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)