NeoProphet 1.5.1

Everything from 1.5.0, whose wheel builds failed before reaching PyPI, plus the fixes below.

pip install --upgrade neoprophet

New

• Local temperature as a regressor: forecast(df, horizon="60D", weather="London") (or weather=(51.5, -0.13)) fetches temperature from Open-Meteo, free and without an API key: recorded values for the history, the weather forecast for the next 16 days, then climatology (the average for that day of year over the previous 5 years). Responses are cached on disk and every value keeps its source. Open-Meteo's free API is for non-commercial use; see its terms.
• Easier regressors: regressors=["promo", "price"] for columns in your data (future values via future=), a dict of Series or a DataFrame, with regressor_options= for mode and prior scale. Missing future values fail with the exact timestamps that are missing.
• Skipped days and hours: skip_days="weekends" (or "Sat", 5, ...) and skip_hours=range(0, 8) drop closed periods from training, the forecast and every chart; charts join Friday to Monday with no empty gap.
• New outputs on every forecast() result and in prophet.visuals:
  • result.plot_backtest(): the forecast against what actually happened on a held-out period, with the accuracy in plain English.
  • result.plot_regressor_effects(): how much each regressor moves the forecast, labelling weather forecast versus climatology.
  • result.report(): one A4 page with the forecast, key numbers, components, regressor effects and backtest accuracy; save it as PDF.
  • result.dashboard("forecast.html"): an interactive, self-contained HTML dashboard that works offline (needs plotly).

Fixes

• The "80% interval" label no longer crosses the forecast boundary in narrow charts.
• Plotly component charts show compact percentage ticks.

Fixes in 1.5.1

• A plotly-only test now skips when plotly is not installed; it failed every 1.5.0 wheel build.
• CI type checks pass without the optional mlx extra, and the MLX install hint names neoprophet[mlx].
• CI uses standard macOS runners.