Instruction:
Here you have a function in Python 3, explain what it does
def project_gdf(gdf, to_crs=None, to_latlong=False):
    assert len(gdf) > 0, 'You cannot project an empty GeoDataFrame.'
    start_time = time.time()

    # if gdf has no gdf_name attribute, create one now
    if not hasattr(gdf, 'gdf_name'):
        gdf.gdf_name = 'unnamed'

    # if to_crs was passed-in, use this value to project the gdf
    if to_crs is not None:
        projected_gdf = gdf.to_crs(to_crs)

    # if to_crs was not passed-in, calculate the centroid of the geometry to
    # determine UTM zone
    else:
        if to_latlong:
            # if to_latlong is True, project the gdf to latlong
            latlong_crs = settings.default_crs
            projected_gdf = gdf.to_crs(latlong_crs)
            log('Projected the GeoDataFrame "{}" to default_crs in {:,.2f} seconds'.format(gdf.gdf_name, time.time()-start_time))
        else:
            # else, project the gdf to UTM
            # if GeoDataFrame is already in UTM, just return it
            if (gdf.crs is not None) and ('proj' in gdf.crs) and (gdf.crs['proj'] == 'utm'):
                return gdf

            # calculate the centroid of the union of all the geometries in the
            # GeoDataFrame
            avg_longitude = gdf['geometry'].unary_union.centroid.x

            # calculate the UTM zone from this avg longitude and define the UTM
            # CRS to project
            utm_zone = int(math.floor((avg_longitude + 180) / 6.) + 1)
            utm_crs = {'datum': 'WGS84',
                       'ellps': 'WGS84',
                       'proj' : 'utm',
                       'zone' : utm_zone,
                       'units': 'm'}

            # project the GeoDataFrame to the UTM CRS
            projected_gdf = gdf.to_crs(utm_crs)
            log('Projected the GeoDataFrame "{}" to UTM-{} in {:,.2f} seconds'.format(gdf.gdf_name, utm_zone, time.time()-start_time))

    projected_gdf.gdf_name = gdf.gdf_name
    return projected_gdf

Response: Project a GeoDataFrame to the UTM zone appropriate for its geometries.


Instruction:
How would you explain what the following Python 3 function does
def project_graph(G, to_crs=None):

    G_proj = G.copy()
    start_time = time.time()

    # create a GeoDataFrame of the nodes, name it, convert osmid to str
    nodes, data = zip(*G_proj.nodes(data=True))
    gdf_nodes = gpd.GeoDataFrame(list(data), index=nodes)
    gdf_nodes.crs = G_proj.graph['crs']
    gdf_nodes.gdf_name = '{}_nodes'.format(G_proj.name)

    # create new lat/lon columns just to save that data for later, and create a
    # geometry column from x/y
    gdf_nodes['lon'] = gdf_nodes['x']
    gdf_nodes['lat'] = gdf_nodes['y']
    gdf_nodes['geometry'] = gdf_nodes.apply(lambda row: Point(row['x'], row['y']), axis=1)
    log('Created a GeoDataFrame from graph in {:,.2f} seconds'.format(time.time()-start_time))

    # project the nodes GeoDataFrame to UTM
    gdf_nodes_utm = project_gdf(gdf_nodes, to_crs=to_crs)

    # extract data for all edges that have geometry attribute
    edges_with_geom = []
    for u, v, key, data in G_proj.edges(keys=True, data=True):
        if 'geometry' in data:
            edges_with_geom.append({'u':u, 'v':v, 'key':key, 'geometry':data['geometry']})

    # create an edges GeoDataFrame and project to UTM, if there were any edges
    # with a geometry attribute. geom attr only exists if graph has been
    # simplified, otherwise you don't have to project anything for the edges
    # because the nodes still contain all spatial data
    if len(edges_with_geom) > 0:
        gdf_edges = gpd.GeoDataFrame(edges_with_geom)
        gdf_edges.crs = G_proj.graph['crs']
        gdf_edges.gdf_name = '{}_edges'.format(G_proj.name)
        gdf_edges_utm = project_gdf(gdf_edges, to_crs=to_crs)

    # extract projected x and y values from the nodes' geometry column
    start_time = time.time()
    gdf_nodes_utm['x'] = gdf_nodes_utm['geometry'].map(lambda point: point.x)
    gdf_nodes_utm['y'] = gdf_nodes_utm['geometry'].map(lambda point: point.y)
    gdf_nodes_utm = gdf_nodes_utm.drop('geometry', axis=1)
    log('Extracted projected node geometries from GeoDataFrame in {:,.2f} seconds'.format(time.time()-start_time))

    # clear the graph to make it a blank slate for the projected data
    start_time = time.time()
    edges = list(G_proj.edges(keys=True, data=True))
    graph_name = G_proj.graph['name']
    G_proj.clear()

    # add the projected nodes and all their attributes to the graph
    G_proj.add_nodes_from(gdf_nodes_utm.index)
    attributes = gdf_nodes_utm.to_dict()
    for label in gdf_nodes_utm.columns:
        nx.set_node_attributes(G_proj, name=label, values=attributes[label])

    # add the edges and all their attributes (including reconstructed geometry,
    # when it exists) to the graph
    for u, v, key, attributes in edges:
        if 'geometry' in attributes:
            row = gdf_edges_utm[(gdf_edges_utm['u']==u) & (gdf_edges_utm['v']==v) & (gdf_edges_utm['key']==key)]
            attributes['geometry'] = row['geometry'].iloc[0]

        # attributes dict contains key, so we don't need to explicitly pass it here
        G_proj.add_edge(u, v, **attributes)

    # set the graph's CRS attribute to the new, projected CRS and return the
    # projected graph
    G_proj.graph['crs'] = gdf_nodes_utm.crs
    G_proj.graph['name'] = '{}_UTM'.format(graph_name)
    if 'streets_per_node' in G.graph:
        G_proj.graph['streets_per_node'] = G.graph['streets_per_node']
    log('Rebuilt projected graph in {:,.2f} seconds'.format(time.time()-start_time))
    return G_proj

Response:
Project a networkx graph to UTM zone appropriate for its geographic
    location.


Instruction:
Write a Python 3 function for
plotting a GeoDataFrame of place boundary geometries.

Response:
def plot_shape(gdf, fc='#cbe0f0', ec='#999999', linewidth=1, alpha=1,
               figsize=(6,6), margin=0.02, axis_off=True):
    """
    Plot a GeoDataFrame of place boundary geometries.

    Parameters
    ----------
    gdf : GeoDataFrame
        the gdf containing the geometries to plot
    fc : string or list
        the facecolor (or list of facecolors) for the polygons
    ec : string or list
        the edgecolor (or list of edgecolors) for the polygons
    linewidth : numeric
        the width of the polygon edge lines
    alpha : numeric
        the opacity
    figsize : tuple
        the size of the plotting figure
    margin : numeric
        the size of the figure margins
    axis_off : bool
        if True, disable the matplotlib axes display

    Returns
    -------
    fig, ax : tuple
    """

    # if facecolor or edgecolor is a string instead of a list, make sure we have
    # as many colors as gdf elements
    if isinstance(fc, str):
        fc = [fc] * len(gdf)
    if isinstance(ec, str):
        ec = [ec] * len(gdf)

    # plot the geometries one at a time
    fig, ax = plt.subplots(figsize=figsize)
    for geometry, facecolor, edgecolor in zip(gdf['geometry'], fc, ec):
        if isinstance(geometry, (Polygon, MultiPolygon)):
            if isinstance(geometry, Polygon):
                geometry = MultiPolygon([geometry])
            for polygon in geometry:
                patch = PolygonPatch(polygon, fc=facecolor, ec=edgecolor, linewidth=linewidth, alpha=alpha)
                ax.add_patch(patch)
        else:
            raise ValueError('All geometries in GeoDataFrame must be shapely Polygons or MultiPolygons')

    # adjust the axis margins and limits around the image and make axes
    # equal-aspect
    west, south, east, north = gdf.unary_union.bounds
    margin_ns = (north - south) * margin
    margin_ew = (east - west) * margin
    ax.set_ylim((south - margin_ns, north + margin_ns))
    ax.set_xlim((west - margin_ew, east + margin_ew))
    ax.set_aspect(aspect='equal', adjustable='box')
    if axis_off:
        ax.axis('off')

    plt.show()
    return fig, ax


Instruction:
Make a summary of the following Python 3 code
def rgb_color_list_to_hex(color_list):
    color_list_rgb = [[int(x*255) for x in c[0:3]] for c in color_list]
    color_list_hex = ['#{:02X}{:02X}{:02X}'.format(rgb[0], rgb[1], rgb[2]) for rgb in color_list_rgb]
    return color_list_hex

Response: Convert a list of RGBa colors to a list of hexadecimal color codes.


Instruction:
Write a Python 3 function that can
return n - length list of RGBa colors from the passed colormap name and alpha.

Response:
def get_colors(n, cmap='viridis', start=0., stop=1., alpha=1., return_hex=False):
    """
    Return n-length list of RGBa colors from the passed colormap name and alpha.

    Parameters
    ----------
    n : int
        number of colors
    cmap : string
        name of a 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 a hexadecimal string

    Returns
    -------
    colors : list
    """
    colors = [cm.get_cmap(cmap)(x) for x in np.linspace(start, stop, n)]
    colors = [(r, g, b, alpha) for r, g, b, _ in colors]
    if return_hex:
        colors = rgb_color_list_to_hex(colors)
    return colors


Instruction:
Can you tell what is the following Python 3 function doing
def get_node_colors_by_attr(G, attr, num_bins=None, cmap='viridis', start=0, stop=1, na_color='none'):
    if num_bins is None:
        num_bins=len(G.nodes())
    bin_labels = range(num_bins)
    attr_values = pd.Series([data[attr] for node, data in G.nodes(data=True)])
    cats = pd.qcut(x=attr_values, q=num_bins, labels=bin_labels)
    colors = get_colors(num_bins, cmap, start, stop)
    node_colors = [colors[int(cat)] if pd.notnull(cat) else na_color for cat in cats]
    return node_colors

Response: Get a list of node colors by binning some continuous - variable attribute into a random variety of quantiles.


Instruction:
Make a summary of the following Python 3 code
def get_edge_colors_by_attr(G, attr, num_bins=5, cmap='viridis', start=0, stop=1, na_color='none'):
    if num_bins is None:
        num_bins=len(G.edges())
    bin_labels = range(num_bins)
    attr_values = pd.Series([data[attr] for u, v, key, data in G.edges(keys=True, data=True)])
    cats = pd.qcut(x=attr_values, q=num_bins, labels=bin_labels)
    colors = get_colors(num_bins, cmap, start, stop)
    edge_colors = [colors[int(cat)] if pd.notnull(cat) else na_color for cat in cats]
    return edge_colors

Response: Get a list of edge colors by binning some continuous - variable attribute into a random network.


Instruction:
Given the following Python 3 function, write the documentation
def save_and_show(fig, ax, save, show, close, filename, file_format, dpi, axis_off):
    # save the figure if specified
    if save:
        start_time = time.time()

        # create the save folder if it doesn't already exist
        if not os.path.exists(settings.imgs_folder):
            os.makedirs(settings.imgs_folder)
        path_filename = os.path.join(settings.imgs_folder, os.extsep.join([filename, file_format]))

        if file_format == 'svg':
            # if the file_format is svg, prep the fig/ax a bit for saving
            ax.axis('off')
            ax.set_position([0, 0, 1, 1])
            ax.patch.set_alpha(0.)
            fig.patch.set_alpha(0.)
            fig.savefig(path_filename, bbox_inches=0, format=file_format, facecolor=fig.get_facecolor(), transparent=True)
        else:
            if axis_off:
                # if axis is turned off, constrain the saved figure's extent to
                # the interior of the axis
                extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
            else:
                extent = 'tight'
            fig.savefig(path_filename, dpi=dpi, bbox_inches=extent, format=file_format, facecolor=fig.get_facecolor(), transparent=True)
        log('Saved the figure to disk in {:,.2f} seconds'.format(time.time()-start_time))

    # show the figure if specified
    if show:
        start_time = time.time()
        plt.show()
        log('Showed the plot in {:,.2f} seconds'.format(time.time()-start_time))
    # if show=False, close the figure if close=True to prevent display
    elif close:
        plt.close()

    return fig, ax

Response: Save a figure to disk and show it as specified.


Instruction:
Implement a function in Python 3 to
plot a networkx spatial graph.

Response:
def plot_graph(G, bbox=None, fig_height=6, fig_width=None, margin=0.02,
               axis_off=True, equal_aspect=False, bgcolor='w', show=True,
               save=False, close=True, file_format='png', filename='temp',
               dpi=300, annotate=False, node_color='#66ccff', node_size=15,
               node_alpha=1, node_edgecolor='none', node_zorder=1,
               edge_color='#999999', edge_linewidth=1, edge_alpha=1,
               use_geom=True):
    """
    Plot a networkx spatial graph.

    Parameters
    ----------
    G : networkx multidigraph
    bbox : tuple
        bounding box as north,south,east,west - if None will calculate from
        spatial extents of data. if passing a bbox, you probably also want to
        pass margin=0 to constrain it.
    fig_height : int
        matplotlib figure height in inches
    fig_width : int
        matplotlib figure width in inches
    margin : float
        relative margin around the figure
    axis_off : bool
        if True turn off the matplotlib axis
    equal_aspect : bool
        if True set the axis aspect ratio equal
    bgcolor : string
        the background color of the figure and axis
    show : bool
        if True, show the figure
    save : bool
        if True, save the figure as an image file to disk
    close : bool
        close the figure (only if show equals False) to prevent display
    file_format : string
        the format of the file to save (e.g., 'jpg', 'png', 'svg')
    filename : string
        the name of the file if saving
    dpi : int
        the resolution of the image file if saving
    annotate : bool
        if True, annotate the nodes in the figure
    node_color : string
        the color of the nodes
    node_size : int
        the size of the nodes
    node_alpha : float
        the opacity of the nodes
    node_edgecolor : string
        the color of the node's marker's border
    node_zorder : int
        zorder to plot nodes, edges are always 2, so make node_zorder 1 to plot
        nodes beneath them or 3 to plot nodes atop them
    edge_color : string
        the color of the edges' lines
    edge_linewidth : float
        the width of the edges' lines
    edge_alpha : float
        the opacity of the edges' lines
    use_geom : bool
        if True, use the spatial geometry attribute of the edges to draw
        geographically accurate edges, rather than just lines straight from node
        to node

    Returns
    -------
    fig, ax : tuple
    """

    log('Begin plotting the graph...')
    node_Xs = [float(x) for _, x in G.nodes(data='x')]
    node_Ys = [float(y) for _, y in G.nodes(data='y')]

    # get north, south, east, west values either from bbox parameter or from the
    # spatial extent of the edges' geometries
    if bbox is None:
        edges = graph_to_gdfs(G, nodes=False, fill_edge_geometry=True)
        west, south, east, north = edges.total_bounds
    else:
        north, south, east, west = bbox

    # if caller did not pass in a fig_width, calculate it proportionately from
    # the fig_height and bounding box aspect ratio
    bbox_aspect_ratio = (north-south)/(east-west)
    if fig_width is None:
        fig_width = fig_height / bbox_aspect_ratio

    # create the figure and axis
    fig, ax = plt.subplots(figsize=(fig_width, fig_height), facecolor=bgcolor)
    ax.set_facecolor(bgcolor)

    # draw the edges as lines from node to node
    start_time = time.time()
    lines = []
    for u, v, data in G.edges(keys=False, data=True):
        if 'geometry' in data and use_geom:
            # if it has a geometry attribute (a list of line segments), add them
            # to the list of lines to plot
            xs, ys = data['geometry'].xy
            lines.append(list(zip(xs, ys)))
        else:
            # if it doesn't have a geometry attribute, the edge is a straight
            # line from node to node
            x1 = G.nodes[u]['x']
            y1 = G.nodes[u]['y']
            x2 = G.nodes[v]['x']
            y2 = G.nodes[v]['y']
            line = [(x1, y1), (x2, y2)]
            lines.append(line)

    # add the lines to the axis as a linecollection
    lc = LineCollection(lines, colors=edge_color, linewidths=edge_linewidth, alpha=edge_alpha, zorder=2)
    ax.add_collection(lc)
    log('Drew the graph edges in {:,.2f} seconds'.format(time.time()-start_time))

    # scatter plot the nodes
    ax.scatter(node_Xs, node_Ys, s=node_size, c=node_color, alpha=node_alpha, edgecolor=node_edgecolor, zorder=node_zorder)

    # set the extent of the figure
    margin_ns = (north - south) * margin
    margin_ew = (east - west) * margin
    ax.set_ylim((south - margin_ns, north + margin_ns))
    ax.set_xlim((west - margin_ew, east + margin_ew))

    # configure axis appearance
    xaxis = ax.get_xaxis()
    yaxis = ax.get_yaxis()

    xaxis.get_major_formatter().set_useOffset(False)
    yaxis.get_major_formatter().set_useOffset(False)

    # if axis_off, turn off the axis display set the margins to zero and point
    # the ticks in so there's no space around the plot
    if axis_off:
        ax.axis('off')
        ax.margins(0)
        ax.tick_params(which='both', direction='in')
        xaxis.set_visible(False)
        yaxis.set_visible(False)
        fig.canvas.draw()

    if equal_aspect:
        # make everything square
        ax.set_aspect('equal')
        fig.canvas.draw()
    else:
        # if the graph is not projected, conform the aspect ratio to not stretch the plot
        if G.graph['crs'] == settings.default_crs:
            coslat = np.cos((min(node_Ys) + max(node_Ys)) / 2. / 180. * np.pi)
            ax.set_aspect(1. / coslat)
            fig.canvas.draw()

    # annotate the axis with node IDs if annotate=True
    if annotate:
        for node, data in G.nodes(data=True):
            ax.annotate(node, xy=(data['x'], data['y']))

    # save and show the figure as specified
    fig, ax = save_and_show(fig, ax, save, show, close, filename, file_format, dpi, axis_off)
    return fig, ax


Instruction:
Create a Python 3 function for
giving a list of nodes return a list of lines that together follow the path .

Response:
def node_list_to_coordinate_lines(G, node_list, use_geom=True):
    """
    Given a list of nodes, return a list of lines that together follow the path
    defined by the list of nodes.

    Parameters
    ----------
    G : networkx multidigraph
    route : list
        the route as a list of nodes
    use_geom : bool
        if True, use the spatial geometry attribute of the edges to draw
        geographically accurate edges, rather than just lines straight from node
        to node

    Returns
    -------
    lines : list of lines given as pairs ( (x_start, y_start), (x_stop, y_stop) )
    """
    edge_nodes = list(zip(node_list[:-1], node_list[1:]))
    lines = []
    for u, v in edge_nodes:
        # if there are parallel edges, select the shortest in length
        data = min(G.get_edge_data(u, v).values(), key=lambda x: x['length'])

        # if it has a geometry attribute (ie, a list of line segments)
        if 'geometry' in data and use_geom:
            # add them to the list of lines to plot
            xs, ys = data['geometry'].xy
            lines.append(list(zip(xs, ys)))
        else:
            # if it doesn't have a geometry attribute, the edge is a straight
            # line from node to node
            x1 = G.nodes[u]['x']
            y1 = G.nodes[u]['y']
            x2 = G.nodes[v]['x']
            y2 = G.nodes[v]['y']
            line = [(x1, y1), (x2, y2)]
            lines.append(line)
    return lines


Instruction:
Create a Python 3 function for
plotting a route along a networkx spatial graph.

Response:
def plot_graph_route(G, route, bbox=None, fig_height=6, fig_width=None,
                     margin=0.02, bgcolor='w', axis_off=True, show=True,
                     save=False, close=True, file_format='png', filename='temp',
                     dpi=300, annotate=False, node_color='#999999',
                     node_size=15, node_alpha=1, node_edgecolor='none',
                     node_zorder=1, edge_color='#999999', edge_linewidth=1,
                     edge_alpha=1, use_geom=True, origin_point=None,
                     destination_point=None, route_color='r', route_linewidth=4,
                     route_alpha=0.5, orig_dest_node_alpha=0.5,
                     orig_dest_node_size=100, orig_dest_node_color='r',
                     orig_dest_point_color='b'):
    """
    Plot a route along a networkx spatial graph.

    Parameters
    ----------
    G : networkx multidigraph
    route : list
        the route as a list of nodes
    bbox : tuple
        bounding box as north,south,east,west - if None will calculate from
        spatial extents of data. if passing a bbox, you probably also want to
        pass margin=0 to constrain it.
    fig_height : int
        matplotlib figure height in inches
    fig_width : int
        matplotlib figure width in inches
    margin : float
        relative margin around the figure
    axis_off : bool
        if True turn off the matplotlib axis
    bgcolor : string
        the background color of the figure and axis
    show : bool
        if True, show the figure
    save : bool
        if True, save the figure as an image file to disk
    close : bool
        close the figure (only if show equals False) to prevent display
    file_format : string
        the format of the file to save (e.g., 'jpg', 'png', 'svg')
    filename : string
        the name of the file if saving
    dpi : int
        the resolution of the image file if saving
    annotate : bool
        if True, annotate the nodes in the figure
    node_color : string
        the color of the nodes
    node_size : int
        the size of the nodes
    node_alpha : float
        the opacity of the nodes
    node_edgecolor : string
        the color of the node's marker's border
    node_zorder : int
        zorder to plot nodes, edges are always 2, so make node_zorder 1 to plot
        nodes beneath them or 3 to plot nodes atop them
    edge_color : string
        the color of the edges' lines
    edge_linewidth : float
        the width of the edges' lines
    edge_alpha : float
        the opacity of the edges' lines
    use_geom : bool
        if True, use the spatial geometry attribute of the edges to draw
        geographically accurate edges, rather than just lines straight from node
        to node
    origin_point : tuple
        optional, an origin (lat, lon) point to plot instead of the origin node
    destination_point : tuple
        optional, a destination (lat, lon) point to plot instead of the
        destination node
    route_color : string
        the color of the route
    route_linewidth : int
        the width of the route line
    route_alpha : float
        the opacity of the route line
    orig_dest_node_alpha : float
        the opacity of the origin and destination nodes
    orig_dest_node_size : int
        the size of the origin and destination nodes
    orig_dest_node_color : string
        the color of the origin and destination nodes
    orig_dest_point_color : string
        the color of the origin and destination points if being plotted instead
        of nodes

    Returns
    -------
    fig, ax : tuple
    """

    # plot the graph but not the route
    fig, ax = plot_graph(G, bbox=bbox, fig_height=fig_height, fig_width=fig_width,
                         margin=margin, axis_off=axis_off, bgcolor=bgcolor,
                         show=False, save=False, close=False, filename=filename,
                         dpi=dpi, annotate=annotate, node_color=node_color,
                         node_size=node_size, node_alpha=node_alpha,
                         node_edgecolor=node_edgecolor, node_zorder=node_zorder,
                         edge_color=edge_color, edge_linewidth=edge_linewidth,
                         edge_alpha=edge_alpha, use_geom=use_geom)

    # the origin and destination nodes are the first and last nodes in the route
    origin_node = route[0]
    destination_node = route[-1]

    if origin_point is None or destination_point is None:
        # if caller didn't pass points, use the first and last node in route as
        # origin/destination
        origin_destination_lats = (G.nodes[origin_node]['y'], G.nodes[destination_node]['y'])
        origin_destination_lons = (G.nodes[origin_node]['x'], G.nodes[destination_node]['x'])
    else:
        # otherwise, use the passed points as origin/destination
        origin_destination_lats = (origin_point[0], destination_point[0])
        origin_destination_lons = (origin_point[1], destination_point[1])
        orig_dest_node_color = orig_dest_point_color

    # scatter the origin and destination points
    ax.scatter(origin_destination_lons, origin_destination_lats, s=orig_dest_node_size,
               c=orig_dest_node_color, alpha=orig_dest_node_alpha, edgecolor=node_edgecolor, zorder=4)

    # plot the route lines
    lines = node_list_to_coordinate_lines(G, route, use_geom)

    # add the lines to the axis as a linecollection
    lc = LineCollection(lines, colors=route_color, linewidths=route_linewidth, alpha=route_alpha, zorder=3)
    ax.add_collection(lc)

    # save and show the figure as specified
    fig, ax = save_and_show(fig, ax, save, show, close, filename, file_format, dpi, axis_off)
    return fig, ax


Instruction:
Explain what the following Python 3 code does
def make_folium_polyline(edge, edge_color, edge_width, edge_opacity, popup_attribute=None):

    """
    Turn a row from the gdf_edges GeoDataFrame into a folium PolyLine with
    attributes.

    Parameters
    ----------
    edge : GeoSeries
        a row from the gdf_edges GeoDataFrame
    edge_color : string
        color of the edge lines
    edge_width : numeric
        width of the edge lines
    edge_opacity : numeric
        opacity of the edge lines
    popup_attribute : string
        edge attribute to display in a pop-up when an edge is clicked, if None,
        no popup

    Returns
    -------
    pl : folium.PolyLine
    """

    # check if we were able to import folium successfully
    if not folium:
        raise ImportError('The folium package must be installed to use this optional feature.')

    # locations is a list of points for the polyline
    # folium takes coords in lat,lon but geopandas provides them in lon,lat
    # so we have to flip them around
    locations = list([(lat, lon) for lon, lat in edge['geometry'].coords])

    # if popup_attribute is None, then create no pop-up
    if popup_attribute is None:
        popup = None
    else:
        # folium doesn't interpret html in the html argument (weird), so can't
        # do newlines without an iframe
        popup_text = json.dumps(edge[popup_attribute])
        popup = folium.Popup(html=popup_text)

    # create a folium polyline with attributes
    pl = folium.PolyLine(locations=locations, popup=popup,
                         color=edge_color, weight=edge_width, opacity=edge_opacity)
    return pl

Response: Turn a row from the gdf_edges GeoDataFrame into a folium PolyLine with optional attributes.


Instruction:
Make a summary of the following Python 3 code
def plot_graph_folium(G, graph_map=None, popup_attribute=None,
                      tiles='cartodbpositron', zoom=1, fit_bounds=True,
                      edge_color='#333333', edge_width=5, edge_opacity=1):
    """
    Plot a graph on an interactive folium web map.

    Note that anything larger than a small city can take a long time to plot and
    create a large web map file that is very slow to load as JavaScript.

    Parameters
    ----------
    G : networkx multidigraph
    graph_map : folium.folium.Map
        if not None, plot the graph on this preexisting folium map object
    popup_attribute : string
        edge attribute to display in a pop-up when an edge is clicked
    tiles : string
        name of a folium tileset
    zoom : int
        initial zoom level for the map
    fit_bounds : bool
        if True, fit the map to the boundaries of the route's edges
    edge_color : string
        color of the edge lines
    edge_width : numeric
        width of the edge lines
    edge_opacity : numeric
        opacity of the edge lines

    Returns
    -------
    graph_map : folium.folium.Map
    """

    # check if we were able to import folium successfully
    if not folium:
        raise ImportError('The folium package must be installed to use this optional feature.')

    # create gdf of the graph edges
    gdf_edges = graph_to_gdfs(G, nodes=False, fill_edge_geometry=True)

    # get graph centroid
    x, y = gdf_edges.unary_union.centroid.xy
    graph_centroid = (y[0], x[0])

    # create the folium web map if one wasn't passed-in
    if graph_map is None:
        graph_map = folium.Map(location=graph_centroid, zoom_start=zoom, tiles=tiles)

    # add each graph edge to the map
    for _, row in gdf_edges.iterrows():
        pl = make_folium_polyline(edge=row, edge_color=edge_color, edge_width=edge_width,
                                  edge_opacity=edge_opacity, popup_attribute=popup_attribute)
        pl.add_to(graph_map)

    # if fit_bounds is True, fit the map to the bounds of the route by passing
    # list of lat-lng points as [southwest, northeast]
    if fit_bounds:
        tb = gdf_edges.total_bounds
        bounds = [(tb[1], tb[0]), (tb[3], tb[2])]
        graph_map.fit_bounds(bounds)

    return graph_map

Response: Plot a graph on an interactive folium web map.


Instruction:
Can you write a function in Python 3 where it
plots a route on an interactive folium web map.

Response:
def plot_route_folium(G, route, route_map=None, popup_attribute=None,
                      tiles='cartodbpositron', zoom=1, fit_bounds=True,
                      route_color='#cc0000', route_width=5, route_opacity=1):
    """
    Plot a route on an interactive folium web map.

    Parameters
    ----------
    G : networkx multidigraph
    route : list
        the route as a list of nodes
    route_map : folium.folium.Map
        if not None, plot the route on this preexisting folium map object
    popup_attribute : string
        edge attribute to display in a pop-up when an edge is clicked
    tiles : string
        name of a folium tileset
    zoom : int
        initial zoom level for the map
    fit_bounds : bool
        if True, fit the map to the boundaries of the route's edges
    route_color : string
        color of the route's line
    route_width : numeric
        width of the route's line
    route_opacity : numeric
        opacity of the route lines

    Returns
    -------
    route_map : folium.folium.Map
    """

    # check if we were able to import folium successfully
    if not folium:
        raise ImportError('The folium package must be installed to use this optional feature.')

    # create gdf of the route edges
    gdf_edges = graph_to_gdfs(G, nodes=False, fill_edge_geometry=True)
    route_nodes = list(zip(route[:-1], route[1:]))
    index = [gdf_edges[(gdf_edges['u']==u) & (gdf_edges['v']==v)].index[0] for u, v in route_nodes]
    gdf_route_edges = gdf_edges.loc[index]

    # get route centroid
    x, y = gdf_route_edges.unary_union.centroid.xy
    route_centroid = (y[0], x[0])

    # create the folium web map if one wasn't passed-in
    if route_map is None:
        route_map = folium.Map(location=route_centroid, zoom_start=zoom, tiles=tiles)

    # add each route edge to the map
    for _, row in gdf_route_edges.iterrows():
        pl = make_folium_polyline(edge=row, edge_color=route_color, edge_width=route_width,
                                  edge_opacity=route_opacity, popup_attribute=popup_attribute)
        pl.add_to(route_map)

    # if fit_bounds is True, fit the map to the bounds of the route by passing
    # list of lat-lng points as [southwest, northeast]
    if fit_bounds:
        tb = gdf_route_edges.total_bounds
        bounds = [(tb[1], tb[0]), (tb[3], tb[2])]
        route_map.fit_bounds(bounds)

    return route_map


Instruction:
Implement a function in Python 3 to
plot a figure - ground diagram of a street network.

Response:
def plot_figure_ground(G=None, address=None, point=None, dist=805,
                       network_type='drive_service', street_widths=None,
                       default_width=4, fig_length=8, edge_color='w',
                       bgcolor='#333333', smooth_joints=True, filename=None,
                       file_format='png', show=False, save=True, close=True,
                       dpi=300):
    """
    Plot a figure-ground diagram of a street network, defaulting to one square
    mile.

    Parameters
    ----------
    G : networkx multidigraph
    address : string
        the address to geocode as the center point if G is not passed in
    point : tuple
        the center point if address and G are not passed in
    dist : numeric
        how many meters to extend north, south, east, and west from the center
        point
    network_type : string
        what type of network to get
    street_widths : dict
        where keys are street types and values are widths to plot in pixels
    default_width : numeric
        the default street width in pixels for any street type not found in
        street_widths dict
    fig_length : numeric
        the height and width of this square diagram
    edge_color : string
        the color of the streets
    bgcolor : string
        the color of the background
    smooth_joints : bool
        if True, plot nodes same width as streets to smooth line joints and
        prevent cracks between them from showing
    filename : string
        filename to save the image as
    file_format : string
        the format of the file to save (e.g., 'jpg', 'png', 'svg')
    show : bool
        if True, show the figure
    save : bool
        if True, save the figure as an image file to disk
    close : bool
        close the figure (only if show equals False) to prevent display
    dpi : int
        the resolution of the image file if saving

    Returns
    -------
    fig, ax : tuple
    """

    multiplier = 1.2

    # if G was passed-in, use this graph in the plot, centered on the centroid
    # of its nodes
    if G is not None:
        gdf_nodes = 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 the network by either address or point, whichever was
    # passed-in, using a distance multiplier to make sure 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_from_address(address, distance=dist*multiplier, distance_type='bbox', network_type=network_type,
                                      simplify=False, truncate_by_edge=True, return_coords=True)
        G = simplify_graph(G, strict=False)
    elif point is not None:
        G = graph_from_point(point, distance=dist*multiplier, distance_type='bbox', network_type=network_type,
                             simplify=False, truncate_by_edge=True)
        G = simplify_graph(G, strict=False)
    else:
        raise ValueError('You must pass an address or lat-long point or graph.')

    # if user did not pass in custom street widths, create a dict of default
    # values
    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}

    # we need an undirected graph to find every edge incident to a node
    G_undir = G.to_undirected()

    # for each network edge, get a linewidth according to street type (the OSM
    # 'highway' value)
    edge_linewidths = []
    for _, _, data in G_undir.edges(keys=False, data=True):
        street_type = data['highway'][0] if isinstance(data['highway'], list) else data['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 G_undir.nodes():
            # first, identify all the highway types of this node's incident edges
            incident_edges_data = [G_undir.get_edge_data(node, neighbor) for neighbor in G_undir.neighbors(node)]
            edge_types = [data[0]['highway'] for data in incident_edges_data]
            if len(edge_types) < 1:
                # if node has no incident edges, make size zero
                node_widths[node] = 0
            else:
                # flatten the list of edge types
                edge_types_flat = []
                for et in edge_types:
                    if isinstance(et, list):
                        edge_types_flat.extend(et)
                    else:
                        edge_types_flat.append(et)

                # for each edge type in the flattened list, lookup the
                # corresponding width
                edge_widths = [street_widths[edge_type] if edge_type in street_widths else default_width for edge_type in edge_types_flat]

                # the node diameter will be the biggest of the edge widths, to make joints perfectly smooth
                # alternatively, use min (?) to pervent anything larger from extending past smallest street's line
                circle_diameter = max(edge_widths)

                # mpl circle marker sizes are in area, so it is the diameter
                # squared
                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 G_undir.nodes()]
    else:
        node_sizes = 0

    # define the spatial extents of the plotting figure to make it square, in
    # projected units, and cropped to the desired area
    bbox = bbox_from_point(point, dist, project_utm=False)

    # create a filename if one was not passed
    if filename is None and save:
        filename = 'figure_ground_{}_{}'.format(point, network_type)

    # plot the figure
    fig, ax = plot_graph(G_undir, bbox=bbox, fig_height=fig_length,
                         margin=0, axis_off=True, equal_aspect=False,
                         bgcolor=bgcolor, node_size=node_sizes,
                         node_color=edge_color, edge_linewidth=edge_linewidths,
                         edge_color=edge_color, show=show, save=save,
                         close=close, filename=filename, file_format=file_format,
                         dpi=dpi)

    return fig, ax


Instruction:
Implement a Python 3 function for
saving a response json object to the cache.

Response:
def save_to_cache(url, response_json):
    """
    Save an HTTP response json object to the cache.

    If the request was sent to server via POST instead of GET, then URL should
    be a GET-style representation of request. Users should always pass
    OrderedDicts instead of dicts of parameters into request functions, so that
    the parameters stay in the same order each time, producing the same URL
    string, and thus the same hash. Otherwise the cache will eventually contain
    multiple saved responses for the same request because the URL's parameters
    appeared in a different order each time.

    Parameters
    ----------
    url : string
        the url of the request
    response_json : dict
        the json response

    Returns
    -------
    None
    """
    if settings.use_cache:
        if response_json is None:
            log('Saved nothing to cache because response_json is None')
        else:
            # create the folder on the disk if it doesn't already exist
            if not os.path.exists(settings.cache_folder):
                os.makedirs(settings.cache_folder)

            # hash the url (to make filename shorter than the often extremely
            # long url)
            filename = hashlib.md5(url.encode('utf-8')).hexdigest()
            cache_path_filename = os.path.join(settings.cache_folder, os.extsep.join([filename, 'json']))

            # dump to json, and save to file
            json_str = make_str(json.dumps(response_json))
            with io.open(cache_path_filename, 'w', encoding='utf-8') as cache_file:
                cache_file.write(json_str)

            log('Saved response to cache file "{}"'.format(cache_path_filename))


Instruction:
Given the following Python 3 function, write the documentation
def get_from_cache(url):
    # if the tool is configured to use the cache
    if settings.use_cache:
        # determine the filename by hashing the url
        filename = hashlib.md5(url.encode('utf-8')).hexdigest()

        cache_path_filename = os.path.join(settings.cache_folder, os.extsep.join([filename, 'json']))
        # open the cache file for this url hash if it already exists, otherwise
        # return None
        if os.path.isfile(cache_path_filename):
            with io.open(cache_path_filename, encoding='utf-8') as cache_file:
                response_json = json.load(cache_file)
            log('Retrieved response from cache file "{}" for URL "{}"'.format(cache_path_filename, url))
            return response_json

Response: Retrieve a HTTP response json object from the cache.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def get_http_headers(user_agent=None, referer=None, accept_language=None):

    if user_agent is None:
        user_agent = settings.default_user_agent
    if referer is None:
        referer = settings.default_referer
    if accept_language is None:
        accept_language = settings.default_accept_language

    headers = requests.utils.default_headers()
    headers.update({'User-Agent': user_agent, 'referer': referer, 'Accept-Language': accept_language})
    return headers

Response: Returns the HTTP headers for the current OSMnx server.


Instruction:
Given the following Python 3 function, write the documentation
def nominatim_request(params, type = "search", pause_duration=1, timeout=30, error_pause_duration=180):

    known_requests = {"search", "reverse", "lookup"}
    if type not in known_requests:
        raise ValueError("The type of Nominatim request is invalid. Please choose one of {{'search', 'reverse', 'lookup'}}")

    # prepare the Nominatim API URL and see if request already exists in the
    # cache
    url = 'https://nominatim.openstreetmap.org/{}'.format(type)
    prepared_url = requests.Request('GET', url, params=params).prepare().url
    cached_response_json = get_from_cache(prepared_url)

    if cached_response_json is not None:
        # found this request in the cache, just return it instead of making a
        # new HTTP call
        return cached_response_json

    else:
        # if this URL is not already in the cache, pause, then request it
        log('Pausing {:,.2f} seconds before making API GET request'.format(pause_duration))
        time.sleep(pause_duration)
        start_time = time.time()
        log('Requesting {} with timeout={}'.format(prepared_url, timeout))
        response = requests.get(url, params=params, timeout=timeout, headers=get_http_headers())

        # get the response size and the domain, log result
        size_kb = len(response.content) / 1000.
        domain = re.findall(r'//(?s)(.*?)/', url)[0]
        log('Downloaded {:,.1f}KB from {} in {:,.2f} seconds'.format(size_kb, domain, time.time()-start_time))

        try:
            response_json = response.json()
            save_to_cache(prepared_url, response_json)
        except Exception:
            #429 is 'too many requests' and 504 is 'gateway timeout' from server
            # overload - handle these errors by recursively calling
            # nominatim_request until we get a valid response
            if response.status_code in [429, 504]:
                # pause for error_pause_duration seconds before re-trying request
                log('Server at {} returned status code {} and no JSON data. Re-trying request in {:.2f} seconds.'.format(domain,
                                                                                response.status_code,
                                                                                error_pause_duration),
                                                                                level=lg.WARNING)
                time.sleep(error_pause_duration)
                response_json = nominatim_request(params=params, pause_duration=pause_duration, timeout=timeout)

            # else, this was an unhandled status_code, throw an exception
            else:
                log('Server at {} returned status code {} and no JSON data'.format(domain, response.status_code), level=lg.ERROR)
                raise Exception('Server returned no JSON data.\n{} {}\n{}'.format(response, response.reason, response.text))

        return response_json

Response: Send a request to the Nominatim API and return the JSON response.


Instruction:
How would you code a function in Python 3 to
send a request to the Overpass API and return the JSON response.

Response:
def overpass_request(data, pause_duration=None, timeout=180, error_pause_duration=None):
    """
    Send a request to the Overpass API via HTTP POST and return the JSON
    response.

    Parameters
    ----------
    data : dict or OrderedDict
        key-value pairs of parameters to post to the API
    pause_duration : int
        how long to pause in seconds before requests, if None, will query API
        status endpoint to find when next slot is available
    timeout : int
        the timeout interval for the requests library
    error_pause_duration : int
        how long to pause in seconds before re-trying requests if error

    Returns
    -------
    dict
    """

    # define the Overpass API URL, then construct a GET-style URL as a string to
    # hash to look up/save to cache
    url = 'http://overpass-api.de/api/interpreter'
    prepared_url = requests.Request('GET', url, params=data).prepare().url
    cached_response_json = get_from_cache(prepared_url)

    if cached_response_json is not None:
        # found this request in the cache, just return it instead of making a
        # new HTTP call
        return cached_response_json

    else:
        # if this URL is not already in the cache, pause, then request it
        if pause_duration is None:
            this_pause_duration = get_pause_duration()
        log('Pausing {:,.2f} seconds before making API POST request'.format(this_pause_duration))
        time.sleep(this_pause_duration)
        start_time = time.time()
        log('Posting to {} with timeout={}, "{}"'.format(url, timeout, data))
        response = requests.post(url, data=data, timeout=timeout, headers=get_http_headers())

        # get the response size and the domain, log result
        size_kb = len(response.content) / 1000.
        domain = re.findall(r'//(?s)(.*?)/', url)[0]
        log('Downloaded {:,.1f}KB from {} in {:,.2f} seconds'.format(size_kb, domain, time.time()-start_time))

        try:
            response_json = response.json()
            if 'remark' in response_json:
                log('Server remark: "{}"'.format(response_json['remark'], level=lg.WARNING))
            save_to_cache(prepared_url, response_json)
        except Exception:
            #429 is 'too many requests' and 504 is 'gateway timeout' from server
            # overload - handle these errors by recursively calling
            # overpass_request until we get a valid response
            if response.status_code in [429, 504]:
                # pause for error_pause_duration seconds before re-trying request
                if error_pause_duration is None:
                    error_pause_duration = get_pause_duration()
                log('Server at {} returned status code {} and no JSON data. Re-trying request in {:.2f} seconds.'.format(domain,
                                                                                response.status_code,
                                                                                error_pause_duration),
                                                                                level=lg.WARNING)
                time.sleep(error_pause_duration)
                response_json = overpass_request(data=data, pause_duration=pause_duration, timeout=timeout)

            # else, this was an unhandled status_code, throw an exception
            else:
                log('Server at {} returned status code {} and no JSON data'.format(domain, response.status_code), level=lg.ERROR)
                raise Exception('Server returned no JSON data.\n{} {}\n{}'.format(response, response.reason, response.text))

        return response_json


Instruction:
Can you write a function in Python 3 where it
creates a GeoDataFrame from a single place name query.

Response:
def gdf_from_place(query, gdf_name=None, which_result=1, buffer_dist=None):
    """
    Create a GeoDataFrame from a single place name query.

    Parameters
    ----------
    query : string or dict
        query string or structured query dict to geocode/download
    gdf_name : string
        name attribute metadata for GeoDataFrame (this is used to save shapefile
        later)
    which_result : int
        max number of results to return and which to process upon receipt
    buffer_dist : float
        distance to buffer around the place geometry, in meters

    Returns
    -------
    GeoDataFrame
    """
    # if no gdf_name is passed, just use the query
    assert (isinstance(query, dict) or isinstance(query, str)), 'query must be a dict or a string'
    if (gdf_name is None) and isinstance(query, dict):
        gdf_name = ', '.join(list(query.values()))
    elif (gdf_name is None) and isinstance(query, str):
        gdf_name = query

    # get the data from OSM
    data = osm_polygon_download(query, limit=which_result)
    if len(data) >= which_result:

        # extract data elements from the JSON response
        result = data[which_result - 1]
        bbox_south, bbox_north, bbox_west, bbox_east = [float(x) for x in result['boundingbox']]
        geometry = result['geojson']
        place = result['display_name']
        features = [{'type': 'Feature',
                     'geometry': geometry,
                     'properties': {'place_name': place,
                                    'bbox_north': bbox_north,
                                    'bbox_south': bbox_south,
                                    'bbox_east': bbox_east,
                                    'bbox_west': bbox_west}}]

        # if we got an unexpected geometry type (like a point), log a warning
        if geometry['type'] not in ['Polygon', 'MultiPolygon']:
            log('OSM returned a {} as the geometry.'.format(geometry['type']), level=lg.WARNING)

        # create the GeoDataFrame, name it, and set its original CRS to default_crs
        gdf = gpd.GeoDataFrame.from_features(features)
        gdf.gdf_name = gdf_name
        gdf.crs = settings.default_crs

        # if buffer_dist was passed in, project the geometry to UTM, buffer it
        # in meters, then project it back to lat-long
        if buffer_dist is not None:
            gdf_utm = project_gdf(gdf)
            gdf_utm['geometry'] = gdf_utm['geometry'].buffer(buffer_dist)
            gdf = project_gdf(gdf_utm, to_latlong=True)
            log('Buffered the GeoDataFrame "{}" to {} meters'.format(gdf.gdf_name, buffer_dist))

        # return the gdf
        log('Created GeoDataFrame with {} row for query "{}"'.format(len(gdf), query))
        return gdf
    else:
        # if there was no data returned (or fewer results than which_result
        # specified)
        log('OSM returned no results (or fewer than which_result) for query "{}"'.format(query), level=lg.WARNING)
        gdf = gpd.GeoDataFrame()
        gdf.gdf_name = gdf_name
        return gdf


Instruction:
Here you have a function in Python 3, explain what it does
def gdf_from_places(queries, gdf_name='unnamed', buffer_dist=None):
    # create an empty GeoDataFrame then append each result as a new row
    gdf = gpd.GeoDataFrame()
    for query in queries:
        gdf = gdf.append(gdf_from_place(query, buffer_dist=buffer_dist))

    # reset the index, name the GeoDataFrame
    gdf = gdf.reset_index().drop(labels='index', axis=1)
    gdf.gdf_name = gdf_name

    # set the original CRS of the GeoDataFrame to default_crs, and return it
    gdf.crs = settings.default_crs
    log('Finished creating GeoDataFrame with {} rows from {} queries'.format(len(gdf), len(queries)))
    return gdf

Response: Create a GeoDataFrame from a list of query strings or structured query dicts to geocode and download them.


Instruction:
Can you generate the documentation for the following Python 3 function
def get_osm_filter(network_type):
    filters = {}

    # driving: filter out un-drivable roads, service roads, private ways, and
    # anything specifying motor=no. also filter out any non-service roads that
    # are tagged as providing parking, driveway, private, or emergency-access
    # services
    filters['drive'] = ('["area"!~"yes"]["highway"!~"cycleway|footway|path|pedestrian|steps|track|corridor|'
                        'proposed|construction|bridleway|abandoned|platform|raceway|service"]'
                        '["motor_vehicle"!~"no"]["motorcar"!~"no"]{}'
                        '["service"!~"parking|parking_aisle|driveway|private|emergency_access"]').format(settings.default_access)

    # drive+service: allow ways tagged 'service' but filter out certain types of
    # service ways
    filters['drive_service'] = ('["area"!~"yes"]["highway"!~"cycleway|footway|path|pedestrian|steps|track|corridor|'
                                'proposed|construction|bridleway|abandoned|platform|raceway"]'
                                '["motor_vehicle"!~"no"]["motorcar"!~"no"]{}'
                                '["service"!~"parking|parking_aisle|private|emergency_access"]').format(settings.default_access)

    # walking: filter out cycle ways, motor ways, private ways, and anything
    # specifying foot=no. allow service roads, permitting things like parking
    # lot lanes, alleys, etc that you *can* walk on even if they're not exactly
    # pleasant walks. some cycleways may allow pedestrians, but this filter ignores
    # such cycleways.
    filters['walk'] = ('["area"!~"yes"]["highway"!~"cycleway|motor|proposed|construction|abandoned|platform|raceway"]'
                       '["foot"!~"no"]["service"!~"private"]{}').format(settings.default_access)

    # biking: filter out foot ways, motor ways, private ways, and anything
    # specifying biking=no
    filters['bike'] = ('["area"!~"yes"]["highway"!~"footway|steps|corridor|motor|proposed|construction|abandoned|platform|raceway"]'
                       '["bicycle"!~"no"]["service"!~"private"]{}').format(settings.default_access)

    # to download all ways, just filter out everything not currently in use or
    # that is private-access only
    filters['all'] = ('["area"!~"yes"]["highway"!~"proposed|construction|abandoned|platform|raceway"]'
                      '["service"!~"private"]{}').format(settings.default_access)

    # to download all ways, including private-access ones, just filter out
    # everything not currently in use
    filters['all_private'] = '["area"!~"yes"]["highway"!~"proposed|construction|abandoned|platform|raceway"]'

    # no filter, needed for infrastructures other than "highway"
    filters['none'] = ''

    if network_type in filters:
        osm_filter = filters[network_type]
    else:
        raise UnknownNetworkType('unknown network_type "{}"'.format(network_type))

    return osm_filter

Response: Returns a filter to query OSM for the specified network type.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def osm_net_download(polygon=None, north=None, south=None, east=None, west=None,
                     network_type='all_private', timeout=180, memory=None,
                     max_query_area_size=50*1000*50*1000, infrastructure='way["highway"]',
                     custom_filter=None):
    """
    Download OSM ways and nodes within some bounding box from the Overpass API.

    Parameters
    ----------
    polygon : shapely Polygon or MultiPolygon
        geographic shape to fetch the street network within
    north : float
        northern latitude of bounding box
    south : float
        southern latitude of bounding box
    east : float
        eastern longitude of bounding box
    west : float
        western longitude of bounding box
    network_type : string
        {'walk', 'bike', 'drive', 'drive_service', 'all', 'all_private'} what
        type of street network to get
    timeout : int
        the timeout interval for requests and to pass to API
    memory : int
        server memory allocation size for the query, in bytes. If none, server
        will use its default allocation size
    max_query_area_size : float
        max area for any part of the geometry, in the units the geometry is in:
        any polygon bigger will get divided up for multiple queries to API
        (default is 50,000 * 50,000 units [ie, 50km x 50km in area, if units are
        meters])
    infrastructure : string
        download infrastructure of given type. default is streets, ie,
        'way["highway"]') but other infrastructures may be selected like power
        grids, ie, 'way["power"~"line"]'
    custom_filter : string
        a custom network filter to be used instead of the network_type presets

    Returns
    -------
    response_jsons : list
    """

    # check if we're querying by polygon or by bounding box based on which
    # argument(s) where passed into this function
    by_poly = polygon is not None
    by_bbox = not (north is None or south is None or east is None or west is None)
    if not (by_poly or by_bbox):
        raise InsufficientNetworkQueryArguments(
            'You must pass a polygon or north, south, east, and west')

    # create a filter to exclude certain kinds of ways based on the requested
    # network_type
    if custom_filter:
        osm_filter = custom_filter
    else:
        osm_filter = get_osm_filter(network_type)
    response_jsons = []

    # pass server memory allocation in bytes for the query to the API
    # if None, pass nothing so the server will use its default allocation size
    # otherwise, define the query's maxsize parameter value as whatever the
    # caller passed in
    if memory is None:
        maxsize = ''
    else:
        maxsize = '[maxsize:{}]'.format(memory)

    # define the query to send the API
    # specifying way["highway"] means that all ways returned must have a highway
    # key. the {filters} then remove ways by key/value. the '>' makes it recurse
    # so we get ways and way nodes. maxsize is in bytes.
    if by_bbox:
        # turn bbox into a polygon and project to local UTM
        polygon = Polygon([(west, south), (east, south), (east, north), (west, north)])
        geometry_proj, crs_proj = project_geometry(polygon)

        # subdivide it if it exceeds the max area size (in meters), then project
        # back to lat-long
        geometry_proj_consolidated_subdivided = consolidate_subdivide_geometry(geometry_proj, max_query_area_size=max_query_area_size)
        geometry, _ = project_geometry(geometry_proj_consolidated_subdivided, crs=crs_proj, to_latlong=True)
        log('Requesting network data within bounding box from API in {:,} request(s)'.format(len(geometry)))
        start_time = time.time()

        # loop through each polygon rectangle in the geometry (there will only
        # be one if original bbox didn't exceed max area size)
        for poly in geometry:
            # represent bbox as south,west,north,east and round lat-longs to 6
            # decimal places (ie, ~100 mm) so URL strings aren't different
            # due to float rounding issues (for consistent caching)
            west, south, east, north = poly.bounds
            query_template = '[out:json][timeout:{timeout}]{maxsize};({infrastructure}{filters}({south:.6f},{west:.6f},{north:.6f},{east:.6f});>;);out;'
            query_str = query_template.format(north=north, south=south,
                                              east=east, west=west,
                                              infrastructure=infrastructure,
                                              filters=osm_filter,
                                              timeout=timeout, maxsize=maxsize)
            response_json = overpass_request(data={'data':query_str}, timeout=timeout)
            response_jsons.append(response_json)
        log('Got all network data within bounding box from API in {:,} request(s) and {:,.2f} seconds'.format(len(geometry), time.time()-start_time))

    elif by_poly:
        # project to utm, divide polygon up into sub-polygons if area exceeds a
        # max size (in meters), project back to lat-long, then get a list of
        # polygon(s) exterior coordinates
        geometry_proj, crs_proj = project_geometry(polygon)
        geometry_proj_consolidated_subdivided = consolidate_subdivide_geometry(geometry_proj, max_query_area_size=max_query_area_size)
        geometry, _ = project_geometry(geometry_proj_consolidated_subdivided, crs=crs_proj, to_latlong=True)
        polygon_coord_strs = get_polygons_coordinates(geometry)
        log('Requesting network data within polygon from API in {:,} request(s)'.format(len(polygon_coord_strs)))
        start_time = time.time()

        # pass each polygon exterior coordinates in the list to the API, one at
        # a time
        for polygon_coord_str in polygon_coord_strs:
            query_template = '[out:json][timeout:{timeout}]{maxsize};({infrastructure}{filters}(poly:"{polygon}");>;);out;'
            query_str = query_template.format(polygon=polygon_coord_str, infrastructure=infrastructure, filters=osm_filter, timeout=timeout, maxsize=maxsize)
            response_json = overpass_request(data={'data':query_str}, timeout=timeout)
            response_jsons.append(response_json)
        log('Got all network data within polygon from API in {:,} request(s) and {:,.2f} seconds'.format(len(polygon_coord_strs), time.time()-start_time))

    return response_jsons

Response: Download OSM ways and nodes within some bounding box from Overpass API.


Instruction:
Can you create a Python 3 function that
extracts exterior coordinates from polygon or multiPolygons and return a list of strings

Response:
def get_polygons_coordinates(geometry):
    """
    Extract exterior coordinates from polygon(s) to pass to OSM in a query by
    polygon. Ignore the interior ("holes") coordinates.

    Parameters
    ----------
    geometry : shapely Polygon or MultiPolygon
        the geometry to extract exterior coordinates from

    Returns
    -------
    polygon_coord_strs : list
    """

    # extract the exterior coordinates of the geometry to pass to the API later
    polygons_coords = []
    if isinstance(geometry, Polygon):
        x, y = geometry.exterior.xy
        polygons_coords.append(list(zip(x, y)))
    elif isinstance(geometry, MultiPolygon):
        for polygon in geometry:
            x, y = polygon.exterior.xy
            polygons_coords.append(list(zip(x, y)))
    else:
        raise TypeError('Geometry must be a shapely Polygon or MultiPolygon')

    # convert the exterior coordinates of the polygon(s) to the string format
    # the API expects
    polygon_coord_strs = []
    for coords in polygons_coords:
        s = ''
        separator = ' '
        for coord in list(coords):
            # round floating point lats and longs to 6 decimal places (ie, ~100 mm),
            # so we can hash and cache strings consistently
            s = '{}{}{:.6f}{}{:.6f}'.format(s, separator, coord[1], separator, coord[0])
        polygon_coord_strs.append(s.strip(separator))

    return polygon_coord_strs


Instruction:
How would you explain what the following Python 3 function does
def get_node(element):

    node = {}
    node['y'] = element['lat']
    node['x'] = element['lon']
    node['osmid'] = element['id']
    if 'tags' in element:
        for useful_tag in settings.useful_tags_node:
            if useful_tag in element['tags']:
                node[useful_tag] = element['tags'][useful_tag]
    return node

Response: Convert an OSM node element into the format for a networkx node.


Instruction:
Write a Python 3 function for
converting an OSM way element into a networkx graph path.

Response:
def get_path(element):
    """
    Convert an OSM way element into the format for a networkx graph path.

    Parameters
    ----------
    element : dict
        an OSM way element

    Returns
    -------
    dict
    """

    path = {}
    path['osmid'] = element['id']

    # remove any consecutive duplicate elements in the list of nodes
    grouped_list = groupby(element['nodes'])
    path['nodes'] = [group[0] for group in grouped_list]

    if 'tags' in element:
        for useful_tag in settings.useful_tags_path:
            if useful_tag in element['tags']:
                path[useful_tag] = element['tags'][useful_tag]
    return path


Instruction:
Implement a Python 3 function for
parsing the Osm nodes and paths from the Overpass API response.

Response:
def parse_osm_nodes_paths(osm_data):
    """
    Construct dicts of nodes and paths with key=osmid and value=dict of
    attributes.

    Parameters
    ----------
    osm_data : dict
        JSON response from from the Overpass API

    Returns
    -------
    nodes, paths : tuple
    """

    nodes = {}
    paths = {}
    for element in osm_data['elements']:
        if element['type'] == 'node':
            key = element['id']
            nodes[key] = get_node(element)
        elif element['type'] == 'way': #osm calls network paths 'ways'
            key = element['id']
            paths[key] = get_path(element)

    return nodes, paths


Instruction:
Can you tell what is the following Python 3 function doing
def remove_isolated_nodes(G):

    isolated_nodes = [node for node, degree in dict(G.degree()).items() if degree < 1]
    G.remove_nodes_from(isolated_nodes)
    log('Removed {:,} isolated nodes'.format(len(isolated_nodes)))
    return G

Response: Removes isolated nodes from a networkx multidigraph G.


Instruction:
Make a summary of the following Python 3 code
def truncate_graph_dist(G, source_node, max_distance=1000, weight='length', retain_all=False):

    # get the shortest distance between the node and every other node, then
    # remove every node further than max_distance away
    start_time = time.time()
    G = G.copy()
    distances = nx.shortest_path_length(G, source=source_node, weight=weight)
    distant_nodes = {key:value for key, value in dict(distances).items() if value > max_distance}
    G.remove_nodes_from(distant_nodes.keys())
    log('Truncated graph by weighted network distance in {:,.2f} seconds'.format(time.time()-start_time))

    # remove any isolated nodes and retain only the largest component (if
    # retain_all is True)
    if not retain_all:
        G = remove_isolated_nodes(G)
        G = get_largest_component(G)

    return G

Response: Truncate a networkx graph by weighted network distance.


Instruction:
Create a Python 3 function to
remove every node in graph that falls outside a bounding box. Needed because overpass returns entire ways that also include nodes outside the bbox if the way (that is, a way with a single OSM ID) has a node inside the bbox at some point. Parameters ---------- G : networkx multidigraph north : float northern latitude of bounding box south : float southern latitude of bounding box east : float eastern longitude of bounding box west : float western longitude of bounding box truncate_by_edge : bool if True retain node if it's outside bbox but at least one of node's neighbors are within bbox retain_all : bool if True, return the entire graph even if it is not connected Returns ------- networkx multidigraph

Response:
def truncate_graph_bbox(G, north, south, east, west, truncate_by_edge=False, retain_all=False):
    """
    Remove every node in graph that falls outside a bounding box.

    Needed because overpass returns entire ways that also include nodes outside
    the bbox if the way (that is, a way with a single OSM ID) has a node inside
    the bbox at some point.

    Parameters
    ----------
    G : networkx multidigraph
    north : float
        northern latitude of bounding box
    south : float
        southern latitude of bounding box
    east : float
        eastern longitude of bounding box
    west : float
        western longitude of bounding box
    truncate_by_edge : bool
        if True retain node if it's outside bbox but at least one of node's
        neighbors are within bbox
    retain_all : bool
        if True, return the entire graph even if it is not connected

    Returns
    -------
    networkx multidigraph
    """

    start_time = time.time()
    G = G.copy()
    nodes_outside_bbox = []

    for node, data in G.nodes(data=True):
        if data['y'] > north or data['y'] < south or data['x'] > east or data['x'] < west:
            # this node is outside the bounding box
            if not truncate_by_edge:
                # if we're not truncating by edge, add node to list of nodes
                # outside the bounding box
                nodes_outside_bbox.append(node)
            else:
                # if we're truncating by edge, see if any of node's neighbors
                # are within bounding box
                any_neighbors_in_bbox = False
                neighbors = list(G.successors(node)) + list(G.predecessors(node))
                for neighbor in neighbors:
                    x = G.nodes[neighbor]['x']
                    y = G.nodes[neighbor]['y']
                    if y < north and y > south and x < east and x > west:
                        any_neighbors_in_bbox = True
                        break

                # if none of its neighbors are within the bounding box, add node
                # to list of nodes outside the bounding box
                if not any_neighbors_in_bbox:
                    nodes_outside_bbox.append(node)

    G.remove_nodes_from(nodes_outside_bbox)
    log('Truncated graph by bounding box in {:,.2f} seconds'.format(time.time()-start_time))

    # remove any isolated nodes and retain only the largest component (if
    # retain_all is True)
    if not retain_all:
        G = remove_isolated_nodes(G)
        G = get_largest_component(G)

    return G


Instruction:
Explain what the following Python 3 code does
def quadrat_cut_geometry(geometry, quadrat_width, min_num=3, buffer_amount=1e-9):

    # create n evenly spaced points between the min and max x and y bounds
    west, south, east, north = geometry.bounds
    x_num = math.ceil((east-west) / quadrat_width) + 1
    y_num = math.ceil((north-south) / quadrat_width) + 1
    x_points = np.linspace(west, east, num=max(x_num, min_num))
    y_points = np.linspace(south, north, num=max(y_num, min_num))

    # create a quadrat grid of lines at each of the evenly spaced points
    vertical_lines = [LineString([(x, y_points[0]), (x, y_points[-1])]) for x in x_points]
    horizont_lines = [LineString([(x_points[0], y), (x_points[-1], y)]) for y in y_points]
    lines = vertical_lines + horizont_lines

    # buffer each line to distance of the quadrat width divided by 1 billion,
    # take their union, then cut geometry into pieces by these quadrats
    buffer_size = quadrat_width * buffer_amount
    lines_buffered = [line.buffer(buffer_size) for line in lines]
    quadrats = unary_union(lines_buffered)
    multipoly = geometry.difference(quadrats)

    return multipoly

Response: Splits a shapely Polygon or MultiPolygon into smaller sub - polygons of a specified size and buffer them into smaller quadrats.


Instruction:
Explain what the following Python 3 code does
def intersect_index_quadrats(gdf, geometry, quadrat_width=0.05, min_num=3, buffer_amount=1e-9):

    # create an empty dataframe to append matches to
    points_within_geometry = pd.DataFrame()

    # cut the geometry into chunks for r-tree spatial index intersecting
    multipoly = quadrat_cut_geometry(geometry, quadrat_width=quadrat_width, buffer_amount=buffer_amount, min_num=min_num)

    # create an r-tree spatial index for the nodes (ie, points)
    start_time = time.time()
    sindex = gdf['geometry'].sindex
    log('Created r-tree spatial index for {:,} points in {:,.2f} seconds'.format(len(gdf), time.time()-start_time))

    # loop through each chunk of the geometry to find approximate and then
    # precisely intersecting points
    start_time = time.time()
    for poly in multipoly:

        # buffer by the tiny distance to account for any space lost in the
        # quadrat cutting, otherwise may miss point(s) that lay directly on
        # quadrat line
        buffer_size = quadrat_width * buffer_amount
        poly = poly.buffer(buffer_size).buffer(0)

        # find approximate matches with r-tree, then precise matches from those
        # approximate ones
        if poly.is_valid and poly.area > 0:
            possible_matches_index = list(sindex.intersection(poly.bounds))
            possible_matches = gdf.iloc[possible_matches_index]
            precise_matches = possible_matches[possible_matches.intersects(poly)]
            points_within_geometry = points_within_geometry.append(precise_matches)

    if len(points_within_geometry) > 0:
        # drop duplicate points, if buffered poly caused an overlap on point(s)
        # that lay directly on a quadrat line
        points_within_geometry = points_within_geometry.drop_duplicates(subset='node')
    else:
        # after simplifying the graph, and given the requested network type,
        # there are no nodes inside the polygon - can't create graph from that
        # so throw error
        raise Exception('There are no nodes within the requested geometry')

    log('Identified {:,} nodes inside polygon in {:,.2f} seconds'.format(len(points_within_geometry), time.time()-start_time))
    return points_within_geometry

Response: Intersects points with a polygon using an r - tree spatial index and cutting the polygon up into smaller sub - polygons for r - tree acceleration


Instruction:
Can you create a Python 3 function that
removes every node in graph that lie outside the polygon or MultiPolygon.

Response:
def truncate_graph_polygon(G, polygon, retain_all=False, truncate_by_edge=False, quadrat_width=0.05, min_num=3, buffer_amount=1e-9):
    """
    Remove every node in graph that falls outside some shapely Polygon or
    MultiPolygon.

    Parameters
    ----------
    G : networkx multidigraph
    polygon : Polygon or MultiPolygon
        only retain nodes in graph that lie within this geometry
    retain_all : bool
        if True, return the entire graph even if it is not connected
    truncate_by_edge : bool
        if True retain node if it's outside polygon but at least one of node's
        neighbors are within polygon (NOT CURRENTLY IMPLEMENTED)
    quadrat_width : numeric
        passed on to intersect_index_quadrats: the linear length (in degrees) of
        the quadrats with which to cut up the geometry (default = 0.05, approx
        4km at NYC's latitude)
    min_num : int
        passed on to intersect_index_quadrats: the minimum number of linear
        quadrat lines (e.g., min_num=3 would produce a quadrat grid of 4
        squares)
    buffer_amount : numeric
        passed on to intersect_index_quadrats: buffer the quadrat grid lines by
        quadrat_width times buffer_amount

    Returns
    -------
    networkx multidigraph
    """

    start_time = time.time()
    G = G.copy()
    log('Identifying all nodes that lie outside the polygon...')

    # get a GeoDataFrame of all the nodes, for spatial analysis
    node_geom = [Point(data['x'], data['y']) for _, data in G.nodes(data=True)]
    gdf_nodes = gpd.GeoDataFrame({'node':pd.Series(G.nodes()), 'geometry':node_geom})
    gdf_nodes.crs = G.graph['crs']

    # find all the nodes in the graph that lie outside the polygon
    points_within_geometry = intersect_index_quadrats(gdf_nodes, polygon, quadrat_width=quadrat_width, min_num=min_num, buffer_amount=buffer_amount)
    nodes_outside_polygon = gdf_nodes[~gdf_nodes.index.isin(points_within_geometry.index)]

    # now remove from the graph all those nodes that lie outside the place
    # polygon
    start_time = time.time()
    G.remove_nodes_from(nodes_outside_polygon['node'])
    log('Removed {:,} nodes outside polygon in {:,.2f} seconds'.format(len(nodes_outside_polygon), time.time()-start_time))

    # remove any isolated nodes and retain only the largest component (if retain_all is False)
    if not retain_all:
        G = remove_isolated_nodes(G)
        G = get_largest_component(G)

    return G


Instruction:
Make a summary of the following Python 3 code
def add_edge_lengths(G):

    start_time = time.time()

    # first load all the edges' origin and destination coordinates as a
    # dataframe indexed by u, v, key
    coords = np.array([[u, v, k, G.nodes[u]['y'], G.nodes[u]['x'], G.nodes[v]['y'], G.nodes[v]['x']] for u, v, k in G.edges(keys=True)])
    df_coords = pd.DataFrame(coords, columns=['u', 'v', 'k', 'u_y', 'u_x', 'v_y', 'v_x'])
    df_coords[['u', 'v', 'k']] = df_coords[['u', 'v', 'k']].astype(np.int64)
    df_coords = df_coords.set_index(['u', 'v', 'k'])

    # then calculate the great circle distance with the vectorized function
    gc_distances = great_circle_vec(lat1=df_coords['u_y'],
                                    lng1=df_coords['u_x'],
                                    lat2=df_coords['v_y'],
                                    lng2=df_coords['v_x'])

    # fill nulls with zeros and round to the millimeter
    gc_distances = gc_distances.fillna(value=0).round(3)
    nx.set_edge_attributes(G, name='length', values=gc_distances.to_dict())

    log('Added edge lengths to graph in {:,.2f} seconds'.format(time.time()-start_time))
    return G

Response: Adds length attribute to each edge in the graph G by great circle distance between nodes u and v.


Instruction:
Given the following Python 3 function, write the documentation
def add_path(G, data, one_way):

    # extract the ordered list of nodes from this path element, then delete it
    # so we don't add it as an attribute to the edge later
    path_nodes = data['nodes']
    del data['nodes']

    # set the oneway attribute to the passed-in value, to make it consistent
    # True/False values
    data['oneway'] = one_way

    # zip together the path nodes so you get tuples like (0,1), (1,2), (2,3)
    # and so on
    path_edges = list(zip(path_nodes[:-1], path_nodes[1:]))
    G.add_edges_from(path_edges, **data)

    # if the path is NOT one-way
    if not one_way:
        # reverse the direction of each edge and add this path going the
        # opposite direction
        path_edges_opposite_direction = [(v, u) for u, v in path_edges]
        G.add_edges_from(path_edges_opposite_direction, **data)

Response: Adds a path to the multidigraph G.


Instruction:
Can you generate the documentation for the following Python 3 function
def add_paths(G, paths, bidirectional=False):

    # the list of values OSM uses in its 'oneway' tag to denote True
    osm_oneway_values = ['yes', 'true', '1', '-1']

    for data in paths.values():

        # if this path is tagged as one-way and if it is not a walking network,
        # then we'll add the path in one direction only
        if ('oneway' in data and data['oneway'] in osm_oneway_values) and not bidirectional:
            if data['oneway'] == '-1':
                # paths with a one-way value of -1 are one-way, but in the
                # reverse direction of the nodes' order, see osm documentation
                data['nodes'] = list(reversed(data['nodes']))
            # add this path (in only one direction) to the graph
            add_path(G, data, one_way=True)

        elif ('junction' in data and data['junction'] == 'roundabout') and not bidirectional:
            # roundabout are also oneway but not tagged as is
            add_path(G, data, one_way=True)

        # else, this path is not tagged as one-way or it is a walking network
        # (you can walk both directions on a one-way street)
        else:
            # add this path (in both directions) to the graph and set its
            # 'oneway' attribute to False. if this is a walking network, this
            # may very well be a one-way street (as cars/bikes go), but in a
            # walking-only network it is a bi-directional edge
            add_path(G, data, one_way=False)

    return G

Response: Adds a collection of paths to the OSM tree G.


Instruction:
Can you generate the documentation for the following Python 3 function
def create_graph(response_jsons, name='unnamed', retain_all=False, bidirectional=False):

    log('Creating networkx graph from downloaded OSM data...')
    start_time = time.time()

    # make sure we got data back from the server requests
    elements = []
    for response_json in response_jsons:
        elements.extend(response_json['elements'])
    if len(elements) < 1:
        raise EmptyOverpassResponse('There are no data elements in the response JSON objects')

    # create the graph as a MultiDiGraph and set the original CRS to default_crs
    G = nx.MultiDiGraph(name=name, crs=settings.default_crs)

    # extract nodes and paths from the downloaded osm data
    nodes = {}
    paths = {}
    for osm_data in response_jsons:
        nodes_temp, paths_temp = parse_osm_nodes_paths(osm_data)
        for key, value in nodes_temp.items():
            nodes[key] = value
        for key, value in paths_temp.items():
            paths[key] = value

    # add each osm node to the graph
    for node, data in nodes.items():
        G.add_node(node, **data)

    # add each osm way (aka, path) to the graph
    G = add_paths(G, paths, bidirectional=bidirectional)

    # retain only the largest connected component, if caller did not
    # set retain_all=True
    if not retain_all:
        G = get_largest_component(G)

    log('Created graph with {:,} nodes and {:,} edges in {:,.2f} seconds'.format(len(list(G.nodes())), len(list(G.edges())), time.time()-start_time))

    # add length (great circle distance between nodes) attribute to each edge to
    # use as weight
    if len(G.edges) > 0:
        G = add_edge_lengths(G)

    return G

Response: Create a networkx graph from the OSM data.


Instruction:
Given the following Python 3 function, write the documentation
def bbox_from_point(point, distance=1000, project_utm=False, return_crs=False):

    # reverse the order of the (lat,lng) point so it is (x,y) for shapely, then
    # project to UTM and buffer in meters
    lat, lng = point
    point_proj, crs_proj = project_geometry(Point((lng, lat)))
    buffer_proj = point_proj.buffer(distance)

    if project_utm:
        west, south, east, north = buffer_proj.bounds
        log('Created bounding box {} meters in each direction from {} and projected it: {},{},{},{}'.format(distance, point, north, south, east, west))
    else:
        # if project_utm is False, project back to lat-long then get the
        # bounding coordinates
        buffer_latlong, _ = project_geometry(buffer_proj, crs=crs_proj, to_latlong=True)
        west, south, east, north = buffer_latlong.bounds
        log('Created bounding box {} meters in each direction from {}: {},{},{},{}'.format(distance, point, north, south, east, west))

    if return_crs:
        return north, south, east, west, crs_proj
    else:
        return north, south, east, west

Response: Create a bounding box around a point


Instruction:
Can you generate the documentation for the following Python 3 function
def graph_from_bbox(north, south, east, west, network_type='all_private',
                    simplify=True, retain_all=False, truncate_by_edge=False,
                    name='unnamed', timeout=180, memory=None,
                    max_query_area_size=50*1000*50*1000, clean_periphery=True,
                    infrastructure='way["highway"]', custom_filter=None):
    """
    Create a networkx graph from OSM data within some bounding box.

    Parameters
    ----------
    north : float
        northern latitude of bounding box
    south : float
        southern latitude of bounding box
    east : float
        eastern longitude of bounding box
    west : float
        western longitude of bounding box
    network_type : string
        what type of street network to get
    simplify : bool
        if true, simplify the graph topology
    retain_all : bool
        if True, return the entire graph even if it is not connected
    truncate_by_edge : bool
        if True retain node if it's outside bbox but at least one of node's
        neighbors are within bbox
    name : string
        the name of the graph
    timeout : int
        the timeout interval for requests and to pass to API
    memory : int
        server memory allocation size for the query, in bytes. If none, server
        will use its default allocation size
    max_query_area_size : float
        max size for any part of the geometry, in square degrees: any polygon
        bigger will get divided up for multiple queries to API
    clean_periphery : bool
        if True (and simplify=True), buffer 0.5km to get a graph larger than
        requested, then simplify, then truncate it to requested spatial extent
    infrastructure : string
        download infrastructure of given type (default is streets (ie, 'way["highway"]') but other
        infrastructures may be selected like power grids (ie, 'way["power"~"line"]'))
    custom_filter : string
        a custom network filter to be used instead of the network_type presets

    Returns
    -------
    networkx multidigraph
    """

    if clean_periphery and simplify:
        # create a new buffered bbox 0.5km around the desired one
        buffer_dist = 500
        polygon = Polygon([(west, north), (west, south), (east, south), (east, north)])
        polygon_utm, crs_utm = project_geometry(geometry=polygon)
        polygon_proj_buff = polygon_utm.buffer(buffer_dist)
        polygon_buff, _ = project_geometry(geometry=polygon_proj_buff, crs=crs_utm, to_latlong=True)
        west_buffered, south_buffered, east_buffered, north_buffered = polygon_buff.bounds

        # get the network data from OSM then create the graph
        response_jsons = osm_net_download(north=north_buffered, south=south_buffered,
                                          east=east_buffered, west=west_buffered,
                                          network_type=network_type, timeout=timeout,
                                          memory=memory, max_query_area_size=max_query_area_size,
                                          infrastructure=infrastructure, custom_filter=custom_filter)
        G_buffered = create_graph(response_jsons, name=name, retain_all=retain_all,
                                  bidirectional=network_type in settings.bidirectional_network_types)
        G = truncate_graph_bbox(G_buffered, north, south, east, west, retain_all=True, truncate_by_edge=truncate_by_edge)

        # simplify the graph topology
        G_buffered = simplify_graph(G_buffered)

        # truncate graph by desired bbox to return the graph within the bbox
        # caller wants
        G = truncate_graph_bbox(G_buffered, north, south, east, west, retain_all=retain_all, truncate_by_edge=truncate_by_edge)

        # count how many street segments in buffered graph emanate from each
        # intersection in un-buffered graph, to retain true counts for each
        # intersection, even if some of its neighbors are outside the bbox
        G.graph['streets_per_node'] = count_streets_per_node(G_buffered, nodes=G.nodes())

    else:
        # get the network data from OSM
        response_jsons = osm_net_download(north=north, south=south, east=east,
                                          west=west, network_type=network_type,
                                          timeout=timeout, memory=memory,
                                          max_query_area_size=max_query_area_size,
                                          infrastructure=infrastructure, custom_filter=custom_filter)

        # create the graph, then truncate to the bounding box
        G = create_graph(response_jsons, name=name, retain_all=retain_all,
                         bidirectional=network_type in settings.bidirectional_network_types)
        G = truncate_graph_bbox(G, north, south, east, west, retain_all=retain_all, truncate_by_edge=truncate_by_edge)

        # simplify the graph topology as the last step. don't truncate after
        # simplifying or you may have simplified out to an endpoint
        # beyond the truncation distance, in which case you will then strip out
        # your entire edge
        if simplify:
            G = simplify_graph(G)

    log('graph_from_bbox() returning graph with {:,} nodes and {:,} edges'.format(len(list(G.nodes())), len(list(G.edges()))))
    return  G

Response: Create a networkx graph from OSM data within some bounding box.


Instruction:
Can you write a function in Python 3 where it
creates a networkx graph from OSM data within some distance of some center point.

Response:
def graph_from_point(center_point, distance=1000, distance_type='bbox',
                     network_type='all_private', simplify=True, retain_all=False,
                     truncate_by_edge=False, name='unnamed', timeout=180,
                     memory=None, max_query_area_size=50*1000*50*1000,
                     clean_periphery=True, infrastructure='way["highway"]',
                     custom_filter=None):
    """
    Create a networkx graph from OSM data within some distance of some (lat,
    lon) center point.

    Parameters
    ----------
    center_point : tuple
        the (lat, lon) central point around which to construct the graph
    distance : int
        retain only those nodes within this many meters of the center of the
        graph, with distance determined according to distance_type argument
    distance_type : string
        {'network', 'bbox'} if 'bbox', retain only those nodes within a bounding
        box of the distance parameter. if 'network', retain only those nodes
        within some network distance from the center-most node.
    network_type : string
        what type of street network to get
    simplify : bool
        if true, simplify the graph topology
    retain_all : bool
        if True, return the entire graph even if it is not connected
    truncate_by_edge : bool
        if True retain node if it's outside bbox but at least one of node's
        neighbors are within bbox
    name : string
        the name of the graph
    timeout : int
        the timeout interval for requests and to pass to API
    memory : int
        server memory allocation size for the query, in bytes. If none, server
        will use its default allocation size
    max_query_area_size : float
        max size for any part of the geometry, in square degrees: any polygon
        bigger will get divided up for multiple queries to API
    clean_periphery : bool,
        if True (and simplify=True), buffer 0.5km to get a graph larger than
        requested, then simplify, then truncate it to requested spatial extent
    infrastructure : string
        download infrastructure of given type (default is streets (ie, 'way["highway"]') but other
        infrastructures may be selected like power grids (ie, 'way["power"~"line"]'))
    custom_filter : string
        a custom network filter to be used instead of the network_type presets

    Returns
    -------
    networkx multidigraph
    """

    if distance_type not in ['bbox', 'network']:
        raise InvalidDistanceType('distance_type must be "bbox" or "network"')

    # create a bounding box from the center point and the distance in each
    # direction
    north, south, east, west = bbox_from_point(center_point, distance)

    # create a graph from the bounding box
    G = graph_from_bbox(north, south, east, west, network_type=network_type, simplify=simplify,
                        retain_all=retain_all, truncate_by_edge=truncate_by_edge, name=name,
                        timeout=timeout, memory=memory, max_query_area_size=max_query_area_size,
                        clean_periphery=clean_periphery, infrastructure=infrastructure,
                        custom_filter=custom_filter)

    # if the network distance_type is network, find the node in the graph
    # nearest to the center point, and truncate the graph by network distance
    # from this node
    if distance_type == 'network':
        centermost_node = get_nearest_node(G, center_point)
        G = truncate_graph_dist(G, centermost_node, max_distance=distance)

    log('graph_from_point() returning graph with {:,} nodes and {:,} edges'.format(len(list(G.nodes())), len(list(G.edges()))))
    return G


Instruction:
Can you write a function in Python 3 where it
creates a networkx graph from OSM data within some distance of some address.

Response:
def graph_from_address(address, distance=1000, distance_type='bbox',
                       network_type='all_private', simplify=True, retain_all=False,
                       truncate_by_edge=False, return_coords=False,
                       name='unnamed', timeout=180, memory=None,
                       max_query_area_size=50*1000*50*1000,
                       clean_periphery=True, infrastructure='way["highway"]',
                       custom_filter=None):
    """
    Create a networkx graph from OSM data within some distance of some address.

    Parameters
    ----------
    address : string
        the address to geocode and use as the central point around which to
        construct the graph
    distance : int
        retain only those nodes within this many meters of the center of the
        graph
    distance_type : string
        {'network', 'bbox'} if 'bbox', retain only those nodes within a bounding
        box of the distance parameter.
        if 'network', retain only those nodes within some network distance from
        the center-most node.
    network_type : string
        what type of street network to get
    simplify : bool
        if true, simplify the graph topology
    retain_all : bool
        if True, return the entire graph even if it is not connected
    truncate_by_edge : bool
        if True retain node if it's outside bbox but at least one of node's
        neighbors are within bbox
    return_coords : bool
        optionally also return the geocoded coordinates of the address
    name : string
        the name of the graph
    timeout : int
        the timeout interval for requests and to pass to API
    memory : int
        server memory allocation size for the query, in bytes. If none, server
        will use its default allocation size
    max_query_area_size
        float, max size for any part of the geometry, in square degrees: any
        polygon bigger will get divided up for multiple queries to API
    clean_periphery : bool,
        if True (and simplify=True), buffer 0.5km to get a graph larger than
        requested, then simplify, then truncate it to requested spatial extent
    infrastructure : string
        download infrastructure of given type (default is streets (ie, 'way["highway"]') but other
        infrastructures may be selected like power grids (ie, 'way["power"~"line"]'))
    custom_filter : string
        a custom network filter to be used instead of the network_type presets

    Returns
    -------
    networkx multidigraph or tuple
        multidigraph or optionally (multidigraph, tuple)
    """

    # geocode the address string to a (lat, lon) point
    point = geocode(query=address)

    # then create a graph from this point
    G = graph_from_point(point, distance, distance_type, network_type=network_type,
                         simplify=simplify, retain_all=retain_all, truncate_by_edge=truncate_by_edge,
                         name=name, timeout=timeout, memory=memory,
                         max_query_area_size=max_query_area_size,
                         clean_periphery=clean_periphery, infrastructure=infrastructure,
                         custom_filter=custom_filter)
    log('graph_from_address() returning graph with {:,} nodes and {:,} edges'.format(len(list(G.nodes())), len(list(G.edges()))))

    if return_coords:
        return G, point
    else:
        return G