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@Julian-Hochhaus
Julian-Hochhaus committed Apr 27, 2023
2 parents 258843a + f9d79af commit 1e03582
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Showing 2 changed files with 385 additions and 114 deletions.
143 changes: 101 additions & 42 deletions Python/helpers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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()
Expand All @@ -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
Expand All @@ -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):
Expand Down Expand Up @@ -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:
Expand All @@ -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()):
Expand All @@ -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:
Expand All @@ -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()
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