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assign ml node in the 950,000 range #351

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merged 3 commits into from
May 28, 2024
Merged

assign ml node in the 950,000 range #351

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fd5b28e
assign ml node in the 950,000 range
yueshuaing Apr 19, 2024
6cadef6
Merge branch 'develop_with_ranch' into develop_with_ranch_ml_node_num…
i-am-sijia Apr 30, 2024
422e149
add timeofday feature to roadway property change
yueshuaing May 8, 2024
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149 changes: 127 additions & 22 deletions network_wrangler/roadwaynetwork.py
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Original file line number Diff line number Diff line change
Expand Up @@ -158,6 +158,8 @@ class RoadwayNetwork(object):
SP_WEIGHT_FACTOR = 100
MANAGED_LANES_NODE_ID_SCALAR = 500000
MANAGED_LANES_LINK_ID_SCALAR = 1000000
MANAGED_LANES_NODE_ID_MIN= 950001
MANAGED_LANES_NODE_ID_MAX= 1000000

SELECTION_REQUIRES = ["link"]

Expand Down Expand Up @@ -1393,6 +1395,8 @@ def apply_roadway_feature_change(
# if there is a set command, add that property to network
self.validate_properties(properties)

p =1

for i, p in enumerate(properties):
attribute = p["property"]

Expand All @@ -1407,26 +1411,119 @@ def apply_roadway_feature_change(
"selected links".format(attribute)
)

if in_place:
if "set" in p.keys():
self.links_df.loc[link_idx, attribute] = p["set"]
else:
self.links_df.loc[link_idx, attribute] = (
self.links_df.loc[link_idx, attribute] + p["change"]
)
else:
if i == 0:
updated_network = copy.deepcopy(self)
attr_value = ""

if "timeofday" in p.keys():
for idx in link_idx:
attr_value = {}

if "set" in p.keys():
attr_value["default"] = p["set"]
elif "change" in p.keys():
# the attribute could have become an object column
# if a ML project has been applied before to the network
if isinstance(self.links_df.at[idx, attribute], dict):
attr_value["default"] = self.links_df.at[idx, attribute]["default"] + p["change"]
elif isinstance(self.links_df.at[idx, attribute], str):
attr_value["default"] = int(self.links_df.at[idx, attribute]) + p["change"]
else:
attr_value["default"] = self.links_df.at[idx, attribute] + p["change"]

attr_value["timeofday"] = []

for tod in p["timeofday"]:
if "set" in tod.keys():
attr_value["timeofday"].append(
{
"time": parse_time_spans(tod["time"]),
"value": tod["set"]
}
)
elif "change" in tod.keys():
# the attribute could have become an object column
# if a ML project has been applied before to the network
if isinstance(self.links_df.at[idx, attribute], dict):
attr_value["timeofday"].append(
{
"time": parse_time_spans(tod["time"]),
"value": self.links_df.at[idx, attribute]["default"] + tod["change"]
}
)
elif isinstance(self.links_df.at[idx, attribute], str):
attr_value["timeofday"].append(
{
"time": parse_time_spans(tod["time"]),
"value": int(self.links_df.at[idx, attribute]) + tod["change"]
}
)
else:
attr_value["timeofday"].append(
{
"time": parse_time_spans(tod["time"]),
"value": self.links_df.at[idx, attribute] + tod["change"]
}
)

if in_place:
if attribute in self.links_df.columns and not isinstance(
attr_value, numbers.Number
):
# if the attribute already exists
# and the attr value we are trying to set is not numeric
# then change the attribute type to object
self.links_df[attribute] = self.links_df[attribute].astype(object)

if attribute not in self.links_df.columns:
# if it is a new attribute then initialize with NaN values
self.links_df[attribute] = "NaN"

self.links_df.at[idx, attribute] = attr_value

else:
if i == 1:
updated_network = copy.deepcopy(self)

if attribute in self.links_df.columns and not isinstance(
attr_value, numbers.Number
):
# if the attribute already exists
# and the attr value we are trying to set is not numeric
# then change the attribute type to object
updated_network.links_df[attribute] = updated_network.links_df[
attribute
].astype(object)

if attribute not in updated_network.links_df.columns:
# if it is a new attribute then initialize with NaN values
updated_network.links_df[attribute] = "NaN"

updated_network.links_df.at[idx, attribute] = attr_value

if "set" in p.keys():
updated_network.links_df.loc[link_idx, attribute] = p["set"]
if p == len(properties):
return updated_network
else:
p = p + 1
else:
if in_place:
if "set" in p.keys():
self.links_df.loc[link_idx, attribute] = p["set"]
else:
self.links_df.loc[link_idx, attribute] = (
self.links_df.loc[link_idx, attribute] + p["change"]
)
else:
updated_network.links_df.loc[link_idx, attribute] = (
updated_network.links_df.loc[link_idx, attribute] + p["change"]
)
if i == 0:
updated_network = copy.deepcopy(self)

if "set" in p.keys():
updated_network.links_df.loc[link_idx, attribute] = p["set"]
else:
updated_network.links_df.loc[link_idx, attribute] = (
updated_network.links_df.loc[link_idx, attribute] + p["change"]
)

if i == len(properties) - 1:
return updated_network
if i == len(properties) - 1:
return updated_network

def apply_managed_lane_feature_change(
self, link_idx: list, properties: dict, in_place: bool = True
Expand Down Expand Up @@ -2172,12 +2269,20 @@ def _update_location_reference(location_reference: list):
)
return out_location_reference

ml_links_df["A"] = (
ml_links_df["A"] + RoadwayNetwork.MANAGED_LANES_NODE_ID_SCALAR
)
ml_links_df["B"] = (
ml_links_df["B"] + RoadwayNetwork.MANAGED_LANES_NODE_ID_SCALAR
# ml_links_df["A"] = (
# ml_links_df["A"] + RoadwayNetwork.MANAGED_LANES_NODE_ID_SCALAR
# )
# ml_links_df["B"] = (
# ml_links_df["B"] + RoadwayNetwork.MANAGED_LANES_NODE_ID_SCALAR
# )
unique_ids = pd.concat([ml_links_df['A'], ml_links_df['B']]).unique()
available_ids = (iter(range(RoadwayNetwork.MANAGED_LANES_NODE_ID_MIN,
RoadwayNetwork.MANAGED_LANES_NODE_ID_MAX))
)
ml_node_mapping = {original_id: next(available_ids) for original_id in unique_ids}
ml_links_df["A"] = ml_links_df['A'].map(ml_node_mapping)
ml_links_df["B"] = ml_links_df['B'].map(ml_node_mapping)

ml_links_df[RoadwayNetwork.UNIQUE_LINK_KEY] = (
ml_links_df[RoadwayNetwork.UNIQUE_LINK_KEY]
+ RoadwayNetwork.MANAGED_LANES_LINK_ID_SCALAR
Expand Down