Saravanan666/pypi-org huggingface dataset:
Package: coregeist
bitgeistbitgeist is a Python library to convert deep neural network models into c source code.InstallationUse the package managerpipto install bitgeist.pipinstallcoregeistUsageimportcoregeistascg# transform a pytorch model to c codeprint(cg.transform(model))Example:...importcoregeistascg...classMnistModel(nn.Module):def__init__(self):super(MnistModel,self).__init__()inputs=28*28hidden=120output=10self.l1=cg.Linear(inputs,hidden,downsample=downsample)self.action=nn.PReLU(hidden)self.l2=cg.Linear(hidden,output,downsample=downsample)defforward(self,x):out=self.l1(x)out=self.action(out)out=self.l2(out)returnoutmodel=MnistModel().to(device)# train the model...print(cg.transform(model))# print or write to file ......ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: coregen
CoregenThis distribution’s development is in alpha state.
Files location / package structure is prone to changes until v1.0.0 is released.This library does not support py2Free software: MIT licenseDocumentation:https://coregen.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-06-07)First release on PyPI.
Package: core-gps-visualization-app
GPS Visualization feature for the curator core project.Configuration1. Add “core_gps_visualization_app” to your INSTALLED_APPS setting like thisINSTALLED_APPS=[..."core_gps_visualization_app",]2. Include the core_gps_visualization_app URLconf in your project urls.py like thisurl(r'^',include("core_gps_visualization_app.urls")),
Package: corehttp
Core HTTP shared client library for Pythoncorehttpprovides shared exceptions and modules for Python SDK client libraries.Getting startedTypically, you will not need to installcorehttp, as it will be installed when you install one of the client libraries using it.TransportsTo usecorehttp, you will need to choose a transport implementation.corehttpprovides the following transports:Synchronous transports:RequestsTransport- A synchronous transport based on theRequestslibrary.HttpXTransport- An synchronous transport based on theHTTPXlibrary.Asynchronous transports:AioHttpTransport- An asynchronous transport based on theaiohttplibrary.AsyncHttpXTransport-  An asynchronous transport based on theHTTPXlibrary.Each transport has its own dependencies, which you can install using thecorehttpextras:# Install individually.pipinstallcorehttp[requests]pipinstallcorehttp[aiohttp]pipinstallcorehttp[httpx]# Install multiple.pipinstallcorehttp[requests,httpx]If no transports are specified,corehttpwill default to usingRequestsTransportfor synchronous pipeline requests andAioHttpTransportfor asynchronous pipeline requests.ContributingThis project welcomes contributions and suggestions. Most contributions require
you to agree to a Contributor License Agreement (CLA) declaring that you have
the right to, and actually do, grant us the rights to use your contribution.
For details, visithttps://cla.microsoft.com.When you submit a pull request, a CLA-bot will automatically determine whether
you need to provide a CLA and decorate the PR appropriately (e.g., label,
comment). Simply follow the instructions provided by the bot. You will only
need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information, see theCode of Conduct FAQor contactopencode@microsoft.comwith any
additional questions or comments.Release History1.0.0b4 (2024-02-23)Other ChangesRelax type checking inPipelineconstructors to only check that each user-supplied policy object has either asendmethod or both anon_requestandon_responsemethod. This allows for more flexible policy implementations.#342961.0.0b3 (2024-02-01)Features AddedSupport tuple input forfilesvalues tocorehttp.rest.HttpRequest#34082Support simultaneousfilesanddatafield entry intocorehttp.rest.HttpRequest#340821.0.0b2 (2023-11-14)Features AddedAdded the initial implementation of the HTTPX transport.#32813Breaking ChangesRemovedrequestsas a default dependency ofcorehttp. This is now an "extras" dependency and can be installed viacorehttp[requests].#32813Renamed theaioextra toaiohttp.#32813Bugs FixedFixed an issue withmultipart/form-datain the async transport wheredatawas not getting encoded into the request body.#32473Fixed an issue withconnection_verify,connection_cert, andconnection_timeoutnot being propagated to underlying transports.#33057Fixed an issue with theaiohttptransport not using SSL by default.#33057Other ChangesAdded extras forhttpx.#328131.0.0b1 (2023-10-18)Initial Release
Package: core-https
CORE-HTTPThis project/library contains common elements related to HTTP & API services...Execution EnvironmentInstall librariespip install --upgrade pip 
pip install virtualenvCreate the Python Virtual Environment.virtualenv --python=python3.11 .venvActivate the Virtual Environment.source .venv/bin/activateInstall required libraries.pip install .Check tests and coverage...python manager.py run-test
python manager.py run-coverage
Package: core-humanresources
CORE HUMAN RESOURCESIt is a module of the Research CORE ERM which implements the models related to human resources.People private infoContracts proposalsContractsInstallationpipinstallcore-humanresourcesEnterprise Resources Management- Research CORECORE is an ERM platform developed in within a research environment and aims to automatize
internal management processes, like human resources, orders, budgets, suppliers, funds, equipment \rooms accesses and maintenance and publications.The project is distributed as an open-source project to facilitate the adaptation of the
ERM to each institution's needs.
Package: coreir
Ultralight Python bindings forcoreirusing ctypes.pip install coreirThe Python package comes with a wheel that contains a pre-built CoreIR binary supporting manylinux and MacOS.  If the package finds a pre-existing installation of CoreIR (e.g. compiled from source), it will default to using that rather than the shipped binary (this enables you to override the package binary if, for example, you're using a development version to test a new feature).CHANGELOGDevelopment SetupInstall a local working copy to your python packages usingpip install -e .NOTE:When working with an editable link, as of 7/31/19,pip uninstall coreirwill not correctly remove the installed script (seehttps://github.com/pypa/pip/issues/5997), a workaround is to remove the scrip
manually when uninstalling, (e.g.rm ~/miniconda3/bin/coreir)To run the testspip install pytest
pytest
Package: corejet.jira
CoreJet test runner JIRA integrationThis package provides a requirements catalogue source forcorejet.testrunnerthat can fetch requirements from JIRA.To use it, make sure it is installed in the working set of the testrunner.
If using Buildout, you can do this with:[test]
recipe = corejet.testrunner
eggs =
    corejet.jira
    <other packages>
defaults = ['--auto-color', '--auto-progress']Here is an example command line invocation:./bin/test -s corejet.core --corejet="jira,url=https://acme.jira.com,username=corejet,password=secret,project=Acme Corp,filter=10151,pointsField=10060,epicField=10061,acceptanceCriteriaField=10088"The--corejetoption must start withjira,followed by a set of
parameters that indicate how to connect to JIRA. The parameters are:url=<url>URL of JIRA instanceusername=<username>username to use to connectpassword=<password>password to use to connectproject=<name>Name of projectfilter=<id>Numeric id of filter that returns storiespointsField=<id>Numeric id of field containing story pointsepicField=<id>Numeric id of field indicating epic for a storyacceptanceCriteriaField=<id>Numeric id of field containing acceptance criteria (scenarios)This presumes JIRA is set up with a filter that returns all stories you want
to include (e.g. return all valid issues of typeStoryin the project, if
using GreenHopper). The various field ids describe the fields that provide
story points, epic/theme (either a string or a reference to another issue),
and a field with acceptance criteria.The first two are standard GreenHopper fields. The acceptance criteria field
must be added manually. It should be a plain text field containing scenarios
in simple Gherkin syntax, e.g.:Scenario: First scenario
Given a precondition
  And another precondition
When something happens
  And something else happens
Then a result is expected
  And another result is expected

Scenario: Second scenario
Given another precondition
When something else happens
Then a different result is expectedThe parser is relatively forgiving, but note:The parser is case-insensitiveZero or more scenarios may be presentScenarios must start with “Scenario: “ followed by a nameThe “Given” clause is optional, but must come first in a scenarioThe “When” clause is required, and must come before the “Then” clauseThe “Then”” clause is also requiredAn “And” clause can come after any “Given”, “When” or “Then”, but not
first.Changelog1.0a3Fix broken package1.0a1First release
[optilude]
Package: corejet.recipe.testrunner
IntroductionThis package provides a buildout recipe based onzc.recipe.testrunnerto
install a test script forcorejet.testrunner.UsageIn your buildout, add a part like this:[buildout]
parts =
    ...
    test

...

[test]
recipe = corejet.recipe.testrunner
eggs =
    my.package
defaults = ['--auto-color', '--auto-progress']The recipe accepts the same options aszc.recipe.testrunner, so look at
its documentation for details.When buildout is run, you should have a script inbin/testand a directoryparts/test.To run the tests, use thebin/testscript. If you pass the--xmloption, test reports will be written toparts/test/testreportsdirectory:$ bin/test --xml -s my.packageIf you are using Hudson, you can now configure the build to publish JUnit
test reports for<buildoutdir>/parts/test/testreports/*.xml.To output a CoreJet report, do:$ bin/test --corejet="file,path/to/corejet/file.xml" -s my.packageThe CoreJet report and output XML file will be placed inparts/test/corejet. You can combine--xmland--corejet.The example above uses thefileCoreJet repository source, which expects
to find a CoreJet XML file at the path specified after the comma.Changelog1.0.0 (2012-05-26)Copied recipe from corejet.testrunner to fix the recipe requiring lxml
[datakurre]
Package: corejet.robot
Robot Framework support for CoreJetA work in progress. Has rough edges. Depends onrobotsuite.Is auto-imported bycorejet.testrunner >= 1.0.1just before
the generation of CoreJet test report (until a better way for
plugins is discovered).
Provides IStory-adapter forrobotsuite’sRobotTestCaseto do the magic.Generating test skeletonscorejet.robotships with an XSLT stylesheet for generating test skeletons
for Python unittest. If you are using buildout, you can install a helper
script for executing the XSLT-transformation with:[corejet2robot]
recipe = zc.recipe.egg
eggs = corejet.robot
scripts = corejet2robotAnd execute it with:bin/corejet2robot path/to/corejet.xmlTrybin/corejet2robot--helpfor more information.Changelog0.5.0 (2012-07-22)Fixed to support the latestrobotsuite.Fixed to prepend story level steps into scenarios to allocorejet.testrunnerto count them correctly.0.4.0 (2012-05-31)Fixed test id and title metadata to default to empty string instead of None.0.3.0 (2012-05-30)Fixed the XSLT and the adapter to use ‘Meta: Title’ instead to ‘Meta: Name’
to be more coherent with @story-decorator.0.1.0 (2012-05-30)Proof of concept.
Package: corejet.testrunner
IntroductionThis package provides an extension to the test runner to the one that ships
withzope.testrunner.It is based on (and can be used as a wholesale replacement for),collective.xmltestreport.The test runner is identical to the one inzope.testrunner, except:it is capable of writing test reports in the XML format output by JUnit/Ant.
This allows the test results to be analysed by tools such as the
Hudson/Jenkins continuous integration server.it can output reports in the CoreJet XML format - seecorejet.coreUsageIn your buildout, add a part like this:[buildout]
parts =
    ...
    test

...

[test]
recipe = corejet.recipe.testrunner
eggs =
    my.package
defaults = ['--auto-color', '--auto-progress']The recipe (corejet.recipe.testrunner) accepts the same options aszc.recipe.testrunner, so look at its documentation for details.When buildout is run, you should have a script inbin/testand a directoryparts/test.To run the tests, use thebin/testscript. If you pass the--xmloption, test reports will be written toparts/test/testreportsdirectory:$ bin/test --xml -s my.packageIf you are using Hudson, you can now configure the build to publish JUnit
test reports for<buildoutdir>/parts/test/testreports/*.xml.To output a CoreJet report, do:$ bin/test --corejet="file,path/to/corejet/file.xml" -s my.packageThe CoreJet report and output XML file will be placed inparts/test/corejet. You can combine--xmland--corejet.The example above uses thefileCoreJet repository source, which expects
to find a CoreJet XML file at the path specified after the comma.Repository sourcesOther repository sources can be registered via entry points: Packages must
register an entry point undercorejet.repositorysourceidentifying a
callable that is passed the stringafterthe comma with a unique name and
which should return acorejet.core.model.RequirementsCatalogueobject.Hence, thefilesource is defined as:def fileSource(path):
    catalogue = RequirementsCatalogue()
    with open(path) as stream:
        catalogue.populate(stream)
    return catalogueand registered with:[corejet.repositorysource]
file = corejet.testrunner.filesource:fileSourceUsebin/test--helpfor a full list of options.Changelog1.0.1 (2012-05-30)Fixed the check for non-corejet tests to rely on adaptation instead of magic.
[datakurre]1.0.0 (2012-05-26)Fixed to allow requirements-sources with no options
[datakurre]Fixed to work with test-suites containing non-corejet-tests
[datakurre]Added support for story-level steps
[datakurre]1.0a4Python 2.5 compatibility
[jodok]MANIFEST.in to include CHANGES.txt in sdist
[jodok]1.0a3Fix broken package1.0a1First release
[optilude]
Package: corejet.visualization
CoreJet VisualisationThis package contains a skeleton for generating a graphical CoreJet report
from a CoreJet XML file using XSLT.Callcorejet.visualization.generateReportFromCatalogue()with anIRequirementsCatalogueas the first argument and a target directory name
as the second argument to put a report in that directory.Changelog1.0.0Fixed ‘corejet-to-jit.xsl’ to prefix scenario-ids with story-ids
to allow identically named scenarios in different stories
[datakurre]Fixed ‘corejet-to-jit.xsl’ to produce cleaner javascript
[datakurre]1.0a5Python 2.5 compatibility
[jodok]MANIFEST.in to include CHANGES.txt in sdist
[jodok]1.0a4Ensure output works even if there are no story points
[optilude]1.0a3Fix broken package
[optilude]1.0a2Use lxml to output XSLT, avoiding the need for command-line invocation of
xsltproc
[optilude]1.0a1First release
[optilude]
Package: core-json-app
Add JSON support to the curator core project.Quick start1. Add “core_json_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_json_app',]2. Include the core_json_app URLconf in your project urls.pyre_path(r'^',include('core_json_app.urls')),re_path(r'^',include("core_main_app.urls")),Url for core_json_app should be placed above core_main_app.
Package: corek
CorekAn expression parser and evaluator for simple comparison expressions in Python. Only supports numeric comparisons.ExamplefromcorekimportRulesEngineengine=RulesEngine()input_data={'a':10,'b':2,'c':3}print(engine.evaluate_rule('a == 10',input_data))# Trueprint(engine.evaluate_rule('a > 10',input_data))# Falseprint(engine.evaluate_rule('(a + b) > 10',input_data))# True
Package: corella-lib
CorellaThe Corella Low Power Wide Area Network (LPWAN) module allows developers to connect a range of IoT devices to the Taggle network.The Taggle network is an LPWAN solution based on world leading Australian developed technology operating in the 916-928MHz Low Interference Potential Device (LIPD) class licence band, which has been developed by Taggle Systems to provide one of the lowest cost, lowest power, longest range, and highest capacity LPWAN solutions available. The Taggle network is based on one-way transmissions from endpoint nodes to the Taggle receiver network, and is particularly well suited to battery powered endpoint applications with low data rate requirements, such as automatic meter reading, wireless sensors for smart agriculture and environmental monitoring, and cost sensitive smart city applications.Corella is based on the popular XBee module format, and provides a single serial port with a simple “AT” command style interface to allow rapid integration of the module into both lab prototypes and volume production IoT devices. The module has a single radio transmit output via an SMA connector, and can be supplied with a 2dBi - wave dipole antenna to enable rapid connection to the Taggle network.All receive functions are handled seamlessly by the Taggle network, with the user’s receive data presented via a custom web portal. A range of data plans are available depending on the number of endpoints connected to the Taggle network and the frequency of messages per endpoint.Requirementscorella_lib requires Python >= 3.5.InstallationTo install corella_lib, simply usepip:$pipinstallcorella_libUsage# Import Corellafromcorella_lib.serialimportCorella# Initialize Corellacorella=Corella('<device-port-goes-here>')# Send a message through Taggle network with packet IDcorella.send(<packet-id-goes-here>,'<12-bytes-data-goes-here>')Examples# Initialize Corella>>>corella=Corella('/dev/ttyAMA0')# Send a message through Taggle network with packet ID 2>>>corella.send(2,'temp:23C')True# Check if the device is connected>>>corella.connectedTrue# Get device ID>>>corella.id'130065'# Get device version information>>>corella.version{'F.W':'1.1.01','H.W':'REV_A'}# Get device firmware version>>>corella.firmware_version'1.0.31'# Get device hardware version>>>corella.hardware_version'REV_A'# Get device diagnostics information>>>corella.diagnostics{'CURR_TEMP':'32','BATT':'3.21V','MAX_TEMP':'58','MIN_TEMP':'31'}# Get device supply voltage>>>corella.battery3.21# Get device current temperature in degrees Celsius>>>corella.curr_temp58.0# Get device max temperature in degrees Celsius>>>corella.max_temp58.0# Get device min temperature in degrees Celsius>>>corella.min_temp31.0# Turn off device LEDs>>>corella.turn_off_leds()True# Turn on device LEDs>>>corella.turn_on_leds()TrueDocumentationDocumentation is available athttp://corella.taggle.com.au.
Package: corels
Welcome to the Python binding of the Certifiably Optimal RulE ListS (CORELS) algorithm!Documentation for this package is hosted at:https://corels.eecs.harvard.edu/corels/pycorels/For information on CORELS, please visit our website:https://corels.eecs.harvard.edu.For the code of this package, visit:https://github.com/fingoldin/pycorelsCORELS uses Python, Numpy, GMP, and a C++ compiler (g++ is a great choice).
GMP (GNU Multiple Precision library) is not required, but it ishighly recommended, as it improves performance. If it is not installed, CORELS will run slower.
Package: core-main-registry-app
Core Main Registry AppThis Django reusable app contains the main functionalities for the registry core project.Pre-requisitesInstall gitInstall pythonInstall pipCreate a virtual envInstallationAutomated installationWarningThe automated installation is not yet available.Manual installation$gitclonehttps://myrepo.com/core_main_registry_app.git$cdcore_main_registry_app$pythonsetup.py$pipinstallsdist/*.tar.gzConfiguration1. Add “core_main_registry_app”, “mptt” to your INSTALLED_APPS setting like thisINSTALLED_APPS=[..."core_main_registry_app","mptt",]2. Include the core_main_registry_app URLconf in your project urls.py like thisurl(r'^',include("core_main_registry_app.urls")),3. Launch migration: create table and constraints.$ python manage.py migrate
Package: core-mixins
core-mixinsThis project contains common functions, decorators, and mixin classes
as the base for other projects or libraries...Execution EnvironmentInstall librariespip install --upgrade pip 
pip install virtualenvCreate the Python Virtual Environment.virtualenv --python=python3.11 .venvActivate the Virtual Environment.source .venv/bin/activateInstall required libraries.pip install .Check tests and coverage...python manager.py test
python manager.py coverage
Package: coremltools
Core MLis an Apple framework that allows developers to easily integrate
machine learning (ML) models into apps. Core ML is available on iOS, iPadOS,
watchOS, macOS, and tvOS. Core ML introduces a public file format (.mlmodel)
for a broad set of ML methods including deep neural networks (convolutional
and recurrent), tree ensembles (boosted trees, random forest, decision trees),
and generalized linear models. Core ML models can be directly integrated into
apps within Xcode.coremltoolsis a python package for creating, examining, and testing models in
the .mlmodel format. In particular, it can be used to:Convert trained models from popular machine learning tools into Core ML format
(.mlmodel).Write models to Core ML format with a simple API.Making predictions using the Core ML framework (on select platforms) to
verify conversion.More Informationcoremltools user guide and examplesCore ML framework documentationMachine learning at AppleLicenseCopyright (c) 2020, Apple Inc. All rights reserved.Use of this source code is governed by the3-Clause BSD Licensethat can be found in the LICENSE.txt file.
Package: coremltools_windows
Core MLis an Apple framework which allows developers to simply and easily integrate
machine learning (ML) models into apps running on Apple devices (including iOS,
watchOS, macOS, and tvOS).  Core ML introduces a public file format (.mlmodel)
for a broad set of ML methods including deep neural networks (both
convolutional and recurrent), tree ensembles with boosting, and generalized
linear models. Models in this format can be directly integrated into apps
through Xcode.coremltoolsis a python package for creating, examining, and testing models in
the .mlmodel format.  In particular, it can be used to:Convert existing models to .mlmodel format from popular machine learning tools including Keras, Caffe, scikit-learn, libsvm, and XGBoost.Express models in .mlmodel format through a simple API.Make predictions with an .mlmodel (on select platforms for testing purposes).InstallationThe method for installingcoremltoolsfollows thestandard python package installation steps.
Once you have set up a python environment, run:pip install -U coremltoolsThe packagedocumentationcontains
more details on how to use coremltools.Dependenciescoremltoolshas the following dependencies:numpy (1.10.0+)protobuf (3.1.0+)In addition, it has the following soft dependencies that are only needed when
you are converting models of these formats:Keras (1.2.2, 2.0.4+) with corresponding Tensorflow versionXgboost (0.7+)scikit-learn (0.17+)libSVMMore InformationCore ML framework documentationCore ML model specificationMachine learning at AppleLicenseCopyright (c) 2018, Apple Inc. All rights reserved.Use of this source code is governed by the3-Clause BSD Licensethat can be found in the LICENSE.txt file.
Package: core-module-advanced-blob-host-app
core_module_advanced_blob_host_appAdvanced Blob Host module for the parser core project.Quick start1. Add “core_module_advanced_blob_host_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_advanced_blob_host_app',]2. Include the core_module_advanced_blob_host_app URLconf in your project urls.pyurl(r'^',include('core_module_advanced_blob_host_app.urls')),
Package: core-module-auto-key-integer-sequence-app
core_module_auto_key_integer_sequence_appAuto Key (sequence of integers) module for the parser core project.Quick start1. Add “core_module_auto_key_integer_sequence_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_auto_key_integer_sequence_app',]2. Include the core_module_auto_key_integer_sequence_app URLconf in your project urls.pyurl(r'^',include('core_module_auto_key_integer_sequence_app.urls')),