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restitution_packing_graphs.py
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restitution_packing_graphs.py
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"""
@author: Jack Richard Grogan
___ ________ ________ ___ __ ________
|\ \|\ __ \|\ ____\|\ \|\ \ |\ ____\
\ \ \ \ \|\ \ \ \___|\ \ \/ /|_ \ \ \___|
__ \ \ \ \ __ \ \ \ \ \ ___ \ \ \ \ ___
|\ \\_\ \ \ \ \ \ \ \____\ \ \\ \ \ \ \ \|\ \
\ \________\ \__\ \__\ \_______\ \__\\ \__\ \ \_______\
\|________|\|__|\|__|\|_______|\|__| \|__| \|_______|
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.legend_handler import HandlerTuple
# Setting out graph colours
colors = ['k','b', 'limegreen', 'r']
# Reading in data
df = pd.read_csv(r"final_paper_packing_results_restitution.csv")
df.rename({'Unnamed: 0': 'restitution_pw'}, axis=1, inplace=True)
df_restitution_pw = np.asarray(df['restitution_pw'])
s0_r0_columns = [column for column in df.columns if column.startswith("sliding_0_rolling_0")]
s1_r0_columns = [column for column in df.columns if column.startswith("sliding_1_rolling_0")]
s0_r1_columns = [column for column in df.columns if column.startswith("sliding_0_rolling_1")]
s1_r1_columns = [column for column in df.columns if column.startswith("sliding_1_rolling_1")]
df_s0_r0= df[s0_r0_columns].T
df_s1_r0= df[s1_r0_columns].T
df_s0_r1= df[s0_r1_columns].T
df_s1_r1= df[s1_r1_columns].T
# Converting data into arrays
repeats_s0_r0 = [0]*len(df_restitution_pw)
repeats_s1_r0 = [0]*len(df_restitution_pw)
repeats_s0_r1 = [0]*len(df_restitution_pw)
repeats_s1_r1 = [0]*len(df_restitution_pw)
for i in range(len(df_restitution_pw)):
repeats_s0_r0[i] = np.asarray(df_s0_r0[i])
repeats_s1_r0[i] = np.asarray(df_s1_r0[i])
repeats_s0_r1[i] = np.asarray(df_s0_r1[i])
repeats_s1_r1[i] = np.asarray(df_s1_r1[i])
data_s0_r0 = [0]*len(repeats_s0_r0[0])
data_s1_r0 = [0]*len(repeats_s1_r0[0])
data_s0_r1 = [0]*len(repeats_s0_r1[0])
data_s1_r1 = [0]*len(repeats_s1_r1[0])
for i in range(len(repeats_s0_r0[0])):
data_s0_r0[i] = np.asarray(df[s0_r0_columns[i]])
data_s1_r0[i] = np.asarray(df[s1_r0_columns[i]])
data_s0_r1[i] = np.asarray(df[s0_r1_columns[i]])
data_s1_r1[i] = np.asarray(df[s1_r1_columns[i]])
# Creating figure
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 7), layout = 'constrained')
# Creating scatterplot
scatter_s0_r0 = []
scatter_s0_r0_x_points = []
scatter_s1_r0 = []
scatter_s1_r0_x_points = []
scatter_s0_r1 = []
scatter_s0_r1_x_points = []
scatter_s1_r1 = []
scatter_s1_r1_x_points = []
for i in data_s0_r0:
for j in i:
scatter_s0_r0.append(j)
for i in range(len(data_s0_r0)):
for j in df_restitution_pw:
scatter_s0_r0_x_points.append(j)
for i in data_s1_r0:
for j in i:
scatter_s1_r0.append(j)
for i in range(len(data_s1_r0)):
for j in df_restitution_pw:
scatter_s1_r0_x_points.append(j)
for i in data_s0_r1:
for j in i:
scatter_s0_r1.append(j)
for i in range(len(data_s0_r1)):
for j in df_restitution_pw:
scatter_s0_r1_x_points.append(j)
for i in data_s1_r1:
for j in i:
scatter_s1_r1.append(j)
for i in range(len(data_s1_r1)):
for j in df_restitution_pw:
scatter_s1_r1_x_points.append(j)
s1 = plt.scatter(scatter_s0_r0_x_points, scatter_s0_r0, color = colors[0], alpha = 0.4, marker = 'x')#, label = "Particle-Particle SLiding Friction = 0 \nParticle-Particle Rolling Friction = 0 ")
s2 = plt.scatter(scatter_s1_r0_x_points, scatter_s1_r0, color = colors[1], alpha = 0.4, marker = 'x')#, label = "Particle-Particle SLiding Friction = 1 \nParticle-Particle Rolling Friction = 0 ")
s3 = plt.scatter(scatter_s0_r1_x_points, scatter_s0_r1, color = colors[2], alpha = 0.4, marker = 'x')#, label = "Particle-Particle SLiding Friction = 0 \nParticle-Particle Rolling Friction = 1 ")
s4 = plt.scatter(scatter_s1_r1_x_points, scatter_s1_r1, color = colors[3], alpha = 0.4, marker = 'x')#, label = "Particle-Particle SLiding Friction = 1 \nParticle-Particle Rolling Friction = 1")
# Creating line plot of means
mean_s0_r0 = []
std_s0_r0 = []
mean_s1_r0 = []
std_s1_r0 = []
mean_s0_r1 = []
std_s0_r1 = []
mean_s1_r1 = []
std_s1_r1 = []
for i in repeats_s0_r0:
mean_s0_r0.append(np.mean(i))
std_s0_r0.append(np.std(i))
for i in repeats_s1_r0:
mean_s1_r0.append(np.mean(i))
std_s1_r0.append(np.std(i))
for i in repeats_s0_r1:
mean_s0_r1.append(np.mean(i))
std_s0_r1.append(np.std(i))
for i in repeats_s1_r1:
mean_s1_r1.append(np.mean(i))
std_s1_r1.append(np.std(i))
# Creating shade of +- 1 standard deviation either side of the mean
top_line_s0_r0 = np.asarray(mean_s0_r0) + np.asarray(std_s0_r0)
bottom_line_s0_r0 = np.asarray(mean_s0_r0) - np.asarray(std_s0_r0)
top_line_s1_r0 = np.asarray(mean_s1_r0) + np.asarray(std_s1_r0)
bottom_line_s1_r0 = np.asarray(mean_s1_r0) - np.asarray(std_s1_r0)
top_line_s0_r1 = np.asarray(mean_s0_r1) + np.asarray(std_s0_r1)
bottom_line_s0_r1 = np.asarray(mean_s0_r1) - np.asarray(std_s0_r1)
top_line_s1_r1 = np.asarray(mean_s1_r1) + np.asarray(std_s1_r1)
bottom_line_s1_r1 = np.asarray(mean_s1_r1) - np.asarray(std_s1_r1)
p1, = plt.plot(df_restitution_pw, mean_s0_r0, color = colors[0], linewidth = 0.7)
f1 = plt.fill_between(df_restitution_pw, top_line_s0_r0, bottom_line_s0_r0, color=colors[0], alpha=.2)#, label = '1 Standard Deviation')
p2, = plt.plot(df_restitution_pw, mean_s1_r0, color = colors[1], linewidth = 0.7)
f2 = plt.fill_between(df_restitution_pw, top_line_s1_r0, bottom_line_s1_r0, color=colors[1], alpha=.2)#, label = '1 Standard Deviation')
p3, = plt.plot(df_restitution_pw, mean_s0_r1, color = colors[2], linewidth = 0.7)
f3 = plt.fill_between(df_restitution_pw, top_line_s0_r1, bottom_line_s0_r1, color=colors[2], alpha=.2)#, label = '1 Standard Deviation')
p4, = plt.plot(df_restitution_pw, mean_s1_r1, color = colors[3], linewidth = 0.7)
f4 = plt.fill_between(df_restitution_pw, top_line_s1_r1, bottom_line_s1_r1, color=colors[3], alpha=.2)#, label = '1 Standard Deviation')
# Figure formatting
plt.grid(which='major', color='k', linestyle='-', alpha = 0.5)
plt.grid(which='minor', color='black', linestyle='-', alpha = 0.2)
plt.minorticks_on()
plt.xlabel("Particle-Wall Restitution Coefficient (-)", fontsize = 11.5)
plt.ylabel("Final Packing Density (-)", fontsize = 11.5)
plt.xlim([0.1,1])
plt.ylim([0.55,0.67])
plt.tick_params(axis = 'both', labelsize = 11.5)
handles, labels = ax.get_legend_handles_labels()
legend_points = [(p1, s1), f1, (p2, s2), f2, (p3, s3), f3, (p4, s4), f4]
legend_labels = ["Particle-Particle: \nSliding Friction = 0 \nRolling Friction = 0",
"1 Standard \nDeviation",
"Particle-Particle \nSliding Friction = 1 \nRolling Friction = 0",
"1 Standard \nDeviation",
"Particle-Particle: \nSliding Friction = 0 \nRolling Friction = 1",
"1 Standard \nDeviation",
"Particle-Particle: \nSliding Friction = 1 \nRolling Friction = 1 ",
"1 Standard \nDeviation"]
plt.legend(legend_points, legend_labels, loc="upper left", bbox_to_anchor =(1, 1), ncol = 1, numpoints=8, handler_map={tuple: HandlerTuple(ndivide=None)}, frameon = False, labelspacing=1.9, fontsize = 11.5)
plt.savefig('method_4_procedure_1_restitution_graph', bbox_inches="tight")