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Handle urban area with missing daily VMT per capita #342

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Feb 3, 2023
Merged

Handle urban area with missing daily VMT per capita #342

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13 changes: 9 additions & 4 deletions prereise/gather/demanddata/transportation_electrification/generate_scaling_factors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,16 +129,18 @@ def load_dot_vmt_per_capita(path):
)

return (
df_ua.squeeze().loc[lambda x: x != "[no data]"],
df_ua.squeeze().loc[~df_ua.index.str.contains("\[no data\]")], # noqa: W605
df_state.rename(index=state2abv).squeeze(),
)


def calculate_vmt_for_ua(census_ua, tht_ua):
def calculate_vmt_for_ua(census_ua, tht_ua, tht_state):
"""Calculate the total annual Vehicle Miles Traveled (VMT) in urban areas

:param dict census_ua: dictionary as returned by :func:`load_census_ua`
:param pandas.Series tht_ua: vmt per capita in urban areas as returned by
:param pandas.Series tht_ua: daily vmt per capita in urban areas as returned by
:func:`load_dot_vmt_per_capita`
:param pandas.Series tht_state: annual vmt per capita in state as returned by
:func:`load_dot_vmt_per_capita`
:return: (*dict*) -- keys are state abbreviations and values are data frame giving
annual vmt by urban areas.
Expand All @@ -159,7 +161,10 @@ def calculate_vmt_for_ua(census_ua, tht_ua):
vmt_for_ua[s] = pd.DataFrame(
{
"Annual VMT": [
365 * tht_ua_format.loc[i] * census_ua_format.loc[i] for i in common
365 * tht_ua_format.loc[i] * census_ua_format.loc[i]
if tht_ua_format.loc[i] != "[no data]"
else tht_state.loc[s] * census_ua_format.loc[i]
for i in common
]
},
index=list(common),
Expand Down