diff --git a/python/tests/unit_test_data/json-fixer.ipynb b/python/tests/unit_test_data/json-fixer.ipynb
deleted file mode 100644
index 7c5a5cb1..00000000
--- a/python/tests/unit_test_data/json-fixer.ipynb
+++ /dev/null
@@ -1,287 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "with open('./resample_tests.json', 'r') as file:\n",
-    "    before = json.load(file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_dict(dictionary, key, value):\n",
-    "    if value is not None:\n",
-    "        dictionary[key] = value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "after = {}\n",
-    "for i in before.keys(): # i is test class\n",
-    "    if i == \"__SharedData\":\n",
-    "        continue\n",
-    "    after[i] = {}\n",
-    "    for j in before[i].keys(): # j is test method\n",
-    "        after[i][j] = {}\n",
-    "        for k in before[i][j].keys(): # input, expected, etc.\n",
-    "            tsdf = {}\n",
-    "            update_dict(tsdf, \"ts_col\", before[i][j][k].get(\"ts_col\", None))\n",
-    "            update_dict(tsdf, \"other_ts_cols\", before[i][j][k].get(\"other_ts_cols\", None))\n",
-    "            update_dict(tsdf, \"partition_cols\", before[i][j][k].get(\"partition_cols\", None))\n",
-    "            update_dict(tsdf,  \"sequence_col\", before[i][j][k].get(\"sequence_col\", None))\n",
-    "            update_dict(tsdf, \"start_ts\", before[i][j][k].get(\"start_ts\", None))\n",
-    "            update_dict(tsdf, \"end_ts\", before[i][j][k].get(\"end_ts\", None))\n",
-    "            update_dict(tsdf, \"series\", before[i][j][k].get(\"series\", None))\n",
-    "            sdf = {}\n",
-    "            update_dict(sdf, \"schema\", before[i][j][k].get(\"schema\", None))\n",
-    "            update_dict(sdf, \"ts_convert\", before[i][j][k].get(\"ts_convert\", None))\n",
-    "            update_dict(sdf, \"data\", before[i][j][k].get(\"data\", None))\n",
-    "            after[i][j][k] = {\n",
-    "                        \"tsdf\": tsdf,\n",
-    "                        \"df\": sdf,\n",
-    "                        \"$ref\": before[i][j][k].get(\"$ref\", None)\n",
-    "                    }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "after_2 = {}\n",
-    "for i in before.keys(): # i is test class\n",
-    "    if i != \"__SharedData\":\n",
-    "        continue\n",
-    "    after_2[i] = {}\n",
-    "    for j in before[i].keys(): # j is test method\n",
-    "        tsdf = {}\n",
-    "        update_dict(tsdf, \"ts_col\", before[i][j].get(\"ts_col\", None))\n",
-    "        update_dict(tsdf, \"other_ts_cols\", before[i][j].get(\"other_ts_cols\", None))\n",
-    "        update_dict(tsdf, \"partition_cols\", before[i][j].get(\"partition_cols\", None))\n",
-    "        update_dict(tsdf,  \"sequence_col\", before[i][j].get(\"sequence_col\", None))\n",
-    "        update_dict(tsdf, \"start_ts\", before[i][j].get(\"start_ts\", None))\n",
-    "        update_dict(tsdf, \"end_ts\", before[i][j].get(\"end_ts\", None))\n",
-    "        update_dict(tsdf, \"series\", before[i][j].get(\"series\", None))\n",
-    "        sdf = {}\n",
-    "        update_dict(sdf, \"schema\", before[i][j].get(\"schema\", None))\n",
-    "        update_dict(sdf, \"ts_convert\", before[i][j].get(\"ts_convert\", None))\n",
-    "        update_dict(sdf, \"data\", before[i][j].get(\"data\", None))\n",
-    "        after_2[i][j] = {\n",
-    "                    \"tsdf\": tsdf,\n",
-    "                    \"df\": sdf,\n",
-    "                }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'__SharedData': {'input_data': {'tsdf': {'ts_col': 'event_ts',\n",
-       "    'partition_cols': ['symbol']},\n",
-       "   'df': {'schema': 'symbol string, date string, event_ts string, trade_pr float, trade_pr_2 float',\n",
-       "    'data': [['S1', 'SAME_DT', '2020-08-01 00:00:10', 349.21, 10.0],\n",
-       "     ['S1', 'SAME_DT', '2020-08-01 00:00:11', 340.21, 9.0],\n",
-       "     ['S1', 'SAME_DT', '2020-08-01 00:01:12', 353.32, 8.0],\n",
-       "     ['S1', 'SAME_DT', '2020-08-01 00:01:13', 351.32, 7.0],\n",
-       "     ['S1', 'SAME_DT', '2020-08-01 00:01:14', 350.32, 6.0],\n",
-       "     ['S1', 'SAME_DT', '2020-09-01 00:01:12', 361.1, 5.0],\n",
-       "     ['S1', 'SAME_DT', '2020-09-01 00:19:12', 362.1, 4.0]]}}}}"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "after_2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'ResampleUnitTests': {'test_appendAggKey_freq_is_none': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'}},\n",
-       "  'test_appendAggKey_freq_microsecond': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'}},\n",
-       "  'test_appendAggKey_freq_is_invalid': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'}},\n",
-       "  'test_aggregate_floor': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'},\n",
-       "   'expected_data': {'tsdf': {'ts_col': 'event_ts',\n",
-       "     'partition_cols': ['symbol']},\n",
-       "    'df': {'schema': 'symbol string, event_ts string, date string, trade_pr float, trade_pr_2 float',\n",
-       "     'data': [['S1', '2020-08-01 00:00:00', 'SAME_DT', 349.21, 10.0],\n",
-       "      ['S1', '2020-09-01 00:00:00', 'SAME_DT', 361.1, 5.0]]},\n",
-       "    '$ref': None}},\n",
-       "  'test_aggregate_average': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'},\n",
-       "   'expected_data': {'tsdf': {'ts_col': 'event_ts',\n",
-       "     'partition_cols': ['symbol']},\n",
-       "    'df': {'schema': 'symbol string, event_ts string, trade_pr double, trade_pr_2 double',\n",
-       "     'data': [['S1', '2020-08-01 00:00:00', 348.8760009765625, 8.0],\n",
-       "      ['S1', '2020-09-01 00:00:00', 361.6000061035156, 4.5]]},\n",
-       "    '$ref': None}},\n",
-       "  'test_aggregate_min': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'},\n",
-       "   'expected_data': {'tsdf': {'ts_col': 'event_ts',\n",
-       "     'partition_cols': ['symbol']},\n",
-       "    'df': {'schema': 'symbol string, event_ts string, date string, trade_pr float, trade_pr_2 float',\n",
-       "     'data': [['S1', '2020-08-01 00:00:00', 'SAME_DT', 340.21, 6.0],\n",
-       "      ['S1', '2020-09-01 00:00:00', 'SAME_DT', 361.1, 4.0]]},\n",
-       "    '$ref': None}},\n",
-       "  'test_aggregate_min_with_prefix': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'},\n",
-       "   'expected_data': {'tsdf': {'ts_col': 'event_ts',\n",
-       "     'partition_cols': ['symbol']},\n",
-       "    'df': {'schema': 'symbol string, event_ts string, min_date string, min_trade_pr float, min_trade_pr_2 float',\n",
-       "     'data': {'$ref': '#/ResampleUnitTests/test_aggregate_min/expected_data/data'}},\n",
-       "    '$ref': None}},\n",
-       "  'test_aggregate_min_with_fill': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'},\n",
-       "   'expected_data': {'tsdf': {'ts_col': 'event_ts',\n",
-       "     'partition_cols': ['symbol']},\n",
-       "    'df': {'schema': 'symbol string, event_ts string, date string, trade_pr float, trade_pr_2 float',\n",
-       "     'data': [['S1', '2020-08-01 00:00:00', 'SAME_DT', 340.21, 6.0],\n",
-       "      ['S1', '2020-08-02 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-03 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-04 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-05 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-06 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-07 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-08 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-09 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-10 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-11 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-12 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-13 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-14 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-15 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-16 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-17 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-18 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-19 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-20 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-21 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-22 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-23 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-24 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-25 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-26 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-27 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-28 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-29 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-30 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-08-31 00:00:00', None, 0.0, 0.0],\n",
-       "      ['S1', '2020-09-01 00:00:00', 'SAME_DT', 361.1, 4.0]]},\n",
-       "    '$ref': None}},\n",
-       "  'test_aggregate_max': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'},\n",
-       "   'expected_data': {'tsdf': {'ts_col': 'event_ts',\n",
-       "     'partition_cols': ['symbol']},\n",
-       "    'df': {'schema': 'symbol string, event_ts string, date string, trade_pr float, trade_pr_2 float',\n",
-       "     'data': [['S1', '2020-08-01 00:00:00', 'SAME_DT', 353.32, 10.0],\n",
-       "      ['S1', '2020-09-01 00:00:00', 'SAME_DT', 362.1, 5.0]]},\n",
-       "    '$ref': None}},\n",
-       "  'test_aggregate_ceiling': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'},\n",
-       "   'expected_data': {'tsdf': {'ts_col': 'event_ts',\n",
-       "     'partition_cols': ['symbol']},\n",
-       "    'df': {'schema': 'symbol string, event_ts string, date string, trade_pr float, trade_pr_2 float',\n",
-       "     'data': [['S1', '2020-08-01 00:00:00', 'SAME_DT', 350.32, 6.0],\n",
-       "      ['S1', '2020-09-01 00:00:00', 'SAME_DT', 362.1, 4.0]]},\n",
-       "    '$ref': None}},\n",
-       "  'test_aggregate_invalid_func_arg': {'input_data': {'tsdf': {},\n",
-       "    'df': {},\n",
-       "    '$ref': '#/__SharedData/input_data'},\n",
-       "   'expected_data': {'tsdf': {'ts_col': 'event_ts',\n",
-       "     'partition_cols': ['symbol']},\n",
-       "    'df': {'schema': 'symbol string, event_ts string, date string, trade_pr float, trade_pr_2 float',\n",
-       "     'data': [['S1', '2020-07-31 20:00:00', 'SAME_DT', 348.88, 8.0],\n",
-       "      ['S1', '2020-08-31 20:00:00', 'SAME_DT', 361.6, 4.5]]},\n",
-       "    '$ref': None}}},\n",
-       " '__SharedData': {'input_data': {'tsdf': {'ts_col': 'event_ts',\n",
-       "    'partition_cols': ['symbol']},\n",
-       "   'df': {'schema': 'symbol string, date string, event_ts string, trade_pr float, trade_pr_2 float',\n",
-       "    'data': [['S1', 'SAME_DT', '2020-08-01 00:00:10', 349.21, 10.0],\n",
-       "     ['S1', 'SAME_DT', '2020-08-01 00:00:11', 340.21, 9.0],\n",
-       "     ['S1', 'SAME_DT', '2020-08-01 00:01:12', 353.32, 8.0],\n",
-       "     ['S1', 'SAME_DT', '2020-08-01 00:01:13', 351.32, 7.0],\n",
-       "     ['S1', 'SAME_DT', '2020-08-01 00:01:14', 350.32, 6.0],\n",
-       "     ['S1', 'SAME_DT', '2020-09-01 00:01:12', 361.1, 5.0],\n",
-       "     ['S1', 'SAME_DT', '2020-09-01 00:19:12', 362.1, 4.0]]}}}}"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "combined = after | after_2\n",
-    "combined"
-   ]
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": ""
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv142",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}