scipy - Trying to interpolate linearly in python -

i have 3 arrays: a, b, c length 15.

a=[950, 850, 750, 675, 600, 525, 460, 400, 350, 300, 250, 225, 200, 175, 150]   b = [16, 12, 9, -35, -40, -40, -40, -45, -50, -55, -60, -65, -70, -75, -80]  c=[32.0, 22.2, 12.399999999999999, 2.599999999999998, -7.200000000000003, -17.0, -26.800000000000004, -36.60000000000001, -46.400000000000006, -56.2, -66.0, -75.80000000000001, -85.60000000000001, -95.4, -105.20000000000002]  

i trying find value of @ index b=c. t

the problem there no place b=c need linearly interpolate between values in array find value of b=c. make sense?

i thinking using scipy.interpolate interpolation.

i having hard time wrappying mind around how solve problem. ideas on great!

here's simpler variation of function another answer of mine:

from __future__ import division  import numpy np   def find_roots(t, y):     """     given input signal `y` samples @ times `t`,     find times `y` 0.      `t` , `y` must 1-d numpy arrays.      linear interpolation used estimate time `t` between     samples @ sign changes in `y` occur.     """     # find y crosses 0.     transition_indices = np.where(np.sign(y[1:]) != np.sign(y[:-1]))[0]      # linearly interpolate time values transition occurs.     t0 = t[transition_indices]     t1 = t[transition_indices + 1]     y0 = y[transition_indices]     y1 = y[transition_indices + 1]     slope = (y1 - y0) / (t1 - t0)     transition_times = t0 - y0/slope      return transition_times 

that function can used t = a , y = b - c. example, here data, entered numpy arrays:

in [354]: = np.array([950, 850, 750, 675, 600, 525, 460, 400, 350, 300, 250, 225, 200, 175, 150])  in [355]: b = np.array([16, 12, 9, -35, -40, -40, -40, -45, -50, -55, -60, -65, -70, -75, -80])  in [356]: c = np.array([32.0, 22.2, 12.399999999999999, 2.599999999999998, -7.200000000000003, -17.0, -26.800000000000004, -3      ...: 6.60000000000001, -46.400000000000006, -56.2, -66.0, -75.80000000000001, -85.60000000000001, -95.4, -105.2000000000      ...: 0002]) 

the place "b = c" place "b - c = 0", pass b - c y:

in [357]: find_roots(a, b - c) out[357]: array([ 312.5]) 

so linearly interpolated value of a 312.5.

with following matplotlib commands:

in [391]: plot(a, b, label="b") out[391]: [<matplotlib.lines.line2d @ 0x11eac8780>]  in [392]: plot(a, c, label="c") out[392]: [<matplotlib.lines.line2d @ 0x11f23aef0>]  in [393]: roots = find_roots(a, b - c)  in [394]: [axvline(root, color='k', alpha=0.2) root in roots] out[394]: [<matplotlib.lines.line2d @ 0x11f258208>]  in [395]: grid()  in [396]: legend(loc="best") out[396]: <matplotlib.legend.legend @ 0x11f260ba8>  in [397]: xlabel("a") out[397]: <matplotlib.text.text @ 0x11e71c470> 

i plot