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Projectile Velocity Comparison.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Feb 16 16:04:16 2022
@author: alfie
"""
import csv
import numpy as np
from matplotlib import pyplot as plt
from scipy.stats import linregress
plt.rcParams['figure.figsize'] = [6, 4]
plt.rcParams['figure.dpi'] = 250
M1Path = 'Model + Exp. Data CSVs/PR17.0g-BORE12.7mm-C8.0g.csv'
E1Path = 'Model + Exp. Data CSVs/Riad Model 1 8g12.7mm17g Projectile Velocity.csv'
def ReadIn(path, n=1, m=1e6):
""" Reads in data from a 2 column csv file and returns the columns values
as two lists """
x_data = []
y_data = []
with open(path, "r", encoding='utf-8-sig') as file:
reader = csv.reader(file)
for i, line in enumerate(reader):
x = float(line[0])
y = float(line[n]) / m
x_data.append(x)
y_data.append(y)
file.close()
return(x_data, y_data)
def Compare(modelPath, expPath, dx_e=0, dy_e=0):
""" Compares the model and experimental data. Can shift the experimental
in both x and y as specified"""
mod_x, mod_y = ReadIn(modelPath, n=4, m=1)
exp_x_unaltered, exp_y_unaltered = ReadIn(expPath, m=1)
exp_x = [x + dx_e for x in exp_x_unaltered]
exp_y = [y + dy_e for y in exp_y_unaltered]
xs = [exp_x_unaltered, exp_x, mod_x, ]
ys = [exp_y_unaltered, exp_y, mod_y]
labels = ['Unaltered Experimental', 'Altered Experimental', 'Model']
linestyles = ['-', '-', '-']
colors = ['lightgrey', 'orange', 'blue']
title = 'Projectile Velocity vs Time: Model vs Experimental'
xtitle = 'Time (s)'
ytitle = 'Velocity (mps)'
Plot(xs, ys, labels, linestyles, colors, title, xtitle, ytitle)
exp_x_trimmed = []
exp_y_trimmed = []
for x, y in zip(exp_x, exp_y):
if x > min(mod_x) and x < max(mod_x):
exp_x_trimmed.append(x)
exp_y_trimmed.append(y)
mod_y_aligned = np.interp(exp_x_trimmed, mod_x, mod_y)
mod_x_aligned = exp_x_trimmed
xs = [exp_x_trimmed, mod_x_aligned]
ys = [exp_y_trimmed, mod_y_aligned]
labels = ['Experimental', 'Model']
linestyles = ['-', '-']
colors = ['orange', 'blue']
title = 'Projectile Velocity vs Time: Model vs Experimental'
xtitle = 'Time (s)'
ytitle = 'Piston Velocity (mps)'
Plot(xs, ys, labels, linestyles, colors, title, xtitle, ytitle)
slope, intercept, r_value_alt, p_value, std_err = linregress(exp_y_trimmed, mod_y_aligned)
R2_alt = r_value_alt**2
print('Adusted Datasets have an R squared value of: ', R2_alt)
exp_x_unaltered_trimmed = []
exp_y_unaltered_trimmed = []
for x, y in zip(exp_x_unaltered, exp_y_unaltered):
if x > min(mod_x) and x < max(mod_x):
exp_x_unaltered_trimmed.append(x)
exp_y_unaltered_trimmed.append(y)
mod_y_aligned2 = np.interp(exp_x_unaltered_trimmed, mod_x, mod_y)
slope, intercept, r_value_unalt, p_value, std_err = linregress(exp_y_unaltered_trimmed, mod_y_aligned2)
R2_unalt = r_value_unalt**2
print('Unadusted Datasets have an R squared value of: ', R2_unalt)
xs = [exp_y_trimmed, exp_y_unaltered_trimmed, [0, max(mod_y_aligned)]]
ys = [mod_y_aligned, mod_y_aligned2, [0, max(mod_y_aligned)]]
labels = ['Altered Model vs Exp. (R2={})'.format(round(R2_alt, 3)), 'Unaltered Model vs Exp. (R2={})'.format(round(R2_unalt, 3)), 'x=y (R=1)']
linestyles = ['-', '-', '--']
colors = ['red', 'blue', 'k']
title = 'Projectile Velocity vs Time: Model vs Experimental'
xtitle = 'Experimental Piston Velocity (mps)'
ytitle = 'Model Piston Velocity (mps)'
Plot(xs, ys, labels, linestyles, colors, title, xtitle, ytitle, 2, True)
meansquared_sum = 0
meanvalue_sum = 0
for exp, mod in zip(exp_y_trimmed, mod_y_aligned):
squared_diff = (exp - mod)**2
meansquared_sum += squared_diff
meanvalue_sum += exp + mod
meansquared = meansquared_sum / len(exp_y_unaltered_trimmed)
meanvalue = meanvalue_sum / (2 * len(exp_y_unaltered_trimmed))
print('Mean Squared Difference = ', meansquared)
print('Mean value = ', meanvalue)
print(max(exp_y_trimmed))
print(max(mod_y_aligned))
print(-max(exp_y_trimmed) + max(mod_y_aligned))
def Plot(xs, ys, labels, linestyles, colors, title='', xtitle='', ytitle='', n_col=4, scatter=False):
fig, ax = plt.subplots()
fig.suptitle(title)
ax.set_xlabel(xtitle)
ax.set_ylabel(ytitle)
for n in range(0, len(xs)):
if n != len(xs) - 1 and scatter == True:
ax.scatter(xs[n], ys[n], label=labels[n], linestyle=linestyles[n], color=colors[n], s=4)
else:
ax.plot(xs[n], ys[n], label=labels[n], linestyle=linestyles[n], color=colors[n])
ax.grid()
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), fancybox=True,
shadow=True, ncol=n_col)
def DeltaTPeak(modelPath, expPath):
mod_x, mod_y = ReadIn(modelPath, n=3, m=1)
exp_x, exp_y = ReadIn(expPath, m=1)
mod_max = max(mod_y)
mod_max_index = mod_y.index(mod_max)
exp_max = max(exp_y)
exp_max_index = exp_y.index(exp_max)
timeshift = exp_x[exp_max_index] - mod_x[mod_max_index]
return(timeshift)
dt = -0.0019084999999999998 # DeltaTPeak(M1Path, E1Path)
Compare(M1Path, E1Path, dx_e=-dt)
"""
plt.rcParams['figure.figsize'] = [6, 4]
plt.rcParams['figure.dpi'] = 250
x1_data = []
y1_data = []
with open("Experimental 8g17g12.7mm.csv", "r", encoding='utf-8-sig') as file_exp:
reader = csv.reader(file_exp)
for i, line in enumerate(reader):
x1 = float(line[0])
y1 = float(line[1])
x1_data.append(x1)
y1_data.append(y1)
file_exp.close()
xValues = np.linspace(0, max(x1_data), 500)
yValues = np.interp(xValues, x1_data, y1_data)
fig, ax = plt.subplots()
ax.plot(x1_data, y1_data, color='Orange', label='Input Data')
ax.plot(xValues, yValues, label='Interpolated', linestyle='--', color='red')
ax.grid(True, color='dimgray', linestyle='--', linewidth=0.5)
ax.set_axisbelow(True)
ax.set_ylabel('y')
ax.set_xlabel('x')
ax.legend()
"""