test_notebook.ipynb

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   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_at_registration\n",
       "34.0    14\n",
       "33.0    12\n",
       "28.0    10\n",
       "31.0    10\n",
       "37.0     9\n",
       "26.0     8\n",
       "39.0     8\n",
       "32.0     8\n",
       "27.0     7\n",
       "36.0     6\n",
       "35.0     6\n",
       "43.0     5\n",
       "29.0     5\n",
       "41.0     3\n",
       "30.0     3\n",
       "40.0     3\n",
       "25.0     3\n",
       "38.0     3\n",
       "54.0     2\n",
       "23.0     2\n",
       "42.0     2\n",
       "47.0     2\n",
       "53.0     2\n",
       "24.0     1\n",
       "Name: count, dtype: int64"
      ]
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top level run file\n",
    "# Run the code from here\n",
    "\n",
    "# Import the user defined functions\n",
    "\n",
    "from func_mors_dfs import create_mors_df_dict, filter_bab\n",
    "from func_read_data import read_data\n",
    "from func_score_agg import score_agg\n",
    "from process_mors import process_mors\n",
    "\n",
    "# File path and names. Names are used as keys in the data_dict\n",
    "path = \"/Users/ellengoddard/Desktop/working_data/\"\n",
    "f_names = [\n",
    "    \"core_10_table\",\n",
    "    \"mors_table\",\n",
    "    \"pai_table_v2\",\n",
    "    \"swemwebs_table\",\n",
    "    \"whooley_table\",\n",
    "]\n",
    "ext = \".csv\"\n",
    "# List of NaN indicators used on read in of data\n",
    "na_list = [999, \"\", \"Not applicable\", \"NULL\", \"Undefined\", \"Unknown\"]\n",
    "\n",
    "# Read in all datasets\n",
    "data_dict = read_data(path, f_names, ext, na_list)\n",
    "\n",
    "# Run the sub functions to aggregate the scores for each measure\n",
    "# and invert where necessary so a higher score is always better\n",
    "data_dict = score_agg(data_dict, f_names)\n",
    "\n",
    "# Define MORS dataset\n",
    "mors_df = data_dict[\"mors_table\"]\n",
    "\n",
    "# Remove Baby Steps data from MORS\n",
    "mors_df_filtered = filter_bab(mors_df)\n",
    "\n",
    "# Separate MORS into dataframes based on assessment tool\n",
    "mors_df_dict = create_mors_df_dict(mors_df_filtered)\n",
    "\n",
    "\n",
    "processed_mors_dict = process_mors(mors_df_dict)\n",
    "\n",
    "# Combine the original data_dict with the mors_df_dict\n",
    "combined_data_dict = {**data_dict, **processed_mors_dict}\n",
    "\n",
    "\n",
    "combined_data_dict[\"PTT - MORS\"][\"age_at_registration\"].value_counts()"
   ]
  }
 ],
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