Saravanan666/pypi-org huggingface dataset:
Package: panpipelines
PANpipelinesThis repository contains all the necessary scripts for reproducing the steps taken to preprocess and analyze MRI data collected during the Precision Aging Network (PAN) project.The panpipelines packageThe PAN Pipelines use a set of python modules packaged under the mainpanpipelinespackage to run all the preprocessing and analysis workflows which are based on NiPype.InstallationIt is recommended that a python environment manager likecondaorvirtualenvis used to install thepanpipelinespackage. Assuming you have created a conda environment calledpanpythonthen the package can be installed as follows:conda activate panpython
pip install panpipelinesDeploymentFor an example of using the package to process MRI data please refer to the./deploymentfolder. All the necessary parameters for running the pipelines are described in aconfigfile in the./configsubdirectory which is passed as a parameter to the main modulepan_processing.py. In the example provided this file is namepanpipeconfig_slurm.config.
Package: panpipes
Panpipes - multimodal single cell pipelinesCreated and Maintained by Charlotte Rich-Griffin and Fabiola CurionAdditional contributors: Sarah Ouologuem, Devika Agarwal, and Tom ThomasSee ourdocumentationand ourpreprint:Panpipes: a pipeline for multiomic single-cell data analysisCharlotte Rich-Griffin*, Fabiola Curion*, Tom Thomas, Devika Agarwal, Fabian J. Theis, Calliope A. Dendrou.bioRxiv 2023.03.11.532085;doi:https://doi.org/10.1101/2023.03.11.532085IntroductionThese workflows use cgat-core pipeline softwareAvailable workflows:"ingest" : for the ingestion of data and computation of QC metrics"preprocess" : for filtering and normalising of each modality"integration" : integrate and batch correction using  single and multimodal methods"clustering" : cell clustering on single modalities"refmap" : transfer scvi-tools models from published data to your data"vis" : visualize metrics from other pipelines in the context of experiment metadata"qc_spatial" : for the ingestion of spatial transcriptomics (ST) data (Vizgen, Visium) and computation of QC metrics"preprocess_spatial" : for filtering and normalizing ST data"deconvolution_spatial" : for the cell type deconvolution of ST slidesInstallation and configurationSeeinstallation instructions hereOxford BMRC Rescomp users find additional advice indocs/installation_rescompReleasespanpipes v0.4.0is outnow!Theingestworkflow now expects different headers for the RNA and Protein modalities.
Check the examplesubmission fileand thedocumentationfor more detailed instructions.
Package: pan-python
pan-pythonis a Python package for Palo Alto Networks
Next-Generation Firewalls, WildFire and AutoFocus.  It provides
several components:Python and command line interface to the PAN-OS and Panorama XML APICommand line program for managing PAN-OS XML configurationsPython and command line interface to the WildFire APIPython and command line interface to the AutoFocus APIPython and command line interface to the PAN-OS licensing APIDocumentationRendered reStructuredText from GitHub:https://github.com/kevinsteves/pan-python/tree/master/docHTML from source distribution:doc/*.htmlInstallpan-pythonis available as areleaseon GitHub and as apackageon PyPi.There are no external modules required to usepan-python.
Package: panqt
# QtPandas### Utilities to use pandas (the data analysis/manipulation library for Python) with Qt.## Project Information[![Join the chat at https://gitter.im/qtpandas/Lobby#](https://badges.gitter.im/qtpandas/lobby.svg)](https://gitter.im/qtpandas/Lobby#)## Build Status[![Travis status](https://travis-ci.org/draperjames/QtPandas.svg?branch=master)](https://travis-ci.org/draperjames/QtPandas)Requirements;> Python 3.x> Pandas 20.0> PyQt 4.7.8To install run the following in the command prompt;```pip install git+https://github.com/zbarge/QtPandas.gitpip install --upgrade git+https://github.com/robertlugg/easygui.git```To use, create a new Python script containing the following:```from PyQt4.QtCore import *from PyQt4.QtGui import *from panqt.views.CSVDialogs import CSVImportDialogif __name__ == "__main__":from sys import argv, exitapp = QApplication(argv)dialog = CSVImportDialog()dialog.show()app.exec_()```Several examples can also be found in the exmples directory.# Development## Wanna contribute?Join us on [gitter](https://gitter.im/qtpandas/Lobby#)### TO DO:- [x] Reach out to @kaotika and @datalyze-solutions- [ ] Secure qtpandas namespace on pip.- [ ] Wait for reply- [ ] If no reply create new repo for QtPandas.- [x] Create .travis.yml file.- [x] integrate into README.md- [ ] Add documentation.- [ ] Add screen shots- [ ] Create Wiki- [ ] Make verison agnostic.- [ ] Create specific Python version tests.- [ ] Add Windows, Apple, and Linux tests.- [ ] Consider adding functions seen in [Spyder IDE's dataframeeditor](https://github.com/spyder-ide/spyder/blob/f2b36f00f873cf4080087bfb529e6256b3e24792/spyder/widgets/variableexplorer/dataframeeditor.py)- [ ] Sort- [ ] Color codingForked from @datalyze-solutions's [master](https://github.com/datalyze-solutions/panqt).
Package: pansat
PansatAccessing satellite and reanalysis data made easy.pansatprovides a unified interface for a wide range of satellite, reanalysis and other meteorological data with a focus on data that is relevant for atmospheric research. Many of these datasets appear in different formats and are difficult to download and access in a consistent way. Therefore,pansattries to overcome this difficulties and provides user-friendly download functionalities that can be used within python or from the command line. Currently,pansatsupports various gridded and ungridded datasets from different data providers and allows for a quick implementation of new data products. The user can specify time ranges, regions and variables for the datasets to be downloaded. This facilitates the access the data needed for case studies, climatological time series or collocations of different datasets.Installationpip install pansatDocumentationOnline documentation and Examples for Pansat are availablehere.Hurricane Laura (2020) as seen by GOESWithpansatyou can, for example, quickly find images for specific storm cases.
Package: pansi
Pansi is a clean and simple ANSI escape code library for Python.General usagePansi provides an object calledansithrough which all escape codes can be selected.
This object exposes the codes as both attributes (e.g.ansi.red) and items (e.g.ansi["red"]).This object can therefore be used in several different ways, but the simplest is through the stringformatmethod.
Here, if the object is supplied as a simple named argument, all references need to be prefixed:Alternatively, passing the object with the**operator removes the need for prefixes and thus makes the template string shorter.
However, this does introduce a greater chance of clashing with other parameters, and doesn’t signal which parameters are which.Ultimately, it is a subjective choice between these two options.ColoursFor foreground text, the standard set of colours can be selected using the lower case name for normal brightness
and the upper case name for high intensity.
To select as the background colour instead, simply prefix withbg., e.g.bg.blue.ColourForegroundBackgroundBlackblackbg.blackRedredbg.redGreengreenbg.greenYellowyellowbg.yellowBluebluebg.yellowMagentamagentabg.magentaCyancyanbg.cyanWhitewhitebg.whiteBright blackBLACKbg.BLACKBright redREDbg.REDBright greenGREENbg.GREENBright yellowYELLOWbg.YELLOWBright blueBLUEbg.YELLOWBright magentaMAGENTAbg.MAGENTABright cyanCYANbg.CYANBright whiteWHITEbg.WHITEFull 24-bit colour support is also available (on those terminals that support it) by using thergbselector.Foreground and background colours can be inverted and then set back to normal using therevand_revtags respectively.To reset foreground and background back to their defaults, usefg.resetandbg.reset.Text Weightweight.normalweight.boldweight.lightb(alias forweight.bold)_b(alias forweight.normal)Text Stylestyle.normalstyle.italicstyle.frakturi(alias forstyle.italic)_i(alias forstyle.normal)Text decorationu(underline)uu(double underline)_u(no underline)o(overline)_o(no overline)s(strike through)_s(no strike through)Blinkingblink(blink)BLINK(blink fast)_blink(no blink)Hide & showhideshow
Package: pansql
DISCLAIMERThis project is not maintained. It is merely a fork ofyhat/pandasqland all credit goes to the group. This fork just resolves an issue of compatibility with SQLAlchemy v2.x.x.A PR was requestedfor this to be included in the mainpandasqlproject but it seems to be dormant. This sparked the creation of this fork.pansqlpansqlallows you to querypandasDataFrames using SQL syntax. It works
similarly tosqldfin R.pansqlseeks to provide a more familiar way of
manipulating and cleaning data for people new to Python orpandas.Installation$ pip install -U pansqlBasicsThe main function used in pansql issqldf.sqldfaccepts 2 parametrsa sql query stringa set of session/environment variables (locals()orglobals())Specifyinglocals()orglobals()can get tedious. You can define a short
helper function to fix this.from pansql import sqldf
pysqldf = lambda q: sqldf(q, globals())QueryingpansqlusesSQLite syntax. Anypandasdataframes will be automatically detected bypansql. You can query them as
you would any regular SQL table.$ python
>>> from pansql import sqldf, load_meat, load_births
>>> pysqldf = lambda q: sqldf(q, globals())
>>> meat = load_meat()
>>> births = load_births()
>>> print pysqldf("SELECT * FROM meat LIMIT 10;").head()
                  date  beef  veal  pork  lamb_and_mutton broilers other_chicken turkey
0  1944-01-01 00:00:00   751    85  1280               89     None          None   None
1  1944-02-01 00:00:00   713    77  1169               72     None          None   None
2  1944-03-01 00:00:00   741    90  1128               75     None          None   None
3  1944-04-01 00:00:00   650    89   978               66     None          None   None
4  1944-05-01 00:00:00   681   106  1029               78     None          None   Nonejoins and aggregations are also supported>>> q = """SELECT
        m.date, m.beef, b.births
     FROM
        meats m
     INNER JOIN
        births b
           ON m.date = b.date;"""
>>> joined = pyqldf(q)
>>> print joined.head()
                    date    beef  births
403  2012-07-01 00:00:00  2200.8  368450
404  2012-08-01 00:00:00  2367.5  359554
405  2012-09-01 00:00:00  2016.0  361922
406  2012-10-01 00:00:00  2343.7  347625
407  2012-11-01 00:00:00  2206.6  320195

>>> q = "select
           strftime('%Y', date) as year
           , SUM(beef) as beef_total
           FROM
              meat
           GROUP BY
              year;"
>>> print pysqldf(q).head()
   year  beef_total
0  1944        8801
1  1945        9936
2  1946        9010
3  1947       10096
4  1948        8766More information and code samples available in theexamplesfolder or onour blog.
Package: panstamps
panstampsDownload stacked and/or warp image stamps from the STScI PanSTARRS image server via CL or Python API.Documentation for panstamps is hosted byRead the Docs(development versionandmaster version). The code lives ongithub. Please report any issues you findhere... note::
    If working with warped PS1 images then you need to work off a machine that has an IP address whitelisted by the `Pan-STARRS1 data archive <https://panstarrs.stsci.edu/>`_, otherwise only stacked images will be available to you. Also *w*-band images are not (yet) accessible from the data archive.FeaturesHow to cite panstampsIf you usepanstampsin your work, please cite using the following BibTeX entry:@software{Young_panstamps,author={Young, David R.},doi={10.5281/zenodo.8037665},license={GPL-3.0-only},title={{panstamps}},url={https://github.com/thespacedoctor/panstamps}}
Package: panstat
PanStat: A Python toolkit for the statistical and visualization of core and pan genes in Pan-genomeInstallationpython3-mpipinstallpanstatUsagepanstat-h1. statUsage: panstat stat [OPTIONS]

  Calculate statistics for core and pan genes

Options:
  -i, --input-file PATH           Path to the input data file  [required]
  -o, --output-file PATH          Path where the results will be saved  [default: output_stat.txt]
  -n, --num-samples INTEGER       Number of samples to compute  [required]
  -t, --share_type [intersection|union]
                                  Type of share to compute
  --header INTEGER                Row number to use as the column names  [default: 0]
  --sep TEXT                      Delimiter to use for reading the input file (e.g., "\t" for tab)
  --start-col INTEGER             Column index to start reading sample data from  [default: 1]
  --show-progress BOOLEAN         Show progress
  --chunksize INTEGER             The chunksize lines to read
  --chunk INTEGER                 The index of chunk
  -h, -?, --help                  Show this message and exit.


examples:
    panstat stat -h
    panstat stat -i input.txt -o output.txt -n 13 -t intersection
    panstat stat -i input.txt -o output.txt -n 13 -t intersection --chunksize 100 --chunk 2 [read 101-200 lines]2. plotUsage:panstatplot[OPTIONS]RESULT_DIRGenerateBoxplotwithstatisticsresults

Options:-R,--RscriptTEXTPathtotheexecutableRscript[default:Rscript]-w,--writeTEXTWritetheRcodetoafile--optionTEXTOptionsintheformatkey=valueforboxplot,eg.title="Demo Stats",x_lab="Shared_Numbers",y_lab="Data"-h,-?,--helpShowthismessageandexit.



examples:panstatplot-hpanstatplotout/resultpanstatplotout/result--writeboxplot.Rpanstatplotout/result--writeboxplot.R--optionx_lab=XXX--optionwidth=30--optiondpi=500defaultoptions:infile='processed_stats.tsv'output='boxplot'x_lab='Genomes'y_lab='Families'title='BoxPlot'legend_title='Type'dpi=300width=14height=73. batchUsage:panstatbatch[OPTIONS]GeneratebatchshellsandSJMjob

Options:-i,--input-filePATHPathtotheinputdatafile-sep,--sepTEXTDelimitertouseforreadingtheinputfile(e.g.,"\t"fortab)-s,--start-colINTEGERColumnindextostartreadingsampledatafrom[default:1]-t,--thresholdINTEGERThethresholdtodividethecombinations[default:200000]-O,--output-dirPATHPathtotheoutputdirectory[default:.]--jobTEXTGenerateSJMJob--no-checkDonotcheckqueuesforSJM-h,-?,--helpShowthismessageandexit.


examples:panstatbatch-hpanstatbatch-iinput.txt-t200000-Ooutpanstatbatch-iinput.txt-t200000--jobrun.jobResultprefixx: core genes (intersection)y: pan genes (union)shell directoryshell/
├── plot.sh
├── x2
│   └──stat.x2_1.sh
├── y2
│   └──stat.y2_1.sh
...
├── x14
│   ├── stat.x14_1.sh
│   ├── stat.x14_2.sh
│   ├── ...
│   └── stat.x14_100.sh
...
└── y29
    └──stat.y29_1.shstatistical resultresult/
├── x2
│   └──x2_1.txt
├── y2
│   └──y2_1.txt
...
├── x14
│   ├── x14_1.txt
│   ├── x14_2.txt
│   ├── ...
│   └── x14_100.txt
...
└── y29
    └──y29_1.txtvisualization resultprocessed_stats.tsv
plot.R
pointplot.png
pointplot.pdf
Package: pansys
pansysWork interactively with ANSYS mechanical APDL and python.The pansys module helps you interact with ansys through python. Starting a
panys session is as easy as setting a variable.frompansysimportAnsysans=Ansys()Now you're ready to send commands to your newly created ansys session.ans.send("/prep7")ans.send("""n,,1n,,2""")As you must have guessed,ans.sendcommand will let you send commands
from python to ansys in string format.You can get data out of Ansys as well.nmax=ans.get("node","","num","max")ncount=ans.get("node","","count","")Usingget_list()function, you can get any ansys list item as well.nodes=ans.get_list("nlist")You can also start an ansys session in a remote machine. You will have to set up your ssh keys for this to work.ans=Ansys(host="remotesystem")Look into the documentation to get to know the API better.InstallationAs of now, pansys is supported only on Linux systems. You will also need ANSYS installation on your computer
(or on the remote system, if you are using thehostoption when you're starting pansys).pipinstallpansysThe module assumes that you have ANSYS already installed in your machine. Also, the default command that the module is using
isansys150. You can override it with thestart_commandkeyword argument.a=Ansys(startcommand='my_ansys_command')If you want to change the default start command fromansys150, you can set an environment variablePANSYS_STARTCOMMAND.
The value of this environment variable will be used as the start command. This value will be overridden if you explicitly callAnsyssession with astartcommand.DocumentationYou can find the documentation atreadthedocs
Package: pantab
Getting HelpDocumentationOfficial documentation for the project and all of its features is hosted at:https://pantab.readthedocs.io/en/latest/index.htmlCommunityIf you'd like to chat with the developer(s) or other users in the community, freel free to join theHyper API slack channel.Warranty / ConsultingAs dictated by the license, pantab comes without warranty. innobi LLC fortunately offers consulting services and direct support for pantab and on anything which integrates Python and Tableau. Reach out tosales@innobi.iofor more details.
Package: pantarei
PantareiA minimal and flexible workflow managerFeaturessubmit jobs on local slurm schedulerhandle task dependenciessubmit on remote clusterQuick startPantarei builds on three different execution units:functionsare stateless, Python callablestasksare wrapped functions for shared-memory environments (single node)jobsare wrapped tasks for distributed-memory environments (HPC clusters)To see it in action, say you have a Python functiondeff(x):importtimetime.sleep(2)returnxWrap the function with a Task and call it with a range of argumentsfrompantareiimport*task=Task(f)forxin[1,2]:task(x=x)The task's results are cached: a successive execution will just the resultsresults=task(x=1)We wrap the task with a Job and submit jobs to a local scheduler (like SLURM)job=Job(task)forxin[3,4]:job(x=x)Once the jobs are done, we can get the results (which are cached too)job.scheduler.wait()results=job(x=3)DocumentationCheck out thetutorialfor more examples and thepublic APIfor full details.InstallationFrom pypipip install pantareiContributingContributions to the project are welcome. If you wish to contribute, check outthese guidelines.AuthorsDaniele Coslovich
Package: pantera
panteraPantera is a tool to test the resilience of cloud applications. It’s based on
EC2 instances, and process management. It’s able to destroy, stop and reboot
instances, and also able to kill process locally and remotely.Installation$ pip install panteraUsageFirst open the interactive python shell:$ pythonThen:>>> from pantera import random_run
>>> random_run("ec2-access-key", "ec2-secret-key")This will run a random action, using the given parameters.You can also run a specific action:>>> from pantera import run
>>> run("terminate", "ec2-access-key", "ec2-secret-key")To check the list of available actions:>>> from pantera import list_actions
>>> list_actions()
Package: panTeX
panTeXGenerating pretty reports usingpandasandmatplotlib,SeabornorAltair.Installationpip install pantexto install the main applicationInstallMiKTeX(on Windows) or TBD on LinuxInstallpandocInstallBrowsersync(for browser mode):npm install -g browser-syncInstall chromdriver.exe forAltairand make sure it's on your system pathQuickstartTo create apantex.Managerobject:m=pantex.Manager('mytemplate.md')To save context tomytemplate.pkl:m.save_context({'my_header':'Hello World!','my_table':df.head()})To append to context in a Python script:m.save_context({'my_footer':'Goodbye!','an_image':mplFigure},append=True)To generate a pretty pdf report:m.save_to_pdf()To read the current context in a Python script:m.get_context()m.get_context()To generate a pdf report using ain-memorycontext dictionary:m=pantex.Manager('mytemplate.md',{'my_header':'Hello World!','my_table':df.head()})m.save_to_pdf()Quickstart via Command LineTo run in browser mode:python -m pantex.edit mytemplate.mdTo prduce a pretty LaTeX pdf report:python -m pantex.publish mytemplate.mdHow it worksInspired by traditional website rendering, a report generation process is divided into two parts:A markdown template withPython-native template variables.A context dictionary, which is saved atmytemplate.pklFor example, say you have a markdown file at./mytemplate.md, containing:# ${my_header}${my_table}
Some textIn Python, you can write:m=pantex.Manager('mytemplate.md')m.save_context({'my_header':'Hello World!','my_table':df.head()})m.save_to_pdf()panTeX will combine the markdown template and the Python context and produce a pretty pdf LaTeX report.
Note that pandas DataFrame objects will be automatically rendered as a table.  Matplotlib/Seaborn
charts and LaTeX style equations are also supported.  (The LaTeX equations must be contained in\begin{equation}/\end{equation}tags.)matplotlibandSeabornplot can also be added to the context.  For example,importseabornassnssns.set_theme()df=sns.load_dataset("tips")sns_plot=sns.relplot(data=df,x="total_bill",y="tip",col="time",hue="smoker",style="smoker",size="size",)m=pantex.Manager('mytemplate.md')m.save_context({'pretty_figure':sns_plot},append=True)# "Pretty Figure" will be the image captionBehind the scenes, panTeX saves the image atassets/pretty_figure.epsand adds![Pretty Figure](assets/pretty_figure.eps)to therenderedversion of markdown file.The Development Server (Edit Mode)python -m pantex.edit mytemplate.mdenables an in-browser version of the LaTeX report.  It's not quite as pretty as the pdf version, but it offers a near real time rendering experience.  (Rendering of pdf reports is typically too slow for editing and requires
the file to re-opened after each render.) If you need the html files, they can be found atmytemplate.htmlandassets/.
Package: panther
PantherIs A Fast &  Friendly Web Framework For Building Async APIs With Python 3.10+📚 Full Documentation:PantherPy.GitHub.ioWhy Use Panther ?Document-oriented Databases ODM (PantherDB, MongoDB)Built-in Websocket SupportCache APIs (In Memory, In Redis)Built-in Authentication Classes (Customizable)Built-in Permission Classes (Customizable)Handle Custom MiddlewaresHandle Custom ThrottlingVisual API Monitoring (In Terminal)Supported byBenchmarkFrameworkThroughput (Request/Second)Blacksheep5,339Muffin5,320Panther5,112Sanic3,660FastAPI3,260Tornado2,081Bottle2,045Django821Flask749More Detail:https://GitHub.com/PantherPy/frameworks-benchmarkInstallation1. Create a Virtual Environment$ python3 -m venv .venv2. Active The Environment* Linux & Mac$ source .venv/bin/activate* Windows$ .\.venv\Scripts\activate3.Install Panther- ⬇ Normal Installation$ pip install panther-  ⬇ Include full requirements (MongoDB, JWTAuth, Ruff, Redis, bpython)$ pip install panther[full]UsageCreate Project$panthercreateRun Project$pantherrun--reload* Panther usesUvicornas ASGI (Asynchronous Server Gateway Interface) but you can run the project withGranian,daphneor any ASGI server tooMonitoring Requests$panthermonitorPython Shell$panthershellSingle-File Structure ExampleCreatemain.pyfromdatetimeimportdatetime,timedeltafrompantherimportversion,status,Pantherfrompanther.appimportAPIfrompanther.requestimportRequestfrompanther.responseimportResponsefrompanther.throttlingimportThrottlingInfoThrottling=Throttling(rate=5,duration=timedelta(minutes=1))@API()asyncdefhello_world():return{'detail':'Hello World'}@API(cache=True,throttling=InfoThrottling)asyncdefinfo(request:Request):data={'panther_version':version(),'datetime_now':datetime.now().isoformat(),'user_agent':request.headers.user_agent}returnResponse(data=data,status_code=status.HTTP_202_ACCEPTED)url_routing={'':hello_world,'info':info,}app=Panther(__name__,configs=__name__,urls=url_routing)Run the project:$ panther run --reloadNow you can see these two urls:http://127.0.0.1:8000/http://127.0.0.1:8000/info/Next Step:First CRUDReal Word Example:Https://GitHub.com/PantherPy/panther-exampleRoadmapIf you find this project useful, please give it a star ⭐️.