Saravanan666/pypi-org huggingface dataset:
Package: pdfy
PdfyPdfy is a Python library for converting HTML (and anything Chrome can render) into PDF. It uses Chrome printing functionality, so the PDFs will be rendered exactly as printed in the browser.InstallationTo install the library, you need to run.pip install pdfyAdditionally,you will need to install Chrome Driver.UsageUsing the library is as easy as:from pdfy import Pdfy
p = Pdfy()
p.html_to_pdf("html_file.htm", pdf_path="pdf_file.pdf")More control over the PDF layoutIf you need to have more control over the layout, you can pass additional parameters to html_to_pdfoptions = {"paperWidth": 8.3, "paperHeight":11.7}
p.html_to_pdf("html_file.htm", pdf_path="pdf_file.pdf" options=options)The full list of parameters is available onChrome's Developer site.Not saving the PDFIn the absence of the pdf_path argument, the html_to_pdf function will return the PDF as a base64 encoded string.pdf = p.html_to_pdf("html_file.htm")Multiple instancesThe library will run Chrome in the background in the remote debug mode. This means that if your project requires multiple initialized Pdfy objects, you might need to change the port used for debugging. This can be done by passing the port number to Pdfy() as follows:p = Pdfy(debug_port=9222) #9222 is the default portCreditsThis library is released underthe Apache 2.0 License.(C) Copyright 2018-2022Mika HämäläinenNeed for NLP solutions for your business?My company,Rootroo offers consulting related to multilingual NLP tasks. We have a strong academic background in the state-of-the-art AI solutions for every NLP need. Just contact us, we won't bite.Cite@software{mika_hamalainen_2020_4108770,
  author       = {Mika Hämäläinen and
                  Hiromu Hota and
                  Mike and
                  Mirza Delic},
  title        = {mikahama/pdfy 1.0.50},
  month        = oct,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {1.0.50},
  doi          = {10.5281/zenodo.4108770},
  url          = {https://doi.org/10.5281/zenodo.4108770}
}
Package: pdg
The Python packagepdgprovides programmatic access to the data
published by the  Particle Data Group (PDG) in theReview of Particle Physics.PLEASE NOTE: This software is a beta release for testing and is still under active development.
The data provided by the present version should not be used in any scientific publications
without careful checking of every single data item retrieved from the PDG API.For documentation of the PDG API seehttps://pdgapi.lbl.gov/doc.The source code for this package can be found athttps://github.com/particledatagroup/api.TheReview of Particle Physicsis available online from the PDG website athttps://pdg.lbl.gov.General information about PDG and theReview of Particle Physicscan be found athttps://pdg.lbl.gov/about.To provide feedback or report bugs, please contactapi@pdg.lbl.gov.
Package: pdgid
pdgidA command-line tool to quickly get a particle's name from its PDGID and vice versa.All elementary SM particles are included as well as some (hopefully) relevant
hadrons and BSM particles. The data was sourced from a combination ofthe PDG 2020 review,the CERN twiki, andrivet.If you want additional particles added, extra features or find a bug, please
submit an MR or create a issue! I am waiting to see if this package gains traction.Installationpipinstallpdgidpdgidhas no dependencies so you are free to install it outside of a virtual
environment and not worry about package conflicts.Requirespython3.6or higher.UsagePass the command an id to get the associated particle name:>pdgid14ν_μOr pass a particle name to get the associated id:>pdgidν_μ14[ν_μ]Note the particle name can often be written in many forms and unicode is
accepted (e.g. "nu_mu", "Vμ", "ν_μ", "muon neutrino", etc...). The name that's been matched is displayed in the square brackets to help with cases of ambiguity.Finally, when ran without arguments the tool will print a table of all the particles it
knows (along with their id).LimitationsSearching by name doesn't support using "bar" to represent antiparticle.Order matters for particles with multiple super/subscripts. This could be
fixed with even more regex.LicenseMIT
Package: pdg-mp3-converter
mp4 to mp3 converter / mp3 to mp4 converter by pdghow to use:convert mp4 to mp3frompdg_mp3_converterimportconverterfilepath='path to mp4 file'filename='name of the file'converter.mp4ToMp3(path=filepath,name=filename)convert mp3 (must have been mp4 before) back to mp4frompdg_mp3_converterimportconverterfilepath='path to mp3 file'filename='name of the file'converter.mp3ToMp4(path=filepath,name=filename)
Package: pdgrid
A higly performant back end for AG-Grid front end.This API is written to deliver fast responses to requests from a grid front end client on data sets that are too big to be effectively transformed on the front end.The ag-grid endpoint takes a Pandas dataframe and the corresponding ag-grid request and performs the transformations to the dataframe specified in the request, returning a dictionary formatted as expected by ag-grid.The source code is availableherePerformanceWhen benchmarked against a purpose-built Java server PDGrid generally outperformed Java. In some cases by a large factor, depending on the size of the data set. Pandas is extremely performant when performing grouping, aggregation, filtering and sorting commands. The biggest bottleneck in most cases will be the loading of the data from db or otherwise. By caching the data in Apache Parquet format this can be speeded up significantly (see examplehere).Supported ag-grid featuresPivoting, Pagination, Grouping, Sorting, Aggregrations, Filtering, Asynchronous fetching of set filter values.RequirementsPython 3.8 or 3.9.Installation:From source:$gitclonegit@github.com:slindal/pdgrid.git
$cdpdgrid
$python3.9-mvenvenv
$.env/bin/activate
$pythonsetup.pyinstallUsing package manager (in a python3 venv):$python-mpipinstallpdgridRunning a test serverSet up mysql database and add a table:$cdexamples
$msyql-uroot
$mysql>createdatabasesample_data;$mysql>source./create_athletes.sql;Install the python packages needed to run the server:$python-mpipinstall-rserver_requirements.txtFinally start up the server (from example directory):$exportFLASK_APP=server
$python-mflaskrun--port=8000If you have started up a front end client (followthese stepsyou should be able to see the data sethttp://localhost:4000Using other dataNo changes are necessary on the server side, beyond changing the function that loads the data from database or elsewhere. The front end client must be updated to handle any changes to the data set.
Package: pd-helper
pd-helperA helpful package to streamline Pandas DataFrame optimization.Save 50-75% on DataFrame memory usage by running the optimizer.Autoconfigure dtypes for appropriate data types in each column withhelper.Generate a random DataFrame of controlled random variables for testing withmaker.Installpipinstallpd-helperBasic Usage to Iterate over DataFramefrompd_helper.makerimportMakeDatafrompd_helper.helperimportoptimizefaker=MakeData()if__name__=="__main__":# MakeData() generates a fake dataframe, convenient for testingdf=faker.make_df()df=optimize(df)Better Usage With Multiprocessingfrompd_helper.makerimportMakeDatafrompd_helper.helperimportoptimizefaker=MakeData()if__name__=="__main__":# MakeData() generates a fake dataframe, convenient for testingdf=faker.make_df()df=optimize(df,enable_mp=True)Specify Special Mappingsfrompd_helper.makerimportMakeDatafrompd_helper.helperimportoptimizefaker=MakeData()if__name__=="__main__":# MakeData() generates a fake dataframe, convenient for testingdf=faker.make_df()special_mappings={'string':['object_id'],'category':['item_name']}# special mappings will be applied instead of by optimize ruleset, they will be returned.df=optimize(df,enable_mp=True,special_mappings=special_mappings)Sample Results with HelperStartingwith175.63MBmemory.

Afteroptmization.Endingwith65.33MBmemory.Generating a Randomly Imperfect DataFrame with MakerMaker provides a class, MakeData(), to generate a table of made-up records.Each row is an event where an item was retrieved.Options to make the table imperfectly random in various ways.Sample table below:Retrieved DateItem NameRetrievedConditionSectorExample2019-01-01, 2019-03-4Toaster, LighterTrue, FalseJunk, Excellent1, 2Data TypeStringStringStringStringIntegerReferencesPandas Categorical:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Categorical.htmlPandas Pickle:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_pickle.htmlPandas CSV:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.htmlPandas Datetime:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.htmlTODOImprove efficiency of iterating on DataFrame.Allow user to toggle logging.Provide tools for imputing missing data.
Package: pdic
PDICPDIC:(IDL)Transpiler Source-to-source compiler C/C++ to YAML Portable Data Interface DescriptionProject related toPDIFrom C/C++ to Yaml DescriptionSimple binaryPython librarystatic binaryPython 3./pdic file.cimport pdic; result:str = pdic.files_to_pdi()InstalationBinarycmake-S.-DBINARY:BOOL=ON-Bbuild&&cmake--buildbuild---j`nproc`Google Testcmake-S.-DTEST:BOOL=ON-Bbuild&&cmake--buildbuild---j`nproc`cdbuild&&ctestPythonpythonsetup.pybuild-G"Unix Makefiles"python3setup.pybdist_wheel-G"Unix Makefiles"-j`nproc`pythonsetup.pybuild_ext-G"Unix Makefiles"--inplace-j`nproc`pytestUsage#pragma pdi ontypedefstructVar8{#pragma pdi type : int64intmy_int;charchar_tab[20];charmy_char;}var;#pragma pdi size:[42]int**array_of_pointer_of_array[21];varmy_var;#pragma pdi offWill be see and describe as followstructs:
 Var8:
  type: record
  name: Var8
  alias: [var]
  fieldsize: 3
  buffersize: 28
  packed: false
  members:
   char_tab: { type: array, subtype: char, size: 20 }
   my_char:
    offset: 24   
    type: char
   my_int:
    type: int64
data:
 array_of_pointer_of_array: { type: array, subtype:  { type: pointer, subtype:  { type: array, subtype: int, size: 42 } }, size: 21 }
 my_var:
  type: record
  name: Var8
  alias: [var]
  fieldsize: 3
  buffersize: 28
  packed: false
  members:
   char_tab: { type: array, subtype: char, size: 20 }
   my_char:
    offset: 24   
    type: char
   my_int:
    type: int64Python Usageimportosimportpathlibimportyamlimportpdichere=pathlib.Path(__file__).parent.resolve()# pip show -f pdicif__name__=="__main__":pdi_yml_description:str=pdic.files_to_pdi([os.path.join(here,"level_1.c"),os.path.join(here,"level_2.c")])print(pdi_yml_description)print(yaml.dump(yaml.load(pdi_yml_description)))IndexAbstractUsageBinaryPythonDeveloppementCodeDocumentationTestDeployLinksAutre DocumentationBiblioCode exampleExtraClang LLVMMind MapGraphUMLContact / CreditsCreditsBarre Kevinneudinger(Software Scientist)CEA pdi team
Package: pdict
pdict has a dictionary like interface and a sqlite backend.
It uses pickle to store Python objects and strings, which are then compressed with zlib for storage in sqlite.
Multithreaded interaction is supported.Depends on python 2.5+Example use:>>> filename = 'cache.db'
>>> cache = pdict.PersistentDict(filename)
>>> url = 'http://google.com/abc'
>>> html = '<html>abc</html>'
>>>
>>> url in cache
False
>>> cache[url] = html
>>> url in cache
True
>>> cache[url] == html
True
>>> cache.get(url)['value'] == html
True
>>> now = datetime.datetime.now()
>>> cache.meta(url)
{}
>>> cache.meta(url, 'meta')
>>> cache.meta(url)
'meta'
>>> del cache[url]
>>> url in cache
False
Package: pdicts
pdict is an implementation of a persistent dictionary using sqlite3 and json libraries. The library is silly and it sucks and as such it shouldn't be used by anyone. :)Usage:::>>> import pdicts>>> d = pdicts.PersistentDict("test.sqlite")>>> d['moto'] = "Let the beauty of what you love be what you do">>> d['moto']u'Let the beauty of what you love be what you do'
Package: pdiff
pdiffPretty side-by-side diff.Inspired byydiffandicdiff.ExampleInstallationpip install pdiffOr:brew install nkouevda/nkouevda/pdiffUsageusage: pdiff [<options>] [--] <left file> <right file>

Pretty side-by-side diff

optional arguments:
  -h, --help            show this help message and exit
  -b, --background, --no-background
                        highlight background instead of foreground (default: True)
  -l, --line-numbers, --no-line-numbers
                        show line number columns (default: True)
  -t <n>, --expand-tabs <n>
                        expand tabs to <n> spaces (default: 8)
  -s, --signs, --no-signs
                        show sign columns (default: True)
  -U <n>, --unified <n>
                        show <n> lines of context (default: 3)
  -v, --version         show program's version number and exit
  -w <n>, --width <n>   fit output to <n> columns (default: autodetect)GitConfigure apdiffdifftool, and add some aliases to your liking, e.g.:[difftool "pdiff"]
	cmd = "pdiff -- \"$LOCAL\" \"$REMOTE\" | less --Raw-control-chars"

[alias]
	dfp = difftool --tool=pdiff
	dfpc = difftool --tool=pdiff --cachedLicenseMIT License
Package: pdiffer
A Python interface to the`perceptualdiff<http://pdiff.sourceforge.net/>`__ tool, plus
helpers for making pdiff-based assertions in tests. ByZack.Install using pip:pip install pdifferYou need to haveperceptualdiffinstalled somewhere on your computer
for this library to work.UsageCreate yourPDifferinstance, optionally specifying a path to the
binary:frompdifferimportPDifferpdiff=PDiffer(bin='/usr/local/bin/perceptualdiff')# or, justfrompdifferimportpdiffDiff two images by pathname:result=pdiff('image1.png','image2.png')If the images are similar enough,resultwill be falsy; that is, the
following code will print ‘SAME’:>>>ifresult:...print'DIFFERENT'...else:...print'SAME'SAMEYou can get the string output of the command by just callingunicode/str(or evenprint) on the result:>>>print(result)PASS: Images are binary identicalIf the images are different, you’ll see something else:>>>result=pdiff('image1.png','image3.png')>>>ifresult:...print(result)FAIL: Image dimensions do not matchParametersThere are a few parameters you can customize in the pdiff invocation:pdiff('image1.png','image2.png',fov=65,threshold=40,gamma=2.2,luminance=100,luminanceonly=True,colorfactor=1.0,downsample=0,output='o.ppm')Documentation on all of these options can be found by runningperceptualdiff-help.Test AssertionsPerceptualDiff is mostly useful in automated testing, and therefore some
basic assertions are provided for checking image similarity (under both
PEP8 and camelCase names):frompdifferimportassertImagesSimilar,assertImagesDifferentfrompdifferimportassert_images_similar,assert_images_differentdeftest_something():assert_images_different('image1.png','image2.png')assert_images_similar('image1.png','image3.png')These assertions take parameters just likepdiff(), e.g.:assert_images_different('image1.png','image2.png',fov=89.9,threshold=40)UnlicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.In jurisdictions that recognize copyright laws, the author or authors of
this software dedicate any and all copyright interest in the software to
the public domain. We make this dedication for the benefit of the public
at large and to the detriment of our heirs and successors. We intend
this dedication to be an overt act of relinquishment in perpetuity of
all present and future rights to this software under copyright law.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 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.For more information, please refer tohttp://unlicense.org/
Package: pdiffutils
A small package for manipulating powder diffraction dataFree software: Apache Software License 2.0Installationpip install pdiffutilsYou can also install the in-development version with:pip install https://github.com/rowlesmr/pdiffutils/archive/master.zipDocumentationhttps://pdiffutils.readthedocs.io/DevelopmentJust ask me, and I can probably help.Changelog0.0.3 (2022-02-02)Fixed incorrect output in split on zero for DiffractionExperiment0.0.2 (2021-12-01)I don’t know what I’ve added in terms of featuresImplemented a bunch of unit testsSquashed various bugs found by unit tests0.0.1 (2021-11-06)Fixed function exports0.0.0 (2021-11-06)First release on PyPI.
Package: pd.imalyzer
[name:недописано]* Mon May 12 2008 Andrey Orlov <dbdtdreambot.ru> 0.0.4All utilities united into imalyzer utilityA few bugs fixedDebugging code removed* Sat May 10 2008 Andrey Orlov <dbdt@dreambot.ru> 0.0.3Many utilities and libraries developed. It start to likesome working prototype* Tue May 06 2008 Andrey Orlov <dbdt@dreambot.ru> 0.0.2- first official release
Package: pdir2
pdir2: Pretty dir() printing with joyHave you ever dreamed of a better output ofdir()? I do. So I created this.FeaturesAttributes are grouped by types/functionalities, with beautiful colors.Support color customization,here's how.Support all platforms including Windows(Thanks tocolorama).Supportipython,ptpython,bpythonandJupyter Notebook! Seewikifor details.The return value ofpdir()can still be used as a list of names.✨ Attribute searchingYou can search for certain names with.s()or.search():Search is case-insensitive by default.search(name, case_sensitive=True)does case-sensitive searching.:star2: Attribute filteringproperties: Find properties/variables defined in the inspected object.methods: Find methods/functions defined in the inspected object.public: Find public attributes.own: Find attributes that are not inherited from parent classes.These filterscan be chained!Order doesNOTmatter.For example, usepdir(obj).public.own.methodsto find all public own methods.You can also callsearchon the returned results.See acomplete example.InstallGenericpip install pdir2About the name. I wanted to call it "pdir", but there's already one with this
name on pypi. Mine is better, of course.Fedoradnf install python3-pdir2Automatic ImportAs a better alternative ofdir(), it's more convenient to automatically import
pdir2 when launching REPL. Luckily, Python provides a way to do this. In you.bashrc(or.zshrc), add this line:export PYTHONSTARTUP=$HOME/.pythonstartupThen, create.pythonstartupin your home folder. Add one line:import pdirNext time you launch REPL,pdir()is already there, Hooray!DevelopmentSet up development environmentPDM: pdir2 usesPDMto manage dependencies, so you want to make sure it's installed.pyenv: Since you need to test pdir2 on multiple Python versions,pyenvis highly recommended. Make sure you have Python 3.8, 3.9, 3.10 and 3.11 installed.Install dev dependenciesSimply runpdm install.If you want to work on a specific Python version, runpdm use [PYTHON_VERSION]first to switch PDM to that version (e.g.pdm use 3.9if you want to debug a Python 3.9 specific issue).Run testsRunpdm run toxThe guide may be incomplete. Please file bugs if you encounter any issues.
Package: pdir-athesto
Pretty-dirPretty-dirorpdiris a pretty print function for the dir() command.It checks the size of the screen to shows a better output of dir.
As the commandlsin Linux.It uses colors to diferencepublicprivatedprotectedanddunder/magicelementsHow to installpip install pdir-athestoHow to useImport the command and use the syntaxpdir(<data>)
        pdir(<data>, <str>)where<data>is any object and<str>must be"public"or"protected"or"private"or"magic"or"dunder"ExampleUse the manual>>>help(pdir)Extra: Automatic importAs a common function you should be able to use it in any moment
without the need to import the package everytime.So my recommendation is to use thePYTHONSTARTUPvariable.Copy this line into your.bashrcexportPYTHONSTARTUP=$HOME/.pythonrcand create the.pythonrcwith your favorite editor(*Cof, *Cof, Vim)and
paste this line#!/usr/bin/env python3try:frompdirimportpdirexcept:passSuggestionsIf you found something that should be thereOr you think that the redaction needs to be more specificOr you think that you need something more (a feature)Or there is something elsePlease open anissueand tell me your idea.LicenceGPLv3AuthorHi, I'm Gustavo Mejía and if it's works to you.
Please let me know in twitter in@im_tavo.
Package: pdisk
PDisk.pro API WrapperA Python package to interact with the unofficial API of PDisk.pro.InstallationTo install the package, you can use pip:pipinstallpdiskUsageTo use the package, you must first create an instance of the Pdisk class, passing your API key as a parameter:frompdiskimportPdiskapi_key="YOUR_API_KEY_HERE"pdisk=Pdisk(api_key)AccountYou can retrieve your account info and stats using the following methods:account_info=awaitpdisk.account_info()print(account_info.email,account_info.balance,account_info.storage_used)account_stats=awaitpdisk.account_stats()forstatinaccount_stats:print(f"{stat.profit_total}:{stat.downloads}:{stat.refs}")File UploadYou can upload files using the following methods:# Upload a file from local storageresponses=awaitpdisk.upload_file("/path/to/file")forresponseinresponses:print(response)# Upload a remote fileresponses=awaitpdisk.upload_remote_file("https://example.com/file.mp4",folder_id=12345)forresponseinresponses:print(response)You can check the upload status of a file using the following method:file_code="FILE_CODE"status=awaitpdisk.check_upload_status(file_code)print(status)File ManagementYou can retrieve information about a file using the following method:file_code="FILE_CODE"file_info=awaitpdisk.file_info(file_code)forinfoinfile_info:print(info)You can also retrieve a list of files using the following method:files=awaitpdisk.get_file_list(page=1,per_page=20)forfileinfiles:print(file)Check Documentaion For More ...comming soon ...LicenseThis project is licensed under the MIT License (c) 2023 kalanakt.
Package: pdivas
PDIVAS : Pathogenicity Predictor for Deep-Intronic Variants causing Aberrant SplicingSumaryPDIVAS is a pathogenicity predictor for deep-intronic variants causing aberrant splicing.The deep-intronic variants can cause pathogenic pseudoexons or extending exons which disturb the normal gene expression and can be the causal of patiens with Mendelian diseases.PDIVAS efficiently prioritizes the causal candidates from a vast number of deep-intronic variants detected by whole-genome sequencing.The scope of PDIVAS prediction is variants in protein-coding genes on autosomes and X chromosome.This command-line interface is compatible with variant files in VCF format.PDIVAS is modeled on random forest algorism to classify pathogenic and benign variants with referring to features fromSplicing predictorsofSpliceAI(Jaganathan et al., Cell 2019) andMaxEntScan(Yao and Berge, j. Comput. Biol. 2004)(*)The output module of SpliceAI was customed for PDIVAS features (see the Option2, for the details).Human splicing constraint scoreofConSplice(Cormier et al., BMC Bioinfomatics 2022).Reference & contactbioarxiv?a0160561@yahoo.co.jp(Ryo Kurosawa at Kyoto University)DetailsPlease view the detailed usage and methods athttps://github.com/shiro-kur/PDIVASand medRxiv.