def partition_nd(columns, filter_value, possible_values):
# note that since we don't iterate through the columns many times,
# it's not worth it to copy non contiguous columns in this version
d = group_indices_nd(columns, filter_value)
if len(columns) > 1:
pvalues = product(*possible_values)
else:
pvalues = possible_values[0]
empty_list = np.empty(0, dtype=int)
# XXX: It would be nice to print a warning if d contains keys not in
# pvalues but that might be hard to implement efficiently in the python
# version (and I am not eager to diverge too much).
return [d.get(pv, empty_list) for pv in pvalues]
def partition_nd(columns, filter_value, possible_values):
assert len(columns) > 0
assert all(isinstance(c, np.ndarray) for c in columns), \
"not all columns are ndarrays: " + \
', '.join(str(type(c)) for c in columns)
# note that since we don't iterate through the columns many times,
# it's not worth it to copy non contiguous columns in this version
d = group_indices_nd(columns, filter_value)
if len(columns) > 1:
pvalues = product(*possible_values)
else:
pvalues = possible_values[0]
empty_list = np.empty(0, dtype=int)
# XXX: It would be nice to print a warning if d contains keys not in
# pvalues but that might be hard to implement efficiently in the python
# version (and I am not eager to diverge too much).
return [d.get(pv, empty_list) for pv in pvalues]
def group_context(used_variables, setfilter, context):
"""
return a dict of the form:
{'field1': array1, 'field2': array2, 'idx': array_of_arrays_of_ids}
"""
names = sorted(used_variables)
columns = [context[name] for name in names]
# group_indices_nd returns a dict {value_or_tuple: array_of_indices}
d = group_indices_nd(columns, setfilter)
keylists = zip(*d.keys()) if len(columns) > 1 else [d.keys()]
keyarrays = [np.array(c) for c in keylists]
# we want a 1d array of arrays, not the 2d array that np.array(d.values())
# produces if we have a list of arrays with all the same length
idcol = context['id']
ids_by_group = np.empty(len(d), dtype=object)
ids_by_group[:] = [idcol[v] for v in d.values()]
result = dict(zip(names, keyarrays))
result['__ids__'] = ids_by_group
result['__len__'] = len(d)
return result