def get_singleyr_data(fc_project, projtyp, adt, posted_speedlim, out_dict={}):
pcl_pt_fc = p.parcel_pt_fc_yr(2016)
pcl_poly_fc = p.parcel_poly_fc_yr(2016)
accdata = acc.get_acc_data(fc_project, p.accdata_fc, projtyp, get_ej=False)
collision_data = coll.get_collision_data(fc_project, projtyp, p.collisions_fc, adt)
complete_street_score = {'complete_street_score': -1} if projtyp == p.ptype_fwy else \
cs.complete_streets_idx(pcl_pt_fc, fc_project, projtyp, posted_speedlim, p.trn_svc_fc)
truck_route_pct = {'pct_proj_STAATruckRoutes': 1} if projtyp == p.ptype_fwy else \
linex.get_line_overlap(fc_project, p.freight_route_fc, p.freight_route_fc) # all freeways are STAA truck routes
ag_acres = luac.get_lutype_acreage(fc_project, projtyp, pcl_poly_fc, p.lutype_ag)
pct_adt_truck = {"pct_truck_aadt": -1} if projtyp != p.ptype_fwy else truck_fwy.get_tmc_truck_data(fc_project, projtyp)
intersxn_data = intsxn.intersection_density(fc_project, p.intersections_base_fc, projtyp)
npmrds_data = npmrds.get_npmrds_data(fc_project, projtyp)
transit_data = trnsvc.transit_svc_density(fc_project, p.trn_svc_fc, projtyp)
bikeway_data = bnmi.get_bikeway_mileage_share(fc_project, p.ptype_sgr)
infill_status = urbn.projarea_infill_status(fc_project, p.comm_types_fc)
# total job + du density (base year only, for state-of-good-repair proj eval only)
job_du_dens = lu_pt_buff.point_sum_density(pcl_pt_fc, fc_project, projtyp,
[p.col_emptot, p.col_du], p.ilut_sum_buffdist)
comb_du_dens = sum(list(job_du_dens.values()))
job_du_dens['job_du_perNetAcre'] = comb_du_dens
# get EJ data
ej_data = lu_pt_buff.point_sum(pcl_pt_fc, fc_project, projtyp, [p.col_pop_ilut],
p.ilut_sum_buffdist, p.col_ej_ind, case_excs_list=[])
ej_flag_dict = {0: "Pop_NonEJArea", 1: "Pop_EJArea"} # rename keys from 0/1 to more human-readable names
ej_data = utils.rename_dict_keys(ej_data, ej_flag_dict)
ej_data["Pct_PopEJArea"] = ej_data["Pop_EJArea"] / sum(list(ej_data.values()))
accdata_ej = acc.get_acc_data(fc_project, p.accdata_fc, projtyp, get_ej=True) # EJ accessibility data
ej_data.update(accdata_ej)
# for base dict, add items that only have a base year value (no future year values)
for d in [accdata, collision_data, complete_street_score, truck_route_pct, pct_adt_truck, ag_acres, intersxn_data,
npmrds_data, transit_data, bikeway_data, infill_status, job_du_dens, ej_data]:
out_dict_base.update(d)
outdf = pd.DataFrame.from_dict(out_dict_base, orient='index')
return outdf
"pct_truck_aadt": -1
} if project_type != p.ptype_fwy else truck_fwy.get_tmc_truck_data(
project_fc, project_type)
intersxn_data = intsxn.intersection_density(project_fc,
p.intersections_base_fc,
project_type)
npmrds_data = npmrds.get_npmrds_data(project_fc, project_type)
transit_data = trnsvc.transit_svc_density(project_fc, p.trn_svc_fc,
project_type)
bikeway_data = bnmi.get_bikeway_mileage_share(project_fc, p.ptype_sgr)
infill_status = urbn.projarea_infill_status(project_fc, p.comm_types_fc)
# total job + du density (base year only, for state-of-good-repair proj eval only)
job_du_dens = lu_pt_buff.point_sum_density(p.parcel_pt_fc, project_fc,
project_type,
[p.col_emptot, p.col_du],
p.ilut_sum_buffdist)
comb_du_dens = sum(list(job_du_dens.values()))
job_du_dens['job_du_perNetAcre'] = comb_du_dens
# get EJ data
ej_data = lu_pt_buff.point_sum(p.parcel_pt_fc,
project_fc,
project_type, [p.col_pop_ilut],
p.ilut_sum_buffdist,
p.col_ej_ind,