def _scan2recarray(self):
# unwrapping/assembling table from stats
stats = self.scanstats
xkeys = sorted( stats.keys() )
ids = sorted( stats[xkeys[0]].keys() )
fieldnames = sorted( stats[xkeys[0]][ids[0]].keys() )
self.scanarrays = {}
for id in ids:
a = {}
for y in fieldnames:
a[y] = [stats[x][id][y] for x in xkeys]
self.scanarrays[id] = numpy.rec.fromarrays(
[a[y] for y in fieldnames],names=fieldnames)
def show(self):
print "Statistics S"
print "------------"
pp.pprint(self.S)
print "Data in D"
print "---------"
pp.pprint(sorted(self.D.keys()))
def __init__(self,hoppinggraphs,normalization="maxabs",verbosity=1,prune='default'):
"""Create a 'heatmap' for the Hopgraph statistics from a dictionary of CombinedGraphs.
>>> hm = HeatmapAnalysis(hg,normalize="maxabs")
:Arguments:
HoppingGraphs Dictionary of HoppingGraph instances. The key is used to label
the simulation in the heat map and thus should be expressive.
normalization Method to normalize the data across observables. Can be None
(not recommended), 'maxabs', or 'zscore'. See the normalize()
method for documentation.
NOTE that the normalization strongly influences the clustering
in the heat map.
verbosity Chattiness; use at least 1 in order to be notified if you
should install additional packages. Otherwise a less powerful
alternative is chosen silently,
prune dict with keys that are removed from the heat map; see
prune_default class attribute.
:Methods:
plot plot the heat map
normalize normalize using the 'normalize' method
labels dictionary of row, column names (and the normalization constants
as strings)
annotation 'enumerate' dictionaries of labels but not stringified
"""
# TODO: input a dict of HopGraph or CombinedGraph instances
# and a dict with metadata
# Currently assumes CombinedGraph as input
set_verbosity(verbosity)
hg = hoppinggraphs
self._filename = None # holds default filename
if prune is 'default':
prune = self.prune_default
elif prune is None:
prune = []
else:
try: 'foo' in prune
except: raise TypeError("prune must support 'in' lookup")
self.columns = hg.keys() # labels for sim: 'R13_1_plm', 'R13_1_apo', ...
self.names = []
self.heatmap = None
self.normalization_method = normalization
# boring data reorganization
table = {}
for sim,hopgraph in hg.items():
for k,v in hopgraph.stats().items():
try:
table[k].append(v)
except:
table[k] = [v]
self.names = [k for k in sorted(table.keys()) if k not in prune]
self.data = numpy.array([table[k] for k in self.names])
# reference values, tagged with row name
# colum offsets are the indices in self.columns
self.taggeddata = numpy.rec.fromarrays(self.data,names=self.names)
self.heatmap = self.data.copy()
self.normalizations = numpy.zeros(len(self.heatmap))
self.normalize(method=normalization) # modifies heatmap and normalizations
# set up annotation
self.annotation()
