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boxplot.py
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## numpy is used for creating fake data
import numpy as np
import os
import sys
import pandas as pd
import statistics as st
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="whitegrid")
###############################################################
save = True
LIM_Y_MAX = 0.15
LIM_Y_MIN = 0.05
DPI = 5000
input_dir = "/home/pablo/ws/log/errors"
print("Reading from", input_dir)
save_dir = "/home/pablo/ws/log"
save = True
ylabel_x = -0.11
ylabel_y = 0.5
###############################################################
ex = []
ex_trajs = []
ex_modes = []
ey = []
ey_trajs = []
ey_modes = []
for i in range(1, 6):
######### no pred
errors_no_pred_linear = pd.read_csv(input_dir + "/errors_NO_pred_l{}.csv".format( i))
errors_no_pred_circular = pd.read_csv(input_dir + "/errors_NO_pred_c{}.csv".format( i))
# linear
ex_no_pred_linear = errors_no_pred_linear['ex_real'].tolist()
ex.append(st.mean(ex_no_pred_linear))
ex_trajs.append('linear')
ex_modes.append('w/o pred')
ey_no_pred_linear = errors_no_pred_linear['ey_real'].tolist()
ey.append(st.mean(ey_no_pred_linear))
ey_trajs.append('linear')
ey_modes.append('w/o pred')
# circular
ex_no_pred_circular = errors_no_pred_circular['ex_real'].tolist()
ex.append(st.mean(ex_no_pred_circular))
ex_trajs.append('circular')
ex_modes.append('w/o pred')
ey_no_pred_circular = errors_no_pred_circular['ey_real'].tolist()
ey.append(st.mean(ey_no_pred_circular))
ey_trajs.append('circular')
ey_modes.append('w/o pred')
######### pred
errors_pred_linear = pd.read_csv(input_dir + "/errors_pred_l{}.csv".format( i))
errors_pred_circular = pd.read_csv(input_dir + "/errors_pred_c{}.csv".format( i))
# linear
ex_pred_linear = errors_pred_linear['ex_real'].tolist()
ex.append(st.mean(ex_pred_linear))
ex_trajs.append('linear')
ex_modes.append('w/ pred')
ey_pred_linear = errors_pred_linear['ey_real'].tolist()
ey.append(st.mean(ey_pred_linear))
ey_trajs.append('linear')
ey_modes.append('w/ pred')
# circular
ex_pred_circular = errors_pred_circular['ex_real'].tolist()
ex.append(st.mean(ex_pred_circular))
ex_trajs.append('circular')
ex_modes.append('w/ pred')
ey_pred_circular = errors_pred_circular['ey_real'].tolist()
ey.append(st.mean(ey_pred_circular))
ey_trajs.append('circular')
ey_modes.append('w/ pred')
###################################################################3
fig = plt.figure()
ex_dic = {'traj': ex_trajs,
'mode': ex_modes,
'ex': ex}
df_ex = pd.DataFrame(ex_dic, columns= ['traj', 'mode', 'ex'])
ax = sns.boxplot(y='ex', x='traj',
data=df_ex,
palette="colorblind",
hue='mode',
width=0.5)
ax.set(xlabel='trajectory', ylabel='error in x axis (m)')
ax.yaxis.set_label_coords(ylabel_x, ylabel_y)
for i,artist in enumerate(ax.artists):
# Set the linecolor on the artist to the facecolor, and set the facecolor to None
col = artist.get_facecolor()
artist.set_edgecolor(col)
if save:
fig.savefig(os.path.join(save_dir, "boxplot_ex.pdf"), format='pdf', dpi=DPI)
###################################################################
fig = plt.figure()
ey_dic = {'traj': ey_trajs,
'mode': ey_modes,
'ey': ey}
df_ey = pd.DataFrame(ey_dic, columns= ['traj', 'mode', 'ey'])
ax = sns.boxplot(y='ey', x='traj',
data=df_ey,
palette="colorblind",
hue='mode',
width=0.5)
ax.set(xlabel='trajectory', ylabel='error in y axis (m)')
ax.yaxis.set_label_coords(ylabel_x, ylabel_y)
for i,artist in enumerate(ax.artists):
# Set the linecolor on the artist to the facecolor, and set the facecolor to None
col = artist.get_facecolor()
print(col)
artist.set_edgecolor(col)
if save:
fig.savefig(os.path.join(save_dir, "boxplot_ey.pdf"), format='pdf', dpi=DPI)
plt.show()