21.09

apteronotus_code/sin_all_normal.py

@@ -0,0 +1,64 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pylab
+from IPython import embed
+from scipy.optimize import curve_fit
+from jar_functions import gain_curve_fit
+from jar_functions import avgNestedLists
+
+
+identifier = [#'2018lepto1',
+              #'2018lepto4',
+              #'2018lepto5',
+              #'2018lepto76',
+              '2018lepto98',
+              '2019lepto03',
+              #'2019lepto24',
+              #'2019lepto27',
+              #'2019lepto30',
+              #'2020lepto04',
+              #'2020lepto06',
+              '2020lepto16',
+              '2020lepto19',
+              '2020lepto20'
+              ]
+
+tau = []
+f_c = []
+for ID in identifier:
+    print(ID)
+    amf = np.load('5Hz_amf_%s.npy' %ID)
+    gain = np.load('5Hz_gain_%s.npy' %ID)
+
+    sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
+    #print('tau:', sinv[0])
+    tau.append(sinv[0])
+    f_cutoff = abs(1 / (2*np.pi*sinv[0]))
+    print('f_cutoff:', f_cutoff)
+    f_c.append(f_cutoff)
+
+
+amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
+
+all = []
+
+for ident in identifier:
+    data = np.load('5Hz_gain_%s.npy' %ident)
+    all.append(data)
+
+av = avgNestedLists(all)
+
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.plot(amf, av, 'o')
+ax.set_xscale('log')
+ax.set_yscale('log')
+ax.set_title('gaincurve_average_allfish_5Hz')
+ax.set_ylabel('gain [Hz/(mV/cm)]')
+ax.set_xlabel('envelope_frequency [Hz]')
+ax.set_ylim(0.0008, )
+ax.plot(f_c, np.full((len(identifier)), 0.0015), 'o', label = 'cutoff frequencies')
+ax.legend()
+
+plt.show()
+embed()

apteronotus_code/sin_all_uniform.py

@@ -0,0 +1,97 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pylab
+from IPython import embed
+from scipy.optimize import curve_fit
+from jar_functions import gain_curve_fit
+from jar_functions import avgNestedLists
+
+
+identifier_uniform = ['2018lepto1',
+              # '2018lepto4',
+              # '2018lepto5',
+              #'2018lepto76',
+              '2018lepto98',
+              # '2019lepto03',
+              '2019lepto24',
+              #'2019lepto27',
+              # '2019lepto30',
+              '2020lepto04',
+              # '2020lepto06',
+              # '2020lepto16',
+              '2020lepto19',
+              # '2020lepto20'
+              ]
+identifier = ['2018lepto1',
+              '2018lepto4',
+              '2018lepto5',
+              '2018lepto76',
+              '2018lepto98',
+              '2019lepto03',
+              '2019lepto24',
+              '2019lepto27',
+              '2019lepto30',
+              '2020lepto04',
+              '2020lepto06',
+              '2020lepto16',
+              '2020lepto19',
+              '2020lepto20'
+              ]
+
+tau = []
+f_c = []
+for ID in identifier:
+    print(ID)
+    amf = np.load('amf_%s.npy' %ID)
+    gain = np.load('gain_%s.npy' %ID)
+
+    sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
+    #print('tau:', sinv[0])
+    tau.append(sinv[0])
+    f_cutoff = abs(1 / (2*np.pi*sinv[0]))
+    print('f_cutoff:', f_cutoff)
+    f_c.append(f_cutoff)
+
+tau_uniform = []
+f_c_uniform = []
+for ID in identifier_uniform:
+    #print(ID)
+    amf = np.load('amf_%s.npy' %ID)
+    gain = np.load('gain_%s.npy' %ID)
+
+    sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
+    #print('tau:', sinv[0])
+    tau_uniform.append(sinv[0])
+    f_cutoff = abs(1 / (2*np.pi*sinv[0]))
+    #print('f_cutoff:', f_cutoff)
+    f_c_uniform.append(f_cutoff)
+amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
+
+all = []
+new_all = []
+for ident in identifier:
+    data = np.load('gain_%s.npy' %ident)
+    all.append(data)
+for ident in identifier_uniform:
+    data = np.load('gain_%s.npy' % ident)
+    new_all.append(data)
+
+av = avgNestedLists(all)
+new_av = avgNestedLists(new_all)
+lim = 0.001
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.plot(amf, av, 'o', color = 'orange', label = 'normal')
+ax.plot(amf, new_av, 'o', color = 'blue', label = 'uniformed')
+ax.set_xscale('log')
+ax.set_yscale('log')
+ax.set_title('gaincurve_average_allfish')
+ax.set_ylabel('gain [Hz/(mV/cm)]')
+ax.set_xlabel('envelope_frequency [Hz]')
+ax.set_ylim(0.0008, )
+ax.plot(f_c, np.full((len(identifier)), 0.0015), 'o', color = 'orange', label = 'all cutoff frequencies')
+ax.plot(f_c_uniform, np.full((len(identifier_uniform)), 0.001), 'o', color = 'blue', label = 'uniformed cutoff frequencies')
+ax.legend()
+
+plt.show()
+embed()

eigenmannia_code/step_response_eigen.py

@@ -43,12 +43,13 @@ for infodataset in datasets:
 
 for ID in identifier:
     base_path = 'D:\\jar_project\\JAR\\eigenmannia\\step\\%s' %ID
-    res_df = []
+    response = []
+    stim_ampl = []
     for idx, dataset in enumerate(os.listdir(base_path)):
         dataset = os.path.join(base_path, dataset, 'beats-eod.dat')
         print(dataset)
         #input of the function
-        frequency, time, amplitude, eodf, deltaf, stimulusf, duration, pause = parse_dataset(dataset)
+        frequency, time, amplitude, eodf, deltaf, stimulusf, stimulusamplitude, duration, pause = parse_dataset(dataset)
         dm = np.mean(duration)
         pm = np.mean(pause)
         timespan = dm + pm
@@ -57,20 +58,15 @@ for ID in identifier:
         if len(frequency) == 5:
             continue
 
-        norm, base, jar = norm_function(frequency, time, onset_point=dm - dm, offset_point=dm)  # dm-dm funktioniert nur wenn onset = 0 sec
+        mf, tnew = mean_traces(start, stop, timespan, frequency, time) # maybe fixed timespan/sampling rate
-        print(jar)
-        if jar[0] == 0.0:
-            continue
-
-        mf, tnew = mean_traces(start, stop, timespan, norm, time) # maybe fixed timespan/sampling rate
 
         cf, ct = mean_noise_cut_eigen(mf, tnew, n=1250)
 
-        cf_arr = np.array(cf)
+        onset_point = dm - dm
-        ct_arr = np.array(ct)
+        offset_point = dm
+        onset_end = onset_point - 10
+        offset_start = offset_point - 10
 
-        freq_all.append(cf_arr)
-        time_all.append(ct_arr)
 
         b = []
         for index, i in enumerate(ct):
@@ -84,15 +80,37 @@ for ID in identifier:
                 print(h)
                 print(indexx)
                 print(cf[indexx])
+
+        ''' sounds good, doesnt work somehow: in norm devision by 0 (jar) or index doesnt fit
+                norm, base, jar = norm_function(frequency, time, onset_point=dm - dm,
+                                                offset_point=dm)  # dm-dm funktioniert nur wenn onset = 0 sec
+                b = []
+                for index, i in enumerate(ct):
+                    if i > -45 and i < -5:
+                        b.append(cf[index])
+
+                j = []
+                for indexx, h in enumerate(ct):
+                    if h > 195 and h < 145:
+                        j.append(cf[indexx])
+                        print(h)
+                        print(indexx)
+                        print(cf[indexx])
+        b = np.median(cf[(ct >= onset_end) & (ct < onset_point)])
+
+        j = np.median(cf[(ct >= offset_start) & (ct < offset_point)])
+
+        '''
+
         r = np.median(j) - np.median(b)
-        #response.append(r)
+        response.append(r)
-        embed()
+        stim_ampl.append(stimulusamplitude)
+    res_ampl = sorted(zip(stim_ampl, response))
     base_line = plt.axhline(y = 0, color = 'black', ls = 'dotted', linewidth = '1')
 
-    plt.xlim([-10,220])
+    plt.xlabel('Stimulusamplitude')
-    plt.xlabel('time [s]')
+    plt.ylabel('absolute JAR magnitude')
-    plt.ylabel('rel. JAR magnitude')
+    plt.title('absolute JAR')
-    plt.title('relative JAR')
     plt.savefig('relative JAR')
     plt.legend(loc = 'lower right')
     plt.show()

notes

@@ -1,10 +1,11 @@
-+ größe/gewicht/dominanz/temp in csv und über split aufteilen und mit ID verknüpfen oder mit pandar,
+- zeigen: sin_all_uniform - sin_all_normal (also 5Hz, let away 0.001Hz?, gain_fit), eigenmannia_jar, plot_eigenmannia_jar(compare res_df_%s / res_df_%s_new),
-eod basefrequenz rausziehen, scatter plot gegen cutoff frequency, ...
+fish_properties(step_response_eigen does not work (norming/mean_trace)), phaseshift_all, gewicht größe von vanessa
+
++ größe/gewicht/dominanz/temp/eod basefrequenz/... , scatter plot gegen cutoff frequency, ...
     - cutoff - dominance score
     - cutoff - basefrequency
-    - gain - dominance_score: für gain predict machen pro fish,
+    - gain - dominance_score: für gain predict machen pro fish, passt diese zeitkonstante?
-    hab ich dazu die richtige zeitckonstante aus gain_fit?
+    - mit daten befassen (fish_properties!
-    ... da ich ja prediction auch über sin und nicht step mache dann
 + eigenmannia: specgram von pre_data neben specgram von data machen um zu sehen ob analyse fehler oder fehler in import_data
     - erkenntnis: hab bei bm/jm nicht den gleichen mean abgezogen..
     - an sich res_df besser, jedoch immer noch relativ variabel
@@ -13,6 +14,7 @@ eod basefrequenz rausziehen, scatter plot gegen cutoff frequency, ...
 + look at step eigen data
     - norming of data: what if in norm = ground / jar with jar == 0.0?
 + look at 5Hz data - compare
++ to step response eigenmannia and eigenmannia response to deltaf, to now absolute JAR --> should i go to relative? (relative didnt work for me somehow)
 
 long term:
 - extra datei mit script drin um fertige daten darzustellen, den fit-code nur zur datenverarbeitung verwenden