cleanup

        self.frag_series = pd.DataFrame({'group' : group_arr, 'replicate' : rep_arr, 'frag_len' : frag_lens, 'occurences' : frag_counts})

        # ---------- Data - Peak stats --------- #

        # create number of peaks df

        # unique_groups = self.seacr_beds.group.unique()

        # unique_replicates = self.seacr_beds.replicate.unique()

        # self.df_no_peaks = pd.DataFrame(index=range(0,(len(unique_groups)*len(unique_replicates))), columns=['group','replicate','all_peaks'])

        # k=0 # counter

        # for i in list(range(len(unique_groups))):

        #     for j in list(range(len(unique_replicates))):

        #         self.df_no_peaks.at[k,'all_peaks'] = self.seacr_beds[(self.seacr_beds['group']==unique_groups[i]) & (self.seacr_beds['replicate']==unique_replicates[j])].shape[0]

        #         self.df_no_peaks.at[k,'group'] = unique_groups[i]

        #         self.df_no_peaks.at[k,'replicate'] = unique_replicates[j]

        #         k=k+1

        # print(self.seacr_beds)

        # print("peaks df")

        # print(self.df_no_peaks)

        # unique_groups = self.seacr_beds.group.unique()

        # seacr_beds_group_rep = self.seacr_beds[['group','replicate']].groupby(['group','replicate']).size().reset_index()

        self.seacr_beds_group_rep = self.seacr_beds[['group','replicate']].groupby(['group','replicate']).size().reset_index().rename(columns={0:'all_peaks'})

        # print("test df")

        # print(test_df)

        # self.df_no_peaks =

        # ---------- Data - Reproducibility of peaks between replicates --------- #

        # empty dataframe to fill in loop

        unique_groups = self.seacr_beds.group.unique()

        print(unique_groups)

        unique_replicates = self.seacr_beds.replicate.unique()

        # print(unique_replicates)

        # rep_permutations = array_permutate(range(len(unique_replicates)))

        # test_permutations = array_permutate(range(5))

        # print(test_permutations)

        # print(rep_permutations)

        # print("this is seacr bed")

        # print(self.seacr_beds)

        self.replicate_number = 1 #len(unique_replicates)

        # print("this is rep_permutations")

        # print(rep_permutations)

        # get replicate number for each group

        # seacr_beds_group_rep_df = self.seacr_beds[['group','replicate']] #, as_index = False).sum()

        # print(seacr_beds_group_rep_df)

        print(self.seacr_beds_group_rep)

        if self.replicate_number >= 1:

        self.replicate_number = 1

        self.multiple_reps = True

        if (len(unique_groups) == self.seacr_beds_group_rep.shape[0]):

            self.multiple_reps = False

        if self.multiple_reps:

            idx_count=0

            for i in list(range(len(unique_groups))):

                group_i = unique_groups[i]

                        self.reprod_peak_stats.at[idx_count, 'no_peaks_reproduced'] = len(unique_pyr_starts)

                    idx_count = idx_count + 1

            print("denominator")

            print(self.reprod_peak_stats['all_peaks'])

            print('numerator')

            print(self.reprod_peak_stats['no_peaks_reproduced'])

            fill_reprod_rate = (self.reprod_peak_stats['no_peaks_reproduced'] / self.reprod_peak_stats['all_peaks'])*100

            self.reprod_peak_stats['peak_reproduced_rate'] = fill_reprod_rate

        data["peak_widths"] = data7b

        # Plot 7c

        # if self.replicate_number > 1:

        if self.multiple_reps:

            plot7c, data7c = self.reproduced_peaks()

            plots["reproduced_peaks"] = plot7c

            data["reproduced_peaks"] = data7c