def render( self ):
'''supply the :class:`Renderer.Renderer` with the data to render.
The data supplied will depend on the ``groupby`` option.
returns a ResultBlocks data structure.
'''
self.debug( "%s: rendering data started for %i items" % (self,
len(self.data)))
# get number of levels required by renderer
try:
renderer_nlevels = self.renderer.nlevels
except AttributeError:
# set to -1 to avoid any grouping
# important for user renderers that are functions
# and have no level attribute.
renderer_nlevels = -1
# initiate output structure
results = ResultBlocks( title = "")
# convert to data series
# The data is melted, i.e,
# BMW price 10000
# BMW speed 100
# Golf price 5000
# Golf speed 50
dataframe = DataTree.asDataFrame( self.data )
# dataframe.write_csv( "test.csv" )
if dataframe is None:
self.warn( "%s: no data after conversion" % self )
raise ValueError( "no data for renderer" )
# special patch: set column names to pruned levels
# if there are no column names
if len(dataframe.columns) == len(self.pruned):
if list(dataframe.columns) == list(range( len(dataframe.columns))):
dataframe.columns = [x[1] for x in self.pruned]
index = dataframe.index
def getIndexLevels( index ):
try:
# hierarchical index
nlevels = len(index.levels)
except AttributeError:
nlevels = 1
index = [ (x,) for x in index]
#raise ValueError('data frame without MultiIndex' )
return nlevels
nlevels = getIndexLevels( index )
self.debug( "%s: rendering data started. levels=%i, required levels>=%i, group_level=%s" %\
(self, nlevels,
renderer_nlevels,
str(self.group_level) ) )
if renderer_nlevels < 0 and self.group_level <= 0:
# no grouping for renderers that will accept
# a dataframe with any level of indices and no explicit
# grouping has been asked for.
results.append( self.renderer( dataframe, path = () ) )
else:
# user specified group level by default
group_level = self.group_level
# set group level to maximum allowed by renderer
if renderer_nlevels >= 0:
group_level = max(nlevels - renderer_nlevels, group_level)
# add additional level if necessary
if nlevels < group_level:
prefix = tuple(["level%i" % x for x in range( group_level - nlevels)])
dataframe.index = pandas.MultiIndex.from_tuples( [ prefix + x for x in dataframe.index ] )
# used to be: group_level + 1
# hierarchical index
# numpy.unique converts everything to a string
# which is not consistent with selecting later
paths = map( tuple, DataTree.unique( [ x[:group_level] for x in dataframe.index.unique() ] ))
pathlength = len(paths[0]) - 1
is_hierarchical = isinstance( dataframe.index, pandas.core.index.MultiIndex )
if is_hierarchical:
# Note: can only sort hierarchical indices
dataframe = dataframe.sortlevel()
if dataframe.index.lexsort_depth < pathlength:
raise ValueError('could not sort data frame: sort depth=%i < pathlength=%i, dataframe=%s' \
% (dataframe.index.lexsort_depth,
pathlength,
dataframe))
for path in paths:
if path:
if len(path) == nlevels:
# extract with loc in order to obtain dataframe
work = dataframe.loc[[path]]
else:
# select data frame as cross-section
work = dataframe.xs(path, axis=0 )
else:
# empty tuple - use full data set
work = dataframe
# remove columns and rows in work that are all Na
work = work.dropna( axis=1, how='all').dropna( axis=0, how='all')
if is_hierarchical and renderer_nlevels >= 0:
work_levels = getIndexLevels( work.index )
# reduce levels of indices required to that required
# for Renderer. This occurs if groupby=none.
if work_levels > renderer_nlevels:
sep = work_levels - (renderer_nlevels - 1)
tuples = [ ( DataTree.path2str( x[:sep] ), ) + x[sep:] \
for x in work.index ]
work.index = pandas.MultiIndex.from_tuples( tuples )
try:
results.append( self.renderer( work,
path = path ))
except:
self.error( "%s: exception in rendering" % self )
results.append( ResultBlocks( Utils.buildException( "rendering" ) ) )
if len(results) == 0:
self.warn("renderer returned no data.")
raise ValueError( "renderer returned no data." )
self.debug( "%s: rendering data finished with %i blocks" % (self.tracker, len(results)))
return results
def __call__( self, data ):
result = DataTree.asDataFrame( data )
return odict( ( ('all', result),) )
