Saravanan666/pypi-org huggingface dataset:
Package: carbonnaranow
Carbonara API Wrapper⬇️ Installing Carbon-Now via 🐍 pip:pip install carbonnaranow🆕 Upgrading Carbon-Now via 🐍 pip:pip install -U carbonnaranow⬇️ Installing Carbon-Now via 🌐 source:git clone https://github.com/OpenRestfulAPI/carbon-now-sh-API-Wrapper carbon-now
cd carbon-now
make install📖 Example Usage:import asyncio

from carbonnaranow import Carbon

code = """
import asyncio

async def func():
    return "Hello from Carbonara"

if __name__ == '__main__':
    asyncio.run(func())
"""

async def main():
    carbon = Carbon(
        code=code,
    )
    print(await carbon.save('carbon_photo'))

if __name__ == '__main__':
    asyncio.run(main())📖 Advance Usage Example:Checkexample.py©️ CopyrightsLicensed with GPLv3,
This Project was made by an indivial onTelegram. The person who made this API wrapper has nothing to do withCarbonorCarbonara. Therefore All rights reserved toCarbonandCarbonara's Rightful OwnerPeter SolopovHimself.
Package: carbonnow
Carbonara API Wrapper⬇️ Installing Carbon-Now via 🐍 pip:pip install carbonnow🆕 Upgrading Carbon-Now via 🐍 pip:pip install -U carbonnow⬇️ Installing Carbon-Now via 🌐 source:git clone https://github.com/OpenRestfulAPI/carbon-now-sh-API-Wrapper carbon-now
cd carbon-now
make install📖 Example Usage:import asyncio

from carbonnow import Carbon

code = """
import asyncio

async def func():
    return "Hello from Carbonara"

if __name__ == '__main__':
    asyncio.run(func())
"""

async def main():
    carbon = Carbon(
        code=code,
    )
    print(await carbon.save('carbon_photo'))

if __name__ == '__main__':
    asyncio.run(main())📖 Advance Usage Example:Checkexample.py©️ CopyrightsLicensed with GPLv3,
This Project was made by an indivial onTelegram. The person who made this API wrapper has nothing to do withCarbonorCarbonara. Therefore All rights reserved toCarbonandCarbonara's Rightful OwnerPeter SolopovHimself.
Package: carbonplan
carbonplan / carbonplan-pythonnamespace package for python utilities and subprojectsThis repository includes thecarbonplannamespace Python package. The package itself includes very little of substance, and is distributed to act as the top-level namespace for other Python packages developed by CarbonPlan. Other packages include:PackageImportGitHub Repocarbonplan-dataimport carbonplan.datahttps://github.com/carbonplan/datacarbonplan-forest-offsetsimport carbonplan.forest_offsetshttps://github.com/carbonplan/forest-offsetscarbonplan-forest-risksimport carbonplan.forest_riskshttps://github.com/carbonplan/forest-riskscarbonplan-stylesimport carbonplan.styleshttps://github.com/carbonplan/stylesinstallpipinstallcarbonplan[all]# or individual sub packagespipinstallcarbonplan[styles]usagefromcarbonplan.styles.mplimportset_theme# this is the same things asfromcarbonplan_styles.mplimportset_themelicenseAll the code in this repository isMITlicensed. We include attribution for this content, and we please request that you also maintain that attribution if using this data.about usCarbonPlan is a non-profit organization that uses data and science for climate action. We aim to improve the transparency and scientific integrity of carbon removal and climate solutions through open data and tools. Find out more atcarbonplan.orgor get in touch byopening an issueorsending us an email.
Package: carbonplan-data
carbonplan / datadata catalog and curationThis repository includes our main data catalog as well as our pre-processing utilities.installpipcarbonplan[data]usageThe CarbonPlan data archives are currently mirrored on Google Cloud Storage (US-Central) and
Microsoft Azure (US-West). Set theCARBONPLAN_DATAenvironment variable before using the
Intake catalog below:# google (us-central)exportCARBONPLAN_DATA="https://storage.googleapis.com/carbonplan-data"# or# azure (us-west)exportCARBONPLAN_DATA="https://carbonplan.blob.core.windows.net/carbonplan-data"# open the top level catalogfromcarbonplan.dataimportcat# extract an entry as a Dask-backed Xarray Datasetcat.mtbs["raster"](region="conus",resolution="4000m").to_dask()developer documentationTo run the unit and integration tests for this API, run:$py.test-vCatalog entries scan be marked as eitherskiporxfailby setting thecikey in the metadata dictionary:foo:description:'skipthisentryintheCItests'metadata:ci:skiplicenseAll the code in this repository isMITlicensed. When possible, the data is licensed using theCC-BY-4.0license. We include attribution and additional license information for third party datasets, and we request that you also maintain that attribution if using this data.about usCarbonPlan is a non-profit organization that uses data and science for climate action. We aim to improve the transparency and scientific integrity of carbon removal and climate solutions through open data and tools. Find out more atcarbonplan.orgor get in touch byopening an issueorsending us an email.
Package: carbonplan-forest-offsets
carbonplan / forest-offsetsanalysis of forest offset projectsThis repository includes our libraries and scripts for analyzing California's compliance forest carbon offsets program. This work is described in more detail in aweb articleand apreprint. See thecarbonplan/forest-offsets-paperrepository for Jupyter notebooks that specifically recreate all the figures in the preprint. You can also browse some of these data and results in aninteractive web map.installFrom PyPI:pipinstallcarbonplan_forest_offsetsor from source:pipinstallgit+https://github.com/carbonplan/forest-offsets.gitdata sourcesAll data sources used in this project are described inthisIntake Catalog. A schematic representing the primary input datasets and outputs is shown below for reference.data productsSee the following Zenodo archives for descriptions of the data products produced by this project:G Badgley, J Freeman, J J Hamman, B Haya, D Cullenward (2021) California improved forest management offset project database (Version 1.0)https://doi.org/10.5281/zenodo.4630684.G Badgley, et al. (2021) Systematic over-crediting in California’s forest carbon offsets programhttps://doi.org/10.5281/zenodo.4630711.environmentsThis project uses the Python and R programing languages. Environment specifications are included in theenvs/directory and pre-built Docker images are available on Dockerhub:retro-python-notebookretro-r-notebooklicenseAll the code in this repository isMITlicensed. When possible, the data used by this project is licensed using theCC-BY-4.0license. We include attribution and additional license information for third party datasets, and we request that you also maintain that attribution if using this data.about usCarbonPlan is a non-profit organization that uses data and science for climate action. We aim to improve the transparency and scientific integrity of carbon removal and climate solutions through open data and tools. Find out more atcarbonplan.orgor get in touch byopening an issueorsending us an email.contributorsThis project is being developed by CarbonPlan staff and the following outside contributors:Grayson Badgley (@badgley)
Package: carbonplan-forest-risks
carbonplan / forest-risksforest carbon potential and risksThis repository includes our libraries and scripts for mapping forest carbon potential and risks.installpipinstallcarbonplan[forest-risks]usageThis codebase is organized into modules that implement data loading and model fitting as well as utitlies for plotting and other common tasks. Most anlayses involve some combination of theloadandfitmodules.Thescriptsfolder contains tools to import data, run models, and parse results.Theregrid.pyandconvert.pyscripts are for converting the results to zarr files for storage and geojson for visualization purposes.Several notebooks are additionally provided that show the use of these tools for fitting models and inspecting model outputs. Notebooks are organized by the model type, e.g.biomass,fire, etc.data productsAs part of this project we have created derived data products for five key variables relevant to evaluating forest carbon storage potential and risks.biomassThe potential carbon storage in forests assuming continued growth of existing forests.fireThe risks associated with forest fires.droughtThe risk to forests from insect-related tree mortality.insectsThe risk to forests from insect-related tree mortality.Gridded rasters for each of these layers are available for the continental United States at a 4km spatial scale. For biomass and fire, projections are shown through the end of the 21st century in decadal increments. Drought and insect models are still in development so we currently only show historical risks for these disturbance types.  All data are accessible via thiscatalog. Additional formats and download options will be provided in the future.licenseAll the code in this repository isMITlicensed. When possible, the data used by this project is licensed using theCC-BY-4.0license. We include attribution and additional license information for third party datasets, and we request that you also maintain that attribution if using this data.about usCarbonPlan is a non-profit organization that uses data and science for climate action. We aim to improve the transparency and scientific integrity of carbon removal and climate solutions through open data and tools. Find out more atcarbonplan.orgor get in touch byopening an issueorsending us an email.contributorsThis project is being developed by CarbonPlan staff and the following outside contributors:Bill Anderegg (@anderegg)Grayson Badgley (@badgley)Anna Trugman
Package: carbonplan-forests
carbonplan / forestsforest carbon potential and risksNote: This project is under active development. We expect to make many breaking changes to the utilities and APIs included in this repository. Feel free to look around, but use at your own risk.This repository includes our libraries and scripts for mapping forest carbon potential and risks.installpipinstallcarbonplan[forests]usageThis codebase is organized into modules that implement data loading and model fitting as well as utitlies for plotting and other common tasks. Most anlayses involve some combination of theloadandfitmodules.There are four scripts in thescriptsfolder that use these tools to import data, run models, and parse results.biomass.pyfire.pydrought.pyinsects.pyAnd there are two additional scriptsregrid.pyandconvert.pyfor converting the results to zarr files for storage and geojson for visualization purposes.Several notebooks are additionally provided that show the use of these tools for fitting models and inspecting model outputs. Notebooks are organized by the model type, e.g.biomass,fire, etc.licenseAll the code in this repository isMITlicensed. Some of the data provided by this API is sourced from content made available under aCC-BY-4.0license. We include attribution for this content, and we please request that you also maintain that attribution if using this data.about usCarbonPlan is a non-profit organization that uses data and science for carbon removal. We aim to improve the transparency and scientific integrity of carbon removal and climate solutions through open data and tools. Find out more atcarbonplan.orgor get in touch byopening an issueorsending us an email.contributorsThis project is being developed by CarbonPlan staff and the following outside contributors:Bill Anderegg (@anderegg)Grayson Badgley (@badgley)Anna Trugman
Package: carbonplan-styles
carbonplan / stylesplot styles for matplotlib and altairA collection of plotting styles forMatplotlibandAltair.Try it out on Binder:installpipinstallcarbonplan[styles]usageThere are currently two separate styles/themes available in this package:carbonplan_dark: black background, lighter colorscarbonplan_light: white background, darker colorsBoth styels/themes are automatically made available in Matplotlib and Altair.matplotlibfromcarbonplanimportstylesstyles.mpl.set_theme(style='carbonplan_dark')altairimportaltairasaltalt.themes.enable('carbonplan_dark')licenseAll the code in this repository isMITlicensed. We include attribution for this content, and we please request that you also maintain that attribution if using this data.about usCarbonPlan is a non-profit organization that uses data and science for climate action. We aim to improve the transparency and scientific integrity of carbon removal and climate solutions through open data and tools. Find out more atcarbonplan.orgor get in touch byopening an issueorsending us an email.
Package: carbonplan-trace
carbonplan / traceworking repo for carbonplan's climate trace projectThis repository includes example Jupyter notebooks and other utilities for a collaborative project CarbonPlan is working on involving tracking emissions related to biomass losses from forests.This project is a work in progress. Nothing here is final or complete.We have completed the scripts and notebooks for delivery of version 0 of the dataset (carbonplan_trace/v0) which largely reproduces and extends work by Zarin et al (2016) as hosted on the Global Forest Watch platform.Input datasets include:aboveground biomass for year 2000 (Zarin et al., 2016)binary masks of tree cover loss year for 2001-2020 (Hansen et al (2013))Suomi NPP (VIIRS) Fire Masks for 2011-2020 (Schroeder et al., 2014)country boundary shapefile from the Database of Global Administrative Areas (GADM) version 3.6.Note: Geopolitical boundaries that have changed over the period of record will be tagged to the static country designation as defined in GADM v3.6.Some tips for reproducing this effort:Thescripts/aggregate_emissions.v0.pyscript can be run to reproduce both the 3 km global emissions raster dataset and the country-average estimates. As a warning, depending on the size of the machine you're running on, you might encounter memory issues when dealing with the 30m datasets. For that reason, we opted to process the 30m tiles in serial. If you are struggling, you might want to check to ensure that your cluster isn't getting overloaded.We recommend using the sample notebooknotebooks/blogpost_sample_notebook.ipynbas a starting point to introduce yourself to the structure of the data and how to work with a high resolution global product. We emphasize that the 3km product may be sufficient for some users.licenseAll the code in this repository isMITlicensed. When possible, the data used by this project is licensed using theCC-BY-4.0license. We include attribution and additional license information for third party datasets, and we request that you also maintain that attribution if using this data.about usCarbonPlan is a non-profit organization that uses data and science for climate action. We aim to improve the transparency and scientific integrity of carbon removal and climate solutions through open data and tools. Find out more atcarbonplan.orgor get in touch byopening an issueorsending us an email.
Package: carbonpy
CarbonpyA module which names straight/branched chain organic compounds, suggests conversions from one type to another, etc.Usage-Naming compounds-Instantiate the classNamer(), which takes a string which contains the hydrocarbon (condensed form) and then call it with a method namedanalyser()to get the IUPAC name of the compound.Example:a = Namer('CH~CH')
a.analyser()
>>> EthyneDue to limitations in expressing a hydrocarbon easily, we have selected this pathSingle bond:- -Double bond:- =Triple bond:- ~Branches:- -([compound] (more branches...))([another branch from same carbon])- and so on(Branches support coming soon)You can also get the molecular formula of the compound:compound = Namer('CH~C-C~C-CH=C=C=CH2')
compound.molecular_formula()
>>> Câ‚ˆHâ‚„Example- 2-Methylpropane would be expressed as (once branches are supported):a = Namer('CH3-CH(CH3)-CH3').analyser()
>>> 2-MethylpropaneWill support naming with functional groups in the future.
Package: carbonsh
carbonshPython module for carbon.now.shInstallpipinstallcarbonsh**pyppeteerrequires to download Chromium the first time you use it, go to its documentation for more informationUsageimportcarbonshimportasynciocode=("const test = 'test';""let x = 0.1 + 0.2;""console.log(test, x)")config=carbonsh.Config(language=carbonsh.languages.JAVASCRIPT)# returns >>> 'https://carbon.now.sh/?bg=rgba(...'carbon_url=carbonsh.code_to_url(code,config)loop=asyncio.get_event_loop()# saves the image as carbon.png where expectedloop.run_until_complete(carbonsh.url_to_file(carbon_url,'/path/to/save/screenshot/'))loop.run_until_complete(carbonsh.code_to_file(code,config,'/path/to/save/screenshot/'))
Package: carbonstat
Metrics collection agent forCarbon. It allow you to collect some
metrics about your code and measure execution time.InstallationInstall via pip:pip install carbonstatBasic usageYou can just importcarbonstat.statinstance and play with it:fromcarbonstatimportstatdeffoo():print'Hello, world!'foo()foo()stat['foo.count'].add(2)# save `foo.count` metric as execution counterstat.send()# send packet to Carbon with `foo.count` metric valueAll packets are sent via udp to127.0.0.1:2003by default. You can
change default destination address via environment variables$CARBOH_HOSTand$CARBON_PORT.You can combine multiple metrics in oneCarbonStatinstance too:deffoo():print'Hello from foo!'defbar():print'Hello from bar!'foo()foo()bar()stat['foo.count'].add(2)stat['bar.count'].add(1)stat.send()# send packet to Carbon with two metricsAdvanced usageYou can measure execution time of code blocks with convenient context
manager:stat=CarbonStat(host=192.168.0.1,port=2003)deffoo(sec):sleep(sec)print'Hello after%dseconds!'%secwithstat.timer('foo.time')astimer:timer.start()# measure firstfoo(3)timer.stop()timer.start()# measure againfoo(5)timer.stop()stat.send()# send packet:#               heartbeat    0 timespamp#               foo.time.min 3 timestamp#               foo.time.avg 4 timestamp#               foo.time.max 5 timestampOr you can do it simpler:deffoo(sec):sleep(sec)print'Hello after%dseconds!'%secwithstat.timer('foo.time'):foo(3)withstat.timer('foo.time'):foo(5)stat.send()# send packet like aboveYou can even decorate your function and measure it’s execution time
while calling it:@stat.timeit('foo.time')deffoo(sec):sleep(sec)print'Hello after%dseconds!'%secfoo(3)foo(5)stat.send()# send packet like aboveExtraIn some cases you may need to save the value of any metric after sending
the packet to Carbon. You can do it by settingaccumulateattribute
toTrue:```
Package: carbox
What is this?A basicContent Addressable aRchive(CAR) v1 reader and writer. Enough to get you reading from theATProtofirehose and emitting blocks from your own PDS.More details on ATProto's CAR usage here.InstallationpipinstallcarboxBasic Usagefromdatetimeimportdatetimefromcarbox.carimportBlock,read_car,write_carfromcarbox.messageimportread_event_pairimportdag_cbor# Where websocket_msg is a message comes from the firehose.header,event=read_event_pair(websocket_msg)roots,blocks=read_car(event['blocks'])records=[dag_cbor.decode(block)forblockinblocks]record={'$type':'app.bsky.feed.post','text':'Hello, world!','createdAt':datetime.now().isoformat(),}block=Block(decoded=record)car_bytes=write_car(block.cid,[block])Changelog0.2 - 2023-09-07Add write support viacarbox.write_car. See usage example above for details.0.0.1 - 2023-05-07Initial release.Release instructionsHere's how to package, test, and ship a new release.Run the unit tests.source[path_to_virtualenv]/bin/activate.csh
pytestBump the version number inpyproject.toml.git grepthe old version number to make sure it only appears in the changelog. Change the current changelog entry inREADME.mdfor this new version fromunreleasedto the current date.git commit -am 'release vX.Y'Upload totest.pypi.orgfor testing.poetrybuild
setenvverX.Y
twineupload-rpypitestdist/carbox-$ver*Install from test.pypi.org.cd/tmp
python3-mvenvlocalsource[path_to_virtualenv]/bin/activate.csh# make sure we force pip to use the uploaded versionpip3uninstallcarbox
pip3install--upgradepip
pip3install-ihttps://test.pypi.org/simple--extra-index-urlhttps://pypi.org/simplecarbox==$verdeactivateSmoke test that the code trivially loads and runs:fromcarbox.carimportBlock,read_car,write_carblock=Block(decoded={'foo':['bar',2,3.14]})car_bytes=write_car([block.cid],[block])assertread_car(car_bytes)==([block.cid],[block])Tag the release in git. In the tag message editor, delete the generated comments at bottom, leave the first line blank (to omit the release "title" in github), put### Notable changeson the second line, then copy and paste this version's changelog contents below it.gittag-av$ver--cleanup=verbatim
gitpush&&gitpush--tagsClick here to draft a new release on GitHub.EntervX.Yin theTag versionbox. LeaveRelease titleempty. Copy### Notable changesand the changelog contents into the description text box.Upload topypi.org!twineuploaddist/carbox-$ver*Wait for the docs to build on Read the Docs, then check that they look ok.On theVersions page, check that the new version is active, If it's not, activate it in theActivate a Versionsection.
Package: carbspec
carbspec: pH and Alkalinity Determination by Spectral FittingSubmitted ManuscriptHigh-Precision characterisation of indicator dyes by Spectral Fitting: Application to Swawater pH and Alkalinity measurements. Branson, O. & Ellwood, M.L&O MethodsElectronic supplements are available in theSI Folder.Installationpip install  carbspecExample Usagefromcarbspec.ioimportload_spectrum# for reading filesfromcarbspecimportspectro# for doing the workspec=load_spectrum('./SI/data/pH/CRM1_DICKSON_D10_CRM_100211_03_12_2019.dat')# this data file is in the 'SI' folder of the reposiitory.# NB Yo don't have to use 'load_spectrum' - the following function just requires two arrays containing wavelength and absorption.spectro.pH_from_spectrum(s['wavelength'],s['Abs'])>3.570124144350996+/-0.006193756635537494Further examples of pH and TA calculation on batches of files may be found in theFiguresSupplement to the manusript.
Package: carbuapi
CarbuAPISince 99.9% potential users of this code are spanish speakers this README and associated docs are written in spanish. However contributions are welcomed.Módulo de consulta a los datos abiertos sobre precios de carburantes del Ministerio de Consumo del gobierno de España:https://sede.serviciosmin.gob.es/es-ES/datosabiertos/catalogo/precios-carburantesInspirado por la crisis de los precios energéticos de 2022 y este post dedomology.es.Características✅ Consulta de precios sobre todas las gasolineras de España.✅ Filtrado por provincia, producto (i. ex. Gasolina 95, Gasoil A, etc…) y distancia del usuario (requiere localización base).✅ Ordenación por distancia o precioPor implementar❌ Caché.❌ Interfaz asíncrono.
Package: carburetor
Carburetor: anonymous browsing made easyDiscover anonymous browsing with Carburetor on your phones and computers. Tailored for GNOME, it's your hidden superhero for online adventures, powered by TOR. Don't get your hands dirty with system files any more – just tap in the app, keeping your online world safe and private. Customize settings if you want, but you don't have to. Carburetor is Free Software and puts you in control. No worries, just enjoy your anonymous browsing!Carburetor complies with theGNOME Code of Conduct.InstallCarburetor is natively packaged for some distros:If you're on any other distribution or OS, Tractor team welcomes you to make build recepie of yours.You can also get it fromFlathub.Runyou can runcarburetorby command line or through your desktop environment.ContributeContributing is always appreciated. SeeCONTRIBUTINGfor more information.