Saravanan666/pypi-org huggingface dataset:
Package: resifdatareporter
resif data reporterCet outil collecte sur un ou plusieurs espaces disques en structure SDS l'espace occupé par les réseaux sismo.Mises à jourPour publier une nouvelle version :incrémenter le numéro de version danssetup.pyFaire un commit et un push
L'intégration continue se charge de construire le paquet python et de le publier.Installationpython3 -m pip install resifdatareporter --extra-index-url https://gricad-gitlab.univ-grenoble-alpes.fr/api/v4/projects/13239/packages/pypi/simpleÀ proposlicence : Ce projet est livré sous licence GPLv3 ou plusauteur : Jonathan Schaefferjonathan.schaeffer@univ-grenoble-alpes.frCollecte des volumesLes données sont dans un ou plusieurs dossier(s) contenant un sous-dossier par année et pour chaque année, un dossier par réseau.Dans le rapport final, on souhaite distinguer les réseaux permanents des expériences temporaires.Les données collectées sont écrites en YAML dans un fichier cache.Les données sont ensuite écrites dans une base postgres.ConfigurationLe script de collecte a besoin d'un fichier de configuration. Le fichierconfig.yml.examplefournit toute la documentation nécessaire pour un paramétrage initial.Par défaut, le script collecte les données (en faisant desdu), les compile et les écrit dans le fichierdata.yaml.Si les données contenues dansdata.yamlne sont pas plus anciennes que ce que spécifie la configurationdata_cache_ttl(en jour), alors le script scanne le fichier data.json pour le restituer et n'effectue pas le scan des volumes.PostgresQuelle config sur le serveur postgres ? Quelle base et quelles tables créer ?CREATETYPEsismo_data_typeASENUM('bud','validated')CREATETABLEdataholdings(networkvarchar(2),yearvarchar(4),stationvarchar(5),channelvarchar(3),qualityvarchar(1),typesismo_data_type,sizebigint,is_permanentboolean,datedate);TesterLancer des tests unitairespipinstall-e.
pytestLancer un docker postgresdockerpullpostgres
dockerrun--namepg-p5432:5432-ePOSTGRES_PASSWORD=mysecretpassword-dpostgresConfigurationUn fichier de Configuration pour les tests :volumes:-path:/tmp/pytest-of-schaeffj/pytest-22/validated/type:validated-path:/tmp/pytest-of-schaeffj/pytest-22/budtype:budpostgres:host:localhostdatabase:statsport:5432user:postgrespassword:mysecretpasswordmetadata:# Information about the gathered data. Used to tag the timeserie valuespermanent_networks:#  List all permanent networks here. Otherwise, they will be considered as temporary-CL-GL-MQ-ND-PF-RD-FR-G-RA-WITester le programme complet :python resif_data_reporter.py --config config.yml
Package: resif-delivery-stats-plotter
resif-delivery-stats-plotteris a command line tool to plot statistics about requests and metadata/data deliveries
by the datacenter about a seismic network handled by Résif. This tool is mostly designed to help the seismic networks’ PI
to build their annual activity reports.More details about Résif are available onhttp://seismology.resif.fr/InstallationFrom PyPIresif-delivery-stats-plotteris distributed onPyPIas a universal wheel and is available on
Linux/macOS and Windows and supports Python 3.5+ and PyPy.First, create and activate a virtualenv:$python3-mvenvvenv$sourcevenv/bin/activateInstall/Update thewheelpackage to allow installation of the binary version of dependencies.$pipinstall--upgradepipsetuptoolswheelThen, install theresif-delivery-stats-plotterpackage:$pipinstallresif-delivery-stats-plotterFrom sourcesresif-delivery-stats-plottersources are distributed under the terms ofGPLv3 licenceand are available athttps://gitlab.com/resif/resif-delivery-stats-plotterFirst, ensure Git and Python 3 are installed on your system.$sudoaptinstallgit$sudoaptinstallpython3python3-virtualenvpython3-pippython3-setuptoolspython3-wheelThen, clone the project from the Gitlab repository:$gitclonehttps://gitlab.com/resif/resif-delivery-stats-plotter.git$cdresif-delivery-stats-plotterThen, create and activate a virtualenv:$python3-mvenvvenv$sourcevenv/bin/activateFinally, install the project and its dependencies:$pipinstall--upgradepipsetuptoolswheel$pipinstall-e.UsageGeneral syntax:$resif-delivery-stats-plotter[OPTIONS]COMMAND[ARGS]Use the–helpoption to show the specific syntax of each command.Build an HTML report$resif-delivery-stats-plotterreport[OPTIONS]NETWORKYEARBuild an individual plotUse the following commands:plot-network-availabilityplot-map-network-stationsplot-data-send-monthlyplot-data-send-yearlyplot-data-stored-yearlyplot-requests-monthlyplot-requests-yearlyplot-map-clients-by-countryplot-map-requests-by-countryOther operationsUse the following commands:list-networkslist-stationslist-channelsclear-requests-cache
Package: resiland
Resiland is a testbed that allows one to modify resilience of a multistable networked dynamical system in a controlled manner and to generate time series of observables that may be used to evaluate the suitability of data-driven indicators of resilience. Resiland is a noval ansatz for simulation of a modification of system’s resilience (Resi-) in a potential landscape (-land). Self-induced transitions allow state changes without a change of resilience and distinguishable dynamics allow to simulate real-world systems with different functionings, structures and feedbacks. Furthermore, a change of the potential landscape influence resilience and simulations up to a system crises are possbile.Examples: Take a look into the examples to get started and for reference.Issue Tracker: Please report any bugs here. Also feel free to ask for new features.Installation instructions: In most cases, pip3 install resiland or similar should do the job.
Package: resilient
IBM SOAR Python LibraryOverviewThis package is a Python client module for the IBM SOAR REST API.DocumentationThis library is used in ourresilient-circuitsandresilient-sdklibraries to handle connections to IBM SOAR REST API.For more information on how to develop custom Apps for IBM SOAR see the IBM SOAR App Developer's Guide. The guide is available on the IBM Knowledge Center atibm.biz/soar-docs. On this web page, select your IBM SOAR platform version. On the follow-on page, you can find the App Developer's Guide by expandingAppsin the Table of Contents pane.For our API Documentation on this library, please seeibm.biz/soar-python-docs.Change LogWe are continuously adding to this package and our change log can be found atibm.biz/resilient-changes.License and TermsCopyright © IBM Corporation 2023Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to
deal in the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
sell copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
IN THE SOFTWARE.
Package: resilient-circuits
IBM SOAR Resilient Circuits FrameworkOverviewThe IBM SOARresilient-circuitsFramework is used to run IBM SOAR Apps and Integrations.DocumentationThe IBM SOAR App Developer's Guide provides information on using the IBM SOAR Resilient Circuits Framework to run Apps and Integrations. The guide is available on the IBM Knowledge Center atibm.biz/soar-docs. On this web page, select your IBM SOAR platform version. On the follow-on page, you can find the App Developer's Guide by expandingAppsin the Table of Contents pane.For our API Documentation on this library, please seeibm.biz/soar-python-docs.Change LogWe are continuously adding to this package and our change log can be found atibm.biz/resilient-circuits-changes.License and TermsCopyright © IBM Corporation 2023Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to
deal in the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
sell copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
IN THE SOFTWARE.
Package: resilient-lib
IBM SOAR Common LibraryOverviewThe IBM SOAR Common Library, formerly theIBM Resilient Common Library, package contains common library calls which facilitate the development of Apps for IBM SOAR.DocumentationView our documentation for this library at:ibm.biz/soar-python-docs.Change LogWe are continuously adding to this package and our change log can be found atibm.biz/resilient-lib-changes.License and TermsCopyright © IBM Corporation 2023Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to
deal in the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
sell copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
IN THE SOFTWARE.
Package: resilient-sdk
IBM SOAR SDKOverviewThe IBM SOAR SDK, formerly the IBM Resilient SDK, provides the tools and infrastructure you need to develop IBM SOAR Apps, which you can then post on ourApp Exchange.InstallationTo install the IBM SOAR SDK, execute the following command:$ pip install resilient-sdkUsage and DocumentationFor details on the commands provided with the SDK, use the-hoption on the command line. For example,resilient-sdk -handresilient-sdk codegen -h.For more examples on its usage seeibm.biz/soar-python-docs.The IBM SOAR App Developer's Guide provides information on using the IBM SOAR SDK to develop and package apps. The guide is available on the IBM Knowledge Center atibm.biz/soar-docs. On this web page, select your IBM SOAR platform version. On the follow-on page, you can find the App Developer's Guide by expandingAppsin the Table of Contents pane.Change LogOur change log can be found atibm.biz/resilient-sdk-changes.License and TermsCopyright © IBM Corporation 2023Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to
deal in the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
sell copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
IN THE SOFTWARE.
Package: resilipy
ReadmeResiliPyprovides an accessible machine-learning based framework for classifying mice according to their stress resilience based on tracking data.MotivationResilience research in mice using the chronic social defeat paradigm commonly depends on the social interaction (SI) ratio to group animals into those that are susceptible and resilient to stress. The SI ratio only considers the time spent near the aggressor mouse during the social interaction test, not considering many possible indicators of resilient or susceptible behaviour.HereResiliPyoffers an alternative labelling approach that does not consider the distance to the aggressor animal directly, but rather takes the relative distances for each time point into consideration, setting up a distribution of relative distance measures. Using percentiles from this distribution gives more detailed information about the overall behaviour of the animals. In combination with preassigned resilience labels, a machine learning model is trained to classify new animals by their resilience.To simplify use, the classification and pre-processing process has been integrated into a graphical user interface. Accordingly, no programming knowledge is required to useResiliPy.StructureResiliPyconsists of three main modules:LabellerThe Labeller performs the classification task itself on a dataset of unlabelled animals. Based on a loaded machine-learning model, an imported pre-processed (see Preprocessor) dataset can be classified.PreprocessorThe given classification approach requires relative distance distributions extracted from the tracking data. This process if performed with the Preprocessor. Given raw Ethovision tracking files, the coordinates are transformed into the needed format. The extracted dataset can subsequently be used for labelling.BuilderFor classification, it is highly advisable to use a model that has been trained with comparable data. New machine learning models can be imported and used inResiliPy. An optimised model can be imported into the Builder together with training data to create a.modelfile. This is then imported into the Labeller for classification.Installation & LaunchWith Python installed,ResiliPycan be installed via the Python Packaging Index (PyPI). In the terminal or command promt, type:pipinstallresilipyResiliPycan be launched by typing in the terminal:python-mresilipyReferencesLicenseMIT License (MIT). SeeLicensefile for details.
Package: resim
ReSim (chemoREsistance SIMulator) is a python package that simulates cancer
chemoresistance dynamics based on a stochastic model. The model describes the
interaction between chemotherapeutics and various cancer cell subpopulations
(including sensitive, primary resistant, acquired resistant and quiescent
cancer cells).ReSim provides both GUI (powered byFlask <http://flask.pocoo.org>) and CUI.Remarks: Simulation result is greatly depended on input params, therefore
ReSim is intended to use in parallel within vitroassay. Procedures or
details in identifying input paramsin vitrois out of scope here and will be
documented in elsewhere (hope to get it publish soon).DependenciesflaskFlask-WTFnumpyscipypandasmatplotlibseabornplotlysdeintInstallationWestronglyrecommend everyone to install resim within a virtual
environment. Setup the venv as usual and run$ pip install resimOtherwise, install it locally.Getting Start - GUIOpen your command prompt, simply run$ resim-flaskReSim is now available athttp://localhost:5000/resim.NoteGUI provides a simplified & handy version of resim with interactive
plotly plots. Please use CUI if you need more control of input params.Advanced UsageUsing defaults params (calibrated from the case of sorafenib and liver cancer):>>> import resim

>>> model = resim.Simulator()
>>> res = model.simulate()

>>> snsplt_cells = resim.plot_cells(res, subplot=True)
>>> snsplt_drug = resim.plot_drug(res)
>>> snsplt_fht = resim.plot_fht(res)

>>> snsplt_cells.figure.savefig('resim_cells.png')
>>> snsplt_drug.figure.savefig('resim_drug.png')
>>> snsplt_fht.figure.savefig('resim_fht.png')You may set your own params. For examples, input the growth rate of sensitive cells and resistant cells determinedin vitro:>>> import resim

>>> model = resim.Simulator(gr=[0.2, 0.1, 0.1])
>>> res = model.simulate()model.simulate()returns a dict of dictionary containingpd.DataFrameornp.array. To extract the result,:>>>  df_cells = res['case']['cells']
>>>  df_drugs = res['case']['drugs']

>>> l_fht_control = res['control']['fht'])
>>> l_fht_treatment = res['case']['fht'])NotesGUI outputs are generated byPlotly <https://plot.ly/>. But bothplot_cells,plot_drugandplot_fhtin CUI returnseaborn
<https://seaborn.pydata.org/>plot object. You may customize your own theme,
title, layout.DocumentationAvailable athttps://resim.readthedocs.io/.TODOAdd class methods for conventional first-line/second-line chemos in HCC, PC.Add tool for converting tumor’s diameter/volume to number of cells.Add tool for converting doubling time to growth rate.Add tool for converting half-life to k.Add command line module.Integrate parallel programming sde solver modulensim
<https://github.com/mattja/nsim>to speed up the computation.
Package: resin-release-tool
resin-release-toolThis tool is to set releases to release groups and canaries in balenaCloudInstallationpip install resin-release-toolBuild / Run locallyYou need poetry to build the projecthttps://python-poetry.org/poetry install
poetry build
poetry run resin-release-tool
etc..Example usageOne canary groupMark relevant devices with device tags with name "release_group" and value "canary" on balenaCloud.To deploy a canary commit to them, run:resin-release-tool--app$APP_IDrelease-c$CANARY_COMMIT-gcanaryTo deploy said commit to rest of devices, run:resin-release-tool--app$APP_IDrelease-c$NEW_RELEASE_COMMIT-a
resin-release-tool--app$APP_IDunpincanary(Note: Running the unpin command is not necessary if canary is already onNEW_RELEASE_COMMIT, however, without it, it won't track the latest app-wide release.)Staggered release with multiple groupsMark relevant devices with device tags with name "release_group" and value "release_group_1/2/3" on balenaCloud.To deploy a commit to all devices in a staggered way:(Add appropriate wait or checks between commands as appropriate for your usecase.)resin-release-tool--app$APP_IDrelease-c$NEW_RELEASE_COMMIT-grelease_group_1
resin-release-tool--app$APP_IDrelease-c$NEW_RELEASE_COMMIT-grelease_group_2
resin-release-tool--app$APP_IDrelease-c$NEW_RELEASE_COMMIT-grelease_group_3

resin-release-tool--app$APP_IDrelease-c$NEW_RELEASE_COMMIT-a
resin-release-tool--app$APP_IDunpinrelease_group_1release_group_2release_group_3UsageUsage: resin-release-tool [OPTIONS] COMMAND [ARGS]...

  You can set app and token as environment variables, using RESIN_APP and
  RESIN_TOKEN

Options:
  --token TOKEN  balenaCloud auth token  [required]
  --app APP_ID   balenaCloud app ID  [required]
  --help         Show this message and exit.

Commands:
  disable_rolling      Disables rolling releases in the application
  enable_rolling       Enables rolling releases in the application
  info                 Information of the application
  release              Sets release commits for a given release or app
  releases             Show successful releases of the application
  show_devices_status  Show the status of the devices in the app
  show_group_versions  Show the release versions of the devices in release groups
  unpin                Unpins the version of one or more releasesDevelopmentThe config file used by the balena_sdk is located at$HOME/.balena/balena.cfgTo format the code run:black <path to files >Tests can be run withpoetry run pytestsTo debug/run commands in pycharm configureresin_release_tool/cli.pyas the script path and the command you want to  run as parameter (credentials can be added as envs)Publishing a new versionPre-release stepsupddate the changelog and runmakerelease<version># e.g. v0.3.1to update the version inpyproject.tomlRelease stepAfter merging these changes, tag the commit on master usinggit tag <version>. This must match the new version in thepyprojct.tomlpush the new tag to Githubgit push origin <version>this should trigger thepublish-to-pypiworkflowNew versions are uploaded tohttps://pypi.org/project/resin-release-tool/
Package: resippy
ReSIPPyRemote Sensing and Image Processing in PythonReSIPPy is a set of tools to perform image processing with a focus on overhead and spectral remote sensing applications.
Some features include:Navigation from world coordinate to camera pixel coordinatesNavigation from pixel coordinates to world coordinatesOrthorectificationSpectral matched filtersReading spectral librariesInstallationThe installation of ReSIPPy requires two tricky dependencies:GDALPyProjUsing condaWe prefer to use conda to manage our Python environments and dependencies.
To install Anaconda (or Miniconda), follow the instructions in theAnaconda Docs.
Once you have a working conda installation, clone this repo and create an environment for working with ReSIPPy:$condaenvcreate-fenvironment.ymlThen, activate the environment to work with ReSIPPy:$condaactivateresippyUsing pipReSIPPy is also available on PyPi and can be installed using pip:$pipinstallresippyInstallation using pip may require the user to download and install additional system level dependencies.DocumentationThis project uses Sphinx for documentation.
To build HTML documentation, navigate to thedocsfolder and run the following:$makehtmlWhen completed, the HTML documentation will be available indocs/_build/html.
Package: resipy
ResIPy a python wrapper around R* codesThis package is a Python wrapper around the R* family of codes (R2, cR2, R3t, cR3t, seehttp://www.es.lancs.ac.uk/people/amb/Freeware/R2/R2.htmfor more details) and provides an API (Application Programming Interface) for inverting/modelling resistivity and IP data.It aims to be(1)simple and intuitiveto use (you can invert your data in one-click with all default settings)(2) but still allowing the user to havefull controlon data filtering, error modelling, mesh parameters and inversion settingsThis is an open-source project, everyone is welcome to contribute.In addition to this python package that provideds the API,
a multi-platform standalone Graphical User Interface (GUI) is also available to download.Please see the gitlab repository for more information and to download the executable for the GUI:https://gitlab.com/hkex/resipyOr the online documentation:https://hkex.gitlab.io/resipy