diff --git a/rainfallwrangler/src/lib/python/json2tfrecord.py b/rainfallwrangler/src/lib/python/json2tfrecord.py
old mode 100644
new mode 100755
index eedf795..667a98e
--- a/rainfallwrangler/src/lib/python/json2tfrecord.py
+++ b/rainfallwrangler/src/lib/python/json2tfrecord.py
@@ -1,34 +1,82 @@
+#!/usr/bin/env python3
 import sys
 import os
+import math
 import gzip
 import json
 import argparse
 
 import tensorflow as tf
 
+# TO PARSE:
+@tf.function
+def parse_item(item):
+	parsed = tf.io.parse_single_example(item, features={
+		"rainfallradar": tf.io.FixedLenFeature([], tf.string),
+		"waterdepth": tf.io.FixedLenFeature([], tf.string)
+	})
+	rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32)
+	water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
+	
+	# TODO: The shape of the resulting tensor can't be statically determined, so we need to reshape here
+	
+	# TODO: Any other additional parsing here, since multiple .map() calls are not optimal
+	return rainfall, water
+
+def parse_example(filenames, compression_type="GZIP", parallel_reads_multiplier=1.5):
+	return tf.data.TFRecordDataset(filenames,
+		compression_type=compression_type,
+		num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier)
+	).map(parse_item, num_parallel_calls=tf.data.AUTOTUNE)
+
 def parse_args():
 	parser = argparse.ArgumentParser(description="Convert a generated .jsonl.gz file to a .tfrecord.gz file")
 	parser.add_argument("--input", "-i", help="Path to the input file to convert.", required=True)
 	parser.add_argument("--output", "-o", help="Path to the output file to write to.", required=True)
-	
-	return parser.parse_args(args=sys.argv[2:])
+	return parser.parse_args(args=sys.argv[1:])
 
 def convert(filepath_in, filepath_out):
-	with gzip.open(filepath_in, "r") as handle, tf.io.TFRecordWriter(filepath_out) as writer:
+	options = tf.io.TFRecordOptions(compression_type="GZIP", compression_level=9)
+	with gzip.open(filepath_in, "r") as handle, tf.io.TFRecordWriter(filepath_out, options=options) as writer:
+		i = -1
 		for line in handle:
+			i += 1
 			if len(line) == 0:
 				continue
 			
+			###
+			## 1: Parse JSON
+			###
 			obj = json.loads(line)
 			
-			rainfall = tf.constant(obj.rainfallradar, dtype=tf.float32)
-			water = tf.constant(obj.waterdepth, dtype=tf.float32)
+			###
+			## 2: Convert to tensor
+			###
+			rainfall = tf.constant(obj["rainfallradar"], dtype=tf.float32)
+			water = tf.constant(obj["waterdepth"], dtype=tf.float32)
 			
+			###
+			## 3: Print shape definitions (required when parsing)
+			###
+			if i == 0:
+				print("SHAPES\t"+json.dumps({ "rainfallradar": rainfall.shape.as_list(), "waterdepth": water.shape.as_list() }))
+			
+			###
+			## 4: Serialise tensors
+			###
+			rainfall = tf.train.BytesList(value=[tf.io.serialize_tensor(rainfall, name="rainfall").numpy()])
+			water = tf.train.BytesList(value=[tf.io.serialize_tensor(water, name="water").numpy()])
+			
+			###
+			## 5: Write to .tfrecord.gz file
+			###
 			record = tf.train.Example(features=tf.train.Features(feature={
-				"rainfallradar": tf.train.BytesList(bytes_list=tf.io.serialize_tensor(rainfall)),
-				"waterdepth": tf.train.BytesList(bytes_list=tf.io.serialize_tensor(water))
+				"rainfallradar": tf.train.Feature(bytes_list=rainfall),
+				"waterdepth": tf.train.Feature(bytes_list=water)
 			}))
 			writer.write(record.SerializeToString())
+			
+			print(i)
 
 
 def main():
@@ -39,4 +87,10 @@ def main():
 		sys.exit(2)
 	
 	
-	convert(args.input, args.output)
\ No newline at end of file
+	convert(args.input, args.output)
+
+if __name__ == "__main__":
+    main()
+else:
+    print("This script must be run directly. It cannot be imported.")
+    exit(1)