19.10

apteronotus_code/base_eodf.py

@@ -7,11 +7,12 @@ 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
+from jar_functions import adjust_eodf
 
 base_path = 'D:\\jar_project\\JAR\\sin'
 
-identifier = [#'2018lepto1',
+identifier = ['2018lepto1',
-              #'2018lepto4',
+              '2018lepto4',
               '2018lepto5',
               '2018lepto76',
               '2018lepto98',
@@ -25,6 +26,7 @@ identifier = [#'2018lepto1',
               '2020lepto19',
               '2020lepto20'
               ]
+eod = []
 for ID in identifier:
     base = []
 
@@ -56,10 +58,19 @@ for ID in identifier:
         ff = np.mean(f)
         base.append(ff)
 
-        plt.plot(ct, cf)
+        #plt.plot(ct, cf)
-        plt.show()
+        #plt.show()
     base_eod = np.mean(base)
     print(ID)
     print(base_eod)
+    eod.append(base_eod)
 
-    embed()
+temp = np.load('temperature.npy')
+
+eod_temp = zip(eod, temp)
+
+Q10_eod = []
+for et in eod_temp:
+    Q10 = adjust_eodf(et[0], et[1])
+    Q10_eod.append(Q10)
+embed()

apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py

@@ -7,7 +7,7 @@ from matplotlib.mlab import specgram
 import os
 from jar_functions import gain_curve_fit
 
-identifier = ['2020lepto06']
+identifier = ['2020lepto19']
 
 tau = []
 f_c = []
@@ -19,7 +19,7 @@ for ID in identifier:
     gain = np.load('gain_%s.npy' %ID)
     print(gain)
 
-    sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
+    sinv, sinc = curve_fit(gain_curve_fit, amf, gain, [2, 3])
     print('tau:', sinv[0])
     tau.append(sinv[0])
     f_cutoff = abs(1 / (2*np.pi*sinv[0]))
@@ -35,8 +35,8 @@ for ID in identifier:
     fig = plt.figure()
     ax = fig.add_subplot()
     ax.plot(amf, gain,'o' , label = 'gain')
-    ax.plot(amf, predict, label = 'fit')
+    #ax.plot(amf, predict, label = 'fit')
-    ax.axvline(x=f_cutoff, ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
+    #ax.axvline(x=f_cutoff, ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
     ax.set_xscale('log')
     ax.set_yscale('log')
     ax.set_ylabel('gain [Hz/(mV/cm)]')

apteronotus_code/figure_apteronotus_jar_filter_fit.py

@@ -14,6 +14,8 @@ from jar_functions import sin_response
 from jar_functions import mean_noise_cut
 from jar_functions import gain_curve_fit
 
+plt.rcParams.update({'font.size': 12})
+
 def take_second(elem):      # function for taking the names out of files
     return elem[1]
 
@@ -35,16 +37,16 @@ for ident in identifier:
     currf = None
     idxlist = []
 
-    data = sorted(np.load('5Hz_%s files.npy' %ident), key = take_second)      # list with filenames in it
+    data = sorted(np.load('%s files.npy' %ident), key = take_second)      # list with filenames in it
 
     for i, d in enumerate(data):
         dd = list(d)
-        if dd[1] == '0.5':
+        if dd[1] == '0.05':
-            jar = np.load('5Hz_%s.npy' %dd)     # load data for every file name
+            jar = np.load('%s.npy' %dd)     # load data for every file name
             jm = jar - np.mean(jar)         # low-pass filtering by subtracting mean
             print(dd)
 
-            time = np.load('5Hz_%s time.npy' %dd)       # time file
+            time = np.load('%s time.npy' %dd)       # time file
             dt = time[1] - time[0]
 
             n = int(1/float(d[1])/dt)
@@ -67,7 +69,7 @@ for ident in identifier:
             # jar trace
             plt.plot(time, jar, color = 'C0')
             #plt.hlines(y=np.min(jar) - 2, xmin=0, xmax=400, lw=2.5, color='r', label='stimulus duration')
-            plt.title('JAR trace 2018lepto98, AM-frequency:%sHz, deltaf = -5Hz' % float(d[1]))
+            plt.title('JAR trace 2018lepto98, AM-frequency: %sHz' % float(d[1]))
             plt.xlabel('time[s]')
             plt.ylabel('frequency[Hz]')
             plt.show()
@@ -80,22 +82,29 @@ for ident in identifier:
             # plt.show()
 
             # filter by running average
-            plt.plot(time, jm, color = 'C0', label = 'JAR: subtracted by mean')
+            fig = plt.figure(figsize = (8,14))
-            plt.plot(time, jm - cutf, color = 'darkorange', label = 'JAR: subtracted by mean and step response')
+            fig.suptitle('JAR trace spectogram 2018lepto98:\n subtraction of mean and running average')
-            plt.title('JAR trace spectogram 2018lepto98: subtraction of mean and step response, deltaf = -5Hz')
+            ax = fig.add_subplot(211)
-            plt.xlabel('time[s]')
+            ax.plot(time, jm, color = 'C0', label = '1)')
-            plt.ylabel('frequency[Hz]')
+            ax.plot(time, jm - cutf, color = 'darkorange', label = '2)')
-            plt.legend()
+            ax.set_ylabel('frequency[Hz]')
-            plt.show()
+            ax.set_ylim(-10.5, 10.5)
+            ax.axes.xaxis.set_ticklabels([])
+            plt.legend(loc='upper right')
+            plt.text(-0.1, 1.05, "A)", fontweight=550, transform=ax.transAxes)
 
             # jar trace and fit
-            plt.plot(time, jm - cutf, color = 'darkorange', label = 'JAR: subtracted by mean and step response')
+            ax1 = fig.add_subplot(212)
+            ax1.plot(time, jm - cutf, color = 'darkorange', label = '2)')
             phase_gain = [(((p % (2 * np.pi)) * 360) / (2 * np.pi)), A]
-
+            print(phase_gain)
-            plt.plot(time, sin_response(time, *sinv), color = 'limegreen', label='fit: phaseshift=%.2f°, gain=%.2f[Hz/(mV/cm)]' % tuple(phase_gain))
+            ax1.plot(time, sin_response(time, *sinv), color = 'forestgreen', label='3)')
-            plt.title('JAR trace spectogram 2018lepto98 with fit, deltaf = -5Hz')
+
-            plt.xlabel('time[s]')
+            ax1.set_xlabel('time[s]')
-            plt.ylabel('frequency[Hz]')
+            ax1.set_ylabel('frequency[Hz]')
-            plt.legend()
+            ax1.set_ylim(-10.5,10.5)
+            plt.legend(loc = 'upper right')
+            plt.text(-0.1, 1.05, "B)", fontweight=550, transform=ax1.transAxes)
             plt.show()
+            plt.savefig('test_fig.png')
             embed()

apteronotus_code/figure_apteronotus_rms_gaincurve.py

@@ -14,10 +14,12 @@ from jar_functions import sin_response
 from jar_functions import mean_noise_cut
 from jar_functions import gain_curve_fit
 
+plt.rcParams.update({'font.size': 12})
+
 def take_second(elem):      # function for taking the names out of files
     return elem[1]
 
-identifier = ['2019lepto03']
+identifier = ['2018lepto4']
 for ident in identifier:
 
     predict = []
@@ -35,16 +37,16 @@ for ident in identifier:
     currf = None
     idxlist = []
 
-    data = sorted(np.load('5Hz_%s files.npy' %ident), key = take_second)      # list with filenames in it
+    data = sorted(np.load('%s files.npy' %ident), key = take_second)      # list with filenames in it
 
     for i, d in enumerate(data):
         dd = list(d)
 
-        jar = np.load('5Hz_%s.npy' %dd)     # load data for every file name
+        jar = np.load('%s.npy' %dd)     # load data for every file name
         jm = jar - np.mean(jar)         # low-pass filtering by subtracting mean
         print(dd)
 
-        time = np.load('5Hz_%s time.npy' %dd)       # time file
+        time = np.load('%s time.npy' %dd)       # time file
         dt = time[1] - time[0]
 
         n = int(1/float(d[1])/dt)
@@ -122,26 +124,27 @@ for ident in identifier:
     # condition needed to be fulfilled: RMS < threshold or RMS < mean(RMS)
     idx_arr = (rootmeansquare_arr < threshold_arr) | (rootmeansquare_arr < np.mean(rootmeansquare_arr))
 
-    fig = plt.figure()
+    fig = plt.figure(figsize = (8,14))
+    fig.suptitle('gaincurve and RMS 2018lepto4')
     ax0 = fig.add_subplot(2, 1, 1)
-    ax0.plot(amfreq_arr[idx_arr], mgain_arr[idx_arr], 'o')
+    ax0.plot(amfreq_arr, mgain_arr, 'o')
     ax0.set_yscale('log')
     ax0.set_xscale('log')
-    ax0.set_title('gaincurve 2019lepto03, deltaf = -5Hz')
     ax0.set_ylabel('gain [Hz/(mV/cm)]')
-    ax0.set_xlabel('envelope_frequency [Hz]')
+    ax0.axes.xaxis.set_ticklabels([])
-    #plt.savefig('%s gain' % data[0][0])
+    plt.text(-0.1, 1.05, "A)", fontweight=550, transform=ax0.transAxes)
 
     ax1 = fig.add_subplot(2, 1, 2, sharex = ax0)
     ax1.plot(amfreq, threshold, 'o-', label = 'threshold', color = 'b')
     ax1.set_xscale('log')
     ax1.plot(amfreq, rootmeansquare, 'o-', label = 'RMS', color ='orange')
     ax1.set_xscale('log')
-    ax1.set_xlabel('envelope_frequency [Hz]')
+    ax1.set_xlabel('envelope frequency [Hz]')
     ax1.set_ylabel('RMS [Hz]')
+    plt.text(-0.1, 1.05, "B)", fontweight=550, transform=ax1.transAxes)
     plt.legend()
     pylab.show()
-
+    #fig.savefig('test.pdf')
     #np.save('phaseshift_%s' % ident, mphaseshift_arr[idx_arr])
     #np.save('gain_%s' %ident, mgain_arr[idx_arr])
     #np.save('amf_%s' %ident, amfreq_arr[idx_arr])

apteronotus_code/sin_all_normal.py

@@ -18,20 +18,49 @@ identifier = [#'2018lepto1',
               #'2019lepto30',
               #'2020lepto04',
               #'2020lepto06',
-              '2020lepto16',
+              #'2020lepto16',
               '2020lepto19',
-              '2020lepto20'
+              #'2020lepto20'
               ]
 
 amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
 
+custom_f = np.logspace(-2, -1, 10)
+custom_alpha = np.logspace(1.5, 1, 10)
+c_gain = []
+custom_tau = abs(1 / (2 * np.pi * custom_f))
+for t, a in zip(custom_tau, custom_alpha):
+    custom_gain = []
+    for am in amf:
+        custom_g = gain_curve_fit(am, t, a)
+        custom_gain.append(custom_g)
+    c_gain.append(custom_gain)
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.set_xscale('log')
+ax.set_yscale('log')
+for cc, c in enumerate(c_gain): 
+    ax.plot(amf, c)
+    ax.axvline(x=custom_f[cc], ymin=0, ymax=5, alpha=0.8)  # colors_uniform[ff])
+
+plt.show()
+
+mean = avgNestedLists(c_gain)
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.set_xscale('log')
+ax.set_yscale('log')
+ax.plot(amf, mean)
+plt.show()
+
 all = []
 
 for ident in identifier:
-    data = np.load('5Hz_gain_%s.npy' %ident)
+    data = np.load('gain_%s.npy' %ident)
     all.append(data)
 
 av = avgNestedLists(all)
+embed()
 
 fig = plt.figure()
 ax = fig.add_subplot(111)
@@ -45,10 +74,10 @@ fit = []
 fit_amf = []
 for ID in identifier:
     print(ID)
-    amf = np.load('5Hz_amf_%s.npy' %ID)
+    amf = np.load('amf_%s.npy' %ID)
-    gain = np.load('5Hz_gain_%s.npy' %ID)
+    gain = np.load('gain_%s.npy' %ID)
 
-    sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
+    sinv, sinc = curve_fit(gain_curve_fit, amf, gain, [2, 3])
     #print('tau:', sinv[0])
     tau.append(sinv[0])
     f_cutoff = abs(1 / (2*np.pi*sinv[0]))
@@ -63,7 +92,7 @@ for ff ,f in enumerate(fit):
 
 ax.set_xscale('log')
 ax.set_yscale('log')
-ax.set_title('gain average all fish, deltaf: -5Hz')
+ax.set_title('gain average all fish')
 ax.set_ylabel('gain [Hz/(mV/cm)]')
 ax.set_xlabel('envelope_frequency [Hz]')
 ax.set_ylim(0.0008, )

apteronotus_code/sin_all_uniform.py

@@ -93,6 +93,7 @@ for ID in identifier_uniform:
     tau_uniform.append(sinv[0])
     f_cutoff = abs(1 / (2*np.pi*sinv[0]))
     print('f_cutoff:', f_cutoff)
+    print('alpha:', sinv[1])
     f_c_uniform.append(f_cutoff)
     fit_uniform.append(gain_curve_fit(amf, *sinv))
     fit_amf_uniform.append(amf)

apteronotus_code/stimulus_model.py

@@ -0,0 +1,45 @@
+from IPython import embed
+import numpy as np
+import matplotlib.pyplot as plt
+from jar_functions import sin_response
+
+plt.rcParams.update({'font.size': 12})
+
+# AM model
+lower = 0
+upper = 200
+sample = 1000
+x = np.linspace(lower, upper, sample)
+y1 = (sin_response(np.linspace(lower, upper, sample), 0.02, -np.pi/2, -0.75) - 1)
+y2 = (sin_response(np.linspace(lower, upper, sample), 0.02, -np.pi/2, 0.75) + 1)
+
+fig = plt.figure(figsize = (12,6))
+ax = fig.add_subplot(121)
+ax.plot(x, y1, c = 'red')
+ax.plot(x, y2, c = 'red')
+ax.fill_between(x, y1, y2)
+
+ax.set_xlabel('time[s]')
+ax.set_ylabel('amplitude')
+ax.set_xlim(0,200)
+ax.axes.yaxis.set_ticks([])
+plt.text(-0.1, 1.05, "A)", fontweight=550, transform=ax.transAxes)
+
+# carrier
+lower = 0
+upper = 10
+sample = 1000
+x = np.linspace(lower, upper, sample)
+y1 = (sin_response(np.linspace(lower, upper, sample), 800, np.pi, -0.75) - 1)
+
+ax1 = fig.add_subplot(122)
+ax1.plot(x, y1)
+ax1.axhline(y = -0.25, c = 'red', lw = 2)
+ax1.axhline(y = -1.75, c = 'red', lw = 2)
+
+ax1.set_xlabel('time[ms]')
+ax1.set_xlim(0,10)
+ax1.axes.get_yaxis().set_visible(False)
+plt.text(-0.1, 1.05, "B)", fontweight=550, transform=ax1.transAxes)
+
+plt.show()

apteronotus_code/temperature.py

@@ -3,7 +3,6 @@ import numpy as np
 from IPython import embed
 import matplotlib.pyplot as plt
 from jar_functions import parse_infodataset
-from jar_functions import adjust_eodf
 
 base_path = 'D:\\jar_project\\JAR\\sin'
 
@@ -36,5 +35,5 @@ for ID in identifier:
     print(i)
     print(np.mean(temperature))
     av_temperature.append(np.mean(temperature))
-
+np.save('temperature.npy', av_temperature)
 embed()

eigenmannia_code/eigenmannia_jar_stacked.py

@@ -12,10 +12,14 @@ from jar_functions import get_time_zeros
 from jar_functions import import_data_eigen
 from scipy.signal import savgol_filter
 
+plt.rcParams.update({'font.size': 18})
+
 base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
 
 #2015eigen8 no nix files
-identifier = ['2015eigen16', '2013eigen13','2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
+identifier = [#'2013eigen13',
+              '2015eigen16','2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
+
 
 response = []
 deltaf = []
@@ -28,8 +32,8 @@ for ID in identifier:
         delta_f, duration = parse_stimuli_dat(stimuli_dat)
         dur = int(duration[0][0:2])
         print(delta_f)
-        if delta_f ==[-2.0]:
+        if delta_f != [4.0]:
-            print('HANDLE WITH CARE -2Hz:', datapath)
+            continue
         data, pre_data, dt = import_data_eigen(datapath)
 
         #hstack concatenate: 'glue' pre_data and data
@@ -49,7 +53,7 @@ for ID in identifier:
         eodf4 = eodf * 4
 
         lim0 = eodf4 - 40
-        lim1 = eodf4 + 40
+        lim1 = eodf4 + 60
 
         df = freqs[1] - freqs[0]
         ix0 = int(np.floor(lim0/df))    # back to index
@@ -60,16 +64,6 @@ for ID in identifier:
 
         cut_time_jar = times[:len(jar4)]
         ID_delta_f = [ID, str(delta_f[0]).split('.')[0]]
-        plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0] - 10, times[-1] - 10, lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
-        plt.plot((cut_time_jar - 10), jar4, 'k', label = 'jar trace', lw = 2)
-        plt.hlines(y=lim0 + 5, xmin=0, xmax=60, lw=2.5, color='gold', label='stimulus duration')
-        plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
-        plt.xlim(right=times[-1] - 10)
-        plt.legend()
-        #plt.show()
-        delta_f_ID = [str(delta_f[0]).split('.')[0], ID]
-        plt.savefig('%sHz_specgram_jar_%s' %tuple(delta_f_ID))
-        plt.close()
 
         b = []
         for idx, i in enumerate(times):
@@ -81,10 +75,28 @@ for ID in identifier:
                 j.append(jar4[idx])
 
         r = np.median(j) - np.median(b)
-        print(r)
+        print('response:', r)
         deltaf.append(delta_f[0])
         response.append(r)
 
+        plt.figure(figsize = (14,8))
+        plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
+        plt.plot(cut_time_jar, jar4, 'k', label = 'peak detection trace', lw = 2)
+        plt.hlines(y=lim0 + 5, xmin=10, xmax=70, lw=4, color='yellow', label='stimulus duration')
+        plt.hlines(y=lim0 + 5, xmin=0, xmax=10, lw=4, color='red', label='pause')
+        plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
+        plt.xlim(times[0],times[-1])
+        #embed()
+        #plt.xticks((times[0], 10, 20, 30, 40, 50, 60, times[-1]), [0, 10, 20, 30 ,40, 50, 60, 70])
+        plt.xlabel('time [s]')
+        plt.ylabel('frequency [Hz]')
+        plt.legend(loc = 'best')
+        plt.show()
+        delta_f_ID = [str(delta_f[0]).split('.')[0], ID]
+
+        plt.close()
+
+
     res_df = sorted(zip(deltaf,response))
 
     #np.save('res_df_%s_new' %ID, res_df)

eigenmannia_code/eigenmannia_jar_subplot.py

@@ -0,0 +1,138 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import nix_helpers as nh
+from IPython import embed
+from matplotlib.mlab import specgram
+#from tqdm import tqdm
+from jar_functions import parse_stimuli_dat
+from jar_functions import norm_function_eigen
+from jar_functions import mean_noise_cut_eigen
+from jar_functions import get_time_zeros
+from jar_functions import import_data_eigen
+from scipy.signal import savgol_filter
+
+plt.rcParams.update({'font.size': 18})
+
+base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
+
+#2015eigen8 no nix files
+identifier = [#'2013eigen13',
+              '2015eigen16'] #,'2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
+
+
+response = []
+deltaf = []
+
+specs = []
+jars = []
+sub_times = []
+sub_lim0 = []
+sub_lim1 = []
+
+for ID in identifier:
+    for dataset in os.listdir(os.path.join(base_path, ID)):
+        datapath = os.path.join(base_path, ID, dataset, '%s.nix' % dataset)
+        #print(datapath)
+        stimuli_dat = os.path.join(base_path, ID, dataset, 'manualjar-eod.dat')
+        #print(stimuli_dat)
+        delta_f, duration = parse_stimuli_dat(stimuli_dat)
+        dur = int(duration[0][0:2])
+        if delta_f == [-2.0] or delta_f == [2.0] or delta_f == [-10.0] or delta_f == [10.0]:
+            print(delta_f)
+
+            data, pre_data, dt = import_data_eigen(datapath)
+            # hstack concatenate: 'glue' pre_data and data
+            dat = np.hstack((pre_data, data))
+
+            # data
+            nfft = 2 ** 17
+            spec, freqs, times = specgram(dat[0], Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
+            dbspec = 10.0 * np.log10(spec)  # in dB
+            power = dbspec[:, 25]
+
+            fish_p = power[(freqs > 200) & (freqs < 1000)]
+            fish_f = freqs[(freqs > 200) & (freqs < 1000)]
+
+            index = np.argmax(fish_p)
+            eodf = fish_f[index]
+            eodf4 = eodf * 4
+
+            lim0 = eodf4 - 40
+            lim1 = eodf4 + 40
+
+            df = freqs[1] - freqs[0]
+            ix0 = int(np.floor(lim0 / df))  # back to index
+            ix1 = int(np.ceil(lim1 / df))  # back to index
+            spec4 = dbspec[ix0:ix1, :]
+            freq4 = freqs[ix0:ix1]
+            jar4 = freq4[np.argmax(spec4, axis=0)]  # all freqs at max specs over axis 0
+
+            cut_time_jar = times[:len(jar4)]
+            ID_delta_f = [ID, str(delta_f[0]).split('.')[0]]
+
+            b = []
+            for idx, i in enumerate(times):
+                if i > 0 and i < 10:
+                    b.append(jar4[idx])
+            j = []
+            for idx, i in enumerate(times):
+                if i > 15 and i < 55:
+                    j.append(jar4[idx])
+
+            r = np.median(j) - np.median(b)
+            print('response:', r)
+            deltaf.append(delta_f[0])
+            response.append(r)
+            specs.append(spec4)
+            jars.append(jar4)
+            sub_times.append(cut_time_jar)
+            sub_lim0.append(lim0)
+            sub_lim1.append(lim1)
+        if len(specs) == 4:
+            break
+
+    # plt.imshow(specs[0], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[0], sub_lim1[1]), aspect='auto', vmin=-80, vmax=-10)
+    # plt.plot(sub_times[0], jars[0], 'k', label = 'peak detection trace', lw = 2)
+    # plt.hlines(y=lim0 + 5, xmin=10, xmax=70, lw=4, color='yellow', label='stimulus duration')
+    # plt.hlines(y=lim0 + 5, xmin=0, xmax=10, lw=4, color='red', label='pause')
+    # plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
+    # plt.xlim(times[0],times[-1])
+
+fig = plt.figure(figsize = (20,20))
+ax0 = fig.add_subplot(221)
+ax0.imshow(specs[0], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[0], sub_lim1[0]), aspect='auto', vmin=-80, vmax=-10)
+ax0.plot(sub_times[0], jars[0], 'k', label = 'peak detection trace', lw = 2)
+ax0.set_xlim(times[0],times[-1])
+ax0.set_ylabel('frequency [Hz]')
+ax0.axes.xaxis.set_ticklabels([])
+ax0.set_title('∆F -2 Hz')
+
+ax1 = fig.add_subplot(222)
+ax1.imshow(specs[1], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[1], sub_lim1[1]), aspect='auto', vmin=-80, vmax=-10)
+ax1.plot(sub_times[1], jars[1], 'k', label = 'peak detection trace', lw = 2)
+ax1.set_xlim(times[0],times[-1])
+ax1.axes.xaxis.set_ticklabels([])
+ax1.axes.yaxis.set_ticklabels([])
+ax1.set_title('∆F -10 Hz')
+
+ax2 = fig.add_subplot(223)
+ax2.imshow(specs[2], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[2], sub_lim1[2]), aspect='auto', vmin=-80, vmax=-10)
+ax2.plot(sub_times[2], jars[2], 'k', label = 'peak detection trace', lw = 2)
+ax2.set_xlim(times[0],times[-1])
+ax2.set_ylabel('frequency [Hz]')
+ax2.set_xlabel('time [s]')
+ax2.set_title('∆F 2 Hz')
+
+ax3 = fig.add_subplot(224)
+ax3.imshow(specs[3], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[3], sub_lim1[3]), aspect='auto', vmin=-80, vmax=-10)
+ax3.plot(sub_times[3], jars[3], 'k', label = 'peak detection trace', lw = 2)
+ax3.set_xlim(times[0],times[-1])
+ax3.set_xlabel('time [s]')
+ax3.axes.yaxis.set_ticklabels([])
+ax3.set_title('∆F 10 Hz')
+
+plt.show()
+
+embed()
+

notes

@@ -1,11 +1,11 @@
 machen:
-- phaseshift (sin_all) nochmal, nicht richtiges Dings verwendet ( sinv[2]/p) und dann auch in phaseshift
+- wenn benötigt sin response fit mit neuer phaseshift berechnung nochmal durchlaufen lassen, dann phasshift all
 - abbildung erstellen mit custom cutoff frequencies über ganzen bereich (0.001Hz-1Hz) um hoffentlich zu zeigen dass dabei lineare
 Gerade entsteht, vergleichen mit uniformen Bereich bei Daten bei dem es sich auch eher linear verhält um zu zeigen auf was wir hinaus wollen
 - filter: zieht mean von einer amfreq periode ab wodurch alles was nicht damit in Verbindung steht herausfiltert,
 auch JAR. problematisch wird dies eher wenn JAR-Anstieg schneller abläuft als eine amfreq periode
 - wenn fit nicht funktioniert einfach weglassen, wenn sättigung vorhanden nochmal anschauen
-
+- base_eod und q10 und temp für eigenmannia
 
 + figures:
     apteronotus: fundament by tims bachelor thesis, important that apteronotus only shifts his frequency up (as eigenmannia doesnt --> natalies measurements)