30.06

jar_functions.py

@@ -33,7 +33,6 @@ 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
@@ -42,7 +41,6 @@ def parse_dataset(dataset_name):
             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
@@ -58,7 +56,7 @@ def parse_dataset(dataset_name):
 
 
 
-def noise_reduce(dataset_name, n):
+'''
     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
@@ -74,10 +72,15 @@ def noise_reduce(dataset_name, n):
         if len(l) > 0 and l[0] is not '#':
             temporary = list(map(float, l.split()))
             frequencies.append(temporary[1])
+'''
 
-    for k in np.arange(0, len(frequencies), n): # sollte nach k+n weitergehen?
+def mean_noise_cut(frequencies, time, n):
+    cutf = []
+    cutt = []
+    for k in np.arange(0, len(time), n):
         f = frequencies[k:k+n]
+        t = time[k]
         mean = np.mean(f)
         cutf.append(mean)
-
+        cutt.append(t)
-    return cutf
+    return cutf, cutt

scratch.py

@@ -1,7 +1,7 @@
 import os
 import numpy as np
 from IPython import embed
-from jar_functions import noise_reduce
+from jar_functions import mean_noise_cut
 
 datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
 

second_try.py

@@ -5,7 +5,7 @@ import IPython
 import numpy as np
 from IPython import embed
 from jar_functions import parse_dataset
-from jar_functions import noise_reduce
+from jar_functions import mean_noise_cut
 
 
 datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
@@ -26,7 +26,6 @@ timespan = 210
 for dataset in datasets:
     #input of the function
     t, f, a, e, d, s= parse_dataset(dataset)
-    cf = noise_reduce(dataset, n = 10)
 
     'times'
     # same for time in both loops
@@ -46,20 +45,15 @@ for dataset in datasets:
     # interpolation
     f0new = np.interp(tnew, t0, f0)
 
-    f1 = f[1][:minimumf]
+    f1 = f[1] #[:minimumf]
     # interpolation
     f1new = np.interp(tnew, t1, f1)
 
     #new array with frequencies of both loops as two lists put together
-    frequency = np.array([[f0new], [f1new]])
+    frequency = np.array([f0new, f1new])
-    #making a mean over both loops with the axis 0 (=averaged in y direction, axis=1 would be over x axis)
-    mf = np.mean(frequency, axis=0).T           #.T as transition (1,0) -> (0,1)
-
-    #other variant for transition by reshaping in needed dimension
-    mfreshape = np.reshape(mf, (minimumf, 1))           #as ploting is using the first dimension, number of datapoints has to be in the first
-    treshape = np.reshape(tnew, (minimumf, 1))
-
 
+    #making a mean over both loops with the axis 0 (=averaged in y direction, axis=1 would be over x axis)
+    mf = np.mean(frequency, axis=0)           #.T as transition (1,0) -> (0,1)
 
     #appending data
     eodf.append(e)
@@ -70,31 +64,39 @@ for dataset in datasets:
     frequency_mean.append(mf)
     time.append(tnew)
 
+for i in range(len(frequency_mean)):
+    for n in [10, 50, 100, 1000, 10000, 20000, 30000]:
+        cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=n)
+        plt.plot(ct, cf, label='n=%d' % n)
 
 
-'''
-'controll of interpolation'
-fig=plt.figure()
-ax=fig.add_subplot(1,1,1)
-
-ax.plot(tnew, mf, c = 'r', marker = 'o', ls = 'solid', label = 'new')
-ax.plot(t0, f0, c = 'b', marker = '+', ls = '-', label = 'loop_0')
-ax.plot(t1, f1, c= 'g', marker = '+', ls = '-', label = 'loop_1')
-plt.legend(loc = 'best')
-#plt.plot(tnew, mf, marker = 'r-o', label = new, t0, f0, marker = 'b-+', label = loop_0, t1, f1, marker = 'g-+', label = loop_1)
-plt.show()
-'''
 
 'plotting'
-'''why does append put in a 3rd dimension? plt.plot(time, frequency_mean) '''
-
-plt.plot(tnew, mf)
 plt.xlim([-10,200])
 #plt.ylim([400, 1000])
 plt.xlabel('time [s]')
 plt.ylabel('frequency [Hz]')
+#plt.title('noise_cut_n=100')
+#plt.savefig('noise_cut_n=100')
+plt.legend()
 plt.show()
 
 
+def double_exp(t, a1, a2, tau1, tau2):
+    return a1*np.exp(-t/tau1)
+# plotten mit manual values for a1, ...
+# auch mal a1 oder a2 auf Null setzen.
+
 #evtl. normiert darstellen (frequency / baseline frequency?)?
 #Zeitkonstante: von sec. 0 bis 63%? relative JAR
+'''
+'controll of interpolation'
+fig=plt.figure()
+ax=fig.add_subplot(1,1,1)
+ax.plot(tnew, mf, c = 'r', marker = 'o', ls = 'solid', label = 'new')
+ax.plot(t0, f0, c = 'b', marker = '+', ls = '-', label = 'loop_0')
+ax.plot(t1, f1, c= 'g', marker = '+', ls = '-', label = 'loop_1')
+plt.legend(loc = 'best')
+#plt.plot(tnew, mf, marker = 'r-o', label = new, t0, f0, marker = 'b-+', label = loop_0, t1, f1, marker = 'g-+', label = loop_1)
+plt.show()
+'''