"""Visualize spatial footprints, street networks, routes, and orientations."""

from pathlib import Path

import networkx as nx
import numpy as np
import pandas as pd

from . import bearing
from . import graph
from . import projection
from . import settings
from . import simplification
from . import utils
from . import utils_geo
from . import utils_graph

# matplotlib is an optional dependency needed for visualization
try:
    import matplotlib.cm as cm
    import matplotlib.colors as colors
    import matplotlib.pyplot as plt
    from matplotlib import colormaps
except ImportError:  # pragma: no cover
    cm = colors = plt = colormaps = None


def get_colors(n, cmap="viridis", start=0.0, stop=1.0, alpha=1.0, return_hex=False):
    """
    Get `n` evenly-spaced colors from a matplotlib colormap.

    Parameters
    ----------
    n : int
        number of colors
    cmap : string
        name of a matplotlib colormap
    start : float
        where to start in the colorspace
    stop : float
        where to end in the colorspace
    alpha : float
        opacity, the alpha channel for the RGBa colors
    return_hex : bool
        if True, convert RGBa colors to HTML-like hexadecimal RGB strings. if
        False, return colors as (R, G, B, alpha) tuples.

    Returns
    -------
    color_list : list
    """
    _verify_mpl()
    color_list = [colormaps[cmap](x) for x in np.linspace(start, stop, n)]
    if return_hex:
        color_list = [colors.to_hex(c) for c in color_list]
    else:
        color_list = [(r, g, b, alpha) for r, g, b, _ in color_list]
    return color_list


def get_node_colors_by_attr(
    G, attr, num_bins=None, cmap="viridis", start=0, stop=1, na_color="none", equal_size=False
):
    """
    Get colors based on node attribute values.

    Parameters
    ----------
    G : networkx.MultiDiGraph
        input graph
    attr : string
        name of a numerical node attribute
    num_bins : int
        if None, linearly map a color to each value. otherwise, assign values
        to this many bins then assign a color to each bin.
    cmap : string
        name of a matplotlib colormap
    start : float
        where to start in the colorspace
    stop : float
        where to end in the colorspace
    na_color : string
        what color to assign nodes with missing attr values
    equal_size : bool
        ignored if num_bins is None. if True, bin into equal-sized quantiles
        (requires unique bin edges). if False, bin into equal-spaced bins.

    Returns
    -------
    node_colors : pandas.Series
        series labels are node IDs and values are colors
    """
    _verify_mpl()
    vals = pd.Series(nx.get_node_attributes(G, attr))
    return _get_colors_by_value(vals, num_bins, cmap, start, stop, na_color, equal_size)


def get_edge_colors_by_attr(
    G, attr, num_bins=None, cmap="viridis", start=0, stop=1, na_color="none", equal_size=False
):
    """
    Get colors based on edge attribute values.

    Parameters
    ----------
    G : networkx.MultiDiGraph
        input graph
    attr : string
        name of a numerical edge attribute
    num_bins : int
        if None, linearly map a color to each value. otherwise, assign values
        to this many bins then assign a color to each bin.
    cmap : string
        name of a matplotlib colormap
    start : float
        where to start in the colorspace
    stop : float
        where to end in the colorspace
    na_color : string
        what color to assign edges with missing attr values
    equal_size : bool
        ignored if num_bins is None. if True, bin into equal-sized quantiles
        (requires unique bin edges). if False, bin into equal-spaced bins.

    Returns
    -------
    edge_colors : pandas.Series
        series labels are edge IDs (u, v, key) and values are colors
    """
    _verify_mpl()
    vals = pd.Series(nx.get_edge_attributes(G, attr))
    return _get_colors_by_value(vals, num_bins, cmap, start, stop, na_color, equal_size)


def plot_graph(
    G,
    ax=None,
    figsize=(8, 8),
    bgcolor="#111111",
    node_color="w",
    node_size=15,
    node_alpha=None,
    node_edgecolor="none",
    node_zorder=1,
    edge_color="#999999",
    edge_linewidth=1,
    edge_alpha=None,
    show=True,
    close=False,
    save=False,
    filepath=None,
    dpi=300,
    bbox=None,
):
    """
    Visualize a graph.

    Parameters
    ----------
    G : networkx.MultiDiGraph
        input graph
    ax : matplotlib axis
        if not None, plot on this preexisting axis
    figsize : tuple
        if ax is None, create new figure with size (width, height)
    bgcolor : string
        background color of plot
    node_color : string or list
        color(s) of the nodes
    node_size : int
        size of the nodes: if 0, then skip plotting the nodes
    node_alpha : float
        opacity of the nodes, note: if you passed RGBA values to node_color,
        set node_alpha=None to use the alpha channel in node_color
    node_edgecolor : string
        color of the nodes' markers' borders
    node_zorder : int
        zorder to plot nodes: edges are always 1, so set node_zorder=0 to plot
        nodes below edges
    edge_color : string or list
        color(s) of the edges' lines
    edge_linewidth : float
        width of the edges' lines: if 0, then skip plotting the edges
    edge_alpha : float
        opacity of the edges, note: if you passed RGBA values to edge_color,
        set edge_alpha=None to use the alpha channel in edge_color
    show : bool
        if True, call pyplot.show() to show the figure
    close : bool
        if True, call pyplot.close() to close the figure
    save : bool
        if True, save the figure to disk at filepath
    filepath : string
        if save is True, the path to the file. file format determined from
        extension. if None, use settings.imgs_folder/image.png
    dpi : int
        if save is True, the resolution of saved file
    bbox : tuple
        bounding box as (north, south, east, west). if None, will calculate
        from spatial extents of plotted geometries.

    Returns
    -------
    fig, ax : tuple
        matplotlib figure, axis
    """
    _verify_mpl()
    max_node_size = max(node_size) if hasattr(node_size, "__iter__") else node_size
    max_edge_lw = max(edge_linewidth) if hasattr(edge_linewidth, "__iter__") else edge_linewidth
    if max_node_size <= 0 and max_edge_lw <= 0:  # pragma: no cover
        raise ValueError("Either node_size or edge_linewidth must be > 0 to plot something.")

    # create fig, ax as needed
    utils.log("Begin plotting the graph...")
    if ax is None:
        fig, ax = plt.subplots(figsize=figsize, facecolor=bgcolor, frameon=False)
        ax.set_facecolor(bgcolor)
    else:
        fig = ax.figure

    if max_edge_lw > 0:
        # plot the edges' geometries
        gdf_edges = utils_graph.graph_to_gdfs(G, nodes=False)["geometry"]
        ax = gdf_edges.plot(ax=ax, color=edge_color, lw=edge_linewidth, alpha=edge_alpha, zorder=1)

    if max_node_size > 0:
        # scatter plot the nodes' x/y coordinates
        gdf_nodes = utils_graph.graph_to_gdfs(G, edges=False, node_geometry=False)[["x", "y"]]
        ax.scatter(
            x=gdf_nodes["x"],
            y=gdf_nodes["y"],
            s=node_size,
            c=node_color,
            alpha=node_alpha,
            edgecolor=node_edgecolor,
            zorder=node_zorder,
        )

    # get spatial extents from bbox parameter or the edges' geometries
    padding = 0
    if bbox is None:
        try:
            west, south, east, north = gdf_edges.total_bounds
        except NameError:
            west, south = gdf_nodes.min()
            east, north = gdf_nodes.max()
        bbox = north, south, east, west
        padding = 0.02  # pad 2% to not cut off peripheral nodes' circles

    # configure axis appearance, save/show figure as specified, and return
    ax = _config_ax(ax, G.graph["crs"], bbox, padding)
    fig, ax = _save_and_show(fig, ax, save, show, close, filepath, dpi)
    utils.log("Finished plotting the graph")
    return fig, ax


def plot_graph_route(
    G,
    route,
    route_color="r",
    route_linewidth=4,
    route_alpha=0.5,
    orig_dest_size=100,
    ax=None,
    **pg_kwargs,
):
    """
    Visualize a route along a graph.

    Parameters
    ----------
    G : networkx.MultiDiGraph
        input graph
    route : list
        route as a list of node IDs
    route_color : string
        color of the route
    route_linewidth : int
        width of the route line
    route_alpha : float
        opacity of the route line
    orig_dest_size : int
        size of the origin and destination nodes
    ax : matplotlib axis
        if not None, plot route on this preexisting axis instead of creating a
        new fig, ax and drawing the underlying graph
    pg_kwargs
        keyword arguments to pass to plot_graph

    Returns
    -------
    fig, ax : tuple
        matplotlib figure, axis
    """
    _verify_mpl()
    if ax is None:
        # plot the graph but not the route, and override any user show/close
        # args for now: we'll do that later
        override = {"show", "save", "close"}
        kwargs = {k: v for k, v in pg_kwargs.items() if k not in override}
        fig, ax = plot_graph(G, show=False, save=False, close=False, **kwargs)
    else:
        fig = ax.figure

    # scatterplot origin and destination points (first/last nodes in route)
    x = (G.nodes[route[0]]["x"], G.nodes[route[-1]]["x"])
    y = (G.nodes[route[0]]["y"], G.nodes[route[-1]]["y"])
    ax.scatter(x, y, s=orig_dest_size, c=route_color, alpha=route_alpha, edgecolor="none")

    # assemble the route edge geometries' x and y coords then plot the line
    x = []
    y = []
    for u, v in zip(route[:-1], route[1:]):
        # if there are parallel edges, select the shortest in length
        data = min(G.get_edge_data(u, v).values(), key=lambda d: d["length"])
        if "geometry" in data:
            # if geometry attribute exists, add all its coords to list
            xs, ys = data["geometry"].xy
            x.extend(xs)
            y.extend(ys)
        else:
            # otherwise, the edge is a straight line from node to node
            x.extend((G.nodes[u]["x"], G.nodes[v]["x"]))
            y.extend((G.nodes[u]["y"], G.nodes[v]["y"]))
    ax.plot(x, y, c=route_color, lw=route_linewidth, alpha=route_alpha)

    # save and show the figure as specified, passing relevant kwargs
    sas_kwargs = {"save", "show", "close", "filepath", "file_format", "dpi"}
    kwargs = {k: v for k, v in pg_kwargs.items() if k in sas_kwargs}
    fig, ax = _save_and_show(fig, ax, **kwargs)
    return fig, ax


def plot_graph_routes(G, routes, route_colors="r", route_linewidths=4, **pgr_kwargs):
    """
    Visualize several routes along a graph.

    Parameters
    ----------
    G : networkx.MultiDiGraph
        input graph
    routes : list
        routes as a list of lists of node IDs
    route_colors : string or list
        if string, 1 color for all routes. if list, the colors for each route.
    route_linewidths : int or list
        if int, 1 linewidth for all routes. if list, the linewidth for each route.
    pgr_kwargs
        keyword arguments to pass to plot_graph_route

    Returns
    -------
    fig, ax : tuple
        matplotlib figure, axis
    """
    _verify_mpl()

    # check for valid arguments
    if not all(isinstance(r, list) for r in routes):  # pragma: no cover
        raise ValueError("routes must be a list of route lists")
    if len(routes) < 2:  # pragma: no cover
        raise ValueError("You must pass more than 1 route")
    if isinstance(route_colors, str):
        route_colors = [route_colors] * len(routes)
    if len(routes) != len(route_colors):  # pragma: no cover
        raise ValueError("route_colors list must have same length as routes")
    if isinstance(route_linewidths, int):
        route_linewidths = [route_linewidths] * len(routes)
    if len(routes) != len(route_linewidths):  # pragma: no cover
        raise ValueError("route_linewidths list must have same length as routes")

    # plot the graph and the first route
    override = {"route", "route_color", "route_linewidth", "show", "save", "close"}
    kwargs = {k: v for k, v in pgr_kwargs.items() if k not in override}
    fig, ax = plot_graph_route(
        G,
        route=routes[0],
        route_color=route_colors[0],
        route_linewidth=route_linewidths[0],
        show=False,
        save=False,
        close=False,
        **kwargs,
    )

    # plot the subsequent routes on top of existing ax
    override.update({"ax"})
    kwargs = {k: v for k, v in pgr_kwargs.items() if k not in override}
    r_rc_rlw = zip(routes[1:], route_colors[1:], route_linewidths[1:])
    for route, route_color, route_linewidth in r_rc_rlw:
        fig, ax = plot_graph_route(
            G,
            route=route,
            route_color=route_color,
            route_linewidth=route_linewidth,
            show=False,
            save=False,
            close=False,
            ax=ax,
            **kwargs,
        )

    # save and show the figure as specified, passing relevant kwargs
    sas_kwargs = {"save", "show", "close", "filepath", "file_format", "dpi"}
    kwargs = {k: v for k, v in pgr_kwargs.items() if k in sas_kwargs}
    fig, ax = _save_and_show(fig, ax, **kwargs)
    return fig, ax


def plot_figure_ground(
    G=None,
    address=None,
    point=None,
    dist=805,
    network_type="drive_service",
    street_widths=None,
    default_width=4,
    figsize=(8, 8),
    edge_color="w",
    smooth_joints=True,
    **pg_kwargs,
):
    """
    Plot a figure-ground diagram of a street network.

    Parameters
    ----------
    G : networkx.MultiDiGraph
        input graph, must be unprojected
    address : string
        address to geocode as the center point if G is not passed in
    point : tuple
        center point if address and G are not passed in
    dist : numeric
        how many meters to extend north, south, east, west from center point
    network_type : string
        what type of street network to get
    street_widths : dict
        dict keys are street types and values are widths to plot in pixels
    default_width : numeric
        fallback width in pixels for any street type not in street_widths
    figsize : numeric
        (width, height) of figure, should be equal
    edge_color : string
        color of the edges' lines
    smooth_joints : bool
        if True, plot nodes same width as streets to smooth line joints and
        prevent cracks between them from showing
    pg_kwargs
        keyword arguments to pass to plot_graph

    Returns
    -------
    fig, ax : tuple
        matplotlib figure, axis
    """
    _verify_mpl()
    multiplier = 1.2

    # if user did not pass in custom street widths, create a dict of defaults
    if street_widths is None:
        street_widths = {
            "footway": 1.5,
            "steps": 1.5,
            "pedestrian": 1.5,
            "service": 1.5,
            "path": 1.5,
            "track": 1.5,
            "motorway": 6,
        }

    # if G was passed in, plot it centered on its node centroid
    if G is not None:
        gdf_nodes = utils_graph.graph_to_gdfs(G, edges=False, node_geometry=True)
        lnglat_point = gdf_nodes.unary_union.centroid.coords[0]
        point = tuple(reversed(lnglat_point))

    # otherwise get network by address or point (whichever was passed) using a
    # dist multiplier to ensure we get more than enough network. simplify in
    # non-strict mode to not combine multiple street types into single edge
    elif address is not None:
        G, point = graph.graph_from_address(
            address,
            dist=dist * multiplier,
            dist_type="bbox",
            network_type=network_type,
            simplify=False,
            truncate_by_edge=True,
            return_coords=True,
        )
        G = simplification.simplify_graph(G, strict=False)
    elif point is not None:
        G = graph.graph_from_point(
            point,
            dist=dist * multiplier,
            dist_type="bbox",
            network_type=network_type,
            simplify=False,
            truncate_by_edge=True,
        )
        G = simplification.simplify_graph(G, strict=False)
    else:  # pragma: no cover
        raise ValueError("You must pass an address or lat-lng point or graph.")

    # we need an undirected graph to find every edge incident on a node
    Gu = utils_graph.get_undirected(G)

    # for each edge, get a linewidth according to street type
    edge_linewidths = []
    for _, _, d in Gu.edges(keys=False, data=True):
        street_type = d["highway"][0] if isinstance(d["highway"], list) else d["highway"]
        if street_type in street_widths:
            edge_linewidths.append(street_widths[street_type])
        else:
            edge_linewidths.append(default_width)

    if smooth_joints:
        # for each node, get a nodesize according to the narrowest incident edge
        node_widths = {}
        for node in Gu.nodes:
            # first, identify all the highway types of this node's incident edges
            ie_data = [Gu.get_edge_data(node, nbr) for nbr in Gu.neighbors(node)]
            edge_types = [d[min(d)]["highway"] for d in ie_data]
            if not edge_types:
                # if node has no incident edges, make size zero
                node_widths[node] = 0
            else:
                # flatten the list of edge types
                et_flat = []
                for et in edge_types:
                    if isinstance(et, list):
                        et_flat.extend(et)
                    else:
                        et_flat.append(et)

                # lookup corresponding width for each edge type in flat list
                edge_widths = [
                    street_widths[et] if et in street_widths else default_width for et in et_flat
                ]

                # node diameter should equal largest edge width to make joints
                # perfectly smooth. alternatively use min(?) to prevent
                # anything larger from extending past smallest street's line.
                # circle marker sizes are in area, so use diameter squared.
                circle_diameter = max(edge_widths)
                circle_area = circle_diameter**2
                node_widths[node] = circle_area

        # assign the node size to each node in the graph
        node_sizes = [node_widths[node] for node in Gu.nodes]
    else:
        node_sizes = 0

    # define the view extents of the plotting figure
    bbox = utils_geo.bbox_from_point(point, dist, project_utm=False)

    # plot the figure
    override = {"bbox", "node_size", "node_color", "edge_linewidth"}
    kwargs = {k: v for k, v in pg_kwargs.items() if k not in override}
    fig, ax = plot_graph(
        G=Gu,
        bbox=bbox,
        figsize=figsize,
        node_size=node_sizes,
        node_color=edge_color,
        edge_color=edge_color,
        edge_linewidth=edge_linewidths,
        **kwargs,
    )
    return fig, ax


def plot_footprints(
    gdf,
    ax=None,
    figsize=(8, 8),
    color="orange",
    edge_color="none",
    edge_linewidth=0,
    alpha=None,
    bgcolor="#111111",
    bbox=None,
    save=False,
    show=True,
    close=False,
    filepath=None,
    dpi=600,
):
    """
    Visualize a GeoDataFrame of geospatial entities' footprints.

    Parameters
    ----------
    gdf : geopandas.GeoDataFrame
        GeoDataFrame of footprints (shapely Polygons and MultiPolygons)
    ax : axis
        if not None, plot on this preexisting axis
    figsize : tuple
        if ax is None, create new figure with size (width, height)
    color : string
        color of the footprints
    edge_color : string
        color of the edge of the footprints
    edge_linewidth : float
        width of the edge of the footprints
    alpha : float
        opacity of the footprints
    bgcolor : string
        background color of the plot
    bbox : tuple
        bounding box as (north, south, east, west). if None, will calculate
        from the spatial extents of the geometries in gdf
    save : bool
        if True, save the figure to disk at filepath
    show : bool
        if True, call pyplot.show() to show the figure
    close : bool
        if True, call pyplot.close() to close the figure
    filepath : string
        if save is True, the path to the file. file format determined from
        extension. if None, use settings.imgs_folder/image.png
    dpi : int
        if save is True, the resolution of saved file

    Returns
    -------
    fig, ax : tuple
        matplotlib figure, axis
    """
    _verify_mpl()

    if ax is None:
        fig, ax = plt.subplots(figsize=figsize, facecolor=bgcolor, frameon=False)
        ax.set_facecolor(bgcolor)
    else:
        fig = ax.figure

    # retain only Polygons and MultiPolygons, then plot
    gdf = gdf[gdf["geometry"].type.isin({"Polygon", "MultiPolygon"})]
    ax = gdf.plot(
        ax=ax, facecolor=color, edgecolor=edge_color, linewidth=edge_linewidth, alpha=alpha
    )

    # determine figure extents
    if bbox is None:
        west, south, east, north = gdf.total_bounds
    else:
        north, south, east, west = bbox

    # configure axis appearance, save/show figure as specified, and return
    ax = _config_ax(ax, gdf.crs, (north, south, east, west), 0)
    fig, ax = _save_and_show(fig, ax, save, show, close, filepath, dpi)
    return fig, ax


def plot_orientation(
    Gu,
    num_bins=36,
    min_length=0,
    weight=None,
    ax=None,
    figsize=(5, 5),
    area=True,
    color="#003366",
    edgecolor="k",
    linewidth=0.5,
    alpha=0.7,
    title=None,
    title_y=1.05,
    title_font=None,
    xtick_font=None,
):
    """
    Plot a polar histogram of a spatial network's bidirectional edge bearings.

    Ignores self-loop edges as their bearings are undefined.

    For more info see: Boeing, G. 2019. "Urban Spatial Order: Street Network
    Orientation, Configuration, and Entropy." Applied Network Science, 4 (1),
    67. https://doi.org/10.1007/s41109-019-0189-1

    Parameters
    ----------
    Gu : networkx.MultiGraph
        undirected, unprojected graph with `bearing` attributes on each edge
    num_bins : int
        number of bins; for example, if `num_bins=36` is provided, then each
        bin will represent 10° around the compass
    min_length : float
        ignore edges with `length` attributes less than `min_length`
    weight : string
        if not None, weight edges' bearings by this (non-null) edge attribute
    ax : matplotlib.axes.PolarAxesSubplot
        if not None, plot on this preexisting axis; must have projection=polar
    figsize : tuple
        if ax is None, create new figure with size (width, height)
    area : bool
        if True, set bar length so area is proportional to frequency,
        otherwise set bar length so height is proportional to frequency
    color : string
        color of histogram bars
    edgecolor : string
        color of histogram bar edges
    linewidth : float
        width of histogram bar edges
    alpha : float
        opacity of histogram bars
    title : string
        title for plot
    title_y : float
        y position to place title
    title_font : dict
        the title's fontdict to pass to matplotlib
    xtick_font : dict
        the xtick labels' fontdict to pass to matplotlib

    Returns
    -------
    fig, ax : tuple
        matplotlib figure, axis
    """
    _verify_mpl()

    if title_font is None:
        title_font = {"family": "DejaVu Sans", "size": 24, "weight": "bold"}
    if xtick_font is None:
        xtick_font = {
            "family": "DejaVu Sans",
            "size": 10,
            "weight": "bold",
            "alpha": 1.0,
            "zorder": 3,
        }

    # get the bearings' distribution's bin counts and edges
    bin_counts, bin_edges = bearing._bearings_distribution(Gu, num_bins, min_length, weight)

    # positions: where to center each bar. ignore the last bin edge, because
    # it's the same as the first (i.e., 0° = 360°)
    positions = np.radians(bin_edges[:-1])

    # width: make bars fill the circumference without gaps or overlaps
    width = 2 * np.pi / num_bins

    # radius: how long to make each bar
    bin_frequency = bin_counts / bin_counts.sum()
    if area:
        # set bar length so area is proportional to frequency
        radius = np.sqrt(bin_frequency)
    else:
        # set bar length so height is proportional to frequency
        radius = bin_frequency

    # create ax (if necessary) then set N at top and go clockwise
    if ax is None:
        fig, ax = plt.subplots(figsize=figsize, subplot_kw={"projection": "polar"})
    else:
        fig = ax.figure
    ax.set_theta_zero_location("N")
    ax.set_theta_direction("clockwise")
    ax.set_ylim(top=radius.max())

    # configure the y-ticks and remove their labels
    ax.set_yticks(np.linspace(0, radius.max(), 5))
    ax.set_yticklabels(labels="")

    # configure the x-ticks and their labels
    xticklabels = ["N", "", "E", "", "S", "", "W", ""]
    ax.set_xticks(ax.get_xticks())
    ax.set_xticklabels(labels=xticklabels, fontdict=xtick_font)
    ax.tick_params(axis="x", which="major", pad=-2)

    # draw the bars
    ax.bar(
        positions,
        height=radius,
        width=width,
        align="center",
        bottom=0,
        zorder=2,
        color=color,
        edgecolor=edgecolor,
        linewidth=linewidth,
        alpha=alpha,
    )

    if title:
        ax.set_title(title, y=title_y, fontdict=title_font)
    fig.tight_layout()
    return fig, ax


def _get_colors_by_value(vals, num_bins, cmap, start, stop, na_color, equal_size):
    """
    Map colors to the values in a series.

    Parameters
    ----------
    vals : pandas.Series
        series labels are node/edge IDs and values are attribute values
    num_bins : int
        if None, linearly map a color to each value. otherwise, assign values
        to this many bins then assign a color to each bin.
    cmap : string
        name of a matplotlib colormap
    start : float
        where to start in the colorspace
    stop : float
        where to end in the colorspace
    na_color : string
        what color to assign to missing values
    equal_size : bool
        ignored if num_bins is None. if True, bin into equal-sized quantiles
        (requires unique bin edges). if False, bin into equal-spaced bins.

    Returns
    -------
    color_series : pandas.Series
        series labels are node/edge IDs and values are colors
    """
    if len(vals) == 0:
        raise ValueError("There are no attribute values.")

    if num_bins is None:
        # calculate min/max values based on start/stop and data range
        vals_min = vals.dropna().min()
        vals_max = vals.dropna().max()
        full_range = (vals_max - vals_min) / (stop - start)
        full_min = vals_min - full_range * start
        full_max = full_min + full_range

        # linearly map a color to each attribute value
        normalizer = colors.Normalize(full_min, full_max)
        scalar_mapper = cm.ScalarMappable(normalizer, colormaps[cmap])
        color_series = vals.map(scalar_mapper.to_rgba)
        color_series.loc[pd.isnull(vals)] = na_color

    else:
        # otherwise, bin values then assign colors to bins
        cut_func = pd.qcut if equal_size else pd.cut
        bins = cut_func(vals, num_bins, labels=range(num_bins))
        bin_colors = get_colors(num_bins, cmap, start, stop)
        color_list = [bin_colors[b] if pd.notnull(b) else na_color for b in bins]
        color_series = pd.Series(color_list, index=bins.index)

    return color_series


def _save_and_show(fig, ax, save=False, show=True, close=True, filepath=None, dpi=300):
    """
    Save a figure to disk and/or show it, as specified by args.

    Parameters
    ----------
    fig : figure
        matplotlib figure
    ax : axis
        matplotlib axis
    save : bool
        if True, save the figure to disk at filepath
    show : bool
        if True, call pyplot.show() to show the figure
    close : bool
        if True, call pyplot.close() to close the figure
    filepath : string
        if save is True, the path to the file. file format determined from
        extension. if None, use settings.imgs_folder/image.png
    dpi : int
        if save is True, the resolution of saved file

    Returns
    -------
    fig, ax : tuple
        matplotlib figure, axis
    """
    fig.canvas.draw()
    fig.canvas.flush_events()

    if save:
        # default filepath, if none provided
        if filepath is None:
            filepath = Path(settings.imgs_folder) / "image.png"
        else:
            filepath = Path(filepath)

        # if save folder does not already exist, create it
        filepath.parent.mkdir(parents=True, exist_ok=True)

        # get the file extension and figure facecolor
        ext = filepath.suffix.strip(".")
        fc = fig.get_facecolor()

        if ext == "svg":
            # if the file format is svg, prep the fig/ax for saving
            ax.axis("off")
            ax.set_position([0, 0, 1, 1])
            ax.patch.set_alpha(0.0)
            fig.patch.set_alpha(0.0)
            fig.savefig(filepath, bbox_inches=0, format=ext, facecolor=fc, transparent=True)
        else:
            # constrain saved figure's extent to interior of the axis
            extent = ax.bbox.transformed(fig.dpi_scale_trans.inverted())

            # temporarily turn figure frame on to save with facecolor
            fig.set_frameon(True)
            fig.savefig(
                filepath, dpi=dpi, bbox_inches=extent, format=ext, facecolor=fc, transparent=True
            )
            fig.set_frameon(False)  # and turn it back off again
        utils.log(f"Saved figure to disk at {filepath}")

    if show:
        plt.show()

    if close:
        plt.close()

    return fig, ax


def _config_ax(ax, crs, bbox, padding):
    """
    Configure axis for display.

    Parameters
    ----------
    ax : matplotlib axis
        the axis containing the plot
    crs : dict or string or pyproj.CRS
        the CRS of the plotted geometries
    bbox : tuple
        bounding box as (north, south, east, west)
    padding : float
        relative padding to add around the plot's bbox

    Returns
    -------
    ax : matplotlib axis
        the configured/styled axis
    """
    # set the axis view limits to bbox + relative padding
    north, south, east, west = bbox
    padding_ns = (north - south) * padding
    padding_ew = (east - west) * padding
    ax.set_ylim((south - padding_ns, north + padding_ns))
    ax.set_xlim((west - padding_ew, east + padding_ew))

    # set margins to zero, point ticks inward, turn off ax border and x/y axis
    # so there is no space around the plot
    ax.margins(0)
    ax.tick_params(which="both", direction="in")
    _ = [s.set_visible(False) for s in ax.spines.values()]
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    # set aspect ratio
    if projection.is_projected(crs):
        # if projected, make equal aspect ratio
        ax.set_aspect("equal")
    else:
        # if not projected, conform aspect ratio to not stretch plot
        cos_lat = np.cos((south + north) / 2 / 180 * np.pi)
        ax.set_aspect(1 / cos_lat)

    return ax


def _verify_mpl():
    """
    Verify that matplotlib is installed and successfully imported.

    Returns
    -------
    None
    """
    if cm is None or colors is None or plt is None or colormaps is None:  # pragma: no cover
        raise ImportError(
            "matplotlib must be installed as an optional dependency for visualization"
        )
