Document the preprocessing module

src/preprocessing.py

@@ -3,9 +3,13 @@ from Bio.SeqIO import parse
 from numpy.random import random
 from tensorflow.io import TFRecordWriter
 from tensorflow.train import BytesList, Example, Feature, Features, FloatList
+from typing import List
 
 
-def generate_example(sequence, weight_matrix):
+def generate_example(sequence, weight_matrix) -> bytes:
+    """
+    Create a binary-string for each sequence containing the sequence and the bases' frequency
+    """
     schema = {
         "sequence": Feature(bytes_list=BytesList(value=[sequence.encode()])),
         "A_counts": Feature(float_list=FloatList(value=[weight_matrix["A"][0]])),
@@ -17,7 +21,10 @@ def generate_example(sequence, weight_matrix):
     return example.SerializeToString()
 
 
-def parse_data(filepath):
+def parse_data(filepath) -> List[bytes]:
+    """
+    Parse a FASTQ file and generate a List of serialized Examples
+    """
     examples = []
     with open(filepath) as handle:
         for row in parse(handle, "fastq"):
@@ -28,7 +35,10 @@ def parse_data(filepath):
     return examples
 
 
-def create_dataset(filepath):
+def create_dataset(filepath) -> None:
+    """
+    Create a training and test dataset with a 70/30 split respectively
+    """
     data = parse_data(filepath)
     train_test_split = 0.7
     with TFRecordWriter("data/train_data.tfrecords") as train_writer, TFRecordWriter(