16.09

apteronotus_code/base_eodf.py

@@ -0,0 +1,65 @@
+import os
+import numpy as np
+from IPython import embed
+import matplotlib.pyplot as plt
+import nix_helpers as nh
+from jar_functions import get_time_zeros
+from jar_functions import parse_dataset
+from jar_functions import mean_traces
+from jar_functions import mean_noise_cut_eigen
+
+base_path = 'D:\\jar_project\\JAR\\sin'
+
+identifier = ['2018lepto1',
+              '2018lepto4',
+              '2018lepto5',
+              '2018lepto76',
+              '2018lepto98',
+              '2019lepto03',
+              '2019lepto24',
+              '2019lepto27',
+              '2019lepto30',
+              '2020lepto04',
+              '2020lepto06',
+              '2020lepto16',
+              '2020lepto19',
+              '2020lepto20'
+              ]
+for ID in identifier:
+    base = []
+
+    for dataset in os.listdir(os.path.join(base_path, ID)):
+        if dataset == 'prerecordings':
+            continue
+        datapath = os.path.join(base_path, ID, dataset, 'beats-eod.dat')
+        print(datapath)
+        try:
+            o = open(datapath)
+        except IOError:
+            continue
+        frequency, time, amplitude, eodf, deltaf, stimulusf, duration, pause = parse_dataset(datapath)
+
+        dm = np.mean(duration)
+        pm = np.mean(pause)
+        timespan = dm + pm
+        start = np.mean([t[0] for t in time])
+        stop = np.mean([t[-1] for t in time])
+
+        mf, tnew = mean_traces(start, stop, timespan, frequency, time)  # maybe fixed timespan/sampling rate
+
+        cf, ct = mean_noise_cut_eigen(mf, tnew, 1250)
+
+        f = []
+        for idx, i in enumerate(ct):
+            if i > -45 and i < -5:
+                f.append(cf[idx])
+        ff = np.mean(f)
+        base.append(ff)
+
+        plt.plot(ct, cf)
+        plt.show()
+    base_eod = np.mean(base)
+    print(ID)
+    print(base_eod)
+
+embed()