08.07 analysis step_stimulus not perfect, but works

jar_functions.py

@@ -73,7 +73,7 @@ def parse_infodataset(dataset_name):
             identifier.append((l.split(':')[-1].strip()[1:12]))
     return identifier
 
-def mean_loops(start, stop, timespan, frequencies, time):
+def mean_traces(start, stop, timespan, frequencies, time):
     minimumt = min(len(time[0]), len(time[1]))
     # new time with wished timespan because it varies for different loops
     tnew = np.arange(start, stop, timespan / minimumt)  # 3rd input is stepspacing:
@@ -139,6 +139,13 @@ def JAR_eod(frequencies, time, offset_point):
 
     return jar_eod
 
+def sort_values(values):
+    a = values[:2]
+    tau = np.array(sorted(values[2:], reverse=False))
+    values = np.array([a, tau])
+    values_flat = values.flatten()
+    return values_flat
+
 
 
 

second_try.py

@@ -7,10 +7,11 @@ from IPython import embed
 from scipy.optimize import curve_fit
 from jar_functions import parse_dataset
 from jar_functions import parse_infodataset
-from jar_functions import mean_loops
+from jar_functions import mean_traces
 from jar_functions import mean_noise_cut
 from jar_functions import norm_function
 from jar_functions import step_response
+from jar_functions import sort_values
 
 #nicht: 19-aa, 22-ae, 22-ad (?)
 datasets = [#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-aa\\beats-eod.dat')),   #-5Hz delta f, horrible fit
@@ -31,9 +32,6 @@ infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat')),
 time_all = []
 freq_all = []
 
-constant_factors = []
-time_constants = []
-
 ID = []
 
 for infodataset in infodatasets:
@@ -48,9 +46,9 @@ for dataset in datasets:
     dm = np.mean(duration)
     pm = np.mean(pause)
     timespan = dm + pm
-    start = -10
-    stop = 200
-    mf , tnew = mean_loops(start, stop, timespan, frequency, time)
+    start = (time[0][0] + time[1][0]) / 2
+    stop = (time[0][-1] + time[1][-1]) / 2
+    mf , tnew = mean_traces(start, stop, timespan, frequency, time) # maybe fixed timespan/sampling rate
 
     #for i in range(len(mf)):
 
@@ -64,34 +62,40 @@ for dataset in datasets:
     freq_all.append(norm.tolist())
     time_all.append(ct_arr)
 
-    plt.plot(ct_arr, norm) #, label='fish=%s' % ID)
+    plt.plot(ct_arr, norm, color = 'grey', label='fish=%s' % ID)
+
+    # fit function
+    sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], norm[ct_arr < dm]) #step_values and step_cov
+
+    # sorted a and tau
+    values = sort_values(sv)
+
+    '''
+    plt.plot(ct_arr[ct_arr < 100], step_response(ct_arr, *sv)[ct_arr < 100], color='orange',
+             label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
+    '''
+
+    print('fish: a1, a2, tau1, tau2', values)
 
-    sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], norm[ct_arr < dm], maxfev = 2000) #step_values and step_cov
+# average over all fish
+mf_all , tnew_all = mean_traces(start, stop, timespan, freq_all, time_all)
 
-    a = sv[:2]
-    tau = np.array(sorted(sv[2:], reverse=False))
-    values = np.array([a, tau])
-    values_flat = values.flatten()
+plt.plot(tnew_all, mf_all, color = 'b', label = 'average', ls = 'dashed')
 
-    plt.plot(ct_arr [ct_arr < 100], step_response(ct_arr, *sv)[ct_arr < 100], label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_flat))
+# fit for average
+sv_all, sc_all = curve_fit(step_response, tnew_all[tnew_all < dm], mf_all[tnew_all < dm]) #step_values and step_cov
 
-    print('a1, a2, tau1, tau2', values_flat)
-    constant_factors.append(a)
-    time_constants.append(tau)
-fr = []
-for j in freq_all:
-    fr.append(freq_all[j])
-    embed()
-    minimumf_all = min(len(freq_all[j]))
-    f_all = freq_all[j][:minimumf_all]
-    print(freq_all[0])
-print(len((freq_all[j])))
+values_all = sort_values(sv_all)
 
-#f_all_arr = np.array([f0_all], [f1_all])
-#f_mean_all = np.mean(freq_all, axis = 0)
-#t_mean_all = np.mean(time_all, axis = 0)
+plt.plot(tnew_all[tnew_all < 100], step_response(tnew_all, *sv_all)[tnew_all < 100], color='orange',
+         label='average_fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_all))
+
+print('average: a1, a2, tau1, tau2', values_all)
+
+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')
 
-const_line = plt.axhline(y=0.632)
 plt.xlim([-10,220])
 plt.xlabel('time [s]')
 plt.ylabel('rel. JAR magnitude')
@@ -101,9 +105,8 @@ plt.legend(loc = 'lower right')
 plt.show()
 embed()
 
-
-# alle daten einlesen durch große for schleife (auch average über alle fische?)
-# für einzelne fische fit kontrollieren
-
-# Fragen: wie offset point wenn nicht start bei 0 sec? über zeitdatenpunkt?
-# wie zip ich ID liste mit plot (für eine for schleife) zusammen?
+# Fragen:
+# 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?