Saravanan666/pypi-org huggingface dataset:
Package: pjlog
PJLOGThis is a simple Logarithm Library to make working with Logarithm easy and with better useful functions.InstallationRun the following to installpipinstallpjlogUsageimportpjlogaspl# Make a Logarithm Objecta=pl.log(value=100,base=10)# Printing the Objectprint(a)# To get valueprint(a.Lget_value())# To addc=a+pl.log(value=4,base=2)
Package: pjplan
pjplan: Python library for project planning and analysisWhat is it?pjplanturns yourJupyter Notebookinto project management software.
This Python package gives you a simple API with following features:Describe Work Burn-down Structure (WBS) of your project in code or load it from external source.Manipulate tasks: link them as successors/predecessors, find, delete, reorder, etc. A lot of batch operations are supported via simple commands.Assign resources and estimates to tasksDefine work calendars for each resourceCalculate project schedule in several waysFind critical pathVisualize WBS as Gantt or Network diagramSave/Load your WBS to/from csv filesExport WBS topandasWhy to use it?There are several use cases for pjplan:Work with project plan as code with your favourite IDE, VSC, etc.Load tasks data from your tracker (Jira, Asure Devops, Trello, etc.) to pjplan,
link/modify/reorder/delete tasks using rich pjplan API, then create schedule and Gantt for them.Create several schedules for same project and find optimal one.Where to use it?pjplancreated specially for use insideJupyter Notebookor similar software. All library objects (WBS, Task, Schedule, Calendar) have great visualisations
so you can easily work with them in notebooks.But, of course, you can use pjplan in any other python applications.InstallationpipinstallpjplanGetting startedLet's define simple project WBS:frompjplanimportWBS,TaskwithWBS()asprj:prj//Task(1,'Task 1',estimate=40,resource_name='Developer')prj//Task(2,'Task 2',predecessors=[prj[1]],estimate=20,resource_name='Developer')withprj//Task(3,'Task 3')ast3:t3//Task(4,'Task 4',predecessors=[prj[2]],estimate=100,resource_name='Tester')t3//Task(5,'Task 5',predecessors=[prj[2]],estimate=50,resource_name='Tester')Let's define resources:frompjplanimportResource,WeeklyCalendarwork_calendar=WeeklyCalendar(days=[0,1,2,3,4],units_per_day=8)developer=Resource(name='Developer',calendar=work_calendar)tester=Resource(name='Tester',calendar=work_calendar)Now we can create schedule for our project:fromdatetimeimportdatetimefrompjplanimportForwardSchedulerschedule=ForwardScheduler(start=datetime.now(),resources=[developer,tester]).calc(prj)visualise it asMermaidGantt:frompjplanimportMermaidGanttMermaidGantt(schedule.schedule)or Network:frompjplanimportMermaidNetworkMermaidNetwork(schedule.schedule)save schedule to csv:frompjplanimportwrite_csvwrite_csv(schedule.schedule,"schedule.csv")More examplesMore examples you can find atexamplesdirectory.
Package: pj-rfnotifier
RobotSlackNotificationsSend notifications to Slack or Mattermost using Robot Framework Listener.InstallationThe recommended installation method is pip:pip install robotframework-notificationsRunning this command installs also the latest version of RequestsImport LibraryTo use RobotSlackNotifications in Robot Framework, the library needs to be imported using theLibrarysetting as any other library. The library needs the webhook url from Slack or Mattermost as an argument.You can retrieve this webhook url in Slack or Mattermost.Slackhttps://slack.com/intl/en-lv/help/articles/115005265063-incoming-webhooks-for-slackMattermosthttps://docs.mattermost.com/developer/webhooks-incoming.html#simple-incoming-webhookUsageImport library then add it on the listener during Robot Framework test run.
Package: pj-rfreport
RobotSlackNotificationsSend notifications to Slack or Mattermost using Robot Framework Listener.InstallationThe recommended installation method is pip:pip install robotframework-notificationsRunning this command installs also the latest version of RequestsImport LibraryTo use RobotSlackNotifications in Robot Framework, the library needs to be imported using theLibrarysetting as any other library. The library needs the webhook url from Slack or Mattermost as an argument.You can retrieve this webhook url in Slack or Mattermost.Slackhttps://slack.com/intl/en-lv/help/articles/115005265063-incoming-webhooks-for-slackMattermosthttps://docs.mattermost.com/developer/webhooks-incoming.html#simple-incoming-webhookUsageImport library then add it on the listener during Robot Framework test run.
Package: pj-rf-report
RobotSlackNotificationsSend notifications to Slack or Mattermost using Robot Framework Listener.InstallationThe recommended installation method is pip:pip install robotframework-notificationsRunning this command installs also the latest version of RequestsImport LibraryTo use RobotSlackNotifications in Robot Framework, the library needs to be imported using theLibrarysetting as any other library. The library needs the webhook url from Slack or Mattermost as an argument.You can retrieve this webhook url in Slack or Mattermost.Slackhttps://slack.com/intl/en-lv/help/articles/115005265063-incoming-webhooks-for-slackMattermosthttps://docs.mattermost.com/developer/webhooks-incoming.html#simple-incoming-webhookUsageImport library then add it on the listener during Robot Framework test run.
Package: pj-rf-testrail
RobotTestRailUpdateUpdate Test Rail execution status using Robot Framework.InstallationThe recommended installation method is pip:pip install robotframework-notificationsRunning this command installs also the latest version of RequestsImport LibraryTo use RobotSlackNotifications in Robot Framework, the library needs to be imported using theLibrarysetting as any other library. The library needs the webhook url from Slack or Mattermost as an argument.You can retrieve this webhook url in Slack or Mattermost.Slackhttps://slack.com/intl/en-lv/help/articles/115005265063-incoming-webhooks-for-slackMattermosthttps://docs.mattermost.com/developer/webhooks-incoming.html#simple-incoming-webhookUsageImport library then add it on the listener during Robot Framework test run.
Package: pj-robotnotifier
RobotSlackNotificationsSend notifications to Slack or Mattermost using Robot Framework Listener.InstallationThe recommended installation method is pip:pip install robotframework-notificationsRunning this command installs also the latest version of RequestsImport LibraryTo use RobotSlackNotifications in Robot Framework, the library needs to be imported using theLibrarysetting as any other library. The library needs the webhook url from Slack or Mattermost as an argument.You can retrieve this webhook url in Slack or Mattermost.Slackhttps://slack.com/intl/en-lv/help/articles/115005265063-incoming-webhooks-for-slackMattermosthttps://docs.mattermost.com/developer/webhooks-incoming.html#simple-incoming-webhookUsageImport library then add it on the listener during Robot Framework test run.
Package: pjscan
PJScanIntroductionThis framework helps you discover security vulnerabilities by executing graph traversals on the Code Property Graph.NetworkX is a Python package for the creation, manipulation,
and study of the structure, dynamics, and functions
of complex networks.Website (including documentation):TODOMailing list:TODOSource:https://github.com/EnhancedPHPJoern/PJScanBug reports:https://github.com/EnhancedPHPJoern/PJScan/issueTutorial:TODOGitHub Discussions:https://github.com/EnhancedPHPJoern/PJScan/discussionsInstallInstall the latest version of NetworkX::$ pip install pjscanInstall with all optional dependencies::$ pip install pjscan[all]For additional details, please seeINSTALL.rst.BugsPlease report any bugs that you findhere <https://github.com/EnhancedPHPJoern/PJScan/issues>.
Or, even better, fork the repository onGitHub <https://github.com/EnhancedPHPJoern/PJScan>and create a pull request (PR). We welcome all changes, big or small, and we
will help you make the PR if you are new togit(just ask on the issue and/or
seeCONTRIBUTING.rst).LicenseReleased under the 3-Clause BSD license (seeLICENSE.txt)::
Package: pj-scraper
noneFree software: MIT licenseInstallationpip install pj-scraperDocumentationTo use the project:frompj_scraper.scraperimportScrapers=Scraper()products=s.get_all_products_from_category("smartklokker")sellers_and_prices=s.get_sellers_and_prices_of_product_list(products["product_number"])Changelog0.0.0 (2021-04-09)First release on PyPI.
Package: pjscript
A single file, dependency free language written in python. The goal of this project is to show that
programming languages are not a black box and that you could learn the basic concpets required to implement your own language.
While this language is not a real programming language however a lot of the internal concepts displayed in this
language is taken from other real programming languages.how to Runpython-mpjscript<file.js>#or you could use the lexer, parser and interpreter in your python filefrompjcriptimport*lexer=Lexer("var x = 1;")tree=Parser(lexer)interpreter=Interpreter(tree)interpreter.interpret()Data types:- Int (positive & negitive)
- String
- Array
- Object
- NullBasic statemnets/* this is a comment */varage=2;varname="jack";varnewage=age;name="jack and me";log(1,"it's working fuck yea",name);varx=add(age,add(2,3));log(x);Functions and Scopevarx="hello world";functionprintme(st1,st2,st3){log(st1);log(st2);log(st3);};functiontestfunc(){varx="new thing";printme(x,1,3);};testfunc();log(x);Expressionsvarx=0+add(2,3);varx=x+add(2,3);varx=(x*2)/2;log(x);/* should log 10*/functionMul(){returnadd(2,add(2*3,2));};varresult=Mul();log(result);/* post-increment and decrement */varx=2;log("x: ",x);vary=x++*2;log("y: ",y);/* output y: 4 */log("x: ",x);/* output y: 3 *//* pre-increment and decrement */varx=2;log("x: ",x);vary=++x*2;log("y: ",y);/* output y: 6 */log("x: ",x);/* output y: 3 *//* Comparison Operators */varx=2;vary=x>=2;log("y: ",y);/* True */log("x: ",x);/* 2 */For Loopsfor(vari=0;i<5;i++){log(i);};/* or */vari=0;for(i=2;i<5;i++){log(i);};/* infinite loop */vari=0;for(;;i++){log(i);};IF ELSEvarx=0;if(x==3){log("if");}elseif(x==2){log("else if");}elseif(x==1){log("else if second");}else{log("else");};vary=2;if(x==2){log("if only");};for(vari=0;i<5;i++){if(i==3){log(i);};};Array// numbersvarnumbers=[1,2,3];log(numbers);log(numbers[2]);// Stringsvarperson=["jack","jay","nick"];for(vari=0;i<3;i++){log(person[i]);};Object// Objectvarperson={name:"jack",age:23};log(person.name);log(person.age);
Package: pjsekai-background-gen-pillow
pjsekai_background_gen_pillowGenerates PJSekai background image from Image.Installationpip install pjsekai_background_gen_pillowExamplefromPILimportImageimportpjsekai_background_gen_pillowaspjbggenerator=pjbg.Generator()generator.generate(Image.open("path/to/image.png")).save("path/to/output.png")CLIYou can runpjsekai_background_gen_pillow,pjbgorpython -m pjsekai_background_gen_pillowfrom command line.usage: pjsekai_background_gen_pillow [-h] [-b BACKGROUND] [-f FORMAT] base output

Generates PJSekai background image from Image.

positional arguments:
  base                  Base image file path, or "-" for stdin.
  output                Output image file path, or "-" for stdout.

optional arguments:
  -h, --help            show this help message and exit
  -b BACKGROUND, --background BACKGROUND
                        Background image file path.
  -f FORMAT, --format FORMAT
                        Output image format.APIGeneratorGenerater for background images.Parametersbase : PIL.ImageBase background image.
Defaults to Built-in background image..generate(image)Generate background image from source image.Parameterssource : PIL.ImageSource image.ReturnsPIL.ImageLicenseThis library is licensed under GPLv3.test.pngis licensed under CC-BY-SA 4.0.
Package: pjsekai.scores
Project Sekai Scoresプロセカの譜面ファイル解析し、ベクトル画像（SVG）を生成する。Installationpip でインストール：pip install pjsekai.scoresまた、手動でビルドしてインストール：python -m build
pip install dist/pjsekai.scores-*.whlUsageProject Sekai Scores には、デフォルトの起動スクリプトが含まれており、ローカルの SUS 譜面ファイルを読み込み、それを SVG ベクター画像に変換することができます。以下のコマンドを使って簡単に実行できます：python -m pjsekai.scores <xxx.sus>以下はパッケージとして使用して譜面画像を生成する例です：importpjsekai.scoresscore=pjsekai.scores.Score.open('1.sus',encoding='UTF-8')drawing=pjsekai.scores.Drawing(score=score)drawing.svg().saveas('1.svg')譜面ファイルを個性化して豊かにするカスタマイズ機能を提供しています。下記の文書をご参照ください：BPM、拍子、セクションを変換歌詞を追加スタイルシートをカスタマイズLicenseProject Sekai Scores は MIT ライセンスのもとで提供されています。詳細については、LICENSEファイルをご覧ください。Project Sekai Scores は SEGA・Colorful Palette・プロジェクトセカイとは一切関係がありません。
Package: p-js-rs
PJSPJS is a utility class for writing and reading JSON data in Python.Example Usagefromp_jsimportPJSdata={"key":"value"}# Write compressed dataPJS.write_compressed_data(data,"output")# Read compressed dataread_data=PJS.read_compressed_data("output")print(read_data)# Githubhttps://github.com/Rajveer-Singh8124/P_JS
Package: pjtree
Make any directory and files by json.
Make directory hierarchy to json.RequirementsPython 2.7Python 3.3FeaturesnonthingCommandsjreadjwriteExamplesTarget directory to json.$ jread [Target directory] [-f, --file] [--encoding]Make any directory and files by json.$ jwrite [Json] [Make path] [--encoding] [--force] [--notoverwrite]Make any directory and files.Installationeasy_install:$ easy_install pjtreepip:$ pip install pjtreeLicenseCopyright (c) 2013, Kazuki Hasegawa All rights reserved.Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.HistoryVersion 0.1.0 (9-15-2013)Responded to the python3.xVersion 0.0.3 (9-14-2013)Added overwrite actionVersion 0.0.2 (7-11-2013)Responded to the python3.3Version 0.0.1 (7-9-2013)First release
Package: pjv
PJV helps validating and find erroneous line in JSON file (as far as it can) .InstallationBy using pip:pip install pjvExampleGiven sample.json file content:{"scores":
    {"hvn": "9"},
    {"nvh": "6"},
}Use pjv to validate it:$ pjv sample.json
Error: Expecting property name: line 3 column 5 (char 33)
2     {"hvn": "9"},
3     {"nvh": "6"}, <----- this or maybe the line above is informal, such as redundant or missing `,`. See http://json.org for JSON format.
4 }AuthorsViet Hung Nguyen <hvn@familug.org>ContributionAll contribution are greatly welcome :+1:
Package: pk1
IntroductionPackOne, inspired by the “serverless” trend in cloud computing and big data processing, has the ambitions to bridge the IaaS to Apache Ambari seamlessly and coordinate Ambari Services into an elastic high-level workspace.InstallChoose a linux host with python 3.6, and create a postgresql db with its information (db_user, db_passwd, db_host, db_port, db_name) collected. Then run:pip install pk1pip install -U pip setuptoolspk1 setup –database db_user:db_passwd:db_host:b_port:db_nameRunpk1 start [–listening 127.0.0.1:11001]Stoppk1 stopUninstallpk1 uninstall
Package: pk1-jet
IntroductionPackOne, inspired by the “serverless” trend in cloud computing and big data processing, has the ambitions to bridge the IaaS to Apache Ambari seamlessly and coordinate Ambari Services into a high-level workspace.InstallChoose a linux host which network can reach the target openstack group resource, and run:pip install pk1pk1 setup [–database pk1_db_user:pk1_db_passwd:pk1_db_host:pk1_db_port:pk1_db_name:]Runpk1 start [–listening 127.0.0.1:11001]Stoppk1 stopUninstallpk1 uninstallAcknowledgeNational key Research Program of China: Scientific Big Data Management System (No.2016YFB1000600)
Package: pkai
pKAIA fast and interpretable deep learning approach to accurate electrostatics-driven pKa prediction@article{pkai,
author = {Reis, Pedro B. P. S. and Bertolini, Marco and Montanari, Floriane and Machuqueiro, Miguel and Clevert, Djork-Arné},
title = {pKAI: A fast and interpretable deep learning approach to accurate electrostatics-driven pKa prediction},
note = {in preparation}
}Installation & Basic UsageWe recommend installing pKAI on a conda enviroment. The pKAI+ model will be downloaded on the first execution and saved for subsequent runs.python3 -m pip install pKAI

pKAI <pdbfile>It can also be used as python function,from pKAI.pKAI import pKAI

pks = pKAI(pdb)where each element of the returned list is a tuple of size 4. (chain, resnumb, resname, pk)pKAI+ vs pKAI modelspKAI+ (default model) aims to predict experimental pKavalues from a single conformation. To do such, the interactions characterized in the input structure are given less weight and, as a consequence, the predictions are closer to the pKavalues of the residues in water. This effect is comparable to an increase in the dielectric constant of the protein in Poisson-Boltzmann models. In these models, the dielectric constant tries to capture, among others, electronic polarization and side-chain reorganization. When including conformational sampling explicitly, one should use a lower value for the dielectric constant of the protein. Likewise, one should use pKAI -- instead of pKAI+ -- as in this model there is no penalization of the interactions' impact on the predicted pKavalues.tl;dr versionuse pKAI+ for pKapredictions arising from a single structureuse pKAI for pKapredictions arising from multiple conformationsChange the model to be used in the calculation by evoking themodelargument:pKAI <pdbfile> --model pKAIBenchmarkPerformed on 736 experimental values taken from the PKAD database1.MethodRMSEMAEQuantile 0.9Error < 0.5 (%)Null21.090.721.5152.3PROPKA31.110.731.5851.1PypKa41.070.711.4852.6pKAI1.150.751.6649.3pKAI+0.980.641.3755.0[1] Pahari, Swagata et al. "PKAD: a database of experimentally measured pKa values of ionizable groups in proteins." doi:10.1093/database/baz024[2] Thurlkill, Richard L et al. “pK values of the ionizable groups of proteins.” doi:10.1110/ps.051840806[3] Olsson, Mats H M et al. “PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions.” doi:10.1021/ct100578z[4] Reis, Pedro B P S et al. “PypKa: A Flexible Python Module for Poisson-Boltzmann-Based pKa Calculations.” doi:10.1021/acs.jcim.0c00718LicenseThis source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.ContactsPlease submit a github issue to report bugs and to request new features. Alternatively, you mayemail the developer directly.
Package: pkbar
pkbarKeras style progressbar for pytorch (PK Bar)1. Showpkbar.Pbar(progress bar)loading and processing dataset
10/10  [==============================] - 1.0spkbar.Kbar(keras bar)Epoch: 1/3
100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269
Epoch: 2/3
100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261
Epoch: 3/3
100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.42542. Installpip install pkbar3. Usagepkbar.Pbar(progress bar)importpkbarimporttimepbar=pkbar.Pbar(name='loading and processing dataset',target=10)foriinrange(10):time.sleep(0.1)pbar.update(i)loading and processing dataset
10/10  [==============================] - 1.0spkbar.Kbar(keras bar)for a concreate exampleimportpkbarimporttorch# training looptrain_per_epoch=num_of_batches_per_epochforepochinrange(num_epochs):################################### Initialization ########################################kbar=pkbar.Kbar(target=train_per_epoch,epoch=epoch,num_epochs=num_epochs,width=8,always_stateful=False)# By default, all metrics are averaged over time. If you don't want this behavior, you could either:# 1. Set always_stateful to True, or# 2. Set stateful_metrics=["loss", "rmse", "val_loss", "val_rmse"], Metrics in this list will be displayed as-is.# All others will be averaged by the progbar before display.############################################################################################ trainingforiinrange(train_per_epoch):outputs=model(inputs)train_loss=criterion(outputs,targets)train_rmse=torch.sqrt(train_loss)optimizer.zero_grad()train_loss.backward()optimizer.step()############################# Update after each batch ##################################kbar.update(i,values=[("loss",train_loss),("rmse",train_rmse)])######################################################################################### validationoutputs=model(inputs)val_loss=criterion(outputs,targets)val_rmse=torch.sqrt(val_loss)################################ Add validation metrics ###################################kbar.add(1,values=[("val_loss",val_loss),("val_rmse",val_rmse)])###########################################################################################Epoch: 1/3
100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269
Epoch: 2/3
100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261
Epoch: 3/3
100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.42544. AcknowledgeKeras progbar's code fromtf.keras.utils.Progbar