Saravanan666/pypi-org huggingface dataset:
Package: pyromongo
pyromongoINTRODUCTIONPersistent Session Storage for Pyrogram with MongoDBfrompyrogram.clientimportClientfrompyromongoimportMongoStoragefrommotor.motor_asyncioimportAsyncIOMotorClientclient=Client(..)mongo=AsyncIOMotorClient("mongodb+srv://..")client.storage=MongoStorage(mongo["pyrogram"],remove_peers=False)# use remove_peers=True to remove all peers on logoutclient.run()Note:❌ It will not work with the pyrogram context manager.asyncdefmain():asyncwithClient(..)asclient:client.storage=MongoStorage(..)# It will only store peersINSTALLATION:pipinstallpyromongo

pipinstallgit+https://github.com/animeshxd/pyromongoInstall dnspython formongo+srv://..URIspipinstalldnspython# or pip install -U pyromongo[srv]# for Termux use dnspython forkaptinstallresolv-conf
pipinstallgit+https://github.com/animeshxd/dnspythonInstall with all extra dependenciespipinstall"pyromongo[gssapi,aws,ocsp,snappy,srv,zstd,encryption]"Check other required dependencies for motorhttps://motor.readthedocs.io/en/stable/installation.html#dependencies
Package: pyromorphite
⛏pyromorphite💎Process mining in python.Get the Source CodePyromorphite is actively developed on GitHub, where the code isalways available.You can clone the public repository:$ git clone git://github.com/xcavation/pyromorphite.gitOnce you have a copy of the source, you can embed it in your own Python package, or install it into your site-packages easily:$ cd requests
$ pip install .QuickstartThis is a quick introduction to Pyromorphite. Before proceeding, make sure that Pyromorphite isinstalled.Import a Log 📜Reading in event log files in Pyromorphite is super easy. It supportsXES, CSV, and Excel files.XES FilesBegin by importing the Pyromorphite module:>>>importpyromorphiteaspmWe'll try now to get an xes file from a web repository. Forthisdataset:>>>log=pm.read_xes("https://data.4tu.nl/repository/uuid:c1e9137e-2877-410d-a76a-21ce7f97a239/DATA1")Similarly we would do if we would like to read a local file. Let's assume that under/path/to/file.xeslies our file. We can then do:>>>log=pm.read_xes("/path/to/file.xes")CSV FilesAlthough not part of Pyromorphite, reading a csv file can be done with thepandaslibrary.Begin by import the pandas module:>>>importpandasaspdWe'll try now to get an xes file from a web repository. Forthisdataset:>>>log=pd.read_csv("https://data.4tu.nl/repository/uuid:d5ccb355-ca67-480f-8739-289b9b593aaf/DATA")Construct a Bag 🎒Having parsed a log into a pandasDataFramewe can simply extract the traces of events, with
togehter with their frequency in the log.>>>bag=pm.as_bag(log)If we consider that everybody might use different column names, we can specify the case, timestamp
and activity columns when creating the bag in the following way:>>>bag=pm.as_bag(log,case='CI Name (aff)',time='Actual Start',activity='Change Type')Does this Trace Conform to the Event Log?For this task we are going to use a model calledLog Skeleton.>>>bag=pm.as_bag(pm.read_xes(os.path.join("B1.xes")))>>>traces=[("a1","a4","a5","a7")]>>>pred=pm.skeleton.classify(bag,traces)>>>[False]NOTE: LogB1.xesis equal to the multisetthat can be foundhere.
Package: pyronear
PyroNear: early wildfire detectionThe increasing adoption of mobile phones have significantly shortened the time required for firefighting agents to be alerted of a starting wildfire. In less dense areas, limiting and minimizing this duration remains critical to preserve forest areas.PyroNear aims at offering an wildfire early detection system with state-of-the-art performances at minimal deployment costs.Table of ContentsGetting StartedPrerequisitesInstallationUsageReferencesDocumentationContributingCreditsLicenseGetting startedPrerequisitesPython 3.6 (or more recent)pipInstallationUse pip to install the package from gitpipinstallgit+https://github.com/frgfm/PyroNear@masterUsagedatasetsAccess all PyroNear datasets just like anytorchvision.datasets.VisionDataset:frompyronear.datasetsimportOpenFiredataset=OpenFire('./data',download=True)ReferencesYou are free to use any training script, but some are already provided for reference. In order to use them, install the specific requirements and check script options as follows:pipinstall-rreferences/classification/fastai/requirements.txt
pythonreferences/classification/fastai/train.py--helpYou can then run the script with your own arguments:pythonreferences/classification/fastai/train.py--data-path./data--lr3e-3--epochs4--pretrained--deterministicPlease note that most tasks are provided with two training scripts (and theirrequirements.txt): one usingfastaiand the other without it.DocumentationThe full package documentation is availableherefor detailed specifications. The documentation was built withSphinxusing athemeprovided byRead the Docs.ContributingPlease refer toCONTRIBUTINGif you wish to contribute to this project.CreditsThis project is developed and maintained by the repo owner and volunteers fromData for Good.LicenseDistributed under the MIT License. SeeLICENSEfor more information.
Package: pyronic
pyronicRun a program and silence its output, unless it fails.Usageusage: pyronic [-h] [-V] [-v] [-s] ...

positional arguments:
  command

optional arguments:
  -h, --help      show this help message and exit
  -V, --version   show program's version number and exit
  -v, --verbose   Include additional output on failure
  -s, --separate  Separate stdout/stderr rather than combiningBackgroundpyronic is a replacement forchronic.One key difference is that pyronic, in its default mode, will preserve the
relative order of the lines written by program to stdout and stderr. Some
programs aren't consistent in printing to stdout or stderr, and this order can
make errors much eaiser to comprehend.$ chronic ./test/interleave
1 stdout
2 stdout
4 stdout
7 stdout
9 stdout
3 stderr
5 stderr
6 stderr
8 stderr

$ pyronic ./test/interleave
1 stdout
2 stdout
3 stderr
4 stdout
5 stderr
6 stderr
7 stdout
8 stderr
9 stdout
Package: pyronn-torch
pyronn-torchThis repository provides PyTorch bindings forPYRO-NN,
a collection of back-propagatable projectors for CT reconstruction.Feel free to cite our publication:@article{PYRONN2019,author={Syben, Christopher and Michen, Markus and Stimpel, Bernhard and Seitz, Stephan and Ploner, Stefan and Maier, Andreas K.},title={Technical Note: PYRO-NN: Python reconstruction operators in neural networks},year={2019},journal={Medical Physics},}InstallationFrom PyPI:pipinstallpyronn-torchFrom this repository:gitclone--recurse-submodules--recursivehttps://github.com/theHamsta/pyronn-torch.gitcdpyronn-torchpipinstalltorchpipinstall-e.You can build a binary wheel usingpythonsetup.pybdist_wheelImportantIf you’re using an older CUDA version you might get an error about'-allow-unsupported-compiler'not being a
valid compiler option. In that case remove that compiler option from this project’ssetup.py.Usageimportpyronn_torchimportnumpyasnpprojector=pyronn_torch.ConeBeamProjector((128,128,128),# volume shape(2.0,2.0,2.0),# volume spacing in mm(-127.5,-127.5,-127.5),# volume origin in mm(2,480,620),# projection_shape (n, width, height)[1.0,1.0],# projection_spacing in mm(0,0),# projection_originnp.array([[[-3.10e+2,-1.20e+03,0.00e+00,1.86e+5],[-2.40e+2,0.00e+00,1.20e+03,1.44e+5],[-1.00e+00,0.00e+00,0.00e+00,6.00e+2]],[[-2.89009888e+2,-1.20522754e+3,-1.02473585e-13,1.86000000e+5],[-2.39963440e+2,-4.18857765e+0,1.20000000e+3,1.44000000e+5],[-9.99847710e-01,-1.74524058e-2,0.00000000e+0,6.00000000e+2]]])# two projection matrices in shape (n, 3, 4)# optionally: source_isocenter_distance=1, source_detector_distance=1 for a scalar weighting the projections)projection=projector.new_projection_tensor(requires_grad=True)projection=projection+1.result=projector.project_backward(projection,use_texture=True)assertprojection.requires_gradassertresult.requires_gradloss=result.mean()loss.backward()Or easier withPyCONRAD(pip install pyconrad)projector=pyronn_torch.ConeBeamProjector.from_conrad_config()The configuration can then be done usingCONRAD(startable usingconradfrom command line)
Package: pyronos
PyronosSimple and sweet load testing module.Installationpip3installpyronosCommand Line Interpreter (CLI)Creates a"results"folder in the current directory to store all of the load testings.# Simple usage.pyronoshttps://my.websiteget25simple# Send head request.pyronoshttps://my.websitehead25simple# Dump logs.pyronoshttps://my.websiteget25simple-d# Send requests sequentially.pyronoshttps://my.websiteget25simple-s# Print progress of sequential requests.pyronoshttps://my.websiteget25simple-s-pModulefrompyronosimportPyronosURL='https://my.website'HTTP_METHOD='get'# or 'head', 'options', 'delete', 'post', 'put'.NUM_OF_REQS=25DATA={}# Add request body.HEADERS={}# Add request headers.FIGURE='simple'# or 'stem', 'step'.OUTPUT='csv'# or 'json', 'yml'.SEQUENTIAL=FalsePRINT_PROGRESS=FalseDUMP_LOGS=Trueload_tester=Pyronos(URL,HTTP_METHOD,num_requests=NUM_OF_REQS,figure=FIGURE,output=OUTPUT,sequential=SEQUENTIAL,print_progress=PRINT_PROGRESS,dump_log=DUMP_LOGS)load_tester.dispatcher()# Dispatch requests to start load testing.Example Stem FigureExample JSON Output{'starting_time':'30/12/201902:56:38','finishing_time':'30/12/201902:56:42','response_times':[0.314406,0.268947,0.286585,0.303832,0.30449,0.276836,0.294791,0.308027,0.270716,0.289745,0.304603,0.276385,0.303491,0.29894,0.275576],'response_codes':[200,200,200,200,200,200,200,200,200,200,200,200,200,200,200],'num_of_failed_responses':0,'q1':0.2766105,'median':0.294791,'q3':0.304161,'std':0.014522718628258128}PyPIhttps://pypi.org/project/pyronos/
Package: pyron-rs
pyron-rsA rust-based game development platform integrated into a python module, inspired by Godot.https://github.com/godotengine/godotSmall Test Project (Included)Show ExampleA terminal based example, which is included the tests folderThe displayed FPS counter ishere.Input (here)OutputAs you can see, it has a very simple and pythonic syntaxAnd generates a very simple terminal 'game'ProjectYou can find more project detailshereBuilding from SourceYou can find the guide to building from sourcehereHow to useTo use this module, you have a few options:Wait for official releases on PyPI.Download the wheel files from Github Actions. (Currently Broken)Download the source and compile yourself (SeeBUILDING.md).Wait for any releases from Github.
Package: pyrootutils
pyrootutilsA simple python package to solve all your problems with pythonpath, working directory, file paths, module imports and environment variables.Why pyrootutils?Problem:I would like to be able to:Run my python scripts from anywhereAlways import python modules relatively to the project root directoryAlways access files relatively to the project root so I don't have to specify a series of../to get to the dataAlways have access to environment variables from.envfile without having to load them manuallyHave all the above benefits in notebooks even if they're nested in subdirectoriesSolution:Thepyrootutilspackage provides a flexible way to setup the python project with a simple one-liner. It finds the project root based on the location of specified file name, e.g..project-rootor.git.The package is tiny and continuosly maintained, so you can use it without worrying it gets deprecated in the future.SetuppipinstallpyrootutilsUsageimportpyrootutils# find absolute root path (searches for directory containing .project-root file)# search starts from current file and recursively goes over parent directories# returns pathlib objectpath=pyrootutils.find_root(search_from=__file__,indicator=".project-root")# find absolute root path (searches for directory containing any of the files on the list)path=pyrootutils.find_root(search_from=__file__,indicator=[".git","setup.cfg"])# take advantage of the pathlib syntaxdata_dir=path/"data"assertdata_dir.exists(),f"path doesn't exist:{data_dir}"# set root directorypyrootutils.set_root(path=path# path to the root directoryproject_root_env_var=True,# set the PROJECT_ROOT environment variable to root directorydotenv=True,# load environment variables from .env if exists in root directorypythonpath=True,# add root directory to the PYTHONPATH (helps with imports)cwd=True,# change current working directory to the root directory (helps with filepaths))importpyrootutils# combines find_root() and set_root() into one methodroot=pyrootutils.setup_root(search_from=__file__,indicator="pyproject.toml"project_root_env_var=True,dotenv=True,pythonpath=True,cwd=True,)InspirationsThis package is heavily inspired by:https://github.com/chendaniely/pyprojroothttps://github.com/pashminacameron/py-repo-roothttps://github.com/EduardKononov/from-roothttps://github.com/eddieantonio/project-paths
Package: pyroot-zen
pyroot_zenis a wrapper aroundPyROOTwith better syntax simplification. By design, it does NOT introduce any new APIs, in order to make its usage more intuitive, as well as being compatible with other packages.Why should you use this? as the Zen of Python says: “Readability counts”.To use, you need only one line:>> import pyroot_zenwhere all objects inROOTnamespace are now patched.Features at a glance:Pythonic property instead of Getter/Setter.e.g.,hist.xaxis.title = "mass"instead ofhist.GetAxis().SetTitle("mass")Automatic (awkwardC++topython) arguments conversion.e.g.,ROOT.TPolyLine(3, [1,2,3], [1,4,9])without usingarray.Pythonic iterable as expected.How does it work? Basically intercepting__getattr__, __getattribute__, __init__call where necessary.Tested with: ROOT 6.08.06 (OSX homebrew), python 2.7.13, pytest 3.1.1.Relationship with other packagesROOT: This is required to be installed by the user, andPyROOTshould also be available.RooStats: If this is installed, the features will also be available automatically.rootpy: While on the surface it looks like they share a lot of similarity, the 2 packages usages are very different:rootpyrequires the user to use the new classes/functions from its namespace to benefit from its functionality, whereaspyroot_zeninjects the functionality into objects inPyROOTdirectly, and focusing only on syntaxes reduction.
Package: pyrop
pyropOverviewImperative-style railway-oriented programming in PythonGetting StartedInstallpyrop:pip install pyropA simple example:importpyrop# Do something with pyropSee amore in-depth tutorial here.LinksSee thedocumentation here.Development StatusThis project is currently in early-stage development. There may be
breaking changes often. While the major version is 0, minor version
upgrades will often have breaking changes.DevelopingSee thedevelopment guidefor development details.AuthorCreated by Nick DeRobertis. MIT License.
Package: pyropatch
PyropatchAn advanced monkeypatcher add-on for PyrogramInstallationInstall pyropatch with pippip install pyropatchUsage/ExamplesAll patchesCommand HandlerFlood HandlerListenfrompyropatchimportpyropatch#apply all patchesfrompyrogramimportClientapp=Client(...)Command Handlerfrompyropatchimportcommand_handler#apply command handlerfrompyrogramimportClientapp=Client(...)#pass info along with commands in command filter@app.on_message(filters.command(commands='start',info='Check Bot is Alive'))# to set bot commands from the command available on botapp.auto_set_commands()# to get all the commands available in botapp.commandsFlood Handlerfrompyropatchimportflood_handler#apply flood handlerfrompyrogramimportClientapp=Client(...)# all floodwaits will automatically handledapp.send_message("me","Flood handled with **Pyropatch**!")Listenfrompyropatchimportlisten#apply listenfrompyrogramimportClientapp=Client(...)# listen for a message in a particular chatm=app.listen_message(chat_id=chat_id,filters=filters,timeout=timeout)# listen for a callback data in a particular messageu=app.listen_callback(chat_id=chat_id,message_id=message_id,filters=filters,timeout=timeout)u=app.listen_callback(inline_message_id=inline_message_id,filters=filters,timout=timeout)# listen for an inline queryu=app.listen_inline_query(user_id=user_id,filters=filters,timeout=timeout)# listen for an inline resultu=app.listen_inline_result(user_id=user_id,filters=filters,timeout=timeout)More Comming SoonSpecial thanks toPyromod
Package: pyro-ppl
Getting Started|Documentation|Community|ContributingPyro is a flexible, scalable deep probabilistic programming library built on PyTorch.  Notably, it was designed with these principles in mind:Universal: Pyro is a universal PPL - it can represent any computable probability distribution.Scalable: Pyro scales to large data sets with little overhead compared to hand-written code.Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level abstractions to express generative and inference models, while allowing experts easy-access to customize inference.Pyro was originally developed at Uber AI and is now actively maintained by community contributors, including a dedicated team at theBroad Institute.
In 2019, Pyrobecamea project of the Linux Foundation, a neutral space for collaboration on open source software, open standards, open data, and open hardware.For more information about the high level motivation for Pyro, check out ourlaunch blog post.
For additional blog posts, check out work onexperimental designandtime-to-event modelingin Pyro.InstallingInstalling a stable Pyro releaseInstall using pip:pipinstallpyro-pplInstall from source:gitclonegit@github.com:pyro-ppl/pyro.gitcdpyro
gitcheckoutmaster# master is pinned to the latest releasepipinstall.Install with extra packages:To install the dependencies required to run the probabilistic models included in theexamples/tutorialsdirectories, please use the following command:pipinstallpyro-ppl[extras]Make sure that the models come from the same release version of thePyro source codeas you have installed.Installing Pyro dev branchFor recent features you can install Pyro from source.Install Pyro using pip:pipinstallgit+https://github.com/pyro-ppl/pyro.gitor, with theextrasdependency to run the probabilistic models included in theexamples/tutorialsdirectories:pipinstallgit+https://github.com/pyro-ppl/pyro.git#egg=project[extras]Install Pyro from source:gitclonehttps://github.com/pyro-ppl/pyrocdpyro
pipinstall.# pip install .[extras] for running models in examples/tutorialsRunning Pyro from a Docker ContainerRefer to the instructionshere.CitationIf you use Pyro, please consider citing:@article{bingham2019pyro,
  author    = {Eli Bingham and
               Jonathan P. Chen and
               Martin Jankowiak and
               Fritz Obermeyer and
               Neeraj Pradhan and
               Theofanis Karaletsos and
               Rohit Singh and
               Paul A. Szerlip and
               Paul Horsfall and
               Noah D. Goodman},
  title     = {Pyro: Deep Universal Probabilistic Programming},
  journal   = {J. Mach. Learn. Res.},
  volume    = {20},
  pages     = {28:1--28:6},
  year      = {2019},
  url       = {http://jmlr.org/papers/v20/18-403.html}
}
Package: pyror
pyrorA client for Research Organization Registry’s (ROR) APIRORROR API docsInstallationFrom PyPipip install pyrorDev versionpip install git+git://github.com/sckott/pyror.git#egg=pyror#ORgit clone git@github.com:sckott/pyror.git
cd pyror
make installUsageSee thepyror docsMetaPlease note that this project is released with aContributor Code of Conduct. By participating in this project you agree to abide by its terms.License: MIT; seeLICENSE file
Package: pyroredis
APyrogramsession storage implementation backed by Redis.Note: The hooks will not work untilPyrogrammerge#6b37046or use patchedmy pyrogram patched version.UsageThis session implementation can store multiple Sessions in the same key hive.Installingpip3installpyroredisUpgradingpip3install-UpyroredisQuick startfrompyrogramimportClientfrompyroredisimportRedisSessionimportredis# These example values won't work. You must get your own api_id and# api_hash from https://my.telegram.org, under API Development.api_key=12345api_hash='0123456789abcdef0123456789abcdef'session_name="798xxxxxxx7"redis_connector=redis.Redis(host='localhost',port=6379,db=0,decode_responses=False)redis_session=RedisSession(redis_connector)client=Client(session_name=session_name,api_key=(api_key,api_hash))client.load_session_hook=redis_session.load_sessionclient.save_session_hook=redis_session.save_sessionclient.start()
Package: pyrorl
PyroRLPyroRL is a new reinforcement learning OpenAI Gym environment built for the simulation of wildfire evacuation. Check out thedocsand thedemo.How to UseFirst, install our package:pipinstallpyrorlTo use our wildfire evacuation environment, define the dimensions of your grid, where the populated areas are, the paths, and which populated areas can use which path. See an example below.# Create environmentkwargs={'num_rows':num_rows,'num_cols':num_cols,'populated_areas':populated_areas,'paths':paths,'paths_to_pops':paths_to_pops}env=gymnasium.make('wildfire_evac/WildfireEvacuation-v0',**kwargs)# Run a simple loop of the environmentenv.reset()for_inrange(10):# Take action and observationaction=env.action_space.sample()observation,reward,terminated,truncated,info=env.step(action)# Render environment and print rewardenv.render()print("Reward: "+str(reward))For more examples, check out theexamples/folder.How to ContributeFor information on how to contribute, check out ourcontribution guide.
Package: pyrosalind
# PyROSALIND
Parsing of [ROSALIND](http://rosalind.info) website, in order to get user information programatically.To run an example on [aluriak user](http://rosalind.info/users/aluriak), run:makeThe main script gives example of the main API, which is currently:from pyrosalind import Useraluriak = User.from_name(‘aluriak’)
print(aluriak)
print(aluriak.solved)Usepip install -r requirements.txtto install thebeautiful soupandrequestsdependencies.## Retrieved data
Input data is the username.[X] problems solved[ ] badges[ ] achievements[ ] source codes submitted for each problem[ ] scores, statistics[ ] global and country ranks[ ] account details (last solvel, member for, country, time zone, language)
Package: pyroSAR
pyroSARA Python Framework for Large-Scale SAR Satellite Data ProcessingThe pyroSAR package aims at providing a complete solution for the scalable organization and processing of SAR satellite data:Reading of data from various past and present satellite missionsHandling of acquisition metadataUser-friendly access to processing utilities inSNAPandGAMMA Remote SensingsoftwareFormatting of the preprocessed data for further analysisExport to Data Cube solutionsHead on over toreadthedocsfor installation instructions,
examples and API reference.
Package: pyrosbag
ROS Bag Python ControllerProgrammatically controlROS Bagfiles with
Python.Also look atrosbag_pandas,
also available on PyPI, for an good package which allows you to work with the
data directly.Free software: MIT licenseDocumentation:https://pyrosbag.readthedocs.io.FeaturesGeneral Bag classrosbag playTo docheckcompressdecompressfilterfixhelpinforecordreindexCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.3 (2017-09-05)Fix failing to import properly.0.1.2 (2017-01-09)Fix Continuous Integration.Fix Code Coverage metrics.0.1.0 (2017-01-09)First release on PyPI.