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-0.1.25.tar.gz (191.9 kB view details)

Uploaded Source

Built Distribution

digitalarztools-0.1.25-py3-none-any.whl (233.7 kB view details)

Uploaded Python 3

File details

Details for the file digitalarztools-0.1.25.tar.gz.

File metadata

  • Download URL: digitalarztools-0.1.25.tar.gz
  • Upload date:
  • Size: 191.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.6

File hashes

Hashes for digitalarztools-0.1.25.tar.gz
Algorithm Hash digest
SHA256 1931d0f9b4e7a4d73b331481715147bb5ae505a315815130fddf1812795e103e
MD5 117428d890f4f1bd89e618e8654d08f9
BLAKE2b-256 ba237f9ec4e781826b189f2702c91cb4de873c419c432b2adf9aecec3ccca43e

See more details on using hashes here.

File details

Details for the file digitalarztools-0.1.25-py3-none-any.whl.

File metadata

File hashes

Hashes for digitalarztools-0.1.25-py3-none-any.whl
Algorithm Hash digest
SHA256 cbc08d663ebf4b2f0926f66860857c839ada6d9043ac8d0c6073a3acad3b263a
MD5 69f13f878f3fbbcda49644517529f63a
BLAKE2b-256 c1efec4c478073a75bea1527bbe4a8a9350e67f064d1f36c4670fa815eee057c

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