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

code/plot_functions.py

@@ -72,7 +72,7 @@ functions_path = r"C:\Users\diana\OneDrive - UT Cloud\Master\GPs\GP1_Grewe\Proje
 sys.path.append(functions_path)
 import useful_functions as u
 
-def plot_highlighted_integrals(frequency, power, points, color_mapping, points_categories, delta = 2.5):
+def plot_highlighted_integrals(frequency, power, points, color_mapping, points_categories, delta=2.5):
     """
     Plot the power spectrum and highlight integrals that exceed the threshold.
 
@@ -82,12 +82,10 @@ def plot_highlighted_integrals(frequency, power, points, color_mapping, points_c
         An array of frequencies corresponding to the power values.
     power : np.array
         An array of power spectral density values.
-    exceeding_points : list
-        A list of harmonic frequencies that exceed the threshold.
+    points : list
+        A list of harmonic frequencies to check and highlight.
     delta : float
         Half-width of the range for integration around each point.
-    threshold : float
-        Threshold value to compare integrals with local mean.
     color_mapping : dict
         A dictionary mapping each category to its color.
     points_categories : dict
@@ -111,9 +109,15 @@ def plot_highlighted_integrals(frequency, power, points, color_mapping, points_c
         if valid:  
             # 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.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
-            print(f"Integral around {point:.2f} Hz: {integral:.5e}")
+
+            # Print out point and color
+            print(f"Integral around {point:.2f} Hz: {integral:.5e}, Color: {color}")
+            
+            # Annotate the plot with the point and its color
+            ax.text(point, max(power) * 0.9, f'{point:.2f}', color=color, fontsize=10, ha='center')
 
             # Define left and right boundaries of adjacent regions
             left_boundary = frequency[np.where((frequency >= point - 5 * delta) & (frequency < point - delta))[0][0]]
@@ -132,3 +136,4 @@ def plot_highlighted_integrals(frequency, power, points, color_mapping, points_c
     return fig
 
 
+

code/useful_functions.py

@@ -32,7 +32,7 @@ def all_coming_together(freq_array, power_array, points_list, categories, num_ha
     Returns
     -------
     valid_points : list
-        A list of valid points with their harmonics.
+        A continuous list of harmonics for all valid points.
     color_mapping : dict
         A dictionary mapping categories to corresponding colors.
     category_harmonics : dict
@@ -40,7 +40,7 @@ def all_coming_together(freq_array, power_array, points_list, categories, num_ha
     messages : list
         A list of messages for each point, stating whether it was valid or not.
     """
-    valid_points = []
+    valid_points = []  # A continuous list of harmonics for valid points
     color_mapping = {}
     category_harmonics = {}
     messages = []
@@ -58,7 +58,7 @@ def all_coming_together(freq_array, power_array, points_list, categories, num_ha
         if valid:
             # Step 3: Prepare harmonics if the point is valid
             harmonics, color_map, category_harm = prepare_harmonic(point, category, num_harmonics, color)
-            valid_points.append((point, harmonics))
+            valid_points.extend(harmonics)  # Use extend() to append harmonics in a continuous manner
             color_mapping.update(color_map)
             category_harmonics.update(category_harm)
             messages.append(f"The point {point} is valid.")
@@ -67,6 +67,8 @@ def all_coming_together(freq_array, power_array, points_list, categories, num_ha
     
     return valid_points, color_mapping, category_harmonics, messages
 
+
+
 def AM(EODf, stimulus):
     """
     Calculates the Amplitude Modulation and Nyquist frequency
@@ -425,8 +427,7 @@ def spike_times(stim):
     dt = ti.sampling_interval  
     return spikes, stim_dur, dt # se changed spike_times to spikes so its not the same as name of function
 
-
-def valid_integrals(integral, local_mean, point, threshold = 0.3):
+def valid_integrals(integral, local_mean, point, threshold = 0.1):
     """
     Check if the integral exceeds the threshold compared to the local mean and 
     provide feedback on whether the given point is valid or not.