Saravanan666/pypi-org huggingface dataset:
Package: pr-keepachangelog
DocumentationThe package can be installed withpip install pr-keepachangelog.
For help and to use it:python -m pr_keepachangelog.Based on a pull request that is merged on a certain branch, you can populate the changelog automatically. The category in which the changelog is populated depends on the prefix of the specified branch:hotfix/ -> Fixedfeature/ -> Fixedbugfix/ -> Fixedadditional branch typesdeprecated/ -> Deprecatedchanged/ -> Changedremoved/ -> Removedsecurity/ -> SecurityKeepachangelog categoriesKeepachangelog will guess the new version number when you make a release with--release:Added: for new features. Results in minor update.Changed: for changes in existing functionality. Results in major update.Deprecated: for soon-to-be removed features. Results in major update.Removed: for now removed features. Results in major update.Fixed: for any bug fixes (hotfix/bugfix). Results in patch update.Security: in case of vulnerabilities. Results in patch update.
Package: prlps-logger
че этопример простейшего логгера, который будет выводить логги в консоль и записывать их в лог-файл.содержит пример обработки строк:удаление из логов токенов телеграм-ботовисключение из логгированияgetUpdatesиsendChatActionподсветка цветами разных событий и уровней логгирования в консоли (и в html-логе)может создать html-файл для удобного просмотра последнего логакак использовать:сначала установить:pip install prlps-loggerпример использования:importos# импортируем библиотеку:fromprlps_loggerimportLogger# указываем путь до файла с логами куда будет все записываться:current_py_file=os.path.abspath(__file__)# путь до текущего файлаworkdir=os.path.dirname(current_py_file)# путь до рабочей папки с этим файломos.makedirs(os.path.join(workdir,"logs"),exist_ok=True)# создание папки для логовlog_file_name=f"{os.path.splitext(os.path.basename(current_py_file))[0]}_{os.path.basename(os.path.dirname(current_py_file))}.log"# создание имени для файла логовlog_file=os.path.join(workdir,"logs",log_file_name)# установка пути до файла логов# код выше можно пропустить и указать любой путь до файла где будут логи (создавать его не нужно):# log_file = "C:/Users/User/Desktop/logfile.txt"# создание экземпляра класса с нужным уровнем логгирования:logger=Logger(log_file,'DEBUG')# уровень логгирования можно не указывать, тогда по умолчанию будет 'WARN':# logger = Logger(log_file)# предположим далее какой-то твой код# если в коде возникнет не перехваченное исключение, то оно с трейсбеком будет записано в файл логов и выведено в консоль# для подробного логгирования ты можешь сам вызывать логгер с нужным уровнем в необходимых участках кода:logger.warn('https://api.telegram.org/bot123232324234/ неправильный токен!')logger.error('https://api.telegram.org/bot856764534234/ ошибка соединенеия')logger.info('получен ответ от api:{response}')logger.crit('https://api.telegram.org/bot856764534234/ flood control')logger.event('результаты получены:{results}')try:my_iq=((0+0)*0)**0/0exceptExceptionase:# тут твоя какая-то обработка исключения...logger.error(f'ты больной?\n{e}')# логгирование# посмотрим как логгируются не обработанные исключения:# my_iq = ((0 + 0) * 0) ** 0 / 0# лог (только созданный этим логгером) можно передать в виде html-файла для удобного просмотра или вывода куда-то, например на страницу через fastapi и т.п.html_log=logger.log_to_html()# создаст файл для текущего лога, аналогично вызову logger.log_to_html(log_file)# или создать html-файл с указанием пути до конкретного лог-файла, не обязательно текущего# html_log = logger.log_to_html(log_file)  # для текущего лога# html_log = logger.log_to_html('/path/to/file.log.3')  # для какого-то другого лог-файла, например после ротации логов# метод возвращает полный путь до созданного файла html, можно посмотреть или потом что-то с этим сделать:print(html_log)играйся 😊
Package: prlsnapshotter
Parallels SnapshotterSnapshots of parallels per command line; quick create by templateInstallationpip3installprlsnapshotterCreate a template parallel machine. Then make a shortcut for it:prl-snap-t<template-machine>-p<machine_prefix>shortcut<path>
prl-snap-tpostgres_template-ppostgres_shortcutpgsnap
Package: prm
PRMpip repository managerUse$prmlistpypihttps://pypi.org/simpledoubanhttps://pypi.douban.com/simpletencenthttps://mirrors.cloud.tencent.com/pypi/simplealiyunhttps://mirrors.aliyun.com/pypi/simple/$prmshowCurrent:https://mirrors.cloud.tencent.com/pypi/simple$prmusepypiSettingtopypi$prmshowCurrent:https://pypi.org/simple$prm--helpUsage:prm[OPTIONS]COMMAND[ARGS]...Options:--helpShowthismessageandexit.Commands:listshowuseInstallpipinstallprmAuthorYixian Du
Package: prman
PR Man!TLDR> prman -m "This PR is created with prman!"

The repository name: simulation
The project id: oilrig/simulation
The current branch: f/OIL-123-add-tests
The PR name: OIL 123: add tests
Fetching the project...
Fetching PR's...
Fetching approvers...
Select approvers:
* lemar_david_99 (Lemar David)
* jFISHER_20 (Jared Fisher)
* choiii (Peyton Choi)

> david_99;fisher_20;peyton

Pushing to the origin...
Creating the PR...
The PR is created:
https://gitlab.com/oilrig/simulation/merge_requests/42Why?Creating a PR in GitLab UI takes so much time.
You need to click a button, select options like 'Squash', 'Remove source branch when merged'.
You need to select minimal approvers count.
You need to select approvers.
You should set approvers as assignee to give them email about your PR.It is a pure nightmare.
Just runprmanin your repo directory.
Type approvers likedavid_99; fisher_20; peyton(search by substring, multiple items divided by;).
It pushes your branch to the origin automatically.
A Pull Request is done!How to install it?Installpipx.
It is like a pip+venv for CLI apps.python3 -m pip install --user pipx.pipx install prman.Go toGitLab Access Tokens UI.Name:prman; Scopes:api; Create personal access token.prman config set gitlab.token YOUR_TOKEN(the config is stored in~/.config/prman/config.json).
Package: prms6bmi
PRMS6-BMI Test Projects and python evaluation codeTo build and install the prms6-bmi python package used in python evaluation:Create a conda environment from the environment.yml file.Activate the environmentcd to pkg/prms6-bmirun "pip install ."Or if you are developing the code inside of the prms6-bmi package -run "pip install -e ."The pipestem project used in testing is found in /prms/pipestem. To test the bmi's copy the /pipestem folder and and save as:pipestem_surface - control file: control_surface.simple1pipestem_soil - control file: control_soil.simple1pipestem_groundwater - control file: control_groundwater.simple1pipestem_streamflow - control file: control_streamflow.simple1The git repositories for each BMI can be found athttps://github.com/nhm-usgsbmi-prms6-surfacebmi-prms6-soilbmi-prms6-groundwaterbmi-prms6-streamflowEach BMI depends on the PRMS6 model and library found at the prms repository currently using the 6.0.0_dev_bmi branch
Package: prms-python
PRMS-Python provides a Python interface to PRMS data files and manages PRMS simulations. This module aims to improve the efficiency of PRMS simulations while providing “pythonic” tools to do scenario-based PRMS simulations. By “scenario-based” we mean testing model hypotheses associated with model inputs, outputs, and model structure. For example, parameter sensitivity analysis, where each “scenario” is an iterative perturbation of one or many parameters. Another example “scenario-based” modeling exercise would be climate scenario modeling: what will happen to modeled outputs if the input meteorological data were to change?
Package: prmt
prmtDescriptionSimple and robust command line prompts for most of the values.
The formatting of the questions and prompts is completely customizable.
This lib has no dependencies and no magic.Features:Prompt bool from user. (prmt.confirm)Prompt string from user. (prmt.string)Prompt string from user, opening an editor window. (prmt.string_from_editor)Prompt integer from user. (prmt.integer)Prompt list of strings from user. (prmt.list_of_str)Prompt user to select an item from a list/dict of items. (prmt.select)Optional features:Set default values.Customize formatting for each prompt.Customize formatting for all prompts via theprmt.Prompt()class.Open default Text Editor for the user to enter text.Blacklist values. (If the user enters blacklisted values she will be prompted again)RequirementsPython>=3.7Installpip install prmtorpoetry install prmtExamplesLook into thetestsfolder for code examples.To see it in action on the command line, run:pipenv run python make.py testDevelopmentgit clone https://github.com/feluxe/prmt.git

cd prmt

pipenv install --dev --pre

pipenv run python make.py --help
Package: prntscraper
prntscraperprntscraperwill allow you to scrape images from the prnt.sc website as they have predictable subheadings we can freely attempt to get random images or images in a sequential order from a specified amount to a max limit and download them into a folder. This process is also known asFuskeringThis is a security issue from their end as It's highly common people store sensitive information that could easily be stolen if these images are found by the wrong people.This module is no longer being worked on.DISCLAIMERI am not responsible for the information that may be found with this package.InstallationThis module is compatible with Python3+.Install the latest version from git repository using pip:$pipinstallgit+https://github.com/ItzBlinkzy/prntscraper.gitInstall from PyPI:$pipinstallprntscraperHow to useCopy the below code into a python file and run it. This should automatically create a folder called"images"in the same directory as this file. This is also why it is advised you make a new folder for ease of access.fromprntscraperimportPrntScrapermy_scraper=PrntScraper()# Gets random images and store them in ./images folderrandom_images=my_scraper.get_random_images()print(random_images)# Takes inputted images and stores them in ./images folder.input_images=my_scraper.get_input_images()print(input_images)# Gets the count of all the images in the ./images folder.size=my_scraper.folder_size()print(size)Outputs:get_random_images()`Howmanyimageswouldyoulike?:5Completed:100%|██████████████████████████████████████████████████|6/6[00:09<00:00,1.58s/images]Successfullydownloaded5images.Lookat./imagesfolder.get_input_images()Enteranytworandomlettersinthealphabet.Example[gy]:vkEnteraPointbelow9999tostartfrom:100EnteranEndPoint(inclusive)below9999toend:109Completed:100%|██████████████████████████████████████████████████|10/10[00:13<00:00,1.31s/images]Successfullydownloaded10images.Lookat./imagesfolder.folder_size()Therearecurrently15imagesinthefolder.Pretty simple right?IssuesFeel free to open an issue or make a pull request if you have any problems using this module.CreditsItzBlinkzyYou can be here too if you'd like to contribute.Repo* https://github.com/ItzBlinkzy/prntscraper/LicenseGNU General Public License v3
Package: pro
PRO runs processes and restarts them when it observes file changes.PRO only does two things, but it does them well: it runs processes,and restarts select processes when their dependent files change.PRO is designed to be useful for running processes in development, butthere are probably better options for running your processes in production.Installation------------pip install proUsage-----pro runfile.pyYour runfile contains the description of which processes to run, andan optional list of files to observe for each process group. A processcan be defined as a command to run as a string, or as a CMD object,which can specify a directory in which to run.Example runfile.py:import osfrom pro import CMD, runrun(CMD('python manage.py runserver 8080', cd='example_project'))run('python -m SimpleHTTPServer 8888', watch_list=['watchme'])sass_file = 'static/css/style.sass'run('sass %s' % sass_file, watch_list=[sass_file])template_dir="static/templates"handlebars_inputs=os.path.join(template_dir, '*.handlebars')handlebars_output=os.path.join(template_dir, 'templates.js')cmd = 'handlebars `ls %s` -f "%s"' % (handlebars_inputs, handlebars_output)run(cmd, watch_list=[handlebars_inputs])
Package: proactuary
ProActuary –Package to read and operate on actuarial file formatsThis Python package provides utilities tailored for specialized text formats frequently encountered in actuarial work.BackgroundWithin the actuarial domain, there's specific file formats that, at a glance, appears similar to a CSV. However, it incorporates some unique characteristics:Allows for preamble content before the header.Allows for type annotations.Supports metadata appended after the main content.Starting any data line with an asterisk.Enforces headers not to be surrounded by quotes, while all other data strings should be.Given the intricate nature of this format and the emphasis on data integrity in actuarial work, there's a demand for a toolset that can seamlessly handle such files. Enter ProActuary.FeaturesRead Functionality: Efficiently convert specialized actuarial text files into a pandas DataFrame.Write Functionality: Export pandas DataFrames back into the specialized actuarial text format, adhering to its nuances.Flexibility: Additional parameters can be passed via kwargs, making the package adaptable and extensible.Consistency: Adopts a naming convention similar topandas, ensuring familiarity and ease of use.InstallationTo install ProActuary:pip install proactuaryUsageReading Filesfromproactuaryimportread_prodf=read_pro("path_to_file.pro")You can pass additional parameters via kwargs to tailor the reading process to specific needs.Writing Filesfromproactuaryimportto_proto_pro(df,"output_filename.pro")Writing ensures that the file conforms to the specialized format's requirements, such as the specific handling of headers and quoted strings.DependenciesWhile the package is self-sufficient for most tasks, it relies onpandasfor DataFrame operations. Detailed dependencies can be found in therequirements.txtfile.Contribution & Future DevelopmentThis package is an ongoing effort to streamline the process of handling actuarial file formats. Contributions, feedback, and suggestions are most welcome! Fork the repository and submit a pull request or open an issue.LicenseProActuary is licensed under the MIT License. For more details, consult the LICENSE file.
Package: probabilidad
calculo de probabilidad con distribucion binomial y distribucion de Gausspara usarlo basta con importarfromprobabilidadimportGaussian,Bionomialluego crear las respectivas varibles haciendo herencia de las classes importandas sea el casofromprobabilidadimportGaussian,Bionomial#el primer argumento es la media y el segundo es la desviacion estandargaussian_primera=Gaussian(20,5)#metodosgaussian_primera.calculate_mean()#mediagaussian_primera.calculate_stdev()#estandar desviaciongaussian_primera.plot_histogram#generar graficosgaussian_primera.pdf#probabilidad de desidadgaussian_primera.plot_histogram_pdf#generar graficos con probabilidad de densidadgaussian_primera+gaussian_sengunda#suma de desviaciones#el primer valor representa la probabilidad y el segundo nbionomial_primero=Bionomial(.24,28)bionomial_primero.calculate_mean()# calcula la mediabionomial_primero.calculate_stdev()#calcula la desviacionbionomial_primero.replace_stats_with_data()#remplace con datos de un fichero txtbionomial_primero.pdf()#calcula la probabilidad de densidadbionomial_primero.plot_bar_pdf#hace el graficopronto se agregaran mas probabilidades..
Package: probabilistic-reconciliation
reconcileProbabilistic reconciliation of time series forecastsAboutReconcile implements probabilistic time series forecast reconciliation methods introduced inZambon, Lorenzo, Dario Azzimonti, and Giorgio Corani."Probabilistic reconciliation of forecasts via importance sampling."arXiv preprint arXiv:2210.02286 (2022).Panagiotelis, Anastasios, et al."Probabilistic forecast reconciliation: Properties, evaluation and score optimisation."European Journal of Operational Research (2022).The package implements methods to compute summing/aggregation matrices for grouped and hierarchical time series and reconciliation methods for probabilistic forecasts based on sampling and optimization,
and in the near future also some recent forecasting methods, such as proposed inBenavoli,et al.(2021)orCoraniet al., (2020)viaGPJax.ExamplesAn example timeseries forecast application using GPs can be found inexamples/reconciliation.pyand acase study on probabilistic forecast reconciliation of stock index datacan be foundhere.InstallationMake sure to have a workingJAXinstallation. Depending whether you want to use CPU/GPU/TPU,
please followthese instructions.To install the package from PyPI, call:pipinstallprobabilistic-reconciliationTo install the latest GitHub , just call the following on the
command line:pipinstallgit+https://github.com/dirmeier/reconcile@<RELEASE>AuthorSimon Dirmeiersfyrbnd @ pm me
Package: probabilistic-symptom-graph
Probabilistic Symptom Graphpipinstallprobabilistic-symptom-graphfromsrc.ProbabilisticSymptomGraphimportProbabilisticSymptomConditionGraphimportnetworkxasnximportnumpyasnpmedical_condition_gexf="./data/medical-condition-symptom-graph.gexf"graph=nx.read_gexf(medical_condition_gexf)sim_matrix=np.load('./data/md-symptom-sim-mat.npy')symptom_names=[]fornode_id,tpeingraph.nodes(data="type"):iftpe=="Symptom":symptom_names.append(node_id)symptom_names=sorted(symptom_names)condition_names=[]fornode_id,tpeingraph.nodes(data="type"):iftpe=="MedicalCondition":condition_names.append(node_id)condition_names=sorted(condition_names)probabilistic_graph=ProbabilisticSymptomConditionGraph(condition_names,symptom_names,graph,sim_matrix)print(" | ".join(probabilistic_graph.get_all_symptoms()[:10]))probabilistic_graph.get_condition_probs(["acne"])[:5]Graph ImageRed nodes represent medical conditions and green nodes represent symptoms.This graph data was gathered by mining Wikipedia "Medical Condition (New)" infoboxes and extracting medical condition <=> Symptom pairs. Post processing was done using ChatGPT.Data MiningSee./data/WikipediaSymptomExtractor.ipynb
Package: probabilistic-timed-automata
Python library for building and simulating probabilistic timed automata.[![codecov](https://codecov.io/gh/anand-bala/probabilistic-timed-automata/branch/master/graph/badge.svg?token=9JIV7X4YEQ)](https://codecov.io/gh/anand-bala/probabilistic-timed-automata)
[![pypi](https://badge.fury.io/py/probabilistic-timed-automata.svg)](https://badge.fury.io/py/probabilistic-timed-automata)## InstallationTo install usingpipjust use:`shell $ python3-mpip installprobabilistic-timed-automata`For developers, simply clone the repository and run`shell $ python3-mpip install-e. `# Usage
Package: probability-calculator
probability_calculatorCalculate with and analyze random variables / probability densities.UsageInstall the package, e.g.:pip install probability_calculator.Initialization and plottingThe package provides simple ways to define random variables with discrete outcomes, for instance a die:fromprobability_calculatorimportFairDiedensity=FairDie(6)# initialize a fair die with 6 sidesfig,ax=density.plot_outcomes()# plot the outcomes using matplotlibFor the general case, the classRandomVariablescan be used:fromprobability_calculatorimportRandomVariablefromfractionsimportFractiondensity=RandomVariable(outcomes=[{"value":0,"p":Fraction(2,10)},{"value":1,"p":Fraction(3,10)},{"value":Fraction(5,2),"p":Fraction(1,10)},{"value":3,"p":Fraction(4,10)},])# initialize a discrete density with 4 different outcomesfig,ax=density.plot_outcomes()# plot the density using matplotlibNote that only integers and fractions can be used to assure exact calculations.Combine random variablesThe discrete density of throwing a die two times can be modelled by adding the random variable with itself:density_for_one_throw=FairDie(6)density_sum_of_two_throws=density_for_one_throw+density_for_one_throw# same as density_for_one_throw*2fig,ax=density_sum_of_two_throws.plot_outcomes()Note that the operations (+and*) on random variables are always assuming independed random variables, even when using the same variable multiple times.LimitationsContinuous densities / random variables are not supported at the moment.Adding many random variables together might get stuck due to a lot of possible outcomes. In general, adding 10 densities with 10 outcomes each lead to $10^{10}$ outcomes. However, simple cases like the die work, so evenDie(10)*100is no problem.ContributingWe greatly appreciate fixes and new features forprobability_calculator. All contributions to this project should be sent as pull requests on github.LicenseApache License 2.0
Package: probability-distro
This is a simple python package for calculating Gaussian and Binomial Distributions.In this package there are two classes present:GaussianBinomialusefrom probability_distro import Gaussian, BinomialExamples:# parameters are  mean and standard deviation.first_gaussian=Gaussian(25,2)second_gaussian=Gaussian(30,3)# add gaussians using + operator.add_gaussians=first_gaussian+second_gaussian"""Methods:1.read_data_file()Function to read in data from a txt file. The txt file should haveone number (float) per line. The numbers are stored in the data attribute.Args:file_name (string): name of a file to read fromReturns:None2. calculate_mean()Function to calculate the mean of the data set.Args:NoneReturns:float: mean of the data set3.  calculate_stdev()Function to calculate the standard deviation of the data set.Args:sample (bool): whether the data represents a sample or populationReturns:float: standard deviation of the data set"""For more methods please check the source code for this package.
Package: probablepeople
probablepeople is a python library for parsing unstructured romanized name or company strings into components, using conditional random fields.>From the python interpreter:>>> import probablepeople
>>> probablepeople.parse('Mr George "Gob" Bluth II')
[('Mr', 'PrefixMarital'),
 ('George', 'GivenName'),
 ('"Gob"', 'Nickname'),
 ('Bluth', 'Surname'),
 ('II', 'SuffixGenerational')]
>>> probablepeople.parse('Sitwell Housing Inc')
[('Sitwell', 'CorporationName'),
 ('Housing', 'CorporationName'),
 ('Inc', 'CorporationLegalType')]
Package: probadg
probadg PackageThis is a probability package that contains classes to create binomial and Gaussian distributions. It also contains a RandomObject class that simulates all the possible results of a binomial random variable, with their corresponding probabilities.Furthermore, it contains a function that simulates the Bayes Algorithm.Installation:To install the package you could just use:pip install probadgTools and Software UsedNumpy v.1.19.2Matplotlib v.0.23.2MathPandas v.1.1.3LicenseThis software is licensed under theMITlicense. The license can be found in thelicense.txtfile.AcknoledgementsThis package was done under the guidance ofUdacityData Science Nano Degree.Also, I would like to thank the free online education platformsCourseraandEdx, which helped me to get started in this path.