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remove root_dir and update path for scenario network files
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@AshishKuls
AshishKuls committed Jan 29, 2024
1 parent 4249850 commit 36b8066
Showing 1 changed file with 19 additions and 40 deletions.
59 changes: 19 additions & 40 deletions tm2py/acceptance/simulated.py
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Original file line number Diff line number Diff line change
Expand Up @@ -148,8 +148,7 @@ def _validate(self):
return

def _add_model_link_id(self):
file_root = self.scenario_dict["scenario"]["root_dir"]
df = pd.read_csv(os.path.join(file_root, "acceptance", "crosswalks", "transit_link_id_mapping_am.csv"))
df = pd.read_csv(os.path.join("acceptance", "crosswalks", "transit_link_id_mapping_am.csv"))

df = df.rename(
columns={
Expand All @@ -161,12 +160,11 @@ def _add_model_link_id(self):

def _reduce_simulated_transit_by_segment(self):
file_prefix = "transit_segment_"
file_root = self.scenario_dict["scenario"]["root_dir"]

time_period = "am" # am only

df = pd.read_csv(
os.path.join(file_root, "output_summaries", file_prefix + time_period + ".csv"),
os.path.join("output_summaries", file_prefix + time_period + ".csv"),
low_memory=False,
)

Expand Down Expand Up @@ -196,10 +194,9 @@ def _get_operator_name_from_line_name(
def _reduce_simulated_transit_shapes(self):

file_prefix = "boardings_by_segment_"
file_root = self.scenario_dict["scenario"]["root_dir"]
time_period = "am" # am only
gdf = gpd.read_file(
os.path.join(file_root, "output_summaries", file_prefix + time_period + ".geojson")
os.path.join("output_summaries", file_prefix + time_period + ".geojson")
)

gdf["first_row_in_line"] = gdf.groupby("LINE_ID").cumcount() == 0
Expand Down Expand Up @@ -305,18 +302,16 @@ def _join_coordinates_to_stations(self, input_df, input_column_name):

def _read_standard_node(self):

root_dir = self.scenario_dict["scenario"]["root_dir"]
in_file = os.path.join(root_dir,"inputs","trn","standard","v12_node.geojson")
in_file = os.path.join("inputs","trn","standard","v12_node.geojson")
gdf = gpd.read_file(in_file, driver = "GEOJSON")

self.standard_nodes_gdf = gdf

return

def _read_standard_transit_stops(self):

root_dir = self.scenario_dict["scenario"]["root_dir"]
in_file = os.path.join(root_dir, "inputs", "trn", "standard", "v12_stops.txt")

in_file = os.path.join("inputs", "trn", "standard", "v12_stops.txt")

df = pd.read_csv(in_file)

Expand All @@ -326,8 +321,7 @@ def _read_standard_transit_stops(self):

def _read_standard_transit_shapes(self):

root_dir = self.scenario_dict["scenario"]["root_dir"]
in_file = os.path.join(root_dir, "inputs", "trn", "standard", "v12_shapes.txt")
in_file = os.path.join("inputs", "trn", "standard", "v12_shapes.txt")

df = pd.read_csv(in_file)

Expand All @@ -337,8 +331,7 @@ def _read_standard_transit_shapes(self):

def _read_standard_transit_routes(self):

root_dir = self.scenario_dict["scenario"]["root_dir"]
in_file = os.path.join(root_dir, "inputs", "trn", "standard", "v12_routes.txt")
in_file = os.path.join("inputs", "trn", "standard", "v12_routes.txt")

df = pd.read_csv(in_file)

Expand All @@ -351,8 +344,7 @@ def _reduce_simulated_home_work_flows(self):
# if self.simulated_maz_data_df.empty:
# self._make_simulated_maz_data()

root_dir = self.scenario_dict["scenario"]["root_dir"]
in_file = os.path.join(root_dir, "ctramp_output", "wsLocResults_1.csv")
in_file = os.path.join("ctramp_output", "wsLocResults_1.csv")

df = pd.read_csv(in_file)

Expand Down Expand Up @@ -393,12 +385,11 @@ def _reduce_simulated_home_work_flows(self):

def _make_simulated_maz_data(self):

root_dir = self.scenario_dict["scenario"]["root_dir"]
in_file = os.path.join(root_dir, "inputs", "landuse", "maz_data.csv")
in_file = os.path.join("inputs", "landuse", "maz_data.csv")

df = pd.read_csv(in_file)

index_file = os.path.join(root_dir,"inputs", "landuse", "mtc_final_network_zone_seq.csv")
index_file = os.path.join("inputs", "landuse", "mtc_final_network_zone_seq.csv")

index_df = pd.read_csv(index_file)
join_df = index_df.rename(columns={"N": "MAZ_ORIGINAL"})[
Expand All @@ -420,14 +411,13 @@ def _reduce_simulated_rail_access_summaries(self):
self._make_transit_mode_dict()

file_prefix = "transit_segment_"
file_root = self.scenario_dict["scenario"]["root_dir"]

out_df = pd.DataFrame()

for time_period in self.model_time_periods:

df = pd.read_csv(
os.path.join(file_root, "output_summaries", file_prefix + time_period + ".csv"),
os.path.join("output_summaries", file_prefix + time_period + ".csv"),
dtype={"stop_name": str, "mdesc": str},
low_memory=False,
)
Expand Down Expand Up @@ -545,8 +535,6 @@ def _reduce_simulated_station_to_station(self):
# if self.model_time_periods is None:
# self._get_model_time_periods()

root_dir = self.scenario_dict["scenario"]["root_dir"]

path_list = [
"WLK_TRN_WLK",
"WLK_TRN_KNR",
Expand All @@ -570,7 +558,6 @@ def _reduce_simulated_station_to_station(self):
operator_list, self.model_time_periods, path_list
):
input_file_name = os.path.join(
root_dir,
"output_summaries",
f"{operator}_station_to_station_{path}_{time_period}.txt",
)
Expand Down Expand Up @@ -633,13 +620,12 @@ def _join_tm2_mode_codes(self, input_df):
def _reduce_simulated_transit_boardings(self):

file_prefix = "boardings_by_line_"
file_root = self.scenario_dict["scenario"]["root_dir"]

c_df = pd.DataFrame()
for time_period in self.model_time_periods:

df = pd.read_csv(
os.path.join(file_root, "output_summaries", file_prefix + time_period + ".csv")
os.path.join("output_summaries", file_prefix + time_period + ".csv")
)
df["time_period"] = time_period
c_df = pd.concat([c_df,df],axis="rows", ignore_index=True)
Expand Down Expand Up @@ -690,8 +676,7 @@ def _reduce_simulated_transit_boardings(self):

def _reduce_simulated_zero_vehicle_households(self):

root_dir = self.scenario_dict["scenario"]["root_dir"]
in_file = os.path.join(root_dir, "ctramp_output", "householdData_1.csv")
in_file = os.path.join("ctramp_output", "householdData_1.csv")

df = pd.read_csv(in_file)

Expand Down Expand Up @@ -939,7 +924,7 @@ def _make_transit_technology_in_vehicle_table_from_skims(self):

tech_list = self.c.transit_technology_abbreviation_dict.keys()

skim_dir = os.path.join(self.scenario_dict["scenario"]["root_dir"], "skim_matrices","transit")
skim_dir = os.path.join("skim_matrices", "transit")


running_df = None
Expand Down Expand Up @@ -1019,8 +1004,7 @@ def _read_transit_demand(self):
"KNR_TRN_WLK",
"WLK_TRN_KNR",
]
dem_dir = os.path.join(self.scenario_dict["scenario"]["root_dir"], "demand_matrices", "transit")
#dem_dir = '//corp.pbwan.net/us/CentralData/DCCLDA00/Standard/sag/projects/MTC/Acceptance_Criteria/temp/temp_acceptance/demand_matrices'
dem_dir = os.path.join("demand_matrices", "transit")

out_df = pd.DataFrame()
for time_period in self.model_time_periods:
Expand Down Expand Up @@ -1123,16 +1107,13 @@ def _make_district_to_district_transit_summaries(self):
return

def _reduce_simulated_traffic_flow(self):
#TODO:AK - CHeck this
file_root = self.scenario_dict["scenario"]["root_dir"]
time_of_day_df = pd.DataFrame()


for time_period in self.model_time_periods:

emme_scenario = self.network_shapefile_names_dict[time_period]
gdf = gpd.read_file(
os.path.join(file_root, "emme_project", "Media", emme_scenario, "emme_links.shp")
os.path.join("output_summaries", emme_scenario, "emme_links.shp")
)
df = gdf[
["ID", "@flow_da", "@flow_lrgt", "@flow_sr2", "@flow_sr3", "@flow_trk", "@flow_dato", "@flow_lrg0","@flow_sr2t", "@flow_sr3t", "@flow_trkt"]
Expand Down Expand Up @@ -1184,13 +1165,11 @@ def _reduce_simulated_traffic_flow(self):

def _reduce_simulated_roadway_assignment_outcomes(self):

file_root = self.scenario_dict["scenario"]["root_dir"]

# step 1: get the shape
shape_period = "am"
emme_scenario = self.network_shapefile_names_dict[shape_period]
shape_gdf = gpd.read_file(
os.path.join(file_root, "emme_project", "Media", emme_scenario, "emme_links.shp")
os.path.join("output_summaries", emme_scenario, "emme_links.shp")
)
self.simulated_roadway_am_shape_gdf = (
shape_gdf[["INODE", "JNODE", "#link_id", "geometry"]]
Expand All @@ -1213,7 +1192,7 @@ def _reduce_simulated_roadway_assignment_outcomes(self):
else:
emme_scenario = self.network_shapefile_names_dict[t]
gdf = gpd.read_file(
os.path.join(file_root, "emme_project", "Media", emme_scenario, "emme_links.shp")
os.path.join("output_summaries", emme_scenario, "emme_links.shp")
)

df = pd.DataFrame(gdf)[
Expand Down

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