import copy
import functools
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import patheffects
from matplotlib.collections import QuadMesh
import matplotlib.ticker as mticker

from .. import helpers, cv


def _base_plot(plot_func):
    commondoc = """

    Parameters
    ----------
    mode : str, optional
        perspective mode to use for plotting. Can be "local" (default), "geographical", or "camera". For
        "geographical" a velocimetry result that contains "lon" and "lat" coordinates must be provided
        (i.e. produced with known CRS for control points).
    x : str, optional
        Coordinate for *x* axis. If ``None``, use ``darray.dims[1]``.
    y : str, optional
        Coordinate for *y* axis. If ``None``, use ``darray.dims[0]``.
    ax : plt.axes, optional
        If None (default), use the current axes. Not applicable when using facets.
    *args : additional arguments, passed to wrapped Matplotlib function.
    **kwargs : additional keyword arguments to wrapped Matplotlib function.

    Returns
    -------
    artist : matplotlib mappable
        The same type of primitive artist that the wrapped Matplotlib
        function returns.

    """
    # This function is largely based on xarray.Dataset function _dsplot
    # Build on the original docstring
    plot_func.__doc__ = f"{plot_func.__doc__}{commondoc}"

    # apply wrapper to allow for partial update of the function, with updated docstring
    @functools.wraps(plot_func)
    def get_plot_method(
            ref,
            mode="local",
            ax=None,
            add_colorbar=False,
            add_cross_section=True,
            add_text=False,
            text_prefix="",
            text_suffix="",
            kwargs_line={},
            *args,
            **kwargs
    ):
        """Retrieve plot method with all required inputs

        Parameters
        ----------
        ref : xr.Dataset
            velocimetry or transect object
        mode : str, optional
            perspective mode to use for plotting. Can be "local", "geographical", or "camera". For
            "geographical" a velocimetry result that contains "lon" and "lat" coordinates must be provided
            (i.e. produced with known CRS for control points).
        ax : plt.axes object, optional
            If None (default), use the current axes. Not applicable when using facets.
        add_colorbar : boolean, optional
            if True, a colorbar is added to axes (default: False)
        add_cross_section : boolean, optional
            if True, and a transect is plotted, the transect coordinates are plotted (default: True)
        add_text : boolean, optional
            if True, add a text label in the axes displaying information about the video's transect
        text_prefix : str, optional
            string to add in front of standard text on transect plot. Only used if ``add_text=True``
        text_suffix : str, optional
            String to add after standard text on transect plot. Only used if ``add_text=True``
        kwargs_line : dict, optional
            additional keyword arguments passed to matplotlib.pyplot.plot for plotting cross-section.
            (Default value = {})
        *args : additional arguments, passed to wrapped Matplotlib function.
        **kwargs : additional keyword arguments to wrapped Matplotlib function.

        Returns
        -------
        p : matplotlib mappable
            mappable of wrapped matplotlib function

        """
        # in case persistent transform is in kwargs_line, remove this
        if mode == "geographical":
            try:
                import cartopy
            except:
                raise ImportError("Cartopy not found, please install with 'mamba install -c conda-forge cartopy")
        if "transform" in kwargs_line:
            del kwargs_line["transform"]
        ax = _prepare_axes(ax=ax, mode=mode)
        # update ax

        if len(ref._obj["v_x"].shape) > 2:
            raise OverflowError(
                f'Dataset\'s variables should only contain 2 dimensions, this dataset '
                f'contains {len(ref._obj["v_x"].shape)} dimensions. Reduce this by applying a reducer or selecting a time step. '
                f'Reducing can be done e.g. with ds.mean(dim="time", keep_attrs=True) or slicing with ds.isel(time=0)'
            )
        if "time" in ref._obj:
            if isinstance(ref._obj["time"].values.tolist(), list):
                raise OverflowError(
                    f'Dimension "time" exists in dataset and has multiple values. Reduce this by applying a reducer or '
                    f'selecting a time step.'
                )
        if "quantile" in ref._obj:
            if isinstance(ref._obj["quantile"].values.tolist(), list):
                raise OverflowError(
                    f'Dimension "quantile" exists in dataset and contains multiple values. Reduce this by selecting one'
                    f'quantile, e.g. using ds.isel(quantile=2) or ds.sel(quantile=0.5)'
                )
        if plot_func.__name__ == "streamplot":
            if mode != "local":
                raise NotImplementedError(f"Streamplot only works in local mode, not in {mode} mode.")

        # check if dataset is a transect or not
        is_transect = True if "points" in ref._obj.dims else False

        assert mode in ["local", "geographical", "camera"], 'Mode must be "local", "geographical" or "camera"'
        if mode == "local":
            x = ref._obj["x"].values
            y = ref._obj["y"].values
            u, v, s = ref.get_uv_local()
            if not(is_transect) and plot_func.__name__ == "scatter":
                x, y = np.meshgrid(x, y)

            if plot_func.__name__ == "streamplot":
                # flipping of variables is needed
                y = np.flipud(y)
                u = np.flipud(u)
                v = np.flipud(v)
                s = np.flipud(s)

        elif mode == "geographical":
            # import some additional packages
            import cartopy.crs as ccrs
            # add transform for GeoAxes
            kwargs["transform"] = ccrs.PlateCarree()
            kwargs_line["transform"] = ccrs.PlateCarree()

            x = ref._obj["lon"].values
            y = ref._obj["lat"].values
            u, v, s = ref.get_uv_geographical()
        else:
            # mode is camera
            x = ref._obj["xp"].values
            y = ref._obj["yp"].values
            u, v, s = ref.get_uv_camera()
        if plot_func.__name__ in ["quiver", "streamplot"]:
            primitive = plot_func("", x, y, u, v, s, ax, *args, **kwargs)
        else:
            primitive = plot_func("", x, y, s, ax, *args, **kwargs)
        if add_colorbar:
            cb = cbar(ax, primitive)
        if mode == "local":
            ax.set_aspect("equal")
        if is_transect:
            if add_cross_section:
                ax.plot(x, y, "#00FF88", linewidth=3, alpha=0.3, **kwargs_line)
                ax.plot(x, y, "#00FF88", linewidth=2, alpha=0.3, **kwargs_line)
                ax.plot(x, y, "#00FF88", linewidth=1, alpha=0.3, **kwargs_line)
                if mode == "camera":
                    # lens position is needed, so check this
                    if hasattr(ref._obj.camera_config, "lens_position"):
                        x_bottom, y_bottom = ref._obj.transect.get_xyz_perspective()
                        ax.plot(x_bottom, y_bottom, "#0088FF", alpha=0.3, linewidth=3, **kwargs_line)
                        ax.plot(x_bottom, y_bottom, "#0088FF", alpha=0.3, linewidth=2, **kwargs_line)
                        ax.plot(x_bottom, y_bottom, "#0088FF", alpha=0.3, linewidth=1, **kwargs_line)
                if add_text:
                    plot_text(ax, ref._obj, text_prefix, text_suffix)

        if mode == "geographical" and not(is_transect):
            ax.set_extent(
                [
                    x.min() - 0.0001,
                    x.max() + 0.0001,
                    y.min() - 0.0001,
                    y.max() + 0.0001
                ],
                crs=ccrs.PlateCarree()
            )
        return primitive
    return get_plot_method



def _frames_plot(ref, ax=None, mode="local", *args, **kwargs):
    """Creates QuadMesh plot from a RGB or grayscale frame on a new or existing (if ax is not None) axes

    Wraps :py:func:`matplotlib:matplotlib.collections.QuadMesh`.

    Parameters
    ----------
    ax : plt.axes, optional
        If None (default), use the current axes. Not applicable when using facets.
    mode : str, optional
        perspective mode to use for plotting. Can be "local" (default), "geographical", or "camera".
        For "geographical" a frames set from Frames.project should be used that contains "lon" and "lat" coordinates.
        For "camera", a non-projected frames set should be used.
    *args : additional arguments, passed to wrapped Matplotlib function.
    **kwargs : additional keyword arguments to wrapped Matplotlib function.

    Returns
    -------
    p : matplotlib.collections.QuadMesh

    """
    # prepare axes
    if mode == "geographical":
        try:
            import cartopy
        except:
            raise ImportError("Cartopy not found, please install with 'mamba install -c conda-forge cartopy")
    if "time" in ref._obj.coords:
        if ref._obj.time.size > 1:
            raise AttributeError(f'Object contains dimension "time" with length {len(ref._obj.time)}. Reduce dataset by selecting one time step or taking a median, mean or other statistic.')
    if mode == "camera":
        rotation = ref.camera_config.rotation
    else:
        rotation = None
    ax = _prepare_axes(ax=ax, mode=mode, rotation=rotation)
    f = ax.figure  # handle to figure
    if mode == "local":
        x = "x"
        y = "y"
    elif mode == "geographical":
        # import some additional packages
        import cartopy.crs as ccrs
        # add transform for GeoAxes
        kwargs["transform"] = ccrs.PlateCarree()
        x = "lon"
        y = "lat"
    else:
        # mode is camera
        x = "xp"
        y = "yp"
    assert all(v in ref._obj.coords for v in [x, y]), f'required coordinates "{x}" and/or "{y}" are not available'
    if len(ref._obj.shape) == 3 and ref._obj.shape[-1] == 3:
        # looking at an rgb image
        facecolors = ref._obj.values.reshape(ref._obj.shape[0] * ref._obj.shape[1], 3) / 255
        facecolors = np.hstack([facecolors, np.ones((len(facecolors), 1))])
        primitive = ax.pcolormesh(ref._obj[x], ref._obj[y], ref._obj.mean(dim="rgb"), shading="nearest",
                             facecolors=facecolors, *args, **kwargs)
        # remove array values, override .set_array, needed in case GeoAxes is provided, because GeoAxes asserts if
        # array has dims
        QuadMesh.set_array(primitive, None)
    else:
        primitive = ax.pcolormesh(ref._obj[x], ref._obj[y], ref._obj, *args, **kwargs)
    # fix axis limits to min and max of extent of frames
    if mode == "geographical":
        ax.set_extent(
            [
                ref._obj[x].min() - 0.0001,
                ref._obj[x].max() + 0.0001,
                ref._obj[y].min() - 0.0001,
                ref._obj[y].max() + 0.0001,
            ],
            crs=ccrs.PlateCarree()
        )
    else:
        ax.set_xlim([ref._obj[x].min(), ref._obj[x].max()])
        ax.set_ylim([ref._obj[y].min(), ref._obj[y].max()])
    return primitive


class _Transect_PlotMethods:
    """Enables use of ds.velocimetry.plot functions as attributes on a Dataset containing velocimetry results.
    For example, Dataset.velocimetry.plot.pcolormesh. When called without a subfunction, quiver will be used.
    """

    def __init__(self, transect):
        # make the original dataset also available on the plotting object
        self.transect = transect
        self._obj = transect._obj
        # Add to class _PlotMethods

    def __call__(self, method="quiver", *args, **kwargs):
        """

        Parameters
        ----------
        method : str, optional
            plot method to use (default: "quiver")
        *args : additional arguments, passed to wrapped Matplotlib function.
        **kwargs : additional keyword arguments to wrapped Matplotlib function.

        Returns
        -------
        p : matplotlib mappable
            mappable of wrapped matplotlib function

        """
        return getattr(self, method)(*args, **kwargs)


    def get_uv_camera(self, dt=0.1):
        """Get x-directional (u), y-directional (v) and scalar velocity in camera projection from transect dataset.

        Parameters
        ----------
        dt : float, optional
            time step [s] used to transpose velocities over a short distance for projection (default: 0.1)

        Returns
        -------
        u : np.ndarray
            x-directional velocity
        v : np.ndarray
            y-directional velocity
        s : np.ndarray
            scalar velocity

        """
        v_eff = "v_eff"
        v_dir = "v_dir"
        # retrieve the backward transformation array
        transect = self._obj.transect
        camera_config = transect.camera_config
        # M = velocimetry.camera_config.get_M(velocimetry.h_a, to_bbox_grid=True, reverse=True)
        src = camera_config.get_bbox(camera=True, h_a=transect.h_a, expand_exterior=False).exterior.coords[0:4]
        dst_xy = camera_config.get_bbox(expand_exterior=False).exterior.coords[0:4]
        # get geographic coordinates bbox corners
        # get geographic coordinates bbox corners
        dst = cv.transform_to_bbox(dst_xy, camera_config.bbox, camera_config.resolution)
        M = cv.get_M_2D(src, dst, reverse=True)
        # M = transect.camera_config.get_M(transect.h_a, reverse=True, to_bbox_grid=True)

        x, y = self._obj.x, self._obj.y
        _u = self._obj[v_eff] * np.sin(self._obj[v_dir])
        _v = self._obj[v_eff] * np.cos(self._obj[v_dir])
        s = np.abs(self._obj[v_eff].values)
        x_moved, y_moved = x + _u * dt, y + _v * dt
        xp, yp = transect.get_xyz_perspective(M=M, xs=x.values, ys=y.values)
        xp_moved, yp_moved = transect.get_xyz_perspective(M=M, xs=x_moved.values, ys=y_moved.values)
        # remove vectors that have nan on moved pixels
        xp_moved[np.isnan(x_moved)] = np.nan
        yp_moved[np.isnan(y_moved)] = np.nan

        self._obj["xp"][:] = xp[:]
        self._obj["yp"][:] = yp[:]
        u, v = xp_moved - self._obj["xp"], yp_moved - self._obj["yp"]
        return u, v, s


    def get_uv_geographical(self):
        """Get x-directional (u), y-directional (v) and scalar velocity in geographical projection from transect dataset.

        Returns
        -------
        u : np.ndarray
            x-directional velocity
        v : np.ndarray
            y-directional velocity
        s : np.ndarray
            scalar velocity
        """
        v_eff = "v_eff"
        v_dir = "v_dir"
        u = self._obj[v_eff] * np.sin(self._obj[v_dir])
        v = self._obj[v_eff] * np.cos(self._obj[v_dir])
        s = self._obj[v_eff]
        aff = self.transect.camera_config.transform
        theta = np.arctan2(aff.d, aff.a)
        # rotate velocity vectors along angle theta to match the requested projection. this only changes values
        # in case of camera projections
        u, v = helpers.rotate_u_v(u, v, theta)
        return u, v, s


    def get_uv_local(self):
        """Get x-directional (u), y-directional (v) and scalar velocity in local projection from transect dataset.

        Returns
        -------
        u : np.ndarray
            x-directional velocity
        v : np.ndarray
            y-directional velocity
        s : np.ndarray
            scalar velocity
        """
        v_eff = "v_eff"
        v_dir = "v_dir"
        u = self._obj[v_eff] * np.sin(self._obj[v_dir])
        v = self._obj[v_eff] * np.cos(self._obj[v_dir])
        s = self._obj[v_eff]
        return u, v, s


class _Velocimetry_PlotMethods:
    """Enables use of ds.velocimetry.plot functions as attributes on a Dataset containing velocimetry results.
    For example, Dataset.velocimetry.plot.pcolormesh. When called without a subfunction, quiver will be used
    """
    def __init__(self, velocimetry):
        # make the original dataset also available on the plotting object
        self.velocimetry = velocimetry
        self._obj = velocimetry._obj
        # Add to class _PlotMethods

    def __call__(self, method="quiver", *args, **kwargs):
        """
        Parameters
        ----------
        method : str, optional
            plot method to use "quiver", "scatter", "streamplot" or "pcolormesh" (default: "quiver")
        *args : additional arguments, passed to wrapped Matplotlib function.
        **kwargs : additional keyword arguments to wrapped Matplotlib function.

        Returns
        -------
        p : matplotlib mappable
            mappable of wrapped matplotlib function

        """
        return getattr(self, method)(*args, **kwargs)


    def get_uv_geographical(self):
        """Get x-directional (u), y-directional (v) and scalar velocity in camera projection from velocimetry dataset.

        Returns
        -------
        u : np.ndarray
            x-directional velocity
        v : np.ndarray
            y-directional velocity
        s : np.ndarray
            scalar velocity
        """
        # select lon and lat variables as coordinates
        velocimetry = self._obj.velocimetry
        u = self._obj["v_x"]
        v = -self._obj["v_y"]
        s = (u**2 + v**2)**0.5
        aff = velocimetry.camera_config.transform
        theta = np.arctan2(aff.d, aff.a)
        # rotate velocity vectors along angle theta to match the requested projection. this only changes values
        # in case of camera projections
        u, v = helpers.rotate_u_v(u, v, theta)
        return u, v, s


    def get_uv_local(self):
        """Get x-directional (u), y-directional (v) and scalar velocity in local projection from velocimetry dataset.

        Returns
        -------
        u : np.ndarray
            x-directional velocity
        v : np.ndarray
            y-directional velocity
        s : np.ndarray
            scalar velocity
        """
        u = self._obj["v_x"].values
        v = -self._obj["v_y"].values
        s = (u**2 + v**2)**0.5
        return u, v, s


    def get_uv_camera(self, dt=0.1):
        """Get x-directional (u), y-directional (v) and scalar velocity in camera projection from velocimetry dataset.

        Parameters
        ----------
        dt : float, optional
            time step [s] used to transpose velocities over a short distance for projection (default: 0.1)

        Returns
        -------
        u : np.ndarray
            x-directional velocity
        v : np.ndarray
            y-directional velocity
        s : np.ndarray
            scalar velocity

        """
        # retrieve the backward transformation array from x, y to persective row column
        velocimetry = self._obj.velocimetry
        camera_config = velocimetry.camera_config
        # M = velocimetry.camera_config.get_M(velocimetry.h_a, to_bbox_grid=True, reverse=True)
        src = camera_config.get_bbox(camera=True, h_a=velocimetry.h_a, expand_exterior=False).exterior.coords[0:4]
        dst_xy = camera_config.get_bbox(expand_exterior=False).exterior.coords[0:4]
        # get geographic coordinates bbox corners
        dst = cv.transform_to_bbox(dst_xy, camera_config.bbox, camera_config.resolution)
        M = cv.get_M_2D(src, dst, reverse=True)

        # get the shape of the original frames
        shape_y, shape_x = velocimetry.camera_shape
        xi, yi = np.meshgrid(self._obj.x, self._obj.y)
        # flip the y-coordinates to match the row order used by opencv
        yi = np.flipud(yi)

        # follow the velocity vector over a short distance (dt*velocity)
        x_moved, y_moved = xi + self._obj["v_x"] * dt, yi + self._obj["v_y"] * dt
        # project the found displacement points to camera projection
        xp_moved, yp_moved = helpers.xy_to_perspective(
            x_moved.values,
            y_moved.values,
            velocimetry.camera_config.resolution,
            M
        )
        xp, yp = helpers.xy_to_perspective(
            xi,
            yi,
            velocimetry.camera_config.resolution,
            M
        )
        # convert row counts to start at the top of the frame instead of bottom
        yp_moved = shape_y - yp_moved
        yp = shape_y - yp
        # missing values end up at the top-left, replace these with nan
        yp_moved[yp_moved == shape_y] = np.nan  # ds["yp"].values[yp_moved == shape_y]
        xp_moved[xp_moved == 0] = np.nan  # ds["xp"].values[xp_moved == 0]

        # estimate the projected velocity vector
        u, v = xp_moved - xp, yp_moved - yp
        self._obj["xp"][:] = xp[:]
        self._obj["yp"][:] = yp[:]
        s = ((self._obj["v_x"] ** 2 + self._obj["v_y"] ** 2) ** 0.5).values
        return u, v, s


@_base_plot
def quiver(_, x, y, u, v, s=None, ax=None, *args, **kwargs):
    """Creates quiver plot from velocimetry results on new or existing axes

    Wraps :py:func:`matplotlib:matplotlib.pyplot.quiver`.
    """
    if "color" in kwargs:
        # replace scalar colors by one single color
        primitive = ax.quiver(x, y, u, v, *args, **kwargs)
    else:
        primitive = ax.quiver(x, y, u, v, s, *args, **kwargs)
    #
    return primitive


@_base_plot
def scatter(_, x, y, c=None, ax=None, *args, **kwargs):
    """Creates scatter plot of velocimetry or transect results on new or existing axes

    Wraps :py:func:`matplotlib:matplotlib.pyplot.scatter`.
    """
    primitive = ax.scatter(x, y, c=c, *args, **kwargs)
    return primitive


@_base_plot
def streamplot(_, x, y, u, v, s=None, ax=None, linewidth_scale=None, *args, **kwargs):
    """Creates streamplot of velocimetry results on new or existing axes

    Wraps :py:func:`matplotlib:matplotlib.pyplot.streamplot`. Additional input arguments:
    """
    if linewidth_scale is not None:
        kwargs["linewidth"] = s * linewidth_scale
    if "color" in kwargs:
        primitive = ax.streamplot(x, y, u, v, *args, **kwargs)
    else:
        primitive = ax.streamplot(x, y, u, v, color=s, *args, **kwargs)
    return primitive.lines


@_base_plot
def pcolormesh(_, x, y, s=None, ax=None, *args, **kwargs):
    """Creates pcolormesh plot from velocimetry results on new or existing axes

    Wraps :py:func:`matplotlib:matplotlib.pyplot.pcolormesh`.
    """
    primitive = ax.pcolormesh(x, y, s, *args, **kwargs)
    return primitive


def cbar(ax, p, size=12, **kwargs):
    """Add colorbar to existing axes. In case camera mode is used, the colorbar will get a bespoke layout and will
    be placed inside of the axes object.

    Parameters
    ----------
    ax : plt.axes
    p : matplotlib mappable
        used to define colorbar
    size : float, optional
        fontsize, used for colorbar title
    kwargs :
        dict, additional settings passed to plt.colorbar
    **kwargs :


    Returns
    -------
    cbar : matplotlib colorbar
        handle to colorbar
    """
    label_format = '{:,.2f}'
    path_effects = [
        patheffects.Stroke(linewidth=2, foreground="w"),
        patheffects.Normal(),
    ]
    cax = ax.inset_axes([0.05, 0.05,
                         0.02, 0.25])
    cb = ax.figure.colorbar(p, cax=cax, **kwargs)
    ticks_loc = cb.get_ticks().tolist()
    cb.set_ticks(mticker.FixedLocator(ticks_loc))
    cb.set_ticklabels([label_format.format(x) for x in ticks_loc], path_effects=path_effects, fontsize=size)
    cb.set_label(label="velocity [m/s]", size=size, path_effects=path_effects)
    return cb

def plot_text(ax, ds, prefix, suffix):
    if "q" not in ds:
        return
    _ds = copy.deepcopy(ds)
    path_effects = [
        patheffects.Stroke(linewidth=3, foreground="w"),
        patheffects.Normal(),
    ]
    xloc = 0.95
    yloc = 0.95
    _ds.transect.get_river_flow(q_name="q")
    Q = np.abs(_ds.river_flow)
    string = prefix
    string += "Water level: {:1.2f} m\nDischarge: {:1.2f} m3/s".format(_ds.transect.h_a, Q.values)
    if "q_nofill" in ds:
        _ds.transect.get_river_flow(q_name="q_nofill")
        Q_nofill = np.abs(_ds.river_flow)
        perc_measured = Q_nofill / Q * 100  # fraction that is truly measured compared to total
        string += " ({:1.0f}% measured)".format(perc_measured.values)
    # reset river flow if necessary
    string += suffix
    ax.text(
        xloc,
        yloc,
        string,
        size=24,
        horizontalalignment="right",
        verticalalignment="top",
        path_effects=path_effects,
        transform=ax.transAxes
    )


def _prepare_axes(ax=None, mode="local", rotation=None):
    """Prepares the axes, needed to plot results, called from `pyorc.PIV.plot`.

    Parameters
    ----------
    ref : Frames, Transect or Velocimetry
        object to derive plot from
    ax : plt.axes, optional
        if not provided (default), a new axes is prepared (default: None)
    mode : str, optional
        mode to plot, can be "local" (default), "geographical" or "camera".

    Returns
    -------
    ax : plt.axes
    """
    if ax is not None:
        if mode == "geographical":
            # ensure that the axes is a geoaxes
            from cartopy.mpl.geoaxes import GeoAxesSubplot
            assert (
                isinstance(ax, GeoAxesSubplot)), "For mode=geographical, the provided axes must be a cartopy GeoAxesSubplot"
        return ax

    # make a screen filling figure with black edges and faces
    if rotation in [90, 270]:
        f = plt.figure(figsize=(9, 16), frameon=False, facecolor="k")
        f.set_size_inches(9, 16, True)
    else:
        f = plt.figure(figsize=(16, 9), frameon=False, facecolor="k")
        f.set_size_inches(16, 9, True)

    f.patch.set_facecolor("k")
    f.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=None, hspace=None)
    if mode == "geographical":
        import cartopy.crs as ccrs
        ax = f.add_subplot(111, projection=ccrs.PlateCarree())
    else:
        ax = plt.subplot(111)
    return ax

# add all required plot methods below
setattr(_Velocimetry_PlotMethods, "quiver", quiver)
setattr(_Velocimetry_PlotMethods, "pcolormesh", pcolormesh)
setattr(_Velocimetry_PlotMethods, "scatter", scatter)
setattr(_Velocimetry_PlotMethods, "streamplot", streamplot)
setattr(_Transect_PlotMethods, "quiver", quiver)
setattr(_Transect_PlotMethods, "scatter", scatter)
