def deleteSelection(self):
"""
Delete the data object referenced by the current tree selection.
"""
item = self.getSanitizedItemSelectionData()
if item is None:
return
if item[0] is DATA_SET_ITEM:
ids = (item[1], None) if len(item) < 3 else (item[1], item[2])
self.Parent.TopLevelParent.facsPlotPanel.deleteAssociatedSubplots(ids)
self.applyToSelection(DataStore.remove, FacsData.removeClustering)
# if all data deleted, clear axes selectors
if len(DataStore.getData()) == 0:
self.Parent.TopLevelParent.updateAxesList([])
if item[0] is FIGURE_SET_ITEM:
id = item[1]
#TODO: figure out if this is a good shortcut for clearing the figure of subplots
if id == FigureStore.getSelectedIndex():
wx.MessageBox('The currently selected Figure cannot be deleted.', 'Invalid Action', wx.OK | wx.ICON_WARNING)
else:
FigureStore.remove(id)
self.updateTree()
def buildFigureTree(self, root, figures):
for id, fig in figures.iteritems():
child = self.tree.AppendItem(root, fig.name)
if (FigureStore.getSelectedIndex() == id):
self.tree.SetItemBold(child, True)
self.tree.SetPyData(child, (FIGURE_SET_ITEM, id))
self.tree.SetItemImage(child, self.figureicn, wx.TreeItemIcon_Normal)
def selectItemTreeSelection(self, rightClick=False):
"""
Using the selected tree item, set the corresponding object
in the data store as the current selection.
"""
item = self.getSanitizedItemSelectionData()
if item is not None:
if item[0] is DATA_SET_ITEM:
self.applyToSelection(DataStore.selectDataSet, FacsData.selectClustering)
if item[0] is FIGURE_SET_ITEM and not rightClick:
if item[1] != FigureStore.getSelectedIndex():
currFig = FigureStore.getSelectedFigure()
newFig = FigureStore.get(item[1])
switchFigures(self.Parent.TopLevelParent.facsPlotPanel, currFig, newFig, True)
self.Parent.TopLevelParent.selectAxes(newFig.axes)
FigureStore.setSelectedFigure(item[1])
def saveState(dir, filename):
"""
Save a representation of the system state: All the loaded data sets,
their clusterings, any transformations or analyses (future),
and all plots.
The state is saved in JSON format based on a dict of the following form:
data: list of IDs
binfile: the filename of the binary file used to store all the actual data
data-dID: dict of settings belonging to a FacsData instance
clust-dID-cID: a dict of attributes belonging to a clustering
figures: list of figureID strings
fig-ID: A dict for each figure keyed on the ID. The subplot attribute here
is replaced with a list of fig-ID-p-ID strings for locating subplot dicts
fig-ID-p-ID: A dict for each subplot in each figure keyed on fig ID and plot ID.
current-data: data ID
current-figure: figure ID
"""
store = shelve.open(os.path.join(dir, filename))
#store = dbopen(os.path.join(dir, filename), 'c', format='csv')
# The actual numeric data will be stored in a separate binary file using
# the numpy savez() method allowing for efficient storage/retrieval of data
binfile = '%s.npz' % filename
bindata = {}
store['data'] = DataStore.getData().keys()
store['binfile'] = binfile
for dID in DataStore.getData():
fdata = DataStore.get(dID)
dStr = 'data-%i' % dID
dfname = fdata.filename if (fdata.filename is not '') else binfile
bindata[dStr] = fdata.data
store[dStr] = {'filename': dfname,
'displayname': fdata.displayname,
'labels': fdata.labels,
'annotations': fdata.annotations,
'analysis': fdata.analysis,
'ID': fdata.ID,
'parent': fdata.parent,
'children': fdata.children,
'selDims': fdata.selDims,
'clustering': fdata.clustering.keys(),
'nodeExpanded': fdata.nodeExpanded,
'selectedClustering': fdata.selectedClustering}
# clusterings
for cID in fdata.clustering:
cStr = 'clust-%i-%i' % (dID, cID)
csett = {'method': fdata.methodIDs[cID],
'opts': fdata.clusteringOpts[cID],
'clusteringSelDims': fdata.clusteringSelDims[cID],
'infoExpanded': fdata.infoExpanded[cID]}
store[cStr] = csett
bindata[cStr] = fdata.clustering[cID]
# figures
sfigs = []
for figID in FigureStore.getFigures():
fig = FigureStore.get(figID)
fStr = 'fig-%i' % figID
d = dict(fig.__dict__)
splots = packSubplots(store, figID, fig.subplots)
d['subplots'] = splots
store[fStr] = d
sfigs.append(fStr)
store['figures'] = list(sfigs)
# other
store['current-data'] = DataStore.getCurrentIndex()
store['current-figure'] = FigureStore.getSelectedIndex()
# write out settings data
store.close()
# write out numeric data to binary file
np.savez(os.path.join(dir, binfile), **bindata)