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Update README to point to PyPI, docs, etc. #53

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Merged
elainethale merged 4 commits into from
Feb 9, 2026
Merged

Update README to point to PyPI, docs, etc. #53

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bbc0a1e
Add button links to PyPI and documentation
elainethale Feb 9, 2026
d2bea4e
Updating codecov configuration.
elainethale Feb 9, 2026
b684b41
Updating codecov configuration.
elainethale Feb 9, 2026
44a8aca
Add codecov badge
elainethale Feb 9, 2026
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6 changes: 3 additions & 3 deletions .github/workflows/ci.yml
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Expand Up @@ -45,11 +45,11 @@ jobs:
run: |
pytest -v --cov --cov-report=xml
- name: codecov
uses: codecov/codecov-action@v4.2.0
if: ${{ matrix.os == env.DEFAULT_OS && matrix.python-version == env.DEFAULT_PYTHON }}
uses: codecov/codecov-action@v5
if: ${{ matrix.os == env.DEFAULT_OS && matrix.python-version == env.DEFAULT_PYTHON }}
with:
token: ${{ secrets.CODECOV_TOKEN }}
name: chronify-tests
slug: dsgrid/stride
fail_ci_if_error: false
verbose: true
mypy:
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4 changes: 4 additions & 0 deletions README.md
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@@ -1,4 +1,8 @@
# STRIDE
[![PyPI](https://img.shields.io/pypi/v/stride-load-forecast.svg)](https://pypi.org/project/stride-load-forecast/)
[![Documentation](https://img.shields.io/badge/docs-ready-blue.svg)](https://dsgrid.github.io/stride/)
[![codecov](https://codecov.io/gh/dsgrid/stride/branch/main/graph/badge.svg)](https://app.codecov.io/github/dsgrid/stride)


STRIDE (Smart Trending and Resource Insights for Demand Estimation) is a Python tool for assembling annual hourly electricity demand projections at the country-level suitable for grid planning. STRIDE is designed to enable quick assemblage of first-order load forecasts that can then be refined, guided by visual QA/QC of results. The first order load forecasts are based on country-level data describing normalized electricity use, electricity use correlates (e.g., population, human development index, gross domestic product), weather, and load shapes. Alternative scenarios and forecast refinements can be made by layering in user-supplied data at any point in the calculation workflow and/or opting to use more complex forecasting models for certain subsectors/end uses.

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