From c3c92fd42d7604804668fd4ca23be12255b4d46e Mon Sep 17 00:00:00 2001
From: xaver <xaver.roos@t-online.de>
Date: Mon, 29 Jun 2020 16:38:20 +0200
Subject: [PATCH] 29.06

---
 jar_functions.py | 23 ++++++++++------
 scratch.py       | 11 +++++++-
 second_try.py    | 69 +++++++++++++++++++++++++++++++++++++++---------
 3 files changed, 82 insertions(+), 21 deletions(-)

diff --git a/jar_functions.py b/jar_functions.py
index 9b124c9..e09db27 100644
--- a/jar_functions.py
+++ b/jar_functions.py
@@ -52,14 +52,21 @@ def parse_dataset(dataset_name):
     times.append(time)          #append data from one list to another
     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]
+    return times, frequencies, amplitudes, eodfs, deltafs, stimulusfs       #output of the function
 
-    #print(len(time))
-    print(len(times))
-    embed()
 
-    return times, frequencies, amplitudes, eodfs, deltafs, stimulusfs       #output of the function
\ No newline at end of file
+
+def noise_reduce(dataset_name):
+    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()
+
+    n = 10
+    cutf = []
+    for i in np.arange(0, len(dataset_name), n):    #dataset_name sollte Frequenzen sein?
+        mean = np.mean(dataset_name[i:i+n])         #sollte nach i+n weitergehen?
+        cutf.append(mean)
+
+    return cutf
\ No newline at end of file
diff --git a/scratch.py b/scratch.py
index 8e3a54f..ac9900f 100644
--- a/scratch.py
+++ b/scratch.py
@@ -1,4 +1,6 @@
 import numpy as np
+from IPython import embed
+
 """
 #second_try scratch
 minimum = min(len(frequency[0]), len(frequency[1]))
@@ -21,4 +23,11 @@ g = [1, 2]
 
 h = [3, 4]
 
-z = np.array([1, 2], [3, 4], dtype=object)
\ No newline at end of file
+z = np.array([[g], [h]])
+
+mean0 = np.mean(z, axis=0)
+mean1 = np.mean(z, axis=1)
+
+print(mean0)
+print(mean1)
+
diff --git a/second_try.py b/second_try.py
index fa8f88f..0a84651 100644
--- a/second_try.py
+++ b/second_try.py
@@ -5,9 +5,10 @@ import IPython
 import numpy as np
 from IPython import embed
 from jar_functions import parse_dataset
+from jar_functions import noise_reduce
 
 
-datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat'))]
+datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
 #           (os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
 
 eodf = []
@@ -15,31 +16,75 @@ deltaf = []
 stimulusf = []
 
 time = []
-frequency = []
+frequency_mean= []
 amplitude = []
 
+start = -10
+stop = 200
+timespan = 210
 
 for dataset in datasets:
+    #input of the function
     t, f, a, e, d, s= parse_dataset(dataset)
 
-    time.append(t)
-    frequency.append(f)
-    amplitude.append(a)
+    'times'
+    # same for time in both loops
+    minimumt = min(len(t[0]), len(t[1]))
+    t0 = t[0][:minimumt]
+    t1 = t[1][:minimumt]
+
+    # new time with wished timespan because it varies for different loops
+    tnew = np.arange(start, stop, timespan / minimumt)  # 3rd input is stepspacing:
+    # in case complete measuring time devided by total number of datapoints
+
+    'frequencies'
+    # minimum datapoint lenght of both loops of frequencies
+    minimumf = min(len(f[0]), len(f[1]))
+    # new frequencies to minimum for both loops
+    f0 = f[0][:minimumf]
+    # interpolation
+    f0new = np.interp(tnew, t0, f0)
+
+    f1 = f[1][:minimumf]
+    # interpolation
+    f1new = np.interp(tnew, t1, f1)
+
+    #new array with frequencies of both loops as two lists put together as an array
+    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))
+
+
+
+    #appending data
     eodf.append(e)
     deltaf.append(d)
     stimulusf.append(s)
+    amplitude.append(a)
 
-mean = np.mean(frequency, axis=0)
+    frequency_mean.append(mfreshape)
+    time.append(treshape)
 
-#embed()
+    cutfreq = noise_reduce(mfreshape)
+    embed()
 
-#evtl. normiert darstellen (frequency / baseline frequency?)?
-#Zeitkonstante: von sec. 0 bis 63%? relative JAR
 
-plt.plot(time, frequency)
 
+#plotting
+'''why does append put in a 3rd dimension? plt.plot(time, frequency_mean) '''
+
+plt.plot(treshape, mfreshape)
+plt.xlim([-10,200])
+#plt.ylim([400, 1000])
 plt.xlabel('time [s]')
 plt.ylabel('frequency [Hz]')
-plt.xlim([-10,200])
-plt.title('second try because first try was sold out')
 plt.show()
+
+
+#evtl. normiert darstellen (frequency / baseline frequency?)?
+#Zeitkonstante: von sec. 0 bis 63%? relative JAR