Motor thrust/efficiency plotter script

thrust-plot.py

from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter


InterpolatedToFile = "plot.png"

MotorPropThrust = np.array([34,		77,		127,	198,	266,	354,	457,	555,	689,	805,	943,	1064,	1136,	1330])
MotorPropAmps 	= np.array([0.23,	0.48,	0.74,	1.15,	1.67,	2.41,	3.42,	4.29,	5.91,	7.59,	9.24,	10.48,	12.76,	16.38])
MotorPropVolts 	= np.array([16.66, 	16.66, 	16.66, 	16.66, 	16.66, 	16.66, 	16.52, 	16.52, 	16.38,	16.38, 	16.22, 	16.22, 	16.22, 	16.08]);
MotorPropTitle = "Flyduino X2212-1000kv, HQ 9043 Endurance, 16.8V"

#MotorPropThrust2 = np.array([24,		56,		101,	159,	221,	296,	388,	474,	584,	698,	806,	908,	1007,	1170])
#MotorPropAmps2 	= np.array([0.21,	0.41,	0.66,	1.02,	1.47,	2.18,	3.04,	3.74,	5,		6.78,	8.31,	10,		11.39,	14.12])
#MotorPropVolts2 	= np.array([15.5, 	15.5,	15.5,	15.5,	15.35,	15.35,	15.35,	15.35,	15.21,	15.20,	15.06,	15.06,	15.06,	14.91]);
#MotorPropTitle2 = "Flyduino X2212-1000kv, HQ 9043 Endurance, 15.5V"


MotorPropWatts = MotorPropAmps * MotorPropVolts
#MotorPropWatts2 = MotorPropAmps2 * MotorPropVolts2

Coefficients = np.polyfit(MotorPropThrust, MotorPropWatts, 2)
#Coefficients2 = np.polyfit(MotorPropThrust2, MotorPropWatts2, 2)

fig, ax1 = plt.subplots(figsize=(7,6))

fig.suptitle(MotorPropTitle)
plt.xlabel('Thrust [g]')
plt.xlim((0,1500))
plt.ylim((0,300))
plt.grid()

x = np.linspace(0,MotorPropThrust.max(),100)
y = Coefficients[0] * x * x + Coefficients[1] * x + Coefficients[2]
ax1.plot(x,y, color='#0000ff')

#x2 = np.linspace(0,MotorPropThrust2.max(),100)
#y2 = Coefficients2[0] * x2 * x2 + Coefficients2[1] * x2 + Coefficients2[2]
#ax1.plot(x2,y2, color='#8888ff')

plt.scatter(MotorPropThrust, MotorPropWatts, s=30, c='#0000ff')

#plt.scatter(MotorPropThrust2, MotorPropWatts2, s=30, c='#8888ff')

ax1.set_ylabel('Electrical power [W]', color='#000088')
for tl in ax1.get_yticklabels():
    tl.set_color('#000088')

ax2 = ax1.twinx()
ye = x/y
ax2.plot(x,ye,color='#880000')

#ye2 = x2/y2
#ax2.plot(x2,ye2,color='#ff4444')

ax2.set_ylabel('Efficiency [g/W]', color='#880000')
for tl in ax2.get_yticklabels():
    tl.set_color('#880000')

plt.savefig(InterpolatedToFile)