def button_clear_fit(event):
'''Clear the fits curves from the plot'''
nonlocal list_fit_denser_line2D
nonlocal fit_function
clear_line2D(figure, lines2D['fit'], axes, redraw=True)
for line2D in list_fit_denser_line2D:
clear_line2D(figure, line2D, axes, redraw=True)
fit_function = None
def button_add_fit(event):
'''Add the fit to the plot'''
nonlocal fit_function
if fit_interval_ready:
# If a previous fit, clear it
clear_line2D(figure, lines2D['fit'], axes, redraw=False)
if not ivar['not_refresh_flag']:
clear_line2D(figure, lines2D['fit_denser'], axes, redraw=False)
# Get current colors in the axis to not repeat them
colors = [ l.get_color() for l in axes.get_lines() ]
# Fit
if mask['nonfit'] is not None:
if interval['xmin_nonfit'] > interval['xmin_fit'] and interval['xmax_nonfit'] < interval['xmax_fit']:
mask_fit_minus_nonfit = np.logical_xor(mask['fit'], mask['nonfit'])
else:
print('**The non fit interval is outside the fit interval. It will be ignored.**')
mask_fit_minus_nonfit = mask['fit'].copy()
else:
mask_fit_minus_nonfit = mask['fit'].copy()
xfit = x[mask_fit_minus_nonfit]
yfit = y[mask_fit_minus_nonfit]
# Force the fit to match the fist two data points and the last two data points to promote a smoother match with the original
xfit_fixed = np.concatenate([ [xfit[0]], [xfit[1]], [xfit[-2]], [xfit[-1]] ])
yfit_fixed = np.concatenate([ [yfit[0]], [yfit[1]], [yfit[-2]], [yfit[-1]] ])
# Fit
fit_function = np.poly1d( polyfit_with_fixed_points(ivar['polynomial_degree'], xfit, yfit, xfit_fixed, yfit_fixed) )
color = 'lime' if not 'lime' in colors else None
lines2D['fit'], = axes.plot(x[mask['fit']], fit_function(x[mask['fit']]), linestyle='None', marker='o', color=color, markerfacecolor=color, markeredgecolor='None')
# Plot a denser x to evidence possible wiggles between the original data
points_in_between = np.arange(0.0, 1.0, 0.1)
x_mask_diff = np.diff(x[mask['fit']])
x_mask_denser = np.array([])
for shift in points_in_between:
x_mask_denser = np.concatenate( [ x_mask_denser, x[mask['fit']][:-1] + x_mask_diff * shift ] )
# Add the last point
x_mask_denser = np.concatenate( [ x_mask_denser, np.array( [ x[mask['fit']][-1] ] ) ] )
x_mask_denser = np.sort( x_mask_denser )
color = lines2D['fit'].get_color()
lines2D['fit_denser'], = axes.plot(x_mask_denser, fit_function(x_mask_denser), linestyle='solid', marker=None, color=color, label='Polynomial fit n={}'.format(ivar['polynomial_degree']))
list_fit_denser_line2D.append(lines2D['fit_denser'])
# Redraw
axes.legend(loc='best', ncol=1, framealpha=0.5, fontsize=10)
figure.canvas.draw()
def onselect(vmin, vmax):
'''Select the interval for the fit and the interval to be excluded in the fit'''
nonlocal fit_interval_ready
# Interval to fit
# Activate by pressing Enter and then using the left button
if ivar['keystroke'] == 'enter' and ivar['pressed_button'] == 1:
clear_line2D(figure, lines2D['fit_interval'], axes, redraw=False)
# Store the values
interval['xmin_fit'] = vmin
interval['xmax_fit'] = vmax
# Print the interval
print('Interval for the fit:')
print('xmin = {:.3},\t xmax = {:.3}\n'.format(vmin, vmax))
# Get the indices of the values within the selected span
condition1_fit = interval['xmin_fit'] < x
condition2_fit = x < interval['xmax_fit']
mask['fit'] = np.logical_and(condition1_fit, condition2_fit)
# Plot in red the selected span as an aditional Line2D object in lines
if interval['xmin_fit'] != interval['xmax_fit']:
lines2D['fit_interval'], = axes.plot(x[mask['fit']], y[mask['fit']], linestyle='None', marker='o', markerfacecolor='red', markeredgecolor='None', label='Fit interval')
fit_interval_ready = True
else:
fit_interval_ready = False
# Interval to exclude
# Activate by pressing Enter and then using the right button OR pressing Shift+Enter and the using left button
if (ivar['keystroke']=='shift+enter' and ivar['pressed_button']==1) or (ivar['keystroke']=='enter' and ivar['pressed_button']==3):
clear_line2D(figure, lines2D['v1'], axes, redraw=False)
clear_line2D(figure, lines2D['v2'], axes, redraw=False)
interval['xmin_nonfit'] = vmin
interval['xmax_nonfit'] = vmax
# Print the interval
print('Interval to be excluded in the fit:')
print('xmin = {:.3},\t xmax = {:.3}\n'.format(vmin, vmax))
# Get the indices of the values within the selected span
condition1_nonfit = interval['xmin_nonfit'] < x
condition2_nonfit = x < interval['xmax_nonfit']
mask['nonfit'] = np.logical_and(condition1_nonfit, condition2_nonfit)
# Plot in black the selected span
if interval['xmin_nonfit'] != interval['xmax_nonfit']:
lines2D['v1'] = axes.axvline(interval['xmin_nonfit'], label='Nonfit interval', linestyle='dashed', color='black')
lines2D['v2'] = axes.axvline(interval['xmax_nonfit'], linestyle='dashed', color='black')
# Redraw
axes.legend(loc='best', ncol=1, framealpha=0.5, fontsize=10)
figure.canvas.draw()