add description for fit limits to energy calibration

sed/calibrator/energy.py

@@ -578,7 +578,7 @@ def calibrate(
         binning = kwds.pop("binning", self.binning)
 
         if method == "lmfit":
-            self.calibration = fit_energy_calibation(
+            self.calibration = fit_energy_calibration(
                 landmarks,
                 sign * biases,
                 binwidth,
@@ -2085,7 +2085,7 @@ def peakdetect1d(
     return (np.asarray(max_peaks), np.asarray(min_peaks))
 
 
-def fit_energy_calibation(
+def fit_energy_calibration(
     pos: Union[List[float], np.ndarray],
     vals: Union[List[float], np.ndarray],
     binwidth: float,

sed/calibrator/momentum.py

@@ -8,7 +8,6 @@
 from typing import Any
 from typing import Dict
 from typing import List
-from typing import Sequence
 from typing import Tuple
 from typing import Union
 
@@ -399,7 +398,6 @@ def feature_extract(
         direction: str = "ccw",
         feature_type: str = "points",
         rotsym: int = 6,
-        ascale: Union[float, np.ndarray] = None,
         symscores: bool = True,
         **kwds,
     ):

tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data.ipynb

@@ -64,7 +64,7 @@
     "# The Scan directory\n",
     "fdir = data_path + '/Scan049_1'\n",
     "# create sed processor using the config file:\n",
-    "sp = sed.SedProcessor(folder=fdir, config=\"../sed/config/mpes_example_config.yaml\")"
+    "sp = sed.SedProcessor(folder=fdir, config=\"../sed/config/mpes_example_config.yaml\", verbose=True)"
    ]
   },
   {
@@ -463,7 +463,7 @@
    "metadata": {},
    "source": [
     "#### 3. Step:\n",
-    "Next, the detected peak positions and bias voltages are used to determine the calibration function. This can be either done by fitting the functional form d^2/(t-t0)^2 via lmfit (\"lmfit\"), or using a polynomial approxiamtion (\"lstsq\" or \"lsqr\"). Here, one can also define a reference id, and a reference energy. Those define the absolute energy position of the feature used for calibration in the \"reference\" trace, at the bias voltage where the final measurement has been performed. The energy scale can be either \"kientic\" (decreasing energy with increasing TOF), or \"binding\" (increasing energy with increasing TOF).\n",
+    "Next, the detected peak positions and bias voltages are used to determine the calibration function. This can be either done by fitting the functional form d^2/(t-t0)^2 via lmfit (\"lmfit\"), or using a polynomial approxiamtion (\"lstsq\" or \"lsqr\"). Here, one can also define a reference id, and a reference energy. Those define the absolute energy position of the feature used for calibration in the \"reference\" trace, at the bias voltage where the final measurement has been performed. The energy scale can be either \"kinetic\" (decreasing energy with increasing TOF), or \"binding\" (increasing energy with increasing TOF).\n",
     "\n",
     "After calculating the calibration, all traces corrected with the calibration are plotted ontop of each other, the calibration function together with the extracted features is plotted."
    ]
@@ -480,7 +480,17 @@
     "refid=4\n",
     "Eref=-0.5\n",
     "# the lmfit method uses a fit of (d/(t-t0))**2 to determine the energy calibration\n",
-    "sp.calibrate_energy_axis(ref_energy=Eref, ref_id=refid, energy_scale=\"kinetic\", method=\"lmfit\")"
+    "# limits and starting values for the fitting parameters can be provided as dictionaries\n",
+    "sp.calibrate_energy_axis(\n",
+    "    ref_id=refid,\n",
+    "    ref_energy=Eref,\n",
+    "    method=\"lmfit\",\n",
+    "    energy_scale='kinetic',\n",
+    "    d={'value':1.0,'min': .7, 'max':1.2, 'vary':True},\n",
+    "    t0={'value':8e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},\n",
+    "    E0={'value': 0., 'min': -100, 'max': 0, 'vary': True},\n",
+    "    verbose=True,\n",
+    ")"
    ]
   },
   {