import math

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

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


def merfish():
    """
    Merfish dataset from `squidpy`.
    """
    adata = sq.datasets.merfish()
    adata = adata[adata.obs.Bregma == -9].copy()
    return adata


def mibitof():
    """
    Mibitof dataset from `squidpy`.
    """
    adata = sq.datasets.mibitof()
    return adata

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.obps['spatial_distances']` and `adata.obsm['spatial']` afterwards too.
    @return [Graph] generated networkx graph from adata['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

# generate spatial graph from a given dataset
# g, weight = spatial_graph(merfish().obsm['spatial'])
for i in range(1, 10):
    points, seed = random_graph()
    g, weight = spatial_graph(points)
    g = GraphView(g)

    # calculate centrality values
    vp = closeness(g, weight=weight)
    vp.a = np.nan_to_num(vp.a) # correct floating point values
    # ep.a = np.nan_to_num(ep.a) # correct floating point values

    # calculate convex hull
    convex_hull = centrality.convex_hull(g)

    # plot graph with convex_hull
    fig = plt.figure(figsize=(15, 5))
    ax0, ax1 = fig.subplots(1, 2)
    plot.graph_plot(fig, ax0, g, vp, convex_hull, f"Random Graph (seed: {seed})\nCloseness")

    # generate model based on convex hull and associated centrality values
    quantification = plot.quantification_data(g, vp, convex_hull)

    # optimize model's piece-wise linear function
    d = quantification[:, 0]
    C = quantification[:, 1]
    m_opt, c0_opt, b_opt = fitting.fit_piece_wise_linear(d, C)

    # TODO
    # should this be part of the plotting function itself, it should not be necessary for me to do this
    d_curve = np.linspace(min(d), max(d), 500)
    C_curve = np.piecewise(
        d_curve,
        [d_curve <= b_opt, d_curve > b_opt],
        [lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
    )
    # plot model containing modeled piece-wise linear function
    plot.quantification_plot(ax1, quantification, d_curve, C_curve, 'Models')

    # linear regression model
    m_reg, c_reg = fitting.fit_linear_regression(d, C)
    x = np.linspace(min(d), max(d), 500)
    y = m_reg * x + c_reg
    ax1.plot(x, y, color='k', linewidth=1, label="Simple Linear Regression")
    ax1.legend()

    fig.savefig(f"random_point_clouds/{i}_closeness.svg", format='svg')

    # ---------------------------------------------------------------------------------------------

    # calculate centrality values
    vp, ep = betweenness(g, weight=weight)
    vp.a = np.nan_to_num(vp.a) # correct floating point values
    # ep.a = np.nan_to_num(ep.a) # correct floating point values

    # calculate convex hull
    convex_hull = centrality.convex_hull(g)

    # plot graph with convex_hull
    fig = plt.figure(figsize=(15, 5))
    ax0, ax1 = fig.subplots(1, 2)
    plot.graph_plot(fig, ax0, g, vp, convex_hull, f"Random Graph (seed: {seed})\nBetweenness")

    # generate model based on convex hull and associated centrality values
    quantification = plot.quantification_data(g, vp, convex_hull)

    # optimize model's piece-wise linear function
    d = quantification[:, 0]
    C = quantification[:, 1]
    m_opt, c0_opt, b_opt = fitting.fit_piece_wise_linear(d, C)

    # TODO
    # should this be part of the plotting function itself, it should not be necessary for me to do this
    d_curve = np.linspace(min(d), max(d), 500)
    C_curve = np.piecewise(
        d_curve,
        [d_curve <= b_opt, d_curve > b_opt],
        [lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
    )
    # plot model containing modeled piece-wise linear function
    plot.quantification_plot(ax1, quantification, d_curve, C_curve, 'Models')

    # linear regression model
    m_reg, c_reg = fitting.fit_linear_regression(d, C)
    x = np.linspace(min(d), max(d), 500)
    y = m_reg * x + c_reg
    ax1.plot(x, y, color='k', linewidth=1, label="Simple Linear Regression")
    ax1.legend()

    fig.savefig(f"random_point_clouds/{i}_betweenness.svg", format='svg')

    # ---------------------------------------------------------------------------------------------
    
    # calculate centrality values
    vp = pagerank(g, weight=weight)
    vp.a = np.nan_to_num(vp.a) # correct floating point values
    # ep.a = np.nan_to_num(ep.a) # correct floating point values

    # calculate convex hull
    convex_hull = centrality.convex_hull(g)

    # plot graph with convex_hull
    fig = plt.figure(figsize=(15, 5))
    ax0, ax1 = fig.subplots(1, 2)
    plot.graph_plot(fig, ax0, g, vp, convex_hull, f"Random Graph (seed: {seed})\nPageRank")

    # generate model based on convex hull and associated centrality values
    quantification = plot.quantification_data(g, vp, convex_hull)

    # optimize model's piece-wise linear function
    d = quantification[:, 0]
    C = quantification[:, 1]
    m_opt, c0_opt, b_opt = fitting.fit_piece_wise_linear(d, C)

    # TODO
    # should this be part of the plotting function itself, it should not be necessary for me to do this
    d_curve = np.linspace(min(d), max(d), 500)
    C_curve = np.piecewise(
        d_curve,
        [d_curve <= b_opt, d_curve > b_opt],
        [lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
    )
    # plot model containing modeled piece-wise linear function
    plot.quantification_plot(ax1, quantification, d_curve, C_curve, 'Models')

    # linear regression model
    m_reg, c_reg = fitting.fit_linear_regression(d, C)
    x = np.linspace(min(d), max(d), 500)
    y = m_reg * x + c_reg
    ax1.plot(x, y, color='k', linewidth=1, label="Simple Linear Regression")
    ax1.legend()

    fig.savefig(f"random_point_clouds/{i}_pagerank.svg", format='svg')