Saravanan666/pypi-org huggingface dataset:
Package: pdp-lgtm
pdp-lgtmparty of dope pajamas looks good to me.summarythis package provides a cli toolpdp, it composes commands from predefined words and argumentsinstallpip install pdp-lgtmneeds tk to run, make sure os specific python-tk connector is installed.examplerunpdpflip to git tab, pressgitthenfetch --prunepress enterconfigurationedit~/.pdpconfig.yamlto add tabbed sections and change width / height. config file is auto generated after first run. history is saved as~/.pdphistory.yaml.actionsclick a word to append it to the entries.enter: print and run command.esc: print command but don't run.backspace: on an empty entry to remove it.tab: on an entry to move to the next one.up: on an entry to increment if it's an integer.down: on an entry to decrement if it's an integer.shiftor other special keys: flip tab.developto release make a tag like v0.1 that matches package versionpython3 setup.py sdist
python3 -m twine upload dist/*
Package: pd.plonedemografico
ContentsIntroductionThe viewsprenotazione_add_roslots.jsontipologies.jsonCreditsAuthorsChangelog1.0.1 (2014-09-10)1.0.0 (2014-05-05)DownloadIntroductionAdd custom views to the Plone booking productpd.prenotazioni.The functionality of this package will be merged inpd.prenotazioni.The viewsprenotazione_add_roThis is a form with all fields hidden.
It is meant to be used to display the data before final submission.slots.jsonCall this view like this:http://localhost:8080/Plone/agenda/slots.json?booking_date=2014/06/23It will return a list of urls:[
    "http://localhost:8080/Plone/agenda/prenotazione_add?form.booking_date=2014-06-23+09%3A00&",
    "http://localhost:8080/Plone/agenda/prenotazione_add?form.booking_date=2014-06-23+09%3A05&",
    ...
    "http://localhost:8080/Plone/agenda/prenotazione_add?form.booking_date=2014-06-23+17%3A50&",
    "http://localhost:8080/Plone/agenda/prenotazione_add?form.booking_date=2014-06-23+17%3A55&"
]If the parameterbooking_dateis not specified it will default to today.tipologies.jsonCall this view like this:http://localhost:8080/Plone/agenda/@@tipologies.jsonIt will return a json object:{
    "Task for 1 person": 10,
    "Task for 2 people": 20,
    "Task for 3 people": 30
 }The key is the booking tipology name,
the value the booking duration in minutes.CreditsDeveloped with the support ofComune di Padova.Comune di Padova supports thePloneGov initiative.AuthorsThis product was developed by RedTurtle Technology team.Changelog1.0.1 (2014-09-10)Moved custom prenotazioni_context_state to pd.prenotazioni
[alert]1.0.0 (2014-05-05)Initial release [alert]Download
Package: pdpm
Python Development Projects Manager (PDPM)A command line tool for managing ProjectsReferences:ReferenceURLGitHubhttps://github.com/Okirshen/pdpmInitTo init the tool
use this commands:pdpmsetpathThis will set the project directory to be in the current directorypdpmset-launcherLAUNCHERLAUNCHER: code, pycharm, atom, etc...This will set the command that will be used to launch the projectpdpmcreate[OPTIONS]PROJECT_NAME-s --start - If raised the project will be launchedThis will create a project namedPROJECT_NAMEpdpmremovePROJECT_NAMEThis will remove a project namedPROJECT_NAMEpdpmshow
project1
project2
project3
project4This will echo all projects in project folderpdpmstartPROJECT_NAMEThis will launchPROJECT_NAMECreated by: Ofri KirshenEmail:okirshen@gmail.com
Package: pdpolygonapi
pdpolygonapiPython API for Polygon.io returning Pandas Objects.This package provides a class,PolygonApithat provides methods for accessing
polygon.io's REST API and returning data in various Pandas objects (for example, DataFrames and/or Series).This package is in no way endorsed by, nor associated with, polygon.io or its ownership.Some of the methods include:fetch_ohlcvdf()... Returns a DataFrame containing OHLCV data with a Datetime Indexfetch_options_chain()... Returns a DataFrame of all options for an underlying for a range of expiration dates.
The DataFrame is Indexed by Expiration Date, Strike, and Put/Callfetch_quotes()... Returns Bid/Ask BidSize/AskSize data for a Ticker, with a Datetime IndexFor more detailed information see the apiPolygon jupyter notebook in the examples folder.NOTE: It is strongly recommended that you store your Polygon.io APIKEY in an ENVIRONMENT VARIABLE on
      your local machine, and pass the NAME of the ENVIRONMENT VARIABLE into the class constructor.

      (Alternatively you can pass your APIKEY directly into the class constructor, but for security
       reasons this is not recommended.  At any rate, AVOID putting your APIKEY into your code).Seeexamples/apiPolygon.ipynbCopyright 2023, Daniel Goldfarb,dgoldfarb.github@gmail.comLicensed under the Apache License, Version 2.0 (the "License");  you may not
use this package and its associated files except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0A copy of the License may also be found in the pdpolygon package repository.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: pdprpr
PanDas PRePRocessor: Preprocess Pandas Objects for Machine LearningInstallation$ pip install pdprprUsageAssume you have followingDataFrameto be preprocessed:frompandasimportDataFramedf=DataFrame({'num':[1,3,float('nan')],# numerical feature, needs to be scaled in [0, 1]'cat':['p','q','r'],# categorical feature, needs to be transformted to dummy var'bin':[False,False,True],# binary feature, needs to be 0 / 1},columns=['num','cat','bin'])#    num cat    bin# 0  1.0   p  False# 1  3.0   q  False# 2  NaN   r   TrueDefine preprocessing settings:# preprocessing.yml-name:numkind:numerical-name:catkind:categorical-name:binkind:binaryThen createDataFramePreprocessorwith them:importyamlwithopen('preprocessing.yml')asf:settings=yaml.load(f)frompdprprimportDataFramePreprocessorprocessor=DataFramePreprocessor(settings)Finally use it to preprocess theDataFrame:processor.process(df)#    num__VALUE  cat__p  cat__q  cat__r  bin__TRUE# 0         0.0       1       0       0          0# 1         1.0       0       1       0          0# 2         NaN       0       0       1          1DocumentationFor more options please seetestsuntill docs get available…
Package: pdpy11
PDPy11PDPy11 is a cross-platform assembler for PDP-11 computers.RequirementsPython 3.6 or laterNo Python modules requiredAny platform (Windows, Mac OS, Linux)InstallationUse pip.$pip3installpdpy11(some modern systems usepipinstead ofpip3for Python 3 packages)On Windows, installing via pip works too, but pay attention to any warnings. If pip says anything along the lines ofThe script pdpy11.exe is installed in '...' which is not on PATH, add the path pip mentions to the PATH variable. On newer Windows, this can be done by searching for 'Edit the system environment variables' in the Start menu and clicking through 'Environment Variables...', then 'User variables for ...' or 'System variables' depending on whether you want to install the compiler for a single user or globally, followed by 'Path', and adding the path to the end of the list.If you're using Sublime Text, you might also want to install various utilities for syntax highlighting and buildinghere.LicensingPDPy11 is licensed under GNU General Public License version 3 (which is attached to PDPy11 in 'LICENSE' file), or (at your option) any later version.DevelopmentInstall the following packages from pip:coverage pytest mutmut pyfakefs pylint. You can now run tests usingmake test, run tests with coverage usingmake cov, run mutation tests usingmake mutand run linter usingmake lint. You can addPYTHON=...option to set path to or name of Python interpreter.
Package: pdpy-lib
PdPyA Pure Data (Pd) scripting language written in Python.The documentation exists here:pdpy-org.github.io/docInstallationFrom console or better, avirtual environmentpip install pdpy-libThen, from pythonimportpdpy_libaspdpyReferencesThis project is inspired by many discussions and projects:Pure Data to XML: seethis discussionon the pd-list archives.Pure Data to JSON: seethis other oneon the pd-list archives.Pure Data file format specifications were expainedhereNewPd file formatdiscussionon the pd-list archives.sebpiq's repostirories:WebPd_pd-parser, as well aspd-fileutilsdylanburati'spuredata-compilerCopyrightlibpd: Copyright (c) Peter Brinkmann & the libpd team 2010-2021Pure Data: Copyright (c) 1997-2021 Miller Puckette and others.pyaudio: Copyright (c) 2006 Hubert Pham
Package: pdqhash
pdqhash-pythonThese are Python bindings to the PDQ perceptual hash released by Facebook. Note that the bindings are provided under the MIT license but the PDQ source code is licensed separately under its own license (see theThreatExchange/hashing/pdqfolder).Installationpip install pdqhashUsageimportpdqhashimage=cv2.imread(os.path.join('tests','images',image_name))image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)hash_vector,quality=pdqhash.compute(image)# Get all the rotations and flips in one pass.# hash_vectors is a list of vectors in the following order# - Original# - Rotated 90 degrees# - Rotated 180 degrees# - Rotated 270 degrees# - Flipped vertically# - Flipped horizontally# - Rotated 90 degrees and flipped vertically# - Rotated 90 degrees and flipped horizontallyhash_vectors,quality=pdqhash.compute_dihedral(image)# Get the floating point values of the hash.hash_vector_float,quality=pdqhash.compute_float(image)ContributingSet up local development usingmake init(you need to havepipenvinstalled)Run tests usingmake testRun tests in Docker usingmake docker_test
Package: pdr
README.mdThe Planetary Data Reader (pdr)This tool provides a single command---read(‘/path/to/file’)---for ingestingallcommon planetary data types. It is currently in development. Almost every kind
of "primary observational data" product currently archived in the PDS
(under PDS3 or PDS4) should be covered eventually.Currently-supported datasets are listed here.If the software fails while attempting to read from datasets that we have listed as supported, please submit an issue with a link to the file and information about the error (if applicable). There might also be datasets that work but are not listed. We would like to hear about those too. If a dataset is not yet supported that you would like us to consider prioritizing,please fill out this request form.AttributionIf you usepdrin your work, please cite us using our Zenodo DOI:Installationpdris now oncondaandpip. We recommend (and only officially support) installation into acondaenvironment.
You can do this like so:conda create --name pdrenv
conda activate pdrenv
conda install -c conda-forge pdrThe minimum supported version of Python is3.9.Using the conda install will install all dependencies in the environment.yml
file (both required and optional) for pdr. If you'd prefer to forego the
optional dependencies, please use minimal_environment.yml in your
installation. This is not supported through a direct conda install as
described above and will reqiore additional steps. Optional dependencies
and the added functionality they support are listed below:pvl: allowsData.load("LABEL", as_pvl=True)which will load PDS3 labels aspvlobjects rather than plain textastropy: adds support for FITS filesjupyter: allows usage of the Example Jupyter Notebook (and other jupyter notebooks you create)pillow: adds support for TIFF files and browse image renderingmatplotlib: allows usage ofsave_sparklines, an experimental browse functionUsage(You can check out our example Notebook on Binder for a
quick interactive demo of functionality:)Additional information on usage including examples, output data types, notes and caveats, test, etc.
can now be accessed in our documentation on readthedocs at:https://pdr.readthedocs.ioThis work is supported by NASA grant No. 80NSSC21K0885.
Package: pd-read
PDRead provides such and such and so and so. You might find
it most useful for tasks involving <x> and also <y>. Typical usage
often looks like this:#!/usr/bin/env python

from pdread.pd_read import PDRead
PDRead('filename.csv')(Note the double-colon and 4-space indent formatting above.)Paragraphs are separated by blank lines.Italics,bold,
andmonospacelook like this.A SectionLists look like this:FirstSecond. Can be multiple lines
but must be indented properly.A Sub-SectionNumbered lists look like you’d expect:hi theremust be goingUrls arehttp://like.thisand links can be
writtenlike this.
Package: pdrepr
pdreprpdrepr takes a pandas DataFrame as input, andattemptsto output a valid Python expression that will create an identical DataFrame. Supports multiindices for rows and columns, at least for the relatively
simple cases I have tested. DataFrames with datatypes other than strings, ints and floats should work if their_repr__()method also returns a string that can be passed toeval(), resulting in a similar object.MotivationI was tired of having to manually construct DataFrames to be used in testing, especially the reference object to be compared with the resulting DF. With this package, such a code snipped can be created from the resulting DF.InstallationUsing poetry:poetryaddpdreprUsing pipenv:pipenvinstallpdreprUsing pip:pipinstallpdreprUsage>>>frompdreprimportpdrepr>>>pdrepr(df)pd.DataFrame({'character':['The Nude Organist','BBC continuity announcer','The Colonel'],'played by':['Terry Jones and Terry Gilliam','John Cleese','Graham Chapman']}).set_index(['character'])>>>pd.DataFrame({'character':['The Nude Organist','BBC continuity announcer','The Colonel'],'played by':['Terry Jones and Terry Gilliam','John Cleese','Graham Chapman']}).set_index(['character'])playedbycharacterTheNudeOrganistTerryJonesandTerryGilliamBBCcontinuityannouncerJohnCleeseTheColonelGrahamChapman
Package: pd.requires
Dependence analysis utils=========================This packages to provide tools for dependence analysis of python modules.Programs--------find_requiresProgram find all dependences required by python moduleor module set.Used::find_requires [<MODULE> [...]] [-f (RPM|PYPI)] [-r]-fDependencies will be printed in RPM or PYPI format,it is PYPI by default;-rThe name of file will be printedafter dependence emited by them.The program must be run in directory contains module.find_providesProgramm find all dependencies provided by python moduleor module set.Used::find_provides [<MODULE> [...]] [-f (RPM|PYPI)]-fDependencies will be out in RPM or PYPI format, it is PYPI bydefault;The program must be run in directory contains module.imalyzerProgram do analyse dependencies beetween files in moduleset, but it is only experimental version and it using is notrecommended now.Sat Nov 10 2007 Andrey Orlov <cray@neural.ru> 0.0.3- Some documentation enchancement;- Switches to turn on stupid rpm-compatible dependencesformat added. It is turn off by default;Sat Nov 10 2007 Andrey Orlov <cray@neural.ru> 0.0.2- Some bugs fixed* Wed Nov 9 2007 Andrey Orlov <cray@neural.ru> 0.0.1- first official release
Package: pdrle
pdrleInstallationpip install pdrleUsageimportpdrleimportpandasx=pandas.Series(["a","a","b","b","a","a","a","c"])rle=pdrle.encode(x)rle#   vals  runs# 0    a     2# 1    b     2# 2    a     3# 3    c     1y=pdrle.decode(rle.vals,rle.runs)y# 0    a# 1    a# 2    b# 3    b# 4    a# 5    a# 6    a# 7    c# dtype: objectpandas.concat({"x":x,"id":pdrle.get_id(x)},axis=1)#    x  id# 0  a   0# 1  a   0# 2  b   1# 3  b   1# 4  a   2# 5  a   2# 6  a   2# 7  c   3pandas.concat({"x":x,"sn":pdrle.get_sn(x)},axis=1)#    x  sn# 0  a   0# 1  a   1# 2  b   0# 3  b   1# 4  a   0# 5  a   1# 6  a   2# 7  c   0
Package: pdrozd-ssg
pdrozd-ssgpdrozd-ssg is a static site generator created in pythonFeaturesCreates an index based on your html files createdAdded JSON config file supportComplete Markdown Support with the Help ofPython-Markdown/markdownRequirementsPython 3How to Installgo tohttps://pypi.org/project/pdrozd-ssg/or simply run the commandpip install pdrozd-ssgto run use the commandpdrozd [with the command you want]How to UpgradeIf theirs a new version run the commandpip install pdrozd-ssg --upgradeCommandsThe commands of pdrozd-ssg are-h or --help this will display to the user the options they have-c or --config this will specify a config file which contains arguments for SSG to read.-v or --version this will display to the user the current verison of pdrozd-ssg-i or --input this with a combanation of .txt or .md file or directory will output your files as a Static Site
to use put in the format. e.g.pdrozd -i or --input [file.txt\text.md] or [directory\]-l or --lang this at the end of the input command will allow the default language of the HTML files to change. e.g.pdrozd -i or --input [file.txt\text.md] or [directory\] -l or --lang [language]Config FileConfig files should be in legal JSON format with a similar format like{
    "input":"./Sherlock-Holmes-Selected-Stories",
    "lang":"fr"
}
Package: pds4-utils
pds4_utilsUtilities for working with NASA Planetary Data System v4 (PDS4) data filesDependenciesThe following dependencies must be met:python 3pandaspyyamllxmlPDS4 toolsInstallationFirst, clone this repository. If you are using conda, the dependencies can be installed in a new environment using the provided environment file:conda env create -f environment.ymlThe newly created environment can be activated with:conda activate pds4utilsOtherwise, please make sure the dependencies are installed with your system package manager, or a tool likepip. Use of a conda environment or virtualenv is recommended!The package can then be installed with:python setup.py installContentsThe module contains a few simple functions and a class. A brief overview is given here:read_tablereads 2D tables from PDS4 productsone level of group fields are supportedreturns a pandas dataframegroup field data are returned as an array in each pandas celliftable_nameis not given, the first table is returnedthe DataFrame is indexed by the first time field, if anythis can be set using theindex_colparameterread_tablesreads multiple tables usingread_tableuseful for building a large dataframe from many similar data productssetadd_filename=Trueto add the product name to each row, to track which product the data came fromindex_products(directory='.', pattern='*.xml')searches for PDS4 labels recursively indirectorymatchingpatternreturns a pandas DataFrame with one row per productreturned data include:LID + VIDbundle, collection and product identifierstart and stop time, if presentDatabasethis class builds one or more DataFrames containing custom meta-data from a set of PDS4 productsa YAML formatted configuration file is required to determine which attributes to readthe Xpath to each attribute must be knownseeexample.ymlfor more informationif no config file is specified when instantiating the class, a default is looked forpds_dbase.ymlin the user's home directory, or pointed to byAPPDATAorXDG_CONFIG_HOMEeach entry in the configuration file produces one database table (one Pandas dataframe)to see which tables have been loaded, uselist_tables()to return a table, useget_table(table)to save or restore a database usingsave_dbase()orload_dbase()ExampleThe Jupyter notebook included with this repository shows an example of pds4_utils in use. To view the notebook, clickhere.
Package: pdsa
ContentsThe BookIntroductionDependenciesDocumentationLicenseSource codeAuthorsInstall with pipCompile from sourceThe BookEverybody interested in learning more about probabilistic data structures and
algorithms could be referred to our recently published book:Probabilistic Data Structures and Algorithms for Big Data Applicationsby Andrii Gakhov2019, ISBN: 978-3748190486 (paperback) ASIN: B07MYKTY8W (e-book)IntroductionProbabilistic data structures is a common name of data structures
based on different hashing techniques.Unlike regular (or deterministic) data structures, they always provide
approximated answers, but usually with reliable ways to estimate
the error probability.The potential losses or errors are fully compensated by extremely
low memory requirements, constant query time and scaling.GitHub repository:https://github.com/gakhov/pdsaDependenciesPython 3.5+ (http://python.org/download/)Cython 0.28+ (http://cython.org/#download)DocumentationThe latest documentation can be found athttp://pdsa.readthedocs.io/en/latest/Membership problemBloom FilterCounting Bloom FilterCardinality problemLinear counterProbabilistic counter (Flajolet–Martin algorithm)HyperLogLogFrequency problemCount SketchCount-Min SketchRank problemq-digestLicenseMIT LicenseSource codehttps://github.com/gakhov/pdsa/AuthorsMaintainer:Andrii Gakhov <andrii.gakhov@gmail.com>Install with pipInstallation requires a working build environment.Using pip, PDSA releases are currently only available as source packages.$pip3install-UpdsaWhen using pip it is generally recommended to install packages in avirtualenvto avoid modifying system state:$virtualenv.env-ppython3--no-site-packages$source.env/bin/activate$pip3install-Ucython$pip3install-UpdsaCompile from sourceThe other way to install PDSA is to clone itsGitHub repositoryand build it from
source.$gitclonehttps://github.com/gakhov/pdsa.git$cdpdsa$makeinstall$bin/pip3install-rrequirements-dev.txt$maketest
Package: pds-api
NASA PDS Federated APIPDS strives to develop REST-ful web APIs for all online web applications, including data search and DOI management.This repository is the entry point for the NASA PDS Federated API specifications (e.g. followingOpenAPIstandard) and their documentation.Please visit our website athttps://nasa-pds.github.io/pds-api/for user documentation.PrerequisitesPython 3For PDF generation, basiclatex and accompanying libraries are neededbrew install basictex
sudo /Library/TeX/texbin/tlmgr update --self
sudo /Library/TeX/texbin/tlmgr install latexmk
sudo /Library/TeX/texbin/tlmgr install tex-gyre
sudo /Library/TeX/texbin/tlmgr install fncychap
sudo /Library/TeX/texbin/tlmgr install wrapfig
sudo /Library/TeX/texbin/tlmgr install capt-of
sudo /Library/TeX/texbin/tlmgr install framed
sudo /Library/TeX/texbin/tlmgr install needspace
sudo /Library/TeX/texbin/tlmgr install tabulary
sudo /Library/TeX/texbin/tlmgr install varwidth
sudo /Library/TeX/texbin/tlmgr install titlesecGenerate the user's documentationSet the overall version (e.g. build number) indocs/source/conf.pyThe OpenAPI yaml unresolved specifications are stored in thespecsdirectory.The naming of the specification are:
PDS_APIs-{application}-{version}-swagger.yamlFor example:
PDS_APIs-registry-1.0.0-swagger.yamlTo add a new specification or a new version of a specification:Copy the OpenAPI Specification under thespecsdirectory following the above filename convention.Add a new entry for this file indocs/source/specifications.rstIn the filedocs/source/conf.pyadd a section for the new specification in theredocobject.Generate a PDF of the documentation.cd docs/
make latexpdf
cp build/latex/pdsapis.pdf build/html/_staticGenerate the Sphinx documentation:pip install -e '.[dev]'

sphinx-build -b html docs/source docs/build/htmlCode of ConductAll users and developers of the NASA-PDS software are expected to abide by ourCode of Conduct. Please read this to ensure you understand the expectations of our community.DevelopmentTo develop this project, use your favorite text editor, or an integrated development environment with Python support, such asPyCharm.ContributingFor information on how to contribute to NASA-PDS codebases please take a look at ourContributing guidelines.CI/CDThe template repository comes with our two "standard" CI/CD workflows,stable-cicdandunstable-cicd. The unstable build runs on any push tomain(± ignoring changes to specific files) and the stable build runs on push of a release branch of the formrelease/<release version>. Both of these make use of our GitHub actions build step,Roundup. Theunstable-cicdwill generate (and constantly update) a SNAPSHOT release. If you haven't done a formal software release you will end up with av0.0.0-SNAPSHOTrelease (see NASA-PDS/roundup-action#56 for specifics).