threshold = 1e-4 # Threshold, below which ignore differences
diffs = np.zeros((fieldssmallest, 6), dtype=floattype)
for ID, name in fields.items():
max = np.amax(np.abs(new[:fieldssmallest,ID]))
if max < threshold:
diffs[:,ID] = 0
diffsum = 0
print('Detected differences of less than {} when comparing {} field component, therefore set as zero.'.format(threshold, fields[ID]))
else:
diffs[:,ID] = (np.abs(new[:fieldssmallest,ID] - old[:fieldssmallest,ID]) / max) * 100
diffsum = (np.sum(np.abs(new[:fieldssmallest,ID] - old[:fieldssmallest,ID])) / np.sum(np.abs(new[:fieldssmallest,ID]))) * 100
print('Total differences in field component {}: {:.1f}%'.format(name, diffsum))
# Plot new
fig1, plt1 = plot_Ascan(newfile + ' versus ' + oldfile, timenew, new[:,0], new[:,2], new[:,4], new[:,1], new[:,3], new[:,5])
# Add old and set legend
for index, ax in enumerate(fig1.axes):
if plotorder[index] in [0, 2, 4]:
ax.plot(timeold, old[:,plotorder[index]], 'r', label='old', lw=2, ls='--')
else:
ax.plot(timeold, old[:,plotorder[index]], label='old', lw=2, ls='--')
ax.set_xlim(0, timeold[-1])
handles, existlabels = ax.get_legend_handles_labels()
ax.legend(handles, ['Model (new code)', 'Model (old C code)'])
# Plots of differences
fig2, plt2 = plot_Ascan('Deltas: ' + newfile + ' versus ' + oldfile, timenew[:timesmallest], diffs[:,0], diffs[:,2], diffs[:,4], diffs[:,1], diffs[:,3], diffs[:,5])
[ax.set_xlim(0, timenew[timesmallest - 1]) for ax in fig2.axes]
[ax.set_ylim(0, np.ceil(np.amax(np.abs(diffs)))) for ax in fig2.axes]
# Differences
threshold = 1e-4 # Threshold, below which ignore differences
diffs = np.zeros((iterations, 6), dtype=floattype)
for ID, name in fields.items():
max = np.amax(np.abs(analytical[:,ID]))
if max < threshold:
diffs[:,ID] = 0
diffsum = 0
print('Detected differences of less than {} when comparing {} field component, therefore set as zero.'.format(threshold, fields[ID]))
else:
diffs[:,ID] = (np.abs(analytical[:,ID] - model[:,ID]) / max) * 100
diffsum = (np.sum(np.abs(analytical[:,ID] - model[:,ID])) / np.sum(np.abs(analytical[:,ID]))) * 100
print('Total differences in field component {}: {:.1f}%'.format(name, diffsum))
# Plot model
fig1, plt1 = plot_Ascan(modelfile + ' versus analytical solution', time, model[:,0], model[:,1], model[:,2], model[:,3], model[:,4], model[:,5])
# Add analytical solution and set legend
for index, ax in enumerate(fig1.axes):
if index in [0, 2, 4]:
ax.plot(time, analytical[:,plotorder[index]], 'r', label='analytical', lw=2, ls='--')
else:
ax.plot(time, analytical[:,plotorder[index]], label='analytical', lw=2, ls='--')
ax.set_xlim(0, time[-1])
handles, existlabels = ax.get_legend_handles_labels()
ax.legend(handles, ['Model', 'Analytical'])
# Plots of differences
fig2, plt2 = plot_Ascan('Deltas: ' + modelfile + ' versus analytical solution', time, diffs[:,0], diffs[:,1], diffs[:,2], diffs[:,3], diffs[:,4], diffs[:,5])
[ax.set_xlim(0, time[-1]) for ax in fig2.axes]
[ax.set_ylim(0, np.ceil(np.amax(np.abs(diffs)))) for ax in fig2.axes]
if max < threshold:
diffs[:, ID] = 0
diffsum = 0
print(
'Detected differences of less than {} when comparing {} field component, therefore set as zero.'
.format(threshold, fields[ID]))
else:
diffs[:, ID] = (np.abs(analytical[:, ID] - model[:, ID]) / max) * 100
diffsum = (np.sum(np.abs(analytical[:, ID] - model[:, ID])) /
np.sum(np.abs(analytical[:, ID]))) * 100
print('Total differences in field component {}: {:.1f}%'.format(
name, diffsum))
# Plot model
fig1, plt1 = plot_Ascan(modelfile + ' versus analytical solution', time,
model[:, 0], model[:, 1], model[:, 2], model[:, 3],
model[:, 4], model[:, 5])
# Add analytical solution and set legend
for index, ax in enumerate(fig1.axes):
if index in [0, 2, 4]:
ax.plot(time,
analytical[:, plotorder[index]],
'r',
label='analytical',
lw=2,
ls='--')
else:
ax.plot(time,
analytical[:, plotorder[index]],
label='analytical',
