Saravanan666/pypi-org huggingface dataset:
Package: ppuri
ppURIApyparsingbased URI parser/scanner library.Install using pip or your tool of choice e.g.pip install ppuri
poetry add ppuriUsageParsingEither importppuri.uriand use theparsefunction to match and parse against all URI schemes e.g.fromppuriimporturiinfo=uri.parse("https://www.example.com:443/a.path?q=aparam#afragment")print(info)prints{"authority":{"address":"www.example.com","port":"443"},"fragment":"afragment","parameters":[{"name":"q","value":"aparam"}],"path":"/a.path","scheme":"https","uri":"https://www.example.com:443/a.path?q=aparam#afragment"}Or import a specific scheme's parse function.fromppuri.schemeimporthttpinfo=http.parse()and use that to parseScanningTo scan text for URIs use thescanmethodSupported schemesCurrently supports the following schemeshttp(s)urndatafilemailtoaboutaaacoapcridHttp(s)parse_stringreturns a dictionary of the form{"scheme":"http or https","authority":{"address":"hostname or ipv4 address or ipv6 address","port":"port number","username":"user name if provided","password":"pasword if provided"},"path":"path if provided","parameters":[// list of parameters if provided{"name":"parameter name","value":"parameter value or None if not provided"}],"fragment":"fragment if provided","uri":"The full URI"}Urnparse_stringreturns a dictionary of the form{"scheme":"urn","nid":"Namespace Identifier","nss":"Namespace Specific String","uri":"The full URI"}MailToparse_stringreturns a dictionary of the form{"scheme":"mailto","addresses":["List of email addresses",]"parameters":["list of parameters if provided",{"name":"bcc","value":"dave@example.com"}],"uri":"The full URI"}Dataparse_stringreturns a dictionary of the form{"scheme":"data","type":"Mime type","subtype":"Mime Subtype","encoding":"base64 if specified","data":"The actual data","uri":"The full URI"}Fileparse_stringreturns a dictionary of the form{"scheme":"file","path":"The /file/path","uri":"The full URI"}Package Status
Package: ppviz
ppviz (python profiler viz'ualizer)It's a GUI to explore/profile a python file.
(it's a little bit likesnakeviz)
Should work on windows/mac/linux and chromeos/linux$ python -m pip install ppviz
$ ppviz sudoku.pyremark: "sudoku.py" is an example (and can be foundhere), you should use the python file you want to profile ;-)it useshtag(+htbulma) as gui system ;-)
Package: ppw
Python Project WizardA tool for creating skeleton python project, built with popular develop tools and
conform to the best practice.FeaturesThis tool will create Python project with the following features:Poetry: Manage version, dependancy, build and releaseMkdocs: Writting your docs in markdown styleTesting withPytest(unittest is still supported out of the box)Code coverage report and endorsed byCodecovTox: Test your code against environment matrix, lint and artifact check.Format withBlackandIsortLint code withFlake8andFlake8-docstringsPre-commit hooks: Formatting/linting anytime when commit/run local tox/CIMkdocstrings: Auto API doc generation and docstring template (vscode and its extensionautodocStringsis required)Command line interface usingPython Fire(optional)Continuouse Integration/Deployment bygithub actions, includes:publish dev build/official release to TestPyPI/PyPI automatically when CI successpublish documents automatically when CI successextract change log from github and integrate with release notes automaticallyHost your documentation fromGit Pageswith zero-configSupport multiple versions of documentations (bymike)Create repo and push initial commits by repo.sh scriptQuickstartInstall ppw if you haven't install it yet:pip install -U ppwGenerate a Python package project by simple run:ppwThen follow theTutorialto finish configurations.CreditsThis repo is forked fromaudreyr/cookiecutter-pypackage, and borrowed some ideas frombriggySmallsLinkscfg4pycfg4pyis a great tool for managing configuration files, supporting configuration for different environments (dev, prodction and test), automatically converting yaml-based configuration to python class, so, you can access configuration items by attribute, thus, enable auto-completion (by IDE). It also supports live-reload, remoting central configuration, config template and more.
Package: ppwjb
Python Project Wizard with Jupyter BookA tool for creating skeleton python project, built with popular develop tools and
conform to best practice including Jupyter Book and its various integrations.FeaturesThis tool will create Python project with the following features:Poetry: Manage version, dependency, build and releaseJupyter Book: Writing your docs in markdown style and jupyter notebooksTesting withPytest(unittest is still supported out of the box)Code coverage report and endorsed byCodecovTox: Test your code against environment matrix, lint and artifact check.Format withBlackandIsortLint code withFlake8andFlake8-docstringsPre-commit hooks: Formatting/linting anytime when commit/run local tox/CIBandit: Checking for security vulnerabilitiesCommand line interface usingPython Fire(optional)Continuous Integration/Deployment bygithub actions, includes:publish dev build/official release to TestPyPI/PyPI automatically when CI successpublish documents automatically when CI successextract change log from github and integrate with release notes automaticallyHost your documentation fromGit Pageswith zero-configQuickstartInstall ppwjb if you haven't install it yet:pip install -U ppwjbGenerate a Python package project by simple run:ppwjbThen followTutorialto finish other configurations.CreditsWe based this work onzillionare/cookiecutter-pypackageand borrowed some ideas fromaudreyr/cookiecutter-pypackageandbriggySmalls.
Package: ppxml
Command-line tool to validate and pretty-print XML.Setup# Install through pippipinstallppxml# Install through gitpipinstallgit+https://github.com/h2rd/ppxml# or without cachecd/tmpgitclonehttps://github.com/h2rd/ppxmlcdppxmlpythonsetup.pyinstallExamplesJust prettify and colorize xml code:curlhttp://feeds.bbci.co.uk/news/rss.xml|ppxmlUse xpath for get some elements:curlhttp://feeds.bbci.co.uk/news/rss.xml|ppxml-x"//item"Use xpath for get deeper elements:curlhttp://feeds.bbci.co.uk/news/rss.xml|ppxml-x"//item/title"
Package: ppxxh
ppxxh is a Pure Python implementation of the XXHASH family of hash
algorithms.This pure python implementation ofYann Collet’s XXHASHfamily of
non-cryptographic hash algorithms with no dependencies outside of the
python standard library is significantly slower than version 0.8.0 of
thereference implementationof these hash algorithms, but they
produce the same outputs given the same inputs. This equality of outputs
is checked with tests that duplicate portions ofxsum_sanity_check.cfrom thereference implementation.ppxxhis compatible with Python 3.0 and newer.The interface and use ofppxxhis similar to the Python standard
library modulehashlib, while providing XXHASH hash algorithms.
Thus the various hash objects may be created using theppxxh.new()function or directly instantiated from their classes.Hash MethodsThe hash objects have these common methods:update(data)Update the state of the hash object. Data may be passed to the hash
object at initialization and/or by an arbitrary number of calls toupdate().digest()Return the hash digest as a bytes object.hexdigest()Return the hash digest as a string of hexidecimal digits.intdigest()Return the hash digest as an unsigned integer.This method is not part of thehashlibinterface but is the
typical output format of thereference implementation(except for
xxh3_128)copy()Return a copy (clone) of the hash object.Thexxh3_128hash objects also have aintdigest2()method that
returns the hash digest as a pair of 64-bit unsigned integers matching
the typical output format of thereference implementationfor this
hash algorithmAs listed inppxxh.algorithms_guaranteedandppxxh.algorithms_guaranteed, the hash algorithms provided arexxh32,xxh64,xxh3_64, andxxh3_128.Theppxxh.generate_secret()function duplicates theXXH3_generateSecret()function provided by thereference implementationto use a low entropy bytes like object
of arbitrary length to generate a high entropy secret well suited to use
as thesecretparameter ofxxh3_64andxxh3_128.ExampleCalculate the digest in various forms for the bytes object,b'This is a bytes object, not a string!'.  Specify aseedvalue.  If this optional value were not provided, the hash object
would be initialized differently and the various resulting digest
values would all be different.>>> from ppxxh import xxh64
>>> m = xxh64(seed=14414669413082423462)  # seed is optional
>>> m.name
'xxh64'
>>> m.digest_size
8
>>> m.block_size
32
>>> m.update(b'This is a bytes')
>>> m.hexdigest()  # a digest may be requested at any time
'938907256708f46c'
>>> m.update(b' object, not a string!')
>>> m.digest()
b'!^>f7\x84Ii'
>>> m.hexdigest()
'215e3e6637844969'
>>> m.intdigest()
2404427859801426281A more condensed way to get the same result.>>> xxh64(b'This is a bytes object, not a string!',
...       seed=14414669413082423462).hexdigest()
'215e3e6637844969'The reference implementation of the XXHASH family are available athttps://github.com/Cyan4973/xxHash(Python code formatted byblack-l79)Licensed underMIT License
Package: ppy-common
In the name of God, the Most Gracious, the Most Merciful.PPyCommon (PWeb Python Common)DocumentationInstall and update usingpip:pipinstall-Uppy-commonPlease findthe Documentationwith example fromhmtmcse.comDonateBangla Fighterdevelops and supports PPyCommon (PWeb Python Common) and the libraries it uses. In order to grow
the community of contributors and users, and allow the maintainers to devote more time to the projects.ContributingFor guidance on setting up a development environment and how to make a contribution to PPyCommon (PWeb Python Common), see the contributing guidelines.LinksChanges :https://opensource.banglafighter.org/pweb/ppy-commonPyPI Releases :https://pypi.org/project/ppy-commonSource Code :https://github.com/banglafighter/ppy-commonIssue Tracker :https://github.com/banglafighter/ppy-common/issuesWebsite :https://www.banglafighter.com/open-source
Package: ppy-file-text
In the name of God, the Most Gracious, the Most Merciful.PPyFileTextDocumentationInstall and update usingpip:pipinstall-Uppy-file-textPlease findthe Documentationwith example fromhmtmcse.comDonateBangla Fighterdevelops and supports PPyFileText and the libraries it uses. In order to grow
the community of contributors and users, and allow the maintainers to devote more time to the projects.ContributingFor guidance on setting up a development environment and how to make a contribution to PPyFileText, see the contributing guidelines.LinksChanges :https://opensource.banglafighter.org/pweb/ppy-file-textPyPI Releases :https://pypi.org/project/ppy-file-textSource Code :https://github.com/banglafighter/ppy-file-textIssue Tracker :https://github.com/banglafighter/ppy-file-text/issuesWebsite :https://www.problemfighter.com/open-source
Package: ppy-jsonyml
In the name of God, the Most Gracious, the Most Merciful.PPyJsonYMLDocumentationInstall and update usingpip:pipinstall-Uppy-jsonymlPlease findthe Documentationwith example fromhmtmcse.comDonateBangla Fighterdevelops and supports PPyJsonYML and the libraries it uses. In order to grow
the community of contributors and users, and allow the maintainers to devote more time to the projects.ContributingFor guidance on setting up a development environment and how to make a contribution to PPyJsonYML, see the contributing guidelines.LinksChanges :https://opensource.banglafighter.org/pweb/ppy-jsonymlPyPI Releases :https://pypi.org/project/ppy-jsonymlSource Code :https://github.com/banglafighter/ppy-jsonymlIssue Tracker :https://github.com/banglafighter/ppy-jsonyml/issuesWebsite :https://www.banglafighter.com/open-source
Package: ppyPatternRecognition
ppyPatternRecognition[Should read this in github]This is a library about AI model. All the model ideas are come from inclass lecture.You can see the class githubherewhich is created by Aj. Ekapol.Table of contentppyPatternRecognitionTable of contentProvided modelHow to installK-meansLinear regressionLogistic regressionSimple Naïve BayesProvided modelppyPatternRecognitionTable of contentProvided modelHow to installK-meansLinear regressionLogistic regressionSimple Naïve BayesHow to installInstall pythonInstall library usingpippipinstall-UppyPatternRecognitionK-meanscodehereexamplefromppyPatternRecognitionimportKmeansdf=pd.read_csv(...)k_means=Kmeans()# fit the modellabeled_df=k_means.fit(df,k=3)# print the labelprint(labeled_df['label'])# get the last centroidprint(k_means.last_centroid)fitmethod will return the dataframe with label columnlast_centroidis the last centroid of the model after fittingLinear regressioncodehereexamplefromppyPatternRecognitionimportLinearRegressiondf=pd.read_csv(...)X_train,y_train=...linear_regression=LinearRegression()# fit the modellinear_regression.fit(X_train,y_train,epochs=1000,lr=0.01)# predicty_pred=linear_regression.predict(X_test)fitmethod will return the model itselfpredictmethod will return the predicted valueLogistic regressioncodehereexamplefromppyPatternRecognitionimportLogisticRegressiondf=pd.read_csv(...)X_train,y_train=...logistic_regression=LogisticRegression()# fit the modellogistic_regression.fit(X_train,y_train,epochs=1000,lr=0.01)# predicty_pred=logistic_regression.predict(X_test)fitmethod will return the model itselfpredictmethod will return the predicted valueSimple Naïve BayescodehereexamplefromppyPatternRecognitionimportSimpleBayesClassifierdf=pd.read_csv(...)X_train,y_train=...simple_naive_bayes=SimpleBayesClassifier()# fit the modelsimple_naive_bayes.fit(X_train,y_train)# predicty_pred=simple_naive_bayes.predict(X_test)fitmethod will return the model itselffit_gaussianmethod will return the model itselfpredictmethod will return the predicted valuepredict_gaussianmethod will return the predicted value
Package: ppytools2
PPyTools`PPyTools`this name make by myself. It’s group byPP`(my lover’s name first word) + `Py`(Python abbr)  + `Tools.PPyToolsis a Python library(2.7+) utility that inPythonproject. Hope it can help you usefully on work or others.Test ReportRun Test Guideexport PYTHONPATH=your project real path
cd ./test
python -u suites_start.pyInstallpip install ppytools2
from ppytools.xxx import xxxHistory@history
Package: pqam-rmsadtandoc2023
Lattice-DistortionRelease:Compatible with:Tests:License:This ForkThis small repository is a lightweight fork version of the original one by Tandoc. It is only slightly modified to fit automated pipelines of theULTERA Database (ultera.org)infrastrcutre, which expectsmodel.pyscript withpredict(comp: pymatgen.Composition)andpredict(comp: str)function returning an ordered array of output numbers or a labeled dictionary of them.Output Order: [rmsad_Tandoc2023]Output Meaning (all based on10.1038/s41524-023-00993-x):rmsad_Tandoc2023- Root Mean Squared Atomic Displacement in the units of AngstromInstall and UseThere are no dependencies beyond a fairly recent pymatgen (>=2022.1.9). You need to simplypip install pqam-rmsadtandoc2023and you should be good to go! Then you can run it on your compositions in Python likefrom pqam-rmsadtandoc2023 import predict

print(predict('Mo25 Nb25 Hf50'))
print(predict('MoNbHf'))
print(predict('Mo33.3 Nb33.3 Hf33.3'))or from command line likepython -c "import pqam_rmsadtandoc2023 as m;print(m.predict('Mo25 Nb25 Hf50'));print(m.predict('MoNbHf'));print(m.predict('Mo33.3 Nb33.3 Hf33.3'))"Original README by TandocThis repository contains relevant code and data for "Mining lattice distortion, strength, and intrinsic ductility of refractory high-entropy alloys using physics-informed >statistical learning" by Christopher Tandoc, Yong-Jie Hu, Liang Qi, and Peter K. Liaw to be published in npj Computational MaterialsRMSAD_tool.py is a linux command line script written in python that takes a chemical composition in the form of a text string and prints the lattice distortion in angstroms.example usgage:
./RMSAD_tool.py Ti0.5V0.5-This script uses pymatgen (https://pymatgen.org/) to process the input string and is thus a requirement for the script to work. Depending on the version of pymatgen you have >installed, lines 3 and 380 may need to be modified (https://matsci.org/t/python-problem-with-pymatgen/35720)
-numpy (https://numpy.org/) is also a dependency
-This tool is currently only able to make predictions for compositions containing Ti,Zr,Hf,V,Nb,Ta,Mo,W,Re,Ru and will return an error if any other elements are present in >the input
-B2 and elemental feature data are defined in dictionaries at the beginning of the codetraining.ipynb and training_data.csv contains code and data to reproduce the rmsad model training that was performed in the paper
-jupyter notebook is needed to open training.ipynb, dependencies are numpy, pymatgen, matplotlib (https://matplotlib.org/), pandas (https://pandas.pydata.org/), and >sklearn(https://scikit-learn.org/stable/)MiscellaneousLast maintenance check: January 22nd, 2024
Package: pqapi
peperqa-apiPython client for interacting with paperqa appUsageMake sure to set the environment variablePQA_API_TOKENorPQA_API_KEYto your API token.exportPQA_API_TOKEN=pqa-...To query agent:importpqapiresponse=pqapi.agent_query("Are COVID-19 vaccines effective?")to query with a specific bibliography (collection of papers)importpqapiresponse=pqapi.agent_query("Are COVID-19 vaccines effective?","covid")TemplatesYou can use templates to batch multiple queries together. A minimal example would be:The effectiveness of COVID-19 is given below:{{"Are COVID-19 vaccines effective?"|pqa}}Or, more complex examples can use shared bibliographies set by variables names:{%withbib="covid"%}## Info{{"Are COVID-19 vaccines effective?"|pqa(bib)}}## Modality{{"Has there been an AAV COVID-19 vaccine?"|pqa(bib)}}{%endwith%}You render it via:pqa-rendertemplate.jinja>output.mdManaging bibliographiesTo get information about a specific bibliographyimportpqapiresponse=pqapi.get_bibliography("default")print(response)You do not need to explicitly create a bibliography, just adding files will create one. To upload files:importpqapifiles=open("paper.pdf","rb")metadata=pqapi.UploadMetadata(filename="paper.pdf",citation="Test Citation")response=pqapi.upload_file("default",filemetadata)To delete a bibliography:importpqapiresponse=pqapi.delete_bibliography("default")DevelopmentYou can change the server URL endpoint to a local PQA server withexportPQA_URL="http://localhost:8080"
Package: pqc
A sample Python projectA sample project that exists as an aid to thePython Packaging User
Guide'sTutorial on Packaging and Distributing
Projects.This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.Most of the configuration for a Python project is done in thesetup.pyfile,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and can be written usingreStructuredTextormarkdownwith the appropriatekey set. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.
Package: pqca
PQCA (Partitioned Quantum Cellular Automata)A quantum cellular automaton iteratively applies some update circuit to some initial state.
A partitioned quantum cellular automaton (PQCA) derives its update circuit by partitioning
a lattice of qubits into cells, and then applying the same circuit to each cell.
The full update circuit is created by composing several such partitioned updates.
There is a review of Quantum Cellular Automata by Terry Farrelly, published in Quantum, and available atdoi:10.22331/q-2020-11-30-368.This python module allows for the easy creation and execution of partitioned quantum cellular automata.
To create an automaton you will need:A starting state (list of 0s and 1s)Update Frames (seepqca.update_frame)A simulator / quantum computer (seepqca.backend)An Update Frame combines a tessellation with a circuit to be applied
to each cell in that tessellation.
A tessellation just partitions a list of qubits into cells. For examplepqca.tessellation.one_dimensional(10,2)partitions 10 qubits into 5 cells, each of size 2.
The Update Frame would then need to be a circuit on 2 qubits.
For more complicated tessellations you can use, e.g.pqca.tessellation.n_dimensional([4,2,4],[2,2,2])which partitions 32 qubits as though they were arranged in a lattice
of shape4 x 2 x 4, with each cell of size2 x 2 x 2.
The Update Frame would then need to be a circuit on 8 qubits.One can then callnext(automaton)which will advance the internal state of the automaton and return the new state.InstallationInstall viapipfrom the command line with the command:pip install pqcaExampleHere is an example that creates two update frames,
both applying a simple CX gate, but with offset tessellations.# Create circuitcx_circuit=qiskit.QuantumCircuit(2)cx_circuit.cx(0,1)# Create tessellationtes=pqca.tessellation.one_dimensional(10,2)# Create update framesupdate_1=pqca.UpdateFrame(tes,cx_circuit)update_2=pqca.UpdateFrame(tes.shifted_by(1),cx_circuit)# Create initial stateinitial_state=[1]*10# Specify a backend; `pqca.backend.qiskit()` returns IBM's Aer simulator by default# See backend.py for more details and instructions on coding your own backendbackend=pqca.backend.qiskit()# Create the automatonautomaton=pqca.Automaton(initial_state,[update_1,update_2],backend)# The automaton can be called like any other iterator# The following line advances the internal state, and returns the new statenext(automaton)DocumentationDetailed documentation can be found atreadthedocs.ioas well as
in the docstrings of the python files themselves.LicensingThe source code is available under the MIT licence and can be found
onHector Miller-Bakewell's github.AcknowledgementsThis package was created as part of theQuTune Project.