diff --git a/.gitignore b/.gitignore
index 7e7a178..d92c4de 100644
--- a/.gitignore
+++ b/.gitignore
@@ -167,3 +167,4 @@ nbs/saves/
 
 # Data
 *.zip
+corebridge/saves/
diff --git a/corebridge/core.py b/corebridge/core.py
index af8e621..fbfa4a1 100644
--- a/corebridge/core.py
+++ b/corebridge/core.py
@@ -61,7 +61,8 @@ def set_time_index_zone(df:pd.DataFrame, timezone):
         timezone (str): The desired time zone.
 
     Returns:
-        pd.DataFrame: The DataFrame with its index time zone set to the specified time zone.
+        pd.DataFrame: The modified DataFrame with its index time zone set 
+        to the specified time zone.
 
     Raises:
         None
@@ -104,10 +105,12 @@ def timeseries_dataframe(
     """
 
     if isinstance(data, pd.DataFrame):
-        df = data
+        df = data.copy()
+        df.index = pd.DatetimeIndex(df.index).round('ms')
 
     elif isinstance(data, pd.Series):
         df = pd.DataFrame(data)
+        df.index = pd.DatetimeIndex(df.index).round('ms')
 
     elif isinstance(data, dict):
         # assume a dict/mapping of individual arrays representing timeseries 
@@ -194,7 +197,7 @@ def timeseries_dataframe_from_datadict(
     return df
 
 
-# %% ../nbs/00_core.ipynb 20
+# %% ../nbs/00_core.ipynb 23
 def pop_nan_values(data):
     """
     Recursively pop keys with nan values from dict or lists with dicts.
@@ -213,7 +216,7 @@ def pop_nan_values(data):
     else:
         return data
 
-# %% ../nbs/00_core.ipynb 21
+# %% ../nbs/00_core.ipynb 35
 def timeseries_dataframe_to_datadict(
         data:typing.Union[pd.DataFrame, pd.Series, dict], 
         recordformat:str='records', 
@@ -237,8 +240,13 @@ def timeseries_dataframe_to_datadict(
 
     normalized_data = timeseries_dataframe(data, timezone=timezone)
     if isinstance(normalized_data.index, pd.DatetimeIndex):
-        normalized_data.index = normalized_data.index.map(lambda x: x.isoformat())
-    
+        if timezone == 'UTC':
+            print(f"Normalized, UTC")
+            normalized_data.index = normalized_data.index.strftime("%FT%R:%SZ")
+        else:
+            print(f"Normalized, {timezone}")
+            normalized_data.index = normalized_data.index.map(lambda x: x.isoformat(timespec='milliseconds'))
+                 
     if orient == 'records':
         records = normalized_data.reset_index().to_dict(orient='records')
     else:
@@ -250,7 +258,7 @@ def timeseries_dataframe_to_datadict(
     return records    
 
 
-# %% ../nbs/00_core.ipynb 28
+# %% ../nbs/00_core.ipynb 51
 #def interpolate_timeseries(sampler, period, method_args):
 
 
@@ -295,12 +303,12 @@ def timeseries_dataframe_resample(df:pd.DataFrame, period:str, method:str):
 
 
 
-# %% ../nbs/00_core.ipynb 32
+# %% ../nbs/00_core.ipynb 55
 class AICoreModuleBase:
     pass
 
 
-# %% ../nbs/00_core.ipynb 33
+# %% ../nbs/00_core.ipynb 56
 @patch
 def __init__(self:AICoreModuleBase, 
             save_dir:str, # path where the module can keep files 
diff --git a/nbs/00_core.ipynb b/nbs/00_core.ipynb
index 8dfbad1..3b95a28 100644
--- a/nbs/00_core.ipynb
+++ b/nbs/00_core.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +145,8 @@
     "        timezone (str): The desired time zone.\n",
     "\n",
     "    Returns:\n",
-    "        pd.DataFrame: The DataFrame with its index time zone set to the specified time zone.\n",
+    "        pd.DataFrame: The modified DataFrame with its index time zone set \n",
+    "        to the specified time zone.\n",
     "\n",
     "    Raises:\n",
     "        None\n",
@@ -179,70 +180,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2022-01-01 01:00:00+01:00</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-02 01:00:00+01:00</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-03 01:00:00+01:00</th>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                           A\n",
-       "time                        \n",
-       "2022-01-01 01:00:00+01:00  1\n",
-       "2022-01-02 01:00:00+01:00  2\n",
-       "2022-01-03 01:00:00+01:00  3"
+       "DatetimeIndex(['2022-01-01 01:00:00+01:00', '2022-01-02 01:00:00+01:00',\n",
+       "               '2022-01-03 01:00:00+01:00'],\n",
+       "              dtype='datetime64[ns, Europe/Berlin]', name='time', freq=None)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "df = pd.DataFrame({'A': [1, 2, 3]}, index=pd.DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03']))\n",
-    "set_time_index_zone(df, 'Europe/Berlin')"
+    "set_time_index_zone(df, 'Europe/Berlin')\n",
+    "df.index"
    ]
   },
   {
@@ -258,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -281,10 +238,12 @@
     "    \"\"\"\n",
     "\n",
     "    if isinstance(data, pd.DataFrame):\n",
-    "        df = data\n",
+    "        df = data.copy()\n",
+    "        df.index = pd.DatetimeIndex(df.index).round('ms')\n",
     "\n",
     "    elif isinstance(data, pd.Series):\n",
     "        df = pd.DataFrame(data)\n",
+    "        df.index = pd.DatetimeIndex(df.index).round('ms')\n",
     "\n",
     "    elif isinstance(data, dict):\n",
     "        # assume a dict/mapping of individual arrays representing timeseries \n",
@@ -337,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -450,13 +409,13 @@
        "2023-05-04 10:44:53+00:00  16.55"
       ]
      },
-     "execution_count": null,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "timeseries_dataframe_from_datadict([\n",
+    "df = timeseries_dataframe_from_datadict([\n",
     "      {\n",
     "         \"time\":\"2023-05-04T10:04:49.000Z\",\n",
     "         \"value\":16.72\n",
@@ -469,12 +428,35 @@
     "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
     "         \"value\":16.55\n",
     "      }\n",
-    "   ], timecolumns=['time'])"
+    "   ], timecolumns=['time'])\n",
+    "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2023-05-04 10:04:49+00:00', '2023-05-04 10:24:51+00:00',\n",
+       "               '2023-05-04 10:44:53+00:00'],\n",
+       "              dtype='datetime64[ns, UTC]', name='time', freq=None)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -530,13 +512,13 @@
        "2023-05-04 12:44:53+02:00  16.55"
       ]
      },
-     "execution_count": null,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "set_time_index_zone( timeseries_dataframe_from_datadict([\n",
+    "df = set_time_index_zone( timeseries_dataframe_from_datadict([\n",
     "      {\n",
     "         \"time\":\"2023-05-04T10:04:49\",\n",
     "         \"value\":16.72\n",
@@ -549,12 +531,42 @@
     "         \"time\":\"2023-05-04T10:44:53\",\n",
     "         \"value\":16.55\n",
     "      }\n",
-    "   ], timecolumns=['time']), timezone='Europe/Amsterdam')"
+    "   ], timecolumns=['time']), timezone='Europe/Amsterdam')\n",
+    "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2023-05-04 12:04:49+02:00', '2023-05-04 12:24:51+02:00',\n",
+       "               '2023-05-04 12:44:53+02:00'],\n",
+       "              dtype='datetime64[ns, Europe/Amsterdam]', name='time', freq=None)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Handling NaN values in the output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -579,190 +591,922 @@
     "        return data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Timezones"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2018-04-10 09:01:01.123000+02:00',\n",
+       "               '2018-04-10 09:01:02.123000+02:00',\n",
+       "               '2018-04-10 09:01:03.123000+02:00'],\n",
+       "              dtype='datetime64[ns, Europe/Amsterdam]', freq='s')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#| export\n",
-    "def timeseries_dataframe_to_datadict(\n",
-    "        data:typing.Union[pd.DataFrame, pd.Series, dict], \n",
-    "        recordformat:str='records', \n",
-    "        timezone:str='UTC',\n",
-    "        popNaN:bool=False):\n",
-    "\n",
-    "    \"\"\"\n",
-    "    Convert a timeseries DataFrame or Series into a dictionary representation.\n",
-    "\n",
-    "    Args:\n",
-    "        data (Union[pd.DataFrame, pd.Series, dict]): The input data to be converted. It can be a pandas DataFrame, Series, or a dictionary.\n",
-    "        recordformat (str, optional): The format of the output records. Defaults to 'records'.\n",
-    "        timezone (str, optional): The timezone to use for the DataFrame index. Defaults to 'UTC'.\n",
-    "        popNaN (bool, optional): Whether to remove NaN values from the output dictionary. Defaults to False.\n",
-    "\n",
-    "    Returns:\n",
-    "        Union[dict, list]: The converted dictionary representation of the input data. If `popNaN` is True, it returns a dictionary with NaN values removed. Otherwise, it returns a dictionary or a list of dictionaries depending on the `recordformat` parameter.\n",
-    "    \"\"\"\n",
-    "        \n",
-    "    orient = recordformat.lower()\n",
-    "\n",
-    "    normalized_data = timeseries_dataframe(data, timezone=timezone)\n",
-    "    if isinstance(normalized_data.index, pd.DatetimeIndex):\n",
-    "        normalized_data.index = normalized_data.index.map(lambda x: x.isoformat())\n",
-    "    \n",
-    "    if orient == 'records':\n",
-    "        records = normalized_data.reset_index().to_dict(orient='records')\n",
-    "    else:\n",
-    "        records =  normalized_data.to_dict(orient=orient)\n",
-    "\n",
-    "    if popNaN and normalized_data.isna().any(axis=None):\n",
-    "            return pop_nan_values(records)\n",
-    "    \n",
-    "    return records    \n"
+    "rng = pd.date_range(pd.Timestamp(\"2018-04-10T09:01:01.123+02:00\"), periods=3, freq='s').tz_convert('Europe/Amsterdam')\n",
+    "rng"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['2018-04-10T09:01:01+0200', '2018-04-10T09:01:02+0200',\n",
+       "       '2018-04-10T09:01:03+0200'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df = timeseries_dataframe_from_datadict([\n",
-    "      {\n",
-    "         \"time\":\"2023-05-04T10:04:49.000Z\",\n",
-    "         \"value\":16.72\n",
-    "      },\n",
-    "      {\n",
-    "         \"time\":\"2023-05-04T10:24:51.000Z\",\n",
-    "         \"value\":16.65\n",
-    "      },\n",
-    "      {\n",
-    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
-    "         \"value\":16.55\n",
-    "      },\n",
-    "      {\n",
-    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
-    "         \"value\":np.nan\n",
-    "      }\n",
-    "   ], timecolumns=['time'])\n",
-    "\n"
+    "\n",
+    "rng.strftime(\"%FT%R:%S%z\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'time': '2023-05-04T10:04:49+00:00', 'value': 16.72},\n",
-       " {'time': '2023-05-04T10:24:51+00:00', 'value': 16.65},\n",
-       " {'time': '2023-05-04T10:44:53+00:00', 'value': 16.55},\n",
-       " {'time': '2023-05-04T10:44:53+00:00'}]"
+       "DatetimeIndex(['2018-04-10 09:01:01+02:00', '2018-04-10 09:01:02+02:00',\n",
+       "               '2018-04-10 09:01:03+02:00'],\n",
+       "              dtype='datetime64[ns, UTC+02:00]', freq=None)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "timeseries_dataframe_to_datadict(df, recordformat='records', popNaN=True)"
+    "pd.DatetimeIndex(rng.strftime(\"%FT%R:%S%z\")).round('ms')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'index': ['2023-05-04T10:04:49+00:00',\n",
-       "  '2023-05-04T10:24:51+00:00',\n",
-       "  '2023-05-04T10:44:53+00:00',\n",
-       "  '2023-05-04T10:44:53+00:00'],\n",
-       " 'columns': ['value'],\n",
-       " 'data': [[16.72], [16.65], [16.55]],\n",
-       " 'index_names': ['time']}"
+       "Index(['2018-04-10T07:01:01Z', '2018-04-10T07:01:02Z', '2018-04-10T07:01:03Z'], dtype='object')"
       ]
      },
-     "execution_count": null,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "timeseries_dataframe_to_datadict(df, recordformat='tight', popNaN=True)"
+    "\n",
+    "rng.tz_convert('UTC').strftime(\"%FT%R:%SZ\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "test_data = {'index': ['2023-05-04T10:04:49+00:00',\n",
-    "  '2023-05-04T10:24:51+00:00',\n",
-    "  '2023-05-04T10:44:53+00:00',\n",
-    "  '2023-05-04T10:44:53+00:00'],\n",
-    " 'columns': ['value'],\n",
-    " 'data': [[16.72], [16.65], [16.55], [np.nan]],\n",
-    " 'index_names': ['time'],\n",
-    " 'column_names': [None]}"
+    "# .map(lambda x: x.isoformat())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng = pd.date_range(pd.Timestamp(\"2018-04-10T09:01:01.123+02:00\"), periods=30000, freq='s').tz_convert('Europe/Amsterdam')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Which is faster, `strftime()` or `isoformat()`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "{'index': ['2023-05-04T10:04:49+00:00',\n",
-       "  '2023-05-04T10:24:51+00:00',\n",
-       "  '2023-05-04T10:44:53+00:00',\n",
-       "  '2023-05-04T10:44:53+00:00'],\n",
-       " 'columns': ['value'],\n",
-       " 'data': [[16.72], [16.65], [16.55]],\n",
-       " 'index_names': ['time']}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "113 ms ± 728 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
     }
    ],
    "source": [
-    "pop_nan_values(test_data)"
+    "%%timeit\n",
+    "ft = rng.strftime(\"%FT%R:%S%z\")\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "75 ms ± 229 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "ft = rng.map(lambda x: x.isoformat(timespec='milliseconds'))\n"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "#| export\n",
+    "def timeseries_dataframe_to_datadict(\n",
+    "        data:typing.Union[pd.DataFrame, pd.Series, dict], \n",
+    "        recordformat:str='records', \n",
+    "        timezone:str='UTC',\n",
+    "        popNaN:bool=False):\n",
     "\n",
-    "#def interpolate_timeseries(sampler, period, method_args):\n",
+    "    \"\"\"\n",
+    "    Convert a timeseries DataFrame or Series into a dictionary representation.\n",
     "\n",
+    "    Args:\n",
+    "        data (Union[pd.DataFrame, pd.Series, dict]): The input data to be converted. It can be a pandas DataFrame, Series, or a dictionary.\n",
+    "        recordformat (str, optional): The format of the output records. Defaults to 'records'.\n",
+    "        timezone (str, optional): The timezone to use for the DataFrame index. Defaults to 'UTC'.\n",
+    "        popNaN (bool, optional): Whether to remove NaN values from the output dictionary. Defaults to False.\n",
     "\n",
-    "ResamplerMethods = dict(\n",
-    "    count=lambda R: R.count(),\n",
-    "    median=lambda R: R.median(),\n",
-    "    mean=lambda R: R.mean(),\n",
-    "    min=lambda R: R.min(),\n",
-    "    max=lambda R: R.max(),\n",
-    "    sum=lambda R: R.sum(),\n",
+    "    Returns:\n",
+    "        Union[dict, list]: The converted dictionary representation of the input data. If `popNaN` is True, it returns a dictionary with NaN values removed. Otherwise, it returns a dictionary or a list of dictionaries depending on the `recordformat` parameter.\n",
+    "    \"\"\"\n",
+    "        \n",
+    "    orient = recordformat.lower()\n",
+    "\n",
+    "    normalized_data = timeseries_dataframe(data, timezone=timezone)\n",
+    "    if isinstance(normalized_data.index, pd.DatetimeIndex):\n",
+    "        if timezone == 'UTC':\n",
+    "            print(f\"Normalized, UTC\")\n",
+    "            normalized_data.index = normalized_data.index.strftime(\"%FT%R:%SZ\")\n",
+    "        else:\n",
+    "            print(f\"Normalized, {timezone}\")\n",
+    "            normalized_data.index = normalized_data.index.map(lambda x: x.isoformat(timespec='milliseconds'))\n",
+    "                 \n",
+    "    if orient == 'records':\n",
+    "        records = normalized_data.reset_index().to_dict(orient='records')\n",
+    "    else:\n",
+    "        records =  normalized_data.to_dict(orient=orient)\n",
+    "\n",
+    "    if popNaN and normalized_data.isna().any(axis=None):\n",
+    "            return pop_nan_values(records)\n",
+    "    \n",
+    "    return records    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 09:04:49.050000+00:00</th>\n",
+       "      <td>16.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:24:51.010000+00:00</th>\n",
+       "      <td>16.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:44:53+00:00</th>\n",
+       "      <td>16.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:44:53+00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  value\n",
+       "time                                   \n",
+       "2023-05-04 09:04:49.050000+00:00  16.72\n",
+       "2023-05-04 10:24:51.010000+00:00  16.65\n",
+       "2023-05-04 10:44:53+00:00         16.55\n",
+       "2023-05-04 10:44:53+00:00           NaN"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = timeseries_dataframe_from_datadict([\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:04:49.050+01:00\",\n",
+    "         \"value\":16.72\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:24:51.010Z\",\n",
+    "         \"value\":16.65\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
+    "         \"value\":16.55\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
+    "         \"value\":np.nan\n",
+    "      }\n",
+    "   ], timecolumns=['time'])\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2023-05-04 09:04:49.050000+00:00',\n",
+       "               '2023-05-04 10:24:51.010000+00:00',\n",
+       "                      '2023-05-04 10:44:53+00:00',\n",
+       "                      '2023-05-04 10:44:53+00:00'],\n",
+       "              dtype='datetime64[ns, UTC]', name='time', freq=None)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 09:04:49.050000+00:00</th>\n",
+       "      <td>16.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:24:51.010000+00:00</th>\n",
+       "      <td>16.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:44:53+00:00</th>\n",
+       "      <td>16.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:44:53+00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  value\n",
+       "time                                   \n",
+       "2023-05-04 09:04:49.050000+00:00  16.72\n",
+       "2023-05-04 10:24:51.010000+00:00  16.65\n",
+       "2023-05-04 10:44:53+00:00         16.55\n",
+       "2023-05-04 10:44:53+00:00           NaN"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = timeseries_dataframe_from_datadict([\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:04:49.050+01:00\",\n",
+    "         \"value\":16.72\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:24:51.010Z\",\n",
+    "         \"value\":16.65\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
+    "         \"value\":16.55\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
+    "         \"value\":np.nan\n",
+    "      }\n",
+    "   ], timecolumns=['time'])\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2023-05-04 09:04:49.050000+00:00',\n",
+       "               '2023-05-04 10:24:51.010000+00:00',\n",
+       "                      '2023-05-04 10:44:53+00:00',\n",
+       "                      '2023-05-04 10:44:53+00:00'],\n",
+       "              dtype='datetime64[ns, UTC]', name='time', freq=None)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timeseries_dataframe(df, timezone='UTC').index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 09:04:49.050000+00:00</th>\n",
+       "      <td>16.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:24:51.010000+00:00</th>\n",
+       "      <td>16.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:44:53+00:00</th>\n",
+       "      <td>16.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:44:53+00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  value\n",
+       "time                                   \n",
+       "2023-05-04 09:04:49.050000+00:00  16.72\n",
+       "2023-05-04 10:24:51.010000+00:00  16.65\n",
+       "2023-05-04 10:44:53+00:00         16.55\n",
+       "2023-05-04 10:44:53+00:00           NaN"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = timeseries_dataframe_from_datadict([\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:04:49.050+01:00\",\n",
+    "         \"value\":16.72\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:24:51.010Z\",\n",
+    "         \"value\":16.65\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
+    "         \"value\":16.55\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
+    "         \"value\":np.nan\n",
+    "      }\n",
+    "   ], timecolumns=['time'])\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized, UTC\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'time': '2023-05-04T09:04:49Z', 'value': 16.72},\n",
+       " {'time': '2023-05-04T10:24:51Z', 'value': 16.65},\n",
+       " {'time': '2023-05-04T10:44:53Z', 'value': 16.55},\n",
+       " {'time': '2023-05-04T10:44:53Z'}]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timeseries_dataframe_to_datadict(df, recordformat='records', popNaN=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 09:04:49.050000+00:00</th>\n",
+       "      <td>16.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:24:51.010000+00:00</th>\n",
+       "      <td>16.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:44:53+00:00</th>\n",
+       "      <td>16.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:44:53+00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  value\n",
+       "time                                   \n",
+       "2023-05-04 09:04:49.050000+00:00  16.72\n",
+       "2023-05-04 10:24:51.010000+00:00  16.65\n",
+       "2023-05-04 10:44:53+00:00         16.55\n",
+       "2023-05-04 10:44:53+00:00           NaN"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = timeseries_dataframe_from_datadict([\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:04:49.050+01:00\",\n",
+    "         \"value\":16.72\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:24:51.010Z\",\n",
+    "         \"value\":16.65\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
+    "         \"value\":16.55\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
+    "         \"value\":np.nan\n",
+    "      }\n",
+    "   ], timecolumns=['time'])\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized, Europe/Berlin\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'time': '2023-05-04T11:04:49.050+02:00', 'value': 16.72},\n",
+       " {'time': '2023-05-04T12:24:51.010+02:00', 'value': 16.65},\n",
+       " {'time': '2023-05-04T12:44:53.000+02:00', 'value': 16.55},\n",
+       " {'time': '2023-05-04T12:44:53.000+02:00'}]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timeseries_dataframe_to_datadict(df, recordformat='records', popNaN=True, timezone='Europe/Berlin')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2023-05-04 09:04:49.050000+00:00',\n",
+       "               '2023-05-04 10:24:51.010000+00:00',\n",
+       "                      '2023-05-04 10:44:53+00:00',\n",
+       "                      '2023-05-04 10:44:53+00:00'],\n",
+       "              dtype='datetime64[ns, UTC]', name='time', freq=None)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 09:04:49.050000+00:00</th>\n",
+       "      <td>16.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:24:51.010000+00:00</th>\n",
+       "      <td>16.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:44:53+00:00</th>\n",
+       "      <td>16.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-04 10:44:53+00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  value\n",
+       "time                                   \n",
+       "2023-05-04 09:04:49.050000+00:00  16.72\n",
+       "2023-05-04 10:24:51.010000+00:00  16.65\n",
+       "2023-05-04 10:44:53+00:00         16.55\n",
+       "2023-05-04 10:44:53+00:00           NaN"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = timeseries_dataframe_from_datadict([\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:04:49.050+01:00\",\n",
+    "         \"value\":16.72\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:24:51.010Z\",\n",
+    "         \"value\":16.65\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
+    "         \"value\":16.55\n",
+    "      },\n",
+    "      {\n",
+    "         \"time\":\"2023-05-04T10:44:53.000Z\",\n",
+    "         \"value\":np.nan\n",
+    "      }\n",
+    "   ], timecolumns=['time'])\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized, UTC\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'index': ['2023-05-04T09:04:49Z',\n",
+       "  '2023-05-04T10:24:51Z',\n",
+       "  '2023-05-04T10:44:53Z',\n",
+       "  '2023-05-04T10:44:53Z'],\n",
+       " 'columns': ['value'],\n",
+       " 'data': [[16.72], [16.65], [16.55]],\n",
+       " 'index_names': ['time']}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timeseries_dataframe_to_datadict(df, recordformat='tight', popNaN=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_data = {'index': ['2023-05-04T10:04:49+00:00',\n",
+    "  '2023-05-04T10:24:51+00:00',\n",
+    "  '2023-05-04T10:44:53+00:00',\n",
+    "  '2023-05-04T10:44:53+00:00'],\n",
+    " 'columns': ['value'],\n",
+    " 'data': [[16.72], [16.65], [16.55], [np.nan]],\n",
+    " 'index_names': ['time'],\n",
+    " 'column_names': [None]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'index': ['2023-05-04T10:04:49+00:00',\n",
+       "  '2023-05-04T10:24:51+00:00',\n",
+       "  '2023-05-04T10:44:53+00:00',\n",
+       "  '2023-05-04T10:44:53+00:00'],\n",
+       " 'columns': ['value'],\n",
+       " 'data': [[16.72], [16.65], [16.55]],\n",
+       " 'index_names': ['time']}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop_nan_values(test_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "72.2 µs ± 79.6 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "pop_nan_values(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "\n",
+    "#def interpolate_timeseries(sampler, period, method_args):\n",
+    "\n",
+    "\n",
+    "ResamplerMethods = dict(\n",
+    "    count=lambda R: R.count(),\n",
+    "    median=lambda R: R.median(),\n",
+    "    mean=lambda R: R.mean(),\n",
+    "    min=lambda R: R.min(),\n",
+    "    max=lambda R: R.max(),\n",
+    "    sum=lambda R: R.sum(),\n",
     "    std=lambda R: R.std(),\n",
     "    var=lambda R: R.var(),\n",
     "    nearest=lambda R: R.nearest(),\n",
@@ -799,7 +1543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -834,7 +1578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -935,7 +1679,7 @@
        "2023-05-04 11:44:53+00:00  16.38         NaN          NaN"
       ]
      },
-     "execution_count": null,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -953,7 +1697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -964,7 +1708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1015,7 +1759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1026,9 +1770,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "python3",
+   "display_name": ".devenv-corebridge",
    "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": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/01_aicorebridge.ipynb b/nbs/01_aicorebridge.ipynb
index 944eed4..ad860ca 100644
--- a/nbs/01_aicorebridge.ipynb
+++ b/nbs/01_aicorebridge.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -182,7 +182,7 @@
        "{'anumber': array([1., 1., 2.])}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -285,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -368,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -471,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -503,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -564,7 +564,7 @@
        "2023-05-04 10:44:53+00:00      2  16.55"
       ]
      },
-     "execution_count": null,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -576,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -585,7 +585,7 @@
        "{'anumber': array([2., 2., 2.])}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -597,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -614,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -623,7 +623,7 @@
        "array([2., 2., 2.])"
       ]
      },
-     "execution_count": null,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -634,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -643,7 +643,7 @@
        "array([2., 2., 2.])"
       ]
      },
-     "execution_count": null,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -657,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -666,7 +666,7 @@
        "1.5"
       ]
      },
-     "execution_count": null,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -680,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -689,7 +689,7 @@
        "float | numpy.ndarray"
       ]
      },
-     "execution_count": null,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -700,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -709,7 +709,7 @@
        "array([2., 2., 2.])"
       ]
      },
-     "execution_count": null,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -720,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -729,7 +729,7 @@
        "array([2., 2., 2.])"
       ]
      },
-     "execution_count": null,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -747,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -764,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -773,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -791,7 +791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -804,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -819,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -839,7 +839,7 @@
       "]\n",
       "Result Message\n",
       " [\n",
-      "  \"Startup time: 2024-09-13T09:29:58.965350+00:00\",\n",
+      "  \"Startup time: 2025-01-20T12:39:14.709059+00:00\",\n",
       "  \"Corebridge version: 0.3.4\",\n",
       "  \"test_function((data: pandas.core.frame.DataFrame, anumber: float | numpy.ndarray = 0))\",\n",
       "  \"init_args: (1, 2), init_kwargs: {'num_1': 3, 'num_2': 4, 'assets_dir': None, 'save_dir': '/home/fenke/repos/corebridge/nbs/cache'}\",\n",
@@ -876,7 +876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -885,7 +885,7 @@
        "pandas.core.frame.DataFrame"
       ]
      },
-     "execution_count": null,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -896,7 +896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -914,7 +914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -933,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -942,7 +942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -960,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -979,7 +979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -999,7 +999,7 @@
       "]\n",
       "Result Message\n",
       " [\n",
-      "  \"Startup time: 2024-09-13T09:29:59.059278+00:00\",\n",
+      "  \"Startup time: 2025-01-20T12:39:14.823696+00:00\",\n",
       "  \"Corebridge version: 0.3.4\",\n",
       "  \"test_function((data: pandas.core.frame.DataFrame, anumber: float | numpy.ndarray = 0))\",\n",
       "  \"init_args: (1, 2), init_kwargs: {'num_1': 3, 'num_2': 4, 'assets_dir': None, 'save_dir': '/home/fenke/repos/corebridge/nbs/cache'}\",\n",
@@ -1036,13 +1036,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'init_time': datetime.datetime(2024, 9, 13, 9, 29, 59, 59278, tzinfo=datetime.timezone.utc),\n",
+       "{'init_time': datetime.datetime(2025, 1, 20, 12, 39, 14, 823696, tzinfo=datetime.timezone.utc),\n",
        " 'aicorebridge_version': '0.3.4',\n",
        " 'processor': <function __main__.test_function(data: pandas.core.frame.DataFrame, anumber: float | numpy.ndarray = 0)>,\n",
        " 'processor_signature': <Signature (data: pandas.core.frame.DataFrame, anumber: float | numpy.ndarray = 0)>,\n",
@@ -1058,7 +1058,7 @@
        "  'save_dir': '/home/fenke/repos/corebridge/nbs/cache'}}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1083,7 +1083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1097,6 +1097,18 @@
    "display_name": "corebridge.venv",
    "language": "python",
    "name": "corebridge.venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/sidebar.yml b/nbs/sidebar.yml
index 4a11587..031408d 100644
--- a/nbs/sidebar.yml
+++ b/nbs/sidebar.yml
@@ -5,975 +5,3 @@ website:
       - 00_core.ipynb
       - 01_aicorebridge.ipynb
       - 02_rscriptbridge.ipynb
-      - section: saves
-        contents:
-          - section: rscript
-            contents:
-              - section: libs
-                contents:
-                  - section: Deriv
-                    contents:
-                      - section: html
-                        contents:
-                          - saves/rscript/libs/Deriv/html/00Index.html
-                  - section: Formula
-                    contents:
-                      - section: doc
-                        contents: []
-                        href: saves/rscript/libs/Formula/doc/index.html
-                      - section: html
-                        contents:
-                          - saves/rscript/libs/Formula/html/00Index.html
-                  - section: R6
-                    contents:
-                      - section: html
-                        contents:
-                          - saves/rscript/libs/R6/html/00Index.html
-                  - section: RColorBrewer
-                    contents:
-                      - section: html
-                        contents:
-                          - saves/rscript/libs/RColorBrewer/html/00Index.html
-                  - section: Rcpp
-                    contents:
-                      - section: doc
-                        contents: []
-                        href: saves/rscript/libs/Rcpp/doc/index.html
-                      - section: html
-                        contents:
-                          - saves/rscript/libs/Rcpp/html/00Index.html
-                  - section: RcppEigen
-                    contents:
-                      - section: doc
-                        contents: []
-                        href: saves/rscript/libs/RcppEigen/doc/index.html
-                      - section: html
-                        contents:
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