Saravanan666/pypi-org huggingface dataset:
Package: climate-analyzer
climate_analyzerclimate_analyzer is a command line app that downloads NOAA weather station
daily summary data and provides a gui to analyze year-to-year trends.
It also provides for searching a particular region for available NOAA
weather stations. climate_analyzer uses NOAA's Web Services API described
here: ([https://www.ncdc.noaa.gov/cdo-web/webservices]).FeaturesSearch for available NOAA weather stations by state/county.Download NOAA Climate Daily Summary Data by weather station.Store data in off-line database(s) organized by weather station.Visualize with tkinter + matplotlib GUIChecks NOAA Web Services for updates to existing off-line database(s).Usageclimate_analyzer is a command line app that takes one and only one option.
If no option is provided, climate_analyzer checks for updates to existing
databases and then launches the visualization gui.  In order to access NOAA's
Web Services, an access token must be obtained and provided to climate_analyzer.When installed with pip, climate_analyzer will add 'cda.exe' to the Python
Environment in the pythonXX\Scripts directory and a climate_analysis package to
pythonXX\Lib\site-packages.  cda.exe utilizes the climate_analysis package.
Config data is kept in cda.ini, also stored in pythonXX\Lib\site-packages.Options--token [access-token]Set the  used by climate_analyzer when connecting to NOAA's
Web Services.  The  is a 32-character alpha-numeric and can be
obtained from here here:https://www.ncdc.noaa.gov/cdo-web/tokenThis, and other configuration parameters are stored in
..\site-packages\climate_analyzer\cda.ini.--findrgn [state]Set the state & county region used by --find.  For each state, climate_analyzer
prompts with a list of possible counties (or parishes).  The state/county is
converted 5-digit fip_code and stored in cda.ini. See:https://www.census.gov/library/reference/code-lists/ansi.html--find [radius]Display <STATION_ID> and other information for all weather stations within
the state & county set by -findrgn.--home [lat,long]Set internal variable  used to calculate distance of weather station.--station [alias]Set <STATION_ALIAS> and <STATION_ID> used by --getcd.  The user is prompted
for the <STATION_ID>.  Any number of alias & id pairs may be set but a
[alias, id] pair must be set inorder to download its climate data.--getcd [alias]Download all available daily summary climate data for [alias].  Daily summary
consists of [tmin, tmax, tavg, prcp, snow, snwd].  Data is stored in sqlite
database @ %USERPROFILE%/AppData/ClimateData with a filename of [alias].  The
location used to store sqlite databases can be changed by editing cda.ini.
Package: climate-assessment
Please note that ``climate-assessment`` is still in early developmental stages, thus all interfaces are subject to change.The packageclimate-assessmentprovides the possibility to reproduce the climate variable data for the working group III (WGIII or WG3) contribution to the IPCC Sixth Assessment (AR6) report, using climate emulators that were used in the working group I (WGI or WG1) contribution to AR6.
It also allows for assessing new emissions pathways in a way that is fully consistent with AR6.InstallationUsingpippipis Python’s default package management system.Attention!Due to the better dependency resolution installing withpip>=22is recommended.If you install Anaconda, thenpipis also usable.pipcan also be used when Python is installed directly,withoutusing Anaconda.Ensurepipis installed—with Anaconda, or according to the pip documentation.Open a command prompt and run:$ pip install climate-assessmentFrom source(Optional) If you intend to contribute changes toclimate-assessment, installing
directly fromsourceis the way to go.Detailed instructions on how to do this can be found in the documentation underhttps://climate-assessment.readthedocs.io/en/latest/install.html.DocumentationAll documentation, including installation instructions, can be found athttps://climate-assessment.readthedocs.io/.LicenseLicensed under an MIT License. See the LICENSE file for more information.DevelopmentRaising an issueIf you have a suggestion for development, or find a bug, please report this under:https://github.com/iiasa/climate-assessment/issues.Running the testsThe tests can be run withpytest. On a Linux system, you should run something likeMAGICC_PROBABILISTIC_FILE=path/to/probabilistic-filepytest tests.
Note that for the tests to work properly, you must set up your.envfile (see “Environment” section above).
On Windows, the environment variables (likeMAGICC_PROBABILISTIC_FILE=path/to/probabilistic-file) should be set system-wide, and the command readspytest tests.Formatting codeBefore committing or merging code, the following lines should be run to ensure that the formatting is consistent with what is expected by the Continuous Integration setup (for users withmakeinstalled,make checkswill run these for you):blacksrcscriptstestssetup.pyisortsrcscriptstestssetup.pyflake8srcscriptstestssetup.py
Package: climate-indices
Climate Indices in PythonThis project contains Python implementations of various climate index algorithms which provide
a geographical and temporal picture of the severity of precipitation and temperature anomalies
useful for climate monitoring and research.The following indices are provided:SPI,
Standardized Precipitation Index, utilizing both gamma and Pearson Type III distributionsSPEI,
Standardized Precipitation Evapotranspiration Index, utilizing both gamma and Pearson Type III distributionsPET, Potential Evapotranspiration, utilizing eitherThornthwaiteorHargreavesequationsPNP,
Percentage of Normal PrecipitationPCI, Precipitation Concentration IndexThis Python implementation of the above climate index algorithms is being developed
with the following goals in mind:to provide an open source software package to compute a suite of
climate indices commonly used for climate monitoring, with well
documented code that is faithful to the relevant literature and
which produces scientifically verifiable resultsto provide a central, open location for participation and collaboration
for researchers, developers, and users of climate indicesto facilitate standardization and consensus on best-of-breed
climate index algorithms and corresponding compliant implementations in Pythonto provide transparency into the operational code used for climate
monitoring activities at NCEI/NOAA, and consequent reproducibility
of published datasets computed from this packageto incorporate modern software engineering principles and scientific programming
best practicesThis is a developmental/forked version of code that is originally developed and
maintained by NIDIS/NCEI/NOAA. The official release version is available atdrought.gov.DocumentationLicenseDisclaimerCitationYou can citeclimate_indicesin your projects and research papers via the BibTeX
entry below.@misc {climate_indices,
    author = "James Adams",
    title  = "climate_indices, an open source Python library providing reference implementations of commonly used climate indices",
    url    = "https://github.com/monocongo/climate_indices",
    month  = "may",
    year   = "2017--"
}
Package: climate-learn
ClimateLearnClimateLearnis a Python library for accessing state-of-the-art climate data and machine learning models in a standardized, straightforward way. This library provides access to multiple datasets, a zoo of baseline approaches, and a suite of metrics and visualizations for large-scale benchmarking of statistical downscaling and temporal forecasting methods. For further context on our past motivation and future plans, check out our announcementblog post.UsagePython 3.8+is required. The xESMF package has to be installed separately since one of its dependencies, ESMpy, is available only through Conda.conda install -c conda-forge xesmf
pip install climate-learnQuickstartWe have a quickstart notebook in thenotebooksfolder titledQuickstart.ipynb. It is intended for use in Google Colab and can be launched by clicking the Google Colab badge above or this link:https://colab.research.google.com/drive/1LcecQLgLtwaHOwbvJAxw9UjCxfM0RMrX?usp=sharing.We also previewed some key features of ClimateLearn at a spotlight tutorial in the "Tackling Climate Change with Machine Learning" Workshop at the Neural Information Processing Systems 2022 Conference. The slides and recorded talk can be found onClimate Change AI's website.DocumentationFind us onReadTheDocs.About UsClimateLearn is managed by the Machine Intelligence Group at UCLA, headed byProfessor Aditya Grover.ContributingContributions are welcome! See ourcontributing guide.Citing ClimateLearnIf you use ClimateLearn, please see theCITATION.cfffile or use the citation prompt provided by GitHub in the sidebar.
Package: climate-metrics
Climate metricsclimate_metricsimplements standard greenhouse gas methods including Global Warming Potential (GWP) and Average Global Temperature Change Potential (GTP).  The implementation provides flexibility for computing climate metrics for a time series of GHG emissions represented as a time series. You can find a motivating examplehere.DevelopmentTests can be executed on the local copy of climate_metrics by runningpython -m pytestfrom the root directry which adds the current directory tosys.path.Notebooks are checked using nbval:pytest --nbval notebookswill check that the notebook outputs are the same andpytest --nbval-lax notebookswhich check that the notebooks execute without errors.
Package: climatempo
MetroSaoPauloAuthor: Julio BlancoVersion: 0.0.1DescriptionSimple wrapper over climatempo basic api
Originally written for a home assistant sensorLicenseCopyright 2019  Julio Manuel Blanco PérezPermission 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: climatempopy
ClimaTempoPyA Clima Tempo API Python WrapperRetrieving Brazilian current weather and forecast prevision from Clima Tempo site.Exampleimport sys
from climatempopy import ClimaTempo
from aiohttp import ClientSession
import asyncio

YOUR_CLIMA_TEMPO_TOKEN =''
LOCALE_ID = ''

async def main():
    async with ClientSession() as session:
        climatempo = ClimaTempo(session, YOUR_CLIMA_TEMPO_TOKEN , LOCALE_ID)
        await climatempo.update_current_weather()
        await climatempo.update_72_hours_forecast()
        await climatempo.update_15_days_forecast()
    print(climatempo.current)
    print(climatempo.forecast_72_hours)
    print(climatempo.forecast_15_days)

loop = asyncio.get_event_loop()
loop.run_until_complete(main())Scripts of this lib$ pip3 install climatempopy
$ get_localeID YOUR_CLIMATEMPO_TOKEN LAT LON
$ get_weather_by_localeID YOUR_CLIMATEMPO_TOKEN LOCALE_ID
Package: climatereport-zillow
climatereport_zillowUnlike other real estate sites, Zillow listings do not contain a section on the environmental risk of the property in the listing. This package takes a Zillow url as input and uses it to compile an HTML report on the climate risks of the property in the zillow listing. This report will automatically open after running the function. The report includes data provided by the First Street Foundation, under the the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License.Installation$pipinstallclimatereport_zillowHow to Use$fromclimatereport_zillowimportclimatereport_zillow
$climatereport_zillow.climatereport_zillow("https://www.zillow.com/homedetails/13353-Cavandish-Ln-Moreno-Valley-CA-92553/18008847_zpid/")ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licenseclimatereport_zillowwas created by Darci Kovacs. It is licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
license.Creditsclimatereport_zillowwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: climate-resilience
climate-resilienceDownload ExamplesThis file requires adownload_params.ymlfile to specify the download configurations.We cannot directly download the data from the Google Earth Engine directly onto the local machine. So the best option is to download to the drive and then download that data to the local drive.Preprocess ExamplesThe preprocessing functions will expect that the local data drive contains the downloaded data.If the data is on drive, the drive needs to be mounted.
This is easier to do in a google colab session. Once the drive is mounted, the path of the mounted drive can be used with the functions as normal.Expected file and directory structure:The input file and directory structure for functionscalculate_Nth_percentile(),calculate_pr_count_amount(), andcalculate_temporal_mean()in thepreprocessing codeshould be as follows:datadir
├── scenario1_variable1_ensemble
│   ├── name1_state1_scenario1_variable1.csv
│   └── name2_state2_scenario1_variable1.csv
├── scenario1_variable2_ensemble
│   ├── name1_state1_scenario1_variable2.csv
│   └── name2_state2_scenario1_variable2.csv
├── scenario2_variable1_ensemble
│   ├── name1_state1_scenario2_variable1.csv
│   └── name2_state2_scenario2_variable1.csv
└── scenario2_variable2_ensemble
    ├── name1_state1_scenario2_variable2.csv
    └── name2_state2_scenario2_variable2.csvVisualization ExamplesThe visualization code will be easier to be used in a notebook as inline visualizations can be used.Map visualization notebookBelow is a screenshot of the interactive map with the sites marked.Box plot visualization notebookBelow is a screenshot of boxplot of annual precipitation in different regions of the United States.Library Features:DownloaderClass SiteDownloadermember functions:download_model_average_daily()download_historical_daily()download_historical_monthly()download_samples()Preprocessing functionscalculate_Nth_percentile()calculate_pr_count_amount()calculate_temporal_mean()get_climate_ensemble()get_per_year_stats()get_sub_period_stats()Vizualization functionsplot_map()plot_histogram()plot_boxplots()ContributorsSatyarth PraveenZexuan XuHaruko Wainwright
Package: climates
ClimatesCommand-line interfaces made accessible to even simpletons.InstallationpipinstallclimatesUsage# Step 1: import ClimatefromclimatesimportClimate# Step 2: create Climate objectcli=Climate("hello",description="Hello world app.")# Step 3: ???# Step 4: add commands to CLIcli.add_commands(hello,bye)# Step 5: run CLIcli.run()# Step 6: PROFIT!!!Seeexample.pyfor details.FeaturesGenerate CLI help and options from docstrings and type annotationsAutomatic dispatch to command handling functionsLicenseMIT.
Package: climate-search-cli
climate-search-cliA python program that can be run from the command line, and used to search climate policy documents.Task OverviewCreated as an interview technical challenge. The task is to create a cli tool that can be used to search summaries of climate documents.The cli needed to have the following functionality:Load & validate documents into a database at the command lineQuery the documents returning a sequence of document objectsDisplay the documents and some statistics about them as outputOrder by relevance using a relevancy scoreEvaluation CriteriaThe following are the items that are being evaluated:ReadabilityMaintainabilityFunctionalityEfficiencyModularityCommenting and documentationTesting StrategyRunRun poetry to install dependencies, (see below for other ways of running):poetry install
poetry shellData can be loaded via:cs loadThis will also output errors to the same directory, and load the data into a database. A custom file can also be loaded using the --localpath argument.Data can then be queried by passing keywords with the retrieve command:cs retrieve -k green -k energyThis will display the policies that match. Results can also be sorted with:cs retrieve -k forests --sortThe test directory contains both unit and integration tests, these can be run with pytest:pytestSolution OverviewI decided to use click as the cli tool for this project. As well as sqlite as a backend, both of these are simple and portable, although if I could start again, I'd be keen to use a database that had support for arrays. Transformations and schema definitions where done in pandas for convenience, I originally started going down the path of having multiple tables in the database, but decided this was over optimising for what was needed with the given timeframe. Having just one table meant pandas was a straightoferward option for defining the table. The search relevency implementation is just a quick tfidf algorithm on the results.Time takenI worked intermittently on this over the course of a couple of days. I think the total time actively working on the solution was about 6 hours. (Not including time spent reading the brief, researching and planning). I could keep going, but I went over the suggested timeframe, so I'm leaving it here. Some key items I'd like to improve include error handling and the relevancy algorithm.alternate ways of runningDockerThis can also be run via docker:docker build -t climate-search-cli:latest .docker run climate-search-cli:latest load
docker run climate-search-cli:latest retrieve -k cycling -k health --sortpypiAlsoavailable on pypi:pip install climate-search-cli
Package: climateserv
ClimateServ API AccessThis is a python package to access theClimateSERV APIyou can install using conda or pip:conda install -c servir climateservpip install climateservCurrent supported operations:Timeseries CSVVariablesAverageMinMaxDownload Zip file of tifsVariableDownloadDownload Zip file containing a NetCDFVariableNetCDFCurrent supported datasets:CHIRPSRainfallVariable: CHIRPSGEFSAnomaliesVariable: CHIRPS_GEFS_anomPrecipitationMeanVariable: CHIRPS_GEFS_precip_meanCHIRPRainfallVariable: CHIRPeMODISCentral Asia NDVIVariable: CentralAsia_eMODISEast Africa NDVIVariable: EastAfrica_eMODISSouthern Africa NDVIVariable: SouthAfrica_eMODISWest Africa NDVIVariable: WestAfrica_eMODISSeasonal_ForecastVariable: CCSM4SeasonalEnsemble Variable: ens01 thru ens10seasonal_variable: Temperature or PrecipitationVariable: CFSV2SeasonalEnsemble Variable: ens01 thru ens24seasonal_variable: Temperature or PrecipitationIMERG 1 Day (late)Variable: IMERGIMERG 1 Day (early)Variable: IMERG_earlyEvaporative Stress IndexESI 4 weekVariable: ESI_4ESI 12 weekVariable: ESI_12NASA-USDA Enhanced SMAP Global Soil Moisture DataSoil moisture profileVariable: USDA_SMAPSurface soil moistureVariable: USDA_SSMSurface soil moisture anomalyVariable: USDA_SSMASubsurface soil moistureVariable: USDA_SSSMSubsurface soil moisture anomalyVariable: USDA_SSSMASample UsageThis is sample code to produce a time series csv using the CentralAsia_eModis dataset.  If you were to choose the OperationType of Download you would need to change the Outfile from .csv to .zip If you would like the data returned as a json object to a variable set Outfile to 'memory_object' and create a variable to hold the return from the climateserv.api.request_data call.import climateserv.api

x = 81.27   
y = 29.19

GeometryCoords = [[x-.01,y+.01],[x+.01, y+.01],
                  [x+.01, y-.01],[x-.01,y-.01],[x-.01,y+.01]]
                  
DatasetType = 'CentralAsia_eMODIS'
OperationType = 'Average'
EarliestDate = '01/03/2018'
LatestDate = '03/16/2018'
SeasonalEnsemble = '' # only used for Seasonal_Forecast
SeasonalVariable = '' # only used for Seasonal_Forecast
Outfile = 'out.csv'

climateserv.api.request_data(DatasetType, OperationType, 
             EarliestDate, LatestDate,GeometryCoords, 
             SeasonalEnsemble, SeasonalVariable,Outfile)License and DistributionClimateSERVpy is distributed by SERVIR under the terms of the MIT License. SeeLICENSEin this directory for more information.Privacy & Terms of UseClimateSERVpy abides to all of SERVIR's privacy and terms of use as described
athttps://servirglobal.net/Privacy-Terms-of-Use.
Package: climate-toolbox
Tools for climate data wranglingFree software: MIT licenseDocumentation:https://climate-toolbox.readthedocs.io.FeaturesTODOCreditsclimate_toolboxwas created by theClimate Impact LabHistory0.1.4 (current version)Support vectorized indexing for xarray >= 0.10 inclimate_toolbox.climate_toolbox._reindex_spatial_data_to_regions(GH issue #10)Support iteratively increasing bounding box in~climate_toolbox.climate_toolbox._fill_holes_xr(GH issue #11).Support multiple interpolation methods (linear and cubic) in~climate_toolbox.climate_toolbox._fill_holes_xr(GH issue #12).Fix bug causing tests to pass no matter what0.1.3 (2017-08-04)Support passing a dataset (not just a filepath) intoload_baselineandload_bcsd(GH issue #4)0.1.2 (2017-07-25)merge in bug fixes0.1.1 (2017-07-25)Various bug fixes (seeGH issue #2)0.1.0 (2017-07-24)First release on PyPI.