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small improvement

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This commit is contained in:
Ryan Tang 2024-11-07 17:18:02 -05:00
parent fce37c5e69
commit 0183197a7f
1 changed files with 15 additions and 3 deletions
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18
Cleopatra/FitExData.py
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@ -13,6 +13,8 @@ from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from ExtractXsecPy import read_DWBA from ExtractXsecPy import read_DWBA
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
class FitPlotWidget(QWidget): class FitPlotWidget(QWidget):
def __init__(self, figure): def __init__(self, figure):
super().__init__() super().__init__()
@ -60,6 +62,9 @@ class Fitting():
if not line: if not line:
continue continue
if line.startswith("$"):
continue
# Check for excitation energy lines # Check for excitation energy lines
if line.startswith("#="): if line.startswith("#="):
# If there's an existing data block, save it # If there's an existing data block, save it
@ -121,6 +126,8 @@ class Fitting():
fitTheory_lower = [] fitTheory_lower = []
fitTheory_upper = [] fitTheory_upper = []
para = []
perr = []
chi_squared = [] chi_squared = []
for k in range(nFit): for k in range(nFit):
@ -154,14 +161,15 @@ class Fitting():
p0=np.ones(len(xsecID)), # Initial guess for scale parameters p0=np.ones(len(xsecID)), # Initial guess for scale parameters
bounds=(lower_bounds, upper_bounds)) bounds=(lower_bounds, upper_bounds))
perr = np.sqrt(np.diag(pcov)) # Standard deviation of the parameters para.append(popt)
perr.append(np.sqrt(np.diag(pcov))) # Standard deviation of the parameters
# Get the fitted model values # Get the fitted model values
y_fit = fit_func(x_exp, *popt) y_fit = fit_func(x_exp, *popt)
residuals = y_exp - y_fit residuals = y_exp - y_fit
chi_squared.append(np.sum((residuals / y_err) ** 2)) chi_squared.append(np.sum((residuals / y_err) ** 2))
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}") 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}")
# print(f"Fitted scale for fit {k}: {popt} +/- {perr} | Chi^2 : {chi_squared[-1]:.4f}") # print(f"Fitted scale for fit {k}: {popt} +/- {perr} | Chi^2 : {chi_squared[-1]:.4f}")
# Append the theoretical fit for this fit option # Append the theoretical fit for this fit option
@ -186,7 +194,7 @@ class Fitting():
# Plot all fit theories # Plot all fit theories
for i, fit in enumerate(fitTheory): for i, fit in enumerate(fitTheory):
plt.plot(self.dataX, fit, label=f'Chi2:{chi_squared[i]:.3f} | Xsec:{self.fitOption[expDataID][i]} Fit') plt.plot(self.dataX, fit, label=f'Chi2:{chi_squared[i]:.3f} | Xsec:{self.fitOption[expDataID][i]}')
plt.fill_between(self.dataX, fitTheory_lower[i], fitTheory_upper[i], alpha=0.2) plt.fill_between(self.dataX, fitTheory_lower[i], fitTheory_upper[i], alpha=0.2)
# Customize plot # Customize plot
@ -201,6 +209,10 @@ class Fitting():
plt.text(0.05, 0.05, f'Fit for Exp Data : {self.ExList[expDataID]} MeV', transform=plt.gca().transAxes, plt.text(0.05, 0.05, f'Fit for Exp Data : {self.ExList[expDataID]} MeV', transform=plt.gca().transAxes,
fontsize=12, verticalalignment='bottom', horizontalalignment='left', color='black') fontsize=12, verticalalignment='bottom', horizontalalignment='left', color='black')
for i, _ in enumerate(para):
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,
fontsize=12, verticalalignment='bottom', horizontalalignment='left', color=default_colors[i])
return figure_list return figure_list