def plot_with_map(currMicroClusters, dataContext, title):
p = Plotter(currMicroClusters, dataContext)
ax = plt.gca() # we try the one with the mc size
# map
map = Basemap(projection='cyl', )
map.drawmapboundary(fill_color='aqua')
map.fillcontinents(color='gray', lake_color='aqua')
map.drawcoastlines()
# plot
p.plotMicroClustersSize(ax)
fig = plt.gcf()
fig.canvas.manager.window.showMaximized()
fig.suptitle(title, fontweight="bold")
# show
if not generate_gif:
plt.show() # plot figure to see resutls; for testing
return fig
dyclee = Dyclee(dataContext=dataContext,
relativeSize=relativeSize,
uncommonDimensions=uncommonDimensions,
closenessThreshold=closenessThreshold)
# start
X = non_time_series_datasets[datIndx]['dataset']
dName = non_time_series_datasets[datIndx]['name']
k = non_time_series_datasets[datIndx]['k']
baseFolder = getClusteringResultsPath() + dName + '/'
# normalize dataset for easier parameter selection
X = StandardScaler().fit_transform(X)
ac = 0 # processed samples
# iterate over the data points
for dataPoint in X: # column index
ac += 1
dyclee.trainOnElement(dataPoint)
currMicroClusters = dyclee.getClusteringResult(
) # we wanna show the clustering at the end, only once
res = prepareResultFrom(currMicroClusters)
folder = baseFolder + getDycleeName() + '/'
# storeNonTimeSeriesResult(res, folder) # FIXME: uncomment!
# store algo config
algoConfig = dyclee.getConfig()
# storeAlgoConfig(algoConfig, folder) # FIXME: uncomment!
# IMPORTANT: plotting outside dyclee
p = Plotter(currMicroClusters, dataContext)
# p.plotClusters() # FIXME: uncomment if you want all 3 plots to be displayed - and comment the whole following block
ax = plt.gca() # we try the one with the mc size
p.plotMicroClustersSize(ax)
plt.show()
