[RaphaelFakhri]

A logistic model fit with scaling='minmax' and no floor column has its floor set to min(y). seasonality_plot_df always used cap=1, so setup_dataframe raised ValueError: cap must be greater than floor whenever min(y) >= 1. That broke plot_components, plot_weekly, plot_yearly, plot_seasonality, and their plotly versions for these models.

df = pd.DataFrame({
    "ds": pd.date_range("2020-01-01", periods=800),
    "y": 100 + 10 * np.sin(np.arange(800) / 7.0),
})
df["cap"] = 200.0
m = Prophet(growth="logistic", scaling="minmax").fit(df)
m.plot_components(m.predict(m.make_future_dataframe(10).assign(cap=200.0)))
# ValueError: cap must be greater than floor (which defaults to 0).

Changes

• seasonality_plot_df uses y_min as the floor when setup_dataframe will use it (minmax scaling, no logistic floor), and sets cap one unit above the floor. Seasonal components don't depend on cap, so the plotted values don't change for any other model.
• Adds test_logistic_minmax_without_floor, which checks that the seasonality plot data matches the weekly component returned by predict.

Testing

python -m pytest prophet/tests/test_plot.py -k logistic_minmax

• Without the fix: 1 failed (ValueError: cap must be greater than floor).
• With the fix: 1 passed. prophet/tests has 143 passed and 2 skipped. pyrefly check reports the same results before and after the change.

Fixes #2771

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