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Merge pull request #181 from nelsonag/relunc_plot

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Added code to plot_mesh_tally.py which gives an option to plot mean or r...
This commit is contained in:
Paul Romano 2013-06-01 20:11:15 -07:00
commit 3ff930be50
1 changed files with 77 additions and 13 deletions
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90
src/utils/plot_mesh_tally.py
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@ -73,6 +73,17 @@ class AppForm(QMainWindow):
self.axial_level = QComboBox()
self.connect(self.axial_level, SIGNAL('activated(int)'),
self.on_draw)
# Add Option to plot mean or uncertainty
label_mean = QLabel("Mean or Uncertainty:")
self.mean = QComboBox()
self.mean.addItems(['Mean','Absolute Uncertainty',
'Relative Uncertainty'])
# Update window when mean selection is changed
self.connect(self.mean, SIGNAL('activated(int)'),
self.on_draw)
self.label_filters = QLabel("Filter options:")
@ -111,7 +122,9 @@ class AppForm(QMainWindow):
self.grid.addWidget(self.basis, 1, 1)
self.grid.addWidget(label_axial_level, 2, 0)
self.grid.addWidget(self.axial_level, 2, 1)
self.grid.addWidget(self.label_filters, 3, 0)
self.grid.addWidget(label_mean, 3, 0)
self.grid.addWidget(self.mean, 3, 1)
self.grid.addWidget(self.label_filters, 4, 0)
self._update()
self.populate_boxes()
@ -163,6 +176,7 @@ class AppForm(QMainWindow):
# Get selected basis, axial_level and stage
basis = self.basis.currentIndex() + 1
axial_level = self.axial_level.currentIndex() + 1
is_mean = self.mean.currentIndex()
# Create spec_list
spec_list = []
@ -171,24 +185,68 @@ class AppForm(QMainWindow):
continue
index = self.boxes[tally.type].currentIndex()
spec_list.append((tally.type, index))
# Take is_mean and convert it to an index of the score
score_loc = is_mean
if score_loc > 1:
score_loc = 1
if self.basis.currentText() == 'xy':
matrix = np.zeros((self.nx, self.ny))
for i in range(self.nx):
for j in range(self.ny):
matrix[i,j] = self.datafile.get_value(self.tally.currentIndex(), spec_list + [('mesh', (i, j, axial_level))], self.scoreBox.currentIndex())[0]
matrix[i,j] = self.datafile.get_value(self.tally.currentIndex(),
spec_list + [('mesh', (i, j, axial_level))],
self.scoreBox.currentIndex())[score_loc]
# Calculate relative uncertainty from absolute, if
# requested
if is_mean == 2:
# Take care to handle zero means when normalizing
mean_val = self.datafile.get_value(self.tally.currentIndex(),
spec_list + [('mesh', (i, j, axial_level))],
self.scoreBox.currentIndex())[0]
if mean_val > 0.0:
matrix[i,j] = matrix[i,j] / mean_val
else:
matrix[i,j] = 0.0
elif self.basis.currentText() == 'yz':
matrix = np.zeros((self.ny, self.nz))
for i in range(self.ny):
for j in range(self.nz):
matrix[i,j] = self.datafile.get_value(self.tally.currentIndex(), spec_list + [('mesh', (axial_level, i, j))], self.scoreBox.currentIndex())[0]
matrix[i,j] = self.datafile.get_value(self.tally.currentIndex(),
spec_list + [('mesh', (axial_level, i, j))],
self.scoreBox.currentIndex())[score_loc]
# Calculate relative uncertainty from absolute, if
# requested
if is_mean == 2:
# Take care to handle zero means when normalizing
mean_val = self.datafile.get_value(self.tally.currentIndex(),
spec_list + [('mesh', (axial_level, i, j))],
self.scoreBox.currentIndex())[0]
if mean_val > 0.0:
matrix[i,j] = matrix[i,j] / mean_val
else:
matrix[i,j] = 0.0
else:
matrix = np.zeros((self.nx, self.nz))
for i in range(self.nx):
for j in range(self.nz):
matrix[i,j] = self.datafile.get_value(self.tally.currentIndex(), spec_list + [('mesh', (i, axial_level, j))], self.scoreBox.currentIndex())[0]
matrix[i,j] = self.datafile.get_value(self.tally.currentIndex(),
spec_list + [('mesh', (i, axial_level, j))],
self.scoreBox.currentIndex())[score_loc]
# Calculate relative uncertainty from absolute, if
# requested
if is_mean == 2:
# Take care to handle zero means when normalizing
mean_val = self.datafile.get_value(self.tally.currentIndex(),
spec_list + [('mesh', (i, axial_level, j))],
self.scoreBox.currentIndex())[0]
if mean_val > 0.0:
matrix[i,j] = matrix[i,j] / mean_val
else:
matrix[i,j] = 0.0
# print spec_list
@ -197,7 +255,8 @@ class AppForm(QMainWindow):
# Make figure, set up color bar
self.axes = self.fig.add_subplot(111)
cax = self.axes.imshow(matrix, vmin=0.0, vmax=matrix.max(), interpolation="nearest")
cax = self.axes.imshow(matrix, vmin=0.0, vmax=matrix.max(),
interpolation="nearest")
self.fig.colorbar(cax)
self.axes.set_xticks([])
@ -212,7 +271,9 @@ class AppForm(QMainWindow):
'''
# print 'Calling _update...'
self.mesh = self.datafile.meshes[self.datafile.tallies[self.tally.currentIndex()].filters['mesh'].bins[0] - 1]
self.mesh = self.datafile.meshes[
self.datafile.tallies[
self.tally.currentIndex()].filters['mesh'].bins[0] - 1]
self.nx, self.ny, self.nz = self.mesh.dimension
@ -228,7 +289,8 @@ class AppForm(QMainWindow):
self.axial_level.addItems([str(i+1) for i in range(self.ny)])
# Determine maximum value from current tally data set
self.maxvalue = self.datafile.tallies[self.tally.currentIndex()].results.max()
self.maxvalue = self.datafile.tallies[
self.tally.currentIndex()].results.max()
# print self.maxvalue
# Clear and hide old filter labels
@ -245,7 +307,7 @@ class AppForm(QMainWindow):
def populate_boxes(self):
# print 'Calling populate_boxes...'
n = 4
n = 5
labels = {'cell': 'Cell : ',
'cellborn': 'Cell born: ',
'surface': 'Surface: ',
@ -255,7 +317,8 @@ class AppForm(QMainWindow):
# For each filter in newly-selected tally, name a label and fill the
# relevant combobox with options
for element in self.tally_list[self.tally.currentIndex()]:
nextFilter = self.datafile.tallies[self.tally.currentIndex()].filters[element]
nextFilter = self.datafile.tallies[
self.tally.currentIndex()].filters[element]
if element == 'mesh':
continue
@ -274,7 +337,8 @@ class AppForm(QMainWindow):
elif element == 'energyin' or element == 'energyout':
for i in range(nextFilter.length):
text = str(nextFilter.bins[i]) + ' to ' + str(nextFilter.bins[i+1])
text = (str(nextFilter.bins[i]) + ' to ' +
str(nextFilter.bins[i+1]))
combobox.addItem(text)
self.scoreBox.clear()