--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/to_categorical.py	Sat May 01 01:24:32 2021 +0000
@@ -0,0 +1,52 @@
+import argparse
+import json
+import warnings
+
+import numpy as np
+import pandas as pd
+from keras.utils import to_categorical
+
+
+def main(inputs, infile, outfile, num_classes=None):
+    """
+    Parameter
+    ---------
+    input : str
+        File path to galaxy tool parameter
+
+    infile : str
+        File paths of input vector
+
+    outfile : str
+        File path to output matrix
+
+    num_classes : str
+        Total number of classes. If None, this would be inferred as the (largest number in y) + 1
+
+    """
+    warnings.simplefilter("ignore")
+
+    with open(inputs, "r") as param_handler:
+        params = json.load(param_handler)
+
+    input_header = params["header0"]
+    header = "infer" if input_header else None
+
+    input_vector = pd.read_csv(infile, sep="\t", header=header)
+
+    output_matrix = to_categorical(input_vector, num_classes=num_classes)
+
+    np.savetxt(outfile, output_matrix, fmt="%d", delimiter="\t")
+
+
+if __name__ == "__main__":
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-y", "--infile", dest="infile")
+    aparser.add_argument(
+        "-n", "--num_classes", dest="num_classes", type=int, default=None
+    )
+    aparser.add_argument("-o", "--outfile", dest="outfile")
+    args = aparser.parse_args()
+
+    main(args.inputs, args.infile, args.outfile, args.num_classes)