bug fix for FixExData, rewrite PlotWindow.py, makes it as a genric plot window
This commit is contained in:
Ryan@Home
parent
81b79f9582
commit
019877e5ea
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@ -35,7 +35,7 @@ class FitPlotWidget(QWidget):
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class Fitting():
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def __init__(self):
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self.ExList = []
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self.dataName_list = []
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self.fitOption = []
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self.expData = []
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@ -49,7 +49,7 @@ class Fitting():
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print(self.headers)
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def read_expData(self, fileName):
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self.ExList = []
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self.dataName_list = []
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self.fitOption = []
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self.expData = []
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@ -74,7 +74,7 @@ class Fitting():
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# Extract dataSet Name
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dataName = line.split()[1]
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self.ExList.append(dataName)
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self.dataName_list.append(dataName)
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# Check for fit option lines
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elif line.startswith("fit"):
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@ -92,14 +92,14 @@ class Fitting():
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self.expData.append(np.array(current_data, dtype=float))
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# Convert to numpy arrays
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self.ExList = np.array(self.ExList)
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self.dataName_list = np.array(self.dataName_list)
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self.expData = [np.array(data) for data in self.expData]
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# Output the result
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print("=========== Number of data set:", len(self.ExList))
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for i in range(0, len(self.ExList)):
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print("=========== Number of data set:", len(self.dataName_list))
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for i in range(0, len(self.dataName_list)):
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print("-------------------------")
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print(" ExList:", self.ExList[i])
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print(" ExList:", self.dataName_list[i])
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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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@ -127,64 +127,64 @@ class Fitting():
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fitTheory_upper = []
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para = []
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perr = []
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para_err = []
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chi_squared = []
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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 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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# 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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if processFlag == False :
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continue
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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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if processFlag == False :
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continue
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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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# 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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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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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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# 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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# 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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para.append(popt)
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perr.append(np.sqrt(np.diag(pcov))) # Standard deviation of the parameters
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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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# 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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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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print(f"Fitted scale for fit {k}: {', '.join([f'{x:.3f}' for x in popt])} +/- {', '.join([f'{x:.3f}' for x in perr[-1]])} | 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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# 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[k] += 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[k] += (popt[p] + perr[p]) * np.interp(self.dataX, self.dataX, self.data[id])
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fitTheory_lower[k] += (popt[p] - perr[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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@ -206,13 +206,11 @@ class Fitting():
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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.ExList[expDataID]}', transform=plt.gca().transAxes,
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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 perr[i]])}" , transform=plt.gca().transAxes,
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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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@ -1,126 +1,164 @@
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#!/usr/bin/python3
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import os
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import time
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from PyQt6.QtWidgets import (
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QGridLayout, QWidget, QCheckBox
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)
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from PyQt6.QtCore import QUrl
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from PyQt6.QtWebEngineWidgets import QWebEngineView
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import plotly.graph_objects as go
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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, XsecFile):
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def __init__(self, windowTitle):
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super().__init__()
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self.setWindowTitle("DWBA Plot")
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self.setGeometry(100, 100, 800, 600)
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self.setWindowTitle(windowTitle)
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self.resize(800, 600)
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self.x = []
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self.data = []
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self.headers = []
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self.read_data(XsecFile)
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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_plotly_graph)
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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_plotly_graph)
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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_plotly_graph)
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self.showMarker_checkBox.stateChanged.connect(self.plot_graph)
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self.html_file = None
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self.web_view = QWebEngineView()
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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.showMarker_checkBox, 0, 0)
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layout.addWidget(self.log_scale_checkbox, 0, 1)
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layout.addWidget(self.gridline_checkbox, 0, 2)
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layout.addWidget(self.web_view, 1, 0, 5, 3)
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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.plot_plotly_graph()
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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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def read_data(self,file_path):
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self.headers, self.x, self.data = read_DWBA(file_path)
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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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def plot_plotly_graph(self):
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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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if self.html_file and os.path.exists(self.html_file):
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os.remove(self.html_file)
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# Create a Plotly figure
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fig = go.Figure()
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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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if self.showMarker_checkBox.isChecked() :
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plotStyle = 'lines+markers'
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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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plotStyle = 'lines'
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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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# Add traces for each column in data against x
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for i, y in enumerate(self.data):
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fig.add_trace(go.Scatter(x=self.x, y=y, mode=plotStyle, name=self.headers[i + 1])) # Use headers for names
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# Update layout for better presentation
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fig.update_layout(
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xaxis_title="Angle_CM [Deg]",
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yaxis_title="Xsec [mb/sr]",
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template="plotly",
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plot_bgcolor='rgba(0,0,0,0)', # Set plot background to transparent
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paper_bgcolor='rgba(0,0,0,0)', # Set paper background to transparent
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legend=dict(
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x=1, # X position (1 = far right)
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y=1, # Y position (1 = top)
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xanchor='right', # Anchor the legend to the right
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yanchor='top', # Anchor the legend to the top
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bgcolor='rgba(255, 255, 255, 0.5)', # Optional: semi-transparent background for legend
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bordercolor='rgba(0, 0, 0, 0.5)', # Optional: border color
|
||||
borderwidth=1 # Optional: border width
|
||||
),
|
||||
yaxis=dict(
|
||||
# linecolor='black', # Set y-axis line color to black
|
||||
type ='log' if self.log_scale_checkbox.isChecked() else 'linear', # Toggle y-axis scale
|
||||
gridcolor='lightgray', # Set gridline color
|
||||
gridwidth=1, # Set gridline width (in pixels)
|
||||
showgrid = self.gridline_checkbox.isChecked() # Toggle gridlines for y-axis
|
||||
),
|
||||
xaxis=dict(
|
||||
# linecolor='black', # Set x-axis line color to black
|
||||
gridcolor='lightgray', # Set gridline color
|
||||
gridwidth=1, # Set gridline width (in pixels)
|
||||
showgrid = self.gridline_checkbox.isChecked() # Toggle gridlines for x-axis as well
|
||||
),
|
||||
margin=dict(l=40, r=40, t=40, b=40), # Set margins to reduce empty space
|
||||
# width=800, # Optional: set fixed width for the plot
|
||||
# height=600 # Optional: set fixed height for the plot
|
||||
)
|
||||
|
||||
fig.add_shape(
|
||||
# Line with reference to the plot
|
||||
type="rect",
|
||||
xref="paper",
|
||||
yref="paper",
|
||||
x0=0,
|
||||
y0=0,
|
||||
x1=1.0,
|
||||
y1=1.0,
|
||||
line=dict(
|
||||
color="black",
|
||||
width=1,
|
||||
)
|
||||
)
|
||||
|
||||
# Save the plot as an HTML file in a temporary location
|
||||
timestamp = int(time.time() * 1000) # Unique timestamp in milliseconds
|
||||
html_file = f"/tmp/plotwindow_{timestamp}.html"
|
||||
fig.write_html(html_file)
|
||||
self.html_file = html_file # Store for cleanup
|
||||
self.web_view.setUrl(QUrl.fromLocalFile(html_file))
|
||||
|
||||
def __del__(self):
|
||||
if os.path.exists(self.html_file):
|
||||
os.remove(self.html_file)
|
||||
|
|
|
|||
|
|
@ -5,4 +5,5 @@ PyQt6-WebEngine
|
|||
numpy
|
||||
pandas
|
||||
matplotlib
|
||||
kaleido
|
||||
kaleido
|
||||
scipy
|
||||
Loading…
Reference in New Issue
Block a user