def _update(self):
''' Call this method every time VD is changed to update Stark
data. Iter over VD and fill up different lists of keys, each
list contains names from each data type.
'''
# Start from clean lists
self._dim = []
self._elab = []
self._num = []
self._imm = []
self._rate = []
self._curr = []
# Sort md.items() by 'ORD' to have output lists already ordered.
md_items = self._md['vars'].items()
# md_items.sort(key=lambda x: x[1].get('ORD', 0))
for key, val in md_items:
if val['type'] == 'D':
self._dim += utils.unroll({key: val})
elif val['type'] == 'E':
# (Re)evaluate elab columns
self._df[key] = self._eval(self._md['vars'][key]['elab'])
self._elab.append(key)
elif val['type'] == 'N':
# TODO: check that dtypes are really numeric types
self._num.append(key)
elif val['type'] == 'I':
self._imm.append(key)
elif val['type'] == 'C':
self._curr.append(key)
elif val['type'] == 'R':
self._rate.append(key)
def __delitem__(self, key):
del self._df[key]
target = utils.unroll(self._md)
target.remove(key)
self._md = utils.filter_tree(self._md, target)