Saravanan666/pypi-org huggingface dataset:
Package: clime-json-converter
CLIME JSON Converter ProjectA tool to convert JSON files between different formatsTable of ContentsCLIME JSON Converter ProjectTable of ContentsAboutLicensingHow To UseInstallationCommand Line ArguementsAboutThe Software Systems Laboratory (SSL) CLIME JSON Converter project is a tool to convert JSON files between different formats.LicensingThis project is licensed under the BSD-3-Clause. See theLICENSEfor more information.How To UseInstallationYou can install the tool from PyPi with one of the following one liners:pip install clime-metricspip install clime-json-converterCommand Line Arguementsclime-json-converter -husage:CLIMEJSONConverter[-h]-iINPUT[--clipboard][--csv][--excel][--feather][--hdf]--html[--json][--latex][--markdown][--parquet][--pickle][--stata][--tsv]AtooltoconvertJSONfilesbetweendifferentformats

options:-h,--helpshowthishelpmessageandexit-iINPUT,--inputINPUTFiletobeconvertedtootheroutputs--clipboardCopythedatafromthefiletothesystem'sclipboard--csvConverttheinputto*.csv--excelConverttheinputto*.xlsx--featherConverttheinputto*.feather--hdfConverttheinputto*.hdf5--htmlConverttheinputto*.html--jsonConverttheinputto*.json--latexConverttheinputto*.tex--markdownConverttheinputto*.md--parquetConverttheinputto*.parquet--pickleConverttheinputto*.pkl--stataConvertinputto*.dta--tsvConverttheinputto*.tsv

Author(s):NicholasM.Synovic,MatthewHyatt,GeorgeK.Thiruvathukal
Package: clime-metrics
PRIME: Command Line Metrics ToolA complete installer for PRIME (Transitioning from CLIME)Table of ContentsPRIME: Command Line Metrics ToolTable of ContentsAboutLicensingDeveloper ToolingOperating SystemShell SoftwareBundled ProjectsAboutThe Software Systems Laboratory (SSL) PRIME (PRocess Internal Metrics) project is a collection ofpythontools that can be used on any Git repository to generate longitudinal graphs of classical process metrics.You can install the entirety of the PRIME project from Pypi withpip install --upgrade pip clime-metrics.LicensingThese tools can be modified by outside teams or individuals for usage of their own personal projects.This project is licensed under the BSD-3-Clause. See theLICENSEfor more information.Developer ToolingTo maximize the utility of this project and the greater SSL PRIME project, the following dependencies arerequired:Operating SystemAll tools developed for the greater SSL PRIME projecttargetMac OS and Linux. PRIME is not supported or recommended to run on Windowsbut can be modified to do so at your own risk.Shell SoftwareThe following softwareis requiredto run the tools:clocgitpython 3.9.6or newersloccountThe following softwareis optionalto run the tools:jqparallelParallel::ForkManagerPerl packageBundled ProjectsThis projects bundles the followingpythonprojects into onepipinstallable:PRIME BadgesPRIME Bus FactorPRIME CommitsPRIME GitHub Repository SearcherPRIME Issue DensityPRIME Issue SpoilagePRIME IssuesPIME JSON ConverterPRIME Productivity
Package: climen
PyCLIMenuCLI Menu module for Python.UsageStandard usagefrompyclimenuimportmenumain_menu=menu.Menu("Main Menu")main_menu.add_items({"Option 1":callback_1,"Option 2":callback_2})main_menu.display()Alternatively...frompyclimenuimportmenumenu.BasicMenu("Main Menu",{"Option 1":callback_1,"Option 2":callback_2})Option menufrompyclimenuimportmenucolor_menu=menu.OptionMenu("Color")red=(255,0,0)green=(0,255,0)blue=(0,0,255)color_menu.add_items({"Red":red,"Green":green,"Blue":blue})color=color_menu.display()Alternatively...frompyclimenuimportmenucolor_menu=menu.BasicOptionMenu("Color",{"Red":(255,0,0),"Green":(0,255,0),"Blue":(0,0,255)})color=color_menu.selection
Package: climenu
climenuThis project is used to createsimplecommand-line interfaces using
text-based menus.The interface loosely mimicsclick(the use of
decorators to define the interface).Documentation is hostedon ReadTheDocs; releases
are hostedon pypi.Example:from__future__importprint_functionimportclimenu@climenu.menu()defbuild_release():'''Build release'''print("built the release")@climenu.menu()defbuild_package():'''Build the package'''print("built the package")if__name__=='__main__':climenu.run()
Package: climenu-pkg-baely
PyCLIMenuCLI Menu module for Python.UsageStandard usageimportmenumain_menu=menu.Menu("Main Menu")main_menu.add_items({"Option 1":callback_1,"Option 2":callback_2})main_menu.display()Alternatively...importmenumenu.BasicMenu("Main Menu",{"Option 1":callback_1,"Option 2":callback_2})Option menuimportmenucolor_menu=menu.OptionMenu("Color")red=(255,0,0)green=(0,255,0)blue=(0,0,255)color_menu.add_items({"Red":red,"Green":green,"Blue":blue})color=color_menu.display()Alternatively...importmenucolor_menu=menu.BasicOptionMenu("Color",{"Red":(255,0,0),"Green":(0,255,0),"Blue":(0,0,255)})color=color_menu.selection
Package: clime-productivity
CLIME ProductivityA tool to calculate the productivity of a Git repositoryTable of ContentsCLIME ProductivityTable of ContentsAboutLicensingHow To UseInstallationShell CommandsAboutThe Software Systems Laboratory (SSL) CLIME Productivity project is a tool to calculate the productivity of a Git repository.LicensingThis project is licensed under the BSD-3-Clause. See theLICENSEfor more information.How To UseInstallationYou can install this tool with one of the following one liners:pip install --upgrade pip clime-metapip install --upgrade pip clime-productivityShell Commandsclime-productivity-compute -husage:CLIMERepositoryProductivityCalculator[-h][-iINPUT][-oOUTPUT]AtooltocalculatetheproductivityofaGitrepositorywhereproductivityis
definedas:|ΔLOC|/(RepositoryAge)options:-h,--helpshowthishelpmessageandexit-iINPUT,--inputINPUTCommitsJSONfile.DEFAULT:./commits_loc.json-oOUTPUT,--outputOUTPUTOutputJSONfile.DEFAULT:./productivity.json

Author(s):NicholasM.Synovic,MatthewHyatt,GeorgeK.Thiruvathukalclime-productivity-graph -husage:CLIMEProductivityGrapher[-h][-iINPUT][-oOUTPUT][--typeTYPE][--titleTITLE][--x-labelX_LABEL][--y-labelY_LABEL][--stylesheetSTYLESHEET][-v]Atooltographtheproductivityofarepository

options:-h,--helpshowthishelpmessageandexit-iINPUT,--inputINPUTJSONexportfromCLIMEGitHubIssueDensityCompute.DEFAULT:./productivity.json-oOUTPUT,--outputOUTPUTFilenameofthegraph.DEFAULT:./productivity.pdf--typeTYPETypeoffiguretoplot.DEFAULT:line--titleTITLETitleofthefigure.DEFAULT:""--x-labelX_LABELXaxislabelofthefigure.DEFAULT:""--y-labelY_LABELYaxislabelofthefigure.DEFAULT:""--stylesheetSTYLESHEETFilepathofmatplotlibstylesheettouse.DEFAULT:""-v,--versionDisplayversionofthetool

Author(s):NicholasM.Synovic,MatthewHyatt,GeorgeK.Thiruvathukal
Package: clime-repository-searcher
CLIME GitHub Repository SearcherA utility to perform advanced searching on GitHub using both the REST and GraphQL APIsTable of ContentsCLIME GitHub Repository SearcherTable of ContentsAboutLicensingHow To UseInstallationCommand Line ArguementsAboutThe Software Systems Laboratory (SSL) GitHub Repository Searcher is an installable Python project that utilizes both the GitHub REST and GraphQL APIs to allow for the advanced searching of repositories hosted on GitHub.LicensingThis project is licensed under the BSD-3-Clause. See theLICENSEfor more information.How To UseInstallationYou can install the tool from PyPi with one of the following one liners:pip install clime-metricspip install clime-github-repository-searcherCommand Line Arguementsclime-github-repository-searcher -husage:CLIMEGitHubRepositorySearcher[-h][-rREPOSITORY][--topicTOPIC]-oOUTPUT-tTOKEN[--min-starsMIN_STARS][--max-starsMAX_STARS][--min-commitsMIN_COMMITS][--max-commitsMAX_COMMITS][--min-issuesMIN_ISSUES][--max-issuesMAX_ISSUES][--min-pull-requestsMIN_PULL_REQUESTS][--max-pull-requestsMAX_PULL_REQUESTS][--min-forksMIN_FORKS][--max-forksMAX_FORKS][--min-watchersMIN_WATCHERS][--max-watchersMAX_WATCHERS][--min-created-dateMIN_CREATED_DATE][--max-created-dateMAX_CREATED_DATE][--min-pushed-dateMIN_PUSHED_DATE][--max-pushed-dateMAX_PUSHED_DATE][-v]AutilitytoperformadvancedsearchingonGitHubusingboththeRESTand
GraphQLAPIs

options:-h,--helpshowthishelpmessageandexit-rREPOSITORY,--repositoryREPOSITORYAspecificrepositorytobeanalyzed.MustbeinformatOWNER/REPO--topicTOPICTopictoscrape(upto)thetop1000repositoriesfrom-oOUTPUT,--outputOUTPUTJSONfiletodumpdatato-tTOKEN,--tokenTOKENGitHubpersonalaccesstoken--min-starsMIN_STARSMinimumnumberofstarsarepositorymusthave--max-starsMAX_STARSMaximumnumberofstarsarepositorymusthave--min-commitsMIN_COMMITSMinimumnumberofcommitsarepositorymusthave--max-commitsMAX_COMMITSMaximumnumberofcommitsarepositorymusthave--min-issuesMIN_ISSUESMinimumnumberofissuesarepositorymusthave--max-issuesMAX_ISSUESMaximumnumberofissuesarepositorymusthave--min-pull-requestsMIN_PULL_REQUESTSMinimumnumberofpullrequestsarepositorymusthave--max-pull-requestsMAX_PULL_REQUESTSMaximumnumberofpullrequestsarepositorymusthave--min-forksMIN_FORKSMinimumnumberofforksarepositorymusthave--max-forksMAX_FORKSMaximumnumberofforksarepositorymusthave--min-watchersMIN_WATCHERSMinimumnumberofwatchersarepositorymusthave--max-watchersMAX_WATCHERSMaximumnumberofwatchersarepositorymusthave--min-created-dateMIN_CREATED_DATEMinimumdateofcreationarepositorymusthave--max-created-dateMAX_CREATED_DATEMaximumdateofcreationarepositorymusthave--min-pushed-dateMIN_PUSHED_DATEMinimumdateofthelatestpusharepositorymusthave--max-pushed-dateMAX_PUSHED_DATEMaximumdateofthelatestpusharepositorymusthave-v,--versionDisplayversionofthetool

Author(s):NicholasM.Synovic,MatthewHyatt,GeorgeK.Thiruvathukal
Package: climesync
CLI for the TimeSync APIClimesync is a CLI frontend to submit times to a TimeSync implementation.IMPORTANT: Please upgrade your setuptools before installing ClimeSync,
we recommend version >= 23.1.0$ pip install -U setuptoolsTo run the program, type$ climesyncclimesync accepts args like so:$ climesync –connect <timesync baseurl> –user <username> –password <password>or with short options:$ climesync -c <timesync baseurl> -u <username> -p <password>If all three args are provided, climesync will connect to timesync and
authenticate your username and password for you.Once climesync is running, you can complete the following tasks:c - connect
dc - disconnect
s - sign in
so - sign out/reset credentialsct - submit time
ut - update time
gt - get times
st - sum times
dt - delete timecp - create project
up - update project
gp - get projects
dp - delete projectca - create activity
ua - update activity
ga - get activities
da - delete activitycu - create user
uu - update user
gu - get users
du - delete userq - exitSee detailed documentation athttp://climesync.readthedocs.io/en/latest/
Package: climetlab
CliMetLabCliMetLabis a Python package aiming at simplifying access to climate and meteorological datasets, allowing users to focus on science instead of
technical issues such as data access and data formats. It is mostly intended to be used in Jupyter notebooks, and be interoperable with all popular
data analytic packages, such as Numpy, Pandas, Xarray, SciPy, Matplotlib, etc. as well as machine learning frameworks, such as Tensorflow, Keras or PyTorch.The documentation can be found athttps://climetlab.readthedocs.io/.PluginsSeehttps://climetlab.readthedocs.io/en/latest/guide/pluginlist.html.LicenseApache License 2.0In applying this licence, ECMWF does not waive the privileges and immunities
granted to it by virtue of its status as an intergovernmental organisation nor does it submit to any jurisdiction.
Package: climetlab-cems-flood
Climetlab CEMS FloodBuild StatusDownload GloFAS data from the Climate Data Store.importclimetlabascmldataset=cml.load_dataset('glofas-seasonal',model='lisflood',system_version='operational',temporal_filter='2022 01 01',leadtime_hour='24-72',variable="river_discharge_in_the_last_24_hours")ds=dataset.to_xarray()ds(<xarray.Dataset>Dimensions:(realization:51,forecast_reference_time:1,leadtime:3,lat:1500,lon:3600)Coordinates:*realization(realization)int64012345...4647484950*forecast_reference_time(forecast_reference_time)datetime64[ns]2022-01-01*leadtime(leadtime)timedelta64[ns]1days2days3days*lat(lat)float64-59.95-59.85-59.75...89.8589.95*lon(lon)float64-179.9-179.8-179.8...179.8540.0time(forecast_reference_time,leadtime)datetime64[ns]...Datavariables:dis24(realization,forecast_reference_time,leadtime,lat,lon)float32...Attributes:GRIB_edition:2GRIB_centre:ecmfGRIB_centreDescription:EuropeanCentreforMedium-RangeWeatherForecastsGRIB_subCentre:0Conventions:CF-1.7institution:EuropeanCentreforMedium-RangeWeatherForecastshistory:2023-01-02T10:51GRIBtoCDM+CFviacfgrib-0.9.1...,)Moreexample requestsanddocumentation
Package: climetlab-eumetnet-postprocessing-benchmark
The Eumetnet postprocessing benchmark dataset Climetlab pluginA plugin forclimetlabto retrieve the Eumetnet postprocessing benchmark datasets.Ease the download of the dataset time-aligned forecasts, reforecasts (hindcasts) and observations (ERA5 reanalysis).Climetlab plugin version: 0.2.5EUPPBench dataset version: 1.0Base dataset version: 1.0Dataset status:Datasets statusDocumentation of the datasetsThere are currently two sub-datasets available:The EUPPBench datasetThe base dataset over Europe's domainThey are both documentedhere.Using climetlab to access the dataSee thedemo notebooksdemo_training_data_forecasts.ipynbdemo_ensemble_forecasts.ipynbdemo_EUPPBench_germany_station_data.ipynbdemo_EUPPBench_gridded_data.ipynbThe climetlab python package allows easy access to the data with a few lines of code such as:# Uncomment the line below if climetlab and the plugin are not yet installed#!pip install climetlab climetlab-eumetnet-postprocessing-benchmarkimportclimetlabascmlds=cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-surface',"2017-12-02","2t","highres")fcs=ds.to_xarray()which download the deterministic (high-resolution) forecasts for the 2 metres temperature.
Once obtained, the corresponding observations (if available) can be retrieved in thexarrayformat by using theget_observations_as_xarraymethod:obs=ds.get_observations_as_xarray()Support and contributingPlease open aissue on github.LICENSESee theLICENSEfile for the code, and theDATA_LICENSEfor the data.AuthorsSee theCONTRIBUTORS.mdfile.
Package: climetlab-eumetsat
eumetsatA dataset plugin for climetlab for the dataset eumetsat/mydataset.FeaturesIn this README is a description of how to get the eumetsat.Datasets descriptionThere are two datasets:1 :mydataset2TODOUsing climetlab to access the data (supports grib, netcdf and zarr)See the demo notebooks here (https://github.com/ecmwf-lab/climetlab-eumetsat/notebookshttps://github.com/ecmwf-lab/climetlab-eumetsat/notebooks/demo_mydataset.ipynb[nbviewer] (https://nbviewer.jupyter.org/github/climetlab_eumetsat/blob/main/notebooks/demo_mydataset.ipynb)
[colab] (https://colab.research.google.com/github/climetlab_eumetsat/blob/main/notebooks/demo_mydataset.ipynb)The climetlab python package allows easy access to the data with a few lines of code such as:!pip install climetlab climetlab-eumetsat
import climetlab as cml
ds = cml.load_dataset(""eumetsat-mydataset", date='20201231',)
ds.to_xarray()
Package: climetlab-google-drive-source
climetlab-google_drive_sourceA source plugin for climetlab for the source google-drive.FeaturesIn this README is a description of how to use the source provided by the python package google_drive_source.Source descriptionRead a publicly accessible file from google drive as if it was local (i.e. cache the file locally).Using climetlab to access the dataSee thedemo notebooksdemo.ipynbThe climetlab python package allows easy access to the data with a few lines of code such as:!pipinstallclimetlabclimetlab-google-drive-sourceimportclimetlabascmlds=cml.load_source("google-drive",file_id="...")# use ds as you would use cml.load("file", filename)TodoAdd authentification support. Perhaps using the package pydrive2?Support and contributingOpen a issue on github or a pull request.LICENSESee the LICENSE file.
(C) Copyright 2022 European Centre for Medium-Range Weather Forecasts.
This software is licensed under the terms of the Apache Licence Version 2.0
which can be obtained athttp://www.apache.org/licenses/LICENSE-2.0.
In applying this licence, ECMWF does not waive the privileges and immunities
granted to it by virtue of its status as an intergovernmental organisation
nor does it submit to any jurisdiction.AuthorsFlorian Pinault and al.See also the CONTRIBUTORS.md file.
Package: climetlab-intake-plugin
ClimetLab-IntakeSource plugin addedTests addedDataset plugin with cookiecutter guidelines added, repo:https://github.com/esowc/climetlab-intake-dataTODO:matplotlib driver addition along with magics (high priority)Caching mechanism with fsspec andhttps://github.com/ecmwf/climetlab/blob/master/climetlab/core/caching.py(low priority)
Package: climetlab-maelstrom-ens10
ENS10 DatasetA dataset plugin for CliMetLab for the ENS10 dataset from the MAELSTROM project.The ENS10 dataset is designed to help the development of machine learning tools to improve ensemble predictions via post-processing. It consists of the model output data of ECMWF "hindcast" experiments. These are ensemble forecasts with 10 ensemble members that are spread over 20 years (1998-2017) with two forecasts per week.StructureThe dataset is grouped by day-of-year (i.e., a single date contains all 20 years of predictions), where
each date contains three steps: 0, 24, and 48 hour lead time. Thus, files contain three days at a time.
To query the list of dates, runall_dateliston the loaded dataset.In every file, there are 6 dimensions of data (in this order):number(ensemble member),time(year offset from 1998),step(forecast lead time, 0=0h, 1=24h, 2=48h),surface/isobaricInhPa(pressure level),latitude, andlongitude.A smaller (10 GB) dataset is also available through this plugin asmaelstrom-ens5mini. It spans the first 10 years, cropped to Europe, and contains 5 ensemble members. See demo notebook on usage.FeaturesUsing CliMetLab to access the dataSee the demo notebookshereAccessing data is performed on a date basis, where the dataset is organized by day-of-year (i.e., the file of each date contains all 20 forecasts at all of the 20 years). The dataset is also split to surface-level data, and pressure-level data for above-ground forecasts.Theclimetlabpython package allows easy access to the data with a few lines of code:!pipinstallclimetlabclimetlab-maelstrom-ens10importclimetlabascml# Toy datasetds=cml.load_dataset("maelstrom-ens5mini",date='01')# Pressure-level datads=cml.load_dataset("maelstrom-ens10",date='20170226',dtype='pl')# Surface-level datads=cml.load_dataset("maelstrom-ens10",date='20170226',dtype='sfc')# Alternatively, the year can be omitted, and pressure levels are given by default:# ds = cml.load_dataset("maelstrom-ens10", date='0226')# Convert dataset to xarray datads.to_xarray()
Package: climetlab-maelstrom-nogwd
maelstrom-nogwdA CliMetLab (https://climetlab.readthedocs.io) dataset plugin for the dataset maelstrom-nogwd.FeaturesIn this README is a description of how to get the maelstrom-nogwd.Dataset descriptionContains the input/output dataset for learning non-orographic
gravity wave drag, as described inhttps://arxiv.org/abs/2101.08195Data is grouped by forecast start date.Data has been preprocessed into inputs "x" and outputs "y".
"x" contains vertical profiles of winds & temperature plus surface values
of pressure and geopotential.
"y" contains the the wind increments due to parametrised non-orographic
gravity wave drag. The machine learning task is to predict y given x.
Unlike many ML tasks within the field of weather and climate, this task
can be predicted independently for each column of atmosphere.Using climetlab to access the dataData can be accessed either by forecast start-date or dataset type.
With neither argument provided, the first file is loaded, corresponding
to 2015-01-01 (the tier-1 dataset). Incorrect dates will be flagged.
Other dataset types are "training", "validation" & "testing" corresponding
to the date groups outlined inhttps://arxiv.org/abs/2101.08195The climetlab python package allows easy access to the data with a few lines of code such as:!pip install climetlab climetlab_maelstrom_nogwd
import climetlab as cml
cmlds = cml.load_dataset("maelstrom-nogwd", date='2015-01-01')
ds = cmlds.to_xarray()
#or
cmlds = cml.load_dataset("maelstrom-nogwd", dataset='training')
ds = cmlds.to_xarray()Seehttps://git.ecmwf.int/projects/MLFET/repos/maelstrom-nogwd/browse/notebooks/demo_nogwd.ipynbfor a short tutorial.
Package: climetlab-maelstrom-power-production
climetlab-power-productionA dataset plugin for climetlab for the dataset climetlab-plugin-a6/maelstrom-production-forecasts.FeaturesIn this README is a description of how to get the CliMetLab Plugin for A6.InstallationViapippip install climetlab-maelstrom-power-productionor viapoetrygit clone git@github.com:4castRenewables/climetlab-plugin-a6.git
cd climetlab-plugin-a6
poetry install --no-devDatasets descriptionThere are five datasets:maelstrom-constants-a-bmaelstrom-power-productionmaelstrom-weather-model-levelmaelstrom-weather-pressure-levelmaelstrom-weather-surface-levelA detailed description of each dataset (variables, meta data etc.) is availablehere(see Section 3.6).maelstrom-constants-a-bConstants used for calculation of pressure at intermediate model levels.Usageimportclimetlabascmlproduction_data=cml.load_dataset("maelstrom-constants-a-b")ReferencesIFS Documentation – Cy47r1, Operational implementation 30 June 2020, Part III: Dynamics and Numerical Procedures, ECMWF, 2020, p. 6, Eq. 2.11maelstrom-power-productionPower production data of wind turbines located in various regions of Germany.The data were provided byNOTUS energy GmbH & Co. KG.
For a detailed description see the link above.Usageimportclimetlabascmlproduction_data=cml.load_dataset("maelstrom-power-production",wind_turbine_id=1)Thewind_turbine_idis a number1toN, whereNis the maximum number of currently available wind turbines.Currently available: 45 wind turbines.maelstrom-weather-model-levelECMWFIFS HRES model level data for whole Europe.For a detailed description see the link above.Usageimportclimetlabascmlweather_ml=cml.load_dataset("maelstrom-weather-model-level",date="2019-01-01")Currently available dates:2017-01-01until2020-12-31maelstrom-weather-pressure-levelECMWFIF HRES pressure level data for whole Europe.For a detailed description see the link above.Usageimportclimetlabascmlweather_pl=cml.load_dataset("maelstrom-weather-pressure-level",date="2019-01-01")Currently available dates:2017-01-01until2020-12-31maelstrom-weather-surface-levelECMWFIFS HRES surface level data for whole Europe.For a detailed description see the link above.Usageimportclimetlabascmlweather_pl=cml.load_dataset("maelstrom-weather-surface-level",date="2019-01-01")Currently available dates:2017-01-01until2020-12-31Using climetlab to access the data (supports grib, netcdf and zarr)See the demo notebookshere.The climetlab python package allows easy access to the data with a few lines of code such as:!pipinstallclimetlabclimetlab-maelstrom-power-productionimportclimetlabascmldata=cml.load_dataset("maelstrom-power-production",date="2019-01-01")data.to_xarray()Executing the notebooksBefore executing the notebooks, make sure to install the project and the
notebook dependencies correctlypoetry install --extras notebooks