environmentaltools.graphics.bivariate_pdf

environmentaltools.graphics.bivariate_pdf(df_sim: DataFrame, df_obs: DataFrame, variables: list, bins: int = None, levels: list = None, ax=None, file_name: str = None, logx: str = False, logy: str = False, contour: bool = False)[source]

Plot bivariate PDF comparison between observed and simulated data.

Creates side-by-side contour or image plots of the bivariate probability density functions for visual model validation.

Parameters:

  • df_sim (pd.DataFrame) – Simulated time series data.

  • df_obs (pd.DataFrame) – Observed time series data.

  • variables (list) – List of two variable names [var1, var2] to plot.

  • bins (int or list, optional) – Number of bins for histogram. If None, uses [25, 25]. Defaults to None.

  • levels (list, optional) – Contour levels. If None, auto-computed. Defaults to None.

  • ax (matplotlib.axes.Axes, optional) – Axes for the plot. Creates new if None. Defaults to None.

  • file_name (str, optional) – File path to save the plot. If None, displays interactively. Defaults to None.

  • logx (bool) – If True, applies log transform to first variable. Defaults to False.

  • logy (bool) – If True, applies log transform to second variable. Defaults to False.

  • contour (bool) – If True, uses contour plot instead of image. Defaults to False.

Returns:

Array of axes with the plots.

Return type:

matplotlib.axes.Axes