Saravanan666/pypi-org huggingface dataset:
Package: pandoramoon
Fast open-source exomoon transit detection algorithmNamed after the imaginary 5th moon of the gas giant Polyphemus, orbiting Alpha Centauri A, in the filmAvatar.Pandora employs an analytical photodynamical model, including:Stellar limb darkeningFull and partial planet-moon eclipsesBarycentric motion of planet and moonTo search for moons, Pandora can used with nested samplers, e.g.UltraNestordynesty. We will provide an example workflow for injection and retrievals soon. Pandora is fast, calculating 10,000 models and log-likelihood evaluation per second (give or take an order of magnitude, depending on parameters and data). This means that a retrieval with 250 Mio. evaluations until convergence takes about 5 hours on a single core. Scaling with cores is worse than linear. For searches in large amounts of data, it is most efficient to assign one core per lightcurve.Create transit lightcurveimport pandora
params = pandora.model_params()
params.per_bary = 365.25  # [days]
# (...) See tutorials for list of parameters
time = pandora.time(params).grid()
model = pandora.moon_model(params)
flux_total, flux_planet, flux_moon = model.light_curve(time)

plt.plot(time, flux_planet, color="blue")
plt.plot(time, flux_moon, color="red")
plt.plot(time, flux_total, color="black")
plt.show()Create videoWith Pandora, you can create transit videos to understand, teach, and explore exomoon transits. Try it out:video = model.video(
    time=time
    limb_darkening=True, 
    teff=3200,
    planet_color="black",
    moon_color="black",
    ld_circles=100
)
video.save(filename="video.mp4", fps=25, dpi=200)Videos approximate the true lightcurve as calculated by Pandora very well. They are, however, not pixel-perfect due to the underlying Matplotlib render engine. Klick the image to view the video:InstallationInstall withpip install pandora-moon. If you end up in dependency hell, set up a fresh environment:conda create -n pandora_env python=3.9
conda activate pandora_env
conda install numpy matplotlib numba 
pip install pandora-moonFor sampling, the following packages will be useful:conda install cython scipy
pip install ultranest dynesty h5pyAttributionPlease citeHippke & Heller (2022, A&A in press)if you find this code useful in your research. The BibTeX entry for the paper is:@ARTICLE{
}
Package: pandora-plugin-libsgm
Pandora plugin libSGMSemi-Global Matching algorithm plugin for.Overview•Install•Usage•Related•ReferencesOverviewPandorastereo matching framework is designed to provide some state of the art stereo algorithms and to add others one as plugins.ThisPandora pluginaims to optimize the cost volume following Semi-Global Matching algorithm, defined by[Hirschmuller], with thelibSGMlibrary .Installpandora_plugin_libsgmis available on Pypi and can be installed by:pipinstallnumpy
pipinstallpandora_plugin_libsgmThis command will installed required dependencies asPandoraandlibSGM.UsageLet's refer toPandora's readmeoronline documentationfor further information about Pandora general functionalities.More specifically, you can find :SGM configuration file exampledocumentation about SGM theory and parametersRelatedPandora- A stereo matching frameworklibSGM- C++/python implementation of semi-global matching algorithmReferencesPlease cite the following paper when using Pandora and pandora_plugin_libsgm:Cournet, M., Sarrazin, E., Dumas, L., Michel, J., Guinet, J., Youssefi, D., Defonte, V., Fardet, Q., 2020. Ground-truth generation and disparity estimation for optical satellite imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.[Hirschmuller]H. Hirschmuller, "Stereo Processing by Semiglobal Matching and Mutual Information," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 328-341, Feb. 2008. doi: 10.1109/TPAMI.2007.1166
Package: pandoraPlugintools
Python: module plugintools for PandoraFMS DeveloperspandoraPluginTools is a library that aims to help the creation of scripts and their integration in Pandora FMS.PluginTools Reference DocumentationThe package includes the following modules. Each one has different functions that facilitate and automate the data integration in Pandora FMS:generalModule containing general purpose functions, useful in the creation of plugins for PandoraFMS.threadsModule containing threading purpose functions, useful to run parallel functions.agentsModule that contains functions oriented to the creation of Pandora FMS agentsmodulesModule that contains functions oriented to the creation of Pandora FMS modules.transferModule containing functions oriented to file transfer and data sending to Pandora FMS server.discoveryModule containing functions oriented to the creation of Pandora FMS discovery plugins.httpModule containing functions oriented to HTTP API calls.snmpModule containing functions oriented to calls to snmp operations.encryptionModule containing encryption oriented functions.outputModule containing functions oriented to output and return data.ExampleimportpandoraPluginToolsasppt## Define agentserver_name="WIN-SERV"agent=ppt.init_agent({"agent_name":ppt.generate_md5(server_name),"agent_alias":server_name,"description":"Default Windows server"})## Define modulesmodules=[]data=10modules.append({"name":"CPU usage","type":"generic_data","value":data,"desc":"Percentage of CPU utilization","unit":"%"})## Generate and transfer XMLxml_content=ppt.print_agent(agent,modules)xml_file=ppt.write_xml(xml_content,agent["agent_name"])ppt.transfer_xml(xml_file,transfer_mode="tentacle",tentacle_ip="192.168.1.20",tentacle_port="41121",)The package has the following dependencies:Hashlibdatetime.datetimehashlibjsonosprint_agentprint_log_moduleprint_modulequeue.Queuerequests.auth.HTTPBasicAuthrequests.auth.HTTPDigestAuthrequests.sessions.Sessionrequests_ntlm.HttpNtlmAuthshutilsubprocess.Popensysthreading.Threadpycryptoeasysnmppysnmp
Package: pandora-storage-server-client
Pandora Storage Server Python Client 🪐🐍This is the Python Client implementation for the Key-Value Pairs Schemaless Pandora Storage Server.How to Install Pandora Storage Server Python Client*Requirements:Python 3.6+ installed.Make sure you have Python 3.6+ installed and run the following command in your terminal to install the Pandora Storage Server Python Client:For MacOS and GNU/Linux:pip3installpandora_storage_server_clientFor Windows:pipinstallpandora_storage_server_clientHow to use the Pandora Storage Server Python ClientTo use the client import the client module to your Python script as follows:importpandora_storage_server_clientaspandora
Package: pandora-tiktoken
⏳ tiktokentiktoken is a fastBPEtokeniser for use with
OpenAI's models.importtiktokenenc=tiktoken.get_encoding("gpt2")assertenc.decode(enc.encode("hello world"))=="hello world"# To get the tokeniser corresponding to a specific model in the OpenAI API:enc=tiktoken.encoding_for_model("text-davinci-003")The open source version oftiktokencan be installed from PyPI:pip install tiktokenThe tokeniser API is documented intiktoken/core.py.Example code usingtiktokencan be found in theOpenAI Cookbook.Performancetiktokenis between 3-6x faster than a comparable open source tokeniser:Performance measured on 1GB of text using the GPT-2 tokeniser, usingGPT2TokenizerFastfromtokenizers==0.13.2,transformers==4.24.0andtiktoken==0.2.0.Getting helpPlease post questions in theissue tracker.If you work at OpenAI, make sure to check the internal documentation or feel free to contact
@shantanu.Extending tiktokenYou may wish to extendtiktokento support new encodings. There are two ways to do this.Create yourEncodingobject exactly the way you want and simply pass it around.cl100k_base=tiktoken.get_encoding("cl100k_base")# In production, load the arguments directly instead of accessing private attributes# See openai_public.py for examples of arguments for specific encodingsenc=tiktoken.Encoding(# If you're changing the set of special tokens, make sure to use a different name# It should be clear from the name what behaviour to expect.name="cl100k_im",pat_str=cl100k_base._pat_str,mergeable_ranks=cl100k_base._mergeable_ranks,special_tokens={**cl100k_base._special_tokens,"
":100264,"
":100265,})Use thetiktoken_extplugin mechanism to register yourEncodingobjects withtiktoken.This is only useful if you needtiktoken.get_encodingto find your encoding, otherwise prefer
option 1.To do this, you'll need to create a namespace package undertiktoken_ext.Layout your project like this, making sure to omit thetiktoken_ext/__init__.pyfile:my_tiktoken_extension
├── tiktoken_ext
│   └── my_encodings.py
└── setup.pymy_encodings.pyshould be a module that contains a variable namedENCODING_CONSTRUCTORS.
This is a dictionary from an encoding name to a function that takes no arguments and returns
arguments that can be passed totiktoken.Encodingto construct that encoding. For an example, seetiktoken_ext/openai_public.py. For precise details, seetiktoken/registry.py.Yoursetup.pyshould look something like this:fromsetuptoolsimportsetup,find_namespace_packagessetup(name="my_tiktoken_extension",packages=find_namespace_packages(include=['tiktoken_ext*']),install_requires=["tiktoken"],...)Then simplypip install ./my_tiktoken_extensionand you should be able to use your
custom encodings! Make surenotto use an editable install.
Package: pandora-upload
pandora-uploadpandora_upload is a commandline client for pan.do/ra.
You can use it to upload one or more files to your pandora instance.
No conversion is done on the client side.
To upload/sync large repositories use pandora_clientYou can also use pandora-upload as a python script:importpandora_clientitem_id=pandora_client.upload('http://pandora/api/',['/home/example/Videos/video.mp4',{'title':'This is an example','date':'2021-11-15'})
Package: pandots
PanDotsDescriptionPangenome Dotplots. Visualize pangenome chromosome structures by generating dotplots from sequence aligmnets in PAF format.
Sequences should be aligned using an anchoring genome.InstallationIn a conda environmentFist create an environment that includes all dependencies:conda create -c conda-forge -c bioconda -n pandots \
  python pandas numpy seaborn plotnineThen install withpip:conda activate pandots
pip install pandotsWith just pipInstall  withpip:pip install pandotsUsageGenerate input filesCreate PAF pairwise alignments to an anchor genome for all genomes using minimap2 or your prefered method.
Extract your chromosome of interest from all PAF files and concatenate into a single file.Visualize with PanDotsOnce you have your chromosome PAF file, you can generate the plot.python pandots.py --paf <chromosome.paf> --ref <anchor_chromosome> --out <output_suffix>After completion the plot will be saved as<output_suffix>.pandots.svg.Aditional arguments--key: show genome key or not. Default: False--pad: integer value to adjust spacing between genomes. Default: 10000000--flip: designates if PAF column 1 is the anchor or the query. Default: False (column 1 is anchor)--invert: inverts the anchor coordinates in relation to the pangenome. Default: False--reorder: adjust the order of genomes plotted using length and number of inversions. Default False--recolor: recolor genomes to an alternating color scheme. Default: FalseLicensePanDots is licensed under aSalk Institute BSD license
Package: pandus
pandusWhat is it?pandusis a wrapper around popular data analysis librarypandas, with the sole purpose of providing a different name. It provides a drop-in replacement for pandas, with all the same functionality and API, but with the name changed to "pandus".Note that while pandas is a serious library for data analysis, pandus is not intended to be taken seriously and is purely for fun. Use at your own risk!Why?Because of an inside joke.InstallpipinstallpandusUseimportpandusaspd# just use it as you would use pandas
Package: pane
PanePane is part of the Rayse library for hp-FEA.InstallationUse the package managerpipto install Pane:pipinstallpaneLicensePane is licensed underGNU LGPLv3.This software relies on thePyO3library undertheApache-2.0license for ffi and ontherayoncrate under theMITandApache-2.0licenses for parallelism.
Package: panediv-nfw
panediv_nfwGenerate tmux layout string from simple format.Install$ pip3 install panediv-nfw

$ export PATH=$PATH:~/.local/bin/PaneDivAutomate tmux layout generation tools.
With options, print layout information only.
Without options, start tmux with the layout.
Run this script on the outside of tmux.Usage$ panediv -h
usage: panediv [-h] [--tmuxinator] [--show_layout] [--show_commands] [--show_matrix] layout

Generate layout string from simple format.

positional arguments:
  layout               Layout string, ex.) {,}. Without other options, start tmux.

optional arguments:
  -h, --help           show this help message and exit
  --tmuxinator, -i     Export tmuxnator configuration.
  --show_layout, -l    Print layout string.
  --show_commands, -c  Print command list.
  --show_matrix, -m    Print pane number matrix.ExamplesOpen tmux with complex layout from simple string.Simple 3 rowspanediv '[,,]'panediv '[3]'Simple 3 columnspanediv '{,,}'panediv '{3}'Simple 3 rows with commandspanediv '[/usr/games/sl,"figlet \"pane 2\"",]'Specify size, 10 lines, 15 lines, leftpanediv '[(,10),(,15),]'Specify size, 20 percent, 30 percent, leftpanediv '[(,20%),(,30%),]'Specify command and sizepanediv '[(/usr/games/sl,20%),("figlet \"pane 2\"",10),]'Complex layoutpanediv '{[{,[2]},,{[2],}],,[{[2],},,{,[2]}]}'panediv '{([4], 70%),,}'Export TmuxInator configuration.panediv -i /tmp/layout '{/usr/games/sl, [1,"figlet \"pane 3\"",2],}' && tmuxinator start -p /tmp/layoutpdv, pdcSimply split current pane evenly.Usage$ pdv.py
usage: pdv.py [-h] [--vertical] num

Split pane evenly.

positional arguments:
  num             Divide pane into num. default: horiszontally.

optional arguments:
  -h, --help      show this help message and exit
  --vertical, -v  Divide vertically.$ pdc -h
usage: pdc.py [-h] [--file FILE] [--vertical] [--instant] [commands [commands ...]]

Split pane and run commands. Input commands as args, filename(-f) or from pipe.

positional arguments:
  commands              Commands.

optional arguments:
  -h, --help            show this help message and exit
  --file FILE, -f FILE  Commands file or \n separated stdin
  --vertical, -v        Divide vertically.
  --instant, -i         Kill the pane when the command finished.ExamplesSplit current pane horizontallypdv 3Split current pane verticallypdv -v 3Split current pane horizontally and run commandspdc "echo 1" "echo 2"echo "echo 1\necho 2" | pdcfor i in {1..2}; do echo "echo ${i}"; done | pdc 'echo 1' 'echo 2'echo "echo 1\necho 2" > ./commands.txt && pdc -f ./commands.txtSplit current pane verticallypdc -v "echo 1" "echo 2"Split current but close then when command done.pdc -i "echo 1" "echo 2; sleep 3"
Package: panedr
This repository hosts the source files for both the Pyedr and Panedr packages.pyedrandpanedrare compatible with Python 3.6 and greater.PyedrPyedr provides a means of reading aGromacs EDRbinary XDR file and
return its contents as a dictionary ofnumpyarrays. Pyedr exposes
the following functions:edr_to_dict: returns a dictionary of NumPy arrays keyed by the energy
type from a given path to an EDR file.read_edr: parses an EDR file and returns the energy terms
in a nested listget_unit_dictionary: Returns a dictionary that holds the units of each
energy term found in the EDR file.PanedrPanedr uses the Pyedr library to read aGromacs EDRbinary energy XDR file
and returns its contents as apandasdataframe. Panedr exposes the
following functions:edr_to_df: which gets the path to an EDR file and returns a
pandas DataFrame.get_unit_dictionary: Returns a dictionary that holds the units of each
energy term found in the EDR file.ExampleUsingpyedr:importpyedr# Read the EDR filepath='ener.edr'dic=pyedr.edr_to_dict(path)# The `verbose` optional parameter can be set to True to display the# progress on stderrdic=pyedr.edr_to_dict(path,verbose=True)# Get the average pressure after the first 10 nspressure_avg=dic['Pressure'][dic['Time']>10000].mean()# Get the units of the EDR entriesunit_dict=pyedr.get_unit_dictionary(path)unit_dict["Temperature"]# returns "K"Usingpanedr:importpanedr# Read the EDR filepath='ener.edr'df=panedr.edr_to_df(path)# The `verbose` optional parameter can be set to True to display the# progress on stderrdf=panedr.edr_to_df(path,verbose=True)# Get the average pressure after the first 10 nspressure_avg=df['Pressure'][df['Time']>10000].mean()# Get the units of the EDR entriesunit_dict=panedr.get_unit_dictionary(path)unit_dict["Temperature"]# returns "K"InstallYou can installpyedrandpanedrusingpip:pipinstallpyedr# installing panedr automatically installs pyedrpipinstallpanedrIf you are usingcondaandconda-forge, you can install with:condainstall-cconda-forgepyedr# install panedr automatically installs pyedrcondainstall-cconda-forgepanedrTestsThepyedrandpanedrrepositories contains a series of tests.
If you downloaded or cloned the code from the repository, you can run
the tests. To do so, installpytest, and, in the directory of the
panedr source code, run:Forpyedr:pytest-vpyedr/pyedr/testsForpanedr:pytest-vpanedr/panedr/testsLicensePyedr and Panedr translate part of the source code of Gromacs into Python.
Therefore, Panedr is distributed under the same GNU Lesser General
Public License version 2.1 as Gromacs. See thelicensefor more details.
Package: panelapp
panelappAllows for lots of operations for Panelapp queriesInstallpipinstallpanelappImportfrompanelappimportPanelappfrompanelappimportapifrompanelappimportqueriesHow to usefrompanelappimportPanelapppanel=Panelapp.Panel(269)# Create panel object, confidence level defaults to 3, version is the lastestpanel.get_info()# Return dict with general data about the panel# For the 4 following methods you can specify the confidence levelspanel.get_genes()# Return all the gene symbols/idspanel.get_gene_symbols()# Return gene symbols according to confidence level choosen when creating the panel objectpanel.get_hgnc_ids()# Return hgnc ids according to confidence level choosen when creating the panel objectpanel.get_ensembl_ids("GRCh37")# Return ensembl ids according to confidence level choosen when creating the panel objectpanel.update_version("3.2","2")# Update the panel with version and confidence level givenpanel.is_signedoff()# Return date of signedoff or False if not signedoffpanel.get_data()# Return all the data the API sent, you can use that there's something that is lacking in my methodspanel.write()# Write a file for the panel containing the most important data (according to me at least, you can customize this yourself using the .get_data() method)frompanelappimportqueriespanels=queries.get_all_panels()# Return dict {panel_id: Panelapp.Panel} of all panels in Panelappsignedoff_panels=queries.get_all_signedoff_panels()# Return dict {panel_id: Panelapp.Panel} of signedoff panels. Note that this only returns the latest versions on public panels.matches,differences=queries.compare_versions(Panelapp.Panel,"2.7")# Return tuple of match and differences between the given panel and another panel versionpanel=queries.get_signedoff_panel(269)# Return panel object with latest signedoff version
Package: panel-chemistry
🧪 Panel-ChemistryWe want tomake it super simple to doexploratory data analysisand develop high-qualityPaneldata appswithin the domain ofchemistry.We providethepanel-chemistrypython package of chemistry components for Panel.example notebooks and data apps.🚀 Get started in under a minuteYou can install the package withpippipinstallpanel-chemistry[examples]Explore the sample appspnhellopanel-chemistryYou can now find thereferencenotebooks in theexamples/awesome-panel/panel-chemistryfolder. Check them out by runningjupyter lab.📒 Get started on BinderClick the button🏃 Serve a data appAdd the below to a python file or notebookimportpanelaspnfrompanel_chemistry.widgetsimportJSMEEditorpn.extension("jsme",sizing_mode="stretch_width")smiles="N[C@@H](CCC(=O)N[C@@H](CS)C(=O)NCC(=O)O)C(=O)O"editor=JSMEEditor(value=smiles,height=500,format="smiles")editor.servable()Runpanel serve name_of_app.pyorpanel_serve name_of_notebook.ipynb.Install with condacondainstall-cconda-forgepanel-chemistry⭐ SupportPlease supportPanelandawesome-panelby giving the projects a star on Github:holoviz/panel.awesome-panel/awesome-panel.Thanks❤️ ContributeYou can findgood first issuesin theissue tracker. To get started check out theDEVELOPER_GUIDE.I would love to support and receive your contributions. Thanks..🖥️ Monitor
Package: panel-com-g2
panel_com_g2panel_com provides a serial port interface to Michael Reiser's Panel Generation 2 controllers, first adapted from the MATLABPanel_com.mscript.Installation and SetupInstall the package using the following command:python -m pip install panel_com_g2To update the package, use the following command:python -m pip install panel_com_g2 --upgrade
Package: panelctmc
panelctmcContinuous Time Markov Chain for daily panel data and annual transition probabilitiesTable of ContentsInstallationUsageCommandsSupportContributingInstallationThepanelctmcgit repois available asPyPi packagepip install panelctmcUsageCheck theexamplesfolder for notebooks.CommandsCheck syntax:flake8 --ignore=F401Run Unit Tests:python -W ignore -m unittest discoverRemove.pycfiles:find . -type f -name "*.pyc" | xargs rmRemove__pycache__folders:find . -type d -name "__pycache__" | xargs rm -rfUpload to PyPi with twine:python setup.py sdist && twine upload -r pypi dist/*DebuggingNotebooks to profile python code are in theprofilefolderSupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.
Package: panelexpr
panelexprA simple panel data evaluatorInstallation$pipinstallpanelexprUsageSample Datasample_zh_2.csv:DatewindcodeOpenHighLowCloseVolume20170913000001.SZ1221.4880091226.8034481205.541691215.109481668237.4220170914000001.SZ1215.1094811232.1188881194.9108111203.415514883087.7720170915000001.SZ1200.226251203.4155141185.343021200.22625646094.8120170918000001.SZ1195.9738991203.4155141190.6584591195.973899607612.9620170919000001.SZ1195.9738991205.541691177.9014041183.216844764212.6220170913000002.SZ3473.6913923576.6664333431.1283743536.849417467463.7720170914000002.SZ3539.5954183852.6395433508.0164053783.9895161150696.1420170915000002.SZ3775.7515134149.2076623771.6325114022.8916121550495.6120170918000002.SZ3983.0745964083.3036363843.0285393849.8935421063043.0720170919000002.SZ3881.4725554132.7316563858.1315463950.1225821196308.5load dataimportpandasaspddata=pd.read_csv("../data/sample_zh_2.csv")Calculationimportpanelexpraspepe.eval("ma(Open, 2)",data=data,group_tag="windcode")# equivalent todata.groupby("windcode").rolling(2).mean()["Open"].reset_index(drop=True).rename()Output:0            NaN
1    1218.298745
2    1207.667866
3    1198.100075
4    1195.973899
5            NaN
6    3506.643405
7    3657.673466
8    3879.413054
9    3932.273575
dtype: float64User defined operatorfrompanelexprimportTimeSeriesOperatorclassMyMovingAverageOperator(TimeSeriesOperator):defeval(self,series:pd.Series,window)->pd.Series:# all udo must implement this methods=series.rolling(window).mean()returnspe.register("my_ma",MyMovingAverageOperator)# register the operatorpe.eval("my_ma(Open, 2)",data=data,group_tag="windcode")