build.160 log20091106121636Lbuild.160.xml 11/06/2009 12:16:36 cc-seattle_parcel build.160 0]]> 0: completed...0.3 sec]]> 0))]]> 0)): completed...0.7 sec]]> 0....0.0 sec]]> 0....0.0 sec]]> 0....0.0 sec]]> 0)]]> 0): completed...0.6 sec]]> 0))]]> 0)): completed...0.7 sec]]> 0....0.0 sec]]> 0....0.0 sec]]> 0....0.0 sec]]> Cache Directory set to: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data Start simulation run: started on Fri Nov 6 12:57:17 2009 random seed = 0 Starting simulation for year 2000: started on Fri Nov 6 12:57:17 2009 Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/run_model_system.log Logging to file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/year_2000_log.txt Simulate year 2000: started on Fri Nov 6 12:57:17 2009 numpy.logical_and(urbansim_parcel.parcel.unit_price>20.0855, urbansim_parcel.parcel.unit_price<1096.63, urbansim_parcel.parcel.existing_units>100) unit_price = safe_array_divide(parcel.land_value + urbansim_parcel.parcel.improvement_value, urbansim_parcel.parcel.existing_units) urbansim_parcel.parcel.existing_units unit_name = parcel.disaggregate(land_use_type.unit_name)....0.4 sec building_sqft = (parcel.aggregate(urbansim_parcel.building.building_sqft)).astype(int32) urbansim_parcel.building.building_sqft.......0.4 sec building_sqft = (parcel.aggregate(urbansim_parcel.building.building_sqft)).astype(int32): completed...0.7 sec residential_units = (parcel.aggregate(building.residential_units)).astype(int32)....0.1 sec urbansim_parcel.parcel.existing_units: completed.....1.5 sec improvement_value =parcel.aggregate(building.improvement_value)....0.2 sec unit_price = safe_array_divide(parcel.land_value + urbansim_parcel.parcel.improvement_value, urbansim_parcel.parcel.existing_units): completed...1.9 sec numpy.logical_and(urbansim_parcel.parcel.unit_price>20.0855, urbansim_parcel.parcel.unit_price<1096.63, urbansim_parcel.parcel.existing_units>100): completed...2.1 sec Estimating Real Estate Price Model (from urbansim.models.real_estate_price_model): started on Fri Nov 6 12:57:20 2009 numpy.logical_or(urbansim_parcel.parcel.building_sqft, urbansim_parcel.parcel.is_land_use_type_vacant) urbansim_parcel.parcel.is_land_use_type_SSS..........0.0 sec numpy.logical_or(urbansim_parcel.parcel.building_sqft, urbansim_parcel.parcel.is_land_use_type_vacant): completed...0.1 sec art300 = psrc.parcel.distance_to_arterial_in_gridcell<300 psrc.parcel.SSS_in_gridcell..........................0.5 sec art300 = psrc.parcel.distance_to_arterial_in_gridcell<300: completed...0.6 sec art600 = psrc.parcel.distance_to_arterial_in_gridcell<600....0.0 sec hbwavgtmda = parcel.disaggregate(psrc.zone.trip_weighted_average_time_hbw_from_home_am_drive_alone) psrc.zone.trip_weighted_average_time_hbw_from_home_am_drive_alone....0.2 sec hbwavgtmda = parcel.disaggregate(psrc.zone.trip_weighted_average_time_hbw_from_home_am_drive_alone): completed...0.3 sec hwy200 = psrc.parcel.distance_to_highway_in_gridcell<200 psrc.parcel.SSS_in_gridcell..........................0.4 sec hwy200 = psrc.parcel.distance_to_highway_in_gridcell<200: completed...0.4 sec hwy2000 = psrc.parcel.distance_to_highway_in_gridcell<2000....0.0 sec is_pre_1940 = parcel.aggregate(numpy.ma.masked_where(urbansim_parcel.building.has_valid_year_built == 0, building.year_built),function=mean) < 1940 numpy.ma.masked_where(urbansim_parcel.building.has_valid_year_built==0,building.year_built) urbansim_parcel.building.has_valid_year_built....0.0 sec numpy.ma.masked_where(urbansim_parcel.building.has_valid_year_built==0,building.year_built): completed...0.1 sec is_pre_1940 = parcel.aggregate(numpy.ma.masked_where(urbansim_parcel.building.has_valid_year_built == 0, building.year_built),function=mean) < 1940: completed...0.2 sec ln_bldgage = (ln(parcel.aggregate(urbansim_parcel.building.age_masked, function=mean))).astype(float32) urbansim_parcel.building.age_masked..................0.0 sec ln_bldgage = (ln(parcel.aggregate(urbansim_parcel.building.age_masked, function=mean))).astype(float32): completed...0.2 sec ln_invfar = (ln(parcel.parcel_sqft/(urbansim_parcel.parcel.building_sqft).astype(float32))).astype(float32)....0.1 sec lnavginc = (ln(parcel.disaggregate(urbansim_parcel.zone.average_income))).astype(float32) average_income = zone.aggregate(urbansim_parcel.household.income, function=mean) zone_id = household.disaggregate(urbansim_parcel.building.zone_id) zone_id = building.disaggregate(parcel.zone_id)....0.5 sec zone_id = household.disaggregate(urbansim_parcel.building.zone_id): completed...1.5 sec average_income = zone.aggregate(urbansim_parcel.household.income, function=mean): completed...1.8 sec lnavginc = (ln(parcel.disaggregate(urbansim_parcel.zone.average_income))).astype(float32): completed...1.9 sec lnemp10da = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_10_minutes_travel_time_hbw_am_drive_alone))).astype(float32) urbansim_parcel.zone.employment_within_DDD_minutes_travel_time_hbw_am_drive_alone number_of_jobs = zone.number_of_agents(job) zone_id = job.disaggregate(urbansim_parcel.building.zone_id)....1.8 sec number_of_jobs = zone.number_of_agents(job): completed...2.1 sec urbansim_parcel.zone.employment_within_DDD_minutes_travel_time_hbw_am_drive_alone: completed...2.1 sec lnemp10da = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_10_minutes_travel_time_hbw_am_drive_alone))).astype(float32): completed...2.3 sec lnemp10wa = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_10_minutes_travel_time_hbw_am_walk))).astype(float32) urbansim_parcel.zone.employment_within_DDD_minutes_travel_time_hbw_am_walk....0.1 sec lnemp10wa = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_10_minutes_travel_time_hbw_am_walk))).astype(float32): completed...0.1 sec lnemp20da = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_20_minutes_travel_time_hbw_am_drive_alone))).astype(float32) urbansim_parcel.zone.employment_within_DDD_minutes_travel_time_hbw_am_drive_alone....0.0 sec lnemp20da = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_20_minutes_travel_time_hbw_am_drive_alone))).astype(float32): completed...0.1 sec lnemp20tw = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_20_minutes_travel_time_hbw_am_transit_walk))).astype(float32) urbansim_parcel.zone.employment_within_DDD_minutes_travel_time_hbw_am_transit_walk....0.1 sec lnemp20tw = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_20_minutes_travel_time_hbw_am_transit_walk))).astype(float32): completed...0.1 sec lnemp20wa = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_20_minutes_travel_time_hbw_am_walk))).astype(float32) urbansim_parcel.zone.employment_within_DDD_minutes_travel_time_hbw_am_walk....0.0 sec lnemp20wa = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_20_minutes_travel_time_hbw_am_walk))).astype(float32): completed...0.1 sec lnemp30da = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_30_minutes_travel_time_hbw_am_drive_alone))).astype(float32) urbansim_parcel.zone.employment_within_DDD_minutes_travel_time_hbw_am_drive_alone....0.0 sec lnemp30da = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_30_minutes_travel_time_hbw_am_drive_alone))).astype(float32): completed...0.1 sec lnemp30tw = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_30_minutes_travel_time_hbw_am_transit_walk))).astype(float32) urbansim_parcel.zone.employment_within_DDD_minutes_travel_time_hbw_am_transit_walk....0.0 sec lnemp30tw = (ln(parcel.disaggregate(urbansim_parcel.zone.employment_within_30_minutes_travel_time_hbw_am_transit_walk))).astype(float32): completed...0.1 sec lnempden = (ln(parcel.disaggregate(urbansim_parcel.zone.number_of_jobs_per_acre))).astype(float32) urbansim_parcel.zone.number_of_jobs_per_acre acres = zone.aggregate(parcel.parcel_sqft) / 43560.0 ....0.0 sec urbansim_parcel.zone.number_of_jobs_per_acre: completed...0.0 sec lnempden = (ln(parcel.disaggregate(urbansim_parcel.zone.number_of_jobs_per_acre))).astype(float32): completed...0.1 sec lngcdacbd = (ln(parcel.disaggregate(psrc.zone.generalized_cost_hbw_am_drive_alone_to_cbd))).astype(float32) psrc.zone.generalized_cost_hbw_am_drive_alone_to_cbd psrc.zone.generalized_cost_hbw_am_drive_alone_to_DDD destination_id=129+0*zone.zone_id............0.0 sec psrc.zone.generalized_cost_hbw_am_drive_alone_to_DDD: completed...0.1 sec psrc.zone.generalized_cost_hbw_am_drive_alone_to_cbd: completed...0.1 sec lngcdacbd = (ln(parcel.disaggregate(psrc.zone.generalized_cost_hbw_am_drive_alone_to_cbd))).astype(float32): completed...0.2 sec lnlotsqft = (ln(parcel.parcel_sqft)).astype(float32).....0.0 sec lnpopden = (ln(parcel.disaggregate(urbansim_parcel.zone.population_per_acre))).astype(float32) urbansim_parcel.zone.population_per_acre population = zone.aggregate(household.persons)...0.1 sec urbansim_parcel.zone.population_per_acre: completed...0.1 sec lnpopden = (ln(parcel.disaggregate(urbansim_parcel.zone.population_per_acre))).astype(float32): completed...0.2 sec lnsqft = (ln(urbansim_parcel.parcel.building_sqft)).astype(float32)....0.0 sec lnunits = (ln(urbansim_parcel.parcel.residential_units)).astype(float32)....0.0 sec ln_unit_price=ln(urbansim_parcel.parcel.unit_price)......0.0 sec Estimate regression for submodel 2 Number of observations: 577 R-Squared: 0.391119057313 Adjusted R-Squared: 0.386861148623 Suggested |t-value| > 2.5214762078 ----------------------------------------------- Coeff_names estimate SE t-values constant 3.58979 0.0421461 85.1749 is_pre_1940 -0.160337 0.0384098 -4.17437 ln_invfar 0.410281 0.0235912 17.3912 lnemp10wa 0.0247455 0.00601373 4.11483 lnempden 0.117951 0.0157296 7.49865 =============================================== Estimate regression for submodel 3 Number of observations: 3119 WARNING: Estimation may led to singularities. Results may be not correct. R-Squared: 0.495016531529 Adjusted R-Squared: 0.493880278144 Suggested |t-value| > 2.83641811386 ----------------------------------------------- Coeff_names estimate SE t-values constant 6.22552 0.200954 30.9799 art600 0.191088 0.0476275 4.01214 ln_bldgage -0.0782249 0.0100171 -7.80917 ln_invfar 0.444801 0.0127305 34.9398 lnempden 0.080816 0.00822422 9.82658 lngcdacbd -0.50125 0.0537695 -9.3222 lnsqft -0.0694015 0.0100332 -6.91716 lnunits -0.233619 0.0196214 -11.9063 =============================================== Estimate regression for submodel 7 Number of observations: 140 R-Squared: 0.406135982971 Adjusted R-Squared: 0.388540012096 Suggested |t-value| > 2.22298052682 ----------------------------------------------- Coeff_names estimate SE t-values constant 2.24044 1.06045 2.11273 lnemp20tw 0.169451 0.0399933 4.23699 lngcdacbd 0.298906 0.263519 1.13429 lnlotsqft 0.391905 0.0451349 8.68298 lnsqft -0.422357 0.0462911 -9.12395 =============================================== Estimate regression for submodel 9 Number of observations: 41 R-Squared: 0.110478718502 Adjusted R-Squared: 0.0116430205582 Suggested |t-value| > 1.92706306765 ----------------------------------------------- Coeff_names estimate SE t-values constant 4.69474 1.33366 3.52018 ln_bldgage 0.0182196 0.0970336 0.187766 lngcdacbd -0.0130355 0.401479 -0.0324688 lnlotsqft 0.192615 0.161551 1.19229 lnsqft -0.233239 0.140831 -1.65615 =============================================== Estimate regression for submodel 10 Number of observations: 625 R-Squared: 0.549862239562 Adjusted R-Squared: 0.545491970043 Suggested |t-value| > 2.53727248236 ----------------------------------------------- Coeff_names estimate SE t-values constant 7.48533 0.34481 21.7085 hbwavgtmda -0.0862935 0.00873664 -9.87719 ln_bldgage -0.0557824 0.0156163 -3.57206 ln_invfar 0.486597 0.0231987 20.9752 lnempden 0.0377551 0.0150543 2.50793 lngcdacbd -0.542356 0.0978551 -5.54244 lnsqft -0.0945556 0.0140276 -6.7407 =============================================== Estimate regression for submodel 14 Number of observations: 12853 WARNING: Estimation may led to singularities. Results may be not correct. R-Squared: 0.476911372487 Adjusted R-Squared: 0.476218539868 Suggested |t-value| > 3.07592791302 ----------------------------------------------- Coeff_names estimate SE t-values constant 3.85547 0.243361 15.8426 art300 -0.0323448 0.00532154 -6.07809 hbwavgtmda -0.0724224 0.00368149 -19.6721 is_pre_1940 0.0463426 0.00768392 6.03112 ln_bldgage -0.0179048 0.004644 -3.85548 ln_invfar 0.199781 0.00726316 27.506 lnavginc 0.201862 0.00964138 20.9371 lnemp10da -0.190353 0.00667629 -28.5119 lnemp10wa 0.015755 0.000912243 17.2707 lnemp20da 0.128445 0.0103307 12.4333 lnemp20tw 0.259878 0.0586704 4.42946 lnemp20wa -0.245629 0.0582254 -4.21859 lnemp30tw 0.061234 0.0045647 13.4147 lnempden 0.0132776 0.00475701 2.79116 lngcdacbd 0.070786 0.02655 2.66614 lnpopden 0.0662681 0.00603592 10.979 lnsqft -0.198181 0.00772824 -25.6437 lnunits 0.0506865 0.00587558 8.62663 =============================================== Estimate regression for submodel 15 Number of observations: 1035 R-Squared: 0.0984823659812 Adjusted R-Squared: 0.0914529887179 Suggested |t-value| > 2.63479727981 ----------------------------------------------- Coeff_names estimate SE t-values constant 52.7033 20.8746 2.52475 art600 -0.0674286 0.0407863 -1.65322 hwy2000 -0.0606156 0.0326236 -1.85803 lnavginc 0.0961301 0.0607879 1.5814 lnemp10da -0.0756108 0.0191558 -3.94716 lnemp20da 0.25141 0.0996925 2.52185 lnemp30da -3.77471 1.66871 -2.26205 lngcdacbd -0.402046 0.112306 -3.57992 lnunits -0.0816628 0.0143667 -5.68416 =============================================== Estimate regression for submodel 18 Number of observations: 1532 WARNING: Estimation may led to singularities. Results may be not correct. R-Squared: 0.366324956265 Adjusted R-Squared: 0.362996393986 Suggested |t-value| > 2.70819669712 ----------------------------------------------- Coeff_names estimate SE t-values constant 7.72995 0.330246 23.4067 hbwavgtmda -0.0615612 0.00853993 -7.20863 ln_bldgage -0.0738744 0.0117569 -6.28349 ln_invfar 0.348771 0.012464 27.9822 lnemp10da -0.0412084 0.00805978 -5.11284 lnempden 0.0720599 0.0099869 7.21545 lngcdacbd -0.560356 0.0689287 -8.1295 lnlotsqft -0.05149 0.0118578 -4.3423 lnunits -0.171099 0.0206194 -8.29796 =============================================== Estimate regression for submodel 19 Number of observations: 20 R-Squared: 0.501530252236 Adjusted R-Squared: 0.442886752499 Suggested |t-value| > 1.7308183826 ----------------------------------------------- Coeff_names estimate SE t-values constant 4.97817 0.34301 14.5132 art300 -0.179942 0.433122 -0.415453 ln_invfar -0.36841 0.0953852 -3.86234 =============================================== Estimate regression for submodel 20 Number of observations: 357 R-Squared: 0.59277737088 Adjusted R-Squared: 0.589316555335 Suggested |t-value| > 2.42440421171 ----------------------------------------------- Coeff_names estimate SE t-values constant 2.84672 0.0815486 34.9082 hwy200 0.237304 0.0710549 3.33973 ln_invfar 0.584159 0.0283993 20.5695 lnempden 0.339581 0.0189319 17.937 =============================================== Estimate regression for submodel 24 Number of observations: 128049 WARNING: Estimation may led to singularities. Results may be not correct. R-Squared: 0.472558901618 Adjusted R-Squared: 0.472497108045 Suggested |t-value| > 3.42931017584 ----------------------------------------------- Coeff_names estimate SE t-values constant 15.137 0.450431 33.6055 art300 -0.0311443 0.00182645 -17.0518 art600 -0.0545849 0.00165592 -32.9634 hbwavgtmda -0.068664 0.00073828 -93.0053 hwy2000 -0.00458479 0.00191649 -2.39228 is_pre_1940 0.0803943 0.00187763 42.8168 ln_bldgage -0.0183836 0.00127612 -14.4058 ln_invfar 0.172401 0.00208853 82.5465 lnavginc 0.610552 0.0030839 197.981 lnemp10da -0.0879973 0.00132694 -66.316 lnemp20da 0.152882 0.00332027 46.045 lnemp30da -1.14951 0.0356473 -32.2467 lngcdacbd -0.188882 0.00725138 -26.0478 lnpopden 0.108981 0.00187711 58.0577 lnsqft -0.245067 0.00227246 -107.842 lnunits -0.278416 0.00566998 -49.1036 =============================================== Estimate regression for submodel 25 Number of observations: 85 R-Squared: 0.694718317168 Adjusted R-Squared: 0.687272422465 Suggested |t-value| > 2.10775977201 ----------------------------------------------- Coeff_names estimate SE t-values constant 2.05995 0.359365 5.7322 ln_invfar 0.617323 0.0454344 13.5871 lnemp30tw 0.190851 0.0344441 5.54091 =============================================== Estimate regression for submodel 26 Number of observations: 4808 WARNING: Estimation may led to singularities. Results may be not correct. R-Squared: 0.0514766725175 Adjusted R-Squared: 0.0502912653179 Suggested |t-value| > 2.91170679778 ----------------------------------------------- Coeff_names estimate SE t-values constant 1.34944 0.345414 3.90673 art300 0.0695707 0.0197633 3.5202 hwy2000 0.23774 0.0238599 9.96401 lnavginc 0.0558779 0.0172024 3.24827 lnemp10da 0.068263 0.0135233 5.0478 lnemp10wa 0.0209368 0.00314154 6.6645 lngcdacbd 0.362523 0.0648806 5.58755 =============================================== Estimate regression for submodel 28 Number of observations: 1155 R-Squared: 0.0096657786497 Adjusted R-Squared: 0.0079464484043 Suggested |t-value| > 2.6555330205 ----------------------------------------------- Coeff_names estimate SE t-values constant 3.14716 0.284388 11.0665 lnempden 0.0471131 0.014493 3.25074 lngcdacbd 0.244395 0.089292 2.73703 =============================================== Estimate regression for submodel 30 Number of observations: 456 R-Squared: 0.0705799721238 Adjusted R-Squared: 0.0623367789719 Suggested |t-value| > 2.47436715334 ----------------------------------------------- Coeff_names estimate SE t-values constant 5.20743 0.310202 16.7872 hbwavgtmda -0.0339436 0.0171307 -1.98144 ln_bldgage -0.0330234 0.0241652 -1.36657 lnemp20tw -0.0415644 0.0201397 -2.0638 lnunits 0.0804204 0.0249548 3.22264 =============================================== Estimating Real Estate Price Model (from urbansim.models.real_estate_price_model): completed...8.7 sec Simulate year 2000: completed...................................11.4 sec Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/year_2000_log.txt Starting simulation for year 2000: completed........................11.5 sec Start simulation run: completed.........................................11.5 sec Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/run_model_system.log Cache Directory set to: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data Start simulation run: started on Fri Nov 6 12:57:29 2009 random seed = 0 Starting simulation for year 2000: started on Fri Nov 6 12:57:29 2009 Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/run_model_system.log Logging to file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/year_2000_log.txt Simulate year 2000: started on Fri Nov 6 12:57:29 2009 numpy.logical_and(household.building_id>0, household.disaggregate(building.sqft_per_unit>0)) building.sqft_per_unit>0.................................0.2 sec numpy.logical_and(household.building_id>0, household.disaggregate(building.sqft_per_unit>0)): completed...0.2 sec Estimating Household Location Choice Model (from urbansim.models.household_location_choice_model): started on Fri Nov 6 12:57:31 2009 numpy.logical_and(urbansim_parcel.building.is_residential, numpy.logical_and(numpy.logical_and(building.residential_units, building.sqft_per_unit), urbansim_parcel.building.unit_price > 0)) is_residential = building.disaggregate(building_type.is_residential)....0.2 sec unit_price = building.disaggregate(urbansim_parcel.parcel.unit_price) unit_price = safe_array_divide(parcel.land_value + urbansim_parcel.parcel.improvement_value, urbansim_parcel.parcel.existing_units) urbansim_parcel.parcel.existing_units unit_name = parcel.disaggregate(land_use_type.unit_name)....0.3 sec building_sqft = (parcel.aggregate(urbansim_parcel.building.building_sqft)).astype(int32) urbansim_parcel.building.building_sqft....0.1 sec building_sqft = (parcel.aggregate(urbansim_parcel.building.building_sqft)).astype(int32): completed...0.2 sec residential_units = (parcel.aggregate(building.residential_units)).astype(int32)....0.1 sec urbansim_parcel.parcel.existing_units: completed...0.7 sec improvement_value =parcel.aggregate(building.improvement_value)....0.1 sec unit_price = safe_array_divide(parcel.land_value + urbansim_parcel.parcel.improvement_value, urbansim_parcel.parcel.existing_units): completed...0.9 sec unit_price = building.disaggregate(urbansim_parcel.parcel.unit_price): completed...1.3 sec numpy.logical_and(urbansim_parcel.building.is_residential, numpy.logical_and(numpy.logical_and(building.residential_units, building.sqft_per_unit), urbansim_parcel.building.unit_price > 0)): completed...1.5 sec Choice set size: 30 ln_residential_units = ln_bounded(building.residential_units)....0.0 sec submodel: -2 wesml_sampling_correction_variable = safe_array_divide(psrc_parcel.building.observed_building_type_share, psrc_parcel.building.sampled_building_type_share) observed_building_type_share = 0.668443925 * (urbansim.building.building_type_id == 19) + 0.230847302 * (urbansim.building.building_type_id == 12) + 0.019761348 * (urbansim.building.building_type_id == 11) + 0.080947425 * (urbansim.building.building_type_id == 4)....0.0 sec sampled_building_type_share = 0.794019192 * (urbansim.building.building_type_id == 19) + 0.131667039 * (urbansim.building.building_type_id == 12) + 0.014728855 * (urbansim.building.building_type_id == 11) + 0.059584914 * (urbansim.building.building_type_id == 4)....0.0 sec wesml_sampling_correction_variable = safe_array_divide(psrc_parcel.building.observed_building_type_share, psrc_parcel.building.sampled_building_type_share): completed...0.2 sec Convergence achieved. Akaike's Information Criterion (AIC): 4467.71715419 Number of Iterations: 4 *********************************************** Log-likelihood is: -2232.8585771 Null Log-likelihood is: -2295.80823262 Likelihood ratio index: 0.0274193874871 Adj. likelihood ratio index: 0.0269838110371 Number of observations: 675 Suggested |t-value| > 2.55239352195 Convergence statistic is: 3.65132806318e-05 ----------------------------------------------- Coeff_names estimate std err t-values ln_residential_units -0.176446 0.021107 -8.3596 *********************************************** Elapsed time: 0.07 seconds Estimating Household Location Choice Model (from urbansim.models.household_location_choice_model): completed...2.6 sec Simulate year 2000: completed....................................4.9 sec Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/year_2000_log.txt Starting simulation for year 2000: completed.........................5.2 sec Start simulation run: completed..........................................5.2 sec Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/run_model_system.log Cache Directory set to: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data Start simulation run: started on Fri Nov 6 12:57:34 2009 random seed = 0 Starting simulation for year 2000: started on Fri Nov 6 12:57:34 2009 Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/run_model_system.log Logging to file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/year_2000_log.txt Simulate year 2000: started on Fri Nov 6 12:57:34 2009 'agents_grouping_attribute' set to job.building_type. numpy.logical_and(job.building_id>0, job.disaggregate(building.non_residential_sqft) > 0)....0.6 sec Estimating Home_based Employment Location Choice Model (from urbansim.models.employment_location_choice_model): started on Fri Nov 6 12:57:36 2009 urbansim_parcel.building.vacant_home_based_job_space urbansim_parcel.building.total_home_based_job_space sum_minimum_persons_and_2=building.aggregate(urbansim_parcel.household.minimum_persons_and_2) urbansim_parcel.household.minimum_persons_and_2....1.9 sec sum_minimum_persons_and_2=building.aggregate(urbansim_parcel.household.minimum_persons_and_2): completed...2.2 sec is_multi_family_residential=building.disaggregate(building_type.building_type_name =='multi_family_residential') building_type.building_type_name=='multi_family_residential'....0.1 sec is_multi_family_residential=building.disaggregate(building_type.building_type_name =='multi_family_residential'): completed...0.1 sec urbansim_parcel.building.total_home_based_job_space: completed...2.4 sec urbansim_parcel.building.number_of_SSS_jobs urbansim.job.is_building_type_SSS................0.0 sec urbansim_parcel.building.number_of_SSS_jobs: completed...0.2 sec WARNING: Negative values detected in function/method '' WARNING: Minimum: -150.0. Negative values clipped to zero. urbansim_parcel.building.vacant_home_based_job_space: completed...2.6 sec Choice set size: 30 blngcdacbd = (ln(building.disaggregate(psrc.zone.generalized_cost_hbw_am_drive_alone_to_cbd))).astype(float32) psrc.zone.generalized_cost_hbw_am_drive_alone_to_cbd psrc.zone.generalized_cost_hbw_am_drive_alone_to_DDD destination_id=129+0*zone.zone_id............0.0 sec psrc.zone.generalized_cost_hbw_am_drive_alone_to_DDD: completed...0.3 sec psrc.zone.generalized_cost_hbw_am_drive_alone_to_cbd: completed...0.3 sec zone_id = building.disaggregate(parcel.zone_id)......0.8 sec blngcdacbd = (ln(building.disaggregate(psrc.zone.generalized_cost_hbw_am_drive_alone_to_cbd))).astype(float32): completed...1.2 sec submodel: -2 Convergence achieved. Akaike's Information Criterion (AIC): 3545.02425022 Number of Iterations: 5 *********************************************** Log-likelihood is: -1771.51212511 Null Log-likelihood is: -1778.82623061 Likelihood ratio index: 0.00411175941313 Adj. likelihood ratio index: 0.00354959095463 Number of observations: 523 Suggested |t-value| > 2.50191555894 Convergence statistic is: 0.000806215257736 ----------------------------------------------- Coeff_names estimate std err t-values blngcdacbd 0.756991 0.306998 2.46578 *********************************************** Elapsed time: 0.08 seconds Estimating Home_based Employment Location Choice Model (from urbansim.models.employment_location_choice_model): completed...4.8 sec Simulate year 2000: completed....................................7.2 sec Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/year_2000_log.txt Starting simulation for year 2000: completed.........................7.4 sec Start simulation run: completed..........................................7.4 sec Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data/run_model_system.log Cache directory for run 1 set to /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/runs/run_1.2009_11_06_12_57 Running opus_core.store.utils.cache_flt_data.CacheFltData: started on Fri Nov 6 12:57:43 2009 Caching files from '/projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/base_year_data' to '/projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/runs/run_1.2009_11_06_12_57'. Running opus_core.store.utils.cache_flt_data.CacheFltData: completed....12.5 sec Logging to file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/runs/run_1.2009_11_06_12_57/run_multiprocess.log Running simulation for years 2001 thru 2002 Root seed: 0 Running UrbanSim for year 2001 in new process: started on Fri Nov 6 12:57:55 2009 Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/runs/run_1.2009_11_06_12_57/run_multiprocess.log Invoking: /projects/urbansim/third-party/bin/python /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/source/urbansim/model_coordinators/model_system.py -r /tmp/tmpvj8ZXN/resources.pickle --log-file-name run_multiprocess.log Running UrbanSim for year 2001 in new process: completed.........8 min, 41.2 sec Running UrbanSim for year 2002 in new process: started on Fri Nov 6 13:06:36 2009 Closing log file: /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/data/data/seattle_parcel/runs/run_1.2009_11_06_12_57/run_multiprocess.log Invoking: /projects/urbansim/third-party/bin/python /projects/urbansim10/urbbuild/cruisecontrol/sandbox/opus_integration/source/urbansim/model_coordinators/model_system.py -r /tmp/tmpkTTp_M/resources.pickle --log-file-name run_multiprocess.log Running UrbanSim for year 2002 in new process: completed.........4 min, 38.2 sec Done running simulation for years 2001 thru 2002 /projects/urbansim/third-party/lib/python2.5/site-packages/numpy-1.2.1-py2.5-linux-i686.egg/numpy/lib/arraysetops.py:131: UserWarning: The order of the output arguments for `return_index` has changed. Before, the output was (indices, unique_arr), but has now been reversed to be more consistent. warnings.warn("The order of the output arguments for "