import csv
import numpy as np
import os
import matplotlib.pyplot as plt
# This function is used to read the folders mentioned in folders.txt file.
def read_folders(file_name):
# Open folders.txt file and read all the lines in it.
files = []
with open(file_name) as file:
files = file.readlines()
files = [x.split()[0] for x in files]
return files
# This function is useful to read labels for each folder. You can change the label from the labels.txt file.
def readlabels(label_file):
# Open labels.txt file and read all the lines in it.
labels = []
with open(label_file) as file:
labels = file.readlines()
labels = [x.split('\n')[0] for x in labels]
return labels
# Read rot_err, trans_err from csv files.
def read_csv(folder_name):
data = []
# Each folder having results contain test.csv file with all the log.
# Read all data from the csv file.
with open(os.path.join(folder_name, 'test.csv')) as csvfile:
csvreader = csv.reader(csvfile)
for row in csvreader:
row = [float(x) for x in row]
data.append(row)
rot_err, trans_err = [], []
# Log stored is as per following sequence in csv files:
# Sr. No. [0], time taken [1], number of iterations [2], translation error [3], rotation error [4].
if folder_name[5:9]=='PNLK':
for data_i in data:
rot_err.append(data_i[2])
trans_err.append(data_i[1])
else:
for data_i in data:
rot_err.append(data_i[4])
trans_err.append(data_i[3])
return rot_err, trans_err
# It will count the total number of test cases having rotation error below certain threshold.
def count_success_rot(rot_err):
# A dictionary to store:
# key: rotation error for success criteria ([0, 180, 0.5] degree)
# value: total number of cases that passed this criteria
success_dict = {}
# Update dictionary with zero values for all keys.
for i in range(0,1805,5):
success_dict.update({i/10.0:0})
# Find the values for each key value in dictionary.
for rot in rot_err:
idx = -1
for i in range(1800,-5,-5):
if rot>i/10.0:
idx = i+5
break
for j in range(idx, 1805, 5):
success_dict[j/10.0] = success_dict[j/10.0]+1
return success_dict
def make_plot(files, labels):
plt.figure()
AUC = [] # Calculate Area under curve for each test folder. (quantification of success)
for file_idx in range(len(files)):
rot_err, trans_err = read_csv(files[file_idx])
success_dict = count_success_rot(rot_err)
x_range = list(success_dict.keys())
x_range.sort()
success = []
for i in x_range:
success.append(success_dict[i])
# Ratio of successful cases to total test cases.
success = np.array(success)/total_cases
area = np.trapz(success, dx=0.5)
AUC.append(area)
plt.plot(x_range, success, linewidth=6, label=labels[file_idx])
plt.xlabel('Rotation Error for Success Criteria', fontsize=40)
plt.ylabel('Success Ratio', fontsize=40)
plt.tick_params(labelsize=40, width=3, length=10)
plt.xticks(np.arange(0,180.5,30))
plt.yticks(np.arange(0,1.1,0.2))
plt.xlim(-0.5,180)
plt.ylim(0,1.01)
plt.grid(True)
plt.legend(fontsize=30, loc=4)
AUC = np.array(AUC)/180.0
print('Area Under Curve values: {}'.format(AUC))
np.savetxt('auc.txt',AUC)
if __name__=='__main__':
total_cases = 5070.0 # Total number of test cases used.
colors = [[102,102,255], [215,85,85], [128,128,128], [255,153,51], [153,51,255], [102,255,102]]
# colors = [[102,102,255], [215,85,85], [215,85,85], [215,85,85]]
colors = np.array(colors)/255.0
files = read_folders('folders.txt')
labels = readlabels('labels.txt')
make_plot(files, labels)
plt.show()
