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Plotting Module#

The Plotting module provides functions for generating visualizations of bank lines, erosion results, and other data in the D-FAST Bank Erosion software.

Overview#

The Plotting module contains functions for creating various types of plots and visualizations that help users understand the results of bank line detection and erosion calculations. These visualizations include maps of bank lines, erosion profiles, and time series of erosion volumes.

Components#

The Plotting module consists of the following components:

Plotting Functions#

dfastbe.plotting #

Copyright (C) 2020 Stichting Deltares.

This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation version 2.1.

This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with this library; if not, see http://www.gnu.org/licenses/.

contact: delft3d.support@deltares.nl Stichting Deltares P.O. Box 177 2600 MH Delft, The Netherlands

All indications and logos of, and references to, "Delft3D" and "Deltares" are registered trademarks of Stichting Deltares, and remain the property of Stichting Deltares. All rights reserved.

INFORMATION This file is part of D-FAST Bank Erosion: https://github.com/Deltares/D-FAST_Bank_Erosion

chainage_markers(xykm: numpy.ndarray, ax: matplotlib.axes.Axes, ndec: int = 1, scale: float = 1000) -> None #

Add markers indicating the river chainage to a plot.

Arguments#

xykm : numpy.ndarray Array containing the x, y, and chainage; unit m for x and y, km for chainage. ax : matplotlib.axes.Axes Axes object in which to add the markers. ndec : int Number of decimals used for marks. scale: float Indicates whether the axes are in m (1) or km (1000).

Source code in src/dfastbe/plotting.py 
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def chainage_markers(
    xykm: numpy.ndarray, ax: matplotlib.axes.Axes, ndec: int = 1, scale: float = 1000
) -> None:
    """
    Add markers indicating the river chainage to a plot.

    Arguments
    ---------
    xykm : numpy.ndarray
        Array containing the x, y, and chainage; unit m for x and y, km for chainage.
    ax : matplotlib.axes.Axes
        Axes object in which to add the markers.
    ndec : int
        Number of decimals used for marks.
    scale: float
        Indicates whether the axes are in m (1) or km (1000).
    """
    step = 10 ** (-ndec)
    labelstr = " {:." + str(ndec) + "f}"
    km_rescaled = xykm[:, 2] / step
    mask = numpy.isclose(numpy.round(km_rescaled), km_rescaled)
    ax.plot(
        xykm[mask, 0] / scale,
        xykm[mask, 1] / scale,
        linestyle="None",
        marker="+",
        color="k",
    )
    for i in numpy.nonzero(mask)[0]:
        ax.text(
            xykm[i, 0] / scale,
            xykm[i, 1] / scale,
            labelstr.format(xykm[i, 2]),
            fontsize="x-small",
            clip_on=True,
        )

get_colors(cmap_name: str, n: int) -> List[Tuple[float, float, float]] #

Obtain N colors from the specified colormap.

Arguments#

cmap_name : str Name of the color map. n : int Number of colors to be returned.

Returns#

clrcyc : List[Tuple[float, float, float]] List of colour tuplets.

Source code in src/dfastbe/plotting.py 
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def get_colors(cmap_name: str, n: int) -> List[Tuple[float, float, float]]:
    """
    Obtain N colors from the specified colormap.

    Arguments
    ---------
    cmap_name : str
        Name of the color map.
    n : int
        Number of colors to be returned.

    Returns
    -------
    clrcyc : List[Tuple[float, float, float]]
        List of colour tuplets.
    """
    cmap = matplotlib.cm.get_cmap(cmap_name)
    clrs = [cmap(i / (n - 1)) for i in range(n)]
    return clrs

plot1_waterdepth_and_banklines(bbox: Tuple[float, float, float, float], xykm: numpy.ndarray, banklines: geopandas.geodataframe.GeoDataFrame, fn: numpy.ndarray, nnodes: numpy.ndarray, xn: numpy.ndarray, yn: numpy.ndarray, h: numpy.ndarray, hmax: float, xlabel_txt: str, ylabel_txt: str, title_txt: str, waterdepth_txt: str) -> [matplotlib.figure.Figure, matplotlib.axes.Axes] #

Create the bank erosion plot with water depths and initial bank lines.

Tuple[float, float, float, float]

Tuple containing boundary limits (xmin, ymin, xmax, ymax); unit m.

xykm : numpy.ndarray Array containing the x, y, and chainage; unit m for x and y, km for chainage. banklines : geopandas.geodataframe.GeoDataFrame Pandas object containing the bank lines.

numpy.ndarray

N x M array listing the nodes (max M) per face (total N) of the mesh.

nnodes : numpy.ndarray Number of nodes per face (max M). xn : numpy.ndarray X-coordinates of the mesh nodes. yn : numpy.ndarray Y-coordinates of the mesh nodes. h : numpy.ndarray Array of water depth values. hmax : float Water depth value to be used as upper limit for coloring. xlabel_txt : str Label for the x-axis. ylabel_txt : str Label for the y-axis. title_txt : str Label for the axes title. waterdepth_txt : str Label for the color bar.

Results#

fig : matplotlib.figure.Figure Figure object. ax : matplotlib.axes.Axes Axes object.

Source code in src/dfastbe/plotting.py 
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def plot1_waterdepth_and_banklines(
    bbox: Tuple[float, float, float, float],
    xykm: numpy.ndarray,
    banklines: geopandas.geodataframe.GeoDataFrame,
    fn: numpy.ndarray,
    nnodes: numpy.ndarray,
    xn: numpy.ndarray,
    yn: numpy.ndarray,
    h: numpy.ndarray,
    hmax: float,
    xlabel_txt: str,
    ylabel_txt: str,
    title_txt: str,
    waterdepth_txt: str,
) -> [matplotlib.figure.Figure, matplotlib.axes.Axes]:
    """
    Create the bank erosion plot with water depths and initial bank lines.

    bbox : Tuple[float, float, float, float]
        Tuple containing boundary limits (xmin, ymin, xmax, ymax); unit m.
    xykm : numpy.ndarray
        Array containing the x, y, and chainage; unit m for x and y, km for chainage.
    banklines : geopandas.geodataframe.GeoDataFrame
        Pandas object containing the bank lines.

    fn : numpy.ndarray
        N x M array listing the nodes (max M) per face (total N) of the mesh.
    nnodes : numpy.ndarray
        Number of nodes per face (max M).
    xn : numpy.ndarray
        X-coordinates of the mesh nodes.
    yn : numpy.ndarray
        Y-coordinates of the mesh nodes.
    h : numpy.ndarray
        Array of water depth values.
    hmax : float
        Water depth value to be used as upper limit for coloring.
    xlabel_txt : str
        Label for the x-axis.
    ylabel_txt : str
        Label for the y-axis.
    title_txt : str
        Label for the axes title.
    waterdepth_txt : str
        Label for the color bar.

    Results
    -------
    fig : matplotlib.figure.Figure
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    """
    fig, ax = matplotlib.pyplot.subplots()
    setsize(fig)
    ax.set_aspect(1)
    #
    scale = 1000
    chainage_markers(xykm, ax, ndec=0, scale=scale)
    ax.plot(xykm[:, 0] / scale, xykm[:, 1] / scale, linestyle="--", color="k")
    for bl in banklines.geometry:
        bp = numpy.array(bl.coords)
        ax.plot(bp[:, 0] / scale, bp[:, 1] / scale, color="k")
    p = plot_mesh_patches(ax, fn, nnodes, xn, yn, h, 0, hmax)
    cbar = fig.colorbar(p, ax=ax, shrink=0.5, drawedges=False, label=waterdepth_txt)
    #
    set_bbox(ax, bbox)
    ax.set_xlabel(xlabel_txt)
    ax.set_ylabel(ylabel_txt)
    ax.grid(True)
    ax.set_title(title_txt)
    return fig, ax

plot2_eroded_distance_and_equilibrium(bbox: Tuple[float, float, float, float], xykm: numpy.ndarray, bank_crds: List[numpy.ndarray], dn_tot: List[numpy.ndarray], to_right: List[bool], dnav: numpy.ndarray, xy_eq: List[numpy.ndarray], xe: numpy.ndarray, ye: numpy.ndarray, xlabel_txt: str, ylabel_txt: str, title_txt: str, erosion_txt: str, eroclr_txt: str, eqbank_txt: str) -> [matplotlib.figure.Figure, matplotlib.axes.Axes] #

Create the bank erosion plot with predicted bank line shift and equilibrium bank line.

Arguments#

bbox : Tuple[float, float, float, float] Tuple containing boundary limits (xmin, ymin, xmax, ymax); unit m. xykm : numpy.ndarray Array containing the x, y, and chainage; unit m for x and y, km for chainage. bank_crds : List[numpy.ndarray] List of N arrays containing the x- and y-coordinates of the original bank lines. dn_tot : List[numpy.ndarray] List of N arrays containing the total erosion distance values. to_right : List[bool] List of N booleans indicating whether the bank is on the right. dnav : numpy.ndarray Array of N average erosion distance values. xy_eq : List[numpy.ndarray] List of N arrays containing the x- and y-coordinates of the equilibrium bank line. xe : numpy.ndarray M x 2 array of begin/end x-coordinates of mesh edges. ye : numpy.ndarray M x 2 array of begin/end y-coordinates of mesh edges. xlabel_txt : str Label for the x-axis. ylabel_txt : str Label for the y-axis. title_txt : str Label for the axes title. erosion_txt : str Label for the shaded eroded area. eroclr_txt : str Label for the color bar. eqbank_txt : str Label for the equilibrium bank position.

Results#

fig : matplotlib.figure.Figure Figure object. ax : matplotlib.axes.Axes Axes object.

Source code in src/dfastbe/plotting.py 
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def plot2_eroded_distance_and_equilibrium(
    bbox: Tuple[float, float, float, float],
    xykm: numpy.ndarray,
    bank_crds: List[numpy.ndarray],
    dn_tot: List[numpy.ndarray],
    to_right: List[bool],
    dnav: numpy.ndarray,
    xy_eq: List[numpy.ndarray],
    xe: numpy.ndarray,
    ye: numpy.ndarray,
    xlabel_txt: str,
    ylabel_txt: str,
    title_txt: str,
    erosion_txt: str,
    eroclr_txt: str,
    eqbank_txt: str,
) -> [matplotlib.figure.Figure, matplotlib.axes.Axes]:
    """
    Create the bank erosion plot with predicted bank line shift and equilibrium bank line.

    Arguments
    ---------
    bbox : Tuple[float, float, float, float]
        Tuple containing boundary limits (xmin, ymin, xmax, ymax); unit m.
    xykm : numpy.ndarray
        Array containing the x, y, and chainage; unit m for x and y, km for chainage.
    bank_crds : List[numpy.ndarray]
        List of N arrays containing the x- and y-coordinates of the original
        bank lines.
    dn_tot : List[numpy.ndarray]
        List of N arrays containing the total erosion distance values.
    to_right : List[bool]
        List of N booleans indicating whether the bank is on the right.
    dnav : numpy.ndarray
        Array of N average erosion distance values.
    xy_eq : List[numpy.ndarray]
        List of N arrays containing the x- and y-coordinates of the equilibrium
        bank line.
    xe : numpy.ndarray
        M x 2 array of begin/end x-coordinates of mesh edges.
    ye : numpy.ndarray
        M x 2 array of begin/end y-coordinates of mesh edges.
    xlabel_txt : str
        Label for the x-axis.
    ylabel_txt : str
        Label for the y-axis.
    title_txt : str
        Label for the axes title.
    erosion_txt : str
        Label for the shaded eroded area.
    eroclr_txt : str
        Label for the color bar.
    eqbank_txt : str
        Label for the equilibrium bank position.

    Results
    -------
    fig : matplotlib.figure.Figure
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    """
    scale = 1000
    fig, ax = matplotlib.pyplot.subplots()
    setsize(fig)
    ax.set_aspect(1)
    #
    # plot_mesh(ax, xe, ye, scale=scale)
    chainage_markers(xykm, ax, ndec=0, scale=scale)
    dnav_max = dnav.max()
    for ib in range(len(xy_eq)):
        ax.plot(
            xy_eq[ib][:, 0] / scale, xy_eq[ib][:, 1] / scale, linewidth=1, color="k"
        )
        #
        if to_right[ib]:
            bankc = bank_crds[ib]
            dnc = dn_tot[ib]
        else:
            bankc = bank_crds[ib][::-1]
            dnc = dn_tot[ib][::-1]
        nbp = len(bankc)
        #
        dxy = bankc[1:] - bankc[:-1]
        ds = numpy.sqrt((dxy ** 2).sum(axis=1))
        dxy = dxy * (dn_tot[ib] / ds).reshape((nbp - 1, 1))
        #
        x = numpy.zeros(((nbp - 1) * 4,))
        x[0::4] = bankc[:-1, 0]
        x[1::4] = bankc[1:, 0]
        x[2::4] = bankc[:-1, 0] + dxy[:, 1]
        x[3::4] = bankc[1:, 0] + dxy[:, 1]
        #
        y = numpy.zeros(((nbp - 1) * 4,))
        y[0::4] = bankc[:-1, 1]
        y[1::4] = bankc[1:, 1]
        y[2::4] = bankc[:-1, 1] - dxy[:, 0]
        y[3::4] = bankc[1:, 1] - dxy[:, 0]
        #
        tfn = numpy.zeros(((nbp - 1) * 2, 3))
        tfn[0::2, 0] = [4 * i for i in range(nbp - 1)]
        tfn[0::2, 1] = tfn[0::2, 0] + 1
        tfn[0::2, 2] = tfn[0::2, 0] + 2
        #
        tfn[1::2, 0] = tfn[0::2, 0] + 1
        tfn[1::2, 1] = tfn[0::2, 0] + 2
        tfn[1::2, 2] = tfn[0::2, 0] + 3
        #
        tval = numpy.zeros(((nbp - 1) * 2,))
        tval[0::2] = dnc
        tval[1::2] = dnc
        #
        colors = ["lawngreen", "gold", "darkorange"]
        cmap = matplotlib.colors.LinearSegmentedColormap.from_list("mycmap", colors)
        p = ax.tripcolor(
            x / scale,
            y / scale,
            tfn,
            facecolors=tval,
            edgecolors="face",
            linewidth=0.5,
            cmap=cmap,
            vmin=0,
            vmax=2 * dnav_max,
        )
    #
    cbar = fig.colorbar(
        p, ax=ax, shrink=0.5, drawedges=False, label=eroclr_txt
    )
    #
    shaded = matplotlib.patches.Patch(color="gold", linewidth=0.5)
    eqbank = matplotlib.lines.Line2D([], [], color="k", linewidth=1)
    handles = [shaded, eqbank]
    labels = [erosion_txt, eqbank_txt]
    #
    set_bbox(ax, bbox)
    ax.set_xlabel(xlabel_txt)
    ax.set_ylabel(ylabel_txt)
    ax.grid(True)
    ax.set_title(title_txt)
    ax.legend(handles, labels, loc="upper right")
    return fig, ax

plot3_eroded_volume(km_mid: numpy.ndarray, km_step: float, chainage_txt: str, erosion_volume: List[List[numpy.ndarray]], ylabel_txt: str, title_txt: str, qlabel: str, banklabel: str) -> [matplotlib.figure.Figure, matplotlib.axes.Axes] #

Create the bank erosion plot with total eroded volume subdivided per discharge level.

Arguments#

km_mid : numpy.ndarray Array containing the mid points for the chainage bins. km_step : float Bin width. chainage_txt : str Label for the horizontal chainage axes. erosion_volume : List[List[numpy.ndarray]] List of nQ lists of N arrays containing the total erosion distance values ylabel_txt : str Label for the vertical erosion volume axes. title_txt : str Label for axes title. qlabel : str Label for discharge level. banklabel : str Label for bank id.

Results#

fig : matplotlib.figure.Figure Figure object. ax : matplotlib.axes.Axes Axes object.

Source code in src/dfastbe/plotting.py 
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def plot3_eroded_volume(
    km_mid: numpy.ndarray,
    km_step: float,
    chainage_txt: str,
    erosion_volume: List[List[numpy.ndarray]],
    ylabel_txt: str,
    title_txt: str,
    qlabel: str,
    banklabel: str,
) -> [matplotlib.figure.Figure, matplotlib.axes.Axes]:
    """
    Create the bank erosion plot with total eroded volume subdivided per discharge level.

    Arguments
    ---------
    km_mid : numpy.ndarray
        Array containing the mid points for the chainage bins.
    km_step : float
        Bin width.
    chainage_txt : str
        Label for the horizontal chainage axes.
    erosion_volume : List[List[numpy.ndarray]]
        List of nQ lists of N arrays containing the total erosion distance values
    ylabel_txt : str
        Label for the vertical erosion volume axes.
    title_txt : str
        Label for axes title.
    qlabel : str
        Label for discharge level.
    banklabel : str
        Label for bank id.

    Results
    -------
    fig : matplotlib.figure.Figure
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    """
    fig, ax = matplotlib.pyplot.subplots()
    setsize(fig)
    #
    plot3_stacked_per_discharge(ax, km_mid + 0.2 * km_step, km_step, erosion_volume, qlabel, 0.4)
    plot3_stacked_per_bank(ax, km_mid - 0.2 * km_step, km_step, erosion_volume, banklabel, 0.4)
    #
    ax.set_xlabel(chainage_txt)
    ax.set_ylabel(ylabel_txt)
    ax.grid(True)
    ax.set_title(title_txt)
    ax.legend(loc="upper right")
    return fig, ax

plot3_eroded_volume_subdivided_1(km_mid: numpy.ndarray, km_step: float, chainage_txt: str, erosion_volume: List[List[numpy.ndarray]], ylabel_txt: str, title_txt: str, qlabel: str) -> [matplotlib.figure.Figure, matplotlib.axes.Axes] #

Create the bank erosion plot with total eroded volume subdivided per discharge level.

Arguments#

km_mid : numpy.ndarray Array containing the mid points for the chainage bins. km_step : float Bin width. chainage_txt : str Label for the horizontal chainage axes. erosion_volume : List[List[numpy.ndarray]] List of nQ lists of N arrays containing the total erosion distance values ylabel_txt : str Label for the vertical erosion volume axes. title_txt : str Label for axes title. qlabel : str Label for discharge level.

Results#

fig : matplotlib.figure.Figure Figure object. ax : matplotlib.axes.Axes Axes object.

Source code in src/dfastbe/plotting.py 
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def plot3_eroded_volume_subdivided_1(
    km_mid: numpy.ndarray,
    km_step: float,
    chainage_txt: str,
    erosion_volume: List[List[numpy.ndarray]],
    ylabel_txt: str,
    title_txt: str,
    qlabel: str,
) -> [matplotlib.figure.Figure, matplotlib.axes.Axes]:
    """
    Create the bank erosion plot with total eroded volume subdivided per discharge level.

    Arguments
    ---------
    km_mid : numpy.ndarray
        Array containing the mid points for the chainage bins.
    km_step : float
        Bin width.
    chainage_txt : str
        Label for the horizontal chainage axes.
    erosion_volume : List[List[numpy.ndarray]]
        List of nQ lists of N arrays containing the total erosion distance values
    ylabel_txt : str
        Label for the vertical erosion volume axes.
    title_txt : str
        Label for axes title.
    qlabel : str
        Label for discharge level.

    Results
    -------
    fig : matplotlib.figure.Figure
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    """
    fig, ax = matplotlib.pyplot.subplots()
    setsize(fig)
    #
    plot3_stacked_per_discharge(ax, km_mid, km_step, erosion_volume, qlabel, 0.8)
    #
    ax.set_xlabel(chainage_txt)
    ax.set_ylabel(ylabel_txt)
    ax.grid(True)
    ax.set_title(title_txt)
    ax.legend(loc="upper right")
    return fig, ax

plot3_eroded_volume_subdivided_2(km_mid: numpy.ndarray, km_step: float, chainage_txt: str, erosion_volume: List[List[numpy.ndarray]], ylabel_txt: str, title_txt: str, banklabel: str) -> [matplotlib.figure.Figure, matplotlib.axes.Axes] #

Create the bank erosion plot with total eroded volume subdivided per bank.

Arguments#

km_mid : numpy.ndarray Array containing the mid points for the chainage bins. km_step : float Bin width. chainage_txt : str Label for the horizontal chainage axes. erosion_volume : List[List[numpy.ndarray]] List of nQ lists of N arrays containing the total erosion distance values ylabel_txt : str Label for the vertical erosion volume axes. title_txt : str Label for axes title. banklabel : str Label for bank id.

Results#

fig : matplotlib.figure.Figure Figure object. ax : matplotlib.axes.Axes Axes object.

Source code in src/dfastbe/plotting.py 
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def plot3_eroded_volume_subdivided_2(
    km_mid: numpy.ndarray,
    km_step: float,
    chainage_txt: str,
    erosion_volume: List[List[numpy.ndarray]],
    ylabel_txt: str,
    title_txt: str,
    banklabel: str,
) -> [matplotlib.figure.Figure, matplotlib.axes.Axes]:
    """
    Create the bank erosion plot with total eroded volume subdivided per bank.

    Arguments
    ---------
    km_mid : numpy.ndarray
        Array containing the mid points for the chainage bins.
    km_step : float
        Bin width.
    chainage_txt : str
        Label for the horizontal chainage axes.
    erosion_volume : List[List[numpy.ndarray]]
        List of nQ lists of N arrays containing the total erosion distance values
    ylabel_txt : str
        Label for the vertical erosion volume axes.
    title_txt : str
        Label for axes title.
    banklabel : str
        Label for bank id.

    Results
    -------
    fig : matplotlib.figure.Figure
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    """
    fig, ax = matplotlib.pyplot.subplots()
    setsize(fig)
    #
    plot3_stacked_per_bank(ax, km_mid, km_step, erosion_volume, banklabel, 0.8)
    #
    ax.set_xlabel(chainage_txt)
    ax.set_ylabel(ylabel_txt)
    ax.grid(True)
    ax.set_title(title_txt)
    ax.legend(loc="upper right")
    return fig, ax

plot3_stacked_per_bank(ax: matplotlib.axes.Axes, km_mid: numpy.ndarray, km_step: float, erosion_volume: List[List[numpy.ndarray]], banklabel: str, wfrac: float) -> None #

Add a stacked plot of bank erosion with total eroded volume subdivided per bank to the selected axes.

Arguments#

fig : matplotlib.figure.Figure Figure object. ax : matplotlib.axes.Axes Axes object. km_mid : numpy.ndarray Array containing the mid points for the chainage bins. km_step : float Bin width. erosion_volume : List[List[numpy.ndarray]] List of nQ lists of N arrays containing the total erosion distance values banklabel : str Label for bank id. wfrac : float Width fraction for the stacked column.

Results#

None

Source code in src/dfastbe/plotting.py 
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def plot3_stacked_per_bank(
   ax: matplotlib.axes.Axes,
   km_mid: numpy.ndarray,
   km_step: float,
   erosion_volume: List[List[numpy.ndarray]],
   banklabel: str,
   wfrac: float,
) -> None:
    """
    Add a stacked plot of bank erosion with total eroded volume subdivided per bank to the selected axes.

    Arguments
    ---------
    fig : matplotlib.figure.Figure
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    km_mid : numpy.ndarray
        Array containing the mid points for the chainage bins.
    km_step : float
        Bin width.
    erosion_volume : List[List[numpy.ndarray]]
        List of nQ lists of N arrays containing the total erosion distance values
    banklabel : str
        Label for bank id.
    wfrac : float
        Width fraction for the stacked column.

    Results
    -------
    None
    """
    n_banklines = len(erosion_volume[0])
    clrs = get_colors("plasma", n_banklines + 1)
    for ib in range(n_banklines):
        for iq in range(len(erosion_volume)):
            if iq == 0:
                dvq = erosion_volume[iq][ib].copy()
            else:
                dvq = dvq + erosion_volume[iq][ib]
        if ib == 0:
            ax.bar(
                km_mid,
                dvq,
                width=wfrac * km_step,
                color=clrs[ib],
                label=banklabel.format(ib=ib + 1),
            )
            cumdv = dvq
        else:
            ax.bar(
                km_mid,
                dvq,
                width=wfrac * km_step,
                bottom=cumdv,
                color=clrs[ib],
                label=banklabel.format(ib=ib + 1),
            )
            cumdv = cumdv + dvq

plot3_stacked_per_discharge(ax: matplotlib.axes.Axes, km_mid: numpy.ndarray, km_step: float, erosion_volume: List[List[numpy.ndarray]], qlabel: str, wfrac: float) -> None #

Add a stacked plot of bank erosion with total eroded volume subdivided per discharge level to the selected axes.

Arguments#

fig : matplotlib.figure.Figure Figure object. ax : matplotlib.axes.Axes Axes object. km_mid : numpy.ndarray Array containing the mid points for the chainage bins. km_step : float Bin width. erosion_volume : List[List[numpy.ndarray]] List of nQ lists of N arrays containing the total erosion distance values qlabel : str Label for discharge level. wfrac : float Width fraction for the stacked column.

Results#

None

Source code in src/dfastbe/plotting.py 
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def plot3_stacked_per_discharge(
   ax: matplotlib.axes.Axes,
   km_mid: numpy.ndarray,
   km_step: float,
   erosion_volume: List[List[numpy.ndarray]],
   qlabel: str,
   wfrac: float,
) -> None:
    """
    Add a stacked plot of bank erosion with total eroded volume subdivided per discharge level to the selected axes.

    Arguments
    ---------
    fig : matplotlib.figure.Figure
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    km_mid : numpy.ndarray
        Array containing the mid points for the chainage bins.
    km_step : float
        Bin width.
    erosion_volume : List[List[numpy.ndarray]]
        List of nQ lists of N arrays containing the total erosion distance values
    qlabel : str
        Label for discharge level.
    wfrac : float
        Width fraction for the stacked column.

    Results
    -------
    None
    """
    n_levels = len(erosion_volume)
    clrs = get_colors("Blues", n_levels + 1)
    for iq in range(n_levels):
        for ib in range(len(erosion_volume[iq])):
            if ib == 0:
                dvq = erosion_volume[iq][ib].copy()
            else:
                dvq = dvq + erosion_volume[iq][ib]
        if iq == 0:
            ax.bar(
                km_mid,
                dvq,
                width=wfrac * km_step,
                color=clrs[iq + 1],
                label=qlabel.format(iq=iq + 1),
            )
            cumdv = dvq
        else:
            ax.bar(
                km_mid,
                dvq,
                width=wfrac * km_step,
                bottom=cumdv,
                color=clrs[iq + 1],
                label=qlabel.format(iq=iq + 1),
            )
            cumdv = cumdv + dvq

plot4_eroded_volume_eq(km_mid: numpy.ndarray, km_step: float, chainage_txt: str, vol_eq: numpy.ndarray, ylabel_txt: str, title_txt: str) -> [matplotlib.figure.Figure, matplotlib.axes.Axes] #

Create the bank erosion plot with equilibrium eroded volume.

Arguments#

km_mid : numpy.ndarray Array containing the mid points for the chainage bins. km_step : float Bin width. chainage_txt : str Label for the horizontal chainage axes. vol_eq : numpy.ndarray Array containing the equilibrium eroded volume per bin. ylabel_txt : str Label for the vertical erosion volume axes. title_txt : str Label for axes title.

Results#

fig : matplotlib.figure.Figure Figure object. ax : matplotlib.axes.Axes Axes object.

Source code in src/dfastbe/plotting.py 
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def plot4_eroded_volume_eq(
    km_mid: numpy.ndarray,
    km_step: float,
    chainage_txt: str,
    vol_eq: numpy.ndarray,
    ylabel_txt: str,
    title_txt: str,
) -> [matplotlib.figure.Figure, matplotlib.axes.Axes]:
    """
    Create the bank erosion plot with equilibrium eroded volume.

    Arguments
    ---------
    km_mid : numpy.ndarray
        Array containing the mid points for the chainage bins.
    km_step : float
        Bin width.
    chainage_txt : str
        Label for the horizontal chainage axes.
    vol_eq : numpy.ndarray
        Array containing the equilibrium eroded volume per bin.
    ylabel_txt : str
        Label for the vertical erosion volume axes.
    title_txt : str
        Label for axes title.

    Results
    -------
    fig : matplotlib.figure.Figure
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    """
    fig, ax = matplotlib.pyplot.subplots()
    setsize(fig)
    #
    tvol = numpy.zeros(km_mid.shape)
    for i in range(len(km_mid)):
        tvol[i] = vol_eq[i].sum()
    ax.bar(km_mid, tvol, width=0.8 * km_step)
    #
    ax.set_xlabel(chainage_txt)
    ax.set_ylabel(ylabel_txt)
    ax.grid(True)
    ax.set_title(title_txt)
    return fig, ax

plot5series_waterlevels_per_bank(bank_km_mid: List[numpy.ndarray], chainage_txt: str, waterlevel: List[List[numpy.ndarray]], shipwavemax: List[List[numpy.ndarray]], shipwavemin: List[List[numpy.ndarray]], waterlevelq_txt: str, avg_waterlevel_txt: str, shipwave_txt: str, bankheight: List[numpy.ndarray], bankheight_txt: str, bankprotect: List[numpy.ndarray], bankprotect_txt: str, elevation_txt: str, title_txt: str, elevation_unit: str) -> [List[matplotlib.figure.Figure], List[matplotlib.axes.Axes]] #

Create the bank erosion plots with water levels, bank height and bank protection height along each bank.

Arguments#

bank_km_mid : List[numpy.ndarray] List of arrays containing the chainage values per bank (segment) [km]. chainage_txt : str Label for the horizontal chainage axes. waterlevel : List[List[numpy.ndarray]] List of arrays containing the water levels per bank (point) [elevation_unit]. shipmwavemax : numpy.ndarray Maximum bank level subject to ship waves [m] shipwavemin : numpy.ndarray Minimum bank level subject to ship waves [m] waterlevelq_txt : str Label for the water level per discharge level. avg_waterlevel_txt : str Label for the average water level. shipwave_txt : str Label for the elevation range influenced by ship waves. bankheight : List[numpy.ndarray] List of arrays containing the bank heights per bank (segment) [elevation_unit]. bankheight_txt : str Label for the bank height. bankprotect : List[numpy.ndarray] List of arrays containing the bank protection height per bank (point) [elevation_unit]. bankprotect_txt : str Label for the bank protection height. elevation_txt : str General label for elevation data. title_txt : str Label for the axes title. elevation_unit : str Unit used for all elevation data.

Results#

figlist : List[matplotlib.figure.Figure] List of figure objects, one per bank. axlist : List[matplotlib.axes.Axes] List of axes objects, one per bank.

Source code in src/dfastbe/plotting.py 
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def plot5series_waterlevels_per_bank(
    bank_km_mid: List[numpy.ndarray],
    chainage_txt: str,
    waterlevel: List[List[numpy.ndarray]],
    shipwavemax: List[List[numpy.ndarray]],
    shipwavemin: List[List[numpy.ndarray]],
    waterlevelq_txt: str,
    avg_waterlevel_txt: str,
    shipwave_txt: str,
    bankheight: List[numpy.ndarray],
    bankheight_txt: str,
    bankprotect: List[numpy.ndarray],
    bankprotect_txt: str,
    elevation_txt: str,
    title_txt: str,
    elevation_unit: str,
) -> [List[matplotlib.figure.Figure], List[matplotlib.axes.Axes]]:
    """
    Create the bank erosion plots with water levels, bank height and bank protection height along each bank.

    Arguments
    ---------
    bank_km_mid : List[numpy.ndarray]
        List of arrays containing the chainage values per bank (segment) [km].
    chainage_txt : str
        Label for the horizontal chainage axes.
    waterlevel : List[List[numpy.ndarray]]
        List of arrays containing the water levels per bank (point) [elevation_unit].
    shipmwavemax : numpy.ndarray
        Maximum bank level subject to ship waves [m]
    shipwavemin : numpy.ndarray
        Minimum bank level subject to ship waves [m]
    waterlevelq_txt : str
        Label for the water level per discharge level.
    avg_waterlevel_txt : str
        Label for the average water level.
    shipwave_txt : str
        Label for the elevation range influenced by ship waves.
    bankheight : List[numpy.ndarray]
        List of arrays containing the bank heights per bank (segment) [elevation_unit].
    bankheight_txt : str
        Label for the bank height.
    bankprotect : List[numpy.ndarray]
        List of arrays containing the bank protection height per bank (point) [elevation_unit].
    bankprotect_txt : str
        Label for the bank protection height.
    elevation_txt : str
        General label for elevation data.
    title_txt : str
        Label for the axes title.
    elevation_unit : str
        Unit used for all elevation data.

    Results
    -------
    figlist : List[matplotlib.figure.Figure]
        List of figure objects, one per bank.
    axlist : List[matplotlib.axes.Axes]
        List of axes objects, one per bank.
    """
    n_banklines = len(bank_km_mid)
    n_levels = len(waterlevel)
    figlist: List[matplotlib.figure.Figure] = []
    axlist: List[matplotlib.axes.Axes] = []
    clrs = get_colors("Blues", n_levels + 1)
    for ib in range(n_banklines):
        fig, ax = matplotlib.pyplot.subplots()
        setsize(fig)
        bk = bank_km_mid[ib]
        #
        for iq in range(n_levels):
            # shaded range of influence for ship waves
            ax.fill_between(
                bk,
                shipwavemin[iq][ib],
                shipwavemax[iq][ib],
                color=clrs[iq + 1],
                alpha=0.1,
            )
            ax.plot(
                bk,
                shipwavemax[iq][ib],
                color=clrs[iq + 1],
                linestyle="--",
                linewidth=0.5,
            )
            ax.plot(
                bk,
                shipwavemin[iq][ib],
                color=clrs[iq + 1],
                linestyle="--",
                linewidth=0.5,
            )
            # water level line itself
            ax.plot(
                bk,
                waterlevel[iq][ib],
                color=clrs[iq + 1],
                label=waterlevelq_txt.format(iq=iq + 1),
            )
            if iq == 0:
                wl_avg = waterlevel[iq][ib].copy()
            else:
                wl_avg = wl_avg + waterlevel[iq][ib]
        #
        wl_avg = wl_avg / n_levels
        ax.plot(
            bk, wl_avg, color=(0.5, 0.5, 0.5), linewidth=2, label=avg_waterlevel_txt,
        )
        ax.plot(bk, bankheight[ib], color=(0.5, 0.5, 0.5), label=bankheight_txt)
        ymin, ymax = ax.get_ylim()
        #
        # bank protection is only visually included in the plot
        # if it is in the same range as the other quantities
        # don't stretch the vertical scale to squeeze in a very low value.
        #
        ax.plot(
            bk,
            bankprotect[ib],
            color=(0.5, 0.5, 0.5),
            linestyle="--",
            label=bankprotect_txt,
        )
        ax.set_ylim(ymin=ymin, ymax=ymax)
        #
        handles, labels = ax.get_legend_handles_labels()
        #
        # use a slightly higher alpha for the legend to make it stand out better.
        iq = int(n_levels/2)
        shaded = matplotlib.patches.Patch(color=clrs[iq + 1], alpha=0.2)
        handles = [*handles[:-3], shaded, *handles[-3:]]
        labels = [*labels[:-3], shipwave_txt, *labels[-3:]]
        #
        ax.set_xlabel(chainage_txt)
        ax.set_ylabel(elevation_txt + " " + elevation_unit)
        ax.grid(True)
        ax.set_title(title_txt.format(ib=ib + 1))
        ax.legend(handles, labels, loc="upper right")
        figlist.append(fig)
        axlist.append(ax)
    return figlist, axlist

plot6series_velocity_per_bank(bank_km_mid: List[numpy.ndarray], chainage_txt: str, veloc: List[List[numpy.ndarray]], velocq_txt: str, tauc: List[numpy.ndarray], chezy: List[numpy.ndarray], ucrit_txt: str, ylabel_txt: str, title_txt: str, veloc_unit: str) -> [List[matplotlib.figure.Figure], List[matplotlib.axes.Axes]] #

Create the bank erosion plots with velocities and critical velocities along each bank.

Arguments#

bank_km_mid : List[numpy.ndarray] List of arrays containing the chainage values per bank (segment) [km]. chainage_txt : str Label for the horizontal chainage axes. veloc: List[List[numpy.ndarray]] List of arrays containing the velocities per bank (segment) [m/s]. velocq_txt: str, Label for the velocity per discharge level. tauc: List[numpy.ndarray] List of arrays containing the shear stresses per bank (point) [N/m2]. chezy: List[numpy.ndarray] List of arrays containing the Chezy values per bank [m0.5/s]. ucrit_txt: str Label for the critical velocity. ylabel_txt: str Label for the vertical (velocity) axis. title_txt: str Label for the axes title. veloc_unit: str Unit used for all velocities.

Results#

figlist : List[matplotlib.figure.Figure] List of figure objects, one per bank. axlist : List[matplotlib.axes.Axes] List of axes objects, one per bank.

Source code in src/dfastbe/plotting.py 
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def plot6series_velocity_per_bank(
    bank_km_mid: List[numpy.ndarray],
    chainage_txt: str,
    veloc: List[List[numpy.ndarray]],
    velocq_txt: str,
    tauc: List[numpy.ndarray],
    chezy: List[numpy.ndarray],
    ucrit_txt: str,
    ylabel_txt: str,
    title_txt: str,
    veloc_unit: str,
) -> [List[matplotlib.figure.Figure], List[matplotlib.axes.Axes]]:
    """
    Create the bank erosion plots with velocities and critical velocities along each bank.

    Arguments
    ---------
    bank_km_mid : List[numpy.ndarray]
        List of arrays containing the chainage values per bank (segment) [km].
    chainage_txt : str
        Label for the horizontal chainage axes.
    veloc: List[List[numpy.ndarray]]
        List of arrays containing the velocities per bank (segment) [m/s].
    velocq_txt: str,
        Label for the velocity per discharge level.
    tauc: List[numpy.ndarray]
        List of arrays containing the shear stresses per bank (point) [N/m2].
    chezy: List[numpy.ndarray]
        List of arrays containing the Chezy values per bank [m0.5/s].
    ucrit_txt: str
        Label for the critical velocity.
    ylabel_txt: str
        Label for the vertical (velocity) axis.
    title_txt: str
        Label for the axes title.
    veloc_unit: str
        Unit used for all velocities.

    Results
    -------
    figlist : List[matplotlib.figure.Figure]
        List of figure objects, one per bank.
    axlist : List[matplotlib.axes.Axes]
        List of axes objects, one per bank.
    """
    n_banklines = len(bank_km_mid)
    n_levels = len(veloc)
    figlist: List[matplotlib.figure.Figure] = []
    axlist: List[matplotlib.axes.Axes] = []
    clrs = get_colors("Blues", n_levels + 1)
    for ib in range(n_banklines):
        fig, ax = matplotlib.pyplot.subplots()
        setsize(fig)
        bk = bank_km_mid[ib]
        #
        velc = numpy.sqrt(tauc[ib] * chezy[ib] ** 2 / (WATER_DENSITY * g))
        ax.plot(bank_km_mid[ib], velc, color="k", label=ucrit_txt)
        for iq in range(n_levels):
            ax.plot(
                bk,
                veloc[iq][ib],
                color=clrs[iq + 1],
                label=velocq_txt.format(iq=iq + 1),
            )
        #
        ax.set_xlabel(chainage_txt)
        ax.set_ylabel(ylabel_txt + " " + veloc_unit)
        ax.grid(True)
        ax.set_title(title_txt.format(ib=ib + 1))
        ax.legend(loc="upper right")
        figlist.append(fig)
        axlist.append(ax)
    return figlist, axlist

plot7_banktype(bbox: Tuple[float, float, float, float], xykm: numpy.ndarray, bank_crds: List[numpy.ndarray], banktype: List[numpy.ndarray], taucls_str: List[str], xlabel_txt: str, ylabel_txt: str, title_txt: str) -> [matplotlib.figure.Figure, matplotlib.axes.Axes] #

Create the bank erosion plot with colour-coded bank types.

Arguments#

bbox : Tuple[float, float, float, float] Tuple containing boundary limits (xmin, ymin, xmax, ymax); unit m. xykm : numpy.ndarray Array containing the x, y, and chainage; unit m for x and y, km for chainage. bank_crds : List[numpy.ndarray] List of N arrays containing the x- and y-coordinates of the oroginal bank lines. banktype : List[numpy.ndarray] List of N arrays containing the bank type values. taucls_str : List[str] List of strings representing the distinct bank type classes. xlabel_txt : str Label for the x-axis. ylabel_txt : str Label for the y-axis. title_txt : str Label for the axes title.

Results#

fig : matplotlib.figure.Figure Figure object. ax : matplotlib.axes.Axes Axes object.

Source code in src/dfastbe/plotting.py 
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def plot7_banktype(
    bbox: Tuple[float, float, float, float],
    xykm: numpy.ndarray,
    bank_crds: List[numpy.ndarray],
    banktype: List[numpy.ndarray],
    taucls_str: List[str],
    xlabel_txt: str,
    ylabel_txt: str,
    title_txt: str,
) -> [matplotlib.figure.Figure, matplotlib.axes.Axes]:
    """
    Create the bank erosion plot with colour-coded bank types.

    Arguments
    ---------
    bbox : Tuple[float, float, float, float]
        Tuple containing boundary limits (xmin, ymin, xmax, ymax); unit m.
    xykm : numpy.ndarray
        Array containing the x, y, and chainage; unit m for x and y, km for chainage.
    bank_crds : List[numpy.ndarray]
        List of N arrays containing the x- and y-coordinates of the oroginal
        bank lines.
    banktype : List[numpy.ndarray]
        List of N arrays containing the bank type values.
    taucls_str : List[str]
        List of strings representing the distinct bank type classes.
    xlabel_txt : str
        Label for the x-axis.
    ylabel_txt : str
        Label for the y-axis.
    title_txt : str
        Label for the axes title.

    Results
    -------
    fig : matplotlib.figure.Figure
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    """
    fig, ax = matplotlib.pyplot.subplots()
    setsize(fig)
    ax.set_aspect(1)
    #
    scale = 1000
    chainage_markers(xykm, ax, ndec=0, scale=scale)
    clrs = get_colors("plasma", len(taucls_str) + 1)
    for ib in range(len(bank_crds)):
        for ibt in range(len(taucls_str)):
            ibtEdges = numpy.nonzero(banktype[ib] == ibt)[0]
            if len(ibtEdges) > 0:
                nedges = len(ibtEdges)
                nx = max(3 * nedges - 1, 0)
                x = numpy.zeros((nx,)) + numpy.nan
                y = x.copy()
                x[0::3] = bank_crds[ib][ibtEdges, 0].copy() / scale
                y[0::3] = bank_crds[ib][ibtEdges, 1].copy() / scale
                x[1::3] = bank_crds[ib][ibtEdges + 1, 0].copy() / scale
                y[1::3] = bank_crds[ib][ibtEdges + 1, 1].copy() / scale
                #
                if ib == 0:
                    ax.plot(x, y, color=clrs[ibt], label=taucls_str[ibt])
                else:
                    ax.plot(x, y, color=clrs[ibt])
            else:
                if ib == 0:
                    ax.plot(
                        numpy.nan, numpy.nan, color=clrs[ibt], label=taucls_str[ibt]
                    )
    #
    set_bbox(ax, bbox)
    ax.set_xlabel(xlabel_txt)
    ax.set_ylabel(ylabel_txt)
    ax.grid(True)
    ax.set_title(title_txt)
    ax.legend(loc="upper right")
    return fig, ax

plot8_eroded_distance(bank_km_mid: List[numpy.ndarray], chainage_txt: str, dn_tot: List[numpy.ndarray], dn_tot_txt: str, dn_eq: List[numpy.ndarray], dn_eq_txt: str, dn_txt: str, dn_unit: str) -> [matplotlib.figure.Figure, matplotlib.axes.Axes] #

Create the bank erosion plot with total and equilibrium eroded distance.

Arguments#

bank_km_mid : List[numpy.ndarray] List of arrays containing the chainage values per bank (segment) [km]. chainage_txt : str Label for the horizontal chainage axes. dn_tot : List[numpy.ndarray] List of arrays containing the total bank erosion distance per bank (segment) [m]. dn_tot_txt : str Label for the total bank erosion distance. dn_eq : List[numpy.ndarray] List of arrays containing the equilibrium bank erosion distance per bank (segment) [m]. dn_eq_txt : str Label for equilibrium bank erosion distance. dn_txt : str General label for bank erosion distance. dn_unit: str Unit used for bank erosion distance.

Results#

fig : matplotlib.figure.Figure Figure object. ax : matplotlib.axes.Axes Axes object.

Source code in src/dfastbe/plotting.py 
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def plot8_eroded_distance(
    bank_km_mid: List[numpy.ndarray],
    chainage_txt: str,
    dn_tot: List[numpy.ndarray],
    dn_tot_txt: str,
    dn_eq: List[numpy.ndarray],
    dn_eq_txt: str,
    dn_txt: str,
    dn_unit: str,
) -> [matplotlib.figure.Figure, matplotlib.axes.Axes]:
    """
    Create the bank erosion plot with total and equilibrium eroded distance.

    Arguments
    ---------
    bank_km_mid : List[numpy.ndarray]
        List of arrays containing the chainage values per bank (segment) [km].
    chainage_txt : str
        Label for the horizontal chainage axes.
    dn_tot : List[numpy.ndarray]
        List of arrays containing the total bank erosion distance per bank (segment) [m].
    dn_tot_txt : str
        Label for the total bank erosion distance.
    dn_eq : List[numpy.ndarray]
        List of arrays containing the equilibrium bank erosion distance per bank (segment) [m].
    dn_eq_txt : str
        Label for equilibrium bank erosion distance.
    dn_txt : str
        General label for bank erosion distance.
    dn_unit: str
        Unit used for bank erosion distance.

    Results
    -------
    fig : matplotlib.figure.Figure
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    """
    fig, ax = matplotlib.pyplot.subplots()
    setsize(fig)
    #
    n_banklines = len(dn_tot)
    clrs = get_colors("plasma", n_banklines + 1)
    for ib in range(n_banklines):
        bk = bank_km_mid[ib]
        ax.plot(bk, dn_tot[ib], color=clrs[ib], label=dn_tot_txt.format(ib=ib + 1))
        ax.plot(
            bk,
            dn_eq[ib],
            linestyle=":",
            color=clrs[ib],
            label=dn_eq_txt.format(ib=ib + 1),
        )
    #
    ax.set_xlabel(chainage_txt)
    ax.set_ylabel(dn_txt + " " + dn_unit)
    ax.grid(True)
    ax.set_title(dn_txt)
    ax.legend(loc="upper right")
    return fig, ax

plot_detect1(bbox: Tuple[float, float, float, float], xykm: numpy.ndarray, bankareas: List[Polygon], bank: List[LineString], fn: numpy.ndarray, nnodes: numpy.ndarray, xn: numpy.ndarray, yn: numpy.ndarray, h: numpy.ndarray, hmax: float, xlabel_txt: str, ylabel_txt: str, title_txt: str, waterdepth_txt: str, bankarea_txt: str, bankline_txt: str, config_file: ConfigFile) -> [matplotlib.figure.Figure, matplotlib.axes.Axes] #

Create the bank line detection plot.

The figure contains a map of the water depth, the chainage, and detected bank lines.

Arguments#

bbox : Tuple[float, float, float, float] Tuple containing boundary limits (xmin, ymin, xmax, ymax); unit m. xykm : numpy.ndarray Array containing the x, y, and chainage; unit m for x and y, km for chainage. bankareas : List[Polygon] List of bank polygons. bank : List[LineString] List of bank lines. fn : numpy.ndarray N x M array listing the nodes (max M) per face (total N) of the mesh. nnodes : numpy.ndarray Number of nodes per face (max M). xn : numpy.ndarray X-coordinates of the mesh nodes. yn : numpy.ndarray Y-coordinates of the mesh nodes. h : numpy.ndarray Array of water depth values. hmax : float Water depth value to be used as upper limit for coloring. xlabel_txt : str Label for the x-axis. ylabel_txt : str Label for the y-axis. title_txt : str Label for the axes title. waterdepth_txt : str Label for the color bar. bankarea_txt : str Label for the bank search areas. bankline_txt : str Label for the identified bank lines.

Returns#

fig : matplotlib.figure.Figure: Figure object. ax : matplotlib.axes.Axes Axes object.

Source code in src/dfastbe/plotting.py 
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def plot_detect1(
    bbox: Tuple[float, float, float, float],
    xykm: numpy.ndarray,
    bankareas: List[Polygon],
    bank: List[LineString],
    fn: numpy.ndarray,
    nnodes: numpy.ndarray,
    xn: numpy.ndarray,
    yn: numpy.ndarray,
    h: numpy.ndarray,
    hmax: float,
    xlabel_txt: str,
    ylabel_txt: str,
    title_txt: str,
    waterdepth_txt: str,
    bankarea_txt: str,
    bankline_txt: str,
    config_file: ConfigFile,
) -> [matplotlib.figure.Figure, matplotlib.axes.Axes]:
    """
    Create the bank line detection plot.

    The figure contains a map of the water depth, the chainage, and detected
    bank lines.

    Arguments
    ---------
    bbox : Tuple[float, float, float, float]
        Tuple containing boundary limits (xmin, ymin, xmax, ymax); unit m.
    xykm : numpy.ndarray
        Array containing the x, y, and chainage; unit m for x and y, km for chainage.
    bankareas : List[Polygon]
        List of bank polygons.
    bank : List[LineString]
        List of bank lines.
    fn : numpy.ndarray
        N x M array listing the nodes (max M) per face (total N) of the mesh.
    nnodes : numpy.ndarray
        Number of nodes per face (max M).
    xn : numpy.ndarray
        X-coordinates of the mesh nodes.
    yn : numpy.ndarray
        Y-coordinates of the mesh nodes.
    h : numpy.ndarray
        Array of water depth values.
    hmax : float
        Water depth value to be used as upper limit for coloring.
    xlabel_txt : str
        Label for the x-axis.
    ylabel_txt : str
        Label for the y-axis.
    title_txt : str
        Label for the axes title.
    waterdepth_txt : str
        Label for the color bar.
    bankarea_txt : str
        Label for the bank search areas.
    bankline_txt : str
        Label for the identified bank lines.

    Returns
    -------
    fig : matplotlib.figure.Figure:
        Figure object.
    ax : matplotlib.axes.Axes
        Axes object.
    """
    fig, ax = matplotlib.pyplot.subplots()
    setsize(fig)
    ax.set_aspect(1)
    #
    scale = 1 # using scale 1 here because of the geopandas plot commands
    chainage_markers(xykm, ax, ndec=0, scale=scale)
    p = plot_mesh_patches(ax, fn, nnodes, xn, yn, h, 0, hmax, scale=scale)
    for b, bankarea in enumerate(bankareas):
        geopandas.GeoSeries(bankarea, crs=config_file.crs).plot(
            ax=ax, alpha=0.2, color="k"
        )
        geopandas.GeoSeries(bank[b], crs=config_file.crs).plot(ax=ax, color="r")
    cbar = fig.colorbar(p, ax=ax, shrink=0.5, drawedges=False, label=waterdepth_txt)
    #
    shaded = matplotlib.patches.Patch(color="k", alpha=0.2)
    bankln = matplotlib.lines.Line2D([], [], color="r")
    handles = [shaded, bankln]
    labels = [bankarea_txt, bankline_txt]
    #
    set_bbox(ax, bbox, scale=scale)
    ax.set_xlabel(xlabel_txt)
    ax.set_ylabel(ylabel_txt)
    ax.grid(True)
    ax.set_title(title_txt)
    ax.legend(handles, labels, loc="upper right")
    return fig, ax

plot_mesh(ax: matplotlib.axes.Axes, xe: numpy.ndarray, ye: numpy.ndarray, scale: float = 1000) -> None #

Add a mesh to a plot.

Arguments#

ax : matplotlib.axes.Axes Axes object in which to add the mesh. xe : numpy.ndarray M x 2 array of begin/end x-coordinates of mesh edges. ye : numpy.ndarray M x 2 array of begin/end y-coordinates of mesh edges. scale : float Indicates whether the axes are in m (1) or km (1000).

Source code in src/dfastbe/plotting.py 
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def plot_mesh(
    ax: matplotlib.axes.Axes, xe: numpy.ndarray, ye: numpy.ndarray, scale: float = 1000
) -> None:
    """
    Add a mesh to a plot.

    Arguments
    ---------
    ax : matplotlib.axes.Axes
        Axes object in which to add the mesh.
    xe : numpy.ndarray
        M x 2 array of begin/end x-coordinates of mesh edges.
    ye : numpy.ndarray
        M x 2 array of begin/end y-coordinates of mesh edges.
    scale : float
        Indicates whether the axes are in m (1) or km (1000).
    """
    xe1 = xe[:, (0, 1, 1)] / scale
    xe1[:, 2] = numpy.nan
    xev = xe1.reshape((xe1.size,))

    ye1 = ye[:, (0, 1, 1)] / scale
    ye1[:, 2] = numpy.nan
    yev = ye1.reshape((ye1.size,))

    # to avoid OverflowError: In draw_path: Exceeded cell block limit
    # plot the data in chunks ...
    for i in range(0, len(xev), 3000):
        ax.plot(
            xev[i : i + 3000], yev[i : i + 3000], color=(0.5, 0.5, 0.5), linewidth=0.25
        )

plot_mesh_patches(ax: matplotlib.axes.Axes, fn: numpy.ndarray, nnodes: numpy.ndarray, xn: numpy.ndarray, yn: numpy.ndarray, val: numpy.ndarray, minval: Optional[float] = None, maxval: Optional[float] = None, scale: float = 1000) -> matplotlib.collections.PolyCollection #

Add a collection of patches to the plot one for every face of the mesh.

Arguments#

ax : matplotlib.axes.Axes Axes object in which to add the mesh. fn : numpy.ndarray N x M array listing the nodes (max M) per face (total N) of the mesh. nnodes : numpy.ndarray Number of nodes per face (max M). xn : numpy.ndarray X-coordinates of the mesh nodes. yn : numpy.ndarray Y-coordinates of the mesh nodes. val : numpy.ndarray Array of length N containing the value per face. minval : Optional[float] Lower limit for the color scale. maxval : Optional[float] Upper limit for the color scale. scale : float Indicates whether the axes are in m (1) or km (1000).

Returns#

p : matplotlib.collections.PolyCollection Patches object.

Source code in src/dfastbe/plotting.py 
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def plot_mesh_patches(
    ax: matplotlib.axes.Axes,
    fn: numpy.ndarray,
    nnodes: numpy.ndarray,
    xn: numpy.ndarray,
    yn: numpy.ndarray,
    val: numpy.ndarray,
    minval: Optional[float] = None,
    maxval: Optional[float] = None,
    scale: float = 1000,
) -> matplotlib.collections.PolyCollection:
    """
    Add a collection of patches to the plot one for every face of the mesh.

    Arguments
    ---------
    ax : matplotlib.axes.Axes
        Axes object in which to add the mesh.
    fn : numpy.ndarray
        N x M array listing the nodes (max M) per face (total N) of the mesh.
    nnodes : numpy.ndarray
        Number of nodes per face (max M).
    xn : numpy.ndarray
        X-coordinates of the mesh nodes.
    yn : numpy.ndarray
        Y-coordinates of the mesh nodes.
    val : numpy.ndarray
        Array of length N containing the value per face.
    minval : Optional[float]
        Lower limit for the color scale.
    maxval : Optional[float]
        Upper limit for the color scale.
    scale : float
        Indicates whether the axes are in m (1) or km (1000).

    Returns
    -------
    p : matplotlib.collections.PolyCollection
        Patches object.
    """
    tfn_list = []
    tval_list = []
    for n in range(3, max(nnodes) + 1):
        mask = nnodes >= n
        fn_masked = fn[mask, :]
        tfn_list.append(fn_masked[:, (0, n - 2, n - 1)])
        tval_list.append(val[mask])
    tfn = numpy.concatenate(tfn_list, axis=0)
    tval = numpy.concatenate(tval_list, axis=0)
    # cmap = matplotlib.pyplot.get_cmap('Spectral')
    if minval is None:
        minval = numpy.min(tval)
    if maxval is None:
        maxval = numpy.max(tval)
    p = ax.tripcolor(
        xn / scale,
        yn / scale,
        tfn,
        facecolors=tval,
        cmap="Spectral",
        vmin=minval,
        vmax=maxval,
    )
    return p

savefig(fig: matplotlib.figure.Figure, filename: str) -> None #

Save a single figure to file.

Arguments#

fig : matplotlib.figure.Figure Figure to a be saved. filename : str Name of the file to be written.

Source code in src/dfastbe/plotting.py 
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def savefig(fig: matplotlib.figure.Figure, filename: str) -> None:
    """
    Save a single figure to file.

    Arguments
    ---------
    fig : matplotlib.figure.Figure
        Figure to a be saved.
    filename : str
        Name of the file to be written.
    """
    print("saving figure {file}".format(file=filename))
    matplotlib.pyplot.show(block=False)
    fig.savefig(filename, dpi=300)

set_bbox(ax: matplotlib.axes.Axes, bbox: Tuple[float, float, float, float], scale: float = 1000) -> None #

Specify the bounding limits of an axes object.

Arguments#

ax : matplotlib.axes.Axes Axes object to be adjusted. bbox : Tuple[float, float, float, float] Tuple containing boundary limits (xmin, ymin, xmax, ymax); unit m. scale: float Indicates whether the axes are in m (1) or km (1000).

Source code in src/dfastbe/plotting.py 
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def set_bbox(
    ax: matplotlib.axes.Axes,
    bbox: Tuple[float, float, float, float],
    scale: float = 1000,
) -> None:
    """
    Specify the bounding limits of an axes object.

    Arguments
    ---------
    ax : matplotlib.axes.Axes
        Axes object to be adjusted. 
    bbox : Tuple[float, float, float, float]
        Tuple containing boundary limits (xmin, ymin, xmax, ymax); unit m.
    scale: float
        Indicates whether the axes are in m (1) or km (1000).
    """
    ax.set_xlim(xmin=bbox[0] / scale, xmax=bbox[2] / scale)
    ax.set_ylim(ymin=bbox[1] / scale, ymax=bbox[3] / scale)

setsize(fig: matplotlib.figure.Figure) -> None #

Set the size of a figure.

Currently the size is hardcoded, but functionality may be extended in the future.

Arguments#

fig : matplotlib.figure.Figure Figure to a be saved.

Source code in src/dfastbe/plotting.py 
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def setsize(fig: matplotlib.figure.Figure) -> None:
    """
    Set the size of a figure.

    Currently the size is hardcoded, but functionality may be extended in the
    future.

    Arguments
    ---------
    fig : matplotlib.figure.Figure
        Figure to a be saved.
    """
    # the size of an a3 is (16.5, 11.75)
    fig.set_size_inches(11.75, 8.25)  # a4

zoom_x_and_save(fig: matplotlib.figure.Figure, ax: matplotlib.axes.Axes, figbase: str, plot_ext: str, xzoom: List[Tuple[float, float]]) -> None #

Zoom in on subregions of the x-axis and save the figure.

Arguments#

fig : matplotlib.figure.Figure Figure to be processed. ax : matplotlib.axes.Axes Axes to be processed. fig_base : str Base name of the figure to be saved. plot_ext : str File extension of the figure to be saved. xzoom : List[list[float,float]] Values at which to split the x-axis.

Source code in src/dfastbe/plotting.py 
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def zoom_x_and_save(fig: matplotlib.figure.Figure, ax: matplotlib.axes.Axes, figbase: str, plot_ext: str, xzoom: List[Tuple[float,float]]) -> None:
    """
    Zoom in on subregions of the x-axis and save the figure.

    Arguments
    ---------
    fig : matplotlib.figure.Figure
        Figure to be processed.
    ax : matplotlib.axes.Axes
        Axes to be processed.
    fig_base : str
        Base name of the figure to be saved.
    plot_ext : str
        File extension of the figure to be saved.
    xzoom : List[list[float,float]]
        Values at which to split the x-axis.
    """
    xmin, xmax = ax.get_xlim()
    for ix in range(len(xzoom)):
        ax.set_xlim(xmin=xzoom[ix][0], xmax=xzoom[ix][1])
        figfile = (
            figbase
            + ".sub"
            + str(ix + 1)
            + plot_ext
        )
        savefig(fig, figfile)
    ax.set_xlim(xmin=xmin, xmax=xmax)

zoom_xy_and_save(fig: matplotlib.figure.Figure, ax: matplotlib.axes.Axes, figbase: str, plot_ext: str, xyzoom: List[Tuple[float, float, float, float]], scale: float = 1000) -> None #

Zoom in on subregions in x,y-space and save the figure.

Arguments#

fig : matplotlib.figure.Figure Figure to be processed. ax : matplotlib.axes.Axes Axes to be processed. fig_base : str Base name of the figure to be saved. plot_ext : str File extension of the figure to be saved. xyzoom : List[List[float, float, float, float]] List of xmin, xmax, ymin, ymax values to zoom into. scale: float Indicates whether the axes are in m (1) or km (1000).

Source code in src/dfastbe/plotting.py 
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def zoom_xy_and_save(fig: matplotlib.figure.Figure, ax: matplotlib.axes.Axes, figbase: str, plot_ext: str, xyzoom: List[Tuple[float, float, float, float]], scale: float = 1000) -> None:
    """
    Zoom in on subregions in x,y-space and save the figure.

    Arguments
    ---------
    fig : matplotlib.figure.Figure
        Figure to be processed.
    ax : matplotlib.axes.Axes
        Axes to be processed.
    fig_base : str
        Base name of the figure to be saved.
    plot_ext : str
        File extension of the figure to be saved.
    xyzoom : List[List[float, float, float, float]]
        List of xmin, xmax, ymin, ymax values to zoom into.
    scale: float
        Indicates whether the axes are in m (1) or km (1000).
    """
    xmin, xmax = ax.get_xlim()
    ymin, ymax = ax.get_ylim()

    dx_zoom = 0
    xy_ratio = (ymax - ymin) / (xmax - xmin)
    for ix in range(len(xyzoom)):
        xmin0 = xyzoom[ix][0]
        xmax0 = xyzoom[ix][1]
        ymin0 = xyzoom[ix][2]
        ymax0 = xyzoom[ix][3]
        dx = xmax0 - xmin0
        dy = ymax0 - ymin0
        if dy < xy_ratio * dx:
            # x range limiting
            dx_zoom = max(dx_zoom, dx)
        else:
            # y range limiting
            dx_zoom = max(dx_zoom, dy / xy_ratio)
    dy_zoom = dx_zoom * xy_ratio

    for ix in range(len(xyzoom)):
        x0 = (xyzoom[ix][0] + xyzoom[ix][1]) / 2
        y0 = (xyzoom[ix][2] + xyzoom[ix][3]) / 2
        ax.set_xlim(xmin=(x0 - dx_zoom/2) / scale, xmax=(x0 + dx_zoom/2) / scale)
        ax.set_ylim(ymin=(y0 - dy_zoom/2) / scale, ymax=(y0 + dy_zoom/2) / scale)
        figfile = (
            figbase
            + ".sub"
            + str(ix + 1)
            + plot_ext
        )
        savefig(fig, figfile)

    ax.set_xlim(xmin=xmin, xmax=xmax)
    ax.set_ylim(ymin=ymin, ymax=ymax)

The plotting functions component provides functions for creating various types of visualizations, such as:

  • Maps of bank lines and erosion results
  • Profiles of bank erosion
  • Time series of erosion volumes
  • Visualizations of hydrodynamic data

Workflow#

The typical workflow for using the Plotting module is:

  1. Perform bank line detection or erosion calculation
  2. Call the appropriate plotting functions to visualize the results
  3. Display the plots or save them to files

Usage Example#

import matplotlib.pyplot as plt
from dfastbe import plotting as df_plt
from dfastbe.io.config import ConfigFile
from dfastbe.bank_erosion.bank_erosion import Erosion

# Load configuration file
config_file = ConfigFile.read("config.cfg")

# Initialize Erosion object
erosion = Erosion(config_file)

# Run erosion calculation
erosion.run()

# Create a plot of the results
fig, ax = plt.subplots(figsize=(10, 8))
df_plt.plot_bank_lines(ax, bank_lines, color='blue', linewidth=1.5)
df_plt.plot_erosion_results(ax, erosion_results, cmap='viridis')
plt.savefig("erosion_results.png")
plt.show()

For more details on the specific functions, refer to the API reference below.