beta/J_gene_CDR3_anchors.csv'genomic_data=olga.load_model.GenomicDataVDJ()genomic_data.load_igor_genomic_data(params_file_name,V_anchor_pos_file,J_anchor_pos_file)generative_model=olga.load_model.GenerativeModelVDJ()generative_model.load_and_process_igor_model(marginals_file_name)# sequence generationolha_gen=olha.SequenceGeneration(genomic_data,generative_model,error_rate=0.1)olha_gen.gen_rnd_prod_CDR3()# ('TGCGCCAGCAGCTCCATGGACGGCTCCGAAAAACTGTTTTTT', 'CASSSMDGSEKLFF', 49, 3)Comparisonimporttimeitolha_gen=olha.SequenceGeneration(genomic_data,generative_model,error_rate=0.1)olga_gen=olga.sequence_generation.SequenceGenerationVDJ(generative_model,genomic_data)timeit.timeit(olha_gen.gen_rnd_prod_CDR3)# 3.31 μstimeit.timeit(olga_gen.gen_rnd_prod_CDR3)# 103 μs
Package: oli
OliOli is a one liner installer aimed at easing you with the installation of everything you need on different platforms. With no packages neededInstallationRunpip install oliorgitclonehttps://github.com/StanGirard/OlicdOliUsageRun in your basholiorpython3 -m oli.runProgramsLinuxNetdataMacosHomebrewOh My ZshXcode Command LineDevelopment Setup
Package: olib
Welcome to OLIB, an object library.OLIB is an object library you can use to program with objects, uses a JSON
in file database with a versioned readonly storage and reconstructs objects
based on type information in the path.OLIB is placed in the Public Domain and has no COPYRIGHT and no LICENSE.INSTALLOLIB can be found on pypi, seehttp://pypi.org/project/olibinstallation is through pypi:$ sudo pip3 install olib --upgrade --force-reinstallPROGRAMMINGOLIB provides a library you can use to program objects under python3. It
provides a basic Object, that mimics a dict while using attribute access
and provides a save/load to/from json files on disk. Objects can be
searched with a little database module, provides read-only files to
improve persistence and use a type in filename reconstruction.basic usage is this:>>> from obj import Object
>>> o = Object()
>>> o.key = "value"
>>> o.key
'value'
>>> o
{"key": "value"}objects can be saved and loaded to JSON files:>>> from obj import Object, cfg
>>> cfg.wd = "data"
>>> o = Object()
>>> o.key = "value"
>>> path = o.save()
>>> path
'obj.Object/4b58abe2-3757-48d4-986b-d0857208dd96/2021-04-12/21:15:33.734994
>>> oo = Object().load(path)
>>> oo.key
'value'an Object tries to mimic a dictionary while trying to be an object with normal
attribute access as well. Hidden methods are provided as are the basic
methods like get, items, keys, register, set, update, values.great for giving objects peristence by having their state stored in files.CONTACThave fun codingBart Thate (bthate67@gmail.com)botfather on #dunkbots irc.freenode.net
Package: olibpythonproflavio
Nesse curso é ensinado como contribuir com projetos de código abertoLink do cursoPython ProSuportada versão 3 de PythonPara instalar:python3 -m venv .venvsource .venv/bin/activatepip install -r requirements-dev.txtPara conferir qualidade do código:flake8Tópicos a serem abordados:GitVirtualenvPip
Package: oligoMass
DescriptionCalculation of oligonucleotides weights and some properties.Installationfrom Repository on GitHub:pip install git+https://github.com/a1exsan/oligoMass.git#egg=oligoMassfrom PyPi:pip install oligoMass -Uhttps://pypi.org/project/oligoMass/Getting started#### 1) Calculate average and monoisotopic molecular weight of any deoxyoligonucleotide sequense consists of
#### five type bases: dA, dT, dG, dC, dUfrom oligoMass.molmassOligo import oligoNASequenceoligos = oligoNASequence(‘ATGCCGGTuugtU’)print(f”molecular formula: {oligos.getMolecularFormula()}”)print(f”MW: {oligoNASequence(‘ATGCC’).getAvgMass()}”)print(f”monoMW: {oligoNASequence(‘ATGCC’).getMonoMass()}”)#### 2) Calculate average and monoisotopic molecular weight of some deoxyoligonucleotide modifications
#### such as: LNA(+), ribo(r), 2’-O-Me (m) or Phosphorothioated DNA bases as entered (*)print(f” MW: {oligoNASequence(‘+ATrG+CCmG*GTuu+gtU’).getMonoMass()}”)#### 3) Universal sintax to add modifications:print(f” MW: {oligoNASequence(‘ATGC’).getMonoMass()} Da”)#1173.82 Daprint(f” MW: {oligoNASequence(‘ATGC’).getMolecularFormula()} Da”)# C39H50N15O22P3print(f” MW: {oligoNASequence(‘AT{CH2}GC’).getMonoMass()} Da”)#1187.85 Daprint(f” MW: {oligoNASequence(‘AT{CH2}GC’).getMolecularFormula()} Da”)# C40H52N15O22P3print(f” MW: {oligoNASequence(‘AT{S|O}GC’).getMolecularFormula()} Da”)# C39H50N15O21P3Sthat equal (‘AT{S|O}GC’ == ‘ATG*C’)print(f” MW: {oligoNASequence(‘AT[CH2]GC’).getMonoMass()} Da”)#1187.85 Daprint(f” MW: {oligoNASequence(‘AT/CH2/GC’).getMonoMass()} Da”)#1187.85 Da#### 4) getPrefix(index) and getSuffix(index) methods can return prefix and suffix of the sequenceprint(oligoNASequence(‘+ATrG+CCmG*GTuu+gtU’).getPrefix(index=2)())# +ATprint(oligoNASequence(‘+ATrG+CCmG*GTuu+gtU’).getSuffix(index=4)())# CmG*GTuu+gtU#### 5)Calc extinction coef:from oligoMass import dnaextinction = dna.get_simple_ssdna_extinction(“ATGCTT”, dna.get_extinction_dict())print(extinction)# 55100
Package: oligoss
Oligomer Soup Sequencing (OLIGOSS)OLIGOSS is a package forde novosequencing of linear oligomers from
tandem mass spectrometry data.Full documentation can be found here:https://croningroup.gitlab.io/origins/oligoss/index.htmlNote: proper use of this package requires a good working knowledge of both
the chemistry and analytical methods used to obtain results. It isnota
magic bullet to solve all of your oligomer sequencing needs! Please ensure that
all raw mass spectrometry data input to the package is of good quality. All
results should be checked and validated wherever possible, especially if running
data from a new instrument and / or oligomer class.InstallationOLIGOSS is available through Pip (Python Package Index):pip install oligoss --userSource CodeSource code can be viewedon github:https://github.com/croningp/oligoss.git
Package: olimex-ekg-emg
A Python package for gathering and viewing data from theOlimex EKG/EMG Shield.NOTICETHIS SOFTWARE DOES NOT PROVIDE MEDICAL ADVICEThe information provided by this software is not medical advice. By using this software,
You acknowledge that this software does not and should not be construed to provide
health-related or medical advice, or clinical decision support, or to provide,
support or replace any diagnosis, recommendation, advice, treatment or decision by an
appropriately trained and licensed physician, or to create a patient-physician relationship.
You hereby agree that this software will not be relied on or used, in whole or in part,
for any of the preceding purposes by or on Your behalf with respect to any individual(s).
If You believe You suffer from any medical condition, whether or not this software’s
results support this belief, You should immediately seek professional medical advice
or consult with a qualified medical professional.Installationpip install olimex-ekg-emgUsageFirst run both Jupyter Noteobook and Bokeh’s server:In one terminal window:$ jupyter notebookIn another terminal window:$ bokeh serveThen from within a Jupyter notebook cell:from olimex.nb import exg; exg(<port or mock data name>)This will start a new browser tab/window where the EKG will appear. the value
passed to theexgfunction can be a port (eg./dev/tty.usbmodem1411,COM1)
or the name of a mock data file (nsr.bin).To list all available ports that may be sending EKG data, use:from olimex.utils import list_serial_ports

list_serial_ports()To list all mock data:from olimex.utils import get_mock_data_list

_, mock_data = get_mock_data_list()
mock_dataExample OutputFurther DocumentationFurther documentation can be found onRead the Docs.
Package: olinkform
olinkformThe preprocessing lib for olink dataFree software: MIT licenseDocumentation:https://olinkform.readthedocs.io.UsageParse a data filefromolinkformimportparseresult=parse(path,version)# version v1 or v2 are supported,# which are corresponding to the old and new olink formats, individuallyThe result is a dict with the following format{
    "sampleId": {
        "markerId" : {
            "batch": string
            "value": float,
            "LOD": float,
            "MDF": float
        }
    }
}Parse a data file to dataframefromolinkformimportparse_to_dataframedf=parse_to_dataframe(path,version)The result is a dataframe with columns:['batch','sample_id','marker','value','LOD','MDF']CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-06-01)First release on PyPI.
Package: oList
oListA Python 'Object List'. It's basically your standard python list that additionally lets you attach data to it that doesn't affect the actual list items itself.Treat the oList as you would a normal list:my_list=oList([-10,3,4,-11,7,14])my_list.sort()forlist_iteminmy_list:print(str(list_item))oList Methods:oList.attach('data')# can be of any typeoList.get_attachments()# returns a list or the object itself if the attachments are only 1oList.as_dict(_attachments=True)# returns a dict, access values being index, _attachment=True will add attachment data to the dictoList.as_tuple()# returns tupleExample Usage:intel_list=oList(['i5-9600k','i9-10910','i9-10900K',]).attach({'carrier':'Intel','description':'A nice little list of some intel Cpus.'})ryzen_list=oList(['Ryzen 3 1200','Ryzen 5 1400','Ryzen 5 3600x']).attach({'carrier':'AMD','description':'A nice little list of some Ryzen Cpus.'})cpu_list=oList([intel_list,ryzen_list]).attach('List of Intel and Ryzen Cpus')# .... later on in the program ....print("--- "+cpu_list.get_attachments()+" ---\n")forcpusincpu_list:print(cpus.get_attachments())forcpuincpus:print(cpus.get_attachments()['carrier']+' - '+cpu)print('')# OUTPUT:# --- List of Intel and Ryzen Cpus ---## {'carrier': 'Intel', 'description': 'A nice little list of some intel Cpus.'}# Intel - i5-9600k# Intel - i9-10910# Intel - i9-10900K## {'carrier': 'AMD', 'description': 'A nice little list of some Ryzen Cpus.'}# AMD - Ryzen 3 1200# AMD - Ryzen 5 1400# AMD - Ryzen 5 3600x
Package: olist-loafer
olist-loaferis an asynchronous message dispatcher for concurrent tasks processing, with the following features:Encourages decoupling from message providers and consumersEasy to extend and customizeEasy error handling, including integration with sentryEasy to create one or multiple servicesGeneric HandlersAmazon SQS integration:information_source: Currently, only AWS SQS is supportedHow to useA simple message forwader, fromsource-queuetodestination-queue:fromloafer.ext.aws.handlersimportSQSHandlerfromloafer.ext.aws.routesimportSQSRoutefromloafer.managersimportLoaferManagerroutes=[SQSRoute('source-queue',handler=SQSHandler('destination-queue')),]if__name__=='__main__':manager=LoaferManager(routes)manager.run()How to contributeFork this repository, make changes and send us a pull request. We will review your changes and apply them. Before sending us your pull request please check if you wrote and ran tests:maketest
Package: olivar
OlivarOlivar is an open source pipeline for comprehensive design of primer and probe sets for pathogen
sequences. Our pipeline relies on the state-of-the-art primer generating software, and incorporates
low frequency variant information to ensure robust sensitivity and specificity for rapidly evolving
pathogens. Additionally, our software provides visualization and validation capabilities to aid fast
evaluation of the output probe and primer sets.InstallTo use Olivar, simply clone the repository and ensure that the requrements below are installed and in the system path.RequirementsBiopythonPandasJinja2ParsnpPrimer3BlastMAFFTPyVCFpysamAdditional requirements for simulationSamtoolsMinimap2Bowtie2LofreqUsageTo run Olivar and generate probes and primers for a set of sequences and a reference,olivar /path/to/reference.fasta /path/to/input_genomes/ --email <your_email>If you have a set of read datasets you'd like to use to simulate more input data:olivar /path/to/reference.fasta /path/to/input_genomes/ \
    --simulate /path/to/read_directories
    --csv      /path/to/read_metadata.csv
    --email <your_email>NCBI may throttle your blast queries if you only provide your email. If you have an API, you can provide that instead to have a higher allowance. Alternatively, you can supply a local blast database location via--blastdb <database_location>.You can skip the generation of primers by adding the--probes-onlyflag. Please see the--helpdocumentation for more arguments.OutputThe final output reports are present inoutput/final_report. Inoutput, intermediate directories
for the other modules' output is present as well.
Package: olive-oil-ml
[![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/mfinzi/pristine-ml)](https://codeclimate.com/github/mfinzi/pristine-ml)
[![CodeClimate](http://img.shields.io/codeclimate/mfinzi/pristine-ml.svg?style=flat)](https://codeclimate.com/github/mfinzi/pristine-ml“CodeClimate”)# Olive-Oil-MLNuts and bolts deep learning library to make training neural networks easier.
Features:
* Logging functionality not at set time intervals but as a percentage of the total training time
* Convenient specification for Random and Grid hyperparameter search: queued with a single GPU, split over multiple local GPUs, or over a Slurm Cluster
* Clean implementation of popular methods/problems in CV such as Vanilla Image Classification, Regression, the PiModel for Semi-Supervised learning, and SN-GAN; all through the common Trainer abstraction# Installation
To install, runpip install git+https://github.com/mfinzi/olive-oil-ml. Dependencies will be checked and installed from the setup.py file.# Dependencies
* Python 3.7+
* [PyTorch](http://pytorch.org/) 1.3.0+
* [torchvision](https://github.com/pytorch/vision/)
* [tqdm](https://tqdm.github.io/) 4.40+
* [natsort]()
* (optional) [tensorboardX](https://github.com/lanpa/tensorboardX)# Jump into training a single modelTo get a feel for the library, try training a (Classifier, Regressor, PiModel, GAN) model from our recipes.
For classification try running
*python oil/recipes/simpleTrial.py –dataset CIFAR100 –num_epochs 10Or, to train a conditional GAN model:
*python oil/recipes/simpleCGan.py –dataset SVHN –lr 2e-4Or train a PiModel semisupervised on CIFAR10 using only 1k labels:
*python oil/recipes/simplePi.py –dataset CIFAR10 –train 1000You can use-hto see the full range of arguments available. Command line arguments and defaults are automatically inferred
from the code used to construct the trial, so you can make a new trial (that uses some exotic data augmentation strategy for example) and the command line parser will generated for you, see the example recipes for how this works.# Perform a hyperparameter search
Example: Search over hyperparameters for CNN classifier on Cifar100
*python oil/recipes/exampleHyperSearch.py –dataset CIFAR100 –bs [50,32,64] –k [64,96] –num_epochs 100See example code for programmatic way of specifying the hyperparameter search.
Automatically parallelizes the search over multiple GPUs if available.
# Logging Support# Interfacing with external libraries
Package: oliver-codegen
codegencodegen 是一款快速构建项目代码框架的工具.安装$pipxinstalloliver-codegen使用方式$codegencreateyour_project_name-tpython其中-t后的参数是选择什么模板创建项目. 查看支持哪些模板命令codegen list template展示配置输入以下命令, 将在控制台展示暂时支持哪些模板.$codegenlisttemplate卸载软件$pipxuninstalloliver-codegen
Package: oliver_dl
JerryMeet Oliver, your video alchemist. Drop him an M3U8 link, and he'll whip up a lightning-fast concoction, delivering a sleek MP4 straight to your local realm. Oliver's all about that need-for-speed vibe!make sure you haveffmpeginstalledInstall poetrypipinstallpoetryInstall dependanciespoetryinstallCreate and run virtual environmentpoetryshellrunpythonoliver.py
Package: olivertwist
oliver-twistDAG Auditoroliver-twist is a dag auditing tool that audits theDBTDAG and generates a summary report. There is adocs site, including descriptions of allcurrently implemented rules.Getting StartedTo get started, install the package$pipinstallolivertwistand then run it by passing it your dbt manifest JSONolivertwistcheckmanifest.jsonThis will report any failures to the console, and also in HTML format in a directory calledtarget. You can optionally auto-open the report in a browser with:olivertwistcheckmanifest.json--browserFull options are available with:olivertwistcheck--helpConfigurationAll rulesare enabled by default. To change this you need a configuration file calledolivertwist.ymlin the same directory you are runningolivertwist. An example configuration is shown below:version:'1.0'universal:-id:no-rejoin-modelsenabled:false-id:no-disabled-modelsenabled:trueThere is a command to help you generate the config automatically:olivertwistconfigThis will show all the available rules and allow you to toggle the ones that you want to enforce.Local DevelopmentClone this repo and install the project:poetry installInstall pre-commit hooks for lintingThis is optional, but highly recommended to avoid annoying linting failure in CI.poetry run pre-commit installTo run the pre-commit hooks locally:poetry run pre-commit runTo get the latest versions of the dependencies and to update the poetry.lock file run:poetry updateTo run oliver-twist and generate the summary report run:poetry run olivertwist example_manifest.jsonWorking with diagramsTo update and regenerate the images that illustrate rule failures in the documentation follow the next steps:update the diagrams using themermaid syntaxinstallyarncd docs/diagrams./generate.shinspect the generated images in./docs/diagrams/output/if you're happy with the results, run./copy.shso that they are copied over to./docs/imagesyou can now reference those images. e.g. in.docs/rules.mdCreating a distributionpoetry build --format wheel
Package: olivia
OliviaUma simples, amigável e poderosa assistente para requisições HTTPOlivia utiliza a bibliotecarequests, tornando sua execução mais intuitiva.Instalaçãopip install oliviaMétodosNa versão0.3.0, Olivia assite aos métodosPOST, GET, DELETEModo de UsarPOSTfromoliviaimportOliviainfos={"rua":"Verde","numero":"29"}dados=Olivia("http://url@url.com",infos)dados.olivia_salva()Olivia