I am trying to plot a sin function to a data set. I found a tutorial online using scipy.optimize, but it seems not to work even though I copied the code exactly.
At the top:
def func(x, a, b, c, d):
return a * np.sin(b * (x + c)) + d
At the end:
scipy.optimize.curve_fit(func, clean_time, clean_rate)
pylab.show()
There is no line on the output window.
If anyone would like screencaps or the whole code, feel free to comment below.
Thanks!
of course it does not plot anything, curve_fit does not plot.
Look in the documentation, the return values of curve_fit are an array with the estimated parameters and a 2d array with the estimated covariance matrix. You have to plot the fit-function with the estimated parameters yourself.
I also would suggest to fit a*sin(bx +c) +d as then b and c are not correlated.
this works:
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import normal
from scipy.optimize import curve_fit
x_data = np.linspace(0, 2*np.pi, 30)
y_data = np.sin(x_data) + normal(0, 0.2, 30)
def func(x, a, b, c, d):
return a * np.sin(b*x + c) + d
parameter, covariance_matrix = curve_fit(func, x_data, y_data)
x = np.linspace(min(x_data), max(x_data), 1000)
plt.plot(x_data, y_data, 'rx', label='data')
plt.plot(x, func(x, *parameter), 'b-', label='fit') # the star is to unpack the parameter array
plt.show()
this is the result:
np.array([best_a, best_b, best_c, best_d]). you can print them and get every value of the fuction at position x with f(x, *parameter)I have an example code with data in the form a sine curve and a user defined function that fits the data.
Here is the code:
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import math
xdata = np.array([2.65, 2.80, 2.96, 3.80, 3.90, 4.60, 4.80, 4.90, 5.65, 5.92])
ydata = np.sin(xdata)
def func(x,p1,p2,p3): # HERE WE DEFINE A SIN FUNCTION THAT WE THINK WILL FOLLOW THE DATA DISTRIBUTION
return p1*np.sin(x*p2+p3)
# Here you give the initial parameters for p0 which Python then iterates over
# to find the best fit
popt, pcov = curve_fit(func,xdata,ydata,p0=(1.0,1.0,1.0)) #THESE PARAMETERS ARE USER DEFINED
print(popt) # This contains your two best fit parameters
# Performing sum of squares
p1 = popt[0]
p2 = popt[1]
p3 = popt[2]
residuals = ydata - func(xdata,p1,p2,p3)
fres = sum(residuals**2)
print(fres) #THIS IS YOUR CHI-SQUARE VALUE!
xaxis = np.linspace(1,7,100) # we can plot with xdata, but fit will not look good
curve_y = func(xaxis,p1,p2,p3)
plt.plot(xdata,ydata,'*')
plt.plot(xaxis,curve_y,'-')
plt.show()
