xplodnow - 9 months ago 83

Python Question

I am using Scipy to fit my data to a function. The function give me values for 2 parameters, in this case ** a** and

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!

Answer Source

`bounds=[[0,50],[0,0.3]])`

means the second parameter is greater than 50 but smaller then 0.3. Also the first parameter is fixed at zero.

The format is bounds=(lower, upper).