Saravanan666/pypi-org huggingface dataset:
Package: agrc-sweeper
agrc-sweeperThe data cleaning service.Available SweepersAddressesChecks that addresses have minimum required parts and optionally normalizes them.DuplicatesChecks for duplicate features.EmptiesChecks for empty geometries.MetadataChecks to make sure that the metadata meetsthe Basic SGID Metadata Requirements.TagsChecks to make sure that existing tags are cased appropriately. This mean that the are title-cased other than known abbreviations (e.g. UGRC, BLM) and articles (e.g. a, the, of).This check also verifies that the data set contains a tag that matches the database name (e.g.SGID) and the schema (e.g.Cadastre).--try-fixadds missing required tags and title-cases any existing tags.SummaryChecks to make sure that the summary is less than 2048 characters (a limitation of AGOL) and that it is shorter than the description.DescriptionChecks to make sure that the description contains a link to a data page on gis.utah.gov.Use LimitationsChecks to make sure that the text in this section matches theofficial text for UGRC.--try-fixupdates the text to match the official text.Parsing AddressesThis project contains a module that can be used as a standalone address parser,sweeper.address_parser. This allows developer to take advantage of sweepers advanced address parsing and normalization without having to run the entire sweeper process.Usage Examplefromsweeper.address_parserimportAddressaddress=Address('123 South Main Street')print(address)'''--> Parsed Address:{'address_number': '123','normalized': '123 S MAIN ST','prefix_direction': 'S','street_name': 'MAIN','street_type': 'ST'}'''Available Address class propertiesAll properties default to None if there is no parsed value.address_numberaddress_number_suffixprefix_directionstreet_namestreet_directionstreet_typeunit_typeunit_idIf nounit_typeis found, this property is prefixed with#(e.g.# 3). Ifunit_typeis found,#is stripped from this property.cityzip_codepo_boxThe PO Box if a po-box-type address was entered (e.g.po_boxwould be1forp.o. box 1).normalizedA normalized string representing the entire address that was passed into the constructor. PO Boxes are normalized in this formatPO BOX <number>.Installation (requires Pro 2.7+)clone arcgis conda environmentconda create -name sweeper --clone arcgispro-py3activate environmentactivate sweeperinstall sweeperpip install agrc-sweeperDevelopmentclone arcgis conda environmentconda create -name sweeper --clone arcgispro-py3activate environmentactivate sweepertest_metadata.pyuses a SQL database that needs to be restored viasrc/sweeper/tests/data/Sweeper.bakto your local SQL Server.Installing dependenciesclone arcgis conda environmentconda create -name sweeper --clone arcgispro-py3install only required dependencies to run sweeperpip install -e .install required dependencies to work on sweeperpip install -e ".[develop]"install required dependencies to run sweeper testspip install -e ".[tests]"run tests:pytest
Package: agrc-usaddress
usaddress is a python library for parsing unstructured address strings into
address components, using advanced NLP methods.From the python interpreter:>>> import usaddress
>>> usaddress.parse('123 Main St. Suite 100 Chicago, IL')
[('123', 'AddressNumber'),
 ('Main', 'StreetName'),
 ('St.', 'StreetNamePostType'),
 ('Suite', 'OccupancyType'),
 ('100', 'OccupancyIdentifier'),
 ('Chicago,', 'PlaceName'),
 ('IL', 'StateName')]
Package: agreementr
AgreementrIntroAgreementr is a package used to predict the value of agreement of texts.It is based on a fine tuned BERT model.InstallUse pipIfpipis installed, agreementr could be installed directly from it:pip install agreementrDependenciespython>=3.6.0
torch>=0.4.1
numpy
pandas
unidecode
pytorch-pretrained-bert
pytorch-transformersUsage and ExampleNotes: During your first usage, the package will download a model file automatically, which is about 400MB.predictpredictis the core method of this package,
which takes a single text of a list of texts, and returns a list of raw values in[1,5](higher means more agreement, while lower means less).Simplest usageYou may directly importagreementrand use the default predict method, e.g.:>>> import agreementr
>>> agreementr.predict(["I am totally agree with you"])
[4.3568916]Construct from classAlternatively, you may also construct the object from class, where you could customize the model path and device:>>> from agreementr import Agreementr
>>> ar = Agreementr()

# Predict a single text
>>> ar.predict(["I am totally agree with you"])
[4.3568916]

# Predict a list of texts
>>> preds = ar.predict(['I am totally agree with you','I hate you'])
>>> f"Raw values are {preds}"
[4.3568916 2.42935]More detail on how to construct the object is available in docstrings.Model using multiprocessing when preprocessing a large dataset into BERT input featuresIf you want to use several cpu cores via multiprocessing while preprocessing a large dataset, you may construct the object via>>> ar = Agreementr(CPU_COUNT=cpu_cpunt, CHUNKSIZE=chunksize)If you want to faster the code through multi gpus, you may construct the object via>>> ar = Agreementr(is_paralleled=False, BATCH_SIZE = batch_size)ContactJunjie Wu (wujj38@mail2.sysu.edu.cn)
Package: agregator-mess
Агрегатор мессенджеровpython 3.5.2Используемые библиотеки:PyQt5vk_apitelethonpickleТочка входа в приложение core/Manager.pyИнструкция по сборке приложенияУстановить все зависимости с помощью pip install PyQt5 vk_api telethon pickleПерейти в директорию coreЗапустить приложение используя точку входа python Manager.pyЧто сейчас работает:Авторизация  помощью VK user, VK group, telegram userЗагрузка диалогов пользователяЗагрузка изображений диалогов и кеширование ихЗагрузка сообщений из выбранного диалогаМакет пользовательского интерфейсаГлавная страница приложения
Package: agrid
agridA grid for modelling, analyse, map and visualise multidimensional and multivariate data. The module contains a class for generating grid objects with variables and functions that defines a multidimensional space with defined extent.Main features:Labelled dimensions coordinates using xarrayFast process of high resolution and low resolution dataUsing dask arrays2D map plots and cross-sections3D visualisationFeatures for modelling, analysis and visualisationThe repository contains:Module with class for functions and variables.Jupyter notebook tutorials:The grid objectImport dataVisualize dataIntroduction to processing and modelling using grid.Software paper availible here:JORSInstructions:The package can be installed by adding it to your Python path, but can also be an incorporated part of you project.Alternative 1Using pip:pip install agridorpip3 install agridSet up an envirinment for the installation. Use e.g.virtualenvorvenvto set up your environment. For more complex isnallations, have a look atpipenvorpoetry.OSX users can set up an environment directely in conda.conda install -c tobbetripitaka agridAlternative 2Download module and import agrid from local pathSee example in the Jupyter Notebook agrid.To get started:from agrid.grid import Grid
world = Grid()
# The grid is already populated with default coordinates
print(world.ds)Further tutorials are available atGitHub tutorialsMethods to import dataData can be imported from grids, vector data files, rasters or numerically.Methods to export dataData can be exported and saved as netCDF, raster files, text files.Methods to visualise dataVisualisation is not the core use of agrid, but it contains basic functions to plot maps and 3D renderings.Additional functionsAdditional functions are included to download data and structure the content.---If used in publication, please cite:@article{Staal2020a,
abstract = {Researchers use 2D and 3D spatial models of multivariate data of differing resolutions and formats. It can be challenging to work with multiple datasets, and it is time consuming to set up a robust, performant grid to handle such spatial models. We share 'agrid', a Python module which provides a framework for containing multidimensional data and functionality to work with those data. The module provides methods for defining the grid, data import, visualisation, processing capability and export. To facilitate reproducibility, the grid can point to original data sources and provides support for structured metadata. The module is written in an intelligible high level programming language, and uses well documented libraries as numpy, xarray, dask and rasterio.},
author = {St{\aa}l, Tobias and Reading, Anya M.},
doi = {10.5334/JORS.287},
issn = {20499647},
journal = {Journal of Open Research Software},
keywords = {Multivariate processing, Python, Regular grid, Spatial model},
month = {jan},
number = {1},
pages = {1--10},
publisher = {Ubiquity Press, Ltd.},
title = {{A grid for multidimensional and multivariate spatial representation and data processing}},
volume = {8},
year = {2020}
}
Package: agrimetscraper
agrimetscraperintroductionagrimetscraper crawls AgriMet weather data and summarise for later data usage.basic usagePython virtual environment is preferredpip install agrimetscraperstartproject -p <yourproject> -t <select database type: mongodb or sql or atlas>It will create a directory for your project, this folder will contain yourproject-config, yourproject-log, yourproject-database, yourproject-stationsIf you choose 'atlas', the program will prompt you for your atlas connection string and the passwordcd into your project, change .ini file if neededThere are two baseurls for agrimet API: one is for daily, the other is for instant, are all defined in ini filerun project: python runproject.py -f "instant"|"daily" -n dbtableset scheduler in .ini file (this program uses crontab)**For instant data, program will aggregate 15 minutes into 1 hour interval and average the parameters
Package: agrimpy
agrimpyutiliza pyproj para hacer algunas opreaciones con coordenadas geodésicas.InstalaciónEjecutar en la terminal:$ sudo pip install agrimpyUsoVer archivotest.pycon ejemplos.To do…ayudame con un pull-request enhttps://github.com/quijot/agrimpy-packageAutorSantiago Pestarini <santiagonob@gmail.com>Licenciaagrimpyis licensed under theMIT License. See the LICENSE file.
Package: agrivoltaics-supply-side-management
Agrivoltaics Supply Side ManagementOptimizes Supply Side Management with Agrivoltaics (Solar sharing between
Photovoltaics and Agriculture) by Artificial Intelligence.InstallationUse the package managerpipto install
agrivoltaics-supply-side-management.Case 1When using by cloning this GIT repository:pipinstall-e[ABSOLUTEPATHTOTHISPROJECT]Case 2When using a package available in PyPI:pipinstallagrivoltaics-supply-side-managementSolversWe use Python library for mathematical optimization, called Pyomo.
As in any mathematical optimization tool, it requires a solver.For Linear Programming, glpk is used as a solver by default following Pyomo
tutorial.In Macintosh, install it through Homebrew:brewinstallglpkFor other platforms, seehttps://www.gnu.org/software/glpk/Usage[To be added]References[1] P. E. Campana, B. Stridh, S. Amaducci, and M. Colauzzi,
“Optimisation of vertically mounted agrivoltaic systems,”
Journal of Cleaner Production, vol. 325, p. 129091, Nov. 2021,
doi: 10.1016/j.jclepro.2021.129091.
[2] C. B. Honsberg, R. Sampson, R. Kostuk, G. Barron-Gafford,
S. Bowden, and S. Goodnick, “Agrivoltaic Modules Co-Designed
for Electrical and Crop Productivity,”
in 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC),
Jun. 2021, pp. 2163–2166. doi: 10.1109/PVSC43889.2021.9519011.
[3] B. Willockx, B. Uytterhaegen, B. Ronsijn, B. Herteleer, and J. Cappelle,
“A standardized classification and performance indicators of agrivoltaic
systems.,” Oct. 2020. doi: 10.4229/EUPVSEC20202020-6CV.2.47.
[4] H-W, Heldt, ”Plant Biochemistry,” Elsevier Academic Press,
Burlington, MA, USA, 2005.
[5] R. W. Langhans and T. W. Tibbits, “Plant Growth Chamber Handbook
- Chapter 1 Radiation,” in Plant Growth Chamber Handbook,
Iowa State University, 1997.
[6] O. A. Martin, R. Kumar, and J. Lao, "Bayesian Modeling and Computation
in Python," Boca Ratón, 2021.
[Online]. Available:https://bayesiancomputationbook.com[7] R. Retkute, S. E. Smith-Unna, R. W. Smith, A. J. Burgess, O. E. Jensen,
G. N. Johnson, S. P. Preston, E. H. Murchie, ”Exploiting heterogeneous
environments: does photosynthetic acclimation optimize carbon gain in
fluctuating light?,” Journal of Experimental Botany, May 2015, vol. 66,
no. 9, pp. 2437–2447, doi: 10.1093/jxb/erv055.
Package: agro-kit
An API to facilitate interaction with an agriculture tech sensor kit. The sensor kit
contains the following hardware modules in addition to the Raspberry Pi Zero W:Quectel L70-M39 GPSAdafruit MCP3008 ADCSeeed Studio 10102008 Moisture SensorAdafruit TCS34725 Light SensorLicensingFree software: GNU General Public License v3FeaturesSensor readingGPSMoistureLuxCreating sensor profilesLogging data to text filesSending configuration commands GPSSetupEnsure SPI, I2C and UART are enabled on the Raspberry Pi. This can be done using the sudo raspi-config command in the Linux terminal.Install required packages. These can be done as follows:pip3 install Adafruit_GPIO  Adafruit_MCP3008 Adafruit-Blinkapip3 install file-read-backwards requests pynmea2pip3 install RPi.GPIO adafruit-circuitpython-tcs34725pip3 install agro-kitNote that this API can has to be used on a Raspberry Pi.Hardware ConfigurationPin numbers according to datasheets are shown in []Adafruit TCS34735 -> Raspberry PiVDD [1] ->                     GPIO-17 [11]GND [2] ->                     GNDSDA [3] ->                     SDA1_I2C [3]SCL [4] ->                     SCL1_I2C [5]Seeed Studio -> Raspberry Pi[3] -> GPIO23 [16][4] -> GNDMCP3008 -> Seed StudioCHO 0 [1] -> [1]MCP3008 -> Raspberry PiGND [9]  ->                    GNDCS [10]  ->                    GPIO_SPI_CE0 [24]D-IN [11]  ->                  GPIO_SPI_MOSI [19]D-OUT [12] ->                 GPIO_SPI_MISO [21]CLK [13]  ->                   GPIO_SPI_CLK [23]GND [14]  ->                  GNDVREF [15]  ->                  3.3 V [17]VDD [16]   ->                  3.3 V [17]QUECTEL L70-M39TXD [2]  ->                    GPIO_15_UART_RXD [10]RXD [3]    ->                  GPIO_14_UART_TXD [8]VCC [8]    ->                  3.3 V [1]GND [1]      ->                GNDAboutThe library includes 4 main classes: MoistureSensor, light_sensor, GPS and AgroKit.The AgroKit is an aggregated class built with attributes of the other sensor classesThe AgroKit class can use any instance method available in the other sensor classes as well as its own aggregated methodsLink to documentationCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.The pynmea2 python library was used for GPS parsing of NMEA 0183 messages.
Package: agrometeo-geopy
agrometeo-geopyPythonic interface to accessagrometeo.chdata.InstallationThe agrometeo-geopy library requiresgeopandas, which can be installed using conda/mamba as in:condainstall-cconda-forgegeopandasThen, agrometeo-geopy can be installed using pip:pipinstallagrometeo-geopyOverviewimportagrometeoasagmstart_date="2021-08-13"end_date="2021-08-16"agm_ds=agm.AgrometeoDataset(region="Canton de Genève")ts_df=agm_ds.get_ts_df(start_date=start_date,end_date=end_date)ts_dfnameDARDAGNYLA-PLAINESATIGNYPEISSYANIERESLULLYLULLIERBERNEXTROINEXMEINIERtime2021-08-13 00:00:0019.317.818.517.920.618.420.318.619.425.82021-08-13 00:10:0019.617.918.417.720.018.319.618.719.128.62021-08-13 00:20:0019.017.718.217.619.418.419.118.719.224.12021-08-13 00:30:0018.318.018.117.419.118.319.118.618.922.52021-08-13 00:40:0018.718.018.117.619.118.019.018.718.521.5.................................2021-08-16 23:10:0017.517.817.316.917.917.617.317.217.922.22021-08-16 23:20:0017.417.917.417.117.917.617.317.218.022.02021-08-16 23:30:0017.217.917.517.317.817.617.317.318.021.72021-08-16 23:40:0017.217.917.717.117.717.417.217.118.121.92021-08-16 23:50:0017.117.817.517.117.717.317.117.118.122.0576 rows × 10 columnsSeethe user guidefor more details.AcknowledgementsThis package was created withCookiecutterand thezillionare/cookiecutter-pypackageproject template.
Package: agro-met-equations-dynalogic
Agrometeorological Equations PackageLibrary for agrometeorological calculation like evapotranspiration, water balance, degree days, etcAuthors:Bruno Ducraux (bducraux@dynalogic.net)Mariana Gonçalves dos Reis (mreis@dynalogic.net)Based on:PyEtoAboutThis library was adapted to fit some needs of a private project,
and I decided to disponibilize as open source since the original project
that we used as base is.All calculations and formulas were reviewed by the agrometeorologist Mariana Gonçalves dos Reis, based on the documents:Conversion factors and general equations applied in agricultural and forest meteorologyEvapotranspiration_Equation.pdfIn case of questions related to the python code, bug fix ...
please contact Bruno Ducraux, who is the python developer responsible for the project.Installationpip install agro-met-equations-dynalogicUsagefrom AgroMetEquations importSteps to calculate FAO56Day of year: days_passed_on_current_year()Solar declination: sol_dec(day_of_year)Sunset hour angle: sunset_hour_angle(latitude_rad, solar_declination)Daylight hours: daylight_hours(sunset_hour_angle)Inverse relative distance between earth and sun: inv_rel_dist_earth_sun(day_of_year)Extraterrestrial radiation: et_rad(latitude_rad, solar_declination, sunset_hour_angle, inv_rel_dist_earth_sun)Clear sky radiation: cs_rad(altitude, extraterrestrial_radiation)Saturation vapour pressure Min: svp_from_t(temperature_min)Saturation vapour pressure Max: svp_from_t(temperature_max)Saturation vapour pressure: svp(svp_min, svp_max)Actual vapour pressure: avp_from_rhmin_rhmax(svp_min, svp_max, relative_humidity_min, relative_humidity_max)Net outgoing longwave radiation: net_out_lw_rad(temperature_min:kelvin, temperature_max:kelvin, solar_radiation, clear_sky_radiation, actual_vapour_pressure)Net income solar radiation: net_in_sol_rad(solar_radiation)Net radiation at the crop surface: net_rad(net_in_sol_rad, net_outgoing_longwave_radiation)Soil heat flux: soil_heat_flux_by_nightday_period(net_rad)Latent heat: latent_heat(temperature_mean)Delta: delta_svp(temperature_mean)Psychrometric constant: psy_const(atmosphere_pressure, latent_heat)Wind speed measured at different heights: wind_speed_2m(wind_speed, sensor_height)FAO56: fao56_penman_monteith(net_rad, temperature_mean, wind_speed, latent_heat, svp, actual_vapour_pressure, delta, psychrometric_constant, soil_heat_flux)TestingTo test the code you need to have pytest installed.pip install pytestInside the AgroMetEquations folder run the command:pytest
Package: agrossuite
Agros Suite - An application for solution of physical fieldsAgros Suite is a multiplatform application for the solution of physical problems based on the deal.II library, developed by the group at the University of West Bohemia in Pilsen. Agros Suite is distributed under the GNU General Public License.Supported Physical fieldsElectrostaticsElectric CurrentsMagnetic fieldHeat TransferStructural MechanicsAcousticsIncompressible FlowRF FieldKey FeaturesCoupled field solution with the possibility of weak and hard couplingNewton and Picard solvers for nonlinear problemsSteady state, harmonic, or transient analysisAutomatic space (h, p and hp) and time adaptivityTriangle and quad meshing using Triangle or GMSHCurvilinear elements and arbitrary level hanging nodesComputing of the trajectory of charged particles in electromagnetic fieldintuitive and powerful preprocessor and postprocessorvisualization of field variables, calculations of local values, surface and volume integralsscripting using Python
Package: agsadmin
agsadmin provides a convenient Python front-end to several REST calls on the ArcGIS Server 10.1+ REST Admin API.FeaturesArcGIS Server (RestAdmin)Services (Map/Image/Geoprocessing/Geometry/Geocode/GeoData/Globe/Search)start and stopget statisticsget statusget or set the item info (description, summary, tags, etc.)set service propertiesrename servicesMachinesstart and stopSystemDirectorieslist directoriesregister new directoriesunregister directoriesclean directoriesUploadslist uploadsget a specific upload itemupload a fileThese functions can be used to automate management of ArcGIS Services (e.g. start/stop services on a schedule,
start/stop services to perform maintenance on associated datasets, etc.)ArcGIS Portal (SharingAdmin)Contentget itemadd/get/move/delete/update user itemget user/folder contentCommunityget userget groupcreate groupupdate groupExampleThe following is a simplistic example to stop and start a map service.importagsadminhostname="<ServerNameHere>"username="<UsernameHere>"password="<PasswordHere>"rest_admin=agsadmin.RestAdmin(hostname,username,password)service=rest_admin.services.get_service("<MapServiceNameHere>","MapServer","<OptionalFolderHere>")service.stop_service()service.start_service()service.delete()
Package: agsconfig
agsconfig ·agsconfig is a Python library for editing ArcGIS Server service configuration, either before deployment by editing a Service Definition Draft file (generated by either ArcMap, ArcGIS Pro, or via arcpy), or after deployment by editing the JSON configuration provided by ArcGIS Server (via the ArcGIS Server REST Admin API).This helps to programmatically configure services as part of automated service deployment or configuration patching processes.Installationagsconfig is made available on PyPi, simply install it withpip.> pip install agsconfigUsageagsconfig contains many classes to alter the configuration of different service types. However, it is not recommended these classes be instantiated directly. Helper functions are available in the top-level module to load different types of services with either Service Defintion Draft based configuration, or ArcGIS Server JSON configuration. These functions are:agsconfig.load_image_sddraftagsconfig.load_image_serviceagsconfig.load_map_sddraftagsconfig.load_map_serviceagsconfig.load_vector_tile_sddraftagsconfig.load_vector_tile_serviceEach function expects one or more file or file-like objects to be passed in. For functions dealing with Service Definition Drafts, only one file-like object is required. For ArcGIS Server JSON based functions, two are required, the first being the main service JSON, and the second being theItemInfoJSON. Each file should be opened in binary mode, and with write enabled if you wish to save the changes (as opposed to just reading settings). Save changes seeks the file-like object back to the beginning and overwrites the stream.Example: Load/Save a MapServer Service Definiton Draftimportagsconfigsddraft_path="path/to/MyService.sddraft"withopen(sddraft_path,mode="rb+")assddraft:map_service=agsconfig.load_map_sddraft(sddraft)# Edit your map service configurationmap_service.capabilities=[agsconfig.MapServer.Capability.map]map_service.min_instances=3map_service.max_instances=6map_service.summary="This is my awesome map service."map_service.kml_server.enabled=True# Save configuration changesmap_service.save()DevelopmentTo get started on developing agsconfig, simply fork the repository and get it with your favourite Git client. In the root of the repository is a standalone task runner,pie.py, that can excute tasks contained inpie_tasks.py.You'll need a Python install withpipandvirtualenv, but other than that, no pre-installed dependencies are necessary.On a shell, simply run the setup task as follows to create a virtual environment for development work:> python .\pie.py setupTo get a list of all available tasks, exceute the following:> python .\pie.py -l