Merge branch 'dev'

Python/helpers.py

@@ -6,14 +6,28 @@
 from PyQt5 import QtWidgets, QtCore
 import numpy as np
 import os
+import numpy as np
+
 def autoscale_y(ax, margin=0.1):
- """This function rescales the y-axis based on the data that is visible given the current xlim of the axis.
- ax -- a matplotlib axes object
- margin -- the fraction of the total height of the y-data to pad the upper ylims"""
+ """Rescales the y-axis based on the visible data given the current xlim of the axis.
+
+ Args:
+ ax (matplotlib.axes.Axes): The axes object to autoscale.
+ margin (float, optional): The fraction of the total height of the y-data to pad the upper ylims. Default is 0.1.
 
- import numpy as np
+ Returns:
+ None.
+ """
 
  def get_bottom_top(line):
+ """Helper function to get the minimum and maximum y-values for a given line.
+
+ Args:
+ line (matplotlib.lines.Line2D): The line object to get the y-data from.
+
+ Returns:
+ tuple: A tuple containing the minimum and maximum y-values.
+ """
  xd = line.get_xdata()
  yd = line.get_ydata()
  lo, hi = ax.get_xlim()
@@ -23,8 +37,8 @@ def get_bottom_top(line):
  else:
  y_displayed= yd[((xd < lo) & (xd > hi))]
  h = np.max(y_displayed) - np.min(y_displayed)
- if np.min(y_displayed) - 2 * margin * (np.max(y_displayed) - np.min(y_displayed)) > 0:
- bot = np.min(y_displayed) - 2 * margin * (np.max(y_displayed) - np.min(y_displayed))
+ if np.min(y_displayed) - 2 * margin * h > 0:
+ bot = np.min(y_displayed) - 2 * margin * h
  else:
  bot = 0
  top = np.max(y_displayed) + margin * h
@@ -44,39 +58,52 @@ def get_bottom_top(line):
 
  ax.set_ylim(bot, top)
 
+def model_selector(index: int, strind: str, index_pk: int):
+ """
+ Returns a model based on the index parameter.
 
+ Args:
+ index (int): An integer index to select the model.
+ prefix (str): A string prefix to identify the model.
+ index_pk (int): An integer index to identify the peak.
+
+ Returns:
+ Model: A model selected based on the index parameter.
+ """
+ model_options = {
+ 0: GaussianModel(prefix=strind + str(index_pk + 1) + '_'),
+ 1: LorentzianModel(prefix=strind + str(index_pk + 1) + '_'),
+ 2: VoigtModel(prefix=strind + str(index_pk + 1) + '_'),
+ 3: PseudoVoigtModel(prefix=strind + str(index_pk + 1) + '_'),
+ 4: ExponentialGaussianModel(prefix=strind + str(index_pk + 1) + '_'),
+ 5: SkewedGaussianModel(prefix=strind + str(index_pk + 1) + '_'),
+ 6: SkewedVoigtModel(prefix=strind + str(index_pk + 1) + '_'),
+ 7: BreitWignerModel(prefix=strind + str(index_pk + 1) + '_'),
+ 8: LognormalModel(prefix=strind + str(index_pk + 1) + '_'),
+ 9: DoniachModel(prefix=strind + str(index_pk + 1) + '_'),
+ 10: ConvGaussianDoniachDublett(prefix=strind + str(index_pk + 1) + '_'),
+ 11: ConvGaussianDoniachSinglett(prefix=strind + str(index_pk + 1) + '_'),
+ 12: FermiEdgeModel(prefix=strind + str(index_pk + 1) + '_')
+ }
+
+ selected_model = model_options.get(index)
+
+ if selected_model is not None:
+ return selected_model
+ else:
+ raise ValueError(f"No model found for index {index}.")
 
-def modelSelector(index, strind, index_pk):
- if index == 0:
- pk_mod = GaussianModel(prefix=strind + str(index_pk + 1) + '_')
- if index == 1:
- pk_mod = LorentzianModel(prefix=strind + str(index_pk + 1) + '_')
- if index == 2:
- pk_mod = VoigtModel(prefix=strind + str(index_pk + 1) + '_')
- if index == 3:
- pk_mod = PseudoVoigtModel(prefix=strind + str(index_pk + 1) + '_')
- if index == 4:
- pk_mod = ExponentialGaussianModel(prefix=strind + str(index_pk + 1) + '_')
- if index == 5:
- pk_mod = SkewedGaussianModel(prefix=strind + str(index_pk + 1) + '_')
- if index == 6:
- pk_mod = SkewedVoigtModel(prefix=strind + str(index_pk + 1) + '_')
- if index == 7:
- pk_mod = BreitWignerModel(prefix=strind + str(index_pk + 1) + '_')
- if index == 8:
- pk_mod = LognormalModel(prefix=strind + str(index_pk + 1) + '_')
- if index == 9:
- pk_mod = DoniachModel(prefix=strind + str(index_pk + 1) + '_')
- if index == 10:
- pk_mod = ConvGaussianDoniachDublett(prefix=strind + str(index_pk + 1) + '_')
- if index == 11:
- pk_mod = ConvGaussianDoniachSinglett(prefix=strind + str(index_pk + 1) + '_')
- if index == 12:
- pk_mod = FermiEdgeModel(prefix=strind + str(index_pk + 1) + '_')
-
- return pk_mod
 
 class Window_CrossSection(QtWidgets.QWidget):
+ """
+ A class to create a widget for cross-section calculations.
+
+ Attributes:
+ dataset_cross_sections (list): a list of cross-section data.
+ tougaard_params (list): a list of default Tougaard parameters.
+
+ """
+
  dataset_cross_sections=[]
  tougaard_params=['standard value', 0,2886,1643,1,1]
  def __init__(self):
@@ -111,18 +138,25 @@ def __init__(self):
  layout_bottom=QtWidgets.QHBoxLayout()
  btn_add = QtWidgets.QPushButton('Add cross section', self)
  btn_add.resize(btn_add.sizeHint())
- btn_add.clicked.connect(self.addCrossSection)
+ btn_add.clicked.connect(self.add_cross_section)
  layout_bottom.addWidget(btn_add)
- #btn_load = QtWidgets.QPushButton('Load cross section', self)
- #btn_load.resize(btn_load.sizeHint())
- #btn_load.clicked.connect(self.addCrossSection)
  self.btn_cc = QtWidgets.QPushButton('Use current cross-section', self)
  self.btn_cc.resize(self.btn_cc.sizeHint())
  #self.btn_cc.clicked.connect(self.pushToMain)
  layout_bottom.addWidget(self.btn_cc)
  self.layout.addLayout(layout_top)
  self.layout.addLayout(layout_bottom)
  def load_elements(self):
+ """
+ Loads the elements from a CSV file and updates the dataset_cross_sections list.
+
+ Args:
+ None.
+
+ Returns:
+ list: A list of elements loaded from the CSV file.
+
+ """
  dirPath = os.path.dirname(os.path.abspath(__file__))
  temp_elements=[]
  with open (dirPath+'/../CrossSections/cross_sections.csv') as f:
@@ -134,7 +168,17 @@ def load_elements(self):
  if temp not in self.dataset_cross_sections:
  self.dataset_cross_sections.append(temp)
  return(temp_elements)
- def addCrossSection(self):
+ def add_cross_section(self):
+ """
+ Adds a new cross section to the CSV file and updates the list of elements.
+
+ Args:
+ None.
+
+ Returns:
+ None.
+
+ """
  dirPath = os.path.dirname(os.path.abspath(__file__))
  temp_elements = []
  for i in range(self.tougaard_tab.columnCount()):
@@ -153,6 +197,10 @@ def addCrossSection(self):
  print(temp_elements[0]+ ' is already part of the database, please choose a different name!')
 
  def choosenElement(self):
+ """
+ Sets the selected element's parameters in the Tougaard table.
+ """
+
  idx=self.elements.currentIndex()
  for j in range(6):
  if j<4:
@@ -163,14 +211,25 @@ def choosenElement(self):
  self.tougaard_tab.resizeRowsToContents()
 
 class Element:
- def __init__(self, name,atomic_number, tb, tc, tcd, td):
+ """
+ Represents an element with its corresponding Tougaard parameters.
+
+ Args:
+ name (str): The name of the element. Default is 'standard value'.
+ atomic_number (int): The atomic number of the element. Default is 0.
+ tb (int): The value of the Tougaard parameter B for the element. Default is 2866.
+ tc (int): The value of the Tougaard parameter C for the element. Default is 1643.
+ tcd (int): The value of the Tougaard parameter C* for the element. Default is 1.
+ td (int): The value of the Tougaard parameter D for the element. Default is 1.
+ """
+ def __init__(self, name=None, atomic_number=None, tb=None, tc=None, tcd=None, td=None):
  self.name = name if name is not None else 'standard value'
  self.atomic_number = atomic_number if atomic_number is not None else 0
  self.tb = tb if tb is not None else 2866
  self.tc = tc if tc is not None else 1643
  self.tcd = tcd if tcd is not None else 1
  self.td = td if td is not None else 1
- self.tougaard_params = [self.atomic_number,self.tb, self.tc, self.tcd, self.td]
+ self.tougaard_params = [self.atomic_number, self.tb, self.tc, self.tcd, self.td]
 def cross_section():
  window_cross_section = Window_CrossSection()
  window_cross_section.show()