code/am_plots_oneintensityandcell.py

@@ -1,96 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import rlxnix as rlx
-from useful_functions import sam_data, sam_spectrum, calculate_integral, contrast_sorting
-
-# close all open plots
-plt.close('all')
-
-def plot_am_vs_frequency_single_intensity(file, contrast=20):
-    """
-    Plots AM Power vs Stimulus Frequency and Nyquist Frequency vs Stimulus Frequency for 
-    one intensity and one cell (file).
-    
-    Parameters:
-        file (str): Path to the file (one cell).
-        intensity (int): The intensity level (contrast) to filter by.
-    """
-    # Load the dataset for the given file
-    dataset = rlx.Dataset(file)
-    
-    # Get SAMs for the whole recording
-    sam_list = dataset.repro_runs('SAM')
-    
-    # Extract the file tag (first part of the filename) for the legend
-    file_tag = '-'.join(os.path.basename(file).split('-')[0:4])
-    
-    # Sort SAMs by contrast
-    contrast_dict = contrast_sorting(sam_list)
-    
-    # Get the SAMs for 20% contrast
-    sams = contrast_dict[contrast]
-    
-    # Create a figure with 1 row and 2 columns
-    fig, axs = plt.subplots(2, 1, layout='constrained')
-    
-    # Store all stim_freq, peak_power, and am_freq for the given contrast
-    stim_freqs = []
-    peak_powers = []
-    am_freqs = []
-    
-    # Loop over all SAMs of the specified contrast
-    for sam in sams:
-        
-        # Get stim_freq for each SAM
-        _, _, _, _, eodf, nyquist, stim_freq = sam_data(sam)
-        
-        # Skip over empty SAMs
-        if np.isnan(stim_freq):
-            continue
-        
-        # Get power spectrum from one SAM
-        freq, power = sam_spectrum(sam)
-        
-        # get index of 1/2 eodf frequency
-        nyquist_idx = np.searchsorted(freq, nyquist)
-        
-        # get frequencies until 1/2 eodf and powers for those frequencies
-        freqs_before_half_eodf = freq[:nyquist_idx]
-        powers_before_half_eodf = power[:nyquist_idx]
-    
-        # Get the frequency of the highest peak before 1/2 EODf
-        am_peak_f = freqs_before_half_eodf[np.argmax(powers_before_half_eodf)]
-        
-        # Get the power of the highest peak before 1/2 EODf
-        _, _, peak_power = calculate_integral(freq, power, am_peak_f)
-        
-        # Collect data for plotting
-        stim_freqs.append(stim_freq)
-        peak_powers.append(peak_power)
-        am_freqs.append(am_peak_f)
-    
-    # Plot AM Power vs Stimulus Frequency (first column)
-    ax = axs[0]
-    ax.plot(stim_freqs, am_freqs, '-')
-    ax.set_ylabel('AM Frequency [Hz]')
-    ax.grid(True)
-    
-    # Plot AM Frequency vs Stimulus Frequency (second column)
-    ax = axs[1]
-    ax.plot(stim_freqs, peak_powers, '-')
-    ax.set_ylabel('AM Power')
-    ax.grid(True)
-    
-    # Figure settings
-    fig.suptitle(f"Cell: {file_tag}, Contrast: {contrast}%")
-    fig.supxlabel("Stimulus Frequency (df + EODf) [Hz]")
-    plt.show()
-
-
-# Call function
-file = '../data/16-10-24/2024-10-16-ad-invivo-1.nix'
-
-# Call the function to plot the data for one intensity and one cell
-plot_am_vs_frequency_single_intensity(file)
-

code/useful_functions.py

@@ -275,7 +275,7 @@ def extract_stim_data(stimulus):
     stim_dur = stimulus.duration
     # calculates the amplitude modulation
     amp_mod, ny_freq = AM(eodf, stim_freq)
-    return amplitude, df, eodf, stim_freq, stim_dur, amp_mod, ny_freq
+    return amplitude, df, eodf, stim_freq,stim_dur, amp_mod, ny_freq
 
 def find_exceeding_points(frequency, power, points, delta, threshold):
     """