def compute(self, context, expr):
entity = context.entity
baseperiod = entity.base_period
period = context.period - 1
typemap = {bool: int, int: int, float: float}
res_type = typemap[getdtype(expr, context)]
res_size = len(entity.array)
sum_values = np.zeros(res_size, dtype=res_type)
id_to_rownum = context.id_to_rownum
while period >= baseperiod:
ids, values = self.value_for_period(expr,
period,
context,
fill=None)
# filter out lines which are present because there was a value for
# that individual at that period but not for that column
acceptable_rows = hasvalue(values)
acceptable_ids = ids[acceptable_rows]
if len(acceptable_ids):
acceptable_values = values[acceptable_rows]
value_rows = id_to_rownum[acceptable_ids]
period_value = np.zeros(res_size, dtype=np.float)
safe_put(period_value, value_rows, acceptable_values)
sum_values += period_value
period -= 1
return sum_values
def evaluate(self, context):
entity = context['__entity__']
baseperiod = entity.base_period
period = context['period'] - 1
expr = self.expr
typemap = {bool: int, int: int, float: float}
res_type = typemap[dtype(expr, context)]
res_size = len(entity.array)
sum_values = np.zeros(res_size, dtype=res_type)
id_to_rownum = context.id_to_rownum
while period >= baseperiod:
ids, values = entity.value_for_period(expr, period, context,
fill=None)
# filter out lines which are present because there was a value for
# that individual at that period but not for that column
acceptable_rows = hasvalue(values)
acceptable_ids = ids[acceptable_rows]
if len(acceptable_ids):
acceptable_values = values[acceptable_rows]
value_rows = id_to_rownum[acceptable_ids]
period_value = np.zeros(res_size, dtype=np.float)
safe_put(period_value, value_rows, acceptable_values)
sum_values += period_value
period -= 1
return sum_values
def compute(self, context, expr):
entity = context.entity
baseperiod = entity.base_period
period = context.period - 1
res_size = len(entity.array)
num_values = np.zeros(res_size, dtype=np.int)
# current period
last_period_wh_value = np.full(res_size, context.period, dtype=np.int)
sum_values = np.zeros(res_size, dtype=np.float)
id_to_rownum = context.id_to_rownum
while period >= baseperiod:
ids, values = self.value_for_period(expr,
period,
context,
fill=None)
# filter out lines which are present because there was a value for
# that individual at that period but not for that column
acceptable_rows = hasvalue(values)
acceptable_ids = ids[acceptable_rows]
if len(acceptable_ids):
acceptable_values = values[acceptable_rows]
value_rows = id_to_rownum[acceptable_ids]
has_value = np.zeros(res_size, dtype=bool)
safe_put(has_value, value_rows, True)
period_value = np.zeros(res_size, dtype=np.float)
safe_put(period_value, value_rows, acceptable_values)
num_values += has_value * (last_period_wh_value - period)
sum_values += period_value
last_period_wh_value[has_value] = period
period -= 1
return sum_values / num_values
def evaluate(self, context):
entity = context['__entity__']
baseperiod = entity.base_period
period = context['period'] - 1
expr = self.expr
res_size = len(entity.array)
num_values = np.zeros(res_size, dtype=np.int)
last_period_wh_value = np.empty(res_size, dtype=np.int)
last_period_wh_value.fill(context['period']) # current period
sum_values = np.zeros(res_size, dtype=np.float)
id_to_rownum = context.id_to_rownum
while period >= baseperiod:
ids, values = entity.value_for_period(expr, period, context,
fill=None)
# filter out lines which are present because there was a value for
# that individual at that period but not for that column
acceptable_rows = hasvalue(values)
acceptable_ids = ids[acceptable_rows]
if len(acceptable_ids):
acceptable_values = values[acceptable_rows]
value_rows = id_to_rownum[acceptable_ids]
has_value = np.zeros(res_size, dtype=bool)
safe_put(has_value, value_rows, True)
period_value = np.zeros(res_size, dtype=np.float)
safe_put(period_value, value_rows, acceptable_values)
num_values += has_value * (last_period_wh_value - period)
sum_values += period_value
last_period_wh_value[has_value] = period
period -= 1
return sum_values / num_values
