plot_heating_correlations.py 文件源码

def FitPolynomial(fig, x, y, n):

#solution, res = np.polynomial.polynomial.polyfit(x,y,order,full=True)

    solution, C_p = np.polyfit(x, y, n, cov=True)  # C_z is estimated covariance matrix

    # Do the interpolation for plotting:
    xrange = fig.get_xlim()
    t = np.linspace(xrange[0],xrange[1],100)
    # Matrix with rows 1, t, t**2, ...:
    TT = np.vstack([t**(n-i) for i in range(n+1)]).T
    yi = np.dot(TT, solution)  # matrix multiplication calculates the polynomial values
    C_yi = np.dot(TT, np.dot(C_p, TT.T)) # C_y = TT*C_z*TT.T
    sig_yi = np.sqrt(np.diag(C_yi))  # Standard deviations are sqrt of diagonal


    fig.plot(t,yi, 'k-')
    # fig.plot(t,yi+sig_yi, 'k--')
    # fig.plot(t,yi-sig_yi, 'k--')

    return solution

# Take the log of the y value