chore: style

README.md

@@ -14,7 +14,7 @@
 
 In batch processing mode this package will index the raman data files in a chosen folder.
 First, it will try to extract a sample ID and position number from the filenames and create an index of the files in a dataframe. Over this index a preprocessing, fitting and exporting loop will start.
-There are several models, each with a different combination of typical peaks, used for fitting. Each individual typical peak is defined as a class in the deconvolution/default_peaks folder with some added literature reference in the docstring. Here, the individual peak parameter settings can also be easily adjusted for initial values, limits, shape (eg. Lorentzian, Gaussian and Voigt) or be fixed at certain initial values.
+There are several models, each with a different combination of typical peaks, used for fitting. Each individual typical peak is defined as a class in the deconvolution/default_models folder with some added literature reference in the docstring. Here, the individual peak parameter settings can also be easily adjusted for initial values, limits, shape (eg. Lorentzian, Gaussian and Voigt) or be fixed at certain initial values.
 Export is done with plots and excel files for the spectral data and fitting parameters for further analysis.
 
 

src/raman_fitting/__init__.py

@@ -95,18 +95,16 @@
 from raman_fitting.delegating.main_delegator import MainDelegator, make_examples
 
 # Indexer
-from raman_fitting.imports.indexer import MakeRamanFilesIndex as make_index
+from raman_fitting.imports.files.file_indexer import MakeRamanFilesIndex as make_index
 
 # Processing
-from raman_fitting.processing.spectrum_template import SpectrumTemplate
-from raman_fitting.processing.spectrum_constructor import (
-    SpectrumDataLoader,
-    SpectrumDataCollection,
-)
+from raman_fitting.imports.spectrum.spectrum_constructor import SpectrumDataLoader
+from raman_fitting.imports.spectrum.spectra_collection import SpectrumDataCollection
+
 
 # Modelling / fitting
-from raman_fitting.deconvolution_models.init_models import InitializeModels
-from raman_fitting.deconvolution_models.fit_models import Fitter
+from raman_fitting.models.deconvolution.init_models import InitializeModels
+from raman_fitting.models.fit_models import Fitter
 
 
 # Exporting / Plotting

src/raman_fitting/exports/exporter.py

@@ -18,7 +18,9 @@ class Exporter:
 
     """
 
-    def __init__(self, arg, raw_out=True, plot=True, model_names_prefix=["1st", "2nd"]):
+    def __init__(
+        self, arg, raw_out=True, plot=True, model_names_prefix=["first", "second"]
+    ):
         self.raw_out = raw_out
         self.plot = plot
         try:
@@ -87,17 +89,19 @@ def raw_export(self):
     def export_fitting_plotting_models(self):
         pars1, pars2 = [], []
 
-        _1st = {
-            k: val for k, val in self.fitter.FitResults.items() if k.startswith("1st")
+        _first = {
+            k: val for k, val in self.fitter.FitResults.items() if k.startswith("first")
         }
-        _2nd = {
-            k: val for k, val in self.fitter.FitResults.items() if k.startswith("2nd")
+        _second = {
+            k: val
+            for k, val in self.fitter.FitResults.items()
+            if k.startswith("second")
         }
 
-        for modname_2, fitres_2 in _2nd.items():
+        for modname_2, fitres_2 in _second.items():
             self.export_xls_from_spec(fitres_2)
             pars2.append(fitres_2.FitParameters)
-            for modname_1, fitres_1 in _1st.items():
+            for modname_1, fitres_1 in _first.items():
                 self.export_xls_from_spec(fitres_1)
                 try:
                     fit_spectrum_plot(

tests/exporting/test_plotting.py

@@ -13,7 +13,7 @@
 import pytest
 from lmfit import Model
 
-from raman_fitting.deconvolution_models.init_models import InitializeModels
+from raman_fitting.models.deconvolution.init_models import InitializeModels
 from raman_fitting.exports.plot_formatting import (
     get_cmap_list,
     assign_colors_to_peaks,