Saravanan666/pypi-org huggingface dataset:
Package: pduino
DuinolaboDuinolaboest un module à destination des enseignants de physique chimie et de biologie qui permet la réalisation souspythonde systèmes de mesures utilisant la carteArduino.Ce module est très efficace etsimplifie considérablementla réalisation demesures temporellesoupoint par pointà l'aide d'une carteArduino.Duinolaboest constitué de deux bibliothèques :pduinoqui fonctionne sous pythonjuduinoqui permet de créer très simplement des systèmes d'interfaces de mesures graphiques sous jupyter.Sans remplacer un oscilloscope ou une interface de mesures, le système peut néanmoins effectuer des mesures temporelles de 100 ou 200 points jusqu'à une fréquence de 40kHz, ce qui est largement suffisant dans la plupart des cas du programmes de lycée.Dans la mesure où toutes les activités sont basées sur le même modèle, (en particulier avecjuduinosous Jupyter), les élèves, conformément au B.O. Français, se familiarisent rapidement avec l'environnementpythonpour analyser leurs résultats.Sont également fournis plusieurs exemples documentés, fonctionnels et testés avec des élèves, applicables au programme Français de sciences physiques dans les classes de lycée.InstallationSi vous travaillez uniquement avec un IDEphyton(edupython, thonny, spyder, ect...), dans la console de votre environnement python, tapez:pip install pduinoSi vous travaillez avec Jupyter, dans la console de votre environnement Jupyter, tapezpip install pduinopuispip install juduinoGithubDocumentation, exemples et sources sur github :https://github.com/bultec/duinolab
Package: pdupes
pdupesPython script to quickly find duplicate files, inspired by
cwilper/pdupes.Free software: MITDocumentation:https://pdupes.readthedocs.orgFeaturesTODOIntroductionTODODependenciesPython >= 3.6Seerequirements.txtQuickstartpip3 install pdupesTODODevelopment SetupClone the repo:git clonehttps://github.com/n8henrie/pdupes&& cd    pdupesMake a virtualenv:python3-mvenv venvTODOConfigurationTODOAcknowledgementsTODOTroubleshooting / FAQHow can I install an older / specific version of pdupes?Install from a tag:pip install git+git://github.com/n8henrie/pdupes.git@v0.1.0Install from a specific commit:pip install
git+git://github.com/n8henrie/pdupes.git@aabc123def456ghi789Changelog0.2.0 :: 2017-07-24First release on PyPI.
Package: pdusms
PDU Converter PackageAbout this PackageThis library will help you to encode and decode raw SMS dataSupport 7-bit, 8-bit, 16-bitRequires Python 3.6 or betterHow to use (Encode and Decode)Example to number0123456789with messageChào thế giới>>> import smspdu, time
>>> p = smspdu.SMS_DELIVER.create('0123456789', '987654321', 'Chào thế giới', datestamp=time.time(), tp_sri=1, tp_mms=0)
>>> print(p.toPDU())
200A8110325476980008122182514023001A0043006800E0006F0020007400681EBF0020006700691EDB0069
>>> s = smspdu.SMS_DELIVER.fromPDU(p.toPDU(), '0123456789')
>>> print("s.tp_address: " + s.tp_address)
>>> print("s.tp_scts: " + s.tp_scts)
>>> print("s.user_data: " + s.user_data)
s.tp_address: 0123456789
s.tp_scts: 21122815043200
s.user_data: Chào thế giớiThis is upgrade version. The original fromsmspdu 1.0
Package: pdusmsconverter
PDU Converter PackageAbout this PackageThis library will help you to encode and decode raw SMS dataSupport 7-bit, 8-bit, 16-bitRequires Python 3.6 or betterHow to use (Encode and Decode)Example to number0123456789with messageChào thế giới>>> import smspdu, time
>>> p = smspdu.SMS_DELIVER.create('0123456789', '987654321', 'Chào thế giới', datestamp=time.time(), tp_sri=1, tp_mms=0)
>>> print(p.toPDU())
200A8110325476980008122182514023001A0043006800E0006F0020007400681EBF0020006700691EDB0069
>>> s = smspdu.SMS_DELIVER.fromPDU(p.toPDU(), '0123456789')
>>> print("s.tp_address: " + s.tp_address)
>>> print("s.tp_scts: " + s.tp_scts)
>>> print("s.user_data: " + s.user_data)
s.tp_address: 0123456789
s.tp_scts: 21122815043200
s.user_data: Chào thế giớiThis is upgrade version. The original fromsmspdu 1.0
Package: pdutil
Utilities for pandas.importpdutilforsubdfinpdutil.iter.sub_dfs_by_size(df,2):print(subdf)Contents1Installation2Basic Use2.1display2.2iter2.3transform2.4serial3Contributing3.1Installing for development3.2Running the tests3.3Adding documentation4Credits1Installationpipinstallpdutil2Basic Usepdutilis divided into several sub-modules by functionality:2.1displayFunctions related to displayingpandasDataFrames:df_string- Returns a nicely formatted string for the given dataframe.pandas_big_frame_setup- Sets pandas to display really big data frames.df_to_html- Return a nicely formatted HTML code string for the given dataframe.2.2iterFunctions related to iterating overpandasdataframes:sub_dfs_by_size- Get a generator yielding consecutive sub-dataframes of the given size.sub_dfs_by_num- Get a generator yielding num consecutive sub-dataframes of the given df.2.3transformx_y_by_col_lbl- Returns an X dataframe and a y series by the given column name.or_by_masks- Returns a sub-dataframe by the logical or over the given masks.2.4serialSerializationFormat- A mutli-singleton representing different serialization formats of dataframes.3ContributingPackage author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.3.1Installing for developmentClone:gitclonegit@github.com:shaypal5/pdutil.gitInstall in development mode:cdpdutilpipinstall-e.3.2Running the testsTo run the tests use:pipinstallpytestpytest-covcoveragecdpdutilpytest3.3Adding documentationThe project is documented using thenumpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, followthese conventions.4CreditsCreated by Shay Palachy (shay.palachy@gmail.com).
Package: pd-validator
# Pandas DataFrame Validator`pd-validator` is a pandas wrapper library for validating DataFrames against a user-defined schema.```>>> import pandas as pd>>> from pd_validator import Schema>>> from pd_validator import Report>>> df = pd.read_csv('data.csv')>>> dfcol_1  col_2A      B1      BC2      DNaN    A>>> rules = {'col_1': {'dtype': int,'length': 1,'range': [0,1],'required': True,'codes': False,'regex': False},'col_2': {'dtype': str,'length': 1,'range': False,'required': False,'codes': ['A', 'B', 'C'],'regex': False}}>>> schema = Schema(rules=rules)>>> report = Report(df=df, schema=schema())>>> report()col_1  col_2  inval_line  inval_col  inval_val  err_msg1      BC     2           col_2      BC         Invalid length: 1 char max1      BC     2           col_2      BC         Invalid code: ['A', 'B', 'C'] required2      D      3           col_2      D          Invalid code: ['A', 'B', 'C'] requiredA      B      1           col_1      A          Invalid dtype: int requiredA      B      1           col_1      A          Invalid range: [0, 1] required2      D      3           col_1      2          Invalid range: [0, 1] requiredNaN    A      4           col_1      NaN        Missing value: col_1 required```
Package: pdw
The PDW (Public Domain Works) package is the code which runs the Public Domain
Works DB, an open registry of artistic works that are in the public domain:<http://www.publicdomainworks.net/>Though the project itself is focused on recording public domain works the code
and database schema provided by the PDW package is (intentionally) generic and
can be used to hold metadata on any kind of cultural material that fits into a
basic FRBR-like Work-Item-Person schema (see model/frbr.py for more details.)The package also contains a variety of additional material such as:Tools for cleaning up and normalized dataScripts to import data from a wide variety of sources (MARC, Open Library
JSON, NGCOBA)Code for doing statistical analysis of the database
Package: pd-window-decorators
pd-window-decoratorsEasily apply windowing to functions that mutate Pandas DataFrames. Useful for data science projects where you want to apply a function for a DataFrame using smaller chunks of the DataFrame automatically. A moving window can be easily applied to the function using Python decorators.Sliding WindowTo apply a sliding window, importdf_sliding_windowfrompd_window_decoratorsand apply it to your function. The decorator takes atimedeltaobject as a required argument to define the slice size. By default, the decorator will look for a Pandas DataFrame nameddfin the function arguments. The DataFrame must also have a time column. The column name isdsby default. The decorator also expects the function to return a DataFrame.ExampleUsing the decorator with default arguments:@df_sliding_window(window_size=timedelta(days=2))defsum_all(df):df.loc[:,'sum']=df['y'].sum()returndfUsing the decorator with custom arguments:@df_sliding_window(window_size=timedelta(days=2),df_name='my_df',time_column='my_time')defsum_all(my_df):df.loc[:,'sum']=df['y'].sum()returndfNote that in the second example, the DataFrame is namedmy_dfand thedf_nameargument is set tomy_df, so they match.ArgumentsArgumentTypeOptionalDescriptionwindow_sizetimedeltaTrueThe size of the window to apply to the function.df_namestrFalseThe name of the DataFrame variable to pass to the function as an argument. Defaults todf.time_columnstrFalseThe name of the column in the DataFrame that contains the time information. Defaults tods.
Package: pdx
Hello, I'm PDX 👋PDX is a framework for prompt engineering and a dev-ops toolkit.At the core, it provides a mental-model on how to build and manageagents. An agent is a collection of prompts and/or prompt templates with information that is used to interact with the Language Models.Documentation:pdxlabs.io/docsWebsite:pdxlabs.ioQuickstart:create-an-agentInstallationpipinstallpdxQuickstartTo create your first agent, run the following command:pdxcreatemy_first_agentRun and test out the agent by running:pdxtestmy_first_agent--verboseMore information here:PDX - Main ConceptsWhy use PDX?🗃️ Low dependency footprint -> ease of production deployment and maintainance.📂 Mental model to separate prompt templates from the application code. (Similar to Flask blueprint or FastAPI router).📌 Version control the prompts along with their evaluation metrics.📸 Logging and tracing of inputs, prompt render, and model response made easy.🧯 Standardize Error handling and logging.💾 Caching for lowering latency. (Coming soon)📊 Observability out-of-the-box. (Coming soon)📩 Log feedback of the user. (Coming soon)🛎️ A/B testing of prompts. (Coming soon)DemosCheck our the demos in thedemos repository.Models (APIs) currently supported:OpenAIAnthropic
Package: pdxloc
pdxlocpdxlocis a library that provides python functions related to manipulating
Paradox games' localization files.Current features:Read/write localization filesRequirementsPython 3.9 or newerInstallationpip install pdxlocSamplesTake a look at thesamplesdirectory for use-cases.LimitationsWindows - non-EnglishWindows does not support non-English charactersLicenseThis project is licensed, see theLICENSEfile for details.
Package: pdxtra
PDXtra is a data analysis library built on top of Pandas and geared toward
time-series data analysis. The library offers subclasses of traditional
PandasSeriesandDataFrameobjects which provide additional
convenience methods for querying and manipulating data while retaining all
of the functionality of the original Pandas API. Additionally, the library also
providesTimeSeriesandTimeSeriesDataFramesubclasses which are
specifically designed to be used for time-series and financial data analysis.What can the package do?Downcast entire dataframes. Choose whether to downcast integer columns, float
columns, or both.Perform Excel-style x-lookup, and v-lookup.Qickly scan dataframes for column values based on index values, and
optionally search using nearest-match.Compute the intersections of curves.Use inter-quartile range to eliminate outliers in data sets.Compute the linear regression of a series.Get true N-day moving averages.Temporarily set a dataframe index using a context manager.Compute common financial time-series metrics including: relative strength
index, moving average convergence-divergence, and on-balance volume.NoteThis distribution has been developed and tested on a standalone Python build
and could–under rare circumstances–exibit unexpected behavior on standard
CPython. Although no differences between the execution of code in the
standalone build and normal CPython have been found during the process of
development, I nevertheless wish to provide fair warning.Installationpython-mpipinstallpdxtraDocumentationUp-to-date documentation can be found here:https://jammin93.github.io/pdxtra/UsageTimeSeriesandTimeSeriesDataFrameare both subclasses of theSeriesandDataFrameobjects, respectively, and they themselves are
subclasses of the original PandasDataFrameandSeriesobjects. This
means that all of the functionality of the Pandas API is inhereted by these
subclasses.Series ObjectsCreation ofSeriesandDataFrameobjects follows the same syntax as
traditional Pandas.importpdxtraaspdxseries=pdx.Series([1,2,3,4,5])time_series=pdx.TimeSeries([1,2,3,4,5])DataFrame ObjectsCreatingTimeSeriesandTimeSeriesDataFrameobjects is no different
from creating their vanilla counterparts.importpdxtraaspdxdf=pdx.Dataframe({"a":["a","b","c","d","e"],"b":[1,2,3,4,5],})ts_df=pdx.TimeSeriesDataFrame({"a":["a","b","c","d","e"],"b":[1,2,3,4,5],})LicenseGNU General Public License
Package: pdynamics
Python connector for Dynamics 365How to Installpip install pdynamicsUsagefrom pdynamics import crmOn-PremisesADConnect on-premises organizations via user and passwordAD access sample:crmurl='http://servername:port/orgname/'user='domain\\username'password='yourpassword'crmorg=crm.client(crmurl,user,password)crmorg.test_connection()IFDConnect to Dynamics 365 IFD organizations, access_token is required to send requests, it's necessary to create an application group, it has a client id. For example: client id is: 289c7417-83b7-41c3-8fab-68f111b5ba60, and CRM IFD URL ishttps://mycrmorg.company.com:443/Run powershell as Admin on AD FS serverPSC:\windows\system32>Grant-AdfsApplicationPermissioncmdletGrant-AdfsApplicationPermissionatcommandpipelineposition1Supplyvaluesforthefollowingparameters:ClientRoleIdentifier:289c7417-83b7-41c3-8fab-68f111b5ba60ServerRoleIdentifier:https://mycrmorg.company.com:443/What's the token endpoint?When log in CRM, system will redirect to ADFS login page, url is something likehttps://mycrmorg.company.com:5555/adfs/ls/?wa=wsignin1.0&wtrealm=....., then your token endpoint will behttps://mycrmorg.company.com:5555/adfs/oauth2/tokenIFD Connection Sample:crmurl='https://mycrmorg.company.com:443/'tokenurl='https://mycrmorg.company.com:5555/adfs/oauth2/token'user='domain\\username'password='yourpassword'clientid='289c7417-83b7-41c3-8fab-68f111b5ba60'crmorg=crm.client(crmurl,user,password,clientid,tokenurl)crmorg.test_connection()OnlineAccess Dynamics 365 Online:Go to office 365 admin center -> Azure Active Directory orhttps://aad.portal.azure.com/yourcompanydomainAzure Active Directory -> App registrations -> Register an application
a. input a Name
b. select "Accounts in this organizational directory only (Single tenant)" - other options might also work
c. Manage -> Certificates & secrets -> New client secret (keep this client_secret for later use)
d. API permissions -> Add a permission -> Select an API -> Microsoft APIs-> Dynamics CRM
e. Grant admin consent for '{your company}'
f. Find client id (Application ID) in Overview pageDynamics 365 Online Connection Sample:crmurl='https://mycrmorg.crm.dynamics.com/'user='username@mycrmorg.onmicrosoft.com'password='yourpassword'clientid='289c7417-83b7-41c3-8fab-68f111b5ba60'clientsecret='Pq-oEk~_7044Q2V~QGXwaH~2v9k0qlSG1K'crmorg=crm.client(crmurl,user,password,clientid,client_secret=clientsecret)crmorg.test_connection()
Package: pea
pea - The tiniest green vegetable.peais a minimal BDD framework for python, in the style of ruby’scucumberand
python’slettuce. It aims to help you write the same kind of tests - but in straight-up
python code, without all the parsing and indirection and other hoops to jump through. It’s
a lot like ruby’scoulda.Benefits of cucumber-style testing include:You write your tests in clear, english language without inline codeYour tests are human-readable, and hopefully human-editableYou can re-use steps with confidence, because they all do exactly what
they say on the tinBenefits ofpeaoverlettuce,cucumber, etc:It’s a really trivial library (thus the name). It doesn’t do very much,
so it probably doesn’t have many bugsYour features are just python code:No “BDD language parser” neededNo regular expressionsStack traces make senseSyntax highlightingYou can usectagsto jump between test & implementation, as well as
for method completionManaging and renaming functions is much easier than managing regexesYou can use whatever abstractions you likeYou can use rich python objects as arguments, instead of parsing stringsIt doesn’t need its own test runner; so you can just usenoseto run it
alongside your unit testsSo how do I use it?Here’s a minimal example:from pea import *

@step
def I_go_to_the_store():
        world.location='store'
        world.cart = []

@step
def I_buy_some(item):
        world.cart.append(item)

@step
def I_go_home():
        world.location = 'home'

@step
def I_have_some_delicious(item):
        assert item in world.cart
        world.assertEquals(world.location, 'home')

# --------------------

class TestShopping(TestCase):
        def test_buying_some_peas(self):

                Given.I_go_to_the_store()
                When.I_buy_some('peas')
                And.I_go_home()
                Then.I_have_some_delicious('peas')… and when you run it (with nosetests, in verbose mode):Typically you would put your steps in a separate python module (or many),
but it’s your choice.Basics:@stepadds your function to pea’s registry of steps, which allows
them to be called viaGiven,When,And, andThen.To re-use a step from inside another step, just call the function!Stuff to remember:Make sure you inherit frompea.TestCase(and callsuperfromsetUp/tearDown),
as it takes care of resetting theworldbetween tests.You can useTestCaseassertion methods on the world, too
- e.g.world.assertEquals(expected, actual)Pea works well withrednose
Package: pe-accounting-python-api
pe-accounting-python-apiPython package to use the PE Accounting API with Python.PE Accounting is the Swedish bookkeeping system found athttps://www.accounting.pe/sv/var-tjanst. PE's API docs are here:https://api-doc.accounting.pe.pipinstallpe-accounting-python-apifrompe_accounting_python_apiimportRestClientpe_rest_client=RestClient(token=123123)print(pe_rest_client.company())
Package: pe-accounting-python-objects
pe-accounting-python-objectsPython package to use the PE Accounting API with Python.Definitions for business level objects. Currently only User, which models the users found in PE accounting.PE Accounting is the Swedish bookkeeping system found athttps://www.accounting.pe/sv/var-tjanst.PE's API docs are here:https://api-doc.accounting.pe.This projects builds on the low level API project:https://pypi.org/project/pe-accounting-python-api/which will be installed as a requirement.pipinstallpe-accounting-python-objects#!/usr/bin/env python3eUser.pe_credentials=PeCredentials(company_id="1212",api_access_token="123asdfwer")print(PeUser.all_users())
Package: peace
A REST client - inspired by restmod for javascript.Test statusQuick start for contributingvirtualenv -p `which python3` virtualenv
source virtualenv/bin/activate
pip install -r requirements.txt
pip install -r dev_requirements.txt
nosetestsUsagefrom peace import PeaceMaker

def set_authorization_header(request):
    request.headers['Authorization'] = authorization_value
    return request

maker = PeaceMaker('http://api.example.com')
Employee = maker.make(
    'Employee',
    '/employees'
    has: {
        'user': maker.make('Company'),
    },
    intercept: set_authorization_header,
)

employees = Employee.search(last_name='Smith')
bob = Employee.get('100')
bob.last_name = 'Smith-Jones'
bob.save()
bob.refresh()
company = bob.company
print(company.name) # prints namePeace requires the REST API to conform to a particular structure. A JSON
response should look something like this:{
  "data": {
    "id": 100,
    "first_name": "Bob",
    "last_name": "Smith",
  },
  "links": {
    "self": {"href": "/employees/100"},
    "company": {
      "href": "/companies/5",
      "data": {
        "name": "Big Corp",
      },
    },
  },
}Links can be followed (e.g. bob.company). Initially, the data returned
in the original request will be immediately available, but if you try to
access other fields that a direct load is required to make available,
(i.e. were not in the original response), then a load of the linked
object will be done first (e.g. GET /companies/5).
Package: peaceful-pie
Peaceful PieConnect Python with Unity for reinforcement learning!Video:CIExamplesseeexamplesInstallationYou need to install both into Unity project, and into your Python environment.In UnityIn Unity, go to Window | Package Managerchange the dropdown in top left to 'Packages: In Project'check that Newtonsoft Json appearsif Newtonsoft Json is not already in your project:click on the '+' in the top left, and choose 'Add package by name...'put the namecom.unity.nuget.newtonsoft-json, then click 'Add'In Unity, in your project's "Assets" folder, create a "Plugins" folder, if it doesn't already existFirst install AustinHarris.JsonRPC:Downloadhttps://www.nuget.org/api/v2/package/AustinHarris.JsonRpc/1.2.3rename to have filename suffix be.zip(you might need to turn on options to see all file extensions)unzip the resulting zip filecopylib/netstandard2.1/AustinHarris.JsonRpc.dllinto yourPluginsfolderselect the file, in your Plugins, and in 'Inspector' unselect 'validate references', and click 'Apply'Click on 'Releases', on the right of the github pagein the most recent release, downloadPeacefulPie.dllCopyPeacefulPie.dllinto your Unity project'sPluginsfolderIf on Mac silicon:in Unity, goto Plugins, click onPeacefulPie.dllin Inspector, change 'CPU' setting from 'Intel' to 'Any CPU', then click 'Apply'do the same forAustinHarris.JsonRpc.dllYou should be good to go :)In Pythonpip install -U peaceful-pieRequirementscurrently tested with:python 3.10Unity 2021.3.16.f1support Python version: >=3.8.0, <4.0.0please create an issue if your preferred platform is not supported (I'm guessing I might need to downgrade Python a little? Let me know!)DevI'm usng Visual Studio for Mac to write this.
Package: peachapisec-api
The peach proxy module provides a wrapper around thePeach API SecurityAPI.
This module is used for by CI integrations,
test runner plugins and custom traffic generators.RequirementsYou will need the following prerequisites in order to use:Peach API SecurityPython 2 or 3InstallationTo install:$ pip install peachapisec-apiFrom Source$ python setup.py installOffline$ pip install –no-index –find-links ../../deps -r requirements.txt
$ python setup.py installUsageSee the Peach API Security User Guide for more information.
Package: peachapisec-ci
This module provides generic integration with CI systems by running a
command that returns non-zero when testing did not pass.
The vast majority of CI systems support this method of integration.If a specific integration is offered for your CI system that is
preferred over this generic integration.To use, update the configuration at top of python file and
configure your CI system to run.See User Guide -> CI Integration for additional information
about using and configuring the generic CI integration runner.InstallationInstall required python dependencies with internet connection.$ pip install -r requirements.txtOffline InstallationInstall required python dependencies using our offline dependencies
folder.$ pip install –no-index –find-links ../../deps -r requirements.txt