Merge branch 'main' of https://whale.am28.uni-tuebingen.de/git/mbergmann/gpgrewe2024

code/GP_Code.py

@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
 """
-Created on Thu Oct 17 09:23:10 2024
+Created on Tue Oct 22 11:43:41 2024
 
 @author: diana
 """
-# -*- coding: utf-8 -*-
+
 
 import glob
 import os
@@ -101,11 +101,12 @@ def prepare_harmonics(frequencies, categories, num_harmonics, colors):
     return points, color_mapping, points_categories
 
 
-def plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories):
-    """Create a figure of the power spectrum with integrals highlighted around specified points.
+def plot_power_spectrum_with_integrals(frequency, power, points, delta):
+    """
+    Create a figure of the power spectrum and calculate integrals around specified points.
 
-    This function creates a plot of the power spectrum and shades areas around
-    specified harmonic points to indicate the calculated integrals.
+    This function generates the plot of the power spectrum and calculates integrals
+    around specified harmonic points, but it does not color the regions or add vertical lines.
 
     Parameters
     ----------
@@ -117,37 +118,121 @@ def plot_power_spectrum_with_integrals(frequency, power, points, delta, color_ma
         A list of harmonic frequencies to highlight.
     delta : float
         Half-width of the range for integration around each point.
-    color_mapping : dict
-        A mapping of point categories to colors.
-    points_categories : dict
-        A mapping of categories to lists of points.
 
     Returns
     -------
+    integrals : list
+        List of calculated integrals for each point.
+    local_means : list
+        List of local mean values (adjacent integrals).
     fig : matplotlib.figure.Figure
-        The created figure object.
+        The created figure object with the power plot.
+    ax : matplotlib.axes.Axes
+        The axes object for further modifications.
     """
+    
     fig, ax = plt.subplots()
-    ax.plot(frequency, power)
+    ax.plot(frequency, power)  # Plot power spectrum
+    
     integrals = []
+    local_means = []
 
     for point in points:
+        # Define indices for the integration window
         indices = (frequency >= point - delta) & (frequency <= point + delta)
+        # Calculate integral around the point
         integral = np.trapz(power[indices], frequency[indices])
         integrals.append(integral)
-
-        # Get color based on point category
-        color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
-        ax.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
-        print(f"Integral around {point:.2f} Hz: {integral:.5e}")
-
-    ax.set_xlim([0, 1200])
+        
+        # Calculate adjacent region integrals for local mean
+        left_indices = (frequency >= point - 5 * delta) & (frequency < point - delta)
+        right_indices = (frequency > point + delta) & (frequency <= point + 5 * delta)
+        
+        l_integral = np.trapz(power[left_indices], frequency[left_indices])
+        r_integral = np.trapz(power[right_indices], frequency[right_indices])
+        
+        local_mean = np.mean([l_integral, r_integral])
+        local_means.append(local_mean)
+
+    ax.set_xlim([0, 1200])  # Set x-axis limit
     ax.set_xlabel('Frequency (Hz)')
     ax.set_ylabel('Power')
-    ax.set_title('Power Spectrum with marked Integrals')
-    ax.legend()
+    ax.set_title('Power Spectrum with Integrals (Uncolored)')
+
+    return integrals, local_means, fig, ax
+
+
+def highlight_integrals_with_threshold(frequency, power, points, delta, threshold, integrals, local_means, color_mapping, points_categories, fig_orig, ax_orig):
+    """
+    Create a new figure by highlighting integrals that exceed the threshold.
+
+    This function generates a new figure with colored shading around points where the integrals exceed
+    the local mean by a given threshold and adds vertical lines at the boundaries of adjacent regions.
+    It leaves the original figure unchanged.
+
+    Parameters
+    ----------
+    frequency : np.array
+        An array of frequencies corresponding to the power values.
+    power : np.array
+        An array of power spectral density values.
+    points : list
+        A list of harmonic frequencies to highlight.
+    delta : float
+        Half-width of the range for integration around each point.
+    threshold : float
+        Threshold value to compare integrals with local mean.
+    integrals : list
+        List of calculated integrals for each point.
+    local_means : list
+        List of local mean values (adjacent integrals).
+    color_mapping : dict
+        A mapping of point categories to colors.
+    points_categories : dict
+        A mapping of categories to lists of points.
+    fig_orig : matplotlib.figure.Figure
+        The original figure object (remains unchanged).
+    ax_orig : matplotlib.axes.Axes
+        The original axes object (remains unchanged).
+
+    Returns
+    -------
+    fig_new : matplotlib.figure.Figure
+        The new figure object with color highlights and vertical lines.
+    """
+    
+    # Create a new figure based on the original power spectrum
+    fig_new, ax_new = plt.subplots()
+    ax_new.plot(frequency, power)  # Plot the same power spectrum
+    
+    # Loop through each point and check if the integral exceeds the threshold
+    for i, point in enumerate(points):
+        exceeds = integrals[i] > (local_means[i] * threshold)
+
+        if exceeds:
+            # Define color based on the category of the point
+            color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
+            # Shade the region around the point where the integral was calculated
+            ax_new.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
+            print(f"Integral around {point:.2f} Hz: {integrals[i]:.5e}")
+            
+            # Define left and right boundaries of adjacent regions
+            left_boundary = frequency[np.where((frequency >= point - 5 * delta) & (frequency < point - delta))[0][0]]
+            right_boundary = frequency[np.where((frequency > point + delta) & (frequency <= point + 5 * delta))[0][-1]]
+
+            # Add vertical dashed lines at the boundaries of the adjacent regions
+            ax_new.axvline(x=left_boundary, color="k", linestyle="--")
+            ax_new.axvline(x=right_boundary, color="k", linestyle="--")
+
+    # Update plot legend and return the new figure
+    ax_new.set_xlim([0, 1200])
+    ax_new.set_xlabel('Frequency (Hz)')
+    ax_new.set_ylabel('Power')
+    ax_new.set_title('Power Spectrum with Highlighted Integrals')
+    ax_new.legend()
+    
+    return fig_new
 
-    return fig
 
 
 
@@ -184,9 +269,13 @@ categories = ["AM", "EODf", "Stimulus frequency"]
 num_harmonics = [4, 2, 2]
 colors = ["green", "orange", "red"]
 delta = 2.5
+threshold = 10
 
-
-### Peaks im Powerspektrum finden ###
+### 
 points, color_mapping, points_categories = prepare_harmonics(frequencies, categories, num_harmonics, colors)
-fig = plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories)
-plt.show()
+
+# First, create the power spectrum plot with integrals (without coloring)
+integrals, local_means, fig1, ax1 = plot_power_spectrum_with_integrals(frequency, power, points, delta)
+
+# Then, create a new separate figure where integrals exceeding the threshold are highlighted
+fig2 = highlight_integrals_with_threshold(frequency, power, points, delta, threshold, integrals, local_means, color_mapping, points_categories, fig1, ax1)