def as_string(self, context):
tmp_varname = get_tmp_varname()
result = expr_eval(self, context)
if isinstance(result, dict):
indices = result['indices']
values = result['values']
else:
indices = None
if indices is not None:
if isinstance(values, np.ndarray):
res_type = values.dtype.type
else:
res_type = type(values)
result = np.zeros(context_length(context), dtype=res_type)
np.put(result, indices, values)
context[tmp_varname] = result
return tmp_varname
def evaluate(self, context):
expr = self.expr
#FIXME: either take "contextual filter" into account here (by using
# self._getfilter), or don't do it in sum & gini
if self.filter is not None:
filter_values = expr_eval(self.filter, context)
tmp_varname = get_tmp_varname()
context = context.copy()
context[tmp_varname] = filter_values
if getdtype(expr, context) is bool:
# convert expr to int because mul_bbb is not implemented in
# numexpr
expr *= 1
expr *= Variable(tmp_varname)
else:
filter_values = True
values = expr_eval(expr, context)
values = np.asarray(values)
if self.skip_na:
# we should *not* use an inplace operation because filter_values
# can be a simple variable
filter_values = filter_values & ispresent(values)
if filter_values is True:
numrows = len(values)
else:
numrows = np.sum(filter_values)
if numrows:
if self.skip_na:
return na_sum(values) / float(numrows)
else:
return np.sum(values) / float(numrows)
else:
return float('nan')
def evaluate(self, context):
expr = self.expr
#FIXME: either take "contextual filter" into account here (by using
# self._getfilter), or don't do it in grpsum (& grpgini?)
if self.filter is not None:
filter_values = expr_eval(self.filter, context)
tmp_varname = get_tmp_varname()
context = context.copy()
context[tmp_varname] = filter_values
if dtype(expr, context) is bool:
# convert expr to int because mul_bbb is not implemented in
# numexpr
expr *= 1
expr *= Variable(tmp_varname)
else:
filter_values = True
values = expr_eval(expr, context)
filter_values &= np.isfinite(values)
numrows = np.sum(filter_values)
if numrows:
return np.nansum(values) / float(numrows)
else:
return float('nan')
def __init__(self, expr):
CompoundExpression.__init__(self)
self.expr = expr
self.u_varname = get_tmp_varname()