added the tuning curve script

code/tuning_curve_max.py

@@ -0,0 +1,74 @@
+import glob
+import matplotlib.pyplot as plt
+import numpy as np
+import rlxnix as rlx
+import scipy as sp
+import time
+import useful_functions as f
+
+# tatsächliche Power der peaks benutzen
+
+
+
+# variables
+delta = 2.5 # radius for peak detection
+
+# all files we want to use
+files = glob.glob("../data/2024-10-16-af*.nix")
+
+# get only the good and fair filepaths
+new_files = f.remove_poor(files)
+
+
+# loop over all the good files
+for file in new_files:
+    # load a file
+    dataset = rlx.Dataset(file)
+    # extract sams
+    sams = dataset.repro_runs('SAM')
+    # get arrays for frequnecies and power
+    stim_frequencies = np.zeros(len(sams))
+    peak_powers = np.zeros_like(stim_frequencies)
+    # loop over all sams
+    for i, sam in enumerate(sams):
+        # get sam frequency and stimuli
+        avg_dur, _, _, _, _, _, stim_frequency = f.sam_data(sam)
+        print(avg_dur)
+        if np.isnan(avg_dur):
+            continue
+        # use this to change lists basically and add the contrast somewhere
+        else:
+            stimuli = sam.stimuli
+            # lists for the power spectra
+            frequencies = []
+            powers = []
+            # loop over the stimuli
+            for stimulus in stimuli:
+                # get the powerspectrum for each stimuli
+                frequency, power = f.power_spectrum(stimulus)
+                # append the power spectrum data
+                frequencies.append(frequency)
+                powers.append(power)
+            #average over the stimuli
+            sam_frequency = np.mean(frequencies, axis = 0)
+            sam_power = np.mean(powers, axis = 0)
+            # detect and validate peaks
+            integral, surroundings, peak_power = f.calculate_integral(sam_frequency, 
+                                                          sam_power, stim_frequency)
+            valid = f.valid_integrals(integral, surroundings, stim_frequency)
+            #if there is a peak get the power in the peak powers
+            if valid == True:
+                peak_powers[i] = peak_power
+            # add the current stimulus frequency
+            stim_frequencies[i] = stim_frequency
+            
+    # replae zeros with NaN
+    peak_powers = np.where(peak_powers == 0, np.nan, peak_powers)
+    
+plt.plot(stim_frequencies, peak_powers)
+            
+    
+        
+
+    
+