Composite_optimization/visualizer.py at main · HippolyteLBRRR/Composite_optimization · GitHub

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import numpy as np
import scipy as scp
import matplotlib.pyplot as plt

class To_Plot:
    def __init__(self,cost,legend,xaxis=None,extra_points=None):
        self.cost=cost
        self.legend=legend
        if xaxis is None:
            self.xaxis = np.linspace(0,len(cost)-1,len(cost))
        else:
            self.xaxis = xaxis
        self.extra_points = extra_points

def Plot(tab,ite=True,costmin=None,plage=None,fontsize=None,style=None,
         eps=1e-8,colorstyle=None):
    """Fonction d'affichage de courbes de convergence.

    Parameters
    ----------

        tab : array of To_Plot
            Array containing the curves to plot and associated legends.
        ite : boolean, optional
            Boolean which states if the convergence curves are displayed
            according to the number of iterations or according to the
            array tab[?].xaxis
        costmin : float, optional
            Minimum F of the function to minimize.
        plage : array, optional
            Array containing two values [start,finish]. The function displays
            the curves from the start-th iteration to the finish-th iteration.
        fontsize : integer, optional
            Fontsize of the legend.
        style : array, optional
            Array having the same size as tab, containing the curve style of
            each curve. For example if there are two curves, style=['-','--']
            could be a choice.
        eps : float, optional
            Expected precision for the last iterates.
        colorstyle : array, optional
            Array having the same size as tab, containing the color of
            each curve.
    """
    plt.rcParams["figure.figsize"] = (30,12)
    if fontsize==None:
        fontsize=18
    n=len(tab)
    if costmin is None:
        cost_min=np.min(tab[0].cost)
        for i in range(1,n):
            temp=np.min(tab[i].cost)
            if temp<cost_min:cost_min=temp
        cost_min=cost_min*(1-eps)
    else:
        cost_min=costmin
    if plage is None:
        ite_start=0
        ite_end=-1
        xmin = np.infty
        xmax = -np.infty
    else:
        ite_start=int(plage[0])
        ite_end=int(plage[1])
        xmin = plage[0]
        xmax = plage[1]
    tab_leg=[]
    ite_max=0
    for i in range(n):
        if style==None:
            st='-'
        else:
            st=style[i]
        if colorstyle is None:
            col = ['r','b','g','y','k'][i%5]
        else:
            col = colorstyle[i]
        if ite:
            plt.plot(np.log10(tab[i].cost[ite_start:
                                          np.minimum(ite_end,
                                                     len(tab[i].cost))]
                              -cost_min),linestyle=st,color=col,label=tab[i].legend)
            n_ite=len(tab[i].cost[ite_start:
                                  np.minimum(ite_end,len(tab[i].cost))])
            if n_ite>ite_max:ite_max=n_ite
            if tab[i].extra_points is not None:
                x_index = tab[i].extra_points[tab[i].extra_points<len(tab[i].cost)]
                plt.scatter(x_index,np.log10(tab[i].cost[x_index]-cost_min),c=col)
        else:
            plt.plot(tab[i].xaxis,np.log10(tab[i].cost-cost_min),linestyle=st,color=col,label=tab[i].legend)
            if tab[i].extra_points is not None:
                x_index = tab[i].extra_points[tab[i].extra_points<len(tab[i].cost)]
                plt.scatter(tab[i].xaxis[x_index],np.log10(tab[i].cost[x_index]-cost_min),c=col)
            if tab[i].xaxis[0]<xmin and plage is None:xmin=tab[i].xaxis[0]
            if tab[i].xaxis[-1]>xmax and plage is None:xmax=tab[i].xaxis[-1]
    plt.legend(fontsize=fontsize)
    if ite:plt.xlim([0,ite_max])
    if ite==False:plt.xlim([xmin,xmax])
    pass