diff --git a/params.py b/params.py
new file mode 100644
index 0000000..531dcfb
--- /dev/null
+++ b/params.py
@@ -0,0 +1,134 @@
+import matplotlib.colors as mcolors
+import matplotlib as mpl
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+import datetime
+import numpy as np
+from IPython import embed
+
+#############################################################################################
+# default fontsize
+plt.rcParams['font.size'] = 11
+plt.rcParams['axes.linewidth'] = 2
+
+#############################################################################################
+# for boxplots
+props = dict(linewidth=2, color='black')
+props_a = dict(linewidth=2, color='#884ea0')
+props_e = dict(linewidth=2, color='#656565')
+flierprops = dict(marker='o',  markeredgecolor='#656565', markersize=6, linestyle='none')
+
+#############################################################################################
+# time boundaries of day, dusk, night and dawn
+time_bound = ['1900_01_01_6_30', '1900_01_01_16_30', '1900_01_01_18_30',
+              '1900_01_02_4_30', '1900_01_02_6_30', '1900_01_02_16_30']
+time_boxes = []
+for i in range(len(time_bound)):
+    time_boxes.append(datetime.datetime.strptime(time_bound[i], '%Y_%m_%d_%H_%M'))
+datetime_box = mdates.date2num(time_boxes)
+
+# bin length for activity calculations in second
+bin_len = 5
+#############################################################################################
+# colors
+color_efm = ['#2e4053', '#ab1717', '#004d8d', '#000000']
+color_diffdays = ['#1b2631', '#2e4053', '#5d6d7e', '#aeb6bf']
+
+cd = ['#566573', '#2c3e50', '#abb2b9', '#2471a3', '#1a5276', '#a9cce3']
+cm20c = plt.cm.get_cmap('tab20c')
+color2 = ['#f39c12', '#d35400', '#fad7a0', '#e59866', '#c0392b', '#d98880', '#f1c40f']
+color_dadunida = ['#DFD13A',  '#c0392b', '#62285B', '#CE7227']
+
+farben = list(mcolors.CSS4_COLORS)
+color_vec = []
+for i in range(len(farben)):
+    color_vec.append(mcolors.CSS4_COLORS[farben[i]])
+color_vec = sorted(color_vec)
+
+#############################################################################################
+# labels
+labels = ['$\it{Eigenmannia\,sp.}$', '$\it{A.\,macrostomus}\,\u2640$', '$\it{A.\,macrostomus}\,\u2642$', 'unknown']
+
+#############################################################################################
+# plot params
+fs = 11  # font_size
+fs_ticks = 11  # fs_ticks
+lw = 2  # linewidth
+a = 0.3  # alpha
+ms = 7
+h_bins = 30  # hist_bins
+save_path = '../../thesis/Figures/Results/'
+save_path_pres = '../../pres_ergebnisse/figures/'
+
+inch = 2.45
+
+#############################################################################################
+# for nice plots
+def nice_ax(ax):
+    '''
+    Makes nice x and y axis:
+            top and right axis invisible and axis width = 2 and axis ticks labelsize = 11
+    :param ax:
+    :return:
+    '''
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+    ax.tick_params(width=2)
+    ax.tick_params(axis='both', which='major', labelsize=11)
+
+
+mpl.axes.Axes.make_nice_ax = nice_ax
+
+
+def beau_time_channel_axis(ax):
+    '''
+    Makes nice axis for trajectory plots:
+            top and right axis invisible and axis width = 2 and axis ticks labelsize = 11
+            white line for the gap: y = 7.5
+            y ticks and yticklabels: ticks at each electrode, labels at every second
+            x and y labels
+    :param ax:
+    :return:
+    '''
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+    ax.tick_params(width=2)
+    ax.tick_params(axis='both', which='major', labelsize=11)
+    # ax.set_ylim([0, 15])
+    ax.axhline(7.5, xmin=0, xmax=15, color='white', lw=4)
+    # ax.set_yticklabels([1, 3, 5, 7, 14, 16, 18, 20])
+    ax.set_yticks([0, 1, 2, 3, 4, 5, 6, 7, 7.5, 8, 9, 10, 11, 12, 13, 14, 15])
+    ax.set_yticklabels(['1', '', '3', '', '5', '', '7', '', 'gap', '', '10', '', '12', '', '14', '', '16'])
+    # ax.set_yticklabels([0, 0, 2, 4, 6, 13, 15, 17, 19])
+    ax.invert_yaxis()
+    ax.set_xlabel('Time', fontsize=11)
+    ax.set_ylabel('Electrode', fontsize=11)
+
+
+mpl.axes.Axes.beautimechannelaxis = beau_time_channel_axis
+
+
+def timeaxis(ax):
+    '''
+    Makes nice time axis for datetime x values:
+            top and right axis invisible and axis width = 2 and axis ticks labelsize = 11
+            converts the x ticks in the date format into a real time axis of the form H:M
+    :param ax:
+    :return:
+    '''
+
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+    ax.tick_params(width=2)
+    ax.tick_params(axis='both', which='major', labelsize=11)
+    ax.xaxis_date()
+    date_format = mdates.DateFormatter('%H:%M')
+    # date_format = mdates.DateFormatter('%d.%m.%Y %H:%M')
+    ax.xaxis.set_major_formatter(date_format)
+
+
+mpl.axes.Axes.timeaxis = timeaxis
+
+
+
+
diff --git a/plot_activity_freq.py b/plot_activity_freq.py
new file mode 100644
index 0000000..b5efb6e
--- /dev/null
+++ b/plot_activity_freq.py
@@ -0,0 +1,132 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import matplotlib.colors as mcolors
+import matplotlib.gridspec as gridspec
+from IPython import embed
+from scipy import stats
+import os
+from IPython import embed
+from params import *
+import datetime
+import itertools
+from statisitic_functions import *
+
+
+if __name__ == '__main__':
+    ###################################################################################################################
+    # parameter
+    # save_path = '../../thesis/Figures/Results/'
+    # inch = 2.45
+    cat_v1 = [0, 0, 750, 0]
+    cat_v2 = [750, 750, 1000, 1000]
+    cat_v = [0, 580, 750, 1000]
+    cat_n = ['Eigenmannia', 'Apteronotus', 'Apteronotus']
+    pos = [1, 2, 3, 4]
+
+    ###################################################################################################################
+    # load data
+    freq = np.load('../data/f.npy', allow_pickle=True)
+    c = np.load('../data/all_changes.npy', allow_pickle=True)
+    names = np.load('../data/n.npy', allow_pickle=True)
+    stl = np.load('../data/stl.npy', allow_pickle=True)
+
+    ###################################################################################################################
+    # figure
+    fig = plt.figure(figsize=[16 / inch, 8 / inch])
+    spec = gridspec.GridSpec(ncols=3, nrows=1, figure=fig, hspace=0.3, wspace=0.2,
+                             width_ratios=[4,1,2], left=0.08, bottom=0.15, right=0.95, top=0.90)
+    ax = fig.add_subplot(spec[0, 0])
+    ax1 = fig.add_subplot(spec[0, 2])
+    ax2 = fig.add_subplot(spec[0, 1])
+
+    ###############################################################################################################
+    # analysis: separates the activity and frequencies by the species and sex
+    activitys = []
+    freqs = []
+    for p in range(3):
+        f = freq[(names == cat_n[p]) & (freq >= cat_v1[p]) & (freq < cat_v2[p])]
+        a = c[(names == cat_n[p]) & (freq >= cat_v1[p]) & (freq < cat_v2[p])]
+
+        # f = freq[(names == cat_n[p]) & (freq >= cat_v1[p]) & (freq < cat_v2[p]) & (stl==False)]
+        # a = c[(names == cat_n[p]) & (freq >= cat_v1[p]) & (freq < cat_v2[p]) & (stl==False)]
+        #
+        # f_m = freq[(names == cat_n[p]) & (freq >= cat_v1[p]) & (freq < cat_v2[p]) & stl]
+        # a_m = c[(names == cat_n[p]) & (freq >= cat_v1[p]) & (freq < cat_v2[p]) & stl]
+
+        activitys.append(a)
+        freqs.append(f)
+
+        ###############################################################################################################
+        # point plot of freq vs activity
+        ax.plot(f, a, 'o', alpha=0.7, color=color_efm[p], label=labels[p])
+        # ax.plot(f_m, a_m, 'o', alpha=0.7, color=color_efm[p], markeredgecolor='k')
+        ax.set_xlim(400, 1000)
+        ax.set_xticks([400, 600, 800, 1000])
+        ax.set_xticklabels([400, 600, 800, 1000])
+        ax.set_xlabel('EODf [Hz]', fontsize=fs)
+        ax.set_ylabel('Activity [Changes/min]', fontsize=fs)
+        ax.set_ylim(-0.2, 6)
+
+    ###############################################################################################################
+    # boxplot
+    bp = ax1.boxplot(activitys, widths=0.6, showfliers=False)
+    for element in ['boxes', 'medians']:
+        for idx in range(3):
+            bp[element][idx].set(linewidth=2, color=color_efm[idx])
+
+    for element in ['whiskers', 'caps']:
+        for idx, num in enumerate([0,2,4]):
+            for i in [0,1]:
+                bp[element][num+i].set(linewidth=2, color=color_efm[idx])
+                if element == 'whiskers' and i == 1:
+                    ax1.text(idx+1, bp['whiskers'][num+i].get_ydata()[1]+0.3, str(len(activitys[idx])),
+                             horizontalalignment='center')
+
+    ###############################################################################################################
+    # histogram: activity distribution
+    for j in [1,2,0]:
+        n, bins = np.histogram(activitys[j], bins=np.arange(0,8.2,0.2))
+        ax2.hist(activitys[j], bins=np.arange(0,8.2,0.25), orientation='horizontal', histtype='step',
+                 color=color_efm[j], linewidth=2)
+
+    ################################################################################################################
+    # statisitics: cohens d' and mann whitney u
+
+    for subset in itertools.combinations([0,1,2], 2):
+        print(labels[subset[0]], labels[subset[1]])
+        U, p = stats.mannwhitneyu(activitys[subset[0]], activitys[subset[1]])
+        print('U = ', U, 'p = ', p*3)
+        r = 1-(2*U)/(len(activitys[subset[0]])*len(activitys[subset[1]]))
+        print('r = ',r)
+        print('d = ',cohen_d(activitys[subset[0]], activitys[subset[1]]))
+        significance_bar(ax1, p*3, None, subset[0]+1, subset[1]+1, 3.8)
+
+    #################################################################################################################
+    # nice axis
+    tagx = [-0.15, -0.4, -0.17]
+    tagy = [1.05,1.05,1.05]
+    for ax_idx, axis in enumerate([ax,ax2,ax1]):
+        axis.text(tagx[ax_idx], tagy[ax_idx], chr(ord('A') + ax_idx), transform=axis.transAxes, fontsize='large')
+        axis.make_nice_ax()
+
+    ax1.set_xlim(0.5,3.5)
+    ax1.set_xticks([1, 2, 3])
+    ax1.set_xticklabels(['$\it{E}$', '$\it{A}\u2640$', '$\it{A}\u2642$'])
+    ax1.set_xlabel('Species', fontsize=fs)
+
+    ax1.set_ylim(ax.get_ylim())
+    ax1.spines["left"].set_visible(False)
+    ax1.get_yaxis().set_visible(False)
+
+    ax2.set_ylim(ax.get_ylim())
+    ax2.set_yticklabels('')
+    ax2.set_xlabel('n', fontsize=fs)
+
+    fig.align_xlabels([ax,ax1,ax2])
+    fig.legend(loc='upper right', bbox_to_anchor=(0.9, 0.85, 0.1, 0.1))
+
+    # fig.savefig(save_path_pres+'activ_freq.pdf')
+    # fig.savefig(save_path+'activ_freq.pdf')
+    plt.show()
+    embed()
diff --git a/plot_activity_time.py b/plot_activity_time.py
new file mode 100644
index 0000000..9014740
--- /dev/null
+++ b/plot_activity_time.py
@@ -0,0 +1,181 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import matplotlib.colors as mcolors
+import matplotlib.gridspec as gridspec
+from IPython import embed
+from scipy import stats
+import math
+import os
+from IPython import embed
+import helper_functions as hf
+from params import *
+from statisitic_functions import *
+import itertools
+import random
+
+
+def get_recording_number_in_time_bins(time_bins):
+    """
+    Calculates the number of the recordings in the time bins
+
+    :param time_bins: numpy array with borders of the time bins
+    :return: time_bins_recording: numpy array with the number of recordings to that specific time bin
+    """
+    # variables
+    time_bins_recordings = np.zeros(len(time_bins) - 1)
+
+    # load data
+    for index, filename_idx in enumerate([0, 1, 2, 3]):
+        filename = sorted(os.listdir('../data/'))[filename_idx]
+        time_points = np.load('../data/' + filename + '/all_hms.npy', allow_pickle=True)
+
+        # in which bins is this recording, fill time_bins_recordings
+        unique_time_points = np.unique(np.hstack(time_points))
+        for idx, tb in enumerate(time_bins[:-1]):
+            if np.any((unique_time_points >= tb) & (unique_time_points <= time_bins[idx + 1])):
+                time_bins_recordings[idx] += 1
+
+    return time_bins_recordings
+
+
+if __name__ == '__main__':
+    ###################################################################################################################
+    # parameter and variables
+    # plot params
+    c = 0
+    cc = 1
+
+    all_changes = []
+    all_first = []
+    all_last = []
+
+    # time_bins 5 min
+    time_factor = 60 * 60
+    time_bins = np.arange(0, 24 * time_factor + 1, bin_len)
+    # time_bins_recordings = np.zeros(len(time_bins) - 1)
+
+    # percent roaming
+    pr = []
+
+    # kernel
+    kernel_size = 120
+    kernel = np.ones(kernel_size) / kernel_size
+
+    ###################################################################################################################
+    # load data
+    ###################################################################################################################
+    # load all the data of one day
+    cbf = np.load('../data/cbf5.npy', allow_pickle=True)
+    # embed()
+    # quit()
+    N_rec_time_bins = get_recording_number_in_time_bins(time_bins)
+
+    ###################################################################################################################
+    # time zones
+    time_edges = np.array([4.5, 6.5, 16.5, 18.5]) * time_factor
+    day = (time_bins[:-1] >= time_edges[1]) & (time_bins[:-1] <= time_edges[2])
+    dusk = (time_bins[:-1] >= time_edges[2]) & (time_bins[:-1] <= time_edges[3])
+    night = (time_bins[:-1] <= time_edges[0]) | (time_bins[:-1] >= time_edges[3])
+    dawn = (time_bins[:-1] >= time_edges[0]) & (time_bins[:-1] <= time_edges[1])
+
+    ###################################################################################################################
+    # activity in time bins
+    activity = np.full(len(time_bins) - 1, np.nan)
+
+    for i in range(len(activity)):
+        activity[i] = np.nansum(cbf[:2, i])  # * (60/bin_len)
+
+    # activity for boxplot in time zones
+    activ_boxes = [[], [], [], []]
+    activ_boxes_corr = [[], [], [], []]
+    activC = activity / N_rec_time_bins
+    conv_activ = np.convolve(activity, kernel, 'same')
+    conv_N_rec_tb = np.convolve(N_rec_time_bins, kernel, 'same')
+
+    activ_boxes[2].extend(activC[night])  # 18:30 - 04:30
+    activ_boxes[1].extend(activC[dawn])  # 04:30 - 06:30
+    activ_boxes[0].extend(activC[day])  # 06:30 - 16:30
+    activ_boxes[3].extend(activC[dusk])  # 16:30 - 18:30
+
+    # correction if nans in array
+    for j in range(4):
+        b = np.array(activ_boxes[j])
+        activ_boxes_corr[j] = b[~np.isnan(b)]
+    t_b = np.array(time_bins[:-1][day])
+    t_b = t_b[~np.isnan(np.array(activ_boxes[0]))]
+
+    # rolled time axis for nicer plot midnight in the middle start noon
+    rolled_time = np.roll(time_bins[:-1] / time_factor, int(len(time_bins[:-1]) / 2))
+    rolled_activ = np.roll(activC, int(len(time_bins[:-1]) / 2))
+    rolled_conv_activ = np.roll(conv_activ / conv_N_rec_tb, int(len(time_bins[:-1]) / 2) + 1)
+
+    ###################################################################################################################
+    # activity over time
+    fig5 = plt.figure(constrained_layout=True, figsize=[15 / inch, 8 / inch])
+    gs = gridspec.GridSpec(ncols=2, nrows=1, figure=fig5, hspace=0.05, wspace=0.0,
+                           width_ratios=[6, 3], left=0.1, bottom=0.15, right=0.95, top=0.98)
+
+    ax5 = fig5.add_subplot(gs[0, 0])
+    ax6 = fig5.add_subplot(gs[0, 1])
+
+    ax5.plot(rolled_activ, color=color2[5])
+    ax5.plot(rolled_conv_activ, color=color2[4])
+
+    ax5.plot([16.5*time_factor/5, 6.5*time_factor/bin_len], [9, 9], color=color_diffdays[0], lw=7)
+    ax5.plot([16.5*time_factor/5, 18.5*time_factor/bin_len], [9, 9], color=color_diffdays[3], lw=7)
+    ax5.plot([4.5*time_factor/5, 6.5*time_factor/bin_len], [9, 9], color=color_diffdays[3], lw=7)
+
+    # ax5.set_xlim(0, 24)
+    ax5.set_xticks(np.arange(0, 25, 6) * time_factor / bin_len)
+    ax5.set_xticklabels(['12:00', '18:00', '00:00', '06:00', '12:00'])
+    # ax4.set_yticks([0.00, 0.01, 0.02, 0.03])
+    ax5.set_xlabel('Time', fontsize=fs)
+    ax5.set_ylabel('Activity [Changes/ ' + str(bin_len) + ' s]', fontsize=fs)
+
+    for idx_zone, plot_zone, color_zone, day_zone, pos_zone in \
+            zip([0, 1, 2, 3], [day, dusk, night, dawn], [6, 1, 4, 0], ['day', 'dusk', 'night', 'dawn'], [1, 2, 3, 4]):
+
+        # boxplot ax1
+        props_e = dict(linewidth=2, color=color_dadunida[idx_zone])
+        bp = ax6.boxplot(activ_boxes_corr[idx_zone], positions=[pos_zone], widths=0.7, showfliers=False,
+                         boxprops=props_e, medianprops=props_e, capprops=props_e, whiskerprops=props_e)
+    # ax6.boxplot(activ_boxes_corr, positions=[1, 2, 3, 4], widths=0.7, showfliers=False,
+    #             boxprops=props_e, medianprops=props_e, capprops=props_e, whiskerprops=props_e)
+
+    ax6.set_xticks([1, 2, 3, 4])
+    ax6.set_xticklabels(['day', 'dusk', 'night', 'dawn'])
+
+    for ax_idx, axis in enumerate([ax5, ax6]):
+        axis.text(-0.12, 1, chr(ord('A') + ax_idx), transform=axis.transAxes, fontsize='large')
+        axis.make_nice_ax()
+
+    # ax5.set_ylim([ax5.get_ylim()[0], 9.5])
+    ax6.set_ylim(ax5.get_ylim())
+
+    dc = [6.3, 7.2, 8.1, 6.3, 9.0, 6.3]
+    c = 0
+    for subset in itertools.combinations([0, 1, 2, 3], 2):
+        print(subset[0], subset[1])
+        # print(stats.mannwhitneyu(activ_boxes_corr[subset[0]], activ_boxes_corr[subset[1]]))
+        U, p = stats.mannwhitneyu(activ_boxes_corr[subset[0]], activ_boxes_corr[subset[1]])
+        print(U, p * 4)
+        r = 1 - (2 * U) / (len(activ_boxes_corr[subset[0]]) * len(activ_boxes_corr[subset[1]]))
+        # print(r)
+        d = cohen_d(activ_boxes_corr[subset[0]], activ_boxes_corr[subset[1]])
+        significance_bar(ax6, None, d, subset[0] + 1, subset[1] + 1, dc[c])
+        c += 1
+
+    # fig5.savefig(save_path + 'activ_time.png')
+    fig5.savefig(save_path + 'activ_time_box.pdf')
+    plt.show()
+
+    ###################################################################################################################
+    # statistic
+
+    print(stats.pearsonr(activ_boxes_corr[0], t_b))
+    print(stats.pearsonr(activ_boxes_corr[1], time_bins[:-1][dawn]))
+    print(stats.pearsonr(activ_boxes_corr[2], time_bins[:-1][night]))
+    print(stats.pearsonr(activ_boxes_corr[3], time_bins[:-1][dusk]))
+
+    embed()
\ No newline at end of file
diff --git a/plot_aifl.py b/plot_aifl.py
new file mode 100644
index 0000000..72d383a
--- /dev/null
+++ b/plot_aifl.py
@@ -0,0 +1,99 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from IPython import embed
+import helper_functions as hf
+import do_check_for_overlap as cfo
+from params import *
+import matplotlib.colors as mcolors
+import os
+
+if __name__ == '__main__':
+
+    ###################################################################################################################
+    # load data
+    ###################################################################################################################
+    # load all the data of one day
+    filename = sorted(os.listdir('../data/'))[2]
+
+    ident = np.load('../../../data_masterthesis/'+filename+'/all_ident_v.npy', allow_pickle=True)
+    freq = np.load('../../../data_masterthesis/'+filename+'/all_fund_v.npy', allow_pickle=True)
+    timeidx = np.load('../../../data_masterthesis/'+filename+'/all_idx_v.npy', allow_pickle=True)
+    t = np.load('../../../data_masterthesis/'+filename+'/all_times.npy', allow_pickle=True)
+
+    aifl = np.load('../data/'+filename+'/aifl2.npy', allow_pickle=True)
+    faifl = np.load('../data/'+filename+'/faifl2.npy', allow_pickle=True)
+    oofl = np.load('../data/'+filename+'/oofl.npy', allow_pickle=True)
+    faifl = np.delete(faifl, [0], axis=0)
+
+    fish_in_aifl = list(np.unique(np.where(~np.isnan(aifl[:, :, 0]))[1]))
+    fish_in_faifl = list(np.unique(faifl[:, [1, 3]]))
+    correct_aifl = sorted(list(set(fish_in_aifl) - set(fish_in_faifl)))
+
+    dt = datetime.datetime.strptime(filename[-5:], '%H_%M')
+    embed()
+    quit()
+
+    ###################################################################################################################
+    # plot traces in oofl
+    counter = 0
+    oofl = np.array(oofl)
+    for i in range(len(oofl[:, 0])):
+        channel = int(oofl[i, 0])
+        time_diff = timeidx[channel][ident[channel] == oofl[i][1]][-1] - timeidx[channel][ident[channel] == oofl[i][1]][0]
+        if time_diff >= 0: #4800
+            plt.plot(timeidx[channel][ident[channel] == oofl[i][1]],
+                     freq[channel][ident[channel] == oofl[i][1]], Linewidth=2)
+            plt.text(timeidx[channel][ident[channel] == oofl[i][1]][0] + np.random.rand(1) * 0.3,
+                     freq[channel][ident[channel] == oofl[i][1]][0] + np.random.rand(1) * 0.3,
+                     str(oofl[i][0]) + '_' + str(oofl[i][1]), color='blue')
+            counter = counter + 1
+    plt.show()
+
+    ###################################################################################################################
+    # plot overlapping traces
+    new_sorting = cfo.get_list_of_fishN_with_overlap(aifl, fish_in_aifl, timeidx, ident)
+    for fish_number in new_sorting:
+        hf.plot_together(timeidx, freq, ident, aifl, int(fish_number), color_vec[0])
+
+    ###################################################################################################################
+    # plot fish in faifl
+    for i in range(len(faifl)):
+        fishid1 = int(faifl[i, 1])
+        fishid2 = int(faifl[i, 3])
+        hf.plot_all_channels(timeidx, freq, ident, aifl, fishid1, fishN2=fishid2)
+
+    ###################################################################################################################
+    # plot all traces
+
+    fig, ax = plt.subplots(1, 1, figsize=(15 / inch, 8 / inch))
+    fig.subplots_adjust(left=0.12, bottom=0.15, right=0.98, top=0.98)
+    for color_counter, fish_number in enumerate(fish_in_aifl):
+        for channel_idx in [13]:
+            fish1 = aifl[channel_idx, fish_number, ~np.isnan(aifl[channel_idx, fish_number])]
+            r1 = len(fish1)
+            print(fish1)
+            for len_idx1 in range(r1):
+                zeit = t[timeidx[channel_idx][ident[channel_idx] == fish1[len_idx1]]]
+                plot_zeit = []
+                for i in range(len(zeit)):
+                    plot_zeit.append(dt + datetime.timedelta(seconds=zeit[i]))
+                plt.plot(plot_zeit,
+                         freq[channel_idx][ident[channel_idx] == fish1[len_idx1]],
+                         Linewidth=1, label=fish_number, color=color_vec[color_counter+40])
+
+    ax.set_ylim([450, 1000])
+    ax.set_xlabel('Time', fontsize=fs)
+    ax.set_ylabel('EOD frequency [Hz]', fontsize=fs)
+    ax.make_nice_ax()
+    ax.timeaxis()
+    fig.savefig(save_path_pres+'EOD_sorter.pdf')
+    fig.savefig('../../thesis/Figures/Methods/EOD_sorter.pdf')
+    plt.show()
+
+    ###################################################################################################################
+    # plot
+    u = np.unique(faifl[:, [1, 3]])
+    for fish_number in range(len(u)):
+        hf.plot_together(timeidx, freq, ident, aifl, int(u[fish_number]), color_vec[fish_number])
+
+
diff --git a/plot_direction.py b/plot_direction.py
new file mode 100644
index 0000000..a050181
--- /dev/null
+++ b/plot_direction.py
@@ -0,0 +1,149 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import matplotlib.colors as mcolors
+import matplotlib.gridspec as gridspec
+
+from IPython import embed
+from scipy import stats
+import os
+from IPython import embed
+import helper_functions as hf
+from params import *
+import itertools
+from statisitic_functions import *
+
+
+def get_recording_number_in_time_bins(time_bins):
+    """
+    Calculates the number of the recordings in the time bins
+
+    :param time_bins: numpy array with borders of the time bins
+    :return: time_bins_recording: numpy array with the number of recordings to that specific time bin
+    """
+    # variables
+    time_bins_recordings = np.zeros(len(time_bins) - 1)
+
+    # load data
+    for index, filename_idx in enumerate([0, 1, 2, 3]):
+        filename = sorted(os.listdir('../data/'))[filename_idx]
+        time_points = np.load('../data/' + filename + '/all_hms.npy', allow_pickle=True)
+
+        # in which bins is this recording, fill time_bins_recordings
+        unique_time_points = np.unique(np.hstack(time_points))
+        for idx, tb in enumerate(time_bins[:-1]):
+            if np.any((unique_time_points >= tb) & (unique_time_points <= time_bins[idx + 1])):
+                time_bins_recordings[idx] += 1
+
+    return time_bins_recordings
+
+
+if __name__ == '__main__':
+    ###################################################################################################################
+    # parameter and variables
+    # plot params
+    inch = 2.45
+    save_path = '../../thesis/Figures/Results/'
+    kernel_size = 120
+    c = 0
+    cc = 1
+    all_changes = []
+
+    all_first = []
+    all_last = []
+
+    # time_bins 5 min
+    time_factor = 60 * 60
+    time_bins = np.arange(0, 24 * time_factor + 1, bin_len)
+
+    # changes per bin of 5min for all fish
+    cbf = np.full([3, len(time_bins) - 1, 300], np.nan)
+    cbf_counter = 0
+
+    # kernel
+    kernel = np.ones(kernel_size) / kernel_size
+
+    ###################################################################################################################
+    # load data
+    ###################################################################################################################
+    # load all the data of one day
+    cbf = np.load('../data/cbf5.npy', allow_pickle=True)
+    # embed()
+    # quit()
+    N_rec_time_bins = get_recording_number_in_time_bins(time_bins)
+
+    ###################################################################################################################
+    # direction and act plot
+    no = np.full(len(time_bins) - 1, np.nan)
+    down = np.full(len(time_bins) - 1, np.nan)
+    up = np.full(len(time_bins) - 1, np.nan)
+
+    for i in range(len(up)):
+        up[i] = np.nansum(cbf[1, i])  # * (60/bin_len)
+        down[i] = np.nansum(cbf[0, i])  # * (60/bin_len)
+        no[i] = np.nansum(cbf[2, i])  # * (60/bin_len)
+    conv_direct = np.convolve(up - down, kernel, 'same')
+    conv_up = np.convolve(up, kernel, 'same')
+    conv_down = np.convolve(down, kernel, 'same')
+    conv_N_rec_tb = np.convolve(N_rec_time_bins, kernel, 'same')
+
+    # rolled time axis for nicer plot midnight in the middle start noon
+    rolled_time = np.roll(time_bins[:-1] / time_factor, int(len(time_bins[:-1]) / 2))
+    rolled_up = np.roll(up / N_rec_time_bins, int(len(time_bins[:-1]) / 2))
+    rolled_down = np.roll(down * -1 / N_rec_time_bins, int(len(time_bins[:-1]) / 2))
+    rolled_conv_dir = np.roll(conv_direct / conv_N_rec_tb, int(len(time_bins[:-1]) / 2))
+    rolled_conv_up = np.roll(conv_up / conv_N_rec_tb, int(len(time_bins[:-1]) / 2))
+    rolled_conv_down = np.roll(conv_down * -1 / conv_N_rec_tb, int(len(time_bins[:-1]) / 2))
+    ###################################################################################################################
+    # time zones
+    time_edges = np.array([4.5, 6.5, 16.5, 18.5]) * time_factor
+    day = (time_bins[:-1] >= time_edges[1]) & (time_bins[:-1] <= time_edges[2])
+    dusk = (time_bins[:-1] >= time_edges[2]) & (time_bins[:-1] <= time_edges[3])
+    night = (time_bins[:-1] <= time_edges[0]) | (time_bins[:-1] >= time_edges[3])
+    dawn = (time_bins[:-1] >= time_edges[0]) & (time_bins[:-1] <= time_edges[1])
+
+    ###################################################################################################################
+    # activity up down over time
+    fig2, ax2 = plt.subplots(1, 1, figsize=(15 / inch, 8 / inch))
+    fig2.subplots_adjust(left=0.1, bottom=0.15, right=0.95, top=0.98)
+
+    ax2.plot(rolled_up, color=color2[2])
+    ax2.plot(rolled_down, color=color2[3])
+    ax2.plot(rolled_conv_dir, 'k', lw=3)
+    ax2.plot(rolled_conv_up, color=color2[0], lw=3, label='upstream')
+    ax2.plot(rolled_conv_down, color=color2[1], lw=3, label='downstream')
+
+    ax2.plot([16.5*time_factor/5, 6.5*time_factor/bin_len], [5.5, 5.5], color=color_diffdays[0], lw=7)
+    ax2.plot([16.5*time_factor/5, 18.5*time_factor/bin_len], [5.5, 5.5], color=color_diffdays[3], lw=7)
+    ax2.plot([4.5*time_factor/5, 6.5*time_factor/bin_len], [5.5, 5.5], color=color_diffdays[3], lw=7)
+
+    ax2.set_xlim([0, 24 * time_factor / bin_len])
+    ax2.set_xticks(np.arange(0, 25, 3) * time_factor / bin_len)
+    ax2.set_xticklabels(['12:00', '15:00', '18:00', '21:00', '00:00', '03:00', '06:00', '09:00', '12:00'])
+    ax2.make_nice_ax()
+
+    # ax2.set_yticks([-7., -5., -2.5, 0, 2.5, 5., 7.])
+    ax2.set_xlabel('Time', fontsize=fs)
+    ax2.set_ylabel('Activity [Changes/ ' + str(bin_len) + ' s]', fontsize=fs)
+    plt.legend()
+
+    # fig2.savefig(save_path+'activ_updown_time.png')
+    fig2.savefig(save_path + 'activ_updown_time.pdf')
+    fig2.savefig(save_path_pres + 'activ_updown_time.pdf')
+    plt.show()
+
+    ###################################################################################################################
+    # statistics
+
+    # print('night down: ', np.nanmedian(conv_down[night]/conv_N_rec_tb[night]))
+    # print('day down: ', np.nanmedian(conv_down[day]/conv_N_rec_tb[day]))
+
+    print('night', stats.pearsonr(conv_down[night]/conv_N_rec_tb[night], conv_up[night]/conv_N_rec_tb[night]))
+    print('day', stats.pearsonr(conv_down[day]/conv_N_rec_tb[day], conv_up[day]/conv_N_rec_tb[day]))
+    print('dusk', stats.pearsonr(conv_down[dusk]/conv_N_rec_tb[dusk], conv_up[dusk]/conv_N_rec_tb[dusk]))
+    print('dawn', stats.pearsonr(conv_down[dawn]/conv_N_rec_tb[dawn], conv_up[dawn]/conv_N_rec_tb[dawn]))
+    # print('night up: ', np.nanmedian(conv_up[night]/conv_N_rec_tb[night]))
+    # print('day up: ', np.nanmedian(conv_up[day]/conv_N_rec_tb[day]))
+
+    embed()
+    quit()