Added remove_rows() and save functionality in parse_common.py

Author: gopal-msr

scripts/IndexParser/parse_common.py

@@ -1,5 +1,6 @@
 import array
 import struct
+import numpy as np
 
 """Constants"""
 SECTOR_LEN = 4096
@@ -19,7 +20,6 @@ def get_data_type_code_and_size(data_type):
         data_type_size = 1
     else:
         raise Exception("Unsupported data type. Supported data types are float, int8 and uint8")
-    
     return data_type_code, data_type_size
 
 class Node:
@@ -81,11 +81,47 @@ def load_data_only_from_opened_file(self, file, num_rows, num_cols, file_offset_
         self.data = array.array(self.data_format_specifier)
         self.data.fromfile(file, self.num_rows*self.num_cols)
 
+    def remove_rows(self, rows_to_remove):
+        for row in rows_to_remove:
+            if row < 0 or row >= self.num_rows:
+                raise Exception("Invalid row index: " + str(row))
+        
+        new_data = array.array(self.data_format_specifier)
+       
+        # index = 0
+        # rows_to_remove.sort()
+        # while index < len(rows_to_remove):
+        #     if rows_to_remove[index+1] - rows_to_remove[index] == 1:
+        #         index += 1
+        #     else:
+        #         #have to add everything after row
+        #         new_data.extend(self.data[(rows_to_remove[index]+1)*self.num_cols:(rows_to_remove[index+1])*self.num_cols])
+
+        for i in range(self.num_rows):
+            if i not in rows_to_remove:
+                new_data.extend(self.data[i*self.num_cols:(i+1)*self.num_cols])
+
+        self.data = new_data
+        self.num_rows -= len(rows_to_remove)
+    
+    def save_bin(self, file_name):
+        with open(file_name, "wb") as file:
+            self.save_bin_to_opened_file(file)
+
+    def save_bin_to_opened_file(self, file):
+        file.write(struct.pack('I', self.num_rows))
+        file.write(struct.pack('I', self.num_cols))
+        self.data.tofile(file)
+            
+
     def __len__(self):
         return self.num_rows
 
+    def get_vector(self, row):
+        return np.array(self.data[row*self.num_cols:(row+1)*self.num_cols])
 
     def __getitem__(self, key):
         return self.data[key*self.num_cols:(key+1)*self.num_cols]   
+