--- a
+++ b/singlecellmultiomics/statistic/plate2.py
@@ -0,0 +1,150 @@
+import matplotlib.patheffects as path_effects
+import math
+import string
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from .statistic import StatisticHistogram
+import singlecellmultiomics.pyutils as pyutils
+from collections import defaultdict, Counter
+from singlecellmultiomics.utils.plotting import plot_plate
+import matplotlib
+matplotlib.rcParams['figure.dpi'] = 160
+matplotlib.use('Agg')
+
+
+def counter_defaultdict():
+    return defaultdict(Counter)
+
+class PlateStatistic2(object):
+
+    def __init__(self, args):
+        self.args = args
+
+        self.stats = defaultdict(counter_defaultdict)
+
+    def to_csv(self, path):
+
+        export = {}
+        for library, libd in self.stats.items():
+            for (stat,metric), data in  libd.items():
+                print(data)
+                export[library,stat,metric] = data
+
+
+        pd.DataFrame(
+            export).to_csv(
+            path.replace(
+                '.csv',
+                f'plate_metrics.csv'))
+
+    def processRead(self, R1,R2=None, default_lib=None):
+
+        for read in [R1,R2]:
+
+            if read is None:
+                continue
+
+            cell_index = int(read.get_tag('bi'))
+
+            if read.has_tag('MX'):
+                mux= read.get_tag('MX')
+                format = 384 if ('384' in mux or mux.startswith('CS2')) else 96
+                if format==384:
+                    n_rows = 16
+                    n_cols = 24
+                else:
+                    n_rows = 8
+                    n_cols = 12
+            else:
+                n_rows = 16
+                n_cols = 24
+
+            if default_lib is None:
+                if read.has_tag('LY'):
+                    library = read.get_tag('LY')
+
+
+                else:
+                    library = '_'
+            else:
+                library = default_lib
+
+            cell_index = int(cell_index)-1
+            row = int(cell_index/n_cols)
+            col = cell_index - row*n_cols
+
+            cell= read.get_tag('SM')
+            self.stats[library][('total mapped','# reads')][(row,col, cell)] += 1
+
+            if read.is_qcfail:
+                self.stats[library]['qcfail', '# reads'][(row,col,cell)] += 1
+                continue
+
+            if read.is_duplicate:
+                continue
+
+            self.stats[library][('total mapped','# molecules')][(row,col,cell)] += 1
+
+            if read.has_tag('lh') and  read.get_tag('lh') in ('TA','TT','AA'):
+                self.stats[library][read.get_tag('lh'), 'ligated molecules'][(row,col,cell)] += 1
+
+            break
+
+
+    def __repr__(self):
+        return 'Plate statistic'
+
+
+    def __iter__(self):
+        for library, libd in self.stats.items():
+            for name, data in libd.items():
+                for k,v in data.items():
+                    yield (library,name,k),v
+
+
+    def plot(self, target_path, title=None):
+        for library, libd in self.stats.items():
+            for (stat,metric), data in  libd.items():
+                for log in [True,False]:
+
+                    fig, ax, cax = plot_plate(data, log=log, cmap_name='viridis',vmin=1 if  log else 0)
+                    cax.set_ylabel(metric)
+                    ax.set_title(f'{stat}, {metric}')
+                    plt.savefig(
+                        target_path.replace(
+                            '.png',
+                            '') +
+                        f'_{library}_{stat}_{metric.replace("# ","n_")}.{"log" if log else "linear"}.png', bbox_inches='tight', dpi=250)
+                    plt.close()
+            # Create oversequencing plot: (reads / molecule)
+            overseq = {}
+            for coordinate, n_molecules in libd[('total mapped','# molecules')].items():
+                n_reads = libd[('total mapped','# reads')].get(coordinate,0)
+                overseq[coordinate] = (n_reads/n_molecules) if n_reads>0 else 0
+
+            fig, ax, cax = plot_plate(overseq, log=False, cmap_name='viridis', vmin=np.percentile(list(overseq.values()),5))
+            cax.set_ylabel('reads / molecule')
+            ax.set_title(f'Oversequencing')
+            plt.savefig(
+                target_path.replace(
+                    '.png',
+                    '') +
+                f'_{library}_oversequencing.png', bbox_inches='tight', dpi=250)
+            plt.close()
+
+            # Create TA ratio plot: (ta molecules / total molecules)
+            ta_ratio = {}
+            for coordinate, n_ta_molecules in libd[('TA','ligated molecules')].items():
+                n_molecules = libd[('total mapped','# molecules')].get(coordinate,0)
+                ta_ratio[coordinate] = (n_ta_molecules/n_molecules) if n_molecules>0 else 0
+
+            fig, ax, cax = plot_plate(ta_ratio, log=False, cmap_name='viridis', vmin=np.percentile(list(ta_ratio.values()),5))
+            cax.set_ylabel('TA molecules / molecule')
+            ax.set_title(f'TA fraction')
+            plt.savefig(
+                target_path.replace(
+                    '.png',
+                    '') +
+                f'_{library}_ta_fraction.png', bbox_inches='tight', dpi=250)
+            plt.close()