165 lines
5.4 KiB
Python
165 lines
5.4 KiB
Python
#!/usr/bin/python3
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from PyQt6.QtWidgets import (
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QGridLayout, QWidget, QCheckBox
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)
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.backends.backend_qtagg import NavigationToolbar2QT as NavigationToolbar
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from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
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from ExtractXsecPy import read_DWBA
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class PlotWindow(QWidget):
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def __init__(self, windowTitle):
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super().__init__()
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self.setWindowTitle(windowTitle)
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self.resize(800, 600)
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self.default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
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self.log_scale_checkbox = QCheckBox("Use Log Scale for Y-Axis")
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self.log_scale_checkbox.setChecked(True)
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self.log_scale_checkbox.stateChanged.connect(self.plot_graph)
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self.gridline_checkbox = QCheckBox("Show Gridlines")
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self.gridline_checkbox.stateChanged.connect(self.plot_graph)
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self.showMarker_checkBox = QCheckBox("Show Markers")
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self.showMarker_checkBox.stateChanged.connect(self.plot_graph)
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self.figure, self.ax = plt.subplots()
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self.canvas = FigureCanvas(self.figure)
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self.toolbar = NavigationToolbar(self.canvas, self)
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layout = QGridLayout(self)
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layout.addWidget(self.toolbar, 0, 0, 1, 3)
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layout.addWidget(self.showMarker_checkBox, 1, 0)
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layout.addWidget(self.log_scale_checkbox, 1, 1)
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layout.addWidget(self.gridline_checkbox, 1, 2)
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layout.addWidget(self.canvas, 2, 0, 5, 3)
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self.xData = []
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self.yData_list = []
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self.header_list = []
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self.yTitle = ""
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self.x_exp = [] # x positions
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self.x_err = [] # x uncertainties (errors)
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self.y_exp = [] # y positions
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self.y_err = [] # y errors
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self.dataName = ""
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self.fitOption = []
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self.para = [] # fit parameters
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self.perr = [] # fit error
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self.chi_square = [] # fit Chi-squared
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def set_plot_data(self, xData, yData_list, header_list, yTitle):
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self.xData = xData
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self.yData_list = yData_list
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self.header_list = header_list
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self.yTitle = yTitle
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def set_expData(self, expData, fitOption, dataName_list, ID):
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self.x_exp = expData[ID][:, 0]
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self.x_err = expData[ID][:, 1]
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self.y_exp = expData[ID][:, 2]
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self.y_err = expData[ID][:, 3]
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self.dataName = dataName_list[ID]
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self.fitOption = fitOption[ID]
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def read_Xsec(self, file_path):
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headers, dataX, data = read_DWBA(file_path)
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self.xData = dataX
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self.yData_list = data
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self.header_list = headers[1:]
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def set_fitResult(self, para, perr, chi_sq):
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self.para = para
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self.perr = perr
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self.chi_square = chi_sq
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def plot_graph(self):
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self.ax.clear()
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plotStyle = '-' if not self.showMarker_checkBox.isChecked() else '-o'
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for i, y in enumerate(self.yData_list):
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self.ax.plot(self.xData, y, plotStyle, label=self.header_list[i])
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self.ax.set_xlabel('Angle_CM [deg]')
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self.ax.set_ylabel(self.yTitle)
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self.ax.legend(loc='upper right', frameon=True)
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# Apply log scale for y-axis if selected
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if self.log_scale_checkbox.isChecked():
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self.ax.set_yscale('log')
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else:
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self.ax.set_yscale('linear')
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self.ax.autoscale(enable=True, axis='x', tight=True)
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self.figure.tight_layout()
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def plot_Fit(self):
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self.ax.clear()
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self.ax.errorbar(self.x_exp, self.y_exp, xerr=self.x_err, yerr=self.y_err,
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fmt='x', label='Experimental Data', color='black', markersize = 15, elinewidth=2)
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self.ax.set_xlabel('Angle_CM [deg]')
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self.ax.set_ylabel(self.yTitle)
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self.ax.legend(loc='upper right', frameon=True)
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# Apply log scale for y-axis if selected
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if self.log_scale_checkbox.isChecked():
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self.ax.set_yscale('log')
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else:
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self.ax.set_yscale('linear')
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self.ax.autoscale(enable=True, axis='x', tight=True)
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self.figure.tight_layout()
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for k in range(len(self.fitOption)):
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fitTheory = []
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fitTheory_lower = []
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fitTheory_upper = []
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xsecIDStr = self.fitOption[k].strip()
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xsecID = [int(part.strip()) for part in xsecIDStr.split('+')] if '+' in xsecIDStr else [int(xsecIDStr)]
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fitTheory.append(np.zeros_like(self.xData))
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for p, id in enumerate(xsecID):
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fitTheory += self.para[p] * np.interp(self.xData, self.xData, self.yData_list[id])
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fitTheory_upper.append(np.zeros_like(self.xData))
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fitTheory_lower.append(np.zeros_like(self.xData))
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for p, id in enumerate(xsecID):
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fitTheory_upper += (self.para[p] + self.perr[p]) * np.interp(self.xData, self.xData, self.yData_list[id])
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fitTheory_lower += (self.para[p] - self.perr[p]) * np.interp(self.xData, self.xData, self.yData_list[id])
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# Replace plt.title() with plt.text() to position the title inside the plot
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self.ax.text(0.05, 0.05, f'Fit for Exp Data : {self.dataName}', transform=plt.gca().transAxes,
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fontsize=12, verticalalignment='bottom', horizontalalignment='left', color='black')
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for i, fit in enumerate(fitTheory):
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self.ax.plot(self.xData, fit, label=f'Chi2:{self.chi_square[i]:.3f} | Xsec:{self.fitOption[i]}')
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self.ax.fill_between(self.xData, fitTheory_lower[i], fitTheory_upper[i], alpha=0.2)
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for i, _ in enumerate(self.para):
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self.ax.text(0.05, 0.1 + 0.05*i, f"Xsec-{self.fitOption[i].strip()}: {', '.join([f'{x:.3f}' for x in self.para[i]])} +/- {', '.join([f'{x:.3f}' for x in self.perr[i]])}" ,
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transform=plt.gca().transAxes, fontsize=12,
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verticalalignment='bottom', horizontalalignment='left', color=self.default_colors[i])
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self.canvas.draw_idle()
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