From 8220c39386b1428b05b89f3edc80c32fb73ffb8a Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Mon, 30 Oct 2023 09:58:06 -0500 Subject: [PATCH 01/28] SIDARTHE model support --- .../BIOMD0000000955_askenet_request.json | 105 ++++++++++ scratch/hackathon/hackathon_fall_2023_demo.py | 198 +++++++++++++++++- src/funman/api/run.py | 2 +- src/funman/representation/box.py | 14 +- src/funman/representation/constraint.py | 18 +- src/funman/representation/interval.py | 3 +- src/funman/representation/parameter_space.py | 29 ++- src/funman/representation/representation.py | 23 +- src/funman/scenario/parameter_synthesis.py | 7 +- src/funman/scenario/scenario.py | 44 +++- src/funman/search/box_search.py | 80 ++++--- src/funman/search/search.py | 4 +- src/funman/server/query.py | 7 +- src/funman/translate/translate.py | 34 ++- 14 files changed, 488 insertions(+), 80 deletions(-) diff --git a/resources/amr/petrinet/mira/requests/BIOMD0000000955_askenet_request.json b/resources/amr/petrinet/mira/requests/BIOMD0000000955_askenet_request.json index 623ad00d..ca90ebbc 100644 --- a/resources/amr/petrinet/mira/requests/BIOMD0000000955_askenet_request.json +++ b/resources/amr/petrinet/mira/requests/BIOMD0000000955_askenet_request.json @@ -6,6 +6,111 @@ "lb": 0.008799999999999999, "ub": 0.0132 } + }, + { + "name": "gamma", + "interval": { + "lb": 0.3648, + "ub": 0.5472 + } + }, + { + "name": "delta", + "interval": { + "lb": 0.008799999999999999, + "ub": 0.0132 + } + }, + { + "name": "alpha", + "interval": { + "lb": 0.45599999999999996, + "ub": 0.6839999999999999 + } + }, + { + "name": "epsilon", + "interval": { + "lb": 0.1368, + "ub": 0.20520000000000002 + } + }, + { + "name": "zeta", + "interval": { + "lb": 0.1, + "ub": 0.15 + } + }, + { + "name": "lambda", + "interval": { + "lb": 0.027200000000000002, + "ub": 0.0408 + } + }, + { + "name": "eta", + "interval": { + "lb": 0.1, + "ub": 0.15 + } + }, + { + "name": "rho", + "interval": { + "lb": 0.027200000000000002, + "ub": 0.0408 + } + }, + { + "name": "theta", + "interval": { + "lb": 0.2968, + "ub": 0.4452 + } + }, + { + "name": "kappa", + "interval": { + "lb": 0.013600000000000001, + "ub": 0.0204 + } + }, + { + "name": "mu", + "interval": { + "lb": 0.013600000000000001, + "ub": 0.0204 + } + }, + { + "name": "nu", + "interval": { + "lb": 0.0216, + "ub": 0.0324 + } + }, + { + "name": "xi", + "interval": { + "lb": 0.013600000000000001, + "ub": 0.0204 + } + }, + { + "name": "tau", + "interval": { + "lb": 0.008, + "ub": 0.012 + } + }, + { + "name": "sigma", + "interval": { + "lb": 0.013600000000000001, + "ub": 0.0204 + } } ], "structure_parameters": [ diff --git a/scratch/hackathon/hackathon_fall_2023_demo.py b/scratch/hackathon/hackathon_fall_2023_demo.py index e26c030f..1df8b189 100644 --- a/scratch/hackathon/hackathon_fall_2023_demo.py +++ b/scratch/hackathon/hackathon_fall_2023_demo.py @@ -19,12 +19,204 @@ def main(): EXAMPLE_DIR, "requests", "BIOMD0000000955_askenet_request.json" ) + request_dict = { + "parameters": [ + { + "name": "beta", + "interval": { + "lb": 0.011, + "ub": 0.011, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.008799999999999999, "ub": 0.0132}, + }, + { + "name": "gamma", + "interval": { + "lb": 0.456, + "ub": 0.456, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.3648, "ub": 0.5472} + }, + { + "name": "delta", + "interval": { + "lb": 0.011, + "ub": 0.011, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.008799999999999999, "ub": 0.0132}, + }, + { + "name": "alpha", + "interval": { + "lb": 0.57, + "ub": 0.57, + "closed_upper_bound": True, + } + # "interval": { + # "lb": 0.45599999999999996, + # "ub": 0.6839999999999999, + # }, + }, + { + "name": "epsilon", + "interval": {"lb": 0.1368, "ub": 0.20520000000000002}, + "label": "all", + }, + { + "name": "zeta", + "interval": { + "lb": 0.125, + "ub": 0.125, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.1, "ub": 0.15} + }, + { + "name": "lambda", + "interval": { + "lb": 0.034, + "ub": 0.034, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.027200000000000002, "ub": 0.0408}, + }, + { + "name": "eta", + "interval": { + "lb": 0.125, + "ub": 0.125, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.1, "ub": 0.15} + }, + { + "name": "rho", + "interval": { + "lb": 0.034, + "ub": 0.034, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.027200000000000002, "ub": 0.0408}, + }, + { + "name": "theta", + "interval": {"lb": 0.2968, "ub": 0.4452}, + "label": "all", + }, + { + "name": "kappa", + "interval": { + "lb": 0.017, + "ub": 0.017, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.013600000000000001, "ub": 0.0204}, + }, + { + "name": "mu", + "interval": { + "lb": 0.017, + "ub": 0.017, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.013600000000000001, "ub": 0.0204}, + }, + { + "name": "nu", + "interval": { + "lb": 0.027, + "ub": 0.027, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.0216, "ub": 0.0324} + }, + { + "name": "xi", + "interval": { + "lb": 0.017, + "ub": 0.017, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.013600000000000001, "ub": 0.0204}, + }, + { + "name": "tau", + "interval": { + "lb": 0.01, + "ub": 0.01, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.008, "ub": 0.012} + }, + { + "name": "sigma", + "interval": { + "lb": 0.017, + "ub": 0.017, + "closed_upper_bound": True, + } + # "interval": {"lb": 0.013600000000000001, "ub": 0.0204}, + }, + ], + "constraints": [ + { + "name": "theta_epsilon", + "additive_bounds": {"lb": 0}, + "variables": ["theta", "epsilon"], + "weights": [1, -2], + # No timepoints, because the variables are parameters + }, + { + "name": "infected_maximum1", + "variable": "Infected", + "interval": {"ub": 0.2}, + "timepoints": {"lb": 50, "ub": 70, "closed_upper_bound": True}, + }, + { + "name": "infected_maximum2", + "variable": "Infected", + "interval": {"ub": 0.1}, + "timepoints": {"lb": 0, "ub": 50}, + }, + { + "name": "infected_maximum3", + "variable": "Infected", + "interval": {"ub": 0.1}, + "timepoints": {"lb": 71}, + }, + ], + "structure_parameters": [ + { + "name": "schedules", + "schedules": [ + # {"timepoints": [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]} + {"timepoints": [0, 10]} + ], + } + ], + "config": { + "use_compartmental_constraints": True, + "normalization_constant": 1.0, + "tolerance": 1e-1, + "verbosity": 10, + "dreal_mcts": False, + "save_smtlib": False, + "substitute_subformulas": False, + "series_approximation_threshold": None, + "dreal_log_level": "none", + "profile": True, + }, + } + # Use request_dict results = Runner().run( MODEL_PATH, - # request_dict, - REQUEST_PATH, - description="Basic SIR with simple request", + request_dict, + # REQUEST_PATH, + description="SIDARTHE demo", case_out_dir="./out", ) points = results.points() diff --git a/src/funman/api/run.py b/src/funman/api/run.py index d7ac7b20..d15808fa 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -221,7 +221,7 @@ def run_instance( # ).plot(show=False) # plt.savefig(f"{out_dir}/{model.__module__}.png") # plt.close() - sleep(5) + sleep(10) else: results = self._worker.get_results(work_unit.id) break diff --git a/src/funman/representation/box.py b/src/funman/representation/box.py index d975944a..521aa437 100644 --- a/src/funman/representation/box.py +++ b/src/funman/representation/box.py @@ -251,7 +251,15 @@ def __repr__(self): return str(self.model_dump()) def __str__(self): - return f"Box(t_{self.timestep()}={Interval(lb=self.schedule.time_at_step(int(self.timestep().lb)), ub=self.schedule.time_at_step(int(self.timestep().ub)), closed_upper_bound=True)} {self.bounds}), width = {self.width()}" + bounds_str = "\n".join( + [ + f"{k}:\t{str(v)}\t({v.width():.5f})" + for k, v in self.bounds.items() + ] + ) + box_str = f"Box(label: {self.label}\nwidth: {self.width()},\ntimepoints: {Interval(lb=self.schedule.time_at_step(int(self.timestep().lb)), ub=self.schedule.time_at_step(int(self.timestep().ub)), closed_upper_bound=True)},\n{bounds_str}\n)" + return box_str + # return f"Box(t_{self.timestep()}={Interval(lb=self.schedule.time_at_step(int(self.timestep().lb)), ub=self.schedule.time_at_step(int(self.timestep().ub)), closed_upper_bound=True)} {self.bounds}), width = {self.width()}" def finite(self) -> bool: """ @@ -514,6 +522,10 @@ def volume( num_timepoints = Decimal( int(self.timestep().ub) + 1 - int(self.timestep().lb) ).to_integral_exact(rounding=ROUND_CEILING) + if normalize is not None: + num_timepoints = num_timepoints / Decimal( + len(self.schedule.timepoints) + ) elif "num_steps" in widths: del widths["num_steps"] # TODO this timepoint computation could use more thought diff --git a/src/funman/representation/constraint.py b/src/funman/representation/constraint.py index cbb59557..cec2908a 100644 --- a/src/funman/representation/constraint.py +++ b/src/funman/representation/constraint.py @@ -20,7 +20,8 @@ class Constraint(BaseModel): soft: bool = True name: str - # model_config = ConfigDict(extra="forbid") + def time_dependent(self) -> bool: + return False def __hash__(self) -> int: return 1 @@ -36,11 +37,18 @@ class TimedConstraint(Constraint): timepoints: Optional["Interval"] = None def contains_time(self, time: Union[float, int]) -> bool: - return self.timepoints is None or self.timepoints.contains_value(time) + return ( + self.timepoints.contains_value(time) + if self.time_dependent() + else time == 0 + ) def relevant_at_time(self, time: int) -> bool: return self.contains_time(time) + def time_dependent(self) -> bool: + return self.timepoints is not None + class ModelConstraint(Constraint): soft: bool = False @@ -114,12 +122,6 @@ def check_weights( def __hash__(self) -> int: return 4 - def contains_time(self, time: Union[float, int]) -> bool: - return self.timepoints is None or self.timepoints.contains_value(time) - - def relevant_at_time(self, time: int) -> bool: - return self.contains_time(time) - FunmanConstraint = Union[ ModelConstraint, diff --git a/src/funman/representation/interval.py b/src/funman/representation/interval.py index dae0c786..25b86941 100644 --- a/src/funman/representation/interval.py +++ b/src/funman/representation/interval.py @@ -98,7 +98,8 @@ def __repr__(self): return str(self.model_dump()) def __str__(self): - return f"Interval([{self.lb}, {self.ub}))" + ub = "]" if self.closed_upper_bound else ")" + return f"[{self.lb:.5f}, {self.ub:.5f}{ub}" def meets(self, other: "Interval") -> bool: """ diff --git a/src/funman/representation/parameter_space.py b/src/funman/representation/parameter_space.py index 1628a1e5..b83ddb43 100644 --- a/src/funman/representation/parameter_space.py +++ b/src/funman/representation/parameter_space.py @@ -5,6 +5,7 @@ from matplotlib.lines import Line2D from pydantic import BaseModel +from ..constants import LABEL_DROPPED, LABEL_FALSE, LABEL_TRUE from . import Interval, Point from .box import Box from .interval import Interval @@ -25,6 +26,20 @@ class ParameterSpace(BaseModel): false_boxes: List[Box] = [] unknown_points: List[Point] = [] + def __str__(self, dropped_boxes=[]) -> str: + box_labels = {LABEL_TRUE: "+", LABEL_FALSE: "-", LABEL_DROPPED: "x"} + boxes = self.boxes() + steps = {} + for box in boxes + dropped_boxes: + label = box_labels[box.label] + for step in range( + int(box.timestep().lb), int(box.timestep().ub) + 1 + ): + boxes_at_step = steps.get(step, []) + boxes_at_step.append(label) + steps[step] = boxes_at_step + return "\n".join([f"{k}:[{''.join(v)}]" for k, v in steps.items()]) + def true_points(self) -> List[Point]: return [pt for b in self.true_boxes for pt in b.true_points()] @@ -283,17 +298,23 @@ def _compact(self): self.true_boxes = self._box_list_compact(self.true_boxes) self.false_boxes = self._box_list_compact(self.false_boxes) - def labeled_volume(self): - self._compact() + def labeled_volume(self, scenario: "AnalysisScenario"): + # self._compact() labeled_vol = 0 # TODO should actually be able to compact the true and false boxes together, since they are both labeled. # TODO can calculate the percentage of the total parameter space. Is there an efficient way to get the initial PS so we can find the volume of that box? or to access unknown boxes? for box in self.true_boxes: - true_volume = box.volume() + true_volume = box.volume( + parameters=scenario.model_parameters(), + normalize=scenario._original_parameter_widths, + ) labeled_vol += true_volume for box in self.false_boxes: - false_volume = box.volume() + false_volume = box.volume( + parameters=scenario.model_parameters(), + normalize=scenario._original_parameter_widths, + ) labeled_vol += false_volume return labeled_vol diff --git a/src/funman/representation/representation.py b/src/funman/representation/representation.py index e1181619..b89d6856 100644 --- a/src/funman/representation/representation.py +++ b/src/funman/representation/representation.py @@ -4,13 +4,12 @@ """ import logging import math -import sys from typing import Dict, Literal, Optional from pydantic import BaseModel from funman import to_sympy -from funman.constants import LABEL_UNKNOWN, Label +from funman.constants import LABEL_UNKNOWN, NEG_INFINITY, POS_INFINITY, Label l = logging.getLogger(__name__) @@ -58,18 +57,18 @@ def denormalize(self, scenario): return self def __hash__(self): - return int( - sum( - [ - v - for _, v in self.values.items() - if not isinstance(v, EncodingSchedule) - and v != sys.float_info.max - and not math.isinf(v) - ] - ) + my_hash = sum( + [ + v + for _, v in self.values.items() + if not isinstance(v, EncodingSchedule) + and v != POS_INFINITY + and v != NEG_INFINITY + ] ) + return int(my_hash) if not math.isinf(my_hash) else 0 + def __eq__(self, other): if isinstance(other, Point): return all( diff --git a/src/funman/scenario/parameter_synthesis.py b/src/funman/scenario/parameter_synthesis.py index d91bd034..06289eb9 100644 --- a/src/funman/scenario/parameter_synthesis.py +++ b/src/funman/scenario/parameter_synthesis.py @@ -8,6 +8,7 @@ from pandas import DataFrame from pydantic import BaseModel, ConfigDict +from funman.representation.parameter import Schedules from funman.representation.representation import Point from funman.scenario import ( AnalysisScenario, @@ -72,8 +73,12 @@ def solve( self._original_parameter_widths = { p.name: Decimal(minus(p.interval.ub, p.interval.lb)) - for p in self.parameters + for p in self.model_parameters() } + # schedules = self.parameters_of_type(Schedules) + # assert len(schedules) <= 1, "Cannot have more than one Schedules parameter." + # if len(schedules) == 1: + # self._original_parameter_widths[schedules[0]] = [len(s.timepoints) for s in schedules[0].schedules] parameter_space: ParameterSpace = search.search( self, diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index e9fdfcec..283b2eec 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -35,6 +35,7 @@ from funman.model.petrinet import GeneratedPetriNetModel from funman.model.regnet import GeneratedRegnetModel, RegnetModel from funman.representation.constraint import FunmanConstraint +from funman.representation.parameter import NumSteps, Schedules, StepSize l = logging.getLogger(__name__) @@ -153,17 +154,50 @@ def num_dimensions(self): """ return len(self.parameters) - def search_space_volume(self) -> Decimal: + def num_timepoints(self) -> int: + schedules = self.parameters_of_type(Schedules) + if len(schedules) == 1: + num_timepoints = sum( + len(schedule.timepoints) - 1 + for schedule in schedules[0].schedules + ) + else: + # use num_steps and step_size + num_steps = self.parameters_of_type(NumSteps) + step_size = self.parameters_of_type(StepSize) + num_timepoints = (num_steps.width() + 1) * (step_size.width() + 1) + return num_timepoints + + def search_space_volume(self, normalize: bool = False) -> Decimal: bounds = {} - for param in self.parameters: + for param in self.model_parameters(): bounds[param.name] = param.interval - return Box(bounds=bounds).volume() + space_box = Box(bounds=bounds) + + # Normalized volume for a timeslice is 1.0, but compute anyway to verify + space_time_slice_volume = ( + space_box.volume(normalize=self._original_parameter_widths) + if normalize + else space_box.volume() + ) + assert ( + not normalize or space_time_slice_volume == 1.0 + ), f"Normalized space volume is not 1.0, computed = {space_time_slice_volume}" + space_volume = ( + space_time_slice_volume + if normalize + else self.num_timepoints() * space_time_slice_volume + ) + return space_volume def representable_space_volume(self) -> Decimal: bounds = {} - for param in self.parameters: + for param in self.model_parameters(): bounds[param.name] = Interval(lb=NEG_INFINITY, ub=POS_INFINITY) - return Box(bounds=bounds).volume() + space_box = Box(bounds=bounds) + space_time_slice_volume = space_box.volume() + space_volume = self.num_timepoints() * space_time_slice_volume + return space_volume def structure_parameters(self): return self.parameters_of_type(StructureParameter) diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index b872e461..e8cc02d3 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -19,7 +19,7 @@ from pydantic import BaseModel, ConfigDict from pysmt.formula import FNode from pysmt.logics import QF_NRA -from pysmt.shortcuts import And, Implies, Not, Solver +from pysmt.shortcuts import And, Implies, Not, Or, Solver from pysmt.solvers.solver import Model as pysmtModel from funman import ( @@ -260,9 +260,9 @@ def _add_false(self, box: Box, explanation: Explanation = None): def _add_false_point( self, box: Box, point: Point, explanation: Explanation = None ): - l.debug(f"Adding false point: {point}") + l.trace(f"Adding false point: {point}") if point in self._true_points: - l.debug( + l.trace( f"Point: {point} is marked false, but already marked true." ) point.label = LABEL_FALSE @@ -276,9 +276,9 @@ def _add_true(self, box: Box, explanation: Explanation = None): # self.statistics.iteration_operation.put("t") def _add_true_point(self, box: Box, point: Point): - l.debug(f"Adding true point: {point}") + l.trace(f"Adding true point: {point}") if point in self._false_points: - l.debug( + l.trace( f"Point: {point} is marked true, but already marked false." ) point.label = LABEL_TRUE @@ -347,21 +347,17 @@ def _split(self, box: Box, episode: BoxSearchEpisode, points=None): parameters=episode.problem.model_parameters(), ) episode.statistics._iteration_operation.put("s") - bw = box.width( + bw = box.volume( normalize=normalize, parameters=episode.problem.model_parameters() ) - b1w = b1.width( - normalize=normalize, - parameters=episode.problem.model_parameters(), - overwrite_cache=True, + b1w = b1.volume( + normalize=normalize, parameters=episode.problem.model_parameters() ) - b2w = b2.width( - normalize=normalize, - parameters=episode.problem.model_parameters(), - overwrite_cache=True, + b2w = b2.volume( + normalize=normalize, parameters=episode.problem.model_parameters() ) - l.debug( - f"Split box with width = {bw:.5f} into boxes with widths = [{b1w:.5f}, {b2w:.5f}]" + l.trace( + f"Split box with volume = {bw:.5f} into boxes with volumes = [{b1w:.5f}, {b2w:.5f}]" ) return episode._add_unknown([b1, b2]) @@ -555,8 +551,11 @@ def _setup_false_query(self, solver, episode, box, options): ).items() if k.relevant_at_time(timepoint) } + + # Not all assumptions hold formula = Not(And([v for k, v in assumptions.items()])) + # An assumption must hold at all times to hold overall formula1 = And( [ Implies( @@ -569,10 +568,13 @@ def _setup_false_query(self, solver, episode, box, options): v, ) for k, v in assumptions.items() + if k.constraint.time_dependent() ] ) - formula2 = And( + # Each Assumption has held at all times previously, so check that it does not hold currently. + # Need at least one to not hold currently. + formula2 = Or( [ And( [ @@ -588,10 +590,16 @@ def _setup_false_query(self, solver, episode, box, options): ] ) for k, v in assumptions.items() + if k.constraint.time_dependent() + ] + + [ + Not(encoder.encode_assumption(k, options)) + for k, v in assumptions.items() + if not k.constraint.time_dependent() ] ) - formulas = And([formula, formula1, formula2]) + formulas = And([formula, formula1, formula2]).simplify() episode._formula_stack.add_assertion(formulas) @@ -649,6 +657,7 @@ def _setup_true_query(self, solver, episode, box, options): v, ) for k, v in assumptions.items() + if k.constraint.time_dependent() ] ) @@ -661,10 +670,11 @@ def _setup_true_query(self, solver, episode, box, options): ] ) for k, v in assumptions.items() + if k.constraint.time_dependent() ] ) - formulas = And([formula, formula1, formula2]) + formulas = And([formula, formula1, formula2]).simplify() episode._formula_stack.add_assertion(formulas) @@ -921,8 +931,8 @@ def _expand( episode : BoxSearchEpisode Shared search data and statistics. """ - process_name = f"Expander_{idx}_p{os.getpid()}" - l = self._logger(episode.config, process_name=process_name) + process_name = f"Expander_{(idx if idx else 'S')}_p{os.getpid()}" + # l = self._logger(episode.config, process_name=process_name) try: if episode.config.solver == "dreal": @@ -952,7 +962,7 @@ def _expand( try: box: Box = episode._get_unknown() rval.put(box.model_dump()) - l.info(f"{process_name} claimed work") + l.trace(f"{process_name} claimed work") except Empty: exit = self._handle_empty_queue( process_name, @@ -968,6 +978,12 @@ def _expand( else: self._initialize_box(solver, box, episode, options) + l.debug( + "\n" + + all_results["parameter_space"].__str__( + dropped_boxes=all_results["dropped_boxes"] + ) + ) # (point, no_witness_explanation) = self._find_witness_points( # solver, episode, box, rval, options # ) @@ -1003,7 +1019,7 @@ def _expand( episode, points=[true_points, false_points], ): - l.info(f"{process_name} produced work") + l.trace(f"{process_name} produced work") else: rval.put(box.model_dump()) if episode.config.number_of_processes > 1: @@ -1013,7 +1029,8 @@ def _expand( if more_work: with more_work: more_work.notify_all() - l.debug(f"XXX [{box.width()}] Split({box})") + l.debug(f"Split @ {box.timestep().lb}") + l.trace(f"XXX Split:\n{box}") else: # box does not intersect f, so it is in t (true region) curr_step_box = box.current_step() @@ -1022,9 +1039,9 @@ def _expand( explanation=not_false_explanation, ) rval.put(curr_step_box.model_dump()) - l.debug( - f"+++ [{box.width()}] True({curr_step_box})" - ) + l.debug(f"True @ {box.timestep().lb}") + l.trace(f"+++ True:\n{box}") + if episode.config.corner_points: corner_points: List[ Point @@ -1057,7 +1074,8 @@ def _expand( box, explanation=not_true_explanation ) # TODO consider merging lists of boxes - l.debug(f"--- [{box.width()}] False({box})") + l.debug(f"False @ {box.timestep().lb}") + l.trace(f"--- False:\n{box}") if episode.config.corner_points: corner_points: List[ Point @@ -1076,7 +1094,7 @@ def _expand( handler(rval, episode.config, all_results) if "progress" in all_results: l.info(all_results["progress"]) - l.info(f"{process_name} finished work") + l.trace(f"{process_name} finished work") self._initialize_encoding( solver, episode, options, None ) # Reset solver stack to empty @@ -1161,7 +1179,9 @@ def _run_handler_step( # handler.open() while True: try: - result: dict = rval.get(timeout=config.queue_timeout) + result = None + if not rval.empty(): + result: dict = rval.get(timeout=config.queue_timeout) except Empty: break except KeyboardInterrupt: diff --git a/src/funman/search/search.py b/src/funman/search/search.py index f1572a17..3e61ad24 100644 --- a/src/funman/search/search.py +++ b/src/funman/search/search.py @@ -113,9 +113,9 @@ def search( pass def invoke_solver(self, s: Solver) -> Union[pysmtModel, BoxExplanation]: - l.debug("Invoking solver ...") + l.trace("Invoking solver ...") result = s.solve() - l.debug(f"Solver result = {result}") + l.trace(f"Solver result = {result}") if result: result = s.get_model() else: diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 870da0ce..e6aa6086 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -71,6 +71,9 @@ class FunmanProgress(BaseModel): coverage_of_search_space: float = 0.0 coverage_of_representable_space: float = 0.0 + def __str__(self) -> str: + return f"progress: {self.progress:.5f}" + class FunmanWorkUnit(BaseModel): """ @@ -182,9 +185,9 @@ def update_parameter_space( # TODO handle copy? self.parameter_space = results # compute volumes - labeled_volume = results.labeled_volume() + labeled_volume = results.labeled_volume(scenario) # TODO precompute and cache? - search_volume = scenario.search_space_volume() + search_volume = scenario.search_space_volume(normalize=True) # TODO precompute and cache? repr_volume = scenario.representable_space_volume() # compute ratios diff --git a/src/funman/translate/translate.py b/src/funman/translate/translate.py index dd7004ca..297468a1 100644 --- a/src/funman/translate/translate.py +++ b/src/funman/translate/translate.py @@ -301,16 +301,23 @@ def encode_assumed_constraint( ) -> EncodedFormula: assumption = next(a for a in assumptions if a.constraint == constraint) assumption_symbol = self.encode_assumption(assumption, options) - timed_assumption_symbol = self.encode_assumption( - assumption, options, layer_idx=layer_idx - ) - # Assumption is the same at all timepoints and it is equisatisfiable with the encoded_constraint - assumed_constraint = And( - # Iff(assumption_symbol, timed_assumption_symbol), - Implies(assumption_symbol, timed_assumption_symbol), - Iff(timed_assumption_symbol, encoded_constraint[0]), - ) + # The assumption is timed if the constraint has a timed variable + if constraint.time_dependent(): + timed_assumption_symbol = self.encode_assumption( + assumption, options, layer_idx=layer_idx + ) + + # Assumption is the same at all timepoints and it is equisatisfiable with the encoded_constraint + assumed_constraint = And( + # Iff(assumption_symbol, timed_assumption_symbol), + Implies(assumption_symbol, timed_assumption_symbol), + Iff(timed_assumption_symbol, encoded_constraint[0]), + ) + else: + assumed_constraint = And( + Iff(assumption_symbol, encoded_constraint[0]), + ) symbols = {k: v for k, v in encoded_constraint[1].items()} symbols[str(assumption_symbol)] = assumption_symbol return (assumed_constraint, symbols) @@ -711,11 +718,18 @@ def encode_linear_constraint( vars: List[str] = constraint.variables weights: List[int | float] = constraint.weights timestep = options.schedule.time_at_step(layer_idx) + parameters = scenario.parameters + encoded_vars = [ + self._encode_state_var(v) + if len([p for p in parameters if p.name == v]) > 0 + else self._encode_state_var(v, time=timestep) + for v in vars + ] expression = Plus( [ Times( Real(weights[i]), - self._encode_state_var(vars[i], time=timestep), + encoded_vars[i], ) for i in range(len(vars)) ] From 7ffb815455522370137a10849289ba10dc554766 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 1 Nov 2023 10:27:37 +0000 Subject: [PATCH 02/28] halfar example, bug fixes --- .../src/funman_dreal/converter.py | 4 +- notebooks/funman_results.ipynb | 305 ++++++++++++++++++ resources/amr/halfar/halfar.json | 258 +++++++++++++++ .../hackathon_fall_2023_demo_halfar.py | 69 ++++ ... => hackathon_fall_2023_demo_terarrium.py} | 32 +- src/funman/api/run.py | 2 +- src/funman/model/generated_models/petrinet.py | 2 +- src/funman/model/petrinet.py | 3 + src/funman/representation/box.py | 1 + src/funman/representation/parameter_space.py | 9 +- src/funman/scenario/parameter_synthesis.py | 8 +- src/funman/scenario/scenario.py | 22 +- src/funman/search/smt_check.py | 12 +- src/funman/server/__init__.py | 1 + src/funman/server/query.py | 6 + src/funman/server/worker.py | 1 + 16 files changed, 703 insertions(+), 32 deletions(-) create mode 100644 notebooks/funman_results.ipynb create mode 100644 resources/amr/halfar/halfar.json create mode 100644 scratch/hackathon/hackathon_fall_2023_demo_halfar.py rename scratch/hackathon/{hackathon_fall_2023_demo.py => hackathon_fall_2023_demo_terarrium.py} (89%) diff --git a/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py b/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py index 05bce6e3..6acb2973 100644 --- a/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py +++ b/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py @@ -59,8 +59,8 @@ def rewrite_dreal_formula(self, formula: dreal.Formula) -> str: # str_formula = str_formula.replace("pow(beta, 2.0)", "beta^2.0") str_formula = re.sub( - r"pow\([a-z]+\, [0-9.]+\)", - lambda x: x.group().split(",")[0].split("(")[1] + r"pow\([\(\)\-a-z0-9\_ ]+\, [0-9.]+\)", + lambda x: x.group().split(",")[0].split("(", 1)[1] + "^" + x.group().split(",")[1].split(")")[0].strip(), str_formula, diff --git a/notebooks/funman_results.ipynb b/notebooks/funman_results.ipynb new file mode 100644 index 00000000..c6b65275 --- /dev/null +++ b/notebooks/funman_results.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# This notebook illustrates example outputs from Funman, and how to work with the ParameterSpace object it creates.\n", + "\n", + "# Import funman related code\n", + "\n", + "from pathlib import Path\n", + "from funman import FunmanResults\n", + "import json\n", + "from funman import Point, Box, Parameter\n", + "from typing import List, Dict\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model has the symbols: ['Susceptible', 'Diagnosed', 'Infected', 'Ailing', 'Recognized', 'Healed', 'Threatened', 'Extinct', 'beta', 'gamma', 'delta', 'alpha', 'epsilon', 'zeta', 'lambda', 'eta', 'rho', 'theta', 'kappa', 'mu', 'nu', 'xi', 'tau', 'sigma', 't']\n" + ] + } + ], + "source": [ + "SAVED_RESULTS_DIR = Path(\"saved-results\").resolve()\n", + "SAVED_RESULT_FILES = [\n", + " \"d6f61dc8-79a8-44e0-8b9c-f7abc79b45d8.json\"\n", + "]\n", + "SAVED_RESULT_TO_USE = SAVED_RESULTS_DIR / SAVED_RESULT_FILES[0]\n", + "\n", + "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n", + " # Create a FunmanResults object\n", + " results: FunmanResults = FunmanResults.model_validate(json.load(f))\n", + "\n", + "print(f\"Model has the symbols: {results.model._symbols()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the trajectories for each point in the ParameterSpace\n", + "\n", + "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Infected\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "37 Points (+:28, -:9), 37 Boxes (+:28, -:9)\n", + "{beta[0.011, 0.011): 0.011, gamma[0.456, 0.456): 0.456, delta[0.011, 0.011): 0.011, alpha[0.57, 0.57): 0.57, epsilon[0.1368, 0.20520000000000002): 0.1851337291300297, zeta[0.125, 0.125): 0.125, lambda[0.034, 0.034): 0.034, eta[0.125, 0.125): 0.125, rho[0.034, 0.034): 0.034, theta[0.2968, 0.4452): 0.3694555829524995, kappa[0.017, 0.017): 0.017, mu[0.017, 0.017): 0.017, nu[0.027, 0.027): 0.027, xi[0.017, 0.017): 0.017, tau[0.01, 0.01): 0.01, sigma[0.017, 0.017): 0.017}\n", + " Ailing Diagnosed Extinct Healed \\\n", + "time \n", + "0.000000e+00 1.666667e-08 3.333333e-07 0.000000e+00 0.000000e+00 \n", + "3.333333e-09 1.833333e-08 3.033333e-07 3.333333e-09 3.333333e-09 \n", + "6.666667e-09 2.000000e-08 2.733333e-07 6.666667e-09 6.666667e-09 \n", + "1.000000e-08 2.166667e-08 2.433333e-07 1.000000e-08 1.000000e-08 \n", + "1.333333e-08 2.333333e-08 2.133333e-07 1.333333e-08 1.333333e-08 \n", + "1.666667e-08 2.500000e-08 1.833333e-07 1.666667e-08 1.666667e-08 \n", + "2.000000e-08 2.666667e-08 1.533333e-07 2.000000e-08 2.000000e-08 \n", + "2.333333e-08 2.833333e-08 1.233333e-07 2.333333e-08 2.333333e-08 \n", + "2.666667e-08 3.000000e-08 9.333333e-08 2.666667e-08 2.666667e-08 \n", + "3.000000e-08 3.166667e-08 6.333333e-08 3.000000e-08 3.000000e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "\n", + " Infected Recognized Susceptible Threatened alpha \\\n", + "time \n", + "0.000000e+00 3.333333e-06 3.333333e-08 9.999963e-01 0.000000e+00 0.57 \n", + "3.333333e-09 3.003333e-06 3.333333e-08 8.999967e-01 3.333333e-09 0.57 \n", + "6.666667e-09 2.673333e-06 3.333333e-08 7.999970e-01 6.666667e-09 0.57 \n", + "1.000000e-08 2.343333e-06 3.333333e-08 6.999974e-01 1.000000e-08 0.57 \n", + "1.333333e-08 2.013333e-06 3.333333e-08 5.999978e-01 1.333333e-08 0.57 \n", + "1.666667e-08 1.683333e-06 3.333333e-08 4.999982e-01 1.666667e-08 0.57 \n", + "2.000000e-08 1.353333e-06 3.333333e-08 3.999985e-01 2.000000e-08 0.57 \n", + "2.333333e-08 1.023333e-06 3.333333e-08 2.999989e-01 2.333333e-08 0.57 \n", + "2.666667e-08 6.933333e-07 3.333333e-08 1.999993e-01 2.666667e-08 0.57 \n", + "3.000000e-08 3.633333e-07 3.333333e-08 9.999966e-02 3.000000e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "\n", + " beta ... label lambda mu nu rho sigma tau \\\n", + "time ... \n", + "0.000000e+00 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-09 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "6.666667e-09 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "1.000000e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "1.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "1.666667e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "2.000000e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "2.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "2.666667e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.000000e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "\n", + " theta xi zeta \n", + "time \n", + "0.000000e+00 0.369456 0.017 0.125 \n", + "3.333333e-09 0.369456 0.017 0.125 \n", + "6.666667e-09 0.369456 0.017 0.125 \n", + "1.000000e-08 0.369456 0.017 0.125 \n", + "1.333333e-08 0.369456 0.017 0.125 \n", + "1.666667e-08 0.369456 0.017 0.125 \n", + "2.000000e-08 0.369456 0.017 0.125 \n", + "2.333333e-08 0.369456 0.017 0.125 \n", + "2.666667e-08 0.369456 0.017 0.125 \n", + "3.000000e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", + "\n", + "[11 rows x 26 columns]\n" + ] + } + ], + "source": [ + "# Example uses the result object created by FUNMAN\n", + "# - Get the points and boxes\n", + "# - Get the parameters corresponding to a point\n", + "# - Generate a dataframe with the point timeseries\n", + "\n", + "\n", + "\n", + "points: List[Point] = results.points()\n", + "boxes: List[Box] = results.parameter_space.boxes()\n", + "\n", + "num_true_points = len(results.parameter_space.true_points())\n", + "num_false_points = len(results.parameter_space.false_points())\n", + "num_true_boxes = len(results.parameter_space.true_boxes)\n", + "num_false_boxes = len(results.parameter_space.false_boxes)\n", + "\n", + "print(\n", + " f\"{len(points)} Points (+:{num_true_points}, -:{num_false_points}), {len(boxes)} Boxes (+:{num_true_boxes}, -:{num_true_boxes})\"\n", + ")\n", + "if points and len(points) > 0:\n", + " point: Point = points[-1]\n", + " parameters: Dict[Parameter, float] = results.point_parameters(point)\n", + " print(parameters)\n", + " print(results.dataframe([point]))\n", + "else:\n", + " # if there are no points, then we have a box that we found without needing points\n", + "\n", + " box = boxes[0]\n", + " print(json.dumps(box.explain(), indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the corner points of a box\n", + "\n", + "results.parameter_space.false_boxes[0].corner_points" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'method' object is not subscriptable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/danbryce/funman/notebooks/funman_results.ipynb Cell 6\u001b[0m line \u001b[0;36m3\n\u001b[1;32m 1\u001b[0m \u001b[39m# Get the state varibles and parameters from a point (used by the dataframe timeseries function above)\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m results\u001b[39m.\u001b[39;49mparameter_space\u001b[39m.\u001b[39;49mtrue_points[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39mvalues\n", + "\u001b[0;31mTypeError\u001b[0m: 'method' object is not subscriptable" + ] + } + ], + "source": [ + "# Get the state varibles and parameters from a point (used by the dataframe timeseries function above)\n", + "\n", + "results.parameter_space.true_points[0].values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the parameter space\n", + "\n", + "from funman_demo.parameter_space_plotter import ParameterSpacePlotter\n", + "ParameterSpacePlotter(\n", + " results.parameter_space, plot_points=True, parameters=[\"beta\", \"gamma\", \"timestep\"]\n", + " ).plot(show=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the timeseries for each point in the parameter space for the selected variables\n", + "\n", + "results.plot(variables=[\"I\"], label_marker={\"true\":\",\", \"false\": \",\"})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the points from a saved result file.\n", + "\n", + "from funman.server.query import FunmanResults\n", + "import json\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2\n", + "with open(\"out/f13f5edb-41ba-4a70-bcc6-0bb9881ce71c.json\", \"r\") as f:\n", + " results = FunmanResults.model_validate(json.load(f))\n", + " results.plot(variables=[\"I\"], label_marker={\"true\":\",\", \"false\": \",\"})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(results.parameter_space.false_boxes[1].explain( ))\n", + "results.plot(points=results.parameter_space.false_boxes[1].corner_points, variables=[\"I\"], label_marker={\"true\":\",\", \"false\": \",\"})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "funman_venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/resources/amr/halfar/halfar.json b/resources/amr/halfar/halfar.json new file mode 100644 index 00000000..50bb7da4 --- /dev/null +++ b/resources/amr/halfar/halfar.json @@ -0,0 +1,258 @@ +{ + "header": { + "name": "Halfar Model", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.1/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Halfar as Petrinet model created by Dan", + "model_version": "0.1" + }, + "model": { + "states": [ + { + "id": "h_0", + "name": "h_0", + "description": "height" + }, + { + "id": "h_1", + "name": "h_1", + "description": "height" + }, + { + "id": "h_2", + "name": "h_2", + "description": "height" + }, + { + "id": "h_3", + "name": "h_3", + "description": "height" + }, + { + "id": "h_4", + "name": "h_4", + "description": "height" + } + ], + "transitions": [ + { + "id": "w_n_0", + "input": [ + "h_1", + "h_0" + ], + "output": [ + "h_0" + ], + "properties": { + "name": "w_n_0" + } + }, + { + "id": "w_p_0", + "input": [ + "h_2", + "h_1" + ], + "output": [ + "h_0" + ], + "properties": { + "name": "w_p_0" + } + }, + { + "id": "w_n_1", + "input": [ + "h_2", + "h_1" + ], + "output": [ + "h_1" + ], + "properties": { + "name": "w_n_1" + } + }, + { + "id": "w_p_1", + "input": [ + "h_3", + "h_2" + ], + "output": [ + "h_1" + ], + "properties": { + "name": "w_p_1" + } + }, + { + "id": "w_n_2", + "input": [ + "h_3", + "h_2" + ], + "output": [ + "h_2" + ], + "properties": { + "name": "w_n_2" + } + }, + { + "id": "w_p_2", + "input": [ + "h_4", + "h_3" + ], + "output": [ + "h_2" + ], + "properties": { + "name": "w_p_2" + } + }, + { + "id": "w_n_3", + "input": [ + "h_2", + "h_3" + ], + "output": [ + "h_3" + ], + "properties": { + "name": "w_n_3" + } + }, + { + "id": "w_p_3", + "input": [ + "h_4", + "h_3" + ], + "output": [ + "h_3" + ], + "properties": { + "name": "w_p_3" + } + }, + { + "id": "w_n_4", + "input": [ + "h_2", + "h_3" + ], + "output": [ + "h_4" + ], + "properties": { + "name": "w_n_4" + } + }, + { + "id": "w_p_4", + "input": [ + "h_3", + "h_4" + ], + "output": [ + "h_4" + ], + "properties": { + "name": "w_p_4" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "w_n_0", + "expression": "-1*gamma*(h_1-h_0)^3*h_0^5" + }, + { + "target": "w_p_0", + "expression": "gamma*(h_2-h_1)^3*h_1^5" + }, + { + "target": "w_n_1", + "expression": "-1*gamma*(h_2-h_1)^3*h_1^5" + }, + { + "target": "w_p_1", + "expression": "gamma*(h_3-h_2)^3*h_2^5" + }, + { + "target": "w_n_2", + "expression": "-1*gamma*(h_3-h_2)^3*h_2^5" + }, + { + "target": "w_p_2", + "expression": "gamma*(h_4-h_3)^3*h_3^5" + }, + { + "target": "w_n_3", + "expression": "-1*gamma*(h_3-h_2)^3*h_2^5" + }, + { + "target": "w_p_3", + "expression": "gamma*(h_4-h_3)^3*h_3^5" + }, + { + "target": "w_n_4", + "expression": "-1*gamma*(h_3-h_2)^3*h_2^5" + }, + { + "target": "w_p_4", + "expression": "gamma*(h_4-h_3)^3*h_3^5" + } + ], + "initials": [ + { + "target": "h_0", + "expression": "0.1" + }, + { + "target": "h_1", + "expression": "10.0" + }, + { + "target": "h_2", + "expression": "10.0" + }, + { + "target": "h_3", + "expression": "10.0" + }, + { + "target": "h_4", + "expression": "0.1" + } + ], + "parameters": [ + { + "id": "gamma", + "value": 1.0, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.0, + "maximum": 1.0 + } + } + } + ], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + } + } +} \ No newline at end of file diff --git a/scratch/hackathon/hackathon_fall_2023_demo_halfar.py b/scratch/hackathon/hackathon_fall_2023_demo_halfar.py new file mode 100644 index 00000000..60cba9b3 --- /dev/null +++ b/scratch/hackathon/hackathon_fall_2023_demo_halfar.py @@ -0,0 +1,69 @@ +import json +import os + +from funman import Point +from funman.api.run import Runner + + +def main(): + # Setup Paths + + RESOURCES = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "../../resources" + ) + EXAMPLE_DIR = os.path.join(RESOURCES, "amr", "halfar") + MODEL_PATH = os.path.join(EXAMPLE_DIR, "halfar.json") + REQUEST_PATH = os.path.join(EXAMPLE_DIR, "halfar_request.json") + + request_dict = { + "structure_parameters": [ + { + "name": "schedules", + "schedules": [ + # {"timepoints": [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]} + {"timepoints": [0, 1]} + ], + } + ], + "config": { + "use_compartmental_constraints": False, + "normalization_constant": 1.0, + "tolerance": 1e-1, + "verbosity": 5, + "dreal_mcts": True, + "save_smtlib": True, + "substitute_subformulas": False, + "series_approximation_threshold": None, + "dreal_log_level": "info", + "profile": False, + }, + } + + # Use request_dict + results = Runner().run( + MODEL_PATH, + request_dict, + # REQUEST_PATH, + description="SIDARTHE demo", + case_out_dir="./out", + ) + points = results.points() + boxes = results.parameter_space.boxes() + + print( + f"{len(points)} Points (+:{len(results.parameter_space.true_points())}, -:{len(results.parameter_space.false_points())}), {len(boxes)} Boxes (+:{len(results.parameter_space.true_boxes)}, -:{len(results.parameter_space.false_boxes)})" + ) + if points and len(points) > 0: + point: Point = points[-1] + parameters: Dict[Parameter, float] = results.point_parameters(point) + print(parameters) + print(results.dataframe([point])) + else: + # if there are no points, then we have a box that we found without needing points + + box = boxes[0] + print(json.dumps(box.explain(), indent=4)) + + +if __name__ == "__main__": + main() diff --git a/scratch/hackathon/hackathon_fall_2023_demo.py b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py similarity index 89% rename from scratch/hackathon/hackathon_fall_2023_demo.py rename to scratch/hackathon/hackathon_fall_2023_demo_terarrium.py index 1df8b189..7a2f9c09 100644 --- a/scratch/hackathon/hackathon_fall_2023_demo.py +++ b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py @@ -172,28 +172,42 @@ def main(): { "name": "infected_maximum1", "variable": "Infected", - "interval": {"ub": 0.2}, - "timepoints": {"lb": 50, "ub": 70, "closed_upper_bound": True}, + "interval": {"lb": 0.01, "ub": 0.05}, + "timepoints": {"lb": 50, "ub": 75, "closed_upper_bound": True}, }, { "name": "infected_maximum2", "variable": "Infected", - "interval": {"ub": 0.1}, + "interval": {"ub": 0.03}, "timepoints": {"lb": 0, "ub": 50}, }, { "name": "infected_maximum3", "variable": "Infected", - "interval": {"ub": 0.1}, - "timepoints": {"lb": 71}, + "interval": {"ub": 0.03}, + "timepoints": {"lb": 76}, }, ], "structure_parameters": [ { "name": "schedules", "schedules": [ - # {"timepoints": [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]} - {"timepoints": [0, 10]} + { + "timepoints": [ + 0, + 10, + 20, + 30, + 40, + 50, + 60, + 70, + 80, + 90, + 100, + ] + } + # {"timepoints": [0, 10]} ], } ], @@ -206,8 +220,8 @@ def main(): "save_smtlib": False, "substitute_subformulas": False, "series_approximation_threshold": None, - "dreal_log_level": "none", - "profile": True, + "dreal_log_level": "info", + "profile": False, }, } diff --git a/src/funman/api/run.py b/src/funman/api/run.py index d15808fa..7a233b09 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -215,7 +215,7 @@ def run_instance( l.info(f"Dumping results to {outfile}") results = self._worker.get_results(work_unit.id) with open(outfile, "w") as f: - f.write(results.model_dump_json()) + f.write(results.model_dump_json(by_alias=True)) # ParameterSpacePlotter( # results.parameter_space, plot_points=True # ).plot(show=False) diff --git a/src/funman/model/generated_models/petrinet.py b/src/funman/model/generated_models/petrinet.py index eb7089ee..038d6b2b 100644 --- a/src/funman/model/generated_models/petrinet.py +++ b/src/funman/model/generated_models/petrinet.py @@ -10,7 +10,7 @@ class Header(BaseModel): - model_config = ConfigDict(protected_namespaces=()) + model_config = ConfigDict(protected_namespaces=(), populate_by_name=True) name: str schema_: AnyUrl = Field(..., alias="schema") schema_name: Optional[str] = None diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index 6bc01e08..adb4e25b 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -7,6 +7,7 @@ from funman.utils.sympy_utils import substitute, to_sympy +from ..representation.interval import Interval from .generated_models.petrinet import Model as GeneratedPetrinet from .generated_models.petrinet import State, Transition from .model import Model @@ -161,6 +162,7 @@ def compartmental_constraints( "closed_upper_bound": True, }, variables=vars, + timepoints=Interval(lb=0.0), soft=False, ) ] + [ @@ -168,6 +170,7 @@ def compartmental_constraints( name=f"compartmental_{v}_nonnegative", additive_bounds={"lb": 0}, variables=[v], + timepoints=Interval(lb=0.0), soft=False, ) for v in vars diff --git a/src/funman/representation/box.py b/src/funman/representation/box.py index 521aa437..ab66bc1e 100644 --- a/src/funman/representation/box.py +++ b/src/funman/representation/box.py @@ -41,6 +41,7 @@ def from_point(point: Point) -> "Box": p: Interval.from_value(v) for p, v in point.values.items() } box.points.append(point) + box.label = point.label return box def true_points(self) -> List[Point]: diff --git a/src/funman/representation/parameter_space.py b/src/funman/representation/parameter_space.py index b83ddb43..9e109d04 100644 --- a/src/funman/representation/parameter_space.py +++ b/src/funman/representation/parameter_space.py @@ -5,7 +5,7 @@ from matplotlib.lines import Line2D from pydantic import BaseModel -from ..constants import LABEL_DROPPED, LABEL_FALSE, LABEL_TRUE +from ..constants import LABEL_DROPPED, LABEL_FALSE, LABEL_TRUE, LABEL_UNKNOWN from . import Interval, Point from .box import Box from .interval import Interval @@ -27,7 +27,12 @@ class ParameterSpace(BaseModel): unknown_points: List[Point] = [] def __str__(self, dropped_boxes=[]) -> str: - box_labels = {LABEL_TRUE: "+", LABEL_FALSE: "-", LABEL_DROPPED: "x"} + box_labels = { + LABEL_TRUE: "+", + LABEL_FALSE: "-", + LABEL_DROPPED: "x", + LABEL_UNKNOWN: "?", + } boxes = self.boxes() steps = {} for box in boxes + dropped_boxes: diff --git a/src/funman/scenario/parameter_synthesis.py b/src/funman/scenario/parameter_synthesis.py index 06289eb9..51ffcf12 100644 --- a/src/funman/scenario/parameter_synthesis.py +++ b/src/funman/scenario/parameter_synthesis.py @@ -2,8 +2,7 @@ This module defines the Parameter Synthesis scenario. """ import threading -from decimal import Decimal -from typing import Callable, Dict, List, Optional, Union +from typing import Callable, List, Optional, Union from pandas import DataFrame from pydantic import BaseModel, ConfigDict @@ -33,7 +32,6 @@ class ParameterSynthesisScenario(AnalysisScenario, BaseModel): # _assume_model: Optional[FNode] = None # _assume_query: Optional[FNode] = None - _original_parameter_widths: Dict[str, Decimal] = {} @classmethod def get_kind(cls) -> str: @@ -71,10 +69,6 @@ def solve( """ search = self.initialize(config) - self._original_parameter_widths = { - p.name: Decimal(minus(p.interval.ub, p.interval.lb)) - for p in self.model_parameters() - } # schedules = self.parameters_of_type(Schedules) # assert len(schedules) <= 1, "Cannot have more than one Schedules parameter." # if len(schedules) == 1: diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index 283b2eec..b59adec5 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -36,6 +36,7 @@ from funman.model.regnet import GeneratedRegnetModel, RegnetModel from funman.representation.constraint import FunmanConstraint from funman.representation.parameter import NumSteps, Schedules, StepSize +from funman.utils import math_utils l = logging.getLogger(__name__) @@ -67,6 +68,7 @@ class AnalysisScenario(ABC, BaseModel): _smt_encoder: Optional["Encoder"] = None # Encoding for different step sizes (key) _encodings: Optional[Dict["Schedule", "Encoding"]] = {} + _original_parameter_widths: Dict[str, Decimal] = {} def __init__(self, **kwargs): super().__init__(**kwargs) @@ -126,6 +128,11 @@ def initialize(self, config: "FUNMANConfig") -> "Search": self._initialize_encodings(config) + self._original_parameter_widths = { + p.name: Decimal(math_utils.minus(p.interval.ub, p.interval.lb)) + for p in self.model_parameters() + } + return search def _initialize_encodings(self, config: "FUNMANConfig"): @@ -174,12 +181,15 @@ def search_space_volume(self, normalize: bool = False) -> Decimal: bounds[param.name] = param.interval space_box = Box(bounds=bounds) - # Normalized volume for a timeslice is 1.0, but compute anyway to verify - space_time_slice_volume = ( - space_box.volume(normalize=self._original_parameter_widths) - if normalize - else space_box.volume() - ) + if len(bounds) > 0: + # Normalized volume for a timeslice is 1.0, but compute anyway to verify + space_time_slice_volume = ( + space_box.volume(normalize=self._original_parameter_widths) + if normalize + else space_box.volume() + ) + else: + space_time_slice_volume = Decimal(1.0) assert ( not normalize or space_time_slice_volume == 1.0 ), f"Normalized space volume is not 1.0, computed = {space_time_slice_volume}" diff --git a/src/funman/search/smt_check.py b/src/funman/search/smt_check.py index 4b97b3fc..498b244b 100644 --- a/src/funman/search/smt_check.py +++ b/src/funman/search/smt_check.py @@ -62,7 +62,7 @@ def search( parameter_space, schedule, ) - + timestep = len(schedule.timepoints) if result is not None and isinstance(result, pysmtModel): result_dict = result.to_dict() if result else None l.debug(f"Result: {json.dumps(result_dict, indent=4)}") @@ -79,8 +79,9 @@ def search( label=LABEL_TRUE, schedule=schedule, ) - point.values["timestep"] = schedule.time_at_step( - len(schedule.timepoints) - 1 + + point.values["timestep"] = Interval( + lb=timestep, ub=timestep, closed_upper_bound=True ) if config.normalize: denormalized_point = point.denormalize(problem) @@ -91,12 +92,15 @@ def search( elif result is not None and isinstance(result, Explanation): box = Box( bounds={ - p.name: Interval(lb=p.interval.lb, ub=p.interval.ub) + p.name: p.interval.model_copy() for p in problem.parameters }, label=LABEL_FALSE, explanation=result, ) + box.bounds["timestep"] = Interval( + lb=timestep, ub=timestep, closed_upper_bound=True + ) parameter_space.false_boxes.append(box) if resultsCallback: resultsCallback(parameter_space) diff --git a/src/funman/server/__init__.py b/src/funman/server/__init__.py index e69de29b..09db26de 100644 --- a/src/funman/server/__init__.py +++ b/src/funman/server/__init__.py @@ -0,0 +1 @@ +from .query import * diff --git a/src/funman/server/query.py b/src/funman/server/query.py index e6aa6086..56c70850 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -424,6 +424,9 @@ def plot( variables=None, log_y=False, max_time=None, + title="Point Trajectories", + xlabel="Time", + ylabel="Population", label_marker={"true": "+", "false": "o"}, label_color={"true": "g", "false": "r"}, **kwargs, @@ -473,6 +476,9 @@ def plot( ax.set_yscale("symlog") plt.ylim(bottom=0) # plt.show(block=False) + plt.title(title) + plt.xlabel(xlabel) + plt.ylabel(ylabel) return ax def explain(self) -> Explanation: diff --git a/src/funman/server/worker.py b/src/funman/server/worker.py index b8ff35e3..e10df247 100644 --- a/src/funman/server/worker.py +++ b/src/funman/server/worker.py @@ -16,6 +16,7 @@ FunmanWorkRequest, FunmanWorkUnit, ) +from funman.utils import math_utils from ..representation.parameter_space import ParameterSpace From cca821080aa671bd502402508b6023d335081cfc Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 1 Nov 2023 16:14:42 +0000 Subject: [PATCH 03/28] update outputs in notebook --- notebooks/funman_results.ipynb | 200 +++++++++++++----- .../e466f678-c60d-4117-bd58-bed949c512cf.json | 1 + 2 files changed, 145 insertions(+), 56 deletions(-) create mode 100644 notebooks/saved-results/e466f678-c60d-4117-bd58-bed949c512cf.json diff --git a/notebooks/funman_results.ipynb b/notebooks/funman_results.ipynb index c6b65275..ef664c4f 100644 --- a/notebooks/funman_results.ipynb +++ b/notebooks/funman_results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -36,7 +36,7 @@ "source": [ "SAVED_RESULTS_DIR = Path(\"saved-results\").resolve()\n", "SAVED_RESULT_FILES = [\n", - " \"d6f61dc8-79a8-44e0-8b9c-f7abc79b45d8.json\"\n", + " \"e466f678-c60d-4117-bd58-bed949c512cf.json\"\n", "]\n", "SAVED_RESULT_TO_USE = SAVED_RESULTS_DIR / SAVED_RESULT_FILES[0]\n", "\n", @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -58,13 +58,13 @@ "" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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7d+8Vxzh37pwmTJigFi1aKCAgQIMHD1Z+fn5Nvi0AAADUEx4N4rVr1yolJUVpaWnavXu3IiMjlZiYqIKCgnLXb9u2TcOHD9fYsWO1Z88eJSUlKSkpSfv373etKS0tVd++fTVv3rwKX/exxx7TX/7yF61bt07vvfeeTp48qXvuuafG3x8AAADqPptlWZanXjw2NlY9evTQ0qVLJUlOp1OhoaGaOHGipk2bdsX6oUOHqrS0VBs3bnRt69Wrl6KiopSRkeG29tixYwoPD9eePXsUFRXl2l5cXKxWrVpp9erVuvfeeyVJn3/+uW6++WZlZ2erV69elZq9pKREdrtdxcXFCgwMrOpbBwAAwHVW2V7z2BniCxcuaNeuXUpISPjPMF5eSkhIUHZ2drn7ZGdnu62XpMTExArXl2fXrl26ePGi23E6deqkdu3aXfU458+fV0lJidsDAAAA9Z/HgriwsFBlZWVyOBxu2x0Oh/Ly8srdJy8vr0rrKzqGj4+PmjZtWqXjpKeny263ux6hoaGVfk0AAADUXR7/UF19MX36dBUXF7seJ06c8PRIAAAAqAENPPXCLVu2lLe39xV3d8jPz1dwcHC5+wQHB1dpfUXHuHDhgoqKitzOEl/rOL6+vvL19a306wAAAKB+8NgZYh8fH0VHRysrK8u1zel0KisrS3FxceXuExcX57ZekjZv3lzh+vJER0erYcOGbsc5ePCgjh8/XqXjAAAA4MfBY2eIJSklJUWjRo1STEyMevbsqUWLFqm0tFTJycmSpJEjR6pNmzZKT0+XJE2aNEnx8fFasGCBBg0apDVr1mjnzp1avny565inT5/W8ePHdfLkSUnfxa703Znh4OBg2e12jR07VikpKWrevLkCAwM1ceJExcXFVfoOEwAAAPjx8GgQDx06VKdOnVJqaqry8vIUFRWlzMxM1wfnjh8/Li+v/5zE7t27t1avXq2ZM2dqxowZioiI0IYNG9S5c2fXmrfeessV1JI0bNgwSVJaWpqefPJJSdLChQvl5eWlwYMH6/z580pMTNQLL7xQC+8YAAAAdY1H70Ncn3EfYgAAgLqtzt+HGAAAAKgLCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNE8HsTLli1TWFiY/Pz8FBsbq+3bt191/bp169SpUyf5+fmpS5cu2rRpk9vzlmUpNTVVrVu3lr+/vxISEnT48GG3NYcOHdL//u//qmXLlgoMDFTfvn21devWGn9vAAAAqPs8GsRr165VSkqK0tLStHv3bkVGRioxMVEFBQXlrt+2bZuGDx+usWPHas+ePUpKSlJSUpL279/vWjN//nwtWbJEGRkZysnJUePGjZWYmKhz58651tx55526dOmStmzZol27dikyMlJ33nmn8vLyrvt7BgAAQN1isyzL8tSLx8bGqkePHlq6dKkkyel0KjQ0VBMnTtS0adOuWD906FCVlpZq48aNrm29evVSVFSUMjIyZFmWQkJCNGXKFE2dOlWSVFxcLIfDoZUrV2rYsGEqLCxUq1at9P777+snP/mJJOnMmTMKDAzU5s2blZCQUKnZS0pKZLfbVVxcrMDAwB/6rQAAAEANq2yveewM8YULF7Rr1y63APXy8lJCQoKys7PL3Sc7O/uKYE1MTHStz83NVV5entsau92u2NhY15oWLVropptu0u9//3uVlpbq0qVLeumllxQUFKTo6OgK5z1//rxKSkrcHgAAAKj/PBbEhYWFKisrk8PhcNvucDgqvHQhLy/vqusvf73aGpvNpnfffVd79uxRkyZN5Ofnp+eee06ZmZlq1qxZhfOmp6fLbre7HqGhoVV7wwAAAKiTPP6hutpmWZYmTJigoKAgffDBB9q+fbuSkpJ011136euvv65wv+nTp6u4uNj1OHHiRC1ODQAAgOvFY0HcsmVLeXt7Kz8/3217fn6+goODy90nODj4qusvf73ami1btmjjxo1as2aN+vTpo+7du+uFF16Qv7+/Vq1aVeG8vr6+CgwMdHsAAACg/vNYEPv4+Cg6OlpZWVmubU6nU1lZWYqLiyt3n7i4OLf1krR582bX+vDwcAUHB7utKSkpUU5OjmvNt99+K+m765W/z8vLS06n84e/MQAAANQrDTz54ikpKRo1apRiYmLUs2dPLVq0SKWlpUpOTpYkjRw5Um3atFF6erokadKkSYqPj9eCBQs0aNAgrVmzRjt37tTy5cslfXd98OTJkzVnzhxFREQoPDxcs2bNUkhIiJKSkiR9F9XNmjXTqFGjlJqaKn9/f7388svKzc3VoEGDPPJ9AAAAgOd4NIiHDh2qU6dOKTU1VXl5eYqKilJmZqbrQ3HHjx93O5Pbu3dvrV69WjNnztSMGTMUERGhDRs2qHPnzq41jz/+uEpLSzVu3DgVFRWpb9++yszMlJ+fn6TvLtXIzMzUE088odtvv10XL17Urbfeqj//+c+KjIys3W8AAAAAPM6j9yGuz7gPMQAAQN1W5+9DDAAAANQFBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgEMQAAAIxGEAMAAMBoBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgEMQAAAIxGEAMAAMBoBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgEMQAAAIxGEAMAAMBoBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgEMQAAAIxGEAMAAMBoBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgEMQAAAIxGEAMAAMBoBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgEMQAAAIxGEAMAAMBoBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgEMQAAAIxGEAMAAMBoBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgEMQAAAIxGEAMAAMBoBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgEMQAAAIxGEAMAAMBoBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgEMQAAAIxGEAMAAMBoBDEAAACMRhADAADAaAQxAAAAjEYQAwAAwGgNKrtwyZIllT7oo48+Wq1hAAAAgNpmsyzLqszC8PBwtz+fOnVK3377rZo2bSpJKioqUqNGjRQUFKQvvvii0gMsW7ZMv/nNb5SXl6fIyEg9//zz6tmzZ4Xr161bp1mzZunYsWOKiIjQvHnzNHDgQNfzlmUpLS1NL7/8soqKitSnTx+9+OKLioiIcDvO22+/rdmzZ+uTTz6Rn5+f4uPjtWHDhkrPXVJSIrvdruLiYgUGBlZ6PwAAANSOyvZapS+ZyM3NdT3mzp2rqKgoHThwQKdPn9bp06d14MABde/eXU8//XSlh1y7dq1SUlKUlpam3bt3KzIyUomJiSooKCh3/bZt2zR8+HCNHTtWe/bsUVJSkpKSkrR//37Xmvnz52vJkiXKyMhQTk6OGjdurMTERJ07d8615o033tADDzyg5ORk7du3Tx999JF+/vOfV3puAAAA/HhU+gzx93Xs2FGvv/66unXr5rZ9165duvfee5Wbm1up48TGxqpHjx5aunSpJMnpdCo0NFQTJ07UtGnTrlg/dOhQlZaWauPGja5tvXr1UlRUlDIyMmRZlkJCQjRlyhRNnTpVklRcXCyHw6GVK1dq2LBhunTpksLCwvTUU09p7NixVX3rLpwhBgAAqNtq/Azx93399de6dOnSFdvLysqUn59fqWNcuHBBu3btUkJCwn+G8fJSQkKCsrOzy90nOzvbbb0kJSYmutbn5uYqLy/PbY3dbldsbKxrze7du/XVV1/Jy8tL3bp1U+vWrTVgwAC3s8zlOX/+vEpKStweAAAAqP+qFcT9+/fXww8/rN27d7u27dq1S+PHj78iWCtSWFiosrIyORwOt+0Oh0N5eXnl7pOXl3fV9Ze/Xm3N5eubn3zySc2cOVMbN25Us2bN1K9fP50+fbrCedPT02W3212P0NDQSr1PAAAA1G3VCuJXX31VwcHBiomJka+vr3x9fdWzZ085HA698sorNT1jjXI6nZKkJ554QoMHD1Z0dLRWrFghm82mdevWVbjf9OnTVVxc7HqcOHGitkYGAADAdVTp2659X6tWrbRp0yYdOnRIn3/+uSSpU6dOuvHGGyt9jJYtW8rb2/uKSyzy8/MVHBxc7j7BwcFXXX/5a35+vlq3bu22JioqSpJc22+55RbX876+vurQoYOOHz9e4byXwx8AAAA/Lj/oF3OEhYXppptu0sCBA6sUw5Lk4+Oj6OhoZWVlubY5nU5lZWUpLi6u3H3i4uLc1kvS5s2bXevDw8MVHBzstqakpEQ5OTmuNdHR0fL19dXBgwdday5evKhjx46pffv2VXoPAAAAqP+qFcTffvutxo4dq0aNGunWW291nVmdOHGinn322UofJyUlRS+//LJWrVqlAwcOaPz48SotLVVycrIkaeTIkZo+fbpr/aRJk5SZmakFCxbo888/15NPPqmdO3fqkUcekSTZbDZNnjxZc+bM0VtvvaVPP/1UI0eOVEhIiJKSkiRJgYGB+sUvfqG0tDT97W9/08GDBzV+/HhJ0pAhQ6rz7QAAAEA9Vq1LJqZPn659+/bp73//u372s5+5tickJOjJJ58s95Zp5Rk6dKhOnTql1NRU5eXlKSoqSpmZma4PxR0/flxeXv9p9t69e2v16tWaOXOmZsyYoYiICG3YsEGdO3d2rXn88cdVWlqqcePGqaioSH379lVmZqb8/Pxca37zm9+oQYMGeuCBB/Tvf/9bsbGx2rJli5o1a1adbwcAAADqsWrdh7h9+/Zau3atevXqpSZNmmjfvn3q0KGDjhw5ou7duxtxSzLuQwwAAFC3Xdf7EJ86dUpBQUFXbC8tLZXNZqvOIQEAAACPqFYQx8TE6O2333b9+XIEv/LKKxV+IA4AAACoi6p1DfEzzzyjAQMG6LPPPtOlS5e0ePFiffbZZ9q2bZvee++9mp4RAAAAuG6qdYa4b9++2rt3ry5duqQuXbrob3/7m4KCgpSdna3o6OianhEAAAC4bqr1oTrwoToAAIC67rp+qM7b21sFBQVXbP/mm2/k7e1dnUMCAAAAHlGtIK7opPL58+fl4+PzgwYCAAAAalOVPlS3ZMkSSd/dVeKVV15RQECA67mysjK9//776tSpU81OCAAAAFxHVQrihQsXSvruDHFGRobb5RE+Pj4KCwtTRkZGzU4IAAAAXEdVCuLc3FxJ0m233aY333yTX3UMAACAeq9a9yHeunVrTc8BAAAAeES1PlQ3ePBgzZs374rt8+fP15AhQ37wUAAAAEBtqVYQv//++xo4cOAV2wcMGKD333//Bw8FAAAA1JZqBfHZs2fLvb1aw4YNVVJS8oOHAgAAAGpLtYK4S5cuWrt27RXb16xZo1tuueUHDwUAAADUlmp9qG7WrFm65557dPToUd1+++2SpKysLP3f//2f1q1bV6MDAgAAANdTtYL4rrvu0oYNG/TMM8/o9ddfl7+/v7p27ap3331X8fHxNT0jAAAAcN3YrIp+DzOuqqSkRHa7XcXFxQoMDPT0OAAAAPgvle21al1DLElFRUV65ZVXNGPGDJ0+fVqStHv3bn311VfVPSQAAABQ66p1ycQnn3yihIQE2e12HTt2TA8++KCaN2+uN998U8ePH9fvf//7mp4TAAAAuC6qdYY4JSVFo0eP1uHDh+Xn5+faPnDgQO5DDAAAgHqlWkG8Y8cOPfzww1dsb9OmjfLy8n7wUAAAAEBtqVYQ+/r6lvsLOA4dOqRWrVr94KEAAACA2lKtIP6f//kfzZ49WxcvXpQk2Ww2HT9+XL/+9a81ePDgGh0QAAAAuJ6qFcQLFizQ2bNnFRQUpH//+9+Kj4/XDTfcoCZNmmju3Lk1PSMAAABw3VTrLhN2u12bN2/WRx99pH379uns2bPq3r27EhISano+AAAA4LqqdBA3b95chw4dUsuWLTVmzBgtXrxYffr0UZ8+fa7nfAAAAMB1VelLJi5cuOD6IN2qVat07ty56zYUAAAAUFsqfYY4Li5OSUlJio6OlmVZevTRR+Xv71/u2ldffbXGBgQAAACup0oH8R//+EctXLhQR48elc1mU3FxMWeJAQAAUO/ZLMuyqrpTeHi4du7cqRYtWlyPmeqFkpIS2e12FRcXKzAw0NPjAAAA4L9UtteqdZeJ3Nzcag8GAAAA1CXVCmJJysrKUlZWlgoKCuR0Ot2e4xpiAAAA1BfVCuKnnnpKs2fPVkxMjFq3bi2bzVbTcwEAAAC1olpBnJGRoZUrV+qBBx6o6XkAAACAWlWtX9184cIF9e7du6ZnAQAAAGpdtYL4wQcf1OrVq2t6FgAAAKDWVeuSiXPnzmn58uV699131bVrVzVs2NDt+eeee65GhgMAAACut2oF8SeffKKoqChJ0v79+2tyHgAAAKBWVSuIt27dWtNzAAAAAB5RpSC+5557rrnGZrPpjTfeqPZAAAAAQG2qUhDb7fbrNQcAAADgEVUK4hUrVlyvOQAAAACPqNZt1wAAAIAfC4IYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEarE0G8bNkyhYWFyc/PT7Gxsdq+fftV169bt06dOnWSn5+funTpok2bNrk9b1mWUlNT1bp1a/n7+yshIUGHDx8u91jnz59XVFSUbDab9u7dW1NvCQAAAPWEx4N47dq1SklJUVpamnbv3q3IyEglJiaqoKCg3PXbtm3T8OHDNXbsWO3Zs0dJSUlKSkrS/v37XWvmz5+vJUuWKCMjQzk5OWrcuLESExN17ty5K473+OOPKyQk5Lq9PwAAANRtNsuyLE8OEBsbqx49emjp0qWSJKfTqdDQUE2cOFHTpk27Yv3QoUNVWlqqjRs3urb16tVLUVFRysjIkGVZCgkJ0ZQpUzR16lRJUnFxsRwOh1auXKlhw4a59nvnnXeUkpKiN954Q7feeqv27NmjqKioSs1dUlIiu92u4uJiBQYG/oDvAAAAAK6HyvaaR88QX7hwQbt27VJCQoJrm5eXlxISEpSdnV3uPtnZ2W7rJSkxMdG1Pjc3V3l5eW5r7Ha7YmNj3Y6Zn5+vhx56SH/4wx/UqFGja856/vx5lZSUuD0AAABQ/3k0iAsLC1VWViaHw+G23eFwKC8vr9x98vLyrrr+8terrbEsS6NHj9YvfvELxcTEVGrW9PR02e121yM0NLRS+wEAAKBu8/g1xJ7w/PPP68yZM5o+fXql95k+fbqKi4tdjxMnTlzHCQEAAFBbPBrELVu2lLe3t/Lz89225+fnKzg4uNx9goODr7r+8terrdmyZYuys7Pl6+urBg0a6IYbbpAkxcTEaNSoUeW+rq+vrwIDA90eAAAAqP88GsQ+Pj6Kjo5WVlaWa5vT6VRWVpbi4uLK3ScuLs5tvSRt3rzZtT48PFzBwcFua0pKSpSTk+Nas2TJEu3bt0979+7V3r17XbdtW7t2rebOnVuj7xEAAAB1WwNPD5CSkqJRo0YpJiZGPXv21KJFi1RaWqrk5GRJ0siRI9WmTRulp6dLkiZNmqT4+HgtWLBAgwYN0po1a7Rz504tX75ckmSz2TR58mTNmTNHERERCg8P16xZsxQSEqKkpCRJUrt27dxmCAgIkCR17NhRbdu2raV3DgAAgLrA40E8dOhQnTp1SqmpqcrLy1NUVJQyMzNdH4o7fvy4vLz+cyK7d+/eWr16tWbOnKkZM2YoIiJCGzZsUOfOnV1rHn/8cZWWlmrcuHEqKipS3759lZmZKT8/v1p/fwAAAKjbPH4f4vqK+xADAADUbfXiPsQAAACApxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACj1YkgXrZsmcLCwuTn56fY2Fht3779quvXrVunTp06yc/PT126dNGmTZvcnrcsS6mpqWrdurX8/f2VkJCgw4cPu54/duyYxo4dq/DwcPn7+6tjx45KS0vThQsXrsv7AwAAQN3l8SBeu3atUlJSlJaWpt27dysyMlKJiYkqKCgod/22bds0fPhwjR07Vnv27FFSUpKSkpK0f/9+15r58+dryZIlysjIUE5Ojho3bqzExESdO3dOkvT555/L6XTqpZde0j/+8Q8tXLhQGRkZmjFjRq28ZwAAANQdNsuyLE8OEBsbqx49emjp0qWSJKfTqdDQUE2cOFHTpk27Yv3QoUNVWlqqjRs3urb16tVLUVFRysjIkGVZCgkJ0ZQpUzR16lRJUnFxsRwOh1auXKlhw4aVO8dvfvMbvfjii/riiy8qNXdJSYnsdruKi4sVGBhY1bcNAACA66yyvebRM8QXLlzQrl27lJCQ4Nrm5eWlhIQEZWdnl7tPdna223pJSkxMdK3Pzc1VXl6e2xq73a7Y2NgKjyl9F83Nmzev8Pnz58+rpKTE7QEAAID6z6NBXFhYqLKyMjkcDrftDodDeXl55e6Tl5d31fWXv1blmEeOHNHzzz+vhx9+uMJZ09PTZbfbXY/Q0NCrvzkAAADUCx6/htjTvvrqK/3sZz/TkCFD9NBDD1W4bvr06SouLnY9Tpw4UYtTAgAA4HrxaBC3bNlS3t7eys/Pd9uen5+v4ODgcvcJDg6+6vrLXytzzJMnT+q2225T7969tXz58qvO6uvrq8DAQLcHAAAA6j+PBrGPj4+io6OVlZXl2uZ0OpWVlaW4uLhy94mLi3NbL0mbN292rQ8PD1dwcLDbmpKSEuXk5Lgd86uvvlK/fv0UHR2tFStWyMvL+JPlAAAARmrg6QFSUlI0atQoxcTEqGfPnlq0aJFKS0uVnJwsSRo5cqTatGmj9PR0SdKkSZMUHx+vBQsWaNCgQVqzZo127tzpOsNrs9k0efJkzZkzRxEREQoPD9esWbMUEhKipKQkSf+J4fbt2+u3v/2tTp065ZqnojPTAAAA+HHyeBAPHTpUp06dUmpqqvLy8hQVFaXMzEzXh+KOHz/udva2d+/eWr16tWbOnKkZM2YoIiJCGzZsUOfOnV1rHn/8cZWWlmrcuHEqKipS3759lZmZKT8/P0nfnVE+cuSIjhw5orZt27rN4+G70AEAAKCWefw+xPUV9yEGAACo2+rFfYgBAAAATyOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgtDoRxMuWLVNYWJj8/PwUGxur7du3X3X9unXr1KlTJ/n5+alLly7atGmT2/OWZSk1NVWtW7eWv7+/EhISdPjwYbc1p0+f1ogRIxQYGKimTZtq7NixOnv2bI2/NwAAANRtHg/itWvXKiUlRWlpadq9e7ciIyOVmJiogoKCctdv27ZNw4cP19ixY7Vnzx4lJSUpKSlJ+/fvd62ZP3++lixZooyMDOXk5Khx48ZKTEzUuXPnXGtGjBihf/zjH9q8ebM2btyo999/X+PGjbvu7xcAAAB1i82yLMuTA8TGxqpHjx5aunSpJMnpdCo0NFQTJ07UtGnTrlg/dOhQlZaWauPGja5tvXr1UlRUlDIyMmRZlkJCQjRlyhRNnTpVklRcXCyHw6GVK1dq2LBhOnDggG655Rbt2LFDMTExkqTMzEwNHDhQX375pUJCQq45d0lJiex2u4qLixUYGFgT34oK2Z6yXdfjAwAA1CYrrXbys7K95tEzxBcuXNCuXbuUkJDg2ubl5aWEhARlZ2eXu092drbbeklKTEx0rc/NzVVeXp7bGrvdrtjYWNea7OxsNW3a1BXDkpSQkCAvLy/l5OSU+7rnz59XSUmJ2wMAAAD1n0eDuLCwUGVlZXI4HG7bHQ6H8vLyyt0nLy/vqusvf73WmqCgILfnGzRooObNm1f4uunp6bLb7a5HaGhoJd8lAAAA6rIGnh6gvpg+fbpSUlJcfy4pKam1KK6t/1sBAADARB49Q9yyZUt5e3srPz/fbXt+fr6Cg4PL3Sc4OPiq6y9/vdaa//7Q3qVLl3T69OkKX9fX11eBgYFuDwAAANR/Hg1iHx8fRUdHKysry7XN6XQqKytLcXFx5e4TFxfntl6SNm/e7FofHh6u4OBgtzUlJSXKyclxrYmLi1NRUZF27drlWrNlyxY5nU7FxsbW2PsDAABA3efxSyZSUlI0atQoxcTEqGfPnlq0aJFKS0uVnJwsSRo5cqTatGmj9PR0SdKkSZMUHx+vBQsWaNCgQVqzZo127typ5cuXS5JsNpsmT56sOXPmKCIiQuHh4Zo1a5ZCQkKUlJQkSbr55pv1s5/9TA899JAyMjJ08eJFPfLIIxo2bFil7jABAACAHw+PB/HQoUN16tQppaamKi8vT1FRUcrMzHR9KO748ePy8vrPiezevXtr9erVmjlzpmbMmKGIiAht2LBBnTt3dq15/PHHVVpaqnHjxqmoqEh9+/ZVZmam/Pz8XGtee+01PfLII+rfv7+8vLw0ePBgLVmypPbeOAAAAOoEj9+HuL6qzfsQAwAAoOrqxX2IAQAAAE8jiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSCGAAAAEZr4OkB6ivLsiRJJSUlHp4EAAAA5bncaZe7rSIEcTWdOXNGkhQaGurhSQAAAHA1Z86ckd1ur/B5m3WtZEa5nE6nTp48qSZNmshms1331yspKVFoaKhOnDihwMDA6/56qHn8DOs3fn71Hz/D+o+fYf1X2z9Dy7J05swZhYSEyMur4iuFOUNcTV5eXmrbtm2tv25gYCD/Eqjn+BnWb/z86j9+hvUfP8P6rzZ/hlc7M3wZH6oDAACA0QhiAAAAGI0grid8fX2VlpYmX19fT4+CauJnWL/x86v/+BnWf/wM67+6+jPkQ3UAAAAwGmeIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4jrgWXLliksLEx+fn6KjY3V9u3bPT0SKik9PV09evRQkyZNFBQUpKSkJB08eNDTY+EHePbZZ2Wz2TR58mRPj4Iq+Oqrr3T//ferRYsW8vf3V5cuXbRz505Pj4VKKCsr06xZsxQeHi5/f3917NhRTz/9tLgnQN31/vvv66677lJISIhsNps2bNjg9rxlWUpNTVXr1q3l7++vhIQEHT582DPD/v8I4jpu7dq1SklJUVpamnbv3q3IyEglJiaqoKDA06OhEt577z1NmDBBH3/8sTZv3qyLFy/qjjvuUGlpqadHQzXs2LFDL730krp27erpUVAF//rXv9SnTx81bNhQ77zzjj777DMtWLBAzZo18/RoqIR58+bpxRdf1NKlS3XgwAHNmzdP8+fP1/PPP+/p0VCB0tJSRUZGatmyZeU+P3/+fC1ZskQZGRnKyclR48aNlZiYqHPnztXypP/BbdfquNjYWPXo0UNLly6VJDmdToWGhmrixImaNm2ah6dDVZ06dUpBQUF677339P/+3//z9DiogrNnz6p79+564YUXNGfOHEVFRWnRokWeHguVMG3aNH300Uf64IMPPD0KquHOO++Uw+HQ7373O9e2wYMHy9/fX3/84x89OBkqw2azaf369UpKSpL03dnhkJAQTZkyRVOnTpUkFRcXy+FwaOXKlRo2bJhH5uQMcR124cIF7dq1SwkJCa5tXl5eSkhIUHZ2tgcnQ3UVFxdLkpo3b+7hSVBVEyZM0KBBg9z+94j64a233lJMTIyGDBmioKAgdevWTS+//LKnx0Il9e7dW1lZWTp06JAkad++ffrwww81YMAAD0+G6sjNzVVeXp7bv0vtdrtiY2M92jYNPPbKuKbCwkKVlZXJ4XC4bXc4HPr88889NBWqy+l0avLkyerTp486d+7s6XFQBWvWrNHu3bu1Y8cOT4+Cavjiiy/04osvKiUlRTNmzNCOHTv06KOPysfHR6NGjfL0eLiGadOmqaSkRJ06dZK3t7fKyso0d+5cjRgxwtOjoRry8vIkqdy2ufycJxDEQC2ZMGGC9u/frw8//NDTo6AKTpw4oUmTJmnz5s3y8/Pz9DioBqfTqZiYGD3zzDOSpG7dumn//v3KyMggiOuBP/3pT3rttde0evVq3Xrrrdq7d68mT56skJAQfn6oMVwyUYe1bNlS3t7eys/Pd9uen5+v4OBgD02F6njkkUe0ceNGbd26VW3btvX0OKiCXbt2qaCgQN27d1eDBg3UoEEDvffee1qyZIkaNGigsrIyT4+Ia2jdurVuueUWt20333yzjh8/7qGJUBW/+tWvNG3aNA0bNkxdunTRAw88oMcee0zp6emeHg3VcLlf6lrbEMR1mI+Pj6Kjo5WVleXa5nQ6lZWVpbi4OA9OhsqyLEuPPPKI1q9fry1btig8PNzTI6GK+vfvr08//VR79+51PWJiYjRixAjt3btX3t7enh4R19CnT58rbnd46NAhtW/f3kMToSq+/fZbeXm554q3t7ecTqeHJsIPER4eruDgYLe2KSkpUU5Ojkfbhksm6riUlBSNGjVKMTEx6tmzpxYtWqTS0lIlJyd7ejRUwoQJE7R69Wr9+c9/VpMmTVzXR9ntdvn7+3t4OlRGkyZNrrjmu3HjxmrRogXXgtcTjz32mHr37q1nnnlG9913n7Zv367ly5dr+fLlnh4NlXDXXXdp7ty5ateunW699Vbt2bNHzz33nMaMGePp0VCBs2fP6siRI64/5+bmau/evWrevLnatWunyZMna86cOYqIiFB4eLhmzZqlkJAQ150oPMJCnff8889b7dq1s3x8fKyePXtaH3/8sadHQiVJKvexYsUKT4+GHyA+Pt6aNGmSp8dAFfzlL3+xOnfubPn6+lqdOnWyli9f7umRUEklJSXWpEmTrHbt2ll+fn5Whw4drCeeeMI6f/68p0dDBbZu3Vruf/tGjRplWZZlOZ1Oa9asWZbD4bB8fX2t/v37WwcPHvTozNyHGAAAAEbjGmIAAAAYjSAGAACA0QhiAAAAGI0gBgAAgNEIYgAAABiNIAYAAIDRCGIAAAAYjSAGAACA0QhiADDI6NGjPfvrUQGgDmrg6QEAADXDZrNd9fm0tDQtXrxY/IJSAHBHEAPAj8TXX3/t+ue1a9cqNTVVBw8edG0LCAhQQECAJ0YDgDqNSyYA4EciODjY9bDb7bLZbG7bAgICrrhkol+/fpo4caImT56sZs2ayeFw6OWXX1ZpaamSk5PVpEkT3XDDDXrnnXfcXmv//v0aMGCAAgIC5HA49MADD6iwsLCW3zEA1AyCGAAMt2rVKrVs2VLbt2/XxIkTNX78eA0ZMkS9e/fW7t27dccdd+iBBx7Qt99+K0kqKirS7bffrm7dumnnzp3KzMxUfn6+7rvvPg+/EwCoHoIYAAwXGRmpmTNnKiIiQtOnT5efn59atmyphx56SBEREUpNTdU333yjTz75RJK0dOlSdevWTc8884w6deqkbt266dVXX9XWrVt16NAhD78bAKg6riEGAMN17drV9c/e3t5q0aKFunTp4trmcDgkSQUFBZKkffv2aevWreVej3z06FHdeOON13liAKhZBDEAGK5hw4Zuf7bZbG7bLt+9wul0SpLOnj2ru+66S/PmzbviWK1bt76OkwLA9UEQAwCqpHv37nrjjTcUFhamBg34zwiA+o9riAEAVTJhwgSdPn1aw4cP144dO3T06FH99a9/VXJyssrKyjw9HgBUGUEMAKiSkJAQffTRRyorK9Mdd9yhLl26aPLkyWratKm8vPjPCoD6x2bxK4sAAABgMP4qDwAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAoxHEAAAAMBpBDAAAAKMRxAAAADAaQQwAAACjEcQAAAAwGkEMAAAAo/1/RSrsSry1x5oAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -88,7 +88,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "37 Points (+:28, -:9), 37 Boxes (+:28, -:9)\n", + "43 Points (+:34, -:9), 43 Boxes (+:34, -:34)\n", "{beta[0.011, 0.011): 0.011, gamma[0.456, 0.456): 0.456, delta[0.011, 0.011): 0.011, alpha[0.57, 0.57): 0.57, epsilon[0.1368, 0.20520000000000002): 0.1851337291300297, zeta[0.125, 0.125): 0.125, lambda[0.034, 0.034): 0.034, eta[0.125, 0.125): 0.125, rho[0.034, 0.034): 0.034, theta[0.2968, 0.4452): 0.3694555829524995, kappa[0.017, 0.017): 0.017, mu[0.017, 0.017): 0.017, nu[0.027, 0.027): 0.027, xi[0.017, 0.017): 0.017, tau[0.01, 0.01): 0.01, sigma[0.017, 0.017): 0.017}\n", " Ailing Diagnosed Extinct Healed \\\n", "time \n", @@ -103,6 +103,16 @@ "2.666667e-08 3.000000e-08 9.333333e-08 2.666667e-08 2.666667e-08 \n", "3.000000e-08 3.166667e-08 6.333333e-08 3.000000e-08 3.000000e-08 \n", "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 \n", "\n", " Infected Recognized Susceptible Threatened alpha \\\n", "time \n", @@ -117,6 +127,16 @@ "2.666667e-08 6.933333e-07 3.333333e-08 1.999993e-01 2.666667e-08 0.57 \n", "3.000000e-08 3.633333e-07 3.333333e-08 9.999966e-02 3.000000e-08 0.57 \n", "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", + "3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 3.333333e-08 0.57 \n", "\n", " beta ... label lambda mu nu rho sigma tau \\\n", "time ... \n", @@ -131,6 +151,16 @@ "2.666667e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", "3.000000e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", + "3.333333e-08 0.011 ... false 0.034 0.017 0.027 0.034 0.017 0.01 \n", "\n", " theta xi zeta \n", "time \n", @@ -145,8 +175,18 @@ "2.666667e-08 0.369456 0.017 0.125 \n", "3.000000e-08 0.369456 0.017 0.125 \n", "3.333333e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", + "3.333333e-08 0.369456 0.017 0.125 \n", "\n", - "[11 rows x 26 columns]\n" + "[21 rows x 26 columns]\n" ] } ], @@ -205,78 +245,126 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "'method' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/danbryce/funman/notebooks/funman_results.ipynb Cell 6\u001b[0m line \u001b[0;36m3\n\u001b[1;32m 1\u001b[0m \u001b[39m# Get the state varibles and parameters from a point (used by the dataframe timeseries function above)\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m results\u001b[39m.\u001b[39;49mparameter_space\u001b[39m.\u001b[39;49mtrue_points[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39mvalues\n", - "\u001b[0;31mTypeError\u001b[0m: 'method' object is not subscriptable" - ] + "data": { + "text/plain": [ + "{'Recognized_0': 3.33333333e-08,\n", + " 'beta': 0.011,\n", + " 'Healed_0': 0.0,\n", + " 'gamma': 0.456,\n", + " 'Threatened_0': 0.0,\n", + " 'epsilon': 0.14897500000000005,\n", + " 'delta': 0.011,\n", + " 'Extinct_0': 0.0,\n", + " 'alpha': 0.57,\n", + " 'zeta': 0.125,\n", + " 'theta': 0.35245000000000004,\n", + " 'lambda': 0.034,\n", + " 'eta': 0.125,\n", + " 'rho': 0.034,\n", + " 'kappa': 0.017,\n", + " 'mu': 0.017,\n", + " 'nu': 0.027,\n", + " 'xi': 0.017,\n", + " 'tau': 0.01,\n", + " 'sigma': 0.017,\n", + " 'Infected_0': 3.33333333e-06,\n", + " 'timer_t_0': 0.0,\n", + " 'Susceptible_0': 0.9999963,\n", + " 'assume_theta_epsilon': 1.0,\n", + " 'Diagnosed_0': 3.33333333e-07,\n", + " 'assume_infected_maximum2': 1.0,\n", + " 'assume_infected_maximum2_0': 1.0,\n", + " 'Ailing_0': 1.66666666e-08,\n", + " 'timestep': 0.0}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "# Get the state varibles and parameters from a point (used by the dataframe timeseries function above)\n", "\n", - "results.parameter_space.true_points[0].values" + "results.parameter_space.true_points()[0].values" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the parameter space\n", "\n", "from funman_demo.parameter_space_plotter import ParameterSpacePlotter\n", "ParameterSpacePlotter(\n", - " results.parameter_space, plot_points=True, parameters=[\"beta\", \"gamma\", \"timestep\"]\n", - " ).plot(show=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the timeseries for each point in the parameter space for the selected variables\n", - "\n", - "results.plot(variables=[\"I\"], label_marker={\"true\":\",\", \"false\": \",\"})" + " results.parameter_space, plot_points=True, parameters=[\"epsilon\", \"theta\", \"timestep\"]\n", + " ).plot(show=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], - "source": [ - "# Plot the points from a saved result file.\n", - "\n", - "from funman.server.query import FunmanResults\n", - "import json\n", - "\n", - "# %load_ext autoreload\n", - "# %autoreload 2\n", - "with open(\"out/f13f5edb-41ba-4a70-bcc6-0bb9881ce71c.json\", \"r\") as f:\n", - " results = FunmanResults.model_validate(json.load(f))\n", - " results.plot(variables=[\"I\"], label_marker={\"true\":\",\", \"false\": \",\"})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'box': {'beta': {'lb': 0.011, 'ub': 0.011, 'closed_upper_bound': True},\n", + " 'gamma': {'lb': 0.456, 'ub': 0.456, 'closed_upper_bound': True},\n", + " 'delta': {'lb': 0.011, 'ub': 0.011, 'closed_upper_bound': True},\n", + " 'alpha': {'lb': 0.57, 'ub': 0.57, 'closed_upper_bound': True},\n", + " 'epsilon': {'lb': 0.15334018554687504,\n", + " 'ub': 0.16244375000000005,\n", + " 'closed_upper_bound': False},\n", + " 'zeta': {'lb': 0.125, 'ub': 0.125, 'closed_upper_bound': True},\n", + " 'lambda': {'lb': 0.034, 'ub': 0.034, 'closed_upper_bound': True},\n", + " 'eta': {'lb': 0.125, 'ub': 0.125, 'closed_upper_bound': True},\n", + " 'rho': {'lb': 0.034, 'ub': 0.034, 'closed_upper_bound': True},\n", + " 'theta': {'lb': 0.2968,\n", + " 'ub': 0.3059505859375001,\n", + " 'closed_upper_bound': False},\n", + " 'kappa': {'lb': 0.017, 'ub': 0.017, 'closed_upper_bound': True},\n", + " 'mu': {'lb': 0.017, 'ub': 0.017, 'closed_upper_bound': True},\n", + " 'nu': {'lb': 0.027, 'ub': 0.027, 'closed_upper_bound': True},\n", + " 'xi': {'lb': 0.017, 'ub': 0.017, 'closed_upper_bound': True},\n", + " 'tau': {'lb': 0.01, 'ub': 0.01, 'closed_upper_bound': True},\n", + " 'sigma': {'lb': 0.017, 'ub': 0.017, 'closed_upper_bound': True},\n", + " 'timestep': {'lb': 0.0, 'ub': 10.0, 'closed_upper_bound': True}},\n", + " 'relevant_constraints': [{'soft': True,\n", + " 'name': 'theta_epsilon',\n", + " 'timepoints': None,\n", + " 'additive_bounds': {'lb': 0.0,\n", + " 'ub': 1.7976931348623157e+308,\n", + " 'closed_upper_bound': False},\n", + " 'variables': ['theta', 'epsilon'],\n", + " 'weights': [1, -2]}],\n", + " 'expression': '((((((((((assume_theta_epsilon & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 8122187500000003/50000000000000000)) & (theta < 3824382324218751/12500000000000000)) & disj62) & ((((2.0 * epsilon) <= theta) | (! assume_theta_epsilon)) | (! disj62))) & ((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 958376159667969/6250000000000000)) | (! ((2.0 * epsilon) <= theta))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (theta < 3824382324218751/12500000000000000)))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 958376159667969/6250000000000000)))'}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "print(results.parameter_space.false_boxes[1].explain( ))\n", - "results.plot(points=results.parameter_space.false_boxes[1].corner_points, variables=[\"I\"], label_marker={\"true\":\",\", \"false\": \",\"})" + "results.parameter_space.false_boxes[1].explain( )" ] } ], diff --git a/notebooks/saved-results/e466f678-c60d-4117-bd58-bed949c512cf.json b/notebooks/saved-results/e466f678-c60d-4117-bd58-bed949c512cf.json new file mode 100644 index 00000000..c7c6e0d2 --- /dev/null +++ b/notebooks/saved-results/e466f678-c60d-4117-bd58-bed949c512cf.json @@ -0,0 +1 @@ +{"id":"e466f678-c60d-4117-bd58-bed949c512cf","model":{"name":"model_d028717f-e06e-497b-b11e-771be32631fd","init_values":{},"parameter_bounds":{},"petrinet":{"header":{"name":"Giordano2020 - SIDARTHE model of COVID-19 spread in 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(Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! 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(Infected_0 < 29999999999999999/1000000000000000000))) | (! disj30))) & ((assume_theta_epsilon | (! ((2.0 * epsilon) <= theta))) | (! disj31))) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & (((((((((2.0 * epsilon) <= theta) | (theta < 29680000000000001/100000000000000000)) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000)))) & (((! assume_infected_maximum2_0) | (! assume_theta_epsilon)) | (! disj41))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16996328125000007,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3700046875000001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"timestep":0.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.16244375000000005,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3263804687500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 8122187500000003/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & disj32) & disj50) & disj52) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj32))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! 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(Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.14962187500000004,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3857902343750001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.00001118831028494801,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":4.790729161875939e-6,"Ailing_10":4.11336829031736e-6,"Recognized_10":4.996317050366399e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.07603856919607055,"Infected_20":0.0000527051678367253,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998940733660042,"Diagnosed_20":0.000014254236417487181,"Ailing_20":0.0,"Recognized_20":0.00002214306090881688,"Healed_20":0.000015598881265912572,"Threatened_20":8.428115030323341e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1765314514160157,"ub":0.18435014648437506,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.37554083862304705,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":0.0,"ub":0.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"theta_epsilon","timepoints":null,"additive_bounds":{"lb":0.0,"ub":1.7976931348623157e308,"closed_upper_bound":false},"variables":["theta","epsilon"],"weights":[1,-2]}},{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 18435014648437507/100000000000000000)) & disj59) & disj60) & disj74) & ((assume_infected_maximum2_0 | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! disj59))) & ((assume_theta_epsilon | (! ((2.0 * epsilon) <= theta))) | (! disj60))) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & (((((((((2.0 * epsilon) <= theta) | (theta < 29680000000000001/100000000000000000)) | (epsilon < 171/1250)) | (epsilon < 17653145141601571/100000000000000000)) | (theta < 7510816772460941/20000000000000000)) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000)))) & (((! assume_infected_maximum2_0) | (! assume_theta_epsilon)) | (! disj74))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 17653145141601571/100000000000000000))) & (! (theta < 7510816772460941/20000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1804407989501954,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.39113505859375014,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":3.33333333e-8,"assume_infected_maximum2_10":0.0,"Susceptible_10":3.33333333e-8,"Diagnosed_10":3.33333333e-8,"Ailing_10":3.33333333e-8,"Recognized_10":3.33333333e-8,"Healed_10":3.33333333e-8,"Threatened_10":3.33333333e-8,"Extinct_10":3.33333333e-8,"timer_t_10":3.33333333e-8,"funman_lambda":3.33333333e-8,"timestep":0.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.1765314514160157,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3586960937500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":1.0,"ub":1.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (epsilon < 17653145141601571/100000000000000000)) & disj61) & disj77) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj61))) & (((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (epsilon < 17653145141601571/100000000000000000)))) & ((assume_infected_maximum2_10 | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! disj77))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000))) & (! assume_infected_maximum2_10)) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1694876007080079,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.4019480468750001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.0,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.4529200181473426e-6,"Ailing_10":4.110675321578131e-6,"Recognized_10":5.02324673775868e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.19119456404020757,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"timestep":1.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.16244375000000005,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3263804687500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":3.0,"ub":3.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 8122187500000003/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & disj86) & disj97) & disj100) & disj102) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj86))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj97))) & (((Infected_20 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_20)) | (! disj100))) & ((assume_infected_maximum2_30 | (! disj102)) | (! (Infected_30 < 29999999999999999/1000000000000000000)))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (theta < 3263804687500001/10000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! assume_infected_maximum2_30)) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.14962187500000004,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3857902343750001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000011425027528009747,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":4.790729161875939e-6,"Ailing_10":4.11336829031736e-6,"Recognized_10":4.996317050366399e-7,"Healed_10":1.9923392279030494e-6,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.056115176897116655,"Infected_20":0.000057740439705456696,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998927241085045,"Diagnosed_20":0.000014407762109077173,"Ailing_20":0.0,"Recognized_20":0.00002214306090881688,"Healed_20":0.000011036048818594126,"Threatened_20":8.428115030323341e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.00020640014371116688,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995482630264079,"Diagnosed_30":0.00007978392424972967,"Ailing_30":0.00006273186169156044,"Recognized_30":0.00004149718454856439,"Healed_30":0.000055606982896654105,"Threatened_30":7.017250928523561e-6,"Extinct_30":8.546448363453342e-8,"timer_t_30":30.0,"timestep":3.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1765314514160157,"ub":0.18435014648437506,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.37554083862304705,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":1.0,"ub":1.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 18435014648437507/100000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & disj110) & disj129) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj110))) & (((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (epsilon < 17653145141601571/100000000000000000)) | (theta < 7510816772460941/20000000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))))) & ((assume_infected_maximum2_10 | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! disj129))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! assume_infected_maximum2_10)) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (epsilon < 17653145141601571/100000000000000000))) & (! (theta < 7510816772460941/20000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1804407989501954,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41037041931152357,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.0,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.818026625855153e-6,"Ailing_10":4.109271592844326e-6,"Recognized_10":5.037284025096736e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.18415071333219976,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"timestep":1.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.1765314514160157,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3586960937500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (epsilon < 17653145141601571/100000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & disj138) & disj143) & disj145) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj138))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj143))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj145))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1694876007080079,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.4019480468750001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000011293493186527538,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.4529200181473426e-6,"Ailing_10":4.110675321578131e-6,"Recognized_10":5.02324673775868e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.04723933214552918,"Infected_20":0.00005555043936295593,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998934129812498,"Diagnosed_20":0.000016043345186313132,"Ailing_20":0.0,"Recognized_20":0.000023623348894190662,"Healed_20":0.000010473067718449695,"Threatened_20":8.430807999062568e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.16700030522644527,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3341427254199983,"ub":0.3586960937500001,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":0.0,"ub":0.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"theta_epsilon","timepoints":null,"additive_bounds":{"lb":0.0,"ub":1.7976931348623157e308,"closed_upper_bound":false},"variables":["theta","epsilon"],"weights":[1,-2]}},{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (theta < 8967402343750003/25000000000000000)) & disj151) & disj152) & (epsilon < 16700030522644527/100000000000000000)) & disj164) & ((assume_infected_maximum2_0 | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! disj151))) & ((assume_theta_epsilon | (! ((2.0 * epsilon) <= theta))) | (! disj152))) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & ((((((((((2.0 * epsilon) <= theta) | (theta < 29680000000000001/100000000000000000)) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 33414272541999829/100000000000000000)) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (theta < 8967402343750003/25000000000000000))) | (! (epsilon < 16700030522644527/100000000000000000)))) & (((! assume_infected_maximum2_0) | (! assume_theta_epsilon)) | (! disj164))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 33414272541999829/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16455678710937507,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3375657226562501,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"timestep":0.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.16244375000000005,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3263804687500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":4.0,"ub":4.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 8122187500000003/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & assume_infected_maximum2_30) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & disj153) & disj167) & disj170) & disj173) & disj175) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj153))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! 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(Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! 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((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & disj184) & disj204) & disj206) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj184))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (epsilon < 17653145141601571/100000000000000000)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (theta < 7510816772460941/20000000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj204))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj206))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (epsilon < 17653145141601571/100000000000000000))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (theta < 7510816772460941/20000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1804407989501954,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41037041931152357,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000011336308542265614,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.818026625855153e-6,"Ailing_10":4.109271592844326e-6,"Recognized_10":5.037284025096736e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.03186717005608681,"Infected_20":0.000056723129077360236,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998931350249303,"Diagnosed_20":0.000017068979498461317,"Ailing_20":0.0,"Recognized_20":0.000024419878086739077,"Healed_20":7.938314118187088e-6,"Threatened_20":8.432211727796375e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.1765314514160157,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3586960937500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":3.0,"ub":3.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (epsilon < 17653145141601571/100000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & disj214) & disj220) & disj223) & disj225) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj214))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj220))) & (((Infected_20 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_20)) | (! disj223))) & ((assume_infected_maximum2_30 | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! disj225))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! assume_infected_maximum2_30)) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1694876007080079,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.4019480468750001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.00001190693703140173,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.4529200181473426e-6,"Ailing_10":4.110675321578131e-6,"Recognized_10":5.02324673775868e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.02179216607289209,"Infected_20":0.00006120270682530097,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.999889916431114,"Diagnosed_20":0.00001703984637207095,"Ailing_20":0.0,"Recognized_20":0.000023623348894190662,"Healed_20":6.319249376645201e-6,"Threatened_20":8.430807999062568e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.00022636556319700172,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995269575033804,"Diagnosed_30":0.0000947093693099884,"Ailing_30":0.00006846207906071419,"Recognized_30":0.00004396978194934292,"Healed_30":0.000033162400870960394,"Threatened_30":7.354254704364905e-6,"Extinct_30":8.549141332192568e-8,"timer_t_30":30.0,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"timestep":3.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.16700030522644527,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3341427254199983,"ub":0.3586960937500001,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":1.0,"ub":1.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (theta < 8967402343750003/25000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (epsilon < 16700030522644527/100000000000000000)) & disj234) & disj243) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj234))) & ((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (theta < 33414272541999829/100000000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (theta < 8967402343750003/25000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (epsilon < 16700030522644527/100000000000000000)))) & ((assume_infected_maximum2_10 | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! disj243))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! assume_infected_maximum2_10)) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (theta < 33414272541999829/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16472202761322266,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3464194095849992,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.0,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.294067581813355e-6,"Ailing_10":4.11993009442278e-6,"Recognized_10":4.930699009312203e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.19119456404020757,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"timestep":1.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.16244375000000005,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3263804687500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":5.0,"ub":5.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum1","timepoints":{"lb":50.0,"ub":75.0,"closed_upper_bound":true},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.05,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Extinct_0 = 0.0) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 8122187500000003/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30)))))) & (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30)))))) & disj257) & (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40))))) & (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40)))))) & (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40)))))) & (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40)))))) & (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40)))))) & (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40)))))) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & ((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (Infected_40 < 0.0)) | (Diagnosed_40 < 0.0)) | (Susceptible_40 < 0.0)) | (Infected_50 < 50000000000000003/1000000000000000000)) | (Extinct_50 < 0.0)) | (Threatened_50 < 0.0)) | (Healed_50 < 0.0)) | (Recognized_50 < 0.0)) | (Ailing_50 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Infected_40 < 29999999999999999/1000000000000000000))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30))))))) | (! (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30))))))) | (! (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30))))))) | (! (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40)))))) | (! (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40))))))) | (! (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40))))))) | (! (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40))))))) | (! (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40))))))) | (! (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40)))))))) & ((assume_infected_maximum1_50 | (! disj257)) | (! (Infected_50 < 50000000000000003/1000000000000000000)))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (theta < 3263804687500001/10000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! 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3263804687500001/10000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (epsilon 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(theta < 40138193147778523/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.19423061509579428,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41141934151649484,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":3.33333333e-8,"assume_infected_maximum2_10":0.0,"Susceptible_10":3.33333333e-8,"Diagnosed_10":3.33333333e-8,"Ailing_10":3.33333333e-8,"Recognized_10":3.33333333e-8,"Healed_10":3.33333333e-8,"Threatened_10":3.33333333e-8,"Extinct_10":3.33333333e-8,"timer_t_10":3.33333333e-8,"funman_lambda":3.33333333e-8,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"timestep":0.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.18435014648437506,"ub":0.19249860695302495,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.39115094642639175,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":1.0,"ub":1.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (epsilon < 601558146728203/3125000000000000)) & disj293) & disj299) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj293))) & (((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 18435014648437507/100000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (theta < 4889386830329897/12500000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (epsilon < 601558146728203/3125000000000000)))) & ((assume_infected_maximum2_10 | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! disj299))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 18435014648437507/100000000000000000))) & (! assume_infected_maximum2_10)) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (theta < 4889386830329897/12500000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.18842437671870002,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41817547321319587,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.0,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":6.084145884539188e-6,"Ailing_10":4.107970750532584e-6,"Recognized_10":5.050292448214157e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.18024136579802008,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"timestep":1.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1765314514160157,"ub":0.18435014648437506,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.37554083862304705,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":3.0,"ub":3.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 18435014648437507/100000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & disj308) & disj313) & disj316) & disj318) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj308))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (epsilon < 17653145141601571/100000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (theta < 7510816772460941/20000000000000000)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj313))) & (((Infected_20 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_20)) | (! disj316))) & ((assume_infected_maximum2_30 | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! disj318))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (epsilon < 17653145141601571/100000000000000000))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! (theta < 7510816772460941/20000000000000000))) & (! assume_infected_maximum2_30)) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1804407989501954,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41037041931152357,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000011336308542265614,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.818026625855153e-6,"Ailing_10":4.109271592844326e-6,"Recognized_10":5.037284025096736e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.03186717005608681,"Infected_20":0.000056723129077360236,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998931350249303,"Diagnosed_20":0.000017068979498461317,"Ailing_20":0.0,"Recognized_20":0.000024419878086739077,"Healed_20":7.938314118187088e-6,"Threatened_20":8.432211727796375e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.0,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995585352761226,"Diagnosed_30":0.0000929253041313895,"Ailing_30":0.00006529140848349139,"Recognized_30":0.00004160071864041797,"Healed_30":0.00004161648039660679,"Threatened_30":7.460533910525881e-6,"Extinct_30":8.550545060926376e-8,"timer_t_30":30.0,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"timestep":3.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.1765314514160157,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3586960937500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":4.0,"ub":4.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (epsilon < 17653145141601571/100000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & assume_infected_maximum2_30) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & disj327) & disj332) & disj335) & disj338) & disj340) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj327))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj332))) & (((Infected_20 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_20)) | (! disj335))) & (((Infected_30 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_30)) | (! disj338))) & ((assume_infected_maximum2_40 | (! (Infected_40 < 29999999999999999/1000000000000000000))) | (! disj340))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0))) & (! (Infected_30 < 0.0))) & (! (Diagnosed_30 < 0.0))) & (! (Susceptible_30 < 0.0))) & (! assume_infected_maximum2_40)) & (! (Extinct_40 < 0.0))) & (! (Threatened_40 < 0.0))) & (! (Healed_40 < 0.0))) & (! (Recognized_40 < 0.0))) & (! (Ailing_40 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16420471267700204,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.4205496977106491,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000012136048228190806,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.2768237506232435e-6,"Ailing_10":4.107575044542322e-6,"Recognized_10":5.054249508116783e-7,"Healed_10":7.95223938974685e-7,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.020201718193352272,"Infected_20":0.00006312263483437098,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998886436931544,"Diagnosed_20":0.00001681660434687522,"Ailing_20":0.0,"Recognized_20":0.000024154039267324634,"Healed_20":5.8290326908643355e-6,"Threatened_20":8.433908276098379e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.00023551254453217277,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995208972908498,"Diagnosed_30":0.00009375534257654058,"Ailing_30":0.00007605428275467903,"Recognized_30":0.00003789993825440718,"Healed_30":0.000028702096218664743,"Threatened_30":7.267060917363943e-6,"Extinct_30":8.552241609228379e-8,"timer_t_30":30.0,"Infected_40":0.0010890756668970132,"assume_infected_maximum2_40":1.0,"Susceptible_40":0.9978180005168548,"Diagnosed_40":0.0003317707681882455,"Ailing_40":0.0,"Recognized_40":0.00045943205538065505,"Healed_40":0.00012909261493678392,"Threatened_40":0.000028467165866661054,"Extinct_40":8.122285078286781e-7,"timer_t_40":40.0,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"timestep":4.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.16700030522644527,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3341427254199983,"ub":0.3586960937500001,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (theta < 8967402343750003/25000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (epsilon < 16700030522644527/100000000000000000)) & disj348) & disj354) & disj356) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj348))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (theta < 33414272541999829/100000000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (theta < 8967402343750003/25000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (epsilon < 16700030522644527/100000000000000000)))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj354))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj356))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (theta < 33414272541999829/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16472202761322266,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3464194095849992,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000010910071104162921,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.294067581813355e-6,"Ailing_10":4.11993009442278e-6,"Recognized_10":4.930699009312203e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.058741994627970376,"Infected_20":0.000053232247565741264,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.999895574727824,"Diagnosed_20":0.000014895176355501771,"Ailing_20":0.0,"Recognized_20":0.000021166192325158075,"Healed_20":0.000012296672484301488,"Threatened_20":8.421553226217922e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.16244375000000005,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3263804687500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":6.0,"ub":6.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum1","timepoints":{"lb":50.0,"ub":75.0,"closed_upper_bound":true},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.05,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Extinct_0 = 0.0) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 8122187500000003/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & ((((((((Extinct_30 + Threatened_30) + Healed_30) + Recognized_30) + Ailing_30) + Infected_30) + Diagnosed_30) + Susceptible_30) <= 10000100000000001/10000000000000000)) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30)))))) & (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30)))))) & ((((((((Extinct_40 + Threatened_40) + Healed_40) + Recognized_40) + Ailing_40) + Infected_40) + Diagnosed_40) + Susceptible_40) <= 10000100000000001/10000000000000000)) & assume_infected_maximum1_50) & (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40))))) & (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40)))))) & (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40)))))) & (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40)))))) & (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40)))))) & (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40)))))) & (Diagnosed_50 = (Diagnosed_40 + (10.0 * (((-1.0 * (rho * Diagnosed_40)) - (eta * Diagnosed_40)) + (epsilon * Infected_40)))))) & (Susceptible_50 = (Susceptible_40 + (10.0 * ((((-1.0 * ((alpha * Infected_40) * Susceptible_40)) - ((delta * Recognized_40) * Susceptible_40)) - ((gamma * Ailing_40) * Susceptible_40)) - ((beta * Diagnosed_40) * Susceptible_40)))))) & ((((((((Extinct_50 + Threatened_50) + Healed_50) + Recognized_50) + Ailing_50) + Infected_50) + Diagnosed_50) + Susceptible_50) <= 10000100000000001/10000000000000000)) & disj368) & disj370) & (Extinct_60 = (Extinct_50 + (10.0 * (tau * Threatened_50))))) & (Threatened_60 = (Threatened_50 + (10.0 * ((((-1.0 * (sigma * Threatened_50)) - (tau * Threatened_50)) + (nu * Recognized_50)) + (mu * Ailing_50)))))) & (Healed_60 = (Healed_50 + (10.0 * (((((sigma * Threatened_50) + (xi * Recognized_50)) + (kappa * Ailing_50)) + (rho * Diagnosed_50)) + (funman_lambda * Infected_50)))))) & (Recognized_60 = (Recognized_50 + (10.0 * ((((-1.0 * (xi * Recognized_50)) - (nu * Recognized_50)) + (theta * Ailing_50)) + (eta * Diagnosed_50)))))) & (Ailing_60 = (Ailing_50 + (10.0 * ((((-1.0 * (mu * Ailing_50)) - (kappa * Ailing_50)) - (theta * Ailing_50)) + (zeta * Infected_50)))))) & (Infected_60 = (Infected_50 + (10.0 * (((((((-1.0 * (zeta * Infected_50)) - (epsilon * Infected_50)) + ((alpha * Infected_50) * Susceptible_50)) + ((delta * Recognized_50) * Susceptible_50)) + ((gamma * Ailing_50) * Susceptible_50)) + ((beta * Diagnosed_50) * Susceptible_50)) - (funman_lambda * Infected_50)))))) & (Diagnosed_60 = (Diagnosed_50 + (10.0 * (((-1.0 * (rho * Diagnosed_50)) - (eta * Diagnosed_50)) + (epsilon * Infected_50)))))) & (Susceptible_60 = (Susceptible_50 + (10.0 * ((((-1.0 * ((alpha * Infected_50) * Susceptible_50)) - ((delta * Recognized_50) * Susceptible_50)) - ((gamma * Ailing_50) * Susceptible_50)) - ((beta * Diagnosed_50) * Susceptible_50)))))) & ((((((((Extinct_60 + Threatened_60) + Healed_60) + Recognized_60) + Ailing_60) + Infected_60) + Diagnosed_60) + Susceptible_60) <= 10000100000000001/10000000000000000)) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & ((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (Infected_40 < 0.0)) | (Diagnosed_40 < 0.0)) | (Susceptible_40 < 0.0)) | (Extinct_50 < 0.0)) | (Threatened_50 < 0.0)) | (Healed_50 < 0.0)) | (Recognized_50 < 0.0)) | (Ailing_50 < 0.0)) | (Infected_50 < 0.0)) | (Diagnosed_50 < 0.0)) | (Susceptible_50 < 0.0)) | (Infected_60 < 50000000000000003/1000000000000000000)) | (Extinct_60 < 0.0)) | (Threatened_60 < 0.0)) | (Healed_60 < 0.0)) | (Recognized_60 < 0.0)) | (Ailing_60 < 0.0)) | (Diagnosed_60 < 0.0)) | (Susceptible_60 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! ((((((((Extinct_30 + Threatened_30) + Healed_30) + Recognized_30) + Ailing_30) + Infected_30) + Diagnosed_30) + Susceptible_30) <= 10000100000000001/10000000000000000))) | (! (Infected_40 < 29999999999999999/1000000000000000000))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30))))))) | (! (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30))))))) | (! (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30))))))) | (! ((((((((Extinct_40 + Threatened_40) + Healed_40) + Recognized_40) + Ailing_40) + Infected_40) + Diagnosed_40) + Susceptible_40) <= 10000100000000001/10000000000000000))) | (! (Infected_50 < 50000000000000003/1000000000000000000))) | (! (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40)))))) | (! (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40))))))) | (! (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40))))))) | (! (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40))))))) | (! (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40))))))) | (! (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40))))))) | (! (Diagnosed_50 = (Diagnosed_40 + (10.0 * (((-1.0 * (rho * Diagnosed_40)) - (eta * Diagnosed_40)) + (epsilon * Infected_40))))))) | (! (Susceptible_50 = (Susceptible_40 + (10.0 * ((((-1.0 * ((alpha * Infected_40) * Susceptible_40)) - ((delta * Recognized_40) * Susceptible_40)) - ((gamma * Ailing_40) * Susceptible_40)) - ((beta * Diagnosed_40) * Susceptible_40))))))) | (! ((((((((Extinct_50 + Threatened_50) + Healed_50) + Recognized_50) + Ailing_50) + Infected_50) + Diagnosed_50) + Susceptible_50) <= 10000100000000001/10000000000000000))) | (! (Extinct_60 = (Extinct_50 + (10.0 * (tau * Threatened_50)))))) | (! (Threatened_60 = (Threatened_50 + (10.0 * ((((-1.0 * (sigma * Threatened_50)) - (tau * Threatened_50)) + (nu * Recognized_50)) + (mu * Ailing_50))))))) | (! (Healed_60 = (Healed_50 + (10.0 * (((((sigma * Threatened_50) + (xi * Recognized_50)) + (kappa * Ailing_50)) + (rho * Diagnosed_50)) + (funman_lambda * Infected_50))))))) | (! (Recognized_60 = (Recognized_50 + (10.0 * ((((-1.0 * (xi * Recognized_50)) - (nu * Recognized_50)) + (theta * Ailing_50)) + (eta * Diagnosed_50))))))) | (! (Ailing_60 = (Ailing_50 + (10.0 * ((((-1.0 * (mu * Ailing_50)) - (kappa * Ailing_50)) - (theta * Ailing_50)) + (zeta * Infected_50))))))) | (! (Infected_60 = (Infected_50 + (10.0 * (((((((-1.0 * (zeta * Infected_50)) - (epsilon * Infected_50)) + ((alpha * Infected_50) * Susceptible_50)) + ((delta * Recognized_50) * Susceptible_50)) + ((gamma * Ailing_50) * Susceptible_50)) + ((beta * Diagnosed_50) * Susceptible_50)) - (funman_lambda * Infected_50))))))) | (! (Diagnosed_60 = (Diagnosed_50 + (10.0 * (((-1.0 * (rho * Diagnosed_50)) - (eta * Diagnosed_50)) + (epsilon * Infected_50))))))) | (! (Susceptible_60 = (Susceptible_50 + (10.0 * ((((-1.0 * ((alpha * Infected_50) * Susceptible_50)) - ((delta * Recognized_50) * Susceptible_50)) - ((gamma * Ailing_50) * Susceptible_50)) - ((beta * Diagnosed_50) * Susceptible_50))))))) | (! ((((((((Extinct_60 + Threatened_60) + Healed_60) + Recognized_60) + Ailing_60) + Infected_60) + Diagnosed_60) + Susceptible_60) <= 10000100000000001/10000000000000000)))) & (((Infected_50 < 50000000000000003/1000000000000000000) | (! assume_infected_maximum1_50)) | (! disj368))) & ((assume_infected_maximum1_60 | (! disj370)) | (! (Infected_60 < 50000000000000003/1000000000000000000)))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (theta < 3263804687500001/10000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0))) & (! (Infected_30 < 0.0))) & (! (Diagnosed_30 < 0.0))) & (! (Susceptible_30 < 0.0))) & (! (Extinct_40 < 0.0))) & (! (Threatened_40 < 0.0))) & (! (Healed_40 < 0.0))) & (! (Recognized_40 < 0.0))) & (! (Ailing_40 < 0.0))) & (! (Infected_40 < 0.0))) & (! (Diagnosed_40 < 0.0))) & (! (Susceptible_40 < 0.0))) & (! (Extinct_50 < 0.0))) & (! (Threatened_50 < 0.0))) & (! (Healed_50 < 0.0))) & (! (Recognized_50 < 0.0))) & (! (Ailing_50 < 0.0))) & (! (Infected_50 < 0.0))) & (! (Diagnosed_50 < 0.0))) & (! (Susceptible_50 < 0.0))) & (! assume_infected_maximum1_60)) & (! (Extinct_60 < 0.0))) & (! (Threatened_60 < 0.0))) & (! (Healed_60 < 0.0))) & (! (Recognized_60 < 0.0))) & (! (Ailing_60 < 0.0))) & (! (Diagnosed_60 < 0.0))) & (! (Susceptible_60 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.14982221679687502,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3563174209594728,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":0.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000011221190898692687,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":4.797407221765093e-6,"Ailing_10":4.118280425866966e-6,"Recognized_10":4.947195694870338e-7,"Healed_10":2.189497797330955e-6,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.06202993398586859,"Infected_20":0.00005674266046137601,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998938633577079,"Diagnosed_20":0.000013981400862535425,"Ailing_20":2.07040301334975e-6,"Recognized_20":0.00002094795267720741,"Healed_20":0.000011567388317679808,"Threatened_20":8.423202894773735e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.0002022833047375048,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995571826642558,"Diagnosed_30":0.0000767645461351484,"Ailing_30":0.00006491751285149787,"Recognized_30":0.0000365848832914384,"Healed_30":0.00005557553048279299,"Threatened_30":6.622809546433941e-6,"Extinct_30":8.541536227903734e-8,"timer_t_30":30.0,"Infected_40":0.0009968220143491491,"assume_infected_maximum2_40":1.0,"Susceptible_40":0.9980963226842791,"Diagnosed_40":0.0002577797730162086,"Ailing_40":0.00006416334622559782,"Recognized_40":0.00034775665681534274,"Healed_40":0.00022554154484419252,"Threatened_40":0.000025748546642339783,"Extinct_40":7.476963169224315e-7,"timer_t_40":40.0,"Infected_50":0.003692896447181401,"assume_infected_maximum1_50":1.0,"Susceptible_50":0.9920381044107944,"Diagnosed_50":0.0013414024740504534,"Ailing_50":0.0010871387563006144,"Recognized_50":0.0007456233307033548,"Healed_50":0.0010059601269031767,"Threatened_50":0.00012359930700203123,"Extinct_50":3.3225509811564103e-6,"timer_t_50":50.0,"assume_infected_maximum1":1.0,"assume_infected_maximum1_0":1.0,"assume_infected_maximum1_10":1.0,"assume_infected_maximum1_20":1.0,"assume_infected_maximum1_30":1.0,"assume_infected_maximum1_40":1.0,"Infected_60":0.01711826123369208,"assume_infected_maximum1_60":1.0,"Susceptible_60":0.9658499463798447,"Diagnosed_60":0.004818204814468148,"Recognized_60":0.005930269860006755,"Ailing_60":0.001406513736568945,"Healed_60":0.004085417957259275,"Threatened_60":0.00047635802924441626,"Extinct_60":0.000015682481681359533,"timer_t_60":60.0,"timestep":6.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.14992634887695316,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.30107733402252207,"ub":0.3263804687500001,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (theta < 3263804687500001/10000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (epsilon < 14992634887695317/100000000000000000)) & disj379) & disj386) & disj388) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj379))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (theta < 30107733402252207/100000000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (theta < 3263804687500001/10000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (epsilon < 14992634887695317/100000000000000000)))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj386))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj388))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (theta < 30107733402252207/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1433631744384766,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.31372890138626114,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000010797843617771397,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":4.582105810033781e-6,"Ailing_10":4.125378512434109e-6,"Recognized_10":4.876214829198909e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.08775256922308292,"Infected_20":0.00005038655664693856,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.999896268479002,"Diagnosed_20":0.00001297666356048846,"Ailing_20":0.0,"Recognized_20":0.00001894347174832197,"Healed_20":0.00001754019148685688,"Threatened_20":8.416104808206591e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"Infected_60":3.33333333e-8,"assume_infected_maximum1_60":0.0,"Susceptible_60":3.33333333e-8,"Diagnosed_60":3.33333333e-8,"Recognized_60":3.33333333e-8,"Ailing_60":3.33333333e-8,"Healed_60":3.33333333e-8,"Threatened_60":3.33333333e-8,"Extinct_60":3.33333333e-8,"timer_t_60":3.33333333e-8,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.14480762848854067,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.2968,"ub":0.2997639625549317,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (theta < 29976396255493171/100000000000000000)) & (epsilon < 3620190712213517/25000000000000000)) & disj379) & disj386) & disj388) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj379))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (theta < 29976396255493171/100000000000000000))) | (! (epsilon < 3620190712213517/25000000000000000)))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj386))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj388))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 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& (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (epsilon < 9798131161928181/50000000000000000)) & disj398) & 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(Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (epsilon < 9798131161928181/50000000000000000)))) & ((assume_infected_maximum2_10 | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! disj403))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! assume_infected_maximum2_10)) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (epsilon < 601558146728203/3125000000000000))) & (! (theta < 40138193147778523/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.19423061509579428,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.42329096573889263,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.0,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":6.277687163582123e-6,"Ailing_10":4.1071181684483785e-6,"Recognized_10":5.058818269056215e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.17616713556369512,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"Infected_60":3.33333333e-8,"assume_infected_maximum1_60":0.0,"Susceptible_60":3.33333333e-8,"Diagnosed_60":3.33333333e-8,"Recognized_60":3.33333333e-8,"Ailing_60":3.33333333e-8,"Healed_60":3.33333333e-8,"Threatened_60":3.33333333e-8,"Extinct_60":3.33333333e-8,"timer_t_60":3.33333333e-8,"timestep":1.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.18435014648437506,"ub":0.19249860695302495,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.39115094642639175,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 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assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (epsilon < 601558146728203/3125000000000000)) & disj398) & disj404) & disj408) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj398))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 18435014648437507/100000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (theta < 4889386830329897/12500000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (epsilon < 601558146728203/3125000000000000)))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj404))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj408))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 18435014648437507/100000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (theta < 4889386830329897/12500000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.18842437671870002,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41535974365816586,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.00001146485599530368,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":6.084145884539188e-6,"Ailing_10":4.108440035516078e-6,"Recognized_10":5.045599598379218e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.020192068763744974,"Infected_20":0.00005783030607324662,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.999892376749061,"Diagnosed_20":0.000018045323504864536,"Ailing_20":0.0,"Recognized_20":0.000024953519518085272,"Healed_20":6.125262375210261e-6,"Threatened_20":8.433043285124622e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"Infected_60":3.33333333e-8,"assume_infected_maximum1_60":0.0,"Susceptible_60":3.33333333e-8,"Diagnosed_60":3.33333333e-8,"Recognized_60":3.33333333e-8,"Ailing_60":3.33333333e-8,"Healed_60":3.33333333e-8,"Threatened_60":3.33333333e-8,"Extinct_60":3.33333333e-8,"timer_t_60":3.33333333e-8,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1765314514160157,"ub":0.18435014648437506,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.37554083862304705,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":4.0,"ub":4.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 18435014648437507/100000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & assume_infected_maximum2_30) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & disj417) & disj422) & disj425) & disj428) & disj430) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj417))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (epsilon < 17653145141601571/100000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (theta < 7510816772460941/20000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! 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(Infected_0 < 29999999999999999/1000000000000000000))) | (! disj436))) & ((assume_theta_epsilon | (! ((2.0 * epsilon) <= theta))) | (! disj437))) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & ((((((((((2.0 * epsilon) <= theta) | (theta < 29680000000000001/100000000000000000)) | (epsilon < 171/1250)) | (epsilon < 17653145141601571/100000000000000000)) | (theta < 289174415189773/781250000000000)) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000))) | (! (theta < 18602956391870987/50000000000000000)))) & (((! assume_infected_maximum2_0) | (! assume_theta_epsilon)) | (! disj441))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 17653145141601571/100000000000000000))) & (! (theta < 289174415189773/781250000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":0.011,"beta":0.011,"Healed_0":0.011,"gamma":0.456,"Threatened_0":0.011,"epsilon":0.1804407989501954,"delta":0.011,"Extinct_0":0.011,"alpha":0.57,"zeta":0.125,"theta":0.37127621064186117,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":0.011,"timer_t_0":0.011,"Susceptible_0":0.011,"assume_theta_epsilon":1.0,"Diagnosed_0":0.011,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":0.011,"Infected_10":0.011,"assume_infected_maximum2_10":0.0,"Susceptible_10":0.011,"Diagnosed_10":0.011,"Ailing_10":0.011,"Recognized_10":0.011,"Healed_10":0.011,"Threatened_10":0.011,"Extinct_10":0.011,"timer_t_10":0.011,"funman_lambda":0.011,"Infected_20":0.011,"assume_infected_maximum2_20":0.0,"Susceptible_20":0.011,"Diagnosed_20":0.011,"Ailing_20":0.011,"Recognized_20":0.011,"Healed_20":0.011,"Threatened_20":0.011,"Extinct_20":0.011,"timer_t_20":0.011,"Infected_30":0.011,"assume_infected_maximum2_30":0.0,"Susceptible_30":0.011,"Diagnosed_30":0.011,"Ailing_30":0.011,"Recognized_30":0.011,"Healed_30":0.011,"Threatened_30":0.011,"Extinct_30":0.011,"timer_t_30":0.011,"Infected_40":0.011,"assume_infected_maximum2_40":0.0,"Susceptible_40":0.011,"Diagnosed_40":0.011,"Ailing_40":0.011,"Recognized_40":0.011,"Healed_40":0.011,"Threatened_40":0.011,"Extinct_40":0.011,"timer_t_40":0.011,"timestep":0.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.1765314514160157,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3586960937500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":5.0,"ub":5.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum1","timepoints":{"lb":50.0,"ub":75.0,"closed_upper_bound":true},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.05,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Extinct_0 = 0.0) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (epsilon < 17653145141601571/100000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30)))))) & (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30)))))) & (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40))))) & (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40)))))) & (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40)))))) & (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40)))))) & (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40)))))) & (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40)))))) & disj455) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & (((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (epsilon < 17653145141601571/100000000000000000)))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (Infected_40 < 0.0)) | (Diagnosed_40 < 0.0)) | (Susceptible_40 < 0.0)) | (Infected_50 < 50000000000000003/1000000000000000000)) | (Extinct_50 < 0.0)) | (Threatened_50 < 0.0)) | (Healed_50 < 0.0)) | (Recognized_50 < 0.0)) | (Ailing_50 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Infected_40 < 29999999999999999/1000000000000000000))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30))))))) | (! (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30))))))) | (! (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30))))))) | (! (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40)))))) | (! (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40))))))) | (! (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40))))))) | (! (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40))))))) | (! (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40))))))) | (! (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40)))))))) & ((assume_infected_maximum1_50 | (! (Infected_50 < 50000000000000003/1000000000000000000))) | (! disj455))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0))) & (! (Infected_30 < 0.0))) & (! (Diagnosed_30 < 0.0))) & (! (Susceptible_30 < 0.0))) & (! (Extinct_40 < 0.0))) & (! (Threatened_40 < 0.0))) & (! (Healed_40 < 0.0))) & (! (Recognized_40 < 0.0))) & (! (Ailing_40 < 0.0))) & (! (Infected_40 < 0.0))) & (! (Diagnosed_40 < 0.0))) & (! (Susceptible_40 < 0.0))) & (! assume_infected_maximum1_50)) & (! (Extinct_50 < 0.0))) & (! (Threatened_50 < 0.0))) & (! (Healed_50 < 0.0))) & (! (Recognized_50 < 0.0))) & (! (Ailing_50 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16574555501937874,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.43573863525390627,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":0.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000012548567447898177,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.328185161984439e-6,"Ailing_10":4.105043556870841e-6,"Recognized_10":5.079564384831586e-7,"Healed_10":3.313433079061188e-7,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.006285299247378862,"Infected_20":0.00006616105808972704,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998862980024881,"Diagnosed_20":0.00001765510884307625,"Ailing_20":5.077742413097766e-7,"Recognized_20":0.00002483195087412882,"Healed_20":3.717944816799862e-6,"Threatened_20":8.43643976376986e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.00025528983764025584,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995022346177711,"Diagnosed_30":0.00009924311174427083,"Ailing_30":0.00008082276987767385,"Recognized_30":0.00003818846227710685,"Healed_30":0.000018331382384768643,"Threatened_30":7.4068084597926435e-6,"Extinct_30":8.55477309689986e-8,"timer_t_30":30.0,"Infected_40":0.0013481041277377279,"assume_infected_maximum2_40":1.0,"Susceptible_40":0.9976643285100206,"Diagnosed_40":0.00036458232260507515,"Ailing_40":0.00002024517788361512,"Recognized_40":0.0004976266146517239,"Healed_40":0.00008961734305813064,"Threatened_40":0.000029457725869672033,"Extinct_40":8.262285769482629e-7,"timer_t_40":40.0,"Infected_50":0.0052870731518991285,"assume_infected_maximum1_50":1.0,"Susceptible_50":0.9898113571883214,"Diagnosed_50":0.0020193429576421894,"Ailing_50":0.0016101251901598835,"Recognized_50":0.0008227257191020122,"Healed_50":0.00039139494912643217,"Threatened_50":0.00015930500608104059,"Extinct_50":3.7720011639154666e-6,"timer_t_50":50.0,"assume_infected_maximum1":1.0,"assume_infected_maximum1_0":1.0,"assume_infected_maximum1_10":1.0,"assume_infected_maximum1_20":1.0,"assume_infected_maximum1_30":1.0,"assume_infected_maximum1_40":1.0,"Infected_60":3.33333333e-8,"assume_infected_maximum1_60":0.0,"Susceptible_60":3.33333333e-8,"Diagnosed_60":3.33333333e-8,"Recognized_60":3.33333333e-8,"Ailing_60":3.33333333e-8,"Healed_60":3.33333333e-8,"Threatened_60":3.33333333e-8,"Extinct_60":3.33333333e-8,"timer_t_60":3.33333333e-8,"timestep":5.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.16700030522644527,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3341427254199983,"ub":0.3586960937500001,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":3.0,"ub":3.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (theta < 8967402343750003/25000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (epsilon < 16700030522644527/100000000000000000)) & disj464) & disj468) & disj471) & disj473) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj464))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (theta < 33414272541999829/100000000000000000)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (theta < 8967402343750003/25000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! 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\ No newline at end of file From c99adcc6fd82a7c404653e4ebd109a7dfd173d1e Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Wed, 1 Nov 2023 21:11:51 +0000 Subject: [PATCH 04/28] updates for halfar --- resources/amr/halfar/halfar.json | 6 +- .../hackathon_fall_2023_demo_halfar.py | 11 +- .../hackathon_fall_2023_demo_terarrium.py | 14 +- .../hackathon_fall_2023_demo_halfar.ipynb | 206 ++++++++++++++++++ src/funman/representation/parameter_space.py | 2 +- src/funman/search/smt_check.py | 10 +- src/funman/server/query.py | 4 +- 7 files changed, 233 insertions(+), 20 deletions(-) create mode 100644 scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb diff --git a/resources/amr/halfar/halfar.json b/resources/amr/halfar/halfar.json index 50bb7da4..e16121cb 100644 --- a/resources/amr/halfar/halfar.json +++ b/resources/amr/halfar/halfar.json @@ -218,15 +218,15 @@ }, { "target": "h_1", - "expression": "10.0" + "expression": "0.5" }, { "target": "h_2", - "expression": "10.0" + "expression": "1.0" }, { "target": "h_3", - "expression": "10.0" + "expression": "0.5" }, { "target": "h_4", diff --git a/scratch/hackathon/hackathon_fall_2023_demo_halfar.py b/scratch/hackathon/hackathon_fall_2023_demo_halfar.py index 60cba9b3..523f2dc3 100644 --- a/scratch/hackathon/hackathon_fall_2023_demo_halfar.py +++ b/scratch/hackathon/hackathon_fall_2023_demo_halfar.py @@ -20,8 +20,7 @@ def main(): { "name": "schedules", "schedules": [ - # {"timepoints": [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]} - {"timepoints": [0, 1]} + {"timepoints": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]} ], } ], @@ -29,22 +28,22 @@ def main(): "use_compartmental_constraints": False, "normalization_constant": 1.0, "tolerance": 1e-1, - "verbosity": 5, + "verbosity": 10, "dreal_mcts": True, "save_smtlib": True, "substitute_subformulas": False, "series_approximation_threshold": None, - "dreal_log_level": "info", + "dreal_log_level": "none", "profile": False, }, } # Use request_dict - results = Runner().run( + results: FunmanResults = Runner().run( MODEL_PATH, request_dict, # REQUEST_PATH, - description="SIDARTHE demo", + description="Halfar demo", case_out_dir="./out", ) points = results.points() diff --git a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py index 7a2f9c09..3586fee7 100644 --- a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py +++ b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py @@ -172,19 +172,19 @@ def main(): { "name": "infected_maximum1", "variable": "Infected", - "interval": {"lb": 0.01, "ub": 0.05}, + "interval": {"lb": 0.0, "ub": 0.05}, "timepoints": {"lb": 50, "ub": 75, "closed_upper_bound": True}, }, { "name": "infected_maximum2", "variable": "Infected", - "interval": {"ub": 0.03}, + "interval": {"ub": 0.01}, "timepoints": {"lb": 0, "ub": 50}, }, { "name": "infected_maximum3", "variable": "Infected", - "interval": {"ub": 0.03}, + "interval": {"ub": 0.01}, "timepoints": {"lb": 76}, }, ], @@ -200,7 +200,11 @@ def main(): 30, 40, 50, + 55, + 56, + 57, 60, + 65, 70, 80, 90, @@ -214,9 +218,9 @@ def main(): "config": { "use_compartmental_constraints": True, "normalization_constant": 1.0, - "tolerance": 1e-1, + "tolerance": 1e-2, "verbosity": 10, - "dreal_mcts": False, + "dreal_mcts": True, "save_smtlib": False, "substitute_subformulas": False, "series_approximation_threshold": None, diff --git a/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb b/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb new file mode 100644 index 00000000..4a472667 --- /dev/null +++ b/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# This notebook illustrates the halfar ice model\n", + "\n", + "# Import funman related code\n", + "import os\n", + "from pathlib import Path\n", + "from funman import FunmanResults\n", + "import json\n", + "from funman import Point, Box, Parameter\n", + "from typing import List, Dict\n", + "from funman.api.run import Runner\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-01 21:11:08,959 - funman.server.worker - INFO - FunmanWorker running...\n", + "2023-11-01 21:11:08,967 - funman.server.worker - INFO - Starting work on: a99c05ac-e487-4b09-8cbf-7dd0dba678ce\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-01 21:11:10,974 - funman.api.run - INFO - Dumping results to ./out/a99c05ac-e487-4b09-8cbf-7dd0dba678ce.json\n", + "2023-11-01 21:11:12,905 - funman.scenario.consistency - INFO - 16:\t[+]\n", + "2023-11-01 21:11:12,912 - funman.server.worker - INFO - Completed work on: a99c05ac-e487-4b09-8cbf-7dd0dba678ce\n", + "2023-11-01 21:11:22,911 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2023-11-01 21:11:22,920 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2023-11-01 21:11:22,923 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gamma = 0.06325\n", + "1 Points (+:1, -:0), 1 Boxes (+:1, -:0)\n", + "{}\n", + " h_0 h_1 h_2 h_3 h_4 id label\n", + "time \n", + "0.0 0.100000 0.500000 1.000000 0.500000 0.100000 0 true\n", + "1.0 0.100494 0.483693 0.999747 0.500127 0.115940 0 true\n", + "2.0 0.100954 0.467475 0.999523 0.500239 0.131809 0 true\n", + "3.0 0.101380 0.451326 0.999324 0.500338 0.147617 0 true\n", + "4.0 0.101770 0.435144 0.999150 0.500425 0.163459 0 true\n", + "5.0 0.102124 0.419036 0.998998 0.500501 0.179284 0 true\n", + "6.0 0.102443 0.402997 0.998867 0.500567 0.195067 0 true\n", + "7.0 0.102727 0.387021 0.998753 0.500623 0.210811 0 true\n", + "8.0 0.102978 0.371104 0.998656 0.500672 0.226523 0 true\n", + "9.0 0.103199 0.355239 0.998574 0.500713 0.242141 0 true\n", + "10.0 0.103389 0.339511 0.998506 0.500747 0.257780 0 true\n", + "11.0 0.103552 0.323987 0.998448 0.500776 0.273375 0 true\n", + "12.0 0.103691 0.308501 0.998402 0.500799 0.288995 0 true\n", + "13.0 0.103807 0.292805 0.998364 0.500818 0.304477 0 true\n", + "14.0 0.103903 0.277130 0.998334 0.500833 0.320008 0 true\n", + "15.0 0.103980 0.261605 0.998310 0.500845 0.335563 0 true\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "RESOURCES = os.path.join(\n", + " os.getcwd(), \"../../resources\"\n", + ")\n", + "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"halfar\")\n", + "MODEL_PATH = os.path.join(EXAMPLE_DIR, \"halfar.json\")\n", + "REQUEST_PATH = os.path.join(EXAMPLE_DIR, \"halfar_request.json\")\n", + "\n", + "request_dict = {\n", + " \"structure_parameters\": [\n", + " {\n", + " \"name\": \"schedules\",\n", + " \"schedules\": [\n", + " {\"timepoints\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]}\n", + " # {\"timepoints\": [0, 10]}\n", + " ],\n", + " },\n", + " \n", + " ],\n", + " \"constraints\": [\n", + " # {\"name\": \"non-negative_h_0\",\n", + " # \"variable\": \"h_0\",\n", + " # \"interval\": {\"lb\": 0}\n", + " # },\n", + " # {\"name\": \"non-negative_h_1\",\n", + " # \"variable\": \"h_1\",\n", + " # \"interval\": {\"lb\": 0}\n", + " # },\n", + " # {\"name\": \"non-negative_h_2\",\n", + " # \"variable\": \"h_2\",\n", + " # \"interval\": {\"lb\": 0}\n", + " # },\n", + " # {\"name\": \"non-negative_h_3\",\n", + " # \"variable\": \"h_3\",\n", + " # \"interval\": {\"lb\": 0}\n", + " # },\n", + " # {\"name\": \"non-negative_h_4\",\n", + " # \"variable\": \"h_4\",\n", + " # \"interval\": {\"lb\": 0}\n", + " # },\n", + " {\"name\": \"LHS_slope\",\n", + " \"variables\": [\"h_1\", \"h_0\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }, \n", + " {\"name\": \"RHS_slope\",\n", + " \"variables\": [\"h_3\", \"h_4\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }\n", + " ],\n", + " \"config\": {\n", + " \"use_compartmental_constraints\": False,\n", + " \"normalization_constant\": 1.0,\n", + " \"tolerance\": 1e-1,\n", + " \"verbosity\": 20,\n", + " \"dreal_mcts\": True,\n", + " \"save_smtlib\": True,\n", + " \"substitute_subformulas\": False,\n", + " \"series_approximation_threshold\": None,\n", + " \"dreal_log_level\": \"none\",\n", + " \"profile\": False,\n", + " },\n", + "}\n", + "\n", + "# Use request_dict\n", + "results = Runner().run(\n", + " MODEL_PATH,\n", + " request_dict,\n", + " # REQUEST_PATH,\n", + " description=\"Halfar demo\",\n", + " case_out_dir=\"./out\",\n", + ")\n", + "print(f\"gamma = {results.parameter_space.points()[0].values['gamma']:.5f}\")\n", + "results.plot(variables=[\"h_0\", \"h_1\", \"h_2\", \"h_3\", \"h_4\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Height\", legend=[\"h_0\", \"h_1\", \"h_2\", \"h_3\", \"h_4\"],label_color={\"true\": None})\n", + "points = results.points()\n", + "boxes = results.parameter_space.boxes()\n", + "\n", + "print(\n", + " f\"{len(points)} Points (+:{len(results.parameter_space.true_points())}, -:{len(results.parameter_space.false_points())}), {len(boxes)} Boxes (+:{len(results.parameter_space.true_boxes)}, -:{len(results.parameter_space.false_boxes)})\"\n", + ")\n", + "if points and len(points) > 0:\n", + " point: Point = points[-1]\n", + " parameters: Dict[Parameter, float] = results.point_parameters(point)\n", + " print(parameters)\n", + " print(results.dataframe([point]))\n", + "else:\n", + " # if there are no points, then we have a box that we found without needing points\n", + "\n", + " box = boxes[0]\n", + " print(json.dumps(box.explain(), indent=4))\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "funman_venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/funman/representation/parameter_space.py b/src/funman/representation/parameter_space.py index 9e109d04..fb4bb2c9 100644 --- a/src/funman/representation/parameter_space.py +++ b/src/funman/representation/parameter_space.py @@ -43,7 +43,7 @@ def __str__(self, dropped_boxes=[]) -> str: boxes_at_step = steps.get(step, []) boxes_at_step.append(label) steps[step] = boxes_at_step - return "\n".join([f"{k}:[{''.join(v)}]" for k, v in steps.items()]) + return "\n".join([f"{k}:\t[{''.join(v)}]" for k, v in steps.items()]) def true_points(self) -> List[Point]: return [pt for b in self.true_boxes for pt in b.true_points()] diff --git a/src/funman/search/smt_check.py b/src/funman/search/smt_check.py index 498b244b..68526771 100644 --- a/src/funman/search/smt_check.py +++ b/src/funman/search/smt_check.py @@ -1,5 +1,6 @@ import json import logging +import os import sys import threading from typing import Callable, Optional, Tuple, Union @@ -27,7 +28,7 @@ l = logging.getLogger(__file__) -logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) + class SMTCheck(Search): @@ -80,9 +81,8 @@ def search( schedule=schedule, ) - point.values["timestep"] = Interval( - lb=timestep, ub=timestep, closed_upper_bound=True - ) + point.values["timestep"] = timestep + if config.normalize: denormalized_point = point.denormalize(problem) point = denormalized_point @@ -170,6 +170,8 @@ def solve_formula( s.push(1) s.add_assertion(formula) if episode.config.save_smtlib: + filename = os.path.join(os.getcwd(), "dbg_steps.smt2") + l.trace(f"Saving smt file: {filename}") self.store_smtlib( formula, filename=f"dbg_steps.smt2", diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 56c70850..8d72866f 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -429,6 +429,7 @@ def plot( ylabel="Population", label_marker={"true": "+", "false": "o"}, label_color={"true": "g", "false": "r"}, + legend=None, **kwargs, ): """ @@ -471,7 +472,8 @@ def plot( **kwargs, ) ax = df.plot(label=label, marker=label_marker[label], **kwargs) - # plt.legend() + if legend: + plt.legend(legend) if log_y: ax.set_yscale("symlog") plt.ylim(bottom=0) From 778b1d77cf7219f255720e46295f1f1e0ef92526 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Thu, 2 Nov 2023 20:22:38 +0000 Subject: [PATCH 05/28] demo'd version of halfar model --- .../src/funman_demo/generators/halfar.py | 140 ++ notebooks/funman_results.ipynb | 99 +- .../e466f678-c60d-4117-bd58-bed949c512cf.json | 1 - .../068cef77-839d-49be-9191-3502ee6d5240.json | 2 +- .../hackathon_fall_2023_demo_terarrium.py | 31 +- .../hackathon_fall_2023_demo_halfar.ipynb | 1214 ++++++++++++++++- src/funman/config.py | 4 +- src/funman/representation/box.py | 1 + src/funman/representation/constraint.py | 8 +- src/funman/representation/parameter_space.py | 33 +- src/funman/scenario/consistency.py | 3 +- src/funman/scenario/scenario.py | 29 +- src/funman/search/box_search.py | 2 +- src/funman/search/smt_check.py | 5 +- src/funman/server/query.py | 21 +- test/test_param_space.py | 2 +- test/test_petri.py | 9 +- test/test_use_cases.py | 19 +- 18 files changed, 1498 insertions(+), 125 deletions(-) create mode 100644 auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py delete mode 100644 notebooks/saved-results/e466f678-c60d-4117-bd58-bed949c512cf.json diff --git a/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py b/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py new file mode 100644 index 00000000..120cd252 --- /dev/null +++ b/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py @@ -0,0 +1,140 @@ +from decimal import Decimal +from typing import Tuple, List, Dict +from funman.model.generated_models.petrinet import * +from pydantic import AnyUrl, field_validator +import argparse +import json +import os + +from funman.representation.interval import Interval + +class HalfarModel(Model): + pass + + +class Direction(): + Negative: str = "negative" + Positive: str = "positive" + + + +class Coordinate(BaseModel): + vector: List[float] + id: str + neighbors : Dict[str, "Coordinate"] = {} + +class HalfarGenerator(): + range: Interval = Interval(lb=-2.0, ub=2.0) + def coordinates(self, args) -> List[Coordinate]: + coords = [] + step_size = value = self.range.width()/Decimal(int(args.num_discretization_points)-1) + for i in range(args.num_discretization_points): + value = self.range.lb + float(step_size*i) + coords.append(Coordinate(vector=[value], id=str(i))) + for i, coord in enumerate(coords): + coord.neighbors[Direction.Negative]= (coords[i-1] if i > 0 else None) + coord.neighbors[Direction.Positive] = (coords[i+1] if i < len(coords)-1 else None) + return coords + + def transition_expression(self, n1_name: str, n2_name: str, negative=False)-> str: + prefix = "-1*" if negative else "" + return f"{prefix}gamma*({n2_name}-{n1_name})**3*{n1_name}**5" + + def neighbor_gradient(self, coordinate: Coordinate, coordinates: List[Coordinate]) -> Tuple[List[Transition], List[Rate]]: + + # find a triple of coordinates (n0, n1, n2) that are ordered so that dx is positive + if coordinate.neighbors[Direction.Positive] and coordinate.neighbors[Direction.Negative]: + n0 = coordinate.neighbors[Direction.Negative] + elif coordinate.neighbors[Direction.Positive] and not coordinate.neighbors[Direction.Negative]: + n0 = coordinate + elif not coordinate.neighbors[Direction.Positive] and coordinate.neighbors[Direction.Negative]: + n0 = coordinate.neighbors[Direction.Negative].neighbors[Direction.Negative] + else: + raise Exception("Cannot determine the gradient of a coordinate with no neighbors") + n1 = n0.neighbors[Direction.Positive] + n2 = n1.neighbors[Direction.Positive] + + w_p_name = f"w_p_{coordinate.id}" + w_n_name = f"w_n_{coordinate.id}" + n0_name = f"h_{n0.id}" + n1_name = f"h_{n1.id}" + n2_name = f"h_{n2.id}" + + # tp is the gradient wrt. n2, n1 + tp = Transition(id=w_p_name, input=[n2_name, n1_name], output=[w_p_name], properties=Properties(name=w_p_name) ) + + # tn is the gradient wrt. n1, n0 + tn = Transition(id=w_n_name, input=[n1_name, n0_name], output=[w_n_name], properties=Properties(name=w_n_name) ) + + transitions = [tp, tn] + + tpr = Rate(target=w_p_name, expression=self.transition_expression(n1_name, n2_name)) + tnr = Rate(target=w_n_name, expression=self.transition_expression(n0_name, n1_name, negative=True)) + + rates = [tpr, tnr] + return transitions, rates + + + def model(self, args)-> Tuple[Model1, Semantics]: + + coordinates = self.coordinates(args) + + # Create a height variable at each coordinate + states = [State(id=f"h_{i}", name = f"h_{i}", description=f"height at {i}") for i in range(len(coordinates))] + + transitions = [] + rates = [] + for coordinate in coordinates: + coord_transitions, trans_rates = self.neighbor_gradient(coordinate, coordinates) + transitions += coord_transitions + rates += trans_rates + + time = Time(id="t", units=Unit(expression="day", expression_mathml="day")) + + initials = [Initial(target=f"h_{c.id}", expression=f"{1.0/(1.0+abs(c.vector[0]))}") for c in coordinates] + + parameters = [Parameter(id="gamma", value=1.0, distribution=Distribution(type="StandardUniform1", parameters={"minimum":0.0, "maximum":1.0}))] + + return Model1(states=states,transitions=transitions), Semantics(ode=OdeSemantics(rates=rates, initials=initials, parameters=parameters, time=time)) + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "-d", + "--dimensions", + default=1, + type=int, + help=f"Number of spatial dimensions", + ) + parser.add_argument( + "-p", + "--num-discretization-points", + default=5, + type=int, + help=f"Number of discretization points", + ) + parser.add_argument( + "-o", + "--outfile", + default="halfar.json", + help=f"Output filename", + ) + return parser.parse_args() + +def main(): + args = get_args() + generator = HalfarGenerator() + model, semantics = generator.model(args) + halfar_model = HalfarModel(header = Header(name="Halfar Model", + schema_=AnyUrl("https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.1/petrinet/petrinet_schema.json"), + schema_name="petrinet", description="Halfar as Petrinet model created by Dan", + model_version="0.1"), + model=model, semantics=semantics) + j = halfar_model.model_dump_json(indent=4) + # print(j) + with open(args.outfile, "w") as f: + print(f"Writing {os.path.join(os.getcwd(), args.outfile)}") + f.write(j) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/notebooks/funman_results.ipynb b/notebooks/funman_results.ipynb index ef664c4f..22364ceb 100644 --- a/notebooks/funman_results.ipynb +++ b/notebooks/funman_results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -36,7 +36,8 @@ "source": [ "SAVED_RESULTS_DIR = Path(\"saved-results\").resolve()\n", "SAVED_RESULT_FILES = [\n", - " \"e466f678-c60d-4117-bd58-bed949c512cf.json\"\n", + " # \"a8749b58-59ac-476f-848a-486c5ef54010.json\" # Only constrain the parameters\n", + " \"66f19967-f4fb-4b7b-82dd-65176bf41c44.json\"# 60-80: I in [0.1, 0.2)\n", "]\n", "SAVED_RESULT_TO_USE = SAVED_RESULTS_DIR / SAVED_RESULT_FILES[0]\n", "\n", @@ -49,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -58,13 +59,13 @@ "" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -295,12 +296,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -366,6 +367,88 @@ "source": [ "results.parameter_space.false_boxes[1].explain( )" ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model has the symbols: ['Susceptible', 'Diagnosed', 'Infected', 'Ailing', 'Recognized', 'Healed', 'Threatened', 'Extinct', 'beta', 'gamma', 'delta', 'alpha', 'epsilon', 'zeta', 'lambda', 'eta', 'rho', 'theta', 'kappa', 'mu', 'nu', 'xi', 'tau', 'sigma', 't']\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SAVED_RESULTS_DIR = Path(\"saved-results\").resolve()\n", + "SAVED_RESULT_FILES = [\n", + " # \"a8749b58-59ac-476f-848a-486c5ef54010.json\" # Only constrain the parameters\n", + " \"66f19967-f4fb-4b7b-82dd-65176bf41c44.json\"# 60-80: I in [0.1, 0.2)\n", + "]\n", + "SAVED_RESULT_TO_USE = SAVED_RESULTS_DIR / SAVED_RESULT_FILES[0]\n", + "\n", + "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n", + " # Create a FunmanResults object\n", + " results: FunmanResults = FunmanResults.model_validate(json.load(f))\n", + "\n", + "print(f\"Model has the symbols: {results.model._symbols()}\")\n", + "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Infected\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'Path' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/root/funman/notebooks/funman_results.ipynb Cell 10\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m SAVED_RESULTS_DIR \u001b[39m=\u001b[39m Path(\u001b[39m\"\u001b[39m\u001b[39msaved-results\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39mresolve()\n\u001b[1;32m 2\u001b[0m SAVED_RESULT_FILES \u001b[39m=\u001b[39m [\n\u001b[1;32m 3\u001b[0m \n\u001b[1;32m 4\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mea52afa4-3e7b-4124-a89b-170bf99f82ba.json\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m# 60-80: I in [0.0, 0.2)\u001b[39;00m\n\u001b[1;32m 5\u001b[0m ]\n\u001b[1;32m 6\u001b[0m SAVED_RESULT_TO_USE \u001b[39m=\u001b[39m SAVED_RESULTS_DIR \u001b[39m/\u001b[39m SAVED_RESULT_FILES[\u001b[39m0\u001b[39m]\n", + "\u001b[0;31mNameError\u001b[0m: name 'Path' is not defined" + ] + } + ], + "source": [ + "SAVED_RESULTS_DIR = Path(\"saved-results\").resolve()\n", + "SAVED_RESULT_FILES = [\n", + " \n", + " \"ea52afa4-3e7b-4124-a89b-170bf99f82ba.json\"# 60-80: I in [0.0, 0.2)\n", + "]\n", + "SAVED_RESULT_TO_USE = SAVED_RESULTS_DIR / SAVED_RESULT_FILES[0]\n", + "\n", + "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n", + " # Create a FunmanResults object\n", + " results: FunmanResults = FunmanResults.model_validate(json.load(f))\n", + "\n", + "print(f\"Model has the symbols: {results.model._symbols()}\")\n", + "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Infected\")" + ] } ], "metadata": { diff --git a/notebooks/saved-results/e466f678-c60d-4117-bd58-bed949c512cf.json b/notebooks/saved-results/e466f678-c60d-4117-bd58-bed949c512cf.json deleted file mode 100644 index c7c6e0d2..00000000 --- a/notebooks/saved-results/e466f678-c60d-4117-bd58-bed949c512cf.json +++ /dev/null @@ -1 +0,0 @@ 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(Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & disj32) & disj50) & disj52) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj32))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! 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(Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.14962187500000004,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3857902343750001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.00001118831028494801,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":4.790729161875939e-6,"Ailing_10":4.11336829031736e-6,"Recognized_10":4.996317050366399e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.07603856919607055,"Infected_20":0.0000527051678367253,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998940733660042,"Diagnosed_20":0.000014254236417487181,"Ailing_20":0.0,"Recognized_20":0.00002214306090881688,"Healed_20":0.000015598881265912572,"Threatened_20":8.428115030323341e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1765314514160157,"ub":0.18435014648437506,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.37554083862304705,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":0.0,"ub":0.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"theta_epsilon","timepoints":null,"additive_bounds":{"lb":0.0,"ub":1.7976931348623157e308,"closed_upper_bound":false},"variables":["theta","epsilon"],"weights":[1,-2]}},{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 18435014648437507/100000000000000000)) & disj59) & disj60) & disj74) & ((assume_infected_maximum2_0 | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! disj59))) & ((assume_theta_epsilon | (! ((2.0 * epsilon) <= theta))) | (! disj60))) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & (((((((((2.0 * epsilon) <= theta) | (theta < 29680000000000001/100000000000000000)) | (epsilon < 171/1250)) | (epsilon < 17653145141601571/100000000000000000)) | (theta < 7510816772460941/20000000000000000)) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000)))) & (((! assume_infected_maximum2_0) | (! assume_theta_epsilon)) | (! disj74))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 17653145141601571/100000000000000000))) & (! (theta < 7510816772460941/20000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1804407989501954,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.39113505859375014,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":3.33333333e-8,"assume_infected_maximum2_10":0.0,"Susceptible_10":3.33333333e-8,"Diagnosed_10":3.33333333e-8,"Ailing_10":3.33333333e-8,"Recognized_10":3.33333333e-8,"Healed_10":3.33333333e-8,"Threatened_10":3.33333333e-8,"Extinct_10":3.33333333e-8,"timer_t_10":3.33333333e-8,"funman_lambda":3.33333333e-8,"timestep":0.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.1765314514160157,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3586960937500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":1.0,"ub":1.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (epsilon < 17653145141601571/100000000000000000)) & disj61) & disj77) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj61))) & (((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (epsilon < 17653145141601571/100000000000000000)))) & ((assume_infected_maximum2_10 | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! disj77))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000))) & (! assume_infected_maximum2_10)) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1694876007080079,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.4019480468750001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.0,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.4529200181473426e-6,"Ailing_10":4.110675321578131e-6,"Recognized_10":5.02324673775868e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.19119456404020757,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"timestep":1.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.16244375000000005,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3263804687500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":3.0,"ub":3.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 8122187500000003/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & disj86) & disj97) & disj100) & disj102) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj86))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj97))) & (((Infected_20 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_20)) | (! disj100))) & ((assume_infected_maximum2_30 | (! disj102)) | (! (Infected_30 < 29999999999999999/1000000000000000000)))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (theta < 3263804687500001/10000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! assume_infected_maximum2_30)) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.14962187500000004,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3857902343750001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000011425027528009747,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":4.790729161875939e-6,"Ailing_10":4.11336829031736e-6,"Recognized_10":4.996317050366399e-7,"Healed_10":1.9923392279030494e-6,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.056115176897116655,"Infected_20":0.000057740439705456696,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998927241085045,"Diagnosed_20":0.000014407762109077173,"Ailing_20":0.0,"Recognized_20":0.00002214306090881688,"Healed_20":0.000011036048818594126,"Threatened_20":8.428115030323341e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.00020640014371116688,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995482630264079,"Diagnosed_30":0.00007978392424972967,"Ailing_30":0.00006273186169156044,"Recognized_30":0.00004149718454856439,"Healed_30":0.000055606982896654105,"Threatened_30":7.017250928523561e-6,"Extinct_30":8.546448363453342e-8,"timer_t_30":30.0,"timestep":3.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1765314514160157,"ub":0.18435014648437506,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.37554083862304705,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":1.0,"ub":1.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 18435014648437507/100000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & disj110) & disj129) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj110))) & (((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (epsilon < 17653145141601571/100000000000000000)) | (theta < 7510816772460941/20000000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))))) & ((assume_infected_maximum2_10 | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! disj129))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! assume_infected_maximum2_10)) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (epsilon < 17653145141601571/100000000000000000))) & (! (theta < 7510816772460941/20000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1804407989501954,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41037041931152357,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.0,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.818026625855153e-6,"Ailing_10":4.109271592844326e-6,"Recognized_10":5.037284025096736e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.18415071333219976,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"timestep":1.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.1765314514160157,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3586960937500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (epsilon < 17653145141601571/100000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & disj138) & disj143) & disj145) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj138))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj143))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj145))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1694876007080079,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.4019480468750001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000011293493186527538,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.4529200181473426e-6,"Ailing_10":4.110675321578131e-6,"Recognized_10":5.02324673775868e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.04723933214552918,"Infected_20":0.00005555043936295593,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998934129812498,"Diagnosed_20":0.000016043345186313132,"Ailing_20":0.0,"Recognized_20":0.000023623348894190662,"Healed_20":0.000010473067718449695,"Threatened_20":8.430807999062568e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.16700030522644527,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3341427254199983,"ub":0.3586960937500001,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":0.0,"ub":0.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"theta_epsilon","timepoints":null,"additive_bounds":{"lb":0.0,"ub":1.7976931348623157e308,"closed_upper_bound":false},"variables":["theta","epsilon"],"weights":[1,-2]}},{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (theta < 8967402343750003/25000000000000000)) & disj151) & disj152) & (epsilon < 16700030522644527/100000000000000000)) & disj164) & ((assume_infected_maximum2_0 | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! disj151))) & ((assume_theta_epsilon | (! ((2.0 * epsilon) <= theta))) | (! disj152))) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & ((((((((((2.0 * epsilon) <= theta) | (theta < 29680000000000001/100000000000000000)) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 33414272541999829/100000000000000000)) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (theta < 8967402343750003/25000000000000000))) | (! (epsilon < 16700030522644527/100000000000000000)))) & (((! assume_infected_maximum2_0) | (! assume_theta_epsilon)) | (! disj164))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 33414272541999829/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16455678710937507,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3375657226562501,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"timestep":0.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.16244375000000005,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3263804687500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":4.0,"ub":4.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 8122187500000003/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & assume_infected_maximum2_30) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & disj153) & disj167) & disj170) & disj173) & disj175) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj153))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! 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(Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! 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((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & disj184) & disj204) & disj206) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj184))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (epsilon < 17653145141601571/100000000000000000)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (theta < 7510816772460941/20000000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj204))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj206))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (epsilon < 17653145141601571/100000000000000000))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (theta < 7510816772460941/20000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1804407989501954,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41037041931152357,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000011336308542265614,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.818026625855153e-6,"Ailing_10":4.109271592844326e-6,"Recognized_10":5.037284025096736e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.03186717005608681,"Infected_20":0.000056723129077360236,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998931350249303,"Diagnosed_20":0.000017068979498461317,"Ailing_20":0.0,"Recognized_20":0.000024419878086739077,"Healed_20":7.938314118187088e-6,"Threatened_20":8.432211727796375e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.1765314514160157,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3586960937500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":3.0,"ub":3.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (epsilon < 17653145141601571/100000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & disj214) & disj220) & disj223) & disj225) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj214))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj220))) & (((Infected_20 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_20)) | (! disj223))) & ((assume_infected_maximum2_30 | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! disj225))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! assume_infected_maximum2_30)) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1694876007080079,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.4019480468750001,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.00001190693703140173,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.4529200181473426e-6,"Ailing_10":4.110675321578131e-6,"Recognized_10":5.02324673775868e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.02179216607289209,"Infected_20":0.00006120270682530097,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.999889916431114,"Diagnosed_20":0.00001703984637207095,"Ailing_20":0.0,"Recognized_20":0.000023623348894190662,"Healed_20":6.319249376645201e-6,"Threatened_20":8.430807999062568e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.00022636556319700172,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995269575033804,"Diagnosed_30":0.0000947093693099884,"Ailing_30":0.00006846207906071419,"Recognized_30":0.00004396978194934292,"Healed_30":0.000033162400870960394,"Threatened_30":7.354254704364905e-6,"Extinct_30":8.549141332192568e-8,"timer_t_30":30.0,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"timestep":3.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.16700030522644527,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3341427254199983,"ub":0.3586960937500001,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":1.0,"ub":1.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (theta < 8967402343750003/25000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (epsilon < 16700030522644527/100000000000000000)) & disj234) & disj243) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj234))) & ((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (theta < 33414272541999829/100000000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (theta < 8967402343750003/25000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (epsilon < 16700030522644527/100000000000000000)))) & ((assume_infected_maximum2_10 | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! disj243))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! assume_infected_maximum2_10)) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (theta < 33414272541999829/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16472202761322266,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3464194095849992,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.0,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.294067581813355e-6,"Ailing_10":4.11993009442278e-6,"Recognized_10":4.930699009312203e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.19119456404020757,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"timestep":1.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.16244375000000005,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3263804687500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":5.0,"ub":5.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum1","timepoints":{"lb":50.0,"ub":75.0,"closed_upper_bound":true},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.05,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Extinct_0 = 0.0) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 8122187500000003/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30)))))) & (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30)))))) & disj257) & (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40))))) & (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40)))))) & (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40)))))) & (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40)))))) & (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40)))))) & (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40)))))) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & ((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (Infected_40 < 0.0)) | (Diagnosed_40 < 0.0)) | (Susceptible_40 < 0.0)) | (Infected_50 < 50000000000000003/1000000000000000000)) | (Extinct_50 < 0.0)) | (Threatened_50 < 0.0)) | (Healed_50 < 0.0)) | (Recognized_50 < 0.0)) | (Ailing_50 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Infected_40 < 29999999999999999/1000000000000000000))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30))))))) | (! (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30))))))) | (! (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30))))))) | (! (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40)))))) | (! (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40))))))) | (! (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40))))))) | (! (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40))))))) | (! (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40))))))) | (! (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40)))))))) & ((assume_infected_maximum1_50 | (! disj257)) | (! (Infected_50 < 50000000000000003/1000000000000000000)))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (theta < 3263804687500001/10000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! 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3263804687500001/10000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (epsilon 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(theta < 40138193147778523/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.19423061509579428,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41141934151649484,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":3.33333333e-8,"assume_infected_maximum2_10":0.0,"Susceptible_10":3.33333333e-8,"Diagnosed_10":3.33333333e-8,"Ailing_10":3.33333333e-8,"Recognized_10":3.33333333e-8,"Healed_10":3.33333333e-8,"Threatened_10":3.33333333e-8,"Extinct_10":3.33333333e-8,"timer_t_10":3.33333333e-8,"funman_lambda":3.33333333e-8,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"timestep":0.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.18435014648437506,"ub":0.19249860695302495,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.39115094642639175,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":1.0,"ub":1.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (epsilon < 601558146728203/3125000000000000)) & disj293) & disj299) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj293))) & (((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 18435014648437507/100000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (theta < 4889386830329897/12500000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (epsilon < 601558146728203/3125000000000000)))) & ((assume_infected_maximum2_10 | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! disj299))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 18435014648437507/100000000000000000))) & (! assume_infected_maximum2_10)) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (theta < 4889386830329897/12500000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.18842437671870002,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41817547321319587,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.0,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":6.084145884539188e-6,"Ailing_10":4.107970750532584e-6,"Recognized_10":5.050292448214157e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.18024136579802008,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"timestep":1.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1765314514160157,"ub":0.18435014648437506,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.37554083862304705,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":3.0,"ub":3.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 18435014648437507/100000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & disj308) & disj313) & disj316) & disj318) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj308))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (epsilon < 17653145141601571/100000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (theta < 7510816772460941/20000000000000000)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj313))) & (((Infected_20 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_20)) | (! disj316))) & ((assume_infected_maximum2_30 | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! disj318))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (epsilon < 17653145141601571/100000000000000000))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! (theta < 7510816772460941/20000000000000000))) & (! assume_infected_maximum2_30)) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1804407989501954,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41037041931152357,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000011336308542265614,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.818026625855153e-6,"Ailing_10":4.109271592844326e-6,"Recognized_10":5.037284025096736e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.03186717005608681,"Infected_20":0.000056723129077360236,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998931350249303,"Diagnosed_20":0.000017068979498461317,"Ailing_20":0.0,"Recognized_20":0.000024419878086739077,"Healed_20":7.938314118187088e-6,"Threatened_20":8.432211727796375e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.0,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995585352761226,"Diagnosed_30":0.0000929253041313895,"Ailing_30":0.00006529140848349139,"Recognized_30":0.00004160071864041797,"Healed_30":0.00004161648039660679,"Threatened_30":7.460533910525881e-6,"Extinct_30":8.550545060926376e-8,"timer_t_30":30.0,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"timestep":3.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.1765314514160157,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3586960937500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":4.0,"ub":4.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (epsilon < 17653145141601571/100000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & assume_infected_maximum2_30) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & disj327) & disj332) & disj335) & disj338) & disj340) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj327))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj332))) & (((Infected_20 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_20)) | (! disj335))) & (((Infected_30 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_30)) | (! disj338))) & ((assume_infected_maximum2_40 | (! (Infected_40 < 29999999999999999/1000000000000000000))) | (! disj340))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0))) & (! (Infected_30 < 0.0))) & (! (Diagnosed_30 < 0.0))) & (! (Susceptible_30 < 0.0))) & (! assume_infected_maximum2_40)) & (! (Extinct_40 < 0.0))) & (! (Threatened_40 < 0.0))) & (! (Healed_40 < 0.0))) & (! (Recognized_40 < 0.0))) & (! (Ailing_40 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16420471267700204,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.4205496977106491,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000012136048228190806,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.2768237506232435e-6,"Ailing_10":4.107575044542322e-6,"Recognized_10":5.054249508116783e-7,"Healed_10":7.95223938974685e-7,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.020201718193352272,"Infected_20":0.00006312263483437098,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998886436931544,"Diagnosed_20":0.00001681660434687522,"Ailing_20":0.0,"Recognized_20":0.000024154039267324634,"Healed_20":5.8290326908643355e-6,"Threatened_20":8.433908276098379e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.00023551254453217277,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995208972908498,"Diagnosed_30":0.00009375534257654058,"Ailing_30":0.00007605428275467903,"Recognized_30":0.00003789993825440718,"Healed_30":0.000028702096218664743,"Threatened_30":7.267060917363943e-6,"Extinct_30":8.552241609228379e-8,"timer_t_30":30.0,"Infected_40":0.0010890756668970132,"assume_infected_maximum2_40":1.0,"Susceptible_40":0.9978180005168548,"Diagnosed_40":0.0003317707681882455,"Ailing_40":0.0,"Recognized_40":0.00045943205538065505,"Healed_40":0.00012909261493678392,"Threatened_40":0.000028467165866661054,"Extinct_40":8.122285078286781e-7,"timer_t_40":40.0,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"timestep":4.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.16700030522644527,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3341427254199983,"ub":0.3586960937500001,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (theta < 8967402343750003/25000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (epsilon < 16700030522644527/100000000000000000)) & disj348) & disj354) & disj356) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj348))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (theta < 33414272541999829/100000000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (theta < 8967402343750003/25000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (epsilon < 16700030522644527/100000000000000000)))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj354))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj356))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (theta < 33414272541999829/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16472202761322266,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3464194095849992,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000010910071104162921,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.294067581813355e-6,"Ailing_10":4.11993009442278e-6,"Recognized_10":4.930699009312203e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.058741994627970376,"Infected_20":0.000053232247565741264,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.999895574727824,"Diagnosed_20":0.000014895176355501771,"Ailing_20":0.0,"Recognized_20":0.000021166192325158075,"Healed_20":0.000012296672484301488,"Threatened_20":8.421553226217922e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.16244375000000005,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3263804687500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":6.0,"ub":6.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum1","timepoints":{"lb":50.0,"ub":75.0,"closed_upper_bound":true},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.05,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Extinct_0 = 0.0) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 8122187500000003/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & ((((((((Extinct_30 + Threatened_30) + Healed_30) + Recognized_30) + Ailing_30) + Infected_30) + Diagnosed_30) + Susceptible_30) <= 10000100000000001/10000000000000000)) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30)))))) & (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30)))))) & ((((((((Extinct_40 + Threatened_40) + Healed_40) + Recognized_40) + Ailing_40) + Infected_40) + Diagnosed_40) + Susceptible_40) <= 10000100000000001/10000000000000000)) & assume_infected_maximum1_50) & (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40))))) & (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40)))))) & (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40)))))) & (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40)))))) & (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40)))))) & (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40)))))) & (Diagnosed_50 = (Diagnosed_40 + (10.0 * (((-1.0 * (rho * Diagnosed_40)) - (eta * Diagnosed_40)) + (epsilon * Infected_40)))))) & (Susceptible_50 = (Susceptible_40 + (10.0 * ((((-1.0 * ((alpha * Infected_40) * Susceptible_40)) - ((delta * Recognized_40) * Susceptible_40)) - ((gamma * Ailing_40) * Susceptible_40)) - ((beta * Diagnosed_40) * Susceptible_40)))))) & ((((((((Extinct_50 + Threatened_50) + Healed_50) + Recognized_50) + Ailing_50) + Infected_50) + Diagnosed_50) + Susceptible_50) <= 10000100000000001/10000000000000000)) & disj368) & disj370) & (Extinct_60 = (Extinct_50 + (10.0 * (tau * Threatened_50))))) & (Threatened_60 = (Threatened_50 + (10.0 * ((((-1.0 * (sigma * Threatened_50)) - (tau * Threatened_50)) + (nu * Recognized_50)) + (mu * Ailing_50)))))) & (Healed_60 = (Healed_50 + (10.0 * (((((sigma * Threatened_50) + (xi * Recognized_50)) + (kappa * Ailing_50)) + (rho * Diagnosed_50)) + (funman_lambda * Infected_50)))))) & (Recognized_60 = (Recognized_50 + (10.0 * ((((-1.0 * (xi * Recognized_50)) - (nu * Recognized_50)) + (theta * Ailing_50)) + (eta * Diagnosed_50)))))) & (Ailing_60 = (Ailing_50 + (10.0 * ((((-1.0 * (mu * Ailing_50)) - (kappa * Ailing_50)) - (theta * Ailing_50)) + (zeta * Infected_50)))))) & (Infected_60 = (Infected_50 + (10.0 * (((((((-1.0 * (zeta * Infected_50)) - (epsilon * Infected_50)) + ((alpha * Infected_50) * Susceptible_50)) + ((delta * Recognized_50) * Susceptible_50)) + ((gamma * Ailing_50) * Susceptible_50)) + ((beta * Diagnosed_50) * Susceptible_50)) - (funman_lambda * Infected_50)))))) & (Diagnosed_60 = (Diagnosed_50 + (10.0 * (((-1.0 * (rho * Diagnosed_50)) - (eta * Diagnosed_50)) + (epsilon * Infected_50)))))) & (Susceptible_60 = (Susceptible_50 + (10.0 * ((((-1.0 * ((alpha * Infected_50) * Susceptible_50)) - ((delta * Recognized_50) * Susceptible_50)) - ((gamma * Ailing_50) * Susceptible_50)) - ((beta * Diagnosed_50) * Susceptible_50)))))) & ((((((((Extinct_60 + Threatened_60) + Healed_60) + Recognized_60) + Ailing_60) + Infected_60) + Diagnosed_60) + Susceptible_60) <= 10000100000000001/10000000000000000)) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & ((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (theta < 3263804687500001/10000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (Infected_40 < 0.0)) | (Diagnosed_40 < 0.0)) | (Susceptible_40 < 0.0)) | (Extinct_50 < 0.0)) | (Threatened_50 < 0.0)) | (Healed_50 < 0.0)) | (Recognized_50 < 0.0)) | (Ailing_50 < 0.0)) | (Infected_50 < 0.0)) | (Diagnosed_50 < 0.0)) | (Susceptible_50 < 0.0)) | (Infected_60 < 50000000000000003/1000000000000000000)) | (Extinct_60 < 0.0)) | (Threatened_60 < 0.0)) | (Healed_60 < 0.0)) | (Recognized_60 < 0.0)) | (Ailing_60 < 0.0)) | (Diagnosed_60 < 0.0)) | (Susceptible_60 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 8122187500000003/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! ((((((((Extinct_30 + Threatened_30) + Healed_30) + Recognized_30) + Ailing_30) + Infected_30) + Diagnosed_30) + Susceptible_30) <= 10000100000000001/10000000000000000))) | (! (Infected_40 < 29999999999999999/1000000000000000000))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30))))))) | (! (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30))))))) | (! (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30))))))) | (! ((((((((Extinct_40 + Threatened_40) + Healed_40) + Recognized_40) + Ailing_40) + Infected_40) + Diagnosed_40) + Susceptible_40) <= 10000100000000001/10000000000000000))) | (! (Infected_50 < 50000000000000003/1000000000000000000))) | (! (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40)))))) | (! (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40))))))) | (! (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40))))))) | (! (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40))))))) | (! (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40))))))) | (! (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40))))))) | (! (Diagnosed_50 = (Diagnosed_40 + (10.0 * (((-1.0 * (rho * Diagnosed_40)) - (eta * Diagnosed_40)) + (epsilon * Infected_40))))))) | (! (Susceptible_50 = (Susceptible_40 + (10.0 * ((((-1.0 * ((alpha * Infected_40) * Susceptible_40)) - ((delta * Recognized_40) * Susceptible_40)) - ((gamma * Ailing_40) * Susceptible_40)) - ((beta * Diagnosed_40) * Susceptible_40))))))) | (! ((((((((Extinct_50 + Threatened_50) + Healed_50) + Recognized_50) + Ailing_50) + Infected_50) + Diagnosed_50) + Susceptible_50) <= 10000100000000001/10000000000000000))) | (! (Extinct_60 = (Extinct_50 + (10.0 * (tau * Threatened_50)))))) | (! (Threatened_60 = (Threatened_50 + (10.0 * ((((-1.0 * (sigma * Threatened_50)) - (tau * Threatened_50)) + (nu * Recognized_50)) + (mu * Ailing_50))))))) | (! (Healed_60 = (Healed_50 + (10.0 * (((((sigma * Threatened_50) + (xi * Recognized_50)) + (kappa * Ailing_50)) + (rho * Diagnosed_50)) + (funman_lambda * Infected_50))))))) | (! (Recognized_60 = (Recognized_50 + (10.0 * ((((-1.0 * (xi * Recognized_50)) - (nu * Recognized_50)) + (theta * Ailing_50)) + (eta * Diagnosed_50))))))) | (! (Ailing_60 = (Ailing_50 + (10.0 * ((((-1.0 * (mu * Ailing_50)) - (kappa * Ailing_50)) - (theta * Ailing_50)) + (zeta * Infected_50))))))) | (! (Infected_60 = (Infected_50 + (10.0 * (((((((-1.0 * (zeta * Infected_50)) - (epsilon * Infected_50)) + ((alpha * Infected_50) * Susceptible_50)) + ((delta * Recognized_50) * Susceptible_50)) + ((gamma * Ailing_50) * Susceptible_50)) + ((beta * Diagnosed_50) * Susceptible_50)) - (funman_lambda * Infected_50))))))) | (! (Diagnosed_60 = (Diagnosed_50 + (10.0 * (((-1.0 * (rho * Diagnosed_50)) - (eta * Diagnosed_50)) + (epsilon * Infected_50))))))) | (! (Susceptible_60 = (Susceptible_50 + (10.0 * ((((-1.0 * ((alpha * Infected_50) * Susceptible_50)) - ((delta * Recognized_50) * Susceptible_50)) - ((gamma * Ailing_50) * Susceptible_50)) - ((beta * Diagnosed_50) * Susceptible_50))))))) | (! ((((((((Extinct_60 + Threatened_60) + Healed_60) + Recognized_60) + Ailing_60) + Infected_60) + Diagnosed_60) + Susceptible_60) <= 10000100000000001/10000000000000000)))) & (((Infected_50 < 50000000000000003/1000000000000000000) | (! assume_infected_maximum1_50)) | (! disj368))) & ((assume_infected_maximum1_60 | (! disj370)) | (! (Infected_60 < 50000000000000003/1000000000000000000)))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (theta < 3263804687500001/10000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0))) & (! (Infected_30 < 0.0))) & (! (Diagnosed_30 < 0.0))) & (! (Susceptible_30 < 0.0))) & (! (Extinct_40 < 0.0))) & (! (Threatened_40 < 0.0))) & (! (Healed_40 < 0.0))) & (! (Recognized_40 < 0.0))) & (! (Ailing_40 < 0.0))) & (! (Infected_40 < 0.0))) & (! (Diagnosed_40 < 0.0))) & (! (Susceptible_40 < 0.0))) & (! (Extinct_50 < 0.0))) & (! (Threatened_50 < 0.0))) & (! (Healed_50 < 0.0))) & (! (Recognized_50 < 0.0))) & (! (Ailing_50 < 0.0))) & (! (Infected_50 < 0.0))) & (! (Diagnosed_50 < 0.0))) & (! (Susceptible_50 < 0.0))) & (! assume_infected_maximum1_60)) & (! (Extinct_60 < 0.0))) & (! (Threatened_60 < 0.0))) & (! (Healed_60 < 0.0))) & (! (Recognized_60 < 0.0))) & (! (Ailing_60 < 0.0))) & (! (Diagnosed_60 < 0.0))) & (! (Susceptible_60 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.14982221679687502,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.3563174209594728,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":0.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000011221190898692687,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":4.797407221765093e-6,"Ailing_10":4.118280425866966e-6,"Recognized_10":4.947195694870338e-7,"Healed_10":2.189497797330955e-6,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.06202993398586859,"Infected_20":0.00005674266046137601,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998938633577079,"Diagnosed_20":0.000013981400862535425,"Ailing_20":2.07040301334975e-6,"Recognized_20":0.00002094795267720741,"Healed_20":0.000011567388317679808,"Threatened_20":8.423202894773735e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.0002022833047375048,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995571826642558,"Diagnosed_30":0.0000767645461351484,"Ailing_30":0.00006491751285149787,"Recognized_30":0.0000365848832914384,"Healed_30":0.00005557553048279299,"Threatened_30":6.622809546433941e-6,"Extinct_30":8.541536227903734e-8,"timer_t_30":30.0,"Infected_40":0.0009968220143491491,"assume_infected_maximum2_40":1.0,"Susceptible_40":0.9980963226842791,"Diagnosed_40":0.0002577797730162086,"Ailing_40":0.00006416334622559782,"Recognized_40":0.00034775665681534274,"Healed_40":0.00022554154484419252,"Threatened_40":0.000025748546642339783,"Extinct_40":7.476963169224315e-7,"timer_t_40":40.0,"Infected_50":0.003692896447181401,"assume_infected_maximum1_50":1.0,"Susceptible_50":0.9920381044107944,"Diagnosed_50":0.0013414024740504534,"Ailing_50":0.0010871387563006144,"Recognized_50":0.0007456233307033548,"Healed_50":0.0010059601269031767,"Threatened_50":0.00012359930700203123,"Extinct_50":3.3225509811564103e-6,"timer_t_50":50.0,"assume_infected_maximum1":1.0,"assume_infected_maximum1_0":1.0,"assume_infected_maximum1_10":1.0,"assume_infected_maximum1_20":1.0,"assume_infected_maximum1_30":1.0,"assume_infected_maximum1_40":1.0,"Infected_60":0.01711826123369208,"assume_infected_maximum1_60":1.0,"Susceptible_60":0.9658499463798447,"Diagnosed_60":0.004818204814468148,"Recognized_60":0.005930269860006755,"Ailing_60":0.001406513736568945,"Healed_60":0.004085417957259275,"Threatened_60":0.00047635802924441626,"Extinct_60":0.000015682481681359533,"timer_t_60":60.0,"timestep":6.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.14992634887695316,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.30107733402252207,"ub":0.3263804687500001,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (theta < 3263804687500001/10000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (epsilon < 14992634887695317/100000000000000000)) & disj379) & disj386) & disj388) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj379))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (theta < 30107733402252207/100000000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (theta < 3263804687500001/10000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (epsilon < 14992634887695317/100000000000000000)))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj386))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj388))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (theta < 30107733402252207/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.1433631744384766,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.31372890138626114,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000010797843617771397,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":4.582105810033781e-6,"Ailing_10":4.125378512434109e-6,"Recognized_10":4.876214829198909e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.08775256922308292,"Infected_20":0.00005038655664693856,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.999896268479002,"Diagnosed_20":0.00001297666356048846,"Ailing_20":0.0,"Recognized_20":0.00001894347174832197,"Healed_20":0.00001754019148685688,"Threatened_20":8.416104808206591e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"Infected_60":3.33333333e-8,"assume_infected_maximum1_60":0.0,"Susceptible_60":3.33333333e-8,"Diagnosed_60":3.33333333e-8,"Recognized_60":3.33333333e-8,"Ailing_60":3.33333333e-8,"Healed_60":3.33333333e-8,"Threatened_60":3.33333333e-8,"Extinct_60":3.33333333e-8,"timer_t_60":3.33333333e-8,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1368,"ub":0.14480762848854067,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.2968,"ub":0.2997639625549317,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (theta < 29976396255493171/100000000000000000)) & (epsilon < 3620190712213517/25000000000000000)) & disj379) & disj386) & disj388) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj379))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (theta < 29976396255493171/100000000000000000))) | (! (epsilon < 3620190712213517/25000000000000000)))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj386))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj388))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 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& (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (epsilon < 9798131161928181/50000000000000000)) & disj398) & 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(Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (epsilon < 9798131161928181/50000000000000000)))) & ((assume_infected_maximum2_10 | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! disj403))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! assume_infected_maximum2_10)) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (epsilon < 601558146728203/3125000000000000))) & (! (theta < 40138193147778523/100000000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.19423061509579428,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.42329096573889263,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.0,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":6.277687163582123e-6,"Ailing_10":4.1071181684483785e-6,"Recognized_10":5.058818269056215e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.17616713556369512,"Infected_20":3.33333333e-8,"assume_infected_maximum2_20":0.0,"Susceptible_20":3.33333333e-8,"Diagnosed_20":3.33333333e-8,"Ailing_20":3.33333333e-8,"Recognized_20":3.33333333e-8,"Healed_20":3.33333333e-8,"Threatened_20":3.33333333e-8,"Extinct_20":3.33333333e-8,"timer_t_20":3.33333333e-8,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"Infected_60":3.33333333e-8,"assume_infected_maximum1_60":0.0,"Susceptible_60":3.33333333e-8,"Diagnosed_60":3.33333333e-8,"Recognized_60":3.33333333e-8,"Ailing_60":3.33333333e-8,"Healed_60":3.33333333e-8,"Threatened_60":3.33333333e-8,"Extinct_60":3.33333333e-8,"timer_t_60":3.33333333e-8,"timestep":1.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.18435014648437506,"ub":0.19249860695302495,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.39115094642639175,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":2.0,"ub":2.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 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assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (epsilon < 601558146728203/3125000000000000)) & disj398) & disj404) & disj408) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj398))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 18435014648437507/100000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (theta < 4889386830329897/12500000000000000)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (epsilon < 601558146728203/3125000000000000)))) & (((Infected_10 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_10)) | (! disj404))) & ((assume_infected_maximum2_20 | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! disj408))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 18435014648437507/100000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! assume_infected_maximum2_20)) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (theta < 4889386830329897/12500000000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.18842437671870002,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.41535974365816586,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.00001146485599530368,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":6.084145884539188e-6,"Ailing_10":4.108440035516078e-6,"Recognized_10":5.045599598379218e-7,"Healed_10":0.0,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.020192068763744974,"Infected_20":0.00005783030607324662,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.999892376749061,"Diagnosed_20":0.000018045323504864536,"Ailing_20":0.0,"Recognized_20":0.000024953519518085272,"Healed_20":6.125262375210261e-6,"Threatened_20":8.433043285124622e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":3.33333333e-8,"assume_infected_maximum2_30":0.0,"Susceptible_30":3.33333333e-8,"Diagnosed_30":3.33333333e-8,"Ailing_30":3.33333333e-8,"Recognized_30":3.33333333e-8,"Healed_30":3.33333333e-8,"Threatened_30":3.33333333e-8,"Extinct_30":3.33333333e-8,"timer_t_30":3.33333333e-8,"Infected_40":3.33333333e-8,"assume_infected_maximum2_40":0.0,"Susceptible_40":3.33333333e-8,"Diagnosed_40":3.33333333e-8,"Ailing_40":3.33333333e-8,"Recognized_40":3.33333333e-8,"Healed_40":3.33333333e-8,"Threatened_40":3.33333333e-8,"Extinct_40":3.33333333e-8,"timer_t_40":3.33333333e-8,"Infected_50":3.33333333e-8,"assume_infected_maximum1_50":0.0,"Susceptible_50":3.33333333e-8,"Diagnosed_50":3.33333333e-8,"Ailing_50":3.33333333e-8,"Recognized_50":3.33333333e-8,"Healed_50":3.33333333e-8,"Threatened_50":3.33333333e-8,"Extinct_50":3.33333333e-8,"timer_t_50":3.33333333e-8,"assume_infected_maximum1":0.0,"assume_infected_maximum1_0":0.0,"assume_infected_maximum1_10":0.0,"assume_infected_maximum1_20":0.0,"assume_infected_maximum1_30":0.0,"assume_infected_maximum1_40":0.0,"Infected_60":3.33333333e-8,"assume_infected_maximum1_60":0.0,"Susceptible_60":3.33333333e-8,"Diagnosed_60":3.33333333e-8,"Recognized_60":3.33333333e-8,"Ailing_60":3.33333333e-8,"Healed_60":3.33333333e-8,"Threatened_60":3.33333333e-8,"Extinct_60":3.33333333e-8,"timer_t_60":3.33333333e-8,"timestep":2.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.1765314514160157,"ub":0.18435014648437506,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.37554083862304705,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":4.0,"ub":4.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (epsilon < 18435014648437507/100000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & assume_infected_maximum2_30) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & disj417) & disj422) & disj425) & disj428) & disj430) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj417))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (epsilon < 17653145141601571/100000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (theta < 7510816772460941/20000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! 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(Infected_0 < 29999999999999999/1000000000000000000))) | (! disj436))) & ((assume_theta_epsilon | (! ((2.0 * epsilon) <= theta))) | (! disj437))) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & ((((((((((2.0 * epsilon) <= theta) | (theta < 29680000000000001/100000000000000000)) | (epsilon < 171/1250)) | (epsilon < 17653145141601571/100000000000000000)) | (theta < 289174415189773/781250000000000)) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (epsilon < 18435014648437507/100000000000000000))) | (! (theta < 18602956391870987/50000000000000000)))) & (((! assume_infected_maximum2_0) | (! assume_theta_epsilon)) | (! disj441))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 17653145141601571/100000000000000000))) & (! (theta < 289174415189773/781250000000000)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":0.011,"beta":0.011,"Healed_0":0.011,"gamma":0.456,"Threatened_0":0.011,"epsilon":0.1804407989501954,"delta":0.011,"Extinct_0":0.011,"alpha":0.57,"zeta":0.125,"theta":0.37127621064186117,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":0.011,"timer_t_0":0.011,"Susceptible_0":0.011,"assume_theta_epsilon":1.0,"Diagnosed_0":0.011,"assume_infected_maximum2":1.0,"assume_infected_maximum2_0":1.0,"Ailing_0":0.011,"Infected_10":0.011,"assume_infected_maximum2_10":0.0,"Susceptible_10":0.011,"Diagnosed_10":0.011,"Ailing_10":0.011,"Recognized_10":0.011,"Healed_10":0.011,"Threatened_10":0.011,"Extinct_10":0.011,"timer_t_10":0.011,"funman_lambda":0.011,"Infected_20":0.011,"assume_infected_maximum2_20":0.0,"Susceptible_20":0.011,"Diagnosed_20":0.011,"Ailing_20":0.011,"Recognized_20":0.011,"Healed_20":0.011,"Threatened_20":0.011,"Extinct_20":0.011,"timer_t_20":0.011,"Infected_30":0.011,"assume_infected_maximum2_30":0.0,"Susceptible_30":0.011,"Diagnosed_30":0.011,"Ailing_30":0.011,"Recognized_30":0.011,"Healed_30":0.011,"Threatened_30":0.011,"Extinct_30":0.011,"timer_t_30":0.011,"Infected_40":0.011,"assume_infected_maximum2_40":0.0,"Susceptible_40":0.011,"Diagnosed_40":0.011,"Ailing_40":0.011,"Recognized_40":0.011,"Healed_40":0.011,"Threatened_40":0.011,"Extinct_40":0.011,"timer_t_40":0.011,"timestep":0.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.1765314514160157,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3586960937500001,"ub":0.4452,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":5.0,"ub":5.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum1","timepoints":{"lb":50.0,"ub":75.0,"closed_upper_bound":true},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.05,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Extinct_0 = 0.0) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & (epsilon < 17653145141601571/100000000000000000)) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20)))))) & (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20)))))) & (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30))))) & (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30)))))) & (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30)))))) & (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30)))))) & (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30)))))) & (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))) & (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30)))))) & (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30)))))) & (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40))))) & (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40)))))) & (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40)))))) & (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40)))))) & (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40)))))) & (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40)))))) & disj455) & ((Infected_0 < 29999999999999999/1000000000000000000) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))))) & (((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Infected_10 < 29999999999999999/1000000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (epsilon < 17653145141601571/100000000000000000)))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Infected_20 < 29999999999999999/1000000000000000000)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Infected_40 < 29999999999999999/1000000000000000000)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30)))))))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (theta < 8967402343750003/25000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (Infected_30 < 0.0)) | (Diagnosed_30 < 0.0)) | (Susceptible_30 < 0.0)) | (Extinct_40 < 0.0)) | (Threatened_40 < 0.0)) | (Healed_40 < 0.0)) | (Recognized_40 < 0.0)) | (Ailing_40 < 0.0)) | (Infected_40 < 0.0)) | (Diagnosed_40 < 0.0)) | (Susceptible_40 < 0.0)) | (Infected_50 < 50000000000000003/1000000000000000000)) | (Extinct_50 < 0.0)) | (Threatened_50 < 0.0)) | (Healed_50 < 0.0)) | (Recognized_50 < 0.0)) | (Ailing_50 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (epsilon < 17653145141601571/100000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Infected_30 < 29999999999999999/1000000000000000000))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20))))))) | (! (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20))))))) | (! (Diagnosed_30 = (Diagnosed_20 + (10.0 * (((-1.0 * (rho * Diagnosed_20)) - (eta * Diagnosed_20)) + (epsilon * Infected_20))))))) | (! (Susceptible_30 = (Susceptible_20 + (10.0 * ((((-1.0 * ((alpha * Infected_20) * Susceptible_20)) - ((delta * Recognized_20) * Susceptible_20)) - ((gamma * Ailing_20) * Susceptible_20)) - ((beta * Diagnosed_20) * Susceptible_20))))))) | (! (Infected_40 < 29999999999999999/1000000000000000000))) | (! (Extinct_40 = (Extinct_30 + (10.0 * (tau * Threatened_30)))))) | (! (Threatened_40 = (Threatened_30 + (10.0 * ((((-1.0 * (sigma * Threatened_30)) - (tau * Threatened_30)) + (nu * Recognized_30)) + (mu * Ailing_30))))))) | (! (Healed_40 = (Healed_30 + (10.0 * (((((sigma * Threatened_30) + (xi * Recognized_30)) + (kappa * Ailing_30)) + (rho * Diagnosed_30)) + (funman_lambda * Infected_30))))))) | (! (Recognized_40 = (Recognized_30 + (10.0 * ((((-1.0 * (xi * Recognized_30)) - (nu * Recognized_30)) + (theta * Ailing_30)) + (eta * Diagnosed_30))))))) | (! (Ailing_40 = (Ailing_30 + (10.0 * ((((-1.0 * (mu * Ailing_30)) - (kappa * Ailing_30)) - (theta * Ailing_30)) + (zeta * Infected_30))))))) | (! (Infected_40 = (Infected_30 + (10.0 * (((((((-1.0 * (zeta * Infected_30)) - (epsilon * Infected_30)) + ((alpha * Infected_30) * Susceptible_30)) + ((delta * Recognized_30) * Susceptible_30)) + ((gamma * Ailing_30) * Susceptible_30)) + ((beta * Diagnosed_30) * Susceptible_30)) - (funman_lambda * Infected_30))))))) | (! (Diagnosed_40 = (Diagnosed_30 + (10.0 * (((-1.0 * (rho * Diagnosed_30)) - (eta * Diagnosed_30)) + (epsilon * Infected_30))))))) | (! (Susceptible_40 = (Susceptible_30 + (10.0 * ((((-1.0 * ((alpha * Infected_30) * Susceptible_30)) - ((delta * Recognized_30) * Susceptible_30)) - ((gamma * Ailing_30) * Susceptible_30)) - ((beta * Diagnosed_30) * Susceptible_30))))))) | (! (Extinct_50 = (Extinct_40 + (10.0 * (tau * Threatened_40)))))) | (! (Threatened_50 = (Threatened_40 + (10.0 * ((((-1.0 * (sigma * Threatened_40)) - (tau * Threatened_40)) + (nu * Recognized_40)) + (mu * Ailing_40))))))) | (! (Healed_50 = (Healed_40 + (10.0 * (((((sigma * Threatened_40) + (xi * Recognized_40)) + (kappa * Ailing_40)) + (rho * Diagnosed_40)) + (funman_lambda * Infected_40))))))) | (! (Recognized_50 = (Recognized_40 + (10.0 * ((((-1.0 * (xi * Recognized_40)) - (nu * Recognized_40)) + (theta * Ailing_40)) + (eta * Diagnosed_40))))))) | (! (Ailing_50 = (Ailing_40 + (10.0 * ((((-1.0 * (mu * Ailing_40)) - (kappa * Ailing_40)) - (theta * Ailing_40)) + (zeta * Infected_40))))))) | (! (Infected_50 = (Infected_40 + (10.0 * (((((((-1.0 * (zeta * Infected_40)) - (epsilon * Infected_40)) + ((alpha * Infected_40) * Susceptible_40)) + ((delta * Recognized_40) * Susceptible_40)) + ((gamma * Ailing_40) * Susceptible_40)) + ((beta * Diagnosed_40) * Susceptible_40)) - (funman_lambda * Infected_40)))))))) & ((assume_infected_maximum1_50 | (! (Infected_50 < 50000000000000003/1000000000000000000))) | (! disj455))) & (! (theta < 29680000000000001/100000000000000000))) & (! (epsilon < 171/1250))) & (! (epsilon < 8122187500000003/50000000000000000))) & (! (theta < 8967402343750003/25000000000000000))) & (! (Extinct_10 < 0.0))) & (! (Threatened_10 < 0.0))) & (! (Healed_10 < 0.0))) & (! (Recognized_10 < 0.0))) & (! (Ailing_10 < 0.0))) & (! (Infected_10 < 0.0))) & (! (Diagnosed_10 < 0.0))) & (! (Susceptible_10 < 0.0))) & (! (Extinct_20 < 0.0))) & (! (Threatened_20 < 0.0))) & (! (Healed_20 < 0.0))) & (! (Recognized_20 < 0.0))) & (! (Ailing_20 < 0.0))) & (! (Infected_20 < 0.0))) & (! (Diagnosed_20 < 0.0))) & (! (Susceptible_20 < 0.0))) & (! (Extinct_30 < 0.0))) & (! (Threatened_30 < 0.0))) & (! (Healed_30 < 0.0))) & (! (Recognized_30 < 0.0))) & (! (Ailing_30 < 0.0))) & (! (Infected_30 < 0.0))) & (! (Diagnosed_30 < 0.0))) & (! (Susceptible_30 < 0.0))) & (! (Extinct_40 < 0.0))) & (! (Threatened_40 < 0.0))) & (! (Healed_40 < 0.0))) & (! (Recognized_40 < 0.0))) & (! (Ailing_40 < 0.0))) & (! (Infected_40 < 0.0))) & (! (Diagnosed_40 < 0.0))) & (! (Susceptible_40 < 0.0))) & (! assume_infected_maximum1_50)) & (! (Extinct_50 < 0.0))) & (! (Threatened_50 < 0.0))) & (! (Healed_50 < 0.0))) & (! (Recognized_50 < 0.0))) & (! (Ailing_50 < 0.0)))"},"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]},"corner_points":[],"points":[{"type":"point","label":"true","values":{"Recognized_0":3.33333333e-8,"beta":0.011,"Healed_0":0.0,"gamma":0.456,"Threatened_0":0.0,"epsilon":0.16574555501937874,"delta":0.011,"Extinct_0":0.0,"alpha":0.57,"zeta":0.125,"theta":0.43573863525390627,"lambda":0.034,"eta":0.125,"rho":0.034,"kappa":0.017,"mu":0.017,"nu":0.027,"xi":0.017,"tau":0.01,"sigma":0.017,"Infected_0":3.33333333e-6,"timer_t_0":0.0,"Susceptible_0":0.9999963,"assume_theta_epsilon":1.0,"Diagnosed_0":3.33333333e-7,"assume_infected_maximum2":0.0,"assume_infected_maximum2_0":1.0,"Ailing_0":1.66666666e-8,"Infected_10":0.000012548567447898177,"assume_infected_maximum2_10":1.0,"Susceptible_10":0.9999771837374164,"Diagnosed_10":5.328185161984439e-6,"Ailing_10":4.105043556870841e-6,"Recognized_10":5.079564384831586e-7,"Healed_10":3.313433079061188e-7,"Threatened_10":1.1833333313000001e-8,"Extinct_10":0.0,"timer_t_10":10.0,"funman_lambda":0.006285299247378862,"Infected_20":0.00006616105808972704,"assume_infected_maximum2_20":1.0,"Susceptible_20":0.9998862980024881,"Diagnosed_20":0.00001765510884307625,"Ailing_20":5.077742413097766e-7,"Recognized_20":0.00002483195087412882,"Healed_20":3.717944816799862e-6,"Threatened_20":8.43643976376986e-7,"Extinct_20":1.1833333313000002e-9,"timer_t_20":20.0,"Infected_30":0.00025528983764025584,"assume_infected_maximum2_30":1.0,"Susceptible_30":0.9995022346177711,"Diagnosed_30":0.00009924311174427083,"Ailing_30":0.00008082276987767385,"Recognized_30":0.00003818846227710685,"Healed_30":0.000018331382384768643,"Threatened_30":7.4068084597926435e-6,"Extinct_30":8.55477309689986e-8,"timer_t_30":30.0,"Infected_40":0.0013481041277377279,"assume_infected_maximum2_40":1.0,"Susceptible_40":0.9976643285100206,"Diagnosed_40":0.00036458232260507515,"Ailing_40":0.00002024517788361512,"Recognized_40":0.0004976266146517239,"Healed_40":0.00008961734305813064,"Threatened_40":0.000029457725869672033,"Extinct_40":8.262285769482629e-7,"timer_t_40":40.0,"Infected_50":0.0052870731518991285,"assume_infected_maximum1_50":1.0,"Susceptible_50":0.9898113571883214,"Diagnosed_50":0.0020193429576421894,"Ailing_50":0.0016101251901598835,"Recognized_50":0.0008227257191020122,"Healed_50":0.00039139494912643217,"Threatened_50":0.00015930500608104059,"Extinct_50":3.7720011639154666e-6,"timer_t_50":50.0,"assume_infected_maximum1":1.0,"assume_infected_maximum1_0":1.0,"assume_infected_maximum1_10":1.0,"assume_infected_maximum1_20":1.0,"assume_infected_maximum1_30":1.0,"assume_infected_maximum1_40":1.0,"Infected_60":3.33333333e-8,"assume_infected_maximum1_60":0.0,"Susceptible_60":3.33333333e-8,"Diagnosed_60":3.33333333e-8,"Recognized_60":3.33333333e-8,"Ailing_60":3.33333333e-8,"Healed_60":3.33333333e-8,"Threatened_60":3.33333333e-8,"Extinct_60":3.33333333e-8,"timer_t_60":3.33333333e-8,"timestep":5.0},"normalized_values":null,"schedule":{"timepoints":[0,10,20,30,40,50,60,70,80,90,100]}}]},{"type":"box","label":"true","bounds":{"beta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"gamma":{"lb":0.456,"ub":0.456,"closed_upper_bound":true},"delta":{"lb":0.011,"ub":0.011,"closed_upper_bound":true},"alpha":{"lb":0.57,"ub":0.57,"closed_upper_bound":true},"epsilon":{"lb":0.16244375000000005,"ub":0.16700030522644527,"closed_upper_bound":false},"zeta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"lambda":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"eta":{"lb":0.125,"ub":0.125,"closed_upper_bound":true},"rho":{"lb":0.034,"ub":0.034,"closed_upper_bound":true},"theta":{"lb":0.3341427254199983,"ub":0.3586960937500001,"closed_upper_bound":false},"kappa":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"mu":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"nu":{"lb":0.027,"ub":0.027,"closed_upper_bound":true},"xi":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"tau":{"lb":0.01,"ub":0.01,"closed_upper_bound":true},"sigma":{"lb":0.017,"ub":0.017,"closed_upper_bound":true},"timestep":{"lb":3.0,"ub":3.0,"closed_upper_bound":true}},"explanation":{"relevant_assumptions":[{"constraint":{"soft":true,"name":"infected_maximum2","timepoints":{"lb":0.0,"ub":50.0,"closed_upper_bound":false},"variable":"Infected","interval":{"lb":-1.7976931348623157e308,"ub":0.03,"closed_upper_bound":false}}}],"expression":"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_infected_maximum2_0 & (Extinct_0 = 0.0)) & (Threatened_0 = 0.0)) & (Healed_0 = 0.0)) & (Recognized_0 = (333333333.0 * 1/10000000000000000))) & (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000))) & (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000))) & (Diagnosed_0 = (333333333.0 * 1/1000000000000000))) & (Susceptible_0 = 19999925999999999/20000000000000000)) & (sigma = 17000000000000001/1000000000000000000)) & (tau = 1/100)) & (xi = 17000000000000001/1000000000000000000)) & (nu = 27/1000)) & (mu = 17000000000000001/1000000000000000000)) & (kappa = 17000000000000001/1000000000000000000)) & (rho = 17000000000000001/500000000000000000)) & (eta = 1/8)) & (zeta = 1/8)) & (alpha = 11399999999999999/20000000000000000)) & (delta = 10999999999999999/1000000000000000000)) & (gamma = 22800000000000001/50000000000000000)) & (beta = 10999999999999999/1000000000000000000)) & (theta < 22259999999999999/50000000000000000)) & (epsilon < 10260000000000001/50000000000000000)) & (theta < 8967402343750003/25000000000000000)) & assume_infected_maximum2_10) & (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau))))) & (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu)))))) & (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho)))))) & (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta)))))) & (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta)))))) & (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta)))))) & (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta)))))) & (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta)))))) & ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000)) & assume_infected_maximum2_20) & (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10))))) & (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10)))))) & (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda)))))) & (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10)))))) & (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10)))))) & (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda)))))) & (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10)))))) & (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10)))))) & (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20))))) & (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20)))))) & (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20)))))) & (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20)))))) & (Ailing_30 = (Ailing_20 + (10.0 * ((((-1.0 * (mu * Ailing_20)) - (kappa * Ailing_20)) - (theta * Ailing_20)) + (zeta * Infected_20)))))) & (Infected_30 = (Infected_20 + (10.0 * (((((((-1.0 * (zeta * Infected_20)) - (epsilon * Infected_20)) + ((alpha * Infected_20) * Susceptible_20)) + ((delta * Recognized_20) * Susceptible_20)) + ((gamma * Ailing_20) * Susceptible_20)) + ((beta * Diagnosed_20) * Susceptible_20)) - (funman_lambda * Infected_20)))))) & (epsilon < 16700030522644527/100000000000000000)) & disj464) & disj468) & disj471) & disj473) & (((Infected_0 < 29999999999999999/1000000000000000000) | (! assume_infected_maximum2_0)) | (! disj464))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((theta < 29680000000000001/100000000000000000) | (epsilon < 171/1250)) | (epsilon < 8122187500000003/50000000000000000)) | (Extinct_10 < 0.0)) | (Threatened_10 < 0.0)) | (Healed_10 < 0.0)) | (Recognized_10 < 0.0)) | (Ailing_10 < 0.0)) | (Infected_10 < 0.0)) | (Diagnosed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (Extinct_20 < 0.0)) | (Threatened_20 < 0.0)) | (Healed_20 < 0.0)) | (Recognized_20 < 0.0)) | (Ailing_20 < 0.0)) | (Infected_20 < 0.0)) | (Diagnosed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (theta < 33414272541999829/100000000000000000)) | (Infected_30 < 29999999999999999/1000000000000000000)) | (Extinct_30 < 0.0)) | (Threatened_30 < 0.0)) | (Healed_30 < 0.0)) | (Recognized_30 < 0.0)) | (Ailing_30 < 0.0)) | (! (Infected_0 < 29999999999999999/1000000000000000000))) | (! (Extinct_0 = 0.0))) | (! (Threatened_0 = 0.0))) | (! (Healed_0 = 0.0))) | (! (Recognized_0 = (333333333.0 * 1/10000000000000000)))) | (! (Ailing_0 = (16666666599999999.0 * 1/1000000000000000000000000)))) | (! (Infected_0 = (33333333299999999.0 * 1/10000000000000000000000)))) | (! (Diagnosed_0 = (333333333.0 * 1/1000000000000000)))) | (! (Susceptible_0 = 19999925999999999/20000000000000000))) | (! (sigma = 17000000000000001/1000000000000000000))) | (! (tau = 1/100))) | (! (xi = 17000000000000001/1000000000000000000))) | (! (nu = 27/1000))) | (! (mu = 17000000000000001/1000000000000000000))) | (! (kappa = 17000000000000001/1000000000000000000))) | (! (rho = 17000000000000001/500000000000000000))) | (! (eta = 1/8))) | (! (zeta = 1/8))) | (! (alpha = 11399999999999999/20000000000000000))) | (! (delta = 10999999999999999/1000000000000000000))) | (! (gamma = 22800000000000001/50000000000000000))) | (! (beta = 10999999999999999/1000000000000000000))) | (! (theta < 22259999999999999/50000000000000000))) | (! (epsilon < 10260000000000001/50000000000000000))) | (! (theta < 8967402343750003/25000000000000000))) | (! (Infected_10 < 29999999999999999/1000000000000000000))) | (! (Extinct_10 = (Extinct_0 + (10.0 * (Threatened_0 * tau)))))) | (! (Threatened_10 = (Threatened_0 + (10.0 * ((((-1.0 * (Threatened_0 * sigma)) - (Threatened_0 * tau)) + (Recognized_0 * nu)) + (Ailing_0 * mu))))))) | (! (Healed_10 = (Healed_0 + (10.0 * (((((Threatened_0 * sigma) + (Recognized_0 * xi)) + (Ailing_0 * kappa)) + (Infected_0 * funman_lambda)) + (Diagnosed_0 * rho))))))) | (! (Recognized_10 = (Recognized_0 + (10.0 * ((((-1.0 * (Recognized_0 * xi)) - (Recognized_0 * nu)) + (Ailing_0 * theta)) + (Diagnosed_0 * eta))))))) | (! (Ailing_10 = (Ailing_0 + (10.0 * ((((-1.0 * (Ailing_0 * mu)) - (Ailing_0 * kappa)) - (Ailing_0 * theta)) + (Infected_0 * zeta))))))) | (! (Infected_10 = (Infected_0 + (10.0 * ((((((((Recognized_0 * Susceptible_0) * delta) + ((Ailing_0 * Susceptible_0) * gamma)) + ((Infected_0 * Susceptible_0) * alpha)) - (Infected_0 * zeta)) - (Infected_0 * epsilon)) - (Infected_0 * funman_lambda)) + ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! (Diagnosed_10 = (Diagnosed_0 + (10.0 * (((Infected_0 * epsilon) - (Diagnosed_0 * rho)) - (Diagnosed_0 * eta))))))) | (! (Susceptible_10 = (Susceptible_0 + (10.0 * ((((-1.0 * ((Recognized_0 * Susceptible_0) * delta)) - ((Ailing_0 * Susceptible_0) * gamma)) - ((Infected_0 * Susceptible_0) * alpha)) - ((Diagnosed_0 * Susceptible_0) * beta))))))) | (! ((((((((Extinct_10 + Threatened_10) + Healed_10) + Recognized_10) + Ailing_10) + Infected_10) + Diagnosed_10) + Susceptible_10) <= 10000100000000001/10000000000000000))) | (! (Infected_20 < 29999999999999999/1000000000000000000))) | (! (Extinct_20 = (Extinct_10 + (10.0 * (tau * Threatened_10)))))) | (! (Threatened_20 = (Threatened_10 + (10.0 * ((((-1.0 * (sigma * Threatened_10)) - (tau * Threatened_10)) + (nu * Recognized_10)) + (mu * Ailing_10))))))) | (! (Healed_20 = (Healed_10 + (10.0 * (((((sigma * Threatened_10) + (xi * Recognized_10)) + (kappa * Ailing_10)) + (rho * Diagnosed_10)) + (Infected_10 * funman_lambda))))))) | (! (Recognized_20 = (Recognized_10 + (10.0 * ((((-1.0 * (xi * Recognized_10)) - (nu * Recognized_10)) + (theta * Ailing_10)) + (eta * Diagnosed_10))))))) | (! (Ailing_20 = (Ailing_10 + (10.0 * ((((-1.0 * (mu * Ailing_10)) - (kappa * Ailing_10)) - (theta * Ailing_10)) + (zeta * Infected_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((((((-1.0 * (zeta * Infected_10)) - (epsilon * Infected_10)) + ((alpha * Infected_10) * Susceptible_10)) + ((delta * Recognized_10) * Susceptible_10)) + ((gamma * Ailing_10) * Susceptible_10)) + ((beta * Diagnosed_10) * Susceptible_10)) - (Infected_10 * funman_lambda))))))) | (! (Diagnosed_20 = (Diagnosed_10 + (10.0 * (((-1.0 * (rho * Diagnosed_10)) - (eta * Diagnosed_10)) + (epsilon * Infected_10))))))) | (! (Susceptible_20 = (Susceptible_10 + (10.0 * ((((-1.0 * ((alpha * Infected_10) * Susceptible_10)) - ((delta * Recognized_10) * Susceptible_10)) - ((gamma * Ailing_10) * Susceptible_10)) - ((beta * Diagnosed_10) * Susceptible_10))))))) | (! (Extinct_30 = (Extinct_20 + (10.0 * (tau * Threatened_20)))))) | (! (Threatened_30 = (Threatened_20 + (10.0 * ((((-1.0 * (sigma * Threatened_20)) - (tau * Threatened_20)) + (nu * Recognized_20)) + (mu * Ailing_20))))))) | (! (Healed_30 = (Healed_20 + (10.0 * (((((sigma * Threatened_20) + (xi * Recognized_20)) + (kappa * Ailing_20)) + (rho * Diagnosed_20)) + (funman_lambda * Infected_20))))))) | (! (Recognized_30 = (Recognized_20 + (10.0 * ((((-1.0 * (xi * Recognized_20)) - (nu * Recognized_20)) + (theta * Ailing_20)) + (eta * Diagnosed_20))))))) | (! 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\ No newline at end of file diff --git a/resources/cached/068cef77-839d-49be-9191-3502ee6d5240.json b/resources/cached/068cef77-839d-49be-9191-3502ee6d5240.json index cad137b9..fbf20a0e 100644 --- a/resources/cached/068cef77-839d-49be-9191-3502ee6d5240.json +++ b/resources/cached/068cef77-839d-49be-9191-3502ee6d5240.json @@ -361,7 +361,7 @@ "num_steps": 2, "step_size": 1, "num_initial_boxes": 1, - "save_smtlib": true, + "save_smtlib": "my.smt2", "dreal_precision": 0.001, "dreal_log_level": "off", "constraint_noise": 0.0, diff --git a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py index 3586fee7..91b9ff65 100644 --- a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py +++ b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py @@ -169,24 +169,33 @@ def main(): "weights": [1, -2], # No timepoints, because the variables are parameters }, + # { + # "name": "infected_maximum1", + # "variable": "Infected", + # "interval": {"lb": 0.1, "ub": 0.4}, + # "timepoints": {"lb": 60, "ub": 80, "closed_upper_bound": True}, + # }, { "name": "infected_maximum1", "variable": "Infected", - "interval": {"lb": 0.0, "ub": 0.05}, - "timepoints": {"lb": 50, "ub": 75, "closed_upper_bound": True}, + "interval": {"lb": 0.0, "ub": 0.5}, + "timepoints": {"lb": 60, "ub": 80, "closed_upper_bound": True}, }, + + + { "name": "infected_maximum2", "variable": "Infected", - "interval": {"ub": 0.01}, + "interval": {"ub": 0.1}, "timepoints": {"lb": 0, "ub": 50}, }, - { - "name": "infected_maximum3", - "variable": "Infected", - "interval": {"ub": 0.01}, - "timepoints": {"lb": 76}, - }, + # { + # "name": "infected_maximum3", + # "variable": "Infected", + # "interval": {"ub": 0.01}, + # "timepoints": {"lb": 76}, + # }, ], "structure_parameters": [ { @@ -221,10 +230,10 @@ def main(): "tolerance": 1e-2, "verbosity": 10, "dreal_mcts": True, - "save_smtlib": False, + # "save_smtlib": os.path.join(os.path.realpath(__file__), "./out"), "substitute_subformulas": False, "series_approximation_threshold": None, - "dreal_log_level": "info", + "dreal_log_level": "none", "profile": False, }, } diff --git a/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb b/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb index 4a472667..8a89bb0e 100644 --- a/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb +++ b/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -17,65 +17,147 @@ "from typing import List, Dict\n", "from funman.api.run import Runner\n", "\n", + "RESOURCES = os.path.join(\n", + " os.getcwd(), \"../../resources\"\n", + ")\n", + "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"halfar\")\n", + "MODEL_PATH = os.path.join(EXAMPLE_DIR, \"halfar.json\")\n", + "REQUEST_PATH = os.path.join(EXAMPLE_DIR, \"halfar_request.json\")\n", + "\n", + "\n", + "def summarize_results(num_disc, results):\n", + " variables = [f\"h_{d}\" for d in range(num_disc)]\n", + " \n", + " points = results.points()\n", + " boxes = results.parameter_space.boxes()\n", + "\n", + " print(\n", + " f\"{len(points)} Points (+:{len(results.parameter_space.true_points())}, -:{len(results.parameter_space.false_points())}), {len(boxes)} Boxes (+:{len(results.parameter_space.true_boxes)}, -:{len(results.parameter_space.false_boxes)})\"\n", + " )\n", + " if points and len(points) > 0:\n", + " point: Point = points[-1]\n", + " parameters: Dict[Parameter, float] = results.point_parameters(point)\n", + " results.plot(variables=variables, label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Height\", legend=variables,label_color={\"true\": None})\n", + " print(f\"gamma = {results.parameter_space.points()[0].values['gamma']:.5f}\")\n", + " print(parameters)\n", + " print(results.dataframe([point]))\n", + " else:\n", + " # if there are no points, then we have a box that we found without needing points\n", + "\n", + " box = boxes[0]\n", + " print(json.dumps(box.explain(), indent=4))\n", + "\n", + "\n", "# %load_ext autoreload\n", "# %autoreload 2" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-01 21:11:08,959 - funman.server.worker - INFO - FunmanWorker running...\n", - "2023-11-01 21:11:08,967 - funman.server.worker - INFO - Starting work on: a99c05ac-e487-4b09-8cbf-7dd0dba678ce\n" + "2023-11-02 19:49:09,159 - funman.server.worker - INFO - FunmanWorker running...\n", + "2023-11-02 19:49:09,166 - funman.server.worker - INFO - Starting work on: 7fcc6365-6597-4fbb-9a39-9bc0c9ce1d8e\n", + "2023-11-02 19:49:09,230 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 1, 2, 3, 4, 5, 6, 7]\n", + "2023-11-02 19:49:09,233 - funman_dreal.solver - DEBUG - Created new Solver ...\n", + "2023-11-02 19:49:09,237 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-01 21:11:10,974 - funman.api.run - INFO - Dumping results to ./out/a99c05ac-e487-4b09-8cbf-7dd0dba678ce.json\n", - "2023-11-01 21:11:12,905 - funman.scenario.consistency - INFO - 16:\t[+]\n", - "2023-11-01 21:11:12,912 - funman.server.worker - INFO - Completed work on: a99c05ac-e487-4b09-8cbf-7dd0dba678ce\n", - "2023-11-01 21:11:22,911 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2023-11-01 21:11:22,920 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2023-11-01 21:11:22,923 - funman.server.worker - INFO - Worker.stop() completed.\n" + "2023-11-02 19:49:09,411 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 1 to 2\n", + "2023-11-02 19:49:09,546 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 2 to 3\n", + "2023-11-02 19:49:09,657 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 3 to 4\n", + "2023-11-02 19:49:09,750 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 4 to 5\n", + "2023-11-02 19:49:09,842 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 5 to 6\n", + "2023-11-02 19:49:09,936 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 6 to 7\n", + "2023-11-02 19:49:10,303 - /root/funman/src/funman/search/smt_check.py - DEBUG - Result: {\n", + " \"gamma\": 0.40986908191378846,\n", + " \"timer_t_0\": 0.0,\n", + " \"h_0_0\": 0.1,\n", + " \"h_1_0\": 0.5,\n", + " \"h_2_0\": 1.0,\n", + " \"h_3_0\": 0.5,\n", + " \"h_4_0\": 0.1,\n", + " \"h_0_1\": 0.10320183988623906,\n", + " \"h_1_1\": 0.3943308896353139,\n", + " \"h_2_1\": 0.9983605236723448,\n", + " \"h_3_1\": 0.5008197381638276,\n", + " \"h_4_1\": 0.2032870086422747,\n", + " \"timer_t_1\": 1.0,\n", + " \"h_0_3\": 0.10574313031104446,\n", + " \"h_1_3\": 0.18938680069613598,\n", + " \"h_2_3\": 0.9974483429954066,\n", + " \"h_3_3\": 0.5012758285022967,\n", + " \"h_4_3\": 0.41358796055607583,\n", + " \"timer_t_3\": 3.0,\n", + " \"h_0_5\": 0.1059227102997802,\n", + " \"h_1_5\": -0.033748418359347596,\n", + " \"h_2_5\": 0.9974257781434327,\n", + " \"h_3_5\": 0.5012871109282836,\n", + " \"h_4_5\": 0.6364148814971717,\n", + " \"timer_t_5\": 5.0,\n", + " \"h_0_7\": 0.10577056051387802,\n", + " \"h_1_7\": -0.25673602075952506,\n", + " \"h_2_7\": 0.9979205641019937,\n", + " \"h_3_7\": 0.5010397179490031,\n", + " \"h_4_7\": 0.8592828754633821,\n", + " \"timer_t_7\": 7.0,\n", + " \"h_0_2\": 0.105008212401945,\n", + " \"h_1_2\": 0.29235338618593015,\n", + " \"h_2_2\": 0.9976662986286912,\n", + " \"h_3_2\": 0.5011668506856545,\n", + " \"h_4_2\": 0.3038052520977793,\n", + " \"timer_t_2\": 2.0,\n", + " \"h_0_4\": 0.10591532257548826,\n", + " \"h_1_4\": 0.07766478810558348,\n", + " \"h_2_4\": 0.9974238904370984,\n", + " \"h_3_4\": 0.5012880547814508,\n", + " \"h_4_4\": 0.5250163814194893,\n", + " \"timer_t_4\": 4.0,\n", + " \"h_0_6\": 0.10592185835089431,\n", + " \"h_1_6\": -0.14515290037405454,\n", + " \"h_2_6\": 0.9975021517252168,\n", + " \"h_3_6\": 0.5012489241373915,\n", + " \"h_4_6\": 0.7478049042688778,\n", + " \"timer_t_6\": 6.0\n", + "}\n", + "2023-11-02 19:49:10,310 - funman.scenario.consistency - INFO - 7{7}:\t[+]\n", + "2023-11-02 19:49:10,312 - funman.server.worker - INFO - Completed work on: 7fcc6365-6597-4fbb-9a39-9bc0c9ce1d8e\n", + "2023-11-02 19:49:11,168 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2023-11-02 19:49:11,319 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2023-11-02 19:49:11,321 - funman.server.worker - INFO - Worker.stop() completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "gamma = 0.06325\n", "1 Points (+:1, -:0), 1 Boxes (+:1, -:0)\n", + "gamma = 0.40987\n", "{}\n", " h_0 h_1 h_2 h_3 h_4 id label\n", "time \n", "0.0 0.100000 0.500000 1.000000 0.500000 0.100000 0 true\n", - "1.0 0.100494 0.483693 0.999747 0.500127 0.115940 0 true\n", - "2.0 0.100954 0.467475 0.999523 0.500239 0.131809 0 true\n", - "3.0 0.101380 0.451326 0.999324 0.500338 0.147617 0 true\n", - "4.0 0.101770 0.435144 0.999150 0.500425 0.163459 0 true\n", - "5.0 0.102124 0.419036 0.998998 0.500501 0.179284 0 true\n", - "6.0 0.102443 0.402997 0.998867 0.500567 0.195067 0 true\n", - "7.0 0.102727 0.387021 0.998753 0.500623 0.210811 0 true\n", - "8.0 0.102978 0.371104 0.998656 0.500672 0.226523 0 true\n", - "9.0 0.103199 0.355239 0.998574 0.500713 0.242141 0 true\n", - "10.0 0.103389 0.339511 0.998506 0.500747 0.257780 0 true\n", - "11.0 0.103552 0.323987 0.998448 0.500776 0.273375 0 true\n", - "12.0 0.103691 0.308501 0.998402 0.500799 0.288995 0 true\n", - "13.0 0.103807 0.292805 0.998364 0.500818 0.304477 0 true\n", - "14.0 0.103903 0.277130 0.998334 0.500833 0.320008 0 true\n", - "15.0 0.103980 0.261605 0.998310 0.500845 0.335563 0 true\n" + "1.0 0.103202 0.394331 0.998361 0.500820 0.203287 0 true\n", + "2.0 0.105008 0.292353 0.997666 0.501167 0.303805 0 true\n", + "3.0 0.105743 0.189387 0.997448 0.501276 0.413588 0 true\n", + "4.0 0.105915 0.077665 0.997424 0.501288 0.525016 0 true\n", + "5.0 0.105923 -0.033748 0.997426 0.501287 0.636415 0 true\n", + "6.0 0.105922 -0.145153 0.997502 0.501249 0.747805 0 true\n", + "7.0 0.105771 -0.256736 0.997921 0.501040 0.859283 0 true\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -85,20 +167,18 @@ } ], "source": [ - "RESOURCES = os.path.join(\n", - " os.getcwd(), \"../../resources\"\n", - ")\n", - "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"halfar\")\n", - "MODEL_PATH = os.path.join(EXAMPLE_DIR, \"halfar.json\")\n", - "REQUEST_PATH = os.path.join(EXAMPLE_DIR, \"halfar_request.json\")\n", + "# Use a five point model with no constraints\n", + "\n", + "num_disc = 5\n", + "# MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", + "\n", "\n", "request_dict = {\n", " \"structure_parameters\": [\n", " {\n", " \"name\": \"schedules\",\n", " \"schedules\": [\n", - " {\"timepoints\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]}\n", - " # {\"timepoints\": [0, 10]}\n", + " {\"timepoints\": range(0, 8, 1)}\n", " ],\n", " },\n", " \n", @@ -124,26 +204,900 @@ " # \"variable\": \"h_4\",\n", " # \"interval\": {\"lb\": 0}\n", " # },\n", + " # {\"name\": \"LHS_slope\",\n", + " # \"variables\": [\"h_1\", \"h_0\"],\n", + " # \"weights\": [1, -1],\n", + " # \"additive_bounds\": {\"lb\": 0},\n", + " # \"timepoints\": {\"lb\": 0}\n", + " # }, \n", + " # {\"name\": \"RHS_slope\",\n", + " # \"variables\": [\"h_3\", \"h_4\"],\n", + " # \"weights\": [1, -1],\n", + " # \"additive_bounds\": {\"lb\": 0},\n", + " # \"timepoints\": {\"lb\": 0}\n", + " # }\n", + "\n", + "\n", + " # {\"name\": \"melt_h_5\",\n", + " # \"variable\": \"h_5\",\n", + " # \"interval\": {\"lb\": 0, \"ub\": .8},\n", + " # \"timepoints\": {\"lb\": 5}\n", + " # },\n", + "\n", + " ],\n", + " \"config\": {\n", + " \"use_compartmental_constraints\": False,\n", + " \"normalization_constant\": 1.0,\n", + " \"tolerance\": 1e-1,\n", + " \"verbosity\": 10,\n", + " \"dreal_mcts\": True,\n", + " \"dreal_precision\": 0.1,\n", + " # \"save_smtlib\": \"halfar.smt2\",\n", + " \"substitute_subformulas\": False,\n", + " \"series_approximation_threshold\": None,\n", + " \"dreal_log_level\": \"none\",\n", + " \"profile\": False,\n", + " },\n", + "}\n", + "\n", + "# Use request_dict\n", + "results = Runner().run(\n", + " MODEL_PATH,\n", + " request_dict,\n", + " # REQUEST_PATH,\n", + " description=\"Halfar demo\",\n", + " case_out_dir=\"./out\",\n", + ")\n", + "summarize_results(num_disc, results)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-02 19:51:22,577 - funman.server.worker - INFO - FunmanWorker running...\n", + "2023-11-02 19:51:22,583 - funman.server.worker - INFO - Starting work on: bd259d64-f8bf-4714-b82c-df42991e7149\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-02 19:51:22,660 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 1, 2, 3, 4, 5, 6, 7]\n", + "2023-11-02 19:51:22,663 - funman_dreal.solver - DEBUG - Created new Solver ...\n", + "2023-11-02 19:51:22,680 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 1\n", + "2023-11-02 19:51:22,858 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 1 to 2\n", + "2023-11-02 19:51:22,996 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 2 to 3\n", + "2023-11-02 19:51:23,118 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 3 to 4\n", + "2023-11-02 19:51:23,201 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 4 to 5\n", + "2023-11-02 19:51:23,284 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 5 to 6\n", + "2023-11-02 19:51:23,367 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 6 to 7\n", + "2023-11-02 19:51:23,896 - /root/funman/src/funman/search/smt_check.py - DEBUG - Result: {\n", + " \"assume_non-negative_h_0_7\": true,\n", + " \"gamma\": 0.32624452747923555,\n", + " \"assume_non-negative_h_1_7\": true,\n", + " \"timer_t_0\": 0.0,\n", + " \"assume_non-negative_h_2_7\": true,\n", + " \"h_0_0\": 0.1,\n", + " \"h_1_0\": 0.5,\n", + " \"assume_non-negative_h_3_7\": true,\n", + " \"h_2_0\": 1.0,\n", + " \"h_3_0\": 0.5,\n", + " \"assume_non-negative_h_0\": true,\n", + " \"assume_non-negative_h_1\": true,\n", + " \"assume_non-negative_h_2\": true,\n", + " \"assume_non-negative_h_3\": true,\n", + " \"assume_non-negative_h_4\": true,\n", + " \"h_4_0\": 0.1,\n", + " \"assume_non-negative_h_4_7\": true,\n", + " \"h_0_1\": 0.10254857657443395,\n", + " \"h_1_1\": 0.41626665801877544,\n", + " \"h_2_1\": 0.9986949290710601,\n", + " \"h_3_1\": 0.50065253546447,\n", + " \"h_4_1\": 0.1822194685232148,\n", + " \"timer_t_1\": 1.0,\n", + " \"h_0_5\": 0.10578810416480225,\n", + " \"h_1_5\": 0.14531901074692868,\n", + " \"h_2_5\": 0.9975637595928312,\n", + " \"h_3_5\": 0.5012181202035845,\n", + " \"assume_non-negative_h_0_0\": true,\n", + " \"h_4_5\": 0.46194733879600575,\n", + " \"timer_t_5\": 5.0,\n", + " \"assume_non-negative_h_1_0\": true,\n", + " \"assume_non-negative_h_2_0\": true,\n", + " \"assume_non-negative_h_3_0\": true,\n", + " \"assume_non-negative_h_4_0\": true,\n", + " \"assume_non-negative_h_0_1\": true,\n", + " \"assume_non-negative_h_1_1\": true,\n", + " \"assume_non-negative_h_2_1\": true,\n", + " \"assume_non-negative_h_3_1\": true,\n", + " \"assume_non-negative_h_4_1\": true,\n", + " \"assume_non-negative_h_0_2\": true,\n", + " \"h_0_2\": 0.10424001021977046,\n", + " \"h_1_2\": 0.34708115377370463,\n", + " \"h_2_2\": 0.9980177247274781,\n", + " \"assume_non-negative_h_1_2\": true,\n", + " \"h_3_2\": 0.500991137636261,\n", + " \"h_4_2\": 0.2610201985835755,\n", + " \"timer_t_2\": 2.0,\n", + " \"h_0_6\": 0.10581993875703097,\n", + " \"h_1_6\": 0.07868342238159265,\n", + " \"assume_non-negative_h_2_2\": true,\n", + " \"h_2_6\": 0.9975587743114674,\n", + " \"h_3_6\": 0.5012206128442662,\n", + " \"h_4_6\": 0.5285691432331976,\n", + " \"timer_t_6\": 6.0,\n", + " \"assume_non-negative_h_3_2\": true,\n", + " \"assume_non-negative_h_4_2\": true,\n", + " \"assume_non-negative_h_0_3\": true,\n", + " \"assume_non-negative_h_1_3\": true,\n", + " \"assume_non-negative_h_2_3\": true,\n", + " \"assume_non-negative_h_3_3\": true,\n", + " \"assume_non-negative_h_4_3\": true,\n", + " \"assume_non-negative_h_0_4\": true,\n", + " \"h_0_3\": 0.10519878467955732,\n", + " \"h_1_3\": 0.279208677138237,\n", + " \"h_2_3\": 0.9977130460164052,\n", + " \"h_3_3\": 0.5011434769917975,\n", + " \"h_4_3\": 0.32837141267812897,\n", + " \"timer_t_3\": 3.0,\n", + " \"assume_non-negative_h_1_4\": true,\n", + " \"assume_non-negative_h_2_4\": true,\n", + " \"h_0_7\": 0.10582207239864025,\n", + " \"h_1_7\": 0.0,\n", + " \"h_2_7\": 0.9975602843823834,\n", + " \"h_3_7\": 0.5012198578088083,\n", + " \"assume_non-negative_h_3_4\": true,\n", + " \"h_4_7\": 0.5951964503276509,\n", + " \"timer_t_7\": 7.0,\n", + " \"assume_non-negative_h_4_4\": true,\n", + " \"assume_non-negative_h_0_5\": true,\n", + " \"assume_non-negative_h_1_5\": true,\n", + " \"assume_non-negative_h_2_5\": true,\n", + " \"assume_non-negative_h_3_5\": true,\n", + " \"assume_non-negative_h_4_5\": true,\n", + " \"assume_non-negative_h_0_6\": true,\n", + " \"assume_non-negative_h_1_6\": true,\n", + " \"h_0_4\": 0.10563891204503173,\n", + " \"h_1_4\": 0.21208725493695835,\n", + " \"h_2_4\": 0.9975945302506836,\n", + " \"h_3_4\": 0.5012027348746582,\n", + " \"h_4_4\": 0.395256217644169,\n", + " \"timer_t_4\": 4.0,\n", + " \"assume_non-negative_h_2_6\": true,\n", + " \"assume_non-negative_h_3_6\": true,\n", + " \"assume_non-negative_h_4_6\": true\n", + "}\n", + "2023-11-02 19:51:23,901 - funman.scenario.consistency - INFO - 7{7}:\t[+]\n", + "2023-11-02 19:51:23,903 - funman.server.worker - INFO - Completed work on: bd259d64-f8bf-4714-b82c-df42991e7149\n", + "2023-11-02 19:51:24,585 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2023-11-02 19:51:24,907 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2023-11-02 19:51:24,910 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 Points (+:1, -:0), 1 Boxes (+:1, -:0)\n", + "gamma = 0.32624\n", + "{}\n", + " assume_non-negative_h_0 assume_non-negative_h_1 \\\n", + "time \n", + "0.0 1.0 1.0 \n", + "1.0 1.0 1.0 \n", + "2.0 1.0 1.0 \n", + "3.0 1.0 1.0 \n", + "4.0 1.0 1.0 \n", + "5.0 1.0 1.0 \n", + "6.0 1.0 1.0 \n", + "7.0 1.0 1.0 \n", + "\n", + " assume_non-negative_h_2 assume_non-negative_h_3 \\\n", + "time \n", + "0.0 1.0 1.0 \n", + "1.0 1.0 1.0 \n", + "2.0 1.0 1.0 \n", + "3.0 1.0 1.0 \n", + "4.0 1.0 1.0 \n", + "5.0 1.0 1.0 \n", + "6.0 1.0 1.0 \n", + "7.0 1.0 1.0 \n", + "\n", + " assume_non-negative_h_4 h_0 h_1 h_2 h_3 \\\n", + "time \n", + "0.0 1.0 0.100000 0.500000 1.000000 0.500000 \n", + "1.0 1.0 0.102549 0.416267 0.998695 0.500653 \n", + "2.0 1.0 0.104240 0.347081 0.998018 0.500991 \n", + "3.0 1.0 0.105199 0.279209 0.997713 0.501143 \n", + "4.0 1.0 0.105639 0.212087 0.997595 0.501203 \n", + "5.0 1.0 0.105788 0.145319 0.997564 0.501218 \n", + "6.0 1.0 0.105820 0.078683 0.997559 0.501221 \n", + "7.0 1.0 0.105822 0.000000 0.997560 0.501220 \n", + "\n", + " h_4 id label \n", + "time \n", + "0.0 0.100000 0 true \n", + "1.0 0.182219 0 true \n", + "2.0 0.261020 0 true \n", + "3.0 0.328371 0 true \n", + "4.0 0.395256 0 true \n", + "5.0 0.461947 0 true \n", + "6.0 0.528569 0 true \n", + "7.0 0.595196 0 true \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use a five point model with no constraints\n", + "\n", + "num_disc = 5\n", + "# MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", + "\n", + "\n", + "request_dict = {\n", + " \"structure_parameters\": [\n", + " {\n", + " \"name\": \"schedules\",\n", + " \"schedules\": [\n", + " {\"timepoints\": range(0, 8, 1)}\n", + " ],\n", + " },\n", + " \n", + " ],\n", + " \"constraints\": [\n", + " {\"name\": \"non-negative_h_0\",\n", + " \"variable\": \"h_0\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non-negative_h_1\",\n", + " \"variable\": \"h_1\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non-negative_h_2\",\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non-negative_h_3\",\n", + " \"variable\": \"h_3\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non-negative_h_4\",\n", + " \"variable\": \"h_4\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " # {\"name\": \"LHS_slope\",\n", + " # \"variables\": [\"h_1\", \"h_0\"],\n", + " # \"weights\": [1, -1],\n", + " # \"additive_bounds\": {\"lb\": 0},\n", + " # \"timepoints\": {\"lb\": 0}\n", + " # }, \n", + " # {\"name\": \"RHS_slope\",\n", + " # \"variables\": [\"h_3\", \"h_4\"],\n", + " # \"weights\": [1, -1],\n", + " # \"additive_bounds\": {\"lb\": 0},\n", + " # \"timepoints\": {\"lb\": 0}\n", + " # }\n", + "\n", + "\n", + " # {\"name\": \"melt_h_5\",\n", + " # \"variable\": \"h_5\",\n", + " # \"interval\": {\"lb\": 0, \"ub\": .8},\n", + " # \"timepoints\": {\"lb\": 5}\n", + " # },\n", + "\n", + " ],\n", + " \"config\": {\n", + " \"use_compartmental_constraints\": False,\n", + " \"normalization_constant\": 1.0,\n", + " \"tolerance\": 1e-1,\n", + " \"verbosity\": 10,\n", + " \"dreal_mcts\": True,\n", + " \"dreal_precision\": 0.1,\n", + " # \"save_smtlib\": \"halfar.smt2\",\n", + " \"substitute_subformulas\": False,\n", + " \"series_approximation_threshold\": None,\n", + " \"dreal_log_level\": \"none\",\n", + " \"profile\": False,\n", + " },\n", + "}\n", + "\n", + "# Use request_dict\n", + "results = Runner().run(\n", + " MODEL_PATH,\n", + " request_dict,\n", + " # REQUEST_PATH,\n", + " description=\"Halfar demo\",\n", + " case_out_dir=\"./out\",\n", + ")\n", + "summarize_results(num_disc, results)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-02 19:52:46,225 - funman.server.worker - INFO - FunmanWorker running...\n", + "2023-11-02 19:52:46,234 - funman.server.worker - INFO - Starting work on: 49fae446-97b0-41b1-ad13-4cf076fdfd49\n", + "2023-11-02 19:52:46,339 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 1, 2, 3, 4, 5, 6, 7]\n", + "2023-11-02 19:52:46,342 - funman_dreal.solver - DEBUG - Created new Solver ...\n", + "2023-11-02 19:52:46,365 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 1\n", + "2023-11-02 19:52:46,558 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 1 to 2\n", + "2023-11-02 19:52:46,710 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 2 to 3\n", + "2023-11-02 19:52:46,846 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 3 to 4\n", + "2023-11-02 19:52:46,991 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 4 to 5\n", + "2023-11-02 19:52:47,117 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 5 to 6\n", + "2023-11-02 19:52:47,207 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 6 to 7\n", + "2023-11-02 19:52:47,976 - /root/funman/src/funman/search/smt_check.py - DEBUG - Result: {\n", + " \"assume_non-negative_h_0_7\": true,\n", + " \"gamma\": 0.11338518015152209,\n", + " \"assume_non-negative_h_1_7\": true,\n", + " \"timer_t_0\": 0.0,\n", + " \"assume_non-negative_h_2_7\": true,\n", + " \"h_0_0\": 0.1,\n", + " \"h_1_0\": 0.5,\n", + " \"assume_non-negative_h_3_7\": true,\n", + " \"h_2_0\": 1.0,\n", + " \"h_3_0\": 0.5,\n", + " \"assume_non-negative_h_0\": true,\n", + " \"assume_non-negative_h_1\": true,\n", + " \"assume_non-negative_h_2\": true,\n", + " \"assume_non-negative_h_3\": true,\n", + " \"assume_non-negative_h_4\": true,\n", + " \"h_4_0\": 0.1,\n", + " \"assume_non-negative_h_4_7\": true,\n", + " \"h_0_1\": 0.10088574915341848,\n", + " \"h_1_1\": 0.470767955808701,\n", + " \"h_2_1\": 0.9995464592793939,\n", + " \"h_3_1\": 0.500226770360303,\n", + " \"h_4_1\": 0.12857306539818358,\n", + " \"timer_t_1\": 1.0,\n", + " \"h_0_5\": 0.10406190969959184,\n", + " \"h_1_5\": 0.38943248327309743,\n", + " \"h_2_5\": 0.9983807112198245,\n", + " \"h_3_5\": 0.5008096443900878,\n", + " \"assume_non-negative_h_0_0\": true,\n", + " \"h_4_5\": 0.2621967315142081,\n", + " \"timer_t_5\": 5.0,\n", + " \"assume_non-negative_h_1_0\": true,\n", + " \"assume_non-negative_h_2_0\": true,\n", + " \"assume_non-negative_h_3_0\": true,\n", + " \"assume_non-negative_h_4_0\": true,\n", + " \"assume_LHS_slope (h_0 <= h_1)\": true,\n", + " \"assume_RHS_slope (h_3 >= h_4)\": true,\n", + " \"assume_non-negative_h_0_1\": true,\n", + " \"assume_LHS_slope (h_0 <= h_1)_0\": true,\n", + " \"assume_non-negative_h_1_1\": true,\n", + " \"assume_RHS_slope (h_3 >= h_4)_0\": true,\n", + " \"assume_non-negative_h_2_1\": true,\n", + " \"assume_non-negative_h_3_1\": true,\n", + " \"assume_LHS_slope (h_0 <= h_1)_1\": true,\n", + " \"assume_non-negative_h_4_1\": true,\n", + " \"assume_RHS_slope (h_3 >= h_4)_1\": true,\n", + " \"assume_non-negative_h_0_2\": true,\n", + " \"h_0_2\": 0.10174514386870702,\n", + " \"h_1_2\": 0.44170825067816,\n", + " \"h_2_2\": 0.9991645889433753,\n", + " \"assume_non-negative_h_1_2\": true,\n", + " \"h_3_2\": 0.5004177055283123,\n", + " \"h_4_2\": 0.15696431098144537,\n", + " \"assume_LHS_slope (h_0 <= h_1)_2\": true,\n", + " \"timer_t_2\": 2.0,\n", + " \"h_0_6\": 0.10468801205221129,\n", + " \"assume_non-negative_h_2_2\": true,\n", + " \"h_1_6\": 0.37374718500912685,\n", + " \"h_2_6\": 0.9982523988411337,\n", + " \"h_3_6\": 0.5008738005794332,\n", + " \"assume_RHS_slope (h_3 >= h_4)_2\": true,\n", + " \"h_4_6\": 0.3025008738599219,\n", + " \"assume_non-negative_h_3_2\": true,\n", + " \"timer_t_6\": 6.0,\n", + " \"assume_non-negative_h_4_2\": true,\n", + " \"assume_LHS_slope (h_0 <= h_1)_3\": true,\n", + " \"assume_non-negative_h_0_3\": true,\n", + " \"assume_RHS_slope (h_3 >= h_4)_3\": true,\n", + " \"assume_non-negative_h_1_3\": true,\n", + " \"assume_non-negative_h_2_3\": true,\n", + " \"assume_LHS_slope (h_0 <= h_1)_4\": true,\n", + " \"assume_non-negative_h_3_3\": true,\n", + " \"assume_RHS_slope (h_3 >= h_4)_4\": true,\n", + " \"assume_non-negative_h_4_3\": true,\n", + " \"assume_LHS_slope (h_0 <= h_1)_5\": true,\n", + " \"assume_non-negative_h_0_4\": true,\n", + " \"h_0_3\": 0.10258241032671941,\n", + " \"h_1_3\": 0.4211152559456721,\n", + " \"h_2_3\": 0.9988442339988992,\n", + " \"h_3_3\": 0.5005778830005504,\n", + " \"h_4_3\": 0.1852076462251187,\n", + " \"timer_t_3\": 3.0,\n", + " \"assume_non-negative_h_1_4\": true,\n", + " \"assume_non-negative_h_2_4\": true,\n", + " \"assume_RHS_slope (h_3 >= h_4)_5\": true,\n", + " \"h_0_7\": 0.10524268489337923,\n", + " \"h_1_7\": 0.34536219591681205,\n", + " \"assume_LHS_slope (h_0 <= h_1)_6\": true,\n", + " \"assume_non-negative_h_3_4\": true,\n", + " \"h_2_7\": 0.998173633224059,\n", + " \"h_3_7\": 0.5009131833879705,\n", + " \"h_4_7\": 0.342745698386681,\n", + " \"timer_t_7\": 7.0,\n", + " \"assume_non-negative_h_4_4\": true,\n", + " \"assume_RHS_slope (h_3 >= h_4)_6\": true,\n", + " \"assume_non-negative_h_0_5\": true,\n", + " \"assume_LHS_slope (h_0 <= h_1)_7\": true,\n", + " \"assume_non-negative_h_1_5\": true,\n", + " \"assume_non-negative_h_2_5\": true,\n", + " \"assume_RHS_slope (h_3 >= h_4)_7\": true,\n", + " \"assume_non-negative_h_3_5\": true,\n", + " \"assume_non-negative_h_4_5\": true,\n", + " \"assume_non-negative_h_0_6\": true,\n", + " \"assume_non-negative_h_1_6\": true,\n", + " \"h_0_4\": 0.10336099934175262,\n", + " \"h_1_4\": 0.40521313922649793,\n", + " \"h_2_4\": 0.9985762590778945,\n", + " \"h_3_4\": 0.5007118704610528,\n", + " \"h_4_4\": 0.22180641277713464,\n", + " \"timer_t_4\": 4.0,\n", + " \"assume_non-negative_h_2_6\": true,\n", + " \"assume_non-negative_h_3_6\": true,\n", + " \"assume_non-negative_h_4_6\": true\n", + "}\n", + "2023-11-02 19:52:47,980 - funman.scenario.consistency - INFO - 7{7}:\t[+]\n", + "2023-11-02 19:52:47,982 - funman.server.worker - INFO - Completed work on: 49fae446-97b0-41b1-ad13-4cf076fdfd49\n", + "2023-11-02 19:52:48,235 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2023-11-02 19:52:48,486 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2023-11-02 19:52:48,489 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 Points (+:1, -:0), 1 Boxes (+:1, -:0)\n", + "gamma = 0.11339\n", + "{}\n", + " assume_non-negative_h_0 assume_non-negative_h_1 \\\n", + "time \n", + "0.0 1.0 1.0 \n", + "1.0 1.0 1.0 \n", + "2.0 1.0 1.0 \n", + "3.0 1.0 1.0 \n", + "4.0 1.0 1.0 \n", + "5.0 1.0 1.0 \n", + "6.0 1.0 1.0 \n", + "7.0 1.0 1.0 \n", + "\n", + " assume_non-negative_h_2 assume_non-negative_h_3 \\\n", + "time \n", + "0.0 1.0 1.0 \n", + "1.0 1.0 1.0 \n", + "2.0 1.0 1.0 \n", + "3.0 1.0 1.0 \n", + "4.0 1.0 1.0 \n", + "5.0 1.0 1.0 \n", + "6.0 1.0 1.0 \n", + "7.0 1.0 1.0 \n", + "\n", + " assume_non-negative_h_4 h_0 h_1 h_2 h_3 \\\n", + "time \n", + "0.0 1.0 0.100000 0.500000 1.000000 0.500000 \n", + "1.0 1.0 0.100886 0.470768 0.999546 0.500227 \n", + "2.0 1.0 0.101745 0.441708 0.999165 0.500418 \n", + "3.0 1.0 0.102582 0.421115 0.998844 0.500578 \n", + "4.0 1.0 0.103361 0.405213 0.998576 0.500712 \n", + "5.0 1.0 0.104062 0.389432 0.998381 0.500810 \n", + "6.0 1.0 0.104688 0.373747 0.998252 0.500874 \n", + "7.0 1.0 0.105243 0.345362 0.998174 0.500913 \n", + "\n", + " h_4 id label \n", + "time \n", + "0.0 0.100000 0 true \n", + "1.0 0.128573 0 true \n", + "2.0 0.156964 0 true \n", + "3.0 0.185208 0 true \n", + "4.0 0.221806 0 true \n", + "5.0 0.262197 0 true \n", + "6.0 0.302501 0 true \n", + "7.0 0.342746 0 true \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use a five point model with no constraints\n", + "\n", + "num_disc = 5\n", + "# MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", + "\n", + "\n", + "request_dict = {\n", + " \"structure_parameters\": [\n", + " {\n", + " \"name\": \"schedules\",\n", + " \"schedules\": [\n", + " {\"timepoints\": range(0, 8, 1)}\n", + " ],\n", + " },\n", + " \n", + " ],\n", + " \"constraints\": [\n", + " {\"name\": \"non-negative_h_0\",\n", + " \"variable\": \"h_0\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non-negative_h_1\",\n", + " \"variable\": \"h_1\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non-negative_h_2\",\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non-negative_h_3\",\n", + " \"variable\": \"h_3\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non-negative_h_4\",\n", + " \"variable\": \"h_4\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"LHS_slope (h_0 <= h_1)\",\n", + " \"variables\": [\"h_1\", \"h_0\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }, \n", + " {\"name\": \"RHS_slope (h_3 >= h_4)\",\n", + " \"variables\": [\"h_3\", \"h_4\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }\n", + "\n", + "\n", + " # {\"name\": \"melt_h_5\",\n", + " # \"variable\": \"h_5\",\n", + " # \"interval\": {\"lb\": 0, \"ub\": .8},\n", + " # \"timepoints\": {\"lb\": 5}\n", + " # },\n", + "\n", + " ],\n", + " \"config\": {\n", + " \"use_compartmental_constraints\": False,\n", + " \"normalization_constant\": 1.0,\n", + " \"tolerance\": 1e-1,\n", + " \"verbosity\": 10,\n", + " \"dreal_mcts\": True,\n", + " \"dreal_precision\": 0.1,\n", + " # \"save_smtlib\": \"halfar.smt2\",\n", + " \"substitute_subformulas\": False,\n", + " \"series_approximation_threshold\": None,\n", + " \"dreal_log_level\": \"none\",\n", + " \"profile\": False,\n", + " },\n", + "}\n", + "\n", + "# Use request_dict\n", + "results = Runner().run(\n", + " MODEL_PATH,\n", + " request_dict,\n", + " # REQUEST_PATH,\n", + " description=\"Halfar demo\",\n", + " case_out_dir=\"./out\",\n", + ")\n", + "summarize_results(num_disc, results)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-02 19:57:26,405 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 1, 2, 3, 4, 5, 6, 7]\n", + "2023-11-02 19:57:26,410 - funman_dreal.solver - DEBUG - Created new Solver ...\n", + "2023-11-02 19:57:26,436 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 1\n", + "2023-11-02 19:57:27,041 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 1 to 2\n", + "2023-11-02 19:57:27,201 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 2 to 3\n", + "2023-11-02 19:57:27,366 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 3 to 4\n", + "2023-11-02 19:57:27,532 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 4 to 5\n", + "2023-11-02 19:57:27,667 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 5 to 6\n", + "2023-11-02 19:57:27,777 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 6 to 7\n", + "2023-11-02 19:57:28,597 - funman.api.run - INFO - Dumping results to ./out/0a73ca9f-dedc-4972-a109-bb31cfbd6e1e.json\n", + "2023-11-02 19:57:28,711 - funman.scenario.consistency - INFO - 7{7}:\t[-]\n", + "2023-11-02 19:57:28,713 - funman.server.worker - INFO - Completed work on: 0a73ca9f-dedc-4972-a109-bb31cfbd6e1e\n", + "2023-11-02 19:57:38,627 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2023-11-02 19:57:38,722 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2023-11-02 19:57:38,724 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Points (+:0, -:0), 1 Boxes (+:0, -:1)\n", + "{\n", + " \"box\": {\n", + " \"schedules\": {\n", + " \"lb\": -1.7976931348623157e+308,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"gamma\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.0,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"timestep\": {\n", + " \"lb\": 7.0,\n", + " \"ub\": 7.0,\n", + " \"closed_upper_bound\": true\n", + " }\n", + " },\n", + " \"relevant_constraints\": [\n", + " {\n", + " \"soft\": true,\n", + " \"name\": \"non_negative_h_0\",\n", + " \"timepoints\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"variable\": \"h_0\",\n", + " \"interval\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " }\n", + " },\n", + " {\n", + " \"soft\": true,\n", + " \"name\": \"non_negative_h_1\",\n", + " \"timepoints\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"variable\": \"h_1\",\n", + " \"interval\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " }\n", + " },\n", + " {\n", + " \"soft\": true,\n", + " \"name\": \"non_negative_h_2\",\n", + " \"timepoints\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " }\n", + " },\n", + " {\n", + " \"soft\": true,\n", + " \"name\": \"non_negative_h_3\",\n", + " \"timepoints\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"variable\": \"h_3\",\n", + " \"interval\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " }\n", + " },\n", + " {\n", + " \"soft\": true,\n", + " \"name\": \"non_negative_h_4\",\n", + " \"timepoints\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"variable\": \"h_4\",\n", + " \"interval\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " }\n", + " },\n", + " {\n", + " \"soft\": true,\n", + " \"name\": \"LHS_slope\",\n", + " \"timepoints\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"additive_bounds\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"variables\": [\n", + " \"h_1\",\n", + " \"h_0\"\n", + " ],\n", + " \"weights\": [\n", + " 1,\n", + " -1\n", + " ]\n", + " },\n", + " {\n", + " \"soft\": true,\n", + " \"name\": \"RHS_slope\",\n", + " \"timepoints\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"additive_bounds\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"variables\": [\n", + " \"h_3\",\n", + " \"h_4\"\n", + " ],\n", + " \"weights\": [\n", + " 1,\n", + " -1\n", + " ]\n", + " },\n", + " {\n", + " \"soft\": true,\n", + " \"name\": \"melt_h_2\",\n", + " \"timepoints\": {\n", + " \"lb\": 5.0,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 0.8,\n", + " \"closed_upper_bound\": false\n", + " }\n", + " }\n", + " ],\n", + " \"expression\": \"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_melt_h_2 & assume_RHS_slope) & assume_LHS_slope) & assume_non_negative_h_4) & assume_non_negative_h_3) & assume_non_negative_h_2) & assume_non_negative_h_1) & assume_non_negative_h_0) & disj32) & disj38) & disj40) & disj42) & disj48) & disj50) & disj52) & disj54) & disj56) & disj58) & disj60) & disj62) & disj64) & disj66) & disj68) & disj70) & disj72) & disj74) & disj76) & disj78) & disj80) & disj82) & disj84) & disj86) & disj88) & disj94) & disj96) & disj98) & disj100) & disj102) & disj108) & disj110) & disj112) & disj114) & disj116) & disj128) & disj129) & disj130) & disj133) & disj134) & disj135) & disj136) & disj137) & disj138) & disj139) & disj140) & disj141) & disj142) & disj143) & disj144) & disj145) & disj146) & disj147) & disj148) & disj149) & disj150) & disj151) & disj152) & disj153) & disj154) & disj155) & disj156) & disj157) & disj164) & disj165) & disj166) & disj167) & disj168) & disj169) & disj176) & (h_4_5 = ((h_4_4 - ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0)) - (2.0 * ((((gamma * h_2_4) ^ 5.0) * (h_3_4 - h_2_4)) ^ 3.0))))) & (h_3_5 = (h_3_4 - ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0)))) & (h_2_5 = (h_2_4 + (2.0 * ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0))))) & (h_4_4 = ((h_4_3 - ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0)) - (2.0 * ((((gamma * h_2_3) ^ 5.0) * (h_3_3 - h_2_3)) ^ 3.0))))) & (h_3_4 = (h_3_3 - ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0)))) & (h_2_4 = (h_2_3 + (2.0 * ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0))))) & (h_1_4 = (((h_1_3 + (2.0 * ((((gamma * h_2_3) ^ 5.0) * (h_3_3 - h_2_3)) ^ 3.0))) - (2.0 * ((((gamma * h_1_3) ^ 5.0) * (h_2_3 - h_1_3)) ^ 3.0))) + ((((gamma * h_0_3) ^ 5.0) * (h_1_3 - h_0_3)) ^ 3.0)))) & (h_0_4 = ((h_0_3 + (2.0 * ((((gamma * h_1_3) ^ 5.0) * (h_2_3 - h_1_3)) ^ 3.0))) - ((((gamma * h_0_3) ^ 5.0) * (h_1_3 - h_0_3)) ^ 3.0)))) & (h_4_3 = ((h_4_2 - ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0)) - (2.0 * ((((gamma * h_2_2) ^ 5.0) * (h_3_2 - h_2_2)) ^ 3.0))))) & (h_3_3 = (h_3_2 - ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0)))) & (h_2_3 = (h_2_2 + (2.0 * ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0))))) & (h_1_3 = (((h_1_2 + (2.0 * ((((gamma * h_2_2) ^ 5.0) * (h_3_2 - h_2_2)) ^ 3.0))) - (2.0 * ((((gamma * h_1_2) ^ 5.0) * (h_2_2 - h_1_2)) ^ 3.0))) + ((((gamma * h_0_2) ^ 5.0) * (h_1_2 - h_0_2)) ^ 3.0)))) & (h_0_3 = ((h_0_2 + (2.0 * ((((gamma * h_1_2) ^ 5.0) * (h_2_2 - h_1_2)) ^ 3.0))) - ((((gamma * h_0_2) ^ 5.0) * (h_1_2 - h_0_2)) ^ 3.0)))) & (h_4_2 = ((h_4_1 - ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0)) - (2.0 * ((((gamma * h_2_1) ^ 5.0) * (h_3_1 - h_2_1)) ^ 3.0))))) & (h_3_2 = (h_3_1 - ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0)))) & (h_2_2 = (h_2_1 + (2.0 * ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0))))) & (h_1_2 = (((h_1_1 + (2.0 * ((((gamma * h_2_1) ^ 5.0) * (h_3_1 - h_2_1)) ^ 3.0))) - (2.0 * ((((gamma * h_1_1) ^ 5.0) * (h_2_1 - h_1_1)) ^ 3.0))) + ((((gamma * h_0_1) ^ 5.0) * (h_1_1 - h_0_1)) ^ 3.0)))) & (h_0_2 = ((h_0_1 + (2.0 * ((((gamma * h_1_1) ^ 5.0) * (h_2_1 - h_1_1)) ^ 3.0))) - ((((gamma * h_0_1) ^ 5.0) * (h_1_1 - h_0_1)) ^ 3.0)))) & (h_4_1 = ((h_4_0 - ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0)) - (2.0 * ((((gamma * h_2_0) ^ 5.0) * (h_3_0 - h_2_0)) ^ 3.0))))) & (h_3_1 = (h_3_0 - ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0)))) & (h_2_1 = (h_2_0 + (2.0 * ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0))))) & (h_1_1 = (((h_1_0 + (2.0 * ((((gamma * h_2_0) ^ 5.0) * (h_3_0 - h_2_0)) ^ 3.0))) - (2.0 * ((((gamma * h_1_0) ^ 5.0) * (h_2_0 - h_1_0)) ^ 3.0))) + ((((gamma * h_0_0) ^ 5.0) * (h_1_0 - h_0_0)) ^ 3.0)))) & (h_0_1 = ((h_0_0 + (2.0 * ((((gamma * h_1_0) ^ 5.0) * (h_2_0 - h_1_0)) ^ 3.0))) - ((((gamma * h_0_0) ^ 5.0) * (h_1_0 - h_0_0)) ^ 3.0)))) & (h_4_0 = 10000000000000001/100000000000000000)) & (h_3_0 = 1/2)) & (h_2_0 = 1.0)) & (h_1_0 = 1/2)) & (h_0_0 = 10000000000000001/100000000000000000)) & (gamma < 1.0)) & ((assume_melt_h_2_5 | (! assume_melt_h_2)) | (! disj32))) & ((assume_non_negative_h_4_5 | (! assume_non_negative_h_4)) | (! disj38))) & ((assume_non_negative_h_3_5 | (! assume_non_negative_h_3)) | (! disj40))) & ((assume_non_negative_h_2_5 | (! assume_non_negative_h_2)) | (! disj42))) & ((assume_RHS_slope_4 | (! assume_RHS_slope)) | (! disj48))) & ((assume_LHS_slope_4 | (! assume_LHS_slope)) | (! disj50))) & ((assume_non_negative_h_4_4 | (! assume_non_negative_h_4)) | (! disj52))) & ((assume_non_negative_h_3_4 | (! assume_non_negative_h_3)) | (! disj54))) & ((assume_non_negative_h_2_4 | (! assume_non_negative_h_2)) | (! disj56))) & ((assume_non_negative_h_1_4 | (! assume_non_negative_h_1)) | (! disj58))) & ((assume_non_negative_h_0_4 | (! assume_non_negative_h_0)) | (! disj60))) & ((assume_RHS_slope_3 | (! assume_RHS_slope)) | (! disj62))) & ((assume_LHS_slope_3 | (! assume_LHS_slope)) | (! disj64))) & ((assume_non_negative_h_4_3 | (! assume_non_negative_h_4)) | (! disj66))) & ((assume_non_negative_h_3_3 | (! assume_non_negative_h_3)) | (! disj68))) & ((assume_non_negative_h_2_3 | (! assume_non_negative_h_2)) | (! disj70))) & ((assume_non_negative_h_1_3 | (! assume_non_negative_h_1)) | (! disj72))) & ((assume_non_negative_h_0_3 | (! assume_non_negative_h_0)) | (! disj74))) & ((assume_RHS_slope_2 | (! assume_RHS_slope)) | (! disj76))) & ((assume_LHS_slope_2 | (! assume_LHS_slope)) | (! disj78))) & ((assume_non_negative_h_4_2 | (! assume_non_negative_h_4)) | (! disj80))) & ((assume_non_negative_h_3_2 | (! assume_non_negative_h_3)) | (! disj82))) & ((assume_non_negative_h_2_2 | (! assume_non_negative_h_2)) | (! disj84))) & ((assume_non_negative_h_1_2 | (! assume_non_negative_h_1)) | (! disj86))) & ((assume_non_negative_h_0_2 | (! assume_non_negative_h_0)) | (! disj88))) & ((assume_non_negative_h_4_1 | (! assume_non_negative_h_4)) | (! disj94))) & ((assume_non_negative_h_3_1 | (! assume_non_negative_h_3)) | (! disj96))) & ((assume_non_negative_h_2_1 | (! assume_non_negative_h_2)) | (! disj98))) & ((assume_non_negative_h_1_1 | (! assume_non_negative_h_1)) | (! disj100))) & ((assume_non_negative_h_0_1 | (! assume_non_negative_h_0)) | (! disj102))) & ((assume_non_negative_h_4_0 | (! assume_non_negative_h_4)) | (! disj108))) & ((assume_non_negative_h_3_0 | (! assume_non_negative_h_3)) | (! disj110))) & ((assume_non_negative_h_2_0 | (! assume_non_negative_h_2)) | (! disj112))) & ((assume_non_negative_h_1_0 | (! assume_non_negative_h_1)) | (! disj114))) & ((assume_non_negative_h_0_0 | (! assume_non_negative_h_0)) | (! disj116))) & ((conj5 | (! assume_melt_h_2_5)) | (! disj176))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((h_2_5 < 0.0) | (h_4_5 < 0.0)) | (h_3_5 < 0.0)) | (((-1.0 * h_4_4) + h_3_4) < 0.0)) | ((h_1_4 - h_0_4) < 0.0)) | (h_4_4 < 0.0)) | (h_3_4 < 0.0)) | (h_2_4 < 0.0)) | (h_1_4 < 0.0)) | (h_0_4 < 0.0)) | (((-1.0 * h_4_3) + h_3_3) < 0.0)) | ((h_1_3 - h_0_3) < 0.0)) | (h_4_3 < 0.0)) | (h_3_3 < 0.0)) | (h_2_3 < 0.0)) | (h_1_3 < 0.0)) | (h_0_3 < 0.0)) | (((-1.0 * h_4_2) + h_3_2) < 0.0)) | ((h_1_2 - h_0_2) < 0.0)) | (h_4_2 < 0.0)) | (h_3_2 < 0.0)) | (h_2_2 < 0.0)) | (h_1_2 < 0.0)) | (h_0_2 < 0.0)) | (h_4_1 < 0.0)) | (h_3_1 < 0.0)) | (h_2_1 < 0.0)) | (h_1_1 < 0.0)) | (h_0_1 < 0.0)) | (h_4_0 < 0.0)) | (h_3_0 < 0.0)) | (h_2_0 < 0.0)) | (h_1_0 < 0.0)) | (h_0_0 < 0.0)) | (gamma < 0.0)) | (! (h_2_5 < 20000000000000001/25000000000000000))) | (! (h_4_5 = ((h_4_4 - ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0)) - (2.0 * ((((gamma * h_2_4) ^ 5.0) * (h_3_4 - h_2_4)) ^ 3.0)))))) | (! (h_3_5 = (h_3_4 - ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0))))) | (! (h_2_5 = (h_2_4 + (2.0 * ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0)))))) | (! (h_4_4 = ((h_4_3 - ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0)) - (2.0 * ((((gamma * h_2_3) ^ 5.0) * (h_3_3 - h_2_3)) ^ 3.0)))))) | (! (h_3_4 = (h_3_3 - ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0))))) | (! (h_2_4 = (h_2_3 + (2.0 * ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0)))))) | (! (h_1_4 = (((h_1_3 + (2.0 * ((((gamma * h_2_3) ^ 5.0) * (h_3_3 - h_2_3)) ^ 3.0))) - (2.0 * ((((gamma * h_1_3) ^ 5.0) * (h_2_3 - h_1_3)) ^ 3.0))) + ((((gamma * h_0_3) ^ 5.0) * (h_1_3 - h_0_3)) ^ 3.0))))) | (! (h_0_4 = ((h_0_3 + (2.0 * ((((gamma * h_1_3) ^ 5.0) * (h_2_3 - h_1_3)) ^ 3.0))) - ((((gamma * h_0_3) ^ 5.0) * (h_1_3 - h_0_3)) ^ 3.0))))) | (! (h_4_3 = ((h_4_2 - ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0)) - (2.0 * ((((gamma * h_2_2) ^ 5.0) * (h_3_2 - h_2_2)) ^ 3.0)))))) | (! (h_3_3 = (h_3_2 - ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0))))) | (! (h_2_3 = (h_2_2 + (2.0 * ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0)))))) | (! (h_1_3 = (((h_1_2 + (2.0 * ((((gamma * h_2_2) ^ 5.0) * (h_3_2 - h_2_2)) ^ 3.0))) - (2.0 * ((((gamma * h_1_2) ^ 5.0) * (h_2_2 - h_1_2)) ^ 3.0))) + ((((gamma * h_0_2) ^ 5.0) * (h_1_2 - h_0_2)) ^ 3.0))))) | (! (h_0_3 = ((h_0_2 + (2.0 * ((((gamma * h_1_2) ^ 5.0) * (h_2_2 - h_1_2)) ^ 3.0))) - ((((gamma * h_0_2) ^ 5.0) * (h_1_2 - h_0_2)) ^ 3.0))))) | (! (h_4_2 = ((h_4_1 - ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0)) - (2.0 * ((((gamma * h_2_1) ^ 5.0) * (h_3_1 - h_2_1)) ^ 3.0)))))) | (! (h_3_2 = (h_3_1 - ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0))))) | (! (h_2_2 = (h_2_1 + (2.0 * ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0)))))) | (! (h_1_2 = (((h_1_1 + (2.0 * ((((gamma * h_2_1) ^ 5.0) * (h_3_1 - h_2_1)) ^ 3.0))) - (2.0 * ((((gamma * h_1_1) ^ 5.0) * (h_2_1 - h_1_1)) ^ 3.0))) + ((((gamma * h_0_1) ^ 5.0) * (h_1_1 - h_0_1)) ^ 3.0))))) | (! (h_0_2 = ((h_0_1 + (2.0 * ((((gamma * h_1_1) ^ 5.0) * (h_2_1 - h_1_1)) ^ 3.0))) - ((((gamma * h_0_1) ^ 5.0) * (h_1_1 - h_0_1)) ^ 3.0))))) | (! (h_4_1 = ((h_4_0 - ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0)) - (2.0 * ((((gamma * h_2_0) ^ 5.0) * (h_3_0 - h_2_0)) ^ 3.0)))))) | (! (h_3_1 = (h_3_0 - ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0))))) | (! (h_2_1 = (h_2_0 + (2.0 * ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0)))))) | (! (h_1_1 = (((h_1_0 + (2.0 * ((((gamma * h_2_0) ^ 5.0) * (h_3_0 - h_2_0)) ^ 3.0))) - (2.0 * ((((gamma * h_1_0) ^ 5.0) * (h_2_0 - h_1_0)) ^ 3.0))) + ((((gamma * h_0_0) ^ 5.0) * (h_1_0 - h_0_0)) ^ 3.0))))) | (! (h_0_1 = ((h_0_0 + (2.0 * ((((gamma * h_1_0) ^ 5.0) * (h_2_0 - h_1_0)) ^ 3.0))) - ((((gamma * h_0_0) ^ 5.0) * (h_1_0 - h_0_0)) ^ 3.0))))) | (! (h_4_0 = 10000000000000001/100000000000000000))) | (! (h_3_0 = 1/2))) | (! (h_2_0 = 1.0))) | (! (h_1_0 = 1/2))) | (! (h_0_0 = 10000000000000001/100000000000000000))) | (! (gamma < 1.0)))) & ((h_2_5 < 20000000000000001/25000000000000000) | (! conj5))) & (((! assume_non_negative_h_4_5) | (! disj128)) | (! (h_4_5 < 0.0)))) & (((! assume_non_negative_h_3_5) | (! disj129)) | (! (h_3_5 < 0.0)))) & (((! assume_non_negative_h_2_5) | (! disj130)) | (! (h_2_5 < 0.0)))) & (((! assume_RHS_slope_4) | (! disj164)) | (! (((-1.0 * h_4_4) + h_3_4) < 0.0)))) & (((! assume_LHS_slope_4) | (! disj165)) | (! ((h_1_4 - h_0_4) < 0.0)))) & (((! assume_non_negative_h_4_4) | (! disj133)) | (! (h_4_4 < 0.0)))) & (((! assume_non_negative_h_3_4) | (! disj134)) | (! (h_3_4 < 0.0)))) & (((! assume_non_negative_h_2_4) | (! disj135)) | (! (h_2_4 < 0.0)))) & (((! assume_non_negative_h_1_4) | (! disj136)) | (! (h_1_4 < 0.0)))) & (((! assume_non_negative_h_0_4) | (! disj137)) | (! (h_0_4 < 0.0)))) & (((! assume_RHS_slope_3) | (! disj166)) | (! (((-1.0 * h_4_3) + h_3_3) < 0.0)))) & (((! assume_LHS_slope_3) | (! disj167)) | (! ((h_1_3 - h_0_3) < 0.0)))) & (((! assume_non_negative_h_4_3) | (! disj138)) | (! (h_4_3 < 0.0)))) & (((! assume_non_negative_h_3_3) | (! disj139)) | (! (h_3_3 < 0.0)))) & (((! assume_non_negative_h_2_3) | (! disj140)) | (! (h_2_3 < 0.0)))) & (((! assume_non_negative_h_1_3) | (! disj141)) | (! (h_1_3 < 0.0)))) & (((! assume_non_negative_h_0_3) | (! disj142)) | (! (h_0_3 < 0.0)))) & (((! assume_RHS_slope_2) | (! disj168)) | (! (((-1.0 * h_4_2) + h_3_2) < 0.0)))) & (((! assume_LHS_slope_2) | (! disj169)) | (! ((h_1_2 - h_0_2) < 0.0)))) & (((! assume_non_negative_h_4_2) | (! disj143)) | (! (h_4_2 < 0.0)))) & (((! assume_non_negative_h_3_2) | (! disj144)) | (! (h_3_2 < 0.0)))) & (((! assume_non_negative_h_2_2) | (! disj145)) | (! (h_2_2 < 0.0)))) & (((! assume_non_negative_h_1_2) | (! disj146)) | (! (h_1_2 < 0.0)))) & (((! assume_non_negative_h_0_2) | (! disj147)) | (! (h_0_2 < 0.0)))) & (((! assume_non_negative_h_4_1) | (! disj148)) | (! (h_4_1 < 0.0)))) & (((! assume_non_negative_h_3_1) | (! disj149)) | (! (h_3_1 < 0.0)))) & (((! assume_non_negative_h_2_1) | (! disj150)) | (! (h_2_1 < 0.0)))) & (((! assume_non_negative_h_1_1) | (! disj151)) | (! (h_1_1 < 0.0)))) & (((! assume_non_negative_h_0_1) | (! disj152)) | (! (h_0_1 < 0.0)))) & (((! assume_non_negative_h_4_0) | (! disj153)) | (! (h_4_0 < 0.0)))) & (((! assume_non_negative_h_3_0) | (! disj154)) | (! (h_3_0 < 0.0)))) & (((! assume_non_negative_h_2_0) | (! disj155)) | (! (h_2_0 < 0.0)))) & (((! assume_non_negative_h_1_0) | (! disj156)) | (! (h_1_0 < 0.0)))) & (((! assume_non_negative_h_0_0) | (! disj157)) | (! (h_0_0 < 0.0)))) & (! (gamma < 0.0)))\"\n", + "}\n" + ] + } + ], + "source": [ + "# Use a five point model with no constraints\n", + "\n", + "num_disc = 5\n", + "# MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", + "\n", + "\n", + "request_dict = {\n", + " \"structure_parameters\": [\n", + " {\n", + " \"name\": \"schedules\",\n", + " \"schedules\": [\n", + " {\"timepoints\": range(0, 8, 1)}\n", + " ],\n", + " },\n", + " \n", + " ],\n", + " \"constraints\": [\n", + " {\"name\": \"non_negative_h_0\",\n", + " \"variable\": \"h_0\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non_negative_h_1\",\n", + " \"variable\": \"h_1\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non_negative_h_2\",\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non_negative_h_3\",\n", + " \"variable\": \"h_3\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non_negative_h_4\",\n", + " \"variable\": \"h_4\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + "\n", + " # (h_0 <= h_1)\n", " {\"name\": \"LHS_slope\",\n", " \"variables\": [\"h_1\", \"h_0\"],\n", " \"weights\": [1, -1],\n", " \"additive_bounds\": {\"lb\": 0},\n", " \"timepoints\": {\"lb\": 0}\n", " }, \n", + " # (h_3 >= h_4)\n", " {\"name\": \"RHS_slope\",\n", " \"variables\": [\"h_3\", \"h_4\"],\n", " \"weights\": [1, -1],\n", " \"additive_bounds\": {\"lb\": 0},\n", " \"timepoints\": {\"lb\": 0}\n", - " }\n", + " },\n", + "\n", + "\n", + " {\"name\": \"melt_h_2\",\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\"lb\": 0, \"ub\": .8},\n", + " \"timepoints\": {\"lb\": 5}\n", + " },\n", + "\n", " ],\n", " \"config\": {\n", " \"use_compartmental_constraints\": False,\n", " \"normalization_constant\": 1.0,\n", " \"tolerance\": 1e-1,\n", - " \"verbosity\": 20,\n", + " \"verbosity\": 10,\n", " \"dreal_mcts\": True,\n", - " \"save_smtlib\": True,\n", + " \"dreal_precision\": 0.1,\n", + " # \"save_smtlib\": \"halfar.smt2\",\n", " \"substitute_subformulas\": False,\n", " \"series_approximation_threshold\": None,\n", " \"dreal_log_level\": \"none\",\n", @@ -159,24 +1113,176 @@ " description=\"Halfar demo\",\n", " case_out_dir=\"./out\",\n", ")\n", - "print(f\"gamma = {results.parameter_space.points()[0].values['gamma']:.5f}\")\n", - "results.plot(variables=[\"h_0\", \"h_1\", \"h_2\", \"h_3\", \"h_4\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Height\", legend=[\"h_0\", \"h_1\", \"h_2\", \"h_3\", \"h_4\"],label_color={\"true\": None})\n", - "points = results.points()\n", - "boxes = results.parameter_space.boxes()\n", + "summarize_results(num_disc, results)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-02 19:59:37,207 - funman.server.worker - INFO - FunmanWorker running...\n", + "2023-11-02 19:59:37,215 - funman.server.worker - INFO - Starting work on: 64599ed5-ca1a-4308-8f56-ce379fd54cd4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-02 19:59:38,109 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 1, 2, 3, 4, 5, 6, 7]\n", + "2023-11-02 19:59:38,112 - funman_dreal.solver - DEBUG - Created new Solver ...\n", + "2023-11-02 19:59:38,124 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 1\n", + "2023-11-02 19:59:38,243 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 1 to 2\n", + "2023-11-02 19:59:38,338 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 2 to 3\n", + "2023-11-02 19:59:38,424 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 3 to 4\n", + "2023-11-02 19:59:38,512 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 4 to 5\n", + "2023-11-02 19:59:38,597 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 5 to 6\n", + "2023-11-02 19:59:38,670 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 6 to 7\n", + "2023-11-02 19:59:39,353 - funman.api.run - INFO - Dumping results to ./out/64599ed5-ca1a-4308-8f56-ce379fd54cd4.json\n", + "2023-11-02 19:59:39,364 - funman.scenario.consistency - INFO - 7{7}:\t[-]\n", + "2023-11-02 19:59:39,370 - funman.server.worker - INFO - Completed work on: 64599ed5-ca1a-4308-8f56-ce379fd54cd4\n", + "2023-11-02 19:59:49,379 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2023-11-02 19:59:49,882 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2023-11-02 19:59:49,885 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Points (+:0, -:0), 1 Boxes (+:0, -:1)\n", + "{\n", + " \"box\": {\n", + " \"schedules\": {\n", + " \"lb\": -1.7976931348623157e+308,\n", + " \"ub\": 1.7976931348623157e+308,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"gamma\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.0,\n", + " \"closed_upper_bound\": false\n", + " },\n", + " \"timestep\": {\n", + " \"lb\": 7.0,\n", + " \"ub\": 7.0,\n", + " \"closed_upper_bound\": true\n", + " }\n", + " },\n", + " \"relevant_constraints\": [\n", + " {\n", + " \"soft\": true,\n", + " \"name\": \"melt_h_2\",\n", + " \"timepoints\": null,\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\n", + " \"lb\": 0.0,\n", + " \"ub\": 1.0,\n", + " \"closed_upper_bound\": false\n", + " }\n", + " }\n", + " ],\n", + " \"expression\": \"((((((assume_melt_h_2 & disj1047) & (h_2_0 = 1.0)) & ((conj32 | (! assume_melt_h_2)) | (! disj1047))) & (((h_2_0 < 0.0) | (! (h_2_0 < 1.0))) | (! (h_2_0 = 1.0)))) & ((h_2_0 < 1.0) | (! conj32))) & ((! conj32) | (! (h_2_0 < 0.0))))\"\n", + "}\n" + ] + } + ], + "source": [ + "# Use a five point model with no constraints\n", + "\n", + "num_disc = 5\n", + "# MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", + "\n", + "\n", + "request_dict = {\n", + " \"structure_parameters\": [\n", + " {\n", + " \"name\": \"schedules\",\n", + " \"schedules\": [\n", + " {\"timepoints\": range(0, 8, 1)}\n", + " ],\n", + " },\n", + " \n", + " ],\n", + " \"constraints\": [\n", + " {\"name\": \"non_negative_h_0\",\n", + " \"variable\": \"h_0\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non_negative_h_1\",\n", + " \"variable\": \"h_1\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non_negative_h_2\",\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non_negative_h_3\",\n", + " \"variable\": \"h_3\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + " {\"name\": \"non_negative_h_4\",\n", + " \"variable\": \"h_4\",\n", + " \"interval\": {\"lb\": 0},\n", + " \"timepoints\":{\"lb\":0}\n", + " },\n", + "\n", + " # (h_0 <= h_1)\n", + " {\"name\": \"LHS_slope\",\n", + " \"variables\": [\"h_1\", \"h_0\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }, \n", + " # (h_3 >= h_4)\n", + " {\"name\": \"RHS_slope\",\n", + " \"variables\": [\"h_3\", \"h_4\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " },\n", + "\n", + "\n", + " {\"name\": \"melt_h_2\",\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\"lb\": 0, \"ub\": 1}\n", + " },\n", "\n", - "print(\n", - " f\"{len(points)} Points (+:{len(results.parameter_space.true_points())}, -:{len(results.parameter_space.false_points())}), {len(boxes)} Boxes (+:{len(results.parameter_space.true_boxes)}, -:{len(results.parameter_space.false_boxes)})\"\n", + " ],\n", + " \"config\": {\n", + " \"use_compartmental_constraints\": False,\n", + " \"normalization_constant\": 1.0,\n", + " \"tolerance\": 1e-1,\n", + " \"verbosity\": 10,\n", + " \"dreal_mcts\": True,\n", + " \"dreal_precision\": 0.1,\n", + " # \"save_smtlib\": \"halfar.smt2\",\n", + " \"substitute_subformulas\": False,\n", + " \"series_approximation_threshold\": None,\n", + " \"dreal_log_level\": \"none\",\n", + " \"profile\": False,\n", + " },\n", + "}\n", + "\n", + "# Use request_dict\n", + "results = Runner().run(\n", + " MODEL_PATH,\n", + " request_dict,\n", + " # REQUEST_PATH,\n", + " description=\"Halfar demo\",\n", + " case_out_dir=\"./out\",\n", ")\n", - "if points and len(points) > 0:\n", - " point: Point = points[-1]\n", - " parameters: Dict[Parameter, float] = results.point_parameters(point)\n", - " print(parameters)\n", - " print(results.dataframe([point]))\n", - "else:\n", - " # if there are no points, then we have a box that we found without needing points\n", - "\n", - " box = boxes[0]\n", - " print(json.dumps(box.explain(), indent=4))\n", + "summarize_results(num_disc, results)\n", "\n", "\n" ] diff --git a/src/funman/config.py b/src/funman/config.py index 0e7a2d63..5b618773 100644 --- a/src/funman/config.py +++ b/src/funman/config.py @@ -56,9 +56,9 @@ class FUNMANConfig(BaseModel): """Number of initial boxes for BoxSearch""" initial_state_tolerance: float = 0.0 """Factor used to relax initial state values bounds""" - save_smtlib: bool = False + save_smtlib: Optional[str] = None """Whether to save each smt invocation as an SMTLib file""" - dreal_precision: float = 1e-3 + dreal_precision: float = 1e-1 """Precision delta for dreal solver""" dreal_log_level: str = "off" """Constraint noise term to relax constraints""" diff --git a/src/funman/representation/box.py b/src/funman/representation/box.py index ab66bc1e..0a1a9a9a 100644 --- a/src/funman/representation/box.py +++ b/src/funman/representation/box.py @@ -41,6 +41,7 @@ def from_point(point: Point) -> "Box": p: Interval.from_value(v) for p, v in point.values.items() } box.points.append(point) + box.schedule = point.schedule box.label = point.label return box diff --git a/src/funman/representation/constraint.py b/src/funman/representation/constraint.py index cec2908a..c4803519 100644 --- a/src/funman/representation/constraint.py +++ b/src/funman/representation/constraint.py @@ -76,7 +76,7 @@ def relevant_at_time(self, time: int) -> bool: return time == 0 -class QueryConstraint(Constraint): +class QueryConstraint(TimedConstraint): soft: bool = True query: Query @@ -130,3 +130,9 @@ def __hash__(self) -> int: LinearConstraint, QueryConstraint, ] + + +FunmanUserConstraint = Union[ + StateVariableConstraint, + LinearConstraint +] \ No newline at end of file diff --git a/src/funman/representation/parameter_space.py b/src/funman/representation/parameter_space.py index fb4bb2c9..5282057d 100644 --- a/src/funman/representation/parameter_space.py +++ b/src/funman/representation/parameter_space.py @@ -35,15 +35,22 @@ def __str__(self, dropped_boxes=[]) -> str: } boxes = self.boxes() steps = {} + step_timepoints= {} for box in boxes + dropped_boxes: label = box_labels[box.label] for step in range( int(box.timestep().lb), int(box.timestep().ub) + 1 ): + # Boxes may correspond to different schedules, so we combine the steps (and not the timepoints) + time_at_step= box.schedule.time_at_step(step) + step_times = step_timepoints.get(step, set({})) + step_times.add(time_at_step) + step_timepoints[step] = step_times + boxes_at_step = steps.get(step, []) boxes_at_step.append(label) steps[step] = boxes_at_step - return "\n".join([f"{k}:\t[{''.join(v)}]" for k, v in steps.items()]) + return "\n".join([f"{k}{step_timepoints[k]}:\t[{''.join(v)}]" for k, v in steps.items()]) def true_points(self) -> List[Point]: return [pt for b in self.true_boxes for pt in b.true_points()] @@ -303,23 +310,29 @@ def _compact(self): self.true_boxes = self._box_list_compact(self.true_boxes) self.false_boxes = self._box_list_compact(self.false_boxes) - def labeled_volume(self, scenario: "AnalysisScenario"): + def labeled_volume(self, scenario: "AnalysisScenario" = None): # self._compact() labeled_vol = 0 # TODO should actually be able to compact the true and false boxes together, since they are both labeled. # TODO can calculate the percentage of the total parameter space. Is there an efficient way to get the initial PS so we can find the volume of that box? or to access unknown boxes? for box in self.true_boxes: - true_volume = box.volume( - parameters=scenario.model_parameters(), - normalize=scenario._original_parameter_widths, - ) + if scenario: + true_volume = box.volume( + parameters=scenario.model_parameters(), + normalize=scenario._original_parameter_widths, + ) + else: + true_volume = box.volume() labeled_vol += true_volume for box in self.false_boxes: - false_volume = box.volume( - parameters=scenario.model_parameters(), - normalize=scenario._original_parameter_widths, - ) + if scenario: + false_volume = box.volume( + parameters=scenario.model_parameters(), + normalize=scenario._original_parameter_widths, + ) + else: + false_volume = box.volume() labeled_vol += false_volume return labeled_vol diff --git a/src/funman/scenario/consistency.py b/src/funman/scenario/consistency.py index 599847bc..8427592e 100644 --- a/src/funman/scenario/consistency.py +++ b/src/funman/scenario/consistency.py @@ -32,7 +32,8 @@ class ConsistencyScenario(AnalysisScenario, BaseModel): smt_encoder : Encoder, optional method to encode the scenario, by default None """ - + """True if its okay when the volume of the search space is empty (e.g., when it is a point)""" + empty_volume_ok: bool = True _box: Optional[Encoding] = None @classmethod diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index b59adec5..eb77bf39 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -34,7 +34,7 @@ from funman.model.ensemble import EnsembleModel from funman.model.petrinet import GeneratedPetriNetModel from funman.model.regnet import GeneratedRegnetModel, RegnetModel -from funman.representation.constraint import FunmanConstraint +from funman.representation.constraint import FunmanUserConstraint from funman.representation.parameter import NumSteps, Schedules, StepSize from funman.utils import math_utils @@ -48,7 +48,9 @@ class AnalysisScenario(ABC, BaseModel): parameters: List[Parameter] normalization_constant: Optional[float] = None - constraints: Optional[List[Union[FunmanConstraint]]] = None + constraints: Optional[List[Union[FunmanUserConstraint]]] = None + """True if its okay when the volume of the search space is empty (e.g., when it is a point)""" + empty_volume_ok: bool = False model_config = ConfigDict(extra="forbid") model: Union[ @@ -143,7 +145,7 @@ def _initialize_encodings(self, config: "FUNMANConfig"): # Initialize Assumptions # Maintain backward support for query as a single constraint if self.query is not None and not isinstance(self.query, QueryTrue): - query_constraint = QueryConstraint(name="query", query=self.query) + query_constraint = QueryConstraint(name="query", query=self.query, timepoints=Interval(lb=0)) self.constraints += [query_constraint] self._assumptions.append(Assumption(constraint=query_constraint)) @@ -172,7 +174,7 @@ def num_timepoints(self) -> int: # use num_steps and step_size num_steps = self.parameters_of_type(NumSteps) step_size = self.parameters_of_type(StepSize) - num_timepoints = (num_steps.width() + 1) * (step_size.width() + 1) + num_timepoints = (num_steps[0].width() + 1) * (step_size[0].width() + 1) return num_timepoints def search_space_volume(self, normalize: bool = False) -> Decimal: @@ -191,7 +193,7 @@ def search_space_volume(self, normalize: bool = False) -> Decimal: else: space_time_slice_volume = Decimal(1.0) assert ( - not normalize or space_time_slice_volume == 1.0 + self.empty_volume_ok or not normalize or abs(space_time_slice_volume -1.0)<=Decimal(1e-8) ), f"Normalized space volume is not 1.0, computed = {space_time_slice_volume}" space_volume = ( space_time_slice_volume @@ -233,10 +235,19 @@ def _process_parameters(self): # if wrong type, recover structure parameters self.parameters = [ ( - StructureParameter( + NumSteps( name=p.name, interval=Interval(lb=p.lb, ub=p.ub) ) - if (p.name == "num_steps" or p.name == "step_size") + if (p.name == "num_steps") + else p + ) + for p in self.parameters + ] + [ + ( + StepSize( + name=p.name, interval=Interval(lb=p.lb, ub=p.ub) + ) + if (p.name == "step_size") else p ) for p in self.parameters @@ -244,10 +255,10 @@ def _process_parameters(self): if len(self.structure_parameters()) == 0: # Add the structure parameters if still missing self.parameters += [ - StructureParameter( + NumSteps( name="num_steps", interval=Interval(lb=0, ub=0) ), - StructureParameter( + StepSize( name="step_size", interval=Interval(lb=1, ub=1) ), ] diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index e8cc02d3..a56006e7 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -827,7 +827,7 @@ def _find_witness_points( # Generate a witness if episode.config.save_smtlib: self.store_smtlib( - episode, box, filename=f"wp_{episode._iteration}.smt2" + episode, box, filename=os.path.join(episode.config.save_smtlib, f"wp_{episode._iteration}.smt2") ) result = self.invoke_solver(solver) if result is not None and isinstance(result, pysmtModel): diff --git a/src/funman/search/smt_check.py b/src/funman/search/smt_check.py index 68526771..612a4a48 100644 --- a/src/funman/search/smt_check.py +++ b/src/funman/search/smt_check.py @@ -63,7 +63,7 @@ def search( parameter_space, schedule, ) - timestep = len(schedule.timepoints) + timestep = len(schedule.timepoints)-1 if result is not None and isinstance(result, pysmtModel): result_dict = result.to_dict() if result else None l.debug(f"Result: {json.dumps(result_dict, indent=4)}") @@ -101,6 +101,7 @@ def search( box.bounds["timestep"] = Interval( lb=timestep, ub=timestep, closed_upper_bound=True ) + box.schedule = schedule parameter_space.false_boxes.append(box) if resultsCallback: resultsCallback(parameter_space) @@ -170,7 +171,7 @@ def solve_formula( s.push(1) s.add_assertion(formula) if episode.config.save_smtlib: - filename = os.path.join(os.getcwd(), "dbg_steps.smt2") + filename = os.path.join(episode.config.save_smtlib, "dbg_steps.smt2") l.trace(f"Saving smt file: {filename}") self.store_smtlib( formula, diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 8d72866f..5a21cfcc 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -15,7 +15,7 @@ from funman.model.petrinet import GeneratedPetriNetModel, PetrinetModel from funman.model.query import QueryAnd, QueryFunction, QueryLE, QueryTrue from funman.model.regnet import GeneratedRegnetModel, RegnetModel -from funman.representation.constraint import FunmanConstraint +from funman.representation.constraint import FunmanUserConstraint from funman.representation.explanation import Explanation from funman.representation.parameter import ( ModelParameter, @@ -40,7 +40,7 @@ class FunmanWorkRequest(BaseModel): query: Optional[Union[QueryAnd, QueryLE, QueryFunction, QueryTrue]] = None - constraints: Optional[List[FunmanConstraint]] = None + constraints: Optional[List[FunmanUserConstraint]] = None parameters: Optional[List[ModelParameter]] = None config: Optional[FUNMANConfig] = None structure_parameters: Optional[ @@ -51,9 +51,9 @@ class FunmanWorkRequest(BaseModel): @classmethod def check_unique_names( cls, - constraints: Optional[List[FunmanConstraint]], + constraints: Optional[List[FunmanUserConstraint]], info: ValidationInfo, - ) -> Optional[List[FunmanConstraint]]: + ) -> Optional[List[FunmanUserConstraint]]: if constraints is not None and len(constraints) > 0: name_counts = Counter([c.name for c in constraints]) dups = {k: v for k, v in name_counts.items() if v > 1} @@ -185,7 +185,7 @@ def update_parameter_space( # TODO handle copy? self.parameter_space = results # compute volumes - labeled_volume = results.labeled_volume(scenario) + labeled_volume = results.labeled_volume(scenario=scenario) # TODO precompute and cache? search_volume = scenario.search_space_volume(normalize=True) # TODO precompute and cache? @@ -329,17 +329,14 @@ def symbol_timeseries( """ series = self.symbol_values(point, variables) a_series = {} # timeseries as array/list - max_t = max( - [ - max([int(k) for k in tps.keys() if k.isdigit()] + [0]) - for _, tps in series.items() - ] - ) + timestep = point.timestep() + max_t = point.schedule.timepoints[timestep] + a_series["index"] = list(range(0, max_t + 1)) for var, tps in series.items(): vals = [None] * (int(max_t) + 1) for t, v in tps.items(): - if t.isdigit(): + if t.isdigit() and int(t) <= int(max_t): vals[int(t)] = v a_series[var] = vals return a_series diff --git a/test/test_param_space.py b/test/test_param_space.py index d7e2e004..e795aa05 100644 --- a/test/test_param_space.py +++ b/test/test_param_space.py @@ -207,7 +207,7 @@ def test_search_space_volume(self): model=model, query=QueryTrue(), ) - + scenario.initialize(config=FUNMANConfig()) assert ( scenario.search_space_volume() == scenario.representable_space_volume() diff --git a/test/test_petri.py b/test/test_petri.py index 218d6ef5..26d8a89f 100644 --- a/test/test_petri.py +++ b/test/test_petri.py @@ -24,9 +24,12 @@ ) ensemble_files = [ - "/home/danbryce/funman/test/../resources/miranet/ensemble/BIOMD0000000955_miranet.json", - "/home/danbryce/funman/test/../resources/miranet/ensemble/BIOMD0000000960_miranet.json", - "/home/danbryce/funman/test/../resources/miranet/ensemble/BIOMD0000000983_miranet.json", + os.path.join( + os.path.dirname(os.path.abspath(__file__)),"../resources/miranet/ensemble/BIOMD0000000955_miranet.json"), + os.path.join( + os.path.dirname(os.path.abspath(__file__)),"../resources/miranet/ensemble/BIOMD0000000960_miranet.json"), + os.path.join( + os.path.dirname(os.path.abspath(__file__)),"../resources/miranet/ensemble/BIOMD0000000983_miranet.json"), ] diff --git a/test/test_use_cases.py b/test/test_use_cases.py index e1f0668e..a42d220c 100644 --- a/test/test_use_cases.py +++ b/test/test_use_cases.py @@ -25,9 +25,9 @@ ResultCombinedHandler, SimulationScenario, SimulationScenarioResult, - SimulatorModel, - StructureParameter, + SimulatorModel ) +from funman.representation.parameter import StepSize, NumSteps RESOURCES = os.path.join( os.path.dirname(os.path.abspath(__file__)), "../resources" @@ -65,10 +65,10 @@ def does_not_cross_threshold(sim_results): ), query=query, parameters=[ - StructureParameter( + NumSteps( name="num_steps", interval=Interval(lb=3, ub=3) ), - StructureParameter( + StepSize( name="step_size", interval=Interval(lb=1, ub=1) ), ], @@ -169,10 +169,10 @@ def setup_use_case_bilayer_parameter_synthesis(self): scenario = ParameterSynthesisScenario( parameters=[ ModelParameter(name="beta", interval=Interval(lb=lb, ub=ub)), - StructureParameter( + NumSteps( name="num_steps", interval=Interval(lb=3, ub=3) ), - StructureParameter( + StepSize( name="step_size", interval=Interval(lb=1, ub=1) ), ], @@ -190,11 +190,14 @@ def test_use_case_bilayer_parameter_synthesis(self): config=FUNMANConfig( # solver="dreal", # dreal_mcts=True, + save_smtlib="dlp.smt2", + dreal_log_level="info", tolerance=1e-3, number_of_processes=1, normalize=False, simplify_query=False, use_compartmental_constraints=False, + verbosity=5, _handler=ResultCombinedHandler( [ ResultCacheWriter(f"box_search.json"), @@ -218,10 +221,10 @@ def setup_use_case_bilayer_consistency(self): model=model, query=query, parameters=[ - StructureParameter( + NumSteps( name="num_steps", interval=Interval(lb=3, ub=3) ), - StructureParameter( + StepSize( name="step_size", interval=Interval(lb=1, ub=1) ), ], From 1961b3dedf1873d8e4885320c54f2ccf43809fb5 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Thu, 2 Nov 2023 20:59:12 +0000 Subject: [PATCH 06/28] fixing tests --- .../request2_b_default_wo_compartmental_constrs.json | 5 ++--- src/funman/scenario/scenario.py | 2 +- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json b/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json index acd3eada..837480a2 100644 --- a/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json +++ b/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json @@ -20,9 +20,8 @@ } ], "config": { - "solver": "dreal", - "save_smtlib": true, - "substitute_subformulas": true, + "save_smtlib": false, + "substitute_subformulas": false, "use_compartmental_constraints": false } } \ No newline at end of file diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index eb77bf39..e8c2c488 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -193,7 +193,7 @@ def search_space_volume(self, normalize: bool = False) -> Decimal: else: space_time_slice_volume = Decimal(1.0) assert ( - self.empty_volume_ok or not normalize or abs(space_time_slice_volume -1.0)<=Decimal(1e-8) + self.empty_volume_ok or not normalize or abs(space_time_slice_volume -Decimal(1.0))<=Decimal(1e-8) ), f"Normalized space volume is not 1.0, computed = {space_time_slice_volume}" space_volume = ( space_time_slice_volume From 14b11cbce1df57b4e522406a4d062ede42f84dc4 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Fri, 3 Nov 2023 14:37:11 +0000 Subject: [PATCH 07/28] add funman command line --- setup.py | 5 +++++ src/funman/api/run.py | 39 ++++++++++++++++++++++++++++++++------- 2 files changed, 37 insertions(+), 7 deletions(-) diff --git a/setup.py b/setup.py index 165c7979..6c20b8a7 100644 --- a/setup.py +++ b/setup.py @@ -35,4 +35,9 @@ extras_require={"dreal": ["funman_dreal"]}, tests_require=["unittest"], zip_safe=False, + entry_points={ + "console_scripts": [ + "funman=funman.api.run:main", + ], + }, ) diff --git a/src/funman/api/run.py b/src/funman/api/run.py index 7a233b09..065a59c1 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -1,3 +1,4 @@ +import argparse import json import logging import os @@ -242,12 +243,36 @@ def run_instance( return results -if __name__ == "__main__": - model = args[1] - request = args[2] - out_dir = args[3] +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "model", + type=str, + help=f"model json file", + ) + parser.add_argument( + "request", + type=str, + help=f"request json file", + ) + parser.add_argument( + "-o", + "--outdir", + type=str, + default="out", + help=f"Output directory", + ) + return parser.parse_args() + + +def main() -> int: + args = get_args() + if not os.path.exists(args.outdir): + os.mkdir(args.outdir) + + results = Runner().run(args.model, args.request, case_out_dir=args.outdir) + print(results.model_dump_json(indent=4)) - if not os.path.exists(out_dir): - os.mkdir(out_dir) - Runner().run(model, request, case_out_dir=out_dir) +if __name__ == "__main__": + main() From a1a7592986e5a3aa7281bd206222ff032b7181cb Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Fri, 3 Nov 2023 14:38:47 +0000 Subject: [PATCH 08/28] fix tests --- src/funman/representation/parameter_space.py | 15 ++++++---- src/funman/scenario/consistency.py | 1 + src/funman/scenario/scenario.py | 30 +++++++++----------- test/test_petri.py | 12 ++++++-- test/test_use_cases.py | 28 ++++++------------ 5 files changed, 42 insertions(+), 44 deletions(-) diff --git a/src/funman/representation/parameter_space.py b/src/funman/representation/parameter_space.py index 5282057d..6a622305 100644 --- a/src/funman/representation/parameter_space.py +++ b/src/funman/representation/parameter_space.py @@ -35,22 +35,27 @@ def __str__(self, dropped_boxes=[]) -> str: } boxes = self.boxes() steps = {} - step_timepoints= {} + step_timepoints = {} for box in boxes + dropped_boxes: label = box_labels[box.label] for step in range( int(box.timestep().lb), int(box.timestep().ub) + 1 ): # Boxes may correspond to different schedules, so we combine the steps (and not the timepoints) - time_at_step= box.schedule.time_at_step(step) + time_at_step = box.schedule.time_at_step(step) step_times = step_timepoints.get(step, set({})) step_times.add(time_at_step) - step_timepoints[step] = step_times + step_timepoints[step] = step_times boxes_at_step = steps.get(step, []) boxes_at_step.append(label) steps[step] = boxes_at_step - return "\n".join([f"{k}{step_timepoints[k]}:\t[{''.join(v)}]" for k, v in steps.items()]) + return "\n".join( + [ + f"{k}{step_timepoints[k]}:\t[{''.join(v)}]" + for k, v in steps.items() + ] + ) def true_points(self) -> List[Point]: return [pt for b in self.true_boxes for pt in b.true_points()] @@ -321,7 +326,7 @@ def labeled_volume(self, scenario: "AnalysisScenario" = None): parameters=scenario.model_parameters(), normalize=scenario._original_parameter_widths, ) - else: + else: true_volume = box.volume() labeled_vol += true_volume diff --git a/src/funman/scenario/consistency.py b/src/funman/scenario/consistency.py index 8427592e..071c5f48 100644 --- a/src/funman/scenario/consistency.py +++ b/src/funman/scenario/consistency.py @@ -32,6 +32,7 @@ class ConsistencyScenario(AnalysisScenario, BaseModel): smt_encoder : Encoder, optional method to encode the scenario, by default None """ + """True if its okay when the volume of the search space is empty (e.g., when it is a point)""" empty_volume_ok: bool = True _box: Optional[Encoding] = None diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index e8c2c488..8ad4ce4e 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -145,7 +145,9 @@ def _initialize_encodings(self, config: "FUNMANConfig"): # Initialize Assumptions # Maintain backward support for query as a single constraint if self.query is not None and not isinstance(self.query, QueryTrue): - query_constraint = QueryConstraint(name="query", query=self.query, timepoints=Interval(lb=0)) + query_constraint = QueryConstraint( + name="query", query=self.query, timepoints=Interval(lb=0) + ) self.constraints += [query_constraint] self._assumptions.append(Assumption(constraint=query_constraint)) @@ -174,7 +176,9 @@ def num_timepoints(self) -> int: # use num_steps and step_size num_steps = self.parameters_of_type(NumSteps) step_size = self.parameters_of_type(StepSize) - num_timepoints = (num_steps[0].width() + 1) * (step_size[0].width() + 1) + num_timepoints = (num_steps[0].width() + 1) * ( + step_size[0].width() + 1 + ) return num_timepoints def search_space_volume(self, normalize: bool = False) -> Decimal: @@ -193,7 +197,9 @@ def search_space_volume(self, normalize: bool = False) -> Decimal: else: space_time_slice_volume = Decimal(1.0) assert ( - self.empty_volume_ok or not normalize or abs(space_time_slice_volume -Decimal(1.0))<=Decimal(1e-8) + self.empty_volume_ok + or not normalize + or abs(space_time_slice_volume - Decimal(1.0)) <= Decimal(1e-8) ), f"Normalized space volume is not 1.0, computed = {space_time_slice_volume}" space_volume = ( space_time_slice_volume @@ -235,18 +241,14 @@ def _process_parameters(self): # if wrong type, recover structure parameters self.parameters = [ ( - NumSteps( - name=p.name, interval=Interval(lb=p.lb, ub=p.ub) - ) + NumSteps(name=p.name, interval=Interval(lb=p.lb, ub=p.ub)) if (p.name == "num_steps") else p ) for p in self.parameters - ] + [ + ] + [ ( - StepSize( - name=p.name, interval=Interval(lb=p.lb, ub=p.ub) - ) + StepSize(name=p.name, interval=Interval(lb=p.lb, ub=p.ub)) if (p.name == "step_size") else p ) @@ -255,12 +257,8 @@ def _process_parameters(self): if len(self.structure_parameters()) == 0: # Add the structure parameters if still missing self.parameters += [ - NumSteps( - name="num_steps", interval=Interval(lb=0, ub=0) - ), - StepSize( - name="step_size", interval=Interval(lb=1, ub=1) - ), + NumSteps(name="num_steps", interval=Interval(lb=0, ub=0)), + StepSize(name="step_size", interval=Interval(lb=1, ub=1)), ] self._extract_non_overriden_parameters() diff --git a/test/test_petri.py b/test/test_petri.py index 26d8a89f..1628a696 100644 --- a/test/test_petri.py +++ b/test/test_petri.py @@ -25,11 +25,17 @@ ensemble_files = [ os.path.join( - os.path.dirname(os.path.abspath(__file__)),"../resources/miranet/ensemble/BIOMD0000000955_miranet.json"), + os.path.dirname(os.path.abspath(__file__)), + "../resources/miranet/ensemble/BIOMD0000000955_miranet.json", + ), os.path.join( - os.path.dirname(os.path.abspath(__file__)),"../resources/miranet/ensemble/BIOMD0000000960_miranet.json"), + os.path.dirname(os.path.abspath(__file__)), + "../resources/miranet/ensemble/BIOMD0000000960_miranet.json", + ), os.path.join( - os.path.dirname(os.path.abspath(__file__)),"../resources/miranet/ensemble/BIOMD0000000983_miranet.json"), + os.path.dirname(os.path.abspath(__file__)), + "../resources/miranet/ensemble/BIOMD0000000983_miranet.json", + ), ] diff --git a/test/test_use_cases.py b/test/test_use_cases.py index a42d220c..9ad4c592 100644 --- a/test/test_use_cases.py +++ b/test/test_use_cases.py @@ -25,9 +25,9 @@ ResultCombinedHandler, SimulationScenario, SimulationScenarioResult, - SimulatorModel + SimulatorModel, ) -from funman.representation.parameter import StepSize, NumSteps +from funman.representation.parameter import NumSteps, StepSize RESOURCES = os.path.join( os.path.dirname(os.path.abspath(__file__)), "../resources" @@ -65,12 +65,8 @@ def does_not_cross_threshold(sim_results): ), query=query, parameters=[ - NumSteps( - name="num_steps", interval=Interval(lb=3, ub=3) - ), - StepSize( - name="step_size", interval=Interval(lb=1, ub=1) - ), + NumSteps(name="num_steps", interval=Interval(lb=3, ub=3)), + StepSize(name="step_size", interval=Interval(lb=1, ub=1)), ], config=FUNMANConfig( solver="dreal", @@ -169,12 +165,8 @@ def setup_use_case_bilayer_parameter_synthesis(self): scenario = ParameterSynthesisScenario( parameters=[ ModelParameter(name="beta", interval=Interval(lb=lb, ub=ub)), - NumSteps( - name="num_steps", interval=Interval(lb=3, ub=3) - ), - StepSize( - name="step_size", interval=Interval(lb=1, ub=1) - ), + NumSteps(name="num_steps", interval=Interval(lb=3, ub=3)), + StepSize(name="step_size", interval=Interval(lb=1, ub=1)), ], model=model, query=query, @@ -221,12 +213,8 @@ def setup_use_case_bilayer_consistency(self): model=model, query=query, parameters=[ - NumSteps( - name="num_steps", interval=Interval(lb=3, ub=3) - ), - StepSize( - name="step_size", interval=Interval(lb=1, ub=1) - ), + NumSteps(name="num_steps", interval=Interval(lb=3, ub=3)), + StepSize(name="step_size", interval=Interval(lb=1, ub=1)), ], ) return scenario From d71edd0fbf22af3e834ee296bbcb2fb92ff54e5b Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Fri, 3 Nov 2023 14:39:36 +0000 Subject: [PATCH 09/28] separate user constraints --- src/funman/representation/constraint.py | 5 +---- src/funman/server/query.py | 4 ++-- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/src/funman/representation/constraint.py b/src/funman/representation/constraint.py index c4803519..ad42660b 100644 --- a/src/funman/representation/constraint.py +++ b/src/funman/representation/constraint.py @@ -132,7 +132,4 @@ def __hash__(self) -> int: ] -FunmanUserConstraint = Union[ - StateVariableConstraint, - LinearConstraint -] \ No newline at end of file +FunmanUserConstraint = Union[StateVariableConstraint, LinearConstraint] diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 5a21cfcc..a5f08c48 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -15,7 +15,7 @@ from funman.model.petrinet import GeneratedPetriNetModel, PetrinetModel from funman.model.query import QueryAnd, QueryFunction, QueryLE, QueryTrue from funman.model.regnet import GeneratedRegnetModel, RegnetModel -from funman.representation.constraint import FunmanUserConstraint +from funman.representation.constraint import FunmanUserConstraint from funman.representation.explanation import Explanation from funman.representation.parameter import ( ModelParameter, @@ -331,7 +331,7 @@ def symbol_timeseries( a_series = {} # timeseries as array/list timestep = point.timestep() max_t = point.schedule.timepoints[timestep] - + a_series["index"] = list(range(0, max_t + 1)) for var, tps in series.items(): vals = [None] * (int(max_t) + 1) From d6130057f1456480a6fe48c078bcbcc9602986c6 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Fri, 3 Nov 2023 14:40:06 +0000 Subject: [PATCH 10/28] change save_smtlib config to path --- src/funman/search/box_search.py | 7 ++++++- src/funman/search/smt_check.py | 10 +++++----- 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index a56006e7..0b50de1a 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -827,7 +827,12 @@ def _find_witness_points( # Generate a witness if episode.config.save_smtlib: self.store_smtlib( - episode, box, filename=os.path.join(episode.config.save_smtlib, f"wp_{episode._iteration}.smt2") + episode, + box, + filename=os.path.join( + episode.config.save_smtlib, + f"wp_{episode._iteration}.smt2", + ), ) result = self.invoke_solver(solver) if result is not None and isinstance(result, pysmtModel): diff --git a/src/funman/search/smt_check.py b/src/funman/search/smt_check.py index 612a4a48..a6205643 100644 --- a/src/funman/search/smt_check.py +++ b/src/funman/search/smt_check.py @@ -1,7 +1,6 @@ import json import logging import os -import sys import threading from typing import Callable, Optional, Tuple, Union @@ -30,7 +29,6 @@ l = logging.getLogger(__file__) - class SMTCheck(Search): def search( self, @@ -63,7 +61,7 @@ def search( parameter_space, schedule, ) - timestep = len(schedule.timepoints)-1 + timestep = len(schedule.timepoints) - 1 if result is not None and isinstance(result, pysmtModel): result_dict = result.to_dict() if result else None l.debug(f"Result: {json.dumps(result_dict, indent=4)}") @@ -82,7 +80,7 @@ def search( ) point.values["timestep"] = timestep - + if config.normalize: denormalized_point = point.denormalize(problem) point = denormalized_point @@ -171,7 +169,9 @@ def solve_formula( s.push(1) s.add_assertion(formula) if episode.config.save_smtlib: - filename = os.path.join(episode.config.save_smtlib, "dbg_steps.smt2") + filename = os.path.join( + episode.config.save_smtlib, "dbg_steps.smt2" + ) l.trace(f"Saving smt file: {filename}") self.store_smtlib( formula, From 97410a459cf3c5b53ab8ba938142ea0d6cff2345 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Fri, 3 Nov 2023 14:40:19 +0000 Subject: [PATCH 11/28] demo updates --- .../src/funman_demo/generators/halfar.py | 172 +++++++++++++----- notebooks/funman_results.ipynb | 26 +++ ...t2_b_default_wo_compartmental_constrs.json | 1 - .../hackathon_fall_2023_demo_terarrium.py | 53 ++---- 4 files changed, 168 insertions(+), 84 deletions(-) diff --git a/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py b/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py index 120cd252..97a76ced 100644 --- a/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py +++ b/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py @@ -1,56 +1,80 @@ -from decimal import Decimal -from typing import Tuple, List, Dict -from funman.model.generated_models.petrinet import * -from pydantic import AnyUrl, field_validator import argparse -import json import os +from decimal import Decimal +from typing import Dict, List, Tuple + +from pydantic import AnyUrl, field_validator +from funman.model.generated_models.petrinet import * from funman.representation.interval import Interval + class HalfarModel(Model): pass -class Direction(): +class Direction: Negative: str = "negative" Positive: str = "positive" - class Coordinate(BaseModel): vector: List[float] id: str - neighbors : Dict[str, "Coordinate"] = {} + neighbors: Dict[str, "Coordinate"] = {} -class HalfarGenerator(): + +class HalfarGenerator: range: Interval = Interval(lb=-2.0, ub=2.0) + def coordinates(self, args) -> List[Coordinate]: coords = [] - step_size = value = self.range.width()/Decimal(int(args.num_discretization_points)-1) + step_size = value = self.range.width() / Decimal( + int(args.num_discretization_points) - 1 + ) for i in range(args.num_discretization_points): - value = self.range.lb + float(step_size*i) + value = self.range.lb + float(step_size * i) coords.append(Coordinate(vector=[value], id=str(i))) for i, coord in enumerate(coords): - coord.neighbors[Direction.Negative]= (coords[i-1] if i > 0 else None) - coord.neighbors[Direction.Positive] = (coords[i+1] if i < len(coords)-1 else None) + coord.neighbors[Direction.Negative] = ( + coords[i - 1] if i > 0 else None + ) + coord.neighbors[Direction.Positive] = ( + coords[i + 1] if i < len(coords) - 1 else None + ) return coords - - def transition_expression(self, n1_name: str, n2_name: str, negative=False)-> str: + + def transition_expression( + self, n1_name: str, n2_name: str, negative=False + ) -> str: prefix = "-1*" if negative else "" return f"{prefix}gamma*({n2_name}-{n1_name})**3*{n1_name}**5" - def neighbor_gradient(self, coordinate: Coordinate, coordinates: List[Coordinate]) -> Tuple[List[Transition], List[Rate]]: - + def neighbor_gradient( + self, coordinate: Coordinate, coordinates: List[Coordinate] + ) -> Tuple[List[Transition], List[Rate]]: # find a triple of coordinates (n0, n1, n2) that are ordered so that dx is positive - if coordinate.neighbors[Direction.Positive] and coordinate.neighbors[Direction.Negative]: + if ( + coordinate.neighbors[Direction.Positive] + and coordinate.neighbors[Direction.Negative] + ): n0 = coordinate.neighbors[Direction.Negative] - elif coordinate.neighbors[Direction.Positive] and not coordinate.neighbors[Direction.Negative]: - n0 = coordinate - elif not coordinate.neighbors[Direction.Positive] and coordinate.neighbors[Direction.Negative]: - n0 = coordinate.neighbors[Direction.Negative].neighbors[Direction.Negative] + elif ( + coordinate.neighbors[Direction.Positive] + and not coordinate.neighbors[Direction.Negative] + ): + n0 = coordinate + elif ( + not coordinate.neighbors[Direction.Positive] + and coordinate.neighbors[Direction.Negative] + ): + n0 = coordinate.neighbors[Direction.Negative].neighbors[ + Direction.Negative + ] else: - raise Exception("Cannot determine the gradient of a coordinate with no neighbors") + raise Exception( + "Cannot determine the gradient of a coordinate with no neighbors" + ) n1 = n0.neighbors[Direction.Positive] n2 = n1.neighbors[Direction.Positive] @@ -61,41 +85,87 @@ def neighbor_gradient(self, coordinate: Coordinate, coordinates: List[Coordinate n2_name = f"h_{n2.id}" # tp is the gradient wrt. n2, n1 - tp = Transition(id=w_p_name, input=[n2_name, n1_name], output=[w_p_name], properties=Properties(name=w_p_name) ) - + tp = Transition( + id=w_p_name, + input=[n2_name, n1_name], + output=[w_p_name], + properties=Properties(name=w_p_name), + ) + # tn is the gradient wrt. n1, n0 - tn = Transition(id=w_n_name, input=[n1_name, n0_name], output=[w_n_name], properties=Properties(name=w_n_name) ) - + tn = Transition( + id=w_n_name, + input=[n1_name, n0_name], + output=[w_n_name], + properties=Properties(name=w_n_name), + ) + transitions = [tp, tn] - tpr = Rate(target=w_p_name, expression=self.transition_expression(n1_name, n2_name)) - tnr = Rate(target=w_n_name, expression=self.transition_expression(n0_name, n1_name, negative=True)) + tpr = Rate( + target=w_p_name, + expression=self.transition_expression(n1_name, n2_name), + ) + tnr = Rate( + target=w_n_name, + expression=self.transition_expression( + n0_name, n1_name, negative=True + ), + ) rates = [tpr, tnr] return transitions, rates - - def model(self, args)-> Tuple[Model1, Semantics]: - + def model(self, args) -> Tuple[Model1, Semantics]: coordinates = self.coordinates(args) # Create a height variable at each coordinate - states = [State(id=f"h_{i}", name = f"h_{i}", description=f"height at {i}") for i in range(len(coordinates))] + states = [ + State(id=f"h_{i}", name=f"h_{i}", description=f"height at {i}") + for i in range(len(coordinates)) + ] transitions = [] rates = [] for coordinate in coordinates: - coord_transitions, trans_rates = self.neighbor_gradient(coordinate, coordinates) + coord_transitions, trans_rates = self.neighbor_gradient( + coordinate, coordinates + ) transitions += coord_transitions rates += trans_rates - time = Time(id="t", units=Unit(expression="day", expression_mathml="day")) + time = Time( + id="t", + units=Unit(expression="day", expression_mathml="day"), + ) + + initials = [ + Initial( + target=f"h_{c.id}", expression=f"{1.0/(1.0+abs(c.vector[0]))}" + ) + for c in coordinates + ] + + parameters = [ + Parameter( + id="gamma", + value=1.0, + distribution=Distribution( + type="StandardUniform1", + parameters={"minimum": 0.0, "maximum": 1.0}, + ), + ) + ] + + return Model1(states=states, transitions=transitions), Semantics( + ode=OdeSemantics( + rates=rates, + initials=initials, + parameters=parameters, + time=time, + ) + ) - initials = [Initial(target=f"h_{c.id}", expression=f"{1.0/(1.0+abs(c.vector[0]))}") for c in coordinates] - - parameters = [Parameter(id="gamma", value=1.0, distribution=Distribution(type="StandardUniform1", parameters={"minimum":0.0, "maximum":1.0}))] - - return Model1(states=states,transitions=transitions), Semantics(ode=OdeSemantics(rates=rates, initials=initials, parameters=parameters, time=time)) def get_args(): parser = argparse.ArgumentParser() @@ -121,20 +191,30 @@ def get_args(): ) return parser.parse_args() + def main(): args = get_args() generator = HalfarGenerator() model, semantics = generator.model(args) - halfar_model = HalfarModel(header = Header(name="Halfar Model", - schema_=AnyUrl("https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.1/petrinet/petrinet_schema.json"), - schema_name="petrinet", description="Halfar as Petrinet model created by Dan", - model_version="0.1"), - model=model, semantics=semantics) + halfar_model = HalfarModel( + header=Header( + name="Halfar Model", + schema_=AnyUrl( + "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.1/petrinet/petrinet_schema.json" + ), + schema_name="petrinet", + description="Halfar as Petrinet model created by Dan", + model_version="0.1", + ), + model=model, + semantics=semantics, + ) j = halfar_model.model_dump_json(indent=4) # print(j) with open(args.outfile, "w") as f: print(f"Writing {os.path.join(os.getcwd(), args.outfile)}") f.write(j) + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/notebooks/funman_results.ipynb b/notebooks/funman_results.ipynb index 22364ceb..3270746a 100644 --- a/notebooks/funman_results.ipynb +++ b/notebooks/funman_results.ipynb @@ -449,6 +449,32 @@ "print(f\"Model has the symbols: {results.model._symbols()}\")\n", "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Infected\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(((((((((((((((((((((((SVR_1 = (((((SVR_0 + (nu_R * R2_0)) + (nu_R * R_0)) + (nu_v2 * V2_0)) + (nu_v1 * V1_0)) - (mu * SVR_0))) & (nu_R = 68493150684931507/100000000000000000000)) & (R2_1 = ((((R2_0 - (nu_R * R2_0)) - (mu * R2_0)) + (gamma * AR_0)) + (gamma * IR_0)))) & (R_1 = (((((((((((((((((R_0 - (nu_R * R_0)) - (mu * R_0)) + (gamma * AR_0)) + (gamma * IR_0)) + (gamma * A_0)) + (gamma * IV_0)) + (gamma * I_0)) - (((((ai_R * beta_scale) * beta_R) * ai_beta_ratio) * R_0) * AR_0)) - (((((ai_R * beta_scale) * beta_R) * ai_beta_ratio) * R_0) * A_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * IR_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * IV_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * I_0)) - (((((beta_scale * beta_R) * ai_beta_ratio) * R_0) * AR_0) * (1.0 - ai_R))) - (((((beta_scale * beta_R) * ai_beta_ratio) * R_0) * A_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * IR_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * IV_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * I_0) * (1.0 - ai_R))))) & (nu_v2 = 68493150684931507/100000000000000000000)) & (nu_v1 = 27397260273972603/10000000000000000000)) & (mu = (1281237953761247.0 * 1/62500000000000000000))) & (gamma = 2232142857142857/62500000000000000)) & (ai_R = 42499999999999999/50000000000000000)) & (beta_scale = 1.0)) & (beta_R = 4808100000000001/2000000000000000000)) & (ai_beta_ratio = 3.0)) & (SVR_0 = 0.0)) & (R2_0 = 0.0)) & (R_0 = 0.0)) & (V2_0 = 0.0)) & (V1_0 = 0.0)) & (AR_0 = 0.0)) & (IR_0 = 0.0)) & (A_0 = 0.0)) & (IV_0 = 0.0)) & (I_0 = (19999999999999999.0 * 1/20000000000000000000000))) & \n", + " (((((((((((((((((((((\n", + " (! (SVR_1 = (((((SVR_0 + (nu_R * R2_0)) + (nu_R * R_0)) + (nu_v2 * V2_0)) + (nu_v1 * V1_0)) - (mu * SVR_0)))) | \n", + " (! (nu_R = 68493150684931507/100000000000000000000))) | \n", + " (! (R2_1 = ((((R2_0 - (nu_R * R2_0)) - (mu * R2_0)) + (gamma * AR_0)) + (gamma * IR_0))))) | \n", + " (! (R_1 = (((((((((((((((((R_0 - (nu_R * R_0)) - (mu * R_0)) + (gamma * AR_0)) + (gamma * IR_0)) + (gamma * A_0)) + (gamma * IV_0)) + (gamma * I_0)) - (((((ai_R * beta_scale) * beta_R) * ai_beta_ratio) * R_0) * AR_0)) - (((((ai_R * beta_scale) * beta_R) * ai_beta_ratio) * R_0) * A_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * IR_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * IV_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * I_0)) - (((((beta_scale * beta_R) * ai_beta_ratio) * R_0) * AR_0) * (1.0 - ai_R))) - (((((beta_scale * beta_R) * ai_beta_ratio) * R_0) * A_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * IR_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * IV_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * I_0) * (1.0 - ai_R)))))) | \n", + " (! (nu_v2 = 68493150684931507/100000000000000000000))) | \n", + " (! (nu_v1 = 27397260273972603/10000000000000000000))) | \n", + " (! (mu = (1281237953761247.0 * 1/62500000000000000000)))) | \n", + " (! (gamma = 2232142857142857/62500000000000000))) | \n", + " (! (ai_R = 42499999999999999/50000000000000000))) | \n", + " (! (beta_scale = 1.0))) | \n", + " (! (beta_R = 4808100000000001/2000000000000000000))) | \n", + " (! (ai_beta_ratio = 3.0))) | \n", + " (! (SVR_0 = 0.0))) | \n", + " (! (R2_0 = 0.0))) | \n", + " (! (R_0 = 0.0))) | \n", + " (! (V2_0 = 0.0))) | (! (V1_0 = 0.0))) | (! (AR_0 = 0.0))) | (! (IR_0 = 0.0))) | (! (A_0 = 0.0))) | (! (IV_0 = 0.0))) | (! (I_0 = (19999999999999999.0 * 1/20000000000000000000000)))))" + ] } ], "metadata": { diff --git a/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json b/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json index 837480a2..2e5794b3 100644 --- a/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json +++ b/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json @@ -20,7 +20,6 @@ } ], "config": { - "save_smtlib": false, "substitute_subformulas": false, "use_compartmental_constraints": false } diff --git a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py index 91b9ff65..3b9578d9 100644 --- a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py +++ b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py @@ -170,26 +170,23 @@ def main(): # No timepoints, because the variables are parameters }, # { + # "name": "infected_maximum3", + # "variable": "Infected", + # "interval": { "ub": 0.7}, + # "timepoints": {"lb": 130}, + # }, + # { # "name": "infected_maximum1", # "variable": "Infected", - # "interval": {"lb": 0.1, "ub": 0.4}, - # "timepoints": {"lb": 60, "ub": 80, "closed_upper_bound": True}, + # "interval": { "ub": 0.4}, + # "timepoints": {"lb": 70, "ub": 75, "closed_upper_bound": True}, + # }, + # { + # "name": "infected_maximum2", + # "variable": "Infected", + # "interval": {"ub": 0.2}, + # "timepoints": { "ub": 75}, # }, - { - "name": "infected_maximum1", - "variable": "Infected", - "interval": {"lb": 0.0, "ub": 0.5}, - "timepoints": {"lb": 60, "ub": 80, "closed_upper_bound": True}, - }, - - - - { - "name": "infected_maximum2", - "variable": "Infected", - "interval": {"ub": 0.1}, - "timepoints": {"lb": 0, "ub": 50}, - }, # { # "name": "infected_maximum3", # "variable": "Infected", @@ -201,25 +198,7 @@ def main(): { "name": "schedules", "schedules": [ - { - "timepoints": [ - 0, - 10, - 20, - 30, - 40, - 50, - 55, - 56, - 57, - 60, - 65, - 70, - 80, - 90, - 100, - ] - } + {"timepoints": [0, 10, 30, 50, 70, 90, 110, 130, 150]} # {"timepoints": [0, 10]} ], } @@ -227,7 +206,7 @@ def main(): "config": { "use_compartmental_constraints": True, "normalization_constant": 1.0, - "tolerance": 1e-2, + "tolerance": 1e-5, "verbosity": 10, "dreal_mcts": True, # "save_smtlib": os.path.join(os.path.realpath(__file__), "./out"), From e0190a328b29ef00d4ca6010186c92d08c4f602b Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Fri, 3 Nov 2023 15:19:30 +0000 Subject: [PATCH 12/28] fix tests --- .../requests/request2_b_default_wo_compartmental_constrs.json | 3 ++- src/funman/search/smt_check.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json b/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json index 2e5794b3..e1b09d2e 100644 --- a/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json +++ b/resources/amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json @@ -21,6 +21,7 @@ ], "config": { "substitute_subformulas": false, - "use_compartmental_constraints": false + "use_compartmental_constraints": false, + "dreal_log_level": "info" } } \ No newline at end of file diff --git a/src/funman/search/smt_check.py b/src/funman/search/smt_check.py index a6205643..78eb79b0 100644 --- a/src/funman/search/smt_check.py +++ b/src/funman/search/smt_check.py @@ -175,7 +175,7 @@ def solve_formula( l.trace(f"Saving smt file: {filename}") self.store_smtlib( formula, - filename=f"dbg_steps.smt2", + filename=filename, ) l.trace(f"Solving: {formula.serialize()}") result = self.invoke_solver(s) From 8fc3e1cff7a7923df2945e8bab7abe83b376d352 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Fri, 3 Nov 2023 22:03:35 +0000 Subject: [PATCH 13/28] hackathon demos --- notebooks/funman_results.ipynb | 26 - notebooks/funman_terrarium_demo.ipynb | 258 +++++++ .../hackathon_fall_2023_demo_terarrium.py | 35 +- .../hackathon_fall_2023_demo_halfar.ipynb | 654 ++++++++++++++---- src/funman/api/run.py | 36 +- 5 files changed, 824 insertions(+), 185 deletions(-) create mode 100644 notebooks/funman_terrarium_demo.ipynb diff --git a/notebooks/funman_results.ipynb b/notebooks/funman_results.ipynb index 3270746a..22364ceb 100644 --- a/notebooks/funman_results.ipynb +++ b/notebooks/funman_results.ipynb @@ -449,32 +449,6 @@ "print(f\"Model has the symbols: {results.model._symbols()}\")\n", "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Infected\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(((((((((((((((((((((((SVR_1 = (((((SVR_0 + (nu_R * R2_0)) + (nu_R * R_0)) + (nu_v2 * V2_0)) + (nu_v1 * V1_0)) - (mu * SVR_0))) & (nu_R = 68493150684931507/100000000000000000000)) & (R2_1 = ((((R2_0 - (nu_R * R2_0)) - (mu * R2_0)) + (gamma * AR_0)) + (gamma * IR_0)))) & (R_1 = (((((((((((((((((R_0 - (nu_R * R_0)) - (mu * R_0)) + (gamma * AR_0)) + (gamma * IR_0)) + (gamma * A_0)) + (gamma * IV_0)) + (gamma * I_0)) - (((((ai_R * beta_scale) * beta_R) * ai_beta_ratio) * R_0) * AR_0)) - (((((ai_R * beta_scale) * beta_R) * ai_beta_ratio) * R_0) * A_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * IR_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * IV_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * I_0)) - (((((beta_scale * beta_R) * ai_beta_ratio) * R_0) * AR_0) * (1.0 - ai_R))) - (((((beta_scale * beta_R) * ai_beta_ratio) * R_0) * A_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * IR_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * IV_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * I_0) * (1.0 - ai_R))))) & (nu_v2 = 68493150684931507/100000000000000000000)) & (nu_v1 = 27397260273972603/10000000000000000000)) & (mu = (1281237953761247.0 * 1/62500000000000000000))) & (gamma = 2232142857142857/62500000000000000)) & (ai_R = 42499999999999999/50000000000000000)) & (beta_scale = 1.0)) & (beta_R = 4808100000000001/2000000000000000000)) & (ai_beta_ratio = 3.0)) & (SVR_0 = 0.0)) & (R2_0 = 0.0)) & (R_0 = 0.0)) & (V2_0 = 0.0)) & (V1_0 = 0.0)) & (AR_0 = 0.0)) & (IR_0 = 0.0)) & (A_0 = 0.0)) & (IV_0 = 0.0)) & (I_0 = (19999999999999999.0 * 1/20000000000000000000000))) & \n", - " (((((((((((((((((((((\n", - " (! (SVR_1 = (((((SVR_0 + (nu_R * R2_0)) + (nu_R * R_0)) + (nu_v2 * V2_0)) + (nu_v1 * V1_0)) - (mu * SVR_0)))) | \n", - " (! (nu_R = 68493150684931507/100000000000000000000))) | \n", - " (! (R2_1 = ((((R2_0 - (nu_R * R2_0)) - (mu * R2_0)) + (gamma * AR_0)) + (gamma * IR_0))))) | \n", - " (! (R_1 = (((((((((((((((((R_0 - (nu_R * R_0)) - (mu * R_0)) + (gamma * AR_0)) + (gamma * IR_0)) + (gamma * A_0)) + (gamma * IV_0)) + (gamma * I_0)) - (((((ai_R * beta_scale) * beta_R) * ai_beta_ratio) * R_0) * AR_0)) - (((((ai_R * beta_scale) * beta_R) * ai_beta_ratio) * R_0) * A_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * IR_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * IV_0)) - ((((ai_R * beta_scale) * beta_R) * R_0) * I_0)) - (((((beta_scale * beta_R) * ai_beta_ratio) * R_0) * AR_0) * (1.0 - ai_R))) - (((((beta_scale * beta_R) * ai_beta_ratio) * R_0) * A_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * IR_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * IV_0) * (1.0 - ai_R))) - ((((beta_scale * beta_R) * R_0) * I_0) * (1.0 - ai_R)))))) | \n", - " (! (nu_v2 = 68493150684931507/100000000000000000000))) | \n", - " (! (nu_v1 = 27397260273972603/10000000000000000000))) | \n", - " (! (mu = (1281237953761247.0 * 1/62500000000000000000)))) | \n", - " (! (gamma = 2232142857142857/62500000000000000))) | \n", - " (! (ai_R = 42499999999999999/50000000000000000))) | \n", - " (! (beta_scale = 1.0))) | \n", - " (! (beta_R = 4808100000000001/2000000000000000000))) | \n", - " (! (ai_beta_ratio = 3.0))) | \n", - " (! (SVR_0 = 0.0))) | \n", - " (! (R2_0 = 0.0))) | \n", - " (! (R_0 = 0.0))) | \n", - " (! (V2_0 = 0.0))) | (! (V1_0 = 0.0))) | (! (AR_0 = 0.0))) | (! (IR_0 = 0.0))) | (! (A_0 = 0.0))) | (! (IV_0 = 0.0))) | (! (I_0 = (19999999999999999.0 * 1/20000000000000000000000)))))" - ] } ], "metadata": { diff --git a/notebooks/funman_terrarium_demo.ipynb b/notebooks/funman_terrarium_demo.ipynb new file mode 100644 index 00000000..4d574e10 --- /dev/null +++ b/notebooks/funman_terrarium_demo.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# This notebook illustrates example outputs from Funman, and how to work with the ParameterSpace object it creates.\n", + "\n", + "# The file scratch/hackathon/hackathon_fall_2023_demo_terarrium.py was used to generate the outputs rendered here.\n", + "\n", + "# Import funman related code\n", + "import os\n", + "from funman import FunmanResults\n", + "import json\n", + "from funman_demo.parameter_space_plotter import ParameterSpacePlotter\n", + "SAVED_RESULTS_DIR = \"../out\"\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 1: Unconstrained model, constraints = []\n", + "\n", + "# Synthesize parameters epsilon and theta\n", + "\n", + "# {\n", + "# \"name\": \"epsilon\",\n", + "# \"interval\": {\"lb\": 0.1368, \"ub\": 0.20520000000000002},\n", + "# \"label\": \"all\",\n", + "# },\n", + "# {\n", + "# \"name\": \"theta\",\n", + "# \"interval\": {\"lb\": 0.2968, \"ub\": 0.4452},\n", + "# \"label\": \"all\",\n", + "# },\n", + "\n", + "SAVED_RESULT_TO_USE = os.path.join(SAVED_RESULTS_DIR, \"06396b64-b529-4808-a2dc-d74ff9e33657.json\")\n", + "\n", + "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n", + " results: FunmanResults = FunmanResults.model_validate(json.load(f))\n", + "\n", + "# Plot the trajectories\n", + "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Infected\")\n", + "\n", + "# Plot the parameter space\n", + "ParameterSpacePlotter(\n", + " results.parameter_space, plot_points=True, parameters=[\"epsilon\", \"theta\", \"timestep\"]\n", + " ).plot(show=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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cckk8/vjjbWpCR0Qceuih8dRTT8XXv/71tHOfffbZmDZtWpvWh2I2ZMiQOOKIIzq6jBZde+21aRvRY8aMiZkzZ7aqER0R8ZGPfCSeeuqpOPXUUxPnLVu2LCZOnNjaUgEAQM8YioyeMXRdesbQuekZQ+HSN4bOSc+4ZXrGAAC0lZ4xFB99Y+i69I2h89IzhsKlZwydk55xy/SMITNCrwEgS+bPnx+TJ09OnPOxj30s7rvvvujZs2eb1u7Vq1fcf//9sd9++yXOmzRpUrzwwgttWhvY3vDhw+OGG26IN954I370ox/FPvvsk9F6NTU1ceONN8aUKVOirKwsce7ixYvj6quvzmg/IHM/+MEPYtasWc2O7bbbbnmuBtjSsmXL4tRTT42NGze2OKd3797x4IMPxo033hj9+/fPeM9+/frFRRddFK+88krcfvvtGa8HXdVPfvKTtHP+4z/+I6688sq0j5tbUlFRET/4wQ/iq1/9atq5P/7xj9u1BxS6gQMHxoknnhjf/e5346GHHop33303Fi5cGDfccENHl9asd955J+0bMIYPHx6PPPJI7LLLLm1au6qqKm699dY44YQTEufdc8898eijj7ZpbQAAuiY9YygeesaAnjF0XnrGUNj0jaHj6Rl/QM8YAIBs0zOG4qJvDOgbQ+ekZwyFTc8YOp6e8Qf0jCH3hF4DQJZcccUViZ/Q2KdPn5g2bVqrP7V1WzU1NXHHHXdEr169WpzT0NAQV155ZbvWByJGjhwZ//M//xMvvvhinHvuue2+Xlty5plnxqRJk9LOmzRpUqxcuTKrewOt98orr7T4ZNcBBxwQn/vc5/JbELCVc889N15//fUWx3fZZZf405/+FEcddVTW9y4rK8vJutAVzJ07N55//vnEOQcddFD853/+Z1b2+9GPfpT2Bd1/+ctf4pVXXsnKftBZ7bLLLnH88cfHlVdeGb/73e9iyZIl8Y9//CPuvvvu+Pa3vx1HHnlk9O3bt6PLTPTf//3fsWLFihbHKysr44477ogdd9yxXeuXlZXFlClTYsiQIYnzLr300natDwBA16JnDIVPzxiI0DOGzk7PGAqXvjHkn55xenrGAABkk54xFAd9YyBC3xg6Mz1jKFx6xpB/esbp6RlDbgm9BoAs+Pvf/x533XVX4pzvfve7MXDgwIz2GTx4cFxxxRWJc+68885YtGhRRvtAV7PTTjvFddddF/PmzYvTTz+93Z/01hpf/vKX48wzz0ycs2bNmrjjjjtyVgOQ7Lzzzot169Ztd3tFRUXccMMNUVJS0gFVARERv/vd7+I3v/lNi+M9evSIBx54IEaNGpXHqoDWeOyxx9LO+f73v5+1v2dLS0vjqquuSjvPJ6pSjC644IK4//77Y/HixfHGG2/EfffdF5dcckkcc8wx7W7YdpSVK1em/WToiRMnxt57753RPj179kz7CfF//vOf449//GNG+wAAUNz0jKGw6RkDW9Izhs5LzxgKm74x5IeecdvpGQMAkA16xlD49I2BLekbQ+ekZwyFTc8Y8kPPuO30jCF3hF4DQBZMnjw5GhsbWxwfMWJEnHvuuVnZa8KECbHbbru1ON7Y2BiTJ0/Oyl7QVXzhC1+IL3/5y1FeXp6X/b73ve+l/WTne++9Ny+1AFu76aabWnyi/N///d/jQx/6UJ4rAjarr6+Pf//3f0+cc/3118eYMWPyVBHQFjNnzkwc33333eOggw7K6p6HHHJIDB8+PHHOs88+m9U9oTM466yz4rjjjoudd965o0vJ2JQpUxI/fblXr17x7W9/Oyt7HX/88XHwwQcnzvnpT3+alb0AAChOesZQ2PSMgc30jKHz0jOGwqdvDPmhZ9w+esYAAGRKzxgKn74xsJm+MXROesZQ+PSMIT/0jNtHzxhyQ+g1AGSosbExfv3rXyfOufDCC7P2aa7l5eXx1a9+NXHO1KlTo6mpKSv7Adm36667xqmnnpo4549//KPrGPLs7bffjm984xvNju22225x6aWX5rkiYEu//OUvY8GCBS2OH3/88XHaaaflsSKgLV555ZXE8SOOOCIn+x555JGJ43/7299ysi+QHbfeemvi+Lnnnht1dXVZ2y/dC9/uv//+xOY4AABdl54x0FZ6xtA56RlD56ZnDIVP3xhoKz1jAAAKhZ4x0B76xtA56RtD56VnDIVPzxhoKz1jKHxCrwEgQ9OnT4/Fixe3OF5dXR2f+9znsrrn+PHjo7KyssXxN998Mx5//PGs7glk13HHHZc4vnLlynj11VfzVA0QEfHVr341li1b1uzYddddF926dctzRcBmTU1N8cMf/rDF8bKysrj66qvzWBHQVi39HbvZXnvtlZN906377rvv5mRfIHMvv/xyzJgxI3HOOeeck9U9P/WpT0X//v1bHN+wYUPcddddWd0TAIDioGcMtIeeMXQ+esbQeekZQ3HQNwbaQs8YAIBComcMtJe+MXQ++sbQOekZQ3HQMwbaQs8YioPQawDI0P333584fuyxx0aPHj2yumevXr3i6KOPTpyTri6gY3384x9PO+fvf/97HioBIjb9vXnHHXc0O3byySen/eRGILf+93//N15++eUWx0866aTYY4898lgR0FYbNmxIHN9hhx1ysu+OO+6YOL5u3bqc7AtkLt1zW/vuu28MHz48q3uWlpbGZz/72cQ5nnMDAKA5esZAe+gZQ+eiZwydm54xFAd9Y6At9IwBACgkesZAe+kbQ+eibwydl54xFAc9Y6At9IyhOAi9BoAMPfroo4njxx57bE72TbfuI488kpN9gezo06dP4iepR0QsX748P8VAF7dq1aqYMGFCs2O9evWKH//4x/ktCNjOzTffnDj+pS99KU+VAO3Vs2fPxPGampqc7Jtu3bq6upzsC2Susz7n9vvf/z4aGxtzsjcAAIWrsz5+1TOGzk3PGDoPPWPo/PSMoTjoGwNt0Vmfc9MzBgCgOZ318aueMXR++sbQeegbQ+emZwzFQc8YaIvO+pybnjG0jdBrAMjA4sWL44UXXkicc9hhh+Vk78MPPzxxfN68efHWW2/lZG8gO9J9wpxPgoP8uPjii+P1119vduz73/9+7LzzznmuCNjS8uXL46GHHmpxvH///vGJT3wifwUB7dK3b9/E8ffeey8n+6ZbN11dQMdoaGiIJ554InFOrp5zO/jgg6O6urrF8RUrVsSMGTNysjcAAIVJzxjIhJ4xdA56xtC56RlD8dA3BlpLzxgAgEKiZwxkSt8YOgd9Y+i89IyheOgZA62lZwzFQ+g1AGTgmWeeSRwfOHBgDBw4MCd7DxkyJPr37584xwNj6NzWrl2bOJ70P79Adjz11FPxs5/9rNmxj33sY3HeeefluSJgW/fcc09s3LixxfHjjjsuSkpK8lgR0B6jRo1KHM/Vi6nTrbvbbrvlZF8gM/PmzYs1a9a0OF5RURH77bdfTvaurq6OvffeO3GO59wAANiSnjGQCT1j6Hh6xtD56RlD8dA3BlpLzxgAgEKiZwxkSt8YOp6+MXRuesZQPPSMgdbSM4biIfQaADIwc+bMxPF99tknp/uPHTs2cXzWrFk53R9ov1WrVsWKFSsS5/Tu3TtP1UDXtHHjxjj77LMjlUptN1ZeXh433HCDBhd0Ao888kji+Cc/+ck8VQJk4uCDD04c/+Mf/5iTfdN9gutBBx2Uk32BzKR7zm3UqFFRVVWVs/095wYAQFvoGQPtpWcMHU/PGAqDnjEUD31joLX0jAEAKCR6xkAm9I2h4+kbQ+enZwzFQ88YaC09YygeQq8BIAOzZ89OHN9rr71yun+69T0whs5r1qxZzTa/tjRs2LA8VQNd03/+53/GCy+80OzY17/+9fjwhz+c54qA5jz++OOJ4x/96EfzUwiQkU9+8pNRXV3d4vj06dNjw4YNWd1z3bp1MX369BbHS0tL45BDDsnqnkB2eM4NAIBC4vEr0F56xtDx9IyhMOgZQ/HQNwZay3NuAAAUEo9fgUzoG0PH0zeGzk/PGIqHnjHQWp5zg+JR3tEFAEAhe+mllxLHR4wYkdP9hw8fnjj+8ssv53R/oP1+97vfJY7X1dXFoEGD8lQNdD3z58+Pq666qtmxIUOGxGWXXZbnioDm/O1vf4vFixe3ON6rV68YOnRo2nUaGhri5ZdfjoULF8aKFStiw4YN0b179+jRo0cMHDgwhgwZErW1tdksHdhG79694/TTT49f/vKXzY4vX748fvazn8XEiROztuekSZNi5cqVLY5/6lOfigEDBmRtPyB7POcGAEAh8fgVaC89Y+hYesZQGPSMobjoGwOt5Tk3AAAKicevQCb0jaFj6RtD56dnDMVFzxhoLc+5QfEQeg0A7ZRKpWLRokWJc9I9cM1UuvXT1Qd0jMbGxpg2bVrinIMOOihKS0vzVBF0LU1NTXH22WfHxo0bmx2/7rrronv37nmuCmhOuk9fTHo8/O6778Ztt90W999/f/zxj39s8ZqPiCgpKYk999wzDjrooDjhhBPisMMOi8rKyvaWDbTgG9/4Rtx6660tXo/f+9734jOf+UzsuuuuGe/16quvtviis82+/vWvZ7wPkBsLFy5MHO/o59zWrFkT77zzTuy44445rQMAgM5PzxhoLz1j6Fh6xlA49Iyh+OgbA62hZwwAQKHQMwYyoW8MHUvfGAqDnjEUHz1joDX0jKF4eGYLANrp7bffjvXr1yfO2WWXXXJaQ7r116xZE0uWLMlpDUDb3XvvvfHqq68mzjn++OPzVA10PZMnT44///nPzY599rOfjaOPPjrPFQEtmTt3buL4sGHDtrttyZIl8eUvfzkGDRoUEydOjMceeyyxER2x6YWm8+fPj5///Odx7LHHxoABA+KKK66IZcuWZVQ/sLU99tgjLr300hbH33nnnTjuuONi1apVGe2zdOnSOProoxOv4S984Qvx8Y9/PKN9gNxIpVJp/58518+57bzzzmlfHJ6uYQ4AQNegZwy0l54xdCw9YygcesZQfPSNgXT0jAEAKCR6xkAm9I2hY+kbQ2HQM4bio2cMpKNnDMVF6DUAtNObb76Zds7OO++c0xpas35r6gTyp7GxMfHJt4iIysrK+MxnPpOniqBree211+Lb3/52s2M9e/aMH//4x/ktCEg0f/78xPGddtppq+9/+ctfxu677x7XX399rFu3rt37vvPOO3H55ZfHyJEj48Ybb2z3OsD2Lr744jjiiCNaHJ89e3Z85CMfiTlz5rRr/b/85S8xduzYeOGFF1qcM2zYsPjRj37UrvWB3Fu2bFnaN4Dk+jm38vLy6Nu3b+Icz7kBABChZwy0j54xdCw9YygsesZQnPSNgSR6xgAAFBI9Y6C99I2hY+kbQ+HQM4bipGcMJNEzhuIi9BoA2um9995LHK+rq4uqqqqc1tC9e/eora1NnJOuTiC/fvazn6V9Yn38+PHRp0+fPFUEXcuECRNa/ETH733ve9G/f/88VwQkee211xLHd9xxx4iIqK+vj7POOivOPvvsWL58edb2f/fdd+Pcc8+Nk046KVauXJm1daErKysri3vvvTf+9V//tcU5CxYsiP322y+++MUvtrohPWPGjDj99NPjoIMOSvxU1AEDBsSjjz4aPXv2bHPtQH605rmsfv365byObV/0ti3PuQEAEKFnDLSPnjF0LD1jKCx6xlCc9I2BJHrGAAAUEj1joL30jaFj6RtD4dAzhuKkZwwk0TOG4lLe0QUAQKFaunRp4nhdXV1e6qirq4vVq1e3OJ6uTiB/Fi1aFP/xH/+ROKeioiIuuuiiPFUEXcvtt98ev/3tb5sd23///eNLX/pSnisC0lm8eHHieF1dXTQ0NMSpp54ad911V87quPvuu2PhwoXx8MMPv98AB9qvW7du8dBDD8W///u/x3XXXdfsnI0bN8bNN98cN998c+yyyy5x4IEHxogRI6J3795RW1sbq1atimXLlsWCBQviySefjLfffjvtvvvss0/ceeedMWTIkCz/RkA2tea5rHw875ZuD8+5AQAQoWcMtJ2eMXQsPWMoPHrGULz0jYGW6BkDAFBI9IyB9tA3ho6lbwyFRc8YipeeMdASPWMoLkKvAaCdli1bljjeo0ePvNSRbh8PjKFzaGxsjPHjxye+eCQiYuLEiTFs2LA8VQVdx9KlS+NrX/tas2Pl5eVxww03RGlpaZ6rAtJ56623EscrKytjwoQJOW1EbzZr1qz45Cc/GU8++WTeXngKxay6ujomT54cxx13XFx00UXx17/+tcW5b775Ztx5553t3quysjK++tWvxn/+539GZWVlu9cB8iPdc27dunWLsrKynNfhOTcAAFpDzxhoCz1j6Fh6xlCY9IyhuOkbA83RMwYAoJDoGQNtpW8MHUvfGAqPnjEUNz1joDl6xlBc/F82ALTT+vXrE8dramryUkdtbW3ieLo6gfy45JJL4oknnkicM3DgwLjkkkvyVBF0LV//+tdjyZIlzY5deOGFsddee+W5IiCd9evXx4YNGxLn3HHHHXHjjTe2ON6tW7c47rjj4sYbb4znnnsuXn/99diwYUMsWbIknn/++bjzzjvjzDPPjL59+7aqprlz58Ypp5wSqVSqTb8L0LKjjz465syZE3fffXccd9xxUV1dnbW16+rq4ktf+lL87W9/i2uuuUYTGgqE59wAACgkHr8CbaFnDB1LzxgKj54xdB36xsCWPOcGAEAh8fgVaCt9Y+hY+sZQWPSMoevQMwa25Dk3KC7lHV0AABSqjRs3Jo6Xl+fnr9l0+6SrE8i9+++/P6666qrEOSUlJXHTTTfl7dPboSt59NFHY8qUKc2ODR48OC6//PL8FgS0yrp169LO+f3vf9/s7SUlJXHGGWfE1VdfHTvvvPN24zvuuGPsuOOO8eEPfzjGjRsX69ati6uvvjr+67/+K+2+Dz74YEyaNCm++tWvtu4XAdIqKSmJE088Mfbcc8+47bbb4r//+78zavJUVFTEt771rfj2t78d3bp1y2KlQD54zg0AgELi8SvQWnrG0LH0jKEw6RlD16JvDGzmOTcAAAqJx69AW+gbQ8fSN4b/v737jq+6uh8//g6RTVBkOUBwoDKq4AAHCiJK3VL166qCVVxoa92ttc622rrqFhdo66i4tSICIhRQEREn4AAElaEiewSS3x/9dWi5K/feJDd5Ph8P//h6Ts45MdLefl75nk/h0YyhdtGMgX/xzA1qljpVfQAAKFQ+GAPpeP/99+PEE09M+abGc845J/r27VtJp4LaY+XKlXHGGWckHL/jjjuiUaNGlXgiIF0VjVCNGjWKl156KYYNG7bBEL0hDRs2jCuvvDKmTZsW7du3Tzn/V7/6VXz55ZcVOh/wfevWrYuHHnoounTpEh07doxrr70267ealpaWxu9+97vYeuut48wzz4wZM2bk6LRAZfDMDQCAQuLzK5AOzRiqlmYMhUszhtpDNwb+m2duAAAUEp9fgXTpxlC1dGMoTJox1B6aMfDfPHODmsWl1wBQQWVlZUnHi4uLK+UcqfZZv359pZwD+F8LFy6Mww47LJYtW5Z03u677x433HBDJZ0Kapff/va38dlnn21w7Oijj45DDjmkkk8EpKu0tDTjrykpKYmRI0dGv379KrRnhw4dYvz48bH99tsnnbdy5cq4+uqrK7QH8B8vvvhidOjQIQYMGBAffPBBztdfsGBB3HPPPdGpU6c45phj4tNPP835HkDueeYGAEAh8fkVSEUzhqqnGUPh0oyhdtCNgR/yzA0AgELi8yuQDt0Yqp5uDIVJM4baQTMGfsgzN6hZXHoNABWU6i0s69atq5RzpNqnbt26lXIO4PuWL18eBx98cMyePTvpvObNm8cTTzwR9erVq5yDQS0yZcqUuOWWWzY41rRp07j11lsr90BARiryoPm2226LvffeO6t927Rpk9Z/Nw8dOjS+/vrrrPaC2mrVqlVx9tlnx6GHHpry83IulJWVxfDhw6Nr167xwAMP5H0/IDueuQEAUEh8fgWS0Yyh6mnGUNg0Y6jZdGMgEc/cAAAoJD6/AqnoxlD1dGMoXJox1GyaMZCIZ25Qs7j0GgAqKNXDqcr6YJzqzXTiFlS+tWvXRv/+/WPKlClJ5zVs2DCeffbZaNeuXSWdDGqPdevWxWmnnZbwrWi///3vY/PNN6/kUwGZyPRz7OGHHx4DBgzIyd477bRT/Pa3v006Z82aNfHggw/mZD+oTVatWhWHHnpo3HXXXSnnFhcXxwEHHBDXXHNNjBkzJmbOnBnffPNNlJaWxtdffx0zZ86M0aNHx9VXXx19+/aNOnWSP+5evnx5nHrqqTF48OBcfTtAHnjmBgBAIfH5FUhEM4aqpxlD4dOMoebSjYFkPHMDAKCQ+PwKJKMbQ9XTjaGwacZQc2nGQDKeuUHNkvwaewAgoVRvWVm7dm2lnMMHY6he1q9fH8cff3yMGjUq6by6devGE088kfVbIoENu+GGG+Kdd97Z4Fj37t3jrLPOqtwDARnL9HPs7373u5zuf8EFF8TNN98c33zzTcI5Tz75ZFx00UU53RdqsrVr18bhhx8eY8aMSTqvbt26cfrpp8f5558f22yzzQbnNG/ePJo3bx4dOnSIPn36RETEp59+GjfddFMMGTIkaay68847o6ioKG6//faKfzNA3njmBgBAIfH5FdgQzRiqB80YCp9mDDWTbgyk4pkbAACFxOdXIBHdGKoH3RgKm2YMNZNmDKTimRvULMlfRwEAJNSkSZOk48uXL6+UcyxbtizpeKpzArlTXl4ep512Wjz11FNJ59WpUyceeuihOOSQQyrpZFC7fPLJJ3HVVVdtcGyjjTaKe+65J+XbGYGq16hRo7Tn7rPPPtGlS5ec7t+gQYM45ZRTks6ZPHlyfP311zndF2qyK664IuUvbLZr1y7Gjx8ft99+e8IInci2224bd9xxR7z22mvRtm3bpHPvuOOOuPvuuzNaH6gcnrkBAFBIfH4FfkgzhupBM4aaQTOGmkk3BlLxzA0AgELi8yuwIboxVA+6MRQ+zRhqJs0YSMUzN6hZ/C9vAKigTTfdNOn40qVLK+UcqfZJdU4gd37xi1/E0KFDU867++6747jjjsv/gaCWOv3002P16tUbHPvFL34RXbt2rdwDARVSt27dKCkpSWvuwIED83KGVDG6rKws3nzzzbzsDTXNxIkT449//GPSOR06dIi33norevTokdVee+21V0yZMiW23XbbpPMuvPDC+PTTT7PaC8i9VM+ySktLE37ezyXP3AAASIdmDPyQZgzVg2YMNYNmDDWPbgykQzMGAKCQaMbAhujGUD3oxlD4NGOoeTRjIB2aMdQsLr0GgApq3rx50vHvvvuuUs6xZMmSpOOpzgnkxq9//eu47bbbUs678cYbY9CgQZVwIqid7r///nj11Vc3ONauXbuEb2UGqqd0P8vuvffeedm/Y8eOsckmmySd8/bbb+dlb6hpLr300igrK0s4vummm8aLL74YLVq0yMl+LVu2jBdffDHpn+EVK1bERRddlJP9gNxJ57//K+O5W6o9PHMDACBCMwa+TzOG6kEzhppFM4aaRTcG0qEZAwBQSDRj4Id0Y6gedGOoOTRjqFk0YyAdmjHULC69BoAKSvU/jtesWZP3D8bffvttrF27NukcH4wh/37/+9/HH/7wh5Tzrrrqqjj//PMr4URQOy1YsCDpw+Tbb789GjduXIknArKVTpBq1qxZbL/99nnZv6ioKLp37550jje3QmqTJ0+O8ePHJ51z5ZVXRocOHXK67w477BC//e1vk8559tln/TmGaiad//6fP39+3s+Rag/P3AAAiNCMgf/QjKF60Iyh5tGMoebQjYF0acYAABQSzRj4b7oxVA+6MdQsmjHUHJoxkC7NGGoWl14DQAVttdVWKecsWLAgr2dIZ/10zglU3J///Oe47LLLUs676KKLUj4EA7JzzjnnxOLFizc4dtRRR8Whhx5ayScCspXOZ9mOHTtGUVFR3s7QqVOnpONz587N295QUzzwwANJx9u2bRunn356XvY+++yzo02bNgnHy8rK4p577snL3kDFNGrUKGXozfczt5UrV8ayZcuSzmnXrl1ezwAAQGHQjIEIzRiqE80Yah7NGGoO3RhIl2YMAEAh0YyBf9GNofrQjaFm0Yyh5tCMgXRpxlCzuPQaACqoSZMmKT8Yz5kzJ69nmD17dtLxVq1aecsk5NGQIUPivPPOSznvnHPOiT/+8Y/5PxDUYs8991wMHz58g2NNmzaNW2+9tZJPBOTC1ltvnXLOJptsktczNGvWLOn4t99+m9f9oSZ49dVXk44fe+yxUb9+/bzsXb9+/fi///u/pHNGjx6dl72Bimvfvn3S8Xw/c0tn/VRnBACgdtCMAc0Yqg/NGGomzRhqDt0YyIRmDABAodCMgQjdGKoT3RhqHs0Yag7NGMiEZgw1h0uvASALqR6Offzxx3nd/5NPPkk6ns7DO6BiHn744TjzzDNTzjv11FMFMKgE559/fsKxa6+9NrbYYotKPA2QK9tss03KOfmO0anWX7lyZV73h0K3cOHCmDFjRtI5Bx54YF7PkGr9adOmxdKlS/N6BiAz1f2ZW+vWraNRo0Z5PQMAAIWjun9+1YwhfzRjqF40Y6iZNGOoGXRjIFPV/ZmbZgwAwH+r7p9fNWPIL90YqhfdGGoezRhqBs0YyFR1f+amGUP6NqrqAwBAIevcuXO89dZbCcdT/Y/tbKVav3PnznndH2qrJ554Ik455ZQoLy9POu/444+PIUOGRFFRUSWdDGqvr7/+eoN/v2nTplG/fv247777crbX22+/nXT8448/Trlfr169okOHDjk7E9RUXbp0STmnYcOGeT1DqvXXrVuX1/2h0M2aNSvlnO7du+f1DD169Eg6vn79+vj4449j1113zes5gPR17tw5hg8fnnDcMzcAAKoTzRhqJ80Yqh/NGGomzRhqBt0YyJRmDABAIdGMofbSjaH60Y2h5tGMoWbQjIFMacZQc7j0GgCysMsuu8SwYcMSjk+dOjWv+6d6EN6tW7e87g+10XPPPRcnnnhirF+/Pum8/v37x0MPPRR16tSppJMBG7J06dI444wzKnXPiRMnxsSJE5POefDBB4VoSEO3bt2iTp06UVZWlnDOkiVL8nqGVOvnO4ZDofvmm2+SjterVy823njjvJ5hk002ibp160ZpaWnCOanOCVSuXXbZJem4Z24AAFQnmjHUPpoxFBbNGAqbZgw1g24MZEozBgCgkGjGUDvpxlBYdGMoXJox1AyaMZApzRhqDk/FACALqT4Yv/POOyljVUWtW7cupk2blnSOD8aQWy+//HL83//9X9IHWBERBx10UDz22GOx0UbeMQMA2SgpKYntt98+6Zzvvvsur2dYvHhx0vEmTZrkdX8odKn+DDVv3rxSzpFqHyEaqpdUz9zmzZsXCxcuzNv+U6ZMSTrumRsAAP9NM4baRTMGgMqlGUPNoBsDmdKMAQAoJJox1D66MQBUHs0YagbNGMiUZgw1h0uvASALu+22WzRo0CDh+PLly1N+eK2oN998M1auXJlwvEGDBrHrrrvmZW+ojcaOHRv9+/ePNWvWJJ3Xp0+feOqpp6JevXqVdDIAqNl69uyZdDyfD6LTWX/LLbfM6/5Q6IqLi5OOp/p8nSurV69OOl5UVFQp5wDS06ZNm2jXrl3SOWPHjs3L3l9++WXMnDkz6ZxUn08AAKhdNGOoPTRjAKgamjEUPt0YyJRmDABAIdGMoXbRjQGg8mnGUPg0YyBTmjHUHC69BoAsNGjQIPbee++kc1555ZW87D1q1Kik4/vss0/SUA6kb9KkSXHYYYfFqlWrks7r2bNnPPfcc/7sAUAO9evXL+n4hx9+mPSXNLP11ltvJR1P9aAcarvGjRsnHV+8eHGsX78+r2coLS1N+bb2Ro0a5fUMQOb69u2bdLyqnrl16NDBf/8DAPA9mjHUDpoxAFQdzRgKn24MVIRmDABAodCMofbQjQGgamjGUPg0Y6AiNGOoGVx6DQBZOuCAA5KOP/XUU3nZd/jw4UnHDzzwwLzsC7XNlClT4qCDDorly5cnnbf77rvHiy++mPJBGwCQmb59+yZ9e+u6detSBuOKWrlyZbz33ntJ5+y888552Rtqis022yzpeHl5eXzxxRd5PcO8efNSzmndunVezwBkLtUzt+eeey4vv8jimRsAABWhGUPNphkDQNXSjKHw6cZARWjGAAAUEs0Yaj7dGACqjmYMhU8zBipCM4aawaXXAJClo48+Oun422+/HTNmzMjpnu+//37Sh2JFRUUpzwWk9t5770W/fv1iyZIlSeftvPPO8fLLL0fTpk0r6WQAUHtssskmKR/6jhw5Mi97jx49OuVD7h49euRlb6gptt5665RzxowZk9czjB49OuWcdM4JVK5DDjkk6ZvRFy5cmPJtyZn69ttv4+WXX04655hjjsnpngAA1AyaMdRcmjEAVD3NGAqfbgxUhGYMAEAh0YyhZtONAaBqacZQ+DRjoCI0Y6gZXHoNAFnadtttY4899kg657bbbsvpnrfeemvS8b322ivat2+f0z2htpk5c2YccMAB8c033ySd16lTp3jllVeiWbNmlXQyYEO+++67KC8vr5S/rrjiiqRnGTBgQMo1Bg4cWDn/YKCGGDBgQNLx+++/P0pLS3O+71133ZV0vH379rHDDjvkfF+oSVq0aBFt2rRJOmfEiBF5PcNLL72UdHyzzTaLVq1a5fUMQOaaNGkShx9+eNI5uX7mdvfdd8fatWsTjrdt2zb23XffnO4JAEDNoBlDzaQZQ2HRjKFm04yhsOnGQEVoxgAAFBLNGGou3RgKi24MNZdmDIVNMwYqQjOGmsGl1wCQAz/72c+Sjj/44IPx1Vdf5WSvefPmxcMPP5x0jofbkJ3Zs2fH/vvvHwsWLEg6r0OHDjFq1Kho2bJlJZ0MAGqnI444Ilq0aJFwfP78+fHEE0/kdM+PP/445RsYjzzyyJzuCTXVXnvtlXT8qaeeilmzZuVl7+nTp8ezzz6bdM6ee+6Zl72B7KV65vb3v/893nnnnZzstXz58pRx++STT46ioqKc7AcAQM2jGUPNohkDQPWiGUPh042BitCMAQAoJJox1Dy6MQBUH5oxFD7NGKgIzRgKn0uvASAHTjrppKRvalq5cmVceumlOdnrkksuidWrVyccb926dZx00kk52Qtqoy+//DL233//mDdvXtJ57du3jzFjxsTmm29eSScDgNqrQYMG8Ytf/CLpnAsvvDAWL16ck/3Ky8vj9NNPj7KysqTzBg0alJP9oKZL9QbV0tLSuPzyy/Oy92WXXRbr169POuewww7Ly95A9g444IDYaaedEo6Xl5fHeeedl5O9/vCHP8T8+fMTjtevXz/OPffcnOwFAEDNpBlDzaEZA0D1oxlD4dONgYrQjAEAKCSaMdQsujEAVC+aMRQ+zRioCM0YCp9LrwEgB9J5OPbQQw/F008/ndU+f/vb3+KRRx5JOue8886L+vXrZ7UP1FaLFi2K/fffPz777LOk89q0aRNjxoyJNm3aVNLJAIBzzjknNt5444TjX331VZx99tk52evPf/5zjB07NumcAw88MDp16pST/aCmO/zww6NJkyZJ5/z1r3+NIUOG5HTfG2+8MZ566qmkcxo0aOBt6lDNXXLJJUnHX3vttbj55puz2mPixInxxz/+MemcgQMHRuvWrbPaBwCAmk0zhppBMwaA6kszhsKmGwMVpRkDAFAoNGOoOXRjAKieNGMobJoxUFGaMRQ2l14DQI6cd9550bZt26RzBgwYEG+++WaF1n/99dfj1FNPTTqnXbt2KaM4sGHfffddHHjggTF9+vSk8zbbbLMYM2ZMbL311pV0MgAgImKTTTaJq6++Oumcxx57LM4+++woLy+v8D73339/XHDBBUnnFBUVxXXXXVfhPaC2KSkpSeuN5YMHD47HHnssJ3s+8MADcfHFF6ecd8opp0SzZs1ysieQH8cff3zsvvvuSedccskl8fzzz1do/Y8//jiOPvroWLduXcI5JSUlceWVV1ZofQAAahfNGAqbZgwA1ZtmDIVNNwYqSjMGAKCQaMZQ+HRjAKi+NGMobJoxUFGaMRQ2l14DQI40atQobrrppqRzli1bFgceeGC88MILGa397LPPRr9+/WL58uVJ5914443RsGHDjNYGIpYvXx4HHXRQvPPOO0nntWjRIkaPHh0dOnSonIMBAN8zePDg2GWXXZLOueuuu+K4446LRYsWZbT2mjVr4sorr4xBgwZFWVlZ0rlnnnlmdOvWLaP1oba7+OKLk75FPSJi3bp1cfzxx8fgwYNj5cqVFdpn2bJlccopp8Spp56a8s9y48aN41e/+lWF9gEqT1FRUdx+++1RVFSUcE5paWkcc8wxcd9992W09oQJE6JXr17x1VdfJZ13xRVXxGabbZbR2gAA1E6aMRQuzRgACoNmDIVNNwYqQjMGAKCQaMZQ2HRjAKj+NGMobJoxUBGaMRS2ovJsXkkDAPyPE088MR555JGkc4qKiuL444+Pyy+/PHbccceE8z788MO4+uqr4/HHH09r37/85S8ZnxeIOOyww9L6JZHBgwdH165d83+g/2/zzTePQw45pNL2A9Jz5ZVXxlVXXZVwfMCAATF06NDKOxDUMh999FF079495S9qbrLJJnHZZZfFT3/606QPj5cvXx7PP/98XH755fHpp5+m3H+HHXaIt99+Oxo1apTx2aG2u/vuu+Oss85Ka27z5s3j7LPPjtNOOy222mqrlPNnzZoVQ4YMibvvvju+++67tPa4+eab47zzzktrLhSacePGxcyZMzP6mm+++SYuvfTSpHPuvffejM/Sq1evnPxC92WXXRa///3vU8778Y9/HFdffXXStzbPmTMnrr/++rj33nuTvnk54p/nHz16dBQXF2d8ZgAAai/NGAqPZgxkQjOGqqUZQ2HTjaFyaMaaMQA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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 2: Constrain theta and epsilon, so that 2 epsilon <= theta\n", + "\n", + "SAVED_RESULT_TO_USE = os.path.join(SAVED_RESULTS_DIR, \"cddf5173-7555-4641-b74a-23a01cf4f9cf.json\")\n", + "\n", + "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n", + " results: FunmanResults = FunmanResults.model_validate(json.load(f))\n", + "\n", + "# Plot the trajectories\n", + "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Infected\")\n", + "\n", + "# Plot the parameter space\n", + "ParameterSpacePlotter(\n", + " results.parameter_space, plot_points=True, parameters=[\"epsilon\", \"theta\", \"timestep\"]\n", + " ).plot(show=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Sn3pK09LS1K5dO2VmZqpjx4567LHHNGvWLM2fP19vvfVW1XXp6en61a9+5Y+SAAAAApdhhM36pC4WwzAMs4toqsLCQtlsNtnt9qpnVgEAAILe9u1S//5SixZSfn5Q7+Tkbl5j8XwAAIBAEyZbi9ZEKAUAAAg0YTZ0LxFKAQAAAsv589LKlc5jQikAAABMsXq1M5hecYXUu7fZ1fgNoRQAACCQuIbuJ0wI+a1FayKUAgAABJKa+92HEUIpAABAoDh5Utq2zXkcBluL1kQoBQAACBTLljl/TUuT2rY1tRR/I5QCAAAEijAdupcIpQAAAIEhDLcWrYlQCgAAEAi+/VY6flyKjZWGDze7Gr8jlAIAAASCmluLxsaaW4sJCKUAAACBIIyH7iVCKQAAgPlKS6UVK5zHhFIAAACYYs0a6dw553GfPubWYhJCKQAAgNlcQ/czZoTV1qI1EUoBAADM5prkdO215tZhIkIpAACAmU6flrZscR6H2daiNRFKAQAAzOTaWjQ1VWrXztxaTEQoBQAAMFMYby1aE6EUAADALIZRHUonTDC3FpMRSgEAAMyya5d07JhzB6cRI8yuxlSEUgAAALO4eklHjpRatDC3FpMRSgEAAMzC0H0VQikAAIAZ2Fq0FkIpAACAGdaulUpKnMtA9etndjWmI5QCAACYoebQfZhuLVoToRQAAMAMrE9aC6EUAADA386cYWvRixBKAQAA/G3ZMufC+f36Se3bm11NQCCUAgAA+BtD95cglAIAAPiTYUhffuk8JpRWIZQCAAD40+7d0pEjUkyMlJlpdjUBg1AKAADgT66h+9LSsN9atCZCKQAAgD+5hu6fecbcOgIMoRQAAMBfysqk5cudxzxPWguhFAAAwF/WrpXOnpXatmVr0YsQSgEAAPzFNXQ/YYJkJYbVxJ8GAACAv9Tc7x61EEoBAAD84YcfpE2bnMeE0ksQSgEAAPzBtbVo377SFVeYXU3AIZQCAAD4Q83nSXEJQikAAICvGQb73TeCUAoAAOBre/ZIhw9L0dHSyJFmVxOQCKUAAAC+5hq6HzFCioszt5YARSgFAADwNYbuG0UoBQAA8KXycrYWdQOhFAAAwJfWrZOKi6U2baTUVLOrCViEUgAAAF9yDd2PH8/Wog3gTwYAAMCXeJ7ULYRSAAAAX8nLY2tRNxFKAQAAfOWrrySHQ+rdW7rySrOrCWiEUgAAAF9xDd3TS9ooQikAAIAvsLWoRwilAAAAvrBvn3TokBQVJY0aZXY1AY9QCgAA4AuuXtIRI6SWLc2tJQgQSgEAAHyB50k9QigFAADwNrYW9RihFAAAwNvWr5eKiqTLLpMGDDC7mqBAKAUAAPA2thb1GH9KAAAA3vbll85fGbp3G6EUAADAm/LzpQ0bnMdMcnIboRQAAMCbXFuL9uwppaSYXU3QIJQCAAB4E0P3TUIoBQAA8BbDkJYudR4TSj1CKAUAAPCW/fulgwfZWrQJCKUAAADe4hq6z8iQ4uPNrSXIEEoBAAC8xbU+KUP3HiOUAgAAeEN5uXPmvcRSUE1AKAUAAPCGDRukwkIpOVm65hqzqwk6hFIAAABvcD1POn68FBFhbi1BiFAKAADgDa7nSRm6bxJCKQAAQHMVFEjr1zuPCaVNQigFAABoruXLnVuL9ughdepkdjVBiVAKAADQXAzdNxuhFAAAoLnY777ZCKUAAADNsX+/8xUZKY0ebXY1QYtQCgAA0Bw1txZNSDC3liDmt1A6e/ZsZWZmasaMGSovL696Pzc3VxkZGRo1apSmTp2qs2fP+qskAACA5nOFUp4nbRa/hNLc3FwdO3ZM2dnZ6tmzpxYtWlR1rnfv3srJydHKlSs1cOBAffjhh/4oCQAAoPkqKqRly5zHPE/aLH4JpTk5OZp44YuaNGmS1qxZU3UuKiqq6vjcuXPq0aNHve2UlpaqsLCw1gsAAMA0GzdKdrvUqpU0cKDZ1QQ1v4TS/Px8JSYmSpJsNpvy8vJqnf/88881YMAArVixQt26dau3nXnz5slms1W9UlJSfFo3AABAg1xD9+PGsbVoM/kllCYlJVX1atrtdiUnJ9c6P2nSJG3dulXTpk3T/Pnz621n7ty5stvtVa8jR474tG4AAIAGudYnZei+2fwSSjMyMpSVlSVJWrp0qYYPH151rrS0tOrYZrMpLi6u3nZiYmKUmJhY6wUAAGCKwkJp3TrnMZOcms0voTQtLU3t2rVTZmamduzYoWnTpmnWrFmSnEP3o0aN0ujRo/XFF19o5syZ/igJAACgeZYvlyorpe7dpc6dza4m6FkMwzDMLqKpCgsLZbPZZLfb6TUFAAD+9YtfSC+/7Pz1xRfNriZguZvXWDwfAACgKdha1KsIpQAAAJ46cEDau9c5456tRb2CUAoAAOApVy/psGESjxB6BaEUAADAUwzdex2hFAAAwBOVldKFpS5ZCsp7CKUAAACe2LRJKiiQkpKk9HSzqwkZhFIAAABP1NxaNDLS3FpCCKEUAADAE66tRRm69ypCKQAAgLuKiqS1a53HTHLyKkIpAACAu1askCoqpG7dpC5dzK4mpBBKAQAA3MXQvc8QSgEAANzlCqUM3XsdoRQAAMAdhw5Je/Y4txYdM8bsakIOoRQAAMAdrqWghgyRbDZzawlBhFIAAAB3MHTvU4RSAACAxlRWSsuWOY+Z5OQThFIAAIDGbNki5eVJiYnS4MFmVxOSCKUAAACNcQ3ds7WozxBKAQAAGuOa5MTQvc8QSgEAABpSVCTl5DiPmeTkM4RSAACAhqxcKZWXO7cV7drV7GpCFqEUAACgIa6he3pJfYpQCgAA0BDWJ/ULQikAAEB9Dh+Wdu2SrFa2FvUxQikAAEB9XEP3gwdLrVqZW0uII5QCAADUh+dJ/YZQCgAAUJfKStYn9SNCKQAAQF22bnVuLZqQIA0ZYnY1IY9QCgAAUBdXL+nYsVJUlLm1hAFCKQAAQF1cS0ExdO8XhFIAAICLFRdLa9Y4j5nk5BeR7l44aNAgWSwWORwO5efnKz4+XsXFxWrVqpU2bdrkyxoBAAD8a9Uq59ainTtL3bqZXU1YcLundOPGjdqwYYMGDhyoJUuWKDc3V59++qmGDRvmy/oAAAD8r+YuThaLubWECY+H77/55hv17NlTktSjRw9t3rzZ60UBAACYiudJ/c7t4XuXqVOnasyYMerfv7+2b9+uqVOn+qIuAAAAcxw9Ku3c6dxadOxYs6sJGx6H0jlz5uiee+7RgQMH1LlzZ7Vr184XdQEAAJjDtRTUoEFScrK5tYQRj4fvjx8/rl//+td66aWX1Lp1a73++uu+qAsAAMAcDN2bwuNQevfdd2vGjBk6cuSIIiIitGDBAl/UBQAA4H8Oh5SV5TxmKSi/8jiUVlZWavDgwbJcmInmcDi8XhQAAIAptm2TzpyR4uOloUPNriaseBxK+/TpoyeffFJnzpzR008/rdTUVF/UBQAA4H+uofsxY9ha1M88nuj0/PPPa8mSJWrZsqV69uzJ7HsAABA6XJOcGLr3uyY9Uzp58mQ9/vjjmjp1qh555BFf1AUAAOBfZ89Kq1c7jwmlfud2T+m+ffu0e/dubd26VZ9++qkkqaKiQlu2bPFZcQAAAH6zapVUViZ17Ch17252NWHH7VB67Ngxbdq0SYWFhdq0aZMMw1BUVJR+97vf+bI+AAAA/2BrUVO5HUpHjRqlUaNGadq0aerTp48sFosMw9C3337ry/oAAAD8g+dJTeXxM6UPPvhg1XJQFotFDz74oNeLAgAA8Ktjx6QdO5w9pGwtagqPQ+n58+dr/VxSUuK1YgAAAEzh6iVNT5cuu8zcWsKUx0tCTZkyRTfeeKMyMzO1evVqTZkyxRd1AQAA+A9D96azGIZhePqhr7/+Wrt27VKvXr3Ur18/X9TllsLCQtlsNtntdiUmJppWBwAACGIOh3T55dLp09LKldLIkWZXFFLczWseD98XFxfryy+/1LZt29S7d2999tlnzSoUAADAVLm5zkDasiVbi5rI41B65513KiUlRdnZ2YqIiNAf//hHX9QFAADgH66h+zFjpOhoc2sJY03qKb3lllsUdWE/2CaM/gMAAASOmuuTwjQeh9L27dvrjTfe0NmzZ7VgwQJ16NDBF3UBAAD4XkmJlJ3tPJ4wwdxawpzHofTVV1/V2bNnlZ6ervz8fL3yyiu+qAsAAMD3srOdW4umpEg9ephdTVhze0moiRMn6osvvtCjjz6qF1980Zc1AQAA+AdbiwYMt0NpeXm5HnnkES1evFhxcXG1zj3zzDNeLwwAAMDnXKGUoXvTuR1KP/30U23btk05OTmaPHmyL2sCAADwvePHpW++cfaQjhtndjVhz+1Q2qJFCw0bNkzr169XUVGRCgoKmHkPAACCV1aW89eBA6XWrc2tBZ5vM/rggw9q06ZN6tChgwzDkMVi0XvvveeL2gAAAHyHpaACisehNDc3Vzk5Ob6oBQAAwD8cjupF83meNCB4vCRUenq61qxZo7Nnz6qkpEQlJSW+qAsAAMB3tm+XTp1yHg8bZm4tkNSEntItW7Zo69attd776quvvFYQAACAz7mG7idPlmJizK0FkjwIpTfffLMsFota8yAwAAAIdiwFFXDcDqV/+MMffFkHAACAf5w7V721KJOcAobbobRTp06+rAMAAMA/srOl0lKpQwepZ0+zq8EFHk90AgAACGo1h+7ZWjRgEEoBAEB4cS0FxdB9QCGUAgCA8HHihPT1184e0vHjza4GNRBKAQBA+HBtLTpgAFuLBhhCKQAACB9sLRqwCKUAACA8GAbPkwYwQikAAAgP33zjfKY0Lk7KyDC7GlyEUAoAAMKDa+h+1Ci2Fg1AhFIAABAeeJ40oBFKAQBA6Dt/Xlq1ynlMKA1IhFIAABD6Vq92BtMrrpB69TK7GtSBUAoAAEJfzVn3bC0akAilAAAg9PE8acAjlAIAgNB28qS0bZvzeNw4U0tB/fwWSmfPnq3MzEzNmDFD5eXlVe8vXrxYQ4YM0YgRI/TQQw/5qxwAABAuli1z/pqWJrVta2opqJ9fQmlubq6OHTum7Oxs9ezZU4sWLao6l5qaqjVr1mj16tU6deqUNm3a5I+SAABAuHAN3V97rbl1oEF+CaU5OTmaeOEZjkmTJmnNmjVV5zp27KjIyEhJUnR0tKzW+ksqLS1VYWFhrRcAAEC9DKM6lE6YYG4taJBfQml+fr4SExMlSTabTXl5eZdcs3HjRp06dUrXXHNNve3MmzdPNput6pWSkuKzmgEAQAj49lvp+++lFi2k4cPNrgYN8EsoTUpKqurVtNvtSk5OrnX+6NGjevjhh/Xmm2822M7cuXNlt9urXkeOHPFZzQAAIAS4eklHjpRiY82tBQ3ySyjNyMhQVlaWJGnp0qUaXuP/qRQVFWn69OmaP3++2jby8HFMTIwSExNrvQAAAOrFUlBBwy+hNC0tTe3atVNmZqZ27NihadOmadasWZKkZ599VgcOHNADDzyg0aNHa+XKlf4oCQAAhLrSUsmVKwilAc9iGIZhdhFNVVhYKJvNJrvdTq8pAACo7auvnOuStm8vHTvGTk4mcTevsXg+AAAITTVn3RNIAx6hFAAAhKaa+90j4BFKAQBA6Dl9WtqyxXk8fry5tcAthFIAABB6Lqz6o9RUqV07c2uBWwilAAAg9LCLU9AhlAIAgNBiGDxPGoQIpQAAILTs3OlcAio2Vhoxwuxq4CZCKQAACC2uofvMTOee9wgKhFIAABBaGLoPSoRSAADgdZ/t+Uxny876/8alpdKKFc5jQmlQIZQCAACv2vb9Nl3/zvWKnxev3Wd2+/fmOTlSSYnzuF8//94bzUIoBQAAXuMwHBrxevXkor0/7PVvAa6h+zvuYGvRIEMoBQAAXnPvP+/V2XLnsH2kNVJjuozxbwGuSU7XXuvf+6LZCKUAAMArdp/ZrddzX6/6eeG0hWoZ3dJ/BZw5w9aiQYxQCgAAms0wDA15dUit937y/k90svik/4pYtsy5cH6/flL79v67L7yCUAoAAJrtkc8fkb3Ufsn75yvO+68I19A9s+6DEqEUAAA0y+GCw3puw3N1nmsR6afF6w2D/e6DHKEUAAA0mWEYGviXgfWeP1Nyxj+F7N4tHT0qxcQ4d3JC0CGUAgCAJnty+ZMNBk/XTHyfq7m1aFycf+4JryKUAgCAJjlRdEL/k/0/DV6z6/Qu/xTD0H3QI5QCAIAmaWjY3iUqIsr3hZSVsbVoCCCUAgAAj/0h5w86XnS8wWsssmjy1ZN9X8zatdLZs1LbtlL//r6/H3yCUAoAADxScK5A//blvzV63StTX1FCTILvC3IN3Y8fL1mJNsGKbw4AAHhk4CsDZcho9Lr7Ft+no4VHfV8Q65OGBEIpAABw2yubXtF3Bd+5de3NvW/WlQlX+ragH36QNm92HjPJKagRSgEAgFuKS4v10yU/devaDokdNH/KfFksFt8W5dpatE8f6YorfHsv+BShFAAAuGXIq0PcGra3yKIOCR30+f7PfV8UQ/chg1AKAAAa9c7X7+jbM9+6de3wlOFad2ydbv/gdm0+vtl3RRmG9OWXzmNCadAjlAIAgAadrzivOz+8061re7XupdVHVlf93OOyHr4qS9qzRzp8WIqOlkaO9N194BeEUgAA0KCRr4+UQ45Gr4uLirtk7dJTZ0/5qqzqofsRI9haNAQQSgEAQL2W7F6ijcc3unVtl6Quspfaa733/PrnfVGWk2vonln3IYFQCgAA6lReWa4b3r3BrWtT26Vqx+kdl7zfNq6tl6u6oKxMWr7ceXzttb65B/yKUAoAAOo08a2JqjAqGr2ubcu22n5ye53nEmMTvV2W07p1UnGx1KaNlJrqm3vArwilAADgEisPrtSKQyvcutZhOOp95vRHPX7kxapqcA3ds7VoyOBbBAAAtVQ6KjXxLfeWWOrVupfOlJyp89xDgx9Sx6SO3iytGuuThhxCKQAAqOWGhTeozFHW6HWdkzpr55md9Z5/bsNzOlZ4zJulOeXlSRsvTL5iklPIIJQCAIAqm45v0id7P2n0utjIWLcCZ6Q10htl1fbVV86F83v3lq680vvtwxSEUgAAIMn5bGjma5luXWuLsancUd7odYWlhc0t61IM3YckQikAAJAk3fnBnTpfeb7R665qdZVOnj3pVpuxkbHNLas2w6gOpQzdhxRCKQAA0K7Tu/TOjncavS65RbK+y//O7XaTYpOaUVUd9u2TDh2SoqKkUaO82zZMRSgFACDMGYah9L+ku3VtSXmJR22XVTY+YcojNbcWbdnSu23DVIRSAADC3E8/+anOlp9t9Lr28e11vqLx4X2XmIgYj0Nsoxi6D1mEUgAAwtjBgoN6ZcsrjV53efzl+r74e4/aLq0srXenpyYpL6/eWpRJTiGHUAoAQBgbMH9Ao9fERMToRPGJJrV/deurm/S5Oq1fLxUVSZddJg1ovG4EF0IpAABh6vGlj6vgfEGj10VZo5p8j7xzeU3+7CVcQ/dsLRqS+EYBAAhDJ4pO6A/r/tDodW1btlVxeXGT7tGvbT+lX+HeBCq3sD5pSCOUAgAQhvr9uV+j1yRGJ+rU2VNNaj82MlYvXPeCrBYvRY38fLYWDXGEUgAAwsxTq57SmZIzjV7X1B5Sq6w6X3Feo98crZ2ndzapjUt89ZXkcEg9e0opKd5pEwGFUAoAQBjJP5ev/1z+n41elxCdIIfhaNI9HKr+XFPbuARD9yGPUAoAQBjp96fGh+2TYpNUVFbUpPYvHq6Pi4prUju1sLVoWCCUAgAQJl5Y/4KOFR1r8JpIa6RbM/LrEmWNqtUzes+Ae9Q5qXOT2qpl/37p4EHn1qKjRze/PQQkQikAAGGguLRYD37+oM/aj7ZGq9xRXvVzl6Quujv1blksluY37uolzciQ4uOb3x4CEqEUAIAwkDY/rdFr4qPjVeGo8LjtSEukyhzVe9y3jWurAwUHNPKNkTpYcNDj9i7x5ZfOX3meNKQRSgEACHGvb31d+/P3N3hNi8gWKi7zfLa9RRZVGNVBNj4qXqdKnMtItYhooUhrpMdt1lJR4Zx5LxFKQxyhFACAEHa+/Lxmfjyz0evOVZxrUvuGjKrjKGuUSipKqtusPKfSitImtVtlwwapsFBKTmZr0RBHKAUAIISl/yW9VnCsS7Q12iv3slgslywB1exQWnNr0YiI5rWFgEYoBQAgRP3j239ox+kdDV4TFxVX63lQd1lUewJTy6iWKqus3U7fNn2VYmvmQvesTxo2CKUAAISg8spy3bzo5gavscqqkvKSBq+p83MWa63eV1uMTWfLz9a6pqOto7666yslxCR43H6VggLn8L3E+qRhgFAKAEAIGv7a8EZ3U2psWL8uVou1VrutYlvJXmqvdU1yi2QtnLZQiTGJHrdfy/LlUmWl1KOH1LFj89pCwCOUAgAQYrL2Z2nj8Y0NXhNtjW5SKK0ZSG0xNuWfz691Pi4qTu/c9I6u/fu1in06tkk9sVUYug8rhFIAAEJIpaNSkxZMavCa6IjoJj1HWlPLqJaX9JBGWiP17rR3Nf2D6VXblO4+s7vpN2F90rBCKAUAIISM/9t4VRqVDV5z8YSkprj4GVJJ+vtNf9fMxTNr9Z5GWJs4Y37/fucrMlIaNaqpZSKIEEoBAAgR646s04pDKxq8xtrMf/ovnnXv8pepf9FjXzymU2dPVb3XOq61uiR1adqNXL2kGRlSQjMmSyFoEEoBAAgBDsOhkW+MbPCaKGuUHGp48lNTPDP+Gf1u9e90tPBorffPlJxRYWlh0xpl6D7sEEoBAAgBN7xzg8od5fWet8jS4Hl3RFgiLpkcNWf4HL2Z+6b25e+75Ppoa3TTHhWoqJCWLXMesxRU2CCUAgAQ5HK/z9XivYsbvKYpM+1rio6IvuRZ1ZkDZurL/V/Wu0B/maNMreNae36zjRslu11q1UoaOLAp5SIIEUoBAAhihmFoyF+HNHiN1dK8f+5jImIu6fGcevVU7TmzR5tPbG7ws1/s/8LzG7qG7tlaNKwQSgEACGIzPpyh0sr695e3ytroIvoNibZGX9J+RocMlVaWKvtIdqOf79W6l+c3da1PytB9WCGUAgAQpPbn7deC7QsavKY5E5siLZGXrGfaq3UvtY1v63YPaHxMvGc3tduldeucx4TSsEIoBQAgSKX+OdVnbVtkUYVRUeu9KxOuVOrlqfpo10dutfHAoAfU0ebh9qArVji3Fr36aqlzZ88+i6BGKAUAIAj9Yskv6lzA3qW5z5FePDGqVWwrTbxqohZ+s9DtNl7c+KIOFhz07MYM3YctQikAAEHmWOExvbzp5Qavac5zpBdrEdlCt/S5Ra/nvu7xZ8+UnPHsA+x3H7YIpQAABJk+L/fxWdsX79gUaYnUzAEzNX/zfI/birBEqFVsK/c/cOCAtG+fc2vR0aM9vh+CG6EUAIAgMufLObKX2n3StkWWWsP2Fln0s/Sf6cWNL3rcVrQ1WmvuWaOuyV3d/5BrKaihQ6XERI/vieBGKAUAIEicKTmj3+X8zidtXxxIJeln6T/TCxtf8LitllEtNejKQRr616GefZCh+7BGKAUAIEj0frG3T9qtK5DeO+DeRp9brUurmFbq2qqr1hxZo7ioOB2xH3Hvg5WVbC0a5iLNLgAAADRuXvY8nT532uvt1hVIb+t7m/669a8et9WuZTvFRcXp61NfKy4yTvPGzVOKLcW9D2/aJBUUSElJUnq6x/dG8KOnFACAAFdUWqR//+rffdL2xYF0cvfJWvjNwkveb0zHxI4yZOhAwQHFR8erpKJED33+kPbl7XOvAdfQ/bhxzolOCDt+C6WzZ89WZmamZsyYofLy8qr39+zZo7S0NMXGxqq4uNhf5QAAEDR6vdSErTqbYETKCH2+93OPA2n35O7KP5+vU2dPKSE6QcVl1f+eF5wrcK8R1yQnhu7Dll9CaW5uro4dO6bs7Gz17NlTixYtqjrXoUMHrVy5UkOHevgwNAAAYeBPG/6kY0XHfH6ffm37af3R9apUpUef69Omjw4WHFRRWZHiouJUVFZU6/w17a9pvJHCQmntWucxk5zCll9CaU5OjiZe+I9s0qRJWrNmTdW5uLg42Ww2t9opLS1VYWFhrRcAAKHqXNk5/fyzn/v8Pp1tnbX3h70qN8obv7iG1Lap2nF6h8od5YqJiFFJeckl17gVqFeskCoqpG7dpC5dPKoBocMvoTQ/P1+JF9Ybs9lsysvLa1I78+bNk81mq3qlpLj58DQAAEGo75/6+vwebeLa6EzJGZ2vPO/R59LapSn3VK4k5wL7pZWldV5XabjR88rQPeSnUJqUlFTVq2m325WcnNykdubOnSu73V71OnLEzWUmAAAIMgtyF+i7gu98eo+E6ASVV5aruNyzOR392vbTtpPbJDln71cYFfVeu+PUjsYbZH1SyE+hNCMjQ1lZWZKkpUuXavjw4U1qJyYmRomJibVeAACEmrKKMs34aIZP7xEdEa3oiGgVlBZ49LmerXtq+6ntVT83Nimqe3L3hhs8dEjas0eKiJDGjPGoFoQWv4TStLQ0tWvXTpmZmdqxY4emTZumWbNmSXIO7Y8fP165ubmaOnWqPvvsM3+UBABAwBowf4DHM+A9YbVY1Sq2lX4494NHn7sq6SrtOrPLo8+cLT/b8AWuofshQyQ355ggNPltIbDf//73tX6eP3++JKlVq1ZVvagAAIS7xbsW69sz3/r0Hpe3vFzHi4+7fb1FFrWPb6/vCr5TlDVK5Q73J0S1atGq4QsYuscFLJ4PAECAqHRU6ob3bvDpPTomdvQokEYoQsktknW8+LjHgVSSYiJi6j9ZWSm5OqaY5BT2CKUAAASIoa8OlcNw+Kz9TrZOOlx42O3rI62Rio2M1Q/nflCEJcLjQCpJqw6uqv/k5s1Sfr6UmCgNHuxx2wgt7OMFAEAAWHFghTZ9v8ln7XdM7KhD9kNuXx8TEaPSylJVOCpktVjdW9qpDq3jWtd/0vU8KVuLQvSUAgBgOsMwNP6t8T5rv0NCB496SOOi4qrWHbXI0qze2xaRLeo/6XqelKF7iFAKAIDpxr45tsk9kY25vOXlOlp01O3r46Pja+3M1NxVADoldar7RFGRlJPjPGaSE0QoBQDAVJuPb9aKQyt80vZlLS7TibMn3L4+MSZRxWWeLaTfmHonVa1c6dxa9KqrpK5dvXpPBCdCKQAAJjEMQ0P/OtQnbduibR6tQ2qLsamwtNBr90+OTVbOPTkadOWgui9g6B4X4aliAABM8uN3fqwKR/1bdDZVXGSc7GV2t6+3RdtkL7XLIotXFu2/uffNWnDTAkVFRNV/EeuT4iKEUgAATLDr9C4t3rvY6+1GR0SrpKKk8QsvSIhOkL3MO4E0Pjpei25epGu7XSvDMHSu/JxaRNUx0enwYWn3bslqlcaObdY9EToYvgcAwARp89O83maEJUJllWVuXx8XGaeisiKvBNIJV03QqcdO6dpu12rvD3s1+o3RivtNXN0X19xaNCmpWfdF6CCUAgDgZzP+MaNqySVvscji0Qz+aGt1j2pzAmlsRKwWTluoL2Z8oUhrpOZlz1Ofl/to1WHnovlH7XXM/Od5UtSB4XsAAPzoUP4h/X37373erqfBsszhfo9qfYZeOVSf3/m5bLE2rT+6Xnf/827tOrOr6ny7lu1ktV7U/1Vza1GeJ0UN9JQCAOBHvV/ubXYJzRZpjdSfrv+T1t67VlaLVQ9+9qCG/XVYVSC1yCKrrDp59qTaxLWp/eGtW6W8PLYWxSXoKQUAwE9+/snPPZqEFIj6tumrrH/JUrv4dlqyZ4nuXXyvThRXr4UaY41RqaNUhgyN6DhC+efz1bZl2+oGXEP3Y8ZIUQ3MzkfYoacUAAA/OFV0Sn/a/CevtmmRxavXNcRqserpsU9r+8+3S5J+8t5PNOWdKVWBNMISIUkqdZSqVWwrRVmitPrw6tqBVKqe5MTQPS5CTykAAH7Q/aXuXm3P3Rnz3phZ3zmps5bNWKYurbrota2v6aHPH6q181OEIlRpVMoii2zRNuWfz68699V3X2nsVReWfSoultascR4TSnERekoBAPCxuVlzvbpbkrtB0yprswKpRRY9Nuwxfffgd6o0KpX5eqZmfjyzKpBGWpx9W5Wq1GUtLpMhQwVlBbXaWHVwVfUPK1dK5eVS585sLYpL0FMKAIAPFZ4v1G/X/NZr7XkSSB1yNPk+l7e8XEtnLFWv1r00b/U8PbHiiUt2n6owKhRrjVWpo7TeLU0PFhys/qHm0L2l+Y8UILQQSgEA8KGrnr/Ka235K5DOHDBTr0x9RRuPbVTfl/tqT96eOtuOscbovON8g23d0OOG6h/YWhQNIJQCAOAjv13123p7ED3lyTOkTQ2kthiblty+RKmXp+rBzx7USxtfuuT+DjkUFxmnkooSlTrq3wAgrW2acu7Nqd5m9OhRaedOthZFvQilAAD4wLnyc5q7fK5X2vJkslJTnyG9oecNenfau8o6kKWuz3fVqbOnLmnX1Uva0LJWtmibtv98u1JsKbVPuIbuBw2SWrVqUo0IbYRSAAB8oOvz3pvI09zZ8w2JiYjRB7d8oPQr0jX9g+n6cNeHdV7X2CMBVlm19M6lGt91fN0XMHSPRjD7HgAAL3t5w8v6vvh7s8to1IiUEfrh337QybMndfkfL68zkEZe6L9qKJD+ZsxvVPlEZf2B1OGo3lqU/e5RD3pKAQDworKKMv3is1+YXUaDrBarXv/x6xrWYZjG/W2c1h9bX++1Faqo99zkrpP1z9v/qQhrRMM33LZNOnNGio+Xhg5tYtUIdfSUAgDgRT1e6mF2CQ3qmtRV3//r9zpsP6zeL/duMJDWJyUxRfbZdn1y5yeNB1Kpeuh+7Fi2FkW96CkFAMBLFuQuqL0uZ4D5z8z/1JQeU5TxWob25+/3+PPR1mhtm7VNvdr28uyDrlDK0D0aQE8pAABeUOmo1IyPZphdRp3io+K19f6tOllyUkNeHdKkQLpw2kKV/mep54H07Fm2FoVb6CkFAMAL+r7c16ez5Jvqum7X6WfpP9P4t8Y3ac3UB9If0PPXPy9LU3dgWrVKKiuTOnWSundvWhsIC4RSAACaafHuxdr1wy6zy6jFIotenfqqPtz1oX608Ecefz69fbpyZuYoKqKZz4DWXAqKrUXRAEIpAADNYBiGfrzwx2aXUUvrFq01d8RcPfDZAzpXcc6jzybFJGnvL/eqdcvW3inGtWg+z5OiEYRSAACaIf2V9IAatv/R1T/S0cKjevTLRz36nFVWrblnjYameHHJpmPHpB07nD2k48Z5r12EJEIpAABNtPLgSm05scXsMqpM7zNd73/7viqNSo8+9+zEZ/XQsIe8X1DNrUWTk73fPkIKoRQAgCYwDEPj/hYYvX+tYlopLjpOC3cs9OhzN/a4UR/c+kHTJzE1hqF7eIBQCgBAE4x6Y5THPZK+0MnWSYfsh5Rfmu/+ZxI6adcvdyk2KtZ3hTkc1aGUpaDgBkIpAAAe2nJ8i7IPZ5tdhuKi4nTIfsjt66Ot0dr9wG51btXZd0W55OZKp0+ztSjcRigFAMBDQ/46xNT7R1giVGlUqqS8xO3PLJm+RNf3uN6HVV3EtRTU6NFSdLT/7ougRSgFAMAD1//9elU4Kky7v0UWjx4beHzI43pm0jM+rKgeDN3DQ4RSAADctOfMHn22/zNTa3B3+alB7Qdp3X3rZLX4eUfx4mLpn/+Usi883kAohZsIpQAAuKnfn/uZXUKjbNE2HX74sBJbJPrvpuXlzuH6BQucgbTkwmMF3btLV1/tvzoQ1AilAAC4Yfr701VWWWZ2GfWyyqot929RavtU/9zQMKScHOntt6X33pPOnKk+17WrdMcd0n33sbUo3EYoBQCgEUcKjujdb981u4x6zb9uvu4ffL9/bvbtt84e0bfflg4erH6/bVtp+nRnGB00iDAKjxFKAQBoxNUvBeYQ9E96/kTv3/q+72909Ki0cKEzjG7bVv1+fLx0003OIDp2rBRJrEDT8V8PAAANuP/j+3W+4rzZZdTSMbGj9j+4X5ERPvxnPD9f+uADZxBdudI5XC85g+d11zmD6NSpUlyc72pAWCGUAgBQjzPFZ/SXrX8xu4wqUdYoHX3kqNrGt/XNDc6flz75xBlEP/1UKqvxDG1mpjOI/uQn0mWX+eb+CGuEUgAA6tHl+S5ml1Al644sjes2zvsNV1ZKK1Y4g+gHH0iFhdXn+vVzBtHp06VOnbx/b6AGQikAAHV4bOljKi4vNrsMzRk2R/MmzvNuo4YhbdniDKILF0rff199LiVFuv12ZxjtF/hLYCF0EEoBALhIcWmx/rjuj6bWMPDygdp4/0ZZvDmLff9+ZxB94ona77dqJd18szOIjhghWf284D4gQikAAJdI+b8U0+7dMqqlTj92Wi2iW3inwZMnneuILlggrV9f/X5srPSjHzmD6KRJ7E8P0xFKAQCo4dcrf62C0gK/39cii3b9fJeubuOF5aeKiqSPPnIG0aVLq9+3WqXx451B9IYbpEQ/7voENIJQCgDABefLz+u/VvyX3+/76uRXNTN9ZvMaKStzBtAFC6SPP5bOnas+N3iw8znRW2+VLr+8efcBfIRQCgDABSnP+nfY/sfdf6yPbv+o6Q04HNKaNdVbfeblVZ/r3t3ZI3r77c5jIMARSgEAkPTcuud0puRM4xd6QfuW7XX0X4/K2tQJRd98U73V5+HD1e9ffnn1Vp8DB7LVJ4IKoRQAEPYqKiv08NKHfX6fCEuEfvi3H2SLtXn+4cOHpXfecYbR7dur309IkKZNc/aIjh0rRUR4r2DAjwilAICw1+lZ3y8Mv/zO5RrddbRnH8rLkxYtcgbRVauq34+Kkq6/3tkjOmWK1MJLM/UBExFKAQBh7Y2tb+h48XGftf/okEf1h0l/cP8D585Jixc7g+hnn0nl5dXnRo1yBtFp06TkZO8XC5iIUAoACFsOh0P/7+P/55O2+7bpq+0/3974hZJUUSEtX+4Mom++WftcaqpzaP6225y7LQEhilAKAAhbXZ/v6vU2YyNiZZ9jV3RkI4vRG4a0aVP1Vp8nT1af69SpeqvPPn28XiMQiAilAICw9NHOj3TQftCrbX73wHfqclmXhi/au9c5a37BAuexy2WXSbfc4gyiGRnMnEfYIZQCAMKOYRi68b0bvdbe/Ovm6/7B99d/wYkT0rvvOoPoxo3V77do4dxZ6Y47pIkTnROYgDBFKAUAhJ3eL/X2Sjsjrhyh7Huz6z5ZWCh9+KEziH75ZfX7ERHShAnVW33Gx3ulFiDYEUoBAGHlq/1fadcPu5rdzrl/P6fYqNjab5aVOWfML1jgnEF//nz1uaFDq7f6bNu22fcHQg2hFAAQVsb9fVyzPv/zgT/XS1Neqn7D4ZCys53Pib7/vpSfX32uR4/qrT67en9SFRBKCKUAgLCR9nJasz6//F+Wa3SX0c4fvv7a2SP6zjvSkSPVF7Vv71y+6fbbpWuuYcIS4CZCKQAgLKw/sl65p3Ob9Nl2Ldtp1y92KemUXZo3z9kr+s031RckJjoXtL/jDmn0aLb6BJqAUAoACAtDXxvapM/9uMskfXj2R7JMnCqtXl19IjpamjzZGUQnT5ZiY+tvBECjCKUAgJA35JUhTfrcX7/rr3ueypIqPne+YbE4e0LvuEO66SapVSvvFQmEOUIpACCk7Ti5Qxu+3+DRZ2LLpZ0vSJ0Lv3a+MWCA8xnR6dOlDh18UCUAQikAIKT1/XNfj67vd0La8ooU2amL9MsLW3326uWj6gC4EEoBACGp0lGpuF/Huf8BQ/q3zbH6XaeZUvbt0rBhzJwH/IhQCgAIOSeKTqj9/7Z3+3qrIa3s+YxG/OphtvoETEIoBQCElL9u+qvuXXKv29e3jr1MBx85pJbRLX1YFYDGEEo9ZPlvi4wnDLPLAABcxDAMdfm/LjpUdMjtz0zpPkWLb1/sw6oAuItQ6iECKQAEnvziH5T8x9YefebVqa9q5jUzfVQRAE8RSgEAga+8XLLbna+CglqvYbsf07q4AsnNOUmRlkh999B3SrGl+K5eAB4jlAIAfK+01Bki6wiVrvccBXnaX7hPD0Tn6IvWJc5/oayqDps1Q2fN45qPghpqMJx2Seqi/Q/ul4VZ9UDAIZQCABpmGFJJSd2h0m6XIy9Ph4r26c6SRcqJK5Yi1HCYtMgZHmuKltRGUttm1tpA1vzloF/q+eufb+YNAPgKoRQAQp3DIRUXX9ozmZenXQe3aYpe0oFz5Y2HSV30c81g2eLCy111hUcfdl6uvHulRnYa6bsbAGg2QikABLrKSqmwsCpUOvLztWHTco0995TOSc5wWCnPeiZd77mu9SRQ1mzDh2yRNj06+FE9kvmI4mPja50zjOrfkMViUfL/JCvfyL+kDausKv73YrWIaspvEIA/EUoBwNdck3QKCrRi2ccac+LR2ucrL/zqSc/kxdc29re5H3smh7QbolemvqL+V/b3SfuuQOp6LtR+1l5nIO2e1F17HtrjkxoAeJ/fQuns2bOVk5Ojzp0767XXXlPUhR0zKisrdd9992nv3r0aOHCgnn32WX+VBCBcGIZkGHJUVMhhVKqiokzFZ4t0PO+oDhQc0Cdbl2jlqbU6rVMqULHn7TtqHLvbM1lThJv3aShENjLBxx1PDn1Sc0bPUUxMTPMa8pGLe0ddkv6QdMm1vxr2Kz018Sl/lAXAS/wSSnNzc3Xs2DFlZ2fr6aef1qJFi3TbbbdJkj755BNdccUVeu2113Tfffdp7dq1GjZsmD/K8ojlv5mpCaAe1jrec7dnsr7eT0954a+oJ9c9qSfXPdn8hky2+2e7dXXbq80uA4CH6vqr1OtycnI0ceJESdKkSZO0Zs0at85drLS0VIWFhbVeABDULDVeaLbK/6wkkAJByi+hND8/X4mJiZIkm82mvLw8t85dbN68ebLZbFWvlBQWPgYAOBlPGLJa/fLPGgAf8MvwfVJSUlWvpt1uV3JyslvnLjZ37lz967/+a9XPhYWFfgumbC8KAADgO375v5QZGRnKysqSJC1dulTDhw9369zFYmJilJiYWOsFAACA4OeXUJqWlqZ27dopMzNTO3bs0LRp0zRr1ixJ0pQpU3T48GFlZmYqNjY2ICc5AQAAwLcsRs01NoJMYWGhbDab7HY7vaYAAAAByN28xhPhAAAAMB2hFAAAAKYjlAIAAMB0hFIAAACYjlAKAAAA0xFKAQAAYDpCKQAAAExHKAUAAIDpCKUAAAAwHaEUAAAApiOUAgAAwHSEUgAAAJiOUAoAAADTEUoBAABgOkIpAAAATBdpdgHNYRiGJKmwsNDkSgAAAFAXV05z5bb6BHUoLSoqkiSlpKSYXAkAAAAaUlRUJJvNVu95i9FYbA1gDodDx48fV0JCgiwWi8/vV1hYqJSUFB05ckSJiYk+vx/cw/cSuPhuAhPfS+DiuwlMfC/NYxiGioqKdMUVV8hqrf/J0aDuKbVarerQoYPf75uYmMh/lAGI7yVw8d0EJr6XwMV3E5j4XpquoR5SFyY6AQAAwHSEUgAAAJiOUOqBmJgYPfHEE4qJiTG7FNTA9xK4+G4CE99L4OK7CUx8L/4R1BOdAAAAEBroKQUAAIDpCKUAAAAwHaEUAAAApiOUemD27NnKzMzUjBkzVF5ebnY5YW3Dhg0aNmyYRo4cqdtuu03l5eV6//33lZGRoXHjxuno0aNmlxjW3nnnHbVp00aS+F4CxIoVKzRu3DiNGTNGH374oVavXq2MjAyNGDFC27dvN7u8sOVwOHT33XcrMzNTI0aM0K5du/huTGS32zV48GDFx8frm2++kVT332G7du3SyJEjlZGRoWXLlplZcmgx4JZt27YZd9xxh2EYhvHUU08Zb7/9tskVhbfjx48bJSUlhmEYxpw5c4z333/fGDp0qFFaWmqsXr3auP/++02uMHxVVFQYN954ozFgwACjvLyc7yUAlJSUGFOmTDFKS0ur3hs5cqSRl5dnHDp0yLjuuutMrC68bd682Zg+fbphGIaxatUq47777uO7MVFZWZlx6tQp46677jK2b99e799hN954o7Fnzx7DbrcbGRkZJlcdOugpdVNOTo4mTpwoSZo0aZLWrFljckXhrX379mrRooUkKTo6Wrt371avXr0UHR2t4cOH6+uvvza5wvD1zjvv6Oabb5bVatXevXv5XgLA2rVr1aJFC02dOlU33nijvv/+e0VERKhVq1bq2LGj8vLyzC4xbHXo0EGGYcgwDOXn56tly5Z8NyaKioqqGuWRVO/fYcePH1f37t2VmJio5ORknTlzxqySQwqh1E35+flVW4vZbDb+oggQhw4d0hdffKERI0bU2vqtsrLSxKrCV2Vlpd577z3deuutkmr/78Z1Hv538uRJ7du3T4sXL9Z9992nJ554otb3EhkZqbKyMhMrDF+tW7dWVFSUevbsqV/+8pd65JFH+G4CSH1/hzkcjqr3yATeQyh1U1JSkgoLCyU5nzlJTk42uSIUFhZqxowZeuONN9SmTZuq70eSIiIiTKwsfP3973/XLbfcIqvV+VdLzf/dSHwvZklKStLw4cMVHR2tcePGaevWrbW+l4qKCkVHR5tYYfj64osvFBkZqd27d+uDDz7Qo48+yncTQOr7O8z1d5xEJvAmQqmbMjIylJWVJUlaunSphg8fbnJF4a2iokLTp0/XE088oR49eqh79+7auXOnysrKlJOTo/79+5tdYlj69ttv9be//U2TJk3S3r179cILL/C9BIBBgwZp586dMgxD27ZtU+/evVVRUaGCggIdOXKEf1BNZBiGLrvsMknOXtOioiK+mwBS378t7du31/79+1VUVKS8vDy1bt3a5EpDAzs6eeDxxx/XunXr1LFjR73++uv8v1cTvfXWW3r44YfVr18/SdLPfvYzSdJzzz2n2NhYvfnmm0pJSTGzxLCXnp6uTZs26d133+V7CQAvvfSS3n33XVksFr322ms6duyY5syZI4vFopdfflmpqalmlxiWKioqdMcdd+jEiRMqLS3V//7v/6qiooLvxkTXX3+9tm3bpk6dOmnWrFlq0aLFJX+Hffvtt5o1a5YqKyv13//935owYYLZZYcEQikAAABMx/A9AAAATEcoBQAAgOkIpQAAADAdoRQAAACmI5QCAADAdIRSAAAAmI5QCgAAANMRSgHAD86dO6fRo0dr9OjRSkhI0OjRo9W5c2etWbPG7NIAICCweD4A+JlrtysAQDV6SgHAJE8++aQ++eQTHTx4UBkZGbr11lvVp08fvfvuu5oyZYpSU1O1d+9eSdIbb7yhzMxMZWRk6KuvvjK5cgDwvkizCwAASPn5+crOztayZcs0d+5cbdy4UYsXL9Zbb72lhx56SAsXLtSqVatUUlKiyZMna+zYsWaXDABeRSgFgADQu3dvRURE6IorrlDfvn1ltVp15ZVXKisrS/v379eOHTs0ZswYSdLp06dNrhYAvI9QCgABwGKx1HlsGIauuuoq9e/fX5988oksFovKy8vNKBEAfIpQCgABrnXr1po+fbpGjRqliIgI9evXT88//7zZZQGAVzH7HgAAAKZj9j0AAABMRygFAACA6QilAAAAMB2hFAAAAKYjlAIAAMB0hFIAAACYjlAKAAAA0xFKAQAAYDpCKQAAAExHKAUAAIDp/j+pJSeP/93bBwAAAABJRU5ErkJggg==", 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gZwyNR88YmoOeMTQ3PWOgWvrG0LyEUAOMYHfeeWe6urq6jR188MFpaWnp91yHHHJIWltbu43NnTt3QPUBI8dDDz3UY2z69Ol1qAQYDPfcc0/OO++8jbfHjRuXb3zjG/UrCBgyv/jFL3qMveIVr6hDJUA9vPrVr+4x9vOf/7zf8/zsZz/rdrutrS2HH3541XUBw1tvz/m98pWvrGquww47rE/zAwAwdPSMgcGiZwyNRc8YmoeeMTQ3PWOgGnrGAACNR98YGAx6xtBY9IyheegZQ3PTMwaqpW8MzUsINcAIdt999/UY23PPPauaq62tLbvssku3sfnz56ezs7Oq+YCRo7OzMz/4wQ96jB9yyCF1qAYYqM7Ozpx22mlpb2/fOHbhhRf2+gmGwMhWLpdz6623dhubMmVKdt555423n3/++XzpS1/KG9/4xsyYMSOjRo3K+PHjs/vuu+eVr3xlPvrRj+ZXv/pVjxecAiPDG9/4xuy2227dxr761a/m0Ucf7fMcV111Ve64445uY+9617uy/fbbD0qNwPAzmM8p9vZp5vfff39VcwEAUB09Y2Aw6BlDY9EzhuahZwzoGQPV0DMGAGg8+sbAQOkZQ2PRM4bmoWcM6BkD1dI3huYlhBpgBFuwYEGPsc3/U9gfu+66a7fb5XI5CxcurHo+YGT44Q9/mMWLF3cb22GHHXwiGYxQn/nMZ7p9Gthhhx2Ws88+u44VAUPlgQceyLp167qN7bXXXhv//vWvfz0zZszI2WefnZ/+9Kd57LHH0t7enlWrVuXRRx/NLbfcks997nN57Wtfm5e97GX5r//6r1r/E4ABamlpyZe//OVuY6tWrcqxxx6bBx54YJvH//d//3fe8573dBvbcccd85nPfGZQ6wSGl8F8TnHnnXdOqVTqNvbwww9XNRcAANXRMwYGg54xNBY9Y2geesaAnjFQDT1jAIDGo28MDJSeMTQWPWNoHnrGgJ4xUC19Y2heQqgBRrDNmzlJMmPGjKrn6+3Yp556qur5gOFv7dq1+djHPtZj/PTTT09LS0sdKgIG4u67784FF1yw8fbo0aNz+eWXp1j0Xz9oRL096brddttl3bp1efOb35y/+7u/y4oVK/o01z333JMTTzwxZ5xxRjo6Oga7VGAIveENb8gXvvCFFAqFjWP3339/Xv7yl+f9739/rrvuujz11FPp6OjI6tWr8/DDD+fKK6/M61//+pxwwglZs2bNxuOmTJmS6667LlOnTq3HPwWokcF8TrFUKmX69OndxjyfCABQW3rGwEDpGUNj0TOG5qJnDCR6xkD/6RkDADQefWNgIPSMobHoGUNz0TMGEj1joDr6xtC8POMHMII9++yzPcbGjx9f9Xy9Hbt06dKq5wOGv4997GM9Prls++23z0c/+tE6VQRUq6OjI6eddlq3ps65556bffbZp45VAUPpySef7DE2fvz4vOtd78qPf/zjbuOFQiHTpk3LDjvskGXLluWpp55KV1dXj+O/8Y1v5IknnsgPf/hDLyyBEeScc87J3nvvnfe///0bP3V0zZo1+drXvpavfe1rfZrjuOOOy1e+8pW86EUvGsJKgeFgqJ9TbG9vz8qVKwc0JwAAfadnDAyUnjE0Dj1jaD56xsAGesZAf+gZAwA0Hn1jYCD0jKFx6BlD89EzBjbQMwb6S98YmpdH+QAj2KpVq3qMjRkzpur5ejt29erVVc8HDG/XXHNNLrnkkh7jF198sU8kgxHo05/+dO68886Ntw866KD80z/9U/0KAobcc88912PsJz/5Sf7zP/9z4+3Jkyfn3//93/PYY49l8eLFuffee/PEE0/kqaeeyte//vXsvPPOvc5x3nnnDWXpwBA49thj88ADD+Q73/lOXv3qV/fpmGKxmDPOOCN33nlnfvCDH2gMQ5PwnCIAQGPx+A4YCD1jaCx6xtB89IyBTekZA33lOUUAgMbjMR5QLT1jaCx6xtB89IyBTekZA/3hOUVoXkKoAUawTT+BcIPRo0dXPV9vD+La29urng8YvubOnZt3v/vdPcZPPPHEvOc976lDRcBA3HHHHbnwwgs33m5tbc3ll1+eUqlUx6qAobZu3boeY2vWrNn495e//OW555578v/9f/9fj4bPlClT8rd/+7eZN29ejj766B7zfPrTn8699947+EUDQ6ZSqeTnP/95/uM//iM33HBDn47p6urK5Zdfno985CO57rrrhrhCYLjwnCIAQGPx+A6olp4xNBY9Y2hOesbApvSMgb7ynCIAQOPxGA+ohp4xNBY9Y2hOesbApvSMgf7wnCI0LyHUAA2mUCgM6rGVSmUg5QDD0MMPP5w3velNPT4paN999803v/nNOlUFVKu9vT2nnXZaOjs7N4597GMfy8te9rI6VgXUwtYeq0+bNi3XX399dtxxx63OMWHChPz4xz/O3nvv3WPuz372s4NSJzD0Hn744Rx55JE57rjjcv311/do+kyZMiX77rtv9txzz0yaNKnb9zo7O3P99dfn9a9/fd7ylrfkmWeeqWHlwHDhOUUAgMbi8R2wLXrG0Fj0jKF56RkDG+gZAwPlOUUAgMbjMR6wNXrG0Fj0jKF56RkDG+gZA4PBc4rQHIRQA4xgra2tPcY2/USy/urt2La2tqrnA4afxx9/PK997WuzePHibuMzZszIz3/+80yYMKFOlQHVOv/88/PHP/5x4+2XvOQl+fjHP17HioBa6e3/Axt87nOfy9SpU/s0z9ixY/PlL3+5x/iVV16ZFStWVF0fUBt33313Dj300Nx4443dxvfcc898+ctfzmOPPZYlS5Zk3rx5efDBB/Pcc8/lwQcfzGc/+9lMnz692zE/+MEPcuSRR/b4/wLQWDynCADQWDy+A/pLzxgaj54xNC89YyDRMwb6z3OKAACNx2M8oD/0jKHx6BlD89IzBhI9Y6A6nlOE5iWEGmAEGzt2bI+xwX4QN27cuKrnA4aXp59+Oq997WvzyCOPdBvfaaed8stf/jK77bZbnSoDqjVnzpx87nOf23i7VCrl8ssv9yQMNIktPVafMmVK3v72t/drrte85jXZb7/9uo11dnbmpptuqro+YOg9++yzecMb3tDjU4Xf85735I9//GPOPPPM7Lzzzj2O23PPPfPRj34099xzT97whjd0+968efNyyimnpKura0hrB+rHc4oAAI3F4zugP/SMofHoGUNz0zMG9IyBanhOEQCg8XiMB/SVnjE0Hj1jaG56xoCeMVAtzylC8xJCDTCCTZ48ucfYypUrq56vt2N7Owcw8ixdujSvec1rct9993UbnzJlSn75y19m7733rlNlQLXWrVuXv/mbv0lnZ+fGsQ996EM55JBD6lgVUEtbeqz+qle9qqoXiRxzzDE9xjb/xFNgePnnf/7nPP74493G/vqv/zqXXXZZRo8evc3jt99++1xzzTU59NBDu43/7ne/y7e+9a3BLBUYRob6OcW2traMHz++6vkAAOgfPWOgr/SMofHoGQN6xoCeMVANPWMAgMajbwz0hZ4xNB49Y0DPGNAzBqqlbwzNSwg1wAi244479hh77LHHqp5v0aJFfToHMLI899xzOeaYY/KnP/2p2/gOO+yQX/7yl5k9e3adKgMG4tJLL80999yz8fZee+2V888/v44VAbU2ffr0XscPOOCAqubr7bgnnniiqrmAoffMM8/kP/7jP7qNjR49Ol/4whdSKBT6PM/o0aPzxS9+scf4JZdcMtASgWFqMJ9TLJfLefLJJ7c5PwAAQ0fPGOgLPWNoTHrGgJ4xNDc9Y6BaesYAAI1H3xjYFj1jaEx6xoCeMTQ3PWNgIPSNoXm11LsAAKo3c+bMHmOPPvpo1fMtXLiw2+1SqZRdd9216vmA+nv++edzzDHH5I477ug2PmnSpFx//fXZf//961QZMFCbN2xWrFiRV77ylX0+vr29vcfYnDlz8vKXv7zH+J133tnf8oAaePGLX9zr+JY+uXhbejtu6dKlVc0FDL1f/vKXWbduXbex1772tdl55537PdfBBx+c2bNnd3vh2R//+Mc8+eSTW3whCjByzZw5MzfddFO3sUcffTRHHHFEv+d64okn0tnZ2WN+AABqR88Y2BY9Y2hcesaAnjE0Nz1joFp6xgAAjUffGNgaPWNoXHrGgJ4xNDc9Y2Ag9I2heQmhBhjB9tlnnx5jDz30UFVztbe39/h04j322CMtLX5VwEi1fPnyvO51r8vcuXO7jU+cODHXXXddDjrooDpVBgyFxYsXZ/HixQOaY9WqVbnrrrsGqSJgqM2YMSPjx4/PypUru42PGjWqqvlGjx7dY2zt2rVVzQUMvbvvvrvH2Cte8Yqq53vFK17RrTm84Ryaw9B4BvM5xfnz5/cY23fffauaCwCA6ugZA1ujZwzNRc8Ymo+eMTQ3PWOgWnrGAACNR98Y2BI9Y2guesbQfPSMobnpGQMDoW8MzatY7wIAqN4BBxyQYrH7pXzOnDk9PhGkL+bMmZOOjo5uYwceeOCA6gPqZ8WKFTn22GPzhz/8odv4hAkTct111+Uv/uIv6lQZADBYisVir4/Zn3/++armW7ZsWY+xaj/tGBh6vX2C+NSpU6uer7djn3322arnA4av3l4sfvPNN1c11+9///seY55TBACoLT1jYEv0jAGg8ekZQ3PTMwaqpWcMANB49I2B3ugZA0Dj0zOG5qZnDAyEvjE0LyHUACPYuHHjcsABB3QbW7VqVe64445+z3XjjTf2GDvyyCOrrg2on5UrV+b1r399brnllm7j48ePz89+9rMBfWoZADC8HHXUUT3GHnnkkarmWrBgQY+xgTSagKHV26eKr1mzpur5Vq9e3WNs7NixVc8HDF//f3t3Hl11feYP/AkhrIJhERRlExjlDKKCgCKKuNQFHQSsW13AGS0udaZqqwIVPHMUHUetexkdhDIuHZWBAQV1VCzWhU3BhRZlUxRlDWvY8/vDX6nXm0BuFgLf+3qdk3P8Pvf7+Xye3AS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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 3: Constrain peak infected\n", + "\n", + "# {\n", + "# \"name\": \"infected_maximum1\",\n", + "# \"variable\": \"Infected\",\n", + "# \"interval\": { \"lb\": 1e-5, \"ub\": 0.4},\n", + "# \"timepoints\": {\"lb\": 75, \"ub\": 125}\n", + " \n", + "# },\n", + "\n", + "# Tighten epsilon <= 0.18, theta <= 0.4\n", + "\n", + "SAVED_RESULT_TO_USE = os.path.join(SAVED_RESULTS_DIR, \n", + " # \"1a6108d5-58c8-44ed-b41e-93d95dd4ddab.json\"\n", + " \"51b0da9c-c2f1-4070-9722-6c78655e70eb.json\"\n", + " )\n", + "\n", + "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n", + " results: FunmanResults = FunmanResults.model_validate(json.load(f))\n", + "\n", + "# Plot the trajectories\n", + "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Infected\")\n", + "\n", + "# Plot the parameter space\n", + "ParameterSpacePlotter(\n", + " results.parameter_space, plot_points=True, parameters=[\"epsilon\", \"theta\", \"timestep\"]\n", + " ).plot(show=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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i0FAIAAhyD8LZtLMQIMiODW0aCgAY1noYFFAg4OMAKN5QIK8kr87mW1kMYImIiIhuUxXTB1bErrA79mzGWQDm9IE/T/+JUmMpAEClVNXuJKuBASwRERHRbajMWIY1Z9eIxwNaDsCexD2yY1t5tEJ8Zjwc1Y4YGzwWiw4vAgC4OLjAQeVQJ/OtCgawRERERLehqEtRyC7JBmAuj5WQnWA3fSDEKwQAMDp4NNIL0xGbHgsAmNhxItRKdZ3MtyoYwBIRERHdhqzTB+4LuQ9/xf4lO06pUOJE6gkA5uYFi48vFq892LFh1oJlAEtERER0mzGajJIAdlDQIOy6vEt2bBvPNkjJT4G7zh3DWw/HD8d/EK8N/XUoN3ERERERUe07cOUArheay2A1cWqClLwUu+kDge6BAMybvM6kncHV/KvitVFtR8FV61rr860qBrBEREREtxnr1dd7Q+61mz6gUqhw6MohAMDk0Mn46shXkuvrz69Hfml+7U20mhjAEhEREd1GBEHAipjyclmDgwbbTR8I9goWN3r1DeiLpWeW2owxmAy1Ms9bwQCWiIiI6DZyPPU4EnPNLWHdde5IL0yHSTDJjvV18QUAzO05F1GXomRXW4sNxbU32WpiAEtERER0G7FOHxgTPAarzq6SHadRarD/yn4A5vSBzw99LjuOdWCJiIiIqFb9FVOe7zo4aDCiLkXJjgvxDkGRoQitPVqjvXd7bL6w2WaMTq2Di4NLbU212hjAEhEREd0mYtNixZawzhpn5Bbn2k0fsFQXmNRpElafXS2b61psKEZRWVHtTbiaGl5rBSIiIiKqFut0gVHtRmHNuTWy47QqLQ5eOQjAnD7w8OqH7T6z1Fhas5OsAVyBJSIiIrpNWFcfGBo0FDsu7ZAdF+IdAqNgRJhPGDwdPXEk5YjsuOauzdHEuUmtzPVWMIAlIiIiug1cyr6E46nHAZhzV4sMRXbTBzRKDQDz6uvP0T/bbXLwQp8Xameyt4gpBERERES3gVWx5ekDw1sPx99xf8uOc1Q74ujVowCAiZ0mYtBPg+w+003rhhJDCbRqbc1O9hZxBZaIiIjoNrDybHn5rKGthuKfhH9kx7X3bg8BAvq16IeisiJczLpo95mPrn3U7upsfWIAS0RERNTIpeanYk/iHgCAWqmGwWSAUTDKji0xlgAAJneajG+OfnPD5z4Z8SR0al3NTrYGMIWAiIiIqJFbc7a82sBdQXdhQ/wG2XHOGmecSTsDlUKF8e3Ho/2X7W/43Onh02tymjWGK7BEREREjdxfseXNC4a3Ho7tF7fLjgv2CgYADGs9DOczzyOzKNPuMzs06YAIv4ianWgNYQBLRERE1IhlFmWK+a5KhRJKhdJu+kBWcRYAc/OCLw59ccPnTg+bDoVCUbOTrSEMYImIiIgasXVx68SAtX/L/th0YZPsODetGxKyE6BT6zCy7Ui7TQ4AQKVQYWrnqbUy35rAAJaIiIioEVsZW159YESbEdh2cZvsuDYebQAAY4LHYE/iHhQZ7LeIHdFmBJq5NqvZidYgBrBEREREjVR+aT42xZevuGoUGhhMBtmxyXnJAMzpA58d/OyGz22om7csGMASERERNVKb4jeJZbF6+/fGtgT51Vd3rTuuFVyDXqtHH/8+2Hl5p91neug8cE/be/Denvdw+vrpWpn3raqzAHb+/PmIjIzEtGnTUFZWJp6Pi4tDeHg4dDod8vPzxfOurq4YOHAgBg4ciFOnTgEA9uzZgz59+qBfv37iOSIiIqI7lXX1gbvb3G03fSDIIwgAML79eKw+t9ruJi/A3F72TNoZvLj9RYR+FYrcktyanXQNqJMANjo6GsnJydi9ezdCQkKwYsUK8Zq/vz927tyJXr16Se4JDg5GVFQUoqKiEBoaCgB45ZVXsH79evz++++YP39+XUydiIiIqEEqNhTj73Pl7WId1Y4oM5XJjrV025oUWonqA+HT8d2x78RjlUJVA7OtWXUSwO7btw/Dhg0DAIwYMQJ79+4Vrzk5OUGv19vcc+HCBfTv3x+zZ89GcXExioqKoFKp4OHhgRYtWiAz037dspKSEuTm5kr+EREREd1Otl/cjoKyAgBAF98uiLocJTvOy9ELOSU58HXxRRuPNjh13f6v2B2bdEQH7w745eQvAABPnSe0am2Nz/1W1UkAm5WVBTc3NwCAXq+/YfBpER8fj127dqFZs2ZYtGiR5BkAoFarUVpaKnvvggULoNfrxX8BAQE180WIiIiIGgjr6gMj247ElgtbZMf5u/kDAB7s+CB+OvnTDZ/5SPgjWHV2FQrLCgEAT3R7Amplw2vcWicBrLu7u7gKmpOTA09Pz5ve4+XlBQC4//77ER0dLXkGABgMBjg4OMje+9JLLyEnJ0f8l5SUVAPfgoiIiKhhMJgMWHV2lXjs4uBiN30gLiMOgDm39buj38mOAcypAlM6T8GXR74Uz727913kleTV0KxrTp0EsH369MG2beak4s2bN6Nv3743HF9QUACj0ZxcvHv3brRp0wZOTk4wGAzIzs5GUlLSDYNgrVYLNzc3yT8iIiKi28Wuy7vErlodvDtgb9Je2XFNnJqgyFCE1h6toVVpxVJacu5uezcKywqxL2mfeC7QPRDODs41O/kaUCcBbHh4OHx8fBAZGYkzZ85g/PjxmDlzJgBzesGQIUMQHR2N0aNHY+PGjTh//jy6d++O/v37Y8OGDZg7dy4A4K233sLIkSMxceJELFiwoC6mTkRERNTgWKcPjGo3ym76gI+zDwBz7devjnx1w2dOD5uOxccXS8491uUxKBUNr+qqQhAEob4nUdtyc3Oh1+uRk5PD1VgiIiJq1EyCCc0/ao7U/FQAwFuD38Kr/7xqM04BBZQKJYyCESdnnUS/Jf1uWBKr4KUCBH4aiLTCNPH+xOcSxRza2laVeK3hhdREREREZNeh5ENi8NrKoxUOXjkoO66pc1MYBSPCfMJwveD6DYPXp7s/jV2Ju8TgFTBvDKur4LWqGMASERERNSLW6QP3tLvHbvqAh84DQOVbx351WJpi8HjXx29xprWHASwRERFRIyEIAlbElDeE8tR5iq1krSmgwNmMswCAe0PuxaYLm+w+M7RpKALcArD+/HrxXFOnphgSNKQGZ16zGMASERERNRInr51EQnaCeHzs6jHZcb4uvgCAfi364djVYyg1ytfOB8yrr7+d+k3SXnZK5ylo9VkrKN5Q3LmtZImIiIjo1lmnD8zqNgubL2yWHeeodgRw8/QBlUKFyZ0mY9HhRZLz3o7eSCtMQxvPNnB1cK2BmdcsBrBEREREjcRfsX+Jn5s4N5FNH1AqlLiYfREqhQqDgwbjYLL8Ji/AvFErMTcRF7IuiOcGtByANXFrAABPdH0CCoWiBr9BzWAAS0RERNQIxGXE4UzaGQDmBgWnrp2SHWdJHxjaeig2nt8IAfYrpk4Pn45vj34rOTe01VAcSj4EBRTwcPRAmVG+w1d9YgBLRERE1Aisii1vHTs6eLTdjVkKmFdMJ3eafMPmBV6OXhgcNBi/n/pdPOfm4IbEnEQAgAABM/6egcKywpqYfo1iAEtERETUCFhXH/Bx9kGxodhmjEqhQnJeMnRqHTr7dMb5zPN2nzcldArWnluLIkOReG5ip4n488yfknHW1xsKBrBEREREDVxiTiKOXD0CAHDXuSM2PVZ2XDOXZgCA0e1GS1ZW5UwPn45Fh6Sbt5q5NpNUHXBUO8LbyftWpl4rGMASERERNXCrz64WP49sOxKb4uXTB4qN5lXZSZ0mYcmJJXaf19mnM1wcXHAo5ZB4rluzbjbPfSjsIaiV6luYee1gAEtERETUwP0VU159wN/VXzZ9QK1UI70wHXqtHk2cmkjawlY0PWw6fjj+g+TciDYjbCoWvBL5yi3OvHYwgCUiIiJqwK4XXMfuxN0AAGeNs928Vkv6wPj24/HN0W/sPk+tVOPBTg/iu6PfSc5fy78mOfbQeeBIyhEYTIZbmX6tYABLRERE1ICtObtGLIV1d5u77aYP5JTkAAAe6PgAVp5dKTsGAEa1HYXjV48jszhTPDc5dDKWxSyTjMsqzsK4ZeNkV3vrGwNYIiIiogbMunlBC30L2aoADkoH5JbkwtfFFyWGkhuWvpoePt2m81ZLfUvJ5i2VQgUAeKr7U3BxcLnVr1DjGMASERERNVDZxdnYfnE7AECr0iIhO0F2nI+LDwDgwY4P4vPDn9t9nreTN7o16yZpQRvsFYwdCTsk40yCCQAwO2L2Lc2/tjCAJSIiImqg1seth0Ew56AOaz3MbvqAZcPWmOAxNsGotSmhU7D0zFIxQAXMKQUHkg9IxgkQENkiEh2bdrzVr1ArGl5dBCIiIiICAEkua6B7oGz6gFalRbGhGK08WiEhKwFGwWj3eQ91fgjjlo0Tj1UKlZg7a6FRalBmKmuwq68AV2CJiIiIGqTCskKsj1sPwFw5ICk3SXZcE6cmAMytYz8/ZD99IMwnDCXGElzOuSyeG91utCTHFgDKTGVo4tQE49qPq/iIBoMBLBEREVEDtCl+E0qMJQCAwYGDsTl+s+y41PxUAMCQVkMQfS3a7vMeCX8EXx/5WnKutWdrZBdni8eWzVuPdXkMWrX2VqZfqxjAEhERETVAK2PL0wdae7aWTR9wVDvCIBgQ5hOGPYl77D5LrVRjTMgYLD2zVDzn7+qP/Un7JeOMghEKKDAzYmYNfIPawwCWiIiIqIEpNZZizbk1AAClQimuslbkofMAYK7j+u2xb+0+75529yAqIUpc0QWA0cGjse/KPpuxd7e9G4Hugbcw+9rHAJaIiIiogfkn4R/kl+YDMJe0si57ZS0lPwUA0LVZVyTmJNp93vSw6fjs0GeSc0Vl0hVdS/pAQ968ZcEAloiIiKiB+SumfGPVjC4zZBsTOGmcAAD9WvTDqthVdp/VxKkJWnu0xonUE+K5Ia2GYPW51ZJxRsGIlvqWuLvN3TCajLj7t7uheEPBTlxEREREdGNGk1FSGeB64XXZca4OrgCAiZ0m4vfTv9t93pTQKfgx+kfJuRCvEMnmLYsnuj0BlVKFv+P+xqb4TfBy9IIgCFX/ErWMdWCJiIiIGpA9iXuQVZwFAOjZvCe2XdwmO+5awTWoFCoEuAbIBqMWUzpPwZCfh4jHXo5eOJZ6zGacWqnGY10eAwB8cegLAMC9IffCUeNY3a9Sa7gCS0RERNSAWFcfaN+kPQrKCmzGOGucAZi7c1VcXbXWxbcLUvJSJM0KRrcbjX1Jtpu3xrUfBx8XH8SkxWB7grl97Q/Hf0BWUVZ1v0qtYQBLRERE1EAIgoDlMcvF48yiTNlxOrUOAHB/h/ux4fwGu8+bHj7dprmBdRtZAFD+fzho2by16NAi8dq0ztPg4ehRhW9QNxjAEhERETUQh1MO42r+VQBAuG84/kn4R3ZcRlEGAHPbV+vSWNY0Sg0GBw3G9ovbxXN9Avrg77i/JeNMMAe0A1oOQE5xDpacWCJee6bHM9X/MrWIASwRERFRA2GdPtCpSSexlJY1y+atCR0m4OujX9tct7in3T1YF7cOAso3YXVs0lHMrwXKS2d9OuJTKBQK/HjiR7FhQq/mvdC9efdb+0K1hAEsERERUQMgCAKWnVkmHlvnrVpTKBQAgDHBY3DgygG7z3s47GFxMxZgDnzPXD8jGWMUjHDSOOGhsIdgEkyS8c/0bJirrwADWCIiIqIG4UzaGSRkJwAwl7mKuhQlOy63JBd6rR5pBWk2+awWTZyaQK/TIzkvWTw3su1I2c5bkzpNgrvOHVsubEF8VjwAwMfZB/d3uP8Wv1HtYQBLRERE1ABYNy8I9w1HXmmezRhL+sD49uPx7VH7rWOndp6Kb45+IzlnSRewUMC8kmvZvGW9+jo7YjYcVA5V/AZ1hwEsERERUQNgXX1ALvcVAEwm84rrXa3uwtmMs3afNaHDBKyMKc+nDfMJw8b4jZIxAgR09+uObn7dcCHzAtafXw/AvPlrZsTMan+PusAAloiIiKiexWfG40yaOT81yD3IbvpAgaEAvi6+OHntpN1ndW3WFaevn0apqVQ8F+4bLtm8pVRIS2d9efhL8doDHR+Ar4sv0grSqv19ahsDWCIiIqJ6tip2lfi5a7OuyC+zXYF107oBMAeYP5740e6zpodNx2cHPxOPtSotzmecl4wxCSa469zxYKcHUVBagO+Pfy9ee6bHM8gqykLQp0FQvKFgIwMiIiIisrUidoX42VLGqqLismIAQHe/7rhWcE12jEapMa/App0Wzw1vPVx289b0sOlw0jjh15O/IrckFwDQw68Hevr3xPfHvkdBWQE6+3SGu869ul+r1jCAJSIiIqpHV3Kv4FDyIQCAn4sfdl/ebTNGAQVKTaVo7dEaWy9stfus0cGjJaW4AECr1sqOnRUxC4IgSDp1PdPzGRhMBvHcsz2fFct2NSQMYImIiIjq0eqzq8XP3Zt3l68+oDVXH3iw44OSZgcVTe08VdJJq5V7K0knLovBQYMR7B2MnZd3irm3TZ2bYkKHCfgr5i8k5SahqXNTTAqdVN2vVasYwBIRERHVoxUx5ekDpcZS2TEFpQUAgED3QNn8WMBcu9VgNEgC4B7NeyCzOFM8VkK6ecu6dNasbrOgVWvx8YGPAQD9W/RHUZl8OkN9YwBLREREVE/SC9Ox6/IuAIC3ozf2JO6RHWcUjAjzCcNfsX/JXgfMq6+LDi8Sj5VQ4nLOZckYE0xo5tIMY4PHIiknSbJ5bGbETBy4cgAHkw9Co9RgRewKeL7vyU1cRERERFRu7bm1ECAAAHr497hx84IO4/FPwj92nzWizQgxGAaAwa0GY/+V/eKxpXHB410fh0alwddHvoYJ5rqykzpNgp+rn7j6GuQRBAAYGzwWHo4et/IVawUDWCIiIqJ6Yp0+YDQZZcdYglpnjTPKTGWyY7o164b9SfvFYBgoD3wtBAhQKpSY0XUGig3F+Pro1+K1Z3o8g8ScRLEb2KXsSwCA53s/X/UvVQcYwBIRERHVg5ziHGy9aK4o4KZ1w4ErB2zGWBoO9GvRD7+d+s3us6aHT8eXR8qbEfi5+ElWYy1GtxuNAH0Alp5eiswic25st2bd0Mu/Fz4/+DmMghFB7kEoNZYiwi8C/Vr0u6XvWFsYwBIRERHVgw3nN8BgMgAAejbviZySHJsxzhpnAMA9be/B8avHZZ/joHJAC7cWSM1PFc/19O+JjKIM8diSPjA7YjYEQZA0OpjTcw4Kygrw3bHvAEAMbJ/v/XyDLKEFMIAlIiIiqhc32pBlkVeaB5VChfzSfEl6gLUxwWPw88mfJeesg1nAnD7Q2qM1hrYeioPJB3Es9RgA88axBzs+iB9P/Iickhz4OPsgpyQHAW4BGN9+fDW/We1jAEtERERUx4rKirAubh0AwEnjJDYysKZSqACYKxD8eupXu8+a0GEC1p5bKx73Cegju3lrZreZUCqU0tJZEbOgUWnw6cFPzWP/f8V1bs+50Kg01f16tY4BLBEREVEd23JhC0qMJQDM7Vvl0gd0ah0A4L0h74mbqirydfHF9YLrks1dXo5ekjECBGhVWjzS5RGk5qdi6emlAMwB8qyIWVgXtw7xmfFwcXBBan4qXB1c8XjXx2via9YaBrBEREREdcy6+oBKqZIdU1BWAJ1ah4SsBLvPmdZ5GhYdKq/96qHzwP6k/TbjJnScAG8nb3x79FsYBHPe7fgO49HcrblYOssS+D7e9XHodfqqf6k6xACWiIiIqA6VGkux+txqAIBWpcWxq8dsxqgVagDmqgHLziyz+6ze/r1xNuOs5Di9KN1m3OyI2SgzluHLw+WVCub0mIMTqScQdSkKAHA55zKUCiXm9JxTna9Vp+osgJ0/fz4iIyMxbdo0lJWVL3PHxcUhPDwcOp0O+fnm1mgJCQmIjIzEgAEDMGrUKOTkmJfVBw4ciMjISAwcOBC//PJLXU2diIiIqMZEXYpCfqk55unm1w1Zxbadriz5p12bdZW0grUW4RchluGykBvb2aczevv3xsrYlbhWcA0AEO4bjj4BffDJgU8AmFvUAsD9He4XPzdkdRLARkdHIzk5Gbt370ZISAhWrChfNvf398fOnTvRq1cv8Zy7uzv+/vtv7Ny5E2PGjMF3330nXtu4cSOioqIwbdq0upg6ERERUY1aGbtS/KxVaWXHFBmKoNfqZTd3WUwLnYafo8urD4T5hElqyVqXzlIoFPj80OfitTk95uBawTX8cfoPAEBSThIAYF6vedX4RnWvTgLYffv2YdiwYQCAESNGYO/eveI1Jycn6PXSPAsPDw+4u7sDABwcHKBUmqepVCoxcuRIjBkzBpcvS3v7WispKUFubq7kHxEREVF9M5qMYv6rWqnGidQTNmM0SvPq630h92Fj/EbZ5zioHOCqdUVBWYF4zs/VTzJGgAAXBxdMCZ2CE6knsDfJHH95OnpiUugkfHn4S5QaS9HctTmMghF9A/qip3/Pmviata5OAtisrCy4ubkBAPR6PTIz5ZfCK8rOzsZXX32F6dOnAwCWL1+OXbt24fnnn8czzzxj974FCxZAr9eL/wICAm75OxARERHdqv1X9osNBrr4dpFNH7CUsgp0D0SxoVj2OfeG3Ivvj30vHjtrnHE45bDNuKmhU+GqdcXnB8tXX2d2mwkA+OrIVwAgVkCY17t89dUkmHDf0vugeEOBMqN8+9r6VCcBrLu7u7gKmpOTA09Pz5veU1ZWhilTpuCjjz4Sx3t5mXfHDRgwACkpKXbvfemll5CTkyP+S0pKqoFvQURERHRr/oopb17gpHGSHVNqLIWPsw+2Xdxm9zmj2ozCviv7xONe/r2QXiizeav7bGQUZoh1ZFUKFWZHzMZvJ39DemE6PHWeYj7u2OCx4n1rzq7B6rOr4aZ1Q5GhqGpfsg7USQDbp08fbNtm/h9h8+bN6Nu3703veeKJJ/DAAw+gX7/yHryWIDgmJgYeHh5279VqtXBzc5P8IyIiIqpPgiBg6RlzDValQomT107ajLFOH7BuRmCtmUsznM88LzmXV5JnM65PQB909umMH47/gFJjqfhcfzd/sXSWUmEOBT8b8ZlYzksQBLy24zUAQG5JrtjutiGpkwA2PDwcPj4+iIyMxJkzZzB+/HjMnGlevs7KysKQIUMQHR2N0aNHY+PGjdi9ezeWLVuGJUuWYODAgfj0U3N3iMGDByMyMhIzZ87Ehx9+WBdTJyIiIqoRx64ew9X8qwCA0KahsukDgmBuF+uqdYVRMMo+Z2rnqfjm6DficVvPtjiUUr7Zy3rzltFklGzeeqbnM9iesB1n0s5Ap9YhvSgd7jp3PNLlEXFM1KUonE47DQAY134cPB1v/st5XVPX1Ys++OADyfE335j/8B4eHuLqrLWCggKbc0eOHKmdyRERERHVMuvqA25a+V+HDYIBrTxaYd25dXaf0967PdIK08TjlvqWkhVZAQK8HL1wf4f7sS5uHa7kXgFgDpojW0Tinj/uAWDezJWSl4KZ3WbCxcFFvP+1qNfEz/8d8N8qfsu6wUYGRERERHXAkj6ggAJn0s7YXHdQOgAA7ml7D2IzYmWf0bN5T7EJAmBOOZCrZPBol0ehU+vw2cHPxHNzes7BuYxz2HB+AxRQICUvBWqlGs/0KN8Yf+zqMexJ3AMA8Hb0RtjXYXfuJi4iIiKiO1lMWgwuZF0AYF5BzSyyrchkafFqr/IAADzQ4QGsj1svHvdo3kO289bMbjMRmxaLfy79AwBw17ljcuhkMaC1lNya2Gkimrs1F+97Pep18XN6UTr0Wv2du4mLiIiI6E5mnT7g4Si/Ed0kmNDZpzP+jvtb9rpWpYVBMEhyY0sMJTbjhrcejtaerfHFoS/Ec090fQLFhmL8FP0TAOBqnjkX17pxwfmM81gXZ05dcFQ7AgCe6fGM3XSH+sQAloiIiKiWLT29VPwcm2abHmBJHxgcOFjc6FXRvSH34ruj5d1J/V39ceSq7f6g2RGzkVOcgyUnlgAwVxp4svuT+PbotygsK0RTp6YwwYRBgYPQpVkX8b63d78NAeZNZEWGIjhpnDC319xqfNvaxwCWiIiIqBYlZCWIu/pbe7RGZrFt+kCpyVzmKjkv2e5zIltEIj4rXjxu7dnaZoy/mz9GtRuFn6J/En/6Hxs8Fn6ufuKKbG6puSzp872fF++7mncVv54014rVqXQAzGkI3k7elf+idYgBLBEREVEtWnV2lfi5iVMTu+P6BvTFpvhNstf8XP0kq60KKBBzPcZm3BNdn4BSocSnBz4Vzz3T4xmsiFmB5LxkuDq4othQjGCvYNzd9m5xzAf7PhBTE4qNxdAoNVApVDh17VTlv2gdYgBLREREVIuWnVkmfo7LjLO5bkkf6NqsK/JKbRsSAMDk0MmSNIRuft2QVpQmGaNWqvF418ex5cIWXMy+CADo0KQDBrQcIDYusDQrmNd7ntjEIKc4R2wra5nLiDYjsHD/QnT+ujMyCjOq/qVrGQNYIiIiolpyNe8qDiYfBAC00LeQrT5QaiqFSqFCTJrtiqqFj7OPpBqAyWSyGXNvyL1o5toMnx8sb1wwt+dc7L+yH4dTDkOj1CC7OBveTt6Y1nmaOOaLQ1+IlQ9KTaVQKpRik4UZXWfAy8mrit+69jGAJSIiIqolq8+uFj/7OvvaHXdX0F3YdXmX3evWq7hejl44lnrMZszsiNm4kHkBG+I3ADA3S5gSOkVcfXXXuQMAnox4Eo4ac5WBorIifLDP3GxKrTD3txrSagj2JO6BSqHCi/1erMS3rHsMYImIiIhqybKY8sDTegOWhUapAQC08miFMpN8w4C3Br2FwymHxeNg72CbMcFewRgUOAhfHv5SPPdE1yeQVpgm5uCmFaZBq9Liye5PimOWnFiCnJIcAOV1aC2luaZ0noJWHq0q90XrGANYIiIiolqQWZSJnZd2AgB8XXxl0wfKTGXQqXU4lHxI9hlalRYZRdIc1PMZ523GzY6YjcKyQnx3zFxmSwEFnuz+JD4/+DlMgglNnZsCAKZ2ngofFx8AgMFkwJs735Q8Z0DLAdh5eScUUOClfi9V8RvXHQawRERERLXg73N/i3VV/V397Y4b3no4jqcel712b8i9YvMBAOjYpCPSCqWbtxzVjng4/GH8evJXcRPYPe3ugbeTN74//j0A4HrBdQDAc72eE+9bfmY5rhVckzxLrTSnETzQ8QGxW1dDxACWiIiIqBYsj1kufr6YddHmuiVY1Ov0YqBbUZhPmGTl1nKPtUmdJkGv1ePTg+Wls+b2nIslJ5YgtyRXzH0d0WYEOjbtCAAQBAH/2fEfAObVWgDo2bwn/kkwt559tuezaPNZGyjeUCC7OLuyX7nOMIAlIiIiqmF5JXnYfGEzAPOmK7nmBQaTAXqt3u7mreauzSXXXBxcEH0t2mbc7O6zsfPyTsSmmzt8hXiFYEDLAWJAW1haCEDaNnbzhc24kHUBQHkA6+rgCgEC7g25Fzsu7UBaYRqCvYLh6uBa5e9f2xjAEhEREdWwjfEbYTCZN0W10LewO25o66G4lH1J9tqEDhOw9eJW8bi9d3ubMRF+EYjwi5CUzprTcw7WnV+Hi1kX4ah2RKmpFKFNQzGk1RBxzKv/vCp+NsFcksuy+vpcr+fw4f4PAQD/6f8fsXZsQ8IAloiIiKiGLT9Tnj6QmJNoc12lMAeFJsG2nquFTqMTu2MBwKWsSzZjZkfMRlJOkliuy9XBFdPCpsk2LlAozCut+5P24+jVo5J5DAkaAhNMGNFmBPYn7UdGUQbctG5w0jhBEOTTG+oTA1giIiKiGlRsKMa68+sAAHqt3qaKAAAYBSN8nH2wM2Gn7DN6+/fGn6f/FI9be7S26bwFABM7TcTXR74WV1Ef7/o44jLisOvyLqgUKuSX5sPXxReTOk0S77Hkvlrm0c6rHaIuRQEAnu/9vFgXNrckF+OWjZOtnlDfGMASERER1aBtF7eJna1a6lvaXLfknA4MHIiMYvk2rYMCB0lSC5w0TjZj5vacC6VCKbaBVUCBp7o/hU8OfALA3MgAAJ7u/jS0ai0AICYtBtsTtgMANApzDdoW+hYwCAYMChyEY1ePIaMoA1qVefyLfV9kJy4iIiKi292KmBXi5+TcZJvrlgA2vTBd9n6dWidusLI4df2UzbhZEbOw9PRSse3ryLYj4aRxEldus4qz4Kh2xKyIWeI9r0e9Ln4uE8rQQt8Cuy6ZN4r9q/e/xNXXEqO5mcG7e9/lCiwRERHR7azMWIaVsSsBAM4aZ9kVVhNMaOXRCnsT98o+Y2zwWKw5t0Y8DvMJsxkzKHAQgr2CxdVWwLx5a9HhRSgzlcFD5wEAeCT8EXEFNSknCX/F/gWgvBxXO892KDWVAgBOp51GemE6HFQO4jNDm4aKZbgaEgawRERERDVk1+VdYjOBQPdAm+uW1dcIvwgUG4tln9HSvaWYggDIr+LOjpiNg8kHceLaCQBAW8+26BfQD18f+RqAefVVAQWe7fWseM87u98RN40ZTAb4OPtgb5I5iF75wEos3LcQAFBqLBXveWPgG1AqGl642PBmRERERNRI/RXzl/j5av5Vu+MSMhNkz/u7+WNz/Gbx2M/VD+lF0lQDXxdf3Btyr6R01tyec/Hbqd+QUZQBFwcXAMCY4DFo69UWAJBRmIEfjv8AoHz1tVOTTigyFKFbs26Iz4xHWmGaZPW1i28X3Btyb2W+dp1jAEtERERUA0yCCctilonHcrmjAgR0atoJx1KPyT5jdLvRkmYFcj/fP97lcWQUZWDpmaUAzKkK0zpPwycHPwEAcfV2Xu/yxgUfH/gYZaYyAObVV3edOw4mHwQAvNDnBSzcL7/6aim91dAwgCUiIiKqAQevHBRLZoV4hdhct6QPtPNqJ6nvas06dUClUCEmLUZyXalQYka3Gfju6HfiMx7v+jj2X9mPmLQYOKgcYDAZEOEXgcgWkQCAgtKC8rqw/1/3Ndw3HPll+QhtGooruVdwveA6NEqN+J7uft1xJOUIolNtO381BAxgiYiIiGqAdfrA9YLrNtcFmBsCnL5+Wvb+3v69sersKvG4nVc7mzGj2o5CM5dm+OLwF+K5p7o/Ja6+WgLUeb3KGxd8c/QbFJaZ28kaBSOcNc44fvU4AHPlgff3vQ8A4gotANzf4X68uetNhH8TbrdaQn1iAEtERER0iwRBwB+n/wAAaJQaZBbLl57q4dcDcRlxste6+3VHdnG2eCwXBM+OmI1VZ1eJ14a3Hg6DyYBN8ZuggAJFhiIEuAXg/g73AzCnBLy9+20A5cFtt2bdkFOSg2CvYKQVptm8p1fzXpJg3JIz25AwgCUiIiK6RdHXopGSnwLAXEWgIuX/h1zeTt6y9zuqHSW5r16OXjYdvILcgzC8zXBJ6axnez0rHls2b83pOQcalTkd4LdTv4m5uEbBCK1Ki9Np5hVg665b1u4JvgeHUg4BMFdSsDy3IWEAS0RERHSLrFcs5TZvmWCCSqHC8dTjsvePbDsSuxN3i8dyge7MbjNx8tpJ7L+yH4A5oO3arCt+PvkzACCvNA8uDi6Y0XWG+Z2CCa/teA0AxFJY3fy6IbMoE0HuQcgtycW1gmuS3NfIFpFYenqpeHwp+xKyirIq90eoQwxgiYiIiG6RJX1ApVDZ7VzVJ6CP3dJaHjoPsUYrAJs0AweVAx7t8qikdNazvZ7F98e+R7GhGK4OrgDMFQr0Oj0AYO25tbiSewWAOZhVKVSIz4wHYK5QYKk8YJ37elfQXTZdvyyruQ0JA1giIiKiW3Au/ZzY+rWFvoXNdcvqp72GAAFuAdh4fqN43Majjbjhy+L+DvdDqVDil5O/ADCnHEwOnYwvDpk3c+WV5kGpUGJur7kAzDm5L29/WfKMCL8IXC+4Dn83f5QYSpCanyrJbx0UOAi/nvxVck8LtxZw1jjf/I9QxxjAEhEREd0CS+tYAJJNWBYmwQSdWifu/K9oSKshSM4v77aVVWz7k/3siNlYfHyxuFr6aJdHsSl+E67mX4Wj2hEAML79eLH7167LuxCbHit5RmJOIgDg2Z7P4sP9HwIw14S16BPQB/FZ8ZJ73h/6PldgiYiIiG43v5/+HYC5zqtc8AmYS2TllubKXrueX14FwNXB1WbzFmCuDPDpwU/F46e7Py3Wdi0xlgAwb8qyePkf6eprV9+uuJp/FT7OPgDMXcKsV1+HthqKn078JLmnvXd7TOg4QXbO9Y0BLBEREVE1JeYkinVd/d38ba5bmhfklsgHr739e2Nrwlbx2MfFx2bMopGLsCF+A5LzzKu0Q1oNQVphGo5dPQa1Ug2TYEKfgD7o6d8TAHAi9QT2Je2TPON6oTlIntNjjuzqaxffLriSd0Vyz0fDP7Kb9lDfGuasiIiIiBqBVbHljQfyS/NtrgsQoNfqcfLaSdn7g72CJe1bL2RekFx31jhjauep4morYE4BqNhZy3r19dV/XpU8o1PTTriSewVejl5wUDvYrL4Obz0cS04skdzTs3lPDG89XP5LNwAMYImIiIiqyZI+AMjnrgJAl2ZdJDv9LXRqHQ5eOSget3BrYbN5a2rnqUjOTUbUpSgAQEt9SwR7B2P12dUAzOkDrTxaYWzwWABAfGY8NpzfAKB89TevJA+ANO3AevW1vXd7pBWmSd770fCPxE5eDREDWCIiIqJquJZ/DYeSzQX//Vz9bK5bAsjUvFTZ+4e1HobYjPKNVnIruLMjZouVBgBz6awvDn0BAQKcNE7mcz2fhUppXol9Y+cbYhAsQECwVzAu51yGXquHs4MzUvJSxFVbwFx/tuLq6/DWw9EnoA+MJuPN/wj1hAEsERERUTWsObdG/FxUVmRzXYCAps5NcS7jnOz9KpQHkjq1zqb9bG//3gjyCMLiE4vFMePbj8fi4+bjwrJCuOvc8UiXRwAAqfmp+OOUuR6tJXguM5pXfmdHzBY3gRmF8sA0yD0IOSU5kvd+MPQDlBhKEP5NOBRvKFBYVnizP0WdYwBLREREVA2WYBGwnz4Q7BVskxYAmDd8bb1YvnmrmUszmzGzI2bjpxM/odhQDACYHj4dK2JWIK80T1x9ndltptjq9b0974nBqQABge6BuJh9Ec4aZ3joPJCclyxZfR0TPAY/nvhR8s6JnSYi1CcUXx35Cqevn5ZdWW4IGMASERERVVF2cTZ2Je4CADR1ampz3bICein7kuz9ffz7IL+sPGXgcvZlyXUvRy+M7zBesnnryYgn8dmhzwBAXBV9usfTAICc4hx8deQrAIDy/8M7tcK8UeuJbk/g88PmDl7Wq69NHJugoKxAPFYqlHh78NvILs7G/3b9DwDw5sA3xWC5IWEAS0RERFRF6+LWia1f5TZoCRDQQt8CSblJsvcnZCWIn32cfWCCSXL9kfBHsCdxDxKyzeMGBQ5CXEYcLmVfgoPKAQAwJXSKWLrrs0OfifVgTTDBz9UP8Vnx0Kl18HH2wZXcK5LV13tD7sWvp6Rdt57o+gRaebTCu3veRWZRJjo26YiHwx+u0t+lrjCAJSIiIqqiP0//KX62lz7g5yL/83uv5r1w5OoR8diSp2ptZsRMfHLgE/H4uV7P4ZODn0jGW0pnFRuK8cHeDwCUr/y6aMxpBY+GP4ovDps3gVlWXxVQwEXjIga8Fv8d+F8k5iSK731vyHuSclsNCQNYIiIioiooKC3AlgtbAACejp52x8Vlxsme93X1FfNi1Uq1zeatYa2HQalQYmP8RvFcU+em2JO4B0qFEgIEDAochC7NugAAFh9bjLxSc6ksAQKaODVBXGYcNEoN/N38bVZf7wu5D3+e+VPyzvl958PXxRev7XgNJcYSDGg5ACPbjqzsn6TOMYAlIiIiqoJN8ZvEtAGTySQ7pq1nW2QWZdqc16l12J+0Xzy2t3lr0aFF4vHCoQvF3FdLIDqv9zwA5nqur+98HUD56qslqH6o80NiXqxl9VUJJVRKlaQOrLPGGS/2exHRqdH4OfpnAMD7Q99nHVgiIiKi28XSM0vFz9kl2bJjnDXOsucHthyIawXXxOPk3GTJdX83fwwKHIRvjn4DANCqtBjRZgSWnVkGwJxvG+wVLK6OLo9ZLjYhECDAXeeOcxnnoFKoEOQRhKTcJEk72Pva34cVMSsk73w18lW469wxf9t8CBDwYMcH0aN5j8r8KeoNA1giIiKiSioxlGBd3DoAgJvWze44e7VfrZsVeOg8bDZvzeg6A8vOLBOrAzwc9jB+PfkrDCYDtCotAHM+rFKhhCAIeGX7KwDKV199nH0AAJM6TcK3x74FAHGzmUqhQrGhWFLWy1PniTm95mDbxW3YfGEzNEoN3h78NgAgKUd+A1pDwACWiIiIqJK2J2xHkcHctMASNFbUoUkHcYy1ALcAHLhywO6zlVDisS6P4cP9H4rnHu/6uLgaW2IsgZejFx4KewgAsDl+s1ilQIAAZ40zzmWcgwIKtPNqh8ScROnqa8h9WH9+veSdb9/1NnRqHf699d8AgCe7P4nWnq2RUZiBzl93huINBTIKM276d6lrDGCJiIiIKsnyUz4Amw5WFvZasHb26QyDYM49VUBhU73gvvb34ULWBXH1tn+L/jh69SiyirPE1dcnuz8JR40jAOClf16S3B/gFgAAuL/D/fjh+A8AyldfNUqNzfuauzbHY10ewx+n/sDx1ONw07rh1f6vAgBej3od2cXZCPMJg7vO/QZ/kfrBAJaIiIioEgwmA1bGrgRQXqaqIpVChfjMeNlrMWkx4md7m7esS2fN7TVXPC4xlsBB5YCnuj8FADhw5QBOpJ4Qx2pVWpzNOAvAvAJ8OeeyZPV1bPBYbE/YLnnfB0M/gFEw4pV/zGkIL/V7Cd5O3ohJixE3f308/GOolCo0NAxgiYiIiCphT+IesVyVvaCufZP2km5XFhF+EeLP/QBwNf+q5Ho7r3Zo69kWq8+uBmCuIatRaiS5tFNDp8LHxZzj+tJ26eproHsgAHOg+lP0TwDKV18dlA6y73uw04NYdGgRLueYu4DN7TkXAPCvLf+CUTBibPBYDAoaZOevUb8YwBIRERFVwvIzy8XP9tIHcorlz1uv2Lo4uEg2UgHArG6z8M3Rb8Tzz/Z6Fp8f+lwyxlI6KzYtFlGXosTzaqUacRnmmrOhTUNxKfuSZPV1TPAY7E3aK3nWx8M/RnZxNt7a/RYAYPGYxXDUOGJz/GZsjN8ItUKNrOIsKN5QiIFwQ1JnAez8+fMRGRmJadOmoaysvONEXFwcwsPDodPpkJ9fvjPv008/Rd++fTFmzBjk5uYCAPbs2YM+ffqgX79+OHXqVF1NnYiIiO5wJsEkls9yVDvKjtGpdbKtYx3VjjiUfEg8Viuk3a20Kq15NfSwufarg8oBfQL6YOvFreJGseGth6Nj044AgP/s+I/k/iD3IAgQMKLNCPx26jdxvgCgVWpxIeuCZHyEXwTubnM3FuxegOzibHRq2gkPhT0Eg8mAeVvMQfLAoIHYdXkX9Fq9bD3b+lYnAWx0dDSSk5Oxe/duhISEYMWK8vpj/v7+2LlzJ3r16iWeS09Px9q1a7Fnzx48+OCDWLTI/D/oK6+8gvXr1+P333/H/Pnz62LqRERERDicfBgZRebd+BqVRnZMG482sud7+fdCoaFQPK5YO3ZS6CRsv7hdXNWd1nkalpxYAgDiSqqlbWxSThJWnV0l3quAAhezLgIAuvp2RUJ2ApRW4d097e7B8dTjkvd9PPxjXM65LDZHeH/I+1ApVfju6HeISYuBh85DzK/974D/wtvJ285fpf7USQC7b98+DBs2DAAwYsQI7N1bvozt5OQEvV4vGX/48GEMGDAACoVCHF9UVASVSgUPDw+0aNECmZn2/2ugpKQEubm5kn9ERERE1WVd/D+3RD6usG5QIDmfX36+qVNTm+uzI2Zj4f6F4vHUzlPx68lfAZR30BrSaggA4H+7/if5Sb+VRysYBSMGBQ4S28Naass6qhwRk16+cQwA7gq6C/1a9MN/dvwHpcZSDA4ajBFtRiC7OFtc2e3arCvSC9Ph7eh9Z+fAZmVlwc3NXOxXr9ffMPi0N976HACo1WqUlpbK3r9gwQLo9XrxX0BAQA19EyIiIrrTCIKA30//DsD8874cVwdXsSOWNX9Xf0kQmV6YLrnetVlXmEwmnLx2EgDQJ6APdl/ejRJjCTRK80rv4jGLoVCY67H+eOJHyf2JOYkAgB7Ne+Bi1kVJbdoRbUcgNj1WMv7DYR/i+NXj+O2kOdXg/SHmlrFv7XoLGUUZaOXRCrsu7zLPtSgdXb7pgusF12/8B6oHdRLAuru7i6ugOTk58PT0rPJ463MAYDAY4OAg/39EL730EnJycsR/SUkNt5MEERERNWynrp9CSl4KAECn0smO8XP1kz3f2rO1+Fmn1tl03podMVv8KR8A5vSYI+bClpnK4Ovii8mhkwEAC/ctRJmpfB9RoD4QZaYy9A3oK64QWzaBOaudJWW2AHN92DDfMLFl7OTQyejm1w3xmfH47KB5Dt6O3igzlcHFoXzTWROnJnb+MvWnTgLYPn36YNu2bQCAzZs3o2/fvjcc3717d+zatUsy3snJCQaDAdnZ2UhKSrphEKzVauHm5ib5R0RERFQdf8X8JX7OLZVPH0jOTZY9f+pa+aZzrVIruabX6jE4aLDYHMHH2QcFpQW4VnBNXH19uvvT0Kq1KCgtEINMi5R8c1Ddy78XLmRdkKy+Dmk9RFK2S6lQ4t273sWWC1uw9eJWOKgc8NYgcwWCf2/9N8pMZejarCsOpRyCUqGUtLw9k3bGzl+m/tRJABseHg4fHx9ERkbizJkzGD9+PGbOnAnAnC4wZMgQREdHY/To0di4cSOaNGmCUaNGoW/fvvj999/x5JNPAgDeeustjBw5EhMnTsSCBQvqYupERER0h/v1lDkfVa1Uy173dPREflm+zflw33BkFpenTeaUSktsPRT2EH47+ZuY5/psr2fF1dgyUxkc1Y6YFTELAPDl4S8lG8EC3AJQaixFt2bdsObcGgDlq6+uGlccTjksedcjYY8g0D1QbBn7dPenEeQRhKhLUeKmsKyiLNnv6ePsI/+HqUcKQRCEmw9r3HJzc6HX65GTk8PVWCIiIqq08xnn0e6LdgDM9VutVyYtWrm3wsXsizbnw3zCEH0tGgDgqfOUBLMAcGLmCdz1813IKMqARqnB8gnLce/Se6FSqGAUjJgdMRtfjvoSZcYyNF3YFNnF2eK9jmpHFBmKMLfnXHx68FMooBAD2NHtRuPvuL/FsRqlBpeevYRtF7fh4dUPQ6/V48KcC3DXuaP7d91xPPU4+gb0xd6kvdCqtCgxlkjmWfRKEXRq+dSJmlSVeI2NDIiIiIjssE4fkAteAeBK7hXZ86evnxY/WwefADCg5QCcyzgnluaaEjoFi08sBmCuPKCAAs/2ehYA8MvJXyT3+7r4oshQhNCmodhwfgOA8tVXvYMe+6/sl7xrTs858NB54NV/XgUAvBz5MrycvPBz9M84nnocrg6u4lwrBq8eOg8UlhWioWEAS0RERGSHJX1ApZBvHevj7INSk21VpJ7Ne4qpAWqF2mbz1qyIWfhw34fi8bj24/D3ufJV09HBo9HOqx1MgsmmcUFeibmd7YCWA3A+87wk97Vfy36SSgeOake8EvkKPj/0OZJykxDgFoBnejyD/NJ8vPzPywCATk07IackR7ZBQ1ZxFty0De/XawawRERERDKScpLEDUz2fkK3lxeblFNeAaliYNjUuSnaeLbBoRRzd66ezXti68WtECCIjQvm9TJ3xFp7bq1YAQEwVwQoKCtAsFcwtlzYAqB89dVT54ndibsl73ol8hWYBBPe2f0OAOB/g/4HR40j3tvzHlLzU+Hv5o+DyQcBAEWGIpvv4aHzsNsetz7J/9VldO/eHQqFAiaTCVlZWXBxcUF+fj48PDxw5MiR2pwjERERUZ1bFVve8aqgrEB2jHVwaeHn6ic5n1eWJ7n+WJfHsOjQIvF4dsRsPLXhKQDmFrDdmnVD/5b9IQgC5m+Tdh61BJmDggbh6yNfS3Jfu/t1x+aLm8Wxeq0ez/Z6Fq/teA05JTkI8wnD1M5TkZiTKDZOcNe540ruFTioHFBqtF1JzirOgsFkkP3u9anSK7CHDx/GoUOH0K1bN6xfvx7R0dHYsGEDevfuXZvzIyIiIqoXv5z6BUB5O9eK/Fz9xODRmvWufbmf3x/o+AB+O2VuJNDUqSlS81NRUFYgpinM6z0PCoUCuy7vQlxGnHifh84D+aX5CHIPwo6EHQDKV1+9HL2wJ2mP5D1vD34b1wuu44vDXwAA3h9qbhn70vaXUGwoRscmHXH6+mmoFCrZ4BUAJrSfAB+XhleFoMopBKdPn0ZISAgAIDg4GEePHq3xSRERERHVp7SCNBxNMcc49tIH7AV9sWnl3a/yS6Qbv0a1HYUt8VvEhgRP93gaXx75EoB585a/mz8mdJgAAPj3tn9L7jWazDm1dwXdhXMZ5yTXuvh2kawS+zr7Yka3GXh1x6soNZZiaKuhGNZ6GA5eOYjfT5m7ilk2hlm3pq1oeezyBtmJq9IpBBajR4/GoEGD0LlzZ5w6dQqjR4+ujXkRERER1Zs159aIq5v2duFXbAsLAB2bdBTzZhVQ2GzeeqLrE5i53lwLX61Uw8/VD4k5iWLprDk95kCj0iA6NRqHkg+J97k6uCK3NBf+rv42ea5NnJrYnHt/6Ps4de2UGKy+N+Q9CIKA5zY/BwDo1qwbjl49ajd1wCKyRWSD7MRV5QD2xRdfxKOPPoqEhAQEBgbCx6fhLSsTERER3Ypfos3pA9Y5ptaauzZHcp5t962C0vJVUGeNs6TBQUt9S5gEE1LzUwEAEztNxA/HfwBgXn11cXDBjG4zAMAm91WlNKcXDGk1BD9G/yiZV4cmHbDz8k5xbGuP1pjUaRKG/ToMADC181R0adYFf57+E/uv7Iej2hGx6eZV4hsFrwCwO3E30grT0NS56Q3H1bUqpxCkpKTgf//7HxYtWgRvb28sWbKkNuZFREREVC9yinPEfFJ76QO5JbYtZXVqHS7lXBKPK3bnmhUxCx8d+Eg8HhI0BPuv7BfLYD3W5TG469xxMesiNl/YLLk3uzgbPs4+OHDlAIDy3FdfZ1/sTdwrGfvx8I+x9eJW7Li0Q2wZW1RWJAbFId4hKCwrtKmgYF2Oy2Jg4EB4O3nL/g3qU5UD2OnTp2PatGlISkqCSqXCb7/9VhvzIiIiIqoX68+vF/NC5UpLqRQq5JXm2Zxv59nO7jPVCjX6tegn/tTfrVk3bIzfCABi+ay5PecCgNhwwMJD5wEAGNpqKM5mnJVca+XRCgahvEpAmE8Y7m5zt5g/O6fHHLR0b4mP9n+ExJxENHVuiuOpxwHAprpAxZVmnVKHH8b8YHcTW32q8oyMRiN69OgBhcIcpZtM9hN/iYiIiBqbX0/+esPr9n5OT8hOED8rK4RYEzpOwG8nyxf9Hgl/BCtiVojH49qPQ5BHEK7lX8OyM8vE8w4qB2QVZ8FT54kjV6VlS5u5NMOB5AOSc5/d/Rl+PfUrTl8/DQ+dB16OfBlX865iwZ4FAMy5tID8amtFJpjQ+rPWKDYU33RsXatyANuxY0e8/vrrSE9Px9tvv42wsLDamBcRERFRnSssK8S2i9sAAFqVVnZMZlGmzTkfZx/JqmzFzVsPdX4IS06Y0y69HL2QkJUgtowFgOd7Pw8AeHv322IHLwBw0bgAAIa1Hoaz6dLV1+auzSUVBPq37I/uft3FFdxXIl+Bh6O5hWxBWQFae7TGhawLUCqUktVWuWDWTesmdhizVGNoSKq8ieuzzz7D+vXr4ezsjJCQEFYhICIiotvG5vjNYomrEmOJzXUHlYPseRcHF1wruAYAcFI7odBQXrmgY5OOiMuME++bFTELiw6bGxkIENDbvzd6+fdCbkkuvj36rXifWqFGZrE5WI6+Fi15X3PX5jYrsp+O+BSfHvwUyXnJaKlviad6PIXjV4+LgXNOibmjVsWyWRVTBzQKjSTHN8gjyOb71rdq5cCOGjUKL7zwAkaPHo3nnnuuNuZFREREVOcsDQbscde5y55PyCpPH7AOXgFgZreZWLjP3PlKrVTDWeOM7OJsMbfUsvr68f6PJcGxm87cBGF8+/Fi1QCLiqWtxgaPhb+bv5gq8Pbgt6FVaTFvyzwIENChSQekF6bb5LPKphJYnRoSNATNXJrJfuf6VOkV2Pj4eJw7dw7Hjx/Hhg0bAAAGgwHHjh2rtckRERER1ZVSYynWn18PwJw+ILfSmlGYYXOurWdbnM88D8C8elkmlInXnNRO8Nf7Iyk3CQBwf/v7xRVRk2BCkHsQ7g25F8WGYrG9K2AOLDOLMuGscUZMWozkfQFuAThx7YTk3MJhC/HWrreQW5KLcN9wTAqdhDXn1iDqUhQcVA6Iz4wX32mt4uprE8cmSCtKAwA4KB3w2/jfxH1PDUmlV2CTk5Nx5MgR5Obm4siRIzh8+DBOnz6N9957rzbnR0RERFQndiTsEDcsyQWvjmpHSX6qhXVOrHVFAMBcg/XLw1+Kx92bd8f5zPPiSuizvZ6FSqnC98e+R35pedktD0dz5YEhrYbYrL66OLhIjh8OexgKKMT3fDD0AxhMBryw9QUA5koFN6v3CgAaaMTgFQD83Pzgs9CnUvfWtUqvwA4YMAADBgzA+PHj0bFjRygUCgiCgJiYmJvfTERERNTAWbpW2eOocbQpq6VT65BRVL4qW3FFc3TwaIz+w7xfKNwnXFzhNQkm6LV6PBL+CIwmI97Y+YbkvsyiTOhUOsRlxEnOt9S3lAS0KoUK79z1DuZtnocyUxmGtx6OIa2G4KP9HyE+Mx6ejp42m78AQKlQ2qzGajQalJWZV4/berTF+SzzqnJeSR68nLxu+Lepa1XOgZ0zZ464lKxQKDBnzpwanxQRERFRXTKajFh1dhUAQKPUyI6Rqz7Q3LW5+NlR5Si51su/F7Zf3C4eP9DxAfyT8I94PLPbTLhqXbHszDJJW1pPR08A8quvFZsPPNPjGSTnJmPpmaVQQIH3hryH9MJ0vLnzTQDmbmByKgavfi5+YstcrUqLC9kXAJg3rVWsF9sQVDmALS6W1gIrLJTvD0xERETUWOxN2iuWwbJUIbBW8Wd7C0tuKwAUGaWrs4+GP4pvj5mrCnjoPCQroWqlGs/0fAaCIOCl7S9J7sssyoRGqcGFrAuS84H6QJtzr/Z/VUwVeCjsIYT5huG/O/6LnJIc+Lv5S+ZnUXEjlwoqpOSniMd+rn5igFtqLJVtpVvfqhzA3nPPPbjvvvvw0UcfYdy4cbjnnntqY15EREREdeaPU3/c8Lrcbn1vJ28xP7Ri4wLAHAhbVjUfDX8Uf575U7z2YMcH4e/mjy0XtuByzmXxvKXr1uDAwTarrxXzb98Y+AYOXDmAnZd3QqvS4n+D/oeYtBh8c/QbAJDk1Fp/j4qrry7a8uA8tEmopCHDy/1ehq+Lr81z6luV68C+/PLLOHnyJM6ePYs33ngDoaGhtTEvIiIiojohCAKWxZi7X6mVatmfzOVax6oV5WFUxVXKeb3m4aP9HwEw56lCAclmqHm95wGAuHpqkVWcBZVChcTcRMn5IH0QEnLKA0s3BzfM7TkXfRb3AWDeDBagD8Ddv90No2AUmxbYfNcK8/R39ceVvCsAzPm81q1q/V398VI/6epwQ1HlFdj8/Hxs3boVJ06cQIcOHbBx48bamBcRERFRnTh69aiY3yoXvLpp3WTvSy1IFT9XDAy7NesmBpBjg8fi5+ifxWsDAweia7OuOHjlIE5dPyWe12v1AIABLQfYrL5WbOf6v0H/w4qYFYhJi4Gnoyde7PciNsVvwqb4TVAr1JJVXQuVQiU5VkCB5Lxk8biNRxtJ+kRGUQZc33XF9YLrst+/PlU5gJ06dSoCAgKwe/duqFQqfPjhh7UxLyIiIqI6sfT00htelysjFeAWIH52UDpIrg1tNRR/nClPSWjfpD3SCtNs2sZWXH3NKcmBAgpczb9q876rBeXnmjo1xbSwaXgt6jUAwKuRr8LFwQXPbzE/N0AfIBuIV0xB8HT0FAPvrr5dcTrttHjN380fRYYi9G/ZH95O3jbPqm/VWoF94IEHoNGYd+gJQsNL7CUiIiKqDEEQxO5bFVcoLSqufgLShgalJmmAO6HDBKyPM5fL6tikI9aeW2t+FwS082qHkW1H4mz6WexO3C3e4+rgCgCIbBlps/rq7SgNIBcOW4gvD3+JlLwUBLoH4snuT+Lbo98iJi0Grg6ukhxWi4o5vM2cm4nlv3RqneSd7jp3XMm9AieNExaPWWyz6ashqPKMmjVrhh9//BEFBQX47bff4O/vXxvzIiIiIqp1MWkx4oqnXJMCufQBB5WDTbtYCz8XP8Rnxosrm6Pbjcap66fEAHJer3lQKpSYv3W+5D5Ljm1aQZrkfJB7ENKLyktstdS3xNBWQ/HeXnMjqXcGv4MiQxFe22FejXXSONnMSQmlJMVBCaUk/aGLbxdJfVtLwP7uXe+itWdr2e9Z36ocwH7//fcoKChAREQEsrKy8O2339bGvIiIiIhq3bIzy2543VJFwJqXY3lR/4orm490eQRfHfkKgDmnNfpaNADz6quXoxemhU3Dldwr+Dvub/EeS63WXv69bFZfK9ae/XTEp3hnzzvIK81Dt2bd8GCnB/HWrreQUZQBH2cfXCu4ZjNfE6RVB7ydvcWANqJZBPZf2S9e83P1Q7GhGP1b9sdTPZ6y81epf5WuQjBs2DBs2bIFzz//PL744ovanBMRERFRnfjl5C8AzOkDciuwcrmkqfnym7cUUKCJUxNxNXVS6CR8feRr8fqT3Z+Ek8YJT294WnKfJUjOLs6WvCfIPUiSDtCpSSd0aNIB9y+/H4C5ZezFrIv47OBnAOTLZlX8Xp46T3FTloPSAfFZ8eI1N60bUvJSGnTqgEWlA9iysjI899xz+Pvvv+HkJF2efv/992t8YkRERES16WLWRTFAlAteXR1cbcpnuevcxUBTBRWMKL/vvvb3iY0LAKCotPxneQeVA57s/iQyizLFoBkAHNXm9rQRzSJw5OoRybsq7v7/YuQXeOWfV2AwGTCy7UgMChqE+5behzJTGQLcAmSbFlT8XtZB8oCWA7A1Yat4bNms1pBTBywqHcBu2LABJ06cwL59+zBq1KjanBMRERFRrVtxZsUNr8utaJpM5T/HWwevADAocBBWxq4EAIxsOxLLY5eL16aGToWviy9e2PKCZFW3xFhifleZ9F2t3FvhYvZF8bhvQF/o1Dosj1kOpUKJ94a8hx0JO7D67GoooZSUw7JQQCFZ6fV28hZb1nbx7SIJXi3pBw09dcCi0gGso6MjevfujYMHDyIvLw/Z2dmsQEBERESN1o/RPwIwt1at2J1K7hwA5Jbmyj6rjUcbRF2KEo8D9YGS/Nnnej+HgtICfHG4PA1Tq9KixFiCMJ8wMVfWIiUvRXL82YjP8OzmZwEA08Omo713e0xbNQ0A4OviK2kFKzd/F7WLGLxqlBrJ8500TrhWcK1RpA5YVLkT15w5c3DkyBH4+/tDEAQoFAosW3bjBGgiIiKihiQlL0XcMCUXqDqqHVFQViA518SpCdIK02zGAsDUzlPxv13/AwCEeIdgzbk14rXhrYejU9NOeHfPu5KSXJaV2Ipluiquvo5sOxJX8q5gd+JuOKod8cagN/BT9E84kXoCTmonm+BV7jsVGsuD6WGth2H9+fU2YxtD6oBFlQPY6Oho7Nu3rzbmQkRERFQnLD/121MxeAVsKwJYaBQaFBmKxHzTgYED8fWRr8Wf8Of1nocyYxne3fOueI9aoYZBMKBDkw6ISYuRPK9iLusHQz/A+GXjAQDP9XoO7jp3vPLPK+Z3qzRAhX1mFTduuWvdkV2SDQBo79VeErxagvIBLQc0itQBiyqvEUdERGDv3r0oKChAYWEhCgvl66ARERERNVQ/nvgRgG0ZLEC+oYFGqRGDQmWF8OnB0Afx7VHz5i1XB1ccSTFvxhIgoFPTThjaaih+iv4JOSU5Ns+tWOUg0D1Q0s51cqfJ2JO4B2fTz8LbyRv/7vtvvLvnXaTmp8JD5yH7TOvgVavUisGrSqGS1HvVqXVIK0yDk8YJP4z5oVGkDlhUeQX22LFjOH78uOTcP//8U2MTIiIiIqpNGYUZOHb1GABpGSwLjVIDo1G6QctZ4ywGghXrqoZ4h+DXk78CAMYGj8Wvp34Vr83rNQ8CBPxnx3/Ec0ooYRAMaOvZFnEZcZJnJeUkSca9NuA1DPxpIADgtf6vIackBx/u/xCA/Cazihu3rLuEjWw7UlJ/1rKXqTGlDlhUOoCdMGECFAoFvL0bXj9cIiIiospac3aNbOBqUWy0bR1rCV4rCvMJw18xfwEwB49ZxVniNR9nH0wOnYzVZ1dLascqlUqYTCab1d8Wbi2QmJsoHs+OmI2lZ5YiNT8VrT1aY2bETExfPR3FhmLZfNyKG7ec1c4oMJhTIQL1gZLUAU9HT2QWZTa61AGLSgewCxcurM15EBEREdWJJdFLANiuVgLm1Vfrn/AB+XqwFqODR+OtXW8BAAYHDcbG+I3itad7PA0HlYNN21iDyYAg9yDEZVZYfbXKfXVQOuCpHk+h+3fdAQAL7lqAoylH8cfpPwBAdjOZdfCqgEIMXhVQwEnjJF7XKDTILMpslKkDFpUOYFu2bFmb8yAiIiKqdXklediXZN6MLrcKK3fOUqu1Ime1M85nnBePvZ28JUHkrIhZ2HV5l6TblSVAViulIZi/qz+u5F0Rj//d99/4/NDn4mayce3Hoe/ivgCkm7Is7JX9AoARrUdg44XywFqpVALGG6cOGE1G3L/8fqw+uxrCfxte2dTGF3ITERERVdP6uPV2Az1AvnWspUNVxZ/8J4ZOxF+x5vSB1h6tseH8BvHarG6z4O3kjee3PC+5x9I163zmecl56+DVWeOM+0LuEzeGRT0chaVnluJg8kE4KB1k0xmsv5NGqRED8aZOTbEtYZt4Ta/Vo8RYctPUgff3vo/VZ1fDWeOMq3lX7Y6rLwxgiYiI6I5hSR+Q46BysDnnpnUTP1dcnXXTuokBb4/mPSRpBs/1fg7RqdE4evWozfO1aq3kOc1cmkmO3xz0Jt7a/RaMghGj241G9+bd8eK2F80XbYsm2LBOgfB38xePVVAhpyTH3LBgrP2GBQeuHBA3nX0x8gs0c20mO64+MYAlIiKiO0KxoRg7EnbYvW4w2q6+5pbId97qF9BPrDzgrHHGgSsHxGuj241GO692NquvpcZS+Lr4Ij4zXnL+an75CqenzhNdfLtg1dlVUCqUeHfIu/ho/0dIyk2Cs8ZZXA22kCsDZtG/ZX8cSz0mHqtU5vJg7w15D608Wsnek1Ocg0l/TYJRMGJSp0l4OOxhu8+vTwxgiYiI6I6w5cIWmw1a1iqWx6qYp2qtp39PcSPV0FZDkZCdIF57vvfzuJh1EdsTtovnLKuvzhpnyXOaOjeVHL8/9H28uuNVAMBjXR6Du85dbIBg3ZrWwl41BReNCw4nHxaPXR1cUWosxYCWA/Bk9ydl7xEEATPXzcSl7EsIcg/CopGLoFBUYsm3HjCAJSIiojvCkhNVSx+wF8B6O3pjT+IeAOYVUOsV1K7NuqJ/y/7lP/n/v1JjKZo4NcGFrAviOQUUuF5wXTz2d/OHXqvHvqR9cNI44fWBr+PVf15FQVkBnDRONsFqxYYK1jo27Sg2LVBAgbzSvJumDvx44kcsPbMUaqUaP9/3M8b+ORaKNxRivdiGhAEsERER3fbKjGXYFL/J7vWKP80D5pQDOfeF3IeDyQcBAL39e4ufAfPqa1phmri5CygPjl21rpLneDl6SY4/GvYRXtlhbhE7r9c8pOanih3D5FZfK64YW4T5hEnmZAnEb5Q6cC79HJ7e+DQA4H+D/odVsauwO3E33HXuuFZwTfae+lTlTlxEREREjc3OyzvtBqRyHNWOkrar1nJLy/NiXbQu4md/N39M6DABczbOkVQFKDWWwkPngYtZF8VzSiiRXpQuHgd7BSOtIA1xGXFo4tQE/+rzL4z5cwwECFAr1bLVEeRolBokZJWnMzhrnFFQVnDD1IESQwkm/jURhWWFuCvoLoR4h+Cl7S8BAH4c+yN8XXwr9e66xBVYIiIiuu39dOInu9c0So3NOXu1X4e2GopVZ1cBAFroW+Cfi/+I1+b0mIMiQxEWn1gsnrOsvnroPCTP0ev0kuOFQxfijV1vAAD+O+C/2J6wHbsu74JSoax08AoA3fy6SQJsS/rBjRoWvLjtRZxIPQFvJ2+8M/gdPLrmUQDAc72ew9iQsZV+d11iAEtERES3NZNgwppza+xel9vYZa9WbKB7oJhu0KlpJxgEc3Dp4uCCGd1m4IO9H0jSEUqNpXDTuuFidvnqa8WWs939uuNwymFcL7iOtp5t8XDYw3hh6ws3nIecIPcgSTUES2D+3pD37DYs2HB+Az45+AkA4PvR32Pu5rnIKs5Cj+Y98O6Qdyv97rrGAJaIiIhua/uT9tttBSvHUe0oe97f1R/rz68HADhpnHDwSnme6WNdHoOj2hEfH/hYPGcJID0dPSXPcXFwkRy/OfBNLNy/EIC5ZezXR7/GxayLUClUNnOwVzZLqVBKvqOj2hFlpjIMDBxoN3Xgat5VPLzaXCZrTo852JO4BweuHIC7zh1/jv/zhiW66hsDWCIiIrqt/Rz9s91rcpUG7OW+Dm09FCl5KQCAPv59kFGUAcAcVM7tORdfHflKbP0KmFd2nTXOuJR9STynhDTQHNJqCFafW43CskL08u+FyBaR+N+u/wEAjILRZg72ymaF+4YjvbA8p7bIUARnjbPd1AGTYMK0VdOQXpiOcN9wDAwcKAbRS8YuQTPXZui3pB8UbyiqtApcVxjAEhER0W1LEASsiF1h93rF/FK5VU/A3MXqXPo5AOaA9XLOZfHa+A7j0ULfAm/tesvmPm8nb8mxo0a6uvtsz2fx/bHvAQDvD3kfr+98HbkluVVa/fR29Mbxq8fFY+vUAXtVBz7Y+wG2J2yHk8YJHw37CI///TgA80rsvSH3Ys7GOTiUfAieOk9Jqa+GggEsERER3bZOpJ5AZlFmpcfbCxxHtB2BfVf2ATCvdp7PPC9em9drHv44/Ye4ImvhqHaUBLoqqCQrtBM6TMB3x76DUTBibPBYeDh64Juj3wCwv9Iqx0HlII7XqrRi6sDs7rNlxx+8clBslvDJ8E/w6o5XkVmUiQi/CLw/9H0sOb4E3x37DgCQWZyJUoNtibH6VmcB7Pz58xEZGYlp06ahrKw8WdpoNOLRRx9FZGQknn32WQBAQkICBg4ciIEDB6Jz58647777AADTp09H9+7dMXDgQHzwwQd1NXUiIiJqpCztXuXI/bRu2ZRVkXWjA+uqBb39e6OXfy+8vP1l8ZxlFbeJcxPJMzSq8vsUUOCBjg9gzbk1UClUWHDXAjy/5fkq/1zf3rs9UvJTxOMSY8kNUwcsrWINJgMe7Pggzmecx74kc2C+9P6liEmLwax1syT32FuVrk91EsBGR0cjOTkZu3fvRkhICFasKF/KX7duHfz8/LB7924UFBRg//79CAoKQlRUFKKiojBu3Djce++94vglS5YgKioKL7zwQl1MnYiIiBqxP07/YfdaxWDRQWnbjQsw12i1NEHwc/HDoZRD4rV5vedhU/wmJOUmiecsuauJOYniObVCjWJjeR3ax7s+joX7FoqfE7ITsOXClsp+LQDmFd6z6WfL33GThgWCIGD2+tlIyE5AoHsgxrUfhw/2mxcEV0xYAQ+dB8YtHYdSk3TFNbO48ivYdaVOAth9+/Zh2LBhAIARI0Zg7969lboGAGvXrhUDWIVCgRkzZmDo0KGIjo62+76SkhLk5uZK/hEREdGd5Wz6WUmbV2tyqQIVAzeLMJ8wcWOXdTmqIPcg3BdyH/615V/iOUt71wC3AOn7FOXvUyvU6OXfCweTD8JZ44xXIl/B81uer+S3Kuft5C2mDmiUGhhMhhumDvwc/TP+OP0HVAoVPhn+CZ5cb65O8HT3p3Ff+/swbdU0XMq5ZHNfxZXkhqBOAtisrCy4ubkBAPR6PTIzMyt1LSYmBs2bN4deby72u3DhQuzfvx+ff/45nnjiCbvvW7BgAfR6vfgvICDA7lgiIiK6Pf1+6ne71yqbY+qgdBBrq+rUOhxOPixem9tzLo6kHEFMeox4zgQT1Eq1ZEVWrVBLas3O7TUXC/YsAAD8q8+/sObcGslKamW0dGspeYel4sHiMYtlUwfiMuLw1IanAJgbJXyw7wNkFGWga7OuWDhsIRbsXiCWCKvoap78fwTUpzoJYN3d3cVV0JycHHh6elbq2vLlyzFhwgTx2MvL3DM4JCQECoUCRqNteQkAeOmll5CTkyP+S0pKkh1HREREt69fTv5S6bFy5bQAYFjrYUjMNacChPuGi2kAbg5ueLTLo3h287PiWMuqro+zj933OKod4evii/jMePg4++DR8Efx2o7XKj1Py1yT85Jt5v7+0PcR5BFkM77EUIKJKyaioKwAAwMHIr80H3uT9sJN64Zl9y/Drsu7xE1dcuTKedW3Oglg+/Tpg23btgEANm/ejL59+1bq2tq1azF2bHkLM0uge/36dZSWlkKlkk8q1mq1cHNzk/wjIiKiO8fl7MuS+qs3Y69dq3VlgYtZ5d20ZkbMRHJusqTzlQABKoVKGlwq1JKNYfP7zsf7e98HYF4J/eTgJ5KuXJXh5+onPlOlUImpA7MiZsmOf3n7yzieehyejp54rMtjeH+f+f3fj/4eDioHPLj8QbvvCnIPQlffrlWaX12okwA2PDwcPj4+iIyMxJkzZzB+/HjMnDkTAHDPPfcgMTERkZGR0Ol06N27NwAgNjZWkj4AAFOnTkW/fv0wduxYLFy4sC6mTkRERI3Qn6f/rPRYuZ/cAXPuqyVAbe/dXqyHqlKo8EyPZzBvyzybeyquvlqnKui1ehQbipFWmIZ2Xu0woOUAfH7w80rPEzDnvVpvDjMKxhumDmw8vxEfHfgIALBw6EI8t/k5AMCTEU9iTPAYjF82Hlkl8gG0o9oRx2ceh1LZ8KquKgRBqHyhsUYqNzcXer0eOTk5XI0lIiK6A3RY1AGx6bG39IwRrUdg0wVz9YF2Xu0QlxEHAJgcOhnvD3kfAR8HSAJUBRSSY41CgzKhPPf1vbvew+s7X0eRoQgrH1iJn6J/wppzayo9HwUUcFA5oMRYAsAcSBsFIxaNXCTbLjY1PxWdv+qMtMI0PNn9SZy6dgq7E3cj3Dcc+x/bj3mb5+GrI1/ZfdfhGYfRza9bped3q6oSrzW8kJqIiIjoFqTmp9oNXivb4cpV44o9iXsAAE2cmojBK2BuXPDithdtNoL5uvhKjq1TB3ydfXEu4xyKDEXoE9AHeq2+SsErADR3bS4Gr0ooYRSMGBQ4SDZ1wCSY8NCqh5BWmIbQpqFw0bhgd+JuuDq4Ytn9y7D8zHK7wSsAfH3P13UavFYVA1giIiK6raw4Y791bMWg016R/j4BfZBflg/AnHNqMaDlALTyaIU/z9imKFQs2WX9rn/1+Rd+jP4RAPDuXe9KNn9VhrPGGVfyrojHJphr2NprWPDhvg+x9eJWOKod8XSPp/HBPnO91+9Gf4ciQxFmrJ1h910PdX4IT3SzX+2pIWAAS0RERLeVxScWV3qsvR325zLPATC3Zj157aR4fl7veXhz55s2m75uVHmglXsr7Li0AybBhPtC7kNcRhxOXT9V6TkC0mDYEnQvGrlIturA4eTDePkfc2ewNwa+gVf/eRUCBMzqNgvD2wzHuKXjUGIqkX1PB+8O+Om+n6o0t/rAAJaIiIhuG1lFWTiRekL2WmXTB7o16yZWMGjn1U4MHtt6tsXgoMH4+ujXNvdcK7gmfq7Y0Wt299lYf349VAoVXu3/KuZvm1+peVj4OPugsKxQ/A43Sh3IK8kTW8WObz8e6+LWIa0wDWE+Yfhw+IeYvno6LmRdkH2Ps8YZh2Yckr3W0DCAJSIiotvG6rOr7TYpqHjeXkBr3TXLunTWc72ewycHPkGxoVgyvomTtFOVdUevLj5dsOzMMgDAE92ewPIzyyWluW5Go9RIgmMBApw1znZTB57a8BQuZF1AC30LBOoDsStxF1wcXLBswjJ8dvCzG+bdHnr8EJwdnCs9t/rEAJaIiIhuG98d+67SY+UCXU9HTxy7egyAuQZqQVkBAMBD54HJoZPxwd4PbO5JK0wTP2uUGsm18R3G43DKYThrnPFo+KNYuK9qZUB1ap342RKw2mtY8Ev0L/jl5C9QKpR4rudzYvmsb+/5Fldyr+Cl7S/Zfc+v9/2KDk07VGlu9YkBLBEREd0W8kvzcSi5cj+B21t97dCkA0yCeYOU5Wd7AHiy+5P44/QfyC3NlYz3dvKWHFu3jI1sESnm4/6777/x5q43JZUJbsZD54G80jzx2CSY7KYOxGfG48kN5lJaz/d+Hu/seQcCBDzR9QlEtozEhGUTbO6xeCriKUzpPKXS82oIGMASERHRbWH9+fWVbntqL80gOjUagDl4tPx0r1Fq8GTEk/hv1H9txqcXpoufK66+9mvRDxezLsLXxRe9/Xvj77i/KzU3wBxgV+zQZS91oNRYikl/TUJ+aT4iW0TiUPIhpBWmobNPZ7w/9H3cv+x+ZBZnyr6nm283fDHqi0rPq6FgAEtERES3he+OVj59QE43327iiqeHzkM8P6XzFOy/sl/sxGXh6egpObZefR3ZZiS+PfotAHPLWMvqaGW5OLiIny0B6wdDP5BNHXj1n1dxJOUIPB090a1ZN+y8vBPOGmcsu38Z/rPjPziYfFD2He5ad+x9bG+V5tVQMIAlIiKiRq/EUIJdl3fd0jMsm6s0Sg0uZpdv3prXax7+vfXfNuMzi8pXNa1XXxVQINA9EBlFGQjxDoFgEhCfGX/T91vSGpw0TrKpAzMjZtrcs+XCFrHG69wec/HpwU8BAN/c8w2OXT2Gzw/Jt6pVKpQ4NvMYtGrtTefVEDGAJSIiokZv64WtkhXQqvJ19sWlnEsAgBb6FuL5oa2GIr0wXRLQAoC7zl1ybP3u+zvcL+a+vjnwTbyw7YVKzcGS1lBUVmRzTS514Fr+NTy06iEAwPSw6fjyyJcQIODxLo8j3Dccj6x5xO67Vj2wSnY1t7FgAEtERESN3rfHvr2l+5u6NBU/W2rAAuYNUc9tfs5mfHZxtvjZupuXSqGCWqlGsaEYkS0iseH8BrGSQWW4OLiIgaxlRfbLkV/aBJsmwYTpa6bjWsE1dGrSCQnZCbhWcA2hTUPx1uC3MPbPsWLb2Yrm95mPMSFjKj2nhogBLBERETVqBpMBWy9urfb9Kqhw5voZAEBz1+biRrCOTTrCx9kH0deiJePdtG6SY+uNY5NDJ2PpmaUAgLk95+Kn6Jt3tbKsrGqUGuSX5ovnBQgYHDRYNnXgkwOfYFP8JujUOvRv2V/Me116/1I8ueFJu80K+gX0w7tD373pnBo6BrBERETUqO28tNOmuUBVdGzaUQxCc0pyxPPzes/Ds5uftRmfW1JeSst69VWr0iI1PxUmwYTx7cfjzV1v2q12YM1StstoklZQcNY44/vR39ukDhxNOYoXt70IAJgVMQtfHfkKAPD1PV9jw/kNWBm7UvY93k7eiJoeddP5NAYMYImIiKhRq0rzAjmXcy4DAFwdXMUV0KZOTdHXvy92Xt4pGWtdHQCwXX3denEr1Eo1IltE4uS1kzd9t/XGLRNMkmtyVQfySvIw8a+JKDOVYVTbUfjj1B8QIOCxLo+hhb4FXtgqn2+rVqoRPSsaKqVK9npjwwCWiIiIGi2TYML68+urfb+/q7+46uqodhTPP93jabz0j23nKuuf+JVWYZSrgytOpJ4AADzR9Qn8Z8d/bvpuJZQQIECpUEqaJgCwmzrwzMZnEJ8ZjwC3AOSX5ptzYJt2wkv9XsL4pePtrvhumrIJfq5+N51TY8EAloiIiBqtQ8mHJEFlVSkU5hVQtVKN64XmOq9alRYTOk7AmnNrJGOdNc6SY+sV03Htx+F46nG4OrgiuzhbUgbLHsv9ygrhmL2GBb+d/A0/Rf8EpUKJoa2GYuflnXDSOOG3+37Dw6sfRnpROuS8Negt3NXqrpvOpzFhAEtERESN1vfHvq/2vRqlBkm5SQCAJk5NxPPTw6dj4b6FYm6qhXU1Aeug09vRG1GXogAAM7rOwB+n/7jpuy25szqVzqa97AdDP0Cge6Dk3MWsi5i9fjYA4KHOD+HH6B8BAF+N+go/Rv+IvUnyDQmGtx6OV/q/ctP5NDYMYImIiKhREgTB7oalyghyL88vvZp/Vfw8o+sM/Bz9s2SsdXoBIF19HdF6BC7nXIafqx/Wn19/041bSoVSzJ0tNko3n8mlDpQZyzDpr0nIK81DL/9e2BS/yVxGK3w6HNWO+PjAx7Lv8XP1w4bJG244l8aKASwRERE1SievnURWcVa177c0J/B28hbP3dPuHiyPWW7TFKHIUN5cwLLxCjDn0K47vw6AuenBuYxzN32vZWVXrVRLzrs4uMimDry24zUcSj4ED50H1Eo1UgtS0aFJBzzd/Wk8vPph2Xc4KB1wctZJKJW3Z6h3e34rIiIiuu0tPr642vcGuAXAYDL/dJ9TXF4665kez9i0X9WpdZJj6xXWXv69kF2SjRDvECw9vfSm77W0nFUr1eL7LeRSB7Zd3Ib39r4HABjRZgT2JO6Bo9oRP479EZP/miwJrK1FTY+Cl5PXTefTWDGAJSIiokZpWcyyat9rqTzgpHESV1vDfcJx8tpJm4oA9mrMtvVoi7Xn1gIAPHWeNukAFSmgEN9VMXgdHDQYM7tJUwfSCtIwbdU0CBAwJniM2CBh0chFWLhvIeIy42Tf8+nwT9E7oPcN59LYMYAlIiKiRicuIw6p+anVvt/SjMD65/p5vefhnd3vSMY5qBzsPqO1Z2uUmkoR7hOOfVf2VfrdaoV86oClIgJgzu+dvma6+B0PJx+GSTDhobCHkFuSazd4Hx8yHnN6zan0XBorBrBERETU6Px44sdq39vMpRkAcyUASwkuPxc/lBpLbXJqS42lss8IbRqKTRc2AQCS85Jv+k6tSivWfK1M1YHPDn6GDec3QKfWoY9/H1zNv4oQ7xBM7TwV8zbPk31HkHsQlj+w/KZzuR0wgCUiIqJG57dTv1X7XkvFATetm3huTs85+G/UfyXjLPmqciwdudp7t0daYdpN31liLAEAm9JcdwXdZZM6cPzqcfx7278BAMNaDcO+K/vgqHbEV6O+wpQVU2w6dgHmclwnZp2QrOLezhjAEhERUaOSmJOIxJzEat1rWX0FIK62OqmdEOQeZLOSWrESgUVEswjsv7IfaqUaZ9PP3vSdlpqvcqkD34/5XhJ05pfmY+JfE1FqLEVki0isizNXOPhk+Cd4bcdrSCuyDZYVUGD/4/slAfntjgEsERERNSq/nvy12vemF5q7Vem1evHc410fx2tRr0nGVSxxZc3S0MDT0fOmNV81So1Y87Vi6sDCoQttUgfmbpyLuIw4+Ln6IT4zHiaYMCV0Cs5nnsfuxN2y7/hu9HcI9w2/4TxuNwxgiYiIqFH56cRP1brPWe0srqpaqhAooED/lv1t6rdWrBJg0du/N2LTY6FT63C94PpN32lvFfeuoLvwRLcnJOf+PP0nFp9YDKVCCX83f1zNv4pgr2Dc3fZuLNy/UPY5D4c9jMe6PnbTedxuGMASERFRo3G94Lrd8lE3I1fUf1z7cWKdVQvLT/4290Mppi4YTcabvs+SQ1uxMYFc1YGLWRcxc505F3ZQ4CAcSj4EnVqH94e+jxlrZ8g+P8QrBD/e++NN53E7YgBLREREjcYfp/6o9r15pXkApJuz7m9/Pw6nHJaMs/zkX1Ev/15IzkuGTq2zu7JqzTKm4sathUMXoqV7y/JxxjJM/msycktyEdo0FDsSdgAAPhjyAeZvnS/brMBZ44xjM4/ddA63KwawRERE1GgsPlG97lteOnNXKqVCKQaWPZv3tHme0k5opFFqcCbtDAD7jQ3kWLedBeRTB/4b9V8cTD4IN60b0grTYIIJkzpNwv4r+3E2w3aTmAIKHH3iKBw1jpWex+2GASwRERE1CtnF2Th17VS17s0ozgBgLjdl8Uj4I9h2cZtknFyJKgDo2qwrckpy7KYXWLOs8CqgkGzykksd2H5xO97d8y4AoI1HG6Tmp6KdVzt0a9YNv5/+Xfb5v4/7HcHewTedx+2MASwRERE1Cn/F/HXTXf9ynDXO4udCg7lNbAt9C2xP2C55XsXVUgudWodjV80/19tLL7BmWeGtONeKqQPWrWJ7Nu+JY6nHoFVp8Wrkq/j31n/LPvup7k9hYujEm87hdscAloiIiBqFb49+W637LN20nNRO4rlZ3WZhZexKyTh7wXEH7w6VynkFyoPgm6UOCIKAR9Y8gqv5VxHoHogjKUcAAG8Negv/2vov2ZXgrr5d8cXILyo1j9sdA1giIiJq8ApKC3D06tEq36eAQgw+LauvAHAx+2KlVlNdHVxxLLVym6WUUIpB8M1SB7449AXWn18PrUqLYkMxjIIRD3R8AOvi1smW59I76LH/8f2VmsedgAEsERERNXh/x/1dqYCzIieNedXVuvLA3B5z8Uv0L5W6P8AtoNLvspc/WzF1IDo1Gv/a+i8AQFvPtkjNT0Ubzzbwd/XHzsSdss84MfsEHFQOlZ7L7Y4BLBERETV4Xx/5ulr3WbpmWRoTWDZhlRhLbnqvh84DMekxlXpPxVqvFkNaDZGkDhSUFoitYjs06YDTaaehVWnxVMRT+OjAR7LPWDdpnU3HrjsdA1giIiJq0EqNpdiXtK/K9zmqzWWmrKsBTOg4AT+c+KFS97s5uFX6XRVrvQLm1IHvR38vSR14dtOzOJt+Ft5O3jiXbu7+9XK/l/HKP6/IPvfFfi9iVLtRlZ7HnYIBLBERETVoWy5sqfQmKmuWeq0qZXnpqwDXAOSX5t/03qZOTXE593KV32mtYurAsjPL8P3x76GAAiqFCkbBiHHtx+GPM39I8nMt+vr3xYK7FtzSHG5XDGCJiIioQVt0aFGV77HeUGVJH4gMiMSS6CWVut9yT2XeI6di6sCl7Et44m/zcRvPNrhWcA2t3FtBo9DgbLptswIvRy/sfEQ+H5YYwBIREVEDZjQZEXU5qsr3WXJSrTdv9fTvifTC9Jve6+fih8zizEq9R27jVsXUAYPJgMl/TUZOSQ5a6lvifOZ5OKgcMLHTRCyNWWpzv0qhwunZpyUrxyTFAJaIiIgarJ2XdlapdauFQTCvoFpSD1p7tMaymGWVujejKKPK77NWMXXgjag3sP/KfjhrnJGcmwzAXAnh3b3vyt6/ddpW+Lr63tIcbncMYImIiKjBWnS46ukDWqUWACRtX0e2GYnEnMSb3uvn4lepCgX2VEwdiLoUhbd3vw3AXNLLIBgwut1o/Bj9o+zGr7cHvY1BQYOq/f47BQNYIiIiapAEQcCWi1uqfF+JyRyAWurG6rV6bEvYVql7U/JTqvw+C1cHV0nqQHphOqasnAIBAlrqWyKtMA1B7kHILMpEWmGazf3DWg3Dy/1frvb77yQMYImIiKhBOph8sFIVA6xZNlVZt3K9v/39iE2Pvem93o7elXpHxTaxFguHlacOCIKAx9Y+hpS8FDR1borLOZfhoHLA4MDB2Ju01+ZeX2dfbJyysVLvJwawRERE1EB9efjLKt9TcVOVWqlG9LXom96ngALpRTff4AVI28RaDGk1BDO6zhCPvzz8JdaeWwuNUoPMQvOGsEfCHpGtQatWqhHzVAyUSoZllcW/FBERETU4giDg77i/q3///weZY9qNwZGrR2463tL0oDoqpg6cvHYSz2953nxN6wqDYMDw1sPx88mfZe/f88geeDh6VPv9dyIGsERERNTgnL5+GtnF2VW6x5I+YN3W9Xrh9UrdJ9dIoLKsUwcKywoxccVElBhL4Ovii8yiTLTUt8TFzIsoMhTZ3Pvp8E/R079ntd99p2IAS0RERA3OraQPWHb39wvoh72JtvmmFVm3eq2qoa2GSlIHntv0HGLTY+GmdUNqfio0Sg06eHfA+azzNveOCxmHOb3mVPvddzIGsERERNTgrDy7skrj5VZfHdQOsvmqFe+zVCuoKlcHV3w/pjx1YEXMCnx77FsooEBhmXlFd1z7cdh4wXZzVkt9S6x4YEW13kt1GMDOnz8fkZGRmDZtGsrKyvsZG41GPProo4iMjMSzzz4rnnd1dcXAgQMxcOBAnDp1CgCwZ88e9OnTB/369RPPERER0e3lQuYFXC+4+U//1iquvrb1bItdl3dV+r7q+HDYh2ihbwEASMxJxIy/zSuxep0eBpMBAwMHYvmZ5Tb3aVVanJp96pZWfu90dRLARkdHIzk5Gbt370ZISAhWrCj/L45169bBz88Pu3fvRkFBAfbv3w8ACA4ORlRUFKKiohAaGgoAeOWVV7B+/Xr8/vvvmD9/fl1MnYiIiOrYV0e+uuVnBLoHwmAy3HCMvXJYlTG01VA83vVxAOZWsVNWTkF2cTY8HT2RXZwNf1d/nL5+WjZAPjzjMFy1rtV+N9VRALtv3z4MGzYMADBixAjs3bv3ptcuXLiA/v37Y/bs2SguLkZRURFUKhU8PDzQokULZGba71FcUlKC3NxcyT8iIiJqHJadqVzLV3s8dB7Ynbj7puMs6QVVDWQrpg68test7EncA51ah8yiTKgVajR1bor0QtuyXItHL0aoT2iV3ke26iSAzcrKgpubGwBAr9dLgk971+Lj47Fr1y40a9YMixYtkowDALVajdLSUtn3LViwAHq9XvwXEBBQW1+NiIiIalBybjKScpNu6RldfLug2FBc6fE3y5OtyDp1YNflXfjfrv8BAMqM5hTJwUGDcSz1mM19D3V+CI90faRK7yJ5dRLAuru7i6ugOTk58PT0vOk1Ly8vAMD999+P6OhoyTgAMBgMcHBwkH3fSy+9hJycHPFfUtKt/T8CERER1Y3vj31frfssq6hqhRqHUw5X+j61Ql2l91inDmQWZWLKyikwCSa4OrjCKBjRy7+XbPvbdp7t8NN9P1XpXWRfnQSwffr0wbZt5h7EmzdvRt++fW94raCgAEajeUfg7t270aZNGzg5OcFgMCA7OxtJSUmSILgirVYLNzc3yT8iIiJq+H499Wu17rOsovby74W80rxK32cQbpwna806dUAQBDy+9nFcyb0CVwdX5JXmwc/VDydST9jc56h2RPTsm3cDo8qrkwA2PDwcPj4+iIyMxJkzZzB+/HjMnDkTAHDPPfcgMTERkZGR0Ol06N27N86fP4/u3bujf//+2LBhA+bOnQsAeOuttzBy5EhMnDgRCxYsqIupExERUR1JL0xHfGZ8le+zzmGNTY+t9H3KKoZB1qkD3xz9BqvOroJaqUZeaR5UChXUSrVs6kL0rGjo1LoqvYtuTCEIQtUSPxqh3Nxc6PV65OTkcDWWiIiogVq4byFe2PpCte/v4tMFx68dr9RYjVKDMlPZzQf+v6GthmLz1M1QKBQ4ff00un/XHcWGYqgUKhgFIyJ8I3Ak1bZl7dL7l+KBjg9U+j13sqrEa2xkQERERA3Cjyd+vKX7UwtSKz22KsGri4OLmDpQVFaEiSsmothQDCeNE4yCEWFNw2SD1ycjnmTwWksYwBIREVG9yy3JRUxaTLXvD3QPxNX8q5Uaq1Vpq/Tsj4Z9JKYOPL/leZxJOwOdWofCskL4OPvg1HXb5kqdfTpj0ahFVXoPVR4DWCIiIqp3f57+s8rlrKyZTJXrqKWAAiXGkko/17rqwKrYVWKTBUv6QLGh2KZZgYuDC47MsF2RpZrDAJaIiIjq3bdHv632vR5aDyTmJlZqbFXat1qnDiTlJOGxtY8BAFQKFQCgpb4lckpypM+HAjFPxkCj0lT6PVR1DGCJiIioXhWVFcmWn6osd0f3So1TKVQwCZVbqQXKUweMJiOmrJyCrOIsaFVaGAUj2ni0wcXsizb3/D3xbwTo2UCptjGAJSIionq1MnYljIKxWvdqFBokZCdUamxV3jGs1TAxdeCtXW9hd+JuqJVqlBhL4O3kjfgs23JfL/R+AaOCR1X6HVR9DGCJiIioXlnySqujsqudSkXlQx4XBxd8N+Y7KBQK7L68G2/uehMAYDAZoIQSucW5Nvf0bN4T7w97v9LvoFvDAJaIiIjqTZmxDAeTD1b7/ks5lyo1rjqpA9atYi0BsKejJ0pNpZLxeq0eex7ZU+nn061jAEtERET1ZuP5jTCYKt/O1VoLtxZVCkwrw5I6IAgCZvw9A0m5SdAoNTAJJjR3bY70onTJeCWUOPf0OahV6hqdB90YA1giIiKqN58f+rza96bkp9TgTABnjbOYOvDdse+wMnYlFFCgzFQGvVaP5Lxkm3u2PbQNPi4+NToPujkGsERERFQvjCYjdifurta9TZ2aVnvl1p5PRnyCFvoWOHP9DOZumgsAECBAAQVyS2zzXt8Y8AYGBQ2q0TlQ5TCAJSIionoRdSmqSk0FrGWXZNfoXIa2GorHujyGorIiTPprEooNxWLeq06ts2myMLDlQLw28LUanQNVHgNYIiIiqhefHfysWve5alxRaiy9+cBKctY4iw0LXtj6Ak5dPyXWjPXQeaDIUCQZ7+3ojX8e/qfG3k9VxwCWiIiI6pwgCNiesL1a9xqE2kkdWHN2DRYdXgTAXDPWWe2MrOIsyViVQoW4Z+Kq1NGLah4DWCIiIqpzh5IPoaCsoMr3aZQamxXRWzGs9TA81uUxXMm9gkfXPiq5VmCwnd/eR/fCw9Gjxt5P1cMAloiIiOrcJwc+qdZ9DiqHGpuDs8YZ34/+HibBhKkrpyKzKBMKmFdWVQqVzfiPhn6Env49a+z9VH0MYImIiKjObYzfWK37qrNqa88nIz5BgD4AC/YswM7LO6GAAgIEOKocbdrOjmw7Es/1ea7G3k23hgEsERER1anT104jpySnyve5aFxqbA6WqgN7E/fi9ajXAZhLZulUOhQZpSkKzVyaYd2kdTX2brp1DGCJiIioTlU3fSC/LL/G5vDDmB+QU5KDySsnS1Zbi43FknFqpRpxT3PTVkPDAJaIiIjq1Nq4tVW+x0ntVGPv/270d/B388cTfz+BxJxEMe9VzrEnjsFFW3Mrv1QzGMASERFRnbmYdRFphWlVvq/QUFgj7x/Sagge6/IYfjj+A5bHLAdgTh2Q27T1zahvEOoTWiPvpZrFAJaIiIjqzKcHPq3yPTVVecBR5YjFYxbjbPpZzNk4RzyvVqhtNm1N6DABT0Q8USPvpZqnru8JEBER0Z1jReyKKt9TU123Phv5GZo4N8E9f9wjqSVbsTFCC7cWWHr/0hp5J9UOBrBERERUJ1JyU5CSl1Kle5RQwgTTLb97SJA5dWDuprk4ee2k3XEOSgd22moEmEJAREREdcLSprUqBAi3/F5HtSMWj12MdXHr8Pmhz8Xzcpu3Yp6MgVatveV3Uu1iAEtERER14o/Tf1T5npoIYD+/+3MoFUo8suYR8ZylaYG13+77Da29Wt/y+6j2MYWAiIiIal1mUSYSshOqdI9ckFlVdwXdhYfDHsawX4choyhDPF/xuY+EPYLJnSff0ruo7nAFloiIiGrdN0e+qfI9txq86lQ6LBm7BB/s+wA7Lu2wO66tZ1ssvnfxLb2L6hYDWCIiIqp1P0f/XKXxSsWthyhfjPwCyXnJ+M+O/9gdo1PpcHr26Vt+F9UtphAQERFRrcovzce5jHNVusck3FrlgUGBgzC+/Xh0+baLTY1Xa3HPxMFBXTN1ZqnucAWWiIiIatWS40uqlA5wo9aulaFVafHj2B8xa/0sXMq+ZHfcmgfXIEAfcEvvovrBAJaIiIhq1Q/Hf6jS+FvNff1i5BfYnrAdS8/Yb0bwVPenMCZkzC29h+oPUwiIiIio1hQbim/YOKCmDWg5AP0C+qHbd93sjunUtBO+GPlFnc2Jah4DWCIiIqo1f57+s0ZquVaGg9IB34/5HhOWT0BhWaHsGCe1E07MPFEn86HawxQCIiIiqjVfH/m6zt61aNQiLDq0CCdST8heV0CBhLkJUClVdTYnqh1cgSUiIqJaUWYsw5GUI3XyrsgWkfB19sWMgzPsjtkybQuaujStk/lQ7WIAS0RERLVizdk1NyxhVVMclA746P/au/voKOpDjePPbkISVPLCS5FIqOK1RSygFVQSVoJULhC0pSjC5VK1t4HWN1CPAofaVMVDD0c5pEKxVQFFQQjqVaA0GBXzCgpCkBchIIQQIICbN8jrZuf+4SVKEyCQmZ3d7Pdzzp6zmVl+v2fzC/owmZ35z7ka8faIc75mWvw0/aLnLyzPAt/gFAIAAGCJ+V/45oNSL498WdMzputk1clm9/fv1l9/ufMvPskC36DAAgAA03kNr3KLci2fJyEuQe4qtz4+8HGz+zuEddCm5E2W54BvcQoBAAAw3fr961Xvrbd0jnbOdno6/mmNSRvT7H6HHCqcUmjKbWnhX1hRAABgutSNqZbP8eKdL2pK+hR5vJ5m92c/mK2Yy2IszwHfo8ACAABTGYahDQc3WDrHrVfdqo3FG895q9jnE59XfI94SzPAPhRYAABgqtyiXNU01Fg2fogjRGNvGKvlO5Y3u39Q3CD9cfAfLZsf9qPAAgAAU83Nm2vp+CmDU/TMp880uy86IlqZD2ZaOj/sR4EFAACmSt+fbtnYP7/y53p397vN3irWIYeKphbJ4XBYNj/8AwUWAACYZtuxbTpdf9qSsZ0Op27udrPyS/Kb3b8leYuuCL/CkrnhXyiwAADANC/lvmTZ2A/3f1ivbn212X1z75yrm2Jvsmxu+BcKLAAAMM3qvastGbd3l956+6u3m9135zV36vH4xy2ZF/6JAgsAAEyxz71P5bXlpo/rdDgVExEjd427yb5O7TspfaJ159zCP1FgAQCAKV7MedGScX/5k18qpyinyXannDr8+GE+tBWEfFZgp02bJpfLpYkTJ6q+/vtbyzU0NOi3v/2tXC6Xpk6dKkk6cOCAXC6XBg8erKSkJJWXf/evucTERLlcLiUmJmrp0qW+ig4AAFogbXea6WNeG3OtPtjzQbP7djy0QxHtIkyfE/7PJwU2Pz9fxcXFysrKUq9evbRq1arGfWvWrFFsbKyysrJ0+vRp5eXlKTo6WqtXr9Znn32mu+++W6+++v0J2+vWrdOGDRs0ceJEX0QHAAAtUFxRLHd101/xt1aNp0ZeeZtsf2XkK7q+y/Wmz4fA4JMCm5ubq2HDhkmShg8frpycnPPui4mJUXR0tCQpLCxMTud3MZ1Op0aOHKm7775bhYWF55yvtrZWFRUVZz0AAIB1rLh5wc1db1ZxZXGT7Xf95C5NHjDZ9PkQOHxSYEtLSxUZGSlJioqKktvtbtG+srIyLVy4UA888IAkKS0tTZmZmXryySf16KOPnnO+2bNnKyoqqvERFxdnwbsCAABnnOsKAZeq2xXdtKVkS5PtXS/rqg/Hf2jqXAg8Pimw0dHRjUdBy8vL1bFjxwvuq6+v14QJEzR37tzGbZ06dZIkDR48WEeOHDnnfDNmzFB5eXnjo6ioyJL3BQAAJHe1WyWnS0wds7S6tMk2p8Opoif4fzp8VGDj4+OVkZEhSUpPT1dCQsIF902aNEljx47VoEGDGl97puju2rVLMTEx55wvPDxckZGRZz0AAIA15m2cZ+p43S7vppqGmibb9z26T+1C2pk6FwKTTwrsjTfeqK5du8rlcmnnzp0aM2aMJk/+7tyVUaNG6dChQ3K5XIqIiNDAgQOVlZWllStXavHixUpMTFRqaqok6Y477pDL5dLkyZP10kvW3ekDAAC03OKti00bKzI8UkdPH22yfekvl+qamGtMmweBzWEYhmF3CKtVVFQoKipK5eXlHI0FAMBEp2pPqcNfOlg6x73X36uVY1daOgfsdzF9jRsZAACAS7Zw80LTxgp3hjfZFtshlvKKJiiwAADgkplVYMNCwlTrrT1rW4gjRIemHjJlfLQtFFgAAHBJauprdKDsgClj1TXUNdl2aOohhThDTBkfbQsFFgAAXJIl25ZYNnbaPWmKjYy1bHwENgosAAC4JPM2zWv1GA45mmz7Td/f6J4b7mn12Gi7KLAAAOCiebwe7fl2T6vHMXT2xZDiIuP0xug3Wj0u2jYKLAAAuGgrdqwwfcwQR4gOTj1o+rhoeyiwAADgos3JmWP6mMeePCang2qCC+OnBAAAXBSv4dX249tNHXPN+DXqfHlnU8dE20WBBQAAF2Xt3rWmjve7n/9OST9JMnVMtG0UWAAAcFGe++w508a6JuoavXrXq6aNh+BAgQUAAC1mGIY2H91sylihjlDtn7LflLEQXCiwAACgxTYc3GDaWCeeOiGHo+l1YIELocACAIAWe+bTZ0wZJ2NihqLbR5syFoIPBRYAALRYTlFOq8d4uP/DGtpzqAlpEKwosAAAoEU2H2n9ua89o3tqftJ8E9IgmFFgAQBAizz4vw+26s+HOkK177F9JqVBMKPAAgCAC6qqr9KOEztaNUbp06V8aAumoMACAIALGv3O6Fb9+ewHs3VFxBUmpUGwC7U7AAAA8E87j+/U61tf13u731NheeElj/PYgMeU0CPBxGQIdhRYAAAgScorytPrX76utQVrVXK6RIaMVo/ZM7qnUkemmpAO+B4FFgCAIOT1evXRNx/ptS9f08cHPlZpTanpc4Q4QrjTFixBgQUAIAjUeer0/tfv67UvX9PGwxt1qv6U5XNWTq+0fA4EJwosAABt0Km6U3p7+9tatHWRth/frhpPjU/n3/g/G9U+rL1P50TwoMACANAGnKw6qUVbF+mNbW+owF2gem+9bVmm3DJFt3a/1bb50fZRYAEACECFZYV6ZcsrWrFjhQ6VH1KD0WB3JEnSNVHXaN6IeXbHQBtHgbWI41mHjJTWf3oTAADpu0tazf98vj7Y84FKTpXIK6/dkZpwyqlvpn5jdwwEAQqsRSivAIDWyDmUo5c3vayPDnyk0upSUy5pZbXqmdV2R0CQoMACAGAzr9erfxX8S6lfpCqvKE+VdYH36f0vk79UWGiY3TEQJCiwAAD4WJ2nTmm70zR/03zll+Sr2hPYRy4fu+Ux3RR7k90xEEQosAAAWOxU3Skt2bpEf9/yd+39dq/qvHV2RzLNVR2uUuoI7rQF36LAAgBgspNVJzX/8/l6M/9NHSg7YHccSx1+4rDdERCEKLAAALRSYVmhXsx9Ue/uflclp0vkNfzvCgFWqJ1Za3cEBCkKLAAAF2lHyQ69kPWCPvrmI7mr3QFxhQCzbXpgEx/agm0osAAAXED2wWzNypql3MO5AXmFALON/o/RuuXHt9gdA0GMAgsAwA94vV59uOdDzcmdo23HtgX8FQLM5pBD7014z+4YCHIUWABAUKvz1GnxtsWa/8V87T3Ztq4QYAVvSnCc3wv/RoEFAASVU3WnNC9vnpbkL9H+0v12xwkYP77ix1r666V2xwAkUWABAG3c8VPHNStzllbtXqWjp47aHccvOeVU0rVJmjNsjnr9qJfdcYALosACANqUA+4DmvnpTKXvS5e7xm13HL/QKbyTUganaFL/SQpvF253HKDVKLAAgIC2uXiz/vTpn/TZwc9U1VBldxxbJHZP1MJRC9WrK0dPERwosACAgLJu7zo9n/m8Pi/+XA1qsDuO5S4LuUzPJT6nR259hKOnwP+jwAIA/JbX69XibYs1N2+udp3cZXccS/Tr3E/v3PMOR0+Bi0CBBQD4jTpPnebkztGCTQt0rOqY3XFazSmnZt42U8/c8YzatWtndxygzaDAAgBsU1ZVppkbZmrJ5iWqMgLv/NXul3fX2v9aq76xfe2OAgQVCiwAwGcOlx3WI+se0Qd7P7A7SotM6jNJ8++az9FT4P95vV45nU67Y1BgAQDWyT+Wr/vfu1/5J/LtjtJEe7XXJ//9iW679ja7o8AP+UtR8zf+8j2hwAIATLNu7zrdu/xendZpu6NoyFVDlH5/OkdPL4Ci1jy+J/6NAgsAuCRer1cvfPqC/pT9J1vmf//X7+tXfX7V4td7vd7G506ns/Hrf38OwP9RYAEgADQ0NKi6oVruareOnz6ub09/q5LTJTp26piOlB3RwfKD2l+8XyeqT6hUpapTnd2RW+VHjh/p0LRDateu3XnL5sU8P/P1hZ4Hm2B+7whcPiuw06ZNU25urq6++motWrSo8Vc6DQ0NSk5OVkFBgW6++WbNmzdPkpSamqqVK1eqU6dOeuuttxQZGans7Gw9/fTTcjqdWrhwofr06eOr+AD8RF1dndzVbn19/Gutylulfxz4h+pVb3csXKSnej2lOffNafHrW1I8KadA8PBJgc3Pz1dxcbGysrL0wgsvaNWqVRo/frwkac2aNYqNjdWiRYuUnJysvLw8XXfddfrwww+VnZ2tZcuWacGCBZoxY4ZmzpyptWvXqrKyUr///e/1z3/+0xfxL5rjWYfdEQDAVu7H3IqJibE7BoA2yicFNjc3V8OGDZMkDR8+XIsXL24ssLm5uUpKSmrcl5OTo7KyMg0ePFgOh0PDhw/X/fffr+rqaoWEhCgmJkYxMTFyu93nnK+2tla1tbWNX1dUVFj47gAgeBkpht0RAAQhnxTY0tJSdevWTZIUFRV1VvksLS1VZGTkWfsutE2SQkNDVVdXp7CwsCbzzZ49W88+++wl562pqWmcIzT0wt+iH74egP8LV7hiQ2I18MqBGtFnhBKuTtCVkVeqffv2dkcDALSATxpXdHR041HQ8vJydezY8bz7oqOjtW/fvibbfngk1ePxNFteJWnGjBl64oknGr+uqKhQXFxci/NGRES0/M0183qOSAAAAFjHJ2e2x8fHKyMjQ5KUnp6uhISE8+4bMGCAMjMzz9p22WWXyePxqKysTEVFRWeV4H8XHh6uyMjIsx4AAABoG3xSYG+88UZ17dpVLpdLO3fu1JgxYzR58mRJ0qhRo3To0CG5XC5FRERo4MCB6tKli5KSkpSQkKBly5bpoYcekiTNmjVLI0eO1Lhx4zR79mxfRAcAAICfcRiG0eZ/311RUaGoqCiVl5dzNBYAAMAPXUxf4+J4AAAACCgUWAAAAAQUCiwAAAACCgUWAAAAAYUCCwAAgIBCgQUAAEBAocACAAAgoFBgAQAAEFAosAAAAAgoFFgAAAAEFAosAAAAAgoFFgAAAAGFAgsAAICAQoEFAABAQKHAAgAAIKCE2h3AFwzDkCRVVFTYnAQAAADNOdPTzvS28wmKAltZWSlJiouLszkJAAAAzqeyslJRUVHnfY3DaEnNDXBer1dHjhxRhw4d5HA4LJ+voqJCcXFxKioqUmRkpOXzwX6seXBi3YMPax58WHPfMQxDlZWVio2NldN5/rNcg+IIrNPpVPfu3X0+b2RkJD/sQYY1D06se/BhzYMPa+4bFzryegYf4gIAAEBAocACAAAgoFBgLRAeHq6UlBSFh4fbHQU+wpoHJ9Y9+LDmwYc1909B8SEuAAAAtB0cgQUAAEBAocACAAAgoFBgAQAAEFAosBaYNm2aXC6XJk6cqPr6ervjwCKff/65Bg4cqNtvv13jx49XfX290tLSFB8fr6FDh+rw4cN2R4RFli9fri5dukgSax4ENmzYoKFDh2rIkCF6//33lZ2drfj4eA0aNEhfffWV3fFgAa/XqwceeEAul0uDBg3S119/zbr7GwOm2rZtmzFhwgTDMAxj1qxZxrJly2xOBKscOXLEqKqqMgzDMKZPn26kpaUZt912m1FbW2tkZ2cbkyZNsjkhrODxeIzRo0cbN910k1FfX8+at3FVVVXGqFGjjNra2sZtt99+u+F2u43CwkJjxIgRNqaDVbZs2WKMGzfOMAzDyMzMNJKTk1l3P8MRWJPl5uZq2LBhkqThw4crJyfH5kSwSrdu3dS+fXtJUlhYmPbs2aPrr79eYWFhSkhI0Pbt221OCCssX75c9957r5xOpwoKCljzNi4vL0/t27fXXXfdpdGjR+vo0aMKCQlRTEyMevToIbfbbXdEWKB79+4yDEOGYai0tFSXX3456+5nKLAmKy0tbbzVXFRUFD/kQaCwsFDr16/XoEGDzrrNYENDg42pYIWGhgatXLlS9913n6Sz/76f2Y+2paSkRPv27dPq1auVnJyslJSUs9Y8NDRUdXV1NiaEFTp37qx27dqpV69eevTRR/X444+z7n6GAmuy6OhoVVRUSJLKy8vVsWNHmxPBShUVFZo4caKWLFmiLl26NK69JIWEhNiYDFZ46623NHbsWDmd3/2n84d/3yXWvC2Kjo5WQkKCwsLCNHToUG3duvWsNfd4PAoLC7MxIaywfv16hYaGas+ePXr33Xf15JNPsu5+hgJrsvj4eGVkZEiS0tPTlZCQYHMiWMXj8WjcuHFKSUnRT3/6U1133XXavXu36urqlJubq759+9odESbbtWuX3nzzTQ0fPlwFBQV6+eWXWfM2bsCAAdq9e7cMw9C2bdvUu3dveTwelZWVqaioiIMUbZRhGOrUqZOk747GVlZWsu5+hjtxWeCpp57Sxo0b1aNHDy1evJh/pbVRS5cu1dSpU9WnTx9J0h/+8AdJUmpqqiIiIvTGG28oLi7OzoiwUP/+/bV582atWLGCNW/jFixYoBUrVsjhcGjRokUqLi7W9OnT5XA49Le//U39+vWzOyJM5vF4NGHCBB07dky1tbWaO3euPB4P6+5HKLAAAAAIKJxCAAAAgIBCgQUAAEBAocACAAAgoFBgAQAAEFAosAAAAAgoFFgAAAAEFAosAAAAAgoFFgD8THV1tRITE5WYmKgOHTooMTFRV199tXJycuyOBgB+gRsZAIAfO3PHLwDA9zgCCwAB4M9//rPWrFmjgwcPKj4+Xvfdd59uuOEGrVixQqNGjVK/fv1UUFAgSVqyZIlcLpfi4+P1ySef2JwcAMwXancAAMDFKS0tVVZWlj7++GPNmDFDX3zxhVavXq2lS5dqypQpeuedd5SZmamqqiolJSXpjjvusDsyAJiKAgsAAaZ3794KCQlRbGysfvazn8npdOqqq65SRkaG9u/fr507d2rIkCGSpBMnTticFgDMR4EFgADjcDiafW4Yhnr27Km+fftqzZo1cjgcqq+vtyMiAFiKAgsAbUjnzp01btw4DR48WCEhIerTp4/++te/2h0LAEzFVQgAAAAQULgKAQAAAAIKBRYAAAABhQILAACAgEKBBQAAQEChwAIAACCgUGABAAAQUCiwAAAACCgUWAAAAAQUCiwAAAACCgUWAAAAAeX/AA8mC38Pl+PYAAAAAElFTkSuQmCC", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Step 3: Constrain peak infected\n", + "\n", + "# {\n", + "# \"name\": \"infected_maximum1\",\n", + "# \"variable\": \"Infected\",\n", + "# \"interval\": { \"lb\": 1e-5, \"ub\": 0.4},\n", + "# \"timepoints\": {\"lb\": 40, \"ub\": 125}\n", + " \n", + "# },\n", + "\n", + "# Tighten epsilon <= 0.18, theta <= 0.4\n", + "\n", + "SAVED_RESULT_TO_USE = os.path.join(SAVED_RESULTS_DIR, \n", + " # \"1a6108d5-58c8-44ed-b41e-93d95dd4ddab.json\"\n", + " \"29da5af4-9463-44f4-b51b-1b6f28cb79a5.json\"\n", + " )\n", + "\n", + "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n", + " results: FunmanResults = FunmanResults.model_validate(json.load(f))\n", + "\n", + "# Plot the trajectories\n", + "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Infected\")\n", + "\n", + "# Plot the parameter space\n", + "ParameterSpacePlotter(\n", + " results.parameter_space, plot_points=True, parameters=[\"epsilon\", \"theta\", \"timestep\"]\n", + " ).plot(show=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "funman_venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py index 3b9578d9..9cd657f9 100644 --- a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py +++ b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py @@ -62,7 +62,10 @@ def main(): }, { "name": "epsilon", - "interval": {"lb": 0.1368, "ub": 0.20520000000000002}, + "interval": {"lb": 0.1368, + # "ub": 0.20520000000000002 + "ub": 0.18 + }, "label": "all", }, { @@ -103,7 +106,10 @@ def main(): }, { "name": "theta", - "interval": {"lb": 0.2968, "ub": 0.4452}, + "interval": {"lb": 0.2968, + # "ub": 0.4452 + "ub":0.4 + }, "label": "all", }, { @@ -168,6 +174,13 @@ def main(): "variables": ["theta", "epsilon"], "weights": [1, -2], # No timepoints, because the variables are parameters + }, + { + "name": "infected_maximum1", + "variable": "Infected", + "interval": { "lb": 1e-5, "ub": 0.4}, + "timepoints": {"lb": 50, "ub": 125} + }, # { # "name": "infected_maximum3", @@ -175,12 +188,7 @@ def main(): # "interval": { "ub": 0.7}, # "timepoints": {"lb": 130}, # }, - # { - # "name": "infected_maximum1", - # "variable": "Infected", - # "interval": { "ub": 0.4}, - # "timepoints": {"lb": 70, "ub": 75, "closed_upper_bound": True}, - # }, + # { # "name": "infected_maximum2", # "variable": "Infected", @@ -198,7 +206,7 @@ def main(): { "name": "schedules", "schedules": [ - {"timepoints": [0, 10, 30, 50, 70, 90, 110, 130, 150]} + {"timepoints": [0, 10, 30, 50, 70, 90, 110, 130, 150, 170, 190, 210]} # {"timepoints": [0, 10]} ], } @@ -206,13 +214,14 @@ def main(): "config": { "use_compartmental_constraints": True, "normalization_constant": 1.0, - "tolerance": 1e-5, + "tolerance": 1e-2, "verbosity": 10, "dreal_mcts": True, # "save_smtlib": os.path.join(os.path.realpath(__file__), "./out"), "substitute_subformulas": False, "series_approximation_threshold": None, - "dreal_log_level": "none", + "dreal_log_level": "info", + "dreal_precision": 1, "profile": False, }, } @@ -224,6 +233,10 @@ def main(): # REQUEST_PATH, description="SIDARTHE demo", case_out_dir="./out", + dump_plot=False, + parameters_to_plot=["theta", "epsilon", "timestep"], + point_plot_config = {"variables":["Infected"], "label_marker":{"true":",", "false": ","}, "xlabel":"Time", "ylabel":"Infected"}, + num_points=50 ) points = results.points() boxes = results.parameter_space.boxes() diff --git a/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb b/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb index 8a89bb0e..eef8881b 100644 --- a/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb +++ b/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ " if points and len(points) > 0:\n", " point: Point = points[-1]\n", " parameters: Dict[Parameter, float] = results.point_parameters(point)\n", - " results.plot(variables=variables, label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Height\", legend=variables,label_color={\"true\": None})\n", + " results.plot(variables=variables, label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Height\", legend=variables,label_color={\"true\": \"g\", \"false\":\"r\"})\n", " print(f\"gamma = {results.parameter_space.points()[0].values['gamma']:.5f}\")\n", " print(parameters)\n", " print(results.dataframe([point]))\n", @@ -54,123 +54,190 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-11-02 19:49:09,159 - funman.server.worker - INFO - FunmanWorker running...\n", - "2023-11-02 19:49:09,166 - funman.server.worker - INFO - Starting work on: 7fcc6365-6597-4fbb-9a39-9bc0c9ce1d8e\n", - "2023-11-02 19:49:09,230 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 1, 2, 3, 4, 5, 6, 7]\n", - "2023-11-02 19:49:09,233 - funman_dreal.solver - DEBUG - Created new Solver ...\n", - "2023-11-02 19:49:09,237 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 1\n" - ] + "data": { + "image/png": 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0.105008212401945,\n", - " \"h_1_2\": 0.29235338618593015,\n", - " \"h_2_2\": 0.9976662986286912,\n", - " \"h_3_2\": 0.5011668506856545,\n", - " \"h_4_2\": 0.3038052520977793,\n", - " \"timer_t_2\": 2.0,\n", - " \"h_0_4\": 0.10591532257548826,\n", - " \"h_1_4\": 0.07766478810558348,\n", - " \"h_2_4\": 0.9974238904370984,\n", - " \"h_3_4\": 0.5012880547814508,\n", - " \"h_4_4\": 0.5250163814194893,\n", - " \"timer_t_4\": 4.0,\n", - " \"h_0_6\": 0.10592185835089431,\n", - " \"h_1_6\": -0.14515290037405454,\n", - " \"h_2_6\": 0.9975021517252168,\n", - " \"h_3_6\": 0.5012489241373915,\n", - " \"h_4_6\": 0.7478049042688778,\n", - " \"timer_t_6\": 6.0\n", - "}\n", - "2023-11-02 19:49:10,310 - funman.scenario.consistency - INFO - 7{7}:\t[+]\n", - "2023-11-02 19:49:10,312 - funman.server.worker - INFO - Completed work on: 7fcc6365-6597-4fbb-9a39-9bc0c9ce1d8e\n", - "2023-11-02 19:49:11,168 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2023-11-02 19:49:11,319 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2023-11-02 19:49:11,321 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use a five point model with no constraints\n", + "\n", + "num_disc = 5\n", + "MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", + "\n", + "\n", + "request_dict = {\n", + " \"structure_parameters\": [\n", + " {\n", + " \"name\": \"schedules\",\n", + " \"schedules\": [\n", + " {\"timepoints\": range(0, 50, 1)}\n", + " ],\n", + " },\n", + " \n", + " ],\n", + " \"parameters\":[\n", + " {\"name\": \"gamma\",\n", + " \"label\":\"all\",\n", + " \"interval\": {\"lb\":0, \"ub\":0.5}}\n", + " ],\n", + " \"constraints\": [\n", + " {\"name\": \"pos_h_0\",\n", + " \"variable\": \"h_0\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_1\",\n", + " \"variable\": \"h_1\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_2\",\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_3\",\n", + " \"variable\": \"h_3\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_4\",\n", + " \"variable\": \"h_4\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"LHS_slope\",\n", + " \"variables\": [\"h_1\", \"h_0\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }, \n", + " {\"name\": \"RHS_slope\",\n", + " \"variables\": [\"h_3\", \"h_4\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }\n", + "\n", + "\n", + " # {\"name\": \"melt_h_5\",\n", + " # \"variable\": \"h_5\",\n", + " # \"interval\": {\"lb\": 0, \"ub\": .8},\n", + " # \"timepoints\": {\"lb\": 5}\n", + " # },\n", + "\n", + " ],\n", + " \"config\": {\n", + " \"use_compartmental_constraints\": False,\n", + " \"normalization_constant\": 1.0,\n", + " \"tolerance\": 1e-5,\n", + " \"verbosity\": 30,\n", + " \"dreal_mcts\": True,\n", + " \"dreal_precision\": 1,\n", + " # \"save_smtlib\": \"halfar.smt2\",\n", + " \"substitute_subformulas\": False,\n", + " \"series_approximation_threshold\": None,\n", + " \"dreal_log_level\": \"none\",\n", + " \"profile\": False,\n", + " },\n", + "}\n", + "variables = [f\"h_{d}\" for d in range(num_disc)]\n", + " \n", + "# points = results.points()\n", + "# boxes = results.parameter_space.boxes()\n", + "\n", + "# print(\n", + "# f\"{len(points)} Points (+:{len(results.parameter_space.true_points())}, -:{len(results.parameter_space.false_points())}), {len(boxes)} Boxes (+:{len(results.parameter_space.true_boxes)}, -:{len(results.parameter_space.false_boxes)})\"\n", + "# )\n", + "\n", + "# Use request_dict\n", + "results = Runner().run(\n", + " MODEL_PATH,\n", + " request_dict,\n", + " # REQUEST_PATH,\n", + " description=\"Halfar demo\",\n", + " case_out_dir=\"./out\",\n", + " dump_plot=True,\n", + " parameters_to_plot=[\"gamma\", \"timestep\"],\n", + " point_plot_config={\"variables\":variables, \"label_marker\":{\"true\":\",\", \"false\": \",\"}, \"xlabel\":\"Time\", \"ylabel\":\"Height\", \"legend\":variables,\"label_color\":{\"true\": \"g\", \"false\":\"r\"}},\n", + " num_points=1\n", + ")\n", + "# summarize_results(num_disc, results)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 Points (+:1, -:0), 1 Boxes (+:1, -:0)\n", - "gamma = 0.40987\n", - "{}\n", - " h_0 h_1 h_2 h_3 h_4 id label\n", - "time \n", - "0.0 0.100000 0.500000 1.000000 0.500000 0.100000 0 true\n", - "1.0 0.103202 0.394331 0.998361 0.500820 0.203287 0 true\n", - "2.0 0.105008 0.292353 0.997666 0.501167 0.303805 0 true\n", - "3.0 0.105743 0.189387 0.997448 0.501276 0.413588 0 true\n", - "4.0 0.105915 0.077665 0.997424 0.501288 0.525016 0 true\n", - "5.0 0.105923 -0.033748 0.997426 0.501287 0.636415 0 true\n", - "6.0 0.105922 -0.145153 0.997502 0.501249 0.747805 0 true\n", - "7.0 0.105771 -0.256736 0.997921 0.501040 0.859283 0 true\n" - ] + "data": { + "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-03 19:50:53,341 - funman.search.box_search - ERROR - Traceback (most recent call last):\n", + " File \"/root/funman/src/funman/search/box_search.py\", line 1013, in _expand\n", + " ) = self._get_false_points(\n", + " File \"/root/funman/src/funman/search/box_search.py\", line 731, in _get_false_points\n", + " points, explanation = self._get_points(\n", + " File \"/root/funman/src/funman/search/box_search.py\", line 702, in _get_points\n", + " result = self.invoke_solver(solver)\n", + " File \"/root/funman/src/funman/search/search.py\", line 117, in invoke_solver\n", + " result = s.solve()\n", + " File \"/root/funman_venv/lib/python3.8/site-packages/pysmt/decorators.py\", line 64, in clear_pending_pop_wrap\n", + " return f(self, *args, **kwargs)\n", + " File \"/root/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 626, in solve\n", + " raise e\n", + " File \"/root/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 624, in solve\n", + " ans = self.check_sat()\n", + " File \"/root/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 558, in check_sat\n", + " result = self.context.CheckSat()\n", + "RuntimeError: KeyboardInterrupt(SIGINT) Detected.\n", + "\n" + ] } ], "source": [ "# Use a five point model with no constraints\n", "\n", "num_disc = 5\n", - "# MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", + "MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", "\n", "\n", "request_dict = {\n", @@ -178,44 +245,49 @@ " {\n", " \"name\": \"schedules\",\n", " \"schedules\": [\n", - " {\"timepoints\": range(0, 8, 1)}\n", + " {\"timepoints\": range(0, 20, 1)}\n", " ],\n", " },\n", " \n", " ],\n", + " \"parameters\":[\n", + " {\"name\": \"gamma\",\n", + " \"label\":\"all\",\n", + " \"interval\": {\"lb\":0, \"ub\":0.5}}\n", + " ],\n", " \"constraints\": [\n", - " # {\"name\": \"non-negative_h_0\",\n", - " # \"variable\": \"h_0\",\n", - " # \"interval\": {\"lb\": 0}\n", - " # },\n", - " # {\"name\": \"non-negative_h_1\",\n", - " # \"variable\": \"h_1\",\n", - " # \"interval\": {\"lb\": 0}\n", - " # },\n", - " # {\"name\": \"non-negative_h_2\",\n", - " # \"variable\": \"h_2\",\n", - " # \"interval\": {\"lb\": 0}\n", - " # },\n", - " # {\"name\": \"non-negative_h_3\",\n", - " # \"variable\": \"h_3\",\n", - " # \"interval\": {\"lb\": 0}\n", - " # },\n", - " # {\"name\": \"non-negative_h_4\",\n", - " # \"variable\": \"h_4\",\n", - " # \"interval\": {\"lb\": 0}\n", - " # },\n", - " # {\"name\": \"LHS_slope\",\n", - " # \"variables\": [\"h_1\", \"h_0\"],\n", - " # \"weights\": [1, -1],\n", - " # \"additive_bounds\": {\"lb\": 0},\n", - " # \"timepoints\": {\"lb\": 0}\n", - " # }, \n", - " # {\"name\": \"RHS_slope\",\n", - " # \"variables\": [\"h_3\", \"h_4\"],\n", - " # \"weights\": [1, -1],\n", - " # \"additive_bounds\": {\"lb\": 0},\n", - " # \"timepoints\": {\"lb\": 0}\n", - " # }\n", + " {\"name\": \"pos_h_0\",\n", + " \"variable\": \"h_0\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_1\",\n", + " \"variable\": \"h_1\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_2\",\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_3\",\n", + " \"variable\": \"h_3\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_4\",\n", + " \"variable\": \"h_4\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"LHS_slope\",\n", + " \"variables\": [\"h_1\", \"h_0\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }, \n", + " {\"name\": \"RHS_slope\",\n", + " \"variables\": [\"h_3\", \"h_4\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }\n", "\n", "\n", " # {\"name\": \"melt_h_5\",\n", @@ -228,10 +300,10 @@ " \"config\": {\n", " \"use_compartmental_constraints\": False,\n", " \"normalization_constant\": 1.0,\n", - " \"tolerance\": 1e-1,\n", - " \"verbosity\": 10,\n", + " \"tolerance\": 1e-5,\n", + " \"verbosity\": 30,\n", " \"dreal_mcts\": True,\n", - " \"dreal_precision\": 0.1,\n", + " \"dreal_precision\": 1,\n", " # \"save_smtlib\": \"halfar.smt2\",\n", " \"substitute_subformulas\": False,\n", " \"series_approximation_threshold\": None,\n", @@ -239,6 +311,14 @@ " \"profile\": False,\n", " },\n", "}\n", + "variables = [f\"h_{d}\" for d in range(num_disc)]\n", + " \n", + "# points = results.points()\n", + "# boxes = results.parameter_space.boxes()\n", + "\n", + "# print(\n", + "# f\"{len(points)} Points (+:{len(results.parameter_space.true_points())}, -:{len(results.parameter_space.false_points())}), {len(boxes)} Boxes (+:{len(results.parameter_space.true_boxes)}, -:{len(results.parameter_space.false_boxes)})\"\n", + "# )\n", "\n", "# Use request_dict\n", "results = Runner().run(\n", @@ -247,12 +327,310 @@ " # REQUEST_PATH,\n", " description=\"Halfar demo\",\n", " case_out_dir=\"./out\",\n", + " dump_plot=True,\n", + " parameters_to_plot=[\"gamma\", \"timestep\"],\n", + " point_plot_config={\"variables\":variables, \"label_marker\":{\"true\":\",\", \"false\": \",\"}, \"xlabel\":\"Time\", \"ylabel\":\"Height\", \"legend\":variables,\"label_color\":{\"true\": \"g\", \"false\":\"r\"}},\n", + " num_points=1\n", ")\n", - "summarize_results(num_disc, results)\n", + "# summarize_results(num_disc, results)\n", "\n", "\n" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7vGPHjjmDfw11wAEHRN++fRNrrrrqqoKslYbly5fHH/7wh8San/70p9G5c+dGr1VVVZUzjDN+/Ph47bXXGr0W5acS7kn77rtvnH322TmfHxXKz372s1h33XUTazyrglVXCfcjGs+MRBa4H626m266KXHTjAi/eA8AABSHPHV+5IWgMOSpASqH+QjKlzw1lK9K+mxenjp/skJkRSXck+SpoTJUwv2IxjMjkQXuR6tOnhqgdGwQDUBJrVixIv785z8n1px66qlRXV1dkPWaN28eP/7xjxNrRo8eHStXrizIesV05513xtKlS+s9vt5668Vhhx1WsPUOP/zwxAcqS5cujbvvvrtg60Glashr86mnniqL+xBA1piPoDzNnTs37r333sSaAQMGxGqrrZZSR0BTNWrUqMTjxx13XLRv375g65122mmJxx988MHEgHWWPPLII/Hpp5/We7x169bxwx/+sGDr7b777rHVVlsl1tx+++0FWw8qWdu2bePEE09MrJk8eXJ89tlnKXUEUDnMSFB+Vq5cGSNHjkys+c53vhObbLJJSh0BAABNhTx1/uSFoDzJUwMUj/kIypM8NZAV8tT5kxWC8iVPDVA8ZiQoP/LUAKVlg2gASmr8+PHx0Ucf1Xu8pqYmjjjiiIKuOWTIkGjZsmW9xz/88MN44oknCrpmMeR64DB06NBo0aJFwdZr1apVDBkypFE9Af+x3377JR7/4osv4r333kupG4DKYT6C8jR69OhYtGhRYo1vEQWK7e23344XXnghsebYY48t6Jr7779/rLPOOvUeX7JkSfzlL38p6JrFkmvm+f73vx+rr756QdfM9b/H6NGjc35LNfAfuZ5VRUS89tprKXQCUFnMSFB+xo4dG9OnT0+s8ZwKAAAoBnnq/MkLQfmSpwYoDvMRlCd5aiAL5KkbR1YIyps8NUBxmJGg/MhTA5SWDaIBKKkHH3ww8Xj//v2jXbt2BV2zY8eOsc8++yTW5Oqr1GbOnBn//Oc/E2sGDx5c8HVzfUP8M888E7NmzSr4ulBpvvvd7+asmTp1agqdAFQO8xGUr5tuuinxeI8ePWL33XdPqRugqcr1LGibbbaJDTbYoKBrNmvWLA455JDEmqw/o4r4zzdCP/zww4k1xZjDDj744GjevHm9x99///3417/+VfB1oRL16dMn53N4z6oAVo0ZCcpTrudUbdu2jUMPPTSlbgAAgKZEnjo/8kJQ3uSpAQrPfATlS54ayAJ56vzJCkH5k6cGKDwzEpQneWqA0rJBNAAl9dhjjyUe79+/f1HWzXXeRx99tCjrFsq4ceMSv42qW7dusfnmmxd83T59+sR6661X7/GVK1fG+PHjC74uVJpOnTpFy5YtE2vmzJmTTjMAFcJ8BOXpX//6V7z44ouJNcOGDYuqqqqUOgKaqqw+o3r88cdjxYoVRVm7UF588cXE97CtWrWKfv36FXzdNdZYI7bffvvEmqw/44MsWXvttROPe1YFsGrMSFB+Pv/883jggQcSaw455JCora1NqSMAAKApyepnVVl/jiAvBOVNnhqg8MxHUJ7kqYGsyOozKnnq+skKQWHJUwMUlhkJyo88NUDp2SAagJL56KOP4vXXX0+s2WOPPYqy9p577pl4fPLkyfHxxx8XZe1CGDduXOLxYl23hpw71weAwH+sscYaiccXLVqUUicAlcF8BOUp17eINmvWLIYOHZpOM0CTtXz58pgwYUJiTbFmib59+0ZNTU29x+fOnRsvvPBCUdYulFxz2E477RStW7cuytq5nvGZw6Dh1lxzzcTjnlUBrBozEpSf2267LZYuXZpYM3z48JS6AQAAmhJ56vzJC0H5k6cGKCzzEZQneWogC+SpG0dWCCqDPDVAYZmRoPzIUwOUng2iASiZ559/PvF4165do2vXrkVZu0ePHrHOOusk1mT5w6Jc126nnXYq2to77rhj4vEsXzfIkoULFyYeT/pAG4CvMx9B+Vm6dGncdtttiTV77rlndOvWLaWOgKZq8uTJsWDBgnqPt2jRIue3huerpqYmttpqq8SarM8S5jCoDJ5VARSWGQnKT65fvN9oo42K+toFAACaLnnq/HkGA+XPZ1QAhWU+gvIjTw1khTx145jDoDJ4VgVQWGYkKD/y1AClZ4NoAEpm4sSJice33nrroq6/7bbbJh6fNGlSUdfP19KlS2Py5MmJNcW8drmu22uvvRbLli0r2vpQCebNmxdz585NrFlttdVS6gag/JmPoDzdf//9MXPmzMQa3yIKpCHXM6pNN900WrVqVbT1y/UZ1ZdK+Ywv17WbNWtWvP/++0VbHyrJBx98kHjcsyqAVWNGgvLywgsvxKuvvppY4zkVAABQLPLU+ZEXgvInTw1QWOYjKE/y1EBWyFM3jqwQVAZ5aoDCMiNBeZGnBsgGG0QDUDIvv/xy4vHevXsXdf1c58/qh0WTJ09ODMRUV1fHpptuWrT1N99882jWrP4RYunSpTFlypSirQ+VYNKkSVFXV5dY07Nnz5S6ASh/5iMoTzfeeGPi8dVXXz0OPPDAlLoBmjLPqPLXkDBMMa9fp06dYr311kusyfL1g6x4//33c/6imWdVAA1nRoLyk+s5VfPmzeOoo45KqRsAAKCp8VlVfuSFoPzJUwMUlvkIypM8NZAVnlHlT1YIKoM8NUBhmZGg/MhTA2RD81I3AEDT9dZbbyUe33DDDYu6/gYbbJB4/O233y7q+vnKdd26d+8eLVu2LNr6LVu2jK5du8Z7771Xb83bb78dW265ZdF6gHL30EMPJR5v3759dOvWLaVu0jV79uyYOnVqTJ8+PebNmxfLli2L1q1bR5s2bWKttdaKrl27xjrrrJMYDAS+atmyZfHOO+/E+++/H7NmzYrFixdHixYtonXr1tGxY8fo0qVLdO3aNVq3bl3qVovGfATl54MPPohHH300sebII48s6ms3S8xIUFqeUeUvV28tWrSI7t27F7WHDTbYIGbMmFHv8SxfP8iKXM+qIiI222yzFDopjXnz5sU777wT06dPjy+++CKWLFkSNTU10bp161hzzTWjS5cusd5660Xz5j5ah1xWrFgR7777brz//vvx2WefxaJFi6K6ujratGkT7du3/+9zqtra2lK3WlRmJCgvixYtijvuuCOxZr/99ovOnTun1FHpmY8AACBdPqvKj7wQlD95alkhKCR5avMRlCN56q8yI0FpeUaVP1khqAzy1PJCUCjy1P9hRoLyIk/9deYjoFTcVQAoibq6upg2bVpiTa4Pcxor1/lz9Vcq7777buLxYl+3L9dICuzk6hGashUrVsSdd96ZWLPzzjtXVFjlrrvuin/961/xzDPPNOje2rZt2+jTp098+9vfjn322Sf69u3bZMJM0FBTpkyJn/3sZ/H444/Hq6++GkuWLEmsb9asWfTq1Su23Xbb2GOPPWKfffaJtdZaK6Vui898BOXnlltuiZUrVybWDB8+PKVuSsOMBNlR6lki1/kXLFgQn332Way55ppF7SMfua7d+uuvH9XV1UXtYYMNNognn3yy3uPmMMht9OjRicc33XTTTN6DGuORRx6J22+/PZ566ql46623oq6uLrG+pqYmtthii9h+++3je9/7Xuy+++7Rpk2blLqFbHv//ffjvPPOi3HjxsWkSZNi4cKFOf/Ot771rdhmm21i9913j3333bfiNvgwI0F5ueeee2Lu3LmJNZX+nCrCfAQAAKUiT52/Un/G9+Ua8kKQH3nqaTnrZYUgN3nqrzIfQfmRpzYjQZaUepaQp24cWSFoPHlqeSFoDHnqrzMjQXmRp/4P8xGQBTaIBqAkPvnkk1i8eHFizbrrrlvUHnKdf8GCBfHpp59mLuyT64PuYl+3hqzhIQjU77777ksMvEVEHHDAASl1k44zzjhjleoXLFgQzzzzTDzzzDNx2WWXxWqrrRZDhgyJk046qejfNA3l4u67716l+pUrV8Ybb7wRb7zxRtx2223RrFmz+N73vhcnnHBC7LffflFVVVWkTtNhPoLyUldXF7fccktizfbbbx+bb755Og2ViBkJsqGuri7ne7RizxJrr712NGvWLPEXPd59991MhgnNYVD+XnrppXj66acTayrtWVVExMUXX7xK9YsXL44XXnghXnjhhbjmmmuibdu2MXjw4PjhD38YW221VZG6hPLw+OOPx+OPP75Kf2fq1KkxderU/z7j6tu3bxx//PFx6KGHRvPm5R9jMSNBebnpppsSj6+77rqxzz77pNRN6ZiPAACgNOSp8+cZDJQ3eercZIUgN3nqrzIfQXmRp/4PMxJkgzx145jDoPzJUzeMvBDUT57668xIUF7kqf/DfARkQeV8jTYAZeXDDz/MWbP22msXtYeGnL8hfaYtV0/Fvm4NWSOL1w2yYMWKFXHuuecm1rRs2TIOPvjglDoqD7Nnz44rrrgiNtlkkzjmmGPcY6AAVq5cGQ8//HAccMABse2228Zjjz1W6pYaxXwE5eXxxx+PqVOnJtY0hW8RbSwzEhTG7Nmzc/7SfbFniebNm8fqq6+eWJPV17g5DMrfWWedlbPm8MMPT6GT8rJgwYK48cYbY+utt46BAwfGW2+9VeqWoKw99dRTccQRR8Qmm2wSd955Z6nbaTQzEpSPd955J5588snEmiFDhkR1dXVKHZUv8xEAAORHnjp/nsFA+ZKnzo+sEBSePHXhmY+g4eSpC8OMBIUhT9045jAof/LU+ZEXgsKSpy48MxI0jDx14ZiPgEKwQTQAJTFz5szE4+3bt49WrVoVtYc2bdpEbW1tYk2uPkshV09rrbVW0Xvo3Llz4vEsXjfIgj/+8Y8xZcqUxJohQ4ZEp06dUuqovKxYsSJuvPHG2GyzzWLUqFGlbgcqxsSJE2PPPfeMo48+Or744otSt5MX8xGUl1zfItqmTZsYPHhwSt2UPzMSNE5D/o02S9TPHAbl7a9//Ws88sgjiTV77rlnbL755il1VJ7uvffe2HLLLeOyyy6Lurq6UrcDZe3f//53DB48OPbff//4+OOPS91O3sxIUD5uvvnmnP9+H3300Sl1UznMRwAA0HDy1PnzDAbKlzx148gKQXHIUxeG+QgaTp66sMxI0Djy1I1jDoPyJk9dGPJCUDjy1IVjRoKGkacuDvMRkC8bRANQErNmzUo83r59+1T6yLVOrj5LIQvXrhyvG5TatGnT4swzz0ysadGiRZxxxhkpdVS+5syZE0cddVT88Ic/jOXLl5e6HagYN998c3z729+OqVOnlrqVVWY+gvIxd+7cuPfeexNrDj744NTeE1YSMxLkpyH/Rpsl6mcOg/I1d+7cOOGEE3LWnXPOOSl0U/4WL14cP/3pT2PgwIGxYMGCUrcDZe+vf/1rbLPNNvHSSy+VupW8mJGgPKxcuTJGjhyZWLPLLrvEBhtskFJHlcV8BAAADZOF5wgNWSeLzxKycO3K8bpBqclTF46sEBSHPHXjmI+gYeSpi8eMBPmRp24ccxiUL3nqwpIXgsKSp248MxLkJk9dXOYjIB82iAagJGbPnp14vF27dqn0kWudLL6Zz8K1K8frBqW0YsWKGDJkSMyfPz+xbsSIEdGzZ8+Uuip/f/jDH2LgwIECO1BAr7/+euywww4xefLkUreySsxHUD5Gjx4dixYtSqwZPnx4St1UJjMSrJpcc0Tr1q2jurq66H2U6yxRDnPYwoULY8mSJUXvA8rND3/4w5gxY0ZizcEHHxx9+/ZNqaPKMGbMmOjXr1/Mmzev1K1A2fvwww/ju9/9bjzxxBOlbmWVmZGgPIwdOzamT5+eWOM5VeOZjwAAIFkWniM0ZJ0sflaVhWtXjtcNSkmeujhkhaDw5KnzZz6ChpGnLj4zEqwaeerGKYc5TFYIvpk8dXHIC0HhyFM3jhkJcpOnTof5CFgVzUvdAABN0+LFixOPt23bNpU+amtrE4/n6rMUsnDtyvG6QSmdc845MWHChMSarl27Vtw3iNbU1MSOO+4YvXv3ji222CI222yzWGONNaJ9+/bRoUOHWLp0acyaNStmzpwZ06ZNiwkTJsSECRPi5ZdfjpUrVzZojQceeCCOPPLIGD16dFRVVRX5J4Js2HzzzWObbbaJLbbYIrbYYovo2rVrdOjQITp06BAtW7b87+vq008/jeeeey6efPLJeOaZZ+KLL75o0Pk///zz2HPPPeOZZ56J9ddfv8g/TWGYj6B83HjjjYnHe/XqVfGhGTMSZEsW5oiI8p0lsnD9cl27iP/02apVq6L3AuXi+uuvj9tvvz2xpl27dnHZZZel1FF6mjdvHjvssEP06dMnNt9889h8882jc+fO/31fvXLlypg5c2bMnDkzZsyYEU899VRMmDAhXnzxxVi6dGmD1njuuediv/32i0ceecS9h4rXs2fP2GGHHWKLLbaIzTffPNZff/3/vp5at24ds2fP/u9r6sUXX4wnn3wynnrqqfj8888bdP6FCxfG/vvvH+PHj4/tttuuyD9N4ZiRoDzkek7VoUOHGDRoUErdlI75CAAASisLzxEiyvOzqixcu3K8blBK8tSyQlBI8tRfZz6C8iFPbUaCrMnCHBFRvrNEFq6frBCsOnlqeSEoFHnqb2ZGgvIgT/0f5iMgS2wQDUBJ5BpsmzdP55+oXOs0dABPUxauXTleNyiVBx98MH79618n1lRVVcVNN92UyrfcFdsaa6wRAwcOjP79+0e/fv2iTZs29da2bNkyamtro1u3brHVVlvFgAEDIiLi3XffjcsuuyxuuummWLRoUc4177jjjujTp0+cccYZBfs5IEuqq6tjr732iv333z/69+8f3bp1S6zv3LlzdO7cOTbddNPYdddd44wzzojFixfHyJEj49JLL41///vfOdf86KOPYuDAgfGPf/wjampqCvWjFI35CMrDv/71r3jppZcSa44++uiUukmXGQmyKwtzREPWyeoskYXr15A1snr9oBRefPHFOOWUU3LWXX755dG1a9cUOiq+du3axYABA6J///6x9957R4cOHRLru3TpEl26dIktt9wy9t1334iI+PTTT+PKK6+MP/zhDzFnzpyca06YMCFGjBgRf/zjHwvxI0CmfPe7340DDzww+vfvHxtttFFi7ZprrhlrrrlmRETstNNOccopp8SKFSvi7rvvjt/+9rcxadKknOvNnz8/Bg4cGBMnTow11lijID9DsZmRIPs+//zzePDBBxNrDjvssGjdunVKHaXLfAQAANmRhecIDVkni88RsnDtyvG6QanIU8sKQWPJU+dmPoLyIE9tRoIsysIc0ZB1sjpLZOH6yQrBqpGnlheCxpKnzs2MBNknT20+ArKpWakbAKBpysIb+Yask8U38lm4duV43aAUXnvttTj88MOjrq4use5HP/pR7LHHHil1VXhVVVWx0047xW233RbTp0+Pa6+9Nvbff//EoE6S9ddfP37/+9/HtGnTYp999mnQ3zn77LNj4sSJea0HWbXOOuvEOeecE9OmTYuHH344TjzxxJxh5vrU1NTE8ccfH2+++WZcccUV0aJFi5x/Z9KkSXHWWWfltV7azEdQHnJ9i2jz5s1jyJAhKXVTfGYkKA9ZmCMask5WZ4ksXD9hHWi4Dz/8MA488MBYvHhxYt3+++8fw4cPT6mr4undu3f88Y9/jA8//DBGjhwZhxxySM6wTn3WWmut+OUvfxnvv/9+DB06tEF/59prr40HHnggr/Uga1ZbbbU45ZRT4o033ognn3wyfvKTn+QMM9enuro6Bg8eHBMnTozRo0c3aKOPDz74II477ri81isFMxJk36hRo3K+BiphHvr/Mx8BAED2ZOE5QkPWyeJzhCxcu3K8blAK8tSyQtAY8tQNZz6C8iBPbUaCLMrCHNGQdbI6S2Th+skKQcPJU8sLQb7kqVeNGQmyT57afARkkw2iASiJlStXJh6vrq5OpY9c66xYsSKVPlZFFq5dOV43SNunn34a+++/f8ybNy+xbrvttotLL700pa6K4/DDD4+nn346Dj/88GjVqlXBzrvWWmvFQw89FL/61a+iWbPkty4rVqyIH/7whznD41BO3n///bjwwgujS5cuBTtns2bN4pRTTomnn346unfvnrP+6quvjldffbVg6xeL+Qiyb+nSpXH77bcn1uy7776x9tprp9RR8ZmRoDxkYY5oyDpZnSWycP0askZWrx+kaeHChXHggQfGhx9+mFjXo0ePGDlyZEpdFc/OO+8cr7zySpxwwglRW1tbsPO2a9cubr755rj55pujdevWOetPOeWUWLRoUcHWh1J54YUX4oorrsg7xFyfww47LF566aXo3bt3ztoxY8bE3/72t4KuXyxmJMi+m266KfF47969Y9ttt02pm3SYjwAAIJuy8ByhIetk8TlCFq5dOV43SJs8dePJCtHUyVM3nPkIsk+e2owEWZWFOaIh62R1lsjC9ZMVgoaRpy4MeSGaKnnqVWNGguyTpzYfAdlkg2gASiLXtywtX748lT5yrdOQb4NPWxauXTleN0jT/PnzY999941p06Yl1q2++upx9913R8uWLdNprEiK+e18VVVVceaZZ8YNN9yQs/bZZ5/1LVlUlGK+trbffvuYMGFCdO3aNbFu+fLlce655xatj0IxH0H23XfffTFz5szEmkr7FlEzEpSHLMwRDVknq7NEFq5fQ9bI6vWDtCxfvjwOOeSQePHFFxPrampq4u67747VVlstpc6Kp5izWETE0KFDY8yYMTmf602bNi2uv/76ovYCaSjma2rDDTeMJ598MrbccsuctWeffXbR+igkMxJk2/PPPx+vvfZaYk2lPaeKMB8BAEBWZeE5QkPWyeJzhCxcu3K8bpAmeerCkRWiKZOnbjjzEWSfPHVhmZGgcLIwRzRknazOElm4frJCkJs8deHJC9HUyFOvGjMSZJs8dXGYj4BCsEE0ACWRa4hN68OiZcuWJR7PYsgwC9euHK8bpGXp0qUxYMCAeOmllxLrWrduHffff3907949pc7K27Bhw+L000/PWXfppZem0A1Uhm7dusV9990XrVq1Sqx74IEH4u23306pq/yYjyD7cn2L6Nprrx377rtvSt1UDjMSNF4W5oiI8p0lsnD9cl27iOxeP0hDXV1dHHPMMfHQQw8l1jVr1ixGjRpVcd/sXkx77713XH755TnrLr/88lixYkUKHUH56tixYzzwwAOx+uqrJ9ZNmjQpxo0bl1JX+TMjQbblek7VqlWrOOKII1LqprKYjwAAYNVl4TlCRHl+VpWFa1eO1w3SIk9dHLJCUHjy1IVlPoJk8tTFYUaCxsvCHBFRvrNEFq6frBAkk6cuHnkhKBx56sIzI0H95KmLx3wENJYNogEoiVzfoLR06dJU+ijHD4uycO3K8bpBGlasWBGHHXZYPPbYY4l1LVq0iLvvvjt22mmnlDqrDL/4xS9io402Sqx5+umn4913302pIyh/W2+9dZx11lmJNStXrozbbrstpY7yYz6CbPvggw/i0UcfTawZMmRI0b91s1KZkaBxsjBHRJTvLJGF6yesA8lOPfXUGDlyZM66P/7xjzFo0KAUOqosJ510Uuy2226JNe+9915MmDAhpY6gfHXr1i1+97vf5ay79dZbU+imccxIkF2LFi2KP//5z4k1Bx10UHTq1CmljiqP+QgAAFZNFp4jRJTnZ1VZuHbleN0gDfLUxSUrBIUnT1045iOonzx1cZmRoHGyMEdElO8skYXrJysEyeSpi0teCApHnrqwzEjwzeSpi898BDSGDaIBKIna2trE4/Pnz0+lj3nz5iUez9VnKWTh2pXjdYNi+/LbQ++9997EumbNmsWtt94a/fv3T6mzytGyZcv41a9+lbPunnvuSaEbqBw/+9nPYq211kqsyfrrynwE2XbLLbfEypUrE2uOPvrolLqpPGYkaJwszBER5TtLZOH65bp2VVVV0aZNm6L3AVl0/vnnx5VXXpmz7je/+U0cd9xxKXRUmS699NKcNWYxaJgjjzwyevfunVhz//33NyisW0pmJMiue+65J7744ovEmuHDh6fUTeUyHwEAQMNl4TlCRHl+VpWFa1eO1w2KTZ66+GSFoDjkqQvDfAT1k6cuLjMSNE4W5oiI8p0lsnD9ZIWgfvLU6ZAXgsKRpy4cMxJ8M3nqdJiPgHzZIBqAksj1DTG53kQUSq51svhNNlm4duV43aDYTjnllLjlllty1l177bUxePDg4jdUoQ466KDo0aNHYs3jjz+eTjNQIWpqauKEE05IrJkyZUp8+umnKXW06sxHkF11dXVx8803J9b07ds3evXqlVJHlcmMBPnL9W/0smXLYvHixUXvo1xniXKYwzp06BDV1dVF7wOy5ne/+11ccMEFOevOPvvs+NnPfpZCR5Vr6623ju9+97uJNWYxaJiqqqoYMWJEYs3cuXNj0qRJ6TSUJzMSZNeNN96YeLx79+7Rr1+/lLqpXOYjAABouCw8R2jIOln8rCoL164crxsUmzx1OmSFoPDkqQvDfATfTJ46HWYkyJ88deOUwxwmK0RTJU+dHnkhKBx56sIxI8E3k6dOh/kIyJcNogEoidVXXz3x+Jw5c1LpY+7cuYnHc/VZClm4drnWyOJ1g2I666yz4uqrr85Zd9lll8Wxxx6bQkeVq1mzZjFo0KDEmmeffTalbqByHHLIITlr/vnPf6bQSX7MR5Bd48ePj3fffTexxreINp4ZCfLXkH+jzRL1M4dBNl1//fVx2mmn5aw7+eST4xe/+EUKHVW+XO+r33jjjdQ+94ByN2DAgGjRokViTZafU0WYkSCr3nnnnZgwYUJizbBhw6JZM3G6QjAfAQBAw2ThOUKEPHW+PIOBr5KnTo+sEBSHPHXjmY/gm8lTp8OMBPmTp24ccxhkkzx1+uSFoHDkqQvDjARfJ0+dLvMRkA93YABKYo011kg8vmTJkqIPr7NmzYqlS5cm1mTxzXyua/fxxx8XvYdca2TxukGx/OpXv4qLL744Z90FF1wQP/nJT1LoqPLttttuicdnz54dn3zySUrdQGXYbLPNYq211kqseeONN1LqZtWZjyC7brrppsTj7dq1i4MPPjilbiqbGQnyk2uOiDBLJDGHQfbcdtttceKJJ+asO/roo+PKK69MoaOmIdcsVldXF2+++WZK3UB569ixY/Tp0yexJsvPqSLMSJBVN910U9TV1dV7vFmzZjF06ND0Gqpw5iMAAGgYeer8eQYD2SJPnT5ZISg8eerGMx/BN5OnTo8ZCfIjT9045jDIHnnq0pAXgsKRpy4MMxJ8nTx1usxHQD5sEA1ASXTr1i1nTbE/aG3I+RvSZ9py9ZTGB9S51ujevXvRe4AsuPLKK+Pss8/OWXf66afHueeem0JHTcPWW2+ds2batGnFbwQqzFZbbZV4PMuvK/MRZNOcOXPi3nvvTawZPHhwtGnTJqWOKpsZCfLTpk2bnGGOYs8SCxcujHnz5iXWZHWWMIdBtvzlL3+JoUOHxsqVKxPrBg8eHH/605+iqqoqpc4q3yabbBI1NTWJNWYxaLhc72+y/noyI0H2rFixIkaOHJlY069fP6+NAjIfAQBAw8hT588zGMgOeerSkBWC4pCnbhzzEXydPHW6zEiQH3nqxjGHQbbIU5eOvBAUljx145mR4KvkqdNnPgLyYYNoAEqitrY254dF7733XlF7yDUcr7XWWtG2bdui9pCPHj16JB4v9nWLyH3t1l9//aL3AKV2/fXXx4gRI3LW/ehHP4rf/va3xW+oCVl77bVzPgD59NNPU+oGKkeuGSPLryvzEWTT6NGjY/HixYk1w4cPT6mbymdGgvyVepZoyPlz9Vgqpb52EeYw+NJDDz0Uhx12WKxYsSKx7qCDDopRo0ZFs2Y+Ji6kqqqqnAEosxg0XDk/p4owI0EWjR07NmbMmJFY4zlVYZmPAACgYeSp8+cZDGSDPHXpyApBcZTz51TmI8gmeep0mZEgf6WeJeSpG8ccBv8hT11a8kJQWOX8nCrCjARZJE+dPvMRkA/vVAEomVxvlN9+++2irv/vf/878XhW38iX+rpFlO+1g0IZNWpUnHDCCTnrhg8fHldddVUKHTU97du3Tzy+cOHClDqBytGhQ4fE41l+XZmPIJtuvPHGxOObbbZZ7LDDDil10zSYkSA/pZ4lcs0RnTt3jjZt2hS1h3zlunYfffRRLFiwoKg9mMMgYty4cTFo0KBYtmxZYt33vve9uPPOO6N58+Ypdda0lPP7asiacn89mZEge3I9p+rUqVMcdNBB6TTThJT7/RwAANKS9c+qsvocodTXLaJ8rx0Uijx16ckKQeGV83NN8xFkkzx1+sxIkJ9SzxLy1I1jDgN56qwo5/fVkDXl/noyI0H2yFOXRrnfz4H02SAagJLZbLPNEo+/+eabRV0/1/lz9Vcqufr6/PPPY9asWUVbvyHnz+q1g0K4++67Y9iwYVFXV5dYd9hhh8X1118fVVVVKXXWtLRs2TLxeK4P8ICvK+fXlfkIsueVV16JiRMnJtb4FtHCK+d7OZSSZ1T523DDDXPee4p5/erq6nIGzrN8/aAQnn766TjggANi8eLFiXW77rpr3HvvvTlfs+TPLAaFU+6vJzMSZMtnn30WDz74YGLNEUccEa1atUqpo6aj3O/nAACQFp9V5UdeCEpLnjobPH+Bwivn15X5CLJHnro0yvleDqXkGVX+ZIWg9OSps8MsBoVT7q8nMxJkizx16ZT7/RxInw2iASiZrbfeOvH4pEmTirp+rg/Xt9pqq6Kun68ePXrEaqutllhTzGuX67qtvvrq0bVr16KtD6X0wAMPxOGHHx4rVqxIrBswYEDceuut0ayZcbtYFi1alHi8devWKXUClaOcX1fmI8ieXN8i2rJlyzjyyCNT6qbpKOd7OZSSZ1T5a9myZc4wTDGv39tvvx3z5s2r93hVVVVsueWWRVsfSu25556LfffdN+c3hX/nO9+JBx980CxQZGYxKJxyfz2ZkSBbRo0alTM46xfvi6Pc7+cAAJAWn1XlR14ISkeeOjs8f4HCK+fXlfkIskeeujTK+V4OpeQZVf5khaC05KmzxSwGhVPuryczEmSLPHXplPv9HEifhAUAJZPrw6KXX345Z2gwX8uXL49XXnklsSbLHxblunYvvfRS0dbOde4sXzdojLFjx8YhhxyS84HHPvvsE3fccUc0b948pc6anqVLl8bs2bMTa2pra1PqBirHxx9/nHg8668r8xFkx5IlS+L2229PrDnggANijTXWSKmjpsGMBPnLNUdMnz49Pv3006KtX+6zRJbnsJ49e0b79u2Ltj6U0qRJk+J73/teYmAt4j+v0b/97W/mgBSU+/tqyJJKeD2ZkSA7brrppsTj2267bfTu3TulbpqWSrifAwBAGuSp85flZzBZvm7QGPLU2SErBMVR7s81zUeQHfLUpWFGgvzJUzdOlucwWSEqmTx19pT7+2rIkkp4PZmRIDvkqUunEu7nQLpsEA1AyWy77bZRU1NT7/H58+cX7c38888/n/gtgDU1NbHNNtsUZe1C2HnnnROPP/HEE0Vb+/HHH088nqs3KEdPPPFEDBgwIJYsWZJYt/vuu8e9994bLVu2TKmzpmnq1KmxcuXKxJr11lsvpW6gcvz73/9OPJ7115X5CLLjvvvui1mzZiXW+BbRwjMjQf66dOkS3bt3T6wp1izx4YcfxltvvZVYk/VZwhwG6Zs8eXLstddeMWfOnMS6LbbYIh555JHo0KFDOo01YQsXLoyPPvooscYsBg1X7s+pIsxIkBXPPfdcTJ48ObHGc6riMB8BAEDDyVPnzzMYSJc8dbbICkFxlPvnVOYjyA556tIwI0H+5KkbxxwG6ZOnzh55ISiscn9OFWFGgqyQpy4d8xGQDxtEA1AyNTU1sdNOOyXWPProo0VZ+7HHHks83rdv38SwdantscceiccnTJgQS5cuLfi6ixcvjqeffjqxZs899yz4ulBK//znP2P//fePRYsWJdbtvPPO8cADD2T63lEpnnvuuZw1PXr0KH4jUEGWLFkSL7/8cmLN+uuvn04zeTIfQXbk+hbRrl27xl577ZVSN02HGQkaJ9csUapnVBtuuGHOsHWp5bp2r7/+esyYMaMoa+e6fuYwKtHbb78de+yxR3z++eeJdRtvvHE89thjsfrqq6fUWdP2wgsv5Pzlsqy/r4YsyfX+phxeT2YkyIZcz6lat24dhx12WErdNC3mIwAAaDh56vzJC0F65KmzR1YICk+eOn/mI/g6eerSMCNB48hT509WCNIlT51N8kJQWPLUjWNGgv8jT1065iMgHzaIBqCkcr1hvvfee4uy7j333JN4POsfrn/729+Odu3a1Xt8wYIFMXbs2IKv+/DDDyeGOjt06BDbb799wdeFUnnppZdin332ifnz5yfWbbfddvHQQw9F27ZtU+qsaXvooYcSj3/rW9+K2tralLqByjBu3LhYsmRJYk3v3r1T6iY/5iPIhvfffz/nB6dDhw6NZs08liw0MxI0Tq5nVA888ECsWLGi4OuW+zOqiIguXbrExhtvnFhTjGd8EydOjHfffbfe41VVVTmDRFBupk2bFrvvvnt8/PHHiXXf+ta3Yty4cbHWWmul1Bm5ZrGampro1atXSt1AeZsyZUpMmzYtsSbrz6kizEiQBQsXLow77rgjsWbQoEHRoUOHlDpqWsxHAACwauSp8yMvBOmQp84mWSEoPHnq/JmP4KvkqUvHjASNI0+dP1khSI88dXbJC0HhyFM3jhkJ/o88dWmZj4B8+OQAgJIaNGhQ4vGJEyfGm2++WdA1X3vttXj11VfrPV5VVZWzr1Jr3rx5HHTQQYk1o0ePLvi6uc45YMCAaN68ecHXhVJ49dVXY++99465c+cm1m255ZYxduzYaN++fUqdNW2zZs3K+QBkxx13TKkbqBy33npr4vEWLVrEdtttl1I3+TEfQTbccsstid9kWVVVFcOGDUuxo6bBjASN179//2jTpk29xz/99NOcv7CxqmbNmpXzF64OPvjggq5ZLLn6LMUctssuuwhzUlFmzJgR/fr1i+nTpyfWde3aNcaPHx/rrrtuSp2xbNmy+POf/5xYs+2220aLFi1S6gjKW67nVBHl8/7GjASldc8998QXX3yRWDN8+PCUumlazEcAALDq5KnzIy8ExSdPnU2yQlAc8tT5Mx/BV8lTl4YZCRpPnrpxZIWg+OSps0teCApLnrpxzEjwf+SpS8d8BOTLBtEAlFTPnj3j29/+dmLN1VdfXdA1r7rqqsTjO+64Y/To0aOgaxbD4Ycfnnj8L3/5S3z44YcFW+/999+P+++/v1E9Qbl46623Ys8994yZM2cm1m266abx6KOPxmqrrZZSZ1x55ZWxcOHCxJq99947pW6gMrz99ts5v2n9u9/9btTU1KTUUf7MR1BadXV1cfPNNyfW7L777rH++uun1FHTYUaCxqutrY0DDjggsabQz6iuvfbaWLp0ab3Hu3btGt/97ncLumax5Jp5nn322XjxxRcLtt6CBQvipptualRPUE4+/fTT6NevX0ydOjWxbp111onx48dH9+7dU+qMiIjbbrstZ9DcLAYNM3v27LjuuusSa3r27Bk9e/ZMqaPGMSNBad14442JxzfYYIOyec9VbsxHAACw6uSp8ycvBMUjT51dskJQePLU+TMfwVfJU5eOGQkaT566cWSFoLjkqbNNXggKR566ccxI8FXy1KVjPgLyZYNoAEru6KOPTjx+8803x0cffVSQtaZPnx6jRo1KrBk6dGhB1iq2PfbYI7p27Vrv8WXLlsUll1xSsPUuueSSWL58eb3Hu3XrFrvttlvB1oNSmTZtWvTr1y8++eSTxLoNN9wwHnvssVhzzTVT6oypU6fmvK+1atUqDjzwwJQ6gsrw4x//OFasWJFYc8ghh6TUTeOYj6C0xo8fH9OmTUus8S2ihWdGgsLJ9Yzq4Ycfjpdffrkga82fPz9nQPqoo46KqqqqgqxXbBtttFHOb57/5S9/WbD1rr322pg9e3a9x9u2bRuDBg0q2HpQSrNmzYo99tgj3nzzzcS6NddcM8aNGxcbbLBBSp0RETFnzpw466yzctaVy/tqKLUzzzwz5syZk1hTTq8nMxKUzr///e946qmnEmuOPvrosnnPVU7MRwAAkD956vzIC0FxyFNnl6wQFIc8df7MR/BV8tSlYUaCwpGnzp+sEBSPPHW2yQtBYclTN44ZCf6PPHXpmI+AxrBBNAAld+SRR8Zaa61V7/GFCxfGz3/+84KsdcYZZ8TixYvrPd65c+c48sgjC7JWsVVXV8eIESMSa6655pqcD7obYsqUKXHttdcm1px66qlRXV3d6LWglD788MPo169fzm9g6tGjR4wfPz7WWWedlDpj8eLFceihh8aiRYsS6wYPHhzt2rVLqSsof5deemn8/e9/T6xp3759HHrooSl11DjmIyitXN8iutpqq8WAAQNS6qZpMCNBYe25557Ru3fveo/X1dXlnDUa6uKLL46PP/643uOtWrWKk08+uSBrpeW0005LPH7ffffF448/3uh1Pvvss7jooosSa4455pjo2LFjo9eCUvviiy/ie9/7Xrz66quJdZ06dYrHHnssNtlkk5Q6IyJi5cqVcdRRRyXezyMidtlll+jVq1dKXUH5uueee+K6665LrKmuri67X5Q1I0Fp3HTTTVFXV1fv8erq6hgyZEiKHTUN5iMAAGgceer8yAtB4clTZ5esEBSHPHX+zEfwdfLU6TMjQWHJUzeOrBAUnjx1tskLQWHJUzeOGQm+Sp66NMxHQGPZIBqAkqupqYlTTjklsebWW2+NMWPGNGqdu+66K0aPHp1YM2LEiGjVqlWj1pk2bVpUVVUl/nf++ec3ao0vHXfccdGpU6d6jy9btiyOOOKIWLp0ad5rLFmyJI444ojEb3Pv1KlTHHPMMXmvAVnw2WefRb9+/WLq1KmJdV26dInx48dHly5dUuosf7nuRUOHDm3U+Z9++unCNJrD4sWLY+DAgfHiiy8m1lVVVcUZZ5yRSk9QLBMnTswZSiuUkSNHxs9+9rOcdSeddFJ06NChUWuZj6DyzZkzJ+d7tsMPPzxqampS6qh+ZiQgSa7Xy5NPPhmXX355o9b4xz/+Eb/97W8Ta4YOHRqdO3du1DpfKvZ970sHHXRQbLTRRok1w4YNy/kt9knq6upi2LBhMXfu3HprWrRoET/5yU/yXgOyYuHChbHffvvFCy+8kFjXoUOHeOSRRxJ/ISNLevTokXhP2nXXXRt1/n/84x+J4aVCWblyZZxwwgnx4IMP5qw988wzi94PFMOUKVNi9uzZqaz16KOPNmjDoYMPPjh69uxZkDXNSFC5VqxYESNHjkys2WeffWLddddNqaNk5iMAAOBL8tT5kxeCwpGnXnWyQlB48tSNZz6C0pCn/j9mJChv8tT5kxWCwpKnzo+8EBSOPPXQgqxjRoL0yVN/lfkIKCc2iAYgE0aMGBFdu3ZNrBkyZEg8//zzeZ3/2WefzfkNWN27d88ZrM6a2trauOCCCxJrXnzxxRg2bFisXLlylc+/YsWKGDJkSEyaNCmx7qKLLora2tpVPj9kxZw5c2KvvfaKN954I7Fu7bXXjvHjx8f666+fUmfZtt9++0W/fv1i/PjxRVvj7bffjm9/+9vx8MMP56w97rjjfLMrZe/WW2+Nnj17xlVXXRULFiwoyhpLly6NESNGxNChQ3M+xOzcuXPZheDMR1Aat99+eyxevDixpty+lThfZiQob4cddlhst912iTVnnHFGgz6g/SZvv/12DBo0KPEXo9q1a1ewX/ZKU7NmzeLSSy9NrHnvvffi+9//ft6/xHf66afHQw89lFgzYsSI6NatW17nh6xYunRpDBgwIJ566qnEutra2vjb3/4W22yzTUqdZd9JJ50U2267bdx33315vedriI8//jj23HPP+NOf/pSzdu+994699967KH1AsT3yyCPxrW99Ky666KKYOXNmUdaoq6uLX//617HvvvvmfE/ZunXr+NWvflWUPorJjATp+/vf/x4ffvhhYk1TeU4VYT4CAIByI0+dH3khKAx56vzICkHhyVM3nvkISkOe+v+YkaC8yVPnT1YICkeeOn/yQlA48tSFYUaC9MlTf5X5CCgnNogGIBPatGkTv/vd7xJr5s2bF3vttVf89a9/XaVz33///bH33nvH/PnzE+suu+yyaN269SqdOwtOPPHEnN9mOHr06Bg0aFB88cUXDT7v3Llz4/vf/37ceeediXVbbrllHH/88Q0+L2TN/PnzY5999omXX345sW6NNdaIcePGxYYbbphOY2Vi/Pjx0a9fv+jTp09cc801MWvWrIKcd9GiRXH11VfHNttsE6+88krO+nXXXTcuvvjigqwNpfbRRx/FKaecEl27do1TTz21Qa+BhnryySdj5513jiuvvLJB9VdddVV07NixYOunxXwE6bvpppsSj2+99dbRp0+fdJrJADMSlK+qqqr4/e9/H1VVVfXWLFu2LA4++OC44YYbVunczzzzTOyyyy7x0UcfJdadd955sfbaa6/SubNiv/32i/79+yfWPP7447HXXnvlvA7/a8mSJXHcccfFZZddlli3zjrrxDnnnNPg80IWLV++PA499NB45JFHEutat24df/3rX+M73/lOSp2Vj4kTJ8aAAQOiV69e8Zvf/CZmzJhRkPMuX748Ro0aFVtttVWDfnmttrY2rrnmmoKsDaUyZ86cOPfcc6Nbt25x7LHHxjPPPFOwc7/88suxzz77xJlnnpn4y15fOv/888t2sxEzEqQr13Oqzp07x3777ZdSN9lgPgIAgPIhT50/eSFoHHnqxpEVgsKTp2488xGkT576q8xIUL7kqRtHVggaT5668eSFoHDkqQvDjATpkqf+OvMRUC6al7oBAPjSoEGD4gc/+EGMHj263pq5c+fGAQccEIcddlicc845sfHGG9dbO2XKlLjwwgtzBk4iIg4//PAYOHBgXn2XWnV1dYwaNSp22GGHxG8DGzNmTLzwwgtx0UUXxeDBg6OmpuYb6xYtWhSjR4+O8847L+cbmdatW8eoUaOiurq6UT8DlNJhhx0Wzz77bM66Qw89NP7xj3/EP/7xjxS6+s8DxlwPOLPklVdeiR/96Edx6qmnRt++faN///7Rt2/f6NOnT7Ro0aJB51i5cmW8+uqrMWbMmLjmmmvi888/b9Dfa9myZdxzzz2x2mqrNeZHgMyZPXt2XHHFFXHFFVdEr169Yr/99ovdd989vvOd70SnTp0afJ6PP/44xo0bF1dddVU8//zzDf57J598chxyyCH5tF5y5iNI1yuvvBITJ05MrGlK3yL6v8xINBUTJkyIt956a5X+TkO+NX1VA8MREbvsskujfxF1++23jzPPPDPxG9WXLFkSxx57bPzlL3+JCy+8MLbbbrt6a9977734zW9+E3/6059yhnV22WWXGDFiRL6tZ8L1118fffr0ic8++6zemqeffjo233zz+H//7//FMcccE+3atfvGuuXLl8f9998fZ599drz55puJ6zZr1ixuueWWes9F01Hu96RTTz017rvvvpx1Bx54YLz99tvx9ttvr3Jf+WjXrl0ceuihqaxVKO+88078/Oc/j7POOit22GGH6N+/f+yyyy6x7bbb1vv+75u8+eab8dBDD8WVV14Z77//foP/3i233BI9e/bMp3UqRLnfj/7XwoUL44YbbogbbrghunbtGv37948999wzdtxxx1X6RazZs2fHE088EX/84x/j0UcfbfDfO+CAA+L000/Pp/XMMCNRSpV0P8rls88+iwcffDCx5qijjormzZtmZM58BAAA5UGeOj/yQtA48tSFISsEhSdPnT/zEaRLnrp+ZiSaikr7bF6eunFkhSi1cr8nyVMXjrwQpVbu96P/JU/deGYkSqmS7ke5yFMnMx8BWVdVV1dXV+omAOBL8+fPj2233Tbnm+8vbbXVVrH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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use a ten point model with no constraints\n", + "\n", + "num_disc = 10\n", + "MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", + "\n", + "\n", + "request_dict = {\n", + " \"structure_parameters\": [\n", + " {\n", + " \"name\": \"schedules\",\n", + " \"schedules\": [\n", + " {\"timepoints\": range(0, 25, 1)}\n", + " ],\n", + " },\n", + " \n", + " ],\n", + " \"parameters\":[\n", + " {\"name\": \"gamma\",\n", + " \"label\":\"all\",\n", + " \"interval\": {\"lb\":0, \"ub\":0.5}}\n", + " ],\n", + " \"constraints\": [ \n", + " {\"name\": \"pos_h_0\",\n", + " \"variable\": \"h_0\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_1\",\n", + " \"variable\": \"h_1\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_2\",\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_3\",\n", + " \"variable\": \"h_3\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_4\",\n", + " \"variable\": \"h_4\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_5\",\n", + " \"variable\": \"h_5\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_6\",\n", + " \"variable\": \"h_6\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_7\",\n", + " \"variable\": \"h_7\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_8\",\n", + " \"variable\": \"h_8\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_9\",\n", + " \"variable\": \"h_9\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"LHS_slope\",\n", + " \"variables\": [\"h_1\", \"h_0\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }, \n", + " {\"name\": \"RHS_slope\",\n", + " \"variables\": [\"h_8\", \"h_9\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }\n", + "\n", + " ],\n", + " \"config\": {\n", + " \"use_compartmental_constraints\": False,\n", + " \"normalization_constant\": 1.0,\n", + " \"tolerance\": 1e-5,\n", + " \"verbosity\": 30,\n", + " \"dreal_mcts\": True,\n", + " \"dreal_precision\": 1,\n", + " # \"save_smtlib\": \"halfar.smt2\",\n", + " \"substitute_subformulas\": False,\n", + " \"series_approximation_threshold\": None,\n", + " \"dreal_log_level\": \"none\",\n", + " \"profile\": False,\n", + " },\n", + "}\n", + "variables = [f\"h_{d}\" for d in range(num_disc)]\n", + "# Use request_dict\n", + "results = Runner().run(\n", + " MODEL_PATH,\n", + " request_dict,\n", + " # REQUEST_PATH,\n", + " description=\"Halfar demo\",\n", + " case_out_dir=\"./out\",\n", + " dump_plot=True,\n", + " parameters_to_plot=[\"gamma\", \"timestep\"],\n", + " point_plot_config={\"variables\":variables, \"label_marker\":{\"true\":\",\", \"false\": \",\"}, \"xlabel\":\"Time\", \"ylabel\":\"Height\", \"legend\":variables,\"label_color\":{\"true\": \"g\", \"false\":\"r\"}},\n", + " num_points=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use a ten point model with no constraints\n", + "\n", + "num_disc = 20\n", + "MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", + "\n", + "\n", + "request_dict = {\n", + " \"structure_parameters\": [\n", + " {\n", + " \"name\": \"schedules\",\n", + " \"schedules\": [\n", + " {\"timepoints\": range(0, 25, 1)}\n", + " ],\n", + " },\n", + " \n", + " ],\n", + " \"parameters\":[\n", + " {\"name\": \"gamma\",\n", + " \"label\":\"all\",\n", + " \"interval\": {\"lb\":0, \"ub\":0.5}}\n", + " ],\n", + " \"constraints\": [ \n", + " {\"name\": \"pos_h_0\",\n", + " \"variable\": \"h_0\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_1\",\n", + " \"variable\": \"h_1\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_2\",\n", + " \"variable\": \"h_2\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_3\",\n", + " \"variable\": \"h_3\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_4\",\n", + " \"variable\": \"h_4\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_5\",\n", + " \"variable\": \"h_5\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_6\",\n", + " \"variable\": \"h_6\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_7\",\n", + " \"variable\": \"h_7\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_8\",\n", + " \"variable\": \"h_8\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_9\",\n", + " \"variable\": \"h_9\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_10\",\n", + " \"variable\": \"h_10\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_11\",\n", + " \"variable\": \"h_11\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_12\",\n", + " \"variable\": \"h_12\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_13\",\n", + " \"variable\": \"h_13\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_14\",\n", + " \"variable\": \"h_14\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_15\",\n", + " \"variable\": \"h_15\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_16\",\n", + " \"variable\": \"h_16\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_17\",\n", + " \"variable\": \"h_17\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_18\",\n", + " \"variable\": \"h_18\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"pos_h_19\",\n", + " \"variable\": \"h_19\",\n", + " \"interval\": {\"lb\": 0}\n", + " },\n", + " {\"name\": \"LHS_slope\",\n", + " \"variables\": [\"h_1\", \"h_0\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }, \n", + " {\"name\": \"RHS_slope\",\n", + " \"variables\": [\"h_18\", \"h_19\"],\n", + " \"weights\": [1, -1],\n", + " \"additive_bounds\": {\"lb\": 0},\n", + " \"timepoints\": {\"lb\": 0}\n", + " }\n", + "\n", + " ],\n", + " \"config\": {\n", + " \"use_compartmental_constraints\": False,\n", + " \"normalization_constant\": 1.0,\n", + " \"tolerance\": 1e-5,\n", + " \"verbosity\": 30,\n", + " \"dreal_mcts\": True,\n", + " \"dreal_precision\": 1,\n", + " # \"save_smtlib\": \"halfar.smt2\",\n", + " \"substitute_subformulas\": False,\n", + " \"series_approximation_threshold\": None,\n", + " \"dreal_log_level\": \"none\",\n", + " \"profile\": False,\n", + " },\n", + "}\n", + "variables = [f\"h_{d}\" for d in range(num_disc)]\n", + "# Use request_dict\n", + "results = Runner().run(\n", + " MODEL_PATH,\n", + " request_dict,\n", + " # REQUEST_PATH,\n", + " description=\"Halfar demo\",\n", + " case_out_dir=\"./out\",\n", + " dump_plot=True,\n", + " parameters_to_plot=[\"gamma\", \"timestep\"],\n", + " point_plot_config={\"variables\":variables, \"label_marker\":{\"true\":\",\", \"false\": \",\"}, \"xlabel\":\"Time\", \"ylabel\":\"Height\", \"legend\":variables,\"label_color\":{\"true\": \"g\", \"false\":\"r\"}},\n", + " num_points=1\n", + ")" + ] + }, { "cell_type": "code", "execution_count": 9, @@ -448,7 +826,7 @@ "source": [ "# Use a five point model with no constraints\n", "\n", - "num_disc = 5\n", + "num_disc = 10\n", "# MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", "\n", "\n", @@ -457,7 +835,7 @@ " {\n", " \"name\": \"schedules\",\n", " \"schedules\": [\n", - " {\"timepoints\": range(0, 8, 1)}\n", + " {\"timepoints\": range(0, 5, 1)}\n", " ],\n", " },\n", " \n", diff --git a/src/funman/api/run.py b/src/funman/api/run.py index 065a59c1..da5f6b63 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -3,10 +3,14 @@ import logging import os from contextlib import contextmanager +import random from time import sleep from timeit import default_timer from typing import Dict, Tuple, Union +from funman_demo.parameter_space_plotter import ParameterSpacePlotter +from matplotlib import pyplot as plt + import funman from funman.api.settings import Settings from funman.model.generated_models.petrinet import Model as GeneratedPetriNet @@ -145,10 +149,10 @@ def elapsed_timer(self): elapser = None def run( - self, model, request, description="", case_out_dir="." + self, model, request, description="", case_out_dir=".", dump_plot=False, parameters_to_plot=None, point_plot_config={}, num_points=None ) -> FunmanResults: results = self.run_test_case( - (model, request, description), case_out_dir + (model, request, description), case_out_dir, dump_plot=dump_plot,parameters_to_plot=parameters_to_plot, point_plot_config=point_plot_config, num_points=num_points ) return results # ParameterSpacePlotter( @@ -158,7 +162,7 @@ def run( # ).plot(show=False) # plt.savefig(f"{case_out_dir}/scenario1_base_ps_beta_space.png") - def run_test_case(self, case, case_out_dir): + def run_test_case(self, case, case_out_dir, dump_plot=False,parameters_to_plot=None,point_plot_config={}, num_points=None): if not os.path.exists(case_out_dir): os.mkdir(case_out_dir) @@ -169,7 +173,7 @@ def run_test_case(self, case, case_out_dir): self._storage.start(self.settings.data_path) self._worker.start() - results = self.run_instance(case, out_dir=case_out_dir) + results = self.run_instance(case, out_dir=case_out_dir, dump_plot=dump_plot, parameters_to_plot=parameters_to_plot, point_plot_config=point_plot_config, num_points=num_points) self._worker.stop() self._storage.stop() @@ -188,7 +192,7 @@ def get_model(self, model_file: str): raise Exception(f"Could not determine the Model type of {model_file}") def run_instance( - self, case: Tuple[str, Union[str, Dict], str], out_dir="." + self, case: Tuple[str, Union[str, Dict], str], out_dir=".", dump_plot=False, parameters_to_plot=None, point_plot_config={}, num_points=None ): killer = GracefulKiller() (model_file, request_file, description) = case @@ -217,11 +221,23 @@ def run_instance( results = self._worker.get_results(work_unit.id) with open(outfile, "w") as f: f.write(results.model_dump_json(by_alias=True)) - # ParameterSpacePlotter( - # results.parameter_space, plot_points=True - # ).plot(show=False) - # plt.savefig(f"{out_dir}/{model.__module__}.png") - # plt.close() + if dump_plot: + point_plot_filename = f"{out_dir}/{work_unit.id}_points.png" + l.info(f"Creating plot of point trajectories: {point_plot_filename}") + points = results.parameter_space.points() + if len(points)>0: + points_to_plot = random.choices(points, k=min(len(points),num_points)) if num_points else results.parameter_space.points() + results.plot(points=points_to_plot, **point_plot_config) + plt.savefig(point_plot_filename) + plt.close() + + space_plot_filename = f"{out_dir}/{work_unit.id}_parameter_space.png" + l.info(f"Creating plot of parameter space: {space_plot_filename}") + ParameterSpacePlotter( + results.parameter_space, plot_points=False, parameters=parameters_to_plot + ).plot(show=True) + plt.savefig(space_plot_filename) + plt.close() sleep(10) else: results = self._worker.get_results(work_unit.id) From d1668bce7999040c14d55a43e1d78553b42456f1 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Tue, 7 Nov 2023 01:24:57 +0000 Subject: [PATCH 14/28] add depth-first search to box search --- .../hackathon_fall_2023_demo_terarrium.py | 56 ++++--- src/funman/api/run.py | 82 ++++++++-- src/funman/representation/box.py | 41 +++-- src/funman/representation/constraint.py | 4 +- src/funman/search/box_search.py | 145 ++++++++++++++---- 5 files changed, 248 insertions(+), 80 deletions(-) diff --git a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py index 9cd657f9..6e8c45cd 100644 --- a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py +++ b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py @@ -62,10 +62,11 @@ def main(): }, { "name": "epsilon", - "interval": {"lb": 0.1368, - # "ub": 0.20520000000000002 - "ub": 0.18 - }, + "interval": { + "lb": 0.1368, + # "ub": 0.20520000000000002 + "ub": 0.18, + }, "label": "all", }, { @@ -106,10 +107,11 @@ def main(): }, { "name": "theta", - "interval": {"lb": 0.2968, - # "ub": 0.4452 - "ub":0.4 - }, + "interval": { + "lb": 0.2968, + # "ub": 0.4452 + "ub": 0.4, + }, "label": "all", }, { @@ -175,12 +177,11 @@ def main(): "weights": [1, -2], # No timepoints, because the variables are parameters }, - { + { "name": "infected_maximum1", "variable": "Infected", - "interval": { "lb": 1e-5, "ub": 0.4}, - "timepoints": {"lb": 50, "ub": 125} - + "interval": {"lb": 10}, + "timepoints": {"lb": 10, "ub": 10, "closed_upper_bound": True}, }, # { # "name": "infected_maximum3", @@ -188,7 +189,6 @@ def main(): # "interval": { "ub": 0.7}, # "timepoints": {"lb": 130}, # }, - # { # "name": "infected_maximum2", # "variable": "Infected", @@ -206,7 +206,22 @@ def main(): { "name": "schedules", "schedules": [ - {"timepoints": [0, 10, 30, 50, 70, 90, 110, 130, 150, 170, 190, 210]} + { + "timepoints": [ + 0, + 10, + 30, + 50, + 70, + 90, + 110, + 130, + 150, + 170, + 190, + 210, + ] + } # {"timepoints": [0, 10]} ], } @@ -214,7 +229,7 @@ def main(): "config": { "use_compartmental_constraints": True, "normalization_constant": 1.0, - "tolerance": 1e-2, + "tolerance": 0.01, "verbosity": 10, "dreal_mcts": True, # "save_smtlib": os.path.join(os.path.realpath(__file__), "./out"), @@ -235,8 +250,13 @@ def main(): case_out_dir="./out", dump_plot=False, parameters_to_plot=["theta", "epsilon", "timestep"], - point_plot_config = {"variables":["Infected"], "label_marker":{"true":",", "false": ","}, "xlabel":"Time", "ylabel":"Infected"}, - num_points=50 + point_plot_config={ + "variables": ["Infected"], + "label_marker": {"true": ",", "false": ","}, + "xlabel": "Time", + "ylabel": "Infected", + }, + num_points=50, ) points = results.points() boxes = results.parameter_space.boxes() @@ -249,7 +269,7 @@ def main(): parameters: Dict[Parameter, float] = results.point_parameters(point) print(parameters) print(results.dataframe([point])) - else: + elif len(boxes) > 0: # if there are no points, then we have a box that we found without needing points box = boxes[0] diff --git a/src/funman/api/run.py b/src/funman/api/run.py index da5f6b63..4afae3fa 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -2,8 +2,8 @@ import json import logging import os -from contextlib import contextmanager import random +from contextlib import contextmanager from time import sleep from timeit import default_timer from typing import Dict, Tuple, Union @@ -149,10 +149,23 @@ def elapsed_timer(self): elapser = None def run( - self, model, request, description="", case_out_dir=".", dump_plot=False, parameters_to_plot=None, point_plot_config={}, num_points=None + self, + model, + request, + description="", + case_out_dir=".", + dump_plot=False, + parameters_to_plot=None, + point_plot_config={}, + num_points=None, ) -> FunmanResults: results = self.run_test_case( - (model, request, description), case_out_dir, dump_plot=dump_plot,parameters_to_plot=parameters_to_plot, point_plot_config=point_plot_config, num_points=num_points + (model, request, description), + case_out_dir, + dump_plot=dump_plot, + parameters_to_plot=parameters_to_plot, + point_plot_config=point_plot_config, + num_points=num_points, ) return results # ParameterSpacePlotter( @@ -162,7 +175,15 @@ def run( # ).plot(show=False) # plt.savefig(f"{case_out_dir}/scenario1_base_ps_beta_space.png") - def run_test_case(self, case, case_out_dir, dump_plot=False,parameters_to_plot=None,point_plot_config={}, num_points=None): + def run_test_case( + self, + case, + case_out_dir, + dump_plot=False, + parameters_to_plot=None, + point_plot_config={}, + num_points=None, + ): if not os.path.exists(case_out_dir): os.mkdir(case_out_dir) @@ -173,7 +194,14 @@ def run_test_case(self, case, case_out_dir, dump_plot=False,parameters_to_plot=N self._storage.start(self.settings.data_path) self._worker.start() - results = self.run_instance(case, out_dir=case_out_dir, dump_plot=dump_plot, parameters_to_plot=parameters_to_plot, point_plot_config=point_plot_config, num_points=num_points) + results = self.run_instance( + case, + out_dir=case_out_dir, + dump_plot=dump_plot, + parameters_to_plot=parameters_to_plot, + point_plot_config=point_plot_config, + num_points=num_points, + ) self._worker.stop() self._storage.stop() @@ -192,7 +220,13 @@ def get_model(self, model_file: str): raise Exception(f"Could not determine the Model type of {model_file}") def run_instance( - self, case: Tuple[str, Union[str, Dict], str], out_dir=".", dump_plot=False, parameters_to_plot=None, point_plot_config={}, num_points=None + self, + case: Tuple[str, Union[str, Dict], str], + out_dir=".", + dump_plot=False, + parameters_to_plot=None, + point_plot_config={}, + num_points=None, ): killer = GracefulKiller() (model_file, request_file, description) = case @@ -222,19 +256,37 @@ def run_instance( with open(outfile, "w") as f: f.write(results.model_dump_json(by_alias=True)) if dump_plot: - point_plot_filename = f"{out_dir}/{work_unit.id}_points.png" - l.info(f"Creating plot of point trajectories: {point_plot_filename}") + point_plot_filename = ( + f"{out_dir}/{work_unit.id}_points.png" + ) + l.info( + f"Creating plot of point trajectories: {point_plot_filename}" + ) points = results.parameter_space.points() - if len(points)>0: - points_to_plot = random.choices(points, k=min(len(points),num_points)) if num_points else results.parameter_space.points() - results.plot(points=points_to_plot, **point_plot_config) + if len(points) > 0: + points_to_plot = ( + random.choices( + points, k=min(len(points), num_points) + ) + if num_points + else results.parameter_space.points() + ) + results.plot( + points=points_to_plot, **point_plot_config + ) plt.savefig(point_plot_filename) plt.close() - - space_plot_filename = f"{out_dir}/{work_unit.id}_parameter_space.png" - l.info(f"Creating plot of parameter space: {space_plot_filename}") + + space_plot_filename = ( + f"{out_dir}/{work_unit.id}_parameter_space.png" + ) + l.info( + f"Creating plot of parameter space: {space_plot_filename}" + ) ParameterSpacePlotter( - results.parameter_space, plot_points=False, parameters=parameters_to_plot + results.parameter_space, + plot_points=False, + parameters=parameters_to_plot, ).plot(show=True) plt.savefig(space_plot_filename) plt.close() diff --git a/src/funman/representation/box.py b/src/funman/representation/box.py index 0a1a9a9a..33f9f04a 100644 --- a/src/funman/representation/box.py +++ b/src/funman/representation/box.py @@ -67,10 +67,14 @@ def advance(self): if self.timestep().lb == self.timestep().ub: return None else: - box = self.model_copy(deep=True) + box: Box = self.model_copy(deep=True) box.timestep().lb += 1 # Remove points because they correspond to prior timesteps - box.points = [] + box.points = [ + pt + for pt in self.points + if box.timestep().contains_value(pt.timestep()) + ] return box def corners(self, parameters: List[Parameter] = None) -> List[Point]: @@ -234,10 +238,18 @@ def _copy(self): def __lt__(self, other): if isinstance(other, Box): - if self.timestep().lb == other.timestep().lb: - return self.width() > other.width() + # prefer boxes with true points + # prefer boxes later in time + # prefer boxes with smaller width + s_t = len(self.true_points()) + o_t = len(other.true_points()) + if s_t == o_t: + if self.timestep().lb == other.timestep().lb: + return self.width() > other.width() + else: + return self.timestep().lb > other.timestep().lb else: - return self.timestep().lb > other.timestep().lb + return s_t > o_t else: raise Exception(f"Cannot compare __lt__() Box to {type(other)}") @@ -259,7 +271,7 @@ def __str__(self): for k, v in self.bounds.items() ] ) - box_str = f"Box(label: {self.label}\nwidth: {self.width()},\ntimepoints: {Interval(lb=self.schedule.time_at_step(int(self.timestep().lb)), ub=self.schedule.time_at_step(int(self.timestep().ub)), closed_upper_bound=True)},\n{bounds_str}\n)" + box_str = f"Box(\n|+pts|: {len(self.true_points())}\n|-pts|: {len(self.false_points())}\nlabel: {self.label}\nwidth: {self.width()},\ntimepoints: {Interval(lb=self.schedule.time_at_step(int(self.timestep().lb)), ub=self.schedule.time_at_step(int(self.timestep().ub)), closed_upper_bound=True)},\n{bounds_str}\n)" return box_str # return f"Box(t_{self.timestep()}={Interval(lb=self.schedule.time_at_step(int(self.timestep().lb)), ub=self.schedule.time_at_step(int(self.timestep().ub)), closed_upper_bound=True)} {self.bounds}), width = {self.width()}" @@ -376,7 +388,9 @@ def intersects(self, other: "Box") -> bool: ] ) - def _get_max_width_point_Parameter(self, points: List[List[Point]]): + def _get_max_width_point_Parameter( + self, points: List[List[Point]], parameters: List[Parameter] + ): """ Get the parameter that has the maximum average distance from the center point for each parameter and the value for the parameter assigned by each point. @@ -390,10 +404,11 @@ def _get_max_width_point_Parameter(self, points: List[List[Point]]): Parameter parameter (dimension of box) where points are most distant from the center of the box. """ - # + parameter_names = [p.name for p in parameters] group_centers = { p: [average([pt.values[p] for pt in grp]) for grp in points] for p in self.bounds + if p in parameter_names } centers = {p: average(grp) for p, grp in group_centers.items()} # print(points) @@ -408,7 +423,9 @@ def _get_max_width_point_Parameter(self, points: List[List[Point]]): for pt in grp ] parameter_widths = { - p: average([pt[p] for pt in point_distances]) for p in self.bounds + p: average([pt[p] for pt in point_distances]) + for p in self.bounds + if p in parameter_names } # normalized_parameter_widths = { # p: average([pt[p] for pt in point_distances]) @@ -611,14 +628,16 @@ def split( """ p = None if points: - p = self._get_max_width_point_Parameter(points) + p = self._get_max_width_point_Parameter( + points, parameters=parameters + ) if p is not None: mid = self.bounds[p].midpoint( points=[[pt.values[p] for pt in grp] for grp in points] ) if mid == self.bounds[p].lb or mid == self.bounds[p].ub: # Fall back to box midpoint if point-based mid is degenerate - p = self._get_max_width_Parameter() + p = self._get_max_width_Parameter(parameter=parameters) mid = self.bounds[p].midpoint() if p is None: p = self._get_max_width_Parameter( diff --git a/src/funman/representation/constraint.py b/src/funman/representation/constraint.py index ad42660b..5f14db55 100644 --- a/src/funman/representation/constraint.py +++ b/src/funman/representation/constraint.py @@ -40,7 +40,7 @@ def contains_time(self, time: Union[float, int]) -> bool: return ( self.timepoints.contains_value(time) if self.time_dependent() - else time == 0 + else True ) def relevant_at_time(self, time: int) -> bool: @@ -73,7 +73,7 @@ def encodable(self) -> bool: return not isinstance(self.parameter, StructureParameter) def relevant_at_time(self, time: int) -> bool: - return time == 0 + return True # time == 0 class QueryConstraint(TimedConstraint): diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index 0b50de1a..2f3c1ff8 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -19,7 +19,7 @@ from pydantic import BaseModel, ConfigDict from pysmt.formula import FNode from pysmt.logics import QF_NRA -from pysmt.shortcuts import And, Implies, Not, Or, Solver +from pysmt.shortcuts import BOOL, And, Implies, Not, Or, Solver, Symbol from pysmt.solvers.solver import Model as pysmtModel from funman import ( @@ -173,13 +173,19 @@ class BoxSearchEpisode(SearchEpisode): def __init__(self, **kwargs): super().__init__(**kwargs) - self._unknown_boxes = QueueSP() + self._unknown_boxes = PQueueSP() self.statistics = SearchStatistics() if self.config.substitute_subformulas and self.config.simplify_query: self._formula_stack._substitutions = self.problem._encodings[ self.schedule ]._encoder.substitutions(self.schedule) + def get_candiate_point(self, box: Box) -> Point: + return None + + def get_candidate_boxes_for_point(self, point: Point) -> List[Box]: + return [] + def _initialize_boxes(self, expander_count, schedule: EncodingSchedule): # initial_box = self._initial_box() # if not self.add_unknown(initial_box): @@ -187,7 +193,10 @@ def _initialize_boxes(self, expander_count, schedule: EncodingSchedule): # f"Did not add an initial box (of width {initial_box.width()}), try reducing config.tolerance, currently {self.config.tolerance}" # ) initial_boxes = QueueSP() - initial_boxes.put(self._initial_box(schedule)) + initial_box = self._initial_box(schedule) + + initial_boxes.put(initial_box) + num_boxes = 1 while num_boxes < expander_count: b1, b2 = initial_boxes.get().split() @@ -424,7 +433,7 @@ def _simplify_formula( ]._encoder.substitutions(options.step_size) return formula.substitute(substitutions).simplify() - def _initialize_encoding( + def _initialize_model_encoding( self, solver: Solver, episode: BoxSearchEpisode, @@ -454,12 +463,13 @@ def _initialize_encoding( else: time_difference = box.timestep().lb - episode._formula_stack.time - if time_difference < 0: - # Prepare the formula stack by popping irrelevant layers - for i in range(abs(int(time_difference - 1))): - episode._formula_stack.pop() + # if time_difference < 0: + # # Prepare the formula stack by popping irrelevant layers + # for i in range(abs(int(time_difference - 1))): + # episode._formula_stack.pop() - elif time_difference > 0: + # el + if time_difference > 0: # Prepare the formulas for each added layer layer_formulas = [] encoding = episode.problem._encodings[box.schedule] @@ -490,24 +500,36 @@ def _initialize_encoding( assumptions=episode.problem._assumptions, ) ) - formula = And(encoded_constraints) + formula = Implies( + self._solve_at_step_symbol(t), And(encoded_constraints) + ) layer_formulas.append(formula) for layer, formula in enumerate(layer_formulas): episode._formula_stack.push(1) episode._formula_stack.add_assertion(formula) - def _initialize_box( + def _solve_at_step_symbol(self, t: int) -> FNode: + return Symbol(f"solve_step_{t}", BOOL) + + def _initialize_model_for_box( self, solver, box: Box, episode: BoxSearchEpisode, options: EncodingOptions, ): - self._initialize_encoding(solver, episode, options, box) + # Setup the model transitions to evaluate the box + self._initialize_model_encoding(solver, episode, options, box) + formula = self._initialize_box_encoding(box, episode, options) episode._formula_stack.push(1) + episode._formula_stack.add_assertion(formula) + def _initialize_box_encoding( + self, box: Box, episode: SearchEpisode, options: EncodingOptions + ) -> FNode: + # Add constraints for boundaries of the box projected_box = box.project( episode.problem.model_parameters() ).project(episode.problem.model_parameters()) @@ -527,7 +549,7 @@ def _initialize_box( )[0] ) formula = And(parameter_formulas) - episode._formula_stack.add_assertion(formula) + return formula def _setup_false_query(self, solver, episode, box, options): """ @@ -598,11 +620,26 @@ def _setup_false_query(self, solver, episode, box, options): if not k.constraint.time_dependent() ] ) - - formulas = And([formula, formula1, formula2]).simplify() + activate_steps = self.encoding_step_activation_formula(box) + formulas = And( + [formula, formula1, formula2, activate_steps] + ).simplify() episode._formula_stack.add_assertion(formulas) + def encoding_step_activation_formula(self, box: Box) -> FNode: + # Activate all steps up to and inclusive of box.timestep.lb + # Deactivate all steps from box.timestep.lb + 1 to box.timestep.ub + t = int(box.timestep().lb) + tmax = len(box.schedule.timepoints) - 1 # int(box.timestep().ub) + return And( + [self._solve_at_step_symbol(step) for step in range(t + 1)] + + [ + Not(self._solve_at_step_symbol(step)) + for step in range(t + 1, tmax + 1) + ] + ) + def store_smtlib(self, episode, box, filename="dbg"): tmp_name = filename + "_0" if os.path.exists(tmp_name + ".smt2"): @@ -673,8 +710,10 @@ def _setup_true_query(self, solver, episode, box, options): if k.constraint.time_dependent() ] ) - - formulas = And([formula, formula1, formula2]).simplify() + activate_steps = self.encoding_step_activation_formula(box) + formulas = And( + [formula, formula1, formula2, activate_steps] + ).simplify() episode._formula_stack.add_assertion(formulas) @@ -860,20 +899,54 @@ def _find_witness_points( def _get_true_points( self, solver, episode, box, rval, options, my_solver ) -> Optional[Union[List[Point], Explanation]]: - points, explanation = self._get_points( - solver, - box, - box.true_points(), - episode, - rval, - partial(self._setup_true_query, solver, episode, box, options), - episode._add_true_point, - my_solver, - options, - _smtlib_save_fn=partial(self.store_smtlib, episode, box) - if episode.config.save_smtlib - else None, - ) + # At start, the episode._formula_stack will have model constraints up to box.timestep.lb and box constraints + # Each call to self._get_points() will add the "true" assumptions and tries to find a point + # + # While able to find a point, pop the box constraints and add to the model constraints. + original_box_timestep_lb = box.timestep().lb + found_point = True + explanation = None + last_true_point = None + while ( + found_point + and box.timestep().lb <= box.timestep().ub + and len(box.true_points()) == 0 + ): + points, explanation = self._get_points( + solver, + box, + box.true_points(), + episode, + rval, + partial(self._setup_true_query, solver, episode, box, options), + episode._add_true_point, + my_solver, + options, + _smtlib_save_fn=partial(self.store_smtlib, episode, box) + if episode.config.save_smtlib + else None, + ) + if len(box.points) == 0: + # if couldn't find a point, then remove all points from box + found_point = False + box.points = [] + box.timestep().ub = last_true_point.timestep() + elif box.timestep().lb < box.timestep().ub: + episode._formula_stack.pop() # pop the box constraints + last_true_point = box.true_points()[0] + box.points = [] + box.timestep().lb += 1 + self._initialize_model_for_box(solver, box, episode, options) + + # At end, we should either have a true point that goes through the last step, + # or have a true point that ends at an earlier time. + if ( + last_true_point + and last_true_point not in box.points + and box.timestep().contains_value(last_true_point.timestep()) + ): + box.points.append(last_true_point) + box.timestep().lb = original_box_timestep_lb return box.true_points(), explanation def get_box_corners( @@ -981,7 +1054,11 @@ def _expand( else: continue else: - self._initialize_box(solver, box, episode, options) + l.debug(f"Expanding box: {box}") + # Setup the model constraints up to the box.timestep.lb and add box constraints + self._initialize_model_for_box( + solver, box, episode, options + ) l.debug( "\n" @@ -1093,14 +1170,14 @@ def _expand( my_solver, ) rval.put(box.model_dump()) - episode._formula_stack.pop() # Remove box from solver + episode._formula_stack.pop() # Remove box constraints from solver episode._on_iteration() if handler: handler(rval, episode.config, all_results) if "progress" in all_results: l.info(all_results["progress"]) l.trace(f"{process_name} finished work") - self._initialize_encoding( + self._initialize_model_encoding( solver, episode, options, None ) # Reset solver stack to empty except KeyboardInterrupt: From 5bb3c7ddab9b530bfa599f709c9010f01cd52fde Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Tue, 7 Nov 2023 01:33:59 +0000 Subject: [PATCH 15/28] ignores --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 99026b4e..99955825 100644 --- a/.gitignore +++ b/.gitignore @@ -315,3 +315,4 @@ gmon.out unsat.core .gitignore core.dimacs +notebooks/saved-results/out From 2477075ac59460e48270f06b76497f22b74e580e Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Fri, 10 Nov 2023 20:08:41 +0000 Subject: [PATCH 16/28] halfar request --- resources/amr/halfar/halfar_request.json | 36 ++++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 resources/amr/halfar/halfar_request.json diff --git a/resources/amr/halfar/halfar_request.json b/resources/amr/halfar/halfar_request.json new file mode 100644 index 00000000..3678e03e --- /dev/null +++ b/resources/amr/halfar/halfar_request.json @@ -0,0 +1,36 @@ +{ + "structure_parameters": [ + { + "name": "schedules", + "schedules": [ + { + "timepoints": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ] + } + ] + } + ], + "config": { + "use_compartmental_constraints": false, + "normalization_constant": 1.0, + "tolerance": 1e-1, + "verbosity": 20, + "dreal_mcts": true, + "save_smtlib": "./out", + "substitute_subformulas": false, + "series_approximation_threshold": null, + "dreal_log_level": "none", + "profile": false + } +} \ No newline at end of file From e38ac993a0b4309c3680dcaaec8c6a5ed10f0447 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 10 Nov 2023 20:10:24 +0000 Subject: [PATCH 17/28] use normalized widths more carefully. --- .../funman_demo/parameter_space_plotter.py | 4 +- .../hackathon_fall_2023_demo_terarrium.py | 34 ++--- src/funman/api/run.py | 41 +++--- src/funman/config.py | 7 + src/funman/representation/box.py | 135 +++++++++++------- src/funman/representation/interval.py | 30 ++-- src/funman/representation/parameter.py | 5 + src/funman/scenario/scenario.py | 3 +- src/funman/search/box_search.py | 132 +++++++++++------ src/funman/search/search.py | 5 + src/funman/translate/petrinet.py | 14 +- src/funman/utils/sympy_utils.py | 7 +- test/test_plot_parameter_space.py | 13 +- test/test_use_cases.py | 2 +- 14 files changed, 271 insertions(+), 161 deletions(-) diff --git a/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py b/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py index 54d09385..885a585f 100644 --- a/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py +++ b/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py @@ -26,6 +26,7 @@ def __init__( alpha=0.2, plot_points=False, parameters=None, + dpi=100, ): if isinstance(parameter_space, ParameterSpace): self.ps = parameter_space @@ -55,6 +56,7 @@ def __init__( Line2D([0], [0], color="g", lw=4, alpha=alpha), Line2D([0], [0], color="r", lw=4, alpha=alpha), ] + self.dpi = dpi def computeBounds(self, interval: Interval = Interval(lb=-2000, ub=2000)): box = Box(bounds={p: interval for p in self.parameters}) @@ -68,7 +70,7 @@ def initialize_figure(self): self.dim, self.dim, squeeze=False, - dpi=600, + dpi=self.dpi, figsize=(10, 10), ) self.fig = fig diff --git a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py index 6e8c45cd..a25d47c8 100644 --- a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py +++ b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py @@ -64,8 +64,8 @@ def main(): "name": "epsilon", "interval": { "lb": 0.1368, - # "ub": 0.20520000000000002 - "ub": 0.18, + "ub": 0.20520000000000002 + # "ub": 0.18, }, "label": "all", }, @@ -109,8 +109,8 @@ def main(): "name": "theta", "interval": { "lb": 0.2968, - # "ub": 0.4452 - "ub": 0.4, + "ub": 0.4452 + # "ub": 0.4, }, "label": "all", }, @@ -177,12 +177,12 @@ def main(): "weights": [1, -2], # No timepoints, because the variables are parameters }, - { - "name": "infected_maximum1", - "variable": "Infected", - "interval": {"lb": 10}, - "timepoints": {"lb": 10, "ub": 10, "closed_upper_bound": True}, - }, + # { + # "name": "infected_maximum1", + # "variable": "Infected", + # "interval": {"lb": 1e-5}, + # "timepoints": {"lb": 10, "ub": 10, "closed_upper_bound": True}, + # }, # { # "name": "infected_maximum3", # "variable": "Infected", @@ -222,21 +222,21 @@ def main(): 210, ] } - # {"timepoints": [0, 10]} + # {"timepoints": [0, 10, 20]} ], } ], "config": { "use_compartmental_constraints": True, "normalization_constant": 1.0, - "tolerance": 0.01, + "tolerance": 0.2, "verbosity": 10, "dreal_mcts": True, - # "save_smtlib": os.path.join(os.path.realpath(__file__), "./out"), + "save_smtlib": os.path.join(os.path.dirname(__file__), "./out"), "substitute_subformulas": False, "series_approximation_threshold": None, - "dreal_log_level": "info", - "dreal_precision": 1, + "dreal_log_level": "none", + "dreal_precision": 1e-1, "profile": False, }, } @@ -247,8 +247,8 @@ def main(): request_dict, # REQUEST_PATH, description="SIDARTHE demo", - case_out_dir="./out", - dump_plot=False, + case_out_dir=os.path.join(os.path.dirname(__file__), "./out"), + dump_plot=True, parameters_to_plot=["theta", "epsilon", "timestep"], point_plot_config={ "variables": ["Infected"], diff --git a/src/funman/api/run.py b/src/funman/api/run.py index 4afae3fa..300a1ce5 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -256,14 +256,15 @@ def run_instance( with open(outfile, "w") as f: f.write(results.model_dump_json(by_alias=True)) if dump_plot: - point_plot_filename = ( - f"{out_dir}/{work_unit.id}_points.png" - ) - l.info( - f"Creating plot of point trajectories: {point_plot_filename}" - ) points = results.parameter_space.points() if len(points) > 0: + point_plot_filename = ( + f"{out_dir}/{work_unit.id}_points.png" + ) + l.info( + f"Creating plot of point trajectories: {point_plot_filename}" + ) + points_to_plot = ( random.choices( points, k=min(len(points), num_points) @@ -277,19 +278,21 @@ def run_instance( plt.savefig(point_plot_filename) plt.close() - space_plot_filename = ( - f"{out_dir}/{work_unit.id}_parameter_space.png" - ) - l.info( - f"Creating plot of parameter space: {space_plot_filename}" - ) - ParameterSpacePlotter( - results.parameter_space, - plot_points=False, - parameters=parameters_to_plot, - ).plot(show=True) - plt.savefig(space_plot_filename) - plt.close() + boxes = results.parameter_space.boxes() + if len(boxes) > 0: + space_plot_filename = ( + f"{out_dir}/{work_unit.id}_parameter_space.png" + ) + l.info( + f"Creating plot of parameter space: {space_plot_filename}" + ) + ParameterSpacePlotter( + results.parameter_space, + plot_points=False, + parameters=parameters_to_plot, + ).plot(show=False) + plt.savefig(space_plot_filename) + plt.close() sleep(10) else: results = self._worker.get_results(work_unit.id) diff --git a/src/funman/config.py b/src/funman/config.py index 5b618773..d36f11aa 100644 --- a/src/funman/config.py +++ b/src/funman/config.py @@ -3,6 +3,7 @@ analysis. """ import logging +import os from typing import Optional, Union from pydantic import BaseModel, ConfigDict, field_validator, model_validator @@ -106,4 +107,10 @@ def check_use_compartmental_constraints(self) -> "FUNMANConfig": assert ( self.normalization_constant ), "Need to set normalization_constant in configuration to enforce compartmental constraints. The normalization_constant provides the population size used in the constraint upper bound." + + if self.save_smtlib: + assert os.path.exists( + os.path.dirname(self.save_smtlib) + ), "save_smtlib option must be an existing path" + return self diff --git a/src/funman/representation/box.py b/src/funman/representation/box.py index 33f9f04a..34f58751 100644 --- a/src/funman/representation/box.py +++ b/src/funman/representation/box.py @@ -1,6 +1,7 @@ import copy import logging from decimal import ROUND_CEILING, Decimal +from pickle import FALSE from statistics import mean as average from typing import Dict, List, Literal, Optional, Union @@ -10,7 +11,7 @@ import funman.utils.math_utils as math_utils from funman.constants import LABEL_FALSE, LABEL_TRUE, LABEL_UNKNOWN, Label -from . import EncodingSchedule, Interval, Point +from . import EncodingSchedule, Interval, Point, Timestep from .explanation import BoxExplanation from .interval import Interval from .parameter import ModelParameter, Parameter @@ -29,10 +30,10 @@ class Box(BaseModel): label: Label = LABEL_UNKNOWN bounds: Dict[str, Interval] = {} explanation: Optional[BoxExplanation] = None - cached_width: Optional[float] = Field(default=None, exclude=True) schedule: Optional[EncodingSchedule] = None corner_points: List[Point] = [] points: List[Point] = [] + _points_at_step: Dict[Timestep, List[Point]] = {} @staticmethod def from_point(point: Point) -> "Box": @@ -45,11 +46,28 @@ def from_point(point: Point) -> "Box": box.label = point.label return box - def true_points(self) -> List[Point]: - return [p for p in self.points if p.label == LABEL_TRUE] + def add_point(self, point: Point) -> None: + if point not in self.points: + timestep = point.timestep() + step_points = self._points_at_step.get(timestep, []) + step_points.append(point) + self._points_at_step[timestep] = step_points + self.points.append(point) + + def true_points(self, step=None) -> List[Point]: + return [ + p + for p in self.points + if p.label == LABEL_TRUE and (step is None or p.timestep() == step) + ] - def false_points(self) -> List[Point]: - return [p for p in self.points if p.label == LABEL_FALSE] + def false_points(self, step=None) -> List[Point]: + return [ + p + for p in self.points + if p.label == LABEL_FALSE + and (step is None or p.timestep() == step) + ] def explain(self) -> "BoxExplanation": expl = {"box": {k: v.model_dump() for k, v in self.bounds.items()}} @@ -75,6 +93,10 @@ def advance(self): for pt in self.points if box.timestep().contains_value(pt.timestep()) ] + box._points_at_step = { + step: [p for p in pts if p in box.points] + for step, pts in box._points_at_step.items() + } return box def corners(self, parameters: List[Parameter] = None) -> List[Point]: @@ -245,7 +267,7 @@ def __lt__(self, other): o_t = len(other.true_points()) if s_t == o_t: if self.timestep().lb == other.timestep().lb: - return self.width() > other.width() + return self.normalized_width() > other.normalized_width() else: return self.timestep().lb > other.timestep().lb else: @@ -267,7 +289,7 @@ def __repr__(self): def __str__(self): bounds_str = "\n".join( [ - f"{k}:\t{str(v)}\t({v.width():.5f})" + f"{k}:\t{str(v)}\t({v.normalized_width():.5f})" for k, v in self.bounds.items() ] ) @@ -415,18 +437,28 @@ def _get_max_width_point_Parameter( # print(centers) point_distances = [ { - p: abs(pt.values[p] - centers[p]) + p: Decimal(abs(pt.values[p] - centers[p])) for p in pt.values if p in centers } for grp in points for pt in grp ] + parameter_widths = { p: average([pt[p] for pt in point_distances]) for p in self.bounds if p in parameter_names } + parameter_widths = { + p: ( + v / self.bounds[p].original_width + if self.bounds[p].original_width > 0.0 + else 0.0 + ) + for p, v in parameter_widths.items() + } + # normalized_parameter_widths = { # p: average([pt[p] for pt in point_distances]) # / (self.bounds[p].width()) @@ -442,26 +474,18 @@ def _get_max_width_point_Parameter( return max_width_parameter def _get_max_width_Parameter( - self, normalize={}, parameters: List[ModelParameter] = None + self, normalize=False, parameters: List[ModelParameter] = None ) -> Union[str, ModelSymbol]: if parameters: widths = { parameter.name: ( - self.bounds[parameter.name].width( - normalize=normalize[parameter.name] - ) - if parameter.name in normalize - else self.bounds[parameter.name].width() + self.bounds[parameter.name].width(normalize=normalize) ) for parameter in parameters } else: widths = { - p: ( - self.bounds[p].width(normalize=normalize[p]) - if p in normalize - else self.bounds[p].width() - ) + p: self.bounds[p].width(normalize=normalize) for p in self.bounds } if "timestep" in widths: @@ -471,26 +495,18 @@ def _get_max_width_Parameter( return max_width def _get_min_width_Parameter( - self, normalize={}, parameters: List[ModelParameter] = None + self, normalize=FALSE, parameters: List[ModelParameter] = None ) -> Union[str, ModelSymbol]: if parameters: widths = { parameter.name: ( - self.bounds[parameter.name].width( - normalize=normalize[parameter.name] - ) - if parameter.name in normalize - else self.bounds[parameter.name].width() + self.bounds[parameter.name].width(normalize=normalize) ) for parameter in parameters } else: widths = { - p: ( - self.bounds[p].width(normalize=normalize[p]) - if p in normalize - else self.bounds[p].width() - ) + p: (self.bounds[p].width(normalize=normalize)) for p in self.bounds } min_width = min(widths, key=widths.get) @@ -499,7 +515,7 @@ def _get_min_width_Parameter( def volume( self, - normalize=None, + normalize=False, parameters: List[ModelParameter] = None, *, ignore_zero_width_dimensions=True, @@ -519,16 +535,9 @@ def volume( if len(pnames) <= 0: return Decimal("nan") - # if no parameters are normalized then default to an empty dict - if normalize is None: - normalize = {} - # get a mapping of parameters to widths # use normalize.get(p.name, None) to select between default behavior and normalization - widths = { - p: self.bounds[p].width(normalize=normalize.get(p, None)) - for p in pnames - } + widths = {p: self.bounds[p].width(normalize=normalize) for p in pnames} if ignore_zero_width_dimensions: # filter widths of zero from the widths = {p: w for p, w in widths.items() if w != 0.0} @@ -572,10 +581,16 @@ def volume( product *= num_timepoints return product + def normalized_width(self, parameters: List[ModelParameter] = None): + p = self._get_max_width_Parameter( + normalize=True, parameters=parameters + ) + norm_width = self.bounds[p].normalized_width() + return norm_width + def width( self, - normalize={}, - overwrite_cache=False, + normalize=False, parameters: List[ModelParameter] = None, ) -> float: """ @@ -586,14 +601,13 @@ def width( float Max{p: parameter}(p.ub-p.lb) """ - if self.cached_width is None or overwrite_cache: + if normalize: + return self.normalized_width(parameters=parameters) + else: p = self._get_max_width_Parameter( normalize=normalize, parameters=parameters ) - self.cached_width = self.bounds[p].width( - normalize=normalize.get(p, None) - ) - return self.cached_width + return self.bounds[p].width(normalize=normalize) def variance(self, overwrite_cache=False) -> float: """ @@ -631,6 +645,13 @@ def split( p = self._get_max_width_point_Parameter( points, parameters=parameters ) + if ( + p is not None + and self.bounds[p].normalized_width() + < Decimal(0.5) * self.normalized_width() + ): + # Discard selected parameter if its width is much smaller than box width + p = None if p is not None: mid = self.bounds[p].midpoint( points=[[pt.values[p] for pt in grp] for grp in points] @@ -639,28 +660,42 @@ def split( # Fall back to box midpoint if point-based mid is degenerate p = self._get_max_width_Parameter(parameter=parameters) mid = self.bounds[p].midpoint() + if p is None: p = self._get_max_width_Parameter( normalize=normalize, parameters=parameters ) mid = self.bounds[p].midpoint() - # print(f"Split({p}[{self.bounds[p].lb, mid}][{mid, self.bounds[p].ub}])") b1 = self.model_copy(deep=True) b2 = self.model_copy(deep=True) # b1 is lower half assert math_utils.lte(b1.bounds[p].lb, mid) b1.bounds[p] = Interval(lb=b1.bounds[p].lb, ub=mid) - b1.width(overwrite_cache=True) + b1.bounds[p].original_width = self.bounds[p].original_width b1.points = [pt for pt in b1.points if b1.contains_point(pt)] + b1._points_at_step = { + step: [p for p in pts if p in b1.points] + for step, pts in b1._points_at_step.items() + } # b2 is upper half assert math_utils.lte(mid, b2.bounds[p].ub) b2.bounds[p] = Interval(lb=mid, ub=b2.bounds[p].ub) - b2.width(overwrite_cache=True) + b2.bounds[p].original_width = self.bounds[p].original_width b2.points = [pt for pt in b2.points if b2.contains_point(pt)] + b2._points_at_step = { + step: [p for p in pts if p in b2.points] + for step, pts in b2._points_at_step.items() + } + l.info( + f"Split({p}[{self.bounds[p].lb, mid}][{mid, self.bounds[p].ub}])" + ) + l.info( + f"widths: {self.width():.5f} -> {b1.width():.5f} {b2.width():.5f} (raw), {self.normalized_width():.5f} -> {b1.normalized_width():.5f} {b2.normalized_width():.5f} (norm)" + ) return [b2, b1] def intersect(self, b2: "Box", param_list: List[str] = None): diff --git a/src/funman/representation/interval.py b/src/funman/representation/interval.py index 25b86941..ecdabb02 100644 --- a/src/funman/representation/interval.py +++ b/src/funman/representation/interval.py @@ -19,7 +19,7 @@ class Interval(BaseModel): lb: Optional[Union[float, str]] = NEG_INFINITY ub: Optional[Union[float, str]] = POS_INFINITY closed_upper_bound: bool = False - cached_width: Optional[float] = Field(default=None, exclude=True) + original_width: Optional[Decimal] = None @staticmethod def from_value(v: Union[float, str]): @@ -49,9 +49,17 @@ def disjoint(self, other: "Interval") -> bool: def is_point(self) -> bool: return self.lb == self.ub and self.closed_upper_bound - def width( - self, normalize: Optional[Union[Decimal, float]] = None - ) -> Decimal: + def normalized_width(self) -> Decimal: + assert ( + self.original_width is not None + ), f"{self} cannot be normalized without knowing the original width." + return ( + self.width() / self.original_width + if self.original_width > 0.0 + else 0.0 + ) + + def width(self, normalize=False) -> Decimal: """ The width of an interval is ub - lb. @@ -60,16 +68,10 @@ def width( float ub - lb """ - if self.cached_width is None: - self.cached_width = Decimal(self.ub) - Decimal(self.lb) - if normalize is not None: - return ( - self.cached_width / normalize - if normalize > 0 - else Decimal(0.0) - ) + if normalize: + return self.normalized_width() else: - return self.cached_width + return Decimal(self.ub) - Decimal(self.lb) def is_unbound(self) -> bool: return self.lb == NEG_INFINITY and self.ub == POS_INFINITY @@ -330,5 +332,7 @@ def check_interval(self) -> str: f"{self} has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])" ) self.closed_upper_bound = True + if self.original_width is None: + self.original_width = self.width() return self diff --git a/src/funman/representation/parameter.py b/src/funman/representation/parameter.py index e6c95015..8bd7f900 100644 --- a/src/funman/representation/parameter.py +++ b/src/funman/representation/parameter.py @@ -35,6 +35,11 @@ def is_unbound(self) -> bool: def __hash__(self): return abs(hash(self.name)) + @model_validator(mode="after") + def set_interval_original_width(self) -> str: + self.interval.original_width = self.interval.width() + return self + class LabeledParameter(Parameter): label: Literal["any", "all"] = LABEL_ANY diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index 8ad4ce4e..c8b201c4 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -131,8 +131,7 @@ def initialize(self, config: "FUNMANConfig") -> "Search": self._initialize_encodings(config) self._original_parameter_widths = { - p.name: Decimal(math_utils.minus(p.interval.ub, p.interval.lb)) - for p in self.model_parameters() + p.name: p.interval.original_width for p in self.model_parameters() } return search diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index 2f3c1ff8..9b0893b6 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -2,6 +2,7 @@ This module defines the BoxSearch class and supporting classes. """ +import glob import logging import multiprocessing as mp import os @@ -19,7 +20,19 @@ from pydantic import BaseModel, ConfigDict from pysmt.formula import FNode from pysmt.logics import QF_NRA -from pysmt.shortcuts import BOOL, And, Implies, Not, Or, Solver, Symbol +from pysmt.shortcuts import ( + BOOL, + REAL, + And, + Bool, + Equals, + Implies, + Not, + Or, + Real, + Solver, + Symbol, +) from pysmt.solvers.solver import Model as pysmtModel from funman import ( @@ -207,7 +220,7 @@ def _initialize_boxes(self, expander_count, schedule: EncodingSchedule): b = initial_boxes.get() if not self._add_unknown(b): l.exception( - f"Did not find add an initial box (of width {b.width()}), try reducing config.tolerance, currently {self.config.tolerance}" + f"Did not find add an initial box (box had width {b.normalized_width()}), try reducing config.tolerance, currently {self.config.tolerance}" ) # l.debug(f"Initial box: {b}") @@ -232,9 +245,7 @@ def _on_iteration(self): def _add_unknown_box(self, box: Box) -> bool: if ( box.width( - parameters=self.problem.model_parameters(), - normalize=self.problem._original_parameter_widths, - overwrite_cache=True, + parameters=self.problem.model_parameters(), normalize=True ) > self.config.tolerance ): @@ -298,11 +309,11 @@ def _get_unknown(self): self.statistics._num_unknown.value = ( self.statistics._num_unknown.value - 1 ) - self.statistics._current_residual.value = box.width() + self.statistics._current_residual.value = box.normalized_width() else: self.statistics._num_unknown += 1 - self.statistics._current_residual = box.width() - self.statistics._residuals.put(box.width()) + self.statistics._current_residual = box.normalized_width() + self.statistics._residuals.put(box.normalized_width()) this_time = datetime.now() # FIXME self.statistics.iteration_time.put(this_time - self.statistics.last_time.value) # FIXME self.statistics.last_time[:] = str(this_time) @@ -500,10 +511,26 @@ def _initialize_model_encoding( assumptions=episode.problem._assumptions, ) ) + formula_encoded_constraints = And(encoded_constraints) formula = Implies( - self._solve_at_step_symbol(t), And(encoded_constraints) + self._solve_at_step_symbol(t), formula_encoded_constraints + ) + + symbols = formula_encoded_constraints.get_free_variables() + neg_formula = Implies( + Not(self._solve_at_step_symbol(t)), + And( + [ + Equals(s, Real(0.0)) + for s in symbols + if s.symbol_type() == REAL + and str(s) + not in episode.problem.model._parameter_names() + ] # + [Not(s) for s in symbols if s.symbol_type() == BOOL] + ), ) - layer_formulas.append(formula) + + layer_formulas.append(And(formula, neg_formula)) for layer, formula in enumerate(layer_formulas): episode._formula_stack.push(1) @@ -641,14 +668,27 @@ def encoding_step_activation_formula(self, box: Box) -> FNode: ) def store_smtlib(self, episode, box, filename="dbg"): - tmp_name = filename + "_0" - if os.path.exists(tmp_name + ".smt2"): - filename, count = tmp_name.rsplit("_", 1) - count = int(count) + 1 - filename = f"{filename}_{count}" - else: - filename = tmp_name - filename = filename + ".smt2" + iteration_files = glob.glob(filename + "*") + last_index = ( + max( + [ + int(f.rsplit("_", 1)[1].split(".")[0]) + for f in iteration_files + ] + ) + + 1 + if len(iteration_files) > 0 + else "0" + ) + filename = f"{filename}_{last_index}.smt2" + + # if os.path.exists(tmp_name + ".smt2"): + # filename, count = tmp_name.rsplit("_", 1) + # count = int(count) + 1 + # filename = f"{filename}_{count}" + # else: + # filename = tmp_name + # filename = filename + ".smt2" with open(filename, "w") as f: print(f"Saving smtlib file: {filename}") smtlibscript_from_formula_list( @@ -737,7 +777,12 @@ def _get_points( # print("Checking false query") _encoding_fn() if _smtlib_save_fn: - _smtlib_save_fn(filename=f"box_search_{episode._iteration}") + _smtlib_save_fn( + filename=os.path.join( + episode.config.save_smtlib, + f"box_search_{episode._iteration}", + ) + ) result = self.invoke_solver(solver) if result is not None and isinstance(result, pysmtModel): # If substituted formulas are on the stack, then add the original formulas to compute the values of all variables @@ -756,8 +801,7 @@ def _get_points( point = point.denormalize(episode.problem) _point_handler_fn(box, point) # rval.put(point.model_dump()) - if point not in box.points: - box.points.append(point) + box.add_point(point) else: # unsat explanation = result explanation.check_assumptions(episode, my_solver, options) @@ -770,7 +814,7 @@ def _get_false_points( points, explanation = self._get_points( solver, box, - box.false_points(), + box.false_points(step=box.timestep().lb), episode, rval, partial(self._setup_false_query, solver, episode, box, options), @@ -786,7 +830,7 @@ def _get_false_points( else None, ) - return box.false_points(), explanation + return box.false_points(step=box.timestep().lb), explanation def _point_assumptions( self, @@ -906,16 +950,12 @@ def _get_true_points( original_box_timestep_lb = box.timestep().lb found_point = True explanation = None - last_true_point = None - while ( - found_point - and box.timestep().lb <= box.timestep().ub - and len(box.true_points()) == 0 - ): + + while found_point and box.timestep().lb <= box.timestep().ub: points, explanation = self._get_points( solver, box, - box.true_points(), + box.true_points(step=box.timestep().lb), episode, rval, partial(self._setup_true_query, solver, episode, box, options), @@ -926,28 +966,28 @@ def _get_true_points( if episode.config.save_smtlib else None, ) - if len(box.points) == 0: + if len(box.true_points(step=box.timestep().lb)) == 0: + # Could not find a point at the current step, so there won't be any at subsequent steps + # fall out of loop, after setting the upper bound on the box timestep # if couldn't find a point, then remove all points from box found_point = False - box.points = [] - box.timestep().ub = last_true_point.timestep() + + # Cannot find a point at this time, so box.timestep().ub is previous step + # box.timestep().ub = max(box.timestep().lb - 1, 0) elif box.timestep().lb < box.timestep().ub: + # Found a true point, and there are more steps to consider episode._formula_stack.pop() # pop the box constraints - last_true_point = box.true_points()[0] - box.points = [] box.timestep().lb += 1 self._initialize_model_for_box(solver, box, episode, options) + explanation = None + else: + # lb == ub and have a point, so break + break - # At end, we should either have a true point that goes through the last step, - # or have a true point that ends at an earlier time. - if ( - last_true_point - and last_true_point not in box.points - and box.timestep().contains_value(last_true_point.timestep()) - ): - box.points.append(last_true_point) + # reinstate the original lower bound on timestep so that we will check + # whether no false points exist in the main loop of the box search box.timestep().lb = original_box_timestep_lb - return box.true_points(), explanation + return box.true_points(step=box.timestep().lb), explanation def get_box_corners( self, solver, episode, box, rval, options, my_solver @@ -1111,7 +1151,9 @@ def _expand( if more_work: with more_work: more_work.notify_all() - l.debug(f"Split @ {box.timestep().lb}") + l.debug( + f"Split @ {box.timestep().lb}, (width: {box.width():.5f} (raw) {box.normalized_width():.5f} (norm))" + ) l.trace(f"XXX Split:\n{box}") else: # box does not intersect f, so it is in t (true region) diff --git a/src/funman/search/search.py b/src/funman/search/search.py index 3e61ad24..24502a16 100644 --- a/src/funman/search/search.py +++ b/src/funman/search/search.py @@ -2,6 +2,7 @@ import logging import threading from abc import ABC, abstractmethod +from decimal import Decimal from multiprocessing import Array, Queue, Value from queue import Queue as SQueue from typing import Callable, List, Optional, Union @@ -95,6 +96,10 @@ def _initial_box(self, schedule: EncodingSchedule) -> Box: box.bounds["timestep"] = Interval( lb=0, ub=len(schedule.timepoints) - 1, closed_upper_bound=True ) + box.bounds["timestep"].original_width = Decimal( + len(schedule.timepoints) - 1 + ) + return box diff --git a/src/funman/translate/petrinet.py b/src/funman/translate/petrinet.py index d9d6ee93..0562e5bb 100644 --- a/src/funman/translate/petrinet.py +++ b/src/funman/translate/petrinet.py @@ -195,12 +195,14 @@ def _encode_next_step( # If any variables depend upon time, then time updates need to be encoded. if time_var is not None: - time_increment = ( - Plus(current_time_var, Real(step_size)) - .substitute(substitutions) - .simplify() - ) - time_update = Equals(next_time_var, time_increment) + # time_increment = ( + # Plus(current_time_var, Real(step_size)) + # .substitute(substitutions) + # .simplify() + # ) + next_time = Real(next_step) + # time_update = Equals(next_time_var, time_increment) + time_update = Equals(next_time_var, next_time) if self.config.substitute_subformulas: substitutions[next_time_var] = time_increment else: diff --git a/src/funman/utils/sympy_utils.py b/src/funman/utils/sympy_utils.py index f2ac55d8..952fb427 100644 --- a/src/funman/utils/sympy_utils.py +++ b/src/funman/utils/sympy_utils.py @@ -258,7 +258,12 @@ def sympy_to_pysmt_real(expr, numerator_digits=6): def sympy_to_pysmt_symbol(op, expr, op_type=None): - return op(str(expr), op_type) if op_type else op(str(expr)) + s_expr = str(expr) + reserved = [s for s in reserved_words if s in s_expr] + if len(reserved) > 0: + for r in reserved: + s_expr = s_expr.replace(f"funman_{r}", r) + return op(s_expr, op_type) if op_type else op(str(expr)) if __name__ == "__main__": diff --git a/test/test_plot_parameter_space.py b/test/test_plot_parameter_space.py index 9b572218..bdf7dd2b 100644 --- a/test/test_plot_parameter_space.py +++ b/test/test_plot_parameter_space.py @@ -23,12 +23,13 @@ class TestPlotParameterSpace(unittest.TestCase): def test_plot(self): for job in jobs: results_file = os.path.join(RESOURCES, "cached", f"{job}.json") - results: FunmanResults = FunmanResults.parse_file(results_file) - ParameterSpacePlotter( - results.parameter_space, plot_points=True - ).plot(show=False) - plt.savefig(f"{out_dir}/{results.id}.png") - plt.close() + with open(results_file, "r") as f: + results: FunmanResults = FunmanResults.model_validate(f) + ParameterSpacePlotter( + results.parameter_space, plot_points=True + ).plot(show=False) + plt.savefig(f"{out_dir}/{results.id}.png") + plt.close() if __name__ == "__main__": diff --git a/test/test_use_cases.py b/test/test_use_cases.py index 9ad4c592..ec6e3602 100644 --- a/test/test_use_cases.py +++ b/test/test_use_cases.py @@ -182,7 +182,7 @@ def test_use_case_bilayer_parameter_synthesis(self): config=FUNMANConfig( # solver="dreal", # dreal_mcts=True, - save_smtlib="dlp.smt2", + # save_smtlib="./out", dreal_log_level="info", tolerance=1e-3, number_of_processes=1, From 4fb1c5f01edbc4ca222bc2717d446e34fbb28464 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 10 Nov 2023 20:18:44 +0000 Subject: [PATCH 18/28] fix test --- resources/cached/068cef77-839d-49be-9191-3502ee6d5240.json | 1 - test/test_plot_parameter_space.py | 5 ++++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/resources/cached/068cef77-839d-49be-9191-3502ee6d5240.json b/resources/cached/068cef77-839d-49be-9191-3502ee6d5240.json index fbf20a0e..aa089af8 100644 --- a/resources/cached/068cef77-839d-49be-9191-3502ee6d5240.json +++ b/resources/cached/068cef77-839d-49be-9191-3502ee6d5240.json @@ -361,7 +361,6 @@ "num_steps": 2, "step_size": 1, "num_initial_boxes": 1, - "save_smtlib": "my.smt2", "dreal_precision": 0.001, "dreal_log_level": "off", "constraint_noise": 0.0, diff --git a/test/test_plot_parameter_space.py b/test/test_plot_parameter_space.py index bdf7dd2b..20e36279 100644 --- a/test/test_plot_parameter_space.py +++ b/test/test_plot_parameter_space.py @@ -1,3 +1,4 @@ +import json import os import unittest @@ -24,7 +25,9 @@ def test_plot(self): for job in jobs: results_file = os.path.join(RESOURCES, "cached", f"{job}.json") with open(results_file, "r") as f: - results: FunmanResults = FunmanResults.model_validate(f) + results: FunmanResults = FunmanResults.model_validate( + json.load(f) + ) ParameterSpacePlotter( results.parameter_space, plot_points=True ).plot(show=False) From 1c95da167edf5192515d92bcb155cc3643662c77 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Tue, 28 Nov 2023 21:31:53 +0000 Subject: [PATCH 19/28] small improvements to run --- .../hackathon_fall_2023_demo_terarrium.py | 32 +++++++++++++------ src/funman/api/run.py | 7 ++-- src/funman/search/box_search.py | 5 +-- 3 files changed, 30 insertions(+), 14 deletions(-) diff --git a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py index 6e8c45cd..eab4d1ac 100644 --- a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py +++ b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py @@ -177,12 +177,12 @@ def main(): "weights": [1, -2], # No timepoints, because the variables are parameters }, - { - "name": "infected_maximum1", - "variable": "Infected", - "interval": {"lb": 10}, - "timepoints": {"lb": 10, "ub": 10, "closed_upper_bound": True}, - }, + # { + # "name": "infected_maximum1", + # "variable": "Infected", + # "interval": {"lb": 0.0, "ub":0.1}, + # "timepoints": {"lb": 10, "ub": 10, "closed_upper_bound": True}, + # }, # { # "name": "infected_maximum3", # "variable": "Infected", @@ -207,6 +207,20 @@ def main(): "name": "schedules", "schedules": [ { + # "timepoints": [ + # 0, + # 10, + # 30, + # 50, + # 70, + # 90, + # 110, + # 130, + # 150, + # 170, + # 190, + # 210, + # ] "timepoints": [ 0, 10, @@ -229,10 +243,10 @@ def main(): "config": { "use_compartmental_constraints": True, "normalization_constant": 1.0, - "tolerance": 0.01, + "tolerance": 0.1, "verbosity": 10, "dreal_mcts": True, - # "save_smtlib": os.path.join(os.path.realpath(__file__), "./out"), + "save_smtlib": os.path.join(os.path.realpath(__file__), "./out"), "substitute_subformulas": False, "series_approximation_threshold": None, "dreal_log_level": "info", @@ -248,7 +262,7 @@ def main(): # REQUEST_PATH, description="SIDARTHE demo", case_out_dir="./out", - dump_plot=False, + dump_plot=True, parameters_to_plot=["theta", "epsilon", "timestep"], point_plot_config={ "variables": ["Infected"], diff --git a/src/funman/api/run.py b/src/funman/api/run.py index 4afae3fa..97507a90 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -259,11 +259,12 @@ def run_instance( point_plot_filename = ( f"{out_dir}/{work_unit.id}_points.png" ) - l.info( - f"Creating plot of point trajectories: {point_plot_filename}" - ) + points = results.parameter_space.points() if len(points) > 0: + l.info( + f"Creating plot of point trajectories: {point_plot_filename}" + ) points_to_plot = ( random.choices( points, k=min(len(points), num_points) diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index 2f3c1ff8..dc99ca57 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -926,12 +926,13 @@ def _get_true_points( if episode.config.save_smtlib else None, ) - if len(box.points) == 0: + if len(box.true_points()) == 0: # if couldn't find a point, then remove all points from box found_point = False box.points = [] - box.timestep().ub = last_true_point.timestep() + box.timestep().ub = last_true_point.timestep() if last_true_point else box.timestep().lb elif box.timestep().lb < box.timestep().ub: + # found a true point and look forward in time episode._formula_stack.pop() # pop the box constraints last_true_point = box.true_points()[0] box.points = [] From aa3dd13f5785e03a90f8a58f6c5923fd4df32b01 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 1 Dec 2023 16:35:25 +0000 Subject: [PATCH 20/28] avoid plotting parameters against self --- .../funman_demo/parameter_space_plotter.py | 88 ++++++++++++++----- 1 file changed, 67 insertions(+), 21 deletions(-) diff --git a/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py b/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py index 885a585f..c8c788a4 100644 --- a/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py +++ b/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py @@ -62,13 +62,22 @@ def computeBounds(self, interval: Interval = Interval(lb=-2000, ub=2000)): box = Box(bounds={p: interval for p in self.parameters}) return box - def initialize_figure(self): + def map_param_idx_to_plot_loc(self, i, j, plot_diagonal): + if plot_diagonal: + return i, j + elif i == 0 or j == self.dim - 1: + return None, None + else: + return i - 1, j + + def initialize_figure(self, plot_diagonal): if self.dim == 0: return + dim_to_plot = self.dim if plot_diagonal else self.dim - 1 fig, axs = plt.subplots( - self.dim, - self.dim, + dim_to_plot, + dim_to_plot, squeeze=False, dpi=self.dpi, figsize=(10, 10), @@ -91,43 +100,74 @@ def initialize_figure(self): self.fig.tight_layout(pad=3.0) self.data = [[None] * self.dim] * self.dim + for i in range(self.dim): for j in range(self.dim): - if j > i: - axs[i, j].axis("off") + i_coord, j_coord = self.map_param_idx_to_plot_loc( + i, j, plot_diagonal + ) + if i_coord is None or j_coord is None: + continue + + if j_coord > i_coord: + axs[i_coord, j_coord].axis("off") else: - (self.data[i][j],) = self.axs[i, j].plot([], []) - axs[i, j].set_xlabel(f"{self.parameters[i]}") - axs[i, j].set_ylabel(f"{self.parameters[j]}") + (self.data[i][j],) = self.axs[i_coord, j_coord].plot( + [], [] + ) + axs[i_coord, j_coord].set_xlabel(f"{self.parameters[i]}") + axs[i_coord, j_coord].set_ylabel(f"{self.parameters[j]}") self.fig.suptitle(self.title) plt.legend(self.custom_lines, ["true", "false"]) - def plot(self, show=False): - self.initialize_figure() + def plot(self, show=False, plot_diagonal=False): + self.initialize_figure(plot_diagonal) t = "true" f = "false" for b in self.ps.false_boxes: - self.plotNDBox(b, self.color_map[f]) + self.plotNDBox(b, self.color_map[f], plot_diagonal=plot_diagonal) for b in self.ps.true_boxes: - self.plotNDBox(b, self.color_map[t]) + self.plotNDBox(b, self.color_map[t], plot_diagonal=plot_diagonal) if self.plot_points: for p in self.ps.false_points(): - self.plot_add_point(p, self.color_map[f], self.shape_map[f]) + self.plot_add_point( + p, + self.color_map[f], + self.shape_map[f], + plot_diagonal=plot_diagonal, + ) true_points = self.ps.true_points() for p in true_points: - self.plot_add_point(p, self.color_map[t], self.shape_map[t]) + self.plot_add_point( + p, + self.color_map[t], + self.shape_map[t], + plot_diagonal=plot_diagonal, + ) if show: plt.show(block=False) - def plot_add_point(self, point: Point, color="r", shape="x", alpha=0.9): + def plot_add_point( + self, + point: Point, + color="r", + shape="x", + alpha=0.9, + plot_diagonal=False, + ): for i in range(self.dim): for j in range(self.dim): - if i < j: + i_coord, j_coord = self.map_param_idx_to_plot_loc( + i, j, plot_diagonal + ) + if i_coord is None or j_coord is None: + continue + if j_coord > i_coord: continue yval = ( point.values[self.parameters[j]] if self.dim > 1 else 0.0 ) - self.axs[i, j].scatter( + self.axs[i_coord, j_coord].scatter( point.values[self.parameters[i]], yval, color=color, @@ -138,17 +178,23 @@ def plot_add_point(self, point: Point, color="r", shape="x", alpha=0.9): # self.fig.canvas.draw() # self.fig.canvas.flush_events() - def plotNDBox(self, box, color="g", alpha=0.2): + def plotNDBox(self, box, color="g", alpha=0.2, plot_diagonal=False): for i in range(self.dim): for j in range(self.dim): - if i < j: + i_coord, j_coord = self.map_param_idx_to_plot_loc( + i, j, plot_diagonal + ) + if i_coord is None or j_coord is None: continue + if j_coord > i_coord: + continue + x_limits = box.bounds[self.parameters[i]] y_limits = box.bounds[self.parameters[j]] if i == j: # Plot a line segment - self.axs[i, j].plot( + self.axs[i_coord, j_coord].plot( [x_limits.lb, x_limits.ub], [x_limits.lb, x_limits.ub], color=color, @@ -164,7 +210,7 @@ def plotNDBox(self, box, color="g", alpha=0.2): x = np.linspace( float(x_limits.lb), float(x_limits.ub), 1000 ) - self.axs[i, j].fill_between( + self.axs[i_coord, j_coord].fill_between( x, y_limits.lb, y_limits.ub, From e90adad4eae1a118675f885532708978bfb9c882 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 1 Dec 2023 16:35:25 +0000 Subject: [PATCH 21/28] support absolute value --- src/funman/utils/sympy_utils.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/src/funman/utils/sympy_utils.py b/src/funman/utils/sympy_utils.py index 952fb427..992febc3 100644 --- a/src/funman/utils/sympy_utils.py +++ b/src/funman/utils/sympy_utils.py @@ -16,6 +16,7 @@ And, Div, Equals, + Ite, Plus, Pow, Real, @@ -24,7 +25,7 @@ get_env, ) from pysmt.walkers import IdentityDagWalker -from sympy import Add, Expr, Rational, exp, series, symbols, sympify +from sympy import Abs, Add, Expr, Rational, exp, series, symbols, sympify l = logging.getLogger(__name__) @@ -191,6 +192,8 @@ def sympy_to_pysmt(expr): return sympy_to_pysmt_op(Times, expr) elif func.is_Add: return sympy_to_pysmt_op(Plus, expr) + elif isinstance(expr, Abs): + return sympy_to_pysmt_abs(expr) elif func.is_Symbol: return sympy_to_pysmt_symbol(Symbol, expr, op_type=REAL) elif func.is_Pow: @@ -221,6 +224,13 @@ def sympy_to_pysmt_op(op, expr, explode=False): return op(*terms) if explode else op(terms) +def sympy_to_pysmt_abs(expr): + p_expr = sympy_to_pysmt(expr.args[0]) + return Pow( + Pow(p_expr, Real(2.0)), Real(0.5) + ) # Ite(GE(p_expr, Real(0.0)), p_expr, Times(Real(-1.0), p_expr)) + + def sympy_to_pysmt_pow(expr): base = expr.args[0] exponent = expr.args[1] From 788dfa082b009c135532f61d88a0d05e70400308 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 1 Dec 2023 16:35:25 +0000 Subject: [PATCH 22/28] refine plotting --- src/funman/api/run.py | 114 ++++++++++++++++++++++++------------- src/funman/server/query.py | 3 +- 2 files changed, 78 insertions(+), 39 deletions(-) diff --git a/src/funman/api/run.py b/src/funman/api/run.py index 300a1ce5..b184d406 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -249,55 +249,40 @@ def run_instance( sleep(2) # need to sleep until worker has a chance to start working outfile = f"{out_dir}/{work_unit.id}.json" + plotted = False while not killer.kill_now: if self._worker.is_processing_id(work_unit.id): l.info(f"Dumping results to {outfile}") results = self._worker.get_results(work_unit.id) with open(outfile, "w") as f: f.write(results.model_dump_json(by_alias=True)) - if dump_plot: - points = results.parameter_space.points() - if len(points) > 0: - point_plot_filename = ( - f"{out_dir}/{work_unit.id}_points.png" - ) - l.info( - f"Creating plot of point trajectories: {point_plot_filename}" - ) - - points_to_plot = ( - random.choices( - points, k=min(len(points), num_points) - ) - if num_points - else results.parameter_space.points() - ) - results.plot( - points=points_to_plot, **point_plot_config - ) - plt.savefig(point_plot_filename) - plt.close() - - boxes = results.parameter_space.boxes() - if len(boxes) > 0: - space_plot_filename = ( - f"{out_dir}/{work_unit.id}_parameter_space.png" - ) - l.info( - f"Creating plot of parameter space: {space_plot_filename}" - ) - ParameterSpacePlotter( - results.parameter_space, - plot_points=False, - parameters=parameters_to_plot, - ).plot(show=False) - plt.savefig(space_plot_filename) - plt.close() + points = results.parameter_space.points() + boxes = results.parameter_space.boxes() + if dump_plot and (len(points) > 0 or len(boxes) > 0): + plotted = True + self.create_plots( + results, + out_dir, + work_unit, + num_points, + point_plot_config, + parameters_to_plot, + ) sleep(10) else: results = self._worker.get_results(work_unit.id) break + if not plotted and dump_plot: + self.create_plots( + results, + out_dir, + work_unit, + num_points, + point_plot_config, + parameters_to_plot, + ) + if killer.kill_now: l.info( "Requesting that worker stop because received kill signal ..." @@ -313,6 +298,59 @@ def run_instance( return results + def create_plots( + self, + results, + out_dir, + work_unit, + num_points, + point_plot_config, + parameters_to_plot, + ): + points = results.parameter_space.points() + if len(points) > 0: + point_plot_filename = f"{out_dir}/{work_unit.id}_points.png" + l.info( + f"Creating plot of point trajectories: {point_plot_filename}" + ) + + points_to_plot = ( + random.choices( + points, + k=min( + len(points), + ( + num_points + if num_points is not None + else len(points) + ), + ), + ) + if num_points + else results.parameter_space.points() + ) + results.plot(points=points_to_plot, **point_plot_config) + plt.show() + plt.savefig(point_plot_filename) + plt.close() + + boxes = results.parameter_space.boxes() + assert ( + len(parameters_to_plot) > 1 + ), "Cannot plot a parameter space for one parameter" + if len(boxes) > 0 and len(parameters_to_plot) > 1: + space_plot_filename = ( + f"{out_dir}/{work_unit.id}_parameter_space.png" + ) + l.info(f"Creating plot of parameter space: {space_plot_filename}") + ParameterSpacePlotter( + results.parameter_space, + plot_points=False, + parameters=parameters_to_plot, + ).plot(show=True) + plt.savefig(space_plot_filename) + plt.close() + def get_args(): parser = argparse.ArgumentParser() diff --git a/src/funman/server/query.py b/src/funman/server/query.py index a5f08c48..98e82140 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -427,6 +427,7 @@ def plot( label_marker={"true": "+", "false": "o"}, label_color={"true": "g", "false": "r"}, legend=None, + dpi=100, **kwargs, ): """ @@ -448,7 +449,7 @@ def plot( points = self.points() df = self.dataframe(points, max_time=max_time) - fig, ax = plt.subplots(figsize=(8, 6)) + fig, ax = plt.subplots(figsize=(8, 6), dpi=dpi) groups = df.groupby("label") for label, group in groups: if variables is not None: From 3a430bee5a5fc3e16f37906394036f852fa1237b Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 1 Dec 2023 16:35:25 +0000 Subject: [PATCH 23/28] logging --- src/funman/search/box_search.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index 9b0893b6..be10a43b 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -1070,7 +1070,7 @@ def _expand( with my_solver() as solver: episode._formula_stack._solver = solver - l.info(f"{process_name} entering process loop") + l.debug(f"{process_name} entering process loop") # print("Starting initializing dynamics of model") # self._initialize_encoding(solver, episode, [0]) # print("Initialized dynamics of model") From aaa0c0d175c978e334eaed2e24fd7a474ff4e269 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 1 Dec 2023 16:35:25 +0000 Subject: [PATCH 24/28] update unsat core parsing --- .../funman_dreal/src/funman_dreal/converter.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py b/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py index 6acb2973..b6fd9579 100644 --- a/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py +++ b/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py @@ -6,7 +6,7 @@ import dreal from pysmt.decorators import catch_conversion_error from pysmt.formula import FNode -from pysmt.shortcuts import Symbol +from pysmt.shortcuts import FALSE, Symbol from pysmt.smtlib.parser import SmtLibParser, Tokenizer from pysmt.solvers.solver import ( Converter, @@ -59,10 +59,14 @@ def rewrite_dreal_formula(self, formula: dreal.Formula) -> str: # str_formula = str_formula.replace("pow(beta, 2.0)", "beta^2.0") str_formula = re.sub( - r"pow\([\(\)\-a-z0-9\_ ]+\, [0-9.]+\)", - lambda x: x.group().split(",")[0].split("(", 1)[1] + r"pow\([\(\)\-a-z0-9\_\+\*\.\^ ]+\, [\-0-9a-z.]+\)", + lambda x: "(" + + x.group().split(",")[0].split("(", 1)[1] + + ")" + "^" - + x.group().split(",")[1].split(")")[0].strip(), + + "(" + + x.group().split(",")[1].split(")")[0].strip() + + ")", str_formula, ) From 3b129789a26427309a42ac707b4a1f25956cf707 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 1 Dec 2023 16:35:25 +0000 Subject: [PATCH 25/28] cleanup halfar generator --- .../src/funman_demo/generators/halfar.py | 138 ++++++++++++++---- 1 file changed, 108 insertions(+), 30 deletions(-) diff --git a/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py b/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py index 97a76ced..4008a2ae 100644 --- a/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py +++ b/auxiliary_packages/funman_demo/src/funman_demo/generators/halfar.py @@ -1,11 +1,30 @@ +""" +This script will generate instances of the Halfar ice model as Petrinet AMR models. The options control the number of discretization points. +""" + import argparse import os from decimal import Decimal from typing import Dict, List, Tuple -from pydantic import AnyUrl, field_validator - -from funman.model.generated_models.petrinet import * +from pydantic import AnyUrl, BaseModel + +from funman.model.generated_models.petrinet import ( + Distribution, + Header, + Initial, + Model, + Model1, + OdeSemantics, + Parameter, + Properties, + Rate, + Semantics, + State, + Time, + Transition, + Unit, +) from funman.representation.interval import Interval @@ -19,15 +38,26 @@ class Direction: class Coordinate(BaseModel): + """ + Coordinates are N-D points in Cartesian space, as denoted by the vector attribute. The neighbors are coordinates that are at the next point in each direction. + """ + vector: List[float] id: str neighbors: Dict[str, "Coordinate"] = {} class HalfarGenerator: + """ + This generator class constructs the AMR instance. The coordinates are equally spaced across the range. + """ + range: Interval = Interval(lb=-2.0, ub=2.0) def coordinates(self, args) -> List[Coordinate]: + """ + Generate coordinates for the range. + """ coords = [] step_size = value = self.range.width() / Decimal( int(args.num_discretization_points) - 1 @@ -47,13 +77,19 @@ def coordinates(self, args) -> List[Coordinate]: def transition_expression( self, n1_name: str, n2_name: str, negative=False ) -> str: + """ + Custom rate change + """ prefix = "-1*" if negative else "" - return f"{prefix}gamma*({n2_name}-{n1_name})**3*{n1_name}**5" + gamma = "(2/(n+2))*A*(rho*g)**n" + return f"{prefix}{gamma}*(({n2_name}-{n1_name})**3)*({n1_name}**5)" def neighbor_gradient( self, coordinate: Coordinate, coordinates: List[Coordinate] ) -> Tuple[List[Transition], List[Rate]]: - # find a triple of coordinates (n0, n1, n2) that are ordered so that dx is positive + """ + Find a triple of coordinates (n0, n1, n2) that are ordered so that dx = n2-n1 and dx = n1-n0 is positive + """ if ( coordinate.neighbors[Direction.Positive] and coordinate.neighbors[Direction.Negative] @@ -83,12 +119,13 @@ def neighbor_gradient( n0_name = f"h_{n0.id}" n1_name = f"h_{n1.id}" n2_name = f"h_{n2.id}" + h_name = f"h_{coordinate.id}" # tp is the gradient wrt. n2, n1 tp = Transition( id=w_p_name, input=[n2_name, n1_name], - output=[w_p_name], + output=[h_name], properties=Properties(name=w_p_name), ) @@ -96,7 +133,7 @@ def neighbor_gradient( tn = Transition( id=w_n_name, input=[n1_name, n0_name], - output=[w_n_name], + output=[h_name], properties=Properties(name=w_n_name), ) @@ -117,6 +154,9 @@ def neighbor_gradient( return transitions, rates def model(self, args) -> Tuple[Model1, Semantics]: + """ + Generate the AMR Model + """ coordinates = self.coordinates(args) # Create a height variable at each coordinate @@ -127,7 +167,7 @@ def model(self, args) -> Tuple[Model1, Semantics]: transitions = [] rates = [] - for coordinate in coordinates: + for coordinate in coordinates[1:-1]: coord_transitions, trans_rates = self.neighbor_gradient( coordinate, coordinates ) @@ -141,20 +181,49 @@ def model(self, args) -> Tuple[Model1, Semantics]: initials = [ Initial( - target=f"h_{c.id}", expression=f"{1.0/(1.0+abs(c.vector[0]))}" + target=f"h_{c.id}", + expression=( + "0.0" + if (c == coordinates[0] or c == coordinates[-1]) + else f"{1.0/(1.0+abs(c.vector[0]))}" + ), ) for c in coordinates ] parameters = [ Parameter( - id="gamma", - value=1.0, + id="n", + value=3.0, distribution=Distribution( type="StandardUniform1", - parameters={"minimum": 0.0, "maximum": 1.0}, + parameters={"minimum": 3.0, "maximum": 3.0}, ), - ) + ), + Parameter( + id="rho", + value=910.0, + distribution=Distribution( + type="StandardUniform1", + parameters={"minimum": 910.0, "maximum": 910.0}, + ), + ), + Parameter( + id="g", + value=9.8, + distribution=Distribution( + type="StandardUniform1", + parameters={"minimum": 9.8, "maximum": 9.8}, + ), + ), + Parameter( + id="A", + value=1e-16, + distribution=Distribution( + type="StandardUniform1", + parameters={"minimum": 1e-20, "maximum": 1e-12}, + ), + ), ] return Model1(states=states, transitions=transitions), Semantics( @@ -169,13 +238,13 @@ def model(self, args) -> Tuple[Model1, Semantics]: def get_args(): parser = argparse.ArgumentParser() - parser.add_argument( - "-d", - "--dimensions", - default=1, - type=int, - help=f"Number of spatial dimensions", - ) + # parser.add_argument( + # "-d", + # "--dimensions", + # default=1, + # type=int, + # help=f"Number of spatial dimensions", + # ) parser.add_argument( "-p", "--num-discretization-points", @@ -192,25 +261,34 @@ def get_args(): return parser.parse_args() +def header(): + return Header( + name="Halfar Model", + schema_=AnyUrl( + "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.1/petrinet/petrinet_schema.json" + ), + schema_name="petrinet", + description="Halfar as Petrinet model created by Dan Bryce and Drisana Mosiphir", + model_version="0.1", + ) + + def main(): args = get_args() + + assert ( + args.num_discretization_points > 2 + ), "Need to have use at least 3 discretization points to properly define the gradients." + generator = HalfarGenerator() model, semantics = generator.model(args) halfar_model = HalfarModel( - header=Header( - name="Halfar Model", - schema_=AnyUrl( - "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.1/petrinet/petrinet_schema.json" - ), - schema_name="petrinet", - description="Halfar as Petrinet model created by Dan", - model_version="0.1", - ), + header=header(), model=model, semantics=semantics, ) j = halfar_model.model_dump_json(indent=4) - # print(j) + with open(args.outfile, "w") as f: print(f"Writing {os.path.join(os.getcwd(), args.outfile)}") f.write(j) From 5a7d5dbe771485a4215c604aa9270418e465675a Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 1 Dec 2023 16:35:25 +0000 Subject: [PATCH 26/28] move halfar examples --- .../amr/halfar/hand_generated/halfar_5.json | 207 +++++++++++++++ .../hand_generated/halfar_5_one_way.json | 239 ++++++++++++++++++ 2 files changed, 446 insertions(+) create mode 100644 resources/amr/halfar/hand_generated/halfar_5.json create mode 100644 resources/amr/halfar/hand_generated/halfar_5_one_way.json diff --git a/resources/amr/halfar/hand_generated/halfar_5.json b/resources/amr/halfar/hand_generated/halfar_5.json new file mode 100644 index 00000000..7a58e0c2 --- /dev/null +++ b/resources/amr/halfar/hand_generated/halfar_5.json @@ -0,0 +1,207 @@ +{ + "header": { + "name": "Halfar Model", + "schema_": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.1/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Halfar as Petrinet model created by Dan Bryce and Drisana Mosiphir", + "model_version": "0.1" + }, + "properties": null, + "model": { + "states": [ + { + "id": "h_0", + "name": "h_0", + "description": "height at 0", + "grounding": null, + "units": null + }, + { + "id": "h_1", + "name": "h_1", + "description": "height at 1", + "grounding": null, + "units": null + }, + { + "id": "h_2", + "name": "h_2", + "description": "height at 2", + "grounding": null, + "units": null + }, + { + "id": "h_3", + "name": "h_3", + "description": "height at 3", + "grounding": null, + "units": null + }, + { + "id": "h_4", + "name": "h_4", + "description": "height at 4", + "grounding": null, + "units": null + } + ], + "transitions": [ + { + "id": "w_0", + "input": [ + "h_1" + ], + "output": [], + "grounding": null, + "properties": { + "name": "w_0", + "description": null, + "grounding": null + } + }, + { + "id": "w_1", + "input": [ + "h_2" + ], + "output": [ + "h_0" + ], + "grounding": null, + "properties": { + "name": "w_1", + "description": null, + "grounding": null + } + }, + { + "id": "w_2", + "input": [ + "h_3" + ], + "output": [ + "h_1" + ], + "grounding": null, + "properties": { + "name": "w_2", + "description": null, + "grounding": null + } + }, + { + "id": "w_3", + "input": [ + "h_4" + ], + "output": [ + "h_2" + ], + "grounding": null, + "properties": { + "name": "w_3", + "description": null, + "grounding": null + } + }, + { + "id": "w_4", + "input": [], + "output": [ + "h_3" + ], + "grounding": null, + "properties": { + "name": "w_4", + "description": null, + "grounding": null + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "w_0", + "expression": "141850999326.4*A*((abs((h_1)*0.5)**2)*((h_1)*0.5)*(h_0**5))", + "expression_mathml": null + }, + { + "target": "w_1", + "expression": "141850999326.4*A*((abs((h_2-h_0)*0.5)**2)*((h_2-h_0)*0.5)*(h_1**5))", + "expression_mathml": null + }, + { + "target": "w_2", + "expression": "141850999326.4*A*((abs((h_3-h_1)*0.5)**2)*((h_3-h_1)*0.5)*(h_2**5))", + "expression_mathml": null + }, + { + "target": "w_3", + "expression": "141850999326.4*A*((abs((h_4-h_2)*0.5)**2)*((h_4-h_2)*0.5)*(h_3**5))", + "expression_mathml": null + }, + { + "target": "w_4", + "expression": "141850999326.4*A*((abs((-h_3)*0.5)**2)*((-h_3)*0.5)*(h_4**5))", + "expression_mathml": null + } + ], + "initials": [ + { + "target": "h_0", + "expression": "0.25", + "expression_mathml": null + }, + { + "target": "h_1", + "expression": "0.5", + "expression_mathml": null + }, + { + "target": "h_2", + "expression": "1.0", + "expression_mathml": null + }, + { + "target": "h_3", + "expression": "0.5", + "expression_mathml": null + }, + { + "target": "h_4", + "expression": "0.25", + "expression_mathml": null + } + ], + "parameters": [ + { + "id": "A", + "name": null, + "description": null, + "value": 1e-16, + "grounding": null, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 1e-20, + "maximum": 1e-12 + } + }, + "units": null + } + ], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + }, + "typing": null, + "span": null + }, + "metadata": null +} \ No newline at end of file diff --git a/resources/amr/halfar/hand_generated/halfar_5_one_way.json b/resources/amr/halfar/hand_generated/halfar_5_one_way.json new file mode 100644 index 00000000..63e048f5 --- /dev/null +++ b/resources/amr/halfar/hand_generated/halfar_5_one_way.json @@ -0,0 +1,239 @@ +{ + "header": { + "name": "Halfar Model", + "schema_": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.1/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Halfar as Petrinet model created by Dan Bryce and Drisana Mosiphir", + "model_version": "0.1" + }, + "properties": null, + "model": { + "states": [ + { + "id": "h_0", + "name": "h_0", + "description": "height at 0", + "grounding": null, + "units": null + }, + { + "id": "h_1", + "name": "h_1", + "description": "height at 1", + "grounding": null, + "units": null + }, + { + "id": "h_2", + "name": "h_2", + "description": "height at 2", + "grounding": null, + "units": null + }, + { + "id": "h_3", + "name": "h_3", + "description": "height at 3", + "grounding": null, + "units": null + }, + { + "id": "h_4", + "name": "h_4", + "description": "height at 4", + "grounding": null, + "units": null + }, + { + "id": "h_b", + "name": "h_b", + "description": "height at boundary", + "grounding": null, + "units": null + } + ], + "transitions": [ + { + "id": "w_0", + "input": [ + "h_0" + ], + "output": [], + "grounding": null, + "properties": { + "name": "w_0", + "description": null, + "grounding": null + } + }, + { + "id": "w_1", + "input": [ + "h_1" + ], + "output": [ + "h_0" + ], + "grounding": null, + "properties": { + "name": "w_1", + "description": null, + "grounding": null + } + }, + { + "id": "w_2", + "input": [ + "h_2" + ], + "output": [ + "h_1" + ], + "grounding": null, + "properties": { + "name": "w_2", + "description": null, + "grounding": null + } + }, + { + "id": "w_3", + "input": [ + "h_3" + ], + "output": [ + "h_2" + ], + "grounding": null, + "properties": { + "name": "w_3", + "description": null, + "grounding": null + } + }, + { + "id": "w_4", + "input": [ + "h_4" + ], + "output": [ + "h_3" + ], + "grounding": null, + "properties": { + "name": "w_4", + "description": null, + "grounding": null + } + }, + { + "id": "w_b", + "input": [], + "output": [ + "h_b" + ], + "grounding": null, + "properties": { + "name": "w_b", + "description": null, + "grounding": null + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "w_0", + "expression": "283701998652.8*A*((abs(h_1-h_0)**2)*(h_1-h_0)*(h_0**5))", + "expression_mathml": null + }, + { + "target": "w_1", + "expression": "283701998652.8*A*((abs(h_2-h_1)**2)*(h_2-h_1)*(h_1**5))", + "expression_mathml": null + }, + { + "target": "w_2", + "expression": "283701998652.8*A*((abs(h_3-h_2)**2)*(h_3-h_2)*(h_2**5))", + "expression_mathml": null + }, + { + "target": "w_3", + "expression": "283701998652.8*A*((abs(h_4-h_3)**2)*(h_4-h_3)*(h_3**5))", + "expression_mathml": null + }, + { + "target": "w_4", + "expression": "283701998652.8*A*((abs(h_b-h_4)**2)*(h_b-h_4)*(h_4**5))", + "expression_mathml": null + }, + { + "target": "w_b", + "expression": "0", + "expression_mathml": null + } + ], + "initials": [ + { + "target": "h_0", + "expression": "0.0", + "expression_mathml": null + }, + { + "target": "h_1", + "expression": "0.5", + "expression_mathml": null + }, + { + "target": "h_2", + "expression": "1.0", + "expression_mathml": null + }, + { + "target": "h_3", + "expression": "0.5", + "expression_mathml": null + }, + { + "target": "h_4", + "expression": "0.0", + "expression_mathml": null + }, + { + "target": "h_b", + "expression": "0.0", + "expression_mathml": null + } + ], + "parameters": [ + { + "id": "A", + "name": null, + "description": null, + "value": 1e-16, + "grounding": null, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 1e-20, + "maximum": 1e-12 + } + }, + "units": null + } + ], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + }, + "typing": null, + "span": null + }, + "metadata": null +} \ No newline at end of file From a7f324ce837d3ef86f6a28673316d9c429f8eb4c Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 1 Dec 2023 16:35:25 +0000 Subject: [PATCH 27/28] fix core parsing and handling abs, use pysmt fork --- .../src/funman_dreal/converter.py | 85 +- requirements-dev-extras.txt | 2 +- .../hackathon_fall_2023_demo_halfar.ipynb | 987 ++++-------------- setup.py | 3 +- src/funman/search/box_search.py | 2 +- src/funman/translate/translate.py | 14 +- src/funman/utils/sympy_utils.py | 11 +- 7 files changed, 294 insertions(+), 810 deletions(-) diff --git a/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py b/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py index b6fd9579..ba070fe5 100644 --- a/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py +++ b/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py @@ -6,6 +6,17 @@ import dreal from pysmt.decorators import catch_conversion_error from pysmt.formula import FNode +from pysmt.parsing import ( + ClosePar, + Constant, + GrammarSymbol, + HRLexer, + Identifier, + InfixOpAdapter, + PrattParser, + Rule, + UnaryOpAdapter, +) from pysmt.shortcuts import FALSE, Symbol from pysmt.smtlib.parser import SmtLibParser, Tokenizer from pysmt.solvers.solver import ( @@ -18,8 +29,70 @@ from pysmt.walkers import DagWalker +def CoreParser(env=None): + return PrattParser(CoreLexer) + + +class BinaryLiteralExpr(GrammarSymbol): + """Adapter for unary operator.""" + + def __init__(self, operator, lbp): + GrammarSymbol.__init__(self) + self.operator = operator + self.lbp = lbp + + def nud(self, parser): + parser.advance() # OpenPar + parser.advance() # OpenPar + if type(parser.token) != ClosePar: + r = parser.expression() + if type(parser.token) != ClosePar: + raise SyntaxError("Expected ')'") + parser.advance() # ClosePar + if type(parser.token) != ClosePar: + raise SyntaxError("Expected ')'") + parser.advance() # ClosePar + return r + + def __repr__(self): + return repr(self.operator) + + +class CoreLexer(HRLexer): + def __init__(self, env=None): + super().__init__(env=env) + + self.identifier_map = {"and": "&", "or": "|", "==": "="} + + self.rules = [ + Rule(r"(pow)", InfixOpAdapter(self.mgr.Pow, 80), False), # pow + Rule( + r"(and)", InfixOpAdapter(self.AndOrBVAnd, 40), False + ), # conjunction + Rule( + r"(or)", InfixOpAdapter(self.OrOrBVOr, 30), False + ), # disjunction + Rule( + r"(b)", BinaryLiteralExpr(self.BinaryLiteral, 50), False + ), # b() + Rule(r"(==)", InfixOpAdapter(self.mgr.Equals, 60), False), # eq + ] + self.rules + self.compile() + + def BinaryLiteral(self, x): + return x + + def int_constant(self, read): + return Constant(self.mgr.Real(int(read))) + + def identifier(self, read): + r = self.identifier_map.get(read, read) + return Identifier(r, env=self.env) + + class DRealConverter(Converter, DagWalker): def __init__(self, environment): + # self.__setattr__("walk_abs", walk_abs) DagWalker.__init__(self, environment) self.backconversion = {} self.mgr = environment.formula_manager @@ -81,12 +154,10 @@ def create_dreal_symbols(self, rewritten_formula: str) -> List[Symbol]: return symbols def back(self, dreal_formula: dreal.Formula) -> FNode: - from pysmt.parsing import parse - try: - rewritten_formula = self.rewrite_dreal_formula(dreal_formula) - new_symbols = self.create_dreal_symbols(rewritten_formula) - formula = parse(rewritten_formula) + # rewritten_formula = self.rewrite_dreal_formula(dreal_formula) + new_symbols = self.create_dreal_symbols(str(dreal_formula)) + formula = CoreParser().parse(str(dreal_formula)) except Exception as e: raise e return formula @@ -163,6 +234,10 @@ def walk_pow(self, formula, args, **kwargs): res = dreal.pow(args[0], exponent) return res + def walk_abs(self, formula, args, **kwargs): + res = abs(args[0]) + return res + def bool_to_formula(self, value): if isinstance(value, bool): value = dreal.Formula.TRUE() if value else dreal.Formula.FALSE() diff --git a/requirements-dev-extras.txt b/requirements-dev-extras.txt index e751ddff..33e039a6 100644 --- a/requirements-dev-extras.txt +++ b/requirements-dev-extras.txt @@ -1,7 +1,7 @@ fastapi multiprocess openapi-python-client -pysmt +git+https://github.com/danbryce/pysmt@add-abs-value#egg=pysmt uvicorn -e ./ diff --git a/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb b/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb index eef8881b..c57b6a79 100644 --- a/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb +++ b/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb @@ -25,9 +25,7 @@ "REQUEST_PATH = os.path.join(EXAMPLE_DIR, \"halfar_request.json\")\n", "\n", "\n", - "def summarize_results(num_disc, results):\n", - " variables = [f\"h_{d}\" for d in range(num_disc)]\n", - " \n", + "def summarize_results(variables, results):\n", " points = results.points()\n", " boxes = results.parameter_space.boxes()\n", "\n", @@ -38,12 +36,11 @@ " point: Point = points[-1]\n", " parameters: Dict[Parameter, float] = results.point_parameters(point)\n", " results.plot(variables=variables, label_marker={\"true\":\",\", \"false\": \",\"}, xlabel=\"Time\", ylabel=\"Height\", legend=variables,label_color={\"true\": \"g\", \"false\":\"r\"})\n", - " print(f\"gamma = {results.parameter_space.points()[0].values['gamma']:.5f}\")\n", + " print(f\"A = {results.parameter_space.points()[0].values['A']:.5f}\")\n", " print(parameters)\n", " print(results.dataframe([point]))\n", " else:\n", " # if there are no points, then we have a box that we found without needing points\n", - "\n", " box = boxes[0]\n", " print(json.dumps(box.explain(), indent=4))\n", "\n", @@ -58,69 +55,204 @@ "metadata": {}, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving smtlib file: ./out/box_search_0_128.smt2\n", + "Saving smtlib file: ./out/box_search_0_129.smt2\n", + "Saving smtlib file: ./out/box_search_0_130.smt2\n", + "Saving smtlib file: ./out/box_search_0_131.smt2\n", + "Saving smtlib file: ./out/box_search_0_132.smt2\n" + ] }, { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-01 16:28:36,642 - funman.api.run - INFO - Dumping results to ./out/d06c4301-7759-40f1-9a11-f39f88e6fdef.json\n", + "2023-12-01 16:28:36,651 - funman.search.box_search - ERROR - Traceback (most recent call last):\n", + " File \"/home/danbryce/funman/src/funman/search/box_search.py\", line 1118, in _expand\n", + " ) = self._get_true_points(\n", + " File \"/home/danbryce/funman/src/funman/search/box_search.py\", line 955, in _get_true_points\n", + " points, explanation = self._get_points(\n", + " File \"/home/danbryce/funman/src/funman/search/box_search.py\", line 786, in _get_points\n", + " result = self.invoke_solver(solver)\n", + " File \"/home/danbryce/funman/src/funman/search/search.py\", line 122, in invoke_solver\n", + " result = s.solve()\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/pysmt/decorators.py\", line 64, in clear_pending_pop_wrap\n", + " return f(self, *args, **kwargs)\n", + " File \"/home/danbryce/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 626, in solve\n", + " raise e\n", + " File \"/home/danbryce/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 624, in solve\n", + " ans = self.check_sat()\n", + " File \"/home/danbryce/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 558, in check_sat\n", + " result = self.context.CheckSat()\n", + "RuntimeError: KeyboardInterrupt(SIGINT) Detected.\n", + "\n", + "2023-12-01 16:28:36,655 - funman.server.worker - INFO - Completed work on: d06c4301-7759-40f1-9a11-f39f88e6fdef\n", + "2023-12-01 16:28:46,660 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2023-12-01 16:28:46,691 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2023-12-01 16:28:46,693 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Points (+:0, -:0), 0 Boxes (+:0, -:0)\n" + ] + }, + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/danbryce/funman/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb Cell 2\u001b[0m line \u001b[0;36m8\n\u001b[1;32m 71\u001b[0m \u001b[39m# Use request_dict\u001b[39;00m\n\u001b[1;32m 72\u001b[0m results \u001b[39m=\u001b[39m Runner()\u001b[39m.\u001b[39mrun(\n\u001b[1;32m 73\u001b[0m MODEL_PATH,\n\u001b[1;32m 74\u001b[0m request_dict,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 82\u001b[0m \n\u001b[1;32m 83\u001b[0m )\n\u001b[0;32m---> 85\u001b[0m summarize_results(variables, results)\n", + "\u001b[1;32m/home/danbryce/funman/scratch/notebooks/hackathon_fall_2023_demo_halfar.ipynb Cell 2\u001b[0m line \u001b[0;36m3\n\u001b[1;32m 33\u001b[0m \u001b[39mprint\u001b[39m(results\u001b[39m.\u001b[39mdataframe([point]))\n\u001b[1;32m 34\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 35\u001b[0m \u001b[39m# if there are no points, then we have a box that we found without needing points\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m box \u001b[39m=\u001b[39m boxes[\u001b[39m0\u001b[39;49m]\n\u001b[1;32m 37\u001b[0m \u001b[39mprint\u001b[39m(json\u001b[39m.\u001b[39mdumps(box\u001b[39m.\u001b[39mexplain(), indent\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m))\n", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] } ], "source": [ "# Use a five point model with no constraints\n", "\n", "num_disc = 5\n", - "MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", "\n", + "MODEL_PATH = os.path.join(\"../../resources/amr/halfar/hand_generated\", f\"halfar_{num_disc}.json\")\n", + "\n", + "locations = list(range(num_disc))\n", + "height_bounds = [\n", + " {\"name\": f\"pos_h_{i}\",\n", + " \"variable\": f\"h_{i}\",\n", + " \"interval\": {\"lb\": 0, \"ub\": 1.01}\n", + " }\n", + " for i in locations\n", + "]\n", "\n", "request_dict = {\n", " \"structure_parameters\": [\n", " {\n", " \"name\": \"schedules\",\n", " \"schedules\": [\n", - " {\"timepoints\": range(0, 50, 1)}\n", + " {\"timepoints\": range(0,10, 1)}\n", " ],\n", " },\n", " \n", " ],\n", " \"parameters\":[\n", - " {\"name\": \"gamma\",\n", + " {\"name\": \"A\",\n", " \"label\":\"all\",\n", - " \"interval\": {\"lb\":0, \"ub\":0.5}}\n", + " \"interval\": {\"lb\":1e-20, \"ub\":1e-5}}\n", + " # \"interval\": {\"lb\":0, \"ub\":1}}\n", " ],\n", - " \"constraints\": [\n", - " {\"name\": \"pos_h_0\",\n", - " \"variable\": \"h_0\",\n", - " \"interval\": {\"lb\": 0}\n", + " \"constraints\": height_bounds + \n", + " [\n", + " # {\"name\": \"LHS_slope\",\n", + " # \"variables\": [\"h_1\", \"h_0\"],\n", + " # \"weights\": [1, -1],\n", + " # \"additive_bounds\": {\"lb\": 0},\n", + " # \"timepoints\": {\"lb\": 0}\n", + " # }, \n", + " # {\"name\": \"RHS_slope\",\n", + " # \"variables\": [\"h_3\", \"h_4\"],\n", + " # \"weights\": [1, -1],\n", + " # \"additive_bounds\": {\"lb\": 0},\n", + " # \"timepoints\": {\"lb\": 0}\n", + " # }\n", + "\n", + "\n", + " # {\"name\": \"melt_h_5\",\n", + " # \"variable\": \"h_5\",\n", + " # \"interval\": {\"lb\": 0, \"ub\": .8},\n", + " # \"timepoints\": {\"lb\": 5}\n", + " # },\n", + "\n", + " ],\n", + " \"config\": {\n", + " \"use_compartmental_constraints\": False,\n", + " \"normalization_constant\": 1.0,\n", + " \"tolerance\": 1e-3,\n", + " \"verbosity\": 20,\n", + " # \"dreal_mcts\": False,\n", + " \"dreal_precision\": 0.1,\n", + " \"save_smtlib\": \"./out\",\n", + " \"substitute_subformulas\": False,\n", + " \"series_approximation_threshold\": None,\n", + " \"dreal_log_level\": \"none\",\n", + " \"profile\": False,\n", + " },\n", + "}\n", + "variables = [f\"h_{d}\" for d in range(num_disc)]\n", + "\n", + "# Use request_dict\n", + "results = Runner().run(\n", + " MODEL_PATH,\n", + " request_dict,\n", + " # REQUEST_PATH,\n", + " description=\"Halfar demo\",\n", + " case_out_dir=\"./out\",\n", + " dump_plot=True,\n", + " parameters_to_plot=[\"A\", \"timestep\"],\n", + " point_plot_config={\"variables\":variables, \"label_marker\":{\"true\":\",\", \"false\": \",\"}, \"xlabel\":\"Time\", \"ylabel\":\"Height\", \"legend\":variables,\"label_color\":{\"true\": \"g\", \"false\":\"r\"}},\n", + " num_points=None,\n", + " \n", + ")\n", + "\n", + "summarize_results(variables, results)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.dataframe(points=results.parameter_space.points()[4:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use a five point model with no constraints\n", + "\n", + "num_disc = 10\n", + "\n", + "MODEL_PATH = os.path.join(\"../..\", f\"halfar_{num_disc}.json\")\n", + "\n", + "height_bounds = [\n", + " {\"name\": f\"pos_h_{i}\",\n", + " \"variable\": f\"h_{i}\",\n", + " \"interval\": {\"lb\": 0, \"ub\": 1.01}\n", " },\n", - " {\"name\": \"pos_h_1\",\n", - " \"variable\": \"h_1\",\n", - " \"interval\": {\"lb\": 0}\n", - " },\n", - " {\"name\": \"pos_h_2\",\n", - " \"variable\": \"h_2\",\n", - " \"interval\": {\"lb\": 0}\n", - " },\n", - " {\"name\": \"pos_h_3\",\n", - " \"variable\": \"h_3\",\n", - " \"interval\": {\"lb\": 0}\n", - " },\n", - " {\"name\": \"pos_h_4\",\n", - " \"variable\": \"h_4\",\n", - " \"interval\": {\"lb\": 0}\n", + " for i in range(num_disc)\n", + "]\n", + "\n", + "request_dict = {\n", + " \"structure_parameters\": [\n", + " {\n", + " \"name\": \"schedules\",\n", + " \"schedules\": [\n", + " {\"timepoints\": range(0, 10, 1)}\n", + " ],\n", " },\n", + " \n", + " ],\n", + " \"parameters\":[\n", + " {\"name\": \"A\",\n", + " \"label\":\"all\",\n", + " # \"interval\": {\"lb\":1e-18, \"ub\":1e-14}}\n", + " \"interval\": {\"lb\":0, \"ub\":1}}\n", + " ],\n", + " \"constraints\": \n", + " height_bounds + [\n", + " \n", " {\"name\": \"LHS_slope\",\n", " \"variables\": [\"h_1\", \"h_0\"],\n", " \"weights\": [1, -1],\n", @@ -128,7 +260,7 @@ " \"timepoints\": {\"lb\": 0}\n", " }, \n", " {\"name\": \"RHS_slope\",\n", - " \"variables\": [\"h_3\", \"h_4\"],\n", + " \"variables\": [\"h_8\", \"h_9\"],\n", " \"weights\": [1, -1],\n", " \"additive_bounds\": {\"lb\": 0},\n", " \"timepoints\": {\"lb\": 0}\n", @@ -146,9 +278,9 @@ " \"use_compartmental_constraints\": False,\n", " \"normalization_constant\": 1.0,\n", " \"tolerance\": 1e-5,\n", - " \"verbosity\": 30,\n", + " \"verbosity\": 10,\n", " \"dreal_mcts\": True,\n", - " \"dreal_precision\": 1,\n", + " # \"dreal_precision\": 1,\n", " # \"save_smtlib\": \"halfar.smt2\",\n", " \"substitute_subformulas\": False,\n", " \"series_approximation_threshold\": None,\n", @@ -157,13 +289,6 @@ " },\n", "}\n", "variables = [f\"h_{d}\" for d in range(num_disc)]\n", - " \n", - "# points = results.points()\n", - "# boxes = results.parameter_space.boxes()\n", - "\n", - "# print(\n", - "# f\"{len(points)} Points (+:{len(results.parameter_space.true_points())}, -:{len(results.parameter_space.false_points())}), {len(boxes)} Boxes (+:{len(results.parameter_space.true_boxes)}, -:{len(results.parameter_space.false_boxes)})\"\n", - "# )\n", "\n", "# Use request_dict\n", "results = Runner().run(\n", @@ -173,66 +298,21 @@ " description=\"Halfar demo\",\n", " case_out_dir=\"./out\",\n", " dump_plot=True,\n", - " parameters_to_plot=[\"gamma\", \"timestep\"],\n", + " parameters_to_plot=[\"A\", \"timestep\"],\n", " point_plot_config={\"variables\":variables, \"label_marker\":{\"true\":\",\", \"false\": \",\"}, \"xlabel\":\"Time\", \"ylabel\":\"Height\", \"legend\":variables,\"label_color\":{\"true\": \"g\", \"false\":\"r\"}},\n", - " num_points=1\n", + " num_points=None\n", ")\n", - "# summarize_results(num_disc, results)\n", + "\n", + "summarize_results(variables, results)\n", "\n", "\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-11-03 19:50:53,341 - funman.search.box_search - ERROR - Traceback (most recent call last):\n", - " File \"/root/funman/src/funman/search/box_search.py\", line 1013, in _expand\n", - " ) = self._get_false_points(\n", - " File \"/root/funman/src/funman/search/box_search.py\", line 731, in _get_false_points\n", - " points, explanation = self._get_points(\n", - " File \"/root/funman/src/funman/search/box_search.py\", line 702, in _get_points\n", - " result = self.invoke_solver(solver)\n", - " File \"/root/funman/src/funman/search/search.py\", line 117, in invoke_solver\n", - " result = s.solve()\n", - " File \"/root/funman_venv/lib/python3.8/site-packages/pysmt/decorators.py\", line 64, in clear_pending_pop_wrap\n", - " return f(self, *args, **kwargs)\n", - " File \"/root/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 626, in solve\n", - " raise e\n", - " File \"/root/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 624, in solve\n", - " ans = self.check_sat()\n", - " File \"/root/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 558, in check_sat\n", - " result = self.context.CheckSat()\n", - "RuntimeError: KeyboardInterrupt(SIGINT) Detected.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Use a five point model with no constraints\n", "\n", @@ -339,20 +419,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Use a ten point model with no constraints\n", "\n", @@ -461,30 +530,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Use a ten point model with no constraints\n", "\n", @@ -633,196 +681,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-11-02 19:51:22,577 - funman.server.worker - INFO - FunmanWorker running...\n", - "2023-11-02 19:51:22,583 - funman.server.worker - INFO - Starting work on: bd259d64-f8bf-4714-b82c-df42991e7149\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-11-02 19:51:22,660 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 1, 2, 3, 4, 5, 6, 7]\n", - "2023-11-02 19:51:22,663 - funman_dreal.solver - DEBUG - Created new Solver ...\n", - "2023-11-02 19:51:22,680 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 1\n", - "2023-11-02 19:51:22,858 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 1 to 2\n", - "2023-11-02 19:51:22,996 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 2 to 3\n", - "2023-11-02 19:51:23,118 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 3 to 4\n", - "2023-11-02 19:51:23,201 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 4 to 5\n", - "2023-11-02 19:51:23,284 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 5 to 6\n", - "2023-11-02 19:51:23,367 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 6 to 7\n", - "2023-11-02 19:51:23,896 - /root/funman/src/funman/search/smt_check.py - DEBUG - Result: {\n", - " \"assume_non-negative_h_0_7\": true,\n", - " \"gamma\": 0.32624452747923555,\n", - " \"assume_non-negative_h_1_7\": true,\n", - " \"timer_t_0\": 0.0,\n", - " \"assume_non-negative_h_2_7\": true,\n", - " \"h_0_0\": 0.1,\n", - " \"h_1_0\": 0.5,\n", - " \"assume_non-negative_h_3_7\": true,\n", - " \"h_2_0\": 1.0,\n", - " \"h_3_0\": 0.5,\n", - " \"assume_non-negative_h_0\": true,\n", - " \"assume_non-negative_h_1\": true,\n", - " \"assume_non-negative_h_2\": true,\n", - " \"assume_non-negative_h_3\": true,\n", - " \"assume_non-negative_h_4\": true,\n", - " \"h_4_0\": 0.1,\n", - " \"assume_non-negative_h_4_7\": true,\n", - " \"h_0_1\": 0.10254857657443395,\n", - " \"h_1_1\": 0.41626665801877544,\n", - " \"h_2_1\": 0.9986949290710601,\n", - " \"h_3_1\": 0.50065253546447,\n", - " \"h_4_1\": 0.1822194685232148,\n", - " \"timer_t_1\": 1.0,\n", - " \"h_0_5\": 0.10578810416480225,\n", - " \"h_1_5\": 0.14531901074692868,\n", - " \"h_2_5\": 0.9975637595928312,\n", - " \"h_3_5\": 0.5012181202035845,\n", - " \"assume_non-negative_h_0_0\": true,\n", - " \"h_4_5\": 0.46194733879600575,\n", - " \"timer_t_5\": 5.0,\n", - " \"assume_non-negative_h_1_0\": true,\n", - " \"assume_non-negative_h_2_0\": true,\n", - " \"assume_non-negative_h_3_0\": true,\n", - " \"assume_non-negative_h_4_0\": true,\n", - " \"assume_non-negative_h_0_1\": true,\n", - " \"assume_non-negative_h_1_1\": true,\n", - " \"assume_non-negative_h_2_1\": true,\n", - " \"assume_non-negative_h_3_1\": true,\n", - " \"assume_non-negative_h_4_1\": true,\n", - " \"assume_non-negative_h_0_2\": true,\n", - " \"h_0_2\": 0.10424001021977046,\n", - " \"h_1_2\": 0.34708115377370463,\n", - " \"h_2_2\": 0.9980177247274781,\n", - " \"assume_non-negative_h_1_2\": true,\n", - " \"h_3_2\": 0.500991137636261,\n", - " \"h_4_2\": 0.2610201985835755,\n", - " \"timer_t_2\": 2.0,\n", - " \"h_0_6\": 0.10581993875703097,\n", - " \"h_1_6\": 0.07868342238159265,\n", - " \"assume_non-negative_h_2_2\": true,\n", - " \"h_2_6\": 0.9975587743114674,\n", - " \"h_3_6\": 0.5012206128442662,\n", - " \"h_4_6\": 0.5285691432331976,\n", - " \"timer_t_6\": 6.0,\n", - " \"assume_non-negative_h_3_2\": true,\n", - " \"assume_non-negative_h_4_2\": true,\n", - " \"assume_non-negative_h_0_3\": true,\n", - " \"assume_non-negative_h_1_3\": true,\n", - " \"assume_non-negative_h_2_3\": true,\n", - " \"assume_non-negative_h_3_3\": true,\n", - " \"assume_non-negative_h_4_3\": true,\n", - " \"assume_non-negative_h_0_4\": true,\n", - " \"h_0_3\": 0.10519878467955732,\n", - " \"h_1_3\": 0.279208677138237,\n", - " \"h_2_3\": 0.9977130460164052,\n", - " \"h_3_3\": 0.5011434769917975,\n", - " \"h_4_3\": 0.32837141267812897,\n", - " \"timer_t_3\": 3.0,\n", - " \"assume_non-negative_h_1_4\": true,\n", - " \"assume_non-negative_h_2_4\": true,\n", - " \"h_0_7\": 0.10582207239864025,\n", - " \"h_1_7\": 0.0,\n", - " \"h_2_7\": 0.9975602843823834,\n", - " \"h_3_7\": 0.5012198578088083,\n", - " \"assume_non-negative_h_3_4\": true,\n", - " \"h_4_7\": 0.5951964503276509,\n", - " \"timer_t_7\": 7.0,\n", - " \"assume_non-negative_h_4_4\": true,\n", - " \"assume_non-negative_h_0_5\": true,\n", - " \"assume_non-negative_h_1_5\": true,\n", - " \"assume_non-negative_h_2_5\": true,\n", - " \"assume_non-negative_h_3_5\": true,\n", - " \"assume_non-negative_h_4_5\": true,\n", - " \"assume_non-negative_h_0_6\": true,\n", - " \"assume_non-negative_h_1_6\": true,\n", - " \"h_0_4\": 0.10563891204503173,\n", - " \"h_1_4\": 0.21208725493695835,\n", - " \"h_2_4\": 0.9975945302506836,\n", - " \"h_3_4\": 0.5012027348746582,\n", - " \"h_4_4\": 0.395256217644169,\n", - " \"timer_t_4\": 4.0,\n", - " \"assume_non-negative_h_2_6\": true,\n", - " \"assume_non-negative_h_3_6\": true,\n", - " \"assume_non-negative_h_4_6\": true\n", - "}\n", - "2023-11-02 19:51:23,901 - funman.scenario.consistency - INFO - 7{7}:\t[+]\n", - "2023-11-02 19:51:23,903 - funman.server.worker - INFO - Completed work on: bd259d64-f8bf-4714-b82c-df42991e7149\n", - "2023-11-02 19:51:24,585 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2023-11-02 19:51:24,907 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2023-11-02 19:51:24,910 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 Points (+:1, -:0), 1 Boxes (+:1, -:0)\n", - "gamma = 0.32624\n", - "{}\n", - " assume_non-negative_h_0 assume_non-negative_h_1 \\\n", - "time \n", - "0.0 1.0 1.0 \n", - "1.0 1.0 1.0 \n", - "2.0 1.0 1.0 \n", - "3.0 1.0 1.0 \n", - "4.0 1.0 1.0 \n", - "5.0 1.0 1.0 \n", - "6.0 1.0 1.0 \n", - "7.0 1.0 1.0 \n", - "\n", - " assume_non-negative_h_2 assume_non-negative_h_3 \\\n", - "time \n", - "0.0 1.0 1.0 \n", - "1.0 1.0 1.0 \n", - "2.0 1.0 1.0 \n", - "3.0 1.0 1.0 \n", - "4.0 1.0 1.0 \n", - "5.0 1.0 1.0 \n", - "6.0 1.0 1.0 \n", - "7.0 1.0 1.0 \n", - "\n", - " assume_non-negative_h_4 h_0 h_1 h_2 h_3 \\\n", - "time \n", - "0.0 1.0 0.100000 0.500000 1.000000 0.500000 \n", - "1.0 1.0 0.102549 0.416267 0.998695 0.500653 \n", - "2.0 1.0 0.104240 0.347081 0.998018 0.500991 \n", - "3.0 1.0 0.105199 0.279209 0.997713 0.501143 \n", - "4.0 1.0 0.105639 0.212087 0.997595 0.501203 \n", - "5.0 1.0 0.105788 0.145319 0.997564 0.501218 \n", - "6.0 1.0 0.105820 0.078683 0.997559 0.501221 \n", - "7.0 1.0 0.105822 0.000000 0.997560 0.501220 \n", - "\n", - " h_4 id label \n", - "time \n", - "0.0 0.100000 0 true \n", - "1.0 0.182219 0 true \n", - "2.0 0.261020 0 true \n", - "3.0 0.328371 0 true \n", - "4.0 0.395256 0 true \n", - "5.0 0.461947 0 true \n", - "6.0 0.528569 0 true \n", - "7.0 0.595196 0 true \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Use a five point model with no constraints\n", "\n", @@ -917,208 +778,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-11-02 19:52:46,225 - funman.server.worker - INFO - FunmanWorker running...\n", - "2023-11-02 19:52:46,234 - funman.server.worker - INFO - Starting work on: 49fae446-97b0-41b1-ad13-4cf076fdfd49\n", - "2023-11-02 19:52:46,339 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 1, 2, 3, 4, 5, 6, 7]\n", - "2023-11-02 19:52:46,342 - funman_dreal.solver - DEBUG - Created new Solver ...\n", - "2023-11-02 19:52:46,365 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 1\n", - "2023-11-02 19:52:46,558 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 1 to 2\n", - "2023-11-02 19:52:46,710 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 2 to 3\n", - "2023-11-02 19:52:46,846 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 3 to 4\n", - "2023-11-02 19:52:46,991 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 4 to 5\n", - "2023-11-02 19:52:47,117 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 5 to 6\n", - "2023-11-02 19:52:47,207 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 6 to 7\n", - "2023-11-02 19:52:47,976 - /root/funman/src/funman/search/smt_check.py - DEBUG - Result: {\n", - " \"assume_non-negative_h_0_7\": true,\n", - " \"gamma\": 0.11338518015152209,\n", - " \"assume_non-negative_h_1_7\": true,\n", - " \"timer_t_0\": 0.0,\n", - " \"assume_non-negative_h_2_7\": true,\n", - " \"h_0_0\": 0.1,\n", - " \"h_1_0\": 0.5,\n", - " \"assume_non-negative_h_3_7\": true,\n", - " \"h_2_0\": 1.0,\n", - " \"h_3_0\": 0.5,\n", - " \"assume_non-negative_h_0\": true,\n", - " \"assume_non-negative_h_1\": true,\n", - " \"assume_non-negative_h_2\": true,\n", - " \"assume_non-negative_h_3\": true,\n", - " \"assume_non-negative_h_4\": true,\n", - " \"h_4_0\": 0.1,\n", - " \"assume_non-negative_h_4_7\": true,\n", - " \"h_0_1\": 0.10088574915341848,\n", - " \"h_1_1\": 0.470767955808701,\n", - " \"h_2_1\": 0.9995464592793939,\n", - " \"h_3_1\": 0.500226770360303,\n", - " \"h_4_1\": 0.12857306539818358,\n", - " \"timer_t_1\": 1.0,\n", - " \"h_0_5\": 0.10406190969959184,\n", - " \"h_1_5\": 0.38943248327309743,\n", - " \"h_2_5\": 0.9983807112198245,\n", - " \"h_3_5\": 0.5008096443900878,\n", - " \"assume_non-negative_h_0_0\": true,\n", - " \"h_4_5\": 0.2621967315142081,\n", - " \"timer_t_5\": 5.0,\n", - " \"assume_non-negative_h_1_0\": true,\n", - " \"assume_non-negative_h_2_0\": true,\n", - " \"assume_non-negative_h_3_0\": true,\n", - " \"assume_non-negative_h_4_0\": true,\n", - " \"assume_LHS_slope (h_0 <= h_1)\": true,\n", - " \"assume_RHS_slope (h_3 >= h_4)\": true,\n", - " \"assume_non-negative_h_0_1\": true,\n", - " \"assume_LHS_slope (h_0 <= h_1)_0\": true,\n", - " \"assume_non-negative_h_1_1\": true,\n", - " \"assume_RHS_slope (h_3 >= h_4)_0\": true,\n", - " \"assume_non-negative_h_2_1\": true,\n", - " \"assume_non-negative_h_3_1\": true,\n", - " \"assume_LHS_slope (h_0 <= h_1)_1\": true,\n", - " \"assume_non-negative_h_4_1\": true,\n", - " \"assume_RHS_slope (h_3 >= h_4)_1\": true,\n", - " \"assume_non-negative_h_0_2\": true,\n", - " \"h_0_2\": 0.10174514386870702,\n", - " \"h_1_2\": 0.44170825067816,\n", - " \"h_2_2\": 0.9991645889433753,\n", - " \"assume_non-negative_h_1_2\": true,\n", - " \"h_3_2\": 0.5004177055283123,\n", - " \"h_4_2\": 0.15696431098144537,\n", - " \"assume_LHS_slope (h_0 <= h_1)_2\": true,\n", - " \"timer_t_2\": 2.0,\n", - " \"h_0_6\": 0.10468801205221129,\n", - " \"assume_non-negative_h_2_2\": true,\n", - " \"h_1_6\": 0.37374718500912685,\n", - " \"h_2_6\": 0.9982523988411337,\n", - " \"h_3_6\": 0.5008738005794332,\n", - " \"assume_RHS_slope (h_3 >= h_4)_2\": true,\n", - " \"h_4_6\": 0.3025008738599219,\n", - " \"assume_non-negative_h_3_2\": true,\n", - " \"timer_t_6\": 6.0,\n", - " \"assume_non-negative_h_4_2\": true,\n", - " \"assume_LHS_slope (h_0 <= h_1)_3\": true,\n", - " \"assume_non-negative_h_0_3\": true,\n", - " \"assume_RHS_slope (h_3 >= h_4)_3\": true,\n", - " \"assume_non-negative_h_1_3\": true,\n", - " \"assume_non-negative_h_2_3\": true,\n", - " \"assume_LHS_slope (h_0 <= h_1)_4\": true,\n", - " \"assume_non-negative_h_3_3\": true,\n", - " \"assume_RHS_slope (h_3 >= h_4)_4\": true,\n", - " \"assume_non-negative_h_4_3\": true,\n", - " \"assume_LHS_slope (h_0 <= h_1)_5\": true,\n", - " \"assume_non-negative_h_0_4\": true,\n", - " \"h_0_3\": 0.10258241032671941,\n", - " \"h_1_3\": 0.4211152559456721,\n", - " \"h_2_3\": 0.9988442339988992,\n", - " \"h_3_3\": 0.5005778830005504,\n", - " \"h_4_3\": 0.1852076462251187,\n", - " \"timer_t_3\": 3.0,\n", - " \"assume_non-negative_h_1_4\": true,\n", - " \"assume_non-negative_h_2_4\": true,\n", - " \"assume_RHS_slope (h_3 >= h_4)_5\": true,\n", - " \"h_0_7\": 0.10524268489337923,\n", - " \"h_1_7\": 0.34536219591681205,\n", - " \"assume_LHS_slope (h_0 <= h_1)_6\": true,\n", - " \"assume_non-negative_h_3_4\": true,\n", - " \"h_2_7\": 0.998173633224059,\n", - " \"h_3_7\": 0.5009131833879705,\n", - " \"h_4_7\": 0.342745698386681,\n", - " \"timer_t_7\": 7.0,\n", - " \"assume_non-negative_h_4_4\": true,\n", - " \"assume_RHS_slope (h_3 >= h_4)_6\": true,\n", - " \"assume_non-negative_h_0_5\": true,\n", - " \"assume_LHS_slope (h_0 <= h_1)_7\": true,\n", - " \"assume_non-negative_h_1_5\": true,\n", - " \"assume_non-negative_h_2_5\": true,\n", - " \"assume_RHS_slope (h_3 >= h_4)_7\": true,\n", - " \"assume_non-negative_h_3_5\": true,\n", - " \"assume_non-negative_h_4_5\": true,\n", - " \"assume_non-negative_h_0_6\": true,\n", - " \"assume_non-negative_h_1_6\": true,\n", - " \"h_0_4\": 0.10336099934175262,\n", - " \"h_1_4\": 0.40521313922649793,\n", - " \"h_2_4\": 0.9985762590778945,\n", - " \"h_3_4\": 0.5007118704610528,\n", - " \"h_4_4\": 0.22180641277713464,\n", - " \"timer_t_4\": 4.0,\n", - " \"assume_non-negative_h_2_6\": true,\n", - " \"assume_non-negative_h_3_6\": true,\n", - " \"assume_non-negative_h_4_6\": true\n", - "}\n", - "2023-11-02 19:52:47,980 - funman.scenario.consistency - INFO - 7{7}:\t[+]\n", - "2023-11-02 19:52:47,982 - funman.server.worker - INFO - Completed work on: 49fae446-97b0-41b1-ad13-4cf076fdfd49\n", - "2023-11-02 19:52:48,235 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2023-11-02 19:52:48,486 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2023-11-02 19:52:48,489 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 Points (+:1, -:0), 1 Boxes (+:1, -:0)\n", - "gamma = 0.11339\n", - "{}\n", - " assume_non-negative_h_0 assume_non-negative_h_1 \\\n", - "time \n", - "0.0 1.0 1.0 \n", - "1.0 1.0 1.0 \n", - "2.0 1.0 1.0 \n", - "3.0 1.0 1.0 \n", - "4.0 1.0 1.0 \n", - "5.0 1.0 1.0 \n", - "6.0 1.0 1.0 \n", - "7.0 1.0 1.0 \n", - "\n", - " assume_non-negative_h_2 assume_non-negative_h_3 \\\n", - "time \n", - "0.0 1.0 1.0 \n", - "1.0 1.0 1.0 \n", - "2.0 1.0 1.0 \n", - "3.0 1.0 1.0 \n", - "4.0 1.0 1.0 \n", - "5.0 1.0 1.0 \n", - "6.0 1.0 1.0 \n", - "7.0 1.0 1.0 \n", - "\n", - " assume_non-negative_h_4 h_0 h_1 h_2 h_3 \\\n", - "time \n", - "0.0 1.0 0.100000 0.500000 1.000000 0.500000 \n", - "1.0 1.0 0.100886 0.470768 0.999546 0.500227 \n", - "2.0 1.0 0.101745 0.441708 0.999165 0.500418 \n", - "3.0 1.0 0.102582 0.421115 0.998844 0.500578 \n", - "4.0 1.0 0.103361 0.405213 0.998576 0.500712 \n", - "5.0 1.0 0.104062 0.389432 0.998381 0.500810 \n", - "6.0 1.0 0.104688 0.373747 0.998252 0.500874 \n", - "7.0 1.0 0.105243 0.345362 0.998174 0.500913 \n", - "\n", - " h_4 id label \n", - "time \n", - "0.0 0.100000 0 true \n", - "1.0 0.128573 0 true \n", - "2.0 0.156964 0 true \n", - "3.0 0.185208 0 true \n", - "4.0 0.221806 0 true \n", - "5.0 0.262197 0 true \n", - "6.0 0.302501 0 true \n", - "7.0 0.342746 0 true \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Use a five point model with no constraints\n", "\n", @@ -1213,194 +875,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-11-02 19:57:26,405 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 1, 2, 3, 4, 5, 6, 7]\n", - "2023-11-02 19:57:26,410 - funman_dreal.solver - DEBUG - Created new Solver ...\n", - "2023-11-02 19:57:26,436 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 1\n", - "2023-11-02 19:57:27,041 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 1 to 2\n", - "2023-11-02 19:57:27,201 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 2 to 3\n", - "2023-11-02 19:57:27,366 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 3 to 4\n", - "2023-11-02 19:57:27,532 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 4 to 5\n", - "2023-11-02 19:57:27,667 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 5 to 6\n", - "2023-11-02 19:57:27,777 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 6 to 7\n", - "2023-11-02 19:57:28,597 - funman.api.run - INFO - Dumping results to ./out/0a73ca9f-dedc-4972-a109-bb31cfbd6e1e.json\n", - "2023-11-02 19:57:28,711 - funman.scenario.consistency - INFO - 7{7}:\t[-]\n", - "2023-11-02 19:57:28,713 - funman.server.worker - INFO - Completed work on: 0a73ca9f-dedc-4972-a109-bb31cfbd6e1e\n", - "2023-11-02 19:57:38,627 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2023-11-02 19:57:38,722 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2023-11-02 19:57:38,724 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 Points (+:0, -:0), 1 Boxes (+:0, -:1)\n", - "{\n", - " \"box\": {\n", - " \"schedules\": {\n", - " \"lb\": -1.7976931348623157e+308,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"gamma\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.0,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"timestep\": {\n", - " \"lb\": 7.0,\n", - " \"ub\": 7.0,\n", - " \"closed_upper_bound\": true\n", - " }\n", - " },\n", - " \"relevant_constraints\": [\n", - " {\n", - " \"soft\": true,\n", - " \"name\": \"non_negative_h_0\",\n", - " \"timepoints\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"variable\": \"h_0\",\n", - " \"interval\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " }\n", - " },\n", - " {\n", - " \"soft\": true,\n", - " \"name\": \"non_negative_h_1\",\n", - " \"timepoints\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"variable\": \"h_1\",\n", - " \"interval\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " }\n", - " },\n", - " {\n", - " \"soft\": true,\n", - " \"name\": \"non_negative_h_2\",\n", - " \"timepoints\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"variable\": \"h_2\",\n", - " \"interval\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " }\n", - " },\n", - " {\n", - " \"soft\": true,\n", - " \"name\": \"non_negative_h_3\",\n", - " \"timepoints\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"variable\": \"h_3\",\n", - " \"interval\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " }\n", - " },\n", - " {\n", - " \"soft\": true,\n", - " \"name\": \"non_negative_h_4\",\n", - " \"timepoints\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"variable\": \"h_4\",\n", - " \"interval\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " }\n", - " },\n", - " {\n", - " \"soft\": true,\n", - " \"name\": \"LHS_slope\",\n", - " \"timepoints\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"additive_bounds\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"variables\": [\n", - " \"h_1\",\n", - " \"h_0\"\n", - " ],\n", - " \"weights\": [\n", - " 1,\n", - " -1\n", - " ]\n", - " },\n", - " {\n", - " \"soft\": true,\n", - " \"name\": \"RHS_slope\",\n", - " \"timepoints\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"additive_bounds\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"variables\": [\n", - " \"h_3\",\n", - " \"h_4\"\n", - " ],\n", - " \"weights\": [\n", - " 1,\n", - " -1\n", - " ]\n", - " },\n", - " {\n", - " \"soft\": true,\n", - " \"name\": \"melt_h_2\",\n", - " \"timepoints\": {\n", - " \"lb\": 5.0,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"variable\": \"h_2\",\n", - " \"interval\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 0.8,\n", - " \"closed_upper_bound\": false\n", - " }\n", - " }\n", - " ],\n", - " \"expression\": \"(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((assume_melt_h_2 & assume_RHS_slope) & assume_LHS_slope) & assume_non_negative_h_4) & assume_non_negative_h_3) & assume_non_negative_h_2) & assume_non_negative_h_1) & assume_non_negative_h_0) & disj32) & disj38) & disj40) & disj42) & disj48) & disj50) & disj52) & disj54) & disj56) & disj58) & disj60) & disj62) & disj64) & disj66) & disj68) & disj70) & disj72) & disj74) & disj76) & disj78) & disj80) & disj82) & disj84) & disj86) & disj88) & disj94) & disj96) & disj98) & disj100) & disj102) & disj108) & disj110) & disj112) & disj114) & disj116) & disj128) & disj129) & disj130) & disj133) & disj134) & disj135) & disj136) & disj137) & disj138) & disj139) & disj140) & disj141) & disj142) & disj143) & disj144) & disj145) & disj146) & disj147) & disj148) & disj149) & disj150) & disj151) & disj152) & disj153) & disj154) & disj155) & disj156) & disj157) & disj164) & disj165) & disj166) & disj167) & disj168) & disj169) & disj176) & (h_4_5 = ((h_4_4 - ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0)) - (2.0 * ((((gamma * h_2_4) ^ 5.0) * (h_3_4 - h_2_4)) ^ 3.0))))) & (h_3_5 = (h_3_4 - ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0)))) & (h_2_5 = (h_2_4 + (2.0 * ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0))))) & (h_4_4 = ((h_4_3 - ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0)) - (2.0 * ((((gamma * h_2_3) ^ 5.0) * (h_3_3 - h_2_3)) ^ 3.0))))) & (h_3_4 = (h_3_3 - ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0)))) & (h_2_4 = (h_2_3 + (2.0 * ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0))))) & (h_1_4 = (((h_1_3 + (2.0 * ((((gamma * h_2_3) ^ 5.0) * (h_3_3 - h_2_3)) ^ 3.0))) - (2.0 * ((((gamma * h_1_3) ^ 5.0) * (h_2_3 - h_1_3)) ^ 3.0))) + ((((gamma * h_0_3) ^ 5.0) * (h_1_3 - h_0_3)) ^ 3.0)))) & (h_0_4 = ((h_0_3 + (2.0 * ((((gamma * h_1_3) ^ 5.0) * (h_2_3 - h_1_3)) ^ 3.0))) - ((((gamma * h_0_3) ^ 5.0) * (h_1_3 - h_0_3)) ^ 3.0)))) & (h_4_3 = ((h_4_2 - ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0)) - (2.0 * ((((gamma * h_2_2) ^ 5.0) * (h_3_2 - h_2_2)) ^ 3.0))))) & (h_3_3 = (h_3_2 - ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0)))) & (h_2_3 = (h_2_2 + (2.0 * ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0))))) & (h_1_3 = (((h_1_2 + (2.0 * ((((gamma * h_2_2) ^ 5.0) * (h_3_2 - h_2_2)) ^ 3.0))) - (2.0 * ((((gamma * h_1_2) ^ 5.0) * (h_2_2 - h_1_2)) ^ 3.0))) + ((((gamma * h_0_2) ^ 5.0) * (h_1_2 - h_0_2)) ^ 3.0)))) & (h_0_3 = ((h_0_2 + (2.0 * ((((gamma * h_1_2) ^ 5.0) * (h_2_2 - h_1_2)) ^ 3.0))) - ((((gamma * h_0_2) ^ 5.0) * (h_1_2 - h_0_2)) ^ 3.0)))) & (h_4_2 = ((h_4_1 - ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0)) - (2.0 * ((((gamma * h_2_1) ^ 5.0) * (h_3_1 - h_2_1)) ^ 3.0))))) & (h_3_2 = (h_3_1 - ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0)))) & (h_2_2 = (h_2_1 + (2.0 * ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0))))) & (h_1_2 = (((h_1_1 + (2.0 * ((((gamma * h_2_1) ^ 5.0) * (h_3_1 - h_2_1)) ^ 3.0))) - (2.0 * ((((gamma * h_1_1) ^ 5.0) * (h_2_1 - h_1_1)) ^ 3.0))) + ((((gamma * h_0_1) ^ 5.0) * (h_1_1 - h_0_1)) ^ 3.0)))) & (h_0_2 = ((h_0_1 + (2.0 * ((((gamma * h_1_1) ^ 5.0) * (h_2_1 - h_1_1)) ^ 3.0))) - ((((gamma * h_0_1) ^ 5.0) * (h_1_1 - h_0_1)) ^ 3.0)))) & (h_4_1 = ((h_4_0 - ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0)) - (2.0 * ((((gamma * h_2_0) ^ 5.0) * (h_3_0 - h_2_0)) ^ 3.0))))) & (h_3_1 = (h_3_0 - ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0)))) & (h_2_1 = (h_2_0 + (2.0 * ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0))))) & (h_1_1 = (((h_1_0 + (2.0 * ((((gamma * h_2_0) ^ 5.0) * (h_3_0 - h_2_0)) ^ 3.0))) - (2.0 * ((((gamma * h_1_0) ^ 5.0) * (h_2_0 - h_1_0)) ^ 3.0))) + ((((gamma * h_0_0) ^ 5.0) * (h_1_0 - h_0_0)) ^ 3.0)))) & (h_0_1 = ((h_0_0 + (2.0 * ((((gamma * h_1_0) ^ 5.0) * (h_2_0 - h_1_0)) ^ 3.0))) - ((((gamma * h_0_0) ^ 5.0) * (h_1_0 - h_0_0)) ^ 3.0)))) & (h_4_0 = 10000000000000001/100000000000000000)) & (h_3_0 = 1/2)) & (h_2_0 = 1.0)) & (h_1_0 = 1/2)) & (h_0_0 = 10000000000000001/100000000000000000)) & (gamma < 1.0)) & ((assume_melt_h_2_5 | (! assume_melt_h_2)) | (! disj32))) & ((assume_non_negative_h_4_5 | (! assume_non_negative_h_4)) | (! disj38))) & ((assume_non_negative_h_3_5 | (! assume_non_negative_h_3)) | (! disj40))) & ((assume_non_negative_h_2_5 | (! assume_non_negative_h_2)) | (! disj42))) & ((assume_RHS_slope_4 | (! assume_RHS_slope)) | (! disj48))) & ((assume_LHS_slope_4 | (! assume_LHS_slope)) | (! disj50))) & ((assume_non_negative_h_4_4 | (! assume_non_negative_h_4)) | (! disj52))) & ((assume_non_negative_h_3_4 | (! assume_non_negative_h_3)) | (! disj54))) & ((assume_non_negative_h_2_4 | (! assume_non_negative_h_2)) | (! disj56))) & ((assume_non_negative_h_1_4 | (! assume_non_negative_h_1)) | (! disj58))) & ((assume_non_negative_h_0_4 | (! assume_non_negative_h_0)) | (! disj60))) & ((assume_RHS_slope_3 | (! assume_RHS_slope)) | (! disj62))) & ((assume_LHS_slope_3 | (! assume_LHS_slope)) | (! disj64))) & ((assume_non_negative_h_4_3 | (! assume_non_negative_h_4)) | (! disj66))) & ((assume_non_negative_h_3_3 | (! assume_non_negative_h_3)) | (! disj68))) & ((assume_non_negative_h_2_3 | (! assume_non_negative_h_2)) | (! disj70))) & ((assume_non_negative_h_1_3 | (! assume_non_negative_h_1)) | (! disj72))) & ((assume_non_negative_h_0_3 | (! assume_non_negative_h_0)) | (! disj74))) & ((assume_RHS_slope_2 | (! assume_RHS_slope)) | (! disj76))) & ((assume_LHS_slope_2 | (! assume_LHS_slope)) | (! disj78))) & ((assume_non_negative_h_4_2 | (! assume_non_negative_h_4)) | (! disj80))) & ((assume_non_negative_h_3_2 | (! assume_non_negative_h_3)) | (! disj82))) & ((assume_non_negative_h_2_2 | (! assume_non_negative_h_2)) | (! disj84))) & ((assume_non_negative_h_1_2 | (! assume_non_negative_h_1)) | (! disj86))) & ((assume_non_negative_h_0_2 | (! assume_non_negative_h_0)) | (! disj88))) & ((assume_non_negative_h_4_1 | (! assume_non_negative_h_4)) | (! disj94))) & ((assume_non_negative_h_3_1 | (! assume_non_negative_h_3)) | (! disj96))) & ((assume_non_negative_h_2_1 | (! assume_non_negative_h_2)) | (! disj98))) & ((assume_non_negative_h_1_1 | (! assume_non_negative_h_1)) | (! disj100))) & ((assume_non_negative_h_0_1 | (! assume_non_negative_h_0)) | (! disj102))) & ((assume_non_negative_h_4_0 | (! assume_non_negative_h_4)) | (! disj108))) & ((assume_non_negative_h_3_0 | (! assume_non_negative_h_3)) | (! disj110))) & ((assume_non_negative_h_2_0 | (! assume_non_negative_h_2)) | (! disj112))) & ((assume_non_negative_h_1_0 | (! assume_non_negative_h_1)) | (! disj114))) & ((assume_non_negative_h_0_0 | (! assume_non_negative_h_0)) | (! disj116))) & ((conj5 | (! assume_melt_h_2_5)) | (! disj176))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((h_2_5 < 0.0) | (h_4_5 < 0.0)) | (h_3_5 < 0.0)) | (((-1.0 * h_4_4) + h_3_4) < 0.0)) | ((h_1_4 - h_0_4) < 0.0)) | (h_4_4 < 0.0)) | (h_3_4 < 0.0)) | (h_2_4 < 0.0)) | (h_1_4 < 0.0)) | (h_0_4 < 0.0)) | (((-1.0 * h_4_3) + h_3_3) < 0.0)) | ((h_1_3 - h_0_3) < 0.0)) | (h_4_3 < 0.0)) | (h_3_3 < 0.0)) | (h_2_3 < 0.0)) | (h_1_3 < 0.0)) | (h_0_3 < 0.0)) | (((-1.0 * h_4_2) + h_3_2) < 0.0)) | ((h_1_2 - h_0_2) < 0.0)) | (h_4_2 < 0.0)) | (h_3_2 < 0.0)) | (h_2_2 < 0.0)) | (h_1_2 < 0.0)) | (h_0_2 < 0.0)) | (h_4_1 < 0.0)) | (h_3_1 < 0.0)) | (h_2_1 < 0.0)) | (h_1_1 < 0.0)) | (h_0_1 < 0.0)) | (h_4_0 < 0.0)) | (h_3_0 < 0.0)) | (h_2_0 < 0.0)) | (h_1_0 < 0.0)) | (h_0_0 < 0.0)) | (gamma < 0.0)) | (! (h_2_5 < 20000000000000001/25000000000000000))) | (! (h_4_5 = ((h_4_4 - ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0)) - (2.0 * ((((gamma * h_2_4) ^ 5.0) * (h_3_4 - h_2_4)) ^ 3.0)))))) | (! (h_3_5 = (h_3_4 - ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0))))) | (! (h_2_5 = (h_2_4 + (2.0 * ((((gamma * h_3_4) ^ 5.0) * (h_4_4 - h_3_4)) ^ 3.0)))))) | (! (h_4_4 = ((h_4_3 - ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0)) - (2.0 * ((((gamma * h_2_3) ^ 5.0) * (h_3_3 - h_2_3)) ^ 3.0)))))) | (! (h_3_4 = (h_3_3 - ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0))))) | (! (h_2_4 = (h_2_3 + (2.0 * ((((gamma * h_3_3) ^ 5.0) * (h_4_3 - h_3_3)) ^ 3.0)))))) | (! (h_1_4 = (((h_1_3 + (2.0 * ((((gamma * h_2_3) ^ 5.0) * (h_3_3 - h_2_3)) ^ 3.0))) - (2.0 * ((((gamma * h_1_3) ^ 5.0) * (h_2_3 - h_1_3)) ^ 3.0))) + ((((gamma * h_0_3) ^ 5.0) * (h_1_3 - h_0_3)) ^ 3.0))))) | (! (h_0_4 = ((h_0_3 + (2.0 * ((((gamma * h_1_3) ^ 5.0) * (h_2_3 - h_1_3)) ^ 3.0))) - ((((gamma * h_0_3) ^ 5.0) * (h_1_3 - h_0_3)) ^ 3.0))))) | (! (h_4_3 = ((h_4_2 - ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0)) - (2.0 * ((((gamma * h_2_2) ^ 5.0) * (h_3_2 - h_2_2)) ^ 3.0)))))) | (! (h_3_3 = (h_3_2 - ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0))))) | (! (h_2_3 = (h_2_2 + (2.0 * ((((gamma * h_3_2) ^ 5.0) * (h_4_2 - h_3_2)) ^ 3.0)))))) | (! (h_1_3 = (((h_1_2 + (2.0 * ((((gamma * h_2_2) ^ 5.0) * (h_3_2 - h_2_2)) ^ 3.0))) - (2.0 * ((((gamma * h_1_2) ^ 5.0) * (h_2_2 - h_1_2)) ^ 3.0))) + ((((gamma * h_0_2) ^ 5.0) * (h_1_2 - h_0_2)) ^ 3.0))))) | (! (h_0_3 = ((h_0_2 + (2.0 * ((((gamma * h_1_2) ^ 5.0) * (h_2_2 - h_1_2)) ^ 3.0))) - ((((gamma * h_0_2) ^ 5.0) * (h_1_2 - h_0_2)) ^ 3.0))))) | (! (h_4_2 = ((h_4_1 - ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0)) - (2.0 * ((((gamma * h_2_1) ^ 5.0) * (h_3_1 - h_2_1)) ^ 3.0)))))) | (! (h_3_2 = (h_3_1 - ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0))))) | (! (h_2_2 = (h_2_1 + (2.0 * ((((gamma * h_3_1) ^ 5.0) * (h_4_1 - h_3_1)) ^ 3.0)))))) | (! (h_1_2 = (((h_1_1 + (2.0 * ((((gamma * h_2_1) ^ 5.0) * (h_3_1 - h_2_1)) ^ 3.0))) - (2.0 * ((((gamma * h_1_1) ^ 5.0) * (h_2_1 - h_1_1)) ^ 3.0))) + ((((gamma * h_0_1) ^ 5.0) * (h_1_1 - h_0_1)) ^ 3.0))))) | (! (h_0_2 = ((h_0_1 + (2.0 * ((((gamma * h_1_1) ^ 5.0) * (h_2_1 - h_1_1)) ^ 3.0))) - ((((gamma * h_0_1) ^ 5.0) * (h_1_1 - h_0_1)) ^ 3.0))))) | (! (h_4_1 = ((h_4_0 - ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0)) - (2.0 * ((((gamma * h_2_0) ^ 5.0) * (h_3_0 - h_2_0)) ^ 3.0)))))) | (! (h_3_1 = (h_3_0 - ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0))))) | (! (h_2_1 = (h_2_0 + (2.0 * ((((gamma * h_3_0) ^ 5.0) * (h_4_0 - h_3_0)) ^ 3.0)))))) | (! (h_1_1 = (((h_1_0 + (2.0 * ((((gamma * h_2_0) ^ 5.0) * (h_3_0 - h_2_0)) ^ 3.0))) - (2.0 * ((((gamma * h_1_0) ^ 5.0) * (h_2_0 - h_1_0)) ^ 3.0))) + ((((gamma * h_0_0) ^ 5.0) * (h_1_0 - h_0_0)) ^ 3.0))))) | (! (h_0_1 = ((h_0_0 + (2.0 * ((((gamma * h_1_0) ^ 5.0) * (h_2_0 - h_1_0)) ^ 3.0))) - ((((gamma * h_0_0) ^ 5.0) * (h_1_0 - h_0_0)) ^ 3.0))))) | (! (h_4_0 = 10000000000000001/100000000000000000))) | (! (h_3_0 = 1/2))) | (! (h_2_0 = 1.0))) | (! (h_1_0 = 1/2))) | (! (h_0_0 = 10000000000000001/100000000000000000))) | (! (gamma < 1.0)))) & ((h_2_5 < 20000000000000001/25000000000000000) | (! conj5))) & (((! assume_non_negative_h_4_5) | (! disj128)) | (! (h_4_5 < 0.0)))) & (((! assume_non_negative_h_3_5) | (! disj129)) | (! (h_3_5 < 0.0)))) & (((! assume_non_negative_h_2_5) | (! disj130)) | (! (h_2_5 < 0.0)))) & (((! assume_RHS_slope_4) | (! disj164)) | (! (((-1.0 * h_4_4) + h_3_4) < 0.0)))) & (((! assume_LHS_slope_4) | (! disj165)) | (! ((h_1_4 - h_0_4) < 0.0)))) & (((! assume_non_negative_h_4_4) | (! disj133)) | (! (h_4_4 < 0.0)))) & (((! assume_non_negative_h_3_4) | (! disj134)) | (! (h_3_4 < 0.0)))) & (((! assume_non_negative_h_2_4) | (! disj135)) | (! (h_2_4 < 0.0)))) & (((! assume_non_negative_h_1_4) | (! disj136)) | (! (h_1_4 < 0.0)))) & (((! assume_non_negative_h_0_4) | (! disj137)) | (! (h_0_4 < 0.0)))) & (((! assume_RHS_slope_3) | (! disj166)) | (! (((-1.0 * h_4_3) + h_3_3) < 0.0)))) & (((! assume_LHS_slope_3) | (! disj167)) | (! ((h_1_3 - h_0_3) < 0.0)))) & (((! assume_non_negative_h_4_3) | (! disj138)) | (! (h_4_3 < 0.0)))) & (((! assume_non_negative_h_3_3) | (! disj139)) | (! (h_3_3 < 0.0)))) & (((! assume_non_negative_h_2_3) | (! disj140)) | (! (h_2_3 < 0.0)))) & (((! assume_non_negative_h_1_3) | (! disj141)) | (! (h_1_3 < 0.0)))) & (((! assume_non_negative_h_0_3) | (! disj142)) | (! (h_0_3 < 0.0)))) & (((! assume_RHS_slope_2) | (! disj168)) | (! (((-1.0 * h_4_2) + h_3_2) < 0.0)))) & (((! assume_LHS_slope_2) | (! disj169)) | (! ((h_1_2 - h_0_2) < 0.0)))) & (((! assume_non_negative_h_4_2) | (! disj143)) | (! (h_4_2 < 0.0)))) & (((! assume_non_negative_h_3_2) | (! disj144)) | (! (h_3_2 < 0.0)))) & (((! assume_non_negative_h_2_2) | (! disj145)) | (! (h_2_2 < 0.0)))) & (((! assume_non_negative_h_1_2) | (! disj146)) | (! (h_1_2 < 0.0)))) & (((! assume_non_negative_h_0_2) | (! disj147)) | (! (h_0_2 < 0.0)))) & (((! assume_non_negative_h_4_1) | (! disj148)) | (! (h_4_1 < 0.0)))) & (((! assume_non_negative_h_3_1) | (! disj149)) | (! (h_3_1 < 0.0)))) & (((! assume_non_negative_h_2_1) | (! disj150)) | (! (h_2_1 < 0.0)))) & (((! assume_non_negative_h_1_1) | (! disj151)) | (! (h_1_1 < 0.0)))) & (((! assume_non_negative_h_0_1) | (! disj152)) | (! (h_0_1 < 0.0)))) & (((! assume_non_negative_h_4_0) | (! disj153)) | (! (h_4_0 < 0.0)))) & (((! assume_non_negative_h_3_0) | (! disj154)) | (! (h_3_0 < 0.0)))) & (((! assume_non_negative_h_2_0) | (! disj155)) | (! (h_2_0 < 0.0)))) & (((! assume_non_negative_h_1_0) | (! disj156)) | (! (h_1_0 < 0.0)))) & (((! assume_non_negative_h_0_0) | (! disj157)) | (! (h_0_0 < 0.0)))) & (! (gamma < 0.0)))\"\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "# Use a five point model with no constraints\n", "\n", @@ -1498,79 +975,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-11-02 19:59:37,207 - funman.server.worker - INFO - FunmanWorker running...\n", - "2023-11-02 19:59:37,215 - funman.server.worker - INFO - Starting work on: 64599ed5-ca1a-4308-8f56-ce379fd54cd4\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-11-02 19:59:38,109 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 1, 2, 3, 4, 5, 6, 7]\n", - "2023-11-02 19:59:38,112 - funman_dreal.solver - DEBUG - Created new Solver ...\n", - "2023-11-02 19:59:38,124 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 1\n", - "2023-11-02 19:59:38,243 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 1 to 2\n", - "2023-11-02 19:59:38,338 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 2 to 3\n", - "2023-11-02 19:59:38,424 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 3 to 4\n", - "2023-11-02 19:59:38,512 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 4 to 5\n", - "2023-11-02 19:59:38,597 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 5 to 6\n", - "2023-11-02 19:59:38,670 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 6 to 7\n", - "2023-11-02 19:59:39,353 - funman.api.run - INFO - Dumping results to ./out/64599ed5-ca1a-4308-8f56-ce379fd54cd4.json\n", - "2023-11-02 19:59:39,364 - funman.scenario.consistency - INFO - 7{7}:\t[-]\n", - "2023-11-02 19:59:39,370 - funman.server.worker - INFO - Completed work on: 64599ed5-ca1a-4308-8f56-ce379fd54cd4\n", - "2023-11-02 19:59:49,379 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2023-11-02 19:59:49,882 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2023-11-02 19:59:49,885 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 Points (+:0, -:0), 1 Boxes (+:0, -:1)\n", - "{\n", - " \"box\": {\n", - " \"schedules\": {\n", - " \"lb\": -1.7976931348623157e+308,\n", - " \"ub\": 1.7976931348623157e+308,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"gamma\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.0,\n", - " \"closed_upper_bound\": false\n", - " },\n", - " \"timestep\": {\n", - " \"lb\": 7.0,\n", - " \"ub\": 7.0,\n", - " \"closed_upper_bound\": true\n", - " }\n", - " },\n", - " \"relevant_constraints\": [\n", - " {\n", - " \"soft\": true,\n", - " \"name\": \"melt_h_2\",\n", - " \"timepoints\": null,\n", - " \"variable\": \"h_2\",\n", - " \"interval\": {\n", - " \"lb\": 0.0,\n", - " \"ub\": 1.0,\n", - " \"closed_upper_bound\": false\n", - " }\n", - " }\n", - " ],\n", - " \"expression\": \"((((((assume_melt_h_2 & disj1047) & (h_2_0 = 1.0)) & ((conj32 | (! assume_melt_h_2)) | (! disj1047))) & (((h_2_0 < 0.0) | (! (h_2_0 < 1.0))) | (! (h_2_0 = 1.0)))) & ((h_2_0 < 1.0) | (! conj32))) & ((! conj32) | (! (h_2_0 < 0.0))))\"\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "# Use a five point model with no constraints\n", "\n", diff --git a/setup.py b/setup.py index 6c20b8a7..aefebfca 100644 --- a/setup.py +++ b/setup.py @@ -27,11 +27,12 @@ "multiprocess", "tenacity", "pyparsing", - "pysmt", + "pysmt @ git+https://github.com/danbryce/pysmt@add-abs-value#egg=pysmt-0.9.6.dev1", "pandas", "matplotlib" # "automates @ https://github.com/danbryce/automates/archive/e5fb635757aa57007615a75371f55dd4a24851e0.zip#sha1=f9b3c8a7d7fa28864952ccdd3293d02894614e3f" ], + # dependency_links=["git+https://github.com/danbryce/pysmt/pysmt@add-abs-value#egg=pysmt-0.9.6.dev1"], extras_require={"dreal": ["funman_dreal"]}, tests_require=["unittest"], zip_safe=False, diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index be10a43b..38de2666 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -524,7 +524,7 @@ def _initialize_model_encoding( Equals(s, Real(0.0)) for s in symbols if s.symbol_type() == REAL - and str(s) + and s.symbol_name() not in episode.problem.model._parameter_names() ] # + [Not(s) for s in symbols if s.symbol_type() == BOOL] ), diff --git a/src/funman/translate/translate.py b/src/funman/translate/translate.py index 297468a1..351267cc 100644 --- a/src/funman/translate/translate.py +++ b/src/funman/translate/translate.py @@ -319,7 +319,7 @@ def encode_assumed_constraint( Iff(assumption_symbol, encoded_constraint[0]), ) symbols = {k: v for k, v in encoded_constraint[1].items()} - symbols[str(assumption_symbol)] = assumption_symbol + symbols[assumption_symbol.symbol_name()] = assumption_symbol return (assumed_constraint, symbols) def encode_assumption( @@ -409,7 +409,7 @@ def encode_init_layer(self) -> EncodedFormula: initial_state = self._timed_model_elements["init"] initial_symbols = initial_state.get_free_variables() - return (initial_state, {str(s): s for s in initial_symbols}) + return (initial_state, {s.symbol_name(): s for s in initial_symbols}) def encode_transition_layer( self, @@ -702,7 +702,10 @@ def encode_parameter( closed_upper_bound=False, infinity_constraints=False, ) - return (formula, {str(s): s for s in formula.get_free_variables()}) + return ( + formula, + {s.symbol_name(): s for s in formula.get_free_variables()}, + ) else: return None @@ -1061,7 +1064,10 @@ def _encode_state_variable_constraint( else: formula = TRUE() - return (formula, {str(v): v for v in formula.get_free_variables()}) + return ( + formula, + {v.symbol_name(): v for v in formula.get_free_variables()}, + ) def _encode_query_le( self, diff --git a/src/funman/utils/sympy_utils.py b/src/funman/utils/sympy_utils.py index 992febc3..9e16ea41 100644 --- a/src/funman/utils/sympy_utils.py +++ b/src/funman/utils/sympy_utils.py @@ -7,12 +7,9 @@ import pysmt.typing as types import sympy from pysmt.formula import FNode, FormulaManager +from pysmt.shortcuts import GE, GT, LE, LT, REAL +from pysmt.shortcuts import Abs as pysmt_Abs from pysmt.shortcuts import ( - GE, - GT, - LE, - LT, - REAL, And, Div, Equals, @@ -226,9 +223,7 @@ def sympy_to_pysmt_op(op, expr, explode=False): def sympy_to_pysmt_abs(expr): p_expr = sympy_to_pysmt(expr.args[0]) - return Pow( - Pow(p_expr, Real(2.0)), Real(0.5) - ) # Ite(GE(p_expr, Real(0.0)), p_expr, Times(Real(-1.0), p_expr)) + return pysmt_Abs(p_expr) def sympy_to_pysmt_pow(expr): From 0cc9abc1b1b799b0b1deae70808257972812dd36 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 1 Dec 2023 19:21:43 +0000 Subject: [PATCH 28/28] fix tests --- .../src/funman_dreal/converter.py | 21 ++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py b/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py index ba070fe5..8a1322a1 100644 --- a/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py +++ b/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py @@ -1,10 +1,12 @@ import functools +import logging import re from fractions import Fraction from typing import List import dreal from pysmt.decorators import catch_conversion_error +from pysmt.exceptions import UndefinedSymbolError from pysmt.formula import FNode from pysmt.parsing import ( ClosePar, @@ -28,6 +30,8 @@ ) from pysmt.walkers import DagWalker +l = logging.getLogger(__name__) + def CoreParser(env=None): return PrattParser(CoreLexer) @@ -42,7 +46,7 @@ def __init__(self, operator, lbp): self.lbp = lbp def nud(self, parser): - parser.advance() # OpenPar + # parser.advance() # OpenPar parser.advance() # OpenPar if type(parser.token) != ClosePar: r = parser.expression() @@ -73,9 +77,12 @@ def __init__(self, env=None): r"(or)", InfixOpAdapter(self.OrOrBVOr, 30), False ), # disjunction Rule( - r"(b)", BinaryLiteralExpr(self.BinaryLiteral, 50), False + r"(b\()", BinaryLiteralExpr(self.BinaryLiteral, 50), False ), # b() Rule(r"(==)", InfixOpAdapter(self.mgr.Equals, 60), False), # eq + Rule( + r"(-?\d+\.\d+e-?\d+)", self.real_constant, True + ), # decimals scientific ] + self.rules self.compile() @@ -87,7 +94,12 @@ def int_constant(self, read): def identifier(self, read): r = self.identifier_map.get(read, read) - return Identifier(r, env=self.env) + try: + return Identifier(r, env=self.env) + except UndefinedSymbolError as e: + l.exception( + f"Could not resolve parsed identifier: {r}, because:\n{e}" + ) class DRealConverter(Converter, DagWalker): @@ -156,6 +168,9 @@ def create_dreal_symbols(self, rewritten_formula: str) -> List[Symbol]: def back(self, dreal_formula: dreal.Formula) -> FNode: try: # rewritten_formula = self.rewrite_dreal_formula(dreal_formula) + l.debug( + f"Extracting dreal unsat core expression: {str(dreal_formula)}" + ) new_symbols = self.create_dreal_symbols(str(dreal_formula)) formula = CoreParser().parse(str(dreal_formula)) except Exception as e: