import matplotlib.pyplot as plt
import numpy as np
import cascade_creation
import rip_condition


def plot_rip_numberofnodes(max_proba, n_min, n_max, p_init, n_cascades, K_max):
    """
    Plots the RIP constant for varying number of nodes (n_max included)
    """
    x = np.arange(n_min, n_max+1)
    y = []

    for n_nodes in x:
        print n_nodes
        G = cascade_creation.InfluenceGraph(max_proba=.3)
        G.erdos_init(n=n_nodes, p=.1) #TODO: handle different inits!
        cascades = cascade_creation.generate_cascades(G, p_init=p_init,
                                                      n_cascades=n_cascades)
        M, __ = cascade_creation.icc_matrixvector_for_node(cascades, None)
        M = cascade_creation.normalize_matrix(M)
        y.append(rip_condition.find_kth_rip_constants(M, 4)) #

    print y

    plt.clf()
    plt.plot(x, y)
    #plt.show()

    return x, y


def test():
    """
    unit test
    """
    plot_rip_numberofnodes(max_proba=.3, n_min=30, n_max=30,
                            p_init=.01, n_cascades=100, K_max=4)

if __name__=="__main__":
    test()