Get the inverse function of a polyfit in numpy

Here's an example that shows how you can combine your polywith my answer at Inverse function of numpy.polyval().

First some data:

In [44]: xOut[44]: array([0, 1, 2, 3, 4, 5, 6, 7])In [45]: yOut[45]: array([ 9,  4,  0, -1, -1,  4,  8, 16])

Fit a polynomial to the data:

In [46]: poly = np.polyfit(x, y, 2)

Find where the polynomial has the value y0

In [47]: y0 = 4

To do that, create a poly1d object:

In [48]: p = np.poly1d(poly)

And find the roots of p - y0:

In [49]: (p - y0).rootsOut[49]: array([ 5.21787721,  0.90644711])

Check:

In [54]: x0 = (p - y0).rootsIn [55]: p(x0)Out[55]: array([ 4.,  4.])

python numpy polynomials

np.polyfit returns the coefficients of the best fit polynomial, highest first. Thus your poly contains c2, c1, c0 and you have to solve

.      2.   c x + c x + c  = y.    2     1     0

The solution can be found in many places for example here.

python numpy polynomials

Following on Warren's answer

    p = np.poly1d(m1)    print(p)    y0 = returns[sr.argmax()]    x0 = p(y0)    #x0 = p((p - y0).roots)    print(x0)    print(y0)

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