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pymdea.plot

Plotting functions.

DeaPlotter(model, theme=None)

Plot DEA results.

Parameters:

  • model

    (Self @ DeaEngine) –

    Object containing the results of a DEA analysis to be plotted.

  • theme

    (None | str, default: None ) –

    Must be either None or a string corresponding to a matplotlib.pyplot style.

Source code in src/pymdea/plot.py 
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def __init__(
    self: Self,
    model: DeaEngine,
    theme: None | str = None,
) -> Self:
    """Plot DEA results.

    Parameters
    ----------
    model : Self@DeaEngine
        Object containing the results of a DEA analysis to be plotted.
    theme : None | str, optional, default: None
        Must be either None or a string corresponding to a
        matplotlib.pyplot style.

    """
    if theme is not None:
        plt.style.use(style=theme)
    self.window_lengths = model.window_lengths
    self.entropies = model.entropies
    self.delta = model.fit_coefficients[0]
    self.y_intercept = model.fit_coefficients[1]
    self.mu1 = model.mu1
    self.mu2 = model.mu2
    self.mu3 = model.mu3

mu_candidates(fig_width=5, fig_height=3)

Plot the possible values of mu.

Parameters:

  • fig_width

    (int, default: 4 ) –

    Width, in inches, of the figure.

  • fig_height

    (int, default: 3 ) –

    Height, in inches, of the figure.

Source code in src/pymdea/plot.py 
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def mu_candidates(self: Self, fig_width: int = 5, fig_height: int = 3) -> None:
    """Plot the possible values of mu.

    Parameters
    ----------
    fig_width : int, optional, default: 4
        Width, in inches, of the figure.
    fig_height : int, optional, default: 3
        Height, in inches, of the figure.

    """
    x1 = np.linspace(1, 2, 100)
    x2 = np.linspace(2, 3, 100)
    x3 = np.linspace(3, 4, 100)
    y1 = x1 - 1
    y2 = 1 / (x2 - 1)
    y3 = np.full(100, 0.5)

    fig, ax = plt.subplots(figsize=(fig_width, fig_height), layout="constrained")
    ax.plot(x1, y1, color="k")
    ax.plot(x2, y2, color="k")
    ax.plot(x3, y3, color="k")
    ax.plot(
        self.mu1,
        self.delta,
        marker="o",
        fillstyle="none",
        markersize=5,
        markeredgewidth=2,
        linestyle="none",
        label=f"$\\mu$ = {np.round(self.mu1, 2)}",
    )
    ax.plot(
        self.mu2,
        self.delta,
        marker="o",
        fillstyle="none",
        markersize=5,
        markeredgewidth=2,
        linestyle="none",
        label=f"$\\mu$ = {np.round(self.mu2, 2)}",
    )
    ax.plot(
        self.mu3,
        self.delta,
        marker="o",
        fillstyle="none",
        markersize=5,
        markeredgewidth=2,
        linestyle="none",
        label=f"$\\mu$ = {np.round(self.mu3, 2)}",
    )
    ax.set_xticks(ticks=np.linspace(1, 4, 7))
    ax.set_yticks(ticks=np.linspace(0, 1, 5))
    ax.set_xlabel("$\\mu$")
    ax.set_ylabel("$\\delta$")
    ax.legend(loc="center left", bbox_to_anchor=(1.0, 0.5))
    ax.grid(visible=True)
    sns.despine(left=True, bottom=True)
    self.fig_mu_candidates = fig

s_vs_l(fig_width=5, fig_height=3)

Plot the slope of entropy vs window length.

Parameters:

  • fig_width

    (int, default: 4 ) –

    Width, in inches, of the figure.

  • fig_height

    (int, default: 3 ) –

    Height, in inches, of the figure.

Source code in src/pymdea/plot.py 
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def s_vs_l(self: Self, fig_width: int = 5, fig_height: int = 3) -> None:
    """Plot the slope of entropy vs window length.

    Parameters
    ----------
    fig_width : int, optional, default: 4
        Width, in inches, of the figure.
    fig_height : int, optional, default: 3
        Height, in inches, of the figure.

    """
    x_line = np.linspace(start=1, stop=np.max(self.window_lengths), num=3)
    fig, ax = plt.subplots(figsize=(fig_width, fig_height), layout="constrained")
    ax.plot(
        self.window_lengths,
        self.entropies,
        linestyle="none",
        marker="o",
        markersize=3,
        fillstyle="none",
    )
    ax.plot(
        x_line,
        self.delta * np.log(x_line) + self.y_intercept,
        color="k",
        label=f"$\\delta = {np.round(self.delta, 3)}$",
    )
    ax.set_xscale("log")
    ax.set_xlabel("$\\ln(L)$")
    ax.set_ylabel("$S(L)$")
    ax.legend(loc="center left", bbox_to_anchor=(1.0, 0.5))
    sns.despine(trim=True)
    self.fig_s_vs_l = fig