MyStripes

MyStripes turns a sequence of places and periods into climate strips based on ERA5-Land monthly temperature data. It works well for personal life stations, multi-home stories, projects, teams, tours, campaigns, or any other place-based timeline.

It is directly inspired by Show Your Stripes, Ed Hawkins' widely shared warming-stripes project. If you have not seen that page yet, go check it out. It is excellent.

The exported strips are intentionally lightweight, so they can be reused in email signatures, presentation decks, reports, posters, profile pages, and web embeds.

MyStripes is an open project and new contributors are very welcome. Ideas, bug reports, docs fixes, tests, and small code cleanups all help. You do not need to be an expert in climate science or Streamlit to join in. Start with CONTRIBUTING.md.

Why this stack

• Streamlit keeps the main UI simple enough for non-technical users and easy to deploy from GitHub.
• mystripes.api exposes a small Python API for reusable scripts and notebooks.
• cdsapi uses the official Copernicus Climate Data Store API flow for ERA5-Land downloads.
• matplotlib gives exact export control for PNG, SVG, and PDF.

What MyStripes does

• Accepts a flexible number of periods and places across one timeline.
• Keeps the personal-life workflow easy, with labels and hints that fit birth, moves, study years, work phases, and current home.
• Lets users search for cities, regions, and countries by name or enter coordinates manually.
• Auto-fills coordinates from the geocoder result, using an area centroid when the result is a polygon or multipolygon.
• Pulls ERA5-Land monthly 2m_temperature data for each period.
• Uses the nearest single ERA5-Land grid cell by default to keep downloads small.
• Optionally averages grid cells in a chosen radius or inside the selected municipality, district, region, or other place boundary when the geocoder returns a usable area geometry.
• Expands monthly values to daily series before merging periods and aggregating stripe values.
• Offers full calendar years only as the default stripe period, or a 365-day moving average sampled monthly by default or at an evenly spaced fixed strip count.
• Uses a per-location day-of-year climatology built from each location's full downloaded timeline before merging periods into one timeline.
• Caches identical ERA5-Land download requests locally, so regenerating the same strips reuses prior monthly data instead of calling CDS again.
• Exports the graphic as PNG, SVG, and PDF.
• Includes a small Python API for preparing stripe data and plotting or saving the result.

Local run

1. Install dependencies:

   pip install -r requirements.txt

2. Choose one credential option:

   Option A: save a token from the app sidebar. The app can store your CDS token in .streamlit/local_cds_credentials.toml, which is gitignored.

   Option B: enter a session-only token or legacy user_id plus API key in the sidebar. These values stay only in Streamlit session state, are never written to disk by the app, and are intended as a fallback if the default configured token fails.

   Option C: create .streamlit/secrets.toml from the example file:

   CDSAPI_URL = "https://cds.climate.copernicus.eu/api"
   CDSAPI_KEY = "your-personal-access-token"

3. Make sure the CDS licence for reanalysis-era5-land-monthly-means has been accepted for the account whose token you use.

4. Start the Streamlit app:

   streamlit run app.py

Packaging

This repository now includes pyproject.toml so the mystripes package can be built and published on PyPI.

Typical release steps:

python -m build
python -m twine check dist/*
python -m twine upload dist/*

Trusted Publishing is prepared in .github/workflows/release.yml. After adding this repository as a trusted publisher on PyPI, publishing a release is:

1. Bump the version in pyproject.toml.
2. Commit and push to main.
3. Create and push a tag such as v0.1.1.
4. Let GitHub Actions build and publish the package to PyPI.

The workflow uses the GitHub environment name pypi, so you can add deployment protection rules there if you want a manual approval step before upload.

Python API

For scripts and notebooks, use build_stripe_data(...) to turn periods plus monthly temperature frames into a stripe-data bundle, then pass that bundle to plot_stripes(...) to render or save the figure.

from mystripes.api import build_stripe_data, plot_stripes

stripe_data = build_stripe_data(periods=periods, period_data=monthly_frames)
figure = plot_stripes(stripe_data, output_path="mystripes.svg")

The first function accepts periods as dicts, lists, or pandas DataFrames, and monthly data as lists, dicts, or pandas DataFrames. The second function plots from the returned bundle and can save PNG, SVG, or PDF.

Pixi

If you use Pixi, this repository includes pixi.toml with the development tools and ready-to-run tasks:

pixi run start
pixi run test
pixi run coverage
pixi run coverage-html
pixi run build
pixi run lock

Deployment

Recommended host for the UI: Streamlit Community Cloud.

Why:

• It deploys directly from a public GitHub repository.
• It supports per-app secrets in the web UI, which is enough for the CDS API key.
• It is the lowest-friction option for a small public Python app with a Streamlit frontend.

Deployment steps:

1. Push this repository to GitHub, ideally under a repo name such as mystripes.
2. Create a new app in Streamlit Community Cloud and point it at app.py.
3. Add CDSAPI_KEY, optional CDSAPI_URL, and GEOAPIFY_API_KEY in the app secrets.
4. Redeploy.

When Streamlit secrets are available, the app prefers them over any locally saved credential file. A user can still enter a session-only override during their own browser session without changing the deployed secret.

Fallback host: Hugging Face Spaces with the Streamlit SDK if you want another free public host.

uv.lock

If you want Streamlit Community Cloud to use uv.lock for faster installs, you do not need to adopt uv as your normal workflow. After editing dependencies in pyproject.toml, create or refresh the lockfile once from the repo root:

uv lock

If you do not normally use uv, a simple one-off approach is:

python -m pip install uv
uv lock
python -m pip uninstall uv

If you use Pixi here, the same refresh is available as:

pixi run lock

Streamlit Community Cloud checks dependency files in this order: uv.lock, Pipfile, environment.yml, requirements.txt, then pyproject.toml. So committing uv.lock is enough to make the deployment prefer that lockfile over requirements.txt.

Data and method notes

• Climate data source: ERA5-Land monthly means from the Copernicus Climate Data Store.
• Geocoding source: Geoapify geocoding in hosted deployments when GEOAPIFY_API_KEY is configured, with OpenStreetMap Nominatim as a fallback.
• Single-cell mode requests only the nearest native 0.1 degree ERA5-Land grid cell, not a station record.
• Radius mode and boundary mode request the minimal bounding area needed for the selected cells, then average the matching grid cells for each month.
• Boundary mode uses the place polygon returned by Nominatim when available; Geoapify-backed searches fall back to the result bounding box because the hosted geocoding response does not provide the same polygon geometry.
• Full-calendar-year mode omits partial first and current years. The 365-day moving-average mode expands monthly values to daily coverage, applies a daily rolling mean, and samples the smoothed series monthly or at an evenly spaced fixed count. When prior data is available, the app keeps up to one year before the displayed start date for the rolling calculation and crops only after smoothing.
• If the timeline starts before the dataset start date, the stripes start at the first available ERA5-Land monthly date.

Operational notes

• The CDS operator must accept the dataset terms of use in their CDS account before the API key works.
• Current CDS tokens are personal access tokens. They should be stored as the bare token string, not user:token.
• Public Nominatim is not a good fit for Streamlit Community Cloud. Configure GEOAPIFY_API_KEY for deployment and keep Nominatim as the local fallback.
• Downloaded monthly series are cached under .mystripes-cache/, which is local-only and gitignored.
• Geocoding results are cached under the same .mystripes-cache/ directory to avoid repeated provider requests for identical place searches.

Credits and licenses

• This project was inspired by ShowYourStripes.info, the #ShowYourStripes project by Professor Ed Hawkins and the University of Reading.
• The app uses ERA5-Land monthly averaged reanalysis and ERA5 monthly averaged data on single levels from the Copernicus Climate Data Store. Their catalogue entry DOIs are 10.24381/cds.68d2bb30 and 10.24381/cds.f17050d7, and the app includes the Copernicus acknowledgement recommended by ECMWF / C3S.
• The historical fallback uses NOAA PSL's 20CRv3 monthly 2 m air temperature. NOAA PSL asks users to include the requested 20CRv3 acknowledgment text and the official references listed on their How to Cite Use of 20CR Data page.
• The reference-period warming note uses NASA GISTEMP v4. NASA GISS asks users to cite the webpage with an access date and to credit NASA's Goddard Institute for Space Studies or NASA GISS/GISTEMP.
• See NOTICE.md for the project data credit notice and dataset references.
• The software is licensed under LICENSE.
• The generated graphic composition is offered under LICENSE-graphics-CC0.md, with the important caveat that source-data attribution may still apply to Copernicus, NOAA PSL / 20CRv3, and NASA GISS / GISTEMP content.

Time-series summary: MyStripes loads monthly near-surface air temperatures for each place, using ERA5-Land from 1950 onward, ERA5 for 1940-1949, and 20CRv3 before 1940 when needed. Historical slices are anomaly-aligned to ERA5-Land, monthly values are expanded to daily values within each period, merged along the active period schedule, and then compared with the selected reference period to compute the climatology and anomalies behind the stripes.