import math

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

from src import centrality
from src import plot
from src import fitting


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 apply(g, seed, weight, convex_hull, ax, ax2, method):
    # calculate centrality values
    vp, ep = method(g, weight=weight)
    vp.a = np.nan_to_num(vp.a) # correct floating point values

    # euklidian distance
    quantification = plot.quantification_data(g, vp, convex_hull)
    plot.quantification_plot(ax, quantification, None, None, "Euklidian Distance", None)

    # generate model based on convex hull and associated centrality values
    # path distance
    quantification = plot.quantification_data_path_distance(g, weight, vp, convex_hull)
    plot.quantification_plot(ax2, quantification, None, None, "Shortest Path Distance", None)


points, seed = random_graph(n=5000)
g, weight = spatial_graph(points)
g = GraphView(g)
# calculate convex hull
convex_hull = centrality.convex_hull(g)

fig = plt.figure(figsize=(21, 5))
ax1, ax2, ax3 = fig.subplots(1, 3)

# plot graph with convex_hull
# draw without any centrality measure `vp`
vp, ep = betweenness(g, weight=weight)
vp.a = np.nan_to_num(vp.a) # correct floating point values

plot.graph_plot(fig, ax1, g, vp, convex_hull, f"Pointcloud (seed: {seed})")

apply(g, seed, weight, convex_hull, ax2, ax3, betweenness)

fig.savefig(f"Distance_5000_betweenness_euklidian.svg", format='svg')