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Merge pull request #234 from nelsonag/plotmesh_updates
Plot_mesh_tally revisions for usability & accuracy
This commit is contained in:
Paul Romano
commit
8bb077f3ae
2 changed files with 208 additions and 165 deletions
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@ -16,123 +16,161 @@ from matplotlib.backends.backend_qt4agg import NavigationToolbar2QTAgg as Naviga
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import numpy as np
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class AppForm(QMainWindow):
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def __init__(self, parent=None):
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def __init__(self, argv, parent=None):
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QMainWindow.__init__(self, parent)
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# Read data from source or leakage fraction file
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self.get_file_data()
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self.main_frame = QWidget()
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self.setCentralWidget(self.main_frame)
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# Create the Figure, Canvas, and Axes
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self.dpi = 100
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self.fig = Figure((5.0, 15.0), dpi=self.dpi)
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self.canvas = FigureCanvas(self.fig)
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self.canvas.setParent(self.main_frame)
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self.axes = self.fig.add_subplot(111)
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# Create the navigation toolbar, tied to the canvas
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self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)
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self.all_good = False
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while not self.all_good:
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if len(argv) > 1:
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cl_file = str(argv[1])
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else:
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cl_file = None
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self.get_file_data(cl_file)
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# Check that there are any mesh tallies at all
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if len(self.tally_ids) != 0:
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self.all_good = True
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else:
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# if there are not, the user will be given the choice to choose
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# another file (but only if using interactive chooser)
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if cl_file is None:
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choice = QMessageBox.critical(None, "Invalid StatePoint File",
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"File Does Not Contain Mesh " +
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"Tallies!" +
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"\nSelect Another File Or Quit",
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QMessageBox.Retry,
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QMessageBox.Abort)
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if choice == QMessageBox.Abort:
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self.all_good = False
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break
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else:
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print("Invalid StatePoint File; File Does Not Contain " +
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"Mesh Tallies!")
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self.all_good = False
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break
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# Grid layout at bottom
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self.grid = QGridLayout()
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# Overall layout
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self.vbox = QVBoxLayout()
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self.vbox.addWidget(self.canvas)
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self.vbox.addWidget(self.mpl_toolbar)
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self.vbox.addLayout(self.grid)
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self.main_frame.setLayout(self.vbox)
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if self.all_good:
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# Set maximum colorbar value by maximum tally data value
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self.maxvalue = self.datafile.tallies[0].results.max()
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# Tally selections
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label_tally = QLabel("Tally:")
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self.tally = QComboBox()
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self.tally.addItems([(str(i + 1)) for i in range(self.n_tallies)])
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self.connect(self.tally, SIGNAL('activated(int)'),
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self._update)
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self.connect(self.tally, SIGNAL('activated(int)'),
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self.populate_boxes)
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self.connect(self.tally, SIGNAL('activated(int)'),
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self.on_draw)
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self.main_frame = QWidget()
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self.setCentralWidget(self.main_frame)
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# Planar basis
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label_basis = QLabel("Basis:")
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self.basis = QComboBox()
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self.basis.addItems(['xy', 'yz', 'xz'])
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# Create the Figure, Canvas, and Axes
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self.dpi = 100
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self.fig = Figure((5.0, 15.0), dpi=self.dpi)
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self.canvas = FigureCanvas(self.fig)
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self.canvas.setParent(self.main_frame)
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self.axes = self.fig.add_subplot(111)
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# Update window when 'Basis' selection is changed
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self.connect(self.basis, SIGNAL('activated(int)'),
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self._update)
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self.connect(self.basis, SIGNAL('activated(int)'),
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self.populate_boxes)
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self.connect(self.basis, SIGNAL('activated(int)'),
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self.on_draw)
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# Create the navigation toolbar, tied to the canvas
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self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)
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# Axial level within selected basis
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label_axial_level = QLabel("Axial Level:")
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self.axial_level = QComboBox()
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self.connect(self.axial_level, SIGNAL('activated(int)'),
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self.on_draw)
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# Add Option to plot mean or uncertainty
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label_mean = QLabel("Mean or Uncertainty:")
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self.mean = QComboBox()
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self.mean.addItems(['Mean','Absolute Uncertainty',
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'Relative Uncertainty'])
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# Update window when mean selection is changed
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self.connect(self.mean, SIGNAL('activated(int)'),
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self.on_draw)
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# Grid layout at bottom
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self.grid = QGridLayout()
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self.label_filters = QLabel("Filter options:")
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# Overall layout
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self.vbox = QVBoxLayout()
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self.vbox.addWidget(self.canvas)
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self.vbox.addWidget(self.mpl_toolbar)
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self.vbox.addLayout(self.grid)
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self.main_frame.setLayout(self.vbox)
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# Labels for all possible filters
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self.labels = {'cell': 'Cell: ', 'cellborn': 'Cell born: ',
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'surface': 'Surface: ', 'material': 'Material',
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'universe': 'Universe: ', 'energyin': 'Energy in: ',
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'energyout': 'Energy out: '}
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# Empty reusable labels
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self.qlabels = {}
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for j in range(8):
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self.nextLabel = QLabel
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self.qlabels[j] = self.nextLabel
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# Reusable comboboxes labelled with filter names
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self.boxes = {}
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for key in self.labels.keys():
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self.nextBox = QComboBox()
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self.connect(self.nextBox, SIGNAL('activated(int)'),
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# Tally selections
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label_tally = QLabel("Tally:")
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self.tally = QComboBox()
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# Only show options for the tallies with meshes
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self.tally.addItems([str(i + 1) for i in self.tally_ids])
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self.connect(self.tally, SIGNAL('activated(int)'),
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self._update)
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self.connect(self.tally, SIGNAL('activated(int)'),
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self.populate_boxes)
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self.connect(self.tally, SIGNAL('activated(int)'),
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self.on_draw)
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self.boxes[key] = self.nextBox
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# Combobox to select among scores
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self.score_label = QLabel("Score:")
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self.scoreBox = QComboBox()
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for item in self.tally_scores[0]:
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self.scoreBox.addItems(str(item))
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self.connect(self.scoreBox, SIGNAL('activated(int)'),
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self.on_draw)
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# Planar basis
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label_basis = QLabel("Basis:")
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self.basis = QComboBox()
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self.basis.addItems(['xy', 'yz', 'xz'])
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# Fill layout
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self.grid.addWidget(label_tally, 0, 0)
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self.grid.addWidget(self.tally, 0, 1)
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self.grid.addWidget(label_basis, 1, 0)
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self.grid.addWidget(self.basis, 1, 1)
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self.grid.addWidget(label_axial_level, 2, 0)
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self.grid.addWidget(self.axial_level, 2, 1)
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self.grid.addWidget(label_mean, 3, 0)
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self.grid.addWidget(self.mean, 3, 1)
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self.grid.addWidget(self.label_filters, 4, 0)
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# Update window when 'Basis' selection is changed
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self.connect(self.basis, SIGNAL('activated(int)'),
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self._update)
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self.connect(self.basis, SIGNAL('activated(int)'),
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self.populate_boxes)
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self.connect(self.basis, SIGNAL('activated(int)'),
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self.on_draw)
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self._update()
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self.populate_boxes()
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self.on_draw()
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# Axial level within selected basis
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label_axial_level = QLabel("Axial Level:")
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self.axial_level = QComboBox()
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self.connect(self.axial_level, SIGNAL('activated(int)'),
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self.on_draw)
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def get_file_data(self):
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# Add Option to plot mean or uncertainty
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label_mean = QLabel("Mean or Uncertainty:")
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self.mean = QComboBox()
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self.mean.addItems(['Mean','Absolute Uncertainty',
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'Relative Uncertainty'])
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# Update window when mean selection is changed
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self.connect(self.mean, SIGNAL('activated(int)'),
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self.on_draw)
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self.label_filters = QLabel("Filter options:")
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# Labels for all possible filters
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self.labels = {'cell': 'Cell: ', 'cellborn': 'Cell born: ',
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'surface': 'Surface: ', 'material': 'Material',
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'universe': 'Universe: ', 'energyin': 'Energy in: ',
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'energyout': 'Energy out: '}
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# Empty reusable labels
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self.qlabels = {}
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for j in range(8):
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self.nextLabel = QLabel
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self.qlabels[j] = self.nextLabel
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# Reusable comboboxes labelled with filter names
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self.boxes = {}
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for key in self.labels.keys():
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self.nextBox = QComboBox()
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self.connect(self.nextBox, SIGNAL('activated(int)'),
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self.on_draw)
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self.boxes[key] = self.nextBox
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# Combobox to select among scores
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self.score_label = QLabel("Score:")
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self.scoreBox = QComboBox()
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for item in self.tally_scores[0]:
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self.scoreBox.addItems(str(item))
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self.connect(self.scoreBox, SIGNAL('activated(int)'),
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self.on_draw)
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# Fill layout
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self.grid.addWidget(label_tally, 0, 0)
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self.grid.addWidget(self.tally, 0, 1)
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self.grid.addWidget(label_basis, 1, 0)
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self.grid.addWidget(self.basis, 1, 1)
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self.grid.addWidget(label_axial_level, 2, 0)
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self.grid.addWidget(self.axial_level, 2, 1)
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self.grid.addWidget(label_mean, 3, 0)
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self.grid.addWidget(self.mean, 3, 1)
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self.grid.addWidget(self.label_filters, 4, 0)
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self._update()
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self.populate_boxes()
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self.on_draw()
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def get_file_data(self, cl_file=None):
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# Get data file name from "open file" browser
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filename = QFileDialog.getOpenFileName(self, 'Select statepoint file', '.')
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if cl_file is None:
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filename = QFileDialog.getOpenFileName(self,
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'Select statepoint file', '.')
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else:
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filename = cl_file
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# Create StatePoint object and read in data
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self.datafile = StatePoint(str(filename))
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@ -141,32 +179,31 @@ class AppForm(QMainWindow):
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self.setWindowTitle('Core Map Tool : ' + str(self.datafile.path))
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# Set maximum colorbar value by maximum tally data value
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self.maxvalue = self.datafile.tallies[0].results.max()
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self.labelList = []
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# Read mesh dimensions
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# for mesh in self.datafile.meshes:
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# self.nx, self.ny, self.nz = mesh.dimension
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# for mesh in self.datafile.meshes:
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# self.nx, self.ny, self.nz = mesh.dimension
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# Read filter types from statepoint file
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# Find which tallies have meshes so the rest can be ignored,
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# and for these tallies read the filter and score types
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self.tally_ids = []
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self.n_tallies = len(self.datafile.tallies)
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self.tally_list = []
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for tally in self.datafile.tallies:
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self.filter_types = []
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for f in tally.filters:
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self.filter_types.append(f)
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self.tally_list.append(self.filter_types)
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# Read score types from statepoint file
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self.tally_scores = []
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for tally in self.datafile.tallies:
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self.score_types = []
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for s in tally.scores:
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self.score_types.append(s)
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self.tally_scores.append(self.score_types)
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# print 'self.tally_scores = ', self.tally_scores
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for itally, tally in enumerate(self.datafile.tallies):
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if 'mesh' in tally.filters:
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# Then we have a good tally, store the ID, filters and
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# scores
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self.tally_ids.append(itally)
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self.filter_types = []
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for f in tally.filters:
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self.filter_types.append(f)
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self.tally_list.append(self.filter_types)
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self.score_types = []
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for s in tally.scores:
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self.score_types.append(s)
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self.tally_scores.append(self.score_types)
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def on_draw(self):
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""" Redraws the figure
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@ -178,70 +215,73 @@ class AppForm(QMainWindow):
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axial_level = self.axial_level.currentIndex() + 1
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is_mean = self.mean.currentIndex()
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# get current tally index
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tally_id = self.tally_ids[self.tally.currentIndex()]
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# Create spec_list
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spec_list = []
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for tally in self.datafile.tallies[self.tally.currentIndex()].filters.values():
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for tally in self.datafile.tallies[tally_id].filters.values():
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if tally.type == 'mesh':
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continue
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index = self.boxes[tally.type].currentIndex()
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spec_list.append((tally.type, index))
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# Take is_mean and convert it to an index of the score
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score_loc = is_mean
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if score_loc > 1:
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score_loc = 1
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if self.basis.currentText() == 'xy':
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matrix = np.zeros((self.nx, self.ny))
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for i in range(self.nx):
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for j in range(self.ny):
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matrix[i,j] = self.datafile.get_value(self.tally.currentIndex(),
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spec_list + [('mesh', (i, j, axial_level))],
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matrix[i,j] = self.datafile.get_value(tally_id,
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spec_list + [('mesh', (i + 1, j + 1, axial_level))],
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self.scoreBox.currentIndex())[score_loc]
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# Calculate relative uncertainty from absolute, if
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# requested
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if is_mean == 2:
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# Take care to handle zero means when normalizing
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mean_val = self.datafile.get_value(self.tally.currentIndex(),
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spec_list + [('mesh', (i, j, axial_level))],
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mean_val = self.datafile.get_value(tally_id,
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spec_list + [('mesh', (i + 1, j + 1, axial_level))],
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self.scoreBox.currentIndex())[0]
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if mean_val > 0.0:
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matrix[i,j] = matrix[i,j] / mean_val
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else:
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matrix[i,j] = 0.0
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elif self.basis.currentText() == 'yz':
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matrix = np.zeros((self.ny, self.nz))
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for i in range(self.ny):
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for j in range(self.nz):
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matrix[i,j] = self.datafile.get_value(self.tally.currentIndex(),
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spec_list + [('mesh', (axial_level, i, j))],
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matrix[i,j] = self.datafile.get_value(tally_id,
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spec_list + [('mesh', (axial_level, i + 1, j + 1))],
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self.scoreBox.currentIndex())[score_loc]
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# Calculate relative uncertainty from absolute, if
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# requested
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if is_mean == 2:
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# Take care to handle zero means when normalizing
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mean_val = self.datafile.get_value(self.tally.currentIndex(),
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spec_list + [('mesh', (axial_level, i, j))],
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mean_val = self.datafile.get_value(tally_id,
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spec_list + [('mesh', (axial_level, i + 1, j + 1))],
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self.scoreBox.currentIndex())[0]
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if mean_val > 0.0:
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matrix[i,j] = matrix[i,j] / mean_val
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else:
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matrix[i,j] = 0.0
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else:
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matrix = np.zeros((self.nx, self.nz))
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for i in range(self.nx):
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for j in range(self.nz):
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matrix[i,j] = self.datafile.get_value(self.tally.currentIndex(),
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spec_list + [('mesh', (i, axial_level, j))],
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matrix[i,j] = self.datafile.get_value(tally_id,
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spec_list + [('mesh', (i + 1, axial_level, j + 1))],
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self.scoreBox.currentIndex())[score_loc]
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# Calculate relative uncertainty from absolute, if
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# requested
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if is_mean == 2:
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# Take care to handle zero means when normalizing
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mean_val = self.datafile.get_value(self.tally.currentIndex(),
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spec_list + [('mesh', (i, axial_level, j))],
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mean_val = self.datafile.get_value(tally_id,
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spec_list + [('mesh', (i + 1, axial_level, j + 1))],
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self.scoreBox.currentIndex())[0]
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if mean_val > 0.0:
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matrix[i,j] = matrix[i,j] / mean_val
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@ -255,8 +295,8 @@ class AppForm(QMainWindow):
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# Make figure, set up color bar
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self.axes = self.fig.add_subplot(111)
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cax = self.axes.imshow(matrix, vmin=0.0, vmax=matrix.max(),
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interpolation="nearest")
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cax = self.axes.imshow(matrix.transpose(), vmin=0.0, vmax=matrix.max(),
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interpolation="nearest", origin='lower')
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self.fig.colorbar(cax)
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self.axes.set_xticks([])
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@ -271,9 +311,11 @@ class AppForm(QMainWindow):
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'''
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# print 'Calling _update...'
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# get current tally index
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tally_id = self.tally_ids[self.tally.currentIndex()]
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self.mesh = self.datafile.meshes[
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self.datafile.tallies[
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self.tally.currentIndex()].filters['mesh'].bins[0] - 1]
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self.datafile.tallies[tally_id].filters['mesh'].bins[0] - 1]
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self.nx, self.ny, self.nz = self.mesh.dimension
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@ -289,8 +331,7 @@ 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[tally_id].results.max()
|
||||
# print self.maxvalue
|
||||
|
||||
# Clear and hide old filter labels
|
||||
|
|
@ -307,6 +348,9 @@ class AppForm(QMainWindow):
|
|||
def populate_boxes(self):
|
||||
# print 'Calling populate_boxes...'
|
||||
|
||||
# get current tally index
|
||||
tally_id = self.tally_ids[self.tally.currentIndex()]
|
||||
|
||||
n = 5
|
||||
labels = {'cell': 'Cell : ',
|
||||
'cellborn': 'Cell born: ',
|
||||
|
|
@ -317,8 +361,7 @@ 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[tally_id].filters[element]
|
||||
if element == 'mesh':
|
||||
continue
|
||||
|
||||
|
|
@ -337,7 +380,7 @@ class AppForm(QMainWindow):
|
|||
|
||||
elif element == 'energyin' or element == 'energyout':
|
||||
for i in range(nextFilter.length):
|
||||
text = (str(nextFilter.bins[i]) + ' to ' +
|
||||
text = (str(nextFilter.bins[i]) + ' to ' +
|
||||
str(nextFilter.bins[i+1]))
|
||||
combobox.addItem(text)
|
||||
|
||||
|
|
@ -351,9 +394,10 @@ class AppForm(QMainWindow):
|
|||
|
||||
def main():
|
||||
app = QApplication(sys.argv)
|
||||
form = AppForm()
|
||||
form.show()
|
||||
app.exec_()
|
||||
form = AppForm(app.arguments())
|
||||
if form.all_good:
|
||||
form.show()
|
||||
app.exec_()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
#!/usr/bin/env python2
|
||||
|
||||
import struct
|
||||
from math import sqrt
|
||||
from collections import OrderedDict
|
||||
|
||||
import numpy as np
|
||||
|
|
@ -10,10 +9,10 @@ import scipy.stats
|
|||
filter_types = {1: 'universe', 2: 'material', 3: 'cell', 4: 'cellborn',
|
||||
5: 'surface', 6: 'mesh', 7: 'energyin', 8: 'energyout'}
|
||||
|
||||
score_types = {-1: 'flux',
|
||||
score_types = {-1: 'flux',
|
||||
-2: 'total',
|
||||
-3: 'scatter',
|
||||
-4: 'nu-scatter',
|
||||
-4: 'nu-scatter',
|
||||
-5: 'scatter-n',
|
||||
-6: 'scatter-pn',
|
||||
-7: 'transport',
|
||||
|
|
@ -276,7 +275,7 @@ class StatePoint(object):
|
|||
f.bins = self._get_int(path=base+'bins')
|
||||
else:
|
||||
f.bins = self._get_int(f.length, path=base+'bins')
|
||||
|
||||
|
||||
base = 'tallies/tally' + str(i+1) + '/'
|
||||
|
||||
# Read nuclide bins
|
||||
|
|
@ -379,7 +378,7 @@ class StatePoint(object):
|
|||
Calculates the sample mean and standard deviation of the mean for each
|
||||
tally bin.
|
||||
"""
|
||||
|
||||
|
||||
# Determine number of realizations
|
||||
n = self.n_realizations
|
||||
|
||||
|
|
@ -387,14 +386,14 @@ class StatePoint(object):
|
|||
for i in range(len(self.global_tallies)):
|
||||
# Get sum and sum of squares
|
||||
s, s2 = self.global_tallies[i]
|
||||
|
||||
|
||||
# Calculate sample mean and replace value
|
||||
s /= n
|
||||
self.global_tallies[i,0] = s
|
||||
|
||||
# Calculate standard deviation
|
||||
if s != 0.0:
|
||||
self.global_tallies[i,1] = t_value*sqrt((s2/n - s*s)/(n-1))
|
||||
self.global_tallies[i,1] = t_value*np.sqrt((s2/n - s*s)/(n-1))
|
||||
|
||||
# Regular tallies
|
||||
for t in self.tallies:
|
||||
|
|
@ -402,14 +401,14 @@ class StatePoint(object):
|
|||
for j in range(t.results.shape[1]):
|
||||
# Get sum and sum of squares
|
||||
s, s2 = t.results[i,j]
|
||||
|
||||
|
||||
# Calculate sample mean and replace value
|
||||
s /= n
|
||||
t.results[i,j,0] = s
|
||||
|
||||
# Calculate standard deviation
|
||||
if s != 0.0:
|
||||
t.results[i,j,1] = t_value*sqrt((s2/n - s*s)/(n-1))
|
||||
t.results[i,j,1] = t_value*np.sqrt((s2/n - s*s)/(n-1))
|
||||
|
||||
def get_value(self, tally_index, spec_list, score_index):
|
||||
"""Returns a tally score given a list of filters to satisfy.
|
||||
|
|
@ -458,7 +457,7 @@ class StatePoint(object):
|
|||
filter_index += value*t.filters[f_type].stride
|
||||
else:
|
||||
filter_index += f_index*t.filters[f_type].stride
|
||||
|
||||
|
||||
# Return the desired result from Tally.results. This could be the sum and
|
||||
# sum of squares, or it could be mean and stdev if self.generate_stdev()
|
||||
# has been called already.
|
||||
|
|
@ -531,7 +530,7 @@ class StatePoint(object):
|
|||
for i in range(n_filters):
|
||||
|
||||
# compute indices for filter combination
|
||||
filters[:,n_filters - i - 1] = np.floor((np.arange(n_bins) %
|
||||
filters[:,n_filters - i - 1] = np.floor((np.arange(n_bins) %
|
||||
np.prod(filtmax[0:i+2]))/(np.prod(filtmax[0:i+1]))) + 1
|
||||
|
||||
# append in dictionary bin with filter
|
||||
|
|
@ -544,14 +543,14 @@ class StatePoint(object):
|
|||
dims.reverse()
|
||||
dims = np.asarray(dims)
|
||||
if score_str == 'current':
|
||||
dims += 1
|
||||
meshmax[1:4] = dims
|
||||
dims += 1
|
||||
meshmax[1:4] = dims
|
||||
mesh_bins = np.zeros((n_bins,3))
|
||||
mesh_bins[:,2] = np.floor(((filters[:,n_filters - i - 1] - 1) %
|
||||
mesh_bins[:,2] = np.floor(((filters[:,n_filters - i - 1] - 1) %
|
||||
np.prod(meshmax[0:2]))/(np.prod(meshmax[0:1]))) + 1
|
||||
mesh_bins[:,1] = np.floor(((filters[:,n_filters - i - 1] - 1) %
|
||||
mesh_bins[:,1] = np.floor(((filters[:,n_filters - i - 1] - 1) %
|
||||
np.prod(meshmax[0:3]))/(np.prod(meshmax[0:2]))) + 1
|
||||
mesh_bins[:,0] = np.floor(((filters[:,n_filters - i - 1] - 1) %
|
||||
mesh_bins[:,0] = np.floor(((filters[:,n_filters - i - 1] - 1) %
|
||||
np.prod(meshmax[0:4]))/(np.prod(meshmax[0:3]))) + 1
|
||||
data.update({'mesh':zip(mesh_bins[:,0],mesh_bins[:,1],
|
||||
mesh_bins[:,2])})
|
||||
|
|
@ -576,7 +575,7 @@ class StatePoint(object):
|
|||
def _get_data(self, n, typeCode, size):
|
||||
return list(struct.unpack('={0}{1}'.format(n,typeCode),
|
||||
self._f.read(n*size)))
|
||||
|
||||
|
||||
def _get_int(self, n=1, path=None):
|
||||
if self._hdf5:
|
||||
return [int(v) for v in self._f[path].value]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue