def groupSets(groups, for_each = []):
""" groups a list of DataSet objects into a list of lists
this function groups a list of DataSet objects into a list of lists, according to the values of the properties given in the for_ech argument. DataSet objects with the same values of the properties given in for_each are grouped together.
The parameters are:
data: the data to be grouped
for_each: the properties according to which the data is grouped
"""
dd = depth(groups)
if dd > 1:
hgroups = flatten(groups, -1)
hgroups_idcs = hgroups.indices()
else:
hgroups = [groups]
hgroups_idcs = [0]
for idx in hgroups_idcs:
sets = hgroups[idx]
for_each_sets = {}
for iset in sets:
fe_par_set = tuple((iset.props[m] for m in for_each))
if fe_par_set in for_each_sets:
for_each_sets[fe_par_set].append(iset)
else:
for_each_sets[fe_par_set] = [iset]
hgroups[idx] = for_each_sets.values()
if dd > 1:
return groups
else:
return hgroups[0]
def ResultsToXY(sets,x,y,foreach=[]):
""" combines observable x and y to build a list of DataSet with y vs x
this function is used to collect data from a hierarchy of DataSet objects, to prepare plots or evaluation.
the inner-most list has to contain one DataSet with props['observable'] = x and one props['observable'] = y,
this will be the pair x-y used in the collection.
The parameters are:
sets: hierarchy of datasets where the inner-most list must contain to pair x-y
x: the name of the observable to be used as x-value of the collected results
y: the name of the observable to be used as y-value of the collected results
foreach: an optional list of properties used for grouping the results. A separate DataSet object is created for each unique set of values of the specified parameers.
The function returns a list of DataSet objects.
"""
dd = depth(sets)
if dd < 2:
raise Exception('The input hierarchy does not provide a unique pair x-y. The input structure has to be a list of lists as minimum. pyalps.groupSets might help you.')
hgroups = flatten(sets, fdepth=-1)
foreach_sets = {}
for gg in hgroups:
xset = None
yset = None
for d in gg:
if d.props['observable'] == x:
xset = d
if d.props['observable'] == y:
yset = d
if xset is None or yset is None:
continue
common_props = dict_intersect([d.props for d in gg])
fe_par_set = tuple((common_props[m] for m in foreach))
if not fe_par_set in foreach_sets:
foreach_sets[fe_par_set] = DataSet()
foreach_sets[fe_par_set].props = common_props
foreach_sets[fe_par_set].props['xlabel'] = x
foreach_sets[fe_par_set].props['ylabel'] = y
if len(xset.y) == len(yset.y):
foreach_sets[fe_par_set].x = np.concatenate((foreach_sets[fe_par_set].x, xset.y))
foreach_sets[fe_par_set].y = np.concatenate((foreach_sets[fe_par_set].y, yset.y))
elif len(xset.y) == 1:
foreach_sets[fe_par_set].x = np.concatenate((foreach_sets[fe_par_set].x, np.array( [xset.y[0]]*len(yset.y) )))
foreach_sets[fe_par_set].y = np.concatenate((foreach_sets[fe_par_set].y, yset.y))
for k, res in foreach_sets.items():
order = np.argsort(res.x, kind = 'mergesort')
res.x = res.x[order]
res.y = res.y[order]
res.props['label'] = ''
for p in foreach:
res.props['label'] += '%s = %s ' % (p, res.props[p])
return foreach_sets.values()