resolving master merge

code/plot_chirp_bodylegth.py

@@ -173,13 +173,15 @@ def main(datapath: str):
 
             size_winners = []
             for l in ['l1', 'l2', 'l3']:
-                size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0]
+                size_winner = size_rows[size_rows['fish']
+                                        == winner_fish1][l].values[0]
                 size_winners.append(size_winner)
             mean_size_winner = np.nanmean(size_winners)
 
             size_losers = []
             for l in ['l1', 'l2', 'l3']:
-                size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0]
+                size_loser = size_rows[size_rows['fish']
+                                       == winner_fish2][l].values[0]
                 size_losers.append(size_loser)
             mean_size_loser = np.nanmean(size_losers)
 
@@ -191,13 +193,15 @@ def main(datapath: str):
 
             size_winners = []
             for l in ['l1', 'l2', 'l3']:
-                size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0]
+                size_winner = size_rows[size_rows['fish']
+                                        == winner_fish2][l].values[0]
                 size_winners.append(size_winner)
             mean_size_winner = np.nanmean(size_winners)
 
             size_losers = []
             for l in ['l1', 'l2', 'l3']:
-                size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0]
+                size_loser = size_rows[size_rows['fish']
+                                       == winner_fish1][l].values[0]
                 size_losers.append(size_loser)
             mean_size_loser = np.nanmean(size_losers)
 
@@ -210,8 +214,8 @@ def main(datapath: str):
         chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
         chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
 
-        freq_winner  = np.nanmedian(bh.freq[bh.ident==winner_fish_id])
+        freq_winner = np.nanmedian(bh.freq[bh.ident == winner_fish_id])
-        freq_loser  = np.nanmedian(bh.freq[bh.ident==loser_fish_id])
+        freq_loser = np.nanmedian(bh.freq[bh.ident == loser_fish_id])
 
         chirps_winner.append(chirp_winner)
         chirps_loser.append(chirp_loser)
@@ -219,7 +223,8 @@ def main(datapath: str):
         size_chirps_diffs.append(chirp_winner - chirp_loser)
         freq_diffs.append(freq_winner - freq_loser)
 
-    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 5))
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(22*ps.cm, 12*ps.cm), width_ratios=[1.5, 1,1])
+    plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
     scatterwinner = 1.15
     scatterloser = 1.85
     chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
@@ -234,24 +239,24 @@ def main(datapath: str):
     ax1.scatter(np.ones(len(chirps_loser)) *
                 scatterloser, chirps_loser, color='r')
     ax1.set_xticklabels(['winner', 'loser'])
-    ax1.text(0.9, 0.9, f'n = {len(chirps_winner)}',
+    ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}',
              transform=ax1.transAxes, color=ps.white)
 
     for w, l in zip(chirps_winner, chirps_loser):
         ax1.plot([scatterwinner, scatterloser], [w, l],
                  color='r', alpha=0.5, linewidth=0.5)
+    ax1.set_ylabel('Chirps [n]', color=ps.white)
 
     colors1 = ps.red
     ps.set_boxplot_color(bplot1, colors1)
     colors1 = ps.orange
     ps.set_boxplot_color(bplot2, colors1)
-    ax1.set_ylabel('Chirpscounts [n]')
-    embed()
     ax2.scatter(size_diffs, size_chirps_diffs, color='r')
     ax2.set_xlabel('Size difference [mm]')
     ax2.set_ylabel('Chirps difference [n]')
 
-    ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
+    ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
+    # ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
     ax3.set_xlabel('Frequency difference [Hz]')
     ax3.set_yticklabels([])
     ax3.set

code/plot_chirp_size.py

@@ -0,0 +1,211 @@
+import numpy as np
+
+import os
+
+import numpy as np
+import matplotlib.pyplot as plt
+from thunderfish.powerspectrum import decibel
+
+from IPython import embed
+from pandas import read_csv
+from modules.logger import makeLogger
+from modules.plotstyle import PlotStyle
+from modules.behaviour_handling import Behavior, correct_chasing_events
+
+ps = PlotStyle()
+
+logger = makeLogger(__name__)
+
+
+def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    winner_row = order_meta_df[order_meta_df['recording'] == foldername]
+    winner = winner_row['winner'].values[0].astype(int)
+    winner_fish1 = winner_row['fish1'].values[0].astype(int)
+    winner_fish2 = winner_row['fish2'].values[0].astype(int)
+
+    if winner > 0:
+        if winner == winner_fish1:
+            winner_fish_id = winner_row['rec_id1'].values[0]
+            loser_fish_id = winner_row['rec_id2'].values[0]
+
+        elif winner == winner_fish2:
+            winner_fish_id = winner_row['rec_id2'].values[0]
+            loser_fish_id = winner_row['rec_id1'].values[0]
+
+        chirp_winner = len(
+            Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
+        chirp_loser = len(
+            Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+        return chirp_winner, chirp_loser
+    else:
+        return np.nan, np.nan
+
+
+def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    folder_row = order_meta_df[order_meta_df['recording'] == foldername]
+    fish1 = folder_row['fish1'].values[0].astype(int)
+    fish2 = folder_row['fish2'].values[0].astype(int)
+
+    groub = folder_row['group'].values[0].astype(int)
+    size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & (
+        id_meta_df['fish'] == fish1)]
+    size_fish2_row = id_meta_df[(id_meta_df['group'] == groub) & (
+        id_meta_df['fish'] == fish2)]
+
+    size_winners = [size_fish1_row[col].values[0]
+                    for col in ['l1', 'l2', 'l3']]
+    mean_size_winner = np.nanmean(size_winners)
+
+    size_losers = [size_fish2_row[col].values[0] for col in ['l1', 'l2', 'l3']]
+    mean_size_loser = np.nanmean(size_losers)
+
+    if mean_size_winner > mean_size_loser:
+        size_diff_bigger = mean_size_winner - mean_size_loser
+        size_diff_smaller = mean_size_loser - mean_size_winner
+        winner_fish_id = folder_row['rec_id1'].values[0]
+        loser_fish_id = folder_row['rec_id2'].values[0]
+
+    elif mean_size_winner < mean_size_loser:
+        size_diff_bigger = mean_size_loser - mean_size_winner
+        size_diff_smaller = mean_size_winner - mean_size_loser
+        winner_fish_id = folder_row['rec_id2'].values[0]
+        loser_fish_id = folder_row['rec_id1'].values[0]
+    else:
+        size_diff = np.nan
+        winner_fish_id = np.nan
+        loser_fish_id = np.nan
+
+    chirp_winner = len(
+        Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
+    chirp_loser = len(
+        Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+    
+    return size_diff_bigger, chirp_winner,  size_diff_smaller, chirp_loser
+
+
+def get_chirp_freq(folder_name, Behavior, order_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    folder_row = order_meta_df[order_meta_df['recording'] == foldername]
+    fish1 = folder_row['rec_id1'].values[0].astype(int)
+    fish2 = folder_row['rec_id2'].values[0].astype(int)
+    chirp_freq_fish1 = np.nanmedian(
+        Behavior.freq[Behavior.ident == fish1])
+    chirp_freq_fish2 = np.nanmedian(
+        Behavior.freq[Behavior.ident == fish2])
+
+    if chirp_freq_fish1 > chirp_freq_fish2:
+        freq_diff = chirp_freq_fish1 - chirp_freq_fish2
+        winner_fish_id = folder_row['rec_id1'].values[0]
+        loser_fish_id = folder_row['rec_id2'].values[0]
+
+    elif chirp_freq_fish1 < chirp_freq_fish2:
+        freq_diff = chirp_freq_fish2 - chirp_freq_fish1
+        winner_fish_id = folder_row['rec_id2'].values[0]
+        loser_fish_id = folder_row['rec_id1'].values[0]
+
+    chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
+        Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+    return freq_diff, chirp_diff
+
+
+def main(datapath: str):
+
+    foldernames = [
+        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+    path_order_meta = (
+        '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
+    order_meta_df = read_csv(path_order_meta)
+    order_meta_df['recording'] = order_meta_df['recording'].str[1:-1]
+    path_id_meta = (
+        '/').join(foldernames[0].split('/')[:-2]) + '/id_meta.csv'
+    id_meta_df = read_csv(path_id_meta)
+
+    chirps_winner = []
+    size_diffs = []
+    size_chirps_diffs = []
+    chirps_loser = []
+    freq_diffs = []
+    freq_chirps_diffs = []
+
+    for foldername in foldernames:
+        # behabvior is pandas dataframe with all the data
+        if foldername == '../data/mount_data/2020-05-12-10_00/':
+            continue
+        bh = Behavior(foldername)
+        # chirps are not sorted in time (presumably due to prior groupings)
+        # get and sort chirps and corresponding fish_ids of the chirps
+        category = bh.behavior
+        timestamps = bh.start_s
+        # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
+        # Get rid of tracking faults (two onsets or two offsets after another)
+        category, timestamps = correct_chasing_events(category, timestamps)
+
+        winner_chirp, loser_chirp = get_chirp_winner_loser(
+            foldername,  bh, order_meta_df)
+        chirps_winner.append(winner_chirp)
+        chirps_loser.append(loser_chirp)
+        size_diff, chirp_diff = get_chirp_size(
+            foldername, bh, order_meta_df, id_meta_df)
+        size_diffs.append(size_diff)
+        size_chirps_diffs.append(chirp_diff)
+
+        freq_diff, freq_chirps_diff = get_chirp_freq(
+            foldername, bh, order_meta_df)
+        freq_diffs.append(freq_diff)
+        freq_chirps_diffs.append(freq_chirps_diff)
+
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(22*ps.cm, 12*ps.cm), width_ratios=[1.5, 1,1])
+    plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
+    scatterwinner = 1.15
+    scatterloser = 1.85
+    chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
+    chirps_loser = np.asarray(chirps_loser)[~np.isnan(chirps_loser)]
+
+    bplot1 = ax1.boxplot(chirps_winner, positions=[
+        1], showfliers=False, patch_artist=True)
+    bplot2 = ax1.boxplot(chirps_loser,  positions=[
+        2], showfliers=False, patch_artist=True)
+    ax1.scatter(np.ones(len(chirps_winner)) *
+                scatterwinner, chirps_winner, color='r')
+    ax1.scatter(np.ones(len(chirps_loser)) *
+                scatterloser, chirps_loser, color='r')
+    ax1.set_xticklabels(['winner', 'loser'])
+    ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}',
+             transform=ax1.transAxes, color=ps.white)
+
+    for w, l in zip(chirps_winner, chirps_loser):
+        ax1.plot([scatterwinner, scatterloser], [w, l],
+                 color='r', alpha=0.5, linewidth=0.5)
+    ax1.set_ylabel('Chirps [n]', color=ps.white)
+
+    colors1 = ps.red
+    ps.set_boxplot_color(bplot1, colors1)
+    colors1 = ps.orange
+    ps.set_boxplot_color(bplot2, colors1)
+    ax2.scatter(size_diffs, size_chirps_diffs, color='r')
+    ax2.set_xlabel('Size difference [mm]')
+    ax2.set_ylabel('Chirps difference [n]')
+
+    #ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
+    # ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
+    ax3.set_xlabel('Frequency difference [Hz]')
+    ax3.set_yticklabels([])
+    ax3.set
+
+    #plt.savefig('../poster/figs/chirps_winner_loser.pdf')
+    plt.show()
+
+
+if __name__ == '__main__':
+
+    # Path to the data
+    datapath = '../data/mount_data/'
+
+    main(datapath)

code/plot_event_timeline.py

@@ -20,89 +20,90 @@ logger = makeLogger(__name__)
 def main(datapath: str):
     
     foldernames = [datapath + x + '/'  for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
-    #for foldername in foldernames:
+    for foldername in foldernames:
-    foldername = foldernames[0]
+    #foldername = foldernames[0]
-    # if foldername == '../data/mount_data/2020-05-12-10_00/':
+        if foldername == '../data/mount_data/2020-05-12-10_00/':
-    #     continue
+            continue
-    # behabvior is pandas dataframe with all the data
+        #behabvior is pandas dataframe with all the data
-    bh = Behavior(foldername)
+        bh = Behavior(foldername)
-
+        #2020-06-11-10
-    category = bh.behavior
+        category = bh.behavior
-    timestamps = bh.start_s
+        timestamps = bh.start_s
-    # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
+        # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
-    # Get rid of tracking faults (two onsets or two offsets after another)
+        # Get rid of tracking faults (two onsets or two offsets after another)
-    category, timestamps = correct_chasing_events(category, timestamps)
+        category, timestamps = correct_chasing_events(category, timestamps)
-
+
-    # split categories
+        # split categories
-    chasing_onset = (timestamps[category == 0]/ 60) /60
+        chasing_onset = (timestamps[category == 0]/ 60) /60
-    chasing_offset = (timestamps[category == 1]/ 60) /60
+        chasing_offset = (timestamps[category == 1]/ 60) /60
-    physical_contact = (timestamps[category == 2] / 60) /60
+        physical_contact = (timestamps[category == 2] / 60) /60
-
+
-    all_fish_ids = np.unique(bh.chirps_ids)
+        all_fish_ids = np.unique(bh.chirps_ids)
-    fish1_id = all_fish_ids[0]
+        fish1_id = all_fish_ids[0]
-    fish2_id = all_fish_ids[1]
+        fish2_id = all_fish_ids[1]
-    # Associate chirps to inidividual fish
+        # Associate chirps to inidividual fish
-    fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) /60
+        fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) /60
-    fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) /60
+        fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) /60
-    fish1_color = ps.red
+        fish1_color = ps.red
-    fish2_color = ps.orange
+        fish2_color = ps.orange
-
+
-    fig, ax = plt.subplots(4, 1, figsize=(28*ps.cm, 13*ps.cm), height_ratios=[0.5, 0.5, 0.5, 6], sharex=True)
+        fig, ax = plt.subplots(4, 1, figsize=(21*ps.cm, 13*ps.cm), height_ratios=[0.5, 0.5, 0.5, 6], sharex=True)
-    # marker size 
+        # marker size 
-    s = 200
+        s = 200
-    ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='firebrick', marker='|', s=s)
+        ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='firebrick', marker='|', s=s)
-    ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', marker='|', s=s )
+        ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', marker='|', s=s )
-    ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color=fish1_color, marker='|', s=s)
+        ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color=fish1_color, marker='|', s=s)
-    ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color=fish2_color, marker='|', s=s)
+        ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color=fish2_color, marker='|', s=s)
-
+
-
+
-    freq_temp = bh.freq[bh.ident==fish1_id]
+        freq_temp = bh.freq[bh.ident==fish1_id]
-    time_temp = bh.time[bh.idx[bh.ident==fish1_id]]
+        time_temp = bh.time[bh.idx[bh.ident==fish1_id]]
-    ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish1_color)
+        ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish1_color)
-
+
-    freq_temp = bh.freq[bh.ident==fish2_id]
+        freq_temp = bh.freq[bh.ident==fish2_id]
-    time_temp = bh.time[bh.idx[bh.ident==fish2_id]]
+        time_temp = bh.time[bh.idx[bh.ident==fish2_id]]
-    ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish2_color)
+        ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish2_color)
-
+
-    #ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower')
+        #ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower')
-
+
-        # Hide grid lines
+            # Hide grid lines
-    ax[0].grid(False)
+        ax[0].grid(False)
-    ax[0].set_frame_on(False)
+        ax[0].set_frame_on(False)
-    ax[0].set_xticks([])
+        ax[0].set_xticks([])
-    ax[0].set_yticks([])
+        ax[0].set_yticks([])
-    ps.hide_ax(ax[0])
+        ps.hide_ax(ax[0])
-
+
-
+
-    ax[1].grid(False)
+        ax[1].grid(False)
-    ax[1].set_frame_on(False)
+        ax[1].set_frame_on(False)
-    ax[1].set_xticks([])
+        ax[1].set_xticks([])
-    ax[1].set_yticks([])
+        ax[1].set_yticks([])
-    ps.hide_ax(ax[1])
+        ps.hide_ax(ax[1])
-
+
-    ax[2].grid(False)
+        ax[2].grid(False)
-    ax[2].set_frame_on(False)
+        ax[2].set_frame_on(False)
-    ax[2].set_yticks([])
+        ax[2].set_yticks([])
-    ax[2].set_xticks([])
+        ax[2].set_xticks([])
-    ps.hide_ax(ax[2])
+        ps.hide_ax(ax[2])
-
+
-
+
-
+
-    ax[3].axvspan(3, 6, 0, 5, facecolor='grey', alpha=0.5)
+        ax[3].axvspan(3, 6, 0, 5, facecolor='grey', alpha=0.5)
-    ax[3].set_xticks(np.arange(0, 6.1, 0.5))
+        ax[3].set_xticks(np.arange(0, 6.1, 0.5))
-
+
-    labelpad = 40
+        labelpad = 40
-    ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad)
+        fsize = 12 
-    ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad)
+        ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad, fontsize=fsize)
-    ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad)
+        ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad, fontsize=fsize)
-    ax[3].set_ylabel('EODf')
+        ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad, fontsize=fsize)
-
+        ax[3].set_ylabel('EODf')
-    ax[3].set_xlabel('Time [h]')
+
-    #ax[0].set_title(foldername.split('/')[-2])
+        ax[3].set_xlabel('Time [h]')
-    # 2020-03-31-9_59
+        ax[0].set_title(foldername.split('/')[-2])
-    plt.subplots_adjust(left=0.13, right=0.987, top=0.97)
+        # 2020-03-31-9_59
-    plt.savefig('../poster/figs/timeline.pdf')
+        plt.subplots_adjust(left=0.158, right=0.987, top=0.918)
-    plt.show()
+        #plt.savefig('../poster/figs/timeline.pdf')
+        plt.show()
 
 
     # plot chirps

poster/main.tex

@@ -23,7 +23,10 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
     with single - or physically separated - individuals.
     \begin{tikzfigure}[]
         \label{griddrawing}
-        \includegraphics[width=1\linewidth]{figs/introplot}
+<<<<<<< HEAD
+=======
+        \includegraphics[width=0.8\linewidth]{figs/introplot}
+>>>>>>> cdcf9564df07914cf57225de5a8bdaa642fbad0e
     \end{tikzfigure}
 }
 \myblock[TranspBlock]{Chirp detection}{
@@ -41,11 +44,26 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
         \includegraphics[width=\linewidth]{figs/timeline.pdf}
     \end{tikzfigure}
     \noindent
-    \begin{tikzfigure}[]
+    \begin{itemize}
-        \label{fig:example_b}
+        \setlength\itemsep{0.5em}
-        \includegraphics[width=\linewidth]{figs/chirps_winner_loser.pdf}
+        \item Two fish compete for one hidding place in one tank,
-    \end{tikzfigure}
+        \item Experiment had a 3 hour long darkphase and a 3 hour long light phase.
+    \end{itemize}
+
     \noindent
+    \begin{minipage}[c]{0.7\linewidth}
+        \begin{tikzfigure}[]
+            \label{fig:example_b}
+            \includegraphics[width=\linewidth]{figs/chirps_winner_loser.pdf}
+        \end{tikzfigure}
+    \end{minipage} % no space if you would like to put them side by side
+    \begin{minipage}[c]{0.2\linewidth}
+        \begin{itemize}
+            \setlength\itemsep{0.5em}
+            \item Fish who won the competition chirped more often than the fish who lost.
+            \item
+        \end{itemize}
+    \end{minipage}
 }
 
 \myblock[TranspBlock]{Interactions at modulations}{
@@ -55,19 +73,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
         \includegraphics[width=0.5\linewidth]{example-image-c}
     \end{tikzfigure}
 
-    \begin{multicols}{2}
+
-        \begin{itemize}
-            \setlength\itemsep{0.5em}
-            \item $\Delta$EOD$f$ does not appear to decrease during synchronous modulations ().
-            \item Individuals that rise their EOD$f$ first appear to rise their frequency higher compared to reactors (\textbf{B}).
-            \vfill
-            \null
-            \columnbreak
-            \item Synchronized fish keep distances below 1 m (\textbf{C}) but distances over 3 m also occur (see \textbf{movie}).
-            \item Spatial interactions increase \textbf{after} the start of a synchronous modulation (\textbf{D}).
-        \end{itemize}
-    \end{multicols}
-    \vspace{-1cm}
 }
 
 \myblock[GrayBlock]{Conclusion}{