pcp_code.py

# reference:https://benalexkeen.com/parallel-coordinates-in-matplotlib/ 

import numpy as np 
import matplotlib.pyplot as plt
from matplotlib import ticker

# the reference set loaded as a numpy matrix
all_soln = np.loadtxt(open("GAA-reference-set.csv"), delimiter=",")
nd_indices = np.loadtxt(open("non-dominated-index.csv"), delimiter=",")

# identify and list the objectives of the reference set
objs = ['NOISE', 'WEMP', 'DOC', 'ROUGH', 'WFUEL', 'PURCH', 'RANGE', 'LDMAX', 'VCMAX', 'PFPF']

# create an array of integers ranging from 0 to the number of objectives
x = [i for i, _ in enumerate(objs)]

# sharey=False indicates that all the subplot y-axes will be set to different values
fig, ax = plt.subplots(1,len(x)-1, sharey=False, figsize=(15,5))

min_max_range = {}
for i in range(len(objs)):
    all_soln[:,i] = np.true_divide(all_soln[:,i] - min(all_soln[:,i]), np.ptp(all_soln[:,i]))
    min_max_range[objs[i]] = [min(all_soln[:,i]), max(all_soln[:,i]), np.ptp(all_soln[:,i])]
    
# enumerate through all the axes in the figure and plot the data
# only the first line of each 
# blue for the nondominated solutions
# grey or everthing else
for i, ax_i in enumerate(ax):
    for d in range(len(all_soln)):
        if ((d in nd_indices)== False):
            if (d == 0):
                ax_i.plot(objs, all_soln[d, :], color='lightgrey', alpha=0.3, label='Dominated', linewidth=3)
            else:
                ax_i.plot(objs, all_soln[d, :], color='lightgrey', alpha=0.3, linewidth=3)
    ax_i.set_xlim([x[i], x[i+1]])

for i, ax_i in enumerate(ax):
    for d in range(len(all_soln)):
        if (d in nd_indices):
            if (d == nd_indices[0]):
                ax_i.plot(objs, all_soln[d, :], color='c', alpha=0.7, label='Nondominated', linewidth=3)
            else:
                ax_i.plot(objs, all_soln[d, :], color='c', alpha=0.7, linewidth=3)
    ax_i.set_xlim([x[i], x[i+1]])

# function for setting ticks and tick_lables along the y-axis of each subplot
def set_ticks_for_axis(dim, ax_i, ticks):
    min_val, max_val, v_range = min_max_range[objs[dim]]
    step = v_range/float(ticks)
    tick_labels = [round(min_val + step*i, 2) for i in range(ticks)]
    norm_min = min(all_soln[:,dim])
    norm_range = np.ptp(all_soln[:,dim])
    norm_step =(norm_range/float(ticks-1))
    ticks = [round(norm_min + norm_step*i, 2) for i in range(ticks)]
    ax_i.yaxis.set_ticks(ticks)
    ax_i.set_yticklabels(tick_labels)

# enumerating over each axis in fig2
for i in range(len(ax)):
    ax[i].xaxis.set_major_locator(ticker.FixedLocator([i]))  # set tick locations along the x-axis
    set_ticks_for_axis(i, ax[i], ticks=10)   # set ticks along the y-axis

# create a twin axis on the last subplot of fig2
# this will enable you to label the last axis with y-ticks 
ax2 = plt.twinx(ax[-1])
dim = len(ax)
ax2.xaxis.set_major_locator(ticker.FixedLocator([x[-2], x [-1]]))
set_ticks_for_axis(dim, ax2, ticks=10)
ax2.set_xticklabels([objs[-2], objs[-1]])

plt.subplots_adjust(wspace=0, hspace=0.2, left=0.1, right=0.85, bottom=0.1, top=0.9)
ax[8].legend(bbox_to_anchor=(1.35, 1), loc='upper left', prop={'size': 14})
ax[0].set_ylabel("$\leftarrow$ Direction of preference", fontsize=12)
plt.title("PCP Example", fontsize=12)
plt.savefig("PCP_example.png")
plt.show()