From bbc0a1e6ca1c6d9756c1983c8a54b97282879539 Mon Sep 17 00:00:00 2001 From: Elaine Hale Date: Mon, 9 Feb 2026 13:34:05 -0700 Subject: [PATCH 1/4] Add button links to PyPI and documentation --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 9aa0294..a9feba9 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,7 @@ # 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/) + 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. From d2bea4e8acb012b9f02c4b45c5a3aaf1393795e8 Mon Sep 17 00:00:00 2001 From: Elaine Hale Date: Mon, 9 Feb 2026 13:49:45 -0700 Subject: [PATCH 2/4] Updating codecov configuration. --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index c79ee2a..a3a11c4 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -49,7 +49,7 @@ jobs: if: ${{ matrix.os == env.DEFAULT_OS && matrix.python-version == env.DEFAULT_PYTHON }} with: token: ${{ secrets.CODECOV_TOKEN }} - name: chronify-tests + name: codecov-umbrella fail_ci_if_error: false verbose: true mypy: From b684b414420b42ae85829d031a4aeab62c8214e9 Mon Sep 17 00:00:00 2001 From: Elaine Hale Date: Mon, 9 Feb 2026 13:55:44 -0700 Subject: [PATCH 3/4] Updating codecov configuration. --- .github/workflows/ci.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index a3a11c4..33454ae 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -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: codecov-umbrella + slug: dsgrid/stride fail_ci_if_error: false verbose: true mypy: From 44a8aca869b9adc7ee30127d051c45a1ad954c03 Mon Sep 17 00:00:00 2001 From: Elaine Hale Date: Mon, 9 Feb 2026 14:26:42 -0700 Subject: [PATCH 4/4] Add codecov badge --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index a9feba9..9686aa2 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ # 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.