map.py

import warnings

import matplotlib.colors
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.collections import LineCollection, PathCollection
from matplotlib.path import Path

from ..utils import geometry
from . import plotutil

warnings.simplefilter("always", PendingDeprecationWarning)


class PlotMapView:
    """
    Class to create a map of the model. Delegates plotting
    functionality based on model grid type.

    Parameters
    ----------
    modelgrid : flopy.discretization.Grid
        The modelgrid class can be StructuredGrid, VertexGrid,
        or UnstructuredGrid (Default is None)
    ax : matplotlib.pyplot axis
        The plot axis.  If not provided it, plt.gca() will be used.
        If there is not a current axis then a new one will be created.
    model : flopy.modflow object
        flopy model object. (Default is None)
    layer : int
        Layer to plot.  Default is 0.  Must be between 0 and nlay - 1.
    extent : tuple of floats
        (xmin, xmax, ymin, ymax) will be used to specify axes limits.  If None
        then these will be calculated based on grid, coordinates, and rotation.

    Notes
    -----


    """

    def __init__(
        self, model=None, modelgrid=None, ax=None, layer=0, extent=None
    ):

        self.model = model
        self.layer = layer
        self.mg = None

        if modelgrid is not None:
            self.mg = modelgrid
        elif model is not None:
            self.mg = model.modelgrid
        else:
            err_msg = "A model grid instance must be provided to PlotMapView"
            raise AssertionError(err_msg)

        if ax is None:
            try:
                self.ax = plt.gca()
                self.ax.set_aspect("equal")
            except (AttributeError, ValueError):
                self.ax = plt.subplot(1, 1, 1, aspect="equal", axisbg="white")
        else:
            self.ax = ax

        if extent is not None:
            self._extent = extent
        else:
            self._extent = None

    @property
    def extent(self):
        if self._extent is None:
            self._extent = self.mg.extent
        return self._extent

    def plot_array(self, a, masked_values=None, **kwargs):
        """
        Plot an array.  If the array is three-dimensional, then the method
        will plot the layer tied to this class (self.layer).

        Parameters
        ----------
        a : numpy.ndarray
            Array to plot.
        masked_values : iterable of floats, ints
            Values to mask.
        **kwargs : dictionary
            keyword arguments passed to matplotlib.pyplot.pcolormesh

        Returns
        -------
        quadmesh : matplotlib.collections.QuadMesh or
            matplotlib.collections.PatchCollection

        """

        if not isinstance(a, np.ndarray):
            a = np.array(a)

        # Use the model grid to pass back an array of the correct shape
        plotarray = self.mg.get_plottable_layer_array(a, self.layer)

        # if masked_values are provided mask the plotting array
        if masked_values is not None:
            for mval in masked_values:
                plotarray = np.ma.masked_values(plotarray, mval)

        # add NaN values to mask
        plotarray = np.ma.masked_where(np.isnan(plotarray), plotarray)

        ax = kwargs.pop("ax", self.ax)

        # use cached patch collection for plotting
        polygons = self.mg.map_polygons
        if isinstance(polygons, dict):
            polygons = polygons[self.layer]

        if len(polygons) == 0:
            return

        if not isinstance(polygons[0], Path):
            collection = ax.pcolormesh(
                self.mg.xvertices, self.mg.yvertices, plotarray
            )

        else:
            plotarray = plotarray.ravel()
            collection = PathCollection(polygons)
            collection.set_array(plotarray)

        # set max and min
        vmin = kwargs.pop("vmin", None)
        vmax = kwargs.pop("vmax", None)

        # set matplotlib kwargs
        collection.set_clim(vmin=vmin, vmax=vmax)
        collection.set(**kwargs)
        ax.add_collection(collection)

        # set limits
        ax.set_xlim(self.extent[0], self.extent[1])
        ax.set_ylim(self.extent[2], self.extent[3])
        return collection

    def contour_array(self, a, masked_values=None, **kwargs):
        """
        Contour an array.  If the array is three-dimensional, then the method
        will contour the layer tied to this class (self.layer).

        Parameters
        ----------
        a : numpy.ndarray
            Array to plot.
        masked_values : iterable of floats, ints
            Values to mask.
        **kwargs : dictionary
            keyword arguments passed to matplotlib.pyplot.pcolormesh

        Returns
        -------
        contour_set : matplotlib.pyplot.contour

        """
        import matplotlib.tri as tri

        a = np.copy(a)
        if not isinstance(a, np.ndarray):
            a = np.array(a)

        # Use the model grid to pass back an array of the correct shape
        plotarray = self.mg.get_plottable_layer_array(a, self.layer)

        # work around for tri-contour ignore vmin & vmax
        # necessary block for tri-contour NaN issue
        if "levels" not in kwargs:
            vmin = kwargs.pop("vmin", np.nanmin(plotarray))
            vmax = kwargs.pop("vmax", np.nanmax(plotarray))
            levels = np.linspace(vmin, vmax, 7)
            kwargs["levels"] = levels

        # workaround for tri-contour nan issue
        # use -2**31 to allow for 32 bit int arrays
        plotarray[np.isnan(plotarray)] = -(2**31)
        if masked_values is None:
            masked_values = [-(2**31)]
        else:
            masked_values = list(masked_values)
            if -(2**31) not in masked_values:
                masked_values.append(-(2**31))

        ismasked = None
        if masked_values is not None:
            for mval in masked_values:
                if ismasked is None:
                    ismasked = np.isclose(plotarray, mval)
                else:
                    t = np.isclose(plotarray, mval)
                    ismasked += t

        ax = kwargs.pop("ax", self.ax)

        if "colors" in kwargs.keys():
            if "cmap" in kwargs.keys():
                kwargs.pop("cmap")

        filled = kwargs.pop("filled", False)
        plot_triplot = kwargs.pop("plot_triplot", False)
        tri_mask = kwargs.pop("tri_mask", False)

        # Get vertices for the selected layer
        xcentergrid = self.mg.get_xcellcenters_for_layer(self.layer)
        ycentergrid = self.mg.get_ycellcenters_for_layer(self.layer)

        if "extent" in kwargs:
            extent = kwargs.pop("extent")

            idx = (
                (xcentergrid >= extent[0])
                & (xcentergrid <= extent[1])
                & (ycentergrid >= extent[2])
                & (ycentergrid <= extent[3])
            )
            plotarray = plotarray[idx]
            xcentergrid = xcentergrid[idx]
            ycentergrid = ycentergrid[idx]

        plotarray = plotarray.flatten()
        xcentergrid = xcentergrid.flatten()
        ycentergrid = ycentergrid.flatten()
        triang = tri.Triangulation(xcentergrid, ycentergrid)
        analyze = tri.TriAnalyzer(triang)
        mask = analyze.get_flat_tri_mask(rescale=False)

        # mask out holes, optional???
        if tri_mask:
            triangles = triang.triangles
            for i in range(2):
                for ix, nodes in enumerate(triangles):
                    neighbors = self.mg.neighbors(nodes[i], as_nodes=True)
                    isin = np.isin(nodes[i + 1 :], neighbors)
                    if not np.alltrue(isin):
                        mask[ix] = True

        if ismasked is not None:
            ismasked = ismasked.flatten()
            mask2 = np.any(
                np.where(ismasked[triang.triangles], True, False), axis=1
            )
            mask[mask2] = True

        triang.set_mask(mask)

        if filled:
            contour_set = ax.tricontourf(triang, plotarray, **kwargs)
        else:
            contour_set = ax.tricontour(triang, plotarray, **kwargs)

        if plot_triplot:
            ax.triplot(triang, color="black", marker="o", lw=0.75)

        ax.set_xlim(self.extent[0], self.extent[1])
        ax.set_ylim(self.extent[2], self.extent[3])

        return contour_set

    def plot_inactive(self, ibound=None, color_noflow="black", **kwargs):
        """
        Make a plot of inactive cells.  If not specified, then pull ibound
        from the self.ml

        Parameters
        ----------
        ibound : numpy.ndarray
            ibound array to plot.  (Default is ibound in 'BAS6' package.)

        color_noflow : string
            (Default is 'black')

        Returns
        -------
        quadmesh : matplotlib.collections.QuadMesh

        """

        if ibound is None:
            if self.mg.idomain is None:
                raise AssertionError("Ibound/Idomain array must be provided")

            ibound = self.mg.idomain

        plotarray = np.zeros(ibound.shape, dtype=int)
        idx1 = ibound == 0
        plotarray[idx1] = 1
        plotarray = np.ma.masked_equal(plotarray, 0)
        cmap = matplotlib.colors.ListedColormap(["0", color_noflow])
        bounds = [0, 1, 2]
        norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
        quadmesh = self.plot_array(plotarray, cmap=cmap, norm=norm, **kwargs)
        return quadmesh

    def plot_ibound(
        self,
        ibound=None,
        color_noflow="black",
        color_ch="blue",
        color_vpt="red",
        **kwargs,
    ):
        """
        Make a plot of ibound.  If not specified, then pull ibound from the
        self.ml

        Parameters
        ----------
        ibound : numpy.ndarray
            ibound array to plot.  (Default is ibound in the modelgrid)
        color_noflow : string
            (Default is 'black')
        color_ch : string
            Color for constant heads (Default is 'blue'.)
        color_vpt: string
            Color for vertical pass through cells (Default is 'red')

        Returns
        -------
        quadmesh : matplotlib.collections.QuadMesh

        """

        if ibound is None:
            if self.model is not None:
                if self.model.version == "mf6":
                    color_ch = color_vpt

            if self.mg.idomain is None:
                raise AssertionError("Ibound/Idomain array must be provided")

            ibound = self.mg.idomain

        plotarray = np.zeros(ibound.shape, dtype=int)
        idx1 = ibound == 0
        idx2 = ibound < 0
        plotarray[idx1] = 1
        plotarray[idx2] = 2
        plotarray = np.ma.masked_equal(plotarray, 0)
        cmap = matplotlib.colors.ListedColormap(["0", color_noflow, color_ch])
        bounds = [0, 1, 2, 3]
        norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
        quadmesh = self.plot_array(plotarray, cmap=cmap, norm=norm, **kwargs)
        return quadmesh

    def plot_grid(self, **kwargs):
        """
        Plot the grid lines.

        Parameters
        ----------
        kwargs : ax, colors.  The remaining kwargs are passed into the
            the LineCollection constructor.

        Returns
        -------
        lc : matplotlib.collections.LineCollection

        """
        ax = kwargs.pop("ax", self.ax)
        colors = kwargs.pop("colors", "grey")
        colors = kwargs.pop("color", colors)
        colors = kwargs.pop("ec", colors)
        colors = kwargs.pop("edgecolor", colors)

        grid_lines = self.mg.grid_lines
        if isinstance(grid_lines, dict):
            grid_lines = grid_lines[self.layer]

        collection = LineCollection(grid_lines, colors=colors, **kwargs)

        ax.add_collection(collection)
        ax.set_xlim(self.extent[0], self.extent[1])
        ax.set_ylim(self.extent[2], self.extent[3])
        return collection

    def plot_bc(
        self,
        name=None,
        package=None,
        kper=0,
        color=None,
        plotAll=False,
        **kwargs,
    ):
        """
        Plot boundary conditions locations for a specific boundary
        type from a flopy model

        Parameters
        ----------
        name : string
            Package name string ('WEL', 'GHB', etc.). (Default is None)
        package : flopy.modflow.Modflow package class instance
            flopy package class instance. (Default is None)
        kper : int
            Stress period to plot
        color : string
            matplotlib color string. (Default is None)
        plotAll : bool
            Boolean used to specify that boundary condition locations for all
            layers will be plotted on the current ModelMap layer.
            (Default is False)
        **kwargs : dictionary
            keyword arguments passed to matplotlib.collections.PatchCollection

        Returns
        -------
        quadmesh : matplotlib.collections.QuadMesh

        """
        if "ftype" in kwargs and name is None:
            name = kwargs.pop("ftype")

        # Find package to plot
        if package is not None:
            p = package
            name = p.name[0]
        elif self.model is not None:
            if name is None:
                raise Exception("ftype not specified")
            name = name.upper()
            p = self.model.get_package(name)
        else:
            raise Exception("Cannot find package to plot")

        # trap for mf6 'cellid' vs mf2005 'k', 'i', 'j' convention
        if isinstance(p, list) or p.parent.version == "mf6":
            if not isinstance(p, list):
                p = [p]

            idx = np.array([])
            for pp in p:
                if pp.package_type in ("lak", "sfr", "maw", "uzf"):
                    t = plotutil.advanced_package_bc_helper(pp, self.mg, kper)
                else:
                    try:
                        mflist = pp.stress_period_data.array[kper]
                    except Exception as e:
                        raise Exception(
                            f"Not a list-style boundary package: {e!s}"
                        )
                    if mflist is None:
                        return

                    t = np.array(
                        [list(i) for i in mflist["cellid"]], dtype=int
                    ).T

                if len(idx) == 0:
                    idx = np.copy(t)
                else:
                    idx = np.append(idx, t, axis=1)

        else:
            # modflow-2005 structured and unstructured grid
            if p.package_type in ("uzf", "lak"):
                idx = plotutil.advanced_package_bc_helper(p, self.mg, kper)
            else:
                try:
                    mflist = p.stress_period_data[kper]
                except Exception as e:
                    raise Exception(
                        f"Not a list-style boundary package: {e!s}"
                    )
                if mflist is None:
                    return
                if len(self.mg.shape) == 3:
                    idx = [mflist["k"], mflist["i"], mflist["j"]]
                else:
                    idx = mflist["node"]

        nlay = self.mg.nlay

        plotarray = np.zeros(self.mg.shape, dtype=int)
        if plotAll and len(self.mg.shape) > 1:
            pa = np.zeros(self.mg.shape[1:], dtype=int)
            pa[tuple(idx[1:])] = 1
            for k in range(nlay):
                plotarray[k] = pa.copy()
        elif len(self.mg.shape) > 1:
            plotarray[tuple(idx)] = 1
        else:
            plotarray[idx] = 1

        # mask the plot array
        plotarray = np.ma.masked_equal(plotarray, 0)

        # set the colormap
        if color is None:
            # modflow 6 ftype fix, since multiple packages append _0, _1, etc:
            key = name[:3].upper()
            if key in plotutil.bc_color_dict:
                c = plotutil.bc_color_dict[key]
            else:
                c = plotutil.bc_color_dict["default"]
        else:
            c = color

        cmap = matplotlib.colors.ListedColormap(["0", c])
        bounds = [0, 1, 2]
        norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)

        # create normalized quadmesh or patch object depending on grid type
        quadmesh = self.plot_array(plotarray, cmap=cmap, norm=norm, **kwargs)

        return quadmesh

    def plot_shapefile(self, shp, **kwargs):
        """
        Plot a shapefile.  The shapefile must be in the same coordinates as
        the rotated and offset grid.

        Parameters
        ----------
        shp : string or pyshp shapefile object
            Name of the shapefile to plot

        kwargs : dictionary
            Keyword arguments passed to plotutil.plot_shapefile()

        """
        return self.plot_shapes(shp, **kwargs)

    def plot_shapes(self, obj, **kwargs):
        """
        Plot shapes is a method that facilitates plotting a collection
        of geospatial objects

        Parameters
        ----------
        obj : collection object
            obj can accept the following types

            str : shapefile name
            shapefile.Reader object
            list of [shapefile.Shape, shapefile.Shape,]
            shapefile.Shapes object
            flopy.utils.geometry.Collection object
            list of [flopy.utils.geometry, ...] objects
            geojson.GeometryCollection object
            geojson.FeatureCollection object
            shapely.GeometryCollection object
            list of [[vertices], ...]
        kwargs : dictionary
            keyword arguments passed to plotutil.plot_shapefile()

        Returns
        -------
            matplotlib.Collection object
        """
        ax = kwargs.pop("ax", self.ax)
        patch_collection = plotutil.plot_shapefile(obj, ax, **kwargs)
        return patch_collection

    def plot_cvfd(self, verts, iverts, **kwargs):
        """
        Plot a cvfd grid.  The vertices must be in the same
        coordinates as the rotated and offset grid.

        Parameters
        ----------
        verts : ndarray
            2d array of x and y points.
        iverts : list of lists
            should be of len(ncells) with a list of vertex number for each cell

        kwargs : dictionary
            Keyword arguments passed to plotutil.plot_cvfd()

        """
        warnings.warn(
            "plot_cvfd will be deprecated and will be removed in version "
            "3.3.5. Use plot_grid or plot_array",
            PendingDeprecationWarning,
        )
        a = kwargs.pop("a", None)
        if a is None:
            return self.plot_grid(**kwargs)
        else:
            return self.plot_array(a, **kwargs)

    def contour_array_cvfd(self, vertc, a, masked_values=None, **kwargs):
        """
        Contour a cvfd array.  If the array is three-dimensional,
        then the method will contour the layer tied to this class (self.layer).
        The vertices must be in the same coordinates as the rotated and
        offset grid.

        Parameters
        ----------
        vertc : np.ndarray
            Array with of size (nc, 2) with centroid location of cvfd
        a : numpy.ndarray
            Array to plot.
        masked_values : iterable of floats, ints
            Values to mask.
        **kwargs : dictionary
            keyword arguments passed to matplotlib.pyplot.pcolormesh

        Returns
        -------
        contour_set : matplotlib.pyplot.contour

        """
        warnings.warn(
            "contour_cvfd will be deprecated and removed in version 3.3.5. "
            " Use contour_array",
            PendingDeprecationWarning,
        )

        return self.contour_array(a, masked_values=masked_values, **kwargs)

    def plot_vector(
        self,
        vx,
        vy,
        istep=1,
        jstep=1,
        normalize=False,
        masked_values=None,
        **kwargs,
    ):
        """
        Plot a vector.

        Parameters
        ----------
        vx : np.ndarray
            x component of the vector to be plotted (non-rotated)
            array shape must be (nlay, nrow, ncol) for a structured grid
            array shape must be (nlay, ncpl) for a unstructured grid
        vy : np.ndarray
            y component of the vector to be plotted (non-rotated)
            array shape must be (nlay, nrow, ncol) for a structured grid
            array shape must be (nlay, ncpl) for a unstructured grid
        istep : int
            row frequency to plot (default is 1)
        jstep : int
            column frequency to plot (default is 1)
        normalize : bool
            boolean flag used to determine if vectors should be normalized
            using the vector magnitude in each cell (default is False)
        masked_values : iterable of floats
            values to mask
        kwargs : matplotlib.pyplot keyword arguments for the
            plt.quiver method

        Returns
        -------
        quiver : matplotlib.pyplot.quiver
            result of the quiver function

        """
        pivot = kwargs.pop("pivot", "middle")
        ax = kwargs.pop("ax", self.ax)

        # get ibound array to mask inactive cells
        ib = np.ones((self.mg.nnodes,), dtype=int)
        if self.mg.idomain is not None:
            ib = self.mg.idomain.ravel()

        xcentergrid = self.mg.get_xcellcenters_for_layer(self.layer)
        ycentergrid = self.mg.get_ycellcenters_for_layer(self.layer)
        vx = self.mg.get_plottable_layer_array(vx, self.layer)
        vy = self.mg.get_plottable_layer_array(vy, self.layer)
        ib = self.mg.get_plottable_layer_array(ib, self.layer)

        try:
            x = xcentergrid[::istep, ::jstep]
            y = ycentergrid[::istep, ::jstep]
            u = vx[::istep, ::jstep]
            v = vy[::istep, ::jstep]
            ib = ib[::istep, ::jstep]
        except IndexError:
            x = xcentergrid[::jstep]
            y = ycentergrid[::jstep]
            u = vx[::jstep]
            v = vy[::jstep]
            ib = ib[::jstep]

        # if necessary, copy to avoid changing the passed values
        if masked_values is not None or normalize:
            u = np.copy(u)
            v = np.copy(v)

        # mask values
        if masked_values is not None:
            for mval in masked_values:
                to_mask = np.logical_or(u == mval, v == mval)
                u[to_mask] = np.nan
                v[to_mask] = np.nan

        # normalize
        if normalize:
            vmag = np.sqrt(u**2.0 + v**2.0)
            idx = vmag > 0.0
            u[idx] /= vmag[idx]
            v[idx] /= vmag[idx]

        u[ib == 0] = np.nan
        v[ib == 0] = np.nan

        # rotate and plot, offsets must be zero since
        # these are vectors not locations
        urot, vrot = geometry.rotate(u, v, 0.0, 0.0, self.mg.angrot_radians)
        quiver = ax.quiver(x, y, urot, vrot, pivot=pivot, **kwargs)
        return quiver

    def plot_pathline(self, pl, travel_time=None, **kwargs):
        """
        Plot the MODPATH pathlines.

        Parameters
        ----------
        pl : list of rec arrays or a single rec array
            rec array or list of rec arrays is data returned from
            modpathfile PathlineFile get_data() or get_alldata()
            methods. Data in rec array is 'x', 'y', 'z', 'time',
            'k', and 'particleid'.
        travel_time : float or str
            travel_time is a travel time selection for the displayed
            pathlines. If a float is passed then pathlines with times
            less than or equal to the passed time are plotted. If a
            string is passed a variety logical constraints can be added
            in front of a time value to select pathlines for a select
            period of time. Valid logical constraints are <=, <, >=, and
            >. For example, to select all pathlines less than 10000 days
            travel_time='< 10000' would be passed to plot_pathline.
            (default is None)
        kwargs : layer, ax, colors.  The remaining kwargs are passed
            into the LineCollection constructor. If layer='all',
            pathlines are output for all layers

        Returns
        -------
        lc : matplotlib.collections.LineCollection

        """

        from matplotlib.collections import LineCollection

        # make sure pathlines is a list
        if not isinstance(pl, list):
            pids = np.unique(pl["particleid"])
            if len(pids) > 1:
                pl = [pl[pl["particleid"] == pid] for pid in pids]
            else:
                pl = [pl]

        if "layer" in kwargs:
            kon = kwargs.pop("layer")
            if isinstance(kon, bytes):
                kon = kon.decode()
            if isinstance(kon, str):
                if kon.lower() == "all":
                    kon = -1
                else:
                    kon = self.layer
        else:
            kon = self.layer

        marker = kwargs.pop("marker", None)
        markersize = kwargs.pop("markersize", None)
        markersize = kwargs.pop("ms", markersize)
        markercolor = kwargs.pop("markercolor", None)
        markerevery = kwargs.pop("markerevery", 1)
        ax = kwargs.pop("ax", self.ax)

        if "colors" not in kwargs:
            kwargs["colors"] = "0.5"

        linecol = []
        markers = []
        for p in pl:
            tp = plotutil.filter_modpath_by_travel_time(p, travel_time)

            # transform data!
            x0r, y0r = geometry.transform(
                tp["x"],
                tp["y"],
                self.mg.xoffset,
                self.mg.yoffset,
                self.mg.angrot_radians,
            )
            # build polyline array
            arr = np.vstack((x0r, y0r)).T
            # select based on layer
            if kon >= 0:
                kk = p["k"].copy().reshape(p.shape[0], 1)
                kk = np.repeat(kk, 2, axis=1)
                arr = np.ma.masked_where((kk != kon), arr)
            else:
                arr = np.ma.asarray(arr)
            # append line to linecol if there is some unmasked segment
            if not arr.mask.all():
                linecol.append(arr)
                if not arr.mask.all():
                    linecol.append(arr)
                    if marker is not None:
                        for xy in arr[::markerevery]:
                            if not np.all(xy.mask):
                                markers.append(xy)
        # create line collection
        lc = None
        if len(linecol) > 0:
            lc = LineCollection(linecol, **kwargs)
            ax.add_collection(lc)
            if marker is not None:
                markers = np.array(markers)
                ax.plot(
                    markers[:, 0],
                    markers[:, 1],
                    lw=0,
                    marker=marker,
                    color=markercolor,
                    ms=markersize,
                )

        ax.set_xlim(self.extent[0], self.extent[1])
        ax.set_ylim(self.extent[2], self.extent[3])
        return lc

    def plot_timeseries(self, ts, travel_time=None, **kwargs):
        """
        Plot the MODPATH timeseries.

        Parameters
        ----------
        ts : list of rec arrays or a single rec array
            rec array or list of rec arrays is data returned from
            modpathfile TimeseriesFile get_data() or get_alldata()
            methods. Data in rec array is 'x', 'y', 'z', 'time',
            'k', and 'particleid'.
        travel_time : float or str
            travel_time is a travel time selection for the displayed
            pathlines. If a float is passed then pathlines with times
            less than or equal to the passed time are plotted. If a
            string is passed a variety logical constraints can be added
            in front of a time value to select pathlines for a select
            period of time. Valid logical constraints are <=, <, >=, and
            >. For example, to select all pathlines less than 10000 days
            travel_time='< 10000' would be passed to plot_pathline.
            (default is None)
        kwargs : layer, ax, colors.  The remaining kwargs are passed
            into the LineCollection constructor. If layer='all',
            pathlines are output for all layers

        Returns
        -------
            lo : list of Line2D objects
        """
        if "color" in kwargs:
            kwargs["markercolor"] = kwargs["color"]

        return self.plot_pathline(ts, travel_time=travel_time, **kwargs)

    def plot_endpoint(
        self,
        ep,
        direction="ending",
        selection=None,
        selection_direction=None,
        **kwargs,
    ):
        """
        Plot the MODPATH endpoints.

        Parameters
        ----------
        ep : rec array
            A numpy recarray with the endpoint particle data from the
            MODPATH 6 endpoint file
        direction : str
            String defining if starting or ending particle locations should be
            considered. (default is 'ending')
        selection : tuple
            tuple that defines the zero-base layer, row, column location
            (l, r, c) to use to make a selection of particle endpoints.
            The selection could be a well location to determine capture zone
            for the well. If selection is None, all particle endpoints for
            the user-sepcified direction will be plotted. (default is None)
        selection_direction : str
            String defining is a selection should be made on starting or
            ending particle locations. If selection is not None and
            selection_direction is None, the selection direction will be set
            to the opposite of direction. (default is None)

        kwargs : ax, c, s or size, colorbar, colorbar_label, shrink. The
            remaining kwargs are passed into the matplotlib scatter
            method. If colorbar is True a colorbar will be added to the plot.
            If colorbar_label is passed in and colorbar is True then
            colorbar_label will be passed to the colorbar set_label()
            method. If shrink is passed in and colorbar is True then
            the colorbar size will be set using shrink.

        Returns
        -------
        sp : matplotlib.pyplot.scatter

        """

        ax = kwargs.pop("ax", self.ax)

        tep, _, xp, yp = plotutil.parse_modpath_selection_options(
            ep, direction, selection, selection_direction
        )
        # scatter kwargs that users may redefine
        if "c" not in kwargs:
            c = tep["time"] - tep["time0"]
        else:
            c = np.empty((tep.shape[0]), dtype="S30")
            c.fill(kwargs.pop("c"))

        s = kwargs.pop("s", np.sqrt(50))
        s = float(kwargs.pop("size", s)) ** 2.0

        # colorbar kwargs
        createcb = kwargs.pop("colorbar", False)
        colorbar_label = kwargs.pop("colorbar_label", "Endpoint Time")
        shrink = float(kwargs.pop("shrink", 1.0))

        # transform data!
        x0r, y0r = geometry.transform(
            tep[xp],
            tep[yp],
            self.mg.xoffset,
            self.mg.yoffset,
            self.mg.angrot_radians,
        )
        # build array to plot
        arr = np.vstack((x0r, y0r)).T

        # plot the end point data
        sp = ax.scatter(arr[:, 0], arr[:, 1], c=c, s=s, **kwargs)

        # add a colorbar for travel times
        if createcb:
            cb = plt.colorbar(sp, ax=ax, shrink=shrink)
            cb.set_label(colorbar_label)
        return sp