Saravanan666/pypi-org huggingface dataset:
Package: gojson
# gojsonA python client forhttps://db.neelr.dev/## Usage# import library
from gojson import Client# initialise client
c = Client(“521726728521c3df546d8ad699750d78”)# add data
c.store({‘hello’:’world’})# retrieve data
c.retrieve()# delete data
c.delete()see gojson/__init__.py for more it’s literally 19 lines
Package: gokart
gokartGokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.Documentationfor the latest release is hosted on readthedocs.About gokartHere are some good things about gokart.The following meta data for each Task is stored separately in apklfile with hash valuetask output dataimported all module versionstask processing timerandom seed in taskdisplayed logall parameters set as class variables in the taskAutomatically rerun the pipeline if parameters of Tasks are changed.Support GCS and S3 as a data store for intermediate results of Tasks in the pipeline.The above output is exchanged between tasks as an intermediate file, which is memory-friendlypandas.DataFrametype and column checking during I/ODirectory structure of saved files is automatically determined from structure of scriptSeeds for numpy and random are automatically fixedCan code while adhering toSOLIDprinciples as much as possibleTasks are locked via redis even if they run in parallelAll the functions above are created for constructing Machine Learning batches. Provides an excellent environment for reproducibility and team development.Here are some non-goal / downside of the gokart.Batch execution in parallel is supported, but parallel and concurrent execution of task in memory.Gokart is focused on reproducibility. So, I/O and capacity of data storage can become a bottleneck.No support for task visualize.Gokart is not an experiment management tool. The management of the execution result is cut out asThunderbolt.Gokart does not recommend writing pipelines in toml, yaml, json, and more. Gokart is preferring to write them in Python.Getting StartedWithin the activated Python environment, use the following command to install gokart.pip install gokartQuickstartA minimal gokart tasks looks something like this:importgokartclassExample(gokart.TaskOnKart):defrun(self):self.dump('Hello, world!')task=Example()output=gokart.build(task)print(output)gokart.buildreturn the result of dump bygokart.TaskOnKart. The example will output the following.Hello, world!This is an introduction to some of the gokart.
There are still more useful features.Please SeeDocumentation.Have a good gokart life.AchievementsGokart is a proven product.It's actually been used bym3.incfor over 3 yearsNatural Language Processing Competition byNishika.inc2nd prize :Solution RepositoryThanksgokart is a wrapper for luigi. Thanks to luigi and dependent projects!luigi
Package: gokartcorelujobi
gokart-coreThe python package created from python bindings ofgokart-core.https://pypi.org/project/gokartpyBuildingBe aware that building can (currently) only happen withinade.If your build requirements aren't satisfied yet, run:makeinstall_depBuilding the complete and uploading a new version to pypi ist done with the commandmakepackageDevelopingA summary on how to create new python bindings can be found inHOW_TO_BINDINGS.md.LicensePybind11 is provided under a BSD-style license that can be found in the LICENSE file. By using, distributing, or
contributing to this project, you agree to the terms and conditions of this license.
Package: gokart-pipeliner
gokart-pipelinergokart pipeline projectUsagePlease showSampleTask.pyorEaxmple.ipynbfromgokart_pipelinerimportGokartPipelinerfromExampleTasksimport*# make pipelinepreprocess=[TaskA,{'task_b':TaskB,'task_c':TaskC},TaskD]modeling=preprocess+[TaskE,{'task_f':TaskF},TaskF]predict=[{'model':modeling,'task_a':TaskA},TaskG]# instantiation (setting static params)params={'TaskA':{'param1':0.1,'param2':'sample'},'TaskD':{'param1':'foo'}}config_path_list=['./conf/param.ini']gp=GokartPipeliner(params=params,config_path_list=config_path_list)# run (setting dynamic params)running_params={'TaskB':{'param1':'bar'}}gp.run(predict,params=running_params)task exampleclassTask(gokart.TaskOnKart):foo=gokart.TaskInstanceParameter()defrun(self):x=self.load('foo')self.dump(x)get task resultWe can get result of latest pipeline tasks.task_b_result=gp.run([TaskA,TaskB],return_value=True)write requiresIf you say "want to write requires" or "want to reuse existing tasks", we can useoverride_requiresparameter.params={'ExistingTask':{'override_requires':False}}gp.run([ExistingTask],params=params)for jupyter notebookoff loggergp.run([Task],params=params,verbose=False)Developpip install poetry
pip install poetry-dynamic-versioning

# poetry install
poetry build
# poetry lock
Package: gokinjo
# gokinjoA feature extraction library based on k-nearest neighbor algorithm in Python
- k-NN based feature has experience of being used in 1st place solution of Kaggle competitionSwitchable backend of k-NN algorithm
- [scikit-learn](https://github.com/scikit-learn/scikit-learn) (default)
- [annoy](https://github.com/spotify/annoy)FYI: ‘gokinjo’ is meant neighborhood in japanese.### PrerequisitePython 3.6 or latersetuptools >= 30.0.3.0### How to install#### From PyPI`bash $ pip install gokinjo `##### With annoy backend`bash $ pip install "gokinjo[annoy]" `#### From source code`bash $ pip installgit+https://github.com/momijiame/gokinjo.git`### Usage examplePlease see [examples](https://github.com/momijiame/gokinjo/tree/master/examples) in GitHub repository.### How to setup of development environment`bash $ pip install-e".[develop]"$ pytest `### ReferencesThe competition which k-NN feature was used on 1st place solution
-https://www.kaggle.com/c/otto-group-product-classification-challenge/discussion/14335R implementation
-https://github.com/davpinto/fastknn/blob/master/R/extract.RAnother Python implementation
-https://github.com/upura/knnFeat
Package: gokulang
gokulang日本語文章をアニメ版ドラゴンボールの孫悟空風に変換すっぞ。To translate Japanese sentences into Son Goku accent.ReuirementsJanome 0.3.7pykakasi 0.94semidbm 0.5.1six 1.12.0Install(venv) $ pip install gokulangUsagefrom gokulang.gokulang import GokuLang

g = GokuLang()
t = g.translate('FF外から失礼します')
print(t)FFげぇからしつれぇすっぞDetail悟空語ジェネレーターをぺぇそんでも作ってみたぞ！えーぴーえぇ化もしたぞ！ - QiitaWeb APIhttps://gokulang.herokuapp.com/Referenceこのモジュールはきんみさん(@_kinmi)の作成された悟空語ジェネレーターを参考に作成しました。フロント明るくないおじさんがWebアプリ作った話【悟空語ジェネレーター】 - Qiita
Package: gokuzen
PyPi:https://pypi.org/project/gokuzen/สวัสดีจ้า gokusen คืออะไร สามารถตรวจสอบข้อมูลว่า gokusen คืออะไร ###
วิธีติดตั้งเปิด CMD / Terminalpipinstallgokuzenวิธีใช้งานแพ็คเพจนี้เปิด IDLE ขึ้นมาแล้วพิมพ์…fromgokuzenimportgokuzengokuzen=gokuzen()gokuzen.show_name()#เราชื่ออะไรgokuzen.show_mail()#email ของเราgokuzen.about()#gokuzen คืออะไรgokuzen.show_ascii()#ภาพศิลปะพัฒนาโดย: Gokuzen
Package: golangenv
#golangenvThis tool is inspired by [nodeenv](https://github.com/ekalinin/nodeenv) and allows you to install golang into your virtualenvs.Unlike nodeenv, golangenvonlyworks inside virtualenvs.## Install` $ virtualenv myenv $ source myenv/bin/activate $ pip install golangenv $ golangenv install 1.5.1 `## Example` $ mkdir frontend $ echo 'use_env frontend' >frontenv/.env$ cd frontend $ pip install nodeenv golangenv $ golangenv install 1.5.1 $ go version `
Package: golangish
Port golang’s CSP semantics to Python, namely the behavior of thego,<-->andselectconstructs.InstallationTo install use pip:$ pip3 install golangishOr clone the repo:$ git clone github.com/edouardklein/golangish
$ python3 setup.py installUsageDocumentationRead the documentationhttp://golangish.readthedocs.io/en/latest/to understand how to use golangish.In the cloned repoHelper targetsTo build the documentation, run:$ make docTo run the test, run:$ make testTo check the code’s superficial cleanliness run:$ make lintTo run tests each time a Python file is edited$ make live
Package: golden
# goldenPython library built upon [requests](https://github.com/kennethreitz/requests) to access and parse data from [Golden](https://golden.com)'s collection.[Golden](https://golden.com) is a Wikipedia alternative that focuses on emerging tech, startups and ideas.Extract company summaries, timeline of recent events, key people and more !## Usage```python>>> from golden import golden>>> search = golden.download('Apple')>>> golden.title(search)# 'Apple (company)'>>> golden.summary(search)# Apple Inc. is a public company designing and selling personal computers, smartphones, consumer electronics, and software. Its headquarters is located in Cupertino, California and it was founded in 1976.﻿Apple Inc. is a California-based electronics company with a focus producing on consumer devices.>>> golden.content(search, sentences=3)#'﻿Apple Inc. is a California-based electronics company with a focus producing on consumer devices. ﻿Products﻿Products and devices produced by Apple Inc. include iPad, iPhone, AirPods,Apple Watch, HomePod, and MacBook.﻿ Each product can give users access to one or more forms of media or technology including television, music, data storage, and computer applications.The products run on the Mac operating system, which has special features thare not available on non-Mac systems. Furthermore, the devices use continuity, which allows for all the devices owned by a user to beconnected.﻿﻿﻿The company also produces software as a service and media options.'>>> events = golden.timeline(search, events=1)>>> for event in events:>>>     print(event["date"], " : ", event["subtitle"], "\n", event["content"])# March 25, 2019  :  Apple Card# On March 25, 2019 during their keynote event Apple, in partnership with Goldman Sachs and Mastercard, announced Apple Card. A credit card by Apple with no fees—no annual, cash-advance, over-the-limit, international, or late fees— thats gives Apple users the ability to sign up for Apple Card using the Apple Wallet application.>>> other_search = golden.download("jetpack aviation")>>> people = golden.people(other_search)>>> for p in people:>>>     print(p["name"], p["role"])# Boris Jarry Employee# Daniel Schwarzbaum Employee# David Mayman Founder, CEO, Test Pilot, Project Manager# Nelson Tyler Founder, Principle Designer# Sergey Samchik Employee>>> ceo = golden.people(other_search, "CEO")>>> print(ceo)# David Mayman : Founder, CEO, Test Pilot, Project Manager```## InstallationTo install golden run following command :```$ pip install golden```## TO DO* Add new queries :* Commpany Url* Products* Country* Improve suggestion while querying* Hide html output when downloading a new page* Improve content query output:* Sentences nb query parameter doesn't always work## LICENCEAuthored and maintained by [Terros Designs](https://terrosdesigns.com)MIT licensed. See the [LICENSE](https://github.com/terrosdesigns/golden/blob/master/LICENSE) file for full details.
Package: goldendoodle
READMEGoldendoodleis a universal spider for analyzing wether domains include a search string.It is made for a first analyze of a unknown site. The analyze one search string is done for one search string on a list of start URLs. The allowed domain(s) are extracted from the starting URLs.The result is a Scrapy output file with Goldendoodle items.How to execute:From goldendoodle run:scrapycrawlrecherchedechaîne_gldnddl-O<file>[-agldnddlSearchString=<string>]-aoption=<None|regex|email>-astart_urls=<url>[,<url>]p. e.:scrapycrawlrecherchedechaîne_gldnddl-O../reports/findings.json-agldnddlSearchString='(Bereich.*obe[nr].*?)(?=<)'-astart_urls=https://www.drta-archiv.de/blauaugenkaerpflinge/2>../reports/stderr.logoptionoption is optional, which meansNone.If gldnddlSearchString is a regular expression, than you should set option=regex. Otherwise findingElements and findingElementsQuery will not be filled. With option=regex you will get findingElements and findingElementsQuery only for the first finding in the site.With option=emailnogldnddlSearchStringis necessary. There is a search algorithm that gives a result for the most common types of email coding. If you find an email that is not recognized, please let me know so I can adjust the algorithm.With option=email AND gldnddlSearchStringset, the gldnddlSearchString is used, but the results are prepared, as if emails have been searched.example:scrapycrawlrecherchedechaîne_gldnddl-Oreports/findings.json-agldnddlSearchString='for use in illustrative examples in documents'-astart_urls=https://example.com/|teereports/log.txtPrerequisitessee requirements.txtDocumentationseeGoldendoodle docsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Package: golden-frame
Golden Frame"กรอบทอง Generator"Examples+=+=Command for Above Examplesgolden-framebuildgolden_frame.pngexample/MasterIceZ.jpg--output=example/New-MasterIceZ.png

golden-framebuildwessuwan.pngexample/honami-stella.jpg--output=example/honami-wessuwan.pngOther commandsUsegolden-frame --helporgolden-frame build helpAdding ImagesCurrently, to add images, add them directly in assets folder.You can get its location with this commandimportosfrompkg_resourcesimportget_distributionlocation=get_distribution("golden-frame").locationASSET_PATH=os.path.join(location,"golden_frame/assets")print(ASSET_PATH)Note that this is temporary and can be overwrite when installing new version.file_name.jsonSchema{
  "name": "Golden Frame ทพจร",
  "pos": [
    [122, 122],
    [620, 122],
    [620, 844],
    [112, 844]
  ],
  // Optional, for image scaling to ensure good quality
  "defaultMultiplier": 2
}Position is from Top-Left rotate clockwise.Reminder that x goes from left to right and y from top to bottomLive DemoAvailable at (Coming Soon)Coming Soon { flex: true }Adding this properties allow images to be stretch into the same aspect ratio as the input image
Package: goldenhinges
Golden Hinges (full documentationhere) is a Python library to find sets of overhangs (also called junctions, or protrusions) for multipart DNA assembly such as Golden Gate assembly.Given a set of constraints (GC content bounds, differences between overhangs,
mandatory and forbidden overhangs) Golden Hinges enables to find:Maximal sets of valid and inter-compatible overhangs.Sequence decompositions (i.e. position of cuts) which produce valid and
inter-compatible overhangs, for type-2S DNA assembly.Sequence mutations (subject to constraints) which enable the sequence
decomposition, in extreme cases where the original sequence does not allow
for such decomposition.InfosPIP installation:pipinstallgoldenhingesWeb documentation:https://edinburgh-genome-foundry.github.io/GoldenHinges/Github Page (with examples)https://github.com/Edinburgh-Genome-Foundry/GoldenHingesLive demo:http://cuba.genomefoundry.org/design_overhangsLicense:MIT, Copyright Edinburgh Genome FoundryMore biology softwareGolden Hinges is part of theEGF Codonssynthetic biology software suite for DNA design, manufacturing and validation.
Package: goldenowl
goldenowl: Financial analysis toolkitWhat is it?goldenowlis a Python package that provides most commonly used utilities for analysis in the financial domain such as those related to investments and trading.Main FeaturesHere are few things this package does[Holding Analysis]Calculate XIRR of multi touch point investments, portfolio returns etcSIP returns calculations for individual holdingsPortfolio construction, rebalance, XIRR and SIP calculations[Asset Analysis]Transform data to different time framesPerform basic computations such as range, returns etcWhere to get itThe source code is currently hosted on GitHub at:https://github.com/nishanth-/goldenowlInstallers for the latest released version are available at thePython
Package Index (PyPI)pipinstallgoldenowlDependenciesxirr - For calculation of xirrpandas - For data manipulationnumpy - For numerical operationsLicenseMIT
Package: golden-ratio-stats
Golden Ratio in StatisticsTo determine skewness, mean and deviation with a new approach on continuous dataInstallationpip install golden-ratio-in-statisticsGet startedHow to get GRiS result for dataset with this lib:# Library importfromgolden_ratio_stats.golden_ratio_approchimportGoldenRatio# This line of code will allow shorter importsfromgolden_ratio_statsimportGoldenRatio# Instantiate a GoldenRatio object"""Firstly, import the excel file, then run the libraryP.S. Excel file must include name of each column. Like that;name1	name2	name3	name4 ...73,36	72,64	68,45	66,52 ...78,97	67,04	60,85	70,96 ......     ...     ...     ...   ..."""importosimportpandasaspdpath=r'C:\data'os.chdir(path)df=pd.read_excel("my_dataset.xlsx")# GRiS outputprint(GoldenRatio(df))
Package: goldenretriever-core
🦮 Golden RetrieverHow to useInstall the library fromPyPI:pipinstallgoldenretrieveror from source:gitclonehttps://github.com/Riccorl/golden-retriever.gitcdgoldenretriever
pipinstall-e.UsageHow to run an experimentTrainingHere a simple example on how to train a DPR-like Retriever on the NQ dataset.
First download the dataset from (DPR)[]. The run the following code:fromgoldenretriever.trainerimportTrainerfromgoldenretrieverimportGoldenRetrieverfromgoldenretriever.data.datasetsimportInBatchNegativesDataset# create a retrieverretriever=GoldenRetriever(question_encoder="intfloat/e5-small-v2",passage_encoder="intfloat/e5-small-v2")# create a datasettrain_dataset=InBatchNegativesDataset(name="webq_train",path="path/to/webq_train.json",tokenizer=retriever.question_tokenizer,question_batch_size=64,passage_batch_size=400,max_passage_length=64,shuffle=True,)val_dataset=InBatchNegativesDataset(name="webq_dev",path="path/to/webq_dev.json",tokenizer=retriever.question_tokenizer,question_batch_size=64,passage_batch_size=400,max_passage_length=64,)trainer=Trainer(retriever=retriever,train_dataset=train_dataset,val_dataset=val_dataset,max_steps=25_000,wandb_online_mode=True,wandb_project_name="golden-retriever-dpr",wandb_experiment_name="e5-small-webq",max_hard_negatives_to_mine=5,)# start trainingtrainer.train()Evaluationfromgoldenretriever.trainerimportTrainerfromgoldenretrieverimportGoldenRetrieverfromgoldenretriever.data.datasetsimportInBatchNegativesDatasetretriever=GoldenRetriever(question_encoder="",document_index="",device="cuda",precision="16",)test_dataset=InBatchNegativesDataset(name="test",path="",tokenizer=retriever.question_tokenizer,question_batch_size=64,passage_batch_size=400,max_passage_length=64,)trainer=Trainer(retriever=retriever,test_dataset=test_dataset,log_to_wandb=False,top_k=[20,100])trainer.test()InferencefromgoldenretrieverimportGoldenRetrieverretriever=GoldenRetriever(question_encoder="path/to/question/encoder",passage_encoder="path/to/passage/encoder",document_index="path/to/document/index")# retrieve documentsretriever.retrieve("What is the capital of France?",k=5)Data formatInput dataThe retriever expects a jsonl file similar toDPR:[{"question":"....","answers":["...","...","..."],"positive_ctxs":[{"title":"...","text":"...."}],"negative_ctxs":["..."],"hard_negative_ctxs":["..."]},...]Index dataThe document to index can be either a jsonl file or a tsv file similar toDPR:jsonl: each line is a json object with the following keys:id,text,metadatatsv: each line is a tab-separated string with theidandtextcolumn,
followed by any other column that will be stored in themetadatafieldjsonl example:[{"id":"...","text":"...","metadata":["{...}"]},...]tsv example:id \t text \t any other column
...
Package: goldensphinx
# Golden SphinxBuild and serve Sphinx docs.Example usage:```sh$ goldensphinx build serve```You can also just build:```sh$ goldensphinx build```or just serve:```sh$ goldensphinx serve```To see the options you can control, run a command with `--help`:```sh$ goldensphinx build --helpUsage: goldensphinx build [OPTIONS]Options:--source_dir PATH  Directory containing documentation source--dest_dir PATH    Directory to build documentation to--help             Show this message and exit.$ goldensphinx serve --helpUsage: goldensphinx serve [OPTIONS]Options:--static_dir PATH      Static directory to serve--host TEXT            Hostname to serve on--port INTEGER         Port to serve on--threads INTEGER      Number of threads--help                 Show this message and exit.```All options can be also be set using environment variables:- `source_dir` and `static_dir` by `STATIC_DIR`- `dest_dir` by `DOCS_DIR`- `port` by `PORT`- `host` by `HOST`- `threads` by `NUM_SERVER_THREADS`For example:```sh$ export PORT=8080$ goldensphinx serve --host 127.0.0.1[2016-09-06 10:02:14 +0300] [55096] [INFO] Starting gunicorn 19.6.0[2016-09-06 10:02:14 +0300] [55096] [INFO] Listening at: http://127.0.0.1:8080 (87302)```You can also set the environment variable `GOLDENSPHINX` to `false` if you want to prevent goldensphinx from running.## InstallStable:```shpip install goldensphinx```Latest:```shpip install git+https://github.com/yoavram/goldensphinx.git```## Authors- Yoav Ram (@yoavram)## Acknowledgements- The project is inspired by, and the `serve` command is modified from [kennethreitz/goldenarch](https://github.com/kennethreitz/goldenarch).- Many thanks to the authors of Python, Gunicorn, Sphinx, Click, Static and everything else we build our apps with.
Package: goldfinch
Aboutgoldfinch is simple package which removes invalid filename characters from a filename.  Pass in a string or unicode (with or without invalid characters) and get valid filename returned (as unicode).In general, goldfinch will remove invalid filename characters like <>:”/|?* and characters above the 0-255 character set.  The default is is to attempt to convert to ascii characters using _unicodedata_.Installpip install goldfinch
or
easy_install -U goldfinchWhy use goldfinchThe only real usecase for this package is when one creates filenames on the fly and there is no way of knowing what exactly will be passed as the filename.  I find it particularly useful when I scrape websites.So, instead of this happening:>>> fileName = 'this is a filename with some invalid characters in it <>:"/\|?*'
>>> file = open(fileName,"w")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IOError: [Errno 2] No such file or directory: 'this is a filename with some invalid characters in it <>:"/\\|?*'Do this:>>> from goldfinch import validFileName as vfn
>>> fileName = 'this is a filename with some invalid characters in it <>:"/\|?*'
>>> file = open(vfn(fileName),"w")ExamplesThere are three (space, initCap, and ascii) options available when normalizing a filename.The default is ‘space=”underscore”, initCap=True, ascii=True’.For space the options are underscore, remove, and keep.  The default is underscore which will replace a space with an underscore (_).initCap is a True & False option.  True which is the default will convert the first character in a word to cap and leave all others lowercase.ascii is also a True & False option.  Once again True is the default which will convert something like this ÅåÄäÖ to this AaAaO. False will leave characters as-is.Use default setting:>>> fileName = 'THIS IS a filename with ÅåÄäÖ some characters that will not work like these: <>:"/\|?*'
>>> vfn(fileName)
'This_Is_A_Filename_With_Aaaao_Some_Characters_That_Will_Not_Work_Like_These'Default with unicode input:>>> fileName = u'THIS IS a filename with ÅåÄäÖ some characters that will not work like these: <>:"/\|?*'
>>> vfn(fileName)
'This_Is_A_Filename_With_Aaaao_Some_Characters_That_Will_Not_Work_Like_These'With initCap = False:>>> vfn(fileName, initCap = False)
'THIS_IS_a_filename_with_AaAaO_some_characters_that_will_not_work_like_these_'With space = ‘remove’:>>> vfn(fileName, space = 'remove')
'ThisIsAFilenameWithAaaaoSomeCharactersThatWillNotWorkLikeThese'