Add cell count script

scripts/analysis/__init__.py

+from .counts import cell_counts

scripts/analysis/counts.py

+import os
+import numpy as np
+import pandas as pd
+
+
+# TODO need better cell nucleus mapping
+def cell_counts(table_folder):
+    """ Count the number of cells.
+
+    Compute the following counts:
+        - total number of cells
+        - cells that have overlap with specific regions
+        - muscle cells
+    """
+
+    # load the  nuclei mapping table
+    nuclei_table = os.path.join(table_folder, 'cells_to_nuclei.csv')
+    nuclei_table = pd.read_csv(nuclei_table, sep='\t')
+
+    # filter for the cell ids that corresponds to actual cells
+    # based on the nucleus mapping
+    cell_mask = nuclei_table['nucleus_id'].values != 0
+    n_cells = cell_mask.sum()
+
+    # load the region / tissue / semantic table
+    region_table = os.path.join(table_folder, 'regions.csv')
+    region_table = pd.read_csv(region_table, sep='\t')
+    assert region_table.shape[0] == cell_mask.shape[0]
+
+    # count the number of cells for individual regions/muscle
+    count_dict = {}
+    names = ['gut', 'muscle', 'crypticsegment', 'pns', 'head', 'pygidium',
+             'stomodeum', 'vnc']
+    for col_name in region_table.columns:
+        if col_name in names:
+            col = region_table[col_name].values
+            col_mask = col != 0
+            col_sum = np.logical_and(cell_mask, col_mask).sum()
+            count_dict[col_name] = col_sum
+
+    return n_cells, count_dict
+
+
+if __name__ == '__main__':
+    n_cells, count_dict = cell_counts('../../data/0.1.1/tables/sbem-6dpf-1-whole-segmented-cells-labels')
+    print("Number of cells:", n_cells)
+    for name, count in count_dict.items():
+        print("Number", name, "cells:", count)