new file: lmfit_test.py

	deleted:    src/fitter/background_fitter.rs
	modified:   src/fitter/fit_handler.rs
	modified:   src/fitter/fit_settings.rs
	modified:   src/fitter/main_fitter.rs
	modified:   src/fitter/mod.rs
	deleted:    src/fitter/models/double_exponential.rs
	deleted:    src/fitter/models/exponential.rs
	modified:   src/fitter/models/gaussian.rs
	modified:   src/fitter/models/mod.rs
	deleted:    src/fitter/models/polynomial.rs
	modified:   src/histoer/histo1d/context_menu.rs
	modified:   src/histoer/histo1d/histogram1d.rs
	modified:   src/histoer/histo1d/keybinds.rs
	modified:   src/histoer/histo1d/peak_finder.rs
	modified:   src/histogram_scripter/manual_histogram_script.rs

lmfit_test.py

@@ -0,0 +1,125 @@
+import lmfit
+import numpy as np
+import polars as pl
+import matplotlib.pyplot as plt
+
+def gaussian(x, amplitude, mean, sigma):
+    return amplitude * np.exp(-(x - mean)**2 / (2 * sigma**2))
+
+# Multiple Gaussian fitting function
+def MultipleGaussianFit(x_data: list, y_data: list, peak_markers: list, equal_sigma:bool=True, free_position:bool=True, background_type:str='linear'):
+
+    # Initialize the model with or without a background based on the flag
+    if background_type == 'linear':
+        model = lmfit.models.LinearModel(prefix='bg_')
+        params = model.make_params(slope=0, intercept=0)  # Initial guesses for linear background
+    elif background_type == 'quadratic':
+        model = lmfit.models.QuadraticModel(prefix='bg_')
+        params = model.make_params(a=0, b=1, c=0)  # Initial guesses for quadratic background
+    elif background_type == 'exponential':
+        model = lmfit.models.ExponentialModel(prefix='bg_')
+        params = model.make_params(amplitude=1, decay=100)  # Initial guesses for exponential background
+    elif background_type == 'powerlaw':
+        model = lmfit.models.PowerLawModel(prefix='bg_')
+        params = model.make_params(amplitude=1, exponent=-0.5)  # Initial guesses for power-law background
+    elif background_type is None:
+        model = None
+        params = lmfit.Parameters()
+    else:
+        raise ValueError("Unsupported background model")
+
+    first_gaussian = lmfit.Model(gaussian, prefix=f'g0_')
+
+    if model is None:
+        model = first_gaussian
+    else:
+        model += first_gaussian
+
+    params.update(first_gaussian.make_params(amplitude=1, mean=peak_markers[0], sigma=1))
+    params['g0_sigma'].set(min=0)  # Initial constraint for the first Gaussian's sigma
+
+    # Fix the center of the first Gaussian if free_position=False
+    if not free_position:
+        params['g0_mean'].set(vary=False)
+
+    # Add additional Gaussians
+    for i, peak in enumerate(peak_markers[1:], start=1):
+        print(f'Adding Gaussian {i} at {peak}')
+        g = lmfit.Model(gaussian, prefix=f'g{i}_')
+        model += g
+        params.update(g.make_params(amplitude=1, mean=peak))
+
+        # Set the sigma parameter depending on whether equal_sigma is True or False
+        if equal_sigma:
+            params[f'g{i}_sigma'].set(expr='g0_sigma')  # Constrain sigma to be the same as g1_sigma
+        else:
+            params[f'g{i}_sigma'].set(min=0)  # Allow different sigmas for each Gaussian
+
+        params[f'g{i}_amplitude'].set(min=0)
+
+        # Fix the center of the Gaussian if free_position=False
+        if not free_position:
+            params[f'g{i}_mean'].set(vary=False)
+
+    # Fit the model to the data
+    result = model.fit(y_data, params, x=x_data)
+
+    print(result.fit_report())
+
+    # Create an array of (name, value, uncertainty) tuples
+    param_array = [(param, result.params[param].value, result.params[param].stderr) for param in result.params]
+
+
+
+    x_result = x_data
+    y_result = result.best_fit
+
+    composition_points = [x_data, result.best_fit]
+
+
+    # Return the fit result
+    return param_array, 
+
+# Load the data using Polars
+df = pl.read_parquet("/home/alconley/git_workspace/ICESPICE/207Bi/exp_data/207Bi_noICESPICE_f9mm_g0mm_run_13.parquet")
+
+df = df.with_columns([
+    (pl.col("PIPS1000Energy") * 0.5395 + 2.5229).alias("PIPS1000EnergyCalibrated")
+])
+
+
+# Create a histogram from the x_data using np.histogram
+counts, bin_edges = np.histogram(df["PIPS1000EnergyCalibrated"], bins=1200, range=(0, 1200))
+
+# Get the bin centers
+bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])
+
+# Filter the bin centers and counts between 1018 and 1044
+fit_mask = (bin_centers >= 520) & (bin_centers <= 600)
+bin_centers_filtered = bin_centers[fit_mask]
+counts_filtered = counts[fit_mask]
+
+
+# Define the peak markers (this is where you mark the initial guesses for the peak positions)
+peak_markers = [554, 567]  # Replace with actual peak guesses
+
+# Fit the data
+param = MultipleGaussianFit(bin_centers_filtered, counts_filtered, peak_markers, equal_sigma=True, free_position=False, background_type='powerlaw')
+
+# # Plot the original data and the fit
+# plt.figure(figsize=(8, 6))
+
+# # Plot the original data
+# plt.step(bin_centers, counts, where="mid", label="Data")
+
+# # Plot the Gaussian fit only in the selected range
+# plt.plot(bin_centers_filtered, result.best_fit, color="red")
+
+# # Add labels and legend
+# plt.xlabel("PIPS1000Energy")
+# plt.ylabel("Counts")
+# plt.title("Gaussian Fit to Data")
+# plt.legend()
+
+# # Show the plot
+# plt.show()