28.06

jar_functions.py

@@ -1,5 +1,5 @@
 import os   #compability with windows
-
+from IPython import embed
 
 def parse_dataset(dataset_name):
     assert(os.path.exists(dataset_name))        #see if data exists
@@ -7,15 +7,18 @@ def parse_dataset(dataset_name):
     lines = f.readlines()                       #read data
     f.close()                                   #?
 
-    eodfs = []          #metadata lists for every loop
+    # metadata lists for every loop
+    eodfs = []
     deltafs = []
     stimulusfs = []
 
-    times = []          #data itself
+    # data itself
+    times = []
     frequencies = []
     amplitudes = []
 
-    time = []           #temporary lists with data we put in the lists above
+    # temporary lists with data we put in the lists above
+    time = []
     ampl = []
     freq = []
 
@@ -29,13 +32,16 @@ def parse_dataset(dataset_name):
             stimulusfs.append(float(l.split(':')[-1].strip()[:-2]))
 
         if '#Key' in l:
+            #print('KEY')
             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 = []
+
+            time = []                       #temporary lists to overwrite the lists with the same name we made before
+            ampl = []                       #so they are empty again
             freq = []
+            print(len(times))
 
         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
@@ -44,7 +50,16 @@ def parse_dataset(dataset_name):
             ampl.append(temporary[2])
 
     times.append(time)          #append data from one list to another
-    amplitudes.append(ampl)
+    amplitudes.append(ampl)     #these append the data from the first loop to the final lists, because we overwrite them (?)
     frequencies.append(freq)
+    embed()
+
+    minimum = min(len(frequency[0]), len(frequency[1]))
+    f1 = frequencies[0][:minimum]
+    f2 = frequencies[1][:minimum]
+
+    #print(len(time))
+    print(len(times))
+    embed()
 
     return times, frequencies, amplitudes, eodfs, deltafs, stimulusfs       #output of the function

scratch.py

@@ -1,4 +1,5 @@
-
+import numpy as np
+"""
 #second_try scratch
 minimum = min(len(frequency[0]), len(frequency[1]))
 f1 = frequency[0][:minimum]
@@ -14,4 +15,10 @@ for i in range(len(minimum)):
 for f in frequency:
     print(np.mean(f))
 
-# mean_f = np.mean(x) for x in zip(freqeuncies1, frequencies2)
+# mean_f = np.mean(x) for x in zip(freqeuncies1, frequencies2)
+"""
+g = [1, 2]
+
+h = [3, 4]
+
+z = np.array([1, 2], [3, 4], dtype=object)

second_try.py

@@ -29,22 +29,17 @@ for dataset in datasets:
     deltaf.append(d)
     stimulusf.append(s)
 
-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
-
-
-
-
+mean = np.mean(frequency, axis=0)
 
+#embed()
 
+#evtl. normiert darstellen (frequency / baseline frequency?)?
+#Zeitkonstante: von sec. 0 bis 63%? relative JAR
 
-'''
-plt.plot(t, f)
+plt.plot(time, frequency)
 
 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()