Saravanan666/pypi-org huggingface dataset:
Package: pyroscope-beta
Pyroscope Python Integration --Beta--note: This is a beta release. It requires local compilation, might be
buggy and is frequently updated. For the initial implementation, find ithere. Please report anyissues.InstallationYou need the Rust toolchain to compile the library locally. To install
Rust:curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain stable -y 
export PATH=$PATH:/root/.cargo/binlibunwind8-dev is also required. For Ubuntu:apt-get install -y libunwind8-devBuilding/Insalling from PyPi packagepip install pyroscope_betaBuilding/Installing from sourceChange directory topyroscope_ffi/pythonand runmake installConfigurationConfiguration is similar to the old package except forapplication_name:import pyroscope_beta

pyroscope_beta.configure(
  application_name       = "python.app",
  server_address         = "http://localhost:4040",

  tags = {
    "key": "value",
  }
)Adding tagsTags passed to configure are global. To tag code locally, you can use:with pyroscope_beta.tag_wrapper({ "profile": "profile-1" }):
    // Tagged profile
Package: pyroscope-io
Pyroscope Python PackagePyroscope integration for PythonWhat is PyroscopePyroscopeis a tool that lets you continuously profile your applications to prevent and debug performance issues in your code. It consists of a low-overhead agent which sends data to the Pyroscope server which includes a custom-built storage engine. This allows for you to store and query any applications profiling data in an extremely efficient and cost effective way.How to install Pyroscope for Python Applicationspip install pyroscope-ioMinimal ConfigurationAdd the following code to your application. This code will initialize pyroscope profiler and start profiling:importpyroscopepyroscope.configure(application_name="my.python.app",# replace this with some name for your applicationserver_address="http://my-pyroscope-server:4040",# replace this with the address of your pyroscope server)Full ConfigurationOptionally, you can configure several parameters:importpyroscopepyroscope.configure(application_name="my.python.app",# replace this with some name for your applicationserver_address="http://my-pyroscope-server:4040",# replace this with the address of your pyroscope serverauth_token="{YOUR_API_KEY}",# optional, if authentication is enabled, specify the API keysample_rate=100,# default is 100detect_subprocesses=False,# detect subprocesses started by the main process; default is Falseoncpu=True# report cpu time only; default is Truenative=False# profile native extensions; default is Falsegil_only=True# only include traces for threads that are holding on to the Global Interpreter Lock; default is Truelog_level="info"# default is info, possible values: trace, debug, info, warn, error and criticaltags={"region":'{os.getenv("REGION")}',})TagsYou can add tags to certain parts of your code:# You can use a wrapper:withpyroscope.tag_wrapper({"controller":"slow_controller_i_want_to_profile"}):slow_code()ExampleCheck out thisexample python project in our repositoryfor examples of how you can use these features.
Package: pyrose
pyROSE is an easy to install and run python library of ROSE (Rank Ordering of Super-Enhancers)http://younglab.wi.mit.edu/super_enhancer_code.htmlIf you use this library for your analysis please cite the following papers:Warren A. Whyte, David A. Orlando, Denes Hnisz, Brian J. Abraham, Charles Y. Lin, Michael H. Kagey, Peter B. Rahl, Tong Ihn Lee and Richard A. Young, Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes, Cell 153, 307-319, April 11, 2013Jakob Lovén, Heather A. Hoke, Charles Y. Lin, Ashley Lau, David A. Orlando, Christopher R. Vakoc, James E. Bradner, Tong Ihn Lee, and Richard A. Young, Selective Inhibition of Tumor Oncogenes by Disruption of Super-enhancers, Cell 153, 320-334, April 11, 2013How to install?Install from PyPipip install pyroseInstall from the sourcepython setup.py installHow to run?pyrose –help
Package: pyrosenv
pyrosenvSet an environment for easy work with ROS in python without setting things upInstallationpip install pyrosenvUsageFor easy working with ROS in Python 3 in your IDE (Pycharm, VSCode, etc.) Do one or more of the following imports.
Actually after import pyrosenv you can import anything that you would import in ROS environemnt.# set things up and then importimportpyrosenvimportrospy# ...#import ros librariesfrompyrosenvimportrospyfrompyrosenvimportrosbagfrompyrosenvimportroslaunchfrompyrosenvimportrosgraphfrompyrosenvimportroslibfrompyrosenvimportgenpyfrompyrosenvimportcv_bridge# import std msgsfrompyrosenv.std_msgs.msgimportInt16frompyrosenv.std_msgs.msgimportBool# ...# import sensor msgsfrompyrosenv.sensor_msgs.msgimportPointCloud2msg=PointCloud2()frompyrosenv.sensor_msgsimportpoint_cloud2point_cloud2.read_points(msg)frompyrosenv.cv_bridgeimportCvBridge,CvBridgeError# ...Then you can just use rospy, rosbag, std_msgs, sensor_msgs, etc. as you would in ROS environment.
Package: pyrosim
pyrosimPyrosimis an open-source, user-friendly Python 3 library designed for simulating data related to vehicle routing, capacitated vehicle routing, and drone routing problems. Utilizing the Google API, Pyrosim extracts real distances and estimated times between locations, enhancing the accuracy and realism of simulations.Check documentation of thePyrosim.InstallationTo installPyrosim, simply use pip to download and install the library fromPyPI(Python Package Index). The library is at PyPI at this pagehttps://pypi.org/project/pyrosim.Install Pyrosim with the following command:pipinstallpyrosimFeaturesGeographical RealismIntegrate with geographical data sources or APIs to retrieve real-world maps, roads, and locations.Allow users to specify regions of interest, and generate routing problems based on actual geographical data.Customizable Parameters:Provide a wide range of parameters for users to customize, such as the number of locations, distribution of customers, vehicle capacities, time windows, and drone capabilities.Allow users to set constraints and preferences for their specific scenarios.Seasonal Variations:Introduce the concept of seasonal variations, where the characteristics of the routing problem (e.g., demand, road conditions) change based on seasons.Enable users to simulate how routing problems may differ during different times of the year.Traffic Conditions:Incorporate options to simulate varying traffic conditions throughout the day.Allow users to set peak hours, road closures, or traffic congestion parameters to make the generated data more dynamic.Dynamic Customer Behavior:Model dynamic customer behavior, such as changing demand patterns, cancellations, or urgency levels.Introduce randomness in customer behavior to simulate real-world uncertainties.Integration with External APIs:Allow users to integrate external APIs for additional data, such as weather conditions, special events, or road closures, that can impact routing decisions.Drone-Specific Parameters:Extend the data generation for Drone Routing Problems by including parameters specific to drones, such as flight range, recharge time, and restrictions on flying over certain areas.Data Validation:Implement mechanisms to validate generated data for consistency and realism.Provide feedback to users if the specified parameters result in unrealistic or impractical routing problems.Exporting to Standard Formats:Provide a visual preview of the generated data, allowing users to inspect the distribution of customers, road networks, and other relevant details before exporting.Documentation and Examples:Include comprehensive documentation with examples demonstrating how to use the data generation features effectively.Provide guidance on how users can incorporate the generated data into their own projects.UsageExamples here.DocumentationDocumentation sources here.LicenseMy license here.
Package: pyrosimple
pyrosimpleA overhauled Python 3 fork of thepyrocore
tools, for working with therTorrent client.Installationpipinstallpyrosimple# pip install 'pyrosimple[torque]' # Optional dependencies for using pyrotorqueSee thedocumentation for usage.
If you've used rtcontrol/rtxmlrpc before, you should feel right at home.What's the point of this?The pyrocore tools are great, but being stuck on python 2, along with
the complicated install procedure made integrating both the tools and
the code into other processes very painful.Differences from pyrocoreThe following lists are not exhaustive, and don't cover many of the
internal improvements and refactoring.Only supports python 3 and rTorrent 0.9.6+ (this includesrTorrent-PS,rTorrent-PS-CHandjesec/rtorrent)pypy is supported, but not as well testedSimpler poetry-based build system, with a single package to installPerformance improvements (faster templating and only fetching
required fields)Replaced Tempita withJinja2New featuresMulti-instance support for rtcontrol/rtxmlrpcSupport for JSON-RPC (currently only implemented byjesec/rtorrent)Actions to move torrents between paths, or torrents between hostsSeehttps://kannibalox.github.io/pyrosimple/migrate/for how to
migrate scripts to the new features.Legacy branchIf you just want to use the pyrocore tools on python 3 without all the
new features, you can use therelease-1.Xbranch (1.3 is the latest
release at time of writing).  These releases will only receive bug
fixes or changes to maintain compatibility with the original pyrocore
tools.
Package: pyrost
Current build statuspyrostPython Robust Speckle Tracking (pyrost) is a library for wavefront metrology
and sample imaging based on ptychographic speckle tracking algorithm. This
project takes over Andrew Morgan'sspeckle_trackingproject as an improved version aiming to add robustness to the optimisation
algorithm in the case of the high noise present in the measured data.The documentation can be found onRead the Docs.Read the open access scientific article about the technical details of the developed technique published inOptica Express.DependenciesPython3.6 or later (Python 2.x isnotsupported).GNU Scientific Library2.4 or later.LLVM's OpenMP library10.0.0 or later.FFTW3.3.8 or later.h5py2.10.0 or later.NumPy1.19.0 or later.SciPy1.5.2 or later.tqdm4.56.0 or later.InstallationWe recommendnotbuilding from source, but install the release via thecondamanager:conda install -c conda-forge pyrostThe package is available inconda-forgeon OS X and Linux.Also you can install the release frompypiwith the pip package installer:pip install pyrostThe source distribution is available inpypias for now.Installation from sourceIn order to build the package from source simply execute the following command:python setup.py installor:pip install -r requirements.txt -e . -vThat cythonizes the Cython extensions and builds them into/pyrost/bin.
Package: pyros-telemetry
TelemetryPyROS aimed telemetry client. This library gives definition of telemetry concepts:stream (logger, stream definition): TelemetryLogger, TelemetryStreamDefinition...client: TelemetryClientstorage: TelemetryStorage,and implementaions.StreamStream represents concept of well formed data (fixed len byte record) logged by source app, service or program;
that contains data of interest. Stream can be defined using subclass of TelemetryStreamDefinition:MQTTLocalPipeTelemetryLogger provides all-in-one client to start logging your data to local server over local unix pipe
(it is expected server to be already running locally). It uses MQTT to negotiate creation of stream from given definition.In telemetry-server package check SocketTelemetryServer class which allows you to get instance of TelemetryLogger
to start logging to locally running TCP server.Example:logger=MQTTLocalPipeTelemetryLogger('test-stream',host="172.24.1.185",port=1883)logger.add_byte('some-status')logger.add_double('x-coordinate')logger.add_double('y-coordinate')logger.init()# ...logger.log(1,55,0.0,0.0)# first argument is timestamplogger.log(1.23,0,2.1,0.0)logger.log(1.90,0,2.2,1.0)ClientClient provides way of accessing telemetry data. For instance:client=MQTTTelemetryClient(host=host,port=port,topic=topic)test_stream=Nonetest_stream_data=Nonedefreceive_stream_def(received_stream):globaltest_streamtest_stream=received_streamdefreceive_test_stream_data(received_records):globaltest_stream_datatest_stream_data=received_recordsclient.getStreamDefinition('test-stream',process_stream_def)whiletest_streamisNone:time.sleep(0.01)# second argument is start timestamp, third end timestamp and last callback method to deliver dataclient.retrieve(test_stream,0,time.time(),receive_test_stream_data)Client functions operate with callbacks. Methods would request data but result is delivered asynchronously.StorageStorage is mostly used internally by client and server implementations
Package: pyrostest
:fire: pyrostest :fire:=========The most :fire: lit :fire: ros testing framework.Pyrostest is a (more) pythonic framework for writing ros tests.## Why pyrostest?Writing rostests and rostunit tests is annoying. You have to write launch filesfor all of your tests and then do strange things in your unittests and thensomehow the rostests and unittests will communicate. To be honest, I never couldget rostest to work. So I built something better. `pyrostest` allows you to testyour ros nodes in pure python, no need for xml files.## UsageHere's a minimal example of a pyrostest test. Assume you have a ros node,`add_one` in the ros package `pkg`. The node subscribes to `input`, a Float64message, and published to `output`, another Float64, it adds one to the inputand publishes it. This would test that functionality```pythonfrom pyrostest import RosTest, launch_node, mock_nodefrom std_msgs.msg import Float64class TestAddOne(RosTest):@launch_node('pkg', 'add_one')def test_add_one(self):with self.mock_pub('/input', Float64, queue_size=0) as p:with self.check_topic('/output', Float64) as r:p.send(Float64(7))assert r.message.data == 8  # or self.assertEqual```This code will spin up an isolated instance of Roscore, as well as all of thenodes needed for testing. It will then publish the float 7, and wait until thedata has been recieved, and assert about its value.
Package: pyrostorage
API ClientClient for thedata curation APIGetting startedPrerequisitesPython 3.6 (or more recent)pipInstallationYou can clone and install the project dependencies as follows:$gitclonehttps://github.com/pyronear/pyro-storage.git$pipinstall-epyro-storage/client/.UsageImport the clientfrompyrostorageimportclientCreate a client object by handling him the API keysAPI_URL="http://pyronear-storage.herokuapp.com"CREDENTIALS_LOGIN="George Abitbol"CREDENTIALS_PASSWORD="AStrong Password"api_client=client.Client(API_URL,CREDENTIALS_LOGIN,CREDENTIALS_PASSWORD)Use it to query alerts:## Create a mediamedia_id=api_client.create_media().json()["id"]## Upload an image on the mediadummy_image="https://ec.europa.eu/jrc/sites/jrcsh/files/styles/normal-responsive/"\+"public/growing-risk-future-wildfires_adobestock_199370851.jpeg"image_data=requests.get(dummy_image)api_client.upload_media(media_id=media_id,media_data=image_data.content)## Create an annotationdummy_annotation="https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/"\+"master/imagenet-simple-labels.json"annotation_id=api_client.create_annotation(media_id=media_id).json()["id"]annot_data=requests.get(dummy_annotation)api_client.upload_annotation(annotation_id=annotation_id,annotation_data=annot_data.content)LicenseDistributed under the Apache 2.0 License. SeeLICENSEfor more information.DocumentationThe full project documentation is availableherefor detailed specifications. The documentation was built withSphinxusing athemeprovided byRead the Docs.
Package: pyrotation
pyrotationInterconversion of Euler angles, axis angles, quaternions, and rotation matrix.
Supportsnumpy.ndarrayandtorch.Tensor, and usesplum-dispatchto decorate functions of the same name.
In addition, it supportsarbitraryshapes.Installationpipinstallpyrotationconversionwe useplum-dispatchto decorate functions of the same name, so you can usepyrotation.conversionto convert between different representations.
We supportnumpy.ndarrayandtorch.Tensor, and the input can be of arbitrary shape.quaternionmatrixeuler_angleaxis_anglequaternion-quaternion_from_matrixquaternion_from_euler_anglequaternion_from_axis_anglematrixmatrix_from_quaternion-matrix_from_euler_anglematrix_from_axis_angleeuler_angleeuler_angle_from_quaternioneuler_angle_from_matrix-euler_angle_from_axis_angleaxis_angleaxis_angle_from_quaternionaxis_angle_from_matrixaxis_angle_from_euler_angle-importtorchfrompyrotation.conversionimportquaternion_from_matrix,euler_angle_from_matrix# convert a 10x3x3 rotation matrix to a quaternionR=torch.rand(10,3,3)quat=quaternion_from_matrix(R)euler_angle=euler_angle_from_matrix(R)print(quat.shape)# torch.Size([10, 4])print(euler_angle.shape)# torch.Size([10, 3])We also support conversions betweenso3andSO3,se3andSE3. You can useSO3_from_so3andSE3_from_se3to convertso3andse3toSO3andSE3,
respectively. Similarly, you can useso3_from_SO3andse3_from_SE3to convertSO3andSE3toso3andse3, respectively.
Package: pyRotd
pyRotdAcceleration response spectrum calculations implemented in Python.IntroductionSimple Python functions for calculating psuedo-spectral acceleration and
rotated psuedo-spectral acceleration. The response of the
single-degree-of-freedom oscillator is computed in the frequency domain along
with frequency-domain interpolation to accurately capture the high-frequency
characteristics.The calculation of the response spectrum is performed using frequency domain
transfer functions, as well as frequency domain interpolation methods to insure
that the time step of the motions is greater than 10 times the natural
frequency of the oscillator frequency. Two perpendicular ground motions can be
rotated to compute the response at various percentiles. For example, the
minimum, median, and maximum could be computed using percentiles of  0, 50,
and 100. The orientation of both the minimum and maximum percentile are
provided, but not orientation is provided for other percentiles because the
rotate spectral acceleration is not monotonically increasing.Installationpyrotdis available from the Python Cheese Shop:pip install pyrotdExampleSpectral accelerations may be computed for a single time series:osc_damping = 0.05
osc_freqs = np.logspace(-1, 2, 91)
spec_accels = pyrotd.calc_spec_accels(
    time_step, accels, osc_freqs, osc_damping)Rotated spectral accelerations may be computed for a pair of time series:rot_osc_resps = pyrotd.calc_rotated_spec_accels(
    time_step, accels_a, accels_b, osc_freqs, osc_damping)A more detailed example is inthisJupyter Notebook.CitationPlease cite this software using theDOI.History0.6.1 (2023-06-30)Tests np.version to accommodate change in keywords of np.percentile0.6.0 (2023-06-19)Added optimization based on Stewart et al. (2017) algorithmAdded support for computing rotated PGA, PGV, and PGD0.5.4 (2018-07-27)Added MANIFEST.in0.5.3 (2018-07-27)Fix packaging issues0.5.2 (2018-03-26)Bump version for PyPi conflict0.5.1 (2018-03-23)Added tests/ directoryAdded support for computing: SA, PSV, SV, and SD.0.5.0 (2017-06-15)Added support for Python 2.7Added support for multiprocessingChanged from using setup.cfg for metadata.0.4.6 (2017-04-19)Changed to use setup.cfgIncremented version several times due to issues releasing.0.4.1 (2017-04-11)Fix release via Travis.0.4.0 (2017-04-06)Changed to return numpy.recarrays0.3.0 (2017-04-05)Added exampleAdded support for typing0.2.0 (2017-02-20)Changed from camelCase to underscoresAdded PEER NGA West2 test casesFixed: numpy.array creation0.1.0 (2016-03-04)First release on PyPI.
Package: pyrotel
pyrotel 0.9.0vpyrotel is a library for telegram bots.Install and Update:pipinstallpyrotelSTART:frompyrotelimportClientapp=Client("API_TOKEN")asyncdefbot():formsginapp.on_message():ifmsg.text=="/start":app.send_message(msg.chat_id,"hello my dear.\nwelcome to my bot :)",reply_to_message_id=msg.message_id)Social Media:TELEGRAMGITHUB
Package: pyrotest
pyroTest GuidelinesPrecondition:System Requirements:Operating System : Ubuntu Server 18.04.5 LTS.
Processor (CPU)  : 1 or 2 CPU cores minimum. 64-bit CPU required, Intel or AMD.
Memory (RAM)     : 4GB RAM minimum
HDD (Storage)    : 30GB disk size minimum.Packages Requirements:There are certain preconditions which we need to take into consideration before start running
the automated regression suite. We need to install the below packages to satisfy the precondition requirements.For Ubuntu & Debian MachinesHow to Install Chrome Driver:wget https://chromedriver.storage.googleapis.com/2.41/chromedriver_linux64.zip
unzip chromedriver_linux64.zip
sudo mv chromedriver /usr/bin/chromedriver
sudo chown root:root /usr/bin/chromedriver
sudo chmod +x /usr/bin/chromedriver
sudo rm -fr chromedriver_linux64.zipHow to Install Gecko Driver:wget https://github.com/mozilla/geckodriver/releases/download/v0.26.0/geckodriver-v0.26.0-linux64.tar.gz
tar xvzf geckodriver-v0.26.0-linux64.tar.gz
sudo chmod 777 geckodriver
sudo mv geckodriver /usr/bin/How to install firefox Driver:wget https://ftp.mozilla.org/pub/firefox/releases/66.0.3/linux-x86_64/en-US/firefox-66.0.3.tar.bz2
 tar -xjf firefox-66.0.3.tar.bz2
 sudo rm -rf /opt/firefox
 sudo mv firefox/ /opt
 sudo mv /usr/bin/firefox /usr/bin/firefox_old
 sudo ln -s /opt/firefox/firefox /usr/bin/firefoxHow to create Virtual Environment:How to create 3-site topologyPrecondition to create 3-site topology in KVMBefore creating topology we need to create KVM hypervisor, We can create KVM hypervisor in below mentioned ways
1. Create a Hypervisor in Virtual Box/ESXi in private infrastructure
2. Create a Hypervisor in Cloud infrastructure using GCPAs of now we have automated the topology creation in GCP environment for 3-site.
Package: pyRothC
pyRothCPython version of The Rothamsted carbon model (RothC) 26.3.Documentation:Rothamsted RothC ModelSource Code:https://github.com/mishagrol/pyRothCpyRothc is a Python version of The Rothamsted carbon model (RothC) 26.3.RothC is a model for the turnover of organic carbon in non-waterlogged topsoil that allows for the effects of soil type, temperature, soil moisture and plant cover on the turnover process.Inspired by SoilR versionSoilR RothCRequirementsPython 3.7+SciPyNumPyPandasInstallation$pipinstallpyRothCExampleBelow is an example of how theRothCclass should be used. It needsmatplotliblibrary to be installed in
order to draw the graphs.importnumpyasnpimportmatplotlib.pyplotaspltfrompyRothC.RothCimportRothCTemp=np.array([-0.4,0.3,4.2,8.3,13.0,15.9,18.0,17.5,13.4,8.7,3.9,0.6])Precip=np.array([49,39,44,41,61,58,71,58,51,48,50,58])Evp=np.array([12,18,35,58,82,90,97,84,54,31,14,10])soil_thick=25#Soil thickness (organic layer topsoil), in cmSOC=69.7#Soil organic carbon in Mg/haclay=48#Percent clayinput_carbon=2.7#Annual C inputs to soil in Mg/ha/yrIOM=0.049*SOC**(1.139)# Falloon et al. (1998)rothC=RothC(temperature=Temp,precip=Precip,evaporation=Evp,clay=48,input_carbon=input_carbon,pE=1.0,C0=np.array([0,0,0,0,IOM]))df=rothC.compute()df.index=rothC.tfig,ax=plt.subplots(1,1,figsize=(6,4))df.plot(ax=ax)ax.set_ylabel('C stocks (Mg/ha)')ax.set_ylabel('Years')plt.show()TestingIf you need to run the test suite, first install the package in "editable" mode with thetestoptional dependencies:gitclonegit@github.com:mishagrol/pyRothC.gitcdpyRothC
pipinstall-e".[test]"Now you can run the tests by simply running this command:pytesttestsStructure of the RothC modelCredits:Theoretical Ecosystem Ecology group of the Max Planck Institute for BiogeochemistryEquations$$
\begin{aligned}
& \frac{d \boldsymbol{C}}{\mathrm{d} t}=I\left(\begin{array}{c}
\gamma \
1-\gamma \
0 \
0 \
0
\end{array}\right)
+\left(\begin{array}{ccccc}
-k_1 & 0 & 0 & 0 & 0 \
0 & -k_2 & 0 & 0 & 0 \
a_{3,1} & a_{3,2} & -k_3+a_{3,3} & a_{3,4} & 0 \
a_{4,1} & a_{4,2} & a_{4,3} & -k_4+a_{4,4} & 0 \
0 & 0 & 0 & 0 & 0
\end{array}\right)\left(\begin{array}{l}
C_1 \
C_2 \
C_3 \
C_4 \
C_5
\end{array}\right) \
&
\end{aligned}
$$Optional DependenciesMatplotlibLicenseThis project is licensed under the terms of the CC0 1.0 Universal license.