From 54ef23e8f1af1b913b14111e60f84dcb6b95a8a2 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Thu, 23 May 2024 21:10:02 +0000 Subject: [PATCH 01/93] add simulation to check points in consistency problems --- src/funman/api/run.py | 2 +- src/funman/model/petrinet.py | 36 +++++++ src/funman/representation/constraint.py | 8 ++ src/funman/representation/representation.py | 46 ++++++++- src/funman/scenario/consistency.py | 5 + src/funman/scenario/scenario.py | 104 +++++++++++++++++++- src/funman/search/simulate.py | 57 +++++++++++ src/funman/server/query.py | 6 +- src/funman/translate/translate.py | 24 +++++ 9 files changed, 284 insertions(+), 4 deletions(-) create mode 100644 src/funman/search/simulate.py diff --git a/src/funman/api/run.py b/src/funman/api/run.py index e9dfde89..8547aceb 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -339,7 +339,7 @@ def run_instance( point_plot_config, parameters_to_plot, ) - + sleep(10) elif not self._worker.is_processing_id(work_unit.id): results = self._worker.get_results(work_unit.id) diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index 2097d05f..0321fffe 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -176,6 +176,42 @@ def compartmental_constraints( for v in vars ] + def derivative(self, var_name, t, var_to_value, param_to_value): + param_at_t = {p: pv(t).item() for p, pv in param_to_value.items()} + # FIXME assumes each transition has only one rate + pos_rates = [ + self._transition_rate(trans)[0].evalf( + subs={**var_to_value, **param_at_t} + ) + for trans in self._transitions() + for var in trans.output + if var_name == var + ] + neg_rates = [ + self._transition_rate(trans)[0].evalf( + subs={**var_to_value, **param_at_t} + ) + for trans in self._transitions() + for var in trans.input + if var_name == var + ] + + return sum(pos_rates) - sum(neg_rates) + + def gradient(self, y, t, *p): + # FIXME support time varying paramters by treating parameters as a function + var_to_value = { + var: y[i] for i, var in enumerate(self._state_var_names()) + } + param_to_value = { + param: p[i] for i, param in enumerate(self._parameter_names()) + } + grad = [ + self.derivative(var, t, var_to_value, param_to_value) + for var in self._state_var_names() + ] + return grad + class GeneratedPetriNetModel(AbstractPetriNetModel): model_config = ConfigDict(arbitrary_types_allowed=True) diff --git a/src/funman/representation/constraint.py b/src/funman/representation/constraint.py index f32c505c..23cc018a 100644 --- a/src/funman/representation/constraint.py +++ b/src/funman/representation/constraint.py @@ -1,5 +1,7 @@ from typing import List, Optional, Union +import numpy as np +import pandas as pd from pydantic import ( BaseModel, ConfigDict, @@ -15,6 +17,7 @@ from .interval import Interval from .parameter import ModelParameter, StructureParameter +from .representation import Timeseries class Constraint(BaseModel): @@ -40,6 +43,11 @@ def check_name(self) -> "FUNMANConfig": return self +class TimeseriesConstraint(Constraint): + soft: bool = False + timeseries: Timeseries + + class TimedConstraint(Constraint): timepoints: Optional["Interval"] = None diff --git a/src/funman/representation/representation.py b/src/funman/representation/representation.py index 383e448b..7cd88513 100644 --- a/src/funman/representation/representation.py +++ b/src/funman/representation/representation.py @@ -5,19 +5,29 @@ import logging import math -from typing import Dict, Literal, Optional, Set +from typing import Dict, List, Literal, Optional, Set from pydantic import BaseModel from funman import to_sympy from funman.constants import LABEL_UNKNOWN, NEG_INFINITY, POS_INFINITY, Label +from . import Timepoint + l = logging.getLogger(__name__) from . import EncodingSchedule, PointValue +class Timeseries(BaseModel): + data: List[List[float]] + columns: List[str] + + def __getitem__(self, key): + return self.data[self.columns.index(key)] + + class Point(BaseModel): type: Literal["point"] = "point" label: Label = LABEL_UNKNOWN @@ -38,6 +48,17 @@ def __str__(self): def __repr__(self) -> str: return str(self.model_dump()) + def values_at(self, tp: Timepoint) -> Dict[str, float]: + v = { + k.rsplit("_", 1)[0]: v + for k, v in self.values.items() + if self._is_state_variable(k) and int(k.rsplit("_", 1)[-1]) == tp + } + return v + + def value_of(self, var) -> float: + return self.values[var] if var in self.values else None + def relevant_timesteps(self) -> Set[int]: steps = { int(k.rsplit("_", 1)[-1]) @@ -46,6 +67,29 @@ def relevant_timesteps(self) -> Set[int]: } return steps + def _is_state_variable(self, s: str) -> bool: + return ( + not s.startswith("solve_step") + and not s.startswith("assume_") + and "_" in s + ) + + def state_values(self) -> Dict[str, float]: + return { + k: v for k, v in self.values.items() if self._is_state_variable(k) + } + + def relevant_timepoints(self) -> List[int]: + steps = list( + { + int(k.rsplit("_", 1)[-1]) + for k, v in self.values.items() + if self._is_state_variable(k) + } + ) + steps.sort() + return steps + def remove_irrelevant_steps(self, untimed_symbols: Set[str]): relevant = self.relevant_timesteps() relevant_timepoints = [ diff --git a/src/funman/scenario/consistency.py b/src/funman/scenario/consistency.py index ffc5235a..91a414b7 100644 --- a/src/funman/scenario/consistency.py +++ b/src/funman/scenario/consistency.py @@ -78,6 +78,7 @@ def solve( haltEvent=haltEvent, resultsCallback=resultsCallback, ) + parameter_space.num_dimensions = len(self.parameters) l.info(parameter_space) scenario_result = ConsistencyScenarioResult( @@ -87,6 +88,10 @@ def solve( ) scenario_result._models = models + assert self.check_simulation( + config, scenario_result + ), "Simulation of solution is invalid." + return scenario_result diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index 452312f5..b23c0a6a 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -4,7 +4,9 @@ from decimal import Decimal from typing import Dict, List, Optional, Union +import numpy as np from pydantic import BaseModel, ConfigDict +from pysmt.shortcuts import TRUE, And, Solver from funman import ( NEG_INFINITY, @@ -34,9 +36,18 @@ from funman.model.ensemble import EnsembleModel from funman.model.petrinet import GeneratedPetriNetModel from funman.model.regnet import GeneratedRegnetModel, RegnetModel -from funman.representation.constraint import FunmanUserConstraint +from funman.representation import Point +from funman.representation.constraint import ( + FunmanUserConstraint, + TimeseriesConstraint, +) from funman.representation.parameter import NumSteps, Schedules, StepSize +from funman.search.simulate import Simulator, Timeseries +from funman.translate.translate import EncodingOptions from funman.utils import math_utils +from funman.utils.sympy_utils import to_sympy + +from ..representation import Point l = logging.getLogger(__name__) @@ -333,6 +344,97 @@ def _set_normalization(self, config): self.normalization_constant = 1.0 l.warning("Warning: The scenario is not normalized!") + def check_point_simulation(self, point: Point, tvect) -> Timeseries: + init = { + var: value + for var, value in point.values_at(0).items() + if var != "timer_t" + } + parameters = { + p: point.value_of(p) for p in self.model._parameter_names() + } + simulator = Simulator( + model=self.model, init=init, parameters=parameters, tvect=tvect + ) + timeseries = simulator.sim() + # timeseries = np.array([[tvect[t], timeseries[t]] for t in range(len(tvect))]) + return timeseries + + def simulation_tvects(self, config) -> List[Union[float, int]]: + num_steps = self.structure_parameter("num_steps") + step_size = self.structure_parameter("step_size") + schedules = self.structure_parameter("schedules") + + tvects = [] + if schedules: + for s in schedules.schedules: + tvects.append(s.timepoints) + else: + min_steps = num_steps.interval.lb + max_steps = num_steps.interval.ub + min_size = step_size.interval.lb + max_size = step_size.interval.ub + for ss in range(int(min_size), int(max_size) + 1): + tvects.append(np.arange(0, int(max_steps * ss) + 1, int(ss))) + + return tvects + + def check_simulation( + self, config: "FUNMANConfig", results: "AnalysisScenarioResult" + ): + # Check solution with simulation + sim_results = [] + for point in results.parameter_space.points(): + timeseries = self.check_point_simulation( + point, point.relevant_timepoints() + ) + sim_results.append((point, timeseries)) + + for sim_result in sim_results: + with Solver() as solver: + sim_encoding = self.encode_timeseries_verification( + *sim_result + ) + solver.add_assertion(sim_encoding) + result = solver.solve() + if result: + l.info("simulation passed verification") + else: + l.info("simulation failed verification") + return result + + def encode_timeseries_verification( + self, point: Point, timeseries: Timeseries + ): + # Get constraints needed to check timeseries + ts_constraint = TimeseriesConstraint( + name="simulation", timeseries=timeseries + ) + timeseries_constraints = [ + TimeseriesConstraint(name="simulation", timeseries=timeseries) + ] + [c for c in self.constraints if not isinstance(c, ModelConstraint)] + + encoded_constraints = [] + timepoints = timeseries["time"] + encoding = self._smt_encoder.initialize_encodings( + self, len(point.schedule.timepoints) + ) + for c in timeseries_constraints: + for timestep, timepoint in enumerate(timepoints): + if c.encodable() and c.relevant_at_time(timepoint): + encoded_constraints.append( + encoding.construct_encoding( + self, + c, + EncodingOptions(schedule=point.schedule), + layers=[timestep], + assumptions=self._assumptions, + ) + ) + formula = And(encoded_constraints) + + return formula + class AnalysisScenarioResult(ABC): """ diff --git a/src/funman/search/simulate.py b/src/funman/search/simulate.py new file mode 100644 index 00000000..9b438a0d --- /dev/null +++ b/src/funman/search/simulate.py @@ -0,0 +1,57 @@ +from typing import Dict, List, Union + +import numpy as np +import pandas as pd +import sympy +from pydantic import BaseModel +from scipy.integrate import odeint + +from funman import FunmanModel + +from ..representation.representation import Timeseries + +numeric = Union[int, float] + + +class Simulator(BaseModel): + model: FunmanModel + init: Dict[str, Union[float, str]] + parameters: Dict[str, float] + tvect: List[numeric] + + def model_args(self) -> List[float]: + def make_param_func(pname, value): + def pfunc(t): + return np.piecewise(t, [t >= 0], [value]) + + pfunc.__name__ = pname + return pfunc + + params = [make_param_func(p, pv) for p, pv in self.parameters.items()] + return tuple(params) + + def initial_state(self) -> List[float]: + init_state = [ + ( + sympy.sympify(v).evalf(subs=self.parameters) + if isinstance(v, str) + else v + ) + for var, v in self.init.items() + ] + return tuple(init_state) + + def sim(self): + # gradient_fn = partial(self.model.gradient, self.model) # hide the self reference to self.model from odeint + timeseries = odeint( + self.model.gradient, + self.initial_state(), + self.tvect, + args=self.model_args(), + ) + + ts = Timeseries( + data=[self.tvect] + timeseries.T.tolist(), + columns=["time"] + self.model._state_var_names(), + ) + return ts diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 5c470a66..3979aa7d 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -308,7 +308,11 @@ def point_parameters( if scenario is None: scenario = self._scenario() parameters = scenario.model_parameters() - return {p: point.values[p.name] for p in parameters} + return { + p: point.values[p.name] + for p in parameters + if p.name in point.values + } def dataframe( self, points: List[Point], interpolate="linear", max_time=None diff --git a/src/funman/translate/translate.py b/src/funman/translate/translate.py index d973b85b..20aa1e6f 100644 --- a/src/funman/translate/translate.py +++ b/src/funman/translate/translate.py @@ -47,6 +47,7 @@ ParameterConstraint, QueryConstraint, StateVariableConstraint, + TimeseriesConstraint, ) from funman.translate.simplifier import FUNMANSimplifier from funman.utils import math_utils @@ -225,6 +226,7 @@ def __init__(self, **kwargs): StateVariableConstraint: self.encode_query_layer, QueryConstraint: self.encode_query_layer, LinearConstraint: self.encode_linear_constraint, + TimeseriesConstraint: self.encode_timeseries, } def step_size_index(self, step_size: int) -> int: @@ -711,6 +713,28 @@ def encode_parameter( else: return None + def encode_timeseries( + self, + scenario: "AnalysisScenario", + constraint: TimeseriesConstraint, + layer_idx: int, + options: EncodingOptions, + assumptions: List[Assumption], + ) -> Optional[EncodedFormula]: + timepoint = constraint.timeseries["time"][layer_idx] + formulas = [] + for sv in constraint.timeseries.columns: + if sv == "time": + continue + + sv_formula = Equals( + self._encode_state_var(sv, time=int(timepoint)), + Real(constraint.timeseries[sv][layer_idx]), + ) + formulas.append(sv_formula) + formula = And(formulas) + return (formula, {str(s): s for s in formula.get_free_variables()}) + def encode_linear_constraint( self, scenario: "AnalysisScenario", From be1d43055d8bd11a79245eebdd3dc58d7e2dbadd Mon Sep 17 00:00:00 2001 From: Drisana Mosaphir Date: Wed, 31 Jul 2024 13:43:41 -0500 Subject: [PATCH 02/93] SIDARTHE original unit test --- ...ust_2024_eval_6_month_eval_s2_q1_b_i.ipynb | 231 ++++++++++++++++++ 1 file changed, 231 insertions(+) create mode 100644 scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_i.ipynb diff --git a/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_i.ipynb b/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_i.ipynb new file mode 100644 index 00000000..b0ded7ca --- /dev/null +++ b/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_i.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import scipy\n", + "from scipy.integrate import odeint\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "import sir_model\n", + "import json\n", + "from random import randint" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize recording of parameter choices and true/false\n", + "\n", + "\n", + "# USER: set bounds\n", + "theta_search_bounds = [0.371, 0.371]\n", + "epsilon_search_bounds = [0.171, 0.171]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# USER: list how many points for each parameter you'd like to synthesize\n", + "\n", + "theta_values_to_synthesize = 1\n", + "epsilon_values_to_synthesize = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "search_points_theta = np.linspace(theta_search_bounds[0], theta_search_bounds[1], theta_values_to_synthesize)\n", + "search_points_epsilon = np.linspace(epsilon_search_bounds[0], epsilon_search_bounds[1], epsilon_values_to_synthesize)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "alpha_val = 0.57\n", + "beta_val = 0.011\n", + "delta_val = 0.011\n", + "gamma_val = 0.456\n", + "\n", + "# epsilon_val = 0.05 #0.171\n", + "# theta_val = 0.371\n", + "\n", + "zeta_val = 0.125\n", + "eta_val = 0.125\n", + "\n", + "mu_val = 0.017\n", + "nu_val = 0.027\n", + "lamb_val = 0.034\n", + "rho_val = 0.034\n", + "\n", + "kappa_val = 0.017\n", + "xi_val = 0.017\n", + "sigma_val = 0.017\n", + "\n", + "tau_val = 0.01" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "max I percentage: 0.5972694944533659\n", + "argmax I: 47\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'theta': 0.371, 'epsilon': 0.171, 'assignment': '1'}\n" + ] + } + ], + "source": [ + "# set parameters\n", + "def ps(param_synth_method):\n", + " param_choices_true_false = []\n", + " for i in range(len(search_points_theta)):\n", + " theta_val = search_points_theta[i]\n", + " for j in range(len(search_points_epsilon)):\n", + " epsilon_val = search_points_epsilon[j]\n", + "\n", + " # parameters\n", + " # set parameter values\n", + " def alpha(t): return np.piecewise(t, [t>=0], [alpha_val])\n", + " def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n", + " def delta(t): return np.piecewise(t, [t>=0], [delta_val])\n", + " def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n", + "\n", + " def epsilon(t): return np.piecewise(t, [t>=0], [epsilon_val])\n", + " def theta(t): return np.piecewise(t, [t>=0], [theta_val])\n", + "\n", + " def zeta(t): return np.piecewise(t, [t>=0], [zeta_val])\n", + " def eta(t): return np.piecewise(t, [t>=0], [eta_val])\n", + "\n", + " def mu(t): return np.piecewise(t, [t>=0], [mu_val])\n", + " def nu(t): return np.piecewise(t, [t>=0], [nu_val])\n", + " def lamb(t): return np.piecewise(t, [t>=0], [lamb_val])\n", + " def rho(t): return np.piecewise(t, [t>=0], [rho_val])\n", + "\n", + " def kappa(t): return np.piecewise(t, [t>=0], [kappa_val])\n", + " def xi(t): return np.piecewise(t, [t>=0], [xi_val])\n", + " def sigma(t): return np.piecewise(t, [t>=0], [sigma_val])\n", + "\n", + " def tau(t): return np.piecewise(t, [t>=0], [tau_val])\n", + "\n", + "\n", + " # USER: set initial conditions\n", + " I0, D0, A0, R0, T0, H0, E0 = 200/(60e6), 20/(60e6), 1/(60e6), 2/(60e6), 0, 0, 0\n", + " S0 = 1-I0-D0-A0-R0-T0-H0-E0\n", + " y0 = S0, I0, D0, A0, R0, T0, H0, E0 # Initial conditions vector\n", + " # USER: set simulation parameters\n", + " dt = 1\n", + " tstart = 0\n", + " tend = 100\n", + " tvect = np.arange(tstart, tend, dt)\n", + " # simulate/solve ODEs\n", + " sim = odeint(sir_model.SIDARTHE_model, y0, tvect, args=(alpha, beta, gamma, delta, epsilon, mu, zeta, lamb, eta, rho, theta, kappa, nu, xi, sigma, tau))\n", + " S, I, D, A, R, T, H, E = sim.T\n", + " print('max I percentage:', max(I+D+A+R+T))\n", + " print('argmax I:', np.argmax(I+D+A+R+T))\n", + " # plot results - uncomment next line to plot time series. not recommended for large numbers of points\n", + " sir_model.plotSIDARTHE(tvect, S, I, D, A, R, T, H, E)\n", + " # USER: write query condition.\n", + " query_condition = (0.55 <= max(I+D+A+R+T) <= 0.65) and (40 <= np.argmax(I+D+A+R+T) <= 50)\n", + " query = '1' if query_condition else '0'\n", + " param_assignments = {'theta': theta_val, 'epsilon': epsilon_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\n", + " print(param_assignments)\n", + " param_choices_true_false.append(param_assignments)\n", + " if param_synth_method == \"any\" and query == '1':\n", + " return param_choices_true_false\n", + " return param_choices_true_false\n", + " \n", + "param_choices_true_false = ps(\"all\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'I' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mmax\u001b[39m(\u001b[43mI\u001b[49m))\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(np\u001b[38;5;241m.\u001b[39margmax(I))\n", + "\u001b[0;31mNameError\u001b[0m: name 'I' is not defined" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 608db5288b8a8da08ac6c88de617632072726513 Mon Sep 17 00:00:00 2001 From: Drisana Mosaphir Date: Wed, 31 Jul 2024 13:46:18 -0500 Subject: [PATCH 03/93] SIDARTHE step function unit test --- ...st_2024_eval_6_month_eval_s2_q1_b_ii.ipynb | 158 ++++++++++++++++++ 1 file changed, 158 insertions(+) create mode 100644 scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_ii.ipynb diff --git a/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_ii.ipynb b/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_ii.ipynb new file mode 100644 index 00000000..29a7c678 --- /dev/null +++ b/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_ii.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import scipy\n", + "from scipy.integrate import odeint\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "import sir_model\n", + "import json\n", + "from random import randint" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "max I percentage: 0.00192296687623966\n", + "argmax I: 50\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query: 1\n" + ] + } + ], + "source": [ + "# parameters\n", + "# set parameter values\n", + "\n", + "# np.piecewise(x, [x < 0, x >= 0], [-1, 1])\n", + "# alpha_val = 0.57\n", + "# beta_val = 0.011\n", + "# delta_val = 0.011\n", + "# gamma_val = 0.456\n", + "\n", + "# # epsilon_val = 0.05 #0.171\n", + "# # theta_val = 0.371\n", + "\n", + "# zeta_val = 0.125\n", + "# eta_val = 0.125\n", + "\n", + "# mu_val = 0.017\n", + "# nu_val = 0.027\n", + "# lamb_val = 0.034\n", + "# rho_val = 0.034\n", + "\n", + "# kappa_val = 0.017\n", + "# xi_val = 0.017\n", + "# sigma_val = 0.017\n", + "\n", + "# tau_val = 0.01\n", + "\n", + "\n", + "\n", + "def alpha(t): return np.piecewise(t, [0<=t<=4, 428], [0.57, 0.422, 0.36, 0.210]) # checked\n", + "def beta(t): return np.piecewise(t, [0<=t<=4, 422], [0.011, 0.0057, 0.005]) # checked\n", + "def delta(t): return np.piecewise(t, [0<=t<=4, 422], [0.011, 0.0057, 0.005]) # checked\n", + "def gamma(t): return np.piecewise(t, [0<=t<=4, 428], [0.456, 0.285, 0.2, 0.110]) # checked\n", + "\n", + "def epsilon(t): return np.piecewise(t, [0<=t<=12, 1238], [0.171, 0.143, 0.2])\n", + "def theta(t): return np.piecewise(t, [t>=0], [0.371]) # checked\n", + "\n", + "def zeta(t): return np.piecewise(t, [0<=t<=22, 2238], [0.125, 0.034, 0.025])\n", + "def eta(t): return np.piecewise(t, [0<=t<=22, 2238], [0.125, 0.034, 0.025])\n", + "\n", + "def mu(t): return np.piecewise(t, [0<=t<=22, t>22], [0.017, 0.008])\n", + "def nu(t): return np.piecewise(t, [0<=t<=22, t>22], [0.027, 0.015])\n", + "def lamb(t): return np.piecewise(t, [0<=t<=22, t>22], [0.034, 0.08])\n", + "def rho(t): return np.piecewise(t, [0<=t<=22, 2238], [0.034, 0.017, 0.02]) # checked\n", + "\n", + "def kappa(t): return np.piecewise(t, [0<=t<=22, 2238], [0.017, 0.017, 0.02]) # checked\n", + "def xi(t): return np.piecewise(t, [0<=t<=22, 2238], [0.017, 0.017, 0.02]) # checked\n", + "def sigma(t): return np.piecewise(t, [0<=t<=22, 2238], [0.017, 0.017, 0.01]) # checked\n", + "\n", + "def tau(t): return np.piecewise(t, [t>=0], [0.01]) # checked\n", + "\n", + "\n", + "# USER: set initial conditions\n", + "I0, D0, A0, R0, T0, H0, E0 = 200/(60e6), 20/(60e6), 1/(60e6), 2/(60e6), 0, 0, 0\n", + "S0 = 1-I0-D0-A0-R0-T0-H0-E0\n", + "y0 = S0, I0, D0, A0, R0, T0, H0, E0 # Initial conditions vector\n", + "# USER: set simulation parameters\n", + "dt = 1\n", + "tstart = 0\n", + "tend = 100\n", + "tvect = np.arange(tstart, tend, dt)\n", + "# simulate/solve ODEs\n", + "sim = odeint(sir_model.SIDARTHE_model, y0, tvect, args=(alpha, beta, gamma, delta, epsilon, mu, zeta, lamb, eta, rho, theta, kappa, nu, xi, sigma, tau))\n", + "S, I, D, A, R, T, H, E = sim.T\n", + "print('max I percentage:', max(I+D+A+R+T))\n", + "print('argmax I:', np.argmax(I+D+A+R+T))\n", + "# plot results - uncomment next line to plot time series. not recommended for large numbers of points\n", + "sir_model.plotSIDARTHE(tvect, S, I, D, A, R, T, H, E)\n", + "# USER: write query condition.\n", + "query_condition = (0.0015 <= max(I+D+A+R+T) <= 0.0025) and (45 <= np.argmax(I+D+A+R+T) <= 55)\n", + "query = '1' if query_condition else '0'\n", + "print('query:', query)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 50bb68ac5ac0a02aa5c8485358d532a5105fa27b Mon Sep 17 00:00:00 2001 From: Drisana Mosaphir Date: Wed, 31 Jul 2024 15:01:32 -0500 Subject: [PATCH 04/93] AMRs for Aug2024 demo --- .../q1a_ii/eval_scenario1_1_ii_1.json | 371 + .../q1a_ii/eval_scenario1_1_ii_2.json | 387 + .../q1a_ii/eval_scenario1_1_ii_3.json | 706 + .../q1a_ii/eval_scenario1_base.json | 355 + .../q2c/eval_scenario1_2_sirhd.json | 311 + .../q2c/eval_scenario1_2_sirhd_age.json | 11999 ++++++++++++++++ .../baseModel/eval_scenario2_base.json | 355 + .../q1b/eval_scenario2_1_b.json | 6336 ++++++++ .../q1b/part_1/BIOMD0000000955_askenet.json | 694 + .../q1b/part_2/scenario2_a.json | 1489 ++ .../part_2/scenario2_a_beta_scale_var.json | 1545 ++ .../scenario2_a_beta_scale_var_fixed.json | 1545 ++ .../q2a/scenario2_sidarthe_v.json | 546 + .../2024-08/scenarios_amr_links.md | 39 + 14 files changed, 26678 insertions(+) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_1.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_2.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q2c/eval_scenario1_2_sirhd.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q2c/eval_scenario1_2_sirhd_age.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/baseModel/eval_scenario2_base.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/q1b/eval_scenario2_1_b.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_1/BIOMD0000000955_askenet.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_2/scenario2_a.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_2/scenario2_a_beta_scale_var.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_2/scenario2_a_beta_scale_var_fixed.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q2a/scenario2_sidarthe_v.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/scenarios_amr_links.md diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_1.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_1.json new file mode 100644 index 00000000..e917f6a2 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_1.json @@ -0,0 +1,371 @@ +{ + "name": "Evaluation Scenario 1. Part 1 (ii) Masking type 1", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 1", + "model_version": "0.1", + "properties": {}, + "model": { + "states": [ + { + "id": "S", + "name": "S", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I", + "name": "I", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E", + "name": "E", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R", + "name": "R", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H", + "name": "H", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D", + "name": "D", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1", + "input": [ + "I", + "S" + ], + "output": [ + "I", + "E" + ], + "properties": { + "name": "t1" + } + }, + { + "id": "t2", + "input": [ + "E" + ], + "output": [ + "I" + ], + "properties": { + "name": "t2" + } + }, + { + "id": "t3", + "input": [ + "I" + ], + "output": [ + "R" + ], + "properties": { + "name": "t3" + } + }, + { + "id": "t4", + "input": [ + "I" + ], + "output": [ + "H" + ], + "properties": { + "name": "t4" + } + }, + { + "id": "t5", + "input": [ + "H" + ], + "output": [ + "R" + ], + "properties": { + "name": "t5" + } + }, + { + "id": "t6", + "input": [ + "H" + ], + "output": [ + "D" + ], + "properties": { + "name": "t6" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1", + "expression": "I*S*beta*(-c_m*eps_m + 1)/N", + "expression_mathml": "ISbetac_meps_m1N" + }, + { + "target": "t2", + "expression": "E*r_E_to_I", + "expression_mathml": "Er_E_to_I" + }, + { + "target": "t3", + "expression": "I*p_I_to_R*r_I_to_R", + "expression_mathml": "Ip_I_to_Rr_I_to_R" + }, + { + "target": "t4", + "expression": "I*p_I_to_H*r_I_to_H", + "expression_mathml": "Ip_I_to_Hr_I_to_H" + }, + { + "target": "t5", + "expression": "H*p_H_to_R*r_H_to_R", + "expression_mathml": "Hp_H_to_Rr_H_to_R" + }, + { + "target": "t6", + "expression": "H*p_H_to_D*r_H_to_D", + "expression_mathml": "Hp_H_to_Dr_H_to_D" + } + ], + "initials": [ + { + "target": "S", + "expression": "19339995.0000000", + "expression_mathml": "19339995.0" + }, + { + "target": "I", + "expression": "4.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E", + "expression": "1.00000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D", + "expression": "0.0", + "expression_mathml": "0.0" + } + ], + "parameters": [ + { + "id": "N", + "value": 19340000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta", + "value": 0.4, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "c_m", + "value": 0.5, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "eps_m", + "value": 0.5, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_E_to_I", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_R", + "value": 0.8, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_R", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_H", + "value": 0.2, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_H", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_R", + "value": 0.88, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_R", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_D", + "value": 0.12, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_D", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + } + ], + "observables": [], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + } + }, + "metadata": { + "annotations": { + "license": null, + "authors": [], + "references": [], + "time_scale": null, + "time_start": null, + "time_end": null, + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_2.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_2.json new file mode 100644 index 00000000..f6d4b798 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_2.json @@ -0,0 +1,387 @@ +{ + "name": "Evaluation Scenario 1. Part 1 (ii) Masking type 2", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 2", + "model_version": "0.1", + "properties": {}, + "model": { + "states": [ + { + "id": "S", + "name": "S", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I", + "name": "I", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E", + "name": "E", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R", + "name": "R", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H", + "name": "H", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D", + "name": "D", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1", + "input": [ + "I", + "S" + ], + "output": [ + "I", + "E" + ], + "properties": { + "name": "t1" + } + }, + { + "id": "t2", + "input": [ + "E" + ], + "output": [ + "I" + ], + "properties": { + "name": "t2" + } + }, + { + "id": "t3", + "input": [ + "I" + ], + "output": [ + "R" + ], + "properties": { + "name": "t3" + } + }, + { + "id": "t4", + "input": [ + "I" + ], + "output": [ + "H" + ], + "properties": { + "name": "t4" + } + }, + { + "id": "t5", + "input": [ + "H" + ], + "output": [ + "R" + ], + "properties": { + "name": "t5" + } + }, + { + "id": "t6", + "input": [ + "H" + ], + "output": [ + "D" + ], + "properties": { + "name": "t6" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1", + "expression": "I*S*kappa*(beta_c + (-beta_c + beta_s)/(1 + exp(-k*(-t + t_0))))/N", + "expression_mathml": "ISkappabeta_cbeta_cbeta_s1kt_0tN" + }, + { + "target": "t2", + "expression": "E*r_E_to_I", + "expression_mathml": "Er_E_to_I" + }, + { + "target": "t3", + "expression": "I*p_I_to_R*r_I_to_R", + "expression_mathml": "Ip_I_to_Rr_I_to_R" + }, + { + "target": "t4", + "expression": "I*p_I_to_H*r_I_to_H", + "expression_mathml": "Ip_I_to_Hr_I_to_H" + }, + { + "target": "t5", + "expression": "H*p_H_to_R*r_H_to_R", + "expression_mathml": "Hp_H_to_Rr_H_to_R" + }, + { + "target": "t6", + "expression": "H*p_H_to_D*r_H_to_D", + "expression_mathml": "Hp_H_to_Dr_H_to_D" + } + ], + "initials": [ + { + "target": "S", + "expression": "19339995.0000000", + "expression_mathml": "19339995.0" + }, + { + "target": "I", + "expression": "4.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E", + "expression": 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+ }, + { + "id": "r_E_to_I", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_R", + "value": 0.8, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_R", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_H", + "value": 0.2, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_H", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_R", + "value": 0.88, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_R", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_D", + "value": 0.12, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_D", + "value": 0.1, + "units": { + "expression": "1/day", + 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Part 1 (ii) Masking type 3", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", + "model_version": "0.1", + "properties": {}, + "model": { + "states": [ + { + "id": "S_compliant", + "name": "S_compliant", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_compliant", + "name": "I_compliant", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_compliant", + "name": "E_compliant", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_noncompliant", + "name": "I_noncompliant", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "noncompliant" + 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"mira_rate_law": "Infected*Susceptible*alpha", + "mira_rate_law_mathml": "InfectedSusceptiblealpha", + "mira_parameters": "{\"alpha\": 0.57}", + "mira_parameter_distributions": "{\"alpha\": null}" + }, + "rate": 0.57 + }, + { + "tname": "t5", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "epsilon", + "parameter_value": 0.171, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Infected*epsilon\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Infected\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {}}, \"outcome\": {\"name\": \"Diagnosed\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"provenance\": []}", + "mira_rate_law": "Infected*epsilon", + "mira_rate_law_mathml": "Infectedepsilon", + "mira_parameters": "{\"epsilon\": 0.171}", + "mira_parameter_distributions": "{\"epsilon\": null}" + }, + "rate": 0.171 + }, + { + "tname": "t6", + "tprop": { + "template_type": 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{\"ido\": \"0000511\"}, \"context\": {}}, \"outcome\": {\"name\": \"Healed\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Infected*XXlambdaXX", + "mira_rate_law_mathml": "InfectedXXlambdaXX", + "mira_parameters": "{\"lambda\": 0.034}", + "mira_parameter_distributions": "{\"lambda\": null}" + }, + "rate": 0.034 + }, + { + "tname": "t8", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "eta", + "parameter_value": 0.125, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Diagnosed*eta\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Diagnosed\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"outcome\": {\"name\": \"Recognized\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"provenance\": []}", + "mira_rate_law": "Diagnosed*eta", + "mira_rate_law_mathml": "Diagnosedeta", + "mira_parameters": "{\"eta\": 0.125}", + "mira_parameter_distributions": "{\"eta\": null}" + }, + "rate": 0.125 + }, + { + "tname": "t9", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "rho", + "parameter_value": 0.034, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Diagnosed*rho\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Diagnosed\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"outcome\": {\"name\": \"Healed\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Diagnosed*rho", + "mira_rate_law_mathml": "Diagnosedrho", + "mira_parameters": "{\"rho\": 0.034}", + "mira_parameter_distributions": "{\"rho\": null}" + }, + "rate": 0.034 + }, + { + "tname": "t10", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "theta", + "parameter_value": 0.371, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Ailing*theta\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Ailing\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25269\", \"diagnosis\": \"ncit:C113725\"}}, \"outcome\": {\"name\": \"Recognized\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"provenance\": []}", + "mira_rate_law": "Ailing*theta", + "mira_rate_law_mathml": "Ailingtheta", + "mira_parameters": "{\"theta\": 0.371}", + "mira_parameter_distributions": "{\"theta\": null}" + }, + "rate": 0.371 + }, + { + "tname": "t11", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "kappa", + "parameter_value": 0.017, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Ailing*kappa\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Ailing\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25269\", \"diagnosis\": \"ncit:C113725\"}}, \"outcome\": {\"name\": \"Healed\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Ailing*kappa", + "mira_rate_law_mathml": "Ailingkappa", + "mira_parameters": "{\"kappa\": 0.017}", + "mira_parameter_distributions": "{\"kappa\": null}" + }, + "rate": 0.017 + }, + { + "tname": "t12", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "mu", + "parameter_value": 0.017, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Ailing*mu\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Ailing\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25269\", \"diagnosis\": \"ncit:C113725\"}}, \"outcome\": {\"name\": \"Threatened\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25467\"}}, \"provenance\": []}", + "mira_rate_law": "Ailing*mu", + "mira_rate_law_mathml": "Ailingmu", + "mira_parameters": "{\"mu\": 0.017}", + "mira_parameter_distributions": "{\"mu\": null}" + }, + "rate": 0.017 + }, + { + "tname": "t13", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "nu", + "parameter_value": 0.027, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Recognized*nu\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Recognized\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"outcome\": {\"name\": \"Threatened\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25467\"}}, \"provenance\": []}", + "mira_rate_law": "Recognized*nu", + "mira_rate_law_mathml": "Recognizednu", + "mira_parameters": "{\"nu\": 0.027}", + "mira_parameter_distributions": "{\"nu\": null}" + }, + "rate": 0.027 + }, + { + "tname": "t14", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "xi", + "parameter_value": 0.017, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Recognized*xi\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Recognized\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"outcome\": {\"name\": \"Healed\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Recognized*xi", + "mira_rate_law_mathml": "Recognizedxi", + "mira_parameters": "{\"xi\": 0.017}", + "mira_parameter_distributions": "{\"xi\": null}" + }, + "rate": 0.017 + }, + { + "tname": "t15", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "tau_2", + "parameter_value": 0.01, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Threatened*tau_2\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Threatened\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25467\"}}, \"outcome\": {\"name\": \"Extinct\", \"identifiers\": {\"ncit\": \"C28554\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Threatened*tau_2", + "mira_rate_law_mathml": "Threatenedtau_2", + "mira_parameters": "{\"tau_2\": 0.01}", + "mira_parameter_distributions": "{\"tau_2\": null}" + }, + "rate": 0.01 + }, + { + "tname": "t16", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "sigma", + "parameter_value": 0.017, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Threatened*sigma\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Threatened\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25467\"}}, \"outcome\": {\"name\": \"Healed\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Threatened*sigma", + "mira_rate_law_mathml": "Threatenedsigma", + "mira_parameters": "{\"sigma\": 0.017}", + "mira_parameter_distributions": "{\"sigma\": null}" + }, + "rate": 0.017 + }, + { + "tname": "t17", + "tprop": { + "template_type": 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\"outcome\": {\"name\": \"Extinct\", \"identifiers\": {\"ncit\": \"C28554\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Recognized*tau_1", + "mira_rate_law_mathml": "Recognizedtau_1", + "mira_parameters": "{\"tau_1\": 0.00333}", + "mira_parameter_distributions": "{\"tau_1\": null}" + }, + "rate": 0.00333 + } + ], + "I": [ + { + "is": 3, + "it": 1 + }, + { + "is": 1, + "it": 1 + }, + { + "is": 4, + "it": 2 + }, + { + "is": 1, + "it": 2 + }, + { + "is": 5, + "it": 3 + }, + { + "is": 1, + "it": 3 + }, + { + "is": 2, + "it": 4 + }, + { + "is": 1, + "it": 4 + }, + { + "is": 2, + "it": 5 + }, + { + "is": 2, + "it": 6 + }, + { + "is": 2, + "it": 7 + }, + { + "is": 3, + "it": 8 + }, + { + "is": 3, + "it": 9 + }, + { + "is": 4, + "it": 10 + }, + { + "is": 4, + "it": 11 + }, + { + "is": 4, + "it": 12 + }, + { + "is": 5, + "it": 13 + }, + { + "is": 5, + "it": 14 + }, + { + "is": 7, + "it": 15 + }, + { + "is": 7, + "it": 16 + }, + { + "is": 1, + "it": 17 + }, + { + "is": 5, + "it": 18 + } + ], + "O": [ + { + "os": 3, + "ot": 1 + }, + { + "os": 2, + "ot": 1 + }, + { + "os": 4, + "ot": 2 + }, + { + "os": 2, + "ot": 2 + }, + { + "os": 5, + "ot": 3 + }, + { + "os": 2, + "ot": 3 + }, + { + "os": 2, + "ot": 4 + }, + { + "os": 2, + "ot": 4 + }, + { + "os": 3, + "ot": 5 + }, + { + "os": 4, + "ot": 6 + }, + { + "os": 6, + "ot": 7 + }, + { + "os": 5, + "ot": 8 + }, + { + "os": 6, + "ot": 9 + }, + { + "os": 5, + "ot": 10 + }, + { + "os": 6, + "ot": 11 + }, + { + "os": 7, + "ot": 12 + }, + { + "os": 7, + "ot": 13 + }, + { + "os": 6, + "ot": 14 + }, + { + "os": 8, + "ot": 15 + }, + { + "os": 6, + "ot": 16 + }, + { + "os": 9, + "ot": 17 + }, + { + "os": 8, + "ot": 18 + } + ] +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-08/scenarios_amr_links.md b/resources/amr/petrinet/monthly-demo/2024-08/scenarios_amr_links.md new file mode 100644 index 00000000..4213b862 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/scenarios_amr_links.md @@ -0,0 +1,39 @@ +Scenarios: +• 6Mo Eval S2: Q1b (ingest the model with whatever process makes the most sense, and do unit tests), Q2a +• 12Mo Eval S1: Q1a.ii, Q2c +• 12Mo Eval S2 : Q1b, Q2 +Scenario Materials: +1. (6Mo Eval Scenarios) https://github.com/DARPA-ASKEM/program-milestones/tree/main/6-month-milestone/evaluation +2. (12Mo Eval Scenarios): https://github.com/DARPA-ASKEM/program-milestones/tree/main/12-month-milestone/evaluation +For any simulation questions, you may simplify model configuration and just use reasonable values. We want to do some simulation with these models to demonstrate that the stratified or transformed versions give reasonable results that make sense, and can be used to compare outcomes for different age groups, or compare results before and after stratification. Ignore any calibration questions or comparison with other questions outside of this subset. + +AMRs + +6 Month Eval S2 + +Q1b.(i): SIDARTHE, original parameters https://github.com/siftech/funman/blob/d909110cf4c82b62569d37979703c36260909c41/resources/amr/petrinet/mira/models/BIOMD0000000955_askenet.json + +Q1b.(ii): SIDARTHE, updated (step function) parameters +• https://github.com/siftech/funman/blob/d909110cf4c82b62569d37979703c36260909c41/resources/amr/petrinet/mira/models/scenario2_a.json#L1029 +• https://github.com/siftech/funman/blob/d909110cf4c82b62569d37979703c36260909c41/resources/amr/petrinet/mira/models/scenario2_a_beta_scale_var.json#L1029 +• https://github.com/siftech/funman/blob/d909110cf4c82b62569d37979703c36260909c41/resources/amr/petrinet/mira/models/scenario2_a_beta_scale_var_fixed.json#L1029 + +Q2a. SIDARTHE-V: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.01/scenario2_sidarthe_v.json + + +12 Month Eval S1: +Q1a.ii: +(0): Original SEIRHD (may not be necessary): https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_base.json +(1): SEIRHD: modify beta  beta*(1-epsilon_m*c_m): https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_1_ii_1.json +(2): SEIRHD: time-varying beta: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_1_ii_2.json +(3): SEIRHD: stratify by masking: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_1_ii_3.json +Q2c: SIRHD (removed E component for simplicity), stratified into 18 age groups: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_2_sirhd_age.json +Reference for Q2c (may not need to use this): SIRHD not yet stratified by age: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_2_sirhd.json + + +12 Month Eval S2 : +Base model: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario2_base.json +Q1b: SEIRHD with multiple vaccine components: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario2_1_b.json +Q2: SEIRHD extension to include contact matrices – age-stratified 0-9, 10-19, 20-29: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario2_2_a.json + + From ad25e2622e6df8769dfb7c0d9d86c9dd197c73e6 Mon Sep 17 00:00:00 2001 From: dmosaphir <105988905+dmosaphir@users.noreply.github.com> Date: Wed, 31 Jul 2024 15:05:06 -0500 Subject: [PATCH 05/93] AMR - Aug 2024 demo --- .../eval_scenario2_2_a.json | 20713 ++++++++++++++++ 1 file changed, 20713 insertions(+) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/eval_scenario2_2_a.json diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/eval_scenario2_2_a.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/eval_scenario2_2_a.json new file mode 100644 index 00000000..acf84266 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/eval_scenario2_2_a.json @@ -0,0 +1,20713 @@ +{ + "name": "Evaluation Scenario 2 Base model", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 2 Base model", + "model_version": "0.1", + "properties": {}, + "model": { + "states": [ + { + "id": "S_0_9_masked", + "name": "S_0_9_masked", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "age": "0_9", + "masking": "masked" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_0_9_masked", + "name": "I_0_9_masked", + 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+} \ No newline at end of file From d4bd565b8ecaf96d553a38e40606e59aae542ed0 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Mon, 5 Aug 2024 19:31:55 +0000 Subject: [PATCH 06/93] change theme for docs to avoid dep conflict --- docker/dev/root/Dockerfile.root | 3 ++- docker/dev/user/Dockerfile | 3 ++- docs/source/conf.py | 2 +- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/docker/dev/root/Dockerfile.root b/docker/dev/root/Dockerfile.root index c77e3cf2..790f9976 100644 --- a/docker/dev/root/Dockerfile.root +++ b/docker/dev/root/Dockerfile.root @@ -36,7 +36,8 @@ RUN pip install --no-cache-dir wheel RUN pip install --no-cache-dir pytest==7.1.2 RUN pip install --no-cache-dir sphinx==5.2.2 -RUN pip install --no-cache-dir sphinx-rtd-theme==1.0.0 +RUN pip install --no-cache-dir myst-parser +RUN pip install --no-cache-dir autodoc-pydantic RUN pip install --no-cache-dir twine RUN pip install --no-cache-dir build RUN pip install --no-cache-dir pylint diff --git a/docker/dev/user/Dockerfile b/docker/dev/user/Dockerfile index 19f1c636..080164bc 100644 --- a/docker/dev/user/Dockerfile +++ b/docker/dev/user/Dockerfile @@ -44,7 +44,8 @@ RUN pip install --no-cache-dir wheel RUN pip install --no-cache-dir pytest==7.1.2 RUN pip install --no-cache-dir sphinx==5.2.2 -RUN pip install --no-cache-dir sphinx-rtd-theme==1.0.0 +RUN pip install --no-cache-dir myst-parser +RUN pip install --no-cache-dir autodoc-pydantic RUN pip install --no-cache-dir twine RUN pip install --no-cache-dir build RUN pip install --no-cache-dir pylint diff --git a/docs/source/conf.py b/docs/source/conf.py index a1b2d89d..607acdc0 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -45,7 +45,7 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output # html_theme = 'pydata_sphinx_theme' -html_theme = "sphinx_rtd_theme" +html_theme = "classic" html_static_path = ["_static"] # Napoleon configuration for handling numpy docstrings From 13cc0e58f61ead9150b513f986ea21b51ca72043 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 7 Aug 2024 13:41:56 +0000 Subject: [PATCH 07/93] setup demo --- docker/docker-bake.hcl | 2 +- .../monthly-demos/funman_aug_2024_demo.ipynb | 468 ++++++++++++++++++ .../funman_july_2024_demo.ipynb | 0 .../BIOMD0000000955_askenet_request.json | 29 ++ 4 files changed, 498 insertions(+), 1 deletion(-) create mode 100644 notebooks/monthly-demos/funman_aug_2024_demo.ipynb rename notebooks/{ => monthly-demos}/funman_july_2024_demo.ipynb (100%) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_1/BIOMD0000000955_askenet_request.json diff --git a/docker/docker-bake.hcl b/docker/docker-bake.hcl index 3e55db84..06e46b01 100644 --- a/docker/docker-bake.hcl +++ b/docker/docker-bake.hcl @@ -20,7 +20,7 @@ variable "DREAL_REPO_URL" { default = "https://github.com/danbryce/dreal4.git" } variable "DREAL_COMMIT_TAG" { - default = "43706295350b9676b786e2d9d8607b1c6e19afee" + default = "844d64fd7427d5d2ce3b01a2f83231cefc8709a4" } variable "AUTOMATES_COMMIT_TAG" { default = "e5fb635757aa57007615a75371f55dd4a24851e0" diff --git a/notebooks/monthly-demos/funman_aug_2024_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_demo.ipynb new file mode 100644 index 00000000..a708d00a --- /dev/null +++ b/notebooks/monthly-demos/funman_aug_2024_demo.ipynb @@ -0,0 +1,468 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# This notebook illustrates handling the August 2024 Demo of the 6mo Hackathon Scenario 2, described at: https://github.com/DARPA-ASKEM/program-milestones/issues/74\n", + "\n", + "# Import funman related code\n", + "import os\n", + "from funman.api.run import Runner\n", + "from funman_demo import summarize_results\n", + "from funman import FunmanWorkRequest, EncodingSchedule \n", + "import json\n", + "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", + "from funman.representation import Interval\n", + "import pandas as pd\n", + "\n", + "RESOURCES = \"../../resources\"\n", + "SAVED_RESULTS_DIR = \"./out\"\n", + "\n", + "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"monthly-demo\", \"2024-08\", \"6_month_scenario_2\", \"q1b\", \"part_1\")\n", + "MODEL_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"BIOMD0000000955_askenet.json\"\n", + ")\n", + "REQUEST_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"BIOMD0000000955_askenet_request.json\"\n", + ")\n", + "\n", + "request_params = {}\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Constants for the scenario\n", + "STATES = [\"Susceptible\", \"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Healed\", \"Threatened\", \"Extinct\"]\n", + "\n", + "MAX_TIME=50\n", + "STEP_SIZE=5\n", + "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper functions to setup FUNMAN for different steps of the scenario\n", + "\n", + "def get_request():\n", + " with open(REQUEST_PATH, \"r\") as request:\n", + " funman_request = FunmanWorkRequest.model_validate(json.load(request))\n", + " return funman_request\n", + "\n", + "def set_timepoints(funman_request, timepoints):\n", + " funman_request.structure_parameters[0].schedules = [EncodingSchedule(timepoints=timepoints)]\n", + "\n", + "def unset_all_labels(funman_request):\n", + " for p in funman_request.parameters:\n", + " p.label = \"any\"\n", + " \n", + "def set_config_options(funman_request, debug=False, dreal_precision=1e-1):\n", + " # Overrides for configuration\n", + " #\n", + " # funman_request.config.substitute_subformulas = True\n", + " # funman_request.config.use_transition_symbols = True\n", + " # funman_request.config.use_compartmental_constraints=False\n", + " if debug:\n", + " funman_request.config.save_smtlib=\"./out\"\n", + " funman_request.config.tolerance = 0.01\n", + " funman_request.config.dreal_precision = dreal_precision\n", + " # funman_request.config.verbosity = 10\n", + " # funman_request.config.dreal_log_level = \"debug\"\n", + " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", + "\n", + "def get_synthesized_vars(funman_request):\n", + " return [p.name for p in funman_request.parameters if p.label == \"all\"]\n", + "\n", + "def run(funman_request, plot=False):\n", + " to_synthesize = get_synthesized_vars(funman_request)\n", + " return Runner().run(\n", + " MODEL_PATH,\n", + " funman_request,\n", + " description=\"SIDARTHE Eval Scenario 2.1.b.1 6mo\",\n", + " case_out_dir=SAVED_RESULTS_DIR,\n", + " dump_plot=plot,\n", + " print_last_time=True,\n", + " parameters_to_plot=to_synthesize\n", + " )\n", + "\n", + "def setup_common(funman_request, synthesize=False, debug=False, dreal_precision=1e-1):\n", + " set_timepoints(funman_request, timepoints)\n", + " if not synthesize:\n", + " unset_all_labels(funman_request)\n", + " set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision)\n", + " \n", + "\n", + "def set_compartment_bounds(funman_request, upper_bound=9830000.0, error=0.01):\n", + " # Add bounds to compartments\n", + " for var in STATES:\n", + " funman_request.constraints.append(StateVariableConstraint(name=f\"{var}_bounds\", variable=var, interval=Interval(lb=0, ub=upper_bound, closed_upper_bound=True),soft=False))\n", + "\n", + " # Add sum of compartments\n", + " funman_request.constraints.append(LinearConstraint(name=f\"compartment_bounds\", variables=STATES, additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True))\n", + "\n", + "def relax_parameter_bounds(funman_request, factor = 0.1):\n", + " # Relax parameter bounds\n", + " parameters = funman_request.parameters\n", + " for p in parameters:\n", + " interval = p.interval\n", + " width = float(interval.width())\n", + " interval.lb = interval.lb - (factor/2 * width)\n", + " interval.ub = interval.ub + (factor/2 * width)\n", + "\n", + "def plot_last_point(results):\n", + " pts = results.parameter_space.points() \n", + " print(f\"{len(pts)} points\")\n", + "\n", + " if len(pts) > 0:\n", + " # Get a plot for last point\n", + " df = results.dataframe(points=pts[-1:])\n", + " ax = df[STATES].plot()\n", + " ax.set_yscale(\"log\")\n", + "\n", + "def get_last_point_parameters(results):\n", + " pts = results.parameter_space.points()\n", + " if len(pts) > 0:\n", + " pt = pts[-1]\n", + " parameters = results.model._parameter_names()\n", + " param_values = {k:v for k, v in pt.values.items() if k in parameters }\n", + " return param_values\n", + "\n", + "def pretty_print_request_params(params):\n", + " # print(json.dump(params, indent=4))\n", + " if len(params)>0:\n", + "\n", + " df = pd.DataFrame(params)\n", + " print(df.T)\n", + "\n", + "\n", + "def report(results, name):\n", + " plot_last_point(results)\n", + " param_values = get_last_point_parameters(results)\n", + " print(f\"Point parameters: {param_values}\")\n", + " if param_values is not None:\n", + " request_params[name] = param_values\n", + " pretty_print_request_params(request_params)\n", + "\n", + "def add_unit_test(funman_request):\n", + " funman_request.constraints.append(LinearConstraint(name=\"unit_test\", variables = [\n", + " \"Infected\",\n", + " \"Diagnosed\",\n", + " \"Ailing\",\n", + " \"Recognized\",\n", + " \"Threatened\"\n", + " ],\n", + " additive_bounds= {\n", + " \"lb\": 0.55,\n", + " \"ub\": 0.65\n", + " },\n", + " timepoints={\n", + " \"lb\": 45,\n", + " \"ub\": 55\n", + " }\n", + " ))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + 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unconstrained
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" + ], + "text/plain": [ + " unconstrained\n", + "alpha 0.570\n", + "beta 0.011\n", + "delta 0.011\n", + "epsilon 0.171\n", + "eta 0.125\n", + "gamma 0.456\n", + "kappa 0.017\n", + "lambda 0.034\n", + "mu 0.017\n", + "nu 0.027\n", + "rho 0.034\n", + "sigma 0.017\n", + "tau 0.010\n", + "theta 0.371\n", + "xi 0.017\n", + "zeta 0.125" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = {'unconstrained': {'beta': 0.011, 'gamma': 0.45599999999999996, 'delta': 0.011, 'alpha': 0.5699999999999998, 'epsilon': 0.17099999999999999, 'zeta': 0.125, 'lambda': 0.034, 'eta': 0.125, 'rho': 0.034, 'theta': 0.371, 'kappa': 0.017, 'mu': 0.017, 'nu': 0.027, 'xi': 0.017, 'tau': 0.009999999999999998, 'sigma': 0.017}}\n", + "pd.DataFrame(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Automatic initialization of gaol... done\n", + "2024-08-06 21:13:47,838 - funman.scenario.consistency - INFO - 10{50}:\t[+]\n", + "2024-08-06 21:13:47,864 - funman.server.worker - INFO - Completed work on: 83bc1dbc-f847-473c-bbe3-1abdad721bce\n", + "2024-08-06 21:13:48,055 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-08-06 21:13:48,371 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-08-06 21:13:48,380 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 points\n", + "Point parameters: {'beta': 0.011, 'gamma': 0.45599999999999996, 'delta': 0.011, 'alpha': 0.5699999999999998, 'epsilon': 0.17099999999999999, 'zeta': 0.125, 'lambda': 0.034, 'eta': 0.125, 'rho': 0.034, 'theta': 0.371, 'kappa': 0.017, 'mu': 0.017, 'nu': 0.027, 'xi': 0.017, 'tau': 0.009999999999999998, 'sigma': 0.017}\n" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "# Find a single parameterization of the model where sum(IDART) is approx 60% around day 47.\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=True, dreal_precision=1e0)\n", + "# add_unit_test(funman_request)\n", + "results = run(funman_request)\n", + "report(results, \"unconstrained\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add bounds [0, N] to the STATE compartments. \n", + "# Add bounds sum(STATE) in [N-e, N+e], for a small e.\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=True)\n", + "set_compartment_bounds(funman_request)\n", + "results = run(funman_request)\n", + "report(results, \"compartmental_constrained\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Relax the bounds on the parameters to allow additional parameterizations\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request)\n", + "set_compartment_bounds(funman_request)\n", + "relax_parameter_bounds(funman_request, factor = 0.75)\n", + "results = run(funman_request)\n", + "report(results, \"relaxed_bounds\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "funman_request = get_request()\n", + "setup_common(funman_request, synthesize=True)\n", + "set_compartment_bounds(funman_request)\n", + "# relax_parameter_bounds(funman_request, factor=0.75)\n", + "# funman_request.config.verbosity=10\n", + "results = run(funman_request, plot=True)\n", + "report(results, \"synthesis\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import pandas as pd\n", + "# df1 = pd.DataFrame({\"ltp\": ltp, \"gtp\": gtp})\n", + "\n", + "# # df2 = \n", + "# # df1.ltp.N == df1.gtp.N\n", + "# # df2.loc[df2].sort_index()[0:60]\n", + "\n", + "# #(= H_10 (+ H_8 (* 2 (+ (* r_HD (- 1) H_8) (* r_HR (- 1) H_8) (* r_IH_v I_v_8) (* r_IH_u I_u_8)))))\n", + "\n", + "# df1['same'] = df1['ltp'] == df1[\"gtp\"]\n", + "# df1[0:20]\n", + "# # df1.loc[df1.index.str.endswith(\"_6\")].sort_values(by=\"same\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # Get points (trajectories generated)\n", + "# pts = results.parameter_space.points() \n", + "# print(f\"{len(pts)} points\")\n", + "\n", + "# # Get a plot for last point\n", + "# df = results.dataframe(points=pts[-1:])\n", + "# ax = df[STATES].plot()\n", + "# ax.set_yscale(\"log\")\n", + "\n", + "\n", + "# # Get the values of the point\n", + "# gtp=pts[-1].values\n", + "\n", + "\n", + "# # Output the model diagram\n", + "# #\n", + "# # results_unconstrained_point.model.to_dot()\n", + "# # gtp" + ] + } + ], + "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/notebooks/funman_july_2024_demo.ipynb b/notebooks/monthly-demos/funman_july_2024_demo.ipynb similarity index 100% rename from notebooks/funman_july_2024_demo.ipynb rename to notebooks/monthly-demos/funman_july_2024_demo.ipynb diff --git a/resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_1/BIOMD0000000955_askenet_request.json b/resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_1/BIOMD0000000955_askenet_request.json new file mode 100644 index 00000000..a601b0a9 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_1/BIOMD0000000955_askenet_request.json @@ -0,0 +1,29 @@ +{ + "constraints": [ + + ], + "parameters": [], + "structure_parameters": [ + { + "name": "schedules", + "schedules": [ + { + "timepoints": [ + 0, + 10, + 20, + 30, + 40, + 50 + ] + } + ] + } + ], + "config": { + "tolerance": 1e-2, + "dreal_precision": 0.001, + "normalization_constant": 1.0, + "use_compartmental_constraints": true + } +} \ No newline at end of file From 80978efd2e853788fe594293d2ba18d416022d2d Mon Sep 17 00:00:00 2001 From: Drisana Mosaphir Date: Wed, 7 Aug 2024 08:57:06 -0500 Subject: [PATCH 08/93] SIDARTHE original unit test --- ...ust_2024_eval_6_month_eval_s2_q1_b_i.ipynb | 231 ++++++++++++++++++ 1 file changed, 231 insertions(+) create mode 100644 scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_i.ipynb diff --git a/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_i.ipynb b/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_i.ipynb new file mode 100644 index 00000000..b0ded7ca --- /dev/null +++ b/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_i.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import scipy\n", + "from scipy.integrate import odeint\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "import sir_model\n", + "import json\n", + "from random import randint" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize recording of parameter choices and true/false\n", + "\n", + "\n", + "# USER: set bounds\n", + "theta_search_bounds = [0.371, 0.371]\n", + "epsilon_search_bounds = [0.171, 0.171]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# USER: list how many points for each parameter you'd like to synthesize\n", + "\n", + "theta_values_to_synthesize = 1\n", + "epsilon_values_to_synthesize = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "search_points_theta = np.linspace(theta_search_bounds[0], theta_search_bounds[1], theta_values_to_synthesize)\n", + "search_points_epsilon = np.linspace(epsilon_search_bounds[0], epsilon_search_bounds[1], epsilon_values_to_synthesize)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "alpha_val = 0.57\n", + "beta_val = 0.011\n", + "delta_val = 0.011\n", + "gamma_val = 0.456\n", + "\n", + "# epsilon_val = 0.05 #0.171\n", + "# theta_val = 0.371\n", + "\n", + "zeta_val = 0.125\n", + "eta_val = 0.125\n", + "\n", + "mu_val = 0.017\n", + "nu_val = 0.027\n", + "lamb_val = 0.034\n", + "rho_val = 0.034\n", + "\n", + "kappa_val = 0.017\n", + "xi_val = 0.017\n", + "sigma_val = 0.017\n", + "\n", + "tau_val = 0.01" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "max I percentage: 0.5972694944533659\n", + "argmax I: 47\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'theta': 0.371, 'epsilon': 0.171, 'assignment': '1'}\n" + ] + } + ], + "source": [ + "# set parameters\n", + "def ps(param_synth_method):\n", + " param_choices_true_false = []\n", + " for i in range(len(search_points_theta)):\n", + " theta_val = search_points_theta[i]\n", + " for j in range(len(search_points_epsilon)):\n", + " epsilon_val = search_points_epsilon[j]\n", + "\n", + " # parameters\n", + " # set parameter values\n", + " def alpha(t): return np.piecewise(t, [t>=0], [alpha_val])\n", + " def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n", + " def delta(t): return np.piecewise(t, [t>=0], [delta_val])\n", + " def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n", + "\n", + " def epsilon(t): return np.piecewise(t, [t>=0], [epsilon_val])\n", + " def theta(t): return np.piecewise(t, [t>=0], [theta_val])\n", + "\n", + " def zeta(t): return np.piecewise(t, [t>=0], [zeta_val])\n", + " def eta(t): return np.piecewise(t, [t>=0], [eta_val])\n", + "\n", + " def mu(t): return np.piecewise(t, [t>=0], [mu_val])\n", + " def nu(t): return np.piecewise(t, [t>=0], [nu_val])\n", + " def lamb(t): return np.piecewise(t, [t>=0], [lamb_val])\n", + " def rho(t): return np.piecewise(t, [t>=0], [rho_val])\n", + "\n", + " def kappa(t): return np.piecewise(t, [t>=0], [kappa_val])\n", + " def xi(t): return np.piecewise(t, [t>=0], [xi_val])\n", + " def sigma(t): return np.piecewise(t, [t>=0], [sigma_val])\n", + "\n", + " def tau(t): return np.piecewise(t, [t>=0], [tau_val])\n", + "\n", + "\n", + " # USER: set initial conditions\n", + " I0, D0, A0, R0, T0, H0, E0 = 200/(60e6), 20/(60e6), 1/(60e6), 2/(60e6), 0, 0, 0\n", + " S0 = 1-I0-D0-A0-R0-T0-H0-E0\n", + " y0 = S0, I0, D0, A0, R0, T0, H0, E0 # Initial conditions vector\n", + " # USER: set simulation parameters\n", + " dt = 1\n", + " tstart = 0\n", + " tend = 100\n", + " tvect = np.arange(tstart, tend, dt)\n", + " # simulate/solve ODEs\n", + " sim = odeint(sir_model.SIDARTHE_model, y0, tvect, args=(alpha, beta, gamma, delta, epsilon, mu, zeta, lamb, eta, rho, theta, kappa, nu, xi, sigma, tau))\n", + " S, I, D, A, R, T, H, E = sim.T\n", + " print('max I percentage:', max(I+D+A+R+T))\n", + " print('argmax I:', np.argmax(I+D+A+R+T))\n", + " # plot results - uncomment next line to plot time series. not recommended for large numbers of points\n", + " sir_model.plotSIDARTHE(tvect, S, I, D, A, R, T, H, E)\n", + " # USER: write query condition.\n", + " query_condition = (0.55 <= max(I+D+A+R+T) <= 0.65) and (40 <= np.argmax(I+D+A+R+T) <= 50)\n", + " query = '1' if query_condition else '0'\n", + " param_assignments = {'theta': theta_val, 'epsilon': epsilon_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\n", + " print(param_assignments)\n", + " param_choices_true_false.append(param_assignments)\n", + " if param_synth_method == \"any\" and query == '1':\n", + " return param_choices_true_false\n", + " return param_choices_true_false\n", + " \n", + "param_choices_true_false = ps(\"all\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'I' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mmax\u001b[39m(\u001b[43mI\u001b[49m))\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(np\u001b[38;5;241m.\u001b[39margmax(I))\n", + "\u001b[0;31mNameError\u001b[0m: name 'I' is not defined" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5343b7b93979f55aa6648f1ad8e7b39a28933303 Mon Sep 17 00:00:00 2001 From: Drisana Mosaphir Date: Wed, 7 Aug 2024 08:57:06 -0500 Subject: [PATCH 09/93] SIDARTHE step function unit test --- ...st_2024_eval_6_month_eval_s2_q1_b_ii.ipynb | 158 ++++++++++++++++++ 1 file changed, 158 insertions(+) create mode 100644 scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_ii.ipynb diff --git a/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_ii.ipynb b/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_ii.ipynb new file mode 100644 index 00000000..29a7c678 --- /dev/null +++ b/scratch/notebooks/sidarthe_august_2024_eval_6_month_eval_s2_q1_b_ii.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import scipy\n", + "from scipy.integrate import odeint\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "import sir_model\n", + "import json\n", + "from random import randint" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "max I percentage: 0.00192296687623966\n", + "argmax I: 50\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query: 1\n" + ] + } + ], + "source": [ + "# parameters\n", + "# set parameter values\n", + "\n", + "# np.piecewise(x, [x < 0, x >= 0], [-1, 1])\n", + "# alpha_val = 0.57\n", + "# beta_val = 0.011\n", + "# delta_val = 0.011\n", + "# gamma_val = 0.456\n", + "\n", + "# # epsilon_val = 0.05 #0.171\n", + "# # theta_val = 0.371\n", + "\n", + "# zeta_val = 0.125\n", + "# eta_val = 0.125\n", + "\n", + "# mu_val = 0.017\n", + "# nu_val = 0.027\n", + "# lamb_val = 0.034\n", + "# rho_val = 0.034\n", + "\n", + "# kappa_val = 0.017\n", + "# xi_val = 0.017\n", + "# sigma_val = 0.017\n", + "\n", + "# tau_val = 0.01\n", + "\n", + "\n", + "\n", + "def alpha(t): return np.piecewise(t, [0<=t<=4, 428], [0.57, 0.422, 0.36, 0.210]) # checked\n", + "def beta(t): return np.piecewise(t, [0<=t<=4, 422], [0.011, 0.0057, 0.005]) # checked\n", + "def delta(t): return np.piecewise(t, [0<=t<=4, 422], [0.011, 0.0057, 0.005]) # checked\n", + "def gamma(t): return np.piecewise(t, [0<=t<=4, 428], [0.456, 0.285, 0.2, 0.110]) # checked\n", + "\n", + "def epsilon(t): return np.piecewise(t, [0<=t<=12, 1238], [0.171, 0.143, 0.2])\n", + "def theta(t): return np.piecewise(t, [t>=0], [0.371]) # checked\n", + "\n", + "def zeta(t): return np.piecewise(t, [0<=t<=22, 2238], [0.125, 0.034, 0.025])\n", + "def eta(t): return np.piecewise(t, [0<=t<=22, 2238], [0.125, 0.034, 0.025])\n", + "\n", + "def mu(t): return np.piecewise(t, [0<=t<=22, t>22], [0.017, 0.008])\n", + "def nu(t): return np.piecewise(t, [0<=t<=22, t>22], [0.027, 0.015])\n", + "def lamb(t): return np.piecewise(t, [0<=t<=22, t>22], [0.034, 0.08])\n", + "def rho(t): return np.piecewise(t, [0<=t<=22, 2238], [0.034, 0.017, 0.02]) # checked\n", + "\n", + "def kappa(t): return np.piecewise(t, [0<=t<=22, 2238], [0.017, 0.017, 0.02]) # checked\n", + "def xi(t): return np.piecewise(t, [0<=t<=22, 2238], [0.017, 0.017, 0.02]) # checked\n", + "def sigma(t): return np.piecewise(t, [0<=t<=22, 2238], [0.017, 0.017, 0.01]) # checked\n", + "\n", + "def tau(t): return np.piecewise(t, [t>=0], [0.01]) # checked\n", + "\n", + "\n", + "# USER: set initial conditions\n", + "I0, D0, A0, R0, T0, H0, E0 = 200/(60e6), 20/(60e6), 1/(60e6), 2/(60e6), 0, 0, 0\n", + "S0 = 1-I0-D0-A0-R0-T0-H0-E0\n", + "y0 = S0, I0, D0, A0, R0, T0, H0, E0 # Initial conditions vector\n", + "# USER: set simulation parameters\n", + "dt = 1\n", + "tstart = 0\n", + "tend = 100\n", + "tvect = np.arange(tstart, tend, dt)\n", + "# simulate/solve ODEs\n", + "sim = odeint(sir_model.SIDARTHE_model, y0, tvect, args=(alpha, beta, gamma, delta, epsilon, mu, zeta, lamb, eta, rho, theta, kappa, nu, xi, sigma, tau))\n", + "S, I, D, A, R, T, H, E = sim.T\n", + "print('max I percentage:', max(I+D+A+R+T))\n", + "print('argmax I:', np.argmax(I+D+A+R+T))\n", + "# plot results - uncomment next line to plot time series. not recommended for large numbers of points\n", + "sir_model.plotSIDARTHE(tvect, S, I, D, A, R, T, H, E)\n", + "# USER: write query condition.\n", + "query_condition = (0.0015 <= max(I+D+A+R+T) <= 0.0025) and (45 <= np.argmax(I+D+A+R+T) <= 55)\n", + "query = '1' if query_condition else '0'\n", + "print('query:', query)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0c9e7a04f263dcd99605afd71ce992315da33b32 Mon Sep 17 00:00:00 2001 From: Drisana Mosaphir Date: Wed, 7 Aug 2024 08:57:06 -0500 Subject: [PATCH 10/93] AMRs for Aug2024 demo --- .../q1a_ii/eval_scenario1_1_ii_1.json | 371 + .../q1a_ii/eval_scenario1_1_ii_2.json | 387 + .../q1a_ii/eval_scenario1_1_ii_3.json | 706 + .../q1a_ii/eval_scenario1_base.json | 355 + .../q2c/eval_scenario1_2_sirhd.json | 311 + .../q2c/eval_scenario1_2_sirhd_age.json | 11999 ++++++++++++++++ .../baseModel/eval_scenario2_base.json | 355 + .../q1b/eval_scenario2_1_b.json | 6336 ++++++++ .../q1b/part_1/BIOMD0000000955_askenet.json | 694 + .../q1b/part_2/scenario2_a.json | 1489 ++ .../part_2/scenario2_a_beta_scale_var.json | 1545 ++ .../scenario2_a_beta_scale_var_fixed.json | 1545 ++ .../q2a/scenario2_sidarthe_v.json | 546 + .../2024-08/scenarios_amr_links.md | 39 + 14 files changed, 26678 insertions(+) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_1.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_2.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q2c/eval_scenario1_2_sirhd.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q2c/eval_scenario1_2_sirhd_age.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/baseModel/eval_scenario2_base.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/q1b/eval_scenario2_1_b.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_1/BIOMD0000000955_askenet.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_2/scenario2_a.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_2/scenario2_a_beta_scale_var.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_2/scenario2_a_beta_scale_var_fixed.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q2a/scenario2_sidarthe_v.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/scenarios_amr_links.md diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_1.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_1.json new file mode 100644 index 00000000..e917f6a2 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_1.json @@ -0,0 +1,371 @@ +{ + "name": "Evaluation Scenario 1. Part 1 (ii) Masking type 1", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 1", + "model_version": "0.1", + "properties": {}, + "model": { + "states": [ + { + "id": "S", + "name": "S", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I", + "name": "I", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E", + "name": "E", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R", + "name": "R", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H", + "name": "H", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D", + "name": "D", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1", + "input": [ + "I", + "S" + ], + "output": [ + "I", + "E" + ], + "properties": { + "name": "t1" + } + }, + { + "id": "t2", + "input": [ + "E" + ], + "output": [ + "I" + ], + "properties": { + "name": "t2" + } + }, + { + "id": "t3", + "input": [ + "I" + ], + "output": [ + "R" + ], + "properties": { + "name": "t3" + } + }, + { + "id": "t4", + "input": [ + "I" + ], + "output": [ + "H" + ], + "properties": { + "name": "t4" + } + }, + { + "id": "t5", + "input": [ + "H" + ], + "output": [ + "R" + ], + "properties": { + "name": "t5" + } + }, + { + "id": "t6", + "input": [ + "H" + ], + "output": [ + "D" + ], + "properties": { + "name": "t6" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1", + "expression": "I*S*beta*(-c_m*eps_m + 1)/N", + "expression_mathml": "ISbetac_meps_m1N" + }, + { + "target": "t2", + "expression": "E*r_E_to_I", + "expression_mathml": "Er_E_to_I" + }, + { + "target": "t3", + "expression": "I*p_I_to_R*r_I_to_R", + "expression_mathml": "Ip_I_to_Rr_I_to_R" + }, + { + "target": "t4", + "expression": "I*p_I_to_H*r_I_to_H", + "expression_mathml": "Ip_I_to_Hr_I_to_H" + }, + { + "target": "t5", + "expression": "H*p_H_to_R*r_H_to_R", + "expression_mathml": "Hp_H_to_Rr_H_to_R" + }, + { + "target": "t6", + "expression": "H*p_H_to_D*r_H_to_D", + "expression_mathml": "Hp_H_to_Dr_H_to_D" + } + ], + "initials": [ + { + "target": "S", + "expression": "19339995.0000000", + "expression_mathml": "19339995.0" + }, + { + "target": "I", + "expression": "4.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E", + "expression": "1.00000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D", + "expression": "0.0", + "expression_mathml": "0.0" + } + ], + "parameters": [ + { + "id": "N", + "value": 19340000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta", + "value": 0.4, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "c_m", + "value": 0.5, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "eps_m", + "value": 0.5, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_E_to_I", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_R", + "value": 0.8, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_R", + "value": 0.07, + "units": { + 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"time_start": null, + "time_end": null, + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_2.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_2.json new file mode 100644 index 00000000..f6d4b798 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_2.json @@ -0,0 +1,387 @@ +{ + "name": "Evaluation Scenario 1. Part 1 (ii) Masking type 2", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 2", + "model_version": "0.1", + "properties": {}, + "model": { + "states": [ + { + "id": "S", + "name": "S", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I", + "name": "I", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E", + "name": "E", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R", + "name": "R", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H", + "name": "H", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D", + "name": "D", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1", + "input": [ + "I", + "S" + ], + "output": [ + "I", + "E" + ], + "properties": { + "name": "t1" + } + }, + { + "id": "t2", + "input": [ + "E" + ], + "output": [ + "I" + ], + "properties": { + "name": "t2" + } + }, + { + "id": "t3", + "input": [ + "I" + ], + "output": [ + "R" + ], + "properties": { + "name": "t3" + } + }, + { + "id": "t4", + "input": [ + "I" + ], + "output": [ + "H" + ], + "properties": { + "name": "t4" + } + }, + { + "id": "t5", + "input": [ + "H" + ], + "output": [ + "R" + ], + "properties": { + "name": "t5" + } + }, + { + "id": "t6", + "input": [ + "H" + ], + "output": [ + "D" + ], + "properties": { + "name": "t6" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1", + "expression": "I*S*kappa*(beta_c + (-beta_c + beta_s)/(1 + exp(-k*(-t + t_0))))/N", + "expression_mathml": "ISkappabeta_cbeta_cbeta_s1kt_0tN" + }, + { + "target": "t2", + "expression": "E*r_E_to_I", + "expression_mathml": "Er_E_to_I" + }, + { + "target": "t3", + "expression": "I*p_I_to_R*r_I_to_R", + "expression_mathml": "Ip_I_to_Rr_I_to_R" + }, + { + "target": "t4", + "expression": "I*p_I_to_H*r_I_to_H", + "expression_mathml": "Ip_I_to_Hr_I_to_H" + }, + { + "target": "t5", + "expression": "H*p_H_to_R*r_H_to_R", + "expression_mathml": "Hp_H_to_Rr_H_to_R" + }, + { + "target": "t6", + "expression": "H*p_H_to_D*r_H_to_D", + "expression_mathml": "Hp_H_to_Dr_H_to_D" + } + ], + "initials": [ + { + "target": "S", + "expression": "19339995.0000000", + "expression_mathml": "19339995.0" + }, + { + "target": "I", + "expression": "4.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E", + "expression": 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+ }, + { + "id": "r_E_to_I", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_R", + "value": 0.8, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_R", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_H", + "value": 0.2, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_H", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_R", + "value": 0.88, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_R", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_D", + "value": 0.12, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_D", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + } + ], + "observables": [], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + } + }, + "metadata": { + "annotations": { + "license": null, + "authors": [], + "references": [], + "time_scale": null, + "time_start": null, + "time_end": null, + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3.json new file mode 100644 index 00000000..6a511899 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3.json @@ -0,0 +1,706 @@ +{ + "name": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", + "model_version": "0.1", + "properties": {}, + "model": { + "states": [ + { + "id": "S_compliant", + "name": "S_compliant", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_compliant", + "name": "I_compliant", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_compliant", + "name": "E_compliant", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_noncompliant", + "name": "I_noncompliant", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "noncompliant" + 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{\"ido\": \"0000511\"}, \"context\": {}}, \"outcome\": {\"name\": \"Healed\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Infected*XXlambdaXX", + "mira_rate_law_mathml": "InfectedXXlambdaXX", + "mira_parameters": "{\"lambda\": 0.034}", + "mira_parameter_distributions": "{\"lambda\": null}" + }, + "rate": 0.034 + }, + { + "tname": "t8", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "eta", + "parameter_value": 0.125, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Diagnosed*eta\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Diagnosed\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"outcome\": {\"name\": \"Recognized\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"provenance\": []}", + "mira_rate_law": "Diagnosed*eta", + "mira_rate_law_mathml": "Diagnosedeta", + "mira_parameters": "{\"eta\": 0.125}", + "mira_parameter_distributions": "{\"eta\": null}" + }, + "rate": 0.125 + }, + { + "tname": "t9", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "rho", + "parameter_value": 0.034, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Diagnosed*rho\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Diagnosed\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"outcome\": {\"name\": \"Healed\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Diagnosed*rho", + "mira_rate_law_mathml": "Diagnosedrho", + "mira_parameters": "{\"rho\": 0.034}", + "mira_parameter_distributions": "{\"rho\": null}" + }, + "rate": 0.034 + }, + { + "tname": "t10", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "theta", + "parameter_value": 0.371, + "parameter_distribution": null, + "mira_template": 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\"outcome\": {\"name\": \"Healed\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Ailing*kappa", + "mira_rate_law_mathml": "Ailingkappa", + "mira_parameters": "{\"kappa\": 0.017}", + "mira_parameter_distributions": "{\"kappa\": null}" + }, + "rate": 0.017 + }, + { + "tname": "t12", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "mu", + "parameter_value": 0.017, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Ailing*mu\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Ailing\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25269\", \"diagnosis\": \"ncit:C113725\"}}, \"outcome\": {\"name\": \"Threatened\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25467\"}}, \"provenance\": []}", + "mira_rate_law": "Ailing*mu", + "mira_rate_law_mathml": "Ailingmu", + "mira_parameters": "{\"mu\": 0.017}", + "mira_parameter_distributions": "{\"mu\": null}" + }, + "rate": 0.017 + }, + { + "tname": "t13", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "nu", + "parameter_value": 0.027, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Recognized*nu\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Recognized\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"outcome\": {\"name\": \"Threatened\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25467\"}}, \"provenance\": []}", + "mira_rate_law": "Recognized*nu", + "mira_rate_law_mathml": "Recognizednu", + "mira_parameters": "{\"nu\": 0.027}", + "mira_parameter_distributions": "{\"nu\": null}" + }, + "rate": 0.027 + }, + { + "tname": "t14", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "xi", + "parameter_value": 0.017, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Recognized*xi\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Recognized\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"diagnosis\": \"ncit:C15220\"}}, \"outcome\": {\"name\": \"Healed\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Recognized*xi", + "mira_rate_law_mathml": "Recognizedxi", + "mira_parameters": "{\"xi\": 0.017}", + "mira_parameter_distributions": "{\"xi\": null}" + }, + "rate": 0.017 + }, + { + "tname": "t15", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "tau_2", + "parameter_value": 0.01, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Threatened*tau_2\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Threatened\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25467\"}}, \"outcome\": {\"name\": \"Extinct\", \"identifiers\": {\"ncit\": \"C28554\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Threatened*tau_2", + "mira_rate_law_mathml": "Threatenedtau_2", + "mira_parameters": "{\"tau_2\": 0.01}", + "mira_parameter_distributions": "{\"tau_2\": null}" + }, + "rate": 0.01 + }, + { + "tname": "t16", + "tprop": { + "template_type": "NaturalConversion", + "parameter_name": "sigma", + "parameter_value": 0.017, + "parameter_distribution": null, + "mira_template": "{\"rate_law\": \"Threatened*sigma\", \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"Threatened\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {\"disease_severity\": \"ncit:C25467\"}}, \"outcome\": {\"name\": \"Healed\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Threatened*sigma", + "mira_rate_law_mathml": "Threatenedsigma", + "mira_parameters": "{\"sigma\": 0.017}", + "mira_parameter_distributions": "{\"sigma\": null}" + }, + "rate": 0.017 + }, + { + "tname": "t17", + "tprop": { + "template_type": 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\"outcome\": {\"name\": \"Extinct\", \"identifiers\": {\"ncit\": \"C28554\"}, \"context\": {}}, \"provenance\": []}", + "mira_rate_law": "Recognized*tau_1", + "mira_rate_law_mathml": "Recognizedtau_1", + "mira_parameters": "{\"tau_1\": 0.00333}", + "mira_parameter_distributions": "{\"tau_1\": null}" + }, + "rate": 0.00333 + } + ], + "I": [ + { + "is": 3, + "it": 1 + }, + { + "is": 1, + "it": 1 + }, + { + "is": 4, + "it": 2 + }, + { + "is": 1, + "it": 2 + }, + { + "is": 5, + "it": 3 + }, + { + "is": 1, + "it": 3 + }, + { + "is": 2, + "it": 4 + }, + { + "is": 1, + "it": 4 + }, + { + "is": 2, + "it": 5 + }, + { + "is": 2, + "it": 6 + }, + { + "is": 2, + "it": 7 + }, + { + "is": 3, + "it": 8 + }, + { + "is": 3, + "it": 9 + }, + { + "is": 4, + "it": 10 + }, + { + "is": 4, + "it": 11 + }, + { + "is": 4, + "it": 12 + }, + { + "is": 5, + "it": 13 + }, + { + "is": 5, + "it": 14 + }, + { + "is": 7, + "it": 15 + }, + { + "is": 7, + "it": 16 + }, + { + "is": 1, + "it": 17 + }, + { + "is": 5, + "it": 18 + } + ], + "O": [ + { + "os": 3, + "ot": 1 + }, + { + "os": 2, + "ot": 1 + }, + { + "os": 4, + "ot": 2 + }, + { + "os": 2, + "ot": 2 + }, + { + "os": 5, + "ot": 3 + }, + { + "os": 2, + "ot": 3 + }, + { + "os": 2, + "ot": 4 + }, + { + "os": 2, + "ot": 4 + }, + { + "os": 3, + "ot": 5 + }, + { + "os": 4, + "ot": 6 + }, + { + "os": 6, + "ot": 7 + }, + { + "os": 5, + "ot": 8 + }, + { + "os": 6, + "ot": 9 + }, + { + "os": 5, + "ot": 10 + }, + { + "os": 6, + "ot": 11 + }, + { + "os": 7, + "ot": 12 + }, + { + "os": 7, + "ot": 13 + }, + { + "os": 6, + "ot": 14 + }, + { + "os": 8, + "ot": 15 + }, + { + "os": 6, + "ot": 16 + }, + { + "os": 9, + "ot": 17 + }, + { + "os": 8, + "ot": 18 + } + ] +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-08/scenarios_amr_links.md b/resources/amr/petrinet/monthly-demo/2024-08/scenarios_amr_links.md new file mode 100644 index 00000000..4213b862 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/scenarios_amr_links.md @@ -0,0 +1,39 @@ +Scenarios: +• 6Mo Eval S2: Q1b (ingest the model with whatever process makes the most sense, and do unit tests), Q2a +• 12Mo Eval S1: Q1a.ii, Q2c +• 12Mo Eval S2 : Q1b, Q2 +Scenario Materials: +1. (6Mo Eval Scenarios) https://github.com/DARPA-ASKEM/program-milestones/tree/main/6-month-milestone/evaluation +2. (12Mo Eval Scenarios): https://github.com/DARPA-ASKEM/program-milestones/tree/main/12-month-milestone/evaluation +For any simulation questions, you may simplify model configuration and just use reasonable values. We want to do some simulation with these models to demonstrate that the stratified or transformed versions give reasonable results that make sense, and can be used to compare outcomes for different age groups, or compare results before and after stratification. Ignore any calibration questions or comparison with other questions outside of this subset. + +AMRs + +6 Month Eval S2 + +Q1b.(i): SIDARTHE, original parameters https://github.com/siftech/funman/blob/d909110cf4c82b62569d37979703c36260909c41/resources/amr/petrinet/mira/models/BIOMD0000000955_askenet.json + +Q1b.(ii): SIDARTHE, updated (step function) parameters +• https://github.com/siftech/funman/blob/d909110cf4c82b62569d37979703c36260909c41/resources/amr/petrinet/mira/models/scenario2_a.json#L1029 +• https://github.com/siftech/funman/blob/d909110cf4c82b62569d37979703c36260909c41/resources/amr/petrinet/mira/models/scenario2_a_beta_scale_var.json#L1029 +• https://github.com/siftech/funman/blob/d909110cf4c82b62569d37979703c36260909c41/resources/amr/petrinet/mira/models/scenario2_a_beta_scale_var_fixed.json#L1029 + +Q2a. SIDARTHE-V: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.01/scenario2_sidarthe_v.json + + +12 Month Eval S1: +Q1a.ii: +(0): Original SEIRHD (may not be necessary): https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_base.json +(1): SEIRHD: modify beta  beta*(1-epsilon_m*c_m): https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_1_ii_1.json +(2): SEIRHD: time-varying beta: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_1_ii_2.json +(3): SEIRHD: stratify by masking: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_1_ii_3.json +Q2c: SIRHD (removed E component for simplicity), stratified into 18 age groups: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_2_sirhd_age.json +Reference for Q2c (may not need to use this): SIRHD not yet stratified by age: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario1_2_sirhd.json + + +12 Month Eval S2 : +Base model: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario2_base.json +Q1b: SEIRHD with multiple vaccine components: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario2_1_b.json +Q2: SEIRHD extension to include contact matrices – age-stratified 0-9, 10-19, 20-29: https://github.com/gyorilab/mira/blob/main/notebooks/evaluation_2023.07/eval_scenario2_2_a.json + + From 047618425c60043cf1d9d1ab91eeccdc92988e10 Mon Sep 17 00:00:00 2001 From: dmosaphir <105988905+dmosaphir@users.noreply.github.com> Date: Wed, 7 Aug 2024 08:57:06 -0500 Subject: [PATCH 11/93] AMR - Aug 2024 demo --- .../eval_scenario2_2_a.json | 20713 ++++++++++++++++ 1 file changed, 20713 insertions(+) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/eval_scenario2_2_a.json diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/eval_scenario2_2_a.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/eval_scenario2_2_a.json new file mode 100644 index 00000000..acf84266 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_2/eval_scenario2_2_a.json @@ -0,0 +1,20713 @@ +{ + "name": "Evaluation Scenario 2 Base model", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 2 Base model", + "model_version": "0.1", + "properties": {}, + "model": { + "states": [ + { + "id": "S_0_9_masked", + "name": "S_0_9_masked", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "age": "0_9", + "masking": "masked" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_0_9_masked", + "name": "I_0_9_masked", + 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+} \ No newline at end of file From d5994a19b433d6dd003576a9a54072cb338e9aec Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 7 Aug 2024 08:57:06 -0500 Subject: [PATCH 12/93] setup demo --- docker/docker-bake.hcl | 2 +- .../monthly-demos/funman_aug_2024_demo.ipynb | 468 ++++++++++++++++++ .../funman_july_2024_demo.ipynb | 0 .../BIOMD0000000955_askenet_request.json | 29 ++ 4 files changed, 498 insertions(+), 1 deletion(-) create mode 100644 notebooks/monthly-demos/funman_aug_2024_demo.ipynb rename notebooks/{ => monthly-demos}/funman_july_2024_demo.ipynb (100%) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_1/BIOMD0000000955_askenet_request.json diff --git a/docker/docker-bake.hcl b/docker/docker-bake.hcl index 3e55db84..06e46b01 100644 --- a/docker/docker-bake.hcl +++ b/docker/docker-bake.hcl @@ -20,7 +20,7 @@ variable "DREAL_REPO_URL" { default = "https://github.com/danbryce/dreal4.git" } variable "DREAL_COMMIT_TAG" { - default = "43706295350b9676b786e2d9d8607b1c6e19afee" + default = "844d64fd7427d5d2ce3b01a2f83231cefc8709a4" } variable "AUTOMATES_COMMIT_TAG" { default = "e5fb635757aa57007615a75371f55dd4a24851e0" diff --git a/notebooks/monthly-demos/funman_aug_2024_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_demo.ipynb new file mode 100644 index 00000000..a708d00a --- /dev/null +++ b/notebooks/monthly-demos/funman_aug_2024_demo.ipynb @@ -0,0 +1,468 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# This notebook illustrates handling the August 2024 Demo of the 6mo Hackathon Scenario 2, described at: https://github.com/DARPA-ASKEM/program-milestones/issues/74\n", + "\n", + "# Import funman related code\n", + "import os\n", + "from funman.api.run import Runner\n", + "from funman_demo import summarize_results\n", + "from funman import FunmanWorkRequest, EncodingSchedule \n", + "import json\n", + "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", + "from funman.representation import Interval\n", + "import pandas as pd\n", + "\n", + "RESOURCES = \"../../resources\"\n", + "SAVED_RESULTS_DIR = \"./out\"\n", + "\n", + "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"monthly-demo\", \"2024-08\", \"6_month_scenario_2\", \"q1b\", \"part_1\")\n", + "MODEL_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"BIOMD0000000955_askenet.json\"\n", + ")\n", + "REQUEST_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"BIOMD0000000955_askenet_request.json\"\n", + ")\n", + "\n", + "request_params = {}\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Constants for the scenario\n", + "STATES = [\"Susceptible\", \"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Healed\", \"Threatened\", \"Extinct\"]\n", + "\n", + "MAX_TIME=50\n", + "STEP_SIZE=5\n", + "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper functions to setup FUNMAN for different steps of the scenario\n", + "\n", + "def get_request():\n", + " with open(REQUEST_PATH, \"r\") as request:\n", + " funman_request = FunmanWorkRequest.model_validate(json.load(request))\n", + " return funman_request\n", + "\n", + "def set_timepoints(funman_request, timepoints):\n", + " funman_request.structure_parameters[0].schedules = [EncodingSchedule(timepoints=timepoints)]\n", + "\n", + "def unset_all_labels(funman_request):\n", + " for p in funman_request.parameters:\n", + " p.label = \"any\"\n", + " \n", + "def set_config_options(funman_request, debug=False, dreal_precision=1e-1):\n", + " # Overrides for configuration\n", + " #\n", + " # funman_request.config.substitute_subformulas = True\n", + " # funman_request.config.use_transition_symbols = True\n", + " # funman_request.config.use_compartmental_constraints=False\n", + " if debug:\n", + " funman_request.config.save_smtlib=\"./out\"\n", + " funman_request.config.tolerance = 0.01\n", + " funman_request.config.dreal_precision = dreal_precision\n", + " # funman_request.config.verbosity = 10\n", + " # funman_request.config.dreal_log_level = \"debug\"\n", + " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", + "\n", + "def get_synthesized_vars(funman_request):\n", + " return [p.name for p in funman_request.parameters if p.label == \"all\"]\n", + "\n", + "def run(funman_request, plot=False):\n", + " to_synthesize = get_synthesized_vars(funman_request)\n", + " return Runner().run(\n", + " MODEL_PATH,\n", + " funman_request,\n", + " description=\"SIDARTHE Eval Scenario 2.1.b.1 6mo\",\n", + " case_out_dir=SAVED_RESULTS_DIR,\n", + " dump_plot=plot,\n", + " print_last_time=True,\n", + " parameters_to_plot=to_synthesize\n", + " )\n", + "\n", + "def setup_common(funman_request, synthesize=False, debug=False, dreal_precision=1e-1):\n", + " set_timepoints(funman_request, timepoints)\n", + " if not synthesize:\n", + " unset_all_labels(funman_request)\n", + " set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision)\n", + " \n", + "\n", + "def set_compartment_bounds(funman_request, upper_bound=9830000.0, error=0.01):\n", + " # Add bounds to compartments\n", + " for var in STATES:\n", + " funman_request.constraints.append(StateVariableConstraint(name=f\"{var}_bounds\", variable=var, interval=Interval(lb=0, ub=upper_bound, closed_upper_bound=True),soft=False))\n", + "\n", + " # Add sum of compartments\n", + " funman_request.constraints.append(LinearConstraint(name=f\"compartment_bounds\", variables=STATES, additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True))\n", + "\n", + "def relax_parameter_bounds(funman_request, factor = 0.1):\n", + " # Relax parameter bounds\n", + " parameters = funman_request.parameters\n", + " for p in parameters:\n", + " interval = p.interval\n", + " width = float(interval.width())\n", + " interval.lb = interval.lb - (factor/2 * width)\n", + " interval.ub = interval.ub + (factor/2 * width)\n", + "\n", + "def plot_last_point(results):\n", + " pts = results.parameter_space.points() \n", + " print(f\"{len(pts)} points\")\n", + "\n", + " if len(pts) > 0:\n", + " # Get a plot for last point\n", + " df = results.dataframe(points=pts[-1:])\n", + " ax = df[STATES].plot()\n", + " ax.set_yscale(\"log\")\n", + "\n", + "def get_last_point_parameters(results):\n", + " pts = results.parameter_space.points()\n", + " if len(pts) > 0:\n", + " pt = pts[-1]\n", + " parameters = results.model._parameter_names()\n", + " param_values = {k:v for k, v in pt.values.items() if k in parameters }\n", + " return param_values\n", + "\n", + "def pretty_print_request_params(params):\n", + " # print(json.dump(params, indent=4))\n", + " if len(params)>0:\n", + "\n", + " df = pd.DataFrame(params)\n", + " print(df.T)\n", + "\n", + "\n", + "def report(results, name):\n", + " plot_last_point(results)\n", + " param_values = get_last_point_parameters(results)\n", + " print(f\"Point parameters: {param_values}\")\n", + " if param_values is not None:\n", + " request_params[name] = param_values\n", + " pretty_print_request_params(request_params)\n", + "\n", + "def add_unit_test(funman_request):\n", + " funman_request.constraints.append(LinearConstraint(name=\"unit_test\", variables = [\n", + " \"Infected\",\n", + " \"Diagnosed\",\n", + " \"Ailing\",\n", + " \"Recognized\",\n", + " \"Threatened\"\n", + " ],\n", + " additive_bounds= {\n", + " \"lb\": 0.55,\n", + " \"ub\": 0.65\n", + " },\n", + " timepoints={\n", + " \"lb\": 45,\n", + " \"ub\": 55\n", + " }\n", + " ))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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unconstrained
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" + ], + "text/plain": [ + " unconstrained\n", + "alpha 0.570\n", + "beta 0.011\n", + "delta 0.011\n", + "epsilon 0.171\n", + "eta 0.125\n", + "gamma 0.456\n", + "kappa 0.017\n", + "lambda 0.034\n", + "mu 0.017\n", + "nu 0.027\n", + "rho 0.034\n", + "sigma 0.017\n", + "tau 0.010\n", + "theta 0.371\n", + "xi 0.017\n", + "zeta 0.125" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = {'unconstrained': {'beta': 0.011, 'gamma': 0.45599999999999996, 'delta': 0.011, 'alpha': 0.5699999999999998, 'epsilon': 0.17099999999999999, 'zeta': 0.125, 'lambda': 0.034, 'eta': 0.125, 'rho': 0.034, 'theta': 0.371, 'kappa': 0.017, 'mu': 0.017, 'nu': 0.027, 'xi': 0.017, 'tau': 0.009999999999999998, 'sigma': 0.017}}\n", + "pd.DataFrame(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Automatic initialization of gaol... done\n", + "2024-08-06 21:13:47,838 - funman.scenario.consistency - INFO - 10{50}:\t[+]\n", + "2024-08-06 21:13:47,864 - funman.server.worker - INFO - Completed work on: 83bc1dbc-f847-473c-bbe3-1abdad721bce\n", + "2024-08-06 21:13:48,055 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-08-06 21:13:48,371 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-08-06 21:13:48,380 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 points\n", + "Point parameters: {'beta': 0.011, 'gamma': 0.45599999999999996, 'delta': 0.011, 'alpha': 0.5699999999999998, 'epsilon': 0.17099999999999999, 'zeta': 0.125, 'lambda': 0.034, 'eta': 0.125, 'rho': 0.034, 'theta': 0.371, 'kappa': 0.017, 'mu': 0.017, 'nu': 0.027, 'xi': 0.017, 'tau': 0.009999999999999998, 'sigma': 0.017}\n" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "# Find a single parameterization of the model where sum(IDART) is approx 60% around day 47.\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=True, dreal_precision=1e0)\n", + "# add_unit_test(funman_request)\n", + "results = run(funman_request)\n", + "report(results, \"unconstrained\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add bounds [0, N] to the STATE compartments. \n", + "# Add bounds sum(STATE) in [N-e, N+e], for a small e.\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=True)\n", + "set_compartment_bounds(funman_request)\n", + "results = run(funman_request)\n", + "report(results, \"compartmental_constrained\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Relax the bounds on the parameters to allow additional parameterizations\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request)\n", + "set_compartment_bounds(funman_request)\n", + "relax_parameter_bounds(funman_request, factor = 0.75)\n", + "results = run(funman_request)\n", + "report(results, \"relaxed_bounds\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "funman_request = get_request()\n", + "setup_common(funman_request, synthesize=True)\n", + "set_compartment_bounds(funman_request)\n", + "# relax_parameter_bounds(funman_request, factor=0.75)\n", + "# funman_request.config.verbosity=10\n", + "results = run(funman_request, plot=True)\n", + "report(results, \"synthesis\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import pandas as pd\n", + "# df1 = pd.DataFrame({\"ltp\": ltp, \"gtp\": gtp})\n", + "\n", + "# # df2 = \n", + "# # df1.ltp.N == df1.gtp.N\n", + "# # df2.loc[df2].sort_index()[0:60]\n", + "\n", + "# #(= H_10 (+ H_8 (* 2 (+ (* r_HD (- 1) H_8) (* r_HR (- 1) H_8) (* r_IH_v I_v_8) (* r_IH_u I_u_8)))))\n", + "\n", + "# df1['same'] = df1['ltp'] == df1[\"gtp\"]\n", + "# df1[0:20]\n", + "# # df1.loc[df1.index.str.endswith(\"_6\")].sort_values(by=\"same\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # Get points (trajectories generated)\n", + "# pts = results.parameter_space.points() \n", + "# print(f\"{len(pts)} points\")\n", + "\n", + "# # Get a plot for last point\n", + "# df = results.dataframe(points=pts[-1:])\n", + "# ax = df[STATES].plot()\n", + "# ax.set_yscale(\"log\")\n", + "\n", + "\n", + "# # Get the values of the point\n", + "# gtp=pts[-1].values\n", + "\n", + "\n", + "# # Output the model diagram\n", + "# #\n", + "# # results_unconstrained_point.model.to_dot()\n", + "# # gtp" + ] + } + ], + "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/notebooks/funman_july_2024_demo.ipynb b/notebooks/monthly-demos/funman_july_2024_demo.ipynb similarity index 100% rename from notebooks/funman_july_2024_demo.ipynb rename to notebooks/monthly-demos/funman_july_2024_demo.ipynb diff --git a/resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_1/BIOMD0000000955_askenet_request.json b/resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_1/BIOMD0000000955_askenet_request.json new file mode 100644 index 00000000..a601b0a9 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/6_month_scenario_2/q1b/part_1/BIOMD0000000955_askenet_request.json @@ -0,0 +1,29 @@ +{ + "constraints": [ + + ], + "parameters": [], + "structure_parameters": [ + { + "name": "schedules", + "schedules": [ + { + "timepoints": [ + 0, + 10, + 20, + 30, + 40, + 50 + ] + } + ] + } + ], + "config": { + "tolerance": 1e-2, + "dreal_precision": 0.001, + "normalization_constant": 1.0, + "use_compartmental_constraints": true + } +} \ No newline at end of file From 4551053d6935f4b83b03e2107e29614b1ccbd764 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 7 Aug 2024 08:57:06 -0500 Subject: [PATCH 13/93] upgrade build --- docker/ibex/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker/ibex/Dockerfile b/docker/ibex/Dockerfile index f8181b61..ef5dd84d 100644 --- a/docker/ibex/Dockerfile +++ b/docker/ibex/Dockerfile @@ -1,4 +1,4 @@ -FROM ubuntu:20.04 +FROM ubuntu:22.04 ARG IBEX_BRANCH ARG ENABLE_DEBUG=no From 0e6cd7690c5e02316f48634c55e8fa24d41ed1ee Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 14 Aug 2024 13:16:29 -0500 Subject: [PATCH 14/93] use cpp 3.10 for dreal wheel --- docker/dreal4/Dockerfile.dreal4 | 2 +- tools/update-dreal.user | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docker/dreal4/Dockerfile.dreal4 b/docker/dreal4/Dockerfile.dreal4 index 7be71dcb..a803d035 100644 --- a/docker/dreal4/Dockerfile.dreal4 +++ b/docker/dreal4/Dockerfile.dreal4 @@ -75,7 +75,7 @@ RUN cd /dreal4 \ && pip3 install --upgrade setuptools \ && pip3 install --upgrade pip \ && python3 setup.py bdist_wheel \ - && DREAL_WHEEL=dreal-$(python setup.py --version)-cp38-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl \ + && DREAL_WHEEL=dreal-$(python setup.py --version)-cp310-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl \ && cp ./dist/$DREAL_WHEEL /tmp/$DREAL_WHEEL \ && pip3 install ./dist/$DREAL_WHEEL \ && bazel clean --expunge \ diff --git a/tools/update-dreal.user b/tools/update-dreal.user index 5335614d..710d8d2d 100644 --- a/tools/update-dreal.user +++ b/tools/update-dreal.user @@ -10,7 +10,7 @@ if [ ! -d $DREAL_ROOT ] ; then exit 1 fi -DREAL_WHEEL=dreal-*-cp38-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl \ +DREAL_WHEEL=dreal-$(python ${DREAL_ROOT}/setup.py --version)-cp310-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl \ FUNMAN_ENV_PATH=$HOME/funman_venv FUNMAN_PYTHON=${FUNMAN_ENV_PATH}/bin/python From a02ff637ffa824d9e460882806cb924f8b168f79 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 14 Aug 2024 13:19:28 -0500 Subject: [PATCH 15/93] update usage of pandas to infer_objects --- src/funman/server/query.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 20d3d74b..5b5cdd11 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -381,7 +381,9 @@ def dataframe( # df = df.reindex(range(max_time+1), fill_value=None) if interpolate: - df = df.interpolate(method=interpolate) + df = df.infer_objects(copy=False).interpolate( + method=interpolate + ) if time_var and any("timer_t" in x for x in df.columns): df = ( df.rename(columns={"timer_t": "time"}) From c9a465a3cec930a934d1e9734bfc07ef7cca0bda Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Mon, 19 Aug 2024 17:42:56 -0500 Subject: [PATCH 16/93] scenarios --- .../funman_aug_2024_12mo_eval_1_q1_demo.ipynb | 1014 +++++++++++++++++ ...nman_aug_2024_6mo_hack_2_q1b_1_demo.ipynb} | 247 ++-- .../monthly-demos/funman_july_2024_demo.ipynb | 282 +---- .../q1a_ii/eval_scenario1_base.json | 699 ++++++------ .../q1a_ii/eval_scenario1_base_request.json | 28 + 5 files changed, 1517 insertions(+), 753 deletions(-) create mode 100644 notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb rename notebooks/monthly-demos/{funman_aug_2024_demo.ipynb => funman_aug_2024_6mo_hack_2_q1b_1_demo.ipynb} (67%) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base_request.json diff --git a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb new file mode 100644 index 00000000..218e72c2 --- /dev/null +++ b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb @@ -0,0 +1,1014 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# This notebook illustrates handling the August 2024 Demo of the 12mo Evaluation Scenario 1\n", + "\n", + "# Import funman related code\n", + "import os\n", + "from funman.api.run import Runner\n", + "from funman_demo import summarize_results\n", + "from funman import FunmanWorkRequest, EncodingSchedule \n", + "import json\n", + "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", + "from funman.representation import Interval\n", + "import pandas as pd\n", + "\n", + "RESOURCES = \"../../resources\"\n", + "SAVED_RESULTS_DIR = \"./out\"\n", + "\n", + "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"monthly-demo\", \"2024-08\", \"12_month_scenario_1\", \"q1a_ii\")\n", + "MODEL_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario1_base.json\"\n", + ")\n", + "REQUEST_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario1_base_request.json\"\n", + ")\n", + "\n", + "\n", + "request_params = {}\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Constants for the scenario\n", + "STATES = [\"S\", \"I\", \"E\", \"R\", \"H\", \"D\"]\n", + "# IDART = [\"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Threatened\"]\n", + "\n", + "MAX_TIME=10\n", + "STEP_SIZE=2\n", + "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper functions to setup FUNMAN for different steps of the scenario\n", + "\n", + "def get_request():\n", + " with open(REQUEST_PATH, \"r\") as request:\n", + " funman_request = FunmanWorkRequest.model_validate(json.load(request))\n", + " return funman_request\n", + "\n", + "def set_timepoints(funman_request, timepoints):\n", + " funman_request.structure_parameters[0].schedules = [EncodingSchedule(timepoints=timepoints)]\n", + "\n", + "def unset_all_labels(funman_request):\n", + " for p in funman_request.parameters:\n", + " p.label = \"any\"\n", + " \n", + "def set_config_options(funman_request, debug=False, dreal_precision=1e-1):\n", + " # Overrides for configuration\n", + " #\n", + " # funman_request.config.substitute_subformulas = True\n", + " # funman_request.config.use_transition_symbols = True\n", + " # funman_request.config.use_compartmental_constraints=False\n", + " if debug:\n", + " funman_request.config.save_smtlib=\"./out\"\n", + " funman_request.config.tolerance = 0.01\n", + " funman_request.config.dreal_precision = dreal_precision\n", + " # funman_request.config.verbosity = 10\n", + " # funman_request.config.dreal_log_level = \"debug\"\n", + " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", + "\n", + "def get_synthesized_vars(funman_request):\n", + " return [p.name for p in funman_request.parameters if p.label == \"all\"]\n", + "\n", + "def run(funman_request, plot=False):\n", + " to_synthesize = get_synthesized_vars(funman_request)\n", + " return Runner().run(\n", + " MODEL_PATH,\n", + " funman_request,\n", + " description=\"SIERHD Eval 12mo Scenario 1 q1\",\n", + " case_out_dir=SAVED_RESULTS_DIR,\n", + " dump_plot=plot,\n", + " print_last_time=True,\n", + " parameters_to_plot=to_synthesize\n", + " )\n", + "\n", + "def setup_common(funman_request, synthesize=False, debug=False, dreal_precision=1e-1):\n", + " set_timepoints(funman_request, timepoints)\n", + " if not synthesize:\n", + " unset_all_labels(funman_request)\n", + " set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision)\n", + " \n", + "\n", + "def set_compartment_bounds(funman_request, upper_bound=9830000.0, error=0.01):\n", + " # Add bounds to compartments\n", + " for var in STATES:\n", + " funman_request.constraints.append(StateVariableConstraint(name=f\"{var}_bounds\", variable=var, interval=Interval(lb=0, ub=upper_bound, closed_upper_bound=True),soft=False))\n", + "\n", + " # Add sum of compartments\n", + " funman_request.constraints.append(LinearConstraint(name=f\"compartment_bounds\", variables=STATES, additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True))\n", + "\n", + "def relax_parameter_bounds(funman_request, factor = 0.1):\n", + " # Relax parameter bounds\n", + " parameters = funman_request.parameters\n", + " for p in parameters:\n", + " interval = p.interval\n", + " width = float(interval.width())\n", + " interval.lb = interval.lb - (factor/2 * width)\n", + " interval.ub = interval.ub + (factor/2 * width)\n", + "\n", + "def plot_last_point(results):\n", + " pts = results.parameter_space.points() \n", + " print(f\"{len(pts)} points\")\n", + "\n", + " if len(pts) > 0:\n", + " # Get a plot for last point\n", + " df = results.dataframe(points=pts[-1:])\n", + " ax = df[STATES].plot()\n", + " ax.set_yscale(\"log\")\n", + "\n", + "def get_last_point_parameters(results):\n", + " pts = results.parameter_space.points()\n", + " if len(pts) > 0:\n", + " pt = pts[-1]\n", + " parameters = results.model._parameter_names()\n", + " param_values = {k:v for k, v in pt.values.items() if k in parameters }\n", + " return param_values\n", + "\n", + "def pretty_print_request_params(params):\n", + " # print(json.dump(params, indent=4))\n", + " if len(params)>0:\n", + "\n", + " df = pd.DataFrame(params)\n", + " print(df.T)\n", + "\n", + "\n", + "def report(results, name):\n", + " plot_last_point(results)\n", + " param_values = get_last_point_parameters(results)\n", + " # print(f\"Point parameters: {param_values}\")\n", + " if param_values is not None:\n", + " request_params[name] = param_values\n", + " pretty_print_request_params(request_params)\n", + "\n", + "def add_unit_test(funman_request):\n", + " pass\n", + " # funman_request.constraints.append(LinearConstraint(name=\"unit_test\", variables = [\n", + " # \"Infected\",\n", + " # \"Diagnosed\",\n", + " # \"Ailing\",\n", + " # \"Recognized\",\n", + " # \"Threatened\"\n", + " # ],\n", + " # additive_bounds= {\n", + " # \"lb\": 0.55,\n", + " # \"ub\": 0.65\n", + " # },\n", + " # timepoints={\n", + " # \"lb\": 45,\n", + " # \"ub\": 55\n", + " # }\n", + " # ))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-08-16 18:16:57,654 - funman.scenario.consistency - INFO - 5{10}:\t[+]\n", + "2024-08-16 18:16:57,662 - funman.server.worker - INFO - Completed work on: 2af8847c-8400-4942-bf3d-d879a49c5f47\n", + "2024-08-16 18:16:59,512 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-08-16 18:16:59,676 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-08-16 18:16:59,679 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + } + ], + "source": [ + "# Find a single parameterization of the model where sum(IDART) is approx 60% around day 47.\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=True, dreal_precision=1e0)\n", + "add_unit_test(funman_request)\n", + "results = run(funman_request)\n", + "# report(results, \"unconstrained\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 points\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/danbryce/funman/src/funman/server/query.py:384: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n", + " df = df.infer_objects(copy=False).interpolate(\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " D E H I R S id \\\n", + "time \n", + "0.0 0.000000 1.000000 0.000000 4.000000 0.000000 1.934000e+07 0 \n", + "1.0 0.000000 2.400000 0.080000 3.896000 0.224000 1.933999e+07 0 \n", + "2.0 0.000000 3.799999 0.160000 3.792000 0.448000 1.933999e+07 0 \n", + "3.0 0.001920 4.556799 0.219840 4.263808 0.674432 1.933999e+07 0 \n", + "4.0 0.003840 5.313598 0.279680 4.735616 0.900864 1.933999e+07 0 \n", + "5.0 0.007196 6.145124 0.346424 5.438429 1.190670 1.933999e+07 0 \n", + "6.0 0.010552 6.976649 0.413169 6.141241 1.480477 1.933998e+07 0 \n", + "7.0 0.015510 8.037814 0.494677 7.069837 1.860745 1.933998e+07 0 \n", + "8.0 0.020468 9.098979 0.576185 7.998432 2.241013 1.933998e+07 0 \n", + "9.0 0.027383 10.478553 0.678535 9.210347 2.739630 1.933998e+07 0 \n", + "10.0 0.034297 11.858127 0.780885 10.422262 3.238246 1.933997e+07 0 \n", + "\n", + " label p_H_to p_I_to r_E_to r_H_to r_I_to \n", + "time \n", + "0.0 true None None None None None \n", + "1.0 true None None None None None \n", + "2.0 true None None None None None \n", + "3.0 true None None None None None \n", + "4.0 true None None None None None \n", + "5.0 true None None None None None \n", + "6.0 true None None None None None \n", + "7.0 true None None None None None \n", + "8.0 true None None None None None \n", + "9.0 true None None None None None \n", + "10.0 true None None None None None " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results\n", + "# get_last_point_parameters(results)\n", + "# plot_last_point(results)\n", + "# def plot_last_point(results):\n", + "# pts = results.parameter_space.points() \n", + "# print(f\"{len(pts)} points\")\n", + "\n", + "# if len(pts) > 0:\n", + "# # Get a plot for last point\n", + "# df = results.dataframe(points=pts[-1:])\n", + "# ax = df[STATES].plot()\n", + "# ax.set_yscale(\"log\")\n", + "pts = results.parameter_space.points() \n", + "print(f\"{len(pts)} points\")\n", + "df = results.dataframe(points=pts[-1:])\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# plt.plot(df.Infected)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " a b\n", + "0 0 3\n", + "1 1 4" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.DataFrame({\"a\": [0, 1], \"b\": [3, 4]})\n", + "l = list(df.a.values)\n", + "# l = [float(v) for v in l]\n", + "type(l[0])\n", + "\n", + "# p = plt.plot(df.a, df.b)\n", + "df\n", + "# print(p)\n", + "# plt.plot(l)\n", + "\n", + "# plt.plot(df.a.values)\n", + "# plt.plot(df['a'])\n", + "# df.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-16 18:16:59,767 - funman.server.worker - INFO - FunmanWorker running...\n", + "2024-08-16 18:16:59,769 - funman.server.worker - INFO - Starting work on: 7692d692-8ab5-4a84-9e25-f0b2e01c1187\n", + "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-08-16 18:16:59,929 - funman.scenario.consistency - INFO - 5{10}:\t[-]\n", + "2024-08-16 18:16:59,930 - funman.server.worker - INFO - Completed work on: 7692d692-8ab5-4a84-9e25-f0b2e01c1187\n", + "2024-08-16 18:17:01,776 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-08-16 18:17:01,947 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-08-16 18:17:01,958 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 points\n" + ] + } + ], + "source": [ + "# Add bounds [0, N] to the STATE compartments. \n", + "# Add bounds sum(STATE) in [N-e, N+e], for a small e.\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=True)\n", + "set_compartment_bounds(funman_request)\n", + "results = run(funman_request)\n", + "report(results, \"compartmental_constrained\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-16 18:17:01,990 - funman.server.worker - INFO - FunmanWorker running...\n", + "2024-08-16 18:17:01,993 - funman.server.worker - INFO - Starting work on: 9756fa28-57d3-4d09-a095-ce13262126d4\n", + "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-08-16 18:17:02,140 - funman.scenario.consistency - INFO - 5{10}:\t[-]\n", + "2024-08-16 18:17:02,141 - funman.server.worker - INFO - Completed work on: 9756fa28-57d3-4d09-a095-ce13262126d4\n", + "2024-08-16 18:17:03,995 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-08-16 18:17:04,156 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-08-16 18:17:04,157 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 points\n" + ] + } + ], + "source": [ + "# Relax the bounds on the parameters to allow additional parameterizations\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request)\n", + "set_compartment_bounds(funman_request)\n", + "relax_parameter_bounds(funman_request, factor = 0.75)\n", + "results = run(funman_request)\n", + "report(results, \"relaxed_bounds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-16 18:17:04,177 - funman.server.worker - INFO - FunmanWorker running...\n", + "2024-08-16 18:17:04,179 - funman.server.worker - INFO - Starting work on: 32faba99-2ef3-40a1-ad37-59ed7d39e7e9\n", + "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-08-16 18:17:04,360 - funman.scenario.consistency - INFO - 5{10}:\t[-]\n", + "2024-08-16 18:17:04,361 - funman.server.worker - INFO - Completed work on: 32faba99-2ef3-40a1-ad37-59ed7d39e7e9\n", + "2024-08-16 18:17:06,184 - funman.api.run - WARNING - Cannot plot a parameter space for zero boxes or zero parameters\n", + "2024-08-16 18:17:06,185 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-08-16 18:17:06,378 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-08-16 18:17:06,379 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 points\n" + ] + } + ], + "source": [ + "funman_request = get_request()\n", + "setup_common(funman_request, synthesize=True)\n", + "set_compartment_bounds(funman_request)\n", + "# relax_parameter_bounds(funman_request, factor=0.75)\n", + "# funman_request.config.verbosity=10\n", + "results = run(funman_request, plot=True)\n", + "report(results, \"synthesis\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# import pandas as pd\n", + "# df1 = pd.DataFrame({\"ltp\": ltp, \"gtp\": gtp})\n", + "\n", + "# # df2 = \n", + "# # df1.ltp.N == df1.gtp.N\n", + "# # df2.loc[df2].sort_index()[0:60]\n", + "\n", + "# #(= H_10 (+ H_8 (* 2 (+ (* r_HD (- 1) H_8) (* r_HR (- 1) H_8) (* r_IH_v I_v_8) (* r_IH_u I_u_8)))))\n", + "\n", + "# df1['same'] = df1['ltp'] == df1[\"gtp\"]\n", + "# df1[0:20]\n", + "# # df1.loc[df1.index.str.endswith(\"_6\")].sort_values(by=\"same\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "petrinet\n", + "\n", + "\n", + "\n", + "t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]\n", + "\n", + "t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]\n", + "\n", + "\n", + "\n", + "I\n", + "\n", + "I\n", + "\n", + "\n", + "\n", + "t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]->I\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "E\n", + "\n", + "E\n", + "\n", + "\n", + "\n", + "t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]->E\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "S\n", + "\n", + "S\n", + "\n", + "\n", + "\n", + "S->t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t2([E*r_E_to_I]) = [0.2*E]\n", + "\n", + "t2([E*r_E_to_I]) = [0.2*E]\n", + "\n", + "\n", + "\n", + "t2([E*r_E_to_I]) = [0.2*E]->I\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3([I*p_I_to_R*r_I_to_R]) = [0.056*I]\n", + "\n", + "t3([I*p_I_to_R*r_I_to_R]) = [0.056*I]\n", + "\n", + "\n", + "\n", + "R\n", + "\n", + "R\n", + "\n", + "\n", + "\n", + "t3([I*p_I_to_R*r_I_to_R]) = [0.056*I]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4([I*p_I_to_H*r_I_to_H]) = [0.02*I]\n", + "\n", + "t4([I*p_I_to_H*r_I_to_H]) = [0.02*I]\n", + "\n", + "\n", + "\n", + "H\n", + "\n", + "H\n", + "\n", + "\n", + "\n", + "t4([I*p_I_to_H*r_I_to_H]) = [0.02*I]->H\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", + "\n", + "t5([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", + "\n", + "\n", + "\n", + "t5([H*p_H_to_R*r_H_to_R]) = [0.088*H]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t6([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", + "\n", + "t6([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "t6([H*p_H_to_D*r_H_to_D]) = [0.012*H]->D\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I->t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I->t3([I*p_I_to_R*r_I_to_R]) = [0.056*I]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I->t4([I*p_I_to_H*r_I_to_H]) = [0.02*I]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "E->t2([E*r_E_to_I]) = [0.2*E]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t5([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t6([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# # Get points (trajectories generated)\n", + "# pts = results.parameter_space.points() \n", + "# print(f\"{len(pts)} points\")\n", + "\n", + "# # Get a plot for last point\n", + "# df = results.dataframe(points=pts[-1:])\n", + "# ax = df[STATES].plot()\n", + "# ax.set_yscale(\"log\")\n", + "\n", + "\n", + "# # Get the values of the point\n", + "# gtp=pts[-1].values\n", + "\n", + "\n", + "# # Output the model diagram\n", + "# #\n", + "results.model.to_dot()\n", + "# # gtp" + ] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/monthly-demos/funman_aug_2024_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_6mo_hack_2_q1b_1_demo.ipynb similarity index 67% rename from notebooks/monthly-demos/funman_aug_2024_demo.ipynb rename to notebooks/monthly-demos/funman_aug_2024_6mo_hack_2_q1b_1_demo.ipynb index a708d00a..16550b6e 100644 --- a/notebooks/monthly-demos/funman_aug_2024_demo.ipynb +++ b/notebooks/monthly-demos/funman_aug_2024_6mo_hack_2_q1b_1_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -37,21 +37,22 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Constants for the scenario\n", "STATES = [\"Susceptible\", \"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Healed\", \"Threatened\", \"Extinct\"]\n", + "IDART = [\"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Threatened\"]\n", "\n", "MAX_TIME=50\n", - "STEP_SIZE=5\n", + "STEP_SIZE=2\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -151,7 +152,7 @@ "def report(results, name):\n", " plot_last_point(results)\n", " param_values = get_last_point_parameters(results)\n", - " print(f\"Point parameters: {param_values}\")\n", + " # print(f\"Point parameters: {param_values}\")\n", " if param_values is not None:\n", " request_params[name] = param_values\n", " pretty_print_request_params(request_params)\n", @@ -179,164 +180,19 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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unconstrained
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beta0.011
delta0.011
epsilon0.171
eta0.125
gamma0.456
kappa0.017
lambda0.034
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sigma0.017
tau0.010
theta0.371
xi0.017
zeta0.125
\n", - "
" - ], - "text/plain": [ - " unconstrained\n", - "alpha 0.570\n", - "beta 0.011\n", - "delta 0.011\n", - "epsilon 0.171\n", - "eta 0.125\n", - "gamma 0.456\n", - "kappa 0.017\n", - "lambda 0.034\n", - "mu 0.017\n", - "nu 0.027\n", - "rho 0.034\n", - "sigma 0.017\n", - "tau 0.010\n", - "theta 0.371\n", - "xi 0.017\n", - "zeta 0.125" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d = {'unconstrained': {'beta': 0.011, 'gamma': 0.45599999999999996, 'delta': 0.011, 'alpha': 0.5699999999999998, 'epsilon': 0.17099999999999999, 'zeta': 0.125, 'lambda': 0.034, 'eta': 0.125, 'rho': 0.034, 'theta': 0.371, 'kappa': 0.017, 'mu': 0.017, 'nu': 0.027, 'xi': 0.017, 'tau': 0.009999999999999998, 'sigma': 0.017}}\n", - "pd.DataFrame(d)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Automatic initialization of gaol... done\n", - "2024-08-06 21:13:47,838 - funman.scenario.consistency - INFO - 10{50}:\t[+]\n", - "2024-08-06 21:13:47,864 - funman.server.worker - INFO - Completed work on: 83bc1dbc-f847-473c-bbe3-1abdad721bce\n", - "2024-08-06 21:13:48,055 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-08-06 21:13:48,371 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-08-06 21:13:48,380 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 points\n", - "Point parameters: {'beta': 0.011, 'gamma': 0.45599999999999996, 'delta': 0.011, 'alpha': 0.5699999999999998, 'epsilon': 0.17099999999999999, 'zeta': 0.125, 'lambda': 0.034, 'eta': 0.125, 'rho': 0.034, 'theta': 0.371, 'kappa': 0.017, 'mu': 0.017, 'nu': 0.027, 'xi': 0.017, 'tau': 0.009999999999999998, 'sigma': 0.017}\n" - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", - "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", - "\u001b[1;31mClick here for more info. \n", - "\u001b[1;31mView Jupyter log for further details." + "2024-08-16 18:17:23,491 - funman.server.worker - INFO - FunmanWorker running...\n", + "2024-08-16 18:17:23,495 - funman.server.worker - INFO - Starting work on: 81205490-5e77-4321-bbd4-d38e2c4828f0\n", + "2024-08-16 18:17:31,582 - funman.api.run - INFO - Dumping results to ./out/81205490-5e77-4321-bbd4-d38e2c4828f0.json\n", + "2024-08-16 18:17:31,632 - funman.scenario.consistency - INFO - 25{50}:\t[+]\n", + "2024-08-16 18:17:31,679 - funman.server.worker - INFO - Completed work on: 81205490-5e77-4321-bbd4-d38e2c4828f0\n", + "2024-08-16 18:17:41,625 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-08-16 18:17:41,754 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-08-16 18:17:41,757 - funman.server.worker - INFO - Worker.stop() completed.\n" ] } ], @@ -345,9 +201,76 @@ "\n", "funman_request = get_request()\n", "setup_common(funman_request, debug=True, dreal_precision=1e0)\n", - "# add_unit_test(funman_request)\n", + "add_unit_test(funman_request)\n", "results = run(funman_request)\n", - "report(results, \"unconstrained\")" + "# report(results, \"unconstrained\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results\n", + "# get_last_point_parameters(results)\n", + "# plot_last_point(results)\n", + "# def plot_last_point(results):\n", + "# pts = results.parameter_space.points() \n", + "# print(f\"{len(pts)} points\")\n", + "\n", + "# if len(pts) > 0:\n", + "# # Get a plot for last point\n", + "# df = results.dataframe(points=pts[-1:])\n", + "# ax = df[STATES].plot()\n", + "# ax.set_yscale(\"log\")\n", + "pts = results.parameter_space.points() \n", + "print(f\"{len(pts)} points\")\n", + "df = results.dataframe(points=pts[-1:])\n", + "# df[IDART].loc(df.index==50.0)\n", + "IDART_by_day = df[IDART].sum(axis=1)\n", + "error = 0.02\n", + "diff_from_target = abs(IDART_by_day - 0.60)\n", + "close_to_target = any(diff_from_target < error)\n", + "close_to_target\n", + "# IDART_by_day\n", + "# abs(day45_IDART - 47)\n", + "# df[IDART].sum(axis=1).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# plt.plot(df.Infected)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.DataFrame({\"a\": [0, 1], \"b\": [3, 4]})\n", + "l = list(df.a.values)\n", + "# l = [float(v) for v in l]\n", + "type(l[0])\n", + "\n", + "# p = plt.plot(df.a, df.b)\n", + "df\n", + "# print(p)\n", + "# plt.plot(l)\n", + "\n", + "# plt.plot(df.a.values)\n", + "# plt.plot(df['a'])\n", + "# df.plot()" ] }, { @@ -439,7 +362,7 @@ "\n", "# # Output the model diagram\n", "# #\n", - "# # results_unconstrained_point.model.to_dot()\n", + "results.model.to_dot()\n", "# # gtp" ] } @@ -460,7 +383,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/notebooks/monthly-demos/funman_july_2024_demo.ipynb b/notebooks/monthly-demos/funman_july_2024_demo.ipynb index 0ca00a6d..a87a7a6e 100644 --- a/notebooks/monthly-demos/funman_july_2024_demo.ipynb +++ b/notebooks/monthly-demos/funman_july_2024_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -18,7 +18,7 @@ "from funman.representation import Interval\n", "import pandas as pd\n", "\n", - "RESOURCES = \"../resources\"\n", + "RESOURCES = \"../../resources\"\n", "SAVED_RESULTS_DIR = \"./out\"\n", "\n", "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"monthly-demo\", \"2024-07\")\n", @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -45,13 +45,13 @@ "STATES = [\"S_u\", \"I_u\", \"E_u\", \"S_v\", \"E_v\", \"I_v\", \"H\", \"R\", \"D\"]\n", "\n", "MAX_TIME=28\n", - "STEP_SIZE=7\n", + "STEP_SIZE=28\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -157,50 +157,16 @@ " pretty_print_request_params(request_params)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Automatic initialization of gaol... done\n", - "[9830000.00000, 9830000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.30000, 0.30000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-07-24 18:45:00,403 - funman.api.run - INFO - Dumping results to ./out/30859b53-7cf9-40f3-8066-675251190783.json\n", - "2024-07-24 18:45:06,166 - funman.scenario.consistency - INFO - 4{28}:\t[+]\n", - "2024-07-24 18:45:06,177 - funman.server.worker - INFO - Completed work on: 30859b53-7cf9-40f3-8066-675251190783\n", - "2024-07-24 18:45:10,451 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-07-24 18:45:10,710 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-07-24 18:45:10,711 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 points\n", - "Point parameters: {'N': 9830000.0, 'NPI_mult': 1.0, 'beta': 0.15249999999999997, 'vacc_mult': 0.30000000000000004, 'r_Sv': 10000.0, 'r_SvSu': 0.002, 'r_EI': 0.13160484699686698, 'r_IH_u': 0.00435, 'r_IH_v': 0.0013499999999999999, 'r_HR': 0.17341744342913468, 'r_HD': 0.011, 'r_IR_u': 0.185, 'r_IR_v': 0.185}\n", - " N NPI_mult beta r_EI r_HD r_HR \\\n", - "unconstrained 9830000.0 1.0 0.1525 0.131605 0.011 0.173417 \n", - "\n", - " r_IH_u r_IH_v r_IR_u r_IR_v r_Sv r_SvSu vacc_mult \n", - "unconstrained 0.00435 0.00135 0.185 0.185 10000.0 0.002 0.3 \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Find a single parameterization of the model that allows H <= 3000 for all timepoints.\n", "# Do not assume the default compartmental constraints because model has vital dynamics.\n", @@ -209,61 +175,33 @@ "\n", "funman_request = get_request()\n", "setup_common(funman_request)\n", + "# funman_request.model.to_dot()\n", "results = run(funman_request)\n", - "report(results, \"unconstrained\")" + "results.model.to_dot().write(\"SEIRHD-vacc-strat.dot\")\n", + "# report(results, \"unconstrained\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-24 18:45:11,106 - funman.server.worker - INFO - FunmanWorker running...\n", - "2024-07-24 18:45:11,109 - funman.server.worker - INFO - Starting work on: 4fb6b7ce-21b0-4f02-b255-eb94134dba6d\n", - "[9830000.00000, 9830000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.30000, 0.30000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-07-24 18:45:18,791 - funman.api.run - INFO - Dumping results to ./out/4fb6b7ce-21b0-4f02-b255-eb94134dba6d.json\n", - "2024-07-24 18:45:18,804 - funman.scenario.consistency - INFO - 4{28}:\t[+]\n", - "2024-07-24 18:45:18,818 - funman.server.worker - INFO - Completed work on: 4fb6b7ce-21b0-4f02-b255-eb94134dba6d\n", - "2024-07-24 18:45:28,831 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-07-24 18:45:28,900 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-07-24 18:45:28,902 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 points\n", - "Point parameters: {'N': 9830000.0, 'NPI_mult': 1.0, 'beta': 0.15250000000000002, 'vacc_mult': 0.30000000000000004, 'r_Sv': 10000.0, 'r_SvSu': 0.002, 'r_EI': 0.1508337675718892, 'r_IH_u': 0.00435, 'r_IH_v': 0.00135, 'r_HR': 0.1549189131819147, 'r_HD': 0.011, 'r_IR_u': 0.185, 'r_IR_v': 0.185}\n", - " N NPI_mult beta vacc_mult r_Sv \\\n", - "unconstrained 9830000.0 1.0 0.1525 0.3 10000.0 \n", - "compartmental_constrained 9830000.0 1.0 0.1525 0.3 10000.0 \n", - "\n", - " r_SvSu r_EI r_IH_u r_IH_v r_HR \\\n", - "unconstrained 0.002 0.131605 0.00435 0.00135 0.173417 \n", - "compartmental_constrained 0.002 0.150834 0.00435 0.00135 0.154919 \n", - "\n", - " r_HD r_IR_u r_IR_v \n", - "unconstrained 0.011 0.185 0.185 \n", - "compartmental_constrained 0.011 0.185 0.185 \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], + "source": [ + "results.model.to_dot().render(\"SEIRHD-vacc-strat\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Add bounds [0, N] to the STATE compartments. \n", "# Add bounds sum(STATE) in [N-e, N+e], for a small e.\n", @@ -277,58 +215,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-24 18:45:29,268 - funman.server.worker - INFO - FunmanWorker running...\n", - "2024-07-24 18:45:29,271 - funman.server.worker - INFO - Starting work on: ecbf77a9-062b-4470-b67f-48928653308b\n", - "[9830000.00000, 9830000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.30000, 0.30000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-07-24 18:45:35,834 - funman.api.run - INFO - Dumping results to ./out/ecbf77a9-062b-4470-b67f-48928653308b.json\n", - "2024-07-24 18:45:35,855 - funman.scenario.consistency - INFO - 4{28}:\t[+]\n", - "2024-07-24 18:45:35,867 - funman.server.worker - INFO - Completed work on: ecbf77a9-062b-4470-b67f-48928653308b\n", - "2024-07-24 18:45:45,875 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-07-24 18:45:45,919 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-07-24 18:45:45,924 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 points\n", - "Point parameters: {'N': 9830000.0, 'NPI_mult': 1.0, 'beta': 0.15239796545843914, 'vacc_mult': 0.30000000000000004, 'r_Sv': 10000.0, 'r_SvSu': 0.002, 'r_EI': 0.14105188985883776, 'r_IH_u': 0.004350000000000001, 'r_IH_v': 0.00135, 'r_HR': 0.14635253130970075, 'r_HD': 0.011, 'r_IR_u': 0.185, 'r_IR_v': 0.185}\n", - " N NPI_mult beta vacc_mult r_Sv \\\n", - "unconstrained 9830000.0 1.0 0.152500 0.3 10000.0 \n", - "compartmental_constrained 9830000.0 1.0 0.152500 0.3 10000.0 \n", - "relaxed_bounds 9830000.0 1.0 0.152398 0.3 10000.0 \n", - "\n", - " r_SvSu r_EI r_IH_u r_IH_v r_HR \\\n", - "unconstrained 0.002 0.131605 0.00435 0.00135 0.173417 \n", - "compartmental_constrained 0.002 0.150834 0.00435 0.00135 0.154919 \n", - "relaxed_bounds 0.002 0.141052 0.00435 0.00135 0.146353 \n", - "\n", - " r_HD r_IR_u r_IR_v \n", - "unconstrained 0.011 0.185 0.185 \n", - "compartmental_constrained 0.011 0.185 0.185 \n", - "relaxed_bounds 0.011 0.185 0.185 \n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Relax the bounds on the parameters to allow additional parameterizations\n", "\n", @@ -342,100 +231,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-24 18:45:46,223 - funman.server.worker - INFO - FunmanWorker running...\n", - "2024-07-24 18:45:46,225 - funman.server.worker - INFO - Starting work on: a654ce35-3219-47bc-9515-4acdbed918ea\n", - "[9830000.00000, 9830000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.30000, 0.30000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-07-24 18:45:48,239 - funman.api.run - INFO - Dumping results to ./out/a654ce35-3219-47bc-9515-4acdbed918ea.json\n", - "2024-07-24 18:46:11,345 - funman.api.run - INFO - Dumping results to ./out/a654ce35-3219-47bc-9515-4acdbed918ea.json\n", - "2024-07-24 18:46:11,366 - funman.search.box_search - INFO - progress: 0.20000\n", - "2024-07-24 18:46:11,384 - funman.api.run - INFO - Creating plot of point trajectories: ./out/a654ce35-3219-47bc-9515-4acdbed918ea_points.png\n", - "2024-07-24 18:46:11,418 - funman.search.box_search - INFO - progress: 0.40000\n", - "2024-07-24 18:46:11,475 - funman.search.box_search - INFO - progress: 0.60000\n", - "2024-07-24 18:46:11,548 - funman.search.box_search - INFO - progress: 0.80000\n", - "2024-07-24 18:46:11,630 - funman.search.box_search - INFO - progress: 1.00000\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-24 18:46:11,728 - funman.api.run - INFO - Creating plot of parameter space: ./out/a654ce35-3219-47bc-9515-4acdbed918ea_parameter_space.png\n", - "2024-07-24 18:46:12,675 - funman.server.worker - INFO - Completed work on: a654ce35-3219-47bc-9515-4acdbed918ea\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-24 18:46:24,876 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-07-24 18:46:25,277 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-07-24 18:46:25,279 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5 points\n", - "Point parameters: {'N': 9830000.0, 'NPI_mult': 1.0, 'vacc_mult': 0.30000000000000004, 'r_Sv': 10000.0, 'r_SvSu': 0.002, 'beta': 0.15250000000000002, 'r_EI': 0.14691128985675908, 'r_IH_u': 0.00435, 'r_IH_v': 0.00135, 'r_HR': 0.1549189131819147, 'r_HD': 0.011, 'r_IR_u': 0.185, 'r_IR_v': 0.185}\n", - " N NPI_mult beta vacc_mult r_Sv \\\n", - "unconstrained 9830000.0 1.0 0.152500 0.3 10000.0 \n", - "compartmental_constrained 9830000.0 1.0 0.152500 0.3 10000.0 \n", - "relaxed_bounds 9830000.0 1.0 0.152398 0.3 10000.0 \n", - "synthesis 9830000.0 1.0 0.152500 0.3 10000.0 \n", - "\n", - " r_SvSu r_EI r_IH_u r_IH_v r_HR \\\n", - "unconstrained 0.002 0.131605 0.00435 0.00135 0.173417 \n", - "compartmental_constrained 0.002 0.150834 0.00435 0.00135 0.154919 \n", - "relaxed_bounds 0.002 0.141052 0.00435 0.00135 0.146353 \n", - "synthesis 0.002 0.146911 0.00435 0.00135 0.154919 \n", - "\n", - " r_HD r_IR_u r_IR_v \n", - "unconstrained 0.011 0.185 0.185 \n", - "compartmental_constrained 0.011 0.185 0.185 \n", - "relaxed_bounds 0.011 0.185 0.185 \n", - "synthesis 0.011 0.185 0.185 \n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "funman_request = get_request()\n", "setup_common(funman_request, synthesize=True)\n", @@ -448,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -468,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -509,7 +307,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base.json index 2437c382..88dbb5c0 100644 --- a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base.json +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base.json @@ -1,355 +1,356 @@ { - "name": "Evaluation Scenario 1 Base model", - "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", - "schema_name": "petrinet", - "description": "Evaluation Scenario 1 Base model", - "model_version": "0.1", - "properties": {}, - "model": { - "states": [ - { - "id": "S", - "name": "S", - "grounding": { - "identifiers": { - "ido": "0000514" - }, - "modifiers": {} + "header": { + "name": "Evaluation Scenario 1 Base model", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1 Base model", + "model_version": "0.1" }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "I", - "name": "I", - "grounding": { - "identifiers": { - "ido": "0000511" - }, - "modifiers": {} - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "E", - "name": "E", - "grounding": { - "identifiers": { - "apollosv": "0000154" - }, - "modifiers": {} - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "R", - "name": "R", - "grounding": { - "identifiers": { - "ido": "0000592" - }, - "modifiers": {} - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "H", - "name": "H", - "grounding": { - "identifiers": { - "ido": "0000511" - }, - "modifiers": { - "property": "ncit:C25179" - } - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "D", - "name": "D", - "grounding": { - "identifiers": { - "ncit": "C28554" - }, - "modifiers": {} - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - } - ], - "transitions": [ - { - "id": "t1", - "input": [ - "I", - "S" - ], - "output": [ - "I", - "E" - ], - "properties": { - "name": "t1" - } - }, - { - "id": "t2", - "input": [ - "E" - ], - "output": [ - "I" - ], - "properties": { - "name": "t2" - } - }, - { - "id": "t3", - "input": [ - "I" - ], - "output": [ - "R" - ], - "properties": { - "name": "t3" - } - }, - { - "id": "t4", - "input": [ - "I" - ], - "output": [ - "H" - ], - "properties": { - "name": "t4" - } - }, - { - "id": "t5", - "input": [ - "H" - ], - "output": [ - "R" - ], - "properties": { - "name": "t5" - } - }, - { - "id": "t6", - "input": [ - "H" - ], - "output": [ - "D" - ], - "properties": { - "name": "t6" - } - } - ] - }, - "semantics": { - "ode": { - "rates": [ - { - "target": "t1", - "expression": "I*S*beta/N", - "expression_mathml": "ISbetaN" - }, - { - "target": "t2", - "expression": "E*r_E_to_I", - "expression_mathml": "Er_E_to_I" - }, - { - "target": "t3", - "expression": "I*p_I_to_R*r_I_to_R", - "expression_mathml": "Ip_I_to_Rr_I_to_R" + "model": { + "states": [ + { + "id": "S", + "name": "S", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I", + "name": "I", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E", + "name": "E", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R", + "name": "R", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H", + "name": "H", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D", + "name": "D", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1", + "input": [ + "I", + "S" + ], + "output": [ + "I", + "E" + ], + "properties": { + "name": "t1" + } + }, + { + "id": "t2", + "input": [ + "E" + ], + "output": [ + "I" + ], + "properties": { + "name": "t2" + } + }, + { + "id": "t3", + "input": [ + "I" + ], + "output": [ + "R" + ], + "properties": { + "name": "t3" + } + }, + { + "id": "t4", + "input": [ + "I" + ], + "output": [ + "H" + ], + "properties": { + "name": "t4" + } + }, + { + "id": "t5", + "input": [ + "H" + ], + "output": [ + "R" + ], + "properties": { + "name": "t5" + } + }, + { + "id": "t6", + "input": [ + "H" + ], + "output": [ + "D" + ], + "properties": { + "name": "t6" + } + } + ] }, - { - "target": "t4", - "expression": "I*p_I_to_H*r_I_to_H", - "expression_mathml": "Ip_I_to_Hr_I_to_H" + "semantics": { + "ode": { + "rates": [ + { + "target": "t1", + "expression": "I*S*beta/N", + "expression_mathml": "ISbetaN" + }, + { + "target": "t2", + "expression": "E*r_E_to_I", + "expression_mathml": "Er_E_to_I" + }, + { + "target": "t3", + "expression": "I*p_I_to_R*r_I_to_R", + "expression_mathml": "Ip_I_to_Rr_I_to_R" + }, + { + "target": "t4", + "expression": "I*p_I_to_H*r_I_to_H", + "expression_mathml": "Ip_I_to_Hr_I_to_H" + }, + { + "target": "t5", + "expression": "H*p_H_to_R*r_H_to_R", + "expression_mathml": "Hp_H_to_Rr_H_to_R" + }, + { + "target": "t6", + "expression": "H*p_H_to_D*r_H_to_D", + "expression_mathml": "Hp_H_to_Dr_H_to_D" + } + ], + "initials": [ + { + "target": "S", + "expression": "19339995.0000000", + "expression_mathml": "19339995.0" + }, + { + "target": "I", + "expression": "4.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E", + "expression": "1.00000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D", + "expression": "0.0", + "expression_mathml": "0.0" + } + ], + "parameters": [ + { + "id": "N", + "value": 19340000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta", + "value": 0.4, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "r_E_to_I", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_R", + "value": 0.8, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_R", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_H", + "value": 0.2, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_H", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_R", + "value": 0.88, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_R", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_D", + "value": 0.12, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_D", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + } + ], + "observables": [], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + } }, - { - "target": "t5", - "expression": "H*p_H_to_R*r_H_to_R", - "expression_mathml": "Hp_H_to_Rr_H_to_R" - }, - { - "target": "t6", - "expression": "H*p_H_to_D*r_H_to_D", - "expression_mathml": "Hp_H_to_Dr_H_to_D" - } - ], - "initials": [ - { - "target": "S", - "expression": "19339995.0000000", - "expression_mathml": "19339995.0" - }, - { - "target": "I", - "expression": "4.00000000000000", - "expression_mathml": "4.0" - }, - { - "target": "E", - "expression": "1.00000000000000", - "expression_mathml": "1.0" - }, - { - "target": "R", - "expression": "0.0", - "expression_mathml": "0.0" - }, - { - "target": "H", - "expression": "0.0", - "expression_mathml": "0.0" - }, - { - "target": "D", - "expression": "0.0", - "expression_mathml": "0.0" - } - ], - "parameters": [ - { - "id": "N", - "value": 19340000.0, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "beta", - "value": 0.4, - "units": { - "expression": "1/(day*person)", - "expression_mathml": "1dayperson" - } - }, - { - "id": "r_E_to_I", - "value": 0.2, - "units": { - "expression": "1/day", - "expression_mathml": "day-1" - } - }, - { - "id": "p_I_to_R", - "value": 0.8, - "units": { - "expression": "1", - "expression_mathml": "1" - } - }, - { - "id": "r_I_to_R", - "value": 0.07, - "units": { - "expression": "1/day", - "expression_mathml": "day-1" - } - }, - { - "id": "p_I_to_H", - "value": 0.2, - "units": { - "expression": "1", - "expression_mathml": "1" - } - }, - { - "id": "r_I_to_H", - "value": 0.1, - "units": { - "expression": "1/day", - "expression_mathml": "day-1" - } - }, - { - "id": "p_H_to_R", - "value": 0.88, - "units": { - "expression": "1", - "expression_mathml": "1" - } - }, - { - "id": "r_H_to_R", - "value": 0.1, - "units": { - "expression": "1/day", - "expression_mathml": "day-1" - } - }, - { - "id": "p_H_to_D", - "value": 0.12, - "units": { - "expression": "1", - "expression_mathml": "1" - } - }, - { - "id": "r_H_to_D", - "value": 0.1, - "units": { - "expression": "1/day", - "expression_mathml": "day-1" - } - } - ], - "observables": [], - "time": { - "id": "t", - "units": { - "expression": "day", - "expression_mathml": "day" + "metadata": { + "annotations": { + "license": null, + "authors": [], + "references": [], + "time_scale": null, + "time_start": null, + "time_end": null, + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } } - } - } - }, - "metadata": { - "annotations": { - "license": null, - "authors": [], - "references": [], - "time_scale": null, - "time_start": null, - "time_end": null, - "locations": [], - "pathogens": [], - "diseases": [], - "hosts": [], - "model_types": [] - } - } } \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base_request.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base_request.json new file mode 100644 index 00000000..24e3a285 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base_request.json @@ -0,0 +1,28 @@ +{ + "constraints": [], + "parameters": [], + "structure_parameters": [ + { + "name": "schedules", + "schedules": [ + { + "timepoints": [ + 0, + 10, + 20, + 30, + 40, + 50 + ] + } + ] + } + ], + "config": { + "tolerance": 1e-2, + "dreal_precision": 0.001, + "normalization_constant": 19340000.0, + "normalize": false, + "use_compartmental_constraints": true + } +} \ No newline at end of file From 63dde37a64f1c8cc8098d6f2a9159692d0a24f6c Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Tue, 20 Aug 2024 12:58:13 -0500 Subject: [PATCH 17/93] fix tests (asym meets, None values in points) --- .../funman_dreal/src/funman_dreal/solver.py | 13 +++-- src/funman/representation/__init__.py | 4 +- src/funman/representation/box.py | 16 +++++-- src/funman/representation/interval.py | 37 +++++++++----- src/funman/search/box_search.py | 48 +++++++++++-------- test/test_use_cases.py | 2 +- 6 files changed, 76 insertions(+), 44 deletions(-) diff --git a/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py b/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py index 7f175dcd..dd725e2c 100644 --- a/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py +++ b/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py @@ -600,8 +600,11 @@ def get_model(self): for sn in self.symbols: s = self.symbols[sn][0] if s.is_term(): - v = self.get_value(self.symbols[sn]) - assignment[s] = v + try: + v = self.get_value(self.symbols[sn]) + assignment[s] = v + except ValueError as e: + l.error(f"DRealNative.get_model(): {e}") return EagerModel(assignment=assignment, environment=self.environment) def get_value(self, symbol_pair): @@ -615,7 +618,11 @@ def get_value(self, symbol_pair): lb = self.model[item].lb() mid = (ub - lb) / 2.0 mid = mid + lb - if not isinstance(mid, int) and ( + + if math.isnan(lb) and math.isnan(ub): + # Value was not assigned + return None + elif not isinstance(mid, int) and ( isinstance(ub, int) or isinstance(lb, int) ): return Real(lb) if isinstance(lb, int) else Real(ub) diff --git a/src/funman/representation/__init__.py b/src/funman/representation/__init__.py index e99e2bbf..ce7e9599 100644 --- a/src/funman/representation/__init__.py +++ b/src/funman/representation/__init__.py @@ -2,7 +2,7 @@ Classes for representing analysis elements, such as parameter, intervals, boxes, and parmaeter spaces. """ -from typing import Union +from typing import Union, Optional Timepoint = Union[int, float] Timestep = int @@ -10,7 +10,7 @@ from .encoding_schedule import EncodingSchedule -PointValue = Union[float, int, EncodingSchedule] +PointValue = Optional[Union[float, int, EncodingSchedule]] from .parameter import * from .assumption import * from .constraint import * diff --git a/src/funman/representation/box.py b/src/funman/representation/box.py index ce1f9f1c..f3dde87a 100644 --- a/src/funman/representation/box.py +++ b/src/funman/representation/box.py @@ -206,7 +206,9 @@ def _merge(self, other: "Box") -> "Box": """ bounds = {p: None for p in self.bounds.keys()} for p in bounds: - if self.bounds[p].meets(other.bounds[p]): + if self.bounds[p].meets(other.bounds[p]) or other.bounds[p].meets( + self.bounds[p] + ): bounds[p] = Interval( lb=min(self.bounds[p].lb, other.bounds[p].lb), ub=max(self.bounds[p].ub, other.bounds[p].ub), @@ -242,7 +244,10 @@ def _get_merge_candidates(self, boxes: Dict[ModelParameter, List["Box"]]): continue if ( - sorted[i].bounds[p].meets(self.bounds[p]) + ( + sorted[i].bounds[p].meets(self.bounds[p]) + or self.bounds[p].meets(sorted[i].bounds[p]) + ) and sorted[i] not in disqualified_set and sorted[i].schedule == self.schedule ): @@ -264,9 +269,10 @@ def _get_merge_candidates(self, boxes: Dict[ModelParameter, List["Box"]]): if sorted[i] in equals_set: equals_set.remove(sorted[i]) disqualified_set.add(sorted[i]) - if sorted[i].bounds[p].disjoint( - self.bounds[p] - ) and not sorted[i].bounds[p].meets(self.bounds[p]): + if sorted[i].bounds[p].disjoint(self.bounds[p]) and not ( + sorted[i].bounds[p].meets(self.bounds[p]) + or self.bounds[p].meets(sorted[i].bounds[p]) + ): break # Because sorted, no further checking needed if len(boxes.keys()) == 1: # 1D candidates = meets_set diff --git a/src/funman/representation/interval.py b/src/funman/representation/interval.py index 5ad5598e..d2df0ff4 100644 --- a/src/funman/representation/interval.py +++ b/src/funman/representation/interval.py @@ -2,7 +2,7 @@ from decimal import Decimal from typing import List, Optional, Union -from numpy import average, nextafter +from numpy import average, finfo, nextafter from pydantic import ( BaseModel, Field, @@ -148,18 +148,31 @@ def meets(self, other: "Interval") -> bool: bool Does self meet other? """ - return ( - (self.ub == other.lb and not self.closed_upper_bound) - or ( - nextafter(self.ub, POS_INFINITY) == other.lb - and self.closed_upper_bound - ) - or (self.lb == other.ub and not other.closed_upper_bound) - or ( - self.ub == nextafter(other.lb, POS_INFINITY) - and other.closed_upper_bound + l.debug(f"Interval.meets(): {self} {other}") + # return self.ub == other.lb or self.lb == other.ub + if self.closed_upper_bound: + # cannot be equal to other.lb + # Make sure that we don't use 0.0 with nextafter + self_ub = ( + finfo("f8").smallest_normal + if self.ub == 0.0 + else nextafter(self.ub, POS_INFINITY) ) - ) + return self.ub == other.lb + else: + return self.ub == other.lb + # return ( + # (self.ub == other.lb and not self.closed_upper_bound) + # or ( + # nextafter(self.ub, POS_INFINITY) == other.lb + # and self.closed_upper_bound + # ) + # or (self.lb == other.ub and not other.closed_upper_bound) + # or ( + # self.ub == nextafter(other.lb, POS_INFINITY) + # and other.closed_upper_bound + # ) + # ) def finite(self) -> bool: """ diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index fc461bb4..8bc524b5 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -332,9 +332,15 @@ def _extract_point(self, model, box: Box): point = Point( values={ p[0].symbol_name(): ( - float(p[1].constant_value()) - if p[1].is_real_constant() - else p[1].constant_value() + ( + ( + float(p[1].constant_value()) + if p[1].is_real_constant() + else p[1].constant_value() + ) + if p[1] is not None + else None + ) ) for p in model }, @@ -1215,15 +1221,15 @@ def _expand( l.trace(f"+++ True:\n{box}") if episode.config.corner_points: - corner_points: List[Point] = ( - self.get_box_corners( - solver, - episode, - curr_step_box, - rval, - options, - my_solver, - ) + corner_points: List[ + Point + ] = self.get_box_corners( + solver, + episode, + curr_step_box, + rval, + options, + my_solver, ) # Advance a true box to be considered for later timesteps @@ -1269,15 +1275,15 @@ def _expand( l.debug(f"False @ {box.timestep().lb}") l.trace(f"--- False:\n{box}") if episode.config.corner_points: - corner_points: List[Point] = ( - self.get_box_corners( - solver, - episode, - box, - rval, - options, - my_solver, - ) + corner_points: List[ + Point + ] = self.get_box_corners( + solver, + episode, + box, + rval, + options, + my_solver, ) rval.put(box.model_dump()) else: # Timeout FIXME copy of split code diff --git a/test/test_use_cases.py b/test/test_use_cases.py index 0ae3b126..f2124c8d 100644 --- a/test/test_use_cases.py +++ b/test/test_use_cases.py @@ -187,7 +187,7 @@ def test_use_case_bilayer_parameter_synthesis(self): config=FUNMANConfig( # solver="dreal", # dreal_mcts=True, - save_smtlib="./out", + # save_smtlib="./out", # dreal_log_level="info", tolerance=1e-3, number_of_processes=1, From 22a9a7085d5805bdd25c9516b0f04f36bac2eb1c Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 21 Aug 2024 09:58:31 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z#G74zt@~@=Ys&Fz6Z7kx^{s@3Ad3zfpC&SmXd6b6w=JqxK;3pvKCk6nenWLkv11J3}d*~H4wb!>bGdFbjWMgki z^>-==y`J22G`F#S{cmXeI#oCs85o%v*csSf89(emhR;<0sqcRnOaG9Fg!EtMg^lSS zoBvq Date: Fri, 23 Aug 2024 09:43:10 -0500 Subject: [PATCH 19/93] build updates --- docker/api/Dockerfile | 2 +- docker/base/Dockerfile | 2 +- docker/dev/root/Dockerfile.root | 4 ++-- docker/dev/user/Dockerfile | 8 ++++---- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docker/api/Dockerfile b/docker/api/Dockerfile index 29597ce8..4f2970d0 100644 --- a/docker/api/Dockerfile +++ b/docker/api/Dockerfile @@ -1,5 +1,5 @@ ARG SIFT_REGISTRY_ROOT -ARG FROM_IMAGE=funman-pypi +ARG FROM_IMAGE=funman-dreal4 ARG FROM_TAG=${TARGETOS}-${TARGETARCH} FROM ${SIFT_REGISTRY_ROOT}${FROM_IMAGE}:${FROM_TAG} diff --git a/docker/base/Dockerfile b/docker/base/Dockerfile index 689938dc..3ca7edb1 100644 --- a/docker/base/Dockerfile +++ b/docker/base/Dockerfile @@ -47,6 +47,6 @@ RUN pip install --no-cache-dir fastapi>=0.103.1 RUN pip install --no-cache-dir --upgrade setuptools pip RUN pip install --no-cache-dir wheel -RUN pip install /dreal4/dreal-*.whl +RUN pip install /dreal4/dreal-4.21.6.2-cp310-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl CMD [ "/bin/bash" ] diff --git a/docker/dev/root/Dockerfile.root b/docker/dev/root/Dockerfile.root index 790f9976..c21de852 100644 --- a/docker/dev/root/Dockerfile.root +++ b/docker/dev/root/Dockerfile.root @@ -34,8 +34,8 @@ ENV PYTHONPYCACHEPREFIX="/.cache/pycache/" RUN pip install --no-cache-dir --upgrade setuptools pip RUN pip install --no-cache-dir wheel -RUN pip install --no-cache-dir pytest==7.1.2 -RUN pip install --no-cache-dir sphinx==5.2.2 +RUN pip install --no-cache-dir pytest +RUN pip install --no-cache-dir sphinx RUN pip install --no-cache-dir myst-parser RUN pip install --no-cache-dir autodoc-pydantic RUN pip install --no-cache-dir twine diff --git a/docker/dev/user/Dockerfile b/docker/dev/user/Dockerfile index 080164bc..ac380b3d 100644 --- a/docker/dev/user/Dockerfile +++ b/docker/dev/user/Dockerfile @@ -42,8 +42,8 @@ ENV PYTHONPYCACHEPREFIX="/home/$UNAME/.cache/pycache/" RUN pip install --no-cache-dir --upgrade setuptools pip RUN pip install --no-cache-dir wheel -RUN pip install --no-cache-dir pytest==7.1.2 -RUN pip install --no-cache-dir sphinx==5.2.2 +RUN pip install --no-cache-dir pytest +RUN pip install --no-cache-dir sphinx RUN pip install --no-cache-dir myst-parser RUN pip install --no-cache-dir autodoc-pydantic RUN pip install --no-cache-dir twine @@ -51,9 +51,9 @@ RUN pip install --no-cache-dir build RUN pip install --no-cache-dir pylint RUN pip install --no-cache-dir black RUN pip install --no-cache-dir uvicorn -RUN pip install --no-cache-dir pydantic==2.3.* +RUN pip install --no-cache-dir pydantic RUN pip install --no-cache-dir pydantic-settings -RUN pip install --no-cache-dir fastapi==0.103.* +RUN pip install --no-cache-dir fastapi RUN pip install --no-cache-dir pre-commit RUN pip install --no-cache-dir pycln RUN pip install --no-cache-dir openapi-python-client From dbc3be2ca0ff4bd1b54395636c4edb41916bb92c Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 23 Aug 2024 09:48:41 -0500 Subject: [PATCH 20/93] improve dataframe generation --- .../funman_aug_2024_12mo_eval_1_q1_demo.ipynb | 936 +++++------------- src/funman/search/box_search.py | 36 +- src/funman/server/query.py | 24 +- src/funman/server/worker.py | 8 +- 4 files changed, 305 insertions(+), 699 deletions(-) diff --git a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb index 218e72c2..c77526c4 100644 --- a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb +++ b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,218 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H_odeint = [0.0,\n", + " 0.07584066320978722,\n", + " 0.14636646265873351,\n", + " 0.21517239301596403,\n", + " 0.2849940641423008,\n", + " 0.35803162126534144,\n", + " 0.43617188357763786,\n", + " 0.5211433080571843,\n", + " 0.6146269952842578,\n", + " 0.7183393339849907,\n", + " 0.8340968480485813,\n", + " 0.9638704485112929,\n", + " 1.1098340730821774,\n", + " 1.2744112432601529,\n", + " 1.460322143778306,\n", + " 1.6706331916641786,\n", + " 1.9088107460125325,\n", + " 2.178780372444439,\n", + " 2.4849930017839568,\n", + " 2.832499327828298,\n", + " 3.227033820607022,\n", + " 3.6751098523322363,\n", + " 4.184127609093503,\n", + " 4.762496481974214,\n", + " 5.419774326784966,\n", + " 6.166825487583933,\n", + " 7.016000445528594,\n", + " 7.981340143729251,\n", + " 9.078808185052266,\n", + " 10.326554856871647,\n", + " 11.745217338261286,\n", + " 13.358261021733336,\n", + " 15.192367626771185,\n", + " 17.277876514948733,\n", + " 19.64928647253589,\n", + " 22.345826309466535,\n", + " 25.41210364728401,\n", + " 28.898842636443945,\n", + " 32.863722838161294,\n", + " 37.37233298620304,\n", + " 42.49925554848381,\n", + " 48.32929991918692,\n", + " 54.95890451703756,\n", + " 62.49773100251825,\n", + " 71.07047694046699,\n", + " 80.81893664895041,\n", + " 91.9043442331066,\n", + " 104.51003757795193,\n", + " 118.84448659151495,\n", + " 135.14473616311494,\n", + " 153.6803198995054,\n", + " 174.75770892931388,\n", + " 198.7253686880845,\n", + " 225.9795066077193,\n", + " 256.9706043940812,\n", + " 292.2108414225009,\n", + " 332.28253040812973,\n", + " 377.8477020062484,\n", + " 429.65899353366785,\n", + " 488.57201806502917,\n", + " 555.5594130725682,\n", + " 631.7267930288757,\n", + " 718.33086148102,\n", + " 816.7999711100919,\n", + " 928.7574539225811,\n", + " 1056.0480899336421,\n", + " 1200.768126390387,\n", + " 1365.2993106701535,\n", + " 1552.3474521757955,\n", + " 1764.986108867584,\n", + " 2006.7060366726942,\n", + " 2281.4711317587603,\n", + " 2593.781675837228,\n", + " 2948.74577656625,\n", + " 3352.159985334878,\n", + " 3810.6001762079927,\n", + " 4331.523867699931,\n", + " 4923.385255496248,\n", + " 5595.764320925713,\n", + " 6359.511470886844,\n", + " 7226.909216017238,\n", + " 8211.852427514215,\n", + " 9330.048735030014,\n", + " 10599.240502623044,\n", + " 12039.44975726627,\n", + " 13673.24714065712,\n", + " 15526.045525426567,\n", + " 17626.41847934686,\n", + " 20006.442805113635,\n", + " 22702.063309712732,\n", + " 25753.476547071572,\n", + " 29205.528188227865,\n", + " 33108.11624152888,\n", + " 37516.58927032621,\n", + " 42492.12476694649,\n", + " 48102.06815038656,\n", + " 54420.207487559084,\n", + " 61526.95241138342,\n", + " 69509.3788326499,\n", + " 78461.09204661413,\n", + " 88481.85690086993,\n", + " 99676.93033933835,\n", + " 112156.03116953523,\n", + " 126031.8764591974,\n", + " 141418.21409965339,\n", + " 158427.29086936626,\n", + " 177166.7077374985,\n", + " 197735.64179407313,\n", + " 220220.4490700916,\n", + " 244689.7128999469,\n", + " 271188.8609711989,\n", + " 299734.54505555454,\n", + " 330309.0420578748,\n", + " 362855.0097454492,\n", + " 397270.9706782105,\n", + " 433407.9323314221,\n", + " 471067.5318962573,\n", + " 510002.05000426056,\n", + " 549916.5325942405,\n", + " 590473.1326448151,\n", + " 631297.6155083764,\n", + " 671987.805397328,\n", + " 712123.5855505902,\n", + " 751277.9419388921,\n", + " 789028.4531510238,\n", + " 824968.6089066564,\n", + " 858718.3673693967,\n", + " 889933.4450316473,\n", + " 918312.9510473183,\n", + " 943605.1145011623,\n", + " 965610.9989351172,\n", + " 984186.2260247911,\n", + " 999240.8449867101,\n", + " 1010737.5652130407,\n", + " 1018688.6250969241,\n", + " 1023151.5961628385,\n", + " 1024224.424518873,\n", + " 1022039.9954607659,\n", + " 1016760.4778537896,\n", + " 1008571.6676818144,\n", + " 997677.5091145145,\n", + " 984294.9309318912,\n", + " 968649.0965921708,\n", + " 950969.132919035,\n", + " 931484.3726190757,\n", + " 910421.1206448845,\n", + " 887999.9365984657,\n", + " 864433.4099252748,\n", + " 839924.3952308263,\n", + " 814664.6678259049,\n", + " 788833.956208313,\n", + " 762599.306145181,\n", + " 736114.7329186859,\n", + " 709521.118576635,\n", + " 682946.3131058007,\n", + " 656505.4041417397,\n", + " 630301.1204001798,\n", + " 604424.3395841966,\n", + " 578954.674586528,\n", + " 553961.1153107659,\n", + " 529502.705890681,\n", + " 505629.24208586576,\n", + " 482381.97446456744,\n", + " 459794.3062235543,\n", + " 437892.47709088976,\n", + " 416696.2258780569,\n", + " 396219.4261885734,\n", + " 376470.6921664646,\n", + " 357453.95052101125,\n", + " 339168.9775714914,\n", + " 321611.9004232435,\n", + " 304775.66215856594,\n", + " 288650.4511800376,\n", + " 273224.0956361533,\n", + " 258482.42403353218,\n", + " 244409.59338260329,\n", + " 230988.3864887854,\n", + " 218200.48005631237,\n", + " 206026.6853722251,\n", + " 194447.16337089916,\n", + " 183441.61586876688,\n", + " 172989.45475247476,\n", + " 163069.95085616622,\n", + " 153662.36416343783,\n", + " 144746.05696746017,\n", + " 136300.5914987689,\n", + " 128305.81345597992,\n", + " 120741.9227885983,\n", + " 113589.53299236886,\n", + " 106829.72009170499,\n", + " 100444.06240763294,\n", + " 94414.6720882853,\n", + " 88724.21934978342,\n", + " 83355.95026825309,\n", + " 78293.69889597349,\n", + " 73521.89440676449,\n", + " 69025.56391080657,\n", + " 64790.33151791613,\n", + " 60802.414180610635,\n", + " 57048.61477377206]\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(H_odeint)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -46,14 +257,14 @@ "STATES = [\"S\", \"I\", \"E\", \"R\", \"H\", \"D\"]\n", "# IDART = [\"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Threatened\"]\n", "\n", - "MAX_TIME=10\n", - "STEP_SIZE=2\n", + "MAX_TIME=150\n", + "STEP_SIZE=5\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -131,8 +342,14 @@ " if len(pts) > 0:\n", " # Get a plot for last point\n", " df = results.dataframe(points=pts[-1:])\n", + " # pd.options.plotting.backend = \"plotly\"\n", " ax = df[STATES].plot()\n", - " ax.set_yscale(\"log\")\n", + " \n", + " \n", + " fig = plt.figure()\n", + " # fig.set_yscale(\"log\")\n", + " fig.savefig(\"save_file_name.pdf\")\n", + " plt.close()\n", "\n", "def get_last_point_parameters(results):\n", " pts = results.parameter_space.points()\n", @@ -180,330 +397,38 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-08-16 18:16:57,654 - funman.scenario.consistency - INFO - 5{10}:\t[+]\n", - "2024-08-16 18:16:57,662 - funman.server.worker - INFO - Completed work on: 2af8847c-8400-4942-bf3d-d879a49c5f47\n", - "2024-08-16 18:16:59,512 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-08-16 18:16:59,676 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-08-16 18:16:59,679 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - } - ], + "outputs": [], "source": [ - "# Find a single parameterization of the model where sum(IDART) is approx 60% around day 47.\n", + "# i) (TA1 Search and Discovery Workflow, 1 Hr. Time Limit) Find estimates on the\n", + "# efficacy of surgical masks in preventing onward transmission of SARS-CoV-2 (preferred)\n", + "# or comparable viral respiratory pathogens (e.g., MERS-CoV, SARS), including any\n", + "# information about uncertainty in these estimates. The term surgical mask here refers to\n", + "# the commonly available, disposable procedure mask, not an N95-type respirator. Find 3\n", + "# credible documents that provide estimates and use your judgment to determine what\n", + "# value (in the deterministic case) or distribution (in the probabilistic case) to use in your\n", + "# forecasts in 1.a.iii.\n", + "\n", + "# The base model assumes no masking inverventions, and we measure the efficacy in terms of the number of hospitalized.\n", "\n", "funman_request = get_request()\n", - "setup_common(funman_request, debug=True, dreal_precision=1e0)\n", + "setup_common(funman_request, debug=True, dreal_precision=1e-3)\n", "add_unit_test(funman_request)\n", "results = run(funman_request)\n", - "# report(results, \"unconstrained\")" + "report(results, \"unconstrained\")\n", + "pass\n", + "# pts = results.parameter_space.points() \n", + "# print(f\"{len(pts)} points\")\n", + "# df = results.dataframe(points=pts[-1:])\n", + "# df" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 points\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/danbryce/funman/src/funman/server/query.py:384: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n", - " df = df.infer_objects(copy=False).interpolate(\n" - ] - }, - { - "data": { - "text/html": [ - "
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(I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-08-16 18:16:59,929 - funman.scenario.consistency - INFO - 5{10}:\t[-]\n", - "2024-08-16 18:16:59,930 - funman.server.worker - INFO - Completed work on: 7692d692-8ab5-4a84-9e25-f0b2e01c1187\n", - "2024-08-16 18:17:01,776 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-08-16 18:17:01,947 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-08-16 18:17:01,958 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 points\n" - ] - } - ], + "outputs": [], "source": [ "# Add bounds [0, N] to the STATE compartments. \n", "# Add bounds sum(STATE) in [N-e, N+e], for a small e.\n", @@ -660,41 +506,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-08-16 18:17:01,990 - funman.server.worker - INFO - FunmanWorker running...\n", - "2024-08-16 18:17:01,993 - funman.server.worker - INFO - Starting work on: 9756fa28-57d3-4d09-a095-ce13262126d4\n", - "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-08-16 18:17:02,140 - funman.scenario.consistency - INFO - 5{10}:\t[-]\n", - "2024-08-16 18:17:02,141 - funman.server.worker - INFO - Completed work on: 9756fa28-57d3-4d09-a095-ce13262126d4\n", - "2024-08-16 18:17:03,995 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-08-16 18:17:04,156 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-08-16 18:17:04,157 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 points\n" - ] - } - ], + "outputs": [], "source": [ "# Relax the bounds on the parameters to allow additional parameterizations\n", "\n", @@ -708,42 +522,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-08-16 18:17:04,177 - funman.server.worker - INFO - FunmanWorker running...\n", - "2024-08-16 18:17:04,179 - funman.server.worker - INFO - Starting work on: 32faba99-2ef3-40a1-ad37-59ed7d39e7e9\n", - "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-08-16 18:17:04,360 - funman.scenario.consistency - INFO - 5{10}:\t[-]\n", - "2024-08-16 18:17:04,361 - funman.server.worker - INFO - Completed work on: 32faba99-2ef3-40a1-ad37-59ed7d39e7e9\n", - "2024-08-16 18:17:06,184 - funman.api.run - WARNING - Cannot plot a parameter space for zero boxes or zero parameters\n", - "2024-08-16 18:17:06,185 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-08-16 18:17:06,378 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-08-16 18:17:06,379 - funman.server.worker - INFO - Worker.stop() completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 points\n" - ] - } - ], + "outputs": [], "source": [ "funman_request = get_request()\n", "setup_common(funman_request, synthesize=True)\n", @@ -756,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -776,198 +557,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "petrinet\n", - "\n", - "\n", - "\n", - "t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]\n", - "\n", - "t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]\n", - "\n", - "\n", - "\n", - "I\n", - "\n", - "I\n", - "\n", - "\n", - "\n", - "t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]->I\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "E\n", - "\n", - "E\n", - "\n", - "\n", - "\n", - "t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]->E\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "S\n", - "\n", - "S\n", - "\n", - "\n", - "\n", - "S->t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "t2([E*r_E_to_I]) = [0.2*E]\n", - "\n", - "t2([E*r_E_to_I]) = [0.2*E]\n", - "\n", - "\n", - "\n", - "t2([E*r_E_to_I]) = [0.2*E]->I\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t3([I*p_I_to_R*r_I_to_R]) = [0.056*I]\n", - "\n", - "t3([I*p_I_to_R*r_I_to_R]) = [0.056*I]\n", - "\n", - "\n", - "\n", - "R\n", - "\n", - "R\n", - "\n", - "\n", - "\n", - "t3([I*p_I_to_R*r_I_to_R]) = [0.056*I]->R\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t4([I*p_I_to_H*r_I_to_H]) = [0.02*I]\n", - "\n", - "t4([I*p_I_to_H*r_I_to_H]) = [0.02*I]\n", - "\n", - "\n", - "\n", - "H\n", - "\n", - "H\n", - "\n", - "\n", - "\n", - "t4([I*p_I_to_H*r_I_to_H]) = [0.02*I]->H\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t5([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", - "\n", - "t5([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", - "\n", - "\n", - "\n", - "t5([H*p_H_to_R*r_H_to_R]) = [0.088*H]->R\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t6([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", - "\n", - "t6([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", - "\n", - "\n", - "\n", - "D\n", - "\n", - "D\n", - "\n", - "\n", - "\n", - "t6([H*p_H_to_D*r_H_to_D]) = [0.012*H]->D\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "I->t1([I*S*beta/N]) = [2.06825232678387e-8*I*S]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I->t3([I*p_I_to_R*r_I_to_R]) = [0.056*I]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I->t4([I*p_I_to_H*r_I_to_H]) = [0.02*I]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "E->t2([E*r_E_to_I]) = [0.2*E]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "H->t5([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "H->t6([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# # Get points (trajectories generated)\n", "# pts = results.parameter_space.points() \n", diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index 8bc524b5..ebf22da1 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -1221,15 +1221,15 @@ def _expand( l.trace(f"+++ True:\n{box}") if episode.config.corner_points: - corner_points: List[ - Point - ] = self.get_box_corners( - solver, - episode, - curr_step_box, - rval, - options, - my_solver, + corner_points: List[Point] = ( + self.get_box_corners( + solver, + episode, + curr_step_box, + rval, + options, + my_solver, + ) ) # Advance a true box to be considered for later timesteps @@ -1275,15 +1275,15 @@ def _expand( l.debug(f"False @ {box.timestep().lb}") l.trace(f"--- False:\n{box}") if episode.config.corner_points: - corner_points: List[ - Point - ] = self.get_box_corners( - solver, - episode, - box, - rval, - options, - my_solver, + corner_points: List[Point] = ( + self.get_box_corners( + solver, + episode, + box, + rval, + options, + my_solver, + ) ) rval.put(box.model_dump()) else: # Timeout FIXME copy of split code diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 5b5cdd11..549a30f0 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -1,5 +1,6 @@ import logging import random +import re from collections import Counter from datetime import datetime, timedelta from typing import Dict, List, Optional, Tuple, Union @@ -324,6 +325,9 @@ def _scenario(self) -> AnalysisScenario: scenario = FunmanWorkUnit( id=self.id, model=self.model, request=self.request ).to_scenario() + + # Needed to extract + scenario._process_parameters() return scenario def point_parameters( @@ -365,6 +369,12 @@ def dataframe( for i, point in enumerate(points): timeseries = self.symbol_timeseries(point, to_plot) df = pd.DataFrame.from_dict(timeseries) + + if interpolate: + df = df.infer_objects(copy=False).interpolate( + method=interpolate + ) + df["id"] = i parameters = self.point_parameters(point=point, scenario=scenario) for p, v in parameters.items(): @@ -375,15 +385,12 @@ def dataframe( ): df[p.name] = v df["label"] = point.label + df["label"] = df["label"].astype(str) # if max_time: # if time_var: # df = df.at[max_time, :] = None # df = df.reindex(range(max_time+1), fill_value=None) - if interpolate: - df = df.infer_objects(copy=False).interpolate( - method=interpolate - ) if time_var and any("timer_t" in x for x in df.columns): df = ( df.rename(columns={"timer_t": "time"}) @@ -454,12 +461,17 @@ def symbol_values( return vals def _symbols( - self, point: Point, variables: List[str] + self, + point: Point, + variables: List[str], + time_pattern: str = f"_[0-9]+$", ) -> Dict[str, Dict[str, str]]: symbols = {} # vars.sort(key=lambda x: x.symbol_name()) + vars_pattern = "|".join(variables) + pattern = re.compile(f"[{vars_pattern}].*{time_pattern}") for var in point.values: - if any(f"{v}_" in var for v in variables): + if re.match(pattern, var): var_name, timepoint = self._split_symbol(var) if timepoint: if var_name not in symbols: diff --git a/src/funman/server/worker.py b/src/funman/server/worker.py index 28afbed9..d6e9a045 100644 --- a/src/funman/server/worker.py +++ b/src/funman/server/worker.py @@ -180,7 +180,7 @@ def get_current(self) -> Optional[str]: return self.current_id def _update_current_results( - self, scenario: AnalysisScenario, results: ParameterSpace + self, scenario: AnalysisScenario, parameter_space: ParameterSpace ) -> FunmanProgress: with self._results_lock: if self.current_results is None: @@ -194,7 +194,7 @@ def _update_current_results( "Cannot update current_results as it is already finalized" ) return self.current_results.update_parameter_space( - scenario, results + scenario, parameter_space ) def _run(self, stop_event: threading.Event): @@ -246,7 +246,9 @@ def _run(self, stop_event: threading.Event): ), ) with self._results_lock: - self.current_results.finalize_result(scenario, result) + self.current_results.finalize_result( + result.scenario, result + ) l.info(f"Completed work on: {work.id}") except Exception as e: l.error(f"Internal Server Error ({work.id}):") From 87132a07e3dd1a09c2e83a14166c2ce4ae21b1ee Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Tue, 27 Aug 2024 21:25:50 +0000 Subject: [PATCH 21/93] add flag to use entropy vs baseline box comparison for box searcha --- .../sir_request_param_synth_entropy_case.json | 93 +++++++++++++++ setup.py | 1 + src/funman/api/run.py | 2 +- src/funman/config.py | 6 + src/funman/representation/box.py | 111 ++++++++++-------- src/funman/search/search.py | 1 + src/funman/translate/bilayer.py | 6 + 7 files changed, 169 insertions(+), 51 deletions(-) create mode 100644 resources/amr/petrinet/evaluation/sir_request_param_synth_entropy_case.json diff --git a/resources/amr/petrinet/evaluation/sir_request_param_synth_entropy_case.json b/resources/amr/petrinet/evaluation/sir_request_param_synth_entropy_case.json new file mode 100644 index 00000000..ef5e521f --- /dev/null +++ b/resources/amr/petrinet/evaluation/sir_request_param_synth_entropy_case.json @@ -0,0 +1,93 @@ +{ + "constraints": [ + { + "name": "ibounds", + "variable": "I", + "interval": { + "lb": 0.2, + "ub": 0.4 + }, + "timepoints": { + "lb": 35, + "ub": 40, + "closed_upper_bound": true + } + } + ], + "parameters": [ + { + "name": "beta", + "interval": { + "lb": 0.01, + "ub": 0.3 + }, + "label": "all" + }, + { + "name": "gamma", + "interval": { + "lb": 0.01, + "ub": 0.3 + }, + "label": "all" + }, + { + "name": "S0", + "interval": { + "lb": 0.990, + "ub": 0.990 + }, + "label": "any" + }, + { + "name": "I0", + "interval": { + "lb": 0.010, + "ub": 0.010 + }, + "label": "any" + }, + { + "name": "R0", + "interval": { + "lb": 0, + "ub": 0 + }, + "label": "any" + }, + { + "name": "N", + "interval": { + "lb": 1, + "ub": 1 + }, + "label": "any" + } + ], + "structure_parameters": [ + { + "name": "num_steps", + "interval": { + "lb": 8, + "ub": 8 + }, + "label": "all" + }, + { + "name": "step_size", + "interval": { + "lb": 5, + "ub": 5 + }, + "label": "all" + } + ], + "config": { + "tolerance": 1e-2, + "normalization_constant": 1, + "use_compartmental_constraints": true, + "prioritize_box_entropy": false, + "dreal_precision": 1, + "normalize": false + } +} \ No newline at end of file diff --git a/setup.py b/setup.py index ffb7bb73..cd7cc5ff 100644 --- a/setup.py +++ b/setup.py @@ -31,6 +31,7 @@ "pandas", "matplotlib", "pydantic>=2", + "scipy", "sympy", # "automates @ https://github.com/danbryce/automates/archive/e5fb635757aa57007615a75371f55dd4a24851e0.zip#sha1=f9b3c8a7d7fa28864952ccdd3293d02894614e3f" ], diff --git a/src/funman/api/run.py b/src/funman/api/run.py index 559de4f7..e124953c 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -482,7 +482,7 @@ def get_args(): help=f"Create parameter space plot with only the last timestep.", ) - parser.set_defaults(plot=False) + parser.set_defaults(plot=None) return parser.parse_args() diff --git a/src/funman/config.py b/src/funman/config.py index 3a30b3f9..ff4eb614 100644 --- a/src/funman/config.py +++ b/src/funman/config.py @@ -128,6 +128,12 @@ class FUNMANConfig(BaseModel): dreal_prefer_parameters: List[str] = [] """ Prefer to split the listed parameters in dreal """ + point_based_evaluation: bool = False + """ Evaluate parameters using point-based simulation over interval-based SMT encoding """ + + prioritize_box_entropy: bool = True + """ When comparing boxes, prefer those with low entropy """ + @field_validator("solver") @classmethod def import_dreal(cls, v: str) -> str: diff --git a/src/funman/representation/box.py b/src/funman/representation/box.py index f3dde87a..f9ee46b5 100644 --- a/src/funman/representation/box.py +++ b/src/funman/representation/box.py @@ -36,6 +36,7 @@ class Box(BaseModel): corner_points: List[Point] = [] points: List[Point] = [] _points_at_step: Dict[Timestep, List[Point]] = {} + _prioritize_entropy: bool = False @staticmethod def from_point( @@ -280,14 +281,6 @@ def _get_merge_candidates(self, boxes: Dict[ModelParameter, List["Box"]]): candidates = meets_set.intersection(equals_set) return candidates - def _copy(self): - c = Box( - bounds={ - p: Interval(lb=b.lb, ub=b.ub) for p, b in self.bounds.items() - } - ) - return c - def point_entropy(self, bias=1.0) -> float: """ Calculate the entropy of a box in terms of the point labels. Assumes only binary labels, so that p = |true|/(|true|+|false|), and the entropy is H = -(p log p) - ((1-p) log (1-p)) @@ -311,51 +304,69 @@ def point_entropy(self, bias=1.0) -> float: H = -(p * log2(p)) - ((1.0 - p) * log2(1.0 - p)) return H + def _lt_base_(self, other): + # print("b") + s_t = len(self.true_points()) + o_t = len(other.true_points()) + + s_ts = self.timestep().lb + o_ts = other.timestep().lb + + s_nv = self.normalized_volume() + o_nv = other.normalized_volume() + + if s_t == o_t: + if s_ts == o_ts: + return s_nv > o_nv + else: + return s_ts > o_ts + else: + return s_t > o_t + + def _lt_entropy_(self, other): + # print("e") + + s_t = len(self.true_points()) + o_t = len(other.true_points()) + + s_et = (1.0 - self.point_entropy()) * (s_t + 1) + o_et = (1.0 - other.point_entropy()) * (o_t + 1) + + s_nv = self.normalized_volume() + o_nv = other.normalized_volume() + + s_ts = self.timestep().lb + o_ts = other.timestep().lb + # return s_t < o_t + # if self.timestep().lb == other.timestep().lb: + # if s_t == o_t: + if s_et == o_et: + if s_ts == o_ts: + return s_nv > o_nv + else: + return s_ts > o_ts + else: + return s_et > o_et + + # if s_t == o_t: + # if s_nv == o_nv: + # return s_ts > o_ts + # else: + # return s_nv > o_nv + # else: + # return s_t > o_t + # else: + # return s_t > o_t + # else: + # return self.timestep().lb > other.timestep().lb + def __lt__(self, other): if isinstance(other, Box): - # prefer boxes with true points - # prefer boxes later in time - # prefer boxes with smaller width - # s_t = ( - # float(max(len(self.true_points()), len(self.false_points()))) - # / float(len(self.points)) - # if len(self.points) > 0 - # else 0.0 - # ) - # o_t = ( - # float(max(len(other.true_points()), len(other.false_points()))) - # / float(len(other.points)) - # if len(other.points) > 0 - # else 0.0 - # ) - # s_t = ( - # 2.0 * (1.0 - self.point_entropy()) - # + 0.5 * (len(self.true_points())) - # + 0.5 * (len(self.false_points())) - # ) - # o_t = ( - # 2.0 * (1.0 - other.point_entropy()) - # + 0.5 * (len(other.true_points())) - # + 0.5 * (len(other.false_points())) - # ) - s_t = (1.0 - self.point_entropy()) * (len(self.true_points()) + 1) - o_t = (1.0 - other.point_entropy()) * ( - len(other.true_points()) + 1 + return ( + self._lt_entropy_(other) + if self._prioritize_entropy + else self._lt_base_(other) ) - if self.timestep().lb == other.timestep().lb: - # s_residual_volume = self.timestep().width()*self.normalized_volume()*Decimal(s_t) - # o_residual_volume = other.timestep().width()*other.normalized_volume()*Decimal(o_t) - # return s_residual_volume > o_residual_volume - if s_t == o_t: - - # if s_t == o_t: - return self.normalized_volume() > other.normalized_volume() - # else: - # return s_t > o_t - else: - return s_t > o_t - else: - return self.timestep().lb > other.timestep().lb else: raise Exception(f"Cannot compare __lt__() Box to {type(other)}") diff --git a/src/funman/search/search.py b/src/funman/search/search.py index 110385d5..8a504b80 100644 --- a/src/funman/search/search.py +++ b/src/funman/search/search.py @@ -139,6 +139,7 @@ def _initial_box(self, schedule: EncodingSchedule) -> Box: }, schedule=schedule, ) + box._prioritize_entropy = self.config.prioritize_box_entropy box.bounds["timestep"] = Interval( lb=0, ub=len(schedule.timepoints) - 1, closed_upper_bound=True ) diff --git a/src/funman/translate/bilayer.py b/src/funman/translate/bilayer.py index 3b705644..b55376f7 100644 --- a/src/funman/translate/bilayer.py +++ b/src/funman/translate/bilayer.py @@ -212,6 +212,9 @@ def encode_model( for p in timed_parameters } ) + parameter_box._prioritize_entropy = ( + self.config.prioritize_box_entropy + ) else: parameter_box = Box( bounds={ @@ -219,6 +222,9 @@ def encode_model( for p in parameters } ) + parameter_box._prioritize_entropy = ( + self.config.prioritize_box_entropy + ) parameter_constraints = self.box_to_smt( parameter_box # , closed_upper_bound=True ) From b0860283000e8994adcbcc36dc3d44786726e32c Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Tue, 27 Aug 2024 21:26:18 +0000 Subject: [PATCH 22/93] plot anytime performance of box search --- notebooks/anytime_box_priority.ipynb | 265 +++++++++++++++++++++++++++ 1 file changed, 265 insertions(+) create mode 100644 notebooks/anytime_box_priority.ipynb diff --git a/notebooks/anytime_box_priority.ipynb b/notebooks/anytime_box_priority.ipynb new file mode 100644 index 00000000..d46c424d --- /dev/null +++ b/notebooks/anytime_box_priority.ipynb @@ -0,0 +1,265 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_57680/3063950419.py:39: FutureWarning: DataFrame.interpolate with method=pad is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + " comparison_df = comparison_df.interpolate(method=\"pad\", limit_direction=\"forward\")\n" + ] + }, + { + "data": { + "text/plain": [ + "entropy 46.034117\n", + "baseline 43.599075\n", + "dtype: float64" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from funman.server.query import FunmanResults\n", + "import json\n", + "from scipy import integrate\n", + "\n", + "ENTROPY_RESULTS_FILE = \"../out/6480bf47-debc-4072-a950-aa30e58c1a58.json\"\n", + "BASELINE_RESULTS_FILE = \"../out/66ded044-11d8-4513-bb8d-0f0b81b90a34.json\"\n", + "\n", + "def open_results(results_file_path):\n", + " with open(results_file_path, \"r\") as results_file:\n", + " results = FunmanResults.model_validate(json.load(results_file))\n", + " return results\n", + "\n", + "entropy_results = open_results(ENTROPY_RESULTS_FILE)\n", + "baseline_results = open_results(BASELINE_RESULTS_FILE)\n", + "\n", + "entropy_timing = entropy_results.timing\n", + "baseline_timing = baseline_results.timing\n", + "\n", + "import pandas as pd\n", + "from datetime import timedelta\n", + "\n", + "def progress_to_dataframe(timing, name):\n", + " df = pd.DataFrame(timing.progress_timeseries).rename(columns={0:\"time\", 1:name})\n", + " # .set_index(\"time\")\n", + " df.time = df.time.apply(lambda x: x-timing.start_time)\n", + " df = pd.concat([pd.DataFrame({\"time\":[timedelta()], name:0.0}), df])\n", + " # df.loc[timing.start_time] = 0.0\n", + " df = df.set_index(\"time\").sort_index()\n", + " return df\n", + "\n", + "entropy_df = progress_to_dataframe(entropy_timing, \"entropy\").rolling(2).apply(integrate.trapezoid)\n", + "baseline_df = progress_to_dataframe(baseline_timing, \"baseline\").rolling(2).apply(integrate.trapezoid)\n", + "comparison_df = entropy_df.join(baseline_df, how=\"outer\")\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "comparison_df = comparison_df.interpolate(method=\"pad\", limit_direction=\"forward\")\n", + "comparison_df.to_json(\"results.json\")\n", + "comparison_df.cumsum().max()\n", + "\n", + "# entropy_df\n", + "# baseline_df\n", + "\n", + "# comparison_df.rolling(2).apply(integrate.trapezoid)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Progress %/100')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " entropy baseline\n", + "0 0.000000 0.000000\n", + "1005 0.000000 0.778212\n", + "1034 0.779330 0.778212\n", + "2033 0.779330 0.778417\n", + "2054 0.779806 0.778417\n", + "... ... ...\n", + "106593 0.792214 0.938429\n", + "107760 0.792214 0.938646\n", + "109048 0.792214 0.938731\n", + "110332 0.792214 0.938867\n", + "111529 0.792214 0.938867\n", + "\n", + "[151 rows x 2 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comparison_df\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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 62713f3aa40ca308da82a7967b885f0fadfb710a Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Tue, 27 Aug 2024 21:30:40 +0000 Subject: [PATCH 23/93] add point-based simulation --- notebooks/point_based_evaluation.ipynb | 388 +++++++++++++++++++ src/funman/model/ensemble.py | 3 + src/funman/model/model.py | 10 + src/funman/model/petrinet.py | 2 + src/funman/representation/representation.py | 20 +- src/funman/scenario/consistency.py | 4 + src/funman/scenario/scenario.py | 40 +- src/funman/search/__init__.py | 2 + src/funman/search/simulate.py | 30 +- src/funman/search/simulator_check.py | 405 ++++++++++++++++++++ src/funman/search/smt_check.py | 1 + src/funman/server/query.py | 13 +- 12 files changed, 869 insertions(+), 49 deletions(-) create mode 100644 notebooks/point_based_evaluation.ipynb create mode 100644 src/funman/search/simulator_check.py diff --git a/notebooks/point_based_evaluation.ipynb b/notebooks/point_based_evaluation.ipynb new file mode 100644 index 00000000..4589711a --- /dev/null +++ b/notebooks/point_based_evaluation.ipynb @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# This notebook illustrates improvements to scalability gained by point-based evaluation\n", + "\n", + "# Import funman related code\n", + "import os\n", + "from funman.api.run import Runner\n", + "from funman_demo import summarize_results\n", + "from funman import FunmanWorkRequest, EncodingSchedule \n", + "import json\n", + "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", + "from funman.representation import Interval\n", + "import pandas as pd\n", + "\n", + "RESOURCES = \"../resources\"\n", + "SAVED_RESULTS_DIR = \"./out\"\n", + "\n", + "# EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"amr-examples\")\n", + "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"evaluation\")\n", + "MODEL_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"sir.json\"\n", + ")\n", + "# REQUEST_PATH = os.path.join(\n", + "# EXAMPLE_DIR, \"sir_request_param_synth.json\"\n", + "# )\n", + "REQUEST_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"sir_request_consistency.json\"\n", + ")\n", + "\n", + "\n", + "request_params = {}\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper functions to setup FUNMAN for different steps of the scenario\n", + "\n", + "def get_request():\n", + " with open(REQUEST_PATH, \"r\") as request:\n", + " funman_request = FunmanWorkRequest.model_validate(json.load(request))\n", + " return funman_request\n", + "\n", + "def set_timepoints(funman_request, num_steps, step_size):\n", + " funman_request.structure_parameters[0].interval.lb = num_steps\n", + " funman_request.structure_parameters[0].interval.ub = num_steps\n", + " funman_request.structure_parameters[1].interval.lb = step_size\n", + " funman_request.structure_parameters[1].interval.ub = step_size\n", + " \n", + " # funman_request.structure_parameters[0].schedules = [EncodingSchedule(timepoints=timepoints)]\n", + "\n", + "def unset_all_labels(funman_request):\n", + " for p in funman_request.parameters:\n", + " p.label = \"any\"\n", + " \n", + "def set_all_labels(funman_request):\n", + " for p in funman_request.parameters:\n", + " p.label = \"all\" \n", + " \n", + "def set_config_options(funman_request, point_based=False, debug=False, dreal_precision=1, prioritize_box_entropy=False):\n", + " # Overrides for configuration\n", + " #\n", + " # funman_request.config.substitute_subformulas = True\n", + " # funman_request.config.use_transition_symbols = True\n", + " # funman_request.config.use_compartmental_constraints=False\n", + " if debug:\n", + " funman_request.config.save_smtlib=\"./out\"\n", + " funman_request.config.verbosity = 10\n", + " funman_request.config.tolerance = 0.1\n", + " funman_request.config.dreal_precision = dreal_precision\n", + " funman_request.config.point_based_evaluation=point_based\n", + " funman_request.config.normalize=False\n", + " funman_request.config.prioritize_box_entropy=prioritize_box_entropy\n", + " \n", + " # funman_request.config.dreal_log_level = \"debug\"\n", + " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", + "\n", + "def get_synthesized_vars(funman_request):\n", + " return [p.name for p in funman_request.parameters if p.label == \"all\"]\n", + "\n", + "def run(funman_request, plot=False):\n", + " to_synthesize = get_synthesized_vars(funman_request)\n", + " return Runner().run(\n", + " MODEL_PATH,\n", + " funman_request,\n", + " description=\"SIERHD Eval 12mo Scenario 1 q1\",\n", + " case_out_dir=SAVED_RESULTS_DIR,\n", + " dump_plot=plot,\n", + " print_last_time=True,\n", + " parameters_to_plot=to_synthesize\n", + " )\n", + "\n", + "def setup_common(funman_request, num_steps, step_size, point_based=False, synthesize=False, debug=False, dreal_precision=1e-1, prioritize_box_entropy=False):\n", + " set_timepoints(funman_request, num_steps, step_size)\n", + " if not synthesize:\n", + " unset_all_labels(funman_request)\n", + " else:\n", + " set_all_labels(funman_request)\n", + " set_config_options(funman_request, point_based=point_based, debug=debug, dreal_precision=dreal_precision, prioritize_box_entropy=prioritize_box_entropy)\n", + " \n", + "\n", + "def set_compartment_bounds(funman_request, upper_bound=9830000.0, error=0.01):\n", + " # Add bounds to compartments\n", + " for var in STATES:\n", + " funman_request.constraints.append(StateVariableConstraint(name=f\"{var}_bounds\", variable=var, interval=Interval(lb=0, ub=upper_bound, closed_upper_bound=True),soft=False))\n", + "\n", + " # Add sum of compartments\n", + " funman_request.constraints.append(LinearConstraint(name=f\"compartment_bounds\", variables=STATES, additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True))\n", + "\n", + "def relax_parameter_bounds(funman_request, factor = 0.1):\n", + " # Relax parameter bounds\n", + " parameters = funman_request.parameters\n", + " for p in parameters:\n", + " interval = p.interval\n", + " width = float(interval.width())\n", + " interval.lb = interval.lb - (factor/2 * width)\n", + " interval.ub = interval.ub + (factor/2 * width)\n", + "\n", + "def plot_last_point(results):\n", + " pts = results.parameter_space.points() \n", + " print(f\"{len(pts)} points\")\n", + "\n", + " if len(pts) > 0:\n", + " # Get a plot for last point\n", + " df = results.dataframe(points=pts[-1:])\n", + " # pd.options.plotting.backend = \"plotly\"\n", + " ax = df[STATES].plot()\n", + " \n", + " \n", + " fig = plt.figure()\n", + " # fig.set_yscale(\"log\")\n", + " fig.savefig(\"save_file_name.pdf\")\n", + " plt.close()\n", + "\n", + "def get_last_point_parameters(results):\n", + " pts = results.parameter_space.points()\n", + " if len(pts) > 0:\n", + " pt = pts[-1]\n", + " parameters = results.model._parameter_names()\n", + " param_values = {k:v for k, v in pt.values.items() if k in parameters }\n", + " return param_values\n", + "\n", + "def pretty_print_request_params(params):\n", + " # print(json.dump(params, indent=4))\n", + " if len(params)>0:\n", + "\n", + " df = pd.DataFrame(params)\n", + " print(df.T)\n", + "\n", + "\n", + "def report(results, name):\n", + " # plot_last_point(results)\n", + " param_values = get_last_point_parameters(results)\n", + " param_values[\"runtime\"] = results.timing.total_time\n", + " # print(f\"Point parameters: {param_values}\")\n", + " if param_values is not None:\n", + " request_params[name] = param_values\n", + " pretty_print_request_params(request_params)\n", + "\n", + "def add_unit_test(funman_request):\n", + " pass\n", + " # funman_request.constraints.append(LinearConstraint(name=\"unit_test\", variables = [\n", + " # \"Infected\",\n", + " # \"Diagnosed\",\n", + " # \"Ailing\",\n", + " # \"Recognized\",\n", + " # \"Threatened\"\n", + " # ],\n", + " # additive_bounds= {\n", + " # \"lb\": 0.55,\n", + " # \"ub\": 0.65\n", + " # },\n", + " # timepoints={\n", + " # \"lb\": 45,\n", + " # \"ub\": 55\n", + " # }\n", + " # ))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Constants for the scenario\n", + "STATES = [\"S\", \"I\", \"R\"]\n", + "\n", + "MAX_TIME=250\n", + "STEP_SIZE=1\n", + "NUM_STEPS=MAX_TIME/STEP_SIZE\n", + "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[0.20154, 0.20154) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.07109, 0.07109) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.99000, 0.99000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.01000, 0.01000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.00000, 0.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[1.00000, 1.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[12.00000, 12.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[12.00000, 12.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[12.00000, 12.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[5.00000, 5.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[5.00000, 5.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[5.00000, 5.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-08-26 02:12:51,567 - funman.server.worker - INFO - FunmanWorker running...\n", + "2024-08-26 02:12:51,569 - funman.server.worker - INFO - Starting work on: 1e441f8d-1937-461c-a18f-c86ee9aa6f1a\n", + "2024-08-26 02:17:36,377 - funman.api.run - INFO - Dumping results to ./out/1e441f8d-1937-461c-a18f-c86ee9aa6f1a.json\n", + "2024-08-26 02:17:36,479 - funman.scenario.consistency - INFO - 250{250}:\t[+]\n", + "2024-08-26 02:17:39,233 - funman.scenario.scenario - INFO - simulation passed verification\n", + "2024-08-26 02:17:39,318 - funman.server.worker - INFO - Completed work on: 1e441f8d-1937-461c-a18f-c86ee9aa6f1a\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation Time: 0:00:02.754625\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-26 02:17:46,450 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-08-26 02:17:46,872 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-08-26 02:17:46,876 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total # of ibex-fwdbwd Pruning @ Pruning level = 316260\n", + "Total # of ibex-fwdbwd Pruning (zero-effect) @ Pruning level = 296057\n", + "Total time spent in Pruning @ Pruning level = 0.227849 sec\n", + "Total time spent in making constraints @ Pruning level = 0.000000 sec\n", + "Total # of Convert @ Ibex Converter = 1255\n", + "Total time spent in Converting @ Ibex Converter = 0.018036 sec\n", + " beta gamma S0 I0 R0 N runtime\n", + "point-based 0.201544 0.071086 0.99 0.01 0.0 1.0 0:04:47.749004\n" + ] + } + ], + "source": [ + "# Solve using point-based method\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, NUM_STEPS, STEP_SIZE, debug=False, point_based=True, synthesize=True, prioritize_box_entropy=False)\n", + "# add_unit_test(funman_request)\n", + "results = run(funman_request)\n", + "report(results, \"point-based\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "250\t0:00:02.754625\t0:04:44.994379\n" + ] + } + ], + "source": [ + "from datetime import timedelta, datetime\n", + "\n", + "total= \"0:04:47.749004\"\n", + "sim = \"0:00:02.754625\"\n", + "format = \"%H:%M:%S.%f\"\n", + "\n", + "nonsim = datetime.strptime(total, format) - datetime.strptime(sim, format)\n", + "print(f\"{MAX_TIME}\\t{sim}\\t{nonsim}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[1000.00000, 1000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[1.00000, 1.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.00000, 0.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[1.00000, 1.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[1.00000, 1.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[1.00000, 1.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-08-23 15:08:26,279 - funman.server.worker - INFO - FunmanWorker running...\n", + "2024-08-23 15:08:26,282 - funman.server.worker - INFO - Starting work on: 100f7f32-6527-426f-8d4e-62a447cad847\n", + "2024-08-23 15:08:26,636 - funman.scenario.consistency - INFO - 20{100}:\t[+]\n", + "2024-08-23 15:08:27,885 - funman.scenario.scenario - INFO - simulation passed verification\n", + "2024-08-23 15:08:27,894 - funman.server.worker - INFO - Completed work on: 100f7f32-6527-426f-8d4e-62a447cad847\n", + "2024-08-23 15:08:28,286 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-08-23 15:08:28,400 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-08-23 15:08:28,403 - funman.server.worker - INFO - Worker.stop() completed.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " beta gamma S0 I0 R0 runtime\n", + "point-based 0.0 0.14 1000.0 1.0 0.0 0:00:01.710106\n", + "interval-based 0.0 0.14 1000.0 1.0 0.0 0:00:01.612061\n" + ] + } + ], + "source": [ + "# Solve using interval-based method\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, NUM_STEPS, STEP_SIZE, point_based=False, synthesize=False)\n", + "# add_unit_test(funman_request)\n", + "results = run(funman_request)\n", + "report(results, \"interval-based\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FunmanResultsTiming(start_time=datetime.datetime(2024, 8, 23, 15, 8, 26, 282907), end_time=datetime.datetime(2024, 8, 23, 15, 8, 27, 894968), total_time=datetime.timedelta(seconds=1, microseconds=612061), solver_time=None, encoding_time=None, progress_timeseries=[(datetime.datetime(2024, 8, 23, 15, 8, 27, 886926), 0.0)])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.timing" + ] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/funman/model/ensemble.py b/src/funman/model/ensemble.py index 8044b3f2..441a343c 100644 --- a/src/funman/model/ensemble.py +++ b/src/funman/model/ensemble.py @@ -125,3 +125,6 @@ def to_dot(self, values={}): dot.subgraph(m.to_dot(), values=values) return dot + + def gradient(self, y, t, *p): + raise NotImplementedError diff --git a/src/funman/model/model.py b/src/funman/model/model.py index 8a755d0a..5d505820 100644 --- a/src/funman/model/model.py +++ b/src/funman/model/model.py @@ -3,6 +3,7 @@ """ import copy +import re import uuid from abc import ABC from typing import Dict, List, Optional, Union @@ -50,6 +51,14 @@ def _wrap_with_internal_model( return model +def is_state_variable( + var_string, model: "FunmanModel", time_pattern: str = f"_[0-9]+$" +) -> bool: + vars_pattern = "|".join(model._state_var_names()) + pattern = re.compile(f"[{vars_pattern}].*{time_pattern}") + return re.match(pattern, var_string) + + class FunmanModel(ABC, BaseModel): """ The abstract base class for Models. @@ -63,6 +72,7 @@ class FunmanModel(ABC, BaseModel): _normalization_constant: Optional[float] = None _extra_constraints: FNode = None _normalization_term: Optional[FNode] = None + _is_differentiable: bool = False # @abstractmethod # def default_encoder(self, config: "FUNMANConfig") -> "Encoder": diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index aa8c8e82..e490cf1c 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -15,6 +15,8 @@ class AbstractPetriNetModel(FunmanModel): + _is_differentiable: bool = True + def _num_flow_from_state_to_transition( self, state_id: Union[str, int], transition_id: Union[str, int] ) -> int: diff --git a/src/funman/representation/representation.py b/src/funman/representation/representation.py index 586cece7..4d4823ef 100644 --- a/src/funman/representation/representation.py +++ b/src/funman/representation/representation.py @@ -11,6 +11,7 @@ from funman import to_sympy from funman.constants import LABEL_UNKNOWN, NEG_INFINITY, POS_INFINITY, Label +from funman.model.model import FunmanModel, is_state_variable from . import Timepoint @@ -48,11 +49,11 @@ def __str__(self): def __repr__(self) -> str: return str(self.model_dump()) - def values_at(self, tp: Timepoint) -> Dict[str, float]: + def values_at(self, tp: Timepoint, model: FunmanModel) -> Dict[str, float]: v = { k.rsplit("_", 1)[0]: v for k, v in self.values.items() - if self._is_state_variable(k) and int(k.rsplit("_", 1)[-1]) == tp + if is_state_variable(k, model) and int(k.rsplit("_", 1)[-1]) == tp } return v @@ -67,24 +68,19 @@ def relevant_timesteps(self) -> Set[int]: } return steps - def _is_state_variable(self, s: str) -> bool: - return ( - not s.startswith("solve_step") - and not s.startswith("assume_") - and "_" in s - ) - def state_values(self) -> Dict[str, float]: return { - k: v for k, v in self.values.items() if self._is_state_variable(k) + k: v + for k, v in self.values.items() + if is_state_variable(k, self.problem.model) } - def relevant_timepoints(self) -> List[int]: + def relevant_timepoints(self, model: FunmanModel) -> List[int]: steps = list( { int(k.rsplit("_", 1)[-1]) for k, v in self.values.items() - if self._is_state_variable(k) + if is_state_variable(k, model) } ) steps.sort() diff --git a/src/funman/scenario/consistency.py b/src/funman/scenario/consistency.py index 91a414b7..9ae83b1d 100644 --- a/src/funman/scenario/consistency.py +++ b/src/funman/scenario/consistency.py @@ -4,6 +4,7 @@ import logging import threading +from datetime import datetime from typing import Callable, Dict, Optional import matplotlib.pyplot as plt @@ -88,9 +89,12 @@ def solve( ) scenario_result._models = models + start_time = datetime.now() assert self.check_simulation( config, scenario_result ), "Simulation of solution is invalid." + duration = datetime.now() - start_time + l.info(f"Simulation Time: {duration}") return scenario_result diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index b473003e..18c1c08e 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -348,10 +348,12 @@ def _set_normalization(self, config): self.normalization_constant = 1.0 l.warning("Warning: The scenario is not normalized!") - def check_point_simulation(self, point: Point, tvect) -> Timeseries: + def check_point_simulation( + self, point: Point, tvect + ) -> Optional[Timeseries]: init = { var: value - for var, value in point.values_at(0).items() + for var, value in point.values_at(0, self.model).items() if var != "timer_t" } parameters = { @@ -390,22 +392,30 @@ def check_simulation( sim_results = [] for point in results.parameter_space.points(): timeseries = self.check_point_simulation( - point, point.relevant_timepoints() + point, point.relevant_timepoints(results.scenario.model) ) sim_results.append((point, timeseries)) - for sim_result in sim_results: - with Solver() as solver: - sim_encoding = self.encode_timeseries_verification( - *sim_result - ) - solver.add_assertion(sim_encoding) - result = solver.solve() - if result: - l.info("simulation passed verification") - else: - l.info("simulation failed verification") - return result + for point, timeseries in sim_results: + if timeseries is None: + l.warning( + f"Skipping point validation because there is no timeseries ..." + ) + continue + + with Solver() as solver: + sim_encoding = self.encode_timeseries_verification( + point, timeseries + ) + solver.add_assertion(sim_encoding) + result = solver.solve() + if result: + l.info("simulation passed verification") + else: + l.info("simulation failed verification") + return False + + return True def encode_timeseries_verification( self, point: Point, timeseries: Timeseries diff --git a/src/funman/search/__init__.py b/src/funman/search/__init__.py index 2302f547..652e39de 100644 --- a/src/funman/search/__init__.py +++ b/src/funman/search/__init__.py @@ -6,3 +6,5 @@ from .search import * from .smt_check import * from .box_search import * + +# from .simulator_check import * diff --git a/src/funman/search/simulate.py b/src/funman/search/simulate.py index 9b438a0d..34b807cc 100644 --- a/src/funman/search/simulate.py +++ b/src/funman/search/simulate.py @@ -1,4 +1,4 @@ -from typing import Dict, List, Union +from typing import Dict, List, Optional, Union import numpy as np import pandas as pd @@ -41,17 +41,21 @@ def initial_state(self) -> List[float]: ] return tuple(init_state) - def sim(self): + def sim(self) -> Optional[Timeseries]: # gradient_fn = partial(self.model.gradient, self.model) # hide the self reference to self.model from odeint - timeseries = odeint( - self.model.gradient, - self.initial_state(), - self.tvect, - args=self.model_args(), - ) - - ts = Timeseries( - data=[self.tvect] + timeseries.T.tolist(), - columns=["time"] + self.model._state_var_names(), - ) + + if self.model._is_differentiable: + timeseries = odeint( + self.model.gradient, + self.initial_state(), + self.tvect, + args=self.model_args(), + ) + + ts = Timeseries( + data=[self.tvect] + timeseries.T.tolist(), + columns=["time"] + self.model._state_var_names(), + ) + else: + ts = None return ts diff --git a/src/funman/search/simulator_check.py b/src/funman/search/simulator_check.py new file mode 100644 index 00000000..0895aa78 --- /dev/null +++ b/src/funman/search/simulator_check.py @@ -0,0 +1,405 @@ +import logging +import os +import threading +from typing import Callable, Optional, Tuple, Union + +from pysmt.formula import FNode +from pysmt.logics import QF_NRA +from pysmt.shortcuts import BOOL, REAL, And, Bool, Equals, Real, Solver, Symbol +from pysmt.solvers.solver import Model as pysmtModel + +from funman.config import FUNMANConfig +from funman.constants import LABEL_FALSE, LABEL_TRUE +from funman.representation.encoding_schedule import EncodingSchedule +from funman.representation.explanation import Explanation +from funman.translate.translate import EncodingOptions +from funman.utils.smtlib_utils import smtlibscript_from_formula_list + +from ..representation import Interval, Point +from ..representation.box import Box +from ..representation.parameter_space import ParameterSpace + +# import funman.search as search +from .search import Search, SearchEpisode + +# from funman.utils.sympy_utils import sympy_to_pysmt, to_sympy + + +l = logging.getLogger(__file__) + + +class SimulatorCheck(Search): + def search( + self, + problem, + config: Optional["FUNMANConfig"] = None, + haltEvent: Optional[threading.Event] = None, + resultsCallback: Optional[Callable[["ParameterSpace"], None]] = None, + ) -> "SearchEpisode": + parameter_space = ParameterSpace( + num_dimensions=problem.num_dimensions() + ) + models = {} + consistent = {} + + # problem._initialize_encodings(config) + for schedule in problem._smt_encoder._timed_model_elements[ + "schedules" + ].schedules: + l.debug(f"Solving schedule: {schedule}") + schedule_length = len(schedule.timepoints) + episode = SearchEpisode( + config=config, problem=problem, schedule=schedule + ) + options = EncodingOptions( + schedule=schedule, + normalize=config.normalize, + normalization_constant=config.normalization_constant, + ) + + # self._initialize_encoding(solver, episode, box_timepoint, box) + model_result, explanation_result = self.expand( + problem, + episode, + options, + parameter_space, + schedule, + ) + point = None + timestep = len(schedule.timepoints) - 1 + # if model_result is not None and isinstance( + # model_result, pysmtModel + # ): + # # result_dict = model_result.to_dict() if model_result else None + # # l.debug(f"Result: {json.dumps(result_dict, indent=4)}") + # # if result_dict is not None: + # # parameter_values = { + # # k: v + # # for k, v in result_dict.items() + # # # if k in [p.name for p in problem.parameters] + # # } + # # # for k, v in structural_configuration.items(): + # # # parameter_values[k] = v + # point_label = ( + # LABEL_TRUE if explanation_result is None else LABEL_FALSE + # ) + # results_dict = model_result.to_dict() + # point = Point( + # values=results_dict, + # label=point_label, + # schedule=schedule, + # ) + # point.values["timestep"] = timestep + + # if config.normalize: + # denormalized_point = point.denormalize(problem) + # point = denormalized_point + + # if point_label == LABEL_TRUE: + # models[point] = model_result + # consistent[point] = results_dict + + # box = Box.from_point(point) + # # parameter_space.true_boxes.append(Box.from_point(point)) + # else: + # box = Box( + # bounds={ + # p.name: p.interval.model_copy() + # for p in problem.model_parameters() + # }, + # label=LABEL_FALSE, # lack of a point means this must be a false box + # points=[], + # ) + # box._prioritize_entropy = self.config.prioritize_box_entropy + # box.bounds["timestep"] = Interval( + # lb=timestep, ub=timestep, closed_upper_bound=True + # ) + # box.schedule = schedule + + # if explanation_result is not None and isinstance( + # explanation_result, Explanation + # ): + # box.explanation = explanation_result + + # if box.label == LABEL_TRUE: + # parameter_space.true_boxes.append(box) + # else: + # parameter_space.false_boxes.append(box) + + if resultsCallback: + resultsCallback(parameter_space) + + return parameter_space, models, consistent + + def build_formula( + self, + episode: SearchEpisode, + schedule: EncodingSchedule, + options: EncodingOptions, + ) -> Tuple[FNode, FNode]: + encoding = episode.problem._encodings[schedule] + layer_formulas = [] + simplified_layer_formulas = [] + assumption_formulas = [] + for a in episode.problem._assumptions: + assumption_formulas.append( + encoding._encoder.encode_assumption(a, options) + ) + + for timestep, timepoint in enumerate(schedule.timepoints): + encoded_constraints = [] + for constraint in episode.problem.constraints: + if constraint.encodable() and constraint.relevant_at_time( + timepoint + ): + encoded_constraints.append( + encoding.construct_encoding( + episode.problem, + constraint, + options, + layers=[timestep], + assumptions=episode.problem._assumptions, + ) + ) + formula = And(encoded_constraints) + layer_formulas.append(formula) + + # Simplify formulas if needed + if ( + episode.config.simplify_query + and episode.config.substitute_subformulas + ): + substitutions = encoding._encoder.substitutions(schedule) + simplified_layer_formulas = [ + x.substitute(substitutions).simplify() + for x in layer_formulas + ] + + model_formula = And([f for f in layer_formulas]) + + all_layers_formula = And(And(assumption_formulas), model_formula) + + all_simplified_layers_formula = ( + And( + And(assumption_formulas), + And([f for f in simplified_layer_formulas]), + ) + if len(simplified_layer_formulas) > 0 + else None + ) + return all_layers_formula, all_simplified_layers_formula, model_formula + + def solve_formula( + self, s: Solver, formula: FNode, episode + ) -> Union[pysmtModel, Explanation]: + s.push(1) + s.add_assertion(formula) + if episode.config.save_smtlib: + filename = os.path.join( + episode.config.save_smtlib, "dbg_steps.smt2" + ) + l.trace(f"Saving smt file: {filename}") + self.store_smtlib( + formula, + filename=filename, + ) + l.trace(f"Solving: {formula.serialize()}") + result = self.invoke_solver(s, timeout=episode.config.solver_timeout) + s.pop(1) + l.trace(f"Result: {type(result)}") + return result + + def eval_point( + self, + beta_val, + gamma_val, + query_condition=query_1, + plot=False, + rtol=1, + atol=1, + mxstep=10, + mxordn=1, + mxords=1, + hmin=1, + ): + # parameters + def beta(t): + return np.piecewise(t, [t >= 0], [beta_val]) + + def gamma(t): + return np.piecewise(t, [t >= 0], [gamma_val]) + + # USER: set initial conditions + I0, R0 = 0.01, 0 + S0 = 1 - I0 - R0 + y0 = S0, I0, R0 # Initial conditions vector + # USER: set simulation parameters + dt = 1 + tstart = 0 + tend = 60 + tvect = np.arange(tstart, tend + 1, dt) + # simulate/solve ODEs + sim = odeint( + sir_model.SIR_model, + y0, + tvect, + args=(beta, gamma), + mxstep=mxstep, + rtol=rtol, + atol=atol, + ) + S, I, R = sim.T + + # print(list(zip(range(tstart, tend+1, dt), I))) + + # plot results - uncomment next line to plot time series. not recommended for large numbers of points + if plot: + sir_model.plotSIR(tvect, S, I, R) + + query = ( + "1" + if query_condition(sim, tstart, tend, dt, beta_val, gamma_val) + else "0" + ) + param_assignments = { + "beta": beta_val, + "gamma": gamma_val, + "assignment": query, + } # for "all", go through every option. for "any", only need one good parameter choice. + return param_assignments, sim + + # set parameters + def ps( + self, + parameter_space, + num_dim_points=10, # parameter_search_bounds + # , query_condition=query_1, rtol=1e-3, #num_dim_points=10, plot=False + ): + parameters = ( + parameter_space.model_parameters() + ) # list(parameter_search_bounds.keys()) + parameter_points = { + p: np.linspace( + parameter_space.bounds[p].lb, + parameter_space.bounds[p].ub, + num_dim_points, + ) + for p in parameters + } + points = itertools.product(*[parameter_points[p] for p in parameters]) + param_choices_true_false = [] + coverage = [] + + for i, point in enumerate(points): + point_values = {p: point[i] for i, p in enumerate(parameters)} + + param_assignments, _ = eval_point( + point_values["beta"], + point_values["gamma"], + query_condition=query_condition, + rtol=rtol, + plot=plot, + ) + param_choices_true_false.append(param_assignments) + coverage.append((datetime.datetime.now(), i)) + + if param_assignments["assignment"] == "1": + return param_choices_true_false, coverage + return param_choices_true_false, coverage + + def expand( + self, + problem, + episode, + options: EncodingOptions, + parameter_space, + schedule: EncodingSchedule, + ): + model_result = None + explanation_result = None + if episode.config.solver == "dreal": + opts = { + "dreal_precision": episode.config.dreal_precision, + "dreal_log_level": episode.config.dreal_log_level, + "dreal_mcts": episode.config.dreal_mcts, + "preferred": episode.config.dreal_prefer_parameters, # [p.name for p in problem.model_parameters()]if episode.config.dreal_prefer_parameters else [], + } + else: + opts = {} + # s1 = Solver( + # name=episode.config.solver, + # logic=QF_NRA, + # solver_options=opts, + # ) + gridpoints = [] + for gridpoint in gridpoints: + + with Solver( + name=episode.config.solver, + logic=QF_NRA, + solver_options=opts, + ) as s: + formula, simplified_formula, model_formula = ( + self.build_formula(episode, schedule, options) + ) + + if simplified_formula is not None: + # If using a simplified formula, we need to solve it and use its values in the original formula to get the values of all variables + result = self.solve_formula(s, simplified_formula, episode) + if result is not None and isinstance(result, pysmtModel): + model_result = result + assigned_vars = model_result.to_dict() + substitution = { + Symbol( + p, (REAL if isinstance(v, float) else BOOL) + ): (Real(v) if isinstance(v, float) else Bool(v)) + for p, v in assigned_vars.items() + } + result_assignment = And( + [ + ( + Equals(Symbol(p, REAL), Real(v)) + if isinstance(v, float) + else ( + Symbol(p, BOOL) + if v + else Not(Symbol(p, BOOL)) + ) + ) + for p, v in assigned_vars.items() + ] + + [ + Equals(Symbol(p.name, REAL), Real(0.0)) + for p in episode.problem.model_parameters() + if p.is_unbound() + and p.name not in assigned_vars + ] + ) + formula_w_params = And( + formula.substitute(substitution), result_assignment + ) + model_result = self.solve_formula( + s, formula_w_params, episode + ) + elif result is not None and isinstance(result, str): + explanation_result = result + # Unsat core + else: + model_result = self.solve_formula(s, formula, episode) + if isinstance(model_result, Explanation): + explanation_result = model_result + model_result.check_assumptions(episode, s, options) + + # If formula with assumptions is unsat, then we need to generate a trace of the model by giving up on the assumptions. + model_result = self.solve_formula( + s, model_formula, episode + ) + + return model_result, explanation_result + + def store_smtlib(self, formula, filename="dbg.smt2"): + with open(filename, "w") as f: + smtlibscript_from_formula_list( + [formula], + logic=QF_NRA, + ).serialize(f, daggify=False) diff --git a/src/funman/search/smt_check.py b/src/funman/search/smt_check.py index c60618fb..36f7dd0b 100644 --- a/src/funman/search/smt_check.py +++ b/src/funman/search/smt_check.py @@ -109,6 +109,7 @@ def search( label=LABEL_FALSE, # lack of a point means this must be a false box points=[], ) + box._prioritize_entropy = episode.config.prioritize_box_entropy box.bounds["timestep"] = Interval( lb=timestep, ub=timestep, closed_upper_bound=True ) diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 952c4bd7..227b3790 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -1,6 +1,5 @@ import logging import random -import re from collections import Counter from datetime import datetime, timedelta from typing import Dict, List, Optional, Tuple, Union @@ -16,6 +15,7 @@ from funman.model.encoded import EncodedModel from funman.model.ensemble import EnsembleModel from funman.model.generated_models.petrinet import Model as GeneratedPetriNet +from funman.model.model import is_state_variable from funman.model.petrinet import GeneratedPetriNetModel, PetrinetModel from funman.model.query import QueryAnd, QueryFunction, QueryLE, QueryTrue from funman.model.regnet import GeneratedRegnetModel, RegnetModel @@ -174,6 +174,7 @@ class FunmanResultsTiming(BaseModel): solver_time: Optional[timedelta] = None encoding_time: Optional[timedelta] = None progress_timeseries: List[Tuple[datetime, float]] = [] + additional_time: Dict[str, timedelta] = {} def update_progress( self, progress, granularity=timedelta(seconds=1) @@ -465,17 +466,11 @@ def symbol_values( return vals def _symbols( - self, - point: Point, - variables: List[str], - time_pattern: str = f"_[0-9]+$", + self, point: Point, variables: List[str] ) -> Dict[str, Dict[str, str]]: symbols = {} - # vars.sort(key=lambda x: x.symbol_name()) - vars_pattern = "|".join(variables) - pattern = re.compile(f"[{vars_pattern}].*{time_pattern}") for var in point.values: - if re.match(pattern, var): + if is_state_variable(var, self.model): var_name, timepoint = self._split_symbol(var) if timepoint: if var_name not in symbols: From efd77d43e7fe88b075d7a0e89f988c484a8fe91a Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 28 Aug 2024 13:05:45 -0500 Subject: [PATCH 24/93] change to stratified model --- .../funman_aug_2024_12mo_eval_1_q1_demo.ipynb | 1682 +++++++++++------ .../q1a_ii/eval_scenario1_1_ii_3.json | 1408 +++++++------- 2 files changed, 1783 insertions(+), 1307 deletions(-) diff --git a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb index c77526c4..772296e5 100644 --- a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb +++ b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb @@ -1,606 +1,1080 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This notebook illustrates handling the August 2024 Demo of the 12mo Evaluation Scenario 1\n", - "\n", - "# Import funman related code\n", - "import os\n", - "from funman.api.run import Runner\n", - "from funman_demo import summarize_results\n", - "from funman import FunmanWorkRequest, EncodingSchedule \n", - "import json\n", - "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", - "from funman.representation import Interval\n", - "import pandas as pd\n", - "\n", - "RESOURCES = \"../../resources\"\n", - "SAVED_RESULTS_DIR = \"./out\"\n", - "\n", - "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"monthly-demo\", \"2024-08\", \"12_month_scenario_1\", \"q1a_ii\")\n", - "MODEL_PATH = os.path.join(\n", - " EXAMPLE_DIR, \"eval_scenario1_base.json\"\n", - ")\n", - "REQUEST_PATH = os.path.join(\n", - " EXAMPLE_DIR, \"eval_scenario1_base_request.json\"\n", - ")\n", - "\n", - "\n", - "request_params = {}\n", - "\n", - "# %load_ext autoreload\n", - "# %autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H_odeint = [0.0,\n", - " 0.07584066320978722,\n", - " 0.14636646265873351,\n", - " 0.21517239301596403,\n", - " 0.2849940641423008,\n", - " 0.35803162126534144,\n", - " 0.43617188357763786,\n", - " 0.5211433080571843,\n", - " 0.6146269952842578,\n", - " 0.7183393339849907,\n", - " 0.8340968480485813,\n", - " 0.9638704485112929,\n", - " 1.1098340730821774,\n", - " 1.2744112432601529,\n", - " 1.460322143778306,\n", - " 1.6706331916641786,\n", - " 1.9088107460125325,\n", - " 2.178780372444439,\n", - " 2.4849930017839568,\n", - " 2.832499327828298,\n", - " 3.227033820607022,\n", - " 3.6751098523322363,\n", - " 4.184127609093503,\n", - " 4.762496481974214,\n", - " 5.419774326784966,\n", - " 6.166825487583933,\n", - " 7.016000445528594,\n", - " 7.981340143729251,\n", - " 9.078808185052266,\n", - " 10.326554856871647,\n", - " 11.745217338261286,\n", - " 13.358261021733336,\n", - " 15.192367626771185,\n", - " 17.277876514948733,\n", - " 19.64928647253589,\n", - " 22.345826309466535,\n", - " 25.41210364728401,\n", - " 28.898842636443945,\n", - " 32.863722838161294,\n", - " 37.37233298620304,\n", - " 42.49925554848381,\n", - " 48.32929991918692,\n", - " 54.95890451703756,\n", - " 62.49773100251825,\n", - " 71.07047694046699,\n", - " 80.81893664895041,\n", - " 91.9043442331066,\n", - " 104.51003757795193,\n", - " 118.84448659151495,\n", - " 135.14473616311494,\n", - " 153.6803198995054,\n", - " 174.75770892931388,\n", - " 198.7253686880845,\n", - " 225.9795066077193,\n", - " 256.9706043940812,\n", - " 292.2108414225009,\n", - " 332.28253040812973,\n", - " 377.8477020062484,\n", - " 429.65899353366785,\n", - " 488.57201806502917,\n", - " 555.5594130725682,\n", - " 631.7267930288757,\n", - " 718.33086148102,\n", - " 816.7999711100919,\n", - " 928.7574539225811,\n", - " 1056.0480899336421,\n", - " 1200.768126390387,\n", - " 1365.2993106701535,\n", - " 1552.3474521757955,\n", - " 1764.986108867584,\n", - " 2006.7060366726942,\n", - " 2281.4711317587603,\n", - " 2593.781675837228,\n", - " 2948.74577656625,\n", - " 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271188.8609711989,\n", - " 299734.54505555454,\n", - " 330309.0420578748,\n", - " 362855.0097454492,\n", - " 397270.9706782105,\n", - " 433407.9323314221,\n", - " 471067.5318962573,\n", - " 510002.05000426056,\n", - " 549916.5325942405,\n", - " 590473.1326448151,\n", - " 631297.6155083764,\n", - " 671987.805397328,\n", - " 712123.5855505902,\n", - " 751277.9419388921,\n", - " 789028.4531510238,\n", - " 824968.6089066564,\n", - " 858718.3673693967,\n", - " 889933.4450316473,\n", - " 918312.9510473183,\n", - " 943605.1145011623,\n", - " 965610.9989351172,\n", - " 984186.2260247911,\n", - " 999240.8449867101,\n", - " 1010737.5652130407,\n", - " 1018688.6250969241,\n", - " 1023151.5961628385,\n", - " 1024224.424518873,\n", - " 1022039.9954607659,\n", - " 1016760.4778537896,\n", - " 1008571.6676818144,\n", - " 997677.5091145145,\n", - " 984294.9309318912,\n", - " 968649.0965921708,\n", - " 950969.132919035,\n", - " 931484.3726190757,\n", - " 910421.1206448845,\n", - " 887999.9365984657,\n", - " 864433.4099252748,\n", - " 839924.3952308263,\n", - " 814664.6678259049,\n", - " 788833.956208313,\n", - " 762599.306145181,\n", - " 736114.7329186859,\n", - " 709521.118576635,\n", - " 682946.3131058007,\n", - " 656505.4041417397,\n", - " 630301.1204001798,\n", - " 604424.3395841966,\n", - " 578954.674586528,\n", - " 553961.1153107659,\n", - " 529502.705890681,\n", - " 505629.24208586576,\n", - " 482381.97446456744,\n", - " 459794.3062235543,\n", - " 437892.47709088976,\n", - " 416696.2258780569,\n", - " 396219.4261885734,\n", - " 376470.6921664646,\n", - " 357453.95052101125,\n", - " 339168.9775714914,\n", - " 321611.9004232435,\n", - " 304775.66215856594,\n", - " 288650.4511800376,\n", - " 273224.0956361533,\n", - " 258482.42403353218,\n", - " 244409.59338260329,\n", - " 230988.3864887854,\n", - " 218200.48005631237,\n", - " 206026.6853722251,\n", - " 194447.16337089916,\n", - " 183441.61586876688,\n", - " 172989.45475247476,\n", - " 163069.95085616622,\n", - " 153662.36416343783,\n", - " 144746.05696746017,\n", - " 136300.5914987689,\n", - " 128305.81345597992,\n", - " 120741.9227885983,\n", - " 113589.53299236886,\n", - " 106829.72009170499,\n", - " 100444.06240763294,\n", - " 94414.6720882853,\n", - " 88724.21934978342,\n", - " 83355.95026825309,\n", - " 78293.69889597349,\n", - " 73521.89440676449,\n", - " 69025.56391080657,\n", - " 64790.33151791613,\n", - " 60802.414180610635,\n", - " 57048.61477377206]\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(H_odeint)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Constants for the scenario\n", - "STATES = [\"S\", \"I\", \"E\", \"R\", \"H\", \"D\"]\n", - "# IDART = [\"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Threatened\"]\n", - "\n", - "MAX_TIME=150\n", - "STEP_SIZE=5\n", - "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Helper functions to setup FUNMAN for different steps of the scenario\n", - "\n", - "def get_request():\n", - " with open(REQUEST_PATH, \"r\") as request:\n", - " funman_request = FunmanWorkRequest.model_validate(json.load(request))\n", - " return funman_request\n", - "\n", - "def set_timepoints(funman_request, timepoints):\n", - " funman_request.structure_parameters[0].schedules = [EncodingSchedule(timepoints=timepoints)]\n", - "\n", - "def unset_all_labels(funman_request):\n", - " for p in funman_request.parameters:\n", - " p.label = \"any\"\n", - " \n", - "def set_config_options(funman_request, debug=False, dreal_precision=1e-1):\n", - " # Overrides for configuration\n", - " #\n", - " # funman_request.config.substitute_subformulas = True\n", - " # funman_request.config.use_transition_symbols = True\n", - " # funman_request.config.use_compartmental_constraints=False\n", - " if debug:\n", - " funman_request.config.save_smtlib=\"./out\"\n", - " funman_request.config.tolerance = 0.01\n", - " funman_request.config.dreal_precision = dreal_precision\n", - " # funman_request.config.verbosity = 10\n", - " # funman_request.config.dreal_log_level = \"debug\"\n", - " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", - "\n", - "def get_synthesized_vars(funman_request):\n", - " return [p.name for p in funman_request.parameters if p.label == \"all\"]\n", - "\n", - "def run(funman_request, plot=False):\n", - " to_synthesize = get_synthesized_vars(funman_request)\n", - " return Runner().run(\n", - " MODEL_PATH,\n", - " funman_request,\n", - " description=\"SIERHD Eval 12mo Scenario 1 q1\",\n", - " case_out_dir=SAVED_RESULTS_DIR,\n", - " dump_plot=plot,\n", - " print_last_time=True,\n", - " parameters_to_plot=to_synthesize\n", - " )\n", - "\n", - "def setup_common(funman_request, synthesize=False, debug=False, dreal_precision=1e-1):\n", - " set_timepoints(funman_request, timepoints)\n", - " if not synthesize:\n", - " unset_all_labels(funman_request)\n", - " set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision)\n", - " \n", - "\n", - "def set_compartment_bounds(funman_request, upper_bound=9830000.0, error=0.01):\n", - " # Add bounds to compartments\n", - " for var in STATES:\n", - " funman_request.constraints.append(StateVariableConstraint(name=f\"{var}_bounds\", variable=var, interval=Interval(lb=0, ub=upper_bound, closed_upper_bound=True),soft=False))\n", - "\n", - " # Add sum of compartments\n", - " funman_request.constraints.append(LinearConstraint(name=f\"compartment_bounds\", variables=STATES, additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True))\n", - "\n", - "def relax_parameter_bounds(funman_request, factor = 0.1):\n", - " # Relax parameter bounds\n", - " parameters = funman_request.parameters\n", - " for p in parameters:\n", - " interval = p.interval\n", - " width = float(interval.width())\n", - " interval.lb = interval.lb - (factor/2 * width)\n", - " interval.ub = interval.ub + (factor/2 * width)\n", - "\n", - "def plot_last_point(results):\n", - " pts = results.parameter_space.points() \n", - " print(f\"{len(pts)} points\")\n", - "\n", - " if len(pts) > 0:\n", - " # Get a plot for last point\n", - " df = results.dataframe(points=pts[-1:])\n", - " # pd.options.plotting.backend = \"plotly\"\n", - " ax = df[STATES].plot()\n", - " \n", - " \n", - " fig = plt.figure()\n", - " # fig.set_yscale(\"log\")\n", - " fig.savefig(\"save_file_name.pdf\")\n", - " plt.close()\n", - "\n", - "def get_last_point_parameters(results):\n", - " pts = results.parameter_space.points()\n", - " if len(pts) > 0:\n", - " pt = pts[-1]\n", - " parameters = results.model._parameter_names()\n", - " param_values = {k:v for k, v in pt.values.items() if k in parameters }\n", - " return param_values\n", - "\n", - "def pretty_print_request_params(params):\n", - " # print(json.dump(params, indent=4))\n", - " if len(params)>0:\n", - "\n", - " df = pd.DataFrame(params)\n", - " print(df.T)\n", - "\n", - "\n", - "def report(results, name):\n", - " plot_last_point(results)\n", - " param_values = get_last_point_parameters(results)\n", - " # print(f\"Point parameters: {param_values}\")\n", - " if param_values is not None:\n", - " request_params[name] = param_values\n", - " pretty_print_request_params(request_params)\n", - "\n", - "def add_unit_test(funman_request):\n", - " pass\n", - " # funman_request.constraints.append(LinearConstraint(name=\"unit_test\", variables = [\n", - " # \"Infected\",\n", - " # \"Diagnosed\",\n", - " # \"Ailing\",\n", - " # \"Recognized\",\n", - " # \"Threatened\"\n", - " # ],\n", - " # additive_bounds= {\n", - " # \"lb\": 0.55,\n", - " # \"ub\": 0.65\n", - " # },\n", - " # timepoints={\n", - " # \"lb\": 45,\n", - " # \"ub\": 55\n", - " # }\n", - " # ))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# i) (TA1 Search and Discovery Workflow, 1 Hr. Time Limit) Find estimates on the\n", - "# efficacy of surgical masks in preventing onward transmission of SARS-CoV-2 (preferred)\n", - "# or comparable viral respiratory pathogens (e.g., MERS-CoV, SARS), including any\n", - "# information about uncertainty in these estimates. The term surgical mask here refers to\n", - "# the commonly available, disposable procedure mask, not an N95-type respirator. Find 3\n", - "# credible documents that provide estimates and use your judgment to determine what\n", - "# value (in the deterministic case) or distribution (in the probabilistic case) to use in your\n", - "# forecasts in 1.a.iii.\n", - "\n", - "# The base model assumes no masking inverventions, and we measure the efficacy in terms of the number of hospitalized.\n", - "\n", - "funman_request = get_request()\n", - "setup_common(funman_request, debug=True, dreal_precision=1e-3)\n", - "add_unit_test(funman_request)\n", - "results = run(funman_request)\n", - "report(results, \"unconstrained\")\n", - "pass\n", - "# pts = results.parameter_space.points() \n", - "# print(f\"{len(pts)} points\")\n", - "# df = results.dataframe(points=pts[-1:])\n", - "# df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results\n", - "# get_last_point_parameters(results)\n", - "# plot_last_point(results)\n", - "# def plot_last_point(results):\n", - "# pts = results.parameter_space.points() \n", - "# print(f\"{len(pts)} points\")\n", - "\n", - "# if len(pts) > 0:\n", - "# # Get a plot for last point\n", - "# df = results.dataframe(points=pts[-1:])\n", - "# ax = df[STATES].plot()\n", - "# ax.set_yscale(\"log\")\n", - "pts = results.parameter_space.points() \n", - "print(f\"{len(pts)} points\")\n", - "df = results.dataframe(points=pts[-1:])\n", - "\n", - "df['H_odeint'] = pd.Series(H_odeint[0:151])\n", - "df[\"H_diff\"] = df.H - df.H_odeint\n", - "df[[\"H\", \"H_odeint\", \"H_diff\"]]\n", - "# df.H[100.0:150.0]\n", - "# results.parameter_space.points()[0].values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# df.columns\n", - "# import matplotlib.pyplot as plt\n", - "# plt.plot(H_odeint)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "df = pd.DataFrame({\"a\": [0, 1], \"b\": [3, 4]})\n", - "l = list(df.a.values)\n", - "# l = [float(v) for v in l]\n", - "type(l[0])\n", - "\n", - "# p = plt.plot(df.a, df.b)\n", - "df\n", - "# print(p)\n", - "# plt.plot(l)\n", - "\n", - "# plt.plot(df.a.values)\n", - "# plt.plot(df['a'])\n", - "# df.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add bounds [0, N] to the STATE compartments. \n", - "# Add bounds sum(STATE) in [N-e, N+e], for a small e.\n", - "\n", - "funman_request = get_request()\n", - "setup_common(funman_request, debug=True)\n", - "set_compartment_bounds(funman_request)\n", - "results = run(funman_request)\n", - "report(results, \"compartmental_constrained\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Relax the bounds on the parameters to allow additional parameterizations\n", - "\n", - "funman_request = get_request()\n", - "setup_common(funman_request)\n", - "set_compartment_bounds(funman_request)\n", - "relax_parameter_bounds(funman_request, factor = 0.75)\n", - "results = run(funman_request)\n", - "report(results, \"relaxed_bounds\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "funman_request = get_request()\n", - "setup_common(funman_request, synthesize=True)\n", - "set_compartment_bounds(funman_request)\n", - "# relax_parameter_bounds(funman_request, factor=0.75)\n", - "# funman_request.config.verbosity=10\n", - "results = run(funman_request, plot=True)\n", - "report(results, \"synthesis\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import pandas as pd\n", - "# df1 = pd.DataFrame({\"ltp\": ltp, \"gtp\": gtp})\n", - "\n", - "# # df2 = \n", - "# # df1.ltp.N == df1.gtp.N\n", - "# # df2.loc[df2].sort_index()[0:60]\n", - "\n", - "# #(= H_10 (+ H_8 (* 2 (+ (* r_HD (- 1) H_8) (* r_HR (- 1) H_8) (* r_IH_v I_v_8) (* r_IH_u I_u_8)))))\n", - "\n", - "# df1['same'] = df1['ltp'] == df1[\"gtp\"]\n", - "# df1[0:20]\n", - "# # df1.loc[df1.index.str.endswith(\"_6\")].sort_values(by=\"same\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# # Get points (trajectories generated)\n", - "# pts = results.parameter_space.points() \n", - "# print(f\"{len(pts)} points\")\n", - "\n", - "# # Get a plot for last point\n", - "# df = results.dataframe(points=pts[-1:])\n", - "# ax = df[STATES].plot()\n", - "# ax.set_yscale(\"log\")\n", - "\n", - "\n", - "# # Get the values of the point\n", - "# gtp=pts[-1].values\n", - "\n", - "\n", - "# # Output the model diagram\n", - "# #\n", - "results.model.to_dot()\n", - "# # gtp" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "funman_venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This notebook illustrates handling the August 2024 Demo of the 12mo Evaluation Scenario 1\n", + "\n", + "# Import funman related code\n", + "import os\n", + "from funman.api.run import Runner\n", + "from funman_demo import summarize_results\n", + "from funman import FunmanWorkRequest, EncodingSchedule \n", + "import json\n", + "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", + "from funman.representation import Interval\n", + "import pandas as pd\n", + "\n", + "RESOURCES = \"../../resources\"\n", + "SAVED_RESULTS_DIR = \"./out\"\n", + "\n", + "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"monthly-demo\", \"2024-08\", \"12_month_scenario_1\", \"q1a_ii\")\n", + "MODEL_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario1_1_ii_3.json\"\n", + ")\n", + "REQUEST_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario1_base_request.json\"\n", + ")\n", + "\n", + "\n", + "request_params = {}\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H_odeint = [0.0,\n", + " 0.07584066320978722,\n", + " 0.14636646265873351,\n", + " 0.21517239301596403,\n", + " 0.2849940641423008,\n", + " 0.35803162126534144,\n", + " 0.43617188357763786,\n", + " 0.5211433080571843,\n", + " 0.6146269952842578,\n", + " 0.7183393339849907,\n", + " 0.8340968480485813,\n", + " 0.9638704485112929,\n", + " 1.1098340730821774,\n", + " 1.2744112432601529,\n", + " 1.460322143778306,\n", + " 1.6706331916641786,\n", + " 1.9088107460125325,\n", + " 2.178780372444439,\n", + " 2.4849930017839568,\n", + " 2.832499327828298,\n", + " 3.227033820607022,\n", + " 3.6751098523322363,\n", + " 4.184127609093503,\n", + " 4.762496481974214,\n", + " 5.419774326784966,\n", + " 6.166825487583933,\n", + " 7.016000445528594,\n", + " 7.981340143729251,\n", + " 9.078808185052266,\n", + " 10.326554856871647,\n", + " 11.745217338261286,\n", + " 13.358261021733336,\n", + " 15.192367626771185,\n", + " 17.277876514948733,\n", + " 19.64928647253589,\n", + " 22.345826309466535,\n", + " 25.41210364728401,\n", + " 28.898842636443945,\n", + " 32.863722838161294,\n", + " 37.37233298620304,\n", + " 42.49925554848381,\n", + " 48.32929991918692,\n", + " 54.95890451703756,\n", + " 62.49773100251825,\n", + " 71.07047694046699,\n", + " 80.81893664895041,\n", + " 91.9043442331066,\n", + " 104.51003757795193,\n", + " 118.84448659151495,\n", + " 135.14473616311494,\n", + " 153.6803198995054,\n", + " 174.75770892931388,\n", + " 198.7253686880845,\n", + " 225.9795066077193,\n", + " 256.9706043940812,\n", + " 292.2108414225009,\n", + " 332.28253040812973,\n", + " 377.8477020062484,\n", + " 429.65899353366785,\n", + " 488.57201806502917,\n", + " 555.5594130725682,\n", + " 631.7267930288757,\n", + " 718.33086148102,\n", + " 816.7999711100919,\n", + " 928.7574539225811,\n", + " 1056.0480899336421,\n", + " 1200.768126390387,\n", + " 1365.2993106701535,\n", + " 1552.3474521757955,\n", + " 1764.986108867584,\n", + " 2006.7060366726942,\n", + " 2281.4711317587603,\n", + " 2593.781675837228,\n", + " 2948.74577656625,\n", + " 3352.159985334878,\n", + " 3810.6001762079927,\n", + " 4331.523867699931,\n", + " 4923.385255496248,\n", + " 5595.764320925713,\n", + " 6359.511470886844,\n", + " 7226.909216017238,\n", + " 8211.852427514215,\n", + " 9330.048735030014,\n", + " 10599.240502623044,\n", + " 12039.44975726627,\n", + " 13673.24714065712,\n", + " 15526.045525426567,\n", + " 17626.41847934686,\n", + " 20006.442805113635,\n", + " 22702.063309712732,\n", + " 25753.476547071572,\n", + " 29205.528188227865,\n", + " 33108.11624152888,\n", + " 37516.58927032621,\n", + " 42492.12476694649,\n", + " 48102.06815038656,\n", + " 54420.207487559084,\n", + " 61526.95241138342,\n", + " 69509.3788326499,\n", + " 78461.09204661413,\n", + " 88481.85690086993,\n", + " 99676.93033933835,\n", + " 112156.03116953523,\n", + " 126031.8764591974,\n", + " 141418.21409965339,\n", + " 158427.29086936626,\n", + " 177166.7077374985,\n", + " 197735.64179407313,\n", + " 220220.4490700916,\n", + " 244689.7128999469,\n", + " 271188.8609711989,\n", + " 299734.54505555454,\n", + " 330309.0420578748,\n", + " 362855.0097454492,\n", + " 397270.9706782105,\n", + " 433407.9323314221,\n", + " 471067.5318962573,\n", + " 510002.05000426056,\n", + " 549916.5325942405,\n", + " 590473.1326448151,\n", + " 631297.6155083764,\n", + " 671987.805397328,\n", + " 712123.5855505902,\n", + " 751277.9419388921,\n", + " 789028.4531510238,\n", + " 824968.6089066564,\n", + " 858718.3673693967,\n", + " 889933.4450316473,\n", + " 918312.9510473183,\n", + " 943605.1145011623,\n", + " 965610.9989351172,\n", + " 984186.2260247911,\n", + " 999240.8449867101,\n", + " 1010737.5652130407,\n", + " 1018688.6250969241,\n", + " 1023151.5961628385,\n", + " 1024224.424518873,\n", + " 1022039.9954607659,\n", + " 1016760.4778537896,\n", + " 1008571.6676818144,\n", + " 997677.5091145145,\n", + " 984294.9309318912,\n", + " 968649.0965921708,\n", + " 950969.132919035,\n", + " 931484.3726190757,\n", + " 910421.1206448845,\n", + " 887999.9365984657,\n", + " 864433.4099252748,\n", + " 839924.3952308263,\n", + " 814664.6678259049,\n", + " 788833.956208313,\n", + " 762599.306145181,\n", + " 736114.7329186859,\n", + " 709521.118576635,\n", + " 682946.3131058007,\n", + " 656505.4041417397,\n", + " 630301.1204001798,\n", + " 604424.3395841966,\n", + " 578954.674586528,\n", + " 553961.1153107659,\n", + " 529502.705890681,\n", + " 505629.24208586576,\n", + " 482381.97446456744,\n", + " 459794.3062235543,\n", + " 437892.47709088976,\n", + " 416696.2258780569,\n", + " 396219.4261885734,\n", + " 376470.6921664646,\n", + " 357453.95052101125,\n", + " 339168.9775714914,\n", + " 321611.9004232435,\n", + " 304775.66215856594,\n", + " 288650.4511800376,\n", + " 273224.0956361533,\n", + " 258482.42403353218,\n", + " 244409.59338260329,\n", + " 230988.3864887854,\n", + " 218200.48005631237,\n", + " 206026.6853722251,\n", + " 194447.16337089916,\n", + " 183441.61586876688,\n", + " 172989.45475247476,\n", + " 163069.95085616622,\n", + " 153662.36416343783,\n", + " 144746.05696746017,\n", + " 136300.5914987689,\n", + " 128305.81345597992,\n", + " 120741.9227885983,\n", + " 113589.53299236886,\n", + " 106829.72009170499,\n", + " 100444.06240763294,\n", + " 94414.6720882853,\n", + " 88724.21934978342,\n", + " 83355.95026825309,\n", + " 78293.69889597349,\n", + " 73521.89440676449,\n", + " 69025.56391080657,\n", + " 64790.33151791613,\n", + " 60802.414180610635,\n", + " 57048.61477377206]\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(H_odeint)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Constants for the scenario\n", + "STATES = [\"S\", \"I\", \"E\", \"R\", \"H\", \"D\"]\n", + "# IDART = [\"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Threatened\"]\n", + "\n", + "MAX_TIME=150\n", + "STEP_SIZE=5\n", + "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper functions to setup FUNMAN for different steps of the scenario\n", + "\n", + "def get_request():\n", + " with open(REQUEST_PATH, \"r\") as request:\n", + " funman_request = FunmanWorkRequest.model_validate(json.load(request))\n", + " return funman_request\n", + "\n", + "def set_timepoints(funman_request, timepoints):\n", + " funman_request.structure_parameters[0].schedules = [EncodingSchedule(timepoints=timepoints)]\n", + "\n", + "def unset_all_labels(funman_request):\n", + " for p in funman_request.parameters:\n", + " p.label = \"any\"\n", + " \n", + "def set_config_options(funman_request, debug=False, dreal_precision=1e-1):\n", + " # Overrides for configuration\n", + " #\n", + " # funman_request.config.substitute_subformulas = True\n", + " # funman_request.config.use_transition_symbols = True\n", + " # funman_request.config.use_compartmental_constraints=False\n", + " if debug:\n", + " funman_request.config.save_smtlib=\"./out\"\n", + " funman_request.config.tolerance = 0.01\n", + " funman_request.config.dreal_precision = dreal_precision\n", + " # funman_request.config.verbosity = 10\n", + " # funman_request.config.dreal_log_level = \"debug\"\n", + " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", + "\n", + "def get_synthesized_vars(funman_request):\n", + " return [p.name for p in funman_request.parameters if p.label == \"all\"]\n", + "\n", + "def run(funman_request, plot=False):\n", + " to_synthesize = get_synthesized_vars(funman_request)\n", + " return Runner().run(\n", + " MODEL_PATH,\n", + " funman_request,\n", + " description=\"SIERHD Eval 12mo Scenario 1 q1\",\n", + " case_out_dir=SAVED_RESULTS_DIR,\n", + " dump_plot=plot,\n", + " print_last_time=True,\n", + " parameters_to_plot=to_synthesize\n", + " )\n", + "\n", + "def setup_common(funman_request, synthesize=False, debug=False, dreal_precision=1e-1):\n", + " set_timepoints(funman_request, timepoints)\n", + " if not synthesize:\n", + " unset_all_labels(funman_request)\n", + " set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision)\n", + " \n", + "\n", + "def set_compartment_bounds(funman_request, upper_bound=9830000.0, error=0.01):\n", + " # Add bounds to compartments\n", + " for var in STATES:\n", + " funman_request.constraints.append(StateVariableConstraint(name=f\"{var}_bounds\", variable=var, interval=Interval(lb=0, ub=upper_bound, closed_upper_bound=True),soft=False))\n", + "\n", + " # Add sum of compartments\n", + " funman_request.constraints.append(LinearConstraint(name=f\"compartment_bounds\", variables=STATES, additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True))\n", + "\n", + "def relax_parameter_bounds(funman_request, factor = 0.1):\n", + " # Relax parameter bounds\n", + " parameters = funman_request.parameters\n", + " for p in parameters:\n", + " interval = p.interval\n", + " width = float(interval.width())\n", + " interval.lb = interval.lb - (factor/2 * width)\n", + " interval.ub = interval.ub + (factor/2 * width)\n", + "\n", + "def plot_last_point(results):\n", + " pts = results.parameter_space.points() \n", + " print(f\"{len(pts)} points\")\n", + "\n", + " if len(pts) > 0:\n", + " # Get a plot for last point\n", + " df = results.dataframe(points=pts[-1:])\n", + " # pd.options.plotting.backend = \"plotly\"\n", + " ax = df[STATES].plot()\n", + " \n", + " \n", + " fig = plt.figure()\n", + " # fig.set_yscale(\"log\")\n", + " fig.savefig(\"save_file_name.pdf\")\n", + " plt.close()\n", + "\n", + "def get_last_point_parameters(results):\n", + " pts = results.parameter_space.points()\n", + " if len(pts) > 0:\n", + " pt = pts[-1]\n", + " parameters = results.model._parameter_names()\n", + " param_values = {k:v for k, v in pt.values.items() if k in parameters }\n", + " return param_values\n", + "\n", + "def pretty_print_request_params(params):\n", + " # print(json.dump(params, indent=4))\n", + " if len(params)>0:\n", + "\n", + " df = pd.DataFrame(params)\n", + " print(df.T)\n", + "\n", + "\n", + "def report(results, name):\n", + " plot_last_point(results)\n", + " param_values = get_last_point_parameters(results)\n", + " # print(f\"Point parameters: {param_values}\")\n", + " if param_values is not None:\n", + " request_params[name] = param_values\n", + " pretty_print_request_params(request_params)\n", + "\n", + "def add_unit_test(funman_request):\n", + " pass\n", + " # funman_request.constraints.append(LinearConstraint(name=\"unit_test\", variables = [\n", + " # \"Infected\",\n", + " # \"Diagnosed\",\n", + " # \"Ailing\",\n", + " # \"Recognized\",\n", + " # \"Threatened\"\n", + " # ],\n", + " # additive_bounds= {\n", + " # \"lb\": 0.55,\n", + " # \"ub\": 0.65\n", + " # },\n", + " # timepoints={\n", + " # \"lb\": 45,\n", + " # \"ub\": 55\n", + " # }\n", + " # ))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# i) (TA1 Search and Discovery Workflow, 1 Hr. Time Limit) Find estimates on the\n", + "# efficacy of surgical masks in preventing onward transmission of SARS-CoV-2 (preferred)\n", + "# or comparable viral respiratory pathogens (e.g., MERS-CoV, SARS), including any\n", + "# information about uncertainty in these estimates. The term surgical mask here refers to\n", + "# the commonly available, disposable procedure mask, not an N95-type respirator. Find 3\n", + "# credible documents that provide estimates and use your judgment to determine what\n", + "# value (in the deterministic case) or distribution (in the probabilistic case) to use in your\n", + "# forecasts in 1.a.iii.\n", + "\n", + "# The base model assumes no masking inverventions, and we measure the efficacy in terms of the number of hospitalized.\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=True, dreal_precision=1e-3)\n", + "add_unit_test(funman_request)\n", + "results = run(funman_request)\n", + "report(results, \"unconstrained\")\n", + "pass\n", + "# pts = results.parameter_space.points() \n", + "# print(f\"{len(pts)} points\")\n", + "# df = results.dataframe(points=pts[-1:])\n", + "# df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results\n", + "# get_last_point_parameters(results)\n", + "# plot_last_point(results)\n", + "# def plot_last_point(results):\n", + "# pts = results.parameter_space.points() \n", + "# print(f\"{len(pts)} points\")\n", + "\n", + "# if len(pts) > 0:\n", + "# # Get a plot for last point\n", + "# df = results.dataframe(points=pts[-1:])\n", + "# ax = df[STATES].plot()\n", + "# ax.set_yscale(\"log\")\n", + "pts = results.parameter_space.points() \n", + "print(f\"{len(pts)} points\")\n", + "df = results.dataframe(points=pts[-1:])\n", + "\n", + "df['H_odeint'] = pd.Series(H_odeint[0:151])\n", + "df[\"H_diff\"] = df.H - df.H_odeint\n", + "df[[\"H\", \"H_odeint\", \"H_diff\"]]\n", + "# df.H[100.0:150.0]\n", + "# results.parameter_space.points()[0].values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# df.columns\n", + "# import matplotlib.pyplot as plt\n", + "# plt.plot(H_odeint)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.DataFrame({\"a\": [0, 1], \"b\": [3, 4]})\n", + "l = list(df.a.values)\n", + "# l = [float(v) for v in l]\n", + "type(l[0])\n", + "\n", + "# p = plt.plot(df.a, df.b)\n", + "df\n", + "# print(p)\n", + "# plt.plot(l)\n", + "\n", + "# plt.plot(df.a.values)\n", + "# plt.plot(df['a'])\n", + "# df.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add bounds [0, N] to the STATE compartments. \n", + "# Add bounds sum(STATE) in [N-e, N+e], for a small e.\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=True)\n", + "set_compartment_bounds(funman_request)\n", + "results = run(funman_request)\n", + "report(results, \"compartmental_constrained\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Relax the bounds on the parameters to allow additional parameterizations\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request)\n", + "set_compartment_bounds(funman_request)\n", + "relax_parameter_bounds(funman_request, factor = 0.75)\n", + "results = run(funman_request)\n", + "report(results, \"relaxed_bounds\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "funman_request = get_request()\n", + "setup_common(funman_request, synthesize=True)\n", + "set_compartment_bounds(funman_request)\n", + "# relax_parameter_bounds(funman_request, factor=0.75)\n", + "# funman_request.config.verbosity=10\n", + "results = run(funman_request, plot=True)\n", + "report(results, \"synthesis\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import pandas as pd\n", + "# df1 = pd.DataFrame({\"ltp\": ltp, \"gtp\": gtp})\n", + "\n", + "# # df2 = \n", + "# # df1.ltp.N == df1.gtp.N\n", + "# # df2.loc[df2].sort_index()[0:60]\n", + "\n", + "# #(= H_10 (+ H_8 (* 2 (+ (* r_HD (- 1) H_8) (* r_HR (- 1) H_8) (* r_IH_v I_v_8) (* r_IH_u I_u_8)))))\n", + "\n", + "# df1['same'] = df1['ltp'] == df1[\"gtp\"]\n", + "# df1[0:20]\n", + "# # df1.loc[df1.index.str.endswith(\"_6\")].sort_values(by=\"same\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "petrinet\n", + "\n", + "\n", + "\n", + "t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]\n", + "\n", + "t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]\n", + "\n", + "\n", + "\n", + "I_compliant\n", + "\n", + "I_compliant\n", + "\n", + "\n", + "\n", + "t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]->I_compliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "E_compliant\n", + "\n", + "E_compliant\n", + "\n", + "\n", + "\n", + "t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]->E_compliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "S_compliant\n", + "\n", + "S_compliant\n", + "\n", + "\n", + "\n", + "S_compliant->t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]\n", + "\n", + "t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]\n", + "\n", + "\n", + "\n", + "S_compliant->t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t14([S_compliant*p_compliant_noncompliant]) = [0.1*S_compliant]\n", + "\n", + "t14([S_compliant*p_compliant_noncompliant]) = [0.1*S_compliant]\n", + "\n", + "\n", + "\n", + "S_compliant->t14([S_compliant*p_compliant_noncompliant]) = [0.1*S_compliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]->E_compliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_noncompliant\n", + "\n", + "I_noncompliant\n", + "\n", + "\n", + "\n", + "t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]->I_noncompliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]\n", + "\n", + "t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]\n", + "\n", + "\n", + "\n", + "t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]->I_noncompliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "E_noncompliant\n", + "\n", + "E_noncompliant\n", + "\n", + "\n", + "\n", + "t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]->E_noncompliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]\n", + "\n", + "t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]\n", + "\n", + "\n", + "\n", + "t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]->I_compliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]->E_noncompliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5([E_compliant*r_E_to_I]) = [0.2*E_compliant]\n", + "\n", + "t5([E_compliant*r_E_to_I]) = [0.2*E_compliant]\n", + "\n", + "\n", + "\n", + "t5([E_compliant*r_E_to_I]) = [0.2*E_compliant]->I_compliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t6([E_noncompliant*r_E_to_I]) = [0.2*E_noncompliant]\n", + "\n", + "t6([E_noncompliant*r_E_to_I]) = [0.2*E_noncompliant]\n", + "\n", + "\n", + "\n", + "t6([E_noncompliant*r_E_to_I]) = [0.2*E_noncompliant]->I_noncompliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t7([I_compliant*p_I_to_R*r_I_to_R]) = [0.056*I_compliant]\n", + "\n", + "t7([I_compliant*p_I_to_R*r_I_to_R]) = [0.056*I_compliant]\n", + "\n", + "\n", + "\n", + "R\n", + "\n", + "R\n", + "\n", + "\n", + "\n", + "t7([I_compliant*p_I_to_R*r_I_to_R]) = [0.056*I_compliant]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t8([I_noncompliant*p_I_to_R*r_I_to_R]) = [0.056*I_noncompliant]\n", + "\n", + "t8([I_noncompliant*p_I_to_R*r_I_to_R]) = [0.056*I_noncompliant]\n", + "\n", + "\n", + "\n", + "t8([I_noncompliant*p_I_to_R*r_I_to_R]) = [0.056*I_noncompliant]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t9([I_compliant*p_I_to_H*r_I_to_H]) = [0.02*I_compliant]\n", + "\n", + "t9([I_compliant*p_I_to_H*r_I_to_H]) = [0.02*I_compliant]\n", + "\n", + "\n", + "\n", + "H\n", + "\n", + "H\n", + "\n", + "\n", + "\n", + "t9([I_compliant*p_I_to_H*r_I_to_H]) = [0.02*I_compliant]->H\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t10([I_noncompliant*p_I_to_H*r_I_to_H]) = [0.02*I_noncompliant]\n", + "\n", + "t10([I_noncompliant*p_I_to_H*r_I_to_H]) = [0.02*I_noncompliant]\n", + "\n", + "\n", + "\n", + "t10([I_noncompliant*p_I_to_H*r_I_to_H]) = [0.02*I_noncompliant]->H\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t11([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", + "\n", + "t11([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", + "\n", + "\n", + "\n", + "t11([H*p_H_to_R*r_H_to_R]) = [0.088*H]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t12([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", + "\n", + "t12([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "t12([H*p_H_to_D*r_H_to_D]) = [0.012*H]->D\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t13([S_noncompliant*p_noncompliant_compliant]) = [0.1*S_noncompliant]\n", + "\n", + "t13([S_noncompliant*p_noncompliant_compliant]) = [0.1*S_noncompliant]\n", + "\n", + "\n", + "\n", + "t13([S_noncompliant*p_noncompliant_compliant]) = [0.1*S_noncompliant]->S_compliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "S_noncompliant\n", + "\n", + "S_noncompliant\n", + "\n", + "\n", + "\n", + "t14([S_compliant*p_compliant_noncompliant]) = [0.1*S_compliant]->S_noncompliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t15([E_noncompliant*p_noncompliant_compliant]) = [0.1*E_noncompliant]\n", + "\n", + "t15([E_noncompliant*p_noncompliant_compliant]) = [0.1*E_noncompliant]\n", + "\n", + "\n", + "\n", + "t15([E_noncompliant*p_noncompliant_compliant]) = [0.1*E_noncompliant]->E_compliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t16([E_compliant*p_compliant_noncompliant]) = [0.1*E_compliant]\n", + "\n", + "t16([E_compliant*p_compliant_noncompliant]) = [0.1*E_compliant]\n", + "\n", + "\n", + "\n", + "t16([E_compliant*p_compliant_noncompliant]) = [0.1*E_compliant]->E_noncompliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t17([I_noncompliant*p_noncompliant_compliant]) = [0.1*I_noncompliant]\n", + "\n", + "t17([I_noncompliant*p_noncompliant_compliant]) = [0.1*I_noncompliant]\n", + "\n", + "\n", + "\n", + "t17([I_noncompliant*p_noncompliant_compliant]) = [0.1*I_noncompliant]->I_compliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t18([I_compliant*p_compliant_noncompliant]) = [0.1*I_compliant]\n", + "\n", + "t18([I_compliant*p_compliant_noncompliant]) = [0.1*I_compliant]\n", + "\n", + "\n", + "\n", + "t18([I_compliant*p_compliant_noncompliant]) = [0.1*I_compliant]->I_noncompliant\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_compliant->t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_compliant->t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_compliant->t7([I_compliant*p_I_to_R*r_I_to_R]) = [0.056*I_compliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_compliant->t9([I_compliant*p_I_to_H*r_I_to_H]) = [0.02*I_compliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_compliant->t18([I_compliant*p_compliant_noncompliant]) = [0.1*I_compliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "E_compliant->t5([E_compliant*r_E_to_I]) = [0.2*E_compliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "E_compliant->t16([E_compliant*p_compliant_noncompliant]) = [0.1*E_compliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_noncompliant->t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_noncompliant->t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_noncompliant->t8([I_noncompliant*p_I_to_R*r_I_to_R]) = [0.056*I_noncompliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_noncompliant->t10([I_noncompliant*p_I_to_H*r_I_to_H]) = [0.02*I_noncompliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_noncompliant->t17([I_noncompliant*p_noncompliant_compliant]) = [0.1*I_noncompliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_noncompliant->t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_noncompliant->t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_noncompliant->t13([S_noncompliant*p_noncompliant_compliant]) = [0.1*S_noncompliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "E_noncompliant->t6([E_noncompliant*r_E_to_I]) = [0.2*E_noncompliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "E_noncompliant->t15([E_noncompliant*p_noncompliant_compliant]) = [0.1*E_noncompliant]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t11([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t12([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# # Get points (trajectories generated)\n", + "# pts = results.parameter_space.points() \n", + "# print(f\"{len(pts)} points\")\n", + "\n", + "# # Get a plot for last point\n", + "# df = results.dataframe(points=pts[-1:])\n", + "# ax = df[STATES].plot()\n", + "# ax.set_yscale(\"log\")\n", + "\n", + "\n", + "# # Get the values of the point\n", + "# gtp=pts[-1].values\n", + "\n", + "\n", + "# # Output the model diagram\n", + "# #\n", + "results.model.to_dot()\n", + "# # gtp" + ] + } + ], + "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": "3.10.12" + } }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3.json index 6a511899..3b4a2a31 100644 --- a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3.json +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3.json @@ -1,706 +1,708 @@ { - "name": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", - "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", - "schema_name": "petrinet", - "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", - "model_version": "0.1", - "properties": {}, - "model": { - "states": [ - { - "id": "S_compliant", - "name": "S_compliant", - "grounding": { - "identifiers": { - "ido": "0000514" - }, - "modifiers": { - "masking": "compliant" - } - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "I_compliant", - "name": "I_compliant", - "grounding": { - "identifiers": { - "ido": "0000511" - }, - "modifiers": { - "masking": "compliant" - } - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "E_compliant", - "name": "E_compliant", - "grounding": { - "identifiers": { - "apollosv": "0000154" - }, - "modifiers": { - "masking": "compliant" - } - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "I_noncompliant", - "name": "I_noncompliant", - "grounding": { - "identifiers": { - "ido": "0000511" - }, - "modifiers": { - "masking": "noncompliant" - } - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "S_noncompliant", - "name": "S_noncompliant", - "grounding": { - "identifiers": { - "ido": "0000514" - }, - "modifiers": { - "masking": "noncompliant" - } - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "E_noncompliant", - "name": "E_noncompliant", - "grounding": { - "identifiers": { - "apollosv": "0000154" - }, - "modifiers": { - "masking": "noncompliant" - } - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "R", - "name": "R", - "grounding": { - "identifiers": { - "ido": "0000592" - }, - "modifiers": {} - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "H", - "name": "H", - "grounding": { - "identifiers": { - "ido": "0000511" - }, - "modifiers": { - "property": "ncit:C25179" - } - }, - "units": { - "expression": "person", - "expression_mathml": "person" - 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"expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "eps_m_2", + "value": 0.5, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "c_m_3", + "value": 0.5, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "eps_m_3", + "value": 0.5, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_E_to_I", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_R", + "value": 0.8, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_R", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_H", + "value": 0.2, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_H", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_R", + "value": 0.88, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_R", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_D", + "value": 0.12, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_D", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_noncompliant_compliant", + "value": 0.1 + }, + { + "id": "p_compliant_noncompliant", + "value": 0.1 + } + ], + "observables": [], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + } + }, + "metadata": { + "annotations": { + "license": null, + "authors": [], + "references": [], + "time_scale": null, + "time_start": null, + "time_end": null, + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } } - } - } - }, - "metadata": { - "annotations": { - "license": null, - "authors": [], - "references": [], - "time_scale": null, - "time_start": null, - "time_end": null, - "locations": [], - "pathogens": [], - "diseases": [], - "hosts": [], - "model_types": [] - } - } } \ No newline at end of file From f52c5ca78d8805690dd85f9e2c3530b18566c877 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Thu, 29 Aug 2024 20:44:07 +0000 Subject: [PATCH 25/93] roll back base image, fix infinite interval, catch timing diff exception --- docker/base/Dockerfile | 6 ++--- docker/deploy/Dockerfile.deploy | 2 +- docker/dev/root/Dockerfile.root | 2 +- docker/dev/user/Dockerfile | 2 +- docker/dreal4/Dockerfile.dreal4 | 2 +- docker/ibex/Dockerfile | 4 +-- src/funman/api/run.py | 2 +- src/funman/representation/interval.py | 16 +++++++++++- src/funman/search/box_search.py | 36 +++++++++++++-------------- src/funman/server/query.py | 15 +++++++++-- tools/update-dreal.user | 2 +- 11 files changed, 56 insertions(+), 33 deletions(-) diff --git a/docker/base/Dockerfile b/docker/base/Dockerfile index 689938dc..067f028a 100644 --- a/docker/base/Dockerfile +++ b/docker/base/Dockerfile @@ -38,11 +38,11 @@ ARG AUTOMATES_COMMIT_TAG # && git checkout ${AUTOMATES_COMMIT_TAG} # RUN pip install -e automates -RUN pip install --no-cache-dir z3-solver +RUN pip install --no-cache-dir z3-solver==4.12.6 RUN pip install --no-cache-dir graphviz -RUN pip install --no-cache-dir pydantic>=2.3.0 +RUN pip install --no-cache-dir pydantic RUN pip install --no-cache-dir pydantic-settings -RUN pip install --no-cache-dir fastapi>=0.103.1 +RUN pip install --no-cache-dir fastapi RUN pip install --no-cache-dir --upgrade setuptools pip RUN pip install --no-cache-dir wheel diff --git a/docker/deploy/Dockerfile.deploy b/docker/deploy/Dockerfile.deploy index 14157a49..2e0c4bab 100644 --- a/docker/deploy/Dockerfile.deploy +++ b/docker/deploy/Dockerfile.deploy @@ -34,7 +34,7 @@ RUN git clone --depth=1 https://github.com/danbryce/automates.git automates \ && git checkout e5fb635757aa57007615a75371f55dd4a24851e0 RUN pip install -e automates -RUN pip install --no-cache-dir z3-solver +RUN pip install --no-cache-dir z3-solver==4.12.6 RUN pip install --no-cache-dir graphviz # Install funman dev packages diff --git a/docker/dev/root/Dockerfile.root b/docker/dev/root/Dockerfile.root index 790f9976..fd9f82a0 100644 --- a/docker/dev/root/Dockerfile.root +++ b/docker/dev/root/Dockerfile.root @@ -59,7 +59,7 @@ WORKDIR /root # && git checkout e5fb635757aa57007615a75371f55dd4a24851e0 # RUN pip install -e automates -RUN pip install --no-cache-dir z3-solver +RUN pip install --no-cache-dir z3-solver==4.12.6 RUN pip install --no-cache-dir graphviz RUN pip install /dreal4/dreal-*.whl diff --git a/docker/dev/user/Dockerfile b/docker/dev/user/Dockerfile index 080164bc..6c21904d 100644 --- a/docker/dev/user/Dockerfile +++ b/docker/dev/user/Dockerfile @@ -67,7 +67,7 @@ WORKDIR /home/$UNAME # && git checkout e5fb635757aa57007615a75371f55dd4a24851e0 # RUN pip install -e automates -RUN pip install --no-cache-dir z3-solver +RUN pip install --no-cache-dir z3-solver==4.12.6 RUN pip install --no-cache-dir graphviz RUN pip install /dreal4/dreal-*.whl diff --git a/docker/dreal4/Dockerfile.dreal4 b/docker/dreal4/Dockerfile.dreal4 index a803d035..7be71dcb 100644 --- a/docker/dreal4/Dockerfile.dreal4 +++ b/docker/dreal4/Dockerfile.dreal4 @@ -75,7 +75,7 @@ RUN cd /dreal4 \ && pip3 install --upgrade setuptools \ && pip3 install --upgrade pip \ && python3 setup.py bdist_wheel \ - && DREAL_WHEEL=dreal-$(python setup.py --version)-cp310-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl \ + && DREAL_WHEEL=dreal-$(python setup.py --version)-cp38-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl \ && cp ./dist/$DREAL_WHEEL /tmp/$DREAL_WHEEL \ && pip3 install ./dist/$DREAL_WHEEL \ && bazel clean --expunge \ diff --git a/docker/ibex/Dockerfile b/docker/ibex/Dockerfile index ef5dd84d..d0b9fe9f 100644 --- a/docker/ibex/Dockerfile +++ b/docker/ibex/Dockerfile @@ -1,10 +1,8 @@ -FROM ubuntu:22.04 +FROM ubuntu:20.04 ARG IBEX_BRANCH ARG ENABLE_DEBUG=no -ARG ENABLE_DEBUG=no - ARG DEBIAN_FRONTEND=noninteractive # Install base dependencies RUN apt update && apt install -y --no-install-recommends \ diff --git a/src/funman/api/run.py b/src/funman/api/run.py index 559de4f7..e124953c 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -482,7 +482,7 @@ def get_args(): help=f"Create parameter space plot with only the last timestep.", ) - parser.set_defaults(plot=False) + parser.set_defaults(plot=None) return parser.parse_args() diff --git a/src/funman/representation/interval.py b/src/funman/representation/interval.py index d2df0ff4..431a8f82 100644 --- a/src/funman/representation/interval.py +++ b/src/funman/representation/interval.py @@ -1,10 +1,12 @@ import logging from decimal import Decimal -from typing import List, Optional, Union +from math import isinf +from typing import Any, List, Optional, Union from numpy import average, finfo, nextafter from pydantic import ( BaseModel, + ConfigDict, Field, SerializerFunctionWrapHandler, field_serializer, @@ -24,6 +26,7 @@ class Interval(BaseModel): An interval is a pair [lb, ub) that is open (i.e., an interval specifies all points x where lb <= x and ub < x). """ + model_config = ConfigDict(ser_json_inf_nan="constants") lb: Optional[Union[float, str]] = NEG_INFINITY ub: Optional[Union[float, str]] = POS_INFINITY closed_upper_bound: bool = False @@ -403,6 +406,17 @@ def _denormalize(self): self.ub = self.ub if not self.unnormalized_ub else self.unnormalized_ub self.unnormalized_ub = None + @model_validator(mode="before") + @classmethod + def check_original_width_inf(cls, data: Any) -> Any: + if ( + "original_width" in data + and isinstance(data["original_width"], float) + and isinf(data["original_width"]) + ): + data["original_width"] = None + return data + @model_validator(mode="after") def check_interval(self) -> str: if self.lb is None: diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index 8bc524b5..ebf22da1 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -1221,15 +1221,15 @@ def _expand( l.trace(f"+++ True:\n{box}") if episode.config.corner_points: - corner_points: List[ - Point - ] = self.get_box_corners( - solver, - episode, - curr_step_box, - rval, - options, - my_solver, + corner_points: List[Point] = ( + self.get_box_corners( + solver, + episode, + curr_step_box, + rval, + options, + my_solver, + ) ) # Advance a true box to be considered for later timesteps @@ -1275,15 +1275,15 @@ def _expand( l.debug(f"False @ {box.timestep().lb}") l.trace(f"--- False:\n{box}") if episode.config.corner_points: - corner_points: List[ - Point - ] = self.get_box_corners( - solver, - episode, - box, - rval, - options, - my_solver, + corner_points: List[Point] = ( + self.get_box_corners( + solver, + episode, + box, + rval, + options, + my_solver, + ) ) rval.put(box.model_dump()) else: # Timeout FIXME copy of split code diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 5b5cdd11..f224ab52 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -195,7 +195,12 @@ def update_progress( def finalize(self): """Calculate total time""" - self.total_time = self.end_time - self.start_time + try: + self.total_time = self.end_time - self.start_time + except Exception as e: + l.exception( + f"Exception in FunmanResultsTiming:finalize() start_time: {self.start_time} end_time: {self.end_time}" + ) class FunmanResults(BaseModel): @@ -278,7 +283,11 @@ def update_parameter_space( self.progress.progress = coverage_of_search_space self.progress.coverage_of_search_space = coverage_of_search_space self.progress.coverage_of_representable_space = coverage_of_repr_space - self.timing.update_progress(self.progress.coverage_of_search_space) + + try: + self.timing.update_progress(self.progress.coverage_of_search_space) + except Exception as e: + l.exception(f"Unable to update progress due to exception: {e}") self.contract_model() @@ -544,7 +553,9 @@ def plot( **kwargs, ) else: + # data = [state_vars[c].tolist() for c in state_vars.columns] ax = plt.plot( + # data, # state_vars, label=label, marker=label_marker[label], diff --git a/tools/update-dreal.user b/tools/update-dreal.user index 710d8d2d..26040ea2 100644 --- a/tools/update-dreal.user +++ b/tools/update-dreal.user @@ -10,7 +10,7 @@ if [ ! -d $DREAL_ROOT ] ; then exit 1 fi -DREAL_WHEEL=dreal-$(python ${DREAL_ROOT}/setup.py --version)-cp310-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl \ +DREAL_WHEEL=dreal-$(python ${DREAL_ROOT}/setup.py --version)-cp38-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl \ FUNMAN_ENV_PATH=$HOME/funman_venv FUNMAN_PYTHON=${FUNMAN_ENV_PATH}/bin/python From 7df1ad4451129fda26c2090edb3432dea607f330 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Sat, 31 Aug 2024 23:20:32 +0000 Subject: [PATCH 26/93] hand coded abstraction with bounds --- .../funman_aug_2024_12mo_eval_1_q1_demo.ipynb | 886 +++++------------- ...val_scenario1_1_ii_3_destratified_all.json | 621 ++++++++++++ .../q1a_ii/eval_scenario1_base_request.json | 7 +- src/funman/api/api.py | 2 +- src/funman/api/run.py | 2 +- src/funman/config.py | 2 +- src/funman/model/model.py | 6 +- src/funman/representation/representation.py | 17 + src/funman/scenario/scenario.py | 1 + 9 files changed, 887 insertions(+), 657 deletions(-) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json diff --git a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb index 772296e5..6d34a99a 100644 --- a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb +++ b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,6 +17,8 @@ "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", "from funman.representation import Interval\n", "import pandas as pd\n", + "import logging\n", + "import matplotlib.pyplot as plt\n", "\n", "RESOURCES = \"../../resources\"\n", "SAVED_RESULTS_DIR = \"./out\"\n", @@ -25,6 +27,9 @@ "MODEL_PATH = os.path.join(\n", " EXAMPLE_DIR, \"eval_scenario1_1_ii_3.json\"\n", ")\n", + "MODEL_DESTRATIFIED_ALL_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_all.json\"\n", + ")\n", "REQUEST_PATH = os.path.join(\n", " EXAMPLE_DIR, \"eval_scenario1_base_request.json\"\n", ")\n", @@ -38,9 +43,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "H_odeint = [0.0,\n", " 0.07584066320978722,\n", @@ -249,22 +275,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Constants for the scenario\n", - "STATES = [\"S\", \"I\", \"E\", \"R\", \"H\", \"D\"]\n", + "# STATES = [\"S\", \"I\", \"E\", \"R\", \"H\", \"D\"]\n", + "STATES = [\"S_compliant\", \"S_noncompliant\", \"I_compliant\", \"I_noncompliant\", \"E_compliant\", \"E_noncompliant\",\"R\", \"H\", \"D\"]\n", + "STATES_DESTRATIFIED_ALL = [\"S_lb\", \"S_ub\", \"I_lb\", \"I_ub\", \"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"]\n", "# IDART = [\"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Threatened\"]\n", "\n", - "MAX_TIME=150\n", - "STEP_SIZE=5\n", + "MAX_TIME=20\n", + "STEP_SIZE=1\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -292,17 +320,17 @@ " funman_request.config.save_smtlib=\"./out\"\n", " funman_request.config.tolerance = 0.01\n", " funman_request.config.dreal_precision = dreal_precision\n", - " # funman_request.config.verbosity = 10\n", + " funman_request.config.verbosity = logging.INFO\n", " # funman_request.config.dreal_log_level = \"debug\"\n", " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", "\n", "def get_synthesized_vars(funman_request):\n", " return [p.name for p in funman_request.parameters if p.label == \"all\"]\n", "\n", - "def run(funman_request, plot=False):\n", + "def run(funman_request, plot=False, model=MODEL_PATH):\n", " to_synthesize = get_synthesized_vars(funman_request)\n", " return Runner().run(\n", - " MODEL_PATH,\n", + " model,\n", " funman_request,\n", " description=\"SIERHD Eval 12mo Scenario 1 q1\",\n", " case_out_dir=SAVED_RESULTS_DIR,\n", @@ -335,7 +363,7 @@ " interval.lb = interval.lb - (factor/2 * width)\n", " interval.ub = interval.ub + (factor/2 * width)\n", "\n", - "def plot_last_point(results):\n", + "def plot_last_point(results, states=STATES):\n", " pts = results.parameter_space.points() \n", " print(f\"{len(pts)} points\")\n", "\n", @@ -343,7 +371,7 @@ " # Get a plot for last point\n", " df = results.dataframe(points=pts[-1:])\n", " # pd.options.plotting.backend = \"plotly\"\n", - " ax = df[STATES].plot()\n", + " ax = df[states].plot()\n", " \n", " \n", " fig = plt.figure()\n", @@ -367,39 +395,78 @@ " print(df.T)\n", "\n", "\n", - "def report(results, name):\n", - " plot_last_point(results)\n", + "def report(results, name, states=STATES):\n", + " plot_last_point(results, states=states)\n", " param_values = get_last_point_parameters(results)\n", " # print(f\"Point parameters: {param_values}\")\n", " if param_values is not None:\n", " request_params[name] = param_values\n", " pretty_print_request_params(request_params)\n", "\n", - "def add_unit_test(funman_request):\n", - " pass\n", - " # funman_request.constraints.append(LinearConstraint(name=\"unit_test\", variables = [\n", - " # \"Infected\",\n", - " # \"Diagnosed\",\n", - " # \"Ailing\",\n", - " # \"Recognized\",\n", - " # \"Threatened\"\n", - " # ],\n", - " # additive_bounds= {\n", - " # \"lb\": 0.55,\n", - " # \"ub\": 0.65\n", - " # },\n", - " # timepoints={\n", - " # \"lb\": 45,\n", - " # \"ub\": 55\n", - " # }\n", - " # ))\n" + "def add_unit_test(funman_request, model=MODEL_PATH):\n", + " if model == MODEL_DESTRATIFIED_ALL_PATH:\n", + " funman_request.constraints.append(LinearConstraint(name=\"compartment_lb\", soft=False, variables = [s for s in STATES_DESTRATIFIED_ALL if s.endswith(\"_lb\")],\n", + " additive_bounds= {\n", + " \"ub\": 19340000.5\n", + " }\n", + " ))\n", + " funman_request.constraints.append(LinearConstraint(name=\"compartment_ub\", soft=False, variables = [s for s in STATES_DESTRATIFIED_ALL if s.endswith(\"_ub\")],\n", + " additive_bounds= {\n", + " \"lb\": 0\n", + " }\n", + " ))\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-08-31 23:13:28,225 - funman.scenario.consistency - INFO - 10{10}:\t[+]\n", + "2024-08-31 23:13:28,549 - funman.api.run - INFO - Dumping results to ./out/467cf538-f62e-48b8-9a12-9bf36424d777.json\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 15\u001b[0m\n\u001b[1;32m 13\u001b[0m setup_common(funman_request, debug\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dreal_precision\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-0\u001b[39m)\n\u001b[1;32m 14\u001b[0m add_unit_test(funman_request)\n\u001b[0;32m---> 15\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunman_request\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m report(results, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munconstrained\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# pass\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# pts = results.parameter_space.points() \u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# print(f\"{len(pts)} points\")\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# df = results.dataframe(points=pts[-1:])\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# df\u001b[39;00m\n", + "Cell \u001b[0;32mIn[3], line 34\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(funman_request, plot, model)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(funman_request, plot\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, model\u001b[38;5;241m=\u001b[39mMODEL_PATH):\n\u001b[1;32m 33\u001b[0m to_synthesize \u001b[38;5;241m=\u001b[39m get_synthesized_vars(funman_request)\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mRunner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 36\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunman_request\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 37\u001b[0m \u001b[43m \u001b[49m\u001b[43mdescription\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSIERHD Eval 12mo Scenario 1 q1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 38\u001b[0m \u001b[43m 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\u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 153\u001b[0m model: Union[\u001b[38;5;28mstr\u001b[39m, funman\u001b[38;5;241m.\u001b[39mFunmanModel, Dict],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 162\u001b[0m print_last_time: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 163\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m FunmanResults:\n\u001b[1;32m 164\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;124;03m Run a FUNMAN scenario.\u001b[39;00m\n\u001b[1;32m 166\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;124;03m Analysis results\u001b[39;00m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 194\u001b[0m results \u001b[38;5;241m=\u001b[39m 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dump_results, print_last_time)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_storage\u001b[38;5;241m.\u001b[39mstart(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msettings\u001b[38;5;241m.\u001b[39mdata_path)\n\u001b[1;32m 231\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_worker\u001b[38;5;241m.\u001b[39mstart()\n\u001b[0;32m--> 233\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_instance\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 234\u001b[0m \u001b[43m \u001b[49m\u001b[43mcase\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 235\u001b[0m \u001b[43m \u001b[49m\u001b[43mout_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcase_out_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 236\u001b[0m \u001b[43m 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242\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 244\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_worker\u001b[38;5;241m.\u001b[39mstop()\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_storage\u001b[38;5;241m.\u001b[39mstop()\n", + "File \u001b[0;32m~/funman/src/funman/api/run.py:355\u001b[0m, in \u001b[0;36mRunner.run_instance\u001b[0;34m(self, case, out_dir, dump_plot, parameters_to_plot, point_plot_config, num_points, dump_results, print_last_time)\u001b[0m\n\u001b[1;32m 344\u001b[0m plotted \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 345\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_plots(\n\u001b[1;32m 346\u001b[0m results,\n\u001b[1;32m 347\u001b[0m out_dir,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 352\u001b[0m print_last_time,\n\u001b[1;32m 353\u001b[0m )\n\u001b[0;32m--> 355\u001b[0m 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Time Limit) Find estimates on the\n", "# efficacy of surgical masks in preventing onward transmission of SARS-CoV-2 (preferred)\n", @@ -413,11 +480,11 @@ "# The base model assumes no masking inverventions, and we measure the efficacy in terms of the number of hospitalized.\n", "\n", "funman_request = get_request()\n", - "setup_common(funman_request, debug=True, dreal_precision=1e-3)\n", + "setup_common(funman_request, debug=False, dreal_precision=1e-0)\n", "add_unit_test(funman_request)\n", "results = run(funman_request)\n", "report(results, \"unconstrained\")\n", - "pass\n", + "# pass\n", "# pts = results.parameter_space.points() \n", "# print(f\"{len(pts)} points\")\n", "# df = results.dataframe(points=pts[-1:])\n", @@ -426,633 +493,160 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results\n", - "# get_last_point_parameters(results)\n", - "# plot_last_point(results)\n", - "# def plot_last_point(results):\n", - "# pts = results.parameter_space.points() \n", - "# print(f\"{len(pts)} points\")\n", - "\n", - "# if len(pts) > 0:\n", - "# # Get a plot for last point\n", - "# df = results.dataframe(points=pts[-1:])\n", - "# ax = df[STATES].plot()\n", - "# ax.set_yscale(\"log\")\n", - "pts = results.parameter_space.points() \n", - "print(f\"{len(pts)} points\")\n", - "df = results.dataframe(points=pts[-1:])\n", - "\n", - "df['H_odeint'] = pd.Series(H_odeint[0:151])\n", - "df[\"H_diff\"] = df.H - df.H_odeint\n", - "df[[\"H\", \"H_odeint\", \"H_diff\"]]\n", - "# df.H[100.0:150.0]\n", - "# results.parameter_space.points()[0].values" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], - "source": [ - "# df.columns\n", - "# import matplotlib.pyplot as plt\n", - "# plt.plot(H_odeint)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "df = pd.DataFrame({\"a\": [0, 1], \"b\": [3, 4]})\n", - "l = list(df.a.values)\n", - "# l = [float(v) for v in l]\n", - "type(l[0])\n", - "\n", - "# p = plt.plot(df.a, df.b)\n", - "df\n", - "# print(p)\n", - "# plt.plot(l)\n", - "\n", - "# plt.plot(df.a.values)\n", - "# plt.plot(df['a'])\n", - "# df.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-31 23:15:51,114 - funman.server.worker - INFO - FunmanWorker running...\n", + "2024-08-31 23:15:51,118 - funman.server.worker - INFO - Starting work on: fcf2d654-c2ab-4234-afec-d0c7cbc7ba3e\n", + "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-08-31 23:15:59,864 - funman.api.run - INFO - Dumping results to ./out/fcf2d654-c2ab-4234-afec-d0c7cbc7ba3e.json\n", + "2024-08-31 23:15:59,935 - funman.scenario.consistency - INFO - 20{20}:\t[+]\n", + "2024-08-31 23:16:09,941 - funman.api.run - INFO - Dumping results to ./out/fcf2d654-c2ab-4234-afec-d0c7cbc7ba3e.json\n", + "2024-08-31 23:16:20,032 - funman.api.run - INFO - Dumping results to ./out/fcf2d654-c2ab-4234-afec-d0c7cbc7ba3e.json\n", + "2024-08-31 23:16:24,337 - funman.scenario.scenario - INFO - simulation passed verification\n", + "2024-08-31 23:16:24,338 - funman.scenario.consistency - INFO - Simulation Time: 0:00:24.402322\n", + "2024-08-31 23:16:24,389 - funman.server.worker - INFO - Completed work on: fcf2d654-c2ab-4234-afec-d0c7cbc7ba3e\n", + "2024-08-31 23:16:30,088 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-08-31 23:16:30,447 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-08-31 23:16:30,452 - funman.server.worker - INFO - Worker.stop() completed.\n", + "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 points\n", + " N beta c_m_lb c_m_ub eps_m_lb eps_m_ub p_H_to_D \\\n", + "destratified 19340000.0 0.4 0.4 0.6 0.4 0.6 0.12 \n", + "\n", + " p_H_to_R p_I_to_H p_I_to_R r_E_to_I r_H_to_D r_H_to_R \\\n", + "destratified 0.88 0.2 0.8 0.2 0.1 0.1 \n", + "\n", + " r_I_to_H r_I_to_R \n", + "destratified 0.1 0.07 \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Add bounds [0, N] to the STATE compartments. \n", - "# Add bounds sum(STATE) in [N-e, N+e], for a small e.\n", + "# i) (TA1 Search and Discovery Workflow, 1 Hr. Time Limit) Find estimates on the\n", + "# efficacy of surgical masks in preventing onward transmission of SARS-CoV-2 (preferred)\n", + "# or comparable viral respiratory pathogens (e.g., MERS-CoV, SARS), including any\n", + "# information about uncertainty in these estimates. The term surgical mask here refers to\n", + "# the commonly available, disposable procedure mask, not an N95-type respirator. Find 3\n", + "# credible documents that provide estimates and use your judgment to determine what\n", + "# value (in the deterministic case) or distribution (in the probabilistic case) to use in your\n", + "# forecasts in 1.a.iii.\n", "\n", - "funman_request = get_request()\n", - "setup_common(funman_request, debug=True)\n", - "set_compartment_bounds(funman_request)\n", - "results = run(funman_request)\n", - "report(results, \"compartmental_constrained\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Relax the bounds on the parameters to allow additional parameterizations\n", + "# The base model assumes no masking inverventions, and we measure the efficacy in terms of the number of hospitalized.\n", "\n", "funman_request = get_request()\n", - "setup_common(funman_request)\n", - "set_compartment_bounds(funman_request)\n", - "relax_parameter_bounds(funman_request, factor = 0.75)\n", - "results = run(funman_request)\n", - "report(results, \"relaxed_bounds\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "funman_request = get_request()\n", - "setup_common(funman_request, synthesize=True)\n", - "set_compartment_bounds(funman_request)\n", - "# relax_parameter_bounds(funman_request, factor=0.75)\n", - "# funman_request.config.verbosity=10\n", - "results = run(funman_request, plot=True)\n", - "report(results, \"synthesis\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import pandas as pd\n", - "# df1 = pd.DataFrame({\"ltp\": ltp, \"gtp\": gtp})\n", - "\n", - "# # df2 = \n", - "# # df1.ltp.N == df1.gtp.N\n", - "# # df2.loc[df2].sort_index()[0:60]\n", - "\n", - "# #(= H_10 (+ H_8 (* 2 (+ (* r_HD (- 1) H_8) (* r_HR (- 1) H_8) (* r_IH_v I_v_8) (* r_IH_u I_u_8)))))\n", - "\n", - "# df1['same'] = df1['ltp'] == df1[\"gtp\"]\n", - "# df1[0:20]\n", - "# # df1.loc[df1.index.str.endswith(\"_6\")].sort_values(by=\"same\")" + "setup_common(funman_request, debug=True, dreal_precision=1e-1)\n", + "# add_unit_test(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", + "results = run(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", + "report(results, \"destratified\", states=STATES_DESTRATIFIED_ALL)\n", + "# pass\n", + "# pts = results.parameter_space.points() \n", + "# print(f\"{len(pts)} points\")\n", + "# df = results.dataframe(points=pts[-1:])\n", + "# df" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "petrinet\n", - "\n", - "\n", - "\n", - "t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]\n", - "\n", - "t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]\n", - "\n", - "\n", - "\n", - "I_compliant\n", - "\n", - "I_compliant\n", - "\n", - "\n", - "\n", - "t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]->I_compliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "E_compliant\n", - "\n", - "E_compliant\n", - "\n", - "\n", - "\n", - "t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]->E_compliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "S_compliant\n", - "\n", - "S_compliant\n", - "\n", - "\n", - "\n", - "S_compliant->t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]\n", - "\n", - "t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]\n", - "\n", - "\n", - "\n", - "S_compliant->t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "t14([S_compliant*p_compliant_noncompliant]) = [0.1*S_compliant]\n", - "\n", - "t14([S_compliant*p_compliant_noncompliant]) = [0.1*S_compliant]\n", - "\n", - "\n", - "\n", - "S_compliant->t14([S_compliant*p_compliant_noncompliant]) = [0.1*S_compliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]->E_compliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "I_noncompliant\n", - "\n", - "I_noncompliant\n", - "\n", - "\n", - "\n", - "t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]->I_noncompliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]\n", - "\n", - "t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]\n", - "\n", - "\n", - "\n", - "t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]->I_noncompliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "E_noncompliant\n", - "\n", - "E_noncompliant\n", - "\n", - "\n", - "\n", - "t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]->E_noncompliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]\n", - "\n", - "t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]\n", - "\n", - "\n", - "\n", - "t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]->I_compliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]->E_noncompliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t5([E_compliant*r_E_to_I]) = [0.2*E_compliant]\n", - "\n", - "t5([E_compliant*r_E_to_I]) = [0.2*E_compliant]\n", - "\n", - "\n", - "\n", - "t5([E_compliant*r_E_to_I]) = [0.2*E_compliant]->I_compliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t6([E_noncompliant*r_E_to_I]) = [0.2*E_noncompliant]\n", - "\n", - "t6([E_noncompliant*r_E_to_I]) = [0.2*E_noncompliant]\n", - "\n", - "\n", - "\n", - "t6([E_noncompliant*r_E_to_I]) = [0.2*E_noncompliant]->I_noncompliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t7([I_compliant*p_I_to_R*r_I_to_R]) = [0.056*I_compliant]\n", - "\n", - "t7([I_compliant*p_I_to_R*r_I_to_R]) = [0.056*I_compliant]\n", - "\n", - "\n", - "\n", - "R\n", - "\n", - "R\n", - "\n", - "\n", - "\n", - "t7([I_compliant*p_I_to_R*r_I_to_R]) = [0.056*I_compliant]->R\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t8([I_noncompliant*p_I_to_R*r_I_to_R]) = [0.056*I_noncompliant]\n", - "\n", - "t8([I_noncompliant*p_I_to_R*r_I_to_R]) = [0.056*I_noncompliant]\n", - "\n", - "\n", - "\n", - "t8([I_noncompliant*p_I_to_R*r_I_to_R]) = [0.056*I_noncompliant]->R\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t9([I_compliant*p_I_to_H*r_I_to_H]) = [0.02*I_compliant]\n", - "\n", - "t9([I_compliant*p_I_to_H*r_I_to_H]) = [0.02*I_compliant]\n", - "\n", - "\n", - "\n", - "H\n", - "\n", - "H\n", - "\n", - "\n", - "\n", - "t9([I_compliant*p_I_to_H*r_I_to_H]) = [0.02*I_compliant]->H\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t10([I_noncompliant*p_I_to_H*r_I_to_H]) = [0.02*I_noncompliant]\n", - "\n", - "t10([I_noncompliant*p_I_to_H*r_I_to_H]) = [0.02*I_noncompliant]\n", - "\n", - "\n", - "\n", - "t10([I_noncompliant*p_I_to_H*r_I_to_H]) = [0.02*I_noncompliant]->H\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t11([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", - "\n", - "t11([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", - "\n", - "\n", - "\n", - "t11([H*p_H_to_R*r_H_to_R]) = [0.088*H]->R\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t12([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", - "\n", - "t12([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", - "\n", - "\n", - "\n", - "D\n", - "\n", - "D\n", - "\n", - "\n", - "\n", - "t12([H*p_H_to_D*r_H_to_D]) = [0.012*H]->D\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t13([S_noncompliant*p_noncompliant_compliant]) = [0.1*S_noncompliant]\n", - "\n", - "t13([S_noncompliant*p_noncompliant_compliant]) = [0.1*S_noncompliant]\n", - "\n", - "\n", - "\n", - "t13([S_noncompliant*p_noncompliant_compliant]) = [0.1*S_noncompliant]->S_compliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "S_noncompliant\n", - "\n", - "S_noncompliant\n", - "\n", - "\n", - "\n", - "t14([S_compliant*p_compliant_noncompliant]) = [0.1*S_compliant]->S_noncompliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t15([E_noncompliant*p_noncompliant_compliant]) = [0.1*E_noncompliant]\n", - "\n", - "t15([E_noncompliant*p_noncompliant_compliant]) = [0.1*E_noncompliant]\n", - "\n", - "\n", - "\n", - "t15([E_noncompliant*p_noncompliant_compliant]) = [0.1*E_noncompliant]->E_compliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t16([E_compliant*p_compliant_noncompliant]) = [0.1*E_compliant]\n", - "\n", - "t16([E_compliant*p_compliant_noncompliant]) = [0.1*E_compliant]\n", - "\n", - "\n", - "\n", - "t16([E_compliant*p_compliant_noncompliant]) = [0.1*E_compliant]->E_noncompliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t17([I_noncompliant*p_noncompliant_compliant]) = [0.1*I_noncompliant]\n", - "\n", - "t17([I_noncompliant*p_noncompliant_compliant]) = [0.1*I_noncompliant]\n", - "\n", - "\n", - "\n", - "t17([I_noncompliant*p_noncompliant_compliant]) = [0.1*I_noncompliant]->I_compliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t18([I_compliant*p_compliant_noncompliant]) = [0.1*I_compliant]\n", - "\n", - "t18([I_compliant*p_compliant_noncompliant]) = [0.1*I_compliant]\n", - "\n", - "\n", - "\n", - "t18([I_compliant*p_compliant_noncompliant]) = [0.1*I_compliant]->I_noncompliant\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "I_compliant->t1([I_compliant*S_compliant*beta*(-c_m_0*eps_m_0 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_compliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I_compliant->t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I_compliant->t7([I_compliant*p_I_to_R*r_I_to_R]) = [0.056*I_compliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I_compliant->t9([I_compliant*p_I_to_H*r_I_to_H]) = [0.02*I_compliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I_compliant->t18([I_compliant*p_compliant_noncompliant]) = [0.1*I_compliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "E_compliant->t5([E_compliant*r_E_to_I]) = [0.2*E_compliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "E_compliant->t16([E_compliant*p_compliant_noncompliant]) = [0.1*E_compliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I_noncompliant->t2([I_noncompliant*S_compliant*beta*(-c_m_1*eps_m_1 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_compliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I_noncompliant->t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I_noncompliant->t8([I_noncompliant*p_I_to_R*r_I_to_R]) = [0.056*I_noncompliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I_noncompliant->t10([I_noncompliant*p_I_to_H*r_I_to_H]) = [0.02*I_noncompliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I_noncompliant->t17([I_noncompliant*p_noncompliant_compliant]) = [0.1*I_noncompliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "S_noncompliant->t3([I_noncompliant*S_noncompliant*beta*(-c_m_2*eps_m_2 + 1)/N]) = [1.5511892450879e-8*I_noncompliant*S_noncompliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "S_noncompliant->t4([I_compliant*S_noncompliant*beta*(-c_m_3*eps_m_3 + 1)/N]) = [1.5511892450879e-8*I_compliant*S_noncompliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "S_noncompliant->t13([S_noncompliant*p_noncompliant_compliant]) = [0.1*S_noncompliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "E_noncompliant->t6([E_noncompliant*r_E_to_I]) = [0.2*E_noncompliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "E_noncompliant->t15([E_noncompliant*p_noncompliant_compliant]) = [0.1*E_noncompliant]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "H->t11([H*p_H_to_R*r_H_to_R]) = [0.088*H]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "H->t12([H*p_H_to_D*r_H_to_D]) = [0.012*H]\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], + "image/png": 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", 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" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "# # Get points (trajectories generated)\n", - "# pts = results.parameter_space.points() \n", - "# print(f\"{len(pts)} points\")\n", - "\n", - "# # Get a plot for last point\n", - "# df = results.dataframe(points=pts[-1:])\n", - "# ax = df[STATES].plot()\n", - "# ax.set_yscale(\"log\")\n", - "\n", - "\n", - "# # Get the values of the point\n", - "# gtp=pts[-1].values\n", - "\n", - "\n", - "# # Output the model diagram\n", - "# #\n", - "results.model.to_dot()\n", - "# # gtp" + "point = results.points()[0]\n", + "vars = [\"S\", \"E\", \"I\", \"R\", \"D\", \"H\"]\n", + "# values = point.values #.simulation.dataframe().T\n", + "# values[\"S_ub_1\"]- values[\"S_lb_1\"]\n", + "df = point.simulation.dataframe().T\n", + "\n", + "# sns.FacetGrid(df)\n", + "\n", + "# var = \"R\"\n", + "# lb = df[[f\"{var}_lb\"]]\n", + "# ub = df[[f\"{var}_ub\"]]\n", + "\n", + "# plt.title(f\"Bounds\")\n", + "# ax, fig = plt.multiplot\n", + "# plt.plot(lb, linestyle=\"dotted\", color=\"black\")\n", + "# plt.plot(ub, linestyle=\"solid\", color=\"black\")\n", + "# plt.scatter(lb, ub)\n", + "\n", + "# plt.figure(figsize=(10,len(vars)*10))\n", + "\n", + "fig, axs = plt.subplots(len(vars))\n", + "fig.set_figheight(3*len(vars))\n", + "fig.suptitle('Variable Bounds over time')\n", + "for i, var in enumerate(vars):\n", + " labels = [f\"{var}_lb\", f\"{var}_ub\"]\n", + " lb = df[labels]\n", + " # axs[i].set_label(labels)\n", + " \n", + " # ub = df[[]]\n", + " axs[i].set_title(f\"{var} Bounds\")\n", + " # axs[i].legend(labels[0])\n", + " axs[i].plot(lb, label=labels) #,linestyle=\"dotted\", color=\"black\")\n", + " axs[i].legend(loc=\"lower left\")\n", + " # axs[i].set_yscale('logit')\n", + " # axs[1].plot(ub, linestyle=\"solid\", color=\"black\")\n", + "fig.tight_layout()" ] } ], @@ -1072,9 +666,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json new file mode 100644 index 00000000..b73ba709 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json @@ -0,0 +1,621 @@ +{ + "header": { + "name": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", + "model_version": "0.1", + "properties": {} + }, + "model": { + "states": [ + { + "id": "S_lb", + "name": "S_lb", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_lb", + "name": "I_lb", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_lb", + "name": "E_lb", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_ub", + "name": "I_ub", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "S_ub", + "name": "S_ub", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_ub", + "name": "E_ub", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_lb", + "name": "R_lb", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_ub", + "name": "R_ub", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_lb", + "name": "H_lb", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_ub", + "name": "H_ub", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D_lb", + "name": "D_lb", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D_ub", + "name": "D_ub", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1_to_4_lb", + "input": [ + "I_ub", + "S_ub" + ], + "output": [ + "I_ub", + "E_lb" + ], + "properties": { + "name": "t1_to_4_lb" + } + }, + { + "id": "t1_to_4_ub", + "input": [ + "I_lb", + "S_lb" + ], + "output": [ + "I_lb", + "E_ub" + ], + "properties": { + "name": "t1_to_4_ub" + } + }, + { + "id": "t5_to_6_lb", + "input": [ + "E_ub" + ], + "output": [ + "I_lb" + ], + "properties": { + "name": "t5_to_6_lb" + } + }, + { + "id": "t5_to_6_ub", + "input": [ + "E_lb" + ], + "output": [ + "I_ub" + ], + "properties": { + "name": "t5_to_6_ub" + } + }, + { + "id": "t7_to_8_lb", + "input": [ + "I_ub" + ], + "output": [ + "R_lb" + ], + "properties": { + "name": "t7_to_8_lb" + } + }, + { + "id": "t7_to_8_ub", + "input": [ + "I_lb" + ], + "output": [ + "R_ub" + ], + "properties": { + "name": "t7_to_8_ub" + } + }, + { + "id": "t9_to_10_lb", + "input": [ + "I_ub" + ], + "output": [ + "H_lb" + ], + "properties": { + "name": "t9_to_10_lb" + } + }, + { + "id": "t9_to_10_ub", + "input": [ + "I_lb" + ], + "output": [ + "H_ub" + ], + "properties": { + "name": "t9_to_10_ub" + } + }, + { + "id": "t11_lb", + "input": [ + "H_ub" + ], + "output": [ + "R_lb" + ], + "properties": { + "name": "t11_lb" + } + }, + { + "id": "t11_ub", + "input": [ + "H_lb" + ], + "output": [ + "R_ub" + ], + "properties": { + "name": "t11_ub" + } + }, + { + "id": "t12_lb", + "input": [ + "H_ub" + ], + "output": [ + "D_lb" + ], + "properties": { + "name": "t12_lb" + } + }, + { + "id": "t12_ub", + "input": [ + "H_lb" + ], + "output": [ + "D_ub" + ], + "properties": { + "name": "t12_ub" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1_to_4_lb", + "expression": "I_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" + }, + { + "target": "t1_to_4_ub", + "expression": "I_ub*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" + }, + { + "target": "t5_to_6_lb", + "expression": "E_lb*r_E_to_I", + "expression_mathml": "E_lbr_E_to_I" + }, + { + "target": "t5_to_6_ub", + "expression": "E_ub*r_E_to_I", + "expression_mathml": "E_ubr_E_to_I" + }, + { + "target": "t7_to_8_lb", + "expression": "I_lb*p_I_to_R*r_I_to_R", + "expression_mathml": "I_lbp_I_to_Rr_I_to_R" + }, + { + "target": "t7_to_8_ub", + "expression": "I_ub*p_I_to_R*r_I_to_R", + "expression_mathml": "I_ubp_I_to_Rr_I_to_R" + }, + { + "target": "t9_to_10_lb", + "expression": "I_lb*p_I_to_H*r_I_to_H", + "expression_mathml": "I_lbp_I_to_Hr_I_to_H" + }, + { + "target": "t9_to_10_ub", + "expression": "I_ub*p_I_to_H*r_I_to_H", + "expression_mathml": "I_ubp_I_to_Hr_I_to_H" + }, + { + "target": "t11_lb", + "expression": "H_lb*p_H_to_R*r_H_to_R", + "expression_mathml": "H_lbp_H_to_Rr_H_to_R" + }, + { + "target": "t11_ub", + "expression": "H_ub*p_H_to_R*r_H_to_R", + "expression_mathml": "H_ubp_H_to_Rr_H_to_R" + }, + { + "target": "t12_lb", + "expression": "H_lb*p_H_to_D*r_H_to_D", + "expression_mathml": "Hp_H_to_Dr_H_to_D" + }, + { + "target": "t12_ub", + "expression": "H_ub*p_H_to_D*r_H_to_D", + "expression_mathml": "H_ubp_H_to_Dr_H_to_D" + } + ], + "initials": [ + { + "target": "S_lb", + "expression": "19339995.00000000", + "expression_mathml": "9669997.5" + }, + { + "target": "I_lb", + "expression": "4.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E_lb", + "expression": "1.000000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "S_ub", + "expression": "19339995.00000000", + "expression_mathml": "9669997.5" + }, + { + "target": "I_ub", + "expression": "4.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E_ub", + "expression": "1.000000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R_ub", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H_ub", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D_ub", + "expression": "0.0", + "expression_mathml": "0.0" + } + ], + "parameters": [ + { + "id": "N", + "value": 19340000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta", + "value": 0.4, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "eps_m_lb", + "value": 0.4, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "eps_m_ub", + "value": 0.6, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "c_m_lb", + "value": 0.4, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "c_m_ub", + "value": 0.6, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_E_to_I", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_R", + "value": 0.8, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_R", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_H", + "value": 0.2, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_H", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_R", + "value": 0.88, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_R", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_D", + "value": 0.12, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_D", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + } + ], + "observables": [], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + } + }, + "metadata": { + "annotations": { + "license": null, + "authors": [], + "references": [], + "time_scale": null, + "time_start": null, + "time_end": null, + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base_request.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base_request.json index 24e3a285..ecd42c80 100644 --- a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base_request.json +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_base_request.json @@ -9,10 +9,7 @@ "timepoints": [ 0, 10, - 20, - 30, - 40, - 50 + 20 ] } ] @@ -23,6 +20,6 @@ "dreal_precision": 0.001, "normalization_constant": 19340000.0, "normalize": false, - "use_compartmental_constraints": true + "use_compartmental_constraints": false } } \ No newline at end of file diff --git a/src/funman/api/api.py b/src/funman/api/api.py index 746cec27..6674e07c 100644 --- a/src/funman/api/api.py +++ b/src/funman/api/api.py @@ -74,7 +74,7 @@ def get_worker(): def _key_auth(api_key: str, token: str, *, name: str = "API"): # bypass key auth if no token is provided if api_key is None: - print(f"WARNING: Running without {name} token") + l.warning(f"WARNING: Running without {name} token") return # ensure the token is a non-empty string diff --git a/src/funman/api/run.py b/src/funman/api/run.py index e124953c..83cbef2e 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -445,7 +445,7 @@ def create_plots( plt.savefig(space_plot_filename) plt.close() else: - l.warn( + l.warning( "Cannot plot a parameter space for zero boxes or zero parameters" ) diff --git a/src/funman/config.py b/src/funman/config.py index ff4eb614..9021feb9 100644 --- a/src/funman/config.py +++ b/src/funman/config.py @@ -117,7 +117,7 @@ class FUNMANConfig(BaseModel): """ Compute Corner points of each box """ verbosity: int = logging.INFO - """ Verbosity (INFO, DEBUG, WARN, ERROR)""" + """ Verbosity (INFO, DEBUG, TRACE, WARN, ERROR)""" use_transition_symbols: bool = False """ Use transition symbols in encoding transition functions """ diff --git a/src/funman/model/model.py b/src/funman/model/model.py index 5d505820..ecd454eb 100644 --- a/src/funman/model/model.py +++ b/src/funman/model/model.py @@ -52,11 +52,11 @@ def _wrap_with_internal_model( def is_state_variable( - var_string, model: "FunmanModel", time_pattern: str = f"_[0-9]+$" + var_string, model: "FunmanModel", time_pattern: str = f"[\\d]+$" ) -> bool: vars_pattern = "|".join(model._state_var_names()) - pattern = re.compile(f"[{vars_pattern}].*{time_pattern}") - return re.match(pattern, var_string) + pattern = re.compile(f"^(?:{vars_pattern}).*_{time_pattern}") + return re.match(pattern, var_string) is not None class FunmanModel(ABC, BaseModel): diff --git a/src/funman/representation/representation.py b/src/funman/representation/representation.py index 4d4823ef..6cfa2998 100644 --- a/src/funman/representation/representation.py +++ b/src/funman/representation/representation.py @@ -7,6 +7,8 @@ import math from typing import Dict, List, Literal, Optional, Set +import matplotlib.pyplot as plt +import pandas as pd from pydantic import BaseModel from funman import to_sympy @@ -28,6 +30,20 @@ class Timeseries(BaseModel): def __getitem__(self, key): return self.data[self.columns.index(key)] + def dataframe(self): + df = pd.DataFrame( + [ + pd.Series(col, name=self.columns[i + 1], index=self.data[0]) + for i, col in enumerate(self.data[1:]) + ] + ) + return df + + def plot(self, **kwargs): + data = self.dataframe() + ax = data.T.plot(xlabel=self.columns[0], **kwargs) + return ax + class Point(BaseModel): type: Literal["point"] = "point" @@ -35,6 +51,7 @@ class Point(BaseModel): values: Dict[str, PointValue] normalized_values: Optional[Dict[str, float]] = None schedule: Optional[EncodingSchedule] = None + simulation: Optional[Timeseries] = None # def __init__(self, **kw) -> None: # super().__init__(**kw) diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index 18c1c08e..49dc59cd 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -395,6 +395,7 @@ def check_simulation( point, point.relevant_timepoints(results.scenario.model) ) sim_results.append((point, timeseries)) + point.simulation = timeseries for point, timeseries in sim_results: if timeseries is None: From 717b03152bc4d6c20286a9d6bbb7a607435c6c09 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 4 Sep 2024 15:27:56 +0000 Subject: [PATCH 27/93] bounds for abstractions --- .../funman_aug_2024_12mo_eval_1_q1_demo.ipynb | 973 ++++++++++-------- ...eval_scenario1_1_ii_3_destratified_SE.json | 879 ++++++++++++++++ ...val_scenario1_1_ii_3_destratified_all.json | 12 +- src/funman/representation/interval.py | 2 +- src/funman/search/box_search.py | 2 +- src/funman/search/simulate.py | 8 +- 6 files changed, 1461 insertions(+), 415 deletions(-) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_SE.json diff --git a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb index 6d34a99a..c414ce51 100644 --- a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb +++ b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb @@ -20,22 +20,47 @@ "import logging\n", "import matplotlib.pyplot as plt\n", "\n", + "\n", + "\n", "RESOURCES = \"../../resources\"\n", "SAVED_RESULTS_DIR = \"./out\"\n", "\n", "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"monthly-demo\", \"2024-08\", \"12_month_scenario_1\", \"q1a_ii\")\n", - "MODEL_PATH = os.path.join(\n", - " EXAMPLE_DIR, \"eval_scenario1_1_ii_3.json\"\n", - ")\n", - "MODEL_DESTRATIFIED_ALL_PATH = os.path.join(\n", - " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_all.json\"\n", - ")\n", "REQUEST_PATH = os.path.join(\n", - " EXAMPLE_DIR, \"eval_scenario1_base_request.json\"\n", - ")\n", + " EXAMPLE_DIR, \"eval_scenario1_base_request.json\")\n", "\n", + "models = {\n", + " \"original_stratified\": os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario1_1_ii_3.json\"),\n", + " \"destratified_SEI\": os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_all.json\"\n", + "),\n", + " \"destratified_SE\": os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_SE.json\"\n", + ")\n", + "}\n", + "\n", + "states = {\n", + " \"original_stratified\": [\"S_compliant\", \"S_noncompliant\", \"I_compliant\", \"I_noncompliant\", \"E_compliant\", \"E_noncompliant\",\"R\", \"H\", \"D\"],\n", + " \"destratified_SEI\": [\"S_lb\", \"S_ub\", \"I_lb\", \"I_ub\", \"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", + " \"destratified_SE\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"]\n", + "}\n", + "\n", + "basevar_map = [\n", + " ['S_compliant','S_noncompliant', 'S_lb', 'S_ub'], \n", + " ['I_compliant','I_noncompliant','I_lb','I_ub','I_compliant_lb', 'I_noncompliant_ub', 'I_compliant_ub', 'I_noncompliant_lb'],\n", + " ['E_compliant','E_noncompliant','E_lb', 'E_ub'],\n", + " ['R','R_lb', 'R_ub'],\n", + " ['H','H_lb', 'H_ub'],\n", + " ['D','D_lb', 'D_ub']\n", + " ]\n", "\n", "request_params = {}\n", + "request_results = {}\n", + "\n", + "# Cycle styles for lines\n", + "plt.rcParams['axes.prop_cycle'] = (\"cycler('color', 'rgb') +\"\n", + " \"cycler('lw', [1, 2, 3])\")\n", "\n", "# %load_ext autoreload\n", "# %autoreload 2" @@ -45,247 +70,11 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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I9FYLrqbvRkX4IcJfg5Y2I74tuSS6HHIRDCNERIPg2MU6AM7dKgKYp/jOGGFuHeEUX7IXhhEiokFw7Lxl8KpzhxHgclcNx42QvTCMEBENAks3jbPOpLnSjOGhkDp28a3QtYouh1wAwwgR0QCrbtTjYl0LJAlOs1PvtQT5qDG+o7uJrSNkDwwjREQD7HjH+iIJoT7w8/QQXI19WLtquE8N2QHDCBHRADvWEUaSXaCLxsISRg6croLRxKXhqX8YRoiIBtiJMnMYuc7JZ9JcaUJsIPw0KlxqbsOJjvEwRH3FMEJENMBOlZl36k1ygfEiFh5KBaYNDwHAcSPUfwwjREQD6FKTARfrWgAAY10ojABXjhthGKH+YRghIhpAJztaReJCvF1m8KrFzI7Fz46U1qFR3y64GnJmfQojmzZtQnx8PDw9PZGamorDhw9f8/yNGzdi1KhR8PLyQmxsLB577DG0tnJuOhG5vpOW8SIu1ioCALHB3ogL8Ua7Scbh4hrR5ZATszmM7NixAxkZGVi3bh1ycnKQnJyMOXPmoLKyssvz33zzTTzxxBNYt24d8vLy8Morr2DHjh34zW9+0+/iiYgcnaVl5Lpo1xm8eqXpw0MBAAdOM4xQ39kcRjZs2IDly5dj2bJlGDt2LDZv3gxvb29s3bq1y/MPHjyI6dOn4+6770Z8fDxuvfVWLFmypMfWFCIiV2BpGXG18SIW0xPNYeTgGa43Qn1nUxgxGAzIzs5Genr65RdQKJCeno6srKwunzNt2jRkZ2dbw8fZs2fx8ccf47bbbuv2ffR6PXQ6XacHEZGzadK342x1EwDX7KYBgLTEEOvS8FUNetHlkJOyKYxUV1fDaDQiIiKi0/GIiAhotdoun3P33Xfj6aefxg033AAPDw8kJibipptuumY3zfr16xEQEGB9xMbG2lImEZFDyNfqIMtAuJ8G4X6eossZEME+aoyNMgctto5QXw34bJp9+/bhmWeewd///nfk5OTgvffew65du/D73/++2+esXr0a9fX11sf58+cHukwiIru7PF7ENVtFLG7oGDfyVRHDCPWNypaTQ0NDoVQqUVFR0el4RUUFIiMju3zOmjVr8JOf/AQPPPAAAGDcuHFoamrCgw8+iN/+9rdQKK7OQxqNBhqNxpbSiIgczsmLrj141WLa8FD8Y/9ZfFVUA1mWIUmS6JLIydjUMqJWq5GSkoLMzEzrMZPJhMzMTKSlpXX5nObm5qsCh1KpBADIMvczICLXdbLcdaf1XmlKfBDUSgUu1rXgXE2z6HLICdncTZORkYEtW7bgtddeQ15eHlasWIGmpiYsW7YMALB06VKsXr3aev6CBQvw8ssvY/v27SguLsaePXuwZs0aLFiwwBpKiIhcTbvRhMKKRgCuO5PGwlutwsShgQCAA+yqoT6wqZsGABYvXoyqqiqsXbsWWq0WEyZMwO7du62DWktLSzu1hDz55JOQJAlPPvkkLl68iLCwMCxYsAB//OMf7XcVREQOpqSmCYZ2E7zVSsQGeYsuZ8DdMDwUh4prcfBMNX58fZzocsjJSLIT9JXodDoEBASgvr4e/v6u/T8MInINHx4twyNvHcHEoYF4/6HpossZcNnnLuHOlw8i0NsDOU/eAoWC40ao95/f3JuGiGgA5GvNg1dHR7rHf6CShwTAV6NCXXMbTpVzbSiyDcMIEdEAyC9vAACMifITXMngUCkVuD4hGADHjZDtGEaIiAZAvtYcRtylZQS4vE8N1xshWzGMEBHZWX1LGy7WtQAARkW6R8sIcDmMfFNSC327UXA15EwYRoiI7Kygo1UkJtALAV4egqsZPCPCfRHmp0Frmwk55+pEl0NOhGGEiMjOLg9edZ9WEQCQJAnTE0MAsKuGbMMwQkRkZ3kdg1dHu8ng1StZumo4iJVswTBCRGRn7jat90qWMHLsQh10rW2CqyFnwTBCRGRHJpNsHTPiLtN6rxQd6IWEUB+YZODrMzWiyyEnwTBCRGRH5y81o9lghFqlQHyIj+hyhJg23Dxu5CDDCPUSwwgRkR1Z1hcZGeELldI9f8TewHEjZCP3/E4hIhoglpVX3XG8iMX1CSGQJKCoshEVulbR5ZATYBghIrIjd53We6VAbzXGxQQA4BRf6h2GESIiO8q3Dl5135YRAJiWyK4a6j2GESIiO2k2tKOkpgmAe7eMAMB0yyDWohrIsiy4GnJ0DCNERHZSWNEIWQbC/DQI8dWILkeoyXHBUCsV0Opacba6SXQ55OAYRoiI7CS/nONFLLzUSqTEBQEADrKrhnrAMEJEZCccL9KZpavmqyKuN0LXxjBCRGQneWwZ6WRax3ojWWdrYDRx3Ah1j2GEiMgOZFm2toy48xojVxofEwA/jQr1LW04WVYvuhxyYAwjRER2oNW1or6lDSqFhMRw91wG/rtUSgVSE4IBsKuGro1hhIjIDiwrryaG+UKjUgquxnFY1hs5eIaDWKl7DCNERHZQUNGxJw3Hi3QyvWPcyDcltdC3GwVXQ46KYYSIyA4KO8aLjIrwFVyJYxkZ4YtQXw1a20zIOVcnuhxyUAwjRER2YG0ZiWDLyJUkSbq8Giu7aqgbDCNERP1kNMkoqmwEAIxiN81VpneMG+GmedQdhhEion4qrW2Gvt0ETw8FYoO8RZfjcKZ1tIwcvVCPhtY2wdWQI2IYISLqp4KO8SIjwv2gUEiCq3E8Q4K8ERfiDaNJxqGztaLLIQfEMEJE1E+FHC/SI8sU3684boS6wDBCRNRPlsGroyI5k6Y71kGsXPyMusAwQkTUT5ZpvSPYMtItS8tIQUUDqhr0gqshR8MwQkTUD4Z2E4qrmwAAoxhGuhXso8bYjt2MOcWXvothhIioH4qrm9BukuGnUSEqwFN0OQ6NXTXUHYYRIqJ+uHIZeEniTJprmTacg1ipawwjRET9YBkvMpLLwPdoanwwVAoJFy61oLSmWXQ55EAYRoiI+oHTenvPR6PCxKGBAIADXI2VrsAwQkTUD5YwwsGrvcP1RqgrDCNERH3UYjDiXK25u2Ek96TplRtGmMNI1pkamEyy4GrIUTCMEBH1UVFlI2TZPG011FcjuhynkDwkEN5qJWqbDMjvGG9DxDBCRNRH1pk0HLzaa2qVAlOHBQPgeiN0GcMIEVEfneZ4kT6Zbhk3wkGs1IFhhIioj65cY4R6b1rH4meHimvRZjQJroYcAcMIEVEfXV5jhGHEFmMi/RHso0azwYjc83WiyyEHwDBCRNQHutY2lNW3AgBGhjOM2EKhkJCWYG4dYVcNAQwjRER9crqiEQAQ6e+JAG8PwdU4n+kdS8NznxoCGEaIiPqkkONF+sWyad6R85fQbGgXXA2JxjBCRNQHBZbxIuGc1tsXQ4O9ERPohTajjMPFtaLLIcEYRoiI+oAtI/0jSZK1deTgGXbVuDuGESKiPuAGef1nGTfCQazEMEJEZKOaRj2qGw0AuPpqf6QlmltGTpXrUNtkEFwNicQwQkRko8KOmTSxwV7wVqsEV+O8wv08MTLCF7Js3jiP3BfDCBGRjQq5DLzdWLtquE+NW2MYISKyUQHHi9iNZZ+agxw34tYYRoiIbGTdII8zafotNSEYSoWEkppmXKxrEV0OCcIwQkRkA1mWrWuMjOAy8P3m5+mB8UMCAHBWjTvrUxjZtGkT4uPj4enpidTUVBw+fPia59fV1WHlypWIioqCRqPByJEj8fHHH/epYCIikSp0euha26FUSEgI8xFdjktgVw3ZHEZ27NiBjIwMrFu3Djk5OUhOTsacOXNQWVnZ5fkGgwG33HILSkpK8M4776CgoABbtmxBTExMv4snIhpslsGr8SHe8PRQCq7GNUzrWPzsqzM1kGVZcDUkgs1z0jZs2IDly5dj2bJlAIDNmzdj165d2Lp1K5544omrzt+6dStqa2tx8OBBeHiYN5OKj4/vX9VERIJwsTP7S4kLgqeHAlUNehRWNHIsjhuyqWXEYDAgOzsb6enpl19AoUB6ejqysrK6fM5///tfpKWlYeXKlYiIiEBSUhKeeeYZGI3Gbt9Hr9dDp9N1ehAROQLrnjQMI3ajUSmROszcOvLl6SrB1ZAINoWR6upqGI1GREREdDoeEREBrVbb5XPOnj2Ld955B0ajER9//DHWrFmDl156CX/4wx+6fZ/169cjICDA+oiNjbWlTCKiAVPImTQDYsYI87iR/ac5bsQdDfhsGpPJhPDwcPzzn/9ESkoKFi9ejN/+9rfYvHlzt89ZvXo16uvrrY/z588PdJlERD0ymWScrjSvvsqWEfuaOTIMAHDobA1a27pvOSfXZNOYkdDQUCiVSlRUVHQ6XlFRgcjIyC6fExUVBQ8PDyiVlwd6jRkzBlqtFgaDAWq1+qrnaDQaaDQaW0ojIhpwF+ta0GwwQq1UID7EW3Q5LmVEuC8i/DWo0Onxbckl3NDRUkLuwaaWEbVajZSUFGRmZlqPmUwmZGZmIi0trcvnTJ8+HUVFRTCZTNZjhYWFiIqK6jKIEBE5Kst4kYQwH6iUXKbJniRJwowR5tYRjhtxPzZ/N2VkZGDLli147bXXkJeXhxUrVqCpqck6u2bp0qVYvXq19fwVK1agtrYWjz76KAoLC7Fr1y4888wzWLlypf2ugohoEBRwvMiAsowb+aKQYcTd2Dy1d/HixaiqqsLatWuh1WoxYcIE7N692zqotbS0FArF5YwTGxuLTz75BI899hjGjx+PmJgYPProo1i1apX9roKIaBCc5rTeAXXD8FBIEpCvbUClrhXh/p6iS6JBIslOsMKMTqdDQEAA6uvr4e/vL7ocInJT8/78JfLKddiydDJuGRvR8xPIZgv+egDHL9Zjw4+SsWjSENHlUD/19vObnZ5ERL3QbjThTMdMmlFsGRkwlq6aLznF160wjBAR9UJJTTMMRhO8PJQYEuQluhyXdXkQazVMJodvuCc7YRghIuoFy3iRERG+UCgkwdW4rklxgfBWK1HdqEd+x+wlcn0MI0REvVDAwauDQqNS4voELg3vbhhGiIh6wboMPMPIgOO4EffDMEJE1AvWDfK4xsiAs4wbOVxSixYDl4Z3BwwjREQ90LcbUVLTDAAYGeEruBrXlxjmg+gATxjaTThUXCO6HBoEDCNERD04W9UEo0mGn6cKkVyIa8BduTT8/kJ21bgDhhEioh5cOV5EkjiTZjBYdvH9orBScCU0GBhGiIh6UGid1svxIoPlhhGhUCoknKlqwvnaZtHl0ABjGCEi6kGB1rLyKseLDJYALw+kDA0CAOwrYOuIq2MYISLqgaVlhDNpBteNo8xdNfsKuN6Iq2MYISK6hmZDO0o7ugm4xsjguqkjjBw8U4PWNk7xdWUMI0RE11DUsTleiI8aIb4awdW4l7FR/gj306ClzYhvSmpFl0MDiGGEiOgarIudsVVk0EmShBtHsqvGHTCMEBFdg3VaL8eLCDFrdDgADmJ1dQwjRETXUFBh7qZhy4gY04dziq87YBghIrqG09bdejmtVwRO8XUPDCNERN2ob2lDeX0rAC54JhKn+Lo+hhEiom5YWkWiAjwR4OUhuBr3xSm+ro9hhIioGwVcBt4hcIqv62MYISLqxukKLgPvCCRJsraOsKvGNTGMEBF1I1+rA8CZNI7gplGc4uvKGEaIiLogyzLyOxY8GxPlL7gaunIX39IaTvF1NQwjRERdqNDpUdfcBqVCwvBwdtOI5u/pgclx5im+e/MqBFdD9sYwQkTUhbxycxdNQqgPPD2UgqshALhlbAQAIDOfYcTVMIwQEXUhr2O8yGh20TiM2WPMYeTQ2VroWtsEV0P2xDBCRNSF/HLzeJHR3JPGYQwL9UFCmA/aTTL2F3JWjSthGCEi6oJlJs2YKIYRR5Le0TqSmcdZNa6EYYSI6Dv07UacqWoCwJk0jmZ2xy6+nxdUot1oElwN2QvDCBHRdxRVNsJokhHg5YFIf0/R5dAVUuKCEODlgbrmNmSfuyS6HLIThhEiou+4cryIJEmCq6ErqZQKzOpYjTUzn101roJhhIjoOyzTetlF45jSO6b4cr0R18EwQkT0HZaVVzmTxjHNHBkGlULC2aomFFc3iS6H7IBhhIjoOy7PpGHLiCPy9/RAakIwACCTrSMugWGEiOgKVQ16VDcaIEncIM+RzR7NrhpXwjBCRHQFS6vIsBAfeKm5DLyjsqw38k3JJdQ3czVWZ8cwQkR0BetMGi525tCGhnhjRLgvjCYZ+wo5q8bZMYwQEV3BuidNJMeLODrLXjV7TrGrxtkxjBARXSGPe9I4jTnXmcPI5/mVaG0zCq6G+oNhhIioQ5vRhKJKcxjhTBrHlzwkEJH+nmgyGHHwTLXocqgfGEaIiDqcrWpCm1GGr0aFIUFeosuhHigUkrV1ZPcJreBqqD8YRoiIOuRbx4twGXhnMScpEoB53Ag3znNeDCNERB3yOJPG6UyND0aQtwcuNbfhcEmt6HKojxhGiIg65HMmjdNRKRW4pWOvmk/YVeO0GEaIiDpY1hjh4FXnMrejq+aTkxUwmWTB1VBfMIwQEQG41GSAVtcKABjFab1OZVpiKHw1Kmh1rci9UCe6HOoDhhEiIgB55eYumqHB3vDVqARXQ7bw9FBi1uhwAOyqcVYMI0REAI5frAcAJMWwi8YZzb3O3FWz+6QWssyuGmfDMEJEBOBEmbll5LroAMGVUF/cNCoMapUC52qaka9tEF0O2YhhhIgIwElrywjDiDPy0agwc0QYAC6A5owYRojI7TW0tuFsdRMAICma3TTO6vKsGoYRZ8MwQkRuz7LYWXSAJ0J8NYKrob5KHxMOlUJCvrYBZ6saRZdDNmAYISK3d6Kji+Y6dtE4tUBvNaYNDwUA7DpWLrgaskWfwsimTZsQHx8PT09PpKam4vDhw7163vbt2yFJEu64446+vC0R0YA4UdYxXoSDV53egvFRAIAPj5UJroRsYXMY2bFjBzIyMrBu3Trk5OQgOTkZc+bMQWVl5TWfV1JSgscffxwzZszoc7FERAPhBKf1uoxbr4uEWqlAYUUjCjirxmnYHEY2bNiA5cuXY9myZRg7diw2b94Mb29vbN26tdvnGI1G3HPPPXjqqaeQkJDQr4KJiOypxWBEUaV5fAFn0ji/AC8P3DjKPKvmw6NsHXEWNoURg8GA7OxspKenX34BhQLp6enIysrq9nlPP/00wsPDcf/99/fqffR6PXQ6XacHEdFAyNPqYJKBUF8Nwv04eNUVLEiOBgB8dKyMC6A5CZvCSHV1NYxGIyIiIjodj4iIgFbb9VSqAwcO4JVXXsGWLVt6/T7r169HQECA9REbG2tLmUREvXbyii4aSZIEV0P2MHt0ODw9FCipacaJi/zPrDMY0Nk0DQ0N+MlPfoItW7YgNDS0189bvXo16uvrrY/z588PYJVE5M4sH1bj2EXjMnw0KsweY/5PMweyOgebdoMKDQ2FUqlERUVFp+MVFRWIjIy86vwzZ86gpKQECxYssB4zmUzmN1apUFBQgMTExKuep9FooNGwuZSIBp5lJg2XgXctC8ZHY9excnx0tAxPzB0NhYKtXo7MppYRtVqNlJQUZGZmWo+ZTCZkZmYiLS3tqvNHjx6N48ePIzc31/q4/fbbMWvWLOTm5rL7hYiE0rcbUVhhnnHBmTSu5aZRYfDVqFBW34qc0kuiy6Ee2LxPdkZGBu69915MnjwZU6dOxcaNG9HU1IRly5YBAJYuXYqYmBisX78enp6eSEpK6vT8wMBAALjqOBHRYCvUNqLNKCPQ2wMxgV6iyyE78vRQ4tbrIvBezkV8eLQMk+ODRZdE12BzGFm8eDGqqqqwdu1aaLVaTJgwAbt377YOai0tLYVCwYVdicjxXbnYGQevup4FydF4L+cidh0vx5rvjYVKyc8mR2VzGAGAhx9+GA8//HCXX9u3b981n7tt27a+vCURkd1dXgaeXTSu6IbhoQj09kB1owGHimsxfXjvJ1LQ4GJMJCK3daLMPJOGy8C7Jg+lAvOSzMvD/zeXs2ocGcMIEbmlNqMJeeUdYYTTel3W7R0LoH18vBytbUbB1VB3GEaIyC2dqWqEod0EP40KccHeosuhAZI6LBgxgV5o0Lfj01MVPT+BhGAYISK3dPyCebzI2Gh/rkHhwhQKCYsmxQAA3s2+ILga6g7DCBG5pZNl7KJxF4smDQEAfHm6CpW6VsHVUFcYRojILR2/Yk8acm3DQn2QEhcEkwzszL0ouhzqAsMIEbmdNqPJOq03eUig2GJoUNzZ0TrybvZF7uTrgBhGiMjtFGgboG83wd9ThfgQH9Hl0CCYPz4KapUCBRUN1i46chwMI0TkdnLP1wEAkmMDOXjVTQR4eeDWseaVwt/hQFaHwzBCRG7naEcYmRAbKLQOGlx3ppi7av57tAyGdpPgauhKDCNE5HasLSMcL+JWZgwPRZifBrVNBnxRWCW6HLoCwwgRuZWG1jYUVTUCMHfTkPtQKRW4Y4J5RVauOeJYGEaIyK0cv1gPWQZiAr0Q5qcRXQ4NMktXTWZ+BS41GQRXQxYMI0TkVnI5XsStjY70R1KMP9qMMt47wjVHHAXDCBG5laPWmTRcedVd3TVlKADgzUPnuOaIg2AYISK3cvS8ebGzCbFBgishURZOiIa3WokzVU04XFwruhwCwwgRuZGyuhZoda1QKiQuA+/G/Dw9sLBjIOtbh0sFV0MAwwgRuZGc0ksAgLFR/vBWqwRXQyItmWruqvn4hJYDWR0AwwgRuY3sc+YwMmlooNhCSLjxQwKRFOMPQ7sJ7+Zwmq9oDCNE5DZyLGEkjuNFCLh7ahwA4M3DpRzIKhjDCBG5hRaD0bpBWgrDCAG4fUI0fNRKnK1qwtdnOZBVJIYRInILxy7Uod0kI8Jfg5hAL9HlkAPw1aiwcGIMAA5kFY1hhIjcQk5pHQBzq4gkcadeMru7YyDr7hNa1HIgqzAMI0TkFi4PXmUXDV2WFBOA8UMCYDCa8E72edHluC2GESJyebIsW6f1cvAqfZeldeT1r0thNHEgqwgMI0Tk8kpqmlHbZIBapcB10VzsjDpbOCEGAV4eKK1txmf5laLLcUsMI0Tk8r4pMc+UGB8TAI1KKbgacjReaiXuTjW3jmw9UCy4GvfEMEJELs+y/8jUYcGCKyFHtTQtDkqFhKyzNThZVi+6HLfDMEJELo9hhHoSFeCF28ZFAQBe/apEbDFuiGGEiFxaeX0LSmuboZC42Bld233T4wEA/80tQ1WDXmwxboZhhIhcmqVV5LroAPh5egiuhhzZxKFBmDg0EAajCW8cOie6HLfCMEJELo1dNGSL+6YPAwC8/vU56NuNgqtxHwwjROTSGEbIFnOTIhEV4InqRgM+PFouuhy3wTBCRC6rplGP05WNAIAp8Qwj1DMPpQJL0+IBAK8cKOZuvoOEYYSIXNY3JeZVV0dG+CLYRy24GnIWS6bGwtNDgbxyHbLO1Iguxy0wjBCRyzpUbP4gYasI2SLQW40fTY4FAGzaVyS4GvfAMEJELutgkTmMTEsMFVwJOZsHZyZApZDwVVENjnTsa0QDh2GEiFxSVYMeBRUNAIC0xBDB1ZCzGRLkjYUTYgAAmz4/I7ga18cwQkQuKeusuVVkTJQ/x4tQnzw0KxGSBOzNq0C+Vie6HJfGMEJELulgUTUAYDpbRaiPEsN8cVuSeYn4v7N1ZEAxjBCRS/rqTEcYGc7xItR3D81KBAB8dKwMJdVNgqtxXQwjRORyztc243xtC1QKCVO42Bn1w3XRAZg1KgwmGXh5H1tHBgrDCBG5nIMdrSLJsYHw1agEV0PO7uGbhwMA3jtyAWV1LYKrcU0MI0Tkcr7qmNLL8SJkDylxwbg+IRhtRhn/3H9WdDkuiWGEiFyKySTjq47Bq9M4XoTs5OFZIwAAbx4uZevIAGAYISKXcqKsHjVNBvhqVJg0NEh0OeQipg8PwdRhwTC0m/CXzNOiy3E5DCNE5FK+KKgCAExLDIFaxR9xZB+SJGHV3FEAgLezL+BsVaPgilwLv1OJyKV8UWgOIzeNChdcCbmalLhgzB4dDqNJxkt7CkWX41IYRojIZdQ3tyGnYx+RmSM5XoTs7/E5oyBJwK5j5ThxsV50OS6DYYSIXMaBomqYZGB4uC+GBHmLLodc0Jgof9yeHA0AeOGTAsHVuA6GESJyGV8UVgIAbhoZJrgScmUZt4yESiHhi8IqHOrYA4n6h2GEiFyCLMvW8SI3jmIYoYETF+KDxVNiAQDPf1IAWZYFV+T8GEaIyCWcLNOhQqeHl4cSU+K5BDwNrF/MHgFPDwWyz13CnlMVostxegwjROQSLB8IM0eGwtNDKbgacnUR/p64b/owAMAfP86Dvt0ouCLnxjBCRC7BEkbSx0QIroTcxUOzhiPcT4NzNc3YeqBEdDlOrU9hZNOmTYiPj4enpydSU1Nx+PDhbs/dsmULZsyYgaCgIAQFBSE9Pf2a5xMR2epiXQtOleugkIDZDCM0SHw1KqyaOxoA8LfPTqNS1yq4IudlcxjZsWMHMjIysG7dOuTk5CA5ORlz5sxBZWVll+fv27cPS5Ysweeff46srCzExsbi1ltvxcWLF/tdPBERAOztaBWZHBeMYB+14GrInXx/YgwmxAaiyWDEs7vzRZfjtGwOIxs2bMDy5cuxbNkyjB07Fps3b4a3tze2bt3a5flvvPEGHnroIUyYMAGjR4/Gv/71L5hMJmRmZva7eCIi4IoumrFcdZUGl0Ih4Xe3XwcAeC/nIo50LLpHtrEpjBgMBmRnZyM9Pf3yCygUSE9PR1ZWVq9eo7m5GW1tbQgO7n60u16vh06n6/QgIuqKrrUNX3es9XDL2EjB1ZA7mhAbiDsnDQEA/O7DUzCZONXXVjaFkerqahiNRkREdO6TjYiIgFar7dVrrFq1CtHR0Z0CzXetX78eAQEB1kdsbKwtZRKRG/k8vxLtJhmJYT4YFuojuhxyU6vmjoKPWomj5+vw3hEOQ7DVoM6mefbZZ7F9+3a8//778PT07Pa81atXo76+3vo4f/78IFZJRM5k17FyAMC8pCjBlZA7C/f3xCOzRwAAnv1fHuqaDYIrci42hZHQ0FAolUpUVHRe4KWiogKRkdduHn3xxRfx7LPP4tNPP8X48eOvea5Go4G/v3+nBxHRdzW0tmFfx6qr88czjJBYy6bHY3i4L6obDfjjrjzR5TgVm8KIWq1GSkpKp8GnlsGoaWlp3T7v+eefx+9//3vs3r0bkydP7nu1RERXyMyrhKHdhIQwH4yO9BNdDrk5jUqJ5+4cB0kC3s6+gAOnq0WX5DRs7qbJyMjAli1b8NprryEvLw8rVqxAU1MTli1bBgBYunQpVq9ebT3/ueeew5o1a7B161bEx8dDq9VCq9WisbHRfldBRG7po44umu+Ni4IkSYKrIQJS4oKx9Po4AMDq94+h2dAuuCLnYHMYWbx4MV588UWsXbsWEyZMQG5uLnbv3m0d1FpaWory8nLr+S+//DIMBgN+8IMfICoqyvp48cUX7XcVROR2dK1t2G/tookWXA3RZb+eOxrRAZ44X9uCDZ8Wii7HKUiyE2w3qNPpEBAQgPr6eo4fISIAwHs5F5Dxn6MYHu6LPY/NZMsIOZTP8yuxbNs3UEjA+w9NR3JsoOiShOjt5zf3piEip/RBbhkAYD67aMgBzRodjoUTomGSgVXvHoOh3SS6JIfGMEJETqdS14ovT5u7aL4/MUZwNURdW/u9sQjy9kC+tgF/++y06HIcGsMIETmdD3LLYJKBlLggxHOhM3JQIb4aPL0wCQDwt8+LcLi4VnBFjothhIiciizLeDfnAgBg0SS2ipBjW5AcjUWTYmCSgcd25KK+pU10SQ6JYYSInMqpch3ytQ1QqxT43jjOoiHH9/TCJAwN9sbFuhY8ufMEnGDeyKBjGCEip/Jejnnfj1vGRCDA20NwNUQ989Wo8Oe7JkCpkPDh0TLrv2G6jGGEiJyGvt2InR2bkLGLhpzJxKFB+GXH3jVrPziBczVNgityLAwjROQ0PjlZgZomAyL9PXHjyDDR5RDZ5KFZwzE1PhhNBiN+sT0X+naj6JIcBsMIETmNN74+BwC4a2osVEr++CLnolRI+NNdE+DvqcLR83V46sNToktyGPxuJiKnUFTZgEPFtVAqJNw1Zajocoj6JCbQC39ZMhGSBLx5qBRvHS4VXZJDYBghIqfwxiHzD+3Zo8MRGeApuBqivrtpVDgev3UUAGDdByeRU3pJcEXiMYwQkcNrMRjxbrZ5bZF7OnZEJXJmD92UiLnXRcJgNGHF69mobGgVXZJQDCNE5PDeyT4PXWs7hgZ7Y8bwUNHlEPWbJEl48UfJGB7uiwqdHivfyHHr/WsYRojIoRlNMl45UAwAuG96PBQKbopHrsFXo8I/fpICP40K35RcwpM7j7vtgmgMI0Tk0PbmVaCkphn+nir8cHKs6HKI7CoxzBd/XjIBCgn4z7cXsHGve26oxzBCRA7tlS/NrSL3XB8HH41KcDVE9nfz6Aj8/g7zhnp/zjztljNsGEaIyGHlnq/D4ZJaeCgl/HRavOhyiAbMPalxeOTm4QCAJ3eewGf5FYIrGlwMI0TksP72mbnJ+vbkGET4czovubaMW0biBylDYDTJWPnGERw9Xye6pEHDMEJEDun4hXrszauEQgJWzkoUXQ7RgJMkCesXjcPMkWFoaTNi2bZvUKBtEF3WoGAYISKH9OdMc6vIwgkxSAjzFVwN0eDwUCrw93smYfyQANQ2GXD3lq/dIpAwjBCRwzlxsR578yqgkICHO/rRidyFr0aF/7svFUkx/qjpCCSFFa4dSBhGiMjhvPRpAQDg9uRoJLJVhNxQgLcHXr8/FddFXw4kp104kDCMEJFDOVhUjc8LqqBSSHg0faTocoiECfRW440HUjE2yh/VjQYs2XLIZVtIGEaIyGGYTDL++HEeAODH18dhWKiP4IqIxLIEkjFR/qhu1OOHm7OQfa5WdFl2xzBCRA5jZ+5FnCzTwU+jwi9mjxBdDpFDCPJR480HUjFxaCDqW9pw95ZD2HPKtdYhYRghIofQpG/HC5+Yx4o8NGs4gn3UgisichxBPuYWkptHh0PfbsLP/u9bbHehlVoZRojIIWzcW4jy+lbEBnth2fR40eUQORxvtXljvR+mDIFJBp547zg27i10ic31GEaISLhTZTps/aoEAPD7hUnw9FCKLYjIQXkoFXj+B+Px8CzzlPeNe0/j4TePoEnfLriy/mEYISKhTCYZv915HEaTjPnjonDTqHDRJRE5NEmS8PicUXjm++PgoZSw63g5Fv39IM7VNIkurc8YRohIqK1fFeNIaR18NSqs+d5Y0eUQOY27U4fireXXI8xPg4KKBiz46wHsK6gUXVafMIwQkTCFFQ14vmPQ6m/nj0FkADfDI7LF5PhgfPTIDZg4NBC61nYs2/YNNu4tRLvRJLo0mzCMEJEQhnYTHtuRC0O7CTePDsddU2JFl0TklCL8PbH9weuxZGosZNk8jmTxP7/G+dpm0aX1GsMIEQnxwif5OFmmQ5C3B569cxwkSRJdEpHT0qiUWL9oPDYungA/jQrZ5y5h3p+/xPtHLogurVcYRoho0O0+UY4tXxYDANYvGo9wP3bPENnDHRNj8PGjMzA5LgiN+nY8tuMoHnnrCGoa9aJLuyaGESIaVGerGvH428cAAA/OTMDcpEjBFRG5lthgb2x/8Hpk3DISSoWED4+WIX3DF3g3+4LDrknCMEJEg6a+uQ3L//0tGvXtmBofjP83Z5TokohckkqpwC9mj8C7K6ZhdKQfLjW34VdvH8WPXzmEkmrHmwLMMEJEg0LfbsTPXv8WZ6qaEBXgib/dPREqJX8EEQ2kCbGB+PCRG7Bq7mhoVAp8VVSDORv3Y+PeQjQbHGehNP4kIKIBZzLJWPXOMXx9tha+GhVeXTYF4f4cJ0I0GDyUCqy4KRGfPjYTNwwPhb7dhI17T2PWi/vwTvYFmEziu24YRohoQMmyjCc/OIGduWVQKiT8/Z5JGB3pL7osIrcTF+KD/7t/Kv5290TEBnuhQqfH428fxYK/HcDBM9VCa2MYIaIBI8synvrwFN48VAqFBGz4UTJmjgwTXRaR25IkCd8bH429GTfiN7eNhp+nCifLdLh7yyG8my1uGjDDCBENiHajCavfO45tB0sgScDzP0jGwgkxossiIpjXJXlwZiK++PUs3JsWh0h/T8wROLNNkh11ns8VdDodAgICUF9fD39/Nu8SObrWNiN+8dYRfHqqAgoJePbO8fjRZK6wSuSoWgxGeKntv1t2bz+/VXZ/ZyJya9r6Vvzs9WwcPV8HtUqBv9w1kWuJEDm4gQgitmAYISK7yT5XixWv56CyQY8ALw9s/nEK0hJDRJdFRA6OYYSI+s1okvHyviL8ae9pGE0yRkb4YsvSyYgL8RFdGhE5AYYRIuqXs1WNeOLd4zhcUgsAWJAcjfWLxsFXwx8vRNQ7/GlBRH2ibzdiy/6z+MtnRTC0m+CjVuLphUlYNCmGO/ASkU0YRojIJrIsY/cJLdb/Lx+ltc0AgJkjw/DHO5IQG+wtuDoickYMI0TUK7IsY19BFTbuLcTRC/UAgHA/DX5z2xgsnBDN1hAi6jOGESK6pmZDOz7ILcO/s84hr1wHAPDyUGL5jGH42Y2J8OHYECLqJ/4UIaIunalqxOtfn8M72RfQ0Gre3dPLQ4mlaXFYPjMBob4awRUSkatgGCEiq6LKBuw+ocX/TmhxskxnPR4f4o0fXx+HH6QMQaC3WmCFROSKGEaI3FiTvh3flNQi62wNPsurxOnKRuvXlAoJs0aF4Sdp8ZgxPBQKBceEENHAYBghchOyLOPCpRacLKvH0Qv1+PpsDY5dqIfRdHl7Kg+lhOnDQzEvKRK3jI1EsA9bQYho4PUpjGzatAkvvPACtFotkpOT8de//hVTp07t9vy3334ba9asQUlJCUaMGIHnnnsOt912W5+LJqLuybKMqgY9zlQ14Wx1I85WNaFA24ATZfWoa2676vwhQV5ISwjB9OGhmDU6HAFeHgKqJiJ3ZnMY2bFjBzIyMrB582akpqZi48aNmDNnDgoKChAeHn7V+QcPHsSSJUuwfv16fO9738Obb76JO+64Azk5OUhKSrLLRRC5izajCXXNbahtMqBC1wptfSvK6ls6fm2Ftr4FZXWtaNS3d/l8D6WEkRF+SIoOwOT4IFyfEMK1QYhIOEmWZbnn0y5LTU3FlClT8Le//Q0AYDKZEBsbi0ceeQRPPPHEVecvXrwYTU1N+Oijj6zHrr/+ekyYMAGbN2/u1Xv2dgtiIkchyzLajDIMRhMM7Vc8jEbo203Qt5vQajCiyWBEk74dTYZ2NOuNaNS3o9nQjka9Ec2GdtS3tOFScxsuNRlwqdlgndXSE4UEDAnyRkKYDxJCfTEiwhdJ0QEYGekLjUrs7pxE5D56+/ltU8uIwWBAdnY2Vq9ebT2mUCiQnp6OrKysLp+TlZWFjIyMTsfmzJmDnTt3dvs+er0eer3e+medTtftuf3xyoFinO9YQbI7381qXSW3ruKc/J0zuz6n59f57lldvs4Avv93X6erv4Cu/05s/3vr6+t0XVPP12+SZRhNll9l66+dfi8DpquOyeZjsgyTCZ2OGdpN0HcEkIEiSUCAlwci/DwRFeiJqABPRPp7WX8fFeCJ2GBvhg4icho2hZHq6moYjUZERER0Oh4REYH8/Pwun6PVars8X6vVdvs+69evx1NPPWVLaX2y61gZckrrBvx9iFQKCWqVwvxQmn/1VivhrVbBR6OEj1oFH03n33urlfD39ECQjxrBPh4I9FYj2FsNfy8PKDmzhYhciEPOplm9enWn1hSdTofY2Fi7v8+dKUMwLTG007GuVrS+6lAXJ3X10fDd06Quzrr6nF68Ti+X3R6w9+/F63RdT89/b735++/ydfpw3ySYp68qJQkKhQSlAlBIUudjHX9WWI/hqmNKhWR93ncDh0algIdSwfBARHQNNoWR0NBQKJVKVFRUdDpeUVGByMjILp8TGRlp0/kAoNFooNEM/OqO96TGDfh7EBER0bUpbDlZrVYjJSUFmZmZ1mMmkwmZmZlIS0vr8jlpaWmdzgeAPXv2dHs+ERERuRebu2kyMjJw7733YvLkyZg6dSo2btyIpqYmLFu2DACwdOlSxMTEYP369QCARx99FDfeeCNeeuklzJ8/H9u3b8e3336Lf/7zn/a9EiIiInJKNoeRxYsXo6qqCmvXroVWq8WECROwe/du6yDV0tJSKBSXG1ymTZuGN998E08++SR+85vfYMSIEdi5cyfXGCEiIiIAfVhnRASuM0JEROR8evv5bdOYESIiIiJ7YxghIiIioRhGiIiISCiGESIiIhKKYYSIiIiEYhghIiIioRhGiIiISCiGESIiIhKKYYSIiIiEsnk5eBEsi8TqdDrBlRAREVFvWT63e1rs3SnCSENDAwAgNjZWcCVERERkq4aGBgQEBHT7dafYm8ZkMqGsrAx+fn6QJMlur6vT6RAbG4vz58+77J43vEbn5+rXB/AaXYGrXx/g+tc4ENcnyzIaGhoQHR3daRPd73KKlhGFQoEhQ4YM2Ov7+/u75D+sK/EanZ+rXx/Aa3QFrn59gOtfo72v71otIhYcwEpERERCMYwQERGRUG4dRjQaDdatWweNRiO6lAHDa3R+rn59AK/RFbj69QGuf40ir88pBrASERGR63LrlhEiIiISj2GEiIiIhGIYISIiIqEYRoiIiEgotw4jmzZtQnx8PDw9PZGamorDhw+LLqlP1q9fjylTpsDPzw/h4eG44447UFBQ0Omcm266CZIkdXr8/Oc/F1Sx7X73u99dVf/o0aOtX29tbcXKlSsREhICX19f3HnnnaioqBBYse3i4+OvukZJkrBy5UoAzncP9+/fjwULFiA6OhqSJGHnzp2dvi7LMtauXYuoqCh4eXkhPT0dp0+f7nRObW0t7rnnHvj7+yMwMBD3338/GhsbB/Eqru1a19jW1oZVq1Zh3Lhx8PHxQXR0NJYuXYqysrJOr9HVfX/22WcH+Uq619N9/OlPf3pV/XPnzu10jiPfx56ur6vvSUmS8MILL1jPceR72JvPh978/CwtLcX8+fPh7e2N8PBw/PrXv0Z7e7vd6nTbMLJjxw5kZGRg3bp1yMnJQXJyMubMmYPKykrRpdnsiy++wMqVK/H1119jz549aGtrw6233oqmpqZO5y1fvhzl5eXWx/PPPy+o4r657rrrOtV/4MAB69cee+wxfPjhh3j77bfxxRdfoKysDIsWLRJYre2++eabTte3Z88eAMAPf/hD6znOdA+bmpqQnJyMTZs2dfn1559/Hn/5y1+wefNmHDp0CD4+PpgzZw5aW1ut59xzzz04efIk9uzZg48++gj79+/Hgw8+OFiX0KNrXWNzczNycnKwZs0a5OTk4L333kNBQQFuv/32q859+umnO93XRx55ZDDK75We7iMAzJ07t1P9b731VqevO/J97On6rryu8vJybN26FZIk4c477+x0nqPew958PvT089NoNGL+/PkwGAw4ePAgXnvtNWzbtg1r1661X6Gym5o6daq8cuVK65+NRqMcHR0tr1+/XmBV9lFZWSkDkL/44gvrsRtvvFF+9NFHxRXVT+vWrZOTk5O7/FpdXZ3s4eEhv/3229ZjeXl5MgA5KytrkCq0v0cffVROTEyUTSaTLMvOfQ8ByO+//771zyaTSY6MjJRfeOEF67G6ujpZo9HIb731lizLsnzq1CkZgPzNN99Yz/nf//4nS5IkX7x4cdBq763vXmNXDh8+LAOQz507Zz0WFxcn/+lPfxrY4uykq2u899575YULF3b7HGe6j725hwsXLpRvvvnmTsec6R5+9/OhNz8/P/74Y1mhUMhardZ6zssvvyz7+/vLer3eLnW5ZcuIwWBAdnY20tPTrccUCgXS09ORlZUlsDL7qK+vBwAEBwd3Ov7GG28gNDQUSUlJWL16NZqbm0WU12enT59GdHQ0EhIScM8996C0tBQAkJ2djba2tk73c/To0Rg6dKjT3k+DwYDXX38d9913X6fNIZ39HloUFxdDq9V2umcBAQFITU213rOsrCwEBgZi8uTJ1nPS09OhUChw6NChQa/ZHurr6yFJEgIDAzsdf/bZZxESEoKJEyfihRdesGvz92DYt28fwsPDMWrUKKxYsQI1NTXWr7nSfayoqMCuXbtw//33X/U1Z7mH3/186M3Pz6ysLIwbNw4RERHWc+bMmQOdToeTJ0/apS6n2CjP3qqrq2E0Gjv9xQJAREQE8vPzBVVlHyaTCb/85S8xffp0JCUlWY/ffffdiIuLQ3R0NI4dO4ZVq1ahoKAA7733nsBqey81NRXbtm3DqFGjUF5ejqeeegozZszAiRMnoNVqoVarr/oBHxERAa1WK6bgftq5cyfq6urw05/+1HrM2e/hlSz3pavvQcvXtFotwsPDO31dpVIhODjYKe9ra2srVq1ahSVLlnTahOwXv/gFJk2ahODgYBw8eBCrV69GeXk5NmzYILDa3ps7dy4WLVqEYcOG4cyZM/jNb36DefPmISsrC0ql0qXu42uvvQY/P7+ruoCd5R529fnQm5+fWq22y+9Vy9fswS3DiCtbuXIlTpw40Wk8BYBO/bPjxo1DVFQUZs+ejTNnziAxMXGwy7TZvHnzrL8fP348UlNTERcXh//85z/w8vISWNnAeOWVVzBv3jxER0dbjzn7PXRnbW1t+NGPfgRZlvHyyy93+lpGRob19+PHj4darcbPfvYzrF+/3imWHb/rrrusvx83bhzGjx+PxMRE7Nu3D7NnzxZYmf1t3boV99xzDzw9PTsdd5Z72N3ngyNwy26a0NBQKJXKq0YLV1RUIDIyUlBV/ffwww/jo48+wueff44hQ4Zc89zU1FQAQFFR0WCUZneBgYEYOXIkioqKEBkZCYPBgLq6uk7nOOv9PHfuHPbu3YsHHnjgmuc58z203JdrfQ9GRkZeNaC8vb0dtbW1TnVfLUHk3Llz2LNnT49bs6empqK9vR0lJSWDU6CdJSQkIDQ01Prv0lXu45dffomCgoIevy8Bx7yH3X0+9ObnZ2RkZJffq5av2YNbhhG1Wo2UlBRkZmZaj5lMJmRmZiItLU1gZX0jyzIefvhhvP/++/jss88wbNiwHp+Tm5sLAIiKihrg6gZGY2Mjzpw5g6ioKKSkpMDDw6PT/SwoKEBpaalT3s9XX30V4eHhmD9//jXPc+Z7OGzYMERGRna6ZzqdDocOHbLes7S0NNTV1SE7O9t6zmeffQaTyWQNYo7OEkROnz6NvXv3IiQkpMfn5ObmQqFQXNW14SwuXLiAmpoa679LV7iPgLm1MiUlBcnJyT2e60j3sKfPh978/ExLS8Px48c7hUpLsB47dqzdCnVL27dvlzUajbxt2zb51KlT8oMPPigHBgZ2Gi3sLFasWCEHBATI+/btk8vLy62P5uZmWZZluaioSH766aflb7/9Vi4uLpY/+OADOSEhQZ45c6bgynvvV7/6lbxv3z65uLhY/uqrr+T09HQ5NDRUrqyslGVZln/+85/LQ4cOlT/77DP522+/ldPS0uS0tDTBVdvOaDTKQ4cOlVetWtXpuDPew4aGBvnIkSPykSNHZADyhg0b5CNHjlhnkjz77LNyYGCg/MEHH8jHjh2TFy5cKA8bNkxuaWmxvsbcuXPliRMnyocOHZIPHDggjxgxQl6yZImoS7rKta7RYDDIt99+uzxkyBA5Nze30/emZQbCwYMH5T/96U9ybm6ufObMGfn111+Xw8LC5KVLlwq+ssuudY0NDQ3y448/LmdlZcnFxcXy3r175UmTJskjRoyQW1tbra/hyPexp3+nsizL9fX1sre3t/zyyy9f9XxHv4c9fT7Ics8/P9vb2+WkpCT51ltvlXNzc+Xdu3fLYWFh8urVq+1Wp9uGEVmW5b/+9a/y0KFDZbVaLU+dOlX++uuvRZfUJwC6fLz66quyLMtyaWmpPHPmTDk4OFjWaDTy8OHD5V//+tdyfX292MJtsHjxYjkqKkpWq9VyTEyMvHjxYrmoqMj69ZaWFvmhhx6Sg4KCZG9vb/n73/++XF5eLrDivvnkk09kAHJBQUGn4854Dz///PMu/13ee++9siybp/euWbNGjoiIkDUajTx79uyrrrumpkZesmSJ7OvrK/v7+8vLli2TGxoaBFxN1651jcXFxd1+b37++eeyLMtydna2nJqaKgcEBMienp7ymDFj5GeeeabTB7lo17rG5uZm+dZbb5XDwsJkDw8POS4uTl6+fPlV/6lz5PvY079TWZblf/zjH7KXl5dcV1d31fMd/R729Pkgy737+VlSUiLPmzdP9vLykkNDQ+Vf/epXcltbm93qlDqKJSIiIhLCLceMEBERkeNgGCEiIiKhGEaIiIhIKIYRIiIiEophhIiIiIRiGCEiIiKhGEaIiIhIKIYRIiIiEophhIiIiIRiGCEiIiKhGEaIiIhIKIYRIiIiEur/AwS6+jMr9ag8AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "H_odeint = [0.0,\n", - " 0.07584066320978722,\n", - " 0.14636646265873351,\n", - " 0.21517239301596403,\n", - " 0.2849940641423008,\n", - " 0.35803162126534144,\n", - " 0.43617188357763786,\n", - " 0.5211433080571843,\n", - " 0.6146269952842578,\n", - " 0.7183393339849907,\n", - " 0.8340968480485813,\n", - " 0.9638704485112929,\n", - " 1.1098340730821774,\n", - " 1.2744112432601529,\n", - " 1.460322143778306,\n", - " 1.6706331916641786,\n", - " 1.9088107460125325,\n", - " 2.178780372444439,\n", - " 2.4849930017839568,\n", - " 2.832499327828298,\n", - " 3.227033820607022,\n", - " 3.6751098523322363,\n", - " 4.184127609093503,\n", - " 4.762496481974214,\n", - " 5.419774326784966,\n", - " 6.166825487583933,\n", - " 7.016000445528594,\n", - " 7.981340143729251,\n", - " 9.078808185052266,\n", - " 10.326554856871647,\n", - " 11.745217338261286,\n", - " 13.358261021733336,\n", - " 15.192367626771185,\n", - " 17.277876514948733,\n", - " 19.64928647253589,\n", - " 22.345826309466535,\n", - " 25.41210364728401,\n", - " 28.898842636443945,\n", - " 32.863722838161294,\n", - " 37.37233298620304,\n", - " 42.49925554848381,\n", - " 48.32929991918692,\n", - " 54.95890451703756,\n", - " 62.49773100251825,\n", - " 71.07047694046699,\n", - " 80.81893664895041,\n", - " 91.9043442331066,\n", - " 104.51003757795193,\n", - " 118.84448659151495,\n", - " 135.14473616311494,\n", - " 153.6803198995054,\n", - " 174.75770892931388,\n", - " 198.7253686880845,\n", - " 225.9795066077193,\n", - " 256.9706043940812,\n", - " 292.2108414225009,\n", - " 332.28253040812973,\n", - " 377.8477020062484,\n", - " 429.65899353366785,\n", - " 488.57201806502917,\n", - " 555.5594130725682,\n", - " 631.7267930288757,\n", - " 718.33086148102,\n", - " 816.7999711100919,\n", - " 928.7574539225811,\n", - " 1056.0480899336421,\n", - " 1200.768126390387,\n", - " 1365.2993106701535,\n", - " 1552.3474521757955,\n", - " 1764.986108867584,\n", - " 2006.7060366726942,\n", - " 2281.4711317587603,\n", - " 2593.781675837228,\n", - " 2948.74577656625,\n", - " 3352.159985334878,\n", - " 3810.6001762079927,\n", - " 4331.523867699931,\n", - " 4923.385255496248,\n", - " 5595.764320925713,\n", - " 6359.511470886844,\n", - " 7226.909216017238,\n", - " 8211.852427514215,\n", - " 9330.048735030014,\n", - " 10599.240502623044,\n", - " 12039.44975726627,\n", - " 13673.24714065712,\n", - " 15526.045525426567,\n", - " 17626.41847934686,\n", - " 20006.442805113635,\n", - " 22702.063309712732,\n", - " 25753.476547071572,\n", - " 29205.528188227865,\n", - " 33108.11624152888,\n", - " 37516.58927032621,\n", - " 42492.12476694649,\n", - " 48102.06815038656,\n", - " 54420.207487559084,\n", - " 61526.95241138342,\n", - " 69509.3788326499,\n", - " 78461.09204661413,\n", - " 88481.85690086993,\n", - " 99676.93033933835,\n", - " 112156.03116953523,\n", - " 126031.8764591974,\n", - " 141418.21409965339,\n", - " 158427.29086936626,\n", - " 177166.7077374985,\n", - " 197735.64179407313,\n", - " 220220.4490700916,\n", - " 244689.7128999469,\n", - " 271188.8609711989,\n", - " 299734.54505555454,\n", - " 330309.0420578748,\n", - " 362855.0097454492,\n", - " 397270.9706782105,\n", - " 433407.9323314221,\n", - " 471067.5318962573,\n", - " 510002.05000426056,\n", - " 549916.5325942405,\n", - " 590473.1326448151,\n", - " 631297.6155083764,\n", - " 671987.805397328,\n", - " 712123.5855505902,\n", - " 751277.9419388921,\n", - " 789028.4531510238,\n", - " 824968.6089066564,\n", - " 858718.3673693967,\n", - " 889933.4450316473,\n", - " 918312.9510473183,\n", - " 943605.1145011623,\n", - " 965610.9989351172,\n", - " 984186.2260247911,\n", - " 999240.8449867101,\n", - " 1010737.5652130407,\n", - " 1018688.6250969241,\n", - " 1023151.5961628385,\n", - " 1024224.424518873,\n", - " 1022039.9954607659,\n", - " 1016760.4778537896,\n", - " 1008571.6676818144,\n", - " 997677.5091145145,\n", - " 984294.9309318912,\n", - " 968649.0965921708,\n", - " 950969.132919035,\n", - " 931484.3726190757,\n", - " 910421.1206448845,\n", - " 887999.9365984657,\n", - " 864433.4099252748,\n", - " 839924.3952308263,\n", - " 814664.6678259049,\n", - " 788833.956208313,\n", - " 762599.306145181,\n", - " 736114.7329186859,\n", - " 709521.118576635,\n", - " 682946.3131058007,\n", - " 656505.4041417397,\n", - " 630301.1204001798,\n", - " 604424.3395841966,\n", - " 578954.674586528,\n", - " 553961.1153107659,\n", - " 529502.705890681,\n", - " 505629.24208586576,\n", - " 482381.97446456744,\n", - " 459794.3062235543,\n", - " 437892.47709088976,\n", - " 416696.2258780569,\n", - " 396219.4261885734,\n", - " 376470.6921664646,\n", - " 357453.95052101125,\n", - " 339168.9775714914,\n", - " 321611.9004232435,\n", - " 304775.66215856594,\n", - " 288650.4511800376,\n", - " 273224.0956361533,\n", - " 258482.42403353218,\n", - " 244409.59338260329,\n", - " 230988.3864887854,\n", - " 218200.48005631237,\n", - " 206026.6853722251,\n", - " 194447.16337089916,\n", - " 183441.61586876688,\n", - " 172989.45475247476,\n", - " 163069.95085616622,\n", - " 153662.36416343783,\n", - " 144746.05696746017,\n", - " 136300.5914987689,\n", - " 128305.81345597992,\n", - " 120741.9227885983,\n", - " 113589.53299236886,\n", - " 106829.72009170499,\n", - " 100444.06240763294,\n", - " 94414.6720882853,\n", - " 88724.21934978342,\n", - " 83355.95026825309,\n", - " 78293.69889597349,\n", - " 73521.89440676449,\n", - " 69025.56391080657,\n", - " 64790.33151791613,\n", - " 60802.414180610635,\n", - " 57048.61477377206]\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(H_odeint)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, "outputs": [], "source": [ "# Constants for the scenario\n", - "# STATES = [\"S\", \"I\", \"E\", \"R\", \"H\", \"D\"]\n", - "STATES = [\"S_compliant\", \"S_noncompliant\", \"I_compliant\", \"I_noncompliant\", \"E_compliant\", \"E_noncompliant\",\"R\", \"H\", \"D\"]\n", - "STATES_DESTRATIFIED_ALL = [\"S_lb\", \"S_ub\", \"I_lb\", \"I_ub\", \"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"]\n", - "# IDART = [\"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Threatened\"]\n", "\n", - "MAX_TIME=20\n", + "MAX_TIME=5\n", "STEP_SIZE=1\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] @@ -298,6 +87,9 @@ "source": [ "# Helper functions to setup FUNMAN for different steps of the scenario\n", "\n", + "from email.mime import base\n", + "\n", + "\n", "def get_request():\n", " with open(REQUEST_PATH, \"r\") as request:\n", " funman_request = FunmanWorkRequest.model_validate(json.load(request))\n", @@ -327,9 +119,9 @@ "def get_synthesized_vars(funman_request):\n", " return [p.name for p in funman_request.parameters if p.label == \"all\"]\n", "\n", - "def run(funman_request, plot=False, model=MODEL_PATH):\n", + "def run(funman_request, plot=False, model=models['original_stratified']):\n", " to_synthesize = get_synthesized_vars(funman_request)\n", - " return Runner().run(\n", + " results = Runner().run(\n", " model,\n", " funman_request,\n", " description=\"SIERHD Eval 12mo Scenario 1 q1\",\n", @@ -338,6 +130,7 @@ " print_last_time=True,\n", " parameters_to_plot=to_synthesize\n", " )\n", + " return results\n", "\n", "def setup_common(funman_request, synthesize=False, debug=False, dreal_precision=1e-1):\n", " set_timepoints(funman_request, timepoints)\n", @@ -346,13 +139,13 @@ " set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision)\n", " \n", "\n", - "def set_compartment_bounds(funman_request, upper_bound=9830000.0, error=0.01):\n", + "def set_compartment_bounds(funman_request, model, upper_bound=9830000.0, error=0.01):\n", " # Add bounds to compartments\n", - " for var in STATES:\n", + " for var in states[model]:\n", " funman_request.constraints.append(StateVariableConstraint(name=f\"{var}_bounds\", variable=var, interval=Interval(lb=0, ub=upper_bound, closed_upper_bound=True),soft=False))\n", "\n", " # Add sum of compartments\n", - " funman_request.constraints.append(LinearConstraint(name=f\"compartment_bounds\", variables=STATES, additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True))\n", + " funman_request.constraints.append(LinearConstraint(name=f\"compartment_bounds\", variables=states[model], additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True))\n", "\n", "def relax_parameter_bounds(funman_request, factor = 0.1):\n", " # Relax parameter bounds\n", @@ -363,7 +156,7 @@ " interval.lb = interval.lb - (factor/2 * width)\n", " interval.ub = interval.ub + (factor/2 * width)\n", "\n", - "def plot_last_point(results, states=STATES):\n", + "def plot_last_point(results, states):\n", " pts = results.parameter_space.points() \n", " print(f\"{len(pts)} points\")\n", "\n", @@ -395,26 +188,70 @@ " print(df.T)\n", "\n", "\n", - "def report(results, name, states=STATES):\n", - " plot_last_point(results, states=states)\n", + "def report(results, name, states):\n", + " request_results[name] = results\n", + " plot_last_point(results, states)\n", " param_values = get_last_point_parameters(results)\n", " # print(f\"Point parameters: {param_values}\")\n", " if param_values is not None:\n", " request_params[name] = param_values\n", " pretty_print_request_params(request_params)\n", + " \n", "\n", - "def add_unit_test(funman_request, model=MODEL_PATH):\n", - " if model == MODEL_DESTRATIFIED_ALL_PATH:\n", - " funman_request.constraints.append(LinearConstraint(name=\"compartment_lb\", soft=False, variables = [s for s in STATES_DESTRATIFIED_ALL if s.endswith(\"_lb\")],\n", + "def add_unit_test(funman_request, model=\"original_stratified\"):\n", + " if model == \"destratified_SEI\":\n", + " mstates = states[\"destratified_SEI\"]\n", + " funman_request.constraints.append(LinearConstraint(name=\"compartment_lb\", soft=False, variables = [s for s in mstates if s.endswith(\"_lb\")],\n", " additive_bounds= {\n", " \"ub\": 19340000.5\n", " }\n", " ))\n", - " funman_request.constraints.append(LinearConstraint(name=\"compartment_ub\", soft=False, variables = [s for s in STATES_DESTRATIFIED_ALL if s.endswith(\"_ub\")],\n", + " funman_request.constraints.append(LinearConstraint(name=\"compartment_ub\", soft=False, variables = [s for s in mstates if s.endswith(\"_ub\")],\n", " additive_bounds= {\n", " \"lb\": 0\n", " }\n", - " ))\n" + " ))\n", + " \n", + "def plot_bounds(point, fig=None, axs=None, vars = [\"S\", \"E\", \"I\", \"R\", \"D\", \"H\"], basevar_map={}, **kwargs):\n", + " \n", + " df = point.simulation.dataframe().T\n", + " print(df)\n", + "\n", + " # Drop the ub vars because they are paired with the lb vars \n", + " no_ub_vars = [v for v in vars if not v.endswith(\"_ub\")]\n", + "\n", + " if fig is None and axs is None:\n", + " fig, axs = plt.subplots(len(basevar_map))\n", + " fig.set_figheight(3*len(basevar_map))\n", + " fig.suptitle('Variable Bounds over time')\n", + " \n", + " for var in no_ub_vars:\n", + " # print(var)\n", + " # Get index of list containing var\n", + " i = next(iter([i for i, bv in enumerate(basevar_map) if var in bv]))\n", + " # print(i)\n", + " if var.endswith(\"_lb\"):\n", + " # var is lower bound\n", + " basevar = var.split(\"_lb\")[0]\n", + " lb = f\"{basevar}_lb\"\n", + " ub = f\"{basevar}_ub\"\n", + " labels = [lb, ub]\n", + " elif var.endswith(\"_ub\"):\n", + " # skip, handled as part of lb\n", + " continue\n", + " else:\n", + " # var is not of the form varname_lb\n", + " basevar = var\n", + " labels = basevar\n", + " \n", + " \n", + " # print(labels)\n", + " data = df[labels]\n", + " axs[i].set_title(f\"{basevar} Bounds\")\n", + " axs[i].plot(data, label=labels, **kwargs)\n", + " axs[i].legend(loc=\"lower left\")\n", + " fig.tight_layout()\n", + " return fig, axs\n" ] }, { @@ -426,45 +263,91 @@ "name": "stderr", "output_type": "stream", "text": [ - "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-08-31 23:13:28,225 - funman.scenario.consistency - INFO - 10{10}:\t[+]\n", - "2024-08-31 23:13:28,549 - funman.api.run - INFO - Dumping results to ./out/467cf538-f62e-48b8-9a12-9bf36424d777.json\n" + "2024-09-04 15:23:20,192 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,193 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,193 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,193 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,194 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,194 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,194 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,195 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,196 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,196 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,196 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,197 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,197 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,198 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,198 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,198 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,199 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,199 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,199 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,200 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,200 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:20,702 - funman.scenario.consistency - INFO - 5{5}:\t[+]\n", + "2024-09-04 15:23:22,206 - funman.api.run - INFO - Dumping results to ./out/bbdc4d3f-ab58-4a43-a9c2-8153f3df374b.json\n", + "2024-09-04 15:23:32,255 - funman.api.run - INFO - Dumping results to ./out/bbdc4d3f-ab58-4a43-a9c2-8153f3df374b.json\n", + "2024-09-04 15:23:37,351 - funman.scenario.scenario - INFO - simulation passed verification\n", + "2024-09-04 15:23:37,352 - funman.scenario.consistency - INFO - Simulation Time: 0:00:16.649416\n", + "2024-09-04 15:23:37,374 - funman.server.worker - INFO - Completed work on: bbdc4d3f-ab58-4a43-a9c2-8153f3df374b\n", + "2024-09-04 15:23:42,312 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-09-04 15:23:42,405 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-09-04 15:23:42,408 - funman.server.worker - INFO - Worker.stop() completed.\n", + "2024-09-04 15:23:42,410 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,411 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,412 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,413 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,413 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,414 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,415 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,416 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,416 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,417 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,418 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,419 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,420 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,420 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,422 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,423 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,424 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,424 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,425 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,426 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,427 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 15\u001b[0m\n\u001b[1;32m 13\u001b[0m setup_common(funman_request, debug\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dreal_precision\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-0\u001b[39m)\n\u001b[1;32m 14\u001b[0m add_unit_test(funman_request)\n\u001b[0;32m---> 15\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunman_request\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m report(results, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munconstrained\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# pass\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# pts = results.parameter_space.points() \u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# print(f\"{len(pts)} points\")\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# df = results.dataframe(points=pts[-1:])\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# df\u001b[39;00m\n", - "Cell \u001b[0;32mIn[3], line 34\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(funman_request, plot, model)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(funman_request, plot\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, model\u001b[38;5;241m=\u001b[39mMODEL_PATH):\n\u001b[1;32m 33\u001b[0m to_synthesize \u001b[38;5;241m=\u001b[39m get_synthesized_vars(funman_request)\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mRunner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 36\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunman_request\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 37\u001b[0m \u001b[43m \u001b[49m\u001b[43mdescription\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSIERHD Eval 12mo Scenario 1 q1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 38\u001b[0m \u001b[43m \u001b[49m\u001b[43mcase_out_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mSAVED_RESULTS_DIR\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[43m \u001b[49m\u001b[43mdump_plot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 40\u001b[0m \u001b[43m \u001b[49m\u001b[43mprint_last_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters_to_plot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mto_synthesize\u001b[49m\n\u001b[1;32m 42\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/funman/src/funman/api/run.py:194\u001b[0m, in \u001b[0;36mRunner.run\u001b[0;34m(self, model, request, description, case_out_dir, dump_plot, parameters_to_plot, point_plot_config, num_points, dump_results, print_last_time)\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 153\u001b[0m model: Union[\u001b[38;5;28mstr\u001b[39m, funman\u001b[38;5;241m.\u001b[39mFunmanModel, Dict],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 162\u001b[0m print_last_time: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 163\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m FunmanResults:\n\u001b[1;32m 164\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;124;03m Run a FUNMAN scenario.\u001b[39;00m\n\u001b[1;32m 166\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;124;03m Analysis results\u001b[39;00m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 194\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_test_case\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 195\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdescription\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 196\u001b[0m \u001b[43m \u001b[49m\u001b[43mcase_out_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 197\u001b[0m \u001b[43m \u001b[49m\u001b[43mdump_plot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdump_plot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters_to_plot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparameters_to_plot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 199\u001b[0m \u001b[43m \u001b[49m\u001b[43mpoint_plot_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpoint_plot_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 200\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_points\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_points\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 201\u001b[0m \u001b[43m \u001b[49m\u001b[43mdump_results\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdump_results\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 202\u001b[0m \u001b[43m \u001b[49m\u001b[43mprint_last_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprint_last_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 203\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results\n", - "File \u001b[0;32m~/funman/src/funman/api/run.py:233\u001b[0m, in \u001b[0;36mRunner.run_test_case\u001b[0;34m(self, case, case_out_dir, dump_plot, parameters_to_plot, point_plot_config, num_points, dump_results, print_last_time)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_storage\u001b[38;5;241m.\u001b[39mstart(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msettings\u001b[38;5;241m.\u001b[39mdata_path)\n\u001b[1;32m 231\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_worker\u001b[38;5;241m.\u001b[39mstart()\n\u001b[0;32m--> 233\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_instance\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 234\u001b[0m \u001b[43m \u001b[49m\u001b[43mcase\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 235\u001b[0m \u001b[43m \u001b[49m\u001b[43mout_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcase_out_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 236\u001b[0m \u001b[43m \u001b[49m\u001b[43mdump_plot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdump_plot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 237\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters_to_plot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparameters_to_plot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 238\u001b[0m \u001b[43m \u001b[49m\u001b[43mpoint_plot_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpoint_plot_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 239\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_points\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_points\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 240\u001b[0m \u001b[43m \u001b[49m\u001b[43mdump_results\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdump_results\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 241\u001b[0m \u001b[43m \u001b[49m\u001b[43mprint_last_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprint_last_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 242\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 244\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_worker\u001b[38;5;241m.\u001b[39mstop()\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_storage\u001b[38;5;241m.\u001b[39mstop()\n", - "File \u001b[0;32m~/funman/src/funman/api/run.py:355\u001b[0m, in \u001b[0;36mRunner.run_instance\u001b[0;34m(self, case, out_dir, dump_plot, parameters_to_plot, point_plot_config, num_points, dump_results, print_last_time)\u001b[0m\n\u001b[1;32m 344\u001b[0m plotted \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 345\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_plots(\n\u001b[1;32m 346\u001b[0m results,\n\u001b[1;32m 347\u001b[0m out_dir,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 352\u001b[0m print_last_time,\n\u001b[1;32m 353\u001b[0m )\n\u001b[0;32m--> 355\u001b[0m \u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_worker\u001b[38;5;241m.\u001b[39mis_processing_id(work_unit\u001b[38;5;241m.\u001b[39mid):\n\u001b[1;32m 357\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_worker\u001b[38;5;241m.\u001b[39mget_results(work_unit\u001b[38;5;241m.\u001b[39mid)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "1 points\n", + " N beta c_m_0 c_m_1 c_m_2 c_m_3 eps_m_0 \\\n", + "original_stratified 19340000.0 0.4 0.5 0.5 0.5 0.5 0.5 \n", + "\n", + " eps_m_1 eps_m_2 eps_m_3 ... p_H_to_R p_I_to_H \\\n", + "original_stratified 0.5 0.5 0.5 ... 0.88 0.2 \n", + "\n", + " p_I_to_R p_compliant_noncompliant \\\n", + "original_stratified 0.8 0.1 \n", + "\n", + " p_noncompliant_compliant r_E_to_I r_H_to_D r_H_to_R \\\n", + "original_stratified 0.1 0.2 0.1 0.1 \n", + "\n", + " r_I_to_H r_I_to_R \n", + "original_stratified 0.1 0.07 \n", + "\n", + "[1 rows x 21 columns]\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -482,66 +365,59 @@ "funman_request = get_request()\n", "setup_common(funman_request, debug=False, dreal_precision=1e-0)\n", "add_unit_test(funman_request)\n", - "results = run(funman_request)\n", - "report(results, \"unconstrained\")\n", - "# pass\n", - "# pts = results.parameter_space.points() \n", - "# print(f\"{len(pts)} points\")\n", - "# df = results.dataframe(points=pts[-1:])\n", - "# df" + "results = run(funman_request, model=models['original_stratified'])\n", + "report(results, \"original_stratified\", states[\"original_stratified\"])" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-08-31 23:15:51,114 - funman.server.worker - INFO - FunmanWorker running...\n", - "2024-08-31 23:15:51,118 - funman.server.worker - INFO - Starting work on: fcf2d654-c2ab-4234-afec-d0c7cbc7ba3e\n", - "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-08-31 23:15:59,864 - funman.api.run - INFO - Dumping results to ./out/fcf2d654-c2ab-4234-afec-d0c7cbc7ba3e.json\n", - "2024-08-31 23:15:59,935 - funman.scenario.consistency - INFO - 20{20}:\t[+]\n", - "2024-08-31 23:16:09,941 - funman.api.run - INFO - Dumping results to ./out/fcf2d654-c2ab-4234-afec-d0c7cbc7ba3e.json\n", - "2024-08-31 23:16:20,032 - funman.api.run - INFO - Dumping results to ./out/fcf2d654-c2ab-4234-afec-d0c7cbc7ba3e.json\n", - "2024-08-31 23:16:24,337 - funman.scenario.scenario - INFO - simulation passed verification\n", - "2024-08-31 23:16:24,338 - funman.scenario.consistency - INFO - Simulation Time: 0:00:24.402322\n", - "2024-08-31 23:16:24,389 - funman.server.worker - INFO - Completed work on: fcf2d654-c2ab-4234-afec-d0c7cbc7ba3e\n", - "2024-08-31 23:16:30,088 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-08-31 23:16:30,447 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-08-31 23:16:30,452 - funman.server.worker - INFO - Worker.stop() completed.\n", - "[19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "[0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n" + "2024-09-04 15:23:42,710 - funman.server.worker - INFO - FunmanWorker running...\n", + "2024-09-04 15:23:42,713 - funman.server.worker - INFO - Starting work on: d609bf39-37bf-4a71-88cb-707eb9a1237d\n", + "2024-09-04 15:23:42,714 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,714 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,715 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,715 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,715 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,715 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,716 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,716 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,717 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,717 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,718 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,718 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,718 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,719 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:42,720 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:43,220 - funman.scenario.consistency - INFO - 5{5}:\t[+]\n", + "2024-09-04 15:23:44,724 - funman.api.run - INFO - Dumping results to ./out/d609bf39-37bf-4a71-88cb-707eb9a1237d.json\n", + "2024-09-04 15:23:52,979 - funman.scenario.scenario - INFO - simulation passed verification\n", + "2024-09-04 15:23:52,979 - funman.scenario.consistency - INFO - Simulation Time: 0:00:09.758635\n", + "2024-09-04 15:23:52,999 - funman.server.worker - INFO - Completed work on: d609bf39-37bf-4a71-88cb-707eb9a1237d\n", + "2024-09-04 15:23:54,791 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-09-04 15:23:55,017 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-09-04 15:23:55,019 - funman.server.worker - INFO - Worker.stop() completed.\n", + "2024-09-04 15:23:55,022 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,023 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,023 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,024 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,025 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,026 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,027 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,027 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,028 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,029 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,029 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,030 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,030 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,031 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,031 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n" ] }, { @@ -549,49 +425,297 @@ "output_type": "stream", "text": [ "1 points\n", - " N beta c_m_lb c_m_ub eps_m_lb eps_m_ub p_H_to_D \\\n", - "destratified 19340000.0 0.4 0.4 0.6 0.4 0.6 0.12 \n", + " N beta c_m_0 eps_m_0 c_m_1 eps_m_1 c_m_2 \\\n", + "original_stratified 19340000.0 0.4 0.5 0.5 0.5 0.5 0.5 \n", + "destratified_SEI 19340000.0 0.4 NaN NaN NaN NaN NaN \n", + "\n", + " eps_m_2 c_m_3 eps_m_3 ... p_H_to_R r_H_to_R \\\n", + "original_stratified 0.5 0.5 0.5 ... 0.88 0.1 \n", + "destratified_SEI NaN NaN NaN ... 0.88 0.1 \n", + "\n", + " p_H_to_D r_H_to_D p_noncompliant_compliant \\\n", + "original_stratified 0.12 0.1 0.1 \n", + "destratified_SEI 0.12 0.1 NaN \n", + "\n", + " p_compliant_noncompliant eps_m_lb eps_m_ub c_m_lb \\\n", + "original_stratified 0.1 NaN NaN NaN \n", + "destratified_SEI NaN 0.4 0.6 0.4 \n", "\n", - " p_H_to_R p_I_to_H p_I_to_R r_E_to_I r_H_to_D r_H_to_R \\\n", - "destratified 0.88 0.2 0.8 0.2 0.1 0.1 \n", + " c_m_ub \n", + "original_stratified NaN \n", + "destratified_SEI 0.6 \n", "\n", - " r_I_to_H r_I_to_R \n", - "destratified 0.1 0.07 \n" + "[2 rows x 25 columns]\n", + " S_lb I_lb E_lb I_ub S_ub E_ub \\\n", + "0.0 1.934000e+07 4.000000 1.000000 4.000000 1.934000e+07 1.000000 \n", + "1.0 1.933999e+07 3.971775 1.711692 4.006552 1.933999e+07 2.060627 \n", + "2.0 1.933999e+07 4.059282 2.251305 4.210187 1.933999e+07 3.008676 \n", + "3.0 1.933999e+07 4.220733 2.682140 4.587644 1.933999e+07 3.903399 \n", + "4.0 1.933999e+07 4.426770 3.053780 5.127773 1.933999e+07 4.781040 \n", + "5.0 1.933999e+07 4.657683 3.408531 5.826721 1.933999e+07 5.660551 \n", + "\n", + " R_lb R_ub H_lb H_ub D_lb D_ub \n", + "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1.0 0.225941 0.226573 0.075633 0.075854 0.000462 0.000463 \n", + "2.0 0.460189 0.465522 0.144729 0.146571 0.001789 0.001800 \n", + "3.0 0.707717 0.726702 0.209686 0.216141 0.003919 0.003976 \n", + "4.0 0.971842 1.019198 0.272003 0.287859 0.006811 0.006996 \n", + "5.0 1.254785 1.351797 0.332577 0.364572 0.010440 0.010904 \n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# i) (TA1 Search and Discovery Workflow, 1 Hr. Time Limit) Find estimates on the\n", - "# efficacy of surgical masks in preventing onward transmission of SARS-CoV-2 (preferred)\n", - "# or comparable viral respiratory pathogens (e.g., MERS-CoV, SARS), including any\n", - "# information about uncertainty in these estimates. The term surgical mask here refers to\n", - "# the commonly available, disposable procedure mask, not an N95-type respirator. Find 3\n", - "# credible documents that provide estimates and use your judgment to determine what\n", - "# value (in the deterministic case) or distribution (in the probabilistic case) to use in your\n", - "# forecasts in 1.a.iii.\n", + "# Remove all stratification\n", "\n", - "# The base model assumes no masking inverventions, and we measure the efficacy in terms of the number of hospitalized.\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=True, dreal_precision=1e-0)\n", + "# add_unit_test(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", + "results = run(funman_request, model=models['destratified_SEI'])\n", + "report(results, \"destratified_SEI\", states=states['destratified_SEI'])\n", + "# plot_bounds(results.points()[0], vars=[\"S\", \"E\", \"I\", \"R\", \"H\", \"D\"])\n", + "vars = results.model._state_var_names()\n", + "point = results.points()[0]\n", + "fig, axs = plot_bounds(point, vars=vars, basevar_map=basevar_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-04 15:23:55,838 - funman.server.worker - INFO - FunmanWorker running...\n", + "2024-09-04 15:23:55,842 - funman.server.worker - INFO - Starting work on: 54faa50d-80a7-4b89-8ee0-20987c4818a0\n", + "2024-09-04 15:23:55,842 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,843 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,843 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,844 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,844 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,845 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,845 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,846 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,847 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,847 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,848 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,848 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,849 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,851 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,851 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,852 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:55,853 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:23:56,822 - funman.scenario.consistency - INFO - 5{5}:\t[+]\n", + "2024-09-04 15:23:57,847 - funman.api.run - INFO - Dumping results to ./out/54faa50d-80a7-4b89-8ee0-20987c4818a0.json\n", + "2024-09-04 15:24:07,898 - funman.api.run - INFO - Dumping results to ./out/54faa50d-80a7-4b89-8ee0-20987c4818a0.json\n", + "2024-09-04 15:24:17,968 - funman.api.run - INFO - Dumping results to ./out/54faa50d-80a7-4b89-8ee0-20987c4818a0.json\n", + "2024-09-04 15:24:23,075 - funman.scenario.scenario - INFO - simulation passed verification\n", + "2024-09-04 15:24:23,076 - funman.scenario.consistency - INFO - Simulation Time: 0:00:26.253224\n", + "2024-09-04 15:24:23,101 - funman.server.worker - INFO - Completed work on: 54faa50d-80a7-4b89-8ee0-20987c4818a0\n", + "2024-09-04 15:24:28,039 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", + "2024-09-04 15:24:28,137 - funman.server.worker - INFO - FunmanWorker exiting...\n", + "2024-09-04 15:24:28,140 - funman.server.worker - INFO - Worker.stop() completed.\n", + "2024-09-04 15:24:28,143 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,144 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,145 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,145 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,146 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,147 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,147 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,148 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,148 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,150 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,150 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,151 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,152 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,152 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,153 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,153 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", + "2024-09-04 15:24:28,154 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 points\n", + " N beta c_m_0 eps_m_0 c_m_1 eps_m_1 c_m_2 \\\n", + "original_stratified 19340000.0 0.4 0.5 0.5 0.5 0.5 0.5 \n", + "destratified_SEI 19340000.0 0.4 NaN NaN NaN NaN NaN \n", + "destratified_SE 19340000.0 0.4 NaN NaN NaN NaN NaN \n", + "\n", + " eps_m_2 c_m_3 eps_m_3 ... p_H_to_R r_H_to_R \\\n", + "original_stratified 0.5 0.5 0.5 ... 0.88 0.1 \n", + "destratified_SEI NaN NaN NaN ... 0.88 0.1 \n", + "destratified_SE NaN NaN NaN ... 0.88 0.1 \n", + "\n", + " p_H_to_D r_H_to_D p_noncompliant_compliant \\\n", + "original_stratified 0.12 0.1 0.1 \n", + "destratified_SEI 0.12 0.1 NaN \n", + "destratified_SE 0.12 0.1 0.1 \n", + "\n", + " p_compliant_noncompliant eps_m_lb eps_m_ub c_m_lb \\\n", + "original_stratified 0.1 NaN NaN NaN \n", + "destratified_SEI NaN 0.4 0.6 0.4 \n", + "destratified_SE 0.1 0.4 0.6 0.4 \n", + "\n", + " c_m_ub \n", + "original_stratified NaN \n", + "destratified_SEI 0.6 \n", + "destratified_SE 0.6 \n", + "\n", + "[3 rows x 25 columns]\n", + " S_lb I_compliant_lb I_noncompliant_lb E_lb \\\n", + "0.0 1.934000e+07 2.000000 2.000000 1.000000 \n", + "1.0 1.933999e+07 1.856914 1.856914 1.312741 \n", + "2.0 1.933999e+07 1.734484 1.734484 0.966472 \n", + "3.0 1.933999e+07 1.637028 1.637028 -0.145628 \n", + "4.0 1.933999e+07 1.569596 1.569596 -2.207799 \n", + "5.0 1.933998e+07 1.538748 1.538748 -5.499358 \n", + "\n", + " I_compliant_ub I_noncompliant_ub S_ub E_ub R_lb \\\n", + "0.0 2.000000 2.000000 1.934000e+07 1.000000 0.000000 \n", + "1.0 2.176475 2.176475 1.933999e+07 2.388952 0.219132 \n", + "2.0 2.614585 2.614585 1.933999e+07 3.983537 0.429208 \n", + "3.0 3.346622 3.346622 1.933999e+07 5.968534 0.631626 \n", + "4.0 4.439931 4.439931 1.933999e+07 8.562023 0.828268 \n", + "5.0 6.002302 6.002302 1.933999e+07 12.040496 1.021535 \n", + "\n", + " R_ub H_lb H_ub D_lb D_ub \n", + "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1.0 0.234965 0.073151 0.078884 0.000452 0.000474 \n", + "2.0 0.511299 0.133083 0.163189 0.001703 0.001915 \n", + "3.0 0.860794 0.179870 0.264931 0.003594 0.004460 \n", + "4.0 1.321923 0.212641 0.397988 0.005964 0.008398 \n", + "5.0 1.944254 0.229418 0.579655 0.008634 0.014205 \n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Remove SE stratification\n", "\n", "funman_request = get_request()\n", - "setup_common(funman_request, debug=True, dreal_precision=1e-1)\n", + "setup_common(funman_request, debug=True, dreal_precision=1e-0)\n", "# add_unit_test(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", - "results = run(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", - "report(results, \"destratified\", states=STATES_DESTRATIFIED_ALL)\n", - "# pass\n", - "# pts = results.parameter_space.points() \n", - "# print(f\"{len(pts)} points\")\n", - "# df = results.dataframe(points=pts[-1:])\n", - "# df" + "results = run(funman_request, model=models['destratified_SE'])\n", + "report(results, \"destratified_SE\", states=states['destratified_SE'])\n", + "# plot_bounds(results.points()[0], vars=[\"S\", \"E\", \"I\", \"R\", \"H\", \"D\"])\n", + "vars = results.model._state_var_names()\n", + "point = results.points()[0]\n", + "fig, axs = plot_bounds(point, vars=vars, basevar_map=basevar_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "[[0.0 1.000000\n", + " 1.0 1.900946\n", + " 2.0 2.659294\n", + " 3.0 3.339236\n", + " 4.0 3.986401\n", + " 5.0 4.634126\n", + " dtype: float64],\n", + " [None, None],\n", + " [0.0 1.000000\n", + " 1.0 1.711692\n", + " 2.0 2.251305\n", + " 3.0 2.682140\n", + " 4.0 3.053780\n", + " 5.0 3.408531\n", + " Name: E_lb_destratified_SEI, dtype: float64,\n", + " 0.0 1.000000\n", + " 1.0 2.060627\n", + " 2.0 3.008676\n", + " 3.0 3.903399\n", + " 4.0 4.781040\n", + " 5.0 5.660551\n", + " Name: E_ub_destratified_SEI, dtype: float64]]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "var = \"E\"\n", + "var_df = pd.DataFrame()\n", + "for name, result in request_results.items():\n", + " # vars = list(set([v.rsplit(\"_\", 1)[0] for v in result.model._state_var_names()]))\n", + " vars = result.model._state_var_names()\n", + " point = result.points()[0]\n", + " tdf = result.points()[0].simulation.dataframe().T\n", + "\n", + " var_cols = tdf.columns.map(lambda x: x.startswith(var))\n", + " # tdf.loc[var_cols.T]\n", + " var_data = tdf.T.loc[var_cols].T\n", + " var_data.columns = [f\"{col}_{name}\" for col in var_data.columns]\n", + " print()\n", + " var_df = pd.concat([var_df, var_data], axis=1)\n", + "\n", + "\n", + "og = var_df[f\"{var}_compliant_original_stratified\"] + var_df[f\"{var}_noncompliant_original_stratified\"] if f\"{var}_compliant_original_stratified\" in var_df.columns else None\n", + "ub_SE = var_df[f\"{var}_noncompliant_ub_destratified_SE\"] + var_df[f\"{var}_compliant_ub_destratified_SE\"] if f\"{var}_noncompliant_ub_destratified_SE\" in var_df.columns else None\n", + "lb_SE = var_df[f\"{var}_noncompliant_lb_destratified_SE\"] + var_df[f\"{var}_compliant_lb_destratified_SE\"] if f\"{var}_noncompliant_lb_destratified_SE\" in var_df.columns else None\n", + "ub_SEI = var_df[f\"{var}_ub_destratified_SEI\"] if f\"{var}_ub_destratified_SEI\" in var_df.columns else None\n", + "lb_SEI = var_df[f\"{var}_lb_destratified_SEI\"] if f\"{var}_lb_destratified_SEI\" in var_df.columns else None\n", + "[[og], [lb_SE, ub_SE], [lb_SEI, ub_SEI]]" ] }, { @@ -599,9 +723,68 @@ "execution_count": 8, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " S_compliant I_compliant E_compliant I_noncompliant S_noncompliant \\\n", + "0.0 9.669998e+06 2.000000 0.500000 2.000000 9.669998e+06 \n", + "1.0 9.669997e+06 1.995317 0.950473 1.995317 9.669997e+06 \n", + "2.0 9.669996e+06 2.070171 1.329647 2.070171 9.669996e+06 \n", + "3.0 9.669996e+06 2.208319 1.669618 2.208319 9.669996e+06 \n", + "4.0 9.669995e+06 2.399924 1.993201 2.399924 9.669995e+06 \n", + "5.0 9.669994e+06 2.639622 2.317063 2.639622 9.669994e+06 \n", + "\n", + " E_noncompliant R H D \n", + "0.0 0.500000 0.000000 0.000000 0.000000 \n", + "1.0 0.950473 0.226285 0.075753 0.000462 \n", + "2.0 1.329647 0.463073 0.145723 0.001795 \n", + "3.0 1.669618 0.717932 0.213148 0.003950 \n", + "4.0 1.993201 0.997238 0.280472 0.006910 \n", + "5.0 2.317063 1.306723 0.349626 0.010688 \n", + " S_lb I_lb E_lb I_ub S_ub E_ub \\\n", + "0.0 1.934000e+07 4.000000 1.000000 4.000000 1.934000e+07 1.000000 \n", + "1.0 1.933999e+07 3.971775 1.711692 4.006552 1.933999e+07 2.060627 \n", + "2.0 1.933999e+07 4.059282 2.251305 4.210187 1.933999e+07 3.008676 \n", + "3.0 1.933999e+07 4.220733 2.682140 4.587644 1.933999e+07 3.903399 \n", + "4.0 1.933999e+07 4.426770 3.053780 5.127773 1.933999e+07 4.781040 \n", + "5.0 1.933999e+07 4.657683 3.408531 5.826721 1.933999e+07 5.660551 \n", + "\n", + " R_lb R_ub H_lb H_ub D_lb D_ub \n", + "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1.0 0.225941 0.226573 0.075633 0.075854 0.000462 0.000463 \n", + "2.0 0.460189 0.465522 0.144729 0.146571 0.001789 0.001800 \n", + "3.0 0.707717 0.726702 0.209686 0.216141 0.003919 0.003976 \n", + "4.0 0.971842 1.019198 0.272003 0.287859 0.006811 0.006996 \n", + "5.0 1.254785 1.351797 0.332577 0.364572 0.010440 0.010904 \n", + " S_lb I_compliant_lb I_noncompliant_lb E_lb \\\n", + "0.0 1.934000e+07 2.000000 2.000000 1.000000 \n", + "1.0 1.933999e+07 1.856914 1.856914 1.312741 \n", + "2.0 1.933999e+07 1.734484 1.734484 0.966472 \n", + "3.0 1.933999e+07 1.637028 1.637028 -0.145628 \n", + "4.0 1.933999e+07 1.569596 1.569596 -2.207799 \n", + "5.0 1.933998e+07 1.538748 1.538748 -5.499358 \n", + "\n", + " I_compliant_ub I_noncompliant_ub S_ub E_ub R_lb \\\n", + "0.0 2.000000 2.000000 1.934000e+07 1.000000 0.000000 \n", + "1.0 2.176475 2.176475 1.933999e+07 2.388952 0.219132 \n", + "2.0 2.614585 2.614585 1.933999e+07 3.983537 0.429208 \n", + "3.0 3.346622 3.346622 1.933999e+07 5.968534 0.631626 \n", + "4.0 4.439931 4.439931 1.933999e+07 8.562023 0.828268 \n", + "5.0 6.002302 6.002302 1.933999e+07 12.040496 1.021535 \n", + "\n", + " R_ub H_lb H_ub D_lb D_ub \n", + "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1.0 0.234965 0.073151 0.078884 0.000452 0.000474 \n", + "2.0 0.511299 0.133083 0.163189 0.001703 0.001915 \n", + "3.0 0.860794 0.179870 0.264931 0.003594 0.004460 \n", + "4.0 1.321923 0.212641 0.397988 0.005964 0.008398 \n", + "5.0 1.944254 0.229418 0.579655 0.008634 0.014205 \n" + ] + }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -611,42 +794,26 @@ } ], "source": [ - "point = results.points()[0]\n", - "vars = [\"S\", \"E\", \"I\", \"R\", \"D\", \"H\"]\n", - "# values = point.values #.simulation.dataframe().T\n", - "# values[\"S_ub_1\"]- values[\"S_lb_1\"]\n", - "df = point.simulation.dataframe().T\n", - "\n", - "# sns.FacetGrid(df)\n", - "\n", - "# var = \"R\"\n", - "# lb = df[[f\"{var}_lb\"]]\n", - "# ub = df[[f\"{var}_ub\"]]\n", - "\n", - "# plt.title(f\"Bounds\")\n", - "# ax, fig = plt.multiplot\n", - "# plt.plot(lb, linestyle=\"dotted\", color=\"black\")\n", - "# plt.plot(ub, linestyle=\"solid\", color=\"black\")\n", - "# plt.scatter(lb, ub)\n", - "\n", - "# plt.figure(figsize=(10,len(vars)*10))\n", - "\n", - "fig, axs = plt.subplots(len(vars))\n", - "fig.set_figheight(3*len(vars))\n", - "fig.suptitle('Variable Bounds over time')\n", - "for i, var in enumerate(vars):\n", - " labels = [f\"{var}_lb\", f\"{var}_ub\"]\n", - " lb = df[labels]\n", - " # axs[i].set_label(labels)\n", + "\n", + "\n", + "# point = results.points()[0]\n", + "# plot_bounds(point)\n", + "# points = {k:v.points()[0] for k,v in request_results.items()}\n", + "fig = None\n", + "axs = None\n", + "\n", + "\n", + "for name, result in request_results.items():\n", + " # vars = list(set([v.rsplit(\"_\", 1)[0] for v in result.model._state_var_names()]))\n", + " vars = result.model._state_var_names()\n", + " point = result.points()[0]\n", + " # print(name)\n", + " # print(vars)\n", + " # print(point.simulation.dataframe())\n", + " \n", + " fig, axs = plot_bounds(point, vars=vars, fig=fig, axs=axs, basevar_map=basevar_map, alpha=0.5, linewidth=3)\n", " \n", - " # ub = df[[]]\n", - " axs[i].set_title(f\"{var} Bounds\")\n", - " # axs[i].legend(labels[0])\n", - " axs[i].plot(lb, label=labels) #,linestyle=\"dotted\", color=\"black\")\n", - " axs[i].legend(loc=\"lower left\")\n", - " # axs[i].set_yscale('logit')\n", - " # axs[1].plot(ub, linestyle=\"solid\", color=\"black\")\n", - "fig.tight_layout()" + "# request_results" ] } ], diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_SE.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_SE.json new file mode 100644 index 00000000..87375e65 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_SE.json @@ -0,0 +1,879 @@ +{ + "header": { + "name": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", + "model_version": "0.1", + "properties": {} + }, + "model": { + "states": [ + { + "id": "S_lb", + "name": "S_lb", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_compliant_lb", + "name": "I_compliant_lb", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_noncompliant_lb", + "name": "I_noncompliant_lb", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_lb", + "name": "E_lb", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_compliant_ub", + "name": "I_compliant_ub", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_noncompliant_ub", + "name": "I_noncompliant_ub", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "S_ub", + "name": "S_ub", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_ub", + "name": "E_ub", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_lb", + "name": "R_lb", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_ub", + "name": "R_ub", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_lb", + "name": "H_lb", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_ub", + "name": "H_ub", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D_lb", + "name": "D_lb", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D_ub", + "name": "D_ub", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1_and_4_lb", + "input": [ + "I_compliant_ub", + "S_ub" + ], + "output": [ + "I_compliant_ub", + "E_lb" + ], + "properties": { + "name": "t1_and_4_lb" + } + }, + { + "id": "t1_and_4_ub", + "input": [ + "I_compliant_lb", + "S_lb" + ], + "output": [ + "I_compliant_lb", + "E_ub" + ], + "properties": { + "name": "t1_and_4_ub" + } + }, + { + "id": "t2_and_3_lb", + "input": [ + "I_noncompliant_ub", + "S_ub" + ], + "output": [ + "I_noncompliant_ub", + "E_lb" + ], + "properties": { + "name": "t2_and_3_lb" + } + }, + { + "id": "t2_and_3_ub", + "input": [ + "I_noncompliant_lb", + "S_lb" + ], + "output": [ + "I_noncompliant_lb", + "E_ub" + ], + "properties": { + "name": "t2_and_3_ub" + } + }, + { + "id": "t5_lb", + "input": [ + "E_ub" + ], + "output": [ + "I_compliant_lb" + ], + "properties": { + "name": "t5_lb" + } + }, + { + "id": "t5_ub", + "input": [ + "E_lb" + ], + "output": [ + "I_compliant_ub" + ], + "properties": { + "name": "t5_ub" + } + }, + { + "id": "t6_lb", + "input": [ + "E_ub" + ], + "output": [ + "I_noncompliant_lb" + ], + "properties": { + "name": "t6_lb" + } + }, + { + "id": "t6_ub", + "input": [ + "E_lb" + ], + "output": [ + "I_noncompliant_ub" + ], + "properties": { + "name": "t6_ub" + } + }, + { + "id": "t7_lb", + "input": [ + "I_compliant_ub" + ], + "output": [ + "R_lb" + ], + "properties": { + "name": "t7_lb" + } + }, + { + "id": "t7_ub", + "input": [ + "I_compliant_lb" + ], + "output": [ + "R_ub" + ], + "properties": { + "name": "t7_ub" + } + }, + { + "id": "t8_lb", + "input": [ + "I_noncompliant_ub" + ], + "output": [ + "R_lb" + ], + "properties": { + "name": "t8_lb" + } + }, + { + "id": "t8_ub", + "input": [ + "I_noncompliant_lb" + ], + "output": [ + "R_ub" + ], + "properties": { + "name": "t8_ub" + } + }, + { + "id": "t9_lb", + "input": [ + "I_compliant_ub" + ], + "output": [ + "H_lb" + ], + "properties": { + "name": "t9_lb" + } + }, + { + "id": "t9_ub", + "input": [ + "I_compliant_lb" + ], + "output": [ + "H_ub" + ], + "properties": { + "name": "t9_ub" + } + }, + { + "id": "t10_lb", + "input": [ + "I_noncompliant_ub" + ], + "output": [ + "H_lb" + ], + "properties": { + "name": "t10_lb" + } + }, + { + "id": "t10_ub", + "input": [ + "I_noncompliant_lb" + ], + "output": [ + "H_ub" + ], + "properties": { + "name": "t10_ub" + } + }, + { + "id": "t11_lb", + "input": [ + "H_ub" + ], + "output": [ + "R_lb" + ], + "properties": { + "name": "t11_lb" + } + }, + { + "id": "t11_ub", + "input": [ + "H_lb" + ], + "output": [ + "R_ub" + ], + "properties": { + "name": "t11_ub" + } + }, + { + "id": "t12_lb", + "input": [ + "H_lb" + ], + "output": [ + "D_lb" + ], + "properties": { + "name": "t12_lb" + } + }, + { + "id": "t12_ub", + "input": [ + "H_ub" + ], + "output": [ + "D_ub" + ], + "properties": { + "name": "t12_ub" + } + }, + { + "id": "t17_lb", + "input": [ + "I_noncompliant_ub" + ], + "output": [ + "I_compliant_lb" + ], + "properties": { + "name": "t17_lb" + } + }, + { + "id": "t17_ub", + "input": [ + "I_noncompliant_lb" + ], + "output": [ + "I_compliant_ub" + ], + "properties": { + "name": "t17_ub" + } + }, + { + "id": "t18_lb", + "input": [ + "I_compliant_ub" + ], + "output": [ + "I_noncompliant_lb" + ], + "properties": { + "name": "t18_lb" + } + }, + { + "id": "t18_ub", + "input": [ + "I_compliant_lb" + ], + "output": [ + "I_noncompliant_ub" + ], + "properties": { + "name": "t18_ub" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1_and_4_lb", + "expression": "I_compliant_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" + }, + { + "target": "t1_and_4_ub", + "expression": "I_compliant_ub*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" + }, + { + "target": "t2_and_3_lb", + "expression": "I_noncompliant_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" + }, + { + "target": "t2_and_3_ub", + "expression": "I_noncompliant_ub*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" + }, + { + "target": "t5_lb", + "expression": "0", + "expression_mathml": "E_lbr_E_to_I" + }, + { + "target": "t5_ub", + "expression": "E_ub*r_E_to_I", + "expression_mathml": "E_ubr_E_to_I" + }, + { + "target": "t6_lb", + "expression": "0", + "expression_mathml": "E_lbr_E_to_I" + }, + { + "target": "t6_ub", + "expression": "E_ub*r_E_to_I", + "expression_mathml": "E_ubr_E_to_I" + }, + { + "target": "t7_lb", + "expression": "I_compliant_lb*p_I_to_R*r_I_to_R", + "expression_mathml": "I_lbp_I_to_Rr_I_to_R" + }, + { + "target": "t7_ub", + "expression": "I_compliant_ub*p_I_to_R*r_I_to_R", + "expression_mathml": "I_ubp_I_to_Rr_I_to_R" + }, + { + "target": "t8_lb", + "expression": "I_noncompliant_lb*p_I_to_R*r_I_to_R", + "expression_mathml": "I_lbp_I_to_Rr_I_to_R" + }, + { + "target": "t8_ub", + "expression": "I_noncompliant_ub*p_I_to_R*r_I_to_R", + "expression_mathml": "I_ubp_I_to_Rr_I_to_R" + }, + { + "target": "t9_lb", + "expression": "I_compliant_lb*p_I_to_H*r_I_to_H", + "expression_mathml": "I_lbp_I_to_Hr_I_to_H" + }, + { + "target": "t9_ub", + "expression": "I_compliant_ub*p_I_to_H*r_I_to_H", + "expression_mathml": "I_ubp_I_to_Hr_I_to_H" + }, + { + "target": "t10_lb", + "expression": "I_noncompliant_lb*p_I_to_H*r_I_to_H", + "expression_mathml": "I_lbp_I_to_Hr_I_to_H" + }, + { + "target": "t10_ub", + "expression": "I_noncompliant_ub*p_I_to_H*r_I_to_H", + "expression_mathml": "I_ubp_I_to_Hr_I_to_H" + }, + { + "target": "t11_lb", + "expression": "H_lb*p_H_to_R*r_H_to_R", + "expression_mathml": "H_lbp_H_to_Rr_H_to_R" + }, + { + "target": "t11_ub", + "expression": "H_ub*p_H_to_R*r_H_to_R", + "expression_mathml": "H_ubp_H_to_Rr_H_to_R" + }, + { + "target": "t12_lb", + "expression": "H_lb*p_H_to_D*r_H_to_D", + "expression_mathml": "Hp_H_to_Dr_H_to_D" + }, + { + "target": "t12_ub", + "expression": "H_ub*p_H_to_D*r_H_to_D", + "expression_mathml": "H_ubp_H_to_Dr_H_to_D" + }, + { + "target": "t17_lb", + "expression": "I_noncompliant_ub*p_noncompliant_compliant", + "expression_mathml": "I_noncompliantp_noncompliant_compliant" + }, + { + "target": "t17_ub", + "expression": "I_noncompliant_lb*p_noncompliant_compliant", + "expression_mathml": "I_noncompliantp_noncompliant_compliant" + }, + { + "target": "t18_lb", + "expression": "I_compliant_ub*p_compliant_noncompliant", + "expression_mathml": "I_compliantp_compliant_noncompliant" + }, + { + "target": "t18_ub", + "expression": "I_compliant_lb*p_compliant_noncompliant", + "expression_mathml": "I_compliantp_compliant_noncompliant" + } + ], + "initials": [ + { + "target": "S_lb", + "expression": "19339995.00000000", + "expression_mathml": "9669997.5" + }, + { + "target": "I_compliant_lb", + "expression": "2.00000000000000", + "expression_mathml": "2.0" + }, + { + "target": "I_noncompliant_lb", + "expression": "2.00000000000000", + "expression_mathml": "2.0" + }, + { + "target": "E_lb", + "expression": "1.000000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "S_ub", + "expression": "19339995.00000000", + "expression_mathml": "9669997.5" + }, + { + "target": "I_compliant_ub", + "expression": "2.00000000000000", + "expression_mathml": "2.0" + }, + { + "target": "I_noncompliant_ub", + "expression": "2.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E_ub", + "expression": "1.000000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R_ub", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H_ub", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D_ub", + "expression": "0.0", + "expression_mathml": "0.0" + } + ], + "parameters": [ + { + "id": "N", + "value": 19340000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta", + "value": 0.4, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "eps_m_lb", + "value": 0.4, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "eps_m_ub", + "value": 0.6, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "c_m_lb", + "value": 0.4, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "c_m_ub", + "value": 0.6, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_E_to_I", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_R", + "value": 0.8, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_R", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_H", + "value": 0.2, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_H", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_R", + "value": 0.88, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_R", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_D", + "value": 0.12, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_D", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_noncompliant_compliant", + "value": 0.1 + }, + { + "id": "p_compliant_noncompliant", + "value": 0.1 + } + ], + "observables": [], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + } + }, + "metadata": { + "annotations": { + "license": null, + "authors": [], + "references": [], + "time_scale": null, + "time_start": null, + "time_end": null, + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json index b73ba709..626f1e7d 100644 --- a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json @@ -322,7 +322,7 @@ { "id": "t12_lb", "input": [ - "H_ub" + "H_lb" ], "output": [ "D_lb" @@ -334,7 +334,7 @@ { "id": "t12_ub", "input": [ - "H_lb" + "H_ub" ], "output": [ "D_ub" @@ -350,12 +350,12 @@ "rates": [ { "target": "t1_to_4_lb", - "expression": "I_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression": "I_ub*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" }, { "target": "t1_to_4_ub", - "expression": "I_ub*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression": "I_lb*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" }, { @@ -390,12 +390,12 @@ }, { "target": "t11_lb", - "expression": "H_lb*p_H_to_R*r_H_to_R", + "expression": "H_ub*p_H_to_R*r_H_to_R", "expression_mathml": "H_lbp_H_to_Rr_H_to_R" }, { "target": "t11_ub", - "expression": "H_ub*p_H_to_R*r_H_to_R", + "expression": "H_lb*p_H_to_R*r_H_to_R", "expression_mathml": "H_ubp_H_to_Rr_H_to_R" }, { diff --git a/src/funman/representation/interval.py b/src/funman/representation/interval.py index d2df0ff4..c52963dd 100644 --- a/src/funman/representation/interval.py +++ b/src/funman/representation/interval.py @@ -16,7 +16,7 @@ import funman.utils.math_utils as math_utils from funman.constants import NEG_INFINITY, POS_INFINITY -l = logging.Logger(__name__) +l = logging.getLogger(__name__) class Interval(BaseModel): diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index ebf22da1..e5dd5450 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -408,7 +408,7 @@ def _logger(self, config, process_name=None): else: if not process_name: process_name = "BoxSearch" - l = logging.Logger(process_name) + l = logging.getLogger(process_name) return l def _handle_empty_queue( diff --git a/src/funman/search/simulate.py b/src/funman/search/simulate.py index 34b807cc..8509bba2 100644 --- a/src/funman/search/simulate.py +++ b/src/funman/search/simulate.py @@ -33,11 +33,11 @@ def pfunc(t): def initial_state(self) -> List[float]: init_state = [ ( - sympy.sympify(v).evalf(subs=self.parameters) - if isinstance(v, str) - else v + sympy.sympify(self.init[var]).evalf(subs=self.parameters) + if isinstance(self.init[var], str) + else self.init[var] ) - for var, v in self.init.items() + for var in self.model._state_var_names() ] return tuple(init_state) From e1e5a24af7bb225b2759d46541f767164d122407 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 4 Sep 2024 17:21:50 +0000 Subject: [PATCH 28/93] fix log level inheritance, speedup odeint with sympy lambdas instead of evalf, model tweaks --- .../funman_aug_2024_12mo_eval_1_q1_demo.ipynb | 447 +++++++----------- ...val_scenario1_1_ii_3_destratified_all.json | 8 +- src/funman/config.py | 2 +- src/funman/model/petrinet.py | 35 +- src/funman/representation/interval.py | 2 + src/funman/utils/logging.py | 4 + 6 files changed, 210 insertions(+), 288 deletions(-) diff --git a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb index c414ce51..6e380c24 100644 --- a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb +++ b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb @@ -59,8 +59,8 @@ "request_results = {}\n", "\n", "# Cycle styles for lines\n", - "plt.rcParams['axes.prop_cycle'] = (\"cycler('color', 'rgb') +\"\n", - " \"cycler('lw', [1, 2, 3])\")\n", + "# plt.rcParams['axes.prop_cycle'] = (\"cycler('color', 'rgb') +\"\n", + " # \"cycler('lw', [1, 2, 3])\")\n", "\n", "# %load_ext autoreload\n", "# %autoreload 2" @@ -74,14 +74,14 @@ "source": [ "# Constants for the scenario\n", "\n", - "MAX_TIME=5\n", - "STEP_SIZE=1\n", + "MAX_TIME=30\n", + "STEP_SIZE=5\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ " funman_request.config.save_smtlib=\"./out\"\n", " funman_request.config.tolerance = 0.01\n", " funman_request.config.dreal_precision = dreal_precision\n", - " funman_request.config.verbosity = logging.INFO\n", + " funman_request.config.verbosity = logging.ERROR\n", " # funman_request.config.dreal_log_level = \"debug\"\n", " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", "\n", @@ -256,64 +256,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-09-04 15:23:20,192 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,193 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,193 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,193 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,194 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,194 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,194 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,195 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,196 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,196 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,196 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,197 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,197 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,198 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,198 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,198 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,199 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,199 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,199 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,200 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,200 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:20,702 - funman.scenario.consistency - INFO - 5{5}:\t[+]\n", - "2024-09-04 15:23:22,206 - funman.api.run - INFO - Dumping results to ./out/bbdc4d3f-ab58-4a43-a9c2-8153f3df374b.json\n", - "2024-09-04 15:23:32,255 - funman.api.run - INFO - Dumping results to ./out/bbdc4d3f-ab58-4a43-a9c2-8153f3df374b.json\n", - "2024-09-04 15:23:37,351 - funman.scenario.scenario - INFO - simulation passed verification\n", - "2024-09-04 15:23:37,352 - funman.scenario.consistency - INFO - Simulation Time: 0:00:16.649416\n", - "2024-09-04 15:23:37,374 - funman.server.worker - INFO - Completed work on: bbdc4d3f-ab58-4a43-a9c2-8153f3df374b\n", - "2024-09-04 15:23:42,312 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-09-04 15:23:42,405 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-09-04 15:23:42,408 - funman.server.worker - INFO - Worker.stop() completed.\n", - "2024-09-04 15:23:42,410 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,411 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,412 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,413 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,413 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,414 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,415 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,416 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,416 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,417 - funman.representation.interval - WARNING - [0.50000, 0.50000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,418 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,419 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,420 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,420 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,422 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,423 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,424 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,424 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,425 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,426 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,427 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n" + "2024-09-04 17:17:41,879 - funman.server.worker - INFO - FunmanWorker running...\n", + "2024-09-04 17:17:41,883 - funman.server.worker - INFO - Starting work on: 753f64af-4c62-46ab-ad6f-4723d0aff0c3\n" ] }, { @@ -341,7 +292,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -371,55 +322,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-09-04 15:23:42,710 - funman.server.worker - INFO - FunmanWorker running...\n", - "2024-09-04 15:23:42,713 - funman.server.worker - INFO - Starting work on: d609bf39-37bf-4a71-88cb-707eb9a1237d\n", - "2024-09-04 15:23:42,714 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,714 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,715 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,715 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,715 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,715 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,716 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,716 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,717 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,717 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,718 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,718 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,718 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,719 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:42,720 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:43,220 - funman.scenario.consistency - INFO - 5{5}:\t[+]\n", - "2024-09-04 15:23:44,724 - funman.api.run - INFO - Dumping results to ./out/d609bf39-37bf-4a71-88cb-707eb9a1237d.json\n", - "2024-09-04 15:23:52,979 - funman.scenario.scenario - INFO - simulation passed verification\n", - "2024-09-04 15:23:52,979 - funman.scenario.consistency - INFO - Simulation Time: 0:00:09.758635\n", - "2024-09-04 15:23:52,999 - funman.server.worker - INFO - Completed work on: d609bf39-37bf-4a71-88cb-707eb9a1237d\n", - "2024-09-04 15:23:54,791 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-09-04 15:23:55,017 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-09-04 15:23:55,019 - funman.server.worker - INFO - Worker.stop() completed.\n", - "2024-09-04 15:23:55,022 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,023 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,023 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,024 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,025 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,026 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,027 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,027 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,028 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,029 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,029 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,030 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,030 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,031 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,031 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -446,26 +351,37 @@ "destratified_SEI 0.6 \n", "\n", "[2 rows x 25 columns]\n", - " S_lb I_lb E_lb I_ub S_ub E_ub \\\n", - "0.0 1.934000e+07 4.000000 1.000000 4.000000 1.934000e+07 1.000000 \n", - "1.0 1.933999e+07 3.971775 1.711692 4.006552 1.933999e+07 2.060627 \n", - "2.0 1.933999e+07 4.059282 2.251305 4.210187 1.933999e+07 3.008676 \n", - "3.0 1.933999e+07 4.220733 2.682140 4.587644 1.933999e+07 3.903399 \n", - "4.0 1.933999e+07 4.426770 3.053780 5.127773 1.933999e+07 4.781040 \n", - "5.0 1.933999e+07 4.657683 3.408531 5.826721 1.933999e+07 5.660551 \n", + " S_lb I_lb E_lb I_ub S_ub \\\n", + "0.0 1.934000e+07 4.000000 1.000000 4.000000 1.934000e+07 \n", + "5.0 1.933999e+07 4.497161 2.733767 6.016389 1.933999e+07 \n", + "10.0 1.933997e+07 0.990951 -5.282224 17.363698 1.933998e+07 \n", + "15.0 1.933990e+07 -45.330407 -81.668704 80.882919 1.934000e+07 \n", + "20.0 1.933952e+07 -409.351989 -650.824792 496.780914 1.934022e+07 \n", + "25.0 1.933701e+07 -3031.710702 -4702.217340 3362.960448 1.934191e+07 \n", + "30.0 1.931968e+07 -21550.678810 -33227.157234 23385.814862 1.935400e+07 \n", "\n", - " R_lb R_ub H_lb H_ub D_lb D_ub \n", - "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1.0 0.225941 0.226573 0.075633 0.075854 0.000462 0.000463 \n", - "2.0 0.460189 0.465522 0.144729 0.146571 0.001789 0.001800 \n", - "3.0 0.707717 0.726702 0.209686 0.216141 0.003919 0.003976 \n", - "4.0 0.971842 1.019198 0.272003 0.287859 0.006811 0.006996 \n", - "5.0 1.254785 1.351797 0.332577 0.364572 0.010440 0.010904 \n" + " E_ub R_lb R_ub H_lb H_ub \\\n", + "0.0 1.000000 0.000000 0.000000 0.000000 0.000000 \n", + "5.0 6.468573 1.242755 1.365454 0.325790 0.371883 \n", + "10.0 23.307062 2.455659 4.541286 0.367607 1.179461 \n", + "15.0 121.639896 -1.733940 16.899058 -2.191968 5.209236 \n", + "20.0 779.832491 -53.778527 86.570912 -24.938423 31.340085 \n", + "25.0 5343.699263 -459.065706 546.065956 -193.020999 211.544110 \n", + "30.0 37279.471642 -3373.544262 3719.819389 -1387.597058 1471.237197 \n", + "\n", + " D_lb D_ub \n", + "0.0 0.000000 0.000000 \n", + "5.0 0.010369 0.010981 \n", + "10.0 0.034436 0.052471 \n", + "15.0 0.009014 0.210011 \n", + "20.0 -0.585975 1.070793 \n", + "25.0 -5.564188 6.709912 \n", + "30.0 -41.988538 45.643410 \n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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" ] @@ -500,61 +416,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-09-04 15:23:55,838 - funman.server.worker - INFO - FunmanWorker running...\n", - "2024-09-04 15:23:55,842 - funman.server.worker - INFO - Starting work on: 54faa50d-80a7-4b89-8ee0-20987c4818a0\n", - "2024-09-04 15:23:55,842 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,843 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,843 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,844 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,844 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,845 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,845 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,846 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,847 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,847 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,848 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,848 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,849 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,851 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,851 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,852 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:55,853 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:23:56,822 - funman.scenario.consistency - INFO - 5{5}:\t[+]\n", - "2024-09-04 15:23:57,847 - funman.api.run - INFO - Dumping results to ./out/54faa50d-80a7-4b89-8ee0-20987c4818a0.json\n", - "2024-09-04 15:24:07,898 - funman.api.run - INFO - Dumping results to ./out/54faa50d-80a7-4b89-8ee0-20987c4818a0.json\n", - "2024-09-04 15:24:17,968 - funman.api.run - INFO - Dumping results to ./out/54faa50d-80a7-4b89-8ee0-20987c4818a0.json\n", - "2024-09-04 15:24:23,075 - funman.scenario.scenario - INFO - simulation passed verification\n", - "2024-09-04 15:24:23,076 - funman.scenario.consistency - INFO - Simulation Time: 0:00:26.253224\n", - "2024-09-04 15:24:23,101 - funman.server.worker - INFO - Completed work on: 54faa50d-80a7-4b89-8ee0-20987c4818a0\n", - "2024-09-04 15:24:28,039 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n", - "2024-09-04 15:24:28,137 - funman.server.worker - INFO - FunmanWorker exiting...\n", - "2024-09-04 15:24:28,140 - funman.server.worker - INFO - Worker.stop() completed.\n", - "2024-09-04 15:24:28,143 - funman.representation.interval - WARNING - [19340000.00000, 19340000.00000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,144 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,145 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,145 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,146 - funman.representation.interval - WARNING - [0.40000, 0.40000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,147 - funman.representation.interval - WARNING - [0.60000, 0.60000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,147 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,148 - funman.representation.interval - WARNING - [0.80000, 0.80000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,148 - funman.representation.interval - WARNING - [0.07000, 0.07000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,150 - funman.representation.interval - WARNING - [0.20000, 0.20000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,150 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,151 - funman.representation.interval - WARNING - [0.88000, 0.88000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,152 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,152 - funman.representation.interval - WARNING - [0.12000, 0.12000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,153 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,153 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n", - "2024-09-04 15:24:28,154 - funman.representation.interval - WARNING - [0.10000, 0.10000) has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -586,34 +450,37 @@ "destratified_SE 0.6 \n", "\n", "[3 rows x 25 columns]\n", - " S_lb I_compliant_lb I_noncompliant_lb E_lb \\\n", - "0.0 1.934000e+07 2.000000 2.000000 1.000000 \n", - "1.0 1.933999e+07 1.856914 1.856914 1.312741 \n", - "2.0 1.933999e+07 1.734484 1.734484 0.966472 \n", - "3.0 1.933999e+07 1.637028 1.637028 -0.145628 \n", - "4.0 1.933999e+07 1.569596 1.569596 -2.207799 \n", - "5.0 1.933998e+07 1.538748 1.538748 -5.499358 \n", + " S_lb I_compliant_lb I_noncompliant_lb E_lb \\\n", + "0.0 1.934000e+07 2.000000 2.000000 1.000000 \n", + "5.0 1.933998e+07 1.538748 1.538748 -5.499358 \n", + "10.0 1.933993e+07 2.403047 2.403047 -61.659290 \n", + "15.0 1.933969e+07 8.735326 8.735326 -352.858362 \n", + "20.0 1.933848e+07 41.446223 41.446223 -1806.600211 \n", + "25.0 1.933248e+07 204.708540 204.708540 -9032.715141 \n", + "30.0 1.930267e+07 1016.201991 1016.201991 -44932.903807 \n", "\n", - " I_compliant_ub I_noncompliant_ub S_ub E_ub R_lb \\\n", - "0.0 2.000000 2.000000 1.934000e+07 1.000000 0.000000 \n", - "1.0 2.176475 2.176475 1.933999e+07 2.388952 0.219132 \n", - "2.0 2.614585 2.614585 1.933999e+07 3.983537 0.429208 \n", - "3.0 3.346622 3.346622 1.933999e+07 5.968534 0.631626 \n", - "4.0 4.439931 4.439931 1.933999e+07 8.562023 0.828268 \n", - "5.0 6.002302 6.002302 1.933999e+07 12.040496 1.021535 \n", + " I_compliant_ub I_noncompliant_ub S_ub E_ub \\\n", + "0.0 2.000000 2.000000 1.934000e+07 1.000000 \n", + "5.0 6.002302 6.002302 1.933999e+07 12.040496 \n", + "10.0 29.263668 29.263668 1.933999e+07 61.181677 \n", + "15.0 145.260976 145.260976 1.933997e+07 304.408321 \n", + "20.0 721.575492 721.575492 1.933992e+07 1512.461075 \n", + "25.0 3584.553250 3584.553250 1.933966e+07 7513.575138 \n", + "30.0 17806.618357 17806.618357 1.933836e+07 37323.775445 \n", "\n", - " R_ub H_lb H_ub D_lb D_ub \n", - "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1.0 0.234965 0.073151 0.078884 0.000452 0.000474 \n", - "2.0 0.511299 0.133083 0.163189 0.001703 0.001915 \n", - "3.0 0.860794 0.179870 0.264931 0.003594 0.004460 \n", - "4.0 1.321923 0.212641 0.397988 0.005964 0.008398 \n", - "5.0 1.944254 0.229418 0.579655 0.008634 0.014205 \n" + " R_lb R_ub H_lb H_ub D_lb D_ub \n", + "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "5.0 1.021535 1.944254 0.229418 0.579655 0.008634 0.014205 \n", + "10.0 2.078499 10.835730 -0.123649 3.349768 0.016724 0.109833 \n", + "15.0 4.214900 55.198690 -2.938781 17.806471 -0.051529 0.631444 \n", + "20.0 12.160518 276.291622 -18.005395 90.761773 -0.561586 3.324661 \n", + "25.0 48.980738 1375.948949 -94.261809 454.785150 -3.339744 16.887494 \n", + "30.0 228.832997 6840.843005 -475.103888 2265.362927 -17.486658 84.561137 \n" ] }, { "data": { - "image/png": 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SEhpNWIyMHq1QEEVRVcbLPPQkJixERAagqZdlpKhLly5IT09vtI6joyMAoKioCD169AAAtQW4T8rOzoabmxsA4M6dO7h48SJ8fHy0Fi9JExMWIiLSitu3b+PNN9/E5MmT4evrCysrK5w6dQrJycmIiIhotK25uTn69u2LpKQkeHh4oKSkBB9++GGDdZcsWQJ7e3s4OTnhL3/5CxwcHBAZGdkCZ0RSwoSFiIi0Qi6XIzAwECtXrsSVK1dQXV0NV1dXTJs2DQsWLHhm+02bNmHKlCno1asXvL29kZycjFdffbVevaSkJMyaNQuXLl2Cv78/vv32W5iamrbEKZGECOKTFwwNVHl5OaytrVFWVoa2bdvqOxwioufy4MEDFBQUwMPDAzKZTN/hED23p/1Na/L9zeewEBERkeQxYSEiohb39ddfqx4s9/uta9eu+g6PDADXsBARUYsbNWoUAgMDGzzWpk0bHUdDhogJCxERtTgrKytYWVnpOwwyYLwkRERERJLHhIWIiIgkjwkLERERSR4TFiIiIpI8JixEREQkeUxYiIiI/r+srCwIgoDS0lIAQFpaGmxsbPQaEwBER0ervS9p8ODBmD17tt7i0QcmLEREpDW//2I1dGPHjsXFixe12mdhYSEEQXjq26ipYXwOCxER0VOYm5vD3Nxc32EQOMNCRCRpdXXArVv63erqWuK86pCcnAxPT0+YmZnBzc0Nn3zyier42bNnMWTIEJibm8Pe3h7Tp09HRUWF6vjjmZxly5bByckJNjY2WLJkCWpqahAXFwc7Ozt06NABmzdvVrV5PLOxfft29OvXDzKZDN26dcPhw4efGufvLwlduXIFERERcHJyglwuR+/evXHo0CG1Nu7u7li2bBkmT54MKysruLm5Yf369arjHh4eAIAePXpAEAQMHjy4WZ9hTU0NYmJiYG1tDQcHByxcuBCt4H3GT8UZFiIiCbt9G2jXTr8xlJQAjo7a7TM+Ph5fffUVVq5ciQEDBqCoqAj5+fkAgMrKSoSGhiIoKAgnT55ESUkJpk6dipiYGKSlpan6+P7779GhQwccOXIER48exZQpU/Dzzz9j4MCBOH78OHbs2IF33nkHw4YNQ4cOHVTt4uLisGrVKnTp0gUpKSkIDw9HQUEB7O3tnxl3RUUFXnvtNXzyyScwMzPD1q1bER4ejgsXLsDNzU1Vb8WKFfj444+xYMEC/OMf/8CMGTMwaNAgeHt748SJE+jTpw8OHTqErl27wtTUtFmf4ZYtWzBlyhScOHECp06dwvTp0+Hm5oZp06Y1qz/JEzV0+PBhceTIkaKzs7MIQNy9e3ej9X/44QcRQL2tqKhIrd6aNWvEjh07imZmZmKfPn3E48ePNzmmsrIyEYBYVlam6ekQEUnO/fv3xfPnz4v3798XS0pEEdDvVlLS9NijoqLEiIiIRuuUl5eLZmZm4ldffdXg8fXr14u2trZiRUWFqmz//v2ikZGRqFQqVeN07NhRrK2tVdXx9vYWX3nlFdV+TU2NaGlpKX7zzTeiKIpiQUGBCEBMSkpS1amurhY7dOggLl++XBTF/31n3blzRxRFUdy8ebNobW3d6Pl07dpV/OKLL1T7HTt2FN966y3Vfl1dndiuXTtx7dq1anGcOXOm0X6f9PvPddCgQaKPj49YV1enKps/f77o4+PT5D516cm/6Sdp8v2t8SWhyspK+Pn5ITU1VaN2Fy5cQFFRkWpr98Q/GXbs2IHY2FgkJCTg9OnT8PPzQ2hoKEpKSjQNj4iIJC4vLw9VVVUYOnToU4/7+fnB0tJSVda/f3/U1dXhwoULqrKuXbvCyOh/X2NOTk7o3r27at/Y2Bj29vb1vkuCgoJUP5uYmCAgIAB5eXlNir2iogJz586Fj48PbGxsIJfLkZeXh6tXr6rV8/X1Vf0sCAIUCoXWv9P69u0LQRBU+0FBQbh06RJqa2u1Oo5UaHxJKCwsDGFhYRoP1K5du6feGpaSkoJp06Zh0qRJAIB169Zh//792LRpEz744AONxyIiIunS1iLW37/lWRCEBsvqtLgIZ+7cuTh48CA+++wzeHp6wtzcHG+88QYePnz4zNi0GceLSGdrWPz9/VFVVYVu3bph8eLF6N+/PwDg4cOHyMnJQXx8vKqukZERQkJCcOzYsQb7qqqqQlVVlWq/vLy8ZYMnItITe/tHa0j0HYM2eXl5wdzcHJmZmZg6dWq94z4+PkhLS0NlZaVqluXo0aMwMjKCt7f3c4+fnZ2NgQMHAni0cDUnJwcxMTFNanv06FFER0fj9ddfB/BoxqWwsFCj8R+vWXnemZDjx4+r7WdnZ8PLywvGxsbP1a9UtXjC4uzsjHXr1iEgIABVVVXYsGEDBg8ejOPHj6Nnz5747bffUFtbCycnJ7V2Tk5OqgVYv5eYmIiPPvqopUMnItI7IyPtL3jVN5lMhvnz52PevHkwNTVF//79cevWLfz666+YMmUKJkyYgISEBERFRWHx4sW4desW3n//fbz99tv1viuaIzU1FV5eXvDx8cHKlStx584dTJ48uUltvby8sGvXLoSHh0MQBCxcuFDjmZN27drB3NwcGRkZ6NChA2QyGaytrTU+j6tXryI2NhbvvPMOTp8+jS+++AIrVqzQuB9D0eIJi7e3t1pG3K9fP1y5cgUrV67E3/72t2b1GR8fj9jYWNV+eXk5XF1dnztWIiLSjYULF8LExASLFi3CzZs34ezsjHfffRcAYGFhge+++w6zZs1C7969YWFhgdGjRyMlJUUrYyclJSEpKQm5ubnw9PTE3r174eDg0KS2KSkpmDx5Mvr16wcHBwfMnz9f41l+ExMTrF69GkuWLMGiRYvwyiuvICsrS+PzmDhxIu7fv48+ffrA2NgYs2bNwvTp0zXux1AIotj8m7YFQcDu3bs1fqphXFwcfvrpJxw7dgwPHz6EhYUF/vGPf6j1ExUVhdLSUuzZs+eZ/ZWXl8Pa2hplZWVo27athmdBRCQtDx48QEFBATw8PCCTyfQdTqtRWFgIDw8PnDlzBv7+/voO54XytL9pTb6/9fLguNzcXDg7OwN4dC2vV69eyMzMVB2vq6tDZmam2kpuIiIienFpfEmooqICly9fVu0XFBQgNzcXdnZ2cHNzQ3x8PG7cuIGtW7cCAFatWgUPDw907doVDx48wIYNG/D999/jX//6l6qP2NhYREVFISAgAH369MGqVatQWVmpumuIiIgMx9WrV9GlS5enHj9//rzaQ9YIkMvlTz124MABvPLKKzqMRpo0TlhOnTqF4OBg1f7jtSRRUVFIS0tDUVGR2v3oDx8+xJw5c3Djxg1YWFjA19cXhw4dUutj7NixuHXrFhYtWgSlUgl/f39kZGRoZXEVERHplouLS6Mv9nNxcdFdME9wd3eX7KPrG/u82rdvr7tAJOy51rBIBdewEFFrwjUs1NoY7BoWIiIiIk0wYSEiIiLJY8JCREREkseEhYiIiCSPCQsRERFJHhMWIiKi/y8rKwuCIKC0tBQAkJaWBhsbG73G1FTu7u5YtWqVvsNoMUxYiIhIa6KjozV+XYuUjR07FhcvXtRqn4WFhRAEodFnr1B9Lf7yQyIiIkNlbm4Oc3NzfYdB4AwLERHpQV1dHZKTk+Hp6QkzMzO4ubnhk08+UR0/e/YshgwZAnNzc9jb22P69OmoqKhQHX88k7Ns2TI4OTnBxsYGS5YsQU1NDeLi4mBnZ4cOHTpg8+bNqjaPZza2b9+Ofv36QSaToVu3bjh8+PBT4/z9JaErV64gIiICTk5OkMvl6N27Nw4dOqTWxt3dHcuWLcPkyZNhZWUFNzc3rF+/XnXcw8MDANCjRw8IgoDBgwc/8/MaPHgwZs+erVYWGRmJ6OhotbK7d+9i/PjxsLS0RPv27ZGamvrMvg0FExYiItK5+Ph4JCUlYeHChTh//jy2bdumeh1LZWUlQkNDYWtri5MnT2Lnzp04dOgQYmJi1Pr4/vvvcfPmTRw5cgQpKSlISEjAyJEjYWtri+PHj+Pdd9/FO++8g+vXr6u1i4uLw5w5c3DmzBkEBQUhPDwct2/fblLcFRUVeO2115CZmYkzZ85g+PDhCA8PV3slDQCsWLECAQEBOHPmDN577z3MmDEDFy5cAACcOHECAHDo0CEUFRVh165dzfoMG/Lpp5/Cz88PZ86cwQcffIBZs2bh4MGDWutfn3hJiIjIENy7B+Tn637czp0BCwutdnn37l18/vnnWLNmDaKiogAAL730EgYMGAAA2LZtGx48eICtW7fC0tISALBmzRqEh4dj+fLlqsTGzs4Oq1evhpGREby9vZGcnIx79+5hwYIFAP6XFP30008YN26cavyYmBiMHj0aALB27VpkZGRg48aNmDdv3jNj9/Pzg5+fn2r/448/xu7du7F37161hOq1117De++9BwCYP38+Vq5ciR9++AHe3t5wdHQEANjb20OhUDTvQ3yK/v3744MPPgAAvPzyyzh69ChWrlyJYcOGaXUcfWDCQkRkCPLzgV69dD9uTg7Qs6dWu8zLy0NVVRWGDh361ON+fn6qZAV49EVcV1eHCxcuqBKWrl27wsjofxcKnJyc0K1bN9W+sbEx7O3tUVJSotZ/UFCQ6mcTExMEBAQgLy+vSbFXVFRg8eLF2L9/P4qKilBTU4P79+/Xm2Hx9fVV/SwIAhQKRb04WsKT5/Z4v7XcOcSEhYjIEHTu/Ch50Me4WqatRaxt2rRR2xcEocGyuro6rYwHAHPnzsXBgwfx2WefwdPTE+bm5njjjTfw8OHDZ8b2PHEYGRnVe9N0dXV1s/szRExYiIgMgYWF1mc69MXLywvm5ubIzMzE1KlT6x338fFBWloaKisrVbMsR48eVV36eV7Z2dkYOHAgAKCmpgY5OTn11sc8zdGjRxEdHY3XX38dwKMZl8LCQo3GNzU1BQDU1tY2uY2joyOKiopU+7W1tTh37hyCg4PV6mVnZ9fb9/Hx0Sg+qeKiWyIi0imZTIb58+dj3rx52Lp1K65cuYLs7Gxs3LgRADBhwgTIZDJERUXh3Llz+OGHH/D+++/j7bffVl0Oeh6pqanYvXs38vPzMXPmTNy5cweTJ09uUlsvLy/s2rULubm5+Pe//40//vGPGs+ctGvXDubm5sjIyEBxcTHKysqe2WbIkCHYv38/9u/fj/z8fMyYMUP1cLsnHT16FMnJybh48SJSU1Oxc+dOzJo1S6P4pIoJCxER6dzChQsxZ84cLFq0CD4+Phg7dqxqjYeFhQW+++47/Pe//0Xv3r3xxhtvYOjQoVizZo1Wxk5KSkJSUhL8/Pzw008/Ye/evXBwcGhS25SUFNja2qJfv34IDw9HaGgoemo482ViYoLVq1fjyy+/hIuLCyIiIp7ZZvLkyYiKisLEiRMxaNAgdOrUqd7sCgDMmTMHp06dQo8ePbB06VKkpKQgNDRUo/ikShB/f1HMAJWXl8Pa2hplZWVo27atvsMhInouDx48QEFBATw8PCCTyfQdTqtRWFgIDw8PnDlzBv7+/voO54XytL9pTb6/OcNCREREkseEhYiItOrq1auQy+VP3X5/CzCh0c/rxx9/1Hd4ksC7hIiISKtcXFwafbGfi4uL7oJ5gru7e71bg6Wisc+rffv2ugtEwpiwEBGRVpmYmMDT01PfYRgUfl7PxktCREREJHlMWIiIiEjymLAQERGR5DFhISIiIsljwkJERESSx4SFiIhIx7KysiAIgup9QGlpabCxsdFrTAAQHR2NyMhI1f7gwYMxe/ZsvcXzJCYsRESkNb//wqOmGTt2LC5evKjVPgsLCyEIQqPPeDEkfA4LERGRnpmbm8Pc3FzfYUgaZ1iIiEjnBg8ejD/96U+YN28e7OzsoFAosHjxYrU6V69eRUREBORyOdq2bYsxY8aguLhYdXzx4sXw9/fH3/72N7i7u8Pa2hrjxo3D3bt3VXXq6uqQnJwMT09PmJmZwc3NDZ988onq+NmzZzFkyBCYm5vD3t4e06dPR0VFher44xmjZcuWwcnJCTY2NliyZAlqamoQFxcHOzs7dOjQAZs3b1a1eTyzsX37dvTr1w8ymQzdunXD4cOHn/p5/P6S0JUrVxAREQEnJyfI5XL07t0bhw4dUmvj7u6OZcuWYfLkybCysoKbmxvWr1+vOu7h4QEA6NGjBwRBwODBgxv/pTxFTU0NYmJiYG1tDQcHByxcuFAvTwxmwkJERHqxZcsWWFpa4vjx40hOTsaSJUtw8OBBAI8SjYiICPz3v//F4cOHcfDgQfznP//B2LFj1fq4cuUK0tPTsW/fPuzbtw+HDx9GUlKS6nh8fDySkpKwcOFCnD9/Htu2bYOTkxMAoLKyEqGhobC1tcXJkyexc+dOHDp0CDExMWpjfP/997h58yaOHDmClJQUJCQkYOTIkbC1tcXx48fx7rvv4p133sH169fV2sXFxWHOnDk4c+YMgoKCEB4ejtu3bzfps6moqMBrr72GzMxMnDlzBsOHD0d4eHi99zCtWLECAQEBOHPmDN577z3MmDEDFy5cAACcOHECAHDo0CEUFRVh165dTRr797Zs2QITExOcOHECn3/+OVJSUrBhw4Zm9fU8eEmIiMgABKwPgLJCqfNxFXIFTk0/1SJ9+/r6IiEhAQDg5eWFNWvWIDMzE8OGDUNmZibOnj2LgoICuLq6AgC2bt2Krl274uTJk+jduzeAR4lNWloarKysAABvv/02MjMz8cknn+Du3bv4/PPPsWbNGkRFRQEAXnrpJQwYMAAAsG3bNjx48ABbt26FpaUlAGDNmjUIDw/H8uXLVYmNnZ0dVq9eDSMjI3h7eyM5ORn37t3DggULAPwvKfrpp58wbtw41fnFxMRg9OjRAIC1a9ciIyMDGzduxLx585752fj5+cHPz0+1//HHH2P37t3Yu3evWkL12muv4b333gMAzJ8/HytXrsQPP/wAb29vODo6AgDs7e2hUCia/ov5HVdXV6xcuRKCIMDb2xtnz57FypUrMW3atGb32Rwaz7AcOXIE4eHhcHFxgSAISE9Pb7T+rl27MGzYMDg6OqJt27YICgrCd999p1Zn8eLFEARBbevcubOmoRERtVrKCiVu3L2h860lkyRfX1+1fWdnZ5SUlAAA8vLy4OrqqkpWAKBLly6wsbFBXl6eqszd3V2VrDTUR1VVFYYOHdrg+Hl5efDz81MlKwDQv39/1NXVqWYpAKBr164wMvrf16WTkxO6d++u2jc2Noa9vb1q3MeCgoJUP5uYmCAgIEAt9sZUVFRg7ty58PHxgY2NDeRyOfLy8urNsDz5GQqCAIVCUS+O59W3b18IgqDaDwoKwqVLl1BbW6vVcZ5F4xmWyspK+Pn5YfLkyfjDH/7wzPpHjhzBsGHDsGzZMtjY2GDz5s0IDw/H8ePH0aNHD1W9rl27ql2fMzHh5A8R0WMKefP/hSzVcdu0aaO2LwgC6urqtNaHthaxNjSGNmJvzNy5c3Hw4EF89tln8PT0hLm5Od544w08fPjwmbFpMw4p0TgrCAsLQ1hYWJPrr1q1Sm1/2bJl2LNnD7799lu1hMXExOS5pqyIiFqzlrosI1U+Pj64du0arl27ppplOX/+PEpLS9GlS5cm9eHl5QVzc3NkZmZi6tSpDY6RlpaGyspK1SzL0aNHVZd+nld2djYGDhwI4NHC1ZycnHrrY57m6NGjiI6Oxuuvvw7g0YxLYWGhRuObmpoCwHPPhBw/flxtPzs7G15eXjA2Nn6ufjWl80W3dXV1uHv3Luzs7NTKL126BBcXF3Tq1AkTJkyoN+31pKqqKpSXl6ttRETUeoSEhKB79+6YMGECTp8+jRMnTmDixIkYNGgQAgICmtSHTCbD/PnzMW/ePGzduhVXrlxBdnY2Nm7cCACYMGECZDIZoqKicO7cOfzwww94//338fbbb6vWrzyP1NRU7N69G/n5+Zg5cybu3LmDyZMnN6mtl5cXdu3ahdzcXPz73//GH//4R41nTtq1awdzc3NkZGSguLgYZWVlzTkNXL16FbGxsbhw4QK++eYbfPHFF5g1a1az+noeOk9YPvvsM1RUVGDMmDGqssDAQKSlpSEjIwNr165FQUEBXnnlFbVb056UmJgIa2tr1fbkNU4iIjJ8giBgz549sLW1xcCBAxESEoJOnTphx44dGvWzcOFCzJkzB4sWLYKPjw/Gjh2rWuNhYWGB7777Dv/973/Ru3dvvPHGGxg6dCjWrFmjlXNISkpCUlIS/Pz88NNPP2Hv3r1wcHBoUtuUlBTY2tqiX79+CA8PR2hoKHr27KnR+CYmJli9ejW+/PJLuLi4ICIiojmngYkTJ+L+/fvo06cPZs6ciVmzZmH69OnN6ut5COJz3EwtCAJ2797d5Kcabtu2DdOmTcOePXsQEhLy1HqlpaXo2LEjUlJSMGXKlHrHq6qqUFVVpdovLy+Hq6srysrK0LZtW43Pg4hISh48eICCggJ4eHhAJpPpOxzSUGFhITw8PHDmzBn4+/vrOxxJeNrfdHl5OaytrZv0/a2zla3bt2/H1KlTsXPnzkaTFQCwsbHByy+/jMuXLzd43MzMDGZmZi0RJhEREUmQTi4JffPNN5g0aRK++eYbjBgx4pn1KyoqcOXKFTg7O+sgOiIi0qarV69CLpc/dWtsjSK1rMZ+Lz/++KO+w2uUxjMsFRUVajMfBQUFyM3NhZ2dHdzc3BAfH48bN25g69atAB5dBoqKisLnn3+OwMBAKJWP7uk3NzeHtbU1gEe3b4WHh6Njx464efMmEhISYGxsjPHjx2vjHImISIdcXFwafeGei4uL7oLRA3d3d708ur4pGvu9tG/fXneBNIPGCcupU6cQHBys2o+NjQUAREVFIS0tDUVFRWrZ8/r161FTU4OZM2di5syZqvLH9QHg+vXrGD9+PG7fvg1HR0cMGDAA2dnZqqf0ERGR4TAxMYGnp6e+w6AGGPLv5bkW3UqFJot2iIikjotuqbXRxqJbvvyQiIiIJI8JCxEREUkeExYiIiKSPCYsREREJHlMWIiIiHQsKysLgiCgtLQUAJCWlgYbGxu9xtRU7u7u9V5srAtMWIiISGuio6Ob/LoW+p+xY8fi4sWLWu2zsLAQgiA0+uwVQ6KzR/MTERFRw8zNzWFubq7vMCSNMyxERBJWVwfcuqXfra5O++c1ePBg/OlPf8K8efNgZ2cHhUKBxYsXq9W5evUqIiIiIJfL0bZtW4wZMwbFxcWq44sXL4a/vz/+9re/wd3dHdbW1hg3bhzu3r37xOdXh+TkZHh6esLMzAxubm745JNPVMfPnj2LIUOGwNzcHPb29pg+fToqKipUxx/PGC1btgxOTk6wsbHBkiVLUFNTg7i4ONjZ2aFDhw7YvHmzqs3jmY3t27ejX79+kMlk6NatGw4fPvzUz+P3l4SuXLmCiIgIODk5QS6Xo3fv3jh06JBaG3d3dyxbtgyTJ0+GlZUV3NzcsH79etVxDw8PAECPHj0gCAIGDx7c+C8Fj34vs2fPViuLjIxEdHS0Wtndu3cxfvx4WFpaon379khNTX1m38+LCQsRkYTdvg20a6ff7fbtljm3LVu2wNLSEsePH0dycjKWLFmCgwcPAniUaEREROC///0vDh8+jIMHD+I///kPxo4dq9bHlStXkJ6ejn379mHfvn04fPgwkpKSVMfj4+ORlJSEhQsX4vz589i2bRucnJwAAJWVlQgNDYWtrS1OnjyJnTt34tChQ4iJiVEb4/vvv8fNmzdx5MgRpKSkICEhASNHjoStrS2OHz+Od999F++88w6uX7+u1i4uLg5z5szBmTNnEBQUhPDwcNxu4odZUVGB1157DZmZmThz5gyGDx+O8PDweu9hWrFiBQICAnDmzBm89957mDFjBi5cuAAAOHHiBADg0KFDKCoqwq5du5o0dlN8+umn8PPzw5kzZ/DBBx9g1qxZqt9dixFbgbKyMhGAWFZWpu9QiIie2/3798Xz58+L9+/fF0tKRBHQ71ZS0vTYo6KixIiIiGfWGzRokDhgwAC1st69e4vz588XRVEU//Wvf4nGxsbi1atXVcd//fVXEYB44sQJURRFMSEhQbSwsBDLy8tVdeLi4sTAwEBRFEWxvLxcNDMzE7/66qsGY1i/fr1oa2srVlRUqMr2798vGhkZiUqlUnU+HTt2FGtra1V1vL29xVdeeUW1X1NTI1paWorffPONKIqiWFBQIAIQk5KSVHWqq6vFDh06iMuXLxdFURR/+OEHEYB4584dURRFcfPmzaK1tXWjn1nXrl3FL774QrXfsWNH8a233lLt19XVie3atRPXrl2rFseZM2ca7fdJgwYNEmfNmqVWFhERIUZFRamNO3z4cLU6Y8eOFcPCwp7a75N/00/S5PubMyxERKQXvr6+avvOzs4oKSkBAOTl5cHV1RWurq6q4126dIGNjQ3y8vJUZe7u7rCysnpqH1VVVRg6dGiD4+fl5cHPzw+Wlpaqsv79+6Ourk41SwEAXbt2hZHR/74unZyc0L17d9W+sbEx7O3tVeM+FhQUpPrZxMQEAQEBarE3pqKiAnPnzoWPjw9sbGwgl8uRl5dXb4blyc9QEAQoFIp6cbSEJ8/t8X5Tz625uOiWiIj0ok2bNmr7giCgTsMFM431oa1FrA2NoY3YGzN37lwcPHgQn332GTw9PWFubo433ngDDx8+fGZszxOHkZFRvTdNV1dXN7s/bWLCQkQkYfb2gA7+wfzMGHTNx8cH165dw7Vr11SzLOfPn0dpaSm6dOnSpD68vLxgbm6OzMxMTJ06tcEx0tLSUFlZqZplOXr0KIyMjODt7f3c55CdnY2BAwcCAGpqapCTk1NvfczTHD16FNHR0Xj99dcBPJpxKSws1Gh8U1NTAEBtbW2T2zg6OqKoqEi1X1tbi3PnziE4OFitXnZ2dr19Hx8fjeLTFBMWIiIJMzICHB31HYXuhYSEoHv37pgwYQJWrVqFmpoavPfeexg0aBACAgKa1IdMJsP8+fMxb948mJqaon///rh16xZ+/fVXTJkyBRMmTEBCQgKioqKwePFi3Lp1C++//z7efvtt1cLc55GamgovLy/4+Phg5cqVuHPnDiZPntyktl5eXti1axfCw8MhCAIWLlyo8cxJu3btYG5ujoyMDHTo0AEymQzW1taNthkyZAhiY2Oxf/9+vPTSS0hJSVE93O5JR48eRXJyMiIjI3Hw4EHs3LkT+/fv1yg+TXENCxERSY4gCNizZw9sbW0xcOBAhISEoFOnTtixY4dG/SxcuBBz5szBokWL4OPjg7Fjx6rWeFhYWOC7777Df//7X/Tu3RtvvPEGhg4dijVr1mjlHJKSkpCUlAQ/Pz/89NNP2Lt3LxwcHJrUNiUlBba2tujXrx/Cw8MRGhqKnj17ajS+iYkJVq9ejS+//BIuLi6IiIh4ZpvJkycjKioKEydOxKBBg9CpU6d6sysAMGfOHJw6dQo9evTA0qVLkZKSgtDQUI3i05Qg/v5ilQEqLy+HtbU1ysrK0LZtW32HQ0T0XB48eICCggJ4eHhAJpPpOxzSUGFhITw8PHDmzBn4+/vrOxxJeNrftCbf35xhISIiIsljwkJERFp19epVyOXyp26/vzWXdKex38uPP/6o7/AaxUW3RESkVS4uLo2+cM/FxUV3weiBu7t7vVuDpaKx30v79u11F0gzMGEhIiKtMjExgaenp77DoAYY8u+Fl4SIiIhI8piwEBERkeQxYSEiIiLJY8JCREREkseEhYiIiCSPCQsREUmCIAhIT08H8OhpsYIgNHobLr1YmLAQEZHWREdHQxCEetvw4cP1HRoZOD6HhYiItGr48OHYvHmzWpmZmZmeoqHWgjMsRESkVWZmZlAoFGqbra1ts/rKz89Hv379IJPJ0K1bNxw+fFjL0ZKhYMJCRESSFRcXhzlz5uDMmTMICgpCeHg4bt++re+wSA94SYiIyBDcuwfk5+t+3M6dAQsLjZrs27cPcrlcrWzBggVYsGCBxsPHxMRg9OjRAIC1a9ciIyMDGzduxLx58zTuiwwbExYiIkOQnw/06qX7cXNygJ49NWoSHByMtWvXqpXZ2dk1a/igoCDVzyYmJggICEBeXl6z+iLDxoSFiMgQdO78KHnQx7gasrS0NOiX7JE0aZywHDlyBJ9++ilycnJQVFSE3bt3IzIystE2WVlZiI2Nxa+//gpXV1d8+OGHiI6OVquTmpqKTz/9FEqlEn5+fvjiiy/Qp08fTcMjImqdLCw0nuloDbKzszFw4EAAQE1NDXJychATE6PnqEgfNF50W1lZCT8/P6SmpjapfkFBAUaMGIHg4GDk5uZi9uzZmDp1Kr777jtVnR07diA2NhYJCQk4ffo0/Pz8EBoaipKSEk3DIyIiPauqqoJSqVTbfvvtt2b1lZqait27dyM/Px8zZ87EnTt3MHnyZC1HTIZA4xmWsLAwhIWFNbn+unXr4OHhgRUrVgAAfHx88NNPP2HlypUIDQ0FAKSkpGDatGmYNGmSqs3+/fuxadMmfPDBB5qGSEREepSRkQFnZ2e1Mm9vb+Q3Y9FwUlISkpKSkJubC09PT+zduxcODg7aCpUMSIuvYTl27BhCQkLUykJDQzF79mwAwMOHD5GTk4P4+HjVcSMjI4SEhODYsWMN9llVVYWqqirVfnl5ufYDJyIijaWlpSEtLa1ZbUVRVP3s7u6u2h8/frw2QiMD1+LPYVEqlXByclIrc3JyQnl5Oe7fv4/ffvsNtbW1DdZRKpUN9pmYmAhra2vV5urq2mLxExERkf4Z5IPj4uPjUVZWptquXbum75CIiKgRX3/9NeRyeYNb165d9R0eGYAWvySkUChQXFysVlZcXIy2bdvC3NwcxsbGMDY2brCOQqFosE8zMzO+l4KIyICMGjUKgYGBDR5r06aNjqMhQ9TiCUtQUBD++c9/qpUdPHhQ9TAgU1NT9OrVC5mZmarbo+vq6pCZmclb14iIWgkrKytYWVnpOwwyYBpfEqqoqEBubi5yc3MBPLptOTc3F1evXgXw6HLNxIkTVfXfffdd/Oc//8G8efOQn5+Pv/71r/j73/+OP//5z6o6sbGx+Oqrr7Blyxbk5eVhxowZqKysVN01RERERC82jWdYTp06heDgYNV+bGwsACAqKgppaWkoKipSJS8A4OHhgf379+PPf/4zPv/8c3To0AEbNmxQ3dIMAGPHjsWtW7ewaNEiKJVK+Pv7IyMjo95CXCIiInoxCeKT95EZqPLyclhbW6OsrAxt27bVdzhERM/lwYMHKCgogIeHB2Qymb7DIXpuT/ub1uT72yDvEiIiIqIXCxMWIiIikjwmLERERCR5TFiIiMggZGVlQRAElJaW6jsU0gMmLEREpDXR0dEQBKHeNnz4cH2HRgauxR8cR0REL5bhw4dj8+bNamV8Ojk9L86wEBGRVpmZmUGhUKhttra2jbYpLCyEIAiqh5ICQGlpKQRBQFZWllrdo0ePwtfXFzKZDH379sW5c+da4CxIajjDQkRkAALWB0BZ0fAb7FuSQq7AqemndD5uY+Li4vD5559DoVBgwYIFCA8Px8WLF/lOolaOCQsRkQFQVihx4+4NfYfRJPv27YNcLlcrW7BgARYsWKCV/hMSEjBs2DAAwJYtW9ChQwfs3r0bY8aM0Ur/JE1MWIiIDIBC3vDb66U4bnBwMNauXatWZmdnp62QVC/Pfdyvt7c38vLytNY/SRMTFiIiAyC1yzKNsbS0hKenp0ZtjIweLal88m0x1dXVWo2LDBsX3RIRkd45OjoCAIqKilRlTy7AfVJ2drbq5zt37uDixYvw8fFp0fhI/zjDQkREWlVVVQWlUn2BsImJCRwcHJ7axtzcHH379kVSUhI8PDxQUlKCDz/8sMG6S5Ysgb29PZycnPCXv/wFDg4OiIyM1OYpkARxhoWIiLQqIyMDzs7OatuAAQOe2W7Tpk2oqalBr169MHv2bCxdurTBeklJSZg1axZ69eoFpVKJb7/9Fqampto+DZIYQXzygqGB0uT11EREUvfgwQMUFBTAw8MDMplM3+EQPben/U1r8v3NGRYiIiKSPCYsRETU4r7++mvI5fIGt65du+o7PDIAXHRLREQtbtSoUQgMDGzwGJ9QS03BhIWIiFqclZUVrKys9B0GGTBeEiIiIiLJY8JCREREkseEhYiIiCSPCQsRERFJHhMWIiIikjwmLEREJAmFhYUQBEH10sOsrCwIgoDS0lK9xkXSwISFiIi0Jjo6GoIgQBAEtGnTBh4eHpg3bx4ePHig79DIwPE5LEREElZXB9y+rd8Y7O0BIw3+eTt8+HBs3rwZ1dXVyMnJQVRUFARBwPLly1suSGr1mLAQEUnY7dtAu3b6jaGkBHB0bHp9MzMzKBQKAICrqytCQkJw8ODBZicsR48eRXx8PC5evAh/f39s2LAB3bp1a1ZfZLh4SYiIiFrMuXPn8PPPP8PU1LTZfcTFxWHFihU4efIkHB0dER4ejurqai1GSYaAMyxERKRV+/btg1wuR01NDaqqqmBkZIQ1a9Y0u7+EhAQMGzYMALBlyxZ06NABu3fvxpgxY7QVMhkAJixERKRVwcHBWLt2LSorK7Fy5UqYmJhg9OjRze4vKChI9bOdnR28vb2Rl5enjVDJgDBhISKSMHv7R2tI9B2DJiwtLeHp6QkA2LRpE/z8/LBx40ZMmTKlBaKjF0Wz1rCkpqbC3d0dMpkMgYGBOHHixFPrDh48WHWL25PbiBEjVHWevA3u8TZ8+PDmhEZE1KoYGT1a8KrPTZM7hOrHb4QFCxbgww8/xP3795vVR3Z2turnO3fu4OLFi/Dx8Wl+UGSQNP4z3LFjB2JjY5GQkIDTp0/Dz88PoaGhKHnKPwF27dqFoqIi1Xbu3DkYGxvjzTffVKs3fPhwtXrffPNN886IiIgk5c0334SxsTFSU1Ob1X7JkiXIzMzEuXPnEB0dDQcHB0RGRmo3SJI8jROWlJQUTJs2DZMmTUKXLl2wbt06WFhYYNOmTQ3Wt7Ozg0KhUG0HDx6EhYVFvYTl8W1wjzdbW9vmnREREUmKiYkJYmJikJycjMrKSo3bJyUlYdasWejVqxeUSiW+/fbb57rriAyTIIqi2NTKDx8+hIWFBf7xj3+oZbdRUVEoLS3Fnj17ntlH9+7dERQUhPXr16vKoqOjkZ6eDlNTU9ja2mLIkCFYunQp7J9y4bSqqgpVVVWq/fLycri6uqKsrAxt27Zt6ukQEUnSgwcPUFBQAA8PD8hkMn2HQ/TcnvY3XV5eDmtr6yZ9f2s0w/Lbb7+htrYWTk5OauVOTk5QKpXPbH/ixAmcO3cOU6dOVSsfPnw4tm7diszMTCxfvhyHDx9GWFgYamtrG+wnMTER1tbWqs3V1VWT0yAiIiIDo9MHx23cuBHdu3dHnz591MrHjRuHUaNGoXv37oiMjMS+fftw8uRJZGVlNdhPfHw8ysrKVNu1a9d0ED0RET2PZcuWQS6XN7iFhYXpOzySOI1ua3ZwcICxsTGKi4vVyouLi1WPYX6ayspKbN++HUuWLHnmOJ06dYKDgwMuX76MoUOH1jtuZmYGMzMzTUInIiI9e/fdd5/6sDdzc3MdR0OGRqOExdTUFL169UJmZqZqDUtdXR0yMzMRExPTaNudO3eiqqoKb7311jPHuX79Om7fvg1nZ2dNwiMiIgmzs7ODnZ2dvsMgA6XxJaHY2Fh89dVX2LJlC/Ly8jBjxgxUVlZi0qRJAICJEyciPj6+XruNGzciMjKy3kLaiooKxMXFITs7G4WFhcjMzERERAQ8PT0RGhrazNMiIiKi1kTjJ92OHTsWt27dwqJFi6BUKuHv74+MjAzVQtyrV6/C6HdPGbpw4QJ++ukn/Otf/6rXn7GxMX755Rds2bIFpaWlcHFxwauvvoqPP/6Yl32IiIgIgIa3NUuVJrdFERFJHW9rptZG57c1ExEREekDExYiIiKSPCYsRERkMNLS0mBjY6PvMEgPmLAQEZHWREdHQxAECIKANm3awMPDA/PmzcODBw/0HRoZOI3vEiIiImrM8OHDsXnzZlRXVyMnJwdRUVEQBAHLly/Xd2hkwDjDQkREWmVmZgaFQgFXV1dERkYiJCQEBw8efGa7rKwsCIKA0tJSVVlubi4EQUBhYaFa3fT0dHh5eUEmkyE0NJSvaHkBcIaFiMgQ3LsH5OfrftzOnQELi2Y3P3fuHH7++Wd07NhRayHdu3cPn3zyCbZu3QpTU1O89957GDduHI4ePaq1MUh6mLAQERmC/HygVy/dj5uTA/TsqVGTffv2QS6Xo6amBlVVVTAyMsKaNWu0FlJ1dTXWrFmDwMBAAMCWLVvg4+ODEydO1Hu5LrUeTFiIiAxB586Pkgd9jKuh4OBgrF27FpWVlVi5ciVMTEwwevRorYVkYmKC3r17PxFiZ9jY2CAvL48JSyvGhIWIyBBYWGg806EvlpaW8PT0BABs2rQJfn5+2LhxI6ZMmdJou8evdXnyAezV1dUtFygZFC66JSKiFmNkZIQFCxbgww8/xP379xut6+joCAAoKipSleXm5tarV1NTg1OnTqn2L1y4gNLSUvj4+GgnaJIkJixERNSi3nzzTRgbGyM1NbXRep6ennB1dcXixYtx6dIl7N+/HytWrKhXr02bNnj//fdx/Phx5OTkIDo6Gn379uXloFaOCQsREbUoExMTxMTEIDk5GZWVlU+t16ZNG3zzzTfIz8+Hr68vli9fjqVLl9arZ2Fhgfnz5+OPf/wj+vfvD7lcjh07drTkKZAE8G3NREQSw7c1U2vDtzUTERHRC4EJCxER6cSyZcsgl8sb3MLCwvQdHkkcb2smIiKdePfddzFmzJgGj5mbm+s4GjI0TFiIiEgn7OzsYGdnp+8wyEDxkhARERFJHhMWIiIikjwmLERERCR5TFiIiIhI8piwEBERkeQxYSEiIslIS0uDjY2Nan/x4sXw9/fXWzwkHUxYiIhIa6KjoxEZGVmvPCsrC4IgoLS0VOcxUevAhIWIiIgkjwkLERFJ3pdffglXV1dYWFhgzJgxKCsr03dIpGN80i0RkQEIWB8AZYVS5+Mq5Aqcmn5K5+M+6fLly/j73/+Ob7/9FuXl5ZgyZQree+89fP3113qNi3SLCQsRkQFQVihx4+4NfYfRJPv27YNcLlcrq62tbXZ/Dx48wNatW9G+fXsAwBdffIERI0ZgxYoVUCgUzxUrGQ4mLEREBkAh188Xc3PGDQ4Oxtq1a9XKjh8/jrfeeqtZMbi5uamSFQAICgpCXV0dLly4wITlBcKEhYjIAOj7sowmLC0t4enpqVZ2/fp1PUVDrQUX3RIRkaRdvXoVN2/eVO1nZ2fDyMgI3t7eeoyKdI0JCxERSZpMJkNUVBT+/e9/48cff8Sf/vQnjBkzhpeDXjDNSlhSU1Ph7u4OmUyGwMBAnDhx4ql109LSIAiC2iaTydTqiKKIRYsWwdnZGebm5ggJCcGlS5eaExoREbUynp6e+MMf/oDXXnsNr776Knx9ffHXv/5V32GRjgmiKIqaNNixYwcmTpyIdevWITAwEKtWrcLOnTtx4cIFtGvXrl79tLQ0zJo1CxcuXPjfoIIAJycn1f7y5cuRmJiILVu2wMPDAwsXLsTZs2dx/vz5eslNQ8rLy2FtbY2ysjK0bdtWk9MhIpKcBw8eoKCgAB4eHk36byCR1D3tb1qT72+NZ1hSUlIwbdo0TJo0CV26dMG6detgYWGBTZs2PbWNIAhQKBSq7clkRRRFrFq1Ch9++CEiIiLg6+uLrVu34ubNm0hPT9c0PCIiImqFNEpYHj58iJycHISEhPyvAyMjhISE4NixY09tV1FRgY4dO8LV1RURERH49ddfVccKCgqgVCrV+rS2tkZgYOBT+6yqqkJ5ebnaRkRE0hcWFga5XN7gtmzZMn2HRxKm0W3Nv/32G2pra9VmSADAyckJ+fn5Dbbx9vbGpk2b4Ovri7KyMnz22Wfo168ffv31V3To0AFKpVLVx+/7fHzs9xITE/HRRx9pEjoREUnAhg0bcP/+/QaP2dnZ6TgaMiQt/hyWoKAgBAUFqfb79esHHx8ffPnll/j444+b1Wd8fDxiY2NV++Xl5XB1dX3uWImIqGU9+QA4Ik1odEnIwcEBxsbGKC4uVisvLi5u8u1lbdq0QY8ePXD58mUAULXTpE8zMzO0bdtWbSMiIqLWS6OExdTUFL169UJmZqaqrK6uDpmZmWqzKI2pra3F2bNn4ezsDADw8PCAQqFQ67O8vBzHjx9vcp9ERETUuml8SSg2NhZRUVEICAhAnz59sGrVKlRWVmLSpEkAgIkTJ6J9+/ZITEwEACxZsgR9+/aFp6cnSktL8emnn+L//t//i6lTpwJ4dAfR7NmzsXTpUnh5ealua3ZxcUFkZKT2zpSIiIgMlsYJy9ixY3Hr1i0sWrQISqUS/v7+yMjIUC2avXr1KoyM/jdxc+fOHUybNg1KpRK2trbo1asXfv75Z3Tp0kVVZ968eaisrMT06dNRWlqKAQMGICMjg88fICIiIgDNeHCcFPHBcUTUmvDBcdTa6OXBcURERPoUHR3NJQMvICYsRESkNU9LJrKysiAIAkpLS3UeE7UOLf4cFiIiar66OuD2bf3GYG8PGPGft6RnTFiIiCTs9m2ggffK6lRJCeDoqJuxFi9ejPT0dOTm5qrKVq1ahVWrVqGwsFCt7kcffYQ1a9agqqoKf/zjH7F69WqYmprqJlDSOSYsRERkcDIzMyGTyZCVlYXCwkJMmjQJ9vb2+OSTT/QdGrUQJixERKRV+/btg1wuVyurra3V6himpqbYtGkTLCws0LVrVyxZsgRxcXH4+OOP1R6tQa0HExYiItKq4OBgrF27Vq3s+PHjeOutt7Q2hp+fHywsLFT7QUFBqKiowLVr19CxY0etjUPSwYSFiEjC7O0frSHRdwyasLS0hKenp1rZ9evXm9TWyMgIv388WHV1tWYBUKvEhIWISMKMjHS34FUKHB0doVQqIYoiBEEAALUFuI/9+9//xv3792Fubg4AyM7Ohlwuh6urqy7DJR3ihT4iIpKMwYMH49atW0hOTsaVK1eQmpqKAwcO1Kv38OFDTJkyBefPn8c///lPJCQkICYmhutXWjH+ZomISDJ8fHzw17/+FampqfDz88OJEycwd+7cevWGDh0KLy8vDBw4EGPHjsWoUaOwePFi3QdMOsN3CRERSQzfJUStDd8lRERERC8EJixERKQzYWFhkMvlDW7Lli3Td3gkYbxLiIiIdGbDhg24f/9+g8fs7Ox0HA0ZEiYsRESkM+3bt9d3CGSgeEmIiIiIJI8JCxEREUkeExYiIiKSPCYsREREJHlMWIiIiEjymLAQEZGkCIKA9PR0AEBhYSEEQWjwBYj0YmHCQkREWhMdHQ1BECAIAtq0aQMnJycMGzYMmzZtQl1dnb7DIwPGhIWIiLRq+PDhKCoqQmFhIQ4cOIDg4GDMmjULI0eORE1Njb7DIwPFhIWIiLTKzMwMCoUC7du3R8+ePbFgwQLs2bMHBw4cQFpaWrP6zM/PR79+/SCTydCtWzccPnxYu0GT5DFhISKiFjdkyBD4+flh165dzWofFxeHOXPm4MyZMwgKCkJ4eDhu376t5ShJyvhofiIiQ3DvHpCfr/txO3cGLCy01FVn/PLLL81qGxMTg9GjRwMA1q5di4yMDGzcuBHz5s3TSmwkfUxYiIgMQX4+0KuX7sfNyQF69tRKV6IoQhCEZrUNCgpS/WxiYoKAgADk5eVpJS4yDExYiIgMQefOj5IHfYyrJXl5efDw8NBaf/RiYcJCRGQILCy0NtOhD99//z3Onj2LP//5z81qn52djYEDBwIAampqkJOTg5iYGG2GSBLHhIWIiLSqqqoKSqUStbW1KC4uRkZGBhITEzFy5EhMnDixWX2mpqbCy8sLPj4+WLlyJe7cuYPJkydrOXKSMiYsRESkVRkZGXB2doaJiQlsbW3h5+eH1atXIyoqCkZGzbs5NSkpCUlJScjNzYWnpyf27t0LBwcHLUdOUtasv5zU1FS4u7tDJpMhMDAQJ06ceGrdr776Cq+88gpsbW1ha2uLkJCQevWffDLi42348OHNCY2IiPQoLS0NoihCFEVUV1ejpKQEBw8exKRJk5qcrIiiiMjISACAu7s7RFHE+PHjcfz4cVRVVeHXX39FcHBwC54FSZHGCcuOHTsQGxuLhIQEnD59Gn5+fggNDUVJSUmD9bOysjB+/Hj88MMPOHbsGFxdXfHqq6/ixo0bavUePxnx8fbNN98074yIiIio1dE4YUlJScG0adMwadIkdOnSBevWrYOFhQU2bdrUYP2vv/4a7733Hvz9/dG5c2ds2LABdXV1yMzMVKv3+MmIjzdbW9vmnREREUnW119/Dblc3uDWtWtXfYdHEqbRGpaHDx8iJycH8fHxqjIjIyOEhITg2LFjTerj3r17qK6uhp2dnVp5VlYW2rVrB1tbWwwZMgRLly6Fvb19g31UVVWhqqpKtV9eXq7JaRARkZ6MGjUKgYGBDR5r06aNjqMhQ6JRwvLbb7+htrYWTk5OauVOTk7Ib+ITGOfPnw8XFxeEhISoyoYPH44//OEP8PDwwJUrV7BgwQKEhYXh2LFjMDY2rtdHYmIiPvroI01CJyIiCbCysoKVlZW+wyADpNO7hJKSkrB9+3ZkZWVBJpOpyseNG6f6uXv37vD19cVLL72ErKwsDB06tF4/8fHxiI2NVe2Xl5fD1dW1ZYMnIiIivdFoDYuDgwOMjY1RXFysVl5cXAyFQtFo288++wxJSUn417/+BV9f30brdurUCQ4ODrh8+XKDx83MzNC2bVu1jYiIiFovjRIWU1NT9OrVS23B7OMFtE++5+H3kpOT8fHHHyMjIwMBAQHPHOf69eu4ffs2nJ2dNQmPiIiIWimN7xKKjY3FV199hS1btiAvLw8zZsxAZWUlJk2aBACYOHGi2qLc5cuXY+HChdi0aRPc3d2hVCqhVCpRUVEBAKioqEBcXByys7NRWFiIzMxMREREwNPTE6GhoVo6TSIiIjJkGq9hGTt2LG7duoVFixZBqVTC398fGRkZqoW4V69eVXs40Nq1a/Hw4UO88cYbav0kJCRg8eLFMDY2xi+//IItW7agtLQULi4uePXVV/Hxxx/DzMzsOU+PiIiIWgNBFEVR30E8r/LyclhbW6OsrIzrWYjI4D148AAFBQXw8PBQu0GBHsnKykJwcDDu3LkDGxsbfYdDTfC0v2lNvr+b91IHIiKiBjz5qpU2bdrAyckJw4YNw6ZNm1BXV6fv8MiAMWEhIiKtevyqlcLCQhw4cADBwcGYNWsWRo4ciZqaGn2HRwaKCQsREWnV41ettG/fHj179sSCBQuwZ88eHDhwAGlpaY22LSwshCAIyM3NVZWVlpZCEARkZWWp1T169Ch8fX0hk8nQt29fnDt3TvsnQ5LBhIWIiFrckCFD4Ofnh127dmmtz7i4OKxYsQInT56Eo6MjwsPDUV1drbX+SVp0+qRbIiJqnoD1AVBWKHU+rkKuwKnpp7TSV+fOnfHLL79opS/g0d2mw4YNAwBs2bIFHTp0wO7duzFmzBitjUHSwYSFiMgAKCuUuHH3hr7DeC6iKEIQBK319+QDS+3s7ODt7Y28vDyt9U/SwoSFiMgAKOSNv/7EEMbNy8uDh4dHo3UeP8frySdu8DIPAUxYiIgMgrYuy+jL999/j7Nnz+LPf/5zo/UcHR0BAEVFRejRowcAqC3AfVJ2djbc3NwAAHfu3MHFixfh4+OjvaBJUpiwEBGRVlVVVUGpVKK2thbFxcXIyMhAYmIiRo4ciYkTJzba1tzcHH379kVSUhI8PDxQUlKCDz/8sMG6S5Ysgb29PZycnPCXv/wFDg4OiIyMbIEzIingXUJERKRVGRkZcHZ2hru7O4YPH44ffvgBq1evxp49e2BsbPzM9ps2bUJNTQ169eqF2bNnY+nSpQ3WS0pKwqxZs9CrVy8olUp8++23MDU11fbpkETw0fxERBLDR/NTa8NH8xMREdELgQkLERHpzNdffw25XN7g1rVrV32HRxLGRbdERKQzo0aNQmBgYIPH2rRpo+NoyJAwYSEiIp2xsrKClZWVvsMgA8RLQkREEtUK7okgAqCdv2UmLEREEvP40si9e/f0HAmRdjz+W36ey368JEREJDHGxsawsbFBSUkJAMDCwkKr7+Ah0hVRFHHv3j2UlJTAxsamSc/heRomLEREEqRQPHqHz+OkhciQ2djYqP6mm4sJCxGRBAmCAGdnZ7Rr144v/yOD1qZNm+eaWXmMCQsRkYQZGxtr5T/2RIaOi26JiIhI8piwEBERkeQxYSEiIiLJY8JCREREkseEhYiIiCSPCQsRERFJHhMWIiIikjwmLERERCR5TFiIiIhI8piwEBERkeQxYSEiIiLJa1bCkpqaCnd3d8hkMgQGBuLEiRON1t+5cyc6d+4MmUyG7t2745///KfacVEUsWjRIjg7O8Pc3BwhISG4dOlSc0IjIiKiVkjjlx/u2LEDsbGxWLduHQIDA7Fq1SqEhobiwoULaNeuXb36P//8M8aPH4/ExESMHDkS27ZtQ2RkJE6fPo1u3boBAJKTk7F69Wps2bIFHh4eWLhwIUJDQ3H+/HnIZLLnP8tmePAAOHRIL0MTERFJVkgIoJevZlFDffr0EWfOnKnar62tFV1cXMTExMQG648ZM0YcMWKEWllgYKD4zjvviKIoinV1daJCoRA//fRT1fHS0lLRzMxM/Oabb5oUU1lZmQhALCsr0/R0nurbb0UR4MaNGzdu3Lg9uX37rda+ajX6/tZohuXhw4fIyclBfHy8qszIyAghISE4duxYg22OHTuG2NhYtbLQ0FCkp6cDAAoKCqBUKhESEqI6bm1tjcDAQBw7dgzjxo2r12dVVRWqqqpU++Xl5ZqchkaE6T1hIr/RYv0TEREZipqK9gBO62VsjRKW3377DbW1tXByclIrd3JyQn5+foNtlEplg/WVSqXq+OOyp9X5vcTERHz00UeahN5sJvIbqG5bopOxiIiIpKyNHsfWeA2LFMTHx6vN2pSXl8PV1bVFxqqpaK/XXxAREZFUPJph0Q+NEhYHBwcYGxujuLhYrby4uBgKhaLBNgqFotH6j/+3uLgYzs7OanX8/f0b7NPMzAxmZmaahK6xkBDg228BfU19ERERSdETKzh0SqOExdTUFL169UJmZiYiIyMBAHV1dcjMzERMTEyDbYKCgpCZmYnZs2eryg4ePIigoCAAgIeHBxQKBTIzM1UJSnl5OY4fP44ZM2ZofkZaIpMBI0fqbXgiIiJ6gsaXhGJjYxEVFYWAgAD06dMHq1atQmVlJSZNmgQAmDhxItq3b4/ExEQAwKxZszBo0CCsWLECI0aMwPbt23Hq1CmsX78eACAIAmbPno2lS5fCy8tLdVuzi4uLKikiIiKiF5vGCcvYsWNx69YtLFq0CEqlEv7+/sjIyFAtmr169SqMjP73PLp+/fph27Zt+PDDD7FgwQJ4eXkhPT1d9QwWAJg3bx4qKysxffp0lJaWYsCAAcjIyNDbM1iIiIhIWgRRFEV9B/G8ysvLYW1tjbKyMrRt21bf4RAREVETaPL9zXcJERERkeQxYSEiIiLJY8JCREREkseEhYiIiCSPCQsRERFJHhMWIiIikjwmLERERCR5TFiIiIhI8piwEBERkeRp/Gh+KXr8sN7y8nI9R0JERERN9fh7uykP3W8VCcvdu3cBAK6urnqOhIiIiDR19+5dWFtbN1qnVbxLqK6uDjdv3oSVlRUEQdBq3+Xl5XB1dcW1a9f4nqIWxM9ZN/g56w4/a93g56wbLfU5i6KIu3fvwsXFRe3FyQ1pFTMsRkZG6NChQ4uO0bZtW/6fQQf4OesGP2fd4WetG/ycdaMlPudnzaw8xkW3REREJHlMWIiIiEjymLA8g5mZGRISEmBmZqbvUFo1fs66wc9Zd/hZ6wY/Z92QwufcKhbdEhERUevGGRYiIiKSPCYsREREJHlMWIiIiEjymLAQERGR5DFheYbU1FS4u7tDJpMhMDAQJ06c0HdIrcqRI0cQHh4OFxcXCIKA9PR0fYfUKiUmJqJ3796wsrJCu3btEBkZiQsXLug7rFZn7dq18PX1VT1cKygoCAcOHNB3WK1eUlISBEHA7Nmz9R1Kq7N48WIIgqC2de7cWS+xMGFpxI4dOxAbG4uEhAScPn0afn5+CA0NRUlJib5DazUqKyvh5+eH1NRUfYfSqh0+fBgzZ85EdnY2Dh48iOrqarz66quorKzUd2itSocOHZCUlIScnBycOnUKQ4YMQUREBH799Vd9h9ZqnTx5El9++SV8fX31HUqr1bVrVxQVFam2n376SS9x8LbmRgQGBqJ3795Ys2YNgEfvLHJ1dcX777+PDz74QM/RtT6CIGD37t2IjIzUdyit3q1bt9CuXTscPnwYAwcO1Hc4rZqdnR0+/fRTTJkyRd+htDoVFRXo2bMn/vrXv2Lp0qXw9/fHqlWr9B1Wq7J48WKkp6cjNzdX36FwhuVpHj58iJycHISEhKjKjIyMEBISgmPHjukxMqLnV1ZWBuDRlym1jNraWmzfvh2VlZUICgrSdzit0syZMzFixAi1/06T9l26dAkuLi7o1KkTJkyYgKtXr+oljlbx8sOW8Ntvv6G2thZOTk5q5U5OTsjPz9dTVETPr66uDrNnz0b//v3RrVs3fYfT6pw9exZBQUF48OAB5HI5du/ejS5duug7rFZn+/btOH36NE6ePKnvUFq1wMBApKWlwdvbG0VFRfjoo4/wyiuv4Ny5c7CystJpLExYiF4wM2fOxLlz5/R2Hbq18/b2Rm5uLsrKyvCPf/wDUVFROHz4MJMWLbp27RpmzZqFgwcPQiaT6TucVi0sLEz1s6+vLwIDA9GxY0f8/e9/1/llTiYsT+Hg4ABjY2MUFxerlRcXF0OhUOgpKqLnExMTg3379uHIkSPo0KGDvsNplUxNTeHp6QkA6NWrF06ePInPP/8cX375pZ4jaz1ycnJQUlKCnj17qspqa2tx5MgRrFmzBlVVVTA2NtZjhK2XjY0NXn75ZVy+fFnnY3MNy1OYmpqiV69eyMzMVJXV1dUhMzOT16PJ4IiiiJiYGOzevRvff/89PDw89B3SC6Ourg5VVVX6DqNVGTp0KM6ePYvc3FzVFhAQgAkTJiA3N5fJSguqqKjAlStX4OzsrPOxOcPSiNjYWERFRSEgIAB9+vTBqlWrUFlZiUmTJuk7tFajoqJCLVMvKChAbm4u7Ozs4ObmpsfIWpeZM2di27Zt2LNnD6ysrKBUKgEA1tbWMDc313N0rUd8fDzCwsLg5uaGu3fvYtu2bcjKysJ3332n79BaFSsrq3rrrywtLWFvb891WVo2d+5chIeHo2PHjrh58yYSEhJgbGyM8ePH6zwWJiyNGDt2LG7duoVFixZBqVTC398fGRkZ9RbiUvOdOnUKwcHBqv3Y2FgAQFRUFNLS0vQUVeuzdu1aAMDgwYPVyjdv3ozo6GjdB9RKlZSUYOLEiSgqKoK1tTV8fX3x3XffYdiwYfoOjahZrl+/jvHjx+P27dtwdHTEgAEDkJ2dDUdHR53HwuewEBERkeRxDQsRERFJHhMWIiIikjwmLERERCR5TFiIiIhI8piwEBERkeQxYSEiIiLJY8JCREREkseEhYiIiCSPCQsRERFJHhMWIiIikjwmLERERCR5TFiIiIhI8v4fP35P2vIVU24AAAAASUVORK5CYII=", + "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -648,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -663,38 +530,56 @@ { "data": { "text/plain": [ - "[[0.0 1.000000\n", - " 1.0 1.900946\n", - " 2.0 2.659294\n", - " 3.0 3.339236\n", - " 4.0 3.986401\n", - " 5.0 4.634126\n", + "[[0.0 4.000000\n", + " 5.0 5.279244\n", + " 10.0 9.108868\n", + " 15.0 16.124701\n", + " 20.0 28.603764\n", + " 25.0 50.748897\n", + " 30.0 90.039893\n", + " dtype: float64],\n", + " [0.0 4.000000\n", + " 5.0 3.077496\n", + " 10.0 4.806094\n", + " 15.0 17.470652\n", + " 20.0 82.892447\n", + " 25.0 409.417079\n", + " 30.0 2032.403982\n", + " dtype: float64,\n", + " 0.0 4.000000\n", + " 5.0 12.004604\n", + " 10.0 58.527336\n", + " 15.0 290.521953\n", + " 20.0 1443.150983\n", + " 25.0 7169.106500\n", + " 30.0 35613.236715\n", " dtype: float64],\n", - " [None, None],\n", - " [0.0 1.000000\n", - " 1.0 1.711692\n", - " 2.0 2.251305\n", - " 3.0 2.682140\n", - " 4.0 3.053780\n", - " 5.0 3.408531\n", - " Name: E_lb_destratified_SEI, dtype: float64,\n", - " 0.0 1.000000\n", - " 1.0 2.060627\n", - " 2.0 3.008676\n", - " 3.0 3.903399\n", - " 4.0 4.781040\n", - " 5.0 5.660551\n", - " Name: E_ub_destratified_SEI, dtype: float64]]" + " [0.0 4.000000\n", + " 5.0 4.497161\n", + " 10.0 0.990951\n", + " 15.0 -45.330407\n", + " 20.0 -409.351989\n", + " 25.0 -3031.710702\n", + " 30.0 -21550.678810\n", + " Name: I_lb_destratified_SEI, dtype: float64,\n", + " 0.0 4.000000\n", + " 5.0 6.016389\n", + " 10.0 17.363698\n", + " 15.0 80.882919\n", + " 20.0 496.780914\n", + " 25.0 3362.960448\n", + " 30.0 23385.814862\n", + " Name: I_ub_destratified_SEI, dtype: float64]]" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", - "var = \"E\"\n", + "var = \"I\"\n", "var_df = pd.DataFrame()\n", "for name, result in request_results.items():\n", " # vars = list(set([v.rsplit(\"_\", 1)[0] for v in result.model._state_var_names()]))\n", @@ -720,71 +605,87 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " S_compliant I_compliant E_compliant I_noncompliant S_noncompliant \\\n", - "0.0 9.669998e+06 2.000000 0.500000 2.000000 9.669998e+06 \n", - "1.0 9.669997e+06 1.995317 0.950473 1.995317 9.669997e+06 \n", - "2.0 9.669996e+06 2.070171 1.329647 2.070171 9.669996e+06 \n", - "3.0 9.669996e+06 2.208319 1.669618 2.208319 9.669996e+06 \n", - "4.0 9.669995e+06 2.399924 1.993201 2.399924 9.669995e+06 \n", - "5.0 9.669994e+06 2.639622 2.317063 2.639622 9.669994e+06 \n", + " S_compliant I_compliant E_compliant I_noncompliant S_noncompliant \\\n", + "0.0 9.669998e+06 2.000000 0.500000 2.000000 9.669998e+06 \n", + "5.0 9.669994e+06 2.639622 2.317063 2.639622 9.669994e+06 \n", + "10.0 9.669989e+06 4.554434 4.313768 4.554434 9.669989e+06 \n", + "15.0 9.669980e+06 8.062351 7.682373 8.062351 9.669980e+06 \n", + "20.0 9.669963e+06 14.301882 13.634369 14.301882 9.669963e+06 \n", + "25.0 9.669934e+06 25.374449 24.191037 25.374449 9.669934e+06 \n", + "30.0 9.669883e+06 45.019947 42.920325 45.019947 9.669883e+06 \n", + "\n", + " E_noncompliant R H D \n", + "0.0 0.500000 0.000000 0.000000 0.000000 \n", + "5.0 2.317063 1.306723 0.349626 0.010688 \n", + "10.0 4.313768 3.503480 0.774253 0.043551 \n", + "15.0 7.682373 7.419919 1.458829 0.108756 \n", + "20.0 13.634369 14.393184 2.638764 0.228412 \n", + "25.0 24.191037 26.782143 4.712217 0.443085 \n", + "30.0 42.920325 48.773419 8.378989 0.825399 \n", + " S_lb I_lb E_lb I_ub S_ub \\\n", + "0.0 1.934000e+07 4.000000 1.000000 4.000000 1.934000e+07 \n", + "5.0 1.933999e+07 4.497161 2.733767 6.016389 1.933999e+07 \n", + "10.0 1.933997e+07 0.990951 -5.282224 17.363698 1.933998e+07 \n", + "15.0 1.933990e+07 -45.330407 -81.668704 80.882919 1.934000e+07 \n", + "20.0 1.933952e+07 -409.351989 -650.824792 496.780914 1.934022e+07 \n", + "25.0 1.933701e+07 -3031.710702 -4702.217340 3362.960448 1.934191e+07 \n", + "30.0 1.931968e+07 -21550.678810 -33227.157234 23385.814862 1.935400e+07 \n", "\n", - " E_noncompliant R H D \n", - "0.0 0.500000 0.000000 0.000000 0.000000 \n", - "1.0 0.950473 0.226285 0.075753 0.000462 \n", - "2.0 1.329647 0.463073 0.145723 0.001795 \n", - "3.0 1.669618 0.717932 0.213148 0.003950 \n", - "4.0 1.993201 0.997238 0.280472 0.006910 \n", - "5.0 2.317063 1.306723 0.349626 0.010688 \n", - " S_lb I_lb E_lb I_ub S_ub E_ub \\\n", - "0.0 1.934000e+07 4.000000 1.000000 4.000000 1.934000e+07 1.000000 \n", - "1.0 1.933999e+07 3.971775 1.711692 4.006552 1.933999e+07 2.060627 \n", - "2.0 1.933999e+07 4.059282 2.251305 4.210187 1.933999e+07 3.008676 \n", - "3.0 1.933999e+07 4.220733 2.682140 4.587644 1.933999e+07 3.903399 \n", - "4.0 1.933999e+07 4.426770 3.053780 5.127773 1.933999e+07 4.781040 \n", - "5.0 1.933999e+07 4.657683 3.408531 5.826721 1.933999e+07 5.660551 \n", + " E_ub R_lb R_ub H_lb H_ub \\\n", + "0.0 1.000000 0.000000 0.000000 0.000000 0.000000 \n", + "5.0 6.468573 1.242755 1.365454 0.325790 0.371883 \n", + "10.0 23.307062 2.455659 4.541286 0.367607 1.179461 \n", + "15.0 121.639896 -1.733940 16.899058 -2.191968 5.209236 \n", + "20.0 779.832491 -53.778527 86.570912 -24.938423 31.340085 \n", + "25.0 5343.699263 -459.065706 546.065956 -193.020999 211.544110 \n", + "30.0 37279.471642 -3373.544262 3719.819389 -1387.597058 1471.237197 \n", "\n", - " R_lb R_ub H_lb H_ub D_lb D_ub \n", - "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1.0 0.225941 0.226573 0.075633 0.075854 0.000462 0.000463 \n", - "2.0 0.460189 0.465522 0.144729 0.146571 0.001789 0.001800 \n", - "3.0 0.707717 0.726702 0.209686 0.216141 0.003919 0.003976 \n", - "4.0 0.971842 1.019198 0.272003 0.287859 0.006811 0.006996 \n", - "5.0 1.254785 1.351797 0.332577 0.364572 0.010440 0.010904 \n", - " S_lb I_compliant_lb I_noncompliant_lb E_lb \\\n", - "0.0 1.934000e+07 2.000000 2.000000 1.000000 \n", - "1.0 1.933999e+07 1.856914 1.856914 1.312741 \n", - "2.0 1.933999e+07 1.734484 1.734484 0.966472 \n", - "3.0 1.933999e+07 1.637028 1.637028 -0.145628 \n", - "4.0 1.933999e+07 1.569596 1.569596 -2.207799 \n", - "5.0 1.933998e+07 1.538748 1.538748 -5.499358 \n", + " D_lb D_ub \n", + "0.0 0.000000 0.000000 \n", + "5.0 0.010369 0.010981 \n", + "10.0 0.034436 0.052471 \n", + "15.0 0.009014 0.210011 \n", + "20.0 -0.585975 1.070793 \n", + "25.0 -5.564188 6.709912 \n", + "30.0 -41.988538 45.643410 \n", + " S_lb I_compliant_lb I_noncompliant_lb E_lb \\\n", + "0.0 1.934000e+07 2.000000 2.000000 1.000000 \n", + "5.0 1.933998e+07 1.538748 1.538748 -5.499358 \n", + "10.0 1.933993e+07 2.403047 2.403047 -61.659290 \n", + "15.0 1.933969e+07 8.735326 8.735326 -352.858362 \n", + "20.0 1.933848e+07 41.446223 41.446223 -1806.600211 \n", + "25.0 1.933248e+07 204.708540 204.708540 -9032.715141 \n", + "30.0 1.930267e+07 1016.201991 1016.201991 -44932.903807 \n", "\n", - " I_compliant_ub I_noncompliant_ub S_ub E_ub R_lb \\\n", - "0.0 2.000000 2.000000 1.934000e+07 1.000000 0.000000 \n", - "1.0 2.176475 2.176475 1.933999e+07 2.388952 0.219132 \n", - "2.0 2.614585 2.614585 1.933999e+07 3.983537 0.429208 \n", - "3.0 3.346622 3.346622 1.933999e+07 5.968534 0.631626 \n", - "4.0 4.439931 4.439931 1.933999e+07 8.562023 0.828268 \n", - "5.0 6.002302 6.002302 1.933999e+07 12.040496 1.021535 \n", + " I_compliant_ub I_noncompliant_ub S_ub E_ub \\\n", + "0.0 2.000000 2.000000 1.934000e+07 1.000000 \n", + "5.0 6.002302 6.002302 1.933999e+07 12.040496 \n", + "10.0 29.263668 29.263668 1.933999e+07 61.181677 \n", + "15.0 145.260976 145.260976 1.933997e+07 304.408321 \n", + "20.0 721.575492 721.575492 1.933992e+07 1512.461075 \n", + "25.0 3584.553250 3584.553250 1.933966e+07 7513.575138 \n", + "30.0 17806.618357 17806.618357 1.933836e+07 37323.775445 \n", "\n", - " R_ub H_lb H_ub D_lb D_ub \n", - "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1.0 0.234965 0.073151 0.078884 0.000452 0.000474 \n", - "2.0 0.511299 0.133083 0.163189 0.001703 0.001915 \n", - "3.0 0.860794 0.179870 0.264931 0.003594 0.004460 \n", - "4.0 1.321923 0.212641 0.397988 0.005964 0.008398 \n", - "5.0 1.944254 0.229418 0.579655 0.008634 0.014205 \n" + " R_lb R_ub H_lb H_ub D_lb D_ub \n", + "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "5.0 1.021535 1.944254 0.229418 0.579655 0.008634 0.014205 \n", + "10.0 2.078499 10.835730 -0.123649 3.349768 0.016724 0.109833 \n", + "15.0 4.214900 55.198690 -2.938781 17.806471 -0.051529 0.631444 \n", + "20.0 12.160518 276.291622 -18.005395 90.761773 -0.561586 3.324661 \n", + "25.0 48.980738 1375.948949 -94.261809 454.785150 -3.339744 16.887494 \n", + "30.0 228.832997 6840.843005 -475.103888 2265.362927 -17.486658 84.561137 \n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json index 626f1e7d..c3b0bfa8 100644 --- a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json @@ -350,12 +350,12 @@ "rates": [ { "target": "t1_to_4_lb", - "expression": "I_ub*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression": "I_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" }, { "target": "t1_to_4_ub", - "expression": "I_lb*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression": "I_ub*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" }, { @@ -390,12 +390,12 @@ }, { "target": "t11_lb", - "expression": "H_ub*p_H_to_R*r_H_to_R", + "expression": "H_lb*p_H_to_R*r_H_to_R", "expression_mathml": "H_lbp_H_to_Rr_H_to_R" }, { "target": "t11_ub", - "expression": "H_lb*p_H_to_R*r_H_to_R", + "expression": "H_ub*p_H_to_R*r_H_to_R", "expression_mathml": "H_ubp_H_to_Rr_H_to_R" }, { diff --git a/src/funman/config.py b/src/funman/config.py index 9021feb9..d902892e 100644 --- a/src/funman/config.py +++ b/src/funman/config.py @@ -116,7 +116,7 @@ class FUNMANConfig(BaseModel): corner_points: bool = False """ Compute Corner points of each box """ - verbosity: int = logging.INFO + verbosity: int = logging.ERROR """ Verbosity (INFO, DEBUG, TRACE, WARN, ERROR)""" use_transition_symbols: bool = False diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index e490cf1c..5bede49b 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -1,4 +1,4 @@ -from typing import Dict, List, Optional, Union +from typing import Dict, List, Optional, Union, Callable import graphviz import sympy @@ -179,21 +179,28 @@ def compartmental_constraints( for v in vars ] - def derivative(self, var_name, t, var_to_value, param_to_value): - param_at_t = {p: pv(t).item() for p, pv in param_to_value.items()} + def derivative(self, var_name, t, values, params): #var_to_value, param_to_value): + # param_at_t = {p: pv(t).item() for p, pv in param_to_value.items()} # FIXME assumes each transition has only one rate pos_rates = [ - self._transition_rate(trans)[0].evalf( - subs={**var_to_value, **param_at_t} + # self._transition_rate(trans)[0].evalf( + # subs={**var_to_value, **param_at_t} + # ) + self._transition_rate(trans, getLambda=True)[0]( + *values, *params ) for trans in self._transitions() for var in trans.output if var_name == var ] neg_rates = [ - self._transition_rate(trans)[0].evalf( - subs={**var_to_value, **param_at_t} + # self._transition_rate(trans)[0].evalf( + # subs={**var_to_value, **param_at_t} + # ) + self._transition_rate(trans, getLambda=True)[0]( + *values, *params ) + for trans in self._transitions() for var in trans.input if var_name == var @@ -209,8 +216,13 @@ def gradient(self, y, t, *p): param_to_value = { param: p[i] for i, param in enumerate(self._parameter_names()) } + # values = [ + # y[i] for i, _ in enumerate(self._symbols()) + # ] + params = [param_to_value[str(p)](t) for p in self._symbols() if str(p) in param_to_value] + [t] + grad = [ - self.derivative(var, t, var_to_value, param_to_value) + self.derivative(var, t, y, params) #var_to_value, param_to_value) for var in self._state_var_names() ] return grad @@ -221,6 +233,7 @@ class GeneratedPetriNetModel(AbstractPetriNetModel): petrinet: GeneratedPetrinet _transition_rates_cache: Dict[str, Union[sympy.Expr, str]] = {} + _transition_rates_lambda_cache: Dict[str, Union[Callable, str]] = {} def default_encoder( self, config: "FUNMANConfig", scenario: "AnalysisScenario" @@ -340,7 +353,7 @@ def _edge_target(self, edge): def _output_edges(self): return [(t.id, o) for t in self._transitions() for o in t.output] - def _transition_rate(self, transition, sympify=False): + def _transition_rate(self, transition, sympify=False, getLambda=False): if hasattr(self.petrinet.semantics, "ode"): if transition.id not in self._transition_rates_cache: t_rates = [ @@ -364,8 +377,10 @@ def _transition_rate(self, transition, sympify=False): ) for t in t_rates ] + t_rates_lambda = [sympy.lambdify(self._symbols(), t) for t in t_rates] self._transition_rates_cache[transition.id] = t_rates - return self._transition_rates_cache[transition.id] + self._transition_rates_lambda_cache[transition.id] = t_rates_lambda + return self._transition_rates_cache[transition.id] if not getLambda else self._transition_rates_lambda_cache[transition.id] else: return transition.id diff --git a/src/funman/representation/interval.py b/src/funman/representation/interval.py index c52963dd..2532d8df 100644 --- a/src/funman/representation/interval.py +++ b/src/funman/representation/interval.py @@ -13,6 +13,7 @@ from pydantic.functional_serializers import WrapSerializer from typing_extensions import Annotated +from funman.utils.logging import inherit_level import funman.utils.math_utils as math_utils from funman.constants import NEG_INFINITY, POS_INFINITY @@ -412,6 +413,7 @@ def check_interval(self) -> str: # Assume that intervals where lb == ub imply closed_upper_bound if self.lb == self.ub and not self.closed_upper_bound: + inherit_level(l) l.warning( f"{self} has equal lower and upper bounds, so assuming the upper bound is closed. (I.e., [lb, ub) is actually [lb, ub])" ) diff --git a/src/funman/utils/logging.py b/src/funman/utils/logging.py index 20161d00..c0454315 100644 --- a/src/funman/utils/logging.py +++ b/src/funman/utils/logging.py @@ -14,6 +14,10 @@ def set_level(level: int): for h in handlers: h.setLevel(level) +def inherit_level(subLogger: logging.Logger): + rootLogger = logging.getLogger() + rootLevel = rootLogger.level + subLogger.level = rootLevel def add_handler(): logging.basicConfig() From 80d7588fdaa64389057d9df731b80dd3e030d0a2 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 4 Sep 2024 17:22:20 +0000 Subject: [PATCH 29/93] formatting --- src/funman/model/petrinet.py | 39 ++++++++++++++++----------- src/funman/representation/interval.py | 2 +- src/funman/utils/logging.py | 2 ++ 3 files changed, 27 insertions(+), 16 deletions(-) diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index 5bede49b..701a9944 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -1,4 +1,4 @@ -from typing import Dict, List, Optional, Union, Callable +from typing import Callable, Dict, List, Optional, Union import graphviz import sympy @@ -179,16 +179,16 @@ def compartmental_constraints( for v in vars ] - def derivative(self, var_name, t, values, params): #var_to_value, param_to_value): + def derivative( + self, var_name, t, values, params + ): # var_to_value, param_to_value): # param_at_t = {p: pv(t).item() for p, pv in param_to_value.items()} # FIXME assumes each transition has only one rate pos_rates = [ # self._transition_rate(trans)[0].evalf( # subs={**var_to_value, **param_at_t} # ) - self._transition_rate(trans, getLambda=True)[0]( - *values, *params - ) + self._transition_rate(trans, getLambda=True)[0](*values, *params) for trans in self._transitions() for var in trans.output if var_name == var @@ -197,10 +197,7 @@ def derivative(self, var_name, t, values, params): #var_to_value, param_to_value # self._transition_rate(trans)[0].evalf( # subs={**var_to_value, **param_at_t} # ) - self._transition_rate(trans, getLambda=True)[0]( - *values, *params - ) - + self._transition_rate(trans, getLambda=True)[0](*values, *params) for trans in self._transitions() for var in trans.input if var_name == var @@ -219,10 +216,14 @@ def gradient(self, y, t, *p): # values = [ # y[i] for i, _ in enumerate(self._symbols()) # ] - params = [param_to_value[str(p)](t) for p in self._symbols() if str(p) in param_to_value] + [t] - + params = [ + param_to_value[str(p)](t) + for p in self._symbols() + if str(p) in param_to_value + ] + [t] + grad = [ - self.derivative(var, t, y, params) #var_to_value, param_to_value) + self.derivative(var, t, y, params) # var_to_value, param_to_value) for var in self._state_var_names() ] return grad @@ -377,10 +378,18 @@ def _transition_rate(self, transition, sympify=False, getLambda=False): ) for t in t_rates ] - t_rates_lambda = [sympy.lambdify(self._symbols(), t) for t in t_rates] + t_rates_lambda = [ + sympy.lambdify(self._symbols(), t) for t in t_rates + ] self._transition_rates_cache[transition.id] = t_rates - self._transition_rates_lambda_cache[transition.id] = t_rates_lambda - return self._transition_rates_cache[transition.id] if not getLambda else self._transition_rates_lambda_cache[transition.id] + self._transition_rates_lambda_cache[transition.id] = ( + t_rates_lambda + ) + return ( + self._transition_rates_cache[transition.id] + if not getLambda + else self._transition_rates_lambda_cache[transition.id] + ) else: return transition.id diff --git a/src/funman/representation/interval.py b/src/funman/representation/interval.py index 2532d8df..cace3660 100644 --- a/src/funman/representation/interval.py +++ b/src/funman/representation/interval.py @@ -13,9 +13,9 @@ from pydantic.functional_serializers import WrapSerializer from typing_extensions import Annotated -from funman.utils.logging import inherit_level import funman.utils.math_utils as math_utils from funman.constants import NEG_INFINITY, POS_INFINITY +from funman.utils.logging import inherit_level l = logging.getLogger(__name__) diff --git a/src/funman/utils/logging.py b/src/funman/utils/logging.py index c0454315..761c5641 100644 --- a/src/funman/utils/logging.py +++ b/src/funman/utils/logging.py @@ -14,11 +14,13 @@ def set_level(level: int): for h in handlers: h.setLevel(level) + def inherit_level(subLogger: logging.Logger): rootLogger = logging.getLogger() rootLevel = rootLogger.level subLogger.level = rootLevel + def add_handler(): logging.basicConfig() ch = logging.StreamHandler() From 62532c62e02153618b92e94e62d6470c146e389b Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 4 Sep 2024 17:31:56 -0500 Subject: [PATCH 30/93] bounds write up --- notes/abstraction/fig/seirhd-aug.pdf | Bin 0 -> 17559 bytes notes/abstraction/fig/seirhd_bounds.pdf | Bin 0 -> 137165 bytes notes/abstraction/main.pdf | Bin 0 -> 287055 bytes notes/abstraction/stratify-example.tex | 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SsgZ@5si`5Cs;aBM8y5f}T#`Zn literal 0 HcmV?d00001 diff --git a/notes/abstraction/stratify-example.tex b/notes/abstraction/stratify-example.tex index 4687efc9..e2e91612 100644 --- a/notes/abstraction/stratify-example.tex +++ b/notes/abstraction/stratify-example.tex @@ -1,148 +1,74 @@ -The SIERHD model from the July monthly demo uses the model summarized by the Petrinet diagram in Figure \ref{fig:seirhd}. +The SIERHD model from the August monthly demo uses the model summarized by the Petrinet diagram in Figure \ref{fig:seirhd}. \begin{figure} - \includegraphics[width=\linewidth]{fig/seirhd} + \includegraphics[width=\linewidth]{fig/seirhd-aug} \caption{\label{fig:seirhd} SEIRHD Model Petrinet} \end{figure} -The following transitions connect the variables $S_u$, $S_v$, $E_u$, $E_v$, $I_u$, and $I_v$: +The following transitions connect the variables $S_c$, $S_{nc}$, $E_c$, $E_{nc}$, $I_c$, and $I_{nc}$, $R$, $H$, $D$: \begin{eqnarray*} - (I_u, S_u) &\xrightarrow[]{r_1}& (I_u, E_u)\\ - (I_u, S_v) &\xrightarrow[]{r_2}& (I_u, E_v)\\ - (I_v, S_u) &\xrightarrow[]{r_3}& (I_v, E_u)\\ - (I_v, S_v) &\xrightarrow[]{r_4}& (I_v, E_v)\\ - (S_u) &\xrightarrow[]{r_5}& (S_v)\\ - (S_v) &\xrightarrow[]{r_6}& (S_u)\\ - (E_u) &\xrightarrow[]{r_7}& (I_u)\\ - (E_v) &\xrightarrow[]{r_8}& (I_v) + t_1: (I_c, S_c) &\xrightarrow[]{r_1}& (I_c, E_c) \\ + t_2: (I_{nc}, S_c) &\xrightarrow[]{r_2}& (I_{nc}, E_c)\\ + t_3: (I_{nc}, S_{nc}) &\xrightarrow[]{r_3}& (I_{nc}, E_{nc})\\ + t_4: (I_c, S_{nc}) &\xrightarrow[]{r_4}& (I_c, E_{nc})\\ + t_5: (E_c) &\xrightarrow[]{r_5}& (I_c)\\ + t_6:(E_{nc}) &\xrightarrow[]{r_6}& (I_{nc})\\ + t_7:(I_c) &\xrightarrow[]{r_7}& (R)\\ + t_8: (I_{nc}) &\xrightarrow[]{r_8}& (R)\\ + t_9:(I_c) &\xrightarrow[]{r_9}& (H)\\ + t_{10}:(I_{nc}) &\xrightarrow[]{r_{10}}& (H)\\ + t_{11}:(H) &\xrightarrow[]{r_{11}}& (R)\\ + t_{12}:(H) &\xrightarrow[]{r_{12}}& (D)\\ + t_{13}:(S_{nc}) &\xrightarrow[]{r_{13}}& (S_c)\\ + t_{14}:(S_c) &\xrightarrow[]{r_{14}}& (S_{nc})\\ + t_{15}:(E_{nc}) &\xrightarrow[]{r_{15}}& (E_c)\\ + t_{16}:(E_c) &\xrightarrow[]{r_{16}}& (E_{nc})\\ + t_{17}:(I_{nc}) &\xrightarrow[]{r_{17}}& (I_c)\\ + t_{18}:(I_c) &\xrightarrow[]{r_{18}}& (I_{nc}) \end{eqnarray*} -Recovering the original, unstratified model corresponds to an abstraction where $S = (S_u, S_v)$, $I = (I_u, I_v)$, and $E = (E_u, E_v)$: +Abstracting this base model involves merging variables, such as $S_c$ and $S_{nc}$ into a composite variable $S$ where $S = S_c + S_{nc}$. In this approach, a composite variable can represent any number of stratified copies of a variable (e.g., $S$ stratified by ten age groups). We also abstract the base model transitions so that their source and target variables are composite variables. For example, if we define composite variables $S$, $I$, and $E$ for the corresponding stratified variables in the base model, then transitions $t_1$ to $t_4$ become the composite transition $t_{1:4}$: \begin{eqnarray*} - (I, S) &\xrightarrow[]{r_1}& (I, E)\\ - (I, S) &\xrightarrow[]{r_2}& (I, E)\\ - (I, S) &\xrightarrow[]{r_3}& (I, E)\\ - (I, S) &\xrightarrow[]{r_4}& (I, E)\\ - (S) &\xrightarrow[]{r_5}& (S)\\ - (S) &\xrightarrow[]{r_6}& (S)\\ - (E) &\xrightarrow[]{r_7}& (I)\\ - (E) &\xrightarrow[]{r_8}& (I) + t_{1:4}:(I, S) &\xrightarrow[]{r_{1:4}}& (I, E) \end{eqnarray*} -In order for the abstraction to preserve the semantics of the stratified model, it must define $S^t = S_u^t + S_v^t$, $I^t = I_u^t + I_v^t$, and $E^t = E_u^t + E_v^t$ for all time points $t$. If we look at the definitions for these terms, we have: +\noindent where $r_{1:4}$ is the composite rate of flow and $r_{1:4} = \sum_{i=1}^4 r_i$. The composite rate of flow $r_{1:4}$ between the base variables due to the base transitions becomes difficult to track because we no longer separately model each base variable. For example $r_1 = \frac{I_c S_c \beta(-c_{m_0}*\epsilon_{m_0} + 1)}{N}$, depends upon $I_c$ and $S_c$, and they are no longer variables in the abstract model. We express $r_{1:4}$ in terms of the abstract variables by bounding it. Bounding the rate expressions implies that we must also bound each of the variables. We denote the upper and lower bounds with the $ub$ and $lb$ superscripts. -\begin{eqnarray*} - \frac{\partial S_u}{\partial t} &=& - I_u S_u r_1 - I_v S_u r_3 - S_u r_5 + S_v r_6\\ - \frac{\partial S_v}{\partial t} &=& - I_u S_v r_2 - I_v S_v r_4 + S_u r_5 - S_v r_6\\ - \frac{\partial S}{\partial t} &=& \frac{\partial S_u}{\partial t} + \frac{\partial S_v}{\partial t} \\ - &=& - I_u S_u r_1 - I_v S_u r_3 - S_u r_5 + S_v r_6 - I_u S_v r_2 - I_v S_v r_4 + S_u r_5 - S_v r_6\\ - &=& - I_u S_u r_1 - I_v S_u r_3 - I_u S_v r_2 - I_v S_v r_4 \\ -\end{eqnarray*} - -\begin{eqnarray*} - \frac{\partial I_u}{\partial t} &=& I_u S_u r_1 - I_u S_u r_1 + I_u S_v r_2 - I_u S_v r_2 + E_u r_7\\ - &=& E_u r_7\\ - \frac{\partial I_v}{\partial t} &=& I_v S_u r_3 - I_v S_u r_3 + I_v S_v r_4 - I_v S_v r_4 + E_v r_8\\ - &=& E_v r_8\\ - \frac{\partial I}{\partial t} &=& \frac{\partial I_u}{\partial t} + \frac{\partial I_v}{\partial t} \\ - &=& E_u r_7 + E_v r_8 -\end{eqnarray*} +For example, we express the rate $r_{1:4}$ with a pair of rates $r^{lb}_{1:4}$ and $r^{ub}_{1:4}$. If we assume that the composite rate preserves the total flow between base model variables, we define bounds on the rate as follows: \begin{eqnarray*} - \frac{\partial E_u}{\partial t} &=& I_u S_u r_1 + I_v S_u r_3 - E_u r_7\\ - \frac{\partial E_v}{\partial t} &=& I_u S_v r_2 + I_v S_v r_4 - E_v r_8\\ - \frac{\partial E}{\partial t} &=& \frac{\partial E_u}{\partial t} + \frac{\partial E_v}{\partial t} \\ - &=& I_u S_u r_1 + I_v S_u r_3 - E_u r_7 + I_u S_v r_2 + I_v S_v r_4 - E_v r_8 + S(t+dt) &=& S_c(t+dt) + S_{nc}(t+dt)\\ + &=& S_c(t) - (r_1+ r_2)dt + S_{nc}(t) - (r_3+ r_4)dt\\ + &=& S(t) - (r_1+ r_2 +r_3+ r_4)dt\\ + &=& S(t) - \biggl(\frac{I_c S_c \beta(1-c_{m_0}\epsilon_{m_0})}{N}+\frac{I_{nc} S_c \beta(1-c_{m_1}\epsilon_{m_1} )}{N}+\\ + &&\qquad\qquad\frac{I_{nc} S_{nc} \beta(1-c_{m_2}\epsilon_{m_2} )}{N}+\frac{I_c S_{nc} \beta(1-c_{m_3}\epsilon_{m_3} )}{N}\biggl)dt\\ + &\leq& S(t) - \biggl(\frac{I_c S_c \beta(1-c^{ub}_{m}\epsilon^{ub}_{m} )}{N}+\frac{I_{nc} S_c \beta(1-c^{ub}_{m}\epsilon^{ub}_{m})}{N}+\\ + &&\qquad\qquad\frac{I_{nc} S_{nc} \beta(1-c^{ub}_{m}\epsilon^{ub}_{m})}{N}+\frac{I_c S_{nc} \beta(1-c^{ub}_{m}\epsilon^{ub}_{m} )}{N}\biggl)dt\\ + &=& S(t) - \frac{\beta(1-c^{ub}_{m}\epsilon^{ub}_{m} )}{N}(I_c S_c +I_{nc} S_c +I_{nc} S_{nc} +I_c S_{nc} )dt\\ + &=& S(t) - \frac{\beta(1-c^{ub}_{m}\epsilon^{ub}_{m} )}{N}((I_c+I_{nc}) (S_c + S_{nc}) )dt\\ + &=& S(t) - \frac{\beta(1-c^{ub}_{m}\epsilon^{ub}_{m} )}{N}(IS)dt\\ + &\leq& S^{ub}(t) - \frac{I^{lb}S^{lb}\beta(1-c^{ub}_{m}\epsilon^{ub}_{m} )}{N}dt\\ + &=& S^{ub}(t+dt) + % && \end{eqnarray*} -Abstraction implies that we allow additional behaviors in the more abstract model (i.e., overapproximate). In Petrinet models, overapproximation corresponds to cases where the abstract compartment may take on additional values beyond those possible when aggregating the corresponding refined compartments. +\noindent where the upper bound $S^{ub}(t+dt)$ assumes that the negative rate terms have minimal magnitude (i.e., the upper bound decreases by the least amount). The terms are minimal when they are replaced by the appropriate bounds $I^{lb}$, $S^{lb}$, $c^{ub}_{m}$, $\epsilon^{ub}_{m}$. The lower bound $S^{ub}(t+dt)$ uses a similar approach, instead selecting bounds with a maximum magnitude and decreasing the lower bound by greatest amount. Positive rate terms are handled similarly so that they are maximal when used to compute upper bounds and minimal for lower bounds. +The abstract model that de-stratifies the base model defines 12 compartments (lower and upper bound for each variable after defining the composite variables), and 12 transitions (lower and upper bound for each composite transition). While the resulting model has fewer transitions, it has more compartments. However, we would have constructed the same size abstraction for a stratified model with an arbitrary number of levels. For example, if the base model used ten levels instead of two, then it would have 33 compartments and significantly more transitions. +We developed two abstract models from the base model. The first, as described above, de-stratifies the $S$, $E$, and $I$ variables. The second, de-stratifies only $S$ and $E$, allowing $I$ to remain stratified. -\begin{eqnarray*} - \underline{\frac{\partial S_u}{\partial t}} &\geq& - \overline{I_u} \overline{S_u} r_1 - \overline{I_v} \overline{S_u} r_3 - \overline{S_u} r_5 + \underline{S_v} r_6\\ - &=& - N^2 r_1 - N^2 r_3 - N r_5 + 0 r_6\\ - &=& - S_u^0 (N r_1 + N r_3 + r_5)\\ - \\ - \overline{\frac{\partial S_u}{\partial t}} &\leq& - \underline{I_u} \underline{S_u} r_1 - \underline{I_v} \underline{S_u} r_3 - \underline{S_u} r_5 + \overline{S_v} r_6\\ - &=& - 0 0 r_1 - 0 0 r_3 - 0 r_5 + S_v^0 r_6\\ - &=& S_v^0 r_6\\ - \\ - \underline{\frac{\partial S_v}{\partial t}} &\geq& - \overline{I_u} \overline{S_v} r_2 - \overline{I_v} \overline{S_v} r_4 + \underline{S_u} r_5 +-\overline{S_v} r_6\\ - &=& - N S_v^0 r_1 - N S_v^0 r_4 - 0 r_5 + S_v^0 r_6\\ - &=& - S_v^0 (N r_1 + N r_4 + r_6)\\ - \\ - \overline{\frac{\partial S_v}{\partial t}} &\leq& - \underline{I_u} \underline{S_v} r_2 - \underline{I_v} \underline{S_v} r_4 + \overline{S_u} r_5 -\underline{S_v} r_6\\ - &=& - 0 0 r_1 -0 0 r_4 + S_u^0 r_5 - 0 r_6\\ - &=& S_u^0 r_5\\ - \\ - \frac{\partial S}{\partial t} &\leq& \overline{\frac{\partial S_u}{\partial t}} + \overline{\frac{\partial S_v}{\partial t}}\\ - &\leq& S_v^0 r_6 + S_u^0 r_5\\ - \frac{\partial S}{\partial t} &\geq& \underline{\frac{\partial S_u}{\partial t}} + \underline{\frac{\partial S_v}{\partial t}}\\ - &\geq& - S_u^0 (N r_1 + N r_3 + r_5) - S_v^0 (N r_1 + N r_4 + r_6) - \\ -\end{eqnarray*} - -\begin{eqnarray*} - \underline{\frac{\partial I_u}{\partial t}} &\geq& \underline{I_u} \underline{S_u} r_1 - \overline{I_u} \overline{S_u} r_1 + \underline{I_u} \underline{S_v} r_2 - \overline{I_u} \overline{S_v} r_2 + \underline{E_u} r_7\\ - &=& 0 0 r_1 - N S_u^0 r_1 + 0 0 r_2 - N S_v^0 r_2 + 0 r_7\\ - &=& - N (S_u^0 r_1 + S_v^0 r_2)\\ - \\ - \overline{\frac{\partial I_u}{\partial t}} &\leq& \overline{I_u} \overline{S_u} r_1 - \underline{I_u} \underline{S_u} r_1 + \overline{I_u} \overline{S_v} r_2 - \underline{I_u} \underline{S_v} r_2 + \overline{E_u} r_7\\ - &=& N S_u^0 r_1 - 0 0 r_1 + N S_v^0 r_2 - 0 0 r_2 + N r_7\\ - &=& N (S_u^0 r_1 + S_v^0 r_2 + r_7)\\ - \\ - \underline{\frac{\partial I_v}{\partial t}} &\geq& \underline{I_v} \underline{S_u} r_3 - \overline{I_v} \overline{S_u} r_3 + \underline{I_v} \underline{S_v} r_4 - \overline{I_v} \overline{S_v} r_4 + \underline{E_v} r_8\\ - &=& 0 0 r_3 - N S_u^0 r_3 + 0 0 r_4 - N S_v^0 r_4 + 0 r_8\\ - &=& - N (S_u^0 r_3 + S_v^0 r_4)\\ - \\ - \overline{\frac{\partial I_v}{\partial t}} &\leq& \overline{I_v} \overline{S_u} r_3 - \underline{I_v} \underline{S_u} r_3 + \overline{I_v} \overline{S_v} r_4 - \underline{I_v} \underline{S_v} r_4 + \overline{E_v} r_8\\ - &=& N S_u^0 r_3 - 0 0 r_3 + N S_v^0 r_4 - 0 0 r_4 + N r_8\\ - &=& N (S_u^0 r_3 + S_v^0 r_4 + r_8)\\ - \\ - \frac{\partial I}{\partial t} &\leq& \overline{\frac{\partial I_u}{\partial t}} + \overline{\frac{\partial I_v}{\partial t}}\\ - &\leq& N (S_u^0 r_1 + S_v^0 r_2 + r_7) + N (S_u^0 r_3 + S_v^0 r_4 + r_8)\\ - &=& N (S_u^0 r_1 + S_v^0 r_2 + r_7 + S_u^0 r_3 + S_v^0 r_4 + r_8)\\ - \frac{\partial I}{\partial t} &\geq& \underline{\frac{\partial I_u}{\partial t}} + \underline{\frac{\partial I_v}{\partial t}}\\ - &\geq& - N (S_u^0 r_1 + S_v^0 r_2 + S_u^0 r_3 + S_v^0 r_4) - \\ -\end{eqnarray*} - -\begin{eqnarray*} - \underline{\frac{\partial E_u}{\partial t}} &\geq& \underline{I_u} \underline{S_u} r_1 + \underline{I_v} \underline{S_u} r_3 - \overline{E_u} r_7\\ - &=& 0 0 r_1 + 0 0 r_3 - N r_7\\ - &=& - N r_7\\ - \\ - \overline{\frac{\partial E_u}{\partial t}} &\leq& \overline{I_u} \overline{S_u} r_1 + \overline{I_v} \overline{S_u} r_3 - \underline{E_u} r_7\\ - &=& N S_u^0 r_1 + N S_u^0 r_3 - 0 r_7\\ - &=& N S_u^0 (r_1 + r_3)\\ - \\ - \underline{\frac{\partial E_v}{\partial t}} &\geq& \underline{I_u} \underline{S_v} r_2 + \underline{I_v} \underline{S_v} r_4 - \overline{E_v} r_8\\ - &=& 0 0 r_2 + 0 0 r_4 -N r_8\\ - &=& -N r_8\\ - \\ - \overline{\frac{\partial E_v}{\partial t}} &\leq& \overline{I_u} \overline{S_v} r_2 + \overline{I_v} \overline{S_v} r_4 - \underline{E_v} r_8\\ - &=& N S_v^0 r_2 +N S_v^0 r_4 - 0 r_8\\ - &=& N S_v^0 (r_2 + r_4)\\ - \\ - \frac{\partial E}{\partial t} &\leq& \overline{\frac{\partial E_u}{\partial t}} + \overline{\frac{\partial E_v}{\partial t}}\\ - &\leq& N S_u^0 (r_1 + r_3) +N S_v^0 (r_2 + r_4)\\ - \frac{\partial E}{\partial t} &\geq& \underline{\frac{\partial E_u}{\partial t}} + \underline{\frac{\partial E_v}{\partial t}}\\ - &\geq& - N (r_7 + r_8)\\ - \\ -\end{eqnarray*} - -\begin{eqnarray*} - S^{t+dt} &=& S^t + \frac{\partial S}{\partial t}dt\\ - S^{t+dt} &\leq& S^t + \overline{\frac{\partial S}{\partial t}}dt\\ - &=& S^t + (- \underline{I_u} \underline{S_u} r_1 - \underline{I_v} \underline{S_u} r_3 - \underline{S_u} r_5 + \overline{S_v} r_6)dt -\end{eqnarray*} - -Assume that all compartments are population constrained. Use information about monotonicity. +\begin{figure} + \includegraphics[width=\linewidth]{fig/seirhd_bounds.pdf} + \caption{\label{fig:seirhd-bounds} SEIRHD Model Bounds} +\end{figure} -\[\frac{\partial S_u}{\partial t} \leq 0, 0 \leq S_u \leq N\] -\[\frac{\partial S_v}{\partial t} \leq 0, 0 \leq S_v \leq N\] -\[ 0 \leq I_u \leq N\] -\[ 0 \leq I_v \leq N\] \ No newline at end of file +Figure \ref{fig:seirhd-bounds} illustrates the bounds computed by simulating the base and abstract models with FUNMAN. Each subplot is one compartment variable, and each series is one of the bounds or base model value. For example, the second plot illustrates $I$, which includes: +\begin{itemize} + \item Base model: $I$\_compliant and $I$\_\text{noncompliant}, + \item De-stratified $S$, $E$, and $I$: $I$\_\text{lb} and $I$\_\text{ub}, + \item De-stratified $S$, and $E$: $I$\_compliant\_lb, $I$\_compliant\_ub,$I$\_noncompliant\_lb and $I$\_noncompliant\_ub, +\end{itemize} +The abstractions provide different bounds for each variable. In general, as abstraction increases, the model will provide looser bounds, but with a smaller model. Using abstraction refinement techniques, it is possible to start with an abstract model and only refine the relevant variables. Selectively refining models will trade off multiple abstract model simulations against a single, potentially large model simulation. In cases where the bounds are enough to answer a query (or check a constraint), the abstract model simulation can lead to significant scale up. \ No newline at end of file From 53a5f6200c772da44ec63d91dc7b55f07bce6745 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Thu, 5 Sep 2024 22:34:14 +0000 Subject: [PATCH 31/93] use odeint exclusively, and improved plotting --- .../funman_aug_2024_12mo_eval_1_q1_demo.ipynb | 424 ++++--- .../eval_scenario1_1_ii_3_destratified_S.json | 1065 +++++++++++++++++ src/funman/config.py | 4 + src/funman/constants.py | 4 + src/funman/scenario/consistency.py | 56 +- src/funman/scenario/scenario.py | 75 +- 6 files changed, 1382 insertions(+), 246 deletions(-) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_S.json diff --git a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb index 6e380c24..fed41f64 100644 --- a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb +++ b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -37,24 +37,36 @@ "),\n", " \"destratified_SE\": os.path.join(\n", " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_SE.json\"\n", + "),\n", + " \"destratified_S\": os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_S.json\"\n", ")\n", "}\n", "\n", "states = {\n", " \"original_stratified\": [\"S_compliant\", \"S_noncompliant\", \"I_compliant\", \"I_noncompliant\", \"E_compliant\", \"E_noncompliant\",\"R\", \"H\", \"D\"],\n", " \"destratified_SEI\": [\"S_lb\", \"S_ub\", \"I_lb\", \"I_ub\", \"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", - " \"destratified_SE\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"]\n", + " \"destratified_SE\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", + " \"destratified_S\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_compliant_lb\", \"E_compliant_ub\",\"E_noncompliant_lb\", \"E_noncompliant_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"]\n", "}\n", "\n", "basevar_map = [\n", " ['S_compliant','S_noncompliant', 'S_lb', 'S_ub'], \n", " ['I_compliant','I_noncompliant','I_lb','I_ub','I_compliant_lb', 'I_noncompliant_ub', 'I_compliant_ub', 'I_noncompliant_lb'],\n", - " ['E_compliant','E_noncompliant','E_lb', 'E_ub'],\n", + " ['E_compliant','E_noncompliant','E_lb', 'E_ub', 'E_compliant_lb','E_noncompliant_lb', 'E_compliant_ub','E_noncompliant_ub',],\n", " ['R','R_lb', 'R_ub'],\n", " ['H','H_lb', 'H_ub'],\n", " ['D','D_lb', 'D_ub']\n", " ]\n", "\n", + "hatches= {\n", + " \"original_stratified\": '/', \n", + " \"destratified_SEI\": '\\\\', \n", + " \"destratified_SE\" : '|', \n", + " \"destratified_S\" : '-'\n", + " #, '+', 'x', 'o', 'O', '.', '*'\n", + "}\n", + "\n", "request_params = {}\n", "request_results = {}\n", "\n", @@ -68,27 +80,25 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Constants for the scenario\n", "\n", - "MAX_TIME=30\n", - "STEP_SIZE=5\n", + "MAX_TIME=200\n", + "STEP_SIZE=10\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Helper functions to setup FUNMAN for different steps of the scenario\n", "\n", - "from email.mime import base\n", - "\n", "\n", "def get_request():\n", " with open(REQUEST_PATH, \"r\") as request:\n", @@ -169,7 +179,7 @@ " \n", " fig = plt.figure()\n", " # fig.set_yscale(\"log\")\n", - " fig.savefig(\"save_file_name.pdf\")\n", + " # fig.savefig(\"save_file_name.pdf\")\n", " plt.close()\n", "\n", "def get_last_point_parameters(results):\n", @@ -212,20 +222,21 @@ " }\n", " ))\n", " \n", - "def plot_bounds(point, fig=None, axs=None, vars = [\"S\", \"E\", \"I\", \"R\", \"D\", \"H\"], basevar_map={}, **kwargs):\n", + "def plot_bounds(point, fig=None, axs=None, vars = [\"S\", \"E\", \"I\", \"R\", \"D\", \"H\"], model=None, basevar_map={}, **kwargs):\n", " \n", " df = point.simulation.dataframe().T\n", - " print(df)\n", + " # print(df)\n", "\n", " # Drop the ub vars because they are paired with the lb vars \n", " no_ub_vars = [v for v in vars if not v.endswith(\"_ub\")]\n", + " no_strat_vars = [v for v in no_ub_vars if not \"_noncompliant\" in v]\n", "\n", " if fig is None and axs is None:\n", " fig, axs = plt.subplots(len(basevar_map))\n", " fig.set_figheight(3*len(basevar_map))\n", " fig.suptitle('Variable Bounds over time')\n", " \n", - " for var in no_ub_vars:\n", + " for var in no_strat_vars:\n", " # print(var)\n", " # Get index of list containing var\n", " i = next(iter([i for i, bv in enumerate(basevar_map) if var in bv]))\n", @@ -244,55 +255,102 @@ " basevar = var\n", " labels = basevar\n", " \n", + " \n", + " if \"_compliant\" in basevar:\n", + " basevar = basevar.split(\"_\")[0]\n", + " if isinstance(labels, list):\n", + " lb = df[f\"{basevar}_compliant_lb\"] + df[f\"{basevar}_noncompliant_lb\"]\n", + " ub = df[f\"{basevar}_compliant_ub\"] + df[f\"{basevar}_noncompliant_ub\"]\n", + " labels = [f\"{basevar}_lb\", f\"{basevar}_ub\"]\n", + " data = pd.concat([lb, ub],axis=1, keys=labels)\n", + " \n", + " else:\n", + " data = df[f\"{basevar}_compliant\"] + df[f\"{basevar}_noncompliant\"]\n", + " labels = f\"{basevar}\"\n", + " else:\n", + " # print(labels)\n", + " data = df[labels]\n", + " if \"_compliant\" in basevar:\n", + " basevar = basevar.split(\"_\")[0]\n", + " labels = f\"{basevar}\"\n", + " \n", " \n", - " # print(labels)\n", - " data = df[labels]\n", + " legend_labels = labels\n", + " if model is not None:\n", + " legend_labels = [f\"{model}_{k.rsplit('_', 1)[0]}\" for k in labels[0:1]][0] if isinstance(labels, list) else f\"{model}_{labels}\"\n", + " \n", + " \n", + " \n", + " \n", + " # Fill between lb and ub\n", + " if isinstance(labels, list):\n", + " axs[i].fill_between(data.index, data[labels[0]], data[labels[1]], label=legend_labels, **kwargs)\n", + " else:\n", + " if \"hatch\" in kwargs:\n", + " del kwargs[\"hatch\"]\n", + " if \"alpha\" in kwargs:\n", + " del kwargs[\"alpha\"]\n", + " axs[i].plot(data, label=legend_labels, **kwargs)\n", " axs[i].set_title(f\"{basevar} Bounds\")\n", - " axs[i].plot(data, label=labels, **kwargs)\n", - " axs[i].legend(loc=\"lower left\")\n", + "\n", + " \n", + "\n", + " \n", + " # axs[i].legend(loc=\"outer\")\n", + " axs[i].legend(loc='center left', bbox_to_anchor=(1, 0.5),\n", + " ncol=1, fancybox=True, shadow=True, prop={'size': 8}, markerscale=2)\n", + " # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + "\n", " fig.tight_layout()\n", " return fig, axs\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-09-04 17:17:41,879 - funman.server.worker - INFO - FunmanWorker running...\n", - "2024-09-04 17:17:41,883 - funman.server.worker - INFO - Starting work on: 753f64af-4c62-46ab-ad6f-4723d0aff0c3\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "1 points\n", - " N beta c_m_0 c_m_1 c_m_2 c_m_3 eps_m_0 \\\n", - "original_stratified 19340000.0 0.4 0.5 0.5 0.5 0.5 0.5 \n", + " N beta c_m_0 eps_m_0 c_m_1 eps_m_1 c_m_2 \\\n", + "original_stratified 19340000.0 0.4 0.5 0.5 0.5 0.5 0.5 \n", + "destratified_SEI 19340000.0 0.4 NaN NaN NaN NaN NaN \n", + "destratified_SE 19340000.0 0.4 NaN NaN NaN NaN NaN \n", + "destratified_S 19340000.0 0.4 NaN NaN NaN NaN NaN \n", "\n", - " eps_m_1 eps_m_2 eps_m_3 ... p_H_to_R p_I_to_H \\\n", - "original_stratified 0.5 0.5 0.5 ... 0.88 0.2 \n", + " eps_m_2 c_m_3 eps_m_3 ... p_H_to_R r_H_to_R \\\n", + "original_stratified 0.5 0.5 0.5 ... 0.88 0.1 \n", + "destratified_SEI NaN NaN NaN ... 0.88 0.1 \n", + "destratified_SE NaN NaN NaN ... 0.88 0.1 \n", + "destratified_S NaN NaN NaN ... 0.88 0.1 \n", "\n", - " p_I_to_R p_compliant_noncompliant \\\n", - "original_stratified 0.8 0.1 \n", + " p_H_to_D r_H_to_D p_noncompliant_compliant \\\n", + "original_stratified 0.12 0.1 0.1 \n", + "destratified_SEI 0.12 0.1 NaN \n", + "destratified_SE 0.12 0.1 0.1 \n", + "destratified_S 0.12 0.1 0.1 \n", "\n", - " p_noncompliant_compliant r_E_to_I r_H_to_D r_H_to_R \\\n", - "original_stratified 0.1 0.2 0.1 0.1 \n", + " p_compliant_noncompliant eps_m_lb eps_m_ub c_m_lb \\\n", + "original_stratified 0.1 NaN NaN NaN \n", + "destratified_SEI NaN 0.4 0.6 0.4 \n", + "destratified_SE 0.1 0.4 0.6 0.4 \n", + "destratified_S 0.1 0.4 0.6 0.4 \n", "\n", - " r_I_to_H r_I_to_R \n", - "original_stratified 0.1 0.07 \n", + " c_m_ub \n", + "original_stratified NaN \n", + "destratified_SEI 0.6 \n", + "destratified_SE 0.6 \n", + "destratified_S 0.6 \n", "\n", - "[1 rows x 21 columns]\n" + "[4 rows x 25 columns]\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -322,7 +380,46 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_odepack_py.py:248: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " warnings.warn(warning_msg, ODEintWarning)\n" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "# Remove all stratification\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=True, dreal_precision=1e-3)\n", + "# add_unit_test(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", + "results = run(funman_request, model=models['destratified_SEI'])\n", + "report(results, \"destratified_SEI\", states=states['destratified_SEI'])\n", + "# plot_bounds(results.points()[0], vars=[\"S\", \"E\", \"I\", \"R\", \"H\", \"D\"])\n", + "vars = results.model._state_var_names()\n", + "point = results.points()[0]\n", + "fig, axs = plot_bounds(point, vars=vars, basevar_map=basevar_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -333,55 +430,34 @@ " N beta c_m_0 eps_m_0 c_m_1 eps_m_1 c_m_2 \\\n", "original_stratified 19340000.0 0.4 0.5 0.5 0.5 0.5 0.5 \n", "destratified_SEI 19340000.0 0.4 NaN NaN NaN NaN NaN \n", + "destratified_SE 19340000.0 0.4 NaN NaN NaN NaN NaN \n", "\n", " eps_m_2 c_m_3 eps_m_3 ... p_H_to_R r_H_to_R \\\n", "original_stratified 0.5 0.5 0.5 ... 0.88 0.1 \n", "destratified_SEI NaN NaN NaN ... 0.88 0.1 \n", + "destratified_SE NaN NaN NaN ... 0.88 0.1 \n", "\n", " p_H_to_D r_H_to_D p_noncompliant_compliant \\\n", "original_stratified 0.12 0.1 0.1 \n", "destratified_SEI 0.12 0.1 NaN \n", + "destratified_SE 0.12 0.1 0.1 \n", "\n", " p_compliant_noncompliant eps_m_lb eps_m_ub c_m_lb \\\n", "original_stratified 0.1 NaN NaN NaN \n", "destratified_SEI NaN 0.4 0.6 0.4 \n", + "destratified_SE 0.1 0.4 0.6 0.4 \n", "\n", " c_m_ub \n", "original_stratified NaN \n", "destratified_SEI 0.6 \n", + "destratified_SE 0.6 \n", "\n", - "[2 rows x 25 columns]\n", - " S_lb I_lb E_lb I_ub S_ub \\\n", - "0.0 1.934000e+07 4.000000 1.000000 4.000000 1.934000e+07 \n", - "5.0 1.933999e+07 4.497161 2.733767 6.016389 1.933999e+07 \n", - "10.0 1.933997e+07 0.990951 -5.282224 17.363698 1.933998e+07 \n", - "15.0 1.933990e+07 -45.330407 -81.668704 80.882919 1.934000e+07 \n", - "20.0 1.933952e+07 -409.351989 -650.824792 496.780914 1.934022e+07 \n", - "25.0 1.933701e+07 -3031.710702 -4702.217340 3362.960448 1.934191e+07 \n", - "30.0 1.931968e+07 -21550.678810 -33227.157234 23385.814862 1.935400e+07 \n", - "\n", - " E_ub R_lb R_ub H_lb H_ub \\\n", - "0.0 1.000000 0.000000 0.000000 0.000000 0.000000 \n", - "5.0 6.468573 1.242755 1.365454 0.325790 0.371883 \n", - "10.0 23.307062 2.455659 4.541286 0.367607 1.179461 \n", - "15.0 121.639896 -1.733940 16.899058 -2.191968 5.209236 \n", - "20.0 779.832491 -53.778527 86.570912 -24.938423 31.340085 \n", - "25.0 5343.699263 -459.065706 546.065956 -193.020999 211.544110 \n", - "30.0 37279.471642 -3373.544262 3719.819389 -1387.597058 1471.237197 \n", - "\n", - " D_lb D_ub \n", - "0.0 0.000000 0.000000 \n", - "5.0 0.010369 0.010981 \n", - "10.0 0.034436 0.052471 \n", - "15.0 0.009014 0.210011 \n", - "20.0 -0.585975 1.070793 \n", - "25.0 -5.564188 6.709912 \n", - "30.0 -41.988538 45.643410 \n" + "[3 rows x 25 columns]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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YmZmZzJw5kyZNmtgEyBdffJFNmzbRq1cvatWqxYkTJ/jf//5H9erVueOOOwrsv3nz5oSFhfH++++TkJDAXXfdRVRUFPPnz6dfv36EhITYtO/Zsyeenp489dRT1oCdW926dXn55ZcJDw/n4MGD9OvXD09PTw4cOMDy5ct56KGHaN26NSEhIdbwXFitWrUC4PHHHyc0NNT6kFBxuP/++1m6dCmPPPIIGzZsoEOHDmRlZfHnn3+ydOlS1q5dm2fsPhEphJv/IK5I2TJr1iwDMNq2bZtn2YULF4yJEycaVatWNdzc3IwOHToYkZGRxl133WXcdddd1nY5Q5osW7YsTx/5DXfyxx9/GF26dDE8PDyMypUrG2PGjLEO5fHxxx9b24WFhRnu7u7G/v37jW7duhkVKlQwqlSpYkyePDnPMCxcNpyFYRjG8ePHjbFjxxo1atQwnJycjICAAKNz587G+++/f9XjcvlwJ2az2fD39zfuvfdemyFIcnz//fdGhw4dDDc3N8PLy8vo06eP8ccff+Rp99133xm33nqr4ezsbDRo0MBYuHBhgcOd1KlTx6hZs6bh4uJiBAYGGl26dDF8fX1tht0wDMP48ccfjVatWhnOzs5GnTp1jHbt2tnU7uDgYFSuXNmoVq2a4e/vbzg7OxuBgYHGvffea+zdu/eqxyIjI8OYOnWqUbt2bcPJycmoUaOGER4ebly4cCHf9sOGDTMAo0uXLgX2+cUXXxh33HGH4e7ubri7uxsNGzY0xo4da8TGxlp/M82bNzeaNGly1fpyZGZmGuPHjzf8/PwMk8lkc0wv/33kHPPLh3/J+c1d7q677spTS3p6uvHaa68ZTZo0MVxcXIyKFSsarVq1MqZOnWokJiYWum4RucRkGNdxF7GISAm2ZcsWQkJCqFmzJmFhYQQEBHD48GF++eUX9u/fz759+664/ogRI1iyZIn1yd3MzEz279/PnDlzyMjIyHdw6ZJk48aNhISEsGHDBjp16mTvckTkJtKlWBEpc1555RW8vb359ddfbd7CAIUfRsPR0dHmPbcA7dq1o3fv3nzzzTeMGTOmqMoVESkyenhCRMqc/fv306RJkzyhDsDf3/+6+y3oPbd///03gwYNwtfXlwoVKtCuXTvrwMM55s2bh8lkyjN2YH7vAs55x+sff/xBSEgIFSpUoFq1akyfPj1PTUeOHKFfv364u7vj7+/Pk08+me/DHH/99RcDBgwgICAAV1dXqlevztChQ/MdT1FESi+dsRORMqdWrVpERkaya9cubr311uvuJ+c9t1lZWfz99988++yzVKpUid69e1vbHD9+nNtvv53U1FQef/xxKlWqxPz58+nbty+ff/4599xzz3Vt++zZs3Tv3p3+/fszePBgPv/8c5599lmaNm1Kjx49gOynRzt37syhQ4d4/PHHCQwMZMGCBfzwww82faWnpxMaGkpaWhrjx48nICCAo0ePsmrVKhISEvD29r7OIyQiJY69b/ITESlq3333neHg4GA4ODgY7du3N5555hlj7dq1Rnp6eqHWz3nH7uVTtWrVjOjoaJu2TzzxhAHYvPP03LlzRu3atY2goCDrQyoff/yxARgHDhywWT+/h2Ny3vH6ySefWOelpaUZAQEBxoABA6zz3nrrLQMwli5dap2XkpJi1KtXz6bP3377rcCHc0SkbNGlWBEpc7p27UpkZCR9+/bl999/Z/r06YSGhlKtWjW+/vrrQvXh6upqfc/t2rVree+99/Dw8KBnz57WAZ4h+12wbdu2tRnyxMPDg4ceeoiDBw/yxx9/XNc+eHh42Nzj5+zsTNu2bW3et7p69WqqVq3KwIEDrfMqVKjAQw89ZNNXzhm5tWvXkpqael31iEjpoGB3HTZt2kSfPn0IDAzEZDKxYsWKa1p/ypQpmEymPFPO4KAicuPatGnDl19+ydmzZ4mKiiI8PJxz584xcODAQoUtBwcH63tuu3XrxkMPPcT3339PYmIi4eHh1nb//PNPge+5zVl+PapXr57njQy533Ob03e9evXytLu8ntq1azNhwgTmzp1L5cqVCQ0NZdasWbq/TqQMUrC7DikpKTRv3pxZs2Zd1/pPPfUUcXFxNlPjxo1t3gcpIkXD2dmZNm3a8OqrrzJ79mwyMjJYtmzZdfVVvXp1GjRowKZNm655XXu+5xbg9ddfZ8eOHTz33HOcP3+exx9/nCZNmnDkyJHr6k9ESiYFu+vQo0cPXn755QJvik5LS+Opp56iWrVquLu7ExwcbPPEm4eHBwEBAdbp+PHj/PHHH4waNeom7YFI+ZTzJoO4uLjr7iMzM9P6nlvIflCjoPfc5iyH7LNtkPd1Xdd7Ri+n7/379+cJe/nVA9C0aVP++9//smnTJn766SeOHj3KnDlzrnv7IlLyKNgVg3HjxhEZGcmSJUvYsWMHgwYNonv37vm+aBxg7ty51K9fnzvvvPMmVypSNm3YsCHfM1urV68G8l6qLKy9e/cSGxtr86qunj17EhUVRWRkpHVeSkoK77//PkFBQTRu3BjIfhUYYHO2Lysri/fff/+6asnZ9rFjx/j888+t81JTU/P0mZSURGZmps28pk2bYjabr/ieWxEpfTTcSRE7dOgQH3/8MYcOHbKOTP/UU0+xZs0aPv74Y1599VWb9hcuXGDRokX85z//sUe5ImXS+PHjSU1N5Z577qFhw4akp6ezZcsWPvvsM4KCghg5cuRV+8jMzLS+59ZisXDw4EHmzJmDxWJh8uTJ1nb/+c9/+PTTT+nRowePP/44vr6+zJ8/nwMHDvDFF19gNmf/+7lJkya0a9eO8PBwzpw5g6+vL0uWLMkTuK7FmDFjePfdd3nggQeIjo6matWqLFiwgAoVKti0++GHHxg3bhyDBg2ifv36ZGZmsmDBgnzfRSsipZuCXRHbuXMnWVlZ1K9f32Z+WloalSpVytN++fLlnDt3jrCwsJtVokiZ93//938sW7aM1atX8/7775Oenk7NmjV57LHH+O9//5vvwMWXS0tL4/7777d+9/Lyok2bNixYsIDOnTtb51epUoUtW7bw7LPPMnPmTC5cuECzZs1YuXIlvXr1sulz0aJFPPzww0ybNg0fHx9GjRpFSEgIXbt2va79rFChAuvXr2f8+PHMnDmTChUqMGzYMHr06EH37t2t7Zo3b05oaCgrV67k6NGjVKhQgebNm/Ptt9/Srl2769q2iJRMelfsDTKZTCxfvpx+/foB8NlnnzFs2DB2796d5+bnnHvrcuvcuTNeXl4sX778ZpUsIiIiZZTO2BWx2267jaysLE6cOHHVe+YOHDjAhg0bCj2uloiIiMiVKNhdh+TkZPbt22f9fuDAAWJiYvD19aV+/foMGzaMBx54gNdff53bbruNkydPsn79epo1a2Zzaeajjz6iatWq1tcDiYiIiNwIXYq9Dhs3biQkJCTP/LCwMObNm0dGRgYvv/wyn3zyCUePHqVy5cq0a9eOqVOn0rRpUyD7ZuxatWrxwAMP8Morr9zsXRAREZEySMFOREREpIzQOHYiIiIiZYSCnYiIiEgZoYcnCslisXDs2DE8PT0LfOejiIiISFEzDINz584RGBhoHfT8So2LzI8//mj07t3bqFq1qgEYy5cvv+o67777rtGwYUPD1dXVqF+/vjF//nyb5V988YXRqlUrw9vb26hQoYLRvHlz45NPPrFpExYWZgA2U2hoqE2bWrVq5WkTERFR6H07fPhwnvU1adKkSZMmTZpu1nT48OGr5pUiPWOXkpJC8+bNefDBB+nfv/9V28+ePZvw8HA++OAD2rRpQ1RUFGPGjKFixYr06dMHAF9fX55//nkaNmyIs7Mzq1atYuTIkfj7+xMaGmrtq3v37nz88cfW7y4uLnm29+KLLzJmzBjrd09Pz0LvW07bw4cP4+XlVej1RERERG5EUlISNWrUKFRuKdJg16NHj2sak23BggU8/PDDDBkyBIA6derw66+/8tprr1mDXadOnWzW+fe//838+fPZvHmzTbBzcXHJ81aHy3l6el61TUFyLr96eXkp2ImIiMhNV5hbwez68ERaWhqurq4289zc3IiKiiIjIyNPe8MwWL9+PbGxsXTs2NFm2caNG/H396dBgwY8+uijnD59Os/606ZNo1KlStx2223MmDHjhl6+LSIiIlLS2PXhidDQUObOnUu/fv1o2bIl0dHRzJ07l4yMDE6dOkXVqlUBSExMpFq1aqSlpeHg4MD//vc/m5dmd+/enf79+1O7dm3279/Pc889R48ePYiMjLS+r/Xxxx+nZcuW+Pr6smXLFsLDw4mLi+ONN97It7a0tDTS0tKs35OSkorxSIiIiIjcOLsGu0mTJhEfH0+7du0wDIMqVaoQFhbG9OnTbZ768PT0JCYmhuTkZNavX8+ECROoU6eO9TLt0KFDrW2bNm1Ks2bNqFu3Lhs3bqRz584ATJgwwdqmWbNmODs78/DDDxMREZHv/XgRERFMnTq1mPZcREREpOjZ9VKsm5sbH330EampqRw8eJBDhw4RFBSEp6cnfn5+1nZms5l69erRokULJk6cyMCBA4mIiCiw3zp16lC5cmWb97leLjg4mMzMTA4ePJjv8vDwcBITE63T4cOHr3s/RURERG6GEjGOnZOTE9WrVwdgyZIl9O7d+4rjtFgsFpvLpJc7cuQIp0+ftl7KzU9MTAxmsxl/f/98l7u4uOR7Jk9ERESkpCrSYJecnGxzluzAgQPExMTg6+tLzZo1CQ8P5+jRo3zyyScA7N27l6ioKIKDgzl79ixvvPEGu3btYv78+dY+IiIiaN26NXXr1iUtLY3Vq1ezYMECZs+ebd3m1KlTGTBgAAEBAezfv59nnnmGevXqWZ+ajYyMZOvWrYSEhODp6UlkZCRPPvkkw4cPp2LFikV5CERERETspkiD3bZt2wgJCbF+z7mvLSwsjHnz5hEXF8ehQ4esy7Oysnj99deJjY3FycmJkJAQtmzZQlBQkLVNSkoKjz32GEeOHMHNzY2GDRuycOFC6xApDg4O7Nixg/nz55OQkEBgYCDdunXjpZdesp5xc3FxYcmSJUyZMoW0tDRq167Nk08+aXPfnYiIiMg1Sz0D+3+ApgPtXQkAJsMwDHsXURokJSXh7e1NYmKixrETERERSDgECwfAqb0w8CO4dUCxbOZaMkiJuMdOREREpFSJ3wkLB0JyPHhVB/8m9q4IULATERERuTYHNsGSYZCWBP6NYdjn4F3N3lUBCnYiIiIihbfrS1j+MGSlQ60OMHQxuPnYuyoru45jJyIiIlJq/DIbPn8wO9Q16gvDvyxRoQ50xk5ERETkyiwWWD8Ffn47+3ubMdDjNTA72LWs/CjYiYiIiBQkMx2+Hgc7Psv+3vkFuGMCmEz2rasACnYiIiIi+Uk7B0sfyB6nzuQAd78LLe6zd1VXpGAnIiIicrnkE7BoIMT9Dk4VYPAncEtXe1d1VQp2IiIiIrmd3g8L+8PZg1ChMgxbCtVa2buqQlGwExEREclxNBoWDYbUU+BTC+5fDpXq2ruqQlOwExEREQH46/vse+oyUqBq8+yBhz387V3VNVGwExEREYlZDF+PB0sm1AmBIQvAxdPeVV0zDVAsIiIi5ZdhwE9vwIpHs0Nd08Fw39JSGepAZ+xERESkvLJkwZr/QNT72d9vfxy6TAVz6T3vpWAnIiIi5U/GBVj+EPzxVfb30Aho/5h9ayoCCnYiIiJSvpxPgCX3wT8/g4Mz3DMHbh1g76qKhIKdiIiIlB9Jx2DhADjxB7h4wdBFULujvasqMgp2IiIiUj6c+DM71CUdAY8AGP45BDS1d1VFSsFOREREyr5Dv8DiIXAhASrdAsO/gIq17F1VkVOwExERkbJtzyr4YhRkXoDqbbKHM6nga++qikWRPs+7adMm+vTpQ2BgICaTiRUrVlx1nVmzZtGoUSPc3Nxo0KABn3zyic3yL7/8ktatW+Pj44O7uzstWrRgwYIFNm1GjBiByWSymbp3727T5syZMwwbNgwvLy98fHwYNWoUycnJN7zPIiIiUoL9+iEsvT871NXvAQ98XWZDHRTxGbuUlBSaN2/Ogw8+SP/+/a/afvbs2YSHh/PBBx/Qpk0boqKiGDNmDBUrVqRPnz4A+Pr68vzzz9OwYUOcnZ1ZtWoVI0eOxN/fn9DQUGtf3bt35+OPP7Z+d3FxsdnWsGHDiIuLY926dWRkZDBy5EgeeughFi9eXER7LyIiIiWGYcCGV2HT9OzvLR+AXm+CQ9m+WGkyDMMolo5NJpYvX06/fv0KbHP77bfToUMHZsyYYZ03ceJEtm7dyubNmwtcr2XLlvTq1YuXXnoJyD5jl5CQUOAZwj179tC4cWN+/fVXWrduDcCaNWvo2bMnR44cITAw8Kr7k5SUhLe3N4mJiXh5eV21vYiIiNhJViasegJ+u3iF767/QKf/gMlk17Ku17VkELsOrZyWloarq6vNPDc3N6KiosjIyMjT3jAM1q9fT2xsLB072j6avHHjRvz9/WnQoAGPPvoop0+fti6LjIzEx8fHGuoAunTpgtlsZuvWrQXWlpSUZDOJiIhICZeeCp8Nyw51JjP0fhNCwkttqLtWdg12oaGhzJ07l+joaAzDYNu2bcydO5eMjAxOnTplbZeYmIiHhwfOzs706tWLmTNn0rVrV+vy7t2788knn7B+/Xpee+01fvzxR3r06EFWVhYA8fHx+Pv722zb0dERX19f4uPj860tIiICb29v61SjRo1iOAIiIiJSZFJOwyd9Ye8acHSFIQuh9YP2ruqmsuuF5kmTJhEfH0+7du0wDIMqVaoQFhbG9OnTMed6T5unpycxMTEkJyezfv16JkyYQJ06dejUqRMAQ4cOtbZt2rQpzZo1o27dumzcuJHOnTtfV23h4eFMmDDB+j0pKUnhTkREpKQ6+w8s7A+n94GrT/aTrzWD7V3VTWfXM3Zubm589NFHpKamcvDgQQ4dOkRQUBCenp74+flZ25nNZurVq0eLFi2YOHEiAwcOJCIiosB+69SpQ+XKldm3bx8AAQEBnDhxwqZNZmYmZ86cISAgIN8+XFxc8PLysplERESkBIrbAR92zQ513jVg1HflMtSBnYNdDicnJ6pXr46DgwNLliyhd+/eNmfsLmexWEhLSytw+ZEjRzh9+jRVq1YFoH379iQkJBAdHW1t88MPP2CxWAgOLp9/8SIiImXC3z/Cxz0h+Tj4N8kOdX4N7F2V3RTppdjk5GTrWTKAAwcOEBMTg6+vLzVr1iQ8PJyjR49ax6rbu3cvUVFRBAcHc/bsWd544w127drF/PnzrX1ERETQunVr6tatS1paGqtXr2bBggXMnj3bus2pU6cyYMAAAgIC2L9/P8888wz16tWzDofSqFEjunfvzpgxY5gzZw4ZGRmMGzeOoUOHFuqJWBERESmBdn4Oyx8BSwbUuiP7va9uPvauyq6KNNht27aNkJAQ6/ece9TCwsKYN28ecXFxHDp0yLo8KyuL119/ndjYWJycnAgJCWHLli0EBQVZ26SkpPDYY49x5MgR3NzcaNiwIQsXLmTIkCEAODg4sGPHDubPn09CQgKBgYF069aNl156yWYsu0WLFjFu3Dg6d+6M2WxmwIABvPPOO0W5+yIiInKzRM6Ctc9lf27cD+55D5xcr7hKeVBs49iVNRrHTkREpASwWGDdJIh8N/t724eh+zS4wi1cpd21ZJCyPfyyiIiIlB2Z6fDVY7BzWfb3LlOgwxPlZoy6wlCwExERkZIv7Rx8Nhz+3ghmR7h7FjQfetXVyhsFOxERESnZzh2HRQMhfgc4ucOQT6BeF3tXVSIp2ImIiEjJdXo/LLgHEv6BCpVh2DKo1tLeVZVYCnYiIiJSMh2JhsWDIPU0VKwNw7+ASnXtXVWJpmAnIiIiJc/e72BZGGSkQtUWMOxz8PC76mrlnYKdiIiIlCy/LYKvx4ORBXU7w+BPwMXD3lWVCgp2IiIiUjIYBvz0f/DDy9nfmw2Fu98FByf71lWKKNiJiIiI/Vmy4Ntn4Ne52d87PJE9Tp3GqLsmCnYiIiJiXxkX4MvRsGclYIIer0Hww/auqlRSsBMRERH7OX8WPr0PDm0BB2fo/z40ucfeVZVaCnYiIiJiH4lHYeEAOLkHXLxg6GKofae9qyrVFOxERETk5juxJzvUJR0Fz6rZw5kE3Grvqko9BTsRERG5uf7ZAp8OhQuJULk+DP8SfGrYu6oyQcFOREREbp4/voYvRkNWGtQIhnuXQAVfe1dVZijYiYiIyM0R9QGsfhowoEFPGPgROLnZu6oyRcFOREREipdhZA86/NP/ZX9vNQJ6vg4OiiFFTUdUREREik9WBqx6An5bmP2903Nw1zMaeLiYKNiJiIhI8UhPgWUj4K/vwGSG3m9BqzB7V1WmmYuys02bNtGnTx8CAwMxmUysWLHiquvMmjWLRo0a4ebmRoMGDfjkk09sln/55Ze0bt0aHx8f3N3dadGiBQsWLCiwv0ceeQSTycRbb71lMz8oKAiTyWQzTZs27Xp2U0RERK4m5RTM75Md6hzdsseoU6grdkV6xi4lJYXmzZvz4IMP0r9//6u2nz17NuHh4XzwwQe0adOGqKgoxowZQ8WKFenTpw8Avr6+PP/88zRs2BBnZ2dWrVrFyJEj8ff3JzQ01Ka/5cuX88svvxAYGJjv9l588UXGjBlj/e7p6XkDeysiIiL5OnsQFvSHM/vBrSLctxRqtLV3VeVCkQa7Hj160KNHj0K3X7BgAQ8//DBDhgwBoE6dOvz666+89tpr1mDXqVMnm3X+/e9/M3/+fDZv3mwT7I4ePcr48eNZu3YtvXr1ynd7np6eBAQEXONeiYiISKHF/Q4LB0LKCfCukT1GnV99e1dVbhTppdhrlZaWhqurq808Nzc3oqKiyMjIyNPeMAzWr19PbGwsHTt2tM63WCzcf//9PP300zRp0qTA7U2bNo1KlSpx2223MWPGDDIzM4tuZ0RERMq7/Rvg417Zoa7KrTBqnULdTWbXhydCQ0OZO3cu/fr1o2XLlkRHRzN37lwyMjI4deoUVatWBSAxMZFq1aqRlpaGg4MD//vf/+jatau1n9deew1HR0cef/zxArf1+OOP07JlS3x9fdmyZQvh4eHExcXxxhtv5Ns+LS2NtLQ06/ekpKQi2msREZEyaMcyWPEoWDIg6E4Yughcve1dVblj12A3adIk4uPjadeuHYZhUKVKFcLCwpg+fTpm86WTiZ6ensTExJCcnMz69euZMGECderUoVOnTkRHR/P222+zfft2TFd4dHrChAnWz82aNcPZ2ZmHH36YiIgIXFxc8rSPiIhg6tSpRbvDIiIiZdGWmfDdf7M/N7kH7nkPHPP+f6sUP7teinVzc+Ojjz4iNTWVgwcPcujQIYKCgvD09MTPz8/azmw2U69ePVq0aMHEiRMZOHAgERERAPz000+cOHGCmjVr4ujoiKOjI//88w8TJ04kKCiowG0HBweTmZnJwYMH810eHh5OYmKidTp8+HBR7rqIiEjpZ7HA2ucvhbp2j8GAjxTq7KhEjGPn5ORE9erVAViyZAm9e/e2OWN3OYvFYr1Mev/999OlSxeb5aGhodx///2MHDmywD5iYmIwm834+/vnu9zFxSXfM3kiIiICZKbBisdg1+fZ37u+BLeP18DDdlakwS45OZl9+/ZZvx84cICYmBh8fX2pWbMm4eHhHD161DpW3d69e4mKiiI4OJizZ8/yxhtvsGvXLubPn2/tIyIigtatW1O3bl3S0tJYvXo1CxYsYPbs2QBUqlSJSpUq2dTh5OREQEAADRo0ACAyMpKtW7cSEhKCp6cnkZGRPPnkkwwfPpyKFSsW5SEQEREp+y4kwWfD4MAmMDvC3f+D5kPsXZVQxMFu27ZthISEWL/n3NcWFhbGvHnziIuL49ChQ9blWVlZvP7668TGxuLk5ERISAhbtmyxuYSakpLCY489xpEjR3Bzc6Nhw4YsXLjQOkRKYbi4uLBkyRKmTJlCWloatWvX5sknn7S5705EREQK4Vw8LBoI8TvB2QMGfwL1Otu7KrnIZBiGYe8iSoOkpCS8vb1JTEzEy8vL3uWIiIjcfKf+goX9IeEQuPvBsGUQeJu9qyrzriWDlIh77ERERKSEO7INFg2C82fAtw4M/yL7TylRFOxERETkymLXwLIRkHkeAltmvyLMw++qq8nNp2AnIiIiBdu+AFb+G4wsqNcFBs0HFw97VyUFULATERGRvAwDNv0fbHg5+3vz+6DvO+DgZN+65IoU7ERERMSWJQtWPwXbPsr+fudE+NckjVFXCijYiYiIyCUZ5+GL0fDnKsAEPaZD8EP2rkoKScFOREREsqWegU/vhcO/gIMz9P8AmvSzd1VyDRTsREREBBKPwMIBcPJPcPGGexdD0B32rkqukYKdiIhIeXf8j+xQd+4YeAbC8M+hShN7VyXXQcFORESkPDu4GT69D9ISoXIDuP9L8K5u76rkOinYiYiIlFd/fAVfjIGsNKjRDu79FCr42rsquQEKdiIiIuWJYUDyCdj1Oax9HjCgYW8YMBec3OxdndwgBTsREZGyJCe4JRyChH8u/plrSjwMmRcutW/9IPT8PzA72K9mKTIKdiIiIqXJtQa3/JjM4FUN2j4Et4/XwMNliIKdiIhISVJUwc0zEHxq5j95VQNH55uzP3JTKdiJiIjcTApuUowU7ERERIpSUV4qvVJwc3C6OfsjpYqCnYiIyLWwWCDlRK6wdnl4O5w9fMiVKLhJMVGwExERyU3BTUoxBTsRESlfFNykDCvSYLdp0yZmzJhBdHQ0cXFxLF++nH79+l1xnVmzZvHuu+9y8OBBatasyfPPP88DDzxgXf7ll1/y6quvsm/fPjIyMrjllluYOHEi999/f779PfLII7z33nu8+eabPPHEE9b5Z86cYfz48axcuRKz2cyAAQN4++238fDwKIpdFxGRkkLBTcqxIg12KSkpNG/enAcffJD+/ftftf3s2bMJDw/ngw8+oE2bNkRFRTFmzBgqVqxInz59APD19eX555+nYcOGODs7s2rVKkaOHIm/vz+hoaE2/S1fvpxffvmFwMDAPNsaNmwYcXFxrFu3joyMDEaOHMlDDz3E4sWLi2bnRUTk5lBwEymQyTAMo1g6Npmuesbu9ttvp0OHDsyYMcM6b+LEiWzdupXNmzcXuF7Lli3p1asXL730knXe0aNHCQ4OZu3atfTq1YsnnnjCesZuz549NG7cmF9//ZXWrVsDsGbNGnr27MmRI0fyDYKXS0pKwtvbm8TERLy8vK7aXkTELiwWsGReNmVd/DPjsu+5lmdl5NP+SlOuNlkZ+fdp/XyF5VlXqMlSQE0XkhTcpFy5lgxi13vs0tLScHV1tZnn5uZGVFQUGRkZODnZ/kdnGAY//PADsbGxvPbaa9b5FouF+++/n6effpomTZrk2U5kZCQ+Pj7WUAfQpUsXzGYzW7du5Z577sm3trS0S//DkZSUdN37WRgXzqcQM//pghvkGhTcyPXFRP6jhRuYim4gcZPJtoB8ysrvXweXr2FYC7r2wi7fl8Icg9wrFbRFU4HHtTB1XNbKlPtjIfbxSvXl85dX0KZNuWcU8JdeYG95+ilg3VzNTSYwZf/CLm7SuLQsV3uTcakcE8bFzyZMF38tJlOuzwA5fZrAjMHFjza1Wf+8WENOj1j7z6kt17ZMl/V/sU3O/Pz6xjAubuPy42Nc2r9855swGYZNP5cYF8PJFQLLdYWsywJWvv81lkEKbiL5smuwCw0NZe7cufTr14+WLVsSHR3N3LlzycjI4NSpU1StWhWAxMREqlWrRlpaGg4ODvzvf/+ja9eu1n5ee+01HB0defzxx/PdTnx8PP7+/jbzHB0d8fX1JT4+Pt91IiIimDp1ahHt6dWlp12gXfyim7Y9ESlfDLMj5JpMNt8dbJbhYNs2z/I8U67lDk75tM/9Pb/ludo4OBWwTYeL6zqCcwUFN5EC2DXYTZo0ifj4eNq1a4dhGFSpUoWwsDCmT5+O2Wy2tvP09CQmJobk5GTWr1/PhAkTqFOnDp06dSI6Opq3336b7du3YyrCd92Fh4czYcIE6/ekpCRq1KhRZP1fztnZhV+q3Jvvssv//W26wjKbdle4ym5ctqY9++Sa7gYw8v14xXZX2GzOWR/b1lffRt7ZudcpxLbJte1C7X7+NRVYx1XqNl3+t5Wnff5HxDAurWlc3APDsG2d+7sFExi518/+bF1uXPqV5Mw3DMN6zs3Is27O+THDWouByXoML/WRa72Ldef8Qi25D5FhyrOO7Tk2UwGf829z6fxd7uNxaXkmDmRhtv6ZgeOl70bOfAcyyf354jpG7nUdyMD2e6Zhtq57aXn2+habc5PZZxOdHcw4O5pxcTTn+uyAs6MZZ1P25GK+tCynrbXNZfOdbfrJ1e6y+dZ1HC4tdzDrPaUiRcWuwc7NzY2PPvqI9957j+PHj1O1alXef/99PD098fPzs7Yzm83Uq1cPgBYtWrBnzx4iIiLo1KkTP/30EydOnKBmzZrW9llZWUycOJG33nqLgwcPEhAQwIkTJ2y2nZmZyZkzZwgICMi3NhcXF1xcXIphr/PnWsGDdo/OuWnbE5GCGYaBYYDFMHKFvux5uT/bLL+4Tk44tVxslxNssywGmVkGaZlZpGVaSM+ykJ5pyf6cM2Vl2cyzLsuykJZxaXmeZTbzsqx9526XaTFy7R+kXVx2zk7HODdHs6mAkOiQKyheCog+FZxpVNWTxlW9aFjVCw8XjdwlkqNE/Nfg5ORE9erVAViyZAm9e/e2OWN3OYvFYr3/7f7776dLly42y0NDQ7n//vsZOXIkAO3btychIYHo6GhatWoFwA8//IDFYiE4OLg4dklESjGTKfseVfN13A9aUmVZDDIuBsS0XAHxUmjMHQazbIJj3gCaT7vLwmbuvnIH0Jw2uWVaDDLTs0hNz7qufatVqQKNq3rRqKpX9p+BXgR6uxbpVRyR0qJIg11ycjL79u2zfj9w4AAxMTH4+vpSs2ZNwsPDOXr0KJ988gkAe/fuJSoqiuDgYM6ePcsbb7zBrl27mD9/vrWPiIgIWrduTd26dUlLS2P16tUsWLCA2bNnA1CpUiUqVapkU4eTkxMBAQE0aNAAgEaNGtG9e3fGjBnDnDlzyMjIYNy4cQwdOrRQT8SKiJR2DmYTDmYHXJ0cAPvem2YYBhlZhk1AtAmHBYTNnIB4POkCe+LO8cexJOKTLvDP6VT+OZ3Kt7su3TPt7eZ0KewFetGoqie3+Hvi7FjwSQORsqBIg922bdsICQmxfs+5Ry0sLIx58+YRFxfHoUOHrMuzsrJ4/fXXiY2NxcnJiZCQELZs2UJQUJC1TUpKCo899hhHjhzBzc2Nhg0bsnDhQoYMGXJNtS1atIhx48bRuXNn6wDF77zzzo3tsIiIXDOTyYSzY/blV27wjpczKensiUvij2NJ2X/GJbHvRDKJ5zOI/Ps0kX+ftrZ1cjBRz9/Tehk3J/hVdHe+wT0SKTmKbRy7skbj2ImIlA5pmVn8dTzZGvRyQl/Shcx82wd6u+Y6s5cd+Gr6VsCshzqkhLiWDKJgV0gKdiIipZdhGBxNOG+9hPtHXCJ74s5x6Exqvu3dnR1omOusXuNALxpU8cTN2eEmVy6iYFcsFOxERMqecxcy+DP+nM2l3D/jz+V5wAPAbILald1pHOhtcznXz9NFD2pIsVKwKwYKdiIi5UNmloUDp1KyL+PmupR7Kjk93/aVPZytl3BzLufWqeyOo4Me1JCioWBXDBTsRETKtxPnLlwMeecuBr5EDpxKwZLP/4s6O5ppUMXTJuw1rOqJl6veliHXTsGuGCjYiYjI5c6nZxF7/Jz1ydw/4pL4My6JlALG5Kvh63bxEu7Fy7mBXlTzcdOlXLkiBbtioGAnIiKFYbEYHDqTmuep3GOJF/Jt7+XqmOep3FuqeODiqAc1JJuCXTFQsBMRkRtxNiWdPfGXzuztiTvHvhPnyMjK+3/DjmYT9fw98ty756sx98olBbtioGAnIiJFLT3Twr4TyReD3qXQl3g+I9/2AV6u1ku4OZdzgyq5a8y9Mk7Brhgo2ImIyM1gGAZxiRdshmD5Iy6Jf07nP+ZeBWcHGgR42oy51zDAkwrOJeJ18FIEFOyKgYKdiIjYU3JaJn/GJdncu/dn/DnS8hlzz2SC2pXcaRToZR1vr3GgF/4ac69UUrArBgp2IiJS0mRmWTh4OoU/rG/UyA5+J8+l5dve19354pm97Mu53ZtU1ds0SgEFu2KgYCciIqXFyXNpeZ7K3X8yOc+Ye82qe7P04fa4OinclWQKdsVAwU5EREqzCxlZ7D1+6fVpX/1+jITUDIa2qcG0Ac3sXZ5cwbVkEN1ZKSIiUg64OjnQrLoPzar7ANClcRUe+CiKJb8epmXNigxuU8O+BUqR0IvsREREyqE7b/HjqW4NAPjvV7vYdTTRzhVJUVCwExERKacevasuXRr5k55p4ZGF0SSkptu7JLlBCnYiIiLllNls4vXBLahVqQJHzp7nic9isFz+hIWUKgp2IiIi5Zi3mxOzh7XC1cnMxtiTvPPDX/YuSW6Agp2IiEg51zjQi1f6NQXg7fV/sSH2hJ0rkuulYCciIiIMaFWd4e1qYhjwxJIYDp/J/xVmUrIVabDbtGkTffr0ITAwEJPJxIoVK666zqxZs2jUqBFubm40aNCATz75xGb5l19+SevWrfHx8cHd3Z0WLVqwYMECmzZTpkyhYcOGuLu7U7FiRbp06cLWrVtt2gQFBWEymWymadOm3fA+i4iIlBWTejemeQ0fEs9n8OiiaC5kZNm7JLlGRRrsUlJSaN68ObNmzSpU+9mzZxMeHs6UKVPYvXs3U6dOZezYsaxcudLaxtfXl+eff57IyEh27NjByJEjGTlyJGvXrrW2qV+/Pu+++y47d+5k8+bNBAUF0a1bN06ePGmzvRdffJG4uDjrNH78+KLZcRERkTLAxdGB2cNa4uvuzK6jSUz+are9S5JrVGxvnjCZTCxfvpx+/foV2Ob222+nQ4cOzJgxwzpv4sSJbN26lc2bNxe4XsuWLenVqxcvvfRSvstzRmj+/vvv6dy5M5B9xu6JJ57giSeeuK790ZsnRESkvPh53ynu/3ArFgOm9W/K0LY17V1SuXYtGcSu99ilpaXh6upqM8/NzY2oqCgyMjLytDcMg/Xr1xMbG0vHjh3z7TM9PZ33338fb29vmjdvbrNs2rRpVKpUidtuu40ZM2aQmZl5xdqSkpJsJhERkfKgQ73KTLw4ePELX+9m5xENXlxa2DXYhYaGMnfuXKKjozEMg23btjF37lwyMjI4deqUtV1iYiIeHh44OzvTq1cvZs6cSdeuXW36WrVqFR4eHri6uvLmm2+ybt06KleubF3++OOPs2TJEjZs2MDDDz/Mq6++yjPPPFNgbREREXh7e1unGjX0qhURESk/sgcvrmIdvPhsigYvLg3sein2/PnzjB07lgULFmAYBlWqVGH48OFMnz6d+Ph4qlSpAoDFYuHvv/8mOTmZ9evX89JLL7FixQo6depk7SslJYW4uDhOnTrFBx98wA8//MDWrVvx9/fPd9sfffQRDz/8MMnJybi4uORZnpaWRlpamvV7UlISNWrU0KVYEREpNxLPZ9D33c38czqVu+r78dGINjiYTfYuq9wpNZdi3dzc+Oijj0hNTeXgwYMcOnSIoKAgPD098fPzs7Yzm83Uq1ePFi1aMHHiRAYOHEhERIRNX+7u7tSrV4927drx4Ycf4ujoyIcffljgtoODg8nMzOTgwYP5LndxccHLy8tmEhERKU+83ZyYMzx78OIf957knfUavLikKxHj2Dk5OVG9enUcHBxYsmQJvXv3xmwuuDSLxWJzNu162sTExGA2mws8oyciIiLQqKoXEf1zDV78pwYvLskci7Kz5ORk9u3bZ/1+4MABYmJi8PX1pWbNmoSHh3P06FHrWHV79+4lKiqK4OBgzp49yxtvvMGuXbuYP3++tY+IiAhat25N3bp1SUtLY/Xq1SxYsIDZs2cD2ZdgX3nlFfr27UvVqlU5deoUs2bN4ujRowwaNAiAyMhItm7dSkhICJ6enkRGRvLkk08yfPhwKlasWJSHQEREpMy557bqbP8ngQW//MMTn8Wwavwd1PCtYO+yJB9FGuy2bdtGSEiI9fuECRMACAsLY968ecTFxXHo0CHr8qysLF5//XViY2NxcnIiJCSELVu2EBQUZG2TkpLCY489xpEjR3Bzc6Nhw4YsXLiQIUOGAODg4MCff/7J/PnzOXXqFJUqVaJNmzb89NNPNGnSBMi+rLpkyRKmTJlCWloatWvX5sknn7TWJyIiIlc2qXdjdh1L5LdDCTyyMJovHr0dVycHe5cllym2hyfKGo1jJyIi5V1c4nl6v7OZ0ynpDGpVnekDm2Ey6WGK4lZqHp4QERGR0qOqtxsz770NswmWRR9hya+H7V2SXEbBTkRERArt9nqVeSo0e/DiyV/tZseRBPsWJDYU7EREROSaPHpXXbo1rkJ6loVHF27njAYvLjEU7EREROSamEwm/m9wc2pXdudownn+veQ3siy6Zb8kULATERGRa+bl6sTs4S1xdTLz01+nePv7vfYuSVCwExERkevUMMCLaf2bAfDOD/tYv+e4nSsSBTsRERG5bv1uq0ZY+1oAPPlZDIdOp9q5ovJNwU5ERERuyPO9GtOypg9JFzJ5ZGE0FzKy7F1SuaVgJyIiIjfE2dHMrGEtqeTuzB9xSfx3xS70/gP7ULATERGRG5Z78OLPo4/waZQGL7YHBTsREREpErfXq8wz3RsCMOXr3fx+OMG+BZVDCnYiIiJSZB7uWIfQJjmDF0dr8OKbTMFOREREiozJZGLGoOzBi48lXtDgxTeZgp2IiIgUKS9XJ+YMb4WbkwM//XWKN9dp8OKbRcFOREREilyDAE+mDWgKwLsb9vH9Hxq8+GZQsBMREZFicXeLaoy4PQiAJ5fG8M/pFPsWVA4o2ImIiEixea5nI1rW9OHchUweWbid8+kavLg4KdiJiIhIsXF2NPO/Ya2o7OHMnrgknl+xU4MXFyMFOxERESlWAd6uzLy3JQ5mE19uP8qirYfsXVKZVaTBbtOmTfTp04fAwEBMJhMrVqy46jqzZs2iUaNGuLm50aBBAz755BOb5V9++SWtW7fGx8cHd3d3WrRowYIFC2zaTJkyhYYNG+Lu7k7FihXp0qULW7dutWlz5swZhg0bhpeXFz4+PowaNYrk5OQb3mcRERG5uvZ1K/FMaAMAXlz5BzEavLhYFGmwS0lJoXnz5syaNatQ7WfPnk14eDhTpkxh9+7dTJ06lbFjx7Jy5UprG19fX55//nkiIyPZsWMHI0eOZOTIkaxdu9bapn79+rz77rvs3LmTzZs3ExQURLdu3Th58qS1zbBhw9i9ezfr1q1j1apVbNq0iYceeqjodl5ERESu6KGOdejeJID0LAuPLYzmdHKavUsqc0xGMV3oNplMLF++nH79+hXY5vbbb6dDhw7MmDHDOm/ixIls3bqVzZs3F7hey5Yt6dWrFy+99FK+y5OSkvD29ub777+nc+fO7Nmzh8aNG/Prr7/SunVrANasWUPPnj05cuQIgYGBV92fnD4TExPx8vK6ansRERHJ69yFDO5+92f+PpXCHfUqM//BtjiYTfYuq0S7lgxi13vs0tLScHV1tZnn5uZGVFQUGRkZedobhsH69euJjY2lY8eO+faZnp7O+++/j7e3N82bNwcgMjISHx8fa6gD6NKlC2azOc8lWxERESk+nq5OzLk/e/DizftO8ca6WHuXVKbYNdiFhoYyd+5coqOjMQyDbdu2MXfuXDIyMjh16pS1XWJiIh4eHjg7O9OrVy9mzpxJ165dbfpatWoVHh4euLq68uabb7Ju3ToqV64MQHx8PP7+/jbtHR0d8fX1JT4+Pt/a0tLSSEpKsplERETkxtWv4slrA5sBMGvDftZp8OIiY9dgN2nSJHr06EG7du1wcnLi7rvvJiwsLLsw86XSPD09iYmJ4ddff+WVV15hwoQJbNy40aavkJAQYmJi2LJlC927d2fw4MGcOHHiumuLiIjA29vbOtWoUeO6+xIRERFbfZsHWgcvnvBZDAdPafDiomDXYOfm5sZHH31EamoqBw8e5NChQwQFBeHp6Ymfn5+1ndlspl69erRo0YKJEycycOBAIiIibPpyd3enXr16tGvXjg8//BBHR0c+/PBDAAICAvKEvMzMTM6cOUNAQEC+tYWHh5OYmGidDh8+XMR7LyIiUr4917MRrWtV5FxaJo8sjNbgxUWgRIxj5+TkRPXq1XFwcGDJkiX07t3b5ozd5SwWC2lpV36SJneb9u3bk5CQQHR0tHX5Dz/8gMViITg4ON/1XVxc8PLysplERESk6Dg7mpk1rCWVPVz4M/4czy/X4MU3yrEoO0tOTmbfvn3W7wcOHCAmJgZfX19q1qxJeHg4R48etY5Vt3fvXqKioggODubs2bO88cYb7Nq1i/nz51v7iIiIoHXr1tStW5e0tDRWr17NggULmD17NpA9xMorr7xC3759qVq1KqdOnWLWrFkcPXqUQYMGAdCoUSO6d+/OmDFjmDNnDhkZGYwbN46hQ4cW6olYERERKR5VvFx5977bGDZ3K1/+dpTbalXk/na17F1WqVWkwW7btm2EhIRYv0+YMAGAsLAw5s2bR1xcHIcOXRptOisri9dff53Y2FicnJwICQlhy5YtBAUFWdukpKTw2GOPceTIEdzc3GjYsCELFy5kyJAhADg4OPDnn38yf/58Tp06RaVKlWjTpg0//fQTTZo0sfazaNEixo0bR+fOnTGbzQwYMIB33nmnKHdfRERErkO7OpX4T/eGvLJ6Dy+u3E2TQC9a1qxo77JKpWIbx66s0Th2IiIixccwDMYu3s7qnfFU9XZl1fg7qOThYu+ySoRSM46diIiICGS/2GD6wObU8XMnLvEC4z/9jcwsi73LKnUU7ERERKRE8HBx5L3hrajg7MCW/ad5fd1ee5dU6ijYiYiISIlxSxVPXhuQPXjx7I37+W53/i8SkPwp2ImIiEiJ0qd5IA92qA3AxKW/c0CDFxeagp2IiIiUOOE9G9ImKHvw4kcXRpOanmnvkkoFBTsREREpcZwczMy679Lgxc99qcGLC0PBTkREREokfy9XZt13Gw5mEytijrHgl3/sXVKJp2AnIiIiJVZwnUqE92gIwEur/iD6n7N2rqhkU7ATERGREm3UHbXp2TSAjCyDsYu2cyr5yu+LL88U7ERERKREyxm8uK6fO/FJFxi/WIMXF0TBTkREREo8DxdH3ru/Fe7ODkT+fZr/+06DF+dHwU5ERERKhXr+nkwf2ByAOT/uZ80uDV58OQU7ERERKTV6NavKqDuyBy9+atnv/H0y2c4VlSyO9i6grMnKyiIjI8PeZZRaTk5OODg42LsMEREpwf7ToyE7jyQSdfAMjy7czvKxt1PBWZEGFOyKjGEYxMfHk5CQYO9SSj0fHx8CAgIwmUz2LkVEREogJwcz7953G71mbib2+DnCv9zJW0Na6P83ULArMjmhzt/fnwoVKujHdR0MwyA1NZUTJ04AULVqVTtXJCIiJVX24MUtufeDX/gq5hgta1Yk7PYge5dldwp2RSArK8sa6ipVqmTvcko1Nzc3AE6cOIG/v78uy4qISIHa1vYlvEdDXv5mDy+t+oNbq3nRqpavvcuyKz08UQRy7qmrUKGCnSspG3KOo+5VFBGRqxl1R216NatKpsXgsUXbOXmufA9erGBXhHT5tWjoOIqISGGZTCamD2hGPX8PjielMf7T7eV68GIFOxERESnV3F0cmTM8e/DiX/4+w4y1sfYuyW6KNNht2rSJPn36EBgYiMlkYsWKFVddZ9asWTRq1Ag3NzcaNGjAJ598YrP8yy+/pHXr1vj4+ODu7k6LFi1YsGCBdXlGRgbPPvssTZs2xd3dncDAQB544AGOHTtm009QUBAmk8lmmjZtWpHsd3mV++/44MGDmEwmYmJi7FqTiIiUT/X8PZgxKHvw4vc2/c2aXXF2rsg+ijTYpaSk0Lx5c2bNmlWo9rNnzyY8PJwpU6awe/dupk6dytixY1m5cqW1ja+vL88//zyRkZHs2LGDkSNHMnLkSNauXQtAamoq27dvZ9KkSWzfvp0vv/yS2NhY+vbtm2d7L774InFxcdZp/PjxRbPjpdjJkyd59NFHqVmzJi4uLgQEBBAaGsrPP/9s79JERESuSc+mVRlzZ87gxTvYXw4HLy7Sp2J79OhBjx49Ct1+wYIFPPzwwwwZMgSAOnXq8Ouvv/Laa6/Rp08fADp16mSzzr///W/mz5/P5s2bCQ0Nxdvbm3Xr1tm0effdd2nbti2HDh2iZs2a1vmenp4EBARc596VTQMGDCA9PZ358+dTp04djh8/zvr16zl9+rS9SxMREblmz3ZvyO9HEok6cIZHF0az/LEOuLuUn0FA7HqPXVpaGq6urjbz3NzciIqKyveJSMMwWL9+PbGxsXTs2LHAfhMTEzGZTPj4+NjMnzZtGpUqVeK2225jxowZZGZmXrG2pKQkm6msSUhI4KeffuK1114jJCSEWrVq0bZtW8LDw/M941kYf/75J7fffjuurq7ceuut/Pjjj0VctYiISMEcLw5e7O/pwt7jyfzny50YhmHvsm4auwa70NBQ5s6dS3R0NIZhsG3bNubOnUtGRganTp2ytktMTMTDwwNnZ2d69erFzJkz6dq1a759XrhwgWeffZZ7770XLy8v6/zHH3+cJUuWsGHDBh5++GFeffVVnnnmmQJri4iIwNvb2zrVqFHjmvbNMAxS0zPtMhX2B+zh4YGHhwcrVqwgLa1oHg9/+umnmThxIr/99hvt27enT58+OvsnIiI3lb+nK7OGtcTRbGLl78eYt+WgvUu6aex6bnLSpEnEx8fTrl07DMOgSpUqhIWFMX36dMzmS5nT09OTmJgYkpOTWb9+PRMmTKBOnTp5LtNmZGQwePBgDMNg9uzZNssmTJhg/dysWTOcnZ15+OGHiYiIwMXFJU9t4eHhNuskJSVdU7g7n5FF4xfWFrp9UfrjxdBCvTPP0dGRefPmMWbMGObMmUPLli256667GDp0KM2aNbuubY8bN44BAwYA2fdQrlmzhg8//PCKIVpERKSotQny5bmejXhx1R+88s0emlbzpnVQ2R+82K5n7Nzc3Pjoo49ITU3l4MGDHDp0iKCgIDw9PfHz87O2M5vN1KtXjxYtWjBx4kQGDhxIRESETV85oe6ff/5h3bp1Nmfr8hMcHExmZiYHDx7Md7mLiwteXl42U1k0YMAAjh07xtdff0337t3ZuHEjLVu2ZN68edfVX/v27a2fHR0dad26NXv27CmiakVERApvZIcgeucavPjEuQv2LqnYlYi7CZ2cnKhevToAS5YsoXfv3jZn7C5nsVhsLh3mhLq//vqLDRs2FOq1XjExMZjNZvz9/W98B/Lh5uTAHy+GFkvfhdn2tXB1daVr16507dqVSZMmMXr0aCZPnsyIESOKp0AREZGbwGQy8dqAZsTGn+OvE8mMX/wbi0YH4+hQdofxLdJgl5yczL59+6zfDxw4QExMDL6+vtSsWZPw8HCOHj1qHatu7969REVFERwczNmzZ3njjTfYtWsX8+fPt/YRERFB69atqVu3LmlpaaxevZoFCxZYL7VmZGQwcOBAtm/fzqpVq8jKyiI+Ph7IHirF2dmZyMhItm7dSkhICJ6enkRGRvLkk08yfPhwKlasWJSHwMpkMhXqcmhJ1Lhx40KNQZifX375xfpgS2ZmJtHR0YwbN64IqxMRESk8dxdHZg9vRb9ZP7P1wBmmr43luZ6N7F1WsSnS5LFt2zZCQkKs33PuUQsLC2PevHnExcVx6NAh6/KsrCxef/11YmNjcXJyIiQkhC1bthAUFGRtk5KSwmOPPcaRI0dwc3OjYcOGLFy40DpEytGjR/n6668BaNGihU09GzZsoFOnTri4uLBkyRKmTJlCWloatWvX5sknn7S5h648On36NIMGDeLBBx+kWbNmeHp6sm3bNqZPn87dd999XX3OmjWLW265hUaNGvHmm29y9uxZHnzwwSKuXEREpPDq+XswY2AzHl20nfc3/c1tNXzo0bSqvcsqFiajPD0DfAOSkpLw9vYmMTExz/12Fy5c4MCBA9SuXTvP8C0lWVpaGlOmTOG7775j//79ZGRkUKNGDQYNGsRzzz2Hm5vbFdc3mUwsX76cfv36cfDgQWrXrs3ixYt56623iImJoV69erz77rs2Yb8wSuvxFBGRku3V1Xt4f9PfuDs78NW4O6jn72HvkgrlShnkcgp2hVQWg11JpeMpIiLFITPLwrC5W9l64Az1/D34amzpGLz4WoJd2b17UERERCSX7MGLW1LFy4V9J5J59osdZW7wYgU7ydeiRYusAxhfPjVp0sTe5YmIiFwXP08X/ndx8OJVO+L46OeD9i6pSJX8849iF3379iU4ODjfZU5OTje5GhERkaLTqpYvz/dqxNSVfxCxeg/NqnvTpowMXqxgJ/ny9PTE09PT3mWIiIgUixG3B/HboQS+/v0YYxdtZ9Xjd+DvWfrv69alWBERESl3TCYTEf2bUr+KByfOpTFu8W9kZFnsXdYNU7ATERGRcsndxZE5w1vh4eJI1IEzvPbtn/Yu6YYp2ImIiEi5VcfPg/8b1AyAuZsP8M2OODtXdGMU7ERERKRc635rVR7uWAeAZz7/nX0nztm5ouunYCciIiLl3tOhDWhXx5eU9CweXhBNclqmvUu6Lgp2Umw2btyIyWQiISHB3qWIiIhckaODmZn3Zg9evP9kCs9+XjoHL1awK+dOnjzJo48+Ss2aNXFxcSEgIIDQ0FB+/vlne5cmIiJyU+UevPibnXF8uPmAvUu6Zgp25dyAAQP47bffmD9/Pnv37uXrr7+mU6dOnD592t6liYiI3HStavkyqXdjACK+/ZOoA2fsXNG1UbArxxISEvjpp5947bXXCAkJoVatWrRt25bw8HD69u17xXUPHjyIyWQiJibGpj+TycTGjRtt2v788880a9YMV1dX2rVrx65du4phb0RERIrGA+1rcXeLQLIsBmMXb+dE0gV7l1RoCnbFxTAgPcU+UyHvCch59+uKFStIS0srtkPx9NNP8/rrr/Prr7/i5+dHnz59yMjIKLbtiYiI3IicwYsbVPHk5Lk0xi7eXmoGL9YrxYpLRiq8GmifbT93DJzdr9rM0dGRefPmMWbMGObMmUPLli256667GDp0KM2aNSuyciZPnkzXrl0BmD9/PtWrV2f58uUMHjy4yLYhIiJSlCo4OzJ7eEvufvdnfj14lmnf/mm9RFuS6YxdOTdgwACOHTvG119/Tffu3dm4cSMtW7Zk3rx5RbaN9u3bWz/7+vrSoEED9uzZU2T9i4iIFIc6fh7MGNQcgA83H2DVjmN2rujqdMauuDhVyD5zZq9tXwNXV1e6du1K165dmTRpEqNHj2by5MmMGDGiwHXM5ux/E+R+FFyXV0VEpKzpfmsAj9xVlzk/7ueZz3fQoIont1TxtHdZBdIZu+JiMmVfDrXHZDLdUOmNGzcmJSXlim38/PwAiIu79OqV3A9S5PbLL79YP589e5a9e/fSqFGjG6pRRETkZnmqW33a16lEanoWjyws2YMXF2mw27RpE3369CEwMBCTycSKFSuuus6sWbNo1KgRbm5uNGjQgE8++cRm+Zdffknr1q3x8fHB3d2dFi1asGDBAuvyjIwMnn32WZo2bYq7uzuBgYE88MADHDtme7bszJkzDBs2DC8vL3x8fBg1ahTJyclFst+l1enTp/nXv/7FwoUL2bFjBwcOHGDZsmVMnz6du++++4rrurm50a5dO6ZNm8aePXv48ccf+e9//5tv2xdffJH169eza9cuRowYQeXKlenXr18x7JGIiEjRc3QwM/O+2wjwcmX/yRSe+fz3Ejt4cZEGu5SUFJo3b86sWbMK1X727NmEh4czZcoUdu/ezdSpUxk7diwrV660tvH19eX5558nMjKSHTt2MHLkSEaOHMnatWsBSE1NZfv27UyaNInt27fz5ZdfEhsbm2e4jmHDhrF7927WrVvHqlWr2LRpEw899FDR7Xwp5OHhQXBwMG+++SYdO3bk1ltvZdKkSYwZM4Z33333qut/9NFHZGZm0qpVK5544glefvnlfNtNmzaNf//737Rq1Yr4+HhWrlyJs7NzUe+OiIhIsans4cKsYS1xcjCxemc8c38qmYMXm4xiipwmk4nly5df8czM7bffTocOHZgxY4Z13sSJE9m6dSubN28ucL2WLVvSq1cvXnrppXyX//rrr7Rt25Z//vmHmjVrsmfPHho3bsyvv/5K69atAVizZg09e/bkyJEjBAZe/enVpKQkvL29SUxMxMvLy2bZhQsXOHDgALVr18bV1fWqfcmV6XiKiEhJ9UnkQV74ajcOZhOLRwcTXKdSsW/zShnkcna9xy4tLS3P/3G7ubkRFRWV7434hmGwfv16YmNj6dixY4H9JiYmYjKZ8PHxASAyMhIfHx9rqAPo0qULZrOZrVu3Fs3OiIiISJl3f7ta9LMOXvwbx0vY4MV2DXahoaHMnTuX6OhoDMNg27ZtzJ07l4yMDE6dOmVtl5iYiIeHB87OzvTq1YuZM2dax0W73IULF3j22We59957rak2Pj4ef39/m3aOjo74+voSHx+fbz9paWkkJSXZTOXJokWLrAMYXz41adLE3uWJiIjYhclk4tWLgxefSk5j7KKSNXixXYc7mTRpEvHx8bRr1w7DMKhSpQphYWFMnz7dOpwGgKenJzExMSQnJ7N+/XomTJhAnTp16NSpk01/GRkZDB48GMMwmD179g3VFhERwdSpU2+oj9Ksb9++BAcH57vMycnpJlcjIiJSclRwdmTO/a3oO3Mz2/45y6ur9zC5T8k46WHXM3Zubm589NFHpKamcvDgQQ4dOkRQUBCenp7W4TQge8y0evXq0aJFCyZOnMjAgQOJiIiw6Ssn1P3zzz+sW7fO5hp0QEAAJ06csGmfmZnJmTNnCAgIyLe28PBwEhMTrdPhw4eLcM9LPk9PT+rVq5fvVKtWLXuXJyIiYle1K7vz+uDswYs//vkgX/9eMgYvLhHj2Dk5OVG9enUcHBxYsmQJvXv3tjljdzmLxWLzbtOcUPfXX3/x/fffU6mS7Y2M7du3JyEhgejoaOu8H374AYvFUuBZKRcXF7y8vGwmERERkRzdmgTwaKe61PFzp1FAyRi0uEgvxSYnJ7Nv3z7r9wMHDhATE4Ovry81a9YkPDyco0ePWseq27t3L1FRUQQHB3P27FneeOMNdu3axfz58619RERE0Lp1a+rWrUtaWhqrV69mwYIF1kutGRkZDBw4kO3bt7Nq1SqysrKs9835+vri7OxMo0aN6N69u/WdqBkZGYwbN46hQ4cW6onYwrJYSs419tJMx1FEREqLiV3rMzakHh4uJeNlXkVaxbZt2wgJCbF+nzBhAgBhYWHMmzePuLg4Dh06ZF2elZXF66+/TmxsLE5OToSEhLBlyxaCgoKsbVJSUnjsscc4cuQIbm5uNGzYkIULFzJkyBAAjh49ytdffw1AixYtbOrZsGGD9T68RYsWMW7cODp37ozZbGbAgAG88847RbLfzs7OmM1mjh07hp+fH87Ozphu8O0P5ZFhGKSnp3Py5EnMZrPGuhMRkRLP0cGMh0OJuAAKFOM4dmXN1caQSU9PJy4ujtTUVDtUV7ZUqFCBqlWrKtiJiIhwbePYlYzzhmWAs7MzNWvWJDMzk6ysLHuXU2o5ODjg6OioM54iIiLXQcGuCJlMJpycnDQciIiIiNhFybkoLCIiIiI3RMFOREREpIxQsBMREREpI3SPXSHlPDxc3t4ZKyIiIvaVkz0KM5CJgl0hnTt3DoAaNWrYuRIREREpj86dO4e3t/cV22gcu0KyWCwcO3YMT0/PYhuKIykpiRo1anD48GG9wuwG6VgWDR3HoqNjWTR0HIuOjmXRuBnH0TAMzp07R2Bg4BVfuQo6Y1doZrOZ6tWr35Rt6d20RUfHsmjoOBYdHcuioeNYdHQsi0ZxH8ernanLoYcnRERERMoIBTsRERGRMkLBrgRxcXFh8uTJuLi42LuUUk/HsmjoOBYdHcuioeNYdHQsi0ZJO456eEJERESkjNAZOxEREZEyQsFOREREpIxQsBMREREpIxTsRERERMoIBbsSYtasWQQFBeHq6kpwcDBRUVH2LqnUmTJlCiaTyWZq2LChvcsqFTZt2kSfPn0IDAzEZDKxYsUKm+WGYfDCCy9QtWpV3Nzc6NKlC3/99Zd9ii3BrnYcR4wYkec32r17d/sUW4JFRETQpk0bPD098ff3p1+/fsTGxtq0uXDhAmPHjqVSpUp4eHgwYMAAjh8/bqeKS67CHMtOnTrl+V0+8sgjdqq45Jo9ezbNmjWzDkTcvn17vv32W+vykvKbVLArAT777DMmTJjA5MmT2b59O82bNyc0NJQTJ07Yu7RSp0mTJsTFxVmnzZs327ukUiElJYXmzZsza9asfJdPnz6dd955hzlz5rB161bc3d0JDQ3lwoULN7nSku1qxxGge/fuNr/RTz/99CZWWDr8+OOPjB07ll9++YV169aRkZFBt27dSElJsbZ58sknWblyJcuWLePHH3/k2LFj9O/f345Vl0yFOZYAY8aMsfldTp8+3U4Vl1zVq1dn2rRpREdHs23bNv71r39x9913s3v3bqAE/SYNsbu2bdsaY8eOtX7PysoyAgMDjYiICDtWVfpMnjzZaN68ub3LKPUAY/ny5dbvFovFCAgIMGbMmGGdl5CQYLi4uBiffvqpHSosHS4/joZhGGFhYcbdd99tl3pKsxMnThiA8eOPPxqGkf37c3JyMpYtW2Zts2fPHgMwIiMj7VVmqXD5sTQMw7jrrruMf//73/YrqhSrWLGiMXfu3BL1m9QZOztLT08nOjqaLl26WOeZzWa6dOlCZGSkHSsrnf766y8CAwOpU6cOw4YN49ChQ/YuqdQ7cOAA8fHxNr9Rb29vgoOD9Ru9Dhs3bsTf358GDRrw6KOPcvr0aXuXVOIlJiYC4OvrC0B0dDQZGRk2v8mGDRtSs2ZN/Sav4vJjmWPRokVUrlyZW2+9lfDwcFJTU+1RXqmRlZXFkiVLSElJoX379iXqN+l4U7cmeZw6dYqsrCyqVKliM79KlSr8+eefdqqqdAoODmbevHk0aNCAuLg4pk6dyp133smuXbvw9PS0d3mlVnx8PEC+v9GcZVI43bt3p3///tSuXZv9+/fz3HPP0aNHDyIjI3FwcLB3eSWSxWLhiSeeoEOHDtx6661A9m/S2dkZHx8fm7b6TV5ZfscS4L777qNWrVoEBgayY8cOnn32WWJjY/nyyy/tWG3JtHPnTtq3b8+FCxfw8PBg+fLlNG7cmJiYmBLzm1SwkzKjR48e1s/NmjUjODiYWrVqsXTpUkaNGmXHykSyDR061Pq5adOmNGvWjLp167Jx40Y6d+5sx8pKrrFjx7Jr1y7dL1sECjqWDz30kPVz06ZNqVq1Kp07d2b//v3UrVv3ZpdZojVo0ICYmBgSExP5/PPPCQsL48cff7R3WTZ0KdbOKleujIODQ54nZ44fP05AQICdqiobfHx8qF+/Pvv27bN3KaVazu9Qv9GiV6dOHSpXrqzfaAHGjRvHqlWr2LBhA9WrV7fODwgIID09nYSEBJv2+k0WrKBjmZ/g4GAA/S7z4ezsTL169WjVqhURERE0b96ct99+u0T9JhXs7MzZ2ZlWrVqxfv166zyLxcL69etp3769HSsr/ZKTk9m/fz9Vq1a1dymlWu3atQkICLD5jSYlJbF161b9Rm/QkSNHOH36tH6jlzEMg3HjxrF8+XJ++OEHateubbO8VatWODk52fwmY2NjOXTokH6Tl7nascxPTEwMgH6XhWCxWEhLSytRv0ldii0BJkyYQFhYGK1bt6Zt27a89dZbpKSkMHLkSHuXVqo89dRT9OnTh1q1anHs2DEmT56Mg4MD9957r71LK/GSk5Nt/nV+4MABYmJi8PX1pWbNmjzxxBO8/PLL3HLLLdSuXZtJkyYRGBhIv3797Fd0CXSl4+jr68vUqVMZMGAAAQEB7N+/n2eeeYZ69eoRGhpqx6pLnrFjx7J48WK++uorPD09rfcoeXt74+bmhre3N6NGjWLChAn4+vri5eXF+PHjad++Pe3atbNz9SXL1Y7l/v37Wbx4MT179qRSpUrs2LGDJ598ko4dO9KsWTM7V1+yhIeH06NHD2rWrMm5c+dYvHgxGzduZO3atSXrN3lTn8GVAs2cOdOoWbOm4ezsbLRt29b45Zdf7F1SqTNkyBCjatWqhrOzs1GtWjVjyJAhxr59++xdVqmwYcMGA8gzhYWFGYaRPeTJpEmTjCpVqhguLi5G586djdjYWPsWXQJd6TimpqYa3bp1M/z8/AwnJyejVq1axpgxY4z4+Hh7l13i5HcMAePjjz+2tjl//rzx2GOPGRUrVjQqVKhg3HPPPUZcXJz9ii6hrnYsDx06ZHTs2NHw9fU1XFxcjHr16hlPP/20kZiYaN/CS6AHH3zQqFWrluHs7Gz4+fkZnTt3Nr777jvr8pLymzQZhmHczCApIiIiIsVD99iJiIiIlBEKdiIiIiJlhIKdiIiISBmhYCciIiJSRijYiYiIiJQRCnYiIiIiZYSCnYiIiEgZoWAnIiIiUkYo2ImIiIiUEQp2IiIiImWEgp2IiIhIGaFgJyIiIlJGKNiJiIiIlBEKdiIiIiJlhIKdiIiISBmhYCciIiJSRijYiYiIiJQRCnYiImVEUFAQI0aMsHcZImJHCnYiUqbNmzcPk8nEtm3brthu48aNmEwmm8nX15d27dqxaNGim1StiMiNcbR3ASIiJcnjjz9OmzZtADh9+jSfffYZw4cPJyEhgbFjx9q5OhGRK1OwExHJ5c4772TgwIHW748++ih16tRh8eLFCnYiUuLpUqyIyBU4OztTsWJFHB1t/x2cmZnJSy+9RN26dXFxcSEoKIjnnnuOtLQ0m3Ymk4kpU6bk6ffy++FyLhn//PPPTJgwAT8/P9zd3bnnnns4efKkzbqGYfDyyy9TvXp1KlSoQEhICLt3786zjYyMDKZOncott9yCq6srlSpV4o477mDdunXXf0BEpETTGTsRkVzOnTvHqVOnADhz5gyLFy9m165dfPjhhzbtRo8ezfz58xk4cCATJ05k69atREREsGfPHpYvX37d2x8/fjwVK1Zk8uTJHDx4kLfeeotx48bx2WefWdu88MILvPzyy/Ts2ZOePXuyfft2unXrRnp6uk1fU6ZMISIigtGjR9O2bVuSkpLYtm0b27dvp2vXrtddo4iUXAp2IiK5PPjggzbfzWYzr7zyis3833//nfnz5zN69Gg++OADAB577DH8/f35v//7PzZs2EBISMh1bb9SpUp89913mEwmACwWC++88w6JiYl4e3tz8uRJpk+fTq9evVi5cqW13fPPP8+rr75q09c333xDz549ef/996+rFhEpfXQpVkQklxdeeIF169axbt06PvvsM+69916ef/553n77bWub1atXAzBhwgSbdSdOnAhkB6rr9dBDD1nDGmTf85eVlcU///wDwPfff096ejrjx4+3affEE0/k6cvHx4fdu3fz119/XXc9IlK66IydiEguTZs2pUuXLtbvgwcPJjExkf/85z/cd999+Pn58c8//2A2m6lXr57NugEBAfj4+FhD2PWoWbOmzfeKFSsCcPbsWQBr37fccotNOz8/P2vbHC+++CJ333039evX59Zbb6V79+7cf//9NGvW7LrrE5GSTWfsRESuonPnzly4cIGoqCib+bnPmF2rrKysfOc7ODjkO98wjGveRseOHdm/fz8fffQRt956K3PnzqVly5bMnTv3mvsSkdJBwU5E5CoyMzMBSE5OBqBWrVpYLJY8lziPHz9OQkICtWrVss6rWLEiCQkJNu3S09OJi4u7rlpy+r582ydPnrSe1cvN19eXkSNH8umnn3L48GGaNWuW71O6IlI2KNiJiFzFqlWrAGjevDkAPXv2BOCtt96yaffGG28A0KtXL+u8unXrsmnTJpt277//foFn7K6mS5cuODk5MXPmTJuzeJfXAtkDLOfm4eFBvXr18gzJIiJlh+6xExHJ5aeffuLChQtA9nAnX3/9NT/++CNDhw6lYcOGQHbACwsL4/333ychIYG77rqLqKgo5s+fT79+/WyeiB09ejSPPPIIAwYMoGvXrvz++++sXbuWypUrX1d9fn5+PPXUU0RERNC7d2969uzJb7/9xrfffpunz8aNG9OpUydatWqFr68v27Zt4/PPP2fcuHHXeXREpKRTsBMRyeWdd96xfnZ2dqZOnTq88sorPP300zbt5s6dS506dZg3bx7Lly8nICCA8PBwJk+ebNNuzJgxHDhwgA8//JA1a9Zw5513sm7dOjp37nzdNb788su4uroyZ84cNmzYQHBwMN99953NmULIfj3a119/zXfffUdaWhq1atXi5ZdfzrMvIlJ2mIzruSNXREREREoc3WMnIiIiUkYo2ImIiIiUEQp2IiIiImWEgp2IiIhIGaFgJyIiIlJGKNiJiIiIlBEax66QLBYLx44dw9PT84beDykiIiJyLQzD4Ny5cwQGBmI2X/mcnIJdIR07dowaNWrYuwwREREppw4fPkz16tWv2EbBrpA8PT2B7IPq5eVl52pERESkvEhKSqJGjRrWLHIlCnaFlHP51cvLS8FOREREbrrC3AqmhydEREREyggFOxEREZEyQsFOREREpIxQsBMREREpIxTsRERERMoIBTsRERGRMkLBTkREROR6JcXBnlX2rsJKwU5ERETkelxIgkWD4LNh8NtCe1cDKNiJiIiIXLvMdFh6PxzfCe7+EHSHvSsCFOxEREREro1hwMrH4e+N4OQO930GFYPsXRWgYCciIiJybTa8Ar9/CiYHGDQPqrW0d0VWCnYiIiIihbXtY9g0I/tz7zehfjf71nMZBTsRERGRwti7Fr6ZkP35rmehVZh968mHgp2IiIjI1RzdDstGgGGBFsOgU7i9K8qXgp2IiIjIlZw5AIsHQ0Yq1P0X9HkbTCZ7V5UvBTsRERGRgqSchoUDIOUkBDSFwZ+Ag5O9qyqQgp2IiIhIfjLOw6dD4cx+8K4Bwz4HF097V3VFCnYiIiIil7NkwRej4UgUuHpnhzrPAHtXdVUKdiIiIiK5GQas+Q/8uQocnGHop+Df0N5VFUqxBruIiAjatGmDp6cn/v7+9OvXj9jYWJs2Fy5cYOzYsVSqVAkPDw8GDBjA8ePHbdocOnSIXr16UaFCBfz9/Xn66afJzMy0abNx40ZatmyJi4sL9erVY968eXnqmTVrFkFBQbi6uhIcHExUVFSR77OIiIiUcltmQtT72Z/veQ+COti3nmtQrMHuxx9/ZOzYsfzyyy+sW7eOjIwMunXrRkpKirXNk08+ycqVK1m2bBk//vgjx44do3///tblWVlZ9OrVi/T0dLZs2cL8+fOZN28eL7zwgrXNgQMH6NWrFyEhIcTExPDEE08wevRo1q5da23z2WefMWHCBCZPnsz27dtp3rw5oaGhnDhxojgPgYiIiJQmOz+HdZOyP3d7BW7tf+X2JY1xE504ccIAjB9//NEwDMNISEgwnJycjGXLllnb7NmzxwCMyMhIwzAMY/Xq1YbZbDbi4+OtbWbPnm14eXkZaWlphmEYxjPPPGM0adLEZltDhgwxQkNDrd/btm1rjB071vo9KyvLCAwMNCIiIgpVe2JiogEYiYmJ17jXIiIiUioc+MkwXqxsGJO9DGP1s4Zhsdi7IsMwri2D3NR77BITEwHw9fUFIDo6moyMDLp06WJt07BhQ2rWrElkZCQAkZGRNG3alCpVqljbhIaGkpSUxO7du61tcveR0yanj/T0dKKjo23amM1munTpYm0jIiIi5diJPbDkPshKh0Z9IfSVEjtW3ZU43qwNWSwWnnjiCTp06MCtt94KQHx8PM7Ozvj4+Ni0rVKlCvHx8dY2uUNdzvKcZVdqk5SUxPnz5zl79ixZWVn5tvnzzz/zrTctLY20tDTr96SkpGvcYxERESkVkuJg4UC4kAg12kH/98HsYO+qrstNO2M3duxYdu3axZIlS27WJm9IREQE3t7e1qlGjRr2LklERESK2oUkWDQIko5ApVvg3k/Byc3eVV23mxLsxo0bx6pVq9iwYQPVq1e3zg8ICCA9PZ2EhASb9sePHycgIMDa5vKnZHO+X62Nl5cXbm5uVK5cGQcHh3zb5PRxufDwcBITE63T4cOHr33HRUREpOTKTIel98PxneDuB8M/hwq+9q7qhhRrsDMMg3HjxrF8+XJ++OEHateubbO8VatWODk5sX79euu82NhYDh06RPv27QFo3749O3futHl6dd26dXh5edG4cWNrm9x95LTJ6cPZ2ZlWrVrZtLFYLKxfv97a5nIuLi54eXnZTCIiIlJGGAasfBz+3ghO7nDfUqgYZO+qblix3mM3duxYFi9ezFdffYWnp6f1njhvb2/c3Nzw9vZm1KhRTJgwAV9fX7y8vBg/fjzt27enXbt2AHTr1o3GjRtz//33M336dOLj4/nvf//L2LFjcXFxAeCRRx7h3Xff5ZlnnuHBBx/khx9+YOnSpXzzzTfWWiZMmEBYWBitW7embdu2vPXWW6SkpDBy5MjiPAQiIiJSEm14BX7/FEwOMGgeVGtp74qKRnE+ngvkO3388cfWNufPnzcee+wxo2LFikaFChWMe+65x4iLi7Pp5+DBg0aPHj0MNzc3o3LlysbEiRONjIwMmzYbNmwwWrRoYTg7Oxt16tSx2UaOmTNnGjVr1jScnZ2Ntm3bGr/88kuh90XDnYiIiJQRv36UPaTJZC/D2DbP3tVc1bVkEJNhGIb9YmXpkZSUhLe3N4mJibosKyIiUlrtXQufDgXDAnc9CyHP2buiq7qWDKJ3xYqIiEj5cHQ7LBuRHepaDINO4fauqMgp2ImIiEjZd+YALB4MGalQ91/Q5+1SOQDx1SjYiYiISNmWchoWDoCUkxDQFAZ/Ag5O9q6qWCjYiYiISNmVcT77nroz+8G7Bty3DFw87V1VsVGwExERkbLJkgVfjIYjUeDqDcM+B6+q9q6qWCnYiYiISNljGLDmP/DnKnBwhqGfgn9De1dV7BTsREREpOzZMhOi3s/+fM97ENTBvvXcJAp2IiIiUrbs/BzWTcr+3O0VuLW/feu5iRTsREREpOw4uBlWPJr9OfhRaD/WvvXcZAp2IiIiUjac2ANL7oOsdGjUF0JfKZNj1V2Jgp2IiIiUfklxsHAgXEiEGsHQ/30wO9i7qptOwU5ERERKtwtJsGgQJB2BSvXg3iXg5GbvquxCwU5ERERKr8x0WHo/HN8J7n4w/Auo4GvvquxGwU5ERERKJ8OAlY/D3xvByR3uWwoVg+xdlV0p2ImIiEjptOEV+P1TMDnAoHlQraW9K7I7BTsREREpfbZ9DJtmZH/u/SbU72bfekoIBTsREREpXfauhW8mZn++61loFWbfekoQBTsREREpPY5uh2UjwMiCFsOgU7i9KypRFOxERESkdDhzABYPhoxUqPsv6PN2uRuA+GoU7ERERKTkSzkNCwdAykkIaAqD5oODk72rKnEU7ERERKRkyzgPnw6FM/vBuwbctwxcvexdVYmkYCciIiIllyULvhgNR6LA1RuGfQ5eVe1dVYmlYCciIiIlk2HAmv/An6vAwRmGfgr+De1dVYmmYCciIiIl05aZEPV+9ud73oOgDvatpxRQsBMREZGSZ+fnsG5S9udur8Ct/e1bTymhYCciIiIly8HNsOLR7M/Bj0D7sfatpxRRsBMREZGS48QeWHIfZKVDoz4Q+qrGqrsGCnYiIiJSMiTFwcKBcCERagRD/w/A7GDvqkoVBTsRERGxvwtJsGgQJB2BSvXg3iXg5GbvqkodBTsRERGxr8x0WHo/HN8J7n4w/Auo4GvvqkolBTsRERGxH8OAlY/D3xvByR3uWwoVg+xdVamlYCciIiL2s+EV+P1TMDnAoHlQraW9KyrVFOxERETEPqLnwaYZ2Z97vwn1u9m1nLJAwU5ERERuvr1rYdWE7M8dn4FWYfatp4xQsBMREZGb6+h2WDYCjCxofh+EPGfvisoMBTsRERG5ec4cgMWDISMV6oRA33c0AHERKtZgt2nTJvr06UNgYCAmk4kVK1bYLDcMgxdeeIGqVavi5uZGly5d+Ouvv2zanDlzhmHDhuHl5YWPjw+jRo0iOTnZps2OHTu48847cXV1pUaNGkyfPj1PLcuWLaNhw4a4urrStGlTVq9eXeT7KyIiIleQchoWDoCUkxDQFAZ/Ag5O9q6qTCnWYJeSkkLz5s2ZNWtWvsunT5/OO++8w5w5c9i6dSvu7u6EhoZy4cIFa5thw4axe/du1q1bx6pVq9i0aRMPPfSQdXlSUhLdunWjVq1aREdHM2PGDKZMmcL7779vbbNlyxbuvfdeRo0axW+//Ua/fv3o168fu3btKr6dFxERkUsyzsOnQ+HMfvCuAfctA1cve1dV9hg3CWAsX77c+t1isRgBAQHGjBkzrPMSEhIMFxcX49NPPzUMwzD++OMPAzB+/fVXa5tvv/3WMJlMxtGjRw3DMIz//e9/RsWKFY20tDRrm2effdZo0KCB9fvgwYONXr162dQTHBxsPPzww4WuPzEx0QCMxMTEQq8jIiIihmFkZRrGp/cZxmQvw4ioYRjH99i7olLlWjKI3e6xO3DgAPHx8XTp0sU6z9vbm+DgYCIjIwGIjIzEx8eH1q1bW9t06dIFs9nM1q1brW06duyIs7OztU1oaCixsbGcPXvW2ib3dnLa5GwnP2lpaSQlJdlMIiIico0MA9b8B/5cBQ7OMPRT8G9o76rKLLsFu/j4eACqVKliM79KlSrWZfHx8fj7+9ssd3R0xNfX16ZNfn3k3kZBbXKW5yciIgJvb2/rVKNGjWvdRREREdkyE6Iu3h51z3sQ1MG+9ZRxeiq2AOHh4SQmJlqnw4cP27skERGR0mXn57BuUvbnbi/Drf3tW085YLdgFxAQAMDx48dt5h8/fty6LCAggBMnTtgsz8zM5MyZMzZt8usj9zYKapOzPD8uLi54eXnZTCIiIlJIBzfDikezPwc/Au3H2beecsJuwa527doEBASwfv1667ykpCS2bt1K+/btAWjfvj0JCQlER0db2/zwww9YLBaCg4OtbTZt2kRGRoa1zbp162jQoAEVK1a0tsm9nZw2OdsRERGRInRiDyy5D7LSoVEfCH1VY9XdJMUa7JKTk4mJiSEmJgbIfmAiJiaGQ4cOYTKZeOKJJ3j55Zf5+uuv2blzJw888ACBgYH069cPgEaNGtG9e3fGjBlDVFQUP//8M+PGjWPo0KEEBgYCcN999+Hs7MyoUaPYvXs3n332GW+//TYTJkyw1vHvf/+bNWvW8Prrr/Pnn38yZcoUtm3bxrhx+teDiIhIkUqKg4UD4UIi1AiG/h+A2cHeVZUfxfl47oYNGwwgzxQWFmYYRvaQJ5MmTTKqVKliuLi4GJ07dzZiY2Nt+jh9+rRx7733Gh4eHoaXl5cxcuRI49y5czZtfv/9d+OOO+4wXFxcjGrVqhnTpk3LU8vSpUuN+vXrG87OzkaTJk2Mb7755pr2RcOdiIiIXMX5RMP4X4fsYU3eaWkYKaftXVGZcC0ZxGQYhmHHXFlqJCUl4e3tTWJiou63ExERuVxmOiweBH9vBHc/GP09VAyyd1VlwrVkED0VKyIiIjfGMGDl49mhzskd7luqUGcnCnYiIiJyYza8Ar9/CiYHGDQPqrW0d0XlloKdiIiIXL/oebBpRvbn3m9C/W52Lae8U7ATERGR67N3Lay6OApFx2egVZh96xEFOxEREbkOR7fDshFgZEHz+yDkOXtXJCjYiYiIyLU6cwAWD4aMVKgTAn3f0QDEJYSCnYiIiBReymlYOABSTkJAUxj8CTg42bsquUjBTkRERAon4zx8OhTO7AfvGnDfMnDV2K4liYKdiIiIXJ0lC74YDUeiwNUbhn0OXlXtXZVcRsFORERErswwYE04/LkKHJxh6Kfg39DeVUk+FOxERETkyiLfhaj3sj/fMweCOti3HimQgp2IiIgUbOfn8N1/sz93exluHWDfeuSKFOxEREQkfwc3w4pHsz8HPwLtx9m3HrkqBTsRERHJ68QeWHIfZKVDoz4Q+qrGqisFFOxERETEVlIcLBwIFxKhRjD0/wDMDvauSgpBwU5EREQuuZAEiwZB0hGoVA/uXQJObvauSgpJwU5ERESyZabD0vvh+E5w94PhX0AFX3tXJddAwU5ERESyx6pb+Tj8vRGc3OG+pVAxyN5VyTVSsBMRERHY8Cr8/imYHGDQPKjW0t4VyXVQsBMRESnvoufBpunZn3u/AfW72bUcuX4KdiIiIuXZ3rWwakL2547PQKsRdi1HboyCnYiISHl1dDssGwFGFjS/D0Kes3dFcoMU7ERERMqjMwdg8WDISIU6IdD3HQ1AXAY42rsAERERuQmyMuDM33DyTzi5F2IWQcpJCGgKgz8BByd7VyhFQMFORESkLMk4D6f+gpOxcCr2UpA7sx8smbZtvWvAfcvA1cs+tUqRU7ATEREpjS4kwam92QHu5J+XgtzZfwAj/3Wc3MGvPvg1BL8G0GwoeFW9qWVL8VKwExERKclSTmcHt1OxF0PcxencsYLXcfW5GN5yhbjKDcCrGph1e31ZpmAnIiJib4YB5+IuXTY9+efFs3F/QurpgtfzqHIptPnlTA2zXwemByHKJQU7ERGRm8VigYR/LoW23CEuLang9bxr5gpuOUGuPrhVvHm1S6mgYCciIlLUrE+gxto+xHBqH2Sez38dkxl861x29q0BVLoFXDxubv1SainYiYiIXK+M83B6X6573y4+xJDfE6g5HJyzw1rO/W+VL/5ZqS44utzc+qXMUbATERG5mrRzuS6b5nqA4exBrvoE6uX3v/nUAgf9368UD/2yREREcqSeuXTWLecM3Km9kHS04HVcfWyDW06Q0xOoYgcKdiIiUr4YBpyLt33yNOdsXOqpgtfzqHLpsmnuhxg8/PUEqpQYCnYiIlI2WSyQeMh27Lecy6hXfQL1svvf9ASqlBIKdiIiUrplZWS/0D7367NO/pn9Wq0rPYFasXbeQXz1BKqUcuUu2M2aNYsZM2YQHx9P8+bNmTlzJm3btrV3WSIi5ZdhZIezrPSLU36fc81LOWl79u30frBk5N+3gzNUqpd3EN9K9fQEqpRJ5SrYffbZZ0yYMIE5c+YQHBzMW2+9RWhoKLGxsfj7+9u7PBGRomXJukpYupHPBSzPTC9825zPBYWya+FUwfayac5DDBWD9ASqlCsmwzAKeE677AkODqZNmza8++67AFgsFmrUqMH48eP5z3/+c8V1k5KS8Pb2JjExES8vr5tRrpQVBf0nVuB/eiWwvWEU4s/c/RR2HeMK61yhr2vazrXuR+79Kai/a13n4p9ZmUUcqK4SogxLAX+npYCDS/bZNgeni3/m+uzqDZVvsX2Iwau6nkCVMutaMki5+WdMeno60dHRhIeHW+eZzWa6dOlCZGRknvZpaWmkpaVZvyclXeFG2yKQlHAa1zfrF+s2ikbp+HdAQc+nmQqov+D5+TObSsdxEMnNMDvaBCRTQcHJ5vPVljsXYvk1fjY76ClTketUboLdqVOnyMrKokqVKjbzq1Spwp9//pmnfUREBFOnTr1Z5QHgbCpglHKRUs5iZEdnA9PFKftz7kh9+TLDuiy/efm1yX8bRq54bhRQR06bvPVxhW3krYECluV8zjAcyMCRDBxJv/hnhnHZd7LbpBuO1rbW5calNnnXd7o4z4EMI3d/l9YxsD2j5Wg24ehgwslsxsnRjKPZhJODGScHE44Oeb87OWR/dzRfNs9szu4nd9uL69qsl19bsxlHBwNnh0wcHSw4OqThfLFtznZyr+vkaMbVMbtfEcmr3AS7axUeHs6ECROs35OSkqhRo0axbc/D04f4UdHF1n/+bvK/iG/6v8AL2N4V67jGc31X3KUrbz/vXRAFd2YUWLPtfJsuC7lOobafE8JM2dvIrseEJfey3AEq57vJlN3+4tEzcl9lxch7Bfey+QbZx8mwaZOrr1zzc46ncVlf2PRV8DasPRdYb/7bIE9fBW8jM8sg02IhI8sgMyv7zwyLhcwsg4ysS/MzLQbpWZbsz1kGGRaDjEyLdd0M6/zc62avl5l1ad2si/1lWQwMS95fcKbFINNicAELpOVZXGI5mk3U9K1A7cru2ZNf9p91KntQxcsFk872STlWboJd5cqVcXBw4Pjx4zbzjx8/TkBAQJ72Li4uuLjcvCemzA4OBNSod9O2JyLli+ViiMsdCq2fc8JkZk44zBUgLw+TF9vmCaIXg+elYGlcZd3LAm0+616qJ3t+jkyLwd+nUvj7VEqe/azg7EBQpeywVycn+F0Mfd4VnG7mIRexi3IT7JydnWnVqhXr16+nX79+QPbDE+vXr2fcuHH2LU5EpJiZzSaczSacHUvnJUzDMMi6GE7PpKRz8GKwO5BrOnQmldT0LP6IS+KPuLz3Rfu6O186y1f5YvDzcyeokjuuTg522CuRoldugh3AhAkTCAsLo3Xr1rRt25a33nqLlJQURo4cae/SRETkCkym7PsBHR0g0MeNQB83bq9X2aZNRpaFw2dSrUHv71MpHDiZ/Tk+6QJnUtI5k5JO9D9n8/RfzcfNJvTlnPGr5uOm+/mkVClXwW7IkCGcPHmSF154gfj4eFq0aMGaNWvyPFAhIiKlj5ODmTp+HtTxy/vmiJS0TA6evnh27+Sl4Pf3yWSSLmRyNOE8RxPOs3nfqcv6zLmfz4M6frZn+/w8dT+flDzlahy7G6Fx7EREyh7DMDibmsGBU8n8fdL20u6BUymkZRY8FqC7s8PFBzc8Ll3arexOUGV3vN10P58UnWvJIAp2haRgJyJSvlgsBnFJFy6e4Uu2uafv8JlU8nnQ2KqyR+77+S4GPz93avpW0P18cs0U7IqBgp2IiORIz7RwyHo/X3L2pd2LZ/xOnCt47BiT6dL9fNandv08qFPZnUAfNxzMurQreSnYFQMFOxERKYzktMxLT+2etA1+59IKHoje2cFMrUoVbB7eqF3Zg6DKFfDz0P185ZleKSYiImInHi6O3FrNm1uredvMNwyD0ynp1gc4/s51tu/g6VTSMy38dSKZv04k59unzVAtuR7k8HTV/Xxyic7YFZLO2ImISHHJshgcSzhv8+BGTvA7cvY8V/p/6soeLrku6156ardmpQq4OOp+vrJAl2KLgYKdiIjYw4WMLA6fSb308Eau4VpOJRd8P5/ZBNUqumUP1ZJ7jD7dz1fqKNgVAwU7EREpaZIuZHAw5wzfZcO1JF/pfj5HM7UrufPwXXXo37L6TaxYrofusRMRESkHvFydaFbdh2bVfWzmG4bByeQ069m9S5d2Uzh08X6+2OPnmLD0dywGDGylcFdWKNiJiIiUMSaTCX9PV/w9XQmuU8lmWc79fB/89DefRP7DM5//jquTmd7NAu1UrRQlvQBPRESkHHEwm6jhW4GpfZswtE0NLAY8sSSG9XuO27s0KQIKdiIiIuWQyWTilXuacneLQDItBo8u2s7Pl70rV0ofBTsREZFyysFs4v8GNadb4yqkZ1oYPX8b2w6esXdZcgMU7ERERMoxJwczM++7jTtvqcz5jCxGfvwru44m2rssuU4KdiIiIuWci6MD79/fmra1fTmXlsn9H24lNv6cvcuS66BgJyIiIrg5O/DRiDY0r+HD2dQMhn+4lQOnUuxdllwjBTsREREBst9JO39kGxoGeHLyXBrDPviFI2dT7V2WXAMFOxEREbHyqeDMglHB1PFz51jihf9n787Doqr3P4C/Z2BmWGfYGUlQFDdcEwNpN7miUWlarikq6tWwq1KadlOr2/1hVjfLXCpLvLnTzRY1jYvbLXFDyRVKxSBhwI0ZRNlmvr8/iJMjqGDAGeD9ep7zOHPO55zzOadzr2/PNnhuxX7km4rlbotqiMGOiIiIrHi7arBmQhhaujvi3KVrGLViPy4XlcrdFtUAgx0RERFV0ULniLUTekOvdcAv+Vcx+tP9MF4vk7stugMGOyIiIqpWgKcTVk8Ig6ezGidyTBifcBBFJeVyt0W3wWBHREREtxTk44LPY8KgdbBH6q9XMPHfh1BcZpa7LboFBjsiIiK6rWA/LVaND4Wz2g57z1zC82sOo7TcIndbVA0GOyIiIrqjewPc8enY++CgUmJHej5mbEhDuZnhztYw2BEREVGN9G7jiY9G94LKToEtx3Lx8n+OwWIRcrdFN2CwIyIiohp7pL03Fo/oCTulAv85/Bvmf3MCQjDc2QoGOyIiIqqV/l30ePfZ7lAogM/3/YoF36Uz3NkIBjsiIiKqtUH33oP/e7orAOCjPWexeMdpmTsigMGOiIiI7tKI0ADMfSIYAPCvpJ+x4n9nZe6IGOyIiIjorsU8GIiX+rUHALy55RRW7/tV5o6aNwY7IiIi+lNi+wRhyqNtAQBzvz6OLw//JnNHzReDHREREf0pCoUCsyI7IDq8FYQAXkr8Cd8dy5W7rWaJwY6IiIj+NIVCgflPdsazIS1hEcDf1h/BzvR8udtqdhjsiIiIqE4olQosGNINT3RrgTKzwOTVqdh75qLcbTUrDHZERERUZ+yUCrw3rAciOvmgpNyCCasOIfXXK3K31Www2BEREVGdUtkp8eHInngwyAvXSs0Yu/IAjp83yt1Ws1Bvwe6f//wn7r//fjg5OcHNza3amqysLERFRcHJyQk+Pj6YOXMmysvLrWp27dqFnj17QqPRICgoCAkJCVWWs2TJErRu3RoODg4ICwvDgQMHrKYXFxcjNjYWnp6ecHFxwZAhQ5CXl1dXm0pEREQ3cVDZ4eMxIejVyh2FxeUY89kBnM4vlLutJq/egl1paSmeffZZTJkypdrpZrMZUVFRKC0txd69e7Fq1SokJCRg3rx5Uk1mZiaioqLQp08fpKWlYfr06ZgwYQK2b98u1WzYsAFxcXGYP38+Dh8+jO7duyMyMhL5+X/csDljxgx8++23SExMxO7du5GTk4PBgwfX16YTERERACe1PT4bdx+6tdThclEpRn6yH79eKpK7raZN1LOVK1cKnU5XZfzWrVuFUqkUBoNBGrds2TKh1WpFSUmJEEKIWbNmic6dO1vNN2zYMBEZGSl9Dw0NFbGxsdJ3s9ks/Pz8RHx8vBBCiIKCAqFSqURiYqJUc+rUKQFApKSk1Hg7jEajACCMRmON5yEiIiIhLl8tEf3+tVu0enmzuD8+WZy/ck3ulhqV2mQQ2e6xS0lJQdeuXeHr6yuNi4yMhMlkwokTJ6SaiIgIq/kiIyORkpICoOKsYGpqqlWNUqlERESEVJOamoqysjKrmo4dOyIgIECqISIiovrj7qzG5xNCEejljPMF1zFqxX7kFxbL3VaTJFuwMxgMVqEOgPTdYDDctsZkMuH69eu4ePEizGZztTU3LkOtVle5z+/GmuqUlJTAZDJZDURERHR3fFwdsGZCGO5xc0TmxSKMXnEAV4pK5W6ryalVsJs9ezYUCsVth/T09PrqtUHFx8dDp9NJg7+/v9wtERERNWp+bo5YOzEMPq4aZOQVInrlAZiKy+Ruq0mpVbB78cUXcerUqdsObdq0qdGy9Hp9lSdTK7/r9frb1mi1Wjg6OsLLywt2dnbV1ty4jNLSUhQUFNyypjpz5syB0WiUhuzs7BptFxEREd1aK09nrJkQBg9nNY7+ZkRMwkFcKy2/84xUI7UKdt7e3ujYseNtB7VaXaNlhYeH49ixY1ZPryYlJUGr1SI4OFiqSU5OtpovKSkJ4eHhAAC1Wo2QkBCrGovFguTkZKkmJCQEKpXKqiYjIwNZWVlSTXU0Gg20Wq3VQERERH9eO19X/Ht8KFwd7HHw3BX89fNUFJeZ5W6rSai3e+yysrKQlpaGrKwsmM1mpKWlIS0tDVevXgUA9OvXD8HBwRg9ejR++uknbN++Ha+++ipiY2Oh0WgAAJMnT8bZs2cxa9YspKenY+nSpdi4cSNmzJghrScuLg6ffPIJVq1ahVOnTmHKlCkoKirCuHHjAAA6nQ4xMTGIi4vDzp07kZqainHjxiE8PBy9e/eur80nIiKi2+hyjw4J40LhpLbD/365iKlrj6DMbJG7rcavvh7NjY6OFgCqDDt37pRqzp07JwYMGCAcHR2Fl5eXePHFF0VZWZnVcnbu3Cl69Ogh1Gq1aNOmjVi5cmWVdS1evFgEBAQItVotQkNDxb59+6ymX79+XTz//PPC3d1dODk5iaefflrk5ubWanv4uhMiIqK69+PpC6L937eKVi9vFlPXHhblZovcLdmc2mQQhRBCyJgrGw2TyQSdTgej0cjLskRERHVoZ3o+Jn1+CGVmgaG9WmLB4G5QKhVyt2UzapNB+FuxREREJKs+HX3wwfB7oVQAGw/9hjc2nwTPO90dBjsiIiKS3YCuLfDOs90BAAl7z+Ht7Rkyd9Q4MdgRERGRTRjcsyXeHNQFALB01xks2Xla5o4aHwY7IiIishnP9W6Fvz/eCQDw9vYMfPpDpswdNS72cjfQ1JjNZpSV8S3aNaFSqWBnZyd3G0REZGMmPtwG10rNeO+/P+Mfm0/CSW2HEaEBcrfVKDDY1REhBAwGQ5VfuKDbc3Nzg16vh0LBp5+IiOgPf+sbhGul5fhoz1m8sukYnNR2GNjjHrnbsnkMdnWkMtT5+PjAycmJQeUOhBC4du2a9MsjLVq0kLkjIiKyJQqFArMHdERRaTlW78tC3Maf4KCyQ2TnW/8cKDHY1Qmz2SyFOk9PT7nbaTQcHR0BAPn5+fDx8eFlWSIisqJQKPDGU11wrdSMLw+fxwtrj+CT6F54pL233K3ZLD48UQcq76lzcnKSuZPGp3Kf8b5EIiKqjlKpwMIh3RDVtQVKzRb89fND2H/2ktxt2SwGuzrEy6+1x31GRER3Ym+nxHvDeuCxjj4oLrNgfMJBpGUXyN2WTWKwIyIiIpuntldi6aieuL+tJ4pKzRjz6X6czDHJ3ZbNYbCjGnn00Ucxffp06Xvr1q2xaNEi2fohIqLmx0Flh0/G9ELPADeYissx+tP9OJ1/Ve62bAqDXTM3duxYDBo0SO42iIiIasRZY4+V40LR5R4tLhWV4rkV+5F9+ZrcbdkMBjsiIiJqVHSOKvx7fBja+bjAYCrGyBX7kGu8LndbNoHBju5aYWEhRowYAWdnZ9xzzz1YsmSJ3C0REVEz4eGsxpoJYWjl6YTsy9cxasV+XLxaIndbsmOwqydCCFwrLZdlEEI0yDa+/fbb6N69O44cOYLZs2dj2rRpSEpKapB1ExER+WgdsGZCGPx0Djh7oQjPrdiPgmulcrclK76guJ5cLzMjeN52WdZ98o1IOKnr/z/tAw88gNmzZwMA2rdvjx9//BHvvfce/vKXv9T7uomIiACgpbsT1kzsjWeXpyDdUIjolQexZkIYXDTNM+LwjB3dtfDw8CrfT506JVM3RETUXAV6OWPNhDC4O6nwU3YBxiccxPVSs9xtyaJ5xtkG4Kiyw8k3ImVbNxERUXPSQe+Kf48Pw8hP9uFA5mX8dXUqPhkTAo198/o7kcGunigUiga5HCqnffv2VfneqVMnmbohIqLmrmtLHVaOuw+jPz2APT9fwAtrj2DJqJ5Q2TWfC5TNZ0upzv34449YuHAhfv75ZyxZsgSJiYmYNm2a3G0REVEz1qu1B1ZE94LaXonvT+bhpcSfYLY0zEOFtoDBju7aiy++iEOHDuHee+/Fm2++iX/961+IjJTn8jMREVGlB4K8sGxUT9grFfg6LQevfnWswd4YIbemfa2Q7ighIaFGdbt27bL6fu7cuTrvhYiIqK707eSLRcN74G/rjmDdgWw4qOww74lgKBQKuVurVzxjR0RERE3SE9388NaQbgCAlT+ew7+Sfpa5o/rHYEfIysqCi4vLLYesrCy5WyQiIrorz/byxxsDOwMAFu84jaW7TsvcUf3ipViCn58f0tLSbjudiIiosRoT3hrXSs1Y8F06Fm7LgLPaHtH3t5a7rXrBYEewt7dHUFCQ3G0QERHVm8mPtMW1knJ8sOM05n9zAo4qOwy9z1/utuocL8USERFRszDjL+0x4cFAAMDLXx7FNz/lyNxR3WOwIyIiomZBoVDg71GdMDIsAEIAcRvSkHQyT+626hSDHRERETUbCoUCbw7sgqfvvQflFoHYNYfxwy8X5W6rzjDYERERUbOiVCrw9jPd0L+zHqVmCyb++xAOnrssd1t1gsGOiIiImh17OyU+GHEvHu3gjetlZoxbeRBHfyuQu60/jcGO6oxCocBXX30ldxtEREQ1orZXYvlzIejdxgNXS8ox5rMDSDeY5G7rT2Gwa+bGjh2LQYMGyd0GERGRLBxUdlgRfR96+Luh4FoZnltxAGcvXJW7rbvGYEdERETNmovGHqvGhaJTCy0uXi3BqBX7kX35mtxt3RUGO6qR1q1bY9GiRVbjevTogddee81qXG5uLgYMGABHR0e0adMGX3zxRcM1SUREdJd0Tip8HhOKtt7OyDUWY9SK/cgzFcvdVq3VW7A7d+4cYmJiEBgYCEdHR7Rt2xbz589HaWmpVd3Ro0fx0EMPwcHBAf7+/li4cGGVZSUmJqJjx45wcHBA165dsXXrVqvpQgjMmzcPLVq0gKOjIyIiIvDLL79Y1Vy+fBmjRo2CVquFm5sbYmJicPVqPZ5qFQIoLZJnEKL+tusO5s6diyFDhuCnn37CqFGjMHz4cJw6dUq2foiIiGrKy0WDNRN6I8DDCVmXr2HUiv24dLVE7rZqpd5+Uiw9PR0WiwUfffQRgoKCcPz4cUycOBFFRUV45513AAAmkwn9+vVDREQEli9fjmPHjmH8+PFwc3PDpEmTAAB79+7FiBEjEB8fjyeeeAJr167FoEGDcPjwYXTp0gUAsHDhQnzwwQdYtWoVAgMDMXfuXERGRuLkyZNwcHAAAIwaNQq5ublISkpCWVkZxo0bh0mTJmHt2rX1swPKrgH/J9NvrL6SA6idZVn1s88+iwkTJgAA/vGPfyApKQmLFy/G0qVLZemHiIioNvQ6B6yZEIahH6XgdP5VjP70ANZN6g2do0ru1mqk3s7Y9e/fHytXrkS/fv3Qpk0bPPXUU3jppZfw5ZdfSjVr1qxBaWkpPvvsM3Tu3BnDhw/H3/72N/zrX/+Sat5//330798fM2fORKdOnfCPf/wDPXv2xIcffgig4mzdokWL8Oqrr2LgwIHo1q0b/v3vfyMnJ0d6QvPUqVPYtm0bVqxYgbCwMDz44INYvHgx1q9fj5ycpvdzInIKDw+v8p1n7IiIqDHx93DC6glh8HJR42SuCWNXHkBRSbncbdVIvZ2xq47RaISHh4f0PSUlBQ8//DDUarU0LjIyEm+99RauXLkCd3d3pKSkIC4uzmo5kZGRUmjLzMyEwWBARESENF2n0yEsLAwpKSkYPnw4UlJS4Obmhl69ekk1ERERUCqV2L9/P55++um631iVU8WZMzmonOp8kUqlEuKmS7xlZWV1vh4iIiJb0NbbBZ/HhGH4x/twJKsAE1Ydwspx98FBZSd3a7fVYA9PnD59GosXL8Zf//pXaZzBYICvr69VXeV3g8Fw25obp984361qfHx8rKbb29vDw8NDqrlZSUkJTCaT1VArCkXF5VA5BoWidr3WgLe3N3Jzc6XvJpMJmZmZVer27dtX5XunTp3qvB8iIqL61qmFFv8eHwoXjT1Szl7C5NWpKC23yN3WbdU62M2ePRsKheK2Q3p6utU858+fR//+/fHss89i4sSJddZ8fYqPj4dOp5MGf39/uVuS1WOPPYbPP/8c//vf/3Ds2DFER0fDzq7qv1oSExPx2Wef4eeff8b8+fNx4MABTJ06VYaOiYiI/rzu/m74bOx9cFApsSvjAqatP4Jys+2Gu1oHuxdffBGnTp267dCmTRupPicnB3369MH999+Pjz/+2GpZer0eeXl5VuMqv+v1+tvW3Dj9xvluVZOfn281vby8HJcvX5ZqbjZnzhwYjUZpyM7OvvPOacLmzJmDRx55BE888QSioqIwaNAgtG3btkrd66+/jvXr10v3Oq5btw7BwcEydExERFQ3QgM98PHoXlDbKfHdcQNmfXEUFot8b6C4nVrfY+ft7Q1vb+8a1Z4/fx59+vRBSEgIVq5cCaXSOkeGh4fj73//O8rKyqBSVTxtkpSUhA4dOsDd3V2qSU5OxvTp06X5kpKSpJv0AwMDodfrkZycjB49egCouEy4f/9+TJkyRVpGQUEBUlNTERISAgDYsWMHLBYLwsLCqu1do9FAo9HUbKc0YgkJCTWq02q1WL9+vdW46Ohoq++V9+A9//zzddIbERGRrXi4vTc+HHkvpqw5jC+PnIej2g5vDuoCRT3c/vRn1Ns9dufPn8ejjz6KgIAAvPPOO7hw4QIMBoPVPW0jR46EWq1GTEwMTpw4gQ0bNuD999+3elhi2rRp2LZtG959912kp6fjtddew6FDh6TLewqFAtOnT8ebb76Jb775BseOHcOYMWPg5+cn/VRWp06d0L9/f0ycOBEHDhzAjz/+iKlTp2L48OHw85PplSRERETUqPTrrMe/hnaHQgGs2Z+F/9t6qsqDhXKrt6dik5KScPr0aZw+fRotW7a0mla5E3Q6Hb7//nvExsYiJCQEXl5emDdvnvQOOwC4//77sXbtWrz66qt45ZVX0K5dO3z11VfSO+wAYNasWSgqKsKkSZNQUFCABx98ENu2bZPeYQdUvFpl6tSp6Nu3L5RKJYYMGYIPPvigvja/UcnKyrrt5dKTJ08iICCgATsiIiKyTQN73IOSMgtm/ecoPvlfJpzU9pjxl/ZytyVRCFuLmjbKZDJBp9PBaDRCq9VaTSsuLkZmZiYCAwOtwmRjUV5ejnPnzt1yeuvWrWFvXz//Bmjs+46IiJqnlT9m4vVvTwIA5gzoiL8+UvW+87pyuwxyswZ9jx3ZJnt7ewQFBcndBhERUaMx7oFAXCs14+3tGYj/Lh3uzmoM7SX/GzQY7IiIiIjuQmyfIFwrLcc3P+UgvI2n3O0AYLCrUxaL7b7XxlZxnxERUWP2Ur8OmPRQW+icbOO3ZBns6oBarYZSqUROTg68vb2hVqtt7vFnWyOEQGlpKS5cuAClUmn1s3JERESNhUKhsJlQBzDY1QmlUonAwEDk5uYiJ0em34dtpJycnBAQEFDlHYdERERUewx2dUStViMgIADl5eUwm81yt9Mo2NnZwd7enmc3iYiI6giDXR1SKBRQqVTSr2gQERERNSRe/yIiIiJqIhjsiIiIiJoIBjsiIiKiJoL32NVQ5S+vmUwmmTshIiKi5qQye9TkV2AZ7GqosLAQAODvL//PhRAREVHzU1hYCJ1Od9sahahJ/CNYLBbk5OTA1dW13l7PYTKZ4O/vj+zs7Dv+yC/dHvdl3eB+rDvcl3WD+7HucF/WjYbYj0IIFBYWws/P747vfeUZuxpSKpVo2bJlg6xLq9Xyf2R1hPuybnA/1h3uy7rB/Vh3uC/rRn3vxzudqavEhyeIiIiImggGOyIiIqImgsHOhmg0GsyfPx8ajUbuVho97su6wf1Yd7gv6wb3Y93hvqwbtrYf+fAEERERURPBM3ZERERETQSDHREREVETwWBHRERE1EQw2BERERE1EQx2NmLJkiVo3bo1HBwcEBYWhgMHDsjdUqPz2muvQaFQWA0dO3aUu61GYc+ePXjyySfh5+cHhUKBr776ymq6EALz5s1DixYt4OjoiIiICPzyyy/yNGvD7rQfx44dW+UY7d+/vzzN2rD4+Hjcd999cHV1hY+PDwYNGoSMjAyrmuLiYsTGxsLT0xMuLi4YMmQI8vLyZOrYdtVkXz766KNVjsvJkyfL1LHtWrZsGbp16ya9iDg8PBzfffedNN1WjkkGOxuwYcMGxMXFYf78+Th8+DC6d++OyMhI5Ofny91ao9O5c2fk5uZKww8//CB3S41CUVERunfvjiVLllQ7feHChfjggw+wfPly7N+/H87OzoiMjERxcXEDd2rb7rQfAaB///5Wx+i6desasMPGYffu3YiNjcW+ffuQlJSEsrIy9OvXD0VFRVLNjBkz8O233yIxMRG7d+9GTk4OBg8eLGPXtqkm+xIAJk6caHVcLly4UKaObVfLli2xYMECpKam4tChQ3jssccwcOBAnDhxAoANHZOCZBcaGipiY2Ol72azWfj5+Yn4+HgZu2p85s+fL7p37y53G40eALFp0ybpu8ViEXq9Xrz99tvSuIKCAqHRaMS6detk6LBxuHk/CiFEdHS0GDhwoCz9NGb5+fkCgNi9e7cQouL4U6lUIjExUao5deqUACBSUlLkarNRuHlfCiHEI488IqZNmyZfU42Yu7u7WLFihU0dkzxjJ7PS0lKkpqYiIiJCGqdUKhEREYGUlBQZO2ucfvnlF/j5+aFNmzYYNWoUsrKy5G6p0cvMzITBYLA6RnU6HcLCwniM3oVdu3bBx8cHHTp0wJQpU3Dp0iW5W7J5RqMRAODh4QEASE1NRVlZmdUx2bFjRwQEBPCYvIOb92WlNWvWwMvLC126dMGcOXNw7do1OdprNMxmM9avX4+ioiKEh4fb1DFp36BroyouXrwIs9kMX19fq/G+vr5IT0+XqavGKSwsDAkJCejQoQNyc3Px+uuv46GHHsLx48fh6uoqd3uNlsFgAIBqj9HKaVQz/fv3x+DBgxEYGIgzZ87glVdewYABA5CSkgI7Ozu527NJFosF06dPxwMPPIAuXboAqDgm1Wo13NzcrGp5TN5edfsSAEaOHIlWrVrBz88PR48excsvv4yMjAx8+eWXMnZrm44dO4bw8HAUFxfDxcUFmzZtQnBwMNLS0mzmmGSwoyZjwIAB0udu3bohLCwMrVq1wsaNGxETEyNjZ0QVhg8fLn3u2rUrunXrhrZt22LXrl3o27evjJ3ZrtjYWBw/fpz3y9aBW+3LSZMmSZ+7du2KFi1aoG/fvjhz5gzatm3b0G3atA4dOiAtLQ1GoxFffPEFoqOjsXv3brnbssJLsTLz8vKCnZ1dlSdn8vLyoNfrZeqqaXBzc0P79u1x+vRpuVtp1CqPQx6jda9Nmzbw8vLiMXoLU6dOxebNm7Fz5060bNlSGq/X61FaWoqCggKreh6Tt3arfVmdsLAwAOBxWQ21Wo2goCCEhIQgPj4e3bt3x/vvv29TxySDnczUajVCQkKQnJwsjbNYLEhOTkZ4eLiMnTV+V69exZkzZ9CiRQu5W2nUAgMDodfrrY5Rk8mE/fv38xj9k3777TdcunSJx+hNhBCYOnUqNm3ahB07diAwMNBqekhICFQqldUxmZGRgaysLB6TN7nTvqxOWloaAPC4rAGLxYKSkhKbOiZ5KdYGxMXFITo6Gr169UJoaCgWLVqEoqIijBs3Tu7WGpWXXnoJTz75JFq1aoWcnBzMnz8fdnZ2GDFihNyt2byrV69a/es8MzMTaWlp8PDwQEBAAKZPn44333wT7dq1Q2BgIObOnQs/Pz8MGjRIvqZt0O32o4eHB15//XUMGTIEer0eZ86cwaxZsxAUFITIyEgZu7Y9sbGxWLt2Lb7++mu4urpK9yjpdDo4OjpCp9MhJiYGcXFx8PDwgFarxQsvvIDw8HD07t1b5u5ty5325ZkzZ7B27Vo8/vjj8PT0xNGjRzFjxgw8/PDD6Natm8zd25Y5c+ZgwIABCAgIQGFhIdauXYtdu3Zh+/bttnVMNugzuHRLixcvFgEBAUKtVovQ0FCxb98+uVtqdIYNGyZatGgh1Gq1uOeee8SwYcPE6dOn5W6rUdi5c6cAUGWIjo4WQlS88mTu3LnC19dXaDQa0bdvX5GRkSFv0zbodvvx2rVrol+/fsLb21uoVCrRqlUrMXHiRGEwGORu2+ZUtw8BiJUrV0o1169fF88//7xwd3cXTk5O4umnnxa5ubnyNW2j7rQvs7KyxMMPPyw8PDyERqMRQUFBYubMmcJoNMrbuA0aP368aNWqlVCr1cLb21v07dtXfP/999J0WzkmFUII0ZBBkoiIiIjqB++xIyIiImoiGOyIiIiImggGOyIiIqImgsGOiIiIqIlgsCMiIiJqIhjsiIiIiJoIBjsiIiKiJoLBjoiIiKiJYLAjIiIiaiIY7IiIiIiaCAY7IiIioiaCwY6IiIioiWCwIyIiImoiGOyIiIiImggGOyIiIqImgsGOiIiIqIlgsCMiIiJqIhjsiIiamISEBCgUCpw7d07uVoiogTHYEVGTUxlsbjXs27fvtvM/+uijVvVqtRqBgYGYNGkSsrOzG2griIhqz17uBoiI6ssbb7yBwMDAKuODgoLuOG/Lli0RHx8PACgtLcXJkyexfPlybN++HadOnYKTk1Od90tE9Gcx2BFRkzVgwAD06tXrrubV6XR47rnnrMYFBgZi6tSp+PHHH/GXv/ylLlokIqpTvBRLRFRDer0eAGBvb/1v4iNHjmDAgAHQarVwcXFB3759q1zufe2116BQKKoss7r74Vq3bo0nnngCP/zwA0JDQ+Hg4IA2bdrg3//+d5X5T5w4gcceewyOjo5o2bIl3nzzTVgslip1hw4dQmRkJLy8vODo6IjAwECMHz/+bnYDEdkwnrEjoibLaDTi4sWLVuMUCgU8PT3vOK/ZbJbmLSsrw6lTpzB//nwEBQXhgQcekOpOnDiBhx56CFqtFrNmzYJKpcJHH32ERx99FLt370ZYWNhd9X769Gk888wziImJQXR0ND777DOMHTsWISEh6Ny5MwDAYDCgT58+KC8vx+zZs+Hs7IyPP/4Yjo6OVsvKz89Hv3794O3tjdmzZ8PNzQ3nzp3Dl19+eVe9EZHtYrAjoiYrIiKiyjiNRoPi4uI7zpueng5vb2+rcZ06dcL3338PtVotjXv11VdRVlaGH374AW3atAEAjBkzBh06dMCsWbOwe/fuu+o9IyMDe/bswUMPPQQAGDp0KPz9/bFy5Uq88847AIC33noLFy5cwP79+xEaGgoAiI6ORrt27ayWtXfvXly5cgXff/+91aXpN9988656IyLbxWBHRE3WkiVL0L59e6txdnZ2NZq3devW+OSTTwAA5eXlyMjIwMKFCzFgwAD873//g7e3N8xmM77//nsMGjRICnUA0KJFC4wcORKffPIJTCYTtFptrXsPDg6WQh0AeHt7o0OHDjh79qw0buvWrejdu7cU6irrRo0ahaVLl0rj3NzcAACbN29G9+7doVKpat0PETUODHZE1GSFhobe9cMTzs7OVmf8+vfvjwcffBC9evXCggUL8O677+LChQu4du0aOnToUGX+Tp06wWKxIDs7W7p0WhsBAQFVxrm7u+PKlSvS919//bXaS7039/PII49gyJAheP311/Hee+/h0UcfxaBBgzBy5EhoNJpa90ZEtosPTxAR1VBISAh0Oh327NlT63mre3ACqLiXrzq3OrMohLirdX/xxRdISUnB1KlTcf78eYwfPx4hISG4evVqrZdHRLaLwY6IqBbMZrMUhry9veHk5ISMjIwqdenp6VAqlfD39wdQcbYNAAoKCqzqfv3117vupVWrVvjll1+qjK+uHwDo3bs3/vnPf+LQoUNYs2YNTpw4gfXr19/1+onI9jDYERHV0M6dO3H16lV0794dQMVZtX79+uHrr7+2el1JXl4e1q5diwcffFC6v65t27YAYHW2r6ioCKtWrbrrfh5//HHs27cPBw4ckMZduHABa9assaq7cuVKlTN9PXr0AACUlJTc9fqJyPbwHjsiarK+++47pKenVxl///33Wz3sUB2j0YjVq1cD+OPhiWXLlsHR0RGzZ8+W6t58800kJSXhwQcfxPPPPw97e3t89NFHKCkpwcKFC6W6fv36ISAgADExMZg5cybs7Ozw2WefwdvbG1lZWXe1fbNmzcLnn3+O/v37Y9q0adLrTlq1aoWjR49KdatWrcLSpUvx9NNPo23btigsLMQnn3wCrVaLxx9//K7WTUS2icGOiJqsefPmVTt+5cqVdwx2v/32G0aPHg2g4h41d3d3PPLII5g/f750tgsAOnfujP/973+YM2cO4uPjYbFYEBYWhtWrV1s92KBSqbBp0yY8//zzmDt3LvR6PaZPnw53d3eMGzfurravRYsW2LlzJ1544QUsWLAAnp6emDx5Mvz8/BATEyPVPfLIIzhw4ADWr1+PvLw86HQ6hIaGYs2aNdX+5BoRNV4KcTd34hIRERGRzeE9dkRERERNBIMdERERURPBYEdERETURDDYERERETURDHZERERETUSDBbsFCxZAoVBg+vTp0rji4mLExsbC09MTLi4uGDJkCPLy8qzmy8rKQlRUFJycnODj44OZM2eivLzcqmbXrl3o2bMnNBoNgoKCkJCQUGX9S5YsQevWreHg4ICwsDCrF3oSERERNQUN8h67gwcP4qOPPkK3bt2sxs+YMQNbtmxBYmIidDodpk6disGDB+PHH38EUPHTPVFRUdDr9di7dy9yc3MxZswYqFQq/N///R8AIDMzE1FRUZg8eTLWrFmD5ORkTJgwAS1atEBkZCQAYMOGDYiLi8Py5csRFhaGRYsWITIyEhkZGfDx8anRNlgsFuTk5MDV1fWWv/lIREREVNeEECgsLISfnx+UyjuckxP1rLCwULRr104kJSWJRx55REybNk0IIURBQYFQqVQiMTFRqj116pQAIFJSUoQQQmzdulUolUphMBikmmXLlgmtVitKSkqEEELMmjVLdO7c2Wqdw4YNE5GRkdL30NBQERsbK303m83Cz89PxMfH13g7srOzBQAOHDhw4MCBAwdZhuzs7DvmlXo/YxcbG4uoqChERETgzTfflManpqairKwMERER0riOHTsiICAAKSkp6N27N1JSUtC1a1f4+vpKNZGRkZgyZQpOnDiBe++9FykpKVbLqKypvORbWlqK1NRUzJkzR5quVCoRERGBlJSUGm+Hq6srACA7O1v67UciIiKi+mYymeDv7y9lkdup12C3fv16HD58GAcPHqwyzWAwQK1Ww83NzWq8r68vDAaDVHNjqKucXjntdjUmkwnXr1/HlStXYDabq62p7jckK5WUlFj9OHZhYSEAQKvVMtgRERFRg6vJrWD19vBEdnY2pk2bhjVr1sDBwaG+VlNv4uPjodPppMHf31/uloiIiIhuq96CXWpqKvLz89GzZ0/Y29vD3t4eu3fvxgcffAB7e3v4+vqitLQUBQUFVvPl5eVBr9cDAPR6fZWnZCu/36lGq9XC0dERXl5esLOzq7amchnVmTNnDoxGozRkZ2ff1X4gIiIiaij1Fuz69u2LY8eOIS0tTRp69eqFUaNGSZ9VKhWSk5OleTIyMpCVlYXw8HAAQHh4OI4dO4b8/HypJikpCVqtFsHBwVLNjcuorKlchlqtRkhIiFWNxWJBcnKyVFMdjUYjXXbl5VciIiJqDOrtHjtXV1d06dLFapyzszM8PT2l8TExMYiLi4OHhwe0Wi1eeOEFhIeHo3fv3gCAfv36ITg4GKNHj8bChQthMBjw6quvIjY2FhqNBgAwefJkfPjhh5g1axbGjx+PHTt2YOPGjdiyZYu03ri4OERHR6NXr14IDQ3FokWLUFRUhHHjxtXX5hMRERE1uAZ5j92tvPfee1AqlRgyZAhKSkoQGRmJpUuXStPt7OywefNmTJkyBeHh4XB2dkZ0dDTeeOMNqSYwMBBbtmzBjBkz8P7776Nly5ZYsWKF9A47ABg2bBguXLiAefPmwWAwoEePHti2bVuVByqIiIiIGjOFEELI3URjYDKZoNPpYDQaeVmWiIiIGkxtMgh/K5aIiIjobplygPQtd65rIAx2RERERHejpBBYOxRYPxI4slrubgAw2BERERHVnrkcSBwHGI4Bzt5A6wfl7ggAgx0RERFR7QgBbH0JOJ0E2DsCIzcA7q3l7goAgx0RERFR7fz4PpC6EoACeOZT4J4QuTuSMNgRERER1dTxL4H/zq/43H8B0DFK3n5uwmBHREREVBNZ+4BNkys+h00Bek+Wt59qMNgRERER3cmlM8C6EYC5BOj4BBD5T7k7qhaDHREREdHtFF0C1jwDXL8M+PUEBn8CKO3k7qpaDHZEREREt1J2HVg3HLh8FnALqHgCVu0kd1e3xGBHREREVB2LpeKeut8OAA46YNQXgIuP3F3dFoMdERERUXWSXwNOfgUoVcCwNYB3B7k7uiMGOyIiIqKbHfy04n11ADBwCRD4kLz91BCDHREREdGNfv6+4pclAKDPq0D3YfL2UwsMdkRERESVcn8CEscCwgL0eA54+CW5O6oVBjsiIiIiADD+BqwZCpQVAW0eBZ5cBCgUcndVKwx2RERERMVGYM2zwFUD4N0JGPpvwE4ld1e1xmBHREREzZu5DNgYDeSfBFz0wKjEitebNEIMdkRERNR8CQFsng6c3QmonCteQOzmL3dXd61eg92yZcvQrVs3aLVaaLVahIeH47vvvpOmFxcXIzY2Fp6ennBxccGQIUOQl5dntYysrCxERUXByckJPj4+mDlzJsrLy61qdu3ahZ49e0Kj0SAoKAgJCQlVelmyZAlat24NBwcHhIWF4cCBA/WyzURERNSI/O8d4MhqQKEEnl0J+PWQu6M/pV6DXcuWLbFgwQKkpqbi0KFDeOyxxzBw4ECcOHECADBjxgx8++23SExMxO7du5GTk4PBgwdL85vNZkRFRaG0tBR79+7FqlWrkJCQgHnz5kk1mZmZiIqKQp8+fZCWlobp06djwoQJ2L59u1SzYcMGxMXFYf78+Th8+DC6d++OyMhI5Ofn1+fmExERkS07uhHY8WbF5wELgfaR8vZTF0QDc3d3FytWrBAFBQVCpVKJxMREadqpU6cEAJGSkiKEEGLr1q1CqVQKg8Eg1SxbtkxotVpRUlIihBBi1qxZonPnzlbrGDZsmIiMjJS+h4aGitjYWOm72WwWfn5+Ij4+vsZ9G41GAUAYjcbabTARERHZnsz/CfGGlxDztUJs/7vc3dxWbTJIg91jZzabsX79ehQVFSE8PBypqakoKytDRESEVNOxY0cEBAQgJSUFAJCSkoKuXbvC19dXqomMjITJZJLO+qWkpFgto7KmchmlpaVITU21qlEqlYiIiJBqiIiIqBm58DOwfhRgLgWCBwIRb8jdUZ2xr+8VHDt2DOHh4SguLoaLiws2bdqE4OBgpKWlQa1Ww83Nzare19cXBoMBAGAwGKxCXeX0ymm3qzGZTLh+/TquXLkCs9lcbU16evot+y4pKUFJSYn03WQy1W7DiYiIyPZczQfWPAMUFwAtQ4GnPwKUTedZ0nrfkg4dOiAtLQ379+/HlClTEB0djZMnT9b3av+0+Ph46HQ6afD3b7xPyBARERGA0mvAuuFAwa+AeyAwYh2gcpS7qzpV78FOrVYjKCgIISEhiI+PR/fu3fH+++9Dr9ejtLQUBQUFVvV5eXnQ6/UAAL1eX+Up2crvd6rRarVwdHSEl5cX7Ozsqq2pXEZ15syZA6PRKA3Z2dl3tf1ERERkAyxm4MuJwPlUwNEdGPUF4Owld1d1rsHPPVosFpSUlCAkJAQqlQrJycnStIyMDGRlZSE8PBwAEB4ejmPHjlk9vZqUlAStVovg4GCp5sZlVNZULkOtViMkJMSqxmKxIDk5WaqpjkajkV7TUjkQERFRI/X9XCB9M2CnBoavA7yC5O6oXtTrPXZz5szBgAEDEBAQgMLCQqxduxa7du3C9u3bodPpEBMTg7i4OHh4eECr1eKFF15AeHg4evfuDQDo168fgoODMXr0aCxcuBAGgwGvvvoqYmNjodFoAACTJ0/Ghx9+iFmzZmH8+PHYsWMHNm7ciC1btkh9xMXFITo6Gr169UJoaCgWLVqEoqIijBs3rj43n4iIiGzB/o+AfUsqPj+9HGh16xM7jV29Brv8/HyMGTMGubm50Ol06NatG7Zv346//OUvAID33nsPSqUSQ4YMQUlJCSIjI7F06VJpfjs7O2zevBlTpkxBeHg4nJ2dER0djTfe+OPplcDAQGzZsgUzZszA+++/j5YtW2LFihWIjPzjXTTDhg3DhQsXMG/ePBgMBvTo0QPbtm2r8kAFERERNTHpW4Ftsys+R7wGdBkiazv1TSGEEHI30RiYTCbodDoYjUZeliUiImoMzqcCK6OA8utAyFjgiUWAQiF3V7VWmwzSdJ7vJSIiIqp05Vdg7fCKUBcUATz+bqMMdbXFYEdERERNy/UCYM2zQFE+4NsVeDYBsKv3V/faBAY7IiIiajrKS4ENzwEXMwBXP2DURkDjKndXDYbBjoiIiJoGIYBvXgDO/Q9QuwKjEgGtn9xdNSgGOyIiImoadi0Ajq4HFHbA0ARA30Xujhocgx0RERE1fmlrgd0LKj4/8a+KByaaIQY7IiIiatzO7qq4BAsAD8ZVvNqkmWKwIyIiosYr/xSwYQxgKQe6PAM8NlfujmTFYEdERESNU6Gh4rUmJUYg4H5g0FJA2byjTfPeeiIiImqcSq4Ca4cCxmzAMwgYvgaw18jdlewY7IiIiKhxsZiB/8QAuT8BTp4VrzVx8pC7K5vAYEdERESNhxDAdy8DP28D7B2AERsAjzZyd2UzGOyIiIio8di3FDj4CQAFMPhjwP8+uTuyKQx2RERE1Dic/BrY/veKz/3eBIIHytuPDWKwIyIiItuXfRD4chIAAdw3EQiPlbsjm8RgR0RERLbt8llg3XCgvBho3x/ovwBQKOTuyiYx2BEREZHtuna54l111y4CLboDQz4F7Ozl7spmMdgRERGRbSovAdaPAi6dBnT+wMiNgMZF7q5sGoMdERER2R6LBfjqeSBrL6DRVryrzlUvd1c2r16DXXx8PO677z64urrCx8cHgwYNQkZGhlVNcXExYmNj4enpCRcXFwwZMgR5eXlWNVlZWYiKioKTkxN8fHwwc+ZMlJeXW9Xs2rULPXv2hEajQVBQEBISEqr0s2TJErRu3RoODg4ICwvDgQMH6nybiYiIqA7sfBM4/gWgtAeGfQ74dJK7o0ahXoPd7t27ERsbi3379iEpKQllZWXo168fioqKpJoZM2bg22+/RWJiInbv3o2cnBwMHjxYmm42mxEVFYXS0lLs3bsXq1atQkJCAubNmyfVZGZmIioqCn369EFaWhqmT5+OCRMmYPv27VLNhg0bEBcXh/nz5+Pw4cPo3r07IiMjkZ+fX5+7gIiIiGordRXwv3crPj/5AdDmUVnbaVREA8rPzxcAxO7du4UQQhQUFAiVSiUSExOlmlOnTgkAIiUlRQghxNatW4VSqRQGg0GqWbZsmdBqtaKkpEQIIcSsWbNE586drdY1bNgwERkZKX0PDQ0VsbGx0nez2Sz8/PxEfHx8jXo3Go0CgDAajbXcaiIiIqqxX/4rxGvuQszXCrHjn3J3YxNqk0Ea9B47o9EIAPDwqPg9t9TUVJSVlSEiIkKq6dixIwICApCSkgIASElJQdeuXeHr6yvVREZGwmQy4cSJE1LNjcuorKlcRmlpKVJTU61qlEolIiIipJqblZSUwGQyWQ1ERERUjwzHgY3RgDAD3YYDj86Ru6NGp8GCncViwfTp0/HAAw+gS5cuAACDwQC1Wg03NzerWl9fXxgMBqnmxlBXOb1y2u1qTCYTrl+/josXL8JsNldbU7mMm8XHx0On00mDv7//3W04ERER3Zkpp+K1JqWFQOuHgKcW8111d6HBgl1sbCyOHz+O9evXN9Qq/5Q5c+bAaDRKQ3Z2ttwtERERNU0lhcCaoUBhDuDVoeJhCXu13F01Sg3yhr+pU6di8+bN2LNnD1q2bCmN1+v1KC0tRUFBgdVZu7y8POj1eqnm5qdXK5+avbHm5idp8/LyoNVq4ejoCDs7O9jZ2VVbU7mMm2k0Gmg0mrvbYCIiIqoZczmQOBbIOwY4+1S81sTRXe6uGq16PWMnhMDUqVOxadMm7NixA4GBgVbTQ0JCoFKpkJycLI3LyMhAVlYWwsPDAQDh4eE4duyY1dOrSUlJ0Gq1CA4OlmpuXEZlTeUy1Go1QkJCrGosFguSk5OlGiIiImpgQgBbXwRO/xdQOQEjNwDureTuqlGr1zN2sbGxWLt2Lb7++mu4urpK97PpdDo4OjpCp9MhJiYGcXFx8PDwgFarxQsvvIDw8HD07t0bANCvXz8EBwdj9OjRWLhwIQwGA1599VXExsZKZ9QmT56MDz/8ELNmzcL48eOxY8cObNy4EVu2bJF6iYuLQ3R0NHr16oXQ0FAsWrQIRUVFGDduXH3uAiIiIrqVH98HUhMAKCp+KuyennJ31PjV5+O5AKodVq5cKdVcv35dPP/888Ld3V04OTmJp59+WuTm5lot59y5c2LAgAHC0dFReHl5iRdffFGUlZVZ1ezcuVP06NFDqNVq0aZNG6t1VFq8eLEICAgQarVahIaGin379tV4W/i6EyIiojp07IuKV5rM1wqxb7nc3di02mQQhRBCyBcrGw+TyQSdTgej0QitVit3O0RERI3XrynAvwcC5hKg9/NA/3i5O7Jptckg/K1YIiIiajiXzgDrR1SEuo5PAP3elLujJoXBjoiIiBpG0UVg9RDg+hXgnhBg8CeA0k7urpoUBjsiIiKqf2XXgXUjgCuZgFsrYMR6QO0kd1dNDoMdERER1S+LBdj0V+C3A4CDDhj1BeDiI3dXTRKDHREREdWv/84HTn4NKFXA8LWAd3u5O2qyGOyIiIio/hxcAez9oOLzoKVA6wfl7aeJY7AjIiKi+vHz98DWmRWfH3sV6DZU3n6aAQY7IiIiqns5aRW/ASsswL3PAQ+9JHdHzQKDHREREdWtgmxg7TCgrAho8yjwxCJAoZC7q2aBwY6IiIjqTrERWDsUuGoAfIKBof8G7FRyd9VsMNgRERFR3TCXARvHAPknARc9MCqx4vUm1GAY7IiIiOjPEwLYPB04uwtQOQOjNgK6lnJ31eww2BEREdGft+cd4MhqQKEEnk0AWnSXu6NmicGOiIiI/pyfNgA736z4/Pg7QPt+8vbTjDHYERER0d079wPwdWzF5/v/BtwXI28/zRyDHREREd2dCxnA+pGApQwIHgREvC53R80egx0RERHV3tV8YM0zFa838Q8Dnl4OKBkr5Mb/AkRERFQ7pdeAdcOBgizAow0wfB2gcpS7KwKDHREREdWGxQx8ORE4nwo4egCjvgCcPeXuin5Xr8Fuz549ePLJJ+Hn5weFQoGvvvrKaroQAvPmzUOLFi3g6OiIiIgI/PLLL1Y1ly9fxqhRo6DVauHm5oaYmBhcvXrVqubo0aN46KGH4ODgAH9/fyxcuLBKL4mJiejYsSMcHBzQtWtXbN26tc63l4iIqMn7/lUgfTNgpwFGrAM828rdEd2gXoNdUVERunfvjiVLllQ7feHChfjggw+wfPly7N+/H87OzoiMjERxcbFUM2rUKJw4cQJJSUnYvHkz9uzZg0mTJknTTSYT+vXrh1atWiE1NRVvv/02XnvtNXz88cdSzd69ezFixAjExMTgyJEjGDRoEAYNGoTjx4/X38YTERE1NfuWA/uWVnx+ejkQ0Fvefqgq0UAAiE2bNknfLRaL0Ov14u2335bGFRQUCI1GI9atWyeEEOLkyZMCgDh48KBU89133wmFQiHOnz8vhBBi6dKlwt3dXZSUlEg1L7/8sujQoYP0fejQoSIqKsqqn7CwMPHXv/61xv0bjUYBQBiNxhrPQ0RE1GSc2izEfJ0Q87VC/O89ubtpVmqTQWS7xy4zMxMGgwERERHSOJ1Oh7CwMKSkpAAAUlJS4Obmhl69ekk1ERERUCqV2L9/v1Tz8MMPQ61WSzWRkZHIyMjAlStXpJob11NZU7me6pSUlMBkMlkNREREzdL5VOCLGAACCBkHPDBN7o7oFmQLdgaDAQDg6+trNd7X11eaZjAY4OPjYzXd3t4eHh4eVjXVLePGddyqpnJ6deLj46HT6aTB39+/tptIRETU+F05B6wdBpRfB4IiKn5ZQqGQuyu6BT4Vewtz5syB0WiUhuzsbLlbIiIialjXrwBrhgJFFwB914rfgLWzl7srug3Zgp1erwcA5OXlWY3Py8uTpun1euTn51tNLy8vx+XLl61qqlvGjeu4VU3l9OpoNBpotVqrgYiIqNkoLwU2jAYuZgDae4CRGwGNq9xd0R3IFuwCAwOh1+uRnJwsjTOZTNi/fz/Cw8MBAOHh4SgoKEBqaqpUs2PHDlgsFoSFhUk1e/bsQVlZmVSTlJSEDh06wN3dXaq5cT2VNZXrISIiohsIAXzzAnDuf4DatSLUaf3k7opqoF6D3dWrV5GWloa0tDQAFQ9MpKWlISsrCwqFAtOnT8ebb76Jb775BseOHcOYMWPg5+eHQYMGAQA6deqE/v37Y+LEiThw4AB+/PFHTJ06FcOHD4efX8UBNnLkSKjVasTExODEiRPYsGED3n//fcTFxUl9TJs2Ddu2bcO7776L9PR0vPbaazh06BCmTp1an5tPRETUOO2KB46uBxR2wNBVgL6L3B1RTdXn47k7d+4UAKoM0dHRQoiKV57MnTtX+Pr6Co1GI/r27SsyMjKslnHp0iUxYsQI4eLiIrRarRg3bpwoLCy0qvnpp5/Egw8+KDQajbjnnnvEggULqvSyceNG0b59e6FWq0Xnzp3Fli1barUtfN0JERE1C4dXV7zSZL5WiEMJcndDonYZRCGEEDLmykbDZDJBp9PBaDTyfjsiImqazu4CVg8BLOXAQy8CfefJ3RGhdhmET8USERERkHey4mEJSznQ9Vngsblyd0R3gcGOiIiouSs0AGuHAiUmIOB+YOASvquukWKwIyIias5KrlaEOmM24NkOGL4GsNfI3RXdJQY7IiKi5spcDnwxHsj9CXDyAkYlAk4ecndFfwKDHRERUXMkBLDtZeCX7YC9AzBiPeARKHdX9Ccx2BERETU3QgApHwIHVwBQAIM/Afzvk7srqgP8wTciIqKmSgjAlANcSL9hyKj4s9hYURP5TyD4KXn7pDrDYEdERNTYWSyA6Tcg/6bwdiEDKC2sfh6FHfDgdKD38w3aKtUvBjsiIqLGwmIGCn61Dm4X0oELPwNlRdXPo7QHPNoCPh0B746Ad4eKPz2D+PRrE8RgR0REZGvM5cCVc1Uvn178BSi/Xv08duqK15VUBrfKPz3aAPbqBm2f5MNgR0REJBdzGXD5bNXLpxd/Acwl1c9j7wB4tbMOb94dAfdAwI5/rTd3PAKIiIjqW3kJcOnMTQEuHbh0uuInvKqjcgK82t8U4DoA7q0BpV2Dtk+NB4MdERFRXSkrBi798kd4yz9V8fnyWUCYq59H7VL18ql3R0DnDyj5VjKqHQY7IiKi2iq9Blz8+fcAd+qPIHflHCAs1c+j0VYNbz4dAe09/F1WqjMMdkRERLdSUlgR4G5+jUhBFgBR/TwOboBPp5vOwnUCXPUMcFTvGOyIiIiKjdW8QiQDMGbfeh4nL+szcJWvE3H2ZoAj2TDYERFR83HtcvUBrjDn1vO4+FpfPq0Mc85eDdc3UQ0x2BERUdNTdLHqO+AuZABX8249j6tfRWC78TKqV3vAyaPh+ib6k5pdsFuyZAnefvttGAwGdO/eHYsXL0ZoaKjcbRERUW0JAVzNrxreLpwCrl269Xw6/6oPMXi3Bxx0Ddc7UT1pVsFuw4YNiIuLw/LlyxEWFoZFixYhMjISGRkZ8PHxkbs9IqKmxVwOmEt/H8pq+LnkzrVFFyoCXP4poLjg1ut3a1X1IQav9oDGtcF2AVFDUwghbvFYT9MTFhaG++67Dx9++CEAwGKxwN/fHy+88AJmz55923lNJhN0Oh2MRiO0Wm1DtEtEVJUQFb8Xai6tWQiy+lxW8aLcWgWt0puGGqyjctytXvtRlxTKihf2et8c4NoBauf6Xz9RA6hNBmk2Z+xKS0uRmpqKOXPmSOOUSiUiIiKQkpIiY2cVystKcfbY3rpdaL1k9npYZgP2qbhd/6KypmbLurlv64fgajiPtLbb7YOq0xR3MU9167de3q0WJaBQCCgEAIWAQojf/6xYj+L3oWI5AhCAQiEq5oOQerWer/K/hfhj+VJ/N8wnRMV+/X38H+sQ0vzWy/99nsp9cKs6aVl/1FWuFzdu183rrezpht6r/nnjvr5pmuXm8HNDCCqvLjzdIkTVx/8OG4JSVfF7pnaVf97w2V5dddytPjvo/ghwnkGAylHuLSOyGc0m2F28eBFmsxm+vr5W4319fZGenl6lvqSkBCUlf/xOn8lkqtf+rhUVov03A+t1HUTUNJUrVLAoVbBU/qlUQVT+aaeCUP4eipRqCDsVYKcB7FVQ/B6YlPYVwUrx+2elvRpKleb3zxrYqTRQ2NcwdEmfNVXH8RUgRPWu2QS72oqPj8frr7/eYOtTKBTIhfefXo6og//jFLc/f3OHqZXLqN8e6msZN57Hqe0yb7XNt5/HNnq843zij/NeN52fw83nxCx/nBurGIT1fH/M/0etRVid24OQpilg+b1x6/mrW/cfy61ac2PPCunEWvV9VZ23ct/ddA7yFuuw7ufGmjJhjzLYoxQVf1YMdigVf3wvhapiurC7Ydzv028xf83+V/nn2CsVsLdTQKVUQmWvhL1SAZWdBfZ2JVDZlf3+XVlRY6eEyk4Be2XFnxXjlVBVLsNOWTFOqYC9nRJqu4o/7e0UUN8wvuq8FeNcHVRo7ekED2c1FAyLRFaaTbDz8vKCnZ0d8vKsH3XPy8uDXq+vUj9nzhzExcVJ300mE/z9/eutP1edB1xfO11vyydqioQQN4S0379L04DKb1KNVHv7+XBT3R/Tqlm+1XJuM58Ayi0C5WYLyswC5RYLyio/mwXKLBaUlVtQbhEoM1sqxpktKPt9nnKzQOnvf1bMW1n3R80f4ypqSs1/zFv2+/oql1tuESgrr2b5FgGzpeo/AcotAuUWgWJYgJIqk2Xh6mCPNl7OaO3ljMDfh9aeFd91jiq52yOSRbMJdmq1GiEhIUhOTsagQYMAVDw8kZycjKlTp1ap12g00Gg0DdwlEdWGQqG46eoez97UBcvvIa7shlAoBU3zH+GzzHzroFpusaD096BqFTpvDq53EVQLrpUhx3gdhcXl+Ok3I376zVhlGzyd1RVBr0roc4KTutn81UfNULM6uuPi4hAdHY1evXohNDQUixYtQlFREcaNGyd3a0RENkOpVECtVEBtr5S7lVsqLjPj10vXkHmxCJkXi3Du9z8zLxXhQmEJLhWV4lJRKQ79eqXKvHqtww2hzwmBXi4I9HKCv4cTNPZ2MmwNUd1pVsFu2LBhuHDhAubNmweDwYAePXpg27ZtVR6oICIi2+agskMHvSs66Ku+k+5qSfkfQa8y9F2q+FxwrQwGUzEMpmKknLV+ibFSAdzj7ojWns5VLvHe4+YIezvbDbpElZrVe+z+DL7Hjoio8btSVIrMS0XWwe9SETIvFKGo1HzL+VR2Cvh7OCHQ01k621cZ/vRaByiVvA2A6g/fY0dERFQNd2c13J3V6BngbjVeCIELV0uQeeH3oHfxGjIvXsW5i9eQeakIpeUWnL1QhLMXiqos00GlrLh/z9MZgd7OCPT842yflwuf3KWGxTN2NcQzdkREzZPFIpBrKkbmhSKrs33nLhYh6/I1lFfzFHElF439H/fzeToh0Nv590u9LtA58cldqpnaZBAGuxpisCMiopuVmy347cr1inv4pLN9FcP5guu3/WEddyfVDaHPWQp9gV7OcNbwghr9gcGuHjDYERFRbRSXmZF9+ZrVvXxnfw9/eabbvwzQx1VjdR9f5UMcAR5OcFDxyd3mhvfYERERycxBZYd2vq5o51v1yd2iknKcu1RUcQ/fxat/3NN36RouF5Uiv7AE+YUlOJB52Wo+hQLw0zn+8W6+G8JfS3dHqPjkbrPHM3Y1xDN2RETUEIzXyqR7+c7+fi9f5ZO7hSXlt5zPXlnx5G5rzz/ezVd5ts9P58gndxsxXoqtBwx2REQkJyEELhWVSpd2b3wx87lLRSgus9xyXrW9Eq09naR7+CqHkFbufD9fI8BLsURERE2MQqGAl4sGXi4a3Nfaw2qaxSKQV/jHk7s3PsiRdfkaSsst+DnvKn7Ou2o1X69W7kgYHwoXPqzRZPCMXQ3xjB0RETVG5WYLcgqKcfbi1d8v617D2YtFSD13GUWlZoQFeiBhXCgc1Xwow1bxUmw9YLAjIqKm5KfsAjy3Yj8KS8rxYJAXVkT34hO3Nqo2GYQX1omIiJqh7v5uSBh/H5zUdvjh9EVMWZ2KkvJb/6waNQ4MdkRERM1USCsPfDb2PjiolNiZcQFT1x5BmfnWD2GQ7WOwIyIiasZ6t/HEijH3QW2vRNLJPExfn4ZyhrtGi8GOiIiomXuwnRc+ei4EKjsFthzLxUuJP8F8m9/AJdvFYEdERETo09EHS0b2hL1Sga/ScjD7P0dhYbhrdBjsiIiICADQr7Me7w+/F0oFkJj6G+Z+fRx8eUbjwmBHREREkqhuLfCvoT2gUABr9mfhjc0nGe4aEQY7IiIisjLo3nvw1uBuAICVP57Dgm3pDHeNBIMdERERVTH0Pn+8OagLAOCj3WfxXtLPMndENVFvwe6f//wn7r//fjg5OcHNza3amqysLERFRcHJyQk+Pj6YOXMmysvLrWp27dqFnj17QqPRICgoCAkJCVWWs2TJErRu3RoODg4ICwvDgQMHrKYXFxcjNjYWnp6ecHFxwZAhQ5CXl1dXm0pERNQkPde7FeY/GQwA+GDHaXy44xeZO6I7qbdgV1paimeffRZTpkypdrrZbEZUVBRKS0uxd+9erFq1CgkJCZg3b55Uk5mZiaioKPTp0wdpaWmYPn06JkyYgO3bt0s1GzZsQFxcHObPn4/Dhw+je/fuiIyMRH5+vlQzY8YMfPvtt0hMTMTu3buRk5ODwYMH19emExERNRnjHgjEnAEdAQDvfP8zPtlzVuaO6LZEPVu5cqXQ6XRVxm/dulUolUphMBikccuWLRNarVaUlJQIIYSYNWuW6Ny5s9V8w4YNE5GRkdL30NBQERsbK303m83Cz89PxMfHCyGEKCgoECqVSiQmJko1p06dEgBESkpKjbfDaDQKAMJoNNZ4HiIioqbi/f/+LFq9vFm0enmzWPnDWbnbaVZqk0Fku8cuJSUFXbt2ha+vrzQuMjISJpMJJ06ckGoiIiKs5ouMjERKSgqAirOCqampVjVKpRIRERFSTWpqKsrKyqxqOnbsiICAAKmGiIiIbu9vfdthap8gAMBr357E2v1ZMndE1bGXa8UGg8Eq1AGQvhsMhtvWmEwmXL9+HVeuXIHZbK62Jj09XVqGWq2ucp+fr6+vtJ7qlJSUoKSkRPpuMplqt4FERERNzIv92qPUbMHHe87ilU3HoLJT4Nle/nK3RTeo1Rm72bNnQ6FQ3HaoDFSNXXx8PHQ6nTT4+/PAJSKi5k2hUGDOgI4Ye39rAMCs/xzF12nn5W2KrNTqjN2LL76IsWPH3ramTZs2NVqWXq+v8vRq5ZOqer1e+vPmp1fz8vKg1Wrh6OgIOzs72NnZVVtz4zJKS0tRUFBgddbuxprqzJkzB3FxcdJ3k8nEcEdERM2eQqHA/CeDUVJuwboDWYjb+BPUdkoM6NpC7tYItTxj5+3tjY4dO952UKvVNVpWeHg4jh07ZvX0alJSErRaLYKDg6Wa5ORkq/mSkpIQHh4OAFCr1QgJCbGqsVgsSE5OlmpCQkKgUqmsajIyMpCVlSXVVEej0UCr1VoNREREVBHu/jmoC54JaQmzReCFdUeQdJKvEbMF9fbwRFZWFtLS0pCVlQWz2Yy0tDSkpaXh6tWrAIB+/fohODgYo0ePxk8//YTt27fj1VdfRWxsLDQaDQBg8uTJOHv2LGbNmoX09HQsXboUGzduxIwZM6T1xMXF4ZNPPsGqVatw6tQpTJkyBUVFRRg3bhwAQKfTISYmBnFxcdi5cydSU1Mxbtw4hIeHo3fv3vW1+URERE2aUqnAW0O6YWAPP5RbBGLXHMaujPw7z0j1q74ezY2OjhYAqgw7d+6Uas6dOycGDBggHB0dhZeXl3jxxRdFWVmZ1XJ27twpevToIdRqtWjTpo1YuXJllXUtXrxYBAQECLVaLUJDQ8W+ffuspl+/fl08//zzwt3dXTg5OYmnn35a5Obm1mp7+LoTIiKiqsrKzWLK6kOi1cubRfu/bxU//HJB7paanNpkEIUQ/PG3mjCZTNDpdDAajbwsS0REdIMyswVTVh/Gf0/lwVFlh1XjQxEa6CF3W01GbTIIfyuWiIiI/hSVnRJLRt2LR9p743qZGeNWHkDqr1fkbqtZYrAjIiKiP01jb4ePRofggSBPFJWaMfazAzj6W4HcbTU7DHZERERUJxxUdvhkTC+EtvZAYUk5Rn96ACdz+IL/hsRgR0RERHXGSW2Pz8bdh3sD3GC8XobnPt2Pn/MK5W6r2WCwIyIiojrlorFHwrhQdL1Hh8tFpRj5yX6cuXBV7raaBQY7IiIiqnM6RxU+jwlFpxZaXLxagpGf7MOvl4rkbqvJY7AjIiKieuHmpMbqmFC093VBnqkEIz/Zj9+uXJO7rSaNwY6IiIjqjaeLBqsnhKGNlzPOF1zHyE/2I9d4Xe62miwGOyIiIqpXPq4OWDuxNwI8nJB1+RpGfbIf+aZiudtqkhjsiIiIqN7pdQ5YOzEM97g54uzFIoxasR+XrpbI3VaTw2BHREREDaKluxPWTewNvdYBv+RfxagV+3GlqFTutpoUBjsiIiJqMAGeTlg7MQxeLhqkGwox5rMDMF4vk7utJoPBjoiIiBpUG28XrJ0YBg9nNY6dN2LsygO4WlIud1tNAoMdERERNbj2vq5YHRMGnaMKR7IKMG7lAVwrZbj7sxjsiIiISBbBflqsjgmDq4M9Dp67ggmrDqG4zCx3W40agx0RERHJpmtLHVaND4Wz2g57z1zCpM9TGe7+BAY7IiIiklXPAHesHBcKR5Ud9vx8AVPXHkZpuUXutholBjsiIiKSXWigBz6N7gWNvRL/PZWPaeuPoNzMcFdbDHZERERkE+4P8sLHY3pBbafEd8cNmLHxJ5gtQu62GpV6C3bnzp1DTEwMAgMD4ejoiLZt22L+/PkoLbV+EeHRo0fx0EMPwcHBAf7+/li4cGGVZSUmJqJjx45wcHBA165dsXXrVqvpQgjMmzcPLVq0gKOjIyIiIvDLL79Y1Vy+fBmjRo2CVquFm5sbYmJicPXq1TrfbrPZjOLiYg53OZjNvK+CiKg5e6S9N5aO6gl7pQLf/pSDWV8chYXhrsbs62vB6enpsFgs+OijjxAUFITjx49j4sSJKCoqwjvvvAMAMJlM6NevHyIiIrB8+XIcO3YM48ePh5ubGyZNmgQA2Lt3L0aMGIH4+Hg88cQTWLt2LQYNGoTDhw+jS5cuAICFCxfigw8+wKpVqxAYGIi5c+ciMjISJ0+ehIODAwBg1KhRyM3NRVJSEsrKyjBu3DhMmjQJa9eurZPtFULAYDCgoKCgTpbXnLm5uUGv10OhUMjdChERySAi2BeLR9yLqeuO4D+Hf4PaXoH/e7or/16oAYUQosFi8Ntvv41ly5bh7NmzAIBly5bh73//OwwGA9RqNQBg9uzZ+Oqrr5Ceng4AGDZsGIqKirB582ZpOb1790aPHj2wfPlyCCHg5+eHF198ES+99BIAwGg0wtfXFwkJCRg+fDhOnTqF4OBgHDx4EL169QIAbNu2DY8//jh+++03+Pn53bF3k8kEnU4Ho9EIrVZbZXpubi4KCgrg4+MDJycnHnx3QQiBa9euIT8/H25ubmjRooXcLRERkYy+TjuPGRvSYBFAdHgrvPZU52b59+udMsiN6u2MXXWMRiM8PDyk7ykpKXj44YelUAcAkZGReOutt3DlyhW4u7sjJSUFcXFxVsuJjIzEV199BQDIzMyEwWBARESENF2n0yEsLAwpKSkYPnw4UlJS4ObmJoU6AIiIiIBSqcT+/fvx9NNPV+m1pKQEJSV//DixyWS65XaZzWYp1Hl6etZ8h1AVjo6OAID8/Hz4+PjAzs5O5o6IiEguA3vcgzKzwMwvfsKqlF+htlfilcc7NctwV1MN9vDE6dOnsXjxYvz1r3+VxhkMBvj6+lrVVX43GAy3rblx+o3z3arGx8fHarq9vT08PDykmpvFx8dDp9NJg7+//y23rays4jfunJycbllDNVe5Hyv3KxERNV/PhLTE/z3dFQDwyf8y8c73GWjAi42NTq2D3ezZs6FQKG47VF5GrXT+/Hn0798fzz77LCZOnFhnzdenOXPmwGg0SkN2dvYd5+G/IOoG9yMREd1oRGgAXn+qMwBgyc4z+CD5tMwd2a5aX4p98cUXMXbs2NvWtGnTRvqck5ODPn364P7778fHH39sVafX65GXl2c1rvK7Xq+/bc2N0yvH3XhPVl5eHnr06CHV5OfnWy2jvLwcly9flua/mUajgUajue12EhERUcOIvr81Ssst+OfWU3jvvz9Dba/ElEfbyt2Wzan1GTtvb2907NjxtkPlPXPnz5/Ho48+ipCQEKxcuRJKpfXqwsPDsWfPHqtLbklJSejQoQPc3d2lmuTkZKv5kpKSEB4eDgAIDAyEXq+3qjGZTNi/f79UEx4ejoKCAqSmpko1O3bsgMViQVhYWG13Af1OoVBI9zqeO3cOCoUCaWlpsvZERERN18SH22BmZAcAwFvb0vHpD5kyd2R76u0eu8pQFxAQgHfeeQcXLlyAwWCwuqdt5MiRUKvViImJwYkTJ7Bhwwa8//77Vg9LTJs2Ddu2bcO7776L9PR0vPbaazh06BCmTp0KoCJcTJ8+HW+++Sa++eYbHDt2DGPGjIGfnx8GDRoEAOjUqRP69++PiRMn4sCBA/jxxx8xdepUDB8+vEZPxDZlY8eOrfZyev/+/eVujYiIqIrYPkH4W992AIB/bD6Jz/f9KnNHtqXenopNSkrC6dOncfr0abRs2dJqWuVNjzqdDt9//z1iY2MREhICLy8vzJs3T3qHHQDcf//9WLt2LV599VW88soraNeuHb766ivpHXYAMGvWLBQVFWHSpEkoKCjAgw8+iG3btknvsAOANWvWYOrUqejbty+USiWGDBmCDz74oL42v1Hp378/Vq5caTWOl6GJiMhWzYhoh9JyC5bvPoO5Xx2H2k6BYfcFyN2WTWjQ99g1Zrd7h0xxcTEyMzMRGBhoFSYbg7Fjx6KgoEC6pFobCoUCmzZtwqBBg3Du3DkEBgZi3bp1+OCDD3D48GEEBQVhyZIleOSRR2q13Ma8P4mIqGEIIfCPzafw2Y+ZUCiAfw3tjqfvbXnnGRshm32PXXMihMD1Mnl+HstRZSfbk6UzZ87EokWLEBwcjH/961948sknkZmZyff7ERFRnVIoFJj7RCeUms1YvS8LL278CSo7JZ7o1rxvsWKwqyfXy8wInrddlnWffCMSTuqa/6fdvHkzXFxcrMa98soreOWVV2q97qlTp2LIkCEAKn5ZZNu2bfj0008xa9asWi+LiIjodhQKBd54qgvKygU2HMrGtPVpUNkpEdm5+jdeNAcMdoQ+ffpg2bJlVuNu/IWQ2qh8EhmoeAl0r169cOrUqT/VHxER0a0olQr83+CuKDNb8OWR85i69jA+Gh2Cxzr63nnmJojBrp44quxw8o1I2dZdG87OzggKCqqnboiIiOqXnVKBhc90Q4nZgi1HczF59WGsGNMLD7f3lru1BtdgPynW3CgUCjip7WUZ5Pzlhn379kmfy8vLkZqaik6dOsnWDxERNQ/2dkosGtYD/YJ9UVpuwaTPDyHlzCW522pwDHaEkpIS6R2DlcPFixfvallLlizBpk2bkJ6ejtjYWFy5cgXjx4+v446JiIiqUtkpsXjkvejTwRvFZRbErDqIQ+cuy91Wg2KwI2zbtg0tWrSwGh588MG7WtaCBQuwYMECdO/eHT/88AO++eYbeHl51XHHRERE1dPY22HZcyF4qJ0XrpWaMXblQaRlF8jdVoPhe+xqqKm+x84WcX8SEdGfdb3UjLErD2B/5mVoHeyxdmJvdLlHJ3dbd6U277HjGTsiIiJqchzVdvhs7H0IaeUOU3E5Rn+6H+kGk9xt1TsGO6rWmjVr4OLiUu3QuXNnudsjIiK6I2eNPVaOuw/dW+pw5VoZnluxH6fzr8rdVr3i606oWk899RTCwsKqnaZSqRq4GyIiorujdVDh3+PDMHLFPpzIMWHkJ/uw4a/hCPRylru1esFgR9VydXWFq6ur3G0QERH9aTonFT6PCcOIj/chI68QIz/Zh41/DYe/h5PcrdU5XoolIiKiJs/DWY3VE8LQ1tsZucZijPhkH84XXJe7rTrHYEdERETNgrerBmsn9kZrTyf8duU6Rn2yD3mmYrnbqlMMdkRERNRs+GodsHZib7R0d8S5S9cw8pN9uFBYIndbdYbBjoiIiJoVPzdHrJvYG346B5y5UITnVuzH5aJSuduqEwx2RERE1Oz4ezhhzcTe8HHVICOvEM+t2A/jtTK52/rTGOyo3uzatQsKhQIFBQVyt0JERFRFoJcz1k4Mg5eLGidzTRjz2X4UFjfucMdg18yNHTsWCoWiytC/f3+5WyMiIqp3QT6uWD0hDO5OKvz0mxFjVx5EUUm53G3dtXoNdk899RQCAgLg4OCAFi1aYPTo0cjJybGqOXr0KB566CE4ODjA398fCxcurLKcxMREdOzYEQ4ODujatSu2bt1qNV0IgXnz5qFFixZwdHREREQEfvnlF6uay5cvY9SoUdBqtXBzc0NMTAyuXm3ab5+uqf79+yM3N9dqWLdundxtERERNYiOei0+jwmD1sEeqb9eQcyqg7heapa7rbtSr8GuT58+2LhxIzIyMvCf//wHZ86cwTPPPCNNN5lM6NevH1q1aoXU1FS8/fbbeO211/Dxxx9LNXv37sWIESMQExODI0eOYNCgQRg0aBCOHz8u1SxcuBAffPABli9fjv3798PZ2RmRkZEoLv7jEeZRo0bhxIkTSEpKwubNm7Fnzx5MmjSpPje/0dBoNNDr9VaDu7v7bec5d+4cFAoF0tLSpHEFBQVQKBTYtWuXVe2PP/6Ibt26wcHBAb1797b6b0dERGQLutyjw79jwuCisce+s5cx6fNDKC5rhOFONKCvv/5aKBQKUVpaKoQQYunSpcLd3V2UlJRINS+//LLo0KGD9H3o0KEiKirKajlhYWHir3/9qxBCCIvFIvR6vXj77bel6QUFBUKj0Yh169YJIYQ4efKkACAOHjwo1Xz33XdCoVCI8+fP16h3o9EoAAij0Vhl2vXr18XJkyfF9evX/xhpsQhRclWewWKp0TYJIUR0dLQYOHBgjesrZWZmCgDiyJEj0rgrV64IAGLnzp1CCCF27twpAIhOnTqJ77//Xhw9elQ88cQTonXr1tIxUJ1q9ycREVEDOJh5SXSa+51o9fJmMfaz/aK4rFzulm6bQW7WYD8pdvnyZaxZswb333+/9FujKSkpePjhh6FWq6W6yMhIvPXWW7hy5Qrc3d2RkpKCuLg4q2VFRkbiq6++AgBkZmbCYDAgIiJCmq7T6RAWFoaUlBQMHz4cKSkpcHNzQ69evaSaiIgIKJVK7N+/H08//XSVfktKSlBS8sd7bUwmU+02uOwa8H9+tZunrrySA6hr/ht4mzdvhouLi/UiXnkFr7zySp20M3/+fPzlL38BAKxatQotW7bEpk2bMHTo0DpZPhERUV3p1doDn0bfh3EJB7Az4wJeWHsES0b1hMqucTyWUO9dvvzyy3B2doanpyeysrLw9ddfS9MMBgN8fX2t6iu/GwyG29bcOP3G+W5V4+PjYzXd3t4eHh4eUs3N4uPjodPppMHf379W292Y9OnTB2lpaVbD5MmT62z54eHh0mcPDw906NABp06dqrPlExER1aXwtp74ZEwvqO2V+P5kHqZvSEO52SJ3WzVS6zN2s2fPxltvvXXbmlOnTqFjx44AgJkzZyImJga//vorXn/9dYwZMwabN2+GQqG4u44byJw5c6zOFJpMptqFO5VTxZkzOahq96PGzs7OCAoKqtU8SmXFvwmEENK4srLG/Yg4ERFRpYfaeWP5cz3x189TseVoLtR2SrzzbHfYKW07v9Q62L344osYO3bsbWvatGkjffby8oKXlxfat2+PTp06wd/fH/v27UN4eDj0ej3y8vKs5q38rtfrpT+rq7lxeuW4Fi1aWNX06NFDqsnPz7daRnl5OS5fvizNfzONRgONRnPb7bwthaJWl0MbG29vbwBAbm4u7r33XgCwepDiRvv27UNAQAAA4MqVK/j555/RqVOnBumTiIjobj3W0ReLR/RE7NrD2HTkPFR2CiwY3A1KGw53tQ523t7e0l/qtWWxVJzGrLx3LTw8HH//+99RVlYm3XeXlJSEDh06SE9lhoeHIzk5GdOnT5eWk5SUJF3eCwwMhF6vR3JyshTkTCYT9u/fjylTpkjLKCgoQGpqKkJCQgAAO3bsgMViQVhY2F1tS1NSUlJS5ZK0vb09vLy8bjmPo6MjevfujQULFiAwMBD5+fl49dVXq61944034OnpCV9fX/z973+Hl5cXBg0aVJebQEREVC/6d9Hj/eE98Ld1R7Dx0G9Q2yvxj4FdbPfKY309wbFv3z6xePFiceTIEXHu3DmRnJws7r//ftG2bVtRXFwshKh4etXX11eMHj1aHD9+XKxfv144OTmJjz76SFrOjz/+KOzt7cU777wjTp06JebPny9UKpU4duyYVLNgwQLh5uYmvv76a3H06FExcOBAERgYaPVUZf/+/cW9994r9u/fL3744QfRrl07MWLEiBpvT62fim0koqOjBYAqw41PJt/KyZMnRXh4uHB0dBQ9evQQ33//fbVPxX777beic+fOQq1Wi9DQUPHTTz/ddrmNeX8SEVHT9OXhbNF69mbR6uXN4vVvTghLLd5A8WfV5qnYegt2R48eFX369BEeHh5Co9GI1q1bi8mTJ4vffvvNqu6nn34SDz74oNBoNOKee+4RCxYsqLKsjRs3ivbt2wu1Wi06d+4stmzZYjXdYrGIuXPnCl9fX6HRaETfvn1FRkaGVc2lS5fEiBEjhIuLi9BqtWLcuHGisLCwxtvTVIOdLeL+JCIiW7ThQJZo9XJFuIvfeqrBwl1tgp1CiBvufqdbMplM0Ol0MBqN0Gq1VtOKi4uRmZmJwMBAODg4yNRh08H9SUREturzfb9i7lcVL9r/W992iPtL+3pf5+0yyM0ax0tZqMGtWbMGLi4u1Q6dO3eWuz0iIiJZjO7dCnOfCAYAfJD8C5bsPC1zR9Ya7AXF1Lg89dRTt3ywpPJBFyIiouYo5sFAlJZb8Na2dLy9PQNqOyUmPtzmzjM2AAY7qparqytcXV3lboOIiMgmTXm0LUrLLXjvvz/jn1tPQeeowtD75P8xAwY7IiIiorvwt75BKDWb8c1POQhv6yl3OwAY7OpU5Xv66M/hfiQiosZAoVDgpX4dMOmhttA52cZtSgx2dUCtVkOpVCInJwfe3t5Qq9W2++JCGyaEQGlpKS5cuAClUgm1Wi13S0RERLelUChsJtQBDHZ1QqlUIjAwELm5ucjJken3YZsQJycnBAQESL9HS0RERDXDYFdH1Go1AgICUF5eDrPZLHc7jZadnR3s7e15xpOIiOguMNjVIYVCAZVKxdeBEBERkSx4rYuIiIioiWCwIyIiImoiGOyIiIiImgjeY1dDQggAFT/ES0RERNRQKrNHZRa5HQa7GiosLAQA+PvL/3MhRERE1PwUFhZCp9PdtkYhahL/CBaLBTk5OXB1da23V3GYTCb4+/sjOzsbWq22XtbRXHBf1g3ux7rDfVk3uB/rDvdl3WiI/SiEQGFhIfz8/O74jleesashpVKJli1bNsi6tFot/0dWR7gv6wb3Y93hvqwb3I91h/uybtT3frzTmbpKfHiCiIiIqIlgsCMiIiJqIhjsbIhGo8H8+fOh0WjkbqXR476sG9yPdYf7sm5wP9Yd7su6YWv7kQ9PEBERETURPGNHRERE1EQw2BERERE1EQx2RERERE0Egx0RERFRE8FgZyOWLFmC1q1bw8HBAWFhYThw4IDcLTU6r732GhQKhdXQsWNHudtqFPbs2YMnn3wSfn5+UCgU+Oqrr6ymCyEwb948tGjRAo6OjoiIiMAvv/wiT7M27E77cezYsVWO0f79+8vTrA2Lj4/HfffdB1dXV/j4+GDQoEHIyMiwqikuLkZsbCw8PT3h4uKCIUOGIC8vT6aObVdN9uWjjz5a5bicPHmyTB3brmXLlqFbt27Si4jDw8Px3XffSdNt5ZhksLMBGzZsQFxcHObPn4/Dhw+je/fuiIyMRH5+vtytNTqdO3dGbm6uNPzwww9yt9QoFBUVoXv37liyZEm10xcuXIgPPvgAy5cvx/79++Hs7IzIyEgUFxc3cKe27U77EQD69+9vdYyuW7euATtsHHbv3o3Y2Fjs27cPSUlJKCsrQ79+/VBUVCTVzJgxA99++y0SExOxe/du5OTkYPDgwTJ2bZtqsi8BYOLEiVbH5cKFC2Xq2Ha1bNkSCxYsQGpqKg4dOoTHHnsMAwcOxIkTJwDY0DEpSHahoaEiNjZW+m42m4Wfn5+Ij4+XsavGZ/78+aJ79+5yt9HoARCbNm2SvlssFqHX68Xbb78tjSsoKBAajUasW7dOhg4bh5v3oxBCREdHi4EDB8rST2OWn58vAIjdu3cLISqOP5VKJRITE6WaU6dOCQAiJSVFrjYbhZv3pRBCPPLII2LatGnyNdWIubu7ixUrVtjUMckzdjIrLS1FamoqIiIipHFKpRIRERFISUmRsbPG6ZdffoGfnx/atGmDUaNGISsrS+6WGr3MzEwYDAarY1Sn0yEsLIzH6F3YtWsXfHx80KFDB0yZMgWXLl2SuyWbZzQaAQAeHh4AgNTUVJSVlVkdkx07dkRAQACPyTu4eV9WWrNmDby8vNClSxfMmTMH165dk6O9RsNsNmP9+vUoKipCeHi4TR2T9g26Nqri4sWLMJvN8PX1tRrv6+uL9PR0mbpqnMLCwpCQkIAOHTogNzcXr7/+Oh566CEcP34crq6ucrfXaBkMBgCo9hitnEY1079/fwwePBiBgYE4c+YMXnnlFQwYMAApKSmws7OTuz2bZLFYMH36dDzwwAPo0qULgIpjUq1Ww83NzaqWx+TtVbcvAWDkyJFo1aoV/Pz8cPToUbz88svIyMjAl19+KWO3tunYsWMIDw9HcXExXFxcsGnTJgQHByMtLc1mjkkGO2oyBgwYIH3u1q0bwsLC0KpVK2zcuBExMTEydkZUYfjw4dLnrl27olu3bmjbti127dqFvn37ytiZ7YqNjcXx48d5v2wduNW+nDRpkvS5a9euaNGiBfr27YszZ86gbdu2Dd2mTevQoQPS0tJgNBrxxRdfIDo6Grt375a7LSu8FCszLy8v2NnZVXlyJi8vD3q9XqaumgY3Nze0b98ep0+flruVRq3yOOQxWvfatGkDLy8vHqO3MHXqVGzevBk7d+5Ey5YtpfF6vR6lpaUoKCiwqucxeWu32pfVCQsLAwAel9VQq9UICgpCSEgI4uPj0b17d7z//vs2dUwy2MlMrVYjJCQEycnJ0jiLxYLk5GSEh4fL2Fnjd/XqVZw5cwYtWrSQu5VGLTAwEHq93uoYNZlM2L9/P4/RP+m3337DpUuXeIzeRAiBqVOnYtOmTdixYwcCAwOtpoeEhEClUlkdkxkZGcjKyuIxeZM77cvqpKWlAQCPyxqwWCwoKSmxqWOSl2JtQFxcHKKjo9GrVy+EhoZi0aJFKCoqwrhx4+RurVF56aWX8OSTT6JVq1bIycnB/PnzYWdnhxEjRsjdms27evWq1b/OMzMzkZaWBg8PDwQEBGD69Ol488030a5dOwQGBmLu3Lnw8/PDoEGD5GvaBt1uP3p4eOD111/HkCFDoNfrcebMGcyaNQtBQUGIjIyUsWvbExsbi7Vr1+Lrr7+Gq6urdI+STqeDo6MjdDodYmJiEBcXBw8PD2i1WrzwwgsIDw9H7969Ze7ettxpX545cwZr167F448/Dk9PTxw9ehQzZszAww8/jG7dusncvW2ZM2cOBgwYgICAABQWFmLt2rXYtWsXtm/fblvHZIM+g0u3tHjxYhEQECDUarUIDQ0V+/btk7ulRmfYsGGiRYsWQq1Wi3vuuUcMGzZMnD59Wu62GoWdO3cKAFWG6OhoIUTFK0/mzp0rfH19hUajEX379hUZGRnyNm2Dbrcfr127Jvr16ye8vb2FSqUSrVq1EhMnThQGg0Hutm1OdfsQgFi5cqVUc/36dfH8888Ld3d34eTkJJ5++mmRm5srX9M26k77MisrSzz88MPCw8NDaDQaERQUJGbOnCmMRqO8jdug8ePHi1atWgm1Wi28vb1F3759xffffy9Nt5VjUiGEEA0ZJImIiIiofvAeOyIiIqImgsGOiIiIqIlgsCMiIiJqIhjsiIiIiJoIBjsiIiKiJoLBjoiIiKiJYLAjIiIiaiIY7IiIiIiaCAY7IiIioiaCwY6IiIioiWCwIyIiImoiGOyIiIiImggGOyIiIqImgsGOiIiIqIlgsCMiIiJqIhjsiIiIiJoIBjsiIiKiJoLBjoioiTl37hwUCgUSEhLkboWIGhiDHRE1KQkJCVAoFNJgb2+Pe+65B2PHjsX58+drtIzXXnvNahlKpRItWrTAE088gX379tXzFhAR3T17uRsgIqoPb7zxBgIDA1FcXIx9+/YhISEBP/zwA44fPw4HB4caLWPZsmVwcXGBxWJBdnY2PvnkEzz88MM4cOAAevToUb8bQER0FxjsiKhJGjBgAHr16gUAmDBhAry8vPDWW2/hm2++wdChQ2u0jGeeeQZeXl7S90GDBqFLly5ITExksCMim8RLsUTULDz00EMAgDNnztz1MvR6PQDA3t7638T5+fmIiYmBr68vHBwc0L17d6xatcqqZteuXVAoFNi1a5fV+Oruhxs7dixcXFxw/vx5DBo0CC4uLvD29sZLL70Es9lsNX9BQQHGjh0LnU4HNzc3REdHo6CgoErvBoMB48aNQ8uWLaHRaNCiRQsMHDgQ586du+v9QUS2h2fsiKhZqAww7u7uNZ7n8uXLAACLxYLz58/jH//4BxwcHKzO+F2/fh2PPvooTp8+jalTpyIwMBCJiYkYO3YsCgoKMG3atLvq12w2IzIyEmFhYXjnnXfw3//+F++++y7atm2LKVOmAACEEBg4cCB++OEHTJ48GZ06dcKmTZsQHR1dZXlDhgzBiRMn8MILL6B169bIz89HUlISsrKy0Lp167vqkYhskCAiakJWrlwpAIj//ve/4sKFCyI7O1t88cUXwtvbW2g0GpGdnX3HZcyfP18AqDK4ubmJbdu2WdUuWrRIABCrV6+WxpWWlorw8HDh4uIiTCaTEEKInTt3CgBi586dVvNnZmYKAGLlypXSuOjoaAFAvPHGG1a19957rwgJCZG+f/XVVwKAWLhwoTSuvLxcPPTQQ1bLvHLligAg3n777TtuOxE1brwUS0RNUkREBLy9veHv749nnnkGzs7O+Oabb9CyZcsaL+M///kPkpKS8P3332PlypVo3749hgwZgr1790o1W7duhV6vx4gRI6RxKpUKf/vb33D16lXs3r37rrdh8uTJVt8feughnD171mrd9vb20hk8ALCzs8MLL7xgNZ+joyPUajV27dqFK1eu3HU/RGT7eCmWiJqkJUuWoH379jAajfjss8+wZ88eaDSaWi3j4Ycftnp44plnnkG7du3wwgsvIDU1FQDw66+/ol27dlAqrf+d3KlTJ2n63XBwcIC3t7fVOHd3d6tg9uuvv6JFixZwcXGxquvQoYPVd41Gg7feegsvvvgifH190bt3bzzxxBMYM2aMdN8gETUNPGNHRE1SaGgoIiIiMGTIEHzzzTfo0qULRo4ciatXr971Ml1cXBAWFobDhw+jqKioVvMqFIpqx9/8MEQlOzu7Wvd3O9OnT8fPP/+M+Ph4ODg4YO7cuejUqROOHDlSp+shInkx2BFRk2dnZ4f4+Hjk5OTgww8//FPLKi8vBwApILZq1Qq//PILLBaLVV16ero0HfjjoY2bn1i92zN6lcvOzc2tElYzMjKqrW/bti1efPFFfP/99zh+/DhKS0vx7rvv3vX6icj2MNgRUbPw6KOPIjQ0FIsWLUJxcfFdLePy5cvYu3cv9Ho9fHx8AACPP/44DAYDNmzYINWVl5dj8eLFcHFxwSOPPAKgIoTZ2dlhz549VstcunTpXW5RxbrLy8uxbNkyaZzZbMbixYut6q5du1Zlm9u2bQtXV1eUlJTc9fqJyPbwHjsiajZmzpyJZ599FgkJCVUeTKjOF198ARcXFwghkJOTg08//RRXrlzB8uXLpUurkyZNwkcffYSxY8ciNTUVrVu3xhdffIEff/wRixYtgqurKwBAp9Ph2WefxeLFi6FQKNC2bVts3rwZ+fn5d709Tz75JB544AHMnj0b586dQ3BwML788ksYjUarup9//hl9+/bF0KFDERwcDHt7e2zatAl5eXkYPnz4Xa+fiGwPgx0RNRuDBw9G27Zt8c4772DixIl3vI/txqdNnZ2d0a1bN/zzn//Es88+K413dHTErl27MHv2bKxatQomkwkdOnTAypUrMXbsWKvlLV68GGVlZVi+fDk0Gg2GDh2Kt99+G126dLmr7VEqlfjmm28wffp0rF69GgqFAk899RTeffdd3HvvvVKdv78/RowYgeTkZHz++eewt7dHx44dsXHjRgwZMuSu1k1EtkkhhBByN0FEREREfx7vsSMiIiJqIhjsiIiIiJoIBjsiIiKiJoLBjoiIiKiJYLAjIiIiaiJsKtgtWLAACoUC06dPl8YVFxcjNjYWnp6ecHFxwZAhQ5CXl2c1X1ZWFqKiouDk5AQfHx/MnDlTejt8pV27dqFnz57QaDQICgpCQkJCA2wRERERUcOxmffYHTx4EB999BG6detmNX7GjBnYsmULEhMTodPpMHXqVAwePBg//vgjgIq3rEdFRUGv12Pv3r3Izc3FmDFjoFKp8H//938AgMzMTERFRWHy5MlYs2YNkpOTMWHCBLRo0QKRkZE16s9isSAnJweurq63/M1HIiIioromhEBhYSH8/PygVN7hnJywAYWFhaJdu3YiKSlJPPLII2LatGlCCCEKCgqESqUSiYmJUu2pU6cEAJGSkiKEEGLr1q1CqVQKg8Eg1SxbtkxotVpRUlIihBBi1qxZonPnzlbrHDZsmIiMjKxxj9nZ2QIABw4cOHDgwIGDLEN2dvYd84pNnLGLjY1FVFQUIiIi8Oabb0rjU1NTUVZWhoiICGlcx44dERAQgJSUFPTu3RspKSno2rUrfH19pZrIyEhMmTIFJ06cwL333ouUlBSrZVTW3HjJ904qfxYoOzsbWq32LreUiIiIqHZMJhP8/f2lLHI7sge79evX4/Dhwzh48GCVaQaDAWq1Gm5ublbjfX19YTAYpJobQ13l9Mppt6sxmUy4fv06HB0dq6y7pKTE6sexCwsLAQBarZbBjoiIiBpcTW4Fk/XhiezsbEybNg1r1qyBg4ODnK1UER8fD51OJw3+/v5yt0RERER0W7IGu9TUVOTn56Nnz56wt7eHvb09du/ejQ8++AD29vbw9fVFaWkpCgoKrObLy8uDXq8HAOj1+ipPyVZ+v1ONVqut9mwdAMyZMwdGo1EasrOz62KTiYiIiOqNrMGub9++OHbsGNLS0qShV69eGDVqlPRZpVIhOTlZmicjIwNZWVkIDw8HAISHh+PYsWPIz8+XapKSkqDVahEcHCzV3LiMyprKZVRHo9FIl115+ZWIiIgaA1nvsXN1dUWXLl2sxjk7O8PT01MaHxMTg7i4OHh4eECr1eKFF15AeHg4evfuDQDo168fgoODMXr0aCxcuBAGgwGvvvoqYmNjodFoAACTJ0/Ghx9+iFmzZmH8+PHYsWMHNm7ciC1btjTsBhMRERHVI9kfnriT9957D0qlEkOGDEFJSQkiIyOxdOlSabqdnR02b96MKVOmIDw8HM7OzoiOjsYbb7wh1QQGBmLLli2YMWMG3n//fbRs2RIrVqyo8TvsiIiIiBoDhRBCyN1EY2AymaDT6WA0GnlZloiIiBpMbTKITf2kGBEREVGjYsoBTm2WuwsJgx0RERHR3Sg2AWueBTaMAo6slrsbAAx2RERERLVnLgM2jgHyjgPOPkDrB+XuCACDHREREVHtCAF8Ow04uxNQOQEjNwDureXuCgCDHREREVHt7FoApK0BFErg2QTgnp5ydyRhsCMiIiKqqcOfA7sXVHyO+hfQ3rZencZgR0RERFQTp/9bcQkWAB56Eeg1Tt5+qsFgR0RERHQnuT8BG6MBYQa6DQMemyt3R9VisCMiIiK6nYJsYM1QoPQqEPgw8NSHgEIhd1fVYrAjIiIiupXrV4A1zwBXDYBPMDBsNWCvlrurW2KwIyIiIqpOeQmw/jngQjrg6geMSgQcdHJ3dVsMdkREREQ3s1iAr54Hfv0BULtWhDpdS7m7uiMGOyIiIqKbJb8OHP8CUNoDw/4N6LvI3VGNMNgRERER3ejgCuDHRRWfn1oMtH1M1nZqg8GOiIiIqFL6VmDrzIrPff4O9Bgpbz+1xGBHREREBAC/pQJfjAeEBbh3NPDwTLk7qjUGOyIiIqLLZ4G1Q4Hy60DbvsAT79nsu+puh8GOiIiImreiS8DqZ4BrFwF9N2DoKsBOJXdXd4XBjoiIiJqvsuvA+hHA5TOAzr/itSYaV7m7umuyB7tly5ahW7du0Gq10Gq1CA8Px3fffSdNLy4uRmxsLDw9PeHi4oIhQ4YgLy/PahlZWVmIioqCk5MTfHx8MHPmTJSXl1vV7Nq1Cz179oRGo0FQUBASEhIaYvOIiIjIVlnMwJeTgOz9FS8eHvUF4KqXu6s/RfZg17JlSyxYsACpqak4dOgQHnvsMQwcOBAnTpwAAMyYMQPffvstEhMTsXv3buTk5GDw4MHS/GazGVFRUSgtLcXevXuxatUqJCQkYN68eVJNZmYmoqKi0KdPH6SlpWH69OmYMGECtm/f3uDbS0RERDbi+1eBU98Admpg+FrAp6PcHf1pCiGEkLuJm3l4eODtt9/GM888A29vb6xduxbPPPMMACA9PR2dOnVCSkoKevfuje+++w5PPPEEcnJy4OvrCwBYvnw5Xn75ZVy4cAFqtRovv/wytmzZguPHj0vrGD58OAoKCrBt27Ya9WQymaDT6WA0GqHVaut+o4mIiKjhpCwFts+p+DzkU6DrM/L2cxu1ySCyn7G7kdlsxvr161FUVITw8HCkpqairKwMERERUk3Hjh0REBCAlJQUAEBKSgq6du0qhToAiIyMhMlkks76paSkWC2jsqZyGdUpKSmByWSyGoiIiKgJOPk1sP2Vis8Rr9t0qKstmwh2x44dg4uLCzQaDSZPnoxNmzYhODgYBoMBarUabm5uVvW+vr4wGAwAAIPBYBXqKqdXTrtdjclkwvXr16vtKT4+HjqdThr8/f3rYlOJiIhITln7gP9MBCCA+yYAD0yTu6M6ZRPBrkOHDkhLS8P+/fsxZcoUREdH4+TJk7L2NGfOHBiNRmnIzs6WtR8iIiL6ky7+AqwbDphLgA6PAwMWNsp31d2OvdwNAIBarUZQUBAAICQkBAcPHsT777+PYcOGobS0FAUFBVZn7fLy8qDXVzy1otfrceDAAavlVT41e2PNzU/S5uXlQavVwtHRsdqeNBoNNBpNnWwfERERyexqPrB6CHD9CnBPSMV9dUo7ubuqczZxxu5mFosFJSUlCAkJgUqlQnJysjQtIyMDWVlZCA8PBwCEh4fj2LFjyM/Pl2qSkpKg1WoRHBws1dy4jMqaymUQERFRE1ZaVPGrEgW/Au6tgREbALWT3F3VC9nP2M2ZMwcDBgxAQEAACgsLsXbtWuzatQvbt2+HTqdDTEwM4uLi4OHhAa1WixdeeAHh4eHo3bs3AKBfv34IDg7G6NGjsXDhQhgMBrz66quIjY2VzrhNnjwZH374IWbNmoXx48djx44d2LhxI7Zs2SLnphMREVF9M5dX/P5rzhHA0QN47kvAxVvuruqN7MEuPz8fY8aMQW5uLnQ6Hbp164bt27fjL3/5CwDgvffeg1KpxJAhQ1BSUoLIyEgsXbpUmt/Ozg6bN2/GlClTEB4eDmdnZ0RHR+ONN96QagIDA7FlyxbMmDEDhe40egAAtrpJREFU77//Plq2bIkVK1YgMjKywbeXiIiIGogQwHczgZ+3AfYOwMgNgGdbubuqVzb5HjtbxPfYERERNTL/+xeQ/DoABTDsc6DTk3J3dFca7XvsiIiIiOrE0cTfQx2A/gsabairLQY7IiIialoy9wBfTan4HD4V6D1Z3n4aEIMdERERNR35p4D1zwGWMiB4IPCXf8jdUYNisCMiIqKmwZQLrH4GKDEC/r2Bpz8GlM0r6jSvrSUiIqKmqaQQWPssYPoN8GwHjFgHqBzk7qrBMdgRERFR42YuAzaOAQzHAGdv4LkvACcPubuSBYMdERERNV5CAN9OB87sAFROwMiNFb8u0Uwx2BEREVHjtfstIG01oFACz6wE7ukpd0eyYrAjIiKixunIamBXfMXnqHeBDv3l7ccGMNgRERFR43M6Gfh2WsXnB+OAXuPl7cdGMNgRERFR45J7tOJhCUs50HUo0Hee3B3ZDAY7IiIiajwKsoE1zwKlV4HWDwEDlwAKhdxd2QwGOyIiImocrhdUhLqrBsC7EzBsNWCvlrsrm8JgR0RERLavvATY8Bxw4RTg2qLiXXWObnJ3ZXMY7IiIiMi2WSzA17HAuf8BaldgVCKgayl3VzaJwY6IiIhs2443gGOJgNIeGPZvQN9V7o5sFoMdERER2a6DnwI/vFfx+ckPgLaPyduPjWOwIyIiItuUsQ3Y+lLF50dfAe4dJW8/jYDswS4+Ph733XcfXF1d4ePjg0GDBiEjI8Oqpri4GLGxsfD09ISLiwuGDBmCvLw8q5qsrCxERUXByckJPj4+mDlzJsrLy61qdu3ahZ49e0Kj0SAoKAgJCQn1vXlERER0N86nAl+MA4QFuPc54JFZcnfUKMge7Hbv3o3Y2Fjs27cPSUlJKCsrQ79+/VBUVCTVzJgxA99++y0SExOxe/du5OTkYPDgwdJ0s9mMqKgolJaWYu/evVi1ahUSEhIwb94fLyzMzMxEVFQU+vTpg7S0NEyfPh0TJkzA9u3bG3R7iYiI6A4uZwJrhwFl14C2fYEnFvFddTWkEEIIuZu40YULF+Dj44Pdu3fj4YcfhtFohLe3N9auXYtnnnkGAJCeno5OnTohJSUFvXv3xnfffYcnnngCOTk58PX1BQAsX74cL7/8Mi5cuAC1Wo2XX34ZW7ZswfHjx6V1DR8+HAUFBdi2bdsd+zKZTNDpdDAajdBqtfWz8URERM3dtcvAp38BLp2ueEhi3HeAxlXurmRVmwwi+xm7mxmNRgCAh4cHACA1NRVlZWWIiIiQajp27IiAgACkpKQAAFJSUtC1a1cp1AFAZGQkTCYTTpw4IdXcuIzKmsplEBERkczKrgPrRlSEOp0/MDKx2Ye62rKXu4EbWSwWTJ8+HQ888AC6dOkCADAYDFCr1XBzc7Oq9fX1hcFgkGpuDHWV0yun3a7GZDLh+vXrcHR0tJpWUlKCkpIS6bvJZPrzG0hERETVs1iATX8FsvcBGl3Fu+q0LeTuqtGxqTN2sbGxOH78ONavXy93K4iPj4dOp5MGf39/uVsiIiJqur5/FTj5NWCnBoavAXw6yd1Ro2QzwW7q1KnYvHkzdu7ciZYt/3ibtF6vR2lpKQoKCqzq8/LyoNfrpZqbn5Kt/H6nGq1WW+VsHQDMmTMHRqNRGrKzs//0NhIREVE19i0D9i2p+DxoGRD4kLz9NGKyBzshBKZOnYpNmzZhx44dCAwMtJoeEhIClUqF5ORkaVxGRgaysrIQHh6O/2fvzuOqqPc/jr/OOXBYBUQQJFFRy6VcygXpZjeTGy4t3swyrdRcbl61lBa1xaXNrt72LDMtrdTU7s3KyvJaZr9CTYvczVxSU3AFBGU7Z35/ICeOgKICc4D38/E4D86Z+c7MZ6a5P9+/78x8ByAuLo6NGzdy6NAhV5vly5cTFBREy5YtXW2KrqOwTeE6zuTj40NQUJDbR0RERMrZlo9h2fiC7/GToNVtppZT1Zn+VOw///lP5s+fz8cff0yzZs1c04ODg109acOHD+fzzz9nzpw5BAUFMWrUKAB++OEHoGC4k7Zt2xIVFcXUqVNJSUnh7rvvZsiQITz77LNAwXAnV1xxBSNGjODee+/l66+/5v777+ezzz4jISHhnHXqqVgREZFytncNvHsz5GdD+8HQ83kNa1KC88kgpgc7Syn/Ad955x0GDhwIFAxQ/OCDD7JgwQJycnJISEjg9ddfd11mBfj9998ZPnw4K1euJCAggAEDBvDcc8/h5fXn8yErV65kzJgxbNmyhfr16/PEE0+4tnEuCnYiIiLl6MhvBcOanDoGl3WHO94Hm0c90+kxqlSwqyoU7ERERMpJ5mGYHQ/H90DUVTBwKdgDzK7KY1XpcexERESkGsvNgvm3F4S62o2g3yKFunKkYCciIiKVw5EPHw6GAz+BXyj0/w8EhptdVbWiYCciIiIVzzDgi0fg1y/Ayxfu/ADCmppdVbWjYCciIiIV7/uXYd1swAK3vgUNYs2uqFpSsBMREZGKtfFD+N/Egu/dpkDLm82tpxpTsBMREZGKs+f/YMnwgu+dRkCn4ebWU80p2ImIiEjFOLQNPugHjlxocTPc8LTZFVV7CnYiIiJS/jIOwrzbIDsdomPh1plgVeyoaDrCIiIiUr5yTsD8PpC+D+o0LXgC1tvP7KpqBAU7ERERKT+OPFg0AFI2QkA49P8Q/EPNrqrGULATERGR8mEYsHQ07FwB3v7QbyGExphdVY2iYCciIiLl49up8PP7YLHCbe/AJe3MrqjGUbATERGRi/fzPFj5bMH3Hv+GZt3MraeGUrATERGRi/PbCvj0/oLv14yBDoPNracGU7ATERGRC5eyseBhCWc+tOoD108wu6IaTcFORERELkz6fpjXB3JPQKPOcMt0jVVnMh19EREROX+n0uD92+DEQQhvAXe8D14+ZldV4ynYiYiIyPnJz4GFd8HhrVCrHvRfDH4hZlclKNiJiIjI+TAM+Hgk7PkO7IHQbxGERJtdlZxmerBbtWoVN910E1FRUVgsFpYsWeI23zAMJkyYQL169fDz8yM+Pp4dO3a4tTl27Bj9+/cnKCiIkJAQBg8eTGZmplubDRs20LlzZ3x9fYmOjmbq1KkVvWsiIiLVz9dPwcZFYPWC29+Feq3NrkiKMD3YZWVl0aZNG6ZPn17i/KlTp/LKK68wY8YM1qxZQ0BAAAkJCWRnZ7va9O/fn82bN7N8+XKWLl3KqlWrGDZsmGt+RkYGN9xwAw0bNmT9+vVMmzaNSZMmMXPmzArfPxERkWpj3Tvw3fMF3296BZp2NbceKcZiGIZhdhGFLBYLH330Eb169QIKeuuioqJ48MEHeeihhwBIT08nIiKCOXPm0LdvX7Zu3UrLli358ccfad++PQDLli2jR48e7N+/n6ioKN544w0ee+wxUlJSsNvtAIwbN44lS5awbdu2MtWWkZFBcHAw6enpBAUFlf/Oi4iIeLJfv4QFfcFwwnXj4bpxZldUY5xPBjG9x+5sdu/eTUpKCvHx8a5pwcHBxMbGkpSUBEBSUhIhISGuUAcQHx+P1WplzZo1rjbXXnutK9QBJCQksH37do4fP17itnNycsjIyHD7iIiI1Eh//ASLBxaEuivvgr+ONbsiKYVHB7uUlBQAIiIi3KZHRES45qWkpFC3bl23+V5eXoSGhrq1KWkdRbdxpilTphAcHOz6REfrxlAREamBju+B+bdD3klocj3c+BJYLGZXJaXw6GBnpvHjx5Oenu767Nu3z+ySREREKtfJYwVj1WUdhohW0Gcu2LzNrkrOwqODXWRkJACpqalu01NTU13zIiMjOXTokNv8/Px8jh075tampHUU3caZfHx8CAoKcvuIiIjUGHnZsOBOOLoDguoXjFXnq38LPZ1HB7uYmBgiIyNZsWKFa1pGRgZr1qwhLi4OgLi4ONLS0li/fr2rzddff43T6SQ2NtbVZtWqVeTl5bnaLF++nGbNmlG7du1K2hsREZEqwumEj/4B+1aDTzDc9SEE1TO7KikD04NdZmYmycnJJCcnAwUPTCQnJ7N3714sFgujR4/m6aef5pNPPmHjxo3cc889REVFuZ6cbdGiBd26dWPo0KGsXbuW77//npEjR9K3b1+ioqIA6NevH3a7ncGDB7N582YWLlzIyy+/TGJiokl7LSIi4sGWPwFbloDVG/q+D3VbmF2RlJGX2QWsW7eOLl26uH4Xhq0BAwYwZ84cHnnkEbKyshg2bBhpaWlcc801LFu2DF9fX9cy8+bNY+TIkXTt2hWr1Urv3r155ZVXXPODg4P56quvGDFiBO3atSMsLIwJEya4jXUnIiIiwOoZkPRawfdeb0DMtebWI+fFo8ax82Qax05ERKq9LZ/AonsAA7pOhM66suUJqs04diIiIlJJ9q6B/w4FDGg/GK4ZY3ZFcgEU7ERERGq6I78VvFUiPxsu6wbdp2qsuipKwU5ERKQmyzwM83rDqWMQdRXc9jbYTL8FXy6Qgp2IiEhNlXsSFtxR8HaJkIbQbyHYA8yuSi6Cgp2IiEhN5HTAf4bAH+vBrzbc9R8IrHvu5cSjqa9VRESkJjEMyDwE3z4H2z8Dmw/c+QGEXWp2ZVIOFOxERESqI8OAEwfh8DY4vP3Pv4e2Qnba6UYW6P0WNOhkZqVSjhTsREREqjLDgPT9RcJbYZDbDjnppSxkgdAY6PwQtLylUsuViqVgJyIiUhU4nZC+1733rfBvbmbJy1hsENoYwptBePOCV4OFN4M6TcHbr3Lrl0qhYCciIuJJnI6Cp1TPDHBHfoW8kyUvY/UqCGuFAa7wU6cJePlUavliLgU7ERERMzjyTwe4rWcEuB0FAwWXxGaHOpcW6YE7HeBCG4PNu1LLF8+kYCciIlKRHHlwbFfBQwtFe+GO7gBHbsnLePkWPKUa3vx0iGtR8L12Iw0eLGels0NERKQ85OfA0Z2ne+CKBrjfwJlf8jLe/hB2WZEAd7oXLqQhWG2VW79UCwp2IiIi5yMvu6C37dA296dQj+0Cw1HyMvbAIve/FbkPLjgarHpXgJQfBTsREZGS5J4seGDh8Hb3Xrjje8BwlryMT1Dx8Fa3OQRdAhZLpZYvNZOCnYiI1Gw5mXBk+5/BrbAnLm0vYJS8jG/In0OHFL0PrlakApyYSsFORERqhux0OPxrkcunpy+hpu8rfRn/sDN64JoVBLqAcAU48UgKdiIiUr2cOl78FVqHt8OJA6UvExhR8j1wAWGVV7dIOahxwW769OlMmzaNlJQU2rRpw6uvvkrHjh3NLktERM5X1tEzXqF1OsBlppa+TK2oEgJcM/APrby6RSpQjQp2CxcuJDExkRkzZhAbG8tLL71EQkIC27dvp27dumaXJyJSdTkdBWOy5ecUjNvmyCn47cg7y7Rc9+n5uWeZVuSTdaSgF+7kkdLrCar/5+C9hQEu7DLwC6m0QyJiBothGKXcGVr9xMbG0qFDB1577TUAnE4n0dHRjBo1inHjxp112YyMDIKDg0lPTycoKKgyyhUR+ZPTUSQMFfmUNQy5wlVp03LOCGFFprmts5RppT0lWtFCGpwevLfI5dOwS8FX/3daqo/zySA1pscuNzeX9evXM378eNc0q9VKfHw8SUlJJlZWwOlwcOjA7lLnWwyjtGezTjMoS0YvsclZl3Ofd2ZTSwlVuVVawrr/nFTydo3iDUtodPZ/RNyWLLYewzW5WP1ubQ33NZ2lnqLH3lJsHW5bPXtdxlnmub6Vvg5LkX0rdnzPrMv9IJ2lLsBwnl63UfDXMLBYTv91tS8yr/DYFVmOwuNtGAX3nBvGGest3HbhOp2n1+W+bgCL4cT138cwStm2+zIF63UW2fczlincpmvan/W49sX1myLLFMwrWqvbfrjOVQOrMw+LM7fgr6NIICtLL5dZwelCWb0L3lFq8y54DZbt9PfzmmY/Pf2Mab7BBUEu7DKwB5i9pyIepcYEuyNHjuBwOIiIiHCbHhERwbZt24q1z8nJIScnx/U7IyOjQuvLPJFG5Ox2FboNEak+8vEi3+KNw+JNvrXgr9PihcNqx2H1xmm147R447R5Y1jtOK3eGDY7hrUgQBmnP9jsWAoDlJcPFi87Fi8fLDY7Fi87Vi8fLN52bN6+WLzs2Lx8sHn7YPW24+Xti9XbBy9vH7zsvnh5FyyLza4nRkVMUmOC3fmaMmUKkydPrtRt5hhnf4HzufrjCvsLznfemes+s+W5ly37usu8rKXYl/Na75nrPnM7Z/Zzlb5c6ess63LF97Fs6y9rzWdrV37btvBnX5jl9PyC307XPEuJbZ2n2zpPb//M9TiLTAMLhuHWN4gTq9tvt/UblhJrK239bn+NwnklrZMzli3ar1jS/DPmGSXvWy5e5OH151/Di1y8ycN2+m/htMJ2pU2zcbb/bVScvNOfzFJb2KwWvKwWvG1WvGwF372sBd+9bVa8rBZsReZ7W60FyxSZ720rMs1aZD2nl6nl40WjsAAa1QkgJiyAAB/9UyZSqMb8ryEsLAybzUZqqvvTUqmpqURGRhZrP378eBITE12/MzIyiI6OrrD6gkLqwOSz3AgsIh6j6KX3wq9GafOLtHM4DfKcTvIdBvkOJ/lOg3xHkWlF/uY5jBKnOZwGea5li0w7c72F7dzWUXTan9PziiyTf3p+ntOJo7C+0/PzHEWmOZ0lXrV3OAvqycmvvEvH4bV8iDkd8hqFBRAT5u8Kfr7eet+q1Cw1JtjZ7XbatWvHihUr6NWrF1Dw8MSKFSsYOXJksfY+Pj74+PhUcpUiUhVYilxmLPmKY+m9aX5Un6DhKBIOzxZQi4bRwmB55jTHGSHXNe3MMOpwcvxkHnuOZrH7SBbHsnI5fCKHwydyWLvnWLEao4J9C0JeWIBb+GsQ6o/dS+9oleqnxgQ7gMTERAYMGED79u3p2LEjL730EllZWQwaNMjs0kREqhyb1YLNasPMK6Hpp/LYcyTLFfR2H8liz+m/Gdn5HEjP5kB6Nj/sPOq2nNUCl9T2o1GdABqfDnuF4a9+bT+8bAp9UjXVqGB3xx13cPjwYSZMmEBKSgpt27Zl2bJlxR6oEBGRqiHYz5s20SG0iQ5xm24YBsdP5rkFvd1HC77vOZJFVq6DfcdOse/YKb7b4X4bjJfVQoNQ/yL38fmfvsQbQFSwH1arHgwRz1WjxrG7GBrHTkSkejAMg8OZOew+XNjTd9Kt1+9s9wfavaw0DP0z6MUUeYgjIsjH7TK9SHnROHYiIiKlsFgs1K3lS91avsQ2ruM2z+k0SMnILujlO1qkt+9IFnuPnSQ338mOQ5nsOFT8yWA/b9ufD2/UCXALf3UC7Ap9UinUY1dG6rETEanZHE6DA2mn2FXk8u6e0+Fv3/FTOJyl/3PqGqIlLICYOv7EhP/Z0xfib6/EvZCq6HwyiIJdGSnYiYhIafIcTvYdO1ns0u6uw1kcSD911hfphPh7l/gQR6Mwf2r5nn18U6kZdClWRESkEnnbrDQOD6RxeGCxedl5DvYdO+nq4fvz6d2TpGRkk3Yyj+STaSTvSyu2bFigT4mXdhvVCcDPXn2GzpHyo2AnIiJSgXy9bVwaUYtLI2oVm3cyN5/fj550G6qlsNfvSGaO6/PjnuPFlo0M8qVRmH+xhzga1PHHx0uhr6bSpdgy0qVYERGpTCey89hz5KTbMC27Tge/tJN5pS5nscAlIX6usFf4QEdMWCD1a/vhrTH6qhzdY1cBFOxERMRTpJ3M/fPS7uEsdh896Qp/J3LyS13OZrUQXduPRmEBNA4L5M6O0SX2JIpnUbCrAAp2IiLi6QzD4GhWbomXdvccyeJUnsOtfZCvFwuGdeLyqGCTKpayULCrAAp2IiJSlRmGQWpGjqunb+GP+0jel0ZogJ2Fwzqp586DnU8G0YV2ERGRGsBisRAZ7Etckzrc2bEB7w7uSOv6wRzLyqX/rDXsOZJldolSDhTsREREaqAgX2/mDupIs4haHDqRQ/9Za/gj7ZTZZclFUrATERGpoWoH2Hl/SCyNwwL4I+0U/d9azaGMbLPLkougYCciIlKDhdfyYd7QWOrX9mPP0ZP0n7WGY1m5ZpclF0jBTkREpIarF+zH/CGdiAzyZcehTO6evYb0U6WPlSeeS8FOREREaFDHn/eHxFInwM7mAxkMemctWWcZE088k4KdiIiIANC0biDvD4kl2M+bn/amMWTuOrLPGPtOPJuCnYiIiLi0qBfEu/d2JNDHi6RdR7nv/fXk5CvcVRUKdiIiIuKmTXQIbw/sgK+3lZXbD/PAgmTyHU6zy5IyULATERGRYjrGhPLWPe2x26ws25zCQ4t/weHUy6o8nanB7plnnuHqq6/G39+fkJCQEtvs3buXnj174u/vT926dXn44YfJz3e/mXPlypVcddVV+Pj40LRpU+bMmVNsPdOnT6dRo0b4+voSGxvL2rVrK2CPREREqo/Ol4Yzvf9VeFktLEk+wONLNqI3kXo2U4Ndbm4uffr0Yfjw4SXOdzgc9OzZk9zcXH744Qfmzp3LnDlzmDBhgqvN7t276dmzJ126dCE5OZnRo0czZMgQvvzyS1ebhQsXkpiYyMSJE/npp59o06YNCQkJHDp0qML3UUREpCr7W8sIXryjLVYLLFi7jyeXblG482AWwwP+68yZM4fRo0eTlpbmNv2LL77gxhtv5MCBA0RERAAwY8YMxo4dy+HDh7Hb7YwdO5bPPvuMTZs2uZbr27cvaWlpLFu2DIDY2Fg6dOjAa6+9BoDT6SQ6OppRo0Yxbty4MtV4Pi/gFRERqW4Wr9vHwx9uAGBElyY8nNDc5IpqjvPJIB59j11SUhKtWrVyhTqAhIQEMjIy2Lx5s6tNfHy823IJCQkkJSUBBb2C69evd2tjtVqJj493tREREZGz69M+mqduuRyA6d/sZPo3v5lckZTEy+wCziYlJcUt1AGu3ykpKWdtk5GRwalTpzh+/DgOh6PENtu2bSt12zk5OeTk5Lh+Z2RkXNS+iIiIVHV3xzXiZK6DKV9sY9qX2/H1tjH4mhizy5Iiyr3Hbty4cVgslrN+zhaoPMWUKVMIDg52faKjo80uSURExHT/+GsTHuh6KQBPLd3CgrV7Ta5Iiir3HrsHH3yQgQMHnrVN48aNy7SuyMjIYk+vpqamuuYV/i2cVrRNUFAQfn5+2Gw2bDZbiW0K11GS8ePHk5iY6PqdkZGhcCciIgKMjr+UU3kOZq7axaMfbcTP20avKy8xuyyhAoJdeHg44eHh5bKuuLg4nnnmGQ4dOkTdunUBWL58OUFBQbRs2dLV5vPPP3dbbvny5cTFxQFgt9tp164dK1asoFevXkDBwxMrVqxg5MiRpW7bx8cHHx+fctkPERGR6sRisTC+e3NO5Tp4b/XvPLj4F3y9bXS7ovQOE6kcpj48sXfvXpKTk9m7dy8Oh4Pk5GSSk5PJzMwE4IYbbqBly5bcfffd/PLLL3z55Zc8/vjjjBgxwhW67rvvPnbt2sUjjzzCtm3beP3111m0aBFjxoxxbScxMZG33nqLuXPnsnXrVoYPH05WVhaDBg0yZb9FRESqOovFwuSbL+e2dvVxOA1GLfiJb7ZrGDGzmTrcycCBA5k7d26x6d988w3XXXcdAL///jvDhw9n5cqVBAQEMGDAAJ577jm8vP7sbFy5ciVjxoxhy5Yt1K9fnyeeeKLY5eDXXnuNadOmkZKSQtu2bXnllVeIjY0tc60a7kRERKQ4h9Pg/g9+5rMNB/HxsjJnUEfimtQxu6xq5XwyiEeMY1cVKNiJiIiULM/hZPj76/nf1kP42228NziWdg1rm11WtVFtxrETERERz+dts/Jav6vofGkYJ3MdDHxnLZv+SDe7rBpJwU5EREQumq+3jTfvbkeHRrU5kZ3P3bPX8GvqCbPLqnEU7ERERKRc+Nu9eHtgB9rUD+b4yTz6z1rD7iNZZpdVoyjYiYiISLmp5evN3Hs70jyyFodP5ND/rdXsP37S7LJqDAU7ERERKVch/nbeHxJL4/AADqRn03/WGlIzss0uq0ZQsBMREZFyFxbow/whnYgO9eP3oyfpP2sNRzNzzr2gXBQFOxEREakQkcG+zB/SicggX347lMnds9eSfjLP7LKqNQU7ERERqTDRof7MGxpLWKCdLQczGPDOWjJz8s0uq9pSsBMREZEK1SQ8kPcGxxLs503yvjQGz/mRU7kOs8uqlhTsREREpMK1qBfEu/d2JNDHizW7j/GP99eTk69wV94U7ERERKRStIkO4Z1BHfDztrHq18OMmv8zeQ6n2WVVKwp2IiIiUmk6NArlrXvaY/ey8tWWVB5c9AsOp15bX14U7ERERKRSXXNpGG/0vwovq4VPfjnAo//diFPhrlwo2ImIiEil69oigpf7XonVAgvX7ePJpVswDIW7i6VgJyIiIqbo2boeU29rA8CcH/Yw9cvtCncXScFORERETHNbu/o81esKAN5YuZPXvv7N5IqqNgU7ERERMdXdnRryWI8WADy//FdmfbfL5IqqLgU7ERERMd3QaxszJv4yAJ7+bCvz1+w1uaKqScFOREREPML9XZvyj782BuCxJRv56Of9JldU9ZgW7Pbs2cPgwYOJiYnBz8+PJk2aMHHiRHJzc93abdiwgc6dO+Pr60t0dDRTp04ttq7FixfTvHlzfH19adWqFZ9//rnbfMMwmDBhAvXq1cPPz4/4+Hh27NhRofsnIiIi58disTCuW3MGxDXEMODBRb/wxcaDZpdVpXiZteFt27bhdDp58803adq0KZs2bWLo0KFkZWXx73//G4CMjAxuuOEG4uPjmTFjBhs3buTee+8lJCSEYcOGAfDDDz9w5513MmXKFG688Ubmz59Pr169+Omnn7jiioKbMadOncorr7zC3LlziYmJ4YknniAhIYEtW7bg6+tbrvvlcDjIy8sr13XWNHa7HatVnckiIjWRxWJh4k2XczLXweL1+7n/g5+Z6W2jS/O6ZpdWJVgMD3queNq0abzxxhvs2lVw0+Qbb7zBY489RkpKCna7HYBx48axZMkStm3bBsAdd9xBVlYWS5cuda2nU6dOtG3blhkzZmAYBlFRUTz44IM89NBDAKSnpxMREcGcOXPo27dvmWrLyMggODiY9PR0goKCis03DIOUlBTS0tIu5hAIYLVaiYmJcf03FxGRmsfhNBi9MJlPfzmA3cvKnIEduLppmNllmeJcGaQo03rsSpKenk5oaKjrd1JSEtdee63bP/AJCQn861//4vjx49SuXZukpCQSExPd1pOQkMCSJUsA2L17NykpKcTHx7vmBwcHExsbS1JSUqnBLicnh5ycHNfvjIyMs9ZeGOrq1q2Lv78/FoulzPstf3I6nRw4cICDBw/SoEEDHUcRkRrKZrXwwu1tyM5zsHxLKkPeXcd7gzvSrmHouReuwTwm2P3222+8+uqrrsuwUBCWYmJi3NpFRES45tWuXZuUlBTXtKJtUlJSXO2KLldSm5JMmTKFyZMnl6l2h8PhCnV16tQp0zJSuvDwcA4cOEB+fj7e3t5mlyMiIibxtll5rd+VDJm7ju92HGHg2z8yf2gnWtUPNrs0j1XuNzKNGzcOi8Vy1k/hZdRCf/zxB926daNPnz4MHTq0vEu6IOPHjyc9Pd312bdvX6ltC++p8/f3r6zyqrXCHlqHw2FyJSIiYjYfLxsz725Px0ahnMjJ5+6317A95YTZZXmscu+xe/DBBxk4cOBZ2zRu3Nj1/cCBA3Tp0oWrr76amTNnurWLjIwkNTXVbVrh78jIyLO2KTq/cFq9evXc2rRt27bUGn18fPDx8TnrfpxJlw3Lh46jiIgU5We3MXtge+6avZZf9qXRf9YaFv2jE43DA80uzeOUe49deHg4zZs3P+unsEfmjz/+4LrrrqNdu3a88847xZ6EjIuLY9WqVW5PmS5fvpxmzZpRu3ZtV5sVK1a4Lbd8+XLi4uIAiImJITIy0q1NRkYGa9ascbURERERz1bL15u5gzrQPLIWRzJz6D9rDfuOnTS7LI9j2pgShaGuQYMG/Pvf/+bw4cOkpKS43ffWr18/7HY7gwcPZvPmzSxcuJCXX37Z7WGJBx54gGXLlvH888+zbds2Jk2axLp16xg5ciRQ0PszevRonn76aT755BM2btzIPffcQ1RUFL169ars3a5W9uzZg8ViITk5GYCVK1disVj0ZLCIiFSIEH877w+JpUl4AAfTs+k/aw0p6dlml+VRTAt2y5cv57fffmPFihXUr1+fevXquT6FgoOD+eqrr9i9ezft2rXjwQcfZMKECa4x7ACuvvpq5s+fz8yZM2nTpg0ffvghS5YscY1hB/DII48watQohg0bRocOHcjMzGTZsmXlPoZdVTRw4EDXvY/e3t7ExMTwyCOPkJ2t/6GIiIjnCQv0Yd6QTjQI9WfvsZP0n7WaI5k5516whvCocew82dnGkMnOzmb37t3ExMRUubA4cOBAUlNTeeedd8jLy2P9+vUMGDCA++67j3/9619nXXbPnj3ExMTw888/07ZtW1auXEmXLl04fvw4ISEhF1xTVT6eIiJSOfYdO8ntbyZxMD2bFvWCWDA0lhD/6jn+6fmMY6fh/QUfHx8iIyOJjo6mV69exMfHs3z58gte3/fff0/r1q3x9fWlU6dObNq0qRyrFRERgehQf+YNiSUs0IetBzMY8M6PnMjWm58U7CqIYRiczM035XMxnbCbNm3ihx9+uKi3Pjz88MM8//zz/Pjjj4SHh3PTTTfpNWsiIlLuGocHMm9ILCH+3vyyL43Bc9ZxKrdmD5XlMQMUVzen8hy0nPClKdve8mQC/vay/6ddunQpgYGB5Ofnk5OTg9Vq5bXXXrvg7U+cOJG//e1vAMydO5f69evz0Ucfcfvtt1/wOkVERErSLLIW790bS7+3VrN2zzGGvbeOWQPa4+NlM7s0U6jHTujSpQvJycmsWbOGAQMGMGjQIHr37n3B6ys6jExoaCjNmjVj69at5VGqiIhIMa3qB/POoA74edv4bscRRsz7mTyH0+yyTKEeuwri521jy5MJpm37fAQEBNC0aVMA3n77bdq0acPs2bMZPHhwRZQnIiJS7to3CmXWgPYMmvMj/9uaypiFybzc90ps1po16L167CqIxWLB3+5lyudi3txgtVp59NFHefzxxzl16tQFrWP16tWu78ePH+fXX3+lRYsWF1yTiIhIWfylaRgz7roKb5uFpRsOMu4/G3A6a9bgHwp2UkyfPn2w2WxMnz79gpZ/8sknWbFiBZs2bWLgwIGEhYVpMGgREakU1zeP4OW+V2K1wOL1+5n06eaLeqiwqlGwk2K8vLwYOXIkU6dOJSsr67yXf+6553jggQdo164dKSkpfPrppxf1lK2IiMj56NGqHv/u0waLBd5N+p3nlm2rMeFOAxSXUXUdoNgT6XiKiEh5mLfmdx77qGAs1THxl/FA/KUmV3RhNECxiIiI1Hj9YxvyeM+Ce7xf/N+vvLVql8kVVTwFOynVs88+S2BgYImf7t27m12eiIjIOQ3p3JgH/3YZAM98vpX3V/9uckUVS8OdSKnuu+++UgcV9vPzq+RqRERELszI65tyMs/BGyt38viSTfh52+jdrr7ZZVUIBTspVWhoKKGhoWaXISIiclEsFguPJDTjVK6DOT/s4eEPf8HX20bP1vXMLq3c6VKsiIiIVHsWi4UJN7bk9vb1cRrwwAc/s2JrqtlllTsFOxEREakRrFYLU25tzc1tosh3Ggyf9xP/t+OI2WWVKwU7ERERqTFsVgvP396Gv7WMIDffydB31/HjnmNml1VuFOxERESkRvG2WXmt35Vce1k4p/IcDHrnRzbsTzO7rHKhYCciIiI1jo+XjTfvakfHmFAyc/K55+21bEvJMLusi2ZqsLv55ptp0KABvr6+1KtXj7vvvpsDBw64tdmwYQOdO3fG19eX6Ohopk6dWmw9ixcvpnnz5vj6+tKqVSs+//xzt/mGYTBhwgTq1auHn58f8fHx7Nixo0L3TQrMmTOHkJAQs8sQEREpxs9u4+2BHWgbHULayTzumrWGnYczzS7ropga7Lp06cKiRYvYvn07//nPf9i5cye33Xaba35GRgY33HADDRs2ZP369UybNo1JkyYxc+ZMV5sffviBO++8k8GDB/Pzzz/Tq1cvevXqxaZNm1xtpk6dyiuvvMKMGTNYs2YNAQEBJCQkkJ2dXan764kGDhyIxWLBYrHg7e1NTEwMjzzyiI6NiIjUCIE+Xswd1JGW9YI4kplL/7fWsO/YSbPLunCGB/n4448Ni8Vi5ObmGoZhGK+//rpRu3ZtIycnx9Vm7NixRrNmzVy/b7/9dqNnz55u64mNjTX+8Y9/GIZhGE6n04iMjDSmTZvmmp+Wlmb4+PgYCxYsKHNt6enpBmCkp6cXm3fq1Cljy5YtxqlTp8q8Pk8xYMAAo1u3bsbBgweNvXv3Gh999JERFBRkPPLII+Wy/nfeeccIDg4+r2Wq8vEUEZGq6ciJbKPr8yuNhmOXGtf8a4VxIO2k2SW5nC2DnMlj7rE7duwY8+bN4+qrr8bb2xuApKQkrr32Wux2u6tdQkIC27dv5/jx46428fHxbutKSEggKSkJgN27d5OSkuLWJjg4mNjYWFebms7Hx4fIyEiio6Pp1asX8fHxLF++/JzLrVy5EovFQlpammtacnIyFouFPXv2uLVdsmQJl156Kb6+viQkJLBv375y3gsREZELVyfQh3lDYmlYx599x07Rf9YaDp/IMbus82Z6sBs7diwBAQHUqVOHvXv38vHHH7vmpaSkEBER4da+8HdKSspZ2xSdX3S5ktqUJCcnh4yMDLfPeTEMyM0y52MY51drEZs2beKHH35wC9MX6+TJkzzzzDO8++67fP/996SlpdG3b99yW7+IiEh5iAjyZd6QWKKCfdl1OIu7Z68h7WSu2WWdl3J/pdi4ceP417/+ddY2W7dupXnz5gA8/PDDDB48mN9//53Jkydzzz33sHTpUiwWS3mXdl6mTJnC5MmTL3wFeSfh2ajyK+h8PHoA7AFlbr506VICAwPJz88nJycHq9XKa6+9Vm7l5OXl8dprrxEbGwvA3LlzadGiBWvXrqVjx47lth0REZGLVb+2P/OGduL2N5PYlnKCAW+v5f0hsdTy9Ta7tDIp92D34IMPMnDgwLO2ady4set7WFgYYWFhXHbZZbRo0YLo6GhWr15NXFwckZGRpKa6v+6j8HdkZKTrb0ltis4vnFavXj23Nm3bti21xvHjx5OYmOj6nZGRQXR09Fn3q6rq0qULb7zxBllZWbz44ot4eXnRu3fvclu/l5cXHTp0cP1u3rw5ISEhbN26VcFOREQ8TkxYAPOGxHLHm0n8sj+de+f8yNx7O+JvL/fYVO7KvcLw8HDCw8MvaFmn0wkUXAYFiIuL47HHHiMvL891393y5ctp1qwZtWvXdrVZsWIFo0ePdq1n+fLlxMXFARATE0NkZCQrVqxwBbmMjAzWrFnD8OHDS63Fx8cHHx+fC9oPALz9C3rOzODtf17NAwICaNq0KQBvv/02bdq0Yfbs2QwePPisy1mtBVfyjSKXfvPy8s6zWBEREc9zWUQt3hscy51vrebHPccZ9u56Zg1oj6+3zezSzsq0e+zWrFnDa6+9RnJyMr///jtff/01d955J02aNHGFsn79+mG32xk8eDCbN29m4cKFvPzyy249aQ888ADLli3j+eefZ9u2bUyaNIl169YxcuRIoOClv6NHj+bpp5/mk08+YePGjdxzzz1ERUXRq1evittBi6XgcqgZn4u4jG21Wnn00Ud5/PHHOXXq1FnbFgb4gwcPuqYlJycXa5efn8+6detcv7dv305aWhotWrS44DpFREQq2hWXBDNnUEf87Tb+77cjjJz/E3kOp9llnZVpwc7f35///ve/dO3alWbNmjF48GBat27Nt99+6+opCw4O5quvvmL37t20a9eOBx98kAkTJjBs2DDXeq6++mrmz5/PzJkzadOmDR9++CFLlizhiiuucLV55JFHGDVqFMOGDaNDhw5kZmaybNkyfH19K32/q4I+ffpgs9mYPn36Wds1bdqU6OhoJk2axI4dO/jss894/vnni7Xz9vZm1KhRrFmzhvXr1zNw4EA6deqky7AiIuLx2jWszawB7fHxsvK/rYcYvTAZh/PCH1KsaBbDuIhHKGuQjIwMgoODSU9PJygoyG1ednY2u3fvJiYmpsqFxYEDB5KWlsaSJUvcpj/33HO88MIL7N69m4CA0h/E+P777xk+fDg7duygQ4cO3H///fTp04fdu3fTqFEj5syZw+jRo3n77bd5+OGH+eOPP+jcuTOzZ8+mQYMGJa6zKh9PERGpnr7Zfohh764jz2HQ+6r6TLutNVZr5TzoebYMciYFuzKqrsHOE+l4ioiIJ1q26SAj5v+Mw2lwV6cGPHXLFZUyisf5BDvTx7ETERERqQq6XVGP5/u0wWKB91fvZcoX2/C0/jEFOynVs88+S2BgYImf7t27m12eiIhIpet15SU8+/dWAMxctYuX/rfD5Ircef6ALGKa++67j9tvv73EeX5+fpVcjYiIiGe4s2MDTuY6eGrpFl5esQN/u41//LWJ2WUBCnZyFqGhoYSGhppdhoiIiMcZfE0Mp3Lz+fdXvzLli23UDrBze3vzX2SgYCciIiJyAUZefykncx0s3XCQuMZ1zC4HULArV4VvzpCL42k3ooqIiJTm4YRm/OPaJgT7e8a7ZBXsyoHdbsdqtXLgwAHCw8Ox2+2V8vhzdWQYBocPH8ZisbheIyciIuKpLBaLx4Q6ULArF1arlZiYGA4ePMiBAya9H7YasVgs1K9fH5vNs9/HJyIi4mkU7MqJ3W6nQYMG5Ofn43A4zC6nSvP29laoExERuQAKduWo8PKhLiGKiIiIGTRAsYiIiEg1oWAnIiIiUk0o2ImIiIhUE7rHrowKx1bLyMgwuRIRERGpSQqzR1nGeVWwK6MTJ04AEB1t/utCREREpOY5ceIEwcHBZ21jMTTMf5k4nU4OHDhArVq1Kmzw4YyMDKKjo9m3bx9BQUEVso2aQseyfOg4lh8dy/Kh41h+dCzLR2UcR8MwOHHiBFFRUVitZ7+LTj12ZWS1Wqlfv36lbCsoKEj/IysnOpblQ8ex/OhYlg8dx/KjY1k+Kvo4nqunrpAenhARERGpJhTsRERERKoJBTsP4uPjw8SJE/Hx8TG7lCpPx7J86DiWHx3L8qHjWH50LMuHpx1HPTwhIiIiUk2ox05ERESkmlCwExEREakmFOxEREREqgkFOxEREZFqQsHOQ0yfPp1GjRrh6+tLbGwsa9euNbukKmfSpElYLBa3T/Pmzc0uq0pYtWoVN910E1FRUVgsFpYsWeI23zAMJkyYQL169fDz8yM+Pp4dO3aYU6wHO9dxHDhwYLFztFu3buYU68GmTJlChw4dqFWrFnXr1qVXr15s377drU12djYjRoygTp06BAYG0rt3b1JTU02q2HOV5Vhed911xc7L++67z6SKPdcbb7xB69atXQMRx8XF8cUXX7jme8o5qWDnARYuXEhiYiITJ07kp59+ok2bNiQkJHDo0CGzS6tyLr/8cg4ePOj6/N///Z/ZJVUJWVlZtGnThunTp5c4f+rUqbzyyivMmDGDNWvWEBAQQEJCAtnZ2ZVcqWc713EE6Natm9s5umDBgkqssGr49ttvGTFiBKtXr2b58uXk5eVxww03kJWV5WozZswYPv30UxYvXsy3337LgQMHuPXWW02s2jOV5VgCDB061O28nDp1qkkVe6769evz3HPPsX79etatW8f111/PLbfcwubNmwEPOicNMV3Hjh2NESNGuH47HA4jKirKmDJliolVVT0TJ0402rRpY3YZVR5gfPTRR67fTqfTiIyMNKZNm+aalpaWZvj4+BgLFiwwocKq4czjaBiGMWDAAOOWW24xpZ6q7NChQwZgfPvtt4ZhFJx/3t7exuLFi11ttm7dagBGUlKSWWVWCWceS8MwjL/+9a/GAw88YF5RVVjt2rWNWbNmedQ5qR47k+Xm5rJ+/Xri4+Nd06xWK/Hx8SQlJZlYWdW0Y8cOoqKiaNy4Mf3792fv3r1ml1Tl7d69m5SUFLdzNDg4mNjYWJ2jF2DlypXUrVuXZs2aMXz4cI4ePWp2SR4vPT0dgNDQUADWr19PXl6e2znZvHlzGjRooHPyHM48loXmzZtHWFgYV1xxBePHj+fkyZNmlFdlOBwOPvjgA7KysoiLi/Ooc9KrUrcmxRw5cgSHw0FERITb9IiICLZt22ZSVVVTbGwsc+bMoVmzZhw8eJDJkyfTuXNnNm3aRK1atcwur8pKSUkBKPEcLZwnZdOtWzduvfVWYmJi2LlzJ48++ijdu3cnKSkJm81mdnkeyel0Mnr0aP7yl79wxRVXAAXnpN1uJyQkxK2tzsmzK+lYAvTr14+GDRsSFRXFhg0bGDt2LNu3b+e///2vidV6po0bNxIXF0d2djaBgYF89NFHtGzZkuTkZI85JxXspNro3r2763vr1q2JjY2lYcOGLFq0iMGDB5tYmUiBvn37ur63atWK1q1b06RJE1auXEnXrl1NrMxzjRgxgk2bNul+2XJQ2rEcNmyY63urVq2oV68eXbt2ZefOnTRp0qSyy/RozZo1Izk5mfT0dD788EMGDBjAt99+a3ZZbnQp1mRhYWHYbLZiT86kpqYSGRlpUlXVQ0hICJdddhm//fab2aVUaYXnoc7R8te4cWPCwsJ0jpZi5MiRLF26lG+++Yb69eu7pkdGRpKbm0taWppbe52TpSvtWJYkNjYWQOdlCex2O02bNqVdu3ZMmTKFNm3a8PLLL3vUOalgZzK73U67du1YsWKFa5rT6WTFihXExcWZWFnVl5mZyc6dO6lXr57ZpVRpMTExREZGup2jGRkZrFmzRufoRdq/fz9Hjx7VOXoGwzAYOXIkH330EV9//TUxMTFu89u1a4e3t7fbObl9+3b27t2rc/IM5zqWJUlOTgbQeVkGTqeTnJwcjzondSnWAyQmJjJgwADat29Px44deemll8jKymLQoEFml1alPPTQQ9x00000bNiQAwcOMHHiRGw2G3feeafZpXm8zMxMt//vfPfu3SQnJxMaGkqDBg0YPXo0Tz/9NJdeeikxMTE88cQTREVF0atXL/OK9kBnO46hoaFMnjyZ3r17ExkZyc6dO3nkkUdo2rQpCQkJJlbteUaMGMH8+fP5+OOPqVWrlusepeDgYPz8/AgODmbw4MEkJiYSGhpKUFAQo0aNIi4ujk6dOplcvWc517HcuXMn8+fPp0ePHtSpU4cNGzYwZswYrr32Wlq3bm1y9Z5l/PjxdO/enQYNGnDixAnmz5/PypUr+fLLLz3rnKzUZ3ClVK+++qrRoEEDw263Gx07djRWr15tdklVzh133GHUq1fPsNvtxiWXXGLccccdxm+//WZ2WVXCN998YwDFPgMGDDAMo2DIkyeeeMKIiIgwfHx8jK5duxrbt283t2gPdLbjePLkSeOGG24wwsPDDW9vb6Nhw4bG0KFDjZSUFLPL9jglHUPAeOedd1xtTp06Zfzzn/80ateubfj7+xt///vfjYMHD5pXtIc617Hcu3evce211xqhoaGGj4+P0bRpU+Phhx820tPTzS3cA917771Gw4YNDbvdboSHhxtdu3Y1vvrqK9d8TzknLYZhGJUZJEVERESkYugeOxEREZFqQsFOREREpJpQsBMRERGpJhTsRERERKoJBTsRERGRakLBTkRERKSaULATERERqSYU7ERERESqCQU7ERERkWpCwU5ERESkmlCwExEREakmFOxEREREqgkFOxEREZFqQsFOREREpJpQsBMRERGpJhTsRERERKoJBTsRERGRakLBTkSkGlq5ciUWi4WVK1eaXYqIVCIFOxGpVubMmYPFYmHdunUlzr/uuuu44oorzrmegQMHYrFYXB8vLy+io6Pp27cvW7ZsKe+yRUTKhZfZBYiIeCofHx9mzZoFQH5+Pjt37mTGjBksW7aMLVu2EBUVZXKFIiLuFOxERErh5eXFXXfd5TatU6dO3HjjjXz22WcMHTrUpMpEREqmS7EiIuchMjISKAh9Re3atYs+ffoQGhqKv78/nTp14rPPPnNrU3iZeM+ePW7TS7ofrvCS8ZYtW+jSpQv+/v5ccsklTJ06tVhN+/fvp1evXgQEBFC3bl3GjBlDTk5OsXY7duygd+/eREZG4uvrS/369enbty/p6ekXeDRExNOox05EqqX09HSOHDlSbHpeXt55radwHQ6Hg127djF27Fjq1KnDjTfe6GqTmprK1VdfzcmTJ7n//vupU6cOc+fO5eabb+bDDz/k73//+wXtw/Hjx+nWrRu33nort99+Ox9++CFjx46lVatWdO/eHYBTp07RtWtX9u7dy/33309UVBTvvfceX3/9tdu6cnNzSUhIICcnh1GjRhEZGckff/zB0qVLSUtLIzg4+IJqFBHPomAnItVSfHx8qfMuv/zyMq0jKyuL8PBwt2mXXHIJX331ldv05557jtTUVL777juuueYaAIYOHUrr1q1JTEzklltuwWo9/wskBw4c4N133+Xuu+8GYPDgwTRs2JDZs2e7gt3MmTP59ddfWbRoEX369HFtu02bNm7r2rJlC7t372bx4sXcdtttrukTJkw477pExHPpUqyIVEvTp09n+fLlxT6tW7cu8zp8fX1dy3355Ze8+eabBAYG0qNHD3799VdXu88//5yOHTu6Qh1AYGAgw4YNY8+ePRf8FG1gYKDbPX52u52OHTuya9cut23Xq1fPLaz5+/szbNgwt3UV9sh9+eWXnDx58oLqERHPpx47EamWOnbsSPv27YtNr127domXaEtis9mK9fz16NGDSy+9lPHjx/Of//wHgN9//53Y2Nhiy7do0cI1vyxDrJypfv36WCyWYvVv2LDB9fv333+nadOmxdo1a9bM7XdMTAyJiYm88MILzJs3j86dO3PzzTdz11136TKsSDWiHjsRkfNQv359mjVrxqpVq8572TPDVyGHw1HidJvNVuJ0wzDOe9sAzz//PBs2bODRRx/l1KlT3H///Vx++eXs37//gtYnIp5HwU5E5Dzl5+eTmZnp+t2wYUO2b99erN22bdtc86Ggtw0gLS3Nrd3vv/9+wbU0bNiQnTt3Fgt7JdUD0KpVKx5//HFWrVrFd999xx9//MGMGTMuePsi4lkU7EREzsOvv/7K9u3b3R5O6NGjB2vXriUpKck1LSsri5kzZ9KoUSNatmwJQJMmTQDcevscDgczZ8684Hp69OjBgQMH+PDDD13TTp48WWydGRkZ5Ofnu01r1aoVVqu1xKFRRKRq0j12IiKlyM/P5/333wfA6XSyZ88eZsyYgdPpZOLEia5248aNY8GCBXTv3p3777+f0NBQ5s6dy+7du/nPf/7jeiL28ssvp1OnTowfP55jx44RGhrKBx98UCxwnY+hQ4fy2muvcc8997B+/Xrq1avHe++9h7+/v1u7r7/+mpEjR9KnTx8uu+wy8vPzee+997DZbPTu3fuCty8inkXBTkSkFDk5Oa6hRgCCgoLo0KED7733Hl27dnVNj4iI4IcffmDs2LG8+uqrZGdn07p1az799FN69uzpts558+bxj3/8g+eee46QkBAGDx5Mly5d+Nvf/nZBNfr7+7NixQpGjRrFq6++ir+/P/3796d79+5069bN1a5NmzYkJCTw6aef8scff+Dv70+bNm344osv6NSp0wVtW0Q8j8W40LtwRURERMSj6B47ERERkWpCwU5ERESkmlCwExEREakmFOxEREREqgkFOxEREZFqQsFOREREpJrQOHZl5HQ6OXDgALVq1Sr1fY8iIiIi5c0wDE6cOEFUVJRrwPPSKNiV0YEDB4iOjja7DBEREamh9u3bR/369c/aRsGujGrVqgUUHNSgoCCTqxEREZGaIiMjg+joaFcWORsFuzIqvPwaFBSkYCciIiKVriy3gunhCREREZFqwvRgt2rVKm666SaioqKwWCwsWbLEbf7AgQOxWCxun6IvtgY4duwY/fv3JygoyPVS7czMTLc2GzZsoHPnzvj6+hIdHc3UqVMretdEREREKpXpwS4rK4s2bdowffr0Utt069aNgwcPuj4LFixwm9+/f382b97M8uXLWbp0KatWrWLYsGGu+RkZGdxwww00bNiQ9evXM23aNCZNmsTMmTMrbL9EREREKpvp99h1796d7t27n7WNj48PkZGRJc7bunUry5Yt48cff6R9+/YAvPrqq/To0YN///vfREVFMW/ePHJzc3n77bex2+1cfvnlJCcn88ILL7gFQBEREZGqzPQeu7JYuXIldevWpVmzZgwfPpyjR4+65iUlJRESEuIKdQDx8fFYrVbWrFnjanPttddit9tdbRISEti+fTvHjx+vvB0RERERqUCm99idS7du3bj11luJiYlh586dPProo3Tv3p2kpCRsNhspKSnUrVvXbRkvLy9CQ0NJSUkBICUlhZiYGLc2ERERrnm1a9cutt2cnBxycnJcvzMyMsp710RERETKlccHu759+7q+t2rVitatW9OkSRNWrlxJ165dK2y7U6ZMYfLkyRW2fhEREakGMg7CH+ugxU1mVwJUkUuxRTVu3JiwsDB+++03ACIjIzl06JBbm/z8fI4dO+a6Ly8yMpLU1FS3NoW/S7t3b/z48aSnp7s++/btK+9dERERkaosOx3m3QYL74Kf55ldDVAFg93+/fs5evQo9erVAyAuLo60tDTWr1/vavP111/jdDqJjY11tVm1ahV5eXmuNsuXL6dZs2YlXoaFggc2Cgcj1qDEIiIi4iY/pyDQpW6CgLrQ6C9mVwR4QLDLzMwkOTmZ5ORkAHbv3k1ycjJ79+4lMzOThx9+mNWrV7Nnzx5WrFjBLbfcQtOmTUlISACgRYsWdOvWjaFDh7J27Vq+//57Ro4cSd++fYmKigKgX79+2O12Bg8ezObNm1m4cCEvv/wyiYmJZu22iIiIVFVOJyz5J+xeBfZA6L8YajcyuyoALIZhGGYWsHLlSrp06VJs+oABA3jjjTfo1asXP//8M2lpaURFRXHDDTfw1FNPuR5+gIIBikeOHMmnn36K1Wqld+/evPLKKwQGBrrabNiwgREjRvDjjz8SFhbGqFGjGDt2bJnrzMjIIDg4mPT0dPXeiYiI1GRfPQ4/vApWr4JQ1+T6Ct3c+WQQ04NdVaFgJyIiIiS9Dl+OL/j+9zehTd+zty8H55NBTL8UKyIiIlIlbPrPn6EuflKlhLrzpWAnIiIici67V8FH9xV87/gP+MtoU8spjYKdiIiIyNmkboYP+oMjF1rcDN2mgMVidlUlUrATERERKU3aPni/N+RkQIOr4da3wGozu6pSKdiJiIiIlOTksYJQd+IghDeHO+eDt6/ZVZ2Vgp2IiIjImfKy4YN+cGQ71IqCu/4DfiW/1MCTKNiJiIiIFOV0wH+HwN4k8AmGuz6E4PpmV1UmCnYiIiIihQwDvhgLWz8Fmx36zoOIy82uqswU7EREREQK/d+L8ONbgKVgAOKYzmZXdF4U7EREREQAkhfAiskF37tNgStuNbeeC6BgJyIiIvLbCvhkZMH3q0dBp+Hm1nOBFOxERESkZjuQDIvuAWc+tOoD8U+aXdEFU7ATERGRmuvYbph3G+RmQsxf4ZbXwVp141HVrVxERETkYmQdKRiAOOswRLSCO94HL7vZVV0UBTsRERGpeXKzYP7tcGwnBDeA/ovBN8jsqi6agp2IiIjULI58+PBe+GN9wdsk7voPBNUzu6pyoWAnIiIiNYdhwGdj4Ndl4OULdy6E8MvMrqrcKNiJiIhIzfHtv+Cnd8Fihd6zoUGs2RWVKwU7ERERqRnWz4GVUwq+9/g3tLjR1HIqgoKdiIiIVH/bl8HSMQXfr30YOgw2t54KomAnIiIi1du+H2HxQDCc0PYu6PKY2RVVGAU7ERERqb6O/FYwrEn+KWj6N7jpJbBYzK6qwijYiYiISPV0IhXevxVOHYOoK6HPHLB5m11VhVKwExERkeon5wTM7wNpv0PtGOi3GHwCza6qwinYiYiISPWSnwuL7oGDv4B/WMEAxIHhZldVKRTsREREpPowDPhkFOz8Grz9of8iqNPE7KoqjenBbtWqVdx0001ERUVhsVhYsmSJ23zDMJgwYQL16tXDz8+P+Ph4duzY4dbm2LFj9O/fn6CgIEJCQhg8eDCZmZlubTZs2EDnzp3x9fUlOjqaqVOnVvSuiYiISGVbMRk2fAAWG9z+LlzSzuyKKpXpwS4rK4s2bdowffr0EudPnTqVV155hRkzZrBmzRoCAgJISEggOzvb1aZ///5s3ryZ5cuXs3TpUlatWsWwYcNc8zMyMrjhhhto2LAh69evZ9q0aUyaNImZM2dW+P6JiIhIJVkzE/7vxYLvN78Kl/7N3HpMYDEMwzC7iEIWi4WPPvqIXr16AQW9dVFRUTz44IM89NBDAKSnpxMREcGcOXPo27cvW7dupWXLlvz444+0b98egGXLltGjRw/2799PVFQUb7zxBo899hgpKSnY7XYAxo0bx5IlS9i2bVuZasvIyCA4OJj09HSCgoLKf+dFRETkwm35GBYNAAy4/vGCQYirifPJIKb32J3N7t27SUlJIT4+3jUtODiY2NhYkpKSAEhKSiIkJMQV6gDi4+OxWq2sWbPG1ebaa691hTqAhIQEtm/fzvHjx0vcdk5ODhkZGW4fERER8UC//wD/GQoY0P5e6PyQ2RWZxqODXUpKCgARERFu0yMiIlzzUlJSqFu3rtt8Ly8vQkND3dqUtI6i2zjTlClTCA4Odn2io6MvfodERESkfB3aCgv6giMHmvUseAdsNR6A+Fw8OtiZafz48aSnp7s++/btM7skERERKSr9D3i/N2SnQ3Qs3DYbrDazqzKVRwe7yMhIAFJTU92mp6amuuZFRkZy6NAht/n5+fkcO3bMrU1J6yi6jTP5+PgQFBTk9hEREREPcSoN5t0GGX9A2GVw5wfg7Wd2Vabz6GAXExNDZGQkK1ascE3LyMhgzZo1xMXFARAXF0daWhrr1693tfn6669xOp3Exsa62qxatYq8vDxXm+XLl9OsWTNq165dSXsjIiIi5SI/BxbeBYe2QGBkwQDE/qFmV+URTA92mZmZJCcnk5ycDBQ8MJGcnMzevXuxWCyMHj2ap59+mk8++YSNGzdyzz33EBUV5XpytkWLFnTr1o2hQ4eydu1avv/+e0aOHEnfvn2JiooCoF+/ftjtdgYPHszmzZtZuHAhL7/8MomJiSbttYiIiFwQpxM++gfs+Q7staD/YghpYHZVHsPL7ALWrVtHly5dXL8Lw9aAAQOYM2cOjzzyCFlZWQwbNoy0tDSuueYali1bhq+vr2uZefPmMXLkSLp27YrVaqV379688sorrvnBwcF89dVXjBgxgnbt2hEWFsaECRPcxroTERERD2cY8NVjsPkjsHpD3/ehXmuzq/IoHjWOnSfTOHYiIiIm++FV+Orxgu+3zoLWfcytp5JUm3HsRERERADYsPjPUPe3p2pMqDtfCnYiIiLi2XathCXDC753+idcPcrUcjyZgp2IiIh4rpSN8MFd4MyDy/8ONzxTowcgPhcFOxEREfFMx3+H92+D3BPQ8BroNQOsii5no6MjIiIinufksYK3SmSmQN2W0HceePuee7kaTsFOREREPEveqYL3vx7dAUGXQP8PwS/E7KqqBAU7ERER8RxOB3w4GPatAd/ggrdKBF9idlVVhoKdiIiIeAbDgM8fhu2fgc2n4P2vdVuYXVWVomAnIiIinuG7f8O62YAFer8FDa82u6IqR8FOREREzPfzPPj66YLv3adCy1vMraeKUrATERERc+1YDp+cHnT4L6MhVu9yv1AKdiIiImKeP9bDonvAcEDrvhA/yeyKqjQFOxERETHHsV0w73bIOwlNroebX9VbJS6Sgp2IiIhUvszD8N6tcPIIRLaG298FL7vZVVV5CnYiIiJSuXIyYX4fOL4bQhoUDEDsU8vsqqoFBTsRERGpPI48WDwQDvwMfqFw10dQK8LsqqoNBTsRERGpHIYBn46G35aDlx/0WwRhTc2uqlpRsBMREZHK8c0zkPw+WKzQ5x2I7mB2RdWOgp2IiIhUvB9nw6ppBd9vfBGadTe3nmpKwU5EREQq1rbP4POHCr7/dRy0G2hqOdWZgp2IiIhUnL1r4MN7wXDCVffAdePMrqhaU7ATERGRinH4V1hwB+Rnw6UJ0PNFDUBcwRTsREREpPxlHIT3e8Op43BJu4KHJWxeZldV7SnYiYiISPnKzoB5fSB9L4Q2KRjWxB5gdlU1goKdiIiIlJ/8XFh4F6RuhIBwuOs/EBBmdlU1hoKdiIiIlA+nEz7+J+z+FuyB0H8xhMaYXVWN4vHBbtKkSVgsFrdP8+bNXfOzs7MZMWIEderUITAwkN69e5Oamuq2jr1799KzZ0/8/f2pW7cuDz/8MPn5+ZW9KyIiItXb/ybCxsVg9YLb34WoK82uqMapEncxXn755fzvf/9z/fby+rPsMWPG8Nlnn7F48WKCg4MZOXIkt956K99//z0ADoeDnj17EhkZyQ8//MDBgwe555578Pb25tlnn630fREREamWVr8BP7xS8P3m16BpV3PrqaGqRLDz8vIiMjKy2PT09HRmz57N/Pnzuf766wF45513aNGiBatXr6ZTp0589dVXbNmyhf/9739ERETQtm1bnnrqKcaOHcukSZOw2+2VvTsiIiLVy6b/wrLxBd+7ToS2d5pbTw3m8ZdiAXbs2EFUVBSNGzemf//+7N27F4D169eTl5dHfHy8q23z5s1p0KABSUlJACQlJdGqVSsiIiJcbRISEsjIyGDz5s2lbjMnJ4eMjAy3j4iIiJxhz//BR/8ADOgwFK4ZY3ZFNZrHB7vY2FjmzJnDsmXLeOONN9i9ezedO3fmxIkTpKSkYLfbCQkJcVsmIiKClJQUAFJSUtxCXeH8wnmlmTJlCsHBwa5PdHR0+e6YiIhIVZe6GRb0A0cutLgJuv9LAxCbzOMvxXbv/udLglu3bk1sbCwNGzZk0aJF+Pn5Vdh2x48fT2Jiout3RkaGwp2IiEih9P3w/m2Qkw4N4uDWt8BqM7uqGs/je+zOFBISwmWXXcZvv/1GZGQkubm5pKWlubVJTU113ZMXGRlZ7CnZwt8l3bdXyMfHh6CgILePiIiIUPA2ifd7w4kDENYM+s4H74rrbJGyq3LBLjMzk507d1KvXj3atWuHt7c3K1ascM3fvn07e/fuJS4uDoC4uDg2btzIoUOHXG2WL19OUFAQLVu2rPT6RUREqrS87ILLr4e3Qa16BQMQ+4eaXZWc5vGXYh966CFuuukmGjZsyIEDB5g4cSI2m40777yT4OBgBg8eTGJiIqGhoQQFBTFq1Cji4uLo1KkTADfccAMtW7bk7rvvZurUqaSkpPD4448zYsQIfHx8TN47ERGRKsTpgP8Ohb0/gE9QQagL0W1KnsTjg93+/fu58847OXr0KOHh4VxzzTWsXr2a8PBwAF588UWsViu9e/cmJyeHhIQEXn/9ddfyNpuNpUuXMnz4cOLi4ggICGDAgAE8+eSTZu2SiIhI1WMYsGwcbP0EbHboOw8iLje7KjmDxTAMw+wiqoKMjAyCg4NJT0/X/XYiIlLz/N9LBW+WALjtbbiit6nl1CTnk0Gq3D12IiIiUsl++eDPUJfwrEKdB1OwExERkdLt/Bo+HlHwPW4kxI0wtx45KwU7ERERKdmBZFh4Nzjz4Yrb4G9PmV2RnIOCnYiIiBR3fA/M6wO5mRBzLfR6HayKDZ5O/4VERETEXdbRggGIsw5BxBVwx/vgpSHCqgIFOxEREflT7klYcAcc/Q2Co6H/h+AbbHZVUkYKdiIiIlLAkQ8f3gv7fwTfkIIBiIPqmV2VnAcFOxERESkYgPizRPj1C/DyhX4LIbyZ2VXJeVKwExEREfh2Kvw0FyxW6D0bGnQyuyK5AAp2IiIiNd36ubDy2YLvPaZBixvNrUcumIKdiIhITbZ9GSwdU/C984PQYYi59chFUbATERGpqfavg8UDwXBAm35w/RNmVyQXScFORESkJjryG8y/HfJPQdN4uPkVsFjMrkoukpfZBYiIiEglyDkBR36Fw7/Cke2w8UM4eRTqtYU+c8HmbXaFUg4U7ERERKqTrCNweHtBeDt8+nPkV8j4o3jb2o2g/2LwCaz0MqViKNiJiIhUNU4nZOz/s/etMLwd3g6njpW+XEA4hDWD8MsgvDlccRsE1Km8uqXCKdiJiIh4KkceHNtVpAfudJA7sgPyTpa+XEiD0wGuGYRd9udf/9DKq11MoWAnIiJittws9/vfCnvgju0CZ37Jy1i9oU6TIsHtdE9cnUvB7l+59YvHULATERGpLCePFe99O/wrpO8tfRnvAAi7tCC8uQJcs4L74/TAg5xBwU5ERKQ8GUbBgwpF73sr/HvySOnL+df5s9et6N+gS8Cq0cmkbBTsRERELoQjH47vLvn+t9zM0pcLjna/762wF04PMUg5ULATERE5m7xTBWGt6BAiR36FozvBmVfyMlYvCG1c8v1vGlpEKpCCnYiICMCp4yUPH5K2FzBKXsbL78/734pePg1tDF72Si1fBBTsRESkJjEMOHGw5Pvfsg6Vvpxf7TPufzt9GTU4Wve/iUdRsBMRkerH6YDje0q+/y0no/Tlgi4p5f63ML1HVaqEGhfspk+fzrRp00hJSaFNmza8+uqrdOzY0eyyRETkQuRlw9Hfig8fcnQHOHJLXsZihdoxRcJb89M9cZeBT63KrV+knNWoYLdw4UISExOZMWMGsbGxvPTSSyQkJLB9+3bq1q1rdnkiItWT01EQshy5BW9SKNP3vNLbZB0ucv/b72A4S96ul2/BwwpnDh9Spwl4+VTuMRCpJBbDMEq5I7T6iY2NpUOHDrz22msAOJ1OoqOjGTVqFOPGjTvrshkZGQQHB5Oenk5QUFBllCsiUrrzDkv5FxCuyjDfmXfu5UoLXuXFN7jk+99CGoDVVrHbFqkE55NBakyPXW5uLuvXr2f8+PGuaVarlfj4eJKSkkysrIAjP599O5LLvsDpPF7mVF4sv19Yni/9/w04+/qKb76s2zfO+FvGpUpbf4nTS25b4t00pdZdwvTzaVtaDedRb9mPacnrKHlbp9saBhbL6b+np1lOf4oua7Fwuo3halPQpEh7wwBL4TKnl+P0eossi1G4Dv6choHFwG35whqKL0uROorsR0ltLQV1utqfrtHVzqDI9otvx23dRdfhdnwMLM78c4cl59kCVSWGpYpk9QKbveCtCTZ7Gb+f/mv1Ph3kijyJGlhX97+JnFZjgt2RI0dwOBxERES4TY+IiGDbtm3F2ufk5JCTk+P6nZFxlptty0FWZjqNFnat0G2ISPXlsNhwWrxxWL1xWrxxWr0xrH/+NWzeGFY7hs0bCv+eDkwWLzuW00HK6mXHYvPG4u2D1WbH6l0wzerlg827cL5P2YNYScFMT5GKVJgaE+zO15QpU5g8eXKlbc9isXCM87vEa5Tcp1Ru7c9XVamnpOml93WV1Pbi11vyOkpbb1mXP79jWmK9pfR6uPrGDCjsXzujX841za09Be2dRb672htF+/yKr6foNs+c5r6NktqC062vz1LqNlw1nWU7nLmdEve59G0U9jvmYiPP8CKPwo/N9T3XbboXuXiRZ9jcf+PlWj4X9/WUdv5UBG+bBS+rFS+bBW+bgbctDy+rA29bNl42K15WC3avgr9eNqurvbfNgrfNWjDNasHLVjDfbive1stmKZh+uo13kflBvt40Cgugfm0/vG0KiSJF1ZhgFxYWhs1mIzU11W16amoqkZGRxdqPHz+exMRE1++MjAyio6MrrL5awaEwaV+FrV+kJiu8NF945dQoMv3P74XzjGJXtUuad7Z1UEJ7p2HgcBrkOQ3yHU7yHAb5Tid5+QZ5Tif5jtPTXfOLtHEY5DuM09Oc5BdZR+HvPMfpdTid5OYX/M0/Y35hm8JtuNbpaltY0591OEv4/ywKtuuAUl66UFm8rBYa1PGncVgAMWEBxIQF0jg8gMZhAYTX8sGiy7NSA9WYYGe322nXrh0rVqygV69eQMHDEytWrGDkyJHF2vv4+ODjo6emRKqDwn/gi/87r3/4z8XpLAieeQ73QPpn0DRKCZt/BtKCsOnepnD+n2Hzz3Cbe/pv0cCaVyT0HsvKZc/RLLLznOw6nMWuw1nF6g708Tod9go+BYEvkEZh/tTy9TbhSIpUjhoT7AASExMZMGAA7du3p2PHjrz00ktkZWUxaNAgs0sTEfFIVqsFH6sNHw/718LpNEjJyGb3kSx2Hcli1+FMdh/JYveRLPYdO0lmTj4b/0hn4x/pxZYNr+VD49NhLyasIPDFhAcQXdsfu5cu7UrV5mH/U61Yd9xxB4cPH2bChAmkpKTQtm1bli1bVuyBChER8WxWq4WoED+iQvz4S9Mwt3k5+Q72HTvJrsMFQc/190gWRzJzOHyi4LNm9zG35WxWC9G1/WgcHvhnT19YAI3DA4kI0qVdqRpq1Dh2F0Pj2ImIVH0Z2XnsLhL0ivb0ncx1lLqcv91GozoBrnv4YsIL7umLCQsg2E+XdqVinU8GUbArIwU7EZHqyzAMDp3IYWdh0CsS/vYeO4mjpKdITgsLtLtd0i3s6WtQxx8fLw2QLBdPwa4CKNiJiNRMeQ6n+6XdI1nsPpLJrsNZHDqRU+pyVgvUr+1f5OGN07184QHUC/LFatWlXSkbBbsKoGAnIiJnyszJZ08Jl3V3Hc4iMye/1OV8va1FLu2evqfvdPgL8bdX4h5IVaBgVwEU7EREpKwMw+BwZs4Z9/MV9PTtPXaSPEfp//SGBtjPGKaloKevYR1/fL11abcmUrCrAAp2IiJSHvIdTvYfP1XiAxwH07NLXc5igUtC/Nye1i0MgFEhfth0abfaUrCrAAp2IiJS0U7m5rtCXmFv387T4e9EdumXdu1eVmLqBLhd0m18+snd2v7eGqqlijufDFKjxrETERHxZP52Ly6PCubyqGC36YZhcCwrt+DBjcPuD3D8fvQkuflOtqeeYHvqiWLrDPbzLjIY8589fY3qBOBn16Xd6kY9dmWkHjsREfFEDqfBgbRTfw7VUuQBjj/STpW6nMUCg66O4YkbW6hHz8Opx05ERKSGsFktRIf6Ex3qz3XN3Odl5znYc/TPXr7CBzh2Hcki7WQeb3+/G5sVHu2hcFddKNiJiIhUU77eNppHBtE8sngvz6J1+3jkww289d1ugny9GdX1UhMqlPKmtx2LiIjUQLe3j2bCjS0BeH75r7zz/W6TK5LyoGAnIiJSQ917TQxj4i8DYPKnW1i8bp/JFcnFUrATERGpwe7v2pTB18QAMPY/G/hi40GTK5KLoWAnIiJSg1ksFh7v2YI72kfjNOD+D37m218Pm12WXCAFOxERkRrOYrHw7K2t6Nm6HnkOg3+8t44f9xwzuyy5AAp2IiIigs1q4cXb23Jds3Cy85zc+86PbPoj3eyy5Dwp2ImIiAhQ8GqyN/q3o2NMKCdy8rnn7bX8dijT7LLkPCjYiYiIiIuf3cbsAe1pXT+YY1m53DVrDfuOnTS7LCkjBTsRERFxU8vXmzmDOnJp3UBSMrK5a/YaDmVkm12WlIGCnYiIiBQTGmDn/SGxRIf68fvRk9w9ey1pJ3PNLkvOQcFOREREShQR5Mu8wZ2ICPJhe+oJBrzzI5k5+WaXJWehYCciIiKlalDHn/cHx1Lb35tf9qUxZO6PZOc5zC5LSqFgJyIiImd1aUQt3r03lkAfL1bvOsaIeT+R53CaXZaUQMFOREREzqlV/WBmD2iPj5eVFdsOkbjoFxxOw+yy5AwKdiIiIlImsY3rMOPudnjbLHz6ywEeX7IJw1C48yQKdiIiIlJmXZrV5aU7rsRqgQVr9/LcF9sU7jyIxwe7Ro0aYbFY3D7PPfecW5sNGzbQuXNnfH19iY6OZurUqcXWs3jxYpo3b46vry+tWrXi888/r6xdEBERqVZ6tq7HlFtbAfDmql1M/+Y3kyuSQh4f7ACefPJJDh486PqMGjXKNS8jI4MbbriBhg0bsn79eqZNm8akSZOYOXOmq80PP/zAnXfeyeDBg/n555/p1asXvXr1YtOmTWbsjoiISJV3R4cGPN6zBQD//upX5ny/2+SKBMBieHj/aaNGjRg9ejSjR48ucf4bb7zBY489RkpKCna7HYBx48axZMkStm3bBsAdd9xBVlYWS5cudS3XqVMn2rZty4wZM8pUR0ZGBsHBwaSnpxMUFHRxOyUiIlJNvLj8V15esQOAf/dpw23t6ptcUfVzPhmkSvTYPffcc9SpU4crr7ySadOmkZ//5+CISUlJXHvtta5QB5CQkMD27ds5fvy4q018fLzbOhMSEkhKSqqcHRAREammRsdfyr1/iQHgkQ9/YdmmgyZXVLN5mV3Audx///1cddVVhIaG8sMPPzB+/HgOHjzICy+8AEBKSgoxMTFuy0RERLjm1a5dm5SUFNe0om1SUlJK3W5OTg45OTmu3xkZGeW1SyIiItWGxWLhiRtbkJmTx6J1+xm14GdmD/Di2svCzS6tRjKlx27cuHHFHog481N4GTUxMZHrrruO1q1bc9999/H888/z6quvuoWuijBlyhSCg4Ndn+jo6ArdnoiISFVlsViYcmtreraqR57DYNh761i355jZZdVIpvTYPfjggwwcOPCsbRo3blzi9NjYWPLz89mzZw/NmjUjMjKS1NRUtzaFvyMjI11/S2pTOL8k48ePJzEx0fU7IyND4U5ERKQUNquFF+9oS2ZOPt/+ephB7/zIgmGduOKSYLNLq1FMCXbh4eGEh19YF21ycjJWq5W6desCEBcXx2OPPUZeXh7e3t4ALF++nGbNmlG7dm1XmxUrVrg9gLF8+XLi4uJK3Y6Pjw8+Pj4XVKOIiEhNZPeyMuOudgx4ey1r9xxjwNtrWfiPOJrWDTS7tBrDox+eSEpK4qWXXuKXX35h165dzJs3jzFjxnDXXXe5Qlu/fv2w2+0MHjyYzZs3s3DhQl5++WW33rYHHniAZcuW8fzzz7Nt2zYmTZrEunXrGDlypFm7JiIiUi352W3MGtieKy4J4mhWLnfPXsP+4yfNLqvG8OjhTn766Sf++c9/sm3bNnJycoiJieHuu+8mMTHRrTdtw4YNjBgxgh9//JGwsDBGjRrF2LFj3da1ePFiHn/8cfbs2cOll17K1KlT6dGjR5lr0XAnIiIiZXcsK5fb30zit0OZNKrjz6L74qhby9fssqqk88kgHh3sPImCnYiIyPlJSc+mz5s/sO/YKZpF1GLhPzoR4m8/94LiptqNYyciIiJVT2SwL/MGd6JuLR+2p55gwDs/kpmTf+4F5YIp2ImIiEiFaVDHn/eHxFLb35tf9qUxdO46svMcZpdVbSnYiYiISIW6LKIWc+/tSKCPF0m7jjJy/k/kOZxml1UtKdiJiIhIhWtdP4RZA9rj42Xlf1sP8eCiX3A4dZt/eVOwExERkUrRqXEdZtzVDi+rhU9+OcATH29Cz3CWLwU7ERERqTRdmtflxTvaYrHA/DV7eW7ZNoW7cqRgJyIiIpXqpjZRTPl7KwDe/HYXr6/caXJF1YeCnYiIiFS6vh0b8HjPFgBM+3I7c3/YY25B1YSCnYiIiJhiSOfG3N/1UgAmfrKZ/6zfb3JFVZ+CnYiIiJhmTPylDPpLIwAe/vAXlm1KMbegKk7BTkRERExjsVh4omdLbmtXH6cB9y/4me92HDa7rCpLwU5ERERMZbVaeO7WVnS/IpJch5Nh765n3Z5jZpdVJSnYiYiIiOm8bFZe6tuWay8L51Seg0FzfmTzgXSzy6pyFOxERETEI/h42XjzrnZ0aFSbE9n53DN7LTsPZ5pdVpWiYCciIiIew89uY/bADlxxSRBHs3K5a9Ya9h8/aXZZVYbF0HDPZZKRkUFwcDDp6ekEBQWV2s7hcJCXl1eJlVU/3t7e2Gw2s8sQERETHc3M4fY3k9h5OItGdfxZdF8cdWv5ml2WKcqaQUDBrszOdVANwyAlJYW0tLTKL64aCgkJITIyEovFYnYpIiJikpT0bG6b8QP7j5+ieWQtPhjWiRB/u9llVToFuwpwroN68OBB0tLSqFu3Lv7+/gokF8gwDE6ePMmhQ4cICQmhXr16ZpckIiIm2nMkiz5vJnH4RA5to0N4f0gsgT5eZpdVqc4n2NWsI1NBHA6HK9TVqVPH7HKqPD8/PwAOHTpE3bp1dVlWRKQGaxQWwPuDY7ljZhLJ+9IY9u463h7YAV9v/dtQEj08UQ4K76nz9/c3uZLqo/BY6n5FERFpFlmLuYM6EmC38cPOo4yc/xN5DqfZZXkkBbtypMuv5UfHUkREimoTHcKsAR3w8bLyv62HeGjxLzidupvsTAp2IiIiUiXENanDG3ddhZfVwsfJB3ji403oUQF3CnZyUebMmUNISIjr96RJk2jbtq1p9YiISPV2ffMIXryjLRYLzFuzl38t2252SR5Fwa6GGzhwIL169So2feXKlVgsFg3fIiIiHuemNlE8+/dWAMz4difTv/nN5Io8h4KdiIiIVDl3dmzAoz2aAzDty+28m7TH3II8hIKdVIg333yT6Oho/P39uf3220lP14ucRUSkfA27tgmjrm8KwISPN/Pfn/abXJH5TA12zzzzDFdffTX+/v5u92kVtXfvXnr27Im/vz9169bl4YcfJj8/363NypUrueqqq/Dx8aFp06bMmTOn2HqmT59Oo0aN8PX1JTY2lrVr11bAHv3JMAxO5uab8jH7RtLffvuNRYsW8emnn7Js2TJ+/vln/vnPf5pak4iIVE+Jf7uMgVc3AuDhDzewbFOKuQWZzNQBinNzc+nTpw9xcXHMnj272HyHw0HPnj2JjIzkhx9+4ODBg9xzzz14e3vz7LPPArB792569uzJfffdx7x581ixYgVDhgyhXr16JCQkALBw4UISExOZMWMGsbGxvPTSSyQkJLB9+3bq1q1bIft2Ks9BywlfVsi6z2XLkwn428v+n3bp0qUEBga6TXM4HBe8/ezsbN59910uueQSAF599VV69uzJ888/T2Rk5AWvV0RE5EwWi4UJN7YkMyefD9fv5/4FPzN7YHs6XxpudmmmMLXHbvLkyYwZM4ZWrVqVOP+rr75iy5YtvP/++7Rt25bu3bvz1FNPMX36dHJzcwGYMWMGMTExPP/887Ro0YKRI0dy22238eKLL7rW88ILLzB06FAGDRpEy5YtmTFjBv7+/rz99tuVsp+erkuXLiQnJ7t9Zs2adcHra9CggSvUAcTFxeF0Otm+XU8uiYhI+bNaLTx3ayu6XxFJrsPJsHfXs/7342aXZQqPfqVYUlISrVq1IiIiwjUtISGB4cOHs3nzZq688kqSkpKIj493Wy4hIYHRo0cDBb2C69evZ/z48a75VquV+Ph4kpKSSt12Tk4OOTk5rt8ZGRnnVbuft40tTyac1zLlxe88X7MSEBBA06ZN3abt36/7FEREpOrwsll5qW9bMueu47sdRxj0zlo+GBZHy6izv1u1uvHohydSUlLcQh3g+p2SknLWNhkZGZw6dYojR47gcDhKbFO4jpJMmTKF4OBg1yc6Ovq8ardYLPjbvUz5mP3Whr1793LgwAHX79WrV2O1WmnWrJmJVYmISHXn42Xjzbvb0b5hbTKy87nn7TXsOpxpdlmVqtyD3bhx47BYLGf9bNu2rbw3W+7Gjx9Penq667Nv3z6zS6oyfH19GTBgAL/88gvfffcd999/P7fffrvurxMRkQrnb/di9sAOtKwXxJHMXO6atYY/0k6ZXValKfdLsQ8++CADBw48a5vGjRuXaV2RkZHFnl5NTU11zSv8WzitaJugoCD8/Pyw2WzYbLYS25wtaPj4+ODj41OmOsVd06ZNufXWW+nRowfHjh3jxhtv5PXXXze7LBERqSGC/bx5d3BHbn8ziV2Hs7hr1hoW/SOO8FrV/991i2H22BgUvJZq9OjRxd5y8MUXX3DjjTdy8OBB19OrM2fO5OGHH+bQoUP4+PgwduxYPv/8czZu3Oharl+/fhw7doxly5YBEBsbS8eOHXn11VcBcDqdNGjQgJEjRzJu3Lgy1ZiRkUFwcDDp6ekEBblfr8/Ozmb37t3ExMTg6+t7oYdBitAxFRGRi3Uw/RS3vZHEH2mnaB5Zi4XD4gj29za7rPN2tgxyJlPvsdu7dy/Jycns3bsXh8PheiIzM7PgevgNN9xAy5Ytufvuu/nll1/48ssvefzxxxkxYoSrN+2+++5j165dPPLII2zbto3XX3+dRYsWMWbMGNd2EhMTeeutt5g7dy5bt25l+PDhZGVlMWjQIFP2W0RERCpevWA/5g2JJbyWD9tSTjBwzlqycvLPvWBVZphowIABBlDs880337ja7Nmzx+jevbvh5+dnhIWFGQ8++KCRl5fntp5vvvnGaNu2rWG3243GjRsb77zzTrFtvfrqq0aDBg0Mu91udOzY0Vi9evV51Zqenm4ARnp6erF5p06dMrZs2WKcOnXqvNZZFXTr1s0ICAgo8fPMM89U2Har8zEVEZHKtfVgutF60pdGw7FLjTtnJhmncvPNLum8nC2DnMkjLsVWBTX1Uuwff/zBqVMl33QaGhpKaGhohWy3Oh9TERGpfMn70uj/1mqych3Et4jgjbuuwtvm0YODuJzPpViPHsdOzFd0oGEREZGqqm10CG8NaM/Ad37kf1tTeXjxL7xwe1usVnOHCCtvVSOqioiIiFykq5uE8Xq/q/CyWliSfIAJn2wy/f3q5U3BTkRERGqM+JYRPH97GywWeH/1XqZ+Wb1ed6lgJyIiIjXKLW0v4ZleBe+pf2PlTl5f+ZvJFZUfBTsRERGpcfrFNmB89+YATF22nfeS9phbUDlRsBMREZEa6R9/bcLILk0BeOLjzXz0836TK7p4CnZS4QYOHEivXr3MLkNERKSYB2+4jIFXNwLgocUb+HJzirkFXSQFuxqutNC1cuVKLBZLsde8iYiIVCcWi4UJN7bk1qsuweE0GDX/Z/5vxxGzy7pgCnYiIiJSo1mtFqb2bk3C5RHkOpwMe28d638/bnZZF0TBTi7KpEmTaNu2rdu0l156iUaNGhVrO3nyZMLDwwkKCuK+++4jNze3cooUERE5By+blVfuvJLOl4ZxMtfBoHfWsuVAhtllnTe9eaKiGAbknTRn297+YPGskbRXrFiBr68vK1euZM+ePQwaNIg6derwzDPPmF2aiIgIAD5eNt68ux13z17L+t+Pc8/ba1j0jzgahweaXVqZKdhVlLyT8GyUOdt+9ADYA8rcfOnSpQQGup+0DoejXEuy2+28/fbb+Pv7c/nll/Pkk0/y8MMP89RTT2G1quNYREQ8g7/di7cHduDOmavZcjCDu2atYfHwq7kkxM/s0spE/6IKXbp0ITk52e0za9asct1GmzZt8Pf3d/2Oi4sjMzOTffv2let2RERELlawnzfvDu5I4/AADqRnc9esNRw+kWN2WWWiHruK4u1f0HNm1rbPQ0BAAE2bNnWbtn9/2cbysVqtxd6zl5eXd17bFxER8TRhgT68PziWPjOS2H0ki7tnr2HhsDiC/b3NLu2sFOwqisVyXpdDq6rw8HBSUlIwDAPL6fv6kpOTi7X75ZdfOHXqFH5+BV3Zq1evJjAwkOjo6MosV0REpMyiQvx4f0hBuNuWcoKBc9by/uBYAnw8Nz7pUqxclOuuu47Dhw8zdepUdu7cyfTp0/niiy+KtcvNzWXw4MFs2bKFzz//nIkTJzJy5EjdXyciIh4tJiyA9wZ3JMjXi5/3pjHsvXVk55XvfejlSf+qykVp0aIFr7/+OtOnT6dNmzasXbuWhx56qFi7rl27cumll3Lttddyxx13cPPNNzNp0qTKL1hEROQ8tagXxJx7O+Jvt/H9b0cZteBn8h1Os8sqkcU48wYpKVFGRgbBwcGkp6cTFBTkNi87O5vdu3cTExODr6+vSRVWLzqmIiLiaX747QgD5/xIbr6TW6+8hH/3aYPVWvHDi50tg5xJPXYiIiIiZXB10zCm97sKm9XCf3/+g4mfbC72AKHZFOzkrLp3705gYGCJn2effdbs8kRERCrV31pG8MLtbbBY4L3VvzPty+1ml+TGcx/rEI8wa9YsTp06VeK80NDQSq5GRETEfLe0vYTMnHwe+2gTr6/cSS1fb4Zf18TssgAFOzmHSy65xOwSREREPE7/2IacyM7nuS+28a9l26gTaOf29uYP4aVgJyIiInIB7vtrEzJO5bF0w0HiGtcxuxxAwa5cOZ2e+ehzVaRjKSIiVcHDCc34x7VNPOaNFAp25cBut2O1Wjlw4ADh4eHY7XbXWxjk/BiGQW5uLocPH8ZqtWK3280uSUREpFQWi8VjQh0o2JULq9VKTEwMBw8e5MABk94PW834+/vToEEDvZlCRETkPJga7J555hk+++wzkpOTsdvtpKWlFWtTUs/XggUL6Nu3r+v3ypUrSUxMZPPmzURHR/P4448zcOBAt2WmT5/OtGnTSElJoU2bNrz66qt07Nix3PbFbrfToEED8vPzcTg891UjVYHNZsPLy0u9niIiIufJ1GCXm5tLnz59iIuLY/bs2aW2e+edd+jWrZvrd0hIiOv77t276dmzJ/fddx/z5s1jxYoVDBkyhHr16pGQkADAwoULSUxMZMaMGcTGxvLSSy+RkJDA9u3bqVu3brntj8ViwdvbG29vz+mSFRERkZrDI14pNmfOHEaPHl1qj91HH31Er169Slx27NixfPbZZ2zatMk1rW/fvqSlpbFs2TIAYmNj6dChA6+99hpQcGN+dHQ0o0aNYty4cWWq8Xxe5yEiIiJSXqrdK8VGjBhBWFgYHTt25O2333Z7fUdSUhLx8fFu7RMSEkhKSgIKegXXr1/v1sZqtRIfH+9qU5KcnBwyMjLcPiIiIiKezOMfnnjyySe5/vrr8ff356uvvuKf//wnmZmZ3H///QCkpKQQERHhtkxERAQZGRmcOnWK48eP43A4Smyzbdu2Urc7ZcoUJk+eXP47JCIiIlJByj3YjRs3jn/9619nbbN161aaN29epvU98cQTru9XXnklWVlZTJs2zRXsKsr48eNJTEx0/U5PT6dBgwbquRMREZFKVZg9ynL3XLkHuwcffLDYE6lnaty48QWvPzY2lqeeeoqcnBx8fHyIjIwkNTXVrU1qaipBQUH4+flhs9mw2WwltomMjCx1Oz4+Pvj4+Lh+Fx7U6GjzXxciIiIiNc+JEycIDg4+a5tyD3bh4eGEh4eX92pdkpOTqV27tit0xcXF8fnnn7u1Wb58OXFxcUDBMCTt2rVjxYoVrgcwnE4nK1asYOTIkWXeblRUFPv27aNWrVoVNgxHRkYG0dHR7Nu3Tw9oXCQdy/Kh41h+dCzLh45j+dGxLB+VcRwNw+DEiRNERUWds62p99jt3buXY8eOsXfvXhwOB8nJyQA0bdqUwMBAPv30U1JTU+nUqRO+vr4sX76cZ599loceesi1jvvuu4/XXnuNRx55hHvvvZevv/6aRYsW8dlnn7naJCYmMmDAANq3b0/Hjh156aWXyMrKYtCgQWWu1Wq1Ur9+/XLb97MJCgrS/8jKiY5l+dBxLD86luVDx7H86FiWj4o+jufqqStkarCbMGECc+fOdf2+8sorAfjmm2+47rrr8Pb2Zvr06YwZMwbDMGjatCkvvPACQ4cOdS0TExPDZ599xpgxY3j55ZepX78+s2bNco1hB3DHHXdw+PBhJkyYQEpKCm3btmXZsmXFHqgQERERqco8Yhw7KaCx8sqPjmX50HEsPzqW5UPHsfzoWJYPTzuOVWIcu5rCx8eHiRMnuj20IRdGx7J86DiWHx3L8qHjWH50LMuHpx1H9diJiIiIVBPqsRMRERGpJhTsRERERKoJBTsRERGRakLBTkRERKSaULDzENOnT6dRo0b4+voSGxvL2rVrzS6pypk0aRIWi8XtU9Z3Etd0q1at4qabbiIqKgqLxcKSJUvc5huGwYQJE6hXrx5+fn7Ex8ezY8cOc4r1YOc6jgMHDix2jnbr1s2cYj3YlClT6NChA7Vq1aJu3br06tWL7du3u7XJzs5mxIgR1KlTh8DAQHr37l3s1ZFStmN53XXXFTsv77vvPpMq9lxvvPEGrVu3dg1EHBcXxxdffOGa7ynnpIKdB1i4cCGJiYlMnDiRn376iTZt2pCQkMChQ4fMLq3Kufzyyzl48KDr83//939ml1QlZGVl0aZNG6ZPn17i/KlTp/LKK68wY8YM1qxZQ0BAAAkJCWRnZ1dypZ7tXMcRoFu3bm7n6IIFCyqxwqrh22+/ZcSIEaxevZrly5eTl5fHDTfcQFZWlqvNmDFj+PTTT1m8eDHffvstBw4c4NZbbzWxas9UlmMJMHToULfzcurUqSZV7Lnq16/Pc889x/r161m3bh3XX389t9xyC5s3bwY86Jw0xHQdO3Y0RowY4frtcDiMqKgoY8qUKSZWVfVMnDjRaNOmjdllVHmA8dFHH7l+O51OIzIy0pg2bZprWlpamuHj42MsWLDAhAqrhjOPo2EYxoABA4xbbrnFlHqqskOHDhmA8e233xqGUXD+eXt7G4sXL3a12bp1qwEYSUlJZpVZJZx5LA3DMP76178aDzzwgHlFVWG1a9c2Zs2a5VHnpHrsTJabm8v69euJj493TbNarcTHx5OUlGRiZVXTjh07iIqKonHjxvTv35+9e/eaXVKVt3v3blJSUtzO0eDgYGJjY3WOXoCVK1dSt25dmjVrxvDhwzl69KjZJXm89PR0AEJDQwFYv349eXl5budk8+bNadCggc7JczjzWBaaN28eYWFhXHHFFYwfP56TJ0+aUV6V4XA4+OCDD8jKyiIuLs6jzklT3xUrcOTIERwOR7H31kZERLBt2zaTqqqaYmNjmTNnDs2aNePgwYNMnjyZzp07s2nTJmrVqmV2eVVWSkoKQInnaOE8KZtu3bpx6623EhMTw86dO3n00Ufp3r07SUlJ2Gw2s8vzSE6nk9GjR/OXv/yFK664Aig4J+12OyEhIW5tdU6eXUnHEqBfv340bNiQqKgoNmzYwNixY9m+fTv//e9/TazWM23cuJG4uDiys7MJDAzko48+omXLliQnJ3vMOalgJ9VG9+7dXd9bt25NbGwsDRs2ZNGiRQwePNjEykQK9O3b1/W9VatWtG7dmiZNmrBy5Uq6du1qYmWea8SIEWzatEn3y5aD0o7lsGHDXN9btWpFvXr16Nq1Kzt37qRJkyaVXaZHa9asGcnJyaSnp/Phhx8yYMAAvv32W7PLcqNLsSYLCwvDZrMVe3ImNTWVyMhIk6qqHkJCQrjsssv47bffzC6lSis8D3WOlr/GjRsTFhamc7QUI0eOZOnSpXzzzTfUr1/fNT0yMpLc3FzS0tLc2uucLF1px7IksbGxADovS2C322natCnt2rVjypQptGnThpdfftmjzkkFO5PZ7XbatWvHihUrXNOcTicrVqwgLi7OxMqqvszMTHbu3Em9evXMLqVKi4mJITIy0u0czcjIYM2aNTpHL9L+/fs5evSoztEzGIbByJEj+eijj/j666+JiYlxm9+uXTu8vb3dzsnt27ezd+9enZNnONexLElycjKAzssycDqd5OTkeNQ5qUuxHiAxMZEBAwbQvn17OnbsyEsvvURWVhaDBg0yu7Qq5aGHHuKmm26iYcOGHDhwgIkTJ2Kz2bjzzjvNLs3jZWZmuv1/57t37yY5OZnQ0FAaNGjA6NGjefrpp7n00kuJiYnhiSeeICoqil69eplXtAc623EMDQ1l8uTJ9O7dm8jISHbu3MkjjzxC06ZNSUhIMLFqzzNixAjmz5/Pxx9/TK1atVz3KAUHB+Pn50dwcDCDBw8mMTGR0NBQgoKCGDVqFHFxcXTq1Mnk6j3LuY7lzp07mT9/Pj169KBOnTps2LCBMWPGcO2119K6dWuTq/cs48ePp3v37jRo0IATJ04wf/58Vq5cyZdffulZ52SlPoMrpXr11VeNBg0aGHa73ejYsaOxevVqs0uqcu644w6jXr16ht1uNy655BLjjjvuMH777Tezy6oSvvnmGwMo9hkwYIBhGAVDnjzxxBNGRESE4ePjY3Tt2tXYvn27uUV7oLMdx5MnTxo33HCDER4ebnh7exsNGzY0hg4daqSkpJhdtscp6RgCxjvvvONqc+rUKeOf//ynUbt2bcPf39/4+9//bhw8eNC8oj3UuY7l3r17jWuvvdYIDQ01fHx8jKZNmxoPP/ywkZ6ebm7hHujee+81GjZsaNjtdiM8PNzo2rWr8dVXX7nme8o5aTEMw6jMICkiIiIiFUP32ImIiIhUEwp2IiIiItWEgp2IiIhINaFgJyIiIlJNKNiJiIiIVBMKdiIiIiLVhIKdiIiISDWhYCciIiJSTSjYiYiIiFQTCnYiIiIi1YSCnYiIiEg1oWAnIiIiUk0o2ImIiIhUEwp2IiIiItWEgp2IiIhINaFgJyIiIlJNKNiJiIiIVBMKdiIi1VSjRo0YOHCg2WWISCVSsBORamXOnDlYLBbXx9fXl6ioKBISEnjllVc4ceJEmdazcuVKt/VYLBZCQ0Pp1KkT8+bNq+C9EBG5MF5mFyAiUhGefPJJYmJiyMvLIyUlhZUrVzJ69GheeOEFPvnkE1q3bl2m9dx///106NABgKNHj7Jw4ULuuusu0tLSGDFiREXugojIeVOwE5FqqXv37rRv3971e/z48Xz99dfceOON3HzzzWzduhU/P79zrqdz587cdtttrt/Dhw+ncePGzJ8/X8FORDyOLsWKSI1x/fXX88QTT/D777/z/vvvX9A67HY7tWvXxsvL/f8vzs/P56mnnqJJkyb4+PjQqFEjHn30UXJyctzaWSwWJk2aVGy9Z94PV3hJ+fvvvycxMZHw8HACAgL4+9//zuHDh92WNQyDp59+mvr16+Pv70+XLl3YvHlzsW3k5eUxefJkLr30Unx9falTpw7XXHMNy5cvv6BjISKeR8FORGqUu+++G4CvvvqqTO1PnDjBkSNHOHLkCL/++iuTJk1i06ZNDBgwwK3dkCFDmDBhAldddRUvvvgif/3rX5kyZQp9+/a9qHpHjRrFL7/8wsSJExk+fDiffvopI0eOdGszYcIEnnjiCdq0acO0adNo3LgxN9xwA1lZWW7tJk2axOTJk+nSpQuvvfYajz32GA0aNOCnn366qBpFxHPoUqyI1Cj169cnODiYnTt3lqn9vffe6/bbarXyzDPPuE3/5ZdfmDt3LkOGDOGtt94C4J///Cd169bl3//+N9988w1dunS5oHrr1KnDV199hcViAcDpdPLKK6+Qnp5OcHAwhw8fZurUqfTs2ZNPP/3U1e6xxx7j2WefdVvXZ599Ro8ePZg5c+YF1SIink89diJS4wQGBpb56dgJEyawfPlyli9fzsKFC7nzzjt57LHHePnll11tPv/8cwASExPdln3wwQeBgkB1oYYNG+YKa1Bwz5/D4eD3338H4H//+x+5ubmMGjXKrd3o0aOLrSskJITNmzezY8eOC65HRDybeuxEpMbJzMykbt26ZWrbqlUr4uPjXb9vv/120tPTGTduHP369SM8PJzff/8dq9VK06ZN3ZaNjIwkJCTEFcIuRIMGDdx+165dG4Djx48DuNZ96aWXurULDw93tS305JNPcsstt3DZZZdxxRVX0K1bN+6+++4yPyEsIp5PPXYiUqPs37+f9PT0YiHsfHTt2pXs7GzWrl3rNr1oj9n5cjgcJU632WwlTjcM47y3ce2117Jz507efvttrrjiCmbNmsVVV13FrFmzzntdIuKZFOxEpEZ57733AEhISLjgdeTn5wMFPX8ADRs2xOl0FrvEmZqaSlpaGg0bNnRNq127NmlpaW7tcnNzOXjw4AXVUrjuM7d9+PBhV69eUaGhoQwaNIgFCxawb98+WrduXeJTuiJSNSnYiUiN8fXXX/PUU08RExND//79L3g9S5cuBaBNmzYA9OjRA4CXXnrJrd0LL7wAQM+ePV3TmjRpwqpVq9zazZw5s9Qeu3OJj4/H29ubV1991a0X78xaoGCA5aICAwNp2rRpsSFZRKTq0j12IlItffHFF2zbto38/HxSU1P5+uuvWb58OQ0bNuSTTz7B19e3TOv57rvvyM7OBuDYsWN88sknfPvtt/Tt25fmzZsDBQFvwIABzJw5k7S0NP7617+ydu1a5s6dS69evdyeiB0yZAj33XcfvXv35m9/+xu//PILX375JWFhYRe0n+Hh4Tz00ENMmTKFG2+8kR49evDzzz/zxRdfFFtny5Ytue6662jXrh2hoaGsW7eODz/8sNjwKSJSdSnYiUi1NGHCBKBgQOHQ0FBatWrFSy+9xKBBg6hVq1aZ1/PKK6+4vtvtdho3bswzzzzDww8/7NZu1qxZNG7cmDlz5vDRRx8RGRnJ+PHjmThxolu7oUOHsnv3bmbPns2yZcvo3Lkzy5cvp2vXrhe8r08//TS+vr7MmDGDb775htjYWL766iu3nkIoeD3aJ598wldffUVOTg4NGzbk6aefLrYvIlJ1WYwLuQNXRERERDyO7rETERERqSYU7ERERESqCQU7ERERkWpCwU5ERESkmlCwExEREakmFOxEREREqgmNY1dGTqeTAwcOUKtWrYt6H6SIiIjI+TAMgxMnThAVFYXVevY+OQW7Mjpw4ADR0dFmlyEiIiI11L59+6hfv/5Z2yjYlVHhSPX79u0jKCjI5GpERESkpsjIyCA6OrpMb81RsCujwsuvQUFBCnYiIiJS6cpyK5genhARERGpJhTsRERERKoJBTsRERGRakLBTkRERKSaULATERERqSYU7ERERESqCQU7ERERkQuVcRC2fmp2FS4KdiIiIiIXIjsD5vWBhXfBz/PMrgZQsBMRERE5f/m5sOhuSN0IAeHQ6C9mVwQo2ImIiIicH8OAT0bBrpXgHQD9F0PtRmZXBSjYiYiIiJyfr5+CDR+AxQa3z4WoK82uyEXBTkRERKSsfpwN3z1f8P2ml+HSv5lbzxkU7ERERETKYtvn8PlDBd+vGw9X3W1uPSVQsBMRERE5l/3r4MN7wXDClXfDX8eaXVGJFOxEREREzuboTph/O+SfgqZ/gxtfBIvF7KpKpGAnIiIiUprMw/B+bzh5FOq1hT5zwOZtdlWlUrATERERKUluVkFP3fHdENKwYFgTn0CzqzqrahnsnnvuOSwWC6NHj3ZNy87OZsSIEdSpU4fAwEB69+5NamqqeUWKiIiI53Lkw+JBcOAn8AuFu/4LgXXNruqcql2w+/HHH3nzzTdp3bq12/QxY8bw6aefsnjxYr799lsOHDjArbfealKVIiIi4rEMAz5LhB1fgpcv9FsIYU3NrqpMqlWwy8zMpH///rz11lvUrl3bNT09PZ3Zs2fzwgsvcP3119OuXTveeecdfvjhB1avXm1ixSIiIuJxVv0bfpoLWKD3bIjuaHZFZVatgt2IESPo2bMn8fHxbtPXr19PXl6e2/TmzZvToEEDkpKSSlxXTk4OGRkZbh8RERGp5n6eB988XfC9xzRocaO59ZwnL7MLKC8ffPABP/30Ez/++GOxeSkpKdjtdkJCQtymR0REkJKSUuL6pkyZwuTJkyuiVBEREfFEv62AT+8v+P6X0dBxqKnlXIhq0WO3b98+HnjgAebNm4evr2+5rHP8+PGkp6e7Pvv27SuX9YqIiIgHOvgLLLoHnPnQ6nboOtHsii5ItQh269ev59ChQ1x11VV4eXnh5eXFt99+yyuvvIKXlxcRERHk5uaSlpbmtlxqaiqRkZElrtPHx4egoCC3j4iIiFRDx3+HeX0gNxNiroVbpoO1akakanEptmvXrmzcuNFt2qBBg2jevDljx44lOjoab29vVqxYQe/evQHYvn07e/fuJS4uzoySRURExBOcPAbzboPMVKh7OdzxPnjZza7qglWLYFerVi2uuOIKt2kBAQHUqVPHNX3w4MEkJiYSGhpKUFAQo0aNIi4ujk6dOplRsoiIiJgtLxs+6AdHfoWgSwoGIPYNNruqi1Itgl1ZvPjii1itVnr37k1OTg4JCQm8/vrrZpclIiIiZnA64b9DYW8S+ARD/w8h+BKzq7poFsMwDLOLqAoyMjIIDg4mPT1d99uJiIhUZYYBy8bDmjfAZi94q0RMZ7OrKtX5ZJCqeWegiIiIyIVKml4Q6gB6veHRoe58KdiJiIhIzbHpP/DVYwXf//YUtLrN3HrKmYKdiIiI1Ay7v4OP7iv4HnsfXD3K3HoqgIKdiIiIVH+HtsIH/cGRCy1ugoRnwWIxu6pyp2AnIiIi1VvGAXi/N+SkQ3QnuPWt/2/vzsOjqg+2j3/PTGYmCVkgCwkREgLIoghqgBCtVBQBLVYUFUWUXW2DVbEuaJXa9hEetXWh1qUKuIDihlSfF6pFwVpZFERASRAKsoRECCSBhKxz3j8GQiYLJJDkzHJ/rmuunDnb3Dmc1jvnzPwGbHarU7UIFTsREREJXKWFnm+VKNoDsWfCjW+CI8zqVC1GxU5EREQCU2U5LLwZ8jZBRAKMfQ/CY6xO1aJU7ERERCTwmCYszoTtK8AZAWPehnYpVqdqcSp2IiIiEniWPQob3wZbCFz/KiSda3WiVqFiJyIiIoFlzd/hi6c801c+C92GWJunFanYiYiISODI+j9Ycp9nevBDcN5N1uZpZSp2IiIiEhh2rYF3J4LphvPHwaB7rU7U6lTsRERExP/t3woLRkNlKZw5DH7xl4AcgPhkVOxERETEvx3+Cd64Bo4cgKTz4Lq5YA+xOpUlVOxERETEf5UdhgXXQ8GP0K4zjHkHnG2sTmUZFTsRERHxT1WV8O4EyPkGwmJg7PsQEW91Kkup2ImIiIj/MU34v7vhh48hJMwzAHFsV6tTWU7FTkRERPzPisdh3Wtg2ODaOdCpv9WJfIKKnYiIiPiXb96A5Y95pq94AnpeYW0eH6JiJyIiIv7jh3/BP37jmf7ZNOg/2do8PkbFTkRERPxDznp4+xYwq6DPaLj0EasT+RwVOxEREfF9B3fA/Ougohi6XAy//GtQDkB8Mip2IiIi4ttKDsAb10LxT5DQG65/HUKcVqfySSp2IiIi4rsqjsCbN0D+DxDVEW56F0KjrE7ls1TsRERExDe5q+C9ybBrNYRGw9h3IaqD1al8moqdiIiI+B7ThKXTIesjsDvhhgXQvpfVqXyeip2IiIj4ni9nw5oXPdNXvwidf2ZtHj+hYiciIiK+ZeO78MnDnumh/wO9r7E2jx9RsRMRERHfsf1zWHS7Z3rgr+GCqdbm8TMqdiIiIuIb8r6Dt24CdwWcdZXnap00iYqdiIiIWK9wj2esurIiSM6Aq18Cm2pKU+mIiYiIiLVKC2H+tXAoB+J6eD4B6wi1OpVfUrETERER61SWeW6//vQ9RCR4xqoLj7E6ld9SsRMRERFruN3wwa9hx7/BGeH5Vom2yVan8msqdiIiImKNZb+HTe+CLQRGvw4d+lidyO+p2ImIiEjrW/0S/OcZz/Qv/wpdL7E2T4BQsRMREZHWtflDWHKfZ/qS38G5N1qbJ4AERLGbOXMm/fv3JzIykvbt2zNy5Eiys7O91iktLSUzM5PY2FgiIiIYNWoUeXl5FiUWEREJUjtXw3uTARPSJsBFv7U6UUAJiGK3YsUKMjMzWbVqFZ988gkVFRUMHTqU4uLi6nXuvvtuPvzwQ9555x1WrFhBTk4O11yjrygRERFpNft/gDdHQ2UpdL8crngSDMPqVAHFME3TtDpEc9u3bx/t27dnxYoVDBo0iMLCQuLj41mwYAHXXnstAFlZWfTq1YuVK1cycODAk+6zqKiI6OhoCgsLiYqKaulfQUREJLAcyoNXhkDBTjgjDcZ9CM42VqfyC03pIAFxxa62wsJCAGJiPOPgrF27loqKCoYMGVK9Ts+ePUlOTmblypX17qOsrIyioiKvh4iIiJyCssOw4HpPqWuXCjcuVKlrIQFX7NxuN3fddRcXXnghvXv3BiA3Nxen00nbtm291k1ISCA3N7fe/cycOZPo6OjqR6dOnVo6uoiISOCpqoB3xsHe9RAeC2Pfg4h4q1MFrIArdpmZmWzatIm33nrrtPYzffp0CgsLqx+7du1qpoQiIiJBwjTho7tg678gJAzGvA2xXa1OFdBCrA7QnKZOncpHH33E559/TseOHavnJyYmUl5eTkFBgddVu7y8PBITE+vdl8vlwuVytXRkERGRwLV8FnzzBhg2uG4udOxndaKAFxBX7EzTZOrUqSxatIhPP/2U1NRUr+VpaWk4HA6WLVtWPS87O5udO3eSkZHR2nFFREQC37rXYMUsz/Qv/gw9Lrc2T5AIiCt2mZmZLFiwgMWLFxMZGVn9vrno6GjCwsKIjo5m0qRJTJs2jZiYGKKiorjjjjvIyMho1CdiRUREpAm2fAwf3uWZvui30G+ipXGCSUAMd2I0MAbO3LlzGT9+POAZoPiee+7hzTffpKysjGHDhvG3v/2twVuxtWm4ExERkUbYsw7m/QIqSqDvjTDyeY1Vd5qa0kECoti1BhU7ERGRkziwHV65DIr3QZfBng9LhDitTuX3gn4cOxEREWllxfnwxihPqUs8B65/TaXOAip2IiIicnrKS+DNG+DANojuBGPegVDd3bKCip2IiIicOncVvD8Fdq+B0GjPAMRRHaxOFbRU7EREROTUmCYsuR+yPgK7C258C+J7WJ0qqKnYiYiIyKn5zzPw1d8BA655CVIusDpR0FOxExERkabb8A78a4ZnethjcPZIS+OIh4qdiIiINM1/l8MHv/JMZ0yFjF9bGkeOU7ETERGRxsvdBAtvBncFnH01XPZHqxNJDSp2IiIi0jiFu2H+dVBWBCkXwsgXwKYq4Uv0ryEiIiInd6QA3rgWDuVAfE+4YT44Qq1OJbWo2ImIiMiJVZbBWzfBvs0QkQg3vQth7axOJfVQsRMREZGGud2eD0r8+AU4I2Hsu9C2k9WppAEqdiIiItKwfz0Cm94DWwiMft3zPbDis1TsREREpH6rXoAvZ3umr3oOug62No+clIqdiIiI1PX9Ylj6gGf60keg7w3W5pFGUbETERERbztXwXtTABP6TYKfTbM6kTSSip2IiIgct28LLBgNVWXQ4wq44gkwDKtTSSOp2ImIiIjHoTx4YxSUFsAZ/WDUK2CzW51KmkDFTkRERKDsEMy/Fgp3QkwXGLMQnOFWp5ImUrETEREJdlUV8PY4yN0A4XEw9j1oE2d1KjkFKnYiIiLBzDThwzth2zJwhMNNb3uu2IlfUrETEREJZp89Buvng2GD6+bBGWlWJ5LToGInIiISrNbOg88f90yPeAq6D7M0jpw+FTsREZFgtOWf8NHR8ekG3Qdp4y2NI80jxOoAIiIi0oJMEw7nwb4s2Jd9/Ofur8GsgnNvgsEPWp1SmomKnYiISCAwTSjcXaO8HSty2VBWWP82Zw6DK5/RAMQBRMVORETEn7iroOBHzzdE1LwKt38LlB+ufxvD5vmka3xPiOvu+RnfAzr0VakLMCp2IiIivqiqEg5ur3X1LQv2/wCVpfVvYwuB2G6e0nasvMX3hJiu4Aht3fxiCRU7ERERK1WWQf62ulff9v8A7or6t7G7jl55q13gUsHuaN384lNU7ERERFpDeQnk/1DjFurRInfgv54PMdTHEX68tNW8hdqus77DVeqlYiciItKcyg55rrjV/ATqviw4+CNg1r+NK+pogTt2Be5ogYvqCDaNTCaNp2InIiJyKo4UeN86PVbiCnc1vE1YO4jvVesWag+I7KAPMUizULETERE5keL8uh9g2JcNh3Mb3iYiwVPY4mpdhWsTpwInLUrFTkREpKFBfPdlQ8n+hreLOqPuBxjiukN4TOtlF6lBxU5ERIJHzUF899d6D1xpA4P4ArRN8S5v8T08BS40qvWyizSCip2IiAQet/voIL7ZdYcROdkgvl63T3tA3JngbNO6+UVOkYqdiIj4r3oH8c0+Oojvkfq30SC+EsCCrtg999xzPPHEE+Tm5tK3b19mz57NgAEDrI4lIhIc3FVQVe4ZlLeqwjNddXS6wXnlR+cfnS7ef7zE5W/1zKuPBvGVIBRUxW7hwoVMmzaNF154gfT0dJ5++mmGDRtGdnY27du3tzqeiMjpO1acqsqhsty7JDU4r8y7OHmVrNrzyms86ptX8zXqmdfQQLyn49ggvrVvoWoQXwlChmmaDYyWGHjS09Pp378/f/3rXwFwu9106tSJO+64gwceeOCE2xYVFREdHU1hYSFRUXqzrAQp0/Q8OPrTdB+f9vrprmdefeudZH2vZZzGa3MKWd3Ht21y1mPz8N5XdVE62dUpHytOLcnmgBCX5wqa3em5ymY/ybzQaA3iK0GlKR0kaK7YlZeXs3btWqZPn149z2azMWTIEFauXFln/bKyMsrKyqqfFxUVtWi+w0UHKXwqvcnbnfpoSKfW541T/Dug1XPW2M44wT4aWs87b2PWqf069a93+rlOnvFE653w9c365xuY2I2g+fsv4LiNENx2J26bA7fNiWlzYtodmHbPtKc4OWuVKRdGiBPD7sQIOTrtcGGzO7E5nNhCXBg11sXuhJAa+6kzr8bDq7A5NaabSDMLmmK3f/9+qqqqSEhI8JqfkJBAVlZWnfVnzpzJo48+2lrxcLvdnGHmtdrridRhwX9f3aanRpoYRx81p73nuTGgzjrgxna0itZd/9h6AKZZcx5ey47to+Z+of71PdN113fjuWJkYmCadXMAlBNCBSGU4/D8NO3Hp48tM0NOMK/G+mZIPfurPc9OS/3D2m0GdpuB4+jPELuNEJvhedgrCbFVYbeVHp9vP7rMZiPEfnQbW61lR9e12wwcdtvR/RpH59k8r2U3iHSFkBLbhtS4NiS1DcNuUzkUOSZoil1TTZ8+nWnTplU/LyoqolOnTi32euFtIsn6xXtN26iJf+kaTfw/eKPJtzaauP+m/qXe1N/X8L5W1fC+jBo/6q5n1F7v2LRxfJ2Tva6BgVknv1H/r2TU/pcyvJbV/zvU+rcyjr2u93rHrrsZtdY7nseovcbRp7aj+Q0wDM9dxprPq3/aasz37MdTyGxe63sdvxoauiBc3+yG1627oMF165lfe/ujaRvcR1P2W+k2qXKbVFS5qXJ7nldWmVS53VRU1bfM7flZ73bu6u09y93V05Ve0977997OfXS/R5e5619Wn6qjmRr42EKrcdptdIoJIzWuDZ1j29A5zlP4Ose1oUNUKDaVPgkyQVPs4uLisNvt5OV5XxXLy8sjMTGxzvoulwuXy9Va8QhxOOnZf0irvZ6ISGOYponb5HiprFn63CZVVSYV1SXQu7AeK5h1ltUqnpUnKaxexbPK5GBJBTvyi9mZX0J5lZtt+4rZtq+4TnZXiI2U2HA6xx4ve8emE6JcTf/jUsQPBE2xczqdpKWlsWzZMkaOHAl4bn8uW7aMqVOnWhtORMRHGYaB3QC7D366tMptklNwhB35xezYX8z2/SXV0zsPlFBW6WZL3mG25NUdkDjMYSclNry68KUevdrXOS6c+AiVPvFfQVPsAKZNm8a4cePo168fAwYM4Omnn6a4uJgJEyZYHU1ERJrIbjPoFBNOp5hwLjoz3mtZZZWbPQVH2L7fU/R25Jd4pvOL2X3wCEcqqsjKPURW7qE6+41whXiu9NUofKlxnit/MW2cKn3i04Kq2I0ePZp9+/bxyCOPkJuby7nnnsvSpUvrfKBCRET8W4jdRkpsG1Ji20AP72UVVW52HfBc3du+v+Ro8Stm+/5icgqOcLisku9yivgup+5oCJGhIbXez3f8Vm/bcGcr/XYiDQuqcexOh8axExEJfGWVVew6UFJd+LYfvbW7Y38xOYWlJ9y2bbjj+Pv5Yj23dY/d6o0K1TddyKlrSgdRsWskFTsRkeBWWlHFzgMlNW7vFh+dLiG36MSlL7aNs8aHN8KrpzvHtSHCFVQ3z+QUaIBiERGRZhbqsNM9IZLuCZF1lpWUV/Jjfu2rfCVszy9m36Ey8ovLyS8uZ+2PB+tsGx/pOvpePu/39XWObUOY0/c+tCK+TVfsGklX7ERE5FQcLqusvsJX+9O7+cUnHgkwMSr0+C3dGuP0JceEE+pQ6QsWuhXbAlTsRESkuRUeqeDHGrd0q2/v5hdTUFLR4HaGAUnRYZ6rfF7v6/OUPmeIvjs3kKjYtQAVOxERaU0FJeXVJa/2p3cPlVY2uJ3NgDPahVUXvpTY45/e7RQTjsOu0udvVOxagIqdiIj4AtM0OVBc7lX4an56t7i8qsFt7TaDjkdLX5f4Ntw4ILne9wyKb1GxawEqdiIi4utM02Tf4TLPbd0ahW/7/mJ+zC/hSIV36YsKDeHNWwdydlK0RYmlMVTsWoCKnYiI+DPTNMkrKqu+vbvwq12s31VAbBsnb9+eQdf4CKsjSgOa0kF0o11ERCQIGIZBYnQoGV1juXFAMq9OHMDZSVHkF5cz9uXV7DpQYnVEaQYqdiIiIkEoOszBaxMH0K19BHsLSxn7ymp+OslAy+L7VOxERESCVGyEizcmpdMpJowf80sY+8pqDp5kbD3xbSp2IiIiQSwxOpT5kwaSEOViS95hxs1dw6HShsfQE9+mYiciIhLkkmPDeWNSOjFtnGzYXcikeV9z5ATDpojvUrETERERzkyI5LWJA4h0hbBmxwFue2MtZZUqd/5GxU5EREQA6H1GNHMn9CfMYefzLfu46631VFa5rY4lTaBiJyIiItX6dY7hpVvScNptLNmUy/3vbcTt1pC3/kLFTkRERLxcdGY8s8ech91m8N663Tz64Xfo+wz8g4qdiIiI1DHs7ESevK4PhgGvrvyRJz/OtjqSNIKKnYiIiNTr6vM68seregPw3Gfb+NvyrRYnkpNRsRMREZEGjR2YwvTLewLw+NJsXlu5w9pAckIqdiIiInJCt/28K3dc0g2ARxZ/x7trd1ucSBqiYiciIiInNe2y7oy/oDMA9737LUs27rU2kNRLxU5EREROyjAMHhlxFteldcRtwm/e+oYVW/ZZHUtqUbETERGRRrHZDGaN6sMvzulARZXJba9/zZrtB6yOJTWo2ImIiEij2W0GT40+l8E94imtcDNx3lds2F1gdSw5SsVOREREmsQZYuP5sWmkp8ZwuKyScXPWsCXvkNWxBBU7EREROQWhDjuvjO9P305tOVhSwdiXV/NjfrHVsYKeip2IiIickghXCK9O6E+PhEh+OlTGmL+vZm/hEatjBTUVOxERETllbcOdvD55AJ1jw9lTcISbXl7N/sNlVscKWip2IiIiclraR4byxuR0kqJD+e++Ym5+ZQ2FJRVWxwpKKnYiIiJy2jq2C+eNyenERbjYvLeICfPWUFxWaXWsoKNiJyIiIs2iS3wEr08aQHSYg3U7C7j19a8praiyOlZQUbETERGRZtOrQxTzJvSnjdPOf7bmM3XBN1RUua2OFTRU7ERERKRZnZfcjpfH9ccVYuNfm/O45+1vqXKbVscKCip2IiIi0uwyusby/NjzCbEZ/OPbHH73wUZMU+WupanYiYiISIu4pGcCT99wLjYD3lyzi//5v80qdy1MxU5ERERazIg+Scy6pg8AL3+xnWeXbbU4UWDz+2K3Y8cOJk2aRGpqKmFhYXTt2pUZM2ZQXl7utd6GDRu46KKLCA0NpVOnTjz++OMWJRYREQku1/fvxMMjzgLgqX9t4eV//9fiRIErxOoApysrKwu3282LL75It27d2LRpE1OmTKG4uJgnn3wSgKKiIoYOHcqQIUN44YUX2LhxIxMnTqRt27bceuutFv8GIiIigW/Sz1IpLqvkL59s4U//t5kIVwg3DEi2OlbAMcwAvNn9xBNP8Pzzz/Pf/3r+Inj++ed56KGHyM3Nxel0AvDAAw/wwQcfkJWV1ah9FhUVER0dTWFhIVFRUS2WXUREJFCZpsmsJVm8+Pl/MQx49obzuLJvktWxfF5TOojf34qtT2FhITExMdXPV65cyaBBg6pLHcCwYcPIzs7m4MGD9e6jrKyMoqIir4eIiIicOsMweODyntyUnoxpwt0L1/Ov7/OsjhVQAq7Ybd26ldmzZ3PbbbdVz8vNzSUhIcFrvWPPc3Nz693PzJkziY6Orn506tSp5UKLiIgECcMw+ONVvRl5bhKVbpNfL1jHl1v3Wx0rYPhssXvggQcwDOOEj9q3Uffs2cPw4cO57rrrmDJlymm9/vTp0yksLKx+7Nq167T2JyIiIh42m8ET1/XlsrMSKK90M/m1r1m3s/47aNI0PvvhiXvuuYfx48efcJ0uXbpUT+fk5DB48GAuuOACXnrpJa/1EhMTycvzvtR77HliYmK9+3a5XLhcrlNILiIiIifjsNuYfeN5TH71a77Yup/xc9bw1q0ZnJWk97GfDp8tdvHx8cTHxzdq3T179jB48GDS0tKYO3cuNpv3hciMjAweeughKioqcDgcAHzyySf06NGDdu3aNXt2EREROblQh52Xbknj5lfWsPbHg9wyZzULb8uga3yE1dH8ls/eim2sPXv2cPHFF5OcnMyTTz7Jvn37yM3N9Xrv3JgxY3A6nUyaNInvvvuOhQsX8swzzzBt2jQLk4uIiEi4M4Q54/tzdlIU+w+XM/bl1ew+WGJ1LL/l98OdzJs3jwkTJtS7rOavtmHDBjIzM/nqq6+Ii4vjjjvu4P7772/062i4ExERkZaTf7iM619cybZ9xaTEhvPObRm0jwq1OpZPaEoH8fti11pU7ERERFpWbmEp1734JbsOHKF7QgQLb82gXRvnyTcMcEE/jp2IiIj4n8ToUOZPGkj7SBdb8g4zbu4aDpVWWB3Lr6jYiYiIiM9Ijg1n/uR02oU72LC7kEmvfs2R8iqrY/kN3YptpMZeBq2qqqKiQn9dnC6Hw4Hdbrc6hoiIWGTj7kLG/H0Vh8oq+Xn3eP5+Sz+cIcF5PUrvsWsBJzuopmmSm5tLQUFB64cLUG3btiUxMRHDMKyOIiIiFvhqxwFufmU1pRVuLu+dyOwbzyPEHnzlTsWuBZzsoO7du5eCggLat29PeHi4yshpME2TkpISfvrpJ9q2bUuHDh2sjiQiIhb5fMs+Jr/6NeVVbq5N68jjo/pgswXXf2ObUux8doBif1JVVVVd6mJjY62OExDCwsIA+Omnn2jfvr1uy4qIBKlB3eN59sbzyFywjnfX7ibCFcKMK8/SBZQGBN/1zBZw7D114eHhFicJLMeOp96zKCIS3Ib3TuTJ6/oAMO/LHTz5cbbFiXyXil0z0l8PzUvHU0REjrn6vI78cWRvAJ77bBt/W77V4kS+ScVORERE/MLNA1N44PKeADy+NJvXV+6wNpAPUrGT02YYBh988AEAO3bswDAM1q9fb2kmEREJTLf/vCtTB3cD4OHF3/He2t0WJ/ItKnZBbvz48RiGgWEYOBwOEhISuOyyy5gzZw5ut9vqeCIiInXcM7Q74y/oDMC9737L0k17rQ3kQ1TshOHDh7N371527NjBkiVLGDx4MHfeeScjRoygsrLS6ngiIiJeDMPgkRFncW1aR9wm3PHmN6zYss/qWD5BxU5wuVwkJiZyxhlncP755/Pggw+yePFilixZwrx5805pn1lZWVxwwQWEhobSu3dvVqxY0byhRUQkqNlsBrOuOYcrzkmkosrktte/5qsdB6yOZTkVuxZimiYl5ZWWPJpjzOlLLrmEvn378v7775/S9vfeey/33HMP33zzDRkZGVx55ZXk5+efdi4REZFjQuw2nh59Hhf3iKe0ws3EuV+xcXeh1bEspQGKW8iRiirOeuSflrz2938YRrjz9P9pe/bsyYYNG05p26lTpzJq1CgAnn/+eZYuXcorr7zCfffdd9q5REREjnGG2HhhbBrj5qxh9fYD3DJnNQtvy6B7QqTV0SyhK3bSINM0T3ksuYyMjOrpkJAQ+vXrx+bNm5srmoiISLVQh52Xx/Wjb8doDpZUMPbl1fyYX2x1LEvoil0LCXPY+f4Pwyx77eawefNmUlNTm2VfIiIiLSky1MG8CQO44aVVZOcd4qaXV/PO7Rl0iA6zOlqr0hW7FmIYBuHOEEsezfGNDZ9++ikbN26svp3aVKtWraqerqysZO3atfTq1eu0c4mIiDSkXRsnr08aQOfYcHYfPMLYl1ez/3CZ1bFalYqdUFZWRm5uLnv27GHdunU89thjXHXVVYwYMYJbbrnllPb53HPPsWjRIrKyssjMzOTgwYNMnDixmZOLiIh4ax8VyhuT0+kQHcq2fcXc8soaCo8Ez3eOq9gJS5cupUOHDnTu3Jnhw4fz2Wef8eyzz7J48WLs9lO7rTtr1ixmzZpF3759+eKLL/jHP/5BXFxcMycXERGpq2O7cOZPTicuwsn3e4uYMHcNxWXBMS6rYTbH2BhBoKioiOjoaAoLC4mKivJaVlpayvbt20lNTSU0NNSihIFHx1VERE7H9zlF3PDSSopKK7mwWyyvjOtPaDO9D701naiD1KYrdiIiIhKQzkqK4tWJAwh32vnP1nymLviGiqrA/rpMFTs5ofnz5xMREVHv4+yzz7Y6noiIyAmdl9yOl8f1wxli41+b8/jtO99S5Q7cm5Ua7kRO6Je//CXp6en1LnM4HK2cRkREpOku6BrH8zedz22vr2Xx+hzCnSE8dnXvZhlFwteo2MkJRUZGEhkZnKN3i4hI4Li0VwJPjT6X37z1DW+u2UmEy86DV/QKuHKnW7EiIiISFK7sm8Ssa84B4O//3s7sT7danKj5qdiJiIhI0BjdP5mHR5wFwF8+2cIrX2y3OFHzUrETERGRoDLpZ6ncPaQ7AH/86HsWfrXT4kTNR8VOREREgs5vLu3GlIs834f+wPsb+fDbHIsTNQ8VOxEREQk6hmHw4BW9uHFAMqYJdy9cz7LNeVbHOm0qdtLili9fjmEYFBQUWB1FRESkmmEY/Glkb646N4lKt8mv5q/jy237rY51WlTsgtz48eMxDAPDMHA4HCQkJHDZZZcxZ84c3O7AHp1bRETEbjN48rq+DOmVQHmlm8mvfs26nQetjnXKVOyE4cOHs3fvXnbs2MGSJUsYPHgwd955JyNGjKCyMji+NFlERIKXw27jr2PO48JusZSUVzF+zho27y2yOtYpUbETXC4XiYmJnHHGGZx//vk8+OCDLF68mCVLljBv3rwTbrtjxw4Mw2D9+vXV8woKCjAMg+XLl3ut+5///Ic+ffoQGhrKwIED2bRpU/P/MiIiIqcg1GHnpZv7cX5yW4pKK7n5ldX8d99hq2M1mYpdSzFNKC+25mGe/nfgXXLJJfTt25f333+/GQ6Gx7333suf//xnvvrqK+Lj47nyyiupqKhotv2LiIicjjauEOZOGMBZHaLYf7icsS+vZvfBEqtjNYm+UqylVJTAY0nWvPaDOeBsc9q76dmzJxs2bGiGQB4zZszgsssuA+DVV1+lY8eOLFq0iOuvv77ZXkNEROR0RIc5eG3SAEa/uJJt+4oZ+/Jq3r4tg/ZRoVZHaxRdsZMGmabZrN+hl5GRUT0dExNDjx492Lx5c7PtX0REpDnERbh4Y3I6HduFsSO/hJtfWcPB4nKrYzVKQF2xKysrIz09nW+//ZZvvvmGc889t3rZhg0byMzMrL4NeMcdd3Dfffe1XBhHuOfKmRUc4c2ym82bN5OamnrCdWw2z98GZo3bv7q9KiIi/q5DdBjzJ6dz3Qsryc47xPi5a3hjcjqRoQ6ro51QQF2xu++++0hKqnv7s6ioiKFDh5KSksLatWt54okn+P3vf89LL73UcmEMw3M71IpHM1xl+/TTT9m4cSOjRo064Xrx8fEA7N27t3pezQ9S1LRq1arq6YMHD7JlyxZ69ep12llFRERaQkpsG96YnE67cAff7i5k0qtfc6S8yupYJxQwxW7JkiV8/PHHPPnkk3WWzZ8/n/LycubMmcPZZ5/NDTfcwG9+8xv+8pe/WJDU95SVlZGbm8uePXtYt24djz32GFdddRUjRozglltuOeG2YWFhDBw4kFmzZrF582ZWrFjB7373u3rX/cMf/sCyZcvYtGkT48ePJy4ujpEjR7bAbyQiItI8uidE8trEdCJcIazZfoBfzV9LeaXvjvMaEMUuLy+PKVOm8PrrrxMeXvc25MqVKxk0aBBOp7N63rBhw8jOzubgwfoHISwrK6OoqMjrEaiWLl1Khw4d6Ny5M8OHD+ezzz7j2WefZfHixdjt9pNuP2fOHCorK0lLS+Ouu+7iT3/6U73rzZo1izvvvJO0tDRyc3P58MMPvf5NREREfNE5HaOZM74/oQ4by7P3cdfCb6is8s1y5/fvsTNNk/Hjx3P77bfTr18/duzYUWed3NzcOu8VS0hIqF7Wrl27OtvMnDmTRx99tEUy+5J58+addKy6k+nVqxdffvml17ya77m7+OKLq5+PGDHitF5LRETECgNSY3jx5n5MfvUr/t/GXMKdG3l8VB9stub7kGFz8Nkrdg888ED1V1019MjKymL27NkcOnSI6dOnN+vrT58+ncLCwurHrl27mnX/IiIi4l9+3j2e2Teeh82Ad9fu5g8ffe91IcMX+OwVu3vuuYfx48efcJ0uXbrw6aefsnLlSlwul9eyfv36cdNNN/Hqq6+SmJhIXl6e1/JjzxMTE+vdt8vlqrPPYDR//nxuu+22epelpKTw3XfftXIiERER6wzv3YEnru3LPe98y7wvdxDhCuG3w3pYHauazxa7+Pj46k9cnsizzz7r9Z6unJwchg0bxsKFC0lPTwc846c99NBDVFRU4HB4Pqb8ySef0KNHj3pvw8pxv/zlL6uPY23HjqWIiEgwGZXWkZLySh5e/B1//WwrbVwh/OrirlbHAny42DVWcnKy1/OIiAgAunbtSseOHQEYM2YMjz76KJMmTeL+++9n06ZNPPPMMzz11FOtntffREZGEhkZaXUMERERn3JzRmcOl1Xxv0uz+N+lWcRGOLm+XyerY/l/sWuM6OhoPv74YzIzM0lLSyMuLo5HHnmEW2+91epoIiIi4qd+dXFXDpdV8OG3e8noEmt1HCAAi13nzp3rfSNjnz59+Pe//92ir+12++ZHn/2VjqeIiPi63w7twa0XdSU63DfenhRwxc4KTqcTm81GTk4O8fHxOJ3OZv2O1WBjmibl5eXs27cPm82mse5ERMRnGYbhM6UOVOyahc1mIzU1lb1795KTY9H3wwag8PBwkpOTq7+PVkRERE5Mxa6ZOJ1OkpOTqayspKrKt79Hzh/Y7XZCQkJ05VNERKQJVOyakWEYOBwODQMiIiIiltA9LhEREZEAoWInIiIiEiBU7EREREQChN5j10jHxsYrKiqyOImIiIgEk2Pdo75xemtTsWukQ4cOAdCpk/VfFyIiIiLB59ChQ0RHR59wHcNsTP0T3G43OTk5REZGttgQHEVFRXTq1Ildu3YRFRXVIq8RLHQsm4eOY/PRsWweOo7NR8eyebTGcTRNk0OHDpGUlHTSsV11xa6RbDYbHTt2bJXXioqK0v/ImomOZfPQcWw+OpbNQ8ex+ehYNo+WPo4nu1J3jD48ISIiIhIgVOxEREREAoSKnQ9xuVzMmDEDl8tldRS/p2PZPHQcm4+OZfPQcWw+OpbNw9eOoz48ISIiIhIgdMVOREREJECo2ImIiIgECBU7ERERkQChYiciIiISIFTsfMRzzz1H586dCQ0NJT09nTVr1lgdye/8/ve/xzAMr0fPnj2tjuUXPv/8c6688kqSkpIwDIMPPvjAa7lpmjzyyCN06NCBsLAwhgwZwg8//GBNWB92suM4fvz4Oufo8OHDrQnrw2bOnEn//v2JjIykffv2jBw5kuzsbK91SktLyczMJDY2loiICEaNGkVeXp5FiX1XY47lxRdfXOe8vP322y1K7Luef/55+vTpUz0QcUZGBkuWLKle7ivnpIqdD1i4cCHTpk1jxowZrFu3jr59+zJs2DB++uknq6P5nbPPPpu9e/dWP7744gurI/mF4uJi+vbty3PPPVfv8scff5xnn32WF154gdWrV9OmTRuGDRtGaWlpKyf1bSc7jgDDhw/3OkfffPPNVkzoH1asWEFmZiarVq3ik08+oaKigqFDh1JcXFy9zt13382HH37IO++8w4oVK8jJyeGaa66xMLVvasyxBJgyZYrXefn4449blNh3dezYkVmzZrF27Vq+/vprLrnkEq666iq+++47wIfOSVMsN2DAADMzM7P6eVVVlZmUlGTOnDnTwlT+Z8aMGWbfvn2tjuH3AHPRokXVz91ut5mYmGg+8cQT1fMKCgpMl8tlvvnmmxYk9A+1j6Npmua4cePMq666ypI8/uynn34yAXPFihWmaXrOP4fDYb7zzjvV62zevNkEzJUrV1oV0y/UPpamaZo///nPzTvvvNO6UH6sXbt25ssvv+xT56Su2FmsvLyctWvXMmTIkOp5NpuNIUOGsHLlSguT+acffviBpKQkunTpwk033cTOnTutjuT3tm/fTm5urtc5Gh0dTXp6us7RU7B8+XLat29Pjx49+NWvfkV+fr7VkXxeYWEhADExMQCsXbuWiooKr3OyZ8+eJCcn65w8idrH8pj58+cTFxdH7969mT59OiUlJVbE8xtVVVW89dZbFBcXk5GR4VPnZEirvprUsX//fqqqqkhISPCan5CQQFZWlkWp/FN6ejrz5s2jR48e7N27l0cffZSLLrqITZs2ERkZaXU8v5WbmwtQ7zl6bJk0zvDhw7nmmmtITU1l27ZtPPjgg1x++eWsXLkSu91udTyf5Ha7ueuuu7jwwgvp3bs34DknnU4nbdu29VpX5+SJ1XcsAcaMGUNKSgpJSUls2LCB+++/n+zsbN5//30L0/qmjRs3kpGRQWlpKRERESxatIizzjqL9evX+8w5qWInAePyyy+vnu7Tpw/p6emkpKTw9ttvM2nSJAuTiXjccMMN1dPnnHMOffr0oWvXrixfvpxLL73UwmS+KzMzk02bNun9ss2goWN56623Vk+fc845dOjQgUsvvZRt27bRtWvX1o7p03r06MH69espLCzk3XffZdy4caxYscLqWF50K9ZicXFx2O32Op+cycvLIzEx0aJUgaFt27Z0796drVu3Wh3Frx07D3WONr8uXboQFxenc7QBU6dO5aOPPuKzzz6jY8eO1fMTExMpLy+noKDAa32dkw1r6FjWJz09HUDnZT2cTifdunUjLS2NmTNn0rdvX5555hmfOidV7CzmdDpJS0tj2bJl1fPcbjfLli0jIyPDwmT+7/Dhw2zbto0OHTpYHcWvpaamkpiY6HWOFhUVsXr1ap2jp2n37t3k5+frHK3FNE2mTp3KokWL+PTTT0lNTfVanpaWhsPh8Dons7Oz2blzp87JWk52LOuzfv16AJ2XjeB2uykrK/Opc1K3Yn3AtGnTGDduHP369WPAgAE8/fTTFBcXM2HCBKuj+ZXf/va3XHnllaSkpJCTk8OMGTOw2+3ceOONVkfzeYcPH/b663z79u2sX7+emJgYkpOTueuuu/jTn/7EmWeeSWpqKg8//DBJSUmMHDnSutA+6ETHMSYmhkcffZRRo0aRmJjItm3buO++++jWrRvDhg2zMLXvyczMZMGCBSxevJjIyMjq9yhFR0cTFhZGdHQ0kyZNYtq0acTExBAVFcUdd9xBRkYGAwcOtDi9bznZsdy2bRsLFizgiiuuIDY2lg0bNnD33XczaNAg+vTpY3F63zJ9+nQuv/xykpOTOXToEAsWLGD58uX885//9K1zslU/gysNmj17tpmcnGw6nU5zwIAB5qpVq6yO5HdGjx5tdujQwXQ6neYZZ5xhjh492ty6davVsfzCZ599ZgJ1HuPGjTNN0zPkycMPP2wmJCSYLpfLvPTSS83s7GxrQ/ugEx3HkpISc+jQoWZ8fLzpcDjMlJQUc8qUKWZubq7VsX1OfccQMOfOnVu9zpEjR8xf//rXZrt27czw8HDz6quvNvfu3WtdaB91smO5c+dOc9CgQWZMTIzpcrnMbt26mffee69ZWFhobXAfNHHiRDMlJcV0Op1mfHy8eemll5off/xx9XJfOScN0zTN1iySIiIiItIy9B47ERERkQChYiciIiISIFTsRERERAKEip2IiIhIgFCxExEREQkQKnYiIiIiAULFTkRERCRAqNiJiIiIBAgVOxEREZEAoWInIiIiEiBU7EREREQChIqdiIiISID4/7FlN82Y1osmAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -401,13 +477,13 @@ } ], "source": [ - "# Remove all stratification\n", + "# Remove SE stratification\n", "\n", "funman_request = get_request()\n", "setup_common(funman_request, debug=True, dreal_precision=1e-0)\n", "# add_unit_test(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", - "results = run(funman_request, model=models['destratified_SEI'])\n", - "report(results, \"destratified_SEI\", states=states['destratified_SEI'])\n", + "results = run(funman_request, model=models['destratified_SE'])\n", + "report(results, \"destratified_SE\", states=states['destratified_SE'])\n", "# plot_bounds(results.points()[0], vars=[\"S\", \"E\", \"I\", \"R\", \"H\", \"D\"])\n", "vars = results.model._state_var_names()\n", "point = results.points()[0]\n", @@ -416,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -428,59 +504,38 @@ "original_stratified 19340000.0 0.4 0.5 0.5 0.5 0.5 0.5 \n", "destratified_SEI 19340000.0 0.4 NaN NaN NaN NaN NaN \n", "destratified_SE 19340000.0 0.4 NaN NaN NaN NaN NaN \n", + "destratified_S 19340000.0 0.4 NaN NaN NaN NaN NaN \n", "\n", " eps_m_2 c_m_3 eps_m_3 ... p_H_to_R r_H_to_R \\\n", "original_stratified 0.5 0.5 0.5 ... 0.88 0.1 \n", "destratified_SEI NaN NaN NaN ... 0.88 0.1 \n", "destratified_SE NaN NaN NaN ... 0.88 0.1 \n", + "destratified_S NaN NaN NaN ... 0.88 0.1 \n", "\n", " p_H_to_D r_H_to_D p_noncompliant_compliant \\\n", "original_stratified 0.12 0.1 0.1 \n", "destratified_SEI 0.12 0.1 NaN \n", "destratified_SE 0.12 0.1 0.1 \n", + "destratified_S 0.12 0.1 0.1 \n", "\n", " p_compliant_noncompliant eps_m_lb eps_m_ub c_m_lb \\\n", "original_stratified 0.1 NaN NaN NaN \n", "destratified_SEI NaN 0.4 0.6 0.4 \n", "destratified_SE 0.1 0.4 0.6 0.4 \n", + "destratified_S 0.1 0.4 0.6 0.4 \n", "\n", " c_m_ub \n", "original_stratified NaN \n", "destratified_SEI 0.6 \n", "destratified_SE 0.6 \n", + "destratified_S 0.6 \n", "\n", - "[3 rows x 25 columns]\n", - " S_lb I_compliant_lb I_noncompliant_lb E_lb \\\n", - "0.0 1.934000e+07 2.000000 2.000000 1.000000 \n", - "5.0 1.933998e+07 1.538748 1.538748 -5.499358 \n", - "10.0 1.933993e+07 2.403047 2.403047 -61.659290 \n", - "15.0 1.933969e+07 8.735326 8.735326 -352.858362 \n", - "20.0 1.933848e+07 41.446223 41.446223 -1806.600211 \n", - "25.0 1.933248e+07 204.708540 204.708540 -9032.715141 \n", - "30.0 1.930267e+07 1016.201991 1016.201991 -44932.903807 \n", - "\n", - " I_compliant_ub I_noncompliant_ub S_ub E_ub \\\n", - "0.0 2.000000 2.000000 1.934000e+07 1.000000 \n", - "5.0 6.002302 6.002302 1.933999e+07 12.040496 \n", - "10.0 29.263668 29.263668 1.933999e+07 61.181677 \n", - "15.0 145.260976 145.260976 1.933997e+07 304.408321 \n", - "20.0 721.575492 721.575492 1.933992e+07 1512.461075 \n", - "25.0 3584.553250 3584.553250 1.933966e+07 7513.575138 \n", - "30.0 17806.618357 17806.618357 1.933836e+07 37323.775445 \n", - "\n", - " R_lb R_ub H_lb H_ub D_lb D_ub \n", - "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "5.0 1.021535 1.944254 0.229418 0.579655 0.008634 0.014205 \n", - "10.0 2.078499 10.835730 -0.123649 3.349768 0.016724 0.109833 \n", - "15.0 4.214900 55.198690 -2.938781 17.806471 -0.051529 0.631444 \n", - "20.0 12.160518 276.291622 -18.005395 90.761773 -0.561586 3.324661 \n", - "25.0 48.980738 1375.948949 -94.261809 454.785150 -3.339744 16.887494 \n", - "30.0 228.832997 6840.843005 -475.103888 2265.362927 -17.486658 84.561137 \n" + "[4 rows x 25 columns]\n" ] }, { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] @@ -500,13 +555,13 @@ } ], "source": [ - "# Remove SE stratification\n", + "# Remove S stratification\n", "\n", "funman_request = get_request()\n", "setup_common(funman_request, debug=True, dreal_precision=1e-0)\n", "# add_unit_test(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", - "results = run(funman_request, model=models['destratified_SE'])\n", - "report(results, \"destratified_SE\", states=states['destratified_SE'])\n", + "results = run(funman_request, model=models['destratified_S'])\n", + "report(results, \"destratified_S\", states=states['destratified_S'])\n", "# plot_bounds(results.points()[0], vars=[\"S\", \"E\", \"I\", \"R\", \"H\", \"D\"])\n", "vars = results.model._state_var_names()\n", "point = results.points()[0]\n", @@ -515,13 +570,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "\n", "\n", "\n", "\n" @@ -530,49 +586,44 @@ { "data": { "text/plain": [ - "[[0.0 4.000000\n", - " 5.0 5.279244\n", - " 10.0 9.108868\n", - " 15.0 16.124701\n", - " 20.0 28.603764\n", - " 25.0 50.748897\n", - " 30.0 90.039893\n", + "[[0.0 4.000000\n", + " 10.0 9.108868\n", + " 20.0 28.603764\n", + " 30.0 90.039893\n", + " 40.0 283.430740\n", + " 50.0 892.151043\n", " dtype: float64],\n", - " [0.0 4.000000\n", - " 5.0 3.077496\n", - " 10.0 4.806094\n", - " 15.0 17.470652\n", - " 20.0 82.892447\n", - " 25.0 409.417079\n", - " 30.0 2032.403982\n", + " [0.0 4.000000e+00\n", + " 10.0 4.806094e+00\n", + " 20.0 8.289245e+01\n", + " 30.0 2.032404e+03\n", + " 40.0 5.012936e+04\n", + " 50.0 1.225787e+06\n", " dtype: float64,\n", - " 0.0 4.000000\n", - " 5.0 12.004604\n", - " 10.0 58.527336\n", - " 15.0 290.521953\n", - " 20.0 1443.150983\n", - " 25.0 7169.106500\n", - " 30.0 35613.236715\n", + " 0.0 4.000000e+00\n", + " 10.0 5.852734e+01\n", + " 20.0 1.443151e+03\n", + " 30.0 3.561324e+04\n", + " 40.0 8.781937e+05\n", + " 50.0 2.135255e+07\n", " dtype: float64],\n", - " [0.0 4.000000\n", - " 5.0 4.497161\n", - " 10.0 0.990951\n", - " 15.0 -45.330407\n", - " 20.0 -409.351989\n", - " 25.0 -3031.710702\n", - " 30.0 -21550.678810\n", + " [0.0 4.000000e+00\n", + " 10.0 9.909510e-01\n", + " 20.0 -4.093520e+02\n", + " 30.0 -2.155068e+04\n", + " 40.0 -1.061956e+06\n", + " 50.0 -4.084908e+07\n", " Name: I_lb_destratified_SEI, dtype: float64,\n", - " 0.0 4.000000\n", - " 5.0 6.016389\n", - " 10.0 17.363698\n", - " 15.0 80.882919\n", - " 20.0 496.780914\n", - " 25.0 3362.960448\n", - " 30.0 23385.814862\n", + " 0.0 4.000000e+00\n", + " 10.0 1.736370e+01\n", + " 20.0 4.967809e+02\n", + " 30.0 2.338581e+04\n", + " 40.0 1.151831e+06\n", + " 50.0 5.691454e+07\n", " Name: I_ub_destratified_SEI, dtype: float64]]" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -605,87 +656,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " S_compliant I_compliant E_compliant I_noncompliant S_noncompliant \\\n", - "0.0 9.669998e+06 2.000000 0.500000 2.000000 9.669998e+06 \n", - "5.0 9.669994e+06 2.639622 2.317063 2.639622 9.669994e+06 \n", - "10.0 9.669989e+06 4.554434 4.313768 4.554434 9.669989e+06 \n", - "15.0 9.669980e+06 8.062351 7.682373 8.062351 9.669980e+06 \n", - "20.0 9.669963e+06 14.301882 13.634369 14.301882 9.669963e+06 \n", - "25.0 9.669934e+06 25.374449 24.191037 25.374449 9.669934e+06 \n", - "30.0 9.669883e+06 45.019947 42.920325 45.019947 9.669883e+06 \n", - "\n", - " E_noncompliant R H D \n", - "0.0 0.500000 0.000000 0.000000 0.000000 \n", - "5.0 2.317063 1.306723 0.349626 0.010688 \n", - "10.0 4.313768 3.503480 0.774253 0.043551 \n", - "15.0 7.682373 7.419919 1.458829 0.108756 \n", - "20.0 13.634369 14.393184 2.638764 0.228412 \n", - "25.0 24.191037 26.782143 4.712217 0.443085 \n", - "30.0 42.920325 48.773419 8.378989 0.825399 \n", - " S_lb I_lb E_lb I_ub S_ub \\\n", - "0.0 1.934000e+07 4.000000 1.000000 4.000000 1.934000e+07 \n", - "5.0 1.933999e+07 4.497161 2.733767 6.016389 1.933999e+07 \n", - "10.0 1.933997e+07 0.990951 -5.282224 17.363698 1.933998e+07 \n", - "15.0 1.933990e+07 -45.330407 -81.668704 80.882919 1.934000e+07 \n", - "20.0 1.933952e+07 -409.351989 -650.824792 496.780914 1.934022e+07 \n", - "25.0 1.933701e+07 -3031.710702 -4702.217340 3362.960448 1.934191e+07 \n", - "30.0 1.931968e+07 -21550.678810 -33227.157234 23385.814862 1.935400e+07 \n", - "\n", - " E_ub R_lb R_ub H_lb H_ub \\\n", - "0.0 1.000000 0.000000 0.000000 0.000000 0.000000 \n", - "5.0 6.468573 1.242755 1.365454 0.325790 0.371883 \n", - "10.0 23.307062 2.455659 4.541286 0.367607 1.179461 \n", - "15.0 121.639896 -1.733940 16.899058 -2.191968 5.209236 \n", - "20.0 779.832491 -53.778527 86.570912 -24.938423 31.340085 \n", - "25.0 5343.699263 -459.065706 546.065956 -193.020999 211.544110 \n", - "30.0 37279.471642 -3373.544262 3719.819389 -1387.597058 1471.237197 \n", - "\n", - " D_lb D_ub \n", - "0.0 0.000000 0.000000 \n", - "5.0 0.010369 0.010981 \n", - "10.0 0.034436 0.052471 \n", - "15.0 0.009014 0.210011 \n", - "20.0 -0.585975 1.070793 \n", - "25.0 -5.564188 6.709912 \n", - "30.0 -41.988538 45.643410 \n", - " S_lb I_compliant_lb I_noncompliant_lb E_lb \\\n", - "0.0 1.934000e+07 2.000000 2.000000 1.000000 \n", - "5.0 1.933998e+07 1.538748 1.538748 -5.499358 \n", - "10.0 1.933993e+07 2.403047 2.403047 -61.659290 \n", - "15.0 1.933969e+07 8.735326 8.735326 -352.858362 \n", - "20.0 1.933848e+07 41.446223 41.446223 -1806.600211 \n", - "25.0 1.933248e+07 204.708540 204.708540 -9032.715141 \n", - "30.0 1.930267e+07 1016.201991 1016.201991 -44932.903807 \n", - "\n", - " I_compliant_ub I_noncompliant_ub S_ub E_ub \\\n", - "0.0 2.000000 2.000000 1.934000e+07 1.000000 \n", - "5.0 6.002302 6.002302 1.933999e+07 12.040496 \n", - "10.0 29.263668 29.263668 1.933999e+07 61.181677 \n", - "15.0 145.260976 145.260976 1.933997e+07 304.408321 \n", - "20.0 721.575492 721.575492 1.933992e+07 1512.461075 \n", - "25.0 3584.553250 3584.553250 1.933966e+07 7513.575138 \n", - "30.0 17806.618357 17806.618357 1.933836e+07 37323.775445 \n", - "\n", - " R_lb R_ub H_lb H_ub D_lb D_ub \n", - "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "5.0 1.021535 1.944254 0.229418 0.579655 0.008634 0.014205 \n", - "10.0 2.078499 10.835730 -0.123649 3.349768 0.016724 0.109833 \n", - "15.0 4.214900 55.198690 -2.938781 17.806471 -0.051529 0.631444 \n", - "20.0 12.160518 276.291622 -18.005395 90.761773 -0.561586 3.324661 \n", - "25.0 48.980738 1375.948949 -94.261809 454.785150 -3.339744 16.887494 \n", - "30.0 228.832997 6840.843005 -475.103888 2265.362927 -17.486658 84.561137 \n" - ] - }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -703,8 +679,14 @@ "fig = None\n", "axs = None\n", "\n", - "\n", + "zorders = {\n", + " \"original_stratified\": 10,\n", + " \"destratified_SEI\": 3,\n", + " \"destratified_SE\": 2,\n", + " \"destratified_S\": 9\n", + "}\n", "for name, result in request_results.items():\n", + " zorder = zorders[name]\n", " # vars = list(set([v.rsplit(\"_\", 1)[0] for v in result.model._state_var_names()]))\n", " vars = result.model._state_var_names()\n", " point = result.points()[0]\n", @@ -712,7 +694,9 @@ " # print(vars)\n", " # print(point.simulation.dataframe())\n", " \n", - " fig, axs = plot_bounds(point, vars=vars, fig=fig, axs=axs, basevar_map=basevar_map, alpha=0.5, linewidth=3)\n", + " fig, axs = plot_bounds(point, model=name, vars=vars, fig=fig, axs=axs, basevar_map=basevar_map, alpha=0.2, linewidth=3, \n", + " # hatch=hatches[name], zorder=zorder\n", + " )\n", " \n", "# request_results" ] diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_S.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_S.json new file mode 100644 index 00000000..e948f4c8 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_S.json @@ -0,0 +1,1065 @@ +{ + "header": { + "name": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", + "model_version": "0.1", + "properties": {} + }, + "model": { + "states": [ + { + "id": "S_lb", + "name": "S_lb", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_compliant_lb", + "name": "I_compliant_lb", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_noncompliant_lb", + "name": "I_noncompliant_lb", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_compliant_lb", + "name": "E_compliant_lb", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_noncompliant_lb", + "name": "E_noncompliant_lb", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_compliant_ub", + "name": "I_compliant_ub", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_noncompliant_ub", + "name": "I_noncompliant_ub", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "S_ub", + "name": "S_ub", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_compliant_ub", + "name": "E_compliant_ub", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_noncompliant_ub", + "name": "E_noncompliant_ub", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_lb", + "name": "R_lb", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_ub", + "name": "R_ub", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_lb", + "name": "H_lb", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_ub", + "name": "H_ub", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D_lb", + "name": "D_lb", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D_ub", + "name": "D_ub", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1_lb", + "input": [ + "I_compliant_ub", + "S_ub" + ], + "output": [ + "I_compliant_ub", + "E_compliant_lb" + ], + "properties": { + "name": "t1_lb" + } + }, + { + "id": "t1_ub", + "input": [ + "I_compliant_lb", + "S_lb" + ], + "output": [ + "I_compliant_lb", + "E_compliant_ub" + ], + "properties": { + "name": "t1_ub" + } + }, + { + "id": "t2_lb", + "input": [ + "I_noncompliant_ub", + "S_ub" + ], + "output": [ + "I_noncompliant_ub", + "E_compliant_lb" + ], + "properties": { + "name": "t2_lb" + } + }, + { + "id": "t2_ub", + "input": [ + "I_noncompliant_lb", + "S_lb" + ], + "output": [ + "I_noncompliant_lb", + "E_compliant_ub" + ], + "properties": { + "name": "t2_ub" + } + }, + { + "id": "t3_lb", + "input": [ + "I_noncompliant_ub", + "S_ub" + ], + "output": [ + "I_noncompliant_ub", + "E_noncompliant_lb" + ], + "properties": { + "name": "t3_lb" + } + }, + { + "id": "t3_ub", + "input": [ + "I_noncompliant_lb", + "S_lb" + ], + "output": [ + "I_noncompliant_lb", + "E_noncompliant_ub" + ], + "properties": { + "name": "t3_ub" + } + }, + { + "id": "t4_lb", + "input": [ + "I_compliant_ub", + "S_ub" + ], + "output": [ + "I_compliant_ub", + "E_noncompliant_lb" + ], + "properties": { + "name": "t4_lb" + } + }, + { + "id": "t4_ub", + "input": [ + "I_compliant_lb", + "S_lb" + ], + "output": [ + "I_compliant_lb", + "E_noncompliant_ub" + ], + "properties": { + "name": "t4_ub" + } + }, + { + "id": "t5_lb", + "input": [ + "E_compliant_ub" + ], + "output": [ + "I_compliant_lb" + ], + "properties": { + "name": "t5_lb" + } + }, + { + "id": "t5_ub", + "input": [ + "E_compliant_lb" + ], + "output": [ + "I_compliant_ub" + ], + "properties": { + "name": "t5_ub" + } + }, + { + "id": "t6_lb", + "input": [ + "E_noncompliant_ub" + ], + "output": [ + "I_noncompliant_lb" + ], + "properties": { + "name": "t6_lb" + } + }, + { + "id": "t6_ub", + "input": [ + "E_noncompliant_lb" + ], + "output": [ + "I_noncompliant_ub" + ], + "properties": { + "name": "t6_ub" + } + }, + { + "id": "t7_lb", + "input": [ + "I_compliant_ub" + ], + "output": [ + "R_lb" + ], + "properties": { + "name": "t7_lb" + } + }, + { + "id": "t7_ub", + "input": [ + "I_compliant_lb" + ], + "output": [ + "R_ub" + ], + "properties": { + "name": "t7_ub" + } + }, + { + "id": "t8_lb", + "input": [ + "I_noncompliant_ub" + ], + "output": [ + "R_lb" + ], + "properties": { + "name": "t8_lb" + } + }, + { + "id": "t8_ub", + "input": [ + "I_noncompliant_lb" + ], + "output": [ + "R_ub" + ], + "properties": { + "name": "t8_ub" + } + }, + { + "id": "t9_lb", + "input": [ + "I_compliant_ub" + ], + "output": [ + "H_lb" + ], + "properties": { + "name": "t9_lb" + } + }, + { + "id": "t9_ub", + "input": [ + "I_compliant_lb" + ], + "output": [ + "H_ub" + ], + "properties": { + "name": "t9_ub" + } + }, + { + "id": "t10_lb", + "input": [ + "I_noncompliant_ub" + ], + "output": [ + "H_lb" + ], + "properties": { + "name": "t10_lb" + } + }, + { + "id": "t10_ub", + "input": [ + "I_noncompliant_lb" + ], + "output": [ + "H_ub" + ], + "properties": { + "name": "t10_ub" + } + }, + { + "id": "t11_lb", + "input": [ + "H_ub" + ], + "output": [ + "R_lb" + ], + "properties": { + "name": "t11_lb" + } + }, + { + "id": "t11_ub", + "input": [ + "H_lb" + ], + "output": [ + "R_ub" + ], + "properties": { + "name": "t11_ub" + } + }, + { + "id": "t12_lb", + "input": [ + "H_lb" + ], + "output": [ + "D_lb" + ], + "properties": { + "name": "t12_lb" + } + }, + { + "id": "t12_ub", + "input": [ + "H_ub" + ], + "output": [ + "D_ub" + ], + "properties": { + "name": "t12_ub" + } + }, + { + "id": "t15_lb", + "input": [ + "E_noncompliant_ub" + ], + "output": [ + "E_compliant_lb" + ], + "properties": { + "name": "t15_lb" + } + }, + { + "id": "t15_ub", + "input": [ + "E_noncompliant_lb" + ], + "output": [ + "E_compliant_ub" + ], + "properties": { + "name": "t15_ub" + } + }, + { + "id": "t16_lb", + "input": [ + "E_compliant_ub" + ], + "output": [ + "E_noncompliant_lb" + ], + "properties": { + "name": "t16_lb" + } + }, + { + "id": "t16_ub", + "input": [ + "E_compliant_lb" + ], + "output": [ + "E_noncompliant_ub" + ], + "properties": { + "name": "t16_ub" + } + }, + { + "id": "t17_lb", + "input": [ + "I_noncompliant_ub" + ], + "output": [ + "I_compliant_lb" + ], + "properties": { + "name": "t17_lb" + } + }, + { + "id": "t17_ub", + "input": [ + "I_noncompliant_lb" + ], + "output": [ + "I_compliant_ub" + ], + "properties": { + "name": "t17_ub" + } + }, + { + "id": "t18_lb", + "input": [ + "I_compliant_ub" + ], + "output": [ + "I_noncompliant_lb" + ], + "properties": { + "name": "t18_lb" + } + }, + { + "id": "t18_ub", + "input": [ + "I_compliant_lb" + ], + "output": [ + "I_noncompliant_ub" + ], + "properties": { + "name": "t18_ub" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1_lb", + "expression": "I_compliant_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" + }, + { + "target": "t1_ub", + "expression": "I_compliant_ub*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" + }, + { + "target": "t2_lb", + "expression": "I_noncompliant_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" + }, + { + "target": "t2_ub", + "expression": "I_noncompliant_ub*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" + }, + { + "target": "t4_lb", + "expression": "I_compliant_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" + }, + { + "target": "t4_ub", + "expression": "I_compliant_ub*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" + }, + { + "target": "t3_lb", + "expression": "I_noncompliant_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" + }, + { + "target": "t3_ub", + "expression": "I_noncompliant_ub*S_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" + }, + { + "target": "t5_lb", + "expression": "0", + "expression_mathml": "E_lbr_E_to_I" + }, + { + "target": "t5_ub", + "expression": "E_compliant_ub*r_E_to_I", + "expression_mathml": "E_ubr_E_to_I" + }, + { + "target": "t6_lb", + "expression": "0", + "expression_mathml": "E_lbr_E_to_I" + }, + { + "target": "t6_ub", + "expression": "E_noncompliant_ub*r_E_to_I", + "expression_mathml": "E_ubr_E_to_I" + }, + { + "target": "t7_lb", + "expression": "I_compliant_lb*p_I_to_R*r_I_to_R", + "expression_mathml": "I_lbp_I_to_Rr_I_to_R" + }, + { + "target": "t7_ub", + "expression": "I_compliant_ub*p_I_to_R*r_I_to_R", + "expression_mathml": "I_ubp_I_to_Rr_I_to_R" + }, + { + "target": "t8_lb", + "expression": "I_noncompliant_lb*p_I_to_R*r_I_to_R", + "expression_mathml": "I_lbp_I_to_Rr_I_to_R" + }, + { + "target": "t8_ub", + "expression": "I_noncompliant_ub*p_I_to_R*r_I_to_R", + "expression_mathml": "I_ubp_I_to_Rr_I_to_R" + }, + { + "target": "t9_lb", + "expression": "I_compliant_lb*p_I_to_H*r_I_to_H", + "expression_mathml": "I_lbp_I_to_Hr_I_to_H" + }, + { + "target": "t9_ub", + "expression": "I_compliant_ub*p_I_to_H*r_I_to_H", + "expression_mathml": "I_ubp_I_to_Hr_I_to_H" + }, + { + "target": "t10_lb", + "expression": "I_noncompliant_lb*p_I_to_H*r_I_to_H", + "expression_mathml": "I_lbp_I_to_Hr_I_to_H" + }, + { + "target": "t10_ub", + "expression": "I_noncompliant_ub*p_I_to_H*r_I_to_H", + "expression_mathml": "I_ubp_I_to_Hr_I_to_H" + }, + { + "target": "t11_lb", + "expression": "H_lb*p_H_to_R*r_H_to_R", + "expression_mathml": "H_lbp_H_to_Rr_H_to_R" + }, + { + "target": "t11_ub", + "expression": "H_ub*p_H_to_R*r_H_to_R", + "expression_mathml": "H_ubp_H_to_Rr_H_to_R" + }, + { + "target": "t12_lb", + "expression": "H_lb*p_H_to_D*r_H_to_D", + "expression_mathml": "Hp_H_to_Dr_H_to_D" + }, + { + "target": "t12_ub", + "expression": "H_ub*p_H_to_D*r_H_to_D", + "expression_mathml": "H_ubp_H_to_Dr_H_to_D" + }, + { + "target": "t15_lb", + "expression": "E_noncompliant_ub*p_noncompliant_compliant", + "expression_mathml": "I_noncompliantp_noncompliant_compliant" + }, + { + "target": "t15_ub", + "expression": "E_noncompliant_lb*p_noncompliant_compliant", + "expression_mathml": "I_noncompliantp_noncompliant_compliant" + }, + { + "target": "t16_lb", + "expression": "E_compliant_ub*p_compliant_noncompliant", + "expression_mathml": "I_compliantp_compliant_noncompliant" + }, + { + "target": "t16_ub", + "expression": "E_compliant_lb*p_compliant_noncompliant", + "expression_mathml": "I_compliantp_compliant_noncompliant" + }, + { + "target": "t17_lb", + "expression": "I_noncompliant_ub*p_noncompliant_compliant", + "expression_mathml": "I_noncompliantp_noncompliant_compliant" + }, + { + "target": "t17_ub", + "expression": "I_noncompliant_lb*p_noncompliant_compliant", + "expression_mathml": "I_noncompliantp_noncompliant_compliant" + }, + { + "target": "t18_lb", + "expression": "I_compliant_ub*p_compliant_noncompliant", + "expression_mathml": "I_compliantp_compliant_noncompliant" + }, + { + "target": "t18_ub", + "expression": "I_compliant_lb*p_compliant_noncompliant", + "expression_mathml": "I_compliantp_compliant_noncompliant" + } + ], + "initials": [ + { + "target": "S_lb", + "expression": "19339995.00000000", + "expression_mathml": "9669997.5" + }, + { + "target": "I_compliant_lb", + "expression": "2.00000000000000", + "expression_mathml": "2.0" + }, + { + "target": "I_noncompliant_lb", + "expression": "2.00000000000000", + "expression_mathml": "2.0" + }, + { + "target": "E_compliant_lb", + "expression": "0.500000000000000", + "expression_mathml": "1.0" + }, + { + "target": "E_noncompliant_lb", + "expression": "0.500000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "S_ub", + "expression": "19339995.00000000", + "expression_mathml": "9669997.5" + }, + { + "target": "I_compliant_ub", + "expression": "2.00000000000000", + "expression_mathml": "2.0" + }, + { + "target": "I_noncompliant_ub", + "expression": "2.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E_compliant_ub", + "expression": "0.500000000000000", + "expression_mathml": "1.0" + }, + { + "target": "E_noncompliant_ub", + "expression": "0.500000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R_ub", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H_ub", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D_ub", + "expression": "0.0", + "expression_mathml": "0.0" + } + ], + "parameters": [ + { + "id": "N", + "value": 19340000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta", + "value": 0.4, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "eps_m_lb", + "value": 0.4, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "eps_m_ub", + "value": 0.6, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "c_m_lb", + "value": 0.4, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "c_m_ub", + "value": 0.6, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_E_to_I", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_R", + "value": 0.8, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_R", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_H", + "value": 0.2, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_H", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_R", + "value": 0.88, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_R", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_D", + "value": 0.12, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_D", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_noncompliant_compliant", + "value": 0.1 + }, + { + "id": "p_compliant_noncompliant", + "value": 0.1 + } + ], + "observables": [], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + } + }, + "metadata": { + "annotations": { + "license": null, + "authors": [], + "references": [], + "time_scale": null, + "time_start": null, + "time_end": null, + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } + } +} \ No newline at end of file diff --git a/src/funman/config.py b/src/funman/config.py index d902892e..36b33001 100644 --- a/src/funman/config.py +++ b/src/funman/config.py @@ -9,6 +9,7 @@ from pydantic import BaseModel, ConfigDict, field_validator, model_validator +from funman.constants import MODE_ODEINT, Mode from funman.utils.handlers import ( NoopResultHandler, ResultCombinedHandler, @@ -134,6 +135,9 @@ class FUNMANConfig(BaseModel): prioritize_box_entropy: bool = True """ When comparing boxes, prefer those with low entropy """ + mode: Mode = MODE_ODEINT + """ Mode to run FUNMAN, either funman.constants.MODE_ODEINT or funman.constants.MODE_SMT """ + @field_validator("solver") @classmethod def import_dreal(cls, v: str) -> str: diff --git a/src/funman/constants.py b/src/funman/constants.py index 2c7ddcc7..a89f2473 100644 --- a/src/funman/constants.py +++ b/src/funman/constants.py @@ -17,3 +17,7 @@ LABEL_UNKNOWN: Literal["unknown"] = "unknown" LABEL_DROPPED: Literal["dropped"] = "dropped" Label = Literal["true", "false", "unknown", "dropped"] + +MODE_SMT: Literal["mode_smt"] = "mode_smt" +MODE_ODEINT: Literal["mode_odeint"] = "mode_odeint" +Mode = Literal["mode_smt", "mode_odeint"] diff --git a/src/funman/scenario/consistency.py b/src/funman/scenario/consistency.py index 9ae83b1d..f910503e 100644 --- a/src/funman/scenario/consistency.py +++ b/src/funman/scenario/consistency.py @@ -13,6 +13,8 @@ from pysmt.solvers.solver import Model as pysmt_Model from funman import Point +from funman.constants import MODE_ODEINT, MODE_SMT +from funman.representation.box import Box from funman.scenario import AnalysisScenario, AnalysisScenarioResult from funman.translate import Encoding @@ -73,29 +75,39 @@ def solve( """ search = self.initialize(config) - parameter_space, models, consistent = search.search( - self, - config=config, - haltEvent=haltEvent, - resultsCallback=resultsCallback, - ) - - parameter_space.num_dimensions = len(self.parameters) - l.info(parameter_space) - scenario_result = ConsistencyScenarioResult( - scenario=self, - consistent=consistent, - parameter_space=parameter_space, - ) - scenario_result._models = models - - start_time = datetime.now() - assert self.check_simulation( - config, scenario_result - ), "Simulation of solution is invalid." - duration = datetime.now() - start_time - l.info(f"Simulation Time: {duration}") + if config.mode == MODE_SMT: + parameter_space, models, consistent = search.search( + self, + config=config, + haltEvent=haltEvent, + resultsCallback=resultsCallback, + ) + parameter_space.num_dimensions = len(self.parameters) + l.info(parameter_space) + scenario_result = ConsistencyScenarioResult( + scenario=self, + consistent=consistent, + parameter_space=parameter_space, + ) + scenario_result._models = models + + start_time = datetime.now() + assert self.check_simulation( + config, scenario_result + ), "Simulation of solution is invalid." + duration = datetime.now() - start_time + l.info(f"Simulation Time: {duration}") + elif config.mode == MODE_ODEINT: + point = self.simulate_scenario(config) + parameter_space = ParameterSpace( + num_dimensions=len(self.parameters) + ) + parameter_space.true_boxes.append(Box.from_point(point)) + scenario_result = ConsistencyScenarioResult( + scenario=self, consistent={}, parameter_space=parameter_space + ) + resultsCallback(parameter_space) return scenario_result diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index 49dc59cd..49f13bd7 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -33,6 +33,7 @@ QueryTrue, StructureParameter, ) +from funman.constants import LABEL_TRUE, LABEL_UNKNOWN from funman.model.ensemble import EnsembleModel from funman.model.petrinet import GeneratedPetriNetModel from funman.model.regnet import GeneratedRegnetModel, RegnetModel @@ -348,7 +349,16 @@ def _set_normalization(self, config): self.normalization_constant = 1.0 l.warning("Warning: The scenario is not normalized!") - def check_point_simulation( + def run_scenario_simulation( + self, init, parameters, tvect + ) -> Optional[Timeseries]: + simulator = Simulator( + model=self.model, init=init, parameters=parameters, tvect=tvect + ) + timeseries = simulator.sim() + return timeseries + + def run_point_simulation( self, point: Point, tvect ) -> Optional[Timeseries]: init = { @@ -385,14 +395,71 @@ def simulation_tvects(self, config) -> List[Union[float, int]]: return tvects + def simulate_scenario(self, config: "FUNMANConfig") -> Point: + + init = { + var: float( + self.model._get_init_value(var, self, config).constant_value() + ) + for var in self.model._state_var_names() + } + parameters = { + p: pm.interval.lb + for p in self.model._parameter_names() + for pm in self.parameters + if pm.name == p + } + # parameters = { + # p.name: p.interval.lb + # for p in self.parameters + # } + # timestamped_variables ={f"{var}_{tp}": float(self.model._get_init_value(var, self, config).constant_value()) for var in self.model._state_var_names()} + schedule = self.structure_parameter("schedules").schedules[0] + timepoints = schedule.timepoints + + timeseries = self.run_scenario_simulation(init, parameters, timepoints) + values = { + **{ + f"{var}_{int(timepoint)}": timeseries.data[var_idx][timestep] + for var_idx, var in enumerate(timeseries.columns[1:]) + for timestep, timepoint in enumerate(timeseries.data[0]) + }, + **parameters, + **{"timestep": len(timepoints) - 1}, + } + point = Point( + values=values, + label=LABEL_TRUE, + schedule=schedule, + simulation=timeseries, + ) + + # with Solver() as solver: + # sim_encoding = self.encode_timeseries_verification( + # point, timeseries + # ) + # solver.add_assertion(sim_encoding) + # result = solver.solve() + # if result: + # l.info("simulation passed verification") + # else: + # l.info("simulation failed verification") + # return False + + return point + def check_simulation( self, config: "FUNMANConfig", results: "AnalysisScenarioResult" ): # Check solution with simulation sim_results = [] - for point in results.parameter_space.points(): - timeseries = self.check_point_simulation( - point, point.relevant_timepoints(results.scenario.model) + + points = results.parameter_space.points() + # [Point(label=LABEL_UNKNOWN, values={**{p.name: p.interval.lb for p in self.parameters}, **{f"{var}_0": float(self.model._get_init_value(var, self, config).constant_value()) for var in self.model._state_var_names()},**{f"{var}_{tp}": float(self.model._get_init_value(var, self, config).constant_value()) for var in self.model._state_var_names()}})] if results is None else + + for point in points: + timeseries = self.run_point_simulation( + point, point.relevant_timepoints(self.model) ) sim_results.append((point, timeseries)) point.simulation = timeseries From 6ab8dbd087d0c3eb772736c71e96c0618663659a Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 6 Sep 2024 11:55:31 +0000 Subject: [PATCH 32/93] adjust odeint step --- .../funman_aug_2024_12mo_eval_1_q1_demo.ipynb | 284 ++---------------- src/funman/search/simulate.py | 12 + 2 files changed, 30 insertions(+), 266 deletions(-) diff --git a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb index fed41f64..2b8d2d3e 100644 --- a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb +++ b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -80,20 +80,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Constants for the scenario\n", "\n", - "MAX_TIME=200\n", - "STEP_SIZE=10\n", + "MAX_TIME=10\n", + "STEP_SIZE=5\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -307,58 +307,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 points\n", - " N beta c_m_0 eps_m_0 c_m_1 eps_m_1 c_m_2 \\\n", - "original_stratified 19340000.0 0.4 0.5 0.5 0.5 0.5 0.5 \n", - "destratified_SEI 19340000.0 0.4 NaN NaN NaN NaN NaN \n", - "destratified_SE 19340000.0 0.4 NaN NaN NaN NaN NaN \n", - "destratified_S 19340000.0 0.4 NaN NaN NaN NaN NaN \n", - "\n", - " eps_m_2 c_m_3 eps_m_3 ... p_H_to_R r_H_to_R \\\n", - "original_stratified 0.5 0.5 0.5 ... 0.88 0.1 \n", - "destratified_SEI NaN NaN NaN ... 0.88 0.1 \n", - "destratified_SE NaN NaN NaN ... 0.88 0.1 \n", - "destratified_S NaN NaN NaN ... 0.88 0.1 \n", - "\n", - " p_H_to_D r_H_to_D p_noncompliant_compliant \\\n", - "original_stratified 0.12 0.1 0.1 \n", - "destratified_SEI 0.12 0.1 NaN \n", - "destratified_SE 0.12 0.1 0.1 \n", - "destratified_S 0.12 0.1 0.1 \n", - "\n", - " p_compliant_noncompliant eps_m_lb eps_m_ub c_m_lb \\\n", - "original_stratified 0.1 NaN NaN NaN \n", - "destratified_SEI NaN 0.4 0.6 0.4 \n", - "destratified_SE 0.1 0.4 0.6 0.4 \n", - "destratified_S 0.1 0.4 0.6 0.4 \n", - "\n", - " c_m_ub \n", - "original_stratified NaN \n", - "destratified_SEI 0.6 \n", - "destratified_SE 0.6 \n", - "destratified_S 0.6 \n", - "\n", - "[4 rows x 25 columns]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# i) (TA1 Search and Discovery Workflow, 1 Hr. Time Limit) Find estimates on the\n", "# efficacy of surgical masks in preventing onward transmission of SARS-CoV-2 (preferred)\n", @@ -372,7 +323,7 @@ "# The base model assumes no masking inverventions, and we measure the efficacy in terms of the number of hospitalized.\n", "\n", "funman_request = get_request()\n", - "setup_common(funman_request, debug=False, dreal_precision=1e-0)\n", + "setup_common(funman_request, debug=False)\n", "add_unit_test(funman_request)\n", "results = run(funman_request, model=models['original_stratified'])\n", "report(results, \"original_stratified\", states[\"original_stratified\"])" @@ -380,29 +331,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_odepack_py.py:248: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", - " warnings.warn(warning_msg, ODEintWarning)\n" - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", - "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", - "\u001b[1;31mClick here for more info. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], + "outputs": [], "source": [ "# Remove all stratification\n", "\n", @@ -419,63 +350,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 points\n", - " N beta c_m_0 eps_m_0 c_m_1 eps_m_1 c_m_2 \\\n", - "original_stratified 19340000.0 0.4 0.5 0.5 0.5 0.5 0.5 \n", - "destratified_SEI 19340000.0 0.4 NaN NaN NaN NaN NaN \n", - "destratified_SE 19340000.0 0.4 NaN NaN NaN NaN NaN \n", - "\n", - " eps_m_2 c_m_3 eps_m_3 ... p_H_to_R r_H_to_R \\\n", - "original_stratified 0.5 0.5 0.5 ... 0.88 0.1 \n", - "destratified_SEI NaN NaN NaN ... 0.88 0.1 \n", - "destratified_SE NaN NaN NaN ... 0.88 0.1 \n", - "\n", - " p_H_to_D r_H_to_D p_noncompliant_compliant \\\n", - "original_stratified 0.12 0.1 0.1 \n", - "destratified_SEI 0.12 0.1 NaN \n", - "destratified_SE 0.12 0.1 0.1 \n", - "\n", - " p_compliant_noncompliant eps_m_lb eps_m_ub c_m_lb \\\n", - "original_stratified 0.1 NaN NaN NaN \n", - "destratified_SEI NaN 0.4 0.6 0.4 \n", - "destratified_SE 0.1 0.4 0.6 0.4 \n", - "\n", - " c_m_ub \n", - "original_stratified NaN \n", - "destratified_SEI 0.6 \n", - "destratified_SE 0.6 \n", - "\n", - "[3 rows x 25 columns]\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Remove SE stratification\n", "\n", @@ -492,68 +369,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 points\n", - " N beta c_m_0 eps_m_0 c_m_1 eps_m_1 c_m_2 \\\n", - "original_stratified 19340000.0 0.4 0.5 0.5 0.5 0.5 0.5 \n", - "destratified_SEI 19340000.0 0.4 NaN NaN NaN NaN NaN \n", - "destratified_SE 19340000.0 0.4 NaN NaN NaN NaN NaN \n", - "destratified_S 19340000.0 0.4 NaN NaN NaN NaN NaN \n", - "\n", - " eps_m_2 c_m_3 eps_m_3 ... p_H_to_R r_H_to_R \\\n", - "original_stratified 0.5 0.5 0.5 ... 0.88 0.1 \n", - "destratified_SEI NaN NaN NaN ... 0.88 0.1 \n", - "destratified_SE NaN NaN NaN ... 0.88 0.1 \n", - "destratified_S NaN NaN NaN ... 0.88 0.1 \n", - "\n", - " p_H_to_D r_H_to_D p_noncompliant_compliant \\\n", - "original_stratified 0.12 0.1 0.1 \n", - "destratified_SEI 0.12 0.1 NaN \n", - "destratified_SE 0.12 0.1 0.1 \n", - "destratified_S 0.12 0.1 0.1 \n", - "\n", - " p_compliant_noncompliant eps_m_lb eps_m_ub c_m_lb \\\n", - "original_stratified 0.1 NaN NaN NaN \n", - "destratified_SEI NaN 0.4 0.6 0.4 \n", - "destratified_SE 0.1 0.4 0.6 0.4 \n", - "destratified_S 0.1 0.4 0.6 0.4 \n", - "\n", - " c_m_ub \n", - "original_stratified NaN \n", - "destratified_SEI 0.6 \n", - "destratified_SE 0.6 \n", - "destratified_S 0.6 \n", - "\n", - "[4 rows x 25 columns]\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "\n", "\n", diff --git a/src/funman/search/simulate.py b/src/funman/search/simulate.py index 8509bba2..8c584fba 100644 --- a/src/funman/search/simulate.py +++ b/src/funman/search/simulate.py @@ -12,6 +12,10 @@ numeric = Union[int, float] +import logging + +l = logging.getLogger(__name__) + class Simulator(BaseModel): model: FunmanModel @@ -45,12 +49,20 @@ def sim(self) -> Optional[Timeseries]: # gradient_fn = partial(self.model.gradient, self.model) # hide the self reference to self.model from odeint if self.model._is_differentiable: + full_output = 1 timeseries = odeint( self.model.gradient, self.initial_state(), self.tvect, args=self.model_args(), + full_output=full_output, + rtol=1, + atol=1, ) + if full_output == 1: + timeseries, output = timeseries + + l.debug(f"odeint output: {output}") ts = Timeseries( data=[self.tvect] + timeseries.T.tolist(), From df8a13b1cc7d660cf037e8f56a55786215f1e4d1 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Mon, 9 Sep 2024 14:57:52 +0000 Subject: [PATCH 33/93] demo version --- .../funman_aug_2024_12mo_eval_1_q1_demo.ipynb | 718 ++++++++++++++-- .../q1a_ii/eval_scenario1_1_ii_3.json | 8 +- ...eval_scenario1_1_ii_3_destratified_EI.json | 777 ++++++++++++++++++ .../eval_scenario1_1_ii_3_destratified_S.json | 8 +- ...al_scenario1_1_ii_3_destratified_SEI.json} | 0 src/funman/config.py | 4 +- src/funman/model/petrinet.py | 13 +- src/funman/scenario/consistency.py | 13 +- src/funman/search/simulate.py | 43 +- 9 files changed, 1471 insertions(+), 113 deletions(-) create mode 100644 resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_EI.json rename resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/{eval_scenario1_1_ii_3_destratified_all.json => eval_scenario1_1_ii_3_destratified_SEI.json} (100%) diff --git a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb index 2b8d2d3e..a1e2c128 100644 --- a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb +++ b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -33,11 +33,14 @@ " \"original_stratified\": os.path.join(\n", " EXAMPLE_DIR, \"eval_scenario1_1_ii_3.json\"),\n", " \"destratified_SEI\": os.path.join(\n", - " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_all.json\"\n", + " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_SEI.json\"\n", "),\n", " \"destratified_SE\": os.path.join(\n", " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_SE.json\"\n", "),\n", + " \"destratified_EI\": os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_EI.json\"\n", + "),\n", " \"destratified_S\": os.path.join(\n", " EXAMPLE_DIR, \"eval_scenario1_1_ii_3_destratified_S.json\"\n", ")\n", @@ -47,11 +50,12 @@ " \"original_stratified\": [\"S_compliant\", \"S_noncompliant\", \"I_compliant\", \"I_noncompliant\", \"E_compliant\", \"E_noncompliant\",\"R\", \"H\", \"D\"],\n", " \"destratified_SEI\": [\"S_lb\", \"S_ub\", \"I_lb\", \"I_ub\", \"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", " \"destratified_SE\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", + " \"destratified_EI\": [\"I_lb\", \"I_ub\", \"S_compliant_lb\", \"S_compliant_ub\", \"S_noncompliant_lb\", \"S_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", " \"destratified_S\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_compliant_lb\", \"E_compliant_ub\",\"E_noncompliant_lb\", \"E_noncompliant_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"]\n", "}\n", "\n", "basevar_map = [\n", - " ['S_compliant','S_noncompliant', 'S_lb', 'S_ub'], \n", + " ['S_compliant','S_noncompliant', 'S_lb', 'S_ub','S_compliant_lb', 'S_noncompliant_ub', 'S_compliant_ub', 'S_noncompliant_lb'], \n", " ['I_compliant','I_noncompliant','I_lb','I_ub','I_compliant_lb', 'I_noncompliant_ub', 'I_compliant_ub', 'I_noncompliant_lb'],\n", " ['E_compliant','E_noncompliant','E_lb', 'E_ub', 'E_compliant_lb','E_noncompliant_lb', 'E_compliant_ub','E_noncompliant_ub',],\n", " ['R','R_lb', 'R_ub'],\n", @@ -80,20 +84,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Constants for the scenario\n", "\n", - "MAX_TIME=10\n", - "STEP_SIZE=5\n", + "MAX_TIME=150\n", + "STEP_SIZE=10\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +116,7 @@ " for p in funman_request.parameters:\n", " p.label = \"any\"\n", " \n", - "def set_config_options(funman_request, debug=False, dreal_precision=1e-1):\n", + "def set_config_options(funman_request, debug=False, dreal_precision=1e1):\n", " # Overrides for configuration\n", " #\n", " # funman_request.config.substitute_subformulas = True\n", @@ -200,12 +204,12 @@ "\n", "def report(results, name, states):\n", " request_results[name] = results\n", - " plot_last_point(results, states)\n", + " # plot_last_point(results, states)\n", " param_values = get_last_point_parameters(results)\n", " # print(f\"Point parameters: {param_values}\")\n", " if param_values is not None:\n", " request_params[name] = param_values\n", - " pretty_print_request_params(request_params)\n", + " # pretty_print_request_params(request_params)\n", " \n", "\n", "def add_unit_test(funman_request, model=\"original_stratified\"):\n", @@ -222,9 +226,19 @@ " }\n", " ))\n", " \n", - "def plot_bounds(point, fig=None, axs=None, vars = [\"S\", \"E\", \"I\", \"R\", \"D\", \"H\"], model=None, basevar_map={}, **kwargs):\n", + " \n", + "\n", + "\n", + "def plot_bounds(point, results, timespan=None, fig=None, axs=None, vars = [\"S\", \"E\", \"I\", \"R\", \"D\", \"H\"], model=None, basevar_map={}, **kwargs):\n", " \n", - " df = point.simulation.dataframe().T\n", + " if point.simulation is not None:\n", + " df = point.simulation.dataframe().T\n", + " else:\n", + " df = results.dataframe([point])\n", + " \n", + " if timespan is not None:\n", + " df = df.loc[timespan[0]:timespan[1]]\n", + " \n", " # print(df)\n", "\n", " # Drop the ub vars because they are paired with the lb vars \n", @@ -294,20 +308,20 @@ " axs[i].set_title(f\"{basevar} Bounds\")\n", "\n", " \n", - "\n", + " # axs[i].set_yscale('logit')\n", " \n", " # axs[i].legend(loc=\"outer\")\n", " axs[i].legend(loc='center left', bbox_to_anchor=(1, 0.5),\n", " ncol=1, fancybox=True, shadow=True, prop={'size': 8}, markerscale=2)\n", " # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", "\n", - " fig.tight_layout()\n", + " # fig.tight_layout()\n", " return fig, axs\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -320,108 +334,390 @@ "# value (in the deterministic case) or distribution (in the probabilistic case) to use in your\n", "# forecasts in 1.a.iii.\n", "\n", - "# The base model assumes no masking inverventions, and we measure the efficacy in terms of the number of hospitalized.\n", + "# The Scenario 1.1.ii.3. task is to simulate masking interventions for the SEIRHD model, stratified wrt. the compliant and noncompliant populations. The model stratifies the S, E, and I compartments, and the c_m and eps_m parameters. It also adds two parameters that respectively govern the transition between the compliant and noncompliant populations\n", "\n", "funman_request = get_request()\n", "setup_common(funman_request, debug=False)\n", - "add_unit_test(funman_request)\n", "results = run(funman_request, model=models['original_stratified'])\n", "report(results, \"original_stratified\", states[\"original_stratified\"])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "# Remove all stratification\n", + "# Remove all stratification by combining (Sc, Snc), (Ec, Enc), and (Ic, Inc) into S, E, and I \n", "\n", "funman_request = get_request()\n", - "setup_common(funman_request, debug=True, dreal_precision=1e-3)\n", - "# add_unit_test(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", + "setup_common(funman_request, debug=False)\n", "results = run(funman_request, model=models['destratified_SEI'])\n", - "report(results, \"destratified_SEI\", states=states['destratified_SEI'])\n", - "# plot_bounds(results.points()[0], vars=[\"S\", \"E\", \"I\", \"R\", \"H\", \"D\"])\n", - "vars = results.model._state_var_names()\n", - "point = results.points()[0]\n", - "fig, axs = plot_bounds(point, vars=vars, basevar_map=basevar_map)" + "report(results, \"destratified_SEI\", states=states['destratified_SEI'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "# Remove SE stratification\n", + "# Remove SE stratification by combining (Sc, Snc) and (Ec, Enc) into S and E\n", "\n", "funman_request = get_request()\n", - "setup_common(funman_request, debug=True, dreal_precision=1e-0)\n", - "# add_unit_test(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", + "setup_common(funman_request, debug=False)\n", "results = run(funman_request, model=models['destratified_SE'])\n", - "report(results, \"destratified_SE\", states=states['destratified_SE'])\n", - "# plot_bounds(results.points()[0], vars=[\"S\", \"E\", \"I\", \"R\", \"H\", \"D\"])\n", - "vars = results.model._state_var_names()\n", - "point = results.points()[0]\n", - "fig, axs = plot_bounds(point, vars=vars, basevar_map=basevar_map)" + "report(results, \"destratified_SE\", states=states['destratified_SE'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "# Remove S stratification\n", + "# Remove EI stratification by combining (Ec, Enc) and (Ic, Inc) into E and I\n", "\n", "funman_request = get_request()\n", - "setup_common(funman_request, debug=True, dreal_precision=1e-0)\n", - "# add_unit_test(funman_request, model=MODEL_DESTRATIFIED_ALL_PATH)\n", - "results = run(funman_request, model=models['destratified_S'])\n", - "report(results, \"destratified_S\", states=states['destratified_S'])\n", - "# plot_bounds(results.points()[0], vars=[\"S\", \"E\", \"I\", \"R\", \"H\", \"D\"])\n", - "vars = results.model._state_var_names()\n", - "point = results.points()[0]\n", - "fig, axs = plot_bounds(point, vars=vars, basevar_map=basevar_map)" + "setup_common(funman_request, debug=False)\n", + "results = run(funman_request, model=models['destratified_EI'])\n", + "report(results, \"destratified_EI\", states=states['destratified_EI'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ + "# Remove S stratification by combining (Sc, Snc) in S\n", "\n", - "var = \"I\"\n", - "var_df = pd.DataFrame()\n", - "for name, result in request_results.items():\n", - " # vars = list(set([v.rsplit(\"_\", 1)[0] for v in result.model._state_var_names()]))\n", - " vars = result.model._state_var_names()\n", - " point = result.points()[0]\n", - " tdf = result.points()[0].simulation.dataframe().T\n", - "\n", - " var_cols = tdf.columns.map(lambda x: x.startswith(var))\n", - " # tdf.loc[var_cols.T]\n", - " var_data = tdf.T.loc[var_cols].T\n", - " var_data.columns = [f\"{col}_{name}\" for col in var_data.columns]\n", - " print()\n", - " var_df = pd.concat([var_df, var_data], axis=1)\n", - "\n", - "\n", - "og = var_df[f\"{var}_compliant_original_stratified\"] + var_df[f\"{var}_noncompliant_original_stratified\"] if f\"{var}_compliant_original_stratified\" in var_df.columns else None\n", - "ub_SE = var_df[f\"{var}_noncompliant_ub_destratified_SE\"] + var_df[f\"{var}_compliant_ub_destratified_SE\"] if f\"{var}_noncompliant_ub_destratified_SE\" in var_df.columns else None\n", - "lb_SE = var_df[f\"{var}_noncompliant_lb_destratified_SE\"] + var_df[f\"{var}_compliant_lb_destratified_SE\"] if f\"{var}_noncompliant_lb_destratified_SE\" in var_df.columns else None\n", - "ub_SEI = var_df[f\"{var}_ub_destratified_SEI\"] if f\"{var}_ub_destratified_SEI\" in var_df.columns else None\n", - "lb_SEI = var_df[f\"{var}_lb_destratified_SEI\"] if f\"{var}_lb_destratified_SEI\" in var_df.columns else None\n", - "[[og], [lb_SE, ub_SE], [lb_SEI, ub_SEI]]" + "funman_request = get_request()\n", + "setup_common(funman_request, debug=False)\n", + "results = run(funman_request, model=models['destratified_S'])\n", + "report(results, \"destratified_S\", states=states['destratified_S'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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original_stratifieddestratified_SEIdestratified_SEdestratified_EIdestratified_S
N19340000.0019340000.0019340000.0019340000.0019340000.00
beta0.400.400.400.400.40
c_m_00.50NaNNaNNaNNaN
eps_m_00.40NaNNaNNaNNaN
c_m_10.40NaNNaNNaNNaN
eps_m_10.60NaNNaNNaNNaN
c_m_20.60NaNNaNNaNNaN
eps_m_20.50NaNNaNNaNNaN
c_m_30.50NaNNaNNaNNaN
eps_m_30.50NaNNaNNaNNaN
r_E_to_I0.200.200.200.200.20
p_I_to_R0.800.800.800.800.80
r_I_to_R0.070.070.070.070.07
p_I_to_H0.200.200.200.200.20
r_I_to_H0.100.100.100.100.10
p_H_to_R0.880.880.880.880.88
r_H_to_R0.100.100.100.100.10
p_H_to_D0.120.120.120.120.12
r_H_to_D0.100.100.100.100.10
p_noncompliant_compliant0.10NaN0.100.100.10
p_compliant_noncompliant0.10NaN0.100.100.10
eps_m_lbNaN0.400.400.400.40
eps_m_ubNaN0.600.600.600.60
c_m_lbNaN0.400.400.400.40
c_m_ubNaN0.600.600.600.60
\n", + "
" + ], + "text/plain": [ + " original_stratified destratified_SEI \\\n", + "N 19340000.00 19340000.00 \n", + "beta 0.40 0.40 \n", + "c_m_0 0.50 NaN \n", + "eps_m_0 0.40 NaN \n", + "c_m_1 0.40 NaN \n", + "eps_m_1 0.60 NaN \n", + "c_m_2 0.60 NaN \n", + "eps_m_2 0.50 NaN \n", + "c_m_3 0.50 NaN \n", + "eps_m_3 0.50 NaN \n", + "r_E_to_I 0.20 0.20 \n", + "p_I_to_R 0.80 0.80 \n", + "r_I_to_R 0.07 0.07 \n", + "p_I_to_H 0.20 0.20 \n", + "r_I_to_H 0.10 0.10 \n", + "p_H_to_R 0.88 0.88 \n", + "r_H_to_R 0.10 0.10 \n", + "p_H_to_D 0.12 0.12 \n", + "r_H_to_D 0.10 0.10 \n", + "p_noncompliant_compliant 0.10 NaN \n", + "p_compliant_noncompliant 0.10 NaN \n", + "eps_m_lb NaN 0.40 \n", + "eps_m_ub NaN 0.60 \n", + "c_m_lb NaN 0.40 \n", + "c_m_ub NaN 0.60 \n", + "\n", + " destratified_SE destratified_EI destratified_S \n", + "N 19340000.00 19340000.00 19340000.00 \n", + "beta 0.40 0.40 0.40 \n", + "c_m_0 NaN NaN NaN \n", + "eps_m_0 NaN NaN NaN \n", + "c_m_1 NaN NaN NaN \n", + "eps_m_1 NaN NaN NaN \n", + "c_m_2 NaN NaN NaN \n", + "eps_m_2 NaN NaN NaN \n", + "c_m_3 NaN NaN NaN \n", + "eps_m_3 NaN NaN NaN \n", + "r_E_to_I 0.20 0.20 0.20 \n", + "p_I_to_R 0.80 0.80 0.80 \n", + "r_I_to_R 0.07 0.07 0.07 \n", + "p_I_to_H 0.20 0.20 0.20 \n", + "r_I_to_H 0.10 0.10 0.10 \n", + "p_H_to_R 0.88 0.88 0.88 \n", + "r_H_to_R 0.10 0.10 0.10 \n", + "p_H_to_D 0.12 0.12 0.12 \n", + "r_H_to_D 0.10 0.10 0.10 \n", + "p_noncompliant_compliant 0.10 0.10 0.10 \n", + "p_compliant_noncompliant 0.10 0.10 0.10 \n", + "eps_m_lb 0.40 0.40 0.40 \n", + "eps_m_ub 0.60 0.60 0.60 \n", + "c_m_lb 0.40 0.40 0.40 \n", + "c_m_ub 0.60 0.60 0.60 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params_df = pd.DataFrame(request_params)\n", + "params_df" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Part 1 (ii) Masking type 3", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Evaluation Scenario 1. Part 1 (ii) Masking type 3", + "model_version": "0.1", + "properties": {} + }, + "model": { + "states": [ + { + "id": "S_compliant_lb", + "name": "S_compliant_lb", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "S_compliant_ub", + "name": "S_compliant_ub", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_lb", + "name": "I_lb", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_lb", + "name": "E_lb", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "compliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_ub", + "name": "I_ub", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "S_noncompliant_lb", + "name": "S_noncompliant_lb", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "S_noncompliant_ub", + "name": "S_noncompliant_ub", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E_ub", + "name": "E_ub", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": { + "masking": "noncompliant" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_lb", + "name": "R_lb", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_ub", + "name": "R_ub", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_lb", + "name": "H_lb", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_ub", + "name": "H_ub", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "property": "ncit:C25179" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D_lb", + "name": "D_lb", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D_ub", + "name": "D_ub", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1_to_2_lb", + "input": [ + "I_ub", + "S_compliant_ub" + ], + "output": [ + "I_ub", + "E_lb" + ], + "properties": { + "name": "t1_to_2_lb" + } + }, + { + "id": "t1_to_2_ub", + "input": [ + "I_lb", + "S_compliant_lb" + ], + "output": [ + "I_lb", + "E_ub" + ], + "properties": { + "name": "t1_to_2_ub" + } + }, + { + "id": "t3_to_4_lb", + "input": [ + "I_ub", + "S_noncompliant_ub" + ], + "output": [ + "I_ub", + "E_lb" + ], + "properties": { + "name": "t3_to_4_lb" + } + }, + { + "id": "t3_to_4_ub", + "input": [ + "I_lb", + "S_noncompliant_lb" + ], + "output": [ + "I_lb", + "E_ub" + ], + "properties": { + "name": "t3_to_4_ub" + } + }, + { + "id": "t5_to_6_lb", + "input": [ + "E_ub" + ], + "output": [ + "I_lb" + ], + "properties": { + "name": "t5_to_6_lb" + } + }, + { + "id": "t5_to_6_ub", + "input": [ + "E_lb" + ], + "output": [ + "I_ub" + ], + "properties": { + "name": "t5_to_6_ub" + } + }, + { + "id": "t7_to_8_lb", + "input": [ + "I_ub" + ], + "output": [ + "R_lb" + ], + "properties": { + "name": "t7_to_8_lb" + } + }, + { + "id": "t7_to_8_ub", + "input": [ + "I_lb" + ], + "output": [ + "R_ub" + ], + "properties": { + "name": "t7_to_8_ub" + } + }, + { + "id": "t9_to_10_lb", + "input": [ + "I_ub" + ], + "output": [ + "H_lb" + ], + "properties": { + "name": "t9_to_10_lb" + } + }, + { + "id": "t9_to_10_ub", + "input": [ + "I_lb" + ], + "output": [ + "H_ub" + ], + "properties": { + "name": "t9_to_10_ub" + } + }, + { + "id": "t11_lb", + "input": [ + "H_ub" + ], + "output": [ + "R_lb" + ], + "properties": { + "name": "t11_lb" + } + }, + { + "id": "t11_ub", + "input": [ + "H_lb" + ], + "output": [ + "R_ub" + ], + "properties": { + "name": "t11_ub" + } + }, + { + "id": "t12_lb", + "input": [ + "H_lb" + ], + "output": [ + "D_lb" + ], + "properties": { + "name": "t12_lb" + } + }, + { + "id": "t12_ub", + "input": [ + "H_ub" + ], + "output": [ + "D_ub" + ], + "properties": { + "name": "t12_ub" + } + }, + { + "id": "t13_lb", + "input": [ + "S_noncompliant_ub" + ], + "output": [ + "S_compliant_lb" + ], + "properties": { + "name": "t13_lb" + } + }, + { + "id": "t13_ub", + "input": [ + "S_noncompliant_lb" + ], + "output": [ + "S_compliant_ub" + ], + "properties": { + "name": "t13_ub" + } + }, + { + "id": "t14_lb", + "input": [ + "S_compliant_ub" + ], + "output": [ + "S_noncompliant_lb" + ], + "properties": { + "name": "t14_lb" + } + }, + { + "id": "t14_ub", + "input": [ + "S_compliant_lb" + ], + "output": [ + "S_noncompliant_ub" + ], + "properties": { + "name": "t14_ub" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1_to_2_lb", + "expression": "I_lb*S_compliant_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" + }, + { + "target": "t1_to_2_ub", + "expression": "I_ub*S_compliant_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" + }, + { + "target": "t3_to_4_lb", + "expression": "I_lb*S_noncompliant_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" + }, + { + "target": "t3_to_4_ub", + "expression": "I_ub*S_noncompliant_ub*beta*(-c_m_lb*eps_m_lb + 1)/N", + "expression_mathml": "I_ubS_ubbetac_m_lbeps_m_lb1N" + }, + { + "target": "t5_to_6_lb", + "expression": "E_lb*r_E_to_I", + "expression_mathml": "E_lbr_E_to_I" + }, + { + "target": "t5_to_6_ub", + "expression": "E_ub*r_E_to_I", + "expression_mathml": "E_ubr_E_to_I" + }, + { + "target": "t7_to_8_lb", + "expression": "I_lb*p_I_to_R*r_I_to_R", + "expression_mathml": "I_lbp_I_to_Rr_I_to_R" + }, + { + "target": "t7_to_8_ub", + "expression": "I_ub*p_I_to_R*r_I_to_R", + "expression_mathml": "I_ubp_I_to_Rr_I_to_R" + }, + { + "target": "t9_to_10_lb", + "expression": "I_lb*p_I_to_H*r_I_to_H", + "expression_mathml": "I_lbp_I_to_Hr_I_to_H" + }, + { + "target": "t9_to_10_ub", + "expression": "I_ub*p_I_to_H*r_I_to_H", + "expression_mathml": "I_ubp_I_to_Hr_I_to_H" + }, + { + "target": "t11_lb", + "expression": "H_lb*p_H_to_R*r_H_to_R", + "expression_mathml": "H_lbp_H_to_Rr_H_to_R" + }, + { + "target": "t11_ub", + "expression": "H_ub*p_H_to_R*r_H_to_R", + "expression_mathml": "H_ubp_H_to_Rr_H_to_R" + }, + { + "target": "t12_lb", + "expression": "H_lb*p_H_to_D*r_H_to_D", + "expression_mathml": "Hp_H_to_Dr_H_to_D" + }, + { + "target": "t12_ub", + "expression": "H_ub*p_H_to_D*r_H_to_D", + "expression_mathml": "H_ubp_H_to_Dr_H_to_D" + }, + { + "target": "t13_lb", + "expression": "S_noncompliant_lb*p_noncompliant_compliant", + "expression_mathml": "S_noncompliantp_noncompliant_compliant" + }, + { + "target": "t13_ub", + "expression": "S_noncompliant_ub*p_noncompliant_compliant", + "expression_mathml": "S_noncompliantp_noncompliant_compliant" + }, + { + "target": "t14_lb", + "expression": "S_compliant_lb*p_compliant_noncompliant", + "expression_mathml": "S_compliantp_compliant_noncompliant" + }, + { + "target": "t14_ub", + "expression": "S_compliant_ub*p_compliant_noncompliant", + "expression_mathml": "S_compliantp_compliant_noncompliant" + } + ], + "initials": [ + { + "target": "S_compliant_lb", + "expression": "9669997.50000000", + "expression_mathml": "9669997.5" + }, + { + "target": "S_noncompliant_lb", + "expression": "9669997.50000000", + "expression_mathml": "9669997.5" + }, + { + "target": "I_lb", + "expression": "4.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E_lb", + "expression": "1.000000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D_lb", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "S_compliant_ub", + "expression": "9669997.50000000", + "expression_mathml": "9669997.5" + }, + { + "target": "S_noncompliant_ub", + "expression": "9669997.50000000", + "expression_mathml": "9669997.5" + }, + { + "target": "I_ub", + "expression": "4.00000000000000", + "expression_mathml": "4.0" + }, + { + "target": "E_ub", + "expression": "1.000000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R_ub", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "H_ub", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D_ub", + "expression": "0.0", + "expression_mathml": "0.0" + } + ], + "parameters": [ + { + "id": "N", + "value": 19340000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta", + "value": 0.4, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "eps_m_lb", + "value": 0.4, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "eps_m_ub", + "value": 0.6, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "c_m_lb", + "value": 0.4, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "c_m_ub", + "value": 0.6, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_E_to_I", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_R", + "value": 0.8, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_R", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_I_to_H", + "value": 0.2, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_I_to_H", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_R", + "value": 0.88, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_R", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_H_to_D", + "value": 0.12, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "r_H_to_D", + "value": 0.1, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "p_noncompliant_compliant", + "value": 0.1 + }, + { + "id": "p_compliant_noncompliant", + "value": 0.1 + } + ], + "observables": [], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + } + }, + "metadata": { + "annotations": { + "license": null, + "authors": [], + "references": [], + "time_scale": null, + "time_start": null, + "time_end": null, + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_S.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_S.json index e948f4c8..58385b04 100644 --- a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_S.json +++ b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_S.json @@ -666,7 +666,7 @@ "rates": [ { "target": "t1_lb", - "expression": "I_compliant_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression": "0", "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" }, { @@ -686,7 +686,7 @@ }, { "target": "t4_lb", - "expression": "I_compliant_lb*S_lb*beta*(-c_m_ub*eps_m_ub + 1)/N", + "expression": "0", "expression_mathml": "I_lbS_lbbetac_m_ubeps_m_ub1N" }, { @@ -706,7 +706,7 @@ }, { "target": "t5_lb", - "expression": "0", + "expression": "E_compliant_lb*r_E_to_I", "expression_mathml": "E_lbr_E_to_I" }, { @@ -716,7 +716,7 @@ }, { "target": "t6_lb", - "expression": "0", + "expression": "E_noncompliant_lb*r_E_to_I", "expression_mathml": "E_lbr_E_to_I" }, { diff --git a/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json b/resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_SEI.json similarity index 100% rename from resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_all.json rename to resources/amr/petrinet/monthly-demo/2024-08/12_month_scenario_1/q1a_ii/eval_scenario1_1_ii_3_destratified_SEI.json diff --git a/src/funman/config.py b/src/funman/config.py index 36b33001..e24c4925 100644 --- a/src/funman/config.py +++ b/src/funman/config.py @@ -9,7 +9,7 @@ from pydantic import BaseModel, ConfigDict, field_validator, model_validator -from funman.constants import MODE_ODEINT, Mode +from funman.constants import MODE_ODEINT, MODE_SMT, Mode from funman.utils.handlers import ( NoopResultHandler, ResultCombinedHandler, @@ -135,7 +135,7 @@ class FUNMANConfig(BaseModel): prioritize_box_entropy: bool = True """ When comparing boxes, prefer those with low entropy """ - mode: Mode = MODE_ODEINT + mode: Mode = MODE_SMT """ Mode to run FUNMAN, either funman.constants.MODE_ODEINT or funman.constants.MODE_SMT """ @field_validator("solver") diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index 701a9944..a57302b6 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -184,6 +184,7 @@ def derivative( ): # var_to_value, param_to_value): # param_at_t = {p: pv(t).item() for p, pv in param_to_value.items()} # FIXME assumes each transition has only one rate + # print(f"Calling with args {var_name}; {t}; {values}; {params}") pos_rates = [ # self._transition_rate(trans)[0].evalf( # subs={**var_to_value, **param_at_t} @@ -202,14 +203,16 @@ def derivative( for var in trans.input if var_name == var ] + # print(f"Got rates {pos_rates} {neg_rates}") return sum(pos_rates) - sum(neg_rates) - def gradient(self, y, t, *p): + def gradient(self, t, y, *p): # FIXME support time varying paramters by treating parameters as a function - var_to_value = { - var: y[i] for i, var in enumerate(self._state_var_names()) - } + # var_to_value = { + # var: y[i] for i, var in enumerate(self._state_var_names()) + # } + print(f"y: {y}; t: {t}") param_to_value = { param: p[i] for i, param in enumerate(self._parameter_names()) } @@ -226,6 +229,8 @@ def gradient(self, y, t, *p): self.derivative(var, t, y, params) # var_to_value, param_to_value) for var in self._state_var_names() ] + print(f"vars: {self._state_var_names()}") + print(f"gradient: {grad}") return grad diff --git a/src/funman/scenario/consistency.py b/src/funman/scenario/consistency.py index f910503e..bfa39fde 100644 --- a/src/funman/scenario/consistency.py +++ b/src/funman/scenario/consistency.py @@ -4,7 +4,6 @@ import logging import threading -from datetime import datetime from typing import Callable, Dict, Optional import matplotlib.pyplot as plt @@ -92,12 +91,12 @@ def solve( ) scenario_result._models = models - start_time = datetime.now() - assert self.check_simulation( - config, scenario_result - ), "Simulation of solution is invalid." - duration = datetime.now() - start_time - l.info(f"Simulation Time: {duration}") + # start_time = datetime.now() + # assert self.check_simulation( + # config, scenario_result + # ), "Simulation of solution is invalid." + # duration = datetime.now() - start_time + # l.info(f"Simulation Time: {duration}") elif config.mode == MODE_ODEINT: point = self.simulate_scenario(config) parameter_space = ParameterSpace( diff --git a/src/funman/search/simulate.py b/src/funman/search/simulate.py index 8c584fba..8b43d781 100644 --- a/src/funman/search/simulate.py +++ b/src/funman/search/simulate.py @@ -4,7 +4,7 @@ import pandas as pd import sympy from pydantic import BaseModel -from scipy.integrate import odeint +from scipy.integrate import odeint, solve_ivp from funman import FunmanModel @@ -50,19 +50,34 @@ def sim(self) -> Optional[Timeseries]: if self.model._is_differentiable: full_output = 1 - timeseries = odeint( - self.model.gradient, - self.initial_state(), - self.tvect, - args=self.model_args(), - full_output=full_output, - rtol=1, - atol=1, - ) - if full_output == 1: - timeseries, output = timeseries - - l.debug(f"odeint output: {output}") + use_odeint = False + if use_odeint: + timeseries = odeint( + self.model.gradient, + self.tvect, + self.initial_state(), + args=self.model_args(), + full_output=full_output, + tfirst=True, + rtol=1, + atol=1, + ) + if full_output == 1: + timeseries, output = timeseries + + l.debug(f"odeint output: {output}") + else: + timseries = solve_ivp( + self.model.gradient, + (self.tvect[0], self.tvect[-1]), + self.initial_state(), + args=self.model_args(), + t_eval=self.tvect, + first_step=1.0, + max_step=1.0, + rtol=1.0, + atol=1.0, + ) ts = Timeseries( data=[self.tvect] + timeseries.T.tolist(), From fecd3d48178a3007a1b99509bcbc77cdc24b1721 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Mon, 9 Sep 2024 16:11:20 +0000 Subject: [PATCH 34/93] fix cp version for gihub build --- docker/base/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker/base/Dockerfile b/docker/base/Dockerfile index 2af880a8..9fa607f8 100644 --- a/docker/base/Dockerfile +++ b/docker/base/Dockerfile @@ -47,6 +47,6 @@ RUN pip install --no-cache-dir fastapi RUN pip install --no-cache-dir --upgrade setuptools pip RUN pip install --no-cache-dir wheel -RUN pip install /dreal4/dreal-4.21.6.2-cp310-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl +RUN pip install /dreal4/dreal-4.21.6.2-cp38-none-manylinux_$(ldd --version | grep '^ldd' | sed -E 's/^ldd.*([0-9]+)\.([0-9]+)$/\1_\2/')_$(arch).whl CMD [ "/bin/bash" ] From f2e3e08e90d775e13dc2483885e6a423b6da3614 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 13 Sep 2024 14:47:26 +0000 Subject: [PATCH 35/93] random seed to dreal python binding, improve scipy integration --- .../funman_dreal/src/funman_dreal/solver.py | 1 + .../funman_aug_2024_12mo_eval_1_q1_demo.ipynb | 850 ++++++++++++++---- .../petrinet/terrarium-tests/sir_request.json | 3 +- src/funman/model/petrinet.py | 106 ++- src/funman/representation/interval.py | 2 +- src/funman/scenario/consistency.py | 13 +- src/funman/search/box_search.py | 5 +- src/funman/search/simulate.py | 25 +- src/funman/server/query.py | 9 +- test/test_decapode.py | 11 +- 10 files changed, 785 insertions(+), 240 deletions(-) diff --git a/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py b/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py index dd725e2c..534743bc 100644 --- a/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py +++ b/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py @@ -442,6 +442,7 @@ def __init__( # self.context.config.use_worklist_fixpoint = True self.model = None self.log_level = dreal.LogLevel.OFF + self.config.random_seed = 0 if "solver_options" in options: if ( "preferred" in options["solver_options"] diff --git a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb index a1e2c128..761def07 100644 --- a/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb +++ b/notebooks/monthly-demos/funman_aug_2024_12mo_eval_1_q1_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -11,7 +11,7 @@ "# Import funman related code\n", "import os\n", "from funman.api.run import Runner\n", - "from funman_demo import summarize_results\n", + "from funman import MODE_ODEINT, MODE_SMT\n", "from funman import FunmanWorkRequest, EncodingSchedule \n", "import json\n", "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", @@ -84,20 +84,20 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Constants for the scenario\n", "\n", - "MAX_TIME=150\n", + "MAX_TIME=20\n", "STEP_SIZE=10\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -127,6 +127,7 @@ " funman_request.config.tolerance = 0.01\n", " funman_request.config.dreal_precision = dreal_precision\n", " funman_request.config.verbosity = logging.ERROR\n", + " funman_request.config.mode = MODE_ODEINT\n", " # funman_request.config.dreal_log_level = \"debug\"\n", " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", "\n", @@ -321,9 +322,34 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y: [9.6699975e+06 2.0000000e+00 5.0000000e-01 2.0000000e+00 9.6699975e+06\n", + " 5.0000000e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00]; t: 0.0\n", + "vars: ['S_compliant', 'I_compliant', 'E_compliant', 'I_noncompliant', 'S_noncompliant', 'E_noncompliant', 'R', 'H', 'D']\n", + "gradient: [-1.0000, -0.051999, 0.52400, -0.051999, -1.0000, 0.48000, 0.22400, 0.080000, 0]\n", + "y: [9.66999750e+06 1.99999465e+00 5.00053917e-01 1.99999465e+00\n", + " 9.66999750e+06 5.00049389e-01 2.30482775e-05 8.23153293e-06\n", + " 0.00000000e+00]; t: 0.00010289410642249223\n" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], "source": [ "# i) (TA1 Search and Discovery Workflow, 1 Hr. Time Limit) Find estimates on the\n", "# efficacy of surgical masks in preventing onward transmission of SARS-CoV-2 (preferred)\n", @@ -344,9 +370,79 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-11 18:40:46,147 - funman.funman - ERROR - funman.solve() exiting due to exception: 'ImmutableDenseNDimArray' object has no attribute '_eval_evalf'\n", + "2024-09-11 18:40:46,147 - funman.server.worker - ERROR - Internal Server Error (1d444af2-91ca-4c1a-96a6-e1c314089463):\n", + "Traceback (most recent call last):\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1483, in evalf\n", + " r = rf(x, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1370, in evalf_symbol\n", + " val = options['subs'][x]\n", + "KeyError: beta\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/home/danbryce/funman/src/funman/server/worker.py\", line 240, in _run\n", + " result = f.solve(\n", + " File \"/home/danbryce/funman/src/funman/funman.py\", line 91, in solve\n", + " raise e\n", + " File \"/home/danbryce/funman/src/funman/funman.py\", line 78, in solve\n", + " result = problem.solve(\n", + " File \"/home/danbryce/funman/src/funman/scenario/consistency.py\", line 96, in solve\n", + " assert self.check_simulation(\n", + " File \"/home/danbryce/funman/src/funman/scenario/scenario.py\", line 461, in check_simulation\n", + " timeseries = self.run_point_simulation(\n", + " File \"/home/danbryce/funman/src/funman/scenario/scenario.py\", line 375, in run_point_simulation\n", + " timeseries = simulator.sim()\n", + " File \"/home/danbryce/funman/src/funman/search/simulate.py\", line 70, in sim\n", + " timseries = solve_ivp(\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/ivp.py\", line 557, in solve_ivp\n", + " solver = method(fun, t0, y0, tf, vectorized=vectorized, **options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/rk.py\", line 94, in __init__\n", + " self.f = self.fun(self.t, self.y)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/base.py\", line 138, in fun\n", + " return self.fun_single(t, y)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/base.py\", line 20, in fun_wrapped\n", + " return np.asarray(fun(t, y), dtype=dtype)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/ivp.py\", line 529, in \n", + " fun = lambda t, x, fun=fun: fun(t, x, *args)\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 240, in gradient\n", + " grad = [\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 241, in \n", + " self.derivative(var, t, y, params, var_to_value, param_to_value) # var_to_value, param_to_value)\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 210, in derivative\n", + " neg_rates = [\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 211, in \n", + " self._transition_rate(trans)[0].evalf(\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1648, in evalf\n", + " result = evalf(self, prec + 4, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1483, in evalf\n", + " r = rf(x, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 649, in evalf_mul\n", + " result = evalf(arg, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1488, in evalf\n", + " xe = x._eval_evalf(prec)\n", + "AttributeError: 'ImmutableDenseNDimArray' object has no attribute '_eval_evalf'\n", + "2024-09-11 18:40:46,149 - funman.server.worker - ERROR - Aborting work on: 1d444af2-91ca-4c1a-96a6-e1c314089463\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y: [1.9339995e+07 4.0000000e+00 1.0000000e+00 4.0000000e+00 1.9339995e+07\n", + " 1.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00]; t: 0.0\n" + ] + } + ], "source": [ "# Remove all stratification by combining (Sc, Snc), (Ec, Enc), and (Ic, Inc) into S, E, and I \n", "\n", @@ -358,9 +454,79 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-11 18:40:48,584 - funman.funman - ERROR - funman.solve() exiting due to exception: 'ImmutableDenseNDimArray' object has no attribute '_eval_evalf'\n", + "2024-09-11 18:40:48,584 - funman.server.worker - ERROR - Internal Server Error (a2613db3-ad14-49f0-9b2d-66e3088e36a6):\n", + "Traceback (most recent call last):\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1483, in evalf\n", + " r = rf(x, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1370, in evalf_symbol\n", + " val = options['subs'][x]\n", + "KeyError: beta\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/home/danbryce/funman/src/funman/server/worker.py\", line 240, in _run\n", + " result = f.solve(\n", + " File \"/home/danbryce/funman/src/funman/funman.py\", line 91, in solve\n", + " raise e\n", + " File \"/home/danbryce/funman/src/funman/funman.py\", line 78, in solve\n", + " result = problem.solve(\n", + " File \"/home/danbryce/funman/src/funman/scenario/consistency.py\", line 96, in solve\n", + " assert self.check_simulation(\n", + " File \"/home/danbryce/funman/src/funman/scenario/scenario.py\", line 461, in check_simulation\n", + " timeseries = self.run_point_simulation(\n", + " File \"/home/danbryce/funman/src/funman/scenario/scenario.py\", line 375, in run_point_simulation\n", + " timeseries = simulator.sim()\n", + " File \"/home/danbryce/funman/src/funman/search/simulate.py\", line 70, in sim\n", + " timseries = solve_ivp(\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/ivp.py\", line 557, in solve_ivp\n", + " solver = method(fun, t0, y0, tf, vectorized=vectorized, **options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/rk.py\", line 94, in __init__\n", + " self.f = self.fun(self.t, self.y)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/base.py\", line 138, in fun\n", + " return self.fun_single(t, y)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/base.py\", line 20, in fun_wrapped\n", + " return np.asarray(fun(t, y), dtype=dtype)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/ivp.py\", line 529, in \n", + " fun = lambda t, x, fun=fun: fun(t, x, *args)\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 240, in gradient\n", + " grad = [\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 241, in \n", + " self.derivative(var, t, y, params, var_to_value, param_to_value) # var_to_value, param_to_value)\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 210, in derivative\n", + " neg_rates = [\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 211, in \n", + " self._transition_rate(trans)[0].evalf(\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1648, in evalf\n", + " result = evalf(self, prec + 4, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1483, in evalf\n", + " r = rf(x, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 649, in evalf_mul\n", + " result = evalf(arg, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1488, in evalf\n", + " xe = x._eval_evalf(prec)\n", + "AttributeError: 'ImmutableDenseNDimArray' object has no attribute '_eval_evalf'\n", + "2024-09-11 18:40:48,585 - funman.server.worker - ERROR - Aborting work on: a2613db3-ad14-49f0-9b2d-66e3088e36a6\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y: [1.9339995e+07 2.0000000e+00 2.0000000e+00 1.0000000e+00 2.0000000e+00\n", + " 2.0000000e+00 1.9339995e+07 1.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]; t: 0.0\n" + ] + } + ], "source": [ "# Remove SE stratification by combining (Sc, Snc) and (Ec, Enc) into S and E\n", "\n", @@ -372,9 +538,79 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-11 18:40:50,891 - funman.funman - ERROR - funman.solve() exiting due to exception: 'ImmutableDenseNDimArray' object has no attribute '_eval_evalf'\n", + "2024-09-11 18:40:50,892 - funman.server.worker - ERROR - Internal Server Error (2b08dbde-73e3-45c1-81be-a8ed0b202622):\n", + "Traceback (most recent call last):\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1483, in evalf\n", + " r = rf(x, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1370, in evalf_symbol\n", + " val = options['subs'][x]\n", + "KeyError: p_noncompliant_compliant\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/home/danbryce/funman/src/funman/server/worker.py\", line 240, in _run\n", + " result = f.solve(\n", + " File \"/home/danbryce/funman/src/funman/funman.py\", line 91, in solve\n", + " raise e\n", + " File \"/home/danbryce/funman/src/funman/funman.py\", line 78, in solve\n", + " result = problem.solve(\n", + " File \"/home/danbryce/funman/src/funman/scenario/consistency.py\", line 96, in solve\n", + " assert self.check_simulation(\n", + " File \"/home/danbryce/funman/src/funman/scenario/scenario.py\", line 461, in check_simulation\n", + " timeseries = self.run_point_simulation(\n", + " File \"/home/danbryce/funman/src/funman/scenario/scenario.py\", line 375, in run_point_simulation\n", + " timeseries = simulator.sim()\n", + " File \"/home/danbryce/funman/src/funman/search/simulate.py\", line 70, in sim\n", + " timseries = solve_ivp(\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/ivp.py\", line 557, in solve_ivp\n", + " solver = method(fun, t0, y0, tf, vectorized=vectorized, **options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/rk.py\", line 94, in __init__\n", + " self.f = self.fun(self.t, self.y)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/base.py\", line 138, in fun\n", + " return self.fun_single(t, y)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/base.py\", line 20, in fun_wrapped\n", + " return np.asarray(fun(t, y), dtype=dtype)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/ivp.py\", line 529, in \n", + " fun = lambda t, x, fun=fun: fun(t, x, *args)\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 240, in gradient\n", + " grad = [\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 241, in \n", + " self.derivative(var, t, y, params, var_to_value, param_to_value) # var_to_value, param_to_value)\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 202, in derivative\n", + " pos_rates = [\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 203, in \n", + " self._transition_rate(trans)[0].evalf(\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1648, in evalf\n", + " result = evalf(self, prec + 4, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1483, in evalf\n", + " r = rf(x, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 649, in evalf_mul\n", + " result = evalf(arg, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1488, in evalf\n", + " xe = x._eval_evalf(prec)\n", + "AttributeError: 'ImmutableDenseNDimArray' object has no attribute '_eval_evalf'\n", + "2024-09-11 18:40:50,893 - funman.server.worker - ERROR - Aborting work on: 2b08dbde-73e3-45c1-81be-a8ed0b202622\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y: [9.6699975e+06 9.6699975e+06 4.0000000e+00 1.0000000e+00 4.0000000e+00\n", + " 9.6699975e+06 9.6699975e+06 1.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]; t: 0.0\n" + ] + } + ], "source": [ "# Remove EI stratification by combining (Ec, Enc) and (Ic, Inc) into E and I\n", "\n", @@ -386,9 +622,80 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-11 18:40:53,405 - funman.funman - ERROR - funman.solve() exiting due to exception: 'ImmutableDenseNDimArray' object has no attribute '_eval_evalf'\n", + "2024-09-11 18:40:53,405 - funman.server.worker - ERROR - Internal Server Error (460e0151-6c68-4d31-bd83-3f08b9abfe6f):\n", + "Traceback (most recent call last):\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1483, in evalf\n", + " r = rf(x, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1370, in evalf_symbol\n", + " val = options['subs'][x]\n", + "KeyError: beta\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/home/danbryce/funman/src/funman/server/worker.py\", line 240, in _run\n", + " result = f.solve(\n", + " File \"/home/danbryce/funman/src/funman/funman.py\", line 91, in solve\n", + " raise e\n", + " File \"/home/danbryce/funman/src/funman/funman.py\", line 78, in solve\n", + " result = problem.solve(\n", + " File \"/home/danbryce/funman/src/funman/scenario/consistency.py\", line 96, in solve\n", + " assert self.check_simulation(\n", + " File \"/home/danbryce/funman/src/funman/scenario/scenario.py\", line 461, in check_simulation\n", + " timeseries = self.run_point_simulation(\n", + " File \"/home/danbryce/funman/src/funman/scenario/scenario.py\", line 375, in run_point_simulation\n", + " timeseries = simulator.sim()\n", + " File \"/home/danbryce/funman/src/funman/search/simulate.py\", line 70, in sim\n", + " timseries = solve_ivp(\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/ivp.py\", line 557, in solve_ivp\n", + " solver = method(fun, t0, y0, tf, vectorized=vectorized, **options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/rk.py\", line 94, in __init__\n", + " self.f = self.fun(self.t, self.y)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/base.py\", line 138, in fun\n", + " return self.fun_single(t, y)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/base.py\", line 20, in fun_wrapped\n", + " return np.asarray(fun(t, y), dtype=dtype)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/scipy/integrate/_ivp/ivp.py\", line 529, in \n", + " fun = lambda t, x, fun=fun: fun(t, x, *args)\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 240, in gradient\n", + " grad = [\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 241, in \n", + " self.derivative(var, t, y, params, var_to_value, param_to_value) # var_to_value, param_to_value)\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 210, in derivative\n", + " neg_rates = [\n", + " File \"/home/danbryce/funman/src/funman/model/petrinet.py\", line 211, in \n", + " self._transition_rate(trans)[0].evalf(\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1648, in evalf\n", + " result = evalf(self, prec + 4, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1483, in evalf\n", + " r = rf(x, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 649, in evalf_mul\n", + " result = evalf(arg, prec, options)\n", + " File \"/home/danbryce/funman_venv/lib/python3.8/site-packages/sympy/core/evalf.py\", line 1488, in evalf\n", + " xe = x._eval_evalf(prec)\n", + "AttributeError: 'ImmutableDenseNDimArray' object has no attribute '_eval_evalf'\n", + "2024-09-11 18:40:53,406 - funman.server.worker - ERROR - Aborting work on: 460e0151-6c68-4d31-bd83-3f08b9abfe6f\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y: [1.9339995e+07 2.0000000e+00 2.0000000e+00 5.0000000e-01 5.0000000e-01\n", + " 2.0000000e+00 2.0000000e+00 1.9339995e+07 5.0000000e-01 5.0000000e-01\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00]; t: 0.0\n" + ] + } + ], "source": [ "# Remove S stratification by combining (Sc, Snc) in S\n", "\n", @@ -400,7 +707,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -692,7 +999,7 @@ "c_m_ub 0.60 0.60 0.60 " ] }, - "execution_count": 17, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -704,12 +1011,12 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -751,7 +1058,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -790,209 +1097,376 @@ " \n", " \n", " 0\n", - " 1.000000e+00\n", - " 1.000000e+00\n", - " 1.000000e+00\n", - " 1.000000e+00\n", - " 1.000000e+00\n", - " 1.000000e+00\n", - " 1.000000e+00\n", - " 1.000000e+00\n", - " 1.000000e+00\n", - " 1.000000e+00\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", " \n", " \n", " 1\n", - " 1.824000e+00\n", - " 3.487999e+00\n", - " 1.824000e+00\n", - " 3.487999e+00\n", - " 1.824000e+00\n", - " 3.487999e+00\n", - " 1.824000e+00\n", - " 3.487999e+00\n", - " 1.824000e+00\n", - " 3.487999e+00\n", + " 1.824000\n", + " 3.487999\n", + " 1.824000\n", + " 3.487999\n", + " 1.824000\n", + " 3.487999\n", + " 1.824000\n", + " 3.487999\n", + " 1.824000\n", + " 3.487999\n", " \n", " \n", " 2\n", - " 2.647999e+00\n", - 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151 rows × 10 columns

\n", "" ], "text/plain": [ - " original_stratified_E_lb original_stratified_E_ub \\\n", - "0 1.000000e+00 1.000000e+00 \n", - "1 1.824000e+00 3.487999e+00 \n", - "2 2.647999e+00 5.975999e+00 \n", - "3 3.471999e+00 8.463998e+00 \n", - "4 4.295999e+00 1.095200e+01 \n", - ".. ... ... \n", - "146 4.157384e+08 -9.338146e+10 \n", - "147 3.792124e+08 -1.089595e+11 \n", - "148 3.426864e+08 -1.245376e+11 \n", - "149 3.061603e+08 -1.401157e+11 \n", - "150 2.696343e+08 -1.556938e+11 \n", - "\n", - " destratified_SEI_E_lb destratified_SEI_E_ub destratified_SE_E_lb \\\n", - "0 1.000000e+00 1.000000e+00 1.000000e+00 \n", - "1 1.824000e+00 3.487999e+00 1.824000e+00 \n", - "2 2.647999e+00 5.975999e+00 2.647999e+00 \n", - "3 3.471999e+00 8.463998e+00 3.471999e+00 \n", - "4 4.295999e+00 1.095200e+01 4.295999e+00 \n", - ".. ... ... ... \n", - "146 4.157384e+08 -9.338146e+10 4.157384e+08 \n", - "147 3.792124e+08 -1.089595e+11 3.792124e+08 \n", - "148 3.426864e+08 -1.245376e+11 3.426864e+08 \n", - "149 3.061603e+08 -1.401157e+11 3.061603e+08 \n", - "150 2.696343e+08 -1.556938e+11 2.696343e+08 \n", + " original_stratified_E_lb original_stratified_E_ub destratified_SEI_E_lb \\\n", + "0 1.000000 1.000000 1.000000 \n", + "1 1.824000 3.487999 1.824000 \n", + "2 2.647999 5.975999 2.647999 \n", + "3 3.471999 8.463998 3.471999 \n", + "4 4.295999 10.951997 4.295999 \n", + "5 5.119999 13.439997 5.119999 \n", + "6 5.943998 15.927996 5.943998 \n", + "7 6.767998 18.415995 6.767998 \n", + "8 7.591998 20.903994 7.591998 \n", + "9 8.415998 23.391994 8.415998 \n", + "10 9.239997 25.879993 9.239997 \n", + "11 6.485757 24.357112 6.485757 \n", + "12 3.731517 22.834232 3.731517 \n", + "13 0.977276 21.311351 0.977276 \n", + "14 -1.776964 19.788471 -1.776964 \n", + "15 -4.531204 18.265590 -4.531204 \n", + "16 -7.285444 16.742709 -7.285444 \n", + "17 -10.039685 15.219829 -10.039685 \n", + "18 -12.793925 13.696948 -12.793925 \n", + "19 -15.548165 12.174068 -15.548165 \n", + "20 -18.302406 10.651187 -18.302406 \n", "\n", - " destratified_SE_E_ub destratified_EI_E_lb destratified_EI_E_ub \\\n", - "0 1.000000e+00 1.000000e+00 1.000000e+00 \n", - "1 3.487999e+00 1.824000e+00 3.487999e+00 \n", - "2 5.975999e+00 2.647999e+00 5.975999e+00 \n", - "3 8.463998e+00 3.471999e+00 8.463998e+00 \n", - "4 1.095200e+01 4.295999e+00 1.095200e+01 \n", - ".. ... ... ... \n", - "146 -9.338146e+10 4.157384e+08 -9.338146e+10 \n", - "147 -1.089595e+11 3.792124e+08 -1.089595e+11 \n", - "148 -1.245376e+11 3.426864e+08 -1.245376e+11 \n", - "149 -1.401157e+11 3.061603e+08 -1.401157e+11 \n", - "150 -1.556938e+11 2.696343e+08 -1.556938e+11 \n", + " destratified_SEI_E_ub destratified_SE_E_lb destratified_SE_E_ub \\\n", + "0 1.000000 1.000000 1.000000 \n", + "1 3.487999 1.824000 3.487999 \n", + "2 5.975999 2.647999 5.975999 \n", + "3 8.463998 3.471999 8.463998 \n", + "4 10.951997 4.295999 10.951997 \n", + "5 13.439997 5.119999 13.439997 \n", + "6 15.927996 5.943998 15.927996 \n", + "7 18.415995 6.767998 18.415995 \n", + "8 20.903994 7.591998 20.903994 \n", + "9 23.391994 8.415998 23.391994 \n", + "10 25.879993 9.239997 25.879993 \n", + "11 24.357112 6.485757 24.357112 \n", + "12 22.834232 3.731517 22.834232 \n", + "13 21.311351 0.977276 21.311351 \n", + "14 19.788471 -1.776964 19.788471 \n", + "15 18.265590 -4.531204 18.265590 \n", + "16 16.742709 -7.285444 16.742709 \n", + "17 15.219829 -10.039685 15.219829 \n", + "18 13.696948 -12.793925 13.696948 \n", + "19 12.174068 -15.548165 12.174068 \n", + "20 10.651187 -18.302406 10.651187 \n", "\n", - " destratified_S_E_lb destratified_S_E_ub \n", - "0 1.000000e+00 1.000000e+00 \n", - "1 1.824000e+00 3.487999e+00 \n", - "2 2.647999e+00 5.975999e+00 \n", - "3 3.471999e+00 8.463998e+00 \n", - "4 4.295999e+00 1.095200e+01 \n", - ".. ... ... \n", - "146 4.157384e+08 -9.338146e+10 \n", - "147 3.792124e+08 -1.089595e+11 \n", - "148 3.426864e+08 -1.245376e+11 \n", - "149 3.061603e+08 -1.401157e+11 \n", - "150 2.696343e+08 -1.556938e+11 \n", + " destratified_EI_E_lb destratified_EI_E_ub destratified_S_E_lb \\\n", + "0 1.000000 1.000000 1.000000 \n", + "1 1.824000 3.487999 1.824000 \n", + "2 2.647999 5.975999 2.647999 \n", + "3 3.471999 8.463998 3.471999 \n", + "4 4.295999 10.951997 4.295999 \n", + "5 5.119999 13.439997 5.119999 \n", + "6 5.943998 15.927996 5.943998 \n", + "7 6.767998 18.415995 6.767998 \n", + "8 7.591998 20.903994 7.591998 \n", + "9 8.415998 23.391994 8.415998 \n", + "10 9.239997 25.879993 9.239997 \n", + "11 6.485757 24.357112 6.485757 \n", + "12 3.731517 22.834232 3.731517 \n", + "13 0.977276 21.311351 0.977276 \n", + "14 -1.776964 19.788471 -1.776964 \n", + "15 -4.531204 18.265590 -4.531204 \n", + "16 -7.285444 16.742709 -7.285444 \n", + "17 -10.039685 15.219829 -10.039685 \n", + "18 -12.793925 13.696948 -12.793925 \n", + "19 -15.548165 12.174068 -15.548165 \n", + "20 -18.302406 10.651187 -18.302406 \n", "\n", - "[151 rows x 10 columns]" + " destratified_S_E_ub \n", + "0 1.000000 \n", + "1 3.487999 \n", + "2 5.975999 \n", + "3 8.463998 \n", + "4 10.951997 \n", + "5 13.439997 \n", + "6 15.927996 \n", + "7 18.415995 \n", + "8 20.903994 \n", + "9 23.391994 \n", + "10 25.879993 \n", + "11 24.357112 \n", + "12 22.834232 \n", + "13 21.311351 \n", + "14 19.788471 \n", + "15 18.265590 \n", + "16 16.742709 \n", + "17 15.219829 \n", + "18 13.696948 \n", + "19 12.174068 \n", + "20 10.651187 " ] }, - "execution_count": 39, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } diff --git a/resources/amr/petrinet/terrarium-tests/sir_request.json b/resources/amr/petrinet/terrarium-tests/sir_request.json index a5223d1d..d8769859 100644 --- a/resources/amr/petrinet/terrarium-tests/sir_request.json +++ b/resources/amr/petrinet/terrarium-tests/sir_request.json @@ -81,6 +81,7 @@ "config": { "use_compartmental_constraints": true, "normalization_constant": 1, - "tolerance": 0.02 + "tolerance": 0.02, + "normalize": false } } \ No newline at end of file diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index a57302b6..5a4d5f47 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -5,7 +5,12 @@ from pydantic import BaseModel, ConfigDict from pysmt.shortcuts import REAL, Div, Real, Symbol -from funman.utils.sympy_utils import substitute, sympy_to_pysmt, to_sympy +from funman.utils.sympy_utils import ( + replace_reserved, + substitute, + sympy_to_pysmt, + to_sympy, +) from ..representation.interval import Interval from .generated_models.petrinet import Distribution @@ -180,53 +185,85 @@ def compartmental_constraints( ] def derivative( - self, var_name, t, values, params + self, + var_name, + t, + values, + params, + var_to_value, + param_to_value, + get_lambda=False, ): # var_to_value, param_to_value): # param_at_t = {p: pv(t).item() for p, pv in param_to_value.items()} # FIXME assumes each transition has only one rate # print(f"Calling with args {var_name}; {t}; {values}; {params}") - pos_rates = [ - # self._transition_rate(trans)[0].evalf( - # subs={**var_to_value, **param_at_t} - # ) - self._transition_rate(trans, getLambda=True)[0](*values, *params) - for trans in self._transitions() - for var in trans.output - if var_name == var - ] - neg_rates = [ - # self._transition_rate(trans)[0].evalf( - # subs={**var_to_value, **param_at_t} - # ) - self._transition_rate(trans, getLambda=True)[0](*values, *params) - for trans in self._transitions() - for var in trans.input - if var_name == var - ] + if get_lambda: + pos_rates = [ + self._transition_rate(trans, get_lambda=get_lambda)[0]( + *values, *params + ) + for trans in self._transitions() + for var in trans.output + if var_name == var + ] + neg_rates = [ + self._transition_rate(trans, get_lambda=get_lambda)[0]( + *values, *params + ) + for trans in self._transitions() + for var in trans.input + if var_name == var + ] + else: + pos_rates = [ + self._transition_rate(trans)[0].evalf( + subs={**var_to_value, **param_to_value, "timer_t": t}, n=5 + ) + for trans in self._transitions() + for var in trans.output + if var_name == var + ] + neg_rates = [ + self._transition_rate(trans)[0].evalf( + subs={**var_to_value, **param_to_value, "timer_t": t}, n=5 + ) + for trans in self._transitions() + for var in trans.input + if var_name == var + ] # print(f"Got rates {pos_rates} {neg_rates}") return sum(pos_rates) - sum(neg_rates) def gradient(self, t, y, *p): # FIXME support time varying paramters by treating parameters as a function - # var_to_value = { - # var: y[i] for i, var in enumerate(self._state_var_names()) - # } + var_to_value = { + var: y[i] for i, var in enumerate(self._state_var_names()) + } print(f"y: {y}; t: {t}") param_to_value = { - param: p[i] for i, param in enumerate(self._parameter_names()) + param: p[i](t)[()] + for i, param in enumerate(self._parameter_names()) } # values = [ # y[i] for i, _ in enumerate(self._symbols()) # ] params = [ - param_to_value[str(p)](t) + param_to_value[str(p)] for p in self._symbols() if str(p) in param_to_value ] + [t] grad = [ - self.derivative(var, t, y, params) # var_to_value, param_to_value) + self.derivative( + var, + t, + y, + params, + var_to_value, + param_to_value, + get_lambda=True, + ) # var_to_value, param_to_value) for var in self._state_var_names() ] print(f"vars: {self._state_var_names()}") @@ -359,7 +396,7 @@ def _edge_target(self, edge): def _output_edges(self): return [(t.id, o) for t in self._transitions() for o in t.output] - def _transition_rate(self, transition, sympify=False, getLambda=False): + def _transition_rate(self, transition, sympify=False, get_lambda=False): if hasattr(self.petrinet.semantics, "ode"): if transition.id not in self._transition_rates_cache: t_rates = [ @@ -383,8 +420,13 @@ def _transition_rate(self, transition, sympify=False, getLambda=False): ) for t in t_rates ] + unreserved_symbols = [ + replace_reserved(s) for s in self._symbols() + ] + # convert "t" to "timer_t" + unreserved_symbols[-1] = self._time_var_id(self._time_var()) t_rates_lambda = [ - sympy.lambdify(self._symbols(), t) for t in t_rates + sympy.lambdify(unreserved_symbols, t) for t in t_rates ] self._transition_rates_cache[transition.id] = t_rates self._transition_rates_lambda_cache[transition.id] = ( @@ -392,7 +434,7 @@ def _transition_rate(self, transition, sympify=False, getLambda=False): ) return ( self._transition_rates_cache[transition.id] - if not getLambda + if not get_lambda else self._transition_rates_lambda_cache[transition.id] ) else: @@ -444,10 +486,12 @@ def contract_parameters( new_bounds = parameter_bounds[param.id] if param.distribution: param.distribution.parameters["minimum"] = max( - new_bounds.lb, param.distribution.parameters["minimum"] + new_bounds.lb, + float(param.distribution.parameters["minimum"]), ) param.distribution.parameters["maximum"] = min( - new_bounds.ub, param.distribution.parameters["maximum"] + new_bounds.ub, + float(param.distribution.parameters["maximum"]), ) else: param.distribution = Distribution( diff --git a/src/funman/representation/interval.py b/src/funman/representation/interval.py index 6bd706bf..dc52fa71 100644 --- a/src/funman/representation/interval.py +++ b/src/funman/representation/interval.py @@ -152,7 +152,7 @@ def meets(self, other: "Interval") -> bool: bool Does self meet other? """ - l.debug(f"Interval.meets(): {self} {other}") + l.trace(f"Interval.meets(): {self} {other}") # return self.ub == other.lb or self.lb == other.ub if self.closed_upper_bound: # cannot be equal to other.lb diff --git a/src/funman/scenario/consistency.py b/src/funman/scenario/consistency.py index bfa39fde..f910503e 100644 --- a/src/funman/scenario/consistency.py +++ b/src/funman/scenario/consistency.py @@ -4,6 +4,7 @@ import logging import threading +from datetime import datetime from typing import Callable, Dict, Optional import matplotlib.pyplot as plt @@ -91,12 +92,12 @@ def solve( ) scenario_result._models = models - # start_time = datetime.now() - # assert self.check_simulation( - # config, scenario_result - # ), "Simulation of solution is invalid." - # duration = datetime.now() - start_time - # l.info(f"Simulation Time: {duration}") + start_time = datetime.now() + assert self.check_simulation( + config, scenario_result + ), "Simulation of solution is invalid." + duration = datetime.now() - start_time + l.info(f"Simulation Time: {duration}") elif config.mode == MODE_ODEINT: point = self.simulate_scenario(config) parameter_space = ParameterSpace( diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py index e5dd5450..8c04a026 100644 --- a/src/funman/search/box_search.py +++ b/src/funman/search/box_search.py @@ -346,11 +346,11 @@ def _extract_point(self, model, box: Box): }, schedule=box.schedule, ) - # Timestep is not in the model (implicit) - point.values["timestep"] = box.timestep().lb point.remove_irrelevant_steps( self.problem._smt_encoder._untimed_symbols ) + # Timestep is not in the model (implicit) + point.values["timestep"] = box.timestep().lb return point @@ -1312,6 +1312,7 @@ def _expand( handler(rval, episode.config, all_results) if ( "progress" in all_results + and all_results["progress"] is not None and all_results["progress"].progress > last_progress ): diff --git a/src/funman/search/simulate.py b/src/funman/search/simulate.py index 8b43d781..b6c1bd2b 100644 --- a/src/funman/search/simulate.py +++ b/src/funman/search/simulate.py @@ -59,28 +59,37 @@ def sim(self) -> Optional[Timeseries]: args=self.model_args(), full_output=full_output, tfirst=True, - rtol=1, - atol=1, ) if full_output == 1: timeseries, output = timeseries l.debug(f"odeint output: {output}") + data = ( + timeseries.T.tolist() + if len(timeseries) > 0 + else [[v] for v in self.initial_state()] + ) else: - timseries = solve_ivp( + result = solve_ivp( self.model.gradient, (self.tvect[0], self.tvect[-1]), self.initial_state(), args=self.model_args(), t_eval=self.tvect, - first_step=1.0, - max_step=1.0, - rtol=1.0, - atol=1.0, + # first_step=1.0, + # max_step=1.0, + # rtol=1.0, + # atol=1.0, ) + timeseries = result.y + data = ( + timeseries.tolist() + if len(timeseries) > 0 + else [[v] for v in self.initial_state()] + ) ts = Timeseries( - data=[self.tvect] + timeseries.T.tolist(), + data=[self.tvect] + data, columns=["time"] + self.model._state_var_names(), ) else: diff --git a/src/funman/server/query.py b/src/funman/server/query.py index eee9bf19..7d916bcb 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -243,7 +243,7 @@ def contract_model(self): """ if not isinstance(self.model, GeneratedPetriNetModel): - raise NotImplemented( + raise NotImplementedError( f"Cannot contract model of type {type(self.model)}" ) @@ -291,7 +291,12 @@ def update_parameter_space( except Exception as e: l.exception(f"Unable to update progress due to exception: {e}") - self.contract_model() + try: + self.contract_model() + except NotImplementedError as e: + l.info( + f"Bypassing output of contracted model because it is not implmented for this model type: {type(self.model)}" + ) return self.progress diff --git a/test/test_decapode.py b/test/test_decapode.py index 98c58ef5..049c32cb 100644 --- a/test/test_decapode.py +++ b/test/test_decapode.py @@ -13,6 +13,7 @@ from funman.model.decapode import DecapodeDynamics, DecapodeModel from funman.model.query import Query, QueryAnd, QueryTrue from funman.representation import ModelParameter +from funman.representation.interval import Interval from funman.scenario import ( ConsistencyScenario, ConsistencyScenarioResult, @@ -85,13 +86,18 @@ def setup_use_case_decapode_parameter_synthesis(self, query: Query): model = self.setup_use_case_decapode_common() [lb, ub] = model.parameter_bounds['m_Mo(Other("‾"))'] scenario = ParameterSynthesisScenario( - parameters=[ModelParameter(name='m_Mo(Other("‾"))', lb=lb, ub=ub)], + parameters=[ + ModelParameter( + name='m_Mo(Other("‾"))', interval=Interval(lb=lb, ub=ub) + ) + ], model=model, query=query, ) return scenario + @unittest.skip("tmp") @unittest.expectedFailure def test_use_case_decapode_regression(self): """ @@ -118,6 +124,7 @@ def test_use_case_decapode_regression(self): print(f"Could not solve scenario because: {e}") assert False + @unittest.skip("tmp") @unittest.expectedFailure def test_use_case_decapode_sensitivity_analysis(self): """ @@ -159,6 +166,7 @@ def setup_use_case_decapode_consistency(self, query: Query): scenario = ConsistencyScenario(model=model, query=query) return scenario + @unittest.skip("tmp") @unittest.expectedFailure def test_use_case_decapode_consistency(self): """ @@ -182,6 +190,7 @@ def test_use_case_decapode_consistency(self): print(f"Could not solve scenario because: {e}") assert False + @unittest.skip("tmp") @unittest.expectedFailure def test_use_case_decapode_projection(self): """ From 656c24b2e2d4f2ebf366ef50dca3f78523f4dad8 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 13 Sep 2024 18:46:55 +0000 Subject: [PATCH 36/93] support observations --- docker/docker-bake.hcl | 2 +- .../funman_sep_2024_observables.ipynb | 373 +++++++++ .../SIDARTHE.model.with.observables.json | 775 ++++++++++++++++++ .../2024-09/eval_scenario_base_request.json | 25 + src/funman/model/generated_models/petrinet.py | 8 + src/funman/model/model.py | 5 + src/funman/model/petrinet.py | 23 + src/funman/translate/bilayer.py | 8 + src/funman/translate/ensemble.py | 11 + src/funman/translate/petrinet.py | 56 +- src/funman/translate/translate.py | 13 +- 11 files changed, 1294 insertions(+), 5 deletions(-) create mode 100644 notebooks/monthly-demos/funman_sep_2024_observables.ipynb create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/SIDARTHE.model.with.observables.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/eval_scenario_base_request.json diff --git a/docker/docker-bake.hcl b/docker/docker-bake.hcl index 06e46b01..d0ec4ca0 100644 --- a/docker/docker-bake.hcl +++ b/docker/docker-bake.hcl @@ -20,7 +20,7 @@ variable "DREAL_REPO_URL" { default = "https://github.com/danbryce/dreal4.git" } variable "DREAL_COMMIT_TAG" { - default = "844d64fd7427d5d2ce3b01a2f83231cefc8709a4" + default = "03a1055c7768ba609f33897ad91c361da6582871" } variable "AUTOMATES_COMMIT_TAG" { default = "e5fb635757aa57007615a75371f55dd4a24851e0" diff --git a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb new file mode 100644 index 00000000..dc0a1589 --- /dev/null +++ b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# This notebook illustrates handling the September 2024 Demo of the 18-month epi evaluation scenario 2 question 6b\n", + "\n", + "# Import funman related code\n", + "import os\n", + "from funman.api.run import Runner\n", + "from funman import MODE_ODEINT, MODE_SMT\n", + "from funman import FunmanWorkRequest, EncodingSchedule \n", + "import json\n", + "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", + "from funman.representation import Interval\n", + "import pandas as pd\n", + "import logging\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "\n", + "RESOURCES = \"../../resources\"\n", + "SAVED_RESULTS_DIR = \"./out\"\n", + "\n", + "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"monthly-demo\", \"2024-09\")\n", + "REQUEST_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario_base_request.json\")\n", + "\n", + "models = {\n", + " \"sidarthe_observables\": os.path.join(\n", + " EXAMPLE_DIR, \"SIDARTHE.model.with.observables.json\")\n", + "}\n", + "\n", + "# states = {\n", + "# \"original_stratified\": [\"S_compliant\", \"S_noncompliant\", \"I_compliant\", \"I_noncompliant\", \"E_compliant\", \"E_noncompliant\",\"R\", \"H\", \"D\"],\n", + "# \"destratified_SEI\": [\"S_lb\", \"S_ub\", \"I_lb\", \"I_ub\", \"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", + "# \"destratified_SE\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", + "# \"destratified_EI\": [\"I_lb\", \"I_ub\", \"S_compliant_lb\", \"S_compliant_ub\", \"S_noncompliant_lb\", \"S_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", + "# \"destratified_S\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_compliant_lb\", \"E_compliant_ub\",\"E_noncompliant_lb\", \"E_noncompliant_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"]\n", + "# }\n", + "\n", + "# basevar_map = [\n", + "# ['S_compliant','S_noncompliant', 'S_lb', 'S_ub','S_compliant_lb', 'S_noncompliant_ub', 'S_compliant_ub', 'S_noncompliant_lb'], \n", + "# ['I_compliant','I_noncompliant','I_lb','I_ub','I_compliant_lb', 'I_noncompliant_ub', 'I_compliant_ub', 'I_noncompliant_lb'],\n", + "# ['E_compliant','E_noncompliant','E_lb', 'E_ub', 'E_compliant_lb','E_noncompliant_lb', 'E_compliant_ub','E_noncompliant_ub',],\n", + "# ['R','R_lb', 'R_ub'],\n", + "# ['H','H_lb', 'H_ub'],\n", + "# ['D','D_lb', 'D_ub']\n", + "# ]\n", + "\n", + "# hatches= {\n", + "# \"original_stratified\": '/', \n", + "# \"destratified_SEI\": '\\\\', \n", + "# \"destratified_SE\" : '|', \n", + "# \"destratified_S\" : '-'\n", + "# #, '+', 'x', 'o', 'O', '.', '*'\n", + "# }\n", + "\n", + "request_params = {}\n", + "request_results = {}\n", + "\n", + "# Cycle styles for lines\n", + "# plt.rcParams['axes.prop_cycle'] = (\"cycler('color', 'rgb') +\"\n", + " # \"cycler('lw', [1, 2, 3])\")\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Constants for the scenario\n", + "\n", + "MAX_TIME=2\n", + "STEP_SIZE=1\n", + "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper functions to setup FUNMAN for different steps of the scenario\n", + "\n", + "\n", + "from funman.server.query import FunmanWorkRequest\n", + "\n", + "\n", + "def get_request():\n", + " with open(REQUEST_PATH, \"r\") as request:\n", + " funman_request = FunmanWorkRequest.model_validate(json.load(request))\n", + " return funman_request\n", + "\n", + "def get_model(model):\n", + " return Runner().get_model(model) if isinstance(model, dict) else Runner().get_model(models[model])\n", + "\n", + "def set_timepoints(funman_request, timepoints):\n", + " funman_request.structure_parameters[0].schedules = [EncodingSchedule(timepoints=timepoints)]\n", + "\n", + "def unset_all_labels(funman_request):\n", + " for p in funman_request.parameters:\n", + " p.label = \"any\"\n", + " \n", + "def set_config_options(funman_request, debug=False, dreal_precision=1e1):\n", + " # Overrides for configuration\n", + " #\n", + " # funman_request.config.substitute_subformulas = True\n", + " # funman_request.config.use_transition_symbols = True\n", + " # funman_request.config.use_compartmental_constraints=False\n", + " if debug:\n", + " funman_request.config.save_smtlib=\"./out\"\n", + " funman_request.config.tolerance = 0.01\n", + " funman_request.config.dreal_precision = dreal_precision\n", + " funman_request.config.verbosity = logging.ERROR\n", + " funman_request.config.mode = MODE_ODEINT\n", + " # funman_request.config.dreal_log_level = \"debug\"\n", + " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", + "\n", + "def get_synthesized_vars(funman_request):\n", + " return [p.name for p in funman_request.parameters if p.label == \"all\"]\n", + "\n", + "def run(funman_request, plot=False, model=models['sidarthe_observables']):\n", + " to_synthesize = get_synthesized_vars(funman_request)\n", + " results = Runner().run(\n", + " model,\n", + " funman_request,\n", + " description=\"SIERHD Eval 12mo Scenario 1 q1\",\n", + " case_out_dir=SAVED_RESULTS_DIR,\n", + " dump_plot=plot,\n", + " print_last_time=True,\n", + " parameters_to_plot=to_synthesize\n", + " )\n", + " return results\n", + "\n", + "def setup_common(funman_request, synthesize=False, debug=False, dreal_precision=1e-1):\n", + " set_timepoints(funman_request, timepoints)\n", + " if not synthesize:\n", + " unset_all_labels(funman_request)\n", + " set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision)\n", + " \n", + "\n", + "def set_compartment_bounds(funman_request, model, upper_bound=9830000.0, error=0.01):\n", + " # Add bounds to compartments\n", + " for var in states[model]:\n", + " funman_request.constraints.append(StateVariableConstraint(name=f\"{var}_bounds\", variable=var, interval=Interval(lb=0, ub=upper_bound, closed_upper_bound=True),soft=False))\n", + "\n", + " # Add sum of compartments\n", + " funman_request.constraints.append(LinearConstraint(name=f\"compartment_bounds\", variables=states[model], additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True))\n", + "\n", + "def relax_parameter_bounds(funman_request, factor = 0.1):\n", + " # Relax parameter bounds\n", + " parameters = funman_request.parameters\n", + " for p in parameters:\n", + " interval = p.interval\n", + " width = float(interval.width())\n", + " interval.lb = interval.lb - (factor/2 * width)\n", + " interval.ub = interval.ub + (factor/2 * width)\n", + "\n", + "def plot_last_point(results, states):\n", + " pts = results.parameter_space.points() \n", + " print(f\"{len(pts)} points\")\n", + "\n", + " if len(pts) > 0:\n", + " # Get a plot for last point\n", + " df = results.dataframe(points=pts[-1:])\n", + " # pd.options.plotting.backend = \"plotly\"\n", + " ax = df[states].plot()\n", + " \n", + " \n", + " fig = plt.figure()\n", + " # fig.set_yscale(\"log\")\n", + " # fig.savefig(\"save_file_name.pdf\")\n", + " plt.close()\n", + "\n", + "def get_last_point_parameters(results):\n", + " pts = results.parameter_space.points()\n", + " if len(pts) > 0:\n", + " pt = pts[-1]\n", + " parameters = results.model._parameter_names()\n", + " param_values = {k:v for k, v in pt.values.items() if k in parameters }\n", + " return param_values\n", + "\n", + "def pretty_print_request_params(params):\n", + " # print(json.dump(params, indent=4))\n", + " if len(params)>0:\n", + "\n", + " df = pd.DataFrame(params)\n", + " print(df.T)\n", + "\n", + "\n", + "def report(results, name, states):\n", + " request_results[name] = results\n", + " # plot_last_point(results, states)\n", + " param_values = get_last_point_parameters(results)\n", + " # print(f\"Point parameters: {param_values}\")\n", + " if param_values is not None:\n", + " request_params[name] = param_values\n", + " # pretty_print_request_params(request_params)\n", + " \n", + "\n", + "def add_unit_test(funman_request, model=\"sidarthe_observables\"):\n", + " if model == \"destratified_SEI\":\n", + " mstates = states[\"destratified_SEI\"]\n", + " funman_request.constraints.append(LinearConstraint(name=\"compartment_lb\", soft=False, variables = [s for s in mstates if s.endswith(\"_lb\")],\n", + " additive_bounds= {\n", + " \"ub\": 19340000.5\n", + " }\n", + " ))\n", + " funman_request.constraints.append(LinearConstraint(name=\"compartment_ub\", soft=False, variables = [s for s in mstates if s.endswith(\"_ub\")],\n", + " additive_bounds= {\n", + " \"lb\": 0\n", + " }\n", + " ))\n", + " \n", + " \n", + "\n", + "\n", + "def plot_bounds(point, results, timespan=None, fig=None, axs=None, vars = [\"S\", \"E\", \"I\", \"R\", \"D\", \"H\"], model=None, basevar_map={}, **kwargs):\n", + " \n", + " if point.simulation is not None:\n", + " df = point.simulation.dataframe().T\n", + " else:\n", + " df = results.dataframe([point])\n", + " \n", + " if timespan is not None:\n", + " df = df.loc[timespan[0]:timespan[1]]\n", + " \n", + " # print(df)\n", + "\n", + " # Drop the ub vars because they are paired with the lb vars \n", + " no_ub_vars = [v for v in vars if not v.endswith(\"_ub\")]\n", + " no_strat_vars = [v for v in no_ub_vars if not \"_noncompliant\" in v]\n", + "\n", + " if fig is None and axs is None:\n", + " fig, axs = plt.subplots(len(basevar_map))\n", + " fig.set_figheight(3*len(basevar_map))\n", + " fig.suptitle('Variable Bounds over time')\n", + " \n", + " for var in no_strat_vars:\n", + " # print(var)\n", + " # Get index of list containing var\n", + " i = next(iter([i for i, bv in enumerate(basevar_map) if var in bv]))\n", + " # print(i)\n", + " if var.endswith(\"_lb\"):\n", + " # var is lower bound\n", + " basevar = var.split(\"_lb\")[0]\n", + " lb = f\"{basevar}_lb\"\n", + " ub = f\"{basevar}_ub\"\n", + " labels = [lb, ub]\n", + " elif var.endswith(\"_ub\"):\n", + " # skip, handled as part of lb\n", + " continue\n", + " else:\n", + " # var is not of the form varname_lb\n", + " basevar = var\n", + " labels = basevar\n", + " \n", + " \n", + " if \"_compliant\" in basevar:\n", + " basevar = basevar.split(\"_\")[0]\n", + " if isinstance(labels, list):\n", + " lb = df[f\"{basevar}_compliant_lb\"] + df[f\"{basevar}_noncompliant_lb\"]\n", + " ub = df[f\"{basevar}_compliant_ub\"] + df[f\"{basevar}_noncompliant_ub\"]\n", + " labels = [f\"{basevar}_lb\", f\"{basevar}_ub\"]\n", + " data = pd.concat([lb, ub],axis=1, keys=labels)\n", + " \n", + " else:\n", + " data = df[f\"{basevar}_compliant\"] + df[f\"{basevar}_noncompliant\"]\n", + " labels = f\"{basevar}\"\n", + " else:\n", + " # print(labels)\n", + " data = df[labels]\n", + " if \"_compliant\" in basevar:\n", + " basevar = basevar.split(\"_\")[0]\n", + " labels = f\"{basevar}\"\n", + " \n", + " \n", + " legend_labels = labels\n", + " if model is not None:\n", + " legend_labels = [f\"{model}_{k.rsplit('_', 1)[0]}\" for k in labels[0:1]][0] if isinstance(labels, list) else f\"{model}_{labels}\"\n", + " \n", + " \n", + " \n", + " \n", + " # Fill between lb and ub\n", + " if isinstance(labels, list):\n", + " axs[i].fill_between(data.index, data[labels[0]], data[labels[1]], label=legend_labels, **kwargs)\n", + " else:\n", + " if \"hatch\" in kwargs:\n", + " del kwargs[\"hatch\"]\n", + " if \"alpha\" in kwargs:\n", + " del kwargs[\"alpha\"]\n", + " axs[i].plot(data, label=legend_labels, **kwargs)\n", + " axs[i].set_title(f\"{basevar} Bounds\")\n", + "\n", + " \n", + " # axs[i].set_yscale('logit')\n", + " \n", + " # axs[i].legend(loc=\"outer\")\n", + " axs[i].legend(loc='center left', bbox_to_anchor=(1, 0.5),\n", + " ncol=1, fancybox=True, shadow=True, prop={'size': 8}, markerscale=2)\n", + " # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + "\n", + " # fig.tight_layout()\n", + " return fig, axs\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "\n", + "\n", + "funman_request = get_request()\n", + "model = get_model(\"sidarthe_observables\")\n", + "setup_common(funman_request, debug=False)\n", + "results = run(funman_request, model=models[\"sidarthe_observables\"])\n", + "# report(results, \"sidarthe_observables\", states[\"sidarthe_observables\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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/resources/amr/petrinet/monthly-demo/2024-09/SIDARTHE.model.with.observables.json b/resources/amr/petrinet/monthly-demo/2024-09/SIDARTHE.model.with.observables.json new file mode 100644 index 00000000..154ffa07 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-09/SIDARTHE.model.with.observables.json @@ -0,0 +1,775 @@ +{ + "id": "b2bc5453-626e-4f07-b54f-2680c6378d67", + "createdOn": "2024-08-27T18:51:24.946+00:00", + "updatedOn": "2024-09-09T14:39:31.925+00:00", + "name": "SIDARTHE model with observables", + "fileNames": [], + "temporary": false, + "publicAsset": true, + "header": { + "name": "SIDARTHE model with observables", + "description": "Giordano2020 - SIDARTHE model of COVID-19 spread in Italy", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "model_version": "0.1" + }, + "model": { + "states": [ + { + "id": "Susceptible", + "name": "Susceptible", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "Diagnosed", + "name": "Diagnosed", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "diagnosis": "ncit:C15220" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "Infected", + "name": "Infected", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "Ailing", + "name": "Ailing", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "disease_severity": "ncit:C25269", + "diagnosis": "ncit:C113725" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "Recognized", + "name": "Recognized", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "diagnosis": "ncit:C15220" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "Healed", + "name": "Healed", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "Threatened", + "name": "Threatened", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": { + "disease_severity": "ncit:C25467" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "Extinct", + "name": "Extinct", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1", + "input": [ + "Diagnosed", + "Susceptible" + ], + "output": [ + "Diagnosed", + "Infected" + ], + "properties": { + "name": "t1" + } + }, + { + "id": "t2", + "input": [ + "Ailing", + "Susceptible" + ], + "output": [ + "Ailing", + "Infected" + ], + "properties": { + "name": "t2" + } + }, + { + "id": "t3", + "input": [ + "Recognized", + "Susceptible" + ], + "output": [ + "Recognized", + "Infected" + ], + "properties": { + "name": "t3" + } + }, + { + "id": "t4", + "input": [ + "Infected", + "Susceptible" + ], + "output": [ + "Infected", + "Infected" + ], + "properties": { + "name": "t4" + } + }, + { + "id": "t5", + "input": [ + "Infected" + ], + "output": [ + "Diagnosed" + ], + "properties": { + "name": "t5" + } + }, + { + "id": "t6", + "input": [ + "Infected" + ], + "output": [ + "Ailing" + ], + "properties": { + "name": "t6" + } + }, + { + "id": "t7", + "input": [ + "Infected" + ], + "output": [ + "Healed" + ], + "properties": { + "name": "t7" + } + }, + { + "id": "t8", + "input": [ + "Diagnosed" + ], + "output": [ + "Recognized" + ], + "properties": { + "name": "t8" + } + }, + { + "id": "t9", + "input": [ + "Diagnosed" + ], + "output": [ + "Healed" + ], + "properties": { + "name": "t9" + } + }, + { + "id": "t10", + "input": [ + "Ailing" + ], + "output": [ + "Recognized" + ], + "properties": { + "name": "t10" + } + }, + { + "id": "t11", + "input": [ + "Ailing" + ], + "output": [ + "Healed" + ], + "properties": { + "name": "t11" + } + }, + { + "id": "t12", + "input": [ + "Ailing" + ], + "output": [ + "Threatened" + ], + "properties": { + "name": "t12" + } + }, + { + "id": "t13", + "input": [ + "Recognized" + ], + "output": [ + "Threatened" + ], + "properties": { + "name": "t13" + } + }, + { + "id": "t14", + "input": [ + "Recognized" + ], + "output": [ + "Healed" + ], + "properties": { + "name": "t14" + } + }, + { + "id": "t15", + "input": [ + "Threatened" + ], + "output": [ + "Extinct" + ], + "properties": { + "name": "t15" + } + }, + { + "id": "t16", + "input": [ + "Threatened" + ], + "output": [ + "Healed" + ], + "properties": { + "name": "t16" + } + } + ] + }, + "properties": {}, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1", + "expression": "Diagnosed*Susceptible*beta", + "expression_mathml": "DiagnosedSusceptiblebeta" + }, + { + "target": "t2", + "expression": "Ailing*Susceptible*gamma", + "expression_mathml": "AilingSusceptiblegamma" + }, + { + "target": "t3", + "expression": "Recognized*Susceptible*delta", + "expression_mathml": "RecognizedSusceptibledelta" + }, + { + "target": "t4", + "expression": "Infected*Susceptible*alpha", + "expression_mathml": "InfectedSusceptiblealpha" + }, + { + "target": "t5", + "expression": "Infected*epsilon", + "expression_mathml": "Infectedepsilon" + }, + { + "target": "t6", + "expression": "Infected*zeta", + "expression_mathml": "Infectedzeta" + }, + { + "target": "t7", + "expression": "Infected*lambda", + "expression_mathml": "Infectedlambda" + }, + { + "target": "t8", + "expression": "Diagnosed*eta", + "expression_mathml": "Diagnosedeta" + }, + { + "target": "t9", + "expression": "Diagnosed*rho", + "expression_mathml": "Diagnosedrho" + }, + { + "target": "t10", + "expression": "Ailing*theta", + "expression_mathml": "Ailingtheta" + }, + { + "target": "t11", + "expression": "Ailing*kappa", + "expression_mathml": "Ailingkappa" + }, + { + "target": "t12", + "expression": "Ailing*mu", + "expression_mathml": "Ailingmu" + }, + { + "target": "t13", + "expression": "Recognized*nu", + "expression_mathml": "Recognizednu" + }, + { + "target": "t14", + "expression": "Recognized*xi", + "expression_mathml": "Recognizedxi" + }, + { + "target": "t15", + "expression": "Threatened*tau", + "expression_mathml": "Threatenedtau" + }, + { + "target": "t16", + "expression": "Threatened*sigma", + "expression_mathml": "Threatenedsigma" + } + ], + "initials": [ + { + "target": "Susceptible", + "expression": "0.9999963", + "expression_mathml": "0.99999629999999995" + }, + { + "target": "Diagnosed", + "expression": "3.33333333e-7", + "expression_mathml": "3.33333333e-7" + }, + { + "target": "Infected", + "expression": "3.33333333e-6", + "expression_mathml": "3.3333333299999999e-6" + }, + { + "target": "Ailing", + "expression": "1.66666666e-8", + "expression_mathml": "1.6666666599999999e-8" + }, + { + "target": "Recognized", + "expression": "3.33333333e-8", + "expression_mathml": "3.33333333e-8" + }, + { + "target": "Healed", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "Threatened", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "Extinct", + "expression": "0.0", + "expression_mathml": "0.0" + } + ], + "parameters": [ + { + "id": "alpha", + "name": "alpha", + "description": "alpha", + "value": 0.57, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.5642999999999999, + "maximum": 0.5757 + } + }, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "beta", + "name": "beta", + "description": "beta", + "value": 0.011, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.010889999999999999, + "maximum": 0.01111 + } + }, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "delta", + "name": "delta", + "description": "delta", + "value": 0.011, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.010889999999999999, + "maximum": 0.01111 + } + }, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "epsilon", + "name": "epsilon", + "description": "epsilon", + "value": 0.171, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.16929000000000002, + "maximum": 0.17271 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "eta", + "name": "eta", + "description": "eta", + "value": 0.125, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.12375, + "maximum": 0.12625 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "gamma", + "name": "gamma", + "description": "gamma", + "value": 0.456, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.45144, + "maximum": 0.46056 + } + }, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "kappa", + "name": "kappa", + "description": "kappa", + "value": 0.017, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.01683, + "maximum": 0.01717 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "lambda", + "name": "lambda", + "description": "lambda", + "value": 0.034, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.03366, + "maximum": 0.03434 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "mu", + "name": "mu", + "description": "mu", + "value": 0.017, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.01683, + "maximum": 0.01717 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "nu", + "name": "nu", + "description": "nu", + "value": 0.027, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.02673, + "maximum": 0.02727 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "rho", + "name": "rho", + "description": "rho", + "value": 0.034, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.03366, + "maximum": 0.03434 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "theta", + "name": "theta", + "description": "theta", + "value": 0.371, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.36729, + "maximum": 0.37471 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "xi", + "name": "xi", + "description": "xi", + "value": 0.017, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.01683, + "maximum": 0.01717 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "zeta", + "name": "zeta", + "description": "zeta", + "value": 0.125, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.12375, + "maximum": 0.12625 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "tau", + "name": "tau", + "description": "tau", + "value": 0.01, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.0099, + "maximum": 0.0101 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "sigma", + "name": "sigma", + "description": "sigma", + "value": 0.017, + "distribution": { + "type": "StandardUniform1", + "parameters": { + "minimum": 0.01683, + "maximum": 0.01717 + } + }, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + } + ], + "observables": [ + { + "id": "TotalInfected", + "name": "TotalInfected", + "expression": "Ailing + Diagnosed + Infected + Recognized + Threatened", + "expression_mathml": "AilingDiagnosedInfectedRecognizedThreatened" + }, + { + "id": "R0", + "name": "R0", + "expression": "alpha/(epsilon + eta + lambda) + beta*epsilon/((eta + rho)*(epsilon + eta + lambda)) + delta*epsilon*eta/((eta + rho)*(nu + xi)*(epsilon + eta + lambda)) + delta*theta*zeta/((nu + xi)*(epsilon + eta + lambda)*(kappa + mu + theta)) + gamma*zeta/((epsilon + eta + lambda)*(kappa + mu + theta))", + "expression_mathml": "alphaepsilonetalambdabetaepsilonetarhoepsilonetalambdadeltaepsilonetaetarhonuxiepsilonetalambdadeltathetazetanuxiepsilonetalambdakappamuthetagammazetaepsilonetalambdakappamutheta" + } + ], + "time": { + "id": "t" + } + }, + "typing": null + }, + "metadata": { + "annotations": { + "authors": [], + "references": [], + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-09/eval_scenario_base_request.json b/resources/amr/petrinet/monthly-demo/2024-09/eval_scenario_base_request.json new file mode 100644 index 00000000..8fe10dad --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-09/eval_scenario_base_request.json @@ -0,0 +1,25 @@ +{ + "constraints": [], + "parameters": [], + "structure_parameters": [ + { + "name": "schedules", + "schedules": [ + { + "timepoints": [ + 0, + 10, + 20 + ] + } + ] + } + ], + "config": { + "tolerance": 1e-2, + "dreal_precision": 0.001, + "normalization_constant": 1.0, + "normalize": false, + "use_compartmental_constraints": true + } +} \ No newline at end of file diff --git a/src/funman/model/generated_models/petrinet.py b/src/funman/model/generated_models/petrinet.py index 038d6b2b..d7f4203e 100644 --- a/src/funman/model/generated_models/petrinet.py +++ b/src/funman/model/generated_models/petrinet.py @@ -61,6 +61,13 @@ class Unit(BaseModel): expression_mathml: Optional[str] = None +class Observable(BaseModel): + id: str + name: Optional[str] = None + expression: Optional[str] = None + expression_mathml: Optional[str] = None + + class Parameter(BaseModel): id: str name: Optional[str] = None @@ -80,6 +87,7 @@ class OdeSemantics(BaseModel): rates: Optional[List[Rate]] = None initials: Optional[List[Initial]] = None parameters: Optional[List[Parameter]] = None + observables: Optional[List[Observable]] = None time: Optional[Time] = None diff --git a/src/funman/model/model.py b/src/funman/model/model.py index ecd454eb..78874b09 100644 --- a/src/funman/model/model.py +++ b/src/funman/model/model.py @@ -138,6 +138,11 @@ def variables(self, include_next_state=False): return vars + def observables(self): + raise NotImplementedError( + f"FunmanModel.observables() is abstract and needs to be implemented by subclass: {type(self)}" + ) + def calculate_normalization_constant( self, scenario: "AnalysisScenario", config: "FUNMANConfig" ) -> float: diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index 5a4d5f47..f99cca0c 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -276,8 +276,31 @@ class GeneratedPetriNetModel(AbstractPetriNetModel): petrinet: GeneratedPetrinet _transition_rates_cache: Dict[str, Union[sympy.Expr, str]] = {} + _observables_cache: Dict[str, Union[sympy.Expr, str]] = {} _transition_rates_lambda_cache: Dict[str, Union[Callable, str]] = {} + def observables(self): + return self.petrinet.semantics.ode.observables + + def is_timed_observable(self, observation_id): + (_, e) = self.observable_expression(observation_id) + vars = [str(e) for e in e.get_free_variables()] + obs_state_vars = [v for v in vars if v in self._state_var_names()] + return any(obs_state_vars) + + def observable_expression(self, observation_id): + if observation_id not in self._observables_cache: + observable = next( + iter([o for o in self.observables() if o.id == observation_id]) + ) + self._observables_cache[observation_id] = ( + observable.expression, + sympy_to_pysmt( + to_sympy(observable.expression, self._symbols()) + ), + ) + return self._observables_cache[observation_id] + def default_encoder( self, config: "FUNMANConfig", scenario: "AnalysisScenario" ) -> "Encoder": diff --git a/src/funman/translate/bilayer.py b/src/funman/translate/bilayer.py index b55376f7..1bfe17d9 100644 --- a/src/funman/translate/bilayer.py +++ b/src/funman/translate/bilayer.py @@ -90,6 +90,14 @@ def _encode_next_step( return And(transition, measurements).simplify(), substitutions + def encode_observation( + self, scenario: "AnalysisScenario", step: int, substitutions={} + ): + l.warning( + f"Bilayer model does not support observations. Results omit observations." + ) + return TRUE() + def _encode_untimed_constraints( self, scenario: "AnalysisScenario" ) -> FNode: diff --git a/src/funman/translate/ensemble.py b/src/funman/translate/ensemble.py index 3a4b7b42..4bda6597 100644 --- a/src/funman/translate/ensemble.py +++ b/src/funman/translate/ensemble.py @@ -1,3 +1,4 @@ +import logging from numbers import Number from typing import Dict, Set @@ -9,6 +10,8 @@ from .translate import Encoder, Encoding +l = logging.getLogger(__name__) + class EnsembleEncoder(Encoder): def encode_model(self, scenario: "AnalysisScenario") -> Encoding: @@ -44,6 +47,14 @@ def _encode_next_step( return And(list(model_steps.values())) + def encode_observation( + self, scenario: "AnalysisScenario", step: int, substitutions={} + ): + l.warning( + f"Ensemble model does not support observations. Results omit observations." + ) + return TRUE() + def _submodel_substitution_map( self, model: FunmanModel, step=None, next_step=None ) -> Dict[Symbol, Symbol]: diff --git a/src/funman/translate/petrinet.py b/src/funman/translate/petrinet.py index f2058377..e665a028 100644 --- a/src/funman/translate/petrinet.py +++ b/src/funman/translate/petrinet.py @@ -305,11 +305,65 @@ def _encode_next_step( # for var in state_vars # ]) + next_observations = self.encode_observation( + scenario, next_step, substitutions=substitutions + ) + return ( - And(var_updates + [time_update, normalization_constraint]), + And( + var_updates + + [time_update, normalization_constraint, next_observations] + ), substitutions, ) + def encode_observation( + self, scenario: "AnalysisScenario", step: int, substitutions={} + ): + model = scenario.model + observables = model.observables() + + state_vars = scenario.model._state_vars() + + state = { + scenario.model._state_var_id(s): self._encode_state_var( + scenario.model._state_var_name(s), time=0 + ) + for s in state_vars + } + + timed_observations = And( + [ + Equals( + self._encode_state_var(o.id, time=step), + rate_expr_to_pysmt( + model.observable_expression(o.id)[0], state=state + ), + ) + for o in observables + if model.is_timed_observable(o.id) + ] + ) + untimed_observations = And( + [ + Equals( + self._encode_state_var(o.id), + rate_expr_to_pysmt( + model.observable_expression(o.id)[0], state=state + ), + ) + for o in observables + if step == 0 and not model.is_timed_observable(o.id) + ] + ) + + # f = And([ + # Equals(self._encode_state_var(o.id, time=step), rate_expr_to_pysmt(model.observable_expression(o.id)[0], state=state)) + # for o in observables + # if step == 0 or model.is_timed_observable(o.id) + # ]) + return And(timed_observations, untimed_observations) + def _define_init( self, scenario: "AnalysisScenario", init_time: int = 0 ) -> FNode: diff --git a/src/funman/translate/translate.py b/src/funman/translate/translate.py index b2d79475..50e08cb6 100644 --- a/src/funman/translate/translate.py +++ b/src/funman/translate/translate.py @@ -405,15 +405,22 @@ def encode_model_layer( assumptions: List[Assumption], ) -> EncodedFormula: if layer_idx == 0: - return self.encode_init_layer() + return self.encode_init_layer(scenario) else: return self.encode_transition_layer(scenario, layer_idx, options) - def encode_init_layer(self) -> EncodedFormula: + def encode_init_layer( + self, scenario: "AnalysisScenario" + ) -> EncodedFormula: initial_state = self._timed_model_elements["init"] initial_symbols = initial_state.get_free_variables() - return (initial_state, {s.symbol_name(): s for s in initial_symbols}) + observations = self.encode_observation(scenario, 0, substitutions={}) + + return ( + And(initial_state, observations), + {s.symbol_name(): s for s in initial_symbols}, + ) def encode_transition_layer( self, From f0c23b84151ec17d0cf9e8c13854fff28aa476b1 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 13 Sep 2024 20:47:28 +0000 Subject: [PATCH 37/93] constraints on observables --- .../2024-09/eval_scenario_base_request.json | 20 +++++- src/funman/scenario/consistency.py | 4 +- src/funman/translate/translate.py | 67 +++++++++++++++---- 3 files changed, 72 insertions(+), 19 deletions(-) diff --git a/resources/amr/petrinet/monthly-demo/2024-09/eval_scenario_base_request.json b/resources/amr/petrinet/monthly-demo/2024-09/eval_scenario_base_request.json index 8fe10dad..9e487065 100644 --- a/resources/amr/petrinet/monthly-demo/2024-09/eval_scenario_base_request.json +++ b/resources/amr/petrinet/monthly-demo/2024-09/eval_scenario_base_request.json @@ -1,5 +1,20 @@ { - "constraints": [], + "constraints": [ + { + "name": "TotalInfected_ub", + "variable": "TotalInfected", + "interval": { + "ub": 0.1 + } + }, + { + "name": "R0_lb", + "variable": "R0", + "interval": { + "lb": 0.1 + } + } + ], "parameters": [], "structure_parameters": [ { @@ -20,6 +35,7 @@ "dreal_precision": 0.001, "normalization_constant": 1.0, "normalize": false, - "use_compartmental_constraints": true + "use_compartmental_constraints": true, + "save_smtlib": "./out" } } \ No newline at end of file diff --git a/src/funman/scenario/consistency.py b/src/funman/scenario/consistency.py index f910503e..8efc0f7e 100644 --- a/src/funman/scenario/consistency.py +++ b/src/funman/scenario/consistency.py @@ -12,14 +12,12 @@ from pydantic import BaseModel, ConfigDict from pysmt.solvers.solver import Model as pysmt_Model -from funman import Point +from funman import ParameterSpace, Point from funman.constants import MODE_ODEINT, MODE_SMT from funman.representation.box import Box from funman.scenario import AnalysisScenario, AnalysisScenarioResult from funman.translate import Encoding -from ..representation.parameter_space import ParameterSpace - l = logging.getLogger(__name__) diff --git a/src/funman/translate/translate.py b/src/funman/translate/translate.py index 50e08cb6..eebdfb8c 100644 --- a/src/funman/translate/translate.py +++ b/src/funman/translate/translate.py @@ -816,7 +816,6 @@ def encode_linear_constraint( ), Minus(Real(nxt_t), Real(curr_t)), ).simplify() - pass else: expression = Plus( [ @@ -1112,47 +1111,87 @@ def _normalize(self, scenario: "AnalysisScenario", value): def _encode_state_variable_constraint( self, scenario: "AnalysisScenario", - query: StateVariableConstraint, + constraint: StateVariableConstraint, layer_idx: int, options: EncodingOptions, assumptions: List[Assumption], ): time = options.schedule.time_at_step(layer_idx) - if query.contains_time(time): + if ( + scenario.model.is_timed_observable(constraint.variable) + or constraint.variable in scenario.model._state_var_names() + ) and constraint.contains_time(time): bounds = ( - query.interval.normalize(options.normalization_constant) + constraint.interval.normalize(options.normalization_constant) if options.normalize - else query.interval + else constraint.interval ) - symbol = self._encode_state_var(var=query.variable, time=time) + symbol = self._encode_state_var(var=constraint.variable, time=time) norm = ( scenario.normalization_constant if options.normalize else 1.0 ) lb = ( - math_utils.div(query.interval.lb, norm) - if query.interval.lb != NEG_INFINITY - else query.interval.lb + math_utils.div(constraint.interval.lb, norm) + if constraint.interval.lb != NEG_INFINITY + else constraint.interval.lb ) ub = ( - math_utils.div(query.interval.ub, norm) - if query.interval.ub != POS_INFINITY - else query.interval.ub + math_utils.div(constraint.interval.ub, norm) + if constraint.interval.ub != POS_INFINITY + else constraint.interval.ub ) formula = self.interval_to_smt( - query.variable, + constraint.variable, Interval( lb=lb, ub=ub, - closed_upper_bound=query.interval.closed_upper_bound, + closed_upper_bound=constraint.interval.closed_upper_bound, ), time=time, # closed_upper_bound=query.interval.closed_upper_bound, infinity_constraints=False, ) + elif ( + not scenario.model.is_timed_observable(constraint.variable) + and not constraint.variable in scenario.model._state_var_names() + ) and layer_idx == 0: + bounds = ( + constraint.interval.normalize(options.normalization_constant) + if options.normalize + else constraint.interval + ) + symbol = self._encode_state_var(var=constraint.variable) + + norm = ( + scenario.normalization_constant if options.normalize else 1.0 + ) + + lb = ( + math_utils.div(constraint.interval.lb, norm) + if constraint.interval.lb != NEG_INFINITY + else constraint.interval.lb + ) + ub = ( + math_utils.div(constraint.interval.ub, norm) + if constraint.interval.ub != POS_INFINITY + else constraint.interval.ub + ) + + formula = self.interval_to_smt( + constraint.variable, + Interval( + lb=lb, + ub=ub, + closed_upper_bound=constraint.interval.closed_upper_bound, + ), + time=None, + # closed_upper_bound=query.interval.closed_upper_bound, + infinity_constraints=False, + ) else: formula = TRUE() From 3dc972a8cbcf23de8c10bb2aa0f1668af7f0eb4d Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Tue, 17 Sep 2024 14:33:28 +0000 Subject: [PATCH 38/93] adjust integration parameters to diagnose fpe --- src/funman/model/petrinet.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index f99cca0c..4eb61185 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -12,7 +12,7 @@ to_sympy, ) -from ..representation.interval import Interval +from ..representation import Interval from .generated_models.petrinet import Distribution from .generated_models.petrinet import Model as GeneratedPetrinet from .generated_models.petrinet import State, Transition @@ -217,7 +217,7 @@ def derivative( else: pos_rates = [ self._transition_rate(trans)[0].evalf( - subs={**var_to_value, **param_to_value, "timer_t": t}, n=5 + subs={**var_to_value, **param_to_value, "timer_t": t}, n=10 ) for trans in self._transitions() for var in trans.output @@ -225,7 +225,7 @@ def derivative( ] neg_rates = [ self._transition_rate(trans)[0].evalf( - subs={**var_to_value, **param_to_value, "timer_t": t}, n=5 + subs={**var_to_value, **param_to_value, "timer_t": t}, n=10 ) for trans in self._transitions() for var in trans.input @@ -242,7 +242,7 @@ def gradient(self, t, y, *p): } print(f"y: {y}; t: {t}") param_to_value = { - param: p[i](t)[()] + replace_reserved(param): p[i](t)[()] for i, param in enumerate(self._parameter_names()) } # values = [ @@ -262,7 +262,7 @@ def gradient(self, t, y, *p): params, var_to_value, param_to_value, - get_lambda=True, + get_lambda=False, ) # var_to_value, param_to_value) for var in self._state_var_names() ] From f2a40b9c9cb85cca6d1fb507deeb020c6afce2f6 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Wed, 18 Sep 2024 15:17:10 +0000 Subject: [PATCH 39/93] verbose flag to get result on console --- src/funman/api/run.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/src/funman/api/run.py b/src/funman/api/run.py index 83cbef2e..bfa520f6 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -481,6 +481,13 @@ def get_args(): default=False, help=f"Create parameter space plot with only the last timestep.", ) + parser.add_argument( + "-v", + "--verbose", + help="Write result to console", + default=False, + action="store_true" + ) parser.set_defaults(plot=None) return parser.parse_args() @@ -504,7 +511,8 @@ def main() -> int: parameters_to_plot=to_plot, print_last_time=args.last_time, ) - print(results.model_dump_json(indent=4)) + if args.verbose: + print(results.model_dump_json(indent=4,by_alias=True)) if __name__ == "__main__": From ca752f250749adb4590691eaa3ab7e7369d41260 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Wed, 18 Sep 2024 15:18:10 +0000 Subject: [PATCH 40/93] improve handling of observables in results --- .../funman_sep_2024_observables.ipynb | 95 +++++++++++++------ src/funman/model/model.py | 11 +++ src/funman/model/petrinet.py | 31 +++--- src/funman/representation/representation.py | 4 +- src/funman/scenario/scenario.py | 44 ++++++++- src/funman/server/query.py | 46 ++++++--- 6 files changed, 173 insertions(+), 58 deletions(-) diff --git a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb index dc0a1589..9b03f2f5 100644 --- a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb +++ b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb @@ -34,13 +34,14 @@ " EXAMPLE_DIR, \"SIDARTHE.model.with.observables.json\")\n", "}\n", "\n", - "# states = {\n", + "states = {\n", + " \"sidarthe_observables\": ['Susceptible', 'Diagnosed', 'Infected', 'Ailing', 'Recognized', 'Healed', 'Threatened', 'Extinct']\n", "# \"original_stratified\": [\"S_compliant\", \"S_noncompliant\", \"I_compliant\", \"I_noncompliant\", \"E_compliant\", \"E_noncompliant\",\"R\", \"H\", \"D\"],\n", "# \"destratified_SEI\": [\"S_lb\", \"S_ub\", \"I_lb\", \"I_ub\", \"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", "# \"destratified_SE\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", "# \"destratified_EI\": [\"I_lb\", \"I_ub\", \"S_compliant_lb\", \"S_compliant_ub\", \"S_noncompliant_lb\", \"S_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", "# \"destratified_S\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_compliant_lb\", \"E_compliant_ub\",\"E_noncompliant_lb\", \"E_noncompliant_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"]\n", - "# }\n", + "}\n", "\n", "# basevar_map = [\n", "# ['S_compliant','S_noncompliant', 'S_lb', 'S_ub','S_compliant_lb', 'S_noncompliant_ub', 'S_compliant_ub', 'S_noncompliant_lb'], \n", @@ -78,7 +79,7 @@ "source": [ "# Constants for the scenario\n", "\n", - "MAX_TIME=2\n", + "MAX_TIME=200\n", "STEP_SIZE=1\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" ] @@ -110,7 +111,7 @@ " for p in funman_request.parameters:\n", " p.label = \"any\"\n", " \n", - "def set_config_options(funman_request, debug=False, dreal_precision=1e1):\n", + "def set_config_options(funman_request, debug=False, dreal_precision=1e-3, mode=MODE_SMT):\n", " # Overrides for configuration\n", " #\n", " # funman_request.config.substitute_subformulas = True\n", @@ -121,7 +122,7 @@ " funman_request.config.tolerance = 0.01\n", " funman_request.config.dreal_precision = dreal_precision\n", " funman_request.config.verbosity = logging.ERROR\n", - " funman_request.config.mode = MODE_ODEINT\n", + " funman_request.config.mode = mode\n", " # funman_request.config.dreal_log_level = \"debug\"\n", " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", "\n", @@ -141,11 +142,11 @@ " )\n", " return results\n", "\n", - "def setup_common(funman_request, synthesize=False, debug=False, dreal_precision=1e-1):\n", + "def setup_common(funman_request, synthesize=False, debug=False, dreal_precision=1e-1, mode=MODE_SMT):\n", " set_timepoints(funman_request, timepoints)\n", " if not synthesize:\n", " unset_all_labels(funman_request)\n", - " set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision)\n", + " set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision, mode=mode)\n", " \n", "\n", "def set_compartment_bounds(funman_request, model, upper_bound=9830000.0, error=0.01):\n", @@ -199,12 +200,12 @@ "\n", "def report(results, name, states):\n", " request_results[name] = results\n", - " # plot_last_point(results, states)\n", + " plot_last_point(results, states)\n", " param_values = get_last_point_parameters(results)\n", " # print(f\"Point parameters: {param_values}\")\n", " if param_values is not None:\n", " request_params[name] = param_values\n", - " # pretty_print_request_params(request_params)\n", + " pretty_print_request_params(request_params)\n", " \n", "\n", "def add_unit_test(funman_request, model=\"sidarthe_observables\"):\n", @@ -318,35 +319,75 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "funman_request = get_request()\n", + "model = get_model(\"sidarthe_observables\")\n", + "setup_common(funman_request, debug=False, mode=MODE_ODEINT)\n", + "results = run(funman_request, model=models[\"sidarthe_observables\"])\n", + "# report(results, \"sidarthe_observables\", states[\"sidarthe_observables\"]+[\"TotalInfected\", \"R0\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", - "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", - "\u001b[1;31mClick here for more info. \n", - "\u001b[1;31mView Jupyter log for further details." + "name": "stdout", + "output_type": "stream", + "text": [ + "1 points\n", + " alpha beta delta epsilon eta gamma \\\n", + "sidarthe_observables 0.5643 0.01089 0.01089 0.16929 0.12375 0.45144 \n", + "\n", + " kappa lambda mu nu rho sigma \\\n", + "sidarthe_observables 0.01683 0.03366 0.01683 0.02673 0.03366 0.01683 \n", + "\n", + " tau theta xi zeta \n", + "sidarthe_observables 0.0099 0.36729 0.01683 0.12375 \n" ] + }, + { + "data": { + "image/png": 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P3wK2hc2Mnl5OjkgIIQRIMtLi+Lm74OWqQ1XheFaxs8O5YKxWC2s/fhfVaqXDpZcTPWCQs0MSQghRQZKRFkZRFKICbTNHEjJbzvTefRt+I+3YUVyMbgyf9ICzwxFCCHEGSUZaIHvdSAspYjUVFbKpYvbMoFvuwt3H18kRCSGEOJMkIy1QpH/F9N4Wkoxs+fZrSgry8QuPkJ14hRCiCZJkpAWqXGukJUzvzUlN5u9ffwZg2MQpaHU6J0ckhBDinyQZaYHatqC1Rjb/9ytUq5XIXn2J7NHb2eEIIYSohiQjLVBkRTKSWVhGXkm5k6NpPBlJiRza/DsAl992j5OjEUIIURNJRlogD4OOIE8DcHHXjfy57EtQVTpcejnBbds7OxwhhBA1kGSkhYqyz6i5OKf3psbHEb/jLxRFw6Bb73J2OEIIIc5CkpEWqnJGzYnsEidH0jgqV1rtfPlQ/MMjnByNEEKIs5FkpIUK8zECkJx78SUj2cmniNu2BYBLbrjZydEIIYQ4F0lGWqhwX1sycuoiTEZ2/PwDqCpt+1xCQEQbZ4cjhBDiHCQZaaHCfFyBiy8ZKczJ5sDGdYD0igghRHMhyUgLFX7GMI2qqk6OpuHE/vozFrOZsA6dadWpq7PDEUIIUQuSjLRQId62nhFTuZWc4otjrRFzeTl71v0KQL/rbnRyNEIIIWqrXsnI+++/T2RkJK6urgwYMIBt27adtf2CBQvo2LEjRqORiIgIHn30UUwmU70CFg3DoNPa1xo5lXNxDNXEbf2Tkvw8PPz8addvgLPDEUIIUUt1TkaWLVvGjBkzeP7559m1axc9e/Zk5MiRpKenV9v+66+/5qmnnuL555/n4MGDLFq0iGXLlvHMM8+cd/Di/FTOqLlY6kZi16wCoMeIUWi0WidHI4QQorbqnIy8+eabTJ48mUmTJtGlSxcWLlyIm5sbixcvrrb95s2bueyyy7jzzjuJjIzk6quv5o477jhnb4pofOEX0fTe9MRjJB8+gEarpfvwkc4ORwghRB3UKRkpKytj586djBgx4vQNNBpGjBjBli1bqr1m0KBB7Ny50558HDt2jFWrVnHttdeeR9iiIVRO770YkpHdFb0i0f0H4eHr5+RohBBC1EWd9lPPzMzEYrEQHBzscDw4OJhDhw5Ve82dd95JZmYml19+OaqqYjab+de//nXWYZrS0lJKS0vtP+fn59clTFFLYd4Xx/Te8lIThzZvBKDnVdc4ORohhBB11eizaWJiYpg9ezYffPABu3bt4ocffmDlypW8/PLLNV4zZ84cvL297a+ICFnOuzFcLKuwHt22hbKSEryDQ2jVuZuzwxFCCFFHdeoZCQgIQKvVkpaW5nA8LS2NkJCQaq957rnnuOeee7j//vsB6N69O0VFRUyZMoVnn30WjaZqPvT0008zY8YM+8/5+fmSkDSC0wWszXtm0/7f1wPQZfBwlGr+noQQQjRtdfovt4uLC3379mXdunX2Y1arlXXr1jFw4MBqrykuLq6ScGgrZjrUtNiWwWDAy8vL4SUaXquKmpHMwlJM5RYnR1M/BVmZHN8bC0DXocOdG4wQQoh6qVPPCMCMGTOYMGEC/fr1o3///ixYsICioiImTZoEwPjx4wkPD2fOnDkAjBkzhjfffJPevXszYMAAjh49ynPPPceYMWPsSYlwDm+jHjcXLcVlFlLyTEQFuDs7pDo78McGUFVade6Gd1D1vXNCiPqzWCyUl18cCyOKhqfX6xvks7zOychtt91GRkYGs2bNIjU1lV69erF69Wp7UWtSUpJDT8i///1vFEXh3//+N6dOnSIwMJAxY8bw6quvnnfw4vwoikKYj5Gj6YUk55Y0u2REVVX7PjRdpFdEiAalqiqpqank5uY6OxTRxPn4+BASEoKiKPW+h6I2g41J8vPz8fb2Ji8vT4ZsGtiExdvYeCSD18f14NZLmlddTlpCPF8+9Qg6vQv/+vhLDG5uzg5JiItGSkoKubm5BAUF4ebmdl4fNOLipKoqxcXFpKen4+PjQ2hoaJU2tf38rnPPiLi4NOdVWI/8tQmAqN79JBERogFZLBZ7IuLv7+/scEQTZjTaPkPS09MJCgqq95CNTD1o4cJ9bGuNNLfpvaqq2pORDpde5uRohLi4VNaIuEmSL2qh8u/kfGqLJBlp4UK9bVltSl7zmt6bcTyB3NQUdHoX2vbt7+xwhLgoydCMqI2G+DuRZKSFC/ay9YykFzSvZOTwlj8AiOzVFxdXo5OjEUIIcT4kGWnhgrwMAKTll56jZdPhMEQz8HInRyOEEBfGxIkTGTt2rP3nK664gunTp5/1msjISBYsWNCocTUESUZauGBPW89IXkl5s1n47MwhmnZ9LnF2OEKIJiYjI4MHH3yQ1q1bYzAYCAkJYeTIkfz555/ODq1WEhMTURSF2NhYh+Nvv/02S5YscUpMjU1m07RwXkYdLjoNZWYrGQWlRPg1/YK1+B1bAWjTsw8uxqYfrxDiwho3bhxlZWUsXbqUtm3bkpaWxrp168jKynJ2aOfF29vb2SE0GukZaeEURSG4YqimudSNHNu1DYB2UrgqhPiH3Nxc/vjjD+bOncuwYcNo06YN/fv35+mnn+b666+vttchNzcXRVGIiYkBICcnh7vuuovAwECMRiPR0dF89tln9vYnT57kjjvuwM/PD3d3d/r168fWrVvt53/66Sf69OmDq6srbdu25cUXX8RsNtvPK4rChx9+yDXXXIPRaKRt27Z899139vNRUVEA9O7dG0VRuOKKK4CqwzQAZrOZqVOn4u3tTUBAAM8991yNW61Uvtf777+fwMBAvLy8GD58OLt3767rr7nBSc+IIMjTlRPZJc2ibqQwJ5vU+DjAtr6IEOLCUFWVEicN5Rr12lrP2PDw8MDDw4Ply5dz6aWXYjAY6vy85557jgMHDvDLL78QEBDA0aNHKSmxLX9QWFjI0KFDCQ8PZ8WKFYSEhLBr1y6sVisAf/zxB+PHj+edd95h8ODBxMfHM2XKFACef/55h2e89tprvP3223zxxRfcfvvt7N27l86dO7Nt2zb69+/Pb7/9RteuXXFxcakx1qVLl3Lfffexbds2duzYwZQpU2jdujWTJ0+utv0tt9yC0Wjkl19+wdvbm48++ogrr7ySI0eO4OfnV+ffVUORZESc7hnJb/o9Iwl/7wAgpF00Hr7O+x+OEC1NSbmFLrN+dcqzD7w0EjeX2n1c6XQ6lixZwuTJk1m4cCF9+vRh6NCh3H777fTo0aNW90hKSqJ3797062f7B09kZKT93Ndff01GRgbbt2+3f3i3b9/efv7FF1/kqaeeYsKECQC0bduWl19+mSeffNIhGbnlllvsu9m//PLLrF27lnfffZcPPviAwMBAAPz9/QkJOfueWxEREbz11lsoikLHjh3Zu3cvb731VrXJyKZNm9i2bRvp6en2JG3evHksX76c7777zp40OYMM0wiCKopY0wqafs9I/E7bEE3bPjJEI4So3rhx40hOTmbFihWMGjWKmJgY+vTpU+vizwcffJBvvvmGXr168eSTT7J582b7udjYWHr37l1jL8Lu3bt56aWX7D00Hh4eTJ48mZSUFIqLi+3t/rnT/cCBAzl48GCd3+ull17q0Gs0cOBA4uLisFiq9mLt3r2bwsJC/P39HeJLSEggPj6+zs9uSNIzIuzTe9Ob+DCNuayM43v/BpCFzoS4wIx6LQdeGum0Z9eVq6srV111FVdddRXPPfcc999/P88//zx//GFbo+jMuop/rhx6zTXXcPz4cVatWsXatWu58sorefjhh5k3b559+fOaFBYW8uKLL3LTTTdVG5MzFRYWEhoaaq+NOZOPj88Fj+dMkowIe89IUy9gPXFgL+bSUjz8/AmKbOvscIRoURRFqfVQSVPUpUsXli9fbh8CSUlJoXfv3gBVptACBAYGMmHCBCZMmMDgwYN54oknmDdvHj169ODTTz8lOzu72t6RPn36cPjwYYehm+r89ddfjB8/3uHnyngqa0Sq6934pzMLZyvvEx0dXe0eMX369CE1NRWdTucw9NQUNN+/LNFggptJz8ixXdsBaNv7ElmmWghRraysLG655RbuvfdeevTogaenJzt27OD111/nhhtuwGg0cumll/Laa68RFRVFeno6//73vx3uMWvWLPr27UvXrl0pLS3l559/pnPnzgDccccdzJ49m7FjxzJnzhxCQ0P5+++/CQsLY+DAgcyaNYvrrruO1q1bc/PNN6PRaNi9ezf79u3jlVdesT/j22+/pV+/flx++eV89dVXbNu2jUWLFgEQFBSE0Whk9erVtGrVCldX1xqn9SYlJTFjxgweeOABdu3axbvvvsv8+fOrbTtixAgGDhzI2LFjef311+nQoQPJycmsXLmSG2+80V4j4wxSMyLOqBlp2j0jx/fGAhDZq49zAxFCNFkeHh4MGDCAt956iyFDhtCtWzeee+45Jk+ezHvvvQfA4sWLMZvN9O3bl+nTpzskCWDrmXj66afp0aMHQ4YMQavV8s0339jPrVmzhqCgIK699lq6d+/Oa6+9Zu+JGDlyJD///DNr1qzhkksu4dJLL+Wtt96iTZs2Ds948cUX+eabb+jRoweff/45//nPf+jSpQtgK8J95513+OijjwgLC+OGG26o8f2OHz+ekpIS+vfvz8MPP8wjjzxSYyGqoiisWrWKIUOGMGnSJDp06MDtt9/O8ePHCQ4Ort8vvIEo6tkmJDcR+fn5eHt7k5eXh5eXl7PDuejkFpfR66W1ABx+ZRQGXf22gG5M+ZkZfPLwJBRFw0OLvsbV3cPZIQlx0TKZTCQkJBAVFeX0OoeLkaIo/Pjjj1XWDGmuzvb3UtvPb+kZEXgb9bjobH8KTXWoprJwNaR9tCQiQghxkZFkRKAoCkGelauwNtFkZE8sAG169HZuIEIIIRqcFLAKAIK9XDmZU9IkFz5TrVaS9tmWK27TrZdzgxFCiPPUDKojLjjpGREA9p6RtCaYjGQkJVKSn4fe4Epoh47ODkcIIUQDk2REALaeEWiawzTH99jqRSK6dker0zs5GiGEEA1NkhEBQKC9Z6QJJiMVU3rbdO/l1DiEEEI0DklGBHBmz0jTGqaxmM2cOnwAgIhuPZ0cjRBCiMYgyYgATteMNLWpvekJ8ZhLS3H18CSgVWtnhyOEEKIRSDIigNM9I01tFdYTB/YCEN6pK4pG/lyFEOJiJP91FwAEeNg2ZsotLqfcYnVyNKedOrQfgFaduzo5EiHExURRFJYvX+7sMC6IK664gunTpzs7jLOSZEQA4OvmglZj23wuu6jMydHYWK0WTh2qqBfp0t3J0QghmoOJEyeiKAqKoqDX6wkODuaqq65i8eLFWK2n/6GVkpLCNddc48RIxZkkGREAaDQKfu623pGMJjK9NzPpOKXFRbgYjQS2iXJ2OEKIZmLUqFGkpKSQmJjIL7/8wrBhw3jkkUe47rrrMJvNAISEhGAwGJwcqagkyYiwC/Cw/Q8zs7BpJCMnD+4DIKxjFzTaprd5nxCiaTIYDISEhBAeHk6fPn145pln+Omnn/jll19YsmQJUHWYZubMmXTo0AE3Nzfatm3Lc889R3l5ucN9X3nlFYKCgvD09OT+++/nqaeeolevXvbzEydOZOzYscybN4/Q0FD8/f15+OGHHe6Tk5PD+PHj8fX1xc3NjWuuuYa4uDj7+ePHjzNmzBh8fX1xd3ena9eurFq1yn5+3759XHPNNXh4eBAcHMw999xDZmam/XxRURHjx4/Hw8OD0NBQ5s+f30C/1cYlyYiwq6wbySxsGsM0Jw/YkpFWnaReRAinU1UoK3LOqwGWTx8+fDg9e/bkhx9+qPa8p6cnS5Ys4cCBA7z99tt88sknvPXWW/bzX331Fa+++ipz585l586dtG7dmg8//LDKfTZs2EB8fDwbNmxg6dKlLFmyxJ4AgS1h2bFjBytWrGDLli2oqsq1115rT1gefvhhSktL+f3339m7dy9z587Fw8O2OWhubi7Dhw+nd+/e7Nixg9WrV5OWlsatt95qv/8TTzzBxo0b+emnn1izZg0xMTHs2rXrvH9/jU32phF2lT0jWU2gZ0RVVXvPSKvO3ZwcjRCC8mKYHeacZz+TDC7u532bTp06sWfPnmrP/fvf/7Z/HxkZyeOPP84333zDk08+CcC7777Lfffdx6RJkwCYNWsWa9asobCw0OE+vr6+vPfee2i1Wjp16sTo0aNZt24dkydPJi4ujhUrVvDnn38yaNAgwJbkREREsHz5cm655RaSkpIYN24c3bvb6uTatm1rv/d7771H7969mT17tv3Y4sWLiYiI4MiRI4SFhbFo0SK+/PJLrrzySgCWLl1Kq1atzvdX1+gkGRF2p3tGnJ+M5KQkU1KQj1avJ7hdtLPDEUJcBFRVRVGUas8tW7aMd955h/j4eAoLCzGbzXh5ednPHz58mIceesjhmv79+7N+/XqHY127dkV7xrByaGgoe/falig4ePAgOp2OAQMG2M/7+/vTsWNHDh48CMC0adN48MEHWbNmDSNGjGDcuHH06NEDgN27d7NhwwZ7T8mZ4uPjKSkpoayszOH+fn5+dOzY9Pf0kmRE2J2uGXH+ME1K3CEAgttGo9PLfjRCOJ3ezdZD4axnN4CDBw8SFVW1GH7Lli3cddddvPjii4wcORJvb2+++eabetVb6P/x3ytFURxm8ZzL/fffz8iRI1m5ciVr1qxhzpw5zJ8/n//7v/+jsLCQMWPGMHfu3CrXhYaGcvTo0TrH21RIzYiwa0oFrJXJSGh008/ohWgRFMU2VOKMVw29GXWxfv169u7dy7hx46qc27x5M23atOHZZ5+lX79+REdHc/z4cYc2HTt2ZPv27Q7H/vnzuXTu3Bmz2czWrVvtx7Kysjh8+DBdunSxH4uIiOBf//oXP/zwA4899hiffPIJAH369GH//v1ERkbSvn17h5e7uzvt2rVDr9c73D8nJ4cjR47UKU5nkGRE2Pk3oQLW5LjDAIRJMiKEqKPS0lJSU1M5deoUu3btYvbs2dxwww1cd911jB8/vkr76OhokpKS+Oabb4iPj+edd97hxx9/dGjzf//3fyxatIilS5cSFxfHK6+8wp49e2oc9qlOdHQ0N9xwA5MnT2bTpk3s3r2bu+++m/DwcG644QYApk+fzq+//kpCQgK7du1iw4YNdO7cGbAVt2ZnZ3PHHXewfft24uPj+fXXX5k0aRIWiwUPDw/uu+8+nnjiCdavX8++ffuYOHEimmawerUM0wi7ptIzUm4ykXk8EYDQDp2cGosQovlZvXo1oaGh6HQ6fH196dmzJ++88w4TJkyo9oP5+uuv59FHH2Xq1KmUlpYyevRonnvuOV544QV7m7vuuotjx47x+OOPYzKZuPXWW5k4cSLbtm2rU2yfffaZfc2TsrIyhgwZwqpVq+zDOxaLhYcffpiTJ0/i5eXFqFGj7LN6wsLC+PPPP5k5cyZXX301paWltGnThlGjRtnf1xtvvGEfzvH09OSxxx4jLy+vnr/JC0dR1QaYM9XI8vPz8fb2Ji8vz6GgSDSstHwTA2avQ6tRiHvlGjSa8+8arY8T+/fw35eewcM/gAc+WOKUGIRoyUwmEwkJCURFReHq6urscJqsq666ipCQEL744gtnh+JUZ/t7qe3nt/SMCLvKFVgtVpWc4jL8PZyzOuHpIRrpFRFCNA3FxcUsXLiQkSNHotVq+c9//sNvv/3G2rVrnR3aRaHpDySJC0av1eDjZusqzHLi/jQpFcmIFK8KIZoKRVFYtWoVQ4YMoW/fvvzvf//j+++/Z8SIEc4O7aIgPSPCQYCHgdzicjILSukQ7HnBn6+q6hkzaaRnRAjRNBiNRn777Tdnh3HRkp4R4aBy4bMMJxWx5mekUZyXi0arIziqnVNiEEIIcWFJMiIcOHvhs8ohmqDIKHQuLk6JQQghxIUlyYhw4Oz9aVKP2VYQDG7XwSnPF0IIceFJMiIcOHt/mvSKZCSkbXunPF8IIcSFJ8mIcODMYRrVaiUtoaJnRJIRIYRoMSQZEQ6cuQprTmoKZSUl6PQu+LdqfcGfL4QQwjkkGREOAjwra0YufM9IZa9IYGQUmjO24BZCiMaSmprKVVddhbu7Oz4+Ps4Op4rIyEgWLFjg7DAanSQjwoG/++mpvRd6p4C0+DhAhmiEEPU3ceJExo4dW+v2b731FikpKcTGxjbY7rYtJYFoSLLomXAQWNEzUma2UlBqxstVf8Geba8XiZJkRAhxYcTHx9O3b1+io6OdHUqLJj0jwoGrXouHwZajZhZcuLoR1WolPSEegOB28h8FIcT5u+KKK5g2bRpPPvkkfn5+hISEOOzEGxkZyffff8/nn3+OoihMnDgRgNzcXO6//34CAwPx8vJi+PDh7N692+He//vf/7jkkktwdXUlICCAG2+80f7M48eP8+ijj6IoCopyesPRTZs2MXjwYIxGIxEREUybNo2ioiL7+fT0dMaMGYPRaCQqKoqvvvqq8X45TYwkI6KK09N7L1zdSE5qsq141cWAf3jEBXuuEKJ2VFWluLzYKa/zGTJeunQp7u7ubN26lddff52XXnrJvrnd9u3bGTVqFLfeeispKSm8/fbbANxyyy2kp6fzyy+/sHPnTvr06cOVV15JdnY2ACtXruTGG2/k2muv5e+//2bdunX0798fgB9++IFWrVrx0ksvkZKSQkpKCmDrgRk1ahTjxo1jz549LFu2jE2bNjF16lR7rBMnTuTEiRNs2LCB7777jg8++ID09PR6v/fmRIZpRBV+7i4kZhWTXXThekbSjknxqhBNWYm5hAFfD3DKs7feuRU3vVu9ru3RowfPP/88ANHR0bz33nusW7eOq666isDAQAwGA0ajkZCQEMDWe7Ft2zbS09MxGGzD1vPmzWP58uV89913TJkyhVdffZXbb7+dF1980f6cnj17AuDn54dWq8XT09N+T4A5c+Zw1113MX36dHss77zzDkOHDuXDDz8kKSmJX375hW3btnHJJZcAsGjRIjp37lyv993cSDIiqvCvXIX1Au7cW5mMSL2IEKIh9ejRw+Hn0NDQs/Y27N69m8LCQvz9/R2Ol5SUEB9vG0qOjY1l8uTJdYpj9+7d7Nmzx2HoRVVVrFYrCQkJHDlyBJ1OR9++fe3nO3Xq1CRn+DQGSUZEFZXDNBdyem/G8WMABEW1vWDPFELUnlFnZOudW5327PrS6x2L8BVFwWq11ti+sLCQ0NBQYmJiqpyrTAyMxrrHU1hYyAMPPMC0adOqnGvdunWDzeRpriQZEVX4u1/Y/WlUVSX9eCIAQW0kGRGiKVIUpd5DJc1Jnz59SE1NRafTERkZWW2bHj16sG7dOiZNmlTteRcXFywWS5X7HjhwgPbtq+/97dSpE2azmZ07d9qHaQ4fPkxubm6930tzIgWsogr/ygLWCzRMU5idhakgH0WjkZVXhRBONWLECAYOHMjYsWNZs2YNiYmJbN68mWeffZYdO3YA8Pzzz/Of//yH559/noMHD7J3717mzp1rv0dkZCS///47p06dIjMzE4CZM2eyefNmpk6dSmxsLHFxcfz000/2AtaOHTsyatQoHnjgAbZu3crOnTu5//7769UL0xxJMiKq8HOvHKa5MD0jGccTbM8Na4XOxeWCPFMIIaqjKAqrVq1iyJAhTJo0iQ4dOnD77bdz/PhxgoODAdv03W+//ZYVK1bQq1cvhg8fzrZt2+z3eOmll0hMTKRdu3YEBgYCtt6UjRs3cuTIEQYPHkzv3r2ZNWsWYWFh9us+++wzwsLCGDp0KDfddBNTpkwhKCjowv4CnERR6zFn6v333+eNN94gNTWVnj178u6779qnNVUnNzeXZ599lh9++IHs7GzatGnDggULuPbaa2v1vPz8fLy9vcnLy8PLy6uu4Yo6+vNoJnd9upXoIA/Wzhja6M/b+uN/2fTN53S6bCijpz3R6M8TQpydyWQiISGBqKgoXF1dnR2OaOLO9vdS28/vOteMLFu2jBkzZrBw4UIGDBjAggULGDlyJIcPH642gysrK+Oqq64iKCiI7777jvDwcI4fP95iKoSbo8phmuwLNEyTXtEzEtgm6oI8TwghRNNS52TkzTffZPLkyfbCnYULF7Jy5UoWL17MU089VaX94sWLyc7OZvPmzfaq5pqKgkTTUFnAml1chsWqotUo57ji/FQO0wRJMiKEEC1SnWpGysrK2LlzJyNGjDh9A42GESNGsGXLlmqvWbFiBQMHDuThhx8mODiYbt26MXv27CqVxmcqLS0lPz/f4SUuHF83PYoCqgo5xY3bO1JuMpGTcgqAwEiZSSOEEC1RnZKRzMxMLBaLvYinUnBwMKmpqdVec+zYMb777jssFgurVq3iueeeY/78+bzyyis1PmfOnDl4e3vbXxERsjz4haTTavAx2nqxGnutkcwTx0FVcfP2wd3Ht1GfJYQQomlq9Nk0VquVoKAgPv74Y/r27cttt93Gs88+y8KFC2u85umnnyYvL8/+OnHiRGOHKf7BvgprI8+oyZB6ESGEaPHqVDMSEBCAVqslLS3N4XhaWprDGvxnCg0NRa/Xoz1jv5HOnTuTmppKWVkZLtVM5TQYDPY9AYRz+Lu7cJTGXxJeileFEELUqWfExcWFvn37sm7dOvsxq9XKunXrGDhwYLXXXHbZZRw9etRh+d0jR44QGhpabSIimoaAC9wzIsWrQgjRctV5mGbGjBl88sknLF26lIMHD/Lggw9SVFRkn10zfvx4nn76aXv7Bx98kOzsbB555BGOHDnCypUrmT17Ng8//HDDvQvR4Cqn9zZmz4iqqmSdOA5AQOvIRnuOEEKIpq3OU3tvu+02MjIymDVrFqmpqfTq1YvVq1fbi1qTkpLQaE7nOBEREfz66688+uij9OjRg/DwcB555BFmzpzZcO9CNLjKVVgzG7GAtTA7i9LiIjRaLX7hrRrtOUIIIZq2em2UN3XqVPt6+v9U3U6HAwcO5K+//qrPo4STXIgC1syKXhGfkDC0Ov05WgshxPmLiYlh2LBh5OTk4OPjw5IlS5g+fbp9Q7oXXniB5cuXExsb69Q4WxrZm0ZUK8C98Ydp7EM0EW0a7RlCiJZpy5YtaLVaRo8e7XB80KBBpKSk4O3tXe11jz/+uENdpLgwJBkR1arsGWnMJeEzTyTZniU79QohGtiiRYv4v//7P37//XeSk5Ptx11cXAgJCUFRql9Z2sPDA39//wsVpqggyYioVmUBa2YjDtNknawsXpWeESFEwyksLGTZsmU8+OCDjB49miVLltjPxcTEoCiKfVjmn1544QV69epl/3nixImMHTuWefPmERoair+/Pw8//DDl5eX2NikpKYwePRqj0UhUVBRff/01kZGRLFiwoHHe4EWoXjUj4uIXULE/TYHJTKnZgkGnPccVdaNarWSelJ4RIZoLVVVRS0qc8mzFaKyxJ6M6//3vf+nUqRMdO3bk7rvvZvr06Tz99NN1useZNmzYQGhoKBs2bODo0aPcdttt9OrVi8mTJwO2WaSZmZnExMSg1+uZMWMG6enp9XpWSyXJiKiWl1GHTqNgtqpkF5UR6m1s0PvnZaRjLi1Fq9PhGxLWoPcWQjQ8taSEw336OuXZHXftRHFzq3X7RYsWcffddwMwatQo8vLy2LhxI1dccUW9nu/r68t7772HVqulU6dOjB49mnXr1jF58mQOHTrEb7/9xvbt2+nXrx8An376KdHR0fV6VkslwzSiWoqi2Kf3Nsb+NJVDNH5hrdBoG7bXRQjRch0+fJht27Zxxx13AKDT6bjttttYtGhRve/ZtWtXh1XEQ0ND7T0fhw8fRqfT0adPH/v59u3b4+sre23VhfSMiBr5exhILyhtlLqRzCRbMuIvM2mEaBYUo5GOu3Y67dm1tWjRIsxmM2Fhp3tcVVXFYDDw3nvv1ev5er3j0gOKojisKi7OnyQjokYBFUWsjTGjJquiXkSm9QrRPCiKUqehEmcwm818/vnnzJ8/n6uvvtrh3NixY/nPf/5Dp06dGvSZHTt2xGw28/fff9O3r20Y6+jRo+Tk5DTocy52koyIGvk34jBN5YJn0jMihGgoP//8Mzk5Odx3331V1hEZN24cixYt4o033mjQZ3bq1IkRI0YwZcoUPvzwQ/R6PY899hjGOhbdtnRSMyJq5FcxoyazqGGHaawWC9mnTgDSMyKEaDiLFi1ixIgR1S5oNm7cOHbs2MGePXsa/Lmff/45wcHBDBkyhBtvvJHJkyfj6emJq6trgz/rYqWoqqo6O4hzyc/Px9vbm7y8PLy8vJwdTovx/oajvPHrYW7u24p5t/RssPtmJ5/is0cfQOdiYNrSb1E0khML0ZSYTCYSEhKIioqSD9R6OHnyJBEREfz2229ceeWVzg6n0Z3t76W2n98yTCNqVFkz0tD701T2iviGhUsiIoRo9tavX09hYSHdu3cnJSWFJ598ksjISIYMGeLs0JoNSUZEjfzdG2dJ+Ozkk7b7h0c06H2FEMIZysvLeeaZZzh27Bienp4MGjSIr776qsosHFEzSUZEjU4vCd/AyUhFz4hfeKsGva8QQjjDyJEjGTlypLPDaNakj1zUqLJnJKuolIYsLcqqTEbCpGdECCGEJCPiLCp7RkzlVorLLA1yT1VVyT5VOUwjPSNCCCEkGRFn4eaixVVv+xNpqLVGinKyKSspRlE0+ISGN8g9hRBCNG+SjIgaKYpiH6ppqLVGKodovIOD0UlxlxBCCCQZEedgXxK+gXpGKmfS+MlMGiGEEBUkGRFnZd+5t4F6Ripn0si0XiGEEJUkGRFn5e9RMUzTUD0j9mm9kowIIURNXnjhBXr16tWoz7jiiiuYPn16oz6jtiQZEWfl79Gwm+VVzqTxC5OZNEKIhjdx4kTbDsOKgl6vJyoqiieffBKTyeTs0Ork8ccfZ926dc4O44KRRc/EWQWcsdbI+SotLqIwJxsA/1bSMyKEaByjRo3is88+o7y8nJ07dzJhwgQURWHu3LnODq3WPDw88PDwcHYYF4z0jIizasiekcriVXdfPwxu7ud9PyGEqI7BYCAkJISIiAjGjh3LiBEjWLt2LQBWq5U5c+YQFRWF0WikZ8+efPfddw7X79+/n+uuuw4vLy88PT0ZPHgw8fHx9utfeuklWrVqhcFgoFevXqxevdrh+s2bN9OrVy9cXV3p168fy5cvR1EUYmNjAYiJiUFRFNatW0e/fv1wc3Nj0KBBHD582H6Pfw7TVPb2nPmKjIy0n9+3bx/XXHMNHh4eBAcHc88995CZmWk/X1RUxPjx4/Hw8CA0NJT58+c3xK+6wUgyIs7qdAFrAyQjMkQjRLOlqirlpRanvM5nBeh9+/axefNmXFxs/y2bM2cOn3/+OQsXLmT//v08+uij3H333WzcuBGAU6dOMWTIEAwGA+vXr2fnzp3ce++9mM1mAN5++23mz5/PvHnz2LNnDyNHjuT6668nLi4OsO1SO2bMGLp3786uXbt4+eWXmTlzZrWxPfvss8yfP58dO3ag0+m49957a3wfKSkp9tfRo0dp3769fSO+3Nxchg8fTu/evdmxYwerV68mLS2NW2+91X79E088wcaNG/npp59Ys2YNMTEx7Nq1q96/14YmwzTirAIqClgbYufenJRTAPiFyWJnQjQ35jIrHz+y0SnPnvL2UPQGba3b//zzz3h4eGA2myktLUWj0fDee+9RWlrK7Nmz+e233xg4cCAAbdu2ZdOmTXz00UcMHTqU999/H29vb7755hv7RncdOnSw33vevHnMnDmT22+/HYC5c+eyYcMGFixYwPvvv8/XX3+Noih88sknuLq60qVLF06dOsXkyZOrxPnqq68ydOhQAJ566ilGjx6NyWTC1dW1StuQkBDAlhSOGzcOb29vPvroIwDee+89evfuzezZs+3tFy9eTEREBEeOHCEsLIxFixbx5ZdfcuWVVwKwdOlSWrVqOv8wlGREnFXlME12URlWq4pGo9T7XjnJtmTEV1ZeFUI0omHDhvHhhx9SVFTEW2+9hU6nY9y4cezfv5/i4mKuuuoqh/ZlZWX07t0bgNjYWAYPHlztjrv5+fkkJydz2WWXORy/7LLL2L17NwCHDx+mR48eDglF//79q42zR48e9u9DQ0MBSE9Pp3Xr1jW+t2eeeYYtW7awY8cOjEYjALt372bDhg3V1pjEx8dTUlJCWVkZAwYMsB/38/OjY8eONT7nQpNkRJxV5TCN2aqSbyrHx82l3vfKrugZ8ZWeESGaHZ2LhilvD3Xas+vC3d2d9u3bA7Yegp49e7Jo0SK6desGwMqVKwkPd/zvkMFg6wWu/IC/EM5MeBTF9g89q9VaY/svv/ySt956i5iYGIf4CwsLGTNmTLUFuqGhoRw9erQBo24ckoyIszLotHi66igwmcksLKt3MqJareSmJAPSMyJEc6QoSp2GSpoKjUbDM888w4wZMzhy5AgGg4GkpCT78Mg/9ejRg6VLl1JeXl6ld8TLy4uwsDD+/PNPh+v//PNPe+9Hx44d+fLLLyktLbUnONu3bz/v97Flyxbuv/9+PvroIy699FKHc3369OH7778nMjISna7qx3q7du3Q6/Vs3brV3uuSk5PDkSNHavw9XGhSwCrOyd/99FBNfRVkZ2IuL0Oj1eEdGNxQoQkhxDndcsstaLVaPvroIx5//HEeffRRli5dSnx8PLt27eLdd99l6dKlAEydOpX8/Hxuv/12duzYQVxcHF988YV9pssTTzzB3LlzWbZsGYcPH+app54iNjaWRx55BIA777wTq9XKlClTOHjwIL/++ivz5s0DTvd+1FVqaio33ngjt99+OyNHjiQ1NZXU1FQyMjIAePjhh8nOzuaOO+5g+/btxMfH8+uvvzJp0iQsFgseHh7cd999PPHEE6xfv559+/YxceJENJqmkwJIz4g4J38PA4lZxedVxJpdUS/iExyCRtv8/nUlhGi+dDodU6dO5fXXXychIYHAwEDmzJnDsWPH8PHxoU+fPjzzzDMA+Pv7s379ep544gmGDh2KVqulV69e9jqRadOmkZeXx2OPPUZ6ejpdunRhxYoVREdHA7bek//97388+OCD9OrVi+7duzNr1izuvPPOagtTa+PQoUOkpaWxdOlSe9IE0KZNGxITE+29NTNnzuTqq6+mtLSUNm3aMGrUKHvC8cYbb9iHczw9PXnsscfIy8s7n19rg1LU85kzdYHk5+fj7e1NXl4eXl5ezg6nxZny+Q7WHEjj5bHduOfSNvW6x9+//sz6xQtp128AY594roEjFEI0JJPJREJCAlFRUfX+ABWnffXVV0yaNIm8vLwLWpNyoZzt76W2n9/SMyLOyb8BpvfKTBohREvx+eef07ZtW8LDw9m9ezczZ87k1ltvvSgTkYYiyYg4p4AGWIW1co0RSUaEEBe71NRUZs2aRWpqKqGhodxyyy28+uqrzg6rSZNkRJzT6VVYz79mRBY8E0Jc7J588kmefPJJZ4fRrDSdUlrRZFWuwppZUL+eEXNZGfmZ6YD0jAghhKhKkhFxToGeFclIPWtGctNSQFUxuLnj5u3TgJEJIYS4GEgyIs6psmcko6B+ycjp4tWwes+zF0IIcfGSZEScU2XPSEGpGVO5pc7XZ0vxqhBCiLOQZESck5erDhed7U+lPr0jMpNGCCHE2UgyIs5JURQCK4dq6lE3kpuaAoBPaFiDxiWEEOLiIMmIqJWAyiLWevSM5KbZkhHf4NAGjUkIIZwlJiYGRVHIzc09r/tERkayYMGCBompOZNkRNRKfXtGyk0minKyAfAJkZ4RIUTjmjhxImPHjq1yvKGSB9E4JBkRtRLoaVv47J9rjZSWZnDixBKKixOrva6yV8TVwxNXD49GjVEIIUTzJMmIqJXTPSMm+zGLpYTY2AkciXuZLX+NYPfuyRQVHXO4zl4vEiJDNEKIpmPTpk0MHjwYo9FIREQE06ZNo6ioyH7+iy++oF+/fnh6ehISEsKdd95Jenr6ed0zPT2dMWPGYDQaiYqK4quvvmq099fcSDIiaqVyem/lbBpVVTl0+N8UFh1GozECKplZ69mzdwpWa7n9upzUZAB8pF5EiGZNVVXKTSanvBp6c/n4+HhGjRrFuHHj2LNnD8uWLWPTpk1MnTrV3qa8vJyXX36Z3bt3s3z5chITE5k4ceJ53XPixImcOHGCDRs28N133/HBBx+cM8FpKWRvGlEr9iXhKzbLO5X8H1JTl6MoWnr1XISLSyA7d91GcXECycn/pVWru4DTwzRSLyJE82YuLeWdCTc75dnTln6H/h9b05/Nzz//jMc/hoUtltNrJM2ZM4e77rqL6dOnAxAdHc0777zD0KFD+fDDD3F1deXee++1t2/bti3vvPMOl1xyCYWFhVXuXZt7JiUl8csvv7Bt2zYuueQSABYtWkTnzp1r/b4uZtIzImrlzJ4RVbVw7NhbALRr+xi+vgNwd29LVNQ0AI4lvI3ZXAicHqbxlWEaIcQFMmzYMGJjYx1en376qf387t27WbJkCR4eHvbXyJEjsVqtJCQkALBz507GjBlD69at8fT0ZOjQoQAkJSVV+8xz3fPgwYPodDr69u1rv6ZTp074+Pg03i+iGZGeEVErZ+5Pk5u3i/LybHQ6byIi7rO3CQ+7nRMnllBSkkhS0qe0bTtdakaEuEjoDAamLf3Oac+uC3d3d9q3b+9w7OTJk/bvCwsLeeCBB5g2bVqVa1u3bk1RUREjR45k5MiRfPXVVwQGBpKUlMTIkSMpK6t+w9Bz3fPIkSN1eg8tjSQjolYqh2mKyyykpK6xHQsYhkZz+k9Io9HTvt0T7N33MEknFhMeOpGC7ExAhmmEaO4URanTUElT1qdPHw4cOFAlYam0d+9esrKyeO2114iIiABgx44d53XPTp06YTab2blzp32Y5vDhwzLVuIIM04hacTfocHPRAioZmWsBCAy4qkq7wMCRGF1bY7EUcSJhOagqLkY3jJ5eFzZgIYSowcyZM9m8eTNTp04lNjaWuLg4fvrpJ3uxaevWrXFxceHdd9/l2LFjrFixgpdffvm87tmxY0dGjRrFAw88wNatW9m5cyf3338/RqOx0d9vcyDJiKi1QE8DYe6pmMtOoNG44Oc3uEobRVEIDhkDQHrmSsA2RCO79QohmooePXqwceNGjhw5wuDBg+nduzezZs0iLMzWgxsYGMiSJUv49ttv6dKlC6+99hrz5s07r3sCfPbZZ4SFhTF06FBuuukmpkyZQlBQUKO+1+ZCURt6zlQjyM/Px9vbm7y8PLy85F/YzjLuw82EaL7hpuiV+PsPo1fPT6ttV1gUx9ato0DVsndpO9r1uYIx02de4GiFEPVlMplISEggKioK14tkaEY0nrP9vdT281t6RkStBXoY6BW01/Z9wIga23m4R+Ph0RkUC95R+fgEh1yoEIUQQjRDkoyIWgvzKqGtt21aW0DAlWdtGxJsG6rxbZ8vM2mEEEKclSQjotbC3Y8DUGiJwGAIPGvb4IpkxCOsGI+Auk3LE0II0bLUKxl5//33iYyMxNXVlQEDBrBt27ZaXffNN9+gKEq1OyqKpi/AkAhApqnNOdvqdYEUpRpRFFANcY0cmRBCiOaszsnIsmXLmDFjBs8//zy7du2iZ8+ejBw58pzr6ycmJvL4448zeHDVGRiieXDXxANwojDinG3zM9LJT7ItmVxo2tmocQkhhGje6pyMvPnmm0yePJlJkybRpUsXFi5ciJubG4sXL67xGovFwl133cWLL75I27Ztzytg4Txai62HIy47/Jxtc1NTyD/hDkB2zmas1upXLRRCCCHqlIyUlZWxc+dORow4PZNCo9EwYsQItmzZUuN1L730EkFBQdx33301tjlTaWkp+fn5Di/hXOXluVjNth1496UHnXMXzZzUFEoyXbGWu2KxFJKX9/eFCFMIIUQzVKdkJDMzE4vFQnBwsMPx4OBgUlNTq71m06ZNLFq0iE8++aTWz5kzZw7e3t72V+VyvMJ5Cgr2A5Be7E9eqSu5xeVnbZ+blgwoaMttSyNnZW1s7BCFEEI0U406m6agoIB77rmHTz75hICAgFpf9/TTT5OXl2d/nThxohGjFLVRmYykFtuKV1PzTWdtX7lBnoerbYfKrOzfGzE6IYQQzVmdNsoLCAhAq9WSlpbmcDwtLY2QkKoLW8XHx5OYmMiYMWPsx6xWq+3BOh2HDx+mXbt2Va4zGAwY6rhLo2hcBYUHAMgzRwG2ZKRzaM2r6VUmIwFBV1CQ+TmFhQcpLU3DYAiu8RohhGhsMTExDBs2jJycHHx8fJwdjtNNnDiR3Nxcli9f7tQ46tQz4uLiQt++fVm3bp39mNVqZd26dQwcOLBK+06dOrF3715iY2Ptr+uvv55hw4YRGxsrwy/NSGXPSJliG3ZJy6u5Z8RqsZCXbktYA8I64eXZHYCsrD8aOUohREumKMpZXy+88IKzQyQyMpIFCxY4O4wmp049IwAzZsxgwoQJ9OvXj/79+7NgwQKKioqYNGkSAOPHjyc8PJw5c+bg6upKt27dHK6vzET/eVw0XWZzEcXFCQDoXTsBBWcdpinIysBqMaPV6/H0C8Av73LyC/aQk7OFsLCbL1DUQoiWJiUlxf79smXLmDVrFocPH7Yf8/DwYMeOHXW+b1lZGS4uLg0So6henWtGbrvtNubNm8esWbPo1asXsbGxrF692l7UmpSU5PAHIZq/wsKDgIrBJRhfT9twXFp+aY3tcyqGaLyDQlA0Gnx9L7Udz/3rnLNwhBCivkJCQuwvb29vFEVxOObh4WFvu3PnTvr164ebmxuDBg1ySFpeeOEFevXqxaeffuqw+Vtubi73338/gYGBeHl5MXz4cHbv3m2/Lj4+nhtuuIHg4GA8PDy45JJL+O233+znr7jiCo4fP86jjz5q762ptGnTJgYPHozRaCQiIoJp06ZRVFRkPx8ZGcns2bO599578fT0pHXr1nz88ccO7//EiRPceuut+Pj44Ofnxw033EBiYqL9vMViYcaMGfj4+ODv78+TTz7ZZP6bXK8C1qlTp3L8+HFKS0vZunUrAwYMsJ+LiYlhyZIlNV67ZMkSp49NibopLLT9j9TDszMhXrb/UaadpWeksl6kck8ab+/eKIqe0tJUTCYpRhaiOVJVFWuZxSmvxvjAfPbZZ5k/fz47duxAp9Nx7733Opw/evQo33//PT/88AOxsbEA3HLLLaSnp/PLL7+wc+dO+vTpw5VXXkl2djYAhYWFXHvttaxbt46///6bUaNGMWbMGJKSbHt6/fDDD7Rq1YqXXnqJlJQU+z/c4+PjGTVqFOPGjWPPnj0sW7aMTZs2MXXqVIeY5s+fT79+/fj777956KGHePDBB+1JVHl5OSNHjsTT05M//viDP//8Ew8PD0aNGkVZWZn9+iVLlrB48WI2bdpEdnY2P/74Y4P/buujzsM0ouUpLrEN0bi7tSPYYEtGUs9SM5KbaluPxLciGdFq3fDy6kFe3k5ycrZiNLZu5IiFEA1NLbeSPGuzU54d9tIgFBdtg97z1VdfZejQoQA89dRTjB49GpPJZO8FKSsr4/PPPycw0LYP16ZNm9i2bRvp6en2CRbz5s1j+fLlfPfdd0yZMoWePXvSs2dP+zNefvllfvzxR1asWMHUqVPx8/NDq9Xi6enpMOljzpw53HXXXUyfPh2A6Oho3nnnHYYOHcqHH35oj+naa6/loYceAmDmzJm89dZbbNiwgY4dO7Js2TKsViuffvqpvcfls88+w8fHh5iYGK6++moWLFjA008/zU033QTAwoUL+fXXXxv091pfkoyIc6qsF3FziyJYqUXPSFpFz0hwmP2Yr09/WzKSu5WwsFsaMVohhDi3Hj162L8PDbX9wyk9PZ3WrW3/WGrTpo09EQHYvXs3hYWF+Pv7O9ynpKSE+HjbVhmFhYW88MILrFy5kpSUFMxmMyUlJfaekZrs3r2bPXv28NVXX9mPqaqK1WolISGBzp07V4m5cgiqciuW3bt3c/ToUTw9PR3ubTKZiI+PJy8vj5SUFIeRDJ1OR79+/ZrEUI0kI+KcKpMRo1skRhdbMpJVVEap2YJBV/VfK/8cpgHw8b0Ujn9Ibs5WVFV1GCsVQjR9il5D2EuDnPbshqbX60/fv+K/R5VLTwC4u7s7tC8sLCQ0NJSYmJgq96qcmPH444+zdu1a5s2bR/v27TEajdx88832YZKaFBYW8sADDzBt2rQq5yqTo3/GXBl3ZcyFhYX07dvXIaGpdGZS1VRJMiLOymotw2Q6Cdh6Rgwuely0GsosVjIKSmnl6+bQXrVayUuzrcbrE3K6Z8THuw+KosNUmozJdBKjUaZ1C9GcKIrS4EMlzUmfPn1ITU1Fp9MRGRlZbZs///yTiRMncuONNwK2BOHMAlKwLZFhsViq3PvAgQO0b9/+vOJbtmwZQUFBeHlVvwZUaGgoW7duZciQIQCYzWZ77YuzNeoKrKL5Kyk5iapa0GiMGFyCURSFIC/beGl1QzWFOdmYy8vQaLV4BZzOxrVaN/t6Izm5Wy9M8EII0UBGjBjBwIEDGTt2LGvWrCExMZHNmzfz7LPP2qcLR0dH2wted+/ezZ133unQ2wK2WTG///47p06dIjMzE7DVf2zevJmpU6cSGxtLXFwcP/30U5UC1rO56667CAgI4IYbbuCPP/4gISGBmJgYpk2bxsmTtn9QPvLII7z22mssX76cQ4cO8dBDD5Gbm9swv6DzJMmIOKvK4lU3tyh7V2bljJrUvKrTeyuLV72DgtFoHf8V5VMxxTc3R5IRIUTzoigKq1atYsiQIUyaNIkOHTpw++23c/z4cfvSFm+++Sa+vr4MGjSIMWPGMHLkyCq9Di+99BKJiYm0a9fOPnzSo0cPNm7cyJEjRxg8eDC9e/dm1qxZhIWFVYmjJm5ubvz++++0bt2am266ic6dO3PfffdhMpnsPSWPPfYY99xzDxMmTGDgwIF4enrae3GcTVGbQuXKOeTn5+Pt7U1eXl6N3U+icSQlLSLu6GyCgq6le7d3AXj4612s3JPCc9d14b7Loxza71n3K2s/fpeoXn256ekXHc5lZf1B7O6JuLq24rJBsnGeEE2VyWQiISHBYY0NIWpytr+X2n5+S8+IOKszZ9JUquwZSa9mmMY+kyakakbv7d0HRdFiMp2kpORkY4QrhBCiGZJkRJyVPRkxnk5GgitqRqpbEr5ymObMmTSVdDp3PD1tU9NypW5ECCFEBUlGxFkVlyQCjj0jwV41L3xW3bTeM/n62ua45+Rua8gwhRBCNGOSjIgamc1FlJbapum6uUXaj9e0JLyqqqeTkeAakhGf/gDkSBGrEEKICpKMiBqVlBwHQK/3Q6/3sR8P8a7oGck3OazcV5yXS3mpCUXR4BUYXO09vb37VtSNnMBkSm684IUQQjQbkoyIGp0uXo10OF45TGMqt5JvMtuP51TUi3gGBKL7x0qBlXQ6Dzwr1xuR3hEhhBBIMiLOorriVQBXvRZvoy3ZOHOo5lz1IpXsQzVSxCqEEAJJRsRZnC5ejaxyLrRiqOZUbon9WGUy4nuOZMSnoohVFj8TQggBkoyIszBVrAXiWs0+Mq18jQCcyjkzGamY1ltD8WolH+++gIYSUxImU0oDRSuEEKK5kmRE1KjEdAIAo2t1yYhtg7yTZyYjZ1nw7Ew6nSeenl1t18gUXyFEExcTE4OiKE1mH5eLkSQjolpWaymlpWkAGI2tqpyv7Bk5mVMM/GNa7zmGaUDqRoQQjWPixIm2HYb/8Ro1alStrr/iiiuYPn26w7FBgwaRkpKCt7d3g8WpKArLly9vsPs1dzpnByCaJtvwiYpGY0Sv969yPtynYpimomakpCCf0uIiALyDQ855fx/fASSdWERu7vaGC1oIIYBRo0bx2WefORwzGAz1vp+LiwshIef+75qoP+kZEdUqMdnqRYzGVvbdes/0z2Gayl4RD/8A9C7n/h+9j/clgEJx8TFKSzMaKGohRGNRVZWysjKnvOq6n6vBYCAkJMTh5evrS0xMDC4uLvzxxx/2tq+//jpBQUGkpaUxceJENm7cyNtvv23vUUlMTKwyTLNkyRJ8fHz49ddf6dy5Mx4eHowaNYqUFMcauMWLF9O1a1cMBgOhoaFMnToVgMjISABuvPFGFEWx/9ySSc+IqJa9eNW16hANQHjFME1GQSmmcsvpepFa9IoA6PVeeHh0prDwALm52wgOHt0AUQshGkt5eTmzZ892yrOfeeYZXFxczvs+lUMw99xzD7t37+bYsWM899xzfPvttwQHB/P2229z5MgRunXrxksvvQRAYGAgiYmJVe5VXFzMvHnz+OKLL9BoNNx99908/vjjfPXVVwB8+OGHzJgxg9dee41rrrmGvLw8/vzzTwC2b99OUFAQn332GaNGjUKr1Z73e2vuJBkR1bL3jNSQjPi66XFz0VJcZiE5t4ScFNtMGt9zFK863MOnP4WFB8iRZEQI0YB+/vlnPDw8HI4988wzPPPMM7zyyiusXbuWKVOmsG/fPiZMmMD1118PgLe3Ny4uLri5uZ1zWKa8vJyFCxfSrl07AKZOnWpPYABeeeUVHnvsMR555BH7sUsuuQSwJTgAPj4+MvxTQZIRUS1TiW0mjWs1xatgK74K9zESl17IyZwSCuy79dY+GfHx7c+Jk0tkB18hmgG9Xs8zzzzjtGfXxbBhw/jwww8djvn5+QG2+o+vvvqKHj160KZNG9566616xeTm5mZPRABCQ0NJT08HID09neTkZK688sp63bslkmREVKvEdAqouWcEbDNq4tILOZVbgrUiGfENrUMy4m37V0JRURxlZdm4uPidR8RCiMakKEqDDJVcCO7u7rRv377G85s3bwYgOzub7Oxs3N3d6/yMfyZIiqLYa1uMRmOd79fSSQGrqJbJdPaeETijiDW7yL4vTV2GaVxc/HB3jwaQWTVCiAsiPj6eRx99lE8++YQBAwYwYcIErFar/byLiwsWi+W8nuHp6UlkZCTr1q2rsY1erz/v51xMJBkRVVgsJZSVZQLVL3hWqbKINSUti9Ki2k/rPZOPj21peFlvRAjRUEpLS0lNTXV4ZWZmYrFYuPvuuxk5ciSTJk3is88+Y8+ePcyfP99+bWRkJFu3biUxMZHMzEyHRKUuXnjhBebPn88777xDXFwcu3bt4t1333V4zrp160hNTSUnJ+e833NzJ8mIqMJUMUSj1Xqg03lV2+ZQ9iEy1C2gmMmuKF718A9Ab3Ct07MqFz+TlViFEA1l9erVhIaGOrwuv/xyXn31VY4fP85HH30E2Oo8Pv74Y/7973+ze/duAB5//HG0Wi1dunQhMDCQpKSkesUwYcIEFixYwAcffEDXrl257rrriIuLs5+fP38+a9euJSIigt69e5//m27mFLWuE7idID8/H29vb/Ly8vDyqv7DUTSczMwN7N5zPx4enRnQ/+cq52NOxPBYzGOUWcuwlvnROeESBsYfIqJrD26dVbepf6WlGWz681JAYcjgnej1DbfCoRCifkwmEwkJCURFReHqWrd/YIiW52x/L7X9/JaeEVFFZc+Iq2t4lXMrj61k+obplFnL0Gtc0LhkY1T+AupWL1LJYAjEza0toErdiBBCtFCSjIgq7Bvk/WO33pMFJ/n3pn9jUS2MaTuGmFs3YMkZgleRrapc71+/XisfGaoRQogWTZIRUUVJSfULnv149EfMqplLQi7hlctfwcvgRbBlHJ6Ftlk1m4p31et5vlLEKoQQLZokI6IKk6nqUvBmq5nlccsBuK3jbWgU259OhI+7vWdkTf4fHM4+XOfn+fjaekYKCg5gNhecT+hCCCGaIUlGRBX2npEzhmn+PPUn6SXp+Bp8GRYxzH48ws2KwWpGBfKNZbz797v/vN05uRpCMBpbA1Zyc3ecb/hCCCGaGUlGhAOzuQCzORdwLGD9Pu57AK5vdz0u2tOrMEZobeuLlBs9sGjhj1N/kFaUVufnynojQgjRckkyIhxULgOv1/ui09k2msoozuD3k78DcFP0TQ7t/S35ABTo/egT1AeramVF/Io6P9fX91IAcnK21Dt2IYQQzZMkI8KBfYO8M+pF1h5fi0W10DOwJ2192jq0N5pyAUhXPBnb/kbAVuha1+Vr/HwHAlBQsJ/y8tx6Ri+EEKI5kmREOCgxVZ1JsyPNVscxpNWQKu2tuRkAZGm96Ok7BDedGycKTtivqS2DIRg3t/aASk6ODNUIIURLIsmIcGCqKF6t3CBPVVV2pNoSi0tCLqnSPi/NthR8rs6b5Bwr10RdA8Dyo8vr/Gw/P1vvSHbO5jpfK4QQzqIoCsuXLz+veyxfvpz27duj1WqZPn16g8TVUGJiYlAUhdzc3EZ7hiQjwsHpnhHbTJr43HhySnNw1brSzb+bQ1tVVcmt2K03V+9NQmYhN0bbhmrWJK6huLy4Ts/28x0EQI4kI0KIelAU5ayvF154ocZrExMTURSF2NjYBomjrsnJAw88wM0338yJEyd4+eWXzzuGC5FANCSdswMQTYt9jRGjbSZN5XBLz6Ce6LV6h7bFebmUlZSgopCv8+JYZhF3X9qDcI9wThWeYnPyZka0GVHrZ/v4XApoKC4+hqk0FVdD3XYAFkK0bCkpKfbvly1bxqxZszh8+PTaRx4eHs4I65wKCwtJT09n5MiRhIXVfVuNi4H0jAg7VVXPWH3V1jOyPdW2X8wlwVWHaHJTbf/D13n5YtHoOJZRhKIojGhtS0B+S/qtTs/X673w9OwKQE62zKoRoilRVRWLpdgpr9oWxIeEhNhf3t7eKIpi/zkoKIg333yTVq1aYTAY6NWrF6tXr7ZfGxUVBUDv3r1RFIUrrrgCgO3bt3PVVVcREBCAt7c3Q4cOZdeu2q82Xdnj8sMPPzBs2DDc3Nzo2bMnW7bY/hsXExODp6cnAMOHD0dRFGJiYgDYtGkTgwcPxmg0EhERwbRp0ygqKrLfu7S0lJkzZxIREYHBYKB9+/YsWrSIxMREhg2zrQfl6+uLoihMnDgRAKvVypw5c4iKisJoNNKzZ0++++47h5hXrVpFhw4dMBqNDBs2jMTExFq/3/qSnhFhZzbnYrEUArY1RlRVtfeMVFcvklMxROMRFAoWSMi0/Y9kRJsRLD2wlI0nNlJmKXNYl+Rc/HwHUVCwl5yczYSG3ni+b0kI0UCs1hJiNnZ3yrOvGLoXrdbtvO7x9ttvM3/+fD766CN69+7N4sWLuf7669m/fz/R0dFs27aN/v3789tvv9G1a1dcXGz/3SooKGDChAm8++67qKrK/Pnzufbaa4mLi7MnEbXx7LPPMm/ePKKjo3n22We54447OHr0KIMGDeLw4cN07NiR77//nkGDBuHn50d8fDyjRo3ilVdeYfHixWRkZDB16lSmTp3KZ599BsD48ePZsmUL77zzDj179iQhIYHMzEwiIiL4/vvvGTduHIcPH8bLywuj0QjAnDlz+PLLL1m4cCHR0dH8/vvv3H333QQGBjJ06FBOnDjBTTfdxMMPP8yUKVPYsWMHjz322Hn97mtDkhFhV9kr4uISiFbryrHcY2SbsjFoDXQL6FalfWW9SHCrVnAcTuYUU2q20COwBwHGADJLMtmaspXBrQbXOgY/v8s4nvQR2dl/oqoqiqI0zJsTQrRo8+bNY+bMmdx+++0AzJ07lw0bNrBgwQLef/99AgMDAfD39yck5PQQ8fDhwx3u8/HHH+Pj48PGjRu57rrrav38xx9/nNGjRwPw4osv0rVrV44ePUqnTp0ICgoCwM/Pz/7sOXPmcNddd9mLWaOjo3nnnXcYOnQoH374IUlJSfz3v/9l7dq1jBhh641u2/b00gt+fn4ABAUF4ePjA9h6UmbPns1vv/3GwIED7dds2rSJjz76yH7vdu3aMX/+fAA6duzI3r17mTt3bq3fa31IMiLs/jmtt3KIpldgr2p7N3JSbMlIaEQrPFJ1FJaaScoqJjrYkytbX8myw8tYl7SuTsmIt3c/NBpXSsvSKCo6godHx/N9W0KIBqDRGLli6F6nPft85Ofnk5yczGWXXeZw/LLLLmP37t1nvTYtLY1///vfxMTEkJ6ejsViobi4mKSkpDrF0KNHD/v3oaGhAKSnp9OpU6dq2+/evZs9e/bw1Vdf2Y+pqorVaiUhIYG9e/ei1WoZOnRorWM4evQoxcXFXHXVVQ7Hy8rK6N27NwAHDx5kwIABDucrE5fGJMmIsLMveFYxrffvjL8B6BvSt9r2lTUjvqFhRAWUs/dUHscyi4gO9mREmxEsO7yM9Unree7S59BqtLWKQas14Os7gKysjWRl/y7JiBBNhKIo5z1U0hxNmDCBrKws3n77bdq0aYPBYGDgwIGUlZXV6T56/ekJAJU9vlartcb2hYWFPPDAA0ybNq3KudatW3P06NE6Pb/yngArV64kPDzc4ZzBYKjz/RqSFLAKu8ql4Ct7Rg5lHQKgq3/XKm1VVbXXjPiEhNE20B04XTfSN7gv3gZvckpz2JVe+2IvAD8/W09KdtYf9XgXQgjhyMvLi7CwMP7880+H43/++SddunQBsNeIWCyWKm2mTZvGtddeS9euXTEYDGRmZjZ6zH369OHAgQO0b9++ysvFxYXu3btjtVrZuHFjtddX9366dOmCwWAgKSmpyj0jImyTFjp37sy2bdsc7vXXX3810rs8TZIRYWcynV4K3mQ2kZifCEAnv6rdiMV5uZSbSlAUDd5BIUQF2JKRYxm2zFuv0TMk3LZia+W+NrXl72e7Lid3OxZL3dYqEUKI6jzxxBPMnTuXZcuWcfjwYZ566iliY2N55JFHAFtthdFoZPXq1aSlpZGXlwfYajW++OILDh48yNatW7nrrrvsxaCNaebMmWzevJmpU6cSGxtLXFwcP/30E1OnTgUgMjKSCRMmcO+997J8+XISEhKIiYnhv//9LwBt2rRBURR+/vlnMjIyKCwsxNPTk8cff5xHH32UpUuXEh8fz65du3j33XdZunQpAP/617+Ii4vjiSee4PDhw3z99dcsWbKk0d+vJCPCrqSkomfEGEF8bjwW1YKvwZdAY2CVtjkptraeAYHo9HraBtrm7x9NL7S3GRJhSypiTsTUKQ43t7YVs3nKZGl4IUSDmDZtGjNmzOCxxx6je/furF69mhUrVhAdHQ2ATqfjnXfe4aOPPiIsLIwbbrgBgEWLFpGTk0OfPn245557mDZtmr3gtDH16NGDjRs3cuTIEQYPHkzv3r2ZNWuWwzokH374ITfffDMPPfQQnTp1YvLkyfapv+Hh4bz44os89dRTBAcH25OYl19+meeee445c+bQuXNnRo0axcqVK+1Tm1u3bs3333/P8uXL6dmzJwsXLmT27NmN/n4Vta47mjlBfn4+3t7e5OXl4eXl5exwLkqqqhKzsRtWq4mBl67nl5M7eGHLC1waeimfXP1Jlfb7Nqzl14Vv06ZHb25+9mWOphcw4s3fcXPRsu+FkWg0CgVlBQz5Zghm1czPN/5MG682tY7n4KFnSU7+hlatJtCxw6yGfKtCiHMwmUwkJCQQFRWFq6urs8MRTdzZ/l5q+/ktPSMCgLKyTKxWE6Dg6hrK4RzbqoXVDdEADvUiAJH+7hh0GorLLBzPtg2teLp40jfYVvy68UT145o1qRyqyc6WuhEhhLjYSTIigNP1IgZDCBqNC4ezbclIB98O1bbPrZjW61uRjOi0GjqG2BYAOpiSb283NMI27ayudSN+foNQFB3FxccoLj5ep2uFEEI0L5KMCABKKqb1Go2tsarWWveM+IaeHr/sEmrrgjuQfEYy0sqWjOxM20lBWUGt49HpPPHx7gdAZtb6Wl8nhBCi+ZFkRABQXGJbwMdobM2pwlMUlRfhonEh0juySlvVarUveFY5TAPQuSIZObNnpLVXa6K8ozCrZv5MdpxWdy4BAVcCkJkpyYgQQlzMJBkRAJjsyUiEfYimnU879Bp9lbYFWZmYy0rR6nT4BJ9eNrlLWEXPyBnJCJzuHalr3UhAgG2jp9zcbZjNte9VEUI0jGYwv0E0AQ3xdyLJiAAch2nONUSTdcrW1ickDI329MqqnSpqRlLyTOQWn16dsDIZ+ePUH1isjgsKnY2bWxRubm1RVTNZUsgqxAVTuVpocbGs8yPOrfLv5MxVZutKloMXAJSYTicjh7LXANDRr/ql2LNP2faw8Qtv5XDc01VPaz83krKLOZCSz6B2AQD0CuqFl4sXeaV57M7YTZ/gPrWOKyBgOElJx8jMXEdw0LV1fl9CiLrTarX4+PiQnp4OgJubm2xaKapQVZXi4mLS09Px8fFBq63dth/VqVcy8v777/PGG2+QmppKz549effdd+nfv3+1bT/55BM+//xz9u3bB0Dfvn2ZPXt2je3FhWexlFJamgqA0fX0ME1H35qSEVvi4h8eUeVc51BPWzKSfDoZ0Wl0XB5+OasSVrHx5Ma6JSP+V5KU9ClZWRtRVQuKUv8/diFE7VXuHluZkAhREx8fH4edjuujzsnIsmXLmDFjBgsXLmTAgAEsWLCAkSNHcvjw4WpXpYuJieGOO+5g0KBBuLq6MnfuXK6++mr2799fZaMe4Rymit16tVoPiq0aUopsG+DV2DOSXNEzEtaqyrkuod78uj+NgymONR5XRFxhS0ZObOTRvo/WOjZv7z7odN6Ul+eQm7cLX59Lan2tEKL+FEUhNDSUoKAgysvLnR2OaKL0ev159YhUqnMy8uabbzJ58mQmTZoEwMKFC1m5ciWLFy/mqaeeqtL+zO2PAT799FO+//571q1bx/jx4+sZtmhIJWcUr8blxgEQ7hGOp4tnte0ra0b8augZgapFrIPCBqFVtMTnxXOi4AQRnlWvrY5GoyMgYBipqcvJyPhVkhEhLjCtVtsgHzZCnE2dCljLysrYuXMnI0aMOH0DjYYRI0awZcuWWt2juLiY8vJy/Pz8amxTWlpKfn6+w0s0npIzpvWea4impCCfknzbBlLV9oxUzKg5ml6Aqfx0saq3wds+PFPXBdCCAkcBkJ6+Wqr7hRDiIlSnZCQzMxOLxUJwcLDD8eDgYFJTU2t1j5kzZxIWFuaQ0PzTnDlz8Pb2tr8qtzYWjeN08WoEh7IPATXPpKksXvUMCERfzZ4V4T5GAj0NlFtU9pzMczhX3ym+fn6D0WrdKC1NIb9gT52uFUII0fRd0Km9r732Gt988w0//vjjWTdfevrpp8nLy7O/Tpw4cQGjbHns03pdT0/r7eBX/TLwZ6sXAds4c782vgDsOJ7tcK4yGdmetp3CssIq19ZEq3XF39+25khG+upaXyeEEKJ5qFMyEhAQgFarJS0tzeF4WlraOStp582bx2uvvcaaNWvo0aPHWdsaDAa8vLwcXqLxVA7T6A2hxOfGA+deY+Sf03rP1C/SNgS3IzHH4XikdyRtvNpgtprZklK7Yb1KQUEyVCOEEBerOiUjLi4u9O3bl3Xr1tmPWa1W1q1bx8CBA2u87vXXX+fll19m9erV9OvXr/7Riganqqq9ZyTDDOXWcjz1noS5h1XbPqeiZ6S6ab2VKntGdh7PwWp1TBwqe0diTsTUKU5/v6FoNAZKTEkUFh6s07VCCCGatjoP08yYMYNPPvmEpUuXcvDgQR588EGKiorss2vGjx/P008/bW8/d+5cnnvuORYvXkxkZCSpqamkpqZSWFj7bnrReMrKMrFaSwANx4pyAdsQTU0LHNl7RmoYpgFbEatRryWvpJz4DMf/P1cmI5tObarTaqw6nTv+/rZr09NX1fo6IYQQTV+dk5HbbruNefPmMWvWLHr16kVsbCyrV6+2F7UmJSWRkpJib//hhx9SVlbGzTffTGhoqP01b968hnsXot5KTLYhGlfXUA7nnH2IxlxWRl66bYiuumm9lfRaDb0ifADY/o+hmt7BvfHUe5JtymZv5t46xRpUsQJratr/ZKhGCCEuIvVagXXq1KlMnTq12nMxMTEOPycmJtbnEeICKSmuTEZacTjjHCuvJp8EVcXg7o6bt89Z79sv0pctx7LYcTybOwe0th/Xa/RcFn4ZqxNX8/vJ3+kV1KvWsQYGjECrdcdkOkle3k58fGTITwghLgayUV4LV1ySAICbMdI+k6amlVczkxIBCGwddc59KvqeUTfyT0MjKupGTsbUKVat1khg4NUApKatqNO1Qgghmi5JRlq44mJbMmLRB5JbmotO0dHOp121bTMqkpGA1m3Oed8+bXxRFDieVUx6vsnh3ODwwWgVLXE5cZwoqNu07ZDgGwBb3YjVKktUCyHExUCSkRauuPgYABlm23LPkd6RGLSGattmnjgOQEBE5Dnv6+Wqp3OIbUr25vgsh3PeBm/6hdiGWH47/lud4vX1HYiLSyDl5TlkZ/9Rp2uFEEI0TZKMtGCqaqW4OBGAhJISoObiVTg9TBPQOrJW97+iYyAA6w9V3fXzqtZXAXVPRjQaHcHB1wGQkrq8TtcKIYRomiQZacFMphSsVhOKomd/rm0GVE3JiKmwkMJsWw9HQMS5h2kAhnWy7eK88UgGln+sNzK89XAUFPZk7iG1qHZbCVQKCRkLQGbmWsrLc+t0rRBCiKZHkpEWrLjYNpXXaGzDoRzbbr0dfKtfBr6yV8QrMAiDm1ut7t87wgdvo568knL+TnIsZA10C7TPpFmXtK6aq2vm5dkNT4+uWK1lpErviBBCNHuSjLRglfUiBmNreyFpTTNpMk4kArXvFQHQaTUM7VDzUM2I1rbNEus6VAMQFnYrAKeSl8maI0II0cxJMtKCFVXMpClRvFBRCTIG4efqV23butaLVBrWyZaMbDicUeXciDa2ZGRX+i6ySrKqnD+b4ODr0WhcKSo6Qn5+bJ2uFUII0bRIMtKCVfaMpFfMpKmpVwQgM6liJk0dk5GhHYJQFDiYkk9KXonDuTCPMLr5d8OqWll7fG2d7qvXexEUdA0Aycn/rdO1QgghmhZJRlqwymQkobgYqLl4VVVVMiuGaQLrMEwD4OfuYl8avrqhmmuibAnFqoS67zcTFnYbAGnpP2M2F9T5eiGEEE2DJCMtlNlcRGmpbRbLnjxbktDBr/ri1fyMdMpKStBodfieZYO8mlzdJQSAFbHJVc6NihqFgsLf6X9zqvBUne7r490Pd/doLJZikpO/rXNcQgghmgZJRlqoymXg9Xpf9ufYvu/kW33PSGWviF94K7S6um9ndEOvMBQFtiZkcyK72OFckFsQ/UP6A/BLwi91uq+iKES0mgDAiZOfo6q13wVYCCFE0yHJSAtVXGQbotG4hGGymDDqjER4Vr8Tb3qCrW1gm6h6PSvMx8jAtv4ALP+7au/HtW1tu/HWZ6gmJGQsOp0PJtMJMjPX1ys+IYQQziXJSAtVuSdNsWJbsj3aNxqtRltt29RjtjVIQtpF1/t5N/WxDe/8+PepKlNxr2x9JXqNnricOI7kHKnTfbVaI+EVtSMnTiypd3xCCCGcR5KRFqqo+CgAaRV7zXX261xj27RjtrbBbeufjIzqFoKrXsOxzCJiT+Q6nPM2eDM4fDAAPx/7uc73btXqbhRFS07uXxQUHKh3jEIIIZxDkpEWqrDwEAAHC22zULr6d622XUF2JkU52SiKhqDI+g3TAHgYdIzqaitk/X7XySrnr293PQArjq6gvI678bq6hhEUaJuVk3h8Yb1jFEII4RySjLRAFkuxfZhma7Zthku3gG7Vtk2Lt/WK+Ee0Rm9wPa/n3tzXVpPyw65T5BU7JhxDIobg5+pHlimLP07WfTfeNpEPApCevoqiinoYIYQQzYMkIy1QYeERQEWr8yW9zFa8GuVdfa9HWkW9SHDb9uf93Mva+9MpxJPiMgtfbj3ucE6v0XNDuxsA+CHuhzrf29OjEwEBVwIqx6V3RAghmhVJRlqggkJbXUWZLhiw1YvoNNVP2U2tqBcJaVf9GiR1oSgKU4a0BeCzPxMxlTtOxR0bPRaAP079QXpx1QXSziWyzUMApKYtp6Sk6lCQEEKIpkmSkRaosPAgAGkWFwC6+Heptp2qqqTFV8ykaYCeEYAxPcMI83Yls7C0yjTftt5t6R3UG6tqZUX8ijrf29u7F36+l6GqFhIT32+QeIUQQjQ+SUZaoIICWzJypMi2V0xN9SL5GemUFOSj0eoIqOcaI/+k12q493LbvT7+/Rhmi9Xh/E3RNwHw7eFvsVjrvohZVNtHAEhO+Y6iovjzjFYIIcSFIMlIC6OqFoqKDgOwIzcNqHkmTWW9SEDrNuj0+gaL4fb+rfF103Mss4j/bD/hcG5k5Ei8Dd4kFyUTcyKmzvf28e5LQMAIwEr8sfkNEq8QQojGJclIC1NSkoTFUoyiGDhVWo6H3oPWXq2rbWuvFzmP9UWq42HQ8ehVthqUN9ccJq/k9Mwao87ILR1uAeDLg1/W6/7t2j4GaMjI+JW8vL/PO14hhBCNS5KRFqZyUbByfRAqCl39u6JRqv8zSD5saxvS/vyLV//pzv6tiQ7yIKe4nHfXxTmcu63jbWgVLTvSdnAo+1Cd7+3h0YHQUNtwT9zR16qs+CqEEKJpkWSkhSmoKF7NtBgB6BJQffFqeVkpqUdtS7O36lz9MM750Gk1/Ps627OXbE7kcGqB/VyIewhXtbkKgK8OflWv+7eNegSNxkhe3g5S0346/4CFEEI0GklGWpjCimm9BwsLAegZ0LPadqlxh7GYzbj7+uETEtYosQztEMiIzsGYrSqPfPM3pebTBat3db4LgFXHVtVrmq+raxhRkbapvkePvobZXHCOK4QQQjiLJCMtiKqqFBTsB2B3fhYAfYL7VNv2xIF9ALTq3A1FURotptk3dcPP3YVDqQXMX3N6k7yegT3pHdSbMmsZS/Yvqde9W7e+D6MxkrKyDI4lvNNAEQshhGhokoy0ICUlxykry0RFx8kyDe192uPr6ltt21OHTicjZ1WcDZvfgy9vhtdaw5tdYcl1sGE2FGacM6YgT1fmjusB2Kb6/n7Edo2iKDzQ4wHANs03qySrtm/TTqMx0LHD8wCcPLmU/Pw9db6HEEKIxifJSAuSm7sDgEKNP2YU+gX3q7adxVxO8hHb9N+ILmdJRuLWwgeXwppn4ehaMOVB/klI/AM2zoUF3eGXp6D07EMkV3UJ5s4Bthk9D3+1y14/MihsEN0DumOymFh6YGld3y4A/v5DCAoajapaOHDwSazW0nrdRwghROORZKQFyc3dBkCcyfZzv5Dqk5HUo3GYy0oxenrhFx5RtYGqwprn4KuboTANAjrA1a/ClI1w329w/bsQ1gfMJbD1Q/hoCCSffYrt82O60D/Sj4JSM5M+20Zavsmhd+SbQ9+QY8qp1/vu2OEF9Hp/ioriSEh4t173EEII0XgkGWlBcvO2A7A9LxeAvsF9q2138uA56kV+fwM2V9RgDHgQHvgdBk2FsF4QcQn0GQ+T18Nd34NXK8g+Bp9eBbu+qDE2g07Lx+P70jbQneQ8E3d/upX0fBNDWg2hs19nSswlfLzn43q9bxcXPzp1fBmA40kfy9ojQgjRxEgy0kKUlqZRUpIEKCSUaoj0iiTAGFBt29PJSDVTencugQ2v2r6/5g245jXQG6u2UxSIHgEPboLOY8BaDiumwsY3bD0r1fBxc2HJxP6EeLkSl17ILR9t4WROCY/2fRSw9Y4czz9e7bXnEhQ0kuDg61FVC/v2TaO8PLde9xFCCNHwJBlpIXJzbb0ixRo/TKpS4xBNeVkpJw/ZZty06tLd8eTJHfDzDNv3gx+DAVPO/WCjL9z6ha09wIZXYPVTNSYkrf3d+PZfA2nt58bxrGLGfbgZfVlHLg+/HLNqZsHOBed+Zg06dXwJo7ENptJkDhycKYuhCSFEEyHJSAtRWbwaX6oFqLF49cS+PZhLS/HwDyDwzM3xyorghymgWqDrjTD8udo/XFHgylm2nhQU2LoQfn2mxoQkws+WkHQM9iS9oJTbP/6Ltprb0Sgafkv6jZ1pO2v/7DPodJ507/YuiuJCZuZvJCXVb9hHCCFEw5JkpIWorBfZmZcHwCUhl1TbLn7HVgDa9R3gWC+y5jnIjgfPMLjuLVuCUVcDpsCYt23f//UBrJ1VY0IS7OXKDw8N4roeoZitKu+vKcTHfDkAr/z1CuWW8mqvOxdPz6506GBLpI7Gv0FG5rp63UcIIUTDkWSkBSgvz6Ow0DZV92iphu4B3QlyC6rSTrVaid9pS0ba9+1/+kT8BtixyPb92A9sQy/11XeCLZkBWxHsupdqTEjcDTrevaM3z4/pgkGnISl+KKrFnaO5R/l4z6J6hxAedgfh4XcCKvv3P0pBYd33vxFCCNFwJBlpAbKyYgCVfNWdQqvCsIhh1bZLPRZHUW4OLkYjrbraFiLDXAarnrB9f8n90K76a+uk371w7Tzb95vehJg5NTZVFIVJl0Xx6/QhDGgTgSn1OgAW7v6INzdsorjMXOfHK4pCh+hZ+PoOwmIpYnfsvZSUnKjXWxFCCHH+JBlpAdIz1gCwrcD2wT289fBq28XvsK1DEtmzLzq93nbwrw8gKw7cA+tWJ3Iu/SfDyIokZONciJl71uaRAe58ff+lvHntJHSlnUAx88mh1xj42lpeX32IpKziOj1eo9HTvdt7uLtHU1qWxt+x4yktrfseOEIIIc6fJCMXOYvFRFbWRgBiixVae7amrXfbattWDtG06zfAdiA/GTa+bvv+qpfA6NOwwQ18CK5+xfZ9zGzb+iVnodEoXN8rnB9unY+LxojO7Tglbr/yQUw8Q97YwO0fb2Hp5kRO5ZbU6vF6vTe9ey3F1TWCkpKkioTk3EvYCyGEaFg6ZwcgGld29h9YrSWU4MbJcpWJHYZXu5BZTsopMpMSUTQaonpXzLRZ828oL4JW/aHH7ecdS35mCSnxeRRklVCYW4bFbEWrvQoXv0C8Tn6Pzy8/EWTW4zJ8+lnvE+XTmpcue56n/ngK18ANdPXrS2ycH38dy+avY9k8v2I/XcO8GNE5mCEdAugW7o1Bp632XgZDMH16f87OnbdTVBTHzl2306f3F7i6Ns5OxUIIIaqSZOQil57xKwB/FwHUXC+yd8NaACJ79Mbo4QkJf8C+70HRwOh5oKlfJ1puejEH/0wmbns6BdmmGloFAQ8CoPzXgv/albTu34moXgEEt/FC0VRNnka3Hc2W5C38FP8T+R5L+GnaZ/wVV87aA2nsPJ7D/uR89ifn8/a6OFy0Grq38qZfG196t/alc6gnEb5uaCruazS2pk+f//B37D2UlCSyc+dt9Or1Ge7u7ev1noUQQtSNojaDlZ/y8/Px9vYmLy8PLy8vZ4fTbFit5fyxqT9mcz7vpBvIVQJZf8t6tBrHXgKL2czHD02kOC+X6x97hui+l8DCwZBx0Fa0Onp+nZ+dnVzEluXxJO7JtB/TaBQC23jiF+qOu48BnYsGi1mltKicvMwSso8lU1BkcLiPh6+BzoNC6XxZGJ5+rg7nisuLuWPlHRzLO0Y3/258NuozXHWuZBWWsv5QOusOprM9MZusorIq8Rn1WjoEe9AxxJOoAA9a+RoJ88ynJPVhSk2J6HSedOv2Hv5+l9f5vQshhLCp7ee3JCMXsazsTcTGTsCk6nn6pI5J3e6zL61+pritm1nx5mzcvH2Y8sEStNs/si1KZvSD/9sJbn61fmZpiZktPxzlwKZk24xdBVp38aPL5WFEdPbDxfXsnXGFq+aTvHEDCaWXcNw8kHJzRfvK+1wWRmTPALRaW0/NifwT3LnqTnJLcxkZOZLXh7yORjndi6OqKsezitlxPIedx7PZfSKPoxmFlJmt1T7fQ1/ItN6LaOcTj1XVcKRkIiX6cQR4GAjwNNi+ehgI8HDB01WPtppeGyGEEDa1/fyWYZqL2KlT/wFgZ5EKaLilwy3Vttu73jaU0/WKEWiL02FDxSyXES/UKRE5cTCb9Z8fpDCnFIC2vQK5dGxbfEPca30Pj2sfo4OXhg6/vYBZ1XMsdCYHiodz6kgeSfuzSdqfjYevgW5Dw+l6eTgRXhG8dcVbTF47mV8Tf8XX4MszA56x18UoikJkgDuRAe7c3LcVAGaLlePZxRxOLeBwagFJ2cWczCnmRHYJaQXw+vaHmND1GwaFbaeT22J2pO1k7h93UmKuugePh0GHl6sOL6MeL1c9XkZdxVc9nq46jC5a3PRajC5ajC46jHotbi4VP1d+X3HeRafBRaupfnNCIYS4iEnPyEWqpOQkm7cMA6y8luJK++AhfDjiwyrt8jPT+WTqfaCq3Pv2x/hunAkHV0CrS+DeNbWqFVFVlR2rEtn2vwQAvAJcGT6+M+EdzmNxtB2LK/bBUSH6anKHfsDBHfkc/DOZkgLb6qs6vYaOl4bQY1gEm00beOaPZ1BRGd9lPI/3e7xeH+qlZgvJuSaSsorIyfga99IP0Shmis0BrE+9n90Z0WQWlJJvqvv6JrWl1yq4aDXoK5ITvVaDQWf76qLT2M7rqh7XKgpajYJOq6BRFHQaBa1Gg1aDw1fb8Yq2moq22opjyulztvMaNIotqTvzq0ZRoOKrRgEF29fTxxQUBfs1Co7HlYp2yhn3gNP3PvO8csYz4PTiv/b/79p/rv68PTG1/+zY3v6lFtf98xnwz2vrGIMknuIiJ8M0LVxc3GySTiziaJkL76XpeHf4u1wRcUWVdhuWfMyuX1YQ0bUHt94+FL6+FRQtPPA7hHQ753PMZRbWfX6Qoztsa3R0HRzGoHHtzzkcUysHVsAPk8FsguDucPtXWDwiiNuRxu71J8g8UWhvGtHFj8IOx5mT+hwoKnd1vosnL3nSYcimPvLz97Bv3yOUmJIA2+qt7do9ARpPCkxm8kvKyTeVk19irvh6+ucCUznFZRaKyy2YyiwUl1koKbdQUmahuNxMSZmVkjIzxeWWmhahFS1MlUSrwe7bsHdsyLs1dD6mNPRvr2nfrkF/f99MGUivCJ+GuyEyTNOimc2FJKf8F4D1eQqh7qEMDh9cpV1+Zjq7164CoP/o0bDyX7YTAx+qVSJSlFvKqg/3kH68AI1WYeidHelyWQNOie1yPXiFwX9uh7S98PFQtOMW0WnglXS8NISUo7nsXneShN0ZnDiQDQc8ech3Hht9fmSZ5VsyijOYPXg2Bq3h3M+qgZdXD/r3/5mj8a9z6tSXnEr+D+kZv9K+3UxCQ2/Cz93lvN+mqqqUmq2UWayU27+qlFkslJlVyixWysxWyiu+/vPncouVUrMVq6pisYLFasVsVbFaVcxWFUvFy/69qmKxVHytOG5ra63S1mxVUVUVVQWrqmJVQa2I2Wo/zj/aqBVtKtthb6va76FWc53jzyqOx+H0zgHqGb87x5/P+/8dTvXP99fgN24RWtJ7bVhWJ/6dSM/IRejEiaUciXuJLIuOV5L1PN7vScZ3HV+l3ZqP3mHv+jVEdOnOLT3yUHZ/Bd6t4eG/wOXsdR7px/NZ9cEeivLKcHXXc82/uhEWfR7DMmeTewL+ew8k/w0oMPgxuOIp0NpWic3PLGFPzEkObkqmzGQBoFRbwsGgzVi6ZjL7mhcJcQ857zBycv7i0OHnKS4+CoCHR2fatXscf7+h0t3eRNU1iVFxbH+2c+dMiOp5ndpQH6YNcJuG+nBoiE+Zhvq9NNQnXkPcpqE+fhvqPQV6GnDVV78mU33JME0LVV6ex5a/RlBens13OXoSieKnsT/honX8F3x28imWPPYgqtXK7fdeT/iWJwAFJv4MkWefzhq3I431Sw9iLrfiG+rO6Id64B1YtbizQZlL4ZcnYecS289hveHGjyGwg71JmcnMoS2p7Fl/grwM2yqsVqycCjjE8NG9uOrSy847abBayzhxcimJie9jNhcA4OXZg9ZtJhMYcDUajXQ2CiFEJUlGWqhDh2dx6tRXpJVreT3VhblD5zMycqRDG1VV+fG1F0iI3UnbHt25Ufs1mHLh8hkw4vka762qKttXJrL9Z1uhauuu/oy8vysuxgv4Abz/R/jfdFu8WhdbzINngO70UIxqVTm+L4uta+PIjDu9NLzZr5Arru1Bl/4R6F3OL/svL88hMfFDTp76AqvVto6Jq2sErSMmERp6Mzpd7WcQCSHExUqSkRYoP38P23fcBKi8l27Aw6svn1/zeZXegL9X/4/1n32EVq/nnl5Z+BfuhtBecN9a0FVfA1FeZmH90oMc3WkrVO15ZQSDxrW3r2J6QeUnw4ppcNS2aix+bW1753S6rko1V8qJbP7z7Wq0cX7oVNt7U1xUOvYNpeOloYRH+1S7wmttlZVlcvLkV5w89QXl5TkAaLUeBAVdQ0jIDfj6DEA5zyJaIYRoriQZaWEslmJ27LyVwsKDbC/SsizHnS+v/ZKuAV0d2mWeOM6XT0/HUl7OsG56+ljW23bkvX8d+Lap9t4F2SZWf7T3vApVrcXFmLOysGRlYc7OxpyVhVpcjGq2oJrNqBYzWKwoLi5oXA0oBlcUgwGtpwdaX9/TLy8vFK3WNki6/0dY/RQUptkeEnGprZak7RVVkpJN8X/xzY+rCT/RBa/SAPtxDz8D0X2Dads7kODI6peerw2LpYSU1B9JSlpESUmi/bjBEEpIyA0EBY7E07ObJCZCiBZFkpEWRFUt7Nn7EJmZv1FoUZib6srD/Z7mrs53ObQrzstl2QtPkZ18kqhQV270Xouid4WJK6FVv2rvnXQgi7WLD2AqLK9VoapqsWA6dIjSgwcpjTtKaVwcpUePYk5Pb5g3qyhovb3R+vqiCwxEF+CH3pyELmcHOoMJvdGCrl13dMP/D6Xr9aA9PYRUXF7Mkn1LWbVlPW1Se9IuqxcGi5v9vJu3C217BtK2VyCh0d7o6lHIpapWcnN3kJq2nPT0Vfa6EgC93p8A/6H4BwzDz/dy9Hr5WxZCXNwkGWkhVFXlSNzLnDy5FLMK76Ub6Bx+HXMHz3UYninOz+O/Lz5N1skkPFwV7o7YgrveCrd8Bl1uqHJfc7mF7T8nsGtNEqgQ2NqTUVO64RXgWKiqWq2Y9h+geNs2irdvp3jHDqyFhVXuB6AYDOj8/dH6+6Pz80Pj7o6i14FOh6LTg0ZBLStDNZViLTXZvhYUYM7NwZKTizU/v/a/GEVF56agDwpA1yYafZtodMEh6EOCyffS803OWr7L2EBwbkeisnoSmdcNvfl03YlWryEs2oeITn606uxLQLhHnXtNLJZSMrPWk5b2M9nZm7BYzvy9KHh4dMLHux/ePn3x8e6Hq2tone4vhBBNnSQjLYDFYuLQoWdJTVsOwGeZLljcerFo5CLc9Kf/xZ+dfIoV81+1JSIGK7e22omvG3DzZ9Dp2ir3TYnPY8MXB8lJLQagy+AwBt8abe8psJpMFG3eQsH6dRRuiMGSleVwvcbDA9fu3TBER2No3x5D+2gM7dqi8fI6r9ksank5lrw8LDk5mLNzMGdkYE5LpTw1DXNaGuVpqZhTUjBnZNZ6rpvJ20iqWxnpHhpyfTtQZuyJq7YHGqunQzuDm46Qtt62Vztvgtp41mlhN6u1jNzcHWRlxZCZFUNxcXyVNi4ugXh6dsHTowsenl3x9OiEq2srNBp9rZ8jhBBNiSQjF7nCojj27X+MosL9WFT4PkeP6jWEBcMW2BMRVVXZH/Mb6z/7iPJSE+66cm5tvRs/Lxe44z8QeZnDPXPTivnrp3jid2UA4OblwtA7O9K2VyDmnBwKN8RQsH4dRX9uRi05PUtF4+GB2yWX2F79++PauZOtrsNJVLMZc1oy5q3fU/73r5jj91NeDOZiDeUlWswmA+ZiBdVS/Z++ChS7hZDt15ls307k+ERj/cfCaQoqnm5W/Py1+Ie7ExjlS0DHELyCPGvVg1Jamk5u3k7ycneQm7edgoKDQNXN+xRFh9EYgZtbW9yMkbi5ReHmFoWrawQGQ5AkKkKIJq1Rk5H333+fN954g9TUVHr27Mm7775L//79a2z/7bff8txzz5GYmEh0dDRz587l2mur/ou8JpKMnGYypXAs8T2Sk/+LgpVCCyzJMtCl1Y28MPAF9Fo9qqqSGLuTzd9+RWp8HAARbrlcE3YYz+gBMPZD8IkAbNNgk+Ny2bPBtpKpqtpqPzsNDKFP53LKt22i6Pc/KNm7F6ynPyx1oaF4Dh+O55XDcevXD8Xl/FcibTTF2XDoZ9j7HSRuAtW2/LqlVIO5REO5JgyzNpxyiw9FJRoyM7MoS8/ALacE13KwKhoKPVqR59WWPO8o8rzaUupa/QaCGms5buZc3CnEQ2/C09WCl5cGD18XPPzdcPHzttW8+Pig9fZG4+WF1sMDq6acwsJDFBQcoKBgPwWFBygqOorVajrLG1MwuARhcA3BYAjF1RCCwTUUF70/Li5+6PV+uLj4o9f7odW6Ns7vVgghzqLRkpFly5Yxfvx4Fi5cyIABA1iwYAHffvsthw8fJigoqEr7zZs3M2TIEObMmcN1113H119/zdy5c9m1axfdup17yfG6vJmLVWlpBhnZfxB/4mvKC2NRKtb+212sZVNpKNP6v8iQ8MFkHE/k6F+/c+iPdeRm5QKgUywMDEiiX0QJmitmwiWTKSuzkhKfx4n92cT/nW7fZRcgzLuYjiXb0O1YhyU72yEOQ+fO9gTE0Llz81x1tLTg/9u789goyv8P4O9nZo9ub0pvSsshikoBrVCrEY00HOGnIH61YhMOUQSrolWDGAHxD0sggvEIaqLiN94YKBGvlFulIlfFi/5of4WqdIuA22vb7jGf3x+7O91ptxdsGbb9vJLJzD7PM7Ofp5/dmWdnpzvAqf1A1T6gai9g/SVwO1Mk6gZl4FhYHP5oJpysq4PtXxuimwgxTYRoexQiXGkwIRVkSIMjLA3NYQmgbn70zOhshLnVpk4mRx2MziaY4IDJoMBsIoSZJZgtEowRJlCCDGe8C66YFjii7HBY6tFqPA+HZAMJd4+7LcvhnsGJMQ5G0yAYDDEwGCJhkCMhe+cGQxQMhkjIcqSnzhAF2RAFWbJAlsMghH5nuxhjoanPBiPZ2dmYMGECXn/9dQCAoigYOnQoHnvsMTz77LMd2ufl5aGpqQnbt29Xy2688UaMHz8eb775ZlA7E6qICG53E5pbz+JsYxX+baxAfVMlGhuPA61/wkI2TfuqJhm/nEnAaHcWrmgKQ93pWtTUnIfD2ZZKg3Dj6ph6XJkSDeeI/6AuJgfnTjfj/Gk7/j3r1FxSISsOJNUeRNqfuxFpr1HLpYgIRNx0EyIm3YLIW26BMfnif1L9smM/D9T8DFiPATXHPMvnKhDox57tQqDcZMT/mkyeeVg4Thhl2AUAIkQ2S0iuG4SExkTENscjqjUB4a5EGCkBEmIhRO++UhHkhsFph9HVBNnd2m5qgcFcB0N4HeTwekhRjRBRdojIFiDCAYQ7gAiXZ5KD802sIBkSGSFghAQTZGGCJEyQYPbMpTDv3AxZhEFIJkiyEUIYIckmSJIJwjuXZN9khjCEQZKNkGQzJEOYt61nPSEZISBBCBlCSN4BUduy/4QO7aTQHDAz1o/0yWDE4XAgPDwcn3/+OWbNmqWWz5s3DzabDdu2beuwTnp6OgoLC/HEE0+oZatWrUJxcTF+/vnngM/T2tqK1ta2T+v19fUYOnRo0Acjn7+eDymiEX53hwCE766K5F1o9+cR1K7er43wzIXfsqdAgZDcHSfZDcnggGxshZA6Xi+gbp0AR/1g2M+moeHvUXDaYzppKSBgBCQzIMxAF/eHlBQ3ZHcrDK5myO4WCADCIEOEWSBZLBAWCyRL2ADdmRPgcgBu7+RqBRQX4HZ65orT774jgFsATiHgEIATAi4h4ALgFm3LBEBAgqzIkMh/kiBIguydC5IgICF49/YkCEMrZGMzZJMdstEO2WSHZGiFMDggGVohyd6577GhFZLcVh/KLwEiAZDwzqW2ZXge++o9j33nHIU3v52UeZcpQBkgvAN90W4d+D1vW5ln++3L/Jbb90fTxqcHCaLO2nQsp07bdrJed7H4ba/zg00vttdp2+Dom4so++hNFORg0wb9DybMWhjUbfbJXXvPnj0Lt9uNpKQkTXlSUhKOHz8ecB2r1RqwvdVq7fR5ioqKsHr16t6EdkHMQ/4P4TFB+v2LIFAUCU5nGFpaItDaEokmewyaGuPQ0DAYLpffd/7hji620gog8L/Wds/tWVdpBJrgmVhH3V002u5GaW0U7+RUj1WXjAKgBUBLOIDwbhr7I0iS2zu5IMueuVomuyD7LavtJDeEUCAkBZJ3LoT/shuSIAjJ3Um9Ask7F4IAEIQgzWNJ6n5PLAT5fYDo+ddajA1E/1Zk6vbcl+VdvZYvX47CwkL1se/MSLC1/jkKzrND2n0SCPTJBV3Ue5a1n5485yjUMkWAFNkzkQxSDIAiAYoMcpmgOC0ghxkgE4R6akbAAgGLABKE53Rz26lpCRDC+4mVIAQ89ZJnkmQBSZYgJAmQJM9/thgMEHIwP3Gzi0cAKd7XDHn/HbndnAiAr43iV+ZZh+D5mo9AHefeZb/7xnqfyncX2XZ3k/V7rIbkeybfggKQW43euxDgk6/fm8b/nF+g+oAnIf3OPGlesaRt5rniWvEOOLzLaBuwQPgvEwS8bUW79eB/xrP9mU7/ALXBivZlfu3azij5bVM9+9qxDOSNqzMB6zpr71/e9hcMvP1OttFVLAHbtC0HPBfSPpHdbrvr9l3+rS5Y35wXCRUZQ67svlEf6dVgJD4+HrIso7a2VlNeW1uL5E6uJ0hOTu5VewAwm80wm82d1gfLfx7/b58/B2OMMca61qsbZZhMJmRlZWHnzp1qmaIo2LlzJ3JycgKuk5OTo2kPACUlJZ22Z4wxxtjA0uuvaQoLCzFv3jzccMMNmDhxIl555RU0NTVhwYIFAIC5c+diyJAhKCoqAgAsXboUt956K15++WXMmDEDn3zyCQ4dOoS33347uD1hjDHGWEjq9WAkLy8P//zzD1auXAmr1Yrx48fjm2++US9Sra6uhiS1nXC56aab8NFHH+H555/Hc889h1GjRqG4uLjHvzHCGGOMsf6Nfw6eMcYYY32ip8fvXl0zwhhjjDEWbDwYYYwxxpiueDDCGGOMMV3xYIQxxhhjuuLBCGOMMcZ0xYMRxhhjjOmKByOMMcYY0xUPRhhjjDGmKx6MMMYYY0xXvf45eD34fiS2vr5e50gYY4wx1lO+43Z3P/YeEoORhoYGAMDQoUN1joQxxhhjvdXQ0ICYmJhO60Pi3jSKouD06dOIioqCECJo262vr8fQoUPx559/9tt73vT3PnL/Ql9/7yP3L/T19z72Zf+ICA0NDUhNTdXcRLe9kDgzIkkS0tLS+mz70dHR/fIF5q+/95H7F/r6ex+5f6Gvv/exr/rX1RkRH76AlTHGGGO64sEIY4wxxnQ1oAcjZrMZq1atgtls1juUPtPf+8j9C339vY/cv9DX3/t4OfQvJC5gZYwxxlj/NaDPjDDGGGNMfzwYYYwxxpiueDDCGGOMMV3xYIQxxhhjuhrQg5E33ngDw4YNQ1hYGLKzs/HTTz/pHdIFKSoqwoQJExAVFYXExETMmjUL5eXlmja33XYbhBCaafHixTpF3DsvvPBCh9hHjx6t1re0tKCgoACDBw9GZGQk7r77btTW1uoYce8NGzasQx+FECgoKAAQevnbt28f7rjjDqSmpkIIgeLiYk09EWHlypVISUmBxWJBbm4uTpw4oWlz/vx55OfnIzo6GrGxsVi4cCEaGxsvYS8611X/nE4nli1bhszMTERERCA1NRVz587F6dOnNdsIlPM1a9Zc4p50rrsczp8/v0P806ZN07QJ1RwCCPh+FEJg3bp1apvLOYc9OS70ZN9ZXV2NGTNmIDw8HImJiXjmmWfgcrmCHu+AHYx8+umnKCwsxKpVq3DkyBGMGzcOU6dOxZkzZ/QOrdf27t2LgoIC/PjjjygpKYHT6cSUKVPQ1NSkaffQQw+hpqZGndauXatTxL137bXXamL//vvv1bonn3wSX3zxBTZv3oy9e/fi9OnTmD17to7R9t7Bgwc1/SspKQEA3HPPPWqbUMpfU1MTxo0bhzfeeCNg/dq1a/Hqq6/izTffxIEDBxAREYGpU6eipaVFbZOfn4/ffvsNJSUl2L59O/bt24dFixZdqi50qav+2e12HDlyBCtWrMCRI0ewZcsWlJeX48477+zQ9sUXX9Tk9LHHHrsU4fdIdzkEgGnTpmni//jjjzX1oZpDAJp+1dTU4N1334UQAnfffbem3eWaw54cF7rbd7rdbsyYMQMOhwP79+/H+++/j02bNmHlypXBD5gGqIkTJ1JBQYH62O12U2pqKhUVFekYVXCcOXOGANDevXvVsltvvZWWLl2qX1AXYdWqVTRu3LiAdTabjYxGI23evFkt++OPPwgAlZaWXqIIg2/p0qU0cuRIUhSFiEI7fwBo69at6mNFUSg5OZnWrVunltlsNjKbzfTxxx8TEdHvv/9OAOjgwYNqm6+//pqEEPT3339fsth7on3/Avnpp58IAJ06dUoty8jIoA0bNvRtcEESqI/z5s2jmTNndrpOf8vhzJkz6fbbb9eUhVIO2x8XerLv/Oqrr0iSJLJarWqbjRs3UnR0NLW2tgY1vgF5ZsThcODw4cPIzc1VyyRJQm5uLkpLS3WMLDjq6uoAAHFxcZryDz/8EPHx8RgzZgyWL18Ou92uR3gX5MSJE0hNTcWIESOQn5+P6upqAMDhw4fhdDo1uRw9ejTS09NDNpcOhwMffPABHnjgAc2NIUM5f/6qqqpgtVo1OYuJiUF2draas9LSUsTGxuKGG25Q2+Tm5kKSJBw4cOCSx3yx6urqIIRAbGyspnzNmjUYPHgwrrvuOqxbt65PTn/3pT179iAxMRFXXXUVlixZgnPnzql1/SmHtbW1+PLLL7Fw4cIOdaGSw/bHhZ7sO0tLS5GZmYmkpCS1zdSpU1FfX4/ffvstqPGFxI3ygu3s2bNwu92aPzAAJCUl4fjx4zpFFRyKouCJJ57AzTffjDFjxqjl999/PzIyMpCamopjx45h2bJlKC8vx5YtW3SMtmeys7OxadMmXHXVVaipqcHq1atxyy234Ndff4XVaoXJZOqwk09KSoLVatUn4ItUXFwMm82G+fPnq2WhnL/2fHkJ9P7z1VmtViQmJmrqDQYD4uLiQi6vLS0tWLZsGebMmaO5Cdnjjz+O66+/HnFxcdi/fz+WL1+OmpoarF+/Xsdoe27atGmYPXs2hg8fjsrKSjz33HOYPn06SktLIctyv8rh+++/j6ioqA5f/4ZKDgMdF3qy77RarQHfp766YBqQg5H+rKCgAL/++qvmmgoAmu9pMzMzkZKSgsmTJ6OyshIjR4681GH2yvTp09XlsWPHIjs7GxkZGfjss89gsVh0jKxvvPPOO5g+fTpSU1PVslDO30DmdDpx7733goiwceNGTV1hYaG6PHbsWJhMJjz88MMoKioKiZ8dv++++9TlzMxMjB07FiNHjsSePXswefJkHSMLvnfffRf5+fkICwvTlIdKDjs7LlxOBuTXNPHx8ZBlucNVw7W1tUhOTtYpqov36KOPYvv27di9ezfS0tK6bJudnQ0AqKiouBShBVVsbCyuvPJKVFRUIDk5GQ6HAzabTdMmVHN56tQp7NixAw8++GCX7UI5f768dPX+S05O7nAxucvlwvnz50Mmr76ByKlTp1BSUtLtrdmzs7Phcrlw8uTJSxNgkI0YMQLx8fHqa7I/5BAAvvvuO5SXl3f7ngQuzxx2dlzoyb4zOTk54PvUVxdMA3IwYjKZkJWVhZ07d6pliqJg586dyMnJ0TGyC0NEePTRR7F161bs2rULw4cP73adsrIyAEBKSkofRxd8jY2NqKysREpKCrKysmA0GjW5LC8vR3V1dUjm8r333kNiYiJmzJjRZbtQzt/w4cORnJysyVl9fT0OHDig5iwnJwc2mw2HDx9W2+zatQuKoqgDscuZbyBy4sQJ7NixA4MHD+52nbKyMkiS1OGrjVDx119/4dy5c+prMtRz6PPOO+8gKysL48aN67bt5ZTD7o4LPdl35uTk4JdfftEMKn0D62uuuSboAQ9In3zyCZnNZtq0aRP9/vvvtGjRIoqNjdVcNRwqlixZQjExMbRnzx6qqalRJ7vdTkREFRUV9OKLL9KhQ4eoqqqKtm3bRiNGjKBJkybpHHnPPPXUU7Rnzx6qqqqiH374gXJzcyk+Pp7OnDlDRESLFy+m9PR02rVrFx06dIhycnIoJydH56h7z+12U3p6Oi1btkxTHor5a2hooKNHj9LRo0cJAK1fv56OHj2q/jfJmjVrKDY2lrZt20bHjh2jmTNn0vDhw6m5uVndxrRp0+i6666jAwcO0Pfff0+jRo2iOXPm6NUlja7653A46M4776S0tDQqKyvTvCd9/4Gwf/9+2rBhA5WVlVFlZSV98MEHlJCQQHPnztW5Z2266mNDQwM9/fTTVFpaSlVVVbRjxw66/vrradSoUdTS0qJuI1Rz6FNXV0fh4eG0cePGDutf7jns7rhA1P2+0+Vy0ZgxY2jKlClUVlZG33zzDSUkJNDy5cuDHu+AHYwQEb322muUnp5OJpOJJk6cSD/++KPeIV0QAAGn9957j4iIqquradKkSRQXF0dms5muuOIKeuaZZ6iurk7fwHsoLy+PUlJSyGQy0ZAhQygvL48qKirU+ubmZnrkkUdo0KBBFB4eTnfddRfV1NToGPGF+fbbbwkAlZeXa8pDMX+7d+8O+JqcN28eEXn+vXfFihWUlJREZrOZJk+e3KHf586dozlz5lBkZCRFR0fTggULqKGhQYfedNRV/6qqqjp9T+7evZuIiA4fPkzZ2dkUExNDYWFhdPXVV9NLL72kOZDrras+2u12mjJlCiUkJJDRaKSMjAx66KGHOnyYC9Uc+rz11ltksVjIZrN1WP9yz2F3xwWinu07T548SdOnTyeLxULx8fH01FNPkdPpDHq8whs0Y4wxxpguBuQ1I4wxxhi7fPBghDHGGGO64sEIY4wxxnTFgxHGGGOM6YoHI4wxxhjTFQ9GGGOMMaYrHowwxhhjTFc8GGGMMcaYrngwwhhjjDFd8WCEMcYYY7riwQhjjDHGdMWDEcYYY4zp6v8BjBUmkqCWHBwAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "\n", - "\n", - "funman_request = get_request()\n", - "model = get_model(\"sidarthe_observables\")\n", - "setup_common(funman_request, debug=False)\n", - "results = run(funman_request, model=models[\"sidarthe_observables\"])\n", - "# report(results, \"sidarthe_observables\", states[\"sidarthe_observables\"])" + "report(results, \"sidarthe_observables\", states[\"sidarthe_observables\"]+[\"TotalInfected\"])\n", + "# model = get_model(\"sidarthe_observables\")\n", + "# model\n", + "# model[0]._state_var_names()\n", + "# df = results.dataframe(results.points())\n", + "# df[states[\"sidarthe_observables\"]]\n", + "# df" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "2.3781998884724245" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.points()[0].values[\"R0\"]" + ] } ], "metadata": { diff --git a/src/funman/model/model.py b/src/funman/model/model.py index 78874b09..900d0e81 100644 --- a/src/funman/model/model.py +++ b/src/funman/model/model.py @@ -58,6 +58,14 @@ def is_state_variable( pattern = re.compile(f"^(?:{vars_pattern}).*_{time_pattern}") return re.match(pattern, var_string) is not None +def is_observable( + var_string, model: "FunmanModel", time_pattern: str = f"[\\d]+$" +) -> bool: + vars_pattern = "|".join(model._observable_names()) + pattern = re.compile(f"^(?:{vars_pattern}).*") + return re.match(pattern, var_string) is not None + + class FunmanModel(ABC, BaseModel): """ @@ -227,6 +235,9 @@ def _parameter_names(self) -> List[str]: def _state_var_names(self) -> List[str]: return [] + def _observable_names(self) -> List[str]: + return [] + def _parameter_names(self): return [] diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index 4eb61185..80e2907b 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -4,6 +4,7 @@ import sympy from pydantic import BaseModel, ConfigDict from pysmt.shortcuts import REAL, Div, Real, Symbol +from pysmt.formula import FNode from funman.utils.sympy_utils import ( replace_reserved, @@ -13,7 +14,7 @@ ) from ..representation import Interval -from .generated_models.petrinet import Distribution +from .generated_models.petrinet import Distribution, Observable from .generated_models.petrinet import Model as GeneratedPetrinet from .generated_models.petrinet import State, Transition from .model import FunmanModel @@ -200,7 +201,7 @@ def derivative( if get_lambda: pos_rates = [ self._transition_rate(trans, get_lambda=get_lambda)[0]( - *values, *params + *values, *params, t ) for trans in self._transitions() for var in trans.output @@ -208,7 +209,7 @@ def derivative( ] neg_rates = [ self._transition_rate(trans, get_lambda=get_lambda)[0]( - *values, *params + *values, *params, t ) for trans in self._transitions() for var in trans.input @@ -240,7 +241,7 @@ def gradient(self, t, y, *p): var_to_value = { var: y[i] for i, var in enumerate(self._state_var_names()) } - print(f"y: {y}; t: {t}") + # print(f"y: {y}; t: {t}") param_to_value = { replace_reserved(param): p[i](t)[()] for i, param in enumerate(self._parameter_names()) @@ -262,12 +263,12 @@ def gradient(self, t, y, *p): params, var_to_value, param_to_value, - get_lambda=False, + get_lambda=True, ) # var_to_value, param_to_value) for var in self._state_var_names() ] - print(f"vars: {self._state_var_names()}") - print(f"gradient: {grad}") + # print(f"vars: {self._state_var_names()}") + # print(f"gradient: {grad}") return grad @@ -276,14 +277,14 @@ class GeneratedPetriNetModel(AbstractPetriNetModel): petrinet: GeneratedPetrinet _transition_rates_cache: Dict[str, Union[sympy.Expr, str]] = {} - _observables_cache: Dict[str, Union[sympy.Expr, str]] = {} + _observables_cache: Dict[str, Union[str, FNode, sympy.Expr]] = {} _transition_rates_lambda_cache: Dict[str, Union[Callable, str]] = {} def observables(self): return self.petrinet.semantics.ode.observables def is_timed_observable(self, observation_id): - (_, e) = self.observable_expression(observation_id) + (_, e, _) = self.observable_expression(observation_id) vars = [str(e) for e in e.get_free_variables()] obs_state_vars = [v for v in vars if v in self._state_var_names()] return any(obs_state_vars) @@ -293,11 +294,13 @@ def observable_expression(self, observation_id): observable = next( iter([o for o in self.observables() if o.id == observation_id]) ) + sympy_expr = to_sympy(observable.expression, self._symbols()) self._observables_cache[observation_id] = ( observable.expression, sympy_to_pysmt( - to_sympy(observable.expression, self._symbols()) + sympy_expr ), + sympy_expr ) return self._observables_cache[observation_id] @@ -400,12 +403,18 @@ def _state_vars(self) -> List[State]: def _state_var_names(self) -> List[str]: return [self._state_var_name(s) for s in self._state_vars()] + + def _observable_names(self) -> List[str]: + return [self._observable_name(s) for s in self.observables()] def _transitions(self) -> List[Transition]: return self.petrinet.model.transitions def _state_var_name(self, state_var: State) -> str: return state_var.id + + def _observable_name(self, observable: Observable) -> str: + return observable.id def _input_edges(self): return [(i, t.id) for t in self._transitions() for i in t.input] @@ -449,7 +458,7 @@ def _transition_rate(self, transition, sympify=False, get_lambda=False): # convert "t" to "timer_t" unreserved_symbols[-1] = self._time_var_id(self._time_var()) t_rates_lambda = [ - sympy.lambdify(unreserved_symbols, t) for t in t_rates + sympy.lambdify(unreserved_symbols, t, cse=True) for t in t_rates ] self._transition_rates_cache[transition.id] = t_rates self._transition_rates_lambda_cache[transition.id] = ( diff --git a/src/funman/representation/representation.py b/src/funman/representation/representation.py index 6cfa2998..4cd3428f 100644 --- a/src/funman/representation/representation.py +++ b/src/funman/representation/representation.py @@ -5,7 +5,7 @@ import logging import math -from typing import Dict, List, Literal, Optional, Set +from typing import Dict, List, Literal, Optional, Set, Union import matplotlib.pyplot as plt import pandas as pd @@ -24,7 +24,7 @@ class Timeseries(BaseModel): - data: List[List[float]] + data: List[Union[float, List[float]]] columns: List[str] def __getitem__(self, key): diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index 49f13bd7..4aae3480 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -46,7 +46,8 @@ from funman.search.simulate import Simulator, Timeseries from funman.translate.translate import EncodingOptions from funman.utils import math_utils -from funman.utils.sympy_utils import to_sympy +from funman.utils.sympy_utils import replace_reserved, to_sympy +import sympy from ..representation import Point @@ -394,7 +395,31 @@ def simulation_tvects(self, config) -> List[Union[float, int]]: tvects.append(np.arange(0, int(max_steps * ss) + 1, int(ss))) return tvects - + + + def compute_observables(self, timeseries, parameters): + observables = self.model.observables() + timepoints = timeseries.data[0] + data = {} + unreseved_parameters = {replace_reserved(k): v for k,v in parameters.items() } + for o in observables: + o_name = self.model._observable_name(o) + # o_fn = o.expression + o_fn = self.model.observable_expression(o_name) + # Evaluate o_fn for each time in timeseries + if self.model.is_timed_observable(o_name): + values = [] + for ti, t in enumerate(timepoints): + # state_at_t = [timeseries.data[ci][ti] for ci, c in enumerate(timeseries.columns)] + state_at_t = {c: timeseries.data[ci][ti] for ci, c in enumerate(timeseries.columns) if c != "time"} + value = o_fn[2].evalf(subs={**state_at_t, **parameters}) + values.append(float(value)) + data[o_name] = values + else: + value = o_fn[2].evalf(subs={**unreseved_parameters}) + data[o_name] = float(value) + return data + def simulate_scenario(self, config: "FUNMANConfig") -> Point: init = { @@ -418,11 +443,24 @@ def simulate_scenario(self, config: "FUNMANConfig") -> Point: timepoints = schedule.timepoints timeseries = self.run_scenario_simulation(init, parameters, timepoints) + + observable_timeseries = self.compute_observables(timeseries, parameters) + for k,v in observable_timeseries.items(): + timeseries.data.append(v) + timeseries.columns.append(k) + values = { **{ - f"{var}_{int(timepoint)}": timeseries.data[var_idx][timestep] + f"{var}_{int(timepoint)}": timeseries.data[var_idx+1][timestep] + for var_idx, var in enumerate(timeseries.columns[1:]) + for timestep, timepoint in enumerate(timeseries.data[0]) + if isinstance(timeseries.data[var_idx+1], list) + }, + **{ + var: timeseries.data[var_idx+1] for var_idx, var in enumerate(timeseries.columns[1:]) for timestep, timepoint in enumerate(timeseries.data[0]) + if not isinstance(timeseries.data[var_idx+1], list) }, **parameters, **{"timestep": len(timepoints) - 1}, diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 7d916bcb..01efd41c 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -15,7 +15,7 @@ from funman.model.encoded import EncodedModel from funman.model.ensemble import EnsembleModel from funman.model.generated_models.petrinet import Model as GeneratedPetriNet -from funman.model.model import is_state_variable +from funman.model.model import is_observable, is_state_variable from funman.model.petrinet import GeneratedPetriNetModel, PetrinetModel from funman.model.query import QueryAnd, QueryFunction, QueryLE, QueryTrue from funman.model.regnet import GeneratedRegnetModel, RegnetModel @@ -379,7 +379,7 @@ def dataframe( fails if scenario is not consistent """ scenario = self._scenario() - to_plot = scenario.model._state_var_names() + to_plot = scenario.model._state_var_names() + scenario.model._observable_names() time_var = scenario.model._time_var() if time_var: to_plot += ["timer_t"] @@ -441,11 +441,15 @@ def symbol_timeseries( 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() and int(t) <= int(max_t): - vals[int(t)] = v - a_series[var] = vals + + if isinstance(tps, dict): + vals = [None] * (int(max_t) + 1) + for t, v in tps.items(): + if t.isdigit() and int(t) <= int(max_t): + vals[int(t)] = v + a_series[var] = vals + else: + a_series[var] = [tps]*(int(max_t) + 1) return a_series def symbol_values( @@ -471,12 +475,15 @@ def symbol_values( vals = {} for var in vars: vals[var] = {} - for t in vars[var]: - try: - value = point.values[vars[var][t]] - vals[var][t] = float(value) - except OverflowError as e: - l.warning(e) + if isinstance(vars[var], dict): + for t in vars[var]: + try: + value = point.values[vars[var][t]] + vals[var][t] = float(value) + except OverflowError as e: + l.warning(e) + else: + vals[var] = point.values[vars[var]] return vals def _symbols( @@ -484,16 +491,25 @@ def _symbols( ) -> Dict[str, Dict[str, str]]: symbols = {} for var in point.values: - if is_state_variable(var, self.model): + if is_state_variable(var, self.model) or is_observable(var, self.model): var_name, timepoint = self._split_symbol(var) if timepoint: if var_name not in symbols: symbols[var_name] = {} symbols[var_name][timepoint] = var + elif timepoint is None: + # Could be an observable with not time index + if var_name not in symbols: + symbols[var_name] = {} + symbols[var_name] = var return symbols def _split_symbol(self, symbol: str) -> Tuple[str, str]: - s, t = symbol.rsplit("_", 1) + try: + s, t = symbol.rsplit("_", 1) + except ValueError: + s = symbol + t = None return s, t def plot_trajectories(self, variable: str, num: int = 200): From c5f41a003ccd1858ec7840eb8f672c0c28eb49a7 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Wed, 18 Sep 2024 15:18:30 +0000 Subject: [PATCH 41/93] format results file --- src/funman/server/storage.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/funman/server/storage.py b/src/funman/server/storage.py index b96ccfd8..a8b0e00d 100644 --- a/src/funman/server/storage.py +++ b/src/funman/server/storage.py @@ -63,7 +63,7 @@ def add_result(self, result: FunmanResults): # raise FunmanException(f"Id {id} was already set to a value.") self.results[id] = result with open(self.path / f"{id}.json", "w") as f: - f.write(result.model_dump_json(by_alias=True)) + f.write(result.model_dump_json(indent=4,by_alias=True)) def get_result(self, id: str) -> FunmanResults: with self.lock: From 0693e6430abd457d4005cf4ba8dcb0d01a185b98 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Wed, 18 Sep 2024 15:18:54 +0000 Subject: [PATCH 42/93] format --- src/funman/api/run.py | 4 ++-- src/funman/model/model.py | 2 +- src/funman/model/petrinet.py | 19 ++++++++--------- src/funman/scenario/scenario.py | 37 ++++++++++++++++++++------------- src/funman/server/query.py | 13 ++++++++---- src/funman/server/storage.py | 2 +- 6 files changed, 45 insertions(+), 32 deletions(-) diff --git a/src/funman/api/run.py b/src/funman/api/run.py index bfa520f6..4d03a2c6 100644 --- a/src/funman/api/run.py +++ b/src/funman/api/run.py @@ -486,7 +486,7 @@ def get_args(): "--verbose", help="Write result to console", default=False, - action="store_true" + action="store_true", ) parser.set_defaults(plot=None) @@ -512,7 +512,7 @@ def main() -> int: print_last_time=args.last_time, ) if args.verbose: - print(results.model_dump_json(indent=4,by_alias=True)) + print(results.model_dump_json(indent=4, by_alias=True)) if __name__ == "__main__": diff --git a/src/funman/model/model.py b/src/funman/model/model.py index 900d0e81..01d5d9ec 100644 --- a/src/funman/model/model.py +++ b/src/funman/model/model.py @@ -58,6 +58,7 @@ def is_state_variable( pattern = re.compile(f"^(?:{vars_pattern}).*_{time_pattern}") return re.match(pattern, var_string) is not None + def is_observable( var_string, model: "FunmanModel", time_pattern: str = f"[\\d]+$" ) -> bool: @@ -66,7 +67,6 @@ def is_observable( return re.match(pattern, var_string) is not None - class FunmanModel(ABC, BaseModel): """ The abstract base class for Models. diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index 80e2907b..7d4315a3 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -3,8 +3,8 @@ import graphviz import sympy from pydantic import BaseModel, ConfigDict -from pysmt.shortcuts import REAL, Div, Real, Symbol from pysmt.formula import FNode +from pysmt.shortcuts import REAL, Div, Real, Symbol from funman.utils.sympy_utils import ( replace_reserved, @@ -14,9 +14,9 @@ ) from ..representation import Interval -from .generated_models.petrinet import Distribution, Observable +from .generated_models.petrinet import Distribution from .generated_models.petrinet import Model as GeneratedPetrinet -from .generated_models.petrinet import State, Transition +from .generated_models.petrinet import Observable, State, Transition from .model import FunmanModel @@ -297,10 +297,8 @@ def observable_expression(self, observation_id): sympy_expr = to_sympy(observable.expression, self._symbols()) self._observables_cache[observation_id] = ( observable.expression, - sympy_to_pysmt( - sympy_expr - ), - sympy_expr + sympy_to_pysmt(sympy_expr), + sympy_expr, ) return self._observables_cache[observation_id] @@ -403,7 +401,7 @@ def _state_vars(self) -> List[State]: def _state_var_names(self) -> List[str]: return [self._state_var_name(s) for s in self._state_vars()] - + def _observable_names(self) -> List[str]: return [self._observable_name(s) for s in self.observables()] @@ -412,7 +410,7 @@ def _transitions(self) -> List[Transition]: def _state_var_name(self, state_var: State) -> str: return state_var.id - + def _observable_name(self, observable: Observable) -> str: return observable.id @@ -458,7 +456,8 @@ def _transition_rate(self, transition, sympify=False, get_lambda=False): # convert "t" to "timer_t" unreserved_symbols[-1] = self._time_var_id(self._time_var()) t_rates_lambda = [ - sympy.lambdify(unreserved_symbols, t, cse=True) for t in t_rates + sympy.lambdify(unreserved_symbols, t, cse=True) + for t in t_rates ] self._transition_rates_cache[transition.id] = t_rates self._transition_rates_lambda_cache[transition.id] = ( diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py index 4aae3480..52c63560 100644 --- a/src/funman/scenario/scenario.py +++ b/src/funman/scenario/scenario.py @@ -5,6 +5,7 @@ from typing import Dict, List, Optional, Union import numpy as np +import sympy from pydantic import BaseModel, ConfigDict from pysmt.shortcuts import TRUE, And, Solver @@ -47,7 +48,6 @@ from funman.translate.translate import EncodingOptions from funman.utils import math_utils from funman.utils.sympy_utils import replace_reserved, to_sympy -import sympy from ..representation import Point @@ -395,13 +395,14 @@ def simulation_tvects(self, config) -> List[Union[float, int]]: tvects.append(np.arange(0, int(max_steps * ss) + 1, int(ss))) return tvects - - + def compute_observables(self, timeseries, parameters): observables = self.model.observables() timepoints = timeseries.data[0] data = {} - unreseved_parameters = {replace_reserved(k): v for k,v in parameters.items() } + unreseved_parameters = { + replace_reserved(k): v for k, v in parameters.items() + } for o in observables: o_name = self.model._observable_name(o) # o_fn = o.expression @@ -411,7 +412,11 @@ def compute_observables(self, timeseries, parameters): values = [] for ti, t in enumerate(timepoints): # state_at_t = [timeseries.data[ci][ti] for ci, c in enumerate(timeseries.columns)] - state_at_t = {c: timeseries.data[ci][ti] for ci, c in enumerate(timeseries.columns) if c != "time"} + state_at_t = { + c: timeseries.data[ci][ti] + for ci, c in enumerate(timeseries.columns) + if c != "time" + } value = o_fn[2].evalf(subs={**state_at_t, **parameters}) values.append(float(value)) data[o_name] = values @@ -419,7 +424,7 @@ def compute_observables(self, timeseries, parameters): value = o_fn[2].evalf(subs={**unreseved_parameters}) data[o_name] = float(value) return data - + def simulate_scenario(self, config: "FUNMANConfig") -> Point: init = { @@ -443,24 +448,28 @@ def simulate_scenario(self, config: "FUNMANConfig") -> Point: timepoints = schedule.timepoints timeseries = self.run_scenario_simulation(init, parameters, timepoints) - - observable_timeseries = self.compute_observables(timeseries, parameters) - for k,v in observable_timeseries.items(): + + observable_timeseries = self.compute_observables( + timeseries, parameters + ) + for k, v in observable_timeseries.items(): timeseries.data.append(v) timeseries.columns.append(k) - + values = { **{ - f"{var}_{int(timepoint)}": timeseries.data[var_idx+1][timestep] + f"{var}_{int(timepoint)}": timeseries.data[var_idx + 1][ + timestep + ] for var_idx, var in enumerate(timeseries.columns[1:]) for timestep, timepoint in enumerate(timeseries.data[0]) - if isinstance(timeseries.data[var_idx+1], list) + if isinstance(timeseries.data[var_idx + 1], list) }, **{ - var: timeseries.data[var_idx+1] + var: timeseries.data[var_idx + 1] for var_idx, var in enumerate(timeseries.columns[1:]) for timestep, timepoint in enumerate(timeseries.data[0]) - if not isinstance(timeseries.data[var_idx+1], list) + if not isinstance(timeseries.data[var_idx + 1], list) }, **parameters, **{"timestep": len(timepoints) - 1}, diff --git a/src/funman/server/query.py b/src/funman/server/query.py index 01efd41c..fcde87d8 100644 --- a/src/funman/server/query.py +++ b/src/funman/server/query.py @@ -379,7 +379,10 @@ def dataframe( fails if scenario is not consistent """ scenario = self._scenario() - to_plot = scenario.model._state_var_names() + scenario.model._observable_names() + to_plot = ( + scenario.model._state_var_names() + + scenario.model._observable_names() + ) time_var = scenario.model._time_var() if time_var: to_plot += ["timer_t"] @@ -441,7 +444,7 @@ def symbol_timeseries( a_series["index"] = list(range(0, max_t + 1)) for var, tps in series.items(): - + if isinstance(tps, dict): vals = [None] * (int(max_t) + 1) for t, v in tps.items(): @@ -449,7 +452,7 @@ def symbol_timeseries( vals[int(t)] = v a_series[var] = vals else: - a_series[var] = [tps]*(int(max_t) + 1) + a_series[var] = [tps] * (int(max_t) + 1) return a_series def symbol_values( @@ -491,7 +494,9 @@ def _symbols( ) -> Dict[str, Dict[str, str]]: symbols = {} for var in point.values: - if is_state_variable(var, self.model) or is_observable(var, self.model): + if is_state_variable(var, self.model) or is_observable( + var, self.model + ): var_name, timepoint = self._split_symbol(var) if timepoint: if var_name not in symbols: diff --git a/src/funman/server/storage.py b/src/funman/server/storage.py index a8b0e00d..15a31616 100644 --- a/src/funman/server/storage.py +++ b/src/funman/server/storage.py @@ -63,7 +63,7 @@ def add_result(self, result: FunmanResults): # raise FunmanException(f"Id {id} was already set to a value.") self.results[id] = result with open(self.path / f"{id}.json", "w") as f: - f.write(result.model_dump_json(indent=4,by_alias=True)) + f.write(result.model_dump_json(indent=4, by_alias=True)) def get_result(self, id: str) -> FunmanResults: with self.lock: From 34c129d43182af70e8d12432a5afc17883da7af7 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Wed, 18 Sep 2024 16:02:33 +0000 Subject: [PATCH 43/93] Oct models --- .../monthly-demo/2024-10/sirhd-vac.json | 639 ++++++++++++++++++ .../petrinet/monthly-demo/2024-10/sirhd.json | 317 +++++++++ 2 files changed, 956 insertions(+) create mode 100644 resources/amr/petrinet/monthly-demo/2024-10/sirhd-vac.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-10/sirhd.json diff --git a/resources/amr/petrinet/monthly-demo/2024-10/sirhd-vac.json b/resources/amr/petrinet/monthly-demo/2024-10/sirhd-vac.json new file mode 100644 index 00000000..53b32467 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-10/sirhd-vac.json @@ -0,0 +1,639 @@ +{ + "header": { + "name": "Model", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Model", + "model_version": "0.1" + }, + "properties": {}, + "model": { + "states": [ + { + "id": "S_u", + "name": "S_u", + "grounding": { + "identifiers": {}, + "modifiers": { + "vax": "u" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_u", + "name": "I_u", + "grounding": { + "identifiers": {}, + "modifiers": { + "vax": "u" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I_v", + "name": "I_v", + "grounding": { + "identifiers": {}, + "modifiers": { + "vax": "v" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "S_v", + "name": "S_v", + "grounding": { + "identifiers": {}, + "modifiers": { + "vax": "v" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_u", + "name": "R_u", + "grounding": { + "identifiers": {}, + "modifiers": { + "vax": "u" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_v", + "name": "R_v", + "grounding": { + "identifiers": {}, + "modifiers": { + "vax": "v" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_u", + "name": "H_u", + "grounding": { + "identifiers": {}, + "modifiers": { + "vax": "u" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_v", + "name": "H_v", + "grounding": { + "identifiers": {}, + "modifiers": { + "vax": "v" + } + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D", + "name": "D", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1", + "input": [ + "I_u", + "S_u" + ], + "output": [ + "I_u", + "I_u" + ], + "properties": { + "name": "t1" + } + }, + { + "id": "t2", + "input": [ + "I_v", + "S_u" + ], + "output": [ + "I_v", + "I_u" + ], + "properties": { + "name": "t2" + } + }, + { + "id": "t3", + "input": [ + "I_v", + "S_v" + ], + "output": [ + "I_v", + "I_v" + ], + "properties": { + "name": "t3" + } + }, + { + "id": "t4", + "input": [ + "I_u", + "S_v" + ], + "output": [ + "I_u", + "I_v" + ], + "properties": { + "name": "t4" + } + }, + { + "id": "t5", + "input": [ + "I_u" + ], + "output": [ + "R_u" + ], + "properties": { + "name": "t5" + } + }, + { + "id": "t6", + "input": [ + "I_v" + ], + "output": [ + "R_v" + ], + "properties": { + "name": "t6" + } + }, + { + "id": "t7", + "input": [ + "I_u" + ], + "output": [ + "H_u" + ], + "properties": { + "name": "t7" + } + }, + { + "id": "t8", + "input": [ + "I_v" + ], + "output": [ + "H_v" + ], + "properties": { + "name": "t8" + } + }, + { + "id": "t9", + "input": [ + "H_u" + ], + "output": [ + "D" + ], + "properties": { + "name": "t9" + } + }, + { + "id": "t10", + "input": [ + "H_v" + ], + "output": [ + "D" + ], + "properties": { + "name": "t10" + } + }, + { + "id": "t11", + "input": [ + "H_u" + ], + "output": [ + "R_u" + ], + "properties": { + "name": "t11" + } + }, + { + "id": "t12", + "input": [ + "H_v" + ], + "output": [ + "R_v" + ], + "properties": { + "name": "t12" + } + }, + { + "id": "t13", + "input": [ + "S_u" + ], + "output": [ + "S_v" + ], + "properties": { + "name": "t13" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1", + "expression": "I_u*S_u*beta_0/N", + "expression_mathml": "I_uS_ubeta_0N" + }, + { + "target": "t2", + "expression": "I_v*S_u*beta_1/N", + "expression_mathml": "I_vS_ubeta_1N" + }, + { + "target": "t3", + "expression": "I_v*S_v*beta_2/N", + "expression_mathml": "I_vS_vbeta_2N" + }, + { + "target": "t4", + "expression": "I_u*S_v*beta_3/N", + "expression_mathml": "I_uS_vbeta_3N" + }, + { + "target": "t5", + "expression": "I_u*pir_0*rir", + "expression_mathml": "I_upir_0rir" + }, + { + "target": "t6", + "expression": "I_v*pir_1*rir", + "expression_mathml": "I_vpir_1rir" + }, + { + "target": "t7", + "expression": "I_u*pih_0*rih_0", + "expression_mathml": "I_upih_0rih_0" + }, + { + "target": "t8", + "expression": "I_v*pih_1*rih_1", + "expression_mathml": "I_vpih_1rih_1" + }, + { + "target": "t9", + "expression": "H_u*phd_0*rhd_0", + "expression_mathml": "H_uphd_0rhd_0" + }, + { + "target": "t10", + "expression": "H_v*phd_1*rhd_1", + "expression_mathml": "H_vphd_1rhd_1" + }, + { + "target": "t11", + "expression": "H_u*phr_0*rhr_0", + "expression_mathml": "H_uphr_0rhr_0" + }, + { + "target": "t12", + "expression": "H_v*phr_1*rhr_1", + "expression_mathml": "H_vphr_1rhr_1" + }, + { + "target": "t13", + "expression": "S_u*v_a*v_b", + "expression_mathml": "S_uv_av_b" + } + ], + "initials": [ + { + "target": "S_u", + "expression": "-D0/2 - H0/2 - I0/2 + N/2 - R0/2", + "expression_mathml": "D02H02I02N2R02" + }, + { + "target": "I_u", + "expression": "I0/2", + "expression_mathml": "I02" + }, + { + "target": "I_v", + "expression": "I0/2", + "expression_mathml": "I02" + }, + { + "target": "S_v", + "expression": "-D0/2 - H0/2 - I0/2 + N/2 - R0/2", + "expression_mathml": "D02H02I02N2R02" + }, + { + "target": "R_u", + "expression": "R0/2", + "expression_mathml": "R02" + }, + { + "target": "R_v", + "expression": "R0/2", + "expression_mathml": "R02" + }, + { + "target": "H_u", + "expression": "H0/2", + "expression_mathml": "H02" + }, + { + "target": "H_v", + "expression": "H0/2", + "expression_mathml": "H02" + }, + { + "target": "D", + "expression": "D0", + "expression_mathml": "D0" + } + ], + "parameters": [ + { + "id": "N", + "value": 150000000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta_0", + "value": 0.18, + "units": { + "expression": "person/day", + "expression_mathml": "personday" + } + }, + { + "id": "beta_1", + "value": 0.18, + "units": { + "expression": "person/day", + "expression_mathml": "personday" + } + }, + { + "id": "beta_2", + "value": 0.18, + "units": { + "expression": "person/day", + "expression_mathml": "personday" + } + }, + { + "id": "beta_3", + "value": 0.18, + "units": { + "expression": "person/day", + "expression_mathml": "personday" + } + }, + { + "id": "pir_0", + "value": 0.9, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "pir_1", + "value": 0.9, + "units": { + 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"expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhr_0", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "phr_1", + "value": 0.87, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhr_1", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "v_a", + "value": 0.3, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "v_b", + "value": 1.0, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhi", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "I0", + "value": 1000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R0", + "value": 0.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H0", + "value": 0.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D0", + "value": 781454.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "observables": [], + "time": { + "id": "t" + } + } + }, + "metadata": { + "annotations": {} + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-10/sirhd.json b/resources/amr/petrinet/monthly-demo/2024-10/sirhd.json new file mode 100644 index 00000000..1a16b971 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-10/sirhd.json @@ -0,0 +1,317 @@ +{ + "header": { + "name": "Model", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Model", + "model_version": "0.1" + }, + "properties": {}, + "model": { + "states": [ + { + "id": "S", + "name": "S", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I", + "name": "I", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R", + "name": "R", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H", + "name": "H", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D", + "name": "D", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1", + "input": [ + "I", + "S" + ], + "output": [ + "I", + "I" + ], + "properties": { + "name": "t1" + } + }, + { + "id": "t2", + "input": [ + "I" + ], + "output": [ + "R" + ], + "properties": { + "name": "t2" + } + }, + { + "id": "t3", + "input": [ + "I" + ], + "output": [ + "H" + ], + "properties": { + "name": "t3" + } + }, + { + "id": "t4", + "input": [ + "H" + ], + "output": [ + "D" + ], + "properties": { + "name": "t4" + } + }, + { + "id": "t5", + "input": [ + "H" + ], + "output": [ + "R" + ], + "properties": { + "name": "t5" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1", + "expression": "I*S*beta/N", + "expression_mathml": "ISbetaN" + }, + { + "target": "t2", + "expression": "I*pir*rir", + "expression_mathml": "Ipirrir" + }, + { + "target": "t3", + "expression": "I*pih*rih", + "expression_mathml": "Ipihrih" + }, + { + "target": "t4", + "expression": "H*phd*rhd", + "expression_mathml": "Hphdrhd" + }, + { + "target": "t5", + "expression": "H*phr*rhr", + "expression_mathml": "Hphrrhr" + } + ], + "initials": [ + { + "target": "S", + "expression": "-D0 - H0 - I0 + N - R0", + "expression_mathml": "D0H0I0NR0" + }, + { + "target": "I", + "expression": "I0", + "expression_mathml": "I0" + }, + { + "target": "R", + "expression": "R0", + "expression_mathml": "R0" + }, + { + "target": "H", + "expression": "H0", + "expression_mathml": "H0" + }, + { + "target": "D", + "expression": "D0", + "expression_mathml": "D0" + } + ], + "parameters": [ + { + "id": "N", + "value": 150000000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta", + "value": 0.18, + "units": { + "expression": "person/day", + "expression_mathml": "personday" + } + }, + { + "id": "pir", + "value": 0.9, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "pih", + "value": 0.1, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rih", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "phd", + "value": 0.13, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhd", + "value": 0.3, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "phr", + "value": 0.87, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhr", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "rhi", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "I0", + "value": 1000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R0", + "value": 0.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H0", + "value": 0.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D0", + "value": 781454.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "observables": [], + "time": { + "id": "t" + } + } + }, + "metadata": { + "annotations": {} + } + } \ No newline at end of file From 1cc915fe99590ae02a96c4b9551782bfc3df3079 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Wed, 18 Sep 2024 17:05:35 +0000 Subject: [PATCH 44/93] testing bug fixes --- .../funman_sep_2024_observables.ipynb | 25 ++++++++++--------- src/funman/model/petrinet.py | 16 ++++++------ 2 files changed, 22 insertions(+), 19 deletions(-) diff --git a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb index 9b03f2f5..2121d5b4 100644 --- a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb +++ b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -81,12 +81,13 @@ "\n", "MAX_TIME=200\n", "STEP_SIZE=1\n", - "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))" + "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))\n", + "model_str = \"sidarthe_observables\"" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -317,20 +318,20 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "funman_request = get_request()\n", - "model = get_model(\"sidarthe_observables\")\n", + "model = get_model(model_str)\n", "setup_common(funman_request, debug=False, mode=MODE_ODEINT)\n", - "results = run(funman_request, model=models[\"sidarthe_observables\"])\n", + "results = run(funman_request, model=models[model_str])\n", "# report(results, \"sidarthe_observables\", states[\"sidarthe_observables\"]+[\"TotalInfected\", \"R0\"])" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -350,7 +351,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -360,7 +361,7 @@ } ], "source": [ - "report(results, \"sidarthe_observables\", states[\"sidarthe_observables\"]+[\"TotalInfected\"])\n", + "report(results, model_str, states[model_str]+[\"TotalInfected\"])\n", "# model = get_model(\"sidarthe_observables\")\n", "# model\n", "# model[0]._state_var_names()\n", @@ -371,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -380,7 +381,7 @@ "2.3781998884724245" ] }, - "execution_count": 6, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index 7d4315a3..8863689b 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -201,7 +201,7 @@ def derivative( if get_lambda: pos_rates = [ self._transition_rate(trans, get_lambda=get_lambda)[0]( - *values, *params, t + *values, *params ) for trans in self._transitions() for var in trans.output @@ -209,7 +209,7 @@ def derivative( ] neg_rates = [ self._transition_rate(trans, get_lambda=get_lambda)[0]( - *values, *params, t + *values, *params ) for trans in self._transitions() for var in trans.input @@ -218,7 +218,7 @@ def derivative( else: pos_rates = [ self._transition_rate(trans)[0].evalf( - subs={**var_to_value, **param_to_value, "timer_t": t}, n=10 + subs={**var_to_value, **param_to_value}, n=10 ) for trans in self._transitions() for var in trans.output @@ -226,7 +226,7 @@ def derivative( ] neg_rates = [ self._transition_rate(trans)[0].evalf( - subs={**var_to_value, **param_to_value, "timer_t": t}, n=10 + subs={**var_to_value, **param_to_value}, n=10 ) for trans in self._transitions() for var in trans.input @@ -246,14 +246,16 @@ def gradient(self, t, y, *p): replace_reserved(param): p[i](t)[()] for i, param in enumerate(self._parameter_names()) } + param_to_value["timer_t"] = t # values = [ # y[i] for i, _ in enumerate(self._symbols()) # ] + unreserved_symols = [replace_reserved(s) for s in self._symbols()] params = [ param_to_value[str(p)] - for p in self._symbols() + for p in unreserved_symols if str(p) in param_to_value - ] + [t] + ] grad = [ self.derivative( @@ -339,7 +341,7 @@ def _time_var_id(self, time_var): def _symbols(self): symbols = self._state_var_names() + self._parameter_names() if self._time_var(): - symbols += [self._time_var().id] + symbols += [f"timer_{self._time_var().id}"] return symbols def _get_init_value( From af6ebc74c26272c3fd2acff75940c1cc71b568ea Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Wed, 18 Sep 2024 19:03:30 +0000 Subject: [PATCH 45/93] debug models --- .../funman_sep_2024_observables.ipynb | 60 ++-- .../{2024-10 => 2024-09}/sirhd-vac.json | 52 +-- .../petrinet/monthly-demo/2024-09/sirhd.json | 285 ++++++++++++++++ .../petrinet/monthly-demo/2024-10/sirhd.json | 317 ------------------ src/funman/model/petrinet.py | 1 + 5 files changed, 331 insertions(+), 384 deletions(-) rename resources/amr/petrinet/monthly-demo/{2024-10 => 2024-09}/sirhd-vac.json (92%) create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd.json delete mode 100644 resources/amr/petrinet/monthly-demo/2024-10/sirhd.json diff --git a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb index 2121d5b4..3e7c140b 100644 --- a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb +++ b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -31,11 +31,17 @@ "\n", "models = {\n", " \"sidarthe_observables\": os.path.join(\n", - " EXAMPLE_DIR, \"SIDARTHE.model.with.observables.json\")\n", + " EXAMPLE_DIR, \"SIDARTHE.model.with.observables.json\"),\n", + " \"sirhd\": os.path.join(\n", + " EXAMPLE_DIR, \"sirhd.json\"),\n", + " \"sirhd-vac\": os.path.join(\n", + " EXAMPLE_DIR, \"sirhd-vac.json\"),\n", "}\n", "\n", "states = {\n", - " \"sidarthe_observables\": ['Susceptible', 'Diagnosed', 'Infected', 'Ailing', 'Recognized', 'Healed', 'Threatened', 'Extinct']\n", + " \"sidarthe_observables\": ['Susceptible', 'Diagnosed', 'Infected', 'Ailing', 'Recognized', 'Healed', 'Threatened', 'Extinct'],\n", + " \"sirhd\": [\"S\", \"I\", \"R\", \"H\", \"D\"],\n", + " \"sirhd-vac\": [\"S_v\", \"I_v\", \"R_v\", \"H_v\", \"S_u\", \"I_u\", \"R_u\", \"H_u\", \"D\"],\n", "# \"original_stratified\": [\"S_compliant\", \"S_noncompliant\", \"I_compliant\", \"I_noncompliant\", \"E_compliant\", \"E_noncompliant\",\"R\", \"H\", \"D\"],\n", "# \"destratified_SEI\": [\"S_lb\", \"S_ub\", \"I_lb\", \"I_ub\", \"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", "# \"destratified_SE\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", @@ -73,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -82,12 +88,13 @@ "MAX_TIME=200\n", "STEP_SIZE=1\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))\n", - "model_str = \"sidarthe_observables\"" + "# model_str = \"sidarthe_observables\"\n", + "model_str = \"sirhd-vac\"" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -318,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -331,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -339,19 +346,21 @@ "output_type": "stream", "text": [ "1 points\n", - " alpha beta delta epsilon eta gamma \\\n", - "sidarthe_observables 0.5643 0.01089 0.01089 0.16929 0.12375 0.45144 \n", + " N beta_0 beta_1 beta_2 beta_3 phd_0 phd_1 phr_0 \\\n", + "sirhd-vac 150000000.0 0.18 0.18 0.18 0.18 0.13 0.13 0.87 \n", "\n", - " kappa lambda mu nu rho sigma \\\n", - "sidarthe_observables 0.01683 0.03366 0.01683 0.02673 0.03366 0.01683 \n", + " phr_1 pih_0 ... pir_1 rhd_0 rhd_1 rhr_0 rhr_1 rih_0 rih_1 \\\n", + "sirhd-vac 0.87 0.1 ... 0.9 0.3 0.3 0.07 0.07 0.07 0.07 \n", "\n", - " tau theta xi zeta \n", - "sidarthe_observables 0.0099 0.36729 0.01683 0.12375 \n" + " rir v_a v_b \n", + "sirhd-vac 0.07 0.3 1.0 \n", + "\n", + "[1 rows x 22 columns]\n" ] }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -361,7 +370,7 @@ } ], "source": [ - "report(results, model_str, states[model_str]+[\"TotalInfected\"])\n", + "report(results, model_str, states[model_str])\n", "# model = get_model(\"sidarthe_observables\")\n", "# model\n", "# model[0]._state_var_names()\n", @@ -372,18 +381,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "2.3781998884724245" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "ename": "KeyError", + "evalue": "'R0'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpoints\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mR0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n", + "\u001b[0;31mKeyError\u001b[0m: 'R0'" + ] } ], "source": [ diff --git a/resources/amr/petrinet/monthly-demo/2024-10/sirhd-vac.json b/resources/amr/petrinet/monthly-demo/2024-09/sirhd-vac.json similarity index 92% rename from resources/amr/petrinet/monthly-demo/2024-10/sirhd-vac.json rename to resources/amr/petrinet/monthly-demo/2024-09/sirhd-vac.json index 53b32467..3f6e8cf4 100644 --- a/resources/amr/petrinet/monthly-demo/2024-10/sirhd-vac.json +++ b/resources/amr/petrinet/monthly-demo/2024-09/sirhd-vac.json @@ -373,47 +373,47 @@ "initials": [ { "target": "S_u", - "expression": "-D0/2 - H0/2 - I0/2 + N/2 - R0/2", + "expression": "74608773.0", "expression_mathml": "D02H02I02N2R02" }, { "target": "I_u", - "expression": "I0/2", + "expression": "500", "expression_mathml": "I02" }, { "target": "I_v", - "expression": "I0/2", + "expression": "500", "expression_mathml": "I02" }, { "target": "S_v", - "expression": "-D0/2 - H0/2 - I0/2 + N/2 - R0/2", + "expression": "74608773.0", "expression_mathml": "D02H02I02N2R02" }, { "target": "R_u", - "expression": "R0/2", + "expression": "0.0", "expression_mathml": "R02" }, { "target": "R_v", - "expression": "R0/2", + "expression": "0.0", "expression_mathml": "R02" }, { "target": "H_u", - "expression": "H0/2", + "expression": "0.0", "expression_mathml": "H02" }, { "target": "H_v", - "expression": "H0/2", + "expression": "0.0", "expression_mathml": "H02" }, { "target": "D", - "expression": "D0", + "expression": "781454.0", "expression_mathml": "D0" } ], @@ -587,44 +587,12 @@ } }, { - "id": "rhi", + "id": "rir", "value": 0.07, "units": { "expression": "1/day", "expression_mathml": "day-1" } - }, - { - "id": "I0", - "value": 1000.0, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "R0", - "value": 0.0, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "H0", - "value": 0.0, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "D0", - "value": 781454.0, - "units": { - "expression": "person", - "expression_mathml": "person" - } } ], "observables": [], diff --git a/resources/amr/petrinet/monthly-demo/2024-09/sirhd.json b/resources/amr/petrinet/monthly-demo/2024-09/sirhd.json new file mode 100644 index 00000000..1ecc2aea --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-09/sirhd.json @@ -0,0 +1,285 @@ +{ + "header": { + "name": "Model", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Model", + "model_version": "0.1" + }, + "properties": {}, + "model": { + "states": [ + { + "id": "S", + "name": "S", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I", + "name": "I", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R", + "name": "R", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H", + "name": "H", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D", + "name": "D", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1", + "input": [ + "I", + "S" + ], + "output": [ + "I", + "I" + ], + "properties": { + "name": "t1" + } + }, + { + "id": "t2", + "input": [ + "I" + ], + "output": [ + "R" + ], + "properties": { + "name": "t2" + } + }, + { + "id": "t3", + "input": [ + "I" + ], + "output": [ + "H" + ], + "properties": { + "name": "t3" + } + }, + { + "id": "t4", + "input": [ + "H" + ], + "output": [ + "D" + ], + "properties": { + "name": "t4" + } + }, + { + "id": "t5", + "input": [ + "H" + ], + "output": [ + "R" + ], + "properties": { + "name": "t5" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1", + "expression": "I*S*beta/N", + "expression_mathml": "ISbetaN" + }, + { + "target": "t2", + "expression": "I*pir*rir", + "expression_mathml": "Ipirrir" + }, + { + "target": "t3", + "expression": "I*pih*rih", + "expression_mathml": "Ipihrih" + }, + { + "target": "t4", + "expression": "H*phd*rhd", + "expression_mathml": "Hphdrhd" + }, + { + "target": "t5", + "expression": "H*phr*rhr", + "expression_mathml": "Hphrrhr" + } + ], + "initials": [ + { + "target": "S", + "expression": "149217546.0", + "expression_mathml": "D0H0I0NR0" + }, + { + "target": "I", + "expression": "1000.0", + "expression_mathml": "I0" + }, + { + "target": "R", + "expression": "0.0", + "expression_mathml": "R0" + }, + { + "target": "H", + "expression": "0.0", + "expression_mathml": "H0" + }, + { + "target": "D", + "expression": "781454.0", + "expression_mathml": "D0" + } + ], + "parameters": [ + { + "id": "N", + "value": 150000000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta", + "value": 0.18, + "units": { + "expression": "person/day", + "expression_mathml": "personday" + } + }, + { + "id": "pir", + "value": 0.9, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "pih", + "value": 0.1, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rih", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "phd", + "value": 0.13, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhd", + "value": 0.3, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "phr", + "value": 0.87, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhr", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "rir", + "value": 0.07, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + } + ], + "observables": [], + "time": { + "id": "t" + } + } + }, + "metadata": { + "annotations": {} + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-10/sirhd.json b/resources/amr/petrinet/monthly-demo/2024-10/sirhd.json deleted file mode 100644 index 1a16b971..00000000 --- a/resources/amr/petrinet/monthly-demo/2024-10/sirhd.json +++ /dev/null @@ -1,317 +0,0 @@ -{ - "header": { - "name": "Model", - "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json", - "schema_name": "petrinet", - "description": "Model", - "model_version": "0.1" - }, - "properties": {}, - "model": { - "states": [ - { - "id": "S", - "name": "S", - "grounding": { - "identifiers": {}, - "modifiers": {} - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "I", - "name": "I", - "grounding": { - "identifiers": {}, - "modifiers": {} - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "R", - "name": "R", - "grounding": { - "identifiers": {}, - "modifiers": {} - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "H", - "name": "H", - "grounding": { - "identifiers": {}, - "modifiers": {} - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "D", - "name": "D", - "grounding": { - "identifiers": {}, - "modifiers": {} - }, - "units": { - "expression": "person", - "expression_mathml": "person" - } - } - ], - "transitions": [ - { - "id": "t1", - "input": [ - "I", - "S" - ], - "output": [ - "I", - "I" - ], - "properties": { - "name": "t1" - } - }, - { - "id": "t2", - "input": [ - "I" - ], - "output": [ - "R" - ], - "properties": { - "name": "t2" - } - }, - { - "id": "t3", - "input": [ - "I" - ], - "output": [ - "H" - ], - "properties": { - "name": "t3" - } - }, - { - "id": "t4", - "input": [ - "H" - ], - "output": [ - "D" - ], - "properties": { - "name": "t4" - } - }, - { - "id": "t5", - "input": [ - "H" - ], - "output": [ - "R" - ], - "properties": { - "name": "t5" - } - } - ] - }, - "semantics": { - "ode": { - "rates": [ - { - "target": "t1", - "expression": "I*S*beta/N", - "expression_mathml": "ISbetaN" - }, - { - "target": "t2", - "expression": "I*pir*rir", - "expression_mathml": "Ipirrir" - }, - { - "target": "t3", - "expression": "I*pih*rih", - "expression_mathml": "Ipihrih" - }, - { - "target": "t4", - "expression": "H*phd*rhd", - "expression_mathml": "Hphdrhd" - }, - { - "target": "t5", - "expression": "H*phr*rhr", - "expression_mathml": "Hphrrhr" - } - ], - "initials": [ - { - "target": "S", - "expression": "-D0 - H0 - I0 + N - R0", - "expression_mathml": "D0H0I0NR0" - }, - { - "target": "I", - "expression": "I0", - "expression_mathml": "I0" - }, - { - "target": "R", - "expression": "R0", - "expression_mathml": "R0" - }, - { - "target": "H", - "expression": "H0", - "expression_mathml": "H0" - }, - { - "target": "D", - "expression": "D0", - "expression_mathml": "D0" - } - ], - "parameters": [ - { - "id": "N", - "value": 150000000.0, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "beta", - "value": 0.18, - "units": { - "expression": "person/day", - "expression_mathml": "personday" - } - }, - { - "id": "pir", - "value": 0.9, - "units": { - "expression": "1", - "expression_mathml": "1" - } - }, - { - "id": "pih", - "value": 0.1, - "units": { - "expression": "1", - "expression_mathml": "1" - } - }, - { - "id": "rih", - "value": 0.07, - "units": { - "expression": "1/day", - "expression_mathml": "day-1" - } - }, - { - "id": "phd", - "value": 0.13, - "units": { - "expression": "1", - "expression_mathml": "1" - } - }, - { - "id": "rhd", - "value": 0.3, - "units": { - "expression": "1/day", - "expression_mathml": "day-1" - } - }, - { - "id": "phr", - "value": 0.87, - "units": { - "expression": "1", - "expression_mathml": "1" - } - }, - { - "id": "rhr", - "value": 0.07, - "units": { - "expression": "1/day", - "expression_mathml": "day-1" - } - }, - { - "id": "rhi", - "value": 0.07, - "units": { - "expression": "1/day", - "expression_mathml": "day-1" - } - }, - { - "id": "I0", - "value": 1000.0, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "R0", - "value": 0.0, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "H0", - "value": 0.0, - "units": { - "expression": "person", - "expression_mathml": "person" - } - }, - { - "id": "D0", - "value": 781454.0, - "units": { - "expression": "person", - "expression_mathml": "person" - } - } - ], - "observables": [], - "time": { - "id": "t" - } - } - }, - "metadata": { - "annotations": {} - } - } \ No newline at end of file diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py index 8863689b..23a5f9a2 100644 --- a/src/funman/model/petrinet.py +++ b/src/funman/model/petrinet.py @@ -271,6 +271,7 @@ def gradient(self, t, y, *p): ] # print(f"vars: {self._state_var_names()}") # print(f"gradient: {grad}") + assert not any([not isinstance(v, float) for v in grad]), f"Gradient has a non-float element: {grad}" return grad From 8bcd26cfc3302cf567ad30c5e51f41719dd37127 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Thu, 19 Sep 2024 16:02:28 +0000 Subject: [PATCH 46/93] modify demo notebook --- .../funman_sep_2024_observables.ipynb | 76 +++++-------------- 1 file changed, 20 insertions(+), 56 deletions(-) diff --git a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb index 3e7c140b..5e883588 100644 --- a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb +++ b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -88,13 +88,13 @@ "MAX_TIME=200\n", "STEP_SIZE=1\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))\n", - "# model_str = \"sidarthe_observables\"\n", - "model_str = \"sirhd-vac\"" + "model_str = \"sidarthe_observables\"\n", + "# model_str = \"sirhd-vac\"" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -325,20 +325,7 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "funman_request = get_request()\n", - "model = get_model(model_str)\n", - "setup_common(funman_request, debug=False, mode=MODE_ODEINT)\n", - "results = run(funman_request, model=models[model_str])\n", - "# report(results, \"sidarthe_observables\", states[\"sidarthe_observables\"]+[\"TotalInfected\", \"R0\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -346,21 +333,19 @@ "output_type": "stream", "text": [ "1 points\n", - " N beta_0 beta_1 beta_2 beta_3 phd_0 phd_1 phr_0 \\\n", - "sirhd-vac 150000000.0 0.18 0.18 0.18 0.18 0.13 0.13 0.87 \n", + " alpha beta delta epsilon eta gamma \\\n", + "sidarthe_observables 0.5643 0.01089 0.01089 0.16929 0.12375 0.45144 \n", "\n", - " phr_1 pih_0 ... pir_1 rhd_0 rhd_1 rhr_0 rhr_1 rih_0 rih_1 \\\n", - "sirhd-vac 0.87 0.1 ... 0.9 0.3 0.3 0.07 0.07 0.07 0.07 \n", + " kappa lambda mu nu rho sigma \\\n", + "sidarthe_observables 0.01683 0.03366 0.01683 0.02673 0.03366 0.01683 \n", "\n", - " rir v_a v_b \n", - "sirhd-vac 0.07 0.3 1.0 \n", - "\n", - "[1 rows x 22 columns]\n" + " tau theta xi zeta \n", + "sidarthe_observables 0.0099 0.36729 0.01683 0.12375 \n" ] }, { "data": { - "image/png": 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", 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3Hz/++COPPvooH374IQDdu3dn165dxMXF0aZNm1qDt7c3rVu3Rq/X19p/UVERe/fubVA5PUHCiBBCiGbFYrGQnZ3N4cOH2bp1Ky+99BJXX301V1xxBbfffnud9RMSEsjIyOCbb74hJSWFN998k59++qnWOv/85z+ZPXs2n376Kfv27eOFF15g+/btJ7ztczwJCQlcffXV3H333axZs4akpCRuvfVWWrRowdVXXw3AQw89xG+//UZqaipbt25l+fLldOjQAXA1bi0sLOSmm25i06ZNpKSk8Ntvv3HHHXfgcDjw8fHhrrvu4rHHHmPZsmXs3LmT8ePHo9Gc+5d6uU0jhBCiWVm0aBGRkZHodDoCAwPp0qULb775JuPGjTvuhfmqq67i4YcfZtKkSVgsFkaPHs1//vMfnnnmGfc6t9xyCwcOHGDKlClUV1dz/fXXM378eDZu3Nigsn3yySfuPk+sViuDBg1i4cKF7ts7DoeDiRMncujQIfz8/Bg5cqT7qZ6oqCjWrl3LE088wWWXXYbFYqFly5aMHDnSfV6vvfaa+3aOr68vjz76KCUlJaf5TZ49iqo2wjNTTay0tBR/f39KSkpqNSgSQgjR+Kqrq0lNTSU+Ph6TyeTp4pyzLr30UiIiIvj88889XRSPOtnPS32v31IzIoQQQpxCZWUl77//PiNGjECr1fL111+zZMkSFi9e7OmiNQsSRoQQQohTUBSFhQsX8uKLL1JdXU27du344YcfGD58uKeL1ixIGBFCCCFOwWw2s2TJEk8Xo9k695vYCiGEEKJZkzAihBBCCI+SMCKEEEIIj5IwIoQQQgiPkjAihBBCCI+SMCKEEEIIj5IwIoQQ4oKVnZ3NpZdeire3NwEBAZ4uTh1xcXHMnDnT08VochJGhBBCNBvjx49nzJgx9V7/9ddfJysri23btjXa220vlADRmKTTMyGEEBeslJQUevToQUJCgqeLckGTmhEhhBDN0pAhQ5g8eTKPP/44QUFBRERE1HoTb1xcHD/88AOfffYZiqIwfvx4AIqLi5kwYQKhoaH4+flxySWXkJSUVGvf//d//0evXr0wmUyEhIRwzTXXuI+Znp7Oww8/jKIoKIri3mbNmjUMHDgQs9lMTEwMkydPpqKiwr08NzeXK6+8ErPZTHx8PF9++WXTfTnnGAkjQgghTklVVSptlR4ZzuTl8p9++ine3t5s2LCBV199leeee879crtNmzYxcuRIrr/+erKysnjjjTcAuO6668jNzeXXX39ly5YtdO/enWHDhlFYWAjAggULuOaaa7j88sv5888/Wbp0Kb179wbgxx9/JDo6mueee46srCyysrIAVw3MyJEjGTt2LNu3b2fu3LmsWbOGSZMmucs6fvx4Dh48yPLly/n+++959913yc3NPe1zP5/IbRohhBCnVGWvos9XfTxy7A03b8BL73Va2yYmJvLf//4XgISEBN5++22WLl3KpZdeSmhoKEajEbPZTEREBOCqvdi4cSO5ubkYjUYApk+fzrx58/j++++55557ePHFF7nxxht59tln3cfp0qULAEFBQWi1Wnx9fd37BJg2bRq33HILDz30kLssb775JoMHD+a9994jIyODX3/9lY0bN9KrVy8AZs+eTYcOHU7rvM83EkaEEEI0W4mJibU+R0ZGnrS2ISkpifLycoKDg2vNr6qqIiUlBYBt27Zx9913N6gcSUlJbN++vdatF1VVcTqdpKamsnfvXnQ6HT169HAvb9++/Tn5hE9TkDAihBDilMw6Mxtu3uCxY58uvV5f67OiKDidzhOuX15eTmRkJCtWrKiz7EgwMJsbXp7y8nLuvfdeJk+eXGdZbGxsoz3Jc76SMCKEEOKUFEU57Vsl55Pu3buTnZ2NTqcjLi7uuOskJiaydOlS7rjjjuMuNxgMOByOOvv966+/aNOmzXG3ad++PXa7nS1btrhv0yQnJ1NcXHza53I+kQasQgghRI3hw4fTt29fxowZw++//05aWhrr1q3jX//6F5s3bwbgv//9L19//TX//e9/2b17Nzt27OCVV15x7yMuLo5Vq1Zx+PBh8vPzAXjiiSdYt24dkyZNYtu2bezbt4+ff/7Z3YC1Xbt2jBw5knvvvZcNGzawZcsWJkyYcFq1MOcjCSNCCCFEDUVRWLhwIYMGDeKOO+6gbdu23HjjjaSnpxMeHg64Ht/97rvvmD9/Pl27duWSSy5h48aN7n0899xzpKWl0bp1a0JDQwFXbcrKlSvZu3cvAwcOpFu3bkydOpWoqCj3dp988glRUVEMHjyYa6+9lnvuuYewsLCz+wV4iKKeyTNTZ0lpaSn+/v6UlJTg5+fn6eIIIUSzVl1dTWpqKvHx8ZhMJk8XR5zjTvbzUt/rd4NqRqZNm0avXr3w9fUlLCyMMWPGkJycfMrtvvvuO9q3b4/JZKJz584sXLiwIYcVQgghRDPWoDCycuVKJk6cyB9//MHixYux2WxcdtlltXqQ+7t169Zx0003cdddd/Hnn38yZswYxowZw86dO8+48EIIIYQ4/53RbZq8vDzCwsJYuXIlgwYNOu46N9xwAxUVFfzyyy/ueRdffDFdu3bl/fffr9dx5DaNEEKcPXKbRjTEWb9N83clJSWAq8e5E1m/fj3Dhw+vNW/EiBGsX7/+hNtYLBZKS0trDUIIIYRonk47jDidTh566CH69+/PRRdddML1srOz3S2QjwgPDyc7O/uE20ybNg1/f3/3EBMTc7rFFEIIIcQ57rTDyMSJE9m5cyfffPNNY5YHgKeeeoqSkhL3cPDgwUY/hhBCCCHODafVA+ukSZP45ZdfWLVqFdHR0SddNyIigpycnFrzcnJyar1A6O+MRqP7BUVCCCGEaN4aVDOiqiqTJk3ip59+YtmyZcTHx59ym759+7J06dJa8xYvXkzfvn0bVlIhhBBCNEsNqhmZOHEiX331FT///DO+vr7udh/+/v7uLmtvv/12WrRowbRp0wB48MEHGTx4MDNmzGD06NF88803bN68mQ8++KCRT0UIIYQQ56MG1Yy89957lJSUMGTIECIjI93D3Llz3etkZGSQlZXl/tyvXz+++uorPvjgA7p06cL333/PvHnzTtroVQghhGgKK1asQFEU9wvo5syZ434bL8AzzzxD165dPVK2C1mDakbq0yXJ8V67fN1113Hdddc15FBCCCHEaVu/fj0DBgxg5MiRLFiwwD2/X79+ZGVl4e/vf9ztpkyZwj//+c+zVUxRQ16UJ4QQotmZPXs2//znP1m1ahWZmZnu+QaDgYiICBRFOe52Pj4+BAcHn61iihoSRoQQQjQr5eXlzJ07l/vvv5/Ro0czZ84c97K/36b5u7/fphk/fjxjxoxh+vTpREZGEhwczMSJE7HZbO51srKyGD16NGazmfj4eL766ivi4uKYOXNm05xgM3Raj/YKIYS4sKiqilpV5ZFjK2bzCWsyjufbb7+lffv2tGvXjltvvZWHHnqIp556qkH7ONby5cuJjIxk+fLl7N+/nxtuuIGuXbty9913A64HN/Lz81mxYgV6vZ5HHnmE3Nzc0zrWhUrCiBBCiFNSq6pI7t7DI8dut3ULipdXvdefPXs2t956KwAjR46kpKSElStXMmTIkNM6fmBgIG+//TZarZb27dszevRoli5dyt13382ePXtYsmQJmzZtomfPngB89NFHJCQknNaxLlRym0YIIUSzkZyczMaNG7npppsA0Ol03HDDDcyePfu099mpUye0Wq37c2RkpLvmIzk5GZ1OR/fu3d3L27RpQ2Bg4Gkf70IkNSNCCCFOSTGbabd1i8eOXV+zZ8/GbrcTFRXlnqeqKkajkbfffvu0jq/X62uXR1FwOp2ntS9xfBJGhBBCnJKiKA26VeIJdrudzz77jBkzZnDZZZfVWjZmzBi+/vpr2rdv36jHbNeuHXa7nT///JMePVy3sfbv309RUVGjHqe5kzAihBCiWfjll18oKirirrvuqtOPyNixY5k9ezavvfZaox6zffv2DB8+nHvuuYf33nsPvV7Po48+irmBjW4vdNJmRAghRLMwe/Zshg8fftwOzcaOHcvmzZvZvn17ox/3s88+Izw8nEGDBnHNNddw99134+vri8lkavRjNVeKWp9uVT2stLQUf39/SkpK8PPz83RxhBCiWauuriY1NZX4+Hi5oJ6GQ4cOERMTw5IlSxg2bJini9PkTvbzUt/rt9ymEUIIIc7AsmXLKC8vp3PnzmRlZfH4448TFxfHoEGDPF2084aEESGEEOIM2Gw2nn76aQ4cOICvry/9+vXjyy+/rPMUjjgxCSNCCCHEGRgxYgQjRozwdDHOa9KAVQghhBAeJWFECCGEEB4lYUQIIYQQHiVhRAghhBAeJWFECCGEEB4lYUQIIYQQHiVhRAghhDjHPPPMM3Tt2rVJjzFkyBAeeuihJj1GfUkYEUII0WyMHz/e9YZhRUGv1xMfH8/jjz9OdXW1p4vWIFOmTGHp0qWeLsZZI52eCSGEaFZGjhzJJ598gs1mY8uWLYwbNw5FUXjllVc8XbR68/HxwcfHx9PFOGukZkQIIUSzYjQaiYiIICYmhjFjxjB8+HAWL14MgNPpZNq0acTHx2M2m+nSpQvff/99re137drFFVdcgZ+fH76+vgwcOJCUlBT39s899xzR0dEYjUa6du3KokWLam2/bt06unbtislkomfPnsybNw9FUdi2bRsAK1asQFEUli5dSs+ePfHy8qJfv34kJye79/H32zRHanuOHeLi4tzLd+7cyahRo/Dx8SE8PJzbbruN/Px89/KKigpuv/12fHx8iIyMZMaMGY3xVTcaCSNCCCFOSVVVbBaHR4Yzebn8zp07WbduHQaDAYBp06bx2Wef8f7777Nr1y4efvhhbr31VlauXAnA4cOHGTRoEEajkWXLlrFlyxbuvPNO7HY7AG+88QYzZsxg+vTpbN++nREjRnDVVVexb98+wPWW2iuvvJLOnTuzdetWnn/+eZ544onjlu1f//oXM2bMYPPmzeh0Ou68884TnkdWVpZ72L9/P23atHG/iK+4uJhLLrmEbt26sXnzZhYtWkROTg7XX3+9e/vHHnuMlStX8vPPP/P777+zYsUKtm7detrfa2OT2zRCCCFOyW518sGDKz1y7HveGIzeqK33+r/88gs+Pj7Y7XYsFgsajYa3334bi8XCSy+9xJIlS+jbty8ArVq1Ys2aNcyaNYvBgwfzzjvv4O/vzzfffON+0V3btm3d+54+fTpPPPEEN954IwCvvPIKy5cvZ+bMmbzzzjt89dVXKIrChx9+iMlkomPHjhw+fJi77767TjlffPFFBg8eDMCTTz7J6NGjqa6uxmQy1Vk3IiICcIXCsWPH4u/vz6xZswB4++236datGy+99JJ7/Y8//piYmBj27t1LVFQUs2fP5osvvmDYsGEAfPrpp0RHR9f7O21qEkaEEEI0K0OHDuW9996joqKC119/HZ1Ox9ixY9m1axeVlZVceumltda3Wq1069YNgG3btjFw4MDjvnG3tLSUzMxM+vfvX2t+//79SUpKAiA5OZnExMRagaJ3797HLWdiYqJ7OjIyEoDc3FxiY2NPeG5PP/0069evZ/PmzZjNZgCSkpJYvnz5cduYpKSkUFVVhdVqpU+fPu75QUFBtGvX7oTHOdskjAghhDglnUHDPW8M9tixG8Lb25s2bdoArhqCLl26MHv2bC666CIAFixYQIsWLWptYzQaAdwX+LPh2MCjKArgapNyIl988QWvv/46K1asqFX+8vJyrrzyyuM20I2MjGT//v2NWOqmIWFECCHEKSmK0qBbJecKjUbD008/zSOPPMLevXsxGo1kZGS4b4/8XWJiIp9++ik2m61O7Yifnx9RUVGsXbu21vZr16511360a9eOL774AovF4g44mzZtOuPzWL9+PRMmTGDWrFlcfPHFtZZ1796dH374gbi4OHS6upf11q1bo9fr2bBhg7vWpaioiL17957wezjbpAGrEEKIZu26665Dq9Uya9YspkyZwsMPP8ynn35KSkoKW7du5a233uLTTz8FYNKkSZSWlnLjjTeyefNm9u3bx+eff+5+0uWxxx7jlVdeYe7cuSQnJ/Pkk0+ybds2HnzwQQBuvvlmnE4n99xzD7t37+a3335j+vTpwNHaj4bKzs7mmmuu4cYbb2TEiBFkZ2eTnZ1NXl4eABMnTqSwsJCbbrqJTZs2kZKSwm+//cYdd9yBw+HAx8eHu+66i8cee4xly5axc+dOxo8fj0Zz7kQAqRkRQgjRrOl0OiZNmsSrr75KamoqoaGhTJs2jQMHDhAQEED37t15+umnAQgODmbZsmU89thjDB48GK1WS9euXd3tRCZPnkxJSQmPPvooubm5dOzYkfnz55OQkAC4ak/+7//+j/vvv5+uXbvSuXNnpk6dys0333zchqn1sWfPHnJycvj000/doQmgZcuWpKWluWtrnnjiCS677DIsFgstW7Zk5MiR7sDx2muvuW/n+Pr68uijj1JSUnImX2ujUtQzeWbqLCktLcXf35+SkhL8/Pw8XRwhhGjWqqurSU1NJT4+/rQvoOKoL7/8kjvuuIOSkpKz2iblbDnZz0t9r99SMyKEEEI0os8++4xWrVrRokULkpKSeOKJJ7j++uubZRBpLBJGhBBCiEaUnZ3N1KlTyc7OJjIykuuuu44XX3zR08U6p0kYEUIIIRrR448/zuOPP+7pYpxXzp2mtEIIIYS4IEkYEUIIIYRHSRgRQgghhEdJGBFCCCGER0kYEUIIIYRHSRgRQgghhEdJGBFCCCEaaMWKFSiKQnFx8RntJy4ujpkzZzZKmc5nEkaEEEI0G+PHj2fMmDF15jdWeBBNQ8KIEEIIITxKwogQQogLzpo1axg4cCBms5mYmBgmT55MRUWFe/nnn39Oz5498fX1JSIigptvvpnc3Nwz2mdubi5XXnklZrOZ+Ph4vvzyyyY7v/ONhBEhhBCnpKoqtupqjwyN/XL5lJQURo4cydixY9m+fTtz585lzZo1TJo0yb2OzWbj+eefJykpiXnz5pGWlsb48ePPaJ/jx4/n4MGDLF++nO+//5533333lAHnQqGojf2v3ATq+wpiIYQQZ+54r4S3VVfz5rh/eKQ8kz/9Hv3fXk1/IuPHj+eLL76o8yp7h8NBdXU1RUVFTJkyBa1Wy6xZs9zL16xZw+DBg6moqKizLcDmzZvp1asXZWVl+Pj4sGLFCoYOHUpRUREBAQFMmDDhpPvMyMigXbt2bNy4kV69egGwZ88eOnTowOuvv85DDz10Gt/MueF4Py9H1Pf6LS/KE0II0awMHTqU9957r9a8DRs2cOuttwKQlJTE9u3ba90mUVUVp9NJamoqHTp0YMuWLTzzzDMkJSVRVFSE0+kEICMjg44dO9Y55qn2uXfvXnQ6HT169HAvb9++PQEBAY156uctCSNCCCFOSWc0MvnT7z127Ibw9vamTZs2teYdOnTIPV1eXs69997L5MmT62wbGxtLRUUFI0aMYMSIEXz55ZeEhoaSkZHBiBEjsFqtxz3mqfa5d+/eBp3DhUbCiBBCiFNSFKXet0rOdd27d+evv/6qE1iO2LFjBwUFBbz88svExMQArts0Z7LP9u3bY7fb2bJli/s2TXJysjxqXEMasAohhLigPPHEE6xbt45Jkyaxbds29u3bx88//+xubBobG4vBYOCtt97iwIEDzJ8/n+eff/6M9tmuXTtGjhzJvffey4YNG9iyZQsTJkzAbDY3+fmeDySMCCGEuKAkJiaycuVK9u7dy8CBA+nWrRtTp04lKioKgNDQUObMmcN3331Hx44defnll5k+ffoZ7RPgk08+ISoqisGDB3Pttddyzz33EBYW1qTner6Qp2mEEELUcrKnI4T4u8Z4mkZqRoQQQgjhURJGhBBCCOFREkaEEEII4VESRoQQQgjhURJGhBBCCOFREkaEEEII4VESRoQQQgjhUQ0OI6tWreLKK68kKioKRVGYN2/eSddfsWIFiqLUGbKzs0+3zEIIIYRoRhocRioqKujSpQvvvPNOg7ZLTk4mKyvLPUivc0IIIYSA03hR3qhRoxg1alSDDxQWFiavShZCCOFRK1asYOjQoRQVFck1CRg/fjzFxcWnvMvR1M5am5GuXbsSGRnJpZdeytq1a8/WYYUQQlwgjtck4NjhmWee8XQRiYuLY+bMmZ4uxjmnwTUjDRUZGcn7779Pz549sVgsfPTRRwwZMoQNGzbQvXv3425jsViwWCzuz6WlpU1dTCGEEOe5rKws9/TcuXOZOnUqycnJ7nk+Pj5s3ry5wfu1Wq0YDIZGKaM4viavGWnXrh333nsvPXr0oF+/fnz88cf069eP119//YTbTJs2DX9/f/cQExPT1MUUQghxnouIiHAP/v7+KIpSa56Pj4973S1bttCzZ0+8vLzo169frdDyzDPP0LVrVz766KNaL38rLi5mwoQJhIaG4ufnxyWXXEJSUpJ7u5SUFK6++mrCw8Px8fGhV69eLFmyxL18yJAhpKen8/DDD7tra45Ys2YNAwcOxGw2ExMTw+TJk6moqHAvj4uL46WXXuLOO+/E19eX2NhYPvjgg1rnf/DgQa6//noCAgIICgri6quvJi0tzb3c4XDwyCOPEBAQQHBwMI8//jjnyrtyPfJob+/evdm/f/8Jlz/11FOUlJS4h4MHD57F0gkhhPg7VVVxWh0eGZrigvmvf/2LGTNmsHnzZnQ6HXfeeWet5fv37+eHH37gxx9/ZNu2bQBcd9115Obm8uuvv7Jlyxa6d+/OsGHDKCwsBKC8vJzLL7+cpUuX8ueffzJy5EiuvPJKMjIyAPjxxx+Jjo7mueeecz/MAa4QM3LkSMaOHcv27duZO3cua9asYdKkSbXKNGPGDHr27Mmff/7JAw88wP333+8OUTabjREjRuDr68vq1atZu3YtPj4+jBw5EqvV6t5+zpw5fPzxx6xZs4bCwkJ++umnRv9uT0eT36Y5nm3bthEZGXnC5UajEaPReBZLJIQQ4mRUm5PMqes8cuyo5/qhGLSNus8XX3yRwYMHA/Dkk08yevRoqqur3bUgVquVzz77jNDQUMBVc7Fx40Zyc3Pd16fp06czb948vv/+e+655x66dOlCly5d3Md4/vnn+emnn5g/fz6TJk0iKCgIrVaLr68vERER7vWmTZvGLbfcwkMPPQRAQkICb775JoMHD+a9995zl+nyyy/ngQceAOCJJ57g9ddfZ/ny5bRr1465c+fidDr56KOP3DUun3zyCQEBAaxYsYLLLruMmTNn8tRTT3HttdcC8P777/Pbb7816vd6uhocRsrLy2vVaqSmprJt2zaCgoKIjY3lqaee4vDhw3z22WcAzJw5k/j4eDp16kR1dTUfffQRy5Yt4/fff2+8sxBCCCEaIDEx0T195I/j3NxcYmNjAWjZsqU7iAAkJSVRXl5OcHBwrf1UVVWRkpICuK6PzzzzDAsWLCArKwu73U5VVZW7ZuREkpKS2L59O19++aV7nqqqOJ1OUlNT6dChQ50yH7kFlZub697H/v378fX1rbXv6upqUlJSKCkpISsriz59+riX6XQ6evbseU7cqmlwGNm8eTNDhw51f37kkUcAGDduHHPmzCErK6vWF2+1Wnn00Uc5fPgwXl5eJCYmsmTJklr7EEIIcW5T9BqinuvnsWM3Nr1ef3T/NTUJTqfTPc/b27vW+uXl5URGRrJixYo6+zryiPCUKVNYvHgx06dPp02bNpjNZv7xj3+4b5OcSHl5Offeey+TJ0+us+xIOPp7mY+U+0iZy8vL6dGjR61Ac8Sxoepc1eAwMmTIkJOmqDlz5tT6/Pjjj/P44483uGBCCCHOHYqiNPqtkvNJ9+7dyc7ORqfTERcXd9x11q5dy/jx47nmmmsAV0A4tgEpgMFgwOFw1Nn3X3/9RZs2bc6ofHPnziUsLAw/P7/jrhMZGcmGDRsYNGgQAHa73d32xdPk3TRCCCHEKQwfPpy+ffsyZswYfv/9d9LS0li3bh3/+te/3I8LJyQkuBu8JiUlcfPNN9eqbQHXUzGrVq3i8OHD5OfnA672H+vWrWPSpEls27aNffv28fPPP9dpwHoyt9xyCyEhIVx99dWsXr2a1NRUVqxYweTJkzl06BAADz74IC+//DLz5s1jz549PPDAAxQXFzfOF3SGJIwIIYQQp6AoCgsXLmTQoEHccccdtG3blhtvvJH09HTCw8MB+N///kdgYCD9+vXjyiuvZMSIEXVqHZ577jnS0tJo3bq1+/ZJYmIiK1euZO/evQwcOJBu3boxdepUoqKi6l0+Ly8vVq1aRWxsLNdeey0dOnTgrrvuorq62l1T8uijj3Lbbbcxbtw4+vbti6+vr7sWx9MU9VxouXIKpaWl+Pv7U1JScsLqJyGEEI2jurqa1NTUWn1sCHEiJ/t5qe/1W2pGhBBCCOFREkaEEEII4VESRoQQQgjhURJGhBBCCOFREkaEEEII4VESRoQQQgjhURJGhBBCCOFREkaEEEII4VESRoQQQgjhURJGhBBCiJNYsWIFiqKcM+9xaY4kjAghhGg2xo8f73rD8N+GkSNH1mv7IUOG8NBDD9Wa169fP7KysvD392+0ciqKwrx58xptf+c7nacLIIQQQjSmkSNH8sknn9SaZzQaT3t/BoOBiIiIMy2WOAmpGRFCCNGsGI1GIiIiag2BgYGsWLECg8HA6tWr3eu++uqrhIWFkZOTw/jx41m5ciVvvPGGu0YlLS2tzm2aOXPmEBAQwG+//UaHDh3w8fFh5MiRZGVl1SrHxx9/TKdOnTAajURGRjJp0iQA4uLiALjmmmtQFMX9+UImNSNCCCFOSVVVbDabR46t1+tRFOWM93PkFsxtt91GUlISBw4c4D//+Q/fffcd4eHhvPHGG+zdu5eLLrqI5557DoDQ0FDS0tLq7KuyspLp06fz+eefo9FouPXWW5kyZQpffvklAO+99x6PPPIIL7/8MqNGjaKkpIS1a9cCsGnTJsLCwvjkk08YOXIkWq32jM/tfCdhRAghxCnZbDZeeukljxz76aefxmAw1Hv9X375BR8fnzr7ePrpp3nhhRdYvHgx99xzDzt37mTcuHFcddVVAPj7+2MwGPDy8jrlbRmbzcb7779P69atAZg0aZI7wAC88MILPProozz44IPueb169QJcAQcgICBAbv/UkDAihBCiWRk6dCjvvfderXlBQUGAq/3Hl19+SWJiIi1btuT1118/rWN4eXm5gwhAZGQkubm5AOTm5pKZmcmwYcNO8wwuPBJGhBBCnJJer+fpp5/22LEbwtvbmzZt2pxw+bp16wAoLCyksLAQb2/vMy6ToiioqgqA2Wxu8P4udBJGhBBCnJKiKA26VXKuSklJ4eGHH+bDDz9k7ty5jBs3jiVLlqDRuJ7nMBgMOByOMzqGr68vcXFxLF26lKFDhx53Hb1ef8bHaU7kaRohhBDNisViITs7u9aQn5+Pw+Hg1ltvZcSIEdxxxx188sknbN++nRkzZri3jYuLY8OGDaSlpZGfn4/T6TytMjzzzDPMmDGDN998k3379rF161beeuutWsdZunQp2dnZFBUVnfE5n+8kjAghhGhWFi1aRGRkZK1hwIABvPjii6SnpzNr1izA1c7jgw8+4N///jdJSUkATJkyBa1WS8eOHQkNDSUjI+O0yjBu3DhmzpzJu+++S6dOnbjiiivYt2+fe/mMGTNYvHgxMTExdOvW7cxP+jynqEducp3DSktL8ff3p6SkBD8/P08XRwghmrXq6mpSU1OJj4/HZDJ5ujjiHHeyn5f6Xr+lZkQIIYQQHiVhRAghhBAeJWFECCGEEB4lYUQIIYQQHiVhRAghhBAeJWFECCGEEB4lYUQIIYQQHiVhRAghhBAeJWFECCGEEB4lYUQIIYQ4A4qiMG/evDPax7x582jTpg1arZaHHnqoUcrVWFasWIGiKBQXFzfZMSSMCCGEaBYURTnp8Mwzz5xw27S0NBRFYdu2bY1SjoaGk3vvvZd//OMfHDx4kOeff/6My3A2AkRj0nm6AEIIIURjyMrKck/PnTuXqVOnkpyc7J7n4+PjiWKdUnl5Obm5uYwYMYKoqChPF8cjpGZECCFEsxAREeEe/P39URTF/TksLIz//e9/REdHYzQa6dq1K4sWLXJvGx8fD0C3bt1QFIUhQ4YAsGnTJi699FJCQkLw9/dn8ODBbN26td5lOlLj8uOPPzJ06FC8vLzo0qUL69evB1w1GL6+vgBccsklKIrCihUrAFizZg0DBw7EbDYTExPD5MmTqaiocO/bYrHwxBNPEBMTg9FopE2bNsyePZu0tDSGDh0KQGBgIIqiMH78eACcTifTpk0jPj4es9lMly5d+P7772uVeeHChbRt2xaz2czQoUNJS0ur9/meLgkjQgghTklVVRyOSo8MjfFy+TfeeIMZM2Ywffp0tm/fzogRI7jqqqvYt28fABs3bgRgyZIlZGVl8eOPPwJQVlbGuHHjWLNmDX/88QcJCQlcfvnllJWVNej4//rXv5gyZQrbtm2jbdu23HTTTdjtdvr16+euvfnhhx/IysqiX79+pKSkMHLkSMaOHcv27duZO3cua9asYdKkSe593n777Xz99de8+eab7N69m1mzZuHj40NMTAw//PADAMnJyWRlZfHGG28AMG3aND777DPef/99du3axcMPP8ytt97KypUrATh48CDXXnstV155Jdu2bWPChAk8+eSTZ/DN14/cphFCCHFKTmcVK1Z29sixhwzegVbrdUb7mD59Ok888QQ33ngjAK+88grLly9n5syZvPPOO4SGhgIQHBxMRESEe7tLLrmk1n4++OADAgICWLlyJVdccUW9jz9lyhRGjx4NwLPPPkunTp3Yv38/7du3JywsDICgoCD3sadNm8Ytt9zibsyakJDAm2++yeDBg3nvvffIyMjg22+/ZfHixQwfPhyAVq1auY8XFBQEQFhYGAEBAYCrJuWll15iyZIl9O3b173NmjVrmDVrlnvfrVu3ZsaMGQC0a9eOHTt28Morr9T7XE+HhBEhhBDNWmlpKZmZmfTv37/W/P79+5OUlHTSbXNycvj3v//NihUryM3NxeFwUFlZSUZGRoPKkJiY6J6OjIwEIDc3l/bt2x93/aSkJLZv386XX37pnqeqKk6nk9TUVHbs2IFWq2Xw4MH1LsP+/fuprKzk0ksvrTXfarXSrVs3AHbv3k2fPn1qLT8SXJqShBEhhBCnpNGYGTJ4h8eO7Snjxo2joKCAN954g5YtW2I0Gunbty9Wq7VB+9Hr9e5pRVEAV/uNEykvL+fee+9l8uTJdZbFxsayf//+Bh3/yD4BFixYQIsWLWotMxqNDd5fY5IwIoQQ4pQURTnjWyWe4ufnR1RUFGvXrq1Vk7B27Vp69+4NgMFgAMDhcNTadu3atbz77rtcfvnlgKtNRX5+fpOXuXv37vz111+0adPmuMs7d+6M0+lk5cqV7ts0xzre+XTs2BGj0UhGRsYJa1Q6dOjA/Pnza837448/Tvc06k0asAohhGj2HnvsMV555RXmzp1LcnIyTz75JNu2bePBBx8EXG0rzGYzixYtIicnh5KSEsDVVuPzzz9n9+7dbNiwgVtuuQWzuelrap544gnWrVvHpEmT2LZtG/v27ePnn392N2CNi4tj3Lhx3HnnncybN4/U1FRWrFjBt99+C0DLli1RFIVffvmFvLw8ysvL8fX1ZcqUKTz88MN8+umnpKSksHXrVt566y0+/fRTAO677z727dvHY489RnJyMl999RVz5sxp8vOVMCKEEKLZmzx5Mo888giPPvoonTt3ZtGiRcyfP5+EhAQAdDodb775JrNmzSIqKoqrr74agNmzZ1NUVET37t257bbbmDx5srvBaVNKTExk5cqV7N27l4EDB9KtWzemTp1aqx+S9957j3/84x888MADtG/fnrvvvtv96G+LFi149tlnefLJJwkPD3eHmOeff57//Oc/TJs2jQ4dOjBy5EgWLFjgfrQ5NjaWH374gXnz5tGlSxfef/99XnrppSY/X0VtjGemmlhpaSn+/v6UlJTg5+fn6eIIIUSzVl1dTWpqKvHx8ZhMJk8XR5zjTvbzUt/rt9SMCCGEEMKjJIwIIYQQwqMkjAghhBDCoySMCCGEEMKjJIwIIYQQwqMkjAghhBDCoySMCCGEEMKjJIwIIYQQwqMkjAghhBDCoySMCCGEEMKjJIwIIYRoNsaPH4+iKCiKgl6vJz4+nscff5zq6mr3OoWFhdxyyy34+fkREBDAXXfdRXl5uQdLLSSMCCGEaFZGjhxJVlYWBw4c4PXXX2fWrFn897//dS+/5ZZb2LVrF4sXL+aXX35h1apV3HPPPR4ssZAwIoQQolkxGo1EREQQExPDmDFjGD58OIsXLwZg9+7dLFq0iI8++og+ffowYMAA3nrrLb755hsyMzM9XPILl87TBRBCCHHuU1WVSqfTI8f20mhQFOW0tt25cyfr1q2jZcuWAKxfv56AgAB69uzpXmf48OFoNBo2bNjANddc0yhlFg0jYUQIIcQpVTqdtF61wyPHThnUGW+ttt7r//LLL/j4+GC327FYLGg0Gt5++20AsrOzCQsLq7W+TqcjKCiI7OzsRi23qL8G36ZZtWoVV155JVFRUSiKwrx58065zYoVK+jevTtGo5E2bdowZ86c0yiqEEIIcWpDhw5l27ZtbNiwgXHjxnHHHXcwduxYTxdLnESDa0YqKiro0qULd955J9dee+0p109NTWX06NHcd999fPnllyxdupQJEyYQGRnJiBEjTqvQQgghzi4vjYaUQZ09duyG8Pb2pk2bNgB8/PHHdOnShdmzZ3PXXXcRERFBbm5urfXtdjuFhYVEREQ0WplFwzQ4jIwaNYpRo0bVe/3333+f+Ph4ZsyYAUCHDh1Ys2YNr7/+uoQRIYQ4TyiK0qBbJecKjUbD008/zSOPPMLNN99M3759KS4uZsuWLfTo0QOAZcuW4XQ66dOnj4dLe+Fq8jYj69evZ/jw4bXmjRgxgoceeuiE21gsFiwWi/tzaWlpk5Rt4ldb+SuzFAVAgSPNoxRFQQEUBZSauUfaTh1ZdmTekXXcy2sWKMdu87d9uuYp7mMqCmg1CkadFoNWg1GvOWasdX/2NmoJMBsI8NIT6G0g0MtAiwAzZsP59wtCCCHOluuuu47HHnuMd955hylTpjBy5Ejuvvtu3n//fWw2G5MmTeLGG28kKirK00W9YDV5GMnOziY8PLzWvPDwcEpLS6mqqsJsNtfZZtq0aTz77LNNXTQyi6tIza9o8uM0tXA/Iy2DvYkL9qJ1qA9dYwLoHO2Pl0HaJwshhE6nY9KkSbz66qvcf//9fPnll0yaNIlhw4ah0WgYO3Ysb775pqeLeUE7J69WTz31FI888oj7c2lpKTExMY1+nBfHdKbcYkdVVQBUQFVBRXV9+Ps89zS1tqFmuXpkm+Oso9asqJ5gvw6nisXuxGJ3YrU7sdgdNeOjnyssDooqrRRV2iiutFJYbqXMYien1EJOqYWNqYXuc9NpFHrHBzGsQzhXJEYS7mdq9O9PCCHONSd6QOLJJ5/kySefBFxtSr766quzWCpxKk0eRiIiIsjJyak1LycnBz8/v+PWioCrwxqj0djURaNjlF+TH6OpFVdaSSuoJL2ggrT8SvZkl7I1o4icUgvrUgpYl1LASwt3c1nHcO4cEE+vuCBPF1kIIYSopcnDSN++fVm4cGGteYsXL6Zv375NfegLQoCXga5eBrrGBNSan5pfwdLdOfy6M5st6UX8ujObX3dmM7xDGE+Oak+bMF/PFFgIIYT4mwb3M1JeXs62bdvYtm0b4Hp0d9u2bWRkZACuWyy33367e/377ruPAwcO8Pjjj7Nnzx7effddvv32Wx5++OHGOQNxXPEh3kwY2Iof7u/Hbw8N4qbeMWg1Ckt25zLqjdV8tPqA+zaSEEII4UkNDiObN2+mW7dudOvWDYBHHnmEbt26MXXqVACysrLcwQQgPj6eBQsWsHjxYrp06cKMGTP46KOP5LHes6hdhC/Trk3kt4cGcUn7MGwOlRcW7GbCp5spqbJ5unhCCCEucIp6Hvx5XFpair+/PyUlJfj5nf/tPDxJVVW+2JDB87/8hdXupFOUH1/c1YdAb4OniyaEOEdUV1eTmppKXFzcCdv2CXFEVVUVaWlpxMfHYzLVfliivtdveWvvBUZRFG67uCU/3t+PYG8DuzJLuenDP8gvt5x6YyHEBUGv1wNQWVnp4ZKI88GRn5MjPzen45x8tFc0vYta+PPNPRdz80cb2JNdxl2fbubbey/GqJMO1IS40Gm1WgICAtzdpnt5eZ32W3NF86WqKpWVleTm5hIQEID2DHrolds0F7gDeeVc8+46Sqps3Nwnlpeu8cy7J4QQ5xZVVcnOzqa4uNjTRRHnuICAACIiIo4bWOt7/ZaakQtcq1Af3rixK3fM2cRXGzLoGhPA9T0bv4M5IcT5RVEUIiMjCQsLw2aThu7i+PR6/RnViBwhYUQwpF0YDw9vy/8W7+W/P++if5sQWgRIozUhhOuWTWNcbIQ4GWnAKgCYNLQNveOCqLI5eO7/dnm6OEIIIS4gEkYEABqNwvNjLkKnUfhtVw7L9uSceiMhhBCiEUgYEW7tIny5a0A8AP+dv4tqm8PDJRJCCHEhkDAiapk8LIEIPxMHC6uYu+mgp4sjhBDiAiBhRNTibdQx8ZI2ALy/MgWr3enhEgkhhGjuJIyIOq7rEU2Yr5Gskmp+2HrI08URQgjRzEkYEXWY9FruHdwagHdX7MfmkNoRIYQQTUfCiDium3vHEuxt4GBhFb9sz/R0cYQQQjRjEkbEcZkNWsb3iwPgyz8yPFsYIYQQzZqEEXFCN/SKQatR2JxexN6cMk8XRwghRDMlYUScUJifieEdwgD4eqPUjgghhGgaEkbESd3UOxaAH7celk7QhBBCNAkJI+KkBiaE0iLATEmVjV93Znm6OEIIIZohCSPipLQahRt7xQDw3Wbpc0QIIUTjkzAiTmlMtxYA/HGggLwyi4dLI4QQormRMCJOKSbIi64xAThV5FaNEEKIRidhRNTLFYmRAPyyXcKIEEKIxiVhRNTL5Z1dYWRTWiE5pdUeLo0QQojmRMKIqJeoADM9WgaiqrBwh9SOCCGEaDwSRkS9ya0aIYQQTUHCiKi3I7dqtqQXkVsmt2qEEEI0Dgkjot7C/Ux0ifYHYMWePA+XRgghRHMhYUQ0yCXtwwFYsjvHwyURQgjRXEgYEQ0yrObFeWv258u7aoQQQjQKCSOiQTpF+RHuZ6TS6mBDaqGniyOEEKIZkDAiGkRRFPetmqVyq0YIIUQjkDAiGmx4za2apbtzUVXVw6URQghxvpMwIhqsX+sQjDoNh4ur2JtT7uniCCGEOM/pPF0Acf4xG7Rc3CqYlXvzWL0vj3YRvp4ukhBCNCtOpwOn3YHDbsdht+G022um7TjtNtfY4cBxZPo4y13rHDvffuL9ORwMuOE2AiIiPXK+EkbEaRmYEFITRvKZMLCVp4sjhBCNzul0YLdYsNUMdks1dqsVu82K3WbDYbNit9aM/zbtsNmwWy3YrbZjPltrltfMcy+r2Z+1Zmy3oTqdZ/18u4+6SsKIOL8MSAgBYENqARa7A6NO6+ESCSEuRE6nA2tVVc1Q6RpXV2GzVNcEiepageLY+e7PVgu2ags2a+3lDpvN06fnptFq0Wh1aHU6NDrX2DWtP2a6Zqw9/vKj6+jRaLU18/Tu7fxCQj12fhJGxGlpF+5LqK+RvDILW9KL6Nc6xNNFEkKcJ1RVxW6zYikvp7qiHEtl5TFBohJrZc34bwHjyLStusq1TXUVdovlrJRZZzSiN5rQGQzoDEZ0ej06vQGtoWasN7jmGQxo3ctc81zLDOgMevd6rmXHLHdv5/pcK0xotSia5t3EU8KIOC2KojCgTQg//XmYNfvyJYwIcYFRnU4sVZVYKsqprqioGZdjqaj427jcvezIepaKchx2e6OWR6vToTd7YTSbMZjM6Ewm9AYjepMJncEVJPRGI3qj0R0s9EcCxpHPx65vMtaMTej0BhRFadTyitokjIjT5g4j+/N53NOFEUKcNlVVsVVXUVVWSlVpqWt87HC8eWWlZ9yuQVE0GL29MXp5YTAfGcxHxyZznXlGsxf6mmVGLy/0Nevo9PpG+jaEJ0gYEaftSLuRHYdLKKqwEuht8HCJhBBHqE4n1RXlVBQXUVFcRGXNuKKk2DVdMz4SLE63pkJnMGL09sbk7YPRyxuTjw/GY6e9apb5+GDy8sbo7eP67O2DwWRq9rcfRP1IGBGnLdzPRNtwH/bmlLMupYDRiZ5phS3EhcTpcFBeVEh5YT5lBQWuoFFSREVxcc24JnyUFON0NOz9UTq9AbOfP2ZfP8x+fq7xscPf5pl8fNEZ5I8QceYkjIgzMqBNKHtzylmzP0/CiBBnyG6zUVFUQFlBPmWFBZQX5FNWmE95QUHNOJ+K4mJUtf63R0w+vngHBOIdEICXf2DNdCBe/gF4+we4wkdNyNAbTU14dkKcmIQRcUYGJoTw8dpUVu/LR1VVaeQlxEnYLNWU5uVRmpdDSW4OJXk5lObmUJKXS1lBHpUlxfXaj0arxScoGJ/AYHwCg/AKCMTbP8A1Djg2cPij1UlbCnHukzAizkifVkHotQqHiqpIL6gkLsTb00USwmMcdvvRoJGb45rOy60JHDn1ChtavR7foBB8goNrxiH4BgXXjEPwDQ7By89f2lqIZkXCiDgjXgYd3WMD2ZBayOr9+RJGRLPndDgozculKDuToqxMirMzKcrOpDgrk5K8nFM+YWIwe+EfFo5faDj+oWHuad+QUHyDQzD7+kkNo7jgSBgRZ2xAmxA2pBayZl8et13c0tPFEaJRVJWVUnAog4JDBynMPOQKHVmZlOTm4HSc+MkTndFIQFgEfqFhrsARFo5/aDh+NWOjt7eEDSH+RsKIOGMDEkKYsXgv61IKsDuc6LRSfSzOD6qqUlFcdDR0HD5IwWHXdFVpyQm30+kNBEREEhARRWBklHscGBGFd2CQhA0hGkjCiDhjidEB+Jl0lFbb2X64hO6xgZ4ukhB1WCoryctIJS/dNeQfTKfw8EEsFRUn3MYvNJzgFtEEtYghMLKFO3j4BgVLmw0hGpGEEXHGtBqFfq1DWLQrmzX78iWMCI9SnU5K8nLJSz/gDh556amU5OYcd31F0RAQEUFQi1iCW0QTHB1LcHQsQVHR6E3yqKsQZ4OEEdEoBiQcDSOThyWc0b5U1UFx8RZy836lvGw3VdUHsdmK0GiMaLVemM0t8fZujb9fVwKD+mMyRjTSWYjzjdPpoPDwIbJT9pFzYB+5aankZ6Rirao67vo+wSGEtYwntGU8ITEtCY6OJTCyhXTcJYSHSRgRjWJQguvV01sziii32PExNvxHS1WdZGf/RMqB/2GxZNdZ7nRasNtLsViyKS7ewOHDXwHg492OsPDRhIddgZeXNKBtrlRVpSQ3h+yUva7wkbKPnNQUbNV1g4dWpyM4uiWhNcHDNcRh9vXzQMmFEKciYUQ0ithgL1oGe5FeUMkfKQUM7xjeoO0rKvbz1+4nKC3dBoBO50toyGUEBQ3AbI7FYAhGVe3Y7WVUVB6gojyZouINlJbuoLwimfIDyRw48D+CgwYRHTOO4KBBKIrc0z+fVZYUk7kvmZya8JF9YD/VZaV11tMbTYTFtyaidQLh8a0JbRlPYFQ0Wp38ehPifCH/W0WjGZgQQnpBBqv35TUojBQVb2L79nuw20vRar2Jj5tETMw4NBrjcdf380t0T9tsxeTlLSEn5/8oLFpLQeEqCgpXYTbHERN9O5GRY9HpfM743ETTUp1OCg4fJHPvbjKTd5O5dzdFWZl11tPqdIS2jCe8VQIRrV1DUHQMGo3WA6UWQjQWRVVV1dOFOJXS0lL8/f0pKSnBz0+qWc9Vv+3K5t7Pt9AqxJtlU4bUa5u8vN/ZuetBnE4r/n7d6Nz5HYzGhtWqHFFVlcGhQ1+QmfUtdnsZADpdAC1jJxAdfZuEknOIzWohe18yh2uCR9bePVRXlNdZLzg6log2bYlo3ZaI1gmExMbJq+KFOI/U9/otYUQ0mtJqG92eW4zDqbL68aHEBHmdfP3S7WzZegNOp5WQkOFc1OkNtNozf3rBbq8gO3seBw99QmVlKgB6fSCxMROIjr5VQokH2K1Wsvbt4eBfOzi4awdZ+/bUeWW9zmAksk1botp1pEW7DkQmtMfkI/9WQpzP6nv9lts0otH4mfR0iwlgc3oRa/bnc1Pv2BOua7Hms33H/a4gEnwJiZ3fRVEap6pdp/MmOvoWWrS4kZycX0hNe4vKylRSDrxGxsHZxMdNpEWLm9Fo5AmKpmK32cjel1wTPraTuW8PDput1jregUG0qAkeUe06EtoyXtp5CHGBkv/5olENTAhlc3oRq/flnTCMqKqDnTv/icWSjZdXKzp1+l+jBZFjKYqWiIirCQ+/guyc/yM19S2qqtLYu+95Dh78lNatpxAWdrn0ltkIVFWl4GA6adv/JC1pK4d378Jus9ZaxzsgkJhOicR07ExMp84ERETJdy+EACSMiEY2sG0Iry/Zy5p9+TicKlpN3YvN4cPfUFy8Ea3Wh8TO76PT+TZpmRRFS2TEGMLDriAr63sOpM6kqjqDnbsm45fxEW3aPElgYJ8mLUNzVFlaQvqObaQn/Un69q2UFxXWWu7lH1ATPBKJ6dSZwMgWEj6EEMclYUQ0qsQW/ke7hj9UTLe/9cZqseaTcuA1AFq3noK3d+uzVjaNRkeLFjcSEXEVGRmzSc/4kNKy7Wz982ZCQobRuvVj+HifWYdtzZnT6SBr315S/9xEWtJWclJT4JgmZzqDkeiOFxGX2J2WiV0Jjo6V8CGEqBcJI6JR6bQa+rcJ4ded2azel18njOzf/zJ2exm+vp2IbnGzR8qo1XoRH/9PolrcRGrqm2RmfkN+/lIKClYQFXUDreIfxGAI8UjZzjXWqkrStv/JgS0bObB1E1V/6+cjNDaOll26E5fYnRbtO0pPpkKI03JaYeSdd97htddeIzs7my5duvDWW2/Ru3fv4647Z84c7rjjjlrzjEYj1dXVp3NocR4YmBBaE0byanUNX1KylezsnwCFdu2eb5J2Ig1hNITQvt1zxESPJyXlVfLyF3P48FdkZ88nruV9xMTc0ShP95xvSvNzSdmykZTNGzj0145aT70YvbyJ69qD+K49aJnYDZ/AIA+WVAjRXDQ4jMydO5dHHnmE999/nz59+jBz5kxGjBhBcnIyYWFhx93Gz8+P5ORk92epum3eBia4ahW2ZhRTVm3D1+TqFyI19S0AIiP/gb9fF4+V7++8vVuRmPg+RUUb2bf/RcrKdpJyYDqHD39F69aPER5+RbPvzTX/YDp7/1jL/o3ryMtIq7UsIDyS1j1706p7H1q07yhPvAghGl2Df6v873//4+6773bXdrz//vssWLCAjz/+mCeffPK42yiKQkSEvMzsQhET5EV8iDep+RWsTyngsk4RlJZup6BwFYqiJT7uAU8X8bgCA3vTq+dPZOfMJyXlNaotmez662EOHvyEhIR/ERDQ09NFbDSqqpKfkcbeDWvZ+8daCg8fdC9TFA1R7drTqntvWvfoQ1CLaPkDQgjRpBoURqxWK1u2bOGpp55yz9NoNAwfPpz169efcLvy8nJatmyJ0+mke/fuvPTSS3Tq1OmE61ssFiwWi/tzaWnd91GIc9vAhBBS8ytYvS+fyzpFkJb2LgDh4VdiNp+4/xFPUxQNkRFjCAsdQcbBj0lPn0VpmatzttDQkbRp/fh5+zI+VVXJS09l7x9r2PvHWoqyDruXaXU6WiZ2I6FPf1p174WXn78HSyqEuNA0KIzk5+fjcDgID6/dXXd4eDh79uw57jbt2rXj448/JjExkZKSEqZPn06/fv3YtWsX0dHRx91m2rRpPPvssw0pmjjHDEwI5bP16azel0d5eTJ5+YsBhbiW93u6aPWi1ZqJj5tIVOT1HEidSWbmt+TlLSI/fykx0bcTFzcRvf78uGAXZh5i95oV7Fm7kuLsLPd8rV5PXJcetL24P6179Mbo5e3BUgohLmRNfvO3b9++9O3b1/25X79+dOjQgVmzZvH8888fd5unnnqKRx55xP25tLSUmJiYpi6qaEQXtwpCp1FIK6jkr33vAxAWNgpv7zYeLlnDGI2hdGj/IjHRt7Nv/zQKC1eTcXA2mVk/EB8/iegWt5yTPblWlhSzZ90qdq9eTnbKPvd8nd5AXNcetO07gFbdemH0OnmX/UIIcTY0KIyEhISg1WrJycmpNT8nJ6febUL0ej3dunVj//79J1zHaDRiNB7/ja3i/OBr0tOjZSA7Dh6ipGgRGiA25k5PF+u0+fi0o1vXORQUrGTf/mlUVOxj374XOHTocxLaPElIyKUeb1dhs1Szf/MGdq9eTlrSVlSnEwBFoyGuS3c6DBxK6x69MZjMHi2nEEL8XYPCiMFgoEePHixdupQxY8YA4HQ6Wbp0KZMmTarXPhwOBzt27ODyyy9vcGHF+eXSjuH4On5EgxUfn/b4+XX1dJHOWHDwYAID+5OV9R0pB16nqiqd7TvuJyCgNwltnsbPr/NZLY+qqhz6awe7Vi5l74Z12Kqr3MsiWifQYeAltO83EC//gLNaLiGEaIgG36Z55JFHGDduHD179qR3797MnDmTiooK99M1t99+Oy1atGDatGkAPPfcc1x88cW0adOG4uJiXnvtNdLT05kwYULjnok451zSPgxj0VoAgkOv93jNQWNx9eR6E+HhV5Ke/j4ZBz+muHgjmzaPISJiDK1bPYrJFNWkZagoLmLXyqXsXP47RVmZ7vn+YeF0GDiUDgOGEBR1/DZZQghxrmlwGLnhhhvIy8tj6tSpZGdn07VrVxYtWuRu1JqRkYFGc7RPhqKiIu6++26ys7MJDAykR48erFu3jo4dOzbeWYhzUqB+N1E+OVgcBv4q7sv51Vrk1HQ6H1q3nkKLFjeTkjKD7Jx5ZGfPIzf3V2Jj7qJly3vR6Xwa7XhOp4P07dvYsfQ3UrZswOlwAKA3mWnffxCdBg0jql2HZhP6hBAXDkVVj3m5xDmqtLQUf39/SkpK8PPz83RxRD3t2vUI2Tk/s+rQxRTpp/DmTd08XaQmVVq6nX37XqK4ZBMABkMIreIfJirqujPqbbasIJ+dyxezY/nvlOXnuedHtmlH52EjaNdvoLQDEUKck+p7/ZauFEWTsNvLyc1bBMCqQ/3Jt+ZiczjRa5tvT6Z+fol07/41eXm/sz/lFaqq0tmT/C8OHvqUNq0fJzh4SL1rLVRVJWNnEn8u+oUDWzaiqq7GqCZvHzoMGkrnS0YQGhvXhGcjhBBnj4QR0STy8hbjdFowm+MpdbShrNrGptRC+rVp3i+gUxSFsLARhIQM5dDhL0lNfYuKir0kbZ+Av39P2rR+7KQ9uVqrq/hr5TL+/O2XWr2ixnTsTOdLLqNNn37oDfKkmRCieZEwIppETs58ACIiruKS9uF8t+UQC3dmNfswcoRGYyA25g4iI64hLf09Dh36nJKSzWzZegPBwYNp3WoKvr5H200VZWeybdEv7FyxBGtVJeBqC9Jp8CV0HXEFwS2knx0hRPMlbUZEo7Na81mzth+q6qDvxUvYeNCb8Z9sItjbwIanh6FrxrdqTqTakk1a6ttkZn2LqroanoaFjcaHq0j69Q9StmyAmv+KgZFRdB1xBZ0GD5dOyYQQ5zVpMyI8Jif3V1TVga9vZ7y84unfxkmgl56CCivrDxQwMCHU00U860zGCNq3f4HY2AmkHHid3NxfyM1dQI5zARavAPReIUS37Uu3kVcSl9gNRXPhBTYhxIVLfuOJRue+RRN+FQB6rYZRnSMB+L+kzBNu19zZrBb2rvmLjR9Wsue7eErSfVA0ENKhmItuS+eia7VEdYiRICKEuODIbz3RqKqrMykp2QoohIePds+/MtHVCdiindlY7A4Plc4zrFWVbJj3HR9OvJOls991vazOEkKYeQqd2n5EQEBvVNXGwUNzWLd+CHv3Po/FkuvpYgshxFkjt2lEo8rN+w2AAP+eGI1H3+7cOz6IMF8juWUWVu/NZ3jH8BPtotmorijnz1//j60Lf6a6ohwAv9Aweowew0VDL3X3DRLeYgiFRWtJTX2DkpKtHDw0h8OZXxMVdSNxLe+t9T0KIURzJGFENKq8vMUAhIaNqDVfq1G4IjGKj9em8nNSZrMOI1VlpWxd+DNbf/0/95MxgZEt6HPN9XQYMASNtnYHaIqiEBw0gKDA/hQVreNA6huUlGzh0KFPycz8mqiomySUCCGaNQkjotFYrQUUF7t6Hw0NubTO8jHdXGHkt13ZFFdaCfAynO0iNqmq8jI2z/+BP39b4H5hXXB0LBdfewNt+w5Aozl5L6yKohAU1J/AwH4nCCU30jL2HkymyLNxOkIIcdZIGBGNJj9/GeDE16cTZnPdl7R1buFPx0g//soq5ceth7lzQPzZL2QTsFZXsXXhfDb/349YKisACI1rxcXX3kBCr74NbpBaN5S8SUnJZg4d+ozDh78mImIMLWPvwdu7VVOcjhBCnHUSRkSjOdJeJDS0bq0IuC6yN/WJ5T/zdvL1xgzu6B93Xr/UzW6zsX3Jr2z46VsqS4oBCImNo//1t9K6Z58zPrfaoWQ9qWlvU1y8gays78jK+p7Q0BHEtbwXP7/ERjgbIYTwHAkjolHY7eUUFq4FIDR0xAnXu7prFC8t2M2+3HK2pBfRMy7obBWx0TgdDv5avZz1339FaZ7rqZeA8Ej6XX8L7fsNavRHc12hpB9BQf0oKdlKWvos8vOXkJe3iLy8RQQG9iOu5X0EBvY7r8OdEOLCJWFENIqCgpWoqhWzOQ5v74QTrudn0nNll0i+3XyIrzcePO/CSFrSVlZ89hEFhzIA8AkM4uKxN3HR0EvR6pr+v5O/f3e6JM6ivHwv6RkfkJMzn6KidRQVrcPPN5GWLe8jNPRSFEWe2hdCnD8kjIhGceQWTVjoZaf86/ym3rF8u/kQv2zP5KnL2xPic+YvflOdKvmHy8lOKSE3o4zywmoqS604HSqKAgazDp8AI77BJkJifAmN8SUwwgtFU7+ahILDB1n1xccc2OpqoGvy9qH3NdfTdcRoj7y4zsenLZ06TqdV/MNkHPyIzMxvKS3bzo6dD2A2tyQmZjyREWPR6bzPetmEEKKh5N004ow5nRZWre6Nw1FOzx7f4+/f7aTrq6rKmHfXkXSwmH9e0oZHL2t32scuzqlk97os9m7MprzI0qBtzb56otsFEtclhPguoegNdZ92qSorZf33X5O0eCFOhwONVkvXEVfQd+xNmHx8Trvcjc1qLeDgoU85dOhz7PZSAHQ6X6KibiAmehwmU5SHSyiEuBDV9/otYUScsfz85SRtn4DREE7//mvqdYtg0c4s7vtiK34mHeueGoaPsWGVdCV5lWz8JZW9G3Og5idYb9QS0dqfiHg//ELNePsb0eo0qE4VS5Wd8iILxbmV5B8sIy+jDLvV6d6fzqilVdcQ2vWOILp9IKrqJOn3Baz//mt3h2WtevRm8K13ERTVokFlPZscjkqysn4k4+AnVFWlAaAoWkJDRxAbc+cpg6IQQjQmeVGeOGvy8n4HIKQBbRUu6xhBq1BvDuRV8PWGDO4eVL/HVJ0OJ1t/S2fTgjScDlcKaXlRMO37RhKXGIxOf/K+PI5w2J3kpJaQvquQ/ZtzKM2vZu+GHPZuyEFvyMFhWUZlcZbrvGLjGHL7BFp27lqvfXuSVutFdPSttGhxMwUFK8g4+DFFRevJzV1Ibu5C/Py6EhtzB6GhI9Bo9J4urhBCAFIzIs6QqjpYveZibLZCunX9jKCg/vXe9ttNB3n8h+2E+xlZ9fhQjLqTB4ni3EoWz95FbnoZADEdg7j46laEtTyznwlVVck+UMqu1fvZvep7rBU7XAsUE9EXjeaScWMJjTl/f+7KynZz8OAnZOf8H6pqBcBoCCcq6gaiWtyAyRjh4RIKIZoruU0jzoqioo1s/fMmdDp/Bg7Y0KC/ti12B4NfXUF2aTVTr+h40k7QDicX8eusHVgq7Ri9dAy8oS1te4c3yqOsqtPJzhVLWPXVHKrLXO0tfIK7Y7P3QdG43h8T2zGIXlfEE9HK/4yP5ykWaz6HD33JocNfYrMVAK5bOCEhw4lucYs8GiyEaHQSRsRZsXffCxw8+AkREdfQqeP0Bm//1YYMnv5pBwFeelZOGYq/V90ws2d9Fss/34PTqRIe78fIezrjE9g4T7DkZaSx5KN3yUz+C3Ddkhk+YSIt2nUg+0AJ25Yc5MCfuRz5XxLbKYheo8/vUOJ0WsjN/Y3Dh7+iuGSTe76XVzwtWtxCZMS16PXn7/kJIc4dEkZEk1NVlXXrB1NdfZjEzu8RGnpZg/dhdzi5/M3V7M0p5+6B8fxrdMday/9am8nyz/cAkNAzjEtu74DuOE+9NJTDbuOPH79l47xvcToc6I0m+l13M91GXVWnv5CSvCq2/JrGnj+yUZ2u/y7NIZQAlJcnc+jwV2Rn/4TD4erKXqMxER5+JS2ibsTPr4vUlgghTpuEEdHkSst2smnT1Wg0JgYN3IxWaz6t/axIzmX8J5swaDUseWQwscFeQO0gkjg0mgHXJzTKhTE37QCL3n2dvPRUANr0upih4+/FLyT0pNudKJT0vqIV4fHn98+l3V5Ods58Dh/6gvKKZPd8b+8EoiKvIyLiagyGEA+WUAhxPpIwIppcyoH/kZb2DqGhl5HY+b3T3o+qqtz+8UZW78tnYEIIn93Zm9SkfH6dtQPUxgsiDrudDT99y4af5uJ0ODD5+jH8rvtp13dgg/ZzvFAS3yWEPle1IrjFudP3yOlQVZWSki0cPvw1uXm/4nS6+m5RFB0hIcOIiryOoKCBaDTyIJ4Q4tQkjIgm98eGkVRU7KNjxxlERow5o32l5JVz+RursdidPD8wgcpFmdhtTjoOjGLIze3OOIjkph1g0XszyUs7AEBC734Mu+t+vAMCT3ufJXlVbF6QSvKGbFebEgUSeobT+8p4AsK8zqi85wKbrZSc3F/IyvyO0rLt7vkGQxiRkdcSFfkPvLyax5uXhRBNQ8KIaFKVlams/2M4iqJj4ICNjdLg8aPVB3jj//Zwe5kRL1UhtlMQox9IRKM9/fesOB0ONs77jvU/fIPTYcfk48uwO++jXb9BjdYWojCrgo3/l0rKVtdL8xSNQoe+EfQcHY9vkKlRjuFp5eXJZGZ9T3b2PGy2Qvd8f//uRIRfTVjY5RgM59d7hoQQTU/CiGhSaemzSEl5laDAAXTr9mmj7NNqcTDjqVX4VaqUmxQmvtAfHx/Dae+vND+XhW9N5/Ae15MybXr1ZfiEB86oNuRk8jLK2PB/B0jf4XpsVqNTuGhgC3qMisPL7/TP41zidFrJz19GZtZ3FBSsAly92CqKjuCgQYRHXEVoyPDTbj8khGheJIyIJrVp81hKS7fRrt3zRLe4uVH2ufLrZHauPEy1ovKpj4Wr+sfwwpjOp7Wv5PWrWfzB21gqKzCYzQy78346DBx6Vp4MyUop4Y95KWTuKwZAZ9CQeEkM3S6NxeTdfHo9tVhyycn5heyceZSV7XLP12q9CQ29lIjwMQQG9pX2JUJcwCSMiCZTVXWYdesHAQoD+q/DaAw7433u3ZjN4o//AgWir4rloVXJqCq8dE1nbu4TW+/9WKurWD7nA3YuXwxAZJt2XD75MQLCz24vo6qqcmh3EX/8nOLuMdZg1tHt0hgSL4nBYGpeF+iKiv1k58wnO3s+1dUH3fMNhhDCwkYRFjqKgICeKMqZP5YthDh/SBgRTSY9/QP2p7xCQEAfenT/6oz3V5pfxTcvbMRW7aDn5XH0uaoV7yzfz2u/JaPVKLxzc3dGXnTqMFFwKIP5/5tG4eGDoCj0GXM9ff9xU51+Q84mVVVJTcpnw/wDFGa6+vEw++rpPqIlFw1uUe936ZwvVFWlpHQr2dnzyc1dgM1W5F5mMIQQGjqCsLBRBPj3khoTIS4AEkZEk9m46WrKynY2yi0ap8PJvP/9SVZKCZGt/RnzaHc0GgVVVZny3XZ+2HoIvVbhvVt6MLxj+An3s3v1cn7/8G3sFgvegUGM/ucUYjolnlHZGpPqVNm3JYeN81MpyasCwDvASK/RcbTvF4n2DBrpnqucTiuFhWvJzV1IXv4S7PZS9zK9PoiwI8EkoI8EEyGaKQkjoklUVqax/o9hKIqWAf3XYzAEn9H+Ni1IZeP/pWIwabnh373xCzna8NHhVHl47jbmJ2Wi1yq89o8ujOnWotb2dquV5Z9+wPYliwCIvagLoyc/hpd/wBmVq6k4HE6S12ezaUEq5UWuPjz8Qs30viKehF7haDTNs7dTp9NKYdE6cnMXkZf3O3Z7iXuZXh9ISMgwQkOGExTUH632/H8sWgjhImFENIm0tHdJOTCjUZ6iyT9UxncvbcbpVBl+R0fa9al7K8bucPLQ3G38sj0LgMmXtOGh4W3RaBRK83OZP+Mlcg7sB0Xh4mtvoO8/bkKjOfdvfdhtDnatymTLojSqymwABIR70X1ELG17R6DVNb+akiOcThtFRX/U1JgsrnUrR6MxEhTYn5CQYYSEXNIo7ZGEEJ4jYUQ0iQ0bR1NevocO7acRFXX9ae/H4XDy/cubyT9YTqtuoYy856ITPunidKq8+lsy769MAWBQ21CmJOpYM2sGVaUlmHz9uHzSo8R37XHa5fEUa7WdHSsO8efvGVgq7QD4BBrpdlksHfpHoW+E9/Ccy5xOO8XFG8jLX0p+/hKqqw/XWu7n15XQkGGEhAzD27utvCdHiPOMhBHR6MrLk9mw8fKajs42oNcHnPa+Ni9MZcP8VIzeOm7+78X16ofju80H+fdPO2hbtINBBWvR4CQsrhVXT/k3fqHn91/Q1mo7u1Zlsm1JBpWlVsDV0LXLsBguGhyN0dz821SoqkpFxV7y8peQn7+U0tKkWstNxiiCggcRHDyIoMB+6HS+HiqpEKK+6nv9bv6/4USjycr6AYCQ4KFnFEQKDpezaUEaAAOvb1vvDsGu6RIBa74lY/9qAJK927Ah5hoSqw10Oe3SnBsMJh3dLoul89AW7Fmfzdbf0ikrqOaPeQfY+lsGHQdE0XlIC/yCm29nYoqi4OPTDh+fdsTHTcRiySU/fxn5+UspLFpLtSWTzMxvyMz8BkXR4e/fneAgVzjx8emAojTfW1tCNHdSMyLqxem0sWZtP2y2QhITPyA0ZNjp7cfh5IdXt5CbXkZcYgiX39+5XlXvVWWl/Dz9RQ7v2YWiaND0uYJZBTFU2V09gA5pF8rEoW3o2TKwWVTlOxxO9m/KYcuidIqyKwFXN/OtuobSZVgMEa38msV51pfDUUVR8QYKClZRWLiKysrUWsv1+mCCgwcSFDiAwKC+mIxnt18ZIcTxyW0a0ajy8n5n+477MRhC6N9v7Wk/irllURp/zDuA0UvHTVP74B1gPOU2RdmZ/PTyMxRlZWIwe3HlQ08Q17UHWSVVvPZbMvP+PEzNy3PpEOnHzX1iuTIxkgCvc7MLdtWp4qy04Sy34SizusblVlSLA6fVgWpxoFqdOC0OVIud6hIrFSUWbFV2XPFDQW/QYPbRY/DSoeg0KFoNilYBnWus6DQoeg0akw7FqHWNTVo0Ji2KUYfG5JqnMevQeOtRzrMGs1VVGRQUrKagcBVFRetwOCprLffyakVgYF+CAvsRGNgHvb5pXgEghDg5CSOiUSVtv5f8/CXExk4goc1Tp7WPwqwKvn1xEw67k2HjOtC+b+QptzmcvJufX3ueqrJSfENCufbJZwiJaVlrnfSCCt5fmcKPWw9jqakp0WsVBrQJYXjHcAa0CSE2yOus1SSoqoqz3IYttxJHYTX2gmrshVXYC6txlFpxlluPvNLlnKGYtGh9DGi89Wi89Wh99EenfQ1o/Q1o/Yxo/QznXHBxOq0Ul2yhsGA1hUXrarqmP/YLVvDx6UBQYF8CA/sSENBT2psIcZZIGBGNxmLJY+26/qiqgz59FuHjnXDKbYqri1mXuY6DZQcx68z4GwKo/C6UwowqYjsFc8WkxFOGg+T1a/j1nRk4bDbCWyVwzRNTT/qSu+JKKz9sPcx3mw+yJ7us1rLoQDMD2oTQt3UwF7XwJz7Yu1H69FAdKracCqyHyrBlVWDPqcSWU4Gzwn7KbTVeOjS+BrS+NSHArEMxaNEYNChGrWvaqHVd/DWKa1DAUmUnfWcBqdvzsVba0eBaFBbjS3TbAIIjvFBUUK0OnNUOnNV21GqHq6al2n50XO3AWWVrcDDSeOuPhpPjjQNNaDz4FJDNVkJx8UYKi9ZTVLSeioq9f1tDg69PB/wDehAQ0IsA/14YjaEeKasQzZ2EEdFoUtPe4cCB/+Hn15VePX846brJhcm8tvk1NmZtROXoj1bXw8O5OONKHHorne43M6zj4BPuQ1VVNs3/gdVfzQGgdc8+jP7nY+hNpnqXeX9uOb/uyGL1/nz+zCjC5qj9Y+5t0NIh0o9OUX60CfclPtibuBAvovzNJw0pjgob1tQSLGmlrgByuBzVdpyruQLaIBO6YDO6IJN70AYYXQGkEW6NOGxODiTlsWt1JoeTj/bV4e1voEP/KDr0i6zVidzxqE4VZ5UdZ0XNbaMKW8201T3tKLW6hhILOOr360LjrUcbaEQXaDpmbEIXaEQbYEJjPHthxWLNp6gmmBQVraeqKqPOOmZzy5pg0pOAgJ6YzXEXVJscIZqKhBHRKJxOC2vXDcZqzaNjxxlERow57noWh4U3tr7BV7u/wqE6AEgITKBTcCecBXoiFvVF49SyrPUX7A3bxMAWA3mi9xO09Kt9y8XpcLB09ntsX+rqUbX7qKsYfPtdZ9SRWYXFzsa0Qtbtz2dTWhF7skupPl6AAAw6DbFBXkT4mQj3M9HCy0B7K7QoteOfV42uoLrONopJiyHaF32UN/pwb/QR3ujDzChn8b0zxTmV/LUmk93rs6gut7nnR7bxp12fCFp3DzvjNwarqoqz0o6jxOIOJ3XGxRZUi+OU+9J46Y6Gk0ATuoCacZArtDRlWLFYcigu3kxxySaKizdTXr4HqP1rUK8Pws+vC/5+XfHz74qfbyJ6vfzuEadHVVVUVcXhcOB0OnE6nSecVlW1zvTx5jXF8r59+xIQENCo5y5hRDSKrKwf+Wv3YxgN4fTrtwKNpm6j0BJLCZOXTWZr7lYALmt5GQ/3eJho32gcDic/vLKFvIwywjt4k95vDV/s+QK7045Ja+LpPk8zps0YFEXBUlnJLzNfJi1pKygKQ8fdTfdRVzX6OdkdTlLzK9iZWcJfmaUcyKsgtaCCg4WV2BwqUSj0Q09fdHRDi4HafyGn4mC74iDVoJDtrcXqq8PPy0iAl54Asx4/sx4vgxZvow4vgxYfow4vgw5vo9Y99jbq8NJr0TXyO2mO1Jb8tSaTQ8lF7musRqcQ1zmEdn0iaNkpGK2+6dp9OKvs2IuqcRRVYy+y1B4XW1Cr6nELy1vnDidHAoouyOSqYQkwNmq7FZutlJLSra6AUryJ0tLtqKq1znpeXq3x9+uCn19X/Py74OPdDo3mzAKeaJgjF+4jg91uP6PPR+adLBzUZ7o+650P7rrrLmJiYhp1nxJGxBlTVZWNm66kvHw3rVs9RlzcfXXWySrP4r4l93Gg5AC+el9eHvQyg6IHuZe7Ozc75umZ9NJ0XvjjBf7I+gOA0bHDeSTyHyya9Ql5mdnoDEZGP/g4bXr2OWvnasutpCIpl7KkPJT82rUfRXqFnQaVjU47ay3V5DbiLxaDToNJp8Go12LUaWoGLUa9BlPN2D1Pp8F0ZD29a55Bp0GnUdBrNei0rrFeq6DTaFAq7VTsL6NsTzGWAov7mDqTltAOgUR2CiI8wR+jUYf+b/vQaRS0GqVJblU4q+3ucPL3oGIvrD51WFFA6288JqAYXbfEasKKxteAcgbtgZxOC2Vluykp/ZPS0iRKS5Koqq57a0ejMeHr2wk/vy74+nbC16cjXl6tmvVL/5xOJ3a7vdZgs9lO+Plky0607snCw/lyUa8vjUZz3EFRlJNON9XyHj16SM3IyUgY8YzCwnX8ue02NBozA/qvqdPRWWF1Ibf/ejvppemEeYXx3vD3aBvY1r08/1AZ303bjNPxt3fPlGbiTJrLx6nzeduZh3+ZgZGbwjBYdHhprVwTs4uIcH+I7AqxfaDtSAhpC418YbQXVlP5Zy6V2/Ow5xzzaKhGwRjnh6l9EKb2QehCze6LsqqqVFodFFfZKK60UlJpq5m2UVzl+lxabaPC4qDSaj86tjqotLjGFRY7dufZ/W8X6lDoaNXSwarDVz36PVpQSdE72Kt3kKp3Yv/bV6xRQKtR0CiucKJVFDQ1QUWjKO7QotFwdJmi1NpGo6lZT6lZ73j7UxS0Wtd8k1MlwOIk0Kbib1XxtzpdY4sTX6sT/SmuRw4Fyk1ayk0aKsxa12DSUmXWUuGlw27QoFEUFECjUVAUUFBc7YMVXMuOLFdcnbHpKMagJmNw/oXesQe9czcaKuocW8WAXdsahyYBhzYBuzYBp7YViuJ6hF1Rjv4YK0ce1D7mOz/yc6ZQe72j0xyzTe3tjyxzOuw47TacdjtOhw2n0+6atttxOGw4HXYcdtc6jmPWdTjsOP427RqOznc6T30L7mxSNBo0Gi0ardZ1QdVqXZ/d87RotJq6n7W111GOXJg1GjTK0WnlmOlay7Va93StZe4LfN3tjszTarUoGgVF0Rw37Cuc/Pfcmf4aPNn28SHeeBkaN0xLGBFnRFVV/vzzVoqK/yC6xW20a/dMreWVtkom/D6BHfk7iPSO5LNRnxHhfbSjKbvNwfcvb6HgcDnxXUIYdV9nlIMbYdWrkLIMVNcV5XdLGH+mJ6BzaCj1sTKq5V4udhZRR3Ab6HYrdL0FfE6/63en1UHVrgIqN2djSTn65li0CqY2AZg7h2LuGITGq+mq31VVxepwUmlxUGG1Y7U7qbY5sdgdWOxO12BzTVfbjplnd2CxOWvNt9qd2J1O7A7XPu0OJ3anis3hmmdzOLE5VPc6doeToAqV6AqV2CoFb+fR30xWVFL1TlJ0DtL0DirOrSd43YJQiERDVM3YNe0ah6GgO8Uv8zJUsnC6h0ycZB4zr+4NmroUnIR75dEqII2Wvgdp6XeIGN9DmHR1t3Y4NWRWRJBRFk1GaQsOl0dxuDySUqtfzb5UdDjQ40SvONDjqBk7j5k+Zt6Rz3XWd807W+1unSo40GBHgwMNDlWDA8U1jQb7MZ9d08csr/lsPzLvb8udas0YBQcKTjQ4VKXms2u+0xXZzs7JXiB+fKAf3WMbt08eCSPijBQUrGJb0h0oioG+Fy/BbG7hXmZz2pi8bDJrDq/B3+jPZ6M+o5V/q1rbr/pmLztWHMLsq+eGieF4r5sKe389ukJsP7bZu7Js2U5UVaU4QmFh53RUo45nej7OVV6xcHiLK7ikrQFHzS95jQ7aj4Ye4yF+CGhOfcVUVRXrwTIqN+dQmZRXq4GlsbU/Xt3CmzyAnItUp0p2aikpf+ZyYGseZYW1b08FRnsT0S6Q8HYB+LXwRkXBoao4nCrOmrF7UFWctaapNc9+zDbHbuuadq3rcDhxqq5mLq4Gf+BU1Zp5rs9qzWenevSzytH1cKp4VTnwrnbgXeXAu9qJb7UDn2oHvhYnXrZT/7or1ymUGBSKDRqKDQrFeoVig0KhTqFYB6qi1JSxZgOnHY3ThuKw4qs9TJAhjQBjBoGmQ/ibDmPUVR73OFariYrKACor/KmsCKiZDsDhaJzO+pxocChanEcGtDiVY+ahxaFoasaueY4j26DFUbO+KzC4ltvdAUEDJ+h+/6Tf8Cm+/pMtPtml6lT/qie7yqmn2Pqk257B1dMT53Oq8s4e14vO0f6nOHrDSBgRp01VnWzcdBXl5buJjbmLhISnj1mm8u+1/2Z+ynxMWhMfXvYhXcO61tr+wLY8fn1/BwBXXJJGy71Pgb3a9cur6y04+z3IqkWr2PLLTwB0GjKc/uPv5F/r/83yg8sBuOOiO3iw24NoNVqwlMFfP8PmT+Dw5qMHCk6Avg9Al5tAX/cRVqfVQeWfuZSvy6x1G0YbZMK7exhe3cPRBdX/ceHmTFVV8g+Wc2BbHhm7CshNr91Pi8lbT0zHIKLbBxLdLvCUjwyfq5xWx9F2KoXV2GsGW2EVlUVlVFstVGPDotiOjhUbFmxYFbtr0DmwaRxYsGFx2nCc9NaFisFYiY93IT6+hXh7F+LtXYLJVHbCGgyr1QerNRSHPRynGoZGiUKrbYHBEIjRaMRoNGIwGE44bTAY0Ov1aOoR1IVoahJGxGnLzv6ZXX89gk7nS7++y2t1pf3G1jf4aMdHaBUtbwx9g8ExtfsLKc2v4ttpm7BU2OkavpH+yjTXglZDYdSrWH1jWPjWdFI2bwCg/w230eea61EUBafq5O0/3+bDHR8CMCh6EK8MfAUfg88xhdsBWz6F7XPBUuqa5xUMvSZAr7vBJxR7cTXl67Oo2Jjtbgyp6DWYLwrBq2c4xnj/M2rgeCGoKLFw8K9C0ncWkPFXIda/NSr1DTYR3S6QFu0CadE2EJ/AU3frf7bYbDYqKyupqqqq97iqqurMDqqCER0G9Bi1eowGIyajCZOXGbOPGbOfN96Bvph9vTCZTOj1KpCFw3kQmy2N6uoDVFbuw2LJPuEh9PpAvLxa4eXVCm+v+Jrp1pjNMfJUjzhnSRgRp8VuL+OPDSOxWLLrPEHz5e4veXnjywA82+9Zrk24tta2NouDH151tRMJM6ZybcDjaE1eMOJF6HYbpQV5zHvlOfIy0tDq9Yy4/yE69K/b+dnCAwuZum4qFoeF1v6teWvYW8T4/u1xM0sZ/PkFrH8XSjJQVbAqXSn3upeqohh3Pac2yIRPvyi8e4ajMTXfpxyaktPhJPtACQd3F3E4uYic1FKcf2uA6x9qJrK1PxE1Q1CEd6MFPqfTSVVVFRUVFfUaLBbLqXd6AgaDAS8vL8xmc52xyWTCqOrQ27XoqxT0VaAtV9GWOdEWOXCWWU9Zv67x0rmeBPKv6a3WPW1EG2BE9a6m0pJCecVeKir2UlmRSkVlChZL1gn3qSg6zOYYzOY4zOZYzOYYvMwtMZtjMZmi0Wql9k94joQRcVp2736KzKxvMZti6dNnIVqtqzp+UdoiHl/5OCoq/+z2T+5JvKfWdqqq8tuHO0nZmodZU8T1wY/hExUJ138Gwa3J2pfMvNeep7KkGC//AK6e8m+i2rY/YTl25u9k8rLJ5FXl4W/053+D/0fvyN511lMtVioXLaZ8Szk2a5R7vtH7ID4DojENGoDSyH15XOis1XayUko4nOwKJ7kZZXUuwkYvHeHx/kS29icszpewWD9MPkf/enc4HJSXl9caThQuKisrT3p//XgURTlhqDjR2Gw2o9OdfmBV7U4cxRbsRa5bP47C6qPTRdX1ekUA/C2wBLhCC35OrF45WPWHqOYglZY0KisOUFmVWuclgX9nNEZgNsXUBJVjhxj0+iDpaVY0KQkjosHy85eTtH0CoNC9+9cEBvQCYH3meiYunYjNaePGdjfydJ+n6/wCW//dLrYuzUGDjTFBU4ns1Q1G/w8MXuxauZTFH76Nw2YjNDaOMU9MxS/k1E/E5Fbm8uCyB9lZsBOtomVi14ncedGdaDVaHKVWyv/IpGJjNs4jPY5qwTvgL3zK3kGvSXfNi0iEfv+ETteAVqqym4Kl0kb2gVKyD5RweH8BWRn52BzVODVWnForDo0Vp8aKxmRHMdixY8Fqr9uT7amYzWa8vb3rNZhMpnPuIuu02HEUWbCXWNy91TpKanqurRlUa/360dB46VwvLfTV4wwow+aTjdWcg1Wfg0WThcV5mCrrQRyOuo8fH0ur9cFkisRkisJkjMJkisJ47LQxXG4BiTMiYUQ0iMWSx6ZNV2Ox5hATcwdtE/4NwKbsTTyw5AGqHdVc2vJSXhv0mqtR6TG2fr+B9Utcv/QuCXiPDv+4HHqMx2axsPTj99m1cgkArXr0ZvQ/p2Awe9W7XNX2ap5Z/wwLDiwA4GrzSO6z3gy7K9zvSdH6G/DuG4V3rwi03nooSIH178C2r8Be0xbArwX0udf1FI6pcVuLXwhsNhtlZWWUlpZSWlpKeXk5ZWVldcYNukWiKmgxYNKb8fb2wdfPh4Bgf4JC/fH19akVLry8vNBqPffyvbNBVVXUKjuOUiv24qMBxVFc091+zbzjvgvpePtDxelbjSO4CLt/PnafPKymXKy6bCxKFlZnbj32omA0hmMyRrpCypHBGIHBGI7REIbBECKBRZyQhBFRbw5HFVu33kxp2Xa8vFrRu9d8tFozm7M388DSB6iyVzGwxUBmDp2JQVv7scPtn89j9VrXv0m/0Hl0u288tOhOwaEM/u/1lyk4lIGiaOh73U30ueb603rHjNPuYPXS37D/UUC7qjj3fH1LX3z7t8DcKQRFe5y/gisLYdNs2PgBVNT84jX4QPfboc99ENiy7jYXoOrqanfIODZwHDs0pIGnTqfD19cXHx8f99hs8ka1aLGWaagqUCnNtlOWYz9uB08arUJAuBdBUd4ERXoTFOVNcJQPfiEmNBf4LbcjgcVeYsVZVvMCwzJXWDn62TVgP/mvdqfGis2Uj91UiM27CIdfEXbvIuzGAmyGfKyaPFTFdtJ9uCgYDMEYDeEYjKEYDWEYjeEYjK6x+7MhGEVp3oFS1CVhRNSLqjrYsWMiefmL0esD6dnje7y84liSvoQnVj2B1Wmlb2Rf3hr2Fkbt0ScmVGsVG9+cw+b97QDoHr2Fvo/chWoOZNfKpSz9+D3sFgveAYGMnvwYMZ0SG1w2e4mFio3ZrlsxZa5+RuyKgxV+m/k5cBm6Ft5M6TmFPpGn6DbeVg07voP1b0PeHtc8RQutL4EuN0K7y8FQ/9qa84WqqlRWVtYJFn8PHFZrfbr5coUMf39/fH1964SNY8dGo7Fet0isVXbyDpaRl1FGQWYFhZkVFGVVYDvBi/Y0GgXfEBMBYV74h5nxD/UiIMyMf5gXvsGmk75t+ULjrmU5ElBqQsrfA4uz1HrCmhYVJw5DGTZTAXZTATZTIXZzgeuzuRiHqRibvhiU+vbKqkGvD8RgCMZgCHGN9cEYDMHoj8yr+WwwBKPVNr//kxciCSPilBwOC7v+eoS8vEVoNAa6df0cf/8efP7X50zfPB0VlSExQ3h10KuYdUf7lbBl7GDVe7+zp6gbAL0vOkTP+2+hrKiQJR+9Q+qfrr5AWiZ2Y9TER/AOcD0arDqdOIqLUW02cDpRjEa0vr4o+qNVvKpTxZJSTPkfWVTvLoCa35MaHz0+F0di7h3OvOz/4/XNr1Nmc/WFMSh6EPd3uZ+LQi46+QmrKuxfCuvfggMrjs43+ED7K6DzPyB+EOjOncdUT8ThcFBRUXHcWoxjA4fDUb8Lhclkws/Pzz34+vrW+uzn53dW2mGoTpWyomoKa8JJYdbRkGI/ye0JjVbBL8SMX4gJ3yATvsE1Q5AZ3yAT3v5n9r6a5sxpdeAst+Eot+Ist+GssOEot+Est+KosLnmldtwVFhxVtjc/yfhSGApx24swm4srjU43NNF2I0loDTsUqPRmI+GFkMwen0Qen0Aen0gep2/a6wPqDVoNOf+/90LjYQRcVI2WzHbt99HcckmFEXPRZ3ewOh/MVPXTmXZwWUAXN/2ep7q8xS6Iy/+slso+nUWi37zo9Aei4KTwZdCu6sGs3Xhz2z4aS7Wqiq0Oh19rriWDj5BVCclYdm/H2tqKvb8fDjOxVHj54chri362L5gTADH0UcRDfH+rhDSKbjWm1qLqot4L+k9vk3+Fofq2me/qH7c0uEWBrQYgOYEvUO65e939VWyfS4Upx+db/CB1kNdtSUJl4F3yGl+w6fv7+0zjlebUV5eXu8nTLy9vesEi78HDoOhcXr9bCqqU6W82EJJbiUleVUU51a5p0tyq3DYT96OQqNV8AlyBRWfQCPe/ka8Aww1YyNe/ga8/YxN+jbj5kB1qjir7DgrasLKseGl0oaz0n50XOEaq1ZHTWgpxW4oxVEz2A1lOIwlrrF7nmusautze6gujWJGr/U/GlqMgegNAeh1Aej0/uh1fuh0fuh0vscMrs/HeyO5OHMSRsQJ5ResYM/up7FYc9DpfOl80btsLC3ltU2vkVuZi16j59Gej3Jz+5tdfwmrKvadC9j27Qo25w3DgRGzvpJh49pSWXWYtd99SUmOq7OmUN8AupRZMGzfdeK+h3U619uanAq6iC7oY/qgDeuEUtOeRLVVYTv4B/bMPzDEBWFO7II5sTPmxER0kZG1/jpPK0njwx0fsuDAAncoifaJ5uo2VzM6fjQxfqd4HbaqwsGNrlCyZwGUH9vplAIRnSFugGuI7QteQaf9vauqisVi+f/2zj06qur649/7nsnkZQh5AUFABCG8REmjFbtKFo/SSqu/llLWD7RWq0VLi3VRXAWqfzQoq+qqZUG7loq/ZVHL+im0tLU/3tYSUV5FQCJgeEkSIJBk3nMf+/fHnZnMJJNkAoFhkv1hHc65+zzuPtn3sefcc8/t1MnozvwMURSjj0wSORvZ2dnIzMy8qtdV04FYR6WlMQB3YwDuS+G4MQBPUxCU5IcJHZlKnKOSka3CmaXCkanAmaXAmalGY3ZckoMMK8ZJsR0UM9Zx8cY4MH4dpt+AqXtgCM1hB6UZZsRhUbwwFQ9M1QNT8cJSPFFZd0de2iJCgyRkQhYzIUlZkKVsKEoWZCUbspoNRcuBouZAkjMhSxmQJBckOROSlAFZctnbkqtXf7X5SmBnhGmHz1eLL2p/h4aGvwAAnM6bIRb/BH84ugl7GuxHK6VZpVh570qM6jcKsCyYR/6GY3/ZjE++LEeLWQwAGFDsQcl4wsH/24jmi/bEUM0wMeLcRQy47IlOSdRGjIDrK+XQRoyENmwo5KJiiM4sBGs9CBy9BP/hi3GvMgpaADBPwjizG4HD/4Hl8bTrg5SfD2dZGRxjyuy4rAxyv3444z6DdZ+tw8bjG6OPbwBgZN5I3FVyF+4uuRsTCiZA6ez1XssC6g4An78P1PwDqD/YpoAA9B8JlEwASsbbXxUuGgOoGdHHJh6Pp0Mnw+12d2t+RlePTVwuFy/5nQSWacHbHIK70Q93YwDe5hC8TUF4m4PwNoXsuDkIq4sJn21RHRIcWSqcmQqc4VjLkKFlROIEaZcMqY9Pwk0WMixYAQOW3wAFTHtEJrptwPKb0W0rEIIRdEM3LkM3m2FYzTAFN0w17LwoHpiyD5bigyX77XR0u/uvmXeGQAokZECEE5KQAUnMsOOoA+OCLGdCVjIgKVmQVRckNQOSkgFZzoAoOSCJTkiSE6LoiMaimNxcrBuNa+qMrFq1CitXrkR9fT3GjRuHV155BZMmtV+QKsL69euxdOlSnDx5EsOHD8fzzz+Pb3zjG0nvj52RK4fIQlPTJzh37h00nN8EIhOAgGDmV7HuvA//afwMAKBJGh4e8zAeGv0QHC3n0LxrIz6vPosjlyfBY/UHkQlV+ALZmbVo+PIzGOHvcSiGiSEXmnDzxWaomVlw3XUXMu+5B66v3g2lsBBkWtDrvAh+0YzA0UsInmyOe+Ys3aQhY0IBMsYXQClonbBGloVQbS38/zkI/6cHEfjPQQQ+/xww2i8cJZcUwzm6DI7Ro4Ehg/Cxsw4bfR/ho/Mfw6LWnTllJ+4ovANl+WW4Le82jOo3CgUZBR2f4O56BI9/AM+J3XCfOQRP8yV44IIHGXDDFU674Bay4SMVyX5BNHZ+RiInIysrC06nMy0vPOkKESHoNcIOSjAa+9w6/O4Q/G4dAU8k1tutQNsdZE2C5pRbnRSnDMUhQ9EkKA4JqiZBcchQHZFtGYpDgqJJUB2taUWT+BjpBDIs23EJmrCCJihohGMzLraCIRghN3S9BabeAt30wDTdMMgDk9ww4IUlem0nRvHBkvyw5CAsKQBL9oOkICw5ABKTW9TuyjskQCQVAmkQoUGEAxI0iIIDIhy2wyI4IIkOSJIDoui0Y0mDKDsgyXZakh0QFQck2QlJ1uy04oQkO6BpxT2+Yu81c0beeecdzJs3D2vWrEF5eTlefvllrF+/HjU1NSgoaL+Q1a5duzB58mRUVVXhm9/8JtatW4fnn38e+/btQ1lZFxMOu9kZxiYUakRz8z40XvoQjY3bEQh8Gc2ro3z8+YIftUHb7JqkYVbpVPy3NgrS5/U4W9OMM02lOK8PApkXYRkNEIxaWPoZmGid7+EKhFDa2ILhxYOQe889cH31HjhGj4bZYsA470PojAfBUy3Qz7rbzdaXCzLgGJkH5+h+UEuzkr6gWoEAAkc+Q+DQIfgPfYrAocMI1dYmfBwkaBqkIYPRlO/AqQwfPpXqccrlw+UsFW6XBlPWoFka8sQ8FKlFyBFy4CIXVFOFoAswggYCvgB0Pfln1wIsZMKHTHiRDTey4UO2Q0S2KwNZOTnIzitAdl4B1JwCICPfno+SkW8/+rmCV56Z1EAWIeg3bCfFoyPg1uH32Omgz0DQ1za2Q9vv+1w1AqBoEmRVgqKKkJRILEJRJUiKCFmVIKsiZCUSx8hUqXVbsetJsh1ESbDTSnxakoQ++Xo1mVarExMyQSELVsgE6VZ02wwFYIY80HUvTMMD0/DCMLwwTS9MywfT8sIkP0zywYIfpmDHlhgASTosMQhLCoEke5FAkuxtiMm+rXT1jC15Hf1HTu7RNq+ZM1JeXo4777wTv//97wHY340YNGgQnnzySfzyl79sV3727Nnwer3YtGlTVPaVr3wF48ePx5o1a3q0M30BIhOG4YauN0M3mhDwn0WLtxZu30n4/KcR9NcCxqW4OgELOOjRsO9yLi558+AKZOJWbxFucecgyy3BF1LhIwVkeUDWJZB5EWQ1x7UhCQpclowSS0XpwFtRNKYcSslQQJdgNAVhXPDBaAwACX4xCk4ZWmkWHLfeBMfIPMj9kv/iq2ma0HW9XQiFQtB1HUG3G96Tp+A5cwa+C+fhb25BwOdDSJagKwpCigpdVaArdujwU6mdYMEAZBOCAkgOCbJLhZrphCPTAZcmIou8yDWbkOmvg+Y+C7WlHornPFTTgAKCSgSVAIUIChFktB1DEWyHRMsGtMxwnGVPptWywiGcJzvCQYsJsTIHIKnhWLGdHFFuDYJ4RX8D5uqxLELIn8BJCRjQA2ZrHDShBwyEAib0oC3Tg6a9HTAQCppdf2P+GiIIsB0WWYQkCzHp1u2IQyNKAkTRdmAEUWiVxchFUYAQJ4uk7TY6rScCgihAEOLTomgrKsbmxcUCBCG+bmvZmLy4cnYsCgIgoMdGpcgikBF2anTLTsfElh6CFfLDMPwwdR9Mww/T8ME0/bAMP0wzANPyw7ICMK0ALCsACwFYFIBFQVgUgiWEQOHYEkIgQQeJIZCowxJ1UDiMG/Y68m65s0f6FSHZ+3e3ZtqEQiHs3bsXS5YsicpEUURlZSWqq6sT1qmursaiRYviZNOmTcOGDRs63E8wGIxbybGlpaU7aibN//5+LgRX67wEARRzlwif7YkmRbWRJayXqKxA0WLUtp5gQRBNCJE4GmK2JR2iHOryXkIE6N5c+C4NhP/SAPgaByLPUlAJCqtH9v4VwJNnn4AZIAjIApANQRgCIfJPECBAiup5MRxw8gs7xCIBUABBkyA4ZIgZMsQMBaIq2atBnrJgfmHCNE1YlpUwjqQjTodlJbfaJABAVYH+Xb/9Ius6tGDQDoFgazpBcAQCUBI8GopgiIAh2cEUAZ8ItEiAKebBFFvllgBQTAAAtNsOgoQggMZoni0P26tNXSF8rAn2wdQxAhA51EiIFA1XiD2YBPu/cHE71aZdu42YpcoizaB9O9FNIXJWCHF12m+3kaO1vxGd2pWMq9tzTlaixdg630X7jI61Sb5tKRxiB84JAEEBCRosaOG0CoIcjhVYkW2ogCDDggoSFLsslNZ0OLaggAQZgBRuJxxDsi8Usfsn2K9ZJ7kSbK+FLNjWsMKmI7QepXa6VQ60Pp+m6LnbtrydTJRv1xXa7QMAJAhwAXAl0CFRndb7gAiCCEAURIgQUPhfF5B3S7f+Cj1Gt5yRixcvwjRNFBYWxskLCwtx9OjRhHXq6+sTlq+v7/hT2VVVVXj22We7o9oVoQ74Ahk5ySyJfONhmjJ0XUUw6EIw6EIgkGkHfzY8nptgmjGvqSkhAMlNnOwRIrvrYR9SVVUoitIuqKoKh8PRLmiallAuhkLQ6xtgNl2GefkyzKYmmJcvw4ikm5th+fzQfW4YHg9MnxfkDwCBAMSADiFmMFG27IArexMxSTq6rV2LEQ7qIM30VQgACRIsUQYJMixRhiVKsAQZJIa3w3KKTQsSSJRAgghLsGM7SDFBhCV2khdbT2yfB0G0b+32sEU4bY/+EcQ2aaGT8vHppIiWkzo8U67oDErhwOWls4nv49eDG/IdpCVLlsSNprS0tGDQoC5e0bwCgmeGQ78wAECbX25xR5AQn6aYIhT7+6nVl7UTMfUowdFF8T4zSARZImBJoHCIT4sgUwEZKixdA0gCIEAU7F9xDgFwCkLr0KQoQJRkCKIMUZEgyxJERYakqBBVFZKmQZRleyGo8BAqBHu4FJHhyfDwqaCIgCLZ6QTDMp0NV8bmSZIEURTj4o5ksizHORuS1IOT9WQZ2tAhAIZ0uyoRgUIhkN8PMgw76DpIN0CGDkRkhhGVka7DtEyYpg7dCMG0DJimDssyYZkmiCxYlgkyLVhkgiwTZFkgyx4lak23yokIBLJjsn/t2MdS+FdPOB+W3T7IsmVxaTP8WM2y2yAzLA/Xi7YXbp8o3K59tBPZi14JFN57tBxay0d/hdkyIToyFyOPOndtLt1ECcTULjuBlbqyYhdtJK5PMfLYAU/qaH8d6N1Zu8nQvW531Jer2V+sxAyH9t8kSuZNWwH2yM/VklR/ujEjwS4qwj7SBdiaip2kEU7HDF0CMWXQJha6Uac13b5ORJ64fGftUYJ2Boz6dod/k2tNt5yR/Px8SJKEhoaGOHlDQwOKiooS1ikqKupWeQDQNA2adu1X0vuvn/7PNd8H07sQBAGCpgHX4fhkGIbpK3RrWrSqqpg4cSK2bt0alVmWha1bt6KioiJhnYqKirjyALB58+YOyzMMwzAM07fo9mOaRYsWYf78+bjjjjswadIkvPzyy/B6vXjooYcAAPPmzcOAAQNQVVUFAFi4cCHuvfde/Pa3v8XMmTPx9ttvY8+ePfjjH//Ysz1hGIZhGCYt6bYzMnv2bFy4cAHLli1DfX09xo8fj/fffz86SfX06dNxq0LeddddWLduHX71q1/hmWeewfDhw7Fhw4ak1xhhGIZhGKZ3w8vBMwzDMAxzTUj2/t33ltJjGIZhGOaGgp0RhmEYhmFSCjsjDMMwDMOkFHZGGIZhGIZJKeyMMAzDMAyTUtgZYRiGYRgmpbAzwjAMwzBMSmFnhGEYhmGYlMLOCMMwDMMwKaXby8GngsgisS0tLSnWhGEYhmGYZInct7ta7D0tnBG32w0AGDRoUIo1YRiGYRimu7jdbuTk5HSYnxbfprEsC+fOnUNWVhYEQeixdltaWjBo0CCcOXOm137zprf3kfuX/vT2PnL/0p/e3sdr2T8igtvtRklJSdxHdNuSFiMjoihi4MCB16z97OzsXnmAxdLb+8j9S396ex+5f+lPb+/jtepfZyMiEXgCK8MwDMMwKYWdEYZhGIZhUkqfdkY0TcPy5cuhaVqqVblm9PY+cv/Sn97eR+5f+tPb+3gj9C8tJrAyDMMwDNN76dMjIwzDMAzDpB52RhiGYRiGSSnsjDAMwzAMk1LYGWEYhmEYJqX0aWdk1apVuPnmm+FwOFBeXo6PP/441SpdEVVVVbjzzjuRlZWFgoICfPvb30ZNTU1cma997WsQBCEuPPbYYynSuHv8+te/bqf7yJEjo/mBQAALFixAv379kJmZiQceeAANDQ0p1Lj73Hzzze36KAgCFixYACD97PfBBx/gW9/6FkpKSiAIAjZs2BCXT0RYtmwZiouL4XQ6UVlZiWPHjsWVuXTpEubOnYvs7Gzk5ubi4YcfhsfjuY696JjO+qfrOhYvXowxY8bA5XKhpKQE8+bNw7lz5+LaSGTzFStWXOeedExXNnzwwQfb6T99+vS4MulqQwAJz0dBELBy5cpomRvZhsncF5K5dp4+fRozZ85ERkYGCgoK8PTTT8MwjB7Xt886I++88w4WLVqE5cuXY9++fRg3bhymTZuG8+fPp1q1brNz504sWLAAH330ETZv3gxd1zF16lR4vd64co888gjq6uqi4YUXXkiRxt1n9OjRcbp/+OGH0byf//zn+Otf/4r169dj586dOHfuHO6///4Uatt9Pvnkk7j+bd68GQDw3e9+N1omnezn9Xoxbtw4rFq1KmH+Cy+8gN/97ndYs2YNdu/eDZfLhWnTpiEQCETLzJ07F4cPH8bmzZuxadMmfPDBB3j00UevVxc6pbP++Xw+7Nu3D0uXLsW+ffvw7rvvoqamBvfdd1+7ss8991ycTZ988snroX5SdGVDAJg+fXqc/m+99VZcfrraEEBcv+rq6vDaa69BEAQ88MADceVuVBsmc1/o6tppmiZmzpyJUCiEXbt24Y033sDatWuxbNmynleY+iiTJk2iBQsWRLdN06SSkhKqqqpKoVY9w/nz5wkA7dy5Myq79957aeHChalT6ipYvnw5jRs3LmFeU1MTKYpC69evj8o+++wzAkDV1dXXScOeZ+HChTRs2DCyLIuI0tt+AOi9996LbluWRUVFRbRy5cqorKmpiTRNo7feeouIiI4cOUIA6JNPPomW+cc//kGCINCXX3553XRPhrb9S8THH39MAOjUqVNR2eDBg+mll166tsr1EIn6OH/+fJo1a1aHdXqbDWfNmkVf//rX42TpZMO294Vkrp1///vfSRRFqq+vj5ZZvXo1ZWdnUzAY7FH9+uTISCgUwt69e1FZWRmViaKIyspKVFdXp1CznqG5uRkAkJeXFyf/05/+hPz8fJSVlWHJkiXw+XypUO+KOHbsGEpKSjB06FDMnTsXp0+fBgDs3bsXuq7H2XLkyJEoLS1NW1uGQiG8+eab+OEPfxj3Ych0tl8stbW1qK+vj7NZTk4OysvLozarrq5Gbm4u7rjjjmiZyspKiKKI3bt3X3edr5bm5mYIgoDc3Nw4+YoVK9CvXz9MmDABK1euvCbD39eSHTt2oKCgACNGjMDjjz+OxsbGaF5vsmFDQwP+9re/4eGHH26Xly42bHtfSObaWV1djTFjxqCwsDBaZtq0aWhpacHhw4d7VL+0+FBeT3Px4kWYphn3BwaAwsJCHD16NEVa9QyWZeFnP/sZ7r77bpSVlUXlP/jBDzB48GCUlJTg4MGDWLx4MWpqavDuu++mUNvkKC8vx9q1azFixAjU1dXh2WefxT333INDhw6hvr4eqqq2u8gXFhaivr4+NQpfJRs2bEBTUxMefPDBqCyd7deWiF0SnX+RvPr6ehQUFMTly7KMvLy8tLNrIBDA4sWLMWfOnLiPkP30pz/F7bffjry8POzatQtLlixBXV0dXnzxxRRqmzzTp0/H/fffjyFDhuDEiRN45plnMGPGDFRXV0OSpF5lwzfeeANZWVntHv+miw0T3ReSuXbW19cnPE8jeT1Jn3RGejMLFizAoUOH4uZUAIh7TjtmzBgUFxdjypQpOHHiBIYNG3a91ewWM2bMiKbHjh2L8vJyDB48GH/+85/hdDpTqNm14dVXX8WMGTNQUlISlaWz/foyuq7je9/7HogIq1evjstbtGhRND127Fioqoof//jHqKqqSotlx7///e9H02PGjMHYsWMxbNgw7NixA1OmTEmhZj3Pa6+9hrlz58LhcMTJ08WGHd0XbiT65GOa/Px8SJLUbtZwQ0MDioqKUqTV1fPEE09g06ZN2L59OwYOHNhp2fLycgDA8ePHr4dqPUpubi5uvfVWHD9+HEVFRQiFQmhqaoork662PHXqFLZs2YIf/ehHnZZLZ/tF7NLZ+VdUVNRuMrlhGLh06VLa2DXiiJw6dQqbN2/u8tPs5eXlMAwDJ0+evD4K9jBDhw5Ffn5+9JjsDTYEgH/961+oqanp8pwEbkwbdnRfSObaWVRUlPA8jeT1JH3SGVFVFRMnTsTWrVujMsuysHXrVlRUVKRQsyuDiPDEE0/gvffew7Zt2zBkyJAu6xw4cAAAUFxcfI2163k8Hg9OnDiB4uJiTJw4EYqixNmypqYGp0+fTktbvv766ygoKMDMmTM7LZfO9hsyZAiKioribNbS0oLdu3dHbVZRUYGmpibs3bs3Wmbbtm2wLCvqiN3IRByRY8eOYcuWLejXr1+XdQ4cOABRFNs92kgXzp49i8bGxugxme42jPDqq69i4sSJGDduXJdlbyQbdnVfSObaWVFRgU8//TTOqYw41qNGjepxhfskb7/9NmmaRmvXrqUjR47Qo48+Srm5uXGzhtOFxx9/nHJycmjHjh1UV1cXDT6fj4iIjh8/Ts899xzt2bOHamtraePGjTR06FCaPHlyijVPjqeeeop27NhBtbW19O9//5sqKyspPz+fzp8/T0REjz32GJWWltK2bdtoz549VFFRQRUVFSnWuvuYpkmlpaW0ePHiOHk62s/tdtP+/ftp//79BIBefPFF2r9/f/RtkhUrVlBubi5t3LiRDh48SLNmzaIhQ4aQ3++PtjF9+nSaMGEC7d69mz788EMaPnw4zZkzJ1VdiqOz/oVCIbrvvvto4MCBdODAgbhzMvIGwq5du+ill16iAwcO0IkTJ+jNN9+k/v3707x581Lcs1Y666Pb7aZf/OIXVF1dTbW1tbRlyxa6/fbbafjw4RQIBKJtpKsNIzQ3N1NGRgatXr26Xf0b3YZd3ReIur52GoZBZWVlNHXqVDpw4AC9//771L9/f1qyZEmP69tnnREioldeeYVKS0tJVVWaNGkSffTRR6lW6YoAkDC8/vrrRER0+vRpmjx5MuXl5ZGmaXTLLbfQ008/Tc3NzalVPElmz55NxcXFpKoqDRgwgGbPnk3Hjx+P5vv9fvrJT35CN910E2VkZNB3vvMdqqurS6HGV8Y///lPAkA1NTVx8nS03/bt2xMek/Pnzyci+/XepUuXUmFhIWmaRlOmTGnX78bGRpozZw5lZmZSdnY2PfTQQ+R2u1PQm/Z01r/a2toOz8nt27cTEdHevXupvLyccnJyyOFw0G233Ua/+c1v4m7kqaazPvp8Ppo6dSr179+fFEWhwYMH0yOPPNLux1y62jDCH/7wB3I6ndTU1NSu/o1uw67uC0TJXTtPnjxJM2bMIKfTSfn5+fTUU0+Rrus9rq8QVpphGIZhGCYl9Mk5IwzDMAzD3DiwM8IwDMMwTEphZ4RhGIZhmJTCzgjDMAzDMCmFnRGGYRiGYVIKOyMMwzAMw6QUdkYYhmEYhkkp7IwwDMMwDJNS2BlhGIZhGCalsDPCMAzDMExKYWeEYRiGYZiUws4IwzAMwzAp5f8BtgNpEwXF7bgAAAAASUVORK5CYII=", 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" ] @@ -370,34 +355,13 @@ } ], "source": [ - "report(results, model_str, states[model_str])\n", - "# model = get_model(\"sidarthe_observables\")\n", - "# model\n", - "# model[0]._state_var_names()\n", - "# df = results.dataframe(results.points())\n", - "# df[states[\"sidarthe_observables\"]]\n", - "# df" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'R0'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpoints\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mR0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n", - "\u001b[0;31mKeyError\u001b[0m: 'R0'" - ] - } - ], - "source": [ - "results.points()[0].values[\"R0\"]" + "model = get_model(model_str)\n", + "to_plot = model[0]._state_var_names() + model[0]._observable_names()\n", + "\n", + "funman_request = get_request()\n", + "setup_common(funman_request, debug=False, mode=MODE_ODEINT)\n", + "results = run(funman_request, model=models[model_str])\n", + "report(results, model_str, to_plot)" ] } ], From b71d78c9f7c401e02fc3aa0c4777f42c2f836927 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Thu, 19 Sep 2024 16:06:49 +0000 Subject: [PATCH 47/93] add example of retreiving observations --- .../funman_sep_2024_observables.ipynb | 118 ++++++++++++++++++ 1 file changed, 118 insertions(+) diff --git a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb index 5e883588..ff6ce9f6 100644 --- a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb +++ b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb @@ -363,6 +363,124 @@ "results = run(funman_request, model=models[model_str])\n", "report(results, model_str, to_plot)" ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TotalInfectedR0
00.0000042.3782
10.0000062.3782
20.0000092.3782
30.0000132.3782
40.0000182.3782
.........
1960.0203312.3782
1970.0198102.3782
1980.0193022.3782
1990.0188072.3782
2000.0183252.3782
\n", + "

201 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " TotalInfected R0\n", + "0 0.000004 2.3782\n", + "1 0.000006 2.3782\n", + "2 0.000009 2.3782\n", + "3 0.000013 2.3782\n", + "4 0.000018 2.3782\n", + ".. ... ...\n", + "196 0.020331 2.3782\n", + "197 0.019810 2.3782\n", + "198 0.019302 2.3782\n", + "199 0.018807 2.3782\n", + "200 0.018325 2.3782\n", + "\n", + "[201 rows x 2 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Generate dataframe with values for the observables\n", + "results.dataframe(results.points())[model[0]._observable_names()]" + ] } ], "metadata": { From 144bdc6b1ecc496d6683a3fb18e047e6f0ef9386 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Tue, 24 Sep 2024 15:56:51 +0000 Subject: [PATCH 48/93] clean up notebook --- notebooks/monthly-demos/helpers.py | 244 +++++++++++++++++++++++++++++ 1 file changed, 244 insertions(+) create mode 100644 notebooks/monthly-demos/helpers.py diff --git a/notebooks/monthly-demos/helpers.py b/notebooks/monthly-demos/helpers.py new file mode 100644 index 00000000..a5556558 --- /dev/null +++ b/notebooks/monthly-demos/helpers.py @@ -0,0 +1,244 @@ +# Helper functions to setup FUNMAN for different steps of the scenario + + +from funman.representation.parameter import Schedules +from funman.server.query import FunmanWorkRequest +from funman import MODE_ODEINT, MODE_SMT +from funman import FunmanWorkRequest, EncodingSchedule +from funman.api.run import Runner +from funman.representation import Interval +from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint +from funman import FUNMANConfig + +import json +import logging +import matplotlib.pyplot as plt +import pandas as pd + +def get_request(request_path): + if request_path is None: + return FunmanWorkRequest() + + with open(request_path, "r") as request: + funman_request = FunmanWorkRequest.model_validate(json.load(request)) + return funman_request + +def get_model(model): + return Runner().get_model(model) if isinstance(model, dict) else Runner().get_model(model) + +def set_timepoints(funman_request, timepoints): + if funman_request.structure_parameters is not None and len(funman_request.structure_parameters) > 0: + funman_request.structure_parameters[0].schedules = [EncodingSchedule(timepoints=timepoints)] + else: + funman_request.structure_parameters =[Schedules(schedules = [EncodingSchedule(timepoints=timepoints)])] + +def unset_all_labels(funman_request): + if funman_request.parameters is not None: + for p in funman_request.parameters: + p.label = "any" + +def set_config_options(funman_request, debug=False, dreal_precision=1e-3, mode=MODE_SMT): + if funman_request.config is None: + funman_request.config = FUNMANConfig() + # Overrides for configuration + # + # funman_request.config.substitute_subformulas = True + # funman_request.config.use_transition_symbols = True + # funman_request.config.use_compartmental_constraints=False + if debug: + funman_request.config.save_smtlib="./out" + funman_request.config.tolerance = 0.01 + funman_request.config.dreal_precision = dreal_precision + funman_request.config.verbosity = logging.ERROR + funman_request.config.mode = mode + funman_request.config.normalize=False + # funman_request.config.dreal_log_level = "debug" + # funman_request.config.dreal_prefer_parameters = ["beta","NPI_mult","r_Sv","r_EI","r_IH_u","r_IH_v","r_HR","r_HD","r_IR_u","r_IR_v"] + +def get_synthesized_vars(funman_request): + return [p.name for p in funman_request.parameters if p.label == "all"] if funman_request.parameters is not None else [] + +def run(funman_request, model, models, plot=False, SAVED_RESULTS_DIR="./out"): + to_synthesize = get_synthesized_vars(funman_request) + results = Runner().run( + models[model], + funman_request, + description="SIERHD Eval 12mo Scenario 1 q1", + case_out_dir=SAVED_RESULTS_DIR, + dump_plot=plot, + print_last_time=True, + parameters_to_plot=to_synthesize + ) + return results + +def setup_common(funman_request, timepoints, synthesize=False, debug=False, dreal_precision=1e-1, mode=MODE_SMT): + set_timepoints(funman_request, timepoints) + if not synthesize: + unset_all_labels(funman_request) + set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision, mode=mode) + + +def set_compartment_bounds(funman_request, model, upper_bound=9830000.0, error=0.01): + # Add bounds to compartments + for var in states[model]: + funman_request.constraints.append(StateVariableConstraint(name=f"{var}_bounds", variable=var, interval=Interval(lb=0, ub=upper_bound, closed_upper_bound=True),soft=False)) + + # Add sum of compartments + funman_request.constraints.append(LinearConstraint(name=f"compartment_bounds", variables=states[model], additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True)) + +def relax_parameter_bounds(funman_request, factor = 0.1): + # Relax parameter bounds + parameters = funman_request.parameters + for p in parameters: + interval = p.interval + width = float(interval.width()) + interval.lb = interval.lb - (factor/2 * width) + interval.ub = interval.ub + (factor/2 * width) + +def plot_last_point(results, states): + pts = results.parameter_space.points() + print(f"{len(pts)} points") + + if len(pts) > 0: + # Get a plot for last point + df = results.dataframe(points=pts[-1:]) + # pd.options.plotting.backend = "plotly" + ax = df[states].plot() + + + fig = plt.figure() + # fig.set_yscale("log") + # fig.savefig("save_file_name.pdf") + plt.close() + +def get_last_point_parameters(results): + pts = results.parameter_space.points() + if len(pts) > 0: + pt = pts[-1] + parameters = results.model._parameter_names() + param_values = {k:v for k, v in pt.values.items() if k in parameters } + return param_values + +def pretty_print_request_params(params): + # print(json.dump(params, indent=4)) + if len(params)>0: + + df = pd.DataFrame(params) + print(df.T) + + +def report(results, name, states, request_results, request_params): + request_results[name] = results + plot_last_point(results, states) + param_values = get_last_point_parameters(results) + # print(f"Point parameters: {param_values}") + if param_values is not None: + request_params[name] = param_values + pretty_print_request_params(request_params) + + +def add_unit_test(funman_request, model="sidarthe_observables"): + if model == "destratified_SEI": + mstates = states["destratified_SEI"] + funman_request.constraints.append(LinearConstraint(name="compartment_lb", soft=False, variables = [s for s in mstates if s.endswith("_lb")], + additive_bounds= { + "ub": 19340000.5 + } + )) + funman_request.constraints.append(LinearConstraint(name="compartment_ub", soft=False, variables = [s for s in mstates if s.endswith("_ub")], + additive_bounds= { + "lb": 0 + } + )) + + + + +def plot_bounds(point, results, timespan=None, fig=None, axs=None, vars = ["S", "E", "I", "R", "D", "H"], model=None, basevar_map={}, **kwargs): + + if point.simulation is not None: + df = point.simulation.dataframe().T + else: + df = results.dataframe([point]) + + if timespan is not None: + df = df.loc[timespan[0]:timespan[1]] + + # print(df) + + # Drop the ub vars because they are paired with the lb vars + no_ub_vars = [v for v in vars if not v.endswith("_ub")] + no_strat_vars = [v for v in no_ub_vars if not "_noncompliant" in v] + + if fig is None and axs is None: + fig, axs = plt.subplots(len(basevar_map)) + fig.set_figheight(3*len(basevar_map)) + fig.suptitle('Variable Bounds over time') + + for var in no_strat_vars: + # print(var) + # Get index of list containing var + i = next(iter([i for i, bv in enumerate(basevar_map) if var in bv])) + # print(i) + if var.endswith("_lb"): + # var is lower bound + basevar = var.split("_lb")[0] + lb = f"{basevar}_lb" + ub = f"{basevar}_ub" + labels = [lb, ub] + elif var.endswith("_ub"): + # skip, handled as part of lb + continue + else: + # var is not of the form varname_lb + basevar = var + labels = basevar + + + if "_compliant" in basevar: + basevar = basevar.split("_")[0] + if isinstance(labels, list): + lb = df[f"{basevar}_compliant_lb"] + df[f"{basevar}_noncompliant_lb"] + ub = df[f"{basevar}_compliant_ub"] + df[f"{basevar}_noncompliant_ub"] + labels = [f"{basevar}_lb", f"{basevar}_ub"] + data = pd.concat([lb, ub],axis=1, keys=labels) + + else: + data = df[f"{basevar}_compliant"] + df[f"{basevar}_noncompliant"] + labels = f"{basevar}" + else: + # print(labels) + data = df[labels] + if "_compliant" in basevar: + basevar = basevar.split("_")[0] + labels = f"{basevar}" + + + legend_labels = labels + if model is not None: + legend_labels = [f"{model}_{k.rsplit('_', 1)[0]}" for k in labels[0:1]][0] if isinstance(labels, list) else f"{model}_{labels}" + + + + + # Fill between lb and ub + if isinstance(labels, list): + axs[i].fill_between(data.index, data[labels[0]], data[labels[1]], label=legend_labels, **kwargs) + else: + if "hatch" in kwargs: + del kwargs["hatch"] + if "alpha" in kwargs: + del kwargs["alpha"] + axs[i].plot(data, label=legend_labels, **kwargs) + axs[i].set_title(f"{basevar} Bounds") + + + # axs[i].set_yscale('logit') + + # axs[i].legend(loc="outer") + axs[i].legend(loc='center left', bbox_to_anchor=(1, 0.5), + ncol=1, fancybox=True, shadow=True, prop={'size': 8}, markerscale=2) + # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) + + # fig.tight_layout() + return fig, axs From b83eeeb8eb762948e46e7b4a6deaa7b331585aa1 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Tue, 24 Sep 2024 15:56:59 +0000 Subject: [PATCH 49/93] clean up notebook --- .../funman_sep_2024_observables.ipynb | 448 +++--------------- 1 file changed, 64 insertions(+), 384 deletions(-) diff --git a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb index ff6ce9f6..083f6bab 100644 --- a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb +++ b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb @@ -10,16 +10,17 @@ "\n", "# Import funman related code\n", "import os\n", - "from funman.api.run import Runner\n", - "from funman import MODE_ODEINT, MODE_SMT\n", - "from funman import FunmanWorkRequest, EncodingSchedule \n", + "# from funman.api.run import Runner\n", + "from funman import MODE_ODEINT, MODE_SMT, Interval, LinearConstraint\n", + "# from funman import FunmanWorkRequest, EncodingSchedule \n", "import json\n", - "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", - "from funman.representation import Interval\n", + "# from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n", + "# from funman.representation import Interval\n", "import pandas as pd\n", "import logging\n", "import matplotlib.pyplot as plt\n", "\n", + "from helpers import run, get_model, setup_common, get_request, report\n", "\n", "\n", "RESOURCES = \"../../resources\"\n", @@ -38,40 +39,22 @@ " EXAMPLE_DIR, \"sirhd-vac.json\"),\n", "}\n", "\n", + "requests = {\n", + " \"sidarthe_observables\": REQUEST_PATH,\n", + " \"sirhd\": None,\n", + " \"sirhd-vac\": None\n", + "}\n", + "\n", "states = {\n", " \"sidarthe_observables\": ['Susceptible', 'Diagnosed', 'Infected', 'Ailing', 'Recognized', 'Healed', 'Threatened', 'Extinct'],\n", " \"sirhd\": [\"S\", \"I\", \"R\", \"H\", \"D\"],\n", " \"sirhd-vac\": [\"S_v\", \"I_v\", \"R_v\", \"H_v\", \"S_u\", \"I_u\", \"R_u\", \"H_u\", \"D\"],\n", - "# \"original_stratified\": [\"S_compliant\", \"S_noncompliant\", \"I_compliant\", \"I_noncompliant\", \"E_compliant\", \"E_noncompliant\",\"R\", \"H\", \"D\"],\n", - "# \"destratified_SEI\": [\"S_lb\", \"S_ub\", \"I_lb\", \"I_ub\", \"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", - "# \"destratified_SE\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", - "# \"destratified_EI\": [\"I_lb\", \"I_ub\", \"S_compliant_lb\", \"S_compliant_ub\", \"S_noncompliant_lb\", \"S_noncompliant_ub\",\"E_lb\", \"E_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"],\n", - "# \"destratified_S\": [\"S_lb\", \"S_ub\", \"I_compliant_lb\", \"I_compliant_ub\", \"I_noncompliant_lb\", \"I_noncompliant_ub\",\"E_compliant_lb\", \"E_compliant_ub\",\"E_noncompliant_lb\", \"E_noncompliant_ub\",\"R_lb\", \"R_ub\",\"H_lb\", \"H_ub\", \"D_lb\",\"D_ub\"]\n", "}\n", "\n", - "# basevar_map = [\n", - "# ['S_compliant','S_noncompliant', 'S_lb', 'S_ub','S_compliant_lb', 'S_noncompliant_ub', 'S_compliant_ub', 'S_noncompliant_lb'], \n", - "# ['I_compliant','I_noncompliant','I_lb','I_ub','I_compliant_lb', 'I_noncompliant_ub', 'I_compliant_ub', 'I_noncompliant_lb'],\n", - "# ['E_compliant','E_noncompliant','E_lb', 'E_ub', 'E_compliant_lb','E_noncompliant_lb', 'E_compliant_ub','E_noncompliant_ub',],\n", - "# ['R','R_lb', 'R_ub'],\n", - "# ['H','H_lb', 'H_ub'],\n", - "# ['D','D_lb', 'D_ub']\n", - "# ]\n", - "\n", - "# hatches= {\n", - "# \"original_stratified\": '/', \n", - "# \"destratified_SEI\": '\\\\', \n", - "# \"destratified_SE\" : '|', \n", - "# \"destratified_S\" : '-'\n", - "# #, '+', 'x', 'o', 'O', '.', '*'\n", - "# }\n", "\n", "request_params = {}\n", "request_results = {}\n", "\n", - "# Cycle styles for lines\n", - "# plt.rcParams['axes.prop_cycle'] = (\"cycler('color', 'rgb') +\"\n", - " # \"cycler('lw', [1, 2, 3])\")\n", "\n", "# %load_ext autoreload\n", "# %autoreload 2" @@ -85,242 +68,10 @@ "source": [ "# Constants for the scenario\n", "\n", - "MAX_TIME=200\n", - "STEP_SIZE=1\n", + "MAX_TIME=20\n", + "STEP_SIZE=10\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))\n", - "model_str = \"sidarthe_observables\"\n", - "# model_str = \"sirhd-vac\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Helper functions to setup FUNMAN for different steps of the scenario\n", - "\n", - "\n", - "from funman.server.query import FunmanWorkRequest\n", - "\n", - "\n", - "def get_request():\n", - " with open(REQUEST_PATH, \"r\") as request:\n", - " funman_request = FunmanWorkRequest.model_validate(json.load(request))\n", - " return funman_request\n", - "\n", - "def get_model(model):\n", - " return Runner().get_model(model) if isinstance(model, dict) else Runner().get_model(models[model])\n", - "\n", - "def set_timepoints(funman_request, timepoints):\n", - " funman_request.structure_parameters[0].schedules = [EncodingSchedule(timepoints=timepoints)]\n", - "\n", - "def unset_all_labels(funman_request):\n", - " for p in funman_request.parameters:\n", - " p.label = \"any\"\n", - " \n", - "def set_config_options(funman_request, debug=False, dreal_precision=1e-3, mode=MODE_SMT):\n", - " # Overrides for configuration\n", - " #\n", - " # funman_request.config.substitute_subformulas = True\n", - " # funman_request.config.use_transition_symbols = True\n", - " # funman_request.config.use_compartmental_constraints=False\n", - " if debug:\n", - " funman_request.config.save_smtlib=\"./out\"\n", - " funman_request.config.tolerance = 0.01\n", - " funman_request.config.dreal_precision = dreal_precision\n", - " funman_request.config.verbosity = logging.ERROR\n", - " funman_request.config.mode = mode\n", - " # funman_request.config.dreal_log_level = \"debug\"\n", - " # funman_request.config.dreal_prefer_parameters = [\"beta\",\"NPI_mult\",\"r_Sv\",\"r_EI\",\"r_IH_u\",\"r_IH_v\",\"r_HR\",\"r_HD\",\"r_IR_u\",\"r_IR_v\"]\n", - "\n", - "def get_synthesized_vars(funman_request):\n", - " return [p.name for p in funman_request.parameters if p.label == \"all\"]\n", - "\n", - "def run(funman_request, plot=False, model=models['sidarthe_observables']):\n", - " to_synthesize = get_synthesized_vars(funman_request)\n", - " results = Runner().run(\n", - " model,\n", - " funman_request,\n", - " description=\"SIERHD Eval 12mo Scenario 1 q1\",\n", - " case_out_dir=SAVED_RESULTS_DIR,\n", - " dump_plot=plot,\n", - " print_last_time=True,\n", - " parameters_to_plot=to_synthesize\n", - " )\n", - " return results\n", - "\n", - "def setup_common(funman_request, synthesize=False, debug=False, dreal_precision=1e-1, mode=MODE_SMT):\n", - " set_timepoints(funman_request, timepoints)\n", - " if not synthesize:\n", - " unset_all_labels(funman_request)\n", - " set_config_options(funman_request, debug=debug, dreal_precision=dreal_precision, mode=mode)\n", - " \n", - "\n", - "def set_compartment_bounds(funman_request, model, upper_bound=9830000.0, error=0.01):\n", - " # Add bounds to compartments\n", - " for var in states[model]:\n", - " funman_request.constraints.append(StateVariableConstraint(name=f\"{var}_bounds\", variable=var, interval=Interval(lb=0, ub=upper_bound, closed_upper_bound=True),soft=False))\n", - "\n", - " # Add sum of compartments\n", - " funman_request.constraints.append(LinearConstraint(name=f\"compartment_bounds\", variables=states[model], additive_bounds=Interval(lb=upper_bound-error, ub=upper_bound+error, closed_upper_bound=False), soft=True))\n", - "\n", - "def relax_parameter_bounds(funman_request, factor = 0.1):\n", - " # Relax parameter bounds\n", - " parameters = funman_request.parameters\n", - " for p in parameters:\n", - " interval = p.interval\n", - " width = float(interval.width())\n", - " interval.lb = interval.lb - (factor/2 * width)\n", - " interval.ub = interval.ub + (factor/2 * width)\n", - "\n", - "def plot_last_point(results, states):\n", - " pts = results.parameter_space.points() \n", - " print(f\"{len(pts)} points\")\n", - "\n", - " if len(pts) > 0:\n", - " # Get a plot for last point\n", - " df = results.dataframe(points=pts[-1:])\n", - " # pd.options.plotting.backend = \"plotly\"\n", - " ax = df[states].plot()\n", - " \n", - " \n", - " fig = plt.figure()\n", - " # fig.set_yscale(\"log\")\n", - " # fig.savefig(\"save_file_name.pdf\")\n", - " plt.close()\n", - "\n", - "def get_last_point_parameters(results):\n", - " pts = results.parameter_space.points()\n", - " if len(pts) > 0:\n", - " pt = pts[-1]\n", - " parameters = results.model._parameter_names()\n", - " param_values = {k:v for k, v in pt.values.items() if k in parameters }\n", - " return param_values\n", - "\n", - "def pretty_print_request_params(params):\n", - " # print(json.dump(params, indent=4))\n", - " if len(params)>0:\n", - "\n", - " df = pd.DataFrame(params)\n", - " print(df.T)\n", - "\n", - "\n", - "def report(results, name, states):\n", - " request_results[name] = results\n", - " plot_last_point(results, states)\n", - " param_values = get_last_point_parameters(results)\n", - " # print(f\"Point parameters: {param_values}\")\n", - " if param_values is not None:\n", - " request_params[name] = param_values\n", - " pretty_print_request_params(request_params)\n", - " \n", - "\n", - "def add_unit_test(funman_request, model=\"sidarthe_observables\"):\n", - " if model == \"destratified_SEI\":\n", - " mstates = states[\"destratified_SEI\"]\n", - " funman_request.constraints.append(LinearConstraint(name=\"compartment_lb\", soft=False, variables = [s for s in mstates if s.endswith(\"_lb\")],\n", - " additive_bounds= {\n", - " \"ub\": 19340000.5\n", - " }\n", - " ))\n", - " funman_request.constraints.append(LinearConstraint(name=\"compartment_ub\", soft=False, variables = [s for s in mstates if s.endswith(\"_ub\")],\n", - " additive_bounds= {\n", - " \"lb\": 0\n", - " }\n", - " ))\n", - " \n", - " \n", - "\n", - "\n", - "def plot_bounds(point, results, timespan=None, fig=None, axs=None, vars = [\"S\", \"E\", \"I\", \"R\", \"D\", \"H\"], model=None, basevar_map={}, **kwargs):\n", - " \n", - " if point.simulation is not None:\n", - " df = point.simulation.dataframe().T\n", - " else:\n", - " df = results.dataframe([point])\n", - " \n", - " if timespan is not None:\n", - " df = df.loc[timespan[0]:timespan[1]]\n", - " \n", - " # print(df)\n", - "\n", - " # Drop the ub vars because they are paired with the lb vars \n", - " no_ub_vars = [v for v in vars if not v.endswith(\"_ub\")]\n", - " no_strat_vars = [v for v in no_ub_vars if not \"_noncompliant\" in v]\n", - "\n", - " if fig is None and axs is None:\n", - " fig, axs = plt.subplots(len(basevar_map))\n", - " fig.set_figheight(3*len(basevar_map))\n", - " fig.suptitle('Variable Bounds over time')\n", - " \n", - " for var in no_strat_vars:\n", - " # print(var)\n", - " # Get index of list containing var\n", - " i = next(iter([i for i, bv in enumerate(basevar_map) if var in bv]))\n", - " # print(i)\n", - " if var.endswith(\"_lb\"):\n", - " # var is lower bound\n", - " basevar = var.split(\"_lb\")[0]\n", - " lb = f\"{basevar}_lb\"\n", - " ub = f\"{basevar}_ub\"\n", - " labels = [lb, ub]\n", - " elif var.endswith(\"_ub\"):\n", - " # skip, handled as part of lb\n", - " continue\n", - " else:\n", - " # var is not of the form varname_lb\n", - " basevar = var\n", - " labels = basevar\n", - " \n", - " \n", - " if \"_compliant\" in basevar:\n", - " basevar = basevar.split(\"_\")[0]\n", - " if isinstance(labels, list):\n", - " lb = df[f\"{basevar}_compliant_lb\"] + df[f\"{basevar}_noncompliant_lb\"]\n", - " ub = df[f\"{basevar}_compliant_ub\"] + df[f\"{basevar}_noncompliant_ub\"]\n", - " labels = [f\"{basevar}_lb\", f\"{basevar}_ub\"]\n", - " data = pd.concat([lb, ub],axis=1, keys=labels)\n", - " \n", - " else:\n", - " data = df[f\"{basevar}_compliant\"] + df[f\"{basevar}_noncompliant\"]\n", - " labels = f\"{basevar}\"\n", - " else:\n", - " # print(labels)\n", - " data = df[labels]\n", - " if \"_compliant\" in basevar:\n", - " basevar = basevar.split(\"_\")[0]\n", - " labels = f\"{basevar}\"\n", - " \n", - " \n", - " legend_labels = labels\n", - " if model is not None:\n", - " legend_labels = [f\"{model}_{k.rsplit('_', 1)[0]}\" for k in labels[0:1]][0] if isinstance(labels, list) else f\"{model}_{labels}\"\n", - " \n", - " \n", - " \n", - " \n", - " # Fill between lb and ub\n", - " if isinstance(labels, list):\n", - " axs[i].fill_between(data.index, data[labels[0]], data[labels[1]], label=legend_labels, **kwargs)\n", - " else:\n", - " if \"hatch\" in kwargs:\n", - " del kwargs[\"hatch\"]\n", - " if \"alpha\" in kwargs:\n", - " del kwargs[\"alpha\"]\n", - " axs[i].plot(data, label=legend_labels, **kwargs)\n", - " axs[i].set_title(f\"{basevar} Bounds\")\n", - "\n", - " \n", - " # axs[i].set_yscale('logit')\n", - " \n", - " # axs[i].legend(loc=\"outer\")\n", - " axs[i].legend(loc='center left', bbox_to_anchor=(1, 0.5),\n", - " ncol=1, fancybox=True, shadow=True, prop={'size': 8}, markerscale=2)\n", - " # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", - "\n", - " # fig.tight_layout()\n", - " return fig, axs\n" + "# model_str = \"sidarthe_observables\"\n" ] }, { @@ -333,19 +84,21 @@ "output_type": "stream", "text": [ "1 points\n", - " alpha beta delta epsilon eta gamma \\\n", - "sidarthe_observables 0.5643 0.01089 0.01089 0.16929 0.12375 0.45144 \n", + " N beta_0 beta_1 beta_2 beta_3 phd_0 phd_1 phr_0 \\\n", + "sirhd-vac 150000000.0 0.18 0.18 0.18 0.18 0.13 0.13 0.87 \n", + "\n", + " phr_1 pih_0 ... pir_1 rhd_0 rhd_1 rhr_0 rhr_1 rih_0 rih_1 \\\n", + "sirhd-vac 0.87 0.1 ... 0.9 0.3 0.3 0.07 0.07 0.07 0.07 \n", "\n", - " kappa lambda mu nu rho sigma \\\n", - "sidarthe_observables 0.01683 0.03366 0.01683 0.02673 0.03366 0.01683 \n", + " rir v_a v_b \n", + "sirhd-vac 0.07 0.3 1.0 \n", "\n", - " tau theta xi zeta \n", - "sidarthe_observables 0.0099 0.36729 0.01683 0.12375 \n" + "[1 rows x 22 columns]\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -355,131 +108,58 @@ } ], "source": [ - "model = get_model(model_str)\n", + "# Stratified Scenario that needs parameter adjustment\n", + "\n", + "model_str = \"sirhd-vac\"\n", + "model = get_model(models[model_str])\n", "to_plot = model[0]._state_var_names() + model[0]._observable_names()\n", "\n", - "funman_request = get_request()\n", - "setup_common(funman_request, debug=False, mode=MODE_ODEINT)\n", - "results = run(funman_request, model=models[model_str])\n", - "report(results, model_str, to_plot)" + "funman_request = get_request(requests[model_str])\n", + "setup_common(funman_request, timepoints, debug=False, mode=MODE_SMT)\n", + "results = run(funman_request, model_str, models)\n", + "report(results, model_str, to_plot, request_results, request_params)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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TotalInfectedR0
00.0000042.3782
10.0000062.3782
20.0000092.3782
30.0000132.3782
40.0000182.3782
.........
1960.0203312.3782
1970.0198102.3782
1980.0193022.3782
1990.0188072.3782
2000.0183252.3782
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201 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " TotalInfected R0\n", - "0 0.000004 2.3782\n", - "1 0.000006 2.3782\n", - "2 0.000009 2.3782\n", - "3 0.000013 2.3782\n", - "4 0.000018 2.3782\n", - ".. ... ...\n", - "196 0.020331 2.3782\n", - "197 0.019810 2.3782\n", - "198 0.019302 2.3782\n", - "199 0.018807 2.3782\n", - "200 0.018325 2.3782\n", - "\n", - "[201 rows x 2 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "0 points\n", + " N beta_0 beta_1 beta_2 beta_3 phd_0 phd_1 phr_0 \\\n", + "sirhd-vac 150000000.0 0.18 0.18 0.18 0.18 0.13 0.13 0.87 \n", + "\n", + " phr_1 pih_0 ... pir_1 rhd_0 rhd_1 rhr_0 rhr_1 rih_0 rih_1 \\\n", + "sirhd-vac 0.87 0.1 ... 0.9 0.3 0.3 0.07 0.07 0.07 0.07 \n", + "\n", + " rir v_a v_b \n", + "sirhd-vac 0.07 0.3 1.0 \n", + "\n", + "[1 rows x 22 columns]\n" + ] } ], "source": [ - "# Generate dataframe with values for the observables\n", - "results.dataframe(results.points())[model[0]._observable_names()]" + "# Stratified Scenario that needs parameter adjustment\n", + "# Add constraint to reduce vaccination rate\n", + "\n", + "model_str = \"sirhd-vac\"\n", + "model = get_model(models[model_str])\n", + "to_plot = model[0]._state_var_names() + model[0]._observable_names()\n", + "\n", + "funman_request = get_request(requests[model_str])\n", + "setup_common(funman_request, timepoints, mode=MODE_SMT,debug=True)\n", + "\n", + "# The number of unvaccinated Susceptible is not increasing\n", + "funman_request.constraints =[LinearConstraint(name=\"monotone_vaccination\", variables=[\"S_u\"], additive_bounds=Interval(ub=0, closed_upper_bound=True), derivative=True, soft=False)]\n", + "\n", + "\n", + "results = run(funman_request, model_str, models)\n", + "report(results, model_str, to_plot, request_results, request_params)" ] } ], From 03c8dfd616c37d3b15dce629f11c308b0bb5082c Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Thu, 26 Sep 2024 18:47:11 +0000 Subject: [PATCH 50/93] setup stratification notebook --- .../funman_sep_2024_observables.ipynb | 449 ++++++++- .../funman_sep_2024_strat_abs.ipynb | 930 ++++++++++++++++++ notebooks/monthly-demos/helpers.py | 2 +- .../2024-09/sirhd-vac-request.json | 42 + 4 files changed, 1410 insertions(+), 13 deletions(-) create mode 100644 notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd-vac-request.json diff --git a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb index 083f6bab..a0c0337d 100644 --- a/notebooks/monthly-demos/funman_sep_2024_observables.ipynb +++ b/notebooks/monthly-demos/funman_sep_2024_observables.ipynb @@ -41,8 +41,8 @@ "\n", "requests = {\n", " \"sidarthe_observables\": REQUEST_PATH,\n", - " \"sirhd\": None,\n", - " \"sirhd-vac\": None\n", + " \"sirhd-vac\": os.path.join(EXAMPLE_DIR, \"sirhd-vac-request.json\"),\n", + " \"sirhd\": None\n", "}\n", "\n", "states = {\n", @@ -62,21 +62,21 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Constants for the scenario\n", "\n", - "MAX_TIME=20\n", - "STEP_SIZE=10\n", + "MAX_TIME=3\n", + "STEP_SIZE=1\n", "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))\n", "# model_str = \"sidarthe_observables\"\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -90,15 +90,15 @@ " phr_1 pih_0 ... pir_1 rhd_0 rhd_1 rhr_0 rhr_1 rih_0 rih_1 \\\n", "sirhd-vac 0.87 0.1 ... 0.9 0.3 0.3 0.07 0.07 0.07 0.07 \n", "\n", - " rir v_a v_b \n", - "sirhd-vac 0.07 0.3 1.0 \n", + " rir v_a v_b \n", + "sirhd-vac 0.07 0.449998 1.049996 \n", "\n", "[1 rows x 22 columns]\n" ] }, { "data": { - "image/png": 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", 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ujBKJqJFUFhSgJGW3be2XvXthvb77Ra2GW58+VQEkBkqdTrpCiajBNVoYWbduHZ577jl89dVXiIuLu+mxarUaak48RNSsiKII07lzKK5a+6U8NbVa94vczxfuMTHQxsbCrW9fdr8QtSCNEka+/PJLPPPMM1i3bh0eeeSRxjglETkB0WRC2eHD9gBivnSp2n51p05wj42p6n65C4JMJk2hRCQph8NISUkJzp07Z/89MzMTqamp8PHxQdu2bTFz5kxcvnwZn332GQBb18z48eOxbNkyREdHIycnBwDg4uICT0/PenobROQsKq9ds639kpxi634pKbHvE1QquPaJtq39EhMDZevWElZKRM7C4Ud7k5OTERsbe8P28ePHY82aNZgwYQLOnz+P5ORkAEBMTAxSUlJqPb4u+GgvkfMSRRGm9HT72i/lqamA1WrfL/f1hXbA/XCPjYVbv36QubpKVywRNaq6fn/f0TwjjYVhhMi5iGZzVfeLLYCYL16stl/dsaN97RdNly7sfiFqoZx2nhEiapoqr11D6Z49trVf9vyh+0WphGufPrYAEhMDZWCgdIUSUZPDMEJENRJFEabMTNvU60lJKD96rHr3S6tW0A4YYJv9tF8/yNzcpCuWiJo0hhEishPNZpQdOVq19ksSzBeyqu1Xt28PbWws3GNjoOnald0vRFQvGEaIWjhLURFKdu+xzX66dy+s1y+/oFTCrXdv2+RjMTFQtQmSrlAiarYYRohaIGNGJkqSbXN/lB09Clgs9n1yb++q7pdYuPXvD7mW3S9E1LAYRohaALGy0t79UpKUBNOFC9X2q9tFQhtjW/vFJaorBLlcokqJqCViGCFqpixFRSjZs9cWQPbsubH75Z5etgAyMBaqNm2kK5SIWjyGEaJmxHT+vH3q9bIjR6p3v3h5QTvgflv3y733Qq7VSlgpEdHvGEaImjCxshLlx47ZA4gpM7PaflVkhG3q9dhYuERFsfuFiJwSwwhRE2MpLq6afCwZpbt3w1JU9PtOhQKu9/Syr/2iattWukKJiOqIYYSoCTBlZVVNPpaMssOHgcpK+z65pyfcflv75d57IXd3l7BSIiLHMYwQOSHRYkF5aqo9gJjS06vtV4WH29d+cenWDYKC/1cmoqaLn2BETsJSUoLSvVVPv6TshqWw8PedCgVce/a0BxBVSIhUZRIR1TuGESIJmS5dQsmuJBQn7ULZ4SOA2WzfJ/P0hPa++2xrv9x3H+RcsZqImimGEaJGJFosKP/5Z9vdj+RkGM+eq7ZfFRpqm3o9NgauPXqw+4WIWgR+0hE1MEtJ6e/dL7t3w3Lt2u875XK49uhhDyDqsDDJ6iQikgrDCFEDMF26bJ96vfSnn6p3v3h42LpfYmKgvf8+yD09JayUiEh6DCNE9cicnY3cRYth2Ly52nZVSEjV3Y9YuPboDkGplKhCIiLnwzBCVA+sZWW4+sm/cXXVKogVFYAgVD39Ygsg6nB2vxAR1YZhhOgOiFYrDN9/j9xFi1GZmwsAcO3VC/4zZ8Dlrrskro6I7oTVaoXJZJK6DKemVCohr4dlJhhGiG5T2dFj0CckoOL4cQCAsk0b+L/6KtwffACCIEhcHRHdCZPJhMzMTFitVqlLcXpeXl4ICAi4o889hhEiB5kvX0buokUwbP0BACBzc0Or5/8PPuPGQaZWS1wdEd0pURSRnZ0NuVyO4OBgyGQyqUtySqIooqysDLlVd4Vbt25926/FMEJUR9bSUuR//DEKVq+BaDQCggCvJ4fD7+WXofDzk7o8IqonlZWVKCsrQ2BgIFxdXaUux6m5uLgAAHJzc+Hv73/bXTYMI0S3IFqtKNr0LfKWLEFlXh4AwLV3b+hmzoCmUyeJqyOi+maxWAAAKpVK4kqaht8Cm9lsZhghaghlhw9DnzAPFSdOAACUbdtC99qr0A4axHEhRM0c/z9eN/VxnRhGiGpgunQJuQsXoXjbNgCATKuF7wsvwHvsnyDjv5aIiOoVwwjRdSwlpbj64Yco+PRTiCYTIJPBa8QI+L38EhStWkldHhFRs8QhwkSwLWBXuGED0h96CFc//hiiyQTXvn0QtvEbtJ7zNoMIETUJeXl5eOGFF9C2bVuo1WoEBARg8ODB+PHHH6Uu7aZ4Z4RavNJDh6BPmAdjWhoAQBnSFrrXX4c2NpZ9xkTUpAwfPhwmkwmffvopwsPDodfrkZiYiKtXr0pd2k0xjFCLZbp4EbkL3kXxjh0AAJm7O3z/8hf4jBkNgeNCiKiJKSwsxJ49e5CcnIwBAwYAAEJCQtC7d+9btj1//jzCwsJw7NgxdOvWzf563t7eSEpKQkxMTANWzjBCLZClpARXV65EwaefQTSbAZkM3k+PhO/kyVD4+EhdHhE5GVEUUW62SHJuF6W8zndotVottFotNm3ahD59+kDdhCZhZBihFkO0WFD49dfIW/YeLFW3LN369YP/jNehad9e4uqIyFmVmy3oPGu7JOc++c5guKrq9lWtUCiwZs0aTJo0CStXrkSPHj0wYMAAPP300+jatWsDV3pnOICVWoTSAweQ+cRw5MyaDcvVq1CFhqLNyg8Q/O9PGESIqNkYPnw4rly5gu+++w4PPfQQkpOT0aNHD6xZs0bq0m6Kd0aoWTOdPw/9uwtRkpgIAJB5eMBv8ovwHjUKglIpcXVE1BS4KOU4+c5gyc7tKI1GgwceeAAPPPAA3nrrLTz33HOYPXs2JkyYUGub39bfEUXRvs1sNjt87tvFMELNksVgQP4HK1Hw+eeA2QzI5fB++mn4Tn4RCm9vqcsjoiZEEIQ6d5U4o86dO2PTpk03Pcavan2t7OxsdO/eHQCQmprawJX9ruleXaIaiJWVKNywwTYu5No1AIDbffdB9/prUEdGSlwdEVHDuXr1KkaMGIFnnnkGXbt2hbu7Ow4fPowFCxZg2LBhN23r4uKCPn36YN68eQgLC0Nubi7efPPNRqqcYYSakdJ9+2zzhZw9CwBQRURA9/pr0N5/v8SVERE1PK1Wi+joaCxZsgTp6ekwm80IDg7GpEmT8MYbb9yy/apVq/Dss8+iZ8+e6NChAxYsWIAHH3ywESoHBPH6DqI62L17N959910cOXIE2dnZ2LhxI+Lj42/aJjk5GdOmTcOJEycQHByMN99886Z9V39kMBjg6emJoqIieHh4OFIutQDGzEzkLngXJUlJAAC5pyd8X3oJ3iOf4rgQInJYRUUFMjMzERYWBo1GI3U5Tu9m16uu398OP01TWlqKqKgorFixok7HZ2Zm4pFHHkFsbCxSU1MxdepUPPfcc9i+XZrHpKj5sBQVQZ8wDxlDH7MFEYUC3uPGImL7Nvj8aQyDCBFRE+FwN82QIUMwZMiQOh+/cuVKhIWFYdGiRQCATp06Ye/evViyZAkGD5ZmdDI1bWJlJa7997/If+9fsBQWAgC0AwbA//XXoA4Pl7Y4IiIn9MUXX+D//u//atwXEhKCEydONHJF1TX4mJH9+/cjLi6u2rbBgwdj6tSpDX1qaoZK9uyFfv48mM6lAwBUkRHQvT4D2vvulbgyIiLn9dhjjyE6OrrGfUonuIvc4GEkJycHOp2u2jadTgeDwYDy8nK4uLjc0MZoNMJoNNp/NxgMDV0mOTljRgb08+ejNGU3AEDu5QXfl1+C91NPQVBwHDYR0c24u7vD3d1d6jJq5ZSf4gkJCZgzZ47UZZATsBQWIm/F+7j25ZdAZSWgUMDnT3+C7wvPQ+7pKXV5RERUDxo8jAQEBECv11fbptfr4eHhUeNdEQCYOXMmpk2bZv/dYDAgODi4Qesk5yKazbi2bj3yli+HtagIAKAdOBD+r06HOixM4uqIiKg+NXgY6du3L7Zu3Vpt244dO9C3b99a26jV6ia12iDVr5Ldu6GfNx+mjAwAgLpdO+hmzoBbv34SV0ZERA3B4TBSUlKCc+fO2X/PzMxEamoqfHx80LZtW8ycOROXL1/GZ599BgB4/vnnsXz5crz22mt45plnsGvXLvz3v//Fli1b6u9dULNgPHcO+vkLULpnDwBA7u0NvylT4PXkcI4LISJqxhz+hD98+DBiY2Ptv//WnTJ+/HisWbMG2dnZyMrKsu8PCwvDli1b8Morr2DZsmVo06YNPvnkEz7WS3aV164hf/kKXFu3DrBYAKUSPmPH2saFOPGAKyIiqh8Oh5GYmBjcbNLWmpYpjomJwbFjxxw9FTVzosmEa19+ibwV78Na9cSUNm4QdK++ClVIiMTVERFRY+G9b2p0oiiiJDkZufMXwHT+PABA3bEjdDNmwK1Pzc/BExHRzU2YMAGFhYW3XKHXGTGMUKOqOHMGufPmo3TfPgCAvFUr+E2dAq8nnoAgl0tcHRERScHhtWmIbkdlQQGy58xBZvzjKN23D4JSiVaTnkPE9m3wHjGCQYSIqJGEhoZi6dKl1bZ169YNb7/9tiT1ALwzQg1MNJlQ8MVa5L//PqzFxQAA9wcfhP+r06Hi3DFE1BSIImAuk+bcSldAEKQ5dyNiGKEGIYoiSpKSoJ8/H+YLtqer1J072caF9O4tcXVERA4wlwFzA6U59xtXAJWbNOduRAwjVO8qTp+Gft48lO0/AACQ+/rC/5Wp8IyPZ3cMERHdgGGE6k3l1avIW/YeCjdsAKxWCCoVfCZMQKs//xlybfNP9kTUTCldbXcopDp3PZPJZDdM0WE2m+v9PI5gGKE7ZjWZcO0//0H+BythLSkBALgPeQj+f50OVZsgiasjIrpDgtCsukr8/PyQnZ1t/91gMCAzM1PCihhG6A6IoojinTuRu+BdmC9eBABo7roLupkz4Nqrl8TVERFRTQYOHIg1a9Zg6NCh8PLywqxZsyCXuAudYYRuS0VaGvQJ81B26BAAQOHnB79p0+A57DEIMj4xTkTkrGbOnInMzEw8+uij8PT0xN///nfJ74wI4s3mdncSBoMBnp6eKCoqgoeHh9TltGiV+fnIW7YMhRu+BkQRgloNn2cmwve55yBzaz63MYmo5aqoqEBmZibCwsKg0WikLsfp3ex61fX7m3dGqE6sRiMKPvsMV1d+CGtpKQDA4+GH4f/XaVAGcVwIERHdPoYRuilRFFG8/X/IXbgQ5kuXAACau++GbuZMuPboLnF1RER0vaysLHTu3LnW/SdPnkTbtm0bsaK6YRihWpWfOAF9QgLKDx8BACh0Ovj/dRo8Hn2U40KIiJxQYGAgUlNTb7rfGTGM0A3MubnIW7oMRRs32saFaDRo9eyzaPXsM5C51v8z70REVD8UCgUiIyOlLsNhDCNkZ62oQMGaT5H/0UcQy2zrMHgMHQr/aa9A2bq1xNUREVFzxTBCtnEh27Yh992FMF+xzTKoieqKgJkz4dKtm7TFERFRs8cw0sKVH//VNi7k6FEAgCIgAP5//Ss8Hn0EQgtYKZKIiKTHMNJCmfW5yFuyBEWbNgEABBcXtHruWbR65hnIXFykLY6IiFoUhpEWxlpejqurV+Pqx59ALC8HAHgOGwa/aa9AqdNJXB0REbVEDCMthCiKMGzZitxFi1BZtUCSS/fu0M2cAZeuXSWujoiIWjKGkRag/OefoU+Yh/KqZ88Vga2hmz4d7kOGcFwIEVEzMWHCBBQWFmJTVfd7U8Iw0oyZc3KQu3gxDN99DwAQXF3h++dJ8JkwATKut0BERE6C02g2Q9bycuQtX4H0h4bYg4jn448j4ocf4Pv88wwiREQt1EcffYTAwEBYrdZq24cNG4ZnnnlGoqp4Z6RZEa1WGLZsQe6ixajMyQEAuPTsCd3MmXDpcpfE1RERNU2iKKK8slySc7soXOq1O33EiBF46aWXkJSUhEGDBgEACgoKsG3bNmzdurXezuMohpFmouzYMejnzUPFz78AAJRBQfB/9VW4D36Q40KIiO5AeWU5otdGS3Lug6MPwlVZf8tweHt7Y8iQIVi7dq09jGzYsAG+vr6IjY2tt/M4it00TZz5yhVc/ut0XBg1GhU//wKZqyv8pk1D+NYt8HhoMIMIERFVM2bMGHz99dcwGo0AgC+++AJPP/00ZBIugMo7I02UtbQUV//9b1z99yqIRiMgCPAc/gT8p0yBws9P6vKIiJoNF4ULDo4+KNm569vQoUMhiiK2bNmCe+65B3v27MGSJUvq/TyOYBhpYkSrFUXffYe8xUtQmZsLAHC95x7oZs6ApnNniasjImp+BEGo164SqWk0GjzxxBP44osvcO7cOXTo0AE9evSQtCaGkSak7OhR6OcmoOLXXwEAyjZt4P/aq3B/4AF2xxARUZ2NGTMGjz76KE6cOIE//elPUpfDMNIUmC9fRu6iRTBs/QEAIHNzg+8Lz8N73DjIVCqJqyMioqZm4MCB8PHxwenTpzF69Gipy2EYcWbW0lLkf/wxClathmgyAYIArxEj4PfyS1D4+kpdHhEROZE1a9bU+ViZTIYrV640XDEOYhhxQqLViqKNm5C7dAksefkAANfoaNu4kI4dJa6OiIiofjGMOJmyn36CPmEeKk6eBAAo27aF7vXXoB04kONCiIjoprKystD5Jg8znDx5Em3btm3EiuqGYcRJmC5dQu67C1G8fTsAQKbVwvcvf4H3n8ZwXAgREdVJYGAgUqsWRa1tvzNiGJGYpaQEVz/8CAVr1kA0mwGZDF5PjYDfSy9B0aqV1OUREVETolAoEBkZKXUZDmMYkYhosaBo40bkLl0GS75tXIhbv77wf30GNB3aS1wdERFR42EYkUDpwUPQz5sHY1oaAEAVEgL/Ga9DGxPDcSFERNTi3NZE9CtWrEBoaCg0Gg2io6Nx6NChmx6/dOlSdOjQAS4uLggODsYrr7yCioqK2yq4KTNlZeHSSy8ja/x4GNPSIPPwgG7mDIR//x3cY2MZRIiIqEVy+M7I+vXrMW3aNKxcuRLR0dFYunQpBg8ejNOnT8Pf3/+G49euXYsZM2Zg1apV6NevH86cOYMJEyZAEAQsXry4Xt6Es7MUFyN/5Upc++w/tnEhcjm8R46E70uTofD2lro8IiIiSTkcRhYvXoxJkyZh4sSJAICVK1diy5YtWLVqFWbMmHHD8fv27UP//v3tM7yFhoZi1KhROHhQmkWHGpNosaBww9fIW7YMloICAIBb//7QzXgd6nbtJK6OiIjIOTjUTWMymXDkyBHExcX9/gIyGeLi4rB///4a2/Tr1w9Hjhyxd+VkZGRg69atePjhh++gbOdXeuAAMp8YjpzZs2EpKIAqLAzBH65E8CcfM4gQERFdx6E7I/n5+bBYLNDpdNW263Q6nDp1qsY2o0ePRn5+Pu69916IoojKyko8//zzeOONN2o9j9FohNFotP9uMBgcKVNSpvPnoX93IUoSEwEAMk9P+L34IrxHPQ1BqZS4OiIias7y8vIwa9YsbNmyBXq9Ht7e3oiKisKsWbPQv39/qcurVYM/TZOcnIy5c+fi/fffR3R0NM6dO4cpU6bg73//O956660a2yQkJGDOnDkNXVq9shgMyP9gJQo+/xz4bVzI6NHwe/EvkHt5SV0eERG1AMOHD4fJZMKnn36K8PBw6PV6JCYm4urVq1KXdlMOhRFfX1/I5XLo9fpq2/V6PQICAmps89Zbb2Hs2LF47rnnAAB33303SktL8ec//xl/+9vfIJPd2FM0c+ZMTJs2zf67wWBAcHCwI6U2GrGyEoUbNiBv2XuwXLsGAHAbcD90r70GdUSExNUREVFLUVhYiD179iA5ORkDBgwAAISEhKB37963bDt69GhYLBasX7/evs1sNqN169ZYvHgxxo0b12B1Aw6GEZVKhZ49eyIxMRHx8fEAAKvVisTEREyePLnGNmVlZTcEDrlcDgAQRbHGNmq1Gmq12pHSJFHy44/InTcfxrNnAQCqiAjoZrwO7X33SVwZERHVF1EUIZaXS3JuwcWlztM+aLVaaLVabNq0CX369HHoe3TMmDEYMWIESkpKoNVqAQDbt29HWVkZHn/88duq3REOd9NMmzYN48ePR69evdC7d28sXboUpaWl9qdrxo0bh6CgICQkJAAAhg4disWLF6N79+72bpq33noLQ4cOtYeSpsaYkYncBQtQkpwMAJB7esL35Zfg/dRTHBdCRNTMiOXlON2jpyTn7nD0CARX1zodq1AosGbNGkyaNAkrV65Ejx49MGDAADz99NPo2rXrTdsOHjwYbm5u2LhxI8aOHQvANjXHY489Bnd39zt+H7es3dEGI0eOtA+QycnJQbdu3bBt2zb7oNasrKxqd0LefPNNCIKAN998E5cvX4afnx+GDh2Kf/7zn/X3LhqJpagI+e+/j4Iv1gKVlYBCAZ8xo+H7l79A7ukpdXlERNTCDR8+HI888gj27NmDAwcO4IcffsCCBQvwySefYMKECbW2UygUeOqpp/DFF19g7NixKC0txbfffot169Y1St2CWFtfiRMxGAzw9PREUVERPDw8Gv38YmUlrq1fj/z3/gVLUREAQBsTA//XXoM6PKzR6yEiooZTUVGBzMxMhIWFQaPRNJlumto899xz2LFjBy5cuHDT4/bt24cBAwbg8uXL2LFjB6ZMmYLs7Gwob3HH/4/X63p1/f7m2jS3ULJnL/Tz58F0Lh0AoG4XCf8ZM6B14kekiIio/giCUOeuEmfUuXNnbNq06ZbH9evXD8HBwVi/fj1++OEHjBgx4pZBpL4wjNTCmJEB/fz5KE3ZDQCQe3vDb8rL8HrySQgKXjYiInIuV69exYgRI/DMM8+ga9eucHd3x+HDh7FgwQIMGzasTq8xevRorFy5EmfOnEFSUlIDV/w7fqv+gaWwEHkr3se1tWsBiwVQKuHzpz/B94XnIZegi4iIiKgutFotoqOjsWTJEqSnp8NsNiM4OBiTJk266USj1xszZgz++c9/IiQkpFEnSeOYkSqi2Yxr69Yjb/lyWH8bFzJoEHSvTocqNLRBzklERM7nZmMg6EYcM1IPRFFE6e7d0M9fAFNGBgBA3b49dDNnwK1vX4mrIyIiav5adBjZs+MgZCvfg8+JowAAuY8P/KZMgdeTwyE00TlQiIiI/uiLL77A//3f/9W4LyQkBCdOnGjkiqprsWHEWFYB9auT4V5RgkqZHGVDn0TPv02DguNCiIiomXnssccQHR1d477GemLmZlpsGFFq1CgeMRanUvbhgw5DkC34ou+6k3jr0c7oHMhAQkREzYe7u3ujzKR6u25cpa6FkMkEDHzjJQz//nMMf7QPVAoZ9mdcxSP/2oOZ3/yCvGKj1CUSERG1CC02jAC2iWzc1ApMH9wBu/46AI92bQ1RBL48dBGxC5OxMiUdxkqL1GUSERE1ay06jFyvjbcrlo/ugQ3P90XXNp4oMVZi3g+nELc4BT8cz651hWEiIiK6Mwwjf9Ar1Aeb/tIfi0ZEQeehxsWCcrzwxVGM/OgAfr1cJHV5REREzQ7DSA1kMgHDe7ZB0vQYvDyoHdQKGQ5lFmDo8r14bcPPyC2ukLpEIiKiZoNh5CZcVQpMe6A9dk2PwbBugRBF4L+HLyH23WSsSDqHCjPHkxAREd0phpE6CPJywbKnu+PrF/qhW7AXSk0WvLv9NOIWp2DLLxxPQkRE0pswYYJthWFBgFKpRFhYGF577TVUVDj/3XyGEQf0DPHGNy/0w9KR3RDgocGla+V4ce1RPPXhfhy/xPEkREQkrYceegjZ2dnIyMjAkiVL8OGHH2L27NlSl3VLDCMOkskExHcPwq7pAzA1rh00Shl+On8Nj63Yi+lf/Qy9wfkTKBERNU9qtRoBAQEIDg5GfHw84uLisGPHjlu2S05OhiAIKCwstG9LTU2FIAg4f/58wxVcpcXOwHqnXFUKTI1rj5H3BGPBttPYeOwyNhy5hK3Hs/HCgAhMuj8cGiXXtyEiaupEUUSlySrJuRUqGQRBuK22v/76K/bt24eQkJB6rqr+MYzcodaeLlgyshvG9Q3BO5tP4lhWIRbtOIN1P13E60M6YmjX1rf9h0RERNKrNFnx0ZQUSc7952UDoFTX/R+2mzdvhlarRWVlJYxGI2QyGZYvX96AFdYPdtPUk+5tbeNJ3hvVHYGeGlwuLMfLXx7Dkyv3I/ViodTlERFRCxAbG4vU1FQcPHgQ48ePx8SJEzF8+HCpy7ol3hmpR4Ig4LGoQDzYWYePd2fg/eR0HLlwDfErfsQT3YPw2kMdEeCpkbpMIiJygEIlw5+XDZDs3I5wc3NDZGQkAGDVqlWIiorCv//9bzz77LM3bSeT2c5z/dOhZrPZwWpvH++MNACNUo6XBrVD8qsxGN6jDQDgm2OXEbswGct2nkW5ifOTEBE1FYIgQKmWS/JzJ938MpkMb7zxBt58802Ul5ff9Fg/Pz8AQHZ2tn1bamrqbZ/bUQwjDUjnocGip6Lw3eT+6BXijXKzBUt2nsHARcn4NvUy5ychIqIGNWLECMjlcqxYseKmx0VGRiI4OBhvv/02zp49iy1btmDRokWNVCXDSKPo2sYLXz3fF8tHd0eQlwuyiyowZV0qnvhgH45mXZO6PCIiaqYUCgUmT56MBQsWoLS0tNbjlEolvvzyS5w6dQpdu3bF/Pnz8Y9//KPR6hTEJvDPc4PBAE9PTxQVFcHDw0Pqcu5IhdmCf+/NxIqkcyir6q4Z1i0Qrz/UEYFeLhJXR0REFRUVyMzMRFhYGDQajvO7lZtdr7p+f/POSCPTKOV4MTYSydNj8FSvNhAE4NvUKxi4KBmLd5xBmalS6hKJiIgaFcOIRPw9NFjwZBS+n3wveof5oMJsxXuJZxG7MBnfHL0Eq9Xpb1gREVETMXfuXGi12hp/hgwZInV57KZxBqIoYtuvOZj7QxouFthGPEe18cSsoZ3RM8RH4uqIiFqW5thNU1BQgIKCghr3ubi4ICgo6LZfuz66aTjPiBMQBAFD7m6N2I7+WP3jeaxIOoefLxVh+Af7MTQqEK8/1AFtvF2lLpOIiJooHx8f+Pg47z9u2U3jRDRKOV6IicCu6QPw9D3BEATg+5+vYNCiFCzcfhqlRo4nISKi5odhxAn5u2swb3hXbH7pXvQJ94Gx0orlSecQuzAZXx2+yPEkRETUrDCMOLG7Aj3x5aQ++HBsT4S0ckVusRGvbvgFw1b8iEOZNff9ERERNTUMI05OEAQMvisA/3vlfrzxcEe4qxU4frkIT324Hy9+cRQXC8qkLpGIiOiOMIw0EWqFHH++PwJJr8ZgdHRbyARgy/FsDFqcggXbTqGE40mIiKiJYhhpYny1asx9/G5sefk+9I9sBVOlFe8npyPm3WT896eLsHA8CRERNTEMI01Up9Ye+PzZaHw8rhdCW7kiv8SI177+BY8t34sDGVelLo+IiBrZhAkTIAiCbZVhpRJhYWF47bXXUFFRIXVpt8Qw0oQJgoAHOuvwv1cG4M1HOsFdo8CJKwY8/dEBvPD5EWRd5XgSIqKW5KGHHkJ2djYyMjKwZMkSfPjhh5g9e7bUZd0Sw0gzoFLI8Nx94UieHoOxfUIgE4Affs1B3OIUJPyQhuIKs9QlEhFRI1Cr1QgICEBwcDDi4+MRFxeHHTt23LJdv3798Prrr1fblpeXB6VSid27dzdUuXa3FUZWrFiB0NBQaDQaREdH49ChQzc9vrCwEC+++CJat24NtVqN9u3bY+vWrbdVMNWulVaNv8d3wQ9T7sd97XxhsljxYUoGYhcm48tDWRxPQkR0G0RRhLmiQpKfO1mx5ddff8W+ffugUqlueeyYMWOwbt26audbv349AgMDcd999912DXXl8HTw69evx7Rp07By5UpER0dj6dKlGDx4ME6fPg1/f/8bjjeZTHjggQfg7++PDRs2ICgoCBcuXICXl1d91E816BDgjs+e6Y2k07n4x+Y0ZOSXYuY3x/HpvvOYNbQz+kX4Sl0iEVGTUWk04r3xT0py7pc/3QClA+vjbN68GVqtFpWVlTAajZDJZFi+fPkt2z311FOYOnUq9u7daw8fa9euxahRoyAIwm3XX1cO3xlZvHgxJk2ahIkTJ6Jz585YuXIlXF1dsWrVqhqPX7VqFQoKCrBp0yb0798foaGhGDBgAKKiou64eKqdIAgY2FGH7a/cj1mPdoaHRoFTOcUY/fFB/PmzwzifXyp1iUREVM9iY2ORmpqKgwcPYvz48Zg4cSKGDx9+y3Z+fn548MEH8cUXXwAAMjMzsX//fowZM6ahSwbg4Kq9JpMJrq6u2LBhA+Lj4+3bx48fj8LCQnz77bc3tHn44Yfh4+MDV1dXfPvtt/Dz88Po0aPx+uuvQy6X13geo9EIo9Fo/91gMCA4OLjZrtrbGK6VmrB05xl8ftDWXaOUC5jYPwyTB0bCQ6OUujwiIqfxx1VoRVFE5XXfSY1JoVbX+c7EhAkTUFhYiE2bNgEArFYroqKiMHXqVDz77LO3bL927Vq8/PLLyM7OxoIFC7B+/Xr88ssvt2xXH6v2OnRnJD8/HxaLBTqdrtp2nU6HnJycGttkZGRgw4YNsFgs2Lp1K9566y0sWrQI//jHP2o9T0JCAjw9Pe0/wcHBjpRJNfB2U2HOsC7YNuU+DGjvB7NFxEe7MxD7bjK+OHgBlRar1CUSETklQRCg1Ggk+bmTLhKZTIY33ngDb775JsrLy295/LBhw1BRUYFt27Zh7dq1jXZXBGiEp2msViv8/f3x0UcfoWfPnhg5ciT+9re/YeXKlbW2mTlzJoqKiuw/Fy9ebOgyW4x2Ond8+kxvrJ54DyL83HC11IS/bfwVj/5rL/aezZe6PCIiqkcjRoyAXC7HihUrbnmsm5sb4uPj8dZbbyEtLQ2jRo1qhAptHAojvr6+kMvl0Ov11bbr9XoEBATU2KZ169Zo3759tS6ZTp06IScnByaTqcY2arUaHh4e1X6ofsV28Me2qfdjzmN3wctViVM5xfjTvw/iuU9/QkZeidTlERFRPVAoFJg8eTIWLFiA0tJbjxUcM2YMfv75Z9x3331o27ZtI1Ro41AYUalU6NmzJxITE+3brFYrEhMT0bdv3xrb9O/fH+fOnYPV+ns3wJkzZ9C6des6PW5EDUcpl2F8v1AkT4/BxP6hUMgE7EzLxYNLduPvm0+iqIzzkxARNRVr1qyxjxe53owZM5Cbmws3N7dbvsaQIUMgiiJSUlIaoMLaOdxNM23aNHz88cf49NNPkZaWhhdeeAGlpaWYOHEiAGDcuHGYOXOm/fgXXngBBQUFmDJlCs6cOYMtW7Zg7ty5ePHFF+vvXdAd8XJVYfbQu7Bt6v0Y2NEflVYR/96biZiFSfjP/vMcT0JERA3K4TAycuRILFy4ELNmzUK3bt2QmpqKbdu22Qe1ZmVlITs72358cHAwtm/fjp9++gldu3bFyy+/jClTpmDGjBn19y6oXkT6a7Fqwj349JneaOevxbUyM9769gSGLNuD3WfypC6PiIhu09y5c6HVamv8GTJkiNTlOfZor1Tq+mgQ1Z9KixVfHsrC4h1ncK2qu2ZgR3+88XAnRPprJa6OiKjh3OxR1aaqoKAABQUFNe5zcXFBUFDQbb92fTza6/AMrNQyKOQyjO0biseigvCvXWexZt957DqVi91n8vCnPiGYGtcOXq4c80NE1BT4+PjAx8dH6jJqxYXy6KY8XZV489HO+N8r9yOukw6VVhFr9p3HgHeTsebHTJg5noSIiO4QwwjVSbifFp+M74XPn41GB507isrNePv7k3ho6W4knc6VujwiImrCGEbIIfe288WWl+/FPx/vAh83FdLzSjFx9U8Yv+oQzuqLpS6PiIiaIIYRcphCLsOY6BAkTY/Bn+8Ph1IuIOVMHh5atgezv/0V10prnsyOiIioJgwjdNs8XZR44+FO2PHKADzYWQeLVcSn+y9gwLtJWLWX40mIiKhuGEbojoX6uuGjcb2wdlI0OrX2gKGiEu9sPonBS3dj1yk9msDT40REJCGGEao3/SJ8sfmlezHvibvhq1UhI68Uz6w5jHGrDuF0DseTEBE1pAkTJiA+Pv6G7cnJyRAEAYWFhY1eU10xjFC9kssEPN27LZKmx+D5ARFQyWXYczYfQ5btxpubjuNqiVHqEomIyMkwjFCDcNcoMWNIR+ycNgBDugTAKgKfH8hCzMJkfLInA6ZKjichInImb7/9Nrp161Zt29KlSxEaGtrg5+YMrNSg2rZyxQd/6okDGVfxzvcncTLbgH9sScPnBy7gb490RlwnfwiCIHWZRES1EkURolmaf0AJSlmL+IxkGKFG0Se8Fb5/6V58feQSFmw/jfNXyzDps8PoH9kKbz7SGZ1ac80hInJOotmKK7P2SXLuwHf6QVDJ63z85s2bodVWXz/MYrHUd1n1jt001GjkMgFP3ROM5Fdj8JeYCKgUMvx47ioeeW8P3th4HPkcT0JEdEdiY2ORmppa7eeTTz6Ruqxb4p0RanRatQKvPdQRo3q3xbxtp7Dll2ysPZiF71Ov4KVBkRjfLxRqRd3/JUBE1JAEpQyB7/ST7NyOcHNzQ2RkZLVtly5dqlNbmUx2w1QMZrPZofPfLoYRkkywjytWjO6B8X0L8PfNJ3H8chHmbj2FLw5mYeaQThh8l65F9JUSkXMTBMGhrpKmys/PDzk5ORBF0f7Zm5qa2ijnZjcNSa53mA++fbE/Fo6Igr+7GheuluH5z49g1McHcOJKkdTlERG1CDExMcjLy8OCBQuQnp6OFStW4IcffmiUczOMkFOQyQQ82bMNkqbH4KWBkVArZDiQUYBH/7UXM77+BXnFHE9CRNSQOnXqhPfffx8rVqxAVFQUDh06hOnTpzfKuQWxCczVbTAY4OnpiaKiInh48KmLluDStTLM33Ya3/98BYBtnMmLsZGY2D8UGmXzv11KRNKpqKhAZmYmwsLCoNFopC7H6d3setX1+5t3RsgptfF2xb9GdceG5/siqo0nSoyVmL/tFB5YkoIfjmdzvRsiomaEYYScWq9QH2z8S38sGRmFAA8NLhaU44UvjmLkRwfw62WOJyEiqqshQ4ZAq9XW+DN37lxJa+PTNOT0ZDIBj3dvg8F3BeDDlAx8uDsdhzILMHT5Xozo2QbTH+wAfw/eSiUiuplPPvkE5eXlNe7z8fFp5GqqYxihJsNVpcArD7THyHuCsWDbKWxKvYL/Hr6ELb9k4y+xkXj23jCOJyEiqkVQUJDUJdSK3TTU5AR6uWDp093xzV/6oVuwF0pNFry7/TQGLUrB5l+ucDwJEVETwzBCTVaPtt745oV+WPZ0N7T21OByYTkmrz2GESv345dLhVKXR0REdcQwQk2aTCZgWLcg7PprDF6Jaw8XpRyHL1zDY8t/xF//+zP0hgqpSyQioltgGKFmwUUlx5S4dkiaHoMnetj6Rb8+egkx7ybjX4lnUWF2/lUriYhaKoYRalYCPDVY/FQ3bHqxP3qGeKPcbMGiHWcwcGEyvvuZ40mIiJwRwwg1S92CvbDh+b7416juCPJywZWiCrz85TEM/2AfjmVdk7o8IiK6DsMINVuCIGBoVCAS/zoA0x9sD1eVHEezCvH4+/vwyvpUZBfV/Lw9EVFTNGHCBMTHx9+wPTk5GYIgoLCwsNFrqiuGEWr2NEo5Jg+0jSd5smcbAMDGY5cRuzAZS3eeQbmJ40mIiKTEMEIths5Dg4UjovDd5P64J9QbFWYrlu48i4GLkrHp2GVYrRxPQkQtk8FggIuLC3744Ydq2zdu3Ah3d3eUlZU16Pk5Ayu1OF3beOG//9cXW4/nYO7WNFwuLMfU9alYs+88Zg3tjB5tvaUukYiciCiKMJvNkpxbqVRCEIQGP4+HhwceffRRrF27FkOGDLFv/+KLLxAfHw9XV9cGPT/DCLVIgiDgka6tMaiTP1b9mIkVu84h9WIhnnh/H4Z1C8TrD3VEoJeL1GUSkRMwm82SLST3xhtvQKVS1fn4zZs3Q6vVVttmsdStK3rMmDEYO3YsysrK4OrqCoPBgC1btmDjxo0O1Xw72E1DLZpGKcdfYiKR9GoMRvYKhiAA36ZewcBFyVi84wzKTJVSl0hEVGexsbFITU2t9vPJJ5/Uqe3DDz8MpVKJ7777DgDw9ddfw8PDA3FxcQ1ZMgDeGSECAPi7azD/ya4Y2zcE72w+iUOZBXgv8SzW/5SF1wZ3xOPdgyCTNfytUiJyPkqlEm+88YZk53aEm5sbIiMjq227dOlSndqqVCo8+eSTWLt2LZ5++mmsXbsWI0eOhELR8FGBYYToOl2CPLH+z32w/UQO/rk1DRcLyvHXr37Gp/vPY9ajndErVNpltomo8QmC4FBXSVM2ZswYPPDAAzhx4gR27dqFf/zjH41yXnbTEP2BIAh4qEtr7HhlAGYM6QitWoFfLhXhyZX7MXntUVy61rCjyomIpHL//fcjICAAY8aMQVhYGKKjoxvlvLcVRlasWIHQ0FBoNBpER0fj0KFDdWq3bt06CIJQ46QsRM5Go5Tj+QERSJoeg1G9beNJNv+SjYGLUrBw+2mUGjmehIiaF0EQMGrUKPz8888YM2ZM451XdHCxjvXr12PcuHFYuXIloqOjsXTpUnz11Vc4ffo0/P39a213/vx53HvvvQgPD4ePjw82bdpU53MaDAZ4enqiqKgIHh4ejpRLVG9OXCnCPzanYX/GVQCAn7sarw3ugOE92nA8CVEzUlFRgczMTISFhUGj0UhdjtO72fWq6/e3w3dGFi9ejEmTJmHixIno3LkzVq5cCVdXV6xatarWNhaLBWPGjMGcOXMQHh7u6CmJnMJdgZ5YOykaH43tiZBWrsgrNuLVDb/gsRV7cSizQOryiIiaLIfCiMlkwpEjR6o95iOTyRAXF4f9+/fX2u6dd96Bv78/nn322duvlMgJCIKAB+8KwP9euR9/e7gT3NUK/HrZgKc+3I8XvziKiwUcT0JEzmnIkCHQarU1/kg1j8pvHHqaJj8/HxaLBTqdrtp2nU6HU6dO1dhm7969+Pe//43U1NQ6n8doNMJoNNp/NxgMjpRJ1ODUCjkm3R+Ox3sEYcmOM/jyUBa2HM/GjjQ9nr03DH+JiYC7xrFH8oiIGtInn3yC8vKaFwj18ZH2ScEGfbS3uLgYY8eOxccffwxfX986t0tISMCcOXMasDKi+uGrVeOfj9+NsX1D8PfNJ/Hjuav4IDkdXx2+hBG92iCukz+6BXtDzjElRCSxoKAgqUuolUNhxNfXF3K5HHq9vtp2vV6PgICAG45PT0/H+fPnMXToUPs2q9VqO7FCgdOnTyMiIuKGdjNnzsS0adPsvxsMBgQHBztSKlGj6hjggc+fjUZiWi7+uTUNmfml+CA5HR8kp8PHTYXYDv6I6+SP+9r7Qavm9D5ERNdz6FNRpVKhZ8+eSExMtD+ea7VakZiYiMmTJ99wfMeOHXH8+PFq2958800UFxdj2bJltQYMtVoNtVrtSGlEkhMEAXGddbi/vR9++DUbO9NykXw6FwWlJnx99BK+PnoJKrkM0eE+iOukw6BO/mjj3bCLTxERNQUO/xNt2rRpGD9+PHr16oXevXtj6dKlKC0txcSJEwEA48aNQ1BQEBISEqDRaNClS5dq7b28vADghu1EzYVKIcOwbkEY1i0IZosVP50vQGJaLhLT9Dh/tQx7zuZjz9l8zP7uBDoGuGNQJ38M7KhDt2AvducQUYvkcBgZOXIk8vLyMGvWLOTk5KBbt27Ytm2bfVBrVlYWZDJO7EoEAEq5DP0ifNEvwhdvPtIJ6XmlSEzTI/FULg6fL8CpnGKcyinGiqR0tHJTIbZjVXdOOz+4sTuHiFoIhyc9kwInPaPmqLDMhOTTediZpkfK6TwUXzejq0ouQ5+IVojr5I9BnXQI8nKRsFKiloWTnjmmPiY94z+9iCTi5apCfPcgxHev6s7JLMDOtFwkntLjwtUy7D6Th91n8jDrW1t3zm/jTKLaeHHGVyJqVhhGiJyAUi5Dv0hf9Iv0xVuPdkJ6XoktmKTpceTCNXt3zvKkc/DVqjGwox8GddLhvna+cFXx/8ZEBEyYMAGffvopANsTqz4+PujatStGjRqFCRMmOPUQCn6KETkZQRAQ6e+OSH93PD8gAgWlJiSfzkViWi5SzuQhv8SI/x6+hP8evgSVQoZ+Ea0wqJMOgzr6I5DdOUQt2kMPPYTVq1fDYrFAr9dj27ZtmDJlCjZs2IDvvvsOCoVzfu07Z1VEZOfjpsITPdrgiR5tYKq04lBmAXam6ZF4So+LBeVIPp2H5NN5eAtA59Ye9nEmdwd5sjuHqIVRq9X2eb+CgoLQo0cP9OnTB4MGDcKaNWvw3HPPSVxhzRhGiJoQlUKGe9v54t52vpg9tDPO5pbYgklaLo5mXcPJbANOZhvw3q5z8HNXY1BHWzC5N9IXLiq51OUTNUmiKMJqrXka9YYmk7lAEO7sHxUDBw5EVFQUvvnmG4YRIqpfgiCgvc4d7XXu+EtMJK6WGJF0Og+JaXrsPpOHvGIj1v10Eet+ugi1Qob+kb4Y1MkfgzrqEODJJwSI6spqLUdyyt2SnDtmwHHI5Xc+OWLHjh3xyy+/1ENFDYNhhKiZaKVV48mebfBkzzYwVlpwMKMAiWl67EzLxeXCcuw6lYtdp3LxN/yKuwI9MKiTDnGd/NElkN05RM2dKIp3fIelITGMEDVDaoUc97f3w/3t/fD2YyJO64uRmJaLnWl6pF4sxIkrBpy4YsB7iWeh81BjYEfbHZP+7M4huoFM5oKYAcdvfWADnbs+pKWlISwsrF5eqyEwjBA1c4IgoGOABzoGeODF2EjklxiRdMr2dM7us3nQG4z48tBFfHnoIjRKGfpH+NqezunkD50Hu3OIBEGol64SqezatQvHjx/HK6+8InUptWIYIWphfLVqjOgVjBG9gmGstOBAVXdOYlV3TuKpXCSeygU2AncHeWJQJ3/EddLhrkAPp77NS0SA0WhETk5OtUd7ExIS8Oijj2LcuHFSl1crhhGiFkytkGNAez8MaO+HOY+JOJVTbB9n8vOlQhy/XITjl4uwdOdZBHhoMLCTbe2cfhG+0CjZnUPkbLZt24bWrVtDoVDA29sbUVFReO+99zB+/HinnvSMa9MQUY3yim3dOTvT9NhzNh/lZot9n4tSjv6Rvojr5I+BHf3hz+4caka4No1juDYNETUYP3c1nronGE/dE4wKswX7M67au3OyiyqwM02PnWl6AEBUG0/7OJPOrdmdQ0SO4Z0RInKIKIo4mW1AYtXaOT9fKqq2P9DT1p0zqJMOfcNbsTuHmhzeGXEM74wQUaMTBAF3BXrirkBPvDyoHXINFdh1Khc703Kx91werhRV4PMDWfj8QBZcVXLcG+mLuE46xHb0h5+7WuryicgJMYwQ0R3x99Dg6d5t8XTvtqgwW7AvPR8703KxKy0XOYYK/O+kHv87qYcgAFFtvOxT1Hdq7c7uHCICwDBCRPVIo5RjYEcdBnbUQYwXceJKVXfOKT1+uVSE1IuFSL1YiEU7ziDIy8U22Vonf/SNaAW1gt05RC0VwwgRNQhBENAlyBNdgjwxJa4d9FXdOYlpeuw9l4/LheX4z4EL+M+BC3BVyXFfO9tkawM7+sNXy+4ckl4TGFLpFOrjOjGMEFGj0HloMKp3W4zq3Rblpuu6c07poTcYsf2EHttP2LpzugV7Ia7q6ZwOOnbnUOOSy2136UwmE1xc6mc69uasrKwMAKBUKm/7Nfg0DRFJShRF/HrZgJ1peiSe0uPXy4Zq+9t4u9jHmUSH+7A7hxqcKIrIysqC2WxGYGCgU08WJiVRFFFWVobc3Fx4eXmhdevWNxxT1+9vhhEicio5RRVIPGWbz+THc/kwVlrt+9xUtgUAB3XSIbaDH1qxO4caiMlkQmZmJqxW660PbuG8vLwQEBBQ4x1MhhEiavLKTRbsPZdvm2ztVC7yio32fYIA9GjrbV87p52/lt05VK+sVitMJpPUZTg1pVJp79aqCcMIETUrVquI45eL7GvnnMyu3p0T7OOCQR1t40yiw1pBpeCtdSKpMYwQUbN2pWqF4V1pevyYfhWm67pztGoF7m/vi0EdbZOt+bipJKyUqOViGCGiFqPMVIm9Z/Or5jTJRX7J7905Mnt3jg5xnfwRye4cokbDMEJELZLVKuKX67pz0v7QndPWx9U+zqR3mA+UcnbnEDUUhhEiIgCXC8uxqyqY7E+/CpPl9+4cd7UC93fwQ1wnf8S094c3u3OI6hXDCBHRH5QaK7HnrO3pnKTTucgv+f1JCZkA9ArxwaCqFYcj/NzYnUN0hxhGiIhuwmoVkXqp0PbYcFouTuUUV9sf2soVg6pmgb0nlN05RLeDYYSIyAEXC8qw61QudqbpcTCjoHp3jkaBmA7+9u4cT9fbn/aaqCVhGCEiuk0lxkrsOZOHnWm5SDqdi4LS37tz5DIBvUK8EddJh4Gd/BHhp5WwUiLnxjBCRFQPLFYRqRev2Rb1S8vFaX317pwwXzf72jn3hHpDwe4cIjuGESKiBnCxoMz+2PDBzKswW37/CPWo6s4ZxO4cIgAMI0REDa64wow9Z/OxM02PpFO5uFZmtu+TywTcE2rrzhnUSYcwXzcJKyWSBsMIEVEjslhFHMuydeckpulxNrek2v5wPzdbMOnoj54h7M6hloFhhIhIQheullZNT297OqfS+vtHrZerEjHt/TCokw4DOvjBQ8PuHGqeGEaIiJyEocKM3WfykFj1dE7hdd05CpmA3mE+9rVzQlqxO4eaD4YRIiInVGmx4mhWYdUgWD3S80qr7Y/019rXzunR1htyGWeBpaarQcPIihUr8O677yInJwdRUVH417/+hd69e9d47Mcff4zPPvsMv/76KwCgZ8+emDt3bq3H14RhhIiaq/P5pdhZNQvsT+erd+d4uyoR28H22PD97X3hzu4camIaLIysX78e48aNw8qVKxEdHY2lS5fiq6++wunTp+Hv73/D8WPGjEH//v3Rr18/aDQazJ8/Hxs3bsSJEycQFBRUr2+GiKgpKyo3I+VMHhLT9Eg+nYei8t+7c5RyW3dOdFgrRPprEeGnRaivK9QKuYQVE91cg4WR6Oho3HPPPVi+fDkAwGq1Ijg4GC+99BJmzJhxy/YWiwXe3t5Yvnw5xo0bV6dzMowQUUtTabHi8IVr9rVzMvJLbzhGLhPQ1scVEX5uiPDXItJPa/uvv5aDYskp1PX7W+HIi5pMJhw5cgQzZ860b5PJZIiLi8P+/fvr9BplZWUwm83w8fGp9Rij0Qij0Wj/3WAwOFImEVGTp5DL0Ce8FfqEt8LfHumMjLwSJJ3Ow8krBqTnlSA9twTFxkpk5pciM78UO9Nyq7X3c1cj0k9bdRfFDZH+7ojwd0OAh4arEZPTcSiM5Ofnw2KxQKfTVduu0+lw6tSpOr3G66+/jsDAQMTFxdV6TEJCAubMmeNIaUREzVq4nxbh162DI4oicouNSM8twbmqcHIurwTnckugNxiRV2z72Z9xtdrraNUK252UqrsoEVWBJaSVK1cmJsk4FEbu1Lx587Bu3TokJydDo9HUetzMmTMxbdo0++8GgwHBwcGNUSIRUZMgCAJ0HhroPDToF+lbbV9xhRnpeaXVAkp6XgkuXC1DibESP18qws+Xiqq1UcgEhLRytY9Hiazq7gn300KrbtSvCmqBHPoL8/X1hVwuh16vr7Zdr9cjICDgpm0XLlyIefPmYefOnejatetNj1Wr1VCr1Y6URkREVdw1SnQL9kK3YK9q202VVly4Wop0e0AptQeVMpPFFmDySgFU/4xv7amxB5QIe7ePFn5aNbt8qF44FEZUKhV69uyJxMRExMfHA7ANYE1MTMTkyZNrbbdgwQL885//xPbt29GrV687KpiIiG6PSiFDO5072uncq223WkXkGCrsweRc7u9hJb/EiOyiCmQXVWDvufxq7Tw0iuoDZ6sCSxtvF053Tw5x+N7btGnTMH78ePTq1Qu9e/fG0qVLUVpaiokTJwIAxo0bh6CgICQkJAAA5s+fj1mzZmHt2rUIDQ1FTk4OAECr1UKr1dZ6HiIiahwymYBALxcEerng/vZ+1fYVlZntY1LsQSWvBBcLymCoqMSxrEIcyyqs1kYllyHM1w0R/m72oBLhZ/txUfFRZLqRw2Fk5MiRyMvLw6xZs5CTk4Nu3bph27Zt9kGtWVlZkMl+T8QffPABTCYTnnzyyWqvM3v2bLz99tt3Vj0RETUoT1cleoZ4o2eId7XtFWYLzl8tRXpuqT2gpOeWICO/BBVmK07ri3FaX3zD6wV5udjHo0Rc97RPKy275lsyTgdPRET1xmoVcbmw/Ma7KbkluHbdmjx/5O2q/ENAsf03yMsFMk6J32RxbRoiInIqBaWmGsallODStfJa26gVMoRXmy/F9r9DW7lBo2SXj7NjGCEioiah3GRBRn5VOLnuKZ/M/FKYLNYa28gEINjH9ffHkP20VWNU3OHpytlnnQXDCBERNWkWq4iLBWXV7qb89l9DRWWt7Xy1qhu6eyL8tQj05OyzjY1hhIiImiVRFJFfYqo2cPa3KfKvFFXU2s5VJa96qset2iDakFZuUCn4KHJDYBghIqIWp8RYiYy86+6k5JbiXF4JzueXotJa89edXCYgxMfVvsjg9U/5uHPBwTvCMEJERFTFbLEiq6rL59x1d1LS80pRYqy9y0fnoa7xKR9/d84+WxcMI0RERLcgiiL0BuMNT/icyy1BbrGx1nbuagXCqw2ctYWUtj6unH32OgwjREREd8BQYbYtNnjdEz4ZeSW4UFAGSy1dPkq5gNBWbtUWG4yoCiyuqpa34CDDCBERUQMwVlpw4WrZdUHlt4G0pSg3W2ptF+ipqWFciha+WlWz7fJhGCEiImpEVquI7N8WHKx60ue3uyn5JaZa23m6KKtN6hZhX3DQFfImPvsswwgREZGTKCwz3bAi8rncEly8VobavoVVChnCfd1uWBk53K/pzD7LMEJEROTkKswWZOaX/mFit1Jk5JXAWFnz7LOCALTxdrEFlOsmdYv008LbTdXI7+DmGEaIiIiaKItVxOVr5TfMPHsurwSFN1lwsJWbqmrAbPXJ3QI9pVlwkGGEiIiomRFF8boFB0urzUJ7ubD2BQddlHKE+934lE+oryvUiobr8mEYISIiakHKTJXIyCu94W5KZn4pzJaav+plAtDWxxWR/lpMjWuPLkGe9VpTXb+/W95Dz0RERM2Qq0qBLkGeNwSKSosVF6+V17jgYHFFJc5fLcP5q2WYMqi9RJUzjBARETVrCrkMYb5uCPN1wwPQ2beLooi8YqO9myfC3026GiU7MxEREUlGEAT4e2jg76FBvwhfSWvhBPpEREQkKYYRIiIikhTDCBEREUmKYYSIiIgkxTBCREREkmIYISIiIkkxjBAREZGkGEaIiIhIUgwjREREJCmGESIiIpIUwwgRERFJimGEiIiIJMUwQkRERJJiGCEiIiJJMYwQERGRpBhGiIiISFIMI0RERCQphhEiIiKSFMMIERERSeq2wsiKFSsQGhoKjUaD6OhoHDp06KbHf/XVV+jYsSM0Gg3uvvtubN269baKJSIiouZH4WiD9evXY9q0aVi5ciWio6OxdOlSDB48GKdPn4a/v/8Nx+/btw+jRo1CQkICHn30Uaxduxbx8fE4evQounTpUi9v4naIogirtVyy8xMRETkTmcwFgiBIcm5BFEXRkQbR0dG45557sHz5cgCA1WpFcHAwXnrpJcyYMeOG40eOHInS0lJs3rzZvq1Pnz7o1q0bVq5cWadzGgwGeHp6oqioCB4eHo6UWyuLpQzJKXfXy2sRERE1dTEDjkMud63X16zr97dD3TQmkwlHjhxBXFzc7y8gkyEuLg779++vsc3+/furHQ8AgwcPrvX4xmKxWCQ9PxERkTMpKcqX7NwOddPk5+fDYrFAp9NV267T6XDq1Kka2+Tk5NR4fE5OTq3nMRqNMBqN9t8NBoMjZdaJsRj4ce/T9f66RERETVLhtxj0xEuSnNrhMSONISEhAXPmzGnQcwiCAKtV2aDnICIiaipkglyyczsURnx9fSGXy6HX66tt1+v1CAgIqLFNQECAQ8cDwMyZMzFt2jT77waDAcHBwY6Ueksuni6Y/vL0en1NIiKipsrF00WyczsURlQqFXr27InExETEx8cDsA1gTUxMxOTJk2ts07dvXyQmJmLq1Kn2bTt27EDfvn1rPY9arYZarXakNIfJ5XJofbQNeg4iIiK6NYe7aaZNm4bx48ejV69e6N27N5YuXYrS0lJMnDgRADBu3DgEBQUhISEBADBlyhQMGDAAixYtwiOPPIJ169bh8OHD+Oijj+r3nRAREVGT5HAYGTlyJPLy8jBr1izk5OSgW7du2LZtm32QalZWFmSy3x/S6devH9auXYs333wTb7zxBtq1a4dNmzZJOscIEREROQ+H5xmRQkPMM0JEREQNq0HmGSEiIiKqbwwjREREJCmGESIiIpIUwwgRERFJimGEiIiIJMUwQkRERJJiGCEiIiJJMYwQERGRpBhGiIiISFIMI0RERCQph9emkcJvM9YbDAaJKyEiIqK6+u17+1YrzzSJMFJcXAwACA4OlrgSIiIiclRxcTE8PT1r3d8kFsqzWq24cuUK3N3dIQhCvb2uwWBAcHAwLl68yAX4boHXyjG8XnXHa1V3vFZ1x2tVdw15rURRRHFxMQIDAyGT1T4ypEncGZHJZGjTpk2Dvb6Hhwf/WOuI18oxvF51x2tVd7xWdcdrVXcNda1udkfkNxzASkRERJJiGCEiIiJJtegwolarMXv2bKjVaqlLcXq8Vo7h9ao7Xqu647WqO16runOGa9UkBrASERFR89Wi74wQERGR9BhGiIiISFIMI0RERCSpZh9GVqxYgdDQUGg0GkRHR+PQoUM3Pf6rr75Cx44dodFocPfdd2Pr1q2NVKn0HLlWa9asgSAI1X40Gk0jViud3bt3Y+jQoQgMDIQgCNi0adMt2yQnJ6NHjx5Qq9WIjIzEmjVrGrxOZ+DotUpOTr7h70oQBOTk5DROwRJKSEjAPffcA3d3d/j7+yM+Ph6nT5++ZbuW+Jl1O9eqpX5mffDBB+jatat9DpG+ffvihx9+uGkbKf6mmnUYWb9+PaZNm4bZs2fj6NGjiIqKwuDBg5Gbm1vj8fv27cOoUaPw7LPP4tixY4iPj0d8fDx+/fXXRq688Tl6rQDbBDnZ2dn2nwsXLjRixdIpLS1FVFQUVqxYUafjMzMz8cgjjyA2NhapqamYOnUqnnvuOWzfvr2BK5Weo9fqN6dPn672t+Xv799AFTqPlJQUvPjiizhw4AB27NgBs9mMBx98EKWlpbW2aamfWbdzrYCW+ZnVpk0bzJs3D0eOHMHhw4cxcOBADBs2DCdOnKjxeMn+psRmrHfv3uKLL75o/91isYiBgYFiQkJCjcc/9dRT4iOPPFJtW3R0tPh///d/DVqnM3D0Wq1evVr09PRspOqcFwBx48aNNz3mtddeE++6665q20aOHCkOHjy4AStzPnW5VklJSSIA8dq1a41SkzPLzc0VAYgpKSm1HtOSP7OuV5drxc+s33l7e4uffPJJjfuk+ptqtndGTCYTjhw5gri4OPs2mUyGuLg47N+/v8Y2+/fvr3Y8AAwePLjW45uL27lWAFBSUoKQkBAEBwffNGm3dC317+pOdOvWDa1bt8YDDzyAH3/8UepyJFFUVAQA8PHxqfUY/m3Z1OVaAfzMslgsWLduHUpLS9G3b98aj5Hqb6rZhpH8/HxYLBbodLpq23U6Xa39zzk5OQ4d31zczrXq0KEDVq1ahW+//Raff/45rFYr+vXrh0uXLjVGyU1KbX9XBoMB5eXlElXlnFq3bo2VK1fi66+/xtdff43g4GDExMTg6NGjUpfWqKxWK6ZOnYr+/fujS5cutR7XUj+zrlfXa9WSP7OOHz8OrVYLtVqN559/Hhs3bkTnzp1rPFaqv6kmsVAeOZ++fftWS9b9+vVDp06d8OGHH+Lvf/+7hJVRU9ahQwd06NDB/nu/fv2Qnp6OJUuW4D//+Y+ElTWuF198Eb/++iv27t0rdSlOr67XqiV/ZnXo0AGpqakoKirChg0bMH78eKSkpNQaSKTQbO+M+Pr6Qi6XQ6/XV9uu1+sREBBQY5uAgACHjm8ubuda/ZFSqUT37t1x7ty5hiixSavt78rDwwMuLi4SVdV09O7du0X9XU2ePBmbN29GUlLSLVcrb6mfWb9x5Fr9UUv6zFKpVIiMjETPnj2RkJCAqKgoLFu2rMZjpfqbarZhRKVSoWfPnkhMTLRvs1qtSExMrLWvrG/fvtWOB4AdO3bUenxzcTvX6o8sFguOHz+O1q1bN1SZTVZL/buqL6mpqS3i70oURUyePBkbN27Erl27EBYWdss2LfVv63au1R+15M8sq9UKo9FY4z7J/qYadHisxNatWyeq1WpxzZo14smTJ8U///nPopeXl5iTkyOKoiiOHTtWnDFjhv34H3/8UVQoFOLChQvFtLQ0cfbs2aJSqRSPHz8u1VtoNI5eqzlz5ojbt28X09PTxSNHjohPP/20qNFoxBMnTkj1FhpNcXGxeOzYMfHYsWMiAHHx4sXisWPHxAsXLoiiKIozZswQx44daz8+IyNDdHV1FV999VUxLS1NXLFihSiXy8Vt27ZJ9RYajaPXasmSJeKmTZvEs2fPisePHxenTJkiymQycefOnVK9hUbzwgsviJ6enmJycrKYnZ1t/ykrK7Mfw88sm9u5Vi31M2vGjBliSkqKmJmZKf7yyy/ijBkzREEQxP/973+iKDrP31SzDiOiKIr/+te/xLZt24oqlUrs3bu3eODAAfu+AQMGiOPHj692/H//+1+xffv2okqlEu+66y5xy5YtjVyxdBy5VlOnTrUfq9PpxIcfflg8evSoBFU3vt8eP/3jz2/XZ/z48eKAAQNuaNOtWzdRpVKJ4eHh4urVqxu9bik4eq3mz58vRkREiBqNRvTx8RFjYmLEXbt2SVN8I6vpOgGo9rfCzyyb27lWLfUz65lnnhFDQkJElUol+vn5iYMGDbIHEVF0nr8prtpLREREkmq2Y0aIiIioaWAYISIiIkkxjBAREZGkGEaIiIhIUgwjREREJCmGESIiIpIUwwgRERFJimGEiIiIJMUwQkQNIjk5GYIgoLCwUOpSiMjJcQZWIqoXMTEx6NatG5YuXQoAMJlMKCgogE6ngyAI0hZHRE5NIXUBRNQ8qVSqFrOUPRHdGXbTENEdmzBhAlJSUrBs2TIIggBBELBmzZpq3TRr1qyBl5cXNm/ejA4dOsDV1RVPPvkkysrK8OmnnyI0NBTe3t54+eWXYbFY7K9tNBoxffp0BAUFwc3NDdHR0UhOTpbmjRJRg+CdESK6Y8uWLcOZM2fQpUsXvPPOOwCAEydO3HBcWVkZ3nvvPaxbtw7FxcV44okn8Pjjj8PLywtbt25FRkYGhg8fjv79+2PkyJEAgMmTJ+PkyZNYt24dAgMDsXHjRjz00EM4fvw42rVr16jvk4gaBsMIEd0xT09PqFQquLq62rtmTp06dcNxZrMZH3zwASIiIgAATz75JP7zn/9Ar9dDq9Wic+fOiI2NRVJSEkaOHImsrCysXr0aWVlZCAwMBABMnz4d27Ztw+rVqzF37tzGe5NE1GAYRoio0bi6utqDCADodDqEhoZCq9VW25abmwsAOH78OCwWC9q3b1/tdYxGI1q1atU4RRNRg2MYIaJGo1Qqq/0uCEKN26xWKwCgpKQEcrkcR44cgVwur3bc9QGGiJo2hhEiqhcqlarawNP60L17d1gsFuTm5uK+++6r19cmIufBp2mIqF6Ehobi4MGDOH/+PPLz8+13N+5E+/btMWbMGIwbNw7ffPMNMjMzcejQISQkJGDLli31UDUROQOGESKqF9OnT4dcLkfnzp3h5+eHrKysennd1atXY9y4cfjrX/+KDh06ID4+Hj/99BPatm1bL69PRNLjDKxEREQkKd4ZISIiIkkxjBAREZGkGEaIiIhIUgwjREREJCmGESIiIpIUwwgRERFJimGEiIiIJMUwQkRERJJiGCEiIiJJMYwQERGRpBhGiIiISFIMI0RERCSp/wce4CkO8NG1mgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -115,16 +115,441 @@ "to_plot = model[0]._state_var_names() + model[0]._observable_names()\n", "\n", "funman_request = get_request(requests[model_str])\n", - "setup_common(funman_request, timepoints, debug=False, mode=MODE_SMT)\n", + "setup_common(funman_request, timepoints, debug=True, mode=MODE_SMT, synthesize=False,dreal_precision=0.1)\n", "results = run(funman_request, model_str, models)\n", "report(results, model_str, to_plot, request_results, request_params)" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "petrinet\n", + "\n", + "\n", + "\n", + "t1([I_u*S_u*beta_0/N]) = [1.2e-9*I_u*S_u]\n", + "\n", + "t1([I_u*S_u*beta_0/N]) = [1.2e-9*I_u*S_u]\n", + "\n", + "\n", + "\n", + "I_u\n", + "\n", + "I_u\n", + "\n", + "\n", + "\n", + "t1([I_u*S_u*beta_0/N]) = [1.2e-9*I_u*S_u]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1([I_u*S_u*beta_0/N]) = [1.2e-9*I_u*S_u]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "S_u\n", + "\n", + "S_u\n", + "\n", + "\n", + "\n", + "S_u->t1([I_u*S_u*beta_0/N]) = [1.2e-9*I_u*S_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t2([I_v*S_u*beta_1/N]) = [1.2e-9*I_v*S_u]\n", + "\n", + "t2([I_v*S_u*beta_1/N]) = [1.2e-9*I_v*S_u]\n", + "\n", + "\n", + "\n", + "S_u->t2([I_v*S_u*beta_1/N]) = [1.2e-9*I_v*S_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t13([S_u*v_a*v_b]) = [0.3*S_u]\n", + "\n", + "t13([S_u*v_a*v_b]) = [0.3*S_u]\n", + "\n", + "\n", + "\n", + "S_u->t13([S_u*v_a*v_b]) = [0.3*S_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t2([I_v*S_u*beta_1/N]) = [1.2e-9*I_v*S_u]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_v\n", + "\n", + "I_v\n", + "\n", + "\n", + "\n", + "t2([I_v*S_u*beta_1/N]) = [1.2e-9*I_v*S_u]->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3([I_v*S_v*beta_2/N]) = [1.2e-9*I_v*S_v]\n", + "\n", + "t3([I_v*S_v*beta_2/N]) = [1.2e-9*I_v*S_v]\n", + "\n", + "\n", + "\n", + "t3([I_v*S_v*beta_2/N]) = [1.2e-9*I_v*S_v]->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3([I_v*S_v*beta_2/N]) = [1.2e-9*I_v*S_v]->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4([I_u*S_v*beta_3/N]) = [1.2e-9*I_u*S_v]\n", + "\n", + "t4([I_u*S_v*beta_3/N]) = [1.2e-9*I_u*S_v]\n", + "\n", + "\n", + "\n", + "t4([I_u*S_v*beta_3/N]) = [1.2e-9*I_u*S_v]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4([I_u*S_v*beta_3/N]) = [1.2e-9*I_u*S_v]->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5([I_u*pir_0*rir]) = [0.063*I_u]\n", + "\n", + "t5([I_u*pir_0*rir]) = [0.063*I_u]\n", + "\n", + "\n", + "\n", + "R_u\n", + "\n", + "R_u\n", + "\n", + "\n", + "\n", + "t5([I_u*pir_0*rir]) = [0.063*I_u]->R_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t6([I_v*pir_1*rir]) = [0.063*I_v]\n", + "\n", + "t6([I_v*pir_1*rir]) = [0.063*I_v]\n", + "\n", + "\n", + "\n", + "R_v\n", + "\n", + "R_v\n", + "\n", + "\n", + "\n", + "t6([I_v*pir_1*rir]) = [0.063*I_v]->R_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t7([I_u*pih_0*rih_0]) = [0.007*I_u]\n", + "\n", + "t7([I_u*pih_0*rih_0]) = [0.007*I_u]\n", + "\n", + "\n", + "\n", + "H_u\n", + "\n", + "H_u\n", + "\n", + "\n", + "\n", + "t7([I_u*pih_0*rih_0]) = [0.007*I_u]->H_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t8([I_v*pih_1*rih_1]) = [0.007*I_v]\n", + "\n", + "t8([I_v*pih_1*rih_1]) = [0.007*I_v]\n", + "\n", + "\n", + "\n", + "H_v\n", + "\n", + "H_v\n", + "\n", + "\n", + "\n", + "t8([I_v*pih_1*rih_1]) = [0.007*I_v]->H_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t9([H_u*phd_0*rhd_0]) = [0.039*H_u]\n", + "\n", + "t9([H_u*phd_0*rhd_0]) = [0.039*H_u]\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "t9([H_u*phd_0*rhd_0]) = [0.039*H_u]->D\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t10([H_v*phd_1*rhd_1]) = [0.039*H_v]\n", + "\n", + "t10([H_v*phd_1*rhd_1]) = [0.039*H_v]\n", + "\n", + "\n", + "\n", + "t10([H_v*phd_1*rhd_1]) = [0.039*H_v]->D\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t11([H_u*phr_0*rhr_0]) = [0.0609*H_u]\n", + "\n", + "t11([H_u*phr_0*rhr_0]) = [0.0609*H_u]\n", + "\n", + "\n", + "\n", + "t11([H_u*phr_0*rhr_0]) = [0.0609*H_u]->R_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t12([H_v*phr_1*rhr_1]) = [0.0609*H_v]\n", + "\n", + "t12([H_v*phr_1*rhr_1]) = [0.0609*H_v]\n", + "\n", + "\n", + "\n", + "t12([H_v*phr_1*rhr_1]) = [0.0609*H_v]->R_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "S_v\n", + "\n", + "S_v\n", + "\n", + "\n", + "\n", + "t13([S_u*v_a*v_b]) = [0.3*S_u]->S_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_u->t1([I_u*S_u*beta_0/N]) = [1.2e-9*I_u*S_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_u->t4([I_u*S_v*beta_3/N]) = [1.2e-9*I_u*S_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_u->t5([I_u*pir_0*rir]) = [0.063*I_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_u->t7([I_u*pih_0*rih_0]) = [0.007*I_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v->t2([I_v*S_u*beta_1/N]) = [1.2e-9*I_v*S_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v->t3([I_v*S_v*beta_2/N]) = [1.2e-9*I_v*S_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v->t6([I_v*pir_1*rir]) = [0.063*I_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v->t8([I_v*pih_1*rih_1]) = [0.007*I_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_v->t3([I_v*S_v*beta_2/N]) = [1.2e-9*I_v*S_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_v->t4([I_u*S_v*beta_3/N]) = [1.2e-9*I_u*S_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_u->t9([H_u*phd_0*rhd_0]) = [0.039*H_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_u->t11([H_u*phr_0*rhr_0]) = [0.0609*H_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_v->t10([H_v*phd_1*rhd_1]) = [0.039*H_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_v->t12([H_v*phr_1*rhr_1]) = [0.0609*H_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model[0].to_dot()" + ] + }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-25 15:42:06,500 - funman.funman - ERROR - funman.solve() exiting due to exception: KeyboardInterrupt(SIGINT) Detected.\n", + "2024-09-25 15:42:06,506 - funman.server.worker - ERROR - Internal Server Error (57d77b08-aee2-4617-8130-e39a1b13da5c):\n", + "Traceback (most recent call last):\n", + " File \"/root/funman/src/funman/server/worker.py\", line 240, in _run\n", + " result = f.solve(\n", + " File \"/root/funman/src/funman/funman.py\", line 91, in solve\n", + " raise e\n", + " File \"/root/funman/src/funman/funman.py\", line 78, in solve\n", + " result = problem.solve(\n", + " File \"/root/funman/src/funman/scenario/consistency.py\", line 77, in solve\n", + " parameter_space, models, consistent = search.search(\n", + " File \"/root/funman/src/funman/search/smt_check.py\", line 60, in search\n", + " model_result, explanation_result = self.expand(\n", + " File \"/root/funman/src/funman/search/smt_check.py\", line 285, in expand\n", + " model_result = self.solve_formula(s, formula, episode)\n", + " File \"/root/funman/src/funman/search/smt_check.py\", line 206, in solve_formula\n", + " result = self.invoke_solver(s, timeout=episode.config.solver_timeout)\n", + " File \"/root/funman/src/funman/search/search.py\", line 183, in invoke_solver\n", + " result = self._internal_invoke_solver(s, None)\n", + " File \"/root/funman/src/funman/search/search.py\", line 190, in _internal_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 647, in solve\n", + " raise e\n", + " File \"/root/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 645, in solve\n", + " ans = self.check_sat()\n", + " File \"/root/funman/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py\", line 564, in check_sat\n", + " result = self.context.CheckSat()\n", + "RuntimeError: KeyboardInterrupt(SIGINT) Detected.\n", + "2024-09-25 15:42:06,521 - funman.server.worker - ERROR - Aborting work on: 57d77b08-aee2-4617-8130-e39a1b13da5c\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -136,8 +561,8 @@ " phr_1 pih_0 ... pir_1 rhd_0 rhd_1 rhr_0 rhr_1 rih_0 rih_1 \\\n", "sirhd-vac 0.87 0.1 ... 0.9 0.3 0.3 0.07 0.07 0.07 0.07 \n", "\n", - " rir v_a v_b \n", - "sirhd-vac 0.07 0.3 1.0 \n", + " rir v_a v_b \n", + "sirhd-vac 0.07 0.01 0.01 \n", "\n", "[1 rows x 22 columns]\n" ] diff --git a/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb b/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb new file mode 100644 index 00000000..93f7c122 --- /dev/null +++ b/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# This notebook illustrates handling the September 2024 Demo of the 18-month epi evaluation scenario 2 question 9\n", + "\n", + "# Import funman related code\n", + "import os\n", + "from funman import MODE_ODEINT, MODE_SMT, Interval, LinearConstraint\n", + "import json\n", + "import pandas as pd\n", + "import logging\n", + "import matplotlib.pyplot as plt\n", + "from helpers import run, get_model, setup_common, get_request, report\n", + "\n", + "\n", + "RESOURCES = \"../../resources\"\n", + "SAVED_RESULTS_DIR = \"./out\"\n", + "\n", + "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\",\"monthly-demo\", \"2024-09\")\n", + "REQUEST_PATH = os.path.join(\n", + " EXAMPLE_DIR, \"eval_scenario_base_request.json\")\n", + "\n", + "models = {\n", + " \"sidarthe_observables\": os.path.join(\n", + " EXAMPLE_DIR, \"SIDARTHE.model.with.observables.json\"),\n", + " \"sirhd\": os.path.join(\n", + " EXAMPLE_DIR, \"sirhd.json\"),\n", + " \"sirhd-vac\": os.path.join(\n", + " EXAMPLE_DIR, \"sirhd-vac.json\"),\n", + "}\n", + "\n", + "requests = {\n", + " \"sidarthe_observables\": REQUEST_PATH,\n", + " \"sirhd-vac\": os.path.join(EXAMPLE_DIR, \"sirhd-vac-request.json\"),\n", + " \"sirhd\": None\n", + "}\n", + "\n", + "states = {\n", + " \"sidarthe_observables\": ['Susceptible', 'Diagnosed', 'Infected', 'Ailing', 'Recognized', 'Healed', 'Threatened', 'Extinct'],\n", + " \"sirhd\": [\"S\", \"I\", \"R\", \"H\", \"D\"],\n", + " \"sirhd-vac\": [\"S_v\", \"I_v\", \"R_v\", \"H_v\", \"S_u\", \"I_u\", \"R_u\", \"H_u\", \"D\"],\n", + "}\n", + "\n", + "\n", + "request_params = {}\n", + "request_results = {}\n", + "\n", + "\n", + "# %load_ext autoreload\n", + "# %autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Constants for the scenario\n", + "\n", + "MAX_TIME=3\n", + "STEP_SIZE=1\n", + "timepoints = list(range(0, MAX_TIME+STEP_SIZE, STEP_SIZE))\n", + "# model_str = \"sidarthe_observables\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "petrinet\n", + "\n", + "\n", + "\n", + "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", + "\n", + "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", + "\n", + "\n", + "\n", + "R\n", + "\n", + "R\n", + "\n", + "\n", + "\n", + "t2_u([I_u*pir*rir]) = [0.063*I_u]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_u\n", + "\n", + "I_u\n", + "\n", + "\n", + "\n", + "I_u->t2_u([I_u*pir*rir]) = [0.063*I_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", + "\n", + "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", + "\n", + "\n", + "\n", + "I_u->t3_u([I_u*pih*rih]) = [0.007*I_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", + "\n", + "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", + "\n", + "\n", + "\n", + "I_u->t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]\n", + "\n", + "t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]\n", + "\n", + "\n", + "\n", + "I_u->t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]\n", + "\n", + "transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]\n", + "\n", + "\n", + "\n", + "I_u->transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t2_v([I_v*pir*rir]) = [0.063*I_v]\n", + "\n", + "t2_v([I_v*pir*rir]) = [0.063*I_v]\n", + "\n", + "\n", + "\n", + "t2_v([I_v*pir*rir]) = [0.063*I_v]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "H\n", + "\n", + "H\n", + "\n", + "\n", + "\n", + "t3_u([I_u*pih*rih]) = [0.007*I_u]->H\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3_v([I_v*pih*rih]) = [0.007*I_v]\n", + "\n", + "t3_v([I_v*pih*rih]) = [0.007*I_v]\n", + "\n", + "\n", + "\n", + "t3_v([I_v*pih*rih]) = [0.007*I_v]->H\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_v\n", + "\n", + "I_v\n", + "\n", + "\n", + "\n", + "t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]\n", + "\n", + "t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]\n", + "\n", + "\n", + "\n", + "t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", + "\n", + "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", + "\n", + "\n", + "\n", + "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]->D\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]\n", + "\n", + "transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]\n", + "\n", + "\n", + "\n", + "transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_v->t2_v([I_v*pir*rir]) = [0.063*I_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v->t3_v([I_v*pih*rih]) = [0.007*I_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v->t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v->t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v->transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S\n", + "\n", + "S\n", + "\n", + "\n", + "\n", + "S->t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S->t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S->t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S->t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Stratify model with vaccination status\n", + "\n", + "from typing import Dict, List, Optional\n", + "from funman.model.generated_models.petrinet import Model1, Parameter, Rate, State,Transition, Properties, Model, Semantics, OdeSemantics\n", + "from funman.model.petrinet import GeneratedPetriNetModel, PetrinetModel\n", + "\n", + "\n", + "model_str = \"sirhd\"\n", + "(model, request) = get_model(models[model_str])\n", + "to_plot = model._state_var_names() + model._observable_names()\n", + "\n", + "\n", + "def stratify(self: PetrinetModel, state_var: str, strata: List[str], strata_parameter:Optional[str]=None, strata_transitions=[], self_strata_transition=False):\n", + " \n", + " # get state variable\n", + " state_vars: List[State] = [s for s in self._state_vars() if self._state_var_name(s) == state_var]\n", + " assert len(state_vars) == 1, \"Found more than one State variable for {state_var}\"\n", + " original_var = state_vars[0]\n", + " new_vars = [State(id=f\"{original_var.id}_{level}\", name=f\"{original_var.name}_{level}\", description=f\"{original_var.description} Stratified wrt. {level}\", grounding=original_var.grounding, units=original_var.units) for level in strata]\n", + " \n", + " # get new transitions\n", + " transitions: Dict[str, Transition] = {t.id: t for t in self._transitions() if original_var.id in t.input or original_var.id in t.output}\n", + " other_transitions = {t.id: t for t in self._transitions() if t.id not in transitions}\n", + " \n", + " src_only_transitions: Dict[str, Transition] = {t_id: t for t_id, t in transitions.items() if original_var.id in t.input and original_var.id not in t.output}\n", + " dest_only_transitions: Dict[str, Transition] = {t_id: t for t_id, t in transitions.items() if original_var.id not in t.input and original_var.id in t.output}\n", + " src_and_dest_transitions: Dict[str, Transition] = {t_id: t for t_id, t in transitions.items() if original_var.id in t.input and original_var.id in t.output }\n", + " \n", + " # Replicate transitions where original_var is in source\n", + " new_src_transitions = [\n", + " Transition(id=f\"{t.id}_{level}\", \n", + " input=[(s if s!= original_var.id else f\"{s}_{level}\") for s in t.input], \n", + " output=t.output, \n", + " grounding=t.grounding, \n", + " properties=Properties(name=f\"{t.properties.name}_{level}\", description=(f\"{t.properties.description} Stratified wrt. {level}\" if t.properties.description else t.properties.description))) \n", + " for t_id, t in src_only_transitions.items()\n", + " for level in strata\n", + " ]\n", + " \n", + " # Replicate transitions where original_var is in destination\n", + " new_dest_transitions = [\n", + " Transition(id=f\"{t.id}_{level}\", \n", + " input=t.input, \n", + " output=[(s if s!= original_var.id else f\"{s}_{level}\") for s in t.output], \n", + " grounding=t.grounding, \n", + " properties=Properties(name=f\"{t.properties.name}_{level}\", description=(f\"{t.properties.description} Stratified wrt. {level}\" if t.properties.description else t.properties.description))) \n", + " for t_id,t in dest_only_transitions.items()\n", + " for level in strata\n", + " ]\n", + " \n", + " # Replicate transitions where original_var is in source and destination\n", + " new_src_dest_transitions = [\n", + " Transition(id=f\"{t.id}_{level_s}_{level_t}\", \n", + " input=[(s if s!= original_var.id else f\"{s}_{level_s}\") for s in t.input], \n", + " output=[(s if s!= original_var.id else f\"{s}_{level_t}\") for s in t.output], \n", + " grounding=t.grounding, \n", + " properties=Properties(name=f\"{t.properties.name}_{level_s}_{level_t}\", description=(f\"{t.properties.description} Stratified wrt. {level_s}, {level_t}.\" if t.properties.description else t.properties.description))) \n", + " for t_id,t in src_and_dest_transitions.items()\n", + " for level_s in strata\n", + " for level_t in strata\n", + " if t_id in strata_transitions or level_s == level_t\n", + " ]\n", + " \n", + " new_transitions = new_src_transitions +new_dest_transitions + new_src_dest_transitions\n", + " \n", + " # Modify rates by substituting fresh versions of the strata_parameter\n", + " old_rates = {t_id: self._transition_rate(t) for t_id, t in transitions.items()}\n", + " other_rates = {r.target: r for r in self.petrinet.semantics.ode.rates if r.target in other_transitions}\n", + " \n", + " src_only_rates = [Rate(target=f\"{t_id}_{level}\", expression=(str(r[0]).replace(strata_parameter, f\"{strata_parameter}_{level}\").replace(state_var, f\"{state_var}_{level}\") if strata_parameter else r[0].replace(state_var, f\"{state_var}_{level}\"))) for t_id,r in old_rates.items() if t_id in src_only_transitions for level in strata]\n", + "\n", + " dest_only_rates = [Rate(target=f\"{t_id}_{level}\", expression=(str(r[0]).replace(strata_parameter, f\"{strata_parameter}_{level}\").replace(state_var, f\"{state_var}_{level}\") if strata_parameter else r[0].replace(state_var, f\"{state_var}_{level}\"))) for t_id,r in old_rates.items() if t_id in dest_only_transitions for level in strata]\n", + "\n", + " src_and_dest_rates = [Rate(target=f\"{t_id}_{level_s}_{level_t}\", expression=(str(r[0]).replace(strata_parameter, f\"{strata_parameter}_{level_s}_{level_t}\").replace(state_var, f\"{state_var}_{level_s}_{level_t}\") if strata_parameter else r[0].replace(state_var, f\"{state_var}_{level_s}_{level_t}\"))) for t_id,r in old_rates.items() if t_id in src_and_dest_transitions for level_s in strata for level_t in strata]\n", + "\n", + " new_rates = src_only_rates + dest_only_rates + src_and_dest_rates\n", + "\n", + " new_states = new_vars + [s for s in self.petrinet.model.states.root if s not in state_vars]\n", + " \n", + " # FIXME update with new states by splitting old state values\n", + " new_initials = self.petrinet.semantics.ode.initials\n", + " \n", + " # FIXME update with split parameters\n", + " new_parameters = self.petrinet.semantics.ode.parameters\n", + " \n", + " # FIXME update with splits\n", + " new_observables = self.petrinet.semantics.ode.observables\n", + " \n", + " self_strata_transitions = [Transition(id=f\"transition_{state_var}_{level_s}_{level_t}\", input=[f\"{state_var}_{level_s}\"], output=[f\"{state_var}_{level_t}\"], grounding=None, properties={\"name\": f\"transition_{state_var}_{level_s}_{level_t}\"}) for level_s in strata for level_t in strata if level_s != level_t]\n", + " self_strata_rates = [Rate(target=f\"transition_{state_var}_{level_s}_{level_t}\", expression=f\"{state_var}_{level_s}*transition_{state_var}_{level_s}_{level_t}\") for level_s in strata for level_t in strata if level_s != level_t]\n", + " self_strata_parameters = [Parameter(id=f\"transition_{state_var}_{level_s}_{level_t}\", name=f\"transition_{state_var}_{level_s}_{level_t}\", description=\"Transition rate parameter between {state_var} strata {level_s} and {level_t}.\", value=1.0/float(len(strata))) for level_s in strata for level_t in strata if level_s != level_t]\n", + " \n", + " new_model = GeneratedPetriNetModel(petrinet=Model(header=self.petrinet.header,\n", + " properties=self.petrinet.properties,\n", + " model=Model1(\n", + " states=new_states, \n", + " transitions=[*new_transitions, *other_transitions.values(), *self_strata_transitions]\n", + " ),\n", + " semantics=Semantics(\n", + " ode=OdeSemantics(\n", + " rates=[*new_rates, *other_rates.values(), *self_strata_rates], \n", + " initials=new_initials, \n", + " parameters=new_parameters+self_strata_parameters, \n", + " observables=new_observables,\n", + " time=self.petrinet.semantics.ode.time), \n", + " typing=self.petrinet.semantics.typing, span=self.petrinet.semantics.span),\n", + " metadata=self.petrinet.metadata\n", + " ))\n", + "\n", + " return new_model #new_rates, transitions, new_transitions # dest_only_rates #original_var, new_vars, new_transitions\n", + " \n", + "m = stratify(model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t1\"], self_strata_transition=True)\n", + "m.to_dot()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "petrinet\n", + "\n", + "\n", + "\n", + "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", + "\n", + "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", + "\n", + "\n", + "\n", + "R\n", + "\n", + "R\n", + "\n", + "\n", + "\n", + "t2_u([I_u*pir*rir]) = [0.063*I_u]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_u\n", + "\n", + "I_u\n", + "\n", + "\n", + "\n", + "I_u->t2_u([I_u*pir*rir]) = [0.063*I_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", + "\n", + "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", + "\n", + "\n", + "\n", + "I_u->t3_u([I_u*pih*rih]) = [0.007*I_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t2_v([I_v*pir*rir]) = [0.063*I_v]\n", + "\n", + "t2_v([I_v*pir*rir]) = [0.063*I_v]\n", + "\n", + "\n", + "\n", + "t2_v([I_v*pir*rir]) = [0.063*I_v]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "H\n", + "\n", + "H\n", + "\n", + "\n", + "\n", + "t3_u([I_u*pih*rih]) = [0.007*I_u]->H\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3_v([I_v*pih*rih]) = [0.007*I_v]\n", + "\n", + "t3_v([I_v*pih*rih]) = [0.007*I_v]\n", + "\n", + "\n", + "\n", + "t3_v([I_v*pih*rih]) = [0.007*I_v]->H\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]->D\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_v\n", + "\n", + "I_v\n", + "\n", + "\n", + "\n", + "I_v->t2_v([I_v*pir*rir]) = [0.063*I_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v->t3_v([I_v*pih*rih]) = [0.007*I_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stratify(model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t3\"], self_strata_transition=True).to_dot()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "petrinet\n", + "\n", + "\n", + "\n", + "t1([I*S*beta/N]) = [1.2e-9*I*S]\n", + "\n", + "t1([I*S*beta/N]) = [1.2e-9*I*S]\n", + "\n", + "\n", + "\n", + "I\n", + "\n", + "I\n", + "\n", + "\n", + "\n", + "t1([I*S*beta/N]) = [1.2e-9*I*S]->I\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1([I*S*beta/N]) = [1.2e-9*I*S]->I\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "S\n", + "\n", + "S\n", + "\n", + "\n", + "\n", + "S->t1([I*S*beta/N]) = [1.2e-9*I*S]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t2([I*pir*rir]) = [0.063*I]\n", + "\n", + "t2([I*pir*rir]) = [0.063*I]\n", + "\n", + "\n", + "\n", + "R\n", + "\n", + "R\n", + "\n", + "\n", + "\n", + "t2([I*pir*rir]) = [0.063*I]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3([I*pih*rih]) = [0.007*I]\n", + "\n", + "t3([I*pih*rih]) = [0.007*I]\n", + "\n", + "\n", + "\n", + "H\n", + "\n", + "H\n", + "\n", + "\n", + "\n", + "t3([I*pih*rih]) = [0.007*I]->H\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]->D\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I->t1([I*S*beta/N]) = [1.2e-9*I*S]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I->t2([I*pir*rir]) = [0.063*I]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I->t3([I*pih*rih]) = [0.007*I]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.to_dot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'GeneratedPetriNetModel' object is not subscriptable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mto_dot()\n", + "\u001b[0;31mTypeError\u001b[0m: 'GeneratedPetriNetModel' object is not subscriptable" + ] + } + ], + "source": [ + "model[0].to_dot()" + ] + } + ], + "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/notebooks/monthly-demos/helpers.py b/notebooks/monthly-demos/helpers.py index a5556558..33c76df9 100644 --- a/notebooks/monthly-demos/helpers.py +++ b/notebooks/monthly-demos/helpers.py @@ -71,7 +71,7 @@ def run(funman_request, model, models, plot=False, SAVED_RESULTS_DIR="./out"): ) return results -def setup_common(funman_request, timepoints, synthesize=False, debug=False, dreal_precision=1e-1, mode=MODE_SMT): +def setup_common(funman_request, timepoints, synthesize=False, debug=False, dreal_precision=1e-3, mode=MODE_SMT): set_timepoints(funman_request, timepoints) if not synthesize: unset_all_labels(funman_request) diff --git a/resources/amr/petrinet/monthly-demo/2024-09/sirhd-vac-request.json b/resources/amr/petrinet/monthly-demo/2024-09/sirhd-vac-request.json new file mode 100644 index 00000000..b691cb27 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-09/sirhd-vac-request.json @@ -0,0 +1,42 @@ +{ + "constraints": [], + "parameters": [ + { + "name": "v_a", + "interval": { + "lb": 0.25, + "ub": 0.45 + }, + "label": "all" + }, + { + "name": "v_b", + "interval": { + "lb": 0.95, + "ub": 1.05 + }, + "label": "all" + } + ], + "structure_parameters": [ + { + "name": "schedules", + "schedules": [ + { + "timepoints": [ + 0, + 10, + 20 + ] + } + ] + } + ], + "config": { + "tolerance": 1e-2, + "dreal_precision": 0.001, + "normalization_constant": 150000000.0, + "normalize": false, + "use_compartmental_constraints": true + } +} \ No newline at end of file From 6751cbd784b967ea444e2c8c6c993ffb0e88eb56 Mon Sep 17 00:00:00 2001 From: Drisana Mosaphir Date: Fri, 27 Sep 2024 15:26:43 -0500 Subject: [PATCH 51/93] terarium models --- .../monthly-demo/2024-09/SIRHD_u_v.json | 678 + .../2024-09/sirhd-access-transmission_UV.json | 688 + .../sirhd-access-transmission_UV_dosage.json | 1380 ++ ...cess-transmission_UV_dosage_ethnicity.json | 19059 ++++++++++++++++ .../monthly-demo/2024-09/sirhd-base.json | 217 + 5 files changed, 22022 insertions(+) create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/SIRHD_u_v.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd-access-transmission_UV.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd-access-transmission_UV_dosage.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd-access-transmission_UV_dosage_ethnicity.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd-base.json diff --git a/resources/amr/petrinet/monthly-demo/2024-09/SIRHD_u_v.json b/resources/amr/petrinet/monthly-demo/2024-09/SIRHD_u_v.json new file mode 100644 index 00000000..9959129b --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-09/SIRHD_u_v.json @@ -0,0 +1,678 @@ +{ + "id": "77ad204d-0113-4170-b1b5-5660ea26966a", + "createdOn": "2024-09-18T19:32:49.430+00:00", + "updatedOn": "2024-09-18T19:33:10.504+00:00", + "name": "SIRHD_u_v_3", + "fileNames": [], + "temporary": false, + "publicAsset": true, + "header": { + "name": "SIRHD_u_v_3", + "description": "This is a model from equations", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "model_version": "0.1" + }, + "model": { + "states": [ + { + "id": "S_Vaccinated", + "name": "S", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Vaccinated" + } + } + }, + { + "id": "I_Vaccinated", + "name": "I", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Vaccinated" + } + } + }, + { + "id": "I_Unvaccinated", + "name": "I", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Unvaccinated" + } + } + }, + { + "id": "S_Unvaccinated", + "name": "S", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Unvaccinated" + } + } + }, + { + "id": "R_Vaccinated", + "name": "R", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Vaccinated" + } + } + }, + { + "id": "R_Unvaccinated", + "name": "R", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Unvaccinated" + } + } + }, + { + "id": "H_Vaccinated", + "name": "H", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Vaccinated" + } + } + }, + { + "id": "H_Unvaccinated", + "name": "H", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Unvaccinated" + } + } + }, + { + "id": "D_Vaccinated", + "name": "D", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Vaccinated" + } + } + }, + { + "id": "D_Unvaccinated", + "name": "D", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Unvaccinated" + } + } + } + ], + "transitions": [ + { + "id": "t0_Vaccinated_Vaccinated", + "input": [ + "I_Vaccinated", + "S_Vaccinated" + ], + "output": [ + "I_Vaccinated", + "I_Vaccinated" + ], + "properties": { + "name": "t0_Vaccinated_Vaccinated" + } + }, + { + "id": "t0_Vaccinated_Unvaccinated", + "input": [ + "I_Unvaccinated", + "S_Vaccinated" + ], + "output": [ + "I_Unvaccinated", + "I_Vaccinated" + ], + "properties": { + "name": "t0_Vaccinated_Unvaccinated" + } + }, + { + "id": "t0_Unvaccinated_Vaccinated", + "input": [ + "I_Vaccinated", + "S_Unvaccinated" + ], + "output": [ + "I_Vaccinated", + "I_Unvaccinated" + ], + "properties": { + "name": "t0_Unvaccinated_Vaccinated" + } + }, + { + "id": "t0_Unvaccinated_Unvaccinated", + "input": [ + "I_Unvaccinated", + "S_Unvaccinated" + ], + "output": [ + "I_Unvaccinated", + "I_Unvaccinated" + ], + "properties": { + "name": "t0_Unvaccinated_Unvaccinated" + } + }, + { + "id": "t1_Vaccinated", + "input": [ + "I_Vaccinated" + ], + "output": [ + "R_Vaccinated" + ], + "properties": { + "name": "t1_Vaccinated" + } + }, + { + "id": "t1_Unvaccinated", + "input": [ + "I_Unvaccinated" + ], + "output": [ + "R_Unvaccinated" + ], + "properties": { + "name": "t1_Unvaccinated" + } + }, + { + "id": "t2_Vaccinated", + "input": [ + "H_Vaccinated" + ], + "output": [ + "R_Vaccinated" + ], + "properties": { + "name": "t2_Vaccinated" + } + }, + { + "id": "t2_Unvaccinated", + "input": [ + "H_Unvaccinated" + ], + "output": [ + "R_Unvaccinated" + ], + "properties": { + "name": "t2_Unvaccinated" + } + }, + { + "id": "t3_Vaccinated", + "input": [ + "I_Vaccinated" + ], + "output": [ + "H_Vaccinated" + ], + "properties": { + "name": "t3_Vaccinated" + } + }, + { + "id": "t3_Unvaccinated", + "input": [ + "I_Unvaccinated" + ], + "output": [ + "H_Unvaccinated" + ], + "properties": { + "name": "t3_Unvaccinated" + } + }, + { + "id": "t4_Vaccinated", + "input": [ + "H_Vaccinated" + ], + "output": [ + "D_Vaccinated" + ], + "properties": { + "name": "t4_Vaccinated" + } + }, + { + "id": "t4_Unvaccinated", + "input": [ + "H_Unvaccinated" + ], + "output": [ + "D_Unvaccinated" + ], + "properties": { + "name": "t4_Unvaccinated" + } + }, + { + "id": "t_conv_0_Vaccinated_Unvaccinated", + "input": [ + "S_Vaccinated" + ], + "output": [ + "S_Unvaccinated" + ], + "properties": { + "name": "t_conv_0_Vaccinated_Unvaccinated" + } + }, + { + "id": "t_conv_0_Unvaccinated_Vaccinated", + "input": [ + "S_Unvaccinated" + ], + "output": [ + "S_Vaccinated" + ], + "properties": { + "name": "t_conv_0_Unvaccinated_Vaccinated" + } + }, + { + "id": "t_conv_1_Vaccinated_Unvaccinated", + "input": [ + "I_Vaccinated" + ], + "output": [ + "I_Unvaccinated" + ], + "properties": { + "name": "t_conv_1_Vaccinated_Unvaccinated" + } + }, + { + "id": "t_conv_1_Unvaccinated_Vaccinated", + "input": [ + "I_Unvaccinated" + ], + "output": [ + "I_Vaccinated" + ], + "properties": { + "name": "t_conv_1_Unvaccinated_Vaccinated" + } + }, + { + "id": "t_conv_2_Vaccinated_Unvaccinated", + "input": [ + "R_Vaccinated" + ], + "output": [ + "R_Unvaccinated" + ], + "properties": { + "name": "t_conv_2_Vaccinated_Unvaccinated" + } + }, + { + "id": "t_conv_2_Unvaccinated_Vaccinated", + "input": [ + "R_Unvaccinated" + ], + "output": [ + "R_Vaccinated" + ], + "properties": { + "name": "t_conv_2_Unvaccinated_Vaccinated" + } + }, + { + "id": "t_conv_3_Vaccinated_Unvaccinated", + "input": [ + "H_Vaccinated" + ], + "output": [ + "H_Unvaccinated" + ], + "properties": { + "name": "t_conv_3_Vaccinated_Unvaccinated" + } + }, + { + "id": "t_conv_3_Unvaccinated_Vaccinated", + "input": [ + "H_Unvaccinated" + ], + "output": [ + "H_Vaccinated" + ], + "properties": { + "name": "t_conv_3_Unvaccinated_Vaccinated" + } + }, + { + "id": "t_conv_4_Vaccinated_Unvaccinated", + "input": [ + "D_Vaccinated" + ], + "output": [ + "D_Unvaccinated" + ], + "properties": { + "name": "t_conv_4_Vaccinated_Unvaccinated" + } + }, + { + "id": "t_conv_4_Unvaccinated_Vaccinated", + "input": [ + "D_Unvaccinated" + ], + "output": [ + "D_Vaccinated" + ], + "properties": { + "name": "t_conv_4_Unvaccinated_Vaccinated" + } + } + ] + }, + "properties": {}, + "semantics": { + "ode": { + "rates": [ + { + "target": "t0_Vaccinated_Vaccinated", + "expression": "I_Vaccinated*S_Vaccinated*b_Vaccinated_Vaccinated/N", + "expression_mathml": "I_VaccinatedS_Vaccinatedb_Vaccinated_VaccinatedN" + }, + { + "target": "t0_Vaccinated_Unvaccinated", + "expression": "I_Unvaccinated*S_Vaccinated*b_Vaccinated_Unvaccinated/N", + "expression_mathml": "I_UnvaccinatedS_Vaccinatedb_Vaccinated_UnvaccinatedN" + }, + { + "target": "t0_Unvaccinated_Vaccinated", + "expression": "I_Vaccinated*S_Unvaccinated*b_Unvaccinated_Vaccinated/N", + "expression_mathml": "I_VaccinatedS_Unvaccinatedb_Unvaccinated_VaccinatedN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated", + "expression": "I_Unvaccinated*S_Unvaccinated*b_Unvaccinated_Unvaccinated/N", + "expression_mathml": "I_UnvaccinatedS_Unvaccinatedb_Unvaccinated_UnvaccinatedN" + }, + { + "target": "t1_Vaccinated", + "expression": "I_Vaccinated*p_{IR}_Vaccinated*r_{IR}", + "expression_mathml": "I_Vaccinatedp_{IR}_Vaccinatedr_{IR}" + }, + { + "target": "t1_Unvaccinated", + "expression": "I_Unvaccinated*p_{IR}_Unvaccinated*r_{IR}", + "expression_mathml": "I_Unvaccinatedp_{IR}_Unvaccinatedr_{IR}" + }, + { + "target": "t2_Vaccinated", + "expression": "H_Vaccinated*p_{HR}_Vaccinated*r_{HR}", + "expression_mathml": "H_Vaccinatedp_{HR}_Vaccinatedr_{HR}" + }, + { + "target": "t2_Unvaccinated", + "expression": "H_Unvaccinated*p_{HR}_Unvaccinated*r_{HR}", + "expression_mathml": "H_Unvaccinatedp_{HR}_Unvaccinatedr_{HR}" + }, + { + "target": "t3_Vaccinated", + "expression": "I_Vaccinated*p_{IH}_Vaccinated*r_{IH}", + "expression_mathml": "I_Vaccinatedp_{IH}_Vaccinatedr_{IH}" + }, + { + "target": "t3_Unvaccinated", + "expression": "I_Unvaccinated*p_{IH}_Unvaccinated*r_{IH}", + "expression_mathml": "I_Unvaccinatedp_{IH}_Unvaccinatedr_{IH}" + }, + { + "target": "t4_Vaccinated", + "expression": "H_Vaccinated*p_{HD}_Vaccinated*r_{HD}", + "expression_mathml": "H_Vaccinatedp_{HD}_Vaccinatedr_{HD}" + }, + { + "target": "t4_Unvaccinated", + "expression": "H_Unvaccinated*p_{HD}_Unvaccinated*r_{HD}", + "expression_mathml": "H_Unvaccinatedp_{HD}_Unvaccinatedr_{HD}" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated", + "expression": "S_Vaccinated*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinatedp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated", + "expression": "S_Unvaccinated*p_Unvaccinated_Vaccinated", + "expression_mathml": "S_Unvaccinatedp_Unvaccinated_Vaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated", + "expression": "I_Vaccinated*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinatedp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated", + "expression": "I_Unvaccinated*p_Unvaccinated_Vaccinated", + "expression_mathml": "I_Unvaccinatedp_Unvaccinated_Vaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated", + "expression": "R_Vaccinated*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinatedp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated", + "expression": "R_Unvaccinated*p_Unvaccinated_Vaccinated", + "expression_mathml": "R_Unvaccinatedp_Unvaccinated_Vaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated", + "expression": "H_Vaccinated*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinatedp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated", + "expression": "H_Unvaccinated*p_Unvaccinated_Vaccinated", + "expression_mathml": "H_Unvaccinatedp_Unvaccinated_Vaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated", + "expression": "D_Vaccinated*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinatedp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated", + "expression": "D_Unvaccinated*p_Unvaccinated_Vaccinated", + "expression_mathml": "D_Unvaccinatedp_Unvaccinated_Vaccinated" + } + ], + "initials": [ + { + "target": "S_Vaccinated", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "I_Vaccinated", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "I_Unvaccinated", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "S_Unvaccinated", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "R_Vaccinated", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "R_Unvaccinated", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Unvaccinated", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Unvaccinated", + "expression": "0", + "expression_mathml": "0" + } + ], + "parameters": [ + { + "id": "N", + "name": "N", + "value": 1 + }, + { + "id": "b_Vaccinated_Vaccinated", + "name": "b", + "value": 1 + }, + { + "id": "b_Vaccinated_Unvaccinated", + "name": "b", + "value": 1 + }, + { + "id": "b_Unvaccinated_Vaccinated", + "name": "b", + "value": 1 + }, + { + "id": "b_Unvaccinated_Unvaccinated", + "name": "b", + "value": 1 + }, + { + "id": "p_{IR}_Vaccinated", + "name": "p_{IR}", + "value": 1 + }, + { + "id": "r_{IR}", + "name": "r_{IR}", + "value": 1 + }, + { + "id": "p_{IR}_Unvaccinated", + "name": "p_{IR}", + "value": 1 + }, + { + "id": "p_{HR}_Vaccinated", + "name": "p_{HR}", + "value": 1 + }, + { + "id": "r_{HR}", + "name": "r_{HR}", + "value": 1 + }, + { + "id": "p_{HR}_Unvaccinated", + "name": "p_{HR}", + "value": 1 + }, + { + "id": "p_{IH}_Vaccinated", + "name": "p_{IH}", + "value": 1 + }, + { + "id": "r_{IH}", + "name": "r_{IH}", + "value": 1 + }, + { + "id": "p_{IH}_Unvaccinated", + "name": "p_{IH}", + "value": 1 + }, + { + "id": "p_{HD}_Vaccinated", + "name": "p_{HD}", + "value": 1 + }, + { + "id": "r_{HD}", + "name": "r_{HD}", + "value": 1 + }, + { + "id": "p_{HD}_Unvaccinated", + "name": "p_{HD}", + "value": 1 + }, + { + "id": "p_Vaccinated_Unvaccinated", + "value": 0.1 + }, + { + "id": "p_Unvaccinated_Vaccinated", + "value": 0.1 + } + ], + "observables": [], + "time": { + "id": "t" + } + }, + "typing": null + }, + "metadata": { + "annotations": { + "authors": [], + "references": [], + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + }, + "source": null, + "gollmCard": null, + "gollmExtractions": null, + "templateCard": null + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-09/sirhd-access-transmission_UV.json b/resources/amr/petrinet/monthly-demo/2024-09/sirhd-access-transmission_UV.json new file mode 100644 index 00000000..2f1efc4e --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-09/sirhd-access-transmission_UV.json @@ -0,0 +1,688 @@ +{ + "id": "1e46c4b8-ab56-42e7-9655-b03c07753c72", + "createdOn": "2024-09-25T18:39:34.297+00:00", + "updatedOn": "2024-09-25T18:40:49.503+00:00", + "name": "sirhd-access-transmission_UV", + "fileNames": [], + "temporary": false, + "publicAsset": true, + "header": { + "name": "sirhd-access-transmission_UV", + "description": "This is a model from equations", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "model_version": "0.1" + }, + "model": { + "states": [ + { + "id": "S_Vaccinated", + "name": "S", + "grounding": { + "identifiers": {}, + "modifiers": { + "Vaccination": "Vaccinated" + } + } + }, + { + "id": "I_Vaccinated", + "name": "I", + 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"I_Vaccinated_1dose_Multi_Oth_unknown*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Asian_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Asian_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Asian_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Asian_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_NHBlack_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_NHBlack_White", + "expression": "I_Vaccinated_1dose_White*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_NHBlack_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_NHBlack_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_NHBlack_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_NHBlack_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Hispanic_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Hispanic_White", + "expression": "I_Vaccinated_1dose_White*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Hispanic_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Hispanic_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Hispanic_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_1dose_Hispanic_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_AIAN_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_1dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_1dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_AIAN_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_1dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_1dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_AIAN_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_1dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_1dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_AIAN_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_1dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_1dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_AIAN_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_1dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_1dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_AIAN_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_1dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_1dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_AIAN_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_1dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_1dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_White_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_1dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_1dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_White_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_1dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_1dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_White_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_1dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_1dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_White_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_1dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_1dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_White_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_1dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_1dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_White_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_1dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_1dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_White_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_1dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_1dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHOPI_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_1dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHOPI_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_1dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_1dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHOPI_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_1dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHOPI_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_1dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHOPI_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_1dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHOPI_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_1dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Asian_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Asian_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Asian_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Asian_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Asian_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Asian_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Asian_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_1dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_1dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHBlack_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHBlack_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHBlack_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHBlack_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHBlack_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_NHBlack_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_1dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Hispanic_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Hispanic_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Hispanic_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Hispanic_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Hispanic_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_1dose_2dose_Hispanic_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_1dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_AIAN_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_AIAN_White", + "expression": "I_Vaccinated_1dose_White*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_AIAN_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_AIAN_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_AIAN_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_AIAN_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_AIAN_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_White_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_White_White", + "expression": "I_Vaccinated_1dose_White*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_White_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_White_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_White_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_White_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_White_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_White", + "expression": "I_Vaccinated_1dose_White*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHOPI_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHOPI_White", + "expression": "I_Vaccinated_1dose_White*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHOPI_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHOPI_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHOPI_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHOPI_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Asian_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Asian_White", + "expression": "I_Vaccinated_1dose_White*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Asian_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Asian_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Asian_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Asian_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Asian_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHBlack_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHBlack_White", + "expression": "I_Vaccinated_1dose_White*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHBlack_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHBlack_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHBlack_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_NHBlack_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Hispanic_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Hispanic_White", + "expression": "I_Vaccinated_1dose_White*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Hispanic_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Hispanic_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Hispanic_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_1dose_Hispanic_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_AIAN_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_AIAN_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_AIAN_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_AIAN_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_AIAN_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_AIAN_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_AIAN_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_2dose_AIAN*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_2dose_AIANb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_White_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_White_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_White_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_White_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_White_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_White_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_White_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_2dose_White*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_2dose_Whiteb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHOPI_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHOPI_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHOPI_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHOPI_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHOPI_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHOPI_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_2dose_NHOPIb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Asian_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Asian_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Asian_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Asian_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Asian_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Asian_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Asian_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_2dose_Asian*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_2dose_Asianb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHBlack_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHBlack_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHBlack_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHBlack_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHBlack_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_NHBlack_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_2dose_NHBlackb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Hispanic_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Hispanic_White", + "expression": "I_Vaccinated_2dose_White*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Hispanic_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Hispanic_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Hispanic_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Vaccinated_2dose_2dose_Hispanic_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Vaccinated_2dose_Hispanicb_Vaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_AIAN_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_White_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHOPI_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Asian_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_NHBlack_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_1dose_Hispanic_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_1dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_1dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_AIAN_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_AIAN*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_AIANb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_White_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_White*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_Whiteb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_Multi_Oth_unknown*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_Multi_Oth_unknownb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHOPI_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_NHOPI*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_NHOPIb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Asian_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_Asian*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_Asianb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_NHBlack_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_NHBlack*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_NHBlackb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_White", + "expression": "I_Unvaccinated_White*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_Asian", + "expression": "I_Unvaccinated_Asian*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Vaccinated_Unvaccinated_2dose_Hispanic_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Vaccinated_2dose_Hispanic*b_Vaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Vaccinated_2dose_Hispanicb_Vaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_AIAN_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_AIAN_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_AIAN_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_AIAN_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_AIAN_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_AIAN_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_AIAN_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_White_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_White_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_White_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_White_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_White_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_White_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_White_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Multi_Oth_unknown_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Multi_Oth_unknown_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Multi_Oth_unknown_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHOPI_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHOPI_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHOPI_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHOPI_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHOPI_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHOPI_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Asian_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Asian_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Asian_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Asian_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Asian_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Asian_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Asian_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHBlack_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHBlack_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHBlack_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHBlack_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHBlack_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_NHBlack_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Hispanic_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Hispanic_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Hispanic_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Hispanic_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Hispanic_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_1dose_Hispanic_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_AIAN_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_AIAN_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_AIAN_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_AIAN_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_AIAN_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_AIAN_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_AIAN_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_White_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_White_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_White_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_White_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_White_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_White_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_White_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHOPI_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHOPI_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHOPI_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHOPI_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHOPI_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHOPI_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Asian_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Asian_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Asian_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Asian_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Asian_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Asian_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Asian_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHBlack_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHBlack_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHBlack_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHBlack_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHBlack_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_NHBlack_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Hispanic_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Hispanic_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Hispanic_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Hispanic_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Hispanic_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_1dose_2dose_Hispanic_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_AIAN_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_AIAN_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_AIAN_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_AIAN_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_AIAN_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_AIAN_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_AIAN_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_White_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_White_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_White_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_White_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_White_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_White_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_White_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHOPI_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHOPI_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHOPI_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHOPI_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHOPI_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHOPI_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Asian_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Asian_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Asian_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Asian_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Asian_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Asian_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Asian_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHBlack_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHBlack_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHBlack_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHBlack_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHBlack_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_NHBlack_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Hispanic_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AIANS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Hispanic_White", + "expression": "I_Vaccinated_1dose_White*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_WhiteS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Hispanic_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHOPIS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Hispanic_Asian", + "expression": "I_Vaccinated_1dose_Asian*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_AsianS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Hispanic_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_NHBlackS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_1dose_Hispanic_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_1dose_HispanicS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_AIAN_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_AIAN_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_AIAN_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_AIAN_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_AIAN_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_AIAN_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_AIAN_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_AIAN*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_AIANb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_White_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_White_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_White_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_White_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_White_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_White_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_White_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_White*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_Whiteb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Multi_Oth_unknown_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHOPI_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHOPI_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHOPI_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHOPI_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHOPI_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHOPI_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHOPI_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_NHOPI*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_NHOPIb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Asian_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Asian_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Asian_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Asian_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Asian_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Asian_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Asian_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_Asian*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_Asianb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHBlack_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHBlack_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHBlack_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHBlack_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHBlack_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHBlack_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_NHBlack_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_NHBlack*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_NHBlackb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Hispanic_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AIANS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Hispanic_White", + "expression": "I_Vaccinated_2dose_White*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_WhiteS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Hispanic_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Hispanic_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHOPIS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Hispanic_Asian", + "expression": "I_Vaccinated_2dose_Asian*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_AsianS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Hispanic_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_NHBlackS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Vaccinated_2dose_2dose_Hispanic_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*S_Unvaccinated_Hispanic*b_Unvaccinated_Vaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Vaccinated_2dose_HispanicS_Unvaccinated_Hispanicb_Unvaccinated_Vaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_AIAN_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Unvaccinated_AIAN*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Unvaccinated_AIANb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_AIAN_White", + "expression": "I_Unvaccinated_White*S_Unvaccinated_AIAN*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Unvaccinated_AIANb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_AIAN_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Unvaccinated_AIAN*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Unvaccinated_AIANb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_AIAN_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Unvaccinated_AIAN*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Unvaccinated_AIANb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_AIAN_Asian", + "expression": "I_Unvaccinated_Asian*S_Unvaccinated_AIAN*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Unvaccinated_AIANb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_AIAN_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Unvaccinated_AIAN*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Unvaccinated_AIANb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_AIAN_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Unvaccinated_AIAN*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Unvaccinated_AIANb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_White_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Unvaccinated_White*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Unvaccinated_Whiteb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_White_White", + "expression": "I_Unvaccinated_White*S_Unvaccinated_White*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Unvaccinated_Whiteb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_White_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Unvaccinated_White*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Unvaccinated_Whiteb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_White_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Unvaccinated_White*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Unvaccinated_Whiteb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_White_Asian", + "expression": "I_Unvaccinated_Asian*S_Unvaccinated_White*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Unvaccinated_Whiteb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_White_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Unvaccinated_White*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Unvaccinated_Whiteb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_White_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Unvaccinated_White*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Unvaccinated_Whiteb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Multi_Oth_unknown_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Multi_Oth_unknown_White", + "expression": "I_Unvaccinated_White*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Multi_Oth_unknown_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Multi_Oth_unknown_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Multi_Oth_unknown_Asian", + "expression": "I_Unvaccinated_Asian*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Multi_Oth_unknown_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Multi_Oth_unknown_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Unvaccinated_Multi_Oth_unknown*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Unvaccinated_Multi_Oth_unknownb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHOPI_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Unvaccinated_NHOPI*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Unvaccinated_NHOPIb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHOPI_White", + "expression": "I_Unvaccinated_White*S_Unvaccinated_NHOPI*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Unvaccinated_NHOPIb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHOPI_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Unvaccinated_NHOPI*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Unvaccinated_NHOPIb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHOPI_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Unvaccinated_NHOPI*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Unvaccinated_NHOPIb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHOPI_Asian", + "expression": "I_Unvaccinated_Asian*S_Unvaccinated_NHOPI*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Unvaccinated_NHOPIb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHOPI_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Unvaccinated_NHOPI*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Unvaccinated_NHOPIb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHOPI_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Unvaccinated_NHOPI*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Unvaccinated_NHOPIb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Asian_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Unvaccinated_Asian*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Unvaccinated_Asianb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Asian_White", + "expression": "I_Unvaccinated_White*S_Unvaccinated_Asian*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Unvaccinated_Asianb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Asian_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Unvaccinated_Asian*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Unvaccinated_Asianb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Asian_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Unvaccinated_Asian*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Unvaccinated_Asianb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Asian_Asian", + "expression": "I_Unvaccinated_Asian*S_Unvaccinated_Asian*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Unvaccinated_Asianb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Asian_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Unvaccinated_Asian*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Unvaccinated_Asianb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Asian_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Unvaccinated_Asian*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Unvaccinated_Asianb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHBlack_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Unvaccinated_NHBlack*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Unvaccinated_NHBlackb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHBlack_White", + "expression": "I_Unvaccinated_White*S_Unvaccinated_NHBlack*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Unvaccinated_NHBlackb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHBlack_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Unvaccinated_NHBlack*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Unvaccinated_NHBlackb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHBlack_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Unvaccinated_NHBlack*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Unvaccinated_NHBlackb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHBlack_Asian", + "expression": "I_Unvaccinated_Asian*S_Unvaccinated_NHBlack*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Unvaccinated_NHBlackb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHBlack_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Unvaccinated_NHBlack*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Unvaccinated_NHBlackb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_NHBlack_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Unvaccinated_NHBlack*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Unvaccinated_NHBlackb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Hispanic_AIAN", + "expression": "I_Unvaccinated_AIAN*S_Unvaccinated_Hispanic*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AIANS_Unvaccinated_Hispanicb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Hispanic_White", + "expression": "I_Unvaccinated_White*S_Unvaccinated_Hispanic*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_WhiteS_Unvaccinated_Hispanicb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Hispanic_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*S_Unvaccinated_Hispanic*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownS_Unvaccinated_Hispanicb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Hispanic_NHOPI", + "expression": "I_Unvaccinated_NHOPI*S_Unvaccinated_Hispanic*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHOPIS_Unvaccinated_Hispanicb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Hispanic_Asian", + "expression": "I_Unvaccinated_Asian*S_Unvaccinated_Hispanic*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_AsianS_Unvaccinated_Hispanicb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Hispanic_NHBlack", + "expression": "I_Unvaccinated_NHBlack*S_Unvaccinated_Hispanic*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_NHBlackS_Unvaccinated_Hispanicb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t0_Unvaccinated_Unvaccinated_Hispanic_Hispanic", + "expression": "I_Unvaccinated_Hispanic*S_Unvaccinated_Hispanic*b_Unvaccinated_Unvaccinated*(1 - t_a)*(1 - t_d)/N", + "expression_mathml": "I_Unvaccinated_HispanicS_Unvaccinated_Hispanicb_Unvaccinated_Unvaccinated1t_a1t_dN" + }, + { + "target": "t1_Vaccinated_1dose_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*p_{IR}_Vaccinated_1dose*r_{IR}", + "expression_mathml": "I_Vaccinated_1dose_AIANp_{IR}_Vaccinated_1doser_{IR}" + }, + { + "target": "t1_Vaccinated_1dose_White", + "expression": "I_Vaccinated_1dose_White*p_{IR}_Vaccinated_1dose*r_{IR}", + "expression_mathml": "I_Vaccinated_1dose_Whitep_{IR}_Vaccinated_1doser_{IR}" + }, + { + "target": "t1_Vaccinated_1dose_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*p_{IR}_Vaccinated_1dose*r_{IR}", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownp_{IR}_Vaccinated_1doser_{IR}" + }, + { + "target": "t1_Vaccinated_1dose_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*p_{IR}_Vaccinated_1dose*r_{IR}", + "expression_mathml": "I_Vaccinated_1dose_NHOPIp_{IR}_Vaccinated_1doser_{IR}" + }, + { + "target": "t1_Vaccinated_1dose_Asian", + "expression": "I_Vaccinated_1dose_Asian*p_{IR}_Vaccinated_1dose*r_{IR}", + "expression_mathml": "I_Vaccinated_1dose_Asianp_{IR}_Vaccinated_1doser_{IR}" + }, + { + "target": "t1_Vaccinated_1dose_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*p_{IR}_Vaccinated_1dose*r_{IR}", + "expression_mathml": "I_Vaccinated_1dose_NHBlackp_{IR}_Vaccinated_1doser_{IR}" + }, + { + "target": "t1_Vaccinated_1dose_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*p_{IR}_Vaccinated_1dose*r_{IR}", + "expression_mathml": "I_Vaccinated_1dose_Hispanicp_{IR}_Vaccinated_1doser_{IR}" + }, + { + "target": "t1_Vaccinated_2dose_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*p_{IR}_Vaccinated_2dose*r_{IR}", + "expression_mathml": "I_Vaccinated_2dose_AIANp_{IR}_Vaccinated_2doser_{IR}" + }, + { + "target": "t1_Vaccinated_2dose_White", + "expression": "I_Vaccinated_2dose_White*p_{IR}_Vaccinated_2dose*r_{IR}", + "expression_mathml": "I_Vaccinated_2dose_Whitep_{IR}_Vaccinated_2doser_{IR}" + }, + { + "target": "t1_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*p_{IR}_Vaccinated_2dose*r_{IR}", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownp_{IR}_Vaccinated_2doser_{IR}" + }, + { + "target": "t1_Vaccinated_2dose_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*p_{IR}_Vaccinated_2dose*r_{IR}", + "expression_mathml": "I_Vaccinated_2dose_NHOPIp_{IR}_Vaccinated_2doser_{IR}" + }, + { + "target": "t1_Vaccinated_2dose_Asian", + "expression": "I_Vaccinated_2dose_Asian*p_{IR}_Vaccinated_2dose*r_{IR}", + "expression_mathml": "I_Vaccinated_2dose_Asianp_{IR}_Vaccinated_2doser_{IR}" + }, + { + "target": "t1_Vaccinated_2dose_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*p_{IR}_Vaccinated_2dose*r_{IR}", + "expression_mathml": "I_Vaccinated_2dose_NHBlackp_{IR}_Vaccinated_2doser_{IR}" + }, + { + "target": "t1_Vaccinated_2dose_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*p_{IR}_Vaccinated_2dose*r_{IR}", + "expression_mathml": "I_Vaccinated_2dose_Hispanicp_{IR}_Vaccinated_2doser_{IR}" + }, + { + "target": "t1_Unvaccinated_AIAN", + "expression": "I_Unvaccinated_AIAN*p_{IR}_Unvaccinated*r_{IR}", + "expression_mathml": "I_Unvaccinated_AIANp_{IR}_Unvaccinatedr_{IR}" + }, + { + "target": "t1_Unvaccinated_White", + "expression": "I_Unvaccinated_White*p_{IR}_Unvaccinated*r_{IR}", + "expression_mathml": "I_Unvaccinated_Whitep_{IR}_Unvaccinatedr_{IR}" + }, + { + "target": "t1_Unvaccinated_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*p_{IR}_Unvaccinated*r_{IR}", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownp_{IR}_Unvaccinatedr_{IR}" + }, + { + "target": "t1_Unvaccinated_NHOPI", + "expression": "I_Unvaccinated_NHOPI*p_{IR}_Unvaccinated*r_{IR}", + "expression_mathml": "I_Unvaccinated_NHOPIp_{IR}_Unvaccinatedr_{IR}" + }, + { + "target": "t1_Unvaccinated_Asian", + "expression": "I_Unvaccinated_Asian*p_{IR}_Unvaccinated*r_{IR}", + "expression_mathml": "I_Unvaccinated_Asianp_{IR}_Unvaccinatedr_{IR}" + }, + { + "target": "t1_Unvaccinated_NHBlack", + "expression": "I_Unvaccinated_NHBlack*p_{IR}_Unvaccinated*r_{IR}", + "expression_mathml": "I_Unvaccinated_NHBlackp_{IR}_Unvaccinatedr_{IR}" + }, + { + "target": "t1_Unvaccinated_Hispanic", + "expression": "I_Unvaccinated_Hispanic*p_{IR}_Unvaccinated*r_{IR}", + "expression_mathml": "I_Unvaccinated_Hispanicp_{IR}_Unvaccinatedr_{IR}" + }, + { + "target": "t2_Vaccinated_1dose_AIAN", + "expression": "H_Vaccinated_1dose_AIAN*p_{HR}_Vaccinated_1dose*r_{HR}", + "expression_mathml": "H_Vaccinated_1dose_AIANp_{HR}_Vaccinated_1doser_{HR}" + }, + { + "target": "t2_Vaccinated_1dose_White", + "expression": "H_Vaccinated_1dose_White*p_{HR}_Vaccinated_1dose*r_{HR}", + "expression_mathml": "H_Vaccinated_1dose_Whitep_{HR}_Vaccinated_1doser_{HR}" + }, + { + "target": "t2_Vaccinated_1dose_Multi_Oth_unknown", + "expression": "H_Vaccinated_1dose_Multi_Oth_unknown*p_{HR}_Vaccinated_1dose*r_{HR}", + "expression_mathml": "H_Vaccinated_1dose_Multi_Oth_unknownp_{HR}_Vaccinated_1doser_{HR}" + }, + { + "target": "t2_Vaccinated_1dose_NHOPI", + "expression": "H_Vaccinated_1dose_NHOPI*p_{HR}_Vaccinated_1dose*r_{HR}", + "expression_mathml": "H_Vaccinated_1dose_NHOPIp_{HR}_Vaccinated_1doser_{HR}" + }, + { + "target": "t2_Vaccinated_1dose_Asian", + "expression": "H_Vaccinated_1dose_Asian*p_{HR}_Vaccinated_1dose*r_{HR}", + "expression_mathml": "H_Vaccinated_1dose_Asianp_{HR}_Vaccinated_1doser_{HR}" + }, + { + "target": "t2_Vaccinated_1dose_NHBlack", + "expression": "H_Vaccinated_1dose_NHBlack*p_{HR}_Vaccinated_1dose*r_{HR}", + "expression_mathml": "H_Vaccinated_1dose_NHBlackp_{HR}_Vaccinated_1doser_{HR}" + }, + { + "target": "t2_Vaccinated_1dose_Hispanic", + "expression": "H_Vaccinated_1dose_Hispanic*p_{HR}_Vaccinated_1dose*r_{HR}", + "expression_mathml": "H_Vaccinated_1dose_Hispanicp_{HR}_Vaccinated_1doser_{HR}" + }, + { + "target": "t2_Vaccinated_2dose_AIAN", + "expression": "H_Vaccinated_2dose_AIAN*p_{HR}_Vaccinated_2dose*r_{HR}", + "expression_mathml": "H_Vaccinated_2dose_AIANp_{HR}_Vaccinated_2doser_{HR}" + }, + { + "target": "t2_Vaccinated_2dose_White", + "expression": "H_Vaccinated_2dose_White*p_{HR}_Vaccinated_2dose*r_{HR}", + "expression_mathml": "H_Vaccinated_2dose_Whitep_{HR}_Vaccinated_2doser_{HR}" + }, + { + "target": "t2_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "H_Vaccinated_2dose_Multi_Oth_unknown*p_{HR}_Vaccinated_2dose*r_{HR}", + "expression_mathml": "H_Vaccinated_2dose_Multi_Oth_unknownp_{HR}_Vaccinated_2doser_{HR}" + }, + { + "target": "t2_Vaccinated_2dose_NHOPI", + "expression": "H_Vaccinated_2dose_NHOPI*p_{HR}_Vaccinated_2dose*r_{HR}", + "expression_mathml": "H_Vaccinated_2dose_NHOPIp_{HR}_Vaccinated_2doser_{HR}" + }, + { + "target": "t2_Vaccinated_2dose_Asian", + "expression": "H_Vaccinated_2dose_Asian*p_{HR}_Vaccinated_2dose*r_{HR}", + "expression_mathml": "H_Vaccinated_2dose_Asianp_{HR}_Vaccinated_2doser_{HR}" + }, + { + "target": "t2_Vaccinated_2dose_NHBlack", + "expression": "H_Vaccinated_2dose_NHBlack*p_{HR}_Vaccinated_2dose*r_{HR}", + "expression_mathml": "H_Vaccinated_2dose_NHBlackp_{HR}_Vaccinated_2doser_{HR}" + }, + { + "target": "t2_Vaccinated_2dose_Hispanic", + "expression": "H_Vaccinated_2dose_Hispanic*p_{HR}_Vaccinated_2dose*r_{HR}", + "expression_mathml": "H_Vaccinated_2dose_Hispanicp_{HR}_Vaccinated_2doser_{HR}" + }, + { + "target": "t2_Unvaccinated_AIAN", + "expression": "H_Unvaccinated_AIAN*p_{HR}_Unvaccinated*r_{HR}", + "expression_mathml": "H_Unvaccinated_AIANp_{HR}_Unvaccinatedr_{HR}" + }, + { + "target": "t2_Unvaccinated_White", + "expression": "H_Unvaccinated_White*p_{HR}_Unvaccinated*r_{HR}", + "expression_mathml": "H_Unvaccinated_Whitep_{HR}_Unvaccinatedr_{HR}" + }, + { + "target": "t2_Unvaccinated_Multi_Oth_unknown", + "expression": "H_Unvaccinated_Multi_Oth_unknown*p_{HR}_Unvaccinated*r_{HR}", + "expression_mathml": "H_Unvaccinated_Multi_Oth_unknownp_{HR}_Unvaccinatedr_{HR}" + }, + { + "target": "t2_Unvaccinated_NHOPI", + "expression": "H_Unvaccinated_NHOPI*p_{HR}_Unvaccinated*r_{HR}", + "expression_mathml": "H_Unvaccinated_NHOPIp_{HR}_Unvaccinatedr_{HR}" + }, + { + "target": "t2_Unvaccinated_Asian", + "expression": "H_Unvaccinated_Asian*p_{HR}_Unvaccinated*r_{HR}", + "expression_mathml": "H_Unvaccinated_Asianp_{HR}_Unvaccinatedr_{HR}" + }, + { + "target": "t2_Unvaccinated_NHBlack", + "expression": "H_Unvaccinated_NHBlack*p_{HR}_Unvaccinated*r_{HR}", + "expression_mathml": "H_Unvaccinated_NHBlackp_{HR}_Unvaccinatedr_{HR}" + }, + { + "target": "t2_Unvaccinated_Hispanic", + "expression": "H_Unvaccinated_Hispanic*p_{HR}_Unvaccinated*r_{HR}", + "expression_mathml": "H_Unvaccinated_Hispanicp_{HR}_Unvaccinatedr_{HR}" + }, + { + "target": "t3_Vaccinated_1dose_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*p_{IH}_Vaccinated_1dose*r_{IH}", + "expression_mathml": "I_Vaccinated_1dose_AIANp_{IH}_Vaccinated_1doser_{IH}" + }, + { + "target": "t3_Vaccinated_1dose_White", + "expression": "I_Vaccinated_1dose_White*p_{IH}_Vaccinated_1dose*r_{IH}", + "expression_mathml": "I_Vaccinated_1dose_Whitep_{IH}_Vaccinated_1doser_{IH}" + }, + { + "target": "t3_Vaccinated_1dose_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*p_{IH}_Vaccinated_1dose*r_{IH}", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownp_{IH}_Vaccinated_1doser_{IH}" + }, + { + "target": "t3_Vaccinated_1dose_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*p_{IH}_Vaccinated_1dose*r_{IH}", + "expression_mathml": "I_Vaccinated_1dose_NHOPIp_{IH}_Vaccinated_1doser_{IH}" + }, + { + "target": "t3_Vaccinated_1dose_Asian", + "expression": "I_Vaccinated_1dose_Asian*p_{IH}_Vaccinated_1dose*r_{IH}", + "expression_mathml": "I_Vaccinated_1dose_Asianp_{IH}_Vaccinated_1doser_{IH}" + }, + { + "target": "t3_Vaccinated_1dose_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*p_{IH}_Vaccinated_1dose*r_{IH}", + "expression_mathml": "I_Vaccinated_1dose_NHBlackp_{IH}_Vaccinated_1doser_{IH}" + }, + { + "target": "t3_Vaccinated_1dose_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*p_{IH}_Vaccinated_1dose*r_{IH}", + "expression_mathml": "I_Vaccinated_1dose_Hispanicp_{IH}_Vaccinated_1doser_{IH}" + }, + { + "target": "t3_Vaccinated_2dose_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*p_{IH}_Vaccinated_2dose*r_{IH}", + "expression_mathml": "I_Vaccinated_2dose_AIANp_{IH}_Vaccinated_2doser_{IH}" + }, + { + "target": "t3_Vaccinated_2dose_White", + "expression": "I_Vaccinated_2dose_White*p_{IH}_Vaccinated_2dose*r_{IH}", + "expression_mathml": "I_Vaccinated_2dose_Whitep_{IH}_Vaccinated_2doser_{IH}" + }, + { + "target": "t3_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*p_{IH}_Vaccinated_2dose*r_{IH}", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownp_{IH}_Vaccinated_2doser_{IH}" + }, + { + "target": "t3_Vaccinated_2dose_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*p_{IH}_Vaccinated_2dose*r_{IH}", + "expression_mathml": "I_Vaccinated_2dose_NHOPIp_{IH}_Vaccinated_2doser_{IH}" + }, + { + "target": "t3_Vaccinated_2dose_Asian", + "expression": "I_Vaccinated_2dose_Asian*p_{IH}_Vaccinated_2dose*r_{IH}", + "expression_mathml": "I_Vaccinated_2dose_Asianp_{IH}_Vaccinated_2doser_{IH}" + }, + { + "target": "t3_Vaccinated_2dose_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*p_{IH}_Vaccinated_2dose*r_{IH}", + "expression_mathml": "I_Vaccinated_2dose_NHBlackp_{IH}_Vaccinated_2doser_{IH}" + }, + { + "target": "t3_Vaccinated_2dose_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*p_{IH}_Vaccinated_2dose*r_{IH}", + "expression_mathml": "I_Vaccinated_2dose_Hispanicp_{IH}_Vaccinated_2doser_{IH}" + }, + { + "target": "t3_Unvaccinated_AIAN", + "expression": "I_Unvaccinated_AIAN*p_{IH}_Unvaccinated*r_{IH}", + "expression_mathml": "I_Unvaccinated_AIANp_{IH}_Unvaccinatedr_{IH}" + }, + { + "target": "t3_Unvaccinated_White", + "expression": "I_Unvaccinated_White*p_{IH}_Unvaccinated*r_{IH}", + "expression_mathml": "I_Unvaccinated_Whitep_{IH}_Unvaccinatedr_{IH}" + }, + { + "target": "t3_Unvaccinated_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*p_{IH}_Unvaccinated*r_{IH}", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownp_{IH}_Unvaccinatedr_{IH}" + }, + { + "target": "t3_Unvaccinated_NHOPI", + "expression": "I_Unvaccinated_NHOPI*p_{IH}_Unvaccinated*r_{IH}", + "expression_mathml": "I_Unvaccinated_NHOPIp_{IH}_Unvaccinatedr_{IH}" + }, + { + "target": "t3_Unvaccinated_Asian", + "expression": "I_Unvaccinated_Asian*p_{IH}_Unvaccinated*r_{IH}", + "expression_mathml": "I_Unvaccinated_Asianp_{IH}_Unvaccinatedr_{IH}" + }, + { + "target": "t3_Unvaccinated_NHBlack", + "expression": "I_Unvaccinated_NHBlack*p_{IH}_Unvaccinated*r_{IH}", + "expression_mathml": "I_Unvaccinated_NHBlackp_{IH}_Unvaccinatedr_{IH}" + }, + { + "target": "t3_Unvaccinated_Hispanic", + "expression": "I_Unvaccinated_Hispanic*p_{IH}_Unvaccinated*r_{IH}", + "expression_mathml": "I_Unvaccinated_Hispanicp_{IH}_Unvaccinatedr_{IH}" + }, + { + "target": "t4_Vaccinated_1dose_AIAN", + "expression": "H_Vaccinated_1dose_AIAN*p_{HD}_Vaccinated_1dose*r_{HD}", + "expression_mathml": "H_Vaccinated_1dose_AIANp_{HD}_Vaccinated_1doser_{HD}" + }, + { + "target": "t4_Vaccinated_1dose_White", + "expression": "H_Vaccinated_1dose_White*p_{HD}_Vaccinated_1dose*r_{HD}", + "expression_mathml": "H_Vaccinated_1dose_Whitep_{HD}_Vaccinated_1doser_{HD}" + }, + { + "target": "t4_Vaccinated_1dose_Multi_Oth_unknown", + "expression": "H_Vaccinated_1dose_Multi_Oth_unknown*p_{HD}_Vaccinated_1dose*r_{HD}", + "expression_mathml": "H_Vaccinated_1dose_Multi_Oth_unknownp_{HD}_Vaccinated_1doser_{HD}" + }, + { + "target": "t4_Vaccinated_1dose_NHOPI", + "expression": "H_Vaccinated_1dose_NHOPI*p_{HD}_Vaccinated_1dose*r_{HD}", + "expression_mathml": "H_Vaccinated_1dose_NHOPIp_{HD}_Vaccinated_1doser_{HD}" + }, + { + "target": "t4_Vaccinated_1dose_Asian", + "expression": "H_Vaccinated_1dose_Asian*p_{HD}_Vaccinated_1dose*r_{HD}", + "expression_mathml": "H_Vaccinated_1dose_Asianp_{HD}_Vaccinated_1doser_{HD}" + }, + { + "target": "t4_Vaccinated_1dose_NHBlack", + "expression": "H_Vaccinated_1dose_NHBlack*p_{HD}_Vaccinated_1dose*r_{HD}", + "expression_mathml": "H_Vaccinated_1dose_NHBlackp_{HD}_Vaccinated_1doser_{HD}" + }, + { + "target": "t4_Vaccinated_1dose_Hispanic", + "expression": "H_Vaccinated_1dose_Hispanic*p_{HD}_Vaccinated_1dose*r_{HD}", + "expression_mathml": "H_Vaccinated_1dose_Hispanicp_{HD}_Vaccinated_1doser_{HD}" + }, + { + "target": "t4_Vaccinated_2dose_AIAN", + "expression": "H_Vaccinated_2dose_AIAN*p_{HD}_Vaccinated_2dose*r_{HD}", + "expression_mathml": "H_Vaccinated_2dose_AIANp_{HD}_Vaccinated_2doser_{HD}" + }, + { + "target": "t4_Vaccinated_2dose_White", + "expression": "H_Vaccinated_2dose_White*p_{HD}_Vaccinated_2dose*r_{HD}", + "expression_mathml": "H_Vaccinated_2dose_Whitep_{HD}_Vaccinated_2doser_{HD}" + }, + { + "target": "t4_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "H_Vaccinated_2dose_Multi_Oth_unknown*p_{HD}_Vaccinated_2dose*r_{HD}", + "expression_mathml": "H_Vaccinated_2dose_Multi_Oth_unknownp_{HD}_Vaccinated_2doser_{HD}" + }, + { + "target": "t4_Vaccinated_2dose_NHOPI", + "expression": "H_Vaccinated_2dose_NHOPI*p_{HD}_Vaccinated_2dose*r_{HD}", + "expression_mathml": "H_Vaccinated_2dose_NHOPIp_{HD}_Vaccinated_2doser_{HD}" + }, + { + "target": "t4_Vaccinated_2dose_Asian", + "expression": "H_Vaccinated_2dose_Asian*p_{HD}_Vaccinated_2dose*r_{HD}", + "expression_mathml": "H_Vaccinated_2dose_Asianp_{HD}_Vaccinated_2doser_{HD}" + }, + { + "target": "t4_Vaccinated_2dose_NHBlack", + "expression": "H_Vaccinated_2dose_NHBlack*p_{HD}_Vaccinated_2dose*r_{HD}", + "expression_mathml": "H_Vaccinated_2dose_NHBlackp_{HD}_Vaccinated_2doser_{HD}" + }, + { + "target": "t4_Vaccinated_2dose_Hispanic", + "expression": "H_Vaccinated_2dose_Hispanic*p_{HD}_Vaccinated_2dose*r_{HD}", + "expression_mathml": "H_Vaccinated_2dose_Hispanicp_{HD}_Vaccinated_2doser_{HD}" + }, + { + "target": "t4_Unvaccinated_AIAN", + "expression": "H_Unvaccinated_AIAN*p_{HD}_Unvaccinated*r_{HD}", + "expression_mathml": "H_Unvaccinated_AIANp_{HD}_Unvaccinatedr_{HD}" + }, + { + "target": "t4_Unvaccinated_White", + "expression": "H_Unvaccinated_White*p_{HD}_Unvaccinated*r_{HD}", + "expression_mathml": "H_Unvaccinated_Whitep_{HD}_Unvaccinatedr_{HD}" + }, + { + "target": "t4_Unvaccinated_Multi_Oth_unknown", + "expression": "H_Unvaccinated_Multi_Oth_unknown*p_{HD}_Unvaccinated*r_{HD}", + "expression_mathml": "H_Unvaccinated_Multi_Oth_unknownp_{HD}_Unvaccinatedr_{HD}" + }, + { + "target": "t4_Unvaccinated_NHOPI", + "expression": "H_Unvaccinated_NHOPI*p_{HD}_Unvaccinated*r_{HD}", + "expression_mathml": "H_Unvaccinated_NHOPIp_{HD}_Unvaccinatedr_{HD}" + }, + { + "target": "t4_Unvaccinated_Asian", + "expression": "H_Unvaccinated_Asian*p_{HD}_Unvaccinated*r_{HD}", + "expression_mathml": "H_Unvaccinated_Asianp_{HD}_Unvaccinatedr_{HD}" + }, + { + "target": "t4_Unvaccinated_NHBlack", + "expression": "H_Unvaccinated_NHBlack*p_{HD}_Unvaccinated*r_{HD}", + "expression_mathml": "H_Unvaccinated_NHBlackp_{HD}_Unvaccinatedr_{HD}" + }, + { + "target": "t4_Unvaccinated_Hispanic", + "expression": "H_Unvaccinated_Hispanic*p_{HD}_Unvaccinated*r_{HD}", + "expression_mathml": "H_Unvaccinated_Hispanicp_{HD}_Unvaccinatedr_{HD}" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_1dose_AIAN", + "expression": "S_Vaccinated_1dose_AIAN*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_1dose_AIANp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_1dose_White", + "expression": "S_Vaccinated_1dose_White*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_1dose_Whitep_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown", + "expression": "S_Vaccinated_1dose_Multi_Oth_unknown*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_1dose_Multi_Oth_unknownp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_1dose_NHOPI", + "expression": "S_Vaccinated_1dose_NHOPI*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_1dose_NHOPIp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_1dose_Asian", + "expression": "S_Vaccinated_1dose_Asian*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_1dose_Asianp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_1dose_NHBlack", + "expression": "S_Vaccinated_1dose_NHBlack*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_1dose_NHBlackp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_1dose_Hispanic", + "expression": "S_Vaccinated_1dose_Hispanic*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_1dose_Hispanicp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_2dose_AIAN", + "expression": "S_Vaccinated_2dose_AIAN*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_2dose_AIANp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_2dose_White", + "expression": "S_Vaccinated_2dose_White*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_2dose_Whitep_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown", + "expression": "S_Vaccinated_2dose_Multi_Oth_unknown*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_2dose_Multi_Oth_unknownp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_2dose_NHOPI", + "expression": "S_Vaccinated_2dose_NHOPI*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_2dose_NHOPIp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_2dose_Asian", + "expression": "S_Vaccinated_2dose_Asian*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_2dose_Asianp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_2dose_NHBlack", + "expression": "S_Vaccinated_2dose_NHBlack*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_2dose_NHBlackp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Vaccinated_Unvaccinated_2dose_Hispanic", + "expression": "S_Vaccinated_2dose_Hispanic*p_Vaccinated_Unvaccinated", + "expression_mathml": "S_Vaccinated_2dose_Hispanicp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_1dose_AIAN", + "expression": "S_Unvaccinated_AIAN*p_Unvaccinated_Vaccinated_AIAN", + "expression_mathml": "S_Unvaccinated_AIANp_Unvaccinated_Vaccinated_AIAN" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_1dose_White", + "expression": "S_Unvaccinated_White*p_Unvaccinated_Vaccinated_White", + "expression_mathml": "S_Unvaccinated_Whitep_Unvaccinated_Vaccinated_White" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_1dose_Multi_Oth_unknown", + "expression": "S_Unvaccinated_Multi_Oth_unknown*p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "expression_mathml": "S_Unvaccinated_Multi_Oth_unknownp_Unvaccinated_Vaccinated_Multi_Oth_unknown" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_1dose_NHOPI", + "expression": "S_Unvaccinated_NHOPI*p_Unvaccinated_Vaccinated_NHOPI", + "expression_mathml": "S_Unvaccinated_NHOPIp_Unvaccinated_Vaccinated_NHOPI" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_1dose_Asian", + "expression": "S_Unvaccinated_Asian*p_Unvaccinated_Vaccinated_Asian", + "expression_mathml": "S_Unvaccinated_Asianp_Unvaccinated_Vaccinated_Asian" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_1dose_NHBlack", + "expression": "S_Unvaccinated_NHBlack*p_Unvaccinated_Vaccinated_NHBlack", + "expression_mathml": "S_Unvaccinated_NHBlackp_Unvaccinated_Vaccinated_NHBlack" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_1dose_Hispanic", + "expression": "S_Unvaccinated_Hispanic*p_Unvaccinated_Vaccinated_Hispanic", + "expression_mathml": "S_Unvaccinated_Hispanicp_Unvaccinated_Vaccinated_Hispanic" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_2dose_AIAN", + "expression": "S_Unvaccinated_AIAN*p_Unvaccinated_Vaccinated_AIAN", + "expression_mathml": "S_Unvaccinated_AIANp_Unvaccinated_Vaccinated_AIAN" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_2dose_White", + "expression": "S_Unvaccinated_White*p_Unvaccinated_Vaccinated_White", + "expression_mathml": "S_Unvaccinated_Whitep_Unvaccinated_Vaccinated_White" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "S_Unvaccinated_Multi_Oth_unknown*p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "expression_mathml": "S_Unvaccinated_Multi_Oth_unknownp_Unvaccinated_Vaccinated_Multi_Oth_unknown" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_2dose_NHOPI", + "expression": "S_Unvaccinated_NHOPI*p_Unvaccinated_Vaccinated_NHOPI", + "expression_mathml": "S_Unvaccinated_NHOPIp_Unvaccinated_Vaccinated_NHOPI" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_2dose_Asian", + "expression": "S_Unvaccinated_Asian*p_Unvaccinated_Vaccinated_Asian", + "expression_mathml": "S_Unvaccinated_Asianp_Unvaccinated_Vaccinated_Asian" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_2dose_NHBlack", + "expression": "S_Unvaccinated_NHBlack*p_Unvaccinated_Vaccinated_NHBlack", + "expression_mathml": "S_Unvaccinated_NHBlackp_Unvaccinated_Vaccinated_NHBlack" + }, + { + "target": "t_conv_0_Unvaccinated_Vaccinated_2dose_Hispanic", + "expression": "S_Unvaccinated_Hispanic*p_Unvaccinated_Vaccinated_Hispanic", + "expression_mathml": "S_Unvaccinated_Hispanicp_Unvaccinated_Vaccinated_Hispanic" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_1dose_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_1dose_AIANp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_1dose_White", + "expression": "I_Vaccinated_1dose_White*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_1dose_Whitep_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_1dose_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_1dose_NHOPIp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_1dose_Asian", + "expression": "I_Vaccinated_1dose_Asian*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_1dose_Asianp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_1dose_NHBlack", + "expression": "I_Vaccinated_1dose_NHBlack*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_1dose_NHBlackp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_1dose_Hispanic", + "expression": "I_Vaccinated_1dose_Hispanic*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_1dose_Hispanicp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_2dose_AIAN", + "expression": "I_Vaccinated_2dose_AIAN*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_2dose_AIANp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_2dose_White", + "expression": "I_Vaccinated_2dose_White*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_2dose_Whitep_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown", + "expression": "I_Vaccinated_2dose_Multi_Oth_unknown*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_2dose_Multi_Oth_unknownp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_2dose_NHOPI", + "expression": "I_Vaccinated_2dose_NHOPI*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_2dose_NHOPIp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_2dose_Asian", + "expression": "I_Vaccinated_2dose_Asian*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_2dose_Asianp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_2dose_NHBlack", + "expression": "I_Vaccinated_2dose_NHBlack*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_2dose_NHBlackp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Vaccinated_Unvaccinated_2dose_Hispanic", + "expression": "I_Vaccinated_2dose_Hispanic*p_Vaccinated_Unvaccinated", + "expression_mathml": "I_Vaccinated_2dose_Hispanicp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_1dose_AIAN", + "expression": "I_Unvaccinated_AIAN*p_Unvaccinated_Vaccinated_AIAN", + "expression_mathml": "I_Unvaccinated_AIANp_Unvaccinated_Vaccinated_AIAN" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_1dose_White", + "expression": "I_Unvaccinated_White*p_Unvaccinated_Vaccinated_White", + "expression_mathml": "I_Unvaccinated_Whitep_Unvaccinated_Vaccinated_White" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_1dose_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownp_Unvaccinated_Vaccinated_Multi_Oth_unknown" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_1dose_NHOPI", + "expression": "I_Unvaccinated_NHOPI*p_Unvaccinated_Vaccinated_NHOPI", + "expression_mathml": "I_Unvaccinated_NHOPIp_Unvaccinated_Vaccinated_NHOPI" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_1dose_Asian", + "expression": "I_Unvaccinated_Asian*p_Unvaccinated_Vaccinated_Asian", + "expression_mathml": "I_Unvaccinated_Asianp_Unvaccinated_Vaccinated_Asian" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_1dose_NHBlack", + "expression": "I_Unvaccinated_NHBlack*p_Unvaccinated_Vaccinated_NHBlack", + "expression_mathml": "I_Unvaccinated_NHBlackp_Unvaccinated_Vaccinated_NHBlack" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_1dose_Hispanic", + "expression": "I_Unvaccinated_Hispanic*p_Unvaccinated_Vaccinated_Hispanic", + "expression_mathml": "I_Unvaccinated_Hispanicp_Unvaccinated_Vaccinated_Hispanic" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_2dose_AIAN", + "expression": "I_Unvaccinated_AIAN*p_Unvaccinated_Vaccinated_AIAN", + "expression_mathml": "I_Unvaccinated_AIANp_Unvaccinated_Vaccinated_AIAN" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_2dose_White", + "expression": "I_Unvaccinated_White*p_Unvaccinated_Vaccinated_White", + "expression_mathml": "I_Unvaccinated_Whitep_Unvaccinated_Vaccinated_White" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "I_Unvaccinated_Multi_Oth_unknown*p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "expression_mathml": "I_Unvaccinated_Multi_Oth_unknownp_Unvaccinated_Vaccinated_Multi_Oth_unknown" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_2dose_NHOPI", + "expression": "I_Unvaccinated_NHOPI*p_Unvaccinated_Vaccinated_NHOPI", + "expression_mathml": "I_Unvaccinated_NHOPIp_Unvaccinated_Vaccinated_NHOPI" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_2dose_Asian", + "expression": "I_Unvaccinated_Asian*p_Unvaccinated_Vaccinated_Asian", + "expression_mathml": "I_Unvaccinated_Asianp_Unvaccinated_Vaccinated_Asian" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_2dose_NHBlack", + "expression": "I_Unvaccinated_NHBlack*p_Unvaccinated_Vaccinated_NHBlack", + "expression_mathml": "I_Unvaccinated_NHBlackp_Unvaccinated_Vaccinated_NHBlack" + }, + { + "target": "t_conv_1_Unvaccinated_Vaccinated_2dose_Hispanic", + "expression": "I_Unvaccinated_Hispanic*p_Unvaccinated_Vaccinated_Hispanic", + "expression_mathml": "I_Unvaccinated_Hispanicp_Unvaccinated_Vaccinated_Hispanic" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_1dose_AIAN", + "expression": "R_Vaccinated_1dose_AIAN*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_1dose_AIANp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_1dose_White", + "expression": "R_Vaccinated_1dose_White*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_1dose_Whitep_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown", + "expression": "R_Vaccinated_1dose_Multi_Oth_unknown*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_1dose_Multi_Oth_unknownp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_1dose_NHOPI", + "expression": "R_Vaccinated_1dose_NHOPI*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_1dose_NHOPIp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_1dose_Asian", + "expression": "R_Vaccinated_1dose_Asian*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_1dose_Asianp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_1dose_NHBlack", + "expression": "R_Vaccinated_1dose_NHBlack*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_1dose_NHBlackp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_1dose_Hispanic", + "expression": "R_Vaccinated_1dose_Hispanic*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_1dose_Hispanicp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_2dose_AIAN", + "expression": "R_Vaccinated_2dose_AIAN*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_2dose_AIANp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_2dose_White", + "expression": "R_Vaccinated_2dose_White*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_2dose_Whitep_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown", + "expression": "R_Vaccinated_2dose_Multi_Oth_unknown*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_2dose_Multi_Oth_unknownp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_2dose_NHOPI", + "expression": "R_Vaccinated_2dose_NHOPI*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_2dose_NHOPIp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_2dose_Asian", + "expression": "R_Vaccinated_2dose_Asian*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_2dose_Asianp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_2dose_NHBlack", + "expression": "R_Vaccinated_2dose_NHBlack*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_2dose_NHBlackp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Vaccinated_Unvaccinated_2dose_Hispanic", + "expression": "R_Vaccinated_2dose_Hispanic*p_Vaccinated_Unvaccinated", + "expression_mathml": "R_Vaccinated_2dose_Hispanicp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_1dose_AIAN", + "expression": "R_Unvaccinated_AIAN*p_Unvaccinated_Vaccinated_AIAN", + "expression_mathml": "R_Unvaccinated_AIANp_Unvaccinated_Vaccinated_AIAN" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_1dose_White", + "expression": "R_Unvaccinated_White*p_Unvaccinated_Vaccinated_White", + "expression_mathml": "R_Unvaccinated_Whitep_Unvaccinated_Vaccinated_White" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_1dose_Multi_Oth_unknown", + "expression": "R_Unvaccinated_Multi_Oth_unknown*p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "expression_mathml": "R_Unvaccinated_Multi_Oth_unknownp_Unvaccinated_Vaccinated_Multi_Oth_unknown" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_1dose_NHOPI", + "expression": "R_Unvaccinated_NHOPI*p_Unvaccinated_Vaccinated_NHOPI", + "expression_mathml": "R_Unvaccinated_NHOPIp_Unvaccinated_Vaccinated_NHOPI" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_1dose_Asian", + "expression": "R_Unvaccinated_Asian*p_Unvaccinated_Vaccinated_Asian", + "expression_mathml": "R_Unvaccinated_Asianp_Unvaccinated_Vaccinated_Asian" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_1dose_NHBlack", + "expression": "R_Unvaccinated_NHBlack*p_Unvaccinated_Vaccinated_NHBlack", + "expression_mathml": "R_Unvaccinated_NHBlackp_Unvaccinated_Vaccinated_NHBlack" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_1dose_Hispanic", + "expression": "R_Unvaccinated_Hispanic*p_Unvaccinated_Vaccinated_Hispanic", + "expression_mathml": "R_Unvaccinated_Hispanicp_Unvaccinated_Vaccinated_Hispanic" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_2dose_AIAN", + "expression": "R_Unvaccinated_AIAN*p_Unvaccinated_Vaccinated_AIAN", + "expression_mathml": "R_Unvaccinated_AIANp_Unvaccinated_Vaccinated_AIAN" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_2dose_White", + "expression": "R_Unvaccinated_White*p_Unvaccinated_Vaccinated_White", + "expression_mathml": "R_Unvaccinated_Whitep_Unvaccinated_Vaccinated_White" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "R_Unvaccinated_Multi_Oth_unknown*p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "expression_mathml": "R_Unvaccinated_Multi_Oth_unknownp_Unvaccinated_Vaccinated_Multi_Oth_unknown" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_2dose_NHOPI", + "expression": "R_Unvaccinated_NHOPI*p_Unvaccinated_Vaccinated_NHOPI", + "expression_mathml": "R_Unvaccinated_NHOPIp_Unvaccinated_Vaccinated_NHOPI" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_2dose_Asian", + "expression": "R_Unvaccinated_Asian*p_Unvaccinated_Vaccinated_Asian", + "expression_mathml": "R_Unvaccinated_Asianp_Unvaccinated_Vaccinated_Asian" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_2dose_NHBlack", + "expression": "R_Unvaccinated_NHBlack*p_Unvaccinated_Vaccinated_NHBlack", + "expression_mathml": "R_Unvaccinated_NHBlackp_Unvaccinated_Vaccinated_NHBlack" + }, + { + "target": "t_conv_2_Unvaccinated_Vaccinated_2dose_Hispanic", + "expression": "R_Unvaccinated_Hispanic*p_Unvaccinated_Vaccinated_Hispanic", + "expression_mathml": "R_Unvaccinated_Hispanicp_Unvaccinated_Vaccinated_Hispanic" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_1dose_AIAN", + "expression": "H_Vaccinated_1dose_AIAN*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_1dose_AIANp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_1dose_White", + "expression": "H_Vaccinated_1dose_White*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_1dose_Whitep_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown", + "expression": "H_Vaccinated_1dose_Multi_Oth_unknown*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_1dose_Multi_Oth_unknownp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_1dose_NHOPI", + "expression": "H_Vaccinated_1dose_NHOPI*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_1dose_NHOPIp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_1dose_Asian", + "expression": "H_Vaccinated_1dose_Asian*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_1dose_Asianp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_1dose_NHBlack", + "expression": "H_Vaccinated_1dose_NHBlack*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_1dose_NHBlackp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_1dose_Hispanic", + "expression": "H_Vaccinated_1dose_Hispanic*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_1dose_Hispanicp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_2dose_AIAN", + "expression": "H_Vaccinated_2dose_AIAN*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_2dose_AIANp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_2dose_White", + "expression": "H_Vaccinated_2dose_White*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_2dose_Whitep_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown", + "expression": "H_Vaccinated_2dose_Multi_Oth_unknown*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_2dose_Multi_Oth_unknownp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_2dose_NHOPI", + "expression": "H_Vaccinated_2dose_NHOPI*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_2dose_NHOPIp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_2dose_Asian", + "expression": "H_Vaccinated_2dose_Asian*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_2dose_Asianp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_2dose_NHBlack", + "expression": "H_Vaccinated_2dose_NHBlack*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_2dose_NHBlackp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Vaccinated_Unvaccinated_2dose_Hispanic", + "expression": "H_Vaccinated_2dose_Hispanic*p_Vaccinated_Unvaccinated", + "expression_mathml": "H_Vaccinated_2dose_Hispanicp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_1dose_AIAN", + "expression": "H_Unvaccinated_AIAN*p_Unvaccinated_Vaccinated_AIAN", + "expression_mathml": "H_Unvaccinated_AIANp_Unvaccinated_Vaccinated_AIAN" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_1dose_White", + "expression": "H_Unvaccinated_White*p_Unvaccinated_Vaccinated_White", + "expression_mathml": "H_Unvaccinated_Whitep_Unvaccinated_Vaccinated_White" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_1dose_Multi_Oth_unknown", + "expression": "H_Unvaccinated_Multi_Oth_unknown*p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "expression_mathml": "H_Unvaccinated_Multi_Oth_unknownp_Unvaccinated_Vaccinated_Multi_Oth_unknown" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_1dose_NHOPI", + "expression": "H_Unvaccinated_NHOPI*p_Unvaccinated_Vaccinated_NHOPI", + "expression_mathml": "H_Unvaccinated_NHOPIp_Unvaccinated_Vaccinated_NHOPI" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_1dose_Asian", + "expression": "H_Unvaccinated_Asian*p_Unvaccinated_Vaccinated_Asian", + "expression_mathml": "H_Unvaccinated_Asianp_Unvaccinated_Vaccinated_Asian" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_1dose_NHBlack", + "expression": "H_Unvaccinated_NHBlack*p_Unvaccinated_Vaccinated_NHBlack", + "expression_mathml": "H_Unvaccinated_NHBlackp_Unvaccinated_Vaccinated_NHBlack" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_1dose_Hispanic", + "expression": "H_Unvaccinated_Hispanic*p_Unvaccinated_Vaccinated_Hispanic", + "expression_mathml": "H_Unvaccinated_Hispanicp_Unvaccinated_Vaccinated_Hispanic" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_2dose_AIAN", + "expression": "H_Unvaccinated_AIAN*p_Unvaccinated_Vaccinated_AIAN", + "expression_mathml": "H_Unvaccinated_AIANp_Unvaccinated_Vaccinated_AIAN" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_2dose_White", + "expression": "H_Unvaccinated_White*p_Unvaccinated_Vaccinated_White", + "expression_mathml": "H_Unvaccinated_Whitep_Unvaccinated_Vaccinated_White" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "H_Unvaccinated_Multi_Oth_unknown*p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "expression_mathml": "H_Unvaccinated_Multi_Oth_unknownp_Unvaccinated_Vaccinated_Multi_Oth_unknown" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_2dose_NHOPI", + "expression": "H_Unvaccinated_NHOPI*p_Unvaccinated_Vaccinated_NHOPI", + "expression_mathml": "H_Unvaccinated_NHOPIp_Unvaccinated_Vaccinated_NHOPI" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_2dose_Asian", + "expression": "H_Unvaccinated_Asian*p_Unvaccinated_Vaccinated_Asian", + "expression_mathml": "H_Unvaccinated_Asianp_Unvaccinated_Vaccinated_Asian" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_2dose_NHBlack", + "expression": "H_Unvaccinated_NHBlack*p_Unvaccinated_Vaccinated_NHBlack", + "expression_mathml": "H_Unvaccinated_NHBlackp_Unvaccinated_Vaccinated_NHBlack" + }, + { + "target": "t_conv_3_Unvaccinated_Vaccinated_2dose_Hispanic", + "expression": "H_Unvaccinated_Hispanic*p_Unvaccinated_Vaccinated_Hispanic", + "expression_mathml": "H_Unvaccinated_Hispanicp_Unvaccinated_Vaccinated_Hispanic" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_1dose_AIAN", + "expression": "D_Vaccinated_1dose_AIAN*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_1dose_AIANp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_1dose_White", + "expression": "D_Vaccinated_1dose_White*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_1dose_Whitep_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_1dose_Multi_Oth_unknown", + "expression": "D_Vaccinated_1dose_Multi_Oth_unknown*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_1dose_Multi_Oth_unknownp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_1dose_NHOPI", + "expression": "D_Vaccinated_1dose_NHOPI*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_1dose_NHOPIp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_1dose_Asian", + "expression": "D_Vaccinated_1dose_Asian*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_1dose_Asianp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_1dose_NHBlack", + "expression": "D_Vaccinated_1dose_NHBlack*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_1dose_NHBlackp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_1dose_Hispanic", + "expression": "D_Vaccinated_1dose_Hispanic*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_1dose_Hispanicp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_2dose_AIAN", + "expression": "D_Vaccinated_2dose_AIAN*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_2dose_AIANp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_2dose_White", + "expression": "D_Vaccinated_2dose_White*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_2dose_Whitep_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_2dose_Multi_Oth_unknown", + "expression": "D_Vaccinated_2dose_Multi_Oth_unknown*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_2dose_Multi_Oth_unknownp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_2dose_NHOPI", + "expression": "D_Vaccinated_2dose_NHOPI*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_2dose_NHOPIp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_2dose_Asian", + "expression": "D_Vaccinated_2dose_Asian*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_2dose_Asianp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_2dose_NHBlack", + "expression": "D_Vaccinated_2dose_NHBlack*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_2dose_NHBlackp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Vaccinated_Unvaccinated_2dose_Hispanic", + "expression": "D_Vaccinated_2dose_Hispanic*p_Vaccinated_Unvaccinated", + "expression_mathml": "D_Vaccinated_2dose_Hispanicp_Vaccinated_Unvaccinated" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_1dose_AIAN", + "expression": "D_Unvaccinated_AIAN*p_Unvaccinated_Vaccinated_AIAN", + "expression_mathml": "D_Unvaccinated_AIANp_Unvaccinated_Vaccinated_AIAN" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_1dose_White", + "expression": "D_Unvaccinated_White*p_Unvaccinated_Vaccinated_White", + "expression_mathml": "D_Unvaccinated_Whitep_Unvaccinated_Vaccinated_White" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_1dose_Multi_Oth_unknown", + "expression": "D_Unvaccinated_Multi_Oth_unknown*p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "expression_mathml": "D_Unvaccinated_Multi_Oth_unknownp_Unvaccinated_Vaccinated_Multi_Oth_unknown" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_1dose_NHOPI", + "expression": "D_Unvaccinated_NHOPI*p_Unvaccinated_Vaccinated_NHOPI", + "expression_mathml": "D_Unvaccinated_NHOPIp_Unvaccinated_Vaccinated_NHOPI" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_1dose_Asian", + "expression": "D_Unvaccinated_Asian*p_Unvaccinated_Vaccinated_Asian", + "expression_mathml": "D_Unvaccinated_Asianp_Unvaccinated_Vaccinated_Asian" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_1dose_NHBlack", + "expression": "D_Unvaccinated_NHBlack*p_Unvaccinated_Vaccinated_NHBlack", + "expression_mathml": "D_Unvaccinated_NHBlackp_Unvaccinated_Vaccinated_NHBlack" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_1dose_Hispanic", + "expression": "D_Unvaccinated_Hispanic*p_Unvaccinated_Vaccinated_Hispanic", + "expression_mathml": "D_Unvaccinated_Hispanicp_Unvaccinated_Vaccinated_Hispanic" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_2dose_AIAN", + "expression": "D_Unvaccinated_AIAN*p_Unvaccinated_Vaccinated_AIAN", + "expression_mathml": "D_Unvaccinated_AIANp_Unvaccinated_Vaccinated_AIAN" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_2dose_White", + "expression": "D_Unvaccinated_White*p_Unvaccinated_Vaccinated_White", + "expression_mathml": "D_Unvaccinated_Whitep_Unvaccinated_Vaccinated_White" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "D_Unvaccinated_Multi_Oth_unknown*p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "expression_mathml": "D_Unvaccinated_Multi_Oth_unknownp_Unvaccinated_Vaccinated_Multi_Oth_unknown" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_2dose_NHOPI", + "expression": "D_Unvaccinated_NHOPI*p_Unvaccinated_Vaccinated_NHOPI", + "expression_mathml": "D_Unvaccinated_NHOPIp_Unvaccinated_Vaccinated_NHOPI" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_2dose_Asian", + "expression": "D_Unvaccinated_Asian*p_Unvaccinated_Vaccinated_Asian", + "expression_mathml": "D_Unvaccinated_Asianp_Unvaccinated_Vaccinated_Asian" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_2dose_NHBlack", + "expression": "D_Unvaccinated_NHBlack*p_Unvaccinated_Vaccinated_NHBlack", + "expression_mathml": "D_Unvaccinated_NHBlackp_Unvaccinated_Vaccinated_NHBlack" + }, + { + "target": "t_conv_4_Unvaccinated_Vaccinated_2dose_Hispanic", + "expression": "D_Unvaccinated_Hispanic*p_Unvaccinated_Vaccinated_Hispanic", + "expression_mathml": "D_Unvaccinated_Hispanicp_Unvaccinated_Vaccinated_Hispanic" + }, + { + "target": "t_conv_0_1dose_2dose_AIAN", + "expression": "S_Vaccinated_1dose_AIAN*p_1dose_2dose", + "expression_mathml": "S_Vaccinated_1dose_AIANp_1dose_2dose" + }, + { + "target": "t_conv_0_1dose_2dose_White", + "expression": "S_Vaccinated_1dose_White*p_1dose_2dose", + "expression_mathml": "S_Vaccinated_1dose_Whitep_1dose_2dose" + }, + { + "target": "t_conv_0_1dose_2dose_Multi_Oth_unknown", + "expression": "S_Vaccinated_1dose_Multi_Oth_unknown*p_1dose_2dose", + "expression_mathml": "S_Vaccinated_1dose_Multi_Oth_unknownp_1dose_2dose" + }, + { + "target": "t_conv_0_1dose_2dose_NHOPI", + "expression": "S_Vaccinated_1dose_NHOPI*p_1dose_2dose", + "expression_mathml": "S_Vaccinated_1dose_NHOPIp_1dose_2dose" + }, + { + "target": "t_conv_0_1dose_2dose_Asian", + "expression": "S_Vaccinated_1dose_Asian*p_1dose_2dose", + "expression_mathml": "S_Vaccinated_1dose_Asianp_1dose_2dose" + }, + { + "target": "t_conv_0_1dose_2dose_NHBlack", + "expression": "S_Vaccinated_1dose_NHBlack*p_1dose_2dose", + "expression_mathml": "S_Vaccinated_1dose_NHBlackp_1dose_2dose" + }, + { + "target": "t_conv_0_1dose_2dose_Hispanic", + "expression": "S_Vaccinated_1dose_Hispanic*p_1dose_2dose", + "expression_mathml": "S_Vaccinated_1dose_Hispanicp_1dose_2dose" + }, + { + "target": "t_conv_0_2dose_1dose_AIAN", + "expression": "S_Vaccinated_2dose_AIAN*p_2dose_1dose", + "expression_mathml": "S_Vaccinated_2dose_AIANp_2dose_1dose" + }, + { + "target": "t_conv_0_2dose_1dose_White", + "expression": "S_Vaccinated_2dose_White*p_2dose_1dose", + "expression_mathml": "S_Vaccinated_2dose_Whitep_2dose_1dose" + }, + { + "target": "t_conv_0_2dose_1dose_Multi_Oth_unknown", + "expression": "S_Vaccinated_2dose_Multi_Oth_unknown*p_2dose_1dose", + "expression_mathml": "S_Vaccinated_2dose_Multi_Oth_unknownp_2dose_1dose" + }, + { + "target": "t_conv_0_2dose_1dose_NHOPI", + "expression": "S_Vaccinated_2dose_NHOPI*p_2dose_1dose", + "expression_mathml": "S_Vaccinated_2dose_NHOPIp_2dose_1dose" + }, + { + "target": "t_conv_0_2dose_1dose_Asian", + "expression": "S_Vaccinated_2dose_Asian*p_2dose_1dose", + "expression_mathml": "S_Vaccinated_2dose_Asianp_2dose_1dose" + }, + { + "target": "t_conv_0_2dose_1dose_NHBlack", + "expression": "S_Vaccinated_2dose_NHBlack*p_2dose_1dose", + "expression_mathml": "S_Vaccinated_2dose_NHBlackp_2dose_1dose" + }, + { + "target": "t_conv_0_2dose_1dose_Hispanic", + "expression": "S_Vaccinated_2dose_Hispanic*p_2dose_1dose", + "expression_mathml": "S_Vaccinated_2dose_Hispanicp_2dose_1dose" + }, + { + "target": "t_conv_1_1dose_2dose_AIAN", + "expression": "I_Vaccinated_1dose_AIAN*p_1dose_2dose", + "expression_mathml": "I_Vaccinated_1dose_AIANp_1dose_2dose" + }, + { + "target": "t_conv_1_1dose_2dose_White", + "expression": "I_Vaccinated_1dose_White*p_1dose_2dose", + "expression_mathml": "I_Vaccinated_1dose_Whitep_1dose_2dose" + }, + { + "target": "t_conv_1_1dose_2dose_Multi_Oth_unknown", + "expression": "I_Vaccinated_1dose_Multi_Oth_unknown*p_1dose_2dose", + "expression_mathml": "I_Vaccinated_1dose_Multi_Oth_unknownp_1dose_2dose" + }, + { + "target": "t_conv_1_1dose_2dose_NHOPI", + "expression": "I_Vaccinated_1dose_NHOPI*p_1dose_2dose", + "expression_mathml": 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{ + "target": "H_Vaccinated_1dose_NHOPI", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated_1dose_Asian", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated_1dose_NHBlack", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated_1dose_Hispanic", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated_2dose_AIAN", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated_2dose_White", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated_2dose_NHOPI", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated_2dose_Asian", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated_2dose_NHBlack", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Vaccinated_2dose_Hispanic", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Unvaccinated_AIAN", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Unvaccinated_White", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Unvaccinated_Multi_Oth_unknown", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Unvaccinated_NHOPI", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Unvaccinated_Asian", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Unvaccinated_NHBlack", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "H_Unvaccinated_Hispanic", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_1dose_AIAN", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_1dose_White", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_1dose_Multi_Oth_unknown", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_1dose_NHOPI", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_1dose_Asian", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_1dose_NHBlack", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_1dose_Hispanic", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_2dose_AIAN", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_2dose_White", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_2dose_Multi_Oth_unknown", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_2dose_NHOPI", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_2dose_Asian", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_2dose_NHBlack", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Vaccinated_2dose_Hispanic", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Unvaccinated_AIAN", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Unvaccinated_White", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Unvaccinated_Multi_Oth_unknown", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Unvaccinated_NHOPI", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Unvaccinated_Asian", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Unvaccinated_NHBlack", + "expression": "0", + "expression_mathml": "0" + }, + { + "target": "D_Unvaccinated_Hispanic", + "expression": "0", + "expression_mathml": "0" + } + ], + "parameters": [ + { + "id": "N", + "name": "N", + "value": 1 + }, + { + "id": "b_Vaccinated_Vaccinated", + "name": "b", + "value": 1 + }, + { + "id": "t_a", + "name": "t_a", + "value": 1 + }, + { + "id": "t_d", + "name": "t_d", + "value": 1 + }, + { + "id": "b_Vaccinated_Unvaccinated", + "name": "b", + "value": 1 + }, + { + "id": "b_Unvaccinated_Vaccinated", + "name": "b", + "value": 1 + }, + { + "id": "b_Unvaccinated_Unvaccinated", + "name": "b", + "value": 1 + }, + { + "id": "p_{IR}_Vaccinated_1dose", + "name": "p_{IR}", + "value": 1 + }, + { + "id": "r_{IR}", + "name": "r_{IR}", + "value": 1 + }, + { + "id": "p_{IR}_Vaccinated_2dose", + "name": "p_{IR}", + "value": 1 + }, + { + "id": "p_{IR}_Unvaccinated", + "name": "p_{IR}", + "value": 1 + }, + { + "id": "p_{HR}_Vaccinated_1dose", + "name": "p_{HR}", + "value": 1 + }, + { + "id": "r_{HR}", + "name": "r_{HR}", + "value": 1 + }, + { + "id": "p_{HR}_Vaccinated_2dose", + "name": "p_{HR}", + "value": 1 + }, + { + "id": "p_{HR}_Unvaccinated", + "name": "p_{HR}", + "value": 1 + }, + { + "id": "p_{IH}_Vaccinated_1dose", + "name": "p_{IH}", + "value": 1 + }, + { + "id": "r_{IH}", + "name": "r_{IH}", + "value": 1 + }, + { + "id": "p_{IH}_Vaccinated_2dose", + "name": "p_{IH}", + "value": 1 + }, + { + "id": "p_{IH}_Unvaccinated", + "name": "p_{IH}", + "value": 1 + }, + { + "id": "p_{HD}_Vaccinated_1dose", + "name": "p_{HD}", + "value": 1 + }, + { + "id": "r_{HD}", + "name": "r_{HD}", + "value": 1 + }, + { + "id": "p_{HD}_Vaccinated_2dose", + "name": "p_{HD}", + "value": 1 + }, + { + "id": "p_{HD}_Unvaccinated", + "name": "p_{HD}", + "value": 1 + }, + { + "id": "p_Vaccinated_Unvaccinated", + "value": 0.1 + }, + { + "id": "p_Unvaccinated_Vaccinated_AIAN", + "value": 0.1 + }, + { + "id": "p_Unvaccinated_Vaccinated_White", + "value": 0.1 + }, + { + "id": "p_Unvaccinated_Vaccinated_Multi_Oth_unknown", + "value": 0.1 + }, + { + "id": "p_Unvaccinated_Vaccinated_NHOPI", + "value": 0.1 + }, + { + "id": "p_Unvaccinated_Vaccinated_Asian", + "value": 0.1 + }, + { + "id": "p_Unvaccinated_Vaccinated_NHBlack", + "value": 0.1 + }, + { + "id": "p_Unvaccinated_Vaccinated_Hispanic", + "value": 0.1 + }, + { + "id": "p_1dose_2dose", + "value": 0.1 + }, + { + "id": "p_2dose_1dose", + "value": 0.1 + } + ], + "observables": [], + "time": { + "id": "t" + } + }, + "typing": null + }, + "metadata": { + "annotations": { + "authors": [], + "references": [], + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + }, + "source": null, + "gollmCard": null, + "gollmExtractions": null, + "templateCard": null + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-09/sirhd-base.json b/resources/amr/petrinet/monthly-demo/2024-09/sirhd-base.json new file mode 100644 index 00000000..f4df5879 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-09/sirhd-base.json @@ -0,0 +1,217 @@ +{ + "id": "4dfbce53-e22f-4ea4-a1dc-d681dd6e3d78", + "createdOn": "2024-09-13T21:21:11.132+00:00", + "updatedOn": "2024-09-25T17:45:17.838+00:00", + "name": "sirhd-base", + "fileNames": [], + "temporary": false, + "publicAsset": true, + "header": { + "name": "sirhd-base", + "description": "This is a model from equations", + "schema": "https://github.com/DARPA-ASKEM/Model-Representations/blob/main/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "model_version": "0.1" + }, + "model": { + "states": [ + { + "id": "D", + "name": "D" + }, + { + "id": "H", + "name": "H" + }, + { + "id": "I", + "name": "I" + }, + { + "id": "R", + "name": "R" + }, + { + "id": "S", + "name": "S" + } + ], + "transitions": [ + { + "id": "t0", + "input": [ + "I", + "S" + ], + "output": [ + "I", + "I" + ] + }, + { + "id": "t1", + "input": [ + "I" + ], + "output": [ + "R" + ] + }, + { + "id": "t2", + "input": [ + "H" + ], + "output": [ + "R" + ] + }, + { + "id": "t3", + "input": [ + "I" + ], + "output": [ + "H" + ] + }, + { + "id": "t4", + "input": [ + "H" + ], + "output": [ + "D" + ] + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t0", + "expression": "(S/N)*b*I", + "expression_mathml": "SNbI" + }, + { + "target": "t1", + "expression": "I*r_{IR}*p_{IR}", + "expression_mathml": "Ir_{IR}p_{IR}" + }, + { + "target": "t2", + "expression": "H*r_{HR}*p_{HR}", + "expression_mathml": "Hr_{HR}p_{HR}" + }, + { + "target": "t3", + "expression": "I*r_{IH}*p_{IH}", + "expression_mathml": "Ir_{IH}p_{IH}" + }, + { + "target": "t4", + "expression": "H*r_{HD}*p_{HD}", + "expression_mathml": "Hr_{HD}p_{HD}" + } + ], + "initials": [ + { + "target": "S", + "expression": "0", + "expression_mathml": "" + }, + { + "target": "I", + "expression": "0", + "expression_mathml": "" + }, + { + "target": "R", + "expression": "0", + "expression_mathml": "" + }, + { + "target": "H", + "expression": "0", + "expression_mathml": "" + }, + { + "target": "D", + "expression": "0", + "expression_mathml": "" + } + ], + "parameters": [ + { + "id": "N", + "name": "N", + "value": 1 + }, + { + "id": "b", + "name": "b", + "value": 1 + }, + { + "id": "p_{HD}", + "name": "p_{HD}", + "value": 1 + }, + { + "id": "p_{HR}", + "name": "p_{HR}", + "value": 1 + }, + { + "id": "p_{IH}", + "name": "p_{IH}", + "value": 1 + }, + { + "id": "p_{IR}", + "name": "p_{IR}", + "value": 1 + }, + { + "id": "r_{HD}", + "name": "r_{HD}", + "value": 1 + }, + { + "id": "r_{HR}", + "name": "r_{HR}", + "value": 1 + }, + { + "id": "r_{IH}", + "name": "r_{IH}", + "value": 1 + }, + { + "id": "r_{IR}", + "name": "r_{IR}", + "value": 1 + } + ], + "observables": [], + "time": null + }, + "typing": null + }, + "metadata": { + "annotations": { + "authors": [], + "references": [], + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + }, + "source": null, + "gollmCard": null, + "gollmExtractions": null, + "templateCard": null + } +} \ No newline at end of file From d7d560bae2f6e114268de382a9459df733cefadb Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Fri, 27 Sep 2024 17:01:44 -0500 Subject: [PATCH 52/93] draft of abstraction writeup --- notes/abstraction/background.tex | 170 +++++++++++++++++++++++++++++++ notes/abstraction/main.pdf | Bin 287055 -> 358409 bytes notes/abstraction/main.tex | 7 ++ 3 files changed, 177 insertions(+) create mode 100644 notes/abstraction/background.tex diff --git a/notes/abstraction/background.tex b/notes/abstraction/background.tex new file mode 100644 index 00000000..9a2daf79 --- /dev/null +++ b/notes/abstraction/background.tex @@ -0,0 +1,170 @@ +\begin{definition} + A Petrinet $\Omega$ is a directed graph $(V, E)$ with vertices $V=(V_x, + V_z)$ partitioned into sets $V_x$ of state vertices and $V_z$ of transition + vertices, and edges $E=(E_{in}, E_{out})$ partitioned into sets $E_{out}$ of + flow-out and $E_{in}$ flow-in edges. +\end{definition} + +\begin{definition} +A flow-out edge $e \in E_{out}$ comprises a pair of vertices $(v_x,v_z)$, where +$v_x \in V_x$ is a state vertex, $v_z \in V_z$ is a transition vertex, and the +flow is directed from $v_x$ to $v_z$. +\end{definition} + +\begin{definition} + A flow-in edge $e \in E_{in}$ comprises a pair of vertices $(v_z,v_x)$, + similar to a flow-out edge, except that the flow is directed from $v_z$ to + $v_x$. +\end{definition} + + + +\begin{definition} + The ODE semantics $\Theta$ of the Petrinet $\Omega$ defines a tuple $(P, X, + Z, {\cal I}, {\cal P}, {\cal X}, {\cal Z}, {\cal R})$ where + \begin{itemize} + \item $P$ is a set of parameters; + \item $X$ is a set of state variables; + \item $Z$ is a set of transitions; + \item ${\cal I}: S \rightarrow \reals$ assigns the initial value of + state variables to a real number; + \item ${\cal P}: P \rightarrow \reals \cup \reals \times \reals$ assigns + parameters to a real number, or a pair of real numbers defining an + interval; + \item ${\cal X}: X \rightarrow V_x$ assigns state variables to state + vertices; + \item ${\cal Z}: Z \rightarrow V_z$ assigns transtions to transition + vertices; and + \item ${\cal R}: {\bf P} \times {\bf X} \times Z \rightarrow \reals$ + defines the rate of each transition in $x \in X$ in terms of the set of + parameter vectors ${\bf P}$ and state variable vectors ${\bf X}$. + \end{itemize} + The elements of the Petrinet $\Omega$ and semantics $\Theta$ define the + partial derivative $\frac{d {\bf x}}{dt}$, so that for each state variable + $x \in X$: + + \begin{equation}\label{eqn:flow} + \frac{dx}{dt} = \sum_{v_z \in V_z^{in(x)}} {\cal R}({\bf p}, {\bf x}, z) - \sum_{v_z \in V_z^{out(x)} } {\cal R}({\bf p}, {\bf x}, z) + \end{equation} +\noindent where $V_z^{in(x)} = \{v_z \in V_z | (v_z, v_x) \in E_{in}\}$ and + $V_z^{out(x)}=\{v_z \in V_z| (v_x, v_z) \in E_{out}\}$ are the transition + vertices that flow in and out of the vertex $v_x$, respectively. We denote + by $\nabla_{\Omega, \Theta}({\bf p}, {\bf x}, t) = (\frac{dx_1}{dt}, + \frac{dx_2}{dt}, \ldots)^T$, the gradient comprised of components in + Equation \eqref{eqn:flow}. +\end{definition} + +Using the partial derivatives defined by the Petrinet graph and semantics, we +can define the state vector at given time $t+dt$ with the forward Euler method +as: + +\begin{eqnarray*} + \frac{d {\bf x}}{dt} &=& \nabla_{\Omega, \Theta}({\bf p},{\bf x}, t)\\ + \frac{{\bf x}(t+dt)-{\bf x}(t)}{dt} &=& f_{\Omega, \Theta}({\bf p},{\bf x}, + t)\\ + {\bf x}(t+dt)&=& f_{\Omega, \Theta}({\bf p},{\bf x}, t)dt+ {\bf x}(t) +\end{eqnarray*} + +\begin{definition} + An abstraction $(\Theta', \Omega')$ of a Petrinet and the associated + semantics $(\Theta, \Omega)$ that is produced by the abstraction operator + $A$ has the following properties: + \begin{itemize} + \item State: For each $x \in X$, $A(x) = x'$, where $x' \in + X'$. For each vertex $v_x \in V_x$, $A(v_x) = v_x'$ where $v_x' \in + V_x'$. For each $x\in X$ where ${\cal X}(x) = + V_x$, $A(x) = x'$, and $A(v_x) = v_x'$, then ${\cal X}'(x')= + v_{x'}'$. For each $x' \in X'$, ${\cal X}'(x') = \sum\limits_{x \in X: A(x) = x'} {\cal X}(x)$. + \item Parameters: For each $p \in P$, $A(p) = p'$, where $p'\in P'$. + For each $p' \in P'$, ${\cal P}'(p') = \sum\limits_{p \in P: A(p) = p'} {\cal P}(p)$. + \item Transitions: For each $z \in Z$, $A(z) = z'$, where $z' \in Z'$. + For each vertex $v_z \in V_z$, $A(v_z) = v_z'$, where $v_z' \in V_z'$. + For each $z \in Z$, where ${\cal + Z}(z) = v_z$, $A(z) = z'$, and $A(v_z) = v_z'$, then ${\cal + Z}'(z') = v_{z'}'$. + \item In Edges: For each edge $(v_z, v_x) \in E_{in}$, $A((v_z, v_x)) = + (v_z', v_x')$, $A(v_x) = v_x'$, and $A(v_z) = v_z'$, where $(v_z', + v_x')\in E_{in}'$; + \item Out Edges: For each edge $(v_x, v_z) \in E_{out}$, $A((v_x, v_z)) + = (v_x', v_z')$; $A(v_x) = v_x'$, and $A(v_z) = v_z'$, where $(v_x', + v_z')\in E_{out}'$; + + + \item Transition Rates: For each $z' \in Z'$, ${\cal R}'({\bf p}', {\bf + x}', z') = \sum\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf + x}, z)$. + \end{itemize} +\end{definition} + +The abstraction $(\Theta', \Omega')$ similarly defines the gradient $\nabla_{\Omega', \Theta'}({\bf p}', {\bf x}', t) = (\frac{dx_1'}{dt}, +\frac{dx_2'}{dt}, \ldots)^T$, in terms of Equation \ref{eqn:flow}. +The abstraction thus expresses the gradient by aggregating terms from the +base Petrinet and semantics. It preserves the flow on transitions, but +expresses the transition rates in terms of the base states. As such, the +abstraction compresses the Petrinet graph structure, but at the cost of +expanding the expressions for transition rates. Moreover, the transition +rates refer to state variables and parameters that are not expressed +directly by the Petrinet and semantics, and by extension, the gradient. + +We modify the abstraction in what we call a \emph{bounded abstraction}, so that +it refers to the abstract, and not the base, Petrinet and semantics. This +bounded abstraction replaces base elements with corresponding bounded elements. +For example, if $A(x_1) = x'$ and $A(x_2) = x'$ ($x_1$ and $x_2$ are base +variables represented by $x'$ in the abstraction), a possible transition rate +could be of the form +${\cal R}'({\bf p}', {\bf x}', z') = p_1 x_1 + p_2 x_2$. By construction, we +know that $x_1 + x_2 = x'$. However, in general $p_1 \not= p_2$, and we cannot +say that $p_1 x_1 + p_2 x_2 = p'x'$ for some $p'$. Yet, if we replace $p_1$ and +$p_2$ by $p^{ub} = \max(p_1, p_2)$, then $p^{ub} x_1 + p^{ub} x_2 \geq p'x'$. Simplifying, we +get $p^{ub} x_1 + p^{ub} x_2 = p^{ub}(x_1 + x_2) = p^{ub} x' \geq p'x'$. A +similar argument can be made where $p^{lb} = \min(p_1, p_2)$ and we find that +$p^{lb} x' \leq p'x'$. + +By introducing the bounded parameters, we no longer +rely upon the base state variables or parameters. However, in tracking the +effect of the bounded +parameters, the bounded abstraction must also track bounded rates and bounded +state variables. The resulting bounded abstraction thus over-approximates the +abstraction and base model, wherein we can derive bounds on the state variables +at each time, which may correspond to a larger (hence over-approximation) set of +state trajectories. + +\begin{definition} +A bounded abstraction $(\Theta^B, \Omega^B)$ of an abstraction $(\Theta', +\Omega')$ of $(\Theta, \Omega)$ replaces each element of $(\Theta', +\Omega')$ by a pair of elements denoting the lower and upper bound of that +element (and referred to with the ``$lb$'' and ``$ub$'' superscripts). The +bounded abstraction defines: +\begin{itemize} + \item State: For each $x' \in X'$, $x^{lb}, x^{ub} \in X^B$. For each + $v_{x'}' \in V_x'$, ${\cal X}^B(x^{lb}) = v_{x^{lb}}^B$ and ${\cal X}^B(x^{ub}) = + v_x^{ub}$. For each $x^{lb}, x^{ub} \in X^B$, ${\cal I}^B(x^{lb}) = {\cal + I}^B(x^{ub}) = {\cal I}'(x')$. + \item Parameters: For each $p' \in P'$, + let ${\cal P}^B(p^{lb}) = \min\limits_{p \in P: A(p) = p'} {\cal P}(p)$ and ${\cal P}^B(p^{ub}) = \max\limits_{p \in P: A(p) = p'} {\cal P}(p)$. + + + \item Transitions: For each $z' \in Z'$, $z^{lb}, z^{ub} \in Z^B$. + For each vertex $v_z \in V_z$, $v_{z^{lb}}, v_{z^{ub}} \in V_z^B$. + + \item In Edges: For each edge $(v_{z'}, v_{x'}) \in E_{in}'$, $(v_{z^{lb}}, + v_{x^{lb}}), (v_{z^{ub}}, + v_{x^{ub}}) \in E^B_{in}$. + \item Out Edges: For each edge $(v_{x'}, v_{z'}) \in E_{out}'$, $(v_{x^{ub}}, + v_{z^{lb}}), (v_{x^{lb}}, + v_{z^{ub}}) \in E^B_{out}$. + + + \item Transition Rates: For each $z^{lb} \in Z^B$, ${\cal R}^B({\bf p}^B, {\bf + x}^B, z^{lb}) = \min\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf + x}, z)$ (replacing ${\bf p}$ and $[\bf x]$ of the minimal rate by the + elements in ${\bf p}^B$ and ${\bf x}^B$ respectively, which minimize the + rate), and ${\cal R}^B({\bf p}^B, + {\bf + x}^B, z^{ub}) = \max\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf + x}, z)$ (similarly replacing ${\bf p}$ and ${\bf x}$ of the maximal rate by the + elements in ${\bf p}^B$ and ${\bf x}^B$ respectively, which maximize the + rate). +\end{itemize} + +\end{definition} \ No newline at end of file diff --git a/notes/abstraction/main.pdf b/notes/abstraction/main.pdf index a18b1fb4d15a8d240098cb9f5b70e98a1d266eee..65283b02f1582fe3fe65a258740ae1a8632e1cc9 100644 GIT binary patch delta 187923 zcmb@tQ*_`#yX76*w$-t1+qToOoz6eDZ9D1M?AYwsPRF+9J>RT3b1~=gtDCA?t1g~Z zt1k9__OE6KP$O?JsVJ4jB^g*4Ip8Sg7Kc~i*hrX398GQD`1#?O57M)~=luf7h{Qz$bjIXOm3+@2x20`u!=$Ghc6hg%Qt zecuo1T0%W<_0_G>)m57{R(*NjFh-rQWRE1ab8HR?qVdOr&8Y|cS$A`zK zdyT!mpywPMhCSMd6y7*ry-lrQn~~{Z49PP~c*in<+>A$(J%=eSvR;GoC7ih)>x2 zrOYBd8_a54py}UH%X#adrJkWoNU6HP!r~;4fXkKk)0i5!xg&0bK9yV(mIQ?W5b^7% z#tKl`l{=P2?xV+}o5JBh`k6oW4?9I=#x8!I*&g%KyvLTlYc%JXEx>!$zzciWFIY5H 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zv+_wIZ+0!(*j{qjul|zMch9yjht~0JopvB^)~auxU$0-qc!|G$MV8J)MJIOLrC0ND%(}&~#<+TpX-yeGU!?W30Z2uxd&K|0_3n}LK*_I{u&@NC{VUNv9 zF~O+!t5jRo2Zst?+`KCFh?C#erU>hxwUeYC{oJT{FYcDSh_XTpD3Yc>DQA`7HL@@> OH8tc?Rdw}u;{pH;@Fj}? diff --git a/notes/abstraction/main.tex b/notes/abstraction/main.tex index 700b8032..3e51f7f9 100644 --- a/notes/abstraction/main.tex +++ b/notes/abstraction/main.tex @@ -1,11 +1,14 @@ \documentclass[10pt]{article} \usepackage{amsmath} +\usepackage{} +% \usepackage{mathbb} % \usepackage[amsmath]{ntheorem} \usepackage{amsfonts} \usepackage{graphicx} \usepackage{fullpage} +\newtheorem{definition}{Definition} \newcommand{\funman}{FUNMAN} \newcommand{\region}{\bf X} @@ -25,6 +28,10 @@ \begin{document} \maketitle +\section{Background} +\input{background} + + \section{Stratification Abstraction} \input{stratify-example} From 78f9955adaf174aa8950bf10178c0169feefbd39 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Mon, 30 Sep 2024 13:28:12 -0500 Subject: [PATCH 53/93] example of SIR abstraction --- notes/abstraction/abstraction.tex | 78 +++++++++++ notes/abstraction/background.tex | 161 ++++++++-------------- notes/abstraction/bounded-abstraction.tex | 141 +++++++++++++++++++ notes/abstraction/main.pdf | Bin 358409 -> 199950 bytes notes/abstraction/main.tex | 10 +- 5 files changed, 281 insertions(+), 109 deletions(-) create mode 100644 notes/abstraction/abstraction.tex create mode 100644 notes/abstraction/bounded-abstraction.tex diff --git a/notes/abstraction/abstraction.tex b/notes/abstraction/abstraction.tex new file mode 100644 index 00000000..8d0e2368 --- /dev/null +++ b/notes/abstraction/abstraction.tex @@ -0,0 +1,78 @@ +\begin{definition} + An abstraction $(\Theta', \Omega')$ of a Petrinet and the associated + semantics $(\Theta, \Omega)$ that is produced by the abstraction operator + $A$ has the following properties: + \begin{itemize} + \item State: For each $x \in X$, $A(x) = x'$, where $x' \in + X'$. For each vertex $v_x \in V_x$, $A(v_x) = v_x'$ where $v_x' \in + V_x'$. For each $x\in X$ where ${\cal X}(x) = + V_x$, $A(x) = x'$, and $A(v_x) = v_x'$, then ${\cal X}'(x')= + v_{x'}'$. For each $x' \in X'$, ${\cal X}'(x') = \sum\limits_{x \in X: A(x) = x'} {\cal X}(x)$. + \item Parameters: For each $p \in P$, $A(p) = p'$, where $p'\in P'$. + For each $p' \in P'$, ${\cal P}'(p') = \sum\limits_{p \in P: A(p) = p'} {\cal P}(p)$. + \item Transitions: For each $z \in Z$, $A(z) = z'$, where $z' \in Z'$. + For each vertex $v_z \in V_z$, $A(v_z) = v_z'$, where $v_z' \in V_z'$. + For each $z \in Z$, where ${\cal + Z}(z) = v_z$, $A(z) = z'$, and $A(v_z) = v_z'$, then ${\cal + Z}'(z') = v_{z'}'$. + \item In Edges: For each edge $(v_z, v_x) \in E_{in}$, $A((v_z, v_x)) = + (v_z', v_x')$, $A(v_x) = v_x'$, and $A(v_z) = v_z'$, where $(v_z', + v_x')\in E_{in}'$; + \item Out Edges: For each edge $(v_x, v_z) \in E_{out}$, $A((v_x, v_z)) + = (v_x', v_z')$; $A(v_x) = v_x'$, and $A(v_z) = v_z'$, where $(v_x', + v_z')\in E_{out}'$; + + + \item Transition Rates: For each $z' \in Z'$, ${\cal R}'({\bf p}', {\bf + x}', z') = \sum\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf + x}, z)$. + \end{itemize} +\end{definition} + +\begin{example} + The abstraction $(\Theta', \Omega')$ of the stratified SIR model defines + (with the changed elements highlighted by ``*''): + \begin{eqnarray*} + A &=& \left\{ + \begin{array}{lll} + S &: S_1 &*\\ + S &: S_2&*\\ + I &: I\\ + R &: R\\ + \beta &: \beta_1&*\\ + \beta &: \beta_2&*\\ + \gamma &: \gamma\\ + inf&: inf_1&*\\ + inf&: inf_2&*\\ + rec&: rec\\ + v_S &: v_{S_1}&*\\ + v_S &: v_{S_2}&*\\ + v_I &: v_{I}\\ + v_R &: v_{R}\\ + (v_{S}, v_{inf}) &: (v_{S_1}, v_{inf_1})&*\\ + (v_{S}, v_{inf}) &: (v_{S_2}, v_{inf_2})&*\\ + (v_{I}, v_{inf}) &: (v_{I}, v_{inf_1})&*\\ + (v_{I}, v_{inf}) &: (v_{I}, v_{inf_2})&*\\ + (v_I, v_{rec}) &: (v_I, v_{rec})\\ + (v_{inf}, v_I) &: (v_{inf_1}, v_I)&*\\ + (v_{inf}, v_I) &: (v_{inf_2}, v_I)&*\\ + (v_{rec}, v_R) &: (v_{rec}, v_R)\\ + \end{array}\right.\\ + {\cal R} &=& \left\{ + \begin{array}{lll} + \beta_1 S_1 I + \beta_2 S_2 I& : z_{inf}&*\\ + \gamma I R & : z_{rec}\\ + \end{array}\right.\\ + \end{eqnarray*} +\end{example} + +The abstraction $(\Theta', \Omega')$ similarly defines the gradient $\nabla_{\Omega', \Theta'}({\bf p}', {\bf x}', t) = (\frac{dx_1'}{dt}, +\frac{dx_2'}{dt}, \ldots)^T$, in terms of Equation \ref{eqn:flow}. +The abstraction thus expresses the gradient by aggregating terms from the +base Petrinet and semantics. It preserves the flow on transitions, but +expresses the transition rates in terms of the base states. As such, the +abstraction compresses the Petrinet graph structure, but at the cost of +expanding the expressions for transition rates. Moreover, the transition +rates refer to state variables and parameters that are not expressed +directly by the Petrinet and semantics, and by extension, the gradient. + diff --git a/notes/abstraction/background.tex b/notes/abstraction/background.tex index 9a2daf79..6e046b44 100644 --- a/notes/abstraction/background.tex +++ b/notes/abstraction/background.tex @@ -1,10 +1,12 @@ \begin{definition} A Petrinet $\Omega$ is a directed graph $(V, E)$ with vertices $V=(V_x, V_z)$ partitioned into sets $V_x$ of state vertices and $V_z$ of transition - vertices, and edges $E=(E_{in}, E_{out})$ partitioned into sets $E_{out}$ of + vertices, and edges $E=(E_{in}, E_{out})$ partitioned into collections $E_{out}$ of flow-out and $E_{in}$ flow-in edges. \end{definition} + + \begin{definition} A flow-out edge $e \in E_{out}$ comprises a pair of vertices $(v_x,v_z)$, where $v_x \in V_x$ is a state vertex, $v_z \in V_z$ is a transition vertex, and the @@ -17,7 +19,17 @@ $v_x$. \end{definition} - +\begin{example} + The SIR model that stratifies the $S$ state variable into $S_1$ and $S_2$ + for two susceptible populations and defines $\Omega$ by: + \begin{eqnarray*} + V_x &=& \{v_{S_1}, v_{S_2}, v_{I}, v_{R}\}\\ + V_z &=& \{v_{inf_1}, v_{inf_2}, v_{rec}\}\\ + E_{in} &=& ((v_{inf_1}, v_{S_1}), (v_{inf_1}, v_{I}), (v_{inf_1}, v_{I}), (v_{inf_2}, v_{S_2}), (v_{inf_2}, v_{I}), + (v_{inf_2}, v_{I}), (v_{rec}, v_{R}))\\ + E_{out} &=& ((v_{S_1}, v_{inf_1}), (v_{S_2}, v_{inf_2}),(v_{I}, v_{inf_1}), (v_{I}, v_{rec})) + \end{eqnarray*} +\end{example} \begin{definition} The ODE semantics $\Theta$ of the Petrinet $\Omega$ defines a tuple $(P, X, @@ -54,117 +66,52 @@ Equation \eqref{eqn:flow}. \end{definition} +\begin{example} + The stratified SIR model defines $\Theta$ by: + \begin{eqnarray*} + P &=& \{\beta_1, \beta_2, \gamma\}\\ + X &=& \{S_1, S_2, I, R\}\\ + Z &=& \{inf_1, inf_2, rec\}\\ + {\cal I} &=& \left\{ + \begin{array}{ll} + 0.45& :S_1\\ + 0.45& :S_2\\ + 0.1& :I\\ + 0.0& :R + \end{array}\right.\\ + {\cal P}&=& \left\{ + \begin{array}{ll} + 1e{-7}& :\beta_1\\ + 2e{-7}& :\beta_2\\ + 1e{-5}& :\gamma + \end{array}\right.\\ + \\ + {\cal X} &=& \left\{ + \begin{array}{ll} + v_{x} & : x \in X + \end{array}\right.\\ + {\cal Z} &=& \left\{ + \begin{array}{ll} + v_{z} & : z \in Z + \end{array}\right.\\ + {\cal R} &=& \left\{ + \begin{array}{ll} + \beta_1 S_1 I & : z_{inf_1}\\ + \beta_2 S_2 I & : z_{inf_2}\\ + \gamma I R & : z_{rec}\\ + \end{array}\right.\\ + \end{eqnarray*} +\end{example} + + Using the partial derivatives defined by the Petrinet graph and semantics, we can define the state vector at given time $t+dt$ with the forward Euler method as: \begin{eqnarray*} \frac{d {\bf x}}{dt} &=& \nabla_{\Omega, \Theta}({\bf p},{\bf x}, t)\\ - \frac{{\bf x}(t+dt)-{\bf x}(t)}{dt} &=& f_{\Omega, \Theta}({\bf p},{\bf x}, + \frac{{\bf x}(t+dt)-{\bf x}(t)}{dt} &=& \nabla_{\Omega, \Theta}({\bf p},{\bf x}, t)\\ - {\bf x}(t+dt)&=& f_{\Omega, \Theta}({\bf p},{\bf x}, t)dt+ {\bf x}(t) + {\bf x}(t+dt)&=& \nabla_{\Omega, \Theta}({\bf p},{\bf x}, t)dt+ {\bf x}(t) \end{eqnarray*} -\begin{definition} - An abstraction $(\Theta', \Omega')$ of a Petrinet and the associated - semantics $(\Theta, \Omega)$ that is produced by the abstraction operator - $A$ has the following properties: - \begin{itemize} - \item State: For each $x \in X$, $A(x) = x'$, where $x' \in - X'$. For each vertex $v_x \in V_x$, $A(v_x) = v_x'$ where $v_x' \in - V_x'$. For each $x\in X$ where ${\cal X}(x) = - V_x$, $A(x) = x'$, and $A(v_x) = v_x'$, then ${\cal X}'(x')= - v_{x'}'$. For each $x' \in X'$, ${\cal X}'(x') = \sum\limits_{x \in X: A(x) = x'} {\cal X}(x)$. - \item Parameters: For each $p \in P$, $A(p) = p'$, where $p'\in P'$. - For each $p' \in P'$, ${\cal P}'(p') = \sum\limits_{p \in P: A(p) = p'} {\cal P}(p)$. - \item Transitions: For each $z \in Z$, $A(z) = z'$, where $z' \in Z'$. - For each vertex $v_z \in V_z$, $A(v_z) = v_z'$, where $v_z' \in V_z'$. - For each $z \in Z$, where ${\cal - Z}(z) = v_z$, $A(z) = z'$, and $A(v_z) = v_z'$, then ${\cal - Z}'(z') = v_{z'}'$. - \item In Edges: For each edge $(v_z, v_x) \in E_{in}$, $A((v_z, v_x)) = - (v_z', v_x')$, $A(v_x) = v_x'$, and $A(v_z) = v_z'$, where $(v_z', - v_x')\in E_{in}'$; - \item Out Edges: For each edge $(v_x, v_z) \in E_{out}$, $A((v_x, v_z)) - = (v_x', v_z')$; $A(v_x) = v_x'$, and $A(v_z) = v_z'$, where $(v_x', - v_z')\in E_{out}'$; - - - \item Transition Rates: For each $z' \in Z'$, ${\cal R}'({\bf p}', {\bf - x}', z') = \sum\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf - x}, z)$. - \end{itemize} -\end{definition} - -The abstraction $(\Theta', \Omega')$ similarly defines the gradient $\nabla_{\Omega', \Theta'}({\bf p}', {\bf x}', t) = (\frac{dx_1'}{dt}, -\frac{dx_2'}{dt}, \ldots)^T$, in terms of Equation \ref{eqn:flow}. -The abstraction thus expresses the gradient by aggregating terms from the -base Petrinet and semantics. It preserves the flow on transitions, but -expresses the transition rates in terms of the base states. As such, the -abstraction compresses the Petrinet graph structure, but at the cost of -expanding the expressions for transition rates. Moreover, the transition -rates refer to state variables and parameters that are not expressed -directly by the Petrinet and semantics, and by extension, the gradient. - -We modify the abstraction in what we call a \emph{bounded abstraction}, so that -it refers to the abstract, and not the base, Petrinet and semantics. This -bounded abstraction replaces base elements with corresponding bounded elements. -For example, if $A(x_1) = x'$ and $A(x_2) = x'$ ($x_1$ and $x_2$ are base -variables represented by $x'$ in the abstraction), a possible transition rate -could be of the form -${\cal R}'({\bf p}', {\bf x}', z') = p_1 x_1 + p_2 x_2$. By construction, we -know that $x_1 + x_2 = x'$. However, in general $p_1 \not= p_2$, and we cannot -say that $p_1 x_1 + p_2 x_2 = p'x'$ for some $p'$. Yet, if we replace $p_1$ and -$p_2$ by $p^{ub} = \max(p_1, p_2)$, then $p^{ub} x_1 + p^{ub} x_2 \geq p'x'$. Simplifying, we -get $p^{ub} x_1 + p^{ub} x_2 = p^{ub}(x_1 + x_2) = p^{ub} x' \geq p'x'$. A -similar argument can be made where $p^{lb} = \min(p_1, p_2)$ and we find that -$p^{lb} x' \leq p'x'$. - -By introducing the bounded parameters, we no longer -rely upon the base state variables or parameters. However, in tracking the -effect of the bounded -parameters, the bounded abstraction must also track bounded rates and bounded -state variables. The resulting bounded abstraction thus over-approximates the -abstraction and base model, wherein we can derive bounds on the state variables -at each time, which may correspond to a larger (hence over-approximation) set of -state trajectories. - -\begin{definition} -A bounded abstraction $(\Theta^B, \Omega^B)$ of an abstraction $(\Theta', -\Omega')$ of $(\Theta, \Omega)$ replaces each element of $(\Theta', -\Omega')$ by a pair of elements denoting the lower and upper bound of that -element (and referred to with the ``$lb$'' and ``$ub$'' superscripts). The -bounded abstraction defines: -\begin{itemize} - \item State: For each $x' \in X'$, $x^{lb}, x^{ub} \in X^B$. For each - $v_{x'}' \in V_x'$, ${\cal X}^B(x^{lb}) = v_{x^{lb}}^B$ and ${\cal X}^B(x^{ub}) = - v_x^{ub}$. For each $x^{lb}, x^{ub} \in X^B$, ${\cal I}^B(x^{lb}) = {\cal - I}^B(x^{ub}) = {\cal I}'(x')$. - \item Parameters: For each $p' \in P'$, - let ${\cal P}^B(p^{lb}) = \min\limits_{p \in P: A(p) = p'} {\cal P}(p)$ and ${\cal P}^B(p^{ub}) = \max\limits_{p \in P: A(p) = p'} {\cal P}(p)$. - - - \item Transitions: For each $z' \in Z'$, $z^{lb}, z^{ub} \in Z^B$. - For each vertex $v_z \in V_z$, $v_{z^{lb}}, v_{z^{ub}} \in V_z^B$. - - \item In Edges: For each edge $(v_{z'}, v_{x'}) \in E_{in}'$, $(v_{z^{lb}}, - v_{x^{lb}}), (v_{z^{ub}}, - v_{x^{ub}}) \in E^B_{in}$. - \item Out Edges: For each edge $(v_{x'}, v_{z'}) \in E_{out}'$, $(v_{x^{ub}}, - v_{z^{lb}}), (v_{x^{lb}}, - v_{z^{ub}}) \in E^B_{out}$. - - - \item Transition Rates: For each $z^{lb} \in Z^B$, ${\cal R}^B({\bf p}^B, {\bf - x}^B, z^{lb}) = \min\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf - x}, z)$ (replacing ${\bf p}$ and $[\bf x]$ of the minimal rate by the - elements in ${\bf p}^B$ and ${\bf x}^B$ respectively, which minimize the - rate), and ${\cal R}^B({\bf p}^B, - {\bf - x}^B, z^{ub}) = \max\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf - x}, z)$ (similarly replacing ${\bf p}$ and ${\bf x}$ of the maximal rate by the - elements in ${\bf p}^B$ and ${\bf x}^B$ respectively, which maximize the - rate). -\end{itemize} - -\end{definition} \ No newline at end of file diff --git a/notes/abstraction/bounded-abstraction.tex b/notes/abstraction/bounded-abstraction.tex new file mode 100644 index 00000000..dcc5ff82 --- /dev/null +++ b/notes/abstraction/bounded-abstraction.tex @@ -0,0 +1,141 @@ +We modify the abstraction in what we call a \emph{bounded abstraction}, so that +it refers to the abstract, and not the base, Petrinet and semantics. This +bounded abstraction replaces base elements with corresponding bounded elements. +For example, if $A(x_1) = x'$ and $A(x_2) = x'$ ($x_1$ and $x_2$ are base +variables represented by $x'$ in the abstraction), a possible transition rate +could be of the form +${\cal R}'({\bf p}', {\bf x}', z') = p_1 x_1 + p_2 x_2$. By construction, we +know that $x_1 + x_2 = x'$. However, in general $p_1 \not= p_2$, and we cannot +say that $p_1 x_1 + p_2 x_2 = p'x'$ for some $p'$. Yet, if we replace $p_1$ and +$p_2$ by $p^{ub} = \max(p_1, p_2)$, then $p^{ub} x_1 + p^{ub} x_2 \geq p'x'$. Simplifying, we +get $p^{ub} x_1 + p^{ub} x_2 = p^{ub}(x_1 + x_2) = p^{ub} x' \geq p'x'$. A +similar argument can be made where $p^{lb} = \min(p_1, p_2)$ and we find that +$p^{lb} x' \leq p'x'$. + +By introducing the bounded parameters, we no longer +rely upon the base state variables or parameters. However, in tracking the +effect of the bounded +parameters, the bounded abstraction must also track bounded rates and bounded +state variables. The resulting bounded abstraction thus over-approximates the +abstraction and base model, wherein we can derive bounds on the state variables +at each time, which may correspond to a larger (hence over-approximation) set of +state trajectories. + +\begin{definition} +A bounded abstraction $(\Theta^B, \Omega^B)$ of an abstraction $(\Theta', +\Omega')$ of $(\Theta, \Omega)$ replaces each element of $(\Theta', +\Omega')$ by a pair of elements denoting the lower and upper bound of that +element (and referred to with the ``$lb$'' and ``$ub$'' superscripts). The +bounded abstraction defines: +\begin{itemize} + \item State: For each $x' \in X'$, $x^{lb}, x^{ub} \in X^B$. For each + $v_{x'}' \in V_x'$, ${\cal X}^B(x^{lb}) = v_{x^{lb}}^B$ and ${\cal X}^B(x^{ub}) = + v_x^{ub}$. For each $x^{lb}, x^{ub} \in X^B$, ${\cal I}^B(x^{lb}) = {\cal + I}^B(x^{ub}) = {\cal I}'(x')$. + \item Parameters: For each $p' \in P'$, + let ${\cal P}^B(p^{lb}) = \min\limits_{p \in P: A(p) = p'} {\cal P}(p)$ and ${\cal P}^B(p^{ub}) = \max\limits_{p \in P: A(p) = p'} {\cal P}(p)$. + + + \item Transitions: For each $z' \in Z'$, $z^{lb}, z^{ub} \in Z^B$. + For each vertex $v_z \in V_z$, $v_{z^{lb}}, v_{z^{ub}} \in V_z^B$. + + \item In Edges: For each edge $(v_{z'}, v_{x'}) \in E_{in}'$, $(v_{z^{lb}}, + v_{x^{lb}}), (v_{z^{ub}}, + v_{x^{ub}}) \in E^B_{in}$. + \item Out Edges: For each edge $(v_{x'}, v_{z'}) \in E_{out}'$, $(v_{x^{ub}}, + v_{z^{lb}}), (v_{x^{lb}}, + v_{z^{ub}}) \in E^B_{out}$. + + + \item Transition Rates: For each $z^{lb} \in Z^B$, ${\cal R}^B({\bf p}^B, {\bf + x}^B, z^{lb}) = \min\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf + x}, z)$ (replacing ${\bf p}$ and $[\bf x]$ of the minimal rate by the + elements in ${\bf p}^B$ and ${\bf x}^B$ respectively, which minimize the + rate), and ${\cal R}^B({\bf p}^B, + {\bf + x}^B, z^{ub}) = \max\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf + x}, z)$ (similarly replacing ${\bf p}$ and ${\bf x}$ of the maximal rate by the + elements in ${\bf p}^B$ and ${\bf x}^B$ respectively, which maximize the + rate). +\end{itemize} + +\end{definition} + +\begin{example} + The bounded abstraction $(\Theta^B, \Omega^B)$ of the stratified SIR model defines: + \begin{eqnarray*} + V^B_x &=& \{v_{S}^{lb}, v_{S}^{ub}, v_{I}^{lb}, v_{I}^{ub},v_{R}^{lb}, v_{R}^{ub},\}\\ + V^B_z &=& \{v_{inf}^{lb}, v_{inf}^{ub}, v_{rec}^{lb}, v_{rec}^{ub}\}\\ + E^B_{in} &=& ((v_{inf}^{lb}, v_{S}^{lb}), (v_{inf}^{lb}, + v_{I}^{lb}),(v_{inf}^{lb}, v_{I}^{lb}), (v_{rec}^{lb}, v_{R}^{lb}),(v_{inf}^{ub}, v_{S}^{ub}), (v_{inf}^{ub}, + v_{I}^{ub}),(v_{inf}^{ub}, v_{I}^{ub}), (v_{rec}^{ub}, v_{R}^{ub})\\ + E^B_{out} &=& ((v_{S}^{lb}, v_{inf}^{ub}),(v_{I}^{lb}, v_{inf}^{ub}), + (v_{I}^{lb}, v_{rec}^{ub}), (v_{S}^{ub}, v_{inf}^{lb}),(v_{I}^{ub}, + v_{inf}^{lb}), (v_{I}^{ub}, v_{rec}^{lb}))\\ + P^B &=& \{\beta^{lb}, \beta^{ub}, \gamma^{lb}, \gamma^{ub}\}\\ + X^B &=& \{S^{lb}, S^{ub}, I^{lb},I^{ub}, R^{lb}, R^{ub}\}\\ + Z^B &=& \{inf^{lb}, inf^{ub}, rec^{lb}, rec^{ub}\}\\ + {\cal I}^B &=& \left\{ + \begin{array}{ll} + 0.9& :S^{lb}\\ + 0.9& :S^{ub}\\ + 0.1& :I^{lb}\\ + 0.1& :I^{ub}\\ + 0.0& :R^{lb}\\ + 0.0& :R^{ub} + \end{array}\right.\\ + {\cal P}^B&=& \left\{ + \begin{array}{ll} + 1e{-7}& :\beta^{lb}\\ + 2e{-7}& :\beta^{ub}\\ + 1e{-5}& :\gamma^{lb}\\ + 1e{-5}& :\gamma^{ub}\\ + \end{array}\right.\\ + \\ + {\cal X}^B &=& \left\{ + \begin{array}{ll} + v^{lb}_{x} & : x^{lb} \in X^B\\ + v^{ub}_{x} & : x^{ub} \in X^B + \end{array}\right.\\ + {\cal Z}^B &=& \left\{ + \begin{array}{ll} + v_{z}^{lb} & : z^{lb} \in Z^B\\ + v_{z}^{ub} & : z^{ub} \in Z^B + \end{array}\right.\\ + {\cal R}^{B} &=& \left\{ + \begin{array}{ll} + \beta^{lb} S^{lb} I^{lb} & : z^{lb}_{inf}\\ + \beta^{ub} S^{ub} I^{ub} & : z^{ub}_{inf}\\ + \gamma^{lb} I^{lb} R^{lb} & : z^{lb}_{rec}\\ + \gamma^{ub} I^{ub} R^{ub} & : z^{ub}_{rec} + \end{array}\right.\\ + \end{eqnarray*} + + The gradient for the bounded abstraction defines: + \begin{eqnarray} + \nabla_{\Theta^B, \Omega^B} = \begin{bmatrix} + \frac{dS^{lb}}{dt}\\ + \frac{dS^{ub}}{dt}\\ + \frac{dI^{lb}}{dt}\\ + \frac{dI^{ub}}{dt}\\ + \frac{dR^{lb}}{dt}\\ + \frac{dR^{ub}}{dt} + \end{bmatrix} = \begin{bmatrix} + -{\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{inf}^{ub})\\ + -{\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{inf}^{lb})\\ + {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{inf}^{lb}) - {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{rec}^{ub})\\ + {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{inf}^{ub}) - {\cal + R}^{B}({\bf p}^B, {\bf x}^B, z_{rec}^{lb})\\ + {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{rec}^{lb})\\ + {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{rec}^{ub}) + \end{bmatrix} = \begin{bmatrix} + -\beta^{ub} S^{ub} I^{ub}\\ + -\beta^{lb} S^{lb} I^{lb}\\ + \beta^{lb} S^{lb} I^{lb}-\gamma^{ub} I^{ub} R^{ub}\\ + \beta^{ub} S^{ub} I^{ub}-\gamma^{lb} I^{lb} R^{lb}\\ + \gamma^{lb} I^{lb} R^{lb}\\ + \gamma^{ub} I^{ub} R^{ub} + \end{bmatrix} + \end{eqnarray} + +\end{example} \ No newline at end of file diff --git a/notes/abstraction/main.pdf b/notes/abstraction/main.pdf index 65283b02f1582fe3fe65a258740ae1a8632e1cc9..92bf855c26bf10d54504eb6c76f7c21ec22329d6 100644 GIT binary patch delta 168487 zcmZU4Lv$_-uw-o8wr$(CZQod5Y}-z5Y}>ZY8{1Cie}mQ3s`h>QbX9kC*}7m3e@VECVh9*qtNW6EiT`OX;e0M+@ITT7aU@ zKibUDPQ6Muj(-B=(_{jrORM_hqKZV@+s*r7&9FU}Nt8>~#l@TZU>n)yS;OFJx(Y-A*|2FE`=Hv6c?ye-8Ng@ZJvMorBzmTG6v5j)6<$HJfZo`AB zXIaMq$T|I?e#=8GChvwJDUCbYJ>~53r;> zsu=2KwE%aQ215GQW;wiPTDykDSu)lCOE{pY7{e@%CK1#hedPv&`K^&UB3hy_tY1Xk*_OaPcM}5gmZ1Y*$8tt;!6yj{(Dq67_31;-Sme&mpZox zXv;dcM?#E6R<6kfDN=h3Ngc6+(S1#MsmmA8L_0gg(i%Z9i6m=Z48_6;&iQCFtO$fQ z_Mrj(rgY~-1CO>gjBPyRJIn&HPrO*cbpgB-rBp^)BRs$XkqY(Uh1LoI5Y_;W>S*mp z=IDlIfVlKhf`ChzWz^_$^KF~)$WX>7SJ@(Fb()5YQCw90VDJThycC+(`jyNf~NvBK<) zw&)cpoGbwwoKJ1F%JGy*q_2(w)l%QL-AYWW-a!IxwXf^ZGL``QVg{?ukW!l0N*veQ 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zuz3|2#R9~quT&3pEb&w^9WF^=FJ0_yWdha1yEYXI{{lBpSBA4s{UFX9uDsufe+H5K zsIIeg6s}EV_osMw@k3rl1soJVDR>Q&=-_>D&tBzSzX5ebT-L)kGcK9#S*C@(DBiBM zt%kQ@;fL0T1Jk%Aza0Sz-_$eyyA=2TSK`>X(y$O9NC9kY+=lQh>NY+Wq+b%AMVnNg zjg*6w?Q2oZ(b4T|k>d-zs|L>^=V0mh6>h$Cn}bzSii=m0mxG&=k4=n6 znoW$4SL#bjN{aCalm4$qP=voOQnYZeaXqS85@|oGMmgjr7(6!*x3r zZ9~?PteXcnb8OxF1kY{U2PBP~I!w%R^|qXX5%h$d$Q$***kz9Ps+i!jvSpqjBIkID f|L3#2x|zDTdAV3v!gFx(@UnBkQ&CANOT+&cei=my diff --git a/notes/abstraction/main.tex b/notes/abstraction/main.tex index 3e51f7f9..a391526f 100644 --- a/notes/abstraction/main.tex +++ b/notes/abstraction/main.tex @@ -9,6 +9,7 @@ \usepackage{fullpage} \newtheorem{definition}{Definition} +\newtheorem{example}{Example} \newcommand{\funman}{FUNMAN} \newcommand{\region}{\bf X} @@ -31,9 +32,14 @@ \section{Background} \input{background} +\section{Abstraction} +\input{abstraction} -\section{Stratification Abstraction} -\input{stratify-example} +\section{Bounded Abstraction} +\input{bounded-abstraction} + +% \section{Stratification Abstraction} +% \input{stratify-example} % \section{Parameter Synthesis as Abstraction Refinement} From 9d3be432cb4491fa88b02ae778b1fb97b74a26e5 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Mon, 30 Sep 2024 13:28:22 -0500 Subject: [PATCH 54/93] ignores --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index cb64c9d8..464618df 100644 --- a/.gitignore +++ b/.gitignore @@ -321,3 +321,4 @@ notebooks/saved-results/out **/*.out auxiliary_packages/ibex_tests/num_constraint_test auxiliary_packages/ibex_tests/num_constraint_test.o +notes/.DS_Store From 213acaa6b59b88833eaa18b1657a01a3a4831adc Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Tue, 1 Oct 2024 18:39:16 +0000 Subject: [PATCH 55/93] stratification producing meaningful result --- .../funman_sep_2024_strat_abs.ipynb | 1390 +++++++++++++---- .../2024-09/sirhd-stratified-request.json | 25 + .../2024-09/sirhd_stratified.json | 1 + 3 files changed, 1154 insertions(+), 262 deletions(-) create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd-stratified-request.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd_stratified.json diff --git a/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb b/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb index 93f7c122..4a9e577f 100644 --- a/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb +++ b/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -16,6 +16,8 @@ "import logging\n", "import matplotlib.pyplot as plt\n", "from helpers import run, get_model, setup_common, get_request, report\n", + "from funman import to_sympy\n", + "from funman.model.generated_models.petrinet import Initial\n", "\n", "\n", "RESOURCES = \"../../resources\"\n", @@ -37,7 +39,8 @@ "requests = {\n", " \"sidarthe_observables\": REQUEST_PATH,\n", " \"sirhd-vac\": os.path.join(EXAMPLE_DIR, \"sirhd-vac-request.json\"),\n", - " \"sirhd\": None\n", + " \"sirhd\": None,\n", + " \"sirhd_stratified\": os.path.join(EXAMPLE_DIR, \"sirhd-stratified-request.json\")\n", "}\n", "\n", "states = {\n", @@ -71,7 +74,45 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 points\n", + " N beta phd phr pih pir rhd rhr rih rir\n", + "sirhd 150000000.0 0.18 0.13 0.87 0.1 0.9 0.3 0.07 0.07 0.07\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Base Model\n", + "model_str = \"sirhd\"\n", + "(base_model, request) = get_model(models[model_str])\n", + "to_plot = base_model._state_var_names() + base_model._observable_names()\n", + "\n", + "\n", + "funman_request = get_request(models[model_str])\n", + "setup_common(funman_request, timepoints, debug=True, mode=MODE_SMT, synthesize=False,dreal_precision=0.1)\n", + "results = run(funman_request, model_str, models)\n", + "report(results, model_str, to_plot, request_results, request_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -83,16 +124,189 @@ "\n", "\n", - "\n", + "\n", + "\n", + "petrinet\n", + "\n", + "\n", + "\n", + "t1([I*S*beta/N]) = [1.2e-9*I*S]\n", + "\n", + "t1([I*S*beta/N]) = [1.2e-9*I*S]\n", + "\n", + "\n", + "\n", + "I\n", + "\n", + "I\n", + "\n", + "\n", + "\n", + "t1([I*S*beta/N]) = [1.2e-9*I*S]->I\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1([I*S*beta/N]) = [1.2e-9*I*S]->I\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "S\n", + "\n", + "S\n", + "\n", + "\n", + "\n", + "S->t1([I*S*beta/N]) = [1.2e-9*I*S]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t2([I*pir*rir]) = [0.063*I]\n", + "\n", + "t2([I*pir*rir]) = [0.063*I]\n", + "\n", + "\n", + "\n", + "R\n", + "\n", + "R\n", + "\n", + "\n", + "\n", + "t2([I*pir*rir]) = [0.063*I]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3([I*pih*rih]) = [0.007*I]\n", + "\n", + "t3([I*pih*rih]) = [0.007*I]\n", + "\n", + "\n", + "\n", + "H\n", + "\n", + "H\n", + "\n", + "\n", + "\n", + "t3([I*pih*rih]) = [0.007*I]->H\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]->D\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]->R\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I->t1([I*S*beta/N]) = [1.2e-9*I*S]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I->t2([I*pir*rir]) = [0.063*I]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I->t3([I*pih*rih]) = [0.007*I]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H->t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_model.to_dot()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", "\n", "petrinet\n", - "\n", + "\n", "\n", "\n", "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", - "\n", - "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", + "\n", + "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", "\n", "\n", "\n", @@ -103,69 +317,69 @@ "\n", "\n", "t2_u([I_u*pir*rir]) = [0.063*I_u]->R\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "I_u\n", - "\n", - "I_u\n", + "\n", + "I_u\n", "\n", "\n", "\n", "I_u->t2_u([I_u*pir*rir]) = [0.063*I_u]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", - "\n", - "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", + "\n", + "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", "\n", "\n", "\n", "I_u->t3_u([I_u*pih*rih]) = [0.007*I_u]\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", - "\n", - "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", + "t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]\n", + "\n", + "t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]\n", "\n", - "\n", + "\n", "\n", - "I_u->t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", - "\n", - "\n", + "I_u->t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]\n", - "\n", - "t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]\n", + "t1_u_v([I_u*S*beta_u_v/N]) = [1.2e-9*I_u*S]\n", + "\n", + "t1_u_v([I_u*S*beta_u_v/N]) = [1.2e-9*I_u*S]\n", "\n", - "\n", + "\n", "\n", - "I_u->t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]\n", - "\n", - "\n", + "I_u->t1_u_v([I_u*S*beta_u_v/N]) = [1.2e-9*I_u*S]\n", + "\n", + "\n", "\n", "\n", "\n", "transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]\n", - "\n", - "transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]\n", + "\n", + "transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]\n", "\n", "\n", "\n", "I_u->transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -183,15 +397,15 @@ "\n", "\n", "H\n", - "\n", - "H\n", + "\n", + "H\n", "\n", "\n", "\n", "t3_u([I_u*pih*rih]) = [0.007*I_u]->H\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", @@ -202,83 +416,83 @@ "\n", "\n", "t3_v([I_v*pih*rih]) = [0.007*I_v]->H\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]->I_u\n", - "\n", - "\n", - "1.0\n", + "t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]->I_u\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]->I_u\n", - "\n", - "\n", - "1.0\n", + "t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]->I_u\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "I_v\n", - "\n", - "I_v\n", + "\n", + "I_v\n", "\n", - "\n", + "\n", "\n", - "t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]->I_v\n", - "\n", - "\n", - "1.0\n", + "t1_u_v([I_u*S*beta_u_v/N]) = [1.2e-9*I_u*S]->I_v\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]->I_v\n", - "\n", - "\n", - "1.0\n", + "t1_u_v([I_u*S*beta_u_v/N]) = [1.2e-9*I_u*S]->I_v\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]\n", - "\n", - "t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]\n", + "t1_v_u([I_v*S*beta_v_u/N]) = [1.2e-9*I_v*S]\n", + "\n", + "t1_v_u([I_v*S*beta_v_u/N]) = [1.2e-9*I_v*S]\n", "\n", - "\n", + "\n", "\n", - "t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]->I_u\n", - "\n", - "\n", - "1.0\n", + "t1_v_u([I_v*S*beta_v_u/N]) = [1.2e-9*I_v*S]->I_u\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]->I_u\n", - "\n", - "\n", - "1.0\n", + "t1_v_u([I_v*S*beta_v_u/N]) = [1.2e-9*I_v*S]->I_u\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", - "\n", - "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", + "t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]\n", + "\n", + "t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]\n", "\n", - "\n", + "\n", "\n", - "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]->I_v\n", - "\n", - "\n", - "1.0\n", + "t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]->I_v\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]->I_v\n", - "\n", - "\n", - "1.0\n", + "t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]->I_v\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", @@ -315,118 +529,117 @@ "\n", "\n", "transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]->I_v\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]\n", - "\n", - "transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]\n", + "\n", + "transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]\n", "\n", "\n", "\n", "transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]->I_u\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "I_v->t2_v([I_v*pir*rir]) = [0.063*I_v]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "I_v->t3_v([I_v*pih*rih]) = [0.007*I_v]\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "I_v->t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]\n", - "\n", - "\n", + "I_v->t1_v_u([I_v*S*beta_v_u/N]) = [1.2e-9*I_v*S]\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "I_v->t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", - "\n", - "\n", + "I_v->t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]\n", + "\n", + "\n", "\n", "\n", "\n", "I_v->transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "S\n", - "\n", - "S\n", + "\n", + "S\n", "\n", - "\n", + "\n", "\n", - "S->t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", - "\n", - "\n", + "S->t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "S->t1_u_v([I_u_v*S*beta_u_v/N]) = [6.66666666666667e-9*I_u_v*S*beta_u_v]\n", - "\n", - "\n", + "S->t1_u_v([I_u*S*beta_u_v/N]) = [1.2e-9*I_u*S]\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "S->t1_v_u([I_v_u*S*beta_v_u/N]) = [6.66666666666667e-9*I_v_u*S*beta_v_u]\n", - "\n", - "\n", + "S->t1_v_u([I_v*S*beta_v_u/N]) = [1.2e-9*I_v*S]\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "S->t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", - "\n", - "\n", + "S->t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]\n", + "\n", + "\n", "\n", "\n", "\n", "H->t4([H*phd*rhd]) = [0.039*H]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "H->t5([H*phr*rhr]) = [0.0609*H]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "\n", "# Stratify model with vaccination status\n", "\n", "from typing import Dict, List, Optional\n", - "from funman.model.generated_models.petrinet import Model1, Parameter, Rate, State,Transition, Properties, Model, Semantics, OdeSemantics\n", + "from funman.model.generated_models.petrinet import Model1, Parameter, Rate, State,Transition, Properties, Model, Semantics, OdeSemantics, Initial\n", "from funman.model.petrinet import GeneratedPetriNetModel, PetrinetModel\n", + "from sympy import Symbol\n", "\n", "\n", - "model_str = \"sirhd\"\n", - "(model, request) = get_model(models[model_str])\n", - "to_plot = model._state_var_names() + model._observable_names()\n", "\n", "\n", "def stratify(self: PetrinetModel, state_var: str, strata: List[str], strata_parameter:Optional[str]=None, strata_transitions=[], self_strata_transition=False):\n", @@ -436,6 +649,7 @@ " assert len(state_vars) == 1, \"Found more than one State variable for {state_var}\"\n", " original_var = state_vars[0]\n", " new_vars = [State(id=f\"{original_var.id}_{level}\", name=f\"{original_var.name}_{level}\", description=f\"{original_var.description} Stratified wrt. {level}\", grounding=original_var.grounding, units=original_var.units) for level in strata]\n", + " unchanged_vars = [ s.id for s in self._state_vars() if s != original_var]\n", " \n", " # get new transitions\n", " transitions: Dict[str, Transition] = {t.id: t for t in self._transitions() if original_var.id in t.input or original_var.id in t.output}\n", @@ -486,21 +700,71 @@ " old_rates = {t_id: self._transition_rate(t) for t_id, t in transitions.items()}\n", " other_rates = {r.target: r for r in self.petrinet.semantics.ode.rates if r.target in other_transitions}\n", " \n", - " src_only_rates = [Rate(target=f\"{t_id}_{level}\", expression=(str(r[0]).replace(strata_parameter, f\"{strata_parameter}_{level}\").replace(state_var, f\"{state_var}_{level}\") if strata_parameter else r[0].replace(state_var, f\"{state_var}_{level}\"))) for t_id,r in old_rates.items() if t_id in src_only_transitions for level in strata]\n", + " src_only_rates = [\n", + " Rate(\n", + " target=f\"{t_id}_{level}\", \n", + " expression=(str(r[0]).replace(strata_parameter, f\"{strata_parameter}_{level}\").replace(state_var, f\"{state_var}_{level}\") if strata_parameter else r[0].replace(state_var, f\"{state_var}_{level}\"))) \n", + " for t_id,r in old_rates.items() \n", + " if t_id in src_only_transitions \n", + " for level in strata]\n", "\n", - " dest_only_rates = [Rate(target=f\"{t_id}_{level}\", expression=(str(r[0]).replace(strata_parameter, f\"{strata_parameter}_{level}\").replace(state_var, f\"{state_var}_{level}\") if strata_parameter else r[0].replace(state_var, f\"{state_var}_{level}\"))) for t_id,r in old_rates.items() if t_id in dest_only_transitions for level in strata]\n", + " dest_only_rates = [\n", + " Rate(\n", + " target=f\"{t_id}_{level}\", \n", + " expression=(str(r[0]).replace(strata_parameter, f\"{strata_parameter}_{level}\").replace(state_var, f\"{state_var}_{level}\") if strata_parameter else r[0].replace(state_var, f\"{state_var}_{level}\"))) \n", + " for t_id,r in old_rates.items() \n", + " if t_id in dest_only_transitions \n", + " for level in strata]\n", "\n", - " src_and_dest_rates = [Rate(target=f\"{t_id}_{level_s}_{level_t}\", expression=(str(r[0]).replace(strata_parameter, f\"{strata_parameter}_{level_s}_{level_t}\").replace(state_var, f\"{state_var}_{level_s}_{level_t}\") if strata_parameter else r[0].replace(state_var, f\"{state_var}_{level_s}_{level_t}\"))) for t_id,r in old_rates.items() if t_id in src_and_dest_transitions for level_s in strata for level_t in strata]\n", + " src_and_dest_rates = [\n", + " Rate(\n", + " target=f\"{t_id}_{level_s}_{level_t}\", \n", + " expression=(str(r[0]).replace(strata_parameter, f\"{strata_parameter}_{level_s}_{level_t}\").replace(state_var, f\"{state_var}_{level_s}\") if strata_parameter else r[0].replace(state_var, f\"{state_var}_{level_s}\"))) \n", + " for t_id,r in old_rates.items() \n", + " if t_id in src_and_dest_transitions \n", + " for level_s in strata for level_t in strata\n", + " ]\n", "\n", " new_rates = src_only_rates + dest_only_rates + src_and_dest_rates\n", "\n", " new_states = new_vars + [s for s in self.petrinet.model.states.root if s not in state_vars]\n", " \n", - " # FIXME update with new states by splitting old state values\n", - " new_initials = self.petrinet.semantics.ode.initials\n", + " # update with new states by splitting old state values\n", + " original_init_value = to_sympy(next(i.expression for i in self.petrinet.semantics.ode.initials if i.target == original_var.id), {})\n", + " \n", + " new_initials = [i for i in self.petrinet.semantics.ode.initials if i.target in unchanged_vars] + [\n", + " Initial(target=n.id, expression=str(original_init_value/float(len(new_vars))))\n", + " for n in new_vars\n", + " ]\n", " \n", " # FIXME update with split parameters\n", - " new_parameters = self.petrinet.semantics.ode.parameters\n", + " if strata_parameter is not None:\n", + " original_parameter_value = self._parameter_values()[strata_parameter]\n", + " original_parameter = next(iter([p for p in self.petrinet.semantics.ode.parameters if p.id == strata_parameter]))\n", + " unchanged_parameters = [p for p in self.petrinet.semantics.ode.parameters if p.id != strata_parameter]\n", + " src_only_parameters = list(set([\n", + " f\"{strata_parameter}_{level}\" \n", + " for t_id,r in old_rates.items() \n", + " if t_id in src_only_transitions and Symbol(strata_parameter) in old_rates[t_id][0].free_symbols \n", + " for level in strata\n", + " ]))\n", + "\n", + " dest_only_parameters = list(set([\n", + " f\"{strata_parameter}_{level}\" \n", + " for t_id,r in old_rates.items() \n", + " if t_id in dest_only_transitions and Symbol(strata_parameter) in old_rates[t_id][0].free_symbols \n", + " for level in strata\n", + " ]))\n", + "\n", + " src_and_dest_parameters = [\n", + " Parameter(id=f\"{strata_parameter}_{level_s}_{level_t}\", name = f\"{strata_parameter}_{level_s}_{level_t}\", description=f\"{original_parameter.description} stratified as {strata_parameter}_{level_s}_{level_t}\", value=original_parameter_value, distribution=original_parameter.distribution, units=original_parameter.units, grounding=original_parameter.grounding)\n", + " for t_id,r in old_rates.items() \n", + " if t_id in src_and_dest_transitions and Symbol(strata_parameter) in old_rates[t_id][0].free_symbols \n", + " for level_s in strata for level_t in strata\n", + " ]\n", + " new_parameters = unchanged_parameters + src_only_parameters + dest_only_parameters + src_and_dest_parameters\n", + " else:\n", + " new_parameters = self.petrinet.semantics.ode.parameters\n", " \n", " # FIXME update with splits\n", " new_observables = self.petrinet.semantics.ode.observables\n", @@ -509,13 +773,17 @@ " self_strata_rates = [Rate(target=f\"transition_{state_var}_{level_s}_{level_t}\", expression=f\"{state_var}_{level_s}*transition_{state_var}_{level_s}_{level_t}\") for level_s in strata for level_t in strata if level_s != level_t]\n", " self_strata_parameters = [Parameter(id=f\"transition_{state_var}_{level_s}_{level_t}\", name=f\"transition_{state_var}_{level_s}_{level_t}\", description=\"Transition rate parameter between {state_var} strata {level_s} and {level_t}.\", value=1.0/float(len(strata))) for level_s in strata for level_t in strata if level_s != level_t]\n", " \n", - " new_model = GeneratedPetriNetModel(petrinet=Model(header=self.petrinet.header,\n", - " properties=self.petrinet.properties,\n", - " model=Model1(\n", + " \n", + " \n", + " new_model = GeneratedPetriNetModel(\n", + " petrinet=Model(\n", + " header=self.petrinet.header,\n", + " properties=self.petrinet.properties,\n", + " model=Model1(\n", " states=new_states, \n", " transitions=[*new_transitions, *other_transitions.values(), *self_strata_transitions]\n", " ),\n", - " semantics=Semantics(\n", + " semantics=Semantics(\n", " ode=OdeSemantics(\n", " rates=[*new_rates, *other_rates.values(), *self_strata_rates], \n", " initials=new_initials, \n", @@ -528,13 +796,524 @@ "\n", " return new_model #new_rates, transitions, new_transitions # dest_only_rates #original_var, new_vars, new_transitions\n", " \n", - "m = stratify(model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t1\"], self_strata_transition=True)\n", - "m.to_dot()\n" + "stratified_model = stratify(base_model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t1\"], self_strata_transition=True)\n", + "stratified_model_str = f\"{model_str}_stratified\"\n", + "\n", + "stratified_model_path = os.path.join(EXAMPLE_DIR, stratified_model_str+\".json\")\n", + "models[stratified_model_str] = stratified_model_path\n", + "with open(stratified_model_path, \"w\") as f:\n", + " f.write(stratified_model.petrinet.model_dump_json())\n", + "\n", + "\n", + "stratified_model.to_dot()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 points\n", + " N beta_u_u beta_u_v beta_v_u beta_v_v phd \\\n", + "sirhd_stratified 150000000.0 0.18 0.18 0.18 0.18 0.13 \n", + "\n", + " phr pih pir rhd rhr rih rir transition_I_u_v \\\n", + "sirhd_stratified 0.87 0.1 0.9 0.3 0.07 0.07 0.07 0.5 \n", + "\n", + " transition_I_v_u \n", + "sirhd_stratified 0.5 \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Analyze Stratified Base Model\n", + "\n", + "from funman.server.query import FunmanWorkRequest\n", + "\n", + "\n", + "to_plot = stratified_model._state_var_names() + stratified_model._observable_names()\n", + "\n", + "\n", + "\n", + "stratified_request = get_request(models[model_str])\n", + "# stratified_request = FunmanWorkRequest()\n", + "setup_common(stratified_request, timepoints, debug=True, mode=MODE_SMT, synthesize=False,dreal_precision=0.1)\n", + "results = run(stratified_request, stratified_model_str, models)\n", + "report(results, stratified_model_str, to_plot, request_results, request_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timeseries(data=[[0.0, 1.0, 2.0, 3.0], [500.0, 666.9692227999795, 889.6653629654517, 1186.752633952176], [500.0, 666.9692227999795, 889.6653629654517, 1186.752633952176], [149217546.0, 149217130.93021733, 149216577.328128, 149215838.7957602], [0.0, 73.24600202799245, 171.3870330036324, 302.7185795636775], [0.0, 7.739454251973908, 17.326525975274247, 29.45016764609824], [781454.0, 781454.1458808719, 781454.6275871565, 781455.5302247496]], columns=['time', 'I_u', 'I_v', 'S', 'R', 'H', 'D'])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.points()[0].simulation\n", + "# stratified_request" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "petrinet\n", + "\n", + "\n", + "\n", + "t1_lb([I_lb*S_lb*beta_lb/N_lb]) = [1.2e-9*I_lb*S_lb]\n", + "\n", + "t1_lb([I_lb*S_lb*beta_lb/N_lb]) = [1.2e-9*I_lb*S_lb]\n", + "\n", + "\n", + "\n", + "I_lb\n", + "\n", + "I_lb\n", + "\n", + "\n", + "\n", + "t1_lb([I_lb*S_lb*beta_lb/N_lb]) = [1.2e-9*I_lb*S_lb]->I_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_lb([I_lb*S_lb*beta_lb/N_lb]) = [1.2e-9*I_lb*S_lb]->I_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t2_lb([I_lb*pir_lb*rir_lb]) = [0.063*I_lb]\n", + "\n", + "t2_lb([I_lb*pir_lb*rir_lb]) = [0.063*I_lb]\n", + "\n", + "\n", + "\n", + "R_lb\n", + "\n", + "R_lb\n", + "\n", + "\n", + "\n", + "t2_lb([I_lb*pir_lb*rir_lb]) = [0.063*I_lb]->R_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3_lb([I_lb*pih_lb*rih_lb]) = [0.007*I_lb]\n", + "\n", + "t3_lb([I_lb*pih_lb*rih_lb]) = [0.007*I_lb]\n", + "\n", + "\n", + "\n", + "H_lb\n", + "\n", + "H_lb\n", + "\n", + "\n", + "\n", + "t3_lb([I_lb*pih_lb*rih_lb]) = [0.007*I_lb]->H_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4_lb([H_lb*phd_lb*rhd_lb]) = [0.039*H_lb]\n", + "\n", + "t4_lb([H_lb*phd_lb*rhd_lb]) = [0.039*H_lb]\n", + "\n", + "\n", + "\n", + "D_lb\n", + "\n", + "D_lb\n", + "\n", + "\n", + "\n", + "t4_lb([H_lb*phd_lb*rhd_lb]) = [0.039*H_lb]->D_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5_lb([H_lb*phr_lb*rhr_lb]) = [0.0609*H_lb]\n", + "\n", + "t5_lb([H_lb*phr_lb*rhr_lb]) = [0.0609*H_lb]\n", + "\n", + "\n", + "\n", + "t5_lb([H_lb*phr_lb*rhr_lb]) = [0.0609*H_lb]->R_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_ub([I_ub*S_ub*beta_ub/N_ub]) = [1.2e-9*I_ub*S_ub]\n", + "\n", + "t1_ub([I_ub*S_ub*beta_ub/N_ub]) = [1.2e-9*I_ub*S_ub]\n", + "\n", + "\n", + "\n", + "I_ub\n", + "\n", + "I_ub\n", + "\n", + "\n", + "\n", + "t1_ub([I_ub*S_ub*beta_ub/N_ub]) = [1.2e-9*I_ub*S_ub]->I_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_ub([I_ub*S_ub*beta_ub/N_ub]) = [1.2e-9*I_ub*S_ub]->I_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "S_lb\n", + "\n", + "S_lb\n", + "\n", + "\n", + "\n", + "S_lb->t1_ub([I_ub*S_ub*beta_ub/N_ub]) = [1.2e-9*I_ub*S_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t2_ub([I_ub*pir_ub*rir_ub]) = [0.063*I_ub]\n", + "\n", + "t2_ub([I_ub*pir_ub*rir_ub]) = [0.063*I_ub]\n", + "\n", + "\n", + "\n", + "R_ub\n", + "\n", + "R_ub\n", + "\n", + "\n", + "\n", + "t2_ub([I_ub*pir_ub*rir_ub]) = [0.063*I_ub]->R_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3_ub([I_ub*pih_ub*rih_ub]) = [0.007*I_ub]\n", + "\n", + "t3_ub([I_ub*pih_ub*rih_ub]) = [0.007*I_ub]\n", + "\n", + "\n", + "\n", + "H_ub\n", + "\n", + "H_ub\n", + "\n", + "\n", + "\n", + "t3_ub([I_ub*pih_ub*rih_ub]) = [0.007*I_ub]->H_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4_ub([H_ub*phd_ub*rhd_ub]) = [0.039*H_ub]\n", + "\n", + "t4_ub([H_ub*phd_ub*rhd_ub]) = [0.039*H_ub]\n", + "\n", + "\n", + "\n", + "D_ub\n", + "\n", + "D_ub\n", + "\n", + "\n", + "\n", + "t4_ub([H_ub*phd_ub*rhd_ub]) = [0.039*H_ub]->D_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5_ub([H_ub*phr_ub*rhr_ub]) = [0.0609*H_ub]\n", + "\n", + "t5_ub([H_ub*phr_ub*rhr_ub]) = [0.0609*H_ub]\n", + "\n", + "\n", + "\n", + "t5_ub([H_ub*phr_ub*rhr_ub]) = [0.0609*H_ub]->R_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_lb->t1_ub([I_ub*S_ub*beta_ub/N_ub]) = [1.2e-9*I_ub*S_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_lb->t2_ub([I_ub*pir_ub*rir_ub]) = [0.063*I_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_lb->t3_ub([I_ub*pih_ub*rih_ub]) = [0.007*I_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_lb->t4_ub([H_ub*phd_ub*rhd_ub]) = [0.039*H_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_lb->t5_ub([H_ub*phr_ub*rhr_ub]) = [0.0609*H_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_ub\n", + "\n", + "S_ub\n", + "\n", + "\n", + "\n", + "S_ub->t1_lb([I_lb*S_lb*beta_lb/N_lb]) = [1.2e-9*I_lb*S_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_ub->t1_lb([I_lb*S_lb*beta_lb/N_lb]) = [1.2e-9*I_lb*S_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_ub->t2_lb([I_lb*pir_lb*rir_lb]) = [0.063*I_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_ub->t3_lb([I_lb*pih_lb*rih_lb]) = [0.007*I_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_ub->t4_lb([H_lb*phd_lb*rhd_lb]) = [0.039*H_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_ub->t5_lb([H_lb*phr_lb*rhr_lb]) = [0.0609*H_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# from sympy import sympify, Symbol\n", + "\n", + "def formulate_bounds(self: GeneratedPetriNetModel):\n", + " # Reformulate model into bounded version \n", + " # - replace each state S by S_lb adn S_ub\n", + " # - replace each transition T by T_lb and T_ub\n", + " # - replace related parameters by their lb and ub\n", + " # - replace rates by transition type\n", + " \n", + " # lb subtracts ub rate\n", + " # lb adds lb rate\n", + " \n", + " # ub subtracts lb rate\n", + " # ub adds ub rate\n", + " \n", + " bounded_states = [\n", + " State(id=f\"{s.id}_lb\", name=f\"{s.id}_lb\", \n", + " description=f\"{s.description} lb\", \n", + " grounding=s.grounding, units=s.units) \n", + " for s in self.petrinet.model.states\n", + " ] + [\n", + " State(id=f\"{s.id}_ub\", name=f\"{s.id}_ub\", \n", + " description=f\"{s.description} ub\", \n", + " grounding=s.grounding, units=s.units) \n", + " for s in self.petrinet.model.states\n", + " ]\n", + " bounded_transitions = [\n", + " Transition(id=f\"{t.id}_lb\", \n", + " input=[f\"{s}_ub\" for s in t.input], \n", + " output=[f\"{s}_lb\" for s in t.output], \n", + " grounding=t.grounding, \n", + " properties=Properties(\n", + " name=f\"{t.id}_lb\", \n", + " description=(f\"{t.properties.description} lb\" if t.properties.description else t.properties.description))) \n", + " for t in self.petrinet.model.transitions\n", + " ] + [\n", + " Transition(id=f\"{t.id}_ub\", \n", + " input=[f\"{s}_lb\" for s in t.input], \n", + " output=[f\"{s}_ub\" for s in t.output], \n", + " grounding=t.grounding, \n", + " properties=Properties(\n", + " name=f\"{t.id}_ub\", \n", + " description=(f\"{t.properties.description} ub\" if t.properties.description else t.properties.description)))\n", + " for t in self.petrinet.model.transitions\n", + " ]\n", + " \n", + " \n", + " def bound_expression(e, symbols, bound):\n", + " f = to_sympy(e, symbols)\n", + " f_b = f.subs({s: f\"{s}_{bound}\" for s in symbols})\n", + " return f_b\n", + " \n", + " def lb_expression(e, symbols):\n", + " return bound_expression(e, symbols, \"lb\")\n", + " \n", + " def ub_expression(e, symbols):\n", + " return bound_expression(e, symbols, \"ub\")\n", + " \n", + " symbols = self._symbols()\n", + " \n", + " # lb transitions use rates that will:\n", + " # - decrease ub terms by the least amount\n", + " # - increase lb terms by the least amount\n", + " # ub transitions use rates that will:\n", + " # - vice versa wrt. above\n", + " \n", + " bounded_rates = [\n", + " Rate(target=f\"{r.target}_lb\", \n", + " expression=str(lb_expression(r.expression, symbols))) \n", + " for r in self.petrinet.semantics.ode.rates\n", + " ] + [\n", + " Rate(target=f\"{r.target}_ub\", \n", + " expression=str(ub_expression(r.expression, symbols))) \n", + " for r in self.petrinet.semantics.ode.rates\n", + " ]\n", + " bounded_initials = [\n", + " Initial(target=f\"{r.target}_lb\", \n", + " expression=r.expression,\n", + " expression_mathml=r.expression_mathml) \n", + " for r in self.petrinet.semantics.ode.initials\n", + " ] + [\n", + " Initial(target=f\"{r.target}_ub\", \n", + " expression=r.expression,\n", + " expression_mathml=r.expression_mathml)\n", + " for r in self.petrinet.semantics.ode.initials\n", + " ]\n", + " \n", + " bounded_parameters = [\n", + " Parameter(id=f\"{r.id}_lb\", \n", + " value=r.value,\n", + " units=r.units) \n", + " for r in self.petrinet.semantics.ode.parameters\n", + " ] + [\n", + " Parameter(id=f\"{r.id}_ub\", \n", + " value=r.value,\n", + " units=r.units)\n", + " for r in self.petrinet.semantics.ode.parameters\n", + " ]\n", + " \n", + " bounded_observables = []\n", + " \n", + " \n", + " return GeneratedPetriNetModel(petrinet=Model(\n", + " header=self.petrinet.header,\n", + " properties=self.petrinet.properties,\n", + " model=Model1(\n", + " states=bounded_states, \n", + " transitions=bounded_transitions\n", + " ),\n", + " semantics=Semantics(\n", + " ode=OdeSemantics(\n", + " rates=bounded_rates, \n", + " initials=bounded_initials, \n", + " parameters=bounded_parameters, \n", + " observables=bounded_observables,\n", + " time=self.petrinet.semantics.ode.time), \n", + " typing=self.petrinet.semantics.typing, \n", + " span=self.petrinet.semantics.span\n", + " ),\n", + " metadata=self.petrinet.metadata\n", + " ))\n", + " \n", + "mb = formulate_bounds(base_model)\n", + "mb.to_dot()" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -546,174 +1325,282 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", "petrinet\n", - "\n", + "\n", "\n", "\n", "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", - "\n", - "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", + "\n", + "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", "\n", "\n", - "\n", + "\n", "R\n", - "\n", - "R\n", + "\n", + "R\n", "\n", "\n", - "\n", + "\n", "t2_u([I_u*pir*rir]) = [0.063*I_u]->R\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "I_u\n", - "\n", - "I_u\n", + "\n", + "I_u\n", "\n", "\n", "\n", "I_u->t2_u([I_u*pir*rir]) = [0.063*I_u]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", - "\n", - "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", + "\n", + "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", "\n", "\n", "\n", "I_u->t3_u([I_u*pih*rih]) = [0.007*I_u]\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", + "\n", + "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", + "\n", + "\n", + "\n", + "I_u->t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "transition_I_u_v([]) = []\n", + "\n", + "transition_I_u_v([]) = []\n", + "\n", + "\n", + "\n", + "I_u->transition_I_u_v([]) = []\n", + "\n", + "\n", "\n", "\n", "\n", "t2_v([I_v*pir*rir]) = [0.063*I_v]\n", - "\n", - "t2_v([I_v*pir*rir]) = [0.063*I_v]\n", + "\n", + "t2_v([I_v*pir*rir]) = [0.063*I_v]\n", "\n", "\n", - "\n", + "\n", "t2_v([I_v*pir*rir]) = [0.063*I_v]->R\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", - "\n", + "\n", "H\n", - "\n", - "H\n", + "\n", + "H\n", "\n", "\n", - "\n", + "\n", "t3_u([I_u*pih*rih]) = [0.007*I_u]->H\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "t3_v([I_v*pih*rih]) = [0.007*I_v]\n", - "\n", - "t3_v([I_v*pih*rih]) = [0.007*I_v]\n", + "\n", + "t3_v([I_v*pih*rih]) = [0.007*I_v]\n", "\n", "\n", - "\n", + "\n", "t3_v([I_v*pih*rih]) = [0.007*I_v]->H\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]->I_u\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", + "\n", + "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", + "\n", + "\n", + "\n", + "I_v\n", + "\n", + "I_v\n", + "\n", + "\n", + "\n", + "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]->I_v\n", + "\n", + "\n", + "1.0\n", "\n", "\n", - "\n", + "\n", "t4([H*phd*rhd]) = [0.039*H]\n", - "\n", - "t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]\n", "\n", "\n", - "\n", + "\n", "D\n", - "\n", - "D\n", + "\n", + "D\n", "\n", "\n", - "\n", + "\n", "t4([H*phd*rhd]) = [0.039*H]->D\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", - "\n", + "\n", "t5([H*phr*rhr]) = [0.0609*H]\n", - "\n", - "t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]\n", "\n", "\n", - "\n", + "\n", "t5([H*phr*rhr]) = [0.0609*H]->R\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", - "\n", - "I_v\n", - "\n", - "I_v\n", + "\n", + "\n", + "transition_I_u_v([]) = []->I_v\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "transition_I_v_u([]) = []\n", + "\n", + "transition_I_v_u([]) = []\n", + "\n", + "\n", + "\n", + "transition_I_v_u([]) = []->I_u\n", + "\n", + "\n", + "1.0\n", "\n", "\n", - "\n", + "\n", "I_v->t2_v([I_v*pir*rir]) = [0.063*I_v]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "I_v->t3_v([I_v*pih*rih]) = [0.007*I_v]\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", + "I_v->t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v->transition_I_v_u([]) = []\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S\n", + "\n", + "S\n", + "\n", + "\n", + "\n", + "S->t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S->t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", + "\n", + "\n", + "\n", + "\n", + "\n", "H->t4([H*phd*rhd]) = [0.039*H]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "H->t5([H*phr*rhr]) = [0.0609*H]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stratify(model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t3\"], self_strata_transition=True).to_dot()\n" + "stratify(base_model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t3\"], self_strata_transition=True).to_dot()\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -872,37 +1759,16 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.to_dot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'GeneratedPetriNetModel' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mto_dot()\n", - "\u001b[0;31mTypeError\u001b[0m: 'GeneratedPetriNetModel' object is not subscriptable" - ] - } - ], - "source": [ - "model[0].to_dot()" + "base_model.to_dot()" ] } ], diff --git a/resources/amr/petrinet/monthly-demo/2024-09/sirhd-stratified-request.json b/resources/amr/petrinet/monthly-demo/2024-09/sirhd-stratified-request.json new file mode 100644 index 00000000..c92534cd --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-09/sirhd-stratified-request.json @@ -0,0 +1,25 @@ +{ + "constraints": [], + "parameters": [], + "structure_parameters": [ + { + "name": "schedules", + "schedules": [ + { + "timepoints": [ + 0, + 10, + 20 + ] + } + ] + } + ], + "config": { + "tolerance": 1e-2, + "dreal_precision": 0.001, + "normalization_constant": 150000000.0, + "normalize": false, + "use_compartmental_constraints": true + } +} \ No newline at end of file diff --git a/resources/amr/petrinet/monthly-demo/2024-09/sirhd_stratified.json b/resources/amr/petrinet/monthly-demo/2024-09/sirhd_stratified.json new file mode 100644 index 00000000..180454c1 --- /dev/null +++ b/resources/amr/petrinet/monthly-demo/2024-09/sirhd_stratified.json @@ -0,0 +1 @@ +{"header":{"name":"Model","schema_":"https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json","schema_name":"petrinet","description":"Model","model_version":"0.1"},"properties":{},"model":{"states":[{"id":"I_u","name":"I_u","description":"None Stratified wrt. u","grounding":{"identifiers":{},"modifiers":{}},"units":{"expression":"person","expression_mathml":"person"}},{"id":"I_v","name":"I_v","description":"None Stratified wrt. v","grounding":{"identifiers":{},"modifiers":{}},"units":{"expression":"person","expression_mathml":"person"}},{"id":"S","name":"S","description":null,"grounding":{"identifiers":{},"modifiers":{}},"units":{"expression":"person","expression_mathml":"person"}},{"id":"R","name":"R","description":null,"grounding":{"identifiers":{},"modifiers":{}},"units":{"expression":"person","expression_mathml":"person"}},{"id":"H","name":"H","description":null,"grounding":{"identifiers":{},"modifiers":{}},"units":{"expression":"person","expression_mathml":"person"}},{"id":"D","name":"D","description":null,"grounding":{"identifiers":{},"modifiers":{}},"units":{"expression":"person","expression_mathml":"person"}}],"transitions":[{"id":"t2_u","input":["I_u"],"output":["R"],"grounding":null,"properties":{"name":"t2_u","description":null,"grounding":null}},{"id":"t2_v","input":["I_v"],"output":["R"],"grounding":null,"properties":{"name":"t2_v","description":null,"grounding":null}},{"id":"t3_u","input":["I_u"],"output":["H"],"grounding":null,"properties":{"name":"t3_u","description":null,"grounding":null}},{"id":"t3_v","input":["I_v"],"output":["H"],"grounding":null,"properties":{"name":"t3_v","description":null,"grounding":null}},{"id":"t1_u_u","input":["I_u","S"],"output":["I_u","I_u"],"grounding":null,"properties":{"name":"t1_u_u","description":null,"grounding":null}},{"id":"t1_u_v","input":["I_u","S"],"output":["I_v","I_v"],"grounding":null,"properties":{"name":"t1_u_v","description":null,"grounding":null}},{"id":"t1_v_u","input":["I_v","S"],"output":["I_u","I_u"],"grounding":null,"properties":{"name":"t1_v_u","description":null,"grounding":null}},{"id":"t1_v_v","input":["I_v","S"],"output":["I_v","I_v"],"grounding":null,"properties":{"name":"t1_v_v","description":null,"grounding":null}},{"id":"t4","input":["H"],"output":["D"],"grounding":null,"properties":{"name":"t4","description":null,"grounding":null}},{"id":"t5","input":["H"],"output":["R"],"grounding":null,"properties":{"name":"t5","description":null,"grounding":null}},{"id":"transition_I_u_v","input":["I_u"],"output":["I_v"],"grounding":null,"properties":{"name":"transition_I_u_v","description":null,"grounding":null}},{"id":"transition_I_v_u","input":["I_v"],"output":["I_u"],"grounding":null,"properties":{"name":"transition_I_v_u","description":null,"grounding":null}}]},"semantics":{"ode":{"rates":[{"target":"t2_u","expression":"I_u*pir*rir","expression_mathml":null},{"target":"t2_v","expression":"I_v*pir*rir","expression_mathml":null},{"target":"t3_u","expression":"I_u*pih*rih","expression_mathml":null},{"target":"t3_v","expression":"I_v*pih*rih","expression_mathml":null},{"target":"t1_u_u","expression":"I_u*S*beta_u_u/N","expression_mathml":null},{"target":"t1_u_v","expression":"I_u*S*beta_u_v/N","expression_mathml":null},{"target":"t1_v_u","expression":"I_v*S*beta_v_u/N","expression_mathml":null},{"target":"t1_v_v","expression":"I_v*S*beta_v_v/N","expression_mathml":null},{"target":"t4","expression":"H*phd*rhd","expression_mathml":"Hphdrhd"},{"target":"t5","expression":"H*phr*rhr","expression_mathml":"Hphrrhr"},{"target":"transition_I_u_v","expression":"I_u*transition_I_u_v","expression_mathml":null},{"target":"transition_I_v_u","expression":"I_v*transition_I_v_u","expression_mathml":null}],"initials":[{"target":"S","expression":"149217546.0","expression_mathml":"D0H0I0NR0"},{"target":"R","expression":"0.0","expression_mathml":"R0"},{"target":"H","expression":"0.0","expression_mathml":"H0"},{"target":"D","expression":"781454.0","expression_mathml":"D0"},{"target":"I_u","expression":"500.000000000000","expression_mathml":null},{"target":"I_v","expression":"500.000000000000","expression_mathml":null}],"parameters":[{"id":"N","name":null,"description":null,"value":150000000.0,"grounding":null,"distribution":null,"units":{"expression":"person","expression_mathml":"person"}},{"id":"pir","name":null,"description":null,"value":0.9,"grounding":null,"distribution":null,"units":{"expression":"1","expression_mathml":"1"}},{"id":"pih","name":null,"description":null,"value":0.1,"grounding":null,"distribution":null,"units":{"expression":"1","expression_mathml":"1"}},{"id":"rih","name":null,"description":null,"value":0.07,"grounding":null,"distribution":null,"units":{"expression":"1/day","expression_mathml":"day-1"}},{"id":"phd","name":null,"description":null,"value":0.13,"grounding":null,"distribution":null,"units":{"expression":"1","expression_mathml":"1"}},{"id":"rhd","name":null,"description":null,"value":0.3,"grounding":null,"distribution":null,"units":{"expression":"1/day","expression_mathml":"day-1"}},{"id":"phr","name":null,"description":null,"value":0.87,"grounding":null,"distribution":null,"units":{"expression":"1","expression_mathml":"1"}},{"id":"rhr","name":null,"description":null,"value":0.07,"grounding":null,"distribution":null,"units":{"expression":"1/day","expression_mathml":"day-1"}},{"id":"rir","name":null,"description":null,"value":0.07,"grounding":null,"distribution":null,"units":{"expression":"1/day","expression_mathml":"day-1"}},{"id":"beta_u_u","name":"beta_u_u","description":"None stratified as beta_u_u","value":0.18,"grounding":null,"distribution":null,"units":{"expression":"person/day","expression_mathml":"personday"}},{"id":"beta_u_v","name":"beta_u_v","description":"None stratified as beta_u_v","value":0.18,"grounding":null,"distribution":null,"units":{"expression":"person/day","expression_mathml":"personday"}},{"id":"beta_v_u","name":"beta_v_u","description":"None stratified as beta_v_u","value":0.18,"grounding":null,"distribution":null,"units":{"expression":"person/day","expression_mathml":"personday"}},{"id":"beta_v_v","name":"beta_v_v","description":"None stratified as beta_v_v","value":0.18,"grounding":null,"distribution":null,"units":{"expression":"person/day","expression_mathml":"personday"}},{"id":"transition_I_u_v","name":"transition_I_u_v","description":"Transition rate parameter between {state_var} strata {level_s} and {level_t}.","value":0.5,"grounding":null,"distribution":null,"units":null},{"id":"transition_I_v_u","name":"transition_I_v_u","description":"Transition rate parameter between {state_var} strata {level_s} and {level_t}.","value":0.5,"grounding":null,"distribution":null,"units":null}],"observables":[],"time":{"id":"t","units":null}},"typing":null,"span":null},"metadata":{"annotations":{}}} \ No newline at end of file From ebc587e59bbe8a0ebe1bbc091ab0c66b255d4979 Mon Sep 17 00:00:00 2001 From: Dan Bryce Date: Wed, 2 Oct 2024 17:53:39 +0000 Subject: [PATCH 56/93] checkpoint --- .../funman_sep_2024_strat_abs.ipynb | 1387 +++++++++++++---- .../sirhd_abstract_bounded_stratified.json | 1 + .../monthly-demo/2024-09/sirhd_bounded.json | 1 + .../2024-09/sirhd_bounded_stratified.json | 1 + .../2024-09/sirhd_stratified_bounded.json | 660 ++++++++ 5 files changed, 1726 insertions(+), 324 deletions(-) create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd_abstract_bounded_stratified.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd_bounded.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd_bounded_stratified.json create mode 100644 resources/amr/petrinet/monthly-demo/2024-09/sirhd_stratified_bounded.json diff --git a/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb b/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb index 4a9e577f..6ea860bc 100644 --- a/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb +++ b/notebooks/monthly-demos/funman_sep_2024_strat_abs.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 31, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,8 @@ " \"sidarthe_observables\": REQUEST_PATH,\n", " \"sirhd-vac\": os.path.join(EXAMPLE_DIR, \"sirhd-vac-request.json\"),\n", " \"sirhd\": None,\n", - " \"sirhd_stratified\": os.path.join(EXAMPLE_DIR, \"sirhd-stratified-request.json\")\n", + " \"sirhd_stratified\": os.path.join(EXAMPLE_DIR, \"sirhd-stratified-request.json\"),\n", + " \"sirhd_bounded\": os.path.join(EXAMPLE_DIR, \"sirhd-stratified-request.json\")\n", "}\n", "\n", "states = {\n", @@ -112,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -271,10 +272,10 @@ "
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -285,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -622,10 +623,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -810,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -818,14 +819,17 @@ "output_type": "stream", "text": [ "1 points\n", - " N beta_u_u beta_u_v beta_v_u beta_v_v phd \\\n", - "sirhd_stratified 150000000.0 0.18 0.18 0.18 0.18 0.13 \n", + " N beta pir pih rih phd rhd phr rhr \\\n", + "sirhd 150000000.0 0.18 0.9 0.1 0.07 0.13 0.3 0.87 0.07 \n", + "sirhd_stratified 150000000.0 NaN 0.9 0.1 0.07 0.13 0.3 0.87 0.07 \n", "\n", - " phr pih pir rhd rhr rih rir transition_I_u_v \\\n", - "sirhd_stratified 0.87 0.1 0.9 0.3 0.07 0.07 0.07 0.5 \n", + " rir beta_u_u beta_u_v beta_v_u beta_v_v \\\n", + "sirhd 0.07 NaN NaN NaN NaN \n", + "sirhd_stratified 0.07 0.18 0.18 0.18 0.18 \n", "\n", - " transition_I_v_u \n", - "sirhd_stratified 0.5 \n" + " transition_I_u_v transition_I_v_u \n", + "sirhd NaN NaN \n", + "sirhd_stratified 0.5 0.5 \n" ] }, { @@ -858,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -867,7 +871,7 @@ "Timeseries(data=[[0.0, 1.0, 2.0, 3.0], [500.0, 666.9692227999795, 889.6653629654517, 1186.752633952176], [500.0, 666.9692227999795, 889.6653629654517, 1186.752633952176], [149217546.0, 149217130.93021733, 149216577.328128, 149215838.7957602], [0.0, 73.24600202799245, 171.3870330036324, 302.7185795636775], [0.0, 7.739454251973908, 17.326525975274247, 29.45016764609824], [781454.0, 781454.1458808719, 781454.6275871565, 781455.5302247496]], columns=['time', 'I_u', 'I_v', 'S', 'R', 'H', 'D'])" ] }, - "execution_count": 40, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -879,7 +883,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1176,10 +1180,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1187,6 +1191,9 @@ "source": [ "# from sympy import sympify, Symbol\n", "\n", + "from funman.model.petrinet import GeneratedPetriNetModel\n", + "\n", + "\n", "def formulate_bounds(self: GeneratedPetriNetModel):\n", " # Reformulate model into bounded version \n", " # - replace each state S by S_lb adn S_ub\n", @@ -1200,6 +1207,9 @@ " # ub subtracts lb rate\n", " # ub adds ub rate\n", " \n", + " \n", + " \n", + " \n", " bounded_states = [\n", " State(id=f\"{s.id}_lb\", name=f\"{s.id}_lb\", \n", " description=f\"{s.description} lb\", \n", @@ -1307,8 +1317,910 @@ " metadata=self.petrinet.metadata\n", " ))\n", " \n", - "mb = formulate_bounds(base_model)\n", - "mb.to_dot()" + "bounded_model: GeneratedPetriNetModel = formulate_bounds(base_model)\n", + "# mb.to_dot()\n", + "\n", + "# stratified_model = stratify(base_model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t1\"], self_strata_transition=True)\n", + "bounded_model_str = f\"{model_str}_bounded\"\n", + "\n", + "bounded_model_path = os.path.join(EXAMPLE_DIR, bounded_model_str+\".json\")\n", + "models[bounded_model_str] = bounded_model_path\n", + "with open(bounded_model_path, \"w\") as f:\n", + " f.write(bounded_model.petrinet.model_dump_json())\n", + "\n", + "\n", + "bounded_model.to_dot()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 points\n", + " N beta pir pih rih phd rhd phr rhr \\\n", + "sirhd 150000000.0 0.18 0.9 0.1 0.07 0.13 0.3 0.87 0.07 \n", + "sirhd_stratified 150000000.0 NaN 0.9 0.1 0.07 0.13 0.3 0.87 0.07 \n", + "sirhd_bounded NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " rir ... N_ub beta_ub pir_ub pih_ub rih_ub \\\n", + "sirhd 0.07 ... NaN NaN NaN NaN NaN \n", + "sirhd_stratified 0.07 ... NaN NaN NaN NaN NaN \n", + "sirhd_bounded NaN ... 150000000.0 0.18 0.9 0.1 0.07 \n", + "\n", + " phd_ub rhd_ub phr_ub rhr_ub rir_ub \n", + "sirhd NaN NaN NaN NaN NaN \n", + "sirhd_stratified NaN NaN NaN NaN NaN \n", + "sirhd_bounded 0.13 0.3 0.87 0.07 0.07 \n", + "\n", + "[3 rows x 36 columns]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Analyze Bounded Base Model\n", + "\n", + "\n", + "to_plot = bounded_model._state_var_names() + bounded_model._observable_names()\n", + "\n", + "\n", + "\n", + "bounded_request = get_request(models[bounded_model_str])\n", + "# stratified_request = FunmanWorkRequest()\n", + "setup_common(bounded_request, timepoints, debug=True, mode=MODE_SMT, synthesize=False,dreal_precision=0.1)\n", + "results = run(bounded_request, bounded_model_str, models)\n", + "report(results, bounded_model_str, to_plot, request_results, request_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "petrinet\n", + "\n", + "\n", + "\n", + "t2_u_lb([I_u_lb*pir_lb*rir_lb]) = [0.063*I_u_lb]\n", + "\n", + "t2_u_lb([I_u_lb*pir_lb*rir_lb]) = [0.063*I_u_lb]\n", + "\n", + "\n", + "\n", + "R_lb\n", + "\n", + "R_lb\n", + "\n", + "\n", + "\n", + "t2_u_lb([I_u_lb*pir_lb*rir_lb]) = [0.063*I_u_lb]->R_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t2_v_lb([I_v_lb*pir_lb*rir_lb]) = [0.063*I_v_lb]\n", + "\n", + "t2_v_lb([I_v_lb*pir_lb*rir_lb]) = [0.063*I_v_lb]\n", + "\n", + "\n", + "\n", + "t2_v_lb([I_v_lb*pir_lb*rir_lb]) = [0.063*I_v_lb]->R_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3_u_lb([I_u_lb*pih_lb*rih_lb]) = [0.007*I_u_lb]\n", + "\n", + "t3_u_lb([I_u_lb*pih_lb*rih_lb]) = [0.007*I_u_lb]\n", + "\n", + "\n", + "\n", + "H_lb\n", + "\n", + "H_lb\n", + "\n", + "\n", + "\n", + "t3_u_lb([I_u_lb*pih_lb*rih_lb]) = [0.007*I_u_lb]->H_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3_v_lb([I_v_lb*pih_lb*rih_lb]) = [0.007*I_v_lb]\n", + "\n", + "t3_v_lb([I_v_lb*pih_lb*rih_lb]) = [0.007*I_v_lb]\n", + "\n", + "\n", + "\n", + "t3_v_lb([I_v_lb*pih_lb*rih_lb]) = [0.007*I_v_lb]->H_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_u_u_lb([I_u_lb*S_lb*beta_u_u_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]\n", + "\n", + "t1_u_u_lb([I_u_lb*S_lb*beta_u_u_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]\n", + "\n", + "\n", + "\n", + "I_u_lb\n", + "\n", + "I_u_lb\n", + "\n", + "\n", + "\n", + "t1_u_u_lb([I_u_lb*S_lb*beta_u_u_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]->I_u_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_u_u_lb([I_u_lb*S_lb*beta_u_u_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]->I_u_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t2_u_ub([I_u_ub*pir_ub*rir_ub]) = [0.063*I_u_ub]\n", + "\n", + "t2_u_ub([I_u_ub*pir_ub*rir_ub]) = [0.063*I_u_ub]\n", + "\n", + "\n", + "\n", + "I_u_lb->t2_u_ub([I_u_ub*pir_ub*rir_ub]) = [0.063*I_u_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t3_u_ub([I_u_ub*pih_ub*rih_ub]) = [0.007*I_u_ub]\n", + "\n", + "t3_u_ub([I_u_ub*pih_ub*rih_ub]) = [0.007*I_u_ub]\n", + "\n", + "\n", + "\n", + "I_u_lb->t3_u_ub([I_u_ub*pih_ub*rih_ub]) = [0.007*I_u_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t1_u_u_ub([I_u_ub*S_ub*beta_u_u_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]\n", + "\n", + "t1_u_u_ub([I_u_ub*S_ub*beta_u_u_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]\n", + "\n", + "\n", + "\n", + "I_u_lb->t1_u_u_ub([I_u_ub*S_ub*beta_u_u_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t1_u_v_ub([I_u_ub*S_ub*beta_u_v_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]\n", + "\n", + "t1_u_v_ub([I_u_ub*S_ub*beta_u_v_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]\n", + "\n", + "\n", + "\n", + "I_u_lb->t1_u_v_ub([I_u_ub*S_ub*beta_u_v_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "transition_I_u_v_ub([I_u_ub*transition_I_u_v_ub]) = [0.5*I_u_ub]\n", + "\n", + "transition_I_u_v_ub([I_u_ub*transition_I_u_v_ub]) = [0.5*I_u_ub]\n", + "\n", + "\n", + "\n", + "I_u_lb->transition_I_u_v_ub([I_u_ub*transition_I_u_v_ub]) = [0.5*I_u_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "t1_u_v_lb([I_u_lb*S_lb*beta_u_v_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]\n", + "\n", + "t1_u_v_lb([I_u_lb*S_lb*beta_u_v_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]\n", + "\n", + "\n", + "\n", + "I_v_lb\n", + "\n", + "I_v_lb\n", + "\n", + "\n", + "\n", + "t1_u_v_lb([I_u_lb*S_lb*beta_u_v_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]->I_v_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_u_v_lb([I_u_lb*S_lb*beta_u_v_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]->I_v_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_u_lb([I_v_lb*S_lb*beta_v_u_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]\n", + "\n", + "t1_v_u_lb([I_v_lb*S_lb*beta_v_u_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]\n", + "\n", + "\n", + "\n", + "t1_v_u_lb([I_v_lb*S_lb*beta_v_u_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]->I_u_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_u_lb([I_v_lb*S_lb*beta_v_u_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]->I_u_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_v_lb([I_v_lb*S_lb*beta_v_v_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]\n", + "\n", + "t1_v_v_lb([I_v_lb*S_lb*beta_v_v_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]\n", + "\n", + "\n", + "\n", + "t1_v_v_lb([I_v_lb*S_lb*beta_v_v_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]->I_v_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_v_lb([I_v_lb*S_lb*beta_v_v_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]->I_v_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4_lb([H_lb*phd_lb*rhd_lb]) = [0.039*H_lb]\n", + "\n", + "t4_lb([H_lb*phd_lb*rhd_lb]) = [0.039*H_lb]\n", + "\n", + "\n", + "\n", + "D_lb\n", + "\n", + "D_lb\n", + "\n", + "\n", + "\n", + "t4_lb([H_lb*phd_lb*rhd_lb]) = [0.039*H_lb]->D_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5_lb([H_lb*phr_lb*rhr_lb]) = [0.0609*H_lb]\n", + "\n", + "t5_lb([H_lb*phr_lb*rhr_lb]) = [0.0609*H_lb]\n", + "\n", + "\n", + "\n", + "t5_lb([H_lb*phr_lb*rhr_lb]) = [0.0609*H_lb]->R_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "transition_I_u_v_lb([I_u_lb*transition_I_u_v_lb]) = [0.5*I_u_lb]\n", + "\n", + "transition_I_u_v_lb([I_u_lb*transition_I_u_v_lb]) = [0.5*I_u_lb]\n", + "\n", + "\n", + "\n", + "transition_I_u_v_lb([I_u_lb*transition_I_u_v_lb]) = [0.5*I_u_lb]->I_v_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "transition_I_v_u_lb([I_v_lb*transition_I_v_u_lb]) = [0.5*I_v_lb]\n", + "\n", + "transition_I_v_u_lb([I_v_lb*transition_I_v_u_lb]) = [0.5*I_v_lb]\n", + "\n", + "\n", + "\n", + "transition_I_v_u_lb([I_v_lb*transition_I_v_u_lb]) = [0.5*I_v_lb]->I_u_lb\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "R_ub\n", + "\n", + "R_ub\n", + "\n", + "\n", + "\n", + "t2_u_ub([I_u_ub*pir_ub*rir_ub]) = [0.063*I_u_ub]->R_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t2_v_ub([I_v_ub*pir_ub*rir_ub]) = [0.063*I_v_ub]\n", + "\n", + "t2_v_ub([I_v_ub*pir_ub*rir_ub]) = [0.063*I_v_ub]\n", + "\n", + "\n", + "\n", + "t2_v_ub([I_v_ub*pir_ub*rir_ub]) = [0.063*I_v_ub]->R_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "H_ub\n", + "\n", + "H_ub\n", + "\n", + "\n", + "\n", + "t3_u_ub([I_u_ub*pih_ub*rih_ub]) = [0.007*I_u_ub]->H_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t3_v_ub([I_v_ub*pih_ub*rih_ub]) = [0.007*I_v_ub]\n", + "\n", + "t3_v_ub([I_v_ub*pih_ub*rih_ub]) = [0.007*I_v_ub]\n", + "\n", + "\n", + "\n", + "t3_v_ub([I_v_ub*pih_ub*rih_ub]) = [0.007*I_v_ub]->H_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_u_ub\n", + "\n", + "I_u_ub\n", + "\n", + "\n", + "\n", + "t1_u_u_ub([I_u_ub*S_ub*beta_u_u_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]->I_u_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_u_u_ub([I_u_ub*S_ub*beta_u_u_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]->I_u_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_v_ub\n", + "\n", + "I_v_ub\n", + "\n", + "\n", + "\n", + "t1_u_v_ub([I_u_ub*S_ub*beta_u_v_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]->I_v_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_u_v_ub([I_u_ub*S_ub*beta_u_v_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]->I_v_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_u_ub([I_v_ub*S_ub*beta_v_u_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]\n", + "\n", + "t1_v_u_ub([I_v_ub*S_ub*beta_v_u_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]\n", + "\n", + "\n", + "\n", + "t1_v_u_ub([I_v_ub*S_ub*beta_v_u_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]->I_u_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_u_ub([I_v_ub*S_ub*beta_v_u_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]->I_u_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_v_ub([I_v_ub*S_ub*beta_v_v_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]\n", + "\n", + "t1_v_v_ub([I_v_ub*S_ub*beta_v_v_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]\n", + "\n", + "\n", + "\n", + "t1_v_v_ub([I_v_ub*S_ub*beta_v_v_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]->I_v_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t1_v_v_ub([I_v_ub*S_ub*beta_v_v_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]->I_v_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t4_ub([H_ub*phd_ub*rhd_ub]) = [0.039*H_ub]\n", + "\n", + "t4_ub([H_ub*phd_ub*rhd_ub]) = [0.039*H_ub]\n", + "\n", + "\n", + "\n", + "D_ub\n", + "\n", + "D_ub\n", + "\n", + "\n", + "\n", + "t4_ub([H_ub*phd_ub*rhd_ub]) = [0.039*H_ub]->D_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "t5_ub([H_ub*phr_ub*rhr_ub]) = [0.0609*H_ub]\n", + "\n", + "t5_ub([H_ub*phr_ub*rhr_ub]) = [0.0609*H_ub]\n", + "\n", + "\n", + "\n", + "t5_ub([H_ub*phr_ub*rhr_ub]) = [0.0609*H_ub]->R_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "transition_I_u_v_ub([I_u_ub*transition_I_u_v_ub]) = [0.5*I_u_ub]->I_v_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "transition_I_v_u_ub([I_v_ub*transition_I_v_u_ub]) = [0.5*I_v_ub]\n", + "\n", + "transition_I_v_u_ub([I_v_ub*transition_I_v_u_ub]) = [0.5*I_v_ub]\n", + "\n", + "\n", + "\n", + "transition_I_v_u_ub([I_v_ub*transition_I_v_u_ub]) = [0.5*I_v_ub]->I_u_ub\n", + "\n", + "\n", + "1.0\n", + "\n", + "\n", + "\n", + "I_v_lb->t2_v_ub([I_v_ub*pir_ub*rir_ub]) = [0.063*I_v_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v_lb->t3_v_ub([I_v_ub*pih_ub*rih_ub]) = [0.007*I_v_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v_lb->t1_v_u_ub([I_v_ub*S_ub*beta_v_u_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v_lb->t1_v_v_ub([I_v_ub*S_ub*beta_v_v_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v_lb->transition_I_v_u_ub([I_v_ub*transition_I_v_u_ub]) = [0.5*I_v_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_lb\n", + "\n", + "S_lb\n", + "\n", + "\n", + "\n", + "S_lb->t1_u_u_ub([I_u_ub*S_ub*beta_u_u_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_lb->t1_u_v_ub([I_u_ub*S_ub*beta_u_v_ub/N_ub]) = [1.2e-9*I_u_ub*S_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_lb->t1_v_u_ub([I_v_ub*S_ub*beta_v_u_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_lb->t1_v_v_ub([I_v_ub*S_ub*beta_v_v_ub/N_ub]) = [1.2e-9*I_v_ub*S_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_lb->t4_ub([H_ub*phd_ub*rhd_ub]) = [0.039*H_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_lb->t5_ub([H_ub*phr_ub*rhr_ub]) = [0.0609*H_ub]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_u_ub->t2_u_lb([I_u_lb*pir_lb*rir_lb]) = [0.063*I_u_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_u_ub->t3_u_lb([I_u_lb*pih_lb*rih_lb]) = [0.007*I_u_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_u_ub->t1_u_u_lb([I_u_lb*S_lb*beta_u_u_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_u_ub->t1_u_v_lb([I_u_lb*S_lb*beta_u_v_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_u_ub->transition_I_u_v_lb([I_u_lb*transition_I_u_v_lb]) = [0.5*I_u_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v_ub->t2_v_lb([I_v_lb*pir_lb*rir_lb]) = [0.063*I_v_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v_ub->t3_v_lb([I_v_lb*pih_lb*rih_lb]) = [0.007*I_v_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v_ub->t1_v_u_lb([I_v_lb*S_lb*beta_v_u_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v_ub->t1_v_v_lb([I_v_lb*S_lb*beta_v_v_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I_v_ub->transition_I_v_u_lb([I_v_lb*transition_I_v_u_lb]) = [0.5*I_v_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_ub\n", + "\n", + "S_ub\n", + "\n", + "\n", + "\n", + "S_ub->t1_u_u_lb([I_u_lb*S_lb*beta_u_u_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_ub->t1_u_v_lb([I_u_lb*S_lb*beta_u_v_lb/N_lb]) = [1.2e-9*I_u_lb*S_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_ub->t1_v_u_lb([I_v_lb*S_lb*beta_v_u_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S_ub->t1_v_v_lb([I_v_lb*S_lb*beta_v_v_lb/N_lb]) = [1.2e-9*I_v_lb*S_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_ub->t4_lb([H_lb*phd_lb*rhd_lb]) = [0.039*H_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "H_ub->t5_lb([H_lb*phr_lb*rhr_lb]) = [0.0609*H_lb]\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bounded_stratified_model: GeneratedPetriNetModel = formulate_bounds(stratified_model)\n", + "# mb.to_dot()\n", + "\n", + "# stratified_model = stratify(base_model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t1\"], self_strata_transition=True)\n", + "bounded_stratified_model_str = f\"{model_str}_bounded_stratified\"\n", + "\n", + "bounded_stratified_model_path = os.path.join(EXAMPLE_DIR, bounded_stratified_model_str+\".json\")\n", + "models[bounded_stratified_model_str] = bounded_stratified_model_path\n", + "with open(bounded_stratified_model_path, \"w\") as f:\n", + " f.write(bounded_stratified_model.petrinet.model_dump_json())\n", + "\n", + "\n", + "bounded_stratified_model.to_dot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "append {t}\n", + "append {t}\n", + "append {t}\n", + "append {t}\n", + "append {t}\n", + "append {t}\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 66\u001b[0m\n\u001b[1;32m 45\u001b[0m new_model \u001b[38;5;241m=\u001b[39m GeneratedPetriNetModel(\n\u001b[1;32m 46\u001b[0m petrinet\u001b[38;5;241m=\u001b[39mModel(\n\u001b[1;32m 47\u001b[0m header\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39mheader,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 62\u001b[0m metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39mmetadata\n\u001b[1;32m 63\u001b[0m ))\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_model\n\u001b[0;32m---> 66\u001b[0m abstract_bounded_stratified_model: GeneratedPetriNetModel \u001b[38;5;241m=\u001b[39m \u001b[43mabstract\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstratified_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mI_u\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mI_v\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#formulate_bounds(abstract(stratified_model, {\"I_u\": \"I\", \"I_v\": \"I\"}))\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;66;03m# mb.to_dot()\u001b[39;00m\n\u001b[1;32m 68\u001b[0m \n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# stratified_model = stratify(base_model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t1\"], self_strata_transition=True)\u001b[39;00m\n\u001b[1;32m 70\u001b[0m abstract_bounded_stratified_model_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_abstract_bounded_stratified\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "Cell \u001b[0;32mIn[12], line 45\u001b[0m, in \u001b[0;36mabstract\u001b[0;34m(self, state_abstraction)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 41\u001b[0m \u001b[38;5;66;03m# If no group for t, then make a new group\u001b[39;00m\n\u001b[1;32m 42\u001b[0m grouped_transitions\u001b[38;5;241m.\u001b[39mappend([t])\n\u001b[0;32m---> 45\u001b[0m new_model \u001b[38;5;241m=\u001b[39m \u001b[43mGeneratedPetriNetModel\u001b[49m(\n\u001b[1;32m 46\u001b[0m petrinet\u001b[38;5;241m=\u001b[39mModel(\n\u001b[1;32m 47\u001b[0m header\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39mheader,\n\u001b[1;32m 48\u001b[0m properties\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39mproperties,\n\u001b[1;32m 49\u001b[0m model\u001b[38;5;241m=\u001b[39mModel1(\n\u001b[1;32m 50\u001b[0m states\u001b[38;5;241m=\u001b[39mnew_states, \n\u001b[1;32m 51\u001b[0m transitions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m#[*new_transitions, *other_transitions.values(), *self_strata_transitions]\u001b[39;00m\n\u001b[1;32m 52\u001b[0m ),\n\u001b[1;32m 53\u001b[0m semantics\u001b[38;5;241m=\u001b[39mSemantics(\n\u001b[1;32m 54\u001b[0m ode\u001b[38;5;241m=\u001b[39mOdeSemantics(\n\u001b[1;32m 55\u001b[0m rates\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;66;03m#[*new_rates, *other_rates.values(), *self_strata_rates], \u001b[39;00m\n\u001b[1;32m 56\u001b[0m initials\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;66;03m#new_initials, \u001b[39;00m\n\u001b[1;32m 57\u001b[0m parameters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;66;03m#new_parameters+self_strata_parameters, \u001b[39;00m\n\u001b[1;32m 58\u001b[0m observables\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;66;03m#new_observables,\u001b[39;00m\n\u001b[1;32m 59\u001b[0m time\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39msemantics\u001b[38;5;241m.\u001b[39mode\u001b[38;5;241m.\u001b[39mtime), \n\u001b[1;32m 60\u001b[0m typing\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39msemantics\u001b[38;5;241m.\u001b[39mtyping, \n\u001b[1;32m 61\u001b[0m span\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39msemantics\u001b[38;5;241m.\u001b[39mspan),\n\u001b[1;32m 62\u001b[0m metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39mmetadata\n\u001b[1;32m 63\u001b[0m ))\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_model\n", + "Cell \u001b[0;32mIn[12], line 45\u001b[0m, in \u001b[0;36mabstract\u001b[0;34m(self, state_abstraction)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 41\u001b[0m \u001b[38;5;66;03m# If no group for t, then make a new group\u001b[39;00m\n\u001b[1;32m 42\u001b[0m grouped_transitions\u001b[38;5;241m.\u001b[39mappend([t])\n\u001b[0;32m---> 45\u001b[0m new_model \u001b[38;5;241m=\u001b[39m \u001b[43mGeneratedPetriNetModel\u001b[49m(\n\u001b[1;32m 46\u001b[0m petrinet\u001b[38;5;241m=\u001b[39mModel(\n\u001b[1;32m 47\u001b[0m header\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39mheader,\n\u001b[1;32m 48\u001b[0m properties\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39mproperties,\n\u001b[1;32m 49\u001b[0m model\u001b[38;5;241m=\u001b[39mModel1(\n\u001b[1;32m 50\u001b[0m states\u001b[38;5;241m=\u001b[39mnew_states, \n\u001b[1;32m 51\u001b[0m transitions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m#[*new_transitions, *other_transitions.values(), *self_strata_transitions]\u001b[39;00m\n\u001b[1;32m 52\u001b[0m ),\n\u001b[1;32m 53\u001b[0m semantics\u001b[38;5;241m=\u001b[39mSemantics(\n\u001b[1;32m 54\u001b[0m ode\u001b[38;5;241m=\u001b[39mOdeSemantics(\n\u001b[1;32m 55\u001b[0m rates\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;66;03m#[*new_rates, *other_rates.values(), *self_strata_rates], \u001b[39;00m\n\u001b[1;32m 56\u001b[0m initials\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;66;03m#new_initials, \u001b[39;00m\n\u001b[1;32m 57\u001b[0m parameters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;66;03m#new_parameters+self_strata_parameters, \u001b[39;00m\n\u001b[1;32m 58\u001b[0m observables\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;66;03m#new_observables,\u001b[39;00m\n\u001b[1;32m 59\u001b[0m time\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39msemantics\u001b[38;5;241m.\u001b[39mode\u001b[38;5;241m.\u001b[39mtime), \n\u001b[1;32m 60\u001b[0m typing\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39msemantics\u001b[38;5;241m.\u001b[39mtyping, \n\u001b[1;32m 61\u001b[0m span\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39msemantics\u001b[38;5;241m.\u001b[39mspan),\n\u001b[1;32m 62\u001b[0m metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpetrinet\u001b[38;5;241m.\u001b[39mmetadata\n\u001b[1;32m 63\u001b[0m ))\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_model\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1457\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:701\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1152\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1135\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:312\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/funman_venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/pydevd.py:2070\u001b[0m, in \u001b[0;36mPyDB.do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, exception_type)\u001b[0m\n\u001b[1;32m 2067\u001b[0m from_this_thread\u001b[38;5;241m.\u001b[39mappend(frame_custom_thread_id)\n\u001b[1;32m 2069\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_threads_suspended_single_notification\u001b[38;5;241m.\u001b[39mnotify_thread_suspended(thread_id, thread, stop_reason):\n\u001b[0;32m-> 2070\u001b[0m keep_suspended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_wait_suspend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuspend_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_this_thread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes_tracker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2072\u001b[0m frames_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2074\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keep_suspended:\n\u001b[1;32m 2075\u001b[0m \u001b[38;5;66;03m# This means that we should pause again after a set next statement.\u001b[39;00m\n", + "File \u001b[0;32m~/funman_venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/pydevd.py:2106\u001b[0m, in \u001b[0;36mPyDB._do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\u001b[0m\n\u001b[1;32m 2103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_input_hook()\n\u001b[1;32m 2105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess_internal_commands()\n\u001b[0;32m-> 2106\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcancel_async_evaluation(get_current_thread_id(thread), \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mid\u001b[39m(frame)))\n\u001b[1;32m 2110\u001b[0m \u001b[38;5;66;03m# process any stepping instructions\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "from typing import Dict\n", + "from funman import GeneratedPetriNetModel\n", + "from difflib import SequenceMatcher\n", + "\n", + "def abstract(self, state_abstraction: Dict[str, str])-> GeneratedPetriNetModel:\n", + " # Get existing state variables\n", + " state_objs = {s.id: s for s in self._state_vars()}\n", + " \n", + " # Check that there is a state variable for each key in the state_abstraction\n", + " assert all({k in state_objs for k in state_abstraction.keys()}), f\"There are unknown states in the state_abstraction keys: {[k for k in state_abstraction.keys() if k not in state_objs]}\"\n", + " \n", + " #Check that the state_abstraction maps the keys to a state variable that is not in the state_objs\n", + " assert not any({v in state_objs for v in state_abstraction.values()}), f\"There are unknown states in the state_abstraction values: {[v for v in state_abstraction.values() if v in state_objs]}\"\n", + " \n", + " # Create states for values in state_abstraction\n", + " new_state_objs = {v : State(id=v, name=v, description=None, grounding=None, units=None) for v in set(state_abstraction.values()) }\n", + " \n", + " new_states = [*[v for k, v in state_objs.items() if k not in state_abstraction.keys()], *new_state_objs.values()]\n", + " \n", + " # Replace states in the transitions\n", + " subbed_state_ids = set(state_abstraction.keys())\n", + " subbed_transitions = [ # transitions not involved in abstraction\n", + " t for t in self.petrinet.model.transitions \n", + " if not any([s for s in t.input+t.output if s in subbed_state_ids])\n", + " ] + [ # transitions with substitutions\n", + " Transition(id=t.id, \n", + " input=[(state_abstraction[i] if i in state_abstraction else i ) for i in t.input], \n", + " output=[(state_abstraction[i] if i in state_abstraction else i ) for i in t.output], \n", + " grounding=t.grounding, \n", + " properties=t.properties)\n", + " for t in self.petrinet.model.transitions \n", + " if any([s for s in t.input+t.output if s in subbed_state_ids])\n", + " ]\n", + " grouped_transitions = []\n", + " for t in subbed_transitions:\n", + " # Find group in grouped_transitions for t\n", + " try:\n", + " matching_group = next(iter([i for i, g in enumerate(grouped_transitions) if any([ t.input == gt.input and t.output==gt.output for gt in g])]))\n", + " # print(\"append {t}\")\n", + " grouped_transitions[matching_group].append(t)\n", + " except StopIteration:\n", + " # If no group for t, then make a new group\n", + " grouped_transitions.append([t])\n", + " \n", + "\n", + " grouped_rates = [\n", + " [\n", + " next(Rate(target=r.target,\n", + " expression=str(to_sympy(r.expression, self._symbols()).subs(state_abstraction)),\n", + " expression_mathml=None) \n", + " for r in self.petrinet.semantics.ode.rates if r.target == t.id) \n", + " for t in g\n", + " ] \n", + " for g in grouped_transitions\n", + " ]\n", + " \n", + " # Convert grouped transitions into a single transition\n", + " consolidated_transitions=[]\n", + " for g in grouped_transitions:\n", + " if len(g) == 1:\n", + " consolidated_transitions.append(g[0])\n", + " else:\n", + " sub_sequences = set(SequenceMatcher(None, g[0].id, g[1].id).get_matching_blocks())\n", + " s_sub = list(sub_sequences)\n", + " s_sub.sort(key=lambda x: min(x.a, x.b))\n", + " sub = \"\".join([g[0].id[s.a:s.a+s.size] for s in s_sub if s.size > 0])\n", + " for i, t in enumerate(g[2:]):\n", + " sub_sequences = set(SequenceMatcher(None, sub, t.id).get_matching_blocks())\n", + " s_sub = list(sub_sequences)\n", + " s_sub.sort(key=lambda x: min(x.a, x.b))\n", + " sub = \"\".join([g[0].id[s.a:s.a+s.size] for s in s_sub if s.size > 0])\n", + " if sub.endswith(\"_\"):\n", + " sub = sub[:-1]\n", + " \n", + " consolidated_transitions.append(Transition(id=sub, input=g[0].input, output=g[0].output,grounding=g[0].grounding, properties=g[0].properties))\n", + " \n", + " \n", + " ## Remove self transitions\n", + " new_transitions = [t for t in consolidated_transitions if not (t.input == t.output and len(t.input) == 1)]\n", + " \n", + " \n", + " # reduce(lambda x, y: set(SequenceMatcher(None, x, y)).get_matching_blocks()).intersection( set(s1.get_matching_blocks()))\n", + "\n", + " new_model = GeneratedPetriNetModel(\n", + " petrinet=Model(\n", + " header=self.petrinet.header,\n", + " properties=self.petrinet.properties,\n", + " model=Model1(\n", + " states=new_states, \n", + " transitions=new_transitions #[*new_transitions, *other_transitions.values(), *self_strata_transitions]\n", + " ),\n", + " semantics=Semantics(\n", + " ode=OdeSemantics(\n", + " rates=None, #[*new_rates, *other_rates.values(), *self_strata_rates], \n", + " initials=None, #new_initials, \n", + " parameters=None, #new_parameters+self_strata_parameters, \n", + " observables=None, #new_observables,\n", + " time=self.petrinet.semantics.ode.time), \n", + " typing=self.petrinet.semantics.typing, \n", + " span=self.petrinet.semantics.span),\n", + " metadata=self.petrinet.metadata\n", + " ))\n", + " return new_model\n", + "\n", + "abstract_bounded_stratified_model: GeneratedPetriNetModel = abstract(stratified_model, {\"I_u\": \"I\", \"I_v\": \"I\"}) #formulate_bounds(abstract(stratified_model, {\"I_u\": \"I\", \"I_v\": \"I\"}))\n", + "# mb.to_dot()\n", + "\n", + "# stratified_model = stratify(base_model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t1\"], self_strata_transition=True)\n", + "abstract_bounded_stratified_model_str = f\"{model_str}_abstract_bounded_stratified\"\n", + "\n", + "abstract_bounded_stratified_model_path = os.path.join(EXAMPLE_DIR, abstract_bounded_stratified_model_str+\".json\")\n", + "models[abstract_bounded_stratified_model_str] = abstract_bounded_stratified_model_path\n", + "with open(abstract_bounded_stratified_model_path, \"w\") as f:\n", + " f.write(abstract_bounded_stratified_model.petrinet.model_dump_json())\n", + "\n", + "\n", + "abstract_bounded_stratified_model.to_dot()" ] }, { @@ -1325,271 +2237,271 @@ "\n", "\n", - "\n", + "\n", "\n", "petrinet\n", - "\n", + "\n", "\n", "\n", "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", - "\n", - "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", + "\n", + "t2_u([I_u*pir*rir]) = [0.063*I_u]\n", "\n", "\n", "\n", "R\n", - "\n", - "R\n", + "\n", + "R\n", "\n", "\n", "\n", "t2_u([I_u*pir*rir]) = [0.063*I_u]->R\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "I_u\n", - "\n", - "I_u\n", + "\n", + "I_u\n", "\n", "\n", "\n", "I_u->t2_u([I_u*pir*rir]) = [0.063*I_u]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", - "\n", - "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", + "\n", + "t3_u([I_u*pih*rih]) = [0.007*I_u]\n", "\n", "\n", "\n", "I_u->t3_u([I_u*pih*rih]) = [0.007*I_u]\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", - "\n", - "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", + "t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]\n", + "\n", + "t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]\n", "\n", - "\n", + "\n", "\n", - "I_u->t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", - "\n", - "\n", + "I_u->t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "transition_I_u_v([]) = []\n", - "\n", - "transition_I_u_v([]) = []\n", + "transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]\n", + "\n", + "transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]\n", "\n", - "\n", + "\n", "\n", - "I_u->transition_I_u_v([]) = []\n", - "\n", - "\n", + "I_u->transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]\n", + "\n", + "\n", "\n", "\n", "\n", "t2_v([I_v*pir*rir]) = [0.063*I_v]\n", - "\n", - "t2_v([I_v*pir*rir]) = [0.063*I_v]\n", + "\n", + "t2_v([I_v*pir*rir]) = [0.063*I_v]\n", "\n", "\n", "\n", "t2_v([I_v*pir*rir]) = [0.063*I_v]->R\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "H\n", - "\n", - "H\n", + "\n", + "H\n", "\n", "\n", "\n", "t3_u([I_u*pih*rih]) = [0.007*I_u]->H\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "t3_v([I_v*pih*rih]) = [0.007*I_v]\n", - "\n", - "t3_v([I_v*pih*rih]) = [0.007*I_v]\n", + "\n", + "t3_v([I_v*pih*rih]) = [0.007*I_v]\n", "\n", "\n", "\n", "t3_v([I_v*pih*rih]) = [0.007*I_v]->H\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]->I_u\n", - "\n", - "\n", - "1.0\n", + "t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]->I_u\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]->I_u\n", - "\n", - "\n", - "1.0\n", + "t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]->I_u\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", - "\n", - "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", + "t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]\n", + "\n", + "t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]\n", "\n", "\n", "\n", "I_v\n", - "\n", - "I_v\n", + "\n", + "I_v\n", "\n", - "\n", + "\n", "\n", - "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]->I_v\n", - "\n", - "\n", - "1.0\n", + "t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]->I_v\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]->I_v\n", - "\n", - "\n", - "1.0\n", + "t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]->I_v\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "t4([H*phd*rhd]) = [0.039*H]\n", - "\n", - "t4([H*phd*rhd]) = [0.039*H]\n", + "\n", + "t4([H*phd*rhd]) = [0.039*H]\n", "\n", "\n", "\n", "D\n", - "\n", - "D\n", + "\n", + "D\n", "\n", "\n", "\n", "t4([H*phd*rhd]) = [0.039*H]->D\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "t5([H*phr*rhr]) = [0.0609*H]\n", - "\n", - "t5([H*phr*rhr]) = [0.0609*H]\n", + "\n", + "t5([H*phr*rhr]) = [0.0609*H]\n", "\n", "\n", "\n", "t5([H*phr*rhr]) = [0.0609*H]->R\n", - "\n", - "\n", - "1.0\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "transition_I_u_v([]) = []->I_v\n", - "\n", - "\n", - "1.0\n", + "transition_I_u_v([I_u*transition_I_u_v]) = [0.5*I_u]->I_v\n", + "\n", + "\n", + "1.0\n", "\n", - "\n", + "\n", "\n", - "transition_I_v_u([]) = []\n", - "\n", - "transition_I_v_u([]) = []\n", + "transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]\n", + "\n", + "transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]\n", "\n", - "\n", + "\n", "\n", - "transition_I_v_u([]) = []->I_u\n", - "\n", - "\n", - "1.0\n", + "transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]->I_u\n", + "\n", + "\n", + "1.0\n", "\n", "\n", "\n", "I_v->t2_v([I_v*pir*rir]) = [0.063*I_v]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "I_v->t3_v([I_v*pih*rih]) = [0.007*I_v]\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "I_v->t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", - "\n", - "\n", + "I_v->t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "I_v->transition_I_v_u([]) = []\n", - "\n", - "\n", + "I_v->transition_I_v_u([I_v*transition_I_v_u]) = [0.5*I_v]\n", + "\n", + "\n", "\n", "\n", "\n", "S\n", - "\n", - "S\n", + "\n", + "S\n", "\n", - "\n", + "\n", "\n", - "S->t1_u_u([I_u_u*S*beta_u_u/N]) = [6.66666666666667e-9*I_u_u*S*beta_u_u]\n", - "\n", - "\n", + "S->t1_u_u([I_u*S*beta_u_u/N]) = [1.2e-9*I_u*S]\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "S->t1_v_v([I_v_v*S*beta_v_v/N]) = [6.66666666666667e-9*I_v_v*S*beta_v_v]\n", - "\n", - "\n", + "S->t1_v_v([I_v*S*beta_v_v/N]) = [1.2e-9*I_v*S]\n", + "\n", + "\n", "\n", "\n", "\n", "H->t4([H*phd*rhd]) = [0.039*H]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "H->t5([H*phr*rhr]) = [0.0609*H]\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1597,179 +2509,6 @@ "source": [ "stratify(base_model, \"I\", [\"u\", \"v\"], strata_parameter=\"beta\", strata_transitions=[\"t3\"], self_strata_transition=True).to_dot()\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "petrinet\n", - "\n", - "\n", - "\n", - "t1([I*S*beta/N]) = [1.2e-9*I*S]\n", - "\n", - "t1([I*S*beta/N]) = [1.2e-9*I*S]\n", - "\n", - "\n", - "\n", - "I\n", - "\n", - "I\n", - "\n", - "\n", - "\n", - "t1([I*S*beta/N]) = [1.2e-9*I*S]->I\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t1([I*S*beta/N]) = [1.2e-9*I*S]->I\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "S\n", - "\n", - "S\n", - "\n", - "\n", - "\n", - "S->t1([I*S*beta/N]) = [1.2e-9*I*S]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "t2([I*pir*rir]) = [0.063*I]\n", - "\n", - "t2([I*pir*rir]) = [0.063*I]\n", - "\n", - "\n", - "\n", - "R\n", - "\n", - "R\n", - "\n", - "\n", - "\n", - "t2([I*pir*rir]) = [0.063*I]->R\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t3([I*pih*rih]) = [0.007*I]\n", - "\n", - "t3([I*pih*rih]) = [0.007*I]\n", - "\n", - "\n", - "\n", - "H\n", - "\n", - "H\n", - "\n", - "\n", - "\n", - "t3([I*pih*rih]) = [0.007*I]->H\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t4([H*phd*rhd]) = [0.039*H]\n", - "\n", - "t4([H*phd*rhd]) = [0.039*H]\n", - "\n", - "\n", - "\n", - "D\n", - "\n", - "D\n", - "\n", - "\n", - "\n", - "t4([H*phd*rhd]) = [0.039*H]->D\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "t5([H*phr*rhr]) = [0.0609*H]\n", - "\n", - "t5([H*phr*rhr]) = [0.0609*H]\n", - "\n", - "\n", - "\n", - "t5([H*phr*rhr]) = [0.0609*H]->R\n", - "\n", - "\n", - "1.0\n", - "\n", - "\n", - "\n", - "I->t1([I*S*beta/N]) = [1.2e-9*I*S]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I->t2([I*pir*rir]) = [0.063*I]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "I->t3([I*pih*rih]) = [0.007*I]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "H->t4([H*phd*rhd]) = [0.039*H]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "H->t5([H*phr*rhr]) = [0.0609*H]\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": 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"modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R_ub", + "name": "R_ub", + "description": "None ub", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "H_ub", + "name": "H_ub", + "description": "None ub", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D_ub", + "name": "D_ub", + "description": "None ub", + "grounding": { + "identifiers": {}, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1_lb", + "input": [ + "I_ub", + "S_ub" + ], + "output": [ + "I_lb", + "I_lb" + ], + "grounding": null, + "properties": { + "name": "t1_lb", + "description": null, + "grounding": null + } + }, + { + "id": "t2_lb", + "input": [ + "I_ub" + ], + "output": [ + "R_lb" + ], + "grounding": null, + "properties": { + "name": "t2_lb", + "description": null, + "grounding": null + } + }, + { + "id": "t3_lb", + "input": [ + "I_ub" + ], + "output": [ + "H_lb" + ], + "grounding": null, + "properties": { + "name": "t3_lb", + "description": null, + "grounding": null + } + }, + { + "id": "t4_lb", + "input": [ + "H_ub" + ], + "output": [ + "D_lb" + ], + "grounding": null, + "properties": { + "name": "t4_lb", + "description": null, + "grounding": null + } + }, + { + "id": "t5_lb", + "input": [ + "H_ub" + ], + "output": [ + "R_lb" + ], + "grounding": null, + "properties": { + "name": "t5_lb", + "description": null, + "grounding": null + } + }, + { + "id": "t1_ub", + "input": [ + "I_lb", + "S_lb" + ], + "output": [ + "I_ub", + "I_ub" + ], + "grounding": null, + "properties": { + "name": "t1_ub", + "description": null, + "grounding": null + } + }, + { + "id": "t2_ub", + "input": [ + "I_lb" + ], + "output": [ + "R_ub" + ], + "grounding": null, + "properties": { + "name": "t2_ub", + "description": null, + "grounding": null + } + }, + { + "id": "t3_ub", + "input": [ + "I_lb" + ], + "output": [ + "H_ub" + ], + "grounding": null, + "properties": { + "name": "t3_ub", + "description": null, + "grounding": null + } + }, + { + "id": "t4_ub", + "input": [ + "H_lb" + ], + "output": [ + "D_ub" + ], + "grounding": null, + "properties": { + "name": "t4_ub", + "description": null, + "grounding": null + } + }, + { + "id": "t5_ub", + "input": [ + "H_lb" + ], + "output": [ + "R_ub" + ], + "grounding": null, + "properties": { + "name": "t5_ub", + "description": null, + "grounding": null + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1_lb", + "expression": "I_lb*S_lb*beta_lb/N_lb", + "expression_mathml": null + }, + { + "target": "t2_lb", + "expression": "I_lb*pir_lb*rir_lb", + "expression_mathml": null + }, + { + "target": "t3_lb", + "expression": "I_lb*pih_lb*rih_lb", + "expression_mathml": null + }, + { + "target": "t4_lb", + "expression": "H_lb*phd_lb*rhd_lb", + "expression_mathml": null + }, + { + "target": "t5_lb", + "expression": "H_lb*phr_lb*rhr_lb", + "expression_mathml": null + }, + { + "target": "t1_ub", + "expression": "I_ub*S_ub*beta_ub/N_ub", + "expression_mathml": null + }, + { + "target": "t2_ub", + "expression": "I_ub*pir_ub*rir_ub", + "expression_mathml": null + }, + { + "target": "t3_ub", + "expression": "I_ub*pih_ub*rih_ub", + "expression_mathml": null + }, + { + "target": "t4_ub", + "expression": "H_ub*phd_ub*rhd_ub", + "expression_mathml": null + }, + { + "target": "t5_ub", + "expression": "H_ub*phr_ub*rhr_ub", + "expression_mathml": null + } + ], + "initials": [ + { + "target": "S_lb", + "expression": "149217546.0", + "expression_mathml": "D0H0I0NR0" + }, + { + "target": "I_lb", + "expression": "1000.0", + "expression_mathml": "I0" + }, + { + "target": "R_lb", + "expression": "0.0", + "expression_mathml": "R0" + }, + { + "target": "H_lb", + "expression": "0.0", + "expression_mathml": "H0" + }, + { + "target": "D_lb", + "expression": "781454.0", + "expression_mathml": "D0" + }, + { + "target": "S_ub", + "expression": "149217546.0", + "expression_mathml": "D0H0I0NR0" + }, + { + "target": "I_ub", + "expression": "1000.0", + "expression_mathml": "I0" + }, + { + "target": "R_ub", + "expression": "0.0", + "expression_mathml": "R0" + }, + { + "target": "H_ub", + "expression": "0.0", + "expression_mathml": "H0" + }, + { + "target": "D_ub", + "expression": "781454.0", + "expression_mathml": "D0" + } + ], + "parameters": [ + { + "id": "N_lb", + "name": null, + "description": null, + "value": 150000000.0, + "grounding": null, + "distribution": null, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta_lb", + "name": null, + "description": null, + "value": 0.18, + "grounding": null, + "distribution": null, + "units": { + "expression": "person/day", + "expression_mathml": "personday" + } + }, + { + "id": "pir_lb", + "name": null, + "description": null, + "value": 0.9, + "grounding": null, + "distribution": null, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "pih_lb", + "name": null, + "description": null, + "value": 0.1, + "grounding": null, + "distribution": null, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rih_lb", + "name": null, + "description": null, + "value": 0.07, + "grounding": null, + "distribution": null, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "phd_lb", + "name": null, + "description": null, + "value": 0.13, + "grounding": null, + "distribution": null, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhd_lb", + "name": null, + "description": null, + "value": 0.3, + "grounding": null, + "distribution": null, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "phr_lb", + "name": null, + "description": null, + "value": 0.87, + "grounding": null, + "distribution": null, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhr_lb", + "name": null, + "description": null, + "value": 0.07, + "grounding": null, + "distribution": null, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "rir_lb", + "name": null, + "description": null, + "value": 0.07, + "grounding": null, + "distribution": null, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "N_ub", + "name": null, + "description": null, + "value": 150000000.0, + "grounding": null, + "distribution": null, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta_ub", + "name": null, + "description": null, + "value": 0.18, + "grounding": null, + "distribution": null, + "units": { + "expression": "person/day", + "expression_mathml": "personday" + } + }, + { + "id": "pir_ub", + "name": null, + "description": null, + "value": 0.9, + "grounding": null, + "distribution": null, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "pih_ub", + "name": null, + "description": null, + "value": 0.1, + "grounding": null, + "distribution": null, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rih_ub", + "name": null, + "description": null, + "value": 0.07, + "grounding": null, + "distribution": null, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "phd_ub", + "name": null, + "description": null, + "value": 0.13, + "grounding": null, + "distribution": null, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhd_ub", + "name": null, + "description": null, + "value": 0.3, + "grounding": null, + "distribution": null, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "phr_ub", + "name": null, + "description": null, + "value": 0.87, + "grounding": null, + "distribution": null, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "rhr_ub", + "name": null, + "description": null, + "value": 0.07, + "grounding": null, + "distribution": null, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "rir_ub", + "name": null, + "description": null, + "value": 0.07, + "grounding": null, + "distribution": null, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + } + ], + "observables": [], + "time": { + "id": "t", + "units": null + } + }, + "typing": null, + "span": null + }, + "metadata": { + "annotations": {} + } +} \ No newline at end of file From 29abfb6f7a016e83346976c7e78fc70cc3b50279 Mon Sep 17 00:00:00 2001 From: Daniel Bryce Date: Wed, 2 Oct 2024 12:54:11 -0500 Subject: [PATCH 57/93] doc checkpoint --- notes/abstraction/abstraction.tex | 27 +++-- notes/abstraction/background.tex | 4 +- notes/abstraction/bounded-abstraction.tex | 124 +++++++++++----------- notes/abstraction/main.pdf | Bin 199950 -> 201095 bytes 4 files changed, 81 insertions(+), 74 deletions(-) diff --git a/notes/abstraction/abstraction.tex b/notes/abstraction/abstraction.tex index 8d0e2368..4a1c47a7 100644 --- a/notes/abstraction/abstraction.tex +++ b/notes/abstraction/abstraction.tex @@ -7,12 +7,12 @@ X'$. For each vertex $v_x \in V_x$, $A(v_x) = v_x'$ where $v_x' \in V_x'$. For each $x\in X$ where ${\cal X}(x) = V_x$, $A(x) = x'$, and $A(v_x) = v_x'$, then ${\cal X}'(x')= - v_{x'}'$. For each $x' \in X'$, ${\cal X}'(x') = \sum\limits_{x \in X: A(x) = x'} {\cal X}(x)$. + v_{x'}'$. For each $x' \in X'$, ${\cal I}'(x') = \sum\limits_{x \in X: A(x) = x'} {\cal I}(x)$. \item Parameters: For each $p \in P$, $A(p) = p'$, where $p'\in P'$. For each $p' \in P'$, ${\cal P}'(p') = \sum\limits_{p \in P: A(p) = p'} {\cal P}(p)$. \item Transitions: For each $z \in Z$, $A(z) = z'$, where $z' \in Z'$. For each vertex $v_z \in V_z$, $A(v_z) = v_z'$, where $v_z' \in V_z'$. - For each $z \in Z$, where ${\cal + For each $z \in Z$, if ${\cal Z}(z) = v_z$, $A(z) = z'$, and $A(v_z) = v_z'$, then ${\cal Z}'(z') = v_{z'}'$. \item In Edges: For each edge $(v_z, v_x) \in E_{in}$, $A((v_z, v_x)) = @@ -23,13 +23,16 @@ v_z')\in E_{out}'$; - \item Transition Rates: For each $z' \in Z'$, ${\cal R}'({\bf p}', {\bf + \item Transition Rates: For each $z' \in Z'$, + \begin{equation}\label{eqn:agg-flow} + {\cal R}'({\bf p}', {\bf x}', z') = \sum\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf - x}, z)$. + x}, z) + \end{equation} \end{itemize} \end{definition} -\begin{example} +\begin{example}\label{ex:abstraction} The abstraction $(\Theta', \Omega')$ of the stratified SIR model defines (with the changed elements highlighted by ``*''): \begin{eqnarray*} @@ -60,19 +63,21 @@ \end{array}\right.\\ {\cal R} &=& \left\{ \begin{array}{lll} - \beta_1 S_1 I + \beta_2 S_2 I& : z_{inf}&*\\ + \beta_1 S_1 I + \beta_2 S_2 I& : z_{inf}&* \\ \gamma I R & : z_{rec}\\ \end{array}\right.\\ \end{eqnarray*} \end{example} -The abstraction $(\Theta', \Omega')$ similarly defines the gradient $\nabla_{\Omega', \Theta'}({\bf p}', {\bf x}', t) = (\frac{dx_1'}{dt}, +In Example \ref{ex:abstraction}, the abstraction $(\Theta', \Omega')$ maps the $S_1$ and $S_2$ state variables to the $S$ state variable (effectively de-stratifying the base Petrinet). In combining the state variables, the abstract Petrinet consolidates the transitions $inf_1$ and $inf_2$ and associated rates from susceptible to infected. + +Like the base model, the abstraction $(\Theta', \Omega')$ defines a gradient $\nabla_{\Omega', \Theta'}({\bf p}', {\bf x}', t) = (\frac{dx_1'}{dt}, \frac{dx_2'}{dt}, \ldots)^T$, in terms of Equation \ref{eqn:flow}. -The abstraction thus expresses the gradient by aggregating terms from the -base Petrinet and semantics. It preserves the flow on transitions, but +Via Equation \ref{eqn:agg-flow}, the abstraction thus expresses the gradient by aggregating terms from the +base Petrinet and semantics. It preserves the flow on consolidated transitions, but expresses the transition rates in terms of the base states. As such, the abstraction compresses the Petrinet graph structure, but at the cost of expanding the expressions for transition rates. Moreover, the transition -rates refer to state variables and parameters that are not expressed -directly by the Petrinet and semantics, and by extension, the gradient. +rates refer to state variables and parameters (e.g., $\beta_1$, $\beta_2$, $S_1$, and $S_2$) that are not expressed +directly by the abstract Petrinet and semantics (e.g., as $\beta$ and $S$), and by extension, the gradient. diff --git a/notes/abstraction/background.tex b/notes/abstraction/background.tex index 6e046b44..1dfcb31b 100644 --- a/notes/abstraction/background.tex +++ b/notes/abstraction/background.tex @@ -2,7 +2,7 @@ A Petrinet $\Omega$ is a directed graph $(V, E)$ with vertices $V=(V_x, V_z)$ partitioned into sets $V_x$ of state vertices and $V_z$ of transition vertices, and edges $E=(E_{in}, E_{out})$ partitioned into collections $E_{out}$ of - flow-out and $E_{in}$ flow-in edges. + flow-out and $E_{in}$ flow-in edges (relative to state vertices). \end{definition} @@ -48,7 +48,7 @@ \item ${\cal Z}: Z \rightarrow V_z$ assigns transtions to transition vertices; and \item ${\cal R}: {\bf P} \times {\bf X} \times Z \rightarrow \reals$ - defines the rate of each transition in $x \in X$ in terms of the set of + defines the rate of each transition $z \in Z$ in terms of the set of parameter vectors ${\bf P}$ and state variable vectors ${\bf X}$. \end{itemize} The elements of the Petrinet $\Omega$ and semantics $\Theta$ define the diff --git a/notes/abstraction/bounded-abstraction.tex b/notes/abstraction/bounded-abstraction.tex index dcc5ff82..66b58375 100644 --- a/notes/abstraction/bounded-abstraction.tex +++ b/notes/abstraction/bounded-abstraction.tex @@ -1,20 +1,18 @@ We modify the abstraction in what we call a \emph{bounded abstraction}, so that it refers to the abstract, and not the base, Petrinet and semantics. This bounded abstraction replaces base elements with corresponding bounded elements. -For example, if $A(x_1) = x'$ and $A(x_2) = x'$ ($x_1$ and $x_2$ are base -variables represented by $x'$ in the abstraction), a possible transition rate -could be of the form -${\cal R}'({\bf p}', {\bf x}', z') = p_1 x_1 + p_2 x_2$. By construction, we -know that $x_1 + x_2 = x'$. However, in general $p_1 \not= p_2$, and we cannot -say that $p_1 x_1 + p_2 x_2 = p'x'$ for some $p'$. Yet, if we replace $p_1$ and -$p_2$ by $p^{ub} = \max(p_1, p_2)$, then $p^{ub} x_1 + p^{ub} x_2 \geq p'x'$. Simplifying, we -get $p^{ub} x_1 + p^{ub} x_2 = p^{ub}(x_1 + x_2) = p^{ub} x' \geq p'x'$. A -similar argument can be made where $p^{lb} = \min(p_1, p_2)$ and we find that -$p^{lb} x' \leq p'x'$. +For example, if $A(S_1) = S$ and $A(S_2) = S$ ($S_1$ and $S_2$ are base +variables represented by $S$ in the abstraction), the transition rate associated with the $inf$ transition is + ${\cal R}'({\bf p}', {\bf x}', z_{inf}) = \beta_1 S_1 I + \beta_2 S_2 I$. +By construction, we know that $S_1 + S_2 = S$. However, in general $\beta_1 \not= +\beta_2$, and we cannot say that $\beta_1 S_1 I + \beta_2 S_2 I = \beta S I$ for some definition of $\beta$. Yet, if +we replace $\beta_1$ and $\beta_2$ by $\beta^{ub} = \max(\beta_1, \beta_2)$, then $\beta^{ub} S_1 I + +\beta^{ub} S_2 I \geq \beta S I$. Simplifying, we get $\beta^{ub} S_1 I + \beta^{ub} S_2 I = +\beta^{ub}(S_1 + S_2)I = \beta^{ub} S I \geq \beta S I$. A similar argument can be made for the lower bound where +$\beta^{lb} = \min(\beta_1, \beta_2)$ and we find that $\beta^{lb} S I \leq \beta S I$. -By introducing the bounded parameters, we no longer -rely upon the base state variables or parameters. However, in tracking the -effect of the bounded +By introducing the bounded parameters, we no longer rely upon the base state +variables or parameters. However, in tracking the effect of the bounded parameters, the bounded abstraction must also track bounded rates and bounded state variables. The resulting bounded abstraction thus over-approximates the abstraction and base model, wherein we can derive bounds on the state variables @@ -23,51 +21,51 @@ \begin{definition} A bounded abstraction $(\Theta^B, \Omega^B)$ of an abstraction $(\Theta', -\Omega')$ of $(\Theta, \Omega)$ replaces each element of $(\Theta', -\Omega')$ by a pair of elements denoting the lower and upper bound of that -element (and referred to with the ``$lb$'' and ``$ub$'' superscripts). The -bounded abstraction defines: +\Omega')$ of $(\Theta, \Omega)$ replaces each element of $(\Theta', \Omega')$ by +a pair of elements denoting the lower and upper bound of that element (and +referred to with the ``$lb$'' and ``$ub$'' superscripts). The bounded +abstraction defines: \begin{itemize} \item State: For each $x' \in X'$, $x^{lb}, x^{ub} \in X^B$. For each - $v_{x'}' \in V_x'$, ${\cal X}^B(x^{lb}) = v_{x^{lb}}^B$ and ${\cal X}^B(x^{ub}) = - v_x^{ub}$. For each $x^{lb}, x^{ub} \in X^B$, ${\cal I}^B(x^{lb}) = {\cal - I}^B(x^{ub}) = {\cal I}'(x')$. - \item Parameters: For each $p' \in P'$, - let ${\cal P}^B(p^{lb}) = \min\limits_{p \in P: A(p) = p'} {\cal P}(p)$ and ${\cal P}^B(p^{ub}) = \max\limits_{p \in P: A(p) = p'} {\cal P}(p)$. + $v_{x'}' \in V_x'$, ${\cal X}^B(x^{lb}) = v_{x^{lb}}^B$ and ${\cal + X}^B(x^{ub}) = v_{x^{ub}}^B$. For each $x^{lb}, x^{ub} \in X^B$, ${\cal + I}^B(x^{lb}) = {\cal I}^B(x^{ub}) = {\cal I}'(x')$. + \item Parameters: For each $p' \in P'$, let ${\cal P}^B(p^{lb}) = + \min\limits_{p \in P: A(p) = p'} {\cal P}(p)$ and ${\cal P}^B(p^{ub}) = + \max\limits_{p \in P: A(p) = p'} {\cal P}(p)$. - \item Transitions: For each $z' \in Z'$, $z^{lb}, z^{ub} \in Z^B$. - For each vertex $v_z \in V_z$, $v_{z^{lb}}, v_{z^{ub}} \in V_z^B$. + \item Transitions: For each $z' \in Z'$, $z^{lb}, z^{ub} \in Z^B$. For + each vertex $v_z \in V_z$, if $A(v_z)=v_z'$ then $v_{z^{lb}}^B, v_{z^{ub}}^B \in V_z^B$. - \item In Edges: For each edge $(v_{z'}, v_{x'}) \in E_{in}'$, $(v_{z^{lb}}, - v_{x^{lb}}), (v_{z^{ub}}, - v_{x^{ub}}) \in E^B_{in}$. - \item Out Edges: For each edge $(v_{x'}, v_{z'}) \in E_{out}'$, $(v_{x^{ub}}, - v_{z^{lb}}), (v_{x^{lb}}, - v_{z^{ub}}) \in E^B_{out}$. + \item In Edges: For each edge $(v_{z'}^B, v_{x'}^B) \in E_{in}'$, + $(v_{z^{lb}}^B, v_{x^{lb}}^B), (v_{z^{ub}}^B, v_{x^{ub}}^B) \in E^B_{in}$. + \item Out Edges: For each edge $(v_{x'}^B, v_{z'}^B) \in E_{out}'$, + $(v_{x^{ub}}^B, v_{z^{lb}}^B), (v_{x^{lb}}^B, v_{z^{ub}}^B) \in E^B_{out}$. - \item Transition Rates: For each $z^{lb} \in Z^B$, ${\cal R}^B({\bf p}^B, {\bf - x}^B, z^{lb}) = \min\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf - x}, z)$ (replacing ${\bf p}$ and $[\bf x]$ of the minimal rate by the - elements in ${\bf p}^B$ and ${\bf x}^B$ respectively, which minimize the - rate), and ${\cal R}^B({\bf p}^B, - {\bf - x}^B, z^{ub}) = \max\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf - x}, z)$ (similarly replacing ${\bf p}$ and ${\bf x}$ of the maximal rate by the - elements in ${\bf p}^B$ and ${\bf x}^B$ respectively, which maximize the - rate). + \item Transition Rates: For each $z^{lb} \in Z^B$, ${\cal R}^B({\bf + p}^B, {\bf x}^B, z^{lb}) = \min\limits_{z \in Z: A(z)=z'} {\cal R}({\bf + p}, {\bf x}, z)$ (replacing ${\bf p}$ and ${\bf x}$ of the minimal rate + by the elements in ${\bf p}^B$ and ${\bf x}^B$ respectively, which + minimize the rate), and ${\cal R}^B({\bf p}^B, {\bf x}^B, z^{ub}) = + \max\limits_{z \in Z: A(z)=z'} {\cal R}({\bf p}, {\bf x}, z)$ (similarly + replacing ${\bf p}$ and ${\bf x}$ of the maximal rate by the elements in + ${\bf p}^B$ and ${\bf x}^B$ respectively, which maximize the rate). \end{itemize} \end{definition} \begin{example} - The bounded abstraction $(\Theta^B, \Omega^B)$ of the stratified SIR model defines: + The bounded abstraction $(\Theta^B, \Omega^B)$ of the stratified SIR model + defines: \begin{eqnarray*} - V^B_x &=& \{v_{S}^{lb}, v_{S}^{ub}, v_{I}^{lb}, v_{I}^{ub},v_{R}^{lb}, v_{R}^{ub},\}\\ + V^B_x &=& \{v_{S}^{lb}, v_{S}^{ub}, v_{I}^{lb}, v_{I}^{ub},v_{R}^{lb}, + v_{R}^{ub},\}\\ V^B_z &=& \{v_{inf}^{lb}, v_{inf}^{ub}, v_{rec}^{lb}, v_{rec}^{ub}\}\\ E^B_{in} &=& ((v_{inf}^{lb}, v_{S}^{lb}), (v_{inf}^{lb}, - v_{I}^{lb}),(v_{inf}^{lb}, v_{I}^{lb}), (v_{rec}^{lb}, v_{R}^{lb}),(v_{inf}^{ub}, v_{S}^{ub}), (v_{inf}^{ub}, + v_{I}^{lb}),(v_{inf}^{lb}, v_{I}^{lb}), (v_{rec}^{lb}, + v_{R}^{lb}),(v_{inf}^{ub}, v_{S}^{ub}), (v_{inf}^{ub}, v_{I}^{ub}),(v_{inf}^{ub}, v_{I}^{ub}), (v_{rec}^{ub}, v_{R}^{ub})\\ E^B_{out} &=& ((v_{S}^{lb}, v_{inf}^{ub}),(v_{I}^{lb}, v_{inf}^{ub}), (v_{I}^{lb}, v_{rec}^{ub}), (v_{S}^{ub}, v_{inf}^{lb}),(v_{I}^{ub}, @@ -82,8 +80,7 @@ 0.1& :I^{lb}\\ 0.1& :I^{ub}\\ 0.0& :R^{lb}\\ - 0.0& :R^{ub} - \end{array}\right.\\ + 0.0& :R^{ub} \end{array}\right.\\ {\cal P}^B&=& \left\{ \begin{array}{ll} 1e{-7}& :\beta^{lb}\\ @@ -95,41 +92,36 @@ {\cal X}^B &=& \left\{ \begin{array}{ll} v^{lb}_{x} & : x^{lb} \in X^B\\ - v^{ub}_{x} & : x^{ub} \in X^B - \end{array}\right.\\ + v^{ub}_{x} & : x^{ub} \in X^B \end{array}\right.\\ {\cal Z}^B &=& \left\{ \begin{array}{ll} v_{z}^{lb} & : z^{lb} \in Z^B\\ - v_{z}^{ub} & : z^{ub} \in Z^B - \end{array}\right.\\ + v_{z}^{ub} & : z^{ub} \in Z^B \end{array}\right.\\ {\cal R}^{B} &=& \left\{ \begin{array}{ll} \beta^{lb} S^{lb} I^{lb} & : z^{lb}_{inf}\\ \beta^{ub} S^{ub} I^{ub} & : z^{ub}_{inf}\\ \gamma^{lb} I^{lb} R^{lb} & : z^{lb}_{rec}\\ - \gamma^{ub} I^{ub} R^{ub} & : z^{ub}_{rec} - \end{array}\right.\\ + \gamma^{ub} I^{ub} R^{ub} & : z^{ub}_{rec} \end{array}\right.\\ \end{eqnarray*} The gradient for the bounded abstraction defines: \begin{eqnarray} - \nabla_{\Theta^B, \Omega^B} = \begin{bmatrix} - \frac{dS^{lb}}{dt}\\ + \nabla_{\Theta^B, \Omega^B} = \begin{bmatrix} \frac{dS^{lb}}{dt}\\ \frac{dS^{ub}}{dt}\\ \frac{dI^{lb}}{dt}\\ \frac{dI^{ub}}{dt}\\ \frac{dR^{lb}}{dt}\\ - \frac{dR^{ub}}{dt} - \end{bmatrix} = \begin{bmatrix} - -{\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{inf}^{ub})\\ + \frac{dR^{ub}}{dt} \end{bmatrix} = \begin{bmatrix} -{\cal + R}^{B}({\bf p}^B, {\bf x}^B, z_{inf}^{ub})\\ -{\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{inf}^{lb})\\ - {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{inf}^{lb}) - {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{rec}^{ub})\\ + {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{inf}^{lb}) - {\cal + R}^{B}({\bf p}^B, {\bf x}^B, z_{rec}^{ub})\\ {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{inf}^{ub}) - {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{rec}^{lb})\\ {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{rec}^{lb})\\ - {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{rec}^{ub}) - \end{bmatrix} = \begin{bmatrix} - -\beta^{ub} S^{ub} I^{ub}\\ + {\cal R}^{B}({\bf p}^B, {\bf x}^B, z_{rec}^{ub}) \end{bmatrix} = + \begin{bmatrix} -\beta^{ub} S^{ub} I^{ub}\\ -\beta^{lb} S^{lb} I^{lb}\\ \beta^{lb} S^{lb} I^{lb}-\gamma^{ub} I^{ub} R^{ub}\\ \beta^{ub} S^{ub} I^{ub}-\gamma^{lb} I^{lb} R^{lb}\\ @@ -138,4 +130,14 @@ \end{bmatrix} \end{eqnarray} -\end{example} \ No newline at end of file +\end{example} + +The bounded abstraction defines lower and upper bounds on the abstract state variables, for example: + +\begin{eqnarray*} + \frac{dS^{lb}}{dt} &\leq \frac{dS}{dt} &\leq \frac{dS^{ub}}{dt}\\ + -\beta^{ub} S^{ub} I^{ub} &\leq \frac{dS}{dt} &\leq -\beta^{lb} S^{lb} I^{lb}\\ + -\max(\beta_1, \beta_2) S^{ub} I^{ub} &\leq \frac{d (S_1+S_2)}{dt} &\leq -\min(\beta_1, \beta_2) S^{lb} I^{lb}\\ + -\max(\beta_1, \beta_2) S^{ub} I^{ub} \leq -\max(\beta_1, \beta_2) (S_1+S_2) I^{ub} &\leq \frac{d S_1}{dt} +\frac{d S_2}{dt}&\leq -\min(\beta_1, \beta_2) (S_1+S_2) I^{lb}\leq -\min(\beta_1, \beta_2) S^{lb} I^{lb}\\ + -\max(\beta_1, \beta_2) S^{ub} I^{ub} \leq -\max(\beta_1, \beta_2) (S_1+S_2) I^{ub} &\leq \frac{d S_1}{dt} +\frac{d S_2}{dt}&\leq -\min(\beta_1, \beta_2) (S_1+S_2) I\leq -\min(\beta_1, \beta_2) (S_1+S_2) I^{lb}\leq -\min(\beta_1, \beta_2) S^{lb} I^{lb} 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