[GH2][Correct] Remove precorrection notebook and add fragment

Description

Remove precorrection notebook and use fragment for Gotthard2 correction notebook.

How Has This Been Tested?

Relevant Documents (optional)

With reference runs

Types of changes

• New feature (non-breaking change which adds functionality)

Checklist:

Reviewers

@schmidtp @kluyvert

notebooks/Gotthard2/Correction_Gotthard2_NBC.ipynb

 from cal_tools.step_timing import StepTimer
 from cal_tools.tools import (
     calcat_creation_time,
-    CalibrationMetadata,
+    write_constants_fragment,
 )
 from XFELDetAna.plotting.heatmap import heatmapPlot
 
 out_folder = Path(out_folder)
 out_folder.mkdir(parents=True, exist_ok=True)
 
-metadata = CalibrationMetadata(metadata_folder or out_folder)
-# NOTE: this notebook will not overwrite calibration metadata file
-const_yaml = metadata.get("retrieved-constants", {})
-
 if not karabo_id_control:
     karabo_id_control = karabo_id
 
             const_data[mod]["RelativeGainGotthard2"] = cfile["gain_map"][()].astype(np.float32)
             const_data[mod]["Mask"] = cfile["bpix_ff"][()].astype(np.uint32)
 else:
-    if const_yaml:
-        const_data = dict()
-        for mod in karabo_da:
-            const_data[mod] = dict()
-            for cname, mdata in const_yaml[mod]["constants"].items():
-                const_data[mod][cname] = dict()
-                if mdata["creation-time"]:
-                    with h5py.File(mdata["path"], "r") as cf:
-                        const_data[mod][cname] = np.copy(
-                            cf[f"{mdata['dataset']}/data"])
-    else:
-        mdata_dict = {"constants": dict()}
-
-        constant_names = ["LUTGotthard2", "OffsetGotthard2", "BadPixelsDarkGotthard2"]
-        if gain_correction:
-            constant_names += ["RelativeGainGotthard2", "BadPixelsFFGotthard2"]
+    constant_names = ["LUTGotthard2", "OffsetGotthard2", "BadPixelsDarkGotthard2"]
+    if gain_correction:
+        constant_names += ["RelativeGainGotthard2", "BadPixelsFFGotthard2"]
 
-        # Retrieve metadata for all pnccd constants.
-        const_data = g2_cal.ndarray_map(constant_names)
+    g2_metadata = g2_cal.metadata(calibrations=constant_names)
 
     # Validate the constants availability and raise/warn correspondingly.
-    for mod, calibrations in const_data.items():
+    for mod, calibrations in g2_metadata.items():
 
         dark_constants = {"LUTGotthard2"}
         if offset_correction:
         if gain_correction and missing_gain_constants:
             warning(f"Gain constants {missing_gain_constants} are not retrieved for mod {mod}.")
 
-        # Create the mask array.
-        bpix = const_data[mod].get("BadPixelsDarkGotthard2")
-        if bpix is None:
-            bpix = np.zeros((1280, 2, 3), dtype=np.uint32)
-        if const_data[mod].get("BadPixelsFFGotthard2") is not None:
-            bpix |= const_data[mod]["BadPixelsFFGotthard2"]
-        const_data[mod]["Mask"] = bpix
-
-        # Prepare empty arrays for missing constants.
-        if const_data[mod].get("OffsetGotthard2") is None:
-            const_data[mod]["OffsetGotthard2"] = np.zeros(
-                (1280, 2, 3), dtype=np.float32)
-
-        if const_data[mod].get("RelativeGainGotthard2") is None:
-            const_data[mod]["RelativeGainGotthard2"] = np.ones(
-                (1280, 2, 3), dtype=np.float32)
-            if gain_correction:
-                gain_correction = False
-                warning("Gain correction is disabled for this module.")
-        const_data[mod]["RelativeGainGotthard2"] = const_data[mod]["RelativeGainGotthard2"].astype(  # noqa
-            np.float32, copy=False)  # Old gain constants are not float32.
-
 if not karabo_da:
     raise ValueError("Dark constants are not available for all modules.")
+%% Cell type:code id:ac1cdec5 tags:
+``` python
+# Record constant details in YAML metadata.
+write_constants_fragment(
+    out_folder=(metadata_folder or out_folder),
+    det_metadata=g2_metadata,
+    caldb_root=g2_cal.caldb_root)
+
+# Load constants data for all constants.
+const_data = g2_cal.ndarray_map(metadata=g2_metadata)
+
+# Prepare constant arrays.
+if not constants_file:
+    # Create the mask array.
+    bpix = const_data[mod].get("BadPixelsDarkGotthard2")
+    if bpix is None:
+        bpix = np.zeros((1280, 2, 3), dtype=np.uint32)
+    if const_data[mod].get("BadPixelsFFGotthard2") is not None:
+        bpix |= const_data[mod]["BadPixelsFFGotthard2"]
+    const_data[mod]["Mask"] = bpix
+
+    # Prepare empty arrays for missing constants.
+    if const_data[mod].get("OffsetGotthard2") is None:
+        const_data[mod]["OffsetGotthard2"] = np.zeros(
+            (1280, 2, 3), dtype=np.float32)
+
+    if const_data[mod].get("RelativeGainGotthard2") is None:
+        const_data[mod]["RelativeGainGotthard2"] = np.ones(
+            (1280, 2, 3), dtype=np.float32)
+    const_data[mod]["RelativeGainGotthard2"] = const_data[mod]["RelativeGainGotthard2"].astype(  # noqa
+        np.float32, copy=False)  # Old gain constants are not float32.
 %% Cell type:code id:23fcf7f4-351a-4df7-8829-d8497d94fecc tags: