diff --git a/sed/calibrator/energy.py b/sed/calibrator/energy.py
index fae10ad7..ca555a8c 100644
--- a/sed/calibrator/energy.py
+++ b/sed/calibrator/energy.py
@@ -1987,7 +1987,7 @@ def residual(pars, time, data, binwidth, binning, energy_scale):
         value=E0_pars.get("value", min(vals)),
         min=E0_pars.get("min", -np.inf),
         max=E0_pars.get("max", np.inf),
-        vary=d_pars.get("vary", True),
+        vary=E0_pars.get("vary", True),
     )
     fit = Minimizer(
         residual,
@@ -2225,6 +2225,7 @@ def apply_energy_offset(
     df: Union[pd.DataFrame, dask.dataframe.DataFrame],
     columns: Union[str, Sequence[str]],
     signs: Union[int, Sequence[int]],
+    subtract_mean: Union[bool, Sequence[bool]] = True,
     energy_column: str = None,
     reductions: Union[str, Sequence[str]] = None,
     config: dict = None,
@@ -2255,9 +2256,12 @@ def apply_energy_offset(
         columns = [columns]
     if isinstance(signs, int):
         signs = [signs]
+    if len(signs) != len(columns):
+        raise ValueError("signs and columns must have the same length.")
+    if isinstance(subtract_mean, bool):
+        subtract_mean = [subtract_mean] * len(columns)
     if reductions is None:
         reductions = [None] * len(columns)
-
     columns_: List[str] = []
     reductions_: List[str] = []
     to_roll: List[str] = []
@@ -2280,6 +2284,7 @@ def apply_energy_offset(
         target_column=energy_column,
         offset_columns=columns_,
         signs=signs,
+        subtract_mean=subtract_mean,
         reductions=reductions_,
         inplace=True,
     )
diff --git a/sed/core/dfops.py b/sed/core/dfops.py
index e4d43252..ee2c897b 100644
--- a/sed/core/dfops.py
+++ b/sed/core/dfops.py
@@ -331,6 +331,7 @@ def apply_offset_from_columns(
     offset_columns: Union[str, Sequence[str]],
     signs: Union[int, Sequence[int]],
     reductions: Union[str, Sequence[str]],
+    subtract_mean: Union[bool, Sequence[bool]],
     inplace: bool = True,
 ) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
     """Apply an offset to a column based on the values of other columns.
@@ -359,10 +360,12 @@ def apply_offset_from_columns(
     if len(signs) != len(offset_columns):
         raise ValueError("signs and offset_columns must have the same length!")
 
-    for col, sign, red in zip(offset_columns, signs, reductions):
+    for col, sign, red, submean in zip(offset_columns, signs, reductions, subtract_mean):
         assert col in df.columns, f"{col} not in dataframe!"
         if red is not None:
             df[target_column] = df[target_column] + sign * df[col].agg(red)
         else:
             df[target_column] = df[target_column] + sign * df[col]
+        if submean:
+            df[target_column] = df[target_column] - sign * df[col].mean()
     return df
diff --git a/sed/core/processor.py b/sed/core/processor.py
index 13af0648..7997b43c 100644
--- a/sed/core/processor.py
+++ b/sed/core/processor.py
@@ -1168,6 +1168,7 @@ def apply_energy_offset(
         columns: Union[str, Sequence[str]] = None,
         signs: Union[int, Sequence[int]] = None,
         reductions: Union[str, Sequence[str]] = None,
+        subtract_mean: Union[bool, Sequence[bool]] = None,
     ) -> None:
         """Shift the energy axis of the dataframe by a given amount.
 
@@ -1190,22 +1191,27 @@ def apply_energy_offset(
                 f"Energy column {energy_column} not found in dataframe! "
                 "Run energy calibration first",
             )
-        self._dataframe, metadata = energy.apply_energy_offset(
-            df=self._dataframe,
-            columns=columns,
-            energy_column=energy_column,
-            signs=signs,
-            reductions=reductions,
-            config=self._config,
-        )
-        self._dataframe[energy_column] += constant
-        metadata["offset"] = constant
-        self._attributes.add(
-            metadata,
-            "apply_energy_offset",
-            # TODO: allow only appending when no offset along this column(s) was applied
-            duplicate_policy="append",
-        )
+        metadata = {}
+        if columns is not None:
+            self._dataframe, metadata = energy.apply_energy_offset(
+                df=self._dataframe,
+                columns=columns,
+                energy_column=energy_column,
+                signs=signs,
+                reductions=reductions,
+                subtract_mean=subtract_mean,
+                config=self._config,
+            )
+        if constant is not None:
+            self._dataframe[energy_column] += constant
+            metadata["offset"] = constant
+        if len(metadata) > 0:
+            self._attributes.add(
+                metadata,
+                "apply_energy_offset",
+                # TODO: allow only appending when no offset along this column(s) was applied
+                duplicate_policy="append",
+            )
 
     def append_tof_ns_axis(
         self,
diff --git a/tutorial/5 - hextof workflow.ipynb b/tutorial/5 - hextof workflow.ipynb
index ae5aa5b0..e7265e9c 100644
--- a/tutorial/5 - hextof workflow.ipynb	
+++ b/tutorial/5 - hextof workflow.ipynb	
@@ -12,6 +12,7 @@
     "import sed\n",
     "import numpy as np\n",
     "\n",
+    "\n",
     "%matplotlib inline\n",
     "# %matplotlib ipympl\n",
     "import matplotlib.pyplot as plt"
@@ -49,8 +50,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "config={\"core\": {\"paths\": {\"data_raw_dir\": \"../../flash_test_data/fl1user3/\", \"data_parquet_dir\": \"../../flash_test_data/parquet/\"}}}\n",
-    "sp = SedProcessor(runs=[44638], config=config, user_config=config_file, system_config={}, collect_metadata=False)"
+    "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",
+    "}}}\n",
+    "sp = SedProcessor(runs=[44797], config=config, user_config=config_file, system_config={}, collect_metadata=False)"
    ]
   },
   {
@@ -67,26 +71,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Energy Calibration"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## using lmfit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "axes = ['sampleBias', 'dldTimeSteps']\n",
-    "bins = [6, 500]\n",
-    "ranges = [[0,6], [4000, 8000]]\n",
-    "res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
+    "# time-of-flight spectrum"
    ]
   },
   {
@@ -95,7 +80,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp.load_bias_series(binned_data=res)"
+    "sp.append_tof_ns_axis()"
    ]
   },
   {
@@ -104,9 +89,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ranges=(5500, 6000)\n",
-    "ref_id=3\n",
-    "sp.find_bias_peaks(ranges=ranges, ref_id=ref_id)"
+    "axes = ['sampleBias','dldTime']\n",
+    "bins = [5, 250]\n",
+    "ranges = [[28,33],  [650,800]]\n",
+    "res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
    ]
   },
   {
@@ -115,32 +101,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp.append_tof_ns_axis()"
+    "plt.figure()\n",
+    "res.plot.line(x='dldTime')"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "sp.dataframe.head()"
+    "# Energy Calibration"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "ref_id=3\n",
-    "ref_energy=0\n",
-    "sp.calibrate_energy_axis(\n",
-    "    ref_id=ref_id,\n",
-    "    ref_energy=ref_energy,\n",
-    "    method=\"lmfit\",\n",
-    "    energy_scale='kinetic',\n",
-    ")"
+    "## using lmfit"
    ]
   },
   {
@@ -149,17 +125,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ref_id=3\n",
-    "ref_energy=0\n",
-    "sp.calibrate_energy_axis(\n",
-    "    ref_id=ref_id,\n",
-    "    ref_energy=ref_energy,\n",
-    "    method=\"lmfit\",\n",
-    "    energy_scale='kinetic',\n",
-    "    d={'value':1, 'min': 1, 'max': 1},\n",
-    "    E0={'value':0, 'min': -100, 'max': 100,'vary':True},\n",
-    "    t0={'value':225, 'min': -100, 'max': 250, 'vary':True},\n",
-    ")"
+    "axes = ['sampleBias', 'dldTimeSteps']\n",
+    "bins = [5, 500]\n",
+    "ranges = [[28,33], [4000, 4800]]\n",
+    "res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
    ]
   },
   {
@@ -168,7 +137,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp.append_energy_axis()"
+    "plt.figure()\n",
+    "dres = res.copy()\n",
+    "dres.data = np.gradient(res.data, axis=1)\n",
+    "dres.plot.line(x='dldTimeSteps')"
    ]
   },
   {
@@ -177,7 +149,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp.append_tof_ns_axis()"
+    "sp.load_bias_series(binned_data=dres)"
    ]
   },
   {
@@ -186,7 +158,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp.dataframe[['dldTime','dldTimeSteps','energy','dldSectorID']].head()"
+    "ranges=(4120, 4200)\n",
+    "ref_id=0\n",
+    "sp.find_bias_peaks(ranges=ranges, ref_id=ref_id)"
    ]
   },
   {
@@ -195,9 +169,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes = ['sampleBias', 'energy']\n",
-    "bins = [5, 500]\n",
-    "ranges = [[28,33], [-10,10]]\n",
+    "axes = ['sampleBias', 'dldTimeSteps']\n",
+    "bins = [5, 2000]\n",
+    "ranges = [[28,33], [0, 2000]]\n",
     "res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
    ]
   },
@@ -208,21 +182,7 @@
    "outputs": [],
    "source": [
     "plt.figure()\n",
-    "res.mean('sampleBias').plot.line(x='energy',linewidth=3);\n",
-    "res.plot.line(x='energy',linewidth=1,alpha=.5,label='all');\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sp.apply_energy_offset(\n",
-    "    constant=-31.5,\n",
-    "    columns=['sampleBias'],\n",
-    "    signs=[+1],\n",
-    ")"
+    "res.plot.line(x='dldTimeSteps')"
    ]
   },
   {
@@ -231,30 +191,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes = ['sampleBias', 'energy']\n",
-    "bins = [5, 500]\n",
-    "ranges = [[28,33], [-3,2]]\n",
-    "res_fit = sp.compute(bins=bins, axes=axes, ranges=ranges)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure()\n",
-    "ax = plt.subplot(111)\n",
-    "res_fit.energy.attrs['unit'] = 'eV'\n",
-    "res_fit.mean('sampleBias').plot.line(x='energy',linewidth=3, ax=ax);\n",
-    "res_fit.plot.line(x='energy',linewidth=1,alpha=.5,label='all',ax=ax);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## with poly fit"
+    "# ref_id=0\n",
+    "# ref_energy=0\n",
+    "# sp.calibrate_energy_axis(\n",
+    "#     ref_id=ref_id,\n",
+    "#     ref_energy=ref_energy,\n",
+    "#     method=\"lmfit\",\n",
+    "#     energy_scale='kinetic',\n",
+    "# )"
    ]
   },
   {
@@ -263,7 +207,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp = SedProcessor(runs=[44797], config=config_file, collect_metadata=False)"
+    "from sed.calibrator.energy import tof2ev, tof2ns"
    ]
   },
   {
@@ -272,8 +216,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp.add_jitter()\n",
-    "sp.align_dld_sectors()"
+    "ph_peak = 1e-9 * tof2ns(binwidth=sp.config['dataframe']['tof_binwidth'], binning=3,t=219)\n",
+    "sp.calibrate_energy_axis(\n",
+    "    ref_id=0,\n",
+    "    ref_energy=0,\n",
+    "    method=\"lmfit\",\n",
+    "    energy_scale='kinetic',\n",
+    "    d={'value':1.,'min': .8, 'max':5, 'vary':True},\n",
+    "    t0={'value':ph_peak, 'min': ph_peak*0.1, 'max': ph_peak*1.9, 'vary':False},\n",
+    "    E0={'value': 0., 'min': -100, 'max': 100, 'vary': True},\n",
+    "\n",
+    ")"
    ]
   },
   {
@@ -282,10 +235,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes = ['sampleBias', 'dldTimeSteps']\n",
-    "bins = [6, 500]\n",
-    "ranges = [[28,33], [4000, 4800]]\n",
-    "res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
+    "# tof = np.linspace(4000,5000,500)\n",
+    "# plt.figure()\n",
+    "# plt.plot(tof, tof2ev(\n",
+    "#     t=tof, \n",
+    "#     tof_distance=1, \n",
+    "#     time_offset=1e-9*tof2ns(binwidth=sp.config['dataframe']['tof_binwidth'], binning=3,t=218), \n",
+    "#     energy_offset=0,\n",
+    "#     binning=3,\n",
+    "#     energy_scale='kinetic',\n",
+    "#     binwidth=sp.config['dataframe']['tof_binwidth'])\n",
+    "# )"
    ]
   },
   {
@@ -294,7 +254,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp.load_bias_series(binned_data=res)"
+    "sp.append_energy_axis()"
    ]
   },
   {
@@ -303,9 +263,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ranges=(4250, 4500)\n",
-    "ref_id=3\n",
-    "sp.find_bias_peaks(ranges=ranges, ref_id=ref_id)"
+    "sp.ec.calibration"
    ]
   },
   {
@@ -314,14 +272,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ref_id=3\n",
-    "ref_energy=-0.3\n",
-    "sp.calibrate_energy_axis(\n",
-    "    ref_id=ref_id,\n",
-    "    ref_energy=-0.3,\n",
-    "    method=\"lstsq\",\n",
-    "    order=2,\n",
-    ")"
+    "sp.dataframe[['dldTime','dldTimeSteps','energy','dldSectorID']].head()"
    ]
   },
   {
@@ -330,7 +281,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp.append_energy_axis()"
+    "axes = ['sampleBias', 'energy']\n",
+    "bins = [5, 500]\n",
+    "ranges = [[28,33], [-10,10]]\n",
+    "res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
    ]
   },
   {
@@ -339,10 +293,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "axes = ['sampleBias', 'energy']\n",
-    "bins = [5, 500]\n",
-    "ranges = [[28,33], [-10,10]]\n",
-    "res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
+    "plt.figure()\n",
+    "res.mean('sampleBias').plot.line(x='energy',linewidth=3)\n",
+    "res.plot.line(x='energy',linewidth=1,alpha=.5);"
    ]
   },
   {
@@ -351,9 +304,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure()\n",
-    "res.mean('sampleBias').plot.line(x='energy',linewidth=3);\n",
-    "res.plot.line(x='energy',linewidth=1,alpha=.5,label='all');\n"
+    "sp.dataframe"
    ]
   },
   {
@@ -363,9 +314,10 @@
    "outputs": [],
    "source": [
     "sp.apply_energy_offset(\n",
-    "    constant=-31.5,\n",
-    "    columns=['sampleBias'],\n",
-    "    signs=[+1],\n",
+    "    constant=2,\n",
+    "    columns=['sampleBias','monochromatorPhotonEnergy','tofVoltage'],\n",
+    "    signs=[1,-1,-1],\n",
+    "    subtract_mean=True,\n",
     ")"
    ]
   },
@@ -377,8 +329,8 @@
    "source": [
     "axes = ['sampleBias', 'energy']\n",
     "bins = [5, 500]\n",
-    "ranges = [[28,33], [-3,2]]\n",
-    "res_poly = sp.compute(bins=bins, axes=axes, ranges=ranges)"
+    "ranges = [[28,33], [-10,2]]\n",
+    "res_fit = sp.compute(bins=bins, axes=axes, ranges=ranges)"
    ]
   },
   {
@@ -389,19 +341,16 @@
    "source": [
     "plt.figure()\n",
     "ax = plt.subplot(111)\n",
-    "res_poly.energy.attrs['unit'] = 'eV'\n",
-    "res_poly.mean('sampleBias').plot.line(x='energy',linewidth=3, ax=ax);\n",
-    "res_poly.plot.line(x='energy',linewidth=1,alpha=.5,label='all',ax=ax);"
+    "res_fit.energy.attrs['unit'] = 'eV'\n",
+    "res_fit.mean('sampleBias').plot.line(x='energy',linewidth=3, ax=ax)\n",
+    "res_fit.plot.line(x='energy',linewidth=1,alpha=.5,label='all',ax=ax);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# From manual values\n",
-    "these are obviously not the right values, I think I got them from an other calibraiton file.\n",
-    "They are here to show how the calibration works.\n",
-    "Also, I noticed the parameter energy_scale from the config has no effect..."
+    "# Fit the falling edge at E=0 to evaluate the quality of the energy calibration"
    ]
   },
   {
@@ -410,51 +359,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp = SedProcessor(runs=[44797], config=config_file, collect_metadata=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sp.add_jitter()\n",
-    "sp.align_dld_sectors()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sp.append_energy_axis()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sp.apply_energy_offset(\n",
-    "    constant=+31.5,\n",
-    "    columns=['sampleBias'],\n",
-    "    signs=[-1],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "axes = ['sampleBias', 'energy']\n",
-    "bins = [5, 500]\n",
-    "ranges = [[28,33], [-10,5]]\n",
-    "res_config = sp.compute(bins=bins, axes=axes, ranges=ranges)"
+    "from lmfit.models import Gaussian2dModel, LorentzianModel, LinearModel"
    ]
   },
   {
@@ -463,11 +368,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "curve = res_fit.mean('sampleBias').sel(energy=slice(-0.15,1))\n",
+    "x = curve.energy\n",
+    "y = -np.gradient(curve.data)\n",
+    "full_y = np.gradient(res_fit.data, axis=1)\n",
+    "gm = LorentzianModel()\n",
+    "# lin = LinearModel()\n",
+    "model = gm #+ lin\n",
+    "params = gm.guess(y, x=x)\n",
+    "# params.update(lin.make_params())\n",
+    "result = model.fit(y, params, x=x)\n",
+    "\n",
     "plt.figure()\n",
-    "ax = plt.subplot(111)\n",
-    "res_config.energy.attrs['unit'] = 'eV'\n",
-    "res_config.mean('sampleBias').plot.line(x='energy',linewidth=3, ax=ax);\n",
-    "res_config.plot.line(x='energy',linewidth=1,alpha=.5,label='all',ax=ax);"
+    "plt.plot(x, y, 'bo')\n",
+    "plt.plot(x, result.best_fit, 'r-')\n",
+    "result.best_values\n",
+    "best = result.params"
    ]
   },
   {
@@ -476,19 +392,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp.dataframe['energy'] = - sp.dataframe['energy']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "axes = ['sampleBias', 'energy']\n",
-    "bins = [5, 500]\n",
-    "ranges = [[28,33], [-3,2]]\n",
-    "res_config = sp.compute(bins=bins, axes=axes, ranges=ranges)"
+    "{'amplitude': 62.649384173227375,\n",
+    " 'center': 0.07618539040289204,\n",
+    " 'sigma': 0.0948828689156287}"
    ]
   },
   {
@@ -498,10 +404,15 @@
    "outputs": [],
    "source": [
     "plt.figure()\n",
-    "ax = plt.subplot(111)\n",
-    "res_config.energy.attrs['unit'] = 'eV'\n",
-    "res_config.mean('sampleBias').plot.line(x='energy',linewidth=3, ax=ax);\n",
-    "res_config.plot.line(x='energy',linewidth=1,alpha=.5,label='all',ax=ax);"
+    "for i in range(len(res_fit.sampleBias)):\n",
+    "    x = res_fit.isel(sampleBias=i).sel(energy=slice(-0.15,1)).energy\n",
+    "    y = res_fit.isel(sampleBias=i).sel(energy=slice(-0.15,1)).data\n",
+    "    y = -np.gradient(y)\n",
+    "    y = y/np.max(y)\n",
+    "    result = model.fit(y, best, x=x)\n",
+    "    print(f'{i}: {result.best_values}')\n",
+    "    plt.plot(x, y+i, 'bo')\n",
+    "    plt.plot(x, result.best_fit+i, 'r-')\n"
    ]
   },
   {
@@ -509,32 +420,14 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# compare the two methods\n",
-    "fig, ax = plt.subplots(1,3, figsize=(10,4), layout='constrained')\n",
-    "res_poly.energy.attrs['unit'] = 'eV'\n",
-    "res_poly.mean('sampleBias').plot.line(x='energy',linewidth=3, ax=ax[0], label='all');\n",
-    "res_poly.plot.line(x='energy',linewidth=1,alpha=.5,ax=ax[0]);\n",
-    "ax[0].set_title('poly')\n",
-    "res_fit.energy.attrs['unit'] = 'eV'\n",
-    "res_fit.mean('sampleBias').plot.line(x='energy',linewidth=3, ax=ax[1], label='all');\n",
-    "res_fit.plot.line(x='energy',linewidth=1,alpha=.5,ax=ax[1]);\n",
-    "ax[1].set_title('fit')\n",
-    "res_config.energy.attrs['unit'] = 'eV'\n",
-    "res_config.mean('sampleBias').plot.line(x='energy',linewidth=3, ax=ax[2]);\n",
-    "res_config.plot.line(x='energy',linewidth=1,alpha=.5,label='all',ax=ax[2]);\n",
-    "ax[2].set_title('config')\n",
-    "ax[0].set_xlim(-2, 1.5)\n",
-    "ax[1].set_xlim(-2, 1.5)\n",
-    "ax[2].set_xlim(-2, 1.5)"
-   ]
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".pyenv",
+   "display_name": "sed38",
    "language": "python",
-   "name": "python3"
+   "name": "sed38"
   },
   "language_info": {
    "codemirror_mode": {
@@ -546,7 +439,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/hextof_config.yaml b/tutorial/hextof_config.yaml
index e76da9c0..0a8289af 100644
--- a/tutorial/hextof_config.yaml
+++ b/tutorial/hextof_config.yaml
@@ -5,9 +5,9 @@ core:
   beamline: pg2
   instrument: hextof
   paths:
-    data_raw_dir: "/asap3/flash/gpfs/pg2/2023/data/11019101/raw/hdf/offline/fl1user3"
+    data_raw_dir: "/path/to/data"
     # change this to a local directory where you want to store the parquet files
-    data_parquet_dir: "/home/agustsss/temp/sed_parquet"
+    data_parquet_dir: "/path/to/parquet"
 
 binning:
   num_cores: 10
@@ -28,8 +28,8 @@ dataframe:
   tof_ns_column: dldTime
   corrected_tof_column: "tm"
   bias_column: "sampleBias"
-  tof_binwidth: 2.0576131995767355E-12
-  tof_binning: 7 # with 3, 8 bins per step 2**3
+  tof_binwidth: 2.0576131995767355E-11 # in seconds
+  tof_binning: 3
   sector_id_column: dldSectorID
   sector_delays: [0., 0., 0., 0., 0., 0., 0., 0.]
   jitter_cols: ["dldPosX", "dldPosY", "dldTimeSteps"]
@@ -122,14 +122,19 @@ dataframe:
 
 energy:
   calibration:
-    offset: 4150.0
-    coeffs: [-2.01882455e-05,  1.94714008e-01]
-    Tmat: [[ 1.01040e+06,  1.20000e+02],
-        [ 7.18675e+05,  8.50000e+01],
-        [ 3.82275e+05,  4.50000e+01],
-        [-3.86325e+05, -4.50000e+01]]
-    bvec: [ 3.,  2.,  1., -1.]
+    d: 2.7342492951998603
+    t0: 3.6049383256584405e-08
+    E0: -51.289659014865784
     energy_scale: kinetic
-    axis: -463.3385531514501
-    E0:  -463.3385531514501
-    refid: 3
+    refid: 0
+  offset:
+    constant: 2.0
+    sampleBias:
+      sign: 1
+      substract_mean: True
+    monochromatorPhotonEnergy:
+      sign: -1
+      substract_mean: True
+    tofVoltage:
+      sign: -1
+      substract_mean: True