30.06

jar_functions.py

@@ -1,5 +1,6 @@
 import os   #compability with windows
 from IPython import embed
+import numpy as np
 
 def parse_dataset(dataset_name):
     assert(os.path.exists(dataset_name))        #see if data exists
@@ -57,16 +58,26 @@ def parse_dataset(dataset_name):
 
 
 
-def noise_reduce(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
     f.close()
-
+                                                #len of frequencies is 10 time shorter than before, so worked?
-    n = 10
+                                                #put in frequencies instead of dataset?
+                                                #2nd loop cut frequencies by this function?
     cutf = []
-    for i in np.arange(0, len(dataset_name), n):    #dataset_name sollte Frequenzen sein?
+    frequencies = []
-        mean = np.mean(dataset_name[i:i+n])         #sollte nach i+n weitergehen?
+    for i in range(len(lines)):
+        l = lines[i].strip()
+
+        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?
+        f = frequencies[k:k+n]
+        mean = np.mean(f)
         cutf.append(mean)
 
     return cutf

scratch.py

@@ -1,5 +1,9 @@
+import os
 import numpy as np
 from IPython import embed
+from jar_functions import noise_reduce
+
+datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
 
 """
 #second_try scratch
@@ -19,6 +23,8 @@ for f in frequency:
 
 # mean_f = np.mean(x) for x in zip(freqeuncies1, frequencies2)
 """
+
+'''
 g = [1, 2]
 
 h = [3, 4]
@@ -30,4 +36,7 @@ mean1 = np.mean(z, axis=1)
 
 print(mean0)
 print(mean1)
-
+'''
+for dataset in datasets:
+    cf = noise_reduce(dataset, 10)
+    embed()

second_try.py

@@ -26,6 +26,7 @@ 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
@@ -49,12 +50,11 @@ for dataset in datasets:
     # interpolation
     f1new = np.interp(tnew, t1, f1)
 
-    #new array with frequencies of both loops as two lists put together as an array
+    #new array with frequencies of both loops as two lists put together
     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))
@@ -67,18 +67,28 @@ for dataset in datasets:
     stimulusf.append(s)
     amplitude.append(a)
 
-    frequency_mean.append(mfreshape)
+    frequency_mean.append(mf)
-    time.append(treshape)
+    time.append(tnew)
+
 
-    cutfreq = noise_reduce(mfreshape)
-    embed()
 
+'''
+'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
+'plotting'
 '''why does append put in a 3rd dimension? plt.plot(time, frequency_mean) '''
 
-plt.plot(treshape, mfreshape)
+plt.plot(tnew, mf)
 plt.xlim([-10,200])
 #plt.ylim([400, 1000])
 plt.xlabel('time [s]')