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-0.1.23.tar.gz (191.0 kB view details)

Uploaded Source

Built Distribution

digitalarztools-0.1.23-py3-none-any.whl (232.0 kB view details)

Uploaded Python 3

File details

Details for the file digitalarztools-0.1.23.tar.gz.

File metadata

  • Download URL: digitalarztools-0.1.23.tar.gz
  • Upload date:
  • Size: 191.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.6

File hashes

Hashes for digitalarztools-0.1.23.tar.gz
Algorithm Hash digest
SHA256 addf07b5dd5cedd0d842d36c0746cd96fff1c062fd5d36c206d5289c96fc693f
MD5 7172f86fd435e077a3f8614fd66d3e90
BLAKE2b-256 d5ed672738be38713c07d2c56f03ee76fbd48538601792f671488ce2eaef1e4c

See more details on using hashes here.

File details

Details for the file digitalarztools-0.1.23-py3-none-any.whl.

File metadata

File hashes

Hashes for digitalarztools-0.1.23-py3-none-any.whl
Algorithm Hash digest
SHA256 d14659156c8382cb905fdc0c3bb4da1a5b3c887ec4ab3b91fc28f3c803286186
MD5 4e8fa391cb7cc802fdf2e9e10810aa5b
BLAKE2b-256 628b864e43ee451cec667e2467e1909e55ca56b72f37e95750dc08a807cf29f6

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