I would like to identify the Weibull parameters (i.e. the shape and scale) of my data.
0.022988506
0.114942529
0.218390805
0.114942529
0.149425287
0.114942529
0.068965517
0.068965517
0.034482759
0.022988506
0.022988506
0.022988506
0.022988506
I've already tried what this answer proposed, and I'm using Python 3.4.
import scipy.stats as s
import numpy as np
from scipy import stats
def weib(x,n,a):
return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)
data = np.loadtxt("data1.csv")
print(data)
(loc, scale) = s.exponweib.fit_loc_scale(data, 1, 1)
print('loc is: ',loc, '
scale is: ', scale)
This gives me the following output:
[0.02298851 0.11494253 0.2183908 0.11494253 0.14942529 0.11494253 0.06896552 0.06896552 0.03448276 0.02298851 0.02298851 0.02298851 0.02298851]
loc is: 0.0574417296258
scale is: 0.0179259738449
I assume that the data in my csv file was read as x-input values, instead of the y-values of the Weibull function. When I add a second column (or row) with bin, it gives an error that string values can not be converted into floats.
How do I need to modify my csv file in order to use the data within as the y-values of the Weibull function?
I think my problem might be that I don't understand this line:
(loc, scale) = s.exponweib.fit_loc_scale(data, 1, 1)
What does 1, 1
represent here? The parameters should then not be negative.
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