import math

import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np
from graph_tool.all import *


def random_graph(n=5000, seed=None):
    """
    Uniformly random point cloud generation.
    `n` [int] Number of points to generate. Default 5000 seems like a good starting point in point density and corresponding runtime for the subsequent calculations.
    @return [numpy.ndarray] Array of shape(n, 2) containing the coordinates for each point of the generated point cloud.
    """
    if seed is None:
        import secrets
        seed = secrets.randbits(128)
    rng = np.random.default_rng(seed=seed)
    return rng.random((n, 2)), seed


def spatial_graph(adata):
    """
    Generate the spatial graph using delaunay for the given `adata`.
    `adata` will contain the calculated spatial graph contents in the keys
    adata.obsm['spatial']` in case the `adata` is created from a dataset of *squidpy*.
    @return [Graph] generated networkx graph from adata.obsp['spatial_distances']
    """
    g, pos = graph_tool.generation.triangulation(adata, type="delaunay")
    g.vp["pos"] = pos
    weight = g.new_edge_property("double")
    for e in g.edges():
        weight[e] = math.sqrt(sum(map(abs, pos[e.source()].a - pos[e.target()].a)))**2
    return g, weight


def draw_graph(G, ax, name):
    pos = G.vp["pos"]
    x = []
    y = []
    for v in G.vertices():
        # print(pos[v])
        ver = pos[v]
        x.append(ver[0])
        y.append(ver[1])

    # convex hull -> Bounding-Box
    # ch = LineCollection([convex_hull], colors=['g'], linewidths=1)
    # ax.add_collection(ch)

    # edges
    for e in G.edges():
        ex = [pos[e.source()][0], pos[e.target()][0]]
        ey = [pos[e.source()][1], pos[e.target()][1]]
        ax.add_collection(LineCollection([np.column_stack([ex, ey])], colors=['k'], linewidths=0.1))

    ax.scatter(x, y, s=1) # map closeness values as color mapping on the verticies
    ax.set_title(name)


#
# - Create a random point cloud and calculate a triangulation on it
# - For that graph calculate the convex hull
# - Draw the graph with the convex hull
# - For each centrality measure
#   - apply centrality measure to the next axis
# - Draw the corresponding resulting models into a grid
#
points, seed = random_graph(n=3000)
g, weight = spatial_graph(points)
g = GraphView(g)

# plot graph with convex_hull
fig_graph, ax_graph = plt.subplots(figsize=(15, 12))
draw_graph(g, ax_graph, f"Pointcould (seed: {seed} | n: 500)")
fig_graph.savefig("point_cloud_example.svg", format='svg')