Merge pull request #113 from h2020charisma/calibration_updates

documentation updates

CALIBRATION.md

@@ -0,0 +1,25 @@
+# CHARISMA Calibration protocol
+
+```python
+# Create an instance of CalibrationModel
+calmodel = CalibrationModel(laser_wl=785)
+calmodel.derive_model_x(
+    spe_neon,
+    spe_neon_units='cm-1',
+    ref_neon=None,
+    ref_neon_units='nm',
+    spe_sil=None,
+    spe_sil_units='cm-1',
+    ref_sil=None,
+    ref_sil_units='cm-1'
+    )
+# Store
+calmodel.save(modelfile)
+# Load
+calmodel = CalibrationModel.from_file(modelfile)
+# Apply to new spectrum
+calmodel.apply_calibration_x(
+    spe_to_calibrate,
+    spe_units='m-1'
+    )
+```

src/ramanchada2/__init__.py

@@ -53,18 +53,11 @@
 
 # File formats
 
-## `.cha` vs USID/NSID
+## `.cha`
 [ramanchada][] software package introduced `.cha` file format, which is an [HDF5][]
-with a specific architecture. Two spectroscopy specific file formats -- [USID][] and
-[NSID][] -- were considered as successors of `.cha`. USID and NSID are provided
-by [pycroscopy][] package as an extension of HDF5. Several jupyter notebooks were
-created in order to assess the advantages and disadvantages of such a change. These
-file formats are designed to handle multidimensional spectral data, keeping track
-of the modifications. These file formats does not provide big advantage in our case,
-so currently `ramanchada2` supports `.cha` file format and does not support USID
-or NSID.
-
-## Cache
+with a simple layout.
+
+### Cache in .cha files
 The concept to keep previous variants of data is employed in `ramanchada2`. If
 configured so, the software saves the data for all Spectrum instances to a
 tree-organized `.cha` file. When a particular chain of operations is requested
@@ -75,18 +68,20 @@
 uses [h5py][] library to access local hdf files. It is foreseen to have implementation
 with [h5pyd][] that support network operations.
 
+## Nexus format
+
+The latest ramanchada2 package allows export of a spectrum to [NeXus][] format.
+
 
 [CRYSTAL]: https://www.crystal.unito.it/index.php
 [HDF5]: https://hdfgroup.org/
 [LMFIT]: https://lmfit.github.io/lmfit-py/index.html
-[NSID]: https://pycroscopy.github.io/pyNSID
-[USID]: https://pycroscopy.github.io/USID
 [VASP]: https://www.vasp.at/
 [algorithm]: https://doi.org/10.1103/PhysRevB.54.7830
 [h5py]: https://h5py.org/
 [h5pyd]: https://github.com/HDFGroup/h5pyd
-[pycroscopy]: https://pycroscopy.github.io/pycroscopy/
 [ramanchada]: https://github.com/h2020charisma/ramanchada
+[NeXus]: https://www.nexusformat.org/
 """
 
 from __future__ import annotations
@@ -97,9 +92,10 @@
     'auxiliary',
     'io',
     'misc',
+    'protocols',
     'spectral_components',
     'spectrum',
-    'theoretical_lines',
+    'theoretical_lines'
 ]
 __version__ = '0.0.10'
 

src/ramanchada2/protocols/calibration.py

@@ -204,6 +204,62 @@ def process(self, old_spe: Spectrum, spe_units="nm", convert_back=False):
 
 
 class CalibrationModel(ProcessingModel, Plottable):
+    """
+    A class representing a calibration model for Raman spectrum.
+
+    Parameters:
+        laser_wl (int): The wavelength of the laser used for calibration.
+
+    Methods:
+        __init__(self, laser_wl)
+            Initializes a CalibrationModel instance.
+
+        set_laser_wavelength(self, laser_wl)
+            Sets the wavelength of the laser used for calibration.
+
+        clear(self)
+            Clears the calibration model.
+
+        save(self, filename)
+            Saves the calibration model to a file.
+
+        from_file(filename)
+            Loads a calibration model from a file.
+
+        derive_model_x(self, spe_neon, spe_neon_units="cm-1", ref_neon=None, ref_neon_units="nm",
+                       spe_sil=None, spe_sil_units="cm-1", ref_sil=None, ref_sil_units="cm-1", find_kw={}, fit_kw={})
+            Derives x-calibration models using Neon and Silicon spectra.
+
+        apply_calibration_x(self, old_spe: Spectrum, spe_units="cm-1")
+            Applies the x-calibration model to Raman spectrum.
+
+        peaks(self, spe, profile='Gaussian', wlen=300, width=1)
+            Finds and fits peaks in the spectrum spe.
+
+    Example:
+        # Create an instance of CalibrationModel
+        calmodel = CalibrationModel(laser_wl=785)
+        calmodel.derive_model_x(
+            spe_neon,
+            spe_neon_units="cm-1",
+            ref_neon=None,
+            ref_neon_units="nm",
+            spe_sil=None,
+            spe_sil_units="cm-1",
+            ref_sil=None,
+            ref_sil_units="cm-1"
+            )
+        # Store
+        calmodel.save(modelfile)
+        # Load
+        calmodel = CalibrationModel.from_file(modelfile)
+        # Apply to new spectrum
+        calmodel.apply_calibration_x(
+            spe_to_calibrate,
+            spe_units="cm-1"
+            )
+    """
+
     def __init__(self, laser_wl: int):
         super(ProcessingModel, self).__init__()
         super(Plottable, self).__init__()