added code and screenshots regarding signal approximation

Author: n19k6

.gitignore

@@ -1,4 +1,5 @@
 *~
+.~lock*
 *.pyc
 __pycache__/
 venv/

v11_honda_v1/02_signal_synthesis/bezier.svg

@@ -0,0 +1,57 @@
+# 2*pi is one period use degrees internally
+# signal is 9+3
+
+#https://people.cs.clemson.edu/~dhouse/courses/405/notes/splines.pdf
+#https://www.youtube.com/watch?v=bn8iJKFldq0
+
+#python -m venv venv
+#pip install scipy
+#pip install matplotlib
+
+#def signal_degree(x):
+#    return x % 360
+
+#print(signal_degree(30.1))
+#print(signal_degree(30.1+360))
+
+
+#https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.BPoly.from_derivatives.html
+#https://docs.scipy.org/doc/scipy/tutorial/interpolate.html
+
+from scipy.interpolate import BPoly
+#function1 = BPoly.from_derivatives([0, 1, 30], [[0,0],[1,0],[-1,1]])
+#function2 = BPoly.from_derivatives([0, 1, 30], [[0,0],[1,0],[-1,1]], orders=1)
+#print(function1(1))
+
+#0,3.75,7.5,11.25,15,18.75,22.5,26.25
+#[0,0],[0,0],[0,0.02],[1,0],[0,-2.6],[-1,0],[0,0.02],[0,0]
+
+#function1 = BPoly.from_derivatives(x, y, orders=1)
+#function2 = BPoly.from_derivatives([0,3.75,7.5,11.25,15,18.75,22.5,26.25], [[0,0],[0,0],[0,0.02],[1,0],[0,-0.6],[-1,0],[0,0.02],[0,0]])
+
+
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = [e/100 for e in range(0,36000, 375)]
+x2 = [e/1000 for e in range(0,360000, 375)]
+y = [0,0,0,1,0,-1,0,0]*9+[0,0,0,0,0,0,0,0]*3
+fp = [[0,0],[0,0],[0,0],[1,0],[0,0],[-1,0],[0,0],[0,0]]*9+[[0,0],[0,0],[0,0],[0,0],[0,0],[0,0],[0,0],[0,0]]*3
+gp = [[0,0],[0,0],[0,0.02],[1,0],[0,-0.6],[-1,0],[0,0.02],[0,0]]*9+[[0,0],[0,0],[0,0],[0,0],[0,0],[0,0],[0,0],[0,0]]*3
+hp = [[0,0],[0,0],[0.05,0.02],[1,0],[0,-0.6],[-1,0],[-0.05,0.02],[0,0]]*9+[[0,0],[0,0],[0,0],[0,0],[0,0],[0,0],[0,0],[0,0]]*3
+
+f = BPoly.from_derivatives(x, fp, orders=1)
+g = BPoly.from_derivatives(x, gp)
+h = BPoly.from_derivatives(x, hp)
+
+#x = [0,3.75,7.5,11.25,15,18.75,22.5,26.25]
+#y = [0,0,0,1,0,-1,0,0]
+#y1 = [function1(e) for e in x] 
+#y2 = [function2(e) for e in x] 
+#plt.plot(x, y, 'o', y2)
+#plt.legend(['data', 'linear', 'cubic'], loc='best')
+plt.plot(x, y, 'bo', x, [f(e) for e in x], 'g--', x2, [g(e) for e in x2], 'r', x2, [h(e) for e in x2], 'b')
+plt.axis([0,360, -1.25, 1.25])
+plt.show()