Hello all.
Trying to find slope of function using numpy.
Getting close, but results are a bit off. Hope someone out here can
help.
import numpy as np
def deriv(y):
x = list(range(len(y)))
x.reverse() # Change from [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
x = np.array(x) # to [10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
y = np.array(y) # x.reverse() is used to put point 0 at end of
list.
z = np.polyfit(x, y, 2)
print np.poly1d(z)
# Returns:
# 2
# 3.142 x - 18.85 x + 35.13
# 2
# Should be closer to 3.142 x - 6.283 +
10 ????????????????????
return [z[0] * 2, z[1]]
if __name__=='__main__':
# range(-6,5,1)
# y = 3.141592 * x ** 2 - 6.283184 * x + 10 for x in range(-6, 5,
1)
# 160.796416, 119.95572, 85.398208, 57.12388, 35.132736,
19.424776, 10.0, 6.858408, 10.0, 19.424776, 35.132736
#
# y' = 6.283184 * x - 6.283184 for x in range(-6, 5, 1)
# -43.982288, -37.699104, -31.41592, -25.132736, -18.849552,
-12.566368, -6.283184, 0.0, 6.283184, 12.566368, 18.849552
#
z = deriv([160.796416, 119.95572, 85.398208, 57.12388, 35.132736,
19.424776, 10.0, 6.858408, 10.0, 19.424776, 35.132736])
for x in range(-6,5,1):
print str(w(x)) + ',' ,
# Returns:
# -56.548656, -50.265472, -43.982288, -37.699104, -31.41592,
-25.132736, -18.849552, -12.566368, -6.283184, -1.06581410364e-14,
6.283184
# Should be:
# -43.982288, -37.699104, -31.41592, -25.132736, -18.849552,
-12.566368, -6.283184, 0.0, 6.283184, 12.566368, 18.849552
# Note that the range is offset by 2 positions
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