def render(self, dataframe, path ):
if len(dataframe.columns) < 2:
raise ValueError( "requiring two coordinates, only got %s" % str(dataframe.columns))
plts, legend = [], []
blocks = ResultBlocks()
for xcolumn, ycolumn in itertools.combinations( dataframe.columns, 2 ):
# remove missing data points
xvalues, yvalues = Stats.filterMissing( (dataframe[xcolumn], dataframe[ycolumn]) )
# remove columns with all NaN
if len(xvalues) == 0 or len(yvalues) == 0:
continue
# apply log transformation on data not on plot
if self.logscale:
if "x" in self.logscale:
xvalues = R.log10(xvalues)
if "y" in self.logscale:
yvalues = R.log10(yvalues)
self.startPlot()
# wrap, as pandas series can not
# passed through rpy2.
R.smoothScatter( numpy.array( xvalues, dtype=numpy.float),
numpy.array( yvalues, dtype=numpy.float),
xlab=xcolumn,
ylab=ycolumn,
nbin = self.nbins )
blocks.extend( self.endPlot( dataframe, path ) )
return blocks
def __call__(self, data, path ):
'''iterate over leaves/branches in data structure.
This method will call the :meth:`render` method for
each leaf/branch at level :attr:`nlevels`.
'''
if self.nlevels == None: raise NotImplementedError("incomplete implementation of %s" % str(self))
result = ResultBlocks( title = path2str(path) )
labels = DataTree.getPaths( data )
if len(labels) < self.nlevels:
self.warn( "at %s: expected at least %i levels - got %i: %s" %\
(str(path), self.nlevels, len(labels), str(labels)) )
result.append( EmptyResultBlock( title = path2str(path) ) )
return result
paths = list(itertools.product( *labels[:-self.nlevels] ))
for p in paths:
work = DataTree.getLeaf( data, p )
if not work: continue
try:
result.extend( self.render( work, path + p ) )
except:
self.warn("exeception raised in rendering for path: %s" % str(path+p))
raise
return result
