def buildings(store, households, jobs, building_sqft_per_job, settings):
df = store['buildings']
if settings.get("set_nan_price_to_zero", False):
for col in ["residential_price", "non_residential_price"]:
df[col] = 0
if settings.get("reconcile_residential_units_and_households", False):
# prevent overfull buildings (residential)
df["residential_units"] = pd.concat(
[df.residential_units, households.building_id.value_counts()],
axis=1).max(axis=1)
if settings.get("reconcile_non_residential_sqft_and_jobs", False):
# prevent overfull buildings (non-residential)
tmp_df = pd.concat([
df.non_residential_sqft,
jobs.building_id.value_counts() *
df.building_type_id.fillna(-1).map(building_sqft_per_job)
], axis=1)
df["non_residential_sqft"] = tmp_df.max(axis=1).apply(np.ceil)
fill_nas_cfg = settings.get("table_reprocess", None)
if fill_nas_cfg is not None:
fill_nas_cfg = fill_nas_cfg.get("buildings", None)
if fill_nas_cfg is not None:
df = utils.table_reprocess(fill_nas_cfg, df)
return df
def buildings(store, households, jobs, building_sqft_per_job, settings):
df = store['buildings']
if settings.get("set_nan_price_to_zero", False):
for col in ["residential_price", "non_residential_price"]:
df[col] = 0
if settings.get("reconcile_residential_units_and_households", False):
# prevent overfull buildings (residential)
df["residential_units"] = pd.concat(
[df.residential_units,
households.building_id.value_counts()],
axis=1).max(axis=1)
if settings.get("reconcile_non_residential_sqft_and_jobs", False):
# prevent overfull buildings (non-residential)
tmp_df = pd.concat([
df.non_residential_sqft,
jobs.building_id.value_counts() *
df.building_type_id.fillna(-1).map(building_sqft_per_job)
],
axis=1)
df["non_residential_sqft"] = tmp_df.max(axis=1).apply(np.ceil)
fill_nas_cfg = settings.get("table_reprocess", None)
if fill_nas_cfg is not None:
fill_nas_cfg = fill_nas_cfg.get("buildings", None)
if fill_nas_cfg is not None:
df = utils.table_reprocess(fill_nas_cfg, df)
return df
def jobs(store, settings):
df = store['jobs']
if settings.get("remove_invalid_building_ids", True):
# have to do it this way to prevent circular reference
df.building_id.loc[~df.building_id.isin(store['buildings'].index)] = -1
fill_nas_cfg = settings.get("table_reprocess", {}).get("jobs", None)
if fill_nas_cfg is not None:
df = utils.table_reprocess(fill_nas_cfg, df)
return df
def jobs(store, settings):
df = store['jobs']
if settings.get("remove_invalid_building_ids", True):
# have to do it this way to prevent circular reference
df.building_id.loc[~df.building_id.isin(store['buildings'].index)] = -1
fill_nas_cfg = settings.get("table_reprocess", {}).get("jobs", None)
if fill_nas_cfg is not None:
df = utils.table_reprocess(fill_nas_cfg, df)
return df
