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world3_scenarios_sweeps_multiparam.py
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world3_scenarios_sweeps_multiparam.py
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# Std:
import os
import sys
import logging #en reemplazo de los prints
import functools # for reduce
logger = logging.getLogger("--World3 scenarios sweep--") #un logger especifico para este modulo
logger = logging.getLogger("--World3 scenarios Multiparameter sweep --") #un logger especifico para este modulo
#Mine:
import settings.settings_world3_sweep as world3_settings
import mos_writer.formulas as predef_formulas
import mos_writer.parameter_sweep_settings as parameter_sweep_settings
import mos_writer.mos_script_factory
import filesystem.files_aux as files_aux
import settings.gral_settings as gral_settings
import modelica_interface.run_omc as run_omc
import sweeping.iterationInfo
import readme_writer.readme_writer as readme_writer
import plotting.plot_csv as plot_csv
vanilla_SysDyn_mo_path = world3_settings._sys_dyn_package_vanilla_path.replace("\\","/") # The System Dynamics package without modifications
piecewiseMod_SysDyn_mo_path = world3_settings._sys_dyn_package_pw_fix_path.replace("\\","/") # Piecewise function modified to accept queries for values outside of range. Interpolate linearly using closest 2 values
populationTankNewVar_SysDyn_mo_path = world3_settings._sys_dyn_package_pop_state_var_new.replace("\\","/") # Added a new "population" var that includes an integrator. Numerically it's the same as "population" but with the advantage that now we can calculate sensitivities for it
Run2vermeulenAndJongh_SysDyn_mo_path = world3_settings._sys_dyn_package_v_and_j_run_2.replace("\\","/") # Added a new "population" var that includes an integrator. Numerically it's the same as "population" but with the advantage that now we can calculate sensitivities for it
Run3vermeulenAndJongh_SysDyn_mo_path = world3_settings._sys_dyn_package_v_and_j_run_3.replace("\\","/") # Added a new "population" var that includes an integrator. Numerically it's the same as "population" but with the advantage that now we can calculate sensitivities for it
pseudoffwparam_SysDyn_mo_path = world3_settings._sys_dyn_package_pseudo_ffw_param_path.replace("\\","/") # Added a new "population" var that includes an integrator. Numerically it's the same as "population" but with the advantage that now we can calculate sensitivities for it
def main():
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
#### WORK PACKAGE 1 ####
# testNRResources()
#### WORK PACKAGE 3 ####
# test3Params()
# test3fromTop12RelativeWP2()
# test12fromTop12RelativeWP2OneUpOneDown()
# hugoScolnikParamsCurvi01()
# hugoScolnikParamsCurvi02()
# change2For2000and2For2100RelativeTop() # no sweep
# relativeTop2for2100AndTop8For2000() # no sweep
# Curvi Only Pop
# relativeTop12ParamsNoSweep5PercentOptimizePop() # no sweep
# relativeTop12ParamsNoSweep1PercentOptimizePop() # no sweep
# relativeTop18ParamsNoSweep3PercentOptimizePop() # no sweep
# relativeTop36ParamsNoSweep3PercentOptimizePop() # no sweep
# nrResourcesInitCurviNoSweepOptimizePop() # no sweep
# onlyMeasurableInitValsNoSweep3PercOptimizePop() # no sweep
# onlyMeasurableInitValsNoSweep5PercOptimizePop() # no sweep
# relativeTop12ParamsSweepOf2Params5PercentOptimizePop()
# Curvi pop and hwi
# relativeTop12ParamsNoSweep3PercentOptimizePopAndHWI() #no sweep
# relativeTop12ParamsNoSweep5PercentOptimizePopAndHWI() #no sweep
# ZXPOWL only pop
# nrResourcesInitZXPOWLNoSweepOptimizePop() #no sweep
#### POST - WORK PACKAGE 3 ####
# Policies Triggers with CURVI
# policyTriggers_test31_nosweep() # the parameters are the policy triggers for scenarios 2 to 9. Initial: 2050
# policyTriggers_test32_nosweep() # the parameters are the policy triggers for scenarios 2 to 9. Initial: 2018
# policyTriggers_test33_nosweep() # the parameters are the policy triggers for scenarios 2 to 9. Initial: 2034
hapzardExperiment()
##### TESTS DEFINITIONS #####
def hapzardExperiment():
# Hapzard sweep of 4 paramet
sweep_params_settings_list = [
parameter_sweep_settings . OrigParameterSweepSettings("p_land_yield_fact_1" , predef_formulas . DeltaBeforeAndAfter(0.05), 2), # (param_name , formula_instance , iterations)
parameter_sweep_settings . OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas . DeltaBeforeAndAfter(0.05), 2), # (param_name , formula_instance , iterations)
parameter_sweep_settings . OrigParameterSweepSettings("p_nr_res_use_fact_1" , predef_formulas . DeltaBeforeAndAfter(0.05), 2), # (param_name , formula_instance , iterations)
]
# add the sweepSettings to the list
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [],
"fixed_params_description_str": "",
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def policyTriggers_test33_nosweep():
# Curvi run:
# Formula: -hdi
# Param name & Starting point & Max & Min & Curvi
# t_fert_cont_eff_time & 2034 & 2100 & 2018 & 2076.81717859103
# t_ind_equil_time & 2034 & 2100 & 2018 & 2073.09706915164
# t_zero_pop_grow_time & 2034 & 2100 & 2018 & 2049.83898445364
# t_land_life_time & 2034 & 2100 & 2018 & 2026.08829271848
# t_policy_year & 2034 & 2100 & 2018 & 2034.32051122486
# t_fcaor_time & 2034 & 2100 & 2018 & 2083.36491898977
# With:
# ier = 0 nfu = 13 nit = 0
# Time: ~2m on laptop
sweep_params_settings_list = [ parameter_sweep_settings.OrigParameterSweepSettings("t_fert_cont_eff_time" , predef_formulas.OneValue(2076.81717859103 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_ind_equil_time" , predef_formulas.OneValue(2073.09706915164 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_zero_pop_grow_time" , predef_formulas.OneValue(2049.83898445364 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_land_life_time" , predef_formulas.OneValue(2026.08829271848 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_policy_year" , predef_formulas.OneValue(2034.32051122486 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_fcaor_time" , predef_formulas.OneValue(2083.36491898977 ), 1), # (param_name , formula_instance , iterations)
]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def policyTriggers_test32_nosweep():
# Curvi run:
# Formula: -hdi
# Param name & Starting point & Max & Min & Curvi
# t_fert_cont_eff_time & 2018 & 2100 & 2018 & 2018
# t_ind_equil_time & 2018 & 2100 & 2018 & 2018
# t_zero_pop_grow_time & 2018 & 2100 & 2018 & 2018
# t_land_life_time & 2018 & 2100 & 2018 & 2018
# t_policy_year & 2018 & 2100 & 2018 & 2018
# t_fcaor_time & 2018 & 2100 & 2018 & 2018
# With:
# ier = 0 nfu = 13 nit = 0
# Time: ~2m on laptop
sweep_params_settings_list = [ parameter_sweep_settings.OrigParameterSweepSettings("t_fert_cont_eff_time" , predef_formulas.OneValue(2018 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_ind_equil_time" , predef_formulas.OneValue(2018 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_zero_pop_grow_time" , predef_formulas.OneValue(2018 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_land_life_time" , predef_formulas.OneValue(2018 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_policy_year" , predef_formulas.OneValue(2018 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_fcaor_time" , predef_formulas.OneValue(2018 ), 1), # (param_name , formula_instance , iterations)
]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def policyTriggers_test31_nosweep():
# Curvi run:
# Formula: -hdi
# Param name & Starting point & Max & Min & Curvi
# t_fert_cont_eff_time & 2050 & 2100 & 2018 & 2076.49542873992
# t_ind_equil_time & 2050 & 2100 & 2018 & 2049.20235624306
# t_zero_pop_grow_time & 2050 & 2100 & 2018 & 2046.51075636435
# t_land_life_time & 2050 & 2100 & 2018 & 2061.14818103240
# t_policy_year & 2050 & 2100 & 2018 & 2049.72195968877
# t_fcaor_time & 2050 & 2100 & 2018 & 2086.03299786679
# With:
# ier = 2 nfu = 1325 nit = 26
# Time: ~3h on laptop
sweep_params_settings_list = [ parameter_sweep_settings.OrigParameterSweepSettings("t_fert_cont_eff_time" , predef_formulas.OneValue(2076.49542873992 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_ind_equil_time" , predef_formulas.OneValue(2049.20235624306 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_zero_pop_grow_time" , predef_formulas.OneValue(2046.51075636435 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_land_life_time" , predef_formulas.OneValue(2061.14818103240 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_policy_year" , predef_formulas.OneValue(2049.72195968877 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("t_fcaor_time" , predef_formulas.OneValue(2086.03299786679 ), 1), # (param_name , formula_instance , iterations)
]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def onlyMeasurableInitValsNoSweep5PercOptimizePop():
# Curvi run:
# Optimum x0:
# Param name & Default & CurviVal5% & Curvival5%/Default
# nr_resources_init & 1000000000000.0 & 1049999932898.45 & 1.04999993289845
# pop2_init & 700000000.0 & 734997926.769216 & 1.049997038241737
# industrial_capital_init & 210000000000.0 & 199500310116.404 & 0.9500014767447809
# pot_arable_land_tot & 3200000000.0 & 3359996768.48028 & 1.0499989901500875
# pot_arable_land_init & 2300000000.0 & 2185022565.87536 & 0.9500098112501565
# pop1_init & 650000000.0 & 682495277.053796 & 1.049992733928917
# service_capital_init & 144000000000.0 & 136865897154.419 & 0.9504576191279097
# arable_land_init & 900000000.0 & 854999989.771129 & 0.9499999886345878
# land_fertility_init & 600.0 & 574.830198743534 & 0.9580503312392232
# ppoll_in_1970 & 136000000.0 & 142799625.357823 & 1.0499972452781103
# agr_inp_init & 5000000000.0 & 4765263042.21460 & 0.95305260844292
# urban_ind_land_init & 8200000.0 & 7926983.79713496 & 0.9667053411140195
# pop3_init & 190000000.0 & 190430747.405051 & 1.0022670916055316
# pop4_init & 60000000.0 & 58481379.9505684 & 0.9746896658428067
# pers_pollution_init & 25000000.0 & 25085149.6442214 & 1.003405985768856
# des_res_use_rt_DNRUR & 4800000000.0 & 4753250667.76996 & 0.9902605557854084
# ind_out_in_1970 & 790000000000.0 & 790103354230.892 & 1.0001308281403696
# With:
# ier = 2 nfu = 3271 nit = 23
# fopt(pop) = -0.43238705D+10
sweep_params_settings_list = [ parameter_sweep_settings.OrigParameterSweepSettings("nr_resources_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.04999993289844995 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop2_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.04999703824173696 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("industrial_capital_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.04999852325521914 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pot_arable_land_tot" , predef_formulas.IncreasingByDeltaNotInclusive(0.04999899015008746 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pot_arable_land_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.04999018874984351 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop1_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.04999273392891701 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("service_capital_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.049542380872090286 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("arable_land_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.05000001136541221 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("land_fertility_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.04194966876077677 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("ppoll_in_1970" , predef_formulas.IncreasingByDeltaNotInclusive(0.049997245278110336 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("agr_inp_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.046947391557080054 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("urban_ind_land_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.03329465888598049 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop3_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.0022670916055316237 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop4_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.025310334157193304 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pers_pollution_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.003405985768855979 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("des_res_use_rt_DNRUR" , predef_formulas.IncreasingByDeltaNotInclusive(-0.009739444214591608 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("ind_out_in_1970" , predef_formulas.IncreasingByDeltaNotInclusive(0.00013082814036957657 ), 1), # (param_name , formula_instance , iterations)
]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [2025,2050,2075] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def onlyMeasurableInitValsNoSweep3PercOptimizePop():
# Curvi run:
# Optimum x0:
# Param name & Default & CurviVal3% & Curvival3%/Default
# nr_resources_init & 1000000000000.0 & 1029999969230.68 & 1.02999996923068
# pop2_init & 700000000.0 & 720999966.773269 & 1.0299999525332415
# industrial_capital_init & 210000000000.0 & 203700100936.389 & 0.9700004806494714
# pot_arable_land_tot & 3200000000.0 & 3295999683.65404 & 1.0299999011418874
# pot_arable_land_init & 2300000000.0 & 2231000126.0827 & 0.9700000548185651
# pop1_init & 650000000.0 & 669499907.842704 & 1.0299998582195447
# service_capital_init & 144000000000.0 & 139680024201.879 & 0.9700001680686041
# arable_land_init & 900000000.0 & 873000128.625531 & 0.9700001429172567
# land_fertility_init & 600.0 & 582.003889499973 & 0.9700064824999549
# ppoll_in_1970 & 136000000.0 & 140079294.842117 & 1.0299948150155662
# agr_inp_init & 5000000000.0 & 4854389902.22402 & 0.970877980444804
# urban_ind_land_init & 8200000.0 & 7974013.07276953 & 0.9724406186304305
# pop3_init & 190000000.0 & 194214755.894986 & 1.0221829257630843
# pop4_init & 60000000.0 & 61646702.3275202 & 1.0274450387920033
# pers_pollution_init & 25000000.0 & 25746881.1401298 & 1.029875245605192
# des_res_use_rt_DNRUR & 4800000000.0 & 4908990166.58688 & 1.0227062847055999
# ind_out_in_1970 & 790000000000.0 & 790741017431.495 & 1.0009379967487277
# With:
# ier = 2 nfu = 3271 nit = 23
# fopt(pop) = -0.43238705D+10
sweep_params_settings_list = [ parameter_sweep_settings.OrigParameterSweepSettings("nr_resources_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999969230680046 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop2_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999952533241503 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("industrial_capital_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.02999951935052858 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pot_arable_land_tot" , predef_formulas.IncreasingByDeltaNotInclusive(0.02999990114188744 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pot_arable_land_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029999945181434895 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop1_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.02999985821954465 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("service_capital_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.02999983193139588 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("arable_land_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.02999985708274333 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("land_fertility_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029993517500045086 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("ppoll_in_1970" , predef_formulas.IncreasingByDeltaNotInclusive(0.029994815015566223 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("agr_inp_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029122019555196 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("urban_ind_land_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.02755938136956948 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop3_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.022182925763084338 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop4_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.02744503879200333 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pers_pollution_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.029875245605192058 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("des_res_use_rt_DNRUR" , predef_formulas.IncreasingByDeltaNotInclusive(0.022706284705599877 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("ind_out_in_1970" , predef_formulas.IncreasingByDeltaNotInclusive(0.000937996748727743 ), 1), # (param_name , formula_instance , iterations)
]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [2025,2050,2075] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def relativeTop36ParamsNoSweep3PercentOptimizePop():
# Curvi run:
# Optimum x0:
# Param name & Default & CurviVal3% & Curvival3%/Default
# p_fioa_cons_const_1 & 0.43 & 0.442899987682584 & 1.0299999713548464
# p_ind_cap_out_ratio_1 & 3.0 & 3.08999990752248 & 1.02999996917416
# reproductive_lifetime & 30.0 & 29.1000008660267 & 0.9700000288675567
# life_expect_norm & 28.0 & 28.8399991624904 & 1.0299999700889428
# des_compl_fam_size_norm & 3.8 & 3.91399984153159 & 1.029999958297787
# p_avg_life_ind_cap_1 & 14.0 & 13.5800004005958 & 0.9700000286139857
# subsist_food_pc & 230.0 & 223.100008793897 & 0.9700000382343348
# p_serv_cap_out_ratio_1 & 1.0 & 1.02999952398089 & 1.02999952398089
# max_tot_fert_norm & 12.0 & 12.3599989076594 & 1.0299999089716165
# p_nr_res_use_fact_1 & 1.0 & 0.970000032740317 & 0.970000032740317
# nr_resources_init & 1000000000000.0 & 1029999971346.01 & 1.02999997134601
# p_land_yield_fact_1 & 1.0 & 0.970037952266749 & 0.970037952266749
# pop2_init & 700000000.0 & 720999683.82525 & 1.0299995483217859
# industrial_capital_init & 210000000000.0 & 203700454180.396 & 0.9700021627637905
# pot_arable_land_tot & 3200000000.0 & 3199679898.37683 & 0.9998999682427594
# p_avg_life_serv_cap_1 & 20.0 & 19.4000013300908 & 0.97000006650454
# pot_arable_land_init & 2300000000.0 & 2368999915.46405 & 1.029999963245239
# pop1_init & 650000000.0 & 669493026.868808 & 1.0299892721058586
# ppoll_trans_del & 20.0 & 20.5998524117882 & 1.0299926205894099
# land_fr_harvested & 0.7 & 0.720999977562406 & 1.0299999679462943
# inherent_land_fert & 600.0 & 617.999279020275 & 1.029998798367125
# lifet_perc_del & 20.0 & 20.5996751832082 & 1.02998375916041
# service_capital_init & 144000000000.0 & 139681021490.649 & 0.9700070936850624
# arable_land_init & 900000000.0 & 926974042.126842 & 1.0299711579187134
# assim_half_life_1970 & 1.5 & 1.45500004311963 & 0.97000002874642
# land_fertility_init & 600.0 & 617.268252440442 & 1.02878042073407
# p_ppoll_gen_fact_1 & 1.0 & 0.970000028664767 & 0.970000028664767
# avg_life_land_norm & 1000.0 & 1029.998926769 & 1.029998926769
# fr_agr_inp_pers_mtl & 0.001 & 0.0009700119654734652 & 0.9700119654734651
# agr_mtl_toxic_index & 1.0 & 0.970088902055858 & 0.970088902055858
# ppoll_in_1970 & 136000000.0 & 140079994.330919 & 1.0299999583155808
# social_discount & 0.07 & 0.06790001448018312 & 0.9700002068597589
# income_expect_avg_time & 3.0 & 3.08996667172929 & 1.02998889057643
# social_adj_del & 20.0 & 20.5999929544971 & 1.029999647724855
# hlth_serv_impact_del & 20.0 & 19.4443365627337 & 0.972216828136685
# processing_loss & 0.1 & 0.09727264397846881 & 0.9727264397846881
# With:
# ier = 2 nfu = 21834 nit = 60
# fopt(population) = -0.12185403D+11
sweep_params_settings_list = [ parameter_sweep_settings.OrigParameterSweepSettings("p_fioa_cons_const_1" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999971354846444 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999969174159924 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("reproductive_lifetime" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029999971132443348 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("life_expect_norm" , predef_formulas.IncreasingByDeltaNotInclusive(0.02999997008894284 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("des_compl_fam_size_norm" , predef_formulas.IncreasingByDeltaNotInclusive(0.02999995829778701 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas.IncreasingByDeltaNotInclusive(-0.02999997138601429 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("subsist_food_pc" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029999961765665217 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_serv_cap_out_ratio_1" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999523980889897 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("max_tot_fert_norm" , predef_formulas.IncreasingByDeltaNotInclusive(0.02999990897161653 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_nr_res_use_fact_1" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029999967259683014 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("nr_resources_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999971346009957 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_land_yield_fact_1" , predef_formulas.IncreasingByDeltaNotInclusive(-0.02996204773325095 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop2_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999548321785863 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("industrial_capital_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029997837236209524 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pot_arable_land_tot" , predef_formulas.IncreasingByDeltaNotInclusive(-0.00010003175724060398 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_avg_life_serv_cap_1" , predef_formulas.IncreasingByDeltaNotInclusive(-0.02999993349546004 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pot_arable_land_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999963245239014 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop1_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.029989272105858555 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("ppoll_trans_del" , predef_formulas.IncreasingByDeltaNotInclusive(0.02999262058940988 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("land_fr_harvested" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999967946294337 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("inherent_land_fert" , predef_formulas.IncreasingByDeltaNotInclusive(0.029998798367125046 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("lifet_perc_del" , predef_formulas.IncreasingByDeltaNotInclusive(0.029983759160409962 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("service_capital_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029992906314937562 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("arable_land_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.02997115791871341 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("assim_half_life_1970" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029999971253580004 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("land_fertility_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.028780420734070056 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_ppoll_gen_fact_1" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029999971335233022 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("avg_life_land_norm" , predef_formulas.IncreasingByDeltaNotInclusive(0.029998926768999956 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("fr_agr_inp_pers_mtl" , predef_formulas.IncreasingByDeltaNotInclusive(-0.029988034526534868 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("agr_mtl_toxic_index" , predef_formulas.IncreasingByDeltaNotInclusive(-0.02991109794414204 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("ppoll_in_1970" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999958315580777 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("social_discount" , predef_formulas.IncreasingByDeltaNotInclusive(-0.02999979314024115 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("income_expect_avg_time" , predef_formulas.IncreasingByDeltaNotInclusive(0.02998889057643006 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("social_adj_del" , predef_formulas.IncreasingByDeltaNotInclusive(0.029999647724854972 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("hlth_serv_impact_del" , predef_formulas.IncreasingByDeltaNotInclusive(-0.027783171863315026 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("processing_loss" , predef_formulas.IncreasingByDeltaNotInclusive(-0.02727356021531191 ), 1), # (param_name , formula_instance , iterations)
]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [2025,2050,2075] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def relativeTop18ParamsNoSweep3PercentOptimizePop():
# Curvi run:
# Optimum x0:
# Param name & Default & CurviVal3% & Curvival3%/Default
# p_fioa_cons_const_1 & 0.43 & 0.442899987695998 & 1.0299999713860417
# p_ind_cap_out_ratio_1 & 3.0 & 3.0899999140853 & 1.0299999713617667
# reproductive_lifetime & 30.0 & 29.1000008583085 & 0.9700000286102833
# life_expect_norm & 28.0 & 28.8399991989023 & 1.029999971389368
# des_compl_fam_size_norm & 3.8 & 3.91399989128112 & 1.0299999713897685
# p_avg_life_ind_cap_1 & 14.0 & 13.5800004167702 & 0.9700000297693
# subsist_food_pc & 230.0 & 223.100006581901 & 0.9700000286169609
# p_serv_cap_out_ratio_1 & 1.0 & 1.02999996814411 & 1.02999996814411
# max_tot_fert_norm & 12.0 & 12.35999965249 & 1.0299999710408334
# p_nr_res_use_fact_1 & 1.0 & 0.970000028610385 & 0.970000028610385
# nr_resources_init & 1000000000000.0 & 1029999970165.11 & 1.02999997016511
# p_land_yield_fact_1 & 1.0 & 1.01267089341769 & 1.01267089341769
# pop2_init & 700000000.0 & 720999977.481686 & 1.02999996783098
# industrial_capital_init & 210000000000.0 & 203700006501.859 & 0.9700000309612333
# pot_arable_land_tot & 3200000000.0 & 3104000158.43538 & 0.9700000495110562
# p_avg_life_serv_cap_1 & 20.0 & 19.400001917118 & 0.9700000958559001
# pot_arable_land_init & 2300000000.0 & 2368999933.94436 & 1.0299999712801564
# pop1_init & 650000000.0 & 669499934.855496 & 1.0299998997776862
# With:
# f= -11515475440.7037 (negated)
# ier = 2 nfu = 3289 nit = 21
sweep_params_settings_list = [ parameter_sweep_settings.OrigParameterSweepSettings("p_fioa_cons_const_1" , predef_formulas.IncreasingByDeltaNotInclusive( 0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas.IncreasingByDeltaNotInclusive( 0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("reproductive_lifetime" , predef_formulas.IncreasingByDeltaNotInclusive(-0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("life_expect_norm" , predef_formulas.IncreasingByDeltaNotInclusive( 0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("des_compl_fam_size_norm" , predef_formulas.IncreasingByDeltaNotInclusive( 0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas.IncreasingByDeltaNotInclusive(-0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("subsist_food_pc" , predef_formulas.IncreasingByDeltaNotInclusive(-0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_serv_cap_out_ratio_1" , predef_formulas.IncreasingByDeltaNotInclusive( 0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("max_tot_fert_norm" , predef_formulas.IncreasingByDeltaNotInclusive( 0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_nr_res_use_fact_1" , predef_formulas.IncreasingByDeltaNotInclusive(-0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("nr_resources_init" , predef_formulas.IncreasingByDeltaNotInclusive( 0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_land_yield_fact_1" , predef_formulas.IncreasingByDeltaNotInclusive( 0.01267089341769 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop2_init" , predef_formulas.IncreasingByDeltaNotInclusive( 0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("industrial_capital_init" , predef_formulas.IncreasingByDeltaNotInclusive(-0.03 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pot_arable_land_tot" , predef_formulas.IncreasingByDeltaNotInclusive(-0.03 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("p_avg_life_serv_cap_1" , predef_formulas.IncreasingByDeltaNotInclusive(-0.03 ), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pot_arable_land_init" , predef_formulas.IncreasingByDeltaNotInclusive( 0.03), 1), # (param_name , formula_instance , iterations)
parameter_sweep_settings.OrigParameterSweepSettings("pop1_init" , predef_formulas.IncreasingByDeltaNotInclusive( 0.03), 1), # (param_name , formula_instance , iterations)
]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [2025,2050,2075] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def relativeTop12ParamsNoSweep5PercentOptimizePopAndHWI():
# DEFAULT Curvi Results Curvi5%/def Description
# max_tot_fert_norm & 12.0 & 11.4071116380366 & 0.95059263650305 & "Normal maximal total fertility" \\
# p_fioa_cons_const_1 & 0.43 & 0.451499899371272 & 1.0499997659797022 & "Default frac of industrial output allocated to consumption" \\
# p_ind_cap_out_ratio_1 & 3.0 & 3.14847122749876 & 1.0494904091662534 & "Default industrial capital output ratio" \\
# p_serv_cap_out_ratio_1 & 1.0 & 1.04226292625196 & 1.04226292625196 & "Default fraction of service sector output ratio" \\
# life_expect_norm & 28.0 & 29.3138949698442 & 1.0469248203515786 & "Normal life expectancy" \\
# des_compl_fam_size_norm & 3.8 & 3.85710846315913 & 1.0150285429366133 & "Desired normal complete family size" \\
# industrial_capital_init & 210000000000.0 & 199561334891.506 & 0.9502920709119334 & "Initial industrial investment" \\
# p_land_yield_fact_1 & 1.0 & 0.99487613949118 & 0.99487613949118 & "Default land yield factor" \\
# p_nr_res_use_fact_1 & 1.0 & 1.0456279056156 & 1.0456279056156 & "Default non-recoverable resource utilization factor" \\
# reproductive_lifetime & 30.0 & 28.6225928068215 & 0.95408642689405 & "Reproductive life time" \\
# subsist_food_pc & 230.0 & 220.202588706605 & 0.9574025595939348 & "Available per capita food" \\
# p_avg_life_ind_cap_1 & 14.0 & 14.2937741358301 & 1.020983866845007 & "Default average life of industrial capital"; \\
# Curvi run:
# Optimum x0:
# (in the table above)
# With:
# ier = 2 nfu = 1740 nit = 17
# fopt(pop/1e10+hwi) = -0.14702453D+01
# And +-1% of boundaries
maxTotFertNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("max_tot_fert_norm" , predef_formulas . IncreasingByDeltaNotInclusive(-0.04940736349694996 ), 1) # (param_name , formula_instance , iterations)
fioaConsConst1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_fioa_cons_const_1" , predef_formulas . IncreasingByDeltaNotInclusive( 0.04999976597970224 ), 1) # (param_name , formula_instance , iterations)
indCapOutRatio1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas . IncreasingByDeltaNotInclusive( 0.04949040916625336 ), 1) # (param_name , formula_instance , iterations)
servCapOutRatio1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_serv_cap_out_ratio_1" , predef_formulas . IncreasingByDeltaNotInclusive( 0.04226292625196004 ), 1) # (param_name , formula_instance , iterations)
lifeExpectNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("life_expect_norm" , predef_formulas . IncreasingByDeltaNotInclusive( 0.04692482035157863 ), 1) # (param_name , formula_instance , iterations)
desComplFamSizeNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("des_compl_fam_size_norm" , predef_formulas . IncreasingByDeltaNotInclusive( 0.015028542936613265 ), 1) # (param_name , formula_instance , iterations)
indCapInit_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("industrial_capital_init" , predef_formulas . IncreasingByDeltaNotInclusive(-0.04970792908806665 ), 1) # (param_name , formula_instance , iterations)
landYieldFact1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_land_yield_fact_1" , predef_formulas . IncreasingByDeltaNotInclusive(-0.005123860508819966 ), 1) # (param_name , formula_instance , iterations)
nrResUseFact1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_nr_res_use_fact_1" , predef_formulas . IncreasingByDeltaNotInclusive( 0.04562790561559993 ), 1) # (param_name , formula_instance , iterations)
reproLifetime_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("reproductive_lifetime" , predef_formulas . IncreasingByDeltaNotInclusive(-0.04591357310595001 ), 1) # (param_name , formula_instance , iterations)
subsistFoodPc_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("subsist_food_pc" , predef_formulas . IncreasingByDeltaNotInclusive(-0.04259744040606517 ), 1) # (param_name , formula_instance , iterations)
avgLifeIndCap1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas . IncreasingByDeltaNotInclusive( 0.020983866845007082 ), 1) # (param_name , formula_instance , iterations)
# add the sweepSettings to the list
sweep_params_settings_list = [maxTotFertNorm_sweepSettings, fioaConsConst1_sweepSettings, indCapOutRatio1_sweepSettings, servCapOutRatio1_sweepSettings, lifeExpectNorm_sweepSettings, desComplFamSizeNorm_sweepSettings, indCapInit_sweepSettings, landYieldFact1_sweepSettings, nrResUseFact1_sweepSettings, reproLifetime_sweepSettings, subsistFoodPc_sweepSettings, avgLifeIndCap1_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [2025,2050,2075] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def relativeTop12ParamsNoSweep3PercentOptimizePopAndHWI():
# DEFAULT Curvi Results Curvi3%/def Description
# max_tot_fert_norm & 12.0 & 12.3596702764035 & 1.029972523033625 & "Normal maximal total fertility" \\
# p_fioa_cons_const_1 & 0.43 & 0.442898026311551 & 1.029995410026863 & "Default frac of industrial output allocated to consumption" \\
# p_ind_cap_out_ratio_1 & 3.0 & 3.08991285936927 & 1.02997095312309 & "Default industrial capital output ratio" \\
# p_serv_cap_out_ratio_1 & 1.0 & 1.02992984922938 & 1.02992984922938 & "Default fraction of service sector output ratio" \\
# life_expect_norm & 28.0 & 28.8399987213502 & 1.0299999543339358 & "Normal life expectancy" \\
# des_compl_fam_size_norm & 3.8 & 3.91379583273569 & 1.02994627177255 & "Desired normal complete family size" \\
# industrial_capital_init & 210000000000.0 & 203751279614.653 & 0.9702441886412049 & "Initial industrial investment" \\
# p_land_yield_fact_1 & 1.0 & 0.970035858720122 & 0.970035858720122 & "Default land yield factor" \\
# p_nr_res_use_fact_1 & 1.0 & 0.970179653020971 & 0.970179653020971 & "Default non-recoverable resource utilization factor" \\
# reproductive_lifetime & 30.0 & 29.1019099670657 & 0.9700636655688567 & "Reproductive life time" \\
# subsist_food_pc & 230.0 & 223.100157593592 & 0.9700006851895304 & "Available per capita food" \\
# p_avg_life_ind_cap_1 & 14.0 & 13.5800804459832 & 0.9700057461416571 & "Default average life of industrial capital"; \\
# Curvi run:
# Optimum x0:
# (in the table above)
# With:
# ier = 2 nfu = 5182 nit = 68
# fopt(pop/1e10+hwi) = -0.14813551D+01
# And +-1% of boundaries
maxTotFertNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("max_tot_fert_norm" , predef_formulas . IncreasingByDeltaNotInclusive( 0.029972523033624965 ), 1) # (param_name , formula_instance , iterations)
fioaConsConst1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_fioa_cons_const_1" , predef_formulas . IncreasingByDeltaNotInclusive( 0.0299954100268629 ), 1) # (param_name , formula_instance , iterations)
indCapOutRatio1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas . IncreasingByDeltaNotInclusive( 0.02997095312309006 ), 1) # (param_name , formula_instance , iterations)
servCapOutRatio1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_serv_cap_out_ratio_1" , predef_formulas . IncreasingByDeltaNotInclusive( 0.029929849229380023 ), 1) # (param_name , formula_instance , iterations)
lifeExpectNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("life_expect_norm" , predef_formulas . IncreasingByDeltaNotInclusive( 0.029999954333935763 ), 1) # (param_name , formula_instance , iterations)
desComplFamSizeNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("des_compl_fam_size_norm" , predef_formulas . IncreasingByDeltaNotInclusive( 0.029946271772550048 ), 1) # (param_name , formula_instance , iterations)
indCapInit_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("industrial_capital_init" , predef_formulas . IncreasingByDeltaNotInclusive(-0.029755811358795126 ), 1) # (param_name , formula_instance , iterations)
landYieldFact1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_land_yield_fact_1" , predef_formulas . IncreasingByDeltaNotInclusive(-0.02996414127987801 ), 1) # (param_name , formula_instance , iterations)
nrResUseFact1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_nr_res_use_fact_1" , predef_formulas . IncreasingByDeltaNotInclusive(-0.029820346979029022 ), 1) # (param_name , formula_instance , iterations)
reproLifetime_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("reproductive_lifetime" , predef_formulas . IncreasingByDeltaNotInclusive(-0.029936334431143297 ), 1) # (param_name , formula_instance , iterations)
subsistFoodPc_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("subsist_food_pc" , predef_formulas . IncreasingByDeltaNotInclusive(-0.02999931481046958 ), 1) # (param_name , formula_instance , iterations)
avgLifeIndCap1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas . IncreasingByDeltaNotInclusive(-0.029994253858342868 ), 1) # (param_name , formula_instance , iterations)
# add the sweepSettings to the list
sweep_params_settings_list = [maxTotFertNorm_sweepSettings, fioaConsConst1_sweepSettings, indCapOutRatio1_sweepSettings, servCapOutRatio1_sweepSettings, lifeExpectNorm_sweepSettings, desComplFamSizeNorm_sweepSettings, indCapInit_sweepSettings, landYieldFact1_sweepSettings, nrResUseFact1_sweepSettings, reproLifetime_sweepSettings, subsistFoodPc_sweepSettings, avgLifeIndCap1_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [2025,2050,2075] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def relativeTop12ParamsNoSweep1PercentOptimizePop():
# DEFAULT Value WP2(5%, not 1%) Curvi Results Description
# max_tot_fert_norm & 12.0 & 12.60 & 12.1199998842193 & "Normal maximal total fertility" \\
# p_fioa_cons_const_1 & 0.43 & 0.45 & 0.434299995889143 & "Default frac of industrial output allocated to consumption" \\
# p_ind_cap_out_ratio_1 & 3.0 & 3.15 & 3.02999996986311 & "Default industrial capital output ratio" \\
# p_serv_cap_out_ratio_1 & 1.0 & 1.05 & 1.00999999046000 & "Default fraction of service sector output ratio" \\
# life_expect_norm & 28.0 & 29.40 & 28.2799997316718 & "Normal life expectancy" \\
# des_compl_fam_size_norm & 3.8 & 4.00 & 3.83799996359204 & "Desired normal complete family size" \\
# industrial_capital_init & 210000000000.0 & 199500000000.0 & 207900018721.344 & "Initial industrial investment" \\
# x p_land_yield_fact_1 & 1.0 & 0.95 & 0.990001558054131 & "Default land yield factor" \\
# p_nr_res_use_fact_1 & 1.0 & 0.95 & 0.990000010442315 & "Default non-recoverable resource utilization factor" \\
# reproductive_lifetime & 30.0 & 28.5 & 29.7000002862185 & "Reproductive life time" \\
# subsist_food_pc & 230.0 & 218.5 & 227.700002194902 & "Available per capita food" \\
# p_avg_life_ind_cap_1 & 14.0 & 13.29 & 13.8600001336145 & "Default average life of industrial capital"; \\
# Curvi run:
# Optimum x0:
# (in the table above)
# With:
# ier = 0 nfu = 520 nit = 8
# fopt(pop) = -0.53767719D+10
# And +-1% of boundaries
maxTotFertNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("max_tot_fert_norm" , predef_formulas . IncreasingByDeltaNotInclusive(0.01 ), 1) # (param_name , formula_instance , iterations)
fioaConsConst1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_fioa_cons_const_1" , predef_formulas . IncreasingByDeltaNotInclusive(0.01 ), 1) # (param_name , formula_instance , iterations)
indCapOutRatio1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas . IncreasingByDeltaNotInclusive(0.01 ), 1) # (param_name , formula_instance , iterations)
servCapOutRatio1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_serv_cap_out_ratio_1" , predef_formulas . IncreasingByDeltaNotInclusive(0.01 ), 1) # (param_name , formula_instance , iterations)
lifeExpectNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("life_expect_norm" , predef_formulas . IncreasingByDeltaNotInclusive(0.01 ), 1) # (param_name , formula_instance , iterations)
desComplFamSizeNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("des_compl_fam_size_norm" , predef_formulas . IncreasingByDeltaNotInclusive(0.01 ), 1) # (param_name , formula_instance , iterations)
indCapInit_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("industrial_capital_init" , predef_formulas . IncreasingByDeltaNotInclusive(-0.01), 1) # (param_name , formula_instance , iterations)
landYieldFact1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_land_yield_fact_1" , predef_formulas . IncreasingByDeltaNotInclusive(-0.01), 1) # (param_name , formula_instance , iterations)
nrResUseFact1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_nr_res_use_fact_1" , predef_formulas . IncreasingByDeltaNotInclusive(-0.01), 1) # (param_name , formula_instance , iterations)
reproLifetime_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("reproductive_lifetime" , predef_formulas . IncreasingByDeltaNotInclusive(-0.01), 1) # (param_name , formula_instance , iterations)
subsistFoodPc_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("subsist_food_pc" , predef_formulas . IncreasingByDeltaNotInclusive(-0.01), 1) # (param_name , formula_instance , iterations)
avgLifeIndCap1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas . IncreasingByDeltaNotInclusive(-0.01), 1) # (param_name , formula_instance , iterations)
# add the sweepSettings to the list
sweep_params_settings_list = [maxTotFertNorm_sweepSettings, fioaConsConst1_sweepSettings, indCapOutRatio1_sweepSettings, servCapOutRatio1_sweepSettings, lifeExpectNorm_sweepSettings, desComplFamSizeNorm_sweepSettings, indCapInit_sweepSettings, landYieldFact1_sweepSettings, nrResUseFact1_sweepSettings, reproLifetime_sweepSettings, subsistFoodPc_sweepSettings, avgLifeIndCap1_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [2025,2050,2075] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def relativeTop12ParamsSweepOf2Params5PercentOptimizePop():
# We sweep the 2 params that differ from the single sensitivity calculations of Relative (presented in wp2)
# (similar to the "NoSweep" variant of this run, but sweeping 2 parameters)
# Table from WP2 + curvi results (the ones with an x differ between "individual" (wp2) and "together" (curvi)
# DEFAULT Value WP2 Curvi Results Description
# max_tot_fert_norm & 12.0 & 12.60 & 12.5999994203700 & "Normal maximal total fertility" \\
# p_fioa_cons_const_1 & 0.43 & 0.45 & 0.448380420759870 & "Default frac of industrial output allocated to consumption" \\
# p_ind_cap_out_ratio_1 & 3.0 & 3.15 & 3.14999863042567 & "Default industrial capital output ratio" \\
# p_serv_cap_out_ratio_1 & 1.0 & 1.05 & 1.04559432323735 & "Default fraction of service sector output ratio" \\
# life_expect_norm & 28.0 & 29.40 & 29.3999986573765 & "Normal life expectancy" \\
# des_compl_fam_size_norm & 3.8 & 4.00 & 3.98999981851597 & "Desired normal complete family size" \\
# industrial_capital_init & 210000000000.0 & 199500000000.0 & 199499999088.315 & "Initial industrial investment" \\
# x p_land_yield_fact_1 & 1.0 & 0.95 & 1.04989368154214 & "Default land yield factor" \\
# p_nr_res_use_fact_1 & 1.0 & 0.95 & 0.949999988082543 & "Default non-recoverable resource utilization factor" \\
# reproductive_lifetime & 30.0 & 28.5 & 28.4999996571028 & "Reproductive life time" \\
# subsist_food_pc & 230.0 & 218.5 & 218.499997333924 & "Available per capita food" \\
# x p_avg_life_ind_cap_1 & 14.0 & 13.29 & 14.6999966717931 & "Default average life of industrial capital"; \\
# Curvi run:
# Optimum x0:
# (in the table above)
# With:
# ier = 2 nfu = 2623 nit = 33
# And +-5% of boundaries
sweep_params_settings_list = [
parameter_sweep_settings . OrigParameterSweepSettings("p_land_yield_fact_1" , predef_formulas . DeltaBeforeAndAfter(0.05 ), 3), # (param_name , formula_instance , iterations)
parameter_sweep_settings . OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas . DeltaBeforeAndAfter(0.05), 3), # (param_name , formula_instance , iterations)
]
# add the sweepSettings to the list
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [
("p_nr_res_use_fact_1" ,0.949999988082543),
("max_tot_fert_norm" ,12.5999994203700),
("p_fioa_cons_const_1" ,0.448380420759870),
("p_ind_cap_out_ratio_1" ,3.14999863042567),
("p_serv_cap_out_ratio_1" ,1.04559432323735),
("life_expect_norm" ,29.3999986573765),
("des_compl_fam_size_norm" ,3.98999981851597),
("industrial_capital_init" ,199499999088.315),
("reproductive_lifetime" ,28.4999996571028),
("subsist_food_pc" ,218.499997333924)
],
"fixed_params_description_str": "10 parameters were perturbed to a fixed value. See description.",
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [2025,2050,2075] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def relativeTop12ParamsNoSweep5PercentOptimizePop():
# Table from WP2 + curvi results (the ones with an x differ between "individual" (wp2) and "together" (curvi)
# DEFAULT Value WP2 Curvi Results Description
# max_tot_fert_norm & 12.0 & 12.60 & 12.5999994203700 & "Normal maximal total fertility" \\
# p_fioa_cons_const_1 & 0.43 & 0.45 & 0.448380420759870 & "Default frac of industrial output allocated to consumption" \\
# p_ind_cap_out_ratio_1 & 3.0 & 3.15 & 3.14999863042567 & "Default industrial capital output ratio" \\
# p_serv_cap_out_ratio_1 & 1.0 & 1.05 & 1.04559432323735 & "Default fraction of service sector output ratio" \\
# life_expect_norm & 28.0 & 29.40 & 29.3999986573765 & "Normal life expectancy" \\
# des_compl_fam_size_norm & 3.8 & 4.00 & 3.98999981851597 & "Desired normal complete family size" \\
# industrial_capital_init & 210000000000.0 & 199500000000.0 & 199499999088.315 & "Initial industrial investment" \\
# x p_land_yield_fact_1 & 1.0 & 0.95 & 1.04989368154214 & "Default land yield factor" \\
# p_nr_res_use_fact_1 & 1.0 & 0.95 & 0.949999988082543 & "Default non-recoverable resource utilization factor" \\
# reproductive_lifetime & 30.0 & 28.5 & 28.4999996571028 & "Reproductive life time" \\
# subsist_food_pc & 230.0 & 218.5 & 218.499997333924 & "Available per capita food" \\
# x p_avg_life_ind_cap_1 & 14.0 & 13.29 & 14.6999966717931 & "Default average life of industrial capital"; \\
# Curvi run:
# Optimum x0:
# (in the table above)
# With:
# ier = 2 nfu = 2623 nit = 33
# And +-5% of boundaries
maxTotFertNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("max_tot_fert_norm" , predef_formulas . IncreasingByDeltaNotInclusive(0.0499999516975 ), 1) # (param_name , formula_instance , iterations)
fioaConsConst1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_fioa_cons_const_1" , predef_formulas . IncreasingByDeltaNotInclusive(0.0427451645578372 ), 1) # (param_name , formula_instance , iterations)
indCapOutRatio1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas . IncreasingByDeltaNotInclusive(0.0499995434752234 ), 1) # (param_name , formula_instance , iterations)
servCapOutRatio1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_serv_cap_out_ratio_1" , predef_formulas . IncreasingByDeltaNotInclusive(0.04559432323735 ), 1) # (param_name , formula_instance , iterations)
lifeExpectNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("life_expect_norm" , predef_formulas . IncreasingByDeltaNotInclusive(0.0499999520491607 ), 1) # (param_name , formula_instance , iterations)
desComplFamSizeNorm_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("des_compl_fam_size_norm" , predef_formulas . IncreasingByDeltaNotInclusive(0.0499999522410448 ), 1) # (param_name , formula_instance , iterations)
indCapInit_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("industrial_capital_init" , predef_formulas . IncreasingByDeltaNotInclusive(-0.05 ), 1) # (param_name , formula_instance , iterations)
landYieldFact1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_land_yield_fact_1" , predef_formulas . IncreasingByDeltaNotInclusive(0.04989368154214 ), 1) # (param_name , formula_instance , iterations)
nrResUseFact1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_nr_res_use_fact_1" , predef_formulas . IncreasingByDeltaNotInclusive(-0.05 ), 1) # (param_name , formula_instance , iterations)
reproLifetime_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("reproductive_lifetime" , predef_formulas . IncreasingByDeltaNotInclusive(-0.05 ), 1) # (param_name , formula_instance , iterations)
subsistFoodPc_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("subsist_food_pc" , predef_formulas . IncreasingByDeltaNotInclusive(-0.05 ), 1) # (param_name , formula_instance , iterations)
avgLifeIndCap1_sweepSettings = parameter_sweep_settings . OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas . IncreasingByDeltaNotInclusive(0.0499997622709356 ), 1) # (param_name , formula_instance , iterations)
# add the sweepSettings to the list
sweep_params_settings_list = [maxTotFertNorm_sweepSettings, fioaConsConst1_sweepSettings, indCapOutRatio1_sweepSettings, servCapOutRatio1_sweepSettings, lifeExpectNorm_sweepSettings, desComplFamSizeNorm_sweepSettings, indCapInit_sweepSettings, landYieldFact1_sweepSettings, nrResUseFact1_sweepSettings, reproLifetime_sweepSettings, subsistFoodPc_sweepSettings, avgLifeIndCap1_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","human_welfare_index"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [2025,2050,2075] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def hugoScolnikParamsCurvi02():
# Hugo Scolnik article: "Crítica metodológica al modelo WORLD 3" (Methodological criticisim to the World3 model)
# Perturbed 5 params by 5%
# ICOR= 3.15, Default: ICOR=3
# ALIC= 13.3, Default: ALIC=14
# ALSC= 17.1, Default: ALSC=20
# SCOR= 1.05, Default: SCOR=1
# Run "Perturbed": FFW= 0.231, Default: FFW=0.22
# Run "Perturbed Increasing FFW": FFW= 0.242, Default: FFW=0.22
# Perturbed rest of the params by a scalar of 0.24172080E-12
# This function is based in the results of curvi+w3:
# Optimum x0:
# p_ind_cap_out_ratio_1 - 3.15 ==> +5%
# p_avg_life_ind_cap_1 - 13.3 ==> -5%
# p_avg_life_serv_cap_1 - 19.0 ==> -5%
# p_serv_cap_out_ratio_1 - 1.05 ==> +5%
# With:
# ier = 2 nfu = 1964 nit = 93 fopt(population) = -9985562545.07286
# And +-5% of boundaries
icor_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas.IncreasingByPercentage(5), 2) # (param_name , formula_instance , iterations)
ialic_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas.IncreasingByPercentage(-5), 2) # (param_name , formula_instance , iterations)
ialsc_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_avg_life_serv_cap_1" , predef_formulas.IncreasingByPercentage(-5), 2) # (param_name , formula_instance , iterations)
iscor_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_serv_cap_out_ratio_1" , predef_formulas.IncreasingByPercentage(5), 2) # (param_name , formula_instance , iterations)
# add the sweepSettings to the list
sweep_params_settings_list = [ icor_sweepSettings, ialic_sweepSettings, ialsc_sweepSettings, iscor_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population"],
"stopTime" : 2100 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [2025,2050,2075] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def hugoScolnikParamsCurvi01():
# Hugo Scolnik article: "Crítica metodológica al modelo WORLD 3" (Methodological criticisim to the World3 model)
# Perturbed 5 params by 5%
# ICOR= 3.15, Default: ICOR=3
# ALIC= 13.3, Default: ALIC=14
# ALSC= 17.1, Default: ALSC=20
# SCOR= 1.05, Default: SCOR=1
# Run "Perturbed": FFW= 0.231, Default: FFW=0.22
# Run "Perturbed Increasing FFW": FFW= 0.242, Default: FFW=0.22
# Perturbed rest of the params by a scalar of 0.24172080E-12
# This function is based in the results of curvi+w3:
# Optimum x0:
# p_ind_cap_out_ratio_1 - 3.93944837212699... Default: ICOR=3 ==> +31%
# p_avg_life_ind_cap_1 - 14.4095197725215... Default: ALIC=14 ==> +03%
# p_avg_life_serv_cap_1 - 24.8371810528411... Default: ALSC=20 ==> +24%
# p_serv_cap_out_ratio_1 - 0.500018268440072.. Default: SCOR=1 ==> -50%
# With:
# ier = 2 nfu = 1964 nit = 93 fopt(population) = -9985562545.07286
# And with big boundaries (~50%)
icor_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas.IncreasingByPercentage(16) , 3) # (param_name , formula_instance , iterations)
ialic_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas.IncreasingByPercentage(0.015) , 3) # (param_name , formula_instance , iterations)
ialsc_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_avg_life_serv_cap_1" , predef_formulas.IncreasingByPercentage(12) , 3) # (param_name , formula_instance , iterations)
iscor_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_serv_cap_out_ratio_1" , predef_formulas.IncreasingByPercentage(-25) , 3) # (param_name , formula_instance , iterations)
# add the sweepSettings to the list
sweep_params_settings_list = [ icor_sweepSettings, ialic_sweepSettings, ialsc_sweepSettings, iscor_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population"],
"stopTime" : 2100 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def nrResourcesInitZXPOWLNoSweepOptimizePop():
# Curvi results:
# ier = 132 nfu = 1 nit = 0
# Vector solucion =
#
# 1331113420897.75 ===> 1.33111342089775 ===> +33%
#
# fopt(population) = -0.43936607D+10
nRResInit_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("nr_resources_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.33111342089775), 1) # (param_name , formula_instance , iterations)
# add the sweepSettings to the list
sweep_params_settings_list = [nRResInit_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def nrResourcesInitCurviNoSweepOptimizePop():
# Curvi results:
# ier = 2 nfu = 370 nit = 14
# Vector solucion =
#
# 1322956409277.25 ==> def/curvi = 1.32295640927725 = +32%
#
# fopt(population) = -0.43937738D+10
nRResInit_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("nr_resources_init" , predef_formulas.IncreasingByDeltaNotInclusive(0.32295640927725), 1) # (param_name , formula_instance , iterations)
# add the sweepSettings to the list
sweep_params_settings_list = [nRResInit_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def relativeTop2for2100AndTop8For2000():
# We try to increase the population for both 2100 and 2000 to try and fit the function between 1900 and 2000 and still have an effect on 2100
# Top 2 for 2100 up and top 3-8 for 2000 also up (to try and revert the (-) effect on those top 2). Some of these "top 3-8" for 2000 have a negative effect in 2100.
# Parameter | Position in 2000 sorted for pop | Position in 2100 sorted for pop
# p_fioa_cons_const_1 1 (-0.1896934079) 1 (0.4367021008)
# p_ind_cap_out_ratio_1 2 (-0.1590716648) 2 (0.30914336)
# p_avg_life_ind_cap_1 3 (0.0880944114) 6 (-0.0982760653)
# reproductive_lifetime 4 (-0.0598396094) 3 (-0.1321867349) <- we affect this one negatively (-5%)
# land_fr_harvested 5 (0.0549721817) 20 (-0.0083348982)
# inherent_land_fert 6 (0.0511521159) 21 (-0.0080056655)
# p_land_yield_fact_1 7 (0.0508257086) 12 (-0.0269650958)
# des_compl_fam_size_norm 8 (0.0477282242) 5 (0.1060414143)
fioaConsConst_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_fioa_cons_const_1" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
indCapOutRat_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
avgLifeIndCap_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
reproLifet_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("reproductive_lifetime" , predef_formulas.IncreasingByPercentageNotInclusive(-5), 1) # (param_name , formula_instance , iterations)
landFrHarvested_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("land_fr_harvested" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
inherentLandFert_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("inherent_land_fert" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
landYieldFact1_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_land_yield_fact_1" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
desComplFamSizeNorm_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("des_compl_fam_size_norm" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
# add the sweepSettings to the list
sweep_params_settings_list = [fioaConsConst_sweepSettings, indCapOutRat_sweepSettings, avgLifeIndCap_sweepSettings, reproLifet_sweepSettings, landFrHarvested_sweepSettings, inherentLandFert_sweepSettings, landYieldFact1_sweepSettings, desComplFamSizeNorm_sweepSettings,]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population","ppoll_index","industrial_output","nr_resources","food"],
"stopTime" : 2100 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
"extra_ticks" : [1940] # extra years ticks for the plot(s)
}
setUpSweepsAndRun(**run_kwargs)
def relativeTop2for2100AndManyPositiveFor2000():
# We try to increase the population for 2100 and decrease it for 2000 to try and fit the function between 1900 and 2000 and still have an effect on 2100
# Top 2 for 2100 up and only the positive in both up. The Top 2 for 2100 affect the one in 2000 negatively so we try to revert those changes with only positive for 2000 (that are also positive in 2100)
# Parameter | Position in 2000 sorted for pop | Position in 2100 sorted for pop
# p_fioa_cons_const_1 1 (-0.1896934079) 1 (0.4367021008)
# p_ind_cap_out_ratio_1 2 (-0.1590716648) 2 (0.30914336)
# life_expect_norm 11 (0.0305075556) 4 (0.1315758044)
# des_compl_fam_size_norm 8 (0.0477282242) 5 (0.1060414143)
# max_tot_fert_norm 18 (0.009409269) 9 (0.0345123911)
# lifet_perc_del 22 (0.0044847922) 22 (0.0068062731)
# avg_life_land_norm 26 (0.0020099261) 28 (0.0037855243)
# ppoll_in_1970 29 (0.0012835169) 31 (0.0027901066)
# income_expect_avg_time 21 (0.0050549969) 33 (0.0018008269)
# social_adj_del 24 (0.003111505) 34 (0.0016976242)
fioaConsConst_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_fioa_cons_const_1" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
indCapOutRat_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
lifeExpectNorm_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("life_expect_norm" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
desComplFamSizeNorm_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("des_compl_fam_size_norm" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
maxTotFertNorm_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("max_tot_fert_norm" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
lifetPercDel_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("lifet_perc_del" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
avgLifeLandNorm_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("avg_life_land_norm" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
ppollIn1970_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("ppoll_in_1970" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
incomeExpectAvgTime_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("income_expect_avg_time" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
socialAdjDel_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("social_adj_del" , predef_formulas.IncreasingByPercentageNotInclusive(5), 1) # (param_name , formula_instance , iterations)
# Add the sweepSettings to the following list
sweep_params_settings_list = [ fioaConsConst_sweepSettings, indCapOutRat_sweepSettings, lifeExpectNorm_sweepSettings, desComplFamSizeNorm_sweepSettings, maxTotFertNorm_sweepSettings,lifetPercDel_sweepSettings, avgLifeLandNorm_sweepSettings, ppollIn1970_sweepSettings, incomeExpectAvgTime_sweepSettings, socialAdjDel_sweepSettings,]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population"],
"stopTime" : 2100 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : True, #Choose to plot std run alognside this test results
}
setUpSweepsAndRun(**run_kwargs)
### WP 3 tests ####
def test12fromTop12RelativeWP2OneUpOneDown():
# Declare each parameter settings separately and then add them to the list manually
# Con one up one down
indCapInit_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("industrial_capital_init" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
# Orig
landYieldFact_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_land_yield_fact_1" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
nRResUseFact_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_nr_res_use_fact_1" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
reproLifet_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("reproductive_lifetime" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
subsistFood_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("subsist_food_pc" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
avgLifeIndCap_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_avg_life_ind_cap_1" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
maxTotFertNorm_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("max_tot_fert_norm" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
fioaConsConst_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_fioa_cons_const_1" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
indCapOutRat_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
servCapOutRatio_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_serv_cap_out_ratio_1" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
lifeExpectNorm_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("life_expect_norm" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
desComplFamSizeNorm_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("des_compl_fam_size_norm" , predef_formulas.DeltaOneUpAndOneDown(0.01) , 2) # (param_name , formula_instance , iterations)
sweep_params_settings_list = [indCapInit_sweepSettings, landYieldFact_sweepSettings, nRResUseFact_sweepSettings, reproLifet_sweepSettings, subsistFood_sweepSettings, avgLifeIndCap_sweepSettings, maxTotFertNorm_sweepSettings, fioaConsConst_sweepSettings, indCapOutRat_sweepSettings, servCapOutRatio_sweepSettings, lifeExpectNorm_sweepSettings, desComplFamSizeNorm_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population"],
"stopTime" : 2100 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : False, #Choose to plot std run alognside this test results
}
setUpSweepsAndRun(**run_kwargs)
def test3fromTop12RelativeWP2():
# Declare each parameter settings separately and then add them to the list manually
fioaConsConst_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_fioa_cons_const_1" , predef_formulas.DeltaBeforeAndAfter(0.01) , 5) # (param_name , formula_instance , iterations)
indCapOutRat_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas.DeltaBeforeAndAfter(0.01) , 5) # (param_name , formula_instance , iterations)
reproLifet_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("reproductive_lifetime" , predef_formulas.DeltaBeforeAndAfter(0.01) , 5) # (param_name , formula_instance , iterations)
sweep_params_settings_list = [ fioaConsConst_sweepSettings, indCapOutRat_sweepSettings, reproLifet_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : False, #Choose to plot std run alognside this test results
}
setUpSweepsAndRun(**run_kwargs)
def test3Params():
inExAvgTim_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("income_expect_avg_time" , predef_formulas.DeltaBeforeAndAfter(0.01) , 5) # (param_name , formula_instance , iterations)
indCapOutRat_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("p_ind_cap_out_ratio_1" , predef_formulas.IncreasingByPercentage(5) , 2) # (param_name , formula_instance , iterations)
nRResInit_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("nr_resources_init" , predef_formulas.DeltaBeforeAndAfter(0.1) , 5) # (param_name , formula_instance , iterations)
sweep_params_settings_list = [ inExAvgTim_sweepSettings, indCapOutRat_sweepSettings,nRResInit_sweepSettings]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars" : ["population"],
"stopTime" : 2500 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], #We don't want to change any parameters
"mo_file" : piecewiseMod_SysDyn_mo_path, # mo file with tabular modified (to allow out of tabular interpolation)
"plot_std_run" : False, #Choose to plot std run alognside this test results
}
setUpSweepsAndRun(**run_kwargs)
### WP 1 tests ####
def testNRResources():
nRResInit_sweepSettings = parameter_sweep_settings.OrigParameterSweepSettings("nr_resources_init" , predef_formulas.DeltaBeforeAndAfter(0.1) , 10) # (param_name , formula_instance , iterations)
sweep_params_settings_list = [ nRResInit_sweepSettings ]
run_kwargs = {
"sweep_params_settings_list" : sweep_params_settings_list,
"plot_vars":["Food_Production1Agr_InpIntegrator1y","Arable_Land_Dynamics1Pot_Arable_LandIntegrator1y","Arable_Land_Dynamics1Arable_LandIntegrator1y","population","nr_resources"], # Examples: SPECIAL_policy_years, ["nr_resources_init"]
"stopTime": 2100 ,# year to end the simulation (2100 for example)
"scens_to_run" : [1], #The standard run corresponds to the first scenario
"fixed_params" : [], # No fixed parameter changes. Example: [("nr_resources_init",6.3e9),("des_compl_fam_size_norm",2),...]
"mo_file" : vanilla_SysDyn_mo_path, # Mo without modifications
"plot_std_run": False, #Choose to plot std run alognside this test results
}
setUpSweepsAndRun(**run_kwargs)
# Functions:
def setUpSweepsAndRun(sweep_params_settings_list,fixed_params,plot_vars,stopTime,scens_to_run,mo_file,plot_std_run,fixed_params_description_str=False,extra_ticks=[]):
startTime = 1900 # year to start the simulation. Because W3-Mod needs the starttime to be always 1900, we don't allow the user to change it
#The "root" output folder path.
output_root_path = files_aux.makeOutputPath("modelica_multiparam_sweep")
#Create scenarios from factory
scenarios = []
for scen_num in scens_to_run:
folder_name = "scenario_"+str(scen_num)
logger.info("Running scenario {folder_name}".format(folder_name=folder_name))
# Create main folder
scen_folder_path = os.path.join(output_root_path,folder_name)
os.makedirs(scen_folder_path)
# Create run folder
run_folder_path = os.path.join(scen_folder_path,"run")
os.makedirs(run_folder_path)
# Write 2 copies of the output mos_path: one in the root folder of the scenario and the other inside the 'run' folder. The second one will be the one being executed.
output_mos_copy_path = os.path.join(scen_folder_path,gral_settings.mos_script_filename)