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interactivelatgplot.py
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interactivelatgplot.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jul 26 08:44:40 2022
@author: RTB
"""
import numpy as np
import math
from scipy.signal import butter, lfilter#, freqz
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, RangeSlider, Button
import csv
sign = lambda x: math.copysign(1, x)
#todo
cols = ['latg', 'longg', 'speed', 'slipFL', 'slipFR', 'slipRL', 'slipRR']
rawdata = {}
#import data from csv
with open('lateralgdata.csv', encoding='ISO-8859-1') as rawcsv:
csvobject = csv.reader(rawcsv, delimiter='\t')
names = next(csvobject)
rawdata = {name:[] for name in names}
for row in csvobject:
for name, value in zip(names, row):
rawdata[name].append(abs(float(value)))
rawdata['speed'] = [x*3.6 for x in rawdata['speed']]
def butter_lowpass(cutoff, fs, order=5):
return butter(order, cutoff, fs=fs, btype='low', analog=False)
def butter_lowpass_filter(data, cutoff, fs, order=5):
b, a = butter_lowpass(cutoff, fs, order=order)
y = lfilter(b, a, data)
return y
# Filter requirements.
order = 6 #higher is steeper, see https://stackoverflow.com/questions/63320705/what-are-order-and-critical-frequency-when-creating-a-low-pass-filter-using
fs = 60.0 # sample rate, Hz
cutoff = 5.00 # desired cutoff frequency of the filter, Hz
# Filter the data
data = {}
for col in cols:
data[col] = butter_lowpass_filter(rawdata[col], cutoff, fs, order)
init_frontslip = (0.5, 2.0)
init_rearslip = (0.5, 2.0)
class LatGGraph ():
def __init__ (self):
self.data = data
self.degree = 2
def update_front(self, front):
self.update(front, self.rear_slip_slider.val)
def update_rear(self, rear):
self.update(self.front_slip_slider.val, rear)
# The function to be called anytime a slider's value changes
def update(self, front, rear):
frontminval = front[0]
frontmaxval = front[1]
rearminval = rear[0]
rearmaxval = rear[1]
newdata = [[speed, latg] for speed, latg, slipFL, slipFR, slipRL, slipRR in
zip(self.data['speed'], self.data['latg'],
self.data['slipFL'], self.data['slipFR'],
self.data['slipRL'], self.data['slipRR'])
if slipFL >= frontminval and slipFL <= frontmaxval and
slipRL >= rearminval and slipRL <= rearmaxval]
self.line.set_offsets(newdata)
x = [x[0] for x in sorted(newdata)]
y = [x[1] for x in sorted(newdata)]
z = np.polyfit(x, y, self.degree)
p = np.poly1d(z)
self.trendline.set_xdata(x)
self.trendline.set_ydata(p(x))
self.fig.canvas.draw_idle()
def run (self):
self.fig, self.ax = plt.subplots()
self.line = plt.scatter('speed', 'latg', data=data, s=1)
#create initial trendline
x = self.data['speed']
y = self.data['latg']
z = np.polyfit(x, y, self.degree)
p = np.poly1d(z)
self.trendline, = plt.plot(x, p(x))
self.ax.set_xlabel('Speed [km/h]')
self.ax.set_xlim(left=0)
self.ax.set_ylabel('Lateral G [G]')
self.ax.set_ylim(bottom=0)
self.ax.grid()
#add filter based on slip
# Make a horizontal slider to control the frequency.
self.axfrontslip = plt.axes([0.20, 0.15, 0.65, 0.03])
self.axrearslip = plt.axes([0.20, 0.1, 0.65, 0.03])
self.front_slip_slider = RangeSlider(
ax=self.axfrontslip,
label='Front slip []',
valmin=0.0,
valmax=4.0,
valinit=init_frontslip,
)
self.rear_slip_slider = RangeSlider(
ax=self.axrearslip,
label='Rear slip []',
valmin=0.0,
valmax=4.0,
valinit=init_rearslip,
)
#create space for sliders
plt.subplots_adjust(bottom=0.3)#left=0.25, bottom=0.25)
# register the update function with each slider
self.front_slip_slider.on_changed(self.update_front)
self.rear_slip_slider.on_changed(self.update_rear)
# Create a `matplotlib.widgets.Button` to reset the sliders to initial values.
self.resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
self.button = Button(self.resetax, 'Reset', hovercolor='0.975')
def reset(event):
self.front_slip_slider.reset()
self.rear_slip_slider.reset()
self.button.on_clicked(reset)
plt.ion()
plt.show()
self.update(init_frontslip, init_rearslip)
#track long g relative to speed
#add slider for positive and negative (so accel vs brake)
class LongGGraph ():
def __init__ (self):
self.data = data.copy()
self.degree = 2
# The function to be called anytime a slider's value changes
def update(self, direction):
newdata = [[speed, longg] for speed, longg in
zip(self.data['speed'], self.data['longg'])
if sign(longg) == direction]
self.line.set_offsets(newdata)
x = [x[0] for x in sorted(newdata)]
y = [x[1] for x in sorted(newdata)]
z = np.polyfit(x, y, self.degree)
p = np.poly1d(z)
self.trendline.set_xdata(x)
self.trendline.set_ydata(p(x))
self.fig.canvas.draw_idle()
def run (self):
self.fig, self.ax = plt.subplots()
self.line = plt.scatter('speed', 'longg', data=data, s=1)
#create initial trendline
x = self.data['speed']
y = self.data['longg']
z = np.polyfit(x, y, self.degree)
p = np.poly1d(z)
self.trendline, = plt.plot(x, p(x))
self.ax.set_xlabel('Speed [km/h]')
self.ax.set_xlim(left=0)
self.ax.set_ylabel('Longitudinal G [G]')
# self.ax.set_ylim(bottom=0)
self.ax.grid()
#add filter based on slip
# Make a horizontal slider to control the frequency.
self.axdirectionslip = plt.axes([0.20, 0.15, 0.65, 0.03])
# self.axrearslip = plt.axes([0.20, 0.1, 0.65, 0.03])
self.direction_slider = Slider(
ax=self.axdirectionslip,
label='Direction []',
valmin=-1.0,
valmax=1.0,
valsteps = [-1, 1],
valinit=1,
)
# self.rear_slip_slider = RangeSlider(
# ax=self.axrearslip,
# label='Rear slip []',
# valmin=0.0,
# valmax=4.0,
# valinit=init_rearslip,
# )
#create space for sliders
plt.subplots_adjust(bottom=0.3)#left=0.25, bottom=0.25)
# register the update function with each slider
self.direction_slider.on_changed(self.update)
# self.rear_slip_slider.on_changed(self.update_rear)
# Create a `matplotlib.widgets.Button` to reset the sliders to initial values.
# self.resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
# self.button = Button(self.resetax, 'Reset', hovercolor='0.975')
# def reset(event):
# self.front_slip_slider.reset()
# self.rear_slip_slider.reset()
# self.button.on_clicked(reset)
plt.ion()
plt.show()
self.update(1)
graph = LatGGraph()
graph.run()
# def example ():
# # The parametrized function to be plotted
# def f(t, amplitude, frequency):
# return amplitude * np.sin(2 * np.pi * frequency * t)
# t = np.linspace(0, 1, 1000)
# # Define initial parameters
# init_amplitude = 5
# init_frequency = 3
# # Create the figure and the line that we will manipulate
# fig, ax = plt.subplots()
# line, = plt.plot(t, f(t, init_amplitude, init_frequency), lw=2)
# ax.set_xlabel('Time [s]')
# # adjust the main plot to make room for the sliders
# plt.subplots_adjust(left=0.25, bottom=0.25)
# # Make a horizontal slider to control the frequency.
# axfreq = plt.axes([0.25, 0.1, 0.65, 0.03])
# freq_slider = Slider(
# ax=axfreq,
# label='Frequency [Hz]',
# valmin=0.1,
# valmax=30,
# valinit=init_frequency,
# )
# # Make a vertically oriented slider to control the amplitude
# axamp = plt.axes([0.1, 0.25, 0.0225, 0.63])
# amp_slider = Slider(
# ax=axamp,
# label="Amplitude",
# valmin=0,
# valmax=10,
# valinit=init_amplitude,
# orientation="vertical"
# )
# # The function to be called anytime a slider's value changes
# def update(val):
# line.set_ydata(f(t, amp_slider.val, freq_slider.val))
# fig.canvas.draw_idle()
# # register the update function with each slider
# freq_slider.on_changed(update)
# amp_slider.on_changed(update)
# # Create a `matplotlib.widgets.Button` to reset the sliders to initial values.
# resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
# button = Button(resetax, 'Reset', hovercolor='0.975')
# def reset(event):
# freq_slider.reset()
# amp_slider.reset()
# button.on_clicked(reset)
# plt.show()