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()
def mergeDataSets(dsets):
props = dict_intersect([d.props for d in dsets])
merged = copy.deepcopy(dsets.pop())
for d in dsets:
if (d.x != merged.x).any(): raise ValueError('cannot merge datasets: x values mismatch')
if len(d.y) != len(merged.y): raise ValueError('cannot merge datasets: y shape mismatch')
if isinstance(merged.y,alea.MCVectorData):
merged.y.merge(d.y)
else:
for i in range(len(merged.y)):
merged.y[i].merge(d.y[i])
merged.props = props
return merged
def collectXY(sets,x,y,foreach=[],ignoreProperties=False):
""" collects specified data from a list of DataSet objects
this function is used to collect data from a list of DataSet objects, to prepare plots or evaluation. The parameters are:
sets: the list of datasets
x: the name of the property or measurement to be used as x-value of the collected results
y: the name of the property or measurement 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.
ignoreProperties: setting ignoreProperties=True prevents collectXY() from collecting properties.
The function returns a list of DataSet objects.
"""
foreach_sets = {}
for iset in flatten(sets):
if iset.props['observable'] != y and not y in iset.props:
continue
fe_par_set = tuple((iset.props[m] for m in foreach))
if fe_par_set in foreach_sets:
foreach_sets[fe_par_set].append(iset)
else:
foreach_sets[fe_par_set] = [iset]
for k,v in foreach_sets.items():
common_props = dict_intersect([q.props for q in v])
res = DataSet()
res.props = common_props
for im in range(0,len(foreach)):
m = foreach[im]
res.props[m] = k[im]
res.props['xlabel'] = x
res.props['ylabel'] = y
for data in v:
if data.props['observable'] == y:
if len(data.y)>1:
res.props['line'] = '.'
xvalue = np.array([data.props[x] for i in range(len(data.y))])
if len(res.x) > 0 and len(res.y) > 0:
res.x = np.concatenate((res.x, xvalue ))
res.y = np.concatenate((res.y, data.y))
else:
res.x = xvalue
res.y = data.y
elif not ignoreProperties:
res.props['line'] = '.'
xvalue = np.array([ data.props[x] ])
if len(res.x) > 0 and len(res.y) > 0:
res.x = np.concatenate((res.x, xvalue ))
res.y = np.concatenate((res.y, np.array([ data.props[y] ])))
else:
res.x = xvalue
res.y = np.array([ data.props[y] ])
order = np.argsort(res.x, kind = 'mergesort')
res.x = res.x[order]
res.y = res.y[order]
res.props['label'] = ''
for im in range(0,len(foreach)):
res.props['label'] += '%s = %s ' % (foreach[im], k[im])
foreach_sets[k] = res
return foreach_sets.values()
