imdlib
Download and handle binary grided data from Indian Meterological department.
https://github.com/iamsaswata/imdlib
Category: Atmosphere
Sub Category: Meteorological Observation and Forecast
Keywords
gridded-data imd python
Last synced: about 22 hours ago
JSON representation
Repository metadata
Download and process binary IMD meteorological data in Python
- Host: GitHub
- URL: https://github.com/iamsaswata/imdlib
- Owner: iamsaswata
- License: mit
- Created: 2020-01-21T23:42:54.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-03-21T07:15:53.000Z (about 1 year ago)
- Last Synced: 2025-04-25T12:45:55.100Z (2 days ago)
- Topics: gridded-data, imd, python
- Language: Python
- Homepage: https://imdlib.readthedocs.io
- Size: 8.02 MB
- Stars: 34
- Watchers: 2
- Forks: 19
- Open Issues: 7
- Releases: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
imdlib
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
Video Tutorial
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]
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 metadata
- Name: Saswata Nandi
- Login: iamsaswata
- Email:
- Kind: user
- Description:
- Website:
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/15724605?u=aa4774208259d9f052a9b13b0e21a1f48a095548&v=4
- Repositories: 5
- Last ynced at: 2023-03-10T08:02:20.642Z
- Profile URL: https://github.com/iamsaswata
GitHub Events
Total
- Issues event: 4
- Watch event: 3
- Pull request event: 1
- Fork event: 2
Last Year
- Issues event: 4
- Watch event: 3
- Pull request event: 1
- Fork event: 2
Committers metadata
Last synced: 6 days ago
Total Commits: 220
Total Committers: 3
Avg Commits per committer: 73.333
Development Distribution Score (DDS): 0.068
Commits in past year: 55
Committers in past year: 1
Avg Commits per committer in past year: 55.0
Development Distribution Score (DDS) in past year: 0.0
Name | Commits | |
---|---|---|
iamsaswata | n****7@g****m | 205 |
Pratiman | 3****1 | 9 |
pratiman-91 | p****l@h****m | 6 |
Committer domains:
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 29
Total pull requests: 7
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: 1.93
Average comments per pull request: 0.14
Merged pull request: 5
Bot issues: 0
Bot pull requests: 0
Past year issues: 7
Past year pull requests: 1
Past year average time to close issues: about 1 hour
Past year average time to close pull requests: N/A
Past year issue authors: 4
Past year pull request authors: 1
Past year average comments per issue: 0.43
Past year average comments per pull request: 0.0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- pratiman-91 (12)
- ved-ux (2)
- answerquest (2)
- Quickbeasts51429 (1)
- jeevakir (1)
- rohan472000 (1)
- ronakladhar (1)
- carbform (1)
- y-sheng (1)
- SubhadipDatta (1)
- samyaroy (1)
- pradeep2c1 (1)
- thisisashukla (1)
- bkowshik (1)
- julianwid (1)
Top Pull Request Authors
- pratiman-91 (4)
- answerquest (1)
- samyaroy (1)
- iamsaswata (1)
Top Issue Labels
- enhancement (9)
- bug (2)
- question (2)
- wontfix (1)
- New Feature (1)
Top Pull Request Labels
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 6,537 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
- Homepage: https://github.com/iamsaswata/
- Documentation: https://imdlib.readthedocs.io/
- Licenses: MIT
- Latest release: 0.1.20 (published about 1 year ago)
- Last Synced: 2025-04-25T12:31:13.010Z (2 days ago)
- Versions: 27
- Dependent Packages: 0
- Dependent Repositories: 3
- Downloads: 6,537 Last month
- Docker Downloads: 8
-
Rankings:
- Docker downloads count: 4.264%
- Downloads: 4.887%
- Average: 7.044%
- Dependent repos count: 8.985%
- Dependent packages count: 10.038%
- Maintainers (1)
Dependencies
- imdlib *
- sphinx-automodapi *
- 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
- matplotlib *
- numpy *
- pandas *
- python-dateutil *
- pytz *
- requests *
- scipy *
- six *
- urllib3 *
- xarray *
- actions/checkout v2 composite
- actions/checkout v2 composite
- actions/setup-python v2 composite
Score: 13.599404684001716