def compute(self, dataset_pool):
ds = self.get_dataset()
schools = dataset_pool.get_dataset("school")
values = schools[self.varname]
valid_idx = values > 0
result = mean(values[valid_idx], labels=schools[ds.get_id_name()[0]][valid_idx],
index=ds.get_id_attribute())
is_nan = isnan(result)
result[is_nan] = ds["_school_faz_measure"]
return result
def compute(self, dataset_pool):
zones = self.get_dataset()
hhs = dataset_pool.get_dataset("household")
income = hhs["income"]
valid_income_idx = income > 0
result = mean(income[valid_income_idx], labels=hhs[zones.get_id_name()[0]][valid_income_idx],
index=zones.get_id_attribute())
is_nan = isnan(result)
result[is_nan] = 0
return result
def compute(self, dataset_pool):
ds = self.get_dataset()
schools = dataset_pool.get_dataset("school")
values = schools[self.varname]
valid_idx = values > 0
pcl_school_district = ds['school_district_id']
result = mean(values[valid_idx], labels=schools["school_district_id"][valid_idx], index=pcl_school_district)
is_nan = isnan(result)
result[is_nan] = 0
return result
def compute(self, dataset_pool):
persons = dataset_pool.get_dataset("person")
hhs = self.get_dataset()
where_adult = where(persons.get_attribute("is_adult"))[0]
age = persons.get_attribute("age")
return array(
ndimage.mean(
age[where_adult],
labels=persons.get_attribute("household_id")[where_adult],
index=hhs.get_id_attribute(),
)
)
def compute(self, dataset_pool):
ds = self.get_dataset()
schools = dataset_pool.get_dataset("school")
values = schools[self.varname]
valid_idx = values > 0
result = mean(values[valid_idx], labels=schools[ds.get_id_name()[0]][valid_idx],
index=ds.get_id_attribute())
is_nan = isnan(result)
result[is_nan] = 0
# if any(is_nan):
# result_district = mean(values[valid_idx], labels=schools['school_district_id'][valid_idx],
# index=ds['school_district_id'])
# not_is_nan_district = logical_and(isnan(result_district) == 0,is_nan)
# result[not_is_nan_district] = result_district[not_is_nan_district]
return result
def compute(self, dataset_pool):
ds = self.get_dataset()
parcels = dataset_pool.get_dataset("parcel")
# up = parcels.get_attribute("total_value_per_sqft")
up = parcels.get_attribute("unit_price")
where_zero = where(up == 0)[0]
up[where_zero] = parcels.get_attribute("total_value_per_sqft")[where_zero]
where_positive = where(up > 0)[0]
faz = parcels.get_attribute(self.id_variable)
lut = parcels.get_attribute("land_use_type_id")
fazids = ds.get_dataset(1).get_id_attribute()
lutids = ds.get_dataset(2).get_id_attribute()
result = zeros(ds.size()[0])
for i in range(result.shape[0]): # iterate over large areas
labels = where(faz == fazids[i], lut, 0)
result[i, :] = ndimage.mean(up[where_positive], labels=labels[where_positive], index=lutids)
return result
