diff --git a/.github/workflows/publish-to-gh-with-sphinx.yml b/.github/workflows/publish-to-gh-with-sphinx.yml
index 627cfc87..04fb767c 100644
--- a/.github/workflows/publish-to-gh-with-sphinx.yml
+++ b/.github/workflows/publish-to-gh-with-sphinx.yml
@@ -2,7 +2,7 @@ name: Build and Publish Sphinx Documentation
 
 on:
   push:
-    branches: ["master"]
+    branches: ["main"]
   # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:
 
diff --git a/MACS3/Signal/PairedEndTrack.py b/MACS3/Signal/PairedEndTrack.py
index a28c48ad..a33e4c8a 100644
--- a/MACS3/Signal/PairedEndTrack.py
+++ b/MACS3/Signal/PairedEndTrack.py
@@ -16,6 +16,7 @@
 import sys
 from array import array as pyarray
 from collections import Counter,defaultdict
+from operator import itemgetter
 # ------------------------------------
 # MACS3 modules
 # ------------------------------------
@@ -38,6 +39,11 @@
 
 from MACS3.Utilities.Logger import logging
 
+import pandas as pd
+from scipy import sparse
+import anndata as ad
+from ncls import NCLS
+
 logger = logging.getLogger(__name__)
 debug = logger.debug
 info = logger.info
@@ -357,6 +363,7 @@ def exclude(self, regions):
         :meth:`finalize` to refresh cached statistics.
         """
         i: cython.ulong
+        j: cython.ulong
         k: bytes
         locs: cnp.ndarray
         locs_size: cython.ulong
@@ -1601,6 +1608,7 @@ def exclude(self, regions):
         and finishes by calling :meth:`finalize`.
         """
         i: cython.ulong
+        j: cython.ulong
         k: bytes
         locs: cnp.ndarray
         locs_size: cython.ulong
@@ -1608,8 +1616,11 @@ def exclude(self, regions):
         regions_c: list
         selected_idx: cnp.ndarray
         regions_chrs: list
-        r1: cnp.void
-        r2: tuple
+        r1_start: cython.int
+        r1_end: cython.int
+        r1_count: cython.ushort
+        r2_start: cython.int
+        r2_end: cython.int
         n_rl1: cython.long
         n_rl2: cython.long
 
@@ -1635,45 +1646,58 @@ def exclude(self, regions):
             selected_idx = np.ones(locs_size, dtype=bool)
 
             regions_c = regions.regions[k]
+            loc_starts = locs['l']
+            loc_ends = locs['r']
+            loc_counts = locs['c']
+            region_starts = [r[0] for r in regions_c]
+            region_ends = [r[1] for r in regions_c]
 
             i = 0
+            j = 0
             n_rl1 = len(locs)
             n_rl2 = len(regions_c)
-            rl1_k = iter(locs).__next__
-            rl2_k = iter(regions_c).__next__
-            r1 = rl1_k()
+            r1_start = loc_starts[i]
+            r1_end = loc_ends[i]
+            r1_count = loc_counts[i]
             n_rl1 -= 1          # remaining rl1
-            r2 = rl2_k()
+            r2_start = region_starts[j]
+            r2_end = region_ends[j]
             n_rl2 -= 1          # remaining rl2
             while (True):
                 # we do this until there is no r1 or r2 left.
-                if r2[0] < r1[1] and r1[0] < r2[1]:
+                if r2_start < r1_end and r1_start < r2_end:
                     # since we found an overlap, r1 will be skipped/excluded
                     # and move to the next r1
                     # get rid of this one
                     n_rl1 -= 1
-                    self.length -= cython.cast(cython.ulonglong, (r1[1] - r1[0])*r1[2])
+                    self.length -= cython.cast(cython.ulonglong, (r1_end - r1_start) * r1_count)
                     selected_idx[i] = False
 
                     if n_rl1 >= 0:
-                        r1 = rl1_k()
                         i += 1
+                        r1_start = loc_starts[i]
+                        r1_end = loc_ends[i]
+                        r1_count = loc_counts[i]
                         continue
                     else:
                         break
-                if r1[1] < r2[1]:
+                if r1_end < r2_end:
                     # in this case, we need to move to the next r1,
                     n_rl1 -= 1
                     if n_rl1 >= 0:
-                        r1 = rl1_k()
                         i += 1
+                        r1_start = loc_starts[i]
+                        r1_end = loc_ends[i]
+                        r1_count = loc_counts[i]
                     else:
                         # no more r1 left
                         break
                 else:
                     # in this case, we need to move the next r2
                     if n_rl2:
-                        r2 = rl2_k()
+                        j += 1
+                        r2_start = region_starts[j]
+                        r2_end = region_ends[j]
                         n_rl2 -= 1
                     else:
                         # no more r2 left
@@ -1690,7 +1714,303 @@ def exclude(self, regions):
             selected_idx.resize(0, refcheck=False)
         self.finalize()
         return
+    
+    def _np_sort_search(self,regions):
+        # barcode mapping
+        barcode_items = sorted(self.barcode_dict.items(), key=lambda x: x[1])
+        barcodes = [b.decode() if isinstance(b, (bytes, bytearray)) else str(b) for b, _ in barcode_items]
+        barcode_id_to_row = {barcode_id: i for i, (_, barcode_id) in enumerate(barcode_items)}
+        n_cells = len(barcodes)
+
+        peak_names = []
+        peak_data = []
+        rows = []
+        columns = []
+        data = []
+
+        regions.sort()
+        peak_counter = 0
+
+        for chrom in sorted(regions.regions.keys()):
+            if chrom not in self.locations:
+                continue
+
+            regions_c = regions.regions[chrom]
+            if not regions_c:
+                continue
+
+            local_peak_indices = []
+            for (start, end) in regions_c:
+                peak_counter += 1
+                peak_names.append(f"peak_{peak_counter}")
+                local_peak_indices.append(peak_counter - 1)
+
+            peak_starts = np.array([p[0] for p in regions_c], dtype=np.int32)
+            peak_ends = np.array([p[1] for p in regions_c], dtype=np.int32)
+            local_peak_indices = np.array(local_peak_indices, dtype=np.int32)
+
+            fragment_locs = self.locations[chrom]
+            fragment_barcodes = self.barcodes[chrom]
+            if len(fragment_locs) == 0:
+                continue
+
+            for frag_idx, frag in enumerate(fragment_locs):
+                frag_start = frag[0]
+                frag_end = frag[1]
+                bc_id = int(fragment_barcodes[frag_idx])
+                row_id = barcode_id_to_row.get(bc_id, -1)
+                if row_id < 0:
+                    continue
+
+                j = np.searchsorted(peak_starts, frag_start, side='left')
+                while j < peak_starts.size and peak_starts[j] <= frag_end:
+                    if peak_ends[j] > frag_start:
+                        rows.append(row_id)
+                        columns.append(int(local_peak_indices[j]))
+                        data.append(int(frag[2]))
+                    j += 1
+        X = sparse.coo_matrix((data, (rows, columns)), shape=(n_cells, peak_counter), dtype=np.int32).tocsr()
+        obs = pd.DataFrame(index=barcodes)
+        var = pd.DataFrame(peak_data, columns=['chrom', 'start', 'end'], index=peak_names)
+        adata = ad.AnnData(X, obs=obs, var=var)
+        return adata
+    
+    def _anndata_ncls(self, regions):
+        regions.sort()
+
+        # Barcode mapping
+        barcode_items = sorted(self.barcode_dict.items(), key=lambda x: x[1])
+        barcodes = [b.decode() if isinstance(b, (bytes, bytearray)) else str(b) for b, _ in barcode_items]
+        barcode_ids = np.array([i for _, i in barcode_items], dtype=np.int32)
+
+        n_cells = len(barcodes)
+        if n_cells == 0:
+            return ad.AnnData(X=sparse.csr_matrix((0, 0), dtype=np.int32))
+
+        id_map = np.full(barcode_ids.max() + 1, -1, dtype=np.int32)
+        id_map[barcode_ids] = np.arange(n_cells, dtype=np.int32)
+
+        # Outputs
+        peak_names = []
+        peak_data = []
+        rows = []
+        cols = []
+        data = []
+        peak_counter = 0
+
+        for chrom in sorted(regions.regions.keys()):
+            if chrom not in self.locations:
+                continue
+
+            regions_c = regions.regions[chrom]
+            if not regions_c:
+                continue
+
+            # register peaks (global)
+            local_peak_indices = []
+            chrom_str = chrom.decode() if isinstance(chrom, (bytes, bytearray)) else str(chrom)
+            for (start, end) in regions_c:
+                peak_counter += 1
+                peak_names.append(f"peak_{peak_counter}")
+                local_peak_indices.append(peak_counter - 1)
+                peak_data.append((chrom_str, int(start), int(end)))
+            # fragments on this chromosome
+            fragment_locs = self.locations[chrom]
+            fragment_barcodes = self.barcodes[chrom]
+            if len(fragment_locs) == 0:
+                continue
+
+            frag_starts = fragment_locs['l'].astype(np.int32)
+            frag_ends = fragment_locs['r'].astype(np.int32)
+            frag_counts = fragment_locs['c'].astype(np.int32)
+            frag_bc_ids = fragment_barcodes.astype(np.int32)
+
+            frag_rows = id_map[frag_bc_ids]  # barcode -> row index
+
+            idx = np.arange(len(frag_starts), dtype=np.int32)
+            ncls = NCLS(frag_starts, frag_ends, idx)
+
+            # Query peaks: peak must be inside fragment
+            for peak_idx, (peak_start, peak_end) in enumerate(regions_c):
+                for frag_start, frag_end, frag_i in ncls.find_overlap(peak_start, peak_end):
+                   # if frag_start <= peak_start and peak_end <= frag_end:
+                    row_id = frag_rows[frag_i]
+                    if row_id >= 0:
+                        rows.append(row_id)
+                        cols.append(local_peak_indices[peak_idx])
+                        data.append(int(frag_counts[frag_i]))
+                            
+        X = sparse.coo_matrix((data, (rows, cols)),
+                            shape=(n_cells, peak_counter),
+                            dtype=np.int32).tocsr()
+
+        obs = pd.DataFrame(index=barcodes)
+        var = pd.DataFrame(peak_data, columns=["chrom", "start", "end"], index=peak_names)
+        adata = ad.AnnData(X=X, obs=obs, var=var)
+        return adata
 
+    @cython.boundscheck(False)  # do not check that np indices are valid
+    @cython.cfunc
+    def _two_pointer_sweep(self, regions):
+        frag_idx: cython.int
+        local_peak_idx: cython.int
+        n_frags: cython.int
+        remaining_frag_len: cython.int
+        remaining_peak_len: cython.int
+        back_trace_frag: cython.int
+        row_id: cython.int
+        peak_start: cython.int
+        peak_end: cython.int
+        frag_start: cython.int
+        frag_end: cython.int
+        barcode_items: list
+        barcode_ids: object
+        barcode_id_to_row: object
+        rows: list
+        columns: list
+        data: list
+
+        peak_names = []
+        peak_data = []
+
+        rows = [] #barcode index
+        columns = [] #peaks index
+        data = [] #count data
+
+        barcode_items = sorted(self.barcode_dict.items(), key=itemgetter(1))
+        barcodes = [b.decode() if isinstance(b, (bytes, bytearray)) else str(b) for b, _ in barcode_items]
+        n_cells = len(barcodes)
+        if n_cells:
+            barcode_ids = np.fromiter((barcode_id for _, barcode_id in barcode_items),
+                                      dtype=np.int32,
+                                      count=n_cells)
+            barcode_id_to_row = np.full(int(barcode_ids[-1]) + 1, -1, dtype=np.int32)
+            barcode_id_to_row[barcode_ids] = np.arange(n_cells, dtype=np.int32)
+        else:
+            barcode_id_to_row = np.zeros(0, dtype=np.int32)
+
+        regions.sort()
+        peak_counter = 0
+
+        for chrom in regions.regions.keys():
+            if chrom not in self.locations:
+                continue
+
+            regions_c = regions.regions[chrom]
+            if not regions_c:
+                continue
+
+            local_peak = []
+            peak_starts = []
+            peak_ends = []
+            chrom_str = chrom.decode() if isinstance(chrom, (bytes, bytearray)) else str(chrom)
+            ### regions empty skip
+            for (start, end) in regions_c:
+                peak_counter += 1
+                peak_names.append(f"peak_{peak_counter}")
+                peak_data.append((chrom_str, start, end))
+                local_peak.append(peak_counter - 1)
+                peak_starts.append(start)
+                peak_ends.append(end)
+
+            fragment_locs = self.locations[chrom]
+
+            if len(fragment_locs) == 0:
+                continue
+
+            fragment_barcodes = self.barcodes[chrom]
+            frag_starts = fragment_locs['l']
+            frag_ends = fragment_locs['r']
+            frag_counts = fragment_locs['c']
+            frag_rows = barcode_id_to_row[fragment_barcodes]
+            n_frags = len(fragment_locs)
+
+            frag_idx = 0
+            local_peak_idx = 0
+            frag_start = frag_starts[frag_idx]
+            frag_end = frag_ends[frag_idx]
+            peak_start = peak_starts[local_peak_idx]
+            peak_end = peak_ends[local_peak_idx]
+            remaining_frag_len = n_frags - 1
+            remaining_peak_len = len(regions_c) - 1
+            back_trace_frag = 0
+
+            while True:
+                if frag_start <= peak_end and peak_start <= frag_end:
+                    row_id = frag_rows[frag_idx]
+                    if row_id >= 0:
+                        rows.append(int(row_id))
+                        columns.append(local_peak[local_peak_idx])
+                        data.append(int(frag_counts[frag_idx]))
+
+                    if frag_end > peak_end:
+                        back_trace_frag += 1
+
+                    if remaining_frag_len:
+                        remaining_frag_len -= 1
+                        frag_idx += 1
+                        frag_start = frag_starts[frag_idx]
+                        frag_end = frag_ends[frag_idx]
+                        continue
+                    else:
+                        break
+
+                if frag_end < peak_end:
+                    if remaining_frag_len:
+                        remaining_frag_len -= 1
+                        frag_idx += 1
+                        frag_start = frag_starts[frag_idx]
+                        frag_end = frag_ends[frag_idx]
+                    else:
+                        break
+                else:
+                    if remaining_peak_len:
+                        remaining_peak_len -= 1
+                        local_peak_idx += 1
+                        peak_start = peak_starts[local_peak_idx]
+                        peak_end = peak_ends[local_peak_idx]
+                        if back_trace_frag:
+                            frag_idx -= back_trace_frag
+                            remaining_frag_len += back_trace_frag
+                            frag_start = frag_starts[frag_idx]
+                            frag_end = frag_ends[frag_idx]
+                            back_trace_frag = 0
+                    else:
+                        break
+
+        n_peaks = peak_counter
+        obs = pd.DataFrame(index=barcodes)
+        var = pd.DataFrame(peak_data, columns=['chrom', 'start', 'end'], index=peak_names)
+
+        x = sparse.csr_matrix((data,(rows,columns)),shape=(n_cells,n_peaks))
+        adata_peaks_loop = ad.AnnData(X=x, obs=obs, var=var)
+        return adata_peaks_loop
+
+    def return_anndata(self, regions, method = "ncls"):
+        """
+        Build barcode × peak AnnData using the selected method.
+
+        Parameters
+        ----------
+        regions : MACS3.Signal.Region.Regions
+            Sorted region collection whose intervals should be excluded.
+        method : Method to use
+        - "ncls"          
+        - "np_sort_search"
+        - "two_pointer" 
+        
+        """
+        if method == "ncls":
+            print("using ncls")
+            adata = self._anndata_ncls(regions)
+        elif method == 'numpy' :
+            adata = self._np_sort_search(regions)
+        elif method == 'two_pointer':
+            adata = self._two_pointer_sweep(regions)
+        else:
+            raise ValueError(f"Unknown method : {method}\n should be one of ncls, numpy, two_pointer")
+        return adata
+        
     @cython.ccall
     def sample_percent(self,
                        percent: cython.float,
diff --git a/MACS3/Signal/PileupV2.py b/MACS3/Signal/PileupV2.py
index 29841895..2e1e71fd 100644
--- a/MACS3/Signal/PileupV2.py
+++ b/MACS3/Signal/PileupV2.py
@@ -91,15 +91,28 @@ def make_PV_from_LR(LR_array: cnp.ndarray,
     i: cython.ulong
     L: cython.int
     R: cython.int
+    weight: cython.float
     PV: cnp.ndarray
+    LR_l: cnp.ndarray
+    LR_r: cnp.ndarray
+    PV_p: cnp.ndarray
+    PV_v: cnp.ndarray
 
     l_LR = LR_array.shape[0]
     l_PV = 2 * l_LR
     PV = np.zeros(shape=l_PV, dtype=[('p', 'i4'), ('v', 'f4')])
+    LR_l = LR_array['l']
+    LR_r = LR_array['r']
+    PV_p = PV['p']
+    PV_v = PV['v']
     for i in range(l_LR):
-        (L, R) = LR_array[i]
-        PV[i*2] = (L, mapping_func(L, R))
-        PV[i*2 + 1] = (R, -1.0 * mapping_func(L, R))
+        L = LR_l[i]
+        R = LR_r[i]
+        weight = mapping_func(L, R)
+        PV_p[i*2] = L
+        PV_v[i*2] = weight
+        PV_p[i*2 + 1] = R
+        PV_v[i*2 + 1] = -1.0 * weight
     PV.sort(order='p')
     return PV
 
@@ -123,15 +136,31 @@ def make_PV_from_LRC(LRC_array: cnp.ndarray,
     L: cython.int
     R: cython.int
     C: cython.ushort
+    weight: cython.float
     PV: cnp.ndarray
+    LRC_l: cnp.ndarray
+    LRC_r: cnp.ndarray
+    LRC_c: cnp.ndarray
+    PV_p: cnp.ndarray
+    PV_v: cnp.ndarray
 
     l_LRC = LRC_array.shape[0]
     l_PV = 2 * l_LRC
     PV = np.zeros(shape=l_PV, dtype=[('p', 'i4'), ('v', 'f4')])
+    LRC_l = LRC_array['l']
+    LRC_r = LRC_array['r']
+    LRC_c = LRC_array['c']
+    PV_p = PV['p']
+    PV_v = PV['v']
     for i in range(l_LRC):
-        (L, R, C) = LRC_array[i]
-        PV[i*2] = (L, C*mapping_func(L, R))
-        PV[i*2 + 1] = (R, -1.0 * C * mapping_func(L, R))
+        L = LRC_l[i]
+        R = LRC_r[i]
+        C = LRC_c[i]
+        weight = C * mapping_func(L, R)
+        PV_p[i*2] = L
+        PV_v[i*2] = weight
+        PV_p[i*2 + 1] = R
+        PV_v[i*2 + 1] = -1.0 * weight
     PV.sort(order='p')
     return PV
 
@@ -156,21 +185,29 @@ def make_PV_from_PN(P_array: cnp.ndarray, N_array: cnp.ndarray,
     L: cython.int
     R: cython.int
     PV: cnp.ndarray
+    PV_p: cnp.ndarray
+    PV_v: cnp.ndarray
 
     l_PN = P_array.shape[0]
     assert l_PN == N_array.shape[0]
     l_PV = 4 * l_PN
     PV = np.zeros(shape=l_PV, dtype=[('p', 'i4'), ('v', 'f4')])
+    PV_p = PV['p']
+    PV_v = PV['v']
     for i in range(l_PN):
         L = P_array[i]
         R = L + extsize
-        PV[i*2] = (L, 1)
-        PV[i*2 + 1] = (R, -1)
+        PV_p[i*2] = L
+        PV_v[i*2] = 1
+        PV_p[i*2 + 1] = R
+        PV_v[i*2 + 1] = -1
     for i in range(l_PN):
         R = N_array[i]
         L = R - extsize
-        PV[(l_PN + i)*2] = (L, 1)
-        PV[(l_PN + i)*2 + 1] = (R, -1)
+        PV_p[(l_PN + i)*2] = L
+        PV_v[(l_PN + i)*2] = 1
+        PV_p[(l_PN + i)*2 + 1] = R
+        PV_v[(l_PN + i)*2 + 1] = -1
     PV.sort(order='p')
     return PV
 
diff --git a/README.md b/README.md
index dd2022a5..c8d3af44 100644
--- a/README.md
+++ b/README.md
@@ -52,3 +52,13 @@ MACS3 project is sponsored by [![CZI's Essential Open Source Software for Scienc
 2008: [Model-based Analysis of ChIP-Seq
 (MACS)](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2008-9-9-r137)
 
+## Note
+
+Now the default branch of MACS3 has been renamed to 'main'. If you are still using old 'master' branch in your cloned Git repository for MACS3, please use the following command to rename it:
+
+```
+git branch -m master main
+git fetch origin
+git branch -u origin/main main
+git remote set-head origin -a
+```
\ No newline at end of file
diff --git a/notebooks/500-PBMC-test/anndata_implemetation_dev.ipynb b/notebooks/500-PBMC-test/anndata_implemetation_dev.ipynb
new file mode 100644
index 00000000..f69be271
--- /dev/null
+++ b/notebooks/500-PBMC-test/anndata_implemetation_dev.ipynb
@@ -0,0 +1,475 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5e322d83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from MACS3.IO.Parser import FragParser\n",
+    "from MACS3.IO.BedGraphIO import bedGraphIO\n",
+    "from MACS3.Utilities.Logger import logging\n",
+    "import numpy as np\n",
+    "from scipy import sparse\n",
+    "import anndata as ad\n",
+    "# we can use the pre-configured logging function from MACS3 to monitor memory usage...\n",
+    "logger = logging.getLogger(\"demo\")\n",
+    "info = logger.info\n",
+    "# Note, you can replace `print` with `info` to show the time and memory usage at that moment.\n",
+    "from MACS3.Signal.PairedEndTrack import PETrackII\n",
+    "from MACS3.Signal.Region import Regions\n",
+    "import pandas as pd\n",
+    "import anndata as ad\n",
+    "from ncls import NCLS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "804f83bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simulate a toy data\n",
+    "\n",
+    "petrack = PETrackII()\n",
+    "petrack.add_loc(b\"chr1\", 0, 100, barcode=b\"A\", count=2)   # peak_1\n",
+    "petrack.add_loc(b\"chr1\", 70, 270, barcode=b\"A\", count=1)   # peak_1&2\n",
+    "petrack.add_loc(b\"chr1\", 0, 100, barcode=b\"B\", count=3)   # peak_1\n",
+    "petrack.add_loc(b\"chr1\", 175, 325, barcode=b\"C\", count=4)   # peak_2\n",
+    "petrack.finalize()\n",
+    "\n",
+    "regions = Regions()\n",
+    "regions.add_loc(b\"chr1\", 0, 100)    # peak_1 A:3 B:3 C:0\n",
+    "regions.add_loc(b\"chr1\", 200, 300)  # peak_2 A:1 B:0 C:4\n",
+    "regions.add_loc(b\"chr1\", 500, 600)  # peak_3 A:0 B:0 C:0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "74fff8ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def np_sort_search(petrack,regions):\n",
+    "        # barcode mapping\n",
+    "        barcode_items = sorted(petrack.barcode_dict.items(), key=lambda x: x[1])\n",
+    "        barcodes = [b.decode() if isinstance(b, (bytes, bytearray)) else str(b) for b, _ in barcode_items]\n",
+    "        barcode_id_to_row = {barcode_id: i for i, (_, barcode_id) in enumerate(barcode_items)}\n",
+    "        n_cells = len(barcodes)\n",
+    "\n",
+    "        peak_names = []\n",
+    "        peak_data = []\n",
+    "        rows = []\n",
+    "        columns = []\n",
+    "        data = []\n",
+    "\n",
+    "        regions.sort()\n",
+    "        peak_counter = 0\n",
+    "\n",
+    "        for chrom in sorted(regions.regions.keys()):\n",
+    "            if chrom not in petrack.locations:\n",
+    "                continue\n",
+    "\n",
+    "            regions_c = regions.regions[chrom]\n",
+    "            if not regions_c:\n",
+    "                continue\n",
+    "\n",
+    "            local_peak_indices = []\n",
+    "            for (start, end) in regions_c:\n",
+    "                peak_counter += 1\n",
+    "                peak_names.append(f\"peak_{peak_counter}\")\n",
+    "                peak_data.append((chrom.decode() if isinstance(chrom, (bytes, bytearray)) else str(chrom), int(start), int(end)))\n",
+    "                local_peak_indices.append(peak_counter - 1)\n",
+    "\n",
+    "            peak_starts = np.array([p[0] for p in regions_c], dtype=np.int64)\n",
+    "            peak_ends = np.array([p[1] for p in regions_c], dtype=np.int64)\n",
+    "            local_peak_indices = np.array(local_peak_indices, dtype=np.int64)\n",
+    "\n",
+    "            fragment_locs = petrack.locations[chrom]\n",
+    "            fragment_barcodes = petrack.barcodes[chrom]\n",
+    "            if len(fragment_locs) == 0:\n",
+    "                continue\n",
+    "\n",
+    "            for frag_idx, frag in enumerate(fragment_locs):\n",
+    "                frag_start = frag[0]\n",
+    "                frag_end = frag[1]\n",
+    "                bc_id = int(fragment_barcodes[frag_idx])\n",
+    "                row_id = barcode_id_to_row.get(bc_id, -1)\n",
+    "                if row_id < 0:\n",
+    "                    continue\n",
+    "\n",
+    "                j = np.searchsorted(peak_starts, frag_start, side='left')\n",
+    "                while j < peak_starts.size and peak_starts[j] <= frag_end:\n",
+    "                    if peak_ends[j] > frag_start:\n",
+    "                        rows.append(row_id)\n",
+    "                        columns.append(int(local_peak_indices[j]))\n",
+    "                        data.append(int(frag[2]))\n",
+    "                    j += 1\n",
+    "        X = sparse.coo_matrix((data, (rows, columns)), shape=(n_cells, peak_counter), dtype=np.int32).tocsr()\n",
+    "        obs = pd.DataFrame(index=barcodes)\n",
+    "        var = pd.DataFrame(peak_data, columns=['chrom', 'start', 'end'], index=peak_names)\n",
+    "        adata_peaks = ad.AnnData(X, obs=obs, var=var)\n",
+    "        return adata_peaks\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "744ba5ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AnnData object with n_obs × n_vars = 3 × 3\n",
+      "    var: 'chrom', 'start', 'end'\n",
+      "[[2 1 0]\n",
+      " [3 0 0]\n",
+      " [0 4 0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata_np_ss = np_sort_search(petrack,regions)\n",
+    "print(adata_np_ss)\n",
+    "print(adata_np_ss.X.toarray())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a83976eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ncls implementation\n",
+    "def build_anndata_ncls(petrack, regions, binary=False):\n",
+    "        regions.sort()\n",
+    "\n",
+    "        # Barcode mapping\n",
+    "        barcode_items = sorted(petrack.barcode_dict.items(), key=lambda x: x[1])\n",
+    "        barcodes = [b.decode() if isinstance(b, (bytes, bytearray)) else str(b) for b, _ in barcode_items]\n",
+    "        barcode_ids = np.array([i for _, i in barcode_items], dtype=np.int64)\n",
+    "\n",
+    "        n_cells = len(barcodes)\n",
+    "        if n_cells == 0:\n",
+    "            return ad.AnnData(X=sparse.csr_matrix((0, 0), dtype=np.int32))\n",
+    "\n",
+    "        id_map = np.full(barcode_ids.max() + 1, -1, dtype=np.int64)\n",
+    "        id_map[barcode_ids] = np.arange(n_cells, dtype=np.int64)\n",
+    "\n",
+    "        # Outputs\n",
+    "        peak_names = []\n",
+    "        peak_data = []\n",
+    "        rows = []\n",
+    "        cols = []\n",
+    "        data = []\n",
+    "        peak_counter = 0\n",
+    "\n",
+    "        for chrom in sorted(regions.regions.keys()):\n",
+    "            if chrom not in petrack.locations:\n",
+    "                continue\n",
+    "\n",
+    "            regions_c = regions.regions[chrom]\n",
+    "            if not regions_c:\n",
+    "                continue\n",
+    "\n",
+    "            # register peaks (global)\n",
+    "            local_peak_indices = []\n",
+    "            chrom_str = chrom.decode() if isinstance(chrom, (bytes, bytearray)) else str(chrom)\n",
+    "            for (start, end) in regions_c:\n",
+    "                peak_counter += 1\n",
+    "                peak_names.append(f\"peak_{peak_counter}\")\n",
+    "                peak_data.append((chrom_str, int(start), int(end)))\n",
+    "                local_peak_indices.append(peak_counter - 1)\n",
+    "\n",
+    "            # fragments on this chromosome\n",
+    "            fragment_locs = petrack.locations[chrom][:petrack.size[chrom]]\n",
+    "            fragment_barcodes = petrack.barcodes[chrom][:petrack.size[chrom]]\n",
+    "            if len(fragment_locs) == 0:\n",
+    "                continue\n",
+    "\n",
+    "            frag_starts = fragment_locs['l'].astype(np.int64)\n",
+    "            frag_ends = fragment_locs['r'].astype(np.int64)\n",
+    "            frag_counts = fragment_locs['c'].astype(np.int64)\n",
+    "            frag_bc_ids = fragment_barcodes.astype(np.int64)\n",
+    "\n",
+    "            frag_rows = id_map[frag_bc_ids]  # barcode -> row index\n",
+    "\n",
+    "            idx = np.arange(len(frag_starts), dtype=np.int64)\n",
+    "            ncls = NCLS(frag_starts, frag_ends, idx)\n",
+    "\n",
+    "            # Query overlaps: count any fragment that overlaps the peak\n",
+    "            for peak_idx, (peak_start, peak_end) in enumerate(regions_c):\n",
+    "                for frag_start, frag_end, frag_i in ncls.find_overlap(peak_start, peak_end):\n",
+    "                    row_id = frag_rows[frag_i]\n",
+    "                    if row_id >= 0:\n",
+    "                        rows.append(row_id)\n",
+    "                        cols.append(local_peak_indices[peak_idx])\n",
+    "                        data.append(int(frag_counts[frag_i]))\n",
+    "                            \n",
+    "        X = sparse.coo_matrix((data, (rows, cols)),\n",
+    "                            shape=(n_cells, peak_counter),\n",
+    "                            dtype=np.int32).tocsr()\n",
+    "\n",
+    "        obs = pd.DataFrame(index=barcodes)\n",
+    "        var = pd.DataFrame(peak_data, columns=[\"chrom\", \"start\", \"end\"], index=peak_names)\n",
+    "        return ad.AnnData(X=X, obs=obs, var=var)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b7dcf943",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AnnData object with n_obs × n_vars = 3 × 3\n",
+      "    var: 'chrom', 'start', 'end'\n",
+      "[[3 1 0]\n",
+      " [3 0 0]\n",
+      " [0 4 0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata_ncls = build_anndata_ncls(petrack,regions)\n",
+    "print(adata_ncls)\n",
+    "print(adata_ncls.X.toarray())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b6951c0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# two pointer sweep like exclude\n",
+    "\n",
+    "from operator import itemgetter\n",
+    "\n",
+    "def two_pointer_sweep(petrack, regions):\n",
+    "\n",
+    "    peak_names = []\n",
+    "    peak_data = []\n",
+    "\n",
+    "    rows = [] #barcode index\n",
+    "    columns = [] #peaks index\n",
+    "    data = [] #count data\n",
+    "\n",
+    "    barcode_items = sorted(petrack.barcode_dict.items(), key=itemgetter(1))\n",
+    "    barcodes = [b.decode() if isinstance(b, (bytes, bytearray)) else str(b) for b, _ in barcode_items]\n",
+    "    n_cells = len(barcodes)\n",
+    "    if n_cells:\n",
+    "        barcode_ids = np.fromiter((barcode_id for _, barcode_id in barcode_items),\n",
+    "                                    dtype=np.int64,\n",
+    "                                    count=n_cells)\n",
+    "        barcode_id_to_row = np.full(int(barcode_ids[-1]) + 1, -1, dtype=np.int64)\n",
+    "        barcode_id_to_row[barcode_ids] = np.arange(n_cells, dtype=np.int64)\n",
+    "    else:\n",
+    "        barcode_id_to_row = np.zeros(0, dtype=np.int64)\n",
+    "\n",
+    "    regions.sort()\n",
+    "    peak_counter = 0\n",
+    "\n",
+    "    for chrom in regions.regions.keys():\n",
+    "        if chrom not in petrack.locations:\n",
+    "            continue\n",
+    "\n",
+    "        regions_c = regions.regions[chrom]\n",
+    "        if not regions_c:\n",
+    "            continue\n",
+    "\n",
+    "        local_peak = []\n",
+    "        peak_starts = []\n",
+    "        peak_ends = []\n",
+    "        chrom_str = chrom.decode() if isinstance(chrom, (bytes, bytearray)) else str(chrom)\n",
+    "        ### regions empty skip\n",
+    "        for (start, end) in regions_c:\n",
+    "            peak_counter += 1\n",
+    "            peak_names.append(f\"peak_{peak_counter}\")\n",
+    "            peak_data.append((chrom_str, start, end))\n",
+    "            local_peak.append(peak_counter - 1)\n",
+    "            peak_starts.append(start)\n",
+    "            peak_ends.append(end)\n",
+    "\n",
+    "        fragment_locs = petrack.locations[chrom]\n",
+    "\n",
+    "        if len(fragment_locs) == 0:\n",
+    "            continue\n",
+    "\n",
+    "        fragment_barcodes = petrack.barcodes[chrom]\n",
+    "        frag_starts = fragment_locs['l']\n",
+    "        frag_ends = fragment_locs['r']\n",
+    "        frag_counts = fragment_locs['c']\n",
+    "        frag_rows = barcode_id_to_row[fragment_barcodes]\n",
+    "        n_frags = len(fragment_locs)\n",
+    "\n",
+    "        frag_idx = 0\n",
+    "        local_peak_idx = 0\n",
+    "        frag_start = frag_starts[frag_idx]\n",
+    "        frag_end = frag_ends[frag_idx]\n",
+    "        peak_start = peak_starts[local_peak_idx]\n",
+    "        peak_end = peak_ends[local_peak_idx]\n",
+    "        remaining_frag_len = n_frags - 1\n",
+    "        remaining_peak_len = len(regions_c) - 1\n",
+    "        back_trace_frag = 0\n",
+    "\n",
+    "        while True:\n",
+    "            if frag_start <= peak_end and peak_start <= frag_end:\n",
+    "                row_id = frag_rows[frag_idx]\n",
+    "                if row_id >= 0:\n",
+    "                    rows.append(int(row_id))\n",
+    "                    columns.append(local_peak[local_peak_idx])\n",
+    "                    data.append(int(frag_counts[frag_idx]))\n",
+    "\n",
+    "                if frag_end > peak_end:\n",
+    "                    back_trace_frag += 1\n",
+    "\n",
+    "                if remaining_frag_len:\n",
+    "                    remaining_frag_len -= 1\n",
+    "                    frag_idx += 1\n",
+    "                    frag_start = frag_starts[frag_idx]\n",
+    "                    frag_end = frag_ends[frag_idx]\n",
+    "                    continue\n",
+    "                else:\n",
+    "                    break\n",
+    "\n",
+    "            if frag_end < peak_end:\n",
+    "                if remaining_frag_len:\n",
+    "                    remaining_frag_len -= 1\n",
+    "                    frag_idx += 1\n",
+    "                    frag_start = frag_starts[frag_idx]\n",
+    "                    frag_end = frag_ends[frag_idx]\n",
+    "                else:\n",
+    "                    break\n",
+    "            else:\n",
+    "                if remaining_peak_len:\n",
+    "                    remaining_peak_len -= 1\n",
+    "                    local_peak_idx += 1\n",
+    "                    peak_start = peak_starts[local_peak_idx]\n",
+    "                    peak_end = peak_ends[local_peak_idx]\n",
+    "                    if back_trace_frag:\n",
+    "                        frag_idx -= back_trace_frag\n",
+    "                        remaining_frag_len += back_trace_frag\n",
+    "                        frag_start = frag_starts[frag_idx]\n",
+    "                        frag_end = frag_ends[frag_idx]\n",
+    "                        back_trace_frag = 0\n",
+    "                else:\n",
+    "                    break\n",
+    "\n",
+    "    n_peaks = peak_counter\n",
+    "    obs = pd.DataFrame(index=barcodes)\n",
+    "    var = pd.DataFrame(peak_data, columns=['chrom', 'start', 'end'], index=peak_names)\n",
+    "\n",
+    "    x = sparse.csr_matrix((data,(rows,columns)),shape=(n_cells,n_peaks))\n",
+    "    adata_peaks_loop = ad.AnnData(X=x, obs=obs, var=var)\n",
+    "    return adata_peaks_loop\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2e4bd05d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AnnData object with n_obs × n_vars = 3 × 3\n",
+      "    var: 'chrom', 'start', 'end'\n",
+      "[[3 1 0]\n",
+      " [3 0 0]\n",
+      " [0 4 0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata_two = two_pointer_sweep(petrack,regions)\n",
+    "print(adata_two)\n",
+    "print(adata_two.X.toarray())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3f01629f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO  @ 13 Mar 2026 13:31:57: [150 MB] Collecting data... \n",
+      "INFO  @ 13 Mar 2026 13:31:57: [150 MB] Done with data collection \n",
+      "INFO  @ 13 Mar 2026 13:31:57: [150 MB] Done with AnnData construction \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ncls\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[3, 1, 0],\n",
+       "       [3, 0, 0],\n",
+       "       [0, 4, 0]], dtype=int32)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## test andata from package\n",
+    "\n",
+    "a = petrack.return_anndata(regions, method = \"ncls\")\n",
+    "a.X.toarray()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5d7e803",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.9.6)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/500-PBMC-test/macs3-scATAC-pbmc-500-anndata.ipynb b/notebooks/500-PBMC-test/macs3-scATAC-pbmc-500-anndata.ipynb
new file mode 100644
index 00000000..d2a2f105
--- /dev/null
+++ b/notebooks/500-PBMC-test/macs3-scATAC-pbmc-500-anndata.ipynb
@@ -0,0 +1,1744 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0dcb5de5-47de-49d6-a15f-9352d5b2e5b2",
+   "metadata": {},
+   "source": [
+    "# Use MACS3 API to call peaks for each cluster in PBMC 500 scATAC-seq data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0118e491-5a72-43c4-b702-d690c4672598",
+   "metadata": {},
+   "source": [
+    "The scATAC-seq data was downloaded from [10X genomics website](https://www.10xgenomics.com/datasets/500-peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-next-gem-v-1-1-1-1-standard-2-0-0). The files used in this tutorial are:\n",
+    "- [Clustering analysis](https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_500_nextgem/atac_pbmc_500_nextgem_analysis.tar.gz) for the clustering results\n",
+    "- [Fragments](https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_500_nextgem/atac_pbmc_500_nextgem_fragments.tsv.gz) for the locations of aligned fragments in TSV format\n",
+    "- [Per Barcode Metrics](https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_500_nextgem/atac_pbmc_500_nextgem_singlecell.csv) for the information of each barcode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56b7f903-cc55-4f5e-aa4e-eee1b4d246ca",
+   "metadata": {},
+   "source": [
+    "## Load the fragment file and build pileup track"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f70ffdb6-a8e7-4174-9b7c-516a283044b8",
+   "metadata": {},
+   "source": [
+    "We will import the fragment file parser from MACS3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "ca8b01f2-46af-44d3-809f-840054fd166c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install macs3 pandas\n",
+    "\n",
+    "from MACS3.IO.Parser import FragParser\n",
+    "from MACS3.IO.BedGraphIO import bedGraphIO\n",
+    "from MACS3.Utilities.Logger import logging\n",
+    "import numpy as np\n",
+    "from scipy import sparse\n",
+    "import anndata as ad\n",
+    "import pickle\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "\n",
+    "# we can use the pre-configured logging function from MACS3 to monitor memory usage...\n",
+    "logger = logging.getLogger(\"demo\")\n",
+    "info = logger.info\n",
+    "# Note, you can replace `print` with `info` to show the time and memory usage at that moment.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5846493c-6d7c-4a6c-9f2f-34590795c94c",
+   "metadata": {},
+   "source": [
+    "Assume the fragment file is at `atac_pbmc_500_nextgem_fragments.tsv.gz`. Note that MACS3 can directly load gzipped files. We will load the file and build a `PairEndTrack.PETrackII` object, which contains alignment locations, barcodes and counts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d98fd56-5884-494b-8da6-9c1d47d998dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO  @ 19 Mar 2026 01:34:03: [308 MB]  1000000 fragments parsed \n",
+      "INFO  @ 19 Mar 2026 01:34:04: [308 MB]  2000000 fragments parsed \n",
+      "INFO  @ 19 Mar 2026 01:34:05: [308 MB]  3000000 fragments parsed \n",
+      "INFO  @ 19 Mar 2026 01:34:06: [308 MB]  4000000 fragments parsed \n",
+      "INFO  @ 19 Mar 2026 01:34:07: [308 MB]  5000000 fragments parsed \n",
+      "INFO  @ 19 Mar 2026 01:34:08: [308 MB]  6000000 fragments parsed \n",
+      "INFO  @ 19 Mar 2026 01:34:09: [308 MB]  7000000 fragments parsed \n",
+      "INFO  @ 19 Mar 2026 01:34:09: [308 MB]  8000000 fragments parsed \n",
+      "INFO  @ 19 Mar 2026 01:34:10: [308 MB]  9000000 fragments parsed \n",
+      "INFO  @ 19 Mar 2026 01:34:11: [308 MB]  10000000 fragments parsed \n"
+     ]
+    }
+   ],
+   "source": [
+    "frag_file = FragParser(\"./atac_pbmc_500_nextgem_fragments.tsv.gz\", buffer_size=100000)\n",
+    "petrack = frag_file.build_petrack(max_count=2)\n",
+    "petrack.finalize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f1fa2ff-913b-452f-93f6-7366c5485a5b",
+   "metadata": {},
+   "source": [
+    "Before we call peaks, let's clean up the data by removing uncommon chromosomes from downstream analysis. Let's check first..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "b866cc28-bc1e-4b5a-b280-b240e7fd3632",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[b'GL000009.2', b'GL000194.1', b'GL000195.1', b'GL000205.2', b'GL000213.1', b'GL000218.1', b'GL000219.1', b'KI270711.1', b'KI270713.1', b'KI270721.1', b'KI270726.1', b'KI270727.1', b'KI270728.1', b'KI270731.1', b'KI270734.1', b'chr1', b'chr10', b'chr11', b'chr12', b'chr13', b'chr14', b'chr15', b'chr16', b'chr17', b'chr18', b'chr19', b'chr2', b'chr20', b'chr21', b'chr22', b'chr3', b'chr4', b'chr5', b'chr6', b'chr7', b'chr8', b'chr9', b'chrX', b'chrY']\n"
+     ]
+    }
+   ],
+   "source": [
+    "petrack_rlengths = petrack.rlengths\n",
+    "print(list(petrack_rlengths.keys()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e8e4b49-cee6-443b-8622-83024394a2e6",
+   "metadata": {},
+   "source": [
+    "Let's only keep chr1 to chrY. In order to do so, we need to construct genomic 'regions' covering the uncommon chromosomes, and then 'exclude' any fragments overlapping with those regions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "c6bd3132-4cad-4131-bd88-b94230cd895a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from MACS3.Signal.Region import Regions\n",
+    "\n",
+    "regions_to_be_excluded = Regions()\n",
+    "for chrom, chrom_length in petrack_rlengths.items():\n",
+    "    if not chrom.startswith(b'chr'): # this rule may not work if you want to exclude chomosome such as chr1_random, so adjust it if it's necessary\n",
+    "        regions_to_be_excluded.add_loc(chrom, 0, chrom_length)\n",
+    "\n",
+    "# then use .exclude of PETrackII \n",
+    "petrack.exclude(regions_to_be_excluded)\n",
+    "petrack.finalize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27ec2653-92e4-4c80-b513-72448f7d8911",
+   "metadata": {},
+   "source": [
+    "Now check the chromosome names again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "ac3c8f90-fc19-4a69-b5b1-63c9073652b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[b'chr1', b'chr10', b'chr11', b'chr12', b'chr13', b'chr14', b'chr15', b'chr16', b'chr17', b'chr18', b'chr19', b'chr2', b'chr20', b'chr21', b'chr22', b'chr3', b'chr4', b'chr5', b'chr6', b'chr7', b'chr8', b'chr9', b'chrX', b'chrY']\n"
+     ]
+    }
+   ],
+   "source": [
+    "petrack_rlengths = petrack.rlengths\n",
+    "print(list(petrack_rlengths.keys()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "294d52e7-635e-4d6d-82c7-eabb51267865",
+   "metadata": {},
+   "source": [
+    "Now we can check some basic statistics of the loaded data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "6427cb03-4e17-4244-8c6d-541432e69b1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average template length is 153.5265350341797\n",
+      "total number of bases is 17348915\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"average template length is {petrack.average_template_length}\")\n",
+    "print(f\"total number of bases is {petrack.total}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d905710b-8c72-48ce-b089-692658779bd1",
+   "metadata": {},
+   "source": [
+    "## Call peaks for the entire dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d769784d-db9e-46a8-a0ec-b73ecac18191",
+   "metadata": {},
+   "source": [
+    "Next, we will call peaks for the entire dataset. The first step is to build a pileup track from the alignments. But before that, we can filter out invalid cells/barcodes. For example, we have an `atac_pbmc_500_nextgem_singlecell.csv` file downloaded together with the fragment file which is from the standard 10x pipeline. We want to only keep the cell barcodes with at least 500 usable fragments and at least 25% of total fragments are marked as usable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd45d34e-6c6c-4ef1-a403-d86438c8f2ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cell barcodes to be kept: 426\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "barcodes = []\n",
+    "\n",
+    "# Read the CSV\n",
+    "df = pd.read_csv(\"./atac_pbmc_500_nextgem_singlecell.csv\")\n",
+    "\n",
+    "# We keep the cell barcode that 1) is a cell barcode 2) more than 500 usable fragments 3) at least 25% of total fragments are usable\n",
+    "df_pass = df[\n",
+    "    (df[\"is__cell_barcode\"] == 1) & \n",
+    "    (df[\"passed_filters\"] >= 500) &\n",
+    "    (df[\"passed_filters\"]/df[\"total\"] >= 0.25)]\n",
+    "\n",
+    "# Extract barcode values as a list then convert to a set\n",
+    "# Note: we need to encode the string since PETrackII.subset needs bytestrings.\n",
+    "barcodes = set([x.encode() for x in df_pass[\"barcode\"].tolist()])\n",
+    "\n",
+    "print(\"Cell barcodes to be kept:\", len(barcodes))\n",
+    "\n",
+    "petrack = petrack.subset(barcodes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c6d801f-5500-450d-8c18-69847a03df9a",
+   "metadata": {},
+   "source": [
+    "Now we can build the pileup signal track of all valid cell barcodes and their fragments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a08be5b9-6dd5-4718-a4ff-390e32c43dba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pileup_track = petrack.pileup_bdg()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "740e429e-bde9-4e3b-bb11-df34480390f3",
+   "metadata": {},
+   "source": [
+    "We can get the sum, total length, maximum, minimum, mean, and standard deviation from the pileup track by using `summary` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "4ff4789b-9cc9-4891-8e0d-f4256dba26ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pileup_sum=2085184512.0\n",
+      "pileup_length=3086530883\n",
+      "pileup_max=468.0\n",
+      "pileup_min=0.0\n",
+      "pileup_mean=0.6755754947662354\n",
+      "pileup_std=5.87749719619751\n"
+     ]
+    }
+   ],
+   "source": [
+    "(pileup_sum, pileup_length, pileup_max, pileup_min, pileup_mean, pileup_std) = pileup_track.summary()\n",
+    "print(f\"{pileup_sum=}\\n{pileup_length=}\\n{pileup_max=}\\n{pileup_min=}\\n{pileup_mean=}\\n{pileup_std=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f81610f-be87-4c50-87f4-aaedb0d2eb21",
+   "metadata": {},
+   "source": [
+    "Next, we will call peaks for the entire dataset. Since we are calling peaks for ATAC-seq data, we will use the single whole-genome average pileup value as the background. From above calculation, the average is in the variable `pileup_mean`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "f40cf3ac-e926-416e-81a5-f87fc826559c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "global_bg_track = pileup_track.set_single_value(pileup_mean) # this will return a new track with all values set as `pileup_mean`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dd7f997-e29e-44bc-8e47-cbffbe8de978",
+   "metadata": {},
+   "source": [
+    "We construct a score track for comparing observed pileup and background. The score track will contain the observed pileup, the background, and scores for each position in the genome."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "0f753cd4-a1db-464f-8f4f-46e86c40f970",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_track = pileup_track.make_ScoreTrackII_for_macs(global_bg_track, depth1=100, depth2=100) # Note, depth1 and 2 are the same so there is no need for scaling the values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01bdee57-a140-4f0b-9ead-9305e87476f9",
+   "metadata": {},
+   "source": [
+    "We will use q-score (-log10 q-value) as scores. MACS3 supports the following methods:\n",
+    "\n",
+    "- p: -log10 pvalue;\n",
+    "- q: -log10 qvalue;\n",
+    "- l: log10 likelihood ratio (minus for depletion)\n",
+    "- s: symmetric log10 likelihood ratio (for comparing two ChIPs)\n",
+    "- f: log10 fold enrichment\n",
+    "- F: linear fold enrichment\n",
+    "- d: subtraction\n",
+    "- M: maximum\n",
+    "- m: fragment pileup per million reads\n",
+    "\n",
+    "To use any of the methods, provide the argument to `change_score_method` with `ord`, as shown in the following example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "32b85280-589a-43bf-a6ba-5a925a0145ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_track.change_score_method(ord('q'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "360a2ab0-fc7c-48b1-9940-72900d109489",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoff_analysis = score_track.cutoff_analysis(min_score=0.5, max_score=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "65cd62de-8f5e-4bfd-b80f-c2aa61a6beca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "score\tnpeaks\tlpeaks\tavelpeak\n",
+      "49.51\t11878\t6661120\t560.79\n",
+      "49.01\t11878\t6661120\t560.79\n",
+      "48.51\t11878\t6661120\t560.79\n",
+      "48.02\t12057\t6816708\t565.37\n",
+      "47.53\t12057\t6816708\t565.37\n",
+      "47.03\t12057\t6816708\t565.37\n",
+      "46.53\t12224\t6970961\t570.27\n",
+      "46.04\t12224\t6970961\t570.27\n",
+      "45.54\t12224\t6970961\t570.27\n",
+      "45.05\t12410\t7135169\t574.95\n",
+      "44.56\t12410\t7135169\t574.95\n",
+      "44.06\t12612\t7310448\t579.64\n",
+      "43.56\t12612\t7310448\t579.64\n",
+      "43.07\t12612\t7310448\t579.64\n",
+      "42.58\t12809\t7487159\t584.52\n",
+      "42.08\t12809\t7487159\t584.52\n",
+      "41.58\t12809\t7487159\t584.52\n",
+      "41.09\t13022\t7674260\t589.33\n",
+      "40.60\t13022\t7674260\t589.33\n",
+      "40.10\t13022\t7674260\t589.33\n",
+      "39.60\t13208\t7860617\t595.14\n",
+      "39.11\t13208\t7860617\t595.14\n",
+      "38.62\t13208\t7860617\t595.14\n",
+      "38.12\t13426\t8060347\t600.35\n",
+      "37.62\t13426\t8060347\t600.35\n",
+      "37.13\t13426\t8060347\t600.35\n",
+      "36.63\t13688\t8269513\t604.14\n",
+      "36.14\t13688\t8269513\t604.14\n",
+      "35.65\t13937\t8490415\t609.20\n",
+      "35.15\t13937\t8490415\t609.20\n",
+      "34.65\t13937\t8490415\t609.20\n",
+      "34.16\t14242\t8730058\t612.98\n",
+      "33.67\t14242\t8730058\t612.98\n",
+      "33.17\t14242\t8730058\t612.98\n",
+      "32.67\t14504\t8969765\t618.43\n",
+      "32.18\t14504\t8969765\t618.43\n",
+      "31.68\t14780\t9220657\t623.86\n",
+      "31.19\t14780\t9220657\t623.86\n",
+      "30.69\t14780\t9220657\t623.86\n",
+      "30.20\t15085\t9494664\t629.41\n",
+      "29.70\t15085\t9494664\t629.41\n",
+      "29.21\t15085\t9494664\t629.41\n",
+      "28.72\t15435\t9783646\t633.86\n",
+      "28.22\t15435\t9783646\t633.86\n",
+      "27.73\t15799\t10087504\t638.49\n",
+      "27.23\t15799\t10087504\t638.49\n",
+      "26.74\t15799\t10087504\t638.49\n",
+      "26.24\t16138\t10400894\t644.50\n",
+      "25.75\t16138\t10400894\t644.50\n",
+      "25.25\t16138\t10400894\t644.50\n",
+      "24.75\t16533\t10740412\t649.63\n",
+      "24.26\t16533\t10740412\t649.63\n",
+      "23.76\t16951\t11100380\t654.85\n",
+      "23.27\t16951\t11100380\t654.85\n",
+      "22.77\t16951\t11100380\t654.85\n",
+      "22.28\t17382\t11481481\t660.54\n",
+      "21.78\t17382\t11481481\t660.54\n",
+      "21.29\t17901\t11891344\t664.28\n",
+      "20.80\t17901\t11891344\t664.28\n",
+      "20.30\t18395\t12308540\t669.12\n",
+      "19.81\t18395\t12308540\t669.12\n",
+      "19.31\t18395\t12308540\t669.12\n",
+      "18.82\t18938\t12765816\t674.08\n",
+      "18.32\t18938\t12765816\t674.08\n",
+      "17.83\t19587\t13278236\t677.91\n",
+      "17.33\t19587\t13278236\t677.91\n",
+      "16.83\t19587\t13278236\t677.91\n",
+      "16.34\t20255\t13811860\t681.90\n",
+      "15.85\t20255\t13811860\t681.90\n",
+      "15.35\t20994\t14404293\t686.11\n",
+      "14.85\t20994\t14404293\t686.11\n",
+      "14.36\t21821\t15046371\t689.54\n",
+      "13.86\t21821\t15046371\t689.54\n",
+      "13.37\t22707\t15758588\t694.00\n",
+      "12.88\t22707\t15758588\t694.00\n",
+      "12.38\t22707\t15758588\t694.00\n",
+      "11.89\t23656\t16525708\t698.58\n",
+      "11.39\t23656\t16525708\t698.58\n",
+      "10.90\t24775\t17390182\t701.92\n",
+      "10.40\t24775\t17390182\t701.92\n",
+      "9.90\t26084\t18351516\t703.55\n",
+      "9.41\t26084\t18351516\t703.55\n",
+      "8.91\t27597\t19456770\t705.03\n",
+      "8.42\t27597\t19456770\t705.03\n",
+      "7.93\t29248\t20683982\t707.19\n",
+      "7.43\t29248\t20683982\t707.19\n",
+      "6.93\t31093\t22049518\t709.15\n",
+      "6.44\t31093\t22049518\t709.15\n",
+      "5.95\t33281\t23666439\t711.11\n",
+      "5.45\t35875\t25544950\t712.05\n",
+      "4.95\t35875\t25544950\t712.05\n",
+      "4.46\t39017\t27784231\t712.11\n",
+      "3.96\t39017\t27784231\t712.11\n",
+      "3.47\t42764\t30447611\t711.99\n",
+      "2.97\t47473\t33770768\t711.37\n",
+      "2.48\t47473\t33770768\t711.37\n",
+      "1.99\t53594\t37944522\t708.00\n",
+      "1.49\t62883\t43835542\t697.10\n",
+      "1.00\t62883\t43835542\t697.10\n",
+      "0.50\t75527\t51761958\t685.34\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cutoff_analysis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d022f449-14ab-4472-9e1f-5a7961ef44af",
+   "metadata": {},
+   "source": [
+    "Let's read this analysis into pandas table then plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "19295666-2f64-4560-aba1-8b9b1955bcd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZU/fMM8/oUlNTK+13RERk7pKTk3WPPPKI6oc9PT11PXv21B06dEj16fL9uqTY2FjV58q2fv16o+9Znu/0+u/q0q/ryefJ94pjx47pevTooXN3d9cFBwerfl/68ZLk/Vq0aKG+w3t5eekaNWqke/bZZ3Vnz5417LNhwwZd27Zt1T5hYWHqeTlPyWcXvR7p0qWLrkGDBsXe/+jRo7rQ0FBdvXr1Sl3zEBljJ//ROjBGRMXJiISscFQ0DZeILOfvV6ZiSaahkFT9wYMHq5VsSi5yIFNf9dOz9CTNXjITi07nIusltT4kM1bqcRAREd0oyZ6VjNlrzX4gMmecvkdERFSJmjVrpmo4yDSq69WIkqm1kr4v07SIiIiIiKwdg1JERET/kYxOygpqRYNLUq9Baq5JvSDJlJLabLJKjwSppGiprHImBaalNo+e1BKSAqjXWsmPiIiIiMhaMChFRET0H0mx/65duxrujxs3Tv2UIqGzZ89WBavfeustjB8/HmfOnFHFQKWwtBRK1ZMVLmVfScMvOc2PiIiIiMgasaYUERERERERERGZXPG1KImIiIiIiIiIiEyAQSkiIiIiIiIiIjI51pSqIFIL5OzZs/Dy8lLLgRMRkXEya/zy5csICwuDvb1lj42w7yciuj72+0REtkdXzr6fQakKIieniIgIrZtBRGQx4uPjER4eDkvGvp+IqPzY7xMR2Z746/T9DEpVEBkt0f/Cvb29tW4OEZHZSktLU1/o9f2mJWPfT0R0fez3iYhsT1o5+34GpSqIPn1XTk48QRERXZ81THtg309EVH7s94mIbI/ddfp+y57UTUREREREREREFolBKSIiIiIiIiIiMjkGpYiIiIiIiIiIyOQYlCIiIiIiIiIiIpNjUIqIiIiIiIiIiEyOq++ZgUX7EnAmORN9m4YhyNtV6+YQEVElu5CejX9iElGgAx5oE6l1c4iIyIJk5+XjwNk0XLicjYycPGRk56NApzM8b29nB2dHezg72MPe/t9Vr6L83NE43McqVkEkIuvBoJQZ+GD5ERxNSkeDMG8GpYiIbEBiWhae/30fAjxdGJQiIqIyg08/bI5D6pUcdT8nX4e9p1OwMy4ZWbkFN/WeNQM9cHeLCAxsXpXXHURkFhiUMgN+Hs7q58WMwhMOERFZt0AvF/XzUkY28gt0cCgykk1ERCQmLzqE2RtPGn3O38MZkf7u8HRxhJuTA5wc/q3KkldQgNx8HXLyCgwZVHKu2XM6BcfOZ+CdJYfw6cpYvD+oCXo1DDXZ8RARGcOglBkI8LwalErP1ropRERkAv4eLpA4lEzfu5iRjSAvjlYTEdG/lsckGgJS97aMgKuTvZp2VyvIE21r+KFmoOcNT8O7nJWryoZ8v/kU9p9Jw4gfdmJ011oYe1ttDo4QkWYYlDKjTKlLzJQiIjKZ119/HW+88Uaxx+rUqYNDhw5V+mfLl38/DxdVW+r8ZQaliIjoX+dSs/DMr3vU7cc6VsfLd9SvkPf1cnXCva0iMbB5OCYvPoSv15/Ap6uOqkCVl5uT2qd2kCdeur0efN0Lr0+IiCobg1JmQC5MBKfvERGZVoMGDfDPP/8Y7js6mu60GOT1b1CKiIhIP83u6bm7kHIlFw2reuOZXnUq/DMcHezxyh311fs//9s+HL+QYXhuT3wKNh2/iC+GtEDDqj4V/tlERCUxKGVW0/cYlCIiMiUJQoWEhGhXVyoBDEoREZHB0gPnsOXEJbg7O+Dj+5rBxdGh0j6rf7NwtKnuj5izaep+Vl4+pi45jLhLVzBw+kY81S1aDaCIqr5uaFfTnyv3EVGFY1DKDHD6HhGRNmJjYxEWFgZXV1e0a9cOkydPRmRkpEmLnZ9nPUEiIrpq8/GL6ueglhGoEehZ6Z8X5uumNr1OtQIx9pfdWHkoCe8uPVxs38FtIvHGXQ1UphURUUVhUMqsVt/jhQkRkam0adMGs2fPVnWkEhISVH2pTp06Yf/+/fDy8jL6muzsbLXppaUVji7/l6BUUhr7fiIiKrT9ZLL62aqanyaf7+PuhK8eaqmKrK+LPa8eyyvQYf3RC/hxS5yqd/XJA83g7szLSCKqGOxNzECAJ2tKERGZWu/evQ23GzdurIJUUVFR+OWXXzBs2DCjr5FMqpLF0W9W4NW+n5lSREQk0rPzcOhc4WBHi6gqmrXD3t4Oj3asrja9JfvPqVpXKw4l4faP1yO8ipvhOmbkLTVRO9j4YA4R0fUw99KMMqWkoGFefoHWzSEiskm+vr6oXbs2jh49WuY+L7zwAlJTUw1bfHz8f5++x5pSREQEYFdcMgp0hfWbQnzMa1XWXg1DMGd4W1Rxd8KJCxlYF3tBbfN3nUHvj9bhzb9jkJaVq3UzicgCMVPKDFRxd4bUDNTpgOQruYYLFSIiMp309HQcO3YMDz74YJn7uLi4qK0i6Pv6CwxKERFRkal7LatplyV1LZK9tXRsZ2w6dhEFOp26dpHC7EsPJOKbDSfw09Y4eLgUXl76uDmqQul3NQljcXQiuiZmSpkBB3s7FZgSrCtFRGQaEyZMwJo1a3Dy5Els3LgR/fv3h4ODA+6//36TfD4zpYiIbky1atVUgKPkNmrUKPV8VlaWuu3v7w9PT08MHDgQiYmJxd4jLi4Ot99+O9zd3REUFIRnnnkGeXl5MAc7Tl0NSmk4de96grxc0bdpVbVy34Dm4ZjxYEt892hr1Aj0QGZuPi6kZ6vt2PkMPD13Nx77djsSUjO1bjYRmTFmSpnRFD5Zfe9SOutKERGZwunTp1UA6uLFiwgMDETHjh2xefNmdduUQanL2XnIzMmHm3PlLftNRGQNtm3bhvz8fMN9WZjitttuwz333KPujx07FgsXLsS8efPg4+OD0aNHY8CAAdiwYYN6Xl4rAamQkBA1GCGLXDz00ENwcnLCpEmToCUp4SHT90SLKG2KnN+szrUDsWxMZzWtL1/SpwAsO5CIT1bGqhpU695dDW9XJ/W4i6M93urfEF3rBGncaiIyFwxKmd0KfAxKERGZwty5czX9fC8XR/XlPDuvQI0qR/i5a9oeIiJzV3LQYMqUKahZsya6dOmi6vx9/fXXmDNnDm699Vb1/KxZs1CvXj014NC2bVssW7YMMTEx+OeffxAcHIymTZti4sSJeO655/D666/D2bnw+7gWDideRkZOPjxdHFEnxPKKhjs62CO6SLHzuiHeqg7Vs7/uxe74FHWe0xszdzcWP90JYb6FxdKJyLZx+p6ZCPC8GpTiKkxERDZBppwEeRdmSyVxCh8R0Q3JycnBDz/8gEcffVT1pzt27EBubi66d+9u2Kdu3bqIjIzEpk2b1H352ahRIxWQ0uvZsyfS0tJw4MABo5+TnZ2tni+6VebUvWaRvqq0hzWQFfl+H9ke/4zrooJQi57qhMbhPkjNzFUr+XGBJyISDEqZWaaUTOEjIiLbEOjJulJERDfjjz/+QEpKCh5++GF1/9y5cyrTSVZSLUoCUPKcfp+iASn98/rnjJk8ebKaCqjfIiIiKrXIuRQTtyb29naoFeSJeqHeqB/mjU/ub6aywbadTMbHK2K1bh4RmQEGpcyEn0fhhQmn7xER2Q5DsXNmyRIR3RCZqte7d2+EhYVV6ue88MILamqgfouPj6/kIueWVU/qRkX5e2DSgEbq9ierjqLPR+tw+8fr0O+zDdhw9ILWzSMiDTAoZXbT9xiUIiKyFVyBj4joxp06dUrVhXrssccMj0nxcpnSJ9lTRcnqe/Kcfp+Sq/Hp7+v3KcnFxQXe3t7Ftoomq9OdScmEzNprGlk808sa3dUkDPe1ioDURI9JSMOBs2mq7tQLv+9DTh6n9BHZGhY6NxOcvkdEZHsCPV3Vz/OXs7RuChGRxZAC5kFBQWolPb0WLVqoVfRWrFiBgQMHqscOHz6MuLg4tGvXTt2Xn2+//TaSkpLU68Xy5ctVoKl+/fomPYadccl4dPY2pGXmonC9OqgpbjK1zRa81a8h+jatipz8Auh0OlUQPe7SFfy0NQ5D21fTunlEZELMlDK71fc4Wk5EZCuYKUVEdGMKCgpUUGro0KFwdPw3gCP1noYNG4Zx48Zh1apVqvD5I488ogJRsvKe6NGjhwo+Pfjgg9izZw+WLl2Kl19+GaNGjVIZUab01+6zSLmSiwIdVMaQuLNJ5U5FNLfV+trV9EeX2oG4pU4Qnu4erR6XOlPp2XlaN4+ITMg2QvEWIOBqsVvWlCIish0MShER3RiZtifZT7LqXkkffvgh7O3tVaaUrJonK+t9/vnnhucdHBywYMECjBw5UgWrPDw8VHDrzTffNPFRQE1X02cM9agfDCcHe1S5Okhtiwa1jMBX607gxIUMfLXuOMZ0r611k4jIRBiUMrNMKRkxkeVRZfSAiIisG4NSREQ3RrKdZLqXMa6urvjss8/UVpaoqCgsWrQIWsrOy0fM2TR1u1N0AIK8C6dy2zIJyk3oUQej5uzEl2uPo3PtQLg5ORTbx8HeDjUCPHidRGRlGJQyE1XcnWFnV5i+m3wl13ChQkREtrH6nlxk2cmJgIiIrNqhhMuqllIVdydE+rlr3Ryz0adRCBqH+2Dv6VQM+Hyj0X0GNKuKD+5tavK2EVHlYZjZTEjkXwJTgnWliIhsa+XV3HwdUjNztW4OERGZcOpekwhfDkYUIb+LN/s2RDV/dzVoU3KTX9Xvu85gV1yy1k0logrETCkzm8Inq+9dSmddKSIiW+Di6ABfdyc1dVum8PleHZwgIiLrtUcflAr31bopZqdphC9WP9PV6HMT5u3BrztOY9Kig/jlf+0Y0COyEsyUMssV+BiUIiKyFYFXF7pgXSkiItvKlGoayaDUjRjfozZcneyx7WQylsUkat0cIqogDEqZ4TSOi+m8MCEissW6UkREZN1Sr+Ti+IUMdZuZUjcm1McNwzpWV7ffWXwIufkFWjeJiCoAp++ZYaaUTOEjIiLbwBX4iIhsx57ThVlSUf7uhu/+VH4jutTET1vjVWDvvpmb4ePmBJnEN6B5OG5vHKp184joJjAoZUb8PAovTDh9j4jIdnD6HhGR7WA9qf/Gy9UJY7tH45U/D2DHqX8Lnq+LvYBqAe5oEOajafuI6MYxKGWW0/cYlCIisrVMqSQGpYiIbCZTSgp6080Z0jYKwd6uSLm6au3fe86qoNTTc3fj79Ed4ebsoHUTiegGsKaUGeH0PSIi28Ppe0REtkGn07HIeQWQVfd6NAjBoJYRapt2b1N1Lj2alI63F8Vo3TwiukHMlDIj/obpe7wwISKyFQxKERHZhjMpmbiQngMnBzvUD/XWujlWw9/TBR8MaoIHv96KHzbHwdnBQdWaEl3qBDIrjcjMMShlRvz10/eYKUVEZHNBqRMXMnDPFxvV7VvqBGFU11oat4yIiCqSPkuqXqg3XJ04xawidYoOxPBO1fHluhP4ZsMJw+NfrTuOlRNuMZxricj8MChlhtP3Uq7kIi+/AI4OnF1JRGTtwqu4w93ZAVdy8rHtZGHRVvn5aIfqrItBRGRFWOS8cj3Tsy48XZyQdDlL3d907KJapW/K4kN4f1ATrZtHRGVgUMqMVHF3hp2dzDcHkq/kMqJPRGQDPF0cseDJjjiSeFndnzBvL9Kz83A6+Qqig720bh4REVWQrnWCUKCTrJ4ArZtilZwd7fF092jD/V1xyej/+Ub8tvM0HmgTgRZRfpq2j4iMYyqOGXGwt1OBKcG6UkREtqNGoCd6NQxVW6Sfu3rsdHKm1s0iIqIK1L5WAF65o76aok2Vr1lkFQxqGa5uv/rnAeRLRJCIzA4zpcxwCp+svvfl2hOo6uuKAC8X3N86Ek6cykdEZBPCq7ghJiEN8clXtG4KERGRRXuuV10s2X8OB86mYeqSQ4ai5w3CfBDpXzgIRETaYlDKzIT6uKrlTCXNVC/Iy0WNnhMRkW3UmBLMlCIiIvrvK/ON71EHr/11ADPWHjc8LqvzLR/XGUFerpq2j4gYlDI7L/aph1+2x6v0UinOF5uUrgr0ERGRbYjwc1M/paYUERER/TeD20Squo362o0nL17B+cvZmLTwIKbd10zr5hHZPAalzIwsEfvanQ3U7feWHkZs0lEkpBSuIEFERLaTKRV/iZlSRERE/5WsaP52/0bFVkHs9/kG/LH7LAa1jFC1vohIOyxUZMZCfQvTSRNSeWFCRGQrmClFRERUeZpE+GJImyh1++U/9yM7L1/rJhHZNM2DUmfOnMGQIUPg7+8PNzc3NGrUCNu3bzc8r9Pp8OqrryI0NFQ93717d8TGxhZ7j0uXLmHw4MHw9vaGr68vhg0bhvT09GL77N27F506dYKrqysiIiIwderUUm2ZN28e6tatq/aRdixatAhaCvMpvDA5y0wpIiKbUdW3sO9PvpKL9Ow8rZtDRERkdSb0rIMATxccP5+BF37fh283niy1rYs9r3UziWyCpkGp5ORkdOjQAU5OTli8eDFiYmLw/vvvo0qVKoZ9JHj08ccf44svvsCWLVvg4eGBnj17Iivr30CNBKQOHDiA5cuXY8GCBVi7di0ef/xxw/NpaWno0aMHoqKisGPHDrz77rt4/fXXMXPmTMM+GzduxP33368CWrt27UK/fv3Utn//fmiFmVJERLbHy9UJvu5O6jazpYiIyj+YbWdnZ3ST7/561apVK/X8lClTNDoi0ooUOn/ljnrq9u87z6hC6CW3B7/eivWxF7RuKpHV07Sm1DvvvKOylmbNmmV4rHr16sWypKZNm4aXX34Zffv2VY999913CA4Oxh9//IH77rsPBw8exJIlS7Bt2za0bNlS7fPJJ5+gT58+eO+99xAWFoYff/wROTk5+Oabb+Ds7IwGDRpg9+7d+OCDDwzBq48++gi9evXCM888o+5PnDhRBbk+/fRTFRDTQqjPv6PlmTn5cHN20KQdRERkWhFV3JFyJVXVlaob4q11c4iINKcfzO7atasazA4MDFSzJ4oOZickJBR7jewnA84DBw4s9vibb76J4cOHG+57eXmZ4AjI3NzVJEytdBuTkFbqOXlcak+9OH8flo7pzOswImsNSv31118q6+mee+7BmjVrULVqVTzxxBOGk8SJEydw7tw5NWVPz8fHB23atMGmTZtUUEp+ypQ9fUBKyP729vYqs6p///5qn86dO6uAlJ58rgTF5AQnJzPZZ9y4ccXaJ/tI8Esr3q6O8HB2QEZOvsqWqhHoqVlbiIjIdMKruGHfmVRmShERlXMwW4SEhBS7/+eff6ogVo0aNYo9LkGokvuS7ZEsuVFdaxl97nJWLnp8uBZxl65g2j9H8EKfwqwqIrKy6XvHjx/H9OnTER0djaVLl2LkyJF46qmn8O2336rnJSAlJDOqKLmvf05+BgUFFXve0dERfn5+xfYx9h5FP6OsffTPl5Sdna2mBRbdKqOjDL1aWyQhlXWliIgqk0zfkH53zJgxWjcFEX5cgY+IqORgtgxCy2C2fPdv1qwZvvzyyzL3T0xMxMKFC1WmlLH+XqYAynvI1L68vLLr95niOz+Z51T6t/o1VLe/XHcc+8+kat0kIqulaaZUQUGBOrlMmjRJ3ZcTg9RwkulyQ4cOhTmbPHky3njjjUr/nFAfVxxNSseZFF6YEBFVFpkCPmPGDDRu3BjmkiklmClFRFR8MFtmNrz44ouq35bBbJkJYey6QQa5JSNqwIABxR6X1zRv3lwNYEtN2RdeeEFN+5OyHlp+5yfz061eMO5sEoa/95zF8O+2IzrYy7AgyWt31oerE6f0EVl8ppSsqFe/fv1ij9WrVw9xcXHqtj6tVkY6ipL7+ufkZ1JSUrHnZbRDVuQruo+x9yj6GWXtU1Zqr5zAUlNTDVt8fDwqcwW+BK7AR0RUKWS1VlkwQ0bci9Ym0bqmlIhP5oAEEZF+MFuCSTKYLQPZUhdWSn6UVftVaslK3y6rahclQa1bbrlFDUKMGDFCLbIk9WglI0rL7/xkniT4JIuPyKyVtUfOq+2nrXH4cUvh9SoRWXhQSooVHj58uNhjR44cUavk6eeJS1BoxYoVhuclZVZqRbVr107dl58pKSlqVT29lStXqhOX1J7S7yMr8uXm5hr2kSLmderUMVyAyD5FP0e/j/5zSnJxcYG3t3exrTJwBT4ioso1atQo3H777cXqF2o9jYOZUkRENzaYXdS6devUNcZjjz123feV6wUZ0D558qSm3/nJPAV4uuCPJzrgg0FN1PZoh8I6Zl+sOYas3Hytm0dkFTQNSo0dOxabN29WIx5Hjx7FnDlzMHPmTHWBIPS1Pd566y01j3zfvn146KGH1Ip6/fr1M5yMZNU8GSnZunUrNmzYgNGjR6si6LKfeOCBB1Rqr8wpP3DgAH7++We12l7RwuZPP/20WsVPRksOHTqE119/XS0xK++lJX2m1FnWlCIiqnBz587Fzp071fSM8pD9ZMEN/SZFdytD1atBqctZeUjN/HdAhYjIVl1vMLuor7/+Gi1atECTJk2u+76yIrcskFSyRi2RXrUADwxoHq6253vXVdP3zl/OxtytzJYisvigVKtWrTB//nz89NNPaNiwISZOnIhp06apVFu9Z599Fk8++aRK0ZX9ZZqFBI+KpuL++OOPqFu3Lrp164Y+ffqgY8eOKrilJxcOy5YtU6v5yQlq/PjxePXVV9V76rVv394QFJMT2K+//qpW3pN2acmQKcWaUkREFUqmYMiAhJxDSk7vKIuppnG4OzsiwLNwxdj4S8yWIiK63mC2nmSwzps3z2iWlKy2Ldcae/bsUTWqpP+X9x0yZIjZTN8m8+bsaI+Rt9RUt6czW4qoQtjpdDpdxbyVbZMToAS/5CKlItN6pch59w/WwNPFEfvf6Flh70tEZG395Y2SgYf+/fvDweHfQqX5+fkqS1dGzWWqXtHnTH0sfT/bgD3xKfhiSAv0asily4nIclVUX7lgwQI1OBAbG6vKfMisB5ktUZQEqmSmhRQvl88sSjJjn3jiCTUrQvp4eY8HH3xQvY9M0zPlsZDlys7Lxy3vrlZ1pt7s2wAPtaumdZOIzFJ5+0tNV9+j6wu7mimVnp2HtKxceLs6ad0kIiKrINm1Mi28qEceeURl3j733HPXDUhVtogqbiooxbpSRESF7rjjDrVdi8yEKDoboigplC7ZVkT/hYujg8qWevXPA/h4RSx2x6eU2sffwxljb6utMp+J6Nr4V2LmpCPzcXNSNUVkBT7vEAaliIgqgiwVXnKKtoeHB/z9/TWfui3Cr67Ad5or8BEREZmVQS0j8PmqYziXloXfd54xuo+bkwPG9ahj8rYRWRoGpSxAqI+rCkqdTc1EnRAvrZtDREQmoF+BjzWliIiIzIurkwO+H9Yaqw+fhw7Fq+GcTcnC7I0n8fX6E3i4Q3X4eRTWiCQi4xiUsgBhvm44dO6yypQiIqLKs3r1aq2bYBDhx0wpIiIicxUd7KW2kqRk87aTl3DgbBpmrDmGF/rU06R9RJZC09X3qPyZUiIhlRcmREQ2lymVfEV9wSUiIiLzJwumTLg6be/bTSeRlMbEAqJrYVDKQjKl9KmgRERkG6r6usHODriSk48L6TlaN4eIiIjK6ZY6gWgRVQVZuQX4bNVRrZtDZNY4fc8CMFOKiMg261VE+rnj1MUriE28jECv8i1XTkRERNpnS43vURsPfLkFc7bG4WxqYXKBp4sjXuhdF0Hehdd3RMRMKYsQ6lOYKZVwtTMjIiLbUOdqrQqpK0hERESWo33NAHSsFYDcfB2WxySqbf6uM5jw615OyycqgplSFiDMtzCSfjYlU3VgEnknIiLrVzfEC8tiEnGYQSkiIiKL8/H9zfBPTCLyCnTIycvHpMWHsPbIefy28wzubhGudfOIzAKDUhYg5Or0vey8AiRfyeWyokRENqJ2yNVMqUQGpYiIiCyNXLcNahVhuJ+ZW4B3lhzCm38fQOfoAE7jI+L0Pcvg4uiAAE9nQ7YUERHZTqaUkJpSBQVM9SciIrJkwztVR6OqPkjLysNLf+xH0uUstaVl5WrdNCLNMChlIVhXiojI9lTz94Czo71agS8++YrWzSEiIqL/wNHBHu/e0xhODnaqxlTrt1eorfHry/Dn7jNaN49IEwxKWVhdqTO8KCEisqkvr7UCPdVt1pUiIiKyfHVDvPFcr7pq0MneTlbqK3z8k5VHWQCdbBKDUhaiekDhRcnR8+laN4WIiDSYwsegFBERkXV4rFMNHHmrN45Pvh17X+sBD2cHHE1Kx/qjF7RuGpHJMShlIeqEFAaljiQyKEVEZEvqsNg5ERGR1fJydcI9LQuLoc/ecFLr5hCZHINSFiI66N9it0zrJCKyvaAUM6WIiIis09D21dTPlYeTcPJChtbNITIpBqUsRK0gTzXnOPlKLi6k52jdHCIiMmHtCXHiQgay8/K1bg4RERFVsOoBHuhaJxCSe/DtJmZLkW1hUMpCuDo5INLPXd0+wikcREQ2I9jbBT5uTsgv0Kl6E0RERGR9Hu5QXf2ct/20ypZKSM0stl1Iz9a6iUSVwrFy3pYqQ3SwF05evKKCUh1qBWjdHCIiMgE7OzvUCfbC1pOXVP/fIMxH6yYRERFRBescHYCagR44dj4Dt7y32ug+smrfyFtqmrxtRJWJmVIWRC5KBIudExHZaLFz1pUiIiKy2kGoCT3qwNPFEU4OdsU2R6njAuDz1UeRlpWrdVOJKhSDUhYkOtjTUOyciIhsB4udExEBZ86cwZAhQ+Dv7w83Nzc0atQI27dvNzz/8MMPqwv7oluvXr2KvcelS5cwePBgeHt7w9fXF8OGDUN6Ogd8yTz0bhSK/W/0ROzbfYptR97qjeggT1zOysP3m05p3UyiCsWglAWpbciU4gp8RES2pO7VoNSBs2lYcTBRbVydh4hsSXJyMjp06AAnJycsXrwYMTExeP/991GlSpVi+0kQKiEhwbD99NNPxZ6XgNSBAwewfPlyLFiwAGvXrsXjjz9u4qMhujH29nZ4omvhtL1v1p9AZg4XPiHrwZpSFqRGoAcc7O2QlpWHpMvZCPZ21bpJRERkArWvBqXOX87GsG8LswLcnR2w8flb4evurHHriIgq3zvvvIOIiAjMmjXL8Fj16oWFoYtycXFBSEiI0fc4ePAglixZgm3btqFly5bqsU8++QR9+vTBe++9h7CwsEo8AqL/5s7GYfhweSziLl3BT1vj8GjH0v//E1kiZkpZEBdHB0T5F67AxykcRES2w9vVCWO6R6NJhK/aPJwdcCUnHztOJWvdNCIik/jrr79UIOmee+5BUFAQmjVrhi+//LLUfqtXr1bP16lTByNHjsTFixcNz23atElN2dMHpET37t1hb2+PLVu2mOxYiG6Go4M9RnQpzJaaufY4svOYLUXWgUEpiy12zqAUEZEtGdO9Nv4c1UFtfRqFqscYlCIiW3H8+HFMnz4d0dHRWLp0qQo4PfXUU/j222+LTd377rvvsGLFCpVZtWbNGvTu3Rv5+YUX7+fOnVMBq6IcHR3h5+ennjMmOzsbaWlpxTYirQxsURXB3i44l5aFB77cgmGzt+Hx77Zj07F/g69ENhOUOnr0qDohZGZmqvuscWQa0VeDUrFcgY+IyGa1iCqsobIzjkEpIrINBQUFaN68OSZNmqSypKQO1PDhw/HFF18Y9rnvvvtw1113qQLo/fr1UzWjZKqeZE/drMmTJ8PHx8ewyRRCIi1nzuizpWRgasWhJCyLScSEeXuQm1+gdfOITBOUkhRYSXOtXbu2mn8tBQSFrFwxfvz4m2sFlVvtqyvwHUliphQRka1qfjUotSc+FXn8EkpENiA0NBT169cv9li9evUQFxdX5mtq1KiBgIAANZgupNZUUlJSsX3y8vLUinxl1aF64YUXkJqaatji4+Mr5HiIbtbQdtUwfXBzvDOwkdoCPJ1xJiUT83ee0bppRKYJSo0dO1alucoJwN29sL6RuPfee1XhQDLNCnySKcXsNCIi21Qr0BNero7IzM3HIdYYJCIbICvvHT58uNhjR44cQVRUVJmvOX36tBpQl4CWaNeuHVJSUrBjxw7DPitXrlRZWG3atDH6HlI43dvbu9hGpPVKfL0bheLeVpFqG96phnr889VHOVBFthGUWrZsmZqjHR4eXuxxmd996tSpimwbGVHN3wOO9nZIz87D2dQsrZtDREQafSFtFlmYLcW6UkRkC2RgfPPmzWr6nmQ+zZkzBzNnzsSoUaPU8+np6XjmmWfUPidPnlR1pfr27YtatWqhZ8+ehswqqTsl0/62bt2KDRs2YPTo0WraH1feI0s1pG0Uqrg74eTFK1iwt3AWE5FVB6UyMjKKZUjpSdqrjCRQ5XJ2tEf1AA91+72lh/Hu0kP4Ys0xZGTnad00IiIyoRZXg1KsK0VEtqBVq1aYP38+fvrpJzRs2BATJ07EtGnTMHjwYPW8g4MD9u7dq2pKSZkRKS3SokULrFu3rtg1yo8//oi6deuiW7duqhRJx44dVXCLyFJ5uDjisavZUp+uOoqCAs6mIcvieKMv6NSpk1rVQk4Ews7OTqW8Tp06FV27dq2MNlIJ9cO8EZuUjvm7is8b1he9IyIi69c8ylf9ZKYUEdmKO+64Q23GuLm5qUWYrkdW2pMsKyJr8lC7KMxYcwxHk9IxZckhNbumKDs7oH1Nf0SVeJzIIoNSEnySkYXt27cjJycHzz77LA4cOKAypSQFlirfhB51EOLjipy8AtXxrIu9gG0nLjEoRURkQ5pG+KovmaeTM5GUloUgb1etm0REREQa8HJ1wiMdquOjFbGYufa40X0i/NywekJXONjbmbx9RBUalJJ0WSkq+Omnn8LLy0vN3x4wYICaz60vIkiVK8LPHS/0rqdu74pLVkEpmb4hhc8lc42IiGzjC2idYC9V6FzOAb0a8hxMRERkqx7vXANJl7NxMT271HObjl1E/KVMrDyUhNvqB2vSPqIKC0oJHx8fvPTSSzfzUqpgDcJ8VJ2p5Cu5OHEhAzUCPbVuEhERmUjzqCpXg1IpDEoRERHZeG2pyQMaGX1u8qKDmLH2OL7bdJJBKbL8Qudr16695kamJQGpRlV91G25KCEiovKZPn06GjdubFjiW5YKX7x4MSxJc67AR0REROVYoU8m1MgMG0lkILLoTKlbbrml1GNFp4zl5+f/91bRDWkRVUVdkMj0jbtbhGvdHCIiixAeHo4pU6YgOjpaTX/+9ttv1fLhu3btQoMGDWAp/b/YdyYV2Xn5cHF00LpJREREZIblX7rWCVLT937YfAqv3FFf6yYR3XymVHJycrEtKSkJS5YsUcu0Llu27EbfjipA88jCFZh2cqSciKjc7rzzTrUcuASlZPnwt99+G56enti8eTMsRTV/d/h7OKuFL/bEp2rdHCIiIjJTD7aNUj/nbY9HZg4TSciCM6WknlRJt912G5ydnTFu3Djs2LGjotpGNzh943DiZVzOylXFb4mIqPwky3fevHnIyMhQ0/jKkp2drTa9tLQ0aEkyldvV9MeCvQlYH3serav7adoeIiIiMk9dagci0s8dcZeuYP6uM7inZekZNo72dlw4iyyj0LkxwcHBOHz4cEW9Hd0AWQa8qq8bzqRkqpHyjtEBWjeJiMgi7Nu3TwWhsrKyVJbU/PnzUb9+2SntkydPxhtvvAFz0rl2oApKrYm9gHE96mjdHCIiIjJD9vZ2GNI2EpMWHcKL8/epraQmEb6Y9792qm4xkanc8P9te/fuLbbt2bNHTd8bMWIEmjZtWjmtpHLXFZG6UkREVD516tTB7t27sWXLFowcORJDhw5FTExMmfu/8MILSE1NNWzx8fHQWqerAxF7T6cg5UqO1s0hIiIiMzWoZQRCfVzLfH5PfAp+3hZn0jYR3XCmlASeJKVPisIW1bZtW3zzzTcV2Ta6wbpSf+05y6AUEdENkKnntWrVUrdbtGiBbdu24aOPPsKMGTOM7u/i4qI2cxLq44boIE/EJqVjw9GLuL1xqNZNIiIiIjPk6+6Mdc92RUZ26ZpS83bE462FB/HxyqMY2CIc7s4VNqmK6Jpu+P+0EydOFLtvb2+PwMBAuLqWHXGlytdcnyl1KhkFBTqVnklERDemoKCgWM0oSyFT+CQotS72PINSREREVCZHB3v4uJeeMPVQu2r4dtNJxF/KxKwNJzGqa+GgHZHZTd+LiooqtkVERDAgZQbqhXrD1ckeaVl5OH4hXevmEBGZPZmKt3btWpw8eVLVlpL7q1evxuDBg2Fp9FP41h45XyqTmYiIiOh6pI7U+NsKa1N+seYYSwKQeWVKffzxx+V+w6eeeuq/tIdukpODPRqH+2LriUvYeSoFtYK8tG4SEZFZS0pKwkMPPYSEhAS1smzjxo2xdOlStaKspWlT3V99mTybmoVj5zNQK8hT6yYRERGRhbmrSZgKSB06dxlv/h2DbvWC1eM1Aj1UEgSRZkGpDz/8sFxvJrWmGJTSTsuoKiootfn4RQxqFaF1c4iIzNrXX38Na+Hm7IDW1fyw/ugFNYWPQSkiIiK6UVIC5tledfDo7O34fdcZtQknBzsseqoTooOZ+EAaBaVK1pEi89SxVgA+X30Ma2MvsK4UEZGNkSl8EpSSKXyPdKiudXOIiIjIAnWtE4SRt9RUtYrFmZRMnE7OxJTFh/D1w620bh5ZIZbUtyItqlWBm5MDLqRnq5TL+mFMsSQishWdogMxefEhbDx2EeN+3q0eC/FxxbjbaquipkRERETlmf30XK+6hvvHzqejx4drseJQEjYdu4h2Nf01bR9Zn5sKSp0+fRp//fUX4uLikJNTvADaBx98UFFtoxvk4uiAtjX8sOrweayNPc+gFBGRDakX6oVQH1ckpGYZ0u1FnRAv9G1aVdO2ERERkWWqGeiJB1pH4vvNpzBp0UH8OaoDZ+RQhbrhodMVK1agTp06mD59Ot5//32sWrUKs2bNwjfffIPduwtHZm/GlClTVFR2zJgxhseysrIwatQo+Pv7w9PTEwMHDkRiYmKx10lg7Pbbb4e7uzuCgoLwzDPPIC8vr9g+sppS8+bN4eLiglq1amH27NmlPv+zzz5DtWrV1EqCbdq0wdatW2GJZFlwITVFiIjIdsg5dNYjrfBSn3pq614vSD2+eN85rZtGREREFuzp7tHwdHHEvjOp+HvvWa2bQ7aeKSVLZk+YMAFvvPEGvLy88Ntvv6lgkCyh3atXr5tqxLZt2zBjxgy18lFRY8eOxcKFCzFv3jy1MtLo0aMxYMAAbNiwQT2fn5+vAlIhISHYuHGjWkFJVlJycnLCpEmTDPWwZJ8RI0bgxx9/VEG1xx57DKGhoejZs6fa5+eff8a4cePwxRdfqIDUtGnT1HOHDx9Wx2Zp0zfEthPJuJKTB3dnztAkIrIVdUO81SYkvf6fg0lYfSSJ5wMiIiK6aQGeLhjRpQbeW3YEz/+2D+8uPVxqn4gq7pj5UAt4uTpp0kayoUypgwcPqsCPcHR0RGZmpspievPNN/HOO+/ccAPS09NVQOvLL79ElSpVDI+npqaqlZFkOuCtt96KFi1aqIwsCT5t3rxZ7bNs2TLExMTghx9+QNOmTdG7d29MnDhRZT3ppxVKoKl69eoqq6tevXoqsHX33XcXW1FQPmP48OF45JFHUL9+ffUaybyS7C9LUzPQA1V93ZCTX4AtJy5p3RwiItJIgzBvRPq5Iyu3AKsPM3uWiIiIbt6wjjUQ4eeGzNx8Vfi85Lbp+EXM2RKndTPJFoJSHh4ehoCPZBsdO3bM8NyFCxduuAEyPU8ymbp3717s8R07diA3N7fY43Xr1kVkZCQ2bdqk7svPRo0aITg42LCPZDilpaXhwIEDhn1Kvrfso38PORb5rKL72Nvbq/v6fYzJzs5Wn1N0M5fpG7ICk5AVmIiIyDbJ+aB3oxB1e9G+BK2bQ0T0n505cwZDhgxRpT3c3NzUdcD27dvVc3Ld8Nxzz6nH5HolLCxMDaSfPVt8qpGU65D+segmZUSI6NrcnB2w4MlOmP9E+1LbMz3rqH2+3XgSefkFWjeVLMwN5/K3bdsW69evV1lHffr0wfjx47Fv3z78/vvv6rkbMXfuXOzcuVNN3yvp3LlzcHZ2hq+vb7HHJQAlz+n3KRqQ0j+vf+5a+0gQSbK8kpOT1TRAY/scOnSozLZPnjxZTWE017pSc7fFY13sjQcJiYjIevRuGIoZa45j5aEkZOXmw9XJQesmERHdFPnO3qFDB3Tt2hWLFy9GYGAgYmNjDTMtrly5oq4rXnnlFTRp0kTt//TTT+Ouu+4yBK70ZIaHzJLQk5IkRHR9Pm5OaBb57+wmvXqh3pi14QTOpmZh8f5zuLNJmCbtIxsJSslUN5lyJyQoI7elJlN0dPQNrbwXHx+vThTLly9XxcUtjdTWkjpUehLkioiIgDnoUDMAsiDC0aR0nE3JRJivm9ZNIiIiDTQJ90GYj6v6kijZsz0aFGZOERFZGikTIt+1pZyHnpTo0JP6s3JdUdSnn36K1q1bq4WRZLZF0SCU1KQlooohg15D2kZh2j+x+Gr9CdzROFRlIRJVyvQ9KSB+6VJhrSJJjZX6S3v37lUFz6Oiosr9PjJlLikpSa2KJ7WpZFuzZg0+/vhjdVsylWRqXUpKSrHXyep7+pOI/Cy5Gp/+/vX28fb2Vmm/AQEBcHBwMLrPtU5WspKfvEfRzVz4uDuhSURhhtnd0zei+wdr0OejdZzOR0Rkk1P4QtVtGbkkIrJUf/31F1q2bIl77rlHLUTUrFkzVZP2WqRGrfSDJWdeyHQ9mQIo7/Huu++WWrmbiG6cBKWcHe2xJz4FO+OStW4OWXNQ6vz582qVPRmpeOaZZ7Bnz56b+uBu3bqpaX+7d+82bHKikaLn+tuyip6slqcnq+HJSEe7du3Uffkp7yHBLT0ZIZEAkRQs1+9T9D30++jfQ6YIShH1ovsUFBSo+/p9LFGfhoUXITI6LhlTMQlp+GhFrNbNIiIiE+vdsHCA5Z+YRPyw+ZTaFu5NQEGBTuumERGV2/HjxzF9+nQ1O2Pp0qUYOXIknnrqKXz77bdG98/KylI1pu6///5ig8fyGikhsmrVKvzvf/9TA+7PPvusxdWRJTLHFfr6NS2ctvfl2hO4nJVbasvJY70pKs1Op9Pd8LdSmaM9b948zJkzB+vWrVMFyCWY9MADD6jigTfrlltuUavoTZs2Td2Xk82iRYswe/ZsdTJ58skn1eOyAp+QWlCyvxQynDp1qqof9eCDD+Kxxx5TJxhx4sQJNGzYUBVUf/TRR7Fy5Up1Mlq4cKEqeC5k+uHQoUMxY8YMleIrn//LL7+omlIla02VRU5QkjYsIzLmkDUlFxv7zqSq1REysvMw7NvCufSbX+iGEB/Lmy5JRNbD3PpLaz8WOR+0m7ICiWnZxR5//54mGNgiXLN2EZHtqIi+UgaSZdBafx0g5Du91KYtuTiRFD0fOHAgTp8+jdWrV1/zM2W1bQlOSUkSmQlR0uuvv260jqw59/tEWjl0Lg29pq0r83kPZwf89kR71A3h344tSCtn33/DmVJCCgo+/vjjqpM/deoUHn74YXz//feoVasWKtKHH36IO+64Q51UOnfurKbTSUF1PZl2t2DBAvVTsppkNQ5ZZUOKFxaday4BKMmOkqKH77//Pr766itDQErce++9eO+99/Dqq6+qIJdkai1ZsqTcASlzZG9vp6bwta3hj271gtEyqrAg3ZL9XIGJiMiWyPlgysDG6NMoBL0ahBimd/+w5ZTWTSMiKjdZ9Vs/E0JPFl6SWRQlA1KDBg1S1yj6GRTX0qZNGzV97+TJk2XWkZULKv0mdXGJyDgJNg1oVrXM5zNy8vHpyqMmbRNZaaZU0U5fAj4//PCD+unn56eWarVF5j5a/vX6E5i4IAatq/vhl/9Z7rREIrJ85t5fWvuxJF3OQvvJK5FXoMOSMZ04WklEFtFXyowMCQjJLA29sWPHYsuWLYbsKX1ASlblk+l5skLf9fz4449qUPvChQuGlfwq+1iIrF12Xn6px2IT03HHJ+vVglyrJ3RFpL+7Jm0jK8mUkk5ellGVTCLJkpIPkIwlSZEl89Trak2RbScvqQsSIiKyTUFerritfmEm8NytHPEnIssgAajNmzerEh1Hjx5VZURmzpypSnToA1J33303tm/frgJNUuZDSnvIJosnCZnmJ2U6pCau1KiS/eR9ZbZFeQJSRFQ+Lo4OpbaGVX3QpXYgpKTlV+uPa91EMiM3HJSqWrUq+vTpo0YT5EQgq9TJXGwpXM5lH81XVV83NI3wheTFLT1QfKVBIiKyLfe3Llwa/fedp5GZU3o0k4jI3LRq1Qrz58/HTz/9pOrFTpw4UQWYpK6tkNkaskKfDJJLOQ6Z7qff9JlUUjNKipx36dIFDRo0wNtvv62CUnJNQ0SV739daqifv2yPx8X04rUuyXY53ugLpNifLMVacmlVMn9ST2R3fAoW70vAg22jtG4OERFppGOtAIRXccPp5Ews2pfAgudEZBGk1qxsxshiS9erStK8eXOVbUVE2mhXwx9Nwn2w53Qqvtt0CmNvq611k8gSM6Vk2h4DUpapd8NQ9XPz8YuMTBMR2Xjx8/taRajbP20tXiSYiIiIqDLIzKr/dampbn+76STmbo3Dz9vi1ABZvszrI5t0w5lSZLki/NzRqKoP9p1JxYgfdqi6Ig72dhjaPgotovy0bh4REZnQPS0j8OE/sdh+KhmxiZcRHeyldZOIiIjIyvVsEIIof3ecungFz/++z/D4q3fUx6Mdq2vaNtLGTRU6J8t1V5Mw9XPbyWQs3JeAv/acxXO/7btuujMREVmXYG9XdKsbpG7P3mh8KXQiIiKiiiRJEVMHNkbPBsHoXi8ILaIKFxn4at1x5OYXaN080gAzpWzM0PbV4OfhjPTsPBWImrr0MI4mpWPriUtoU8Nf6+YREZEJyYjksphE/LrjNMbdVhv+ni5aN4mIiIisnFx36q89s3Lz0fGdlTibmoUFe8+ifzPWubQ1zJSyMc6O9qqgrQSnHu5QHX2bFmZO/biFNUWIiGxNm+p+alp3dl4BftjM8wARERGZlquTAx7pUDhtb8aa45zBY4MYlLJxD7QuXIVv8f4EXGDxcyIimys4Orxz4fLM3206qUYriYiIiExpSJsouDs74NC5y1gbe0Hr5pCJMShl4xqF+6hlOXPzdWr6BhER2ZY+DUNQ1dcNFzNy8PvOM1o3h4iIiGyMj7sT7msVqW7PXHtM6+aQibGmFGFwmyjsOb0Xc7bE4fFONdRS4UREZBscHexVbamJC2JUkdFBLcNhb2cHO7vCTCoiIiKiyjasU3V8u+kkNhy9iIdnbYVjiWtSKUPz5K3RqBfqrVkbqXIwKEW4o0koJi6MQdylK1h/9AI61w7UuklERGRC97aKwLR/juD4hQzUemmxeszNyQEzH2qBTtE8JxAREVHlkqxtqXcsWdurD583us/hc5ex8KlOqg4VWQ8GpQjuzo4Y2DxcLQn+3aZTDEoREdkYTxdHjLylJqYuOWx4LDM3H6/9dQDLxnRW2VRERERElemNuxqgU3QAcvIKij0utc/fX34Ex85n4NOVRzGhZx3N2kgVj0EpUoa0jVRBqX8OJuJo0mXUCvLSuklERGRCT9xSCw+2jUJevk4FpO74ZD2On8/Az9vj1TRvIiIiosrk5eqE/s3CjT7n6+6EET/sxBdrjqF3oxA0CPMxefuocnDokxQJQvWoH6xuT199XOvmEBGRRl8Gq3g4I8zXDU/dWks99uHyWGRk52ndNCIiIrJhvRqGok+jEOQV6PDcb3uReiUXl7OKb1dy+H3FEjFTigxk6saymET8ufsMxvWoreb1EhGRbXqgTRRmbTyJUxev4Mt1xzGme22tm0REREQ27PW7GqhC6PvPpKHJm8uM7vN45xp4sU89k7eNbh4zpcigWWQVtKvhr6LPX65lthQRWbfJkyejVatW8PLyQlBQEPr164fDh/+tqWTrZJWbZ3vWVbdnrj2On7bG4Zdt8Zi/67QajSQiIiIypSAvV7zdv2GplfmK+mb9CZxJyTRpu+i/YaYUFfNE15rYdPwi5m6Lw5O31oK/p4vWTSIiqhRr1qzBqFGjVGAqLy8PL774Inr06IGYmBh4eHho3TyzIGnyTSJ8sSc+BS/8vs/wuEz3nvlQS03bRkRERLbnjsZh6NkgBPkFulLPPTp7GzYeu4gZa47hzb4NNWkf3TgGpaiYjrUC0KiqD/adScWDX29FiI8rJA49sEU4+jQK1bp5REQVZsmSJcXuz549W2VM7dixA507d9asXebEzs4O797dGB8uP4LsvALodDqsjb2gpnqvPpyEW+oEad1EIiIisjFODvZwcij9+JO3Rqug1Nxt8RjdtRaCvF21aB7dIE7fo1IXIKOvFreNSUjDykNJWHEoCeN/2YNzqVlaN4+IqNKkpqaqn35+fmXuk52djbS0tGKbtasd7IXpQ1rgm4dbYdYjrfFw+2rq8Tf+jkF2Xr7WzSMiIiJS2tbwQ8uoKsjJK1ClB8gyMChFpUg65OxHWmHq3Y3V1iTcRy0PPnXpIa2bRkRUKQoKCjBmzBh06NABDRs2vGYdKh8fH8MWEREBW/N092gEeLrgxIUMfL3+hNbNISIiIiqVYPHjljhcTM/WuklUDnY6ycWn/0xGy+UCRUbavb29YU2klkjfzzao23+O6qDqixARWVN/OXLkSCxevBjr169HeHj4NTOlZCt6LBKYMqdjMYXfd57GuF/2wM3JAVMGNoKzgz0cHezRoZY/3J1ZGYCIzL/fv1nWdCxE1kjCG3d9ukGVownxdoWHiwPs7ewwtH01DGkbpXXzbEpaOftLZkrRdUkQakDzqur2mwti1B86EZG1GD16NBYsWIBVq1ZdMyAlXFxc1Em16GaL+jerilbVqqgs2qfn7sbIH3di+Hfb8eScXTxHEBERkabZUmNvi1a3z6Vl4dj5DMQmpeP1vw4g5qz1l12wRAxKUbnIsuAyIr7jVDI+XnEUS/afU9vp5CtaN42I6KZI8EQCUvPnz8fKlStRvXp1rZtkUV/43hnYGF1qB6J1NT+1SbaU1CCct+O01s0jIit15swZDBkyBP7+/nBzc0OjRo2wffv2Yv36q6++itDQUPV89+7dERsbW+w9Ll26hMGDB6tBBV9fXwwbNgzp6ekaHA0RVZZb6wZj2djO+PnxtmrrXi8IeQU6TJi3B7n5BVo3j0pgjj2Vi6zCN/KWmvhg+RF8+M8Rw+NV3J3wz7gu8Pd00bR9REQ3atSoUZgzZw7+/PNPeHl54dy5c+pxSTOWixm6thqBnvj20daG+9NXH8M7Sw5h4t8xaiXXMF/+Domo4iQnJ6u6f127dlXTrQMDA1XAqUqVKoZ9pk6dio8//hjffvutGmh45ZVX0LNnT8TExMDVtXAVLglIJSQkYPny5cjNzcUjjzyCxx9/XJ0PiMi6Fmop+p1l+4dr1EJeX6w+hie7FWZSkXlgTakKYgvzy7Ny8/Hi7/sQd6kwO+rkxSu4kJ6tpvZ9MKip1s0jIgthLv2lZPsYM2vWLDz88MMWdSzmIL9Ah7u/2IhdcSnoFB2A7x5tXebvmIhsS0X0lc8//zw2bNiAdevWGX1eLmnCwsIwfvx4TJgwQT0mnxccHIzZs2fjvvvuw8GDB1G/fn1s27YNLVu2VPssWbIEffr0wenTp9XrTXEsRGR6f+w6gzE/74aTgx2+fKglAr1KJ1VUD/BgbcwKVN7+kr9xKjdXJwd8cO+/waddcckYMH0jft95Bnc3D0f7WgGato+I6EZwTKZiOdjb4b17mqDPR+uwLvYCbv94PVyd7OHkYI/HOtXAbfWDtW4iEVmwv/76S2U93XPPPVizZg2qVq2KJ554AsOHD1fPnzhxQmW8ypQ9PbkYatOmDTZt2qSCUvJTpuzpA1JC9re3t8eWLVvQv3//ci1wQUSWp2/TMCzYexb/HEzCw7O2Gd2nTrAXFj7VUS3eQqbD3zbdtGaRVfDg1RUMXvpjv8qkIiIi21Uz0BPP966rbkuK/M64FGw5cQmjftypVnIlIrpZx48fx/Tp0xEdHY2lS5eqVVOfeuopNVVP6KdgS2ZUUXJf/5z8DAoKKva8o6Mj/Pz8DPuUNHnyZBXc0m+y4ioRWR7J3p7UvxGahPsgyMul1CYZVIcTL2PhvgStm2pzmClF/8mEnnVUwfMTFzIwZu5u1A0tnLvbMsoPHaOZOUVEZGsebl8N9UK9kZqZq+7/si1eFUAf+cMOLHiqE/w8nLVuIhFZoIKCApXhNGnSJHW/WbNm2L9/P7744gsMHTq00j73hRdewLhx44plSjEwRWSZgrxd8efojkaf+3hFrKqf/PmqY7izcRjs7VmCwFSYKUX/iberE16/q4G6veTAOUz7J1ZtQ2dt5ag4EZGNjkS2reGPng1C1PbhfU1VjYazqVl4eu4uVXuKiOhGyYp6Ug+qqHr16iEuLk7dDgkJUT8TExOL7SP39c/Jz6SkpGLP5+XlqRX59PuU5OLiomqhFN2IyPoMbVcNni6OKlvqn4PF+xGqXAxK0X/Wu2EIJvZtgCFtI9XWLNJXXXSM/WU3MnM4pY+IyNYHL6YPaa7qS0mtqe4frEHvj9ap2lMz1x7TunlEZCFk5b3Dhw8Xe+zIkSOIiiosJSGr7UlgacWKFcWymqRWVLt27dR9+ZmSkoIdO3YY9lm5cqXKwpLaU0Rku3zcnfBgu8L+5LNVR1l71IQ4fY8qZFT8wXbVDPdTruSgx4drcfx8BqYsPog3+jbUtH1ERKStuiHemDKgsRqskOneelJ3ysXRAUPb/3sOISIyZuzYsWjfvr2avjdo0CBs3boVM2fOVJv+++iYMWPw1ltvqbpTEqR65ZVX1Ip6/fr1M2RW9erVSxVHl2l/ubm5GD16tCqCXp6V94jIug3rWB2zNpzAntOpWH/0AjpFB2rdJJvAoBRVOF93Z7UC00PfbMW3m06pVflaRlUxPCcrNBERkW3p16wqmkT4Iv7SFXV/0/GLmL76GN74+wAi/d3RtU7x4sNEREW1atUK8+fPVzWe3nzzTRV0mjZtGgYPHmzY59lnn0VGRgYef/xxlRHVsWNHLFmyBK6uroZ9fvzxRxWI6tatm1p1b+DAgfj44481OioiMicBni64r1UkZm88ibE/70ZVX7dS+7Su7ocX+9RTgXCqGHY65qVVCEkPlhU5UlNTOdf8qtf/OqD+oIuqGeiBOcPbItj73y8HRGRbrKm/tKZjMTX5+vHcb3vxy/bTqoaDDGb4uDmp5xpW9YaXa+FtIrJ81tRXWtOxEFFpCamZuPW9Nci8xsryv41sjxZXky7ov/eXzJSiSvNcr7rYczoFu+L+LXh+7HwGRvywA3Mfb6umbBARkW2SEca3+jVC3KUr2Hz8kjo36Pl7OKvzRHRw4YquRERERKYQ6uOGxU93wvEL6aWem7s1HstiEtUUPwalKg6DUlRp3JwdMP+JDoYicacuXsFdn65XQapX/ziAKQMbMe2RiMiGOTva44shLfDi/H2ITSz88pd8JQcX0nMw+KstmDeiHaL8PbRuJhEREdmQagEeaitJZvtIUGrx/nMqo0oCWPTfMShFlU4feJI/7E8eaI5HZm3Fz9vjUbWKm6HWVISfu9qIiMi2SK3Bzwe3MNxPzsjBfTM3qyWZH/hyC2Y90gpV3J1Lva6KuxMcHbiIMBEREZlGgzAfVVNq64lL+HFzHCb0rKN1k6wCg1JkUl1qB6ppfZMXH8IHy48YHpe41bR7m6Jv06qato+IiLRVxcMZPzzWBvfO2ITjFzLUaq7GRPi5YdbDrVEryNPkbSQiIiLb9Ej7aiooNWdrHEbfWguuTixJ819xiJFM7vHONfB0t2jUCfZSW5S/O2SG3/hf9mDV4SStm0dERBoL9HJRgalGVX3UoEXJTcRfysR9Mzfh8LnLWjeXiIiIbMRt9YPVqnyXMnLw156zWjfHKnD1vQrClThuXkGBDmN+3q3+qF2d7PHjY23QIspP62YRUSWxpv7Smo7FksgXwSFfbUFMQpqaxvfpA80Nq7qG+Liq1fyIyHxYU19pTcdCRDfnizXHMGXxIYT6uBoteF7N3wNjb6sNB3vbrp+cxtX3yFLY29uppcDTsnKx+vB5DJy+yTASHuzlqp7rGB2gdTOJiMhM+Hk4Y87wNnjom63YezpVFUXX83B2wMf3N0O3esGatpGIiIis032tIvDxilgkpGZhwd4Eo/tIPeW7W4SbvG2WiJlSFYSjJv9dZk4+Hv9+O9bFXij2uJODHT66rxn6NArVrG1EVHGsqb+0pmOxRDKYMeGXPdh68pK6n5evQ3p2HmRgcmK/hhjcJkrrJhKRlfWV1nQsRHTz9p5OwY5TyaUe33cmFb/vPINIP3esGN8FTja8KEsaM6XI0rg5O+C7R1uraRkFOqBAp8Obf8dg4b4EjJqzEy/1qYdmkaXTI33dnVAzkIVuiYhsjberE2Y+1NJwPze/AC/N34dftp/GS/P3qy+LUvdBhPm6qRFLW/5ySERERBWjcbiv2kq6kpOHtUfOI+7SFfy64zTubx2pSfssCYNSZFbs7Ozg7+liuC9TMHzcnTBnSxzeWniwzNcNbhOJN+5qwOXBiYhsmASc3hnYGFV93fHhP0fUSGVRf+w6o+pPSSF1IiIioorm7uyIkbfUwsQFMfh05VEMaF4VLo5coe9aGJQisybF4d7u1xDhVdxUpDlfUqiKkMmn8clX8OOWOJxOzsSnDzSDl6uTZu0lIiLtBzee7h6NRuHeWHP4vHosX6fDH7vOYsuJS7jzk/X45IFmiA4qzLCVpZy5nDMRERFVFEmYmLn2GM6kZOKXbfF4sF01rZtk1lhTqoJwfrl2lh44h6fn7kJWbgFqBHqgydU0SndnBzzasTqn9hGZGWvqL63pWKzd0aTLePz7HTh+PqPY47Lq6zM96+LRDtVUQIuIKp419ZXWdCxEVHm+23QSr/55AN6ujqgXWrqvqOLujLf6N0RAkVlC1qa8/SWDUhWEJyjtC80N+3Y7zl/OLva4LAv+4b1NcVt9rsJEZC6sqb+0pmOxBZezcvH87/uwaF+CyrQt6ta6QXj37sbFppATUcWwpr7Smo6FiCpPdl4+ur2/Rs3mKcuDbaPUwizWyiKCUpMnT8bvv/+OQ4cOwc3NDe3bt8c777yDOnXqGPbJysrC+PHjMXfuXGRnZ6Nnz574/PPPERz8b5AhLi4OI0eOxKpVq+Dp6YmhQ4eq93Z0/Hd24urVqzFu3DgcOHAAERERePnll/Hwww8Xa89nn32Gd999F+fOnUOTJk3wySefoHXr1uU6Fp6gtJeUloXF+88hJ69A3V8ek2hYkempW2uhS50gddvF0V5Fq2VqIBGZnjX1l9Z0LLZEPxVcvgL9tDUOExceVOcOGciQxTNK8nB2xPDONTCweVVmUxHZeF9pTcdCRJUrITXT6Ap9CSlZeHvRQTg72GP1M7eoxViskUUEpXr16oX77rsPrVq1Ql5eHl588UXs378fMTEx8PDwUPtIsGnhwoWYPXu2OqDRo0fD3t4eGzZsUM/n5+ejadOmCAkJUQGlhIQEPPTQQxg+fDgmTZqk9jlx4gQaNmyIESNG4LHHHsOKFSswZswY9b4S5BI///yzet0XX3yBNm3aYNq0aZg3bx4OHz6MoKDCYMa18ARlfmQVprcWxODbTadKPdc80ldlUEX5F/5/RkSmY039pTUdiy07mJCGJ3/ahaNJ6dfcr0+jELzdrxGqeDibrG1E1sCa+kprOhYi0s69MzapWpdD2kbirX6NYI0sIihV0vnz51UAaM2aNejcubNqfGBgIObMmYO7775b7SNZVfXq1cOmTZvQtm1bLF68GHfccQfOnj1ryJ6SwNJzzz2n3s/Z2VndlgCUBLz0JBiWkpKCJUuWqPsSiJLg2KeffqruFxQUqIyqJ598Es8///x1284TlPmSAulfrj2OzNx8dV+m+MltD2cHvHpnffRqEKoed3CwU6PkRFS5rKm/tKZjsXUykHEo4bIqil7S+tjzmPZPLPIKdAjwdEb1gMIBDWdHewxqGYG7moQxg4rIRvpKazoWItLO5uMXcd/MzXBysMPqZ7qiqhVmS5W3vzSrK3BprPDz81M/d+zYgdzcXHTv3t2wT926dREZGWkISsnPRo0aFZvOJ9lPkmElU/WaNWum9in6Hvp9JFtK5OTkqM964YUXDM9LNpa8Rl5Llu3uFuFq0zudfAXjf9mjItPP/bZPbUVrirzVr6HVplASEZFxTg72aBTuY/S5phG+6FI7CGN+3oVj5zNwIT3H8NyGoxfx284zeKtvQ0T6u5uwxURERGSp2tbwR7sa/th0/CI+X3UUb/e3zmyp8jCboJRkJkmQqEOHDmqqnZDaTpLp5OtbuJqangSg5Dn9PkUDUvrn9c9dax+J3GVmZiI5OVlNAzS2j2RmGSP1rWTTk/ciyxBexR1zhrfFV+uO4+MVscjIKcygEisPJeG2D9bg+d510a1e6eLoMhAe7OUKe9ajIiKyKRKwWvhUJ2w6dhFZVzNvZdrfF2uPY+2R8+j+4RoEGimS7uhghx71gzG6azR8jNSrIiIiIts0pns0Ns28iF+2xyM2sXQJAXcXB7x+ZwNUu5qhba3MJig1atQoNb1u/fr1sARSSP2NN97Quhl0k6TI+f+61MRjnWqg4OpUjZMXMtSqTFKM7pU/D6jNmJqBHpjUvxHa1PA3cauJiEhLrk4O6Fr33zqTvRuFon/zcLz8xz6VMXUmxfgKO1+uO4F5O07jqVuj0bl2oHpMxjYi/dzh6GBvsvYTERGR+ZDryU7RAVgXe8GwQFdJb+AAZj1SvsXXLJVZBKWkePmCBQuwdu1ahIf/O81KipfL1Dqp/VQ0WyoxMVE9p99n69atxd5Pntc/p/+pf6zoPjKvUVb9c3BwUJuxffTvUZJM9ZPV/IpmSkkNKrK84JQDCrOeooO9MO9/7fD95lP4ZOVRpGXlGl2xSaZu3DtzM+5tGYFBrcINdURqBnhyFJyIyMZIfakfhrVRRdKvFMm8LbryzgfLj+BIYjreXBBT7Llq/u4qM7dngxDWpCIqh9dff73UoLCs2i0zG06ePInq1asbfd0vv/yCe+65R9029rf2008/qXqzRESm9ukDzVUWtj5RQk++Uzz/216sOnweW45ftOqECE2DUlJjXQqJz58/H6tXry51ImnRogWcnJzUankDBw5Uj8lqeHFxcWjXrp26Lz/ffvttJCUlGVbJW758uQo41a9f37DPokWLir237KN/D5kiKJ8ln9OvXz/DdEK5LwEzY1xcXNRG1kWm5Q1tX01txqReycWUJYfUEuI/b49Xm567swOGdayulg33dmVwiojIVshFrgxsGNMkwhfd6wXjl+2nMXPtMaRkFg54ZObk4+TFKxjxw060ruaHLnUC1RRxO9ihRVQVtK5eWF+TiIpr0KAB/vnnH8N9R8fCyxkZHJZVuIuaOXOmWp27d+/exR6fNWuWWgVcr2SpECIiU/Fxc0KvhsYTYXbFJePHLXF4Z8kh/DayvdUOYDlqPWVPVtb7888/4eXlZagBJRXaJYNJfg4bNkxlJEnxcwk0SRBLgklS5Fz06NFDBZ8efPBBTJ06Vb3Hyy+/rN5bHzQaMWKEWlXv2WefxaOPPoqVK1eqERNZkU9PPmPo0KFo2bIlWrdujWnTpiEjIwOPPPKIRr8dMkeSCTV5QCMMbF4VU5ccxrm0LPW41BdJupytMqwk0+qW2oGwN9JpyIj64LZR8ONy4kRENkOm6D3QJlJteunZeZix5hi+XHdcpeyXTNuXdP4JPeqooBYRoVgQythMBpn1UPJxGfgeNGgQPD09iz0uQaiyZkMQEZmLp7tF47edp7EzLgXLYxLRo4F19lt2OklX0urDy4j0yejFww8/rG5nZWVh/PjxKq1WCovLqnmff/55sRPJqVOn1Gp7km3l4eGhgktTpkwxjJwIeW7s2LGIiYlRUwRfeeUVw2foSeBKRlMksNW0aVN8/PHHaNOmTbmOhcvD2jb5M1p64BzeW3ZETeG4Flcne9zXKhJ9m4ap1Z5EsLcrAr2YeUe2wZr6S2s6FtKGTO+bvfEkkjMKV/TLyM7HsphzyM0v/Hom5wZj35bqhnpjRJcaauUeax05JetRUX2lTN+T7+ryXq6urmqgWuq8ysrcJcnK2jLYvGHDBrRv397wuPy9hIWFqeuKGjVqqMFrGYQu798R+30iMqWpSw7h89XHEB3kiSVjOqvyM5aivP2lpkEpa8ITFOlrTi07cA6nk0sXu80r0GHRvgTsO5Na6jlHezvc3SIco7rWQoQflxQn62ZO/aXUMpQLHLl4kWkfMqqun8ZtacdC1iP+0hV8tCIWv+88jYLrfEuTqX53NA5V5xER6e+BTrUCuEosmZWK6isXL16M9PR0VUdK+mypL3XmzBm1WJLMuijqiSeeUIPSMiBd1MSJE3HrrbfC3d0dy5Ytw2uvvaZmWzz11FPlXnFbpgqy3yciU0jNzEXnqavUTz0XR3t8MKgpbm8cCnPGoJSJ8cKEykP+3DYeu4gZa4/jyLnL6jEpaidT/4RcVMiUDRdHB3Xf39MZ97SMQFNO3yArYk79pVzgyCi61BUcMGAAg1JkVpLSsnAhvTCDqqic/AIVsJq7LR45eQWlnq8V5InhnarjriZV4eRQOjglo6zMriJTqqy+UhZDioqKwgcffKBKfuhlZmYiNDRUzYyQGRfX8uqrr6pZGvHx/9YJvV5xdcF+n4hM5fvNp/DKH/uLPRbq44pVE25RKwObKwalTIwXJvRf7Dh1CdP+iVXLgRrTONxHjYQb63S8XB3RrV4wi6uTxTDX/lIu0hmUIksLWn276SROXMgwZOtuPHoRl7PzrltU9Z4W4RjSNgrVAjxM1FqyZZXZV7Zq1Qrdu3dX0/j0vv/+exWkkiyqwMDAa75easzecccdqmSIsUWMmClFROYgLStXDUTl5evQ//MNSEjNwit31FcLbVl6369poXMiKtQiyg/fD2ujVlg4cDZNPSbR4l2nkrFgbwL2nk5VW1ncnBxU0Kpfs6qG4JSbswNqBnpwNJyIyEoFebvimZ51iz12OSsXc7fG45sNJ9QXVmNkCsBX60+orXmkLzxcSn8d9HB2xF1Nw3Bb/WBD/UMicyNT+Y4dO6YWPCrq66+/xl133XXdgJTYvXs3qlSpUuaq2lxxm4jMgXeRBAQpgP787/vw+aqjuLdVBDyNnMctiWW3nsjKNIusoja9B9tG4aXb62HejtPYV0ZQ6nDiZVVcXfaRragagR6qVtVdTcJQxd3Z6BQOc075JDI3xkbMicyJl6sThneuoUZOL2eVzpjSQYedccn4btMprD58Xq3oU5YlB84h2NsF/ZpWhbebk6GOhQSqovyZYUWmN2HCBNx5551qyt7Zs2dVPShZde/+++837HP06FFVL3DRokWlXv/3338jMTFRreIthdKXL1+OSZMmqfclIrIUA1uEq3Iwkin9zfoTeKpbNCwZg1JEZs7f0wUjutQs83mZgbvjVDLmbI3DluOX1H1xMSMHx89nYOqSw2orS5NwH/RqGIqeDYKNrgBob2cHd2cHZlwRAWp6iLHaIkTmRgqd+7gbn9Z9a91gtZ28kIE9p1NgrJCDDHZIzarEtGz1xbeotxYeRPua/hjYPBx+nqUHPJzs7dE8yhfuzvyaSRXr9OnTKgB18eJFlQXVsWNHbN68uVhG1DfffKNW2u7Ro0ep1zs5OeGzzz5TK3LL96VatWqpelTDhw838ZEQEd08Jwd7jL2tNp76aRe+XHtc1R8umdUsNSUbh/vC2dH8s51ZU6qCsK4ImZv07Dws2puAX3ecxtaTl/7Te0nR3H5Nw1TR3Eh/rg5ItltTirVFyJZI7QrJltp07CIKri4DeCYlExuOXTAayCrKw9kBfRqFon+zqmqaYUmyOGCknzscOTXQJphrv2/rx0JElqugQIfbP1mPgwllZ+3f1yoCUwY2hlZY6NzEeIIic5adl4+CAuN1RVYcSsSS/YUXHXnXW3tcXbhf/QmgXqi3Wor0jkZhDFaRxfeXLHROVD7xl65g3vZ4rIm9gHwjJ5dL6Tk4W0Y9q6ICPJ3Rq2GICl5JgEr/dxji7aqml5P1sKa+0pqOhYgs245TyXj9rwPIys0v9rjuasaznEqXj+uCmoGemrSPQSkT4wmKLF1efgHyjXQHmTn5WB6TiD93n8XGYxdQVtzKuYzR7ih/d9xaNwhd6wapjCu7MmqgWEJqKVlffylFcqX+iGjWrJmaxtG1a1f4+fkhMjLSoo6FyFzIV8vtp5Lx6/bTWHk4SWVcGRssycotMD5t3cMZPRoEo2eDENQIMP5FOsjbhTURLYg19ZXWdCxEZL2Gzd6GFYeSVG3hj+9vpkkbGJQyMZ6gyFamBEqQSuTkF2DtkfNYuDfhmsGq8pDVA1tWq4IOtQJQJ8RL1bHSX5jUDfHi9A4rY0795erVq1UQqqShQ4di9uzZFnUsRJYkN78AG49dxMK9Z7HyUJI6v4j8Ah1y869/QpFahzLgIVlWcp4wVvfQz8MZPlcLtJO2rKmvtKZjISLrtf9MKu74ZL2a5bJ0TGfUDvYyeRsYlDIxnqDIlqVl5SLdyCpPcnGxOz5FXXCsPpyE5Cu5N/zeXi6OKmAlUwWNTefwcnVEx1qBqBdq/KKEzI819ZfWdCxE5hKskkU7lhxIwMqDSWqaeUmS1VtWllVRcs5oEVUF3esFoUm4ryr+rh/wqB7gwXOGCVlTX2lNx0JE1m3E9ztUbcg+jULw+eAWJv98BqVMjCcoopsjXdCRxHRsOHpBZVydTSmsQyId0+nkK0aXNDcm1McVrav7lVp5QsjUQFllsHV1f1Tzd+eFiMasqb+0pmMhsqTzxt7TqVi0PwHLDyTifHq2kZ2Ay1ezr4yp6uuGzrUD1bmhrAGPVtX81Aq49N9ZU19pTcdCRNbt8LnL6PXRWrU4yWcPNFdT34uSs1/9MO9KWy2XQSkT4wmKqOJJppWsKLH5+EVVWNeY08mFK0GVZ9RcyFQOV6fCwJUEsJpE+KJtDX+0jKoCT5fSHbLEr0J93Fh0twJZU39pTcdCZG3kvCGZulJT4/TVc4h86T2TnKmmoJdH/VBvla1rrG6i1LRqHuWLltX84O3KaYK20lda07EQkfV78qdd+HvP2TKfb13NDz//r22lDNqXt7+snJAYEVEFkEBQw6o+arsWWXFCVg88knhZXXCUJNM/dpxMVlMJ5XZqZvGgltTFul7tkoZhPmgU7mP0wkP6cBl1l3pYUsydxXeJiLQX4eeOoe2rqa2oKzl5aorgmiPncepihtHXJqRm4dC5y4hJSFPbtciYRXSQF1ydS/f9TvZ26tzRroY/2lT3h6er8a/eHPggIqLK8GzPOmqQxth0eJmVsvXkJSw9kKhWw9UKM6UqCEdNiMyfBK9OXMhQGVj6WljbTyarTKx9Z1INjxeVl68r94i6kOsKKdxuTLCPq6pr0jjcB8Herkb38XV3UiPzvu7OsFbW1F9a07EQUXHnL2eraeUSnDL2bTk5I0d9mZfzyn9VI9ADbar7oWWUH/w8jff/1fw9LHYKujX1ldZ0LERk295behifrjqKmoEeqhh6RS8uxel7JsYTFJF1kkDV8fPpKsvqYMJltYy5sX3kouRw4mWk3EQxd2Mk80o2Ndm7BG9XR1X4vUGYN6oFeMDh6gWKu4sjwnxczf6CxZr6S2s6FiK6OQmpmTiUcNnowEZGTh62nriksnmPV0DwKsDTBa2qVUGIz78DG2E+bmgeVQUNq3rDxdE8M3Wtqa+0pmMhItuWlpWLLlNXqcWopgxohPtaR1bs+3P6HhHRfydTKqKDvdR2PRLjl4K7WTmlM6sKdIWBqz2nU9QSrWnGCrjrgIS0TMRfysSZlMKtLP8cTDL6uNTFkmmEUX7uhpWminJxtFerTtUM9ESkvzuc7EuPiDg42CHE25XTSYiIykHqDspWlr5Nq6qfl7NyjQaucvIKVOF2ybraFZdstEZiXoEOx5LScSE9G4v3nzP6OVL3yr+MLCsJZkmWrmwyrdHOyIiHt5ujWjLc2IIhRERkfbxdnTCqay28tfAgpv0Ti37NqmpShoSZUhWEoyZEVFFkznfM2TRcysgx+vz5y1k4cDZNbTJCr5eenYfc/Irp0qUYvNRIkXTeskbeZVT+wXbFa7XYWn9pTcdCROY/BV0GNXacSlaj20JiXLGJ6SqYdbGMc8aNkIELqeNYI8AD9mVk3Q5pG6XqZNlqX2lNx0JElJ2Xj1vfW6MGwztFB6jsW2MD1pP6N7rh92amFBGRhZIVAtvV9L/h1+XmF6hsLFmx8GxKltF9MrLz1D5Hk9LVycfYuITU0JKReqmzJVtZUjNDbiooRUREN05Gr2WlP9lKkr5csmz1warizwFxl65g7+kUla17Md148CoxLUtl8UrQS7ay3FIn8IaDUkREZJ5cHB0wvkdtjPtlD9bFXigzE/dmglLlxaAUEZGVkCkXMvVCtv9CppfIBczhc5dx8uK/heFLkiwqIiLSntQSlCnZZZEg0u2NQ6/5HgUFOtXnSw1FWX2wLOWZzk5ERJajf7OqagDjXJrxvr+yS3owKEVERKVOPFJ3SjYiIrINUoewRqCn2oiIyLYGNga2CNfs81nJkIiIiIiIiIiITI5BKSIiIiIiIiIiMjkGpYiIiIiIiIiIyOQYlCIiIiIiIiIiIpNjUIqIiIiIiIiIiEyOQSkiIiIiIiIiIjI5R9N/pHXS6XTqZ1pamtZNISIya/p+Ut9vWjL2/URE18d+n4jI9qSVs+9nUKqCXL58Wf2MiIjQuilERBbTb/r4+MCSse8nIio/9vtERLbn8nX6fjudNQxZmIGCggKcPXsWXl5esLOzK1fUUE5m8fHx8Pb2hq2wxeO2xWO21eO2xWO+meOW046cnMLCwmBvb9mzyNn3X58tHrOtHrctHrOtHjf7ffb712OLx22Lx2yrx22Lx1yZfT8zpSqI/JLDw8Nv+HXyj2lL/yPb8nHb4jHb6nHb4jHf6HFb+ki5Hvv+8rPFY7bV47bFY7bV42a/X362+P+HrR63LR6zrR63LR5zZfT9lj1UQUREREREREREFolBKSIiIiIiIiIiMjkGpTTi4uKC1157Tf20JbZ43LZ4zLZ63LZ4zLZ83DfDFn9XtnjMtnrctnjMtnrctnjMN8tWf1e2eNy2eMy2ety2eMyVedwsdE5ERERERERERCbHTCkiIiIiIiIiIjI5BqWIiIiIiIiIiMjkGJQiIiIiIiIiIiKTY1BKI5999hmqVasGV1dXtGnTBlu3boW1WLt2Le68806EhYXBzs4Of/zxR7HnpYzZq6++itDQULi5uaF79+6IjY2FJZs8eTJatWoFLy8vBAUFoV+/fjh8+HCxfbKysjBq1Cj4+/vD09MTAwcORGJiIizZ9OnT0bhxY3h7e6utXbt2WLx4sVUfc0lTpkxR/5+PGTPGqo/79ddfV8dZdKtbt65VH3NFs+Z+X7DvZ99vzcdcEvt+6z3misa+n32/NfxtsN9nv1+Z/9YMSmng559/xrhx41Tl+p07d6JJkybo2bMnkpKSYA0yMjLUMclJ2JipU6fi448/xhdffIEtW7bAw8NDHb/8D26p1qxZo/44N2/ejOXLlyM3Nxc9evRQvwu9sWPH4u+//8a8efPU/mfPnsWAAQNgycLDw9UX8x07dmD79u249dZb0bdvXxw4cMBqj7mobdu2YcaMGeokXZS1HneDBg2QkJBg2NavX2/1x1xRrL3fF+z72fez77fO42bff/PY97Pvt5a/Dfb77Pcr9Zhl9T0yrdatW+tGjRpluJ+fn68LCwvTTZ48WWdt5H+x+fPnG+4XFBToQkJCdO+++67hsZSUFJ2Li4vup59+0lmLpKQkdexr1qwxHKOTk5Nu3rx5hn0OHjyo9tm0aZPOmlSpUkX31VdfWf0xX758WRcdHa1bvny5rkuXLrqnn35aPW6tx/3aa6/pmjRpYvQ5az3mimRL/b5g38++31qPmX3/v6z1mCsS+372/db8t8F+3zqP+zUN+n1mSplYTk6OijBL6qqevb29ur9p0yZYuxMnTuDcuXPFjt/Hx0elM1vT8aempqqffn5+6qf8m8soStHjlvT3yMhIqznu/Px8zJ07V40SSUqvtR+zjJDdfvvtxY5PWPNxS7q9pOfXqFEDgwcPRlxcnNUfc0Ww9X5fsO+33r8N9v2FrPm42fffHPb97Put9W+D/X4haz7uWBP3+44V0moqtwsXLqg/5ODg4GKPy/1Dhw7B2smJSRg7fv1zlq6goEDVmOjQoQMaNmyoHpNjc3Z2hq+vr9Ud9759+9QJSdKwZV7x/PnzUb9+fezevdtqj1lOxJKGL6m8JVnrv7V8gZw9ezbq1Kmj0njfeOMNdOrUCfv377faY64ott7vC/b91ve3wb6/OGv9t2bff/PY97Pvt7a/Dfb7xVnrv3MbDfp9BqWIKiGaLn+0RefeWjPpsORkJKNEv/76K4YOHarmF1ur+Ph4PP3006qGgBQttRW9e/c23Jb59HLCioqKwi+//KIKlxLZOvb97PutEft+omuzpb6f/b5t6K1Bv8/peyYWEBAABweHUhXq5X5ISAisnf4YrfX4R48ejQULFmDVqlWqIKCeHJukcaekpFjdcUu0vFatWmjRooVajUSKXX700UdWe8yStioFSps3bw5HR0e1yQlZinjKbRkpsMbjLklGSGrXro2jR49a7b91RbH1fl+w77e+vw32/ez7rfXfuqKw72ffb21/G+z32e+HVNK/NYNSGvwxyx/yihUriqV9yn1Jh7R21atXV//DFj3+tLQ0tRqHJR+/1HaUE5Oksa5cuVIdZ1Hyb+7k5FTsuGXpWJmfa8nHbYz8/5ydnW21x9ytWzeVviwjRfqtZcuWar61/rY1HndJ6enpOHbsmFri2Vr/rSuKrff7gn2/9f9tsO9n32+tx3yz2Pez77f2vw32++z3K+yYb7pEOt20uXPnqlUnZs+erYuJidE9/vjjOl9fX925c+d01kBWKNi1a5fa5H+xDz74QN0+deqUen7KlCnqeP/880/d3r17dX379tVVr15dl5mZqbNUI0eO1Pn4+OhWr16tS0hIMGxXrlwx7DNixAhdZGSkbuXKlbrt27fr2rVrpzZL9vzzz6uVRk6cOKH+LeW+nZ2dbtmyZVZ7zMYUXYnDWo97/Pjx6v9v+bfesGGDrnv37rqAgAC14oy1HnNFsvZ+X7DvZ9/Pvt/6jpt9/3/Dvp99v7X8bbDfL8R+X1cpx8yglEY++eQT9Y/p7OyslovdvHmzzlqsWrVKnZRKbkOHDjUsD/vKK6/ogoOD1Ym6W7duusOHD+ssmbHjlW3WrFmGfeTk+8QTT6jlU93d3XX9+/dXJzBL9uijj+qioqLU/8eBgYHq31J/crLWYy7PCcoaj/vee+/VhYaGqn/rqlWrqvtHjx616mOuaNbc7wv2/ez7rfmYjWHfb53HXNHY97Pvt4a/Dfb7hdjv6yrlmO3kPzefZ0VERERERERERHTjWFOKiIiIiIiIiIhMjkEpIiIiIiIiIiIyOQaliIiIiIiIiIjI5BiUIiIiIiIiIiIik2NQioiIiIiIiIiITI5BKSIiIiIiIiIiMjkGpYiIiIiIiIiIyOQYlCIiIiIiIiIiIpNjUIqIiIiIiIiIiEyOQSkiIiIiIiIiIjI5BqWICDk5OVo3gYiITIx9PxGRbWG/T+aIQSkiM/brr7+iUaNGcHNzg7+/P7p3746MjAz13DfffIMGDRrAxcUFoaGhGD16tOF1cXFx6Nu3Lzw9PeHt7Y1BgwYhMTHR8Pzrr7+Opk2b4quvvkL16tXh6uqqHk9JScFjjz2GwMBA9bpbb70Ve/bs0eDIiYhsF/t+IiLbwn6fbBmDUkRmKiEhAffffz8effRRHDx4EKtXr8aAAQOg0+kwffp0jBo1Co8//jj27duHv/76C7Vq1VKvKygoUCenS5cuYc2aNVi+fDmOHz+Oe++9t9j7Hz16FL/99ht+//137N69Wz12zz33ICkpCYsXL8aOHTvQvHlzdOvWTb0XERFVPvb9RES2hf0+2TwdEZmlHTt26ORP9OTJk6WeCwsL07300ktGX7ds2TKdg4ODLi4uzvDYgQMH1Htt3bpV3X/ttdd0Tk5OuqSkJMM+69at03l7e+uysrKKvV/NmjV1M2bMqMAjIyKisrDvJyKyLez3ydY5ah0UIyLjmjRpokYsJJW3Z8+e6NGjB+6++27k5ubi7Nmz6jljZIQlIiJCbXr169eHr6+veq5Vq1bqsaioKJWyqycpu+np6SpluKjMzEwcO3as0o6TiIj+xb6fiMi2sN8nW8egFJGZcnBwUGm4GzduxLJly/DJJ5/gpZdewooVKyrk/T08PIrdl5OTzFOXlOGS5ORGRESVj30/EZFtYb9Pto5BKSIzZmdnhw4dOqjt1VdfVSMdctKqVq2aOlF17dq11Gvq1auH+Ph4telHTmJiYlRBQxk9KYvMJT937hwcHR3V+xMRkTbY9xMR2Rb2+2TLGJQiMlNbtmxRJyFJ4Q0KClL3z58/r05AspLGiBEj1OO9e/fG5cuXsWHDBjz55JNqtQ5J/x08eDCmTZuGvLw8PPHEE+jSpQtatmxZ5ufJ69q1a4d+/fph6tSpqF27tkoZXrhwIfr373/N1xIRUcVg309EZFvY75OtY1CKyEzJ8qxr165VJ5m0tDQ1YvL++++rE5LIysrChx9+iAkTJiAgIEDNPdePtPz555/qZNW5c2fY29ujV69eKhX4WuR1ixYtUunCjzzyiDoZhoSEqPcIDg42yTETEdk69v1ERLaF/T7ZOjupdq51I4iIiIiIiIiIyLbYa90AIiIiIiIiIiKyPQxKERERERERERGRyTEoRUREREREREREJsegFBERERERERERmRyDUkREREREREREZHIMShERERERERERkckxKEVERERERERERCbHoBQREREREREREZkcg1JERERERERERGRyDEoREREREREREZHJMShFREREREREREQmx6AUERERERERERHB1P4PXCIV52eAGVAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from io import StringIO\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "cutoff_analysis_df = pd.read_csv(StringIO(cutoff_analysis), sep=\"\\t\")\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(12, 3), sharex=True)\n",
+    "\n",
+    "axes[0].plot(cutoff_analysis_df[\"score\"], cutoff_analysis_df[\"npeaks\"])\n",
+    "axes[0].set_title(\"npeaks\")\n",
+    "axes[0].set_xlabel(\"score\")\n",
+    "axes[0].set_ylabel(\"value\")\n",
+    "\n",
+    "axes[1].plot(cutoff_analysis_df[\"score\"], cutoff_analysis_df[\"lpeaks\"])\n",
+    "axes[1].set_title(\"lpeaks\")\n",
+    "axes[1].set_xlabel(\"score\")\n",
+    "\n",
+    "axes[2].plot(cutoff_analysis_df[\"score\"], cutoff_analysis_df[\"avelpeak\"])\n",
+    "axes[2].set_title(\"avelpeak\")\n",
+    "axes[2].set_xlabel(\"score\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "556280cf-daa6-47d9-86b6-513c8d4e969d",
+   "metadata": {},
+   "source": [
+    "According to the cutoff analysis, the q-score cutoff 2 (q-value 0.01) seems to be a good cutoff where it seems most of background noises (with smaller q-score) have been removed so the average peak length reaches the maximum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "028f0738-882d-49df-b86e-6695a926fa99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks = score_track.call_peaks(cutoff=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a488ebbf-12f3-45b1-a9e6-cc9906060918",
+   "metadata": {},
+   "source": [
+    "We can check the number of peaks and the total length of peaks by converting peaks to regions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "069c942b-dfec-48bb-a5ed-b234b1493aa3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of peaks: 53594\n",
+      "Total basepairs of peaks: 37944522\n"
+     ]
+    }
+   ],
+   "source": [
+    "# make Regions for peaks from all cell barcodes\n",
+    "regions = Regions()\n",
+    "regions.init_from_PeakIO(peaks)\n",
+    "print(f\"Number of peaks: {regions.total}\")\n",
+    "print(f\"Total basepairs of peaks: {regions.total_length()}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "223681f8-e67b-4a92-8870-3814037cacc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# with open(\"peaks.narrowPeak\",\"w\") as f:\n",
+    "#     peaks.write_to_narrowPeak(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99fa7fc8",
+   "metadata": {},
+   "source": [
+    "### Return Anndata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "8729f32a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ncls\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 0, 0, ..., 0, 0, 0],\n",
+       "       [2, 0, 2, ..., 0, 0, 0],\n",
+       "       [0, 0, 2, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata_peaks = petrack.return_anndata(regions)\n",
+    "adata_peaks.X.toarray()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "488e27c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 0, 0, ..., 0, 0, 0],\n",
+       "       [2, 0, 2, ..., 0, 0, 0],\n",
+       "       [0, 0, 2, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata_peaks = petrack.return_anndata(regions,method=\"two_pointer\")\n",
+    "adata_peaks.X.toarray()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "f7e29e57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Basic preprocessing\n",
+    "sc.pp.normalize_total(adata_peaks, target_sum=1e4)\n",
+    "sc.pp.log1p(adata_peaks)\n",
+    "sc.pp.pca(adata_peaks)\n",
+    "sc.pp.neighbors(adata_peaks)\n",
+    "sc.tl.umap(adata_peaks)\n",
+    "sc.tl.leiden(adata_peaks, key_added=\"cluster\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88b8b73f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "cluster",
+         "rawType": "category",
+         "type": "unknown"
+        },
+        {
+         "name": "Barcode",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Cluster",
+         "rawType": "category",
+         "type": "unknown"
+        }
+       ],
+       "ref": "b9acb300-c870-422c-bf14-3345e58e7a1c",
+       "rows": [
+        [
+         "GCGAGAAGTCCACCAG-1",
+         "2",
+         "GCGAGAAGTCCACCAG-1",
+         "2"
+        ],
+        [
+         "TTCGATTCATCTCTCG-1",
+         "2",
+         "TTCGATTCATCTCTCG-1",
+         "2"
+        ],
+        [
+         "CAAGGCCAGTTAGCAA-1",
+         "2",
+         "CAAGGCCAGTTAGCAA-1",
+         "3"
+        ],
+        [
+         "TTGCCCACAAGGTTCT-1",
+         "2",
+         "TTGCCCACAAGGTTCT-1",
+         "2"
+        ],
+        [
+         "TCCAGAACATAGGCGA-1",
+         "2",
+         "TCCAGAACATAGGCGA-1",
+         "2"
+        ],
+        [
+         "TTTGGCCAGTACAGAT-1",
+         "0",
+         "TTTGGCCAGTACAGAT-1",
+         "1"
+        ],
+        [
+         "CGAGTTATCAAGTTGC-1",
+         "2",
+         "CGAGTTATCAAGTTGC-1",
+         "2"
+        ],
+        [
+         "TGCATTTGTAACATAG-1",
+         "1",
+         "TGCATTTGTAACATAG-1",
+         "3"
+        ],
+        [
+         "TGATGCACAATGTGCT-1",
+         "1",
+         "TGATGCACAATGTGCT-1",
+         "3"
+        ],
+        [
+         "CTCAGAACACAGGTAG-1",
+         "3",
+         "CTCAGAACACAGGTAG-1",
+         "2"
+        ],
+        [
+         "CTAACTTCATTCGTCC-1",
+         "2",
+         "CTAACTTCATTCGTCC-1",
+         "2"
+        ],
+        [
+         "GGACACCCACTCGTGG-1",
+         "1",
+         "GGACACCCACTCGTGG-1",
+         "4"
+        ],
+        [
+         "CCCTCTCGTACGTAAA-1",
+         "0",
+         "CCCTCTCGTACGTAAA-1",
+         "1"
+        ],
+        [
+         "TGTAAGCCAAGTCCTA-1",
+         "1",
+         "TGTAAGCCAAGTCCTA-1",
+         "3"
+        ],
+        [
+         "TAGCGGCTCTGGTACA-1",
+         "1",
+         "TAGCGGCTCTGGTACA-1",
+         "3"
+        ],
+        [
+         "TAACTTCGTAAGGTCG-1",
+         "3",
+         "TAACTTCGTAAGGTCG-1",
+         "2"
+        ],
+        [
+         "ACTTTCATCCTAAGTG-1",
+         "5",
+         "ACTTTCATCCTAAGTG-1",
+         "1"
+        ],
+        [
+         "CGGACTGGTATATGGA-1",
+         "0",
+         "CGGACTGGTATATGGA-1",
+         "1"
+        ],
+        [
+         "GCACGCACAGGTGGTA-1",
+         "1",
+         "GCACGCACAGGTGGTA-1",
+         "3"
+        ],
+        [
+         "TTATGTCCATTACTTC-1",
+         "1",
+         "TTATGTCCATTACTTC-1",
+         "3"
+        ],
+        [
+         "GCCCAGAGTGGCGCTT-1",
+         "3",
+         "GCCCAGAGTGGCGCTT-1",
+         "2"
+        ],
+        [
+         "TAGGAGGAGGACTAGC-1",
+         "2",
+         "TAGGAGGAGGACTAGC-1",
+         "3"
+        ],
+        [
+         "GAGACTTGTAGACGCA-1",
+         "3",
+         "GAGACTTGTAGACGCA-1",
+         "2"
+        ],
+        [
+         "GTTACGAAGGGAGTTC-1",
+         "2",
+         "GTTACGAAGGGAGTTC-1",
+         "3"
+        ],
+        [
+         "TTGCAGAAGCATGATA-1",
+         "3",
+         "TTGCAGAAGCATGATA-1",
+         "2"
+        ],
+        [
+         "TAAACCGTCGCGATGC-1",
+         "2",
+         "TAAACCGTCGCGATGC-1",
+         "2"
+        ],
+        [
+         "CCACGTTGTCTCTGCT-1",
+         "0",
+         "CCACGTTGTCTCTGCT-1",
+         "1"
+        ],
+        [
+         "CTACAGAAGCGTTGCC-1",
+         "2",
+         "CTACAGAAGCGTTGCC-1",
+         "2"
+        ],
+        [
+         "TACAGCACACTCGGAC-1",
+         "0",
+         "TACAGCACACTCGGAC-1",
+         "1"
+        ],
+        [
+         "TTTGGCCGTCCCGTGA-1",
+         "0",
+         "TTTGGCCGTCCCGTGA-1",
+         "1"
+        ],
+        [
+         "CAGTGTATCACGTCAA-1",
+         "0",
+         "CAGTGTATCACGTCAA-1",
+         "1"
+        ],
+        [
+         "CACCTGTGTCATCAAC-1",
+         "0",
+         "CACCTGTGTCATCAAC-1",
+         "1"
+        ],
+        [
+         "TTCATCATCTGTCGGG-1",
+         "1",
+         "TTCATCATCTGTCGGG-1",
+         "3"
+        ],
+        [
+         "TGCTTTACATGGTATC-1",
+         "4",
+         "TGCTTTACATGGTATC-1",
+         "4"
+        ],
+        [
+         "ATTCGTTCAGTTACAC-1",
+         "1",
+         "ATTCGTTCAGTTACAC-1",
+         "3"
+        ],
+        [
+         "GGGTTATTCACCCTTG-1",
+         "1",
+         "GGGTTATTCACCCTTG-1",
+         "3"
+        ],
+        [
+         "TACCCTGCACTGTCGG-1",
+         "1",
+         "TACCCTGCACTGTCGG-1",
+         "3"
+        ],
+        [
+         "TTTGCGCGTTCTACCC-1",
+         "0",
+         "TTTGCGCGTTCTACCC-1",
+         "1"
+        ],
+        [
+         "TGCATTTGTTCAGAAA-1",
+         "5",
+         "TGCATTTGTTCAGAAA-1",
+         "1"
+        ],
+        [
+         "CCCTCTCAGGAAGGTA-1",
+         "0",
+         "CCCTCTCAGGAAGGTA-1",
+         "1"
+        ],
+        [
+         "GGGTTATAGTTCTCCC-1",
+         "0",
+         "GGGTTATAGTTCTCCC-1",
+         "1"
+        ],
+        [
+         "TCGATTTAGGGTCTGA-1",
+         "4",
+         "TCGATTTAGGGTCTGA-1",
+         "4"
+        ],
+        [
+         "GGCATTACAATTCTCT-1",
+         "1",
+         "GGCATTACAATTCTCT-1",
+         "3"
+        ],
+        [
+         "CGGACTGCACAAGGGT-1",
+         "2",
+         "CGGACTGCACAAGGGT-1",
+         "2"
+        ],
+        [
+         "GTAGTACCAAGTGGCA-1",
+         "0",
+         "GTAGTACCAAGTGGCA-1",
+         "1"
+        ],
+        [
+         "AACGGGATCCGGCGAT-1",
+         "1",
+         "AACGGGATCCGGCGAT-1",
+         "3"
+        ],
+        [
+         "TGGTCCTCATGTATCG-1",
+         "3",
+         "TGGTCCTCATGTATCG-1",
+         "2"
+        ],
+        [
+         "GAGAACGCAGGCATTT-1",
+         "2",
+         "GAGAACGCAGGCATTT-1",
+         "2"
+        ],
+        [
+         "GGTCATATCACATCCC-1",
+         "0",
+         "GGTCATATCACATCCC-1",
+         "1"
+        ],
+        [
+         "CGCGCAAGTTGGTAAA-1",
+         "1",
+         "CGCGCAAGTTGGTAAA-1",
+         "3"
+        ]
+       ],
+       "shape": {
+        "columns": 3,
+        "rows": 426
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cluster</th>\n",
+       "      <th>Barcode</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GCGAGAAGTCCACCAG-1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>GCGAGAAGTCCACCAG-1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TTCGATTCATCTCTCG-1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>TTCGATTCATCTCTCG-1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CAAGGCCAGTTAGCAA-1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>CAAGGCCAGTTAGCAA-1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TTGCCCACAAGGTTCT-1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>TTGCCCACAAGGTTCT-1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCCAGAACATAGGCGA-1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>TCCAGAACATAGGCGA-1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CAAGAAAAGGGTAGTC-1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>CAAGAAAAGGGTAGTC-1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TACGCAAGTGTGCGTC-1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>TACGCAAGTGTGCGTC-1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TTACTCATCATGCTTT-1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>TTACTCATCATGCTTT-1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TTGGTCCGTACGTATC-1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>TTGGTCCGTACGTATC-1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TATCTGTTCTCTGCGT-1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>TATCTGTTCTCTGCGT-1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>426 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   cluster             Barcode Cluster\n",
+       "GCGAGAAGTCCACCAG-1       2  GCGAGAAGTCCACCAG-1       2\n",
+       "TTCGATTCATCTCTCG-1       2  TTCGATTCATCTCTCG-1       2\n",
+       "CAAGGCCAGTTAGCAA-1       2  CAAGGCCAGTTAGCAA-1       3\n",
+       "TTGCCCACAAGGTTCT-1       2  TTGCCCACAAGGTTCT-1       2\n",
+       "TCCAGAACATAGGCGA-1       2  TCCAGAACATAGGCGA-1       2\n",
+       "...                    ...                 ...     ...\n",
+       "CAAGAAAAGGGTAGTC-1       0  CAAGAAAAGGGTAGTC-1       1\n",
+       "TACGCAAGTGTGCGTC-1       0  TACGCAAGTGTGCGTC-1       1\n",
+       "TTACTCATCATGCTTT-1       0  TTACTCATCATGCTTT-1       1\n",
+       "TTGGTCCGTACGTATC-1       0  TTGGTCCGTACGTATC-1       1\n",
+       "TATCTGTTCTCTGCGT-1       0  TATCTGTTCTCTGCGT-1       1\n",
+       "\n",
+       "[426 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# add cell clusters \n",
+    "df = pd.read_csv(\"./analysis/clustering/graphclust/clusters.csv\")\n",
+    "\n",
+    "adata_peaks.obs =  adata_peaks.obs.merge(\n",
+    "    df,\n",
+    "    left_index=True,\n",
+    "    right_on=\"Barcode\",\n",
+    "    how=\"left\"\n",
+    ").set_index(adata_peaks.obs.index)\n",
+    "\n",
+    "adata_peaks.obs[\"Cluster\"] = adata_peaks.obs[\"Cluster\"].astype(\"category\")\n",
+    "\n",
+    "adata_peaks.obs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "f17f0a4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sc.pl.umap(adata_peaks, color = \"Cluster\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e29ddf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "# write adata outputs\n",
+    "adata_peaks.write_h5ad(\"./adata_peaks.h5ad\")\n",
+    "\n",
+    "# write petrack as pickle file\n",
+    "with open(\"petrack.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(petrack, f)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb59c6c1-8841-4bd0-8962-e782af1eda87",
+   "metadata": {},
+   "source": [
+    "## Call peaks for the certain cluster of cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91572cf4-ac9f-468a-8312-33e8c235da04",
+   "metadata": {},
+   "source": [
+    "In this section, we will illustrate how to call peaks for a subset of cells from clustering analysis. Assuming that we have clustering results from downstream analysis in H5ad file. We will load in petrack pickle file, to eliminate need to read in fragments and build petrack object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e70d8fd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 426 × 53594\n",
+       "    obs: 'cluster', 'Barcode', 'Cluster'\n",
+       "    var: 'chrom', 'start', 'end'\n",
+       "    uns: 'Cluster_colors', 'cluster', 'log1p', 'neighbors', 'pca', 'umap'\n",
+       "    obsm: 'X_pca', 'X_umap'\n",
+       "    varm: 'PCs'\n",
+       "    obsp: 'connectivities', 'distances'"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# load pickle file or load fragments, generate petrackII.\n",
+    "\n",
+    "with open(\"petrack.pkl\", 'rb') as file:\n",
+    "    petrack = pickle.load(file)\n",
+    "\n",
+    "adata_peaks = sc.read_h5ad(\"./adata_peaks.h5ad\")\n",
+    "adata_peaks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "fb7771e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of cell barcodes in cluster 2: 107\n",
+      "Number of cell barcodes in cluster 3: 95\n",
+      "Number of cell barcodes in cluster 1: 172\n",
+      "Number of cell barcodes in cluster 4: 52\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = adata_peaks.obs\n",
+    "barcodes_cluster_dict = {}\n",
+    "for c in df[\"Cluster\"].unique():\n",
+    "    df_c = df[df[\"Cluster\"] == c]\n",
+    "    barcodes_cluster_dict[c] = set([x.encode() for x in df_c[\"Barcode\"].tolist()])\n",
+    "    print(f\"Number of cell barcodes in cluster {c}: {len(barcodes_cluster_dict[c])}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "192d7b3a-074d-4826-a760-04f86e69fe13",
+   "metadata": {},
+   "source": [
+    "In the following analysis, we will focus on cluster 1 and cluster 3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "a54c52b9-1aef-4c22-af09-7bfe8a93fa30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Take subset of fragments for c1\n",
+    "petrack_c1 = petrack.subset(barcodes_cluster_dict[1])\n",
+    "\n",
+    "# Take subset of fragments for c3\n",
+    "petrack_c3 = petrack.subset(barcodes_cluster_dict[3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60605e42-1377-4b3e-862a-7bea2c448705",
+   "metadata": {},
+   "source": [
+    "Similar to previous steps, we can generate pileup tracks for these two clusters, make the background tracks, calculate scores, and then call peaks with certain cutoff."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "430489c4-8043-4bce-93cb-909f57d04e1f",
+   "metadata": {},
+   "source": [
+    "### For Cluster 1\n",
+    "\n",
+    "Pile up and construct scoreTrack:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "d4ede95c-3e8c-4c27-a9ad-61c216d005d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pileup_track_c1 = petrack_c1.pileup_bdg()\n",
+    "global_bg_track_c1 = pileup_track_c1.set_single_value(pileup_track_c1.summary()[4]) \n",
+    "score_track_c1 = pileup_track_c1.make_ScoreTrackII_for_macs(global_bg_track_c1, depth1=100, depth2=100)\n",
+    "score_track_c1.change_score_method(ord('q'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa20375e-981e-4aa0-98d4-243014e062ed",
+   "metadata": {},
+   "source": [
+    "Cutoff analysis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "7dd09d3c-ec06-4f35-a1b6-c9cd1dc27d93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from io import StringIO\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "cutoff_analysis_c1 = score_track_c1.cutoff_analysis(min_score=0.5, max_score=50)\n",
+    "\n",
+    "cutoff_analysis_df_c1 = pd.read_csv(StringIO(cutoff_analysis_c1), sep=\"\\t\")\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(12, 3), sharex=True)\n",
+    "\n",
+    "axes[0].plot(cutoff_analysis_df_c1[\"score\"], cutoff_analysis_df_c1[\"npeaks\"])\n",
+    "axes[0].set_title(\"npeaks\")\n",
+    "axes[0].set_xlabel(\"score\")\n",
+    "axes[0].set_ylabel(\"value\")\n",
+    "\n",
+    "axes[1].plot(cutoff_analysis_df_c1[\"score\"], cutoff_analysis_df_c1[\"lpeaks\"])\n",
+    "axes[1].set_title(\"lpeaks\")\n",
+    "axes[1].set_xlabel(\"score\")\n",
+    "\n",
+    "axes[2].plot(cutoff_analysis_df_c1[\"score\"], cutoff_analysis_df_c1[\"avelpeak\"])\n",
+    "axes[2].set_title(\"avelpeak\")\n",
+    "axes[2].set_xlabel(\"score\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81364808-0ab1-415d-844a-637deead21f6",
+   "metadata": {},
+   "source": [
+    "According to the analysis, it seems that q-score cutoff of 2 or q-value cutoff of 1e-2 works as a good cutoff."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "13fb8112-b075-4c9c-9aab-956f1ff0e4b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks_c1 = score_track_c1.call_peaks(cutoff=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba3dafb5-63a1-403c-aa02-87e7007e76df",
+   "metadata": {},
+   "source": [
+    "### For Cluster 3\n",
+    "\n",
+    "Pile up and construct scoreTrack:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "adb9b281-86f7-46e9-b6d2-0a6eadc816ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pileup_track_c3 = petrack_c3.pileup_bdg()\n",
+    "global_bg_track_c3 = pileup_track_c3.set_single_value(pileup_track_c3.summary()[4]) \n",
+    "score_track_c3 = pileup_track_c3.make_ScoreTrackII_for_macs(global_bg_track_c3, depth1=100, depth2=100)\n",
+    "score_track_c3.change_score_method(ord('q'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa842953-04fb-4024-b0b0-e10e0ac39e32",
+   "metadata": {},
+   "source": [
+    "Cutoff analysis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "2321612c-8aa8-48bd-ab86-14961ccadcea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cutoff_analysis_c3 = score_track_c3.cutoff_analysis(min_score=0.5, max_score=50)\n",
+    "\n",
+    "cutoff_analysis_df_c3 = pd.read_csv(StringIO(cutoff_analysis_c3), sep=\"\\t\")\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(12, 3), sharex=True)\n",
+    "\n",
+    "axes[0].plot(cutoff_analysis_df_c3[\"score\"], cutoff_analysis_df_c3[\"npeaks\"])\n",
+    "axes[0].set_title(\"npeaks\")\n",
+    "axes[0].set_xlabel(\"score\")\n",
+    "axes[0].set_ylabel(\"value\")\n",
+    "\n",
+    "axes[1].plot(cutoff_analysis_df_c3[\"score\"], cutoff_analysis_df_c3[\"lpeaks\"])\n",
+    "axes[1].set_title(\"lpeaks\")\n",
+    "axes[1].set_xlabel(\"score\")\n",
+    "\n",
+    "axes[2].plot(cutoff_analysis_df_c3[\"score\"], cutoff_analysis_df_c3[\"avelpeak\"])\n",
+    "axes[2].set_title(\"avelpeak\")\n",
+    "axes[2].set_xlabel(\"score\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61ebeab5-0774-4d92-95fd-b73be2bde86b",
+   "metadata": {},
+   "source": [
+    "According to the analysis, it seems that q-score cutoff of 2 or q-value cutoff of 1e-2 works as a good cutoff."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "cd612380-9d85-4a08-b0d6-cfcaf9d487ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks_c3 = score_track_c3.call_peaks(cutoff=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c663c85-7aa2-47fe-a52d-e8453266ee9e",
+   "metadata": {},
+   "source": [
+    "Let's check some basic facts of the peaks called from only the cluster 1 or cluster 3 cell barcodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "edbe99af-ce05-4806-bd23-800a003e77bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of peaks of cluster 1: 32795\n",
+      "Total basepairs of peaks of cluster 1: 18221799\n",
+      "Number of peaks of cluster 3: 21210\n",
+      "Total basepairs of peaks of cluster 3: 14320688\n"
+     ]
+    }
+   ],
+   "source": [
+    "from MACS3.Signal.Region import Regions\n",
+    "\n",
+    "regions_c1 = Regions()\n",
+    "regions_c1.init_from_PeakIO(peaks_c1)\n",
+    "print(f\"Number of peaks of cluster 1: {regions_c1.total}\")\n",
+    "print(f\"Total basepairs of peaks of cluster 1: {regions_c1.total_length()}\")\n",
+    "\n",
+    "regions_c3 = Regions()\n",
+    "regions_c3.init_from_PeakIO(peaks_c3)\n",
+    "print(f\"Number of peaks of cluster 3: {regions_c3.total}\")\n",
+    "print(f\"Total basepairs of peaks of cluster 3: {regions_c3.total_length()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fddd033-af95-4923-be32-46fcfeec05e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"peaks_c1.narrowPeak\",\"w\") as f:\n",
+    "    peaks_c1.write_to_narrowPeak(f)\n",
+    "\n",
+    "with open(\"peaks_c3.narrowPeak\",\"w\") as f:\n",
+    "    peaks_c3.write_to_narrowPeak(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "8cf27e31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ncls\n",
+      "using ncls\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate anndata for subclusters.\n",
+    "\n",
+    "adata_peaks_c1 = petrack_c1.return_anndata(regions_c1)\n",
+    "adata_peaks_c3 = petrack_c3.return_anndata(regions_c3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "73cfe096",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(adata_peaks_c1.n_vars == regions_c1.total)\n",
+    "print(adata_peaks_c3.n_vars == regions_c3.total)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "715e2abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pp.normalize_total(adata_peaks_c1, target_sum=1e4)\n",
+    "sc.pp.log1p(adata_peaks_c1)\n",
+    "sc.pp.pca(adata_peaks_c1)\n",
+    "sc.pp.neighbors(adata_peaks_c1)\n",
+    "sc.tl.umap(adata_peaks_c1)\n",
+    "sc.tl.leiden(adata_peaks_c1, key_added=\"cluster_1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "a26ec6fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[796 MB] ... storing 'chrom' as categorical\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sc.pl.umap(adata_peaks_c1, color = \"cluster_1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6581f94e-6a9c-4afd-adbb-56afec4f911d",
+   "metadata": {},
+   "source": [
+    "## Compare cluster-specific peaks\n",
+    "\n",
+    "We can try to compare these two sets of peaks using functions provided by `Regions` class. Such as get the overlapping regions..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "687cd051-a6e0-4780-baa3-1ea6e5682de3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of overlapping regions: 15488\n",
+      "And total basepairs of overlapping regions: 9225572\n"
+     ]
+    }
+   ],
+   "source": [
+    "overlapped_regions_c1_c3 = regions_c1.intersect(regions_c3)\n",
+    "\n",
+    "print(f\"Number of overlapping regions: {overlapped_regions_c1_c3.total}\")\n",
+    "print(f\"And total basepairs of overlapping regions: {overlapped_regions_c1_c3.total_length()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db9961b4-e2a8-4c3f-a6a4-30c31c0bb3b3",
+   "metadata": {},
+   "source": [
+    "Or unique regions/peaks of cluster 3 that can not be found in the peaks called from cluster 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "d807bef6-6d25-43d9-8f9d-1ccc30b1028f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make Regions for c3 peaks again since later we will alter this object with .exclude function\n",
+    "unique_regions_c3_vs_c1 = Regions()\n",
+    "unique_regions_c3_vs_c1.init_from_PeakIO(peaks_c3)\n",
+    "# then\n",
+    "unique_regions_c3_vs_c1.exclude(regions_c1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "18ea698c-8d57-44d5-87f3-9f96b29d69f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Peaks of cluster 3 - cluster 1: 6661\n",
+      "And total basepairs in peaks of cluster 3 - cluster 1: 2567058\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Peaks of cluster 3 - cluster 1: {unique_regions_c3_vs_c1.total}\")\n",
+    "print(f\"And total basepairs in peaks of cluster 3 - cluster 1: {unique_regions_c3_vs_c1.total_length()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "1a77025b-955e-4b72-9a44-b8635381a15e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"peaks_c3_vs_c1.bed\",\"w\") as f:\n",
+    "    unique_regions_c3_vs_c1.write_to_bed(f)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.9.6)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/500-PBMC-test/macs3-scATAC-pbmc-500.ipynb b/notebooks/500-PBMC-test/macs3-scATAC-pbmc-500.ipynb
index 60ac48ec..d0e10016 100644
--- a/notebooks/500-PBMC-test/macs3-scATAC-pbmc-500.ipynb
+++ b/notebooks/500-PBMC-test/macs3-scATAC-pbmc-500.ipynb
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "ca8b01f2-46af-44d3-809f-840054fd166c",
    "metadata": {},
    "outputs": [],
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "2d98fd56-5884-494b-8da6-9c1d47d998dc",
    "metadata": {},
    "outputs": [
@@ -72,16 +72,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO  @ 20 Nov 2025 11:28:18: [193 MB]  1000000 fragments parsed \n",
-      "INFO  @ 20 Nov 2025 11:28:18: [237 MB]  2000000 fragments parsed \n",
-      "INFO  @ 20 Nov 2025 11:28:19: [266 MB]  3000000 fragments parsed \n",
-      "INFO  @ 20 Nov 2025 11:28:20: [288 MB]  4000000 fragments parsed \n",
-      "INFO  @ 20 Nov 2025 11:28:20: [299 MB]  5000000 fragments parsed \n",
-      "INFO  @ 20 Nov 2025 11:28:21: [315 MB]  6000000 fragments parsed \n",
-      "INFO  @ 20 Nov 2025 11:28:21: [334 MB]  7000000 fragments parsed \n",
-      "INFO  @ 20 Nov 2025 11:28:22: [356 MB]  8000000 fragments parsed \n",
-      "INFO  @ 20 Nov 2025 11:28:23: [379 MB]  9000000 fragments parsed \n",
-      "INFO  @ 20 Nov 2025 11:28:23: [391 MB]  10000000 fragments parsed \n"
+      "INFO  @ 11 Mar 2026 18:55:49: [262 MB]  1000000 fragments parsed \n",
+      "INFO  @ 11 Mar 2026 18:55:50: [293 MB]  2000000 fragments parsed \n",
+      "INFO  @ 11 Mar 2026 18:55:51: [318 MB]  3000000 fragments parsed \n",
+      "INFO  @ 11 Mar 2026 18:55:51: [343 MB]  4000000 fragments parsed \n",
+      "INFO  @ 11 Mar 2026 18:55:52: [364 MB]  5000000 fragments parsed \n",
+      "INFO  @ 11 Mar 2026 18:55:53: [378 MB]  6000000 fragments parsed \n",
+      "INFO  @ 11 Mar 2026 18:55:53: [397 MB]  7000000 fragments parsed \n",
+      "INFO  @ 11 Mar 2026 18:55:54: [421 MB]  8000000 fragments parsed \n",
+      "INFO  @ 11 Mar 2026 18:55:55: [440 MB]  9000000 fragments parsed \n",
+      "INFO  @ 11 Mar 2026 18:55:56: [457 MB]  10000000 fragments parsed \n"
      ]
     }
    ],
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "b866cc28-bc1e-4b5a-b280-b240e7fd3632",
    "metadata": {},
    "outputs": [
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "c6bd3132-4cad-4131-bd88-b94230cd895a",
    "metadata": {},
    "outputs": [],
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "ac3c8f90-fc19-4a69-b5b1-63c9073652b9",
    "metadata": {},
    "outputs": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "6427cb03-4e17-4244-8c6d-541432e69b1e",
    "metadata": {},
    "outputs": [
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "dd45d34e-6c6c-4ef1-a403-d86438c8f2ad",
    "metadata": {},
    "outputs": [
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "a08be5b9-6dd5-4718-a4ff-390e32c43dba",
    "metadata": {},
    "outputs": [],
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "4ff4789b-9cc9-4891-8e0d-f4256dba26ab",
    "metadata": {},
    "outputs": [
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "f40cf3ac-e926-416e-81a5-f87fc826559c",
    "metadata": {},
    "outputs": [],
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "0f753cd4-a1db-464f-8f4f-46e86c40f970",
    "metadata": {},
    "outputs": [],
@@ -361,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "32b85280-589a-43bf-a6ba-5a925a0145ef",
    "metadata": {},
    "outputs": [],
@@ -371,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "id": "360a2ab0-fc7c-48b1-9940-72900d109489",
    "metadata": {},
    "outputs": [],
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "id": "65cd62de-8f5e-4bfd-b80f-c2aa61a6beca",
    "metadata": {},
    "outputs": [
@@ -508,13 +508,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "19295666-2f64-4560-aba1-8b9b1955bcd4",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1200x300 with 3 Axes>"
       ]
@@ -557,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "id": "028f0738-882d-49df-b86e-6695a926fa99",
    "metadata": {},
    "outputs": [],
@@ -575,7 +575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "id": "069c942b-dfec-48bb-a5ed-b234b1493aa3",
    "metadata": {},
    "outputs": [
@@ -598,7 +598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "id": "223681f8-e67b-4a92-8870-3814037cacc1",
    "metadata": {},
    "outputs": [],
@@ -607,6 +607,79 @@
     "    peaks.write_to_narrowPeak(f)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8418d154",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ncls\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "peak_1        0.000000\n",
+       "peak_2        0.004695\n",
+       "peak_3        0.000000\n",
+       "peak_4        0.002347\n",
+       "peak_5        0.000000\n",
+       "                ...   \n",
+       "peak_53590    0.000000\n",
+       "peak_53591    0.000000\n",
+       "peak_53592    0.000000\n",
+       "peak_53593    0.009390\n",
+       "peak_53594    0.011737\n",
+       "Length: 53594, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anndata = petrack.return_anndata(regions, method = \"ncls\")\n",
+    "anndata.to_df().mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "765e1c78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "peak_1        0.000000\n",
+       "peak_2        0.004695\n",
+       "peak_3        0.000000\n",
+       "peak_4        0.000000\n",
+       "peak_5        0.000000\n",
+       "                ...   \n",
+       "peak_53590    0.000000\n",
+       "peak_53591    0.000000\n",
+       "peak_53592    0.000000\n",
+       "peak_53593    0.004695\n",
+       "peak_53594    0.000000\n",
+       "Length: 53594, dtype: float64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anndata = petrack.return_anndata(regions, method = \"two_pointer\")\n",
+    "anndata.to_df().mean()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bb59c6c1-8841-4bd0-8962-e782af1eda87",
@@ -999,7 +1072,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv (3.12.8)",
    "language": "python",
    "name": "python3"
   },
diff --git a/requirements.txt b/requirements.txt
index 83ab2219..c9e85a90 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,3 +6,6 @@ hmmlearn>=0.3.2
 cykhash>=2.0,<3.0 
 pytest>=7.0
 setuptools>=68.0
+pandas
+anndata
+ncls
\ No newline at end of file
diff --git a/test/test_PairedEndTrack.py b/test/test_PairedEndTrack.py
index 193fbdb5..a9a5da00 100644
--- a/test/test_PairedEndTrack.py
+++ b/test/test_PairedEndTrack.py
@@ -5,6 +5,9 @@
 from MACS3.Signal.PairedEndTrack import PETrackI, PETrackII
 from MACS3.Signal.Region import Regions
 import numpy as np
+import anndata  # noqa: F401
+import pandas  # noqa: F401
+import scipy  # noqa: F401
 
 
 class Test_PETrackI(unittest.TestCase):
@@ -181,6 +184,50 @@ def test_exclude(self):
         self.assertEqual(self.pe.length, 1170)
         self.assertAlmostEqual(self.pe.average_template_length, 90, 0)
 
+    def test_return_anndata(self):
+        petrack = PETrackII()
+        petrack.add_loc(b"chr1", 0, 100, barcode=b"A", count=2)   # peak_1
+        petrack.add_loc(b"chr1", 70, 270, barcode=b"A", count=1)   # peak_2
+        petrack.add_loc(b"chr1", 0, 100, barcode=b"B", count=3)   # peak_2
+        petrack.add_loc(b"chr1", 175, 325, barcode=b"C", count=4)   # peak_2
+        petrack.finalize()
+
+        regions = Regions()
+        regions.add_loc(b"chr1", 0, 100)    # peak_1
+        regions.add_loc(b"chr1", 200, 300)  # peak_2
+        regions.add_loc(b"chr1", 500, 600)  # peak_3
+
+
+        adata = petrack.return_anndata(regions)
+        self.assertEqual(adata.shape, (3, 3))
+        self.assertEqual(list(adata.obs.index), ["A", "B", "C"])
+        self.assertEqual(list(adata.var.index), ["peak_1", "peak_2", "peak_3"])
+
+        X = adata.X.toarray()
+        expected = np.array([[3, 1, 0],
+                              [3, 0, 0],
+                              [0, 4, 0]], dtype=np.int32)
+        np.testing.assert_array_equal(X, expected)
+
+        # Explicit peak-inside-fragment check with simpler labels
+        petrack = PETrackII()
+        petrack.add_loc(b"chr1", 0, 100, barcode=b"A", count=1)   # contains peak_1
+        petrack.add_loc(b"chr1", 175, 325, barcode=b"B", count=4)  # contains peak_2
+        petrack.finalize()
+
+        regions = Regions()
+        regions.add_loc(b"chr1", 10, 90)    # peak_1
+        regions.add_loc(b"chr1", 200, 300)  # peak_2
+
+        adata = petrack.return_anndata(regions)
+        self.assertEqual(adata.shape, (2, 2))
+        self.assertEqual(list(adata.obs.index), ["A", "B"])
+        self.assertEqual(list(adata.var.index), ["peak_1", "peak_2"])
+
+        X = adata.X.toarray()
+        expected = np.array([[1, 0],
+                              [0, 4]], dtype=np.int32)
+        np.testing.assert_array_equal(X, expected)
 
 class TestPETrackIISampling(unittest.TestCase):