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imdlib

Download and process binary IMD meteorological data in Python

https://github.com/iamsaswata/imdlib

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 33 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: researchgate.net, zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.1%) to scientific vocabulary

Keywords

gridded-data imd python
Last synced: 6 months ago · JSON representation

Repository

Download and process binary IMD meteorological data in Python

Basic Info
Statistics
  • Stars: 39
  • Watchers: 4
  • Forks: 21
  • Open Issues: 8
  • Releases: 6
Topics
gridded-data imd python
Created about 6 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License

README.md

imdlib

Build Status GitHub PyPI Conda Downloads

This is a python package to download and handle binary grided data from Indian Meterological department (IMD).

Installation

pip install imdlib

or

conda install -c iamsaswata imdlib

or

pip install git+https://github.com/iamsaswata/imdlib.git

Documentation

Tutorial Tutorial

Video Tutorial

IMDLIB - Albedo Foundation

License

imdlib is available under the MIT license.

Citation

If you are using imdlib and would like to cite it in academic publication, we would certainly appreciate it. We recommend to use one of these two DOIs for this purpose:

Nandi, S., Patel, P., and Swain, S. (2024). IMDLIB: An open-source library for retrieval, processing and spatiotemporal exploratory assessments of gridded meteorological observation datasets over India. Environmental Modelling and Software, 71 (105869), [DOI]

Nandi, S., Patel, P., and Swain, S. (2022). IMDLIB: A python library for IMD gridded data. Zenodo. [DOI]

DOI

Publications using IMDLIB

Swain, S., Mishra, P.K., Nandi, S., Pradhan, B., Sahoo, S., Al-Ansari, A. (2024). A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India. Applied Water Science, 14, 36. [DOI]

Jaiswal, S., Balietti, A., & Schäffer, D. (2023). Environmental Protection and Labor Market Composition. University of Heidelberg, Department of Economics [DOI]

Pandey, H.K., Singh, V.K., Singh, R.P. et al. (2023). Soil Loss Estimation Using RUSLE in Hard Rock Terrain: a Case Study of Bundelkhand, India. Water Conserv Sci Eng 8, 55. [DOI]

Vage, S., Gupta, T., Roy, S. (2023). Impact Analysis of Climate Change on Floods in an Indian Region Using Machine Learning. In: ICANN 2023, 14261. [DOI]

Garg, N., Negi, S., Nagar, R., Rao, S., & KR, S. (2023). Multivariate multi-step LSTM model for flood runoff prediction: a case study on the Godavari River Basin in India. Journal of Water and Climate Change, [DOI]

Bora, S., & Hazarika, A. (2023). Rainfall time series forecasting using ARIMA model. In 2023 ATCON-1, (pp. 1-5). IEEE, [DOI]

Panja, A., Garai, S., Zade, S., Veldandi, A., Sahani, S., & Maiti, S. (2023). Climate Data Extraction for Social Science Research: A Step by Step Process. Social Science Dimensions of Climate Resilient Agriculture, [ISBN] (ISBN: 978-81-964762-1-2)

Chakra, S., Ganguly, A., Oza, H., Padhya, V., Pandey, A., & Deshpande, R. D. (2023). Multidecadal summer monsoon rainfall trend reversals in South Peninsular India: a new approach to examining long-term rainfall dataset. Journal of Hydrology, [DOI].

Sardar, P., and Samadder, S. R. (2023).  Long-term ecological vulnerability assessment of indian sundarban region under present and future climatic conditions under CMIP6 model. Ecological Informatics. [DOI]

Roy, P. K., Ghosh, A., Basak, S. K., Mohinuddin, S., & Roy M. B. (2023).  Analysing the Role of AHP Model to Identify Flood Hazard Zonation in a Coastal Island, India. Journal of the Indian Society of Remote Sensing Article, 1-15. [DOI]

Kundu, M., Zafor, A., & Maiti, R. (2023). Assessing the nature of potential groundwater zones through machine learning (ML) algorithm in tropical plateau region, West Bengal, India. Acta Geophysica, 1-16. [DOI]

Venkatesh, S., Kirubakaran, T., Ayaz, R. M., Umar, S. M., & Parimalarenganayaki, S. (2023). Non-parametric Approaches to Identify Rainfall Pattern in Semi-Arid Regions: Ranipet, Vellore, and Tirupathur Districts, Tamil Nadu, India. In River Dynamics and Flood Hazards (pp. 507-525). Springer, Singapore. [DOI]

Swain, S., Mishra, S. K., Pandey, A., & Dayal, D. (2022). Assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, India. Environmental Monitoring and Assessment, 194(12), 1-18. [DOI]

Swain, S., Mishra, S. K., Pandey, A., Dayal, D., & Srivastava, P. K. (2022). Appraisal of historical trends in maximum and minimum temperature using multiple non-parametric techniques over the agriculture-dominated Narmada Basin, India. Environmental Monitoring and Assessment, 194(12), 1-23. [DOI]

Owner

  • Name: Saswata Nandi
  • Login: iamsaswata
  • Kind: user

GitHub Events

Total
  • Issues event: 4
  • Watch event: 7
  • Pull request event: 1
  • Fork event: 4
Last Year
  • Issues event: 4
  • Watch event: 7
  • Pull request event: 1
  • Fork event: 4

Committers

Last synced: 6 months ago

All Time
  • Total Commits: 220
  • Total Committers: 3
  • Avg Commits per committer: 73.333
  • Development Distribution Score (DDS): 0.068
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
iamsaswata n****7@g****m 205
Pratiman 3****1@u****m 9
pratiman-91 p****l@h****m 6

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 27
  • Total pull requests: 8
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 15 days
  • Total issue authors: 16
  • Total pull request authors: 4
  • Average comments per issue: 2.0
  • Average comments per pull request: 0.13
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 3
  • Pull requests: 2
  • Average time to close issues: about 1 hour
  • Average time to close pull requests: N/A
  • Issue authors: 3
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • pratiman-91 (11)
  • answerquest (2)
  • Quickbeasts51429 (1)
  • jeevakir (1)
  • rohan472000 (1)
  • ronakladhar (1)
  • ved-ux (1)
  • carbform (1)
  • y-sheng (1)
  • SubhadipDatta (1)
  • samyaroy (1)
  • pradeep2c1 (1)
  • thisisashukla (1)
  • bkowshik (1)
  • julianwid (1)
Pull Request Authors
  • pratiman-91 (3)
  • samyaroy (2)
  • answerquest (1)
  • iamsaswata (1)
Top Labels
Issue Labels
enhancement (7) bug (2) question (2) wontfix (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 5,808 last-month
  • Total docker downloads: 8
  • Total dependent packages: 0
  • Total dependent repositories: 3
  • Total versions: 27
  • Total maintainers: 1
pypi.org: imdlib

A tool for handling and downloading IMD gridded data

  • Versions: 27
  • Dependent Packages: 0
  • Dependent Repositories: 3
  • Downloads: 5,808 Last month
  • Docker Downloads: 8
Rankings
Docker downloads count: 4.3%
Downloads: 4.9%
Average: 7.0%
Dependent repos count: 9.0%
Dependent packages count: 10.0%
Maintainers (1)
nandi
Last synced: 6 months ago

Dependencies

docs/requirements.txt pypi
  • imdlib *
  • sphinx-automodapi *
requirements.txt pypi
  • certifi >=2019.11.28
  • matplotlib >=3.1.3
  • numpy >=1.18.1
  • pandas >=0.25.3
  • pytest *
  • python-dateutil >=2.8.1
  • pytz >=2019.3
  • requests *
  • scipy >=1.4.1
  • six ==1.14.0
  • urllib3 *
  • xarray >=0.14.1
setup.py pypi
  • matplotlib *
  • numpy *
  • pandas *
  • python-dateutil *
  • pytz *
  • requests *
  • scipy *
  • six *
  • urllib3 *
  • xarray *
.github/workflows/conda.yml actions
  • actions/checkout v2 composite
.github/workflows/pypi.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite