03.07

jar_functions.py

@@ -58,11 +58,10 @@ def parse_dataset(dataset_name):
 def mean_noise_cut(frequencies, time, n):
     cutf = []
     cutt = []
-    for k in np.arange(0, len(time), n):
+    for k in np.arange(0, len(frequencies), n):
-        f = frequencies[k:k+n]
         t = time[k]
-        mean = np.mean(f)
+        f = np.mean(frequencies[k:k+n])
-        cutf.append(mean)
+        cutf.append(f)
         cutt.append(t)
     return cutf, cutt
 
@@ -74,19 +73,21 @@ def step_response(t, a1, a2, tau1, tau2):
 
 
 
-def normalized_JAR(frequencies, time, onset=0, offset=100):
+def base_eod(frequencies, time, onset_point):
-
-    onset_point = onset - 10
-    offset_point = offset - 10
-    embed()
     base_eod = []
-    step_eod = []
 
-    np.mean(f[(time >= onset_point) & time < onset])
+    onset_end = onset_point - 10
-    for i in range(len(frequencies)):
+
-        if time < onset and time > onset_point:
+    base = np.mean(frequencies[(time >= onset_end) & (time < onset_point)])
+    base_eod.append(base)
+    return base_eod
+
+def JAR_eod(frequencies, time, offset_point):
+    jar_eod = []
+
+    offset_start = offset_point - 10
 
-            base_eod.append(frequencies[i])
+    jar = np.mean(frequencies[(time >= offset_start) & (time < offset_point)])
+    jar_eod.append(jar)
 
-        if time[i] < offset and time[i] > offset_range:
+    return jar_eod
-            step_eod.append(frequencies[i])

second_try.py

@@ -7,6 +7,8 @@ from IPython import embed
 from jar_functions import parse_dataset
 from jar_functions import mean_noise_cut
 from jar_functions import step_response
+from jar_functions import JAR_eod
+from jar_functions import base_eod
 
 
 datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
@@ -49,40 +51,43 @@ for dataset in datasets:
     frequency_mean.append(mf)
     time.append(tnew)
 
+"""
+    for a in [0, 1, 2]:
+        for b in [0, 1, 2]:
+            r_step = step_response(t = ct_arr, a1 = a, a2 = b, tau1 = 30, tau2 = 60)
+"""
+
 for i in range(len(frequency_mean)):
-    for n in [10, 50, 100, 1000, 10000, 20000, 30000]:
+    for n in [100, 500, 1000]:
         cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=n)
-        #plt.plot(ct, cf, label='n=%d' % n)
-
-        ct_array = np.array(ct) +10
 
-        r_step = step_response(t=ct_array, a1=0.58, a2=0, tau1=100, tau2=100)
 
-        #plt.plot(r_step)
+        ct_arr = np.array(ct)
+        cf_arr = np.array(cf)
 
+        base = base_eod(cf_arr, ct_arr, onset_point = 0)
+        ground = cf_arr - base
+        jar = JAR_eod(ground, ct_arr, offset_point = 100)
+        norm = ground / jar
 
-    for a in [0, 1, 2]:
+        plt.plot(ct_arr, norm, label='n=%d' % n)
-        for b in [0, 1, 2]:
-            r_step = step_response(t = ct_array, a1 = a, a2 = b, tau1 = 30, tau2 = 60)
-
 
-plt.plot(time[0], frequency_mean[0])
-plt.show()
-embed()
 
+for n in [1480]:
+    cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=n)
+    ct_arr = np.array(ct)
+    cf_arr = np.array(cf)
+    r_step = step_response(t=ct_arr + 10, a1=0.55, a2=0.89, tau1=11.2, tau2= 280)
+    plt.plot(r_step, label='fit: n=%d' % n)
 
 'plotting'
-plt.xlim([-10,200])
+plt.xlim([-10,220])
 #plt.ylim([400, 1000])
 plt.xlabel('time [s]')
-#plt.ylabel('rel. JAR magnitude')
+plt.ylabel('rel. JAR magnitude')
 #plt.title('fit_function(a1=0)')
 #plt.savefig('fit_function(a1=0)')
 plt.legend()
 plt.show()
-
+embed()
-
+# Zeitkonstante: von sec. 0 bis 63%? relative JAR
-
-# normiert darstellen (frequency / mean von baseline frequency?)?
-# Zeitkonstante: von sec. 0 bis 63%? relative JAR
-