diff --git a/scripts/poisson_sampling.py b/scripts/poisson_sampling.py
index 5ca481ee62dfb2217ed407e0ded60f417634e942..737cc828c4c3b4751e086897600c0cbe997101c1 100644
--- a/scripts/poisson_sampling.py
+++ b/scripts/poisson_sampling.py
@@ -1,5 +1,7 @@
 import pandas as pd
 import numpy as np
+import argparse
+
 
 '''
 Sample transcript 
@@ -13,25 +15,32 @@ output: csv file with gene id and count
         gtf file with transcript samples
 '''
 
-def transcript_sampling(total_transcript, transcripts):
-
-    #read file containing representative transcript levels and id
 
-    transcript = pd.read_csv(transcripts)
+def transcript_sampling(total_transcript_number, csv_file, output_csv):
+    df = pd.read_csv(csv_file)
     levels = []
 
-    #poisson sampling for each gene, proportional to expression levels
-
-    for expression_level in transcript['level']:
+    for expression_level in df['level']:
 
-        poisson_sampled = np.random.poisson(total_transcript/expression_level)
+        poisson_sampled = np.random.poisson(total_transcript_number/expression_level)
         levels.append(poisson_sampled)
-        # note: if levels from input are decimals, total trancript should be multiplied by expr level, not divided
-        # poisson_sampled = np.random.poisson(total_transcript*expression_level)
 
-    #write output csv file containing transcript id and count (representative transcript numbers)
+    transcript_numbers = pd.DataFrame({'id': df['id'],'count': levels})
+    pd.DataFrame.to_csv(transcript_numbers, output_csv)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description="Transcript Poisson sampler, csv output",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument("--expression_level", required=True, help="csv file with expression level")
+    parser.add_argument("--output_csv", required=True, help="output csv file")
+    parser.add_argument("--input_csv", required=True, help="input csv file")
+    parser.add_argument("--transcript_number", required=True, help="total number of transcripts to sample")
+    args = parser.parse_args()
+
 
-    transcript_numbers = pd.DataFrame({'id': transcript['id'],'count': levels})
-    pd.DataFrame.to_csv(transcript_numbers, "representative_transcript_numbers.csv")
+    transcript_sampling(args.transcript_number, args.input_csv, args.output_csv, args.transcript_number)
 
 
diff --git a/scripts/writegtf.py b/scripts/writegtf.py
index 426f3f492676ff36bdcd461e0851b351070c7360..d5136a79940666ac4f7e9368b687c83bb011a3e9 100644
--- a/scripts/writegtf.py
+++ b/scripts/writegtf.py
@@ -1,21 +1,53 @@
-'''
-This function writes keeps the representative transcripts from the original input file (gtf)
-and writes them to an output.
-(The representative transcripts being listed in a csv file)
-'''
+import pandas as pd
+import numpy as np
+import argparse
 
-def gtf_representative_transcripts(original_gtf, transcripts):
-    transcript = pd.read_csv(transcripts)
-    representative_transcripts = []
+def transcript_ID_finder (entry):
+    index_transcript_id = entry.find("transcript_id")
+    find_transcript_id_name = re.compile("\"\S{1,25}\"")
+    sub_entry = entry[index_transcript_id:]
+    try_find_transcript_id_name = find_transcript_id_name.search(sub_entry)   
+    
+    try: 
+        transcript_ID = try_find_transcript_id_name[0].replace("\"","")
+    except:
+        transcript_ID = ""
+    return (transcript_ID)
 
-    with open (original_gtf, 'r') as file:
-        for id in transcript['id']:
-            for line in file:
-                if id in line:
-                    representative_transcripts.append(line)
 
+'''gtf_file_writer takes as input the original gtf file and the csv file containing relevant transcripts.
 
-    with open ('output.gtf', 'w') as outputfile:
-        outputfile.write(representative_transcripts)
+    It produces a gtf file containing only the transcript entries of those contained in the csv file
+    
+    based on id'''
 
 
+def gtf_file_writer (original_file, csv_file, output_file): 
+    output = []
+
+    df = pd.read_csv(csv_file)
+    listoftranscripts = df['id'].tolist()
+    if df['id'] == False:
+        print('Error. \'id\' column needed in input csv file.')
+
+    with open(original_file, 'r') as f:
+            for entry in f: 
+                if "\ttranscript\t" in entry:
+                    transcript_id = transcript_ID_finder(entry)
+                    if transcript_id in listoftranscripts:
+                        output.append(entry)
+    with open(output_file, 'w') as last_file:
+        last_file.write(output)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description="gtf output file writer",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument("--annotation", required=True, help="gtf file with genome annotation")
+    parser.add_argument("--output_gtf", required=True, help="output gtf file")
+    parser.add_argument("--input_csv", required=True, help="input csv file")
+    args = parser.parse_args()
+
+    gtf_file_writer(args.annotation, args.input_csv, args.output_gtf)