Update normalization.py

normalization.py

@@ -1,8 +1,3 @@
-import pandas as pd
-import pyarrow as pa
-import pyarrow.parquet as pq
-
-# Define normalization parameters
 norm_params = {
     'LAT': {'min_val': -66.817333, 'max_val': 51.055833},
     'LON': {'min_val': -178.116667, 'max_val': 171.358333},
@@ -15,41 +10,4 @@ norm_params = {
 }
 
 def normalize_column(column, min_val, max_val):
-    """Normalize pandas Series from [min_val, max_val] to [0, 1]."""
-    # Ensure column is treated as float for division to work properly.
-    return (column.astype('float64') - min_val) / (max_val - min_val)
-
-# Load the dataset in chunks
-dataset_path = 'C:/Users/View/Desktop/oetem/dataset/dataset.parquet'
-parquet_file = pq.ParquetFile(dataset_path)
-
-# Determine the output file path
-output_path = 'C:/Users/View/Desktop/oetem/dataset/dataset_normalized.parquet'
-
-# Initialize variables for writing
-writer = None
-schema = None
-
-# Process and normalize chunks
-for i in range(parquet_file.num_row_groups):
-    table = parquet_file.read_row_group(i, columns=list(norm_params.keys()) + [' T'])
-    chunk = table.to_pandas()
-
-    # Normalize the columns
-    for col, params in norm_params.items():
-        chunk[col] = normalize_column(chunk[col], min_val=params['min_val'], max_val=params['max_val'])
-
-    # Convert the DataFrame back to a PyArrow Table for writing
-    #table = pa.Table.from_pandas(chunk)
-    table = pa.Table.from_pandas(chunk, preserve_index=False)
-
-    # If first chunk, initialize the writer with the schema
-    if writer is None:
-        schema = table.schema
-        writer = pq.ParquetWriter(output_path, schema)
-
-    writer.write_table(table)
-
-# Close the writer to finalize the file
-if writer is not None:
-    writer.close()
+    return (column.astype('float64') - min_val) / (max_val - min_val)