Lambda and string fit functions

Uses the last of the alphabetical sorted variables as the x-axis value unless xvar is set.

[1]:

import numpy as np
from smpl import plot
from smpl import stat
from smpl import functions as f
import uncertainties.unumpy as unp
import smpl

[2]:

x= np.linspace(-5,5,10)
y = stat.noisy(np.exp(2*x))+stat.noisy(x)
ff = plot.fit(x, y,"exp(a*x)*phi" ,xvar="a", fmt='.', label='data', xaxis="x in a.u.",yaxis="y in a.u.",sigmas=1)

../../../_images/example_fit_lambda_test_lambda_2_0.png

lambda fit does not work with sigma=N, as it needs unp.exp

[3]:

x= np.linspace(-5,5,10)
y = stat.noisy(np.exp(2*x))+stat.noisy(x)
ff = plot.fit(x, y,lambda x,f_mu,c: unp.exp(x*f_mu)*c , fmt='.', label='data', xaxis="x in a.u.",yaxis="y in a.u.")

../../../_images/example_fit_lambda_test_lambda_4_0.png

[4]:

x= np.linspace(-5,5,10)
y = stat.noisy(np.exp(2*x))+stat.noisy(x)
ff = plot.fit(x, y,lambda x,nu,rho: unp.exp(x*nu)*rho,xvar="nu" , fmt='.', label='data', xaxis="$\\nu$ in a.u.",yaxis="y in a.u.",sigmas=1)

../../../_images/example_fit_lambda_test_lambda_5_0.png

[5]:

x= np.linspace(-5,5,10)
y = stat.noisy(np.exp(2*x))+stat.noisy(x)
def e(nu,x,rho):
    return unp.exp(x*nu)*rho
ff = plot.fit(x, y,e ,xvar="x", fmt='.', label='data', xaxis="x in a.u.",yaxis="y in a.u.",sigmas=1)

../../../_images/example_fit_lambda_test_lambda_6_0.png

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