# <b>interactive_curve_fit</b>
A Python project enables you to do curve fitting on spectrum data interactively on GUI.
You can visualize your spectrum and fit the optional number of peaks on GUI using Scipy.optimize.curve_fit method.
## <b>How to use?</b>
Try [tutorial.py](tutorial.py) with your spectrum data!
## <b>Spectrum data format must be like the table below</b>
| x | y |
|---|---|
|0 | 1 |
|1 | 13 |
|2 | 30 |
|3 | 43 |
|4 | 31 |
|5 | 11 |
|...|...|
## <b>Steps to curve-fit</b>
1. Teach your initial guess of the positions of each peaks roughly to Fitter.
```python
from interactive_curve_fit import read_data, Guessor, Fitter
data = read_data(data, headers=2, sep=',')
guessor = Guessor(data, background=10, method='drag')
guess = guessor.guess()
```
### Initial Guess method
- mouse-dragging (wrap up peak area by mouse-dragging)
- click (click the top and the both edges of each peaks)
### Screenshot
1. Give your spectrum data and your guess to Fitter.
```python
fitter = Fitter(data, guess)
fitter.run(method='gaussian')
```
### Supported fitting functions
- gaussian function
- polynomial function
### Output information includes
- position (x, y) of each peaks
- baseline height of the spectrum
- bandwidth of each peaks with its CI (confidential interval)
1. Save the fitting results
```python
fitter.save_data('out/fitting_result.csv')
```
1. Other features
bmp_to_csv converts bmp file to csv file.
```python
from interactive_curve_fit import bmg_to_csv
bmp_to_csv('data/line_spectrum.bmp')
data = read_data('data/line_spectrum.csv')
```
Fitter can visualize fitting results
```
fitter.plot_fit()
```
Fitter can also display fitting results on terminal
```
fitter.display_results_terminal(ci=2)
```
## <b>Supported supectrum file format</b>
- ascii file(.asc .csv .txt etc..)
- bmp image(.bmp .jpg .png .jpeg etc..)
excel sheet files, table of html are planed to be suported in the near future.
## <b>Features that are planned to be supported!</b>
- baseline correlation
- other fitting functions (e.g. binomical distribution function)
- automated guessor method using wavelet transform and CNN
Raw data
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"description": "# <b>interactive_curve_fit</b>\nA Python project enables you to do curve fitting on spectrum data interactively on GUI.\nYou can visualize your spectrum and fit the optional number of peaks on GUI using Scipy.optimize.curve_fit method.\n\n## <b>How to use?</b>\n\nTry [tutorial.py](tutorial.py) with your spectrum data!\n\n## <b>Spectrum data format must be like the table below</b>\n\n| x | y |\n|---|---|\n|0 | 1 |\n|1 | 13 |\n|2 | 30 |\n|3 | 43 |\n|4 | 31 |\n|5 | 11 |\n|...|...|\n\n## <b>Steps to curve-fit</b>\n\n1. Teach your initial guess of the positions of each peaks roughly to Fitter.\n\n ```python\n from interactive_curve_fit import read_data, Guessor, Fitter\n \n data = read_data(data, headers=2, sep=',')\n guessor = Guessor(data, background=10, method='drag')\n guess = guessor.guess()\n ```\n\n ### Initial Guess method\n - mouse-dragging (wrap up peak area by mouse-dragging)\n - click (click the top and the both edges of each peaks)\n\n ### Screenshot\n \n \n \n \n\n1. Give your spectrum data and your guess to Fitter.\n \n ```python\n fitter = Fitter(data, guess)\n fitter.run(method='gaussian')\n ```\n ### Supported fitting functions\n - gaussian function\n - polynomial function\n\n ### Output information includes\n - position (x, y) of each peaks\n - baseline height of the spectrum\n - bandwidth of each peaks with its CI (confidential interval)\n\n1. Save the fitting results\n ```python\n fitter.save_data('out/fitting_result.csv')\n ```\n\n1. Other features\n \n bmp_to_csv converts bmp file to csv file.\n ```python\n from interactive_curve_fit import bmg_to_csv\n bmp_to_csv('data/line_spectrum.bmp')\n data = read_data('data/line_spectrum.csv')\n ```\n \n Fitter can visualize fitting results\n ```\n fitter.plot_fit()\n ```\n\n Fitter can also display fitting results on terminal\n ```\n fitter.display_results_terminal(ci=2)\n ```\n\n## <b>Supported supectrum file format</b>\n\n- ascii file(.asc .csv .txt etc..)\n- bmp image(.bmp .jpg .png .jpeg etc..)\n\n excel sheet files, table of html are planed to be suported in the near future.\n\n## <b>Features that are planned to be supported!</b>\n\n- baseline correlation\n- other fitting functions (e.g. binomical distribution function)\n- automated guessor method using wavelet transform and CNN\n\n",
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