Upload read_dataset.py

read_dataset.py

@@ -0,0 +1,49 @@
+import pyarrow.parquet as pq
+
+METADATA_FILEPATH = 'path/to/your/metadata_file.parquet'
+EMBEDDINGS_FILEPATH = 'path/to/your/embeddings.dat'
+
+
+def read_embeddings(filepath):
+
+    # Read the lines from the file
+    with open(filepath, 'rb') as f:
+        lines = f.readlines()
+    
+    embedding_dict = {}
+    
+    for index, line in enumerate(lines):
+        line_as_list = line.split()
+        
+        # Decode the byte string to a regular string and convert to float
+        float_list = [float(byte_string.decode('utf-8')) for byte_string in line_as_list]
+        
+        # Save the float list (embeddings) to the dictionary with the index as the key
+        embedding_dict[index] = float_list
+    
+    return embedding_dict
+
+
+#Read the metadata file
+metadata_df = pq.read_table(METADATA_FILEPATH).to_pandas()
+
+## How to reach embeddings - example for a specific row
+unique_id = '390U_535R_2021-08-21064631_69'
+
+# Get the row with the unique_id
+row = metadata_df.loc[metadata_df['unique_id'] == unique_id]
+       
+# Get the values for 'data_row' and 'embedding_file'
+dat_row_value = row['dat_row'] # index of the row in the embedding file
+embedding_file_value = row['embedding_file'] # specific embedding file for the row
+
+# Read the embeddings in the embedding file 
+embeddings = read_embeddings(EMBEDDINGS_FILEPATH)
+vector = embeddings[dat_row_value]
+
+print(f"Row: {row}, Dat Row: {dat_row_value}, Embedding File: {embedding_file_value}, Vector length: {len(vector)}")
+#print(f"Vector: {vector}")
+
+
+
+