Saravanan666/pypi-org huggingface dataset:
Package: octario
# Octario**Openstack Component Testing Ansible Roles**Octario is a collection of Ansible roles and playbooks for testing Openstack Components on RHEL/CentOS.It supports the following testers or test frameworks:* PEP8* unit* functional* fullstack* API (In-Tree)* Tempest* Robot### Requirements1. ansible installed on the system ( ansible >= 2 ).2. Component source code. Can be downloaded using `git clone git://git.openstack.org/openstack/<component_name>.git`.Note: `octario` is not provisioning the node on which it will run the tests. It's assumedthat the tester node is provided by the user by specifying it in the hosts file.## Run OctarioFirst, create an inventory file that will include the IP address or the hostname of your tester node.```vi hosts[tester]my_tester_host ansible_user=cloud-user```Edit `octario.yml` with your component details. Make sure to specify name and version!```component:name: novaversion: 9```Run octario!. Choose one of testers and run the following command in octario root directory```ansible-playbook -vvv -i hosts playbooks/pep8.yml --extra-vars @octario.yml```pep8.yml can be replaced with [unittest, functional, api, scenario].yml### Custom testerYou can specify your own tester```export TESTER=my_new_cool_testeransible-playbook -vvv -i hosts playbooks/custom-tester.yml --extra-vars @octario.yml```### External ROLESYou can use external role with `octario`.```cp -r new_role octario/rolesexport ROLE=new_roleansible-playbook -vvv -i hosts playbooks/custom-role.yml --extra-vars @octario.yml```## How it worksThe following drawing added to simplify work-flow overview of `octario` for simple testersas pep8, unittest and functional.<div align="center"><img src="./doc/octario_workflow.png" alt="Octario work-flow"></div><hr />### Patch RPMsThe following drawing describes specifically how RPM patching done with Octario.To inclue patch_rpm role, run playbooks/<tester>-with-patching.yml<div align="center"><img src="./doc/patch_rpm.png" alt="Octario patch rpm work-flow"></div><hr />## More DocsSee the [/doc](https://github.com/redhat-openstack/octario/tree/master/doc) directory of this repo.
Package: octavia
Team and repository tagsOctaviaOctavia is an operator-grade open source scalable load balancer for use in
large OpenStack deployments.Octavia provides the load balancing API for OpenStack. It supports multiple
“provider drivers” that implement load balancing, including the “amphora”
reference driver included with Octavia.Octavia is distributed under the terms of the Apache License, Version 2.0.
The full terms and conditions of this license are detailed in the LICENSE
file.Project resourcesDeveloper documentation for the Octavia project is available athttps://docs.openstack.org/octavia/latest/Release notes for the Octavia project are available athttps://docs.openstack.org/releasenotes/octavia/The project source code repository is located athttps://opendev.org/openstack/octaviaProject status, bugs, and requests for feature enhancements are tracked onhttps://launchpad.net/octaviaFor more information on project direction and guiding principles for
contributors, please see the CONSTITUTION.rst file in this directory, or
specifications in the specs/ sub-directory.The project roadmap is available athttps://wiki.openstack.org/wiki/Octavia/RoadmapExternal ResourcesOctavia Wiki:https://wiki.openstack.org/wiki/OctaviaFor help on usage and hacking of Octavia, please send an email to
OpenStack-dev Mailing List <mailto:openstack-discuss@lists.openstack.org>
with[Octavia]tag.
Package: octavia-dashboard
Team and repository tagsoctavia-dashboardHorizon panels for OctaviaFree software: Apache licenseDocumentation:https://docs.openstack.org/octavia-dashboard/latest/Source:https://opendev.org/openstack/octavia-dashboardRelease notes:https://docs.openstack.org/releasenotes/octavia-dashboard/Bugs:https://storyboard.openstack.org/#!/project/909FeaturesPlease see octavia repositoryHowtoPackage the octavia_dashboard by running:python setup.py sdistThis will create a python egg in the dist folder, which can be used to
install on the horizon machine or within horizon’s python virtual
environment.Copy_1482_project_load_balancer_panel.pyinoctavia_dashboard/enableddirectory
toopenstack_dashboard/local/enabled:$ cp -a \
  ${OCTAVIA_DASHBOARD_DIR}/octavia_dashboard/enabled/_1482_*.py \
  ${HORIZON_DIR}/openstack_dashboard/local/enabled/(Optional) Generate the policy file and copy into horizon’s policy files
folder, and copy_1499_load_balancer_settings.pyinoctavia_dashboard/local_settings.ddirectory
toopenstack_dashboard/local/local_settings.d:$ oslopolicy-policy-generator \
  --config-file \
  ${OCTAVIA_DIR}/etc/policy/octavia-policy-generator.conf \
  --output-file \
  ${OCTAVIA_DASHBOARD_DIR}/octavia_dashboard/conf/octavia_policy.yaml
$ cp -a \
  ${OCTAVIA_DASHBOARD_DIR}/octavia_dashboard/conf/octavia_policy.yaml \
  ${HORIZON_DIR}/openstack_dashboard/conf/
$ cp -a \
  ${OCTAVIA_DASHBOARD_DIR}/octavia_dashboard/local_settings.d/_1499_*.py \
  ${HORIZON_DIR}/openstack_dashboard/local/local_settings.d/Django has a compressor feature that performs many enhancements for the
delivery of static files. If the compressor feature is enabled in your
environment (COMPRESS_OFFLINE = True), run the following commands:$ ./manage.py collectstatic
$ ./manage.py compressFinally restart your web server to enable octavia-dashboard
in your Horizon:$ sudo service apache2 restart
Package: octavia-lib
Team and repository tagsoctavia-libA library to support Octavia provider drivers.This python module provides a python library for Octavia provider driver
developers.See the provider driver development guide for more information:https://docs.openstack.org/octavia/latest/contributor/guides/providers.htmlOctavia-lib is distributed under the terms of the Apache License, Version 2.0.
The full terms and conditions of this license are detailed in the LICENSE file.Free software: Apache licenseDocumentation:https://docs.openstack.org/octavia-lib/latestSource:https://opendev.org/openstack/octavia-libBugs:https://storyboard.openstack.org/#!/project/openstack/octavia-libRelease Notes:https://docs.openstack.org/releasenotes/octavia-lib/
Package: octavia-ovn-provider
OVN provides virtual networking for Open vSwitch and is a component of the Open
vSwitch project.  This project provides integration between OpenStack Octavia
and OVN.Free software: Apache licenseSource:https://opendev.org/openstack/octavia-ovn-providerBugs:https://bugs.launchpad.net/neutronMailing list:http://lists.openstack.org/cgi-bin/mailman/listinfo/openstack-discussIRC: #openstack-neutron-ovn on Freenode.Docs:https://docs.openstack.org/octavia-ovn-provider/latestTeam and repository tagsRelease notes for the project can be found at:https://docs.openstack.org/releasenotes/octavia-ovn-provider
Package: octavia-proxy
This project translates Octavia API requests into elbv2 and elbv3 of the Open
Telekom Cloud. It is required due to missing possibility to implement elbv3
support as a native Octavia driver (it doesn’t offer admin type of access and
to have primary data source in Octavia).Goalsprovide customer facing load balancer APIs using Octavia APIcover elbv3 and elbv2Admin APIs are not going to be implementedStatusPOC in development phaseRequirementsvalidatetoken (fork of keystonemiddleware.auth_token middleware to
allow token validation in the frontend)python-otcextensions (elb branch)Developer setuptox create venv for octavia-proxysource into itwith the venv python go to otcextensions elb branch and dopython setup.py developadd into the clouds.yamlload_balancer_endpoint_override: http://127.0.0.1:9876/.
IMPORTANT (for now): do not use profile:otcget python-openstackclient with python-octaviaclient (otce overrides
loadbalancer function now, therefore - upstream)python octavia_proxy/cmd/api.py –config-file etc/octavia.confopenstack loadbalancer list
Package: octavia-tempest-plugin
Team and repository tagsTempest integration of OctaviaThis project contains the Tempest plugin for the Octavia project for
OpenStack Load Balancing.For more information about Octavia see:https://docs.openstack.org/octavia/latest/For more information about Tempest plugins see:https://docs.openstack.org/tempest/latest/plugin.htmlFree software: Apache licenseDocumentation:https://docs.openstack.org/octavia-tempest-plugin/latest/Source:https://opendev.org/openstack/octavia-tempest-pluginBugs:https://storyboard.openstack.org/#!/project/openstack/octavia-tempest-pluginInstallingFrom the tempest directory, setup the tempest virtual environment for the
Octavia tempest plugin:$ tox -e venv-tempest -- pip3 install -e <path to octavia-tempest-plugin>For example, when using a typical devstack setup:$ cd /opt/stack/tempest
$ tox -e venv-tempest -- pip3 install -e /opt/stack/octavia-tempest-pluginRunning the testsTo run all the tests from this plugin, call from the tempest repo:$ tox -e all -- octavia_tempest_pluginTo run a single test case, call with full path, for example:$ tox -e all -- octavia_tempest_plugin.tests.scenario.v2.test_traffic_ops.TrafficOperationsScenarioTest.test_basic_trafficTo retrieve a list of all tempest tests, run:$ testr list-tests
Package: octbrowser
Usefull methods for simple web parsing :Using requests for open url and accessing pageSimple history managementDownload ressourceSimple way to get html elements as string with css selectorsOpen the current page in your default browserSimple way to follow linksSimple form managementGithub repository :https://github.com/karec/oct-browserDocumentation :http://octbrowser.readthedocs.org/en/latest/index.html
Package: octconv
Octave ConvolutionImplementation ofDrop an Octave: Reducing Spatial Redundancy in
Convolutional Neural Networks with Octave ConvolutionPaper AbstractIn natural images, information is conveyed at different frequencies
where higher frequencies are usually encoded with fine details and
lower frequencies are usually encoded with global structures.
Similarly, the output feature maps of a convolution layer can also be
seen as a mixture of information at different frequencies.
In this work, we propose to factorize the mixed feature maps by their
frequencies, and design a novel Octave Convolution (OctConv) operation
to store and process feature maps that vary spatially “slower” at a lower
spatial resolution reducing both memory and computation cost. Unlike existing
multi-scale methods, OctConv is formulated as a single, generic, plug-and-play
convolutional unit that can be used as a direct replacement
of (vanilla) convolutions without any adjustments in the network architecture.
It is also orthogonal and complementary to methods that suggest better
topologies or reduce channel-wise redundancy like group or depth-wise convolutions.
We experimentally show that by simply replacing convolutions with OctConv,
we can consistently boost accuracy for both image and video recognition tasks,
while reducing memory and computational cost.
An OctConv-equipped ResNet-152 can achieve 82.9% top-1 classification accuracy on
ImageNet with merely 22.2 GFLOPs.InstallationFrom PyPI:pip install octconvBleeding edge version from github:pip install git+https://github.com/braincreators/octconv.git#egg=octconvUsageimporttorchfromoctconvimportOctConv2d# (batch, channels, height, width)x=torch.rand(5,3,200,200)conv1=OctConv2d(in_channels=3,out_channels=10,kernel_size=3,alpha=(0.,0.5),padding=1)conv2=OctConv2d(in_channels=10,out_channels=20,kernel_size=7,alpha=(0.5,0.8),padding=3)conv3=OctConv2d(in_channels=20,out_channels=1,kernel_size=3,alpha=(0.8,0.),padding=1)out=conv3(conv2(conv1(x)))# shape: (5, 1, 200, 200)Original implementationfacebookresearch/OctConv (MXNET)Citation@article{chen2019drop,
  title={Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution},
  author={Chen, Yunpeng and Fan, Haoqi and Xu, Bing and Yan, Zhicheng and Kalantidis, Yannis and Rohrbach, Marcus and Yan, Shuicheng and Feng, Jiashi},
  journal={arXiv preprint arXiv:1904.05049},
  year={2019}
}
Package: octet
octetObject-oriented way to represent digital sizes (bits and bytes)Examples>>>fromoctetimport*>>>GiB(1024).convert_to(TiB)TebiByte(1)>>>size=1024*KiB()>>>size.to_minor_units()bit(8388608)>>>KiB(1)>1000*B()True>>>KiB(1)>1025*B()False
Package: oct-firecam
Wildfire detectionWildfire detectionsystem leverages machine learning techniques to detect smoke in images from real time cameras from vantage points.  This repository contains all the code for collecting training data, training the model, and fetching images from cameras, checking for smoke, and notifying interested folks.  The repository also contains datasets used for training the model.The followingpaperpublished in Remote Sensing journal provides much of the background for this project.LicensesThe code in this repository is released underApache License 2.0.
The datasets are licensed under theCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License.
The original images were collected from theHPWREN camera networkand the bounding boxes coordiantes are provided by Open Climate Tech.
Package: octid
OCTID: One-Class learning-based tool for Tumor Image DetectionOCTID is a one-class learning-based Python package for tile-level tumor detection. OCTID can capture patterns from the available normal whole slide images (WSIs) to identify and remove normal tiles from the training dataset. Using OCTID, researchers with limited pathology expertise can effectively classify and identify the tumor images based on the readily available tumor adjacent images. Leveraging the power of machine learning, OCTID can enable users to conduct image pre-processing easily on the large-scale histopathological image datasets. Example datasets can be downloadedhere.Getting startedInstall OCTID from PyPI
(You may need to creat a new environment before installing OCTID.)$pipinstalloctidto run your first example
(A test.py file can be foundhere.)fromoctidimportoctid# initialize the classify model with the requiered parametersclassify_model=octid.octid(model='googlenet',customised_model=False,feature_dimension=3,outlier_fraction_of_SVM=0.03,training_dataset='training_dataset_path',validation_dataset='validation_dataset_path',unlabelled_dataset='unlabelled_dataset_path')# run the classify modelclassify_model()Parametersmodel (String or PyTorch model): The default value is "googlenet". The available models are listed below. The pre-defined models or customised models can be loaded when customised_model is set as "False" or "True".customised_model (Boolean): The default value is False. If you want to use your own model, you can set this parameter as "Ture" and load and pass your model to the "model" parameter.feature_dimension (Int): Feature dimension reduced by usingUMAP, and the default value is 3.outlier_fraction_of_SVM (Float) : The default value is 0.03. The rbf kernel is used in one-classSVM. This parameter is an upper bound on the fraction of training errors and a lower bound of the fraction of support vectors, which ranges from 0 to 1.training_dataset (String): The path of your template dataset folder, which should only contain the positive or negative images.validation_dataset (String): The path of your validation dataset folder, which should contain both positive and negative images.unlabelled_dataset (String): The path of the dataset that you want to classify, which will be re-saved to two subfolders, corresponding to two classes.** Dataset folders notes: since we use thetorchvision.datasets.ImageFolderto label the image, please follow the instructions provided to create your image folders.** Images processed here are small tiles rather than the whole slide images, which should be segmented into small images, with sizes such as 500 by 500.Available pre-trained models for OCTID
Package: octoapi
OctoAPIThis package provides a Python interface to theOctopus Energy API.DependenciesPython >= 3.6InstallWIPDeployWIPTestingThe automated tests can be run usingpytestContributePlease look atour contributing guidelinesif you want to contribute to this project. GitHub also has some good guides onhow to contribute.LicenseThis project uses aGPL v3 licence.
Package: octo-api-client
OCTO API clientPython HTTP client for OCTO (Open Connection for Tourism) APIs.More info atoctospec.comAPI Specification:https://docs.octo.travel/docs/octo/r6gduoa5ah5ne-octo-apiInstallationpip install octo-api-clientRequirementsPython v3.7+DevelopmentGetting started$ pip install poetry
$ poetry installRunning tests and lintersTo run linters:$ poetry run ruff octo_client
$ poetry run mypy octo_clientTo run tests:$ poetry run pytestUsagefrom octo_client import OctoClient

client = OctoClient('https://octo-api.mysupplier.com', 'MY-SECRET_TOKEN')
client.get_suppliers()
Package: octoblox
OctoBloxInfoblox provider foroctoDNSOctoBlox provides the glue for enterprise migration toGitOpswithInfoblox.InstallationpipinstalloctobloxConfigureproviders:infoblox:class:octoblox.InfoBloxProviderendpoint:infoblox.example.comusername:adminpassword:env/INFOBLOX_PASSWORD# verify: ./infoblox.pem# apiver: 1.0# dns_view: default# log_change: true# alias_types:#   - A#   - AAAA#   - TXT# create_zones: true# new_zone_fields:#   grid_primary:#     - name: infoblox.example.com#   ns_group: default#   restart_if_needed: true#   soa_default_ttl: 3600#   view: default#   use_grid_zone_timer: truedelegated:class:octoblox.DelegatedProviderendpoint:infoblox.example.comusername:adminpassword:env/INFOBLOX_PASSWORD# verify: ./infoblox.pem# apiver: 1.0# dns_view: default# log_change: true# create_zones: true# new_zone_fields:#   delegate_to:#     - name: ns1.delegated.example.com#       address: 1.1.1.1#     - name: ns2.delegated.example.com#       address: 8.8.8.8#   ns_group: default#   view: defaultAlias Record Update BehaviourInfoblox allows for an alias record per DNS record type.
By default OctoBlox will ensure both A and AAAA records are created.
This can be changed using thealias_typesparameter.In the event that too many or not enough record types exist for a single record,
the discovered target value will haveinvalid.appended to make sure that
a record update is generated. This value was chosen as it's specifically listed
inRFC2606for this purpose.This will result in octoDNS reporting that the value is incorrect when the
reality is that the number of ALIAS records is incorrect. While it is possible
that both are the case this is unlikely and OctoBlox can handle this as well.Use of Lenient Flag for Alias RecordsOctoDNS has implemented a behavior of not accepting alias records for non-root
zone entries by default. To get around this provide the--lenientflag when
dumping from InfoBlox with alias records.When storing alias entries in YAML ensure that you add the octodns lenient
entry to the record like so:---alias:octodns:lenient:truetype:ALIASvalue:www.example.com.Alternatively you can set a zone level lenient flag like so:---example.com.:octodns:lenient:truesources:-yamltargets:-infobloxRefer to theoctoDNS entry on leniencefor more information.
Package: octocat
Octocat – Python client for Github API.Docs are available at https://octocat.readthedocs.org/. Pull requests
with documentation enhancements and/or fixes are awesome and most welcome.ContentsRequirementsInstallationUsageBug trackerContributingContributorsLicenseRequirementspython >= 2.6InstallationOctocatshould be installed using pip:pip install octocatUsageBug trackerIf you have any suggestions, bug reports or
annoyances please report them to the issue tracker
athttps://github.com/klen/octocat/issuesContributingDevelopment of Octocat happens at github:https://github.com/klen/octocatContributorsklen(Kirill Klenov)LicenseLicensed under aBSD license.
Package: octocheese
🐙 🧀 – Copy PyPI Packages to GitHub ReleasesThis is a GitHub action, Python package and command line script that copies distribution files from PyPI (“The Cheese Shop”) and adds them to the relevant tag in GitHub Releases.DocsTestsPyPIActivityQAOtherGitHub Actions UsageIn a GitHub Workflow:name:"CopyPyPItoReleases"on:-pushjobs:releases:runs-on:ubuntu-lateststeps:-uses:domdfcoding/octocheese@masterwith:pypi_name:"domdf_python_tools"env:GITHUB_TOKEN:${{secrets.GITHUB_TOKEN}}Installing as a Python package and scriptOctoCheesecan be installed from PyPI.To install withpip:$python-mpipinstalloctocheese
Package: octoconf
octoconfMulti-profile supported, flexible config library for Python 2 & 3.FeaturesAllow multiple config profiles in one YAML fileAllow include multiple YAML filesOverridable profile selection from code for special use-cases (e.g. config for testing)Inheritable config profiles, what makes profile merges by dictionaries. (the native YAML bookmarking is also available)Can use variables in the config fileInstallationpip install octoconfConfig formatAnoctoconfconfig file is pure YAML file with some reserved keywords:USED_CONFIG>: <node_name>in the file rootyou can specify the name of default config profile<INCLUDE: <yml_path(s)>in the file rootthis octoconf file(s) will be included<BASE: <node_name>in the 2nd levelthis will used for making (merge based) inheritance between profilesThe profile nodes should be on 1st level!UsageYou can load config from string withloads():importoctoconfconfig=octoconf.loads(yaml_string)print(config)Or directly from StringIO (e.g. from file) withload():importoctoconfwithopen('config.yml')asfd:config=octoconf.load(fd)print(config)Please check thefeatures docsfor explainoctoconf’s features.Examples YAML filesUSED_CONFIG>:UserConfig<INCLUDE:vendor.defaults.yml# This config overrides the production preset (from vendor.defaults.yml file)UserConfig:<BASE:ProductionConfigApp:TITLE:"AmazingFoobar"Flask:SQLALCHEMY_DATABASE_URI:"sqlite:///${SERVER}/app.sqlite"For more examples, please check theexamplesdirectory.BugsBugs or suggestions? Visit theissue tracker.
Package: octocruncher
Octocruncher V4Use this to query Octopart using the v4 APITo function with active requests, the OCTOPART_API_KEY environment variable must
be set. This can be done in your Python using the following:importosos.environ['OCTOPART_API_KEY']='xxxxxxxxx'UsagefromoctocruncherimportOctoCruncher# Setup the manufacturer part number herex=OctoCruncher(mpn='mpn')OctoCruncher takes in the argument 'mpn' (manufacturer part number) which
everything else is based off of. Other optional arguments:json_source: You can provide a json.loadsobject instead of querying online.
This is helpful for when you want to cache (use with OctoCruncher.getJSON())file_source: Similar to json_source but will load from the given file pathCallables.getNumItems():This tells you how many results there are.setItemNumber(itemnumber=0)This sets the working item for all other functions.getMPN():Returns working manufacturer part number.getJSON():This will return a json object that can be cached and loaded later.getNumDescriptions():Tells you how many available part descriptions there are.getDescription(n=0):Returns the nth description. If n is not specified, the 0th
description will be returned. Returntype is a description class which has thevalueandsourceelements.getNumDatasheets():Tells you how many datasheets are available.getDatasheet(n=0)Returns the nth Datasheet object. This object has the
following parameters that can be accessed:date_created
last_updated
num_pages
size_bytes
date_created
mimetype
url
source.getManufacturer():This returns a manufacturer object with thenameandhomepage_urlattributes