Saravanan666/pypi-org huggingface dataset:
Package: climetlab-maelstrom-radiation
maelstrom-radiationA dataset plugin for climetlab (https://climetlab.readthedocs.io/en/latest/)
for the dataset maelstrom-radiation.Datasets descriptionThis data is for learning the
emulation of the ECMWF radiation scheme, using the McICA solver, found in the ecRad package
(https://github.com/ecmwf/ecrad). Building an accurate emulator of radiative heating
could accelerate weather and climate models partially by enabling the use of GPU
hardware within our models.There are two datasets, allowing different views on the same data:1 :maelstom-radiationSupports theto_xarraymethod and allows users to explore the data with all structure kept intact.2 :maelstrom-radiation-tfLoads the same data but from a shuffled and repacked into the TFRecord format. This dataset supportsto_tfdatasetwhich uses Tensorflow's dataset object.Using climetlab to access the dataBoth datasets and downloaded and explained in the demo notebook herehttps://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynbThe climetlab python package allows easy access to the data with a few lines of code such as:!pip install climetlab climetlab_maelstrom_radiation
import climetlab as cml
cml_ds = cml.load_dataset("maelstrom-radiation", subset="tier-1")
ds = cml_ds.to_xarray()

!or for the TFdataset version
cml_ds = cml.load_dataset("maelstrom-radiation-tf", subset="tier-1")
ds = cml_ds.to_tfdataset(batch_size=256)
Package: climetlab-maelstrom-yr
maelstrom-yrA dataset plugin for climetlab for the dataset maelstrom-yr. Check out thisnotebook.Datasets descriptionContains gridded weather data for the Nordics. It contains both predictors (gridded weather forecasts) and
predictand (gridded analysis fields). The forecasts are used operationally for the Nordic region onhttps://www.yr.noand currently a simple ML-solution is used, as described inNipen et al. 2020.Using climetlab to access the dataThe data can be loaded by the climetlab package (https://github.com/ecmwf/climetlab). The dataset has the
following arguments:size: Which dataset to load (currently 5GB is supported, but in the future a 5TB dataset will be added)parameter: Which predictand to load (currently "air_temperature" is supported)dates: If left blank, the whole dataset is loaded. Otherwise, provide a list of dates in "YYYY-MM-DD"
format to load a subsetHere is an example of how to load the data:!pip install climetlab climetlab_maelstrom_yr
import climetlab as cml
ds = cml.load_dataset("maelstrom-yr", size="5GB", parameter="air_temperature", dates=['2020-06-29'])
ds.to_xarray()
Package: climetlab-mltc-surface-observation-postprocessing
climetlab-mltc-surface-observation-postprocessingA dataset plugin for climetlab for the dataset mltc-surface-observation-postprocessing-main.FeaturesIn this README is a description of how to get the dataset provided by the python package climetlab_mltc_surface_observation_postprocessing.Datasets descriptionThe dataset is designed for learning forecast errors of 2m-temperature from a weather forecasting model, using station observations as the truth. Currently there are 3 variables in the dataset. For each variable the dataset contains over 5 million datapoints covering a range of different locations (not specified).1 :field = forecast_errorThe predictand for the problem, the difference between the forecasted value of 2m-temperature and the observed value, units degrees C (or equally Kelvin).2 :field = time_of_dayLocal time of day, in decimal hours. Useful for diagnosing the diurnal cycle model bias.3:field = soil_temperatureModel soil temperature, units degrees C.Using climetlab to access the dataSee thedemo notebooksdemo_main.ipynbTODO.The climetlab python package allows easy access to the data with a few lines of code such as:!pipinstallclimetlabclimetlab-mltc-surface-observation-postprocessingimportclimetlabascmlds=cml.load_dataset("mltc-surface-observation-postprocessing",field="forecast_error")ds.to_pandas()Support and contributingEither open a issue on github if this is a github repository, or send an email toemail@example.com.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.AuthorsMatthew Chantry, Fenwick Cooper, Zied Ben Bouallegue, Peter DuebenSee also the CONTRIBUTORS.md file.
Package: climetlab-script-web
# CliMetLab WebA web application to manage the cache content and configuration settings of the [CliMetLab](https://github.com/ecmwf/climetlab) Python package.## InstallationInstallation in a virtual environment is advised.` pip installclimetlab-script-web`## ContributingSeeCONTRIBUTING.md.# LicenceApache 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.
Package: climetlab-weatherbench
climetlab-weatherbenchA dataset plugin for climetlab for the dataset weatherbench. SeeWeatherBenchfor more details.FeaturesIn this README is a description of how to get the dataset(s) provided by the python package climetlab-weatherbench.Datasets descriptionAlternate host for the WeatherBench dataset. Currently only hostinggeopotential_500hPa&temperature_850hPa.Using climetlab to access the dataSee thedemo notebooksdemo_main.ipynbTODO.The climetlab python package allows easy access to the data with a few lines of code such as:!pipinstallclimetlabclimetlab-weatherbenchimportclimetlabascmlds=cml.load_dataset("weatherbench",year=2000,parameter="geopotential_500hPa")ds.to_xarray()Support and contributingEither open a issue on github if this is a github repository, or send an email toemail@example.com.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.AuthorsMatthew Chantry and al.See also the CONTRIBUTORS.md file.
Package: climetlab-wekeo-clms
climetlab-wekeo_clmsA dataset plugin for climetlab for the CLMS datasets available on WEkEO.For installation and usage instructions, please refer tothe documentation.FeaturesThis plugin provides a simple access to CLMS datasets provided by WEkEO.To narrow down the data to download, users can specify a start and an end date.While it is not mandatory, the usage of Dask is recommended.For usage examples, please refer to thedemo notebookDatasets descriptionThere are several datasets:cgls_continents_wb_v1_1kmcgls_daily10_lst_dc_global_v1cgls_daily10_lst_dc_global_v2cgls_daily10_lst_tci_global_v1cgls_daily10_lst_tci_global_v2cgls_global_albh_v1_1kmcgls_global_aldh_v1_1kmcgls_global_ba300_v1_333mcgls_global_ba300_v3cgls_global_dmp_v2_1kmcgls_global_dmp300_v1_333mcgls_global_fapar_v1_1kmcgls_global_fapar_v2_1kmcgls_global_fapar300_v1_333mcgls_global_fcover_v1_1kmcgls_global_fcover_v2_1kmcgls_global_fcover300_v1_333mcgls_global_gdmp_v2_1kmcgls_global_gdmp300_v1_333mcgls_global_lai_v1_1kmcgls_global_lai_v2_1kmcgls_global_lai300_v1_333mcgls_global_ndvi_stcgls_global_ndvi_v2_1km_ltscgls_global_ndvi_v2_1kmcgls_global_ndvi_v3_1kmcgls_global_ndvi300_v1_333mcgls_global_ndvi300_v2_333mcgls_global_swi_static_v1_0_1degreecgls_global_swi10_v3_0_1degreecgls_global_wb300_v1_333mcgls_hourly_lst_global_v1cgls_hourly_lst_global_v2The descriptions can be retrieved directly fromWEkEOUsing climetlab to access the dataSee thedemo notebooksdemo_main.ipynbThe climetlab python package allows easy access to the data with a few lines of code such as:!pipinstallclimetlabclimetlab-wekeo-clmsimportclimetlabascmlds=cml.load_dataset("wekeo-clms-cgls-hourly-lst-global-v2",start="2021-01-18",end="2021-01-19",)ds.to_xarray(xarray_open_mfdataset_kwargs={"chunks":"auto","engine":"netcdf4"})Supportandcontributing------------------------Pleaseopenaissueongithubifthisisagithubrepository.LICENSE-------SeetheLICENSEfile.(C)Copyright2023EuropeanCentreforMedium-RangeWeatherForecasts.ThissoftwareislicensedunderthetermsoftheApacheLicenceVersion2.0whichcanbeobtainedathttp://www.apache.org/licenses/LICENSE-2.0.Inapplyingthislicence,ECMWFdoesnotwaivetheprivilegesandimmunitiesgrantedtoitbyvirtueofitsstatusasanintergovernmentalorganisationnordoesitsubmittoanyjurisdiction.Authors-------GermanoGuerriniandal.SeealsotheCONTRIBUTORS.mdfile.
Package: climetlab-wekeo-datasets
climetlab-wekeo_datasetsA dataset plugin for climetlab for the datasets available on WEkEO.For installation and usage instructions, please refer tothe documentation.Currently, the plugin supports CMLS, ECMWF and CMES datasets.FeaturesThis plugin provides a simple access to the datasets provided by WEkEO.To narrow down the data to download, users can specify a start and an end date.While it is not mandatory, the usage of Dask is recommended.For usage examples, please refer to thedemo notebookDatasets descriptionThe list of supported datasets can be seen in the setup.py file.
Each item in the entry points list refers to a specific datasets.The descriptions can be retrieved directly fromWEkEOUsing climetlab to access the dataSee thedemo notebooksdemo_main.ipynbThe climetlab python package allows easy access to the data with a few lines of code such as:!pipinstallclimetlabclimetlab-wekeo-datasetsimportclimetlabascmlds=cml.load_dataset("wekeo-datasets-cgls-hourly-lst-global-v2",start="2021-01-18",end="2021-01-19",)ds.to_xarray(xarray_open_mfdataset_kwargs={"chunks":"auto","engine":"netcdf4"})Supportandcontributing------------------------Pleaseopenaissueongithubifthisisagithubrepository.LICENSE-------SeetheLICENSEfile.(C)Copyright2023EuropeanCentreforMedium-RangeWeatherForecasts.ThissoftwareislicensedunderthetermsoftheApacheLicenceVersion2.0whichcanbeobtainedathttp://www.apache.org/licenses/LICENSE-2.0.Inapplyingthislicence,ECMWFdoesnotwaivetheprivilegesandimmunitiesgrantedtoitbyvirtueofitsstatusasanintergovernmentalorganisationnordoesitsubmittoanyjurisdiction.Authors-------GermanoGuerriniandal.SeealsotheCONTRIBUTORS.mdfile.
Package: climextremes
Functions for fitting GEV and POT (via point process fitting)
models for extremes in climate data, providing return values, return
probabilities, and return periods for stationary and nonstationary models.
Also provides differences in return values and differences in log return
probabilities for contrasts of covariate values. Functions for estimating risk
ratios for event attribution analyses, including uncertainty. Under the hood,
many of the functions use functions from ‘extRemes’, including for fitting the
statistical models.
Package: climmands
ClimmandsClimmands is a small package that provides a convenient abstraction for cli subcommands.Each command is described by different class. These classes should inherit fromCommandclass. TheCommandLoaderclass is responsible for collecting all commands in module scope.CommandExecutorexecute a proper command and pass it the rest of command line arguments.Warning: it's a toy and untested package. Use only for your own responsibility!Installation$pipinstallclimmandsExample#!/usr/bin/env pythonimportargparseimportclimmandsclassHelloWorldCommand(climmands.Command):name='hello'description='Print Hello World message'defexecute(self,parsed_arguments):print('Hello world')classAddTwoNumbersCommand(climmands.Command):name='add'description='Add two numbers and print result'definitialize_arguments_parser(self,parser):parser.add_argument('first',help='First number')parser.add_argument('second',help='Second number')defexecute(self,parsed_arguments):first=int(parsed_arguments.first)second=int(parsed_arguments.second)result=first+secondprint(f'{first}+{second}={result}')defmain():parser=argparse.ArgumentParser(description='My great command')commands=climmands.CommandLoader(parser).load_commands()executor=climmands.CommandExecutor(commands)parsed_arguments=parser.parse_args()ifnotexecutor.execute(parsed_arguments):parser.print_help()if__name__=='__main__':main()
Package: cli-mock
This package provides two command line utilities:crecordandcreplay.
The former records the output (stdout and stderr) and the return code of a
command and the latter replays the command invocation by reproducing its output
and return code:$ crecord echo foo
foo
$ creplay echo foo
foo
$ crecord ls foo
ls: foo: No such file or directory
$ creplay ls foo
ls: foo: No such file or directory
$ echo $?
1This could be used to mock slow and environment-dependent command invocations
for testing purposes.Pytest pluginThere’s also a pytest plugin contained in cli_mock package. It exposespopen_controllerfixture that can be used to replaycrecordlogs in
response tosubprocess.Popeninvocations (and the APIs that call it under
the hood):def test_foo(popen_controller):
    popen_controller.set_replay_log(my_log)
    output = subprocess.check_output(['foo'])
    assert output == b'bar\n'After the replay log is activated calls tosubprocess.Popenand friends
with the commands that are contained in the log will replay from the log.
Commands that are not in the log will trigger anAssertionError.Non-strict modeIt’s possible to allow executing commands that are not in the log via
activating non-strict mode:popen_controller.set_strict(False)or:popen_controller.set_replay_log(my_log, strict=False)In non-strict mode any command that is not in the log will be passed tosubprocess.Popenwithout modifications and will be executed in a usual
fashion.
Package: climoji
climoji📜 AboutClimoji is an initiative to facilitate the use of the emojis from the command line 🖥.⚒️ InstallationInstallation is so easy, you only need to executepip install climoji🚀 Using climojiUsing climoji is as easy as typeclimoji search rocketAnd a 🚀 will appear 😄🚩 LicenseThe code is available under the MIT license.
Package: climopy
This package is meant to help climate scientists complete a variety of data analysis tasks.
It provides handy functions for objective and statistical analysis methods, deriving physical quantities, and downloading archived observational and model data.
The Github page ishere.Please note this package is a work-in-progress! The examples and documentation
are incomplete, and undiscovered bugs may exist.InstallationThe latest release can be installed or updated from PyPi as follows:pipinstallclimopypipinstall--upgradeclimopyDocumentationThe documentation ispublished on readthedocs.
Package: climpie
ClimpieTools for calculating entities' emissions budgets and associated emissions pathways.[#5 badges here]Full documentation can be found at:climpie.readthedocs.io.
We recommend reading the docs there because the internal documentation links
don't render correctly on GitLab's viewer.InstallationClimpie can be installed with conda or pip:pipinstallclimpie
condainstall-cconda-forgeclimpieAdditional dependencies can be installed using# To add plotting dependenciespipinstallclimpie[plots]# To add notebook dependenciespipinstallclimpie[notebooks]# If you are installing with conda, we recommend# installing the extras by hand because there is no stable# solution yet (issue here: https://github.com/conda/conda/issues/7502)For developersFor development, we rely onpoetryfor all our
dependency management. To get started, you will need to make sure that poetry
is installed
(instructions here,
we found that pipx and pip worked better to install on a Mac).For all of work, we use ourMakefile.
You can read the instructions out and run the commands by hand if you wish,
but we generally discourage this because it can be error prone.
In order to create your environment, runmake virtual-environment.If there are any issues, the messages from theMakefileshould guide you
through. If not, please raise an issue in theissue tracker.For the rest of our developer docs, please see.
Package: climpred
Verification of weather and climate forecasts.docstestspackagelicensecommunitytutorialsWe are actively looking for new contributors for climpred!Rileymoved to McKinsey’s
Climate Analytics team as a climate software engineer.Aaronmoved to XING as a data scientist.
We especially hope for python enthusiasts from seasonal, subseasonal or weather
prediction community. In our past coding journey, collaborative coding, feedbacking
issues and pull requests advanced our code and thinking about forecast verification
more than we could have ever expected.
Feel free to implement your own new feature or take a look at thegood first issuetag in the issues. If you are interested in maintaining climpred, please ping us.InstallationYou can install the latest release ofclimpredusingpiporconda:pipinstallclimpred[complete]condainstall-cconda-forgeclimpredYou can also install the bleeding edge (pre-release versions) by cloning this
repository or installing directly from GitHub:gitclonehttps://github.com/pangeo-data/climpred.gitcdclimpredpipinstall.--upgradepipinstallgit+https://github.com/pangeo-data/climpred.gitDocumentationDocumentation is in development and can be found onreadthedocs.
Package: climpy
climpyClimpy is working to help climate researchers to analyse climate data, write in formats ready to be used with machine learning models and analyse the accuracy of model predictionshttps://github.com/climai/climpy/actions/workflows/python-app.yml/badge.svgThe package is divided into three partsTransform: Thetransformmodule transforms by applying different conditions on your dataset. The class diagram below will detail on the application of the module.---
Transform
---

classDiagram

Hazard <|-- Criterion

class Condition{
    args
    returns_event
    func()
}

class Criterion{
    sequence
    apply_conditions()
    valid_conditions(sequence) bool
}

class Hazard{

    event_locations
    n_events
    apply_conditions()
    valid_conditions()
    get_event(n) Event
    all_events() EventList
}

class DataArray{
    xr.DataArray variables
    xr.DataArray functions()
}

class LinkDataHazard{
    on_events()
    get_values()
}

class Event{
    data
    location
    start_time
    end_time
    
    r
    tau

    set_intensity()
}

class EventList{
    event_list
}

DataArray -- LinkDataHazard
LinkDataHazard -- Hazard
Hazard *-- EventList
EventList *--Event
Condition .. Criterion
Condition .. Hazardml_data: Theml_datamodule creates/writes data to be used conveniently for different kinds of machine learning models. The class diagram below will detail on the application of the module.---
Data ML
---
classDiagram


List *-- DataArray

class X{
    values
    meta
}

class Y{
    values
    meta
}

class MLData{
    X
    Y
    tvt_split()
}


class DataArray{
    xr.DataArray variables
    xr.DataArray functions()
}

class SplitMetrics: Themetricsmodule can be applied to observed and simulated variables. This would include exhaustive set of different metrics that can be used on climate related data
Package: climtrends
ClimTrendsClimTrendsis a python package aimed at making it easy to calculate linear
trends in a variety of statistical models.  The current implementation
includes:Normal Distribution - trend in meanPoisson Distribution - trend in meanExponential Distribution - trend in meanGamma Distribution - trend in mean and standard deviationGEV Distribution - trend in meanThe module utilizesdatetime-like objects as the input time value, which
makes it easy to interoperate with data fromnetCDF4andxarray. All models use a Bayesian
framework (using theemceepackage) and assume
a uniform prior on all model parameters. This module is object-oriented and
designed to be easily extendable for regressions of other distributions.  To do
so, one needs to sub-class theClimTrendModelclass and implement a few
required routines; seeTrendNormalModel.pyor otherTrend*Model.pyfiles
for examples.Getting started# dates - a set of input dates# data - corresponding data from those datesimportclimtrendsimportnumpyasnp# initialize the MCMC modellinear_model=climtrends.TrendNormalModel(dates,data)# run the samplerlinear_model.run_mcmc_sampler(num_samples=1000)# get samples of the slopesslopes=linear_model.get_mean_trend_samples()# get the 5th and 95th percentile slopesslopes_5=np.percentile(slopes,5)slopes_95=np.percentile(slopes,95)Known IssuesThe model assumes that input data are vectors; things will likely break if not.Probably other issues exist.  This code is tested and is verified to work in some base cases,
but it is still in alpha stage and has not been tested across a range of settings.
Package: climux
ClimuxLibrary for writing command-line interfacesInstallationpipinstallclimuxUsagefromclimuximportCli,Commanddefhello(name="stranger"):"""Say hello."""returnf"Hello,{name}!"cli=Cli("hello",description="Hello world app.")cli.add(Command(hello))cli.run()Seeexamples.FeaturesSubcommandsGenerate CLI help and options from function signature and docstringAutomatic dispatch to command handling functionsLicenseMIT.
Package: climy
climyDo CLI applications easily.Instalation$pipinstallclimyBasic UsagefromclimyimportApplication,Option,Commanddefenv_handler(comm:Command):print('do every thing with env')print(comm)print(comm.vars)env_command=Command(name='env',description='Create a perfect environ to application',handler=env_handler)app=Application(name='acme',title='ACME App',description='A teste application',version='0.1.0',option_default_separator=Option.Separators.EQUAL)app.add_command(env_command)app.add_option('host','The server allow host IP number.',short='t',var_type='str')app.add_option('port','Service port.',short='p',var_type='int')app.add_option('path','Execution code path.',var_type='str',var_name='path')app.run()$acmesrv--help
ACMEApp(version0.1.0)Usage:acme<command>[options][arguments]Options:-h,--helpPrintthishelp.-t,--host=[HOST]TheserverallowhostIPnumber.-p,--port=[PORT]Serviceport.--path=[PATH]Executioncodepath.

Commands:envCreateaperfectenvirontoapplicationComponentsTablesTo print tables quickly, you just need to define a column list with the column title, size and align.
Then, preparate a data tuple list with the values in the same columns orders.
Below, an example how to print tables.fromclimy.consoleimportctablecolumns=['ID:10>','Name:50','Birth:15^','Weight:10>','Hight:10']data=[(1,'Mark White','26/12/1977',1.75,80.0),(2,'Eva Wood','30/11/1983',1.59,50.0),(3,'John Apple','26/06/1917',1.20,30.0),(4,'Linda Jansen','09/05/1961',1.63,61.0)]ctable(data,columns=columns)And the results:┌──────────┬──────────────────────────────────────────────────┬───────────────┬──────────┬──────────┐
│       ID │ Name                                             │     Birth     │   Weight │ Hight    │
├──────────┼──────────────────────────────────────────────────┼───────────────┼──────────┼──────────┤
│        1 │ Mark White                                       │   26/12/1977  │     1.75 │ 80.0     │
│        2 │ Eva Wood                                         │   30/11/1983  │     1.59 │ 50.0     │
│        3 │ John Apple                                       │   26/06/1917  │      1.2 │ 30.0     │
│        4 │ Linda Jansen                                     │   09/05/1961  │     1.63 │ 61.0     │
└──────────┴──────────────────────────────────────────────────┴───────────────┴──────────┴──────────┘Progress BarYou can print progresses bars by usingcprogressfunction.fromclimy.consoleimportcprogresscprogress(size=50,max=300.0,value=20.0,color='green')# ━━━───────────────────────────────────────────────