25.06

jar_functions.py

@@ -1,24 +1,50 @@
-import os
+import os   #compability with windows
 
 
 def parse_dataset(dataset_name):
-    assert(os.path.exists(dataset_name))
-    f = open(dataset_name, 'r')
-    lines = f.readlines()
-    f.close()
+    assert(os.path.exists(dataset_name))        #see if data exists
+    f = open(dataset_name, 'r')                 #open data we gave in
+    lines = f.readlines()                       #read data
+    f.close()                                   #?
 
-    time = []
-    frequency = []
-    amplitude = []
+    eodfs = []          #metadata lists for every loop
+    deltafs = []
+    stimulusfs = []
+
+    times = []          #data itself
+    frequencies = []
+    amplitudes = []
+
+    time = []           #temporary lists with data we put in the lists above
+    ampl = []
+    freq = []
 
     for i in range(len(lines)):
-        l = lines[i].strip()
+        l = lines[i].strip()                                        #all lines of textdata, exclude all empty lines (empty () default for spacebar)
+        if "#" in l and "EODf" in l:                                #if line starts with # EODf:
+            eodfs.append(float(l.split(':')[-1].strip()[:-2]))      #append: line splitted by ':' the 2nd part ([-1],
+        if "#" in l and "Delta f" in l:                             #which got striped so we sure there is no space at the end,
+            deltafs.append(float(l.split(':')[-1].strip()[:-2]))    #from that all expect the last two signs (Hz unit)
+        if "#" in l and "StimulusFrequency" in l:                   #this for different metadata in different lists
+            stimulusfs.append(float(l.split(':')[-1].strip()[:-2]))
+
+        if '#Key' in l:
+            if len(time) != 0:              #therefore empty in the first round
+                times.append(time)          #2nd loop means time != 0, so we put the times/amplitudes/frequencies to
+                amplitudes.append(ampl)     #the data of the first loop
+                frequencies.append(freq)
+            time = []                       #temporary lists with the data of the 2nd loop which we put to the lists above
+            ampl = []
+            freq = []
 
-        if len(l) > 0 and l[0] is not '#':
-            temp = list(map(float, l.split()))
+        if len(l) > 0 and l[0] is not '#':              #line not empty and doesnt start with #
+            temporary = list(map(float, l.split()))     #temporary list where we got 3 index splitted by spacebar, map to find them
+            time.append(temporary[0])                   #temporary lists with the data at that place, respectively
+            freq.append(temporary[1])
+            ampl.append(temporary[2])
 
-            time.append(temp[0])
-            frequency.append(temp[1])
-            amplitude.append(temp[2])
+    times.append(time)          #append data from one list to another
+    amplitudes.append(ampl)
+    frequencies.append(freq)
 
-    return time, frequency, amplitude
+    return times, frequencies, amplitudes, eodfs, deltafs, stimulusfs       #output of the function

second_try.py

@@ -7,26 +7,56 @@ from IPython import embed
 from jar_functions import parse_dataset
 
 
-dataset = os.path.join('D:\\', 'jar_project', 'JAR', '2020-06-22-ac', 'beats-eod.dat')
+datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat'))]
+#           (os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
 
-t, f, a = parse_dataset(dataset)
+time = []
+frequency = []
+amplitude = []
 
-avg_frequency = np.mean(f)
-print(avg_frequency)
+for dataset in datasets:
+    t, f, a, e= parse_dataset(dataset)
+    embed()
+    time.append(t)
+    frequency.append(f)
+    amplitude.append(a)
 
 
+minimum = min(len(frequency[0]), len(frequency[1]))
+f1 = frequency[0][:minimum]
+f2 = frequency[1][:minimum]
 
+frequency = f1 + f2
+embed()
+#frequency = np.array(frequency)
 
+mean = np.mean(frequency, axis=0)
 
+for i in range(len(minimum)):
+    mean(frequency[0][i], frequency[1][i])
 
+for f in frequency:
+    print(np.mean(f))
 
+# mean_f = np.mean(x) for x in zip(freqeuncies1, frequencies2)
 
 
+embed()
 
+#nächste Schritt: weitere Messungen einfügen und dann über alle Trials mitteln, evtl. normiert darstellen (frequency / baseline frequency?)?
+#Zeitkonstante: von sec. 0 bis 63%? relative JAR
+
+
+
+
+
+
+
+'''
 plt.plot(t, f)
 
 plt.xlabel('time [s]')
 plt.ylabel('frequency [Hz]')
 plt.xlim([-10,200])
 plt.title('second try because first try was sold out')
-plt.show()
+plt.show()'''