{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h] --annotation ANNOTATION --expression_level\n",
      "                             EXPRESSION_LEVEL --output_csv OUTPUT_CSV\n",
      "                             --output_gtf OUTPUT_GTF --transcript_number\n",
      "                             TRANSCRIPT_NUMBER\n",
      "ipykernel_launcher.py: error: the following arguments are required: --annotation, --expression_level, --output_csv, --output_gtf, --transcript_number\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import argparse\n",
    "import transcript_extractor as te\n",
    "import exon_length_filter as elf\n",
    "import representative as rtcl\n",
    "import representative as rp\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    parser = argparse.ArgumentParser(\n",
    "        description=\"transcript sampler\",\n",
    "        formatter_class=argparse.ArgumentDefaultsHelpFormatter\n",
    "    )\n",
    "    parser.add_argument(\"--annotation\", required=True, help=\"gtf file with genome annotation\")\n",
    "    #parser.add_argument(\"--expression_level\", required=True, help=\"csv file with expression level\")\n",
    "    parser.add_argument(\"--output_csv\", required=True, help=\"output csv file\")\n",
    "    parser.add_argument(\"--output_gtf\", required=True, help=\"output gtf file\")\n",
    "    parser.add_argument(\"--transcript_number\", required=True, help=\"total number of transcripts to sample\")\n",
    "    args = parser.parse_args()\n",
    "\n",
    "def exe(input_file, csv, gtf, transcript_nr):\n",
    "    file_name,source_pathway_name_2,deposit_pathway_name_2 = te.extract_transcript(input_file, deposit_pathway_name = True, Input_free = Input_free)\n",
    "    inter_mediate_file_directory = input_file +\"_intermediate_file.txt\"\n",
    "\n",
    "    print(\"Transcripts are filtered based on transcript score. Please wait...\")\n",
    "\n",
    "    pre_filter_representative_transcripts_dict = rtcl.find_repr_by_SupportLevel(inter_mediate_file_directory)\n",
    "\n",
    "    print(\"Transcripts filtered\\n\")\n",
    "    elf.exon_length_filter(file_name,gen_dict= pre_filter_representative_transcripts_dict, Input_free = True)\n",
    "\n",
    "\n",
    "    #return(file_name,source_pathway_name,deposit_pathway_name)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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