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Open Sustainable Technology

A curated list of open technology projects to sustain a stable climate, energy supply, biodiversity and natural resources.

flowsa

Library that attributes resource use, waste, emissions, and loss to economic sectors.
https://github.com/usepa/flowsa

Category: Industrial Ecology
Sub Category: Life Cycle Assessment

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ord

Last synced: about 12 hours ago
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Repository metadata

Library that attributes resource use, waste, emissions, and loss to economic sectors

README.md

FLOWSA Paper
DOI

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/[email protected]#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 metadata

GitHub Events

Total
Last Year

Committers metadata

Last synced: 8 days ago

Total Commits: 4,758
Total Committers: 19
Avg Commits per committer: 250.421
Development Distribution Score (DDS): 0.423

Commits in past year: 174
Committers in past year: 4
Avg Commits per committer in past year: 43.5
Development Distribution Score (DDS) in past year: 0.213

Name Email Commits
catherinebirney b****e@e****v 2743
Ben Young B****g@e****m 1202
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
Bousquin B****n@e****v 10
ysrivas08 y****a@e****m 8
Daniel L. Young, Ph.D y****l@e****v 8
Caitlin Chiquelin C****n@e****m 7
Andy Chase t****e@g****m 5
jchou18 9****8 1
rwashing523 w****e@e****v 1
Liz e****r@e****m 1
davidemeyer m****d@e****v 1
ealonso-mfa e****o@u****v 1

Committer domains:

Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 61
Total pull requests: 111
Average time to close issues: 5 months
Average time to close pull requests: 19 days
Total issue authors: 6
Total pull request authors: 8
Average comments per issue: 1.67
Average comments per pull request: 1.83
Merged pull request: 92
Bot issues: 0
Bot pull requests: 0

Past year issues: 19
Past year pull requests: 22
Past year average time to close issues: 3 months
Past year average time to close pull requests: 26 days
Past year issue authors: 2
Past year pull request authors: 2
Past year average comments per issue: 0.26
Past year average comments per pull request: 0.55
Past year merged pull request: 17
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/usepa/flowsa

Top Issue Authors

  • bl-young (23)
  • WesIngwersen (20)
  • catherinebirney (8)
  • matthewlchambers (7)
  • wadedavis13 (2)
  • wuqi001s (1)

Top Pull Request Authors

  • catherinebirney (59)
  • bl-young (37)
  • matthewlchambers (8)
  • WesIngwersen (3)
  • ericmbell1 (1)
  • andychase (1)
  • jbousquin (1)
  • ysrivas08 (1)

Top Issue Labels

  • enhancement (16)
  • flowbyactivity (10)
  • flowbysector (10)
  • bug (6)
  • feature branch (4)
  • activitytosectormapping (3)
  • invalid (3)
  • question (1)
  • data source issue (1)

Top Pull Request Labels

Dependencies

requirements.txt pypi
  • 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
setup.py pypi
  • 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
.github/workflows/compare_single_FBS.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • actions/upload-artifact v3.1.1 composite
.github/workflows/generate_FBS.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • actions/upload-artifact v3.1.1 composite
.github/workflows/python-app.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
.github/workflows/test_methods.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • actions/upload-artifact v3.1.1 composite
.github/workflows/generate_FBA.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • actions/upload-artifact v3.1.1 composite

Score: 6.951772164398911