diff --git a/sed/core/processor.py b/sed/core/processor.py
index 83b36932..5cfb92ea 100644
--- a/sed/core/processor.py
+++ b/sed/core/processor.py
@@ -778,11 +778,11 @@ def apply_momentum_calibration(
                 df=self._dataframe,
                 calibration=calibration,
             )
-            if self._timed_dataframe is not None:
-                self._timed_dataframe, _ = self.mc.append_k_axis(
-                    df=self._timed_dataframe,
-                    calibration=calibration,
-                )
+            # if self._timed_dataframe is not None:
+            #     self._timed_dataframe, _ = self.mc.append_k_axis(
+            #         df=self._timed_dataframe,
+            #         calibration=calibration,
+            #     )
 
             # Add Metadata
             self._attributes.add(
@@ -1231,12 +1231,12 @@ def append_energy_axis(
                 calibration=calibration,
                 **kwds,
             )
-            if self._timed_dataframe is not None:
-                self._timed_dataframe, _ = self.ec.append_energy_axis(
-                    df=self._timed_dataframe,
-                    calibration=calibration,
-                    **kwds,
-                )
+            # if self._timed_dataframe is not None:
+            #     self._timed_dataframe, _ = self.ec.append_energy_axis(
+            #         df=self._timed_dataframe,
+            #         calibration=calibration,
+            #         **kwds,
+            #     )
 
             # Add Metadata
             self._attributes.add(
@@ -1438,12 +1438,12 @@ def add_jitter(
             amps=amps,
             **kwds,
         )
-        if self._timed_dataframe is not None:
-            self._timed_dataframe = self._timed_dataframe.map_partitions(
-                apply_jitter,
-                cols=cols,
-                cols_jittered=cols,
-            )
+        # if self._timed_dataframe is not None:
+        #     self._timed_dataframe = self._timed_dataframe.map_partitions(
+        #         apply_jitter,
+        #         cols=cols,
+        #         cols_jittered=cols,
+        #     )
         metadata = []
         for col in cols:
             metadata.append(col)
diff --git a/tutorial/5 - hextof workflow.ipynb b/tutorial/5 - hextof workflow.ipynb
index 9675d266..50b57861 100644
--- a/tutorial/5 - hextof workflow.ipynb	
+++ b/tutorial/5 - hextof workflow.ipynb	
@@ -64,11 +64,29 @@
    "source": [
     "config={\"core\": {\"paths\": {\n",
     "    \"data_raw_dir\": \"/asap3/flash/gpfs/pg2/2023/data/11019101/raw/hdf/offline/fl1user3\", \n",
-    "    \"data_parquet_dir\": \"/home/agustsss/temp/sed_parquet/\"\n",
+    "    \"data_parquet_dir\": \"/home/agustsss/temp/sed_parquet_2/\"\n",
     "}}}\n",
     "sp = SedProcessor(runs=[44797], config=config, user_config=config_file, system_config={}, collect_metadata=False)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp.dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp.timed_dataframe"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -79,6 +97,151 @@
     "sp.align_dld_sectors()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dask.diagnostics import ProgressBar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with ProgressBar():\n",
+    "    tdf = sp.timed_dataframe.compute()\n",
+    "    df = sp.dataframe.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdf['delayStage'].min(), tdf['delayStage'].max(),  tdf['delayStage'].max() - tdf['delayStage'].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "df['delayStage'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f'{len(tdf):,.0f}, {len(df):,.0f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp.append_tof_ns_axis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "axes = ['sampleBias','delayStage','dldTime']\n",
+    "bins = [1, 50, 250]\n",
+    "ranges = [[30.9,31.1],[1461.9626, 1464.9075],  [650,800]]\n",
+    "res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time=\"delayStage\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "res.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "axes = ['delayStage']\n",
+    "bins = [50]\n",
+    "ranges = [[1461.9626, 1464.9075]]\n",
+    "res = sp.compute(bins=bins, axes=axes, ranges=ranges)#, normalize_to_acquisition_time=\"delayStage\")\n",
+    "resnorm = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time=\"delayStage\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "res = res / res.mean() * resnorm.mean()\n",
+    "res.plot()\n",
+    "resnorm.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp._normalization_histogram"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -521,18 +684,6 @@
    "display_name": "sed38",
    "language": "python",
    "name": "sed38"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.18"
   }
  },
  "nbformat": 4,