diff --git a/plot_running_std.py b/plot_running_std.py
new file mode 100644
index 0000000..1160bf6
--- /dev/null
+++ b/plot_running_std.py
@@ -0,0 +1,94 @@
+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 *
+
+if __name__ == '__main__':
+
+    ###################################################################################################################
+    # load data
+    ###################################################################################################################
+    # load all the data of one day
+    filename = sorted(os.listdir('../../../data/'))[1]
+    aifl = np.load('../data/'+filename+'/aifl2.npy', allow_pickle=True)
+
+    # all_max_ch_means = np.load('../data/' + filename + '/all_max_ch_means.npy', allow_pickle=True)
+    all_xticks = np.load('../data/' + filename + '/all_xtickses.npy', allow_pickle=True)
+    all_Ctime_v = np.load('../data/' + filename + '/all_Ctime_v.npy', allow_pickle=True)
+    ipp = np.load('../data/' + filename + '/all_ipp.npy', allow_pickle=True)
+    power_means = np.load('../data/' + filename + '/power_means.npy', allow_pickle=True)
+
+    ###################################################################################################################
+    # parameter and variables
+    ###################################################################################################################
+    # plot params
+    # variables
+    sampling_rate = 1/np.diff(all_Ctime_v[0])[0]  # in sec
+
+    # lists
+    fish_in_aifl = list(np.unique(np.where(~np.isnan(aifl[:, :, 0]))[1]))
+    thresholds = np.full(3, np.nan)
+    all_run_std = []
+    ###################################################################################################################
+    # analysis
+    ###################################################################################################################
+    fig = plt.figure(figsize=(16, 14))
+    spec = gridspec.GridSpec(ncols=2, nrows=2, figure=fig)
+    ax1 = fig.add_subplot(spec[0, 0])
+    ax2 = fig.add_subplot(spec[0, 1])
+    ax3 = fig.add_subplot(spec[1, 0])
+
+    for fish in range(len(fish_in_aifl)):
+
+        run_mean, run_std = hf.running_3binsizes(ipp[fish], sampling_rate)
+        all_run_std.append(run_std)
+
+        if len(np.arange(len(run_mean[2]))[~np.isnan(run_mean[2])]) < 10:
+            continue
+
+        ###########################################################################################################
+        # plot
+        # hf.plot_running(all_xticks, run_mean, run_std, threshold, fish, color_vec, fs, fs_ticks, lw)
+        # plt.show()
+
+        for ax_cntr ,ax in enumerate([ax1, ax2, ax3]):
+            non_nan = np.arange(len(run_mean[ax_cntr]))[~np.isnan(run_mean[ax_cntr])]
+            ax.plot(all_xticks[fish][non_nan], run_std[ax_cntr][non_nan], '.', color=color_vec[fish])
+            ax.timeaxis()
+            ax.make_nice_ax()
+
+        ax1.set_xlabel('Time [h]', fontsize=fs)
+
+    rs = np.array(all_run_std)
+
+    for idx in range(3):
+        rs1 = np.hstack(rs[:, idx])
+        rs1 = rs1[~np.isnan(rs1)]
+        rs1 = rs1[rs1>0]
+        print(np.percentile(rs1, [5, 95, 50]))
+        thresholds[idx] = np.percentile(rs1, 50)
+
+    if filename not in os.listdir('../data/'):
+        os.mkdir('../data/'+filename)
+
+    np.save('../data/' + filename + '/thresholds.npy', thresholds)
+
+    fig2, ax2 = plt.subplots()
+
+    ax2.hist(np.hstack(rs[:,0]), bins=200)
+    ax2.hist(np.hstack(rs[:,1]), bins=200)
+    ax2.hist(np.hstack(rs[:,2]), bins=200)
+    plt.show()
+
+    embed()
+
+
+
diff --git a/plot_trajec_5_47.py b/plot_trajec_5_47.py
new file mode 100644
index 0000000..a2fa5cc
--- /dev/null
+++ b/plot_trajec_5_47.py
@@ -0,0 +1,74 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import matplotlib.gridspec as gridspec
+
+from IPython import embed
+import helper_functions as hf
+from params import *
+import os
+import datetime
+
+if __name__ == '__main__':
+    ###################################################################################################################
+    # parameter and variables
+    # plot params
+    inch = 2.45
+    save_path = '../../thesis/Figures/Results/'
+    kernel_size = 100
+
+    fig = plt.figure(constrained_layout=True, figsize=[13 / inch, 15 / inch])
+    gs = gridspec.GridSpec(ncols=1, nrows=2, figure=fig, hspace=0.05, wspace=0.0,
+                           left=0.1, bottom=0.15, right=0.95, top=0.98)
+
+    ax1 = fig.add_subplot(gs[0, 0])
+    ax2 = fig.add_subplot(gs[1, 0])
+    ###################################################################################################################
+    # load all the data of one day
+
+    for filename_idx in [0]:
+        filename = sorted(os.listdir('../data/'))[filename_idx]
+
+        all_max_ch_means = np.load('../data/' + filename + '/all_max_ch.npy', allow_pickle=True)
+        all_xticks = np.load('../data/' + filename + '/all_xtickses.npy', allow_pickle=True)
+        all_ipp = np.load('../data/' + filename + '/all_ipp.npy', allow_pickle=True)
+        power_means = np.load('../data/' + filename + '/power_means.npy', allow_pickle=True)
+        freq = np.load('../data/' + filename + '/fish_freq_q10.npy', allow_pickle=True)
+        ###############################################################################################################
+        # get fish
+        for fish_number, ax, ax_idx in zip([5,47], [ax1,ax2], [0, 1]):
+            if power_means[fish_number] >= -90.0:
+                ipp = all_ipp[fish_number]
+                x_tickses = all_xticks[fish_number]
+                max_ch_mean = all_max_ch_means[fish_number]
+
+                # smoothing of max channel mean
+                kernel = np.ones(kernel_size) / kernel_size
+                smooth_mcm = np.convolve(max_ch_mean, kernel, 'valid')
+
+                try:
+                    smooth_x = x_tickses[int(np.ceil(kernel_size/2)):-int(np.floor(kernel_size/2))]
+                except:
+                    embed()
+                    quit()
+
+                #####################################################################################################
+                # plot traces
+                ax.imshow(ipp[::20].T[::-1], vmin=-100, vmax=-50, aspect='auto', interpolation='gaussian',
+                           extent=[x_tickses[0], x_tickses[-1], -0.5, 15.5])
+                try:
+                    ax.plot(smooth_x[::20], smooth_mcm[::20], '.', color=color2[4])
+                except:
+                    continue
+
+                ax.beautimechannelaxis()
+                ax.timeaxis()
+                ax.text(-0.12, 0.95, chr(ord('A') + ax_idx), transform=ax.transAxes, fontsize='large')
+
+
+    fig.savefig(save_path + 'trajectory_5_47.pdf')
+    # fig.savefig('../../../goettingen2021_poster/pictures/trajectory_5_47.pdf')
+    plt.show()
+
+
+
diff --git a/plot_transit_fish.py b/plot_transit_fish.py
new file mode 100644
index 0000000..158d680
--- /dev/null
+++ b/plot_transit_fish.py
@@ -0,0 +1,267 @@
+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 *
+
+if __name__ == '__main__':
+    ###################################################################################################################
+    # parameter and variables
+    # plot params
+    inch = 2.45
+    save_path = '../../thesis/Figures/Results/'
+
+    # kernel
+    kernel_size = 120
+    kernel = np.ones(kernel_size) / kernel_size
+
+    # counter
+    stl_counter = 0
+    ax0_counter = 0
+
+    # transit: start, stop, dur
+    transit_data = []
+    # transit: time, trajectory
+    xy_transit = []
+    # transit: metadata: name, freq
+    transit_meta = []
+
+    # average speed
+    speed = []
+
+    ###################################################################################################################
+    # load data
+    ###################################################################################################################
+
+    for day_idx in [0, 1, 2, 3]:
+
+        filename = sorted(os.listdir('../data/'))[day_idx]
+        aifl = np.load('../data/' + filename + '/aifl2.npy', allow_pickle=True)
+
+        all_xticks = np.load('../data/' + filename + '/all_xtickses.npy', allow_pickle=True)
+        all_max_ch_means = np.load('../data/' + filename + '/all_max_ch.npy', allow_pickle=True)
+        power_means = np.load('../data/' + filename + '/power_means.npy', allow_pickle=True)
+        all_hms = np.load('../data/' + filename + '/all_hms.npy', allow_pickle=True)
+        freq = np.load('../data/' + filename + '/fish_freq_q10.npy', allow_pickle=True)
+        names = np.load('../data/' + filename + '/fish_species.npy', allow_pickle=True)
+        ###############################################################################################################
+        # lists
+        fish_in_aifl = list(np.unique(np.where(~np.isnan(aifl[:, :, 0]))[1]))
+        # directs = []
+        flat_x = np.unique(np.hstack(all_xticks))
+
+        ###############################################################################################################
+        # analysis of the changes and trajectories
+        for fish_number in range(len(power_means)):
+            if power_means[fish_number] >= -90.0:
+
+                x_tickses = all_xticks[fish_number]
+                max_ch_mean = all_max_ch_means[fish_number]
+
+                # smoothing of max channel mean
+                kernel = np.ones(kernel_size) / kernel_size
+                smooth_mcm = np.convolve(max_ch_mean, kernel, 'valid')
+
+                smooth_x = x_tickses[int(np.ceil(kernel_size / 2)):-int(np.floor(kernel_size / 2) - 1)]
+
+                # interpolate
+                fish_x = flat_x[np.where(flat_x == x_tickses[0])[0][0]:np.where(flat_x == x_tickses[-1])[0][0] + 1]
+                try:
+                    trajectory = np.round(np.interp(fish_x, smooth_x, smooth_mcm))
+                except:
+                    continue
+
+                # trial duration
+                t_s = datetime.timedelta(np.diff([fish_x[0], fish_x[-1]])[0])
+                trial_dur = t_s.total_seconds() / 60
+
+                # average speed
+                speed.append(np.sum(np.abs(np.diff(trajectory))) / trial_dur)
+
+                # activity vs. time
+                t = np.array(all_hms[fish_number] / 60 / 60)
+
+                # swim through
+                first = fish_x[0]
+                last = fish_x[-1]
+
+                start15 = mdates.date2num(mdates.num2date(first) + datetime.timedelta(seconds=2 * 60))
+                stop15 = mdates.date2num(mdates.num2date(last) - datetime.timedelta(seconds=2 * 60))
+
+                y = trajectory
+                # t = np.roll(t, int(len(np.unique(np.hstack(all_hms)))/2))
+
+                if np.any(trajectory[fish_x < start15] >= 13) and np.any(trajectory[fish_x > stop15] <= 2):
+
+                    xy_transit.append(np.array([t, y]))
+                    transit_data.append(np.array([t[0], t[-1], trial_dur, day_idx]))
+                    transit_meta.append([names[fish_number], freq[fish_number,2]])
+
+                elif np.any(trajectory[fish_x < start15] <= 2) and np.any(trajectory[fish_x > stop15] >= 13):
+                    xy_transit.append(np.array([t, y]))
+                    transit_data.append(np.array([t[0], t[-1], trial_dur, day_idx]))
+                    transit_meta.append([names[fish_number], freq[fish_number,2]])
+
+                elif np.any(trajectory[fish_x < start15] <= 1) \
+                        and np.any(trajectory[fish_x > stop15] >= 6) \
+                        and np.any(trajectory[fish_x > stop15] <= 7):
+
+                    xy_transit.append(np.array([t, y]))
+                    transit_data.append(np.array([t[0], t[-1], trial_dur, day_idx]))
+                    transit_meta.append([names[fish_number], freq[fish_number,2]])
+
+                elif np.any(trajectory[fish_x < start15] >= 6) \
+                        and np.any(trajectory[fish_x < start15] <= 7) \
+                        and np.any(trajectory[fish_x > stop15] <= 1):
+
+                    xy_transit.append(np.array([t, y]))
+                    transit_data.append(np.array([t[0], t[-1], trial_dur, day_idx]))
+                    transit_meta.append([names[fish_number], freq[fish_number,2]])
+
+                elif np.any(trajectory[fish_x < start15] >= 14) \
+                        and np.any(trajectory[fish_x > stop15] >= 8) \
+                        and np.any(trajectory[fish_x > stop15] <= 9):
+
+                    xy_transit.append(np.array([t, y]))
+                    transit_data.append(np.array([t[0], t[-1], trial_dur, day_idx]))
+                    transit_meta.append([names[fish_number], freq[fish_number,2]])
+
+                elif np.any(trajectory[fish_x < start15] >= 8) \
+                        and np.any(trajectory[fish_x < start15] <= 9) \
+                        and np.any(trajectory[fish_x > stop15] >= 14):
+
+                    xy_transit.append(np.array([t, y]))
+                    transit_data.append(np.array([t[0], t[-1], trial_dur, day_idx]))
+                    transit_meta.append([names[fish_number], freq[fish_number,2]])
+
+                else:
+                    continue
+
+    transit_data = np.array(transit_data)
+
+    ###############################################################################################################
+    # figure 1: time points of transit fish and trajectories
+    fig = plt.figure(constrained_layout=True, figsize=[15 / inch, 12 / inch])
+    gs = gridspec.GridSpec(ncols=2, nrows=3, figure=fig, hspace=0.05, wspace=0.0,
+                           height_ratios=[1, 1, 1], left=0.1, bottom=0.15, right=0.95, top=0.95)
+
+    ax0 = fig.add_subplot(gs[0, :])
+    ax1 = fig.add_subplot(gs[1, 0])
+    ax2 = fig.add_subplot(gs[1, 1])
+    ax3 = fig.add_subplot(gs[2, 0])
+    ax4 = fig.add_subplot(gs[2, 1])
+
+    ###############################################################################################################
+    # plotting
+    color_list = [0, 1, 4, 6]
+    for ploti in range(len(transit_data)):
+        if transit_data[ploti, 2] < 3 * 60:
+            # colori = color_list[int(transit_data[ploti, 3])]
+
+            if transit_meta[ploti][0] == 'Eigenmannia':
+                colori = 0
+            elif transit_meta[ploti][1] < 750:
+                colori = 1
+            else:
+                colori = 2
+
+            # plot time when fish is there
+            if transit_data[ploti, 0]<12:
+                roll_factor = 12
+            else:
+                roll_factor = -12
+            ax0.plot(transit_data[ploti, :2] + roll_factor, [ax0_counter, ax0_counter], color=color_efm[colori], lw=3,
+                     label=labels[colori])
+            ax0_counter += 1
+            if transit_data[ploti + 1, 3] != transit_data[ploti, 3]:
+                ax0.plot([0, 24], [ax0_counter, ax0_counter], color=color_diffdays[0], lw=1, linestyle='dashed')
+                print(ax0_counter, transit_data[ploti, 3], transit_data[ploti+1, 3])
+                ax0_counter += 1
+
+            # plot trajectories
+            if transit_data[ploti, 0] > 1 and transit_data[ploti, 1] <= 3.5:
+                ax2.plot(xy_transit[ploti][0], xy_transit[ploti][1], color=color_efm[colori])
+                ax2.set_xlim([1, 3.1])
+                ax2.set_xticks([1,2,3])
+                ax2.set_xticklabels(['01:00', '02:00', '03:00'])
+
+            elif transit_data[ploti, 0] > 2.5 and transit_data[ploti, 1] <= 5:
+                ax4.plot(xy_transit[ploti][0], xy_transit[ploti][1], color=color_efm[colori])
+                ax4.set_xlim([3, 5])
+                ax4.set_xticks([3,4,5])
+                ax4.set_xticklabels(['03:00', '04:00', '05:00'])
+
+            elif transit_data[ploti, 0] > 18 and transit_data[ploti, 1] <= 20:
+                ax1.plot(xy_transit[ploti][0], xy_transit[ploti][1], color=color_efm[colori])
+                ax1.set_xlim([18, 20])
+                ax1.set_xticks([18,19,20])
+                ax1.set_xticklabels(['18:00', '19:00', '20:00'])
+
+            elif transit_data[ploti, 0] > 20 and transit_data[ploti, 1] <= 22:
+                ax3.plot(xy_transit[ploti][0], xy_transit[ploti][1], color=color_efm[colori])
+                ax3.set_xlim([20, 22])
+                ax3.set_xticks([20,21,22])
+                ax3.set_xticklabels(['20:00', '21:00', '22:00'])
+
+    #################################################################################################################
+    # nice axis
+    tagx = [-0.07, -0.17, -0.07, -0.17, -0.07]
+    tagy = [0.95,1.05,1.05,1.05,1.05]
+    for ax_idx, axis in enumerate([ax0,ax1,ax2,ax3,ax4]):
+        axis.text(tagx[ax_idx], tagy[ax_idx], chr(ord('A') + ax_idx), transform=axis.transAxes, fontsize='large')
+        axis.make_nice_ax()
+        axis.invert_yaxis()
+        if axis != ax0:
+            axis.axhline(7.5, xmin=0, xmax=15, color='white', lw=2)
+            # ax.set_yticklabels([1, 3, 5, 7, 14, 16, 18, 20])
+            axis.set_yticks([0, 1, 2, 3, 4, 5, 6, 7, 7.5, 8, 9, 10, 11, 12, 13, 14, 15])
+            axis.set_yticklabels([])
+            if axis == ax1 or axis == ax3:
+                axis.set_yticklabels(['1', '', '', '', '5', '', '', '', 'gap', '', '', '', '12', '', '', '', '16'])
+                axis.set_ylabel('Electrode', fontsize=11)
+
+            if axis == ax3 or axis == ax4:
+                axis.set_xlabel('Time', fontsize=11)
+
+    daytagy = [0.1, 0.5, 0.9]
+    for dayi in [0,1,2]:
+        ax0.text(0.02, daytagy[dayi], 'Day '+ str(dayi), transform=ax0.transAxes, fontsize='small', va='center', ha='left')
+
+    ax0.set_ylabel('Fish')
+    ax0.set_yticks([])
+    ax0.set_xlim([-0.1, 24.1])
+    ax0.set_xticks(np.arange(0, 25, 3))
+    ax0.set_xticklabels(['12:00', '15:00', '18:00', '21:00', '00:00', '03:00', '06:00', '09:00', '12:00'])
+
+    fig.align_ylabels()
+    # handles, labels = ax0.get_legend_handles_labels()
+    # unique = [(h, l) for lwdl_idx, (h, l) in enumerate(zip(handles, labels)) if l not in labels[:lwdl_idx]]
+    # ax0.legend(*zip(*unique), bbox_to_anchor=(1, 1), loc="upper right", bbox_transform=fig.transFigure, ncol=1)
+    # fig.legend()
+    fig.savefig(save_path + 'transit.pdf')
+    plt.show()
+
+
+
+    ###############################################################################################################
+    # speed
+    c = np.load('../data/all_changes.npy', allow_pickle=True)
+    stl = np.load('../data/stl.npy', allow_pickle=True)
+
+    speeds = []
+    for ploti in range(len(transit_data)):
+        if transit_data[ploti, 2] < 3 * 60:
+            speeds.append((np.max(xy_transit[ploti][1])-np.min(xy_transit[ploti][1]))/transit_data[ploti, 2])
+
+    min =(np.mean(speeds)-np.std(speeds))*60*12
+    max =(np.mean(speeds)+np.std(speeds))*60*12/1000
+    embed()
diff --git a/plot_when_are_you.py b/plot_when_are_you.py
new file mode 100644
index 0000000..eb175b7
--- /dev/null
+++ b/plot_when_are_you.py
@@ -0,0 +1,174 @@
+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 *
+
+if __name__ == '__main__':
+
+    ###################################################################################################################
+    # parameter and variables
+    ###################################################################################################################
+    # plot params
+    inch = 2.45
+    save_path = '../../thesis/Figures/Results/'
+
+    all_begins = []
+    all_ends = []
+    begins = []
+    ends = []
+    durations = []
+    all_durations = []
+    start = []
+    stop = []
+
+    # plot
+    fig2, ax2 = plt.subplots(1, 1, figsize=(16 / inch, 6 / inch))
+    fig2.subplots_adjust(left=0.12, bottom=0.2, right=0.95, top=0.97)
+
+    c = 0
+    ###################################################################################################################
+    # load data
+    ###################################################################################################################
+    # load all the data of one day
+    for index, filename_idx in enumerate([0, 1, 2, 3]):
+        filename = sorted(os.listdir('../data/'))[filename_idx]
+        all_xticks = np.load('../data/' + filename + '/all_xtickses.npy', allow_pickle=True)
+        all_Ctime_v = np.load('../data/' + filename + '/all_Ctime_v.npy', allow_pickle=True)
+        power_means = np.load('../data/' + filename + '/power_means.npy', allow_pickle=True)
+        names = np.load('../data/' + filename + '/fish_species.npy', allow_pickle=True)
+
+        # variables
+        sampling_rate = 1 / np.diff(all_Ctime_v[0])[0]  # in sec
+        first = np.min(np.unique(np.hstack(all_xticks)))
+        last = np.max(np.unique(np.hstack(all_xticks)))
+        start15 = mdates.date2num(mdates.num2date(first) + datetime.timedelta(seconds=15 * 60))
+        stop15 = mdates.date2num(mdates.num2date(last) - datetime.timedelta(seconds=15 * 60))
+        trial_dur = (np.max(np.unique(np.hstack(all_Ctime_v))) - np.min(np.unique(np.hstack(all_Ctime_v)))) / 60
+
+        start.extend([first])
+        stop.extend([last])
+
+        # plot
+        ax2.plot(trial_dur, 90, 'o', ms=ms, color=color_diffdays[index])
+        print(trial_dur)
+
+        ###############################################################################################################
+        # analysis
+        ###############################################################################################################
+        for fish in range(len(all_xticks)):
+            if power_means[fish] >= -90 and names[fish] != 'unknown':
+                c += 1
+                if np.any(all_xticks[fish] <= start15) or np.any(all_xticks[fish] >= stop15):
+                    all_durations.append((all_Ctime_v[fish][-1] - all_Ctime_v[fish][0]) / 60)
+
+                else:
+                    durations.append((all_Ctime_v[fish][-1] - all_Ctime_v[fish][0]) / 60)
+                    all_durations.append((all_Ctime_v[fish][-1] - all_Ctime_v[fish][0]) / 60)
+
+
+    ###################################################################################################################
+    # plotting figure 2
+    dur = np.array(durations)
+    all_dur = np.array(all_durations)
+
+    n3, bin_edges3 = np.histogram(dur, bins=np.linspace(0, 950, 20))
+    n4, bin_edges4 = np.histogram(all_dur, bins=np.linspace(0, 950, 20))
+    # n3 = n3 / np.sum(n3) / (bin_edges3[1] - bin_edges3[0])
+
+    ax2.bar(bin_edges4[:-1] + (bin_edges4[1] - bin_edges4[0]) / 2, n4,
+            width=0.9 * (bin_edges4[1] - bin_edges4[0]), label='all', color=color2[5])
+    ax2.bar(bin_edges3[:-1] + (bin_edges3[1] - bin_edges3[0]) / 2, n3,
+            width=0.9 * (bin_edges3[1] - bin_edges3[0]), label='corrected', color=color2[4])
+
+    ax2.set_xlabel('Presence duration [min]', fontsize=fs)
+    ax2.set_ylabel('n', fontsize=fs)
+    ax2.make_nice_ax()
+    # ax2.set_ylim([0, 10**2])
+    ax2.set_yscale('symlog')
+    fig2.legend(loc='center right')
+    # fig2.savefig(save_path+'appearance_dur.png')
+    fig2.savefig(save_path+'duration.pdf')
+    plt.show()
+
+    embed()
+    quit()
+
+    # ###################################################################################################################
+    # # plotting figure 1
+    # fig1, ax1 = plt.subplots(1, 1, figsize=(16 / inch, 8 / inch))
+    # fig1.subplots_adjust(left=0.12, bottom=0.3, right=0.95, top=0.97)
+
+    # n1, bin_edges1 = np.histogram(begins, bins=35)
+    # n2, bin_edges2 = np.histogram(ends, bins=bin_edges1)
+    # n11, bin_edges2 = np.histogram(all_begins, bins=bin_edges1)
+    # n21, bin_edges2 = np.histogram(all_ends, bins=bin_edges1)
+    # n1 = n1 / np.sum(n1) / (bin_edges1[1] - bin_edges1[0])
+    # n2 = n2 / np.sum(n2) / (bin_edges1[1] - bin_edges1[0])
+    # # n11 = n11 / np.sum(n11) / (bin_edges1[1] - bin_edges1[0])
+    # # n21 = n21 / np.sum(n21) / (bin_edges1[1] - bin_edges1[0])
+    #
+    # ax1.bar(bin_edges1[:-1] + (bin_edges1[1] - bin_edges1[0]) / 2, n11,
+    #         width=0.9 * (bin_edges1[1] - bin_edges1[0]), color=color2[2])
+    # ax1.bar(bin_edges1[:-1] + (bin_edges1[1] - bin_edges1[0]) / 2, n1,
+    #         width=0.9 * (bin_edges1[1] - bin_edges1[0]), color=color2[0], label='appearance')
+    #
+    # ax1.bar(bin_edges1[:-1] + (bin_edges1[1] - bin_edges1[0]) / 2, n21 * -1,
+    #         width=0.9 * (bin_edges1[1] - bin_edges1[0]), color=color2[3])
+    # ax1.bar(bin_edges1[:-1] + (bin_edges1[1] - bin_edges1[0]) / 2, n2 * -1,
+    #         width=0.9 * (bin_edges1[1] - bin_edges1[0]), color=color2[1], label='disappearance')
+    # ax1.axhline(y=0.0, xmin=0.0, xmax=1.0, color='k', linewidth=lw)
+    #
+    # ax1.set_xlabel('Time', fontsize=fs)
+    # ax1.set_ylabel('n', fontsize=fs)
+    # ax1.timeaxis()
+    # ax1.set_yscale('symlog')
+    # fig1.autofmt_xdate()
+    # ax1.make_nice_ax()
+    # fig1.legend(loc='upper right')
+
+    # fig1.savefig(save_path+'appearance_disappearance.png')
+    # fig1.savefig(save_path+'appearance_disappearance.pdf')
+    ###################################################################################################################
+    # plotting figure 3
+    # fig3 = plt.figure(figsize=[16 / inch, 12 / inch])
+    # spec = gridspec.GridSpec(ncols=1, nrows=2, figure=fig3, hspace=0.3, wspace=0.15,
+    #                          height_ratios=[1, 1], left=0.1, bottom=0.15, right=0.99, top=0.98)
+    # ax3 = fig3.add_subplot(spec[0, 0])
+    # ax4 = fig3.add_subplot(spec[1, 0])
+    # ax3.bar(bin_edges1[:-1] + (bin_edges1[1] - bin_edges1[0]) / 2, n1,
+    #         width=0.9 * (bin_edges1[1] - bin_edges1[0]), color=color2[0], label='appearance')
+    # ax3.bar(bin_edges1[:-1] + (bin_edges1[1] - bin_edges1[0]) / 2, n2 * -1,
+    #         width=0.9 * (bin_edges1[1] - bin_edges1[0]), color=color2[1], label='disappearance')
+    # ax3.axhline(y=0.0, xmin=0.0, xmax=1.0, color='k', linewidth=lw)
+    #
+    # ax3.set_xlabel('Time', fontsize=fs)
+    # ax3.set_ylabel('n', fontsize=fs)
+    # ax3.timeaxis()
+    # fig3.autofmt_xdate()
+    # ax3.make_nice_ax()
+    # # fig3.legend(loc='upper right')
+    #
+    # ax4.bar(bin_edges4[:-1] + (bin_edges4[1] - bin_edges4[0]) / 2, n4,
+    #         width=0.9 * (bin_edges4[1] - bin_edges4[0]), label='all', color=color2[2])
+    # ax4.bar(bin_edges3[:-1] + (bin_edges3[1] - bin_edges3[0]) / 2, n3,
+    #         width=0.9 * (bin_edges3[1] - bin_edges3[0]), label='corrected', color=color2[0])
+    #
+    # ax4.set_xlabel('Presence duration [min]', fontsize=fs)
+    # ax4.set_ylabel('n', fontsize=fs)
+    # ax4.make_nice_ax()
+    # fig3.legend(loc='upper right')
+    #
+    # fig3.savefig(save_path+'appearance_all.pdf')
+    # exit()
+    # if filename not in os.listdir('../data/'):
+    #     os.mkdir('../data/'+filename)
+    #
+    # np.save('../data/' + filename + '/direction.npy', directs)
diff --git a/replace_channel.py b/replace_channel.py
new file mode 100644
index 0000000..980989c
--- /dev/null
+++ b/replace_channel.py
@@ -0,0 +1,24 @@
+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 *
+
+if __name__ == '__main__':
+    ###################################################################################################################
+    # load data
+    ###################################################################################################################
+    # load all the data of one day
+
+    to_be_changed_ident = np.load('../../../Desktop/2019-10-08-18_07/all_ident_v.npy', allow_pickle=True)
+    correct_ident = np.load('../../../Desktop/2019-10-08-18_07_old/all_ident_v.npy', allow_pickle=True)
+
+    embed()
+    quit()
\ No newline at end of file