def scatterplot2D(subplot, figure, dims):
# set default plot options if necessary
opts = subplot.opts
if len(opts) == 0:
opts['xRange'] = ()
opts['xRangeAuto'] = True
opts['yRange'] = ()
opts['yRangeAuto'] = True
opts['xTransform'] = ''
opts['yTransform'] = ''
opts['transformAuto'] = True
# Set axes transforms
if (opts['transformAuto']):
fcData = DataStore.get(subplot.dataIndex)
tf = ('xTransform', 'yTransform')
for i, dim in enumerate(dims):
t = fcData.getDefaultTransform(dim)
if (t is not None) and ('log' in t.lower()):
opts[tf[i]] = 'log'
else:
opts[tf[i]] = 'linear'
if opts['xRangeAuto']:
opts['xRange'] = (1, np.max(subplot.Data[:,dims[0]])*1.5)
if opts['yRangeAuto']:
opts['yRange'] = (1, np.max(subplot.Data[:,dims[1]])*1.5)
# create the subplot and set its attributes
subplot.axes = figure.add_subplot(subplot.mnp, xlim=opts['xRange'],
ylim=opts['yRange'], autoscale_on=False)
subplot.axes.set_xscale(opts['xTransform'], nonposx='clip')
subplot.axes.set_yscale(opts['yTransform'], nonposy='clip')
subplot.axes.set_xlabel(subplot.Labels[dims[0]])
subplot.axes.set_ylabel(subplot.Labels[dims[1]])
subplot.axes.set_title(subplot.Title)
# draw the supplied FACS data
if (not subplot.isDataClustered()):
subplot.axes.plot(subplot.Data[:,dims[0]],
subplot.Data[:,dims[1]],
'.', ms=1, color='black')
else:
data = separate(subplot.Data, subplot.Clustering)
for i in range(len(data)):
xs = data[i][:,dims[0]]
ys = data[i][:,dims[1]]
subplot.axes.plot(xs, ys, '.', ms=1, color=methods.plotColors[i])
def barplot(subplot, figure, dims=None):
"""
Create a bar chart with one bar for each cluster, and the bar height
equal to the percent of events contained within the cluster.
Some techniques borrowed from:
http://www.scipy.org/Cookbook/Matplotlib/BarCharts
"""
clusters = separate(subplot.Data, subplot.Clustering)
# set default plot options
opts = subplot.opts
if len(opts) == 0:
opts['labelAngle'] = 0 if len(clusters) < 5 else -20
opts['view'] = 'percent'
# correcting for previous version
if 'view' not in opts:
opts['view'] = 'percent'
dataSize = len(subplot.Data)
if opts['view'] == 'toplevel':
dataSize = len(DataStore.getToplevelParent(subplot.dataIndex).data)
if opts['view'] in ['percent', 'toplevel']:
displayNums = [float(len(cluster))/dataSize*100 for cluster in clusters]
else:
displayNums = [len(cluster) for cluster in clusters]
numBars = len(displayNums)
width = 0.5
xlocs = na.array(range(len(displayNums)))+0.5
subplot.axes = figure.add_subplot(subplot.mnp, title=subplot.Title, autoscale_on=False)
subplot.axes.set_xticks(xlocs + width/2)
if opts['view'] == 'counts':
subplot.axes.set_xticklabels(map(lambda x: "%i" % x, displayNums), rotation=opts['labelAngle'])
subplot.axes.set_ylim(0, max(displayNums)*1.2)
else:
subplot.axes.set_xticklabels(map(lambda x: "%.2f%%" % x, displayNums), rotation=opts['labelAngle'])
subplot.axes.set_ylim(0, 100)
subplot.axes.set_xlim(0, xlocs[-1] + width*2)
subplot.axes.xaxis.tick_bottom()
subplot.axes.yaxis.tick_left()
subplot.axes.bar(xlocs, displayNums, width=width, color=methods.plotColors[:numBars])
