[SciPy-User] (no subject)

[nxkryptor nxkr]

 1 from __future__ import division 2 from scipy import optimize 3
import matplotlib.pyplot as plt 4 import numpy as np 5  6 data =
open('data.dat', "r") 7 line = data.readline() 8 while
line.startswith('#'): 9 	line = data.readline()10 data_header =
line.split("\t")11 data_header[-1] = data_header[-1].strip()12 13 14
_data_ = np.genfromtxt('data.dat', skiprows=2, delimiter='\t', names =
data_header, dtype = None, unpack = True).transpose()15 data =
np.array(_data_.tolist())16 m = data.shape[0]17 n = data.shape[1] -
218 print m, n19 y_data = np.empty(shape=(m, n))20 for i in range(0,
m):21 	for j in range(0, n):22 		y_data[i, j] = (data[i, j+2])23 x =
_data_['X']24 z = _data_['Z']25 26 def quadratic_fit(x, a, b, c):27
	return a * x ** 2 + b * x + c28 29 fit_a = np.empty(shape = (m, 1))30
fit_b = np.empty(shape = (m, 1))31 fit_c = np.empty(shape = (m, 1))
z_fit = np.empty(shape=(m, len(z)))for j in range(m):
    x_fit = y_data[j, :]
    y_fit = z
 fit_a[j], fit_b[j], fit_c[j] = optimize.curve_fit(quadratic_fit,
x_fit, y_fit)[0]
fit_a_fit_a, fit_a_fit_b, fit_a_fit_c, =
optimize.curve_fit(quadratic_fit, x, fit_a)[0]
fit_b_fit_a, fit_b_fit_b, fit_b_fit_c, =
optimize.curve_fit(quadratic_fit, x, fit_b)[0]
fit_c_fit_a, fit_c_fit_b, fit_c_fit_c, =
optimize.curve_fit(quadratic_fit, x, fit_c)[0]
fit_a = fit_a_fit_a * x ** 2 + fit_a_fit_b * x + fit_a_fit_c
fit_b = fit_b_fit_a * x ** 2 + fit_b_fit_b * x + fit_b_fit_c
fit_c = fit_c_fit_a * x ** 2 + fit_c_fit_b * x + fit_c_fit_cfor j in
range(m):					z_fit[j, :] = (fit_a[j] * x_fit ** 2) + (fit_b[j] *
x_fit) + fit_c[j]
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