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-0.1.39.tar.gz (229.8 kB view details)

Uploaded Source

Built Distribution

digitalarztools-0.1.39-py3-none-any.whl (354.6 kB view details)

Uploaded Python 3

File details

Details for the file digitalarztools-0.1.39.tar.gz.

File metadata

  • Download URL: digitalarztools-0.1.39.tar.gz
  • Upload date:
  • Size: 229.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.6

File hashes

Hashes for digitalarztools-0.1.39.tar.gz
Algorithm Hash digest
SHA256 1cc74af9117063966b6f40028502b1af59c71b9ffe8b11243f74058cebd75771
MD5 b42fc0562f9a4a42e18b376ccf147d73
BLAKE2b-256 815103da1b88e0f5fa4663570c33e00d243c3aa404503b7c794a0a8ffbc5c0c0

See more details on using hashes here.

File details

Details for the file digitalarztools-0.1.39-py3-none-any.whl.

File metadata

File hashes

Hashes for digitalarztools-0.1.39-py3-none-any.whl
Algorithm Hash digest
SHA256 ce547d98b8211779dca68833420a67076f7b51b0e54b0dcb9d59912b5cb4325d
MD5 a8e6e565893db068ea6aeecd5bc51602
BLAKE2b-256 22d48f5c5ef47a831d831ca661604181d400b47235491f4bc2c2c66e1f5e2b8f

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