statguyuser / baselineremoval Goto Github PK

Getting the following error from the following code:

import pandas as pd
import numpy as np
from BaselineRemoval import BaselineRemoval
import matplotlib.pyplot as plt

the version of numpy and python is 1.26 and 3.10 respectively.
when i ran the example in readme, i got following error:

TypeError Traceback (most recent call last)
......
File ~/miniconda3/envs/xxx/lib/python3.10/site-packages/BaselineRemoval.py:49, in BaselineRemoval.ModPoly(self, degree, repitition, gradient)
46 yold=self.input_array
47 yorig=self.input_array
---> 49 polx=self.poly(list(range(1,len(yorig)+1)),degree)
50 nrep=0
52 while (criteria>=gradient) and (nrep<=repitition):

File ~/miniconda3/envs/xx/lib/python3.10/site-packages/BaselineRemoval.py:27, in BaselineRemoval.poly(self, input_array_for_poly, degree_for_poly)
18 '''qr factorization of a matrix. qis orthonormal andr` is upper-triangular.
19 - QR decomposition is equivalent to Gram Schmidt orthogonalization, which builds a sequence of orthogonal polynomials that approximate your function with minimal least-squares error
20 - in the next step, discard the first column from above matrix.
(...)
24 - concatenate all of these arrays created through loop, as a master array. This is done through (np.vstack)
25 - transpose the master array, so that its more like a tabular form(np.transpose)'''
26 input_array_for_poly = np.array(input_array_for_poly,dtype='object')
---> 27 X = np.transpose(np.vstack((input_array_for_poly**k for k in range(degree_for_poly+1))))
28 return np.linalg.qr(X)[0][:,1:]
...
--> 209 raise TypeError('arrays to stack must be passed as a "sequence" type '
210 'such as list or tuple.')
212 return tuple(arrays)

TypeError: arrays to stack must be passed as a "sequence" type such as list or tuple.
`

Then, i modify the 27th line in BaselineRemoval.py from
X = np.transpose(np.vstack((input_array_for_poly**k for k in range(degree_for_poly+1))))
to X = np.transpose(np.vstack([input_array_for_poly**k for k in range(degree_for_poly+1)]))
and it finally works.

Hi, I've been trying to troubleshoot this for a few days, but can't figure it out. I'm running the same code using baselineremoval with imodpoly, but I'm getting different results on Visual Studio compared to Spyder. It seems Spyder's results are the wrong ones. It specifically happens when the input array gets large (in mycase, ~1300). If I shorten the array to 1200, the results are the same, but once past 1300, then the results are different. I've written about the issues here

For Visual Studio I'm using:
VS: 1.74.2
Python: 3.8.8
Numpy: 1.20.1
Pandas: 1.2.4
sklearn: 0.24.1
scipy: 1.6.2

For Spyder I'm using:
Spyder: 4.2.5
Python: 3.8.8
Numpy: 1.20.1
Pandas: 1.2.4
sklearn: 0.24.1
scipy: 1.6.2

I am using the library for a while now but the error I am getting is new as given in the title.
What does it mean? Is the library performing execution or not? Can I go on with the results obtained after baseline removal?

Hi,

I am currently using zhng fit model for the values mentioned below.
6441.98,6383.192,6325.8344,6269.60528,6214.487936,6159.418643,6105.581316,6053.799992,6001.876262,5950.335251,5898.042302,5847.675511,5796.543563,5741.443815,5684.597475,5629.008258,5580.121147,5536.025881,5495.629406,5456.531057,5419.432093,5381.39263,5344.964945,5307.471515,5272.287292,5237.151761,5204.087811,5172.047914,5142.027145,5112.015012,5083.808431,5056.964689,5031.754624,5008.143863,4985.179697,4965.064712,4944.248882,4924.062719,4902.46232,4882.505008,4862.247234,4842.734536

where the values are raw values which didnt have baseline corrected when i am trying to correct baseline using zhang fit it is showing as tilted curve. Is there anything can you help to make the curve corrected or any suggestion is good enough.

Hello,

I am using your code to baseline some Raman spectra that I have. Running it on my Macbook results in different baselining to when I run it on a Windows PC. I am using identical code and identical data (have triple checked this) so am unsure what the issue is.

I have attached images of the results from both systems.

Thank you.