https://github.com/climateimpactlab/dscim
Data-Driven Spatial Climate Impact Model core component code
Science Score: 46.0%
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✓Academic publication links
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✓Committers with academic emails
5 of 11 committers (45.5%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (14.8%) to scientific vocabulary
Repository
Data-Driven Spatial Climate Impact Model core component code
Basic Info
- Host: GitHub
- Owner: ClimateImpactLab
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://climateimpactlab.github.io/dscim/
- Size: 140 MB
Statistics
- Stars: 5
- Watchers: 5
- Forks: 2
- Open Issues: 22
- Releases: 8
Metadata Files
README.md
DSCIM: The Data-driven Spatial Climate Impact Model
This Python library enables the calculation of sector-specific partial social cost of greenhouse gases (SC-GHG) and SCGHGs that are combined across sectors using a variety of valuation methods and assumptions. The main purpose of this library is to parse the monetized spatial damages from different sectors and integrate them using different options ("menu options") that encompass different decisions, such as discount levels, discount strategies, and different considerations related to economic and climate uncertainty.
Installation
Install with pip using:
shell
pip install dscim
Install the unreleased bleeding-edge version of the package with:
shell
pip install git+https://github.com/climateimpactlab/dscim
Dependencies
dscim requires Python > 3.8. Additional compiled packages are required so we recommend installing dscim into a conda environment along with its dependencies.
- numpy
- pandas
- xarray
- matplotlib
- dask
- distributed
- requests
- statsmodels
- zarr
- netcdf4
- h5netcdf
- impactlab-tools
- p_tqdm
Support
Source code is available online at https://github.com/climateimpactlab/dscim. Please file bugs in the bug tracker.
This software is Open Source and available under the Apache License, Version 2.0.
Structure and logic
The library is split into several components that implement the hierarchy defined by the menu options. These are the main elements of the library and serve as the main classes to call different menu options.
```mermaid graph TD SubGraph1Flow(Storage and I/O) subgraph "Storage utilities" SubGraph1Flow --> A[Stacked_damages] SubGraph1Flow -- Climate Data --> Climate SubGraph1Flow -- Economic Data --> EconData end
subgraph "Recipe Book" A[StackedDamages] --> B[MainMenu] B[MainMenu] --> C[AddingUpRecipe]; B[MainMenu] --> D[RiskAversionRecipe]; B[MainMenu] --> E[EquityRecipe] end ```
StackedDamages takes care of parsing all monetized damage data from several
sectors and read the data using a dask.distributed.Client. At the same time,
this class takes care of ingesting FaIR GMST and GMSL data needed to draw damage
functions and calculate FaIR marginal damages to an additional emission of
carbon. The data can be read using the following components:
Class | Function |
|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Climate | Wrapper class to read all things climate, including GMST and GMSL. You can pass a fair_path with a NetCDF with FaIR control and pulse simulations and median FaIR runs. You can use gmst_path to input a CSV file with model and year anomaly data, for fitting the damage functions. |
| EconVars | Class to ingest sector path related data, this includes GDP and population data. Some intermediate variables are also included in this class, check the documentation for more details |
| StackedDamages | Damages wrapper class. This class contains all the elements above and additionally reads all the computed monetized damages. A single path is needed to read all damages, and sectors must be separated by folders. If necessary, the class will save data in .zarr format to make chunking operations more efficient. Check documentation of the class for more details. |
and these elements can be used for the menu options:
- AddingUpRecipe: Adding up all damages and collapse them to calculate a general SCC without valuing uncertainty.
- RiskAversionRecipe: Add risk aversion certainty equivalent to consumption calculations - Value uncertainty over econometric and climate draws.
- EquityRecipe: Add risk aversion and equity to the consumption calculations. Equity includes taking a certainty equivalent over spatial impact regions.
Owner
- Name: Climate Impact Lab
- Login: ClimateImpactLab
- Kind: organization
- Email: info@impactlab.org
- Website: https://impactlab.org
- Twitter: impact_lab
- Repositories: 77
- Profile: https://github.com/ClimateImpactLab
A team of scientists, economists and engineers measuring the real-world costs of climate change.
GitHub Events
Total
- Create event: 83
- Release event: 1
- Issues event: 6
- Watch event: 2
- Delete event: 92
- Issue comment event: 67
- Push event: 150
- Pull request review comment event: 15
- Pull request review event: 22
- Pull request event: 181
Last Year
- Create event: 83
- Release event: 1
- Issues event: 6
- Watch event: 2
- Delete event: 92
- Issue comment event: 67
- Push event: 150
- Pull request review comment event: 15
- Pull request review event: 22
- Pull request event: 181
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 164
- Total Committers: 11
- Avg Commits per committer: 14.909
- Development Distribution Score (DDS): 0.579
Top Committers
| Name | Commits | |
|---|---|---|
| Brewster Malevich | b****h@r****m | 69 |
| Kit Schwarz | k****z@b****u | 27 |
| JMGilbert | l****e@b****u | 23 |
| JMGilbert | j****1@g****m | 19 |
| Kit Schwarz | k****z@b****u | 12 |
| davidrzhdu | 1****u@u****m | 6 |
| JMGilbert | l****e@s****u | 2 |
| Ruixue Lui | l****e@s****u | 2 |
| Kelly McCusker | k****r@u****m | 2 |
| Kit Schwarz | 5****z@u****m | 1 |
| Brewster Malevich | s****v@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 58
- Total pull requests: 411
- Average time to close issues: about 1 month
- Average time to close pull requests: 15 days
- Total issue authors: 5
- Total pull request authors: 7
- Average comments per issue: 0.95
- Average comments per pull request: 0.79
- Merged pull requests: 258
- Bot issues: 0
- Bot pull requests: 323
Past Year
- Issues: 5
- Pull requests: 164
- Average time to close issues: 1 day
- Average time to close pull requests: 11 days
- Issue authors: 4
- Pull request authors: 3
- Average comments per issue: 0.4
- Average comments per pull request: 0.6
- Merged pull requests: 67
- Bot issues: 0
- Bot pull requests: 155
Top Authors
Issue Authors
- brews (40)
- JMGilbert (5)
- kemccusker (5)
- davidrzhdu (4)
- dependabot[bot] (4)
- austinrwg (2)
Pull Request Authors
- dependabot[bot] (454)
- JMGilbert (40)
- brews (38)
- davidrzhdu (8)
- kemccusker (5)
- kitschwarz (4)
- delgadom (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 146 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 8
- Total maintainers: 2
pypi.org: dscim
Data-Driven Spatial Climate Impact Model core component code
- Homepage: https://github.com/ClimateImpactLab/dscim
- Documentation: https://ClimateImpactLab.github.io/dscim
- License: apache-2.0
-
Latest release: 0.7.0
published 7 months ago
Rankings
Dependencies
- actions/checkout v3 composite
- actions/setup-python v3 composite
- pypa/gh-action-pypi-publish v1.5.0 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- codecov/codecov-action v3 composite
- black *
- click *
- dask *
- dask-jobqueue *
- distributed *
- flake8 *
- geopandas *
- h5netcdf *
- impactlab-tools *
- matplotlib *
- netcdf4 *
- numpy *
- p_tqdm *
- pandas *
- pytest *
- pytest-cov *
- requests *
- seaborn *
- statsmodels *
- xarray *
- zarr *