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viz_data.py

VizData 

VizData(dpi: int = 150, font_size: int = 11, font_family: str = 'Arial')


              flowchart TD
              wmbe.viz_data.VizData[VizData]
              wmbe.viz_base.VizBase[VizBase]

                              wmbe.viz_base.VizBase --> wmbe.viz_data.VizData
                


              click wmbe.viz_data.VizData href "" "wmbe.viz_data.VizData"
              click wmbe.viz_base.VizBase href "" "wmbe.viz_base.VizBase"

Data visualization class.

Methods:

  • channel_generic –

    Plot numerical solutions applied to channel cross-section model

  • channel_refweatheringrate_referosionrate_W –

    Plot numerical solutions applied to channel cross-section model (vertical profiles).

  • create_figure –

    Initialize a Pyplot figure.

  • inoue_w_wetdryN –

    Plot Inoue et al data on weakness \(w\) versus proxy time \(N\)

  • li_w_P –

    Plot Li et al data on weakness \(w\) versus proxy depth.

  • li_w_normed_P –

    Plot Li et al data on weakness \(w\) (normalized using 2D model)

  • li_w_wetdryN –

    Plot Li et al data on weakness \(w\) versus proxy time \(N\)

  • li_w_wetdryN_P –

    Plot Li et al data in 3D on weakness versus proxy depth and confining

Source code in wmbe/viz_base.py 
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def __init__(
        self, 
        dpi: int=150, 
        font_size: int=11, 
        font_family: str="Arial",
    ) -> None:
    self.dpi = dpi
    self.fdict = {}
    try:
        mpl.rc("font", size=font_size, family=font_family)
    except:
        mpl.rc("font", size=font_size, family="")

    self.markers = {
        'o': 'circle', 
        'v': 'triangle_down', 
        '^': 'triangle_up', 
        '<': 'triangle_left', 
        '>': 'triangle_right', 
        #  '1': 'tri_down', '2': 'tri_up', 
        #  '3': 'tri_left', '4': 'tri_right',
        #  's': 'square', 
        '8': 'octagon', 
        'p': 'pentagon', 
        # '*': 'star', 
        'h': 'hexagon1',
        'H': 'hexagon2', 
        #  '+': 'plus', 'x': 'x', 
        'D': 'diamond', 
        'd': 'thin_diamond', 
        '|': 'vline', 
        '_': 'hline', 
        'P': 'plus_filled', 
        'X': 'x_filled', 
        #  0: 'tickleft', 1: 'tickright', 
        #  2: 'tickup', 3: 'tickdown', 
        #  4: 'caretleft', 5: 'caretright', 6: 'caretup',
        #  7: 'caretdown', 8: 'caretleftbase', 
        #  9: 'caretrightbase', 10: 'caretupbase', 11: 'caretdownbase'
    }

channel_generic 

channel_generic(
    name: str,
    title: str | None = None,
    zys: Sequence | None = None,
    do_equal_aspect=False,
    text_labels: Sequence | None = None,
    fig_size: tuple[float, float] = (6, 4),
) -> None

Plot numerical solutions applied to channel cross-section model (vertical profiles).

Source code in wmbe/viz_data.py 
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def channel_generic(
        self, 
        name: str,
        title: str|None=None,
        zys: Sequence|None=None, 
        do_equal_aspect=False,
        text_labels: Sequence|None=None,
        fig_size: tuple[float,float]=(6, 4,),
    ) -> None:            
    """
    Plot numerical solutions applied to channel cross-section model 
    (vertical profiles).
    """            
    # Args:
    #     fig (:obj:`Matplotlib figure <matplotlib.figure.Figure>`): 
    #         reference to :mod:`MatPlotLib/Pyplot <matplotlib.pyplot>` figure
    #     zy_list (list): 
    #         set of numerical solutions to plot
    #     text_label (list): text annotation as list of form (x-y coordinate, string, 
    #         font size)
    #     do_equal_aspect (bool): 
    #         flag whether to use force equal sizing of x and y axis scales

    _ = self.create_figure(name=name, size=fig_size,)
    if title is None:
        plt.title(title, fontdict={"fontsize": 11.5})

    zy=zys[0]
    plt.plot(
        zy[2], zy[0], label=zy[4], color="k",
    )
    plt.ylabel(zy[1])
    plt.xlabel(zy[3])
    axes = plt.gca()
    if do_equal_aspect:
        axes.set_aspect("equal")
    else:
        pass
    if text_labels is not None:
        for text_label in text_labels:
            plt.text(
                *text_label[0], 
                text_label[1], 
                color=text_label[3], 
                size=text_label[2],
                verticalalignment="center", 
                horizontalalignment="center",
                transform=axes.transAxes, 
                rotation=text_label[4],
            )
    plt.grid("on",ls=":")
    if len(zys)>=2:
        zy=zys[1]
        plt.plot(0,0, label=zy[4], color="forestgreen",)
    plt.legend()

    if len(zys)>=2:
        zy=zys[1]
        alt_axes = axes.twiny()
        alt_axes.plot(zy[2], zy[0], label=zy[4], color="forestgreen",)
        alt_axes.set_xlabel(zy[3], color="forestgreen",)
    plt.grid(ls=":")

channel_refweatheringrate_referosionrate_W 

channel_refweatheringrate_referosionrate_W(
    name: str,
    title: str | None = None,
    model: Any | None = None,
    text_label: Sequence | None = None,
    fig_size: tuple[float, float] = (6, 4),
) -> None

Plot numerical solutions applied to channel cross-section model (vertical profiles).

Source code in wmbe/viz_data.py 
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def channel_refweatheringrate_referosionrate_W(
        self, 
        name: str,
        title: str|None=None,
        model: Any|None=None, 
        text_label: Sequence|None=None,
        fig_size: tuple[float,float]=(6,4,),
    ) -> None:   
    """
    Plot numerical solutions applied to channel cross-section model (vertical profiles).
    """            
    # Args:
    #     fig (:obj:`Matplotlib figure <matplotlib.figure.Figure>`): 
    #         reference to :mod:`MatPlotLib/Pyplot <matplotlib.pyplot>` figure 
    #     cw (:class:`~.solve1p1d.ChannelWall`): instance of :mod:`~.solve1p1d` model 
    #                         class that simulates channel cross-sectional geometry
    #     text_label (list): text annotation as list of form (x-y coordinate, string, 
    #                     font size)

    _ = self.create_figure(name=name, size=fig_size,)
    if title is None:
        plt.title(title, fontdict={"fontsize": 11.5})

    plt.plot(
        model.w0_array/model.physical_parameters[k], 
        model.z_array, 
        label=r"$w_0/k$",
    )
    plt.plot(
        model.v0_array, 
        model.z_array, 
        label="${u_0}$",
    )
    x_limits = plt.xlim()
    y_limits = plt.ylim()
    plt.plot(
        (-1, -1,), 
        label=r"${\mathcal{W}}=w_0/{u_0} k$", 
        color="forestgreen",
    )
    plt.plot(
        model.vs_array,
        model.z_array, 
        label=r"$v_s$", 
        color="k", 
        lw=2,
    )
    plt.xlim(x_limits)
    plt.ylim(y_limits)
    plt.xlabel(r"Speeds $w_0(z)/k$, ${u_0}(z)$, $v_s(z)$")
    plt.ylabel(r"Height above bed  $z$")
    plt.legend(loc="upper center")
    plt.grid(ls=":")

    axes = plt.gca()
    alt_axes = axes.twiny()
    alt_axes.plot(
        model.W_array,  
        model.z_array, 
        label="${\\mathcal{W}}$", 
        color="forestgreen",
    )
    alt_axes.set_xlabel(
        r"Weathering number  ${\\mathcal{W}}(z)$", 
        color="forestgreen",
    )
    x_limits = axes.get_xlim()
    axes.set_xlim(x_limits[0],x_limits[1]*1.05)

    if text_label is not None:
        plt.text(
            *text_label[0], 
            text_label[1], 
            color="k", 
            size=text_label[2],
            verticalalignment="center", 
            horizontalalignment="center",
            transform=axes.transAxes,
        )

create_figure 

create_figure(
    name: str, size: tuple[float, float] | None = None, dpi: int | None = None
) -> Figure

Initialize a Pyplot figure.

Set its size and DPI. Append it to the figures dictionary.

Source code in wmbe/viz_base.py 
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def create_figure(
    self,
    name: str,
    size: tuple[float, float] | None = None,
    dpi: int | None = None,
) -> Figure:
    """
    Initialize a Pyplot figure.

    Set its size and DPI. Append it to the figures dictionary.
    """
    # Args:
    #     fig_name:
    #         name of figure; used as key in figures dictionary
    #     fig_size:
    #         optional width and height of figure in inches
    #     dpi:
    #         rasterization resolution

    # Returns:
    #     figure:
    #         reference to MatPlotLib/Pyplot figure

    fig_size_: tuple[float, float] = (
        (6, 4,) if size is None else size
    )
    dpi_: float = self.dpi if dpi is None else dpi
    logging.info(
        "Viz:\n   "
        + f"Creating plot: {name} size={fig_size_} @ {dpi_} dpi"
    )
    fig = plt.figure()
    self.fdict.update({name: fig})
    if fig_size_ is not None:
        fig.set_size_inches(*fig_size_)
    fig.set_dpi(dpi_)
    return fig

inoue_w_wetdryN 

inoue_w_wetdryN(
    name: str,
    title: str | None = None,
    data: DataFrame | None = None,
    model: WeatheringMediatedWeakness | None = None,
    text_label: Sequence | None = None,
    fig_size: tuple[float, float] = (6, 4),
) -> None

Plot Inoue et al data on weakness \(w\) versus proxy time \(N\) (number of wet/dry cycles).

Generate graph of rock weakness \(w\) versus proxy time inferred from Inoue et al (2017)_ data on tensile strength \(\\sigma_T\) (normalized by a reference tensile strength \(\\sigma_\\mathrm{ref}\)) after \(N\) wetting and drying cycles.

Source code in wmbe/viz_data.py 
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def inoue_w_wetdryN(
        self, 
        name: str,
        title: str|None=None,
        data: DataFrame|None=None, 
        model: WeatheringMediatedWeakness|None=None,
        text_label: Sequence|None=None,
        fig_size: tuple[float,float]=(6,4,),
    ) -> None:
    r"""
    Plot Inoue et al data on weakness $w$ versus proxy time $N$ 
    (number of wet/dry cycles).

    Generate graph of rock weakness $w$ versus proxy time inferred from 
    `Inoue et al (2017)`_
    data on tensile strength $\\sigma_T$
    (normalized by a reference tensile strength $\\sigma_\\mathrm{ref}$)
    after $N$ wetting and drying cycles.
    """
    # Args:
    #     fig (:obj:`Matplotlib figure <matplotlib.figure.Figure>`): 
    #         reference to :mod:`MatPlotLib/Pyplot <matplotlib.pyplot>` figure 
    #     ed (:class:`~.data.ExptData`): instance of experimental :mod:`~.data` class
    #                                 containing data sets as :mod:`pandas` dataframes
    #     text_label (list): text annotation as list of form (x-y coordinate, string, 
    #                     font size)

    _ = self.create_figure(name=name, size=fig_size,)
    if title is not None:
        plt.title(title, fontdict={"fontsize": 11.5})

    # sigmaT  = df.sigmaT
    wetdryN = data.wetdryN
    erodibility_sigma2   = data.w_sigma2
    # erodibility_sigma1p5 = df.w_sigma1p5
    erodibility_sigma2_fit = model.fits["default"][0]

    plt.plot(
        wetdryN,
        linear_model(wetdryN, *erodibility_sigma2_fit,),
        color="k",
        label=r"$w \sim 1/\sigma_T^2$",
    )
    plt.errorbar(
        np.unique(wetdryN),
        model.w_s2_means, 
        label="",
        xerr=None,
        yerr=model.w_s2_stds*1,
        ecolor="k", 
        mec="w", 
        color="w", 
        fillstyle="full", 
        alpha=1,
        fmt="o", 
        markersize=0, 
        markeredgewidth=2,
        elinewidth=1.5,
        capthick=3,
        capsize=7,
    )
    plt.plot(
        np.unique(wetdryN),
        model.w_s2_means, 
        label="mean data",
        color="lightgray", 
        fillstyle="full", 
        ls="", 
        marker="o",
        markeredgecolor="k",
        ms=13,
    ) 
    plt.plot(
        wetdryN,
        erodibility_sigma2, 
        label="raw data",  
        color="orange", 
        alpha=0.7,
        ls="", 
        marker="s",
        markeredgecolor="k",
        ms=5,
    )

    plt.legend(loc="upper left")
#     plt.ylim(0,)
    plt.xlabel(
        r"Proxy time (no. wet/dry cycles)  $N$  [-]"
    )
    plt.ylabel(
        r"Weakness  $w=(\sigma_T\left[N\right]/\sigma_{\mathrm{ref}})^{-2}$  [-]"
    )
    # plt.ylabel(r"Weakness  [-]")

    if text_label is not None:
        axes = plt.gca()
        plt.text(
            *text_label[0], 
            text_label[1], 
            color="k", 
            size=text_label[2],
            verticalalignment="center", 
            horizontalalignment="center",
            transform=axes.transAxes,
        )
    plt.grid(ls=":")

li_w_P 

li_w_P(
    name: str,
    title: str | None = None,
    data: DataFrame | None = None,
    model: WeatheringMediatedWeakness | None = None,
    text_label: Sequence | None = None,
    fig_size: tuple[float, float] = (6, 4),
) -> None

Plot Li et al data on weakness \(w\) versus proxy depth.

Generate graph of rock weakness \(w\) versus proxy depth inferred from Li et al (2016)_ data on compressive strength \(\\sigma_C\) at a range of confining pressures \(P\) after \(N\) wetting and drying cycles.

Source code in wmbe/viz_data.py 
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def li_w_P(
        self, 
        name: str,
        title: str|None=None,
        data: DataFrame|None=None, 
        model: WeatheringMediatedWeakness|None=None,
        text_label: Sequence|None=None,
        fig_size: tuple[float,float]=(6,4,),
    ) -> None:
    r"""
    Plot Li et al data on weakness $w$ versus proxy depth.

    Generate graph of rock weakness $w$ versus proxy depth inferred from 
    `Li et al (2016)`_ data on compressive strength $\\sigma_C$ at a range
    of confining pressures $P$ after $N$ wetting and drying cycles.
    """
    # Args:
    #     fig (:obj:`Matplotlib figure <matplotlib.figure.Figure>`): 
    #         reference to :mod:`MatPlotLib/Pyplot <matplotlib.pyplot>` figure 
    #     ed (:class:`~.data.ExptData`): instance of experimental :mod:`~.data` class
    #                                 containing data sets as :mod:`pandas` dataframes
    #     text_label (list): text annotation as list of form (x-y coordinate, string, 
    #                     font size)
    _ = self.create_figure(name=name, size=fig_size,)
    if title is not None:
        plt.title(title, fontdict={"fontsize": 11.5})

    color_list = (
        "darkblue",
        "darkmagenta",
        "firebrick",
        "red",
        "orange",
        "green",
    )
    marker_list = (
        "o", "s", "v", "^", "P", "p",
    )
    n_cols = len(color_list)

    sampled_fit = model.fits2d["default"]

    for idx,wetdryN in enumerate(np.flip(np.unique(data.wetdryN))):
        # wdN_ = df.wetdryN[df.wetdryN==wetdryN]
        P_ = data.P[data.wetdryN==wetdryN]
        w_ = data.w_sigma2[data.wetdryN==wetdryN]
        P_fit = sampled_fit[1]
        w_fit = np.flipud(sampled_fit[2].T)[idx]
        plt.plot(
            P_fit, w_fit, color=color_list[idx%n_cols],
        )
        plt.errorbar(
            P_, 
            w_,
            label=f"N = {wetdryN}",
            xerr=None,
            yerr=None,
            ecolor="k", 
            mec="k", 
            color=color_list[idx%n_cols], 
            fillstyle="full", 
            alpha=0.7,
            fmt=marker_list[idx%n_cols], 
            markersize=7, 
            markeredgewidth=0.5,
            elinewidth=1.5,
            capthick=3,
            capsize=7,
        )   

    plt.legend(loc="upper right")
    plt.ylim(0.,)
    plt.autoscale(enable=True, tight=True, axis="x",)
    x_limits = plt.xlim()
    plt.plot(
        x_limits, (1,1), color="gray", ls=":",
    )
    plt.xlabel("Proxy depth (confining pressure $P$)  [MPa]")
    plt.ylabel(r"Weakness  $w=(\sigma_C[N]/\sigma_\mathrm{ref})^{-2}$  [-]")

    if text_label is not None:
        axes = plt.gca()
        plt.text(
            *text_label[0], 
            text_label[1], 
            color="k", 
            size=text_label[2],
            verticalalignment="center", 
            horizontalalignment="center",
            transform=axes.transAxes,
        )
    plt.grid(ls=":")

li_w_normed_P 

li_w_normed_P(
    name: str,
    title: str | None = None,
    data: DataFrame | None = None,
    model: WeatheringMediatedWeakness | None = None,
    text_label: Sequence | None = None,
    fig_size: tuple[float, float] = (6, 4),
) -> None

Plot Li et al data on weakness \(w\) (normalized using 2D model) versus proxy depth.

Generate graph of rock weakness versus proxy depth inferred from Li et al (2016)_ data on compressive strength \(\\sigma_C\) at a range of confining pressures \(P\) after \(N\) wetting and drying cycles.

Source code in wmbe/viz_data.py 
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def li_w_normed_P(
        self, 
        name: str,
        title: str|None=None,
        data: DataFrame|None=None, 
        model: WeatheringMediatedWeakness|None=None,
        text_label: Sequence|None=None,
        fig_size: tuple[float,float]=(6,4,),
    ) -> None:
    r"""
    Plot Li et al data on weakness $w$ (normalized using 2D model) 
    versus proxy depth.

    Generate graph of rock weakness versus proxy depth inferred from 
    `Li et al (2016)`_ data on compressive strength $\\sigma_C$ at a range
    of confining pressures $P$ after $N$ wetting and drying cycles.
    """        
    # Args:
    #     fig (:obj:`Matplotlib figure <matplotlib.figure.Figure>`): 
    #         reference to :mod:`MatPlotLib/Pyplot <matplotlib.pyplot>` figure 
    #     ed (:class:`~.data.ExptData`): instance of experimental :mod:`~.data` class
    #                                 containing data sets as :mod:`pandas` dataframes
    #     text_label (list): text annotation as list of form (x-y coordinate, string, 
    #                     font size)

    _ = self.create_figure(name=name, size=fig_size,)
    if title is not None:
        plt.title(title, fontdict={"fontsize": 11.5})

    color_list = (
        "darkblue",
        "darkmagenta",
        "firebrick",
        "red",
        "orange",
        "green",
    )
    marker_list = (
        "o", "s", "v", "^", "P", "p",
    )
    n_cols = len(color_list)

    sampled_fit = model.fits2d["default"]
    w_ref_vec = np.flipud(sampled_fit[2].T)[:,0]-1

    P_fit = sampled_fit[1]
    w_fit = (np.flipud(sampled_fit[2].T)[-1]-1)/w_ref_vec[-1]+1

    plt.errorbar(
        np.unique(data.P),
        model.w_s2normed_means, 
        label="mean data",
        xerr=None,
        yerr=model.w_s2normed_stds*2,
        ecolor="k", mec="k", 
        color="lightgray", 
        fillstyle="full", 
        alpha=0.7,
        fmt="o", 
        markersize=13, 
        markeredgewidth=1.5,
        elinewidth=1.5,
        capthick=3,
        capsize=7,
    )

    for idx,wetdryN in enumerate(np.flip(np.unique(data.wetdryN))):
        # wdN_ = df.wetdryN[df.wetdryN==wetdryN]
        P_ = data.P[data.wetdryN==wetdryN]
        w_normed = data.w_s2normed[data.wetdryN==wetdryN]
        plt.errorbar(
            P_, 
            w_normed,
            label="N = {}".format(wetdryN),
            xerr=None,
            yerr=None,
            ecolor="k", 
            mec="k", 
            color=color_list[idx%n_cols], 
            fillstyle="full", 
            alpha=0.7,
            fmt=marker_list[idx%n_cols], 
            markersize=7, 
            markeredgewidth=0.5,
            elinewidth=1.5,
            capthick=3,
            capsize=7,
        )   

    plt.plot(
        P_fit,
        w_fit, 
        color="darkblue", 
        label=r"$w \sim \exp(-k\chi)$",
    )

    plt.legend(loc="upper right")
    plt.ylim(0.8,)
    plt.autoscale(enable=True, tight=True, axis="x")
    x_limits = plt.xlim()
    plt.plot(
        x_limits,(1,1), color="gray", ls=":",
    )
    plt.xlabel("Proxy depth (confining pressure $P$)  [MPa]")
    plt.ylabel("Normalized weakness  $w(\\tau,\\chi)/w(0,\\tau)$  [-]")

    if text_label is not None:
        axes = plt.gca()
        plt.text(
            *text_label[0], 
            text_label[1], 
            color="k", 
            size=text_label[2],
            verticalalignment="center", 
            horizontalalignment="center",
            transform=axes.transAxes,
        )
    plt.grid(ls=":")

li_w_wetdryN 

li_w_wetdryN(
    name: str,
    title: str | None = None,
    data: DataFrame | None = None,
    model: WeatheringMediatedWeakness | None = None,
    text_label: Sequence | None = None,
    fig_size: tuple[float, float] = (6, 4),
) -> None

Plot Li et al data on weakness \(w\) versus proxy time \(N\) (number of wet/dry cycles).

Generate graph of rock weakness \(w\) versus proxy time \(N\) inferred from Li et al (2016)_ data on compressive strength \(\sigma_C\) (normalized by a reference compressive strength \(\sigma_\mathrm{ref}\) after \(N\) wetting and drying cycles at a range of confining pressures \(N\).

Source code in wmbe/viz_data.py 
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def li_w_wetdryN(
        self, 
        name: str,
        title: str|None=None,
        data: DataFrame|None=None, 
        model: WeatheringMediatedWeakness|None=None,
        text_label: Sequence|None=None,
        fig_size: tuple[float,float]=(6,4,),
    ) -> None:
    """
    Plot Li et al  data on weakness $w$ versus proxy time $N$
    (number of wet/dry cycles).

    Generate graph of rock weakness $w$ versus proxy time $N$ inferred 
    from `Li et al (2016)`_ data on compressive strength $\\sigma_C$
    (normalized by a reference compressive strength $\\sigma_\\mathrm{ref}$
    after $N$ wetting and drying cycles at a range of confining pressures $N$.
    """
    # Args:
    #     fig (:obj:`Matplotlib figure <matplotlib.figure.Figure>`): 
    #         reference to :mod:`MatPlotLib/Pyplot <matplotlib.pyplot>` figure 
    #     ed (:class:`~.data.ExptData`): instance of experimental :mod:`~.data` class
    #                                 containing data sets as :mod:`pandas` dataframes
    #     text_label (list): text annotation as list of form (x-y coordinate, string, 
    #                     font size)

    _ = self.create_figure(name=name, size=fig_size,)
    if title is not None:
        plt.title(title, fontdict={"fontsize": 11.5})

    color_list = (
        "darkblue",
        "darkmagenta",
        "firebrick",
        "red",
        "orange",
        "green",
    )
    marker_list = (
        "o", "s", "v", "^", "8", "+",
    )
    n_cols = len(color_list)
    for (idx, P_,) in enumerate(np.unique(data.P)):
        selection_name = f"{'P'}_{P_}"
        # sigma  = df.sigmaC[df.P==P_]/100
        wetdryN = data.wetdryN[data.P==P_]
        erodibility_sigma   = data.w_sigma2[data.P==P_]
        erodibility_sigma_fit = model.fits[selection_name][0]

        plt.plot(
            wetdryN,
            linear_model(wetdryN, *erodibility_sigma_fit,),
            color=color_list[idx%n_cols],
            label="",
        )
        plt.errorbar(
            wetdryN,
            erodibility_sigma, 
            label=r"$P = ${}$\,$MPa".format(P_),
            xerr=None,
            yerr=None,
            ecolor="k", 
            mec="k", 
            color=color_list[idx%n_cols], 
            fillstyle="full", 
            alpha=0.7,
            fmt=marker_list[idx%n_cols], 
            markersize=7,
            markeredgewidth=0.5,
            elinewidth=1.5,
            capthick=3,
            capsize=7,
        )    

    plt.legend(loc="upper left")
    plt.ylim(0,)
    plt.xlabel(
        "Proxy time (no. wet/dry cycles)  $N$  [-]"
    )
    plt.ylabel(
        r"Weakness  $w=(\sigma_C\left[N\right]/\sigma_\mathrm{ref})^{-2}$  [-]"
    )

    if text_label is not None:
        axes = plt.gca()
        plt.text(
            *text_label[0], text_label[1], 
            color="k", 
            size=text_label[2],
            verticalalignment="center", 
            horizontalalignment="center",
            transform=axes.transAxes,
        )
    plt.grid(ls=":")

li_w_wetdryN_P 

li_w_wetdryN_P(
    name: str,
    title: str | None = None,
    data: DataFrame | None = None,
    model: WeatheringMediatedWeakness | None = None,
    surface: str | None = None,
    fig_size: tuple[float, float] = (6, 4),
) -> None

Plot Li et al data in 3D on weakness versus proxy depth and confining pressure.

Generate 3D view of 2D surface model & data of rock weakness versus proxy depth inferred from Li et al (2016)_ data on compressive strength \(\\sigma_C\) at a range of confining pressures \(P\) after \(N\) wetting and drying cycles.

Source code in wmbe/viz_data.py 
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def li_w_wetdryN_P(
        self, 
        name: str,
        title: str|None=None,
        data: DataFrame|None=None, 
        model: WeatheringMediatedWeakness|None=None,
        surface: str|None=None,
        # text_label: Sequence|None=None,
        fig_size: tuple[float,float]=(6,4,),
    ) -> None:
    r"""
    Plot Li et al data in 3D on weakness versus proxy depth and confining 
    pressure.

    Generate 3D view of 2D surface model & data of rock weakness versus 
    proxy depth inferred from `Li et al (2016)`_ data on compressive 
    strength $\\sigma_C$ at a range of confining pressures $P$ after $N$ 
    wetting and drying cycles.
    """        
    # Args:
    #     fig (:obj:`Matplotlib figure <matplotlib.figure.Figure>`): 
    #         reference to :mod:`MatPlotLib/Pyplot <matplotlib.pyplot>` figure 
    #     ed (:class:`~.data.ExptData`): instance of experimental :mod:`~.data` class
    #                                 containing data sets as :mod:`pandas` dataframes
    #     model_surface (str): 
    #             key to dict (stored in :attr:`ed`) reference in  to 
    #             2D  function regressed in 
    #             :meth:`~.data.ExptData.fit_weathering_model` that models
    #             joint dependence of weakness on proxy depth and proxy time
    #     text_label (list): text annotation as list of form (x-y coordinate, string, 
    #                     font size)
    fig = self.create_figure(name=name, size=fig_size,)
    if title is not None:
        plt.title(title, fontdict={"fontsize": 11.5})

    axes = fig.add_subplot(1, 1, 1, projection = "3d",)

    # TBD: need an exception here if model_surface not specified
    (X, Y, Z,) = model.fits[surface]

    axes.scatter(
        data.wetdryN, data.P, data.w_sigma2, color="k", s=40,
    )

    colors_list = ("y", "b",)
    colors_array = np.empty(X.shape, dtype=str,)
    rf = 3
    for y in range(len(X)):
        for x in range(len(Y)):
            colors_array[x, y] = colors_list[(x//rf+y//rf) % 2]
    axes.plot_surface(
        X,
        Y,
        Z,
        facecolors=colors_array,  
        alpha=0.35,
        linewidth=0, 
        antialiased=True,
    )
    plt.xlim(0,)
    plt.ylim(0,)
    axes.xaxis.pane.set_edgecolor("k")
    axes.yaxis.pane.set_edgecolor("k")
    axes.zaxis.pane.set_edgecolor("k")
    # axes.view_init(15, 50)
    axes.view_init(25, 72)
    axes.set_xlabel("Proxy time  $N$  [-]")
    axes.set_ylabel("Proxy depth  $P$ [MPa]")
    axes.set_zlabel("Weakness  $w=$  [-]")
    plt.grid(ls=":")