11.08

jar_functions.py

@@ -163,7 +163,7 @@ def average(freq_all, time_all, start, stop, timespan, dm):
 
     values_all = sort_values(sv_all)
 
-    plt.plot(tnew_all[tnew_all < 100], step_response(tnew_all, *sv_all)[tnew_all < 100], color = 'g',
+    plt.plot(tnew_all[tnew_all < dm], step_response(tnew_all, *sv_all)[tnew_all < dm], color = 'g', lw = 2,
              label='average_fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_all))
 
     print('average: a1, a2, tau1, tau2', values_all)

sin_response_fit.py

@@ -9,6 +9,7 @@ from jar_functions import sin_response
 def take_second(elem):
     return elem[1]
 
+predict = []
 
 gain = []
 mgain = []
@@ -25,12 +26,13 @@ data = sorted(np.load('files.npy'), key = take_second)
 for i, d in enumerate(data):
     dd = list(d)
     jar = np.load('%s.npy' %dd)
+    jm = jar - np.mean(jar)
     print(dd)
 
     time = np.load('time: %s.npy' %dd)
 
     b, a = signal.butter(4, (float(d[1]) / 2) / 10000, 'high', analog=True)
-    y = signal.filtfilt(b, a, jar - np.mean(jar))
+    y = signal.filtfilt(b, a, jm)
     #plt.plot(time, y)
     #plt.plot(time, jar)
 
@@ -41,12 +43,16 @@ for i, d in enumerate(data):
     gain.append(np.sqrt(sinv[2]**2))
     amfreq.append(d[1])
 
+    Rs = []
+    for ix, t in enumerate(time):
+        R = (jm[ix] - sin_response(t, float(d[1]), np.sqrt(sinv[1]**2), np.sqrt(sinv[2]**2)))**2
+        Rs.append(R)
 
+    sigma = sum(Rs)
+    rms = np.sqrt((1/len(time)) * sigma)
     #plt.plot(time, sin_response(time, *sinv), label='fit: f=%f, p=%.2f, A=%.2f' % tuple(sinv))
 
-    # plt.legend()
+    #mean over same amfreqs for phase and gain
-    #plt.show()
-
     if currf is None or currf == d[1]:
         currf = d[1]
         idxlist.append(i)
@@ -74,10 +80,25 @@ meanedp = np.mean(meanp)
 mgain.append(meanedf)
 mphaseshift.append(meanedp)
 
+for f in amf:
+    G = np.max(mgain) / np.sqrt(1 + (2*((np.pi*f*3.14)**2)))
+    predict.append(G)
+
+
+
 fig = plt.figure()
 ax = fig.add_subplot(1, 1, 1)
 ax.plot(amf, mgain, 'o')
+ax.plot(amf, predict)
 ax.set_yscale('log')
+ax.set_xscale('log')
+ax.set_title('2018lepto98')
+ax.set_ylabel('gain [Hz/(mV/cm)]')
+ax.set_xlabel('AM-frequency [Hz]')
+#plt.savefig('2018lepto98_gain')
 pylab.show()
+embed()
 
-#betrag von A
+#phase in degree
+# Q10 / conductivity
+# AM-frequency / envelope-frequency scale title?

sin_response_specto.py

@@ -27,24 +27,16 @@ base_path = 'D:\\jar_project\\JAR\\sin\\2018lepto98'
 
 #dat = glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')
 #infodat = glob.glob('D:\\jar_project\\JAR\\2020*\\info.dat')
-datasets = [#'2020-06-19-aa',   #-5Hz delta f, horrible fit
+datasets = ['2020-07-21-ak',
-            #'2020-06-19-ab',   #-5Hz delta f, bad fit
+'2020-07-21-al',
-            #'2020-06-22-aa',   #-5Hz delta f, bad fit
+'2020-07-21-am',
-            #'2020-06-22-ab',   #-5Hz delta f, bad fit
+'2020-07-21-an',
-            #'2020-06-22-ac',   #-15Hz delta f, good fit
+'2020-07-21-ao',
-            #'2020-06-22-ad',   #-15Hz delta f, horrible fit
+'2020-07-22-ai',
-            #'2020-06-22-ae',   #-15Hz delta f, horrible fit
+'2020-07-22-aj',
-            #'2020-06-22-af',    #-15Hz delta f, good fit
+'2020-07-22-ak',
-            '2020-07-21-al',      #sin
+'2020-07-22-al',
-            '2020-07-21-am',
+'2020-07-22-am',
-            '2020-07-21-ak',
-            '2020-07-21-an',
-            '2020-07-21-ao',
-            '2020-07-22-ai',
-            '2020-07-22-aj',
-            '2020-07-22-ak',
-            '2020-07-22-al',
-            '2020-07-22-am',
             ]
 
 time_all = []
@@ -79,7 +71,7 @@ for idx, dataset in enumerate(datasets):
             i = parse_infodataset(infodataset)
             identifier = i[0]
             if not identifier[1:-2] in ID:
-                ID.append(identifier[1:-2])
+                ID.append(identifier[1:-1])
         file_name.append(ID[0])
 
         amfreq = import_amfreq(datapath)
@@ -120,7 +112,7 @@ for idx, dataset in enumerate(datasets):
         np.save('time: %s.npy' % file_name, cut_times)
         np.save('%s.npy' % file_name, jar4)
 
-        plt.plot(cut_times, jm, '-k')
+        #plt.plot(cut_times, jm, '-k')
 
         #cf, ct = mean_noise_cut(jar4, cut_times, n = int(round(len(jar4)/((times[-1] - times [0]) * amfreq))))
         #plt.plot(ct, cf, '-k')

step_response.py

@@ -16,7 +16,7 @@ from jar_functions import step_response
 from jar_functions import sort_values
 from jar_functions import average
 
-base_path = 'D:\\jar_project\\JAR'
+base_path = 'D:\\jar_project\\JAR\\step\\step_2018lepto98'
 
 #nicht: -5Hz delta f, 19-aa, 22-ae, 22-ad (?)
 datasets = [#'2020-06-19-aa',   #-5Hz delta f, horrible fit
@@ -26,8 +26,20 @@ datasets = [#'2020-06-19-aa',   #-5Hz delta f, horrible fit
             #'2020-06-22-ac',    #-15Hz delta f, good fit
             #'2020-06-22-ad',    #-15Hz delta f, horrible fit
             #'2020-06-22-ae',    #-15Hz delta f, horrible fit
-            '2020-06-22-af'     #-15Hz delta f, good fit
+            #'2020-06-22-af',     #-15Hz delta f, good fit
-            ]
+            '2020-07-13-ad',
+'2020-07-13-ae',
+'2020-07-13-af',
+'2020-07-13-ag',
+'2020-07-13-ah',
+'2020-07-13-ai',
+'2020-07-13-aj',
+#'2020-07-13-ak',
+#'2020-07-13-al',
+'2020-07-13-am',
+#'2020-07-13-an',
+#'2020-07-13-ao'
+]
 
 #dat = glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')
 #infodat = glob.glob('D:\\jar_project\\JAR\\2020*\\info.dat')
@@ -68,7 +80,7 @@ for idx, dataset in enumerate(datasets):
     freq_all.append(cf_arr)
     time_all.append(ct_arr)
 
-    plt.plot(ct_arr, cf_arr, color = col[idx], label='fish=%s' % datasets[idx])
+    plt.plot(ct_arr, cf_arr, label='fish=%s' % datasets[idx]) #, color = col[idx]
 
     sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], cf_arr[ct_arr < dm], [1.0, 1.0, 5.0, 50.0], bounds=(0.0, np.inf))  # step_values and step_cov
 
@@ -76,17 +88,17 @@ for idx, dataset in enumerate(datasets):
     values = sort_values(sv)
 
     # fit for each trace 
-    plt.plot(ct_arr[ct_arr < dm], step_response(ct_arr[ct_arr < dm], *sv), label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
+    #plt.plot(ct_arr[ct_arr < dm], step_response(ct_arr[ct_arr < dm], *sv), label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
     #plt.plot(ft, step_response(ft, *sv), color='orange', label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
 
     print('fish: a1, a2, tau1, tau2', values)
 
 
-'''# average over all fish
+# average over all fish
 mf_all, tnew_all, values_all = average(freq_all, time_all, start, stop, timespan, dm)
-'''
 
-const_line = plt.axhline(y = 0.632)
+
+#const_line = plt.axhline(y = 0.632)
 stimulus_duration = plt.hlines(y = -0.25, xmin = 0, xmax = 100, color = 'r', label = 'stimulus_duration')
 base_line = plt.axhline(y = 0, color = 'black', ls = 'dotted', linewidth = '1')
 
@@ -99,14 +111,5 @@ plt.legend(loc = 'lower right')
 plt.show()
 embed()
 
-# norm vor mean_traces damit cutoff von -5
-# average über alle fische eigentlich mal nicht nötig, auslagern
-# nur bei -15 Hz messen
-# bei verschiedenen amplituden messen (siehe Tim)
-# natalie fragen ob sie bei verschiedenen Amplituden messen kann (siehe tim)
 
-# Fragen:
+# natalie fragen ob sie bei verschiedenen Amplituden messen kann (siehe tim)
-# wie offset point wenn nicht start bei 0 sec? über zeitdatenpunkt? oder einfach immer bei 0 onset..?
-# wie zip ich ID liste mit plot (für eine for schleife) zusammen?
-# welche Stimulusintesität?
-# start/stop/timespan ok?