import numpy as np
import matlab.engine

# Start MATLAB engine
eng = matlab.engine.start_matlab()
eng.addpath('C:/Users/tomoh/Dropbox/SPECTRUM', nargout=0)

# Generate some data

# Generate some data with two overlapping peaks and random noise
x = np.linspace(0, 100, 1000)
peak1 = 1.5 * np.exp(-((x - 45) ** 2) / (2 * 4 ** 2))  # First peak, higher and narrower, centered at x=45
peak2 = np.exp(-((x - 50) ** 2) / (2 * 6 ** 2))         # Second peak, lower and wider, centered at x=50
noise = np.random.normal(0, 0.02, len(x))              # Random noise with SNR ~50

y = peak1 + peak2 + noise

data_matrix = np.array([x, y])
data_matrix_matlab = matlab.double(data_matrix.tolist())
# Define input arguments for peakfit and convert to MATLAB types

center = matlab.double([0]) # Center of region to be fit; zero means to fit the entire signal
window = matlab.double([0]) # Width of region to be fit; zero means to fit the entire signal
NumPeaks = matlab.double([2])# 2 peaks in the model
peakshape = matlab.double([1])# 1=Gaussian, 2=Lorentzian, etc.

# Call the MATLAB function and ensure the figure stays open
try:
    FitResults, GOF = eng.peakfit(data_matrix_matlab, center, window, NumPeaks, peakshape, nargout=2)
    # Generate the figure
    # Display FitResults in table format

    print("\nFit Results:")
    print(f"{'Peak #':<8}{'Position':<12}{'Height':<12}{'Width':<12}{'Area':<12}")
    print("-" * 56)
    for row in FitResults:
        print(f"{int(row[0]):<8}{row[1]:<12.4g}{row[2]:<12.4g}{row[3]:<12.4g}{row[4]:<12.4g}")

    # Display goodness of fit in formatted output
    print("\nGoodness of Fit:")
    print(f"{'% Fitting Error':<18}{'R-squared':<12}")
    print("-" * 30)
    print(f"{GOF[0][0]:<18.4g}{GOF[0][1]:<12.4g}")

    print("The MATLAB figure is displayed. Close it to continue.")
    input("Press Enter after closing the figure...")
except Exception as e:
    print(f"Error: {e}")

# Quit MATLAB engine
eng.quit()