updated final figure

model_full.py

@@ -1,19 +1,24 @@
 ##from update_project import **
+import sys
+
 import numpy as np
+from IPython import embed
 from matplotlib import gridspec, pyplot as plt
 
-from threefish.core_calc_fft import create_full_matrix2
+from threefish.core_calc_fft import create_full_matrix2, get_transfer_from_timesignal
-from threefish.core_reformat import chose_mat_max_value, get_axis_on_full_matrix, get_psds_ROC, get_transfer_from_model, \
+from threefish.core_reformat import chose_mat_max_value, get_axis_on_full_matrix, get_psds_ROC, \
+    get_transfer_for_model_full, get_transfer_from_model, \
     load_b_public, load_stack_data_susept
 from threefish.core_reformat_RAM import convert_csv_str_to_float
-from threefish.core_plot_subplots import colors_suscept_paper_dots, plt_model_full_model2, plt_model_letters, \
+from threefish.core_plot_subplots import colors_suscept_paper_dots, letters_for_full_model, plt_model_full_model2, \
-    plt_psds_ROC, plt_RAM_perc
+    plt_model_letters, \
-from threefish.core_load import save_visualization
+    plt_psds_ROC, plt_RAM_perc, recalc_fr_to_DF1
+from threefish.core_load import resave_small_files, save_visualization
 from threefish.core_values import vals_model_full
 from threefish.core_cell_choice import find_all_dir_cells
 from threefish.core_filenames import version_final
 from threefish.core_plot_suscept import perc_model_full, plt_model_big
-from threefish.core import find_code_vs_not
+from threefish.core import find_code_vs_not, info
 from plotstyle import plot_style
 import time
 from threefish.defaults import default_diagonal_points, default_figsize
@@ -36,12 +41,13 @@ def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], end='all', chose_s
 
     plot_style()
     default_figsize(column=2, length=3.5)#2.7
-    grid = gridspec.GridSpec(2, 2, wspace=0.55, bottom = 0.15, height_ratios = [2, 5], width_ratios = [1.2, 1], hspace=1, top=0.92, left=0.06, right=0.98)#hspace=0.25,
+    grid = gridspec.GridSpec(2, 2, wspace=0.55, bottom = 0.15, height_ratios = [2, 5], width_ratios = [1.2, 1], hspace=0.7, top=0.92, left=0.06, right=0.98)#hspace=0.25,
 
 
 
 
     axes = []
+    a_size = 0.0065#und 0.0005 für das lineare das ist vielleicht für das nichtlienare 0.0065#0.0085#0.01#0.0085#125 # 0.0025# 0.01 0.005 und davor 0.025, funktioniert gut 0.0085
 
 
 
@@ -60,27 +66,76 @@ def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], end='all', chose_s
     #embed()
     fr_waves = 139
     color01, color012, color01_2, color02, color0_burst, color0 = colors_suscept_paper_dots()
+    DF1_frmult, DF2_frmult = vals_model_full(val=0.30833333333333335)
+
 
     #############################################
     # plot coherence
+    loaded_from_calc_RAM_model = False
+    if loaded_from_calc_RAM_model:
         cross = get_transfer_from_model(stack_saved)
         axc = plt.subplot(grid[0, 1])
         axc.plot(stack_saved.index, cross, color = 'black')
         axc.set_xlabel('Frequency [Hz]')
         axc.set_ylabel(xlabel_transfer_hz())
+    else:
+        ############################################################
+        #calculate the noise induced tranferfunction
+        # start_eod_emitter --  the lowest frequency of the emmiter fish, default 20
+
+        # frequently used value
+        # so harmonic is the right one since p-change and wave is some relict so take just harmonic
+
+        models = resave_small_files("models_big_fit_d_right.csv",
+                                    load_folder='calc_model_core')
+        flowchart_cell = '2012-07-03-ak-invivo-1'
+
+        model_params = models[models['cell'] == flowchart_cell].iloc[0]
+        print('cell=' + str(flowchart_cell))
+        model_params = models[models['cell'] == flowchart_cell].iloc[0]
+        print('cell=' + str(flowchart_cell))
+        # amp_frame = pd.read_csv('peak_amplitudes_power.csv')
+        cell = model_params.pop('cell')  # .iloc[0]# Werte für das Paper nachschauen
+        eod_fr = model_params['EODf']  # .iloc[0]
+        deltat = model_params.pop("deltat")  # .iloc[0]
+        v_offset = model_params.pop("v_offset")  # .iloc[0]
+        stimulus_length = 1
+
+        power = 1
+        xlim = [0,300]
+        c_sigs = [0.9]  # '',0,
+        var_types = [
+                     ]  # ''#'additiv_cv_adapt_factor_scaled'
+        #            'additiv_visual_d_4_scaled', '']
+        #a_f = 0.009  # 2
+        a_fes = [a_size, a_size, 0]  # alpha\alpha0.1,
+        trials_nr = 100  # 100
+
+
+        frame = get_transfer_for_model_full([a_size], 0,cell, deltat,
+                                                                                                       eod_fr,
+                                                                                                       model_params,
+                                                                                                       stimulus_length,
+                                                                                                       trials_nr,
+                                                                                                       v_offset,
+                                                                                                       c_sig = 0.9,
+                                                                                                       var_type='additiv_cv_adapt_factor_scaled')
 
+        axc = plt.subplot(grid[0, 1])
+        f_same = frame.index
+        #embed()
+        transfer = frame['0']
+        axc.plot(f_same[f_same< xlim[-1]], np.abs(transfer[f_same< xlim[-1]])**power, color = 'black', zorder = 100)
 
-    ###############################
+        axc.set_xlim(xlim)
-    # data part
+        #if c == 0:
+        axc.set_ylabel(r'$|\chi_1|^2$')
 
 
-    data_square_extra = False
-    if data_square_extra:
-        ax, cell, stack_final = plt_data_matrix(ax, axes, cell, grid, ls, lw, perc, stack_final)
 
     #################
     # power spectra data
-    log = ''#'log'#'log'#'log'#'log'#'log'
+    log = 'log'#'log'#'log'#'log'#'log'#'log'
     ylim_log = (-14.2, 3)#(-14.2, 3)
     nfft = 20000#2 ** 13
     xlim_psd = [0, 300]
@@ -93,18 +148,9 @@ def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], end='all', chose_s
 
     markers = ['s', 'o']
     ms = 14
-    data_extra = False
-    if data_extra:
-        DF1_desired, DF1_frmult, DF2_desired, DF2_frmult, eod_fr, grid0 = plt_data_peaks(DF1_desired_orig,
-                                                                                         DF2_desired_orig, c1,
-                                                                                         chose_score, detections, devs,
-                                                                                         dfs, end, grid, log, ms,
-                                                                                         mult_type, nfft, sorted_on,
-                                                                                         xlim_psd, ylim_log)
 
-    else:
     markers = ['o', 'o', 'o', 'o', ]
-        DF1_frmult, DF2_frmult = vals_model_full(val = 0.30833333333333335)
+
     #0.16666666666666666
     grid0 = gridspec.GridSpecFromSubplotSpec(2, 2, wspace=0.15, hspace=0.4,
                                              subplot_spec=grid[1,1])
@@ -122,31 +168,41 @@ def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], end='all', chose_s
     diagonal = 'diagonal1'
     diagonal = ''
     plus_q = 'plus'  # 'minus'#'plus'##'minus'
-    length = 1#2*40  # 5
+    length = 100#2*40  # 5
     reshuffled = ''  # ,
-
+    array_len = 1
     alphas = [1,0.5]
     #a_size = 0.0065#0.0125#25#0.04#0.015
-    a_size = 0.0065#und 0.0005 für das lineare das ist vielleicht für das nichtlienare 0.0065#0.0085#0.01#0.0085#125 # 0.0025# 0.01 0.005 und davor 0.025, funktioniert gut 0.0085
 
     # ATTENTION: Diese Zelle ('2012-07-03-ak-invivo-1') braucht längere Abschnitte, mindsetesn 5 Sekunden damit das Powerspectrum nicht so niosy ist!
     fr_noise, eod_fr_mm,  axes2 = plt_model_full_model2(grid0, stack_final  = stack_final, reshuffled=reshuffled, dev=0.0005, a_f1s=[a_size], af_2 = a_size,
                                                         stimulus_length=length, plus_q=plus_q, stack_saved = stack_saved,
                                                         diagonal=diagonal, runs=1, nfft = nfft, xlim_psd =  xlim_psd,
                                                         cells=[cell], dev_spikes ='original', markers = markers, DF1_frmult = DF1_frmult, DF2_frmult = DF2_frmult,
-                                                        log = log, ms = ms, clip_on = False, trials_nr_all= [1, 1, 1, 1, 1])  #arrays_len a_f1s=[0.02]"2012-12-13-an-invivo-1"'2013-01-08-aa-invivo-1'
+                                                        log = log, ms = ms, array_len = array_len, f_same = f_same, transfer = transfer, clip_on = False, trials_nr_all= [1, 1, 1, 1, 1])  #arrays_len a_f1s=[0.02]"2012-12-13-an-invivo-1"'2013-01-08-aa-invivo-1'
-
 
 
 
 
 
-
+    # letters for the matrix
-    if data_extra:
-        plt_data_peaks_letters(DF1_desired, DF2_desired, axm, color012, color01_2, eod_fr, fr_noise, fr_waves, markers)
-    else:
     plt_model_letters(DF1_frmult, DF2_frmult, axm, color012, color01_2, fr_noise, markers)
 
+    # letters for the transfer function
+    letters = letters_for_full_model()
+    for f in range(len(letters)):
+        df1_recalc = recalc_fr_to_DF1(DF1_frmult, f, fr_noise)
+        df2_recalc = recalc_fr_to_DF1(DF2_frmult, f, fr_noise)
+
+        pos_df1 = np.argmin(np.abs(f_same - np.abs(df1_recalc)))
+        pos_df2 = np.argmin(np.abs(f_same - np.abs(df2_recalc)))
+        axc.text(df1_recalc, np.mean(transfer[pos_df1-array_len:pos_df1+array_len]),
+                letters[f], color=color01, ha='center',
+                va='center', zorder = 100)  # , alpha = alphas[f]
+        axc.text(df2_recalc, np.mean(transfer[pos_df2-array_len:pos_df2+array_len]),
+                letters[f], color=color02, ha='center',
+                va='center', zorder = 100)  # , alpha = alphas[f]
+
     fig = plt.gcf()#[axes[0], axes[3], axes[4]]
     fig.tag([axes[0], axc, axes2[0], axes2[1], axes2[2], axes2[3]], xoffs=-4.5, yoffs=1.2)  # ax_ams[3],
     #plt.show()
@@ -550,4 +606,5 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
 
 if __name__ == '__main__':
     #embed()
+    sys.excepthook = info
     model_full()

susceptibility1.tex

@@ -597,7 +597,7 @@ We estimated the second-order susceptibility of P-unit responses using RAM stimu
 
 \begin{figure*}[t]
   \includegraphics[width=\columnwidth]{model_full}
-  \caption{\label{fig:model_full} Using second-order susceptibility to predict responses to sine-wave stimuli. \figitem[]{A} Absolute value of the second-order susceptibility,  \Eqnref{eq:susceptibility}, for both positive and negative frequencies. \susceptf{} was estimated from $N=10^6$ trials of model simulations in the noise-split condition (cell 2012-07-03-ak, see table~\ref{modelparams} for model parameters). White lines indicate zero frequencies. Nonlinear responses at \fsum{} are quantified in the upper right and lower left quadrants.  Nonlinear responses at \fdiff{} are quantified in the upper left and lower right quadrants. Baseline firing rate of this cell was at $\fbase=120$\,Hz. The position of the orange/red letters correspond to the beat frequencies used for the stimulation with pure sine-waves in the subsequent panels and indicate the sum/difference of those beat frequencies.  \figitem[]{B} Absolute value of the first-order susceptibility. \figitem{C--F} Black line -- power spectral density of model simulations in response to stimulation with two pure sine waves, \fone{} and \ftwo, in addition to the receiving fish's own EOD (three fish scenario). The contrasts of beat beats is 0.0065.  Colored circles highlight the height of selected peaks in the power spectrum. Black circles highlight the peak height that can be predicted from \panel{A, B}. Grey line -- power spectral density of model in the baseline condition. \figitem{B} The sum of the two beat frequencies match \fbase{}. \figitem{C} The difference of \fone{} and \ftwo{} match \fbase{}. \figitem{D} Only the first beat frequency matches \fbase{}. \figitem{C} None of the two beat frequencies matches \fbase{}.}
+  \caption{\label{fig:model_full} Using second-order susceptibility to predict responses to sine-wave stimuli. \figitem[]{A} Absolute value of the second-order susceptibility,  \Eqnref{eq:susceptibility}, for both positive and negative frequencies. \susceptf{} was estimated from $N=10^6$ trials of model simulations in the noise-split condition (cell 2012-07-03-ak, see table~\ref{modelparams} for model parameters). White lines indicate zero frequencies. Nonlinear responses at \fsum{} are quantified in the upper right and lower left quadrants.  Nonlinear responses at \fdiff{} are quantified in the upper left and lower right quadrants. Baseline firing rate of this cell was at $\fbase=120$\,Hz. The position of the orange/red letters correspond to the beat frequencies used for the stimulation with pure sine-waves in the subsequent panels and indicate the sum/difference of those beat frequencies.  \figitem[]{B} Absolute value of the first-order susceptibility. \figitem{C--F} Black line -- power spectral density of model simulations in response to stimulation with two pure sine waves, \fone{} and \ftwo, in addition to the receiving fish's own EOD (three fish scenario). The contrasts of beat beats is 0.0065.  Colored circles highlight the height of selected peaks in the power spectrum. Black circles highlight the peak height that can be predicted from \panel{A, B}. Grey line -- power spectral density of model in the baseline condition. \figitem{B} The sum of the two beat frequencies match \fbase{}. \figitem{C} The difference of \fone{} and \ftwo{} match \fbase{}. \figitem{D} Only the first beat frequency matches \fbase{}. \figitem{C} None of the two beat frequencies matches \fbase{}. \noteab{Für die Transfer Funktion habe ich jetzt einen Faktor 1, für die Nichtlinearität einen Faktor 30, aber vielleicht wenn ich über mehrere Punkte mitteln muss und das alles so noisy ist das eben noch keine Gute Abschätzung in der Stauration?}}
 \end{figure*}
 
 However, the second-order susceptibility \Eqnref{eq:susceptibility} is a spectral measure that is based on Fourier transforms and thus is also defined for negative stimulus frequencies. The full \susceptf{} matrix is symmetric with respect to the origin. In the upper-right and lower-left quadrants of \susceptf{}, stimulus-evoked responses at \fsum{} are shown, whereas in the lower-right and upper-left quadrants nonlinear responses at the difference \fdiff{} are shown (\figref{fig:model_full}). The vertical and horizontal lines at \foneb{} and \ftwob{} are very pronounced in the upper-right quadrant of \subfigrefb{fig:model_full}{A} for nonlinear responses at \fsum{} and extend into the upper-left quadrant (representing \fdiff) where they fade out towards more negative $f_1$ frequencies. The peak in the response power-spectrum at \fdiff{} evoked by pure sine-wave stimulation (\subfigrefb{fig:motivation}{D}) is predicted by the horizontal line in the upper-left quadrant (\subfigrefb{fig:model_full}{A}, \cite{Schlungbaum2023}). 

susceptibility1.tex.bak

@@ -597,7 +597,7 @@ We estimated the second-order susceptibility of P-unit responses using RAM stimu
 
 \begin{figure*}[t]
   \includegraphics[width=\columnwidth]{model_full}
-  \caption{\label{fig:model_full} Using second-order susceptibility to predict responses to sine-wave stimuli. \figitem[]{A} Absolute value of the second-order susceptibility,  \Eqnref{eq:susceptibility}, for both positive and negative frequencies. \susceptf{} was estimated from $N=10^6$ trials of model simulations in the noise-split condition (cell 2012-07-03-ak, see table~\ref{modelparams} for model parameters). White lines indicate zero frequencies. Nonlinear responses at \fsum{} are quantified in the upper right and lower left quadrants.  Nonlinear responses at \fdiff{} are quantified in the upper left and lower right quadrants. Baseline firing rate of this cell was at $\fbase=120$\,Hz. The position of the orange/red letters correspond to the beat frequencies used for the stimulation with pure sine-waves in the subsequent panels and indicate the sum/difference of those beat frequencies.  \figitem[]{B} Absolute value of the first-order susceptibility. \figitem{C--F} Black line -- power spectral density of model simulations in response to stimulation with two pure sine waves, \fone{} and \ftwo, in addition to the receiving fish's own EOD (three fish scenario). The contrasts of beat beats is 0.0065.  Colored circles highlight the height of selected peaks in the power spectrum. Black circles highlight the peak height that can be predicted from \panel{A, B}. Grey line -- power spectral density of model in the baseline condition. \figitem{B} The sum of the two beat frequencies match \fbase{}. \figitem{C} The difference of \fone{} and \ftwo{} match \fbase{}. \figitem{D} Only the first beat frequency matches \fbase{}. \figitem{C} None of the two beat frequencies matches \fbase{}.}
+  \caption{\label{fig:model_full} Using second-order susceptibility to predict responses to sine-wave stimuli. \figitem[]{A} Absolute value of the second-order susceptibility,  \Eqnref{eq:susceptibility}, for both positive and negative frequencies. \susceptf{} was estimated from $N=10^6$ trials of model simulations in the noise-split condition (cell 2012-07-03-ak, see table~\ref{modelparams} for model parameters). White lines indicate zero frequencies. Nonlinear responses at \fsum{} are quantified in the upper right and lower left quadrants.  Nonlinear responses at \fdiff{} are quantified in the upper left and lower right quadrants. Baseline firing rate of this cell was at $\fbase=120$\,Hz. The position of the orange/red letters correspond to the beat frequencies used for the stimulation with pure sine-waves in the subsequent panels and indicate the sum/difference of those beat frequencies.  \figitem[]{B} Absolute value of the first-order susceptibility. \figitem{C--F} Black line -- power spectral density of model simulations in response to stimulation with two pure sine waves, \fone{} and \ftwo, in addition to the receiving fish's own EOD (three fish scenario). The contrasts of beat beats is 0.0065.  Colored circles highlight the height of selected peaks in the power spectrum. Black circles highlight the peak height that can be predicted from \panel{A, B}. Grey line -- power spectral density of model in the baseline condition. \figitem{B} The sum of the two beat frequencies match \fbase{}. \figitem{C} The difference of \fone{} and \ftwo{} match \fbase{}. \figitem{D} Only the first beat frequency matches \fbase{}. \figitem{C} None of the two beat frequencies matches \fbase{}. \noteab{Für die Transfer Funktion habe ich jetzt einen Faktor 1, für die Nichtlinearität einen Faktor 30, aber vielleicht wenn ich über mehrere Punkte mitteln muss und das alles so noisy ist das eben noch keine Gute Abschätzung in der Stauration?}}
 \end{figure*}
 
 However, the second-order susceptibility \Eqnref{eq:susceptibility} is a spectral measure that is based on Fourier transforms and thus is also defined for negative stimulus frequencies. The full \susceptf{} matrix is symmetric with respect to the origin. In the upper-right and lower-left quadrants of \susceptf{}, stimulus-evoked responses at \fsum{} are shown, whereas in the lower-right and upper-left quadrants nonlinear responses at the difference \fdiff{} are shown (\figref{fig:model_full}). The vertical and horizontal lines at \foneb{} and \ftwob{} are very pronounced in the upper-right quadrant of \subfigrefb{fig:model_full}{A} for nonlinear responses at \fsum{} and extend into the upper-left quadrant (representing \fdiff) where they fade out towards more negative $f_1$ frequencies. The peak in the response power-spectrum at \fdiff{} evoked by pure sine-wave stimulation (\subfigrefb{fig:motivation}{D}) is predicted by the horizontal line in the upper-left quadrant (\subfigrefb{fig:model_full}{A}, \cite{Schlungbaum2023}).