rewrite fit plot to add control
This commit is contained in:
parent
019877e5ea
commit
def7d58217
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@ -4,7 +4,7 @@ import numpy as np
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from scipy.optimize import curve_fit
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from PyQt6.QtWidgets import (
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QVBoxLayout, QWidget
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QVBoxLayout, QGridLayout, QWidget, QCheckBox
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)
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import matplotlib.pyplot as plt
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@ -12,26 +12,9 @@ from matplotlib.backends.backend_qtagg import NavigationToolbar2QT as Navigation
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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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from PlotWindow import FitPlotWindow
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default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
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class FitPlotWidget(QWidget):
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def __init__(self, figure):
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super().__init__()
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self.setWindowTitle("Fit Plot")
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self.resize(800, 600)
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self.canvas = FigureCanvas(figure)
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self.toolbar = NavigationToolbar(self.canvas, self)
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layout = QVBoxLayout(self)
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layout.addWidget(self.toolbar)
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layout.addWidget(self.canvas)
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self.setLayout(layout)
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#========================================================
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class Fitting():
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def __init__(self):
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@ -43,10 +26,16 @@ class Fitting():
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self.data = [] # is a 2D array
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self.headers = []
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# fit parameters for a single data set
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self.para = []
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self.para_err = []
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self.chi_squared = []
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self.plot = []
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def read_data(self,file_path):
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self.headers, self.dataX, self.data = read_DWBA(file_path)
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print(self.headers)
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self.headers = self.headers[1:]
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def read_expData(self, fileName):
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self.dataName_list = []
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@ -73,8 +62,8 @@ class Fitting():
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current_data = []
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# Extract dataSet Name
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dataName = line.split()[1]
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self.dataName_list.append(dataName)
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dataName = line.split()[1:]
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self.dataName_list.append(" ".join(dataName))
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# Check for fit option lines
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elif line.startswith("fit"):
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@ -103,114 +92,74 @@ class Fitting():
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print("Fit Options:", self.fitOption[i])
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print(" Data List:\n", self.expData[i])
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def FitSingleData(self, expDataID):
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print("============================================")
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def FitData(self):
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# Get the number of fit options and cross-sections
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nFit = len(self.fitOption[expDataID])
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nXsec = len(self.data)
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figure_list = []
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# Extract experimental data (x, y, and errors)
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x_exp = self.expData[expDataID][:, 0] # x positions
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y_exp = self.expData[expDataID][:, 2] # y positions
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y_err = self.expData[expDataID][:, 3] # y errors
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for expDataID in range(len(self.expData)):
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self.para = []
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self.para_err = []
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self.chi_squared = []
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print("============================================")
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for k in range(nFit):
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# Get the cross-section IDs for the current fit option and strip extra spaces
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xsecIDStr = self.fitOption[expDataID][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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# Get the number of fit options and cross-sections
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nFit = len(self.fitOption[expDataID])
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nXsec = len(self.data)
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# Ensure all cross-section IDs are valid
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processFlag = True
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for id in range(len(xsecID)):
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if xsecID[id] >= nXsec:
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print(f"Error: Requested Xsec-{xsecID[id]} exceeds the number of available cross-sections ({nXsec})")
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processFlag = False
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# Extract experimental data (x, y, and errors)
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x_exp = self.expData[expDataID][:, 0] # x positions
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x_err = self.expData[expDataID][:, 1] # x uncertainties (errors)
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y_exp = self.expData[expDataID][:, 2] # y positions
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y_err = self.expData[expDataID][:, 3] # y errors
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if processFlag == False :
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continue
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fitTheory = []
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fitTheory_lower = []
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fitTheory_upper = []
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# Define the fitting function: a weighted sum of the selected data
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def fit_func(x, *scale):
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y = np.zeros_like(x)
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for p, id in enumerate(xsecID):
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y += scale[p] * np.interp(x, self.dataX, self.data[id])
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return y
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para = []
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para_err = []
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chi_squared = []
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lower_bounds = [1e-6] * len(xsecID) # Setting a small positive lower bound
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upper_bounds = [np.inf] * len(xsecID) # No upper bound
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for k in range(nFit):
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# Get the cross-section IDs for the current fit option and strip extra spaces
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xsecIDStr = self.fitOption[expDataID][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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# Perform curve fitting using the fit_func and experimental data with y-errors as weights
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popt, pcov = curve_fit(fit_func, x_exp, y_exp, sigma=y_err, absolute_sigma=True,
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p0=np.ones(len(xsecID)), # Initial guess for scale parameters
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bounds=(lower_bounds, upper_bounds))
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# Ensure all cross-section IDs are valid
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processFlag = True
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for id in range(len(xsecID)):
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if xsecID[id] >= nXsec:
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print(f"Error: Requested Xsec-{xsecID[id]} exceeds the number of available cross-sections ({nXsec})")
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processFlag = False
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self.para.append(popt)
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perr = np.sqrt(np.diag(pcov))# Standard deviation of the parameters
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self.para_err.append(perr)
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if processFlag == False :
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continue
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# Get the fitted model values
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y_fit = fit_func(x_exp, *popt)
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residuals = y_exp - y_fit
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self.chi_squared.append(np.sum((residuals / y_err) ** 2))
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# Define the fitting function: a weighted sum of the selected data
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def fit_func(x, *scale):
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y = np.zeros_like(x)
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for p, id in enumerate(xsecID):
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y += scale[p] * np.interp(x, self.dataX, self.data[id])
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return y
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print(f"Fitted scale for fit {k}: {', '.join([f'{x:.3f}' for x in popt])} +/- {', '.join([f'{x:.3f}' for x in perr])} | Chi^2 : {self.chi_squared[-1]:.4f}")
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# print(f"Fitted scale for fit {k}: {popt} +/- {perr} | Chi^2 : {chi_squared[-1]:.4f}")
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lower_bounds = [1e-6] * len(xsecID) # Setting a small positive lower bound
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upper_bounds = [np.inf] * len(xsecID) # No upper bound
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return self.para, self.para_err, self.chi_squared
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# Perform curve fitting using the fit_func and experimental data with y-errors as weights
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popt, pcov = curve_fit(fit_func, x_exp, y_exp, sigma=y_err, absolute_sigma=True,
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p0=np.ones(len(xsecID)), # Initial guess for scale parameters
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bounds=(lower_bounds, upper_bounds))
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para.append(popt)
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perr = np.sqrt(np.diag(pcov))# Standard deviation of the parameters
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para_err.append(perr)
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def plot_fits(self):
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# Get the fitted model values
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y_fit = fit_func(x_exp, *popt)
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residuals = y_exp - y_fit
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chi_squared.append(np.sum((residuals / y_err) ** 2))
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self.plot = []
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print(f"Fitted scale for fit {k}: {', '.join([f'{x:.3f}' for x in popt])} +/- {', '.join([f'{x:.3f}' for x in perr])} | Chi^2 : {chi_squared[-1]:.4f}")
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# print(f"Fitted scale for fit {k}: {popt} +/- {perr} | Chi^2 : {chi_squared[-1]:.4f}")
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# Append the theoretical fit for this fit option
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fitTheory.append(np.zeros_like(self.dataX))
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for p, id in enumerate(xsecID):
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fitTheory[-1] += popt[p] * np.interp(self.dataX, self.dataX, self.data[id])
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# Optionally, you can plot the uncertainty as shaded regions (confidence intervals)
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# Create the upper and lower bounds of the theoretical model with uncertainties
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fitTheory_upper.append(np.zeros_like(self.dataX))
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fitTheory_lower.append(np.zeros_like(self.dataX))
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for p, id in enumerate(xsecID):
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fitTheory_upper[-1] += (popt[p] + perr[p]) * np.interp(self.dataX, self.dataX, self.data[id])
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fitTheory_lower[-1] += (popt[p] - perr[p]) * np.interp(self.dataX, self.dataX, self.data[id])
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fig = plt.figure()
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figure_list.append(fig)
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# Plot results
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plt.errorbar(x_exp, y_exp, xerr=x_err, yerr=y_err, fmt='x', label='Experimental Data', color='black', markersize = 15, elinewidth=2)
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# Plot all fit theories
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for i, fit in enumerate(fitTheory):
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plt.plot(self.dataX, fit, label=f'Chi2:{chi_squared[i]:.3f} | Xsec:{self.fitOption[expDataID][i]}')
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plt.fill_between(self.dataX, fitTheory_lower[i], fitTheory_upper[i], alpha=0.2)
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# Customize plot
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plt.xlabel('Angle_CM [deg]')
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plt.ylabel('X-Sec [a.u.]')
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plt.legend()
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plt.autoscale(enable=True, axis='x', tight=True)
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plt.tight_layout()
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plt.yscale('log')
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# Replace plt.title() with plt.text() to position the title inside the plot
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plt.text(0.05, 0.05, f'Fit for Exp Data : {self.dataName_list[expDataID]}', transform=plt.gca().transAxes,
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fontsize=12, verticalalignment='bottom', horizontalalignment='left', color='black')
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for i, _ in enumerate(para):
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plt.text(0.05, 0.1 + 0.05*i, f"Xsec-{self.fitOption[expDataID][i].strip()}: {', '.join([f'{x:.3f}' for x in para[i]])} +/- {', '.join([f'{x:.3f}' for x in para_err[i]])}" , transform=plt.gca().transAxes,
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fontsize=12, verticalalignment='bottom', horizontalalignment='left', color=default_colors[i])
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return figure_list
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for k , dN in enumerate(self.dataName_list):
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self.FitSingleData(k)
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self.plot.append( FitPlotWindow(f"Data-{k}"))
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self.plot[-1].set_data(k, self.expData, self.fitOption, dN, self.dataX, self.data, self.para, self.para_err, self.chi_squared)
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self.plot[-1].plot_Fit()
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self.plot[-1].show()
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@ -59,7 +59,7 @@ class MatPlotLibWindow(QWidget):
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self.ax.plot(self.x, y, plotStyle, label=self.headers[i + 1])
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self.ax.set_xlabel("Angle_CM [Deg]")
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self.ax.set_ylabel("Xsec [mb/sr]")
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self.ax.set_ylabel(r'$\theta_{cm}$ [deg]')
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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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@ -75,7 +75,9 @@ class MatPlotLibWindow(QWidget):
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self.ax.grid(False)
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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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# self.figure.tight_layout()
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self.figure.subplots_adjust(left=0.1, right=0.95, top=0.95, bottom=0.1)
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self.canvas.draw_idle()
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@ -10,9 +10,10 @@ 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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# Set backend to a Qt-compatible one
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plt.switch_backend('QtAgg') # Or use 'Qt5Agg' if there are still issues
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class PlotWindow(QWidget):
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class FitPlotWindow(QWidget):
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def __init__(self, windowTitle):
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super().__init__()
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@ -23,13 +24,10 @@ class PlotWindow(QWidget):
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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.log_scale_checkbox.stateChanged.connect(self.plot_Fit)
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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.gridline_checkbox.stateChanged.connect(self.plot_Fit)
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self.figure, self.ax = plt.subplots()
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self.canvas = FigureCanvas(self.figure)
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@ -37,34 +35,13 @@ class PlotWindow(QWidget):
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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.log_scale_checkbox, 1, 0)
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layout.addWidget(self.gridline_checkbox, 1, 1)
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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.setLayout(layout)
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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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def set_data(self, ID, expData, fitOption, dataName_list, xData, yData_list, para, perr, chi_square ):
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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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@ -72,37 +49,12 @@ class PlotWindow(QWidget):
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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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self.xData = xData
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self.yData_list = yData_list
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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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self.chi_square = chi_square
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def plot_Fit(self):
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self.ax.clear()
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@ -110,55 +62,57 @@ class PlotWindow(QWidget):
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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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fitTheory = []
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fitTheory_lower = []
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fitTheory_upper = []
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for k in range(len(self.fitOption)):
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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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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 id in xsecID:
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fitTheory[k] += self.para[k] * np.interp(self.xData, self.xData, self.yData_list[id])
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fitTheory_upper[k] += (self.para[k] + self.perr[k]) * np.interp(self.xData, self.xData, self.yData_list[id])
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fitTheory_lower[k] += (self.para[k] - self.perr[k]) * np.interp(self.xData, self.xData, self.yData_list[id])
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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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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]])}" ,
|
||||
transform=plt.gca().transAxes, fontsize=12,
|
||||
verticalalignment='bottom', horizontalalignment='left', color=self.default_colors[i])
|
||||
|
||||
# Replace plt.title() with plt.text() to position the title inside the plot
|
||||
self.ax.text(0.05, 0.05, f'Fit for Exp Data : {self.dataName}', transform=plt.gca().transAxes,
|
||||
fontsize=12, verticalalignment='bottom', horizontalalignment='left', color='black')
|
||||
|
||||
|
||||
# Plot decorator
|
||||
# Apply log scale for y-axis if selected
|
||||
if self.log_scale_checkbox.isChecked():
|
||||
self.ax.set_yscale('log')
|
||||
else:
|
||||
self.ax.set_yscale('linear')
|
||||
|
||||
|
||||
if self.gridline_checkbox.isChecked():
|
||||
self.ax.grid(True, which='both', linestyle='--', linewidth=0.5, color='gray')
|
||||
else:
|
||||
self.ax.grid(False)
|
||||
|
||||
|
||||
self.ax.set_xlabel(r'$\theta_{cm}$ [deg]')
|
||||
self.ax.set_ylabel(r'd$\sigma$/d$\Omega$ [deg]')
|
||||
self.ax.legend(loc='upper right', frameon=True)
|
||||
|
||||
self.ax.autoscale(enable=True, axis='x', tight=True)
|
||||
self.figure.tight_layout()
|
||||
|
||||
for k in range(len(self.fitOption)):
|
||||
fitTheory = []
|
||||
fitTheory_lower = []
|
||||
fitTheory_upper = []
|
||||
|
||||
xsecIDStr = self.fitOption[k].strip()
|
||||
xsecID = [int(part.strip()) for part in xsecIDStr.split('+')] if '+' in xsecIDStr else [int(xsecIDStr)]
|
||||
|
||||
fitTheory.append(np.zeros_like(self.xData))
|
||||
for p, id in enumerate(xsecID):
|
||||
fitTheory += self.para[p] * np.interp(self.xData, self.xData, self.yData_list[id])
|
||||
|
||||
fitTheory_upper.append(np.zeros_like(self.xData))
|
||||
fitTheory_lower.append(np.zeros_like(self.xData))
|
||||
|
||||
for p, id in enumerate(xsecID):
|
||||
fitTheory_upper += (self.para[p] + self.perr[p]) * np.interp(self.xData, self.xData, self.yData_list[id])
|
||||
fitTheory_lower += (self.para[p] - self.perr[p]) * np.interp(self.xData, self.xData, self.yData_list[id])
|
||||
|
||||
# Replace plt.title() with plt.text() to position the title inside the plot
|
||||
self.ax.text(0.05, 0.05, f'Fit for Exp Data : {self.dataName}', transform=plt.gca().transAxes,
|
||||
fontsize=12, verticalalignment='bottom', horizontalalignment='left', color='black')
|
||||
|
||||
for i, fit in enumerate(fitTheory):
|
||||
self.ax.plot(self.xData, fit, label=f'Chi2:{self.chi_square[i]:.3f} | Xsec:{self.fitOption[i]}')
|
||||
self.ax.fill_between(self.xData, fitTheory_lower[i], fitTheory_upper[i], alpha=0.2)
|
||||
|
||||
for i, _ in enumerate(self.para):
|
||||
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]])}" ,
|
||||
transform=plt.gca().transAxes, fontsize=12,
|
||||
verticalalignment='bottom', horizontalalignment='left', color=self.default_colors[i])
|
||||
self.figure.subplots_adjust(left=0.1, right=0.95, top=0.95, bottom=0.1)
|
||||
|
||||
self.canvas.draw_idle()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ from CustomTextEdit import CustomTextEdit
|
|||
from ExtractXsecPy import extract_xsec
|
||||
from ExWindow import ExWindow
|
||||
from MatPlotLibWindow import MatPlotLibWindow
|
||||
from FitExData import Fitting, FitPlotWidget
|
||||
from FitExData import Fitting
|
||||
|
||||
################################################## MainWindow
|
||||
class MyWindow(QMainWindow):
|
||||
|
|
@ -400,15 +400,9 @@ class MyWindow(QMainWindow):
|
|||
def fitData(self):
|
||||
self.SaveExpDataFile()
|
||||
|
||||
self.fitCanvas = []
|
||||
self.fitting.read_expData(self.ExpDataFileName)
|
||||
self.fitting.read_data(self.DWBAFileName + ".Xsec.txt")
|
||||
figures = self.fitting.FitData()
|
||||
|
||||
if figures:
|
||||
for p, fig in enumerate(figures):
|
||||
self.fitCanvas.append(FitPlotWidget(fig))
|
||||
self.fitCanvas[-1].show()
|
||||
self.fitting.plot_fits()
|
||||
|
||||
def closeEvent(self, event):
|
||||
if self.plot_window:
|
||||
|
|
@ -416,9 +410,6 @@ class MyWindow(QMainWindow):
|
|||
if self.Ex_window:
|
||||
self.Ex_window.close() # Close the PlotWindow when MainWindow closes
|
||||
self.Ex_window.__del__()
|
||||
if self.fitCanvas :
|
||||
for x in self.fitCanvas:
|
||||
x.close()
|
||||
print("============== Bye Bye ========== ")
|
||||
event.accept() # Accept the event to proceed with closing the main window
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user