Skip to main content

Digital Arz tools for applications

Project description

DigitalArz Tools

Tools for providing GIS capabilities in the DigitalArz Application. Tools are based on

  1. RasterIO
  2. GeoPandas
  3. Shapely
  4. Scikit-learn

Modules are

Raster

  1. rio_raster: to extract raster information and read and write operation using raster io
  2. rio_process: to perform different process on a raster
  3. rio_extraction : to extract data from different pipelines like GEE
  4. indices

Vector

  1. gpd_vector: to extract vector and perform operation using geopandas

Pipeline

To add the account in the digitalarztool module, you have to open the python console. Activate the venv environment and open python in this environment. In console use following commands

from digitalarztools.pipelines.config.server_settings import ServerSetting
ServerSetting().set_up_account("NASA")

Following piplines are available

  1. gee: pipeline with google earth engine for processing and extracting data

  2. srtm: pipeline to extract SRTM data from

  3. nasa: pipeline to extract NASA data. First need to setup account using

    SeverSetting.set_up_account("NASA")
    

    alos palsar: to extract alos palsar RTC data using earthsat api

  4. grace & gldas: to extract grace and gldas data using ggtools(https://pypi.org/project/ggtools/). Grace data is available at https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2

  5. ClimateServ Date: https://pypi.org/project/climateserv/

  6. CHIRP: download Rainfall data.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

digitalarztools-0.1.35.tar.gz (228.8 kB view details)

Uploaded Source

Built Distribution

digitalarztools-0.1.35-py3-none-any.whl (353.7 kB view details)

Uploaded Python 3

File details

Details for the file digitalarztools-0.1.35.tar.gz.

File metadata

  • Download URL: digitalarztools-0.1.35.tar.gz
  • Upload date:
  • Size: 228.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.6

File hashes

Hashes for digitalarztools-0.1.35.tar.gz
Algorithm Hash digest
SHA256 4a42d762c629929fcb67c0ee9800ef0a7345526c24e3f433ab298af15a7a8d4a
MD5 618677e28959cfbf27628aea46db8c2f
BLAKE2b-256 58a2e444d4c5d2c9fd144605e0ed120d5986843fb990d26653ecc96bab0ba9ce

See more details on using hashes here.

File details

Details for the file digitalarztools-0.1.35-py3-none-any.whl.

File metadata

File hashes

Hashes for digitalarztools-0.1.35-py3-none-any.whl
Algorithm Hash digest
SHA256 41728efce31d27540b847b2ef8ce6f0991a2d76ac2b09bf9c995d04a69786b44
MD5 02e4c558b70cc7b8c1c06df7c8d0f623
BLAKE2b-256 87d8f2b759cdff68d9323d712d49203e2d1454cd6819803289c15576137bb233

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page