flowsa
Library that attributes resource use, waste, emissions, and loss to economic sectors
Science Score: 59.0%
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
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
1 of 19 committers (5.3%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (11.0%) to scientific vocabulary
Keywords
Repository
Library that attributes resource use, waste, emissions, and loss to economic sectors
Basic Info
Statistics
- Stars: 32
- Watchers: 11
- Forks: 24
- Open Issues: 27
- Releases: 29
Topics
Metadata Files
README.md
flowsa
flowsa is a data processing library attributing the flows of resources
(environmental, monetary, and human), wastes, emissions, and losses to sectors, typically
NAICS codes. flowsa aggregates, combines,
and allocates data from a variety of sources. The sources can be found in the
GitHub wiki
under "Flow-By-Activity Datasets".
flowsa helps support
USEEIO
as part of the USEEIO modeling
framework. The USEEIO models estimate potential impacts of goods and
services in the US economy. The
Flow-By-Sector datasets
created in FLOWSA are the environmental inputs to
useeior.
Usage
Flow-By-Activity (FBA) Datasets
Flow-By-Activity datasets are formatted tables from a variety of sources. They are largely unchanged from the original data source, except for formatting. A list of available FBA datasets can be found in the Wiki.
import flowsa \
Return list of all available FBA datasets, including years
flowsa.seeAvailableFlowByModels('FBA') \
Generate and return pandas dataframe for 2014 Energy Information
Administration (EIA) Manufacturing Energy Consumption Survey (MECS) land use \
fba = flowsa.getFlowByActivity(datasource="EIA_MECS_Land", year=2014)
Flow-By-Sector (FBS) Datasets
Flow-By-Sector datasets are tables of environmental and other data attributed to sectors. A list of available FBS datasets can be found in the Wiki.
import flowsa \
Return list of all available FBS datasets
flowsa.seeAvailableFlowByModels('FBS') \
Generate and return pandas dataframe for national water withdrawals
attributed to 6-digit sectors. Download all required FBA datasets from
Data Commons. \
fbs = flowsa.getFlowBySector('Water_national_2015_m1',
download_FBAs_if_missing=True)
Examples
Additional example code can be found in the examples folder.
Installation
pip install git+https://github.com/USEPA/flowsa.git@vX.X.X#egg=flowsa
where vX.X.X can be replaced with the version you wish to install under Releases.
Additional Information on Installation, Examples, Detailed Documentation
For more information on flowsa see the wiki.
Accessing datsets output by FLOWSA
FBA and FBS datasets can be accessed on EPA's Data Commons without running the Python code.
Disclaimer
The United States Environmental Protection Agency (EPA) GitHub project code is provided on an "as is" basis and the user assumes responsibility for its use. EPA has relinquished control of the information and no longer has responsibility to protect the integrity, confidentiality, or availability of the information. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by EPA. The EPA seal and logo shall not be used in any manner to imply endorsement of any commercial product or activity by EPA or the United States Government.
Owner
- Name: U.S. Environmental Protection Agency
- Login: USEPA
- Kind: organization
- Location: United States of America
- Website: https://www.epa.gov
- Twitter: EPA
- Repositories: 449
- Profile: https://github.com/USEPA
GitHub Events
Total
- Fork event: 6
- Create event: 23
- Commit comment event: 3
- Release event: 3
- Issues event: 20
- Watch event: 6
- Delete event: 11
- Member event: 3
- Issue comment event: 38
- Push event: 142
- Pull request review event: 5
- Gollum event: 1
- Pull request event: 29
Last Year
- Fork event: 6
- Create event: 23
- Commit comment event: 3
- Release event: 3
- Issues event: 20
- Watch event: 6
- Delete event: 11
- Member event: 3
- Issue comment event: 38
- Push event: 142
- Pull request review event: 5
- Gollum event: 1
- Pull request event: 29
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| catherinebirney | b****e@e****v | 2,752 |
| Ben Young | B****g@e****m | 1,224 |
| matthewlchambers | m****s@b****v | 326 |
| WesIngwersen | i****y@e****v | 235 |
| melissagqc | m****a@g****m | 130 |
| Eric Bell | e****l@e****m | 24 |
| Jacob Specht | j****b@g****m | 20 |
| Andrew Beck | 8****k | 19 |
| Mo Li | m****i@g****m | 16 |
| ysrivas08 | y****a@e****m | 13 |
| Bousquin | B****n@e****v | 10 |
| Daniel L. Young, Ph.D | y****l@e****v | 8 |
| Caitlin Chiquelin | C****n@e****m | 7 |
| Andy Chase | t****e@g****m | 5 |
| Liz | e****r@e****m | 2 |
| davidemeyer | m****d@e****v | 1 |
| ealonso-mfa | e****o@u****v | 1 |
| rwashing523 | w****e@e****v | 1 |
| jchou18 | 9****8 | 1 |
Issues and Pull Requests
Last synced: 9 months ago
All Time
- Total issues: 65
- Total pull requests: 140
- Average time to close issues: 5 months
- Average time to close pull requests: about 1 month
- Total issue authors: 6
- Total pull request authors: 8
- Average comments per issue: 1.69
- Average comments per pull request: 1.64
- Merged pull requests: 115
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 15
- Pull requests: 32
- Average time to close issues: 25 days
- Average time to close pull requests: 24 days
- Issue authors: 2
- Pull request authors: 2
- Average comments per issue: 0.47
- Average comments per pull request: 0.91
- Merged pull requests: 22
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- bl-young (23)
- WesIngwersen (23)
- catherinebirney (9)
- matthewlchambers (7)
- wadedavis13 (2)
- wuqi001s (1)
Pull Request Authors
- catherinebirney (67)
- bl-young (58)
- matthewlchambers (8)
- WesIngwersen (3)
- ericmbell1 (1)
- andychase (1)
- jbousquin (1)
- ysrivas08 (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- StEWI *
- appdirs >=1.4.3
- bibtexparser >=1.2.0
- esupy *
- fedelemflowlist *
- matplotlib >=3.4.3
- numpy >=1.20.1
- openpyxl >=3.0.7
- pandas >=1.3.2
- pip >=9
- pycountry >=19.8.18
- python-dotenv >=0.19.1
- pyyaml >=5.3
- requests >=2.22.0
- requests_ftp ==0.3.1
- seaborn >=0.11.2
- setuptools >=41
- tabula-py >=2.1.1
- xlrd >=2.0.1
- StEWI *
- appdirs >=1.4.3
- bibtexparser >=1.2.0
- esupy *
- fedelemflowlist *
- matplotlib >=3.4.3
- numpy >=1.20.1
- openpyxl >=3.0.7
- pandas >=1.3.2
- pip >=9
- pycountry >=19.8.18
- python-dotenv *
- pyyaml >=5.3
- requests >=2.22.0
- requests_ftp ==0.3.1
- seaborn >=0.11.2
- setuptools >=41
- tabula-py >=2.1.1
- xlrd >=2.0.1
- actions/checkout v3 composite
- actions/setup-python v3 composite
- actions/upload-artifact v3.1.1 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- actions/upload-artifact v3.1.1 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- actions/upload-artifact v3.1.1 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- actions/upload-artifact v3.1.1 composite