bobbycar/logdata_visualization/analyze.py

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import matplotlib.pyplot as plt
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#import csv
import pandas as pd
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import numpy as np
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import argparse
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def mapRange(value, inMin, inMax, outMin, outMax,constrain=False):
if constrain:
return max(outMin, min(outMax, outMin + (((value - inMin) / (inMax - inMin)) * (outMax - outMin))))
else:
return outMin + (((value - inMin) / (inMax - inMin)) * (outMax - outMin))
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parser = argparse.ArgumentParser(description='Analyzes fixed csv logs from bobbycar')
parser.add_argument('-i', '--input', type=argparse.FileType('r'), required=True, help="input csv log file")
args = parser.parse_args()
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df = pd.read_csv(args.input.name)
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x = df['timestamp']
x = [i-x[0] for i in x] #offset time by starttime
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def template():
scattersize=1
scatteralpha=0.1
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
#plt.scatter(x,df['rpm_FrontL'], s=scattersize, alpha=scatteralpha, label="rpm_FrontL")
ax1.plot(x,np.array(df['temp_Front']), c='#0000ff', alpha=0.5, label="temp_Front")
ax1.plot(x,np.array(df['temp_Rear']), c='#ff0000', alpha=0.5, label="temp_Rear")
ax1.plot(x,np.array(df['temp_Air']), c='#00ff00', alpha=0.5, label="temp_Air")
#ax2.plot(x,np.array(df['throttle']), c='g', alpha=0.5, label="throttle")
#confidence
#confidence_FrontL=[abs(x-df['cmd_FrontL'][i-5 if i>4 else 0])+abs(x-df['cmd_FrontL'][i-10 if i>9 else 0])+abs(x-df['cmd_FrontL'][i-20 if i>19 else 0]) for i,x in enumerate(df['cmd_FrontL'])]
#confidence_FrontL=[mapRange(x,0,50,1.0,0.0,True) for x in confidence_FrontL]
#ax1.scatter(df['cmd_FrontL'],df['rpm_FrontL'], s=scattersize, alpha=scatteralpha, label="FrontL")
#ax1.scatter(df['cmd_FrontR'],df['rpm_FrontR'], s=scattersize, alpha=scatteralpha, label="FrontR")
#ax1.scatter(df['cmd_RearL'],df['rpm_RearL'], s=scattersize, alpha=scatteralpha, label="RearL")
#ax1.scatter(df['cmd_RearR'],df['rpm_RearR'], s=scattersize, alpha=scatteralpha, label="RearR")
ax1.set_xlabel('timestamp')
#plt.ylabel('data')
ax1.set_ylabel('first axis')
ax2.set_ylabel('second axis')
#plt.title('')
ax1.legend(loc='upper left')
ax2.legend(loc='upper right')
plt.show()
def rpmDifference():
scattersize=1
scatteralpha=0.1
fig, ax1 = plt.subplots()
#confidence_FrontL=[abs(x-df['cmd_FrontL'][i-5 if i>4 else 0])+abs(x-df['cmd_FrontL'][i-10 if i>9 else 0])+abs(x-df['cmd_FrontL'][i-20 if i>19 else 0]) for i,x in enumerate(df['cmd_FrontL'])]
frontRPMdiff=np.convolve(np.array(df['rpm_FrontL'])-np.array(df['rpm_FrontR']), np.ones(10)/10, mode='same')
rearRPMdiff=np.convolve(np.array(df['rpm_RearL'])-np.array(df['rpm_RearR']), np.ones(10)/10, mode='same')
ax1.plot(x,frontRPMdiff, c='#0000ff', alpha=0.5, label="rpm Difference Front")
ax1.plot(x,rearRPMdiff, c='#00ff00', alpha=0.5, label="rpm Difference Rear")
ax1.set_xlabel('timestamp')
ax1.set_ylabel('rpm Difference')
ax1.legend(loc='upper left')
plt.show()
def plot_rpmVsCurrent():
scattersize=2
scatteralpha=0.5
#confidence
#confidence_FrontL=[abs(x-df['cmd_FrontL'][i-5 if i>4 else 0])+abs(x-df['cmd_FrontL'][i-10 if i>9 else 0])+abs(x-df['cmd_FrontL'][i-20 if i>19 else 0]) for i,x in enumerate(df['cmd_FrontL'])]
#confidence_FrontL=[mapRange(x,0,50,1.0,0.0,True) for x in confidence_FrontL]
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fig, ax1 = plt.subplots()
ax1.scatter(df['rpm_FrontL'],df['current_FrontL'], s=5, alpha=scatteralpha, label="FrontL")
ax1.scatter(df['rpm_FrontR'],df['current_FrontR'], s=5, alpha=scatteralpha, label="FrontR")
#ax1.scatter(df['cmd_FrontR'],df['rpm_FrontR'], s=scattersize, alpha=scatteralpha, label="FrontR")
#ax1.scatter(df['cmd_RearL'],df['rpm_RearL'], s=scattersize, alpha=scatteralpha, label="RearL")
#ax1.scatter(df['cmd_RearR'],df['rpm_RearR'], s=scattersize, alpha=scatteralpha, label="RearR")
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ax1.set_xlabel('rpm')
ax1.set_ylabel('current (A)')
plt.title('rpm vs current')
ax1.legend(loc='upper left')
plt.show()
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#plot_rpmVsCurrent()
rpmDifference()
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exit()