I am using Scipy to fit my data to a function. The function give me values for 2 parameters, in this case a and b. I want to use the bound argument to limit the values these parameters can take, each have their own range of acceptable values.
Acceptable values: 15< a <50 and 0.05< b <0.2
I want to know how to implement them. The official documentation only shows how to do them for 1 parameter. This question is similiar to: Python curve fit library that allows me to assign bounds to parameters. Which also only tackles boundaries for 1 parameter.
Here is what i tried:
def Ebfit(x,a,b):
Eb_mean = a*(0.0256/kt) # Eb at bake temperature
Eb_sigma = b*Eb_mean
Foursigma = 4*Eb_sigma
Eb_a = np.linspace(Eb_mean-Foursigma,Eb_mean+Foursigma,N_Device)
dEb = Eb_a[1] - Eb_a[0]
pdfEb_a = spys.norm.pdf(Eb_a,Eb_mean,Eb_sigma)
## Retention Time
DMom = np.zeros(len(x),float)
tau = (1/f0)*np.exp(Eb_a)
for bb in range(len(x)):
DMom[bb]= (1 - 2*(sum(pdfEb_a*(1 - np.exp(np.divide(-x[bb],tau))))*dEb))
return DMom
time = datafile['time'][0:501]
Moment = datafile['25Oe'][0:501]
params,extras = curve_fit(Ebfit,time,Moment, p0=[20,0.1], bounds=[(15,50),(0.05,0.2)])
I have also tried the following variations to see if the parenthesis was the issue:
params,extras = curve_fit(Ebfit,time,Moment, p0=[20,0.1], bounds=[[15,50],[0.02,0.2]])
params,extras = curve_fit(Ebfit,time,Moment, p0=[20,0.1], bounds=((15,50),(0.02,0.2)))
But I get the same error for all of these variations
ValueError: Each lower bound mush be strictly less than each upper bound.
It only works with a single bound such as:
params,extras = curve_fit(Ebfit,time,Moment, p0=[20,0.1], bounds=[0,50])
Any help is appreciated. Thank you!
curve_fit.bounds
documentation that you don't understand how to do this? Note that the documentation mentions "2-tuple of array_like", so you may need to change those inner list into tuples. – user707650