Saravanan666/pypi-org huggingface dataset:
Package: ivan-pro
Ivan - The Tenable Security Center Swiss Army KnifeA command-line tool which leverages the Tenable Security Center API to automate common tasks
in Cyber Exposure or Vulnerability Management.*** This tool is not an officially supported Tenable project ***

*** Use of this tool is subject to the terms and conditions identified below,
 and is not subject to any license agreement you may have with Tenable ***Installing IVANpip install ivan-proAuthenticating to Tenable Security Centerivan keys --a <your access key or username> --s <your secret key or password> --h <ipaddresss or FQDN>Are my Keys inputted correctly?In different terminals it can be a challenge to copy the keys to ivan since you can not be sure it copied correctly.Use the below commands to check your keysivan find query "select * from keys;"Alternatively, you could try entering your keys again using the '-clear' command to see what is being copied to the screen.ivan keys -clearUpdate the Ivan Databaseivan update vulnsQuery the database directlyivan find query "select * from vulns;"Show me the assets which have docker installed and the containers runningivan find query "select asset_ip, plugin_id, output from vulns where plugin_id='93561';"How many critical vulns do we have?ivan find query "select count(*) from vulns where severity='critical';"Export data into a CSVivan export query "select asset_ip, plugin_name, description, solution, severity, score from vulns where score <='7';"
Package: ivao
What is IVAO?International Virtual Aviation Organisation (IVAO)
is a non-profit association which operates a free-of-charge online flight-simulation network. Following free registration users can connect to the IVAO Network (IVAN) either as a virtual air traffic controller or as a virtual pilot and engage and interact with each other in a massively multiplayer environment utilising real-world aviation procedures, phraseology and techniques.Library Syntax structreivao
├──raw
|
├──to_json
|
├──servers
|
├──voiceServers
|
├──clients
|  ├──pilot
|  |  ├──raw
|  |  └──callsign
|  ├──atc
|  |   ├──raw
|  |   ├──callsign
|  |   └──atis
|  ├──observer
|  |   ├──raw
|  |   └──callsign
|  └──followMe
|
└──connections
   ├──total
   ├──supervisor
   ├──atc
   ├──observer
   ├──pilot
   └──worldTourUsage/ExamplesATC Callsign ExampleReturn all callsign of ATC in IVAOfrom ivao import ivao

print(ivao.clients.atc.callsign())ATC ATISReturn ATIS of ATC in IVAOfrom ivao import ivao
print(ivao.clients.atc.atis(callsign="VTBD_TWR"))Pilot Callsign ExampleReturn all callsign of Pilot in IVAOfrom ivao import ivao

print(ivao.clients.pilot.callsign())
Package: iva-tpu
IVA TPU Python APIMain entitiesTPUDeviceTPUDevice is a device handleTPUProgramTPUProgram contains TPU instructions and weigths dataTPUInferenceTPUInference contains input/output dataExampleimportasyncioimportnumpyasnpfromiva_tpuimportTPUDevice,TPUProgram,TPUInferencefromiva_applications.resnet50importimage_to_tensorfromiva_applications.imagenetimporttpu_tensor_to_classesfromPILimportImageimage=Image.open('ILSVRC2012_val_00000045.JPEG')tensor=image_to_tensor(image)device=TPUDevice()program=TPUProgram("resnet50.tpu")#default TPUProgramInfo is totally finedevice.load_program(program)inference=TPUInference(program)inference.load_tensors([tensor])status_future=device.load_inference(inference)# device returns future for inference statusevent_loop=asyncio.get_event_loop()status=event_loop.run_until_complete(status_future)assertstatus.is_success# check that there is no errors during inferenceoutput=inference.get_tensors()# get resultstpu_tensor_to_classes(output[0],top=1)TPU Dictionary interface...
program = TPUProgram("resnet50.tpu")
inference = TPUInference(program)
inference.load_tensors_dict({"Placeholder:0": tensor})
...
assert status.is_success
output = inference.get_tensors_dict()
tpu_tensor_to_classes(output["logits:0"], top=1)TPU Blocking interfacestatus = device.load_inference_sync(inference) #would block until completionTPU Raw buffer examplesimport asyncio
from iva_tpu import TPUDevice, TPUProgram, TPUInference
program = TPUProgram("omega_program_dnn_quant_3.0.0.tpu")
device = TPUDevice()
device.load_program(program)
inference = TPUInference(program)

with open("f.bin", "rb") as f:
    buf=f.read()

inference.load_raw([buf])
asyncio.get_event_loop().run_until_complete(device.load_inference(inference))
outputs = inference.get_raw()

for i in range(3):
  o = outputs[i]
  with open(f"o{i}.bin", "wb") as f:
    f.write(o)
Package: ivav
Index view of Amplitude Vectorsivav is tool to simulate quantum circuit using indices of the amplitude vectors. ivav is motivated fromBernstein’s Invited Talk at Indocrypt 2021.Getting StartedInstall Python and virtual env. Then follow the below steps.git clone https://github.com/Gopal-Dahale/ivav.git
cd ivav
virtualenv venv
.
.
.
.venv/bin/activate
pip install -r requirements.txt
pip install -e .pip install ivavTestingTo run tests, run the following command in your virutalenvpip install -e .[dev]
pytest
Package: ivcap-df
ivcap-dfA Python Library for interacting with the IVCAP Data FabricDefining SchemasI need to define a much simpler example. Maybe along the lines of the classical Elementary School
experiment of growing grass seeds with different amount of water added every so often -
with maybe an imge taken as well for every observationfrom ivcap_df import Schema, IdColumn, RefColumn, Column, ColType, Connector

common_ns = 'common'
geo_location_s = Schema('geo_location', common_ns, 'The geographic coordinates of a place or event', [
    Column('elevation', ColType.FLOAT64, description="The elevation of a location (WGS 84). Value alone should be assumed to be in meters.", required=False),
    Column('latitude', ColType.FLOAT64, description="The latitude of a location. For example 37.42242 (WGS 84)."),
    Column('longitude', ColType.FLOAT64, description="The longitude of a location. For example -122.08585 (WGS 84)."),
])

geo_line_s = Schema('geo_line', common_ns, 'The geographic coordinates of a line defined by a from and to locations', [
    RefColumn('from', geo_location_s, description='Location of the beginning of the line'), 
    RefColumn('to', geo_location_s, description='Location of the end of the line'), 
])

image_s = Schema('image', common_ns, 'An image',  [
    Column('url', ColType.STRING, format='uri'),
    Column('description', ColType.STRING, required=False),
    Column('format', ColType.STRING, required=False),
    Column('width', ColType.INT64, description="Image width in pixels", unit='pixel', required=False),
    Column('height', ColType.INT64, description="Image height in pixels", unit='pixel', required=False),
    Column('dateTime', ColType.STRING, description='The date and time this image was taken (in ISO 8601 date format).', format='date-time', required=False),
    RefColumn('location', geo_location_s, required=False), 
])To visualise the schema relationships, execute the following in a Jupyter Notebook:schemas = {
    'geo_location_s': geo_location_s,
    'geo_line_s': geo_line_s,
    'image': image_s,
}

Schema.to_dot(schemas.values(), fontSize=10)To persist the schema definition to IVCAP, run the following:from ivcap_prov import Connector

ivcap_params = {"type": "ivcap",  ...}
db = Connector.create(**ivcap_params)
for name, schema in schemas.items():
    print(name)
    schema.persist(db, verbose=False)
print("...schema done")

db.close()Loading DataFirst we will need to load the respective schemas:from ivcap_prov import Connector,Schema

bg_ns = 'blue_growth'
ivcap_ns = 'ivcap'

db = Connector.create(**ivcap_params)
qimage_s = Schema.load(db, 'quadrat_image', bg_ns)
image_artifact_s = Schema.load(db, 'image_artifact', ivcap_ns)
Package: ivcap-sdk-service
ivcap_sdk_serviceSDK library for building services for the IVCAP platformInstallationpipinstallivcap_sdk_serviceUsageBelow is the obligatoryhello worldservice which simply writes a
few messages to logging.fromtypingimportDictfromivcap_sdk_serviceimportService,Parameter,PythonWorkflow,Type,register_serviceimportloggingSERVICE=Service(name="HelloWorld",description="Simple service which does a few simple things",parameters=[Parameter(name="msg",type=Type.STRING,description="Message to echo"),Parameter(name="times",type=Type.INT,default=2,description="Times to repeat"),],workflow=PythonWorkflow(min_memory='2Gi'))defhello_world(args:Dict,logger:logging):foriinrange(args.times):logger.info(f"({i+1}) Hello{args.msg}")register_service(SERVICE,hello_world)For more useful examples see theexamplesdirectory.ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licenseivcap_sdk_servicewas created by Max Ottmax.ott@csiro.au, Tim Erwintim.erwin@csiro.au. SeeLICENSEfor licensing terms.Creditsivcap_sdk_servicewas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.DevelopmentSetupconda create --name ivcap_service python=3.7 -y
conda activate ivcap_service
pip install poetryAdding dependenciesFor testing:poetry add --group dev _lib_name_
Package: ivchepot
Ivchepot is a simple TCP honeypot written in Python 3.Source codehttps://github.com/Ivche1337/IvchepotDocumentationhttps://ivchepot.rtfd.ioDocumentation can be found in thedocs/directory
as reStructuredText files.Build the docs by runningmakein thedocs/directory, then viewingdocs/_build/html/index.html.AuthorIvche1337 <ivchepro@gmail.com>CreditsThis project was done just for learning purposes
and i am not the original creator  of it.
Original creator:https://www.youtube.com/DevDungeon
Package: ivcorrection
IV curve correction tools (IVcorrection)This package provides the following purposes:Calculate IV curve correction coefficients based on IEC 60891:2021 standardPerform IV curve correctionAn interactive page for this tool will be online soon!The package is still under active development and plan to be integrated withpvlib.If there is any problem, please feel free tocontact us!InstallationInstall with pippip install ivcorrectionWhat is I-V curve correctionI-V curve correction refers to correct the I-V curve measured under different environmental condition to an identical one. Generally, the standard test condition (STC) is adopted as the target condition.Correction methodsThis tool can perform I-V curve correction usingProcedure 1, 2, 4 of IEC 60891:2021 [1]and aproposed Procedure dynamic. The determination of correction coefficients follow the IEC 60891:2021 standard [1].How to use this toolThe calculation proceeds with the following steps:Provide PV module parametersCalculate correction coefficientsCorrect example I-V curve(s) (optional)An example is provided inExample_IV_correction_CEC.ipynb.Who we areWe are a collection of national lab researchers funded under the Durable module materials consortium (DuraMAT).References[1] IEC 60891, Photovoltaic devices - Procedures for temperature and irradiance corrections to measured I-V characteristics, 2021.AuthorsBaojie Li (LBNL)
Package: ivenv
ivenvis an Apache2 licensed Python module for interactive virtual
environments.Thevirtualenvpackage had a clever script calledactivate_this.pywhich
when executed in a Python shell would “activate” the virtual
environment. Sadly,venvlacks this feature soivenvadds it back.Theivenvpackage also adds support for “%activate” and “%deactivate” magic
commands in IPython shells and Jupyter notebooks. This provides a simpler
alternative to installingipykernelin the destination virtual environment
and adding the kernel to Jupyter.FeaturesPure-PythonIPython SupportJupyter SupportDeveloped on Python 3.8Tested on CPython 3.6, 3.7, 3.8 and PyPy, PyPy3Tested using GitHub ActionsQuickstartInstallingivenvis simple withpip:$ pip install ivenvYou can access documentation in the interpreter with Python’s built-in help
function:>>>importivenv>>>help(ivenv)>>>help(ivenv.activate)>>>help(ivenv.deactivate)TutorialTheivenvmodule provides two functions for managing virtual environments:>>>fromivenvimportactivate,deactivateTheactivatefunction accepts a path to a virtual environment directory and
“activates” that virtual environment within the Python shell.>>>activate('path/to/venv/directory')Thedeactivatefunction takes no arguments and “deactivates” the virtual
environment within the Python shell.>>>deactivate()It’s also possible to useivenvfrom IPython or Jupyter notebooks. To begin,
load theivenvextension:%load_extivenvOnce the extension is loaded, the “magic” commands:%activateand%deactivatemay be used just as their corresponding functions.%activatepath/to/venv/directory%deactivateReferenceivenv Documentationivenv at PyPIivenv at GitHubivenv Issue TrackerLicenseCopyright 2020 Grant JenksLicensed under the Apache License, Version 2.0 (the “License”); you may not use
this file except in compliance with the License.  You may obtain a copy of the
License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied.  See the License for the
specific language governing permissions and limitations under the License.
Package: iversions
iversionsA cell magic command for IPython/Jupyter to display name and version of
all imported (non built-in) modules in the current session.Installation$pipinstalliversionsUsageIn[1]:%load_extiversionsIn[2]:importnumpyasnp...:importpandasaspd...:importtensorflowastf...:importos...:importsysIn[3]:%iversionsnumpy1.13.1pandas0.20.3tensorflow1.3.0Python3.6.2[SunAug27,201713:50:43]In[4]:Inspired by amazing similar projects:version_informationwatermarkTODOAdd a feature to output torequirements.txtKNOWN ISSUESNon-module imports do not work e.g.from X import Y
Package: ivis
ivisImplementation of the ivis algorithm as described in the paperStructure-preserving visualisation of high dimensional single-cell datasets. Ivis is designed to reduce dimensionality of very large datasets using a siamese neural network trained on triplets. Both unsupervised and supervised modes are supported.InstallationIvis runs on top of TensorFlow. To install the latest ivis release from PyPi running on the CPU TensorFlow package, run:# TensorFlow 2 packages require a pip version >19.0.
pip install --upgrade pippip install ivis[cpu]If you have CUDA installed and want ivis to use the tensorflow-gpu package, runpip install ivis[gpu]Development version can be installed directly from from github:git clone https://github.com/beringresearch/ivis
cd ivis
pip install -e '.[cpu]'The followingoptional dependenciesare needed if using the visualization callbacks while training the Ivis model:matplotlibseabornUpgradingIvis Python package is updated frequently! To upgrade, run:pip install ivis --upgradeFeaturesScalable:ivis is fast and easily extends to millions of observations and thousands of features.Versatile:numpy arrays, sparse matrices, and hdf5 files are supported out of the box. Additionally, both categorical and continuous features are handled well, making it easy to apply ivis to heterogeneous problems including clustering and anomaly detection.Accurate:ivis excels at preserving both local and global features of a dataset. Often, ivis performs better at preserving global structure of the data than t-SNE, making it easy to visualise and interpret high-dimensional datasets.Generalisable:ivis supports addition of new data points to original embeddings via atransformmethod, making it easy to incorporate ivis into standard sklearn Pipelines.And many more! Seeivis readmefor latest additions and examples.Examplesfrom ivis import Ivis
from sklearn.preprocessing import MinMaxScaler
from sklearn import datasets

iris = datasets.load_iris()
X = iris.data
X_scaled = MinMaxScaler().fit_transform(X)

model = Ivis(embedding_dims=2, k=15)

embeddings = model.fit_transform(X_scaled)Copyright 2022 Bering Limited
Package: ivi-sdk
IVI Python SDKGeneral infoTheIVI Python SDKis a minimal wrapper layer around the underlyingIVI gRPC Streams API.  The primary module isivi_sdk/ivi_client.py.The SDK is installable viapipand should be usable with any released Python >= 3.6.  The includedsetup.pywill download, generate, and install the necessary gRPC and protobuf dependencies, as well as fetch protos and run theprotoccode generator.TheIVIClientmakes use ofasyncioandgrpc.aiofor managing stream processing via coroutines, and also instantiates the unary RPC stubs for convenience.  This SDK doesnotwrap the unary RPC calls - users should refer directly to the proto files and interact directly with the generated protobuf and gRPC Python code for unary calls as documented in the Readme.io guide.  Examples are available there as well as intests/example.py.  However, unlike the example code, it is strongly recommend for users to schedule any unary RPC calls and processing within their own coroutine wrappers, to avoid stalling the main Python thread.It is strongly suggested to set theasycioevent loop global exception handler viaset_exception_handlerand fixing or reporting any errors found therein.  Failing to do so may lead to memory leaks through unbounded accumulation of unhandled errors.pytestbased unit tests for theIVIClientstream processing are contained intests/test_ivi_client.pyscript.  This will also require thepytest-asynciopackage to be installed.Recommended further reading beyond the RPC guide:asyncioprotobuf generated codegRPC PythonandgRPC AsyncIO
Package: ivisit
IVisit 0.60Ein Interaktives VIsuelles SImulation Tool
für Python-basierte generische Modell-Simulation und Optimierung von Modell-Parametern
(c) Andreas Knoblauch, HS Albstadt-SigmaringenAktivierung/SichtbarkeitDer folgende Shell-Befehl macht das IVisit Verzeichnis global sichtbar,
indem es vorne in PYTHONPATH und PATH gestellt wird (ggf. auch in .bashrc einfügen...):source /home/ak/projects/experimental/IVisit/python/BashSrcVersionskontrolle mit Git/GitHubUnter Versionskontrolle stehen die folgenden Dateien/Verzeichnisse:
python: Python Sources von IVisit
documentation: Kurz-Doku zu IVisit
demos: Demo-Skripte zu IVisit
design: Entwurf-Dokumente zu IVisit
README.txt: this file
ToDo.txt: Weitere PlanungNicht unter Versionskontrolle stehen:
shipping: Packages prepared for shippingEinrichten eines GitHub Repositories (wie hier bei IVisit):Im Basisverzeichnis (IVisit): git initIn Unterverzeichnissen geeignete .gitignores definieren (Kopiere von alten Repos)git statusAlle relevanten Dateien zum Repository hinzufügen: git add ...Initial commit: git commit -m "Some Message"Auf GitHub ein Remote-Repository einrichten: Einloggen, "New Rpository" etc.
--> HTTPS-Adresse kopieren (z.B.https://github.com/aknoblauch/IVisit.git)Link local repository to GitHub repository: git remote add originhttps://github.com/aknoblauch/IVisit.gitPush to GitHub: git push origin masterPackaging:see                        :https://packaging.python.org/en/latest/tutorials/packaging-projects/update build system        : python3 -m pip install --upgrade build
build package              : python3 -m build
upload package to test-repo: python3 -m twine upload --repository testpypi dist/*
upload package to PyPI     : python3 -m twine upload dist/*
install from test-repo     : pip3 install --index-urlhttps://test.pypi.org/simple/--no-deps ivisit
install from PyPI          : pip3 install ivisit
upgrade from PyPI to latest version: pip3 install --upgrade ivisit
Package: ivisualizer
i-visualizer (innovata visualizer)##============================================================Description.##============================================================Python에서 사용가능한 모든 시각화 모듈들을 포함하여 제공한다.
각각의 프로젝트에서 각 공식 라이브러리를 직접 사용하는 대신, 본 패키지가 그들의 wrapper로써 역할을 한다.##============================================================IDE##============================================================##============================================================Main Packages.##============================================================matplotlibbokeh
Package: ivlam
Python Wrapper for ivlamThis is a python wrapper of the Fortran code of Russell's Lambert Solver.Installpip install ivlamUsageInitializefromivlamimport*infoload=ivlam.initialize(-1)if(infoload!=0):print('Error in ivlam_initialize')Solve the Problemr1vec=np.array([1.0,2.0,3.0])r2vec=np.array([2.0,-3.0,-4.0])tof=450.0prograde=Truedirection=ivlam.getdirection(prograde,r1vec,r2vec)dimensionV=10v1vec,v2vec,uptonhave,inforeturnstatusn,infohalfrevstatus=ivlam.thrun(r1vec,r2vec,tof,direction,dimensionV,dimensionV)if(inforeturnstatusn!=0):print('Error in ivlam_thrun')if(infohalfrevstatus!=0):print('This example is very close to the nPi transfer')print(v1vec[:,dimensionV-uptonhave:dimensionV+uptonhave+1])print(v2vec[:,dimensionV-uptonhave:dimensionV+uptonhave+1])LicenseGNU General Public License v3 or later (GPLv3+)Reference[1] Russell, Ryan P., "On the Solution to Every Lambert Problem,"
Celestial Mechanics and Dynamical Astronomy, Vol. 131, Article 50, 2019, pp. 1-33,https://dx.doi.org/10.1007/s10569-019-9927-z[2] Russell, Ryan P., "Complete Lambert Solver Including Second-Order Sensitivities,"
Journal of Guidance, Control, and Dynamics, accepted 2021,https://doi.org/10.2514/1.G006089
Package: ivoire
ivoireis anRSpec-like testing framework for
Python. It aims to bring a few minor constructs over to Python in a way that
isn’t overwhelmingly disruptive or counterculture.In case you’ve never heard of RSpec, it’s a RubyBDDframework
that is fairly widely used, and whose tests have a style unique from xUnit’s.InstallationIvoire is onPyPIand can be installed
viapip install ivoire(or via your preferred installation method).At this point you should consider Ivoire to be experimental, and there are
likely plenty of bugs to address, so please file them as you run into them on
theissue tracker.A Small ExampleTo write specs using Ivoire, simply import and useivoire.describe. You can
then execute the spec using the includedivoiretest runner.Here’s an example of what a specification looks like.fromivoireimportdescribe,contextclassCalculator(object):defadd(self,x,y):returnx+ydefdivide(self,x,y):returnx/ywithdescribe(Calculator)asit:@it.beforedefbefore(test):test.calc=Calculator()withit("adds two numbers")astest:test.assertEqual(test.calc.add(2,4),6)withit("multiplies two numbers")astest:test.assertEqual(test.calc.multiply(2,3),6)withcontext(Calculator.divide):withit("divides two numbers")astest:test.assertEqual(test.calc.divide(8,4),2)withit("doesn't divide by zero")astest:withtest.assertRaises(ZeroDivisionError):test.calc.divide(8,0)You can find this example atexamples/calculator_spec.py, alongside a few
others.After installing Ivoire, running the example above withivoire examples/calculator_spec.pyshould produce:If you’d like a more verbose output, try passing the-vcommand line flag.At some point in the (hopefully very near) future, when I’ve sorted out an
import hook, Ivoire will also be able to be run asivoire transform `which nosetests`--testmatch='(?:^|[\b_\./-])[Ss]pec',
which will transform specs automatically into normalunittest.TestCases.
Work on this is in progress.Running the Test SuiteIvoire’s test suite is currently written mostly in itself, but it still has a
small section that is written using the standardunittesttest cases.You can run Ivoire’s test suite by runningtoxin the root of the
repository checkout after installingtoxvia your package manager or withpip install tox. This will run both parts of the suite.ContributingI’m Julian Berman.You can find me on Freenode in#python-testingand various other channels
(nick:tos9) if you’d like to chat.Ivoire is developed onGitHub.Feel free to fork and submit patches or feature requests. Your contributions
are most welcome!If you’d like the best chance for them to be merged quickly try to include
tests with your pull request, and adhere to general Python coding standards and
your own common sense :).