معرفی شرکت ها
BaselineRemoval-0.1.4
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Perform baseline removal, baseline correction and baseline substraction for raman spectra using Modpoly, ImodPoly and Zhang fit. Returns baseline-subtracted spectrum
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | BaselineRemoval-0.1.4 |
| نام | BaselineRemoval |
| نسخه کتابخانه | 0.1.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | StatguyUser |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/StatguyUser/BaselineRemoval |
| آدرس اینترنتی | https://pypi.org/project/BaselineRemoval/ |
| مجوز | - |
What is it?
===========
Companion python library for the machine learning book [Feature Engineering & Selection for Explainable Models A Second Course for Data Scientists](https://statguyuser.github.io/feature-engg-selection-for-explainable-models.github.io/index.html). It is used for baseline correction. It has below 3 methods for baseline removal from spectra.
- **Modpoly** Modified multi-polynomial fit [1]. It has below 3 parameters.
1) `degree`, it refers to polynomial degree, and default value is 2.
2) `repitition`, it refers to how many iterations to run, and default value is 100.
3) `gradient`, it refers to gradient for polynomial loss, default is 0.001. It measures incremental gain over each iteration. If gain in any iteration is less than this, further improvement will stop.
- **IModPoly** Improved ModPoly[2], which addresses noise issue in ModPoly. It has below 3 parameters.
1) `degree`, it refers to polynomial degree, and default value is 2.
2) `repitition`, it refers to how many iterations to run, and default value is 100.
3) `gradient`, it refers to gradient for polynomial loss, and default is 0.001. It measures incremental gain over each iteration. If gain in any iteration is less than this, further improvement will stop.
- **ZhangFit** Zhang fit[3], which doesn’t require any user intervention and prior information, such as detected peaks. It has below 3 parameters.
1) `lambda_`, it can be adjusted by user. The larger lambda is, the smoother the resulting background. Default value is 100.
2) `porder` refers to adaptive iteratively reweighted penalized least squares for baseline fitting. Default value is 1.
3) `repitition` is how many iterations to run, and default value is 15.
We can use the python library to process spectral data through either of the techniques ModPoly, IModPoly or Zhang fit algorithm for baseline subtraction. The functions will return baseline-subtracted spectrum.
How to use it?
=================
```python
from BaselineRemoval import BaselineRemoval
input_array=[10,20,1.5,5,2,9,99,25,47]
polynomial_degree=2 #only needed for Modpoly and IModPoly algorithm
baseObj=BaselineRemoval(input_array)
Modpoly_output=baseObj.ModPoly(polynomial_degree)
Imodpoly_output=baseObj.IModPoly(polynomial_degree)
Zhangfit_output=baseObj.ZhangFit()
print('Original input:',input_array)
print('Modpoly base corrected values:',Modpoly_output)
print('IModPoly base corrected values:',Imodpoly_output)
print('ZhangFit base corrected values:',Zhangfit_output)
Original input: [10, 20, 1.5, 5, 2, 9, 99, 25, 47]
Modpoly base corrected values: [-1.98455800e-04 1.61793368e+01 1.08455179e+00 5.21544654e+00
7.20210508e-02 2.15427531e+00 8.44622093e+01 -4.17691125e-03
8.75511661e+00]
IModPoly base corrected values: [-0.84912125 15.13786196 -0.11351367 3.89675187 -1.33134142 0.70220645
82.99739548 -1.44577432 7.37269705]
ZhangFit base corrected values: [ 8.49924691e+00 1.84994576e+01 -3.31739230e-04 3.49854060e+00
4.97412948e-01 7.49628529e+00 9.74951576e+01 2.34940300e+01
4.54929023e+01
```
Where to get it?
================
`pip install BaselineRemoval`
How to cite?
============
Md Azimul Haque (2022). Feature Engineering & Selection for Explainable Models A Second Course for Data Scientists
Dependencies
============
- [numpy](https://www.numpy.org/])
- [scikit-learn](https://scikit-learn.org/)
- [scipy](https://www.scipy.org/)
References
============
1. [Automated Method for Subtraction of Fluorescence from Biological Raman Spectra](https://www.researchgate.net/publication/8974238_Automated_Method_for_Subtraction_of_Fluorescence_from_Biological_Raman_Spectra) by Lieber & Mahadevan-Jansen (2003)
2. [Automated Autofluorescence Background Subtraction Algorithm for Biomedical Raman Spectroscopy](https://www.researchgate.net/publication/5818031_Automated_Autofluorescence_Background_Subtraction_Algorithm_for_Biomedical_Raman_Spectroscopy) by Zhao, Jianhua, Lui, Harvey, McLean, David I., Zeng, Haishan (2007)
3. [Baseline correction using adaptive iteratively reweighted penalized least squares](https://pubs.rsc.org/is/content/articlelanding/2010/an/b922045c#!divAbstract) by Zhi-Min Zhang, Shan Chena and Yi-Zeng Liang (2010)
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | scikit-learn |
| - | scipy |
نحوه نصب
نصب پکیج whl BaselineRemoval-0.1.4:
pip install BaselineRemoval-0.1.4.whl
نصب پکیج tar.gz BaselineRemoval-0.1.4:
pip install BaselineRemoval-0.1.4.tar.gz