Saravanan666/pypi-org huggingface dataset:
Package: pddl-plus-parser
PDDL Plus ParserA generic parser for PDDL+ and more!RequirementsFeaturesChangelog:version 0.0.1 - Initial release. Support for discrete PDDL domain and problem parsing.version 1.0.0 - Major extension to support PDDL 2.1 in terms of numeric actions.version 1.1.0 - Added support for Metric-FF solutions parsing.version 1.2.0 - Added support for ENHSP solution parsing.version 1.3.0 - Added problem generators for some domains used in the experiments.version 1.4.0 - Added negative preconditions for actions.version 2.0.0 - Added support for multi-agent PDDL domains and problems.version 2.1.0 - Added support for more complex type of multi-agent trajectories to support non-trivial interactions.version 2.2.0 - Added support for domains with disjunctive numeric preconditions.version 3.0.0 - Added support for conditional effect without existential quantification and only conjunctive conditions.version 3.1.0 - Added support for universal effects containing only conjunctive conditions.version 3.1.4 - Added support for inapplicable actions and fixed a minor logical bug in the universal effects.version 3.3.0 - Added support for nested action schemas including universal preconditions.version 3.5.0 - Added simplification of numeric expression. This helps to prevent getting too complicated preconditions.
Package: pddlpy
A PDDL library that, by using an ANTLR 4 grammar to parse PDDL files, provides a very simple interface to interact with domain-problems.
This library publishes one object class whose API exposes methods for obtaining:The initial state.The goals.The list of operators.The positive and negative preconditions and the positive and negative effects.The _grounded_ states of a given operator (grounded variables, preconditions and effects).This is enough for the user to focus on the implementation of state-space or plan-space search algorithms.The development of this tool was inspired from Univerty of Edinburgh’s Artificial Intelligence Planning course by Dr. Gerhard Wickler and Prof. Austin Tate. The terms used in this API (and the API itself) closely resembles the ones proposed by the lecturers.As of today it supports Python 3.11 and up.The orginal grammar file was authored by Zeyn Saigol from University of Birmingham. I cleaned up it, made it language agnostic and upgraded to ANTLR 4.
Package: pddsdk
pddsdk is an open source api for pingduoduo.InstallationpipinstallpddsdkSimple usesfrompddsdk.clientimportClientclient_id='123456'client_secret='123456'params={'sort_type':0,'type':'pdd.ddk.goods.search'}r=Client(client_id,client_secret).call(params)print(r)frompddsdk.ddk_goods_searchimportDdkGoodsSearchclient_id='123456'client_secret='123456'params={'sort_type':0}r=DdkGoodsSearch(client_id,client_secret).call(params)print(r)
Package: pdd-sdk
pdd_sdk原作者github链接:https://github.com/makelove/DDK_SDK多多客接口python3版本增加接口字段细节:hot_pepper:pypi安装:pip install pdd_sdk在setup.py 文件中依次输入如下代码上传pypi：pip install twinepython setup.py sdistpython setup.py installtwine upload dist/*
Package: pd-dwi
PD-DWIPD-DWI is a physiologically-decomposed Diffusion-Weighted MRI machine-learning model for predicting response to neoadjuvant chemotherapy in invasive breast cancer.PD-DWI was developed byTCMLgroup as part ofBMMR2 challengeusingACRIN-6698dataset.This work was accepted to MICCAI 2022 (to be held during Sept 18-22 in Singapore).If you publish any work which uses this package, please cite the following publication:M. Gilad, M. Freiman. PD-DWI: Predicting response to neoadjuvant chemotherapy in invasive breast cancer with Physiologically-Decomposed Diffusion-Weighted MRI machine-learning model. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 to be held during Sept 18-22 in Singapore.A pre-print version of our paper is available athttps://arxiv.org/abs/2206.05695InstallationPD-DWI model can be installed directly from Github:pip install git+https://github.com/TechnionComputationalMRILab/PD-DWI.gitUsagePD-DWI can be used in a Python script or via command line.To explore all CLI options and syntax requirements runpd-dwi --helpin your terminal.ContactPlease contact us onms.maya.gilad@gmail.com
Package: pdec
pdecPackages that usepdecDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-npdec--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallpdecnbdev# activate conda environment$condaactivatepdec# make sure the pdec package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the pdec package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/pdec.gitor fromconda$condainstall-cdsm-72pdecor frompypi$pipinstallpdecDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.A = AliasC = ClassVarQ = OptionalE = (Over)-EngineeredZ = CustomR = RichP = PlotD = DataB = BioM = Machine Learning
Package: p-decision-tree
Visual Decision Tree Based on Categorical AttributesAs you may know "scikit-learn" library in python is not able to make a decision tree based on categorical data, and you have to convert categorical data to numerical before passing them to the classifier method. Also, the resulted decision tree is a binary tree while a decision tree does not need to be binary.Here, we provide a library which is able to make a visual decision tree based on categorical data. You can read more about decision treeshere.FeaturesThe main algorithm which is used is ID3 with the following features:Information gain based onentropyInformation gain based onginiSome pruning capabilities like:Minimum number of samplesMinimum information gainThe resulted tree is not binaryRequirementsYou can find all the requirements in "requirements.txt" file, and it can be installed easily by the following command:pip install -r requirements.txtAlso to be able to see visual tree, you need to install graphviz package.Hereyou can find the right package with respect to your operation system.Usagefromp_decision_tree.DecisionTreeimportDecisionTreeimportpandasaspd#Reading CSV file as data set by Pandasdata=pd.read_csv('playtennis.csv')columns=data.columns#All columns except the last one are descriptive by defaultdescriptive_features=columns[:-1]#The last column is considered as labellabel=columns[-1]#Converting all the columns to stringforcolumnincolumns:data[column]=data[column].astype(str)data_descriptive=data[descriptive_features].valuesdata_label=data[label].values#Calling DecisionTree constructor (the last parameter is criterion which can also be "gini")decisionTree=DecisionTree(data_descriptive.tolist(),descriptive_features.tolist(),data_label.tolist(),"entropy")#Here you can pass pruning features (gain_threshold and minimum_samples)decisionTree.id3(0,0)#Visualizing decision tree by Graphvizdot=decisionTree.print_visualTree(render=True)# When using Jupyter#display( dot )print("System entropy: ",format(decisionTree.entropy))print("System gini: ",format(decisionTree.gini))
Package: pdem
=========================pdem - Process Daemonizer=========================Purpose-------A tool, consists of server and client, used to run long processes, collect information from the processes, such asprogress and time elapsed and astimated.Client could command to run process, kill process, get info about live or already dead process.Install-------::# pip3 install pdemUsage of library----------------Write config file to ~/.config/pdem.conf::$ pdem-server writeConf --conf ~/.config/pdem.conf --daemonize Yes --logLevel WARNING --daemonLogFile /tmp/pdem.logStart server with default params, or by params, written to ~/.config/pdem.conf:::$ pdem-server startStatus of server:::$ pdem-server statusStop server:::$ pdem-server stopDisplay help:::$ pdem-server helpDisplay help onRun as client, send command to server, display result and exit:display list of running processes:::$ pdem-server do proclistdisplay list of running and dead processes:::$ pdem-server do proclist showdeadrun process by a server (bash0 would became it's identifier, "name"):::$ pdem-server runprocess bash0 bash_interpreter local /bin/bashkill running process by a server:::$ pdem-server kill bash0* 2016-11-18 mihanentalpo <mihanentalpo@yandex.ru> 0.1.5- Fixed bugs in package* 2016-10-30 mihanentalpo <mihanentalpo@yandex.ru> 0.1.4- Finished python3 implementation, and uploaded pypi package "pdem"* Sun Sep 08 2013 mihanentalpo <mihanentalpo@yandex.ru> 0.0.12- AFixed bug - when some of process' vars contain newline symbol it lead to a broken output. Now, '\n' converted to '\\n'.* Mon May 27 2013 mihanentalpo <mihanentalpo@yandex.ru> 0.0.11- added functionality to see old dead processes, by send "proclist showdead", and remove them by send "burndead"* Sat May 25 2013 mihanentalpo <mihanentalpo@yandex.ru> 0.0.10- fixed bug with stack opverflow in case of lots of incoming process'es data. But now it will consume more CPU.* Thu Feb 28 2013 mihanentalpo <mihanentalpo@yandex.ru> 0.0.8- added daemonization config option* Wed Feb 27 2013 mihanentalpo <mihanentalpo@yandex.ru> 0.0.5- added [PDEM[var:x=y]PDEM]* Tue Feb 26 2013 mihanentalpo <mihanentalpo@yandex.ru> 0.0.4- working release* Wed Feb 20 2013 mihanentalpo <mihanentalpo@yandex.ru> 0.0.3- First alpha release
Package: pdengine
PDEngine - Process Dynamics EngineProcess Dynamics Engine (PDEngine) is an online, iterative simulator for process control models described by transfer functions or state space representations. PDE is implemented in Python and uses thePython Control Systems Library. Users can interact with the simulation by using an API to send control actions (e.g. increasing a reflux rate or shutting off a valve) and query process variables like temperature and pressure. PDE is intended to be an educational tool in chemical engineering process control courses.UsageModels are defined by inheriting from theModelparent class. An example using the Wood-Berry distillation model (3 inputs - 2 MVs, 1 FF & 2 outputs) is provided in/src/WoodBerryModel.py.A PDEngine Model must include:Name (e.g. Wood-Berry Distillation)Input names (e.g. $R$ - Reflux flow rate, $S$ - Steam flow rate, $F$ - Feed flow rate)Ouput names (e.g. $X_D$ - top composition, $X_B$ - bottom composition)Steady-state values for inputs and outputsValidity limits for inputs and outputsTypical moves for inputsA driver program is then created to set up the simulation and initialize the model. Users must define:The simulation step size, $dt$Simulation speed, in terms of a time delay, $T_D$ between stepswm = WoodBerryModel()
dt=0.02 # in minutes, for the Wood-Berry model
woodberry = wm.create_system(dt=dt) # make the actual generator with step size = 0.02At each iteration of the simulation, users can send values to the simulation by usingsend()with a dictionary with the Input names as keys:# the main engine loop
for i in range(N):        
    y = woodberry.send({'R': 0.01, 'S': 0.01, 'F': 0.05}) # send this value to the model
    time.sleep(0.001) # this is the time delay T_D for the simulation speedAn example driver program is provided in theExample.ipynbnotebook that uses the Wood-Berry distillation example.DevelopmentRecommended environment setup:conda create -n pdengine python=3.10
conda activate pdengine
conda install -c conda-forge ipykernel slycot
python -m pip install control jupyter
python -m ipykernel install --user --name=pdengineFor an editable development environment, navigate to thepd-enginefolder, then run:python -m pip install -e .If everything is working correctly, you should be able to run the example notebooks.
Package: pdepy
PDEPyA Python 3 library for solving initial and boundary value problems of some linear partial differential equations using finite-difference methods.LaplaceImplicit CentralParabolicExplicit CentralExplicit UpwindImplicit CentralImplicit UpwindWaveExplicitImplicitUsageInstallationpip install pdepyExamplesLaplace's Equationimportnumpyasnpfrompdepyimportlaplacexn,xf,yn,yf=30,3.0,40,4.0x=np.linspace(0,xf,xn+1)y=np.linspace(0,yf,yn+1)f=lambdax,y:(x-1)**2-(y-2)**2bound_x0=f(0,y)bound_xf=f(xf,y)bound_y0=f(x,0)bound_yf=f(x,yf)axis=x,yconds=bound_x0,bound_xf,bound_y0,bound_yflaplace.solve(axis,conds,method="ic")Parabolic Equationimportnumpyasnpfrompdepyimportparabolicxn,xf,yn,yf=40,4.0,50,0.5x=np.linspace(0,xf,xn+1)y=np.linspace(0,yf,yn+1)init=x**2-4*x+5bound=5*np.exp(-y)p,q,r,s=1,1,-3,3axis=x,yconds=init,bound,boundparams=p,q,r,sparabolic.solve(axis,params,conds,method="iu")Wave Equationimportnumpyasnpfrompdepyimportwavexn,xf,yn,yf=40,1.0,40,1.0x=np.linspace(0,xf,xn+1)y=np.linspace(0,yf,yn+1)d_init=1init=x*(1-x)bound=y*(1-y)axis=x,yconds=d_init,init,bound,boundwave.solve(axis,conds,method="i")DevelopmentCreate virtual environment and install requirements:bin/setup_venvOther commandsRun command in virtual environment:bin/run <command>Install requirements:bin/install_requirementsFormat codebase:bin/formatLint codebase:bin/lintRun unit tests:bin/testPublishPackage and distribute:bin/publish
Package: pde-rk
pde-rkFunctions for simulating PDE models with an adaptive Runge-Kutta schemeInstallationpip install pde-rkInstructionsThe repository contains an example notebook that demonstrates the method using the PAR polarity model (Goehring et al., 2011).
To run the notebook in Colab, click here:LicenseThis work is licensed under aCreative Commons Attribution 4.0 International License.
Package: pdesy
pDESy: Discrete-Event Simulator in PythonWhat is it?pDESyis a Python package of Discrete Time-Event Simulator (DES). It aims to be the fundamental high-level building block for doing practical, real world engineering project management by using DES and other DES modeling tools.pDESyhas only the function of discrete-event simulation, does not include the function of visual modeling.Where to get itThe source code is currently hosted on GitHub at:https://github.com/pDESy/pDESyBinary installers for the latest released version will be available at the Python package index. Now, please install pDESy as following.pipinstallpDESy# pip install git+ssh://git@github.com/pDESy/pDESy.git # INSTALL FROM GITHUB# conda install -c conda-forge -c taiga4112 pDESy # INSTALL FROM ANACONDALicenseMITDocumentationAPI Documentation ishttps://pDESy.github.io/pDESy/index.html.BackgroundpDESyis developed by a part of next generation DES tool ofpDES.ContributionFork it (http://github.com/pDESy/pDESy/fork)Create your feature branch (git checkout -b my-new-feature)Commit your changes (git commit -am 'Add some feature')Push to the branch (git push origin my-new-feature)Create new Pull RequestIf you want to join this project as a researcher, please contactme.
Package: pd-explain
Explain pdA library  that create explation for dataframes manipulations, using pandas dataframes and explations based onExplainED: Explanations for EDA NotebooksInstallationInstall pd-explain with pip or by git sshpipinstallpd-explainpipinstallgit+ssh://git@github.com/analysis-bots/pd-explain.gitFor cloning this project usegitclonegit@github.com:analysis-bots/pd-explain.gitcdpd_explain

pipinstall-rrequirements.txtUsageYou can use explain dataframe like thisimportpandasaspdimportpd_explainpd.read_...()# All read functions create explainable dataframed={'col1':[1,2],'col2':[3,4]}df=pd.DataFrame(data=d)df=pd_explain.to_explainable(df)DemoFull example NotebookDemo Spotify exampleDocumentationDocumentationArticlesExplainED: Explanations for EDA NotebooksAuthors@Eden Isakov@analysis-bots
Package: pdext
Pandas Extention _____________________ A set of
Python modules intended to be [Extention to
pandas](https://github.com/malneni/PdExt)The primary purpose of this package is to allow developers to use pandas
module for more applicationsApplication:Add pandas DataFrame to word document as tableSplit columns to multiple columns with PandasSplit column to multiple rows with pandasCompare column string with another column string with pandasPrerequisites[panads](https://pypi.org/project/pandas/)[docx](https://pypi.org/project/python-docx/)InstallationWindows (cmd):>python setup.py installDocumentationThe Python API and its documentation are currently being worked. You can
find an early preview [here](https://pdext.readthedocs.io/en/latest/).How to useSplitting columns to multiple columns with Pandas![image](/Pictures/Capture1.PNG)Output![image](/Pictures/Capture1_output.PNG)Splitting columns to multiple Rows with Pandas![image](/Pictures/Capture2.PNG)Output![image](/Pictures/Capture2_output.PNG)Add pandas DataFrame to word document as tableWith DataFrame Header as Table header .. image:: /Pictures/Capture3.PNGOutput![image](/Pictures/Capture3_output.PNG)Without DataFrame Header as Table header .. image::
/Pictures/Capture4.PNGOutput![image](/Pictures/Capture4_output.PNG)
Package: pd-extras
Pandas ExtrasSome functions on top of pandas.Install EnvironmentRunpython -m pip install -U pipandpip install -U pip poetry. Then runpoetry install. If you are facing issues installingmysqlclientorpsycopg2on Ubuntu, it's because you are missing some libraries. Please check their pages. Usually forpsycopg2, it'slibpq-devand formysqlclient, it'spython3-dev default-libmysqlclient-dev build-essential. Check the pages for more specific and accurate commands.Generate Documentation Source FilesYou should not have to do this but in case you want to generate the source ReStructuredText files yourself, here is how. Skip to the next section to simply generate html documentation locally.Change to docs directorycd docs/. Runsphinx-quickstart. Chooseywhen it asks to seperate build and source directories.Change todocs/sourcedirectory. Inconf.py, add the following lines at the start of the script.importosimportsyssys.path.insert(0,os.path.abspath("../.."))and save it. Add"sphinx.ext.autodoc",to theextensionslist. Runpython -m pip install -U sphinx_rtd_themeand sethtml_theme = "sphinx_rtd_theme"(or whatever theme you want).Inindex.rst, addmodulesto toctree. The structure should look like this:.. toctree::
:maxdepth: 2
:caption: Contents:

modulesRun the following to generate the source files.poetry install --with docs
poetry run sphinx-apidoc -f -o source/ ../ ../tests/Generating HTML DocumentationChange todocs/usingcd ..then run.\make cleanand.\make html. Output should be built with no errors or warnings. You will get the html documenation indocs/build/htmldirectory. Openindex.html.
Package: pdf12step
12 Step Recovery Meeting PDF GeneratorThis Python program can create meeting lists in PDF format for distribution on the web or print. In order to populate the meeting data, you must be connected to a site running the12 Step Meeting ListWordPress plugin. This project was developed for theBaltimore Intergroup Council of AAThe tool works byDownloading the JSON meeting data form the WP pluginRendering the meeting list directory as HTML usingJinja2templatesConverting the HTML into a PDF using theWeasyPrintdocument factoryThe CSS and HTML templates are extensible so you can customize the PDF output and make it fit your meeting format.The app also contains aFlaskwebapp that can render the live HTML and PDF content in your browser.To see what it can do, check outthis generated directoryFor full documentation, visithttps://pdf12step.readthedocs.io/
Package: pdf2anki
pdf2ankipdf2ankiis a Python application that enables you to generate Anki flashcards from PDF files utilizing the power of OpenAI.PrerequisitesPython 3.6 or higherAccess to OpenAI API (ensure to set your API key as an environment variable named 'OPENAI_API_KEY')InstallationYou can installpdf2ankifrom PyPI:pipinstallpdf2ankiUsageOnce installed, you can use the pdf2anki command to generate Anki cards from a directory of PDFs. The following arguments are needed:pdf2anki--source-dirSOURCE_DIR--output-dirOUTPUT_DIR--modelMODEL--temperatureTEMPERATURE--max-tokensMAX_TOKENSFor example:pdf2anki--source-dir./pdfs--output-dir./anki-cards--modelgpt-3.5-turbo--temperature0.5--max-tokens1000For DevelopersDevelopers can also use pdf2anki in their Python scripts as follows:importos
frompdf2anki.mainimportread_pdf,create_anki_cards#### Define the source PDF folder and the output folder for the Anki cardssource_dir="path/to/pdf_folder"output_dir="path/to/output_folder"#### Define the parameters for the OpenAI modelmodel="gpt-3.5-turbo"temperature=0.5max_tokens=1000#### Iterate over the PDF files in the source directoryforfile_nameinos.listdir(source_dir):iffile_name.endswith(".pdf"):# Get the full path to the source PDFsource_pdf=os.path.join(source_dir,file_name)# Read the PDF textpdf_text=read_pdf(source_pdf)# Create output file pathoutput_file=os.path.join(output_dir,file_name.rsplit(".",1)[0]+".txt")# Create Anki cards from the PDF textcreate_anki_cards(pdf_text,output_file,model,temperature,max_tokens)LicenseThis project is licensed under the terms of the MIT license. For more details, see the LICENSE file.
Package: pdf2chem
pdf2chemA curator for chemistry-related pdf filesInstallation$pipinstallpdf2chem
$cdedatadownloadin Jupyter or Colab

!pip install pdf2chem
!cde data download
import pdf2chem as p2cFeaturesThis version allows the user to curate a folder of chemistry-related pdf files, extracting known chemicals mentioned in the files to csv files with the names as written in the pdf and the SMILES string for each chemical.  Other outputs (e.g., InChI or other known names for the chemical) are possible and may be incorporated into future versions.The package should automatically detect local vs. hosted runtimes and choose the compatible pdf extraction method in textract.DependenciesThe package directly uses cirpy, ChemDataExtractor, pandas, os, re, time, datetime, and sys in addition to native Python 3.  Many of these in turn have a fair few dependencies of their own.UsageUse use self-contained Colab page athttps://drive.google.com/file/d/1YYZm-Ew-408q86DjDbQTCUoWUNVl_UN3/view?usp=sharingorInstall and import as described abovePlace pdf files of interest (typically journal articles) in an accessible folder.Execute p2c.curate_folder()If the files are not in the current directory, pass the directory to the function as an argument, e.g.
p2c.curate_folder('C:/Users/kfrog/literature')The files will then be analyzed internally before a list of words and phrases suspected to be known chemicals is sent to NIH's servers to be resolved.  Chemicals found and their SMILES strings will be aggregated in a csv file for each pdf.
After each pdf is processed, the data from each csv file will be combined to an aggregated csv file for all the papers in that run.Please note: this program depends on both stable internet access and uptime/responsiveness at NIH's servers.  The latter are often slower or down entirely on the weekends, and sometimes this is seen during the week as well.  We appreciate the team there making the databases as accessible as they do.DocumentationThe official documentation is hosted on Read the Docs:https://pdf2chem.readthedocs.io/en/latest/ContributorsWe welcome and recognize all contributions. You can see a list of current contributors in thecontributors tab.CreditsThis package was created with Cookiecutter and the UBC-MDS/cookiecutter-ubc-mds project template, modified from thepyOpenSci/cookiecutter-pyopensciproject template and theaudreyr/cookiecutter-pypackage.This package makes heavy use of ChemDataExtractor and CIRpy, packages developed by Swain and Cole and released under the MIT license.
Swain, M. C., & Cole, J. M. "ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature", J. Chem. Inf. Model. 2016, 56 (10), pp 1894–1904 10.1021/acs.jcim.6b00207