From fb14bbe64a5097a66ee386cac9d3e0c1496b25b8 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Tue, 19 May 2020 01:49:08 +0000 Subject: [PATCH 1/6] taz assingment --- .../abm/models/initialize_from_usim.py | 153 +++++++----------- austin_mp/configs/settings.yaml | 8 +- austin_mp/simulation_mp.py | 15 +- 3 files changed, 72 insertions(+), 104 deletions(-) diff --git a/activitysim/abm/models/initialize_from_usim.py b/activitysim/abm/models/initialize_from_usim.py index c489d96640..bca04232aa 100644 --- a/activitysim/abm/models/initialize_from_usim.py +++ b/activitysim/abm/models/initialize_from_usim.py @@ -1,7 +1,6 @@ import os import numpy as np import pandas as pd -import os import matplotlib.pyplot as plt import geopandas as gpd import orca @@ -25,6 +24,58 @@ def get_zone_geoms_from_h3(h3_ids): return polygon_shapes +def assign_taz(df, gdf): + ''' + Assigns the gdf index (TAZ ID) for each index in df + Input: + - df columns names x, and y. The index is the ID of the object(blocks, school, college) + - gdf: Geopandas DataFrame with TAZ as index, geometry and area value. + Output: + A series with df index and corresponding gdf id + ''' + df = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.x, df.y), crs = "EPSG:4326") + + # Spatial join + df = gpd.sjoin(df, gdf, how = 'left', op = 'intersects') + + #Drop duplicates and keep the one with the smallest H3 area + df = df.sort_values('area') + index_name = df.index.name + df.reset_index(inplace = True) + df.drop_duplicates(subset = [index_name], keep = 'first', inplace = True) + df.set_index(index_name, inplace = True) + + #Check if there is any assigined object + if df.index_right.isnull().sum()>0: + + #Buffer unassigned ids until they reach a hexbin. + null_values = df[df.index_right.isnull()].drop(columns = ['index_right','area']) + + result_list = [] + for index, value in null_values.iterrows(): + buff_size = 0.0001 + matched = False + geo_value = gpd.GeoDataFrame(value).T + geo_value.crs = "EPSG:4326" + while matched == False: + geo_value.geometry = geo_value.geometry.buffer(buff_size) + result = gpd.sjoin(geo_value, gdf, how = 'left', op = 'intersects') + matched = ~result.index_right.isnull()[0] + buff_size = buff_size + 0.0001 + result_list.append(result.iloc[0:1]) + + null_values = pd.concat(result_list) + + # Concatenate newly assigned values to the main values table + df = df.dropna() + df = pd.concat([df, null_values], axis = 0) + + return df.index_right + + else: + return df.index_right + + # ** 1. CREATE NEW TABLES ** # Zones @@ -88,6 +139,7 @@ def schools(blocks): enrollment = enrollment[[ 'ncessch', 'county_code', 'latitude', 'longitude', 'enrollment']].set_index('ncessch') + enrollment.rename(columns = {'longitude':'x', 'latitude':'y'}, inplace = True) return enrollment.dropna() @@ -126,45 +178,9 @@ def colleges(blocks): @orca.column('blocks', cache = True) def TAZ(blocks, zones): - - # Tranform blocks to a Geopandas dataframe - blocks_df = blocks.to_frame(columns=['x', 'y']) - zones_df = zones.to_frame(columns=['geometry', 'area']) - h3_gpd = gpd.GeoDataFrame(zones_df, crs='EPSG:4326') - - blocks_df = gpd.GeoDataFrame( - blocks_df, geometry=gpd.points_from_xy(blocks_df.x, blocks_df.y), - crs="EPSG:4326") - - # Spatial join - blocks_df = gpd.sjoin(blocks_df, h3_gpd, how='left', op = 'intersects') - - #Drop duplicates and keep the one with the smallest H3 area - blocks_df = blocks_df.sort_values('area') - blocks_df.drop_duplicates(subset = ['x', 'y'], keep = 'first', inplace = True) - - #Buffer unassigned blocks until they reach a hexbin. - null_blocks = blocks_df[blocks_df.index_right.isnull()].drop(columns = ['index_right','area']) - - result_list = [] - for index, block in null_blocks.iterrows(): - buff_size = 0.0001 - matched = False - geo_block = gpd.GeoDataFrame(block).T - while matched == False: - geo_block.geometry = geo_block.geometry.buffer(buff_size) - result = gpd.sjoin(geo_block, h3_gpd, how = 'left', op = 'intersects') - matched = ~result.index_right.isnull()[0] - buff_size = buff_size + 0.0001 - result_list.append(result.iloc[0:1]) - - null_blocks = pd.concat(result_list) - - # Concatenate newly assigned blocks to the main blocks table - blocks_df = blocks_df.dropna() - blocks_df = pd.concat([blocks_df, null_blocks], axis = 0) - - return blocks_df.index_right + blocks_df = blocks.to_frame(columns = ['x', 'y']) + h3_gpd = zones.to_frame(columns = ['geometry', 'area']) + return assign_taz(blocks_df, h3_gpd) @orca.column('blocks') @@ -198,45 +214,9 @@ def RESACRE(blocks): @orca.column('schools', cache = True) def TAZ(schools, zones): - - #Tranform blocks to a Geopandas dataframe h3_gpd = zones.to_frame(columns = ['geometry', 'area']) - - school_gpd = schools.to_frame(columns = ['ncessch','longitude', 'latitude']) - school_gpd = gpd.GeoDataFrame( - school_gpd, - geometry=gpd.points_from_xy(school_gpd.longitude, school_gpd.latitude), - crs="EPSG:4326") - # Spatial join - school_gdf = gpd.sjoin(school_gpd, h3_gpd, how = 'left', op = 'intersects') - - #Drop duplicates and keep the one with the smallest H3 area - school_gdf = school_gdf.sort_values('area') - school_gdf.reset_index(inplace = True) - school_gdf.drop_duplicates(subset = ['ncessch'], keep = 'first', inplace = True) - - #Buffer unassigned blocks until they reach a hexbin. - null_schools = school_gdf[school_gdf.index_right.isnull()].drop(columns = ['index_right','area']) - - result_list = [] - for index, school in null_schools.iterrows(): - buff_size = 0.0001 - matched = False - geo_school = gpd.GeoDataFrame(school).T - while matched == False: - geo_school.geometry = geo_school.geometry.buffer(buff_size) - result = gpd.sjoin(geo_school, h3_gpd, how = 'left', op = 'intersects') - matched = ~result.index_right.isnull().iloc[0] - buff_size = buff_size + 0.0001 - result_list.append(result.iloc[0:1]) - - null_school = pd.concat(result_list) - - # Concatenate newly assigned blocks to the main blocks table - school_gdf = school_gdf.dropna() - school_all = pd.concat([school_gdf, null_school], axis = 0) - school_all.set_index('ncessch', inplace = True) - return school_all.index_right + school_gpd = orca.get_table('schools').to_frame(columns = ['x', 'y']) + return assign_taz(school_gpd, h3_gpd) # Colleges Variables @@ -289,24 +269,11 @@ def part_time_enrollment(): return s -@orca.column('colleges', cache=True) +@orca.column('colleges', cache = True) def TAZ(colleges, zones): - #Tranform blocks to a Geopandas dataframe colleges_df = colleges.to_frame(columns = ['x', 'y']) h3_gpd = zones.to_frame(columns = ['geometry', 'area']) - - colleges_df = gpd.GeoDataFrame( - colleges_df, geometry=gpd.points_from_xy(colleges_df.x, colleges_df.y), - crs="EPSG:4326") - - # Spatial join - colleges_df = gpd.sjoin(colleges_df, h3_gpd, how = 'left', op = 'intersects') - - #Drop duplicates and keep the one with the smallest H3 area - colleges_df = colleges_df.sort_values('area') - colleges_df.drop_duplicates(subset = ['x', 'y'], keep = 'first', inplace = True) - - return colleges_df.index_right + return assign_taz(colleges_df, h3_gpd) # Households Variables diff --git a/austin_mp/configs/settings.yaml b/austin_mp/configs/settings.yaml index 4f792af886..1c26ae0864 100644 --- a/austin_mp/configs/settings.yaml +++ b/austin_mp/configs/settings.yaml @@ -3,11 +3,11 @@ inherit_settings: True beam_skims_url: https://beam-outputs.s3.amazonaws.com/output/austin/austin-prod-200k-skims-with-h3-index-final__2020-04-18_09-44-24_wga/ITERS/it.0/0.skimsOD.UrbanSim.Full.csv.gz -create_skims_from_beam: True +create_skims_from_beam: False usim_zone_geoms: h3 # usim_zone_shapefile: -create_inputs_from_usim_data: True +create_inputs_from_usim_data: False usim_data_store: model_data.h5 @@ -49,7 +49,7 @@ use_shadow_pricing: False ## - example sample households_sample_size: 0 -chunk_size: 400000000 +chunk_size: 5000000000 num_processes: 24 stagger: 2 @@ -61,7 +61,7 @@ trace_od: #trace_od: [5, 11] # to resume after last successful checkpoint, specify resume_after: _ -# resume_after: trip_mode_choice +resume_after: _ models: ### mp_initialize step diff --git a/austin_mp/simulation_mp.py b/austin_mp/simulation_mp.py index 32977f1303..ff5206bae0 100644 --- a/austin_mp/simulation_mp.py +++ b/austin_mp/simulation_mp.py @@ -14,7 +14,8 @@ from activitysim.core import pipeline from activitysim.core import mp_tasks from activitysim.core import chunk - +from activitysim.abm.models import initialize_from_usim +from activitysim.abm.models import initialize_skims_from_beam logger = logging.getLogger('activitysim') @@ -35,17 +36,17 @@ def cleanup_output_files(): def run(run_list, injectables=None): - # Create a new skims.omx file from BEAM (http://beam.lbl.gov/) skims - # if skims do not already exist in the input data directory - if config.setting('create_skims_from_beam'): - pipeline.run(models=['create_skims_from_beam']) - pipeline.close_pipeline() - # Create persons, households, and land use .csv files from UrbanSim # data if these files do not already exist in the input data directory if config.setting('create_inputs_from_usim_data'): pipeline.run(models=['load_usim_data', 'create_inputs_from_usim_data']) pipeline.close_pipeline() + + # Create a new skims.omx file from BEAM (http://beam.lbl.gov/) skims + # if skims do not already exist in the input data directory + if config.setting('create_skims_from_beam'): + pipeline.run(models=['create_skims_from_beam']) + pipeline.close_pipeline() if run_list['multiprocess']: logger.info("run multiprocess simulation") From f7a33695b00c3133b71a8617864d9c5068aa3e76 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Wed, 20 May 2020 19:10:05 +0000 Subject: [PATCH 2/6] minor fix skims --- .../abm/models/initialize_skims_from_beam.py | 2 +- austin_mp/configs/settings.yaml | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/activitysim/abm/models/initialize_skims_from_beam.py b/activitysim/abm/models/initialize_skims_from_beam.py index d2d123c349..bbbe57927f 100644 --- a/activitysim/abm/models/initialize_skims_from_beam.py +++ b/activitysim/abm/models/initialize_skims_from_beam.py @@ -122,7 +122,7 @@ def create_skims_from_beam(raw_beam_skims, data_dir): # all skim values we don't have if beam_asim_transit_measure_map[measure]: vals = tmp_df[ - beam_asim_transit_measure_map[measure]].values + beam_asim_transit_measure_map[measure]].values*100 mx = vals.reshape((num_taz, num_taz)) else: mx = np.zeros((num_taz, num_taz)) diff --git a/austin_mp/configs/settings.yaml b/austin_mp/configs/settings.yaml index 1c26ae0864..e8f6959df5 100644 --- a/austin_mp/configs/settings.yaml +++ b/austin_mp/configs/settings.yaml @@ -3,11 +3,11 @@ inherit_settings: True beam_skims_url: https://beam-outputs.s3.amazonaws.com/output/austin/austin-prod-200k-skims-with-h3-index-final__2020-04-18_09-44-24_wga/ITERS/it.0/0.skimsOD.UrbanSim.Full.csv.gz -create_skims_from_beam: False +create_skims_from_beam: True usim_zone_geoms: h3 # usim_zone_shapefile: -create_inputs_from_usim_data: False +create_inputs_from_usim_data: True usim_data_store: model_data.h5 @@ -48,20 +48,20 @@ use_shadow_pricing: False ## - example sample -households_sample_size: 0 +households_sample_size: 10000 chunk_size: 5000000000 -num_processes: 24 -stagger: 2 +num_processes: 20 +stagger: 0 # - tracing trace_hh_id: trace_od: -#trace_hh_id: 1482966 +trace_hh_id: 195809 #trace_od: [5, 11] # to resume after last successful checkpoint, specify resume_after: _ -resume_after: _ +# resume_after: _ models: ### mp_initialize step From b87eca8384b14f201605c16ea8e74cb59a7fbceb Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Wed, 20 May 2020 19:35:20 +0000 Subject: [PATCH 3/6] merge master --- .../abm/models/initialize_from_usim.py | 106 ------------------ .../abm/models/initialize_skims_from_beam.py | 40 +------ 2 files changed, 1 insertion(+), 145 deletions(-) diff --git a/activitysim/abm/models/initialize_from_usim.py b/activitysim/abm/models/initialize_from_usim.py index 3a0829caa8..aecea5dd2b 100644 --- a/activitysim/abm/models/initialize_from_usim.py +++ b/activitysim/abm/models/initialize_from_usim.py @@ -182,52 +182,9 @@ def colleges(blocks): @orca.column('blocks', cache = True) def TAZ(blocks, zones): -<<<<<<< HEAD blocks_df = blocks.to_frame(columns = ['x', 'y']) h3_gpd = zones.to_frame(columns = ['geometry', 'area']) return assign_taz(blocks_df, h3_gpd) -======= - - # Tranform blocks to a Geopandas dataframe - blocks_df = blocks.to_frame(columns=['x', 'y']) - zones_df = zones.to_frame(columns=['geometry', 'area']) - h3_gpd = gpd.GeoDataFrame(zones_df, crs='EPSG:4326') - - blocks_df = gpd.GeoDataFrame( - blocks_df, geometry=gpd.points_from_xy(blocks_df.x, blocks_df.y), - crs="EPSG:4326") - - # Spatial join - blocks_df = gpd.sjoin(blocks_df, h3_gpd, how='left', op = 'intersects') - - # Drop duplicates and keep the one with the smallest H3 area - blocks_df = blocks_df.sort_values('area') - blocks_df.drop_duplicates(subset = ['x', 'y'], keep = 'first', inplace = True) - - # Buffer unassigned blocks until they reach a hexbin. - null_blocks = blocks_df[blocks_df.index_right.isnull()].drop(columns = ['index_right','area']) - - result_list = [] - for index, block in null_blocks.iterrows(): - buff_size = 0.0001 - matched = False - geo_block = gpd.GeoDataFrame(block, crs='EPSG:4326').T - while matched == False: - geo_block.geometry = geo_block.geometry.buffer(buff_size) - result = gpd.sjoin(geo_block, h3_gpd, how = 'left', op = 'intersects') - matched = ~result.index_right.isnull()[0] - buff_size = buff_size + 0.0001 - result_list.append(result.iloc[0:1]) - - null_blocks = pd.concat(result_list) - - # Concatenate newly assigned blocks to the main blocks table - blocks_df = blocks_df.dropna() - blocks_df = pd.concat([blocks_df, null_blocks], axis = 0) - - return blocks_df.index_right ->>>>>>> master - @orca.column('blocks') def CI_employment(jobs, blocks): @@ -260,53 +217,9 @@ def RESACRE(blocks): @orca.column('schools', cache = True) def TAZ(schools, zones): -<<<<<<< HEAD h3_gpd = zones.to_frame(columns = ['geometry', 'area']) school_gpd = orca.get_table('schools').to_frame(columns = ['x', 'y']) return assign_taz(school_gpd, h3_gpd) -======= - - #Tranform blocks to a Geopandas dataframe - zones_df = zones.to_frame(columns=['geometry', 'area']) - h3_gpd = gpd.GeoDataFrame(zones_df, crs='EPSG:4326') - - school_gpd = schools.to_frame(columns = ['ncessch','longitude', 'latitude']) - school_gpd = gpd.GeoDataFrame( - school_gpd, - geometry=gpd.points_from_xy(school_gpd.longitude, school_gpd.latitude), - crs="EPSG:4326") - # Spatial join - school_gdf = gpd.sjoin(school_gpd, h3_gpd, how = 'left', op = 'intersects') - - #Drop duplicates and keep the one with the smallest H3 area - school_gdf = school_gdf.sort_values('area') - school_gdf.reset_index(inplace = True) - school_gdf.drop_duplicates(subset = ['ncessch'], keep = 'first', inplace = True) - - #Buffer unassigned blocks until they reach a hexbin. - null_schools = school_gdf[school_gdf.index_right.isnull()].drop(columns = ['index_right','area']) - - result_list = [] - for index, school in null_schools.iterrows(): - buff_size = 0.0001 - matched = False - geo_school = gpd.GeoDataFrame(school, crs='EPSG:4326').T - while matched == False: - geo_school.geometry = geo_school.geometry.buffer(buff_size) - result = gpd.sjoin(geo_school, h3_gpd, how = 'left', op = 'intersects') - matched = ~result.index_right.isnull().iloc[0] - buff_size = buff_size + 0.0001 - result_list.append(result.iloc[0:1]) - - null_school = pd.concat(result_list) - - # Concatenate newly assigned blocks to the main blocks table - school_gdf = school_gdf.dropna() - school_all = pd.concat([school_gdf, null_school], axis = 0) - school_all.set_index('ncessch', inplace = True) - return school_all.index_right ->>>>>>> master - # Colleges Variables @@ -361,27 +274,8 @@ def part_time_enrollment(): @orca.column('colleges', cache = True) def TAZ(colleges, zones): colleges_df = colleges.to_frame(columns = ['x', 'y']) -<<<<<<< HEAD h3_gpd = zones.to_frame(columns = ['geometry', 'area']) return assign_taz(colleges_df, h3_gpd) -======= - zones_df = zones.to_frame(columns = ['geometry', 'area']) - h3_gpd = gpd.GeoDataFrame(zones_df, crs="EPSG:4326") - - colleges_df = gpd.GeoDataFrame( - colleges_df, geometry=gpd.points_from_xy(colleges_df.x, colleges_df.y), - crs="EPSG:4326") - - # Spatial join - colleges_df = gpd.sjoin(colleges_df, h3_gpd, how = 'left', op = 'intersects') - - #Drop duplicates and keep the one with the smallest H3 area - colleges_df = colleges_df.sort_values('area') - colleges_df.drop_duplicates(subset = ['x', 'y'], keep = 'first', inplace = True) - - return colleges_df.index_right ->>>>>>> master - # Households Variables diff --git a/activitysim/abm/models/initialize_skims_from_beam.py b/activitysim/abm/models/initialize_skims_from_beam.py index 1e6a8cd018..190a91d807 100644 --- a/activitysim/abm/models/initialize_skims_from_beam.py +++ b/activitysim/abm/models/initialize_skims_from_beam.py @@ -102,44 +102,7 @@ def create_skims_from_beam(raw_beam_skims, data_dir): # Adding car distance skims vals = auto_df[beam_asim_hwy_measure_map['DIST']].values mx = vals.reshape((num_taz, num_taz)) -<<<<<<< HEAD - skims[name] = mx - - for period in periods: - df = skims_df - - # highway skims - for path in hwy_paths: - tmp_df = df[(df['mode'] == 'CAR')] - for measure in beam_asim_hwy_measure_map.keys(): - name = '{0}_{1}__{2}'.format(path, measure, period) - if beam_asim_hwy_measure_map[measure]: - vals = tmp_df[beam_asim_hwy_measure_map[measure]].values - mx = vals.reshape((num_taz, num_taz)) - else: - mx = np.zeros((num_taz, num_taz)) - skims[name] = mx - - # transit skims - for transit_mode in transit_modes: - for access_mode in access_modes: - for egress_mode in egress_modes: - path = '{0}_{1}_{2}'.format( - access_mode, transit_mode, egress_mode) - for measure in beam_asim_transit_measure_map.keys(): - name = '{0}_{1}__{2}'.format(path, measure, period) - - # TO DO: something better than zero-ing out - # all skim values we don't have - if beam_asim_transit_measure_map[measure]: - vals = tmp_df[ - beam_asim_transit_measure_map[measure]].values*100 - mx = vals.reshape((num_taz, num_taz)) - else: - mx = np.zeros((num_taz, num_taz)) - skims[name] = mx - skims.close() -======= + skims['DIST'] = mx # active skims @@ -187,4 +150,3 @@ def create_skims_from_beam(raw_beam_skims, data_dir): mx = np.zeros((num_taz, num_taz)) skims[name] = mx skims.close() ->>>>>>> master From 53e9c0782f380935cdb75ef3821e87cf5e2af9a4 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Thu, 21 May 2020 21:38:07 +0000 Subject: [PATCH 4/6] area_type imputation --- .../abm/models/initialize_from_usim.py | 26 ++++++++++++++----- austin_mp/simulation_mp.py | 14 +++++----- 2 files changed, 26 insertions(+), 14 deletions(-) diff --git a/activitysim/abm/models/initialize_from_usim.py b/activitysim/abm/models/initialize_from_usim.py index aecea5dd2b..2dd182906f 100644 --- a/activitysim/abm/models/initialize_from_usim.py +++ b/activitysim/abm/models/initialize_from_usim.py @@ -9,6 +9,7 @@ from urbansim.utils import misc import requests import openmatrix as omx +from shapely import wkt import logging from activitysim.core import config @@ -622,10 +623,17 @@ def COLLPTE(colleges, zones): @orca.column('zones') -def area_type(): - # Integer, 0=regional core, 1=central business district, 2=urban business, - # 3=urban, 4=suburban, 5=rural - return 0 # Assuming all regional core +def area_type(mpo_taz, zones): + + mpo = mpo_taz.to_frame(columns = ['geometry','area_type','ACRES']) + h3_gpd = zones.to_frame(columns = ['geometry', 'area']) + + join = gpd.sjoin(h3_gpd, mpo, how = 'left',op='intersects') + join.area_type.fillna(5, inplace = True) #Fill non-matched areas with 5 (Rural areas) + join = join.groupby(['TAZ', 'area_type'])['ACRES'].sum().reset_index() + join = join.sort_values(['TAZ', 'ACRES'], ascending = True) + s = join.groupby('TAZ')['area_type'].last() + return s @orca.column('zones') @@ -656,6 +664,8 @@ def load_usim_data(data_dir, settings): persons = hdf['/persons'] blocks = hdf['/blocks'] jobs = hdf['/jobs'] + mpo_taz = hdf['/mpo_taz'] + hdf.close() # add home x,y coords to persons table @@ -667,6 +677,10 @@ def load_usim_data(data_dir, settings): left_on='block_id', right_index=True) persons['home_x'] = persons_w_xy['x'] persons['home_y'] = persons_w_xy['y'] + + #Tranform mpo_taz to a geoDataFrame + mpo_taz['geometry'] = mpo_taz['geometry'].apply(wkt.loads) + mpo_taz = gpd.GeoDataFrame(mpo_taz, geometry='geometry', crs ='EPSG:4326') del persons_w_res_blk del persons_w_xy @@ -675,8 +689,8 @@ def load_usim_data(data_dir, settings): orca.add_table('usim_persons', persons) orca.add_table('blocks', blocks) orca.add_table('jobs', jobs) - - + orca.add_table('mpo_taz', mpo_taz) + # Export households tables @inject.step() def create_inputs_from_usim_data(data_dir): diff --git a/austin_mp/simulation_mp.py b/austin_mp/simulation_mp.py index ff5206bae0..2400a2893b 100644 --- a/austin_mp/simulation_mp.py +++ b/austin_mp/simulation_mp.py @@ -14,8 +14,6 @@ from activitysim.core import pipeline from activitysim.core import mp_tasks from activitysim.core import chunk -from activitysim.abm.models import initialize_from_usim -from activitysim.abm.models import initialize_skims_from_beam logger = logging.getLogger('activitysim') @@ -35,18 +33,18 @@ def cleanup_output_files(): def run(run_list, injectables=None): + + # Create a new skims.omx file from BEAM (http://beam.lbl.gov/) skims + # if skims do not already exist in the input data directory + if config.setting('create_skims_from_beam'): + pipeline.run(models=['create_skims_from_beam']) + pipeline.close_pipeline() # Create persons, households, and land use .csv files from UrbanSim # data if these files do not already exist in the input data directory if config.setting('create_inputs_from_usim_data'): pipeline.run(models=['load_usim_data', 'create_inputs_from_usim_data']) pipeline.close_pipeline() - - # Create a new skims.omx file from BEAM (http://beam.lbl.gov/) skims - # if skims do not already exist in the input data directory - if config.setting('create_skims_from_beam'): - pipeline.run(models=['create_skims_from_beam']) - pipeline.close_pipeline() if run_list['multiprocess']: logger.info("run multiprocess simulation") From 3c36ca79bfabc8644684bc214f1bff95311c7275 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Fri, 22 May 2020 19:46:39 +0000 Subject: [PATCH 5/6] fix crs bug --- .../abm/models/initialize_from_usim.py | 4 + austin_mp/Notebooks/area_type_impute.ipynb | 241 ++++++++++++++++++ austin_mp/configs/settings.yaml | 2 +- 3 files changed, 246 insertions(+), 1 deletion(-) create mode 100644 austin_mp/Notebooks/area_type_impute.ipynb diff --git a/activitysim/abm/models/initialize_from_usim.py b/activitysim/abm/models/initialize_from_usim.py index 2dd182906f..f6532852f2 100644 --- a/activitysim/abm/models/initialize_from_usim.py +++ b/activitysim/abm/models/initialize_from_usim.py @@ -38,7 +38,11 @@ def assign_taz(df, gdf): Output: A series with df index and corresponding gdf id ''' + df = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.x, df.y), crs = "EPSG:4326") + gdf.geometry.crs = "EPSG:4326" + + assert df.geometry.crs == gdf.geometry.crs # Spatial join df = gpd.sjoin(df, gdf, how = 'left', op = 'intersects') diff --git a/austin_mp/Notebooks/area_type_impute.ipynb b/austin_mp/Notebooks/area_type_impute.ipynb new file mode 100644 index 0000000000..cbf4e3e027 --- /dev/null +++ b/austin_mp/Notebooks/area_type_impute.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np \n", + "import geopandas as gpd\n", + "import h3\n", + "import matplotlib.pyplot as plt\n", + "from shapely import wkt\n", + "import orca" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocessing the original MPO .shp files with TAZ \n", + "Objective: Get area type as: \n", + "- 0: Regional core\n", + "- 1: CBD\n", + "- 2: Urban Business\n", + "- 3: Urban\n", + "- 4: Suburban\n", + "- 5: Rural" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#Load MPO TAZs shapefiles\n", + "mpo_taz = gpd.read_file('tazs_austin/2015_2045 CAMPO TAZ SHAPE.shp')\n", + "mpo_taz = mpo_taz.to_crs('EPSG:4326')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#Transformation values: \n", + "mpo_taz = mpo_taz[mpo_taz.SMTDNAME != 'OutofArea']\n", + "area_type_dict = {'CBD': 1, 'UrbIntTravis': 2, 'UrbTravis': 3, \n", + " 'SubTravis': 4, 'RurTravis':5,'UrbIntWilliamson': 2,\n", + " 'UrbWilliamson': 3, 'SubWilliamson': 4,'RurWilliamson': 5,\n", + " 'UrbIntHays': 2, 'UrbHays': 3, 'SubHays': 4, 'RurHays': 5,\n", + " 'UrbIntBastrop': 2, 'UrbBastrop': 3, 'SubBastrop': 4,\n", + " 'RurBastrop': 5, 'UrbCaldwell': 3, 'SubCaldwell': 4, \n", + " 'RurCaldwell': 5, 'UrbBurnet': 3,'SubBurnet':4, 'RurBurnet':5} #Triangle shapes that are not in the region\n", + "\n", + "mpo_taz['area_type'] = mpo_taz.SMTDNAME.replace(area_type_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#Transform geopandas to dataframe\n", + "mpo_taz = pd.DataFrame(mpo_taz)\n", + "mpo_taz['geometry'] = mpo_taz.geometry.astype('str')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Store MPO TAZs in the .h5 file" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# hdf = pd.HDFStore('model_data.h5')\n", + "# hdf.append(key = 'mpo_taz', value = mpo_taz)\n", + "# hdf.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load MPO TAZ file and transform it to a geoDataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "hdf = pd.HDFStore('model_data.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "mpo_taz = hdf['/mpo_taz']\n", + "mpo_taz['geometry'] = mpo_taz['geometry'].apply(wkt.loads)\n", + "mpo_taz = gpd.GeoDataFrame(mpo_taz, geometry='geometry', crs ='EPSG:4326')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Merge H3 hexbins and MPO TAZ" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "h3_gpd = gpd.read_file('h3_hexbis.shp')\n", + "h3_gpd.set_index('TAZ', inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "join = gpd.sjoin(h3_gpd, mpo_taz[['geometry','area_type','ACRES']], how = 'left',op='intersects')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (10,10))\n", + "join[join.area_type.isnull()].plot(ax =ax)\n", + "mpo_taz.plot(ax=ax, color = 'orange', alpha = 0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is ok to assume that unmatched h3 hexbins are rural areas" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TAZ\n", + "1 3.0\n", + "2 4.0\n", + "3 3.0\n", + "4 4.0\n", + "5 4.0\n", + " ... \n", + "999 5.0\n", + "1000 5.0\n", + "1001 5.0\n", + "1002 5.0\n", + "1003 5.0\n", + "Name: area_type, Length: 1003, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "join = gpd.sjoin(h3_gpd, mpo_taz[['geometry','area_type','ACRES']], how = 'left',op='intersects')\n", + "join.area_type.fillna(5, inplace = True) #Fill non-matched areas with 5 (Rural areas)\n", + "join = join.groupby(['TAZ', 'area_type'])['ACRES'].sum().reset_index()\n", + "join = join.sort_values(['TAZ', 'ACRES'], ascending = True)\n", + "s = join.groupby('TAZ')['area_type'].last()\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/austin_mp/configs/settings.yaml b/austin_mp/configs/settings.yaml index 70e1e20457..c10823ffcb 100644 --- a/austin_mp/configs/settings.yaml +++ b/austin_mp/configs/settings.yaml @@ -71,7 +71,7 @@ models: ### mp_households step - school_location - workplace_location - - auto_ownership_simulate +# - auto_ownership_simulate - free_parking - cdap_simulate - mandatory_tour_frequency From 74422923f6bcad48876ed8ef48023b287a967a67 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Fri, 22 May 2020 20:05:24 +0000 Subject: [PATCH 6/6] area type impute --- austin_mp/Notebooks/area_type_impute.ipynb | 241 --------------------- austin_mp/area_type_impute.py | 42 ++++ 2 files changed, 42 insertions(+), 241 deletions(-) delete mode 100644 austin_mp/Notebooks/area_type_impute.ipynb create mode 100644 austin_mp/area_type_impute.py diff --git a/austin_mp/Notebooks/area_type_impute.ipynb b/austin_mp/Notebooks/area_type_impute.ipynb deleted file mode 100644 index cbf4e3e027..0000000000 --- a/austin_mp/Notebooks/area_type_impute.ipynb +++ /dev/null @@ -1,241 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd \n", - "import numpy as np \n", - "import geopandas as gpd\n", - "import h3\n", - "import matplotlib.pyplot as plt\n", - "from shapely import wkt\n", - "import orca" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocessing the original MPO .shp files with TAZ \n", - "Objective: Get area type as: \n", - "- 0: Regional core\n", - "- 1: CBD\n", - "- 2: Urban Business\n", - "- 3: Urban\n", - "- 4: Suburban\n", - "- 5: Rural" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "#Load MPO TAZs shapefiles\n", - "mpo_taz = gpd.read_file('tazs_austin/2015_2045 CAMPO TAZ SHAPE.shp')\n", - "mpo_taz = mpo_taz.to_crs('EPSG:4326')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "#Transformation values: \n", - "mpo_taz = mpo_taz[mpo_taz.SMTDNAME != 'OutofArea']\n", - "area_type_dict = {'CBD': 1, 'UrbIntTravis': 2, 'UrbTravis': 3, \n", - " 'SubTravis': 4, 'RurTravis':5,'UrbIntWilliamson': 2,\n", - " 'UrbWilliamson': 3, 'SubWilliamson': 4,'RurWilliamson': 5,\n", - " 'UrbIntHays': 2, 'UrbHays': 3, 'SubHays': 4, 'RurHays': 5,\n", - " 'UrbIntBastrop': 2, 'UrbBastrop': 3, 'SubBastrop': 4,\n", - " 'RurBastrop': 5, 'UrbCaldwell': 3, 'SubCaldwell': 4, \n", - " 'RurCaldwell': 5, 'UrbBurnet': 3,'SubBurnet':4, 'RurBurnet':5} #Triangle shapes that are not in the region\n", - "\n", - "mpo_taz['area_type'] = mpo_taz.SMTDNAME.replace(area_type_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#Transform geopandas to dataframe\n", - "mpo_taz = pd.DataFrame(mpo_taz)\n", - "mpo_taz['geometry'] = mpo_taz.geometry.astype('str')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Store MPO TAZs in the .h5 file" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# hdf = pd.HDFStore('model_data.h5')\n", - "# hdf.append(key = 'mpo_taz', value = mpo_taz)\n", - "# hdf.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load MPO TAZ file and transform it to a geoDataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "hdf = pd.HDFStore('model_data.h5')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "mpo_taz = hdf['/mpo_taz']\n", - "mpo_taz['geometry'] = mpo_taz['geometry'].apply(wkt.loads)\n", - "mpo_taz = gpd.GeoDataFrame(mpo_taz, geometry='geometry', crs ='EPSG:4326')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Merge H3 hexbins and MPO TAZ" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "h3_gpd = gpd.read_file('h3_hexbis.shp')\n", - "h3_gpd.set_index('TAZ', inplace = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "join = gpd.sjoin(h3_gpd, mpo_taz[['geometry','area_type','ACRES']], how = 'left',op='intersects')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize = (10,10))\n", - "join[join.area_type.isnull()].plot(ax =ax)\n", - "mpo_taz.plot(ax=ax, color = 'orange', alpha = 0.5);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is ok to assume that unmatched h3 hexbins are rural areas" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "TAZ\n", - "1 3.0\n", - "2 4.0\n", - "3 3.0\n", - "4 4.0\n", - "5 4.0\n", - " ... \n", - "999 5.0\n", - "1000 5.0\n", - "1001 5.0\n", - "1002 5.0\n", - "1003 5.0\n", - "Name: area_type, Length: 1003, dtype: float64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "join = gpd.sjoin(h3_gpd, mpo_taz[['geometry','area_type','ACRES']], how = 'left',op='intersects')\n", - "join.area_type.fillna(5, inplace = True) #Fill non-matched areas with 5 (Rural areas)\n", - "join = join.groupby(['TAZ', 'area_type'])['ACRES'].sum().reset_index()\n", - "join = join.sort_values(['TAZ', 'ACRES'], ascending = True)\n", - "s = join.groupby('TAZ')['area_type'].last()\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/austin_mp/area_type_impute.py b/austin_mp/area_type_impute.py new file mode 100644 index 0000000000..902c54f9fe --- /dev/null +++ b/austin_mp/area_type_impute.py @@ -0,0 +1,42 @@ +import pandas as pd +import numpy as np +import geopandas as gpd +import h3 +import matplotlib.pyplot as plt +from shapely import wkt +import orca + +# ## Preprocessing the original MPO .shp files with TAZ +# Objective: Get area type as: +# - 0: Regional core +# - 1: CBD +# - 2: Urban Business +# - 3: Urban +# - 4: Suburban +# - 5: Rural + +#Load MPO TAZs shapefiles +mpo_taz = gpd.read_file('data/tazs_austin/2015_2045 CAMPO TAZ SHAPE.shp') +mpo_taz = mpo_taz.to_crs('EPSG:4326') + +#Transformation values: +mpo_taz = mpo_taz[mpo_taz.SMTDNAME != 'OutofArea'] +area_type_dict = {'CBD': 1, 'UrbIntTravis': 2, 'UrbTravis': 3, + 'SubTravis': 4, 'RurTravis':5,'UrbIntWilliamson': 2, + 'UrbWilliamson': 3, 'SubWilliamson': 4,'RurWilliamson': 5, + 'UrbIntHays': 2, 'UrbHays': 3, 'SubHays': 4, 'RurHays': 5, + 'UrbIntBastrop': 2, 'UrbBastrop': 3, 'SubBastrop': 4, + 'RurBastrop': 5, 'UrbCaldwell': 3, 'SubCaldwell': 4, + 'RurCaldwell': 5, 'UrbBurnet': 3,'SubBurnet':4, 'RurBurnet':5} + +mpo_taz['area_type'] = mpo_taz.SMTDNAME.replace(area_type_dict) + + +#Transform geopandas to dataframe +mpo_taz = pd.DataFrame(mpo_taz) +mpo_taz['geometry'] = mpo_taz.geometry.astype('str') + +#Save it to .H5 file +hdf = pd.HDFStore('model_data.h5') +hdf.append(key = 'mpo_taz', value = mpo_taz) +hdf.close()