I am having some issues with a pretty simple code I have written. I have 4 sets of data, and want to generate polynomial best fit lines using numpy polyfit. 3 of the lists yield numbers when using polyfit, but the third data set yields NAN when using polyfit. Below is the code and the print out. Any ideas?
Code:
ind_1=np.array(ind_1, np.float)
dep_1=np.array(dep_1, np.float)
x_1=np.arange(min(ind_1)-1, max(ind_1)+1, .01)
ind_2=np.array(ind_2, np.float)
dep_2=np.array(dep_2, np.float)
x_2=np.arange(min(ind_2)-1, max(ind_2)+1, .01)
ind_3=np.array(ind_3, np.float)
dep_3=np.array(dep_3, np.float)
x_3=np.arange(min(ind_3)-1, max(ind_3)+1, .01)
ind_4=np.array(ind_4, np.float)
dep_4=np.array(dep_4, np.float)
x_4=np.arange(min(ind_4)-1, max(ind_4)+1, .01)
print(ind_1)
print(dep_1)
print(np.polyfit(ind_1,dep_1,2))
print(ind_2)
print(dep_2)
print(np.polyfit(ind_2,dep_2,2))
print(ind_3)
print(dep_3)
print(np.polyfit(ind_3,dep_3,2))
print(ind_4)
print(dep_4)
print(np.polyfit(ind_4,dep_4,2))
Print out:
[ 1.405 1.871 2.713 ..., 5.367 5.404 2.155]
[ 0.274 0.07 0.043 ..., 0.607 0.614 0.152]
[ 0.01391925 -0.00950728 0.14803846]
[ 0.9760001 2.067 8.8 ..., 1.301 1.625 2.007 ]
[ 0.219 0.05 0.9810001 ..., 0.163 0.161 0.163 ]
[ 0.00886807 -0.00868727 0.17793324]
[ 1.143 0.9120001 2.162 ..., 2.915 2.865 2.739 ]
[ 0.283 0.3 0.27 ..., 0.227 0.213 0.161]
[ nan nan nan]
[ 0.167 0.315 1.938 ..., 2.641 1.799 2.719]
[ 0.6810001 0.7140001 0.309 ..., 0.283 0.313 0.251 ]
[ 0.00382331 0.00222269 0.16940372]
Why are the polyfit constants from the third case listed as NAN? All the data sets have same type of data, and all of the code is consistent. Please help.
Introduction to NumPy NaN. In Python, NumPy NAN stands for not a number and is defined as a substitute for declaring value which are numerical values that are missing values in an array as NumPy is used to deal with arrays in Python and this can be initialized using numpy.
In python, Numpy polyfit() is a method that fits the data within a polynomial function. That is, it least squares the function polynomial fit. For example, a polynomial p(X) of deg degree fits the coordinate points (X, Y). This function returns a coefficient vector p that lessens the squared error in the deg, deg-1,…
NaN stands for Not A Number and is one of the common ways to represent the missing value in the data. It is a special floating-point value and cannot be converted to any other type than float.
p = polyfit( x , y , n ) returns the coefficients for a polynomial p(x) of degree n that is a best fit (in a least-squares sense) for the data in y .
Just looked at your data. This is happening because you have a NaN
in dep_3
(element 713). You can make sure that you only use finite values in the fit like this:
idx = np.isfinite(ind_3) & np.isfinite(dep_3)
print(np.polyfit(ind_3[idx], dep_3[idx], 2))
As for finding for bad values in large datasets, numpy makes that really easy. You can find the indices like this:
print(np.where(~np.isfinite(dep_3)))
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