diff --git a/.gitignore b/.gitignore
index 5a06d236..cb64c9d8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -319,3 +319,5 @@ notebooks/saved-results/out
 **/*.bbl
 **/*.blg
 **/*.out
+auxiliary_packages/ibex_tests/num_constraint_test
+auxiliary_packages/ibex_tests/num_constraint_test.o
diff --git a/auxiliary_packages/funman_benchmarks/src/funman_benchmarks/_version.py b/auxiliary_packages/funman_benchmarks/src/funman_benchmarks/_version.py
index 1388158e..3bca77a1 100644
--- a/auxiliary_packages/funman_benchmarks/src/funman_benchmarks/_version.py
+++ b/auxiliary_packages/funman_benchmarks/src/funman_benchmarks/_version.py
@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "1.8.0"
+__version__ = "1.9.0"
diff --git a/auxiliary_packages/funman_demo/setup.py b/auxiliary_packages/funman_demo/setup.py
index e9925015..8bf72f8f 100644
--- a/auxiliary_packages/funman_demo/setup.py
+++ b/auxiliary_packages/funman_demo/setup.py
@@ -22,7 +22,7 @@
     license="MIT",
     packages=find_packages("src"),
     package_dir={"": "src"},
-    install_requires=["funman", "matplotlib", "jupyter"],
+    install_requires=["funman", "matplotlib", "jupyter", "seaborn"],
     tests_require=["unittest"],
     zip_safe=False,
 )
diff --git a/auxiliary_packages/funman_demo/src/funman_demo/_version.py b/auxiliary_packages/funman_demo/src/funman_demo/_version.py
index 1388158e..3bca77a1 100644
--- a/auxiliary_packages/funman_demo/src/funman_demo/_version.py
+++ b/auxiliary_packages/funman_demo/src/funman_demo/_version.py
@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "1.8.0"
+__version__ = "1.9.0"
diff --git a/auxiliary_packages/funman_demo/src/funman_demo/box_plotter.py b/auxiliary_packages/funman_demo/src/funman_demo/box_plotter.py
index ab2cce75..20ea4ab3 100644
--- a/auxiliary_packages/funman_demo/src/funman_demo/box_plotter.py
+++ b/auxiliary_packages/funman_demo/src/funman_demo/box_plotter.py
@@ -209,6 +209,7 @@ def plot_add_patch(self, box: Box, color="r"):
                     linewidth=1,
                     edgecolor=color,
                     facecolor="none",
+                    zorder=box.timestep().lb * box.timestep().lb,
                 )
 
                 # Add the patch to the Axes
@@ -235,6 +236,7 @@ def plot_add_point(self, point: Point, color="r", shape="x", alpha=0.2):
                     marker=shape,
                     alpha=alpha,
                     s=3,
+                    zorder=point.timestep(),
                 )
                 self.fig.canvas.draw()
                 self.fig.canvas.flush_events()
@@ -311,6 +313,7 @@ def plotNDBox(self, box, color="g", alpha=0.2):
                             y_limits.ub,
                             color=color,
                             alpha=alpha,
+                            zorder=box.timestep().lb * box.timestep().lb,
                         )
         plt.show(block=False)
 
diff --git a/auxiliary_packages/funman_demo/src/funman_demo/example/pde.py b/auxiliary_packages/funman_demo/src/funman_demo/example/pde.py
index d09715d4..641b2afb 100644
--- a/auxiliary_packages/funman_demo/src/funman_demo/example/pde.py
+++ b/auxiliary_packages/funman_demo/src/funman_demo/example/pde.py
@@ -8,10 +8,11 @@
 import pandas as pd
 import seaborn as sns
 from IPython.display import HTML
-from sklearn import preprocessing
 
 from funman.server.query import FunmanResults
 
+# from sklearn import preprocessing
+
 
 def animate_heat_map(my_df, frames):
     fig = plt.figure()
diff --git a/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py b/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py
index c8c788a4..6a1fbf8a 100644
--- a/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py
+++ b/auxiliary_packages/funman_demo/src/funman_demo/parameter_space_plotter.py
@@ -1,5 +1,5 @@
 import logging
-from typing import Dict
+from typing import Dict, List
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -15,6 +15,7 @@ class ParameterSpacePlotter:
     def __init__(
         self,
         parameter_space: ParameterSpace,
+        boxes: List[Box] = None,
         plot_bounds: Box = None,
         title: str = "Feasible Regions",
         color_map: Dict[str, str] = {
@@ -27,6 +28,7 @@ def __init__(
         plot_points=False,
         parameters=None,
         dpi=100,
+        synthesized_parameters=None,
     ):
         if isinstance(parameter_space, ParameterSpace):
             self.ps = parameter_space
@@ -34,16 +36,13 @@ def __init__(
             # FIXME this is a hack to accept ParameterSpace objects from the openapi client
             self.ps = ParameterSpace.model_validate(parameter_space.to_dict())
 
-        # TODO this should be easier to access
-        values = []
-        true_points = self.ps.true_points()
-        false_points = self.ps.false_points()
-        if len(true_points) > 0:
-            values = true_points[0].values
-        elif len(false_points) > 0:
-            values = false_points[0].values
+        self.boxes = boxes
 
-        self.parameters = [k for k in values if parameters and k in parameters]
+        # Expect that parameters are available in the parameter space
+        self.parameters = parameters  # [k for k in scenario_parameters if parameters and k in parameters]
+        self.synthesized_parameters = (
+            synthesized_parameters if synthesized_parameters else None
+        )
         self.dim = len(self.parameters)
         self.plot_points = plot_points
 
@@ -80,7 +79,7 @@ def initialize_figure(self, plot_diagonal):
             dim_to_plot,
             squeeze=False,
             dpi=self.dpi,
-            figsize=(10, 10),
+            figsize=(20, 20),
         )
         self.fig = fig
         self.axs = axs
@@ -90,15 +89,16 @@ def initialize_figure(self, plot_diagonal):
         MEDIUM_SIZE = 10
         BIGGER_SIZE = 12
 
-        plt.rc("font", size=SMALL_SIZE)  # controls default text sizes
+        # plt.rc("font", size=SMALL_SIZE)  # controls default text sizes
         plt.rc("axes", titlesize=SMALL_SIZE)  # fontsize of the axes title
         plt.rc("axes", labelsize=TINY_SIZE)  # fontsize of the x and y labels
         plt.rc("xtick", labelsize=TINY_SIZE)  # fontsize of the tick labels
         plt.rc("ytick", labelsize=TINY_SIZE)  # fontsize of the tick labels
-        plt.rc("legend", fontsize=SMALL_SIZE)  # legend fontsize
-        plt.rc("figure", titlesize=BIGGER_SIZE)  # fontsize of the figure title
+        # plt.rc("legend", fontsize=SMALL_SIZE)  # legend fontsize
+        # plt.rc("figure", titlesize=BIGGER_SIZE)  # fontsize of the figure title
 
-        self.fig.tight_layout(pad=3.0)
+        # self.fig.tight_layout(pad=3.0)
+        self.fig.tight_layout(pad=2)
         self.data = [[None] * self.dim] * self.dim
 
         for i in range(self.dim):
@@ -121,20 +121,36 @@ def initialize_figure(self, plot_diagonal):
         plt.legend(self.custom_lines, ["true", "false"])
 
     def plot(self, show=False, plot_diagonal=False):
-        self.initialize_figure(plot_diagonal)
+        self.initialize_figure((plot_diagonal or len(self.parameters) == 1))
         t = "true"
         f = "false"
-        for b in self.ps.false_boxes:
-            self.plotNDBox(b, self.color_map[f], plot_diagonal=plot_diagonal)
-        for b in self.ps.true_boxes:
-            self.plotNDBox(b, self.color_map[t], plot_diagonal=plot_diagonal)
+        if self.boxes:
+            for b in self.boxes:
+                self.plotNDBox(
+                    b,
+                    self.color_map[b.label],
+                    plot_diagonal=(plot_diagonal or len(self.parameters) == 1),
+                )
+        else:
+            for b in self.ps.false_boxes:
+                self.plotNDBox(
+                    b,
+                    self.color_map[f],
+                    plot_diagonal=(plot_diagonal or len(self.parameters) == 1),
+                )
+            for b in self.ps.true_boxes:
+                self.plotNDBox(
+                    b,
+                    self.color_map[t],
+                    plot_diagonal=(plot_diagonal or len(self.parameters) == 1),
+                )
         if self.plot_points:
             for p in self.ps.false_points():
                 self.plot_add_point(
                     p,
                     self.color_map[f],
                     self.shape_map[f],
-                    plot_diagonal=plot_diagonal,
+                    plot_diagonal=(plot_diagonal or len(self.parameters) == 1),
                 )
             true_points = self.ps.true_points()
             for p in true_points:
@@ -142,7 +158,7 @@ def plot(self, show=False, plot_diagonal=False):
                     p,
                     self.color_map[t],
                     self.shape_map[t],
-                    plot_diagonal=plot_diagonal,
+                    plot_diagonal=(plot_diagonal or len(self.parameters) == 1),
                 )
         if show:
             plt.show(block=False)
@@ -174,11 +190,14 @@ def plot_add_point(
                     marker=shape,
                     alpha=alpha,
                     s=10,
+                    zorder=point.timestep(),
                 )
                 # self.fig.canvas.draw()
                 # self.fig.canvas.flush_events()
 
-    def plotNDBox(self, box, color="g", alpha=0.2, plot_diagonal=False):
+    def plotNDBox(
+        self, box, color="g", alpha=0.2, plot_diagonal=False, max_width=100000
+    ):
         for i in range(self.dim):
             for j in range(self.dim):
                 i_coord, j_coord = self.map_param_idx_to_plot_loc(
@@ -189,6 +208,12 @@ def plotNDBox(self, box, color="g", alpha=0.2, plot_diagonal=False):
                 if j_coord > i_coord:
                     continue
 
+                if self.synthesized_parameters and (
+                    self.parameters[i] not in self.synthesized_parameters
+                    or self.parameters[j] not in self.synthesized_parameters
+                ):
+                    continue
+
                 x_limits = box.bounds[self.parameters[i]]
                 y_limits = box.bounds[self.parameters[j]]
 
@@ -204,11 +229,11 @@ def plotNDBox(self, box, color="g", alpha=0.2, plot_diagonal=False):
                 else:
                     # Plot a box
                     if (
-                        abs(float(x_limits.lb)) < 1000
-                        and abs(float(x_limits.ub)) < 1000
+                        abs(float(x_limits.lb)) < max_width
+                        and abs(float(x_limits.ub)) < max_width
                     ):
                         x = np.linspace(
-                            float(x_limits.lb), float(x_limits.ub), 1000
+                            float(x_limits.lb), float(x_limits.ub), max_width
                         )
                         self.axs[i_coord, j_coord].fill_between(
                             x,
@@ -216,4 +241,15 @@ def plotNDBox(self, box, color="g", alpha=0.2, plot_diagonal=False):
                             y_limits.ub,
                             color=color,
                             alpha=alpha,
+                            zorder=box.timestep().lb,
+                        )
+                        self.axs[i_coord, j_coord].text(
+                            (x_limits.lb + x_limits.ub) / 2,
+                            (y_limits.lb + y_limits.ub) / 2,
+                            # f"[{box.timestep().lb}, {box.timestep().ub}]",
+                            f"{box.timestep().lb}",
+                            ha="center",
+                            va="center",
+                            fontsize=8,
+                            color="blue",
                         )
diff --git a/auxiliary_packages/funman_demo/src/funman_demo/plot.py b/auxiliary_packages/funman_demo/src/funman_demo/plot.py
index d894c51c..ca11e269 100644
--- a/auxiliary_packages/funman_demo/src/funman_demo/plot.py
+++ b/auxiliary_packages/funman_demo/src/funman_demo/plot.py
@@ -3,6 +3,7 @@
 from typing import Dict
 
 import matplotlib.pyplot as plt
+from funman_demo.parameter_space_plotter import ParameterSpacePlotter
 from IPython.display import clear_output
 from matplotlib.lines import Line2D
 
@@ -68,17 +69,19 @@ def plot_cached_search(search_path, alpha: float = 0.2):
     )
 
 
-def summarize_results(variables, results, ylabel="Height"):
+def summarize_results(
+    variables,
+    results,
+    ylabel="Height",
+    parameters_to_plot=None,
+    label_color={"true": "g", "false": "r"},
+    synthesized_parameters=None,
+    print_last_time=False,
+) -> str:
     points = results.points()
     boxes = results.parameter_space.boxes()
 
-    l.info("*" * 80)
-    l.info("*" * 80)
-    l.info("* Analysis Summary ")
-    l.info("*" * 80)
-    l.info(
-        f"{len(points)} Points (+:{len(results.parameter_space.true_points())}, -:{len(results.parameter_space.false_points())}), {len(boxes)} Boxes (+:{len(results.parameter_space.true_boxes)}, -:{len(results.parameter_space.false_boxes)})"
-    )
+    point_info = ""
     if points and len(points) > 0:
         point: Point = points[-1]
         parameters: Dict[Parameter, float] = results.point_parameters(point)
@@ -88,15 +91,48 @@ def summarize_results(variables, results, ylabel="Height"):
             xlabel="Time",
             ylabel=ylabel,
             legend=variables,
-            label_color={"true": "g", "false": "r"},
+            label_color=label_color,
         )
         parameter_values = {p: point.values[p.name] for p in parameters}
-        l.info(f"Parameters = {parameter_values}")
-        l.info(parameters)
-        l.info(results.dataframe([point]))
+        point_info = f"""Parameters = {parameter_values}
+        # {parameters}
+        {results.dataframe([point])}
+        """
     else:
         # if there are no points, then we have a box that we found without needing points
-        l.info(f"Found box with no points")
         box = boxes[0]
-        l.info(json.dumps(box.explain(), indent=4))
-    l.info("*" * 80)
+        point_info = f"""Found box with no points
+        {json.dumps(box.explain(), indent=4)}
+        """
+
+    boxes = (
+        results.parameter_space.boxes()
+        if not print_last_time
+        else results.parameter_space.last_boxes()
+    )
+
+    if parameters_to_plot is None:
+        parameters_to_plot = results.model._parameter_names()
+        if not print_last_time:
+            parameters_to_plot += ["timestep"]
+    if len(boxes) > 0 and len(parameters_to_plot) > 1:
+        ParameterSpacePlotter(
+            results.parameter_space,
+            parameters=parameters_to_plot,
+            dpi=len(parameters_to_plot) * 20,
+            plot_points=True,
+            synthesized_parameters=synthesized_parameters,
+        ).plot(show=True)
+
+    divider = "*" * 80
+
+    summary = f"""{divider}
+{divider}
+* Analysis Summary
+{divider}
+{len(points)} Points (+:{len(results.parameter_space.true_points())}, -:{len(results.parameter_space.false_points())}), {len(boxes)} Boxes (+:{len(results.parameter_space.true_boxes)}, -:{len(results.parameter_space.false_boxes)})
+{point_info}
+{divider}
+    """
+
+    return summary
diff --git a/auxiliary_packages/funman_dreal/setup.py b/auxiliary_packages/funman_dreal/setup.py
index 8545bce8..cd2549ae 100644
--- a/auxiliary_packages/funman_dreal/setup.py
+++ b/auxiliary_packages/funman_dreal/setup.py
@@ -23,7 +23,7 @@
     packages=find_packages("src"),
     package_dir={"": "src"},
     install_requires=["funman", "docker", "tenacity", "pyparsing"],
-    extras_require={"dreal": ["dreal"]},
+    # extras_require={"dreal": ["dreal"]},
     tests_require=["unittest"],
     zip_safe=False,
 )
diff --git a/auxiliary_packages/funman_dreal/src/funman_dreal/_version.py b/auxiliary_packages/funman_dreal/src/funman_dreal/_version.py
index 1388158e..3bca77a1 100644
--- a/auxiliary_packages/funman_dreal/src/funman_dreal/_version.py
+++ b/auxiliary_packages/funman_dreal/src/funman_dreal/_version.py
@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "1.8.0"
+__version__ = "1.9.0"
diff --git a/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py b/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py
index 41c753da..a50a3e70 100644
--- a/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py
+++ b/auxiliary_packages/funman_dreal/src/funman_dreal/converter.py
@@ -88,28 +88,47 @@ def __init__(self, env=None):
 
         self.identifier_map = {"and": "&", "or": "|", "==": "="}
 
-        self.rules = [
-            Rule(r"(pow)", PowOpAdapter(self.mgr.Pow, 80), False),  # pow
-            Rule(
-                r"(and)", InfixOpAdapter(self.AndOrBVAnd, 40), False
-            ),  # conjunction
-            Rule(
-                r"(or)", InfixOpAdapter(self.OrOrBVOr, 30), False
-            ),  # disjunction
-            Rule(
-                r"(abs)", UnaryOpAdapter(self.mgr.Abs, 50), False
-            ),  # absolute value
-            Rule(
-                r"(b\()", BinaryLiteralExpr(self.BinaryLiteral, 50), False
-            ),  # b()
-            Rule(r"(==)", InfixOpAdapter(self.mgr.Equals, 60), False),  # eq
-            Rule(
-                r"(-?\d+\.\d+e\+?\d+)", self.real_constant, True
-            ),  # decimals scientific
-            Rule(
-                r"(-?\d+\.\d+e-?\d+)", self.real_constant, True
-            ),  # decimals scientific
-        ] + self.rules
+        self.rules = (
+            [
+                Rule(r"(pow)", PowOpAdapter(self.mgr.Pow, 80), False),  # pow
+                Rule(
+                    r"(and)", InfixOpAdapter(self.AndOrBVAnd, 40), False
+                ),  # conjunction
+                Rule(
+                    r"(or)", InfixOpAdapter(self.OrOrBVOr, 30), False
+                ),  # disjunction
+                Rule(
+                    r"(abs)", UnaryOpAdapter(self.mgr.Abs, 50), False
+                ),  # absolute value
+                Rule(
+                    r"(b\()", BinaryLiteralExpr(self.BinaryLiteral, 50), False
+                ),  # b()
+                Rule(
+                    r"(==)", InfixOpAdapter(self.mgr.Equals, 60), False
+                ),  # eq
+                Rule(
+                    r"(-?\d+e\+?\d+)", self.real_constant, True
+                ),  # decimals scientific
+                Rule(
+                    r"(-?\d+?e-?\d+)", self.real_constant, True
+                ),  # decimals scientific
+                Rule(
+                    r"(-?\d+\.\d+e\+?\d+)", self.real_constant, True
+                ),  # decimals scientific
+                Rule(
+                    r"(-?\d+\.\d+e-?\d+)", self.real_constant, True
+                ),  # decimals scientific
+            ]
+            + self.rules[0:-1]
+            + [
+                Rule(
+                    r"(([A-Za-z_]|[^\u0000-\u007F])([A-Za-z_]|[^\u0000-\u007F])*)",
+                    self.identifier,
+                    True,
+                ),  # unicode identifiers
+            ]
+            + self.rules[-1:]
+        )
         self.compile()
 
     def BinaryLiteral(self, x):
@@ -184,7 +203,7 @@ def rewrite_dreal_formula(self, formula: dreal.Formula) -> str:
         return str_formula
 
     def create_dreal_symbols(self, rewritten_formula: str) -> List[Symbol]:
-        patterns = ["(disj[0-9]+)", "(conj[0-9]+)", "neg"]
+        patterns = ["(disj[0-9]+)", "(conj[0-9]+)", "neg", "ITE[0-9]*"]
         symbol_names = [
             q for p in patterns for q in list(re.findall(p, rewritten_formula))
         ]
@@ -222,6 +241,22 @@ def walk_implies(self, formula, args, **kwargs):
         # self._check_term_result(res)
         return res
 
+    def walk_ite(self, formula, args, **kwargs):
+        converted_args = []
+        for arg in args:
+            if isinstance(arg, Fraction):
+                converted_args.append(float(arg))
+            elif isinstance(arg, dreal.Variable):
+                converted_args.append(dreal.Expression(arg))
+            else:
+                converted_args.append(arg)
+
+        res = dreal.if_then_else(
+            converted_args[0], converted_args[1], converted_args[2]
+        )
+        # self._check_term_result(res)
+        return res
+
     def walk_or(self, formula, args, **kwargs):
         res = functools.reduce(lambda a, b: dreal.Or(a, b), args)
         # self._check_term_result(res)
@@ -272,7 +307,8 @@ def walk_times(self, formula, args, **kwargs):
 
     def walk_pow(self, formula, args, **kwargs):
         exponent = float(args[1]) if isinstance(args[1], Fraction) else args[1]
-        res = dreal.pow(args[0], exponent)
+        base = float(args[0]) if isinstance(args[0], Fraction) else args[0]
+        res = dreal.pow(base, exponent)
         return res
 
     def walk_abs(self, formula, args, **kwargs):
diff --git a/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py b/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py
index 76f30589..7f175dcd 100644
--- a/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py
+++ b/auxiliary_packages/funman_dreal/src/funman_dreal/solver.py
@@ -20,7 +20,7 @@
 )
 from pysmt.formula import FNode
 from pysmt.logics import QF_NRA
-from pysmt.shortcuts import BOOL, Bool, Real, get_env
+from pysmt.shortcuts import BOOL, TRUE, Bool, Real, get_env
 from pysmt.smtlib.parser import SmtLibParser
 from pysmt.smtlib.script import SmtLibCommand
 from pysmt.smtlib.solver import SmtLibOptions, SmtLibSolver
@@ -443,6 +443,11 @@ def __init__(
         self.model = None
         self.log_level = dreal.LogLevel.OFF
         if "solver_options" in options:
+            if (
+                "preferred" in options["solver_options"]
+                and len(options["solver_options"]["preferred"]) > 0
+            ):
+                self.config.preferred = options["solver_options"]["preferred"]
             if "dreal_precision" in options["solver_options"]:
                 self.config.precision = options["solver_options"][
                     "dreal_precision"
@@ -614,9 +619,12 @@ def get_value(self, symbol_pair):
                 isinstance(ub, int) or isinstance(lb, int)
             ):
                 return Real(lb) if isinstance(lb, int) else Real(ub)
-            elif mid == lb or mid == ub:
+            elif mid == lb:
                 # Midpoint is not representable
                 return Real(lb) if ub == 0.0 else Real(ub)
+            elif mid == ub:
+                # Midpoint is not representable
+                return Real(ub) if lb == 0.0 else Real(lb)
             elif not math.isinf(mid):
                 return Real(mid)
             else:
diff --git a/auxiliary_packages/ibex_tests/Makefile b/auxiliary_packages/ibex_tests/Makefile
index 57d7ee30..d8f18b58 100644
--- a/auxiliary_packages/ibex_tests/Makefile
+++ b/auxiliary_packages/ibex_tests/Makefile
@@ -1,11 +1,19 @@
 .DEFAULT_GOAL := all
 
-num_constraint_test.o: src/num_constraint_test.cc
-	g++ -c -g src/num_constraint_test.cc -I/usr/include/ibex -libex
+build:
+	mkdir -p build
 
-num_constraint_test: num_constraint_test.o
-	g++ -g num_constraint_test.o -libex -o num_constraint_test
+build/num_constraint_test.o: src/num_constraint_test.cc
+	g++ -c -g -pg src/num_constraint_test.cc -I/usr/include/ibex -L/root/ibex-lib/__build__/src/libibex.so  -o build/num_constraint_test.o
 
-test: num_constraint_test
+build/num_constraint_test: build/num_constraint_test.o
+	g++ -g -pg build/num_constraint_test.o -L/root/ibex-lib/__build__/src/libibex.so -libex -o build/num_constraint_test
 
-all: test
\ No newline at end of file
+test: build/num_constraint_test
+	build/num_constraint_test
+
+profile: test
+	gprof --flat-profile build/num_constraint_test
+
+all: build test
+	
\ No newline at end of file
diff --git a/auxiliary_packages/ibex_tests/src/num_constraint_test.cc b/auxiliary_packages/ibex_tests/src/num_constraint_test.cc
index e7787de1..0b4d8618 100644
--- a/auxiliary_packages/ibex_tests/src/num_constraint_test.cc
+++ b/auxiliary_packages/ibex_tests/src/num_constraint_test.cc
@@ -10,7 +10,7 @@ using std::chrono::duration_cast;
 using std::chrono::high_resolution_clock;
 using std::chrono::milliseconds;
 
-NumConstraint func(const Array<const ExprSymbol> &args, const ExprCtr &expr)
+NumConstraint func(const Array<const ExprSymbol> &args, const ExprCtr expr)
 {
     cout << "size = " << args.size() << endl;
     switch (args.size())
@@ -93,15 +93,16 @@ NumConstraint func(const Array<const ExprSymbol> &args, const ExprCtr &expr)
 //     cout << "Made constraint c2: " << c2 << endl;
 // }
 
-duration<double, std::milli> make_constraint(const ExprNode *expr, Array<const ExprSymbol> &vars, bool use_opt)
+duration<double, std::milli> make_constraint(const ExprCtr e, Array<const ExprSymbol> &vars, bool use_opt)
 {
     auto t1 = high_resolution_clock::now();
     // cout << vars.size() << endl;
-    cout << "done expr" << endl;
+    cout << "make_constraint(" << use_opt << ") - start" << endl;
 
-    NumConstraint c1 = use_opt ? func(vars, (*expr < 0)) : NumConstraint(vars, (*expr < 0));
+    // cout << "expr = " << e << endl;
+    NumConstraint c1 = use_opt ? func(vars, e) : NumConstraint(vars, e);
 
-    cout << "done expr" << endl;
+    cout << "make_constraint(" << use_opt << ") - end" << endl;
     auto t2 = high_resolution_clock::now();
     duration<double, std::milli> ms_double = t2 - t1;
     return ms_double;
@@ -117,16 +118,12 @@ const ExprNode *make_expr(Array<const ExprSymbol> &x)
     return expr;
 }
 
-void make_vars(Array<const ExprSymbol> *vars)
+void make_vars(Array<const ExprSymbol> &vars, Variable *x)
 {
-    int num_vars = vars->size();
-    Variable x[num_vars];
-    cout << "num_vars: " << num_vars << endl;
-
-    for (int i = 0; i < num_vars; i++)
+    int num_vars = vars.size();
+    for (int i = 0; i < vars.size(); i++)
     {
-        cout << "i = " << i << endl;
-        vars->set_ref(i, x[i]);
+        vars.set_ref(i, x[i]);
     }
 }
 
@@ -142,46 +139,87 @@ int main()
     // NumConstraint c(x, x + 1 <= 0);
     // cout << "Made constraint: " << c << endl;
     cout << "Starting: " << endl;
-    int num_vars = 20;
-    Variable x[num_vars];
-    Array<const ExprSymbol> vars(num_vars);
-
-    // cout << "Made vars: " << vars.size() << endl;
-    make_vars(&vars);
-    cout << vars[0] << endl;
-    cout << "Made vars: " << vars.size() << endl;
-    const ExprNode *expr = make_expr(vars);
-    cout << "Made expr" << endl;
-    cout << (*expr) << endl;
-    auto time_with = make_constraint(expr, vars, true);
-    auto time_without = make_constraint(expr, vars, false);
-    // summary(vars.size(), time_with.count(), time_without.count());
-    delete expr;
-
-    // auto t1 = high_resolution_clock::now();
-    // c1();
-    // auto t2 = high_resolution_clock::now();
-    // // c2();
-
-    // auto t3 = high_resolution_clock::now();
-
-    // /* Getting number of milliseconds as an integer. */
-    // auto ms_int = duration_cast<milliseconds>(t2 - t1);
-
-    // /* Getting number of milliseconds as a double. */
-    // duration<double, std::milli> ms_double = t2 - t1;
-
-    // std::cout << ms_int.count() << "ms\n";
-    // std::cout << ms_double.count() << "ms\n";
-
-    // /* Getting number of milliseconds as an integer. */
-    // auto ms_int1 = duration_cast<milliseconds>(t3 - t2);
-
-    // /* Getting number of milliseconds as a double. */
-    // duration<double, std::milli> ms_double1 = t3 - t2;
-
-    // // std::cout << ms_int1.count() << "ms\n";
-    // // std::cout << ms_double1.count() << "ms\n";
-
+    for (int num_vars = 20; num_vars <= 20; num_vars++)
+    {
+        // int num_vars = 10;
+        Variable x1[num_vars];
+        Variable x2[num_vars];
+        Array<const ExprSymbol> vars1(num_vars);
+        Array<const ExprSymbol> vars2(num_vars);
+
+        make_vars(vars1, x1);
+        make_vars(vars2, x2);
+
+        // for (int i = 0; i < num_vars; i++)
+        // {
+        //     // cout << "i = " << i << endl;
+        //     vars1.set_ref(i, x1[i]);
+        //     vars2.set_ref(i, x2[i]);
+        // }
+
+        // const ExprNode *expr = &(vars1[0] + 1);
+        // for (int i = 1; i < num_vars; i++)
+        //     expr = &(*expr + vars1[i]);
+        const ExprNode *expr1 = make_expr(vars1);
+        const ExprCtr &e1 = (*expr1 < 0);
+        const ExprNode *expr2 = make_expr(vars2);
+        const ExprCtr &e2 = (*expr2 < 0);
+
+        // const ExprNode expr = ;
+        // const ExprCtr e1 = (vars1[0] + vars1[1] + vars1[2] + vars1[3] + vars1[4] + vars1[5] + vars1[6] + vars1[7] + vars1[8] + vars1[9]) < 0;
+        cout << "e1 = " << e1 << endl;
+        // const ExprCtr e2 = (vars2[0] + vars2[1] + vars2[2] + vars2[3] + vars2[4] + vars2[5] + vars2[6] + vars2[7] + vars2[8] + vars2[9]) < 0;
+        cout << "e2 = " << e2 << endl;
+
+        // auto ctr = func(vars, e);
+        // auto ctr = NumConstraint(vars1[0], vars1[1], vars1[2], vars1[3], vars1[4], vars1[5], vars1[6], vars1[7], vars1[8], vars1[9], e1);
+        // auto ctr = NumConstraint(vars, e);
+        // cout << "Made ctr: " << ctr << endl;
+
+        auto time_without = make_constraint(e1, vars1, false);
+        auto time_with = make_constraint(e2, vars2, true);
+        // delete expr1;
+        // delete expr2;
+
+        // cout << "Made e" << endl;
+        // const ExprNode *expr1 = &(vars[0] + 1);
+        // for (int i = 1; i < num_vars; i++)
+        //     expr1 = &(*expr1 + vars[i]);
+        // cout << (*expr1) << endl;
+        // cout << endl;
+
+        // const ExprNode *expr1 = make_expr(vars);
+        // cout << "Made expr1" << endl;
+        // cout << (*expr) << endl;
+        // const ExprCtr &e1 = (*expr < 0);
+
+        summary(vars1.size(), time_with.count(), time_without.count());
+        // delete expr;
+
+        // auto t1 = high_resolution_clock::now();
+        // c1();
+        // auto t2 = high_resolution_clock::now();
+        // // c2();
+
+        // auto t3 = high_resolution_clock::now();
+
+        // /* Getting number of milliseconds as an integer. */
+        // auto ms_int = duration_cast<milliseconds>(t2 - t1);
+
+        // /* Getting number of milliseconds as a double. */
+        // duration<double, std::milli> ms_double = t2 - t1;
+
+        // std::cout << ms_int.count() << "ms\n";
+        // std::cout << ms_double.count() << "ms\n";
+
+        // /* Getting number of milliseconds as an integer. */
+        // auto ms_int1 = duration_cast<milliseconds>(t3 - t2);
+
+        // /* Getting number of milliseconds as a double. */
+        // duration<double, std::milli> ms_double1 = t3 - t2;
+
+        // // std::cout << ms_int1.count() << "ms\n";
+        // // std::cout << ms_double1.count() << "ms\n";
+    }
     return 0;
 }
diff --git a/auxiliary_packages/pde2petri/src/pde2petri/_version.py b/auxiliary_packages/pde2petri/src/pde2petri/_version.py
index 29654eec..0a0a43a5 100644
--- a/auxiliary_packages/pde2petri/src/pde2petri/_version.py
+++ b/auxiliary_packages/pde2petri/src/pde2petri/_version.py
@@ -1 +1 @@
-__version__ = "1.8.0"
+__version__ = "1.9.0"
diff --git a/docker/base/Dockerfile b/docker/base/Dockerfile
index e0df38f6..689938dc 100644
--- a/docker/base/Dockerfile
+++ b/docker/base/Dockerfile
@@ -32,11 +32,11 @@ ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
 ARG AUTOMATES_COMMIT_TAG
 # Install automates
-RUN git clone --depth=1 https://github.com/danbryce/automates.git automates \
-    && cd automates \
-    && git fetch --depth=1 origin ${AUTOMATES_COMMIT_TAG} \
-    && git checkout ${AUTOMATES_COMMIT_TAG}
-RUN pip install -e automates
+# RUN git clone --depth=1 https://github.com/danbryce/automates.git automates \
+#     && cd automates \
+#     && git fetch --depth=1 origin ${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 graphviz
diff --git a/docker/dev/root/Dockerfile.root b/docker/dev/root/Dockerfile.root
index 8e5d76b2..c77e3cf2 100644
--- a/docker/dev/root/Dockerfile.root
+++ b/docker/dev/root/Dockerfile.root
@@ -52,11 +52,11 @@ RUN pip install --no-cache-dir openapi-python-client
 WORKDIR /root
 
 # Install automates
-RUN git clone --depth=1 https://github.com/danbryce/automates.git automates \
-    && cd automates \
-    && git fetch --depth=1 origin e5fb635757aa57007615a75371f55dd4a24851e0 \
-    && git checkout e5fb635757aa57007615a75371f55dd4a24851e0
-RUN pip install -e automates
+# RUN git clone --depth=1 https://github.com/danbryce/automates.git automates \
+#     && cd automates \
+#     && git fetch --depth=1 origin e5fb635757aa57007615a75371f55dd4a24851e0 \
+#     && git checkout e5fb635757aa57007615a75371f55dd4a24851e0
+# RUN pip install -e automates
 
 RUN pip install --no-cache-dir z3-solver
 RUN pip install --no-cache-dir graphviz
diff --git a/docker/dev/user/Dockerfile b/docker/dev/user/Dockerfile
index 89f22f7f..19f1c636 100644
--- a/docker/dev/user/Dockerfile
+++ b/docker/dev/user/Dockerfile
@@ -50,20 +50,21 @@ 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>=2.3.0
-RUN pip install --no-cache-dir fastapi>=0.103.1
+RUN pip install --no-cache-dir pydantic==2.3.*
+RUN pip install --no-cache-dir pydantic-settings
+RUN pip install --no-cache-dir fastapi==0.103.*
 RUN pip install --no-cache-dir pre-commit
 RUN pip install --no-cache-dir pycln
 RUN pip install --no-cache-dir openapi-python-client
 
 WORKDIR /home/$UNAME
 
-# Install automates
-RUN git clone --depth=1 https://github.com/danbryce/automates.git automates \
-    && cd automates \
-    && git fetch --depth=1 origin e5fb635757aa57007615a75371f55dd4a24851e0 \
-    && git checkout e5fb635757aa57007615a75371f55dd4a24851e0
-RUN pip install -e automates
+# # Install automates
+# RUN git clone --depth=1 https://github.com/danbryce/automates.git automates \
+#     && cd automates \
+#     && git fetch --depth=1 origin e5fb635757aa57007615a75371f55dd4a24851e0 \
+#     && git checkout e5fb635757aa57007615a75371f55dd4a24851e0
+# RUN pip install -e automates
 
 RUN pip install --no-cache-dir z3-solver
 RUN pip install --no-cache-dir graphviz
diff --git a/docker/docker-bake.hcl b/docker/docker-bake.hcl
index 34ebeb02..3e55db84 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 = "cc1a4519cdea5ecec94033b178351424a405b472"
+  default = "43706295350b9676b786e2d9d8607b1c6e19afee"
 }
 variable "AUTOMATES_COMMIT_TAG" {
   default = "e5fb635757aa57007615a75371f55dd4a24851e0"
diff --git a/docker/ibex/Makefile.ibex b/docker/ibex/Makefile.ibex
index 83c660b7..ff74a820 100644
--- a/docker/ibex/Makefile.ibex
+++ b/docker/ibex/Makefile.ibex
@@ -15,11 +15,11 @@ DEPS_WORKSPACE=/tmp/ibex-deps
 ifneq ($(ENABLE_DEBUG),no)
 IBEX_DEBUGGING=--with-debug
 MATHLIB_DEBUGGING=--enable-debug
-GAOL_DEBUGGING=
+GAOL_DEBUGGING=--enable-debug
 else
 IBEX_DEBUGGING=
 MATHLIB_DEBUGGING=
-GAOL_DEBUGGING=
+GAOL_DEBUGGING=--disable-debug
 endif
 
 LP_LIB_ARGS=
diff --git a/notebooks/README.md b/notebooks/README.md
new file mode 100644
index 00000000..9eceaa0f
--- /dev/null
+++ b/notebooks/README.md
@@ -0,0 +1,4 @@
+This directory contains notebooks that illustrate how to use FUNMAN.  The notebooks include:
+
+- [pde_as_petrinet.ipynb](pde_as_petrinet.ipynb)
+- [funman_terrarium_demo.ipynb](funman_terrarium_demo.ipynb)
\ No newline at end of file
diff --git a/notebooks/funman_july_2024_demo.ipynb b/notebooks/funman_july_2024_demo.ipynb
new file mode 100644
index 00000000..0ca00a6d
--- /dev/null
+++ b/notebooks/funman_july_2024_demo.ipynb
@@ -0,0 +1,517 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This notebook illustrates handling the July 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\", \"2024-07\")\n",
+    "MODEL_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"SEIRHD_vacc_petrinet.json\"\n",
+    ")\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"SEIRHD_vacc_petrinet_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 = [\"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",
+    "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):\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 = 1000\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=\"SEIRHD Hackathon 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):\n",
+    "    set_timepoints(funman_request, timepoints)\n",
+    "    if not synthesize:\n",
+    "        unset_all_labels(funman_request)\n",
+    "    set_config_options(funman_request, debug=debug)\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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",
+    "# The chosen parameters result in an impossibly large value for some compartments.\n",
+    "# Further runs need to eliminate these impossible values.\n",
+    "\n",
+    "funman_request = get_request()\n",
+    "setup_common(funman_request)\n",
+    "results = run(funman_request)\n",
+    "report(results, \"unconstrained\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "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",
+    "\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": 6,
+   "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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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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": 7,
+   "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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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "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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aQC8sLFTr1q0lSXfccYfOnDmjTp06qXv37tqzZ0+dNgjg9hIaGiqTyVThJPa19SaTiVtFALihmv4xDADUFDkDoKHKzs7WXXfdJenqHwm7uLjo0UcftXFXAAAAANA41GoCvXPnzjp06JACAwPVs2dPvfLKKwoMDFRqaqp8fctfQhUAquvYsWO2bgEAAAAAGjXDMOTg8N9DPvb29mrWrJkNOwIAAACAxqNWE+hTp07VqVOnJEmJiYl68MEH9dZbb8nJyUlpaWl12R+A20xAQICtWwAAAACARs0wDN1///2WSfSLFy/qoYcekpOTk1UdVxEEAAAAgPLsavOiRx99VGPHjpUkhYWF6fjx4/riiy904sQJjRgxoi77A6rt7/+QwsOlCm5HLUn617+kYcOkob+Wpj8tXSi8pe2hmhYsWKCLFy9aHn/22WcqLi62PD5//rz++Mc/2qI1gJwBUO/IGQD1iYy5fSQmJmrYsGEaMmSIhgwZotmzZ2v48OGWx9cWoK6RMwAAAGgKajWBPnfuXBUVFVkeu7q6qnfv3mrevLnmzp1bZ80B1ZV9SnrvPalb94qfL7oozX1OWrRI2vCe1KqV9Prrt7ZHVE98fLzOnz9veTxo0CCdPHnS8rioqEivvPKKLVrDbY6caXxMJlO16pYtW2a5N2hERIR27dpVZf3bb7+tkJAQubi4qHv37tq0aZPV84ZhKCEhQb6+vmrWrJmio6N1+PDhCscqLi5WaGioTCaTzGazZf2hQ4d03333ydvbWy4uLmrfvr1mzZqly5cvW2rS0tJkMpmsFhcXl2q9ZzRM5EzjU52csUXGzJs3T/369ZOrq6tatGhRbhs//PCDHnzwQfn5+cnZ2Vn+/v6aMmWKCgoKLDXbtm0rlzEmk0k5OTk3fM9omMiY20tiYmK1FqAukTMAAABoKmo1gT5nzhxduHCh3PqioiLNmTPnppsCaqLMkJ57TprxtOTkWHHNjs+kzp2lwMCrj4cPlz788Ja1iBowDKPKx4AtkDONU3XyY926dYqLi1NiYqL27Nmjnj17KiYmRqdPn66wfseOHRo5cqTGjx+vvXv3aujQoRo6dKj2799vqVmwYIGWLl2q1NRU7dy5U82bN1dMTIwuXbpUbrwZM2bIz8+v3HpHR0eNHj1aH330kQ4dOqSUlBS99tpr5Q50e3h46NSpU5bl+PHjN3zPaJjImcbpRjljq4wpKSnR8OHD9cQTT1S4HTs7Ow0ZMkR///vf9e233yotLU1btmzRpEmTytUeOnTIKmdat25dnV2DBoaMAVDfyBkAAAA0JbW6B7phGBWebfHVV1/pzjvvvOmmgJp4a7XUs6d0112V1+TkSL6+/33s5yudPStdKZUc7Ou/RwCNGznTOF27msVPz6i83uLFizVhwgSNGzdOkpSamqqNGzdqxYoVmjlzZrn6F198UQ8++KCefvppSdJzzz2nzZs36+WXX1ZqaqoMw1BKSopmzZpluSzqX//6V3l7e2vDhg16+OGHLWN98MEH+uijj/TOO+/ogw8+sNpO+/bt1b59e8vjgIAAbdu2Tf/617+s6kwmk3x8fGqyW9BAkTON041yxlYZc+2PmtPS0irs64477rCaXA8ICNAf//hHLVy4sFxt69atKzyLHY0LGXP76dWrV7WuksE90FFXyBkAAAA0JTWaQL/jjjssl+7r1KmT1Zex0tJSXbhwocKzFoD6cuSolLFVev01W3cCoKkiZxqORx54pNyB4Mull+Xq5CpHu/+e5lLdA8ElJSXavXu34uPjLevs7OwUHR2tzMzMCl+TmZmpuLg4q3UxMTHasGGDJOnYsWPKyclRdHS05XlPT09FREQoMzPTMrmVm5urCRMmaMOGDXJ1db1hr0eOHFF6erp+85vfWK2/cOGCAgICVFZWpt69e+svf/mLunbtWuEYxcXFKi4utjyu6g8LcGuRMw3H9TljGIZKjVK5ObnJzvTfi3dVJ2dsmTE1lZ2drXfffVdRUVHlngsNDVVxcbG6deumZ599Vvfcc0+l45AzDRMZc3saOnSo5WfDMJSUlKRJkyZx0gPqBTkDAACApqZGE+gpKSkyDEOPPfaY5syZI09PT8tzTk5OCgwMVGRkZJ03CVTGvFc6lS0N/fXVxz/8ID0/7+pfMP/2t/+t8/GRPt/538fZpyQvL/7CuaF6/fXX5ebmJkm6cuWK0tLS5OXlJUlW90cHbgVypuEY+OBAy8+GYSjt5TQ98LsHFOIfIndn9xqPd/bsWZWWlsrb29tqvbe3t7755psKX5OTk1Nh/bV7Al/7t6oawzA0duxYTZo0SeHh4fruu+8q7bFfv37as2ePiouLNXHiRM2dO9fyXOfOnbVixQr16NFD+fn5Sk5OVr9+/XTgwAG1bdu23FhJSUncaqeBImcajopyZtDDg9QtoJuc7J1qNJatMqYmRo4cqffff18XL17UQw89pNd/ciNaX19fpaamKjw8XMXFxXr99dc1cOBA7dy5U717965wPHKmYSJjbk/X3/Zl0aJFmjp1qtUVboC6Qs4AAACgqanRBPqYMWMkSUFBQbrnnnvk4FCrK8ADdea3v7X+MjZxovTII9LAgdZ1kf2k+S9I33139V5bb78txTxwCxtFtbVr106vvfbfP1v38fHRm2++Wa4GuFXImYZjYtxEq8dvvfKWfvn7X2pI5BD5uvtW8qqG56WXXtL58+etzkqtzLp163T+/Hl99dVXevrpp5WcnKwZM2ZIkiIjI63+cLFfv36666679Morr+i5554rN1Z8fLzVma0FBQXy9/evg3eEm0XONBzX58zqV1brV6N/pRH9R8jNyc1GXdWfJUuWKDExUd9++60lI/73f/9X0tU/0uncubOltl+/fjp69KiWLFlS7neza8iZhomMAVDfyBkAAAA0NbWaAY+KitLRo0e1cuVKHT16VC+++KJat26tDz74QO3atav00qHArZSaKnm1kn47TGruKs2eLT31lFRaKnXoIHFyTMNU1ZmYQENDzjRuXl5esre3V25urtX63NzcSu8r7uPjU2X9tX9zc3Pl+5MbPObm5io0NFSStHXrVmVmZsrZ2dlqnPDwcI0aNUqrVq2yrLs28dSlSxeVlpZq4sSJeuqpp2RvX/40HUdHR/Xq1UtHjhypsHdnZ+dy20TDR840XrbKmJrw8fGRj4+PQkJCdOedd6p///6aPXu21dg/1bdvX3366aeVjkfOND5kDID6Rs4AAACgMarVBPr27ds1aNAg3XPPPfrkk080b948tW7dWl999ZXeeOMN/e1vf6vrPoFqefXV//48aZL1c1EDri5oWrp3765NmzZxdhNuGXKm6XByclJYWJgyMjIs9wktKytTRkaGpkyZUuFrIiMjlZGRodjYWMu6zZs3W84EDwoKko+PjzIyMiyTWQUFBdq5c6eeeOIJSdLSpUv1/PPPW16fnZ2tmJgYrVu3ThEREZX2W1ZWpsuXL6usrKzCCfTS0lLt27dPv/jFL2qyG9AAkTNNg60yprbKysokyeoe5tczm82VTq6j8SBjANQ3cgYAAACNXa0m0GfOnKnnn39ecXFxcnf/7z1Hf/azn+nll1+us+YA4Ea+++47Xb582dZtAGik4uLiNGbMGIWHh6tv375KSUlRYWGhxo0bJ0kaPXq02rRpo6SkJEnS1KlTFRUVpUWLFmnw4MFau3atvvzyS736/48SmkwmxcbG6vnnn1fHjh0VFBSk2bNny8/PzzKBdv1tKNzcrl4WukOHDpZ7l7/11ltydHRU9+7d5ezsrC+//FLx8fEaMWKEHB0dJUlz587V3XffreDgYOXl5WnhwoU6fvy4Hn/88XrfbwCqxxYZI0lZWVn68ccflZWVpdLSUpnNZklScHCw3NzctGnTJuXm5qpPnz5yc3PTgQMH9PTTT+uee+5RYGCgJCklJUVBQUHq2rWrLl26pNdff11bt27VRx99dMv2H4DaW7p0qdXjK1euKC0tTV5eXlbr//SnP93KtgAAAACgUajVBPq+ffu0Zs2acutbt26ts2fP3nRTAAAA11v7xlqrx6Wlpdq6YavOmM/I08XTsr4mB4JHjBihM2fOKCEhQTk5OQoNDVV6erq8vb0lXZ2EsrOzs9T369dPa9as0axZs/TnP/9ZHTt21IYNG9StWzdLzYwZM1RYWKiJEycqLy9P9957r9LT0+Xi4lLtvhwcHPTCCy/o22+/lWEYCggI0JQpUzRt2jRLzblz5zRhwgTl5OTojjvuUFhYmHbs2KEuXbpUezsArJXLmSul2vLeFv341Y9ydvjvpcmrmzO2ypiEhASr20H06tVLkvTxxx9r4MCBatasmV577TVNmzZNxcXF8vf3129+8xvNnDnT8pqSkhI99dRTOnnypFxdXdWjRw9t2bJF9913X7XeOwDbWrJkidVjHx8fvfnmm1brTCYTE+gAAAAAUAGTYRhGTV/Utm1brV+/Xv369ZO7u7u++uortW/fXu+9956mT5+uo0eP1kevDUpBQYE8PT2Vn58vDw+PSuuO/nhU+3L3yd+Ty0vj9nOy4KQ6eXVSiFdIpTXV/SxV5qcZ1NRUd998lvWZCksK1dK15S3sDrj1fnX3r8qtu1J2Rc4OzrI3Xb2kuclk0n/+8x+rmpvNmaaquvul6HKRPjn+idyd3OXiUP0/AgAao+tzxjAMlalMLvYuMplMksiZmqjufsm9kKvME5l8Z8Jt6dzFc3K0d1RUYFSlNWRM5aq7bw7/cFgHzxxUW4+2t7A7oGH4vuB7hXiFqLNX50pryBkAAABrtToD/eGHH9Yzzzyjt99+WyaTSWVlZfrss880ffp0jR49uq57BAAA0N8//3u5dSfyTyiibYR83bknL4Cbd33OFF8pVl5xngYEDJCbk5uNugKA+te9e3dt2rRJ/v78IQsAAAAA2N24pLy//OUvCgkJkb+/vy5cuKAuXbqof//+6tevn2bNmlXXPQIAAAAAAKCefPfdd7p8+bK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+      "text/plain": [
+       "<Figure size 2000x2000 with 49 Axes>"
+      ]
+     },
+     "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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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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": 8,
+   "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": [],
+   "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_single_scenario.ipynb b/notebooks/funman_single_scenario.ipynb
new file mode 100644
index 00000000..a71c4451
--- /dev/null
+++ b/notebooks/funman_single_scenario.ipynb
@@ -0,0 +1,2061 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This notebook illustrates example outputs from Funman, and how to work with the ParameterSpace object it creates.\n",
+    "\n",
+    "# The file scratch/hackathon/hackathon_fall_2023_demo_terarrium.py was used to generate the outputs rendered here.\n",
+    "\n",
+    "SAVED_RESULTS_DIR = \"./out\"\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_demo.example.pde import plot_spatial_timeseries\n",
+    "from IPython.display import HTML\n",
+    "import matplotlib.pyplot as plt\n",
+    "import itertools\n",
+    "from funman import FunmanWorkRequest, EncodingSchedule, POS_INFINITY\n",
+    "\n",
+    "import json\n",
+    "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n",
+    "from funman.representation import Interval\n",
+    "from funman import FUNMANConfig\n",
+    "import logging\n",
+    "from funman_demo import summarize_results\n",
+    "\n",
+    "\n",
+    "\n",
+    "RESOURCES = \"../resources\"\n",
+    "\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"terrarium-tests\")\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"terrarium-tests\")\n",
+    "MODEL_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR,\"seird.json\"\n",
+    ")\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"seird_request.json\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"stratified\")\n",
+    "# MODEL_PATH = os.path.join(\n",
+    "#     EXAMPLE_DIR,\"model_amr.json\"\n",
+    "# )\n",
+    "# REQUEST_PATH = os.path.join(\n",
+    "#     EXAMPLE_DIR, \"model_amr_request.json\"\n",
+    "# )\n",
+    "\n",
+    "\n",
+    "# %load_ext autoreload\n",
+    "# %autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-03-20 13:43:05,132 - funman.server.worker - INFO - FunmanWorker running...\n",
+      "2024-03-20 13:43:05,135 - funman.server.worker - INFO - Starting work on: e056aa4b-6a9d-4306-a94f-c6f7b4078bd0\n",
+      "2024-03-20 13:43:05,282 - funman.scenario.consistency - INFO - 1{10}:\t[-]\n",
+      "2024-03-20 13:43:05,283 - funman.server.worker - INFO - Completed work on: e056aa4b-6a9d-4306-a94f-c6f7b4078bd0\n",
+      "2024-03-20 13:43:07,142 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n",
+      "2024-03-20 13:43:07,296 - funman.server.worker - INFO - FunmanWorker exiting...\n",
+      "2024-03-20 13:43:07,297 - funman.server.worker - INFO - Worker.stop() completed.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************************************************************************\n",
+      "********************************************************************************\n",
+      "* Analysis Summary\n",
+      "********************************************************************************\n",
+      "0 Points (+:0, -:0), 1 Boxes (+:0, -:1)\n",
+      "Found box with no points\n",
+      "        {\n",
+      "    \"box\": {\n",
+      "        \"beta\": {\n",
+      "            \"lb\": 1.6,\n",
+      "            \"ub\": 1.6,\n",
+      "            \"closed_upper_bound\": true,\n",
+      "            \"original_width\": 0.0\n",
+      "        },\n",
+      "        \"epsilon\": {\n",
+      "            \"lb\": 0.33333,\n",
+      "            \"ub\": 0.33333,\n",
+      "            \"closed_upper_bound\": true,\n",
+      "            \"original_width\": 0.0\n",
+      "        },\n",
+      "        \"gamma\": {\n",
+      "            \"lb\": 0.125,\n",
+      "            \"ub\": 0.125,\n",
+      "            \"closed_upper_bound\": true,\n",
+      "            \"original_width\": 0.0\n",
+      "        },\n",
+      "        \"alpha\": {\n",
+      "            \"lb\": 0.006,\n",
+      "            \"ub\": 0.006,\n",
+      "            \"closed_upper_bound\": true,\n",
+      "            \"original_width\": 0.0\n",
+      "        },\n",
+      "        \"p_UnVax_Vax\": {\n",
+      "            \"lb\": 0.0,\n",
+      "            \"ub\": 0.0,\n",
+      "            \"closed_upper_bound\": true,\n",
+      "            \"original_width\": 0.0\n",
+      "        },\n",
+      "        \"p_Vax_UnVax\": {\n",
+      "            \"lb\": 0.1,\n",
+      "            \"ub\": 0.1,\n",
+      "            \"closed_upper_bound\": true,\n",
+      "            \"original_width\": 0.0\n",
+      "        },\n",
+      "        \"lambda\": {\n",
+      "            \"lb\": 120480.0,\n",
+      "            \"ub\": 120480.0,\n",
+      "            \"closed_upper_bound\": true,\n",
+      "            \"original_width\": 0.0\n",
+      "        },\n",
+      "        \"mu\": {\n",
+      "            \"lb\": 0.012048000000000001,\n",
+      "            \"ub\": 0.012048000000000001,\n",
+      "            \"closed_upper_bound\": true,\n",
+      "            \"original_width\": 0.0\n",
+      "        },\n",
+      "        \"City\": {\n",
+      "            \"lb\": 1.0,\n",
+      "            \"ub\": 1.0,\n",
+      "            \"closed_upper_bound\": true,\n",
+      "            \"original_width\": 0.0\n",
+      "        },\n",
+      "        \"timestep\": {\n",
+      "            \"lb\": 1.0,\n",
+      "            \"ub\": 1.0,\n",
+      "            \"closed_upper_bound\": true,\n",
+      "            \"original_width\": 0.0\n",
+      "        }\n",
+      "    },\n",
+      "    \"relevant_constraints\": [],\n",
+      "    \"expression\": \"(((((((((((((((((((((((((((Deceased_Vax_10 = (Deceased_Vax_0 + (10.0 * (((-1.0 * (Deceased_Vax_0 * p_Vax_UnVax)) + (Infected_Vax_0 * alpha)) + (Deceased_UnVax_0 * p_UnVax_Vax))))) & (Deceased_UnVax_10 = (Deceased_UnVax_0 + (10.0 * (((Deceased_Vax_0 * p_Vax_UnVax) + (alpha * Infected_UnVax_0)) - (Deceased_UnVax_0 * p_UnVax_Vax)))))) & (Recovered_Vax_10 = (Recovered_Vax_0 + (10.0 * (((-1.0 * (p_Vax_UnVax * Recovered_Vax_0)) + (Infected_Vax_0 * gamma)) + (p_UnVax_Vax * Recovered_UnVax_0)))))) & (Recovered_UnVax_10 = (Recovered_UnVax_0 + (10.0 * (((p_Vax_UnVax * Recovered_Vax_0) - (p_UnVax_Vax * Recovered_UnVax_0)) + (Infected_UnVax_0 * gamma)))))) & (Exposed_Vax_10 = (Exposed_Vax_0 + (10.0 * ((((-1.0 * (p_Vax_UnVax * Exposed_Vax_0)) + (((Infected_Vax_0 * (Total_population_0 ^ -1.0)) * Susceptible_Vax_0) * beta)) + (p_UnVax_Vax * Exposed_UnVax_0)) - (Exposed_Vax_0 * epsilon)))))) & (Deceased_Vax_0 = 0.0)) & (p_Vax_UnVax = 10000000000000001/100000000000000000)) & (Infected_Vax_0 = 1/2)) & (alpha = 30000000000000001/5000000000000000000)) & (Deceased_UnVax_0 = 0.0)) & (p_UnVax_Vax = 0.0)) & (Infected_UnVax_0 = 1/2)) & (Recovered_Vax_0 = 0.0)) & (gamma = 1/8)) & (Recovered_UnVax_0 = 0.0)) & (Exposed_Vax_0 = 10000.0)) & (epsilon = 16666500000000001/50000000000000000)) & (Total_population_0 = 10000000.0)) & (Susceptible_Vax_0 = 9979999/2)) & (beta = 16000000000000001/10000000000000000)) & (Exposed_UnVax_0 = 10000.0)) & ((((((((((((((((((((((((((Deceased_Vax_10 < 0.0) | (Deceased_UnVax_10 < 0.0)) | (Recovered_Vax_10 < 0.0)) | (Recovered_UnVax_10 < 0.0)) | (Exposed_Vax_10 < 0.0)) | (! (Deceased_Vax_10 = (Deceased_Vax_0 + (10.0 * (((-1.0 * (Deceased_Vax_0 * p_Vax_UnVax)) + (Infected_Vax_0 * alpha)) + (Deceased_UnVax_0 * p_UnVax_Vax))))))) | (! (Deceased_UnVax_10 = (Deceased_UnVax_0 + (10.0 * (((Deceased_Vax_0 * p_Vax_UnVax) + (alpha * Infected_UnVax_0)) - (Deceased_UnVax_0 * p_UnVax_Vax))))))) | (! (Recovered_Vax_10 = (Recovered_Vax_0 + (10.0 * (((-1.0 * (p_Vax_UnVax * Recovered_Vax_0)) + (Infected_Vax_0 * gamma)) + (p_UnVax_Vax * Recovered_UnVax_0))))))) | (! (Recovered_UnVax_10 = (Recovered_UnVax_0 + (10.0 * (((p_Vax_UnVax * Recovered_Vax_0) - (p_UnVax_Vax * Recovered_UnVax_0)) + (Infected_UnVax_0 * gamma))))))) | (! (Exposed_Vax_10 = (Exposed_Vax_0 + (10.0 * ((((-1.0 * (p_Vax_UnVax * Exposed_Vax_0)) + (((Infected_Vax_0 * (Total_population_0 ^ -1.0)) * Susceptible_Vax_0) * beta)) + (p_UnVax_Vax * Exposed_UnVax_0)) - (Exposed_Vax_0 * epsilon))))))) | (! (Deceased_Vax_0 = 0.0))) | (! (p_Vax_UnVax = 10000000000000001/100000000000000000))) | (! (Infected_Vax_0 = 1/2))) | (! (alpha = 30000000000000001/5000000000000000000))) | (! (Deceased_UnVax_0 = 0.0))) | (! (p_UnVax_Vax = 0.0))) | (! (Infected_UnVax_0 = 1/2))) | (! (Recovered_Vax_0 = 0.0))) | (! (gamma = 1/8))) | (! (Recovered_UnVax_0 = 0.0))) | (! (Exposed_Vax_0 = 10000.0))) | (! (epsilon = 16666500000000001/50000000000000000))) | (! (Total_population_0 = 10000000.0))) | (! (Susceptible_Vax_0 = 9979999/2))) | (! (beta = 16000000000000001/10000000000000000))) | (! (Exposed_UnVax_0 = 10000.0)))) & (! (Deceased_Vax_10 < 0.0))) & (! (Deceased_UnVax_10 < 0.0))) & (! (Recovered_Vax_10 < 0.0))) & (! (Recovered_UnVax_10 < 0.0))) & (! (Exposed_Vax_10 < 0.0)))\"\n",
+      "}\n",
+      "        \n",
+      "********************************************************************************\n",
+      "    \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Feb 2024 Hackathon Scenario 3 (SEIRD) from Terrarium\n",
+    "\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"terrarium-tests\")\n",
+    "MODEL_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"stratified_seird_model.json\"\n",
+    ")\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"stratified_seird_request.json\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "N = \"Total_population\"\n",
+    "STATES_Vax = [\"Susceptible_Vax\", \"Exposed_Vax\", \"Infected_Vax\", \"Recovered_Vax\", \"Deceased_Vax\"]\n",
+    "STATES_UnVax = [\"Susceptible_UnVax\", \"Exposed_UnVax\", \"Infected_UnVax\", \"Recovered_UnVax\", \"Deceased_UnVax\"]\n",
+    "STATES = STATES_UnVax + STATES_Vax\n",
+    "COMPARTMENTS = STATES_Vax + STATES_UnVax + [N]\n",
+    "MAX_TIME=80\n",
+    "STEP_SIZE=10\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    "    # funman_request.structure_parameters[0].schedules= []\n",
+    "    # for t in range(0, MAX_TIME, STEP_SIZE):\n",
+    "    #     funman_request.structure_parameters[0].schedules.append(EncodingSchedule(timepoints=list(range(0, t+STEP_SIZE, STEP_SIZE))))\n",
+    "    \n",
+    "\n",
+    "    # # funman_request.config.substitute_subformulas = True\n",
+    "    # # funman_request.config.use_transition_symbols = True\n",
+    "    # funman_request.config.use_compartmental_constraints=True\n",
+    "    # funman_request.config.save_smtlib=\"./out\"\n",
+    "    # funman_request.config.tolerance = 0.01\n",
+    "    # funman_request.config.verbosity = 20\n",
+    "    # funman_request.config.dreal_log_level = \"info\"\n",
+    "\n",
+    "    # factor = 1.6 \n",
+    "    to_synthesize = [\"beta\"]\n",
+    "    # keep_constant = [\"p_Vax_UnVax\", \"p_UnVax_Vax\"]\n",
+    "    # for p in [po for po in funman_request.parameters if po.name not in keep_constant # if po.name  in to_synthesize\n",
+    "    # ]:\n",
+    "    #     param = funman_request.parameter(p.name)\n",
+    "    #     param.interval.lb = max(p.interval.lb-factor, 0)\n",
+    "    #     param.interval.ub = min(p.interval.ub+factor, max(1, p.interval.ub+factor))\n",
+    "    #     # param.label = \"all\" if p.name in to_synthesize else \"any\"\n",
+    "    #     param.label = \"any\"\n",
+    "    #     print(param)\n",
+    "\n",
+    "    # # Susceptible is decreasing,  dS/dt <= 0, S_{t+1} - S_t <= 0\n",
+    "    # c1 = LinearConstraint(name=\"c1\", additive_bounds=Interval(ub=0, closed_upper_bounds=True), variables=[\"Susceptible\"], derivative=True)\n",
+    "    # funman_request.constraints.append(c1)\n",
+    "\n",
+    "    # # # R is increasing, dR/dt >= 0,   R_{t+1} - R_t >= 0\n",
+    "    # c2 = LinearConstraint(name=\"c2\", additive_bounds=Interval(lb=0), variables=[\"Recovered\"], derivative=True)\n",
+    "    # funman_request.constraints.append(c2)\n",
+    "\n",
+    "    # # # D is increasing, dD/dt >= 0, dD/dt >= 0,   D_{t+1} - D_t >= 0\n",
+    "    # c3 = LinearConstraint(name=\"c3\", additive_bounds=Interval(lb=0), variables=[\"Deceased\"], derivative=True)\n",
+    "    # funman_request.constraints.append(c3)\n",
+    "\n",
+    "    # # # Population size maintained\n",
+    "    # # c4 = LinearConstraint(name=\"compartment\", additive_bounds=Interval(lb=0, ub=0, closed_upper_bound=True), variables=COMPARTMENTS, weights=[1, 1, 1, 1, 1, -1])\n",
+    "    # # funman_request.constraints.append(c4)\n",
+    "\n",
+    "    # # # Infected peak\n",
+    "    # # c5 = LinearConstraint(name=\"Infected_peak\", additive_bounds=Interval(lb=50), variables=[\"Infected\"], timepoints=Interval(lb=40, ub=40, closed_upper_bound=True))\n",
+    "    # # funman_request.constraints.append(c5)\n",
+    "\n",
+    "    # # for sv in STATES:\n",
+    "    # #     funman_request.constraints.append(StateVariableConstraint(name=f\"{sv}_bounds\", variable=sv, interval=Interval(lb=0, ub=100, closed_upper_bound=True)))\n",
+    "    \n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SEIRD demo\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\", synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Automatic initialization of gaol... done\n",
+      "2024-03-15 21:31:50,369 - /root/funman/src/funman/search/smt_check.py - DEBUG - Solving schedule: timepoints=[0, 10, 20]\n",
+      "2024-03-15 21:31:50,372 - funman_dreal.solver - DEBUG - Created new Solver ...\n",
+      "2024-03-15 21:31:50,402 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 0 to 10\n",
+      "2024-03-15 21:31:50,502 - /root/funman/src/funman/translate/petrinet.py - DEBUG - Encoding step: 10 to 20\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name='beta' interval={'lb': 0, 'ub': 1.833, 'closed_upper_bound': True, 'original_width': 0.0} label='any'\n",
+      "name='epsilon' interval={'lb': 0, 'ub': 1.3333333333333335, 'closed_upper_bound': True, 'original_width': 0.0} label='any'\n",
+      "name='gamma' interval={'lb': 0, 'ub': 1.125, 'closed_upper_bound': True, 'original_width': 0.0} label='any'\n",
+      "name='alpha' interval={'lb': 0, 'ub': 1.006, 'closed_upper_bound': True, 'original_width': 0.0} label='any'\n",
+      "name='lambda' interval={'lb': 120479.0, 'ub': 120481.0, 'closed_upper_bound': True, 'original_width': 0.0} label='any'\n",
+      "name='mu' interval={'lb': 0, 'ub': 1.012048, 'closed_upper_bound': True, 'original_width': 0.0} label='any'\n",
+      "name='City' interval={'lb': 0.0, 'ub': 2.0, 'closed_upper_bound': True, 'original_width': 0.0} label='any'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-03-15 21:31:55,309 - funman.api.run - INFO - Dumping results to ./out/7d5e7746-c011-4d17-a50e-5647db2c0b91.json\n",
+      "2024-03-15 21:31:55,323 - funman.scenario.consistency - INFO - 2{20}:\t[+]\n",
+      "2024-03-15 21:31:55,333 - funman.server.worker - INFO - Completed work on: 7d5e7746-c011-4d17-a50e-5647db2c0b91\n",
+      "2024-03-15 21:32:05,342 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n",
+      "2024-03-15 21:32:05,344 - funman.server.worker - INFO - FunmanWorker exiting...\n",
+      "2024-03-15 21:32:05,347 - funman.server.worker - INFO - Worker.stop() completed.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total # of ibex-fwdbwd Pruning                @ Pruning level        =          210127\n",
+      "Total # of ibex-fwdbwd Pruning (zero-effect)  @ Pruning level        =          159490\n",
+      "Total time spent in Pruning                   @ Pruning level        =        0.265446 sec\n",
+      "Total time spent in making constraints        @ Pruning level        =        0.000000 sec\n",
+      "Total # of Convert                            @ Ibex Converter       =              30\n",
+      "Total time spent in Converting                @ Ibex Converter       =        0.000721 sec\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3200x3200 with 49 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************************************************************************\n",
+      "********************************************************************************\n",
+      "* Analysis Summary\n",
+      "********************************************************************************\n",
+      "1 Points (+:1, -:0), 1 Boxes (+:1, -:0)\n",
+      "Parameters = {beta[0, 1.833): 2.5e-322, epsilon[0, 1.3333333333333335): 0.628988076607717, gamma[0, 1.125): 0.06033634263731719, alpha[0, 1.006): 0.012167379326968451, lambda[120479.0, 120481.0): 120480.75931891351, mu[0, 1.012048): 0.7484558002587655, City[0.0, 2.0): 1.3875134480164828}\n",
+      "        # {beta[0, 1.833): 2.5e-322, epsilon[0, 1.3333333333333335): 0.628988076607717, gamma[0, 1.125): 0.06033634263731719, alpha[0, 1.006): 0.012167379326968451, lambda[120479.0, 120481.0): 120480.75931891351, mu[0, 1.012048): 0.7484558002587655, City[0.0, 2.0): 1.3875134480164828}\n",
+      "                  City  Deceased        Exposed  Infected  Recovered  Susceptible  \\\n",
+      "time                                                                        \n",
+      "0.0   1.387513  0.000000   0.000000e+00  1.000000   0.000000         99.0   \n",
+      "1.0   1.387513  0.012056  1.976263e-323  0.928892   0.060448         99.0   \n",
+      "2.0   1.387513  0.024112  3.952525e-323  0.857784   0.120897         99.0   \n",
+      "3.0   1.387513  0.036167  5.928788e-323  0.786676   0.181345         99.0   \n",
+      "4.0   1.387513  0.048223  7.905050e-323  0.715568   0.241794         99.0   \n",
+      "5.0   1.387513  0.060279  9.881313e-323  0.644459   0.302242         99.0   \n",
+      "6.0   1.387513  0.072335  1.185758e-322  0.573351   0.362691         99.0   \n",
+      "7.0   1.387513  0.084391  1.383384e-322  0.502243   0.423139         99.0   \n",
+      "8.0   1.387513  0.096446  1.581010e-322  0.431135   0.483587         99.0   \n",
+      "9.0   1.387513  0.108502  1.778636e-322  0.360027   0.544036         99.0   \n",
+      "10.0  1.387513  0.120558  1.630417e-322  0.288919   0.604484         99.0   \n",
+      "11.0  1.387513  0.124025  2.420922e-322  0.266583   0.623353         99.0   \n",
+      "12.0  1.387513  0.127493  3.211427e-322  0.244246   0.642222         99.0   \n",
+      "13.0  1.387513  0.130960  4.001932e-322  0.221910   0.661091         99.0   \n",
+      "14.0  1.387513  0.134427  4.792437e-322  0.199574   0.679960         99.0   \n",
+      "15.0  1.387513  0.137895  5.582942e-322  0.177237   0.698829         99.0   \n",
+      "16.0  1.387513  0.141362  6.373447e-322  0.154901   0.717698         99.0   \n",
+      "17.0  1.387513  0.144830  7.163952e-322  0.132565   0.736567         99.0   \n",
+      "18.0  1.387513  0.148297  7.954457e-322  0.110229   0.755435         99.0   \n",
+      "19.0  1.387513  0.151765  8.744962e-322  0.087892   0.774304         99.0   \n",
+      "20.0  1.387513  0.155232  9.387247e-322  0.065556   0.793173         99.0   \n",
+      "\n",
+      "      Total_population     alpha assume_Deceased assume_Exposed  \\\n",
+      "time                                                              \n",
+      "0.0              100.0  0.012167            None           None   \n",
+      "1.0              100.0  0.012167            None           None   \n",
+      "2.0              100.0  0.012167            None           None   \n",
+      "3.0              100.0  0.012167            None           None   \n",
+      "4.0              100.0  0.012167            None           None   \n",
+      "5.0              100.0  0.012167            None           None   \n",
+      "6.0              100.0  0.012167            None           None   \n",
+      "7.0              100.0  0.012167            None           None   \n",
+      "8.0              100.0  0.012167            None           None   \n",
+      "9.0              100.0  0.012167            None           None   \n",
+      "10.0             100.0  0.012167            None           None   \n",
+      "11.0             100.0  0.012167            None           None   \n",
+      "12.0             100.0  0.012167            None           None   \n",
+      "13.0             100.0  0.012167            None           None   \n",
+      "14.0             100.0  0.012167            None           None   \n",
+      "15.0             100.0  0.012167            None           None   \n",
+      "16.0             100.0  0.012167            None           None   \n",
+      "17.0             100.0  0.012167            None           None   \n",
+      "18.0             100.0  0.012167            None           None   \n",
+      "19.0             100.0  0.012167            None           None   \n",
+      "20.0             100.0  0.012167            None           None   \n",
+      "\n",
+      "     assume_Infected assume_Recovered assume_Susceptible           beta  \\\n",
+      "time                                                                      \n",
+      "0.0             None             None               None  2.519735e-322   \n",
+      "1.0             None             None               None  2.519735e-322   \n",
+      "2.0             None             None               None  2.519735e-322   \n",
+      "3.0             None             None               None  2.519735e-322   \n",
+      "4.0             None             None               None  2.519735e-322   \n",
+      "5.0             None             None               None  2.519735e-322   \n",
+      "6.0             None             None               None  2.519735e-322   \n",
+      "7.0             None             None               None  2.519735e-322   \n",
+      "8.0             None             None               None  2.519735e-322   \n",
+      "9.0             None             None               None  2.519735e-322   \n",
+      "10.0            None             None               None  2.519735e-322   \n",
+      "11.0            None             None               None  2.519735e-322   \n",
+      "12.0            None             None               None  2.519735e-322   \n",
+      "13.0            None             None               None  2.519735e-322   \n",
+      "14.0            None             None               None  2.519735e-322   \n",
+      "15.0            None             None               None  2.519735e-322   \n",
+      "16.0            None             None               None  2.519735e-322   \n",
+      "17.0            None             None               None  2.519735e-322   \n",
+      "18.0            None             None               None  2.519735e-322   \n",
+      "19.0            None             None               None  2.519735e-322   \n",
+      "20.0            None             None               None  2.519735e-322   \n",
+      "\n",
+      "       epsilon     gamma  id label         lambda        mu  \n",
+      "time                                                         \n",
+      "0.0   0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "1.0   0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "2.0   0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "3.0   0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "4.0   0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "5.0   0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "6.0   0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "7.0   0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "8.0   0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "9.0   0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "10.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "11.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "12.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "13.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "14.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "15.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "16.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "17.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "18.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "19.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "20.0  0.628988  0.060336   0  true  120480.759319  0.748456  \n",
+      "        \n",
+      "********************************************************************************\n",
+      "    \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Feb 2024 Hackathon Scenario 3 (SEIRD)\n",
+    "\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"mira\")\n",
+    "MODEL_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"models\", \"scenario_3_seird.json\"\n",
+    ")\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"requests\", \"scenario_3_seird_request.json\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "N = \"Total_population\"\n",
+    "STATES = [\"Susceptible\", \"Exposed\", \"Infected\", \"Recovered\", \"Deceased\"]\n",
+    "COMPARTMENTS = STATES + [N]\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    "    funman_request.structure_parameters[0].schedules[0] = EncodingSchedule(timepoints=list(range(0, 30, 10)))\n",
+    "\n",
+    "    # funman_request.config.substitute_subformulas = True\n",
+    "    # funman_request.config.use_transition_symbols = True\n",
+    "    funman_request.config.use_compartmental_constraints=True\n",
+    "    funman_request.config.save_smtlib=\"./out\"\n",
+    "    funman_request.config.tolerance = 0.001\n",
+    "    funman_request.config.verbosity = 10\n",
+    "    funman_request.config.dreal_log_level = \"info\"\n",
+    "\n",
+    "    factor = 1\n",
+    "    to_synthesize = [\"beta\"]\n",
+    "    for p in [po for po in funman_request.parameters if po.name not in []]:\n",
+    "        param = funman_request.parameter(p.name)\n",
+    "        param.interval.lb = max(p.interval.lb-factor, 0)\n",
+    "        param.interval.ub = min(p.interval.ub+factor, max(1, p.interval.ub+factor))\n",
+    "        # param.label = \"all\" if p.name in to_synthesize else \"any\"\n",
+    "        print(param)\n",
+    "\n",
+    "    # Susceptible is decreasing,  dS/dt <= 0, S_{t+1} - S_t <= 0\n",
+    "    c1 = LinearConstraint(name=\"c1\", additive_bounds=Interval(ub=0, closed_upper_bounds=True), variables=[\"Susceptible\"], derivative=True)\n",
+    "    funman_request.constraints.append(c1)\n",
+    "\n",
+    "    # # R is increasing, dR/dt >= 0,   R_{t+1} - R_t >= 0\n",
+    "    c2 = LinearConstraint(name=\"c2\", additive_bounds=Interval(lb=0), variables=[\"Recovered\"], derivative=True)\n",
+    "    funman_request.constraints.append(c2)\n",
+    "\n",
+    "    # # D is increasing, dD/dt >= 0, dD/dt >= 0,   D_{t+1} - D_t >= 0\n",
+    "    c3 = LinearConstraint(name=\"c3\", additive_bounds=Interval(lb=0), variables=[\"Deceased\"], derivative=True)\n",
+    "    funman_request.constraints.append(c3)\n",
+    "\n",
+    "    # # Population size maintained\n",
+    "    # c4 = LinearConstraint(name=\"compartment\", additive_bounds=Interval(lb=0, ub=0, closed_upper_bound=True), variables=COMPARTMENTS, weights=[1, 1, 1, 1, 1, -1])\n",
+    "    # funman_request.constraints.append(c4)\n",
+    "\n",
+    "    # # Infected peak\n",
+    "    # c5 = LinearConstraint(name=\"Infected_peak\", additive_bounds=Interval(lb=50), variables=[\"Infected\"], timepoints=Interval(lb=40, ub=40, closed_upper_bound=True))\n",
+    "    # funman_request.constraints.append(c5)\n",
+    "\n",
+    "    for sv in STATES:\n",
+    "        funman_request.constraints.append(StateVariableConstraint(name=f\"{sv}_bounds\", variable=sv, interval=Interval(lb=0, ub=100, closed_upper_bound=True)))\n",
+    "    \n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SEIRD demo\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\", synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Automatic initialization of gaol... done\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Feb 2024 Hackathon Scenario 3.3 (SEIRD)\n",
+    "\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"mira\")\n",
+    "MODEL_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"models\", \"scenario_3_seird_stratified.json\"\n",
+    ")\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"requests\", \"scenario_3_seird_request_stratified.json\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "N = \"Total_population\"\n",
+    "STATES_Vax = [\"Susceptible_Vax\", \"Exposed_Vax\", \"Infected_Vax\", \"Recovered_Vax\", \"Deceased_Vax\"]\n",
+    "STATES_UnVax = [\"Susceptible_UnVax\", \"Exposed_UnVax\", \"Infected_UnVax\", \"Recovered_UnVax\", \"Deceased_UnVax\"]\n",
+    "STATES = STATES_UnVax + STATES_Vax\n",
+    "COMPARTMENTS = STATES_Vax + STATES_UnVax + [N]\n",
+    "MAX_TIME=100\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    "    funman_request.structure_parameters[0].schedules[0] = EncodingSchedule(timepoints=list(range(0, MAX_TIME, 10)))\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",
+    "    funman_request.config.save_smtlib=\"./out\"\n",
+    "    funman_request.config.tolerance = 0.001\n",
+    "    # funman_request.config.verbosity = 5\n",
+    "    # funman_request.config.dreal_log_level = \"trace\"\n",
+    "\n",
+    "    factor = .1\n",
+    "    to_synthesize = [\"beta_Vax\", \"beta_UnVax\"]\n",
+    "    for p in [po for po in funman_request.parameters if po.name not in [\"t_mask\", \"mask\"]]:\n",
+    "        param = funman_request.parameter(p.name)\n",
+    "        \n",
+    "        param.interval.lb = max(p.interval.lb-factor, min(0, p.interval.lb))\n",
+    "        param.interval.ub = min(p.interval.ub+factor, max(1, p.interval.ub))\n",
+    "    #     # param.label = \"all\" if p.name in to_synthesize else \"any\"\n",
+    "\n",
+    "    param = funman_request.parameter(\"t_mask\")\n",
+    "    param.interval.lb = 20\n",
+    "    param.interval.ub = 20\n",
+    "\n",
+    "    # dVax/dt > 0  -(Vax_{t+1}-Vax_t) < 0\n",
+    "    inc_vax = LinearConstraint(name=\"inc_vax\", additive_bounds=Interval(ub=0), variables=STATES_Vax, derivative=True, weights=[-1]*len(STATES_Vax))\n",
+    "    funman_request.constraints.append(inc_vax)\n",
+    "\n",
+    "    # dUnVax/dt < 0  (UnVax_{t+1}-UnVax_t) < 0\n",
+    "    dec_unvax = LinearConstraint(name=\"dec_unvax\", additive_bounds=Interval(ub=0), variables=STATES_UnVax, derivative=True)\n",
+    "    funman_request.constraints.append(dec_unvax)\n",
+    "\n",
+    "    # Susceptible is decreasing,  dS/dt =< 0, S_{t+1} - S_t =< 0\n",
+    "    c1 = LinearConstraint(name=\"c1\", additive_bounds=Interval(ub=0, closed_upper_bound=True), variables=[\"Susceptible_Vax\", \"Susceptible_UnVax\"], derivative=True, timepoints=Interval(lb=0))\n",
+    "    funman_request.constraints.append(c1)\n",
+    "\n",
+    "    # # R is increasing, dR/dt >= 0\n",
+    "    c2 = LinearConstraint(name=\"c2\", additive_bounds=Interval(lb=0), variables=[\"Recovered_Vax\", \"Recovered_UnVax\"],  derivative=True, timepoints=Interval(lb=0))\n",
+    "    funman_request.constraints.append(c2)\n",
+    "\n",
+    "    # # D is increasing, dD/dt >= 0\n",
+    "    c3 = LinearConstraint(name=\"c3\", additive_bounds=Interval(lb=0), variables=[\"Deceased_Vax\", \"Deceased_UnVax\"], derivative=True,  timepoints=Interval(lb=0))\n",
+    "    funman_request.constraints.append(c3)\n",
+    "    \n",
+    "\n",
+    "    # Population size maintained\n",
+    "    c4 = LinearConstraint(name=\"compartment\", additive_bounds=Interval(lb=0, ub=0, closed_upper_bound=True), variables=COMPARTMENTS, weights=[1]*len(STATES) + [-1])\n",
+    "    funman_request.constraints.append(c4)\n",
+    "\n",
+    "    # # Infected peak\n",
+    "    # c5 = LinearConstraint(name=\"Infected_peak\", additive_bounds=Interval(lb=10), variables=[\"Infected_Vax\", \"Infected_UnVax\"], timepoints=Interval(lb=40, ub=40, closed_upper_bound=True))\n",
+    "    # funman_request.constraints.append(c5)\n",
+    "\n",
+    "    for sv in STATES:\n",
+    "        funman_request.constraints.append(StateVariableConstraint(name=f\"{sv}_bounds\", variable=sv, interval=Interval(lb=0, ub=100, closed_upper_bound=True)))\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SEIRD demo\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\", synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Susceptible_Vax</th>\n",
+       "      <th>Exposed_Vax</th>\n",
+       "      <th>Infected_Vax</th>\n",
+       "      <th>Recovered_Vax</th>\n",
+       "      <th>Deceased_Vax</th>\n",
+       "      <th>Susceptible_UnVax</th>\n",
+       "      <th>Exposed_UnVax</th>\n",
+       "      <th>Infected_UnVax</th>\n",
+       "      <th>Recovered_UnVax</th>\n",
+       "      <th>Deceased_UnVax</th>\n",
+       "      <th>Total_population</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>2.416951</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.943061</td>\n",
+       "      <td>0.033442</td>\n",
+       "      <td>0.047264</td>\n",
+       "      <td>94.713914</td>\n",
+       "      <td>0.866637</td>\n",
+       "      <td>0.902980</td>\n",
+       "      <td>0.028877</td>\n",
+       "      <td>0.043489</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>4.833902</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.886123</td>\n",
+       "      <td>0.066884</td>\n",
+       "      <td>0.094529</td>\n",
+       "      <td>91.427828</td>\n",
+       "      <td>1.733274</td>\n",
+       "      <td>0.805960</td>\n",
+       "      <td>0.057754</td>\n",
+       "      <td>0.086977</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.0</th>\n",
+       "      <td>7.250853</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.829184</td>\n",
+       "      <td>0.100326</td>\n",
+       "      <td>0.141793</td>\n",
+       "      <td>88.141743</td>\n",
+       "      <td>2.599911</td>\n",
+       "      <td>0.708940</td>\n",
+       "      <td>0.086632</td>\n",
+       "      <td>0.130466</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>9.667803</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.772245</td>\n",
+       "      <td>0.133768</td>\n",
+       "      <td>0.189057</td>\n",
+       "      <td>84.855657</td>\n",
+       "      <td>3.466549</td>\n",
+       "      <td>0.611920</td>\n",
+       "      <td>0.115509</td>\n",
+       "      <td>0.173955</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.0</th>\n",
+       "      <td>12.084754</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.715307</td>\n",
+       "      <td>0.167210</td>\n",
+       "      <td>0.236321</td>\n",
+       "      <td>81.569571</td>\n",
+       "      <td>4.333186</td>\n",
+       "      <td>0.514900</td>\n",
+       "      <td>0.144386</td>\n",
+       "      <td>0.217443</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.0</th>\n",
+       "      <td>14.501705</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.658368</td>\n",
+       "      <td>0.200652</td>\n",
+       "      <td>0.283586</td>\n",
+       "      <td>78.283485</td>\n",
+       "      <td>5.199823</td>\n",
+       "      <td>0.417880</td>\n",
+       "      <td>0.173263</td>\n",
+       "      <td>0.260932</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7.0</th>\n",
+       "      <td>16.918656</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.601430</td>\n",
+       "      <td>0.234094</td>\n",
+       "      <td>0.330850</td>\n",
+       "      <td>74.997399</td>\n",
+       "      <td>6.066460</td>\n",
+       "      <td>0.320860</td>\n",
+       "      <td>0.202140</td>\n",
+       "      <td>0.304421</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>19.335607</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.544491</td>\n",
+       "      <td>0.267536</td>\n",
+       "      <td>0.378114</td>\n",
+       "      <td>71.711313</td>\n",
+       "      <td>6.933097</td>\n",
+       "      <td>0.223839</td>\n",
+       "      <td>0.231017</td>\n",
+       "      <td>0.347909</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.0</th>\n",
+       "      <td>21.752558</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.487552</td>\n",
+       "      <td>0.300978</td>\n",
+       "      <td>0.425378</td>\n",
+       "      <td>68.425228</td>\n",
+       "      <td>7.799734</td>\n",
+       "      <td>0.126819</td>\n",
+       "      <td>0.259895</td>\n",
+       "      <td>0.391398</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.0</th>\n",
+       "      <td>24.169508</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.430614</td>\n",
+       "      <td>0.334420</td>\n",
+       "      <td>0.472643</td>\n",
+       "      <td>65.139142</td>\n",
+       "      <td>8.666371</td>\n",
+       "      <td>0.029799</td>\n",
+       "      <td>0.288772</td>\n",
+       "      <td>0.434887</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11.0</th>\n",
+       "      <td>25.776021</td>\n",
+       "      <td>0.213676</td>\n",
+       "      <td>0.394368</td>\n",
+       "      <td>0.355536</td>\n",
+       "      <td>0.494118</td>\n",
+       "      <td>63.517605</td>\n",
+       "      <td>8.131708</td>\n",
+       "      <td>0.361195</td>\n",
+       "      <td>0.282469</td>\n",
+       "      <td>0.436183</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12.0</th>\n",
+       "      <td>27.382534</td>\n",
+       "      <td>0.427351</td>\n",
+       "      <td>0.358123</td>\n",
+       "      <td>0.376652</td>\n",
+       "      <td>0.515593</td>\n",
+       "      <td>61.896068</td>\n",
+       "      <td>7.597046</td>\n",
+       "      <td>0.692591</td>\n",
+       "      <td>0.276166</td>\n",
+       "      <td>0.437479</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13.0</th>\n",
+       "      <td>28.989047</td>\n",
+       "      <td>0.641027</td>\n",
+       "      <td>0.321878</td>\n",
+       "      <td>0.397769</td>\n",
+       "      <td>0.537068</td>\n",
+       "      <td>60.274531</td>\n",
+       "      <td>7.062383</td>\n",
+       "      <td>1.023987</td>\n",
+       "      <td>0.269864</td>\n",
+       "      <td>0.438775</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14.0</th>\n",
+       "      <td>30.595560</td>\n",
+       "      <td>0.854702</td>\n",
+       "      <td>0.285632</td>\n",
+       "      <td>0.418885</td>\n",
+       "      <td>0.558543</td>\n",
+       "      <td>58.652994</td>\n",
+       "      <td>6.527720</td>\n",
+       "      <td>1.355383</td>\n",
+       "      <td>0.263561</td>\n",
+       "      <td>0.440072</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15.0</th>\n",
+       "      <td>32.202073</td>\n",
+       "      <td>1.068378</td>\n",
+       "      <td>0.249387</td>\n",
+       "      <td>0.440001</td>\n",
+       "      <td>0.580018</td>\n",
+       "      <td>57.031458</td>\n",
+       "      <td>5.993057</td>\n",
+       "      <td>1.686779</td>\n",
+       "      <td>0.257259</td>\n",
+       "      <td>0.441368</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16.0</th>\n",
+       "      <td>33.808587</td>\n",
+       "      <td>1.282054</td>\n",
+       "      <td>0.213142</td>\n",
+       "      <td>0.461118</td>\n",
+       "      <td>0.601493</td>\n",
+       "      <td>55.409921</td>\n",
+       "      <td>5.458394</td>\n",
+       "      <td>2.018174</td>\n",
+       "      <td>0.250956</td>\n",
+       "      <td>0.442664</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17.0</th>\n",
+       "      <td>35.415100</td>\n",
+       "      <td>1.495729</td>\n",
+       "      <td>0.176896</td>\n",
+       "      <td>0.482234</td>\n",
+       "      <td>0.622968</td>\n",
+       "      <td>53.788384</td>\n",
+       "      <td>4.923731</td>\n",
+       "      <td>2.349570</td>\n",
+       "      <td>0.244653</td>\n",
+       "      <td>0.443960</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18.0</th>\n",
+       "      <td>37.021613</td>\n",
+       "      <td>1.709405</td>\n",
+       "      <td>0.140651</td>\n",
+       "      <td>0.503350</td>\n",
+       "      <td>0.644443</td>\n",
+       "      <td>52.166847</td>\n",
+       "      <td>4.389068</td>\n",
+       "      <td>2.680966</td>\n",
+       "      <td>0.238351</td>\n",
+       "      <td>0.445257</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19.0</th>\n",
+       "      <td>38.628126</td>\n",
+       "      <td>1.923080</td>\n",
+       "      <td>0.104406</td>\n",
+       "      <td>0.524467</td>\n",
+       "      <td>0.665918</td>\n",
+       "      <td>50.545310</td>\n",
+       "      <td>3.854405</td>\n",
+       "      <td>3.012362</td>\n",
+       "      <td>0.232048</td>\n",
+       "      <td>0.446553</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20.0</th>\n",
+       "      <td>40.234639</td>\n",
+       "      <td>2.136756</td>\n",
+       "      <td>0.068160</td>\n",
+       "      <td>0.545583</td>\n",
+       "      <td>0.687393</td>\n",
+       "      <td>48.923773</td>\n",
+       "      <td>3.319742</td>\n",
+       "      <td>3.343758</td>\n",
+       "      <td>0.225745</td>\n",
+       "      <td>0.447849</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21.0</th>\n",
+       "      <td>41.442926</td>\n",
+       "      <td>2.103367</td>\n",
+       "      <td>0.262388</td>\n",
+       "      <td>0.553422</td>\n",
+       "      <td>0.690961</td>\n",
+       "      <td>46.280319</td>\n",
+       "      <td>4.544584</td>\n",
+       "      <td>3.149179</td>\n",
+       "      <td>0.316784</td>\n",
+       "      <td>0.593839</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>42.651213</td>\n",
+       "      <td>2.069978</td>\n",
+       "      <td>0.456615</td>\n",
+       "      <td>0.561261</td>\n",
+       "      <td>0.694530</td>\n",
+       "      <td>43.636864</td>\n",
+       "      <td>5.769425</td>\n",
+       "      <td>2.954601</td>\n",
+       "      <td>0.407823</td>\n",
+       "      <td>0.739829</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23.0</th>\n",
+       "      <td>43.859499</td>\n",
+       "      <td>2.036589</td>\n",
+       "      <td>0.650842</td>\n",
+       "      <td>0.569100</td>\n",
+       "      <td>0.698098</td>\n",
+       "      <td>40.993409</td>\n",
+       "      <td>6.994266</td>\n",
+       "      <td>2.760023</td>\n",
+       "      <td>0.498862</td>\n",
+       "      <td>0.885819</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24.0</th>\n",
+       "      <td>45.067786</td>\n",
+       "      <td>2.003200</td>\n",
+       "      <td>0.845070</td>\n",
+       "      <td>0.576940</td>\n",
+       "      <td>0.701666</td>\n",
+       "      <td>38.349954</td>\n",
+       "      <td>8.219107</td>\n",
+       "      <td>2.565444</td>\n",
+       "      <td>0.589901</td>\n",
+       "      <td>1.031809</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25.0</th>\n",
+       "      <td>46.276073</td>\n",
+       "      <td>1.969811</td>\n",
+       "      <td>1.039297</td>\n",
+       "      <td>0.584779</td>\n",
+       "      <td>0.705234</td>\n",
+       "      <td>35.706499</td>\n",
+       "      <td>9.443948</td>\n",
+       "      <td>2.370866</td>\n",
+       "      <td>0.680940</td>\n",
+       "      <td>1.177799</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26.0</th>\n",
+       "      <td>47.484360</td>\n",
+       "      <td>1.936422</td>\n",
+       "      <td>1.233524</td>\n",
+       "      <td>0.592618</td>\n",
+       "      <td>0.708802</td>\n",
+       "      <td>33.063045</td>\n",
+       "      <td>10.668789</td>\n",
+       "      <td>2.176287</td>\n",
+       "      <td>0.771979</td>\n",
+       "      <td>1.323788</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27.0</th>\n",
+       "      <td>48.692647</td>\n",
+       "      <td>1.903033</td>\n",
+       "      <td>1.427752</td>\n",
+       "      <td>0.600457</td>\n",
+       "      <td>0.712370</td>\n",
+       "      <td>30.419590</td>\n",
+       "      <td>11.893630</td>\n",
+       "      <td>1.981709</td>\n",
+       "      <td>0.863017</td>\n",
+       "      <td>1.469778</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28.0</th>\n",
+       "      <td>49.900934</td>\n",
+       "      <td>1.869644</td>\n",
+       "      <td>1.621979</td>\n",
+       "      <td>0.608296</td>\n",
+       "      <td>0.715938</td>\n",
+       "      <td>27.776135</td>\n",
+       "      <td>13.118472</td>\n",
+       "      <td>1.787131</td>\n",
+       "      <td>0.954056</td>\n",
+       "      <td>1.615768</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29.0</th>\n",
+       "      <td>51.109221</td>\n",
+       "      <td>1.836255</td>\n",
+       "      <td>1.816206</td>\n",
+       "      <td>0.616135</td>\n",
+       "      <td>0.719506</td>\n",
+       "      <td>25.132680</td>\n",
+       "      <td>14.343313</td>\n",
+       "      <td>1.592552</td>\n",
+       "      <td>1.045095</td>\n",
+       "      <td>1.761758</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30.0</th>\n",
+       "      <td>52.317508</td>\n",
+       "      <td>1.802866</td>\n",
+       "      <td>2.010434</td>\n",
+       "      <td>0.623974</td>\n",
+       "      <td>0.723074</td>\n",
+       "      <td>22.489225</td>\n",
+       "      <td>15.568154</td>\n",
+       "      <td>1.397974</td>\n",
+       "      <td>1.136134</td>\n",
+       "      <td>1.907748</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31.0</th>\n",
+       "      <td>52.868813</td>\n",
+       "      <td>2.095052</td>\n",
+       "      <td>1.984097</td>\n",
+       "      <td>0.715592</td>\n",
+       "      <td>0.822936</td>\n",
+       "      <td>21.650052</td>\n",
+       "      <td>14.853568</td>\n",
+       "      <td>1.862739</td>\n",
+       "      <td>1.148427</td>\n",
+       "      <td>1.968539</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32.0</th>\n",
+       "      <td>53.420118</td>\n",
+       "      <td>2.387239</td>\n",
+       "      <td>1.957760</td>\n",
+       "      <td>0.807209</td>\n",
+       "      <td>0.922798</td>\n",
+       "      <td>20.810879</td>\n",
+       "      <td>14.138982</td>\n",
+       "      <td>2.327503</td>\n",
+       "      <td>1.160720</td>\n",
+       "      <td>2.029331</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33.0</th>\n",
+       "      <td>53.971423</td>\n",
+       "      <td>2.679425</td>\n",
+       "      <td>1.931423</td>\n",
+       "      <td>0.898826</td>\n",
+       "      <td>1.022660</td>\n",
+       "      <td>19.971706</td>\n",
+       "      <td>13.424397</td>\n",
+       "      <td>2.792268</td>\n",
+       "      <td>1.173012</td>\n",
+       "      <td>2.090122</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34.0</th>\n",
+       "      <td>54.522728</td>\n",
+       "      <td>2.971611</td>\n",
+       "      <td>1.905086</td>\n",
+       "      <td>0.990443</td>\n",
+       "      <td>1.122522</td>\n",
+       "      <td>19.132533</td>\n",
+       "      <td>12.709811</td>\n",
+       "      <td>3.257033</td>\n",
+       "      <td>1.185305</td>\n",
+       "      <td>2.150914</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35.0</th>\n",
+       "      <td>55.074033</td>\n",
+       "      <td>3.263797</td>\n",
+       "      <td>1.878749</td>\n",
+       "      <td>1.082060</td>\n",
+       "      <td>1.222384</td>\n",
+       "      <td>18.293360</td>\n",
+       "      <td>11.995225</td>\n",
+       "      <td>3.721798</td>\n",
+       "      <td>1.197598</td>\n",
+       "      <td>2.211705</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36.0</th>\n",
+       "      <td>55.625338</td>\n",
+       "      <td>3.555983</td>\n",
+       "      <td>1.852412</td>\n",
+       "      <td>1.173677</td>\n",
+       "      <td>1.322246</td>\n",
+       "      <td>17.454187</td>\n",
+       "      <td>11.280640</td>\n",
+       "      <td>4.186563</td>\n",
+       "      <td>1.209891</td>\n",
+       "      <td>2.272496</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37.0</th>\n",
+       "      <td>56.176643</td>\n",
+       "      <td>3.848169</td>\n",
+       "      <td>1.826075</td>\n",
+       "      <td>1.265295</td>\n",
+       "      <td>1.422108</td>\n",
+       "      <td>16.615014</td>\n",
+       "      <td>10.566054</td>\n",
+       "      <td>4.651328</td>\n",
+       "      <td>1.222183</td>\n",
+       "      <td>2.333288</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38.0</th>\n",
+       "      <td>56.727947</td>\n",
+       "      <td>4.140355</td>\n",
+       "      <td>1.799738</td>\n",
+       "      <td>1.356912</td>\n",
+       "      <td>1.521970</td>\n",
+       "      <td>15.775841</td>\n",
+       "      <td>9.851468</td>\n",
+       "      <td>5.116093</td>\n",
+       "      <td>1.234476</td>\n",
+       "      <td>2.394079</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39.0</th>\n",
+       "      <td>57.279252</td>\n",
+       "      <td>4.432541</td>\n",
+       "      <td>1.773401</td>\n",
+       "      <td>1.448529</td>\n",
+       "      <td>1.621832</td>\n",
+       "      <td>14.936668</td>\n",
+       "      <td>9.136883</td>\n",
+       "      <td>5.580858</td>\n",
+       "      <td>1.246769</td>\n",
+       "      <td>2.454871</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40.0</th>\n",
+       "      <td>57.830557</td>\n",
+       "      <td>4.724727</td>\n",
+       "      <td>1.747064</td>\n",
+       "      <td>1.540146</td>\n",
+       "      <td>1.721694</td>\n",
+       "      <td>14.097495</td>\n",
+       "      <td>8.422297</td>\n",
+       "      <td>6.045623</td>\n",
+       "      <td>1.259061</td>\n",
+       "      <td>2.515662</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41.0</th>\n",
+       "      <td>58.175258</td>\n",
+       "      <td>4.668013</td>\n",
+       "      <td>2.015709</td>\n",
+       "      <td>1.633495</td>\n",
+       "      <td>1.809269</td>\n",
+       "      <td>12.985796</td>\n",
+       "      <td>8.653512</td>\n",
+       "      <td>5.778653</td>\n",
+       "      <td>1.401785</td>\n",
+       "      <td>2.776975</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42.0</th>\n",
+       "      <td>58.519959</td>\n",
+       "      <td>4.611299</td>\n",
+       "      <td>2.284354</td>\n",
+       "      <td>1.726844</td>\n",
+       "      <td>1.896844</td>\n",
+       "      <td>11.874098</td>\n",
+       "      <td>8.884727</td>\n",
+       "      <td>5.511683</td>\n",
+       "      <td>1.544509</td>\n",
+       "      <td>3.038288</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43.0</th>\n",
+       "      <td>58.864660</td>\n",
+       "      <td>4.554585</td>\n",
+       "      <td>2.553000</td>\n",
+       "      <td>1.820192</td>\n",
+       "      <td>1.984419</td>\n",
+       "      <td>10.762400</td>\n",
+       "      <td>9.115942</td>\n",
+       "      <td>5.244713</td>\n",
+       "      <td>1.687233</td>\n",
+       "      <td>3.299601</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44.0</th>\n",
+       "      <td>59.209361</td>\n",
+       "      <td>4.497871</td>\n",
+       "      <td>2.821645</td>\n",
+       "      <td>1.913541</td>\n",
+       "      <td>2.071994</td>\n",
+       "      <td>9.650702</td>\n",
+       "      <td>9.347157</td>\n",
+       "      <td>4.977743</td>\n",
+       "      <td>1.829956</td>\n",
+       "      <td>3.560914</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45.0</th>\n",
+       "      <td>59.554062</td>\n",
+       "      <td>4.441157</td>\n",
+       "      <td>3.090290</td>\n",
+       "      <td>2.006890</td>\n",
+       "      <td>2.159570</td>\n",
+       "      <td>8.539003</td>\n",
+       "      <td>9.578372</td>\n",
+       "      <td>4.710774</td>\n",
+       "      <td>1.972680</td>\n",
+       "      <td>3.822227</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46.0</th>\n",
+       "      <td>59.898763</td>\n",
+       "      <td>4.384443</td>\n",
+       "      <td>3.358935</td>\n",
+       "      <td>2.100238</td>\n",
+       "      <td>2.247145</td>\n",
+       "      <td>7.427305</td>\n",
+       "      <td>9.809587</td>\n",
+       "      <td>4.443804</td>\n",
+       "      <td>2.115404</td>\n",
+       "      <td>4.083540</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47.0</th>\n",
+       "      <td>60.243464</td>\n",
+       "      <td>4.327728</td>\n",
+       "      <td>3.627581</td>\n",
+       "      <td>2.193587</td>\n",
+       "      <td>2.334720</td>\n",
+       "      <td>6.315607</td>\n",
+       "      <td>10.040802</td>\n",
+       "      <td>4.176834</td>\n",
+       "      <td>2.258128</td>\n",
+       "      <td>4.344853</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48.0</th>\n",
+       "      <td>60.588165</td>\n",
+       "      <td>4.271014</td>\n",
+       "      <td>3.896226</td>\n",
+       "      <td>2.286936</td>\n",
+       "      <td>2.422295</td>\n",
+       "      <td>5.203909</td>\n",
+       "      <td>10.272017</td>\n",
+       "      <td>3.909864</td>\n",
+       "      <td>2.400851</td>\n",
+       "      <td>4.606166</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49.0</th>\n",
+       "      <td>60.932866</td>\n",
+       "      <td>4.214300</td>\n",
+       "      <td>4.164871</td>\n",
+       "      <td>2.380284</td>\n",
+       "      <td>2.509870</td>\n",
+       "      <td>4.092210</td>\n",
+       "      <td>10.503232</td>\n",
+       "      <td>3.642895</td>\n",
+       "      <td>2.543575</td>\n",
+       "      <td>4.867479</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50.0</th>\n",
+       "      <td>61.277567</td>\n",
+       "      <td>4.157586</td>\n",
+       "      <td>4.433516</td>\n",
+       "      <td>2.473633</td>\n",
+       "      <td>2.597445</td>\n",
+       "      <td>2.980512</td>\n",
+       "      <td>10.734447</td>\n",
+       "      <td>3.375925</td>\n",
+       "      <td>2.686299</td>\n",
+       "      <td>5.128792</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Susceptible_Vax  Exposed_Vax  Infected_Vax  Recovered_Vax  Deceased_Vax  \\\n",
+       "time                                                                            \n",
+       "0.0          0.000000     0.000000      1.000000       0.000000      0.000000   \n",
+       "1.0          2.416951     0.000000      0.943061       0.033442      0.047264   \n",
+       "2.0          4.833902     0.000000      0.886123       0.066884      0.094529   \n",
+       "3.0          7.250853     0.000000      0.829184       0.100326      0.141793   \n",
+       "4.0          9.667803     0.000000      0.772245       0.133768      0.189057   \n",
+       "5.0         12.084754     0.000000      0.715307       0.167210      0.236321   \n",
+       "6.0         14.501705     0.000000      0.658368       0.200652      0.283586   \n",
+       "7.0         16.918656     0.000000      0.601430       0.234094      0.330850   \n",
+       "8.0         19.335607     0.000000      0.544491       0.267536      0.378114   \n",
+       "9.0         21.752558     0.000000      0.487552       0.300978      0.425378   \n",
+       "10.0        24.169508     0.000000      0.430614       0.334420      0.472643   \n",
+       "11.0        25.776021     0.213676      0.394368       0.355536      0.494118   \n",
+       "12.0        27.382534     0.427351      0.358123       0.376652      0.515593   \n",
+       "13.0        28.989047     0.641027      0.321878       0.397769      0.537068   \n",
+       "14.0        30.595560     0.854702      0.285632       0.418885      0.558543   \n",
+       "15.0        32.202073     1.068378      0.249387       0.440001      0.580018   \n",
+       "16.0        33.808587     1.282054      0.213142       0.461118      0.601493   \n",
+       "17.0        35.415100     1.495729      0.176896       0.482234      0.622968   \n",
+       "18.0        37.021613     1.709405      0.140651       0.503350      0.644443   \n",
+       "19.0        38.628126     1.923080      0.104406       0.524467      0.665918   \n",
+       "20.0        40.234639     2.136756      0.068160       0.545583      0.687393   \n",
+       "21.0        41.442926     2.103367      0.262388       0.553422      0.690961   \n",
+       "22.0        42.651213     2.069978      0.456615       0.561261      0.694530   \n",
+       "23.0        43.859499     2.036589      0.650842       0.569100      0.698098   \n",
+       "24.0        45.067786     2.003200      0.845070       0.576940      0.701666   \n",
+       "25.0        46.276073     1.969811      1.039297       0.584779      0.705234   \n",
+       "26.0        47.484360     1.936422      1.233524       0.592618      0.708802   \n",
+       "27.0        48.692647     1.903033      1.427752       0.600457      0.712370   \n",
+       "28.0        49.900934     1.869644      1.621979       0.608296      0.715938   \n",
+       "29.0        51.109221     1.836255      1.816206       0.616135      0.719506   \n",
+       "30.0        52.317508     1.802866      2.010434       0.623974      0.723074   \n",
+       "31.0        52.868813     2.095052      1.984097       0.715592      0.822936   \n",
+       "32.0        53.420118     2.387239      1.957760       0.807209      0.922798   \n",
+       "33.0        53.971423     2.679425      1.931423       0.898826      1.022660   \n",
+       "34.0        54.522728     2.971611      1.905086       0.990443      1.122522   \n",
+       "35.0        55.074033     3.263797      1.878749       1.082060      1.222384   \n",
+       "36.0        55.625338     3.555983      1.852412       1.173677      1.322246   \n",
+       "37.0        56.176643     3.848169      1.826075       1.265295      1.422108   \n",
+       "38.0        56.727947     4.140355      1.799738       1.356912      1.521970   \n",
+       "39.0        57.279252     4.432541      1.773401       1.448529      1.621832   \n",
+       "40.0        57.830557     4.724727      1.747064       1.540146      1.721694   \n",
+       "41.0        58.175258     4.668013      2.015709       1.633495      1.809269   \n",
+       "42.0        58.519959     4.611299      2.284354       1.726844      1.896844   \n",
+       "43.0        58.864660     4.554585      2.553000       1.820192      1.984419   \n",
+       "44.0        59.209361     4.497871      2.821645       1.913541      2.071994   \n",
+       "45.0        59.554062     4.441157      3.090290       2.006890      2.159570   \n",
+       "46.0        59.898763     4.384443      3.358935       2.100238      2.247145   \n",
+       "47.0        60.243464     4.327728      3.627581       2.193587      2.334720   \n",
+       "48.0        60.588165     4.271014      3.896226       2.286936      2.422295   \n",
+       "49.0        60.932866     4.214300      4.164871       2.380284      2.509870   \n",
+       "50.0        61.277567     4.157586      4.433516       2.473633      2.597445   \n",
+       "\n",
+       "      Susceptible_UnVax  Exposed_UnVax  Infected_UnVax  Recovered_UnVax  \\\n",
+       "time                                                                      \n",
+       "0.0           98.000000       0.000000        1.000000         0.000000   \n",
+       "1.0           94.713914       0.866637        0.902980         0.028877   \n",
+       "2.0           91.427828       1.733274        0.805960         0.057754   \n",
+       "3.0           88.141743       2.599911        0.708940         0.086632   \n",
+       "4.0           84.855657       3.466549        0.611920         0.115509   \n",
+       "5.0           81.569571       4.333186        0.514900         0.144386   \n",
+       "6.0           78.283485       5.199823        0.417880         0.173263   \n",
+       "7.0           74.997399       6.066460        0.320860         0.202140   \n",
+       "8.0           71.711313       6.933097        0.223839         0.231017   \n",
+       "9.0           68.425228       7.799734        0.126819         0.259895   \n",
+       "10.0          65.139142       8.666371        0.029799         0.288772   \n",
+       "11.0          63.517605       8.131708        0.361195         0.282469   \n",
+       "12.0          61.896068       7.597046        0.692591         0.276166   \n",
+       "13.0          60.274531       7.062383        1.023987         0.269864   \n",
+       "14.0          58.652994       6.527720        1.355383         0.263561   \n",
+       "15.0          57.031458       5.993057        1.686779         0.257259   \n",
+       "16.0          55.409921       5.458394        2.018174         0.250956   \n",
+       "17.0          53.788384       4.923731        2.349570         0.244653   \n",
+       "18.0          52.166847       4.389068        2.680966         0.238351   \n",
+       "19.0          50.545310       3.854405        3.012362         0.232048   \n",
+       "20.0          48.923773       3.319742        3.343758         0.225745   \n",
+       "21.0          46.280319       4.544584        3.149179         0.316784   \n",
+       "22.0          43.636864       5.769425        2.954601         0.407823   \n",
+       "23.0          40.993409       6.994266        2.760023         0.498862   \n",
+       "24.0          38.349954       8.219107        2.565444         0.589901   \n",
+       "25.0          35.706499       9.443948        2.370866         0.680940   \n",
+       "26.0          33.063045      10.668789        2.176287         0.771979   \n",
+       "27.0          30.419590      11.893630        1.981709         0.863017   \n",
+       "28.0          27.776135      13.118472        1.787131         0.954056   \n",
+       "29.0          25.132680      14.343313        1.592552         1.045095   \n",
+       "30.0          22.489225      15.568154        1.397974         1.136134   \n",
+       "31.0          21.650052      14.853568        1.862739         1.148427   \n",
+       "32.0          20.810879      14.138982        2.327503         1.160720   \n",
+       "33.0          19.971706      13.424397        2.792268         1.173012   \n",
+       "34.0          19.132533      12.709811        3.257033         1.185305   \n",
+       "35.0          18.293360      11.995225        3.721798         1.197598   \n",
+       "36.0          17.454187      11.280640        4.186563         1.209891   \n",
+       "37.0          16.615014      10.566054        4.651328         1.222183   \n",
+       "38.0          15.775841       9.851468        5.116093         1.234476   \n",
+       "39.0          14.936668       9.136883        5.580858         1.246769   \n",
+       "40.0          14.097495       8.422297        6.045623         1.259061   \n",
+       "41.0          12.985796       8.653512        5.778653         1.401785   \n",
+       "42.0          11.874098       8.884727        5.511683         1.544509   \n",
+       "43.0          10.762400       9.115942        5.244713         1.687233   \n",
+       "44.0           9.650702       9.347157        4.977743         1.829956   \n",
+       "45.0           8.539003       9.578372        4.710774         1.972680   \n",
+       "46.0           7.427305       9.809587        4.443804         2.115404   \n",
+       "47.0           6.315607      10.040802        4.176834         2.258128   \n",
+       "48.0           5.203909      10.272017        3.909864         2.400851   \n",
+       "49.0           4.092210      10.503232        3.642895         2.543575   \n",
+       "50.0           2.980512      10.734447        3.375925         2.686299   \n",
+       "\n",
+       "      Deceased_UnVax  Total_population  \n",
+       "time                                    \n",
+       "0.0         0.000000             100.0  \n",
+       "1.0         0.043489             100.0  \n",
+       "2.0         0.086977             100.0  \n",
+       "3.0         0.130466             100.0  \n",
+       "4.0         0.173955             100.0  \n",
+       "5.0         0.217443             100.0  \n",
+       "6.0         0.260932             100.0  \n",
+       "7.0         0.304421             100.0  \n",
+       "8.0         0.347909             100.0  \n",
+       "9.0         0.391398             100.0  \n",
+       "10.0        0.434887             100.0  \n",
+       "11.0        0.436183             100.0  \n",
+       "12.0        0.437479             100.0  \n",
+       "13.0        0.438775             100.0  \n",
+       "14.0        0.440072             100.0  \n",
+       "15.0        0.441368             100.0  \n",
+       "16.0        0.442664             100.0  \n",
+       "17.0        0.443960             100.0  \n",
+       "18.0        0.445257             100.0  \n",
+       "19.0        0.446553             100.0  \n",
+       "20.0        0.447849             100.0  \n",
+       "21.0        0.593839             100.0  \n",
+       "22.0        0.739829             100.0  \n",
+       "23.0        0.885819             100.0  \n",
+       "24.0        1.031809             100.0  \n",
+       "25.0        1.177799             100.0  \n",
+       "26.0        1.323788             100.0  \n",
+       "27.0        1.469778             100.0  \n",
+       "28.0        1.615768             100.0  \n",
+       "29.0        1.761758             100.0  \n",
+       "30.0        1.907748             100.0  \n",
+       "31.0        1.968539             100.0  \n",
+       "32.0        2.029331             100.0  \n",
+       "33.0        2.090122             100.0  \n",
+       "34.0        2.150914             100.0  \n",
+       "35.0        2.211705             100.0  \n",
+       "36.0        2.272496             100.0  \n",
+       "37.0        2.333288             100.0  \n",
+       "38.0        2.394079             100.0  \n",
+       "39.0        2.454871             100.0  \n",
+       "40.0        2.515662             100.0  \n",
+       "41.0        2.776975             100.0  \n",
+       "42.0        3.038288             100.0  \n",
+       "43.0        3.299601             100.0  \n",
+       "44.0        3.560914             100.0  \n",
+       "45.0        3.822227             100.0  \n",
+       "46.0        4.083540             100.0  \n",
+       "47.0        4.344853             100.0  \n",
+       "48.0        4.606166             100.0  \n",
+       "49.0        4.867479             100.0  \n",
+       "50.0        5.128792             100.0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_unconstrained_point.parameter_space.points()[0]\n",
+    "df= results_unconstrained_point.dataframe(\n",
+    "    results_unconstrained_point.parameter_space.points()\n",
+    ")\n",
+    "df[COMPARTMENTS]\n",
+    "# df[\"S\"]+df[\"E\"]+df[\"I\"]+df[\"D\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((solve_step_0 & assume_Deceased_bounds) & assume_Recovered_bounds) & assume_Infected_bounds) & assume_Exposed_bounds) & assume_Susceptible_bounds) & assume_compartment) & disj1095) & (alpha <= 10060000000000001/10000000000000000)) & (epsilon <= 2666666666666667/2000000000000000)) & solve_step_2) & solve_step_1) & disj1109) & assume_c3_20) & assume_c2_20) & disj1129) & disj1132) & (beta < 6823503167783207/25000000000000000)) & (gamma < 1/512)) & ((conj1471 | (! assume_Infected_bounds)) | (! disj1092))) & (disj1092 | (! conj1474))) & ((conj1472 | (! assume_Susceptible_bounds)) | (! disj1093))) & (disj1093 | (! conj1474))) & ((conj1474 | (! solve_step_0)) | (! disj1095))) & ((Deceased_0 = 0.0) | (! conj1474))) & ((Recovered_0 = 0.0) | (! conj1474))) & ((Exposed_0 = 0.0) | (! conj1474))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Infected_0 < 0.0) | (Susceptible_0 < 0.0)) | (alpha < -99399999999999999/100000000000000000)) | (gamma < -7/8)) | (epsilon < -33333333333333331/50000000000000000)) | (beta < -2087499999999999/12500000000000000)) | (Deceased_10 = 0.0)) | (Deceased_10 < 0.0)) | (Recovered_10 < 0.0)) | (Infected_10 < 0.0)) | (Exposed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (((((((-1.0 * Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10) + Susceptible_10) < 0.0)) | (Deceased_20 < 0.0)) | (Recovered_20 < 0.0)) | (Infected_20 < 0.0)) | (Exposed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20) + Susceptible_20) < 0.0)) | (gamma < 0.0)) | (beta < 27081256023150169/100000000000000000)) | (! 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(Exposed_10 = (Exposed_0 + (10.0 * (((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta) - (Exposed_0 * epsilon))))))) | (! (Infected_10 = (Infected_0 + (10.0 * (((Exposed_0 * epsilon) - (Infected_0 * alpha)) - (Infected_0 * gamma))))))) | (! (Susceptible_10 = (Susceptible_0 - (10.0 * ((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))))) | (! (Deceased_20 <= 100.0))) | (! (Recovered_20 <= 100.0))) | (! (Infected_20 <= 100.0))) | (! (Exposed_20 <= 100.0))) | (! (Susceptible_20 <= 100.0))) | (! (((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20) + Susceptible_20) <= 0.0))) | (! ((10000000000000001/100000000000000000 * (Deceased_20 - Deceased_30)) < 0.0))) | (! ((10000000000000001/100000000000000000 * (Recovered_20 - Recovered_30)) < 0.0))) | (! (Total_population_20 = Total_population_10))) | (! (Deceased_20 = (Deceased_10 + (10.0 * (alpha * Infected_10)))))) | (! 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(gamma < 1/512)))) & (((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Infected_0 < 0.0) | (Susceptible_0 < 0.0)) | (alpha < -99399999999999999/100000000000000000)) | (gamma < -7/8)) | (epsilon < -33333333333333331/50000000000000000)) | (beta < -2087499999999999/12500000000000000)) | (Recovered_10 = 0.0)) | (Recovered_10 < 0.0)) | (Infected_10 < 0.0)) | (Exposed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (((((((-1.0 * Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10) + Susceptible_10) < 0.0)) | (Deceased_20 < 0.0)) | (Recovered_20 < 0.0)) | (Infected_20 < 0.0)) | (Exposed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20) + Susceptible_20) < 0.0)) | (gamma < 0.0)) | (beta < 27081256023150169/100000000000000000)) | (! (Deceased_0 = 0.0))) | (! (Recovered_0 = 0.0))) | (! (Exposed_0 = 0.0))) | (! (Infected_0 <= 100.0))) | (! (Susceptible_0 <= 100.0))) | (! 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(Susceptible_20 = (Susceptible_10 - (10.0 * (((beta * (Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10)))))) | (! (Deceased_30 = 0.0))) | (! (Recovered_30 = 0.0))) | (! (beta < 6823503167783207/25000000000000000))) | (! 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(Susceptible_10 = (Susceptible_0 - (10.0 * ((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))))) | (! (Deceased_20 <= 100.0))) | (! (Recovered_20 <= 100.0))) | (! (Infected_20 <= 100.0))) | (! (Exposed_20 <= 100.0))) | (! (Susceptible_20 <= 100.0))) | (! (((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20) + Susceptible_20) <= 0.0))) | (! ((10000000000000001/100000000000000000 * (Deceased_20 - Deceased_30)) < 0.0))) | (! ((10000000000000001/100000000000000000 * (Recovered_20 - Recovered_30)) < 0.0))) | (! (Total_population_20 = Total_population_10))) | (! (Exposed_20 = (Exposed_10 + (10.0 * ((-1.0 * (epsilon * Exposed_10)) + (((beta * (Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((-1.0 * (alpha * Infected_10)) - (gamma * Infected_10)) + (epsilon * Exposed_10))))))) | (! (Susceptible_20 = (Susceptible_10 - (10.0 * (((beta * (Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10)))))) | (! (Deceased_30 = 0.0))) | (! (Recovered_30 = 0.0))) | (! (beta < 6823503167783207/25000000000000000))) | (! 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(Susceptible_10 = (Susceptible_0 - (10.0 * ((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))))) | (! (Deceased_20 = 0.0))) | (! (Recovered_20 <= 100.0))) | (! (Infected_20 <= 100.0))) | (! (Exposed_20 <= 100.0))) | (! (Susceptible_20 <= 100.0))) | (! (((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20) + Susceptible_20) <= 0.0))) | (! ((10000000000000001/100000000000000000 * (Recovered_20 - Recovered_30)) < 0.0))) | (! (Total_population_20 = Total_population_10))) | (! (Exposed_20 = (Exposed_10 + (10.0 * ((-1.0 * (epsilon * Exposed_10)) + (((beta * (Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((-1.0 * (alpha * Infected_10)) - (gamma * Infected_10)) + (epsilon * Exposed_10))))))) | (! (Susceptible_20 = (Susceptible_10 - (10.0 * (((beta * (Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10)))))) | (! (Recovered_30 = 0.0))) | (! (beta < 6823503167783207/25000000000000000))) | (! (gamma < 1/512)))) & ((((((((((((((((((((((((((((((((((((((((((((((((((((((Infected_0 < 0.0) | (Susceptible_0 < 0.0)) | (alpha < -99399999999999999/100000000000000000)) | (gamma < -7/8)) | (epsilon < -33333333333333331/50000000000000000)) | (beta < -2087499999999999/12500000000000000)) | (Infected_10 < 0.0)) | (Exposed_10 < 0.0)) | (Susceptible_10 < 0.0)) | (((((((-1.0 * Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10) + Susceptible_10) < 0.0)) | (Infected_20 < 0.0)) | (Exposed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20) + Susceptible_20) < 0.0)) | (gamma < 0.0)) | (beta < 27081256023150169/100000000000000000)) | (! (Deceased_0 = 0.0))) | (! (Recovered_0 = 0.0))) | (! (Exposed_0 = 0.0))) | (! (Infected_0 <= 100.0))) | (! (Susceptible_0 <= 100.0))) | (! (Total_population_0 = 100.0))) | (! (Infected_0 = 1.0))) | (! (Susceptible_0 = 99.0))) | (! (alpha <= 10060000000000001/10000000000000000))) | (! (gamma <= 9/8))) | (! (epsilon <= 2666666666666667/2000000000000000))) | (! (beta <= 9165000000000001/5000000000000000))) | (! (Deceased_10 = 0.0))) | (! (Recovered_10 = 0.0))) | (! (Infected_10 <= 100.0))) | (! (Exposed_10 <= 100.0))) | (! (Susceptible_10 <= 100.0))) | (! (((((((-1.0 * Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10) + Susceptible_10) <= 0.0))) | (! (Total_population_10 = Total_population_0))) | (! (Deceased_10 = (Deceased_0 + (10.0 * (Infected_0 * alpha)))))) | (! (Recovered_10 = (Recovered_0 + (10.0 * (Infected_0 * gamma)))))) | (! (Exposed_10 = (Exposed_0 + (10.0 * (((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta) - (Exposed_0 * epsilon))))))) | (! (Infected_10 = (Infected_0 + (10.0 * (((Exposed_0 * epsilon) - (Infected_0 * alpha)) - (Infected_0 * gamma))))))) | (! (Susceptible_10 = (Susceptible_0 - (10.0 * ((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))))) | (! (Deceased_20 = 0.0))) | (! (Recovered_20 = 0.0))) | (! (Infected_20 <= 100.0))) | (! (Exposed_20 <= 100.0))) | (! (Susceptible_20 <= 100.0))) | (! (((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20) + Susceptible_20) <= 0.0))) | (! ((10000000000000001/100000000000000000 * (Deceased_20 - Deceased_30)) < 0.0))) | (! (Total_population_20 = Total_population_10))) | (! (Exposed_20 = (Exposed_10 + (10.0 * ((-1.0 * (epsilon * Exposed_10)) + (((beta * (Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((-1.0 * (alpha * Infected_10)) - (gamma * Infected_10)) + (epsilon * Exposed_10))))))) | (! (Susceptible_20 = (Susceptible_10 - (10.0 * (((beta * (Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10)))))) | (! (Deceased_30 = 0.0))) | (! (beta < 6823503167783207/25000000000000000))) | (! (gamma < 1/512)))) & ((Infected_0 <= 100.0) | (! conj1471))) & ((Susceptible_0 <= 100.0) | (! conj1472))) & ((Total_population_0 = 100.0) | (! conj1474))) & ((Infected_0 = 1.0) | (! conj1474))) & ((Susceptible_0 = 99.0) | (! conj1474))) & ((gamma <= 9/8) | (! conj1474))) & ((beta <= 9165000000000001/5000000000000000) | (! conj1474))) & ((solve_step_3 | conj1514) | (! disj1132))) & ((conj1485 | (! assume_Deceased_bounds)) | (! disj1103))) & (disj1103 | (! conj1491))) & ((conj1486 | (! assume_Recovered_bounds)) | (! disj1104))) & (disj1104 | (! conj1491))) & ((conj1487 | (! assume_Exposed_bounds)) | (! disj1105))) & (disj1105 | (! conj1491))) & ((conj1488 | (! assume_Infected_bounds)) | (! disj1106))) & (disj1106 | (! conj1491))) & ((conj1489 | (! assume_Susceptible_bounds)) | (! disj1107))) & (disj1107 | (! conj1491))) & ((conj1490 | (! assume_compartment)) | (! disj1108))) & (disj1108 | (! conj1491))) & ((conj1491 | (! solve_step_1)) | (! disj1109))) & ((Deceased_10 <= 100.0) | (! conj1485))) & ((Recovered_10 <= 100.0) | (! conj1486))) & ((Infected_10 <= 100.0) | (! conj1488))) & ((Exposed_10 <= 100.0) | (! conj1487))) & ((Susceptible_10 <= 100.0) | (! conj1489))) & ((((((((-1.0 * Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10) + Susceptible_10) <= 0.0) | (! conj1490))) & ((Total_population_10 = Total_population_0) | (! conj1491))) & ((Deceased_10 = (Deceased_0 + (10.0 * (Infected_0 * alpha)))) | (! conj1491))) & ((Recovered_10 = (Recovered_0 + (10.0 * (Infected_0 * gamma)))) | (! conj1491))) & ((Exposed_10 = (Exposed_0 + (10.0 * (((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta) - (Exposed_0 * epsilon))))) | (! conj1491))) & ((Infected_10 = (Infected_0 + (10.0 * (((Exposed_0 * epsilon) - (Infected_0 * alpha)) - (Infected_0 * gamma))))) | (! conj1491))) & ((Susceptible_10 = (Susceptible_0 - (10.0 * ((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))) | (! conj1491))) & (disj1121 | (! conj1508))) & (disj1122 | (! conj1508))) & ((conj1502 | (! assume_Deceased_bounds)) | (! disj1123))) & (disj1123 | (! conj1508))) & ((conj1503 | (! assume_Recovered_bounds)) | (! disj1124))) & (disj1124 | (! conj1508))) & ((conj1504 | (! assume_Exposed_bounds)) | (! disj1125))) & (disj1125 | (! conj1508))) & ((conj1505 | (! assume_Infected_bounds)) | (! disj1126))) & (disj1126 | (! conj1508))) & ((conj1506 | (! assume_Susceptible_bounds)) | (! disj1127))) & (disj1127 | (! conj1508))) & ((conj1507 | (! assume_compartment)) | (! disj1128))) & (disj1128 | (! conj1508))) & ((conj1508 | (! solve_step_2)) | (! disj1129))) & ((Deceased_20 <= 100.0) | (! conj1502))) & ((Recovered_20 <= 100.0) | (! conj1503))) & ((Infected_20 <= 100.0) | (! conj1505))) & ((Exposed_20 <= 100.0) | (! conj1504))) & ((Susceptible_20 <= 100.0) | (! conj1506))) & ((((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20) + Susceptible_20) <= 0.0) | (! conj1507))) & ((((10000000000000001/100000000000000000 * (Deceased_20 - Deceased_30)) < 0.0) | (! assume_c3_20)) | (! disj1121))) & ((((10000000000000001/100000000000000000 * (Recovered_20 - Recovered_30)) < 0.0) | (! assume_c2_20)) | (! disj1122))) & ((Total_population_20 = Total_population_10) | (! conj1508))) & ((Deceased_20 = (Deceased_10 + (10.0 * (alpha * Infected_10)))) | (! conj1508))) & ((Recovered_20 = (Recovered_10 + (10.0 * (gamma * Infected_10)))) | (! conj1508))) & ((Exposed_20 = (Exposed_10 + (10.0 * ((-1.0 * (epsilon * Exposed_10)) + (((beta * (Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10))))) | (! conj1508))) & ((Infected_20 = (Infected_10 + (10.0 * (((-1.0 * (alpha * Infected_10)) - (gamma * Infected_10)) + (epsilon * Exposed_10))))) | (! conj1508))) & ((Susceptible_20 = (Susceptible_10 - (10.0 * (((beta * (Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10)))) | (! conj1508))) & ((Deceased_30 = 0.0) | (! conj1514))) & ((Recovered_30 = 0.0) | (! conj1514))) & ((! conj1471) | (! (Infected_0 < 0.0)))) & ((! conj1472) | (! (Susceptible_0 < 0.0)))) & ((! conj1474) | (! (gamma < -7/8)))) & ((! conj1474) | (! (beta < -2087499999999999/12500000000000000)))) & ((! conj1485) | (! (Deceased_10 < 0.0)))) & ((! conj1486) | (! (Recovered_10 < 0.0)))) & ((! conj1487) | (! (Exposed_10 < 0.0)))) & ((! conj1488) | (! (Infected_10 < 0.0)))) & ((! conj1489) | (! (Susceptible_10 < 0.0)))) & ((! conj1490) | (! (((((((-1.0 * Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10) + Susceptible_10) < 0.0)))) & ((! conj1502) | (! (Deceased_20 < 0.0)))) & ((! conj1503) | (! (Recovered_20 < 0.0)))) & ((! conj1504) | (! (Exposed_20 < 0.0)))) & ((! conj1505) | (! (Infected_20 < 0.0)))) & ((! conj1506) | (! (Susceptible_20 < 0.0)))) & ((! conj1507) | (! (((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20) + Susceptible_20) < 0.0)))) & (! (alpha < -99399999999999999/100000000000000000))) & (! (epsilon < -33333333333333331/50000000000000000))) & (! solve_step_3)) & (! (gamma < 0.0))) & (! (beta < 27081256023150169/100000000000000000)))'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# results_unconstrained_point.parameter_space.points()[0]\n",
+    "# df= results_unconstrained_point.dataframe(\n",
+    "#     results_unconstrained_point.parameter_space.points()\n",
+    "# )\n",
+    "# df[COMPARTMENTS].describe() #.plot()\n",
+    "results_unconstrained_point.parameter_space.false_boxes[0].explanation.expression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((\n",
+      "((((((((((((((((((((((((((((((((((((solve_step_0 & assume_Deceased_bounds) &\n",
+      "assume_Recovered_bounds) & assume_Infected_bounds) & assume_Exposed_bounds) &\n",
+      "assume_Susceptible_bounds) & assume_compartment) & disj1095) & (alpha <=\n",
+      "10060000000000001/10000000000000000)) & (epsilon <=\n",
+      "2666666666666667/2000000000000000)) & solve_step_2) & solve_step_1) & disj1109)\n",
+      "& assume_c3_20) & assume_c2_20) & disj1129) & disj1132) & (beta <\n",
+      "6823503167783207/25000000000000000)) & (gamma < 1/512)) & ((conj1471 | (!\n",
+      "assume_Infected_bounds)) | (! disj1092))) & (disj1092 | (! conj1474))) &\n",
+      "((conj1472 | (! assume_Susceptible_bounds)) | (! disj1093))) & (disj1093 | (!\n",
+      "conj1474))) & ((conj1474 | (! solve_step_0)) | (! disj1095))) & ((Deceased_0 =\n",
+      "0.0) | (! conj1474))) & ((Recovered_0 = 0.0) | (! conj1474))) & ((Exposed_0 =\n",
+      "0.0) | (! conj1474))) &\n",
+      "(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Infected_0 < 0.0)\n",
+      "| (Susceptible_0 < 0.0)) | (alpha < -99399999999999999/100000000000000000)) |\n",
+      "(gamma < -7/8)) | (epsilon < -33333333333333331/50000000000000000)) | (beta <\n",
+      "-2087499999999999/12500000000000000)) | (Deceased_10 = 0.0)) | (Deceased_10 <\n",
+      "0.0)) | (Recovered_10 < 0.0)) | (Infected_10 < 0.0)) | (Exposed_10 < 0.0)) |\n",
+      "(Susceptible_10 < 0.0)) | (((((((-1.0 * Total_population_10) + Deceased_10) +\n",
+      "Recovered_10) + Exposed_10) + Infected_10) + Susceptible_10) < 0.0)) |\n",
+      "(Deceased_20 < 0.0)) | (Recovered_20 < 0.0)) | (Infected_20 < 0.0)) |\n",
+      "(Exposed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (((((((-1.0 *\n",
+      "Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20)\n",
+      "+ Susceptible_20) < 0.0)) | (gamma < 0.0)) | (beta <\n",
+      "27081256023150169/100000000000000000)) | (! (Deceased_0 = 0.0))) | (!\n",
+      "(Recovered_0 = 0.0))) | (! (Exposed_0 = 0.0))) | (! (Infected_0 <= 100.0))) | (!\n",
+      "(Susceptible_0 <= 100.0))) | (! (Total_population_0 = 100.0))) | (! (Infected_0\n",
+      "= 1.0))) | (! (Susceptible_0 = 99.0))) | (! (alpha <=\n",
+      "10060000000000001/10000000000000000))) | (! (gamma <= 9/8))) | (! (epsilon <=\n",
+      "2666666666666667/2000000000000000))) | (! (beta <=\n",
+      "9165000000000001/5000000000000000))) | (! (Deceased_10 <= 100.0))) | (!\n",
+      "(Recovered_10 <= 100.0))) | (! (Infected_10 <= 100.0))) | (! (Exposed_10 <=\n",
+      "100.0))) | (! (Susceptible_10 <= 100.0))) | (! (((((((-1.0 *\n",
+      "Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10)\n",
+      "+ Susceptible_10) <= 0.0))) | (! (Total_population_10 = Total_population_0))) |\n",
+      "(! (Deceased_10 = (Deceased_0 + (10.0 * (Infected_0 * alpha)))))) | (!\n",
+      "(Recovered_10 = (Recovered_0 + (10.0 * (Infected_0 * gamma)))))) | (!\n",
+      "(Exposed_10 = (Exposed_0 + (10.0 * (((((Total_population_0 ^ -1.0) * Infected_0)\n",
+      "* Susceptible_0) * beta) - (Exposed_0 * epsilon))))))) | (! (Infected_10 =\n",
+      "(Infected_0 + (10.0 * (((Exposed_0 * epsilon) - (Infected_0 * alpha)) -\n",
+      "(Infected_0 * gamma))))))) | (! (Susceptible_10 = (Susceptible_0 - (10.0 *\n",
+      "((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))))) | (!\n",
+      "(Deceased_20 <= 100.0))) | (! (Recovered_20 <= 100.0))) | (! (Infected_20 <=\n",
+      "100.0))) | (! (Exposed_20 <= 100.0))) | (! (Susceptible_20 <= 100.0))) | (!\n",
+      "(((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20)\n",
+      "+ Infected_20) + Susceptible_20) <= 0.0))) | (!\n",
+      "((10000000000000001/100000000000000000 * (Deceased_20 - Deceased_30)) < 0.0))) |\n",
+      "(! ((10000000000000001/100000000000000000 * (Recovered_20 - Recovered_30)) <\n",
+      "0.0))) | (! (Total_population_20 = Total_population_10))) | (! (Deceased_20 =\n",
+      "(Deceased_10 + (10.0 * (alpha * Infected_10)))))) | (! (Recovered_20 =\n",
+      "(Recovered_10 + (10.0 * (gamma * Infected_10)))))) | (! (Exposed_20 =\n",
+      "(Exposed_10 + (10.0 * ((-1.0 * (epsilon * Exposed_10)) + (((beta *\n",
+      "(Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10))))))) | (!\n",
+      "(Infected_20 = (Infected_10 + (10.0 * (((-1.0 * (alpha * Infected_10)) - (gamma\n",
+      "* Infected_10)) + (epsilon * Exposed_10))))))) | (! (Susceptible_20 =\n",
+      "(Susceptible_10 - (10.0 * (((beta * (Total_population_10 ^ -1.0)) * Infected_10)\n",
+      "* Susceptible_10)))))) | (! (Deceased_30 = 0.0))) | (! (Recovered_30 = 0.0))) |\n",
+      "(! (beta < 6823503167783207/25000000000000000))) | (! (gamma < 1/512)))) &\n",
+      "(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Infected_0 < 0.0) |\n",
+      "(Susceptible_0 < 0.0)) | (alpha < -99399999999999999/100000000000000000)) |\n",
+      "(gamma < -7/8)) | (epsilon < -33333333333333331/50000000000000000)) | (beta <\n",
+      "-2087499999999999/12500000000000000)) | (Recovered_10 = 0.0)) | (Recovered_10 <\n",
+      "0.0)) | (Infected_10 < 0.0)) | (Exposed_10 < 0.0)) | (Susceptible_10 < 0.0)) |\n",
+      "(((((((-1.0 * Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10)\n",
+      "+ Infected_10) + Susceptible_10) < 0.0)) | (Deceased_20 < 0.0)) | (Recovered_20\n",
+      "< 0.0)) | (Infected_20 < 0.0)) | (Exposed_20 < 0.0)) | (Susceptible_20 < 0.0)) |\n",
+      "(((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20)\n",
+      "+ Infected_20) + Susceptible_20) < 0.0)) | (gamma < 0.0)) | (beta <\n",
+      "27081256023150169/100000000000000000)) | (! (Deceased_0 = 0.0))) | (!\n",
+      "(Recovered_0 = 0.0))) | (! (Exposed_0 = 0.0))) | (! (Infected_0 <= 100.0))) | (!\n",
+      "(Susceptible_0 <= 100.0))) | (! (Total_population_0 = 100.0))) | (! (Infected_0\n",
+      "= 1.0))) | (! (Susceptible_0 = 99.0))) | (! (alpha <=\n",
+      "10060000000000001/10000000000000000))) | (! (gamma <= 9/8))) | (! (epsilon <=\n",
+      "2666666666666667/2000000000000000))) | (! (beta <=\n",
+      "9165000000000001/5000000000000000))) | (! (Deceased_10 = 0.0))) | (!\n",
+      "(Recovered_10 <= 100.0))) | (! (Infected_10 <= 100.0))) | (! (Exposed_10 <=\n",
+      "100.0))) | (! (Susceptible_10 <= 100.0))) | (! (((((((-1.0 *\n",
+      "Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10)\n",
+      "+ Susceptible_10) <= 0.0))) | (! (Total_population_10 = Total_population_0))) |\n",
+      "(! (Deceased_10 = (Deceased_0 + (10.0 * (Infected_0 * alpha)))))) | (!\n",
+      "(Recovered_10 = (Recovered_0 + (10.0 * (Infected_0 * gamma)))))) | (!\n",
+      "(Exposed_10 = (Exposed_0 + (10.0 * (((((Total_population_0 ^ -1.0) * Infected_0)\n",
+      "* Susceptible_0) * beta) - (Exposed_0 * epsilon))))))) | (! (Infected_10 =\n",
+      "(Infected_0 + (10.0 * (((Exposed_0 * epsilon) - (Infected_0 * alpha)) -\n",
+      "(Infected_0 * gamma))))))) | (! (Susceptible_10 = (Susceptible_0 - (10.0 *\n",
+      "((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))))) | (!\n",
+      "(Deceased_20 <= 100.0))) | (! (Recovered_20 <= 100.0))) | (! (Infected_20 <=\n",
+      "100.0))) | (! (Exposed_20 <= 100.0))) | (! (Susceptible_20 <= 100.0))) | (!\n",
+      "(((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20)\n",
+      "+ Infected_20) + Susceptible_20) <= 0.0))) | (!\n",
+      "((10000000000000001/100000000000000000 * (Deceased_20 - Deceased_30)) < 0.0))) |\n",
+      "(! ((10000000000000001/100000000000000000 * (Recovered_20 - Recovered_30)) <\n",
+      "0.0))) | (! (Total_population_20 = Total_population_10))) | (! (Recovered_20 =\n",
+      "(Recovered_10 + (10.0 * (gamma * Infected_10)))))) | (! (Exposed_20 =\n",
+      "(Exposed_10 + (10.0 * ((-1.0 * (epsilon * Exposed_10)) + (((beta *\n",
+      "(Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10))))))) | (!\n",
+      "(Infected_20 = (Infected_10 + (10.0 * (((-1.0 * (alpha * Infected_10)) - (gamma\n",
+      "* Infected_10)) + (epsilon * Exposed_10))))))) | (! (Susceptible_20 =\n",
+      "(Susceptible_10 - (10.0 * (((beta * (Total_population_10 ^ -1.0)) * Infected_10)\n",
+      "* Susceptible_10)))))) | (! (Deceased_30 = 0.0))) | (! (Recovered_30 = 0.0))) |\n",
+      "(! (beta < 6823503167783207/25000000000000000))) | (! (gamma < 1/512)))) &\n",
+      "(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Infected_0 < 0.0) |\n",
+      "(Susceptible_0 < 0.0)) | (alpha < -99399999999999999/100000000000000000)) |\n",
+      "(gamma < -7/8)) | (epsilon < -33333333333333331/50000000000000000)) | (beta <\n",
+      "-2087499999999999/12500000000000000)) | (Infected_10 < 0.0)) | (Exposed_10 <\n",
+      "0.0)) | (Susceptible_10 < 0.0)) | (((((((-1.0 * Total_population_10) +\n",
+      "Deceased_10) + Recovered_10) + Exposed_10) + Infected_10) + Susceptible_10) <\n",
+      "0.0)) | (Deceased_20 = 0.0)) | (Deceased_20 < 0.0)) | (Recovered_20 < 0.0)) |\n",
+      "(Infected_20 < 0.0)) | (Exposed_20 < 0.0)) | (Susceptible_20 < 0.0)) |\n",
+      "(((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20)\n",
+      "+ Infected_20) + Susceptible_20) < 0.0)) | (gamma < 0.0)) | (beta <\n",
+      "27081256023150169/100000000000000000)) | (! (Deceased_0 = 0.0))) | (!\n",
+      "(Recovered_0 = 0.0))) | (! (Exposed_0 = 0.0))) | (! (Infected_0 <= 100.0))) | (!\n",
+      "(Susceptible_0 <= 100.0))) | (! (Total_population_0 = 100.0))) | (! (Infected_0\n",
+      "= 1.0))) | (! (Susceptible_0 = 99.0))) | (! (alpha <=\n",
+      "10060000000000001/10000000000000000))) | (! (gamma <= 9/8))) | (! (epsilon <=\n",
+      "2666666666666667/2000000000000000))) | (! (beta <=\n",
+      "9165000000000001/5000000000000000))) | (! (Deceased_10 = 0.0))) | (!\n",
+      "(Recovered_10 = 0.0))) | (! (Infected_10 <= 100.0))) | (! (Exposed_10 <=\n",
+      "100.0))) | (! (Susceptible_10 <= 100.0))) | (! (((((((-1.0 *\n",
+      "Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10)\n",
+      "+ Susceptible_10) <= 0.0))) | (! (Total_population_10 = Total_population_0))) |\n",
+      "(! (Deceased_10 = (Deceased_0 + (10.0 * (Infected_0 * alpha)))))) | (!\n",
+      "(Recovered_10 = (Recovered_0 + (10.0 * (Infected_0 * gamma)))))) | (!\n",
+      "(Exposed_10 = (Exposed_0 + (10.0 * (((((Total_population_0 ^ -1.0) * Infected_0)\n",
+      "* Susceptible_0) * beta) - (Exposed_0 * epsilon))))))) | (! (Infected_10 =\n",
+      "(Infected_0 + (10.0 * (((Exposed_0 * epsilon) - (Infected_0 * alpha)) -\n",
+      "(Infected_0 * gamma))))))) | (! (Susceptible_10 = (Susceptible_0 - (10.0 *\n",
+      "((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))))) | (!\n",
+      "(Deceased_20 <= 100.0))) | (! (Recovered_20 <= 100.0))) | (! (Infected_20 <=\n",
+      "100.0))) | (! (Exposed_20 <= 100.0))) | (! (Susceptible_20 <= 100.0))) | (!\n",
+      "(((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20)\n",
+      "+ Infected_20) + Susceptible_20) <= 0.0))) | (!\n",
+      "((10000000000000001/100000000000000000 * (Deceased_20 - Deceased_30)) < 0.0))) |\n",
+      "(! ((10000000000000001/100000000000000000 * (Recovered_20 - Recovered_30)) <\n",
+      "0.0))) | (! (Total_population_20 = Total_population_10))) | (! (Exposed_20 =\n",
+      "(Exposed_10 + (10.0 * ((-1.0 * (epsilon * Exposed_10)) + (((beta *\n",
+      "(Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10))))))) | (!\n",
+      "(Infected_20 = (Infected_10 + (10.0 * (((-1.0 * (alpha * Infected_10)) - (gamma\n",
+      "* Infected_10)) + (epsilon * Exposed_10))))))) | (! (Susceptible_20 =\n",
+      "(Susceptible_10 - (10.0 * (((beta * (Total_population_10 ^ -1.0)) * Infected_10)\n",
+      "* Susceptible_10)))))) | (! (Deceased_30 = 0.0))) | (! (Recovered_30 = 0.0))) |\n",
+      "(! (beta < 6823503167783207/25000000000000000))) | (! (gamma < 1/512)))) &\n",
+      "((((((((((((((((((((((((((((((((((((((((((((((((((((((((Infected_0 < 0.0) |\n",
+      "(Susceptible_0 < 0.0)) | (alpha < -99399999999999999/100000000000000000)) |\n",
+      "(gamma < -7/8)) | (epsilon < -33333333333333331/50000000000000000)) | (beta <\n",
+      "-2087499999999999/12500000000000000)) | (Infected_10 < 0.0)) | (Exposed_10 <\n",
+      "0.0)) | (Susceptible_10 < 0.0)) | (((((((-1.0 * Total_population_10) +\n",
+      "Deceased_10) + Recovered_10) + Exposed_10) + Infected_10) + Susceptible_10) <\n",
+      "0.0)) | (Recovered_20 = 0.0)) | (Recovered_20 < 0.0)) | (Infected_20 < 0.0)) |\n",
+      "(Exposed_20 < 0.0)) | (Susceptible_20 < 0.0)) | (((((((-1.0 *\n",
+      "Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20)\n",
+      "+ Susceptible_20) < 0.0)) | (gamma < 0.0)) | (beta <\n",
+      "27081256023150169/100000000000000000)) | (! (Deceased_0 = 0.0))) | (!\n",
+      "(Recovered_0 = 0.0))) | (! (Exposed_0 = 0.0))) | (! (Infected_0 <= 100.0))) | (!\n",
+      "(Susceptible_0 <= 100.0))) | (! (Total_population_0 = 100.0))) | (! (Infected_0\n",
+      "= 1.0))) | (! (Susceptible_0 = 99.0))) | (! (alpha <=\n",
+      "10060000000000001/10000000000000000))) | (! (gamma <= 9/8))) | (! (epsilon <=\n",
+      "2666666666666667/2000000000000000))) | (! (beta <=\n",
+      "9165000000000001/5000000000000000))) | (! (Deceased_10 = 0.0))) | (!\n",
+      "(Recovered_10 = 0.0))) | (! (Infected_10 <= 100.0))) | (! (Exposed_10 <=\n",
+      "100.0))) | (! (Susceptible_10 <= 100.0))) | (! (((((((-1.0 *\n",
+      "Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10)\n",
+      "+ Susceptible_10) <= 0.0))) | (! (Total_population_10 = Total_population_0))) |\n",
+      "(! (Deceased_10 = (Deceased_0 + (10.0 * (Infected_0 * alpha)))))) | (!\n",
+      "(Recovered_10 = (Recovered_0 + (10.0 * (Infected_0 * gamma)))))) | (!\n",
+      "(Exposed_10 = (Exposed_0 + (10.0 * (((((Total_population_0 ^ -1.0) * Infected_0)\n",
+      "* Susceptible_0) * beta) - (Exposed_0 * epsilon))))))) | (! (Infected_10 =\n",
+      "(Infected_0 + (10.0 * (((Exposed_0 * epsilon) - (Infected_0 * alpha)) -\n",
+      "(Infected_0 * gamma))))))) | (! (Susceptible_10 = (Susceptible_0 - (10.0 *\n",
+      "((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))))) | (!\n",
+      "(Deceased_20 = 0.0))) | (! (Recovered_20 <= 100.0))) | (! (Infected_20 <=\n",
+      "100.0))) | (! (Exposed_20 <= 100.0))) | (! (Susceptible_20 <= 100.0))) | (!\n",
+      "(((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20)\n",
+      "+ Infected_20) + Susceptible_20) <= 0.0))) | (!\n",
+      "((10000000000000001/100000000000000000 * (Recovered_20 - Recovered_30)) < 0.0)))\n",
+      "| (! (Total_population_20 = Total_population_10))) | (! (Exposed_20 =\n",
+      "(Exposed_10 + (10.0 * ((-1.0 * (epsilon * Exposed_10)) + (((beta *\n",
+      "(Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10))))))) | (!\n",
+      "(Infected_20 = (Infected_10 + (10.0 * (((-1.0 * (alpha * Infected_10)) - (gamma\n",
+      "* Infected_10)) + (epsilon * Exposed_10))))))) | (! (Susceptible_20 =\n",
+      "(Susceptible_10 - (10.0 * (((beta * (Total_population_10 ^ -1.0)) * Infected_10)\n",
+      "* Susceptible_10)))))) | (! (Recovered_30 = 0.0))) | (! (beta <\n",
+      "6823503167783207/25000000000000000))) | (! (gamma < 1/512)))) &\n",
+      "((((((((((((((((((((((((((((((((((((((((((((((((((((((Infected_0 < 0.0) |\n",
+      "(Susceptible_0 < 0.0)) | (alpha < -99399999999999999/100000000000000000)) |\n",
+      "(gamma < -7/8)) | (epsilon < -33333333333333331/50000000000000000)) | (beta <\n",
+      "-2087499999999999/12500000000000000)) | (Infected_10 < 0.0)) | (Exposed_10 <\n",
+      "0.0)) | (Susceptible_10 < 0.0)) | (((((((-1.0 * Total_population_10) +\n",
+      "Deceased_10) + Recovered_10) + Exposed_10) + Infected_10) + Susceptible_10) <\n",
+      "0.0)) | (Infected_20 < 0.0)) | (Exposed_20 < 0.0)) | (Susceptible_20 < 0.0)) |\n",
+      "(((((((-1.0 * Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20)\n",
+      "+ Infected_20) + Susceptible_20) < 0.0)) | (gamma < 0.0)) | (beta <\n",
+      "27081256023150169/100000000000000000)) | (! (Deceased_0 = 0.0))) | (!\n",
+      "(Recovered_0 = 0.0))) | (! (Exposed_0 = 0.0))) | (! (Infected_0 <= 100.0))) | (!\n",
+      "(Susceptible_0 <= 100.0))) | (! (Total_population_0 = 100.0))) | (! (Infected_0\n",
+      "= 1.0))) | (! (Susceptible_0 = 99.0))) | (! (alpha <=\n",
+      "10060000000000001/10000000000000000))) | (! (gamma <= 9/8))) | (! (epsilon <=\n",
+      "2666666666666667/2000000000000000))) | (! (beta <=\n",
+      "9165000000000001/5000000000000000))) | (! (Deceased_10 = 0.0))) | (!\n",
+      "(Recovered_10 = 0.0))) | (! (Infected_10 <= 100.0))) | (! (Exposed_10 <=\n",
+      "100.0))) | (! (Susceptible_10 <= 100.0))) | (! (((((((-1.0 *\n",
+      "Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10)\n",
+      "+ Susceptible_10) <= 0.0))) | (! (Total_population_10 = Total_population_0))) |\n",
+      "(! (Deceased_10 = (Deceased_0 + (10.0 * (Infected_0 * alpha)))))) | (!\n",
+      "(Recovered_10 = (Recovered_0 + (10.0 * (Infected_0 * gamma)))))) | (!\n",
+      "(Exposed_10 = (Exposed_0 + (10.0 * (((((Total_population_0 ^ -1.0) * Infected_0)\n",
+      "* Susceptible_0) * beta) - (Exposed_0 * epsilon))))))) | (! (Infected_10 =\n",
+      "(Infected_0 + (10.0 * (((Exposed_0 * epsilon) - (Infected_0 * alpha)) -\n",
+      "(Infected_0 * gamma))))))) | (! (Susceptible_10 = (Susceptible_0 - (10.0 *\n",
+      "((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))))) | (!\n",
+      "(Deceased_20 = 0.0))) | (! (Recovered_20 = 0.0))) | (! (Infected_20 <= 100.0)))\n",
+      "| (! (Exposed_20 <= 100.0))) | (! (Susceptible_20 <= 100.0))) | (! (((((((-1.0 *\n",
+      "Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20)\n",
+      "+ Susceptible_20) <= 0.0))) | (! ((10000000000000001/100000000000000000 *\n",
+      "(Deceased_20 - Deceased_30)) < 0.0))) | (! (Total_population_20 =\n",
+      "Total_population_10))) | (! (Exposed_20 = (Exposed_10 + (10.0 * ((-1.0 *\n",
+      "(epsilon * Exposed_10)) + (((beta * (Total_population_10 ^ -1.0)) * Infected_10)\n",
+      "* Susceptible_10))))))) | (! (Infected_20 = (Infected_10 + (10.0 * (((-1.0 *\n",
+      "(alpha * Infected_10)) - (gamma * Infected_10)) + (epsilon * Exposed_10))))))) |\n",
+      "(! (Susceptible_20 = (Susceptible_10 - (10.0 * (((beta * (Total_population_10 ^\n",
+      "-1.0)) * Infected_10) * Susceptible_10)))))) | (! (Deceased_30 = 0.0))) | (!\n",
+      "(beta < 6823503167783207/25000000000000000))) | (! (gamma < 1/512)))) &\n",
+      "((Infected_0 <= 100.0) | (! conj1471))) & ((Susceptible_0 <= 100.0) | (!\n",
+      "conj1472))) & ((Total_population_0 = 100.0) | (! conj1474))) & ((Infected_0 =\n",
+      "1.0) | (! conj1474))) & ((Susceptible_0 = 99.0) | (! conj1474))) & ((gamma <=\n",
+      "9/8) | (! conj1474))) & ((beta <= 9165000000000001/5000000000000000) | (!\n",
+      "conj1474))) & ((solve_step_3 | conj1514) | (! disj1132))) & ((conj1485 | (!\n",
+      "assume_Deceased_bounds)) | (! disj1103))) & (disj1103 | (! conj1491))) &\n",
+      "((conj1486 | (! assume_Recovered_bounds)) | (! disj1104))) & (disj1104 | (!\n",
+      "conj1491))) & ((conj1487 | (! assume_Exposed_bounds)) | (! disj1105))) &\n",
+      "(disj1105 | (! conj1491))) & ((conj1488 | (! assume_Infected_bounds)) | (!\n",
+      "disj1106))) & (disj1106 | (! conj1491))) & ((conj1489 | (!\n",
+      "assume_Susceptible_bounds)) | (! disj1107))) & (disj1107 | (! conj1491))) &\n",
+      "((conj1490 | (! assume_compartment)) | (! disj1108))) & (disj1108 | (!\n",
+      "conj1491))) & ((conj1491 | (! solve_step_1)) | (! disj1109))) & ((Deceased_10 <=\n",
+      "100.0) | (! conj1485))) & ((Recovered_10 <= 100.0) | (! conj1486))) &\n",
+      "((Infected_10 <= 100.0) | (! conj1488))) & ((Exposed_10 <= 100.0) | (!\n",
+      "conj1487))) & ((Susceptible_10 <= 100.0) | (! conj1489))) & ((((((((-1.0 *\n",
+      "Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) + Infected_10)\n",
+      "+ Susceptible_10) <= 0.0) | (! conj1490))) & ((Total_population_10 =\n",
+      "Total_population_0) | (! conj1491))) & ((Deceased_10 = (Deceased_0 + (10.0 *\n",
+      "(Infected_0 * alpha)))) | (! conj1491))) & ((Recovered_10 = (Recovered_0 + (10.0\n",
+      "* (Infected_0 * gamma)))) | (! conj1491))) & ((Exposed_10 = (Exposed_0 + (10.0 *\n",
+      "(((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta) -\n",
+      "(Exposed_0 * epsilon))))) | (! conj1491))) & ((Infected_10 = (Infected_0 + (10.0\n",
+      "* (((Exposed_0 * epsilon) - (Infected_0 * alpha)) - (Infected_0 * gamma))))) |\n",
+      "(! conj1491))) & ((Susceptible_10 = (Susceptible_0 - (10.0 *\n",
+      "((((Total_population_0 ^ -1.0) * Infected_0) * Susceptible_0) * beta)))) | (!\n",
+      "conj1491))) & (disj1121 | (! conj1508))) & (disj1122 | (! conj1508))) &\n",
+      "((conj1502 | (! assume_Deceased_bounds)) | (! disj1123))) & (disj1123 | (!\n",
+      "conj1508))) & ((conj1503 | (! assume_Recovered_bounds)) | (! disj1124))) &\n",
+      "(disj1124 | (! conj1508))) & ((conj1504 | (! assume_Exposed_bounds)) | (!\n",
+      "disj1125))) & (disj1125 | (! conj1508))) & ((conj1505 | (!\n",
+      "assume_Infected_bounds)) | (! disj1126))) & (disj1126 | (! conj1508))) &\n",
+      "((conj1506 | (! assume_Susceptible_bounds)) | (! disj1127))) & (disj1127 | (!\n",
+      "conj1508))) & ((conj1507 | (! assume_compartment)) | (! disj1128))) & (disj1128\n",
+      "| (! conj1508))) & ((conj1508 | (! solve_step_2)) | (! disj1129))) &\n",
+      "((Deceased_20 <= 100.0) | (! conj1502))) & ((Recovered_20 <= 100.0) | (!\n",
+      "conj1503))) & ((Infected_20 <= 100.0) | (! conj1505))) & ((Exposed_20 <= 100.0)\n",
+      "| (! conj1504))) & ((Susceptible_20 <= 100.0) | (! conj1506))) & ((((((((-1.0 *\n",
+      "Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20)\n",
+      "+ Susceptible_20) <= 0.0) | (! conj1507))) &\n",
+      "((((10000000000000001/100000000000000000 * (Deceased_20 - Deceased_30)) < 0.0) |\n",
+      "(! assume_c3_20)) | (! disj1121))) & ((((10000000000000001/100000000000000000 *\n",
+      "(Recovered_20 - Recovered_30)) < 0.0) | (! assume_c2_20)) | (! disj1122))) &\n",
+      "((Total_population_20 = Total_population_10) | (! conj1508))) & ((Deceased_20 =\n",
+      "(Deceased_10 + (10.0 * (alpha * Infected_10)))) | (! conj1508))) &\n",
+      "((Recovered_20 = (Recovered_10 + (10.0 * (gamma * Infected_10)))) | (!\n",
+      "conj1508))) & ((Exposed_20 = (Exposed_10 + (10.0 * ((-1.0 * (epsilon *\n",
+      "Exposed_10)) + (((beta * (Total_population_10 ^ -1.0)) * Infected_10) *\n",
+      "Susceptible_10))))) | (! conj1508))) & ((Infected_20 = (Infected_10 + (10.0 *\n",
+      "(((-1.0 * (alpha * Infected_10)) - (gamma * Infected_10)) + (epsilon *\n",
+      "Exposed_10))))) | (! conj1508))) & ((Susceptible_20 = (Susceptible_10 - (10.0 *\n",
+      "(((beta * (Total_population_10 ^ -1.0)) * Infected_10) * Susceptible_10)))) | (!\n",
+      "conj1508))) & ((Deceased_30 = 0.0) | (! conj1514))) & ((Recovered_30 = 0.0) | (!\n",
+      "conj1514))) & ((! conj1471) | (! (Infected_0 < 0.0)))) & ((! conj1472) | (!\n",
+      "(Susceptible_0 < 0.0)))) & ((! conj1474) | (! (gamma < -7/8)))) & ((! conj1474)\n",
+      "| (! (beta < -2087499999999999/12500000000000000)))) & ((! conj1485) | (!\n",
+      "(Deceased_10 < 0.0)))) & ((! conj1486) | (! (Recovered_10 < 0.0)))) & ((!\n",
+      "conj1487) | (! (Exposed_10 < 0.0)))) & ((! conj1488) | (! (Infected_10 < 0.0))))\n",
+      "& ((! conj1489) | (! (Susceptible_10 < 0.0)))) & ((! conj1490) | (! (((((((-1.0\n",
+      "* Total_population_10) + Deceased_10) + Recovered_10) + Exposed_10) +\n",
+      "Infected_10) + Susceptible_10) < 0.0)))) & ((! conj1502) | (! (Deceased_20 <\n",
+      "0.0)))) & ((! conj1503) | (! (Recovered_20 < 0.0)))) & ((! conj1504) | (!\n",
+      "(Exposed_20 < 0.0)))) & ((! conj1505) | (! (Infected_20 < 0.0)))) & ((!\n",
+      "conj1506) | (! (Susceptible_20 < 0.0)))) & ((! conj1507) | (! (((((((-1.0 *\n",
+      "Total_population_20) + Deceased_20) + Recovered_20) + Exposed_20) + Infected_20)\n",
+      "+ Susceptible_20) < 0.0)))) & (! (alpha <\n",
+      "-99399999999999999/100000000000000000))) & (! (epsilon <\n",
+      "-33333333333333331/50000000000000000))) & (! solve_step_3)) & (! (gamma < 0.0)))\n",
+      "& (! (beta < 27081256023150169/100000000000000000)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "import textwrap\n",
+    "expr = results_unconstrained_point.parameter_space.false_boxes[0].explanation.expression\n",
+    "print(textwrap.fill(expr, 80))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SEIRHD\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    "    funman_request.structure_parameters[0].schedules[0] = EncodingSchedule(timepoints=[0, 1])\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",
+    "    funman_request.config.save_smtlib=\"./out\"\n",
+    "\n",
+    "    for p in [po.name for po in funman_request.parameters if po.name not in [\"S0\", \"I0\", \"E0\", \"R0\", \"H0\", \"D0\", \"N\", \"beta\"]]:\n",
+    "    #[\"beta\", \"r_H_to_D\", \"r_E_to_I\", \"p_I_to_R\", \"r_I_to_R\",  \"p_I_to_H\", \"r_I_to_H\", \"p_H_to_R\", \"r_H_to_R\",\"p_H_to_D\",\"r_H_to_D\"]:\n",
+    "        param = funman_request.parameter(p)\n",
+    "        param.interval.lb = 0\n",
+    "        param.interval.ub = 1\n",
+    "\n",
+    "    # S is decreasing,  dS/dt < 0, S_{t+1} - S_t < 0\n",
+    "    c1 = LinearConstraint(name=\"c1\", additive_bounds=Interval(ub=0), variables=[\"S\"], derivative=True)\n",
+    "    funman_request.constraints.append(c1)\n",
+    "\n",
+    "    # R is increasing, dR/dt > 0, -dR/dt < 0,   - (R_{t+1} - R_t) < 0\n",
+    "    c2 = LinearConstraint(name=\"c2\", additive_bounds=Interval(ub=0), variables=[\"R\"], weights=[-1], derivative=True)\n",
+    "    funman_request.constraints.append(c2)\n",
+    "\n",
+    "    # D is increasing, dD/dt > 0, -dD/dt < 0,   - (D_{t+1} - D_t) < 0\n",
+    "    c3 = LinearConstraint(name=\"c3\", additive_bounds=Interval(lb=0), variables=[\"D\"], derivative=True)\n",
+    "    funman_request.constraints.append(c3)\n",
+    "\n",
+    "    # Population size maintained\n",
+    "    c4 = LinearConstraint(name=\"compartment\", additive_bounds=Interval(lb=0, ub=0, closed_upper_bound=True), variables=[\"S\", \"E\", \"I\", \"R\", \"D\", \"H\", \"N\"], weights=[1, 1, 1, 1, 1, 1, -1])\n",
+    "    funman_request.constraints.append(c4)\n",
+    "\n",
+    "    funman_request.constraints.append(StateVariableConstraint(name=\"SB\", variable=\"S\", interval=Interval(lb=0, ub=101, closed_upper_bound=True)))\n",
+    "    funman_request.constraints.append(StateVariableConstraint(name=\"EB\", variable=\"E\", interval=Interval(lb=0, ub=101, closed_upper_bound=True)))\n",
+    "    funman_request.constraints.append(StateVariableConstraint(name=\"IB\", variable=\"I\", interval=Interval(lb=0, ub=101, closed_upper_bound=True)))\n",
+    "    funman_request.constraints.append(StateVariableConstraint(name=\"RB\", variable=\"R\", interval=Interval(lb=0, ub=101, closed_upper_bound=True)))\n",
+    "    funman_request.constraints.append(StateVariableConstraint(name=\"DB\", variable=\"D\", interval=Interval(lb=0, ub=101, closed_upper_bound=True)))\n",
+    "    funman_request.constraints.append(StateVariableConstraint(name=\"HB\", variable=\"H\", interval=Interval(lb=0, ub=101, closed_upper_bound=True)))\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SEIRD demo\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results([\"S\", \"E\", \"I\", \"R\", \"H\", \"D\"], results_unconstrained_point, ylabel=\"Population\")\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'soft': True,\n",
+       " 'name': 'c2',\n",
+       " 'timepoints': None,\n",
+       " 'additive_bounds': {'lb': -1.7976931348623157e+308,\n",
+       "  'ub': 0.0,\n",
+       "  'closed_upper_bound': False,\n",
+       "  'original_width': 1.7976931348623157e+308},\n",
+       " 'variables': ['R'],\n",
+       " 'weights': [-1],\n",
+       " 'derivative': True}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json.loads(c2.model_dump_json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "json.loads(results_unconstrained_point.parameter_space.false_boxes[0].explanation.model_dump_json())#['relevant_assumptions']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_unconstrained_point.parameter_space.points()[0]\n",
+    "df= results_unconstrained_point.dataframe(\n",
+    "    results_unconstrained_point.parameter_space.points()\n",
+    ")\n",
+    "df[[\"S\", \"E\", \"I\", \"R\", \"H\", \"D\"]]\n",
+    "# df[\"S\"]+df[\"E\"]+df[\"I\"]+df[\"D\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Stratified\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    "    funman_request.structure_parameters[0].schedules[0] = EncodingSchedule(timepoints=[0,1, 2])\n",
+    "    funman_request.config.substitute_subformulas = True\n",
+    "    funman_request.config.use_transition_symbols = True\n",
+    "#     funman_request.config.use_compartmental_constraints=False\n",
+    "#     r_H_to_D = funman_request.parameter(\"r_H_to_D\")\n",
+    "#     r_H_to_D.interval.lb = 0\n",
+    "#     r_H_to_D.interval.ub = 1.0\n",
+    "\n",
+    "    # r_E_to_I = funman_request.parameter(\"r_E_to_I\")\n",
+    "    # r_E_to_I.interval.lb = 0\n",
+    "    # r_E_to_I.interval.ub = 1.0\n",
+    "\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SEIRD demo\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    # summary = summarize_results([\"S\", \"E\", \"I\", \"R\", \"D\"], results_unconstrained_point, ylabel=\"Population\")\n",
+    "    # print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_unconstrained_point.parameter_space.points()[0]"
+   ]
+  }
+ ],
+ "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_terrarium_demo.ipynb b/notebooks/funman_terrarium_demo.ipynb
index 4d574e10..7c01b585 100644
--- a/notebooks/funman_terrarium_demo.ipynb
+++ b/notebooks/funman_terrarium_demo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,12 +10,35 @@
     "\n",
     "# The file scratch/hackathon/hackathon_fall_2023_demo_terarrium.py was used to generate the outputs rendered here.\n",
     "\n",
+    "SAVED_RESULTS_DIR = \"./out\"\n",
+    "\n",
     "# Import funman related code\n",
     "import os\n",
-    "from funman import FunmanResults\n",
+    "from funman.api.run import Runner\n",
+    "from funman_demo import summarize_results\n",
+    "from funman_demo.example.pde import plot_spatial_timeseries\n",
+    "from IPython.display import HTML\n",
+    "import matplotlib.pyplot as plt\n",
+    "import itertools\n",
+    "from funman import FunmanWorkRequest\n",
     "import json\n",
-    "from funman_demo.parameter_space_plotter import ParameterSpacePlotter\n",
-    "SAVED_RESULTS_DIR = \"../out\"\n",
+    "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n",
+    "from funman.representation import Interval\n",
+    "from funman import FUNMANConfig\n",
+    "import logging\n",
+    "from funman_demo import summarize_results\n",
+    "\n",
+    "\n",
+    "\n",
+    "RESOURCES = \"../resources\"\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"mira\")\n",
+    "MODEL_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"models\", \"BIOMD0000000955_askenet.json\"\n",
+    ")\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"requests\", \"BIOMD0000000955_askenet_request.json\"\n",
+    ")\n",
+    "\n",
     "\n",
     "# %load_ext autoreload\n",
     "# %autoreload 2"
@@ -23,214 +46,307 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peak_time = Interval(lb=90, ub=110, closed_upper_bound=True)\n",
+    "\n",
+    "peak_infections = Interval(lb=1e-2, ub=1e-1)\n",
+    "# ramp_up_infections = Interval(ub=0.1*peak_infections.ub)\n",
+    "# ramp_down_infections = Interval(ub=0.01*peak_infections.ub)\n",
+    "ramp_up_infections = Interval(lb=0.0, ub=0.5*peak_infections.ub)\n",
+    "ramp_down_infections = Interval(lb=0.0, ub=0.5*peak_infections.ub)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-02-21 01:15:34,270 - funman.server.worker - INFO - FunmanWorker running...\n",
+      "2024-02-21 01:15:34,273 - funman.server.worker - INFO - Starting work on: 8fdccfb6-957f-4685-9c5f-951d9ecabd78\n",
+      "2024-02-21 01:15:36,285 - funman.api.run - INFO - Dumping results to ./out/8fdccfb6-957f-4685-9c5f-951d9ecabd78.json\n",
+      "2024-02-21 01:15:39,034 - funman.scenario.consistency - INFO - 7{140}:\t[+]\n",
+      "2024-02-21 01:15:39,038 - funman.server.worker - INFO - Completed work on: 8fdccfb6-957f-4685-9c5f-951d9ecabd78\n",
+      "2024-02-21 01:15:46,331 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n",
+      "2024-02-21 01:15:46,626 - funman.server.worker - INFO - FunmanWorker exiting...\n",
+      "2024-02-21 01:15:46,628 - funman.server.worker - INFO - Worker.stop() completed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 0: Unconstrained model, find any parameter assignment\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIDARTHE demo step 0\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    # import cProfile\n",
+    "\n",
+    "    # with cProfile.Profile() as pr:\n",
+    "summary = summarize_results([\"Infected\"], results_unconstrained_point, ylabel=\"Infected\")\n",
+    "print(summary)\n",
+    "    #     pr.dump_stats(\"summary_profile.stats\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
     {
-     "data": {
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-      "text/plain": [
-       "<Figure size 6000x6000 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-02-21 01:15:49,824 - funman.server.worker - INFO - FunmanWorker running...\n",
+      "2024-02-21 01:15:49,827 - funman.server.worker - INFO - Starting work on: b506b93f-7c9d-415b-87b4-4a8df15fdaf5\n",
+      "2024-02-21 01:15:51,834 - funman.api.run - INFO - Dumping results to ./out/b506b93f-7c9d-415b-87b4-4a8df15fdaf5.json\n",
+      "2024-02-21 01:15:52,912 - funman.search.box_search - INFO - progress: 0.12500\n",
+      "2024-02-21 01:15:53,295 - funman.search.box_search - INFO - progress: 0.25000\n",
+      "2024-02-21 01:15:53,649 - funman.search.box_search - INFO - progress: 0.37500\n",
+      "2024-02-21 01:15:53,987 - funman.search.box_search - INFO - progress: 0.50000\n",
+      "2024-02-21 01:15:54,307 - funman.search.box_search - INFO - progress: 0.62500\n",
+      "2024-02-21 01:15:54,621 - funman.search.box_search - INFO - progress: 0.75000\n",
+      "2024-02-21 01:15:54,920 - funman.search.box_search - INFO - progress: 1.00000\n",
+      "2024-02-21 01:15:56,017 - funman.server.worker - INFO - Completed work on: b506b93f-7c9d-415b-87b4-4a8df15fdaf5\n",
+      "2024-02-21 01:16:01,914 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n",
+      "2024-02-21 01:16:02,073 - funman.server.worker - INFO - FunmanWorker exiting...\n",
+      "2024-02-21 01:16:02,079 - funman.server.worker - INFO - Worker.stop() completed.\n"
+     ]
     }
    ],
    "source": [
-    "# Step 1: Unconstrained model, constraints = []\n",
+    "# Take point from previous solution and define a box around it (used to label the box)\n",
+    "from funman import Box\n",
+    "from funman import ModelParameter, LABEL_ALL\n",
+    "point = results_unconstrained_point.parameter_space.true_points()[0]\n",
+    "# parameters = results_parameter_constraint.point_parameters(point)\n",
+    "parameters = results_unconstrained_point.model._parameter_names()\n",
+    "b = Box.from_point(point, radius = .000001, radius_vars=[p for p in parameters])\n",
+    "# b.bounds[\"epsilon\"]\n",
+    "# b.bounds\n",
     "\n",
-    "# Synthesize parameters epsilon and theta\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request: FunmanWorkRequest = FunmanWorkRequest.model_validate(json.load(request))\n",
     "\n",
-    "# {\n",
-    "#     \"name\": \"epsilon\",\n",
-    "#     \"interval\": {\"lb\": 0.1368, \"ub\": 0.20520000000000002},\n",
-    "#     \"label\": \"all\",\n",
-    "# },\n",
-    "# {\n",
-    "#     \"name\": \"theta\",\n",
-    "#     \"interval\": {\"lb\": 0.2968, \"ub\": 0.4452},\n",
-    "#     \"label\": \"all\",\n",
-    "# },\n",
+    "    funman_request.parameters = [ModelParameter(name=p, interval=b.bounds[p], label=LABEL_ALL) for p in parameters]\n",
+    "    # print(funman_request.parameters[0]  )\n",
+    "    # funman_request.config.use_compartmental_constraints=False \n",
+    "    results_synthesis_around_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIDARTHE demo\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            # dump_results=False\n",
+    "    )\n",
+    "    summary = summarize_results([\"Infected\"], results_synthesis_around_point, ylabel=\"Infected\")\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# View profiling if present (must set `\"profiling\": True` in the config above)\n",
     "\n",
-    "SAVED_RESULT_TO_USE = os.path.join(SAVED_RESULTS_DIR, \"06396b64-b529-4808-a2dc-d74ff9e33657.json\")\n",
+    "import os\n",
+    "from IPython.core.display import Image\n",
+    "from IPython.display import display\n",
+    "profile_file = os.path.join(f\"./summary_profile.stats\")\n",
+    "if os.path.exists(profile_file):\n",
+    "    os.system(f\"gprof2dot -f pstats {profile_file} | dot -Tpng -o summary-profile.png\")\n",
+    "    display(Image(filename='summary-profile.png'))\n",
+    "else:\n",
+    "    print(f\"{profile_file} file not present\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1: Constrain peak infected, find any parameter assignment\n",
     "\n",
-    "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n",
-    "    results: FunmanResults = FunmanResults.model_validate(json.load(f))\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
     "\n",
-    "# Plot the trajectories\n",
-    "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"},  xlabel=\"Time\", ylabel=\"Infected\")\n",
+    "    \n",
+    "    funman_request.constraints = [\n",
+    "        StateVariableConstraint(name=\"peak_infected1\", variable=\"Infected\", interval=ramp_up_infections, timepoints=Interval(ub=peak_time.lb)),\n",
+    "        StateVariableConstraint(name=\"peak_infected2\", variable=\"Infected\", interval=peak_infections, timepoints=peak_time),\n",
+    "        StateVariableConstraint(name=\"peak_infected3\", variable=\"Infected\", interval=ramp_down_infections, timepoints=Interval(lb=peak_time.ub))\n",
+    "        ]\n",
     "\n",
-    "# Plot the parameter space\n",
-    "ParameterSpacePlotter(\n",
-    "        results.parameter_space, plot_points=True, parameters=[\"epsilon\", \"theta\", \"timestep\"]\n",
-    "    ).plot(show=True)"
+    "    results_constrained = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIDARTHE demo step 1\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results([\"Infected\"], results_constrained, ylabel=\"Infected\")\n",
+    "    print(summary)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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cckk8/vjjbWpCR0Qceuih8dRTT8XXv/71tHOfffbZmDZtWpvWh2I2ZMiQOOKIIzq6jBZde+21aRvRY8aMiZkzZ7aqER0R8ZGPfCSeeuqpOPXUUxPnLVu2LCZOnNjaUgEAQM8YioyeMXRdesbQuekZQ+HSN4bOSc+4ZXrGAAC0lZ4xFB99Y+i69I2h89IzhsKlZwydk55xy/SMITNCrwEgS+bPnx+TJ09OnPOxj30s7rvvvujZs2eb1u7Vq1fcf//9sd9++yXOmzRpUrzwwgttWhvY3vDhw+OGG26IN954I370ox/FPvvsk9F6NTU1ceONN8aUKVOirKwsce7ixYvj6quvzmg/IHM/+MEPYtasWc2O7bbbbnmuBtjSsmXL4tRTT42NGze2OKd3797x4IMPxo033hj9+/fPeM9+/frFRRddFK+88krcfvvtGa8HXdVPfvKTtHP+4z/+I6688sq0j5tbUlFRET/4wQ/iq1/9atq5P/7xj9u1BxS6gQMHxoknnhjf/e5346GHHop33303Fi5cGDfccENHl9asd955J+0bMIYPHx6PPPJI7LLLLm1au6qqKm699dY44YQTEufdc8898eijj7ZpbQAAuiY9YygeesaAnjF0XnrGUNj0jaHj6Rl/QM8YAIBs0zOG4qJvDOgbQ+ekZwyFTc8YOp6e8Qf0jCH3hF4DQJZcccUViZ/Q2KdPn5g2bVqrP7V1WzU1NXHHHXdEr169WpzT0NAQV155ZbvWByJGjhwZ//M//xMvvvhinHvuue2+Xlty5plnxqRJk9LOmzRpUqxcuTKrewOt98orr7T4ZNcBBxwQn/vc5/JbELCVc889N15//fUWx3fZZZf405/+FEcddVTW9y4rK8vJutAVzJ07N55//vnEOQcddFD853/+Z1b2+9GPfpT2Bd1/+ctf4pVXXsnKftBZ7bLLLnH88cfHlVdeGb/73e9iyZIl8Y9//CPuvvvu+Pa3vx1HHnlk9O3bt6PLTPTf//3fsWLFihbHKysr44477ogdd9yxXeuXlZXFlClTYsiQIYnzLr300natDwBA16JnDIVPzxiI0DOGzk7PGAqXvjHkn55xenrGAABkk54xFAd9YyBC3xg6Mz1jKFx6xpB/esbp6RlDbgm9BoAs+Pvf/x533XVX4pzvfve7MXDgwIz2GTx4cFxxxRWJc+68885YtGhRRvtAV7PTTjvFddddF/PmzYvTTz+93Z/01hpf/vKX48wzz0ycs2bNmrjjjjtyVgOQ7Lzzzot169Ztd3tFRUXccMMNUVJS0gFVARERv/vd7+I3v/lNi+M9evSIBx54IEaNGpXHqoDWeOyxx9LO+f73v5+1v2dLS0vjqquuSjvPJ6pSjC644IK4//77Y/HixfHGG2/EfffdF5dcckkcc8wx7W7YdpSVK1em/WToiRMnxt57753RPj179kz7CfF//vOf449//GNG+wAAUNz0jKGw6RkDW9Izhs5LzxgKm74x5IeecdvpGQMAkA16xlD49I2BLekbQ+ekZwyFTc8Y8kPPuO30jCF3hF4DQBZMnjw5GhsbWxwfMWJEnHvuuVnZa8KECbHbbru1ON7Y2BiTJ0/Oyl7QVXzhC1+IL3/5y1FeXp6X/b73ve+l/WTne++9Ny+1AFu76aabWnyi/N///d/jQx/6UJ4rAjarr6+Pf//3f0+cc/3118eYMWPyVBHQFjNnzkwc33333eOggw7K6p6HHHJIDB8+PHHOs88+m9U9oTM466yz4rjjjoudd965o0vJ2JQpUxI/fblXr17x7W9/Oyt7HX/88XHwwQcnzvnpT3+alb0AAChOesZQ2PSMgc30jKHz0jOGwqdvDPmhZ9w+esYAAGRKzxgKn74xsJm+MXROesZQ+PSMIT/0jNtHzxhyQ+g1AGSosbExfv3rXyfOufDCC7P2aa7l5eXx1a9+NXHO1KlTo6mpKSv7Adm36667xqmnnpo4549//KPrGPLs7bffjm984xvNju22225x6aWX5rkiYEu//OUvY8GCBS2OH3/88XHaaaflsSKgLV555ZXE8SOOOCIn+x555JGJ43/7299ysi+QHbfeemvi+Lnnnht1dXVZ2y/dC9/uv//+xOY4AABdl54x0FZ6xtA56RlD56ZnDIVP3xhoKz1jAAAKhZ4x0B76xtA56RtD56VnDIVPzxhoKz1jKHxCrwEgQ9OnT4/Fixe3OF5dXR2f+9znsrrn+PHjo7KyssXxN998Mx5//PGs7glk13HHHZc4vnLlynj11VfzVA0QEfHVr341li1b1uzYddddF926dctzRcBmTU1N8cMf/rDF8bKysrj66qvzWBHQVi39HbvZXnvtlZN906377rvv5mRfIHMvv/xyzJgxI3HOOeeck9U9P/WpT0X//v1bHN+wYUPcddddWd0TAIDioGcMtIeeMXQ+esbQeekZQ3HQNwbaQs8YAIBComcMtJe+MXQ++sbQOekZQ3HQMwbaQs8YioPQawDI0P333584fuyxx0aPHj2yumevXr3i6KOPTpyTri6gY3384x9PO+fvf/97HioBIjb9vXnHHXc0O3byySen/eRGILf+93//N15++eUWx0866aTYY4898lgR0FYbNmxIHN9hhx1ysu+OO+6YOL5u3bqc7AtkLt1zW/vuu28MHz48q3uWlpbGZz/72cQ5nnMDAKA5esZAe+gZQ+eiZwydm54xFAd9Y6At9IwBACgkesZAe+kbQ+eibwydl54xFAc9Y6At9IyhOAi9BoAMPfroo4njxx57bE72TbfuI488kpN9gezo06dP4iepR0QsX748P8VAF7dq1aqYMGFCs2O9evWKH//4x/ktCNjOzTffnDj+pS99KU+VAO3Vs2fPxPGampqc7Jtu3bq6upzsC2Susz7n9vvf/z4aGxtzsjcAAIWrsz5+1TOGzk3PGDoPPWPo/PSMoTjoGwNt0Vmfc9MzBgCgOZ318aueMXR++sbQeegbQ+emZwzFQc8YaIvO+pybnjG0jdBrAMjA4sWL44UXXkicc9hhh+Vk78MPPzxxfN68efHWW2/lZG8gO9J9wpxPgoP8uPjii+P1119vduz73/9+7LzzznmuCNjS8uXL46GHHmpxvH///vGJT3wifwUB7dK3b9/E8ffeey8n+6ZbN11dQMdoaGiIJ554InFOrp5zO/jgg6O6urrF8RUrVsSMGTNysjcAAIVJzxjIhJ4xdA56xtC56RlD8dA3BlpLzxgAgEKiZwxkSt8YOgd9Y+i89IyheOgZA62lZwzFQ+g1AGTgmWeeSRwfOHBgDBw4MCd7DxkyJPr37584xwNj6NzWrl2bOJ70P79Adjz11FPxs5/9rNmxj33sY3HeeefluSJgW/fcc09s3LixxfHjjjsuSkpK8lgR0B6jRo1KHM/Vi6nTrbvbbrvlZF8gM/PmzYs1a9a0OF5RURH77bdfTvaurq6OvffeO3GO59wAANiSnjGQCT1j6Hh6xtD56RlD8dA3BlpLzxgAgEKiZwxkSt8YOp6+MXRuesZQPPSMgdbSM4biIfQaADIwc+bMxPF99tknp/uPHTs2cXzWrFk53R9ov1WrVsWKFSsS5/Tu3TtP1UDXtHHjxjj77LMjlUptN1ZeXh433HCDBhd0Ao888kji+Cc/+ck8VQJk4uCDD04c/+Mf/5iTfdN9gutBBx2Uk32BzKR7zm3UqFFRVVWVs/095wYAQFvoGQPtpWcMHU/PGAqDnjEUD31joLX0jAEAKCR6xkAm9I2h4+kbQ+enZwzFQ88YaC09YygeQq8BIAOzZ89OHN9rr71yun+69T0whs5r1qxZzTa/tjRs2LA8VQNd03/+53/GCy+80OzY17/+9fjwhz+c54qA5jz++OOJ4x/96EfzUwiQkU9+8pNRXV3d4vj06dNjw4YNWd1z3bp1MX369BbHS0tL45BDDsnqnkB2eM4NAIBC4vEr0F56xtDx9IyhMOgZQ/HQNwZay3NuAAAUEo9fgUzoG0PH0zeGzk/PGIqHnjHQWp5zg+JR3tEFAEAhe+mllxLHR4wYkdP9hw8fnjj+8ssv53R/oP1+97vfJY7X1dXFoEGD8lQNdD3z58+Pq666qtmxIUOGxGWXXZbnioDm/O1vf4vFixe3ON6rV68YOnRo2nUaGhri5ZdfjoULF8aKFStiw4YN0b179+jRo0cMHDgwhgwZErW1tdksHdhG79694/TTT49f/vKXzY4vX748fvazn8XEiROztuekSZNi5cqVLY5/6lOfigEDBmRtPyB7POcGAEAh8fgVaC89Y+hYesZQGPSMobjoGwOt5Tk3AAAKicevQCb0jaFj6RtD56dnDMVFzxhoLc+5QfEQeg0A7ZRKpWLRokWJc9I9cM1UuvXT1Qd0jMbGxpg2bVrinIMOOihKS0vzVBF0LU1NTXH22WfHxo0bmx2/7rrronv37nmuCmhOuk9fTHo8/O6778Ztt90W999/f/zxj39s8ZqPiCgpKYk999wzDjrooDjhhBPisMMOi8rKyvaWDbTgG9/4Rtx6660tXo/f+9734jOf+UzsuuuuGe/16quvtviis82+/vWvZ7wPkBsLFy5MHO/o59zWrFkT77zzTuy44445rQMAgM5PzxhoLz1j6Fh6xlA49Iyh+OgbA62hZwwAQKHQMwYyoW8MHUvfGAqDnjEUHz1joDX0jKF4eGYLANrp7bffjvXr1yfO2WWXXXJaQ7r116xZE0uWLMlpDUDb3XvvvfHqq68mzjn++OPzVA10PZMnT44///nPzY599rOfjaOPPjrPFQEtmTt3buL4sGHDtrttyZIl8eUvfzkGDRoUEydOjMceeyyxER2x6YWm8+fPj5///Odx7LHHxoABA+KKK66IZcuWZVQ/sLU99tgjLr300hbH33nnnTjuuONi1apVGe2zdOnSOProoxOv4S984Qvx8Y9/PKN9gNxIpVJp/58518+57bzzzmlfHJ6uYQ4AQNegZwy0l54xdCw9YygcesZQfPSNgXT0jAEAKCR6xkAm9I2hY+kbQ2HQM4bio2cMpKNnDMVF6DUAtNObb76Zds7OO++c0xpas35r6gTyp7GxMfHJt4iIysrK+MxnPpOniqBree211+Lb3/52s2M9e/aMH//4x/ktCEg0f/78xPGddtppq+9/+ctfxu677x7XX399rFu3rt37vvPOO3H55ZfHyJEj48Ybb2z3OsD2Lr744jjiiCNaHJ89e3Z85CMfiTlz5rRr/b/85S8xduzYeOGFF1qcM2zYsPjRj37UrvWB3Fu2bFnaN4Dk+jm38vLy6Nu3b+Icz7kBABChZwy0j54xdCw9YygsesZQnPSNgSR6xgAAFBI9Y6C99I2hY+kbQ+HQM4bipGcMJNEzhuIi9BoA2um9995LHK+rq4uqqqqc1tC9e/eora1NnJOuTiC/fvazn6V9Yn38+PHRp0+fPFUEXcuECRNa/ETH733ve9G/f/88VwQkee211xLHd9xxx4iIqK+vj7POOivOPvvsWL58edb2f/fdd+Pcc8+Nk046KVauXJm1daErKysri3vvvTf+9V//tcU5CxYsiP322y+++MUvtrohPWPGjDj99NPjoIMOSvxU1AEDBsSjjz4aPXv2bHPtQH605rmsfv365byObV/0ti3PuQEAEKFnDLSPnjF0LD1jKCx6xlCc9I2BJHrGAAAUEj1joL30jaFj6RtD4dAzhuKkZwwk0TOG4lLe0QUAQKFaunRp4nhdXV1e6qirq4vVq1e3OJ6uTiB/Fi1aFP/xH/+ROKeioiIuuuiiPFUEXcvtt98ev/3tb5sd23///eNLX/pSnisC0lm8eHHieF1dXTQ0NMSpp54ad911V87quPvuu2PhwoXx8MMPv98AB9qvW7du8dBDD8W///u/x3XXXdfsnI0bN8bNN98cN998c+yyyy5x4IEHxogRI6J3795RW1sbq1atimXLlsWCBQviySefjLfffjvtvvvss0/ceeedMWTIkCz/RkA2tea5rHw875ZuD8+5AQAQoWcMtJ2eMXQsPWMoPHrGULz0jYGW6BkDAFBI9IyB9tA3ho6lbwyFRc8YipeeMdASPWMoLkKvAaCdli1bljjeo0ePvNSRbh8PjKFzaGxsjPHjxye+eCQiYuLEiTFs2LA8VQVdx9KlS+NrX/tas2Pl5eVxww03RGlpaZ6rAtJ56623EscrKytjwoQJOW1EbzZr1qz45Cc/GU8++WTeXngKxay6ujomT54cxx13XFx00UXx17/+tcW5b775Ztx5553t3quysjK++tWvxn/+539GZWVlu9cB8iPdc27dunWLsrKynNfhOTcAAFpDzxhoCz1j6Fh6xlCY9IyhuOkbA83RMwYAoJDoGQNtpW8MHUvfGAqPnjEUNz1joDl6xlBc/F82ALTT+vXrE8dramryUkdtbW3ieLo6gfy45JJL4oknnkicM3DgwLjkkkvyVBF0LV//+tdjyZIlzY5deOGFsddee+W5IiCd9evXx4YNGxLn3HHHHXHjjTe2ON6tW7c47rjj4sYbb4znnnsuXn/99diwYUMsWbIknn/++bjzzjvjzDPPjL59+7aqprlz58Ypp5wSqVSqTb8L0LKjjz465syZE3fffXccd9xxUV1dnbW16+rq4ktf+lL87W9/i2uuuUYTGgqE59wAACgkHr8CbaFnDB1LzxgKj54xdB36xsCWPOcGAEAh8fgVaCt9Y+hY+sZQWPSMoevQMwa25Dk3KC7lHV0AABSqjRs3Jo6Xl+fnr9l0+6SrE8i9+++/P6666qrEOSUlJXHTTTfl7dPboSt59NFHY8qUKc2ODR48OC6//PL8FgS0yrp169LO+f3vf9/s7SUlJXHGGWfE1VdfHTvvvPN24zvuuGPsuOOO8eEPfzjGjRsX69ati6uvvjr+67/+K+2+Dz74YEyaNCm++tWvtu4XAdIqKSmJE088Mfbcc8+47bbb4r//+78zavJUVFTEt771rfj2t78d3bp1y2KlQD54zg0AgELi8SvQWnrG0LH0jKEw6RlD16JvDGzmOTcAAAqJx69AW+gbQ8fSN4b/v737jq+6uh8//g6RTVBkOUBwoDKq4AAHCiJK3VL166qCVVxoa92ttc622rrqFhdo66i4tSICIhRQEREn4AAElaEiewSS3x/9dWi5K/feJDd5Ph8P//h6Ts45MdLefl75nk/h0YyhdtGMgX/xzA1qljpVfQAAKFQ+GAPpeP/99+PEE09M+abGc845J/r27VtJp4LaY+XKlXHGGWckHL/jjjuiUaNGlXgiIF0VjVCNGjWKl156KYYNG7bBEL0hDRs2jCuvvDKmTZsW7du3Tzn/V7/6VXz55ZcVOh/wfevWrYuHHnoounTpEh07doxrr70267ealpaWxu9+97vYeuut48wzz4wZM2bk6LRAZfDMDQCAQuLzK5AOzRiqlmYMhUszhtpDNwb+m2duAAAUEp9fgXTpxlC1dGMoTJox1B6aMfDfPHODmsWl1wBQQWVlZUnHi4uLK+UcqfZZv359pZwD+F8LFy6Mww47LJYtW5Z03u677x433HBDJZ0Kapff/va38dlnn21w7Oijj45DDjmkkk8EpKu0tDTjrykpKYmRI0dGv379KrRnhw4dYvz48bH99tsnnbdy5cq4+uqrK7QH8B8vvvhidOjQIQYMGBAffPBBztdfsGBB3HPPPdGpU6c45phj4tNPP835HkDueeYGAEAh8fkVSEUzhqqnGUPh0oyhdtCNgR/yzA0AgELi8yuQDt0Yqp5uDIVJM4baQTMGfsgzN6hZXHoNABWU6i0s69atq5RzpNqnbt26lXIO4PuWL18eBx98cMyePTvpvObNm8cTTzwR9erVq5yDQS0yZcqUuOWWWzY41rRp07j11lsr90BARiryoPm2226LvffeO6t927Rpk9Z/Nw8dOjS+/vrrrPaC2mrVqlVx9tlnx6GHHpry83IulJWVxfDhw6Nr167xwAMP5H0/IDueuQEAUEh8fgWS0Yyh6mnGUNg0Y6jZdGMgEc/cAAAoJD6/AqnoxlD1dGMoXJox1GyaMZCIZ25Qs7j0GgAqKNXDqcr6YJzqzXTiFlS+tWvXRv/+/WPKlClJ5zVs2DCeffbZaNeuXSWdDGqPdevWxWmnnZbwrWi///3vY/PNN6/kUwGZyPRz7OGHHx4DBgzIyd477bRT/Pa3v006Z82aNfHggw/mZD+oTVatWhWHHnpo3HXXXSnnFhcXxwEHHBDXXHNNjBkzJmbOnBnffPNNlJaWxtdffx0zZ86M0aNHx9VXXx19+/aNOnWSP+5evnx5nHrqqTF48OBcfTtAHnjmBgBAIfH5FUhEM4aqpxlD4dOMoebSjYFkPHMDAKCQ+PwKJKMbQ9XTjaGwacZQc2nGQDKeuUHNkvwaewAgoVRvWVm7dm2lnMMHY6he1q9fH8cff3yMGjUq6by6devGE088kfVbIoENu+GGG+Kdd97Z4Fj37t3jrLPOqtwDARnL9HPs7373u5zuf8EFF8TNN98c33zzTcI5Tz75ZFx00UU53RdqsrVr18bhhx8eY8aMSTqvbt26cfrpp8f5558f22yzzQbnNG/ePJo3bx4dOnSIPn36RETEp59+GjfddFMMGTIkaay68847o6ioKG6//faKfzNA3njmBgBAIfH5FdgQzRiqB80YCp9mDDWTbgyk4pkbAACFxOdXIBHdGKoH3RgKm2YMNZNmDKTimRvULMlfRwEAJNSkSZOk48uXL6+UcyxbtizpeKpzArlTXl4ep512Wjz11FNJ59WpUyceeuihOOSQQyrpZFC7fPLJJ3HVVVdtcGyjjTaKe+65J+XbGYGq16hRo7Tn7rPPPtGlS5ec7t+gQYM45ZRTks6ZPHlyfP311zndF2qyK664IuUvbLZr1y7Gjx8ft99+e8IInci2224bd9xxR7z22mvRtm3bpHPvuOOOuPvuuzNaH6gcnrkBAFBIfH4FfkgzhupBM4aaQTOGmkk3BlLxzA0AgELi8yuwIboxVA+6MRQ+zRhqJs0YSMUzN6hZ/C9vAKigTTfdNOn40qVLK+UcqfZJdU4gd37xi1/E0KFDU867++6747jjjsv/gaCWOv3002P16tUbHPvFL34RXbt2rdwDARVSt27dKCkpSWvuwIED83KGVDG6rKws3nzzzbzsDTXNxIkT449//GPSOR06dIi33norevTokdVee+21V0yZMiW23XbbpPMuvPDC+PTTT7PaC8i9VM+ySktLE37ezyXP3AAASIdmDPyQZgzVg2YMNYNmDDWPbgykQzMGAKCQaMbAhujGUD3oxlD4NGOoeTRjIB2aMdQsLr0GgApq3rx50vHvvvuuUs6xZMmSpOOpzgnkxq9//eu47bbbUs678cYbY9CgQZVwIqid7r///nj11Vc3ONauXbuEb2UGqqd0P8vuvffeedm/Y8eOsckmmySd8/bbb+dlb6hpLr300igrK0s4vummm8aLL74YLVq0yMl+LVu2jBdffDHpn+EVK1bERRddlJP9gNxJ57//K+O5W6o9PHMDACBCMwa+TzOG6kEzhppFM4aaRTcG0qEZAwBQSDRj4Id0Y6gedGOoOTRjqFk0YyAdmjHULC69BoAKSvU/jtesWZP3D8bffvttrF27NukcH4wh/37/+9/HH/7wh5Tzrrrqqjj//PMr4URQOy1YsCDpw+Tbb789GjduXIknArKVTpBq1qxZbL/99nnZv6ioKLp37550jje3QmqTJ0+O8ePHJ51z5ZVXRocOHXK67w477BC//e1vk8559tln/TmGaiad//6fP39+3s+Rag/P3AAAiNCMgf/QjKF60Iyh5tGMoebQjYF0acYAABQSzRj4b7oxVA+6MdQsmjHUHJoxkC7NGGoWl14DQAVttdVWKecsWLAgr2dIZ/10zglU3J///Oe47LLLUs676KKLUj4EA7JzzjnnxOLFizc4dtRRR8Whhx5ayScCspXOZ9mOHTtGUVFR3s7QqVOnpONz587N295QUzzwwANJx9u2bRunn356XvY+++yzo02bNgnHy8rK4p577snL3kDFNGrUKGXozfczt5UrV8ayZcuSzmnXrl1ezwAAQGHQjIEIzRiqE80Yah7NGGoO3RhIl2YMAEAh0YyBf9GNofrQjaFm0Yyh5tCMgXRpxlCzuPQaACqoSZMmKT8Yz5kzJ69nmD17dtLxVq1aecsk5NGQIUPivPPOSznvnHPOiT/+8Y/5PxDUYs8991wMHz58g2NNmzaNW2+9tZJPBOTC1ltvnXLOJptsktczNGvWLOn4t99+m9f9oSZ49dVXk44fe+yxUb9+/bzsXb9+/fi///u/pHNGjx6dl72Bimvfvn3S8Xw/c0tn/VRnBACgdtCMAc0Yqg/NGGomzRhqDt0YyIRmDABAodCMgQjdGKoT3RhqHs0Yag7NGMiEZgw1h0uvASALqR6Offzxx3nd/5NPPkk6ns7DO6BiHn744TjzzDNTzjv11FMFMKgE559/fsKxa6+9NrbYYotKPA2QK9tss03KOfmO0anWX7lyZV73h0K3cOHCmDFjRtI5Bx54YF7PkGr9adOmxdKlS/N6BiAz1f2ZW+vWraNRo0Z5PQMAAIWjun9+1YwhfzRjqF40Y6iZNGOoGXRjIFPV/ZmbZgwAwH+r7p9fNWPIL90YqhfdGGoezRhqBs0YyFR1f+amGUP6NqrqAwBAIevcuXO89dZbCcdT/Y/tbKVav3PnznndH2qrJ554Ik455ZQoLy9POu/444+PIUOGRFFRUSWdDGqvr7/+eoN/v2nTplG/fv247777crbX22+/nXT8448/Trlfr169okOHDjk7E9RUXbp0STmnYcOGeT1DqvXXrVuX1/2h0M2aNSvlnO7du+f1DD169Eg6vn79+vj4449j1113zes5gPR17tw5hg8fnnDcMzcAAKoTzRhqJ80Yqh/NGGomzRhqBt0YyJRmDABAIdGMofbSjaH60Y2h5tGMoWbQjIFMacZQc7j0GgCysMsuu8SwYcMSjk+dOjWv+6d6EN6tW7e87g+10XPPPRcnnnhirF+/Pum8/v37x0MPPRR16tSppJMBG7J06dI444wzKnXPiRMnxsSJE5POefDBB4VoSEO3bt2iTp06UVZWlnDOkiVL8nqGVOvnO4ZDofvmm2+SjterVy823njjvJ5hk002ibp160ZpaWnCOanOCVSuXXbZJem4Z24AAFQnmjHUPpoxFBbNGAqbZgw1g24MZEozBgCgkGjGUDvpxlBYdGMoXJox1AyaMZApzRhqDk/FACALqT4Yv/POOyljVUWtW7cupk2blnSOD8aQWy+//HL83//9X9IHWBERBx10UDz22GOx0UbeMQMA2SgpKYntt98+6Zzvvvsur2dYvHhx0vEmTZrkdX8odKn+DDVv3rxSzpFqHyEaqpdUz9zmzZsXCxcuzNv+U6ZMSTrumRsAAP9NM4baRTMGgMqlGUPNoBsDmdKMAQAoJJox1D66MQBUHs0YagbNGMiUZgw1h0uvASALu+22WzRo0CDh+PLly1N+eK2oN998M1auXJlwvEGDBrHrrrvmZW+ojcaOHRv9+/ePNWvWJJ3Xp0+feOqpp6JevXqVdDIAqNl69uyZdDyfD6LTWX/LLbfM6/5Q6IqLi5OOp/p8nSurV69OOl5UVFQp5wDS06ZNm2jXrl3SOWPHjs3L3l9++WXMnDkz6ZxUn08AAKhdNGOoPTRjAKgamjEUPt0YyJRmDABAIdGMoXbRjQGg8mnGUPg0YyBTmjHUHC69BoAsNGjQIPbee++kc1555ZW87D1q1Kik4/vss0/SUA6kb9KkSXHYYYfFqlWrks7r2bNnPPfcc/7sAUAO9evXL+n4hx9+mPSXNLP11ltvJR1P9aAcarvGjRsnHV+8eHGsX78+r2coLS1N+bb2Ro0a5fUMQOb69u2bdLyqnrl16NDBf/8DAPA9mjHUDpoxAFQdzRgKn24MVIRmDABAodCMofbQjQGgamjGUPg0Y6AiNGOoGVx6DQBZOuCAA5KOP/XUU3nZd/jw4UnHDzzwwLzsC7XNlClT4qCDDorly5cnnbf77rvHiy++mPJBGwCQmb59+yZ9e+u6detSBuOKWrlyZbz33ntJ5+y888552Rtqis022yzpeHl5eXzxxRd5PcO8efNSzmndunVezwBkLtUzt+eeey4vv8jimRsAABWhGUPNphkDQNXSjKHw6cZARWjGAAAUEs0Yaj7dGACqjmYMhU8zBipCM4aawaXXAJClo48+Oun422+/HTNmzMjpnu+//37Sh2JFRUUpzwWk9t5770W/fv1iyZIlSeftvPPO8fLLL0fTpk0r6WQAUHtssskmKR/6jhw5Mi97jx49OuVD7h49euRlb6gptt5665RzxowZk9czjB49OuWcdM4JVK5DDjkk6ZvRFy5cmPJtyZn69ttv4+WXX04655hjjsnpngAA1AyaMdRcmjEAVD3NGAqfbgxUhGYMAEAh0YyhZtONAaBqacZQ+DRjoCI0Y6gZXHoNAFnadtttY4899kg657bbbsvpnrfeemvS8b322ivat2+f0z2htpk5c2YccMAB8c033ySd16lTp3jllVeiWbNmlXQyYEO+++67KC8vr5S/rrjiiqRnGTBgQMo1Bg4cWDn/YKCGGDBgQNLx+++/P0pLS3O+71133ZV0vH379rHDDjvkfF+oSVq0aBFt2rRJOmfEiBF5PcNLL72UdHyzzTaLVq1a5fUMQOaaNGkShx9+eNI5uX7mdvfdd8fatWsTjrdt2zb23XffnO4JAEDNoBlDzaQZQ2HRjKFm04yhsOnGQEVoxgAAFBLNGGou3RgKi24MNZdmDIVNMwYqQjOGmsGl1wCQAz/72c+Sjj/44IPx1Vdf5WSvefPmxcMPP5x0jofbkJ3Zs2fH/vvvHwsWLEg6r0OHDjFq1Kho2bJlJZ0MAGqnI444Ilq0aJFwfP78+fHEE0/kdM+PP/445RsYjzzyyJzuCTXVXnvtlXT8qaeeilmzZuVl7+nTp8ezzz6bdM6ee+6Zl72B7KV65vb3v/893nnnnZzstXz58pRx++STT46ioqKc7AcAQM2jGUPNohkDQPWiGUPh042BitCMAQAoJJox1Dy6MQBUH5oxFD7NGKgIzRgKn0uvASAHTjrppKRvalq5cmVceumlOdnrkksuidWrVyccb926dZx00kk52Qtqoy+//DL233//mDdvXtJ57du3jzFjxsTmm29eSScDgNqrQYMG8Ytf/CLpnAsvvDAWL16ck/3Ky8vj9NNPj7KysqTzBg0alJP9oKZL9QbV0tLSuPzyy/Oy92WXXRbr169POuewww7Ly95A9g444IDYaaedEo6Xl5fHeeedl5O9/vCHP8T8+fMTjtevXz/OPffcnOwFAEDNpBlDzaEZA0D1oxlD4dONgYrQjAEAKCSaMdQsujEAVC+aMRQ+zRioCM0YCp9LrwEgB9J5OPbQQw/F008/ndU+f/vb3+KRRx5JOue8886L+vXrZ7UP1FaLFi2K/fffPz777LOk89q0aRNjxoyJNm3aVNLJAIBzzjknNt5444TjX331VZx99tk52evPf/5zjB07NumcAw88MDp16pST/aCmO/zww6NJkyZJ5/z1r3+NIUOG5HTfG2+8MZ566qmkcxo0aOBt6lDNXXLJJUnHX3vttbj55puz2mPixInxxz/+MemcgQMHRuvWrbPaBwCAmk0zhppBMwaA6kszhsKmGwMVpRkDAFAoNGOoOXRjAKieNGMobJoxUFGaMRQ2l14DQI6cd9550bZt26RzBgwYEG+++WaF1n/99dfj1FNPTTqnXbt2KaM4sGHfffddHHjggTF9+vSk8zbbbLMYM2ZMbL311pV0MgAgImKTTTaJq6++Oumcxx57LM4+++woLy+v8D73339/XHDBBUnnFBUVxXXXXVfhPaC2KSkpSeuN5YMHD47HHnssJ3s+8MADcfHFF6ecd8opp0SzZs1ysieQH8cff3zsvvvuSedccskl8fzzz1do/Y8//jiOPvroWLduXcI5JSUlceWVV1ZofQAAahfNGAqbZgwA1ZtmDIVNNwYqSjMGAKCQaMZQ+HRjAKi+NGMobJoxUFGaMRQ2l14DQI40atQobrrppqRzli1bFgceeGC88MILGa397LPPRr9+/WL58uVJ5914443RsGHDjNYGIpYvXx4HHXRQvPPOO0nntWjRIkaPHh0dOnSonIMBAN8zePDg2GWXXZLOueuuu+K4446LRYsWZbT2mjVr4sorr4xBgwZFWVlZ0rlnnnlmdOvWLaP1oba7+OKLk75FPSJi3bp1cfzxx8fgwYNj5cqVFdpn2bJlccopp8Spp56a8s9y48aN41e/+lWF9gEqT1FRUdx+++1RVFSUcE5paWkcc8wxcd9992W09oQJE6JXr17x1VdfJZ13xRVXxGabbZbR2gAA1E6aMRQuzRgACoNmDIVNNwYqQjMGAKCQaMZQ2HRjAKj+NGMobJoxUBGaMRS2ovJsXkkDAPyPE088MR555JGkc4qKiuL444+Pyy+/PHbccceE8z788MO4+uqr4/HHH09r37/85S8ZnxeIOOyww9L6JZHBgwdH165d83+g/2/zzTePQw45pNL2A9Jz5ZVXxlVXXZVwfMCAATF06NDKOxDUMh999FF079495S9qbrLJJnHZZZfFT3/606QPj5cvXx7PP/98XH755fHpp5+m3H+HHXaIt99+Oxo1apTx2aG2u/vuu+Oss85Ka27z5s3j7LPPjtNOOy222mqrlPNnzZoVQ4YMibvvvju+++67tPa4+eab47zzzktrLhSacePGxcyZMzP6mm+++SYuvfTSpHPuvffejM/Sq1evnPxC92WXXRa///3vU8778Y9/HFdffXXStzbPmTMnrr/++rj33nuTvnk54p/nHz16dBQXF2d8ZgAAai/NGAqPZgxkQjOGqqUZQ2HTjaFyaMaaMQA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-      "text/plain": [
-       "<Figure size 6000x6000 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Step 2: Constrain theta and epsilon, so that 2 epsilon <= theta\n",
+    "# Step 2: Constrain theta and epsilon so that 2 epsilon <= theta\n",
     "\n",
-    "SAVED_RESULT_TO_USE = os.path.join(SAVED_RESULTS_DIR, \"cddf5173-7555-4641-b74a-23a01cf4f9cf.json\")\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
     "\n",
-    "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n",
-    "    results: FunmanResults = FunmanResults.model_validate(json.load(f))\n",
+    "    epsilon = funman_request.parameter(\"epsilon\")\n",
+    "    theta = funman_request.parameter(\"theta\")\n",
+    " \n",
     "\n",
-    "# Plot the trajectories\n",
-    "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"},  xlabel=\"Time\", ylabel=\"Infected\")\n",
+    "    funman_request.constraints = [\n",
+    "        StateVariableConstraint(name=\"peak_infected1\", variable=\"Infected\", interval=ramp_up_infections, timepoints=Interval(ub=peak_time.lb)),\n",
+    "        StateVariableConstraint(name=\"peak_infected2\", variable=\"Infected\", interval=peak_infections, timepoints=peak_time),\n",
+    "        StateVariableConstraint(name=\"peak_infected3\", variable=\"Infected\", interval=ramp_down_infections, timepoints=Interval(lb=peak_time.ub)),\n",
+    "        LinearConstraint(name=\"2_eps_le_theta\", variables=[epsilon.name, theta.name], additive_bounds=Interval(lb=0), weights=[-2, 1])\n",
+    "        ]\n",
     "\n",
-    "# Plot the parameter space\n",
-    "ParameterSpacePlotter(\n",
-    "        results.parameter_space, plot_points=True, parameters=[\"epsilon\", \"theta\", \"timestep\"]\n",
-    "    ).plot(show=True)"
+    "    results_parameter_constraint = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIDARTHE demo step 2\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results([\"Infected\"], results_parameter_constraint, ylabel=\"Infected\")\n",
+    "    print(summary)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 6000x6000 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Step 3: Constrain peak infected\n",
+    "# Step 3: Synthesize parameters\n",
     "\n",
-    "# {\n",
-    "#     \"name\": \"infected_maximum1\",\n",
-    "#     \"variable\": \"Infected\",\n",
-    "#     \"interval\": { \"lb\": 1e-5, \"ub\": 0.4},\n",
-    "#     \"timepoints\": {\"lb\": 75, \"ub\": 125}\n",
-    "    \n",
-    "# },\n",
     "\n",
-    "# Tighten epsilon <= 0.18, theta <= 0.4\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
     "\n",
-    "SAVED_RESULT_TO_USE = os.path.join(SAVED_RESULTS_DIR, \n",
-    "                                #    \"1a6108d5-58c8-44ed-b41e-93d95dd4ddab.json\"\n",
-    "                                \"51b0da9c-c2f1-4070-9722-6c78655e70eb.json\"\n",
-    "                                   )\n",
+    "    epsilon = funman_request.parameter(\"epsilon\")\n",
+    "    theta = funman_request.parameter(\"theta\")\n",
+    "    epsilon.label = \"all\"\n",
+    "    theta.label = \"all\"\n",
     "\n",
-    "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n",
-    "    results: FunmanResults = FunmanResults.model_validate(json.load(f))\n",
+    "    # for p in [epsilon]:\n",
+    "    #     p.interval = b.bounds[p.name]\n",
     "\n",
-    "# Plot the trajectories\n",
-    "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"},  xlabel=\"Time\", ylabel=\"Infected\")\n",
     "\n",
-    "# Plot the parameter space\n",
-    "ParameterSpacePlotter(\n",
-    "        results.parameter_space, plot_points=True, parameters=[\"epsilon\", \"theta\", \"timestep\"]\n",
-    "    ).plot(show=True)"
+    "    # adjustment_factor = 0.49\n",
+    "    # epsilon_adjustment = float(epsilon.width())*adjustment_factor\n",
+    "    # epsilon.interval = Interval(lb=epsilon.interval.lb+ epsilon_adjustment, ub=epsilon.interval.ub-epsilon_adjustment)\n",
+    "\n",
+    "    # theta_adjustment = float(theta.width())*adjustment_factor\n",
+    "    # theta.interval = Interval(lb=theta.interval.lb+ theta_adjustment, ub=theta.interval.ub-theta_adjustment)\n",
+    "\n",
+    "    # funman_request.config = FUNMANConfig(verbosity=5)\n",
+    "\n",
+    "    funman_request.constraints = [\n",
+    "        StateVariableConstraint(name=\"peak_infected1\", variable=\"Infected\", interval=ramp_up_infections, timepoints=Interval(ub=peak_time.lb)),\n",
+    "        StateVariableConstraint(name=\"peak_infected2\", variable=\"Infected\", interval=peak_infections, timepoints=peak_time),\n",
+    "        StateVariableConstraint(name=\"peak_infected3\", variable=\"Infected\", interval=ramp_down_infections, timepoints=Interval(lb=peak_time.ub)),\n",
+    "        LinearConstraint(name=\"2_eps_le_theta\", variables=[epsilon.name, theta.name], additive_bounds=Interval(lb=0), weights=[-2, 1])\n",
+    "        ]\n",
+    "\n",
+    "    results_synthesis = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIDARTHE demo\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            # dump_results=False\n",
+    "    )\n",
+    "    summary = summarize_results([\"Infected\"], results_synthesis, ylabel=\"Infected\")\n",
+    "    print(summary)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "image/png": 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-      "text/plain": [
-       "<Figure size 6000x6000 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "# Take point from previous solution and define a box around it (used to label the box)\n",
+    "from funman import Box\n",
+    "point = results_parameter_constraint.parameter_space.true_points()[0]\n",
+    "# parameters = results_parameter_constraint.point_parameters(point)\n",
+    "parameters = results_parameter_constraint.model._parameter_names()\n",
+    "b = Box.from_point(point, radius = 0.01, radius_vars=[p for p in parameters])\n",
+    "# b.bounds[\"epsilon\"]\n",
+    "b.bounds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
+    "# Step 4: Synthesize all Parameters\n",
+    "\n",
+    "\n",
+    "from funman.constants import LABEL_ALL\n",
+    "from funman.representation.parameter import ModelParameter\n",
+    "\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request: FunmanWorkRequest = FunmanWorkRequest.model_validate(json.load(request))\n",
+    "\n",
+    "    # epsilon = funman_request.parameter(\"epsilon\")\n",
+    "    # theta = funman_request.parameter(\"theta\")\n",
+    "    # epsilon.label = \"all\"\n",
+    "    # theta.label = \"all\"\n",
+    "\n",
+    "\n",
+    "    funman_request.parameters = [ModelParameter(name=p, interval=b.bounds[p], label=LABEL_ALL) for p in parameters]\n",
+    "        \n",
     "\n",
-    "# Step 3: Constrain peak infected\n",
     "\n",
-    "# {\n",
-    "#     \"name\": \"infected_maximum1\",\n",
-    "#     \"variable\": \"Infected\",\n",
-    "#     \"interval\": { \"lb\": 1e-5, \"ub\": 0.4},\n",
-    "#     \"timepoints\": {\"lb\": 40, \"ub\": 125}\n",
-    "    \n",
-    "# },\n",
     "\n",
-    "# Tighten epsilon <= 0.18, theta <= 0.4\n",
+    "    # adjustment_factor = 0.49\n",
+    "    # epsilon_adjustment = float(epsilon.width())*adjustment_factor\n",
+    "    # epsilon.interval = Interval(lb=epsilon.interval.lb+ epsilon_adjustment, ub=epsilon.interval.ub-epsilon_adjustment)\n",
     "\n",
-    "SAVED_RESULT_TO_USE = os.path.join(SAVED_RESULTS_DIR, \n",
-    "                                #    \"1a6108d5-58c8-44ed-b41e-93d95dd4ddab.json\"\n",
-    "                                \"29da5af4-9463-44f4-b51b-1b6f28cb79a5.json\"\n",
-    "                                   )\n",
+    "    # theta_adjustment = float(theta.width())*adjustment_factor\n",
+    "    # theta.interval = Interval(lb=theta.interval.lb+ theta_adjustment, ub=theta.interval.ub-theta_adjustment)\n",
     "\n",
-    "with open(SAVED_RESULT_TO_USE, \"r\") as f:\n",
-    "    results: FunmanResults = FunmanResults.model_validate(json.load(f))\n",
+    "    # funman_request.config = FUNMANConfig(verbosity=5)\n",
     "\n",
-    "# Plot the trajectories\n",
-    "results.plot(variables=[\"Infected\"], label_marker={\"true\":\",\", \"false\": \",\"},  xlabel=\"Time\", ylabel=\"Infected\")\n",
+    "    funman_request.constraints = [\n",
+    "        StateVariableConstraint(name=\"peak_infected1\", variable=\"Infected\", interval=ramp_up_infections, timepoints=Interval(ub=peak_time.lb)),\n",
+    "        StateVariableConstraint(name=\"peak_infected2\", variable=\"Infected\", interval=peak_infections, timepoints=peak_time),\n",
+    "        StateVariableConstraint(name=\"peak_infected3\", variable=\"Infected\", interval=ramp_down_infections, timepoints=Interval(lb=peak_time.ub)),\n",
+    "        LinearConstraint(name=\"2_eps_le_theta\", variables=[epsilon.name, theta.name], additive_bounds=Interval(lb=0), weights=[-2, 1])\n",
+    "        ]\n",
     "\n",
-    "# Plot the parameter space\n",
-    "ParameterSpacePlotter(\n",
-    "        results.parameter_space, plot_points=True, parameters=[\"epsilon\", \"theta\", \"timestep\"]\n",
-    "    ).plot(show=True)"
+    "    results_synthesis = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIDARTHE demo\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            # dump_results=False\n",
+    "    )\n",
+    "    summary = summarize_results([\"Infected\"], results_synthesis, ylabel=\"Infected\")\n",
+    "    print(summary)"
    ]
   }
  ],
diff --git a/notebooks/pde_as_petrinet.ipynb b/notebooks/pde_as_petrinet.ipynb
index f4539ea9..f895ae07 100644
--- a/notebooks/pde_as_petrinet.ipynb
+++ b/notebooks/pde_as_petrinet.ipynb
@@ -31,21 +31,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[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",
-      "2024-01-29 21:31:43,958 - funman.scenario.scenario - WARNING - Warning: The scenario is not normalized!\n",
-      "2024-01-29 21:32:55,255 - funman.api.run - INFO - Dumping results to ./out/ad6d238f-5793-4c2d-8f2c-edc1e07c7d3a.json\n",
-      "2024-01-29 21:32:55,278 - funman.scenario.consistency - INFO - 10{100}:\t[+]\n",
-      "2024-01-29 21:32:55,282 - funman.server.worker - INFO - Completed work on: ad6d238f-5793-4c2d-8f2c-edc1e07c7d3a\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Halfar Model\n",
     "\n",
@@ -166,70 +154,550 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-29 19:51:16,944 - funman.server.worker - INFO - FunmanWorker running...\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",
-      "2024-01-29 19:51:16,950 - funman.server.worker - INFO - Starting work on: 302a9155-bf60-42c4-ac43-5d29326f4b99\n",
-      "2024-01-29 19:51:16,953 - funman.scenario.scenario - WARNING - Warning: The scenario is not normalized!\n",
-      "2024-01-29 19:51:18,959 - funman.api.run - INFO - Dumping results to ./out/302a9155-bf60-42c4-ac43-5d29326f4b99.json\n",
-      "2024-01-29 19:51:27,445 - funman.scenario.consistency - INFO - 5{5}:\t[+]\n",
-      "2024-01-29 19:51:27,447 - funman.server.worker - INFO - Completed work on: 302a9155-bf60-42c4-ac43-5d29326f4b99\n",
-      "2024-01-29 19:51:29,011 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n",
-      "2024-01-29 19:51:29,459 - funman.server.worker - INFO - FunmanWorker exiting...\n",
-      "2024-01-29 19:51:29,460 - funman.server.worker - INFO - Worker.stop() completed.\n",
-      "2024-01-29 19:51:29,462 - /home/danbryce/funman/auxiliary_packages/funman_demo/src/funman_demo/plot.py - INFO - ********************************************************************************\n",
-      "2024-01-29 19:51:29,462 - /home/danbryce/funman/auxiliary_packages/funman_demo/src/funman_demo/plot.py - INFO - ********************************************************************************\n",
-      "2024-01-29 19:51:29,463 - /home/danbryce/funman/auxiliary_packages/funman_demo/src/funman_demo/plot.py - INFO - * Analysis Summary \n",
-      "2024-01-29 19:51:29,464 - /home/danbryce/funman/auxiliary_packages/funman_demo/src/funman_demo/plot.py - INFO - ********************************************************************************\n",
-      "2024-01-29 19:51:29,464 - /home/danbryce/funman/auxiliary_packages/funman_demo/src/funman_demo/plot.py - INFO - 1 Points (+:1, -:0), 1 Boxes (+:1, -:0)\n",
-      "2024-01-29 19:51:29,585 - /home/danbryce/funman/auxiliary_packages/funman_demo/src/funman_demo/plot.py - INFO - Parameters = {dx[1.0, 1.0): 1.0, u[0.01, 0.5): 0.08615513766690462}\n",
-      "2024-01-29 19:51:29,586 - /home/danbryce/funman/auxiliary_packages/funman_demo/src/funman_demo/plot.py - INFO - {dx[1.0, 1.0): 1.0, u[0.01, 0.5): 0.08615513766690462}\n",
-      "2024-01-29 19:51:29,593 - /home/danbryce/funman/auxiliary_packages/funman_demo/src/funman_demo/plot.py - INFO -          a_0_0     a_0_1     a_0_2     a_0_3     a_0_4     a_1_0     a_1_1  \\\n",
-      "time                                                                         \n",
-      "0.0   0.333333  0.333333  0.333333  0.333333  0.333333  0.333333  0.500000   \n",
-      "1.0   0.275897  0.304615  0.304615  0.304615  0.304615  0.304615  0.462569   \n",
-      "2.0   0.231619  0.256520  0.282857  0.292500  0.292555  0.257865  0.449656   \n",
-      "3.0   0.203894  0.211123  0.265844  0.291503  0.288740  0.211898  0.453423   \n",
-      "4.0   0.179848  0.191845  0.258577  0.281531  0.287757  0.192433  0.410403   \n",
-      "5.0   0.158992  0.190526  0.230420  0.236211  0.248138  0.156514  0.409941   \n",
-      "\n",
-      "         a_1_2     a_1_3     a_1_4  ...     a_3_4     a_4_0     a_4_1  \\\n",
-      "time                                ...                                 \n",
-      "0.0   0.500000  0.500000  0.333333  ...  0.333333  0.333333  0.333333   \n",
-      "1.0   0.473475  0.502159  0.347693  ...  0.346652  0.304615  0.347693   \n",
-      "2.0   0.427490  0.478838  0.350349  ...  0.319800  0.277978  0.372240   \n",
-      "3.0   0.451633  0.503150  0.317243  ...  0.383354  0.248292  0.415301   \n",
-      "4.0   0.477844  0.507689  0.370156  ...  0.364515  0.248562  0.447840   \n",
-      "5.0   0.409480  0.440894  0.337219  ...  0.344223  0.242787  0.390335   \n",
-      "\n",
-      "         a_4_2     a_4_3     a_4_4   dx  id  label         u  \n",
-      "time                                                          \n",
-      "0.0   0.333333  0.333333  0.333333  1.0   0   true  0.086155  \n",
-      "1.0   0.334114  0.351867  0.333333  1.0   0   true  0.086155  \n",
-      "2.0   0.368988  0.402886  0.364477  1.0   0   true  0.086155  \n",
-      "3.0   0.347869  0.406982  0.383945  1.0   0   true  0.086155  \n",
-      "4.0   0.382367  0.403997  0.428069  1.0   0   true  0.086155  \n",
-      "5.0   0.354806  0.467267  0.457276  1.0   0   true  0.086155  \n",
-      "\n",
-      "[6 rows x 29 columns]\n",
-      "2024-01-29 19:51:29,608 - /home/danbryce/funman/auxiliary_packages/funman_demo/src/funman_demo/plot.py - INFO - ********************************************************************************\n"
+      "2024-02-08 15:12:33,815 - funman.scenario.scenario - WARNING - Warning: The scenario is not normalized!\n",
+      "2024-02-08 15:12:35,505 - funman.search.box_search - INFO - progress: 0.09091\n",
+      "2024-02-08 15:12:35,551 - funman.search.box_search - INFO - progress: 0.18182\n",
+      "2024-02-08 15:12:35,592 - funman.search.box_search - INFO - progress: 0.27273\n",
+      "2024-02-08 15:12:35,628 - funman.search.box_search - INFO - progress: 0.36364\n",
+      "2024-02-08 15:12:35,659 - funman.search.box_search - INFO - progress: 0.45455\n",
+      "2024-02-08 15:12:35,687 - funman.search.box_search - INFO - progress: 0.54545\n",
+      "2024-02-08 15:12:35,712 - funman.search.box_search - INFO - progress: 0.63636\n",
+      "2024-02-08 15:12:35,733 - funman.search.box_search - INFO - progress: 0.72727\n",
+      "2024-02-08 15:12:35,752 - funman.search.box_search - INFO - progress: 0.81818\n",
+      "2024-02-08 15:12:35,768 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:36,405 - funman.api.run - INFO - Dumping results to ./out/4b66c65d-54a1-4542-a660-76a9af861f48.json\n",
+      "2024-02-08 15:12:36,408 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(1e-18, 0.057344315320017415)][(0.057344315320017415, 0.1)])\n",
+      "2024-02-08 15:12:36,410 - funman.representation.box - INFO - widths: 0.10000 -> 0.05734 0.04266 (raw), 1.00000 -> 0.57344 0.42656 (norm)\n",
+      "2024-02-08 15:12:36,412 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:36,673 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.057344315320017415, 0.06800823651259927)][(0.06800823651259927, 0.1)])\n",
+      "2024-02-08 15:12:36,675 - funman.representation.box - INFO - widths: 0.04266 -> 0.01066 0.03199 (raw), 0.42656 -> 0.10664 0.31992 (norm)\n",
+      "2024-02-08 15:12:36,676 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:36,760 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.057344315320017415, 0.06001029564074908)][(0.06001029564074908, 0.06800823651259927)])\n",
+      "2024-02-08 15:12:36,761 - funman.representation.box - INFO - widths: 0.01066 -> 0.00267 0.00800 (raw), 0.10664 -> 0.02666 0.07998 (norm)\n",
+      "2024-02-08 15:12:36,763 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:36,779 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.057344315320017415, 0.05801081042278653)][(0.05801081042278653, 0.06001029564074908)])\n",
+      "2024-02-08 15:12:36,780 - funman.representation.box - INFO - widths: 0.00267 -> 0.00067 0.00200 (raw), 0.02666 -> 0.00666 0.01999 (norm)\n",
+      "2024-02-08 15:12:36,782 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:36,795 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.057344315320017415, 0.0575109391182959)][(0.0575109391182959, 0.05801081042278653)])\n",
+      "2024-02-08 15:12:36,796 - funman.representation.box - INFO - widths: 0.00067 -> 0.00017 0.00050 (raw), 0.00666 -> 0.00167 0.00500 (norm)\n",
+      "2024-02-08 15:12:36,798 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:36,812 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.057344315320017415, 0.05738597129217324)][(0.05738597129217324, 0.0575109391182959)])\n",
+      "2024-02-08 15:12:36,813 - funman.representation.box - INFO - widths: 0.00017 -> 0.00004 0.00012 (raw), 0.00167 -> 0.00042 0.00125 (norm)\n",
+      "2024-02-08 15:12:36,815 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:37,097 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(1e-18, 0.046986718331408224)][(0.046986718331408224, 0.057344315320017415)])\n",
+      "2024-02-08 15:12:37,098 - funman.representation.box - INFO - widths: 0.05734 -> 0.04699 0.01036 (raw), 0.57344 -> 0.46987 0.10358 (norm)\n",
+      "2024-02-08 15:12:37,100 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:37,328 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(1e-18, 0.030061240276837778)][(0.030061240276837778, 0.046986718331408224)])\n",
+      "2024-02-08 15:12:37,329 - funman.representation.box - INFO - widths: 0.04699 -> 0.03006 0.01693 (raw), 0.46987 -> 0.30061 0.16925 (norm)\n",
+      "2024-02-08 15:12:37,331 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:37,474 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.030061240276837778, 0.03757655034604722)][(0.03757655034604722, 0.046986718331408224)])\n",
+      "2024-02-08 15:12:37,475 - funman.representation.box - INFO - widths: 0.01693 -> 0.00752 0.00941 (raw), 0.16925 -> 0.07515 0.09410 (norm)\n",
+      "2024-02-08 15:12:37,477 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:37,510 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.030061240276837778, 0.03522400834970697)][(0.03522400834970697, 0.03757655034604722)])\n",
+      "2024-02-08 15:12:37,511 - funman.representation.box - INFO - widths: 0.00752 -> 0.00516 0.00235 (raw), 0.07515 -> 0.05163 0.02353 (norm)\n",
+      "2024-02-08 15:12:37,513 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:37,527 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.03522400834970697, 0.03651470036792427)][(0.03651470036792427, 0.03757655034604722)])\n",
+      "2024-02-08 15:12:37,532 - funman.representation.box - INFO - widths: 0.00235 -> 0.00129 0.00106 (raw), 0.02353 -> 0.01291 0.01062 (norm)\n",
+      "2024-02-08 15:12:37,534 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:37,551 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.03651470036792427, 0.036837373372478596)][(0.036837373372478596, 0.03757655034604722)])\n",
+      "2024-02-08 15:12:37,553 - funman.representation.box - INFO - widths: 0.00106 -> 0.00032 0.00074 (raw), 0.01062 -> 0.00323 0.00739 (norm)\n",
+      "2024-02-08 15:12:37,556 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:37,570 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.03651470036792427, 0.03665257912908644)][(0.03665257912908644, 0.036837373372478596)])\n",
+      "2024-02-08 15:12:37,571 - funman.representation.box - INFO - widths: 0.00032 -> 0.00014 0.00018 (raw), 0.00323 -> 0.00138 0.00185 (norm)\n",
+      "2024-02-08 15:12:37,575 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:37,591 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.03651470036792427, 0.0366063805682384)][(0.0366063805682384, 0.03665257912908644)])\n",
+      "2024-02-08 15:12:37,592 - funman.representation.box - INFO - widths: 0.00014 -> 0.00009 0.00005 (raw), 0.00138 -> 0.00092 0.00046 (norm)\n",
+      "2024-02-08 15:12:37,595 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:37,863 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.06800823651259927, 0.08133813795815417)][(0.08133813795815417, 0.1)])\n",
+      "2024-02-08 15:12:37,864 - funman.representation.box - INFO - widths: 0.03199 -> 0.01333 0.01866 (raw), 0.31992 -> 0.13330 0.18662 (norm)\n",
+      "2024-02-08 15:12:37,866 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:38,015 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08133813795815417, 0.08733659361768836)][(0.08733659361768836, 0.1)])\n",
+      "2024-02-08 15:12:38,017 - funman.representation.box - INFO - widths: 0.01866 -> 0.00600 0.01266 (raw), 0.18662 -> 0.05998 0.12663 (norm)\n",
+      "2024-02-08 15:12:38,020 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:38,054 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08133813795815417, 0.08417074202211046)][(0.08417074202211046, 0.08733659361768836)])\n",
+      "2024-02-08 15:12:38,055 - funman.representation.box - INFO - widths: 0.00600 -> 0.00283 0.00317 (raw), 0.05998 -> 0.02833 0.03166 (norm)\n",
+      "2024-02-08 15:12:38,057 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:38,072 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08133813795815417, 0.08337927912321598)][(0.08337927912321598, 0.08417074202211046)])\n",
+      "2024-02-08 15:12:38,073 - funman.representation.box - INFO - widths: 0.00283 -> 0.00204 0.00079 (raw), 0.02833 -> 0.02041 0.00791 (norm)\n",
+      "2024-02-08 15:12:38,075 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:12:38,167 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,311 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(1e-18, 0.019261989652061502)][(0.019261989652061502, 0.030061240276837778)])\n",
+      "2024-02-08 15:12:38,312 - funman.representation.box - INFO - widths: 0.03006 -> 0.01926 0.01080 (raw), 0.30061 -> 0.19262 0.10799 (norm)\n",
+      "2024-02-08 15:12:38,314 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,450 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(1e-18, 0.01233080748222482)][(0.01233080748222482, 0.019261989652061502)])\n",
+      "2024-02-08 15:12:38,452 - funman.representation.box - INFO - widths: 0.01926 -> 0.01233 0.00693 (raw), 0.19262 -> 0.12331 0.06931 (norm)\n",
+      "2024-02-08 15:12:38,454 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,477 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.01233080748222482, 0.015413509352781025)][(0.015413509352781025, 0.019261989652061502)])\n",
+      "2024-02-08 15:12:38,478 - funman.representation.box - INFO - widths: 0.00693 -> 0.00308 0.00385 (raw), 0.06931 -> 0.03083 0.03848 (norm)\n",
+      "2024-02-08 15:12:38,480 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,494 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.01233080748222482, 0.014451389277960906)][(0.014451389277960906, 0.015413509352781025)])\n",
+      "2024-02-08 15:12:38,495 - funman.representation.box - INFO - widths: 0.00308 -> 0.00212 0.00096 (raw), 0.03083 -> 0.02121 0.00962 (norm)\n",
+      "2024-02-08 15:12:38,498 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,510 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.014451389277960906, 0.014981534726894927)][(0.014981534726894927, 0.015413509352781025)])\n",
+      "2024-02-08 15:12:38,512 - funman.representation.box - INFO - widths: 0.00096 -> 0.00053 0.00043 (raw), 0.00962 -> 0.00530 0.00432 (norm)\n",
+      "2024-02-08 15:12:38,515 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,527 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.014981534726894927, 0.015114071089128434)][(0.015114071089128434, 0.015413509352781025)])\n",
+      "2024-02-08 15:12:38,529 - funman.representation.box - INFO - widths: 0.00043 -> 0.00013 0.00030 (raw), 0.00432 -> 0.00133 0.00299 (norm)\n",
+      "2024-02-08 15:12:38,530 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,542 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.014981534726894927, 0.015039211523215286)][(0.015039211523215286, 0.015114071089128434)])\n",
+      "2024-02-08 15:12:38,544 - funman.representation.box - INFO - widths: 0.00013 -> 0.00006 0.00007 (raw), 0.00133 -> 0.00058 0.00075 (norm)\n",
+      "2024-02-08 15:12:38,546 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,653 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.06800823651259927, 0.07667267244769271)][(0.07667267244769271, 0.08133813795815417)])\n",
+      "2024-02-08 15:12:38,655 - funman.representation.box - INFO - widths: 0.01333 -> 0.00866 0.00467 (raw), 0.13330 -> 0.08664 0.04665 (norm)\n",
+      "2024-02-08 15:12:38,656 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,719 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.06800823651259927, 0.07350682085776136)][(0.07350682085776136, 0.07667267244769271)])\n",
+      "2024-02-08 15:12:38,721 - funman.representation.box - INFO - widths: 0.00866 -> 0.00550 0.00317 (raw), 0.08664 -> 0.05499 0.03166 (norm)\n",
+      "2024-02-08 15:12:38,722 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,738 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07350682085776136, 0.07488146694405189)][(0.07488146694405189, 0.07667267244769271)])\n",
+      "2024-02-08 15:12:38,739 - funman.representation.box - INFO - widths: 0.00317 -> 0.00137 0.00179 (raw), 0.03166 -> 0.01375 0.01791 (norm)\n",
+      "2024-02-08 15:12:38,742 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,755 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07350682085776136, 0.07443366556814167)][(0.07443366556814167, 0.07488146694405189)])\n",
+      "2024-02-08 15:12:38,756 - funman.representation.box - INFO - widths: 0.00137 -> 0.00093 0.00045 (raw), 0.01375 -> 0.00927 0.00448 (norm)\n",
+      "2024-02-08 15:12:38,758 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,772 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07443366556814167, 0.07466537674573676)][(0.07466537674573676, 0.07488146694405189)])\n",
+      "2024-02-08 15:12:38,773 - funman.representation.box - INFO - widths: 0.00045 -> 0.00023 0.00022 (raw), 0.00448 -> 0.00232 0.00216 (norm)\n",
+      "2024-02-08 15:12:38,775 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,788 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07466537674573676, 0.07472330454013552)][(0.07472330454013552, 0.07488146694405189)])\n",
+      "2024-02-08 15:12:38,789 - funman.representation.box - INFO - widths: 0.00022 -> 0.00006 0.00016 (raw), 0.00216 -> 0.00058 0.00158 (norm)\n",
+      "2024-02-08 15:12:38,790 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:38,924 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08733659361768836, 0.09216868289396064)][(0.09216868289396064, 0.1)])\n",
+      "2024-02-08 15:12:38,925 - funman.representation.box - INFO - widths: 0.01266 -> 0.00483 0.00783 (raw), 0.12663 -> 0.04832 0.07831 (norm)\n",
+      "2024-02-08 15:12:38,927 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:39,000 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.09216868289396064, 0.09487631912791225)][(0.09487631912791225, 0.1)])\n",
+      "2024-02-08 15:12:39,001 - funman.representation.box - INFO - widths: 0.00783 -> 0.00271 0.00512 (raw), 0.07831 -> 0.02708 0.05124 (norm)\n",
+      "2024-02-08 15:12:39,003 - funman.search.box_search - INFO - progress: 0.90981\n",
+      "2024-02-08 15:12:39,089 - funman.search.box_search - INFO - progress: 0.91227\n",
+      "2024-02-08 15:12:39,166 - funman.search.box_search - INFO - progress: 0.92348\n",
+      "2024-02-08 15:12:39,242 - funman.search.box_search - INFO - progress: 0.93330\n",
+      "2024-02-08 15:12:39,324 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.046986718331408224, 0.054754916050278914)][(0.054754916050278914, 0.057344315320017415)])\n",
+      "2024-02-08 15:12:39,325 - funman.representation.box - INFO - widths: 0.01036 -> 0.00777 0.00259 (raw), 0.10358 -> 0.07768 0.02589 (norm)\n",
+      "2024-02-08 15:12:39,327 - funman.search.box_search - INFO - progress: 0.93330\n",
+      "2024-02-08 15:12:39,376 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.046986718331408224, 0.051518167008278194)][(0.051518167008278194, 0.054754916050278914)])\n",
+      "2024-02-08 15:12:39,377 - funman.representation.box - INFO - widths: 0.00777 -> 0.00453 0.00324 (raw), 0.07768 -> 0.04531 0.03237 (norm)\n",
+      "2024-02-08 15:12:39,379 - funman.search.box_search - INFO - progress: 0.93330\n",
+      "2024-02-08 15:12:39,394 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.051518167008278194, 0.05265102917749569)][(0.05265102917749569, 0.054754916050278914)])\n",
+      "2024-02-08 15:12:39,396 - funman.representation.box - INFO - widths: 0.00324 -> 0.00113 0.00210 (raw), 0.03237 -> 0.01133 0.02104 (norm)\n",
+      "2024-02-08 15:12:39,398 - funman.search.box_search - INFO - progress: 0.93330\n",
+      "2024-02-08 15:12:39,410 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.051518167008278194, 0.05212505745929988)][(0.05212505745929988, 0.05265102917749569)])\n",
+      "2024-02-08 15:12:39,411 - funman.representation.box - INFO - widths: 0.00113 -> 0.00061 0.00053 (raw), 0.01133 -> 0.00607 0.00526 (norm)\n",
+      "2024-02-08 15:12:39,413 - funman.search.box_search - INFO - progress: 0.93330\n",
+      "2024-02-08 15:12:39,425 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.05212505745929988, 0.0522767800720553)][(0.0522767800720553, 0.05265102917749569)])\n",
+      "2024-02-08 15:12:39,427 - funman.representation.box - INFO - widths: 0.00053 -> 0.00015 0.00037 (raw), 0.00526 -> 0.00152 0.00374 (norm)\n",
+      "2024-02-08 15:12:39,429 - funman.search.box_search - INFO - progress: 0.93330\n",
+      "2024-02-08 15:12:39,442 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.05212505745929988, 0.05218321779569521)][(0.05218321779569521, 0.0522767800720553)])\n",
+      "2024-02-08 15:12:39,443 - funman.representation.box - INFO - widths: 0.00015 -> 0.00006 0.00009 (raw), 0.00152 -> 0.00058 0.00094 (norm)\n",
+      "2024-02-08 15:12:39,445 - funman.search.box_search - INFO - progress: 0.93330\n",
+      "2024-02-08 15:12:39,522 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,581 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.06001029564074908, 0.06334277099649126)][(0.06334277099649126, 0.06800823651259927)])\n",
+      "2024-02-08 15:12:39,581 - funman.representation.box - INFO - widths: 0.00800 -> 0.00333 0.00467 (raw), 0.07998 -> 0.03332 0.04665 (norm)\n",
+      "2024-02-08 15:12:39,584 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,607 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.06334277099649126, 0.06484238491560972)][(0.06484238491560972, 0.06800823651259927)])\n",
+      "2024-02-08 15:12:39,608 - funman.representation.box - INFO - widths: 0.00467 -> 0.00150 0.00317 (raw), 0.04665 -> 0.01500 0.03166 (norm)\n",
+      "2024-02-08 15:12:39,610 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,624 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.06334277099649126, 0.06405092201636234)][(0.06405092201636234, 0.06484238491560972)])\n",
+      "2024-02-08 15:12:39,625 - funman.representation.box - INFO - widths: 0.00150 -> 0.00071 0.00079 (raw), 0.01500 -> 0.00708 0.00791 (norm)\n",
+      "2024-02-08 15:12:39,627 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,640 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.06334277099649126, 0.06385305629155048)][(0.06385305629155048, 0.06405092201636234)])\n",
+      "2024-02-08 15:12:39,641 - funman.representation.box - INFO - widths: 0.00071 -> 0.00051 0.00020 (raw), 0.00708 -> 0.00510 0.00198 (norm)\n",
+      "2024-02-08 15:12:39,644 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,657 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.06385305629155048, 0.0639806276153153)][(0.0639806276153153, 0.06405092201636234)])\n",
+      "2024-02-08 15:12:39,657 - funman.representation.box - INFO - widths: 0.00020 -> 0.00013 0.00007 (raw), 0.00198 -> 0.00128 0.00070 (norm)\n",
+      "2024-02-08 15:12:39,659 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,686 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.06800823651259927, 0.07154899158266316)][(0.07154899158266316, 0.07350682085776136)])\n",
+      "2024-02-08 15:12:39,687 - funman.representation.box - INFO - widths: 0.00550 -> 0.00354 0.00196 (raw), 0.05499 -> 0.03541 0.01958 (norm)\n",
+      "2024-02-08 15:12:39,689 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,705 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.06800823651259927, 0.07026807136640577)][(0.07026807136640577, 0.07154899158266316)])\n",
+      "2024-02-08 15:12:39,705 - funman.representation.box - INFO - widths: 0.00354 -> 0.00226 0.00128 (raw), 0.03541 -> 0.02260 0.01281 (norm)\n",
+      "2024-02-08 15:12:39,708 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,722 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07026807136640577, 0.07083303007985739)][(0.07083303007985739, 0.07154899158266316)])\n",
+      "2024-02-08 15:12:39,723 - funman.representation.box - INFO - widths: 0.00128 -> 0.00056 0.00072 (raw), 0.01281 -> 0.00565 0.00716 (norm)\n",
+      "2024-02-08 15:12:39,725 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,739 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07026807136640577, 0.07065403970415594)][(0.07065403970415594, 0.07083303007985739)])\n",
+      "2024-02-08 15:12:39,740 - funman.representation.box - INFO - widths: 0.00056 -> 0.00039 0.00018 (raw), 0.00565 -> 0.00386 0.00179 (norm)\n",
+      "2024-02-08 15:12:39,742 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,755 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07065403970415594, 0.07075053178859349)][(0.07075053178859349, 0.07083303007985739)])\n",
+      "2024-02-08 15:12:39,756 - funman.representation.box - INFO - widths: 0.00018 -> 0.00010 0.00008 (raw), 0.00179 -> 0.00096 0.00082 (norm)\n",
+      "2024-02-08 15:12:39,758 - funman.search.box_search - INFO - progress: 0.94185\n",
+      "2024-02-08 15:12:39,837 - funman.search.box_search - INFO - progress: 0.94655\n",
+      "2024-02-08 15:12:39,879 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.09487631912791225, 0.09676125050546823)][(0.09676125050546823, 0.1)])\n",
+      "2024-02-08 15:12:39,880 - funman.representation.box - INFO - widths: 0.00512 -> 0.00188 0.00324 (raw), 0.05124 -> 0.01885 0.03239 (norm)\n",
+      "2024-02-08 15:12:39,882 - funman.search.box_search - INFO - progress: 0.94655\n",
+      "2024-02-08 15:12:39,971 - funman.search.box_search - INFO - progress: 0.94949\n",
+      "2024-02-08 15:12:39,993 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08733659361768836, 0.09021085361745079)][(0.09021085361745079, 0.09216868289396064)])\n",
+      "2024-02-08 15:12:39,994 - funman.representation.box - INFO - widths: 0.00483 -> 0.00287 0.00196 (raw), 0.04832 -> 0.02874 0.01958 (norm)\n",
+      "2024-02-08 15:12:39,996 - funman.search.box_search - INFO - progress: 0.94949\n",
+      "2024-02-08 15:12:40,011 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08733659361768836, 0.08926318093669704)][(0.08926318093669704, 0.09021085361745079)])\n",
+      "2024-02-08 15:12:40,012 - funman.representation.box - INFO - widths: 0.00287 -> 0.00193 0.00095 (raw), 0.02874 -> 0.01927 0.00948 (norm)\n",
+      "2024-02-08 15:12:40,013 - funman.search.box_search - INFO - progress: 0.94949\n",
+      "2024-02-08 15:12:40,106 - funman.search.box_search - INFO - progress: 0.95035\n",
+      "2024-02-08 15:12:40,131 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07667267244769271, 0.07883878143146608)][(0.07883878143146608, 0.08133813795815417)])\n",
+      "2024-02-08 15:12:40,133 - funman.representation.box - INFO - widths: 0.00467 -> 0.00217 0.00250 (raw), 0.04665 -> 0.02166 0.02499 (norm)\n",
+      "2024-02-08 15:12:40,134 - funman.search.box_search - INFO - progress: 0.95035\n",
+      "2024-02-08 15:12:40,150 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07883878143146608, 0.07954693244886679)][(0.07954693244886679, 0.08133813795815417)])\n",
+      "2024-02-08 15:12:40,151 - funman.representation.box - INFO - widths: 0.00250 -> 0.00071 0.00179 (raw), 0.02499 -> 0.00708 0.01791 (norm)\n",
+      "2024-02-08 15:12:40,153 - funman.search.box_search - INFO - progress: 0.95035\n",
+      "2024-02-08 15:12:40,166 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07883878143146608, 0.07909913107154494)][(0.07909913107154494, 0.07954693244886679)])\n",
+      "2024-02-08 15:12:40,167 - funman.representation.box - INFO - widths: 0.00071 -> 0.00026 0.00045 (raw), 0.00708 -> 0.00260 0.00448 (norm)\n",
+      "2024-02-08 15:12:40,169 - funman.search.box_search - INFO - progress: 0.95035\n",
+      "2024-02-08 15:12:40,182 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07883878143146608, 0.07898718072721447)][(0.07898718072721447, 0.07909913107154494)])\n",
+      "2024-02-08 15:12:40,184 - funman.representation.box - INFO - widths: 0.00026 -> 0.00015 0.00011 (raw), 0.00260 -> 0.00148 0.00112 (norm)\n",
+      "2024-02-08 15:12:40,186 - funman.search.box_search - INFO - progress: 0.95035\n",
+      "2024-02-08 15:12:40,198 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07898718072721447, 0.07902428055115157)][(0.07902428055115157, 0.07909913107154494)])\n",
+      "2024-02-08 15:12:40,199 - funman.representation.box - INFO - widths: 0.00011 -> 0.00004 0.00007 (raw), 0.00112 -> 0.00037 0.00075 (norm)\n",
+      "2024-02-08 15:12:40,201 - funman.search.box_search - INFO - progress: 0.95035\n",
+      "2024-02-08 15:12:40,277 - funman.search.box_search - INFO - progress: 0.95447\n",
+      "2024-02-08 15:12:40,353 - funman.search.box_search - INFO - progress: 0.95797\n",
+      "2024-02-08 15:12:40,427 - funman.search.box_search - INFO - progress: 0.96100\n",
+      "2024-02-08 15:12:40,508 - funman.search.box_search - INFO - progress: 0.96388\n",
+      "2024-02-08 15:12:40,522 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08417074202211046, 0.08504551680391034)][(0.08504551680391034, 0.08733659361768836)])\n",
+      "2024-02-08 15:12:40,523 - funman.representation.box - INFO - widths: 0.00317 -> 0.00087 0.00229 (raw), 0.03166 -> 0.00875 0.02291 (norm)\n",
+      "2024-02-08 15:12:40,525 - funman.search.box_search - INFO - progress: 0.96388\n",
+      "2024-02-08 15:12:40,540 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08504551680391034, 0.08597236151534939)][(0.08597236151534939, 0.08733659361768836)])\n",
+      "2024-02-08 15:12:40,541 - funman.representation.box - INFO - widths: 0.00229 -> 0.00093 0.00136 (raw), 0.02291 -> 0.00927 0.01364 (norm)\n",
+      "2024-02-08 15:12:40,544 - funman.search.box_search - INFO - progress: 0.96388\n",
+      "2024-02-08 15:12:40,628 - funman.search.box_search - INFO - progress: 0.96472\n",
+      "2024-02-08 15:12:40,704 - funman.search.box_search - INFO - progress: 0.96708\n",
+      "2024-02-08 15:12:40,788 - funman.search.box_search - INFO - progress: 0.96913\n",
+      "2024-02-08 15:12:40,802 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07667267244769271, 0.07821394229979406)][(0.07821394229979406, 0.07883878143146608)])\n",
+      "2024-02-08 15:12:40,803 - funman.representation.box - INFO - widths: 0.00217 -> 0.00154 0.00062 (raw), 0.02166 -> 0.01541 0.00625 (norm)\n",
+      "2024-02-08 15:12:40,805 - funman.search.box_search - INFO - progress: 0.96913\n",
+      "2024-02-08 15:12:40,819 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07667267244769271, 0.0775995171566614)][(0.0775995171566614, 0.07821394229979406)])\n",
+      "2024-02-08 15:12:40,820 - funman.representation.box - INFO - widths: 0.00154 -> 0.00093 0.00061 (raw), 0.01541 -> 0.00927 0.00614 (norm)\n",
+      "2024-02-08 15:12:40,822 - funman.search.box_search - INFO - progress: 0.96913\n",
+      "2024-02-08 15:12:40,835 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.0775995171566614, 0.07783122833390356)][(0.07783122833390356, 0.07821394229979406)])\n",
+      "2024-02-08 15:12:40,837 - funman.representation.box - INFO - widths: 0.00061 -> 0.00023 0.00038 (raw), 0.00614 -> 0.00232 0.00383 (norm)\n",
+      "2024-02-08 15:12:40,839 - funman.search.box_search - INFO - progress: 0.96913\n",
+      "2024-02-08 15:12:40,851 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.0775995171566614, 0.07773554984243095)][(0.07773554984243095, 0.07783122833390356)])\n",
+      "2024-02-08 15:12:40,853 - funman.representation.box - INFO - widths: 0.00023 -> 0.00014 0.00010 (raw), 0.00232 -> 0.00136 0.00096 (norm)\n",
+      "2024-02-08 15:12:40,856 - funman.search.box_search - INFO - progress: 0.96913\n",
+      "2024-02-08 15:12:40,933 - funman.search.box_search - INFO - progress: 0.97106\n",
+      "2024-02-08 15:12:41,017 - funman.search.box_search - INFO - progress: 0.97297\n",
+      "2024-02-08 15:12:41,030 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08133813795815417, 0.08255657426540869)][(0.08255657426540869, 0.08337927912321598)])\n",
+      "2024-02-08 15:12:41,031 - funman.representation.box - INFO - widths: 0.00204 -> 0.00122 0.00082 (raw), 0.02041 -> 0.01218 0.00823 (norm)\n",
+      "2024-02-08 15:12:41,034 - funman.search.box_search - INFO - progress: 0.97297\n",
+      "2024-02-08 15:12:41,047 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08133813795815417, 0.08215303232623325)][(0.08215303232623325, 0.08255657426540869)])\n",
+      "2024-02-08 15:12:41,048 - funman.representation.box - INFO - widths: 0.00122 -> 0.00081 0.00040 (raw), 0.01218 -> 0.00815 0.00404 (norm)\n",
+      "2024-02-08 15:12:41,050 - funman.search.box_search - INFO - progress: 0.97297\n",
+      "2024-02-08 15:12:41,134 - funman.search.box_search - INFO - progress: 0.97334\n",
+      "2024-02-08 15:12:41,208 - funman.search.box_search - INFO - progress: 0.97515\n",
+      "2024-02-08 15:12:41,221 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.09021085361745079, 0.0909294186173914)][(0.0909294186173914, 0.09216868289396064)])\n",
+      "2024-02-08 15:12:41,222 - funman.representation.box - INFO - widths: 0.00196 -> 0.00072 0.00124 (raw), 0.01958 -> 0.00719 0.01239 (norm)\n",
+      "2024-02-08 15:12:41,224 - funman.search.box_search - INFO - progress: 0.97515\n",
+      "2024-02-08 15:12:41,309 - funman.search.box_search - INFO - progress: 0.97628\n",
+      "2024-02-08 15:12:41,391 - funman.search.box_search - INFO - progress: 0.97806\n",
+      "2024-02-08 15:12:41,405 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08733659361768836, 0.08853680544738114)][(0.08853680544738114, 0.08926318093669704)])\n",
+      "2024-02-08 15:12:41,406 - funman.representation.box - INFO - widths: 0.00193 -> 0.00120 0.00073 (raw), 0.01927 -> 0.01200 0.00726 (norm)\n",
+      "2024-02-08 15:12:41,408 - funman.search.box_search - INFO - progress: 0.97806\n",
+      "2024-02-08 15:12:41,492 - funman.search.box_search - INFO - progress: 0.97915\n",
+      "2024-02-08 15:12:41,505 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.09487631912791225, 0.09595156313183528)][(0.09595156313183528, 0.09676125050546823)])\n",
+      "2024-02-08 15:12:41,506 - funman.representation.box - INFO - widths: 0.00188 -> 0.00108 0.00081 (raw), 0.01885 -> 0.01075 0.00810 (norm)\n",
+      "2024-02-08 15:12:41,509 - funman.search.box_search - INFO - progress: 0.97915\n",
+      "2024-02-08 15:12:41,592 - funman.search.box_search - INFO - progress: 0.98013\n",
+      "2024-02-08 15:12:41,605 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07954693244886679, 0.08026549744916031)][(0.08026549744916031, 0.08133813795815417)])\n",
+      "2024-02-08 15:12:41,606 - funman.representation.box - INFO - widths: 0.00179 -> 0.00072 0.00107 (raw), 0.01791 -> 0.00719 0.01073 (norm)\n",
+      "2024-02-08 15:12:41,608 - funman.search.box_search - INFO - progress: 0.98013\n",
+      "2024-02-08 15:12:41,621 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08026549744916031, 0.08062217645358387)][(0.08062217645358387, 0.08133813795815417)])\n",
+      "2024-02-08 15:12:41,622 - funman.representation.box - INFO - widths: 0.00107 -> 0.00036 0.00072 (raw), 0.01073 -> 0.00357 0.00716 (norm)\n",
+      "2024-02-08 15:12:41,624 - funman.search.box_search - INFO - progress: 0.98013\n",
+      "2024-02-08 15:12:41,639 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08026549744916031, 0.08044318607744129)][(0.08044318607744129, 0.08062217645358387)])\n",
+      "2024-02-08 15:12:41,639 - funman.representation.box - INFO - widths: 0.00036 -> 0.00018 0.00018 (raw), 0.00357 -> 0.00178 0.00179 (norm)\n",
+      "2024-02-08 15:12:41,642 - funman.search.box_search - INFO - progress: 0.98013\n",
+      "2024-02-08 15:12:41,656 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08026549744916031, 0.08039843848340564)][(0.08039843848340564, 0.08044318607744129)])\n",
+      "2024-02-08 15:12:41,657 - funman.representation.box - INFO - widths: 0.00018 -> 0.00013 0.00004 (raw), 0.00178 -> 0.00133 0.00045 (norm)\n",
+      "2024-02-08 15:12:41,659 - funman.search.box_search - INFO - progress: 0.98013\n",
+      "2024-02-08 15:12:41,673 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07488146694405189, 0.07543340817429967)][(0.07543340817429967, 0.07667267244769271)])\n",
+      "2024-02-08 15:12:41,675 - funman.representation.box - INFO - widths: 0.00179 -> 0.00055 0.00124 (raw), 0.01791 -> 0.00552 0.01239 (norm)\n",
+      "2024-02-08 15:12:41,677 - funman.search.box_search - INFO - progress: 0.98013\n",
+      "2024-02-08 15:12:41,691 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07543340817429967, 0.07591505500343425)][(0.07591505500343425, 0.07667267244769271)])\n",
+      "2024-02-08 15:12:41,691 - funman.representation.box - INFO - widths: 0.00124 -> 0.00048 0.00076 (raw), 0.01239 -> 0.00482 0.00758 (norm)\n",
+      "2024-02-08 15:12:41,694 - funman.search.box_search - INFO - progress: 0.98013\n",
+      "2024-02-08 15:12:41,710 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07543340817429967, 0.07572565064236962)][(0.07572565064236962, 0.07591505500343425)])\n",
+      "2024-02-08 15:12:41,715 - funman.representation.box - INFO - widths: 0.00048 -> 0.00029 0.00019 (raw), 0.00482 -> 0.00292 0.00189 (norm)\n",
+      "2024-02-08 15:12:41,721 - funman.search.box_search - INFO - progress: 0.98013\n",
+      "2024-02-08 15:12:41,736 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07572565064236962, 0.07579871125938711)][(0.07579871125938711, 0.07591505500343425)])\n",
+      "2024-02-08 15:12:41,737 - funman.representation.box - INFO - widths: 0.00019 -> 0.00007 0.00012 (raw), 0.00189 -> 0.00073 0.00116 (norm)\n",
+      "2024-02-08 15:12:41,741 - funman.search.box_search - INFO - progress: 0.98013\n",
+      "2024-02-08 15:12:41,754 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08597236151534939, 0.08642276638865912)][(0.08642276638865912, 0.08733659361768836)])\n",
+      "2024-02-08 15:12:41,755 - funman.representation.box - INFO - widths: 0.00136 -> 0.00045 0.00091 (raw), 0.01364 -> 0.00450 0.00914 (norm)\n",
+      "2024-02-08 15:12:41,757 - funman.search.box_search - INFO - progress: 0.98013\n",
+      "2024-02-08 15:12:41,915 - funman.search.box_search - INFO - progress: 0.98096\n",
+      "2024-02-08 15:12:41,992 - funman.search.box_search - INFO - progress: 0.98213\n",
+      "2024-02-08 15:12:42,070 - funman.search.box_search - INFO - progress: 0.98298\n",
+      "2024-02-08 15:12:42,084 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07667267244769271, 0.07728970108796022)][(0.07728970108796022, 0.0775995171566614)])\n",
+      "2024-02-08 15:12:42,085 - funman.representation.box - INFO - widths: 0.00093 -> 0.00062 0.00031 (raw), 0.00927 -> 0.00617 0.00310 (norm)\n",
+      "2024-02-08 15:12:42,087 - funman.search.box_search - INFO - progress: 0.98298\n",
+      "2024-02-08 15:12:42,100 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07667267244769271, 0.07705864078500176)][(0.07705864078500176, 0.07728970108796022)])\n",
+      "2024-02-08 15:12:42,101 - funman.representation.box - INFO - widths: 0.00062 -> 0.00039 0.00023 (raw), 0.00617 -> 0.00386 0.00231 (norm)\n",
+      "2024-02-08 15:12:42,104 - funman.search.box_search - INFO - progress: 0.98298\n",
+      "2024-02-08 15:12:42,118 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07705864078500176, 0.07715513286932903)][(0.07715513286932903, 0.07728970108796022)])\n",
+      "2024-02-08 15:12:42,119 - funman.representation.box - INFO - widths: 0.00023 -> 0.00010 0.00013 (raw), 0.00231 -> 0.00096 0.00135 (norm)\n",
+      "2024-02-08 15:12:42,122 - funman.search.box_search - INFO - progress: 0.98298\n",
+      "2024-02-08 15:12:42,135 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08417074202211046, 0.08447274760046583)][(0.08447274760046583, 0.08504551680391034)])\n",
+      "2024-02-08 15:12:42,136 - funman.representation.box - INFO - widths: 0.00087 -> 0.00030 0.00057 (raw), 0.00875 -> 0.00302 0.00573 (norm)\n",
+      "2024-02-08 15:12:42,138 - funman.search.box_search - INFO - progress: 0.98298\n",
+      "2024-02-08 15:12:42,223 - funman.search.box_search - INFO - progress: 0.98350\n",
+      "2024-02-08 15:12:42,235 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08255657426540869, 0.08286118334222232)][(0.08286118334222232, 0.08337927912321598)])\n",
+      "2024-02-08 15:12:42,237 - funman.representation.box - INFO - widths: 0.00082 -> 0.00030 0.00052 (raw), 0.00823 -> 0.00305 0.00518 (norm)\n",
+      "2024-02-08 15:12:42,239 - funman.search.box_search - INFO - progress: 0.98350\n",
+      "2024-02-08 15:12:42,325 - funman.search.box_search - INFO - progress: 0.98397\n",
+      "2024-02-08 15:12:42,338 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08133813795815417, 0.08184647062698758)][(0.08184647062698758, 0.08215303232623325)])\n",
+      "2024-02-08 15:12:42,340 - funman.representation.box - INFO - widths: 0.00081 -> 0.00051 0.00031 (raw), 0.00815 -> 0.00508 0.00307 (norm)\n",
+      "2024-02-08 15:12:42,342 - funman.search.box_search - INFO - progress: 0.98397\n",
+      "2024-02-08 15:12:42,433 - funman.search.box_search - INFO - progress: 0.98443\n",
+      "2024-02-08 15:12:42,446 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.09595156313183528, 0.09622037413281603)][(0.09622037413281603, 0.09676125050546823)])\n",
+      "2024-02-08 15:12:42,447 - funman.representation.box - INFO - widths: 0.00081 -> 0.00027 0.00054 (raw), 0.00810 -> 0.00269 0.00541 (norm)\n",
+      "2024-02-08 15:12:42,449 - funman.search.box_search - INFO - progress: 0.98443\n",
+      "2024-02-08 15:12:42,539 - funman.search.box_search - INFO - progress: 0.98492\n",
+      "2024-02-08 15:12:42,616 - funman.search.box_search - INFO - progress: 0.98564\n",
+      "2024-02-08 15:12:42,696 - funman.search.box_search - INFO - progress: 0.98633\n",
+      "2024-02-08 15:12:42,780 - funman.search.box_search - INFO - progress: 0.98700\n",
+      "2024-02-08 15:12:42,792 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08853680544738114, 0.08883685840480433)][(0.08883685840480433, 0.08926318093669704)])\n",
+      "2024-02-08 15:12:42,793 - funman.representation.box - INFO - widths: 0.00073 -> 0.00030 0.00043 (raw), 0.00726 -> 0.00300 0.00426 (norm)\n",
+      "2024-02-08 15:12:42,795 - funman.search.box_search - INFO - progress: 0.98700\n",
+      "2024-02-08 15:12:42,879 - funman.search.box_search - INFO - progress: 0.98739\n",
+      "2024-02-08 15:12:42,891 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07954693244886679, 0.07999733732191185)][(0.07999733732191185, 0.08026549744916031)])\n",
+      "2024-02-08 15:12:42,892 - funman.representation.box - INFO - widths: 0.00072 -> 0.00045 0.00027 (raw), 0.00719 -> 0.00450 0.00268 (norm)\n",
+      "2024-02-08 15:12:42,894 - funman.search.box_search - INFO - progress: 0.98739\n",
+      "2024-02-08 15:12:42,908 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07954693244886679, 0.07983917491720144)][(0.07983917491720144, 0.07999733732191185)])\n",
+      "2024-02-08 15:12:42,909 - funman.representation.box - INFO - widths: 0.00045 -> 0.00029 0.00016 (raw), 0.00450 -> 0.00292 0.00158 (norm)\n",
+      "2024-02-08 15:12:42,911 - funman.search.box_search - INFO - progress: 0.98739\n",
+      "2024-02-08 15:12:42,924 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07983917491720144, 0.0799122355342851)][(0.0799122355342851, 0.07999733732191185)])\n",
+      "2024-02-08 15:12:42,926 - funman.representation.box - INFO - widths: 0.00016 -> 0.00007 0.00009 (raw), 0.00158 -> 0.00073 0.00085 (norm)\n",
+      "2024-02-08 15:12:42,929 - funman.search.box_search - INFO - progress: 0.98739\n",
+      "2024-02-08 15:12:42,941 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.09021085361745079, 0.09061960254824909)][(0.09061960254824909, 0.0909294186173914)])\n",
+      "2024-02-08 15:12:42,942 - funman.representation.box - INFO - widths: 0.00072 -> 0.00041 0.00031 (raw), 0.00719 -> 0.00409 0.00310 (norm)\n",
+      "2024-02-08 15:12:42,944 - funman.search.box_search - INFO - progress: 0.98739\n",
+      "2024-02-08 15:12:43,028 - funman.search.box_search - INFO - progress: 0.98776\n",
+      "2024-02-08 15:12:43,040 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08062217645358387, 0.08089098745476313)][(0.08089098745476313, 0.08133813795815417)])\n",
+      "2024-02-08 15:12:43,041 - funman.representation.box - INFO - widths: 0.00072 -> 0.00027 0.00045 (raw), 0.00716 -> 0.00269 0.00447 (norm)\n",
+      "2024-02-08 15:12:43,044 - funman.search.box_search - INFO - progress: 0.98776\n",
+      "2024-02-08 15:12:43,057 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08089098745476313, 0.08104735995616383)][(0.08104735995616383, 0.08133813795815417)])\n",
+      "2024-02-08 15:12:43,058 - funman.representation.box - INFO - widths: 0.00045 -> 0.00016 0.00029 (raw), 0.00447 -> 0.00156 0.00291 (norm)\n",
+      "2024-02-08 15:12:43,061 - funman.search.box_search - INFO - progress: 0.98776\n",
+      "2024-02-08 15:12:43,146 - funman.search.box_search - INFO - progress: 0.98790\n",
+      "2024-02-08 15:12:43,223 - funman.search.box_search - INFO - progress: 0.98855\n",
+      "2024-02-08 15:12:43,236 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07821394229979406, 0.0785992597628194)][(0.0785992597628194, 0.07883878143146608)])\n",
+      "2024-02-08 15:12:43,238 - funman.representation.box - INFO - widths: 0.00062 -> 0.00039 0.00024 (raw), 0.00625 -> 0.00385 0.00240 (norm)\n",
+      "2024-02-08 15:12:43,241 - funman.search.box_search - INFO - progress: 0.98855\n",
+      "2024-02-08 15:12:43,254 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07821394229979406, 0.0784664814484684)][(0.0784664814484684, 0.0785992597628194)])\n",
+      "2024-02-08 15:12:43,256 - funman.representation.box - INFO - widths: 0.00039 -> 0.00025 0.00013 (raw), 0.00385 -> 0.00253 0.00133 (norm)\n",
+      "2024-02-08 15:12:43,258 - funman.search.box_search - INFO - progress: 0.98855\n",
+      "2024-02-08 15:12:43,272 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.0784664814484684, 0.07852961623563699)][(0.07852961623563699, 0.0785992597628194)])\n",
+      "2024-02-08 15:12:43,273 - funman.representation.box - INFO - widths: 0.00013 -> 0.00006 0.00007 (raw), 0.00133 -> 0.00063 0.00070 (norm)\n",
+      "2024-02-08 15:12:43,276 - funman.search.box_search - INFO - progress: 0.98855\n",
+      "2024-02-08 15:12:43,364 - funman.search.box_search - INFO - progress: 0.98911\n",
+      "2024-02-08 15:12:43,442 - funman.search.box_search - INFO - progress: 0.98961\n",
+      "2024-02-08 15:12:43,519 - funman.search.box_search - INFO - progress: 0.99009\n",
+      "2024-02-08 15:12:43,597 - funman.search.box_search - INFO - progress: 0.99055\n",
+      "2024-02-08 15:12:43,675 - funman.search.box_search - INFO - progress: 0.99101\n",
+      "2024-02-08 15:12:43,687 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08597236151534939, 0.0861943095814018)][(0.0861943095814018, 0.08642276638865912)])\n",
+      "2024-02-08 15:12:43,688 - funman.representation.box - INFO - widths: 0.00045 -> 0.00022 0.00023 (raw), 0.00450 -> 0.00222 0.00228 (norm)\n",
+      "2024-02-08 15:12:43,691 - funman.search.box_search - INFO - progress: 0.99101\n",
+      "2024-02-08 15:12:43,781 - funman.search.box_search - INFO - progress: 0.99122\n",
+      "2024-02-08 15:12:43,795 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07909913107154494, 0.07925794435018615)][(0.07925794435018615, 0.07954693244886679)])\n",
+      "2024-02-08 15:12:43,797 - funman.representation.box - INFO - widths: 0.00045 -> 0.00016 0.00029 (raw), 0.00448 -> 0.00159 0.00289 (norm)\n",
+      "2024-02-08 15:12:43,800 - funman.search.box_search - INFO - progress: 0.99122\n",
+      "2024-02-08 15:12:43,814 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07925794435018615, 0.07936273507986617)][(0.07936273507986617, 0.07954693244886679)])\n",
+      "2024-02-08 15:12:43,816 - funman.representation.box - INFO - widths: 0.00029 -> 0.00010 0.00018 (raw), 0.00289 -> 0.00105 0.00184 (norm)\n",
+      "2024-02-08 15:12:43,820 - funman.search.box_search - INFO - progress: 0.99122\n",
+      "2024-02-08 15:12:43,835 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07925794435018615, 0.07931668573761601)][(0.07931668573761601, 0.07936273507986617)])\n",
+      "2024-02-08 15:12:43,836 - funman.representation.box - INFO - widths: 0.00010 -> 0.00006 0.00005 (raw), 0.00105 -> 0.00059 0.00046 (norm)\n",
+      "2024-02-08 15:12:43,839 - funman.search.box_search - INFO - progress: 0.99122\n",
+      "2024-02-08 15:12:43,919 - funman.search.box_search - INFO - progress: 0.99157\n",
+      "2024-02-08 15:12:43,997 - funman.search.box_search - INFO - progress: 0.99192\n",
+      "2024-02-08 15:12:44,081 - funman.search.box_search - INFO - progress: 0.99227\n",
+      "2024-02-08 15:12:44,157 - funman.search.box_search - INFO - progress: 0.99261\n",
+      "2024-02-08 15:12:44,167 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.09061960254824909, 0.09072178978094866)][(0.09072178978094866, 0.0909294186173914)])\n",
+      "2024-02-08 15:12:44,168 - funman.representation.box - INFO - widths: 0.00031 -> 0.00010 0.00021 (raw), 0.00310 -> 0.00102 0.00208 (norm)\n",
+      "2024-02-08 15:12:44,171 - funman.search.box_search - INFO - progress: 0.99261\n",
+      "2024-02-08 15:12:44,256 - funman.search.box_search - INFO - progress: 0.99279\n",
+      "2024-02-08 15:12:44,334 - funman.search.box_search - INFO - progress: 0.99308\n",
+      "2024-02-08 15:12:44,347 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08184647062698758, 0.08197355379419592)][(0.08197355379419592, 0.08215303232623325)])\n",
+      "2024-02-08 15:12:44,348 - funman.representation.box - INFO - widths: 0.00031 -> 0.00013 0.00018 (raw), 0.00307 -> 0.00127 0.00179 (norm)\n",
+      "2024-02-08 15:12:44,351 - funman.search.box_search - INFO - progress: 0.99308\n",
+      "2024-02-08 15:12:44,445 - funman.search.box_search - INFO - progress: 0.99324\n",
+      "2024-02-08 15:12:44,456 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08255657426540869, 0.08273165939697391)][(0.08273165939697391, 0.08286118334222232)])\n",
+      "2024-02-08 15:12:44,457 - funman.representation.box - INFO - widths: 0.00030 -> 0.00018 0.00013 (raw), 0.00305 -> 0.00175 0.00130 (norm)\n",
+      "2024-02-08 15:12:44,460 - funman.search.box_search - INFO - progress: 0.99324\n",
+      "2024-02-08 15:12:44,544 - funman.search.box_search - INFO - progress: 0.99340\n",
+      "2024-02-08 15:12:44,555 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08417074202211046, 0.0843295552996047)][(0.0843295552996047, 0.08447274760046583)])\n",
+      "2024-02-08 15:12:44,556 - funman.representation.box - INFO - widths: 0.00030 -> 0.00016 0.00014 (raw), 0.00302 -> 0.00159 0.00143 (norm)\n",
+      "2024-02-08 15:12:44,559 - funman.search.box_search - INFO - progress: 0.99340\n",
+      "2024-02-08 15:12:44,640 - funman.search.box_search - INFO - progress: 0.99354\n",
+      "2024-02-08 15:12:44,652 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08853680544738114, 0.08873027777183116)][(0.08873027777183116, 0.08883685840480433)])\n",
+      "2024-02-08 15:12:44,653 - funman.representation.box - INFO - widths: 0.00030 -> 0.00019 0.00011 (raw), 0.00300 -> 0.00193 0.00107 (norm)\n",
+      "2024-02-08 15:12:44,655 - funman.search.box_search - INFO - progress: 0.99354\n",
+      "2024-02-08 15:12:44,738 - funman.search.box_search - INFO - progress: 0.99372\n",
+      "2024-02-08 15:12:44,813 - funman.search.box_search - INFO - progress: 0.99399\n",
+      "2024-02-08 15:12:44,825 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07954693244886679, 0.07973259428421171)][(0.07973259428421171, 0.07983917491720144)])\n",
+      "2024-02-08 15:12:44,826 - funman.representation.box - INFO - widths: 0.00029 -> 0.00019 0.00011 (raw), 0.00292 -> 0.00186 0.00107 (norm)\n",
+      "2024-02-08 15:12:44,828 - funman.search.box_search - INFO - progress: 0.99399\n",
+      "2024-02-08 15:12:44,841 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07954693244886679, 0.07966640852478668)][(0.07966640852478668, 0.07973259428421171)])\n",
+      "2024-02-08 15:12:44,842 - funman.representation.box - INFO - widths: 0.00019 -> 0.00012 0.00007 (raw), 0.00186 -> 0.00119 0.00066 (norm)\n",
+      "2024-02-08 15:12:44,845 - funman.search.box_search - INFO - progress: 0.99399\n",
+      "2024-02-08 15:12:44,923 - funman.search.box_search - INFO - progress: 0.99426\n",
+      "2024-02-08 15:12:44,936 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08104735995616383, 0.08115365583180882)][(0.08115365583180882, 0.08133813795815417)])\n",
+      "2024-02-08 15:12:44,937 - funman.representation.box - INFO - widths: 0.00029 -> 0.00011 0.00018 (raw), 0.00291 -> 0.00106 0.00184 (norm)\n",
+      "2024-02-08 15:12:44,939 - funman.search.box_search - INFO - progress: 0.99426\n",
+      "2024-02-08 15:12:45,023 - funman.search.box_search - INFO - progress: 0.99442\n",
+      "2024-02-08 15:12:45,034 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08062217645358387, 0.08077919982891538)][(0.08077919982891538, 0.08089098745476313)])\n",
+      "2024-02-08 15:12:45,035 - funman.representation.box - INFO - widths: 0.00027 -> 0.00016 0.00011 (raw), 0.00269 -> 0.00157 0.00112 (norm)\n",
+      "2024-02-08 15:12:45,038 - funman.search.box_search - INFO - progress: 0.99442\n",
+      "2024-02-08 15:12:45,125 - funman.search.box_search - INFO - progress: 0.99457\n",
+      "2024-02-08 15:12:45,137 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.09595156313183528, 0.096085155039653)][(0.096085155039653, 0.09622037413281603)])\n",
+      "2024-02-08 15:12:45,138 - funman.representation.box - INFO - widths: 0.00027 -> 0.00013 0.00014 (raw), 0.00269 -> 0.00134 0.00135 (norm)\n",
+      "2024-02-08 15:12:45,140 - funman.search.box_search - INFO - progress: 0.99457\n",
+      "2024-02-08 15:12:45,229 - funman.search.box_search - INFO - progress: 0.99469\n",
+      "2024-02-08 15:12:45,241 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07999733732191185, 0.0801099385401731)][(0.0801099385401731, 0.08026549744916031)])\n",
+      "2024-02-08 15:12:45,242 - funman.representation.box - INFO - widths: 0.00027 -> 0.00011 0.00016 (raw), 0.00268 -> 0.00113 0.00156 (norm)\n",
+      "2024-02-08 15:12:45,244 - funman.search.box_search - INFO - progress: 0.99469\n",
+      "2024-02-08 15:12:45,257 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.0801099385401731, 0.0801595676901014)][(0.0801595676901014, 0.08026549744916031)])\n",
+      "2024-02-08 15:12:45,258 - funman.representation.box - INFO - widths: 0.00016 -> 0.00005 0.00011 (raw), 0.00156 -> 0.00050 0.00106 (norm)\n",
+      "2024-02-08 15:12:45,261 - funman.search.box_search - INFO - progress: 0.99469\n",
+      "2024-02-08 15:12:45,339 - funman.search.box_search - INFO - progress: 0.99492\n",
+      "2024-02-08 15:12:45,420 - funman.search.box_search - INFO - progress: 0.99514\n",
+      "2024-02-08 15:12:45,496 - funman.search.box_search - INFO - progress: 0.99535\n",
+      "2024-02-08 15:12:45,508 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08597236151534939, 0.08613719537958747)][(0.08613719537958747, 0.0861943095814018)])\n",
+      "2024-02-08 15:12:45,509 - funman.representation.box - INFO - widths: 0.00022 -> 0.00016 0.00006 (raw), 0.00222 -> 0.00165 0.00057 (norm)\n",
+      "2024-02-08 15:12:45,511 - funman.search.box_search - INFO - progress: 0.99535\n",
+      "2024-02-08 15:12:45,595 - funman.search.box_search - INFO - progress: 0.99550\n",
+      "2024-02-08 15:12:45,672 - funman.search.box_search - INFO - progress: 0.99567\n",
+      "2024-02-08 15:12:45,685 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07936273507986617, 0.07942863608194647)][(0.07942863608194647, 0.07954693244886679)])\n",
+      "2024-02-08 15:12:45,686 - funman.representation.box - INFO - widths: 0.00018 -> 0.00007 0.00012 (raw), 0.00184 -> 0.00066 0.00118 (norm)\n",
+      "2024-02-08 15:12:45,688 - funman.search.box_search - INFO - progress: 0.99567\n",
+      "2024-02-08 15:12:45,701 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07942863608194647, 0.07947130901488655)][(0.07947130901488655, 0.07954693244886679)])\n",
+      "2024-02-08 15:12:45,702 - funman.representation.box - INFO - widths: 0.00012 -> 0.00004 0.00008 (raw), 0.00118 -> 0.00043 0.00076 (norm)\n",
+      "2024-02-08 15:12:45,705 - funman.search.box_search - INFO - progress: 0.99567\n",
+      "2024-02-08 15:12:45,717 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08044318607744129, 0.08048825910844233)][(0.08048825910844233, 0.08062217645358387)])\n",
+      "2024-02-08 15:12:45,718 - funman.representation.box - INFO - widths: 0.00018 -> 0.00005 0.00013 (raw), 0.00179 -> 0.00045 0.00134 (norm)\n",
+      "2024-02-08 15:12:45,721 - funman.search.box_search - INFO - progress: 0.99567\n",
+      "2024-02-08 15:12:45,831 - funman.search.box_search - INFO - progress: 0.99579\n",
+      "2024-02-08 15:12:45,918 - funman.search.box_search - INFO - progress: 0.99593\n",
+      "2024-02-08 15:12:45,999 - funman.search.box_search - INFO - progress: 0.99608\n",
+      "2024-02-08 15:12:46,073 - funman.search.box_search - INFO - progress: 0.99621\n",
+      "2024-02-08 15:12:46,084 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.0843295552996047, 0.08436925861897827)][(0.08436925861897827, 0.08447274760046583)])\n",
+      "2024-02-08 15:12:46,085 - funman.representation.box - INFO - widths: 0.00014 -> 0.00004 0.00010 (raw), 0.00143 -> 0.00040 0.00103 (norm)\n",
+      "2024-02-08 15:12:46,089 - funman.search.box_search - INFO - progress: 0.99621\n",
+      "2024-02-08 15:12:46,212 - funman.search.box_search - INFO - progress: 0.99630\n",
+      "2024-02-08 15:12:46,291 - funman.search.box_search - INFO - progress: 0.99643\n",
+      "2024-02-08 15:12:46,367 - funman.search.box_search - INFO - progress: 0.99655\n",
+      "2024-02-08 15:12:46,379 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.09595156313183528, 0.09605135026636223)][(0.09605135026636223, 0.096085155039653)])\n",
+      "2024-02-08 15:12:46,380 - funman.representation.box - INFO - widths: 0.00013 -> 0.00010 0.00003 (raw), 0.00134 -> 0.00100 0.00034 (norm)\n",
+      "2024-02-08 15:12:46,383 - funman.search.box_search - INFO - progress: 0.99655\n",
+      "2024-02-08 15:12:46,396 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08026549744916031, 0.08034315486479189)][(0.08034315486479189, 0.08039843848340564)])\n",
+      "2024-02-08 15:12:46,398 - funman.representation.box - INFO - widths: 0.00013 -> 0.00008 0.00006 (raw), 0.00133 -> 0.00078 0.00055 (norm)\n",
+      "2024-02-08 15:12:46,400 - funman.search.box_search - INFO - progress: 0.99655\n",
+      "2024-02-08 15:12:46,412 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08273165939697391, 0.08277543067986522)][(0.08277543067986522, 0.08286118334222232)])\n",
+      "2024-02-08 15:12:46,413 - funman.representation.box - INFO - widths: 0.00013 -> 0.00004 0.00009 (raw), 0.00130 -> 0.00044 0.00086 (norm)\n",
+      "2024-02-08 15:12:46,416 - funman.search.box_search - INFO - progress: 0.99655\n",
+      "2024-02-08 15:12:46,494 - funman.search.box_search - INFO - progress: 0.99667\n",
+      "2024-02-08 15:12:46,505 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08184647062698758, 0.0819286841611866)][(0.0819286841611866, 0.08197355379419592)])\n",
+      "2024-02-08 15:12:46,506 - funman.representation.box - INFO - widths: 0.00013 -> 0.00008 0.00004 (raw), 0.00127 -> 0.00082 0.00045 (norm)\n",
+      "2024-02-08 15:12:46,509 - funman.search.box_search - INFO - progress: 0.99667\n",
+      "2024-02-08 15:12:46,586 - funman.search.box_search - INFO - progress: 0.99678\n",
+      "2024-02-08 15:12:46,598 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07954693244886679, 0.07962321692668299)][(0.07962321692668299, 0.07966640852478668)])\n",
+      "2024-02-08 15:12:46,599 - funman.representation.box - INFO - widths: 0.00012 -> 0.00008 0.00004 (raw), 0.00119 -> 0.00076 0.00043 (norm)\n",
+      "2024-02-08 15:12:46,602 - funman.search.box_search - INFO - progress: 0.99678\n",
+      "2024-02-08 15:12:46,678 - funman.search.box_search - INFO - progress: 0.99689\n",
+      "2024-02-08 15:12:46,689 - funman.api.run - INFO - Dumping results to ./out/4b66c65d-54a1-4542-a660-76a9af861f48.json\n",
+      "2024-02-08 15:12:46,690 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07999733732191185, 0.0800710488129263)][(0.0800710488129263, 0.0801099385401731)])\n",
+      "2024-02-08 15:12:46,692 - funman.representation.box - INFO - widths: 0.00011 -> 0.00007 0.00004 (raw), 0.00113 -> 0.00074 0.00039 (norm)\n",
+      "2024-02-08 15:12:46,695 - funman.search.box_search - INFO - progress: 0.99689\n",
+      "2024-02-08 15:12:46,708 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08077919982891538, 0.08081845567274824)][(0.08081845567274824, 0.08089098745476313)])\n",
+      "2024-02-08 15:12:46,715 - funman.representation.box - INFO - widths: 0.00011 -> 0.00004 0.00007 (raw), 0.00112 -> 0.00039 0.00073 (norm)\n",
+      "2024-02-08 15:12:46,725 - funman.search.box_search - INFO - progress: 0.99689\n",
+      "2024-02-08 15:12:46,739 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07973259428421171, 0.07977900974304794)][(0.07977900974304794, 0.07983917491720144)])\n",
+      "2024-02-08 15:12:46,740 - funman.representation.box - INFO - widths: 0.00011 -> 0.00005 0.00006 (raw), 0.00107 -> 0.00046 0.00060 (norm)\n",
+      "2024-02-08 15:12:46,743 - funman.search.box_search - INFO - progress: 0.99689\n",
+      "2024-02-08 15:12:46,756 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08873027777183116, 0.08877864585294366)][(0.08877864585294366, 0.08883685840480433)])\n",
+      "2024-02-08 15:12:46,757 - funman.representation.box - INFO - widths: 0.00011 -> 0.00005 0.00006 (raw), 0.00107 -> 0.00048 0.00058 (norm)\n",
+      "2024-02-08 15:12:46,759 - funman.search.box_search - INFO - progress: 0.99689\n",
+      "2024-02-08 15:12:46,771 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.08104735995616383, 0.08110753530022248)][(0.08110753530022248, 0.08115365583180882)])\n",
+      "2024-02-08 15:12:46,773 - funman.representation.box - INFO - widths: 0.00011 -> 0.00006 0.00005 (raw), 0.00106 -> 0.00060 0.00046 (norm)\n",
+      "2024-02-08 15:12:46,775 - funman.search.box_search - INFO - progress: 0.99689\n",
+      "2024-02-08 15:12:46,789 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.0801595676901014, 0.08020012528214877)][(0.08020012528214877, 0.08026549744916031)])\n",
+      "2024-02-08 15:12:46,790 - funman.representation.box - INFO - widths: 0.00011 -> 0.00004 0.00007 (raw), 0.00106 -> 0.00041 0.00065 (norm)\n",
+      "2024-02-08 15:12:46,793 - funman.search.box_search - INFO - progress: 0.99689\n",
+      "2024-02-08 15:12:46,805 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.09061960254824909, 0.09066988257183799)][(0.09066988257183799, 0.09072178978094866)])\n",
+      "2024-02-08 15:12:46,806 - funman.representation.box - INFO - widths: 0.00010 -> 0.00005 0.00005 (raw), 0.00102 -> 0.00050 0.00052 (norm)\n",
+      "2024-02-08 15:12:46,810 - funman.search.box_search - INFO - progress: 0.99689\n",
+      "2024-02-08 15:12:49,054 - funman.server.worker - INFO - Completed work on: 4b66c65d-54a1-4542-a660-76a9af861f48\n",
+      "2024-02-08 15:12:56,744 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n",
+      "2024-02-08 15:12:57,081 - funman.server.worker - INFO - FunmanWorker exiting...\n",
+      "2024-02-08 15:12:57,083 - funman.server.worker - INFO - Worker.stop() completed.\n"
      ]
     },
     {
      "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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       "text/plain": [
-       "None"
+       "<Figure size 240x240 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************************************************************************\n",
+      "********************************************************************************\n",
+      "* Analysis Summary\n",
+      "********************************************************************************\n",
+      "83 Points (+:39, -:44), 83 Boxes (+:39, -:44)\n",
+      "Parameters = {dx[1.0, 1.0): 1.0, u[1e-18, 0.1): 0.07582035282290193}\n",
+      "        # {dx[1.0, 1.0): 1.0, u[1e-18, 0.1): 0.07582035282290193}\n",
+      "                   a_0       a_1       a_2       a_3       a_4   dx  id  label  \\\n",
+      "time                                                                     \n",
+      "0.0   0.333333  0.500000  1.000000  0.500000  0.333333  1.0   0  false   \n",
+      "1.0   0.308060  0.487363  0.962090  0.537910  0.345970  1.0   0  false   \n",
+      "2.0   0.284703  0.473768  0.926096  0.570072  0.360523  1.0   0  false   \n",
+      "3.0   0.263116  0.459433  0.891800  0.597066  0.376411  1.0   0  false   \n",
+      "4.0   0.243167  0.444549  0.859018  0.619412  0.393141  1.0   0  false   \n",
+      "5.0   0.224730  0.429280  0.827593  0.637579  0.410297  1.0   0  false   \n",
+      "6.0   0.207691  0.413771  0.797392  0.651986  0.427530  1.0   0  false   \n",
+      "7.0   0.191944  0.398146  0.768306  0.663011  0.444548  1.0   0  false   \n",
+      "8.0   0.177390  0.382511  0.740240  0.670994  0.461112  1.0   0  false   \n",
+      "9.0   0.163940  0.366959  0.713117  0.676245  0.477026  1.0   0  false   \n",
+      "10.0  0.151510  0.351566  0.686871  0.679040  0.492130  1.0   0  false   \n",
+      "\n",
+      "            u  \n",
+      "time           \n",
+      "0.0   0.07582  \n",
+      "1.0   0.07582  \n",
+      "2.0   0.07582  \n",
+      "3.0   0.07582  \n",
+      "4.0   0.07582  \n",
+      "5.0   0.07582  \n",
+      "6.0   0.07582  \n",
+      "7.0   0.07582  \n",
+      "8.0   0.07582  \n",
+      "9.0   0.07582  \n",
+      "10.0  0.07582  \n",
+      "        \n",
+      "********************************************************************************\n",
+      "    \n"
+     ]
+    },
     {
      "data": {
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-       "      <label for=\"_anim_radio1_0fb99952c9b04107bb6bb224fb8d09ff\">Once</label>\n",
-       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_0fb99952c9b04107bb6bb224fb8d09ff\"\n",
+       "      <label for=\"_anim_radio1_8a1ec047aa6b42af8fbf35ebcfc68843\">Once</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_8a1ec047aa6b42af8fbf35ebcfc68843\"\n",
        "             checked>\n",
-       "      <label for=\"_anim_radio2_0fb99952c9b04107bb6bb224fb8d09ff\">Loop</label>\n",
-       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_0fb99952c9b04107bb6bb224fb8d09ff\"\n",
+       "      <label for=\"_anim_radio2_8a1ec047aa6b42af8fbf35ebcfc68843\">Loop</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_8a1ec047aa6b42af8fbf35ebcfc68843\"\n",
        "             >\n",
-       "      <label for=\"_anim_radio3_0fb99952c9b04107bb6bb224fb8d09ff\">Reflect</label>\n",
+       "      <label for=\"_anim_radio3_8a1ec047aa6b42af8fbf35ebcfc68843\">Reflect</label>\n",
        "    </form>\n",
        "  </div>\n",
        "</div>\n",
@@ -463,1350 +931,1761 @@
        "  /* Instantiate the Animation class. */\n",
        "  /* The IDs given should match those used in the template above. */\n",
        "  (function() {\n",
-       "    var img_id = \"_anim_img0fb99952c9b04107bb6bb224fb8d09ff\";\n",
-       "    var slider_id = \"_anim_slider0fb99952c9b04107bb6bb224fb8d09ff\";\n",
-       "    var loop_select_id = \"_anim_loop_select0fb99952c9b04107bb6bb224fb8d09ff\";\n",
-       "    var frames = new Array(6);\n",
+       "    var img_id = \"_anim_img8a1ec047aa6b42af8fbf35ebcfc68843\";\n",
+       "    var slider_id = \"_anim_slider8a1ec047aa6b42af8fbf35ebcfc68843\";\n",
+       "    var loop_select_id = \"_anim_loop_select8a1ec047aa6b42af8fbf35ebcfc68843\";\n",
+       "    var frames = new Array(11);\n",
        "    \n",
        "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
        "bGliIHZlcnNpb24zLjcuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/OQEPoAAAACXBIWXMAAA9h\\\n",
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+       "f7jfj/G/h7/QlO/5g67/1pTv4ZVJBRAAoGLcBQwAJeJNcBQhAAJAiXgXMEUIgABQIq7togh/TwAA\\\n",
+       "KkYFEABKRAuYIgRAACgR+Y8itIABACpGBRAASkQLmCIEQAAoEfmPIrSAAQAqRgUQAEpEC5giBEAA\\\n",
+       "KBEBkCK0gAEAKkYFEABKRAGQIgRAACgRLWCKEAABoERc20UR/p4AAFSMCiAAlIgWMEUIgABQIi0Z\\\n",
+       "n+klcADQAgYAqBgVQAAoES1gimgZHx9XKwaapl6vZ2BgIP39/anVajO9HAB2QQAEmmpsbCxtbW0Z\\\n",
+       "HR1Na2vrTC8HgF1wDSAAQMUIgAAAFSMAAgBUjAAINFWtVsunP/1pN4AAvIK5CQQAoGJUAAEAKkYA\\\n",
+       "BACoGAEQAKBiBEAAgIoRAAEAKuagmV4AUB5bt27N+eefn507d6ajoyNf+9rXMnv27JleFgAvoQII\\\n",
+       "NM0RRxyRO+64I0NDQzn22GNz1113zfSSANgFFUCgaZ566qlcfvnlGRkZyRNPPJEFCxbM9JIA2AUV\\\n",
+       "QKBpVq9enbPPPjt33313+vr64jnzAK9MKoBA0yxatChLlizJrbfemkMPPXSmlwPAy/AqOACAitEC\\\n",
+       "BgCoGAEQAKBiBEAAgIoRAAEAKkYABACoGAEQAKBiBEAAgIoRAAEAKsabQICm2bp1a84///zs3Lkz\\\n",
+       "HR0d+drXvpbZs2fP9LIAeAkVQKBpjjjiiNxxxx0ZGhrKsccem7vuumumlwTALqgAAk3z1FNP5fLL\\\n",
+       "L8/IyEieeOKJLFiwYKaXBMAuqAACTbN69eqcffbZufvuu9PX1xevGgd4ZVIBBJpm0aJFWbJkSW69\\\n",
+       "9dYceuihM70cAF5Gy7j/RQcAqBQtYACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgY\\\n",
+       "ARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAq\\\n",
+       "RgAEAKgYARAAoGIEQACAivl/usfNo/J+QkcAAAAASUVORK5CYII=\\\n",
        "\"\n",
        "\n",
        "\n",
        "    /* set a timeout to make sure all the above elements are created before\n",
        "       the object is initialized. */\n",
        "    setTimeout(function() {\n",
-       "        anim0fb99952c9b04107bb6bb224fb8d09ff = new Animation(frames, img_id, slider_id, 1000.0,\n",
+       "        anim8a1ec047aa6b42af8fbf35ebcfc68843 = new Animation(frames, img_id, slider_id, 1000.0,\n",
        "                                 loop_select_id);\n",
        "    }, 0);\n",
        "  })()\n",
@@ -2007,42 +2886,42 @@
        "</style>\n",
        "\n",
        "<div class=\"animation\">\n",
-       "  <img id=\"_anim_img388c481ad78b4ab2b70a344948f50dc1\">\n",
+       "  <img id=\"_anim_imgfd804c7f5da446b98c0da607d899f40e\">\n",
        "  <div class=\"anim-controls\">\n",
-       "    <input id=\"_anim_slider388c481ad78b4ab2b70a344948f50dc1\" type=\"range\" class=\"anim-slider\"\n",
+       "    <input id=\"_anim_sliderfd804c7f5da446b98c0da607d899f40e\" type=\"range\" class=\"anim-slider\"\n",
        "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
-       "           oninput=\"anim388c481ad78b4ab2b70a344948f50dc1.set_frame(parseInt(this.value));\">\n",
+       "           oninput=\"animfd804c7f5da446b98c0da607d899f40e.set_frame(parseInt(this.value));\">\n",
        "    <div class=\"anim-buttons\">\n",
-       "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim388c481ad78b4ab2b70a344948f50dc1.slower()\">\n",
+       "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"animfd804c7f5da446b98c0da607d899f40e.slower()\">\n",
        "          <i class=\"fa fa-minus\"></i></button>\n",
-       "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim388c481ad78b4ab2b70a344948f50dc1.first_frame()\">\n",
+       "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"animfd804c7f5da446b98c0da607d899f40e.first_frame()\">\n",
        "        <i class=\"fa fa-fast-backward\"></i></button>\n",
-       "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim388c481ad78b4ab2b70a344948f50dc1.previous_frame()\">\n",
+       "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"animfd804c7f5da446b98c0da607d899f40e.previous_frame()\">\n",
        "          <i class=\"fa fa-step-backward\"></i></button>\n",
-       "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim388c481ad78b4ab2b70a344948f50dc1.reverse_animation()\">\n",
+       "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"animfd804c7f5da446b98c0da607d899f40e.reverse_animation()\">\n",
        "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
-       "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim388c481ad78b4ab2b70a344948f50dc1.pause_animation()\">\n",
+       "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"animfd804c7f5da446b98c0da607d899f40e.pause_animation()\">\n",
        "          <i class=\"fa fa-pause\"></i></button>\n",
-       "      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim388c481ad78b4ab2b70a344948f50dc1.play_animation()\">\n",
+       "      <button title=\"Play\" aria-label=\"Play\" onclick=\"animfd804c7f5da446b98c0da607d899f40e.play_animation()\">\n",
        "          <i class=\"fa fa-play\"></i></button>\n",
-       "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim388c481ad78b4ab2b70a344948f50dc1.next_frame()\">\n",
+       "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"animfd804c7f5da446b98c0da607d899f40e.next_frame()\">\n",
        "          <i class=\"fa fa-step-forward\"></i></button>\n",
-       "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim388c481ad78b4ab2b70a344948f50dc1.last_frame()\">\n",
+       "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"animfd804c7f5da446b98c0da607d899f40e.last_frame()\">\n",
        "          <i class=\"fa fa-fast-forward\"></i></button>\n",
-       "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim388c481ad78b4ab2b70a344948f50dc1.faster()\">\n",
+       "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"animfd804c7f5da446b98c0da607d899f40e.faster()\">\n",
        "          <i class=\"fa fa-plus\"></i></button>\n",
        "    </div>\n",
-       "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select388c481ad78b4ab2b70a344948f50dc1\"\n",
+       "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_selectfd804c7f5da446b98c0da607d899f40e\"\n",
        "          class=\"anim-state\">\n",
-       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_388c481ad78b4ab2b70a344948f50dc1\"\n",
+       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_fd804c7f5da446b98c0da607d899f40e\"\n",
        "             >\n",
-       "      <label for=\"_anim_radio1_388c481ad78b4ab2b70a344948f50dc1\">Once</label>\n",
-       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_388c481ad78b4ab2b70a344948f50dc1\"\n",
+       "      <label for=\"_anim_radio1_fd804c7f5da446b98c0da607d899f40e\">Once</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_fd804c7f5da446b98c0da607d899f40e\"\n",
        "             checked>\n",
-       "      <label for=\"_anim_radio2_388c481ad78b4ab2b70a344948f50dc1\">Loop</label>\n",
-       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_388c481ad78b4ab2b70a344948f50dc1\"\n",
+       "      <label for=\"_anim_radio2_fd804c7f5da446b98c0da607d899f40e\">Loop</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_fd804c7f5da446b98c0da607d899f40e\"\n",
        "             >\n",
-       "      <label for=\"_anim_radio3_388c481ad78b4ab2b70a344948f50dc1\">Reflect</label>\n",
+       "      <label for=\"_anim_radio3_fd804c7f5da446b98c0da607d899f40e\">Reflect</label>\n",
        "    </form>\n",
        "  </div>\n",
        "</div>\n",
@@ -2052,1571 +2931,1872 @@
        "  /* Instantiate the Animation class. */\n",
        "  /* The IDs given should match those used in the template above. */\n",
        "  (function() {\n",
-       "    var img_id = \"_anim_img388c481ad78b4ab2b70a344948f50dc1\";\n",
-       "    var slider_id = \"_anim_slider388c481ad78b4ab2b70a344948f50dc1\";\n",
-       "    var loop_select_id = \"_anim_loop_select388c481ad78b4ab2b70a344948f50dc1\";\n",
-       "    var frames = new Array(6);\n",
+       "    var img_id = \"_anim_imgfd804c7f5da446b98c0da607d899f40e\";\n",
+       "    var slider_id = \"_anim_sliderfd804c7f5da446b98c0da607d899f40e\";\n",
+       "    var loop_select_id = \"_anim_loop_selectfd804c7f5da446b98c0da607d899f40e\";\n",
+       "    var frames = new Array(11);\n",
        "    \n",
        "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
        "bGliIHZlcnNpb24zLjcuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/OQEPoAAAACXBIWXMAAA9h\\\n",
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-       "AQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYA\\\n",
-       "CAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEE\\\n",
-       "QAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIsh\\\n",
-       "AAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAJosc8/\\\n",
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        "\"\n",
        "\n",
        "\n",
        "    /* set a timeout to make sure all the above elements are created before\n",
        "       the object is initialized. */\n",
        "    setTimeout(function() {\n",
-       "        anim388c481ad78b4ab2b70a344948f50dc1 = new Animation(frames, img_id, slider_id, 1000.0,\n",
+       "        animfd804c7f5da446b98c0da607d899f40e = new Animation(frames, img_id, slider_id, 1000.0,\n",
        "                                 loop_select_id);\n",
        "    }, 0);\n",
        "  })()\n",
@@ -3634,7 +4814,7 @@
     "# Advection Model\n",
     "\n",
     "# Instance parameters (used to identify input file)\n",
-    "dimensions = 2\n",
+    "dimensions = 1\n",
     "boundary_slope = 0.0\n",
     "num_disc = 5\n",
     "# derivative = \"centered\"\n",
@@ -3648,7 +4828,7 @@
     "locations = get_locations(dimensions, num_disc)\n",
     "\n",
     "# Funman parameters\n",
-    "steps = 5\n",
+    "steps = 10\n",
     "dt = 1\n",
     "\n",
     "\n",
@@ -3681,24 +4861,4290 @@
     "            \"interval\": {\"lb\":1, \"ub\":1}},\n",
     "        {\n",
     "            \"name\": \"u\",\n",
-    "            \"label\":\"any\",\n",
+    "            \"label\":\"all\",\n",
     "            #  \"interval\": {\"lb\":1e-18, \"ub\":1e-14}}\n",
     "            # \"interval\": {\"lb\":-1e-1, \"ub\":-1e-1} # Best for centered or forward derivatives\n",
-    "            \"interval\": {\"lb\":1e-2, \"ub\":5e-1} # Best for centered or backward derivatives\n",
+    "            \"interval\": {\"lb\":1e-18, \"ub\":1e-1} # Best for centered or backward derivatives\n",
     "        }\n",
     "    ],\n",
     "    \"constraints\": \n",
     "    height_bounds + \n",
     "    [\n",
+    "        {\"name\": \"advection_magnitude\",\n",
+    "            \"variables\": [variables[-1]],\n",
+    "            \"additive_bounds\": {\"lb\": 0.5},\n",
+    "            \"timepoints\": {\"lb\": steps*dt}\n",
+    "        }, \n",
+    " \n",
     "\n",
-    "        # 0 <= dx - u\n",
-    "        # u <= dx\n",
-    "        # {\"name\": \"dx_gte_u\",\n",
-    "        #     \"variables\": [\"dx\", \"u\"],\n",
-    "        #     \"weights\": [1, -1],\n",
-    "        #     \"additive_bounds\": {\"lb\": 0},\n",
-    "        #     # \"timepoints\": {\"lb\": 0}\n",
-    "        # }, \n",
+    "        ],\n",
+    "    \"config\": {\n",
+    "\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "\n",
+    "\n",
+    "results = Runner().run(\n",
+    "    instance_file,\n",
+    "    request_dict,\n",
+    "    # REQUEST_PATH,\n",
+    "    description=f\"Advection demo {instance}\",\n",
+    "    case_out_dir=\"./out\",\n",
+    "    point_plot_config={\"variables\":variables,},\n",
+    "    num_points=None,\n",
+    "    # dump_results=False\n",
+    ")\n",
+    "\n",
+    "summary = summarize_results(variables, results)\n",
+    "print(summary)\n",
+    "plt.close()\n",
+    "\n",
+    "h, dh, anim_h, anim_dh = plot_spatial_timeseries(results, outdir=\"./out\")\n",
+    "plt.close()\n",
+    "display(h)\n",
+    "plt.close()\n",
+    "display(dh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-02-08 15:14:32,691 - funman.server.worker - INFO - FunmanWorker running...\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",
+      "2024-02-08 15:14:32,696 - funman.server.worker - INFO - Starting work on: 03cbc43b-e9ad-4477-8418-1391656849de\n",
+      "2024-02-08 15:14:32,698 - funman.scenario.scenario - WARNING - Warning: The scenario is not normalized!\n",
+      "2024-02-08 15:14:34,705 - funman.api.run - INFO - Dumping results to ./out/03cbc43b-e9ad-4477-8418-1391656849de.json\n",
+      "2024-02-08 15:14:44,236 - funman.search.box_search - INFO - progress: 0.09091\n",
+      "2024-02-08 15:14:44,304 - funman.search.box_search - INFO - progress: 0.18182\n",
+      "2024-02-08 15:14:44,363 - funman.search.box_search - INFO - progress: 0.27273\n",
+      "2024-02-08 15:14:44,415 - funman.search.box_search - INFO - progress: 0.36364\n",
+      "2024-02-08 15:14:44,463 - funman.search.box_search - INFO - progress: 0.45455\n",
+      "2024-02-08 15:14:44,504 - funman.search.box_search - INFO - progress: 0.54545\n",
+      "2024-02-08 15:14:44,541 - funman.search.box_search - INFO - progress: 0.63636\n",
+      "2024-02-08 15:14:44,581 - funman.search.box_search - INFO - progress: 0.72727\n",
+      "2024-02-08 15:14:44,617 - funman.search.box_search - INFO - progress: 0.81818\n",
+      "2024-02-08 15:14:44,641 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:48,239 - funman.api.run - INFO - Dumping results to ./out/03cbc43b-e9ad-4477-8418-1391656849de.json\n",
+      "2024-02-08 15:14:48,247 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(1e-18, 0.059342142564510945)][(0.059342142564510945, 0.1)])\n",
+      "2024-02-08 15:14:48,249 - funman.representation.box - INFO - widths: 0.10000 -> 0.05934 0.04066 (raw), 1.00000 -> 0.59342 0.40658 (norm)\n",
+      "2024-02-08 15:14:48,254 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:49,602 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(1e-18, 0.039416783360433846)][(0.039416783360433846, 0.059342142564510945)])\n",
+      "2024-02-08 15:14:49,603 - funman.representation.box - INFO - widths: 0.05934 -> 0.03942 0.01993 (raw), 0.59342 -> 0.39417 0.19925 (norm)\n",
+      "2024-02-08 15:14:49,605 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:50,450 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.039416783360433846, 0.049270979200542304)][(0.049270979200542304, 0.059342142564510945)])\n",
+      "2024-02-08 15:14:50,451 - funman.representation.box - INFO - widths: 0.01993 -> 0.00985 0.01007 (raw), 0.19925 -> 0.09854 0.10071 (norm)\n",
+      "2024-02-08 15:14:50,452 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:50,806 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.039416783360433846, 0.04675318835955014)][(0.04675318835955014, 0.049270979200542304)])\n",
+      "2024-02-08 15:14:50,807 - funman.representation.box - INFO - widths: 0.00985 -> 0.00734 0.00252 (raw), 0.09854 -> 0.07336 0.02518 (norm)\n",
+      "2024-02-08 15:14:50,809 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:50,829 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.04675318835955014, 0.04858728960932922)][(0.04858728960932922, 0.049270979200542304)])\n",
+      "2024-02-08 15:14:50,831 - funman.representation.box - INFO - widths: 0.00252 -> 0.00183 0.00068 (raw), 0.02518 -> 0.01834 0.00684 (norm)\n",
+      "2024-02-08 15:14:50,832 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:50,851 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.04858728960932922, 0.049045814921773984)][(0.049045814921773984, 0.049270979200542304)])\n",
+      "2024-02-08 15:14:50,852 - funman.representation.box - INFO - widths: 0.00068 -> 0.00046 0.00023 (raw), 0.00684 -> 0.00459 0.00225 (norm)\n",
+      "2024-02-08 15:14:50,854 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:50,873 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.049045814921773984, 0.04916044624988518)][(0.04916044624988518, 0.049270979200542304)])\n",
+      "2024-02-08 15:14:50,874 - funman.representation.box - INFO - widths: 0.00023 -> 0.00011 0.00011 (raw), 0.00225 -> 0.00115 0.00111 (norm)\n",
+      "2024-02-08 15:14:50,877 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:50,895 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.04916044624988518, 0.049189104081912974)][(0.049189104081912974, 0.049270979200542304)])\n",
+      "2024-02-08 15:14:50,896 - funman.representation.box - INFO - widths: 0.00011 -> 0.00003 0.00008 (raw), 0.00111 -> 0.00029 0.00082 (norm)\n",
+      "2024-02-08 15:14:50,898 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:52,477 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.059342142564510945, 0.07459643048633258)][(0.07459643048633258, 0.1)])\n",
+      "2024-02-08 15:14:52,478 - funman.representation.box - INFO - widths: 0.04066 -> 0.01525 0.02540 (raw), 0.40658 -> 0.15254 0.25404 (norm)\n",
+      "2024-02-08 15:14:52,479 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:53,501 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07459643048633258, 0.08348464326271089)][(0.08348464326271089, 0.1)])\n",
+      "2024-02-08 15:14:53,502 - funman.representation.box - INFO - widths: 0.02540 -> 0.00889 0.01652 (raw), 0.25404 -> 0.08888 0.16515 (norm)\n",
+      "2024-02-08 15:14:53,504 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:53,991 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07459643048633258, 0.0793558040783886)][(0.0793558040783886, 0.08348464326271089)])\n",
+      "2024-02-08 15:14:53,992 - funman.representation.box - INFO - widths: 0.00889 -> 0.00476 0.00413 (raw), 0.08888 -> 0.04759 0.04129 (norm)\n",
+      "2024-02-08 15:14:53,994 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:54,055 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.0793558040783886, 0.08054564747640261)][(0.08054564747640261, 0.08348464326271089)])\n",
+      "2024-02-08 15:14:54,057 - funman.representation.box - INFO - widths: 0.00413 -> 0.00119 0.00294 (raw), 0.04129 -> 0.01190 0.02939 (norm)\n",
+      "2024-02-08 15:14:54,060 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:54,077 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.0793558040783886, 0.07981089852982555)][(0.07981089852982555, 0.08054564747640261)])\n",
+      "2024-02-08 15:14:54,078 - funman.representation.box - INFO - widths: 0.00119 -> 0.00046 0.00073 (raw), 0.01190 -> 0.00455 0.00735 (norm)\n",
+      "2024-02-08 15:14:54,080 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:54,096 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.0793558040783886, 0.07962721129318127)][(0.07962721129318127, 0.07981089852982555)])\n",
+      "2024-02-08 15:14:54,097 - funman.representation.box - INFO - widths: 0.00046 -> 0.00027 0.00018 (raw), 0.00455 -> 0.00271 0.00184 (norm)\n",
+      "2024-02-08 15:14:54,099 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:54,116 - funman.representation.box - INFO - Split[[10.00000, 10.00000]](u[(0.07962721129318127, 0.07969506309687945)][(0.07969506309687945, 0.07981089852982555)])\n",
+      "2024-02-08 15:14:54,117 - funman.representation.box - INFO - widths: 0.00018 -> 0.00007 0.00012 (raw), 0.00184 -> 0.00068 0.00116 (norm)\n",
+      "2024-02-08 15:14:54,119 - funman.search.box_search - INFO - progress: 0.90909\n",
+      "2024-02-08 15:14:54,283 - funman.search.box_search - INFO - progress: 0.94492\n",
+      "2024-02-08 15:14:54,435 - funman.search.box_search - INFO - progress: 0.95994\n",
+      "2024-02-08 15:14:54,592 - funman.search.box_search - INFO - progress: 0.97381\n",
+      "2024-02-08 15:14:54,752 - funman.search.box_search - INFO - progress: 0.98296\n",
+      "2024-02-08 15:14:54,824 - funman.search.box_search - INFO - progress: 0.98963\n",
+      "2024-02-08 15:14:54,985 - funman.search.box_search - INFO - progress: 0.99396\n",
+      "2024-02-08 15:14:55,151 - funman.search.box_search - INFO - progress: 0.99663\n",
+      "2024-02-08 15:14:55,306 - funman.search.box_search - INFO - progress: 0.99830\n",
+      "2024-02-08 15:14:55,460 - funman.search.box_search - INFO - progress: 0.99896\n",
+      "2024-02-08 15:14:55,618 - funman.search.box_search - INFO - progress: 0.99938\n",
+      "2024-02-08 15:14:55,780 - funman.search.box_search - INFO - progress: 0.99963\n",
+      "2024-02-08 15:14:56,025 - funman.search.box_search - INFO - progress: 0.99973\n",
+      "2024-02-08 15:14:56,201 - funman.search.box_search - INFO - progress: 0.99984\n",
+      "2024-02-08 15:14:57,249 - funman.server.worker - INFO - Completed work on: 03cbc43b-e9ad-4477-8418-1391656849de\n",
+      "2024-02-08 15:14:58,266 - funman.server.worker - INFO - Worker.stop() acquiring state lock ....\n",
+      "2024-02-08 15:14:58,761 - funman.server.worker - INFO - FunmanWorker exiting...\n",
+      "2024-02-08 15:14:58,762 - funman.server.worker - INFO - Worker.stop() completed.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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63db3338PtVqNWbNmwc3NTfJjt0nvBr2H7Tn+uLKyEmvXrsXp06cxfPhw2NvbQ6lUIj8/HytXroSfnx9+/vnnfrc1efJkJCUlIS4uDsOGDYOrqytaWloMxjV79mxERUVpnzc0NGD9+vUoKCjo9jp9ictYWwkJCXj77bdRVVXV7f0OJC5PT088evQIHR0dQ2bsNjFu0BO253jjuXPnYteuXXB3d8e8efPw3nvv4eLFiya3BQCff/45nn/+eUyYMAHp6ekoKysbUJx9HSPdl7ZUKhUyMjKwevXqfr/fnm0tW7YMqamp+Oabb0yKj0jHoJ8S91yPR61WQ6lUIj4+HgUFBcjPz4e7uztmz56NDz74AE5OTvD09Ox3WwBQW1sLHx8f/Pjjj/jb3/4GlUplMK7Lly9DLpcjLS0Ny5Ytg5eXF7Zt24ZZs2ZBpVIhPT0dPj4+fYrLWFubNm1CR0cHioqKMHz4cO37HUhcp06dQnFxMUaPHo3JkycjLS0NHh4e/TgaRGroKjEhEkJXiQmREEpYQiSEEpYQCaGE7cX27dvFDoEQLdHL6yzV/v370drair179yI0NBQVFRX43e9+J3ZYxMpRwhpQWVmJ9957D1VVVdi2bRvOnDkjdkiE0CmxIUFBQcjJyUFOTg4++ugjZGZmih0SIXQflhApoR6WEAmhhCVEQihhCZEQSlhCJIQSlhAJoYQlREJEHzgRHR2NkJAQscMYEkpLS1FSUjLor0vHUFjGjqPoCRsSEkILOAlEs4LCYKNjKCxjx1HQBZ1PnjyJ06dPY8+ePTrbCCGmE3RB54SEBAQFBendRggxnaALOvd1m6EFndVMDRvYGNxXpVZBZivTu82UfZtam+Du6I77bfe1K673bNvWRv/fNsYYGJjB7eZ8T1CpAJn+fcnQZFLC9pwEraWlBXK5HHK5XLvNx0d35fLExETtMhNdz9er71fDw9EDrg6uOvswxnC98TomeE/QG0v9w3rIbGXwcvbSu/2nhp8Q5hOm83NeyeOX+36Jx9wew53mO5CvkOskbenPpQjigmAvs9fZv03Vhtv3byPEU/9FF3O+J/z0ExCm+56sQXNzMxoaGoxOrCcFHh4eBifz00fQBZ0BYObMmd22WzrOkUMgF4hLdy9hst9kvT0ssTy1tbUICAiAvb3uH1GpYIyhrKysXwlr9fdheSWPCr4C4T7huMXfAq/kxQ6J9JGUkxUY2HzXot/WERvnyOH0y6dhZ2uH4cOGUw9LdJSXl0OhUGD+/Plih0I9LABtklKyEn3Ky8t1ZhxRq9WixGL1PSyRNjVTo61DuPWEHOwcdK74f/rppygsLERxcTHq6uowdepUuLu7g+M4TJ48GYsWLcKf//xnuLi4IDk5GVu3bsXXX38Nxhg++eQTRERECBYfJSwxukJeTEwMtm3bhuDgYLz44osiR6qrraMN1+qvCdbeBO8JcLJ36vaz1atXw9/fHzt37oSbmxvkcjk8PDywefNmnf2vXLmC69ev4+zZs6ipqcHq1avxxRdfCBYfJSzB7Nmzcf78ee1zzQp56enp6OjoQEREBJqbm7vtY+he+lA3evRo7fpFXS8aaWZaunbtGgoLCzF79mwAgEzg++SUsFLV0tJ5HzY4GHB2FrTprv8jlpSUgOd5NDU1dethDd1LH4rs7e2193ttbf9zuuzh4YHq6moAnYuWzZgxA+PGjUNsbCz+8pe/AADa29sFjYUSVopaWoAZM4A7dwBfX6CgwKSkNbZCXkxMDMrLy7v1wJbEwc7B4MCTgbbXU0REBP73f/8XiYmJ4Hle+/MlS5bgV7/6Ff7+97/D1bVzYMzEiRMxZswYxMbGwtbWFvPmzcP69esFi48SVooUCqCxsXNoYmMjcOMGEBk54OYiIyPx5Zdfap/3LNYICgrSjhG3NLY2tjrfOYXm5uaGc+fO6fzcz88PxcXFOj9PSUlBSkqKWWKh2zpSFBoKjBgB2Nl1Po4ZI3ZEZJBIJmE1I5BoJBI6T3/PnAHWru18FPg7LLFckjgl5pU8ZuyfAW9nb9S31OsdoG9VWlqA+fOBu3eBI0dM/g5LpEPQAvYTJ06gtLQUo0ePhre3N86ePYumpibs3LnTpCA5Rw7BXDAu3L6AXzz2C+tOVqDzO+ydOwDPA4yZ/B2WSIegBew3btzAunXroFAo4ODggLq6OjgL8JefV/K4xd/CBO8JNEBf4+5dQKnsfKTVVqyGSQnbs9qg63OFQoG0tDS9FRV5eXlISkpCUlJSn266c44cCpYXYPfC3XQ6DAD793d/fuCAKGFYC8YY4uPjERcXpzODSnl5OZYsWTJosZiUsD0L2EePHo0dO3Zg7Nix8PHxwY4dO9DWpjvOMzExEbm5ucjNzYW/v3+fXosG6Hfxm990f756tThxWIm7d+8CAL777jt4e3uLGougBezR0dHdts+bN8+U5okhY8d2jnJKSwM2bOh8bq3UakBPpzBgDg6Abfd+7M0330RhYSHi4uIAAB0dHRg5ciSOHTvW7feWL1+OmzdvQiaT4cCBAxg1ahRWrlyJmpoauLi44NChQ3BzczMpPElcJSZ6jB0LpKRYd7ICncl6TbjB/5gwAXDqPhAjIyMD77zzDo4cOQJbW1vY2dnhzTffxLfffosx/74H3t7ejuvXr0Mul8PGxgZqtRq7d+/GnDlzsGLFChw7dgxZWVl45513TAqPElbKBjBjARm4xsZGrF69Gk1NTaipqcGkSZO0CWtvb4/f/va3eOWVVzBixAikpaXh2rVr+P777/HZZ5+hvb292/RJA0UJK2V0dXhQHTlyBIsWLcLKlSuxZs0adF0LXaVSISkpCUuXLsXWrVtx4sQJjBs3DtOnT8crr7wCQJhCAErYXnQdYeU9XNwLDt0IWK1jrB62vr4ely5dQmhoKF566SUhIheWg0PnaayQ7Rkwd+5cvPLKK/jyyy/h1OO0+cGDB4iPj4eNTee0tIcPH4anpydee+017P/3Vf23334bCxcuNCk8SlgjeCWPmOwY+Dj7oPZhLQr/u9AyrlILXK1jrB42JSUFc+fOxe7du4WIXHi2tjrfOYUWFBSE48ePAwB+/PFHne2abWfPntXZ9tlnnwkaCyWsEZoRVt/f/t6ypkAVuFqnp67301UqFTIyMrBu3bpuv2OtBexik8zgfzFY7Agrgat1utbDVlVVaethZ8yYgU2bNqGtrQ1FRUXd9hnIvXRiOuphjeAcOchXyFH/sB6cI2c5Payzc+dp8FdfAc88Y/J3WGP1sDExMSa1TYRFPWwvTBlhZdaSQGdnYNw4q67SEXr6lcHGBnCVn3pYM9FcsBrlMgo1D2pQ9N9FltNDDwEjR47E7du3h8TaOv1BCWsm2gtWNd/jyVFPUrIKzMXFBS4uLmKHMejMVg87ffp0ZGVlISQkBEuXLhUqXsnQXLAK9wlHxb0K8EqekpaYzKSE1dTDpqenA+ish01JSUF6ejqqq6vh7u7ebVpIa6K5YHW/7T5sYEPJSgQh6ILOXZ+3t7dj0aJFOHTokM5+1nIPj3PkDC4STchACLqgc9d62PDwcBw8eFDvzOfWNAk1IUIyaz3sli1bTGmeENKDdX7BJESiKGEJkRBKWEIkxGISlmb276eGhs6xxA0NJjeVn5/fbWW6ffv2YevWrZDL5Th37hy2b9+O7OzsPrXV23E0tl2sfcWMC1lZgJdX52MfGLzolJ6eDpVKherqarPXQtIwvn5qaAACAjrnM/r974HKys6DrsfHH3+M5ORkZGZm4t1339X7O8bqYQF0u9dujOY4BnPBuMXf0pmS1th2sfYVMy5kZQGrVnX+W/P42mtGP2ODPWxKSgrWr1+PadOmGW1ACJphfFfqriDQPZCStTenTgGtrZ0zBra2dva0BgQEBGD+/Pn9mk+o6/30nvfaNfTNLa05jhfvXEQwF6xzHI1tF2tfMeNCz2UoN2xAbwwmbHp6OjIyMqBWq3ttxFT6hvERIwICuj83UI+anp6Od999F3PnzsXLL79ssDlj9bCae+0+Pj7d9tFXD6s5jpN8J+mtHza2Xax9xYwLW7d2f56Whl4xkSUmJjLGGGtqbWIVfAWr5Cv1/p5arWZX664abOfug7us/mG9we1Xaq8Y3Ma38qz6XrXB7YoGBXvU8Ujvttb2Vnaz8abBfSv4CnZfeV/vtgG/p4cPGQsLY8zVtfPx4UPG2H8+y652797NGGMsJyfH4OuYquvrNrU2dXvsydh2sfYVMy6WmcmYl1fn47/pO44aFnPRiWb27yc7O2DYsM5HI5ycnLBlyxY8evRoUMLq7Tga2y7WvmLGhddeA+rre/3uqkHldVKkUABNTZ0TkDU1GZ3TKTk5eXBjI2ZlMT0s6Qdagd1qma0e9rnnnsPRo0dRUVGB1NRUoeIlgOBzOhHpMNv6sFevXoWvr68gQRI9aE4nq2S2etiioiI0NzdDLpfr7Gct9bCm0NwCuKe8By9n/YMiaG0d62O2etiEhAQAgFKp1NmP6mGN04yQ8XP1Q9W9KpxfeV7/VUZaW8fqmLUeFgB9fx0AzQiZ4ppiTBw5kW51ES26SiwiQwPDNSNkIkZGoPJeJY38Ilp0H1YkvJLHjP0z4O3sjfqW+m4DwzUTuLV1tKH5UTP1sESLeliRaE57r9VfMzioHADcHd0HPzhisaiHFYm+hbb61ZMKtD5sz3vp2dnZ4HkegYGBGDlyJM6ePYumpibs3LlzwK9BhEM9rEg4Rw4Fywuwe+Fu3TrJ3mjWh3399c7HlpYBx9HzXnplZSXWrl2L06dPw8HBAXV1dXCme70WgxJWRAMueNC3PuwA9byXPnfuXOzatQvu7u5QKBRIS0uDvb29zn766mGJ+dEpsRRpxhLfvWvyWOKe99LVajWUSiXi4+OhVCqxY8cOvQtO0b10cVDCSpGAY4l73ksHgNjYWO2/582bN+C2ifDolFiqaCyxVaKEJURCKGEJkRCz1cNyHIfCwkK4urrijTfeECpeQqya2eph4+LikJqaisbGRkECJYSYsR4WADIyMvDqq6/q7Ef1sIQMjEk9rLF62MzMTJSVlaGoqEhnP31z2hJCemfWethVmuUHRNa1jI0qX4iUDfmBE5rZG7ycvdDQ0tD/cbuEWJAhf1tHU8b2r/p/6V/fhBAJGfIJqyljG+89Xv/6JgK03/WREHMa8qfEmtkbbt+/jcfcHhO0h+WVPOYcnINRLqNQ+7BWkqfbxuphY2JikJWVhZCQECxdulTsUAmsoIcFzLduD+fIIYgLwtX6q5I93TZWD5uXlwd3d3fY2lrF/yaSMOR7WHPilTzK+XKEeYcNbNYIC2CsHra9vR2LFi3CoUOHdPaje+nioIQ1AefI4dtff4uHjx5ihPMIySUrYLwe1tfXFwcPHoRMJtPZj+phxTEkErZPs+SbCefI4eGjh5JMVqD3etgtW7YMdkjECMknbJ9nySdkCJD81QTNfdYfa39EgHsAJSsZ0iSfsDRLPrEmZquHDQsLw+HDh+Hh4YG33npLoHB10Sz5xJqYrR721KlT2LhxI9ra2gQJ1BiaJZ9YC7PWwxpi6B6el7MXhsmGGXwtfzfDpXicI2f09QPcAwxuc7Z3Nvi6AODr6guZre6tDQCwt7XHKJdRBvf1dvaGvUx3Xl/A9PeEAMPviQxNZlsfdvz48UhLS4OHh4fOfobu4TnbG58B0NXB1eA2BzuHAe9rL7M3mFQA4DLMxeA2ma0Mw4cNN7jdyd5pwHH19p7ganhfMjSZtR528+bNpjRPCOlB8leJCbEmog+cKC0t1Z4WV1VVCTZljDW2VVpaKki7/dX1GALS+bwstS2jx5FZkMTERGpLpLaEZKnvcSi0RafEhEiIRSWs5soxtTX4bQnJUt/jUGjLhjHGBHtlQohZWVQPSwgxTvSrxMbGIz/33HP9auvkyZO4dOkSQkND8dJLL2Ht2rUICAjAiy++iFGjDI9G0uejjz6CWq3GrFmzMHnyZOzbtw+1tbWIjY1FTExMv9r69ttv8cMPP+Dq1avYu3fvgOLKz8/Hnj178P777+uM0e75GQ42OoZ9j8vU4yh6D2tsPHJ/JSQk4O2339YOdxwxYgSam5sHNCeRp6cnHj16hI6ODgBAQ0MD1q9fj4KCgn63NWfOHEybNg3x8fEDjmv27NmIiorSO0a752c42OgY9p2px1H0hB3oeGR9VCoVMjIysHr1agDAhg0bsGbNGhw7dqzfbS1btgypqan45ptvTI4LAM6cOYMFCxaYHJc+psYm9OvTMRyYvsQn+imxsfHI/bVp0yZ0dHTgww8/xIoVK3Dq1CncvHlzQFN0njp1CsXFxbCzs0NVVRW8vLywbds2nelU+kKlUkGtVuPChQsICAgYUFyXL1+GXC7HU089pR2jzfM8zp8/r/0MfXx8+h2bEOgY9p2px5GuEhMiIaKfEhNC+o4SlhAJoYQlREKsNmG3b9+Or776qt/7EMtibcdR9KvEYikrK0NbWxueeeYZxMfHIyAgAOPGjYNCoUBqaio+/vhjyGQyLFu2DDk5OZg6dSrKyspw7NgxMMZw584duLi4wN7eHg0NDWhubqaCfRFY23G02h72iSeegIND5xQsU6ZMweLFixEZGYlRo0bhH//4B9zc3ODi4gJHR0e4uLigubkZTzzxBF544QUUFBTAw8MDDx48AAA8++yz4DgO9+7dE/MtWSVrO45Wm7D+/v5Qq9UAADs7O9jY2GgfZ86ciXv37sHdvXMWRmdnZ1RWVsLf3x/Z2dmYOXMmGhsbERoaCgA4fvw4ampqtL9PBo+1HUe6D2uiAwcO4Omnn+73OFdiWaRyHClhCZEQqz0lJkSKKGEJkRBKWEIkhBKWEAmhhCVEQv4/xoKBduBdoU4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 240x240 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************************************************************************\n",
+      "********************************************************************************\n",
+      "* Analysis Summary\n",
+      "********************************************************************************\n",
+      "22 Points (+:10, -:12), 22 Boxes (+:10, -:12)\n",
+      "Parameters = {dx[1.0, 1.0): 1.0, u[1e-18, 0.1): 0.04915839706115814}\n",
+      "        # {dx[1.0, 1.0): 1.0, u[1e-18, 0.1): 0.04915839706115814}\n",
+      "                   a_0       a_1       a_2       a_3       a_4       a_5       a_6  \\\n",
+      "time                                                                         \n",
+      "0.0   0.289474  0.343750  0.423077  0.550000  0.785714  0.785714  0.550000   \n",
+      "1.0   0.275244  0.341082  0.419177  0.543761  0.774127  0.785714  0.561587   \n",
+      "2.0   0.261713  0.337845  0.415338  0.537636  0.762803  0.785145  0.572605   \n",
+      "3.0   0.248848  0.334103  0.411529  0.531624  0.751734  0.784046  0.583053   \n",
+      "4.0   0.236615  0.329912  0.407723  0.525721  0.740913  0.782458  0.592934   \n",
+      "5.0   0.224983  0.325325  0.403898  0.519920  0.730335  0.780416  0.602250   \n",
+      "6.0   0.213923  0.320393  0.400035  0.514217  0.719991  0.777954  0.611009   \n",
+      "7.0   0.203407  0.315159  0.396120  0.508604  0.709876  0.775104  0.619215   \n",
+      "8.0   0.193408  0.309665  0.392140  0.503074  0.699981  0.771898  0.626879   \n",
+      "9.0   0.183900  0.303950  0.388086  0.497621  0.690302  0.768362  0.634008   \n",
+      "10.0  0.174860  0.298049  0.383950  0.492236  0.680830  0.764525  0.640612   \n",
+      "\n",
+      "           a_7       a_8       a_9   dx  id  label         u  \n",
+      "time                                                          \n",
+      "0.0   0.423077  0.343750  0.289474  1.0   0  false  0.049158  \n",
+      "1.0   0.429316  0.347650  0.292142  1.0   0  false  0.049158  \n",
+      "2.0   0.435818  0.351664  0.294870  1.0   0  false  0.049158  \n",
+      "3.0   0.442543  0.355801  0.297662  1.0   0  false  0.049158  \n",
+      "4.0   0.449450  0.360065  0.300520  1.0   0  false  0.049158  \n",
+      "5.0   0.456503  0.364459  0.303448  1.0   0  false  0.049158  \n",
+      "6.0   0.463668  0.368984  0.306447  1.0   0  false  0.049158  \n",
+      "7.0   0.470911  0.373639  0.309521  1.0   0  false  0.049158  \n",
+      "8.0   0.478202  0.378420  0.312673  1.0   0  false  0.049158  \n",
+      "9.0   0.485510  0.383325  0.315905  1.0   0  false  0.049158  \n",
+      "10.0  0.492810  0.388349  0.319219  1.0   0  false  0.049158  \n",
+      "        \n",
+      "********************************************************************************\n",
+      "    \n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\"\n",
+       "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
+       "<script language=\"javascript\">\n",
+       "  function isInternetExplorer() {\n",
+       "    ua = navigator.userAgent;\n",
+       "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
+       "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
+       "  }\n",
+       "\n",
+       "  /* Define the Animation class */\n",
+       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
+       "    this.img_id = img_id;\n",
+       "    this.slider_id = slider_id;\n",
+       "    this.loop_select_id = loop_select_id;\n",
+       "    this.interval = interval;\n",
+       "    this.current_frame = 0;\n",
+       "    this.direction = 0;\n",
+       "    this.timer = null;\n",
+       "    this.frames = new Array(frames.length);\n",
+       "\n",
+       "    for (var i=0; i<frames.length; i++)\n",
+       "    {\n",
+       "     this.frames[i] = new Image();\n",
+       "     this.frames[i].src = frames[i];\n",
+       "    }\n",
+       "    var slider = document.getElementById(this.slider_id);\n",
+       "    slider.max = this.frames.length - 1;\n",
+       "    if (isInternetExplorer()) {\n",
+       "        // switch from oninput to onchange because IE <= 11 does not conform\n",
+       "        // with W3C specification. It ignores oninput and onchange behaves\n",
+       "        // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
+       "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
+       "        slider.setAttribute('oninput', null);\n",
+       "    }\n",
+       "    this.set_frame(this.current_frame);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.get_loop_state = function(){\n",
+       "    var button_group = document[this.loop_select_id].state;\n",
+       "    for (var i = 0; i < button_group.length; i++) {\n",
+       "        var button = button_group[i];\n",
+       "        if (button.checked) {\n",
+       "            return button.value;\n",
+       "        }\n",
+       "    }\n",
+       "    return undefined;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.set_frame = function(frame){\n",
+       "    this.current_frame = frame;\n",
+       "    document.getElementById(this.img_id).src =\n",
+       "            this.frames[this.current_frame].src;\n",
+       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.next_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.previous_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.first_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(0);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.last_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(this.frames.length - 1);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.slower = function()\n",
+       "  {\n",
+       "    this.interval /= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.faster = function()\n",
+       "  {\n",
+       "    this.interval *= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_forward = function()\n",
+       "  {\n",
+       "    this.current_frame += 1;\n",
+       "    if(this.current_frame < this.frames.length){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.first_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.last_frame();\n",
+       "        this.reverse_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.last_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_reverse = function()\n",
+       "  {\n",
+       "    this.current_frame -= 1;\n",
+       "    if(this.current_frame >= 0){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.last_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.first_frame();\n",
+       "        this.play_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.first_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.pause_animation = function()\n",
+       "  {\n",
+       "    this.direction = 0;\n",
+       "    if (this.timer){\n",
+       "      clearInterval(this.timer);\n",
+       "      this.timer = null;\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.play_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = 1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_forward();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.reverse_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = -1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_reverse();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "</script>\n",
+       "\n",
+       "<style>\n",
+       ".animation {\n",
+       "    display: inline-block;\n",
+       "    text-align: center;\n",
+       "}\n",
+       "input[type=range].anim-slider {\n",
+       "    width: 374px;\n",
+       "    margin-left: auto;\n",
+       "    margin-right: auto;\n",
+       "}\n",
+       ".anim-buttons {\n",
+       "    margin: 8px 0px;\n",
+       "}\n",
+       ".anim-buttons button {\n",
+       "    padding: 0;\n",
+       "    width: 36px;\n",
+       "}\n",
+       ".anim-state label {\n",
+       "    margin-right: 8px;\n",
+       "}\n",
+       ".anim-state input {\n",
+       "    margin: 0;\n",
+       "    vertical-align: middle;\n",
+       "}\n",
+       "</style>\n",
+       "\n",
+       "<div class=\"animation\">\n",
+       "  <img id=\"_anim_img622bdc33fe6e49fa9d62c54e2bf7edfe\">\n",
+       "  <div class=\"anim-controls\">\n",
+       "    <input id=\"_anim_slider622bdc33fe6e49fa9d62c54e2bf7edfe\" type=\"range\" class=\"anim-slider\"\n",
+       "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
+       "           oninput=\"anim622bdc33fe6e49fa9d62c54e2bf7edfe.set_frame(parseInt(this.value));\">\n",
+       "    <div class=\"anim-buttons\">\n",
+       "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim622bdc33fe6e49fa9d62c54e2bf7edfe.slower()\">\n",
+       "          <i class=\"fa fa-minus\"></i></button>\n",
+       "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim622bdc33fe6e49fa9d62c54e2bf7edfe.first_frame()\">\n",
+       "        <i class=\"fa fa-fast-backward\"></i></button>\n",
+       "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim622bdc33fe6e49fa9d62c54e2bf7edfe.previous_frame()\">\n",
+       "          <i class=\"fa fa-step-backward\"></i></button>\n",
+       "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim622bdc33fe6e49fa9d62c54e2bf7edfe.reverse_animation()\">\n",
+       "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
+       "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim622bdc33fe6e49fa9d62c54e2bf7edfe.pause_animation()\">\n",
+       "          <i class=\"fa fa-pause\"></i></button>\n",
+       "      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim622bdc33fe6e49fa9d62c54e2bf7edfe.play_animation()\">\n",
+       "          <i class=\"fa fa-play\"></i></button>\n",
+       "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim622bdc33fe6e49fa9d62c54e2bf7edfe.next_frame()\">\n",
+       "          <i class=\"fa fa-step-forward\"></i></button>\n",
+       "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim622bdc33fe6e49fa9d62c54e2bf7edfe.last_frame()\">\n",
+       "          <i class=\"fa fa-fast-forward\"></i></button>\n",
+       "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim622bdc33fe6e49fa9d62c54e2bf7edfe.faster()\">\n",
+       "          <i class=\"fa fa-plus\"></i></button>\n",
+       "    </div>\n",
+       "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select622bdc33fe6e49fa9d62c54e2bf7edfe\"\n",
+       "          class=\"anim-state\">\n",
+       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_622bdc33fe6e49fa9d62c54e2bf7edfe\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio1_622bdc33fe6e49fa9d62c54e2bf7edfe\">Once</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_622bdc33fe6e49fa9d62c54e2bf7edfe\"\n",
+       "             checked>\n",
+       "      <label for=\"_anim_radio2_622bdc33fe6e49fa9d62c54e2bf7edfe\">Loop</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_622bdc33fe6e49fa9d62c54e2bf7edfe\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio3_622bdc33fe6e49fa9d62c54e2bf7edfe\">Reflect</label>\n",
+       "    </form>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "<script language=\"javascript\">\n",
+       "  /* Instantiate the Animation class. */\n",
+       "  /* The IDs given should match those used in the template above. */\n",
+       "  (function() {\n",
+       "    var img_id = \"_anim_img622bdc33fe6e49fa9d62c54e2bf7edfe\";\n",
+       "    var slider_id = \"_anim_slider622bdc33fe6e49fa9d62c54e2bf7edfe\";\n",
+       "    var loop_select_id = \"_anim_loop_select622bdc33fe6e49fa9d62c54e2bf7edfe\";\n",
+       "    var frames = new Array(10);\n",
+       "    \n",
+       "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
+       "bGliIHZlcnNpb24zLjcuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/OQEPoAAAACXBIWXMAAA9h\\\n",
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+       "SkPBqoG7+NMGnMyOlYfWunZoKWE6t24dsWjFpxV8uospIQ0uCR1gbCJGxyLFapMxVLLuIj+4yMP+\\\n",
+       "/vDn1ssNyUly7W5yzuvlfGd2z7nOdX3OCjNvPp9zztUyOjo6GgAAKuOwqS4AAIDJJQACAFSMAAgA\\\n",
+       "UDECIABAxQiAAAAVIwACAFSMAAgAUDECIABAxQiAAAAVIwACAFSMAAgAUDECIABAxQiAAAAVIwAC\\\n",
+       "AFSMAAgAUDECIABAxQiAAAAVIwACAFSMAAgAUDECIABAxQiAAAAVIwACAFSMAAgAUDECIABAxQiA\\\n",
+       "AAAVIwACAFSMAAgAUDECIABAxQiAAAAVIwACAFSMAAgAUDECIABAxQiAAAAVIwACAFSMAAgAUDEC\\\n",
+       "IABAxQiAAAAVIwACAFSMAAgAUDECIABAxQiAwDjXX399nnnmmbHfr7322nz2s5+dwop275Of/GRe\\\n",
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+       "ADJGAAQAyBgBEAAgY/4PRM6x4i8H0TgAAAAASUVORK5CYII=\\\n",
+       "\"\n",
+       "\n",
+       "\n",
+       "    /* set a timeout to make sure all the above elements are created before\n",
+       "       the object is initialized. */\n",
+       "    setTimeout(function() {\n",
+       "        anim622bdc33fe6e49fa9d62c54e2bf7edfe = new Animation(frames, img_id, slider_id, 1000.0,\n",
+       "                                 loop_select_id);\n",
+       "    }, 0);\n",
+       "  })()\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\"\n",
+       "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
+       "<script language=\"javascript\">\n",
+       "  function isInternetExplorer() {\n",
+       "    ua = navigator.userAgent;\n",
+       "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
+       "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
+       "  }\n",
+       "\n",
+       "  /* Define the Animation class */\n",
+       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
+       "    this.img_id = img_id;\n",
+       "    this.slider_id = slider_id;\n",
+       "    this.loop_select_id = loop_select_id;\n",
+       "    this.interval = interval;\n",
+       "    this.current_frame = 0;\n",
+       "    this.direction = 0;\n",
+       "    this.timer = null;\n",
+       "    this.frames = new Array(frames.length);\n",
+       "\n",
+       "    for (var i=0; i<frames.length; i++)\n",
+       "    {\n",
+       "     this.frames[i] = new Image();\n",
+       "     this.frames[i].src = frames[i];\n",
+       "    }\n",
+       "    var slider = document.getElementById(this.slider_id);\n",
+       "    slider.max = this.frames.length - 1;\n",
+       "    if (isInternetExplorer()) {\n",
+       "        // switch from oninput to onchange because IE <= 11 does not conform\n",
+       "        // with W3C specification. It ignores oninput and onchange behaves\n",
+       "        // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
+       "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
+       "        slider.setAttribute('oninput', null);\n",
+       "    }\n",
+       "    this.set_frame(this.current_frame);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.get_loop_state = function(){\n",
+       "    var button_group = document[this.loop_select_id].state;\n",
+       "    for (var i = 0; i < button_group.length; i++) {\n",
+       "        var button = button_group[i];\n",
+       "        if (button.checked) {\n",
+       "            return button.value;\n",
+       "        }\n",
+       "    }\n",
+       "    return undefined;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.set_frame = function(frame){\n",
+       "    this.current_frame = frame;\n",
+       "    document.getElementById(this.img_id).src =\n",
+       "            this.frames[this.current_frame].src;\n",
+       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.next_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.previous_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.first_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(0);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.last_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(this.frames.length - 1);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.slower = function()\n",
+       "  {\n",
+       "    this.interval /= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.faster = function()\n",
+       "  {\n",
+       "    this.interval *= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_forward = function()\n",
+       "  {\n",
+       "    this.current_frame += 1;\n",
+       "    if(this.current_frame < this.frames.length){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.first_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.last_frame();\n",
+       "        this.reverse_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.last_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_reverse = function()\n",
+       "  {\n",
+       "    this.current_frame -= 1;\n",
+       "    if(this.current_frame >= 0){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.last_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.first_frame();\n",
+       "        this.play_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.first_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.pause_animation = function()\n",
+       "  {\n",
+       "    this.direction = 0;\n",
+       "    if (this.timer){\n",
+       "      clearInterval(this.timer);\n",
+       "      this.timer = null;\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.play_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = 1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_forward();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.reverse_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = -1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_reverse();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "</script>\n",
+       "\n",
+       "<style>\n",
+       ".animation {\n",
+       "    display: inline-block;\n",
+       "    text-align: center;\n",
+       "}\n",
+       "input[type=range].anim-slider {\n",
+       "    width: 374px;\n",
+       "    margin-left: auto;\n",
+       "    margin-right: auto;\n",
+       "}\n",
+       ".anim-buttons {\n",
+       "    margin: 8px 0px;\n",
+       "}\n",
+       ".anim-buttons button {\n",
+       "    padding: 0;\n",
+       "    width: 36px;\n",
+       "}\n",
+       ".anim-state label {\n",
+       "    margin-right: 8px;\n",
+       "}\n",
+       ".anim-state input {\n",
+       "    margin: 0;\n",
+       "    vertical-align: middle;\n",
+       "}\n",
+       "</style>\n",
+       "\n",
+       "<div class=\"animation\">\n",
+       "  <img id=\"_anim_img8de068cc704244dea033a4aec0db04fd\">\n",
+       "  <div class=\"anim-controls\">\n",
+       "    <input id=\"_anim_slider8de068cc704244dea033a4aec0db04fd\" type=\"range\" class=\"anim-slider\"\n",
+       "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
+       "           oninput=\"anim8de068cc704244dea033a4aec0db04fd.set_frame(parseInt(this.value));\">\n",
+       "    <div class=\"anim-buttons\">\n",
+       "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim8de068cc704244dea033a4aec0db04fd.slower()\">\n",
+       "          <i class=\"fa fa-minus\"></i></button>\n",
+       "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim8de068cc704244dea033a4aec0db04fd.first_frame()\">\n",
+       "        <i class=\"fa fa-fast-backward\"></i></button>\n",
+       "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim8de068cc704244dea033a4aec0db04fd.previous_frame()\">\n",
+       "          <i class=\"fa fa-step-backward\"></i></button>\n",
+       "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim8de068cc704244dea033a4aec0db04fd.reverse_animation()\">\n",
+       "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
+       "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim8de068cc704244dea033a4aec0db04fd.pause_animation()\">\n",
+       "          <i class=\"fa fa-pause\"></i></button>\n",
+       "      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim8de068cc704244dea033a4aec0db04fd.play_animation()\">\n",
+       "          <i class=\"fa fa-play\"></i></button>\n",
+       "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim8de068cc704244dea033a4aec0db04fd.next_frame()\">\n",
+       "          <i class=\"fa fa-step-forward\"></i></button>\n",
+       "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim8de068cc704244dea033a4aec0db04fd.last_frame()\">\n",
+       "          <i class=\"fa fa-fast-forward\"></i></button>\n",
+       "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim8de068cc704244dea033a4aec0db04fd.faster()\">\n",
+       "          <i class=\"fa fa-plus\"></i></button>\n",
+       "    </div>\n",
+       "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select8de068cc704244dea033a4aec0db04fd\"\n",
+       "          class=\"anim-state\">\n",
+       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_8de068cc704244dea033a4aec0db04fd\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio1_8de068cc704244dea033a4aec0db04fd\">Once</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_8de068cc704244dea033a4aec0db04fd\"\n",
+       "             checked>\n",
+       "      <label for=\"_anim_radio2_8de068cc704244dea033a4aec0db04fd\">Loop</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_8de068cc704244dea033a4aec0db04fd\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio3_8de068cc704244dea033a4aec0db04fd\">Reflect</label>\n",
+       "    </form>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "<script language=\"javascript\">\n",
+       "  /* Instantiate the Animation class. */\n",
+       "  /* The IDs given should match those used in the template above. */\n",
+       "  (function() {\n",
+       "    var img_id = \"_anim_img8de068cc704244dea033a4aec0db04fd\";\n",
+       "    var slider_id = \"_anim_slider8de068cc704244dea033a4aec0db04fd\";\n",
+       "    var loop_select_id = \"_anim_loop_select8de068cc704244dea033a4aec0db04fd\";\n",
+       "    var frames = new Array(10);\n",
+       "    \n",
+       "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
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+       "0DB33XVX5syZk7Vr16ajoyO+Zx7gyKQDCDTMueeemwULFuSBBx7IscceO9LlAPAm/BQcAEDFGAED\\\n",
+       "AFSMAAgAUDECIABAxQiAAAAVIwACAFSMAAgAUDECIABAxQiAAAAV45dAgIbZtm1b5s2bl927d6et\\\n",
+       "rS3f+MY3Mnr06JEuC4DfogMINMxxxx2Xhx56KN3d3Xn3u9+dRx55ZKRLAmA/dACBhnnllVdy5ZVX\\\n",
+       "ZseOHXnxxRczderUkS4JgP3QAQQa5q677sqcOXOydu3adHR0xE+NAxyZdACBhjn33HOzYMGCPPDA\\\n",
+       "Azn22GNHuhwA3kTTgP9EBwCoFCNgAICKEQABACpGAAQAqBgBEACgYgRAAICKEQABACpGAAQAqBgB\\\n",
+       "EACgYgRAAICKEQABACpGAAQAqBgBEACgYgRAAICKEQABACpGAAQAqBgBEACgYgRAAICKEQABACpG\\\n",
+       "AAQAqBgBEACgYgRAAICK+f8A4y+iuhjxUsIAAAAASUVORK5CYII=\\\n",
+       "\"\n",
+       "\n",
+       "\n",
+       "    /* set a timeout to make sure all the above elements are created before\n",
+       "       the object is initialized. */\n",
+       "    setTimeout(function() {\n",
+       "        anim8de068cc704244dea033a4aec0db04fd = new Animation(frames, img_id, slider_id, 1000.0,\n",
+       "                                 loop_select_id);\n",
+       "    }, 0);\n",
+       "  })()\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Advection Model\n",
+    "\n",
+    "# Instance parameters (used to identify input file)\n",
+    "dimensions = 1\n",
+    "boundary_slope = 0.0\n",
+    "num_disc = 10\n",
+    "# derivative = \"centered\"\n",
+    "derivative = \"backward\"\n",
+    "# derivative = \"forward\"\n",
+    "\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"advection\")\n",
+    "instance = f\"advection_{derivative}_{dimensions}_{boundary_slope}_{num_disc}.json\"\n",
+    "instance_file = os.path.join(EXAMPLE_DIR, instance)\n",
+    "\n",
+    "locations = get_locations(dimensions, num_disc)\n",
+    "\n",
+    "# Funman parameters\n",
+    "steps = 10\n",
+    "dt = 1\n",
+    "\n",
+    "\n",
+    "# The energy at each location is non-negative\n",
+    "variables = [f\"a_{loc}\" for loc in locations]\n",
+    "height_bounds = [\n",
+    "    {\n",
+    "        \"name\": f\"pos_{i}\",\n",
+    "         \"variable\": i,\n",
+    "         \"interval\": {\"lb\": 0}\n",
+    "    }\n",
+    "    for i in variables\n",
+    "]\n",
+    "\n",
+    "request_dict = {\n",
+    "    \"structure_parameters\": [\n",
+    "        {\n",
+    "            \"name\": \"schedules\",\n",
+    "            \"schedules\": [\n",
+    "                {\"timepoints\": range(0,(steps+1)*dt, dt)}\n",
+    "            ],\n",
+    "        },\n",
+    "        \n",
+    "    ],\n",
+    "    \"parameters\":[\n",
+    "        {\n",
+    "            \"name\": \"dx\",\n",
+    "            \"label\":\"any\",\n",
+    "            #  \"interval\": {\"lb\":1e-18, \"ub\":1e-14}}\n",
+    "            \"interval\": {\"lb\":1, \"ub\":1}},\n",
+    "        {\n",
+    "            \"name\": \"u\",\n",
+    "            \"label\":\"all\",\n",
+    "            #  \"interval\": {\"lb\":1e-18, \"ub\":1e-14}}\n",
+    "            # \"interval\": {\"lb\":-1e-1, \"ub\":-1e-1} # Best for centered or forward derivatives\n",
+    "            \"interval\": {\"lb\":1e-18, \"ub\":1e-1} # Best for centered or backward derivatives\n",
+    "        }\n",
+    "    ],\n",
+    "    \"constraints\": \n",
+    "    height_bounds + \n",
+    "    [\n",
+    "        {\"name\": \"advection_magnitude\",\n",
+    "            \"variables\": [variables[-1]],\n",
+    "            \"additive_bounds\": {\"lb\": 0.5},\n",
+    "            \"timepoints\": {\"lb\": steps*dt}\n",
+    "        }, \n",
     " \n",
     "\n",
     "        ],\n",
@@ -3716,11 +9162,12 @@
     "    description=f\"Advection demo {instance}\",\n",
     "    case_out_dir=\"./out\",\n",
     "    point_plot_config={\"variables\":variables,},\n",
-    "    num_points=None\n",
+    "    num_points=None,\n",
+    "    # dump_results=False\n",
     ")\n",
     "\n",
-    "point_plot = summarize_results(variables, results)\n",
-    "display(point_plot)\n",
+    "summary = summarize_results(variables, results)\n",
+    "print(summary)\n",
     "plt.close()\n",
     "\n",
     "h, dh, anim_h, anim_dh = plot_spatial_timeseries(results, outdir=\"./out\")\n",
diff --git a/resources/amr/petrinet/amr-examples/sir_request1.json b/resources/amr/petrinet/amr-examples/sir_request1.json
index f55cabbb..261f2613 100644
--- a/resources/amr/petrinet/amr-examples/sir_request1.json
+++ b/resources/amr/petrinet/amr-examples/sir_request1.json
@@ -68,6 +68,7 @@
     }
   ],
   "config": {
-    "tolerance": 1e-2
+    "tolerance": 1e-2,
+    "normalization_constant": 1001
   }
 }
\ No newline at end of file
diff --git a/resources/amr/petrinet/evaluation/sidarthe.json b/resources/amr/petrinet/evaluation/sidarthe.json
new file mode 100644
index 00000000..244aac8f
--- /dev/null
+++ b/resources/amr/petrinet/evaluation/sidarthe.json
@@ -0,0 +1,694 @@
+{
+   "header": {
+      "name": "Giordano2020 - SIDARTHE model of COVID-19 spread in Italy",
+      "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json",
+      "schema_name": "petrinet",
+      "description": "Giordano2020 - SIDARTHE model of COVID-19 spread in Italy",
+      "model_version": "0.1"
+   },
+   "model": {
+      "states": [
+         {
+            "id": "Susceptible",
+            "name": "Susceptible",
+            "grounding": {
+               "identifiers": {
+                  "ido": "0000514"
+               },
+               "modifiers": {}
+            },
+            "units": {
+               "expression": "1",
+               "expression_mathml": "<cn>1</cn>"
+            }
+         },
+         {
+            "id": "Diagnosed",
+            "name": "Diagnosed",
+            "grounding": {
+               "identifiers": {
+                  "ido": "0000511"
+               },
+               "modifiers": {
+                  "diagnosis": "ncit:C15220"
+               }
+            },
+            "units": {
+               "expression": "1",
+               "expression_mathml": "<cn>1</cn>"
+            }
+         },
+         {
+            "id": "Infected",
+            "name": "Infected",
+            "grounding": {
+               "identifiers": {
+                  "ido": "0000511"
+               },
+               "modifiers": {}
+            },
+            "units": {
+               "expression": "1",
+               "expression_mathml": "<cn>1</cn>"
+            }
+         },
+         {
+            "id": "Ailing",
+            "name": "Ailing",
+            "grounding": {
+               "identifiers": {
+                  "ido": "0000511"
+               },
+               "modifiers": {
+                  "disease_severity": "ncit:C25269",
+                  "diagnosis": "ncit:C113725"
+               }
+            },
+            "units": {
+               "expression": "1",
+               "expression_mathml": "<cn>1</cn>"
+            }
+         },
+         {
+            "id": "Recognized",
+            "name": "Recognized",
+            "grounding": {
+               "identifiers": {
+                  "ido": "0000511"
+               },
+               "modifiers": {
+                  "diagnosis": "ncit:C15220"
+               }
+            },
+            "units": {
+               "expression": "1",
+               "expression_mathml": "<cn>1</cn>"
+            }
+         },
+         {
+            "id": "Healed",
+            "name": "Healed",
+            "grounding": {
+               "identifiers": {
+                  "ido": "0000592"
+               },
+               "modifiers": {}
+            },
+            "units": {
+               "expression": "1",
+               "expression_mathml": "<cn>1</cn>"
+            }
+         },
+         {
+            "id": "Threatened",
+            "name": "Threatened",
+            "grounding": {
+               "identifiers": {
+                  "ido": "0000511"
+               },
+               "modifiers": {
+                  "disease_severity": "ncit:C25467"
+               }
+            },
+            "units": {
+               "expression": "1",
+               "expression_mathml": "<cn>1</cn>"
+            }
+         },
+         {
+            "id": "Extinct",
+            "name": "Extinct",
+            "grounding": {
+               "identifiers": {
+                  "ncit": "C28554"
+               },
+               "modifiers": {}
+            },
+            "units": {
+               "expression": "1",
+               "expression_mathml": "<cn>1</cn>"
+            }
+         }
+      ],
+      "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"
+            }
+         }
+      ]
+   },
+   "semantics": {
+      "ode": {
+         "rates": [
+            {
+               "target": "t1",
+               "expression": "Diagnosed*Susceptible*beta",
+               "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>Susceptible</ci><ci>beta</ci></apply>"
+            },
+            {
+               "target": "t2",
+               "expression": "Ailing*Susceptible*gamma",
+               "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>Susceptible</ci><ci>gamma</ci></apply>"
+            },
+            {
+               "target": "t3",
+               "expression": "Recognized*Susceptible*delta",
+               "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>Susceptible</ci><ci>delta</ci></apply>"
+            },
+            {
+               "target": "t4",
+               "expression": "Infected*Susceptible*alpha",
+               "expression_mathml": "<apply><times/><ci>Infected</ci><ci>Susceptible</ci><ci>alpha</ci></apply>"
+            },
+            {
+               "target": "t5",
+               "expression": "Infected*epsilon",
+               "expression_mathml": "<apply><times/><ci>Infected</ci><ci>epsilon</ci></apply>"
+            },
+            {
+               "target": "t6",
+               "expression": "Infected*zeta",
+               "expression_mathml": "<apply><times/><ci>Infected</ci><ci>zeta</ci></apply>"
+            },
+            {
+               "target": "t7",
+               "expression": "Infected*lamb",
+               "expression_mathml": "<apply><times/><ci>Infected</ci><ci>lamb</ci></apply>"
+            },
+            {
+               "target": "t8",
+               "expression": "Diagnosed*eta",
+               "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>eta</ci></apply>"
+            },
+            {
+               "target": "t9",
+               "expression": "Diagnosed*rho",
+               "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>rho</ci></apply>"
+            },
+            {
+               "target": "t10",
+               "expression": "Ailing*theta",
+               "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>theta</ci></apply>"
+            },
+            {
+               "target": "t11",
+               "expression": "Ailing*kappa",
+               "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>kappa</ci></apply>"
+            },
+            {
+               "target": "t12",
+               "expression": "Ailing*mu",
+               "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>mu</ci></apply>"
+            },
+            {
+               "target": "t13",
+               "expression": "Recognized*nu",
+               "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>nu</ci></apply>"
+            },
+            {
+               "target": "t14",
+               "expression": "Recognized*xi",
+               "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>xi</ci></apply>"
+            },
+            {
+               "target": "t15",
+               "expression": "Threatened*tau",
+               "expression_mathml": "<apply><times/><ci>Threatened</ci><ci>tau</ci></apply>"
+            },
+            {
+               "target": "t16",
+               "expression": "Threatened*sigma",
+               "expression_mathml": "<apply><times/><ci>Threatened</ci><ci>sigma</ci></apply>"
+            }
+         ],
+         "initials": [
+            {
+               "target": "Susceptible",
+               "expression": "0.99",
+               "expression_mathml": "<cn>0.99</cn>"
+            },
+            {
+               "target": "Diagnosed",
+               "expression": "0.0",
+               "expression_mathml": "<cn>0.0/cn>"
+            },
+            {
+               "target": "Infected",
+               "expression": "0.01",
+               "expression_mathml": "<cn>0.01</cn>"
+            },
+            {
+               "target": "Ailing",
+               "expression": "0.0",
+               "expression_mathml": "<cn>0.0</cn>"
+            },
+            {
+               "target": "Recognized",
+               "expression": "0.0",
+               "expression_mathml": "<cn>0.0</cn>"
+            },
+            {
+               "target": "Healed",
+               "expression": "0.0",
+               "expression_mathml": "<cn>0.0</cn>"
+            },
+            {
+               "target": "Threatened",
+               "expression": "0.0",
+               "expression_mathml": "<cn>0.0</cn>"
+            },
+            {
+               "target": "Extinct",
+               "expression": "0.0",
+               "expression_mathml": "<cn>0.0</cn>"
+            }
+         ],
+         "parameters": [
+            {
+               "id": "beta",
+               "value": 0.011,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.008799999999999999,
+                     "maximum": 0.0132
+                  }
+               }
+            },
+            {
+               "id": "gamma",
+               "value": 0.456,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.3648,
+                     "maximum": 0.5472
+                  }
+               }
+            },
+            {
+               "id": "delta",
+               "value": 0.011,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.008799999999999999,
+                     "maximum": 0.0132
+                  }
+               }
+            },
+            {
+               "id": "alpha",
+               "value": 0.57,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.45599999999999996,
+                     "maximum": 0.6839999999999999
+                  }
+               }
+            },
+            {
+               "id": "epsilon",
+               "value": 0.171,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.1368,
+                     "maximum": 0.20520000000000002
+                  }
+               }
+            },
+            {
+               "id": "zeta",
+               "value": 0.125,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.1,
+                     "maximum": 0.15
+                  }
+               }
+            },
+            {
+               "id": "lamb",
+               "value": 0.034,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.027200000000000002,
+                     "maximum": 0.0408
+                  }
+               }
+            },
+            {
+               "id": "eta",
+               "value": 0.125,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.1,
+                     "maximum": 0.15
+                  }
+               }
+            },
+            {
+               "id": "rho",
+               "value": 0.034,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.027200000000000002,
+                     "maximum": 0.0408
+                  }
+               }
+            },
+            {
+               "id": "theta",
+               "value": 0.371,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.2968,
+                     "maximum": 0.4452
+                  }
+               }
+            },
+            {
+               "id": "kappa",
+               "value": 0.017,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.013600000000000001,
+                     "maximum": 0.0204
+                  }
+               }
+            },
+            {
+               "id": "mu",
+               "value": 0.017,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.013600000000000001,
+                     "maximum": 0.0204
+                  }
+               }
+            },
+            {
+               "id": "nu",
+               "value": 0.027,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.0216,
+                     "maximum": 0.0324
+                  }
+               }
+            },
+            {
+               "id": "xi",
+               "value": 0.017,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.013600000000000001,
+                     "maximum": 0.0204
+                  }
+               }
+            },
+            {
+               "id": "tau",
+               "value": 0.01,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.008,
+                     "maximum": 0.012
+                  }
+               }
+            },
+            {
+               "id": "sigma",
+               "value": 0.017,
+               "distribution": {
+                  "type": "StandardUniform1",
+                  "parameters": {
+                     "minimum": 0.013600000000000001,
+                     "maximum": 0.0204
+                  }
+               }
+            }
+         ],
+         "observables": [
+            {
+               "id": "Cases",
+               "name": "Cases",
+               "expression": "Diagnosed + Recognized + Threatened",
+               "expression_mathml": "<apply><plus/><ci>Diagnosed</ci><ci>Recognized</ci><ci>Threatened</ci></apply>"
+            },
+            {
+               "id": "Hospitalizations",
+               "name": "Hospitalizations",
+               "expression": "Recognized + Threatened",
+               "expression_mathml": "<apply><plus/><ci>Recognized</ci><ci>Threatened</ci></apply>"
+            },
+            {
+               "id": "Deaths",
+               "name": "Deaths",
+               "expression": "Extinct",
+               "expression_mathml": "<ci>Extinct</ci>"
+            }
+         ],
+         "time": {
+            "id": "t",
+            "units": {
+               "expression": "day",
+               "expression_mathml": "<ci>day</ci>"
+            }
+         }
+      }
+   },
+   "metadata": {
+      "annotations": {
+         "license": "CC0",
+         "authors": [],
+         "references": [
+            "pubmed:32322102"
+         ],
+         "time_scale": null,
+         "time_start": null,
+         "time_end": null,
+         "locations": [],
+         "pathogens": [
+            "ncbitaxon:2697049"
+         ],
+         "diseases": [
+            "doid:0080600"
+         ],
+         "hosts": [
+            "ncbitaxon:9606"
+         ],
+         "model_types": [
+            "mamo:0000028"
+         ]
+      }
+   }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/evaluation/sidarthe_request_consistency.json b/resources/amr/petrinet/evaluation/sidarthe_request_consistency.json
new file mode 100644
index 00000000..852ef99c
--- /dev/null
+++ b/resources/amr/petrinet/evaluation/sidarthe_request_consistency.json
@@ -0,0 +1,153 @@
+{
+    "constraints": [
+        {
+            "name": "ibounds",
+            "variable": "Infected",
+            "interval": {
+                "lb": 0.2,
+                "ub": 0.4
+            },
+            "timepoints": {
+                "lb": 35,
+                "ub": 40,
+                "closed_upper_bound": true
+            }
+        }
+    ],
+    "parameters": [
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.6155,
+                "ub": 0.6155
+            }
+        },
+        {
+            "name": "lamb",
+            "interval": {
+                "lb": 0.04355,
+                "ub": 0.04356
+            }
+        },
+        {
+            "name": "gamma",
+            "interval": {
+                "lb": 0.0456,
+                "ub": 0.0457
+            }
+        },
+        {
+            "name": "alpha",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1
+            }
+        },
+        {
+            "name": "delta",
+            "interval": {
+                "lb": 0.00011,
+                "ub": 0.00011
+            }
+        },
+        {
+            "name": "epsilon",
+            "interval": {
+                "lb": 0.03,
+                "ub": 0.03
+            }
+        },
+        {
+            "name": "zeta",
+            "interval": {
+                "lb": 0.0125,
+                "ub": 0.0125
+            }
+        },
+        {
+            "name": "eta",
+            "interval": {
+                "lb": 0.0125,
+                "ub": 0.0125
+            }
+        },
+        {
+            "name": "rho",
+            "interval": {
+                "lb": 0.034,
+                "ub": 0.034
+            }
+        },
+        {
+            "name": "theta",
+            "interval": {
+                "lb": 0.571,
+                "ub": 0.571
+            }
+        },
+        {
+            "name": "kappa",
+            "interval": {
+                "lb": 0.017,
+                "ub": 0.017
+            }
+        },
+        {
+            "name": "mu",
+            "interval": {
+                "lb": 0.017,
+                "ub": 0.017
+            }
+        },
+        {
+            "name": "nu",
+            "interval": {
+                "lb": 0.027,
+                "ub": 0.027
+            }
+        },
+        {
+            "name": "xi",
+            "interval": {
+                "lb": 0.017,
+                "ub": 0.017
+            }
+        },
+        {
+            "name": "tau",
+            "interval": {
+                "lb": 0.01,
+                "ub": 0.01
+            }
+        },
+        {
+            "name": "sigma",
+            "interval": {
+                "lb": 0.017,
+                "ub": 0.017
+            }
+        }
+    ],
+    "structure_parameters": [
+        {
+            "name": "num_steps",
+            "interval": {
+                "lb": 20,
+                "ub": 20
+            }
+        },
+        {
+            "name": "step_size",
+            "interval": {
+                "lb": 3,
+                "ub": 3
+            }
+        }
+    ],
+    "config": {
+        "tolerance": 1e-2,
+        "normalization_constant": 1,
+        "dreal_log_level": "info",
+        "use_compartmental_constraints": true
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/evaluation/sidarthe_request_param_synth.json b/resources/amr/petrinet/evaluation/sidarthe_request_param_synth.json
new file mode 100644
index 00000000..28cda9b0
--- /dev/null
+++ b/resources/amr/petrinet/evaluation/sidarthe_request_param_synth.json
@@ -0,0 +1,155 @@
+{
+  "constraints": [
+    {
+      "name": "ibounds",
+      "variable": "Infected",
+      "interval": {
+        "lb": 0.2,
+        "ub": 0.4
+      },
+      "timepoints": {
+        "lb": 35,
+        "ub": 40,
+        "closed_upper_bound": true
+      }
+    }
+  ],
+  "parameters": [
+    {
+      "name": "beta",
+      "interval": {
+        "lb": 0.6160,
+        "ub": 0.6165
+      },
+      "label": "all"
+    },
+    {
+      "name": "lamb",
+      "interval": {
+        "lb": 0.0435,
+        "ub": 0.0440
+      },
+      "label": "all"
+    },
+    {
+      "name": "gamma",
+      "interval": {
+        "lb": 0.0456,
+        "ub": 0.0456
+      }
+    },
+    {
+      "name": "alpha",
+      "interval": {
+        "lb": 0.1,
+        "ub": 0.1
+      }
+    },
+    {
+      "name": "delta",
+      "interval": {
+        "lb": 0.00011,
+        "ub": 0.00011
+      }
+    },
+    {
+      "name": "epsilon",
+      "interval": {
+        "lb": 0.03,
+        "ub": 0.03
+      }
+    },
+    {
+      "name": "zeta",
+      "interval": {
+        "lb": 0.0125,
+        "ub": 0.0125
+      }
+    },
+    {
+      "name": "eta",
+      "interval": {
+        "lb": 0.0125,
+        "ub": 0.0125
+      }
+    },
+    {
+      "name": "rho",
+      "interval": {
+        "lb": 0.034,
+        "ub": 0.034
+      }
+    },
+    {
+      "name": "theta",
+      "interval": {
+        "lb": 0.571,
+        "ub": 0.571
+      }
+    },
+    {
+      "name": "kappa",
+      "interval": {
+        "lb": 0.017,
+        "ub": 0.017
+      }
+    },
+    {
+      "name": "mu",
+      "interval": {
+        "lb": 0.017,
+        "ub": 0.017
+      }
+    },
+    {
+      "name": "nu",
+      "interval": {
+        "lb": 0.027,
+        "ub": 0.027
+      }
+    },
+    {
+      "name": "xi",
+      "interval": {
+        "lb": 0.017,
+        "ub": 0.017
+      }
+    },
+    {
+      "name": "tau",
+      "interval": {
+        "lb": 0.01,
+        "ub": 0.01
+      }
+    },
+    {
+      "name": "sigma",
+      "interval": {
+        "lb": 0.017,
+        "ub": 0.017
+      }
+    }
+  ],
+  "structure_parameters": [
+    {
+      "name": "num_steps",
+      "interval": {
+        "lb": 20,
+        "ub": 20
+      }
+    },
+    {
+      "name": "step_size",
+      "interval": {
+        "lb": 3,
+        "ub": 3
+      }
+    }
+  ],
+  "config": {
+    "tolerance": 1e-2,
+    "normalization_constant": 1,
+    "dreal_log_level": "info",
+    "use_compartmental_constraints": true
+  }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/evaluation/sidarthe_request_parameterization.json b/resources/amr/petrinet/evaluation/sidarthe_request_parameterization.json
new file mode 100644
index 00000000..b5549658
--- /dev/null
+++ b/resources/amr/petrinet/evaluation/sidarthe_request_parameterization.json
@@ -0,0 +1,153 @@
+{
+  "constraints": [
+    {
+      "name": "ibounds",
+      "variable": "Infected",
+      "interval": {
+        "lb": 0.2,
+        "ub": 0.4
+      },
+      "timepoints": {
+        "lb": 35,
+        "ub": 40,
+        "closed_upper_bound": true
+      }
+    }
+  ],
+  "parameters": [
+    {
+      "name": "beta",
+      "interval": {
+        "lb": 0.61,
+        "ub": 0.62
+      }
+    },
+    {
+      "name": "lamb",
+      "interval": {
+        "lb": 0.043,
+        "ub": 0.044
+      }
+    },
+    {
+      "name": "gamma",
+      "interval": {
+        "lb": 0.0456,
+        "ub": 0.0456
+      }
+    },
+    {
+      "name": "alpha",
+      "interval": {
+        "lb": 0.1,
+        "ub": 0.1
+      }
+    },
+    {
+      "name": "delta",
+      "interval": {
+        "lb": 0.00011,
+        "ub": 0.00011
+      }
+    },
+    {
+      "name": "epsilon",
+      "interval": {
+        "lb": 0.03,
+        "ub": 0.03
+      }
+    },
+    {
+      "name": "zeta",
+      "interval": {
+        "lb": 0.0125,
+        "ub": 0.0125
+      }
+    },
+    {
+      "name": "eta",
+      "interval": {
+        "lb": 0.0125,
+        "ub": 0.0125
+      }
+    },
+    {
+      "name": "rho",
+      "interval": {
+        "lb": 0.034,
+        "ub": 0.034
+      }
+    },
+    {
+      "name": "theta",
+      "interval": {
+        "lb": 0.571,
+        "ub": 0.571
+      }
+    },
+    {
+      "name": "kappa",
+      "interval": {
+        "lb": 0.017,
+        "ub": 0.017
+      }
+    },
+    {
+      "name": "mu",
+      "interval": {
+        "lb": 0.017,
+        "ub": 0.017
+      }
+    },
+    {
+      "name": "nu",
+      "interval": {
+        "lb": 0.027,
+        "ub": 0.027
+      }
+    },
+    {
+      "name": "xi",
+      "interval": {
+        "lb": 0.017,
+        "ub": 0.017
+      }
+    },
+    {
+      "name": "tau",
+      "interval": {
+        "lb": 0.01,
+        "ub": 0.01
+      }
+    },
+    {
+      "name": "sigma",
+      "interval": {
+        "lb": 0.017,
+        "ub": 0.017
+      }
+    }
+  ],
+  "structure_parameters": [
+    {
+      "name": "num_steps",
+      "interval": {
+        "lb": 20,
+        "ub": 20
+      }
+    },
+    {
+      "name": "step_size",
+      "interval": {
+        "lb": 3,
+        "ub": 3
+      }
+    }
+  ],
+  "config": {
+    "tolerance": 1e-2,
+    "normalization_constant": 1,
+    "dreal_log_level": "info",
+    "use_compartmental_constraints": true
+  }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/evaluation/sir.json b/resources/amr/petrinet/evaluation/sir.json
new file mode 100644
index 00000000..dd630585
--- /dev/null
+++ b/resources/amr/petrinet/evaluation/sir.json
@@ -0,0 +1,257 @@
+{
+    "header": {
+        "name": "SIR Model",
+        "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.1/petrinet/petrinet_schema.json",
+        "schema_name": "petrinet",
+        "description": "SIR model created by Ben, Micah, Brandon",
+        "model_version": "0.1"
+    },
+    "model": {
+        "states": [
+            {
+                "id": "S",
+                "name": "Susceptible",
+                "description": "Number of individuals that are 'susceptible' to a disease infection",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "I",
+                "name": "Infected",
+                "description": "Number of individuals that are 'infected' by a disease",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "R",
+                "name": "Recovered",
+                "description": "Number of individuals that have 'recovered' from a disease infection",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            }
+        ],
+        "transitions": [
+            {
+                "id": "inf",
+                "input": [
+                    "S",
+                    "I"
+                ],
+                "output": [
+                    "I",
+                    "I"
+                ],
+                "properties": {
+                    "name": "Infection",
+                    "description": "Infective process between individuals"
+                }
+            },
+            {
+                "id": "rec",
+                "input": [
+                    "I"
+                ],
+                "output": [
+                    "R"
+                ],
+                "properties": {
+                    "name": "Recovery",
+                    "description": "Recovery process of a infected individual"
+                }
+            }
+        ]
+    },
+    "semantics": {
+        "ode": {
+            "rates": [
+                {
+                    "target": "inf",
+                    "expression": "S*I*beta/N",
+                    "expression_mathml": "<apply><times/><ci>S</ci><ci>I</ci><ci>beta</ci></apply>"
+                },
+                {
+                    "target": "rec",
+                    "expression": "I*gamma",
+                    "expression_mathml": "<apply><times/><ci>I</ci><ci>gamma</ci></apply>"
+                }
+            ],
+            "initials": [
+                {
+                    "target": "S",
+                    "expression": "S0",
+                    "expression_mathml": "<ci>S0</ci>"
+                },
+                {
+                    "target": "I",
+                    "expression": "I0",
+                    "expression_mathml": "<ci>I0</ci>"
+                },
+                {
+                    "target": "R",
+                    "expression": "R0",
+                    "expression_mathml": "<ci>R0</ci>"
+                }
+            ],
+            "parameters": [
+                {
+                    "id": "beta",
+                    "name": "β",
+                    "description": "infection rate",
+                    "units": {
+                        "expression": "1/(person*day)",
+                        "expression_mathml": "<apply><divide/><cn>1</cn><apply><times/><ci>person</ci><ci>day</ci></apply></apply>"
+                    },
+                    "value": 2.7e-7,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 2.6e-7,
+                            "maximum": 2.8e-7
+                        }
+                    }
+                },
+                {
+                    "id": "gamma",
+                    "name": "γ",
+                    "description": "recovery rate",
+                    "grounding": {
+                        "identifiers": {
+                            "askemo": "0000013"
+                        }
+                    },
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><divide/><cn>1</cn><ci>day</ci></apply>"
+                    },
+                    "value": 0.14,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.1,
+                            "maximum": 0.18
+                        }
+                    }
+                },
+                {
+                    "id": "S0",
+                    "name": "S₀",
+                    "description": "Total susceptible population at timestep 0",
+                    "value": 1000
+                },
+                {
+                    "id": "I0",
+                    "name": "I₀",
+                    "description": "Total infected population at timestep 0",
+                    "value": 1
+                },
+                {
+                    "id": "R0",
+                    "name": "R₀",
+                    "description": "Total recovered population at timestep 0",
+                    "value": 0
+                },
+                {
+                    "id": "N",
+                    "name": "N",
+                    "description": "Total population",
+                    "value": 1001
+                }
+            ],
+            "observables": [
+                {
+                    "id": "noninf",
+                    "name": "Non-infectious",
+                    "states": [
+                        "S",
+                        "R"
+                    ],
+                    "expression": "S+R",
+                    "expression_mathml": "<apply><plus/><ci>S</ci><ci>R</ci></apply>"
+                }
+            ],
+            "time": {
+                "id": "t",
+                "units": {
+                    "expression": "day",
+                    "expression_mathml": "<ci>day</ci>"
+                }
+            }
+        }
+    },
+    "metadata": {
+        "processed_at": 1682964953,
+        "processed_by": "mit:process-node1",
+        "variable_statements": [
+            {
+                "id": "v0",
+                "variable": {
+                    "id": "v0",
+                    "name": "VE",
+                    "metadata": [
+                        {
+                            "type": "text_annotation",
+                            "value": " Vaccine Effectiveness"
+                        },
+                        {
+                            "type": "text_annotation",
+                            "value": " Vaccine Effectiveness"
+                        }
+                    ],
+                    "dkg_groundings": [],
+                    "column": [
+                        {
+                            "id": "9-2",
+                            "name": "new_persons_vaccinated",
+                            "dataset": {
+                                "id": "9",
+                                "name": "usa-vaccinations.csv",
+                                "metadata": "https://github.com/DARPA-ASKEM/program-milestones/blob/main/6-month-milestone/evaluation/scenario_3/ta_1/google-health-data/usa-vaccinations.csv"
+                            }
+                        },
+                        {
+                            "id": "9-3",
+                            "name": "cumulative_persons_vaccinated",
+                            "dataset": {
+                                "id": "9",
+                                "name": "usa-vaccinations.csv",
+                                "metadata": "https://github.com/DARPA-ASKEM/program-milestones/blob/main/6-month-milestone/evaluation/scenario_3/ta_1/google-health-data/usa-vaccinations.csv"
+                            }
+                        }
+                    ],
+                    "paper": {
+                        "id": "COVID-19 Vaccine Effectiveness by Product and Timing in New York State",
+                        "file_directory": "https://www.medrxiv.org/content/10.1101/2021.10.08.21264595v1",
+                        "doi": "10.1101/2021.10.08.21264595"
+                    },
+                    "equations": []
+                },
+                "metadata": [],
+                "provenance": {
+                    "method": "MIT annotation",
+                    "description": "text, dataset, formula annotation (chunwei@mit.edu)"
+                }
+            }
+        ]
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/evaluation/sir_request1.json b/resources/amr/petrinet/evaluation/sir_request1.json
new file mode 100644
index 00000000..f9a81968
--- /dev/null
+++ b/resources/amr/petrinet/evaluation/sir_request1.json
@@ -0,0 +1,91 @@
+{
+    "constraints": [
+        {
+            "name": "ibounds",
+            "variable": "I",
+            "interval": {
+                "lb": 0.450,
+                "ub": 0.550
+            },
+            "timepoints": {
+                "lb": 45,
+                "ub": 55,
+                "closed_upper_bound": true
+            }
+        }
+    ],
+    "parameters": [
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.12,
+                "ub": 0.22
+            },
+            "label": "all"
+        },
+        {
+            "name": "gamma",
+            "interval": {
+                "lb": 0.015,
+                "ub": 0.035
+            },
+            "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": 11,
+                "ub": 11
+            },
+            "label": "all"
+        },
+        {
+            "name": "step_size",
+            "interval": {
+                "lb": 5,
+                "ub": 5
+            },
+            "label": "all"
+        }
+    ],
+    "config": {
+        "tolerance": 1e-2,
+        "normalization_constant": 1,
+        "dreal_log_level": "info",
+        "use_compartmental_constraints": true
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/evaluation/sir_request_consistency.json b/resources/amr/petrinet/evaluation/sir_request_consistency.json
new file mode 100644
index 00000000..3b148deb
--- /dev/null
+++ b/resources/amr/petrinet/evaluation/sir_request_consistency.json
@@ -0,0 +1,92 @@
+{
+    "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": 0.201544,
+                "ub": 0.201544
+            },
+            "label": "any"
+        },
+        {
+            "name": "gamma",
+            "interval": {
+                "lb": 0.071086,
+                "ub": 0.071086
+            },
+            "label": "any"
+        },
+        {
+            "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": 12,
+                "ub": 12
+            },
+            "label": "all"
+        },
+        {
+            "name": "step_size",
+            "interval": {
+                "lb": 5,
+                "ub": 5
+            },
+            "label": "all"
+        }
+    ],
+    "config": {
+        "tolerance": 1e-2,
+        "normalization_constant": 1,
+        "dreal_log_level": "info",
+        "use_compartmental_constraints": true,
+        "dreal_precision": 1e-2
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/evaluation/sir_request_param_synth.json b/resources/amr/petrinet/evaluation/sir_request_param_synth.json
new file mode 100644
index 00000000..03e4fbf6
--- /dev/null
+++ b/resources/amr/petrinet/evaluation/sir_request_param_synth.json
@@ -0,0 +1,92 @@
+{
+    "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": 0.19,
+                "ub": 0.20
+            },
+            "label": "all"
+        },
+        {
+            "name": "gamma",
+            "interval": {
+                "lb": 0.06,
+                "ub": 0.08
+            },
+            "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": 11,
+                "ub": 11
+            },
+            "label": "all"
+        },
+        {
+            "name": "step_size",
+            "interval": {
+                "lb": 5,
+                "ub": 5
+            },
+            "label": "all"
+        }
+    ],
+    "config": {
+        "tolerance": 1e-2,
+        "normalization_constant": 1,
+        "dreal_log_level": "info",
+        "use_compartmental_constraints": true,
+        "dreal_precision": 1e-2
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/evaluation/sir_request_parameterization.json b/resources/amr/petrinet/evaluation/sir_request_parameterization.json
new file mode 100644
index 00000000..2734638a
--- /dev/null
+++ b/resources/amr/petrinet/evaluation/sir_request_parameterization.json
@@ -0,0 +1,92 @@
+{
+    "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": 0.19,
+                "ub": 0.20
+            },
+            "label": "any"
+        },
+        {
+            "name": "gamma",
+            "interval": {
+                "lb": 0.06,
+                "ub": 0.08
+            },
+            "label": "any"
+        },
+        {
+            "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": 30,
+                "ub": 30
+            },
+            "label": "all"
+        },
+        {
+            "name": "step_size",
+            "interval": {
+                "lb": 2,
+                "ub": 2
+            },
+            "label": "all"
+        }
+    ],
+    "config": {
+        "tolerance": 1e-2,
+        "normalization_constant": 1,
+        "dreal_log_level": "info",
+        "use_compartmental_constraints": true,
+        "dreal_precision": 1e-2
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/mira/models/scenario_3_seird.json b/resources/amr/petrinet/mira/models/scenario_3_seird.json
new file mode 100644
index 00000000..3bafe67d
--- /dev/null
+++ b/resources/amr/petrinet/mira/models/scenario_3_seird.json
@@ -0,0 +1,273 @@
+{
+    "header": {
+        "name": "EPI Scenario 3",
+        "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json",
+        "schema_name": "petrinet",
+        "description": "EPI Scenario 3",
+        "model_version": "0.1"
+    },
+    "properties": {},
+    "model": {
+        "states": [
+            {
+                "id": "Susceptible",
+                "name": "Susceptible",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Infected",
+                "name": "Infected",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Exposed",
+                "name": "Exposed",
+                "grounding": {
+                    "identifiers": {
+                        "apollosv": "00000154"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Recovered",
+                "name": "Recovered",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Deceased",
+                "name": "Deceased",
+                "grounding": {
+                    "identifiers": {
+                        "ncit": "C28554"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Total_population",
+                "name": "Total_population",
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            }
+        ],
+        "transitions": [
+            {
+                "id": "t1",
+                "input": [
+                    "Infected",
+                    "Susceptible",
+                    "Total_population"
+                ],
+                "output": [
+                    "Infected",
+                    "Exposed",
+                    "Total_population"
+                ],
+                "properties": {
+                    "name": "t1"
+                }
+            },
+            {
+                "id": "t2",
+                "input": [
+                    "Exposed"
+                ],
+                "output": [
+                    "Infected"
+                ],
+                "properties": {
+                    "name": "t2"
+                }
+            },
+            {
+                "id": "t3",
+                "input": [
+                    "Infected"
+                ],
+                "output": [
+                    "Recovered"
+                ],
+                "properties": {
+                    "name": "t3"
+                }
+            },
+            {
+                "id": "t4",
+                "input": [
+                    "Infected"
+                ],
+                "output": [
+                    "Deceased"
+                ],
+                "properties": {
+                    "name": "t4"
+                }
+            }
+        ]
+    },
+    "semantics": {
+        "ode": {
+            "rates": [
+                {
+                    "target": "t1",
+                    "expression": "Infected*Susceptible*beta/Total_population",
+                    "expression_mathml": "<apply><divide/><apply><times/><ci>Infected</ci><ci>Susceptible</ci><ci>beta</ci></apply><ci>Total_population</ci></apply>"
+                },
+                {
+                    "target": "t2",
+                    "expression": "Exposed*epsilon",
+                    "expression_mathml": "<apply><times/><ci>Exposed</ci><ci>epsilon</ci></apply>"
+                },
+                {
+                    "target": "t3",
+                    "expression": "Infected*gamma",
+                    "expression_mathml": "<apply><times/><ci>Infected</ci><ci>gamma</ci></apply>"
+                },
+                {
+                    "target": "t4",
+                    "expression": "Infected*alpha",
+                    "expression_mathml": "<apply><times/><ci>Infected</ci><ci>alpha</ci></apply>"
+                }
+            ],
+            "initials": [
+                {
+                    "target": "Susceptible",
+                    "expression": "99.0000000",
+                    "expression_mathml": "<cn>14561489.0</cn>"
+                },
+                {
+                    "target": "Infected",
+                    "expression": "1.00000000",
+                    "expression_mathml": "<cn>2123452.0</cn>"
+                },
+                {
+                    "target": "Exposed",
+                    "expression": "0.000000000",
+                    "expression_mathml": "<cn>530863.0</cn>"
+                },
+                {
+                    "target": "Recovered",
+                    "expression": "0.00000000",
+                    "expression_mathml": "<cn>2069902.0</cn>"
+                },
+                {
+                    "target": "Total_population",
+                    "expression": "100.000000000000",
+                    "expression_mathml": "<cn>744.0</cn>"
+                },
+                {
+                    "target": "Deceased",
+                    "expression": "0.0000000000",
+                    "expression_mathml": "<cn>53550.0</cn>"
+                }
+            ],
+            "parameters": [
+                {
+                    "id": "beta",
+                    "value": 0.833,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "epsilon",
+                    "value": 0.3333333333333333,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "gamma",
+                    "value": 0.125,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "alpha",
+                    "value": 0.006,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "lambda",
+                    "value": 120480.0
+                },
+                {
+                    "id": "mu",
+                    "value": 0.012048
+                },
+                {
+                    "id": "City",
+                    "value": 1.0
+                }
+            ],
+            "observables": [],
+            "time": {
+                "id": "t",
+                "units": {
+                    "expression": "day",
+                    "expression_mathml": "<ci>day</ci>"
+                }
+            }
+        }
+    },
+    "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/mira/models/scenario_3_seird_stratified.json b/resources/amr/petrinet/mira/models/scenario_3_seird_stratified.json
new file mode 100644
index 00000000..bad7a642
--- /dev/null
+++ b/resources/amr/petrinet/mira/models/scenario_3_seird_stratified.json
@@ -0,0 +1,564 @@
+{
+    "header": {
+        "name": "EPI Scenario 3",
+        "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json",
+        "schema_name": "petrinet",
+        "description": "EPI Scenario 3",
+        "model_version": "0.1"
+    },
+    "properties": {},
+    "model": {
+        "states": [
+            {
+                "id": "Susceptible_Vax",
+                "name": "Susceptible_Vax",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Infected_Vax",
+                "name": "Infected_Vax",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Exposed_Vax",
+                "name": "Exposed_Vax",
+                "grounding": {
+                    "identifiers": {
+                        "apollosv": "00000154"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Recovered_Vax",
+                "name": "Recovered_Vax",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Deceased_Vax",
+                "name": "Deceased_Vax",
+                "grounding": {
+                    "identifiers": {
+                        "ncit": "C28554"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Total_population",
+                "name": "Total_population",
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Susceptible_UnVax",
+                "name": "Susceptible_UnVax",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Infected_UnVax",
+                "name": "Infected_UnVax",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Exposed_UnVax",
+                "name": "Exposed_UnVax",
+                "grounding": {
+                    "identifiers": {
+                        "apollosv": "00000154"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Recovered_UnVax",
+                "name": "Recovered_UnVax",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Deceased_UnVax",
+                "name": "Deceased_UnVax",
+                "grounding": {
+                    "identifiers": {
+                        "ncit": "C28554"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            }
+        ],
+        "transitions": [
+            {
+                "id": "t1_Vax",
+                "input": [
+                    "Infected_Vax",
+                    "Susceptible_Vax",
+                    "Total_population"
+                ],
+                "output": [
+                    "Infected_Vax",
+                    "Exposed_Vax",
+                    "Total_population"
+                ],
+                "properties": {
+                    "name": "t1_Vax"
+                }
+            },
+            {
+                "id": "t2_Vax",
+                "input": [
+                    "Exposed_Vax"
+                ],
+                "output": [
+                    "Infected_Vax"
+                ],
+                "properties": {
+                    "name": "t2_Vax"
+                }
+            },
+            {
+                "id": "t3_Vax",
+                "input": [
+                    "Infected_Vax"
+                ],
+                "output": [
+                    "Recovered_Vax"
+                ],
+                "properties": {
+                    "name": "t3_Vax"
+                }
+            },
+            {
+                "id": "t4_Vax",
+                "input": [
+                    "Infected_Vax"
+                ],
+                "output": [
+                    "Deceased_Vax"
+                ],
+                "properties": {
+                    "name": "t4_Vax"
+                }
+            },
+            {
+                "id": "t1_UnVax",
+                "input": [
+                    "Infected_UnVax",
+                    "Susceptible_UnVax",
+                    "Total_population"
+                ],
+                "output": [
+                    "Infected_UnVax",
+                    "Exposed_UnVax",
+                    "Total_population"
+                ],
+                "properties": {
+                    "name": "t1_UnVax"
+                }
+            },
+            {
+                "id": "t2_UnVax",
+                "input": [
+                    "Exposed_UnVax"
+                ],
+                "output": [
+                    "Infected_UnVax"
+                ],
+                "properties": {
+                    "name": "t2_UnVax"
+                }
+            },
+            {
+                "id": "t3_UnVax",
+                "input": [
+                    "Infected_UnVax"
+                ],
+                "output": [
+                    "Recovered_UnVax"
+                ],
+                "properties": {
+                    "name": "t3_UnVax"
+                }
+            },
+            {
+                "id": "t4_UnVax",
+                "input": [
+                    "Infected_UnVax"
+                ],
+                "output": [
+                    "Deceased_UnVax"
+                ],
+                "properties": {
+                    "name": "t4_UnVax"
+                }
+            },
+            {
+                "id": "tSusceptible_UnVax_to_Vax",
+                "input": [
+                    "Susceptible_UnVax"
+                ],
+                "output": [
+                    "Susceptible_Vax"
+                ],
+                "properties": {
+                    "name": "tSusceptible_UnVax_to_Vax"
+                }
+            },
+            {
+                "id": "tInfected_UnVax_to_Vax",
+                "input": [
+                    "Infected_UnVax"
+                ],
+                "output": [
+                    "Infected_Vax"
+                ],
+                "properties": {
+                    "name": "tInfected_UnVax_to_Vax"
+                }
+            },
+            {
+                "id": "tExposed_UnVax_to_Vax",
+                "input": [
+                    "Exposed_UnVax"
+                ],
+                "output": [
+                    "Exposed_Vax"
+                ],
+                "properties": {
+                    "name": "tExposed_UnVax_to_Vax"
+                }
+            },
+            {
+                "id": "tRecovered_UnVax_to_Vax",
+                "input": [
+                    "Recovered_UnVax"
+                ],
+                "output": [
+                    "Recovered_Vax"
+                ],
+                "properties": {
+                    "name": "tRecovered_UnVax_to_Vax"
+                }
+            }
+        ]
+    },
+    "semantics": {
+        "ode": {
+            "rates": [
+                {
+                    "target": "t1_Vax",
+                    "expression": "Infected_Vax*Susceptible_Vax*beta_Vax*Piecewise((1, t < t_mask), (0.1, True))/Total_population",
+                    "expression_mathml": "<apply><divide/><apply><times/><ci>Infected_Vax</ci><ci>Susceptible_Vax</ci><ci>beta</ci></apply><ci>Total_population</ci></apply>"
+                },
+                {
+                    "target": "t2_Vax",
+                    "expression": "Exposed_Vax*epsilon_Vax",
+                    "expression_mathml": "<apply><times/><ci>Exposed_Vax</ci><ci>epsilon_Vax</ci></apply>"
+                },
+                {
+                    "target": "t3_Vax",
+                    "expression": "Infected_Vax*gamma_Vax",
+                    "expression_mathml": "<apply><times/><ci>Infected_Vax</ci><ci>gamma_Vax</ci></apply>"
+                },
+                {
+                    "target": "t4_Vax",
+                    "expression": "Infected_Vax*alpha_Vax",
+                    "expression_mathml": "<apply><times/><ci>Infected_Vax</ci><ci>alpha_Vax</ci></apply>"
+                },
+                {
+                    "target": "t1_UnVax",
+                    "expression": "Infected_UnVax*Susceptible_UnVax*beta_UnVax/Total_population",
+                    "expression_mathml": "<apply><divide/><apply><times/><ci>Infected_UnVax</ci><ci>Susceptible_UnVax</ci><ci>beta</ci></apply><ci>Total_population</ci></apply>"
+                },
+                {
+                    "target": "t2_UnVax",
+                    "expression": "Exposed_UnVax*epsilon_UnVax",
+                    "expression_mathml": "<apply><times/><ci>Exposed_UnVax</ci><ci>epsilon_UnVax</ci></apply>"
+                },
+                {
+                    "target": "t3_UnVax",
+                    "expression": "Infected_UnVax*gamma_UnVax",
+                    "expression_mathml": "<apply><times/><ci>Infected_UnVax</ci><ci>gamma_UnVax</ci></apply>"
+                },
+                {
+                    "target": "t4_UnVax",
+                    "expression": "Infected_UnVax*alpha_UnVax",
+                    "expression_mathml": "<apply><times/><ci>Infected_UnVax</ci><ci>alpha_UnVax</ci></apply>"
+                },
+                {
+                    "target": "tSusceptible_UnVax_to_Vax",
+                    "expression": "Susceptible_UnVax*vax",
+                    "expression_mathml": "<apply><times/><ci>Susceptible_UnVax</ci><ci>vax</ci></apply>"
+                },
+                {
+                    "target": "tInfected_UnVax_to_Vax",
+                    "expression": "Infected_UnVax*vax",
+                    "expression_mathml": "<apply><times/><ci>Infected<_UnVax/ci><ci>vax</ci></apply>"
+                },
+                {
+                    "target": "tExposed_UnVax_to_Vax",
+                    "expression": "Exposed_UnVax*vax",
+                    "expression_mathml": "<apply><times/><ci>Exposed_UnVax</ci><ci>vax</ci></apply>"
+                },
+                {
+                    "target": "tRecovered_UnVax_to_Vax",
+                    "expression": "Recovered_UnVax*vax",
+                    "expression_mathml": "<apply><times/><ci>Recovered_UnVax</ci><ci>vax</ci></apply>"
+                }
+            ],
+            "initials": [
+                {
+                    "target": "Susceptible_Vax",
+                    "expression": "0.0000000",
+                    "expression_mathml": "<cn>14561489.0</cn>"
+                },
+                {
+                    "target": "Infected_Vax",
+                    "expression": "1.00000000",
+                    "expression_mathml": "<cn>2123452.0</cn>"
+                },
+                {
+                    "target": "Exposed_Vax",
+                    "expression": "0.000000000",
+                    "expression_mathml": "<cn>530863.0</cn>"
+                },
+                {
+                    "target": "Recovered_Vax",
+                    "expression": "0.00000000",
+                    "expression_mathml": "<cn>2069902.0</cn>"
+                },
+                {
+                    "target": "Total_population",
+                    "expression": "100.000000000000",
+                    "expression_mathml": "<cn>744.0</cn>"
+                },
+                {
+                    "target": "Deceased_Vax",
+                    "expression": "0.0000000000",
+                    "expression_mathml": "<cn>53550.0</cn>"
+                },
+                {
+                    "target": "Susceptible_UnVax",
+                    "expression": "98.0000000",
+                    "expression_mathml": "<cn>14561489.0</cn>"
+                },
+                {
+                    "target": "Infected_UnVax",
+                    "expression": "1.00000000",
+                    "expression_mathml": "<cn>2123452.0</cn>"
+                },
+                {
+                    "target": "Exposed_UnVax",
+                    "expression": "0.000000000",
+                    "expression_mathml": "<cn>530863.0</cn>"
+                },
+                {
+                    "target": "Recovered_UnVax",
+                    "expression": "0.00000000",
+                    "expression_mathml": "<cn>2069902.0</cn>"
+                },
+                {
+                    "target": "Deceased_UnVax",
+                    "expression": "0.0000000000",
+                    "expression_mathml": "<cn>53550.0</cn>"
+                }
+            ],
+            "parameters": [
+                {
+                    "id": "beta_Vax",
+                    "value": 0.833,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "epsilon_Vax",
+                    "value": 0.3333333333333333,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "gamma_Vax",
+                    "value": 0.125,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "alpha_Vax",
+                    "value": 0.006,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "lambda_Vax",
+                    "value": 120480.0
+                },
+                {
+                    "id": "mu_Vax",
+                    "value": 0.012048
+                },
+                {
+                    "id": "City_Vax",
+                    "value": 1.0
+                },
+                {
+                    "id": "beta_UnVax",
+                    "value": 0.833,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "epsilon_UnVax",
+                    "value": 0.3333333333333333,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "gamma_UnVax",
+                    "value": 0.125,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "alpha_UnVax",
+                    "value": 0.006,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "lambda_UnVax",
+                    "value": 120480.0
+                },
+                {
+                    "id": "mu_UnVax",
+                    "value": 0.012048
+                },
+                {
+                    "id": "City_UnVax",
+                    "value": 1.0
+                },
+                {
+                    "id": "vax",
+                    "value": 0.1
+                },
+                {
+                    "id": "t_mask",
+                    "value": 20.0
+                },
+                {
+                    "id": "mask",
+                    "value": 0.5
+                }
+            ],
+            "observables": [],
+            "time": {
+                "id": "t",
+                "units": {
+                    "expression": "day",
+                    "expression_mathml": "<ci>day</ci>"
+                }
+            }
+        }
+    },
+    "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/mira/requests/BIOMD0000000955_askenet_request.json b/resources/amr/petrinet/mira/requests/BIOMD0000000955_askenet_request.json
index ca90ebbc..9b16d748 100644
--- a/resources/amr/petrinet/mira/requests/BIOMD0000000955_askenet_request.json
+++ b/resources/amr/petrinet/mira/requests/BIOMD0000000955_askenet_request.json
@@ -1,116 +1,49 @@
 {
-  "parameters": [
-    {
-      "name": "beta",
-      "interval": {
-        "lb": 0.008799999999999999,
-        "ub": 0.0132
-      }
-    },
-    {
-      "name": "gamma",
-      "interval": {
-        "lb": 0.3648,
-        "ub": 0.5472
-      }
-    },
-    {
-      "name": "delta",
-      "interval": {
-        "lb": 0.008799999999999999,
-        "ub": 0.0132
-      }
+  "constraints": [
+    {
+      "name": "theta_ge_2_epsilon",
+      "additive_bounds": {
+        "lb": 0.0
+      },
+      "variables": [
+        "theta",
+        "epsilon"
+      ],
+      "weights": [
+        1,
+        -2
+      ]
     },
     {
-      "name": "alpha",
-      "interval": {
-        "lb": 0.45599999999999996,
-        "ub": 0.6839999999999999
-      }
-    },
+      "name": "IDART_bound",
+      "additive_bounds": {
+        "ub": 0.33
+      },
+      "variables": [
+        "Infected",
+        "Diagnosed",
+        "Ailing",
+        "Recognized",
+        "Threatened"
+      ]
+    }
+  ],
+  "parameters": [
     {
       "name": "epsilon",
       "interval": {
         "lb": 0.1368,
         "ub": 0.20520000000000002
-      }
-    },
-    {
-      "name": "zeta",
-      "interval": {
-        "lb": 0.1,
-        "ub": 0.15
-      }
-    },
-    {
-      "name": "lambda",
-      "interval": {
-        "lb": 0.027200000000000002,
-        "ub": 0.0408
-      }
-    },
-    {
-      "name": "eta",
-      "interval": {
-        "lb": 0.1,
-        "ub": 0.15
-      }
-    },
-    {
-      "name": "rho",
-      "interval": {
-        "lb": 0.027200000000000002,
-        "ub": 0.0408
-      }
+      },
+      "label": "all"
     },
     {
       "name": "theta",
       "interval": {
         "lb": 0.2968,
         "ub": 0.4452
-      }
-    },
-    {
-      "name": "kappa",
-      "interval": {
-        "lb": 0.013600000000000001,
-        "ub": 0.0204
-      }
-    },
-    {
-      "name": "mu",
-      "interval": {
-        "lb": 0.013600000000000001,
-        "ub": 0.0204
-      }
-    },
-    {
-      "name": "nu",
-      "interval": {
-        "lb": 0.0216,
-        "ub": 0.0324
-      }
-    },
-    {
-      "name": "xi",
-      "interval": {
-        "lb": 0.013600000000000001,
-        "ub": 0.0204
-      }
-    },
-    {
-      "name": "tau",
-      "interval": {
-        "lb": 0.008,
-        "ub": 0.012
-      }
-    },
-    {
-      "name": "sigma",
-      "interval": {
-        "lb": 0.013600000000000001,
-        "ub": 0.0204
-      }
+      },
+      "label": "all"
     }
   ],
   "structure_parameters": [
@@ -120,26 +53,18 @@
         {
           "timepoints": [
             0,
-            1,
             10,
-            20,
-            30,
-            40,
-            50
+            20
           ]
         }
       ]
     }
   ],
   "config": {
-    "use_compartmental_constraints": true,
     "normalization_constant": 1.0,
-    "tolerance": 1e-2,
-    "verbosity": 5,
-    "dreal_mcts": true,
-    "save_smtlib": false,
-    "substitute_subformulas": true,
-    "series_approximation_threshold": 1e-8,
-    "dreal_log_level": "none"
+    "normalize": false,
+    "tolerance": 1e-1,
+    "use_compartmental_constraints": true,
+    "dreal_precision": 1e-10
   }
 }
\ No newline at end of file
diff --git a/resources/amr/petrinet/mira/requests/request2_b.json b/resources/amr/petrinet/mira/requests/request2_b.json
index a9a0018d..da05451d 100644
--- a/resources/amr/petrinet/mira/requests/request2_b.json
+++ b/resources/amr/petrinet/mira/requests/request2_b.json
@@ -100,9 +100,5 @@
       "label": "all"
     }
   ],
-  "config": {
-    "solver": "dreal",
-    "save_smtlib": true,
-    "substitute_subformulas": false
-  }
+  "config": {}
 }
\ No newline at end of file
diff --git a/resources/amr/petrinet/mira/requests/request2_b_synthesize.json b/resources/amr/petrinet/mira/requests/request2_b_synthesize.json
index 10af7554..8ee37774 100644
--- a/resources/amr/petrinet/mira/requests/request2_b_synthesize.json
+++ b/resources/amr/petrinet/mira/requests/request2_b_synthesize.json
@@ -140,5 +140,7 @@
       "label": "all"
     }
   ],
-  "config": {}
+  "config": {
+    "normalization_constant": 1.0
+  }
 }
\ No newline at end of file
diff --git a/resources/amr/petrinet/mira/requests/scenario_3_seird_request.json b/resources/amr/petrinet/mira/requests/scenario_3_seird_request.json
new file mode 100644
index 00000000..b9f1c1cf
--- /dev/null
+++ b/resources/amr/petrinet/mira/requests/scenario_3_seird_request.json
@@ -0,0 +1,88 @@
+{
+    "constraints": [],
+    "parameters": [
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.833,
+                "ub": 0.833,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "epsilon",
+            "interval": {
+                "lb": 0.3333333333333333,
+                "ub": 0.3333333333333333,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "gamma",
+            "interval": {
+                "lb": 0.125,
+                "ub": 0.125,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "alpha",
+            "interval": {
+                "lb": 0.006,
+                "ub": 0.006,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "lambda",
+            "interval": {
+                "lb": 120480.0,
+                "ub": 120480.0,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "mu",
+            "interval": {
+                "lb": 0.012048,
+                "ub": 0.012048,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "City",
+            "interval": {
+                "lb": 1.0,
+                "ub": 1.0,
+                "closed_upper_bound": true
+            }
+        }
+    ],
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10,
+                        20,
+                        30,
+                        40,
+                        50,
+                        60,
+                        70,
+                        80,
+                        90,
+                        100
+                    ]
+                }
+            ]
+        }
+    ],
+    "config": {
+        "use_compartmental_constraints": true,
+        "normalization_constant": 200,
+        "tolerance": 0.1
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/mira/requests/scenario_3_seird_request_stratified.json b/resources/amr/petrinet/mira/requests/scenario_3_seird_request_stratified.json
new file mode 100644
index 00000000..5d91b1c1
--- /dev/null
+++ b/resources/amr/petrinet/mira/requests/scenario_3_seird_request_stratified.json
@@ -0,0 +1,168 @@
+{
+    "constraints": [],
+    "parameters": [
+        {
+            "name": "beta_Vax",
+            "interval": {
+                "lb": 0.015,
+                "ub": 0.015,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "epsilon_Vax",
+            "interval": {
+                "lb": 0.12,
+                "ub": 0.12,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "gamma_Vax",
+            "interval": {
+                "lb": 0.01,
+                "ub": 0.01,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "alpha_Vax",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "lambda_Vax",
+            "interval": {
+                "lb": 120480.0,
+                "ub": 120480.0,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "mu_Vax",
+            "interval": {
+                "lb": 0.7,
+                "ub": 0.7,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "City_Vax",
+            "interval": {
+                "lb": 1.0,
+                "ub": 1.0,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "beta_UnVax",
+            "interval": {
+                "lb": 0.833,
+                "ub": 0.833,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "epsilon_UnVax",
+            "interval": {
+                "lb": 0.01,
+                "ub": 0.01,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "gamma_UnVax",
+            "interval": {
+                "lb": 0.02,
+                "ub": 0.02,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "alpha_UnVax",
+            "interval": {
+                "lb": 0.02,
+                "ub": 0.02,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "lambda_UnVax",
+            "interval": {
+                "lb": 120480.0,
+                "ub": 120480.0,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "mu_UnVax",
+            "interval": {
+                "lb": 0.9,
+                "ub": 0.9,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "City_UnVax",
+            "interval": {
+                "lb": 1.0,
+                "ub": 1.0,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "vax",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "t_mask",
+            "interval": {
+                "lb": 40.0,
+                "ub": 40.0,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "mask",
+            "interval": {
+                "lb": 0.5,
+                "ub": 0.5,
+                "closed_upper_bound": true
+            }
+        }
+    ],
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10,
+                        20,
+                        30,
+                        40,
+                        50,
+                        60,
+                        70,
+                        80,
+                        90,
+                        100
+                    ]
+                }
+            ]
+        }
+    ],
+    "config": {
+        "use_compartmental_constraints": true,
+        "normalization_constant": 200,
+        "tolerance": 0.1
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/monthly-demo/2024-07/SEIRHD_vacc_petrinet.json b/resources/amr/petrinet/monthly-demo/2024-07/SEIRHD_vacc_petrinet.json
new file mode 100644
index 00000000..be7710d2
--- /dev/null
+++ b/resources/amr/petrinet/monthly-demo/2024-07/SEIRHD_vacc_petrinet.json
@@ -0,0 +1,674 @@
+{
+    "header": {
+     "name": "SEIRHD vacc model for LA County t0 = 10/28/2021",
+     "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json",
+     "schema_name": "petrinet",
+     "description": "SEIRHD vacc model for LA County t0 = 10/28/2021",
+     "model_version": "0.1"
+    },
+    "properties": {},
+    "model": {
+     "states": [
+      {
+       "id": "S_u",
+       "name": "S_u",
+       "grounding": {
+        "identifiers": {
+         "ido": "0000514"
+        },
+        "modifiers": {
+         "status": "unvaccinated"
+        }
+       },
+       "units": {
+        "expression": "person",
+        "expression_mathml": "<ci>person</ci>"
+       }
+      },
+      {
+       "id": "I_u",
+       "name": "I_u",
+       "grounding": {
+        "identifiers": {
+         "ido": "0000511"
+        },
+        "modifiers": {
+         "status": "unvaccinated"
+        }
+       },
+       "units": {
+        "expression": "person",
+        "expression_mathml": "<ci>person</ci>"
+       }
+      },
+      {
+       "id": "E_u",
+       "name": "E_u",
+       "grounding": {
+        "identifiers": {
+         "apollosv": "00000154"
+        },
+        "modifiers": {
+         "status": "unvaccinated"
+        }
+       },
+       "units": {
+        "expression": "person",
+        "expression_mathml": "<ci>person</ci>"
+       }
+      },
+      {
+       "id": "S_v",
+       "name": "S_v",
+       "grounding": {
+        "identifiers": {
+         "ido": "0000514"
+        },
+        "modifiers": {
+         "status": "vaccinated"
+        }
+       },
+       "units": {
+        "expression": "person",
+        "expression_mathml": "<ci>person</ci>"
+       }
+      },
+      {
+       "id": "E_v",
+       "name": "E_v",
+       "grounding": {
+        "identifiers": {
+         "apollosv": "00000154"
+        },
+        "modifiers": {
+         "status": "vaccinated"
+        }
+       },
+       "units": {
+        "expression": "person",
+        "expression_mathml": "<ci>person</ci>"
+       }
+      },
+      {
+       "id": "I_v",
+       "name": "I_v",
+       "grounding": {
+        "identifiers": {
+         "ido": "0000511"
+        },
+        "modifiers": {
+         "status": "vaccinated"
+        }
+       },
+       "units": {
+        "expression": "person",
+        "expression_mathml": "<ci>person</ci>"
+       }
+      },
+      {
+       "id": "H",
+       "name": "H",
+       "grounding": {
+        "identifiers": {
+         "ido": "0000511"
+        },
+        "modifiers": {
+         "property": "ncit:C25179"
+        }
+       },
+       "units": {
+        "expression": "person",
+        "expression_mathml": "<ci>person</ci>"
+       }
+      },
+      {
+       "id": "R",
+       "name": "R",
+       "grounding": {
+        "identifiers": {
+         "ido": "0000592"
+        },
+        "modifiers": {}
+       },
+       "units": {
+        "expression": "person",
+        "expression_mathml": "<ci>person</ci>"
+       }
+      },
+      {
+       "id": "D",
+       "name": "D",
+       "grounding": {
+        "identifiers": {
+         "ncit": "C28554"
+        },
+        "modifiers": {}
+       },
+       "units": {
+        "expression": "person",
+        "expression_mathml": "<ci>person</ci>"
+       }
+      },
+      {
+       "id": "Cumulative_cases",
+       "name": "Cumulative_cases",
+       "grounding": {
+        "identifiers": {},
+        "modifiers": {}
+       },
+       "units": {
+        "expression": "person",
+        "expression_mathml": "<ci>person</ci>"
+       }
+      }
+     ],
+     "transitions": [
+      {
+       "id": "t1",
+       "input": [
+        "I_u",
+        "S_u"
+       ],
+       "output": [
+        "I_u",
+        "E_u"
+       ],
+       "properties": {
+        "name": "t1"
+       }
+      },
+      {
+       "id": "t2",
+       "input": [
+        "I_u",
+        "S_v"
+       ],
+       "output": [
+        "I_u",
+        "E_v"
+       ],
+       "properties": {
+        "name": "t2"
+       }
+      },
+      {
+       "id": "t3",
+       "input": [
+        "I_v",
+        "S_u"
+       ],
+       "output": [
+        "I_v",
+        "E_u"
+       ],
+       "properties": {
+        "name": "t3"
+       }
+      },
+      {
+       "id": "t4",
+       "input": [
+        "I_v",
+        "S_v"
+       ],
+       "output": [
+        "I_v",
+        "E_v"
+       ],
+       "properties": {
+        "name": "t4"
+       }
+      },
+      {
+       "id": "t5",
+       "input": [
+        "S_u"
+       ],
+       "output": [
+        "S_v"
+       ],
+       "properties": {
+        "name": "t5"
+       }
+      },
+      {
+       "id": "t6",
+       "input": [
+        "S_v"
+       ],
+       "output": [
+        "S_u"
+       ],
+       "properties": {
+        "name": "t6"
+       }
+      },
+      {
+       "id": "t7",
+       "input": [
+        "E_u"
+       ],
+       "output": [
+        "I_u"
+       ],
+       "properties": {
+        "name": "t7"
+       }
+      },
+      {
+       "id": "t8",
+       "input": [
+        "E_v"
+       ],
+       "output": [
+        "I_v"
+       ],
+       "properties": {
+        "name": "t8"
+       }
+      },
+      {
+       "id": "t9",
+       "input": [
+        "I_u"
+       ],
+       "output": [
+        "H"
+       ],
+       "properties": {
+        "name": "t9"
+       }
+      },
+      {
+       "id": "t10",
+       "input": [
+        "I_v"
+       ],
+       "output": [
+        "H"
+       ],
+       "properties": {
+        "name": "t10"
+       }
+      },
+      {
+       "id": "t11",
+       "input": [
+        "H"
+       ],
+       "output": [
+        "R"
+       ],
+       "properties": {
+        "name": "t11"
+       }
+      },
+      {
+       "id": "t12",
+       "input": [
+        "H"
+       ],
+       "output": [
+        "D"
+       ],
+       "properties": {
+        "name": "t12"
+       }
+      },
+      {
+       "id": "t13",
+       "input": [
+        "E_u",
+        "E_v"
+       ],
+       "output": [
+        "E_u",
+        "E_v",
+        "Cumulative_cases"
+       ],
+       "properties": {
+        "name": "t13"
+       }
+      }
+     ]
+    },
+    "semantics": {
+     "ode": {
+      "rates": [
+       {
+        "target": "t1",
+        "expression": "I_u*NPI_mult*S_u*beta/N",
+        "expression_mathml": "<apply><divide/><apply><times/><ci>I_u</ci><ci>NPI_mult</ci><ci>S_u</ci><ci>beta</ci></apply><ci>N</ci></apply>"
+       },
+       {
+        "target": "t2",
+        "expression": "I_u*NPI_mult*S_v*beta*vacc_mult/N",
+        "expression_mathml": "<apply><divide/><apply><times/><ci>I_u</ci><ci>NPI_mult</ci><ci>S_v</ci><ci>beta</ci><ci>vacc_mult</ci></apply><ci>N</ci></apply>"
+       },
+       {
+        "target": "t3",
+        "expression": "I_v*NPI_mult*S_u*beta/N",
+        "expression_mathml": "<apply><divide/><apply><times/><ci>I_v</ci><ci>NPI_mult</ci><ci>S_u</ci><ci>beta</ci></apply><ci>N</ci></apply>"
+       },
+       {
+        "target": "t4",
+        "expression": "I_v*NPI_mult*S_v*beta*vacc_mult/N",
+        "expression_mathml": "<apply><divide/><apply><times/><ci>I_v</ci><ci>NPI_mult</ci><ci>S_v</ci><ci>beta</ci><ci>vacc_mult</ci></apply><ci>N</ci></apply>"
+       },
+       {
+        "target": "t5",
+        "expression": "r_Sv",
+        "expression_mathml": "<ci>r_Sv</ci>"
+       },
+       {
+        "target": "t6",
+        "expression": "S_v*r_SvSu",
+        "expression_mathml": "<apply><times/><ci>S_v</ci><ci>r_SvSu</ci></apply>"
+       },
+       {
+        "target": "t7",
+        "expression": "E_u*r_EI",
+        "expression_mathml": "<apply><times/><ci>E_u</ci><ci>r_EI</ci></apply>"
+       },
+       {
+        "target": "t8",
+        "expression": "E_v*r_EI",
+        "expression_mathml": "<apply><times/><ci>E_v</ci><ci>r_EI</ci></apply>"
+       },
+       {
+        "target": "t9",
+        "expression": "I_u*r_IH_u",
+        "expression_mathml": "<apply><times/><ci>I_u</ci><ci>r_IH_u</ci></apply>"
+       },
+       {
+        "target": "t10",
+        "expression": "I_v*r_IH_v",
+        "expression_mathml": "<apply><times/><ci>I_v</ci><ci>r_IH_v</ci></apply>"
+       },
+       {
+        "target": "t11",
+        "expression": "H*r_HR",
+        "expression_mathml": "<apply><times/><ci>H</ci><ci>r_HR</ci></apply>"
+       },
+       {
+        "target": "t12",
+        "expression": "H*r_HD",
+        "expression_mathml": "<apply><times/><ci>H</ci><ci>r_HD</ci></apply>"
+       },
+       {
+        "target": "t13",
+        "expression": "r_EI*(E_u + E_v)",
+        "expression_mathml": "<apply><times/><ci>r_EI</ci><apply><plus/><ci>E_u</ci><ci>E_v</ci></apply></apply>"
+       }
+      ],
+      "initials": [
+       {
+        "target": "S_u",
+        "expression": "2324633.64",
+        "expression_mathml": "<cn>2324633.6400000001</cn>"
+       },
+       {
+        "target": "I_u",
+        "expression": "3458.66666666667",
+        "expression_mathml": "<cn>3458.6666666666665</cn>"
+       },
+       {
+        "target": "E_u",
+        "expression": "6917.33333333333",
+        "expression_mathml": "<cn>6917.333333333333</cn>"
+       },
+       {
+        "target": "S_v",
+        "expression": "5977629.36",
+        "expression_mathml": "<cn>5977629.3599999994</cn>"
+       },
+       {
+        "target": "E_v",
+        "expression": "3458.66666666667",
+        "expression_mathml": "<cn>3458.6666666666665</cn>"
+       },
+       {
+        "target": "I_v",
+        "expression": "1729.33333333333",
+        "expression_mathml": "<cn>1729.3333333333333</cn>"
+       },
+       {
+        "target": "H",
+        "expression": "673",
+        "expression_mathml": "<cn>673</cn>"
+       },
+       {
+        "target": "R",
+        "expression": "1484896",
+        "expression_mathml": "<cn>1484896</cn>"
+       },
+       {
+        "target": "D",
+        "expression": "26604",
+        "expression_mathml": "<cn>26604</cn>"
+       },
+       {
+        "target": "Cumulative_cases",
+        "expression": "1490084",
+        "expression_mathml": "<cn>1490084</cn>"
+       }
+      ],
+      "parameters": [
+       {
+        "id": "N",
+        "value": 9830000.0,
+        "units": {
+         "expression": "person",
+         "expression_mathml": "<ci>person</ci>"
+        }
+       },
+       {
+        "id": "NPI_mult",
+        "value": 1.0,
+        "distribution": {
+         "type": "StandardUniform1",
+         "parameters": {
+          "minimum": 0.999,
+          "maximum": 1.001
+         }
+        },
+        "units": {
+         "expression": "1",
+         "expression_mathml": "<cn>1</cn>"
+        }
+       },
+       {
+        "id": "beta",
+        "value": 0.125,
+        "distribution": {
+         "type": "StandardUniform1",
+         "parameters": {
+          "minimum": 0.12,
+          "maximum": 0.135
+         }
+        },
+        "units": {
+         "expression": "1/day",
+         "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+        }
+       },
+       {
+        "id": "vacc_mult",
+        "value": 0.3,
+        "units": {
+         "expression": "1",
+         "expression_mathml": "<cn>1</cn>"
+        }
+       },
+       {
+        "id": "r_Sv",
+        "value": 10000.0,
+        "distribution": {
+         "type": "StandardUniform1",
+         "parameters": {
+          "minimum": 9999.0,
+          "maximum": 10001.0
+         }
+        },
+        "units": {
+         "expression": "1/day",
+         "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+        }
+       },
+       {
+        "id": "r_SvSu",
+        "value": 0.002,
+        "units": {
+         "expression": "1/day",
+         "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+        }
+       },
+       {
+        "id": "r_EI",
+        "value": 0.1424,
+        "distribution": {
+         "type": "StandardUniform1",
+         "parameters": {
+          "minimum": 0.1,
+          "maximum": 0.25
+         }
+        },
+        "units": {
+         "expression": "1/day",
+         "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+        }
+       },
+       {
+        "id": "r_IH_u",
+        "value": 0.0041,
+        "distribution": {
+         "type": "StandardUniform1",
+         "parameters": {
+          "minimum": 0.004,
+          "maximum": 0.0045
+         }
+        },
+        "units": {
+         "expression": "1/day",
+         "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+        }
+       },
+       {
+        "id": "r_IH_v",
+        "value": 0.0013,
+        "distribution": {
+         "type": "StandardUniform1",
+         "parameters": {
+          "minimum": 0.001,
+          "maximum": 0.0015
+         }
+        },
+        "units": {
+         "expression": "1/day",
+         "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+        }
+       },
+       {
+        "id": "r_HR",
+        "value": 0.155,
+        "distribution": {
+         "type": "StandardUniform1",
+         "parameters": {
+          "minimum": 0.12,
+          "maximum": 0.2
+         }
+        },
+        "units": {
+         "expression": "1/day",
+         "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+        }
+       },
+       {
+        "id": "r_HD",
+        "value": 0.0099,
+        "distribution": {
+         "type": "StandardUniform1",
+         "parameters": {
+          "minimum": 0.0095,
+          "maximum": 0.012
+         }
+        },
+        "units": {
+         "expression": "1/day",
+         "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+        }
+       },
+       {
+        "id": "r_IR_u",
+        "value": 0.15,
+        "distribution": {
+         "type": "StandardUniform1",
+         "parameters": {
+          "minimum": 0.1,
+          "maximum": 0.2
+         }
+        }
+       },
+       {
+        "id": "r_IR_v",
+        "value": 0.162,
+        "distribution": {
+         "type": "StandardUniform1",
+         "parameters": {
+          "minimum": 0.15,
+          "maximum": 0.2
+         }
+        }
+       }
+      ],
+      "observables": [
+       {
+        "id": "cases",
+        "name": "cases",
+        "expression": "I_u + I_v",
+        "expression_mathml": "<apply><plus/><ci>I_u</ci><ci>I_v</ci></apply>"
+       },
+       {
+        "id": "hospitalized",
+        "name": "hospitalized",
+        "expression": "H",
+        "expression_mathml": "<ci>H</ci>"
+       },
+       {
+        "id": "deceased",
+        "name": "deceased",
+        "expression": "D",
+        "expression_mathml": "<ci>D</ci>"
+       },
+       {
+        "id": "cumulative_cases",
+        "name": "cumulative_cases",
+        "expression": "Cumulative_cases",
+        "expression_mathml": "<ci>Cumulative_cases</ci>"
+       }
+      ],
+      "time": {
+       "id": "t",
+       "units": {
+        "expression": "day",
+        "expression_mathml": "<ci>day</ci>"
+       }
+      }
+     }
+    },
+    "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/2024-07/SEIRHD_vacc_petrinet_mod.json b/resources/amr/petrinet/monthly-demo/2024-07/SEIRHD_vacc_petrinet_mod.json
new file mode 100644
index 00000000..45466f5b
--- /dev/null
+++ b/resources/amr/petrinet/monthly-demo/2024-07/SEIRHD_vacc_petrinet_mod.json
@@ -0,0 +1,631 @@
+{
+    "header": {
+        "name": "SEIRHD vacc model for LA County t0 = 10/28/2021",
+        "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json",
+        "schema_name": "petrinet",
+        "description": "SEIRHD vacc model for LA County t0 = 10/28/2021",
+        "model_version": "0.1"
+    },
+    "properties": {},
+    "model": {
+        "states": [
+            {
+                "id": "S_u",
+                "name": "S_u",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {
+                        "status": "unvaccinated"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "I_u",
+                "name": "I_u",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "status": "unvaccinated"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "E_u",
+                "name": "E_u",
+                "grounding": {
+                    "identifiers": {
+                        "apollosv": "00000154"
+                    },
+                    "modifiers": {
+                        "status": "unvaccinated"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "S_v",
+                "name": "S_v",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {
+                        "status": "vaccinated"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "E_v",
+                "name": "E_v",
+                "grounding": {
+                    "identifiers": {
+                        "apollosv": "00000154"
+                    },
+                    "modifiers": {
+                        "status": "vaccinated"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "I_v",
+                "name": "I_v",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "status": "vaccinated"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "H",
+                "name": "H",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "property": "ncit:C25179"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "R",
+                "name": "R",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "D",
+                "name": "D",
+                "grounding": {
+                    "identifiers": {
+                        "ncit": "C28554"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            }
+        ],
+        "transitions": [
+            {
+                "id": "t1",
+                "input": [
+                    "I_u",
+                    "S_u"
+                ],
+                "output": [
+                    "I_u",
+                    "E_u"
+                ],
+                "properties": {
+                    "name": "t1"
+                }
+            },
+            {
+                "id": "t2",
+                "input": [
+                    "I_u",
+                    "S_v"
+                ],
+                "output": [
+                    "I_u",
+                    "E_v"
+                ],
+                "properties": {
+                    "name": "t2"
+                }
+            },
+            {
+                "id": "t3",
+                "input": [
+                    "I_v",
+                    "S_u"
+                ],
+                "output": [
+                    "I_v",
+                    "E_u"
+                ],
+                "properties": {
+                    "name": "t3"
+                }
+            },
+            {
+                "id": "t4",
+                "input": [
+                    "I_v",
+                    "S_v"
+                ],
+                "output": [
+                    "I_v",
+                    "E_v"
+                ],
+                "properties": {
+                    "name": "t4"
+                }
+            },
+            {
+                "id": "t5",
+                "input": [
+                    "S_u"
+                ],
+                "output": [
+                    "S_v"
+                ],
+                "properties": {
+                    "name": "t5"
+                }
+            },
+            {
+                "id": "t6",
+                "input": [
+                    "S_v"
+                ],
+                "output": [
+                    "S_u"
+                ],
+                "properties": {
+                    "name": "t6"
+                }
+            },
+            {
+                "id": "t7",
+                "input": [
+                    "E_u"
+                ],
+                "output": [
+                    "I_u"
+                ],
+                "properties": {
+                    "name": "t7"
+                }
+            },
+            {
+                "id": "t8",
+                "input": [
+                    "E_v"
+                ],
+                "output": [
+                    "I_v"
+                ],
+                "properties": {
+                    "name": "t8"
+                }
+            },
+            {
+                "id": "t9",
+                "input": [
+                    "I_u"
+                ],
+                "output": [
+                    "H"
+                ],
+                "properties": {
+                    "name": "t9"
+                }
+            },
+            {
+                "id": "t10",
+                "input": [
+                    "I_v"
+                ],
+                "output": [
+                    "H"
+                ],
+                "properties": {
+                    "name": "t10"
+                }
+            },
+            {
+                "id": "t11",
+                "input": [
+                    "H"
+                ],
+                "output": [
+                    "R"
+                ],
+                "properties": {
+                    "name": "t11"
+                }
+            },
+            {
+                "id": "t12",
+                "input": [
+                    "H"
+                ],
+                "output": [
+                    "D"
+                ],
+                "properties": {
+                    "name": "t12"
+                }
+            }
+        ]
+    },
+    "semantics": {
+        "ode": {
+            "rates": [
+                {
+                    "target": "t1",
+                    "expression": "I_u*NPI_mult*S_u*beta/N",
+                    "expression_mathml": "<apply><divide/><apply><times/><ci>I_u</ci><ci>NPI_mult</ci><ci>S_u</ci><ci>beta</ci></apply><ci>N</ci></apply>"
+                },
+                {
+                    "target": "t2",
+                    "expression": "I_u*NPI_mult*S_v*beta*vacc_mult/N",
+                    "expression_mathml": "<apply><divide/><apply><times/><ci>I_u</ci><ci>NPI_mult</ci><ci>S_v</ci><ci>beta</ci><ci>vacc_mult</ci></apply><ci>N</ci></apply>"
+                },
+                {
+                    "target": "t3",
+                    "expression": "I_v*NPI_mult*S_u*beta/N",
+                    "expression_mathml": "<apply><divide/><apply><times/><ci>I_v</ci><ci>NPI_mult</ci><ci>S_u</ci><ci>beta</ci></apply><ci>N</ci></apply>"
+                },
+                {
+                    "target": "t4",
+                    "expression": "I_v*NPI_mult*S_v*beta*vacc_mult/N",
+                    "expression_mathml": "<apply><divide/><apply><times/><ci>I_v</ci><ci>NPI_mult</ci><ci>S_v</ci><ci>beta</ci><ci>vacc_mult</ci></apply><ci>N</ci></apply>"
+                },
+                {
+                    "target": "t5",
+                    "expression": "S_u*r_Sv",
+                    "expression_mathml": "<ci>r_Sv</ci>"
+                },
+                {
+                    "target": "t6",
+                    "expression": "S_v*r_SvSu",
+                    "expression_mathml": "<apply><times/><ci>S_v</ci><ci>r_SvSu</ci></apply>"
+                },
+                {
+                    "target": "t7",
+                    "expression": "E_u*r_EI",
+                    "expression_mathml": "<apply><times/><ci>E_u</ci><ci>r_EI</ci></apply>"
+                },
+                {
+                    "target": "t8",
+                    "expression": "E_v*r_EI",
+                    "expression_mathml": "<apply><times/><ci>E_v</ci><ci>r_EI</ci></apply>"
+                },
+                {
+                    "target": "t9",
+                    "expression": "I_u*r_IH_u",
+                    "expression_mathml": "<apply><times/><ci>I_u</ci><ci>r_IH_u</ci></apply>"
+                },
+                {
+                    "target": "t10",
+                    "expression": "I_v*r_IH_v",
+                    "expression_mathml": "<apply><times/><ci>I_v</ci><ci>r_IH_v</ci></apply>"
+                },
+                {
+                    "target": "t11",
+                    "expression": "H*r_HR",
+                    "expression_mathml": "<apply><times/><ci>H</ci><ci>r_HR</ci></apply>"
+                },
+                {
+                    "target": "t12",
+                    "expression": "H*r_HD",
+                    "expression_mathml": "<apply><times/><ci>H</ci><ci>r_HD</ci></apply>"
+                }
+            ],
+            "initials": [
+                {
+                    "target": "S_u",
+                    "expression": "2324633.64",
+                    "expression_mathml": "<cn>2324633.6400000001</cn>"
+                },
+                {
+                    "target": "I_u",
+                    "expression": "3458.66666666667",
+                    "expression_mathml": "<cn>3458.6666666666665</cn>"
+                },
+                {
+                    "target": "E_u",
+                    "expression": "6917.33333333333",
+                    "expression_mathml": "<cn>6917.333333333333</cn>"
+                },
+                {
+                    "target": "S_v",
+                    "expression": "5977629.36",
+                    "expression_mathml": "<cn>5977629.3599999994</cn>"
+                },
+                {
+                    "target": "E_v",
+                    "expression": "3458.66666666667",
+                    "expression_mathml": "<cn>3458.6666666666665</cn>"
+                },
+                {
+                    "target": "I_v",
+                    "expression": "1729.33333333333",
+                    "expression_mathml": "<cn>1729.3333333333333</cn>"
+                },
+                {
+                    "target": "H",
+                    "expression": "673",
+                    "expression_mathml": "<cn>673</cn>"
+                },
+                {
+                    "target": "R",
+                    "expression": "1484896",
+                    "expression_mathml": "<cn>1484896</cn>"
+                },
+                {
+                    "target": "D",
+                    "expression": "26604",
+                    "expression_mathml": "<cn>26604</cn>"
+                }
+            ],
+            "parameters": [
+                {
+                    "id": "N",
+                    "value": 9830000.0,
+                    "units": {
+                        "expression": "person",
+                        "expression_mathml": "<ci>person</ci>"
+                    }
+                },
+                {
+                    "id": "NPI_mult",
+                    "value": 1.0,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.999,
+                            "maximum": 1.001
+                        }
+                    },
+                    "units": {
+                        "expression": "1",
+                        "expression_mathml": "<cn>1</cn>"
+                    }
+                },
+                {
+                    "id": "beta",
+                    "value": 0.125,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.12,
+                            "maximum": 0.135
+                        }
+                    },
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "vacc_mult",
+                    "value": 0.3,
+                    "units": {
+                        "expression": "1",
+                        "expression_mathml": "<cn>1</cn>"
+                    }
+                },
+                {
+                    "id": "r_Sv",
+                    "value": 10000.0,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.001,
+                            "maximum": 0.002
+                        }
+                    },
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "r_SvSu",
+                    "value": 0.002,
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "r_EI",
+                    "value": 0.1424,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.1,
+                            "maximum": 0.25
+                        }
+                    },
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "r_IH_u",
+                    "value": 0.0041,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.004,
+                            "maximum": 0.0045
+                        }
+                    },
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "r_IH_v",
+                    "value": 0.0013,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.001,
+                            "maximum": 0.0015
+                        }
+                    },
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "r_HR",
+                    "value": 0.155,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.12,
+                            "maximum": 0.2
+                        }
+                    },
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "r_HD",
+                    "value": 0.0099,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.0095,
+                            "maximum": 0.012
+                        }
+                    },
+                    "units": {
+                        "expression": "1/day",
+                        "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                    }
+                },
+                {
+                    "id": "r_IR_u",
+                    "value": 0.15,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.1,
+                            "maximum": 0.2
+                        }
+                    }
+                },
+                {
+                    "id": "r_IR_v",
+                    "value": 0.162,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.15,
+                            "maximum": 0.2
+                        }
+                    }
+                }
+            ],
+            "observables": [
+                {
+                    "id": "cases",
+                    "name": "cases",
+                    "expression": "I_u + I_v",
+                    "expression_mathml": "<apply><plus/><ci>I_u</ci><ci>I_v</ci></apply>"
+                },
+                {
+                    "id": "hospitalized",
+                    "name": "hospitalized",
+                    "expression": "H",
+                    "expression_mathml": "<ci>H</ci>"
+                },
+                {
+                    "id": "deceased",
+                    "name": "deceased",
+                    "expression": "D",
+                    "expression_mathml": "<ci>D</ci>"
+                }
+            ],
+            "time": {
+                "id": "t",
+                "units": {
+                    "expression": "day",
+                    "expression_mathml": "<ci>day</ci>"
+                }
+            }
+        }
+    },
+    "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/2024-07/SEIRHD_vacc_petrinet_request.json b/resources/amr/petrinet/monthly-demo/2024-07/SEIRHD_vacc_petrinet_request.json
new file mode 100644
index 00000000..1fabfb1f
--- /dev/null
+++ b/resources/amr/petrinet/monthly-demo/2024-07/SEIRHD_vacc_petrinet_request.json
@@ -0,0 +1,177 @@
+{
+    "constraints": [
+        {
+            "name": "max_H",
+            "variables": [
+                "H"
+            ],
+            "additive_bounds": {
+                "ub": 3000
+            },
+            "soft": true
+        },
+        {
+            "name": "increasing_D",
+            "variables": [
+                "D"
+            ],
+            "additive_bounds": {
+                "lb": 0
+            },
+            "derivative": true,
+            "soft": false
+        },
+        {
+            "name": "increasing_R",
+            "variables": [
+                "R"
+            ],
+            "additive_bounds": {
+                "lb": 0
+            },
+            "derivative": true,
+            "soft": false
+        },
+        {
+            "name": "increasing_CC",
+            "variables": [
+                "Cumulative_cases"
+            ],
+            "additive_bounds": {
+                "lb": 0
+            },
+            "derivative": true,
+            "soft": false
+        },
+        {
+            "name": "decreasing_S",
+            "variables": [
+                "S_u",
+                "S_v"
+            ],
+            "weights": [
+                -1,
+                -1
+            ],
+            "additive_bounds": {
+                "lb": 0
+            },
+            "derivative": true,
+            "soft": false
+        }
+    ],
+    "parameters": [
+        {
+            "name": "r_SvSu",
+            "interval": {
+                "lb": 0.002,
+                "ub": 0.002,
+                "closed_upper_bound": true
+            },
+            "label": "any"
+        },
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.15,
+                "ub": 0.155
+            },
+            "label": "all"
+        },
+        {
+            "name": "NPI_mult",
+            "interval": {
+                "lb": 1.0,
+                "ub": 1.0,
+                "closed_upper_bound": true
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_Sv",
+            "interval": {
+                "lb": 10000.0,
+                "ub": 10000.0,
+                "closed_upper_bound": true
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_EI",
+            "interval": {
+                "lb": 0.12,
+                "ub": 0.155
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_IH_u",
+            "interval": {
+                "lb": 0.0042,
+                "ub": 0.0045
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_IH_v",
+            "interval": {
+                "lb": 0.0012,
+                "ub": 0.0015
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_HR",
+            "interval": {
+                "lb": 0.15,
+                "ub": 0.2
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_HD",
+            "interval": {
+                "lb": 0.01,
+                "ub": 0.012
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_IR_u",
+            "interval": {
+                "lb": 0.17,
+                "ub": 0.2
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_IR_v",
+            "interval": {
+                "lb": 0.17,
+                "ub": 0.2
+            },
+            "label": "all"
+        }
+    ],
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10,
+                        20
+                    ]
+                }
+            ]
+        }
+    ],
+    "config": {
+        "normalization_constant": 9830000.0,
+        "normalize": false,
+        "tolerance": 1e-1,
+        "use_compartmental_constraints": true,
+        "dreal_precision": 1e-5
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/monthly-demo/2024-07/SEIRHD_vacc_petrinet_request_mod.json b/resources/amr/petrinet/monthly-demo/2024-07/SEIRHD_vacc_petrinet_request_mod.json
new file mode 100644
index 00000000..d5f4382c
--- /dev/null
+++ b/resources/amr/petrinet/monthly-demo/2024-07/SEIRHD_vacc_petrinet_request_mod.json
@@ -0,0 +1,120 @@
+{
+    "constraints": [
+        {
+            "name": "max_H",
+            "variables": [
+                "H"
+            ],
+            "additive_bounds": {
+                "lb": 3000
+            },
+            "timepoints": {
+                "lb": 45,
+                "ub": 70
+            }
+        }
+    ],
+    "parameters": [
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.012,
+                "ub": 0.155
+            },
+            "label": "all"
+        },
+        {
+            "name": "NPI_mult",
+            "interval": {
+                "lb": 0.999,
+                "ub": 1.001
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_Sv",
+            "interval": {
+                "lb": 0.0001,
+                "ub": 0.1
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_EI",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.25
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_IH_u",
+            "interval": {
+                "lb": 0.004,
+                "ub": 0.0045
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_IH_v",
+            "interval": {
+                "lb": 0.001,
+                "ub": 0.0015
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_HR",
+            "interval": {
+                "lb": 0.12,
+                "ub": 0.2
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_HD",
+            "interval": {
+                "lb": 0.0095,
+                "ub": 0.012
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_IR_u",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.2
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_IR_v",
+            "interval": {
+                "lb": 0.15,
+                "ub": 0.2
+            },
+            "label": "all"
+        }
+    ],
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10,
+                        20
+                    ]
+                }
+            ]
+        }
+    ],
+    "config": {
+        "normalization_constant": 9830000.0,
+        "normalize": false,
+        "tolerance": 1e-1,
+        "use_compartmental_constraints": true,
+        "dreal_precision": 1e-5
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/stratified/model_amr.json b/resources/amr/petrinet/stratified/model_amr.json
new file mode 100644
index 00000000..537effdf
--- /dev/null
+++ b/resources/amr/petrinet/stratified/model_amr.json
@@ -0,0 +1,9110 @@
+{
+  "header": {
+    "name": "Toby's Great Adventure SEIRHD 07-19 11:38",
+    "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.1/petrinet/petrinet_schema.json",
+    "schema_name": "petrinet",
+    "description": "        This model has been stratified by age (4 ways),\n        variant (3 ways), and vaccine status (2 ways).\n\n        The naming convention is to have the base\n        population type (e.g., S, E, I) followed by underscores\n        in the order of age, variant, disase status. All compartments\n        are stratified by all three except for the S compartment, which\n        is only stratified by age and status.\n\n        Because death is stratified, it might be useful to create observables\n        which e.g. take the sum over all statuses/ages/variants.\n\n        We used numerical indexes (encoded as strings) for each of the age,\n        variants, and statuses.\n\n        - Status: (0) unvaccinated and (1) vaccinated\n        - Ages: (0) for 0-19, (1) for 20-49, (2) for 50-64, and (3) for 65+\n        - Variants: (0) wild, (1) delta, and (2) omicron\n        \n        This model also includes the conversion from unvaccinated susceptible\n        individuals to vaccinated susceptible individuals (with conserved age).\n        The parameter alpha ideally is a time dependent function.\n        \n        TODO we can model the reverse process (i.e., waning) of vaccinated\n        susceptible individuals becoming unvaccinated as a decay of vaccine\n        efficacy, which would also be time dependant.\n        ",
+    "model_version": "0.1"
+  },
+  "id": "078607a5-77dd-48d2-8d39-b051c9b88c8f",
+  "timestamp": "2023-07-19 16:29:58",
+  "username": null,
+  "model": {
+    "states": [
+      {
+        "id": "S_0_0",
+        "name": "S_0_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000514"
+          },
+          "modifiers": {
+            "age": "0",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_0_0_0",
+        "name": "I_0_0_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "0",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "E_0_0_0",
+        "name": "E_0_0_0",
+        "grounding": {
+          "identifiers": {
+            "apollosv": "0000154"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "0",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_0_0_1",
+        "name": "I_0_0_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "0",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_1_0_0",
+        "name": "I_1_0_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "1",
+            "variant": "0",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_1_0_1",
+        "name": "I_1_0_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "1",
+            "variant": "0",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_2_0_0",
+        "name": "I_2_0_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "2",
+            "variant": "0",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_2_0_1",
+        "name": "I_2_0_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "2",
+            "variant": "0",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_3_0_0",
+        "name": "I_3_0_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "3",
+            "variant": "0",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_3_0_1",
+        "name": "I_3_0_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "3",
+            "variant": "0",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_0_1_0",
+        "name": "I_0_1_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "1",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "E_0_1_0",
+        "name": "E_0_1_0",
+        "grounding": {
+          "identifiers": {
+            "apollosv": "0000154"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "1",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_0_1_1",
+        "name": "I_0_1_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "1",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_1_1_0",
+        "name": "I_1_1_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "1",
+            "variant": "1",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_1_1_1",
+        "name": "I_1_1_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "1",
+            "variant": "1",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_2_1_0",
+        "name": "I_2_1_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "2",
+            "variant": "1",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_2_1_1",
+        "name": "I_2_1_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "2",
+            "variant": "1",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_3_1_0",
+        "name": "I_3_1_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "3",
+            "variant": "1",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_3_1_1",
+        "name": "I_3_1_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "3",
+            "variant": "1",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_0_2_0",
+        "name": "I_0_2_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "2",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "E_0_2_0",
+        "name": "E_0_2_0",
+        "grounding": {
+          "identifiers": {
+            "apollosv": "0000154"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "2",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_0_2_1",
+        "name": "I_0_2_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "2",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_1_2_0",
+        "name": "I_1_2_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "1",
+            "variant": "2",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_1_2_1",
+        "name": "I_1_2_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "1",
+            "variant": "2",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_2_2_0",
+        "name": "I_2_2_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "2",
+            "variant": "2",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_2_2_1",
+        "name": "I_2_2_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "2",
+            "variant": "2",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_3_2_0",
+        "name": "I_3_2_0",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "3",
+            "variant": "2",
+            "status": "0"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "I_3_2_1",
+        "name": "I_3_2_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000511"
+          },
+          "modifiers": {
+            "age": "3",
+            "variant": "2",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "S_0_1",
+        "name": "S_0_1",
+        "grounding": {
+          "identifiers": {
+            "ido": "0000514"
+          },
+          "modifiers": {
+            "age": "0",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "E_0_0_1",
+        "name": "E_0_0_1",
+        "grounding": {
+          "identifiers": {
+            "apollosv": "0000154"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "0",
+            "status": "1"
+          }
+        },
+        "units": {
+          "expression": "person",
+          "expression_mathml": "<ci>person</ci>"
+        }
+      },
+      {
+        "id": "E_0_1_1",
+        "name": "E_0_1_1",
+        "grounding": {
+          "identifiers": {
+            "apollosv": "0000154"
+          },
+          "modifiers": {
+            "age": "0",
+            "variant": "1",
+            "status": "1"
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+        ],
+        "output": [
+          "D_0_0_0"
+        ],
+        "properties": {
+          "name": "t289"
+        }
+      },
+      {
+        "id": "t290",
+        "input": [
+          "H_0_0_1"
+        ],
+        "output": [
+          "D_0_0_1"
+        ],
+        "properties": {
+          "name": "t290"
+        }
+      },
+      {
+        "id": "t291",
+        "input": [
+          "H_0_1_0"
+        ],
+        "output": [
+          "D_0_1_0"
+        ],
+        "properties": {
+          "name": "t291"
+        }
+      },
+      {
+        "id": "t292",
+        "input": [
+          "H_0_1_1"
+        ],
+        "output": [
+          "D_0_1_1"
+        ],
+        "properties": {
+          "name": "t292"
+        }
+      },
+      {
+        "id": "t293",
+        "input": [
+          "H_0_2_0"
+        ],
+        "output": [
+          "D_0_2_0"
+        ],
+        "properties": {
+          "name": "t293"
+        }
+      },
+      {
+        "id": "t294",
+        "input": [
+          "H_0_2_1"
+        ],
+        "output": [
+          "D_0_2_1"
+        ],
+        "properties": {
+          "name": "t294"
+        }
+      },
+      {
+        "id": "t295",
+        "input": [
+          "H_1_0_0"
+        ],
+        "output": [
+          "D_1_0_0"
+        ],
+        "properties": {
+          "name": "t295"
+        }
+      },
+      {
+        "id": "t296",
+        "input": [
+          "H_1_0_1"
+        ],
+        "output": [
+          "D_1_0_1"
+        ],
+        "properties": {
+          "name": "t296"
+        }
+      },
+      {
+        "id": "t297",
+        "input": [
+          "H_1_1_0"
+        ],
+        "output": [
+          "D_1_1_0"
+        ],
+        "properties": {
+          "name": "t297"
+        }
+      },
+      {
+        "id": "t298",
+        "input": [
+          "H_1_1_1"
+        ],
+        "output": [
+          "D_1_1_1"
+        ],
+        "properties": {
+          "name": "t298"
+        }
+      },
+      {
+        "id": "t299",
+        "input": [
+          "H_1_2_0"
+        ],
+        "output": [
+          "D_1_2_0"
+        ],
+        "properties": {
+          "name": "t299"
+        }
+      },
+      {
+        "id": "t300",
+        "input": [
+          "H_1_2_1"
+        ],
+        "output": [
+          "D_1_2_1"
+        ],
+        "properties": {
+          "name": "t300"
+        }
+      },
+      {
+        "id": "t301",
+        "input": [
+          "H_2_0_0"
+        ],
+        "output": [
+          "D_2_0_0"
+        ],
+        "properties": {
+          "name": "t301"
+        }
+      },
+      {
+        "id": "t302",
+        "input": [
+          "H_2_0_1"
+        ],
+        "output": [
+          "D_2_0_1"
+        ],
+        "properties": {
+          "name": "t302"
+        }
+      },
+      {
+        "id": "t303",
+        "input": [
+          "H_2_1_0"
+        ],
+        "output": [
+          "D_2_1_0"
+        ],
+        "properties": {
+          "name": "t303"
+        }
+      },
+      {
+        "id": "t304",
+        "input": [
+          "H_2_1_1"
+        ],
+        "output": [
+          "D_2_1_1"
+        ],
+        "properties": {
+          "name": "t304"
+        }
+      },
+      {
+        "id": "t305",
+        "input": [
+          "H_2_2_0"
+        ],
+        "output": [
+          "D_2_2_0"
+        ],
+        "properties": {
+          "name": "t305"
+        }
+      },
+      {
+        "id": "t306",
+        "input": [
+          "H_2_2_1"
+        ],
+        "output": [
+          "D_2_2_1"
+        ],
+        "properties": {
+          "name": "t306"
+        }
+      },
+      {
+        "id": "t307",
+        "input": [
+          "H_3_0_0"
+        ],
+        "output": [
+          "D_3_0_0"
+        ],
+        "properties": {
+          "name": "t307"
+        }
+      },
+      {
+        "id": "t308",
+        "input": [
+          "H_3_0_1"
+        ],
+        "output": [
+          "D_3_0_1"
+        ],
+        "properties": {
+          "name": "t308"
+        }
+      },
+      {
+        "id": "t309",
+        "input": [
+          "H_3_1_0"
+        ],
+        "output": [
+          "D_3_1_0"
+        ],
+        "properties": {
+          "name": "t309"
+        }
+      },
+      {
+        "id": "t310",
+        "input": [
+          "H_3_1_1"
+        ],
+        "output": [
+          "D_3_1_1"
+        ],
+        "properties": {
+          "name": "t310"
+        }
+      },
+      {
+        "id": "t311",
+        "input": [
+          "H_3_2_0"
+        ],
+        "output": [
+          "D_3_2_0"
+        ],
+        "properties": {
+          "name": "t311"
+        }
+      },
+      {
+        "id": "t312",
+        "input": [
+          "H_3_2_1"
+        ],
+        "output": [
+          "D_3_2_1"
+        ],
+        "properties": {
+          "name": "t312"
+        }
+      },
+      {
+        "id": "t313",
+        "input": [
+          "S_0_0"
+        ],
+        "output": [
+          "S_0_1"
+        ],
+        "properties": {
+          "name": "t313"
+        }
+      },
+      {
+        "id": "t314",
+        "input": [
+          "S_1_0"
+        ],
+        "output": [
+          "S_1_1"
+        ],
+        "properties": {
+          "name": "t314"
+        }
+      },
+      {
+        "id": "t315",
+        "input": [
+          "S_2_0"
+        ],
+        "output": [
+          "S_2_1"
+        ],
+        "properties": {
+          "name": "t315"
+        }
+      },
+      {
+        "id": "t316",
+        "input": [
+          "S_3_0"
+        ],
+        "output": [
+          "S_3_1"
+        ],
+        "properties": {
+          "name": "t316"
+        }
+      }
+    ]
+  },
+  "schema_name": "petrinet",
+  "model_version": "0.1",
+  "semantics": {
+    "ode": {
+      "rates": [
+        {
+          "target": "t1",
+          "expression": "I_0_0_0*S_0_0*chi_0*d_0_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_0</ci><ci>S_0_0</ci><ci>chi_0</ci><ci>d_0_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t2",
+          "expression": "I_0_0_1*S_0_0*chi_0*d_0_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_1</ci><ci>S_0_0</ci><ci>chi_0</ci><ci>d_0_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t3",
+          "expression": "I_1_0_0*S_0_0*chi_0*d_0_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_0</ci><ci>S_0_0</ci><ci>chi_0</ci><ci>d_0_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t4",
+          "expression": "I_1_0_1*S_0_0*chi_0*d_0_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_1</ci><ci>S_0_0</ci><ci>chi_0</ci><ci>d_0_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t5",
+          "expression": "I_2_0_0*S_0_0*chi_0*d_0_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_0</ci><ci>S_0_0</ci><ci>chi_0</ci><ci>d_0_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t6",
+          "expression": "I_2_0_1*S_0_0*chi_0*d_0_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_1</ci><ci>S_0_0</ci><ci>chi_0</ci><ci>d_0_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t7",
+          "expression": "I_3_0_0*S_0_0*chi_0*d_0_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_0</ci><ci>S_0_0</ci><ci>chi_0</ci><ci>d_0_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t8",
+          "expression": "I_3_0_1*S_0_0*chi_0*d_0_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_1</ci><ci>S_0_0</ci><ci>chi_0</ci><ci>d_0_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t9",
+          "expression": "I_0_1_0*S_0_0*chi_1*d_0_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_0</ci><ci>S_0_0</ci><ci>chi_1</ci><ci>d_0_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t10",
+          "expression": "I_0_1_1*S_0_0*chi_1*d_0_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_1</ci><ci>S_0_0</ci><ci>chi_1</ci><ci>d_0_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t11",
+          "expression": "I_1_1_0*S_0_0*chi_1*d_0_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_0</ci><ci>S_0_0</ci><ci>chi_1</ci><ci>d_0_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t12",
+          "expression": "I_1_1_1*S_0_0*chi_1*d_0_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_1</ci><ci>S_0_0</ci><ci>chi_1</ci><ci>d_0_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t13",
+          "expression": "I_2_1_0*S_0_0*chi_1*d_0_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_0</ci><ci>S_0_0</ci><ci>chi_1</ci><ci>d_0_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t14",
+          "expression": "I_2_1_1*S_0_0*chi_1*d_0_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_1</ci><ci>S_0_0</ci><ci>chi_1</ci><ci>d_0_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t15",
+          "expression": "I_3_1_0*S_0_0*chi_1*d_0_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_0</ci><ci>S_0_0</ci><ci>chi_1</ci><ci>d_0_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t16",
+          "expression": "I_3_1_1*S_0_0*chi_1*d_0_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_1</ci><ci>S_0_0</ci><ci>chi_1</ci><ci>d_0_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t17",
+          "expression": "I_0_2_0*S_0_0*chi_2*d_0_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_0</ci><ci>S_0_0</ci><ci>chi_2</ci><ci>d_0_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t18",
+          "expression": "I_0_2_1*S_0_0*chi_2*d_0_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_1</ci><ci>S_0_0</ci><ci>chi_2</ci><ci>d_0_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t19",
+          "expression": "I_1_2_0*S_0_0*chi_2*d_0_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_0</ci><ci>S_0_0</ci><ci>chi_2</ci><ci>d_0_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t20",
+          "expression": "I_1_2_1*S_0_0*chi_2*d_0_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_1</ci><ci>S_0_0</ci><ci>chi_2</ci><ci>d_0_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t21",
+          "expression": "I_2_2_0*S_0_0*chi_2*d_0_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_0</ci><ci>S_0_0</ci><ci>chi_2</ci><ci>d_0_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t22",
+          "expression": "I_2_2_1*S_0_0*chi_2*d_0_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_1</ci><ci>S_0_0</ci><ci>chi_2</ci><ci>d_0_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t23",
+          "expression": "I_3_2_0*S_0_0*chi_2*d_0_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_0</ci><ci>S_0_0</ci><ci>chi_2</ci><ci>d_0_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t24",
+          "expression": "I_3_2_1*S_0_0*chi_2*d_0_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_1</ci><ci>S_0_0</ci><ci>chi_2</ci><ci>d_0_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t25",
+          "expression": "I_0_0_0*S_0_1*chi_0*d_0_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_0</ci><ci>S_0_1</ci><ci>chi_0</ci><ci>d_0_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t26",
+          "expression": "I_0_0_1*S_0_1*chi_0*d_0_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_1</ci><ci>S_0_1</ci><ci>chi_0</ci><ci>d_0_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t27",
+          "expression": "I_1_0_0*S_0_1*chi_0*d_0_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_0</ci><ci>S_0_1</ci><ci>chi_0</ci><ci>d_0_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t28",
+          "expression": "I_1_0_1*S_0_1*chi_0*d_0_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_1</ci><ci>S_0_1</ci><ci>chi_0</ci><ci>d_0_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t29",
+          "expression": "I_2_0_0*S_0_1*chi_0*d_0_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_0</ci><ci>S_0_1</ci><ci>chi_0</ci><ci>d_0_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t30",
+          "expression": "I_2_0_1*S_0_1*chi_0*d_0_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_1</ci><ci>S_0_1</ci><ci>chi_0</ci><ci>d_0_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t31",
+          "expression": "I_3_0_0*S_0_1*chi_0*d_0_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_0</ci><ci>S_0_1</ci><ci>chi_0</ci><ci>d_0_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t32",
+          "expression": "I_3_0_1*S_0_1*chi_0*d_0_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_1</ci><ci>S_0_1</ci><ci>chi_0</ci><ci>d_0_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t33",
+          "expression": "I_0_1_0*S_0_1*chi_1*d_0_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_0</ci><ci>S_0_1</ci><ci>chi_1</ci><ci>d_0_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t34",
+          "expression": "I_0_1_1*S_0_1*chi_1*d_0_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_1</ci><ci>S_0_1</ci><ci>chi_1</ci><ci>d_0_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t35",
+          "expression": "I_1_1_0*S_0_1*chi_1*d_0_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_0</ci><ci>S_0_1</ci><ci>chi_1</ci><ci>d_0_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t36",
+          "expression": "I_1_1_1*S_0_1*chi_1*d_0_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_1</ci><ci>S_0_1</ci><ci>chi_1</ci><ci>d_0_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t37",
+          "expression": "I_2_1_0*S_0_1*chi_1*d_0_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_0</ci><ci>S_0_1</ci><ci>chi_1</ci><ci>d_0_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t38",
+          "expression": "I_2_1_1*S_0_1*chi_1*d_0_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_1</ci><ci>S_0_1</ci><ci>chi_1</ci><ci>d_0_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t39",
+          "expression": "I_3_1_0*S_0_1*chi_1*d_0_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_0</ci><ci>S_0_1</ci><ci>chi_1</ci><ci>d_0_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t40",
+          "expression": "I_3_1_1*S_0_1*chi_1*d_0_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_1</ci><ci>S_0_1</ci><ci>chi_1</ci><ci>d_0_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t41",
+          "expression": "I_0_2_0*S_0_1*chi_2*d_0_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_0</ci><ci>S_0_1</ci><ci>chi_2</ci><ci>d_0_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t42",
+          "expression": "I_0_2_1*S_0_1*chi_2*d_0_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_1</ci><ci>S_0_1</ci><ci>chi_2</ci><ci>d_0_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t43",
+          "expression": "I_1_2_0*S_0_1*chi_2*d_0_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_0</ci><ci>S_0_1</ci><ci>chi_2</ci><ci>d_0_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t44",
+          "expression": "I_1_2_1*S_0_1*chi_2*d_0_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_1</ci><ci>S_0_1</ci><ci>chi_2</ci><ci>d_0_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t45",
+          "expression": "I_2_2_0*S_0_1*chi_2*d_0_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_0</ci><ci>S_0_1</ci><ci>chi_2</ci><ci>d_0_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t46",
+          "expression": "I_2_2_1*S_0_1*chi_2*d_0_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_1</ci><ci>S_0_1</ci><ci>chi_2</ci><ci>d_0_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t47",
+          "expression": "I_3_2_0*S_0_1*chi_2*d_0_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_0</ci><ci>S_0_1</ci><ci>chi_2</ci><ci>d_0_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t48",
+          "expression": "I_3_2_1*S_0_1*chi_2*d_0_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_1</ci><ci>S_0_1</ci><ci>chi_2</ci><ci>d_0_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t49",
+          "expression": "I_0_0_0*S_1_0*chi_0*d_1_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_0</ci><ci>S_1_0</ci><ci>chi_0</ci><ci>d_1_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t50",
+          "expression": "I_0_0_1*S_1_0*chi_0*d_1_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_1</ci><ci>S_1_0</ci><ci>chi_0</ci><ci>d_1_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t51",
+          "expression": "I_1_0_0*S_1_0*chi_0*d_1_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_0</ci><ci>S_1_0</ci><ci>chi_0</ci><ci>d_1_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t52",
+          "expression": "I_1_0_1*S_1_0*chi_0*d_1_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_1</ci><ci>S_1_0</ci><ci>chi_0</ci><ci>d_1_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t53",
+          "expression": "I_2_0_0*S_1_0*chi_0*d_1_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_0</ci><ci>S_1_0</ci><ci>chi_0</ci><ci>d_1_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t54",
+          "expression": "I_2_0_1*S_1_0*chi_0*d_1_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_1</ci><ci>S_1_0</ci><ci>chi_0</ci><ci>d_1_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t55",
+          "expression": "I_3_0_0*S_1_0*chi_0*d_1_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_0</ci><ci>S_1_0</ci><ci>chi_0</ci><ci>d_1_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t56",
+          "expression": "I_3_0_1*S_1_0*chi_0*d_1_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_1</ci><ci>S_1_0</ci><ci>chi_0</ci><ci>d_1_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t57",
+          "expression": "I_0_1_0*S_1_0*chi_1*d_1_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_0</ci><ci>S_1_0</ci><ci>chi_1</ci><ci>d_1_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t58",
+          "expression": "I_0_1_1*S_1_0*chi_1*d_1_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_1</ci><ci>S_1_0</ci><ci>chi_1</ci><ci>d_1_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t59",
+          "expression": "I_1_1_0*S_1_0*chi_1*d_1_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_0</ci><ci>S_1_0</ci><ci>chi_1</ci><ci>d_1_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t60",
+          "expression": "I_1_1_1*S_1_0*chi_1*d_1_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_1</ci><ci>S_1_0</ci><ci>chi_1</ci><ci>d_1_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t61",
+          "expression": "I_2_1_0*S_1_0*chi_1*d_1_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_0</ci><ci>S_1_0</ci><ci>chi_1</ci><ci>d_1_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t62",
+          "expression": "I_2_1_1*S_1_0*chi_1*d_1_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_1</ci><ci>S_1_0</ci><ci>chi_1</ci><ci>d_1_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t63",
+          "expression": "I_3_1_0*S_1_0*chi_1*d_1_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_0</ci><ci>S_1_0</ci><ci>chi_1</ci><ci>d_1_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t64",
+          "expression": "I_3_1_1*S_1_0*chi_1*d_1_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_1</ci><ci>S_1_0</ci><ci>chi_1</ci><ci>d_1_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t65",
+          "expression": "I_0_2_0*S_1_0*chi_2*d_1_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_0</ci><ci>S_1_0</ci><ci>chi_2</ci><ci>d_1_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t66",
+          "expression": "I_0_2_1*S_1_0*chi_2*d_1_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_1</ci><ci>S_1_0</ci><ci>chi_2</ci><ci>d_1_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t67",
+          "expression": "I_1_2_0*S_1_0*chi_2*d_1_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_0</ci><ci>S_1_0</ci><ci>chi_2</ci><ci>d_1_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t68",
+          "expression": "I_1_2_1*S_1_0*chi_2*d_1_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_1</ci><ci>S_1_0</ci><ci>chi_2</ci><ci>d_1_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t69",
+          "expression": "I_2_2_0*S_1_0*chi_2*d_1_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_0</ci><ci>S_1_0</ci><ci>chi_2</ci><ci>d_1_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t70",
+          "expression": "I_2_2_1*S_1_0*chi_2*d_1_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_1</ci><ci>S_1_0</ci><ci>chi_2</ci><ci>d_1_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t71",
+          "expression": "I_3_2_0*S_1_0*chi_2*d_1_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_0</ci><ci>S_1_0</ci><ci>chi_2</ci><ci>d_1_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t72",
+          "expression": "I_3_2_1*S_1_0*chi_2*d_1_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_1</ci><ci>S_1_0</ci><ci>chi_2</ci><ci>d_1_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t73",
+          "expression": "I_0_0_0*S_1_1*chi_0*d_1_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_0</ci><ci>S_1_1</ci><ci>chi_0</ci><ci>d_1_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t74",
+          "expression": "I_0_0_1*S_1_1*chi_0*d_1_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_1</ci><ci>S_1_1</ci><ci>chi_0</ci><ci>d_1_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t75",
+          "expression": "I_1_0_0*S_1_1*chi_0*d_1_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_0</ci><ci>S_1_1</ci><ci>chi_0</ci><ci>d_1_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t76",
+          "expression": "I_1_0_1*S_1_1*chi_0*d_1_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_1</ci><ci>S_1_1</ci><ci>chi_0</ci><ci>d_1_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t77",
+          "expression": "I_2_0_0*S_1_1*chi_0*d_1_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_0</ci><ci>S_1_1</ci><ci>chi_0</ci><ci>d_1_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t78",
+          "expression": "I_2_0_1*S_1_1*chi_0*d_1_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_1</ci><ci>S_1_1</ci><ci>chi_0</ci><ci>d_1_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t79",
+          "expression": "I_3_0_0*S_1_1*chi_0*d_1_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_0</ci><ci>S_1_1</ci><ci>chi_0</ci><ci>d_1_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t80",
+          "expression": "I_3_0_1*S_1_1*chi_0*d_1_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_1</ci><ci>S_1_1</ci><ci>chi_0</ci><ci>d_1_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t81",
+          "expression": "I_0_1_0*S_1_1*chi_1*d_1_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_0</ci><ci>S_1_1</ci><ci>chi_1</ci><ci>d_1_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t82",
+          "expression": "I_0_1_1*S_1_1*chi_1*d_1_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_1</ci><ci>S_1_1</ci><ci>chi_1</ci><ci>d_1_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t83",
+          "expression": "I_1_1_0*S_1_1*chi_1*d_1_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_0</ci><ci>S_1_1</ci><ci>chi_1</ci><ci>d_1_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t84",
+          "expression": "I_1_1_1*S_1_1*chi_1*d_1_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_1</ci><ci>S_1_1</ci><ci>chi_1</ci><ci>d_1_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t85",
+          "expression": "I_2_1_0*S_1_1*chi_1*d_1_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_0</ci><ci>S_1_1</ci><ci>chi_1</ci><ci>d_1_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t86",
+          "expression": "I_2_1_1*S_1_1*chi_1*d_1_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_1</ci><ci>S_1_1</ci><ci>chi_1</ci><ci>d_1_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t87",
+          "expression": "I_3_1_0*S_1_1*chi_1*d_1_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_0</ci><ci>S_1_1</ci><ci>chi_1</ci><ci>d_1_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t88",
+          "expression": "I_3_1_1*S_1_1*chi_1*d_1_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_1</ci><ci>S_1_1</ci><ci>chi_1</ci><ci>d_1_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t89",
+          "expression": "I_0_2_0*S_1_1*chi_2*d_1_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_0</ci><ci>S_1_1</ci><ci>chi_2</ci><ci>d_1_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t90",
+          "expression": "I_0_2_1*S_1_1*chi_2*d_1_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_1</ci><ci>S_1_1</ci><ci>chi_2</ci><ci>d_1_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t91",
+          "expression": "I_1_2_0*S_1_1*chi_2*d_1_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_0</ci><ci>S_1_1</ci><ci>chi_2</ci><ci>d_1_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t92",
+          "expression": "I_1_2_1*S_1_1*chi_2*d_1_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_1</ci><ci>S_1_1</ci><ci>chi_2</ci><ci>d_1_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t93",
+          "expression": "I_2_2_0*S_1_1*chi_2*d_1_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_0</ci><ci>S_1_1</ci><ci>chi_2</ci><ci>d_1_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t94",
+          "expression": "I_2_2_1*S_1_1*chi_2*d_1_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_1</ci><ci>S_1_1</ci><ci>chi_2</ci><ci>d_1_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t95",
+          "expression": "I_3_2_0*S_1_1*chi_2*d_1_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_0</ci><ci>S_1_1</ci><ci>chi_2</ci><ci>d_1_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t96",
+          "expression": "I_3_2_1*S_1_1*chi_2*d_1_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_1</ci><ci>S_1_1</ci><ci>chi_2</ci><ci>d_1_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t97",
+          "expression": "I_0_0_0*S_2_0*chi_0*d_2_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_0</ci><ci>S_2_0</ci><ci>chi_0</ci><ci>d_2_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t98",
+          "expression": "I_0_0_1*S_2_0*chi_0*d_2_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_1</ci><ci>S_2_0</ci><ci>chi_0</ci><ci>d_2_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t99",
+          "expression": "I_1_0_0*S_2_0*chi_0*d_2_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_0</ci><ci>S_2_0</ci><ci>chi_0</ci><ci>d_2_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t100",
+          "expression": "I_1_0_1*S_2_0*chi_0*d_2_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_1</ci><ci>S_2_0</ci><ci>chi_0</ci><ci>d_2_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t101",
+          "expression": "I_2_0_0*S_2_0*chi_0*d_2_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_0</ci><ci>S_2_0</ci><ci>chi_0</ci><ci>d_2_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t102",
+          "expression": "I_2_0_1*S_2_0*chi_0*d_2_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_1</ci><ci>S_2_0</ci><ci>chi_0</ci><ci>d_2_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t103",
+          "expression": "I_3_0_0*S_2_0*chi_0*d_2_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_0</ci><ci>S_2_0</ci><ci>chi_0</ci><ci>d_2_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t104",
+          "expression": "I_3_0_1*S_2_0*chi_0*d_2_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_1</ci><ci>S_2_0</ci><ci>chi_0</ci><ci>d_2_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t105",
+          "expression": "I_0_1_0*S_2_0*chi_1*d_2_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_0</ci><ci>S_2_0</ci><ci>chi_1</ci><ci>d_2_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t106",
+          "expression": "I_0_1_1*S_2_0*chi_1*d_2_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_1</ci><ci>S_2_0</ci><ci>chi_1</ci><ci>d_2_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t107",
+          "expression": "I_1_1_0*S_2_0*chi_1*d_2_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_0</ci><ci>S_2_0</ci><ci>chi_1</ci><ci>d_2_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t108",
+          "expression": "I_1_1_1*S_2_0*chi_1*d_2_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_1</ci><ci>S_2_0</ci><ci>chi_1</ci><ci>d_2_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t109",
+          "expression": "I_2_1_0*S_2_0*chi_1*d_2_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_0</ci><ci>S_2_0</ci><ci>chi_1</ci><ci>d_2_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t110",
+          "expression": "I_2_1_1*S_2_0*chi_1*d_2_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_1</ci><ci>S_2_0</ci><ci>chi_1</ci><ci>d_2_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t111",
+          "expression": "I_3_1_0*S_2_0*chi_1*d_2_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_0</ci><ci>S_2_0</ci><ci>chi_1</ci><ci>d_2_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t112",
+          "expression": "I_3_1_1*S_2_0*chi_1*d_2_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_1</ci><ci>S_2_0</ci><ci>chi_1</ci><ci>d_2_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t113",
+          "expression": "I_0_2_0*S_2_0*chi_2*d_2_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_0</ci><ci>S_2_0</ci><ci>chi_2</ci><ci>d_2_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t114",
+          "expression": "I_0_2_1*S_2_0*chi_2*d_2_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_1</ci><ci>S_2_0</ci><ci>chi_2</ci><ci>d_2_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t115",
+          "expression": "I_1_2_0*S_2_0*chi_2*d_2_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_0</ci><ci>S_2_0</ci><ci>chi_2</ci><ci>d_2_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t116",
+          "expression": "I_1_2_1*S_2_0*chi_2*d_2_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_1</ci><ci>S_2_0</ci><ci>chi_2</ci><ci>d_2_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t117",
+          "expression": "I_2_2_0*S_2_0*chi_2*d_2_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_0</ci><ci>S_2_0</ci><ci>chi_2</ci><ci>d_2_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t118",
+          "expression": "I_2_2_1*S_2_0*chi_2*d_2_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_1</ci><ci>S_2_0</ci><ci>chi_2</ci><ci>d_2_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t119",
+          "expression": "I_3_2_0*S_2_0*chi_2*d_2_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_0</ci><ci>S_2_0</ci><ci>chi_2</ci><ci>d_2_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t120",
+          "expression": "I_3_2_1*S_2_0*chi_2*d_2_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_1</ci><ci>S_2_0</ci><ci>chi_2</ci><ci>d_2_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t121",
+          "expression": "I_0_0_0*S_2_1*chi_0*d_2_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_0</ci><ci>S_2_1</ci><ci>chi_0</ci><ci>d_2_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t122",
+          "expression": "I_0_0_1*S_2_1*chi_0*d_2_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_1</ci><ci>S_2_1</ci><ci>chi_0</ci><ci>d_2_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t123",
+          "expression": "I_1_0_0*S_2_1*chi_0*d_2_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_0</ci><ci>S_2_1</ci><ci>chi_0</ci><ci>d_2_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t124",
+          "expression": "I_1_0_1*S_2_1*chi_0*d_2_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_1</ci><ci>S_2_1</ci><ci>chi_0</ci><ci>d_2_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t125",
+          "expression": "I_2_0_0*S_2_1*chi_0*d_2_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_0</ci><ci>S_2_1</ci><ci>chi_0</ci><ci>d_2_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t126",
+          "expression": "I_2_0_1*S_2_1*chi_0*d_2_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_1</ci><ci>S_2_1</ci><ci>chi_0</ci><ci>d_2_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t127",
+          "expression": "I_3_0_0*S_2_1*chi_0*d_2_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_0</ci><ci>S_2_1</ci><ci>chi_0</ci><ci>d_2_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t128",
+          "expression": "I_3_0_1*S_2_1*chi_0*d_2_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_1</ci><ci>S_2_1</ci><ci>chi_0</ci><ci>d_2_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t129",
+          "expression": "I_0_1_0*S_2_1*chi_1*d_2_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_0</ci><ci>S_2_1</ci><ci>chi_1</ci><ci>d_2_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t130",
+          "expression": "I_0_1_1*S_2_1*chi_1*d_2_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_1</ci><ci>S_2_1</ci><ci>chi_1</ci><ci>d_2_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t131",
+          "expression": "I_1_1_0*S_2_1*chi_1*d_2_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_0</ci><ci>S_2_1</ci><ci>chi_1</ci><ci>d_2_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t132",
+          "expression": "I_1_1_1*S_2_1*chi_1*d_2_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_1</ci><ci>S_2_1</ci><ci>chi_1</ci><ci>d_2_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t133",
+          "expression": "I_2_1_0*S_2_1*chi_1*d_2_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_0</ci><ci>S_2_1</ci><ci>chi_1</ci><ci>d_2_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t134",
+          "expression": "I_2_1_1*S_2_1*chi_1*d_2_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_1</ci><ci>S_2_1</ci><ci>chi_1</ci><ci>d_2_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t135",
+          "expression": "I_3_1_0*S_2_1*chi_1*d_2_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_0</ci><ci>S_2_1</ci><ci>chi_1</ci><ci>d_2_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t136",
+          "expression": "I_3_1_1*S_2_1*chi_1*d_2_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_1</ci><ci>S_2_1</ci><ci>chi_1</ci><ci>d_2_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t137",
+          "expression": "I_0_2_0*S_2_1*chi_2*d_2_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_0</ci><ci>S_2_1</ci><ci>chi_2</ci><ci>d_2_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t138",
+          "expression": "I_0_2_1*S_2_1*chi_2*d_2_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_1</ci><ci>S_2_1</ci><ci>chi_2</ci><ci>d_2_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t139",
+          "expression": "I_1_2_0*S_2_1*chi_2*d_2_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_0</ci><ci>S_2_1</ci><ci>chi_2</ci><ci>d_2_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t140",
+          "expression": "I_1_2_1*S_2_1*chi_2*d_2_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_1</ci><ci>S_2_1</ci><ci>chi_2</ci><ci>d_2_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t141",
+          "expression": "I_2_2_0*S_2_1*chi_2*d_2_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_0</ci><ci>S_2_1</ci><ci>chi_2</ci><ci>d_2_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t142",
+          "expression": "I_2_2_1*S_2_1*chi_2*d_2_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_1</ci><ci>S_2_1</ci><ci>chi_2</ci><ci>d_2_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t143",
+          "expression": "I_3_2_0*S_2_1*chi_2*d_2_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_0</ci><ci>S_2_1</ci><ci>chi_2</ci><ci>d_2_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t144",
+          "expression": "I_3_2_1*S_2_1*chi_2*d_2_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_1</ci><ci>S_2_1</ci><ci>chi_2</ci><ci>d_2_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t145",
+          "expression": "I_0_0_0*S_3_0*chi_0*d_3_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_0</ci><ci>S_3_0</ci><ci>chi_0</ci><ci>d_3_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t146",
+          "expression": "I_0_0_1*S_3_0*chi_0*d_3_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_1</ci><ci>S_3_0</ci><ci>chi_0</ci><ci>d_3_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t147",
+          "expression": "I_1_0_0*S_3_0*chi_0*d_3_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_0</ci><ci>S_3_0</ci><ci>chi_0</ci><ci>d_3_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t148",
+          "expression": "I_1_0_1*S_3_0*chi_0*d_3_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_1</ci><ci>S_3_0</ci><ci>chi_0</ci><ci>d_3_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t149",
+          "expression": "I_2_0_0*S_3_0*chi_0*d_3_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_0</ci><ci>S_3_0</ci><ci>chi_0</ci><ci>d_3_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t150",
+          "expression": "I_2_0_1*S_3_0*chi_0*d_3_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_1</ci><ci>S_3_0</ci><ci>chi_0</ci><ci>d_3_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t151",
+          "expression": "I_3_0_0*S_3_0*chi_0*d_3_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_0</ci><ci>S_3_0</ci><ci>chi_0</ci><ci>d_3_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t152",
+          "expression": "I_3_0_1*S_3_0*chi_0*d_3_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_1</ci><ci>S_3_0</ci><ci>chi_0</ci><ci>d_3_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t153",
+          "expression": "I_0_1_0*S_3_0*chi_1*d_3_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_0</ci><ci>S_3_0</ci><ci>chi_1</ci><ci>d_3_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t154",
+          "expression": "I_0_1_1*S_3_0*chi_1*d_3_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_1</ci><ci>S_3_0</ci><ci>chi_1</ci><ci>d_3_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t155",
+          "expression": "I_1_1_0*S_3_0*chi_1*d_3_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_0</ci><ci>S_3_0</ci><ci>chi_1</ci><ci>d_3_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t156",
+          "expression": "I_1_1_1*S_3_0*chi_1*d_3_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_1</ci><ci>S_3_0</ci><ci>chi_1</ci><ci>d_3_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t157",
+          "expression": "I_2_1_0*S_3_0*chi_1*d_3_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_0</ci><ci>S_3_0</ci><ci>chi_1</ci><ci>d_3_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t158",
+          "expression": "I_2_1_1*S_3_0*chi_1*d_3_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_1</ci><ci>S_3_0</ci><ci>chi_1</ci><ci>d_3_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t159",
+          "expression": "I_3_1_0*S_3_0*chi_1*d_3_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_0</ci><ci>S_3_0</ci><ci>chi_1</ci><ci>d_3_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t160",
+          "expression": "I_3_1_1*S_3_0*chi_1*d_3_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_1</ci><ci>S_3_0</ci><ci>chi_1</ci><ci>d_3_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t161",
+          "expression": "I_0_2_0*S_3_0*chi_2*d_3_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_0</ci><ci>S_3_0</ci><ci>chi_2</ci><ci>d_3_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t162",
+          "expression": "I_0_2_1*S_3_0*chi_2*d_3_0*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_1</ci><ci>S_3_0</ci><ci>chi_2</ci><ci>d_3_0</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t163",
+          "expression": "I_1_2_0*S_3_0*chi_2*d_3_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_0</ci><ci>S_3_0</ci><ci>chi_2</ci><ci>d_3_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t164",
+          "expression": "I_1_2_1*S_3_0*chi_2*d_3_1*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_1</ci><ci>S_3_0</ci><ci>chi_2</ci><ci>d_3_1</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t165",
+          "expression": "I_2_2_0*S_3_0*chi_2*d_3_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_0</ci><ci>S_3_0</ci><ci>chi_2</ci><ci>d_3_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t166",
+          "expression": "I_2_2_1*S_3_0*chi_2*d_3_2*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_1</ci><ci>S_3_0</ci><ci>chi_2</ci><ci>d_3_2</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t167",
+          "expression": "I_3_2_0*S_3_0*chi_2*d_3_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_0</ci><ci>S_3_0</ci><ci>chi_2</ci><ci>d_3_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t168",
+          "expression": "I_3_2_1*S_3_0*chi_2*d_3_3*phi_0*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_1</ci><ci>S_3_0</ci><ci>chi_2</ci><ci>d_3_3</ci><ci>phi_0</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t169",
+          "expression": "I_0_0_0*S_3_1*chi_0*d_3_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_0</ci><ci>S_3_1</ci><ci>chi_0</ci><ci>d_3_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t170",
+          "expression": "I_0_0_1*S_3_1*chi_0*d_3_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_0_1</ci><ci>S_3_1</ci><ci>chi_0</ci><ci>d_3_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t171",
+          "expression": "I_1_0_0*S_3_1*chi_0*d_3_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_0</ci><ci>S_3_1</ci><ci>chi_0</ci><ci>d_3_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t172",
+          "expression": "I_1_0_1*S_3_1*chi_0*d_3_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_0_1</ci><ci>S_3_1</ci><ci>chi_0</ci><ci>d_3_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t173",
+          "expression": "I_2_0_0*S_3_1*chi_0*d_3_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_0</ci><ci>S_3_1</ci><ci>chi_0</ci><ci>d_3_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t174",
+          "expression": "I_2_0_1*S_3_1*chi_0*d_3_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_0_1</ci><ci>S_3_1</ci><ci>chi_0</ci><ci>d_3_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t175",
+          "expression": "I_3_0_0*S_3_1*chi_0*d_3_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_0</ci><ci>S_3_1</ci><ci>chi_0</ci><ci>d_3_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t176",
+          "expression": "I_3_0_1*S_3_1*chi_0*d_3_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_0_1</ci><ci>S_3_1</ci><ci>chi_0</ci><ci>d_3_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t177",
+          "expression": "I_0_1_0*S_3_1*chi_1*d_3_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_0</ci><ci>S_3_1</ci><ci>chi_1</ci><ci>d_3_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t178",
+          "expression": "I_0_1_1*S_3_1*chi_1*d_3_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_1_1</ci><ci>S_3_1</ci><ci>chi_1</ci><ci>d_3_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t179",
+          "expression": "I_1_1_0*S_3_1*chi_1*d_3_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_0</ci><ci>S_3_1</ci><ci>chi_1</ci><ci>d_3_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t180",
+          "expression": "I_1_1_1*S_3_1*chi_1*d_3_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_1_1</ci><ci>S_3_1</ci><ci>chi_1</ci><ci>d_3_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t181",
+          "expression": "I_2_1_0*S_3_1*chi_1*d_3_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_0</ci><ci>S_3_1</ci><ci>chi_1</ci><ci>d_3_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t182",
+          "expression": "I_2_1_1*S_3_1*chi_1*d_3_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_1_1</ci><ci>S_3_1</ci><ci>chi_1</ci><ci>d_3_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t183",
+          "expression": "I_3_1_0*S_3_1*chi_1*d_3_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_0</ci><ci>S_3_1</ci><ci>chi_1</ci><ci>d_3_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t184",
+          "expression": "I_3_1_1*S_3_1*chi_1*d_3_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_1_1</ci><ci>S_3_1</ci><ci>chi_1</ci><ci>d_3_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t185",
+          "expression": "I_0_2_0*S_3_1*chi_2*d_3_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_0</ci><ci>S_3_1</ci><ci>chi_2</ci><ci>d_3_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t186",
+          "expression": "I_0_2_1*S_3_1*chi_2*d_3_0*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_0_2_1</ci><ci>S_3_1</ci><ci>chi_2</ci><ci>d_3_0</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t187",
+          "expression": "I_1_2_0*S_3_1*chi_2*d_3_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_0</ci><ci>S_3_1</ci><ci>chi_2</ci><ci>d_3_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t188",
+          "expression": "I_1_2_1*S_3_1*chi_2*d_3_1*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_1_2_1</ci><ci>S_3_1</ci><ci>chi_2</ci><ci>d_3_1</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t189",
+          "expression": "I_2_2_0*S_3_1*chi_2*d_3_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_0</ci><ci>S_3_1</ci><ci>chi_2</ci><ci>d_3_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t190",
+          "expression": "I_2_2_1*S_3_1*chi_2*d_3_2*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_2_2_1</ci><ci>S_3_1</ci><ci>chi_2</ci><ci>d_3_2</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t191",
+          "expression": "I_3_2_0*S_3_1*chi_2*d_3_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_0</ci><ci>S_3_1</ci><ci>chi_2</ci><ci>d_3_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t192",
+          "expression": "I_3_2_1*S_3_1*chi_2*d_3_3*phi_1*q",
+          "expression_mathml": "<apply><times/><ci>I_3_2_1</ci><ci>S_3_1</ci><ci>chi_2</ci><ci>d_3_3</ci><ci>phi_1</ci><ci>q</ci></apply>"
+        },
+        {
+          "target": "t193",
+          "expression": "E_0_0_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_0_0_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t194",
+          "expression": "E_0_0_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_0_0_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t195",
+          "expression": "E_0_1_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_0_1_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t196",
+          "expression": "E_0_1_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_0_1_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t197",
+          "expression": "E_0_2_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_0_2_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t198",
+          "expression": "E_0_2_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_0_2_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t199",
+          "expression": "E_1_0_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_1_0_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t200",
+          "expression": "E_1_0_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_1_0_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t201",
+          "expression": "E_1_1_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_1_1_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t202",
+          "expression": "E_1_1_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_1_1_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t203",
+          "expression": "E_1_2_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_1_2_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t204",
+          "expression": "E_1_2_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_1_2_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t205",
+          "expression": "E_2_0_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_2_0_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t206",
+          "expression": "E_2_0_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_2_0_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t207",
+          "expression": "E_2_1_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_2_1_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t208",
+          "expression": "E_2_1_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_2_1_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t209",
+          "expression": "E_2_2_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_2_2_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t210",
+          "expression": "E_2_2_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_2_2_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t211",
+          "expression": "E_3_0_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_3_0_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t212",
+          "expression": "E_3_0_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_3_0_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t213",
+          "expression": "E_3_1_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_3_1_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t214",
+          "expression": "E_3_1_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_3_1_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t215",
+          "expression": "E_3_2_0*rho",
+          "expression_mathml": "<apply><times/><ci>E_3_2_0</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t216",
+          "expression": "E_3_2_1*rho",
+          "expression_mathml": "<apply><times/><ci>E_3_2_1</ci><ci>rho</ci></apply>"
+        },
+        {
+          "target": "t217",
+          "expression": "I_0_0_0*gamma*(1 - eta_0_0_0)",
+          "expression_mathml": "<apply><times/><ci>I_0_0_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_0_0_0</ci></apply></apply>"
+        },
+        {
+          "target": "t218",
+          "expression": "I_0_0_1*gamma*(1 - eta_0_0_1)",
+          "expression_mathml": "<apply><times/><ci>I_0_0_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_0_0_1</ci></apply></apply>"
+        },
+        {
+          "target": "t219",
+          "expression": "I_0_1_0*gamma*(1 - eta_0_1_0)",
+          "expression_mathml": "<apply><times/><ci>I_0_1_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_0_1_0</ci></apply></apply>"
+        },
+        {
+          "target": "t220",
+          "expression": "I_0_1_1*gamma*(1 - eta_0_1_1)",
+          "expression_mathml": "<apply><times/><ci>I_0_1_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_0_1_1</ci></apply></apply>"
+        },
+        {
+          "target": "t221",
+          "expression": "I_0_2_0*gamma*(1 - eta_0_2_0)",
+          "expression_mathml": "<apply><times/><ci>I_0_2_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_0_2_0</ci></apply></apply>"
+        },
+        {
+          "target": "t222",
+          "expression": "I_0_2_1*gamma*(1 - eta_0_2_1)",
+          "expression_mathml": "<apply><times/><ci>I_0_2_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_0_2_1</ci></apply></apply>"
+        },
+        {
+          "target": "t223",
+          "expression": "I_1_0_0*gamma*(1 - eta_1_0_0)",
+          "expression_mathml": "<apply><times/><ci>I_1_0_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_1_0_0</ci></apply></apply>"
+        },
+        {
+          "target": "t224",
+          "expression": "I_1_0_1*gamma*(1 - eta_1_0_1)",
+          "expression_mathml": "<apply><times/><ci>I_1_0_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_1_0_1</ci></apply></apply>"
+        },
+        {
+          "target": "t225",
+          "expression": "I_1_1_0*gamma*(1 - eta_1_1_0)",
+          "expression_mathml": "<apply><times/><ci>I_1_1_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_1_1_0</ci></apply></apply>"
+        },
+        {
+          "target": "t226",
+          "expression": "I_1_1_1*gamma*(1 - eta_1_1_1)",
+          "expression_mathml": "<apply><times/><ci>I_1_1_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_1_1_1</ci></apply></apply>"
+        },
+        {
+          "target": "t227",
+          "expression": "I_1_2_0*gamma*(1 - eta_1_2_0)",
+          "expression_mathml": "<apply><times/><ci>I_1_2_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_1_2_0</ci></apply></apply>"
+        },
+        {
+          "target": "t228",
+          "expression": "I_1_2_1*gamma*(1 - eta_1_2_1)",
+          "expression_mathml": "<apply><times/><ci>I_1_2_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_1_2_1</ci></apply></apply>"
+        },
+        {
+          "target": "t229",
+          "expression": "I_2_0_0*gamma*(1 - eta_2_0_0)",
+          "expression_mathml": "<apply><times/><ci>I_2_0_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_2_0_0</ci></apply></apply>"
+        },
+        {
+          "target": "t230",
+          "expression": "I_2_0_1*gamma*(1 - eta_2_0_1)",
+          "expression_mathml": "<apply><times/><ci>I_2_0_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_2_0_1</ci></apply></apply>"
+        },
+        {
+          "target": "t231",
+          "expression": "I_2_1_0*gamma*(1 - eta_2_1_0)",
+          "expression_mathml": "<apply><times/><ci>I_2_1_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_2_1_0</ci></apply></apply>"
+        },
+        {
+          "target": "t232",
+          "expression": "I_2_1_1*gamma*(1 - eta_2_1_1)",
+          "expression_mathml": "<apply><times/><ci>I_2_1_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_2_1_1</ci></apply></apply>"
+        },
+        {
+          "target": "t233",
+          "expression": "I_2_2_0*gamma*(1 - eta_2_2_0)",
+          "expression_mathml": "<apply><times/><ci>I_2_2_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_2_2_0</ci></apply></apply>"
+        },
+        {
+          "target": "t234",
+          "expression": "I_2_2_1*gamma*(1 - eta_2_2_1)",
+          "expression_mathml": "<apply><times/><ci>I_2_2_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_2_2_1</ci></apply></apply>"
+        },
+        {
+          "target": "t235",
+          "expression": "I_3_0_0*gamma*(1 - eta_3_0_0)",
+          "expression_mathml": "<apply><times/><ci>I_3_0_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_3_0_0</ci></apply></apply>"
+        },
+        {
+          "target": "t236",
+          "expression": "I_3_0_1*gamma*(1 - eta_3_0_1)",
+          "expression_mathml": "<apply><times/><ci>I_3_0_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_3_0_1</ci></apply></apply>"
+        },
+        {
+          "target": "t237",
+          "expression": "I_3_1_0*gamma*(1 - eta_3_1_0)",
+          "expression_mathml": "<apply><times/><ci>I_3_1_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_3_1_0</ci></apply></apply>"
+        },
+        {
+          "target": "t238",
+          "expression": "I_3_1_1*gamma*(1 - eta_3_1_1)",
+          "expression_mathml": "<apply><times/><ci>I_3_1_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_3_1_1</ci></apply></apply>"
+        },
+        {
+          "target": "t239",
+          "expression": "I_3_2_0*gamma*(1 - eta_3_2_0)",
+          "expression_mathml": "<apply><times/><ci>I_3_2_0</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_3_2_0</ci></apply></apply>"
+        },
+        {
+          "target": "t240",
+          "expression": "I_3_2_1*gamma*(1 - eta_3_2_1)",
+          "expression_mathml": "<apply><times/><ci>I_3_2_1</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>eta_3_2_1</ci></apply></apply>"
+        },
+        {
+          "target": "t241",
+          "expression": "I_0_0_0*eta_0_0_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_0_0_0</ci><ci>eta_0_0_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t242",
+          "expression": "I_0_0_1*eta_0_0_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_0_0_1</ci><ci>eta_0_0_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t243",
+          "expression": "I_0_1_0*eta_0_1_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_0_1_0</ci><ci>eta_0_1_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t244",
+          "expression": "I_0_1_1*eta_0_1_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_0_1_1</ci><ci>eta_0_1_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t245",
+          "expression": "I_0_2_0*eta_0_2_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_0_2_0</ci><ci>eta_0_2_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t246",
+          "expression": "I_0_2_1*eta_0_2_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_0_2_1</ci><ci>eta_0_2_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t247",
+          "expression": "I_1_0_0*eta_1_0_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_1_0_0</ci><ci>eta_1_0_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t248",
+          "expression": "I_1_0_1*eta_1_0_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_1_0_1</ci><ci>eta_1_0_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t249",
+          "expression": "I_1_1_0*eta_1_1_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_1_1_0</ci><ci>eta_1_1_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t250",
+          "expression": "I_1_1_1*eta_1_1_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_1_1_1</ci><ci>eta_1_1_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t251",
+          "expression": "I_1_2_0*eta_1_2_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_1_2_0</ci><ci>eta_1_2_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t252",
+          "expression": "I_1_2_1*eta_1_2_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_1_2_1</ci><ci>eta_1_2_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t253",
+          "expression": "I_2_0_0*eta_2_0_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_2_0_0</ci><ci>eta_2_0_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t254",
+          "expression": "I_2_0_1*eta_2_0_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_2_0_1</ci><ci>eta_2_0_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t255",
+          "expression": "I_2_1_0*eta_2_1_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_2_1_0</ci><ci>eta_2_1_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t256",
+          "expression": "I_2_1_1*eta_2_1_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_2_1_1</ci><ci>eta_2_1_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t257",
+          "expression": "I_2_2_0*eta_2_2_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_2_2_0</ci><ci>eta_2_2_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t258",
+          "expression": "I_2_2_1*eta_2_2_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_2_2_1</ci><ci>eta_2_2_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t259",
+          "expression": "I_3_0_0*eta_3_0_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_3_0_0</ci><ci>eta_3_0_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t260",
+          "expression": "I_3_0_1*eta_3_0_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_3_0_1</ci><ci>eta_3_0_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t261",
+          "expression": "I_3_1_0*eta_3_1_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_3_1_0</ci><ci>eta_3_1_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t262",
+          "expression": "I_3_1_1*eta_3_1_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_3_1_1</ci><ci>eta_3_1_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t263",
+          "expression": "I_3_2_0*eta_3_2_0*gamma",
+          "expression_mathml": "<apply><times/><ci>I_3_2_0</ci><ci>eta_3_2_0</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t264",
+          "expression": "I_3_2_1*eta_3_2_1*gamma",
+          "expression_mathml": "<apply><times/><ci>I_3_2_1</ci><ci>eta_3_2_1</ci><ci>gamma</ci></apply>"
+        },
+        {
+          "target": "t265",
+          "expression": "H_0_0_0*(1 - mu_0_0_0)/lr_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_0_0</ci><apply><minus/><cn>1</cn><ci>mu_0_0_0</ci></apply></apply><ci>lr_0</ci></apply>"
+        },
+        {
+          "target": "t266",
+          "expression": "H_0_0_1*(1 - mu_0_0_1)/lr_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_0_1</ci><apply><minus/><cn>1</cn><ci>mu_0_0_1</ci></apply></apply><ci>lr_0</ci></apply>"
+        },
+        {
+          "target": "t267",
+          "expression": "H_0_1_0*(1 - mu_0_1_0)/lr_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_1_0</ci><apply><minus/><cn>1</cn><ci>mu_0_1_0</ci></apply></apply><ci>lr_0</ci></apply>"
+        },
+        {
+          "target": "t268",
+          "expression": "H_0_1_1*(1 - mu_0_1_1)/lr_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_1_1</ci><apply><minus/><cn>1</cn><ci>mu_0_1_1</ci></apply></apply><ci>lr_0</ci></apply>"
+        },
+        {
+          "target": "t269",
+          "expression": "H_0_2_0*(1 - mu_0_2_0)/lr_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_2_0</ci><apply><minus/><cn>1</cn><ci>mu_0_2_0</ci></apply></apply><ci>lr_0</ci></apply>"
+        },
+        {
+          "target": "t270",
+          "expression": "H_0_2_1*(1 - mu_0_2_1)/lr_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_2_1</ci><apply><minus/><cn>1</cn><ci>mu_0_2_1</ci></apply></apply><ci>lr_0</ci></apply>"
+        },
+        {
+          "target": "t271",
+          "expression": "H_1_0_0*(1 - mu_1_0_0)/lr_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_0_0</ci><apply><minus/><cn>1</cn><ci>mu_1_0_0</ci></apply></apply><ci>lr_1</ci></apply>"
+        },
+        {
+          "target": "t272",
+          "expression": "H_1_0_1*(1 - mu_1_0_1)/lr_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_0_1</ci><apply><minus/><cn>1</cn><ci>mu_1_0_1</ci></apply></apply><ci>lr_1</ci></apply>"
+        },
+        {
+          "target": "t273",
+          "expression": "H_1_1_0*(1 - mu_1_1_0)/lr_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_1_0</ci><apply><minus/><cn>1</cn><ci>mu_1_1_0</ci></apply></apply><ci>lr_1</ci></apply>"
+        },
+        {
+          "target": "t274",
+          "expression": "H_1_1_1*(1 - mu_1_1_1)/lr_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_1_1</ci><apply><minus/><cn>1</cn><ci>mu_1_1_1</ci></apply></apply><ci>lr_1</ci></apply>"
+        },
+        {
+          "target": "t275",
+          "expression": "H_1_2_0*(1 - mu_1_2_0)/lr_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_2_0</ci><apply><minus/><cn>1</cn><ci>mu_1_2_0</ci></apply></apply><ci>lr_1</ci></apply>"
+        },
+        {
+          "target": "t276",
+          "expression": "H_1_2_1*(1 - mu_1_2_1)/lr_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_2_1</ci><apply><minus/><cn>1</cn><ci>mu_1_2_1</ci></apply></apply><ci>lr_1</ci></apply>"
+        },
+        {
+          "target": "t277",
+          "expression": "H_2_0_0*(1 - mu_2_0_0)/lr_2",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_2_0_0</ci><apply><minus/><cn>1</cn><ci>mu_2_0_0</ci></apply></apply><ci>lr_2</ci></apply>"
+        },
+        {
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+          "expression": "H_2_0_1*(1 - mu_2_0_1)/lr_2",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_2_0_1</ci><apply><minus/><cn>1</cn><ci>mu_2_0_1</ci></apply></apply><ci>lr_2</ci></apply>"
+        },
+        {
+          "target": "t279",
+          "expression": "H_2_1_0*(1 - mu_2_1_0)/lr_2",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_2_1_0</ci><apply><minus/><cn>1</cn><ci>mu_2_1_0</ci></apply></apply><ci>lr_2</ci></apply>"
+        },
+        {
+          "target": "t280",
+          "expression": "H_2_1_1*(1 - mu_2_1_1)/lr_2",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_2_1_1</ci><apply><minus/><cn>1</cn><ci>mu_2_1_1</ci></apply></apply><ci>lr_2</ci></apply>"
+        },
+        {
+          "target": "t281",
+          "expression": "H_2_2_0*(1 - mu_2_2_0)/lr_2",
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+        },
+        {
+          "target": "t282",
+          "expression": "H_2_2_1*(1 - mu_2_2_1)/lr_2",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_2_2_1</ci><apply><minus/><cn>1</cn><ci>mu_2_2_1</ci></apply></apply><ci>lr_2</ci></apply>"
+        },
+        {
+          "target": "t283",
+          "expression": "H_3_0_0*(1 - mu_3_0_0)/lr_3",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_3_0_0</ci><apply><minus/><cn>1</cn><ci>mu_3_0_0</ci></apply></apply><ci>lr_3</ci></apply>"
+        },
+        {
+          "target": "t284",
+          "expression": "H_3_0_1*(1 - mu_3_0_1)/lr_3",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_3_0_1</ci><apply><minus/><cn>1</cn><ci>mu_3_0_1</ci></apply></apply><ci>lr_3</ci></apply>"
+        },
+        {
+          "target": "t285",
+          "expression": "H_3_1_0*(1 - mu_3_1_0)/lr_3",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_3_1_0</ci><apply><minus/><cn>1</cn><ci>mu_3_1_0</ci></apply></apply><ci>lr_3</ci></apply>"
+        },
+        {
+          "target": "t286",
+          "expression": "H_3_1_1*(1 - mu_3_1_1)/lr_3",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_3_1_1</ci><apply><minus/><cn>1</cn><ci>mu_3_1_1</ci></apply></apply><ci>lr_3</ci></apply>"
+        },
+        {
+          "target": "t287",
+          "expression": "H_3_2_0*(1 - mu_3_2_0)/lr_3",
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+        },
+        {
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+          "expression": "H_3_2_1*(1 - mu_3_2_1)/lr_3",
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+        },
+        {
+          "target": "t289",
+          "expression": "H_0_0_0*mu_0_0_0/ld_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_0_0</ci><ci>mu_0_0_0</ci></apply><ci>ld_0</ci></apply>"
+        },
+        {
+          "target": "t290",
+          "expression": "H_0_0_1*mu_0_0_1/ld_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_0_1</ci><ci>mu_0_0_1</ci></apply><ci>ld_0</ci></apply>"
+        },
+        {
+          "target": "t291",
+          "expression": "H_0_1_0*mu_0_1_0/ld_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_1_0</ci><ci>mu_0_1_0</ci></apply><ci>ld_0</ci></apply>"
+        },
+        {
+          "target": "t292",
+          "expression": "H_0_1_1*mu_0_1_1/ld_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_1_1</ci><ci>mu_0_1_1</ci></apply><ci>ld_0</ci></apply>"
+        },
+        {
+          "target": "t293",
+          "expression": "H_0_2_0*mu_0_2_0/ld_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_2_0</ci><ci>mu_0_2_0</ci></apply><ci>ld_0</ci></apply>"
+        },
+        {
+          "target": "t294",
+          "expression": "H_0_2_1*mu_0_2_1/ld_0",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_0_2_1</ci><ci>mu_0_2_1</ci></apply><ci>ld_0</ci></apply>"
+        },
+        {
+          "target": "t295",
+          "expression": "H_1_0_0*mu_1_0_0/ld_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_0_0</ci><ci>mu_1_0_0</ci></apply><ci>ld_1</ci></apply>"
+        },
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+          "target": "t296",
+          "expression": "H_1_0_1*mu_1_0_1/ld_1",
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+        },
+        {
+          "target": "t297",
+          "expression": "H_1_1_0*mu_1_1_0/ld_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_1_0</ci><ci>mu_1_1_0</ci></apply><ci>ld_1</ci></apply>"
+        },
+        {
+          "target": "t298",
+          "expression": "H_1_1_1*mu_1_1_1/ld_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_1_1</ci><ci>mu_1_1_1</ci></apply><ci>ld_1</ci></apply>"
+        },
+        {
+          "target": "t299",
+          "expression": "H_1_2_0*mu_1_2_0/ld_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_2_0</ci><ci>mu_1_2_0</ci></apply><ci>ld_1</ci></apply>"
+        },
+        {
+          "target": "t300",
+          "expression": "H_1_2_1*mu_1_2_1/ld_1",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_1_2_1</ci><ci>mu_1_2_1</ci></apply><ci>ld_1</ci></apply>"
+        },
+        {
+          "target": "t301",
+          "expression": "H_2_0_0*mu_2_0_0/ld_2",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_2_0_0</ci><ci>mu_2_0_0</ci></apply><ci>ld_2</ci></apply>"
+        },
+        {
+          "target": "t302",
+          "expression": "H_2_0_1*mu_2_0_1/ld_2",
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+        },
+        {
+          "target": "t303",
+          "expression": "H_2_1_0*mu_2_1_0/ld_2",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_2_1_0</ci><ci>mu_2_1_0</ci></apply><ci>ld_2</ci></apply>"
+        },
+        {
+          "target": "t304",
+          "expression": "H_2_1_1*mu_2_1_1/ld_2",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_2_1_1</ci><ci>mu_2_1_1</ci></apply><ci>ld_2</ci></apply>"
+        },
+        {
+          "target": "t305",
+          "expression": "H_2_2_0*mu_2_2_0/ld_2",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_2_2_0</ci><ci>mu_2_2_0</ci></apply><ci>ld_2</ci></apply>"
+        },
+        {
+          "target": "t306",
+          "expression": "H_2_2_1*mu_2_2_1/ld_2",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_2_2_1</ci><ci>mu_2_2_1</ci></apply><ci>ld_2</ci></apply>"
+        },
+        {
+          "target": "t307",
+          "expression": "H_3_0_0*mu_3_0_0/ld_3",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_3_0_0</ci><ci>mu_3_0_0</ci></apply><ci>ld_3</ci></apply>"
+        },
+        {
+          "target": "t308",
+          "expression": "H_3_0_1*mu_3_0_1/ld_3",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_3_0_1</ci><ci>mu_3_0_1</ci></apply><ci>ld_3</ci></apply>"
+        },
+        {
+          "target": "t309",
+          "expression": "H_3_1_0*mu_3_1_0/ld_3",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_3_1_0</ci><ci>mu_3_1_0</ci></apply><ci>ld_3</ci></apply>"
+        },
+        {
+          "target": "t310",
+          "expression": "H_3_1_1*mu_3_1_1/ld_3",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_3_1_1</ci><ci>mu_3_1_1</ci></apply><ci>ld_3</ci></apply>"
+        },
+        {
+          "target": "t311",
+          "expression": "H_3_2_0*mu_3_2_0/ld_3",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_3_2_0</ci><ci>mu_3_2_0</ci></apply><ci>ld_3</ci></apply>"
+        },
+        {
+          "target": "t312",
+          "expression": "H_3_2_1*mu_3_2_1/ld_3",
+          "expression_mathml": "<apply><divide/><apply><times/><ci>H_3_2_1</ci><ci>mu_3_2_1</ci></apply><ci>ld_3</ci></apply>"
+        },
+        {
+          "target": "t313",
+          "expression": "alpha_0",
+          "expression_mathml": "<ci>alpha_0</ci>"
+        },
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+          "target": "t314",
+          "expression": "alpha_1",
+          "expression_mathml": "<ci>alpha_1</ci>"
+        },
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+          "target": "t315",
+          "expression": "alpha_2",
+          "expression_mathml": "<ci>alpha_2</ci>"
+        },
+        {
+          "target": "t316",
+          "expression": "alpha_3",
+          "expression_mathml": "<ci>alpha_3</ci>"
+        }
+      ],
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+          "target": "S_0_0",
+          "expression": "19339995.0000000",
+          "expression_mathml": "<cn>19339995.0</cn>"
+        },
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+          "target": "I_0_0_0",
+          "expression": "4.00000000000000",
+          "expression_mathml": "<cn>4.0</cn>"
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+          "expression": "1.00000000000000",
+          "expression_mathml": "<cn>1.0</cn>"
+        },
+        {
+          "target": "I_0_0_1",
+          "expression": "4.00000000000000",
+          "expression_mathml": "<cn>4.0</cn>"
+        },
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+          "expression_mathml": "<cn>4.0</cn>"
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+        },
+        {
+          "id": "eta_0_2_1",
+          "value": 0.1
+        },
+        {
+          "id": "eta_1_0_0",
+          "value": 0.1
+        },
+        {
+          "id": "eta_1_0_1",
+          "value": 0.1
+        },
+        {
+          "id": "eta_1_1_0",
+          "value": 0.1
+        },
+        {
+          "id": "eta_1_1_1",
+          "value": 0.1
+        },
+        {
+          "id": "eta_1_2_0",
+          "value": 0.1
+        },
+        {
+          "id": "eta_1_2_1",
+          "value": 0.1
+        },
+        {
+          "id": "eta_2_0_0",
+          "value": 0.1
+        },
+        {
+          "id": "eta_2_0_1",
+          "value": 0.1
+        },
+        {
+          "id": "eta_2_1_0",
+          "value": 0.1
+        },
+        {
+          "id": "eta_2_1_1",
+          "value": 0.1
+        },
+        {
+          "id": "eta_2_2_0",
+          "value": 0.1
+        },
+        {
+          "id": "eta_2_2_1",
+          "value": 0.1
+        },
+        {
+          "id": "eta_3_0_0",
+          "value": 0.1
+        },
+        {
+          "id": "eta_3_0_1",
+          "value": 0.1
+        },
+        {
+          "id": "eta_3_1_0",
+          "value": 0.1
+        },
+        {
+          "id": "eta_3_1_1",
+          "value": 0.1
+        },
+        {
+          "id": "eta_3_2_0",
+          "value": 0.1
+        },
+        {
+          "id": "eta_3_2_1",
+          "value": 0.1
+        },
+        {
+          "id": "lr_0",
+          "value": 5.0
+        },
+        {
+          "id": "mu_0_0_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_0_0_1",
+          "value": 0.003
+        },
+        {
+          "id": "mu_0_1_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_0_1_1",
+          "value": 0.003
+        },
+        {
+          "id": "mu_0_2_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_0_2_1",
+          "value": 0.003
+        },
+        {
+          "id": "lr_1",
+          "value": 5.0
+        },
+        {
+          "id": "mu_1_0_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_1_0_1",
+          "value": 0.003
+        },
+        {
+          "id": "mu_1_1_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_1_1_1",
+          "value": 0.003
+        },
+        {
+          "id": "mu_1_2_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_1_2_1",
+          "value": 0.003
+        },
+        {
+          "id": "lr_2",
+          "value": 5.0
+        },
+        {
+          "id": "mu_2_0_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_2_0_1",
+          "value": 0.003
+        },
+        {
+          "id": "mu_2_1_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_2_1_1",
+          "value": 0.003
+        },
+        {
+          "id": "mu_2_2_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_2_2_1",
+          "value": 0.003
+        },
+        {
+          "id": "lr_3",
+          "value": 5.0
+        },
+        {
+          "id": "mu_3_0_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_3_0_1",
+          "value": 0.003
+        },
+        {
+          "id": "mu_3_1_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_3_1_1",
+          "value": 0.003
+        },
+        {
+          "id": "mu_3_2_0",
+          "value": 0.003
+        },
+        {
+          "id": "mu_3_2_1",
+          "value": 0.003
+        },
+        {
+          "id": "ld_0",
+          "value": 9.25
+        },
+        {
+          "id": "ld_1",
+          "value": 9.25
+        },
+        {
+          "id": "ld_2",
+          "value": 9.25
+        },
+        {
+          "id": "ld_3",
+          "value": 9.25
+        },
+        {
+          "id": "alpha_0",
+          "value": 0.000517063081695967
+        },
+        {
+          "id": "alpha_1",
+          "value": 0.000517063081695967
+        },
+        {
+          "id": "alpha_2",
+          "value": 0.000517063081695967
+        },
+        {
+          "id": "alpha_3",
+          "value": 0.000517063081695967
+        }
+      ],
+      "observables": [
+        {
+          "id": "",
+          "name": "",
+          "states": [],
+          "expression": "$S_{0,0} + S_{1,0} + S_{2,0} + S_{3,0}$",
+          "expression_mathml": "<mrow><msub>S<mrow><mn>0</mn><mo separator=\"true\">,</mo><mn>0</mn></mrow></msub><mo>+</mo><msub>S<mrow><mn>1</mn><mo separator=\"true\">,</mo><mn>0</mn></mrow></msub><mo>+</mo><msub>S<mrow><mn>2</mn><mo separator=\"true\">,</mo><mn>0</mn></mrow></msub><mo>+</mo><msub>S<mrow><mo separator=\"true\">,</mo><mn>0</mn></mrow></msub></mrow>"
+        }
+      ],
+      "time": {
+        "id": "t",
+        "units": {
+          "expression": "day",
+          "expression_mathml": "<ci>day</ci>"
+        }
+      }
+    }
+  },
+  "metadata": {
+    "annotations": {
+      "authors": [],
+      "references": [],
+      "locations": [],
+      "pathogens": [],
+      "diseases": [],
+      "hosts": [],
+      "model_types": []
+    }
+  },
+  "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json"
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/stratified/model_amr_request.json b/resources/amr/petrinet/stratified/model_amr_request.json
new file mode 100644
index 00000000..f5a84264
--- /dev/null
+++ b/resources/amr/petrinet/stratified/model_amr_request.json
@@ -0,0 +1,22 @@
+{
+  "query": {},
+  "parameters": [],
+  "structure_parameters": [
+    {
+      "name": "schedules",
+      "schedules": [
+        {
+          "timepoints": [
+            0,
+            1,
+            2
+          ]
+        }
+      ]
+    }
+  ],
+  "config": {
+    "tolerance": 1e-2,
+    "save_smtlib": "./out"
+  }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/constrained_trajectory.json b/resources/amr/petrinet/terrarium-tests/constrained_trajectory.json
new file mode 100644
index 00000000..4fba1bc8
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/constrained_trajectory.json
@@ -0,0 +1,263 @@
+{
+    "model": {
+        "id": "sir-param-in-observables",
+        "header": {
+            "name": "SIR model with beta parameter in the observables definition",
+            "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json",
+            "schema_name": "petrinet",
+            "description": "SIR model with observables2",
+            "model_version": "0.1"
+        },
+        "properties": {},
+        "model": {
+            "states": [
+                {
+                    "id": "S",
+                    "name": "S",
+                    "grounding": {
+                        "identifiers": {
+                            "ido": "0000514"
+                        },
+                        "modifiers": {}
+                    },
+                    "units": {
+                        "expression": "person",
+                        "expression_mathml": "<ci>person</ci>"
+                    }
+                },
+                {
+                    "id": "I",
+                    "name": "I",
+                    "grounding": {
+                        "identifiers": {
+                            "ido": "0000511"
+                        },
+                        "modifiers": {}
+                    },
+                    "units": {
+                        "expression": "person",
+                        "expression_mathml": "<ci>person</ci>"
+                    }
+                },
+                {
+                    "id": "R",
+                    "name": "R",
+                    "grounding": {
+                        "identifiers": {
+                            "ido": "0000592"
+                        },
+                        "modifiers": {}
+                    },
+                    "units": {
+                        "expression": "person",
+                        "expression_mathml": "<ci>person</ci>"
+                    }
+                }
+            ],
+            "transitions": [
+                {
+                    "id": "t1",
+                    "input": [
+                        "I",
+                        "S"
+                    ],
+                    "output": [
+                        "I",
+                        "I"
+                    ],
+                    "properties": {
+                        "name": "t1"
+                    }
+                },
+                {
+                    "id": "t2",
+                    "input": [
+                        "I"
+                    ],
+                    "output": [
+                        "R"
+                    ],
+                    "properties": {
+                        "name": "t2"
+                    }
+                }
+            ]
+        },
+        "semantics": {
+            "ode": {
+                "rates": [
+                    {
+                        "target": "t1",
+                        "expression": "I*S*beta",
+                        "expression_mathml": "<apply><times/><ci>I</ci><ci>S</ci><ci>beta</ci></apply>"
+                    },
+                    {
+                        "target": "t2",
+                        "expression": "I*gamma",
+                        "expression_mathml": "<apply><times/><ci>I</ci><ci>gamma</ci></apply>"
+                    }
+                ],
+                "initials": [
+                    {
+                        "target": "S",
+                        "expression": "999.0",
+                        "expression_mathml": "<cn>999.0</cn>"
+                    },
+                    {
+                        "target": "I",
+                        "expression": "1.0",
+                        "expression_mathml": "<cn>1.0</cn>"
+                    },
+                    {
+                        "target": "R",
+                        "expression": "0.0",
+                        "expression_mathml": "<cn>0.0</cn>"
+                    }
+                ],
+                "parameters": [
+                    {
+                        "id": "beta",
+                        "value": 0.0025,
+                        "distribution": {
+                            "type": "StandardUniform1",
+                            "parameters": {
+                                "minimum": 0.001,
+                                "maximum": 0.003
+                            }
+                        }
+                    },
+                    {
+                        "id": "gamma",
+                        "value": 0.07,
+                        "distribution": {
+                            "type": "StandardUniform1",
+                            "parameters": {
+                                "minimum": 0.04,
+                                "maximum": 0.15
+                            }
+                        },
+                        "units": {
+                            "expression": "1/day",
+                            "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+                        }
+                    }
+                ],
+                "observables": [
+                    {
+                        "id": "incident_cases",
+                        "name": "incident_cases",
+                        "expression": "I*S*beta",
+                        "expression_mathml": "<apply><times/><ci>I</ci><ci>S</ci><ci>beta</ci></apply>"
+                    },
+                    {
+                        "id": "beta_param",
+                        "name": "beta_param",
+                        "expression": "beta",
+                        "expression_mathml": "<ci>beta</ci>"
+                    }
+                ],
+                "time": {
+                    "id": "t",
+                    "units": {
+                        "expression": "day",
+                        "expression_mathml": "<ci>day</ci>"
+                    }
+                }
+            }
+        },
+        "metadata": {
+            "annotations": {
+                "license": null,
+                "authors": [],
+                "references": [],
+                "time_scale": null,
+                "time_start": null,
+                "time_end": null,
+                "locations": [],
+                "pathogens": [],
+                "diseases": [],
+                "hosts": [],
+                "model_types": []
+            }
+        }
+    },
+    "request": {
+        "constraints": [
+            {
+                "name": "I_constraint0",
+                "variable": "I",
+                "interval": {
+                    "lb": 0.9141058921813964,
+                    "ub": 909.6296997070312
+                },
+                "timepoints": {
+                    "lb": 0.0,
+                    "ub": 50.0,
+                    "closed_upper_bound": true
+                }
+            },
+            {
+                "name": "I_constraint1",
+                "variable": "I",
+                "interval": {
+                    "lb": 0.0005660105962306,
+                    "ub": 154.95701599121094
+                },
+                "timepoints": {
+                    "lb": 50.0,
+                    "ub": 100.0,
+                    "closed_upper_bound": true
+                }
+            }
+        ],
+        "parameters": [
+            {
+                "name": "beta",
+                "interval": {
+                    "lb": 0.001,
+                    "ub": 0.003
+                },
+                "label": "all"
+            },
+            {
+                "name": "gamma",
+                "interval": {
+                    "lb": 0.04,
+                    "ub": 0.15
+                },
+                "label": "all"
+            }
+        ],
+        "structure_parameters": [
+            {
+                "name": "schedules",
+                "schedules": [
+                    {
+                        "timepoints": [
+                            0,
+                            5,
+                            10,
+                            15,
+                            20,
+                            25,
+                            30,
+                            40,
+                            50,
+                            60,
+                            70,
+                            80,
+                            90,
+                            100
+                        ]
+                    }
+                ]
+            }
+        ],
+        "config": {
+            "use_compartmental_constraints": true,
+            "normalization_constant": 1000,
+            "normalize": false,
+            "tolerance": 0.005
+        }
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/eval_scenario2.json b/resources/amr/petrinet/terrarium-tests/eval_scenario2.json
new file mode 100644
index 00000000..4cfe2bb3
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/eval_scenario2.json
@@ -0,0 +1,374 @@
+{
+    "id": "cd7046cc-e512-4320-82f1-e7f01877f434",
+    "model": {
+        "petrinet": {
+            "header": {
+                "name": "s2_1",
+                "schema": "https://github.com/DARPA-ASKEM/Model-Representations/blob/main/petrinet/petrinet_schema.json",
+                "schema_name": "PetriNet",
+                "description": "This is a model from equations",
+                "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": [
+                            "H"
+                        ],
+                        "output": [
+                            "D"
+                        ]
+                    },
+                    {
+                        "id": "t2",
+                        "input": [
+                            "I"
+                        ],
+                        "output": [
+                            "H"
+                        ]
+                    },
+                    {
+                        "id": "t3",
+                        "input": [
+                            "I"
+                        ],
+                        "output": [
+                            "R"
+                        ]
+                    },
+                    {
+                        "id": "t4",
+                        "input": [
+                            "H"
+                        ],
+                        "output": [
+                            "R"
+                        ]
+                    }
+                ]
+            },
+            "semantics": {
+                "ode": {
+                    "rates": [
+                        {
+                            "target": "t0",
+                            "expression": "N*β*I*S",
+                            "expression_mathml": "<apply><times/><apply><divide/><ci>S</ci><ci>N</ci></apply><ci>β</ci><ci>I</ci></apply>"
+                        },
+                        {
+                            "target": "t1",
+                            "expression": "d*h*H",
+                            "expression_mathml": "<apply><times/><ci>H</ci><ci>d</ci><ci>h</ci></apply>"
+                        },
+                        {
+                            "target": "t2",
+                            "expression": "b*f*I",
+                            "expression_mathml": "<apply><times/><ci>I</ci><ci>b</ci><ci>f</ci></apply>"
+                        },
+                        {
+                            "target": "t3",
+                            "expression": "a*e*I",
+                            "expression_mathml": "<apply><times/><ci>I</ci><ci>a</ci><ci>e</ci></apply>"
+                        },
+                        {
+                            "target": "t4",
+                            "expression": "c*g*H",
+                            "expression_mathml": "<apply><times/><ci>H</ci><ci>c</ci><ci>g</ci></apply>"
+                        }
+                    ],
+                    "initials": [
+                        {
+                            "target": "S",
+                            "expression": "25550375",
+                            "expression_mathml": "<cn>25550375</cn>"
+                        },
+                        {
+                            "target": "I",
+                            "expression": "48722998",
+                            "expression_mathml": "<cn>48722998</cn>"
+                        },
+                        {
+                            "target": "D",
+                            "expression": "781182",
+                            "expression_mathml": "<cn>781182</cn>"
+                        },
+                        {
+                            "target": "H",
+                            "expression": "27003629",
+                            "expression_mathml": "<cn>27003629</cn>"
+                        },
+                        {
+                            "target": "R",
+                            "expression": "47941816",
+                            "expression_mathml": "<cn>47941816</cn>"
+                        }
+                    ],
+                    "parameters": [
+                        {
+                            "id": "N",
+                            "name": "N",
+                            "description": "Total population",
+                            "value": 1.5E8
+                        },
+                        {
+                            "id": "a",
+                            "name": "a",
+                            "description": "rate from I to R",
+                            "value": 0.07
+                        },
+                        {
+                            "id": "b",
+                            "name": "b",
+                            "description": "rate from I to H",
+                            "value": 0.07
+                        },
+                        {
+                            "id": "c",
+                            "name": "c",
+                            "description": "rate from H to R",
+                            "value": 0.07
+                        },
+                        {
+                            "id": "d",
+                            "name": "d",
+                            "description": "rate from H to D",
+                            "value": 0.30000000000000004
+                        },
+                        {
+                            "id": "e",
+                            "name": "e",
+                            "description": "prob of moving from I to R",
+                            "value": 0.09000000000000001
+                        },
+                        {
+                            "id": "f",
+                            "name": "f",
+                            "description": "prob of moving from I to H",
+                            "value": 0.01
+                        },
+                        {
+                            "id": "g",
+                            "name": "g",
+                            "description": "prob of moving from H to R",
+                            "value": 0.8700000000000001
+                        },
+                        {
+                            "id": "h",
+                            "name": "h",
+                            "description": "prob of moving from H to D",
+                            "value": 0.13
+                        },
+                        {
+                            "id": "β",
+                            "name": "β",
+                            "description": "Number of infections per infected person per day",
+                            "value": 0.18000000000000002
+                        }
+                    ]
+                }
+            },
+            "metadata": {
+                "timeseries": {},
+                "initials": {
+                    "S": {
+                        "name": "Susceptible"
+                    },
+                    "I": {
+                        "name": "Infected"
+                    },
+                    "D": {
+                        "name": "Dead"
+                    },
+                    "H": {
+                        "name": "Hospitalized"
+                    },
+                    "R": {
+                        "name": "Recovered"
+                    }
+                },
+                "parameters": {}
+            },
+            "id": "54478092-dd24-46f8-bcac-eba3af2cf018",
+            "name": "s2_1",
+            "createdOn": "2024-03-25T18:10:58.624+00:00",
+            "updatedOn": "2024-03-25T18:28:48.613+00:00",
+            "temporary": false,
+            "publicAsset": false
+        }
+    },
+    "request": {
+        "constraints": [
+            {
+                "name": "c1",
+                "additive_bounds": {
+                    "lb": -9.9999999999E10,
+                    "ub": 0.0,
+                    "original_width": 9.9999999999E10
+                },
+                "variables": [
+                    "S"
+                ],
+                "weights": [
+                    1
+                ],
+                "derivative": true
+            }
+        ],
+        "parameters": [
+            {
+                "name": "N",
+                "interval": {
+                    "lb": 1.5E8,
+                    "ub": 1.5E8,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "a",
+                "interval": {
+                    "lb": 0,
+                    "ub": 0.05,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                },
+                "label": "all"
+            },
+            {
+                "name": "b",
+                "interval": {
+                    "lb": 0.05,
+                    "ub": 0.15,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                },
+                "label": "all"
+            },
+            {
+                "name": "c",
+                "interval": {
+                    "lb": 0.07,
+                    "ub": 0.07,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "d",
+                "interval": {
+                    "lb": 0.30000000000000004,
+                    "ub": 0.30000000000000004,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "e",
+                "interval": {
+                    "lb": 0.09,
+                    "ub": 0.09,
+                    "closed_upper_bound": true
+                },
+                "label": "all"
+            },
+            {
+                "name": "f",
+                "interval": {
+                    "lb": 0.01,
+                    "ub": 0.01,
+                    "closed_upper_bound": true
+                },
+                "label": "all"
+            },
+            {
+                "name": "g",
+                "interval": {
+                    "lb": 0.8700000000000001,
+                    "ub": 0.8700000000000001,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "h",
+                "interval": {
+                    "lb": 0.13,
+                    "ub": 0.13,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "β",
+                "interval": {
+                    "lb": 0.18,
+                    "ub": 0.22,
+                    "closed_upper_bound": true
+                },
+                "label": "all"
+            }
+        ],
+        "config": {
+            "tolerance": 0.05,
+            "normalization_constant": 1.5E8,
+            "use_compartmental_constraints": true
+        },
+        "structure_parameters": [
+            {
+                "name": "schedules",
+                "schedules": [
+                    {
+                        "timepoints": [
+                            0,
+                            10,
+                            20,
+                            30,
+                            40,
+                            50,
+                            60,
+                            70,
+                            80,
+                            90,
+                            100
+                        ]
+                    }
+                ]
+            }
+        ]
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/eval_scenario2_model.json b/resources/amr/petrinet/terrarium-tests/eval_scenario2_model.json
new file mode 100644
index 00000000..d172e007
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/eval_scenario2_model.json
@@ -0,0 +1,230 @@
+ {
+            "header": {
+                "name": "s2_1",
+                "schema": "https://github.com/DARPA-ASKEM/Model-Representations/blob/main/petrinet/petrinet_schema.json",
+                "schema_name": "PetriNet",
+                "description": "This is a model from equations",
+                "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": [
+                            "H"
+                        ],
+                        "output": [
+                            "D"
+                        ]
+                    },
+                    {
+                        "id": "t2",
+                        "input": [
+                            "I"
+                        ],
+                        "output": [
+                            "H"
+                        ]
+                    },
+                    {
+                        "id": "t3",
+                        "input": [
+                            "I"
+                        ],
+                        "output": [
+                            "R"
+                        ]
+                    },
+                    {
+                        "id": "t4",
+                        "input": [
+                            "H"
+                        ],
+                        "output": [
+                            "R"
+                        ]
+                    }
+                ]
+            },
+            "semantics": {
+                "ode": {
+                    "rates": [
+                        {
+                            "target": "t0",
+                            "expression": "N*β*I*S",
+                            "expression_mathml": "<apply><times/><apply><divide/><ci>S</ci><ci>N</ci></apply><ci>β</ci><ci>I</ci></apply>"
+                        },
+                        {
+                            "target": "t1",
+                            "expression": "d*h*H",
+                            "expression_mathml": "<apply><times/><ci>H</ci><ci>d</ci><ci>h</ci></apply>"
+                        },
+                        {
+                            "target": "t2",
+                            "expression": "b*f*I",
+                            "expression_mathml": "<apply><times/><ci>I</ci><ci>b</ci><ci>f</ci></apply>"
+                        },
+                        {
+                            "target": "t3",
+                            "expression": "a*e*I",
+                            "expression_mathml": "<apply><times/><ci>I</ci><ci>a</ci><ci>e</ci></apply>"
+                        },
+                        {
+                            "target": "t4",
+                            "expression": "c*g*H",
+                            "expression_mathml": "<apply><times/><ci>H</ci><ci>c</ci><ci>g</ci></apply>"
+                        }
+                    ],
+                    "initials": [
+                        {
+                            "target": "S",
+                            "expression": "25550375",
+                            "expression_mathml": "<cn>25550375</cn>"
+                        },
+                        {
+                            "target": "I",
+                            "expression": "48722998",
+                            "expression_mathml": "<cn>48722998</cn>"
+                        },
+                        {
+                            "target": "D",
+                            "expression": "781182",
+                            "expression_mathml": "<cn>781182</cn>"
+                        },
+                        {
+                            "target": "H",
+                            "expression": "27003629",
+                            "expression_mathml": "<cn>27003629</cn>"
+                        },
+                        {
+                            "target": "R",
+                            "expression": "47941816",
+                            "expression_mathml": "<cn>47941816</cn>"
+                        }
+                    ],
+                    "parameters": [
+                        {
+                            "id": "N",
+                            "name": "N",
+                            "description": "Total population",
+                            "value": 150000000
+                        },
+                        {
+                            "id": "a",
+                            "name": "a",
+                            "description": "rate from I to R",
+                            "value": 0.07
+                        },
+                        {
+                            "id": "b",
+                            "name": "b",
+                            "description": "rate from I to H",
+                            "value": 0.07
+                        },
+                        {
+                            "id": "c",
+                            "name": "c",
+                            "description": "rate from H to R",
+                            "value": 0.07
+                        },
+                        {
+                            "id": "d",
+                            "name": "d",
+                            "description": "rate from H to D",
+                            "value": 0.30000000000000004
+                        },
+                        {
+                            "id": "e",
+                            "name": "e",
+                            "description": "prob of moving from I to R",
+                            "value": 0.09000000000000001
+                        },
+                        {
+                            "id": "f",
+                            "name": "f",
+                            "description": "prob of moving from I to H",
+                            "value": 0.01
+                        },
+                        {
+                            "id": "g",
+                            "name": "g",
+                            "description": "prob of moving from H to R",
+                            "value": 0.8700000000000001
+                        },
+                        {
+                            "id": "h",
+                            "name": "h",
+                            "description": "prob of moving from H to D",
+                            "value": 0.13
+                        },
+                        {
+                            "id": "β",
+                            "name": "β",
+                            "description": "Number of infections per infected person per day",
+                            "value": 0.18000000000000002
+                        }
+                    ]
+                }
+            },
+            "metadata": {
+                "timeseries": {},
+                "initials": {
+                    "S": {
+                        "name": "Susceptible"
+                    },
+                    "I": {
+                        "name": "Infected"
+                    },
+                    "D": {
+                        "name": "Dead"
+                    },
+                    "H": {
+                        "name": "Hospitalized"
+                    },
+                    "R": {
+                        "name": "Recovered"
+                    }
+                },
+                "parameters": {}
+            },
+            "id": "54478092-dd24-46f8-bcac-eba3af2cf018",
+            "name": "s2_1",
+            "createdOn": "2024-03-25T18:10:58.624+00:00",
+            "updatedOn": "2024-03-25T18:28:48.613+00:00",
+            "temporary": false,
+            "publicAsset": false
+        }
+    
diff --git a/resources/amr/petrinet/terrarium-tests/eval_scenario2_norm.json b/resources/amr/petrinet/terrarium-tests/eval_scenario2_norm.json
new file mode 100644
index 00000000..0ad5a064
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/eval_scenario2_norm.json
@@ -0,0 +1,551 @@
+{
+    "id": "c5e45960-e2e4-4933-9b20-0f70f9885d5f",
+    "model": {
+        "petrinet": {
+            "header": {
+                "name": "s2_3",
+                "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json",
+                "schema_name": "petrinet",
+                "description": "This is a model from equations",
+                "model_version": "0.1"
+            },
+            "properties": {},
+            "model": {
+                "states": [
+                    {
+                        "id": "S",
+                        "name": "S",
+                        "grounding": {
+                            "identifiers": {},
+                            "modifiers": {}
+                        }
+                    },
+                    {
+                        "id": "I",
+                        "name": "I",
+                        "grounding": {
+                            "identifiers": {},
+                            "modifiers": {}
+                        }
+                    },
+                    {
+                        "id": "H",
+                        "name": "H",
+                        "grounding": {
+                            "identifiers": {},
+                            "modifiers": {}
+                        }
+                    },
+                    {
+                        "id": "D",
+                        "name": "D",
+                        "grounding": {
+                            "identifiers": {},
+                            "modifiers": {}
+                        }
+                    },
+                    {
+                        "id": "R",
+                        "name": "R",
+                        "grounding": {
+                            "identifiers": {},
+                            "modifiers": {}
+                        }
+                    }
+                ],
+                "transitions": [
+                    {
+                        "id": "t0",
+                        "input": [
+                            "I",
+                            "S"
+                        ],
+                        "output": [
+                            "I",
+                            "I"
+                        ],
+                        "properties": {
+                            "name": "t0"
+                        }
+                    },
+                    {
+                        "id": "t1",
+                        "input": [
+                            "H"
+                        ],
+                        "output": [
+                            "D"
+                        ],
+                        "properties": {
+                            "name": "t1"
+                        }
+                    },
+                    {
+                        "id": "t2",
+                        "input": [
+                            "I"
+                        ],
+                        "output": [
+                            "H"
+                        ],
+                        "properties": {
+                            "name": "t2"
+                        }
+                    },
+                    {
+                        "id": "t3",
+                        "input": [
+                            "I"
+                        ],
+                        "output": [
+                            "R"
+                        ],
+                        "properties": {
+                            "name": "t3"
+                        }
+                    },
+                    {
+                        "id": "t4",
+                        "input": [
+                            "H"
+                        ],
+                        "output": [
+                            "R"
+                        ],
+                        "properties": {
+                            "name": "t4"
+                        }
+                    }
+                ]
+            },
+            "semantics": {
+                "ode": {
+                    "rates": [
+                        {
+                            "target": "t0",
+                            "expression": "I*S*beta/N",
+                            "expression_mathml": "<apply><divide/><apply><times/><ci>I</ci><ci>S</ci><ci>beta</ci></apply><ci>N</ci></apply>"
+                        },
+                        {
+                            "target": "t1",
+                            "expression": "H*d*h",
+                            "expression_mathml": "<apply><times/><ci>H</ci><ci>d</ci><ci>h</ci></apply>"
+                        },
+                        {
+                            "target": "t2",
+                            "expression": "I*b*f",
+                            "expression_mathml": "<apply><times/><ci>I</ci><ci>b</ci><ci>f</ci></apply>"
+                        },
+                        {
+                            "target": "t3",
+                            "expression": "I*a*e",
+                            "expression_mathml": "<apply><times/><ci>I</ci><ci>a</ci><ci>e</ci></apply>"
+                        },
+                        {
+                            "target": "t4",
+                            "expression": "H*c*g",
+                            "expression_mathml": "<apply><times/><ci>H</ci><ci>c</ci><ci>g</ci></apply>"
+                        }
+                    ],
+                    "initials": [
+                        {
+                            "target": "S",
+                            "expression": "149808412.0",
+                            "expression_mathml": "<cn>149808412.0</cn>"
+                        },
+                        {
+                            "target": "I",
+                            "expression": "137002.0",
+                            "expression_mathml": "<cn>137002.0</cn>"
+                        },
+                        {
+                            "target": "H",
+                            "expression": "52585.0",
+                            "expression_mathml": "<cn>52585.0</cn>"
+                        },
+                        {
+                            "target": "D",
+                            "expression": "2001.0",
+                            "expression_mathml": "<cn>2001.0</cn>"
+                        },
+                        {
+                            "target": "R",
+                            "expression": "0",
+                            "expression_mathml": "<cn>0</cn>"
+                        }
+                    ],
+                    "parameters": [
+                        {
+                            "id": "N",
+                            "name": "N",
+                            "description": "Total population",
+                            "value": 150000000,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 1500000000,
+                                    "maximum": 1500000000
+                                }
+                            }
+                        },
+                        {
+                            "id": "beta",
+                            "name": "beta",
+                            "description": "Number of infections per infected person per day",
+                            "value": 1,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.1,
+                                    "maximum": 0.25
+                                }
+                            }
+                        },
+                        {
+                            "id": "d",
+                            "name": "d",
+                            "description": "rate from H to D",
+                            "value": 1,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.1,
+                                    "maximum": 0.30000000000000004
+                                }
+                            }
+                        },
+                        {
+                            "id": "h",
+                            "name": "h",
+                            "description": "prob of moving from H to D",
+                            "value": 1,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.12000000000000001,
+                                    "maximum": 0.13
+                                }
+                            }
+                        },
+                        {
+                            "id": "b",
+                            "name": "b",
+                            "description": "rate from I to H",
+                            "value": 0.07,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.07,
+                                    "maximum": 0.33
+                                }
+                            }
+                        },
+                        {
+                            "id": "f",
+                            "name": "f",
+                            "description": "prob of moving from I to H",
+                            "value": 1,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.09000000000000001,
+                                    "maximum": 0.1
+                                }
+                            }
+                        },
+                        {
+                            "id": "a",
+                            "name": "a",
+                            "description": "rate from I to R",
+                            "value": 0.07,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.07,
+                                    "maximum": 0.1
+                                }
+                            }
+                        },
+                        {
+                            "id": "e",
+                            "name": "e",
+                            "description": "prob of moving from I to R",
+                            "value": 1,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.9,
+                                    "maximum": 0.91
+                                }
+                            }
+                        },
+                        {
+                            "id": "c",
+                            "name": "c",
+                            "description": "rate from H to R",
+                            "value": 0.07,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.07,
+                                    "maximum": 0.33
+                                }
+                            }
+                        },
+                        {
+                            "id": "g",
+                            "name": "g",
+                            "description": "prob of moving from H to R",
+                            "value": 1,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.8700000000000001,
+                                    "maximum": 0.8700000000000001
+                                }
+                            }
+                        }
+                    ],
+                    "time": {
+                        "id": "t"
+                    }
+                }
+            },
+            "metadata": {
+                "timeseries": {},
+                "initials": {},
+                "parameters": {}
+            },
+            "id": "348d0c7a-87af-4c5e-9182-75deda5551d9",
+            "name": "s2_3",
+            "createdOn": "2024-03-26T18:34:48.497+00:00",
+            "temporary": false,
+            "publicAsset": false
+        }
+    },
+    "progress": {
+        "progress": 1,
+        "coverage_of_search_space": 1
+    },
+    "request": {
+        "constraints": [
+            {
+                "name": "c1",
+                "additive_bounds": {
+                    "ub": 0,
+                    "closed_upper_bound": true,
+                    "original_width": 99999999999
+                },
+                "variables": [
+                    "S"
+                ],
+                "weights": [
+                    1
+                ],
+                "derivative": true
+            },
+            {
+                "name": "c2",
+                "additive_bounds": {
+                    "ub": 0,
+                    "closed_upper_bound": true,
+                    "original_width": 99999999999
+                },
+                "variables": [
+                    "R"
+                ],
+                "weights": [
+                    -1
+                ],
+                "derivative": true
+            },
+            {
+                "name": "c3",
+                "additive_bounds": {
+                    "ub": 0,
+                    "closed_upper_bound": true,
+                    "original_width": 99999999999
+                },
+                "variables": [
+                    "D"
+                ],
+                "weights": [
+                    -1
+                ],
+                "derivative": true
+            },
+            {
+                "name": "I increase",
+                "timepoints": {
+                    "ub": 50
+                },
+                "variables": [
+                    "I"
+                ],
+                "additive_bounds": {
+                    "lb": 0
+                },
+                "derivative": true
+            },
+            {
+                "name": "I decrease",
+                "timepoints": {
+                    "lb": 60
+                },
+                "variables": [
+                    "I"
+                ],
+                "additive_bounds": {
+                    "lb": 0
+                },
+                "weights": [
+                    -1
+                ],
+                "derivative": true
+            },
+            {
+                "name": "H increase",
+                "timepoints": {
+                    "ub": 55
+                },
+                "variables": [
+                    "H"
+                ],
+                "additive_bounds": {
+                    "lb": 0
+                },
+                "derivative": true
+            },
+            {
+                "name": "H decrease",
+                "timepoints": {
+                    "lb": 65
+                },
+                "variables": [
+                    "H"
+                ],
+                "additive_bounds": {
+                    "lb": 0
+                },
+                "weights": [
+                    -1
+                ],
+                "derivative": true
+            }
+        ],
+        "parameters": [
+            {
+                "name": "N",
+                "interval": {
+                    "lb": 150000000,
+                    "ub": 150000000,
+                    "original_width": 100
+                }
+            },
+            {
+                "name": "beta",
+                "interval": {
+                    "lb": 0.5,
+                    "ub": 0.6,
+                    "original_width": 0.24000000000000002
+                },
+                "label": "all"
+            },
+            {
+                "name": "d",
+                "interval": {
+                    "lb": 0.1,
+                    "ub": 0.9900000000000001,
+                    "original_width": 0.8900000000000001
+                }
+            },
+            {
+                "name": "h",
+                "interval": {
+                    "lb": 0.129,
+                    "ub": 0.13,
+                    "original_width": 0.0010000000000000009
+                }
+            },
+            {
+                "name": "b",
+                "interval": {
+                    "lb": 0.75,
+                    "ub": 1.0,
+                    "original_width": 0.26
+                },
+                "label": "any"
+            },
+            {
+                "name": "f",
+                "interval": {
+                    "lb": 0.09000000000000001,
+                    "ub": 0.1,
+                    "original_width": 0.009999999999999995
+                }
+            },
+            {
+                "name": "a",
+                "interval": {
+                    "lb": 0.07,
+                    "ub": 0.1,
+                    "original_width": 0.03
+                },
+                "label": "any"
+            },
+            {
+                "name": "e",
+                "interval": {
+                    "lb": 0.9,
+                    "ub": 0.91,
+                    "original_width": 0.010000000000000009
+                }
+            },
+            {
+                "name": "c",
+                "interval": {
+                    "lb": 0.07,
+                    "ub": 0.33,
+                    "original_width": 0.26
+                }
+            },
+            {
+                "name": "g",
+                "interval": {
+                    "lb": 0.8700000000000001,
+                    "ub": 0.8710000000000001,
+                    "original_width": 0.0010000000000000009
+                }
+            }
+        ],
+        "config": {
+            "tolerance": 0.01,
+            "normalization_constant": 150000000,
+            "use_compartmental_constraints": true
+        },
+        "structure_parameters": [
+            {
+                "name": "schedules",
+                "schedules": [
+                    {
+                        "timepoints": [
+                            0,
+                            10,
+                            20,
+                            30,
+                            40,
+                            45,
+                            50,
+                            55,
+                            60,
+                            65,
+                            70,
+                            75,
+                            80
+                        ]
+                    }
+                ]
+            }
+        ]
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/eval_scenario2_request.json b/resources/amr/petrinet/terrarium-tests/eval_scenario2_request.json
new file mode 100644
index 00000000..9a6ea072
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/eval_scenario2_request.json
@@ -0,0 +1,171 @@
+ {
+        "constraints": [
+            {
+                "name": "c1",
+                "additive_bounds": {
+                    "lb": -99999999999,
+                    "ub": 0,
+                    "closed_upper_bound": true,
+                    "original_width": 99999999999
+                },
+                "variables": [
+                    "S"
+                ],
+                "weights": [
+                    1
+                ],
+                "derivative": true
+            },
+            {
+                "name": "c2",
+                "additive_bounds": {
+                    "lb": -99999999999,
+                    "ub": 0,
+                    "closed_upper_bound": true,
+                    "original_width": 99999999999
+                },
+                "variables": [
+                    "R"
+                ],
+                "weights": [
+                    -1
+                ],
+                "derivative": true
+            },
+            {
+                "name": "c3",
+                "additive_bounds": {
+                    "lb": -99999999999,
+                    "ub": 0,
+                    "closed_upper_bound": true,
+                    "original_width": 99999999999
+                },
+                "variables": [
+                    "D"
+                ],
+                "weights": [
+                    -1
+                ],
+                "derivative": true
+            }
+        ],
+        "parameters": [
+            {
+                "name": "N",
+                "interval": {
+                    "lb": 150000000,
+                    "ub": 150000000,
+                    "closed_upper_bound": true,
+                    "original_width": 0
+                }
+            },
+            {
+                "name": "a",
+                "interval": {
+                    "lb": 0.01,
+                    "ub": 0.1,
+                    "closed_upper_bound": true,
+                    "original_width": 0
+                },
+                "label": "any"
+            },
+            {
+                "name": "b",
+                "interval": {
+                    "lb": 0.01,
+                    "ub": 0.1,
+                    "closed_upper_bound": true,
+                    "original_width": 0
+                },
+                "label": "any"
+            },
+            {
+                "name": "c",
+                "interval": {
+                    "lb": 0.01,
+                    "ub": 0.1,
+                    "closed_upper_bound": true,
+                    "original_width": 0
+                },
+                "label": "any"
+            },
+            {
+                "name": "d",
+                "interval": {
+                    "lb": 0.2,
+                    "ub": 0.4,
+                    "closed_upper_bound": true,
+                    "original_width": 0
+                },
+                "label": "any"
+            },
+            {
+                "name": "e",
+                "interval": {
+                    "lb": 0.01,
+                    "ub": 0.1,
+                    "closed_upper_bound": true,
+                    "original_width": 0
+                },
+                "label": "any"
+            },
+            {
+                "name": "f",
+                "interval": {
+                    "lb": 0.001,
+                    "ub": 0.02,
+                    "closed_upper_bound": true,
+                    "original_width": 0
+                },
+                "label": "any"
+            },
+            {
+                "name": "g",
+                "interval": {
+                    "lb": 0.8,
+                    "ub": 0.9,
+                    "closed_upper_bound": true,
+                    "original_width": 0
+                },
+                "label": "all"
+            },
+            {
+                "name": "h",
+                "interval": {
+                    "lb": 0.1,
+                    "ub": 0.15,
+                    "closed_upper_bound": true,
+                    "original_width": 0
+                },
+                "label": "all"
+            },
+            {
+                "name": "β",
+                "interval": {
+                    "lb": 0.1,
+                    "ub": 0.2,
+                    "closed_upper_bound": true,
+                    "original_width": 0
+                },
+                "label": "all"
+            }
+        ],
+        "config": {
+            "tolerance": 0.05,
+            "normalization_constant": 150000000,
+            "use_compartmental_constraints": true
+        },
+        "structure_parameters": [
+            {
+                "name": "schedules",
+                "schedules": [
+                    {
+                        "timepoints": [
+                            0,10, 20, 30, 40, 50, 60, 70, 80, 90, 100
+                        
+                        ]
+                    }
+                ]
+            }
+        ]
+    }
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/eval_scenario2_small.json b/resources/amr/petrinet/terrarium-tests/eval_scenario2_small.json
new file mode 100644
index 00000000..ba9ed98c
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/eval_scenario2_small.json
@@ -0,0 +1,439 @@
+{
+    "id": "f7fe6d2c-2876-4f3f-b510-18d515694fa2",
+    "model": {
+        "petrinet": {
+            "header": {
+                "name": "s2_1",
+                "schema": "https://github.com/DARPA-ASKEM/Model-Representations/blob/main/petrinet/petrinet_schema.json",
+                "schema_name": "PetriNet",
+                "description": "This is a model from equations",
+                "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": [
+                            "H"
+                        ],
+                        "output": [
+                            "D"
+                        ]
+                    },
+                    {
+                        "id": "t2",
+                        "input": [
+                            "I"
+                        ],
+                        "output": [
+                            "H"
+                        ]
+                    },
+                    {
+                        "id": "t3",
+                        "input": [
+                            "I"
+                        ],
+                        "output": [
+                            "R"
+                        ]
+                    },
+                    {
+                        "id": "t4",
+                        "input": [
+                            "H"
+                        ],
+                        "output": [
+                            "R"
+                        ]
+                    }
+                ]
+            },
+            "semantics": {
+                "ode": {
+                    "rates": [
+                        {
+                            "target": "t0",
+                            "expression": "N*β*I*S",
+                            "expression_mathml": "<apply><times/><apply><divide/><ci>S</ci><ci>N</ci></apply><ci>β</ci><ci>I</ci></apply>"
+                        },
+                        {
+                            "target": "t1",
+                            "expression": "d*h*H",
+                            "expression_mathml": "<apply><times/><ci>H</ci><ci>d</ci><ci>h</ci></apply>"
+                        },
+                        {
+                            "target": "t2",
+                            "expression": "b*f*I",
+                            "expression_mathml": "<apply><times/><ci>I</ci><ci>b</ci><ci>f</ci></apply>"
+                        },
+                        {
+                            "target": "t3",
+                            "expression": "a*e*I",
+                            "expression_mathml": "<apply><times/><ci>I</ci><ci>a</ci><ci>e</ci></apply>"
+                        },
+                        {
+                            "target": "t4",
+                            "expression": "c*g*H",
+                            "expression_mathml": "<apply><times/><ci>H</ci><ci>c</ci><ci>g</ci></apply>"
+                        }
+                    ],
+                    "initials": [
+                        {
+                            "target": "S",
+                            "expression": "25550375",
+                            "expression_mathml": "<cn>25550375</cn>"
+                        },
+                        {
+                            "target": "I",
+                            "expression": "48722998",
+                            "expression_mathml": "<cn>48722998</cn>"
+                        },
+                        {
+                            "target": "D",
+                            "expression": "781182",
+                            "expression_mathml": "<cn>781182</cn>"
+                        },
+                        {
+                            "target": "H",
+                            "expression": "27003629",
+                            "expression_mathml": "<cn>27003629</cn>"
+                        },
+                        {
+                            "target": "R",
+                            "expression": "47941816",
+                            "expression_mathml": "<cn>47941816</cn>"
+                        }
+                    ],
+                    "parameters": [
+                        {
+                            "id": "N",
+                            "name": "N",
+                            "description": "Total population",
+                            "value": 1.5E8
+                        },
+                        {
+                            "id": "a",
+                            "name": "a",
+                            "description": "rate from I to R",
+                            "value": 0.07
+                        },
+                        {
+                            "id": "b",
+                            "name": "b",
+                            "description": "rate from I to H",
+                            "value": 0.07
+                        },
+                        {
+                            "id": "c",
+                            "name": "c",
+                            "description": "rate from H to R",
+                            "value": 0.07
+                        },
+                        {
+                            "id": "d",
+                            "name": "d",
+                            "description": "rate from H to D",
+                            "value": 0.30000000000000004
+                        },
+                        {
+                            "id": "e",
+                            "name": "e",
+                            "description": "prob of moving from I to R",
+                            "value": 0.09000000000000001
+                        },
+                        {
+                            "id": "f",
+                            "name": "f",
+                            "description": "prob of moving from I to H",
+                            "value": 0.01
+                        },
+                        {
+                            "id": "g",
+                            "name": "g",
+                            "description": "prob of moving from H to R",
+                            "value": 0.8700000000000001
+                        },
+                        {
+                            "id": "h",
+                            "name": "h",
+                            "description": "prob of moving from H to D",
+                            "value": 0.13
+                        },
+                        {
+                            "id": "β",
+                            "name": "β",
+                            "description": "Number of infections per infected person per day",
+                            "value": 0.18000000000000002
+                        }
+                    ]
+                }
+            },
+            "metadata": {
+                "timeseries": {},
+                "initials": {
+                    "S": {
+                        "name": "Susceptible"
+                    },
+                    "I": {
+                        "name": "Infected"
+                    },
+                    "D": {
+                        "name": "Dead"
+                    },
+                    "H": {
+                        "name": "Hospitalized"
+                    },
+                    "R": {
+                        "name": "Recovered"
+                    }
+                },
+                "parameters": {}
+            },
+            "id": "54478092-dd24-46f8-bcac-eba3af2cf018",
+            "name": "s2_1",
+            "createdOn": "2024-03-25T18:10:58.624+00:00",
+            "updatedOn": "2024-03-25T18:28:48.613+00:00",
+            "temporary": false,
+            "publicAsset": false
+        }
+    },
+    "progress": {
+        "progress": 1.0
+    },
+    "request": {
+        "constraints": [],
+        "parameters": [
+            {
+                "name": "N",
+                "interval": {
+                    "lb": 1.5E8,
+                    "ub": 1.5E8,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "a",
+                "interval": {
+                    "lb": 0.07,
+                    "ub": 0.07,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "b",
+                "interval": {
+                    "lb": 0.07,
+                    "ub": 0.07,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "c",
+                "interval": {
+                    "lb": 0.07,
+                    "ub": 0.07,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "d",
+                "interval": {
+                    "lb": 0.30000000000000004,
+                    "ub": 0.30000000000000004,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "e",
+                "interval": {
+                    "lb": 0.09000000000000001,
+                    "ub": 0.09000000000000001,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "f",
+                "interval": {
+                    "lb": 0.01,
+                    "ub": 0.01,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "g",
+                "interval": {
+                    "lb": 0.8700000000000001,
+                    "ub": 0.8700000000000001,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "h",
+                "interval": {
+                    "lb": 0.13,
+                    "ub": 0.13,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            },
+            {
+                "name": "β",
+                "interval": {
+                    "lb": 0.18000000000000002,
+                    "ub": 0.18000000000000002,
+                    "closed_upper_bound": true,
+                    "original_width": 0.0
+                }
+            }
+        ],
+        "config": {
+            "tolerance": 0.2,
+            "normalization_constant": 1.5E8,
+            "use_compartmental_constraints": true
+        },
+        "structure_parameters": [
+            {
+                "name": "schedules",
+                "schedules": [
+                    {
+                        "timepoints": [
+                            0,
+                            1
+                        ]
+                    }
+                ]
+            }
+        ]
+    },
+    "done": true,
+    "parameter_space": {
+        "num_dimensions": 11,
+        "false_boxes": [
+            {
+                "label": "false",
+                "bounds": {
+                    "N": {
+                        "lb": 1.0,
+                        "ub": 1.0,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0,
+                        "normalized": true,
+                        "unnormalized_lb": 1.5E8,
+                        "unnormalized_ub": 1.5E8
+                    },
+                    "a": {
+                        "lb": 0.07,
+                        "ub": 0.07,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0
+                    },
+                    "b": {
+                        "lb": 0.07,
+                        "ub": 0.07,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0
+                    },
+                    "c": {
+                        "lb": 0.07,
+                        "ub": 0.07,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0
+                    },
+                    "d": {
+                        "lb": 0.30000000000000004,
+                        "ub": 0.30000000000000004,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0
+                    },
+                    "e": {
+                        "lb": 0.09000000000000001,
+                        "ub": 0.09000000000000001,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0
+                    },
+                    "f": {
+                        "lb": 0.01,
+                        "ub": 0.01,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0
+                    },
+                    "g": {
+                        "lb": 0.8700000000000001,
+                        "ub": 0.8700000000000001,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0
+                    },
+                    "h": {
+                        "lb": 0.13,
+                        "ub": 0.13,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0
+                    },
+                    "β": {
+                        "lb": 0.18000000000000002,
+                        "ub": 0.18000000000000002,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0
+                    },
+                    "timestep": {
+                        "lb": 1.0,
+                        "ub": 1.0,
+                        "closed_upper_bound": true,
+                        "original_width": 0.0
+                    }
+                },
+                "explanation": {
+                    "expression": "(((((((((((((((S_1 = (S_0 - (((S_0 * I_0) * 'β') * N))) & (R_1 = ((R_0 + ((I_0 * e) * a)) + ((H_0 * g) * c)))) & (S_0 = 17033583333333333/100000000000000000)) & (I_0 = 32481998666666667/100000000000000000)) & ('β' = 9000000000000001/50000000000000000)) & (N = 1.0)) & (e = 90000000000000011/1000000000000000000)) & (a = 70000000000000007/1000000000000000000)) & (H_0 = 18002419333333333/100000000000000000)) & (g = 87000000000000011/100000000000000000)) & (c = 70000000000000007/1000000000000000000)) & (R_0 = 31961210666666667/100000000000000000)) & ((((((((((((((S_1 < 0.0) | (R_1 < 0.0)) | (! (S_1 = (S_0 - (((S_0 * I_0) * 'β') * N))))) | (! (R_1 = ((R_0 + ((I_0 * e) * a)) + ((H_0 * g) * c))))) | (! (S_0 = 17033583333333333/100000000000000000))) | (! (I_0 = 32481998666666667/100000000000000000))) | (! ('β' = 9000000000000001/50000000000000000))) | (! (N = 1.0))) | (! (e = 90000000000000011/1000000000000000000))) | (! (a = 70000000000000007/1000000000000000000))) | (! (H_0 = 18002419333333333/100000000000000000))) | (! (g = 87000000000000011/100000000000000000))) | (! (c = 70000000000000007/1000000000000000000))) | (! (R_0 = 31961210666666667/100000000000000000)))) & (! (S_1 < 0.0))) & (! (R_1 < 0.0)))"
+                },
+                "schedule": {
+                    "timepoints": [
+                        0,
+                        1
+                    ]
+                }
+            }
+        ]
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/feb_bug_model.json b/resources/amr/petrinet/terrarium-tests/feb_bug_model.json
new file mode 100644
index 00000000..7283ea34
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/feb_bug_model.json
@@ -0,0 +1,311 @@
+{
+    "id": "62686ae6-e1da-4896-b581-1cb183eae64f",
+    "temporary": false,
+    "publicAsset": false,
+    "header": {
+        "name": "ES1_2ci_SEIRHD",
+        "description": "Evaluation Scenario 2 Base model",
+        "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json",
+        "schema_name": "petrinet",
+        "model_version": "0.1"
+    },
+    "model": {
+        "states": [
+            {
+                "id": "S",
+                "name": "S",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "I",
+                "name": "I",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "E",
+                "name": "E",
+                "grounding": {
+                    "identifiers": {
+                        "apollosv": "0000154"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "R",
+                "name": "R",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "H",
+                "name": "H",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "property": "ncit:C25179"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "D",
+                "name": "D",
+                "grounding": {
+                    "identifiers": {
+                        "ncit": "C28554"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            }
+        ],
+        "transitions": [
+            {
+                "id": "t1",
+                "input": [
+                    "I",
+                    "S"
+                ],
+                "output": [
+                    "E",
+                    "I"
+                ],
+                "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": "<apply><divide/><apply><times/><ci>I</ci><ci>S</ci><ci>beta</ci></apply><ci>N</ci></apply>"
+                },
+                {
+                    "target": "t2",
+                    "expression": "E*r_E_to_I",
+                    "expression_mathml": "<apply><times/><ci>E</ci><ci>r_E_to_I</ci></apply>"
+                },
+                {
+                    "target": "t3",
+                    "expression": "I*p_I_to_R*r_I_to_R",
+                    "expression_mathml": "<apply><times/><ci>I</ci><ci>p_I_to_R</ci><ci>r_I_to_R</ci></apply>"
+                },
+                {
+                    "target": "t4",
+                    "expression": "I*p_I_to_H*r_I_to_H",
+                    "expression_mathml": "<apply><times/><ci>I</ci><ci>p_I_to_H</ci><ci>r_I_to_H</ci></apply>"
+                },
+                {
+                    "target": "t5",
+                    "expression": "H*p_H_to_R*r_H_to_R",
+                    "expression_mathml": "<apply><times/><ci>H</ci><ci>p_H_to_R</ci><ci>r_H_to_R</ci></apply>"
+                },
+                {
+                    "target": "t6",
+                    "expression": "H*p_H_to_D*r_H_to_D",
+                    "expression_mathml": "<apply><times/><ci>H</ci><ci>p_H_to_D</ci><ci>r_H_to_D</ci></apply>"
+                }
+            ],
+            "initials": [
+                {
+                    "target": "S",
+                    "expression": "14561489.0000000",
+                    "expression_mathml": "<cn>14561489.0</cn>"
+                },
+                {
+                    "target": "I",
+                    "expression": "2123452.00000000",
+                    "expression_mathml": "<cn>2123452.0</cn>"
+                },
+                {
+                    "target": "E",
+                    "expression": "530863.000000000",
+                    "expression_mathml": "<cn>530863.0</cn>"
+                },
+                {
+                    "target": "R",
+                    "expression": "2069902.00000000",
+                    "expression_mathml": "<cn>2069902.0</cn>"
+                },
+                {
+                    "target": "H",
+                    "expression": "744.000000000000",
+                    "expression_mathml": "<cn>744.0</cn>"
+                },
+                {
+                    "target": "D",
+                    "expression": "53550.0000000000",
+                    "expression_mathml": "<cn>53550.0</cn>"
+                }
+            ],
+            "parameters": [
+                {
+                    "id": "N",
+                    "value": 19340000
+                },
+                {
+                    "id": "beta",
+                    "value": 0.8
+                },
+                {
+                    "id": "r_E_to_I",
+                    "value": 0.2
+                },
+                {
+                    "id": "p_I_to_R",
+                    "value": 0.9
+                },
+                {
+                    "id": "r_I_to_R",
+                    "value": 0.07
+                },
+                {
+                    "id": "p_I_to_H",
+                    "value": 0.1
+                },
+                {
+                    "id": "r_I_to_H",
+                    "value": 0.1
+                },
+                {
+                    "id": "p_H_to_R",
+                    "value": 0.85
+                },
+                {
+                    "id": "r_H_to_R",
+                    "value": 0.1
+                },
+                {
+                    "id": "p_H_to_D",
+                    "value": 0.15
+                },
+                {
+                    "id": "r_H_to_D",
+                    "value": 0.1
+                }
+            ],
+            "observables": [],
+            "time": {
+                "id": "t",
+                "units": {
+                    "expression": "day",
+                    "expression_mathml": "<ci>day</ci>"
+                }
+            }
+        }
+    },
+    "metadata": {
+        "annotations": {
+            "authors": [],
+            "references": [],
+            "locations": [],
+            "pathogens": [],
+            "diseases": [],
+            "hosts": [],
+            "model_types": []
+        }
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/feb_bug_request.json b/resources/amr/petrinet/terrarium-tests/feb_bug_request.json
new file mode 100644
index 00000000..fa9689ef
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/feb_bug_request.json
@@ -0,0 +1,120 @@
+{
+    "constraints": [],
+    "parameters": [
+        {
+            "name": "N",
+            "interval": {
+                "lb": 19340000,
+                "ub": 19340000
+            },
+            "label": "any"
+        },
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.8,
+                "ub": 0.8
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_E_to_I",
+            "interval": {
+                "lb": 0.2,
+                "ub": 0.2
+            },
+            "label": "any"
+        },
+        {
+            "name": "p_I_to_R",
+            "interval": {
+                "lb": 0.9,
+                "ub": 0.9
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_I_to_R",
+            "interval": {
+                "lb": 0.07,
+                "ub": 0.07
+            },
+            "label": "any"
+        },
+        {
+            "name": "p_I_to_H",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_I_to_H",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1
+            },
+            "label": "any"
+        },
+        {
+            "name": "p_H_to_R",
+            "interval": {
+                "lb": 0.85,
+                "ub": 0.85
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_H_to_R",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1
+            },
+            "label": "any"
+        },
+        {
+            "name": "p_H_to_D",
+            "interval": {
+                "lb": 0.15,
+                "ub": 0.15
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_H_to_D",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1
+            },
+            "label": "any"
+        }
+    ],
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10,
+                        20,
+                        30,
+                        40,
+                        50,
+                        60,
+                        70,
+                        80,
+                        90,
+                        100
+                    ]
+                }
+            ]
+        }
+    ],
+    "config": {
+        "use_compartmental_constraints": true,
+        "normalization_constant": 1,
+        "tolerance": 0.89
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/funman-mar-25.json b/resources/amr/petrinet/terrarium-tests/funman-mar-25.json
new file mode 100644
index 00000000..07353748
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/funman-mar-25.json
@@ -0,0 +1,854 @@
+{
+    "id": "76832491-9d08-4ee0-8f6b-cef5667e5444",
+    "model": {
+        "petrinet": {
+            "header": {
+                "name": "Giordano2020 - SIDARTHE model of COVID-19 spread in Italy",
+                "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json",
+                "description": "Giordano2020 - SIDARTHE model of COVID-19 spread in Italy",
+                "model_version": "0.1"
+            },
+            "model": {
+                "states": [
+                    {
+                        "id": "Susceptible",
+                        "name": "Susceptible",
+                        "grounding": {
+                            "identifiers": {
+                                "ido": "0000514"
+                            },
+                            "modifiers": {}
+                        },
+                        "units": {
+                            "expression": "1",
+                            "expression_mathml": "<cn>1</cn>"
+                        }
+                    },
+                    {
+                        "id": "Diagnosed",
+                        "name": "Diagnosed",
+                        "grounding": {
+                            "identifiers": {
+                                "ido": "0000511"
+                            },
+                            "modifiers": {
+                                "diagnosis": "ncit:C15220"
+                            }
+                        },
+                        "units": {
+                            "expression": "1",
+                            "expression_mathml": "<cn>1</cn>"
+                        }
+                    },
+                    {
+                        "id": "Infected",
+                        "name": "Infected",
+                        "grounding": {
+                            "identifiers": {
+                                "ido": "0000511"
+                            },
+                            "modifiers": {}
+                        },
+                        "units": {
+                            "expression": "1",
+                            "expression_mathml": "<cn>1</cn>"
+                        }
+                    },
+                    {
+                        "id": "Ailing",
+                        "name": "Ailing",
+                        "grounding": {
+                            "identifiers": {
+                                "ido": "0000511"
+                            },
+                            "modifiers": {
+                                "disease_severity": "ncit:C25269",
+                                "diagnosis": "ncit:C113725"
+                            }
+                        },
+                        "units": {
+                            "expression": "1",
+                            "expression_mathml": "<cn>1</cn>"
+                        }
+                    },
+                    {
+                        "id": "Recognized",
+                        "name": "Recognized",
+                        "grounding": {
+                            "identifiers": {
+                                "ido": "0000511"
+                            },
+                            "modifiers": {
+                                "diagnosis": "ncit:C15220"
+                            }
+                        },
+                        "units": {
+                            "expression": "1",
+                            "expression_mathml": "<cn>1</cn>"
+                        }
+                    },
+                    {
+                        "id": "Healed",
+                        "name": "Healed",
+                        "grounding": {
+                            "identifiers": {
+                                "ido": "0000592"
+                            },
+                            "modifiers": {}
+                        },
+                        "units": {
+                            "expression": "1",
+                            "expression_mathml": "<cn>1</cn>"
+                        }
+                    },
+                    {
+                        "id": "Threatened",
+                        "name": "Threatened",
+                        "grounding": {
+                            "identifiers": {
+                                "ido": "0000511"
+                            },
+                            "modifiers": {
+                                "disease_severity": "ncit:C25467"
+                            }
+                        },
+                        "units": {
+                            "expression": "1",
+                            "expression_mathml": "<cn>1</cn>"
+                        }
+                    },
+                    {
+                        "id": "Extinct",
+                        "name": "Extinct",
+                        "grounding": {
+                            "identifiers": {
+                                "ncit": "C28554"
+                            },
+                            "modifiers": {}
+                        },
+                        "units": {
+                            "expression": "1",
+                            "expression_mathml": "<cn>1</cn>"
+                        }
+                    }
+                ],
+                "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"
+                        }
+                    }
+                ]
+            },
+            "semantics": {
+                "ode": {
+                    "rates": [
+                        {
+                            "target": "t1",
+                            "expression": "Diagnosed*Susceptible*beta",
+                            "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>Susceptible</ci><ci>beta</ci></apply>"
+                        },
+                        {
+                            "target": "t2",
+                            "expression": "Ailing*Susceptible*gamma",
+                            "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>Susceptible</ci><ci>gamma</ci></apply>"
+                        },
+                        {
+                            "target": "t3",
+                            "expression": "Recognized*Susceptible*delta",
+                            "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>Susceptible</ci><ci>delta</ci></apply>"
+                        },
+                        {
+                            "target": "t4",
+                            "expression": "Infected*Susceptible*alpha",
+                            "expression_mathml": "<apply><times/><ci>Infected</ci><ci>Susceptible</ci><ci>alpha</ci></apply>"
+                        },
+                        {
+                            "target": "t5",
+                            "expression": "Infected*epsilon",
+                            "expression_mathml": "<apply><times/><ci>Infected</ci><ci>epsilon</ci></apply>"
+                        },
+                        {
+                            "target": "t6",
+                            "expression": "Infected*zeta",
+                            "expression_mathml": "<apply><times/><ci>Infected</ci><ci>zeta</ci></apply>"
+                        },
+                        {
+                            "target": "t7",
+                            "expression": "Infected*lambda",
+                            "expression_mathml": "<apply><times/><ci>Infected</ci><ci>lambda</ci></apply>"
+                        },
+                        {
+                            "target": "t8",
+                            "expression": "Diagnosed*eta",
+                            "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>eta</ci></apply>"
+                        },
+                        {
+                            "target": "t9",
+                            "expression": "Diagnosed*rho",
+                            "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>rho</ci></apply>"
+                        },
+                        {
+                            "target": "t10",
+                            "expression": "Ailing*theta",
+                            "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>theta</ci></apply>"
+                        },
+                        {
+                            "target": "t11",
+                            "expression": "Ailing*kappa",
+                            "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>kappa</ci></apply>"
+                        },
+                        {
+                            "target": "t12",
+                            "expression": "Ailing*mu",
+                            "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>mu</ci></apply>"
+                        },
+                        {
+                            "target": "t13",
+                            "expression": "Recognized*nu",
+                            "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>nu</ci></apply>"
+                        },
+                        {
+                            "target": "t14",
+                            "expression": "Recognized*xi",
+                            "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>xi</ci></apply>"
+                        },
+                        {
+                            "target": "t15",
+                            "expression": "Threatened*tau",
+                            "expression_mathml": "<apply><times/><ci>Threatened</ci><ci>tau</ci></apply>"
+                        },
+                        {
+                            "target": "t16",
+                            "expression": "Threatened*sigma",
+                            "expression_mathml": "<apply><times/><ci>Threatened</ci><ci>sigma</ci></apply>"
+                        }
+                    ],
+                    "initials": [
+                        {
+                            "target": "Susceptible",
+                            "expression": "0.999996300000000",
+                            "expression_mathml": "<cn>0.99999629999999995</cn>"
+                        },
+                        {
+                            "target": "Diagnosed",
+                            "expression": "3.33333333000000e-7",
+                            "expression_mathml": "<cn>3.33333333e-7</cn>"
+                        },
+                        {
+                            "target": "Infected",
+                            "expression": "3.33333333000000e-6",
+                            "expression_mathml": "<cn>3.3333333299999999e-6</cn>"
+                        },
+                        {
+                            "target": "Ailing",
+                            "expression": "1.66666666000000e-8",
+                            "expression_mathml": "<cn>1.6666666599999999e-8</cn>"
+                        },
+                        {
+                            "target": "Recognized",
+                            "expression": "3.33333333000000e-8",
+                            "expression_mathml": "<cn>3.33333333e-8</cn>"
+                        },
+                        {
+                            "target": "Healed",
+                            "expression": "0.0",
+                            "expression_mathml": "<cn>0.0</cn>"
+                        },
+                        {
+                            "target": "Threatened",
+                            "expression": "0.0",
+                            "expression_mathml": "<cn>0.0</cn>"
+                        },
+                        {
+                            "target": "Extinct",
+                            "expression": "0.0",
+                            "expression_mathml": "<cn>0.0</cn>"
+                        }
+                    ],
+                    "parameters": [
+                        {
+                            "id": "beta",
+                            "value": 0.011000000000000001,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.008799999999999999,
+                                    "maximum": 0.013200000000000002
+                                }
+                            }
+                        },
+                        {
+                            "id": "gamma",
+                            "value": 0.456,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.3648,
+                                    "maximum": 0.5472
+                                }
+                            }
+                        },
+                        {
+                            "id": "delta",
+                            "value": 0.011000000000000001,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.008799999999999999,
+                                    "maximum": 0.013200000000000002
+                                }
+                            }
+                        },
+                        {
+                            "id": "alpha",
+                            "value": 0.5700000000000001,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.45599999999999996,
+                                    "maximum": 0.6839999999999999
+                                }
+                            }
+                        },
+                        {
+                            "id": "epsilon",
+                            "value": 0.171,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.1368,
+                                    "maximum": 0.20520000000000002
+                                }
+                            }
+                        },
+                        {
+                            "id": "zeta",
+                            "value": 0.125,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.1,
+                                    "maximum": 0.15000000000000002
+                                }
+                            }
+                        },
+                        {
+                            "id": "lambda",
+                            "value": 0.034,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.027200000000000002,
+                                    "maximum": 0.0408
+                                }
+                            }
+                        },
+                        {
+                            "id": "eta",
+                            "value": 0.125,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.1,
+                                    "maximum": 0.15000000000000002
+                                }
+                            }
+                        },
+                        {
+                            "id": "rho",
+                            "value": 0.034,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.027200000000000002,
+                                    "maximum": 0.0408
+                                }
+                            }
+                        },
+                        {
+                            "id": "theta",
+                            "value": 0.37100000000000005,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.2968,
+                                    "maximum": 0.44520000000000004
+                                }
+                            }
+                        },
+                        {
+                            "id": "kappa",
+                            "value": 0.017,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.013600000000000001,
+                                    "maximum": 0.0204
+                                }
+                            }
+                        },
+                        {
+                            "id": "mu",
+                            "value": 0.017,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.013600000000000001,
+                                    "maximum": 0.0204
+                                }
+                            }
+                        },
+                        {
+                            "id": "nu",
+                            "value": 0.027000000000000003,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.0216,
+                                    "maximum": 0.032400000000000005
+                                }
+                            }
+                        },
+                        {
+                            "id": "xi",
+                            "value": 0.017,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.013600000000000001,
+                                    "maximum": 0.0204
+                                }
+                            }
+                        },
+                        {
+                            "id": "tau",
+                            "value": 0.01,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.008,
+                                    "maximum": 0.012
+                                }
+                            }
+                        },
+                        {
+                            "id": "sigma",
+                            "value": 0.017,
+                            "distribution": {
+                                "type": "StandardUniform1",
+                                "parameters": {
+                                    "minimum": 0.013600000000000001,
+                                    "maximum": 0.0204
+                                }
+                            }
+                        }
+                    ],
+                    "time": {
+                        "id": "t",
+                        "units": {
+                            "expression": "day",
+                            "expression_mathml": "<ci>day</ci>"
+                        }
+                    }
+                }
+            },
+            "metadata": {
+                "timeseries": {},
+                "initials": {},
+                "parameters": {}
+            },
+            "id": "d56b82ec-538e-4303-8e28-1a512f5ea545",
+            "createdOn": "2024-02-27T23:12:01.763+00:00",
+            "temporary": false,
+            "publicAsset": false
+        }
+    },
+    "progress": {
+        "progress": 1.0
+    },
+    "request": {
+        "constraints": [
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+                    "ub": 100.0,
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+                    "original_width": 100.0
+                },
+                "additive_bounds": {
+                    "lb": 0.0,
+                    "ub": 888.0,
+                    "original_width": 888.0
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+                "variables": [
+                    "beta",
+                    "alpha"
+                ],
+                "weights": [
+                    1,
+                    1
+                ]
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+        ],
+        "parameters": [
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+                    "ub": 0.013200000000000002,
+                    "original_width": 0.004400000000000003
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+                "label": "all"
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+            {
+                "name": "gamma",
+                "interval": {
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+                    "ub": 0.5472,
+                    "original_width": 0.1824
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+            {
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+                "interval": {
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+            {
+                "name": "alpha",
+                "interval": {
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+                    "ub": 0.6839999999999999,
+                    "original_width": 0.228
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+                "label": "all"
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+            {
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+            {
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+                    "ub": 0.15000000000000002,
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+            {
+                "name": "lambda",
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+                    "ub": 0.0408,
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+            },
+            {
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+                    "ub": 0.15000000000000002,
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+            },
+            {
+                "name": "rho",
+                "interval": {
+                    "lb": 0.027200000000000002,
+                    "ub": 0.0408,
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+            },
+            {
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+                }
+            },
+            {
+                "name": "kappa",
+                "interval": {
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+                    "ub": 0.0204,
+                    "original_width": 0.0068000000000000005
+                }
+            },
+            {
+                "name": "mu",
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+                    "ub": 0.0204,
+                    "original_width": 0.0068000000000000005
+                }
+            },
+            {
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+                "interval": {
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+                    "ub": 0.032400000000000005,
+                    "original_width": 0.010800000000000006
+                }
+            },
+            {
+                "name": "xi",
+                "interval": {
+                    "lb": 0.013600000000000001,
+                    "ub": 0.0204,
+                    "original_width": 0.0068000000000000005
+                }
+            },
+            {
+                "name": "tau",
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+                    "ub": 0.012,
+                    "original_width": 0.004
+                }
+            },
+            {
+                "name": "sigma",
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+                    "ub": 0.0204,
+                    "original_width": 0.0068000000000000005
+                }
+            }
+        ],
+        "config": {
+            "tolerance": 0.1,
+            "normalization_constant": 1.0,
+            "use_compartmental_constraints": true
+        },
+        "structure_parameters": [
+            {
+                "name": "schedules",
+                "schedules": [
+                    {
+                        "timepoints": [
+                            0,
+                            10,
+                            20,
+                            30,
+                            40,
+                            50,
+                            60,
+                            70,
+                            80,
+                            90,
+                            100
+                        ]
+                    }
+                ]
+            }
+        ]
+    },
+    "done": true,
+    "parameter_space": {
+        "num_dimensions": 17
+    }
+}
diff --git a/resources/amr/petrinet/terrarium-tests/mar_19.json b/resources/amr/petrinet/terrarium-tests/mar_19.json
new file mode 100644
index 00000000..786c64fe
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/mar_19.json
@@ -0,0 +1,2113 @@
+{
+  "model": {
+    "id": "3713c736-cf95-4d4c-8327-5002fc032cab",
+    "createdOn": "2024-02-28T16:28:54.987+00:00",
+    "temporary": false,
+    "publicAsset": true,
+    "header": {
+      "name": "SEIRHD model NPI Type 1",
+      "description": "SEIRHD model NPI Type 1",
+      "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",
+          "name": "S",
+          "grounding": {
+            "identifiers": {
+              "ido": "0000514"
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+            "modifiers": {}
+          },
+          "units": {
+            "expression": "person",
+            "expression_mathml": "<ci>person</ci>"
+          }
+        },
+        {
+          "id": "I",
+          "name": "I",
+          "grounding": {
+            "identifiers": {
+              "ido": "0000511"
+            },
+            "modifiers": {}
+          },
+          "units": {
+            "expression": "person",
+            "expression_mathml": "<ci>person</ci>"
+          }
+        },
+        {
+          "id": "E",
+          "name": "E",
+          "grounding": {
+            "identifiers": {
+              "apollosv": "0000154"
+            },
+            "modifiers": {}
+          },
+          "units": {
+            "expression": "person",
+            "expression_mathml": "<ci>person</ci>"
+          }
+        },
+        {
+          "id": "R",
+          "name": "R",
+          "grounding": {
+            "identifiers": {
+              "ido": "0000592"
+            },
+            "modifiers": {}
+          },
+          "units": {
+            "expression": "person",
+            "expression_mathml": "<ci>person</ci>"
+          }
+        },
+        {
+          "id": "H",
+          "name": "H",
+          "grounding": {
+            "identifiers": {
+              "ido": "0000511"
+            },
+            "modifiers": {
+              "property": "ncit:C25179"
+            }
+          },
+          "units": {
+            "expression": "person",
+            "expression_mathml": "<ci>person</ci>"
+          }
+        },
+        {
+          "id": "D",
+          "name": "D",
+          "grounding": {
+            "identifiers": {
+              "ncit": "C28554"
+            },
+            "modifiers": {}
+          },
+          "units": {
+            "expression": "person",
+            "expression_mathml": "<ci>person</ci>"
+          }
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+          "input": [
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+            "E"
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+          "properties": {
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+        {
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+          "input": [
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+          "output": [
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+          ],
+          "properties": {
+            "name": "t2"
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+        {
+          "id": "t3",
+          "input": [
+            "I"
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+          "output": [
+            "R"
+          ],
+          "properties": {
+            "name": "t3"
+          }
+        },
+        {
+          "id": "t4",
+          "input": [
+            "I"
+          ],
+          "output": [
+            "H"
+          ],
+          "properties": {
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+          }
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+        {
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+          "input": [
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+          "output": [
+            "R"
+          ],
+          "properties": {
+            "name": "t5"
+          }
+        },
+        {
+          "id": "t6",
+          "input": [
+            "H"
+          ],
+          "output": [
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+          "properties": {
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+      ]
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+    "properties": {},
+    "semantics": {
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+            "expression": "I*S*kappa*(beta_c + (-beta_c + beta_s)/(1 + exp(-k*(-t + t0))))/total_population",
+            "expression_mathml": "<apply><divide/><apply><times/><ci>I</ci><ci>S</ci><ci>kappa</ci><apply><plus/><ci>beta_c</ci><apply><divide/><apply><plus/><apply><minus/><ci>beta_c</ci></apply><ci>beta_s</ci></apply><apply><plus/><cn>1</cn><apply><exp/><apply><minus/><apply><times/><ci>k</ci><apply><minus/><ci>t0</ci><ci>t</ci></apply></apply></apply></apply></apply></apply></apply></apply><ci>total_population</ci></apply>"
+          },
+          {
+            "target": "t2",
+            "expression": "E*delta",
+            "expression_mathml": "<apply><times/><ci>E</ci><ci>delta</ci></apply>"
+          },
+          {
+            "target": "t3",
+            "expression": "I*gamma*(1 - hosp)",
+            "expression_mathml": "<apply><times/><ci>I</ci><ci>gamma</ci><apply><minus/><cn>1</cn><ci>hosp</ci></apply></apply>"
+          },
+          {
+            "target": "t4",
+            "expression": "I*gamma*hosp",
+            "expression_mathml": "<apply><times/><ci>I</ci><ci>gamma</ci><ci>hosp</ci></apply>"
+          },
+          {
+            "target": "t5",
+            "expression": "H*(1 - death_hosp)/los",
+            "expression_mathml": "<apply><divide/><apply><times/><ci>H</ci><apply><minus/><cn>1</cn><ci>death_hosp</ci></apply></apply><ci>los</ci></apply>"
+          },
+          {
+            "target": "t6",
+            "expression": "H*death_hosp/los",
+            "expression_mathml": "<apply><divide/><apply><times/><ci>H</ci><ci>death_hosp</ci></apply><ci>los</ci></apply>"
+          }
+        ],
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+          {
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+            "expression": "total_population - I0",
+            "expression_mathml": "<apply><minus/><ci>total_population</ci><ci>I0</ci></apply>"
+          },
+          {
+            "target": "I",
+            "expression": "I0",
+            "expression_mathml": "<ci>I0</ci>"
+          },
+          {
+            "target": "E",
+            "expression": "40.0000000000000",
+            "expression_mathml": "<cn>40.0</cn>"
+          },
+          {
+            "target": "R",
+            "expression": "0.0",
+            "expression_mathml": "<cn>0.0</cn>"
+          },
+          {
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+            "expression": "0.0",
+            "expression_mathml": "<cn>0.0</cn>"
+          },
+          {
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+            "expression": "0.0",
+            "expression_mathml": "<cn>0.0</cn>"
+          }
+        ],
+        "parameters": [
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+              "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
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+              "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+            }
+          },
+          {
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+              "expression_mathml": "<cn>1</cn>"
+            }
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+          {
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+              "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+            }
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+            "units": {
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+              "expression_mathml": "<ci>day</ci>"
+            }
+          },
+          {
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+            "value": 19340000,
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+              "expression_mathml": "<ci>person</ci>"
+            }
+          },
+          {
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+              "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+            }
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+            }
+          },
+          {
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+              }
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+              "expression_mathml": "<apply><power/><ci>day</ci><cn>-1</cn></apply>"
+            }
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+            }
+          },
+          {
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+              "expression_mathml": "<ci>day</ci>"
+            }
+          }
+        ],
+        "observables": [
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+            "name": "infected",
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+            "expression_mathml": "<ci>E</ci>"
+          },
+          {
+            "id": "hospitalized",
+            "name": "hospitalized",
+            "expression": "H",
+            "expression_mathml": "<ci>H</ci>"
+          },
+          {
+            "id": "dead",
+            "name": "dead",
+            "expression": "D",
+            "expression_mathml": "<ci>D</ci>"
+          }
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+            "expression_mathml": "<ci>day</ci>"
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+    },
+    "metadata": {
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+    ],
+    "parameters": [
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+        "name": "beta_c",
+        "interval": {
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+          "ub": 0.8
+        },
+        "label": "any"
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+        "label": "any"
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+        "label": "any"
+      },
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+        "label": "any"
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+      {
+        "name": "total_population",
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+        "label": "any"
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+        "interval": {
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+          "ub": 0.1
+        },
+        "label": "any"
+      },
+      {
+        "name": "los",
+        "interval": {
+          "lb": 5,
+          "ub": 5
+        },
+        "label": "any"
+      }
+    ],
+    "structure_parameters": [
+      {
+        "name": "schedules",
+        "schedules": [
+          {
+            "timepoints": [
+              0,
+              10,
+              20,
+              30
+            ]
+          }
+        ]
+      }
+    ],
+    "config": {
+      "use_compartmental_constraints": true,
+      "normalization_constant": 19340040,
+      "tolerance": 0.5,
+      "save_smtlib": "./out"
+    }
+  }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/seird.json b/resources/amr/petrinet/terrarium-tests/seird.json
new file mode 100644
index 00000000..65ee9cdf
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/seird.json
@@ -0,0 +1,335 @@
+{
+    "header": {
+        "name": "DC ES1_2ci_SEIRHD",
+        "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json",
+        "schema_name": "petrinet",
+        "description": "DC Evaluation Scenario 2 Base model",
+        "model_version": "0.1"
+    },
+    "model": {
+        "states": [
+            {
+                "id": "S",
+                "name": "S",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "I",
+                "name": "I",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "E",
+                "name": "E",
+                "grounding": {
+                    "identifiers": {
+                        "apollosv": "0000154"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "R",
+                "name": "R",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "H",
+                "name": "H",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "property": "ncit:C25179"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "D",
+                "name": "D",
+                "grounding": {
+                    "identifiers": {
+                        "ncit": "C28554"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            }
+        ],
+        "transitions": [
+            {
+                "id": "t1",
+                "input": [
+                    "I",
+                    "S"
+                ],
+                "output": [
+                    "E",
+                    "I"
+                ],
+                "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": "<apply><divide/><apply><times/><ci>I</ci><ci>S</ci><ci>beta</ci></apply><ci>N</ci></apply>"
+                },
+                {
+                    "target": "t2",
+                    "expression": "E*r_E_to_I",
+                    "expression_mathml": "<apply><times/><ci>E</ci><ci>r_E_to_I</ci></apply>"
+                },
+                {
+                    "target": "t3",
+                    "expression": "I*p_I_to_R*r_I_to_R",
+                    "expression_mathml": "<apply><times/><ci>I</ci><ci>p_I_to_R</ci><ci>r_I_to_R</ci></apply>"
+                },
+                {
+                    "target": "t4",
+                    "expression": "I*p_I_to_H*r_I_to_H",
+                    "expression_mathml": "<apply><times/><ci>I</ci><ci>p_I_to_H</ci><ci>r_I_to_H</ci></apply>"
+                },
+                {
+                    "target": "t5",
+                    "expression": "H*p_H_to_R*r_H_to_R",
+                    "expression_mathml": "<apply><times/><ci>H</ci><ci>p_H_to_R</ci><ci>r_H_to_R</ci></apply>"
+                },
+                {
+                    "target": "t6",
+                    "expression": "H*p_H_to_D*r_H_to_D",
+                    "expression_mathml": "<apply><times/><ci>H</ci><ci>p_H_to_D</ci><ci>r_H_to_D</ci></apply>"
+                }
+            ],
+            "initials": [
+                {
+                    "target": "S",
+                    "expression": "S0",
+                    "expression_mathml": "<cn>S0</cn>"
+                },
+                {
+                    "target": "I",
+                    "expression": "I0",
+                    "expression_mathml": "<cn>I0</cn>"
+                },
+                {
+                    "target": "E",
+                    "expression": "E0",
+                    "expression_mathml": "<cn>E0</cn>"
+                },
+                {
+                    "target": "R",
+                    "expression": "R0",
+                    "expression_mathml": "<cn>R0</cn>"
+                },
+                {
+                    "target": "H",
+                    "expression": "H0",
+                    "expression_mathml": "<cn>H0</cn>"
+                },
+                {
+                    "target": "D",
+                    "expression": "D0",
+                    "expression_mathml": "<cn>D0</cn>"
+                }
+            ],
+            "parameters": [
+                {
+                    "id": "S0",
+                    "value": 100.0
+                },
+                {
+                    "id": "I0",
+                    "value": 1.0
+                },
+                {
+                    "id": "E0",
+                    "value": 0.0
+                },
+                {
+                    "id": "R0",
+                    "value": 0.0
+                },
+                {
+                    "id": "H0",
+                    "value": 0.0
+                },
+                {
+                    "id": "D0",
+                    "value": 0.0
+                },
+                {
+                    "id": "N",
+                    "value": 101.0
+                },
+                {
+                    "id": "beta",
+                    "value": 0.2
+                },
+                {
+                    "id": "r_E_to_I",
+                    "value": 0.2
+                },
+                {
+                    "id": "p_I_to_R",
+                    "value": 0.2
+                },
+                {
+                    "id": "r_I_to_R",
+                    "value": 0.1
+                },
+                {
+                    "id": "p_I_to_H",
+                    "value": 0.1
+                },
+                {
+                    "id": "r_I_to_H",
+                    "value": 0.1
+                },
+                {
+                    "id": "p_H_to_R",
+                    "value": 0.1
+                },
+                {
+                    "id": "r_H_to_R",
+                    "value": 0.1
+                },
+                {
+                    "id": "p_H_to_D",
+                    "value": 0.1
+                },
+                {
+                    "id": "r_H_to_D",
+                    "value": 0.1
+                }
+            ],
+            "time": {
+                "id": "t",
+                "units": {
+                    "expression": "day",
+                    "expression_mathml": "<ci>day</ci>"
+                }
+            }
+        }
+    },
+    "metadata": {
+        "annotations": {
+            "authors": [],
+            "references": [],
+            "locations": [],
+            "pathogens": [],
+            "diseases": [],
+            "hosts": [],
+            "model_types": []
+        }
+    },
+    "id": "4f34b9e4-65c3-470c-ae7d-ba709ff11d90",
+    "createdOn": "2024-02-20T03:52:50.682+00:00",
+    "temporary": false,
+    "publicAsset": false
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/seird_request.json b/resources/amr/petrinet/terrarium-tests/seird_request.json
new file mode 100644
index 00000000..71b23b09
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/seird_request.json
@@ -0,0 +1,190 @@
+{
+    "constraints": [],
+    "parameters": [
+        {
+            "name": "S0",
+            "interval": {
+                "lb": 100.0,
+                "ub": 100.0,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "I0",
+            "interval": {
+                "lb": 1.0,
+                "ub": 1.0,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "E0",
+            "interval": {
+                "lb": 0.0,
+                "ub": 0.0,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "R0",
+            "interval": {
+                "lb": 0.0,
+                "ub": 0.0,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "H0",
+            "interval": {
+                "lb": 0.0,
+                "ub": 0.0,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "D0",
+            "interval": {
+                "lb": 0.0,
+                "ub": 0.0,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "N",
+            "interval": {
+                "lb": 101.0,
+                "ub": 101.0,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.2,
+                "ub": 0.2,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "r_E_to_I",
+            "interval": {
+                "lb": 0.2,
+                "ub": 0.2,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "p_I_to_R",
+            "interval": {
+                "lb": 0.2,
+                "ub": 0.2,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "r_I_to_R",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "p_I_to_H",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "r_I_to_H",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "p_H_to_R",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "r_H_to_R",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "p_H_to_D",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        },
+        {
+            "name": "r_H_to_D",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1,
+                "closed_upper_bound": true,
+                "original_width": 0.0
+            }
+        }
+    ],
+    "config": {
+        "tolerance": 0.1,
+        "normalization_constant": 101.0,
+        "use_compartmental_constraints": true
+    },
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10,
+                        20,
+                        30,
+                        40,
+                        50,
+                        60,
+                        70,
+                        80,
+                        90,
+                        100,
+                        110,
+                        120,
+                        130,
+                        140,
+                        150
+                    ]
+                }
+            ]
+        }
+    ]
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/sidarthe_bug_model.json b/resources/amr/petrinet/terrarium-tests/sidarthe_bug_model.json
new file mode 100644
index 00000000..96fc59db
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/sidarthe_bug_model.json
@@ -0,0 +1,673 @@
+{
+    "id": "43ce7b04-a912-4144-a83d-e7d7937fa35d",
+    "createdOn": "2024-02-28T17:36:13.989+00:00",
+    "temporary": false,
+    "publicAsset": false,
+    "header": {
+        "name": "Gio",
+        "description": "Gio",
+        "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json",
+        "model_version": "0.1"
+    },
+    "model": {
+        "states": [
+            {
+                "id": "Susceptible",
+                "name": "Susceptible",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "1",
+                    "expression_mathml": "<cn>1</cn>"
+                }
+            },
+            {
+                "id": "Diagnosed",
+                "name": "Diagnosed",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "diagnosis": "ncit:C15220"
+                    }
+                },
+                "units": {
+                    "expression": "1",
+                    "expression_mathml": "<cn>1</cn>"
+                }
+            },
+            {
+                "id": "Infected",
+                "name": "Infected",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "1",
+                    "expression_mathml": "<cn>1</cn>"
+                }
+            },
+            {
+                "id": "Ailing",
+                "name": "Ailing",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "disease_severity": "ncit:C25269",
+                        "diagnosis": "ncit:C113725"
+                    }
+                },
+                "units": {
+                    "expression": "1",
+                    "expression_mathml": "<cn>1</cn>"
+                }
+            },
+            {
+                "id": "Recognized",
+                "name": "Recognized",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "diagnosis": "ncit:C15220"
+                    }
+                },
+                "units": {
+                    "expression": "1",
+                    "expression_mathml": "<cn>1</cn>"
+                }
+            },
+            {
+                "id": "Healed",
+                "name": "Healed",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "1",
+                    "expression_mathml": "<cn>1</cn>"
+                }
+            },
+            {
+                "id": "Threatened",
+                "name": "Threatened",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "disease_severity": "ncit:C25467"
+                    }
+                },
+                "units": {
+                    "expression": "1",
+                    "expression_mathml": "<cn>1</cn>"
+                }
+            },
+            {
+                "id": "Extinct",
+                "name": "Extinct",
+                "grounding": {
+                    "identifiers": {
+                        "ncit": "C28554"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "1",
+                    "expression_mathml": "<cn>1</cn>"
+                }
+            }
+        ],
+        "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"
+                }
+            }
+        ]
+    },
+    "semantics": {
+        "ode": {
+            "rates": [
+                {
+                    "target": "t1",
+                    "expression": "Diagnosed*Susceptible*beta",
+                    "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>Susceptible</ci><ci>beta</ci></apply>"
+                },
+                {
+                    "target": "t2",
+                    "expression": "Ailing*Susceptible*gamma",
+                    "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>Susceptible</ci><ci>gamma</ci></apply>"
+                },
+                {
+                    "target": "t3",
+                    "expression": "Recognized*Susceptible*delta",
+                    "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>Susceptible</ci><ci>delta</ci></apply>"
+                },
+                {
+                    "target": "t4",
+                    "expression": "Infected*Susceptible*alpha",
+                    "expression_mathml": "<apply><times/><ci>Infected</ci><ci>Susceptible</ci><ci>alpha</ci></apply>"
+                },
+                {
+                    "target": "t5",
+                    "expression": "Infected*epsilon",
+                    "expression_mathml": "<apply><times/><ci>Infected</ci><ci>epsilon</ci></apply>"
+                },
+                {
+                    "target": "t6",
+                    "expression": "Infected*zeta",
+                    "expression_mathml": "<apply><times/><ci>Infected</ci><ci>zeta</ci></apply>"
+                },
+                {
+                    "target": "t7",
+                    "expression": "Infected*lambdax",
+                    "expression_mathml": "<apply><times/><ci>Infected</ci><ci>lambdax</ci></apply>"
+                },
+                {
+                    "target": "t8",
+                    "expression": "Diagnosed*eta",
+                    "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>eta</ci></apply>"
+                },
+                {
+                    "target": "t9",
+                    "expression": "Diagnosed*rho",
+                    "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>rho</ci></apply>"
+                },
+                {
+                    "target": "t10",
+                    "expression": "Ailing*theta",
+                    "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>theta</ci></apply>"
+                },
+                {
+                    "target": "t11",
+                    "expression": "Ailing*kappa",
+                    "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>kappa</ci></apply>"
+                },
+                {
+                    "target": "t12",
+                    "expression": "Ailing*mu",
+                    "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>mu</ci></apply>"
+                },
+                {
+                    "target": "t13",
+                    "expression": "Recognized*nu",
+                    "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>nu</ci></apply>"
+                },
+                {
+                    "target": "t14",
+                    "expression": "Recognized*xi",
+                    "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>xi</ci></apply>"
+                },
+                {
+                    "target": "t15",
+                    "expression": "Threatened*tau",
+                    "expression_mathml": "<apply><times/><ci>Threatened</ci><ci>tau</ci></apply>"
+                },
+                {
+                    "target": "t16",
+                    "expression": "Threatened*sigma",
+                    "expression_mathml": "<apply><times/><ci>Threatened</ci><ci>sigma</ci></apply>"
+                }
+            ],
+            "initials": [
+                {
+                    "target": "Susceptible",
+                    "expression": "0.999996300000000",
+                    "expression_mathml": "<cn>0.99999629999999995</cn>"
+                },
+                {
+                    "target": "Diagnosed",
+                    "expression": "3.33333333000000e-7",
+                    "expression_mathml": "<cn>3.33333333e-7</cn>"
+                },
+                {
+                    "target": "Infected",
+                    "expression": "3.33333333000000e-6",
+                    "expression_mathml": "<cn>3.3333333299999999e-6</cn>"
+                },
+                {
+                    "target": "Ailing",
+                    "expression": "1.66666666000000e-8",
+                    "expression_mathml": "<cn>1.6666666599999999e-8</cn>"
+                },
+                {
+                    "target": "Recognized",
+                    "expression": "3.33333333000000e-8",
+                    "expression_mathml": "<cn>3.33333333e-8</cn>"
+                },
+                {
+                    "target": "Healed",
+                    "expression": "0.0",
+                    "expression_mathml": "<cn>0.0</cn>"
+                },
+                {
+                    "target": "Threatened",
+                    "expression": "0.0",
+                    "expression_mathml": "<cn>0.0</cn>"
+                },
+                {
+                    "target": "Extinct",
+                    "expression": "0.0",
+                    "expression_mathml": "<cn>0.0</cn>"
+                }
+            ],
+            "parameters": [
+                {
+                    "id": "beta",
+                    "value": 0.011,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.008799999999999999,
+                            "maximum": 0.0132
+                        }
+                    }
+                },
+                {
+                    "id": "gamma",
+                    "value": 0.456,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.3648,
+                            "maximum": 0.5472
+                        }
+                    }
+                },
+                {
+                    "id": "delta",
+                    "value": 0.011,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.008799999999999999,
+                            "maximum": 0.0132
+                        }
+                    }
+                },
+                {
+                    "id": "alpha",
+                    "value": 0.57,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.45599999999999996,
+                            "maximum": 0.6839999999999999
+                        }
+                    }
+                },
+                {
+                    "id": "epsilon",
+                    "value": 0.171,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.1368,
+                            "maximum": 0.20520000000000002
+                        }
+                    }
+                },
+                {
+                    "id": "zeta",
+                    "value": 0.125,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.1,
+                            "maximum": 0.15
+                        }
+                    }
+                },
+                {
+                    "id": "lambdax",
+                    "value": 0.034,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.027200000000000002,
+                            "maximum": 0.0408
+                        }
+                    }
+                },
+                {
+                    "id": "eta",
+                    "value": 0.125,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.1,
+                            "maximum": 0.15
+                        }
+                    }
+                },
+                {
+                    "id": "rho",
+                    "value": 0.034,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.027200000000000002,
+                            "maximum": 0.0408
+                        }
+                    }
+                },
+                {
+                    "id": "theta",
+                    "value": 0.371,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.2968,
+                            "maximum": 0.4452
+                        }
+                    }
+                },
+                {
+                    "id": "kappa",
+                    "value": 0.017,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.013600000000000001,
+                            "maximum": 0.0204
+                        }
+                    }
+                },
+                {
+                    "id": "mu",
+                    "value": 0.017,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.013600000000000001,
+                            "maximum": 0.0204
+                        }
+                    }
+                },
+                {
+                    "id": "nu",
+                    "value": 0.027,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.0216,
+                            "maximum": 0.0324
+                        }
+                    }
+                },
+                {
+                    "id": "xi",
+                    "value": 0.017,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.013600000000000001,
+                            "maximum": 0.0204
+                        }
+                    }
+                },
+                {
+                    "id": "tau",
+                    "value": 0.01,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.008,
+                            "maximum": 0.012
+                        }
+                    }
+                },
+                {
+                    "id": "sigma",
+                    "value": 0.017,
+                    "distribution": {
+                        "type": "StandardUniform1",
+                        "parameters": {
+                            "minimum": 0.013600000000000001,
+                            "maximum": 0.0204
+                        }
+                    }
+                }
+            ],
+            "observables": [
+                {
+                    "id": "Cases",
+                    "name": "Cases",
+                    "expression": "Diagnosed + Recognized + Threatened",
+                    "expression_mathml": "<apply><plus/><ci>Diagnosed</ci><ci>Recognized</ci><ci>Threatened</ci></apply>"
+                },
+                {
+                    "id": "Hospitalizations",
+                    "name": "Hospitalizations",
+                    "expression": "Recognized + Threatened",
+                    "expression_mathml": "<apply><plus/><ci>Recognized</ci><ci>Threatened</ci></apply>"
+                },
+                {
+                    "id": "Deaths",
+                    "name": "Deaths",
+                    "expression": "Extinct",
+                    "expression_mathml": "<ci>Extinct</ci>"
+                }
+            ],
+            "time": {
+                "id": "t",
+                "units": {
+                    "expression": "day",
+                    "expression_mathml": "<ci>day</ci>"
+                }
+            }
+        }
+    },
+    "metadata": {}
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/sidarthe_bug_request.json b/resources/amr/petrinet/terrarium-tests/sidarthe_bug_request.json
new file mode 100644
index 00000000..addf4f51
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/sidarthe_bug_request.json
@@ -0,0 +1,160 @@
+{
+    "constraints": [],
+    "parameters": [
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.008799999999999999,
+                "ub": 0.0132
+            },
+            "label": "any"
+        },
+        {
+            "name": "gamma",
+            "interval": {
+                "lb": 0.3648,
+                "ub": 0.5472
+            },
+            "label": "any"
+        },
+        {
+            "name": "delta",
+            "interval": {
+                "lb": 0.008799999999999999,
+                "ub": 0.0132
+            },
+            "label": "any"
+        },
+        {
+            "name": "alpha",
+            "interval": {
+                "lb": 0.45599999999999996,
+                "ub": 0.6839999999999999
+            },
+            "label": "any"
+        },
+        {
+            "name": "epsilon",
+            "interval": {
+                "lb": 0.1368,
+                "ub": 0.20520000000000002
+            },
+            "label": "any"
+        },
+        {
+            "name": "zeta",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.15
+            },
+            "label": "any"
+        },
+        {
+            "name": "lambdax",
+            "interval": {
+                "lb": 0.027200000000000002,
+                "ub": 0.0408
+            },
+            "label": "any"
+        },
+        {
+            "name": "eta",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.15
+            },
+            "label": "any"
+        },
+        {
+            "name": "rho",
+            "interval": {
+                "lb": 0.027200000000000002,
+                "ub": 0.0408
+            },
+            "label": "any"
+        },
+        {
+            "name": "theta",
+            "interval": {
+                "lb": 0.2968,
+                "ub": 0.4452
+            },
+            "label": "any"
+        },
+        {
+            "name": "kappa",
+            "interval": {
+                "lb": 0.013600000000000001,
+                "ub": 0.0204
+            },
+            "label": "any"
+        },
+        {
+            "name": "mu",
+            "interval": {
+                "lb": 0.013600000000000001,
+                "ub": 0.0204
+            },
+            "label": "any"
+        },
+        {
+            "name": "nu",
+            "interval": {
+                "lb": 0.0216,
+                "ub": 0.0324
+            },
+            "label": "any"
+        },
+        {
+            "name": "xi",
+            "interval": {
+                "lb": 0.013600000000000001,
+                "ub": 0.0204
+            },
+            "label": "any"
+        },
+        {
+            "name": "tau",
+            "interval": {
+                "lb": 0.008,
+                "ub": 0.012
+            },
+            "label": "any"
+        },
+        {
+            "name": "sigma",
+            "interval": {
+                "lb": 0.013600000000000001,
+                "ub": 0.0204
+            },
+            "label": "any"
+        }
+    ],
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10,
+                        20,
+                        30,
+                        40,
+                        50,
+                        60,
+                        70,
+                        80,
+                        90,
+                        100
+                    ]
+                }
+            ]
+        }
+    ],
+    "config": {
+        "use_compartmental_constraints": false,
+        "normalization_constant": 1,
+        "tolerance": 0.4
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/sir_request_constraint_no_ub.json b/resources/amr/petrinet/terrarium-tests/sir_request_constraint_no_ub.json
new file mode 100644
index 00000000..31b6ad72
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/sir_request_constraint_no_ub.json
@@ -0,0 +1,85 @@
+{
+    "constraints": [
+        {
+            "name": "I",
+            "variable": "I",
+            "interval": {
+                "lb": 0.15
+            },
+            "timepoints": {
+                "lb": 50,
+                "ub": 50,
+                "closed_upper_bound": true
+            }
+        }
+    ],
+    "parameters": [
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.08,
+                "ub": 0.1
+            },
+            "label": "all"
+        },
+        {
+            "name": "gamma",
+            "interval": {
+                "lb": 0.02,
+                "ub": 0.03
+            },
+            "label": "all"
+        },
+        {
+            "name": "S0",
+            "interval": {
+                "lb": 0.99,
+                "ub": 0.99
+            },
+            "label": "all"
+        },
+        {
+            "name": "I0",
+            "interval": {
+                "lb": 0.01,
+                "ub": 0.01
+            },
+            "label": "all"
+        },
+        {
+            "name": "R0",
+            "interval": {
+                "lb": 0,
+                "ub": 0
+            },
+            "label": "all"
+        }
+    ],
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10,
+                        20,
+                        30,
+                        40,
+                        50,
+                        60,
+                        70,
+                        80,
+                        90,
+                        100
+                    ]
+                }
+            ]
+        }
+    ],
+    "config": {
+        "use_compartmental_constraints": true,
+        "normalization_constant": 1,
+        "tolerance": 0.1
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/sir_request_param_constraint.json b/resources/amr/petrinet/terrarium-tests/sir_request_param_constraint.json
new file mode 100644
index 00000000..d975f8a7
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/sir_request_param_constraint.json
@@ -0,0 +1,100 @@
+{
+    "constraints": [
+        {
+            "name": "I",
+            "variable": "I",
+            "interval": {
+                "lb": 0.15,
+                "ub": 1.0
+            },
+            "timepoints": {
+                "lb": 50,
+                "ub": 50,
+                "closed_upper_bound": true
+            }
+        },
+        {
+            "name": "4x_gamma_le_beta",
+            "variables": [
+                "gamma",
+                "beta"
+            ],
+            "weights": [
+                -4.75,
+                1
+            ],
+            "additive_bounds": {
+                "lb": 0
+            }
+        }
+    ],
+    "parameters": [
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.08,
+                "ub": 0.1
+            },
+            "label": "all"
+        },
+        {
+            "name": "gamma",
+            "interval": {
+                "lb": 0.02,
+                "ub": 0.03
+            },
+            "label": "all"
+        },
+        {
+            "name": "S0",
+            "interval": {
+                "lb": 0.99,
+                "ub": 0.99
+            },
+            "label": "all"
+        },
+        {
+            "name": "I0",
+            "interval": {
+                "lb": 0.01,
+                "ub": 0.01
+            },
+            "label": "all"
+        },
+        {
+            "name": "R0",
+            "interval": {
+                "lb": 0,
+                "ub": 0
+            },
+            "label": "all"
+        }
+    ],
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10,
+                        20,
+                        30,
+                        40,
+                        50,
+                        60,
+                        70,
+                        80,
+                        90,
+                        100
+                    ]
+                }
+            ]
+        }
+    ],
+    "config": {
+        "use_compartmental_constraints": true,
+        "normalization_constant": 1,
+        "tolerance": 0.02
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/stratified_seird_model.json b/resources/amr/petrinet/terrarium-tests/stratified_seird_model.json
new file mode 100644
index 00000000..e95a5a35
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/stratified_seird_model.json
@@ -0,0 +1,679 @@
+{
+    "header": {
+        "name": "Carcione2020 - Deterministic SEIR simulation of a COVID-19 outbreak",
+        "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.6/petrinet/petrinet_schema.json",
+        "schema_name": "petrinet",
+        "description": "Carcione2020 - Deterministic SEIR simulation of a COVID-19 outbreak",
+        "model_version": "0.1"
+    },
+    "properties": {},
+    "model": {
+        "states": [
+            {
+                "id": "Susceptible_UnVax",
+                "name": "Susceptible",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {
+                        "Status": "UnVax"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Infected_UnVax",
+                "name": "Infected",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "Status": "UnVax"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Total_population",
+                "name": "Total_population",
+                "grounding": {
+                    "identifiers": {},
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Exposed_UnVax",
+                "name": "Exposed",
+                "grounding": {
+                    "identifiers": {
+                        "apollosv": "00000154"
+                    },
+                    "modifiers": {
+                        "Status": "UnVax"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Susceptible_Vax",
+                "name": "Susceptible",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {
+                        "Status": "Vax"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Infected_Vax",
+                "name": "Infected",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "Status": "Vax"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Exposed_Vax",
+                "name": "Exposed",
+                "grounding": {
+                    "identifiers": {
+                        "apollosv": "00000154"
+                    },
+                    "modifiers": {
+                        "Status": "Vax"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Recovered_UnVax",
+                "name": "Recovered",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    },
+                    "modifiers": {
+                        "Status": "UnVax"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Recovered_Vax",
+                "name": "Recovered",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    },
+                    "modifiers": {
+                        "Status": "Vax"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Deceased_UnVax",
+                "name": "Deceased",
+                "grounding": {
+                    "identifiers": {
+                        "ncit": "C28554"
+                    },
+                    "modifiers": {
+                        "Status": "UnVax"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "Deceased_Vax",
+                "name": "Deceased",
+                "grounding": {
+                    "identifiers": {
+                        "ncit": "C28554"
+                    },
+                    "modifiers": {
+                        "Status": "Vax"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            }
+        ],
+        "transitions": [
+            {
+                "id": "t1",
+                "input": [
+                    "Infected_UnVax",
+                    "Total_population",
+                    "Susceptible_UnVax"
+                ],
+                "output": [
+                    "Infected_UnVax",
+                    "Total_population",
+                    "Exposed_UnVax"
+                ],
+                "properties": {
+                    "name": "t1"
+                }
+            },
+            {
+                "id": "t2",
+                "input": [
+                    "Infected_Vax",
+                    "Total_population",
+                    "Susceptible_Vax"
+                ],
+                "output": [
+                    "Infected_Vax",
+                    "Total_population",
+                    "Exposed_Vax"
+                ],
+                "properties": {
+                    "name": "t2"
+                }
+            },
+            {
+                "id": "t3",
+                "input": [
+                    "Exposed_UnVax"
+                ],
+                "output": [
+                    "Infected_UnVax"
+                ],
+                "properties": {
+                    "name": "t3"
+                }
+            },
+            {
+                "id": "t4",
+                "input": [
+                    "Exposed_Vax"
+                ],
+                "output": [
+                    "Infected_Vax"
+                ],
+                "properties": {
+                    "name": "t4"
+                }
+            },
+            {
+                "id": "t5",
+                "input": [
+                    "Infected_UnVax"
+                ],
+                "output": [
+                    "Recovered_UnVax"
+                ],
+                "properties": {
+                    "name": "t5"
+                }
+            },
+            {
+                "id": "t6",
+                "input": [
+                    "Infected_Vax"
+                ],
+                "output": [
+                    "Recovered_Vax"
+                ],
+                "properties": {
+                    "name": "t6"
+                }
+            },
+            {
+                "id": "t7",
+                "input": [
+                    "Infected_UnVax"
+                ],
+                "output": [
+                    "Deceased_UnVax"
+                ],
+                "properties": {
+                    "name": "t7"
+                }
+            },
+            {
+                "id": "t8",
+                "input": [
+                    "Infected_Vax"
+                ],
+                "output": [
+                    "Deceased_Vax"
+                ],
+                "properties": {
+                    "name": "t8"
+                }
+            },
+            {
+                "id": "t9",
+                "input": [
+                    "Infected_UnVax"
+                ],
+                "output": [
+                    "Infected_Vax"
+                ],
+                "properties": {
+                    "name": "t9"
+                }
+            },
+            {
+                "id": "t10",
+                "input": [
+                    "Infected_Vax"
+                ],
+                "output": [
+                    "Infected_UnVax"
+                ],
+                "properties": {
+                    "name": "t10"
+                }
+            },
+            {
+                "id": "t11",
+                "input": [
+                    "Susceptible_UnVax"
+                ],
+                "output": [
+                    "Susceptible_Vax"
+                ],
+                "properties": {
+                    "name": "t11"
+                }
+            },
+            {
+                "id": "t12",
+                "input": [
+                    "Susceptible_Vax"
+                ],
+                "output": [
+                    "Susceptible_UnVax"
+                ],
+                "properties": {
+                    "name": "t12"
+                }
+            },
+            {
+                "id": "t13",
+                "input": [
+                    "Exposed_UnVax"
+                ],
+                "output": [
+                    "Exposed_Vax"
+                ],
+                "properties": {
+                    "name": "t13"
+                }
+            },
+            {
+                "id": "t14",
+                "input": [
+                    "Exposed_Vax"
+                ],
+                "output": [
+                    "Exposed_UnVax"
+                ],
+                "properties": {
+                    "name": "t14"
+                }
+            },
+            {
+                "id": "t15",
+                "input": [
+                    "Recovered_UnVax"
+                ],
+                "output": [
+                    "Recovered_Vax"
+                ],
+                "properties": {
+                    "name": "t15"
+                }
+            },
+            {
+                "id": "t16",
+                "input": [
+                    "Recovered_Vax"
+                ],
+                "output": [
+                    "Recovered_UnVax"
+                ],
+                "properties": {
+                    "name": "t16"
+                }
+            },
+            {
+                "id": "t17",
+                "input": [
+                    "Deceased_UnVax"
+                ],
+                "output": [
+                    "Deceased_Vax"
+                ],
+                "properties": {
+                    "name": "t17"
+                }
+            },
+            {
+                "id": "t18",
+                "input": [
+                    "Deceased_Vax"
+                ],
+                "output": [
+                    "Deceased_UnVax"
+                ],
+                "properties": {
+                    "name": "t18"
+                }
+            }
+        ]
+    },
+    "semantics": {
+        "ode": {
+            "rates": [
+                {
+                    "target": "t1",
+                    "expression": "Infected_UnVax*Susceptible_UnVax*beta/Total_population",
+                    "expression_mathml": "<apply><divide/><apply><times/><ci>Infected_UnVax</ci><ci>Susceptible_UnVax</ci><ci>beta</ci></apply><ci>Total_population</ci></apply>"
+                },
+                {
+                    "target": "t2",
+                    "expression": "Infected_Vax*Susceptible_Vax*beta/Total_population",
+                    "expression_mathml": "<apply><divide/><apply><times/><ci>Infected_Vax</ci><ci>Susceptible_Vax</ci><ci>beta</ci></apply><ci>Total_population</ci></apply>"
+                },
+                {
+                    "target": "t3",
+                    "expression": "Exposed_UnVax*epsilon",
+                    "expression_mathml": "<apply><times/><ci>Exposed_UnVax</ci><ci>epsilon</ci></apply>"
+                },
+                {
+                    "target": "t4",
+                    "expression": "Exposed_Vax*epsilon",
+                    "expression_mathml": "<apply><times/><ci>Exposed_Vax</ci><ci>epsilon</ci></apply>"
+                },
+                {
+                    "target": "t5",
+                    "expression": "Infected_UnVax*gamma",
+                    "expression_mathml": "<apply><times/><ci>Infected_UnVax</ci><ci>gamma</ci></apply>"
+                },
+                {
+                    "target": "t6",
+                    "expression": "Infected_Vax*gamma",
+                    "expression_mathml": "<apply><times/><ci>Infected_Vax</ci><ci>gamma</ci></apply>"
+                },
+                {
+                    "target": "t7",
+                    "expression": "Infected_UnVax*alpha",
+                    "expression_mathml": "<apply><times/><ci>Infected_UnVax</ci><ci>alpha</ci></apply>"
+                },
+                {
+                    "target": "t8",
+                    "expression": "Infected_Vax*alpha",
+                    "expression_mathml": "<apply><times/><ci>Infected_Vax</ci><ci>alpha</ci></apply>"
+                },
+                {
+                    "target": "t9",
+                    "expression": "Infected_UnVax*p_UnVax_Vax",
+                    "expression_mathml": "<apply><times/><ci>Infected_UnVax</ci><ci>p_UnVax_Vax</ci></apply>"
+                },
+                {
+                    "target": "t10",
+                    "expression": "Infected_Vax*p_Vax_UnVax",
+                    "expression_mathml": "<apply><times/><ci>Infected_Vax</ci><ci>p_Vax_UnVax</ci></apply>"
+                },
+                {
+                    "target": "t11",
+                    "expression": "Susceptible_UnVax*p_UnVax_Vax",
+                    "expression_mathml": "<apply><times/><ci>Susceptible_UnVax</ci><ci>p_UnVax_Vax</ci></apply>"
+                },
+                {
+                    "target": "t12",
+                    "expression": "Susceptible_Vax*p_Vax_UnVax",
+                    "expression_mathml": "<apply><times/><ci>Susceptible_Vax</ci><ci>p_Vax_UnVax</ci></apply>"
+                },
+                {
+                    "target": "t13",
+                    "expression": "Exposed_UnVax*p_UnVax_Vax",
+                    "expression_mathml": "<apply><times/><ci>Exposed_UnVax</ci><ci>p_UnVax_Vax</ci></apply>"
+                },
+                {
+                    "target": "t14",
+                    "expression": "Exposed_Vax*p_Vax_UnVax",
+                    "expression_mathml": "<apply><times/><ci>Exposed_Vax</ci><ci>p_Vax_UnVax</ci></apply>"
+                },
+                {
+                    "target": "t15",
+                    "expression": "Recovered_UnVax*p_UnVax_Vax",
+                    "expression_mathml": "<apply><times/><ci>Recovered_UnVax</ci><ci>p_UnVax_Vax</ci></apply>"
+                },
+                {
+                    "target": "t16",
+                    "expression": "Recovered_Vax*p_Vax_UnVax",
+                    "expression_mathml": "<apply><times/><ci>Recovered_Vax</ci><ci>p_Vax_UnVax</ci></apply>"
+                },
+                {
+                    "target": "t17",
+                    "expression": "Deceased_UnVax*p_UnVax_Vax",
+                    "expression_mathml": "<apply><times/><ci>Deceased_UnVax</ci><ci>p_UnVax_Vax</ci></apply>"
+                },
+                {
+                    "target": "t18",
+                    "expression": "Deceased_Vax*p_Vax_UnVax",
+                    "expression_mathml": "<apply><times/><ci>Deceased_Vax</ci><ci>p_Vax_UnVax</ci></apply>"
+                }
+            ],
+            "initials": [
+                {
+                    "target": "Susceptible_UnVax",
+                    "expression": "4989999.5",
+                    "expression_mathml": "<cn>4989999.5</cn>"
+                },
+                {
+                    "target": "Infected_UnVax",
+                    "expression": "0.5",
+                    "expression_mathml": "<cn>0.5</cn>"
+                },
+                {
+                    "target": "Total_population",
+                    "expression": "10000000.0",
+                    "expression_mathml": "<cn>10000000.0</cn>"
+                },
+                {
+                    "target": "Exposed_UnVax",
+                    "expression": "10000.0",
+                    "expression_mathml": "<cn>10000.0</cn>"
+                },
+                {
+                    "target": "Susceptible_Vax",
+                    "expression": "4989999.5",
+                    "expression_mathml": "<cn>4989999.5</cn>"
+                },
+                {
+                    "target": "Infected_Vax",
+                    "expression": "0.5",
+                    "expression_mathml": "<cn>0.5</cn>"
+                },
+                {
+                    "target": "Exposed_Vax",
+                    "expression": "10000.0",
+                    "expression_mathml": "<cn>10000.0</cn>"
+                },
+                {
+                    "target": "Recovered_UnVax",
+                    "expression": "0",
+                    "expression_mathml": "<cn>0</cn>"
+                },
+                {
+                    "target": "Recovered_Vax",
+                    "expression": "0",
+                    "expression_mathml": "<cn>0</cn>"
+                },
+                {
+                    "target": "Deceased_UnVax",
+                    "expression": "0",
+                    "expression_mathml": "<cn>0</cn>"
+                },
+                {
+                    "target": "Deceased_Vax",
+                    "expression": "0",
+                    "expression_mathml": "<cn>0</cn>"
+                }
+            ],
+            "parameters": [
+                {
+                    "id": "beta",
+                    "description": "beta",
+                    "value": 0.8330000000000001,
+                    "distribution": {
+                        "type": "Uniform1",
+                        "parameters": {
+                            "minimum": 0.1,
+                            "maximum": 1
+                        }
+                    }
+                },
+                {
+                    "id": "epsilon",
+                    "description": "epsilon",
+                    "value": 0.33333,
+                    "distribution": {
+                        "type": "Uniform1",
+                        "parameters": {
+                            "minimum": 0.30000000000000004,
+                            "maximum": 0.4
+                        }
+                    }
+                },
+                {
+                    "id": "gamma",
+                    "description": "gamma",
+                    "value": 0.125,
+                    "distribution": {
+                        "type": "Uniform1",
+                        "parameters": {
+                            "minimum": 0.1,
+                            "maximum": 0.15000000000000002
+                        }
+                    }
+                },
+                {
+                    "id": "alpha",
+                    "description": "alpha",
+                    "value": 0.006,
+                    "distribution": {
+                        "type": "Uniform1",
+                        "parameters": {
+                            "minimum": 0.005,
+                            "maximum": 0.007
+                        }
+                    }
+                },
+                {
+                    "id": "p_UnVax_Vax",
+                    "value": 0.1,
+                    "distribution": {
+                        "type": "Uniform1",
+                        "parameters": {
+                            "minimum": 0.05,
+                            "maximum": 0.15000000000000002
+                        }
+                    }
+                },
+                {
+                    "id": "p_Vax_UnVax",
+                    "value": 0.1,
+                    "distribution": {
+                        "type": "Uniform1",
+                        "parameters": {
+                            "minimum": 0.05,
+                            "maximum": 0.15000000000000002
+                        }
+                    }
+                },
+                {
+                    "id": "lambda",
+                    "description": "lambda",
+                    "value": 120480,
+                    "distribution": {
+                        "type": "Uniform1",
+                        "parameters": {
+                            "minimum": 120479,
+                            "maximum": 120481
+                        }
+                    }
+                },
+                {
+                    "id": "mu",
+                    "description": "mu",
+                    "value": 0.012048000000000001,
+                    "distribution": {
+                        "type": "Uniform1",
+                        "parameters": {
+                            "minimum": 0.01,
+                            "maximum": 0.015000000000000001
+                        }
+                    }
+                },
+                {
+                    "id": "City",
+                    "description": "City",
+                    "value": 0
+                }
+            ],
+            "time": {
+                "id": "t"
+            }
+        }
+    },
+    "metadata": {
+        "timeseries": {},
+        "initials": {},
+        "parameters": {}
+    },
+    "id": "bc80510b-c0ad-4bdd-a879-20e0ecc7dad0",
+    "name": "Carcione2020 - Deterministic SEIR simulation of a COVID-19 outbreak",
+    "createdOn": "2024-03-15T17:10:11.422+00:00",
+    "temporary": false,
+    "publicAsset": false
+}
diff --git a/resources/amr/petrinet/terrarium-tests/stratified_seird_request.json b/resources/amr/petrinet/terrarium-tests/stratified_seird_request.json
new file mode 100644
index 00000000..abce468f
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/stratified_seird_request.json
@@ -0,0 +1,105 @@
+{
+    "constraints": [],
+    "parameters": [
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 1.6,
+                "ub": 1.6,
+                "closed_upper_bound": true,
+                "original_width": 0
+            },
+            "label": "all"
+        },
+        {
+            "name": "epsilon",
+            "interval": {
+                "lb": 0.33333,
+                "ub": 0.33333,
+                "closed_upper_bound": true,
+                "original_width": 0
+            }
+        },
+        {
+            "name": "gamma",
+            "interval": {
+                "lb": 0.125,
+                "ub": 0.125,
+                "closed_upper_bound": true,
+                "original_width": 0
+            }
+        },
+        {
+            "name": "alpha",
+            "interval": {
+                "lb": 0.006,
+                "ub": 0.006,
+                "closed_upper_bound": true,
+                "original_width": 0
+            }
+        },
+        {
+            "name": "p_UnVax_Vax",
+            "interval": {
+                "lb": 0,
+                "ub": 0,
+                "closed_upper_bound": true,
+                "original_width": 0
+            }
+        },
+        {
+            "name": "p_Vax_UnVax",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1,
+                "closed_upper_bound": true,
+                "original_width": 0
+            }
+        },
+        {
+            "name": "lambda",
+            "interval": {
+                "lb": 120480,
+                "ub": 120480,
+                "closed_upper_bound": true,
+                "original_width": 0
+            }
+        },
+        {
+            "name": "mu",
+            "interval": {
+                "lb": 0.012048000000000001,
+                "ub": 0.012048000000000001,
+                "closed_upper_bound": true,
+                "original_width": 0
+            }
+        },
+        {
+            "name": "City",
+            "interval": {
+                "lb": 1,
+                "ub": 1,
+                "closed_upper_bound": true,
+                "original_width": 0
+            }
+        }
+    ],
+    "config": {
+        "tolerance": 0.1,
+        "normalization_constant": 20000000,
+        "use_compartmental_constraints": true
+    },
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10
+                    ]
+                }
+            ]
+        }
+    ]
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/stress-test-model.json b/resources/amr/petrinet/terrarium-tests/stress-test-model.json
new file mode 100644
index 00000000..7283ea34
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/stress-test-model.json
@@ -0,0 +1,311 @@
+{
+    "id": "62686ae6-e1da-4896-b581-1cb183eae64f",
+    "temporary": false,
+    "publicAsset": false,
+    "header": {
+        "name": "ES1_2ci_SEIRHD",
+        "description": "Evaluation Scenario 2 Base model",
+        "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json",
+        "schema_name": "petrinet",
+        "model_version": "0.1"
+    },
+    "model": {
+        "states": [
+            {
+                "id": "S",
+                "name": "S",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000514"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "I",
+                "name": "I",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "E",
+                "name": "E",
+                "grounding": {
+                    "identifiers": {
+                        "apollosv": "0000154"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "R",
+                "name": "R",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000592"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "H",
+                "name": "H",
+                "grounding": {
+                    "identifiers": {
+                        "ido": "0000511"
+                    },
+                    "modifiers": {
+                        "property": "ncit:C25179"
+                    }
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            },
+            {
+                "id": "D",
+                "name": "D",
+                "grounding": {
+                    "identifiers": {
+                        "ncit": "C28554"
+                    },
+                    "modifiers": {}
+                },
+                "units": {
+                    "expression": "person",
+                    "expression_mathml": "<ci>person</ci>"
+                }
+            }
+        ],
+        "transitions": [
+            {
+                "id": "t1",
+                "input": [
+                    "I",
+                    "S"
+                ],
+                "output": [
+                    "E",
+                    "I"
+                ],
+                "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": "<apply><divide/><apply><times/><ci>I</ci><ci>S</ci><ci>beta</ci></apply><ci>N</ci></apply>"
+                },
+                {
+                    "target": "t2",
+                    "expression": "E*r_E_to_I",
+                    "expression_mathml": "<apply><times/><ci>E</ci><ci>r_E_to_I</ci></apply>"
+                },
+                {
+                    "target": "t3",
+                    "expression": "I*p_I_to_R*r_I_to_R",
+                    "expression_mathml": "<apply><times/><ci>I</ci><ci>p_I_to_R</ci><ci>r_I_to_R</ci></apply>"
+                },
+                {
+                    "target": "t4",
+                    "expression": "I*p_I_to_H*r_I_to_H",
+                    "expression_mathml": "<apply><times/><ci>I</ci><ci>p_I_to_H</ci><ci>r_I_to_H</ci></apply>"
+                },
+                {
+                    "target": "t5",
+                    "expression": "H*p_H_to_R*r_H_to_R",
+                    "expression_mathml": "<apply><times/><ci>H</ci><ci>p_H_to_R</ci><ci>r_H_to_R</ci></apply>"
+                },
+                {
+                    "target": "t6",
+                    "expression": "H*p_H_to_D*r_H_to_D",
+                    "expression_mathml": "<apply><times/><ci>H</ci><ci>p_H_to_D</ci><ci>r_H_to_D</ci></apply>"
+                }
+            ],
+            "initials": [
+                {
+                    "target": "S",
+                    "expression": "14561489.0000000",
+                    "expression_mathml": "<cn>14561489.0</cn>"
+                },
+                {
+                    "target": "I",
+                    "expression": "2123452.00000000",
+                    "expression_mathml": "<cn>2123452.0</cn>"
+                },
+                {
+                    "target": "E",
+                    "expression": "530863.000000000",
+                    "expression_mathml": "<cn>530863.0</cn>"
+                },
+                {
+                    "target": "R",
+                    "expression": "2069902.00000000",
+                    "expression_mathml": "<cn>2069902.0</cn>"
+                },
+                {
+                    "target": "H",
+                    "expression": "744.000000000000",
+                    "expression_mathml": "<cn>744.0</cn>"
+                },
+                {
+                    "target": "D",
+                    "expression": "53550.0000000000",
+                    "expression_mathml": "<cn>53550.0</cn>"
+                }
+            ],
+            "parameters": [
+                {
+                    "id": "N",
+                    "value": 19340000
+                },
+                {
+                    "id": "beta",
+                    "value": 0.8
+                },
+                {
+                    "id": "r_E_to_I",
+                    "value": 0.2
+                },
+                {
+                    "id": "p_I_to_R",
+                    "value": 0.9
+                },
+                {
+                    "id": "r_I_to_R",
+                    "value": 0.07
+                },
+                {
+                    "id": "p_I_to_H",
+                    "value": 0.1
+                },
+                {
+                    "id": "r_I_to_H",
+                    "value": 0.1
+                },
+                {
+                    "id": "p_H_to_R",
+                    "value": 0.85
+                },
+                {
+                    "id": "r_H_to_R",
+                    "value": 0.1
+                },
+                {
+                    "id": "p_H_to_D",
+                    "value": 0.15
+                },
+                {
+                    "id": "r_H_to_D",
+                    "value": 0.1
+                }
+            ],
+            "observables": [],
+            "time": {
+                "id": "t",
+                "units": {
+                    "expression": "day",
+                    "expression_mathml": "<ci>day</ci>"
+                }
+            }
+        }
+    },
+    "metadata": {
+        "annotations": {
+            "authors": [],
+            "references": [],
+            "locations": [],
+            "pathogens": [],
+            "diseases": [],
+            "hosts": [],
+            "model_types": []
+        }
+    }
+}
\ No newline at end of file
diff --git a/resources/amr/petrinet/terrarium-tests/stress-test-request.json b/resources/amr/petrinet/terrarium-tests/stress-test-request.json
new file mode 100644
index 00000000..1264b6c5
--- /dev/null
+++ b/resources/amr/petrinet/terrarium-tests/stress-test-request.json
@@ -0,0 +1,119 @@
+{
+    "constraints": [],
+    "parameters": [
+        {
+            "name": "N",
+            "interval": {
+                "lb": 19340000,
+                "ub": 19340000
+            },
+            "label": "any"
+        },
+        {
+            "name": "beta",
+            "interval": {
+                "lb": 0.8,
+                "ub": 0.8
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_E_to_I",
+            "interval": {
+                "lb": 0.2,
+                "ub": 0.2
+            },
+            "label": "all"
+        },
+        {
+            "name": "p_I_to_R",
+            "interval": {
+                "lb": 0.9,
+                "ub": 0.9
+            },
+            "label": "all"
+        },
+        {
+            "name": "r_I_to_R",
+            "interval": {
+                "lb": 0.07,
+                "ub": 0.07
+            },
+            "label": "any"
+        },
+        {
+            "name": "p_I_to_H",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_I_to_H",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1
+            },
+            "label": "any"
+        },
+        {
+            "name": "p_H_to_R",
+            "interval": {
+                "lb": 0.85,
+                "ub": 0.85
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_H_to_R",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1
+            },
+            "label": "any"
+        },
+        {
+            "name": "p_H_to_D",
+            "interval": {
+                "lb": 0.15,
+                "ub": 0.15
+            },
+            "label": "any"
+        },
+        {
+            "name": "r_H_to_D",
+            "interval": {
+                "lb": 0.1,
+                "ub": 0.1
+            },
+            "label": "any"
+        }
+    ],
+    "structure_parameters": [
+        {
+            "name": "schedules",
+            "schedules": [
+                {
+                    "timepoints": [
+                        0,
+                        10,
+                        20,
+                        30,
+                        40,
+                        50,
+                        60,
+                        70,
+                        80,
+                        90,
+                        100
+                    ]
+                }
+            ]
+        }
+    ],
+    "config": {
+        "normalization_constant": 1,
+        "tolerance": 0.4
+    }
+}
\ No newline at end of file
diff --git a/resources/smt2/photosynthesis.smt2 b/resources/smt2/photosynthesis.smt2
new file mode 100644
index 00000000..4b0e7b39
--- /dev/null
+++ b/resources/smt2/photosynthesis.smt2
@@ -0,0 +1,173 @@
+(set-logic QF_NRA)
+(declare-fun r_s () Real)
+(declare-fun r_b () Real)
+(declare-fun m () Real)
+(declare-fun A () Real)
+(declare-fun c_s () Real)
+(declare-fun c_a () Real)
+(declare-fun e_s () Real)
+(declare-fun e_t () Real)
+(declare-fun P_atm () Real)
+(declare-fun b () Real)
+(declare-fun C_3 () Bool)
+(declare-fun C_4 () Bool)
+
+(declare-fun w_c () Real)
+(declare-fun w_j () Real)
+(declare-fun w_e () Real)
+(declare-fun Gamma_star () Real)
+(declare-fun V_cmax () Real)
+(declare-fun Gamma_star () Real)
+(declare-fun K_c () Real)
+(declare-fun o_i () Real)
+(declare-fun K_o () Real)
+(declare-fun phi () Real)
+(declare-fun alpha () Real)
+(declare-fun c_i () Real)
+(declare-fun K_c25 () Real)
+(declare-fun a_kc () Real)
+(declare-fun T_v () Real)
+(declare-fun T_f () Real)
+(declare-fun R_gas () Real)
+(declare-fun K_o25 () Real)
+(declare-fun a_ko () Real)
+(declare-fun V_cmax25 () Real)
+(declare-fun N_a () Real)
+(declare-fun F_LNR () Real)
+(declare-fun F_NR () Real)
+(declare-fun a_R25 () Real)
+(declare-fun CN_L () Real)
+(declare-fun SLA_0 () Real)
+(declare-fun SLA_m () Real)
+(declare-fun SLA () Real)
+(declare-fun SLA_sun () Real)
+(declare-fun SLA_sha () Real)
+(declare-fun K () Real)
+(declare-fun L_sun () Real)
+(declare-fun c () Real)
+(declare-fun L () Real)
+(declare-fun L_sha () Real)
+(declare-fun DYL () Real)
+(declare-fun DYL_max () Real)
+(declare-fun f () Real)
+(declare-fun f_T_v () Real)
+(declare-fun f_DYL () Real)
+(declare-fun decl () Real)
+(declare-fun decl_max () Real)
+(declare-fun lat () Real)
+(declare-fun e_a () Real)
+(declare-fun e_a_prime () Real)
+(declare-fun e_i () Real)
+(declare-fun q_s () Real)
+(declare-fun active_bgc () Real)
+(declare-fun beta_t () Real)
+(declare-fun x () Real)
+
+(assert (and 
+
+; 8.1
+(= (/ 1 r_s) (+ (* m (/ A c_s) (/ e_s e_t) P_atm) b))
+(=> (= A 0) (= b 2000))
+(=> C_3 (= m 9))
+(=> C_4 (= m 4))
+(xor C_3 C_4)
+
+; 8.2
+;(= A (minimum w_c w_j w_e))
+(= C_3 (= w_c (/ (* V_cmax (- c_i Gamma_star)) (+ c_i (* K_c (/ (+ 1 o_i) K_o))))))
+(= C_4 (= w_c V_cmax))
+(<= 0 (- c_i Gamma_star))
+
+; 8.3
+(= C_3 (= w_j (/ (* (- c_i Gamma_star) 4.6 phi alpha) (+ c_i (* 2 Gamma_star)))))
+(= C_4 (= w_j (* 4.6 phi alpha)))
+
+; 8.4
+(= C_3 (= w_e (* 0.5 V_cmax)))
+(= C_4 (= w_e (* 4000 V_cmax (/ c_i P_atm))))
+(= o_i (+ 0.209 P_atm))
+
+; 8.5 
+(= K_c (* K_c25 (pow a_kc (/ (- T_v 25) 10))))
+(= K_c25 30.0)
+(= a_kc 2.1)
+
+; 8.6
+(= K_o (* K_o25 (pow a_ko (/ (- T_v 25) 10))))
+(= K_o25 30000.0)
+(= a_ko 1.2)
+
+; 8.7
+(= Gamma_star (* 0.5 (/ K_c K_o) 0.21 o_i))
+
+; 8.8
+(= V_cmax25 (* N_a F_LNR F_NR a_R25))
+(= F_NR 7.16)
+(= a_R25 60)
+
+; 8.9
+(= N_a (/ 1 (* CN_L SLA)))
+
+; 8.10
+(= SLA (+ SLA_0 (* SLA_m x)))
+
+; 8.11
+(= SLA_sun (* (pow K -2) (pow L_sun -1) (+ SLA_m (* K SLA_0) (* -1 SLA_m c) (* -1 K SLA_0 c) (* -1 K L SLA_m c))))
+
+; 8.12
+(= SLA_sha (* (pow L_sha -1) (+ (* L (+ SLA_0 (* (/ 1 2) L SLA_m))) (* -1 L_sun SLA_sun))))
+
+; 8.13
+
+; 8.14
+(= f_T_v (pow (+ 1 (exp (* 1000. (pow R_gas -1) (pow (+ T_f T_v) -1) (+ -220000 (* 710 T_f) (* 710 T_v))))) -1))
+(= R_gas 8314.46759)
+(= T_f 273.15)
+
+; 8.15
+(>= f_DYL 0.01) 
+(<= f_DYL 1) 
+(= f_DYL (* (pow DYL 2) (pow DYL_max -2)))
+
+; 8.16
+(= DYL (* 27501.9742 (arccos (* (pow (cos decl) -1) (pow (cos lat) -1) (sin decl) (sin lat)))))
+(= DYL_max (* 27501.9742 (arccos (* (pow (cos decl_max) -1) (pow (cos lat) -1) (sin decl_max) (sin lat)))))
+(= decl_max 23.4667)
+
+; 8.17
+
+; 8.18
+
+; 8.19
+
+; 8.20
+
+; 8.21
+
+; 8.22
+(= A (* (pow P_atm -1) (pow (+ (* 1.37 r_b) (* 1.65 r_s)) -1) (+ c_a (* -1 c_i))))
+(= A (* 0.72992700729927 (pow P_atm -1) (pow r_b -1) (+ c_a (* -1 c_s))))
+(= A (* 0.606060606060606 (pow P_atm -1) (pow r_s -1) (+ c_s (* -1 c_i))))
+
+; 8.23
+(= (* (pow (+ r_b r_s) -1) (+ e_a_prime (* -1 e_i))) (* (pow r_b -1) (+ e_a_prime (* -1 e_s))))
+(= (* (pow r_b -1) (+ e_a_prime (* -1 e_s))) (* (pow r_s -1) (+ e_s (* -1 e_i))))
+
+; 8.24
+(= e_a (* 1.60771704180064 P_atm q_s))
+
+; 8.25
+(= c_s (+ c_a (* -1.37 A P_atm r_b)))
+
+; 8.26
+(= e_s (* (pow (+ r_b r_s) -1) (+ (* e_a_prime r_s) (* e_i r_b))))
+
+; 8.27
+(= (+ (* -1 r_b) (* r_s (+ -1 (* b r_b) (* A P_atm m r_b (pow c_s -1)))) (* (pow r_s 2) (+ b (* A P_atm e_a_prime m (pow c_s -1) (pow e_i -1))))) 0)
+
+; 8.28
+(= c_i (+ c_s (* -1.65 A P_atm r_s)))
+
+))
+(push 1)
+(check-sat)
diff --git a/resources/terarium-tests.json b/resources/terarium-tests.json
index b4d36ee3..1a2090c8 100644
--- a/resources/terarium-tests.json
+++ b/resources/terarium-tests.json
@@ -1,24 +1,112 @@
 {
     "tests": [
         {
-            "name": "petrinet-sir",
+            "name": "sidarthe-bug",
+            "model-path": "amr/petrinet/terrarium-tests/sidarthe_bug_model.json",
+            "request-path": "amr/petrinet/terrarium-tests/sidarthe_bug_request.json",
+            "expected-outcome": {
+                "true-boxes": 1
+            },
+            "regression": false
+        },
+        {
+            "name": "petrinet-stress-test",
+            "model-path": "amr/petrinet/terrarium-tests/stress-test-model.json",
+            "request-path": "amr/petrinet/terrarium-tests/stress-test-request.json",
+            "expected-outcome": {
+                "true-boxes": 0
+            },
+            "regression": false
+        },
+        {
+            "name": "mar_19",
+            "model-path": "amr/petrinet/terrarium-tests/mar_19.json",
+            "request-path": null,
+            "expected-outcome": {
+                "true-boxes": 0
+            },
+            "regression": false
+        },
+        {
+            "name": "mar_25",
+            "model-path": "amr/petrinet/terrarium-tests/funman-mar-25.json",
+            "request-path": null,
+            "expected-outcome": {
+                "true-boxes": 0
+            },
+            "regression": false
+        },
+        {
+            "name": "norm_test",
+            "model-path": "amr/petrinet/terrarium-tests/eval_scenario2_norm.json",
+            "request-path": null,
+            "expected-outcome": {
+                "true-boxes": 0
+            },
+            "regression": false
+        },
+        {
+            "name": "eval_scenario_2",
+            "model-path": "amr/petrinet/terrarium-tests/eval_scenario2_model.json",
+            "request-path": "amr/petrinet/terrarium-tests/eval_scenario2_request.json",
+            "expected-outcome": {
+                "true-boxes": 0
+            },
+            "regression": false
+        },
+        {
+            "name": "sir",
+            "model-path": "amr/petrinet/terrarium-tests/sir.json",
+            "request-path": "amr/petrinet/terrarium-tests/sir_request.json",
+            "expected-outcome": {
+                "true-boxes": 49
+            },
+            "regression": false
+        },
+        {
+            "name": "sir_request_constraint_no_ub",
             "model-path": "amr/petrinet/terrarium-tests/sir.json",
-            "request-path": "amr/petrinet/terrarium-tests/sir_request.json"
+            "request-path": "amr/petrinet/terrarium-tests/sir_request_constraint_no_ub.json",
+            "expected-outcome": {
+                "true-boxes": 5
+            },
+            "regression": false
+        },
+        {
+            "name": "stratified-seird",
+            "model-path": "amr/petrinet/terrarium-tests/stratified_seird_model.json",
+            "request-path": "amr/petrinet/terrarium-tests/stratified_seird_request.json",
+            "expected-outcome": {
+                "true-boxes": 52
+            },
+            "regression": false
         },
         {
             "name": "petrinet-query-default",
             "model-path": "amr/petrinet/mira/models/scenario1_a.json",
-            "request-path": null
+            "request-path": null,
+            "expected-outcome": {
+                "true-boxes": 1
+            },
+            "regression": true
         },
         {
             "name": "petrinet-query-with-basic-request",
             "model-path": "amr/petrinet/mira/models/scenario2_a_beta_scale_static.json",
-            "request-path": "amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json"
+            "request-path": "amr/petrinet/mira/requests/request2_b_default_wo_compartmental_constrs.json",
+            "expected-outcome": {
+                "true-boxes": 1
+            },
+            "regression": true
         },
         {
             "name": "petrinet-query-parameter-synthesis",
             "model-path": "amr/petrinet/mira/models/scenario2_a_beta_scale_static.json",
-            "request-path": "amr/petrinet/mira/requests/request2_b_synthesize.json"
+            "request-path": "amr/petrinet/mira/requests/request2_b_synthesize.json",
+            "expected-outcome": {
+                "true-boxes": 3
+            },
+            "regression": true
         }
     ]
 }
\ No newline at end of file
diff --git a/scratch/evaluation/evaluation23.py b/scratch/evaluation/evaluation23.py
index 005f491c..081063b0 100644
--- a/scratch/evaluation/evaluation23.py
+++ b/scratch/evaluation/evaluation23.py
@@ -332,7 +332,7 @@ def unit_test_1_query(self, steps, init_values):
         query._formula = And(
             [
                 LT(Symbol(f"S_{steps}", REAL), Real(init_values["S"])),
-                GE(Symbol(f"R_{steps}", REAL), Real(init_values["R"]))
+                GE(Symbol(f"R_{steps}", REAL), Real(init_values["R"])),
                 # GT(
                 #     Symbol(f"R_{steps}", REAL), Real(5.0)
                 # ),  # R is near 10 at day 80
diff --git a/scratch/evaluation/evaluation23_s2.py b/scratch/evaluation/evaluation23_s2.py
index 15f78d4a..7d38ed3c 100644
--- a/scratch/evaluation/evaluation23_s2.py
+++ b/scratch/evaluation/evaluation23_s2.py
@@ -400,7 +400,7 @@ def test_scenario_2_1_d_2d(self):
         # ]
         bounds["theta"] = [
             0.2,
-            0.3
+            0.3,
             # max(2 * bounds["epsilon"][1], bounds["theta"][1]),
         ]
         scenario = self.make_scenario(
diff --git a/scratch/evaluation/evaluation24/sidarthe-funman.ipynb b/scratch/evaluation/evaluation24/sidarthe-funman.ipynb
new file mode 100644
index 00000000..67c5686a
--- /dev/null
+++ b/scratch/evaluation/evaluation24/sidarthe-funman.ipynb
@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SAVED_RESULTS_DIR = \"./out\"\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_demo.example.pde import plot_spatial_timeseries\n",
+    "from IPython.display import HTML\n",
+    "import matplotlib.pyplot as plt\n",
+    "import itertools\n",
+    "from funman import FunmanWorkRequest, EncodingSchedule, POS_INFINITY\n",
+    "\n",
+    "import json\n",
+    "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n",
+    "from funman.representation import Interval\n",
+    "from funman import FUNMANConfig\n",
+    "import logging\n",
+    "from funman_demo import summarize_results\n",
+    "\n",
+    "\n",
+    "\n",
+    "RESOURCES = \"../../../resources\"\n",
+    "\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"evaluation\")\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"evaluation\")\n",
+    "MODEL_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR,\"sidarthe.json\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Consistency M3.3\n",
+    "\n",
+    "STATES = [\"Susceptible\", \"Infected\",\"Diagnosed\",  \"Ailing\", \"Recognized\",\"Threatened\", \"Healed\",  \"Extinct\"]\n",
+    "\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=50\n",
+    "STEP_SIZE=2\n",
+    "to_synthesize=[]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sidarthe_request_consistency.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Consistency\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            dump_results=False\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\", synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameterization (M3.3)\n",
+    "\n",
+    "STATES = [\"Susceptible\", \"Infected\",\"Diagnosed\",  \"Ailing\", \"Recognized\",\"Threatened\", \"Healed\",  \"Extinct\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=50\n",
+    "STEP_SIZE=2\n",
+    "to_synthesize=[]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sidarthe_request_parameterization.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    # funman_request.config.dreal_precision = 1e-3\n",
+    "    # funman_request.config.use_compartmental_constraints = False\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIDARTHE Parameterization\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\", synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from funman import FunmanResults\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "from datetime import datetime\n",
+    "# Load FUNMAN output and plot the progress data\n",
+    "# data = results_unconstrained_point\n",
+    "with open(\"out/c633ce05-39ea-4c99-80c8-7d21163a22a9.json\", \"r\") as f:\n",
+    "    data = FunmanResults.model_validate(json.loads(f.read()))\n",
+    "df = pd.DataFrame.from_records(data.timing.progress_timeseries, columns=[\"time\", \"progress\"])\n",
+    "df.time = pd.to_datetime(df.time)\n",
+    "df.plot.scatter(x=\"time\", y=\"progress\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameter Synthesis (M3.3)\n",
+    "\n",
+    "STATES = [\"Susceptible\", \"Infected\",\"Diagnosed\",  \"Ailing\", \"Recognized\",\"Threatened\", \"Healed\",  \"Extinct\"]\n",
+    "\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=2\n",
+    "to_synthesize=[\"beta\", \"lamb\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sidarthe_request_param_synth.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    # funman_request.parameter(\"gamma\").interval.lb = 0.025\n",
+    "    # funman_request.parameter(\"gamma\").interval.ub = 0.03\n",
+    "    # funman_request.parameter(\"beta\").interval.lb = 0.13\n",
+    "    # funman_request.parameter(\"beta\").interval.ub = 0.16\n",
+    "\n",
+    "    # funman_request.config.substitute_subformulas = True\n",
+    "    # funman_request.config.taylor_series_order = 4\n",
+    "    # series_approximation_threshold = 1e-8\n",
+    "    # funman_request.config.verbosity = 10\n",
+    "    funman_request.config.tolerance = 1e-2\n",
+    "    # funman_request.config.compartmental_constraint_noise = 1e-10\n",
+    "    # funman_request.config.solver_timeout = 10\n",
+    "    # funman_request.config.uniform_box_splits = True\n",
+    "    # funman_request.config.num_initial_boxes = 1\n",
+    "    # funman_request.config.dreal_precision = 1e-1\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Parameter Synthesis\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            dump_plot=True,\n",
+    "            parameters_to_plot=to_synthesize+[\"timestep\"]\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\", parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "funman_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scratch/evaluation/evaluation24/sidarthe-manual.ipynb b/scratch/evaluation/evaluation24/sidarthe-manual.ipynb
new file mode 100644
index 00000000..c3ac4a5f
--- /dev/null
+++ b/scratch/evaluation/evaluation24/sidarthe-manual.ipynb
@@ -0,0 +1,262 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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\n",
+    "import itertools\n",
+    "import datetime\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def query_1(sim, tstart, tend, dt):\n",
+    "    S, I, D, A, R, T, H, E = sim.T\n",
+    "\n",
+    "    # USER: write query condition.\n",
+    "    # query_condition =  0.15 <= max(I) <= 0.3  #((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "    # query_condition = (0.45 <= max(I)) and (max(I) <= 0.55) and (45 <= np.argmax(I)*dt) and (np.argmax(I)*dt <= 55)\n",
+    "    query_values = [0.45 <= I[int(t/dt)] <= 0.55 for t in range(tstart, tend+1, dt) if 45 <= t < 55]\n",
+    "    query_condition = all(query_values) \n",
+    "    # print(I[9:11])\n",
+    "    return query_condition\n",
+    "\n",
+    "def query_2(sim, tstart, tend, dt):\n",
+    "    S, I, D, A, R, T, H, E = sim.T\n",
+    "\n",
+    "    # USER: write query condition.\n",
+    "    # query_condition =  0.15 <= max(I) <= 0.3  #((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "    # query_condition = (0.45 <= max(I)) and (max(I) <= 0.55) and (45 <= np.argmax(I)*dt) and (np.argmax(I)*dt <= 55)\n",
+    "    query_values = [0.25 <= I[int(t/dt)] <= 0.55 for t in range(tstart, tend+1, dt) if 45 <= t < 55]\n",
+    "    query_condition = all(query_values) \n",
+    "    # print(I[9:11])\n",
+    "    return query_condition\n",
+    "\n",
+    "def eval_point(beta_val, lamb_val, query_condition=query_1, plot=False, rtol=1, atol=1, mxstep=10, mxordn = 1, mxords=1, hmin=1):    \n",
+    "    # set parameters\n",
+    "\n",
+    "    # USER: set values for all parameters that you are not synthesizing \n",
+    "    alpha_val = 0.1 # I -> I\n",
+    "    delta_val = 0.00011 # S, R -> I\n",
+    "\n",
+    "    epsilon_val = 0.03 # I -> D\n",
+    "    theta_val = 0.571 # A -> R\n",
+    "\n",
+    "    zeta_val = 0.0125 # I -> A\n",
+    "    eta_val = 0.0125 # D -> R\n",
+    "\n",
+    "    mu_val = 0.017 # A -> T\n",
+    "    nu_val = 0.027 # R -> T\n",
+    "    gamma_val = 0.0456 # S, A -> I\n",
+    "    rho_val = 0.034 # D -> H\n",
+    "\n",
+    "    kappa_val = 0.017 # A -> H\n",
+    "    xi_val = 0.017 # R -> H\n",
+    "    sigma_val = 0.017 # T -> H\n",
+    "    \n",
+    "    tau_val = 0.01 # T -> E\n",
+    "\n",
+    "    # USER: set initial conditions\n",
+    "    I0, D0, A0, R0, T0, H0, E0 = 0.01, 0, 0, 0, 0, 0, 0\n",
+    "    S0 = 1-I0-D0-A0-R0-T0-H0-E0\n",
+    "\n",
+    "    # USER: set simulation parameters\n",
+    "    dt = 1\n",
+    "    tstart = 0\n",
+    "    tend = 100\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",
+    "    y0 = S0, I0, D0, A0, R0, T0, H0, E0 # Initial conditions vector\n",
+    "\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",
+    "\n",
+    "\n",
+    "    # write query condition.\n",
+    "    # query_condition = (0.999 <= (S+I+D+A+R+T+H+E).all() <= 1.001)  # compartmental constraint\n",
+    "    #query_condition =  (0.32 <= max(I) <= 0.33) and (12 <= np.argmax(I)*dt <= 14) # max location and size\n",
+    "    query = '1' if query_condition(sim, tstart, tend, dt) else '0'\n",
+    "    # plot results - uncomment next line to plot time series.  not recommended for large numbers of points\n",
+    "    # if query == '1':\n",
+    "    #     print('beta:', beta_val, 'gamma:', gamma_val)\n",
+    "    if plot:\n",
+    "        sir_model.plotSIDARTHE(tvect, S, I, D, A, R, T, H, E)\n",
+    "    ## end plot\n",
+    "    param_assignments = {'beta': beta_val, 'lambda': lamb_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\n",
+    "    return param_assignments, sim\n",
+    "    \n",
+    "# set parameters\n",
+    "def ps(param_synth_method, parameter_search_bounds, query_condition=query_1, rtol=1e-3, num_dim_points=10, plot=False):\n",
+    "    parameters = list(parameter_search_bounds.keys())\n",
+    "    parameter_points = {p : np.linspace(parameter_search_bounds[p][0], parameter_search_bounds[p][1], num_dim_points) for p in parameters}\n",
+    "    points = itertools.product(*[parameter_points[p] for p in parameters])\n",
+    "    param_choices_true_false = []\n",
+    "    coverage = []\n",
+    "    \n",
+    "    for i, point in enumerate(points):\n",
+    "        point_values = {p: point[i] for i, p in enumerate(parameters)}\n",
+    "        \n",
+    "        param_assignments, _ = eval_point(point_values[\"beta\"], point_values[\"lambda\"], query_condition=query_condition, rtol=rtol, plot=plot)\n",
+    "        param_choices_true_false.append(param_assignments)\n",
+    "        coverage.append((datetime.datetime.now(), i))\n",
+    "        \n",
+    "        if param_synth_method == \"any\" and param_assignments['assignment'] == '1':\n",
+    "            return param_choices_true_false, coverage\n",
+    "    return param_choices_true_false, coverage\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Consistency Query\n",
+    "\n",
+    "# USER: set num_dim_points with how many points for each parameter you'd like to evaluate\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.15510204081632656, 0.15510204081632656] #[0.008,0.01] # S, D -> I\n",
+    "lambda_search_bounds = [0.000, 0.0] #[0.36,0.55] # I -> H\n",
+    "\n",
+    "\n",
+    "param_choices_true_false, coverage = ps(\"any\", {\"beta\": beta_search_bounds, \"lambda\": lambda_search_bounds}, query_condition=query_2, rtol=1e-3, num_dim_points=1, plot=True)\n",
+    "print(param_choices_true_false)\n",
+    "\n",
+    "# id_number = randint(10**5, 10**6 - 1)\n",
+    "# with open(f'sir_query_auto_2_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "#     json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)\n",
+    "\n",
+    "# Plot \"true/false\" points.  \n",
+    "sir_model.plot_two_params(\"beta\", \"lambda\", param_choices_true_false)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameterization\n",
+    "\n",
+    "# USER: set num_dim_points with how many points for each parameter you'd like to evaluate\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.1, 1] #[0.008,0.01] # S, D -> I\n",
+    "lambda_search_bounds = [0.000, 0.1] #[0.36,0.55] # I -> H\n",
+    "\n",
+    "\n",
+    "num_dim_points = 50\n",
+    "\n",
+    "param_choices_true_false, coverage = ps(\"any\", {\"beta\": beta_search_bounds, \"lambda\": lambda_search_bounds}, query_condition=query_2, rtol=1e-3,  num_dim_points=num_dim_points)\n",
+    "# print(param_choices_true_false)\n",
+    "\n",
+    "# id_number = randint(10**5, 10**6 - 1)\n",
+    "# with open(f'sir_query_auto_2_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "#     json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)\n",
+    "\n",
+    "solutions = [p for p in param_choices_true_false if p['assignment']==\"1\"]\n",
+    "if len(solutions) > 0:\n",
+    "    my_params = solutions[0]\n",
+    "    print(my_params)\n",
+    "    param_assignment, sim = eval_point(my_params[\"beta\"], my_params[\"lambda\"], plot=True, mxstep=20, query_condition=query_2)\n",
+    "else:\n",
+    "    print(\"No solutions!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameter Synthesis\n",
+    "\n",
+    "# USER: set num_dim_points with how many points for each parameter you'd like to evaluate\n",
+    "num_dim_points = 10\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.1, 1] #[0.008,0.01] # S, D -> I\n",
+    "lambda_search_bounds = [0.000, 0.1] #[0.36,0.55] # I -> H\n",
+    "\n",
+    "\n",
+    "param_choices_true_false, coverage = ps(\"all\", {\"beta\": beta_search_bounds, \"lambda\": lambda_search_bounds}, query_condition=query_2, rtol=1e-3,  num_dim_points=num_dim_points)\n",
+    "# print(param_choices_true_false)\n",
+    "\n",
+    "# id_number = randint(10**5, 10**6 - 1)\n",
+    "# with open(f'sir_query_auto_2_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "#     json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)\n",
+    "\n",
+    "\n",
+    "# Plot \"true/false\" points.  Is there any way of characterizing these regions?\n",
+    "sir_model.plot_two_params(\"lambda\", \"beta\", param_choices_true_false)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "funman_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scratch/evaluation/evaluation24/sir-eval-smt-encoding.ipynb b/scratch/evaluation/evaluation24/sir-eval-smt-encoding.ipynb
new file mode 100644
index 00000000..2d32956b
--- /dev/null
+++ b/scratch/evaluation/evaluation24/sir-eval-smt-encoding.ipynb
@@ -0,0 +1,753 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Automatic initialization of gaol... done\n",
+      "/root/funman_venv/lib/python3.8/site-packages/numpy/core/getlimits.py:511: UserWarning: Signature b'\\xcc\\xcc\\xcc\\xbd' for <class 'numpy.float32'> does not match any known type: falling back to type probe function.\n",
+      "This warnings indicates broken support for the dtype!\n",
+      "  machar = _get_machar(dtype)\n",
+      "/root/funman_venv/lib/python3.8/site-packages/numpy/core/_machar.py:295: RuntimeWarning: overflow encountered in divide\n",
+      "  xmax = xmax / (xmin*beta*beta*beta)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1:54:49.48 to get a functioning encoding with just compartmental constraints.  Needs more time on probing the parameters\n",
+    "# 0 - 32:00.68: Setup basic encoding\n",
+    "# 32:00.68 - 48:00: Setup z3 solver and get results\n",
+    "# 48:00 - 1:27:15: Setup solution extraction\n",
+    "# 1:27:15 - 1:54:49.48: Setup solution plotting and explore 8 parameter bound settings to test\n",
+    "# 1:54:49.48 - 2:09:43.40: Cleanup code, comments, and improve plotting to interpolate\n",
+    "# 2:09:43.40 - 2:17:19.51: Add dreal and test\n",
+    "\n",
+    "from pysmt.shortcuts import And, Or, Plus, Minus, Times, Div, REAL, LE, LT, GE, GT, Equals, Symbol, Real, Solver\n",
+    "from pysmt.fnode import  FNode\n",
+    "from typing import Dict\n",
+    "import pandas as pd\n",
+    "from decimal import Decimal\n",
+    "import numpy as np\n",
+    "from pysmt.logics import QF_NRA\n",
+    "from funman_dreal.solver import ensure_dreal_in_pysmt\n",
+    "import numpy as np\n",
+    "import itertools\n",
+    "import sir_model\n",
+    "\n",
+    "ensure_dreal_in_pysmt()\n",
+    "\n",
+    "def dataframe(assignment: Dict[Symbol, float], state_variables, parameters, max_step, step_size) -> pd.DataFrame:\n",
+    "    timepoints = list(range(0, max_time+step_size, step_size))\n",
+    "    timeseries = {sv: [None]*len(timepoints) for sv in state_variables}\n",
+    "    for k, v in assignment.items():\n",
+    "        sym = k.symbol_name()\n",
+    "        if \"_\" in sym:\n",
+    "            sv = sym.split(\"_\")[0]\n",
+    "            t = sym.split(\"_\")[1]\n",
+    "            value =Decimal(v.numerator) / Decimal(v.denominator)\n",
+    "            timeseries[sv][timepoints.index(int(t))] = value\n",
+    "        else:\n",
+    "            timeseries[sym] = [v]*len(timepoints)\n",
+    "    df = pd.DataFrame(timeseries, index=timepoints).astype(float)\n",
+    "    return df\n",
+    "\n",
+    "def reindex_and_interpolate(df, new_index):\n",
+    "    df_reindexed = df.reindex(index = new_index)\n",
+    "    df_reindexed.interpolate(method = 'linear', inplace = True)\n",
+    "    return df_reindexed\n",
+    "\n",
+    "\n",
+    "def plot_results(values, max_step, step_size):\n",
+    "    timepoints = list(range(0, max_time+step_size, step_size))\n",
+    "    results: pd.DataFrame = dataframe(values, [\"S\", \"I\", \"R\"], [\"beta\", \"gamma\"], max_step, step_size)\n",
+    "    newindex = np.linspace(timepoints[0], timepoints[-1], timepoints[-1]+1)\n",
+    "    results = reindex_and_interpolate(results, newindex)\n",
+    "    print(f\"beta = {results['beta'][0]}, gamma = {results['gamma'][0]}\")\n",
+    "    print(results[[\"S\", \"I\", \"R\"]])\n",
+    "    ax = results[[\"S\", \"I\", \"R\"]].plot()\n",
+    "    ax.set_xlabel=\"Time\"\n",
+    "    return ax, results\n",
+    "\n",
+    "def run_solver(solver, formula):\n",
+    "    solver.add_assertion(formula)\n",
+    "    result = solver.solve()\n",
+    "    if result:\n",
+    "        model = solver.get_model()\n",
+    "        variables = formula.get_free_variables()\n",
+    "        values = {}\n",
+    "        for var in variables:\n",
+    "            try:\n",
+    "                values[var] = model.get_value(var).constant_value()\n",
+    "            except Exception as e:\n",
+    "                pass\n",
+    "    else:\n",
+    "        print(\"Unsat\")\n",
+    "        values = None\n",
+    "    return values\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate model diagram\n",
+    "\n",
+    "# from funman.api.run import Runner\n",
+    "# import json\n",
+    "\n",
+    "# amr_model = \"../../resources/amr/petrinet/amr-examples/sir.json\"\n",
+    "# m = Runner().get_model(amr_model)\n",
+    "# g = m[0].to_dot()\n",
+    "# g.render(\"sir.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The main encoding code\n",
+    "def encode(initial_values, parameter_bounds, infected_bounds, infected_timepoints, step_size=1, max_time=10, noise=1e-3, strict_upper_bound_parameters = False, use_compartmental=True):\n",
+    "    time_format = lambda t: f\"{t:03d}\"\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Initial States ###############################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    S_0 = Symbol(f\"S_{time_format(0)}\", REAL)\n",
+    "    I_0 = Symbol(f\"I_{time_format(0)}\", REAL)\n",
+    "    R_0 = Symbol(f\"R_{time_format(0)}\", REAL)\n",
+    "\n",
+    "    S_0_value = initial_values[\"S\"]\n",
+    "    I_0_value = initial_values[\"I\"]\n",
+    "    R_0_value = initial_values[\"R\"]\n",
+    "\n",
+    "\n",
+    "    population_size = S_0_value + I_0_value + R_0_value\n",
+    "\n",
+    "    # SIR Model Initial State\n",
+    "    initial_state = And([\n",
+    "        Equals(S_0, Real(S_0_value)),\n",
+    "        Equals(I_0, Real(I_0_value)),\n",
+    "        Equals(R_0, Real(R_0_value))\n",
+    "    ])\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Parameters     ###############################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "\n",
+    "    # Parameters\n",
+    "    beta = Symbol(\"beta\", REAL)\n",
+    "    gamma = Symbol(\"gamma\", REAL)\n",
+    "\n",
+    "    beta_bounds = parameter_bounds[\"beta\"]\n",
+    "    gamma_bounds = parameter_bounds[\"gamma\"]\n",
+    "\n",
+    "    upper_op = LT if strict_upper_bound_parameters else LE\n",
+    "\n",
+    "    parameters = And([\n",
+    "        And(LE(Real(beta_bounds[0]), beta), (upper_op(beta, Real(beta_bounds[1])) if beta_bounds[0] < beta_bounds[1] else LE(beta, Real(beta_bounds[1])))),\n",
+    "        And(LE(Real(gamma_bounds[0]), gamma), (upper_op(gamma, Real(gamma_bounds[1])) if gamma_bounds[0] < gamma_bounds[1] else LE(gamma, Real(gamma_bounds[1]))))\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "    timepoints = list(range(0, max_time+step_size, step_size))\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Transitions ##################################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    S_next = lambda t: Symbol(f\"S_{time_format(t+step_size)}\", REAL)\n",
+    "    S_now = lambda t: Symbol(f\"S_{time_format(t)}\", REAL)\n",
+    "    I_next = lambda t: Symbol(f\"I_{time_format(t+step_size)}\", REAL)\n",
+    "    I_now = lambda t: Symbol(f\"I_{time_format(t)}\", REAL)\n",
+    "    R_next = lambda t: Symbol(f\"R_{time_format(t+step_size)}\", REAL)\n",
+    "    R_now = lambda t: Symbol(f\"R_{time_format(t)}\", REAL)\n",
+    "    dt = Real(float(step_size))\n",
+    "\n",
+    "    S_Trans = lambda t: Equals(S_next(t), \n",
+    "                                    Minus(\n",
+    "                                        S_now(t), \n",
+    "                                        Times([beta, S_now(t), I_now(t), dt])))\n",
+    "\n",
+    "    I_Trans = lambda t: Equals(I_next(t), \n",
+    "                                    Plus(\n",
+    "                                        I_now(t), \n",
+    "                                        Times(\n",
+    "                                            Minus(\n",
+    "                                                Times([beta, S_now(t), I_now(t)]), \n",
+    "                                                Times(gamma, I_now(t))), dt)))\n",
+    "\n",
+    "    R_Trans = lambda t: Equals(R_next(t), \n",
+    "                                    Plus(\n",
+    "                                        R_now(t), \n",
+    "                                        Times(\n",
+    "                                            Times(gamma, I_now(t)), \n",
+    "                                            dt)))\n",
+    "\n",
+    "    Trans = lambda t: And(S_Trans(t), I_Trans(t), R_Trans(t))\n",
+    "\n",
+    "    All_Trans = And([Trans(t) for t in timepoints[:-1]])\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Constraints ##################################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    compartmental_constraint = And([\n",
+    "        And( LE(Real(0.0), Plus(S_now(t), Real(noise))),\n",
+    "        LE(Real(0.0), Plus(I_now(t), Real(noise))),\n",
+    "        LE(Real(0.0), Plus(R_now(t), Real(noise))),\n",
+    "        LT(Plus([S_now(t), I_now(t), R_now(t)]), Plus(Real(population_size), Real(noise))), \n",
+    "        GE(Plus([S_now(t), I_now(t), R_now(t)]), Minus(Real(population_size), Real(noise))))\n",
+    "    for t in timepoints]) if use_compartmental else TRUE()\n",
+    "\n",
+    "    # 10m to add and check\n",
+    "    I_peak = infected_bounds\n",
+    "    I_peak_t = infected_timepoints\n",
+    "    \n",
+    "    peak_I = And([\n",
+    "        And(LE(Real(I_peak[0]), I_now(t)),\n",
+    "        LT(I_now(t), Real(I_peak[1])))\n",
+    "        for t in timepoints \n",
+    "        if I_peak_t[0] <= t and t <= I_peak_t[1]\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Combine Assertions ###########################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    consistency = And([\n",
+    "        initial_state,\n",
+    "        parameters,\n",
+    "        All_Trans,\n",
+    "        compartmental_constraint,\n",
+    "        peak_I\n",
+    "        ])\n",
+    "    # consistency.serialize()\n",
+    "    return consistency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Solve encoding\n",
+    "# with Solver() as solver:\n",
+    "#     values = run_solver(solver)\n",
+    "    \n",
+    "# if values:\n",
+    "#     plot_results(values, timepoints)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unsat\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Consistency using z3 M2.0\n",
+    "\n",
+    "# Configure these bounds (lower, upper) to configure the parameter space\n",
+    "parameter_bounds = {\"beta\": (0.15, 0.15), \"gamma\": (0.025, 0.025)}\n",
+    "\n",
+    "# Set these values for the initial state\n",
+    "initial_values = {\"S\": 0.99, \"I\": 0.01, \"R\": 0.0}\n",
+    "\n",
+    "# Infected constraint bounds\n",
+    "infected_bounds = (0.45, 0.55)\n",
+    "infected_timepoints = (45, 55)\n",
+    "\n",
+    "# Timepoints\n",
+    "step_size = 4\n",
+    "max_time = 60\n",
+    "\n",
+    "noise = 1e-3\n",
+    "\n",
+    "values= None\n",
+    "with Solver(name=\"z3\") as solver:\n",
+    "    values = run_solver(solver, encode(initial_values, parameter_bounds, infected_bounds, infected_timepoints, max_time=max_time, step_size=step_size, noise=noise, use_compartmental=True))\n",
+    "    pass\n",
+    "    \n",
+    "if values:\n",
+    "    ax, results = plot_results(values,  max_time, step_size)\n",
+    "    # f_pop = results.loc[float(max_time)][[\"S\", \"I\", \"R\"]].sum()\n",
+    "    # print(f\"Population at end: {f_pop}\")\n",
+    "    # s = list(results.S.values)\n",
+    "    # print([s[i]-s[i-1] for i in range(1, len(s))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta = 0.15000000000000002, gamma = 0.025\n",
+      "             S         I         R\n",
+      "0.0   0.990000  0.010000  0.000000\n",
+      "1.0   0.988515  0.011235  0.000250\n",
+      "2.0   0.986849  0.012620  0.000531\n",
+      "3.0   0.984981  0.014173  0.000846\n",
+      "4.0   0.982887  0.015912  0.001201\n",
+      "...        ...       ...       ...\n",
+      "56.0  0.117645  0.538458  0.343897\n",
+      "57.0  0.108143  0.534498  0.357359\n",
+      "58.0  0.099473  0.529806  0.370721\n",
+      "59.0  0.091567  0.524466  0.383966\n",
+      "60.0  0.084364  0.518558  0.397078\n",
+      "\n",
+      "[61 rows x 3 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Consistency using dreal M2.1\n",
+    "\n",
+    "opts = {\n",
+    "        \"dreal_precision\": 1e-3,\n",
+    "        \"dreal_log_level\": \"none\",\n",
+    "        \"dreal_mcts\": False,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "# Configure these bounds (lower, upper) to configure the parameter space\n",
+    "parameter_bounds = {\"beta\": (0.15, 0.15), \"gamma\": (0.025, 0.025)}\n",
+    "\n",
+    "# Set these values for the initial state\n",
+    "initial_values = {\"S\": 0.99, \"I\": 0.01, \"R\": 0.0}\n",
+    "\n",
+    "# Infected constraint bounds\n",
+    "infected_bounds = (0.45, 0.55)\n",
+    "infected_timepoints = (45, 55)\n",
+    "\n",
+    "# Timepoints\n",
+    "step_size = 1\n",
+    "max_time = 60\n",
+    "\n",
+    "noise = 1e-3\n",
+    "\n",
+    "values= None\n",
+    "with Solver(name=\"dreal\",\n",
+    "            logic=QF_NRA,\n",
+    "            solver_options=opts\n",
+    "            ) as solver:\n",
+    "    values = run_solver(solver, encode(initial_values, parameter_bounds, infected_bounds, infected_timepoints, max_time=max_time, step_size=step_size, noise=noise, use_compartmental=True))\n",
+    "    pass\n",
+    "    \n",
+    "if values:\n",
+    "    ax, results = plot_results(values,  max_time, step_size)\n",
+    "    # f_pop = results.loc[float(max_time)][[\"S\", \"I\", \"R\"]].sum()\n",
+    "    # print(f\"Population at end: {f_pop}\")\n",
+    "    # s = list(results.S.values)\n",
+    "    # print([s[i]-s[i-1] for i in range(1, len(s))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta = 0.15000000000000002, gamma = 0.025\n",
+      "             S         I         R\n",
+      "0.0   0.990000  0.010000  0.000000\n",
+      "1.0   0.988515  0.011235  0.000250\n",
+      "2.0   0.986849  0.012620  0.000531\n",
+      "3.0   0.984981  0.014173  0.000846\n",
+      "4.0   0.982887  0.015912  0.001201\n",
+      "...        ...       ...       ...\n",
+      "56.0  0.117645  0.538458  0.343897\n",
+      "57.0  0.108143  0.534498  0.357359\n",
+      "58.0  0.099473  0.529806  0.370721\n",
+      "59.0  0.091567  0.524466  0.383966\n",
+      "60.0  0.084364  0.518558  0.397078\n",
+      "\n",
+      "[61 rows x 3 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Consistency using dreal M2.2\n",
+    "\n",
+    "opts = {\n",
+    "        \"dreal_precision\": 1e-3,\n",
+    "        \"dreal_log_level\": \"none\",\n",
+    "        \"dreal_mcts\": True,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "# Configure these bounds (lower, upper) to configure the parameter space\n",
+    "parameter_bounds = {\"beta\": (0.15, 0.15), \"gamma\": (0.025, 0.025)}\n",
+    "\n",
+    "# Set these values for the initial state\n",
+    "initial_values = {\"S\": 0.99, \"I\": 0.01, \"R\": 0.0}\n",
+    "\n",
+    "# Infected constraint bounds\n",
+    "infected_bounds = (0.45, 0.55)\n",
+    "infected_timepoints = (45, 55)\n",
+    "\n",
+    "# Timepoints\n",
+    "step_size = 1\n",
+    "max_time = 60\n",
+    "\n",
+    "noise = 1e-3\n",
+    "\n",
+    "values= None\n",
+    "with Solver(name=\"dreal\",\n",
+    "            logic=QF_NRA,\n",
+    "            solver_options=opts\n",
+    "            ) as solver:\n",
+    "    values = run_solver(solver, encode(initial_values, parameter_bounds, infected_bounds, infected_timepoints, max_time=max_time, step_size=step_size, noise=noise, use_compartmental=True))\n",
+    "    pass\n",
+    "    \n",
+    "if values:\n",
+    "    ax, results = plot_results(values,  max_time, step_size)\n",
+    "    # f_pop = results.loc[float(max_time)][[\"S\", \"I\", \"R\"]].sum()\n",
+    "    # print(f\"Population at end: {f_pop}\")\n",
+    "    # s = list(results.S.values)\n",
+    "    # print([s[i]-s[i-1] for i in range(1, len(s))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta = 0.17496597557264137, gamma = 0.031891187313515444\n",
+      "             S         I         R\n",
+      "0.0   0.990000  0.010000  0.000000\n",
+      "1.0   0.988268  0.011416  0.000317\n",
+      "2.0   0.986536  0.012831  0.000634\n",
+      "3.0   0.984328  0.014535  0.001041\n",
+      "4.0   0.982120  0.016239  0.001447\n",
+      "...        ...       ...       ...\n",
+      "56.0  0.064817  0.467489  0.467939\n",
+      "57.0  0.059510  0.456578  0.484273\n",
+      "58.0  0.054202  0.445667  0.500607\n",
+      "59.0  0.049969  0.436744  0.514973\n",
+      "60.0  0.045736  0.427821  0.529339\n",
+      "\n",
+      "[61 rows x 3 columns]\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "No axis named 50 for object type DataFrame",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m~/funman_venv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(cls, axis)\u001b[0m\n\u001b[1;32m    513\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AXIS_TO_AXIS_NUMBER\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 515\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"No axis named {axis} for object type {cls.__name__}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 50",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_37316/215037759.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;31m# Parameterization using dreal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m opts = {\n\u001b[0m\u001b[1;32m     37\u001b[0m         \u001b[0;34m\"dreal_precision\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1e-2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;34m\"dreal_log_level\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"info\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;34m\"dreal_mcts\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/funman_venv/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, axis)\u001b[0m\n\u001b[1;32m    675\u001b[0m         \u001b[0;31m# we need to return a copy of ourselves\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m         \u001b[0mnew_self\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 679\u001b[0;31m             \u001b[0maxis_int_none\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    680\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    681\u001b[0m             \u001b[0maxis_int_none\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m         \u001b[0mnew_self\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis_int_none\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/funman_venv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(cls, axis)\u001b[0m\n\u001b[1;32m    511\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAxisInt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AXIS_TO_AXIS_NUMBER\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 515\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"No axis named {axis} for object type {cls.__name__}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: No axis named 50 for object type DataFrame"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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0eA/eH94BDycjR5JzuWvWNv774z7O5pu1Lk8IISrM3t4ef39/goODGTZsGP369WPt2rVVekxZm0aIf0mv1zGiWyP6t/XngzVHWLwrkaWRp9h0PI1PRnSiRzMfrUsUQmhMURQKSwqr/biOdo7odLrrfv/BgwfZunUrjRs3rsSq/knCiBCVxMvZxNThHbm7azAvLN1HXFo+o77awVN9m/PszS2wM0hDpBD1VWFJIeELw6v9uDvu24GT0alC71m5ciUuLi6UlJRQXFyMXq9n+vTpVVShSn47ClHJwhp7svLpXozoGoyiwOd/xTBi9nZOnZXJ0oQQNV/fvn2Jiopix44djBkzhrFjxzJ8+PAqPaa0jAhRBZxMdrx/V0d6tfDhlZ8PEHnyLAM/28TUOzsyqGOA1uUJIaqZo50jO+7boclxK8rZ2ZnmzZsD8PXXXxMaGsrcuXN5+OGHK7u8UhJGhKhCg0MD6RTswTOL97I3IYtxC/ewOSaY129vh6PJoHV5QohqotPpKtxdUhPo9XpeeeUVJkyYwH333YejY8XDTbmOUyWfKoQoFezlxA//F8G4vs3Q6WDRzkQGT99M9JkcrUsTQohruvvuuzEYDMyYMaPKjiFhRIhqYDToeWFAa757OBxfV3tiUvMYOmML32w7gaIoWpcnhBBXZGdnx1NPPcUHH3xAfn5+lRxDp9SC34Q5OTm4u7uTnZ2Nm5ub1uUI8a9k5BXzwtL9/HUkFYD+bf34YHhHPJ1NGlcmhKgMRUVFxMfHExISgoODg9blVLmr/bzl/f6WlhEhqpm3iz1zx3Tl9dvbYjLoWXs4hdv+t4kdcRlalyaEEJqQMCKEBnQ6HQ/1CuHnJ3vQ1MeZM9lF3DtnO9PWHpP1bYQQ9Y6EESE01D7InRVP9+LusIbYFPjfuuPcO2c7SVnVP1OjEEJoRcKIEBpztrfjw7tD+WxkJ1zs7dh14izDZmzhwKlsrUsTQohqIWFEiBpiaKcgVj/Tm9b+rqTmFnPPl9v441Cy1mUJIUSVkzAiRA3SyNuJHx+P4MaWDSi0WPm/7yL5alOcXP4rRC1UX/7dVsbPKWFEiBrG1cHI12O6Miq8EYoC76yK5vVfDsnAViFqCaPRCEBBQf1Yj+r8z3n+574eMh28EDWQnUHPO8PaE+LjzLuro/l2+0kSzxbw+b2dcXW4/n/wQoiqZzAY8PDwIDVVnUvIyckJnU6ncVWVT1EUCgoKSE1NxcPDA4Ph+pe4kEnPhKjh1hxM5rkleymy2Gjt78rXD3Yj0KNq1ocQQlQORVFITk4mKytL61KqnIeHB/7+/pcNXOX9/pYwIkQtsC8xi0e+2U1abjG+rvbMHdONDg3dtS5LCHENVqsVi8WidRlVxmg0XrVFRMKIEHXM6axCHpq3i6MpuTgaDfzv3s70b+undVlCCHFFVTod/IwZM2jSpAkODg6Eh4ezc+fOq+7/6aef0qpVKxwdHQkODmb8+PEUFRVdz6GFqLeCPBz58YkIerfwodBi5bFvdzN3c3y9GbEvhKi7KhxGlixZwoQJE5g8eTJ79uwhNDSUAQMGlA7UudTChQt5+eWXmTx5MtHR0cydO5clS5bwyiuv/Ovihahv3ByMfP1gN+47d6XN2ysPM/lXudJGCFG7VbibJjw8nG7dujF9+nQAbDYbwcHBPP3007z88sv/2P+pp54iOjqadevWlW57/vnn2bFjB5s3by7XMaWbRoiyFEVhzqY4pvx2BEWBPq0aMP2+LrjYywVyQoiao0q6acxmM5GRkfTr1+/CB+j19OvXj23btl32PT169CAyMrK0KycuLo7Vq1dz2223XfE4xcXF5OTklLkJIS7Q6XQ8dmMzZo7qgoNRz/qjadw1cytnsmVNGyFE7VOhMJKeno7VasXPr+ygOT8/P5KTLz9t9X333cdbb71Fr169MBqNNGvWjD59+ly1m2bKlCm4u7uX3oKDgytSphD1xq3tA1jyWAQ+LvYcSc5l6PQtHEqSNW2EELVLlc/Aun79et577z2++OIL9uzZw88//8yqVat4++23r/ieiRMnkp2dXXpLTEys6jKFqLVCgz1YPq4HLf1cSM0tZtRXOzianKt1WUIIUW4VCiM+Pj4YDAZSUlLKbE9JScHf3/+y75k0aRL3338/jzzyCB06dOCOO+7gvffeY8qUKdhslx90Z29vj5ubW5mbEOLKGno6sfSJHoQGe5BVYGH03B3Ep+drXZYQQpRLhcKIyWQiLCyszGBUm83GunXriIiIuOx7CgoK0OvLHub8BClySaIQlcfNwciCsd1o7e9KWm4xo+Zs59TZ+rE2hhCidqtwN82ECROYM2cOCxYsIDo6mieeeIL8/HzGjh0LwAMPPMDEiRNL9x88eDAzZ85k8eLFxMfHs3btWiZNmsTgwYP/1Tz2Qoh/8nAy8e3D4TRt4ExSdhGjv9pBao7M6SOEqNkqfB3giBEjSEtL4/XXXyc5OZlOnTqxZs2a0kGtCQkJZVpCXnvtNXQ6Ha+99hqnT5+mQYMGDB48mHfffbfyfgohRKkGrvZ8/0g493y5jRMZBYz6agdL/i8CL2eT1qUJIcRlyXTwQtRRiZkF3D1rG8k5RbQLdGPhozfg7igr/gohqk+VTgcvhKj5gr2c+O6RcLydTRxKyuGh+bvILy7RuiwhhPgHCSNC1GHNfV347pFw3B2NRJ48y6Pf7KbIYtW6LCGEKEPCiBB1XJsANxY81B1nk4GtsRk8+f0ezCWylo0QouaQMCJEPdAp2IOvH+yGg1HPX0dSGb8kShbXE0LUGBJGhKgnwpt68+X9XTEZ9Kw6cIaXfjqAzVbjx68LIeoBCSNC1CM3tWzA5/d1xqDX8dOeU7z+60GZfFAIoTkJI0LUMwPa+TPtnlB0OvhuewJTfjsigUQIoSkJI0LUQ0M7BTHljg4AzN4Yx//WxWhckRCiPpMwIkQ9NbJ7Iybd3haAT/48xpyNcRpXJISorySMCFGPPdwrhP/e0hKAd1dHs2hngsYVCSHqIwkjQtRz4/o254k+zQCYtPwg2+MyNK5ICFHfSBgRop7T6XS8OKAVg0MDKbEpPPn9HhIzC7QuSwhRj0gYEUKg0+n4YHhH2ge5kZlv5tFvdlNglnVshBDVQ8KIEAIAR5OB2fd3xcfFxJHkXP774z655FcIUS0kjAghSgV6ODJzdBhGg47VB5KZ/pdc8iuEqHoSRoQQZXRr4sVbQ9sD8PHaY/xxKFnjioQQdZ2EESHEP9zbvREPRDQGYPySKI6l5GpckRCiLpMwIoS4rEm3tyWiqTf5ZiuPfrObrAKz1iUJIeooCSNCiMsyGvTMGNWFhp6OnMwo4KmFeymx2rQuSwhRB0kYEUJckZeziTkPdMXJZGBzTDpTfjuidUlCiDpIwogQ4qraBLgx7Z5QAOZujmdp5CmNKxJC1DUSRoQQ13Rr+wCeubkFAK/8fIC9CWc1rkgIUZdIGBFClMtzN7fglrZ+mK02/u/bSFJyirQuSQhRR0gYEUKUi16vY9qITrT0cyE1t5jHvo2kyGLVuiwhRB0gYUQIUW4u9nZ89UA3PJyM7EvM4pVlB2TKeCHEvyZhRAhRIY28nZhxXxcMeh0/7znN3M3xWpckhKjlJIwIISqsZ3MfXhvUBoD3Vkez8ViaxhUJIWozCSNCiOvyYI8m3B3WEJsCTy3cw4n0fK1LEkLUUhJGhBDXRafT8c4d7encyIOcohIe+3Y3BeYSrcsSQtRCEkaEENfN3s7Al6PDaOBqz7GUPF5bdlAGtAohKkzCiBDiX/F1c2D6vZ3VAa17T7NwZ4LWJQkhahkJI0KIfy28qTcvDmgFwJu/Hmb/qSxtCxJC1CoSRoQQleKxG5uWztD6xHd7yCowa12SEKKWkDAihKgUOp2OD+8OpbG3E6ezChm/JAqbTcaPCCGuTcKIEKLSuDsamTkqDHs7PX8fTeOL9TFalySEqAUkjAghKlXbQDfeHtYegGlrj7ElJl3jioQQNZ2EESFEpbunazAjugZjU+CZRXtJzpYVfoUQVyZhRAhRJd4c2o62AW5k5JsZt3APFqtN65KEEDWUhBEhRJVwMBqYNToMVwc7Ik+eZcrqI1qXJISooSSMCCGqTCNvJ6bd0wmAr7fEs2r/GW0LEkLUSBJGhBBVqn9bP/7vpqYAvLh0H7FpeRpXJISoaSSMCCGq3Au3tCI8xIt8s5UnvouUBfWEEGVIGBFCVDk7g57P7+tcuqDeq7KgnhDiIhJGhBDVwtf1woJ6y/ae5vsdsqCeEEIlYUQIUW0uXlDvrRWyoJ4QQiVhRAhRrWRBPSHEpSSMCCGqlSyoJ4S4lIQRIUS1u3RBvZkbYrUuSQihIQkjQghNXLyg3idrj3HwdLbGFQkhtCJhRAihmbvDGnJbB39KbAoTfoiiuMSqdUlCCA1IGBFCaEan0/H20Pb4uJg4lpLHtLXHtC5JCKEBCSNCCE15u9gz5c6OAMzeGEfkyUyNKxJCVDcJI0IIzfVv68fwLg1RFJjwwz6ZLl6IekbCiBCiRnh9cFsC3B04mVHA1N+OaF2OEKIaSRgRQtQI7o5GPrwrFIBvtp1k8/F0jSsSQlQXCSNCiBqjVwsf7r+hMQAvLt1HTpFF44qEENVBwogQokaZeFtrGns7kZRdxFsrDmtdjhCiGlxXGJkxYwZNmjTBwcGB8PBwdu7cedX9s7KyGDduHAEBAdjb29OyZUtWr159XQULIeo2J5MdH98dik4HSyNPsfZwitYlCSGqWIXDyJIlS5gwYQKTJ09mz549hIaGMmDAAFJTUy+7v9lspn///pw4cYKlS5dy9OhR5syZQ1BQ0L8uXghRN3Vt4sVjvZsCMPHn/WTmy2J6QtRlOkVRKrRCVXh4ON26dWP69OkA2Gw2goODefrpp3n55Zf/sf+sWbP48MMPOXLkCEaj8bqKzMnJwd3dnezsbNzc3K7rM4QQtUuRxcqQ6Zs5lpLHbR38mXFfF3Q6ndZlCSEqoLzf3xVqGTGbzURGRtKvX78LH6DX069fP7Zt23bZ9/z6669EREQwbtw4/Pz8aN++Pe+99x5W65WnfS4uLiYnJ6fMTQhRvzgYDUy7pxN2eh2rDyTz674krUsSQlSRCoWR9PR0rFYrfn5+Zbb7+fmRnJx82ffExcWxdOlSrFYrq1evZtKkSXz88ce88847VzzOlClTcHd3L70FBwdXpEwhRB3RPsidp//TAoDXfzlESk6RxhUJIapClV9NY7PZ8PX1Zfbs2YSFhTFixAheffVVZs2adcX3TJw4kezs7NJbYmJiVZcphKihnuzbjA5B7mQXWnjpp/1UsGdZCFELVCiM+Pj4YDAYSEkpO7o9JSUFf3//y74nICCAli1bYjAYSre1adOG5ORkzObLD0qzt7fHzc2tzE0IUT8ZDXqm3ROKyU7P+qNpLNkl/zkRoq6pUBgxmUyEhYWxbt260m02m41169YRERFx2ff07NmTmJgYbDZb6bZjx44REBCAyWS6zrKFEPVJCz9XXrilFQBvrzxMYmaBxhUJISpThbtpJkyYwJw5c1iwYAHR0dE88cQT5OfnM3bsWAAeeOABJk6cWLr/E088QWZmJs8++yzHjh1j1apVvPfee4wbN67yfgohRJ33UK8QujfxIt9s5b8/7sNmk+4aIeoKu4q+YcSIEaSlpfH666+TnJxMp06dWLNmTemg1oSEBPT6CxknODiY33//nfHjx9OxY0eCgoJ49tlneemllyrvpxBC1HkGvY6P7g7l1s82siM+k3lbT/BwrxCtyxJCVIIKzzOiBZlnRAhx3vc7TvLqsoPY2+lZ9Uxvmvu6aF2SEOIKqmSeESGE0Np93RtxY8sGFJfYeP7HfZRYbdd+kxCiRpMwIoSoVXQ6HR8M74ibgx37ErOYuT5W65KEEP+ShBEhRK3j7+7AW0PbA/C/v45zOElmaRaiNpMwIoSolYZ2CmRAOz8sVoUJP0RhLpHuGiFqKwkjQohaSafT8e4dHfByNnEkOZfP/zqudUlCiOskYUQIUWv5uNjz9rnumi/Wx7IvMUvbgoQQ10XCiBCiVhvUMYDbOwZgtSn898d9FFmuvCK4EKJmkjAihKj13h7aHh8Xe46n5vHJn8e0LkcIUUESRoQQtZ6ns4kpd3YAYM7GOCJPntW4IiFERUgYEULUCf3b+nFnlyBsCvz3x30UmqW7RojaQsKIEKLOmDy4HX5u9sSn5/PB70e0LkcIUU4SRoQQdYa7o5H3h3cEYN6WE2yPy9C4IiFEeUgYEULUKX1a+TKyWzAALyzdR35xicYVCSGuRcKIEKLOeXVQG4I8HEnMLOS91dFalyOEuAYJI0KIOsfVwciHd6ndNd/vSGDT8TSNKxJCXI2EESFEndSjuQ8PRDQG4KWl+8kpsmhckRA1U1ZRFqvjVmtag52mRxdCiCr00q2tWX80jYTMAt5ZeZgP7grVuqS6rTAL0o5A6mFIPXefdgQUBbyaXuYWAk5eWlddbymKwh8n/+C9He9xtugsQa5BhDbQ5t+IhBEhRJ3lbG/HR3eHMmL2Nn7YfYpb2/vzn9Z+WpdV+5nzIe0opEafCx7RaujIOX3l9xSkw6md/9zu4HGFoNIUnH1Ap6uyH6M+Sy1I5Z3t7/B34t8ANHNvhp1eu0igUxRF0ezo5ZSTk4O7uzvZ2dm4ublpXY4QopZ5e+Vh5m6Ox9fVnj/G34iHk0nrkmoHmxUy4yB5P6QcvhA+zp4ArvDV4dYQfNtcuDVoDXo79XNKb/HqfW7S1Y9vcgWPRuDqBy7+6r1rALj4gav/hXujY2X/5HWWoigsi1nGR7s+IteSi53Ojkc7PsojHR7BZKj8fxfl/f6WMCKEqPOKLFZu+2wTcen53NE5iE9GdNK6pJrHUqQGjeT9kHwAzuyHlENgyb/8/k4+5wJHW/BtDb7t1HsH9/If01wAZ+MvhJOLw0p2IlcMPJdycL98WGnQGpr0BjsJnwCJuYm8ufVNdiTvAKC9d3ve7PkmLT1bVtkxJYwIIcRF9iSc5a6ZW7Ep8OX9YQxo5691SdopPKsGjvOhI3m/2u2iXGYKfTtH8Gt34ebbBhq0AZcGVVtjSbHaApN9CvJSIDdZveUlX/Q4BUqKrv45Du7QciC0HQLN/lMvW1GsNivfR3/P53s/p8hahIPBgac6P8XoNqMx6A1VemwJI0IIcYmpvx1h1oZYfFxM/DH+Jryc68n/mIvzYO+3cGKzGjyyEi6/n6MXBHQE/3O3gI7g3Ryq+AvruikKFGVBbsq5kJICuWfOhZczcHKr+vg8kwu0uEUNJs37g72LZqVXl+NnjzN562QOpB8AoJt/N96IeINGbo2q5fgSRoQQ4hLFJVYGf76ZYyl5DOoQwIxRXbQuqWqZC2D3XNj8qTqA9GIejcqGDv8O4BZUtwaM2qyQuBMO/wLRKyDn1IXX7BygeT9oMwRa3Vqx7qVawGK18NWBr5h9YDYlthJcjC5M6DqB4S2Go9dV36weEkaEEOIyDpzKZtgXW7DaFD6/tzODQwO1LqnyWYogch5smgb5qeo2r6YQNhYCO6nBw9FT0xKrnaLA6T0Q/Qsc/lUdq3Ke3gjN+qrBpPWgWn+58YG0A7y+9XVismIA6NOwD6/d8Bp+ztV/JZmEESGEuIJpfxzlf3/F4OVsYu34G/F2sde6pMpRUgx7voFNH6vdFKC2gNz0EnQcCQaZzQFQg0nyAYj+VQ0m6UcvvKYzQJNe0H44dLgLTM7a1VlBhSWFzNg7g2+jv8Wm2PBy8GJi94kMaDIAnUYtXhJGhBDiCswlNoZM38yR5FyGdgrks5GdtS7p3ykxQ9T3sPGjC10R7sFw43+h0ygwGLWtr6ZLO6qGksO/QMqBC9sd3KHz/dDtYbVlqQbblbyL17e8zqk89c//9qa382K3F/F00LYFTMKIEEJcxb7ELO74Ygs2BeaO6crNbWrhZGhWC+xbDBs/uDAo1TUQbnxe/RK1qyMtPtUpI1YNJXu+uagrR6cOfO3+mHpFjr7mrKRSYith1r5ZzN4/GwUFf2d/Jt0wiRsb3qh1aYCEESGEuKb3Vkcze2Mc/m4OrJ1wI64OtaQFwVoCB36EDe9f+MJ08YNeEyDsQTA6aFpenWCzQcyfsPNL9f48r2ZqKOl0r+aDXpPzk3lp40vsSd0DwJ0t7uSFri/gYqo5VwlJGBFCiGsoNFu59bONnMwoYFR4I969o4PWJV2dzQoHf4YNUyFDHZyIkw/0Gq92JdTDOTSqRUYs7JyjdoUV56jbTC4QOhK6PapO9lbN1ieu57Utr5FdnI2z0ZnJEZMZGDKw2uu4FgkjQghRDltj07lvjjoj5eLHbuCGpt4aV3QFsX/DmomQFq0+d/SCns9C90dr1SDLWq04D/YvVoNJ2pEL20NuhO7/B60GVvmcLGarmU8iP+G76O8AaOvdlg9v/LDa5g2pKAkjQghRThN/PsCinQk08XZizXM34mCsQZN8ZSXA76+qV36A2jXQ42kIfxzsXbWtrb5SFIjfCDtnw9HVoNjU7e7BagtV5wfAufJDbUJOAi9sfIHDGYcBuL/t/YzvMh5jDR6gLGFECCHKKafIQv9pG0jJKebxm5rx8sDqb3b/B0shbPkfbP4ESgpBp1e7BPpOrH9zhNRkWQmway7sWaBOsw9gsIc2g6HLA+raOJUw4PW3+N94c9ub5Fvycbd3592e73JT8E3/+nOrmoQRIYSogD8Pp/DIN7sx6HUsf7InHRpqNDhRUdT/ba+ZCFkn1W2Ne8FtH6hrw4iayVIIB39SW0vO7Luw3TMEutyvXmLtWvH1kApLCpm6cyo/H/8ZgC6+XXj/xvfxd64daytJGBFCiAp6etFeVuxLok2AG78+1ROjoZov4Uw/Dr+9BLHr1OeugTDgHWh3Z92apr0uUxQ4EwWRC+DAUjDnqtt1Bmh5q9pa0rxfuSagO372OC9seIHY7Fh06His42M8Hvo4dvraM3mdhBEhhKig9Lxi+k/bwNkCCy8MaMW4vs2r58DFubDxQ9j2BdgsYDBBxFPQ+/l6sZhbnWXOh0PL1DlLEndc2O4aCJ1HqXPBeDb+x9sUReGn4z8xdedUiq3F+Dj6MLX3VMIDwqux+MohYUQIIa7D8r2neW5JFCaDntXP9qK5bxUOElUUdb6QPyapq86COrnWrVPBu1nVHVdUv9QjaijZtwgKM89t1EHTPmprSetBYGdPrjmXt7a9xZoTawDoGdiTd3u9i7djDb3K6xokjAghxHVQFIWH5u/i76NphDX25Mf/i0Cvr4IukuQDsPpFSNiqPvcMUUNIq1sr/1ii5igphiMr1WASt/7CdidvDra9lRcKj3GqIAU7nR3PdHmGMe3GVOsqu5VNwogQQlynpKxC+k/bQL7ZyptD2jGmR5PK+/CCTPj7Xdj9tXpJqNFJ7Y6JeEpmTq1vMuNh73dYor5ntqGAOR5uWHU6gnQm3u/+GqGt79C6wn+tvN/ftTduCSFEFQn0cOTl29oA8P6aI5w6W1A5H5y0F2Z0h11fqUGk3Z3w1C51QTsJIvWPVwjHuoxgVIsOzPJ0x6rTcWtePj+ciCV08YOweFTZK3PqMAkjQghxGaO6N6J7Ey8KzFZeWXaQf92InLADFgyB/DRo0BrGrIC754F7w8opWNQqJbYSvjrwFSNXjiT67BHc7d358MYP+fCuFbi1uQPQqd05X94IC0fC6UitS65S0k0jhBBXEJuWx8DPNmEusTHtnlDu7HKdwSF+EywcAZZ8aNwT7lsis6fWYyeyT/DqllfZn7YfgD4N+zC5x2R8HH0u7JR6BDZ9pM5dcn6G1+b94aaXILibBlVfHxkzIoQQleCL9TF8sOYoHk5G1o6/iQau9hX7gJg/1eb2kiJo2hdGLgSTU9UUK2o0m2JjYfRCPtvzGUXWIlyMLrzc/WWGNBuC7krzyKQfh00fw/4fQLGq25r2hT4vQ6Mbqq/46yRhRAghKoHFamPYjC0cSsphUMcAZtzXpfxvPrIKfnwQrGZ1wqu7F8jYkHrqVO4pJm2ZxO6U3QDcEHADb/d8u/wzqWbEwqZp6kJ9thJ1W8iNaktJk15VVPW/J2FECCEqycHT2QydsQWrTeHL+8MY0K4cXyAHf4KfH1O/ONoOhTu/AjtT1RcrahRFUVh6fCkf7fqIgpICHO0cmRA2gRGtRly5NeRqzp5Q1yva+706QR6oXX83vaSGkxo2U6+EESGEqEQfrDnCF+tj8XW1Z+2Em3B3vMpKqVEL4Zdxal9/xxEw9ItyTf8t6pbk/GTe2PoGW5K2AOq6Mu/0fIdgt+B//+FZiedCybdqyxtAw27qYopth9aYFjgJI0IIUYmKLFZu+2wTcen5jOwWzNThHS+/4665sGqC+rjLGLj900pZtVXUHoqisDJuJVN2TCHXkotJb+KZLs8wus1oDHpD5R4s+zRs+Qwi54O1WN3m6KkuzBf2IPi0qNzjVZCEESGEqGS7TmRy96xtAHz/SDg9m/uU3WHbDPj9FfVx+OPqjKo1rNlcVK30wnTe3vY2fyX+BUB77/a82+tdmno0rdoD56aos7ruWQDZiRe2N+mthpI2g8GugoOvK4GEESGEqAKv/3KQb7adJNjLkTXP3oiz/bnul40fwl/vqI97jYebJ0sQqWciUyJ5fv3zZBRlYKe348nQJxnbfmz1rrJrs0LMOnWG3+O/X7gs2MkHOt2nBpNqXPdIwogQQlSBvOISBnyykdNZhTzYowlvDG4Lf72tXn4J0PdVuPEFCSL1iKIoLDqyiA93fUiJUkJzj+ZM7T2VVl6ttC0s+xTs+VZtMclNurC9aR8IG6suzme4ytinSiBhRAghqsim42ncP3cnoLCt8zoCor9WX+j/NvR8RtPaRPUqKini7e1v82vsrwAMbDKQN3q8gZOxBs0lYy1RW0l2z1PnveHc176zL3QeDWFjwLNJlRxawogQQlShiUujaBf1FqPt1qkbbvsIuj+qbVGiWiXlJfHc388RnRmNXqdnQtgEHmj7wPVdsltdzp5UW0r2fgt5Kec26qD5zfCf1yCwc6UeThbKE0KIqmKz8iYzGW23Dpui49cmr0gQqWe2n9nOiJUjiM6MxtPek9n9ZzOm3ZiaHUQAPBvDzZNg/CG451t1NlcUtcXEZtOsLLnwXQghKsJaAj8/iunQzyg6A8+Zn2DF0fYEnsikaxMvrasTVUxRFOYfms+nez7Fptho692WT/t8SoBLgNalVYzBCG2HqLfMODiyGoIqMLtwJZOWESGEKC+bDX55Eg79DHojurvnY9/5HhQFXly6nyKLVesKRRUqsBTwwsYXmBY5DZtiY2izoSy4dUHtCyKX8moKPZ7SdNC1hBEhhCgPRYHV/4X9S0BngHu+gbZDeO32tvi52ROXns+0tce0rlJUkYScBEatHsXvJ37HTmfHK+Gv8HbPt3GwqxkzndZ2EkaEEKI81r0Ju+cCOrhzNrS+DQB3RyNT7uwAwFeb4tiTcFbDIkVV2HhqIyNXjSQmKwYfRx/mDpjLva3vrfnjQ2oRCSNCCHEtmz5W1wEBuP0T6HBXmZf/09qPO7sEYVPghR/3SXdNHWFTbMzaN4un1j1FrjmX0AahLLl9CV38tBtbUVddVxiZMWMGTZo0wcHBgfDwcHbu3Fmu9y1evBidTsewYcOu57BCCFH9dsyGdW+pj295B7qOvexur9/elgau9sSm5fPpn8ersUBRFXLNuTz797PMiJqBgsI9Le9h3oB5+Dr5al1anVThMLJkyRImTJjA5MmT2bNnD6GhoQwYMIDU1NSrvu/EiRP897//pXfv3tddrBBCVKuohfDbC+rjm16CHk9fcVcPJxPv3aF218zeGMu+xKxqKFBUhbisOO5bdR/rE9dj0pt4q8dbTIqYhLGKZyutzyocRqZNm8ajjz7K2LFjadu2LbNmzcLJyYmvv/76iu+xWq2MGjWKN998k6ZNq3ixICGEqAyHf4FfxqmPw5+APhOv+Zb+bf0Y2ikQmwL//XEfxSXSXVPb/J3wN/euupcTOSfwd/ZnwcAF3NHiDq3LqvMqFEbMZjORkZH069fvwgfo9fTr149t27Zd8X1vvfUWvr6+PPzww+U6TnFxMTk5OWVuQghRbWL+hKUPq4uMdR4NA94r92WPbwxuh4+LieOpeXy+LqaKCxWVRVEUZu+fzbN/P0tBSQFd/bqyeNBi2vu017q0eqFCYSQ9PR2r1Yqfn1+Z7X5+fiQnJ1/2PZs3b2bu3LnMmTOn3MeZMmUK7u7upbfg4OCKlCmEENfv5FZYPBpsFmg7DAb/D/Tl/1Xp6WzinWHqF9jMDbEcOJVdRYWKynJ+/pDP936OgsLIViOZfctsvB29tS6t3qjSq2lyc3O5//77mTNnDj4+PuV+38SJE8nOzi69JSYmVmGVQghxTtJeWDgCSgqheX+4cw7oDRX+mFvbB3B7xwCsNoUXlu7DXKLdNNvi6s7knWHMmjGl84e8HvE6r97wKka9jA+pThWaDt7HxweDwUBKSkqZ7SkpKfj7+/9j/9jYWE6cOMHgwYNLt9nOzX1vZ2fH0aNHadas2T/eZ29vj729fUVKE0KIfyf1CHx7JxTnQONeMOJbsDNd98e9OaQd22IzOJKcy/S/Y5jQv2UlFisqQ2RKJBPWTyCzKBMvBy+m9ZlGmF+Y1mXVSxVqGTGZTISFhbFu3brSbTabjXXr1hEREfGP/Vu3bs2BAweIiooqvQ0ZMoS+ffsSFRUl3S9CiJohMx6+HQaFmRDYBe5dBEbHf/WR3i72vDVU7a754u8YDiVJd01N8uOxH3nk90fILMqkjVcbFg9aLEFEQxVeKG/ChAmMGTOGrl270r17dz799FPy8/MZO1a99v6BBx4gKCiIKVOm4ODgQPv2ZQf/eHh4APxjuxBCaCInCb4ZCrlnoEEbGP0TOFx5qfOKGNQxgJX7/fntYDIv/LifX57qidEgc01qyWKz8P7O91lydAkAtza5lbd6voWj3b8Ln+LfqXAYGTFiBGlpabz++uskJyfTqVMn1qxZUzqoNSEhAX0FBnsJIYRm8jPgm2GQdRI8Q+CB5eBUuSvvvjW0PdvjMjh8JoeZ62N55uYWlfr5ovwyizJ5fv3z7E7ZjQ4dT3d+mkc6PCLTutcAOkVRFK2LuJacnBzc3d3Jzs7Gza1y/scihKjnirJhwWA4sw9cA+GhNeDZuEoO9UvUaZ5dHIXRoOPXp3rRJkB+j1W3o5lHeeavZ0jKT8LZ6MzU3lPpE9xH67LqvPJ+f0sThhCi/jHnw8KRahBx8oEHfqmyIAIwJDSQ/m39sFjVq2ssVrm6pjqtPbmW+3+7n6T8JIJdg/n+tu8liNQwEkaEEPVLcS58dxckbAV7d7j/Z2hQtVe66HQ63h3WHndHIwdP5/DlhtgqPZ5Q2RQbM6JmMGH9BApLCokIiGDRoEU08/jnVZxCWxJGhBD1R1EOfDf8XBBxUwerBoRWy6F93RyYPLgtAJ/+eZzIk2er5bj1Vb4lnwnrJzBr3ywA7m97P1/0+wJ3e3eNKxOXI2FECFE/FGapl+8m7gAHd7VrJrhbtZZwR+cgbu8YQIlN4emFezibb67W49cXhzIOcd+q+1iXsA6j3sg7Pd/hxW4vYqev8DUboppIGBFC1H0Fmerlu6cjwdETxqyAoC7VXoZOp2PKnR0I8XEmKbuI53/ch81W468hqDUsNgszo2YyetVo4rLjaODYgHm3zmNo86FalyauQcKIEKJuy8+Ab4bAmShw8laDSDV1zVyOq4OR6fd1xmSn568jqczeFKdZLXVJbFYso1eP5ot9X1CilNC/cX9+GvIToQ20+7MW5SdhRAhRd+WlqZfvJh8AZ194cBX4d9C6KtoFuvPG4HYAfPj7UXadyNS4otrLarMy/+B87llxD4czDuNmcuP93u/z8U0f4+ngqXV5opwkjAgh6qbcFFhwO6QeAhd/NYj4ttG6qlL3dg9maKdArDaFpxfuJVPGj1RYYk4iD/3+EB9HfozZZqZXUC+WDV3GbU1vk4nMahkJI0KIuifnDMwfBGlH1AnNxq6u8st3K0qn0/HeHR1o2sCZ5Jwixi+JkvEj5aQoCj8c/YHhK4azJ3UPTnZOvBHxBl/c/AW+Tr5alyeug4QRIUTdkn0K5t8GGcfBrSGMXQXeNXNeCWd7O74Y1QUHo54Nx9KYKfOPXFNyfjKP//k4b29/m8KSQrr6deWnIT8xvOVwaQ2pxSSMCCHqjqwEmHcbZMaBRyO1RcSrqdZVXVVrfzfeGqIuHPrxH0fZEZehcUU1k6IorIhdwZ2/3MnWpK3YG+x5qdtLzB0wl4auDbUuT/xLEkaEEHVDZjzMG3Rh0bsHV1fpFO+V6e6uDbmzSxA2BZ5etJf0vGKtS6pRMgozGL9+PK9sfoVcSy4dfDrww+AfGN12NHqdfI3VBfKnKISo/TJi1TEi2Qng1UxtEfEI1rqqctPpdLwzrD3NfV1IzS1m/JIorDJ+BIB1J9dx5693si5hHXZ6O57u/DTfDPyGpu41u8VLVIyEESFE7ZZ+XO2ayTkNPi3VIOIWqHVVFeZksmPmqC44Gg1sOp7OF3/HaF2SpgpLCnl186s8t/45MosyaeHZgkWDFvFYx8dkJtU6SMKIEKL2Sj2iBpG8ZPBtq16+6+qvdVXXrYWfK28PU8ePfPLnMbbGpmtckTaS85N5cM2D/Br7K3qdnkc6PMLiQYtp7dVa69JEFZEwIoSonVKPqF0z+ang116dWdWl9l/WeVdYQ+4Oa4hNgWcXR5GWW7/Gj0SlRjFy5UgOZxzG096Tr275ime7PIvJYNK6NFGFJIwIIWqfjFh1iveCdPDvqAYRZx+tq6o0bw1tT0s/F9Jyi3l28d56M35kecxyHvr9ITKKMmjp2ZJFty+im3/1LmYotCFhRAhRu5w9oU7xnpcCvu3U1XedvLSuqlI5mgx8MaoLTiYDW2Mz+N+641qXVKVKbCV8sOsDJm2ZhMVm4eZGN/PtwG8JcgnSujRRTSSMCCFqj+zTsGDIhcGqdTCInNfc15V371DHj/zvr+NsPl43x49kF2czbt04vj38LQBPhD7BtD7TcDI6aVyZqE4SRoQQtUNuito1c34ekQd+BZcGWldVpe7o3JCR3YJRFHhuyV5Sc4q0LqlSxWfHM3r1aLYmbcXRzpGPb/qYJzs9KXOH1EPyJy6EqPnyM+CboZARA+7BMOZXcAvQuqpq8caQdrT2dyU9z8zTi/ZSYrVpXVKl2HRqE6NWjeJEzgkCnAP4ZuA33NLkFq3LEhqRMCKEqNkKz8K3wyAtGlwD1CDi0UjrqqqNg9HAjFFdcDYZ2BGfyad/1u7xI4qiMP/gfJ766ylyLbl08e3CokGL5LLdek7CiBCi5irOhe/uguT94NxA7Zqp4WvNVIVmDVx4784OAEz/O4Y1B5M1ruj6FFuLeXXzq3wc+TE2xcbwFsP56pav8Hb01ro0oTEJI0KImslcAAtHwOnd4OgJ9y+HBi21rkozQzsF8WCPJgA8/0MUx1JytS2oglILUhm7Ziwr4lZg0Bl4JfwVJkdMxmgwal2aqAEkjAghah5LESy+F05uAXs3uH8Z+LfXuirNvTqoDRFNvck3W3n0m91kFZi1LqlcDqYf5N6V93Ig/QDu9u582f9L7m19LzqdTuvSRA0hYUQIUbOUmOGHByBuPRidYdRSCOysdVU1gtGgZ8aoLgR5OHIyo4CnF9X8CdFWxq1kzG9jSC1MpZl7MxbdtojwgHCtyxI1jIQRIUTNYS2Bnx6G47+DnQPctwQayRfXxbycTcx+IAwHo55Nx9P5YM0RrUu6rBJbCR/v/piJmyZitpnp07AP3932HcFutWc1ZVF9JIwIIWoGmxWWPw7Rv4LBBCMXQkhvrauqkdoFuvPhXaEAfLkxjl+iTmtcUVnZxdk8+eeTzD80H4BHOzzKZ//5DBeTi7aFiRpLwogQQns2G6x4Bg78CHo7uOcbaH6z1lXVaINDA3miTzMAXly6n4OnszWuSHXs7DFGrhzJtjPbSicye6bLMzKRmbgq+dshhNCWosBvL8Le70Cnh+FfQauBWldVK/z3llb0adWA4hIbj32zm/Q8bVf4XXtyLaNXj+ZU3imCXIL47rbvZCIzUS4SRoQQ2lEUWDsJds0BdDBsFrS7Q+uqag2DXsdnIzvT1MeZpOwinvx+DxYNZmi1KTb+t+d/TFg/gcKSQm4IuIHFgxbT0rP+XootKkbCiBBCGzYbrJkIWz9Xnw/+DEJHaFtTLeTuaGT2A2G42NuxMz6Tt1Ycrtbj55pzeeavZ5hzYA4AY9qOYWa/mXg4eFRrHaJ2kzAihKh+1hL4ZRzsmKk+v+0jCBujbU21WHNfVz4d0QmdDr7dfpLFOxOq5bhx2XHct+o+NpzagL3Bnvd6vcd/u/0XO71dtRxf1B0SRoQQ1ctSpM4jsm8h6Axwx2zo/qjWVdV6/dr6MaGf2i0y6ZeDRJ7MrNLjbUjcULrQnb+zPwsGLmBws8FVekxRd0kYEUJUn+Jc+P4uOLoKDPYw8nvpmqlET/2nOQPb+2OxKjz+3R6Ss4sq/Rg2xcaX+77k6b+eJs+SR5hfGIsHLaadd7tKP5aoPySMCCGqR34GLBgMJzaByRVG/yRXzVQynU7HR3eH0trflbTcYv7v290UWayV9vn5lnyeX/8806Omo6AwstVI5twyRxa6E/+ahBEhRNXLPg3zBkLSXnD0gjG/yoRmVcTZ3o7Z93fFw8nIvlPZvLrsIIry76eMT8xJZPTq0fyZ8CdGvZE3It7g1RtexaiXhe7EvydhRAhRtTJi4etbIf0ouAXBQ2sgqIvWVdVpjbydmH5vF/Q6+GnPKeZtOfGvPm/r6a2MWDWCmKwYGjg24OsBXzO85fDKKVYIJIwIIapS8gE1iGQngFczNYg0aKV1VfVCrxY+vHJbGwDeXR3Nlpj0Cn+GoijMOziPJ9Y9Qa45l44NOrL49sV08u1UydWK+k7CiBCiaiRsh3mDID8V/DqoQcSjkdZV1SsP9wrhzs5BWG0K4xbuITGzoNzvLSwp5KVNLzEtcho2xcYdze9g3oB5+Dr5VmHFor6SMCKEqHzH/4RvhkFxNjSKgAdXgot8iVU3nU7He3d2oGNDd7IKLDy8YBdn883XfF9SXhJjfhvDb/G/Yaez49XwV3mzx5uYDKZqqFrURxJGhBCV6+BPsGgklBRC8/4w+mdw9NC6qnrLwWjgy/vD8HW151hKHmPm7SSnyHLF/Xcl72LkypFEZ0bj5eDF7FtmM7L1SHQ6XTVWLeobCSNCiMqzex4sfRhsFmh3J4xcCCYnrauq9wLcHfn+kXC8nE3sP5XNQ/N2UWAuKbOPoigsOrKIx/54jLPFZ2nj1YbFgxbTzb+bRlWL+kTCiBCicmz+BFY+ByjQ9SF19V07adavKVr4ufLNQ91xc7Bj98mzPLLgwhwkZquZyVsn896O9yhRShgYMpAFAxcQ4BKgcdWivpAwIoT4dxQF1r4Of76hPu/9PAyaBnqDpmWJf2of5M6Ch7rjbDKwNTaDJ76L5HROMmN/H8uymGXodXqeD3ue93u/j6Odo9blinpEp1TGbDhVLCcnB3d3d7Kzs3Fzc9O6HCHEeTYbrP4v7J6rPu//NvR8RtuaxDVtj8vgwXk7MRtO4BmyEDNZuJpc+fDGD+kZ1FPr8kQdUt7vb1laUQhxfWxWWPEs7P0W0MHgz2Tl3VrihqbejLklle9jvsSMFWddEItum01jd7n0WmhDummEEBVnLYFlj6tBRKeHO76UIFJLWGwW3tn+DgvjPkKnt1KS257kI48xa112pUwbL8T1kJYRIUTFWC3w08Nw+BfQ26kDVdvdoXVVohwyCjN4fsPzRKZEAvBUp6fwUwYxfsk+Fu1MwMGo5/Xb28plvKLaSRgRQpRfSTH8+CAcXQ0GE9w9H1oP0roqUQ6HMg7x3N/PkZyfjLPRmam9p9InuA8A5hKFF5fuZ96WEziZDLwwoLW2xYp6R8KIEKJ8LIWwZDTE/Al2DjDie2jRT+uqRDksj1nOO9vfodhaTBO3Jnz2n89o6t609PV7ugZTbLEy6ZdDzPg7Fkejgaf+00LDikV9I2FECHFt5nx1VtX4jWB0gnsXQdM+WlclrsFsNTN151R+PPYjADc2vJEpvafgZvrnVQ33RzSh0GLlvdVH+OiPYzia7Hi4V0h1lyzqKQkjQoirK8qBhfdAwjYwucCoH6FxD62rEtdwJu8ME9ZP4GDGQXToeLLTkzzW8TH0uitft/DYjc0oMFv59M/jvL3yMI5GA/eFyxU2oupJGBFCXFnhWfhuOJyOBHt3GP0TBMv04DXdtqRtvLjxRbKKs3AzufH+je/TK6hXud777M0tKDRb+XJjHK8uP4CDUc+dXRpWccWivpMwIoS4vIJM+GYoJO8HR0+4fzkEdtK6KnEVNsXG3ANzmR41HZtio41XG6b1mUZD1/KHCZ1Ox8sDW1NosfLNtpP898d9OBgN3NZBpoYXVUfCiBDin/JS1SCSehicfGDMr+DXTuuqxFXkmnN5ZfMrrE9cD8CdLe7klfBXsDfYV/izdDodbwxuR6HZyo+Rp3hm0V7s7fTc3MavcosW4pzrmvRsxowZNGnSBAcHB8LDw9m5c+cV950zZw69e/fG09MTT09P+vXrd9X9hRAay0mCebepQcTFH8auliBSwx07e4yRK0eyPnE9Jr2JNyLe4M0eb15XEDlPr9cxdXhHBocGUmJTePy7SH7dl1R5RQtxkQqHkSVLljBhwgQmT57Mnj17CA0NZcCAAaSmpl52//Xr13Pvvffy999/s23bNoKDg7nllls4ffr0vy5eCFHJshLVIJJxHNwaqkGkQSutqxJXsTJuJaNWjSIhN4EA5wC+GfgNw1sOr5TPNuh1TLsnlEEdA7BYFZ5ZtJevN8dXymcLcbEKL5QXHh5Ot27dmD59OgA2m43g4GCefvppXn755Wu+32q14unpyfTp03nggQfKdUxZKE+IapAZDwuGQHYCeDSGMSvAs7HWVYkrsFgtfLj7QxYdWQRAj8AeTO09FU8Hz0o/ls2m8OaKQyzYdhKAJ/o048UBrWSmVnFN5f3+rlDLiNlsJjIykn79Lkx0pNfr6devH9u2bSvXZxQUFGCxWPDy8qrIoYUQVSk1Wm0RyU4A7+Yw9jcJIjVYSn4KY38fWxpEHuv4GF/c/EWVBBFQu2zeGNKOFwaorWQz18fywtL9WKy2KjmeqH8qNIA1PT0dq9WKn1/ZQUx+fn4cOXKkXJ/x0ksvERgYWCbQXKq4uJji4uLS5zk5ORUpUwhREcf/VKd4N+dCg9bwwK/gKgMVa6qdZ3bywsYXyCzKxNXoypTeU7gp+KYqP65Op2Nc3+Y0cLFn4rIDLI08RUZeMTNGdcHJJNdCiH+nWlftnTp1KosXL2bZsmU4ODhccb8pU6bg7u5eegsODq7GKoWoR3Z8CQvvVoNI415qi4gEkRrJptiYd3Aej659lMyiTFp5tmLJ7UuqJYhc7J5uwXw5Ogx7Oz1/H03jvjk7OJtvrtYaRN1ToTDi4+ODwWAgJSWlzPaUlBT8/f2v+t6PPvqIqVOn8scff9CxY8er7jtx4kSys7NLb4mJiRUpUwhxLdYSWPU8/PYiKDboPBruXwZO0n1aE6UXpvPkn08yLXIaNsXGkGZD+Pa2bwl20+Y/av3a+rHw0XDcHY1EJWYxfNZWTp0t0KQWUTdUKIyYTCbCwsJYt25d6Tabzca6deuIiIi44vs++OAD3n77bdasWUPXrl2veRx7e3vc3NzK3IQQlaQwS20N2fUVoIP+b8GQ6WBn0roycRkbT21k+K/D2ZK0BXuDPZNumMQ7Pd/B0c5R07rCGnux9PEIAtwdiEvLZ/jMrRxJli51cX0q3E0zYcIE5syZw4IFC4iOjuaJJ54gPz+fsWPHAvDAAw8wceLE0v3ff/99Jk2axNdff02TJk1ITk4mOTmZvLy8yvsphBDlkxkPc2+B2L/UBe9GfAc9nwW5KqLGKbYWM3XnVMatG0dmUSYtPVuy5PYl3NPqnhpzFUsLP1d+frIHLXxdSMkp5u5Z29gZn6l1WaIWqnAYGTFiBB999BGvv/46nTp1IioqijVr1pQOak1ISODMmTOl+8+cOROz2cxdd91FQEBA6e2jjz6qvJ9CCHFtJ7fBnP9A+lFwDYSH1kCb27WuSlxGzNkY7l11L99Hfw/A6DajWThoIc08mmlc2T8FuDvy4+MRdG3sSW5RCaPn7uD3Q8lalyVqmQrPM6IFmWdEiH8pahGseAasZgjsDCMXgZusNVLTKIrCD0d/4MPdH1JsLcbLwYu3e77NjQ1v1Lq0ayo0W3l60R7+jE5Fr4N3hnWQFX9F1cwzIoSoZWw2+PNNWP64GkTaDIEHV0sQqYHOFp3lmb+f4Z0d71BsLaZnUE9+GvJTrQgiAI4mA7NGhzGiazA2BV5ZdoDP/jxOLfj/rqgB5OJwIeoqcwEsewyiV6jPez8PfV8DvfwfpKbZfmY7r2x6hbTCNIx6I+PDxjOqzSj0utr1Z2Vn0DN1eAd83ez5/K8YPvnzGGl5Rbw5pD0Gfc0Y5yJqJgkjQtRFOWdg0Ug4EwV6Iwz5HDrdq3VV4hIWq4XPoz5n/sH5KCiEuIfwwY0f0NqrtdalXTedTsfzt7Sigas9k389xHfbE0jNKeaje0JxczBqXZ6ooWpX7BZCXFtSlDpQ9UwUOHrBmF8liNRAJ7JPMPq30cw7OA8Fhbtb3s2S25fU6iBysQcimjD93i6YDHr+OJzC4M83c/B0ttZliRpKwogQdUn0Spg3EHKTwKcVPLoOGvfQuipxEUVRWHZ8GfesvIfDGYdxt3fn0z6f8nrE65rPHVLZBnUMYMn/3UCQhyMnMwq484utfLvthIwjEf8gV9MIUReY8+Gvd2D7TECBpn3h7vng6KFxYeJiyfnJfLDrA9aeXAtAN/9uvNfrPfydrz6DdW2XXWDhv0v3sfawOnv3oA4BTBneQbpt6oHyfn9LGBGitovfCL8+DWdPqM+7PQq3TgWDDAmrKXLNucw9MJfvor+j2FqMnc6OcZ3HMbbdWAx6g9blVQtFUfh6ywmmrI6mxKbQ2NuJGfd1oX2Qu9aliSokYUSIuq4oG/6YBHsWqM/dGsLgT6FFf03LEhdYrBZ+OPYDs/bNIqs4C4Auvl14sfuLtPNup21xGtmbcJanFu7ldFYhJoOeSbe3YfQNjWvMrLKickkYEaIuO7oGVo5Xx4YAdHsE+r0B9q6aliVUiqLwx8k/+GzPZyTmqgt9hriHML7LePoE96n3X7yXdtvc1sGfqcM7SrdNHSRhRIi6KD8D1rwEB35Un3s1VS/bbdJL27pEqT0pe/h498fsT98PgLeDN092epI7W9yJnV66zs47320z9bdoLFaFRl5OfDFKum3qGgkjQtQligKHfobVL0JBOuj0EDEO+rwCJietqxNAXHYcn0Z+yt+JfwPgaOfI2HZjGdNuDE5G+TO6kqjELMZ9v6e02+a129twv3Tb1BkSRoSoK3LOwKrn4egq9blvWxg6HYLCtK1LAJBemM7MqJn8dPwnrIoVvU7PnS3u5MnQJ2ng1EDr8moF6bapuySMCFHbKQrs/Q5+fxWKs0FvB73/q07rbmfSurp6r8BSwILDC5h/cD4FJQUA9Anuw/gu42nq0VTj6mofRVGYt+UEU6Tbpk6RMCJEbXb2JKx4FuLUJn8CO8PQGeBXP6/AqEksVgvLY5fzRdQXpBemA9Deuz0Tuk6gm383jaur/S7ttnlpYGvGRDTGziBzdNZGEkaEqI1sVtj1lbrSriUf7Byg7ytwwziZN0RjRSVFLItZxtcHvyY5PxmAhi4NeTbsWQY0HiBjHCrRpd02LXxdeHVQG/q08tW4MlFREkaEqG1i/oQ/XofUQ+rzRj3UK2V8mmtbVz1XYCngh6M/MP/QfDKKMgDwcfTh4fYPc0+rezAZpMusKiiKwvc7Evjoj6NkFVgAuLFlA169rQ2t/OUS9tpCwogQtUXyAXXysvNdMg7u8J9J0PVh0EvTtFayi7NZeGQh30d/T3axusBbgHMAD7d/mGEthmFvsNe4wvohu8DC9L+PM3/rCSxWBb0ORnZvxPh+LWngKn8GNZ2EESFquuzT8Pe7ELUQUMBggu6PqQNUnby0rq7eyizK5NvD37LoyCLyLfkANHZrzCMdHmFQ00EY9XKFhxZOZuQz9bcj/HZQ7SJzsbfjyb7NeKhnCA7G+jGlfm0kYUSImqooB7Z8Ctu+gJJCdVv74XDz6+DZRMvK6rWU/BTmH5rP0mNLKbIWAdDCswWPdniUWxrfUm/WkKnpdsZn8s6qw+w/pbZWBXk48tLA1gzuGCDjdmogCSNC1DRWC0TOh/VT1YnLQB0Xcss70FDmDNHKqdxTfH3wa5bHLMdiU8cmtPduz6MdH6VPcB/0Oukqq2lsNoVf9p3mgzVHOZOtBsfOjTx4bVBbwhp7alyduJiEESFqCkWBI6vgz8mQEaNu824O/d+CVreB/G9OE3HZccw9MJdVcauwKlYAwvzCeKzjY0QERMj/smuBQrOVrzbFMXNDLAVm9c/w9o4BvHRra4K9ZNbbmkDCiBA1wand6uDUhK3qcycf6PMyhD0IBhl7oIWo1CjmHZzH34l/o6D++usZ2JNHOz5KmJ+0UNVGqTlFfPzHMX6ITERRwGSn56GeITzZt5nM4qoxCSNCaCkzHta9pa4nA+p8IRFPQc9nwUH+Dlc3m2Jj46mNzDs4jz2pe0q3/yf4PzzW8THa+chkcnXB4aQc3l19mC0x6iXY7o5GxvRowoM9muDlLJdga0HCiBBaSI+BrZ9B1CKwWQAddLoP+r4K7kFaV1fvmK1mVsWtYv6h+cRlxwFg1BsZ3GwwY9qNoam7TNte1yiKwt9HU3l3VTSxaerVUA5GPSO7NeKR3iE09JTum+okYUSI6nQ6EjZ/CtEr4FzTP037wi1vg38HLSurl3LNuSw9tpTvDn9HamEqAC5GF+5pdQ+j2ozC10lm8qzrrDaF3w8lM3N9LAdOq1feGPQ6BncM4PE+zWjtL98l1UHCiBBVTVEgdp0aQk5surC95UDo9Rw0ukGryuqt1IJUvjv8HT8c+6F0jhBfJ1/ub3M/d7W8CxeTi8YViuqmKApbYzOYuT6WzTHppdv7tmrAE32a062JpwxWrkISRoSoKtYSOLxcnSsk+YC6TW8HHe5Wx4T4ttGyunopNiuW+YfmszJuJSW2EgCauTfjwfYPMihkEEYZLCyAA6eymbUhltUHz3D+m69LIw8ev6kZ/dr4oddLKKlsEkaEqGyWQoj6HrZ+DmdPqNuMTuqVMTc8CR7BWlZX7yiKwp7UPcw/OJ/1p9aXbg/zC2Nsu7H0bthb5ggRl3UiPZ8vN8bxU+QpzFYbAM19Xfi/G5sytFMQJjv5e1NZJIwIUVkKz6or6e74EvLT1G2OXhD+f+r07TJ1e7VKL0xnRewKlsUsIz47HgAdOm5udDMPtn+Q0AahGlcoaovUnCK+3nKC77efJLdYbVELcHfg4V4h3Nu9Ec72slL2vyVhRIh/KycJts1QZ00156nb3BtBj6eg82gwOWtaXn1isVnYfGozy2KWsfHUxtJJyhwMDgxqOogx7cYQ4h6icZWitsopsrBwRwJzN8eTllsMqGvf3NLOj6GdgujZzBs7g7SWXA8JI0JcD0WBk1th99dw+Jdzl+cCvu3UQant7pDJyqpRXHYcy48v59fYX8koyijd3rFBR+5ofge3NrlVBqWKSlNksbJs72lmb4wjPj2/dLuPi4nbOwYypFMgnYM9ZMBrBUgYEaIiirJh32I1hKQdubC9cU/oNR6a95Np26tJnjmP30/8zrKYZexL21e63cvBiyHNhjCs+TCaeTTTsEJR19lsCnsSzvJLVBKrDpwhM99c+lqwlyNDQ4MY2imQFn6uGlZZO0gYEaI8Tu9RA8jBn8BSoG4zOqlXxnR9CAI7aVpefaEoCpEpkSyLWcbak2spPLeasUFnoHdQb+5ocQe9G/bGqJdWKVG9LFYbm2PS+TUqid8PJZeugQPQJsCNYZ0CGRwaSKCHo4ZV1lwSRoS4EnOBGj52z4WkvRe2N2gD3R6GjveAg7t29dUjyfnJrIhdwfKY5STkJpRuD3EP4Y7mdzC42WB8HH00rFCICwrNVv6MTuGXqNNsOJaGxXrh67N7iBdDOwVyW/sAPGXq+VISRoS4VOoRiJynTtVerM7IiMEEbYeprSCNbpCumGpQVFLEuoR1/BLzC9vPbC9drM7JzomBIQMZ1nwYoQ1CpV9e1Ghn8838djCZX6JOsyM+s3S70aCjZ3Mf+rRswE2tfGni7VSv/y5LGBECoMQM0b/C7nlwcvOF7Z5N1ADSaRQ4y/+8q5qiKOxL28cvsb+wJn4NeZa80te6+nVlaPOh3NL4FpyMsm6IqH2SsgpZuT+JX6KSOJSUU+a1Rl5O3NSyATe1bEBEM+96d7mwhBFRf9lscGqXumLuwZ8uzA2i00Or29QQ0rQv6OVSvaqWnJ/MyriV/BLzCydyTpRuD3QOZEjzIQxpNoRgV5ksTtQdMam5rItOZcOxNHadyCzTlWM06Oja2IubWqnhpLW/a51vNZEwIuoXRYEzUWr4OLQcshMvvOYaAF3GQJcHZOXcalBUUsRfCX/xS+wvbEvaVtoN42jnSP/G/RnabChd/bvK7KiizssvLmFbbAYbjqWx/lgqiZmFZV73dbVXW01aNaBXcx88nOreWBMJI6J+SDl8LoD8DJlxF7abXKH1IGh/JzS7GQz1q2m0uimKwv70/fwSo3bD5FpyS1/r4tuFoc2HMqDJAJyNMlGcqJ8UReFERgEbjqay8Xg6W2PTKbLYSl/X6yA02IPeLRoQHuJF50YeOJlq/+8tCSOi7kqPOdcF8zOkRV/YbucIrW6FdndCi/5glEvtqpLZaiYyJZItp7ew4dSGMt0wAc4BDGmmdsM0cmukXZFC1FBFFiu7T5xlwzG1S+dYSl6Z1+30OtoFuRMe4kW3Jl50a+JZK1tOJIyIuiUrAQ4tU1tBzlyYCAuDSZ2QrP1waHkr2MtsnFUpMSeRTac3sSVpC7uSd5XOBwLq1Oz9G/dnaPOhdPPvJt0wQlTAmexCNhxNY3tcBjvjM0nKLvrHPi39XOh+Lpx0D/EiwL3m/4dLwoio3RQFUg/D8T/gyGo4tfPCazoDNOurtoC0HgSOHpqVWdcVWArYnbKbzac3s+X0ljJzgQA0cGxAz6Ce9AzqSa/AXjI1uxCV5NTZAnbGZ7LrRCY74zOJTcv/xz7BXo50a+JV2noS4uNc4wbEShgRtU9xHsRvUAPI8bWQc/qiF3XQpJc6BqTNUHD21qzMukxRFOKy49h8ejObT28mMiUSy/n1eQA7nR2d/TrTM7AnvYJ60dKzZY375SdEXZSeV8zuE5nsjD/LzhMZHE7KwXbJt7e3s4m2gW60C3Q/d+9GiLczer12/0YljIiaT1Eg/bgaPmLWwoktFxamA3UMSNOb1G6YNoPB1V+7WusoRVE4nXeaval71fEfSVtIzk8us0+AcwC9gnrRM6gn4f7h0vohRA2QW2Qh8uRZdp3IZFf8WaISszBbbf/Yz8lkoE2AGkzanQsqLf1cMdlVTzeqhBFRM5kL4MTmc60ff0DWybKve4ZAywHqANTGvcDooE2ddZTVZuXY2WPsSd3D3tS97E3ZS2phapl9THoT3fy7lXa/hLiFSOuHEDVckcXKkeRcDiVlcygph0NJORw5k0NxyT8DitGgo4Wva2nryfmWFJcqmJBNwoioGWw29YqXE1vU8HFiE5RcNDDLYFK7X5r3V0OIt6zGWpkKLAUcTD9YGj72pe0j31K279lOZ0db77Z08u3EDQE30NW/K452NX9gnBDi6kqsNuLS8zmclFMmpGQXWi67/7wHu9G3tW+l1lDe7+/afxGzqFmKc+HUbkjcCYk71JlQi8tOj4xbQ7Xlo+UAaNJbroCpROmF6USlRqnhI2UvRzKPUKKUlNnHxehCqG8oXXy70Nm3M+192kv4EKIOsjPoaennSks/V4Z1Vid8VBSF01mFpcHk8LmQcia7iGYNtPtdLGFEXD9FgbMnLgSPxJ2QegiUS5oFjc7QMEydfKzFLeDbRhak+5cURSGtMI3ojGgOZxzmcOZhojOiSSlI+ce+vk6+hPmG0dmvM519O9PCowUGvUGDqoUQWtPpdDT0dKKhpxMD2l0Yh5eRV4yXhqsNSxgR5VdSrM7xkbjjQvjI++eXHx6NIDj83K07+LaTGVD/BUVROJN/Rg0dGYeJzowmOiOajKKMy+7f3KO52urh15kuvl0IcA6QMR9CiKvydrHX9PjyDSH+yVqitnikRUPaEUg9AmlHIf0oWM1l99UbIbCTGjwadlPv3QK0qLpOsCk2EnMTic5UWzyiM6KJzowmuzj7H/vqdXqaujeljVcb2nq3pY13G1p5tpKrXYQQtY6EkfrMWqKu55J2LmykRZ8LHcfBWnz59zj5XGjxCA5Xg4hMu35dMgozOJ51nONnL9xis2PLzGp6np3ejhYeLWjj3YbWXq1p692Wlp4tZayHEKJOkDBS1ymK2pVy9qR6GW1m/LnwcUQNHbbLj6rG6AQ+LaFBa2jQSh3n0aA1eDaR8R4VVFhSSFxWHMfOHisTPq7UzWJvsKelZ0vaeLWhjbd6a+HRApOh9q1LIYQQ5SFhpC4oPHshbFx6n5VQ9lLaSxmd1LDRoPWFm29rcG8EellbpLwURSG9MJ1Teac4lXuKhNwEYs7GcDzrOAk5CSj88wp6HToaujakhUcLWni2oKVnS1p4tqCRayMZYCqEqFckjNRkigJF2WrLRl4K5KZAXjLkJqtjOrJOwtkEuMx4gjJ0enALAo/GastGg5bQoI0aQtyDJXSUU2FJIadzT3Mq7xSn805zKlcNHucDSJH1yqHPy8GrNHS08GxBC48WNPNohpPRqRp/AiGEqJkkjGjBaoGCDDVUXBo0yjxOvXqrxsWcfdWrWDwbnwsdF927NQQ7aeK/EkVRKCgpIKMwg4yiDDIKM0gvTCe9MP1C6Mg7RXph+lU/R6/TE+AcQEOXhgS5BtHMvVlp+PBx9Kmmn0YIIWofCSP/ltUCBZlquCg8d1+QcW5b5iXbMqDg7LVbMi5l7w6ufuBy7ubqXzZweDQCk/wP+2JWm5Uccw5ZxVllQsbF95mFmaXPr9aqcTFXoysNXRuqN5eGpY+DXYLxd/HHqDdW8U8mhBB1j4QRRQFLgdodUpil3hdlQ1HWlbcVZUHhuftLZxctL51ebc1w8VXDxcVBw8UXXPwvBJB6fLXK+VaL7OJssoqzyCrOIrs4u/T5xdtzinNKH+eacy87TuNqHO0c8XbwxtvRG28Hb3wcfQh0CSwTPtzt3avoJxVCiPrrusLIjBkz+PDDD0lOTiY0NJTPP/+c7t27X3H/H3/8kUmTJnHixAlatGjB+++/z2233XbdRVeaeYPUybuudEVJeen04OgJTt7g6KXeO517fv5Wut0bnLzAwR3qwSBFi9VCQUkB+ZZ8cs25F26W3LLPL7rlmHPIs+SVPrcq1us+vrPRuUzA8Ha85PFF9zJ+QwghtFHhMLJkyRImTJjArFmzCA8P59NPP2XAgAEcPXoUX99/LrCzdetW7r33XqZMmcLtt9/OwoULGTZsGHv27KF9+/aV8kNcP+VCENEZ1IDg6KHel948Ltnuod7OP3fyVp/X0kGgNsVGUUkRxdZiikqKKLJeeFxsLabYWkxhSWHptsKSQvIt+RRYCigoKSgNGoWWQvX5JdtLbCXXLqIcTHoTHvYeuNm74WHvgYe9B+727rjbu1/2+fnH0m0ihBA1X4VX7Q0PD6dbt25Mnz4dAJvNRnBwME8//TQvv/zyP/YfMWIE+fn5rFy5snTbDTfcQKdOnZg1a1a5jlllq/ZmJaitGg7uYHKp8vkzbIoNq82KxWahRCnBYrVgsV10s1oosZVgtpkv+9r5x2arWb3ZzBRbi7FYLRRbizHbzKWvXfr84u0XBw/Lv20VKieT3oSryRVXkytuJjdcTa64mFxKt7kay24//9jV5IqL0QVHO0eZ0lwIIWqZKlm112w2ExkZycSJE0u36fV6+vXrx7Zt2y77nm3btjFhwoQy2wYMGMDy5cuveJzi4mKKiy/MAJqTc53jMq7h26QNnMo9hVWxUmIrUcOCYlVvtrL3JUoJNts/Xy+xlWCxWUofn79ZlXOhw1aC1Xbu/ZcuIFfDGPVGHAwO2NvZ42BwwMHOAXuDPfYG+9LHzkZnnOyccDY642h0xMnOCSejU+m2i587GdWbo52jtFAIIYS4ogqFkfT0dKxWK35+fmW2+/n5ceTIkcu+Jzk5+bL7JycnX/E4U6ZM4c0336xIaddlzYk17E/bX+XHuRqj3qjeDMbSx3Z6O0x6E0aDEZPehJ3erszrRr0Re4M9JoNJvelNFx4bTBde06uPjYZz+5/b73ywuDRwyERbQgghtFAjr6aZOHFimdaUnJwcgoODK/04Q5sNJdw/HDu9HQadAYPeoN5f/Phy2y56fv69dno7jHpj6ePzrxl1ari4eJudzq40dEjXgxBCiPquQmHEx8cHg8FASkrZZeNTUlLw9/e/7Hv8/f0rtD+Avb099vZVv5zxPa3uqfJjCCGEEOLqKnQJiMlkIiwsjHXr1pVus9lsrFu3joiIiMu+JyIiosz+AGvXrr3i/kIIIYSoXyrcTTNhwgTGjBlD165d6d69O59++in5+fmMHTsWgAceeICgoCCmTJkCwLPPPstNN93Exx9/zKBBg1i8eDG7d+9m9uzZlfuTCCGEEKJWqnAYGTFiBGlpabz++uskJyfTqVMn1qxZUzpINSEhAf1Fc2706NGDhQsX8tprr/HKK6/QokULli9fXgPmGBFCCCFETVDheUa0UGXzjAghhBCiypT3+7t2ThsqhBBCiDpDwogQQgghNCVhRAghhBCakjAihBBCCE1JGBFCCCGEpiSMCCGEEEJTEkaEEEIIoSkJI0IIIYTQlIQRIYQQQmiqwtPBa+H8JLE5OTkaVyKEEEKI8jr/vX2tyd5rRRjJzc0FIDg4WONKhBBCCFFRubm5uLu7X/H1WrE2jc1mIykpCVdXV3Q6XaV9bk5ODsHBwSQmJsqaN9cg56pi5HyVn5yr8pNzVX5yrsqvKs+Voijk5uYSGBhYZhHdS9WKlhG9Xk/Dhg2r7PPd3NzkL2s5ybmqGDlf5SfnqvzkXJWfnKvyq6pzdbUWkfNkAKsQQgghNCVhRAghhBCaqtdhxN7ensmTJ2Nvb691KTWenKuKkfNVfnKuyk/OVfnJuSq/mnCuasUAViGEEELUXfW6ZUQIIYQQ2pMwIoQQQghNSRgRQgghhKYkjAghhBBCU/U6jMyYMYMmTZrg4OBAeHg4O3fu1LokzW3cuJHBgwcTGBiITqdj+fLlZV5XFIXXX3+dgIAAHB0d6devH8ePH9emWI1NmTKFbt264erqiq+vL8OGDePo0aNl9ikqKmLcuHF4e3vj4uLC8OHDSUlJ0ahi7cycOZOOHTuWTqoUERHBb7/9Vvq6nKcrmzp1Kjqdjueee650m5wv1RtvvIFOpytza926denrcp7KOn36NKNHj8bb2xtHR0c6dOjA7t27S1/X8vd7vQ0jS5YsYcKECUyePJk9e/YQGhrKgAEDSE1N1bo0TeXn5xMaGsqMGTMu+/oHH3zA//73P2bNmsWOHTtwdnZmwIABFBUVVXOl2tuwYQPjxo1j+/btrF27FovFwi233EJ+fn7pPuPHj2fFihX8+OOPbNiwgaSkJO68804Nq9ZGw4YNmTp1KpGRkezevZv//Oc/DB06lEOHDgFynq5k165dfPnll3Ts2LHMdjlfF7Rr144zZ86U3jZv3lz6mpynC86ePUvPnj0xGo389ttvHD58mI8//hhPT8/SfTT9/a7UU927d1fGjRtX+txqtSqBgYHKlClTNKyqZgGUZcuWlT632WyKv7+/8uGHH5Zuy8rKUuzt7ZVFixZpUGHNkpqaqgDKhg0bFEVRz43RaFR+/PHH0n2io6MVQNm2bZtWZdYYnp6eyldffSXn6Qpyc3OVFi1aKGvXrlVuuukm5dlnn1UURf5eXWzy5MlKaGjoZV+T81TWSy+9pPTq1euKr2v9+71etoyYzWYiIyPp169f6Ta9Xk+/fv3Ytm2bhpXVbPHx8SQnJ5c5b+7u7oSHh8t5A7KzswHw8vICIDIyEovFUuZ8tW7dmkaNGtXr82W1Wlm8eDH5+flERETIebqCcePGMWjQoDLnBeTv1aWOHz9OYGAgTZs2ZdSoUSQkJAByni7166+/0rVrV+6++258fX3p3Lkzc+bMKX1d69/v9TKMpKenY7Va8fPzK7Pdz8+P5ORkjaqq+c6fGzlv/2Sz2Xjuuefo2bMn7du3B9TzZTKZ8PDwKLNvfT1fBw4cwMXFBXt7ex5//HGWLVtG27Zt5TxdxuLFi9mzZw9Tpkz5x2tyvi4IDw9n/vz5rFmzhpkzZxIfH0/v3r3Jzc2V83SJuLg4Zs6cSYsWLfj999954okneOaZZ1iwYAGg/e/3WrFqrxA13bhx4zh48GCZ/mpRVqtWrYiKiiI7O5ulS5cyZswYNmzYoHVZNU5iYiLPPvssa9euxcHBQetyarSBAweWPu7YsSPh4eE0btyYH374AUdHRw0rq3lsNhtdu3blvffeA6Bz584cPHiQWbNmMWbMGI2rq6ctIz4+PhgMhn+Mqk5JScHf31+jqmq+8+dGzltZTz31FCtXruTvv/+mYcOGpdv9/f0xm81kZWWV2b++ni+TyUTz5s0JCwtjypQphIaG8tlnn8l5ukRkZCSpqal06dIFOzs77Ozs2LBhA//73/+ws7PDz89PztcVeHh40LJlS2JiYuTv1SUCAgJo27ZtmW1t2rQp7dbS+vd7vQwjJpOJsLAw1q1bV7rNZrOxbt06IiIiNKysZgsJCcHf37/MecvJyWHHjh318rwpisJTTz3FsmXL+OuvvwgJCSnzelhYGEajscz5Onr0KAkJCfXyfF3KZrNRXFws5+kSN998MwcOHCAqKqr01rVrV0aNGlX6WM7X5eXl5REbG0tAQID8vbpEz549/zH1wLFjx2jcuDFQA36/V/kQ2Rpq8eLFir29vTJ//nzl8OHDymOPPaZ4eHgoycnJWpemqdzcXGXv3r3K3r17FUCZNm2asnfvXuXkyZOKoijK1KlTFQ8PD+WXX35R9u/frwwdOlQJCQlRCgsLNa68+j3xxBOKu7u7sn79euXMmTOlt4KCgtJ9Hn/8caVRo0bKX3/9pezevVuJiIhQIiIiNKxaGy+//LKyYcMGJT4+Xtm/f7/y8ssvKzqdTvnjjz8URZHzdC0XX02jKHK+znv++eeV9evXK/Hx8cqWLVuUfv36KT4+PkpqaqqiKHKeLrZz507Fzs5Oeffdd5Xjx48r33//veLk5KR89913pfto+fu93oYRRVGUzz//XGnUqJFiMpmU7t27K9u3b9e6JM39/fffCvCP25gxYxRFUS//mjRpkuLn56fY29srN998s3L06FFti9bI5c4ToMybN690n8LCQuXJJ59UPD09FScnJ+WOO+5Qzpw5o13RGnnooYeUxo0bKyaTSWnQoIFy8803lwYRRZHzdC2XhhE5X6oRI0YoAQEBislkUoKCgpQRI0YoMTExpa/LeSprxYoVSvv27RV7e3uldevWyuzZs8u8ruXvd52iKErVt78IIYQQQlxevRwzIoQQQoiaQ8KIEEIIITQlYUQIIYQQmpIwIoQQQghNSRgRQgghhKYkjAghhBBCUxJGhBBCCKEpCSNCCCGE0JSEESGEEEJoSsKIEEIIITQlYUQIIYQQmpIwIoQQQghN/T+UmAGSwnfQvwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameterization using dreal\n",
+    "\n",
+    "opts = {\n",
+    "        \"dreal_precision\": 1e-2,\n",
+    "        \"dreal_log_level\": \"info\",\n",
+    "        \"dreal_mcts\": True,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "# Configure these bounds (lower, upper) to configure the parameter space\n",
+    "parameter_bounds = {\"beta\": (0.12, 0.22), \"gamma\": (0.015, 0.035)}\n",
+    "\n",
+    "# Set these values for the initial state\n",
+    "initial_values = {\"S\": 0.99, \"I\": 0.01, \"R\": 0.0}\n",
+    "\n",
+    "# Infected constraint bounds\n",
+    "infected_bounds = (0.45, 0.55)\n",
+    "infected_timepoints = (45, 55)\n",
+    "\n",
+    "# Timepoints\n",
+    "step_size = 2\n",
+    "max_time = 60\n",
+    "\n",
+    "noise = 1e-4\n",
+    "\n",
+    "values= None\n",
+    "with Solver(name=\"dreal\",\n",
+    "            logic=QF_NRA,\n",
+    "            solver_options=opts\n",
+    "            ) as solver:\n",
+    "    values = run_solver(solver, encode(initial_values, parameter_bounds, infected_bounds, infected_timepoints, max_time=max_time, step_size=step_size, noise=noise, use_compartmental=True))\n",
+    "    pass\n",
+    "    \n",
+    "if values:\n",
+    "    ax, results = plot_results(values,  max_time, step_size)\n",
+    "    \n",
+    "    # f_pop = results.loc[float(max_time)][[\"S\", \"I\", \"R\"]].sum()\n",
+    "    # print(f\"Population at end: {f_pop}\")\n",
+    "    # s = list(results.S.values)\n",
+    "    # print([s[i]-s[i-1] for i in range(1, len(s))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving smtlib file: dan.smt2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save SMTLIB file\n",
+    "\n",
+    "# from funman.utils.smtlib_utils import smtlibscript_from_formula_list\n",
+    "\n",
+    "# formula = encode(initial_values, parameter_bounds, infected_bounds, infected_timepoints, max_time=max_time, step_size=step_size, noise=noise, use_compartmental=True)\n",
+    "# filename = \"dan.smt2\"\n",
+    "# with open(filename, \"w\") as f:\n",
+    "#     print(f\"Saving smtlib file: {filename}\")\n",
+    "#     smtlibscript_from_formula_list(\n",
+    "#         [formula],\n",
+    "#         logic=QF_NRA,\n",
+    "#     ).serialize(f, daggify=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eval_point(): beta = 0.12, gamma = 0.015\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.12, gamma = 0.02\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.12, gamma = 0.025\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.12, gamma = 0.030000000000000002\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.12, gamma = 0.035\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.145, gamma = 0.015\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.145, gamma = 0.02\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.145, gamma = 0.025\n",
+      "eval_point(): beta = 0.145, gamma = 0.030000000000000002\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.145, gamma = 0.035\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.16999999999999998, gamma = 0.015\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.16999999999999998, gamma = 0.02\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.16999999999999998, gamma = 0.025\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.16999999999999998, gamma = 0.030000000000000002\n",
+      "eval_point(): beta = 0.16999999999999998, gamma = 0.035\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.195, gamma = 0.015\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.195, gamma = 0.02\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.195, gamma = 0.025\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.195, gamma = 0.030000000000000002\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.195, gamma = 0.035\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.22, gamma = 0.015\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.22, gamma = 0.02\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.22, gamma = 0.025\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.22, gamma = 0.030000000000000002\n",
+      "Unsat\n",
+      "eval_point(): beta = 0.22, gamma = 0.035\n",
+      "Unsat\n",
+      "[{'beta': 0.12, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.02, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.025, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.030000000000000002, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.145, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.145, 'gamma': 0.02, 'assignment': '0'}, {'beta': 0.145, 'gamma': 0.025, 'assignment': '1'}, {'beta': 0.145, 'gamma': 0.030000000000000002, 'assignment': '0'}, {'beta': 0.145, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.16999999999999998, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.16999999999999998, 'gamma': 0.02, 'assignment': '0'}, {'beta': 0.16999999999999998, 'gamma': 0.025, 'assignment': '0'}, {'beta': 0.16999999999999998, 'gamma': 0.030000000000000002, 'assignment': '1'}, {'beta': 0.16999999999999998, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.195, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.195, 'gamma': 0.02, 'assignment': '0'}, {'beta': 0.195, 'gamma': 0.025, 'assignment': '0'}, {'beta': 0.195, 'gamma': 0.030000000000000002, 'assignment': '0'}, {'beta': 0.195, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.02, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.025, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.030000000000000002, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.035, 'assignment': '0'}]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameter Synthesis by points (similar to manual approach)\n",
+    "\n",
+    "def eval_point(beta_val, gamma_val, plot=False):\n",
+    "\n",
+    "    print(f\"eval_point(): beta = {beta_val}, gamma = {gamma_val}\")\n",
+    "\n",
+    "    # parameters\n",
+    "    def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n",
+    "    def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n",
+    "\n",
+    "    opts = {\n",
+    "            \"dreal_precision\": 1e-2,\n",
+    "            \"dreal_log_level\": \"info\",\n",
+    "            \"dreal_mcts\": True,\n",
+    "        }\n",
+    "\n",
+    "    # Set these values for the initial state\n",
+    "    initial_values = {\"S\": 0.99, \"I\": 0.01, \"R\": 0.0}\n",
+    "\n",
+    "    # Configure these bounds (lower, upper) to configure the parameter space\n",
+    "    parameter_bounds = {\"beta\": (beta_val, beta_val), \"gamma\": (gamma_val, gamma_val)}\n",
+    "\n",
+    "    # Infected constraint bounds\n",
+    "    infected_bounds = (0.45, 0.55)\n",
+    "    infected_timepoints = (45, 55)\n",
+    "\n",
+    "    # Timepoints\n",
+    "    step_size = 1\n",
+    "    max_time = 60\n",
+    "\n",
+    "    noise = 1e-4\n",
+    "\n",
+    "    values= None\n",
+    "    with Solver(name=\"dreal\",\n",
+    "                logic=QF_NRA,\n",
+    "                solver_options=opts\n",
+    "                ) as solver:\n",
+    "        values = run_solver(solver, encode(initial_values, parameter_bounds, infected_bounds, infected_timepoints, max_time=max_time, step_size=step_size, noise=noise, use_compartmental=True))\n",
+    "        pass\n",
+    "        \n",
+    "    if values and plot:\n",
+    "        ax, results = plot_results(values,  max_time, step_size)\n",
+    "\n",
+    "    param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'assignment': \"1\" if values else \"0\"} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\n",
+    "    return param_assignments\n",
+    "\n",
+    "\n",
+    "\n",
+    "# set parameters\n",
+    "def ps(param_synth_method, parameter_search_bounds, num_dim_points=10, plot=False):\n",
+    "    parameters = list(parameter_search_bounds.keys())\n",
+    "    parameter_points = {p : np.linspace(parameter_search_bounds[p][0], parameter_search_bounds[p][1], num_dim_points) for p in parameters}\n",
+    "    points = itertools.product(*[parameter_points[p] for p in parameters])\n",
+    "    param_choices_true_false = []\n",
+    "    for point in points:\n",
+    "        point_values = {p: point[i] for i, p in enumerate(parameters)}\n",
+    "\n",
+    "        param_assignments = eval_point(float(point_values[\"beta\"]), float(point_values[\"gamma\"]), plot=plot)\n",
+    "        param_choices_true_false.append(param_assignments)\n",
+    "        if param_synth_method == \"any\" and param_assignments['assignment'] == '1':\n",
+    "            return param_choices_true_false\n",
+    "    return param_choices_true_false\n",
+    "\n",
+    "\n",
+    "# Configure these bounds (lower, upper) to configure the parameter space\n",
+    "parameter_bounds = {\"beta\": (0.12, 0.22), \"gamma\": (0.015, 0.035)}\n",
+    "# parameter_bounds = {\"beta\": (0.17496597557264137, 0.17496597557264137), \"gamma\": (0.031891187313515444, 0.031891187313515444)}\n",
+    "\n",
+    "param_choices = ps(\"all\", parameter_bounds, num_dim_points=10)\n",
+    "print(param_choices)\n",
+    "sir_model.plot_two_params(\"gamma\", \"beta\", param_choices)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "funman_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scratch/evaluation/evaluation24/sir-funman.ipynb b/scratch/evaluation/evaluation24/sir-funman.ipynb
new file mode 100644
index 00000000..112d7aa0
--- /dev/null
+++ b/scratch/evaluation/evaluation24/sir-funman.ipynb
@@ -0,0 +1,656 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SAVED_RESULTS_DIR = \"./out\"\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_demo.example.pde import plot_spatial_timeseries\n",
+    "from IPython.display import HTML\n",
+    "import matplotlib.pyplot as plt\n",
+    "import itertools\n",
+    "from funman import FunmanWorkRequest, EncodingSchedule, POS_INFINITY\n",
+    "\n",
+    "import json\n",
+    "from funman.representation.constraint import LinearConstraint, ParameterConstraint, StateVariableConstraint\n",
+    "from funman.representation import Interval\n",
+    "from funman import FUNMANConfig\n",
+    "import logging\n",
+    "from funman_demo import summarize_results\n",
+    "\n",
+    "\n",
+    "\n",
+    "RESOURCES = \"../../../resources\"\n",
+    "\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"evaluation\")\n",
+    "EXAMPLE_DIR = os.path.join(RESOURCES, \"amr\", \"petrinet\", \"evaluation\")\n",
+    "MODEL_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR,\"sir.json\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Consistency (M3.1)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_consistency.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    funman_request.config.solver = \"z3\"\n",
+    "    funman_request.config.verbosity = 10\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Consistency\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            dump_results=False\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\",  parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Consistency (M3.2)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_consistency.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    \n",
+    "    funman_request.config.dreal_mcts = False\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Consistency\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            dump_results=False\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\",  parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Consistency (M3.3)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_consistency.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Consistency\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\",  parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Consistency (M3.4)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_consistency.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    funman_request.config.substitute_subformulas = True\n",
+    "    funman_request.config.taylor_series_order = 3\n",
+    "    funman_request.config.series_approximation_threshold = 1e-8\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Consistency\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\",  parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameterization (M3.1)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_parameterization.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "\n",
+    "    funman_request.config.solver = \"z3\"\n",
+    "    funman_request.config.verbosity = 10\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Parameterization\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\",  parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameterization (M3.2)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_parameterization.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    funman_request.config.dreal_mcts = False\n",
+    "\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Parameterization\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\",  parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameterization (M3.3)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_parameterization.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Parameterization\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\",  parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameterization (M3.4)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_parameterization.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    funman_request.config.substitute_subformulas = True\n",
+    "    funman_request.config.taylor_series_order = 3\n",
+    "    funman_request.config.series_approximation_threshold = 1e-8\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Parameterization\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\",  parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from funman import FunmanResults\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "from datetime import datetime\n",
+    "# Load FUNMAN output and plot the progress data\n",
+    "# data = results_unconstrained_point\n",
+    "with open(\"out/278ddfbc-6176-48a1-b232-ac471b56c20d.json\", \"r\") as f:\n",
+    "    data = FunmanResults.model_validate(json.loads(f.read()))\n",
+    "df = pd.DataFrame.from_records(data.timing.progress_timeseries, columns=[\"time\", \"progress\"])\n",
+    "df['duration'] = df.time.apply(lambda x: (x-df.time.iloc[0]).total_seconds()/60)\n",
+    "df = df[0:123]\n",
+    "ax = df.plot.scatter(x=\"duration\", y=\"progress\")\n",
+    "ax.set_xlabel(\"Time (m)\")\n",
+    "ax.set_ylabel(\"Normalized Parameter Space Coverage\")\n",
+    "ax.set_title(\"FUNMAN Parameter Space Coverage (SIR)\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameter Synthesis (M3.1), z3 not supported\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_param_synth.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    # funman_request.parameter(\"gamma\").interval.lb = 0.025\n",
+    "    # funman_request.parameter(\"gamma\").interval.ub = 0.03\n",
+    "    # funman_request.parameter(\"beta\").interval.lb = 0.13\n",
+    "    # funman_request.parameter(\"beta\").interval.ub = 0.16\n",
+    "\n",
+    "    # funman_request.config.substitute_subformulas = True\n",
+    "    # funman_request.config.taylor_series_order = 4\n",
+    "    # series_approximation_threshold = 1e-8\n",
+    "\n",
+    "    # funman_request.config.verbosity = 10\n",
+    "    funman_request.config.tolerance = 1e-1\n",
+    "    funman_request.config.compartmental_constraint_noise = 1e-10\n",
+    "    # funman_request.config.solver_timeout = 10\n",
+    "    # funman_request.config.uniform_box_splits = True\n",
+    "    # funman_request.config.num_initial_boxes = 1\n",
+    "    # funman_request.config.save_smtlib = \"./out\"\n",
+    "    # funman_request.config.dreal_log_level = \"info\"\n",
+    "    # funman_request.config.dreal_precision = 1e-1\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Parameter Synthesis\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            dump_plot=True,\n",
+    "            parameters_to_plot=to_synthesize+[\"timestep\"],\n",
+    "            dump_results=True\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\", parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameter Synthesis (M3.2)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_param_synth.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    # funman_request.parameter(\"gamma\").interval.lb = 0.025\n",
+    "    # funman_request.parameter(\"gamma\").interval.ub = 0.03\n",
+    "    # funman_request.parameter(\"beta\").interval.lb = 0.13\n",
+    "    # funman_request.parameter(\"beta\").interval.ub = 0.16\n",
+    "\n",
+    "    # funman_request.config.substitute_subformulas = True\n",
+    "    # funman_request.config.taylor_series_order = 4\n",
+    "    # series_approximation_threshold = 1e-8\n",
+    "\n",
+    "    # funman_request.config.verbosity = 10\n",
+    "    funman_request.config.tolerance = 1e-1\n",
+    "    funman_request.config.compartmental_constraint_noise = 1e-10\n",
+    "    # funman_request.config.solver_timeout = 10\n",
+    "    # funman_request.config.uniform_box_splits = True\n",
+    "    # funman_request.config.num_initial_boxes = 1\n",
+    "    # funman_request.config.save_smtlib = \"./out\"\n",
+    "    # funman_request.config.dreal_log_level = \"info\"\n",
+    "    # funman_request.config.dreal_precision = 1e-1\n",
+    "\n",
+    "    funman_request.config.dreal_mcts = False\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Parameter Synthesis\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            dump_plot=True,\n",
+    "            parameters_to_plot=to_synthesize+[\"timestep\"],\n",
+    "            dump_results=True\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\", parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameter Synthesis (M3.3)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_param_synth.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    # funman_request.parameter(\"gamma\").interval.lb = 0.025\n",
+    "    # funman_request.parameter(\"gamma\").interval.ub = 0.03\n",
+    "    # funman_request.parameter(\"beta\").interval.lb = 0.13\n",
+    "    # funman_request.parameter(\"beta\").interval.ub = 0.16\n",
+    "\n",
+    "    # funman_request.config.substitute_subformulas = True\n",
+    "    # funman_request.config.taylor_series_order = 4\n",
+    "    # series_approximation_threshold = 1e-8\n",
+    "\n",
+    "    # funman_request.config.verbosity = 10\n",
+    "    funman_request.config.tolerance = 1e-1\n",
+    "    funman_request.config.compartmental_constraint_noise = 1e-10\n",
+    "    # funman_request.config.solver_timeout = 10\n",
+    "    # funman_request.config.uniform_box_splits = True\n",
+    "    # funman_request.config.num_initial_boxes = 1\n",
+    "    # funman_request.config.save_smtlib = \"./out\"\n",
+    "    # funman_request.config.dreal_log_level = \"info\"\n",
+    "    # funman_request.config.dreal_precision = 1e-1\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Parameter Synthesis\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            dump_plot=True,\n",
+    "            parameters_to_plot=to_synthesize+[\"timestep\"],\n",
+    "            dump_results=True\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\", parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[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",
+      "[11.00000, 11.00000) has equal lower and upper bounds, so assuming the upper bound is closed.  (I.e., [lb, ub) is actually [lb, ub])\n",
+      "[11.00000, 11.00000) has equal lower and upper bounds, so assuming the upper bound is closed.  (I.e., [lb, ub) is actually [lb, ub])\n",
+      "[11.00000, 11.00000) 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",
+      "2024-05-04 21:14:40,723 - funman.server.worker - INFO - FunmanWorker running...\n",
+      "2024-05-04 21:14:40,737 - funman.server.worker - INFO - Starting work on: 71e84318-77ae-4b2c-a7ad-9152427eaf9d\n",
+      "2024-05-04 21:14:42,803 - funman.api.run - INFO - Dumping results to ./out/71e84318-77ae-4b2c-a7ad-9152427eaf9d.json\n",
+      "2024-05-04 21:14:52,883 - funman.api.run - INFO - Dumping results to ./out/71e84318-77ae-4b2c-a7ad-9152427eaf9d.json\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"
+     ]
+    }
+   ],
+   "source": [
+    "# Parameter Synthesis (M3.4)\n",
+    "\n",
+    "STATES = [\"S\", \"I\", \"R\"]\n",
+    "COMPARTMENTS = STATES\n",
+    "MAX_TIME=40\n",
+    "STEP_SIZE=5\n",
+    "to_synthesize=[\"beta\", \"gamma\"]\n",
+    "\n",
+    "REQUEST_PATH = os.path.join(\n",
+    "    EXAMPLE_DIR, \"sir_request_param_synth.json\"\n",
+    ")\n",
+    "\n",
+    "with open(REQUEST_PATH, \"r\") as request:\n",
+    "    funman_request = FunmanWorkRequest.model_validate(json.load(request))\n",
+    " \n",
+    "    funman_request.structure_parameters[0].interval.lb = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[0].interval.ub = int(MAX_TIME/STEP_SIZE)\n",
+    "    funman_request.structure_parameters[1].interval.lb = STEP_SIZE\n",
+    "    funman_request.structure_parameters[1].interval.ub = STEP_SIZE\n",
+    "\n",
+    "    # funman_request.parameter(\"gamma\").interval.lb = 0.025\n",
+    "    # funman_request.parameter(\"gamma\").interval.ub = 0.03\n",
+    "    # funman_request.parameter(\"beta\").interval.lb = 0.13\n",
+    "    # funman_request.parameter(\"beta\").interval.ub = 0.16\n",
+    "\n",
+    "    # funman_request.config.substitute_subformulas = True\n",
+    "    # funman_request.config.taylor_series_order = 4\n",
+    "    # series_approximation_threshold = 1e-8\n",
+    "\n",
+    "    # funman_request.config.verbosity = 10\n",
+    "    funman_request.config.tolerance = 1e-1\n",
+    "    funman_request.config.compartmental_constraint_noise = 1e-10\n",
+    "    # funman_request.config.solver_timeout = 10\n",
+    "    # funman_request.config.uniform_box_splits = True\n",
+    "    # funman_request.config.num_initial_boxes = 1\n",
+    "    # funman_request.config.save_smtlib = \"./out\"\n",
+    "    # funman_request.config.dreal_log_level = \"info\"\n",
+    "    \n",
+    "    funman_request.config.substitute_subformulas = True\n",
+    "    funman_request.config.taylor_series_order = 4\n",
+    "    funman_request.config.series_approximation_threshold = 1e-10\n",
+    "\n",
+    "\n",
+    "    results_unconstrained_point = Runner().run(\n",
+    "            MODEL_PATH,\n",
+    "            funman_request,\n",
+    "            description=\"SIR Parameter Synthesis\",\n",
+    "            case_out_dir=SAVED_RESULTS_DIR,\n",
+    "            dump_plot=True,\n",
+    "            parameters_to_plot=to_synthesize+[\"timestep\"],\n",
+    "            dump_results=True\n",
+    "    )\n",
+    "    summary = summarize_results(COMPARTMENTS, results_unconstrained_point, ylabel=\"Population\", parameters_to_plot=to_synthesize+[\"timestep\"], synthesized_parameters=to_synthesize+[\"timestep\"])\n",
+    "    print(summary)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "funman_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scratch/evaluation/evaluation24/sir-manual.ipynb b/scratch/evaluation/evaluation24/sir-manual.ipynb
new file mode 100644
index 00000000..74375b19
--- /dev/null
+++ b/scratch/evaluation/evaluation24/sir-manual.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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\n",
+    "import itertools\n",
+    "import datetime\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def query_1(sim, tstart, tend, dt):\n",
+    "    S, I, R = sim.T\n",
+    "    # USER: write query condition.\n",
+    "    # query_condition =  0.15 <= max(I) <= 0.3  #((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "    # query_condition = (0.45 <= max(I)) and (max(I) <= 0.55) and (45 <= np.argmax(I)*dt) and (np.argmax(I)*dt <= 55)\n",
+    "    query_values = [0.45 <= I[int(t/dt)] <= 0.55 for t in range(tstart, tend+1, dt) if 45 <= t < 55]\n",
+    "    query_condition = all(query_values) \n",
+    "    # print(I[9:11])\n",
+    "    return query_condition\n",
+    "\n",
+    "\n",
+    "def query_2(sim, tstart, tend, dt, beta_val, gamma_val):\n",
+    "    S, I, R = sim.T\n",
+    "    # USER: write query condition.\n",
+    "    # query_condition =  0.15 <= max(I) <= 0.3  #((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "    # query_condition = (0.45 <= max(I)) and (max(I) <= 0.55) and (45 <= np.argmax(I)*dt) and (np.argmax(I)*dt <= 55)\n",
+    "    \n",
+    "    inc_I = [(I[(int(t/dt))]-I[(int((t-1)/dt))]>0.0 if t > 0 else True  ) for t in range(tstart, tend+1, dt) if t <= 30]\n",
+    "    dec_I = [(I[(int(t/dt))]-I[(int((t-1)/dt))]<0.0 if t > 0 else True  ) for t in range(tstart, tend+1, dt) if t >= 40]\n",
+    "\n",
+    "    i_query_values = [0.2 <= I[int(t/dt)] <= 0.4 for t in range(tstart, tend+1, dt) if 35 <= t <= 50]\n",
+    "    query_condition = all(i_query_values) #and beta_val > 2 * gamma_val and beta_val < 2*gamma_val +0.1 #and all(inc_I) and all(dec_I)\n",
+    "    # print(I[9:11])\n",
+    "    return query_condition\n",
+    "\n",
+    "def eval_point(beta_val, gamma_val, query_condition=query_1, plot=False, rtol=1, atol=1, mxstep=10, mxordn = 1, mxords=1, hmin=1):\n",
+    "    # parameters\n",
+    "    def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n",
+    "    def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n",
+    "\n",
+    "\n",
+    "    # USER: set initial conditions\n",
+    "    I0, R0 = 0.01, 0\n",
+    "    S0 = 1-I0-R0\n",
+    "    y0 = S0, I0, R0 # Initial conditions vector\n",
+    "    # USER: set simulation parameters\n",
+    "    dt = 1\n",
+    "    tstart = 0\n",
+    "    tend = 60\n",
+    "    tvect = np.arange(tstart, tend+1, dt)\n",
+    "    # simulate/solve ODEs\n",
+    "    sim = odeint(sir_model.SIR_model, y0, tvect, args=(beta, gamma), mxstep=mxstep, rtol=rtol, atol=atol)\n",
+    "    S, I, R = sim.T\n",
+    "\n",
+    "    # print(list(zip(range(tstart, tend+1, dt), I)))\n",
+    "\n",
+    "    # plot results - uncomment next line to plot time series.  not recommended for large numbers of points\n",
+    "    if plot:\n",
+    "        sir_model.plotSIR(tvect, S, I, R)\n",
+    "\n",
+    "    query = '1' if query_condition(sim, tstart, tend, dt, beta_val, gamma_val) else '0'\n",
+    "    param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\n",
+    "    return param_assignments, sim\n",
+    "\n",
+    "\n",
+    "\n",
+    "# set parameters\n",
+    "def ps(param_synth_method, parameter_search_bounds, query_condition=query_1, rtol=1e-3, num_dim_points=10, plot=False):\n",
+    "    parameters = list(parameter_search_bounds.keys())\n",
+    "    parameter_points = {p : np.linspace(parameter_search_bounds[p][0], parameter_search_bounds[p][1], num_dim_points) for p in parameters}\n",
+    "    points = itertools.product(*[parameter_points[p] for p in parameters])\n",
+    "    param_choices_true_false = []\n",
+    "    coverage = []\n",
+    "    \n",
+    "    for i, point in enumerate(points):\n",
+    "        point_values = {p: point[i] for i, p in enumerate(parameters)}\n",
+    "        \n",
+    "        param_assignments, _ = eval_point(point_values[\"beta\"], point_values[\"gamma\"], query_condition=query_condition, rtol=rtol, plot=plot)\n",
+    "        param_choices_true_false.append(param_assignments)\n",
+    "        coverage.append((datetime.datetime.now(), i))\n",
+    "        \n",
+    "        if param_synth_method == \"any\" and param_assignments['assignment'] == '1':\n",
+    "            return param_choices_true_false, coverage\n",
+    "    return param_choices_true_false, coverage\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Consistency Query\n",
+    "\n",
+    "# USER: set num_dim_points with how many points for each parameter you'd like to evaluate\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.186734693877551, 0.186734693877551]\n",
+    "gamma_search_bounds = [0.030510204081632655, 0.030510204081632655]\n",
+    "\n",
+    "param_choices_true_false, coverage = ps(\"any\", {\"beta\": beta_search_bounds, \"gamma\": gamma_search_bounds}, query_condition=query_2, rtol=1e-3, num_dim_points=1, plot=True)\n",
+    "print(param_choices_true_false)\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sir_query_auto_2_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)\n",
+    "\n",
+    "\n",
+    "# Plot \"true/false\" points.  Is there any way of characterizing these regions?\n",
+    "# sir_model.plot_two_params(\"gamma\", \"beta\", param_choices_true_false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameterization\n",
+    "\n",
+    "# USER: set num_dim_points with how many points for each parameter you'd like to evaluate\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.19,0.21]\n",
+    "gamma_search_bounds = [0.06,0.08]\n",
+    "\n",
+    "num_dim_points = 10\n",
+    "\n",
+    "param_choices_true_false, coverage = ps(\"any\", {\"beta\": beta_search_bounds, \"gamma\": gamma_search_bounds}, query_condition=query_2, rtol=1e-3, num_dim_points=num_dim_points)\n",
+    "# print(param_choices_true_false)\n",
+    "\n",
+    "# id_number = randint(10**5, 10**6 - 1)\n",
+    "# with open(f'sir_query_auto_2_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "#     json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)\n",
+    "\n",
+    "solutions = [p for p in param_choices_true_false if p['assignment']==\"1\"]\n",
+    "if len(solutions) > 0:\n",
+    "    my_params = solutions[0]\n",
+    "    print(my_params)\n",
+    "    param_assignment, sim = eval_point(my_params[\"beta\"], my_params[\"gamma\"], plot=True, mxstep=20, query_condition=query_2)\n",
+    "else:\n",
+    "    print(\"No solutions!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameter Synthesis\n",
+    "\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.19,0.21]\n",
+    "gamma_search_bounds = [0.06,0.08]\n",
+    "\n",
+    "# USER: set num_dim_points with how many points for each parameter you'd like to evaluate\n",
+    "num_dim_points = 10\n",
+    "\n",
+    "# beta_search_bounds = [0.13,0.16]\n",
+    "# gamma_search_bounds = [0.025,0.03]\n",
+    "\n",
+    "param_choices_true_false, coverage = ps(\"all\", {\"beta\": beta_search_bounds, \"gamma\": gamma_search_bounds}, query_condition=query_2, rtol=1e-3, num_dim_points=num_dim_points)\n",
+    "# print(param_choices_true_false)\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sir_query_auto_2_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)\n",
+    "\n",
+    "\n",
+    "# Plot \"true/false\" points.  Is there any way of characterizing these regions?\n",
+    "sir_model.plot_two_params(\"gamma\", \"beta\", param_choices_true_false)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# coverage timeseries \n",
+    "\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "df = pd.DataFrame.from_records(coverage, columns=[\"time\", \"point id\"])\n",
+    "df.time = pd.to_datetime(df.time)\n",
+    "df.plot.scatter(x=\"time\", y=\"point id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "my_params = [p for p in param_choices_true_false if p['assignment']==\"1\"][-1]\n",
+    "param_assignment, sim = eval_point(my_params[\"beta\"], my_params[\"gamma\"], plot=True, mxstep=20, query_condition=query_1)\n",
+    "# param_assignment, sim = eval_point(0.25, 0.027755102040816326, plot=True, rtol=1e-10)\n",
+    "S, I, R = sim.T\n",
+    "\n",
+    "print(list(enumerate(S))[10])\n",
+    "# print([S[i]-S[i-1] for i in range(1, len(S))])\n",
+    "print(my_params)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "sir_model.plot_two_params(\"gamma\", \"beta\", param_choices_true_false)\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "funman_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scratch/evaluation/evaluation24/sir_model.py b/scratch/evaluation/evaluation24/sir_model.py
new file mode 100644
index 00000000..11770710
--- /dev/null
+++ b/scratch/evaluation/evaluation24/sir_model.py
@@ -0,0 +1,170 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy
+from scipy.integrate import odeint
+
+
+# simulate SIR model - normalized (populations S,I,R add to 1)
+def SIR_model(y, t, beta, gamma):
+    S, I, R = y
+    dSdt = -S * (beta(t) * I)
+    dIdt = S * (beta(t) * I) - gamma(t) * I
+    dRdt = gamma(t) * I
+
+    return dSdt, dIdt, dRdt
+
+
+#  plot SIR model
+def plotSIR(t, S, I, R):
+    f, ax = plt.subplots(1, 1, figsize=(10, 4))
+    ax.plot(t, S, "b", alpha=0.7, linewidth=2, label="Susceptible")
+    ax.plot(t, I, "r", alpha=0.7, linewidth=2, label="Infected")
+    ax.plot(t, R, "r--", alpha=0.7, linewidth=2, label="Recovered")
+
+    ax.set_xlabel("Time (days)")
+    ax.set_ylabel("Fraction of population")
+
+    ax.yaxis.set_tick_params(length=0)
+    ax.xaxis.set_tick_params(length=0)
+    legend = ax.legend()
+    legend.get_frame().set_alpha(0.5)
+    plt.show()
+
+
+# simulate SIDARTHE model - normalized (populations S,I,R add to 1)
+def SIDARTHE_model(
+    y,
+    t,
+    alpha,
+    beta,
+    gamma,
+    delta,
+    epsilon,
+    mu,
+    zeta,
+    lamb,
+    eta,
+    rho,
+    theta,
+    kappa,
+    nu,
+    xi,
+    sigma,
+    tau,
+):
+    S, I, D, A, R, T, H, E = y
+    dSdt = -S * (alpha(t) * I + beta(t) * D + gamma(t) * A + delta(t) * R)
+    dIdt = (
+        S * (alpha(t) * I + beta(t) * D + gamma(t) * A + delta(t) * R)
+        - (epsilon(t) + zeta(t) + lamb(t)) * I
+    )
+    dDdt = epsilon(t) * I - (eta(t) + rho(t)) * D
+    dAdt = zeta(t) * I - (theta(t) + mu(t) + kappa(t)) * A
+    dRdt = eta(t) * D + theta(t) * A - (nu(t) + xi(t)) * R
+    dTdt = mu(t) * A + nu(t) * R - (sigma(t) + tau(t)) * T
+    dHdt = lamb(t) * I + rho(t) * D + kappa(t) * A + xi(t) * R + sigma(t) * T
+    dEdt = tau(t) * T
+
+    return dSdt, dIdt, dDdt, dAdt, dRdt, dTdt, dHdt, dEdt
+
+
+#  plot SIDARTHE model
+def plotSIDARTHE(t, S, I, D, A, R, T, H, E):
+    f, ax = plt.subplots(1, 1, figsize=(10, 4))
+    ax.plot(t, S, "b", alpha=0.7, linewidth=2, label="Susceptible")
+    ax.plot(
+        t,
+        I,
+        "r",
+        alpha=0.7,
+        linewidth=2,
+        label="Infected (Asymptomatic, Infected, Undetected)",
+    )
+    ax.plot(
+        t,
+        D,
+        "r.",
+        alpha=0.7,
+        linewidth=2,
+        label="Diagnosed (Asymptomatic, Infected, Detected)",
+    )
+    ax.plot(
+        t,
+        A,
+        "r:",
+        alpha=0.7,
+        linewidth=2,
+        label="Ailing (Symptomatic, Infected, Undetected)",
+    )
+    ax.plot(
+        t,
+        R,
+        "r--",
+        alpha=0.7,
+        linewidth=2,
+        label="Recognized (Symptomatic, Infected, Detected)",
+    )
+    ax.plot(
+        t,
+        T,
+        "r-.",
+        alpha=0.7,
+        linewidth=2,
+        label="Threatened (Acutely Symptomatic)",
+    )
+    ax.plot(t, H, "g", alpha=0.7, linewidth=2, label="Healed")
+    ax.plot(t, E, "k", alpha=0.7, linewidth=2, label="Extinct (Dead)")
+
+    ax.set_xlabel("Time (days)")
+    ax.set_ylabel("Fraction of population")
+
+    ax.yaxis.set_tick_params(length=0)
+    ax.xaxis.set_tick_params(length=0)
+    legend = ax.legend()
+    legend.get_frame().set_alpha(0.5)
+    plt.show()
+
+
+def SIHDR_model(y, t, beta, ae, bf, cg, dh, N):
+    S, I, H, D, R = y
+    dSdt = -(beta(t) * S * I / N(t))
+    dIdt = (beta(t) * S * I / N(t)) - ae(t) * I - bf(t) * I
+    dHdt = bf(t) * I - cg(t) * H - dh(t) * H
+    dDdt = dh(t) * H
+    dRdt = ae(t) * I + cg(t) * H
+
+    return dSdt, dIdt, dHdt, dDdt, dRdt
+
+
+#  plot SIHDR model
+def plotSIHDR(t, S, I, H, D, R):
+    f, ax = plt.subplots(1, 1, figsize=(10, 4))
+    ax.plot(t, S, "b", alpha=0.7, linewidth=2, label="Susceptible")
+    ax.plot(t, I, "r", alpha=0.7, linewidth=2, label="Infected")
+    ax.plot(t, H, "g", alpha=0.7, linewidth=2, label="Hospitalized")
+    ax.plot(t, D, "r.", alpha=0.7, linewidth=2, label="Dead")
+    ax.plot(t, R, "r--", alpha=0.7, linewidth=2, label="Recovered")
+
+    ax.set_xlabel("Time (days)")
+    ax.set_ylabel("Fraction of population")
+
+    ax.yaxis.set_tick_params(length=0)
+    ax.xaxis.set_tick_params(length=0)
+    legend = ax.legend()
+    legend.get_frame().set_alpha(0.5)
+    plt.show()
+
+
+def plot_two_params(param1, param2, param_choices_true_false):
+    xsample = [result[param1] for result in param_choices_true_false]
+    ysample = [result[param2] for result in param_choices_true_false]
+    colors = [
+        "green" if result["assignment"] == "1" else "red"
+        for result in param_choices_true_false
+    ]
+    plt.title("Parameter Value Pairings")
+    plt.xlabel(param1)
+    plt.ylabel(param2)
+    area = 5  # size of marker
+    plt.scatter(xsample, ysample, s=area, c=colors, alpha=0.5)
+    plt.show()
diff --git a/scratch/hackathon/hackathon23_s1.py b/scratch/hackathon/hackathon23_s1.py
index c5632ef2..cc449c73 100644
--- a/scratch/hackathon/hackathon23_s1.py
+++ b/scratch/hackathon/hackathon23_s1.py
@@ -418,7 +418,7 @@ def unit_test_1_query(self, steps, init_values):
         query._formula = And(
             [
                 LT(Symbol(f"S_{steps}", REAL), Real(init_values["S"])),
-                GE(Symbol(f"R_{steps}", REAL), Real(init_values["R"]))
+                GE(Symbol(f"R_{steps}", REAL), Real(init_values["R"])),
                 # GT(
                 #     Symbol(f"R_{steps}", REAL), Real(5.0)
                 # ),  # R is near 10 at day 80
diff --git a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py
index 89dce5cc..0936bb48 100644
--- a/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py
+++ b/scratch/hackathon/hackathon_fall_2023_demo_terarrium.py
@@ -27,7 +27,7 @@ def main():
                     "lb": 0.011,
                     "ub": 0.011,
                     "closed_upper_bound": True,
-                }
+                },
                 # "interval": {"lb": 0.008799999999999999, "ub": 0.0132},
             },
             {
@@ -36,7 +36,7 @@ def main():
                     "lb": 0.456,
                     "ub": 0.456,
                     "closed_upper_bound": True,
-                }
+                },
                 #  "interval": {"lb": 0.3648, "ub": 0.5472}
             },
             {
@@ -45,7 +45,7 @@ def main():
                     "lb": 0.011,
                     "ub": 0.011,
                     "closed_upper_bound": True,
-                }
+                },
                 # "interval": {"lb": 0.008799999999999999, "ub": 0.0132},
             },
             {
@@ -54,7 +54,7 @@ def main():
                     "lb": 0.57,
                     "ub": 0.57,
                     "closed_upper_bound": True,
-                }
+                },
                 # "interval": {
                 #     "lb": 0.45599999999999996,
                 #     "ub": 0.6839999999999999,
@@ -64,7 +64,7 @@ def main():
                 "name": "epsilon",
                 "interval": {
                     "lb": 0.1368,
-                    "ub": 0.20520000000000002
+                    "ub": 0.20520000000000002,
                     # "ub": 0.18,
                 },
                 "label": "all",
@@ -75,7 +75,7 @@ def main():
                     "lb": 0.125,
                     "ub": 0.125,
                     "closed_upper_bound": True,
-                }
+                },
                 #  "interval": {"lb": 0.1, "ub": 0.15}
             },
             {
@@ -84,7 +84,7 @@ def main():
                     "lb": 0.034,
                     "ub": 0.034,
                     "closed_upper_bound": True,
-                }
+                },
                 # "interval": {"lb": 0.027200000000000002, "ub": 0.0408},
             },
             {
@@ -93,7 +93,7 @@ def main():
                     "lb": 0.125,
                     "ub": 0.125,
                     "closed_upper_bound": True,
-                }
+                },
                 #  "interval": {"lb": 0.1, "ub": 0.15}
             },
             {
@@ -102,14 +102,14 @@ def main():
                     "lb": 0.034,
                     "ub": 0.034,
                     "closed_upper_bound": True,
-                }
+                },
                 # "interval": {"lb": 0.027200000000000002, "ub": 0.0408},
             },
             {
                 "name": "theta",
                 "interval": {
                     "lb": 0.2968,
-                    "ub": 0.4452
+                    "ub": 0.4452,
                     # "ub": 0.4,
                 },
                 "label": "all",
@@ -120,7 +120,7 @@ def main():
                     "lb": 0.017,
                     "ub": 0.017,
                     "closed_upper_bound": True,
-                }
+                },
                 # "interval": {"lb": 0.013600000000000001, "ub": 0.0204},
             },
             {
@@ -129,7 +129,7 @@ def main():
                     "lb": 0.017,
                     "ub": 0.017,
                     "closed_upper_bound": True,
-                }
+                },
                 # "interval": {"lb": 0.013600000000000001, "ub": 0.0204},
             },
             {
@@ -138,7 +138,7 @@ def main():
                     "lb": 0.027,
                     "ub": 0.027,
                     "closed_upper_bound": True,
-                }
+                },
                 #   "interval": {"lb": 0.0216, "ub": 0.0324}
             },
             {
@@ -147,7 +147,7 @@ def main():
                     "lb": 0.017,
                     "ub": 0.017,
                     "closed_upper_bound": True,
-                }
+                },
                 # "interval": {"lb": 0.013600000000000001, "ub": 0.0204},
             },
             {
@@ -156,7 +156,7 @@ def main():
                     "lb": 0.01,
                     "ub": 0.01,
                     "closed_upper_bound": True,
-                }
+                },
                 #  "interval": {"lb": 0.008, "ub": 0.012}
             },
             {
@@ -165,7 +165,7 @@ def main():
                     "lb": 0.017,
                     "ub": 0.017,
                     "closed_upper_bound": True,
-                }
+                },
                 # "interval": {"lb": 0.013600000000000001, "ub": 0.0204},
             },
         ],
diff --git a/scratch/notebooks/2022-09-14-demo.ipynb b/scratch/notebooks/2022-09-14-demo.ipynb
index 4ec593ba..7f71a512 100644
--- a/scratch/notebooks/2022-09-14-demo.ipynb
+++ b/scratch/notebooks/2022-09-14-demo.ipynb
@@ -88,7 +88,7 @@
     "ps_eq = ParameterSpace.construct_all_equal(ps_b1_b2_b3)\n",
     "# intersect the original parameter space with the ps_eq to get the\n",
     "# valid parameter space where (b1 == b2 == b3)\n",
-    "ps = ParameterSpace.intersect(ps_b1_b2_b3, ps_eq)\n",
+    "ps = ParameterSpace.intersection(ps_b1_b2_b3, ps_eq)\n",
     "# assert the parameters spaces for the original and the constant beta\n",
     "# variant are the same\n",
     "#assert(ParameterSpace.compare(ps_bc, ps))"
diff --git a/scratch/notebooks/mira_epi_eval_sc3_seir.ipynb b/scratch/notebooks/mira_epi_eval_sc3_seir.ipynb
new file mode 100644
index 00000000..dd31a2da
--- /dev/null
+++ b/scratch/notebooks/mira_epi_eval_sc3_seir.ipynb
@@ -0,0 +1,520 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "14d9fa23-f229-4499-889f-8b84bf790223",
+   "metadata": {},
+   "source": [
+    "# Scenario 3: Causal Reasoning with Interventions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80f64161-69df-41c6-a014-3dcd1afbdcb3",
+   "metadata": {},
+   "source": [
+    "### Load MIRA dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "efd146e7-0742-4e39-a374-39fe931df029",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "import sympy\n",
+    "\n",
+    "from mira.metamodel import *\n",
+    "from mira.examples.concepts import susceptible, exposed, infected, recovered\n",
+    "from mira.modeling import Model\n",
+    "from mira.modeling.amr.petrinet import AMRPetriNetModel, template_model_to_petrinet_json\n",
+    "from mira.sources.amr.petrinet import template_model_from_amr_json\n",
+    "from mira.metamodel.io import model_to_json_file, model_from_json_file\n",
+    "\n",
+    "from mira.modeling.ode import OdeModel, simulate_ode_model\n",
+    "import numpy\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82328f7c-76da-4eb1-8241-fe0e23b58683",
+   "metadata": {},
+   "source": [
+    "## Build SEIR model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f42f426-67a8-4ff8-87ae-22dc95e2bbbe",
+   "metadata": {},
+   "source": [
+    "### Define units"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f8d81322-226a-4676-a0b8-923e7220e10d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "person_units = lambda: Unit(expression=sympy.Symbol('person'))\n",
+    "day_units = lambda: Unit(expression=sympy.Symbol('day'))\n",
+    "per_day_units = lambda: Unit(expression=1/sympy.Symbol('day'))\n",
+    "dimensionless_units = lambda: Unit(expression=sympy.Integer('1'))\n",
+    "per_day_per_person_units = lambda: Unit(expression=1/(sympy.Symbol('day')*sympy.Symbol('person')))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6900e964-edb2-4427-bab1-d2c9dcdabc82",
+   "metadata": {},
+   "source": [
+    "# Define and stratify model concepts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5e2c1d9f-51a9-46a4-aa06-5aa862b8b081",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_susceptible = Concept(name='S', units=person_units(), identifiers={'ido': '0000514'})\n",
+    "_exposed = Concept(name='E', units=person_units(), identifiers={'apollosv': '00000154'})\n",
+    "_infected = Concept(name='I', units=person_units(), identifiers={'ido': '0000511'})\n",
+    "_recovered = Concept(name='R', units=person_units(), identifiers={'ido': '0000592'})\n",
+    "\n",
+    "c = {\n",
+    "    'S_y': _susceptible.with_context(status=\"young\"),\n",
+    "    'S_m': _susceptible.with_context(status=\"middle\"),\n",
+    "    'S_o': _susceptible.with_context(status=\"old\"),\n",
+    "    'E_y': _exposed.with_context(status=\"young\"),\n",
+    "    'E_m': _exposed.with_context(status=\"middle\"),\n",
+    "    'E_o': _exposed.with_context(status=\"old\"),\n",
+    "    'I_y': _infected.with_context(status=\"young\"),\n",
+    "    'I_m': _infected.with_context(status=\"middle\"),\n",
+    "    'I_o': _infected.with_context(status=\"old\"),\n",
+    "    'R_y': _recovered.with_context(status=\"young\"),\n",
+    "    'R_m': _recovered.with_context(status=\"middle\"),\n",
+    "    'R_o': _recovered.with_context(status=\"old\"),\n",
+    "}\n",
+    "\n",
+    "for concept in c:\n",
+    "    c[concept].name = concept"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4792ddd4-f7bd-43e5-8cc7-a729573b39eb",
+   "metadata": {},
+   "source": [
+    "### Define parameters with uncertainty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b4774d31-1ad4-4027-a82b-6bee06907e03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To add a small distribution around parameters (for Terarium)\n",
+    "def unif_distribution_create(value: float):\n",
+    "    return Distribution(\n",
+    "        type=\"StandardUniform1\",\n",
+    "        parameters={\"minimum\": value, \"maximum\": value + max(0.01*value,0.1)},\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a902e806-0dad-4bf6-a264-0172324257b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = {\n",
+    "    'beta': Parameter(name='beta', value=sympy.Float(0.2), units=per_day_units(),\n",
+    "                      distribution=Distribution(type='StandardUniform1',\n",
+    "                                                parameters={'minimum': 0.05,\n",
+    "                                                            'maximum': 0.8})),  # Infection rate\n",
+    "    'N': Parameter(name='N', value=sympy.Float(300.0), units=person_units()),  # Total population\n",
+    "    'mew': Parameter(name='mew', value=sympy.Float(0.0), units=per_day_units()),  # Mask efficacy\n",
+    "    'mcw': Parameter(name='mcw', value=sympy.Float(0.0), units=per_day_units()),  # Mask compliance\n",
+    "    'r_EI': Parameter(name='r_EI', value=sympy.Float(0.08), units=per_day_units()),  # Rate of progressing E -> I\n",
+    "    'r_IR': Parameter(name='r_IR', value=sympy.Float(0.06), units=per_day_units()),  # Rate of progressing I -> R\n",
+    "    'Myy': Parameter(name='Myy', value=sympy.Float(38.62), units=per_day_units()),  # Contact rate young -> young\n",
+    "    'Mym': Parameter(name='Mym', value=sympy.Float(20.56), units=per_day_units()),  # Contact rate young -> middle\n",
+    "    'Myo': Parameter(name='Myo', value=sympy.Float(6.12), units=per_day_units()),  # Contact rate young -> old\n",
+    "    'Mmy': Parameter(name='Mmy', value=sympy.Float(20.56), units=per_day_units()),  # Contact rate middle -> young\n",
+    "    'Mmm': Parameter(name='Mmm', value=sympy.Float(28.22), units=per_day_units()),  # Contact rate middle -> middle\n",
+    "    'Mmo': Parameter(name='Mmo', value=sympy.Float(11.6), units=per_day_units()),  # Contact rate middle -> old\n",
+    "    'Moy': Parameter(name='Moy', value=sympy.Float(6.12), units=per_day_units()),  # Contact rate old -> young\n",
+    "    'Mom': Parameter(name='Mom', value=sympy.Float(11.6), units=per_day_units()),  # Contact rate old -> middle\n",
+    "    'Moo': Parameter(name='Moo', value=sympy.Float(20.01), units=per_day_units()),  # Contact rate old -> old\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0e7b89c-8d24-42b2-afe1-e3bddf0bfc5c",
+   "metadata": {},
+   "source": [
+    "### Define variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "802ce5dd-9f50-43c3-8d0b-f1e7a692d7b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S_y, S_m, S_o, E_y, E_m, E_o, I_y, I_m, I_o, R_y, R_m, R_o, beta, N, mew, mcw, r_EI, r_IR, Myy, Mym, Myo, Mmy, Mmm, Mmo, Moy, Mom, Moo = \\\n",
+    "    sympy.symbols(\n",
+    "        'S_y S_m S_o E_y E_m E_o I_y I_m I_o R_y R_m R_o beta N mew mcw r_EI r_IR Myy Mym Myo Mmy Mmm Mmo Moy Mom Moo'\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d3dceac-1113-4c55-92a3-3b5ab34ee960",
+   "metadata": {},
+   "source": [
+    "### Set initial values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f892e033-55da-44c6-b309-459dee0cae1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "initials = {\n",
+    "    \"S_y\": Initial(concept=c[\"S_y\"], expression=99.0),\n",
+    "    \"S_m\": Initial(concept=c[\"S_m\"], expression=99.0),\n",
+    "    \"S_o\": Initial(concept=c[\"S_o\"], expression=99.0),\n",
+    "    \"E_y\": Initial(concept=c[\"E_y\"], expression=1.0),\n",
+    "    \"E_m\": Initial(concept=c[\"E_m\"], expression=1.0),\n",
+    "    \"E_o\": Initial(concept=c[\"E_o\"], expression=1.0),\n",
+    "    \"I_y\": Initial(concept=c[\"I_y\"], expression=0.0),\n",
+    "    \"I_m\": Initial(concept=c[\"I_m\"], expression=0.0),\n",
+    "    \"I_o\": Initial(concept=c[\"I_o\"], expression=0.0),\n",
+    "    \"R_y\": Initial(concept=c[\"R_y\"], expression=0.0),\n",
+    "    \"R_m\": Initial(concept=c[\"R_m\"], expression=0.0),\n",
+    "    \"R_o\": Initial(concept=c[\"R_o\"], expression=0.0),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51ea2d61-7dc7-4aa3-849e-9817708158cc",
+   "metadata": {},
+   "source": [
+    "### Define templates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5f186c10-7b95-4678-8737-ebca86c1f856",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##### S -> E\n",
+    "# Sy -> Ey by Iy\n",
+    "syeyiy = ControlledConversion(\n",
+    "    subject=c['S_y'],\n",
+    "    outcome=c['E_y'],\n",
+    "    controller=c['I_y'],\n",
+    "    rate_law=beta*S_y*(1 - mew*mcw)*(Myy*I_y) / N\n",
+    ")\n",
+    "# Sy -> Ey by Im\n",
+    "syeyim = ControlledConversion(\n",
+    "    subject=c['S_y'],\n",
+    "    outcome=c['E_y'],\n",
+    "    controller=c['I_m'],\n",
+    "    rate_law=beta*S_y*(1 - mew*mcw)*(Mym*I_m) / N\n",
+    ")\n",
+    "# Sy -> Ey by Io\n",
+    "syeyio = ControlledConversion(\n",
+    "    subject=c['S_y'],\n",
+    "    outcome=c['E_y'],\n",
+    "    controller=c['I_o'],\n",
+    "    rate_law=beta*S_y*(1 - mew*mcw)*(Myo*I_o) / N\n",
+    ")\n",
+    "\n",
+    "# Sm -> Em by Iy\n",
+    "smemiy = ControlledConversion(\n",
+    "    subject=c['S_m'],\n",
+    "    outcome=c['E_m'],\n",
+    "    controller=c['I_y'],\n",
+    "    rate_law=beta*S_m*(1 - mew*mcw)*(Mmy*I_y) / N\n",
+    ")\n",
+    "# Sm -> Em by Im\n",
+    "smemim = ControlledConversion(\n",
+    "    subject=c['S_m'],\n",
+    "    outcome=c['E_m'],\n",
+    "    controller=c['I_m'],\n",
+    "    rate_law=beta*S_m*(1 - mew*mcw)*(Mmm*I_m) / N\n",
+    ")\n",
+    "# Sm -> Em by Io\n",
+    "smemio = ControlledConversion(\n",
+    "    subject=c['S_m'],\n",
+    "    outcome=c['E_m'],\n",
+    "    controller=c['I_o'],\n",
+    "    rate_law=beta*S_m*(1 - mew*mcw)*(Mmo*I_o) / N\n",
+    ")\n",
+    "\n",
+    "# So -> Eo by Iy\n",
+    "soeoiy = ControlledConversion(\n",
+    "    subject=c['S_o'],\n",
+    "    outcome=c['E_o'],\n",
+    "    controller=c['I_y'],\n",
+    "    rate_law=beta*S_o*(1 - mew*mcw)*(Moy*I_y) / N\n",
+    ")\n",
+    "# So -> Eo by Im\n",
+    "soeoim = ControlledConversion(\n",
+    "    subject=c['S_o'],\n",
+    "    outcome=c['E_o'],\n",
+    "    controller=c['I_m'],\n",
+    "    rate_law=beta*S_o*(1 - mew*mcw)*(Mom*I_m) / N\n",
+    ")\n",
+    "# So -> Eo by Io\n",
+    "soeoio = ControlledConversion(\n",
+    "    subject=c['S_o'],\n",
+    "    outcome=c['E_o'],\n",
+    "    controller=c['I_o'],\n",
+    "    rate_law=beta*S_o*(1 - mew*mcw)*(Moo*I_o) / N\n",
+    ")\n",
+    "\n",
+    "#### E -> I\n",
+    "# Ey -> Iy\n",
+    "eyiy = NaturalConversion(\n",
+    "    subject=c['E_y'],\n",
+    "    outcome=c['I_y'],\n",
+    "    rate_law=r_EI*E_y\n",
+    ")\n",
+    "# Em -> Im\n",
+    "emim = NaturalConversion(\n",
+    "    subject=c['E_m'],\n",
+    "    outcome=c['I_m'],\n",
+    "    rate_law=r_EI*E_m\n",
+    ")\n",
+    "# Eo -> Io\n",
+    "eoio = NaturalConversion(\n",
+    "    subject=c['E_o'],\n",
+    "    outcome=c['I_o'],\n",
+    "    rate_law=r_EI*E_o\n",
+    ")\n",
+    "\n",
+    "#### I -> R\n",
+    "# Iy -> Ry\n",
+    "iyry = NaturalConversion(\n",
+    "    subject=c['I_y'],\n",
+    "    outcome=c['R_y'],\n",
+    "    rate_law=r_IR*I_y\n",
+    ")\n",
+    "# Im -> Rm\n",
+    "imrm = NaturalConversion(\n",
+    "    subject=c['I_m'],\n",
+    "    outcome=c['R_m'],\n",
+    "    rate_law=r_IR*I_m\n",
+    ")\n",
+    "# Io -> Ro\n",
+    "ioro = NaturalConversion(\n",
+    "    subject=c['I_o'],\n",
+    "    outcome=c['R_o'],\n",
+    "    rate_law=r_IR*I_o\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a845f3f9-fe95-4931-9a4b-c3800774f940",
+   "metadata": {},
+   "source": [
+    "### Define observables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "38e21c50-8b53-4288-a8ef-73f6e02ac63a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observables_seir = {\n",
+    "    'susceptible': Observable(name='susceptible', expression=S_y+S_m+S_o),\n",
+    "    'exposed': Observable(name='exposed', expression=E_y+E_m+E_o),\n",
+    "    'infected': Observable(name='infected', expression=I_y+I_m+I_o),\n",
+    "    'recovered': Observable(name='recovered', expression=R_y+R_m+R_o),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0a0a040-600d-45f4-bc68-be57074b6cac",
+   "metadata": {},
+   "source": [
+    "### Define template model and save as petrinet AMR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4c523830-dfc3-4b8a-b584-5dad7f95c729",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seir_model = TemplateModel(\n",
+    "    templates=[\n",
+    "        syeyiy,\n",
+    "        syeyim,\n",
+    "        syeyio,\n",
+    "        smemiy,\n",
+    "        smemim,\n",
+    "        smemio,\n",
+    "        soeoiy,\n",
+    "        soeoim,\n",
+    "        soeoio,\n",
+    "        eyiy,\n",
+    "        emim,\n",
+    "        eoio,\n",
+    "        iyry,\n",
+    "        imrm,\n",
+    "        ioro,\n",
+    "    ],\n",
+    "    parameters=parameters,\n",
+    "    initials=initials,\n",
+    "    observables=observables_seir\n",
+    ")\n",
+    "\n",
+    "# Save as JSON\n",
+    "with open(\"SEIR_scenario3_petrinet.json\", 'w') as fh:\n",
+    "    json.dump(template_model_to_petrinet_json(seir_model), fh, indent=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "82dda977",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "om = OdeModel(model=Model(seir_model), initialized=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "92ef0baf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "times_test = numpy.linspace(0, 100, 100)\n",
+    "res = simulate_ode_model(ode_model=om, times=times_test,\n",
+    "                                 with_observables=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb65a8f3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "148bec6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#variable_list=list(seir_model.initials.keys())\n",
+    "variable_list = ['S_y', 'I_y', 'E_y', 'I_m', 'I_o', 'S_m', 'E_m', 'S_o', 'E_o', 'R_y', 'R_m', 'R_o', 'S', 'E', 'I', 'R']\n",
+    "cm = plt.cm.bwr(numpy.linspace(0, 1, len(variable_list))) # can also use: bwr, brg, hsv, jet\n",
+    "ax = plt.subplot(111)\n",
+    "marker_list = ['-','--',':','-.']*4\n",
+    "ax.set_prop_cycle(color=list(cm), linestyle=marker_list)\n",
+    "\n",
+    "\n",
+    "ax.legend(handles = plt.plot(times_test,res), labels=variable_list,loc = 'upper left', bbox_to_anchor=(1,1))\n",
+    "plt.xlabel('Day')\n",
+    "plt.ylabel('Population')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cedfa54c-a1c6-4eb5-9674-9ac17d823fb3",
+   "metadata": {},
+   "source": [
+    "## Change parameter values and uncertainty from the AMR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "12219f8d-566f-466d-b35e-6bb70af5cea7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"SEIR_scenario3_petrinet.json\"\n",
+    "with open(filename, 'r') as fh:\n",
+    "    tm = template_model_from_amr_json(json.load(fh))\n",
+    "\n",
+    "# Change `Myy` parameter value\n",
+    "tm.parameters[\"Myy\"].value = 50.0\n",
+    "\n",
+    "# Change `Myy` distribution\n",
+    "tm.parameters[\"Myy\"].distribution = Distribution(type='StandardUniform1',\n",
+    "                                                 parameters={'minimum': 40.0, 'maximum': 80.0})\n",
+    "\n",
+    "# Save as updated AMR\n",
+    "with open('update_parameters_example.json', 'w') as fh:\n",
+    "    json.dump(template_model_to_petrinet_json(tm), fh, indent=1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mira_venv",
+   "language": "python",
+   "name": "mira_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": 5
+}
diff --git a/scratch/notebooks/seirhd_stratified_july_2024_demo.ipynb b/scratch/notebooks/seirhd_stratified_july_2024_demo.ipynb
new file mode 100644
index 00000000..673e6400
--- /dev/null
+++ b/scratch/notebooks/seirhd_stratified_july_2024_demo.ipynb
@@ -0,0 +1,448 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import statements\n",
+    "from scipy.integrate import odeint\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define model\n",
+    "## note: p_EI could be included but it's just 1.\n",
+    "def SEIHRD_model(y, t, beta, r_EI, r_IR, r_IH_u, r_IH_v, r_HR, r_HD, NPI_mult, vacc_mult, N):\n",
+    "    S_u, S_v, E_u, E_v, I_u, I_v, H, R, D = y\n",
+    "    dS_udt = -I_u*NPI_mult(t)*S_u*beta(t)/N(t) - I_v*NPI_mult(t)*S_u*beta(t)/N(t) # done\n",
+    "    dS_vdt = -I_u*NPI_mult(t)*S_v*beta(t)*vacc_mult(t)/N(t) - I_v*NPI_mult(t)*S_v*beta(t)*vacc_mult(t)/N(t) # done\n",
+    "    dE_udt = I_u*NPI_mult(t)*S_u*beta(t)/N(t) + I_v*NPI_mult(t)*S_u*beta(t)/N(t) - E_u*r_EI(t) # done\n",
+    "    dE_vdt = I_u*NPI_mult(t)*S_v*beta(t)*vacc_mult(t)/N(t) + I_v*NPI_mult(t)*S_v*beta(t)*vacc_mult(t)/N(t) - E_v*r_EI(t) # done\n",
+    "    dI_udt = E_u*r_EI(t) - I_u*r_IR(t) - I_u*r_IH_u(t)\n",
+    "    dI_vdt = E_v*r_EI(t) - I_v*r_IR(t) - I_v*r_IH_v(t)\n",
+    "    dHdt = I_u*r_IH_u(t) + I_v*r_IH_v(t) - H*r_HR(t) - H*r_HD(t)\n",
+    "    dRdt = I_u*r_IR(t) + I_v*r_IR(t) + H*r_HR(t)\n",
+    "    dDdt = H*r_HD(t) # done\n",
+    "    # dS_vdt = -I_u*NPI_mult(t)*S_v*beta(t)*vacc_mult(t)/N(t) - I_v*NPI_mult(t)*S_v*beta(t)*vacc_mult(t)/N(t) # done\n",
+    "    # dE_udt = I_u*NPI_mult(t)*S_u*beta(t)/N(t) + I_v*NPI_mult(t)*S_u*beta(t)/N(t) - E_u*r_EI(t) # done\n",
+    "    # dE_vdt = I_u*NPI_mult(t)*S_v*beta(t)*vacc_mult(t)/N(t) + I_v*NPI_mult(t)*S_v*beta(t)*vacc_mult(t)/N(t) - E_v*r_EI(t) # done\n",
+    "    # dI_udt = E_u*r_EI(t) - I_u*r_IR(t) - I_u*r_IH_u(t)\n",
+    "    # dI_vdt = E_v*r_EI(t) - I_v*r_IR(t) - I_v*r_IH_v(t)\n",
+    "    # dHdt = I_u*r_IH_u(t) + I_v*r_IH_v(t) - H*r_HR(t) - H*r_HD(t)\n",
+    "    # dRdt = I_u*r_IR(t) + I_v*r_IR(t) + H*r_HR(t)\n",
+    "    # dDdt = H*r_HD(t) # done\n",
+    "    return dS_udt, dS_vdt, dE_udt, dE_vdt, dI_udt, dI_vdt, dHdt, dRdt, dDdt\n",
+    "\n",
+    "#  plot model\n",
+    "def plotSEIHRD(t, S_u, S_v, E_u, E_v, I_u, I_v, H, R, D):\n",
+    "    f, ax = plt.subplots(1,1,figsize=(10,4))\n",
+    "    ax.plot(t, S_u, 'r', alpha=0.7, linewidth=2, label='Susceptible unvacc')\n",
+    "    ax.plot(t, E_u, 'o', alpha=0.7, linewidth=2, label='Exposed unvacc')\n",
+    "    ax.plot(t, I_u, 'y', alpha=0.7, linewidth=2, label='Infected unvacc')\n",
+    "    ax.plot(t, S_v, 'r', alpha=0.7, linewidth=2, linestyle='dotted', label='Susceptible unvacc')\n",
+    "    ax.plot(t, E_v, 'o', alpha=0.7, linewidth=2, linestyle='dotted', label='Exposed unvacc')\n",
+    "    ax.plot(t, I_v, 'y', alpha=0.7, linewidth=2, linestyle='dotted', label='Infected unvacc')\n",
+    "    ax.plot(t, H, 'g', alpha=0.7, linewidth=2, label='Hospitalized')\n",
+    "    ax.plot(t, R, 'b', alpha=0.7, linewidth=2, label='Recovered')\n",
+    "    ax.plot(t, D, 'p', alpha=0.7, linewidth=2, label='Dead')\n",
+    "\n",
+    "\n",
+    "    ax.set_xlabel('Time (days)')\n",
+    "    ax.set_ylabel('Population')\n",
+    "\n",
+    "    ax.yaxis.set_tick_params(length=0)\n",
+    "    ax.xaxis.set_tick_params(length=0)\n",
+    "    # ax.grid(b=True, which='major', c='w', lw=2, ls='-')\n",
+    "    legend = ax.legend()\n",
+    "    legend.get_frame().set_alpha(0.5)\n",
+    "    \n",
+    "    plt.show();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set parameter values\n",
+    "def beta(t): return np.piecewise(t, [t>=0], [0.125])\n",
+    "def r_EI(t): return np.piecewise(t, [t>=0], [0.1424])\n",
+    "def r_IR(t): return np.piecewise(t, [t>=0], [0.15]) ## stratified in AMR??\n",
+    "def r_IH_u(t): return np.piecewise(t, [t>=0], [0.0041])\n",
+    "def r_IH_v(t): return np.piecewise(t, [t>=0], [0.0013])\n",
+    "def r_HR(t): return np.piecewise(t, [t>=0], [0.155])\n",
+    "def r_HD(t): return np.piecewise(t, [t>=0], [0.0099])\n",
+    "def NPI_mult(t): return np.piecewise(t, [t>=0], [1.0])\n",
+    "def vacc_mult(t): return np.piecewise(t, [t>=0], [0.3])\n",
+    "def N(t): return np.piecewise(t, [t>=0], [9830000.0]) \n",
+    "\n",
+    "# set initial conditions\n",
+    "N0 = 9830000.0\n",
+    "Sv0 = 5977629.36\n",
+    "Su0 = 2324633.64\n",
+    "Ev0 = 3458.66666666667\n",
+    "Eu0 = 6917.33333333333\n",
+    "Iv0 = 1729.33333333333\n",
+    "Iu0 = 3458.66666666667\n",
+    "H0 = 673\n",
+    "R0 = 1484896\n",
+    "D0 = 26604\n",
+    "y0 = Sv0, Su0, Ev0, Eu0, Iv0, Iu0, H0, R0, D0 # Initial conditions vector\n",
+    "\n",
+    "# set simulation parameters\n",
+    "## \n",
+    "dt = 1\n",
+    "tstart = 0\n",
+    "tend = 100\n",
+    "tvect = np.arange(tstart, tend, dt) \n",
+    "\n",
+    "# solve odes\n",
+    "sim = odeint(SEIHRD_model, y0, tvect, args=(beta, r_EI, r_IR, r_IH_u, r_IH_v, r_HR, r_HD, NPI_mult, vacc_mult, N))\n",
+    "Sv, Su, Ev, Eu, Iv, Iu, H, R, D = sim.T\n",
+    "\n",
+    "# plot results\n",
+    "# plotSEIHRD(tvect, S, E, I, H, R, D) ## original\n",
+    "plotSEIHRD(tvect, Sv, Su, Ev, Eu, Iv, Iu, H, R, D) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f9958f2c220>]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tvect, Sv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f9968fe1e80>]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tvect, Su)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f99c8dff4c0>]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tvect, Ev)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f99b8d86a00>]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tvect, Eu)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f99788f7280>]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tvect, Iv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f99a883d340>]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tvect, Iu)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f9948d61070>]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tvect, H)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f99692faa00>]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tvect, R)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f9948d2f910>]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tvect, D)"
+   ]
+  },
+  {
+   "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
+}
diff --git a/scratch/notebooks/sidarthe-eval-smt-encoding.ipynb b/scratch/notebooks/sidarthe-eval-smt-encoding.ipynb
new file mode 100644
index 00000000..3805ff29
--- /dev/null
+++ b/scratch/notebooks/sidarthe-eval-smt-encoding.ipynb
@@ -0,0 +1,526 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 0: copy sir encoding\n",
+    "# 59:53.45 to get an encoding (needing debugging/checking)\n",
+    "# 01:41:13 configure parameter bounds to working test case\n",
+    "# 03:00:00 debug model \n",
+    "# 03:00:00 test solvers and extend compartmental bounds with noise\n",
+    "# +33:37.29 fix step size handling in encoding\n",
+    "# +1:00:00 debug step size and plotting\n",
+    "\n",
+    "from pysmt.shortcuts import And, Or, Plus, Minus, Times, Div, REAL, LE, LT, GE, GT, Equals, Symbol, Real, Solver, TRUE, FALSE\n",
+    "from pysmt.fnode import  FNode\n",
+    "from typing import Dict\n",
+    "import pandas as pd\n",
+    "from decimal import Decimal\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def dataframe(assignment: Dict[Symbol, float], state_variables, parameters, max_step, step_size) -> pd.DataFrame:\n",
+    "    timepoints = list(range(0, max_time+step_size, step_size))\n",
+    "    timeseries = {sv: [None]*len(timepoints) for sv in state_variables}\n",
+    "    for k, v in assignment.items():\n",
+    "        sym = k.symbol_name()\n",
+    "        if \"_\" in sym:\n",
+    "            sv = sym.split(\"_\")[0]\n",
+    "            t = sym.split(\"_\")[1]\n",
+    "            value =Decimal(v.numerator) / Decimal(v.denominator)\n",
+    "            if max_time > 0:\n",
+    "                timepoint = timepoints.index(int(t))\n",
+    "                timeseries[sv][timepoint] = value\n",
+    "            else:\n",
+    "                timeseries[sv][timepoints.index(0)] = value\n",
+    "        else:\n",
+    "            timeseries[sym] = [v]*len(timepoints)\n",
+    "    df = pd.DataFrame(timeseries, index=timepoints).astype(float)\n",
+    "    return df\n",
+    "\n",
+    "def reindex_and_interpolate(df, new_index):\n",
+    "    df_reindexed = df.reindex(index = new_index)\n",
+    "    df_reindexed.interpolate(method = 'linear', inplace = True)\n",
+    "    return df_reindexed\n",
+    "\n",
+    "\n",
+    "def plot_results(values, max_step, step_size):\n",
+    "    timepoints = list(range(0, max_time+step_size, step_size))\n",
+    "    results: pd.DataFrame = dataframe(values, [\"Susceptible\", \"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Healed\", \"Threatened\", \"Extinct\"], [\"beta\", \"gamma\", \"delta\", \"alpha\", \"epsilon\", \"zeta\", \"lambdax\", \"eta\", \"rho\", \"theta\", \"kappa\", \"mu\", \"nu\", \"xi\", \"tau\", \"sigma\"], max_step, step_size)\n",
+    "    newindex = np.linspace(timepoints[0], timepoints[-1], timepoints[-1]+1)\n",
+    "    results = reindex_and_interpolate(results, newindex)\n",
+    "    print(f\"beta = {results['beta'][0]}, lambda = {results['lambdax'][0]}\")\n",
+    "    print(results[[\"Susceptible\", \"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Healed\", \"Threatened\", \"Extinct\"]])\n",
+    "    ax = results[[\"Susceptible\", \"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Healed\", \"Threatened\", \"Extinct\"]].plot()\n",
+    "    ax.set_xlabel=\"Time\"\n",
+    "    return ax, results\n",
+    "\n",
+    "def run_solver(solver, formula):\n",
+    "    solver.add_assertion(formula)\n",
+    "    result = solver.solve()\n",
+    "    if result:\n",
+    "        model = solver.get_model()\n",
+    "        variables = formula.get_free_variables()\n",
+    "        values = {}\n",
+    "        for var in variables:\n",
+    "            try:\n",
+    "                values[var] = model.get_value(var).constant_value()\n",
+    "            except Exception as e:\n",
+    "                pass\n",
+    "    else:\n",
+    "        print(\"Unsat\")\n",
+    "        values = None\n",
+    "    return values\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from funman.api.run import Runner\n",
+    "# import json\n",
+    "\n",
+    "# amr_model = \"../../resources/amr/petrinet/mira/models/BIOMD0000000955_askenet.json\"\n",
+    "# m = Runner().get_model(amr_model)\n",
+    "# m[0].to_dot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure these bounds (lower, upper) to configure the parameter space\n",
+    "\n",
+    "noise = 1e-1\n",
+    "\n",
+    "beta_bounds = (0.2,  0.8)\n",
+    "# gamma_bounds = (0.3648, 0.5472)\n",
+    "# alpha_bounds = ( 0.45599999999999996,0.6839999999999999)\n",
+    "# delta_bounds = (0.008799999999999999, 0.0132)\n",
+    "# epsilon_bounds = (0.1368, 0.20520000000000002)\n",
+    "# zeta_bounds = (0.1, 0.15)\n",
+    "lambdax_bounds = (0.0, 0.1)\n",
+    "# eta_bounds = (0.1,  0.15)\n",
+    "# rho_bounds = (0.027200000000000002, 0.0408)\n",
+    "# theta_bounds = (0.2968, 0.4452)\n",
+    "# kappa_bounds = (0.013600000000000001, 0.0204)\n",
+    "# mu_bounds = (0.013600000000000001, 0.0204)\n",
+    "# nu_bounds = (0.0216, 0.0324)\n",
+    "# xi_bounds = (0.013600000000000001, 0.0204)\n",
+    "# tau_bounds = (0.008, 0.012)\n",
+    "# sigma_bounds = (0.013600000000000001, 0.0204)\n",
+    "\n",
+    "# {\"alpha\":0.570, \"beta\":0.011, \"delta\":0.011, \"gamma\":0.456, \"epsilon\":0.171, \"theta\":0.371, \"eta\":0.125, \"zeta\":0.125,\"mu\":0.017, \"nu\":0.027,\"tau\":0.01, \"lambda\":0.034, \"rho\":0.034, \"kappa\":0.017, \"xi\":0.017,\"sigma\":0.017, \"days\": 1}\n",
+    "\n",
+    "param_noise = 0\n",
+    "# beta_bounds = (0.011-param_noise,  0.011+param_noise)\n",
+    "gamma_bounds = (0.0456-param_noise, 0.0456+param_noise)\n",
+    "alpha_bounds = ( 0.1-param_noise, 0.1+param_noise)\n",
+    "delta_bounds = (0.00011-param_noise, 0.00011+param_noise)\n",
+    "epsilon_bounds = (0.03-param_noise, 0.03+param_noise)\n",
+    "zeta_bounds = (0.0125-param_noise, 0.0125+param_noise)\n",
+    "# lambdax_bounds = (0.0-param_noise, 0.1+param_noise)\n",
+    "eta_bounds = (0.0125-param_noise, 0.0125+param_noise)\n",
+    "rho_bounds = (0.034-param_noise, 0.034+param_noise)\n",
+    "theta_bounds = (0.571-param_noise, 0.571+param_noise) #(epsilon_bounds[0]*2.0, epsilon_bounds[1]*2.0) #(0.2968, 0.4452)\n",
+    "kappa_bounds = (0.017-param_noise, 0.017+param_noise)\n",
+    "mu_bounds = (0.017-param_noise, 0.017+param_noise)\n",
+    "nu_bounds = (0.027-param_noise, 0.027+param_noise)\n",
+    "xi_bounds = (0.017-param_noise, 0.017+param_noise)\n",
+    "tau_bounds = (0.01-param_noise, 0.01+param_noise)\n",
+    "sigma_bounds = (0.017-param_noise, 0.017+param_noise)\n",
+    "\n",
+    "\n",
+    "# Set these values for the initial state\n",
+    "\n",
+    "Susceptible_0_value = 0.99\n",
+    "Diagnosed_0_value = 0.01\n",
+    "Infected_0_value = 0.0\n",
+    "Ailing_0_value = 0.0\n",
+    "Recognized_0_value = 0.0\n",
+    "Healed_0_value = 0\n",
+    "Threatened_0_value = 0\n",
+    "Extinct_0_value = 0\n",
+    "\n",
+    "\n",
+    "# Timepoints\n",
+    "step_size = 2\n",
+    "max_time = 10\n",
+    "\n",
+    "# 11:34 - adapting names of variables and parameters\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def encode(step_size=1, max_time=10, noise=1e-3, strict_upper_bound_parameters = False, use_compartmental=True):\n",
+    "    # The main encoding code, which includes \n",
+    "\n",
+    "    time_format = lambda t: f\"{t:03d}\"\n",
+    "\n",
+    "    timepoints = list(range(0, max_time+step_size, step_size))\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Initial States ###############################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    Susceptible_0 = Symbol(f\"Susceptible_{time_format(0)}\", REAL)\n",
+    "    Diagnosed_0 = Symbol(f\"Diagnosed_{time_format(0)}\", REAL)\n",
+    "    Infected_0 = Symbol(f\"Infected_{time_format(0)}\", REAL)\n",
+    "    Ailing_0 = Symbol(f\"Ailing_{time_format(0)}\", REAL)\n",
+    "    Recognized_0 = Symbol(f\"Recognized_{time_format(0)}\", REAL)\n",
+    "    Healed_0 = Symbol(f\"Healed_{time_format(0)}\", REAL)\n",
+    "    Threatened_0 = Symbol(f\"Threatened_{time_format(0)}\", REAL)\n",
+    "    Extinct_0 = Symbol(f\"Extinct_{time_format(0)}\", REAL)\n",
+    "\n",
+    "    population_size = Susceptible_0_value + Diagnosed_0_value + Infected_0_value + Ailing_0_value + Recognized_0_value + Healed_0_value + Threatened_0_value + Extinct_0_value\n",
+    "\n",
+    "    # SIR Model Initial State\n",
+    "    initial_state = And([\n",
+    "        Equals(Susceptible_0, Real(Susceptible_0_value)),\n",
+    "        Equals(Infected_0, Real(Infected_0_value)),\n",
+    "        Equals(Diagnosed_0, Real(Diagnosed_0_value)),\n",
+    "        Equals(Ailing_0, Real(Ailing_0_value)),\n",
+    "        Equals(Recognized_0, Real(Recognized_0_value)),\n",
+    "        Equals(Threatened_0, Real(Threatened_0_value)),\n",
+    "        Equals(Healed_0, Real(Healed_0_value)),\n",
+    "        Equals(Extinct_0, Real(Extinct_0_value))\n",
+    "    ])\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Parameters     ###############################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "\n",
+    "    # Parameters\n",
+    "    beta = Symbol(\"beta\", REAL)\n",
+    "    gamma = Symbol(\"gamma\", REAL)\n",
+    "    delta = Symbol(\"delta\", REAL)\n",
+    "    alpha = Symbol(\"alpha\", REAL)\n",
+    "    epsilon = Symbol(\"epsilon\", REAL)\n",
+    "    zeta = Symbol(\"zeta\", REAL)\n",
+    "    lambdax = Symbol(\"lambdax\", REAL)\n",
+    "    eta = Symbol(\"eta\", REAL)\n",
+    "    rho = Symbol(\"rho\", REAL)\n",
+    "    theta = Symbol(\"theta\", REAL)\n",
+    "    kappa = Symbol(\"kappa\", REAL)\n",
+    "    mu = Symbol(\"mu\", REAL)\n",
+    "    nu = Symbol(\"nu\", REAL)\n",
+    "    xi = Symbol(\"xi\", REAL)\n",
+    "    tau = Symbol(\"tau\", REAL)\n",
+    "    sigma = Symbol(\"sigma\", REAL)\n",
+    "\n",
+    "\n",
+    "    upper_op = LT if strict_upper_bound_parameters else LE\n",
+    "    parameters = And([\n",
+    "        And(LE(Real(beta_bounds[0]), beta), upper_op(beta, Real(beta_bounds[1]))),\n",
+    "        And(LE(Real(gamma_bounds[0]), gamma), upper_op(gamma, Real(gamma_bounds[1]))),\n",
+    "        And(LE(Real(delta_bounds[0]), delta), upper_op(delta, Real(delta_bounds[1]))),\n",
+    "        And(LE(Real(alpha_bounds[0]), alpha), upper_op(alpha, Real(alpha_bounds[1]))),\n",
+    "        And(LE(Real(epsilon_bounds[0]), epsilon), upper_op(epsilon, Real(epsilon_bounds[1]))),\n",
+    "        And(LE(Real(zeta_bounds[0]), zeta), upper_op(zeta, Real(zeta_bounds[1]))),\n",
+    "        And(LE(Real(lambdax_bounds[0]), lambdax), upper_op(lambdax, Real(lambdax_bounds[1]))),\n",
+    "        And(LE(Real(eta_bounds[0]), eta), upper_op(eta, Real(eta_bounds[1]))),\n",
+    "        And(LE(Real(rho_bounds[0]), rho), upper_op(rho, Real(rho_bounds[1]))),\n",
+    "        And(LE(Real(kappa_bounds[0]), kappa), upper_op(kappa, Real(kappa_bounds[1]))),\n",
+    "        And(LE(Real(theta_bounds[0]), theta), upper_op(theta, Real(theta_bounds[1]))),\n",
+    "        And(LE(Real(mu_bounds[0]), mu), upper_op(mu, Real(mu_bounds[1]))),\n",
+    "        And(LE(Real(nu_bounds[0]), nu), upper_op(nu, Real(nu_bounds[1]))),\n",
+    "        And(LE(Real(xi_bounds[0]), xi), upper_op(xi, Real(xi_bounds[1]))),\n",
+    "        And(LE(Real(tau_bounds[0]), tau), upper_op(tau, Real(tau_bounds[1]))),\n",
+    "        And(LE(Real(sigma_bounds[0]), sigma), upper_op(sigma, Real(sigma_bounds[1])))\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Transitions ##################################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    Susceptible_next = lambda t: Symbol(f\"Susceptible_{time_format(t+step_size)}\", REAL)\n",
+    "    Susceptible_now = lambda t: Symbol(f\"Susceptible_{time_format(t)}\", REAL)\n",
+    "    Diagnosed_next = lambda t: Symbol(f\"Diagnosed_{time_format(t+step_size)}\", REAL)\n",
+    "    Diagnosed_now = lambda t: Symbol(f\"Diagnosed_{time_format(t)}\", REAL)\n",
+    "    Infected_next = lambda t: Symbol(f\"Infected_{time_format(t+step_size)}\", REAL)\n",
+    "    Infected_now = lambda t: Symbol(f\"Infected_{time_format(t)}\", REAL)\n",
+    "    Ailing_next = lambda t: Symbol(f\"Ailing_{time_format(t+step_size)}\", REAL)\n",
+    "    Ailing_now = lambda t: Symbol(f\"Ailing_{time_format(t)}\", REAL)\n",
+    "    Healed_next = lambda t: Symbol(f\"Healed_{time_format(t+step_size)}\", REAL)\n",
+    "    Healed_now = lambda t: Symbol(f\"Healed_{time_format(t)}\", REAL)\n",
+    "    Threatened_next = lambda t: Symbol(f\"Threatened_{time_format(t+step_size)}\", REAL)\n",
+    "    Threatened_now = lambda t: Symbol(f\"Threatened_{time_format(t)}\", REAL)\n",
+    "    Recognized_next = lambda t: Symbol(f\"Recognized_{time_format(t+step_size)}\", REAL)\n",
+    "    Recognized_now = lambda t: Symbol(f\"Recognized_{time_format(t)}\", REAL)\n",
+    "    Extinct_next = lambda t: Symbol(f\"Extinct_{time_format(t+step_size)}\", REAL)\n",
+    "    Extinct_now = lambda t: Symbol(f\"Extinct_{time_format(t)}\", REAL)\n",
+    "    dt = Real(float(step_size))\n",
+    "\n",
+    "\n",
+    "    # t1: D, S -> D, I  Diagnosed*Susceptible*beta\n",
+    "    # t2: A, S -> A, I  Ailing*Susceptible*gamma\n",
+    "    # t3: R, S -> R, I  Recognized*Susceptible*delta\n",
+    "    # t4: I, S -> I, I  Infected*Susceptible*alpha\n",
+    "    # t5: I -> D    Infected*epsilon\n",
+    "    # t6: I -> A    Infected*zeta\n",
+    "    # t7: I -> H    Infected*lambdax\n",
+    "    # t8: D -> R    Diagnosed*eta\n",
+    "    # t9: D -> H    Diagnosed*rho\"\n",
+    "    # t10: A -> R   Ailing*theta\n",
+    "    # t11: A -> H   Ailing*kappa\n",
+    "    # t12: A -> T   Ailing*mu\n",
+    "    # t13: R -> T   Recognized*nu\n",
+    "    # t14: R -> H   Recognized*xi\n",
+    "    # t15: T -> E   Threatened*tau\n",
+    "    # t16: T -> H   Threatened*sigma\n",
+    "\n",
+    "    # -t1 -t2 -t3 -t4\n",
+    "    Susceptible_Trans = lambda t: Equals(Susceptible_next(t), \n",
+    "                                    Plus(\n",
+    "                                        Susceptible_now(t), \n",
+    "                                        Times(Plus([\n",
+    "                                            Times(Real(-1.0), Diagnosed_now(t), Susceptible_now(t), beta), \n",
+    "                                            Times(Real(-1.0), Ailing_now(t), Susceptible_now(t), gamma),\n",
+    "                                            Times(Real(-1.0), Recognized_now(t), Susceptible_now(t), delta),\n",
+    "                                            Times(Real(-1.0), Infected_now(t), Susceptible_now(t), alpha)]), \n",
+    "                                            dt)))\n",
+    "\n",
+    "    # -t1+t1 -t8 -t9 + t5\n",
+    "    Diagnosed_Trans = lambda t: Equals(Diagnosed_next(t), \n",
+    "                                    Plus(\n",
+    "                                        Diagnosed_now(t), \n",
+    "                                        Times(Plus([\n",
+    "                                            Times(Real(-1.0), Diagnosed_now(t), eta), \n",
+    "                                            Times(Real(-1.0), Diagnosed_now(t), rho), \n",
+    "                                            Times( Infected_now(t), epsilon)]),\n",
+    "                                            dt)))\n",
+    "\n",
+    "    # +t1 +t2 +t3 +t4 -t5 -t6 -t7 \n",
+    "    Infected_Trans = lambda t: Equals(Infected_next(t), \n",
+    "                                    Plus(\n",
+    "                                        Infected_now(t), \n",
+    "                                        Times(Plus([\n",
+    "                                            Times(Diagnosed_now(t), Susceptible_now(t), beta), # t1\n",
+    "                                            Times(Ailing_now(t), Susceptible_now(t), gamma), # t2\n",
+    "                                            Times(Recognized_now(t), Susceptible_now(t), delta), # t3\n",
+    "                                            Times(Infected_now(t), Susceptible_now(t), alpha), # t4\n",
+    "                                            Times(Real(-1.0), Infected_now(t), epsilon), # t5\n",
+    "                                            Times(Real(-1.0), Infected_now(t), zeta), # t6\n",
+    "                                            Times(Real(-1.0), Infected_now(t), lambdax)]), #t7\n",
+    "                                            dt)))\n",
+    "\n",
+    "    # -t2 +t2 +t6 -t10 -t11 -t12\n",
+    "    Ailing_Trans = lambda t: Equals(Ailing_next(t), \n",
+    "                                    Plus(\n",
+    "                                        Ailing_now(t), \n",
+    "                                        Times(Plus([\n",
+    "                                            Times(Infected_now(t), zeta), \n",
+    "                                            Times(Real(-1.0), Ailing_now(t), theta),\n",
+    "                                            Times(Real(-1.0), Ailing_now(t), kappa),\n",
+    "                                            Times(Real(-1.0), Ailing_now(t), mu)]),\n",
+    "                                            dt)))\n",
+    "\n",
+    "    # +t3 -t3 +t8 +t10 -t13 -t14 \n",
+    "    Recognized_Trans = lambda t: Equals(Recognized_next(t), \n",
+    "                                    Plus(\n",
+    "                                        Recognized_now(t), \n",
+    "                                        Times(Plus([\n",
+    "                                            Times(Diagnosed_now(t), eta), \n",
+    "                                            Times(Ailing_now(t), theta),\n",
+    "                                            Times(Real(-1.0), Recognized_now(t), nu),\n",
+    "                                            Times(Real(-1.0), Threatened_now(t), xi)]),\n",
+    "                                            dt)))\n",
+    "\n",
+    "    # +t7 +t9 +t11 +t14 +t16\n",
+    "    Healed_Trans = lambda t: Equals(Healed_next(t), \n",
+    "                                    Plus(\n",
+    "                                        Healed_now(t), \n",
+    "                                        Times(Plus([\n",
+    "                                            Times(Infected_now(t), lambdax), \n",
+    "                                            Times(Diagnosed_now(t), rho),\n",
+    "                                            Times(Ailing_now(t), kappa),\n",
+    "                                            Times(Recognized_now(t), xi),\n",
+    "                                            Times(Threatened_now(t), sigma)]),\n",
+    "                                            dt)))\n",
+    "\n",
+    "    # +t12 +t13 -t15 -t16\n",
+    "    Threatened_Trans = lambda t: Equals(Threatened_next(t), \n",
+    "                                    Plus(\n",
+    "                                        Threatened_now(t), \n",
+    "                                        Times(Plus([\n",
+    "                                            Times(Ailing_now(t), mu),\n",
+    "                                            Times(Recognized_now(t), nu),\n",
+    "                                            Times(Real(-1.0), Threatened_now(t), tau),\n",
+    "                                            Times(Real(-1.0), Threatened_now(t), sigma)]),\n",
+    "                                            dt)))\n",
+    "\n",
+    "    # -t15\n",
+    "    Extinct_Trans = lambda t: Equals(Extinct_next(t), \n",
+    "                                    Plus(\n",
+    "                                        Extinct_now(t), \n",
+    "                                        Times(Plus([\n",
+    "                                            Times(Threatened_now(t), tau)]),\n",
+    "                                            dt)))\n",
+    "\n",
+    "\n",
+    "    Trans = lambda t: And([\n",
+    "        Susceptible_Trans(t), Diagnosed_Trans(t), \n",
+    "        Infected_Trans(t), Ailing_Trans(t), Recognized_Trans(t), \n",
+    "        Healed_Trans(t), Threatened_Trans(t), Extinct_Trans(t)\n",
+    "        ])\n",
+    "\n",
+    "    All_Trans = And([Trans(t) for t in timepoints[:-1]])\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Constraints ##################################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    pop_sum = lambda t: Plus([\n",
+    "            Susceptible_now(t), Diagnosed_now(t), Infected_now(t),\n",
+    "            Ailing_now(t), Recognized_now(t), Healed_now(t),\n",
+    "            Threatened_now(t), Extinct_now(t)\n",
+    "        ])\n",
+    "\n",
+    "    compartmental_constraint = And([\n",
+    "        And( \n",
+    "        LE(Real(0.0), Susceptible_now(t)),\n",
+    "        LE(Real(0.0), Diagnosed_now(t)),\n",
+    "        LE(Real(0.0), Infected_now(t)),\n",
+    "        LE(Real(0.0), Ailing_now(t)),\n",
+    "        LE(Real(0.0), Recognized_now(t)),\n",
+    "        LE(Real(0.0), Healed_now(t)),\n",
+    "        LE(Real(0.0), Threatened_now(t)),\n",
+    "        LE(Real(0.0), Extinct_now(t)),\n",
+    "        LE(pop_sum(t), Real(population_size+noise)),\n",
+    "        LE(Real(population_size-noise), pop_sum(t))\n",
+    "        )\n",
+    "    for t in timepoints]) if use_compartmental else TRUE()\n",
+    "\n",
+    "    # 10m to add and check\n",
+    "    I_peak = (0.45, 0.55) # .45, .55\n",
+    "    I_peak_t = (45.0, 55.0) # 45, 55\n",
+    "    peak_I = And([\n",
+    "        And(LE(Real(I_peak[0]), Infected_now(t)),\n",
+    "        LT(Infected_now(t), Real(I_peak_t[1])))\n",
+    "        for t in timepoints \n",
+    "        if I_peak_t[0] <= t and t <= I_peak_t[1]\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Combine Assertions ###########################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    consistency = And([\n",
+    "        initial_state,\n",
+    "        parameters,\n",
+    "        All_Trans,\n",
+    "       compartmental_constraint,\n",
+    "        peak_I\n",
+    "        ])\n",
+    "    # print(consistency.serialize())\n",
+    "    return consistency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# encode(max_time=max_time, step_size=step_size, noise=noise, use_compartmental=False).serialize(\n",
+    "\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use dreal instead of z3\n",
+    "from pysmt.logics import QF_NRA\n",
+    "from funman_dreal.solver import ensure_dreal_in_pysmt\n",
+    "\n",
+    "ensure_dreal_in_pysmt()\n",
+    "\n",
+    "opts = {\n",
+    "        \"dreal_precision\": 1e-3,\n",
+    "        \"dreal_log_level\": \"info\",\n",
+    "        \"dreal_mcts\": True,\n",
+    "    }\n",
+    "\n",
+    "max_time=20\n",
+    "step_size=1\n",
+    "noise = 0.0001\n",
+    "\n",
+    "with Solver(name=\"dreal\",\n",
+    "            logic=QF_NRA,\n",
+    "            solver_options=opts\n",
+    "            ) as solver:\n",
+    "    values = run_solver(solver, encode(max_time=max_time, step_size=step_size, noise=noise, use_compartmental=False))\n",
+    "    \n",
+    "if values:\n",
+    "    ax, results = plot_results(values, max_time, step_size)\n",
+    "    f_pop = results.loc[float(max_time)][[\"Susceptible\", \"Diagnosed\", \"Infected\", \"Ailing\", \"Recognized\", \"Healed\", \"Threatened\", \"Extinct\"  ]].sum()\n",
+    "    print(f\"Population at end: {f_pop}\")\n",
+    "    # results.loc[10.0]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# max_time=20\n",
+    "# step_size=1\n",
+    "\n",
+    "\n",
+    "\n",
+    "# # Solve encoding\n",
+    "# with Solver() as solver:\n",
+    "#     values = run_solver(solver, encode(max_time=max_time, step_size=step_size, noise=noise))\n",
+    "    \n",
+    "# if values:\n",
+    "#     plot_results(values, max_time, step_size)\n",
+    "# values"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "funman_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scratch/notebooks/sidarthe-manual-true-false.ipynb b/scratch/notebooks/sidarthe-manual-true-false.ipynb
new file mode 100644
index 00000000..c9502bed
--- /dev/null
+++ b/scratch/notebooks/sidarthe-manual-true-false.ipynb
@@ -0,0 +1,2555 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize recording of parameter choices and true/false\n",
+    "\n",
+    "param_choices_true_false = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Start here at each iteration by changing the values of the parameters, then run the following cells to observe the simulation and tell whether the parameter settings are what you're looking for (1) or not (0)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 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])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set initial conditions\n",
+    "\n",
+    "N0 = 60e6\n",
+    "I0, D0, A0, R0, T0, H0, E0 = 200/N0, 20/N0, 1/N0, 2/N0, 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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set simulation parameters\n",
+    "dt = .5\n",
+    "tstart = 0\n",
+    "tend = 100\n",
+    "tvect = np.arange(tstart, tend, dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 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",
+    "\n",
+    "# plot results\n",
+    "sir_model.plotSIDARTHE(tvect, S, I, D, A, R, T, H, E)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## log parameters and sat/unsat\n",
+    "\n",
+    "param_assignments = {'alpha': alpha_val, 'beta': beta_val, 'delta': delta_val, 'gamma': gamma_val, 'epsilon': epsilon_val, 'theta': theta_val, 'zeta': zeta_val, 'eta': eta_val, 'mu': mu_val, 'nu':nu_val, 'lamb':lamb_val, 'rho':rho_val, 'kappa':kappa_val, 'xi':xi_val, 'sigma':sigma_val, 'tau':tau_val,  'assignment': input(\"Please enter 0 if the parameter choices were bad and 1 if they were good:\")}\n",
+    "\n",
+    "param_choices_true_false.append(param_assignments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot \"true/false\" points.  Is there any way of characterizing these regions?\n",
+    "\n",
+    "\n",
+    "xsample = [result['beta'] for result in param_choices_true_false]\n",
+    "ysample = [result['gamma'] for result in param_choices_true_false]\n",
+    "colors = ['green' if result['assignment'] == '1' else 'red' for result in param_choices_true_false]\n",
+    "plt.title(\"Parameter Value Pairings\")\n",
+    "plt.xlabel(\"beta\")\n",
+    "plt.ylabel(\"gamma\")\n",
+    "\n",
+    "area = 5  # 0 to 15 point radii\n",
+    "\n",
+    "plt.scatter(xsample, ysample, s=area, c=colors, alpha=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha beta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha delta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha gamma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha epsilon\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha theta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha zeta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha eta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha mu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha nu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha lamb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha kappa\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alpha tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta delta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta gamma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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jSdIGjRsw36uqS5pWIklaUMYNmG8k+QDwT/z6wy69ikySNNK4AbM9XbAMPi7Gy5QlSRs07p38L2ldiCRpYRn3RsvtgJcBD6d7ZD8AVfXSRnVJkua5ce+D+QfggcCzgM8AewI+90KStEHjBsxDq+pPgV9U1bnAs4H/0q4sSdJ8N27A/Kr//ZMkjwDuS/fgS0mSRhr3KrKzkuwKvAG4BLgP8KfNqpIkzXvjBsx9gekryc7of69L8qiqumriVUmS5r1xD5E9BjgeWArsAfwh8BTg3Ule06Y0SdJ8Nu4ezP2AA6rq5wBJ3gRcADwJWAm8rU15kqT5atw9mAcDdw6M/wrYu6puZ+DRMZIkTRt3D+YDwBeT/GM//lzgg0l2BL7epDJJ0rw27qNi/jzJZcAT6L4L5viqWtHPfn6r4iRJ89e4ezBU1Uq68y2SJG3UuOdgJEnaJE0DJskhSa5NsjrJyTO0OzDJ+iRHDUz7TpKvJbkqyYoNLStJ2jqNfYhsUyVZRHdT5jOANcAVSS6pqq+PaHcqcPmI1fxOVd3SqkZJUjst92AOAlZX1XVVdSdwPnD4iHYnAhcCNzesRZI0y1oGzFLg+oHxNf20uyVZChwJnDli+QI+kWRlkuM2tJEkxyVZkWTF2rVrJ1C2JGkSWgZMRkyrofHTgZOqav2Ito+vqgOAQ4FXJnnSqI1U1VlVNVVVU0uWLNmigiVJk9PsHAzdHsteA+N7AjcOtZkCzk8CsDtwWJJ1VXVxVd0IUFU3J7mI7pDbZxvWK0maoJZ7MFcA+yRZnmQb4Gi6R/3fraqWV9WyqlpG92yz/15VFyfZMclOAP3TAp4JXN2wVknShDXbg6mqdUlOoLs6bBFwdlWtSnJ8P3/UeZdpDwAu6vdsFgMfqKqPt6pVkjR5qRo+LTJ/TU1N1YoV3jIjSeNKsrKqplqs2zv5JUlNGDCSpCYMGElSEwaMJKkJA0aS1IQBI0lqwoCRJDVhwEiSmjBgJElNGDCSpCYMGElSEwaMJKkJA0aS1IQBI0lqwoCRJDVhwEiSmjBgJElNGDCSpCYMGElSEwaMJKkJA0aS1IQBI0lqwoCRJDVhwEiSmjBgJElNGDCSpCYMGElSEwaMJKkJA0aS1IQBI0lqwoCRJDVhwEiSmjBgJElNGDCSpCaaBkySQ5Jcm2R1kpNnaHdgkvVJjhqavijJlUkubVmnJGnymgVMkkXAGcChwH7AMUn220C7U4HLR6zmVcA1rWqUJLXTcg/mIGB1VV1XVXcC5wOHj2h3InAhcPPgxCR7As8G3tOwRklSIy0DZilw/cD4mn7a3ZIsBY4Ezhyx/OnAa4C7ZtpIkuOSrEiyYu3atVtUsCRpcloGTEZMq6Hx04GTqmr9ry2YPAe4uapWbmwjVXVWVU1V1dSSJUs2u1hJ0mQtbrjuNcBeA+N7AjcOtZkCzk8CsDtwWJJ1wMHA7yY5DNgO2DnJeVX1gob1SpImqGXAXAHsk2Q5cANwNHDsYIOqWj49nOQc4NKquhi4GHhtP/0pwKsNF0maX5oFTFWtS3IC3dVhi4Czq2pVkuP7+aPOu0iSFohUDZ8Wmb+mpqZqxYoVc12GJM0bSVZW1VSLdXsnvySpCQNGktSEASNJasKAkSQ1YcBIkpowYCRJTRgwkqQmDBhJUhMGjCSpCQNGktSEASNJasKAkSQ1YcBIkpowYCRJTRgwkqQmDBhJUhMGjCSpCQNGktSEASNJasKAkSQ1YcBIkpowYCRJTRgwkqQmDBhJUhMGjCSpCQNGktSEASNJasKAkSQ1YcBIkpowYCRJTRgwkqQmDBhJUhMGjCSpCQNGktREqmqua5iYJGuBXwC3zHUtjezOwu0bLOz+2bf5ayH3b3dgx6pa0mLlCypgAJKsqKqpua6jhYXcN1jY/bNv89dC7l/rvnmITJLUhAEjSWpiIQbMWXNdQEMLuW+wsPtn3+avhdy/pn1bcOdgJElbh4W4ByNJ2goYMJKkJra6gElySJJrk6xOcvKI+Uny1/38ryY5YGDe2UluTnL10DK7JfnnJN/sf+86NP/BSX6e5NXtejb7fUuyf5IvJFmV5GtJtlso/Uty7yTn9v26Jslr52Hffr9/be5KMjU077X9uq5N8qx2PZvdviV5RpKV/eu2MslTW/at3+asvnb9/Pn8mTLT+3LTPlOqaqv5ARYB3wIeAmwDfAXYb6jNYcDHgACPBb40MO9JwAHA1UPLvA04uR8+GTh1aP6FwIeBVy+UvgGLga8Cj+zH7wcsWkD9OxY4vx/eAfgOsGye9e1hwL7Ap4Gpgen79dvYFljeb7vJazcHfXs0sEc//Ajghlbvybno38D8+fyZsqHXbpM/U7a2PZiDgNVVdV1V3QmcDxw+1OZw4H3V+SKwS5IHAVTVZ4EfjVjv4cC5/fC5wBHTM5IcAVwHrJpgP0aZ7b49E/hqVX2lX/6HVbV+kh0aMtv9K2DHJIuB7YE7gZ9NsD+DmvStqq6pqmtHbO9wuvC8o6q+Dazua2hhVvtWVVdW1Y396CpguyTbTrA/w2b7tZv3nykz9G2TP1O2toBZClw/ML6mn7apbYY9oKpuAuh/3x8gyY7AScCbt6Dmcc1q34DfAirJ5Um+nOQ1m135eGa7fxfQPRboJuB7wNuralRATUKrvm3J9iZltvs26HnAlVV1xwTWtSGz2r8F8pmyIZv8mbJ4MzfUSkZMG76Oepw243ozcFpV/TwZtdqJmu2+LQaeABwI3AZ8KsnKqvrUZq5vY2a7fwcB64E9gF2Bf03yyaq6bjPXN5PZ7tsk1zWJbU28niQPB06l+19xS36mbLzNuDb5M2VrC5g1wF4D43sCN25Gm2E/SPKgqrqp3z28uZ9+MHBUkrcBuwB3JfllVb1zczswg9nu2xrgM1V1C0CSy+iOt7YKmNnu37HAx6vqV8DNST4HTNEdmpi0Vn3bku1Nymz3jSR7AhcBL6yqb23uesY02/1bCJ8pM21vkz5TtrZDZFcA+yRZnmQb4GjgkqE2lwAv7K+OeCzw0+lDKDO4BHhRP/wi4B8BquqJVbWsqpYBpwN/0eiNALPcN+ByYP8kO/TnKZ4MfH0SHdmA2e7f94Cn9uvake4E5jcm0ZERWvVtQy4Bjk6ybZLlwD7Av21u8Rsxq31LsgvwUeC1VfW5Lah7XLPavwXymbIhm/6ZMtMVAHPxQ3fVw7/TXR3x+n7a8cDx/XCAM/r5X+PXr3L4IN0x+V/Rpe3L6p6rHT4FfLP/vduI7Z5Cwys+5qJvwAvoTjReDbxtIb12wH3ortJZ1b/J/2Qe9u3IfvwO4AfA5QPLvL5f17XAoQulb8Ab6M6dXTXwc/+F0r+h7Z7C/PxMmel9uUmfKT4qRpLUxNZ2iEyStEAYMJKkJgwYSVITBowkqQkDRpLUhAEjbYEky4afRruR9i9OskfLmqSthQEjza4X0z3eRlrwDBhpyy1O9900X01yQX+n82OSfCbdd55cnuRBSY6ie5zN+5NclWT7JG9MckWSq5OclVl4gJU0W7zRUtoCSZYB3waeUFWfS3I2cA3d3dCHV9XaJP8NeFZVvTTJp+nu7l7RL79b9U+BTvIPwIeq6p/moi/SpG1tD7uU5qPr657nap0HvI7uy7T+ud8hWUT3SI5Rfqd/7PkOwG50j+EwYLQgGDDSlhs+DHArsKqqHjfTQum+bvZv6Z4PdX2SU4CmX2stzSbPwUhb7sFJpsPkGOCLwJLpaUnu3X//CXThs1M/PB0mtyS5D3DUbBUszQYDRtpy1wAvSvJVusNcf0MXFqcm+QrdE4N/u297DnBmkqvonlb7brqn3F5M9/h1acHwJL8kqQn3YCRJTRgwkqQmDBhJUhMGjCSpCQNGktSEASNJasKAkSQ18f8BwpYvV3eFvUQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta epsilon\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta theta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta zeta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta eta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta mu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta nu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta lamb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta kappa\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta gamma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta epsilon\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta theta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta zeta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AeyXZk4c+JmF34MkDj01asO6/7x7urwfYabsduOu+u+Z6OFJzWzvt9npgBV3QXD1SfgfdR2RL2gb7POUQTnrJ6Xzla5/jpS89aa6HIzU3rQeLJnlTVf1xg/EMwgeLStLMPRYeLHpekrclObcf0EFJXjbEgCRJC9+0w4fus3Oe36+vB941yIgkSQvedMPnqVX1XuB+gKq6h4duPpAkaUamGz73JdkZKIAkTwXuHWxUkqQFbbpPOHgH8AlgaZILgB8DXjvUoCRJC9t0w+d04FzgdrrTbSuAdwOfHmJQkqSFbbqn3Q4ETgQmqurvq2oTMMjtd5KkhW+64XM73UcqPCnJ3yX5oeGGJEla6KYbPqmqzf0DRi+m+yC5vYcbliRpIZvuNZ9ztixU1YeTXAe8cZghSZIWumkd+VTVn46tr66qX91auyRHJ7k+ybokp09Rf3CSK5Pcm+S0kfKlST6VZG2SNUlOnaLtaUkqyV7TmYMk6bFjukc+M5ZkEd3DR19K90SEVUlWVtVXRja7DTgFOG6s+WbgzVV1dZLdgNVJLt/SNsnSvt+bhhq/JGk4073msy0OB9ZV1Y1VdR9wIXDs6AZVtbGqVtE/OWGkfENVXd0v3wmsBfYb2eT9wG/R/9GrJGl+GTJ89gNuHllfz38OkGlJsgw4DLiqX/854FtV9cWttDsxyWSSyU2bNs10t5KkAQ0ZPlM9+21GRypJdqW7u25FVd2RZBfgrcDbt9a2qs6tqomqmliyZMlMditJGtiQ4bMeWDqyvj9wy3QbJ9meLnguqKpL+uKn0v3B6xeTfKPv8+ok+8zKiCVJTQx2wwGwCjgoyYHAt4DjgVdNp2GSAB8C1lbV+7aUV9V1jPx9UR9AE1V16yyOW5I0sMHCp6o2JzkZuAxYBJxXVWuSnNTXn9MfsUwCuwMPJlkBLAcOAV4DXJfk2r7LM6rq0qHGK0lqZ8gjH/qwuHSsbPQPVr9Nd+ps3L8wjc8Lqqplj3KIkqQ5MOQ1H0mSpmT4SJKaM3wkSc0ZPpKk5gwfSVJzho8kqTnDR5LUnOEjSWrO8JEkNWf4SJKaM3wkSc0ZPpKk5gwfSVJzho8kqTnDR5LUnOEjSWrO8JEkNWf4SJKaM3wkSc0ZPpKk5gwfSVJzho8kqblBwyfJ0UmuT7IuyelT1B+c5Mok9yY5baR8aZJPJVmbZE2SU0fq/neSryb5UpKPJdljyDlIkmbfYOGTZBFwNnAMsBx4ZZLlY5vdBpwCnDVWvhl4c1U9EzgSeONI28uBZ1fVIcDXgLcMNAVJ0kCGPPI5HFhXVTdW1X3AhcCxoxtU1caqWgXcP1a+oaqu7pfvBNYC+/Xr/1hVm/tNPw/sP+AcJEkDGDJ89gNuHllf35fNSJJlwGHAVVNU/yrw8W0ZnCRp7gwZPpmirGbUQbIrcDGwoqruGKt7K93puQsepu2JSSaTTG7atGkmu5UkDWzI8FkPLB1Z3x+4ZbqNk2xPFzwXVNUlY3UnAC8DXl1VUwZaVZ1bVRNVNbFkyZIZD16SNJwhw2cVcFCSA5PsABwPrJxOwyQBPgSsrar3jdUdDfw28HNVdfcsj1mS1MDioTquqs1JTgYuAxYB51XVmiQn9fXnJNkHmAR2Bx5MsoLuzrhDgNcA1yW5tu/yjKq6FPgAsCNweZdRfL6qThpqHpKk2TdY+AD0YXHpWNk5I8vfZuq71f6Fqa8ZUVVPm80xSpLa8wkHkqTmDB9JUnOGjySpOcNHktSc4SNJas7wkSQ1Z/hIkpozfCRJzRk+kqTmDB9JUnOGjySpOcNHktSc4SNJas7wkSQ1Z/hIkpozfCRJzRk+kqTmDB9JUnOGjySpOcNHktSc4SNJas7wkSQ1Z/hIkpobNHySHJ3k+iTrkpw+Rf3BSa5Mcm+S00bKlyb5VJK1SdYkOXWk7olJLk9yQ/99zyHnIEmafYOFT5JFwNnAMcBy4JVJlo9tdhtwCnDWWPlm4M1V9UzgSOCNI21PB66oqoOAK/p1SdI8MuSRz+HAuqq6saruAy4Ejh3doKo2VtUq4P6x8g1VdXW/fCewFtivrz4WOL9fPh84brAZSJIGMWT47AfcPLK+nocCZNqSLAMOA67qi55UVRugCylg74dpd2KSySSTmzZtmuluJUkDGjJ8MkVZzaiDZFfgYmBFVd0xk7ZVdW5VTVTVxJIlS2bSVJI0sCHDZz2wdGR9f+CW6TZOsj1d8FxQVZeMVH0nyb79NvsCG2dhrJKkhoYMn1XAQUkOTLIDcDywcjoNkwT4ELC2qt43Vr0SOKFfPgH421karySpkcVDdVxVm5OcDFwGLALOq6o1SU7q689Jsg8wCewOPJhkBd2dcYcArwGuS3Jt3+UZVXUp8B7goiSvA24CXjHUHCRJw0jVjC7DzEsTExM1OTk518OQpHklyeqqmhiib59wIElqzvCRJDVn+EiSmjN8JEnNGT6SpOYMH0lSc4aPJKk5w0eS1JzhI0lqzvCRJDVn+EiSmjN8JEnNGT6SpOYMH0lSc4aPJKk5w0eS1JzhI0lqzvCRJDVn+EiSmjN8JEnNGT6SpOYMH0lSc4aPJKk5w0eS1Fyqaq7HMLgkm4D/AG6d67EMaC8W7vyc2/y1kOf3eJjbAVW1ZIgdPC7CByDJZFVNzPU4hrKQ5+fc5q+FPD/n9uh42k2S1JzhI0lq7vEUPufO9QAGtpDn59zmr4U8P+f2KDxurvlIkh47Hk9HPpKkxwjDR5LU3LwJnyRHJ7k+ybokp09RnyR/1Nd/KclzR+rOS7IxyZfH2jwxyeVJbui/7zlW/yNJ7kpy2nAz+8G+ms4vySFJrkyyJsl1SXZaCHNLsn2S8/s5rU3ylqHmNeDcXtG/Lg8mmRire0vf1/VJfnq4mf1gf83ml+SlSVb3r93qJEctlLmN1Dd5T5mD38uZv59U1WP+C1gEfB14CrAD8EVg+dg2PwN8HAhwJHDVSN1PAM8FvjzW5r3A6f3y6cDvj9VfDPw1cNpCmh+wGPgS8Jx+/YeBRQtkbq8CLuyXdwG+ASybZ3N7JvAM4NPAxEj58n4fOwIH9vse5HWbo/kdBjy5X3428K2FMreR+sHfU+bgddum95P5cuRzOLCuqm6sqvuAC4Fjx7Y5FvhIdT4P7JFkX4Cq+gxw2xT9Hguc3y+fDxy3pSLJccCNwJpZnMfDaT2/nwK+VFVf7Nt/t6oemM0JjWg9twKekGQxsDNwH3DHLM5n1CBzq6q1VXX9FPs7li5Y762qfwPW9WMYStP5VdU1VXVLv7oG2CnJjrM4n1GtX7uW7ymt57ZN7yfzJXz2A24eWV/fl810m3FPqqoNAP33vQGSPAH4beCdj2LMM9F0fsDTgUpyWZKrk/zWNo9861rP7aN0j1LaANwEnFVVU4XXbBhqbo9mf7Op9fxG/SJwTVXdOwt9TaXp3Bq/p7R+3bbp/WTxNu6stUxRNn6P+HS2ma53Au+vqruSqbqdda3ntxj4ceB5wN3AFUlWV9UV29jfI2k9t8OBB4AnA3sCn03yyaq6cRv7eySt5zabfc3W/mZ9TEmeBfw+3f+oh7KQ31PmxfvJfAmf9cDSkfX9gVu2YZtx30myb1Vt6A85N/blRwAvT/JeYA/gwSTfr6oPbOsEtqL1/NYD/1xVtwIkuZTuHO8Q4dN6bq8CPlFV9wMbk3wOmKA73THbhprbo9nfbGo9P5LsD3wM+OWq+vq29jMNrefW8j1lLn4vZ/x+Ml9Ou60CDkpyYJIdgOOBlWPbrAR+ub+L40jg37eclnkEK4ET+uUTgL8FqKoXVNWyqloG/AHwuwMGDzSeH3AZcEiSXfprIy8EvjIbE5lC67ndBBzV9/UEuoupX52NiUxhqLk9nJXA8Ul2THIgcBDwhW0d/DQ0nV+SPYB/AN5SVZ97FOOejqZza/ye0vr3ctveT7Z2R8Jj5Yvu7oyv0d3F8da+7CTgpH45wNl9/XX857sx/pLuGsD9dCn9unrorowrgBv670+cYr9nMvDdbnMxP+CX6C58fhl470KZG7Ar3d1Ea/p/AL85D+f28/36vcB3gMtG2ry17+t64Jh5+ns55fyAt9Fdr7t25GvvhTC3sf2eyfB30Lb+vZzx+4mP15EkNTdfTrtJkhYQw0eS1JzhI0lqzvCRJDVn+EiSmjN8pAElOTOP8ATj0fokr03y5Hajk+aO4SM9dryW7rFA0oJn+EizLMlb032WyifpHkFPkqcm+US6z6n5bJKDx9q8nO4xQBckuTbJzknenmRVki8nOTeNHjQotWD4SLMoyY/SPc7kMOAX6B62CHAu8Kaq+lHgNOBPRttV1UeBSeDVVXVoVd0DfKCqnldVz6b7eIiXNZqGNLj58mBRab54AfCxqrobIMlKYCfg+cBfjxy8TOdzan6yfzz9LsAT6R5f8nezPmJpDhg+0uwbf2bVdsDtVXXodDtI9zHEf0L3zK2bk5xJF2LSguBpN2l2fQb4+f6azW7Az9J9xsm/JXkFQP8k4edM0fZOYLd+eUvQ3JpkV+DlA49basrwkWZRVV0N/BXdE5kvBj7bV70aeF2SL9KdPhv/WGOADwPnJLmW7snBH6R74vDf0D0mX1owfKq1JKk5j3wkSc0ZPpKk5gwfSVJzho8kqTnDR5LUnOEjSWrO8JEkNff/ARCZf5gsTewbAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta eta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta mu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta nu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta lamb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta kappa\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "delta tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma epsilon\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma theta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma zeta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma eta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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TPDxR0jQc/PgjOfV5Z/HMpc/k9S//37M9HKmTyV7aevLgSvv32H9m5ocjPXwcu+LVHLvi1bM9DKmzcc9Ikrw5yTbgyCT3tF/bgC3Ax/fICCVJD2njBklV/XFVLQTOBV4HvKddPwq4cg+MT5L0EDfZt//uR/MhxBPb9W3Aeb2MSJI0p0z2HsnRVfX0JDcCVNXd7Z/PlSQ9zE32jOSB9gZ7ASRZBOzsbVSSpDljskHyAZp7IgcleSfweeCPehuVJGnOmOwHEi9OshZ4LhBgZVVt6HVkkqQ5YdKPkW+fr/WVHsciSZqD/MNWkqRODBJJUie9BkmSFUluTrIxyVlj1B+R5Lok9yc5c6B8aZJrk2xIsj7JGWP0PTNJJTmwzzlIksY36XskU9W+Xfg84PnAJmBNktVV9W8Dze4CTgdWjuq+A3hjVd2QZCGwNsk1I32TLG23+82+xi9Jmpw+z0iOBjZW1S1VtR24BDhhsEFVbamqNcADo8o3V9UN7fI2YAOweKDJe4Hfpf1ciyRp9vQZJIuB2wbWN/HDYTApSZbRPNvr+nb9RcC3qupLE/Q7JclwkuGtW/2rwJLUlz6DJGOUTekMIsm+wOXAqqq6J8mjgN8D3jJR36q6oKqGqmpo0aJFU9mtJGkK+gySTcDSgfUlTOHP8ybZiyZELq6qK9rinwYOBb6U5NZ2mzckOXhGRixJmrLebrYDa4DDkhwKfIvmycEvnUzHJAE+DGyoqnNHyqvqJuCggXa3AkNVdecMjluSNAW9BUlV7UhyGnA1MA+4sKrWJzm1rT+/PZMYpnlM/c4kq4DlwJHAScBNSda1mzy7qq7qa7ySpOnp84yE9j/+q0aVnT+w/G2ay1OjfZ6x77GM3v6yjkOUJHXkJ9slSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSeqk1yBJsiLJzUk2JjlrjPojklyX5P4kZw6UL01ybZINSdYnOWOg7k+SfCXJ/0tyZZL9+5yDJGl8vQVJknnAecDxwHLgJUmWj2p2F3A6cM6o8h3AG6vqScCxwOsG+l4DPKWqjgT+HXhzT1OQJE1Cn2ckRwMbq+qWqtoOXAKcMNigqrZU1RrggVHlm6vqhnZ5G7ABWNyuf6qqdrRNvwgs6XEOkqQJ9Bkki4HbBtY3tWVTkmQZcBRw/RjVvwl8cjqDkyTNjD6DJGOU1ZQ2kOwLXA6sqqp7RtX9Hs0lsIt30/eUJMNJhrdu3TqV3UqSpqDPINkELB1YXwLcPtnOSfaiCZGLq+qKUXUnAy8EXlZVY4ZTVV1QVUNVNbRo0aIpD16SNDl9Bska4LAkhyZZAJwIrJ5MxyQBPgxsqKpzR9WtAN4EvKiqvjfDY5YkTdH8vjZcVTuSnAZcDcwDLqyq9UlObevPT3IwMAzsB+xMsormHV5HAicBNyVZ127y7Kq6Cvgg8EjgmiZv+GJVndrXPCRJ4+stSADa//ivGlV2/sDytxn7XVefZ+x7LFTVE2ZyjJKkbvxkuySpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqROeg2SJCuS3JxkY5Kzxqg/Isl1Se5PcuZA+dIk1ybZkGR9kjMG6n4iyTVJvtp+P6DPOUiSxtdbkCSZB5wHHA8sB16SZPmoZncBpwPnjCrfAbyxqp4EHAu8bqDvWcCnq+ow4NPtuiRplvR5RnI0sLGqbqmq7cAlwAmDDapqS1WtAR4YVb65qm5ol7cBG4DFbfUJwEXt8kXAyt5mIEmaUJ9Bshi4bWB9E7vCYNKSLAOOAq5vix5bVZuhCRzgoN30OyXJcJLhrVu3TnW3kqRJ6jNIMkZZTWkDyb7A5cCqqrpnKn2r6oKqGqqqoUWLFk2lqyRpCvoMkk3A0oH1JcDtk+2cZC+aELm4qq4YqLojyePaNo8DtszAWCVJ09RnkKwBDktyaJIFwInA6sl0TBLgw8CGqjp3VPVq4OR2+WTg4zM0XknSNMzva8NVtSPJacDVwDzgwqpan+TUtv78JAcDw8B+wM4kq2je4XUkcBJwU5J17SbPrqqrgHcBlyZ5FfBN4MV9zUGSNLFUTem2xZw0NDRUw8PDsz0MSZpTkqytqqGJ2vnJdklSJwaJJKkTg0SS1IlBIknqxCCRJHVikEiSOjFIJEmdGCSSpE4MEklSJwaJJKkTg0SS1IlBIknqxCCRJHVikEiSOjFIJEmdGCSSpE4MEklSJwaJJKkTg0SS1IlBIknqxCCRJHVikEiSOjFIJEmdGCSSpE5SVbM9ht4l2Qp8Y7bHsYcdCNw524N4CPA47OKx2MVjsct4x+KQqlo00QYeFkHycJRkuKqGZnscs83jsIvHYhePxS4zcSy8tCVJ6sQgkSR1YpD8+LpgtgfwEOFx2MVjsYvHYpfOx8J7JJKkTjwjkSR1YpBIkjoxSOaYJCuS3JxkY5Kzxmn3jCQPJvmNUeXzktyY5BP9j7ZfXY5FkluT3JRkXZLhPTPifnQ8DvsnuSzJV5JsSPKze2bU/ZjusUhyePtaGPm6J8mqPTbwHnR8XbwhyfokX07y10n2HndnVeXXHPkC5gFfAx4PLAC+BCzfTbvPAFcBvzGq7reBjwGfmO35zOaxAG4FDpzteTwEjsNFwKvb5QXA/rM9p9k6FqPqv03zYbxZn9eePhbAYuDrwD7t+qXAK8bbn2ckc8vRwMaquqWqtgOXACeM0e71wOXAlsHCJEuAXwY+1PdA94BOx+LHyLSPQ5L9gF8APgxQVdur6ju9j7g/M/WaeC7wtaqay0/D6Hos5gP7JJkPPAq4fbydGSRzy2LgtoH1TW3ZDyRZDPwqcP4Y/d8H/C6ws6fx7Uldj0UBn0qyNskpvY2yf12Ow+OBrcBftJc7P5Tk0X0OtmddXxMjTgT+esZHt2dN+1hU1beAc4BvApuB71bVp8bbmUEyt2SMstHv334f8KaqevCHOiYvBLZU1dqexranTftYtJ5VVU8Hjgdel+QXZnh8e0qX4zAfeDrwZ1V1FPCfwG6vpc8BXV8TJFkAvAj4m5kd2h7X5f+KA2jOXg4FfhJ4dJKXj7ez+dMfp2bBJmDpwPoSfvSUcwi4JAk0D2N7QZIdwDHAi5K8ANgb2C/JR6tq3BfIQ9i0j0VV/W1V3Q5QVVuSXElzKeBz/Q97xnV5TXwR2FRV17ftLmNuB0mn10RbfzxwQ1Xd0fNY+9bldbEX8PWq2gqQ5ArgmcBHd7u32b4p5NeUbqDNB26h+U1h5Abak8dp/xHGvpn4HOb+zfZpHwvg0cDCgeUvACtme06z8ZoA/hk4vF1+G/Ansz2n2ToWbdklwCtney6zeSxofulcT3NvJDRvyHj9ePvzjGQOqaodSU4DrqZ5t8WFVbU+yalt/XjXfX+sdDwWjwWubH8Tmw98rKr+oe8x92EGXhOvBy5uL+ncAryy1wH3qOuxSPIo4PnAa3ofbM+6HIuquj7JZcANwA7gRiZ4jIqPSJEkdeLNdklSJwaJJKkTg0SS1IlBIknqxCCRJHVikEiSOjFIJEmd+IFEaZqS/AHwMpqH490JrAW+C5xC82nijcBJVfW9JB8B7gOOAA6h+eDfycDPAtdX1Svabd4LnAc8D7gbOBt4N/BTwKqqWp1kGfBXNJ/KBzitqr7Q83Sl3fKMRJqGJEPArwNHAb9G89wigCuq6hlV9VRgA/CqgW4HAMcBbwD+Dngv8GTgvyZ5Wtvm0cBnq+pngG3AO2g+bf2rwB+2bbYAz6/moZP/HfhAH3OUJsszEml6fg74eFXdB5Dk79rypyR5B7A/sC/NIypG/F1VVZKbgDuq6qa273pgGbAO2A6MPK7lJuD+qnqg7bOsLd8L+GAbPg8CT+xhftKkGSTS9Iz1mG5oHn63sqq+lOQVNA/IHHF/+33nwPLI+si/xQdq13OLftCuqna2f2QImjOaO4Cn0lxV+P60ZyHNAC9tSdPzeeBXkuydZF+avzwJsBDYnGQvmvsnfXgMsLmqdgIn0TyUT5o1npFI01BVa5Kspnk89zeAYZob7X8AXN+W3UQTLDPtT4HLk7wYuJbmD1JJs8an/0rTlGTfqrq3ffz454BTquqG2R6XtKd5RiJN3wVJltP8xcmLDBE9XHlGIknqxJvtkqRODBJJUicGiSSpE4NEktSJQSJJ6uT/AyQ72wsJmew5AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma mu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma nu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma lamb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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IiGoSMhERUU1CJiIiqknIRERENW0/6l/Sy4C5NN9ceY/t71brVUREHBDaOpKR9G7gbuCXgV8BVkn6jZodi4iI/V+7RzK/D5ze//XGkn4a+Dpwba2ORUTE/q/dazIbgWda5p8BNox9dyIi4kCyxyMZSReXl48Bd0n6W5prMgtpTp9FRETs1lCny44oP9eVqd/f1ulOREQcSPYYMrYv21sdiYiIA0+7d5f1SLpJ0jck3d8/tbHcPEkPSeqTdMkg9ZJ0Zam/X9KcUj5R0t2S7pO0VtKLwk7S70mypKNbyj5Q1vWQpDe2M7aIiKin3bvLrqe5w+wB4Pl2FpDUBVwFnEtz48A9klba/lZLs/nArDKdAVxdfm4Hzra9VdJ44GuSbrW9qqx7elnvd1q2dxJwAXAycAzwZUkn2N7Z5hgj9gnPPPk4vV9ZzpRpr+SVcxd0ujsRo9JuyGyxvXKY654L9NleDyBpOc0NA60hsxC4zrZpPnszWdIU25uAraXN+DK5ZbkrgPfzwmtDC4HltrcDD0vqK33412H2O6KjPvfZ36J3y30cqvF8dPJLmXpCT6e7FDFi7YbMRyR9DvgHmqMMAGx/cQ/LTOWFtzlvpDlKGarNVGBTORJaDbwCuMr2XQCSzgMes32fpIHrWjXIul5A0iJgEcCxxx67h+5HdMaPntvKBI1nJ8+z/dmtQy8QsQ9rN2QuAl5Jc0TRf7rMwJ5CRoOUud025TTXbEmTgZsknQKsBz4E/OIIt4fta4BrAHp6el5UH9Fp737HFXzp7z/FjGmvYuarzup0dyJGpd2QOc32q4a57o3A9Jb5acDjw21j+ylJdwLzgNuAmUD/Ucw04BuS5ra5vYh93suOP5V3ve8vOt2NiDHR7if+V5UL68NxDzBL0kxJE2guyg+8rrMSuLDcZXYm8LTtTZK6yxEMkiYB5wAP2n7A9kttz7A9gyZY5pSHda4ELpB0qKSZNDcT5AOjEREd1O6RzGuAd0p6mOaajADbPnV3C9jeIWkJzdFHF3Ct7bWSFpf6pcAtwAKgD9hGc1oOYAqwrFyXGQessH3znjpY1r2C5saCHcB7c2dZRERnqbmxa4hG0nGDldt+dMx7tBf19PS4t7e3092IiNivSFptu63bHts6kukPE0kvBSaOom8REXEQafcT/+dJ+nfgYeArwCPArRX7FRERB4B2L/x/FDgT+DfbM4E3AP9SrVcREXFAaDdkfly+sGycpHG2/wmYXa9bERFxIGj37rKnJB0OfBW4XtJmmju4IiIidqvdI5mFwLPA7wJfovlumTfX6lRERBwY2r277Ects8sq9SUiIg4wQ3398jMM8vwvdn0Y88gqvYqIiAPCUN+MecSe6iMiIvak3WsyERERw5aQiYiIahIyERFRTUImIiKqSchEREQ1CZmIiKgmIRMREdUkZCIiopqETEREVJOQiYiIahIyERFRTdWQkTRP0kOS+iRdMki9JF1Z6u+XNKeUT5R0t6T7JK2VdFnLMh8tbddIul3SMaV8hqRnS/kaSUtrji0iIoZWLWQkdQFXAfOBk4C3STppQLP5wKwyLQKuLuXbgbNtn0bzDZzzJJ1Z6j5h+1Tbs4GbgQ+3rG+d7dllWlxhWBERMQw1j2TmAn2219t+DlhO8+VnrRYC17mxCpgsaUqZ31rajC+TAWz/sGX5wxj8qwgiImIfUDNkpgIbWuY3lrK22kjqkrQG2AzcYfuu/kaSPi5pA/B2XngkM1PSvZK+Ium1g3VK0iJJvZJ6t2zZMsKhRUREO2qGjAYpG3jUsds2tneWU2LTgLmSTvlJA/tDtqcD1wNLSvEm4FjbpwMXAzdIetGXqtm+xnaP7Z7u7u7hjikiIoahZshsBKa3zE8DHh9uG9tPAXcC8wbZxg3AW0u77bafLK9XA+uAE0bc+4iIGLWaIXMPMEvSTEkTgAuAlQParAQuLHeZnQk8bXuTpG5JkwEkTQLOAR4s87Nalj+vpby73GyApONpbiZYX210ERExpD1+/fJo2N4haQlwG9AFXGt7raTFpX4pcAuwAOgDtgEXlcWnAMtKaIwDVti+udT9iaQTgeeBR4H+u8jOAi6XtAPYCSy2/f1a44uIiKHJPnhvzurp6XFvb2+nuxERsV+RtNp2Tztt84n/iIioJiETERHVJGQiIqKahExERFSTkImIiGoSMhERUU1CJiIiqknIRERENQmZiIioJiETERHVJGQiIqKahExERFSTkImIiGoSMhERUU1CJiIiqknIRERENQmZiIioJiETERHVJGQiIqKahExERFRTNWQkzZP0kKQ+SZcMUi9JV5b6+yXNKeUTJd0t6T5JayVd1rLMR0vbNZJul3RMS90HyroekvTGmmOLiIihVQsZSV3AVcB84CTgbZJOGtBsPjCrTIuAq0v5duBs26cBs4F5ks4sdZ+wfart2cDNwIfL9k4CLgBOBuYBnyl9iIiIDql5JDMX6LO93vZzwHJg4YA2C4Hr3FgFTJY0pcxvLW3Gl8kAtn/Ysvxh/eVlXcttb7f9MNBX+hARER1SM2SmAhta5jeWsrbaSOqStAbYDNxh+67+RpI+LmkD8HbKkUyb20PSIkm9knq3bNkyknFFRESbaoaMBilzu21s7yynxKYBcyWd8pMG9odsTweuB5YMY3vYvsZ2j+2e7u7uoUcREREjVjNkNgLTW+anAY8Pt43tp4A7aa6zDHQD8NZhbC8iIvaimiFzDzBL0kxJE2guyq8c0GYlcGG5y+xM4GnbmyR1S5oMIGkScA7wYJmf1bL8ef3lZV0XSDpU0kyamwnurjS2iIhowyG1Vmx7h6QlwG1AF3Ct7bWSFpf6pcAtwAKai/TbgIvK4lOAZeXusHHACts3l7o/kXQi8DzwKNC/vrWSVgDfAnYA77W9s9b4IiJiaLJfdNnioNHT0+Pe3t5OdyMiYr8iabXtnnba5hP/ERFRTUImIiKqSchEREQ1CZmIiKgmIRMREdUkZCIiopqETEREVJOQiYiIahIyERFRTUImIiKqSchEREQ1CZmIiKgmIRMREdUkZCIiopqETEREVJOQiYiIahIyERFRTUImIiKqSchEREQ1CZmIiKimashImifpIUl9ki4ZpF6Sriz190uaU8onSrpb0n2S1kq6rGWZT0h6sLS/SdLkUj5D0rOS1pRpac2xRUTE0KqFjKQu4CpgPnAS8DZJJw1oNh+YVaZFwNWlfDtwtu3TgNnAPElnlro7gFNsnwr8G/CBlvWtsz27TIsrDCsiIoah5pHMXKDP9nrbzwHLgYUD2iwErnNjFTBZ0pQyv7W0GV8mA9i+3faOUrcKmFZxDBERMQo1Q2YqsKFlfmMpa6uNpC5Ja4DNwB227xpkG78B3NoyP1PSvZK+Ium1g3VK0iJJvZJ6t2zZMqwBRUTE8NQMGQ1S5nbb2N5pezbNkcpcSae8YEHpQ8AO4PpStAk41vbpwMXADZKOfNHK7Wts99ju6e7uHs54IiJimGqGzEZgesv8NODx4bax/RRwJzCvv0zSO4E3AW+33R9K220/WV6vBtYBJ4zBOCIiYoRqhsw9wCxJMyVNAC4AVg5osxK4sNxldibwtO1Nkrpb7hqbBJwDPFjm5wF/AJxne1v/isoyXeX18TQ3E6yvOL6IiBjCIbVWbHuHpCXAbUAXcK3ttZIWl/qlwC3AAqAP2AZcVBafAiwroTEOWGH75lL3aeBQ4A5JAKvKnWRnAZdL2gHsBBbb/n6t8UVExNBUzjYdlHp6etzb29vpbkRE7Fckrbbd007bfOI/IiKqSchEREQ1CZmIiKgmIRMREdUkZCIiopqETEREVJOQiYiIahIyERFRTUImIiKqSchEREQ1CZmIiKgmIRMREdUkZCIiopqETEREVJOQiYiIahIyERFRzUH9pWWStgCPdrofe9nRwPc63Yl9RPZFI/thl+yLXfa0L46z3d3OSg7qkDkYSept9xvtDnTZF43sh12yL3YZq32R02UREVFNQiYiIqpJyBx8rul0B/Yh2ReN7Iddsi92GZN9kWsyERFRTY5kIiKimoRMRERUk5A5QEiaJ+khSX2SLtlDu1dL2inpVwaUd0m6V9LN9Xtb12j2haRHJD0gaY2k3r3T43pGuS8mS7pR0oOSvi3p5/dOr+sY6b6QdGJ5P/RPP5T0O3ut42NslO+J35W0VtI3Jf2VpIlDbtB2pv18ArqAdcDxwATgPuCk3bT7R+AW4FcG1F0M3ADc3OnxdHJfAI8AR3d6HPvIvlgGvLu8ngBM7vSYOrUvBtR/l+bDiB0f197cD8BU4GFgUplfAbxrqG3mSObAMBfos73e9nPAcmDhIO3eB/w1sLm1UNI04JeAz9Xu6F4wqn1xgBnxvpB0JHAW8BcAtp+z/VT1HtczVu+LNwDrbO+vTwoZ7X44BJgk6RDgJcDjQ20wIXNgmApsaJnfWMp+QtJU4C3A0kGW/3Pg/cDzlfq3N412Xxi4XdJqSYuq9XLvGM2+OB7YAvxlOY36OUmH1exsZaN9X/S7APirMe/d3jPi/WD7MeCTwHeATcDTtm8faoMJmQODBikbeG/6nwN/YHvnCxaU3gRstr26Ut/2thHvi+IXbM8B5gPvlXTWGPdvbxrNvjgEmANcbft04EfAbs/f7wdG+75A0gTgPOD/jW3X9qrR/F9xFM1Rz0zgGOAwSe8YaoOHjKyfsY/ZCExvmZ/Giw9je4DlkqB58N0CSTuAM4DzJC0AJgJHSvqC7SHfPPuoEe8L239j+3EA25sl3URzeuGr9btdxWjeF6uAjbbvKu1uZP8OmVG9L0r9fOAbtp+o3NeaRvOeGA88bHsLgKQvAv8Z+MIet9jpC1GZxuRi3iHAepq/MPov5p28h/afZ/CLmq9n/7/wP+J9ARwGHNHy+uvAvE6PqVPvC+CfgRPL60uBT3R6TJ3aF6VsOXBRp8fSqf1A8wfpWpprMaK5MeR9Q20zRzIHANs7JC0BbqO5K+Ra22slLS71ezrHfEAZ5b74GeCm8hfcIcANtr9Uu8+1jMH74n3A9eU00Xrgoqodrmi0+0LSS4BzgfdU72xFo9kPtu+SdCPwDWAHcC9tPHomj5WJiIhqcuE/IiKqSchEREQ1CZmIiKgmIRMREdUkZCIiopqETEREVJOQiYiIavJhzIgxJumPgLfTPIjwe8Bq4GlgEc2nrPuAX7e9TdLngWeBVwLH0Xzg8Z3AzwN32X5XWedW4CrgHOAHwAeBPwWOBX7H9kpJM4D/Q/O0AoAltr9eebgRe5QjmYgxJKkHeCtwOvDLNM+BAvii7VfbPg34NvCbLYsdBZwN/C7wd8AVwMnAqyTNLm0OA+60/XPAM8DHaD6B/hbg8tJmM3Cumwd8/hfgyhpjjBiOHMlEjK3XAH9r+1kASX9Xyk+R9DFgMnA4zWM9+v2dbUt6AHjC9gNl2bXADGAN8BzQ/4ibB4Dttn9clplRyscDny7BtBM4ocL4IoYlIRMxtgZ7lDo0Dxo83/Z9kt5F8zDSftvLz+dbXvfP9/8b/bF3PQPqJ+1sP1++QAqaI6EngNNozlL8x4hHETFGcrosYmx9DXizpImSDqf5xlGAI4BNksbTXK+p4aeATbafB36d5gGIER2VI5mIMWT7HkkraR6h/ijQS3PR/4+Au0rZAzShM9Y+A/y1pF8F/onmi8YiOipPYY4YY5IOt721PB7+q8Ai29/odL8iOiFHMhFj7xpJJ9F80+iyBEwczHIkExER1eTCf0REVJOQiYiIahIyERFRTUImIiKqSchEREQ1/x9TOBX+2MqDoAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma kappa\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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z0jl8zMysdA4fMzMrncPHzMxK5/AxM7PSOXzMzKx0Dh8zMytdw1+jnb6a+jhg3+qyiFjY8xpmZmb1NRQ+kq4ETicLn7uBKcC/AQ4fMzPrt0Yvu70feBfwm4i4GHgz2VcVmJmZ9Vuj4fNSRGwHtkk6GNgAHFVct8zMbE/W6D2fjvRNodcBy4EXgQeL6pSZme3ZGgqfiKh+w+i1kn4AHBwRDxXXLTMz25P1Z7TbicCY6jqSjo6I2wvql5mZ7cEaHe02HzgRWA1sT4sDcPiYmVm/NXrmMyEijiu0J2Zm9prR6Gi3+yT1O3wkTZa0RlKnpMvrlEvSnFT+kKRxubL5kjZIerhmnVskrUzTE5JW5so+ndpaI+ndueUnS1qVyuZIUn/3xczMBk+j4XMjWQCtSSGxSlKvAw4kDQGuIfuD1OOAC+oE2BRgbJpmAnNzZQuAybXtRsT5EXFSRJwEfId06S+1PQ04Pq33zdQHUrszc9vaqV2zXV1s384vfnwTP22/mq0vvdjq7pgNSKOX3eYDFwKr2HHPpy+nAp0RsRZA0mJgKvDLXJ2pwMKICOB+ScMkHRYR6yNiqaQxPTWezl4+CEzKtbU4IrYAv5LUCZwq6Qmy0Xn3pfUWAucC9zS4H2a7hJVLb+Fr3/8sEUH3b5/kvIuvanWXzJrWaPg8GRHt/Wx7JPBUbr4LGN9AnZHA+gbanwg8ExGP5dq6v05bf0zva5fvRNJMsjMkDj/88Aa6YFaeLX/4PUGwl/bi9y/9rtXdMRuQRsPnUUk3A98FtlQX9jHUut59lWiiTk8uABY10FbD24iIecA8gEql0mg/zEpx8ukfYtqGX/Hi5hc45wOfbXV3zAak0fDZjyx0/jy3rK+h1l3A6Nz8KGBdE3V2IqkNOA84uYG2utL7fm3DbFez9777894ZX2p1N8wGRaNPOLi4ibaXAWMlHQk8TTYY4EM1ddqB2el+0HhgY0Q0csntDODRiMhfTmsHbpb0j8AbyAYWPBgRL0vaJGkC8AAwA/jfTeyPmZkNkkb/yHROncUbgY6IuKveOhGxTdJsYAkwBJgfEaslzUrl15J9PcNZQCewGXgl5CQtIvsah+GSuoArI+L6VDyNV19yI7V9K9mAhm3AxyPi5VR8Kdnouf3IBhp4sIGZWQspG2jWRyVpHvBG4F/SoveRPe1gNLA2Ij5RVAdbpVKpREdHR6u7YWa2W5G0PCIqfdVr9J7P0cCkiNiWGp8L/BA4k2z4tZmZWcMa/SPTkcABufkDgDeky1pb6q9iZmZWX6NnPlcBKyXdSzZ0+TTg7yUdAPyooL6ZmdkeqtHRbtdLupvsqQUCPhMR1eHKnyqqc2Zmtmfq9bKbpDem13HAYWRPI3gSeH3+IaBmZmb90deZzyfJHjfztdyy/PC4SZiZmfVTr2c+ETEzvZ0LTI2IdwL/SvY3Pv+94L6ZmdkeqtHRbp+LiN9JejvZ8OoFvPrrD8zMzBrWaPhUnxTwHuDa9FSDocV0yczM9nSNhs/Tkv6Z7Ptz7pa0Tz/WNTMze5VGA+SDZM9omxwRLwB/iodYm5lZkxr9O5/N5L4+IT15upGnT5uZme3El87MzKx0Dh8zMyudw8fMzErn8DEzs9I5fMzMrHQOHzMzK53Dx8zMSufwMTOz0hUaPpImS1ojqVPS5XXKJWlOKn8o/x1BkuZL2iDp4TrrXZbaXS3pqrRsuqSVuWm7pJNS2b2pfrXs0AJ328zM+tDo12j3m6QhwDVkT8HuApZJao+IX+aqTQHGpmk82ZOyx6eyBcDVwMKadt8JTAVOjIgt1SCJiJuAm1KdE4C7ImJlbtXpEdExmPtoZmbNKfLM51SgMyLWRsRWYDFZaORNBRZG5n5gmKTDACJiKfBcnXYvBb4cEVtSvQ116lwALBqk/TAzs0FWZPiMJPva7aqutKy/dWodA0yU9ICkn0g6pU6d89k5fG5Il9yukKR6DUuaKalDUkd3d3cf3TAzs2YVGT71fsFHE3VqtQGHABPInqx9az5MJI0HNkdE/l7R9Ig4AZiYpgvrNRwR8yKiEhGVESNG9NENMzNrVpHh0wWMzs2PAtY1Uadeu7enS3UPAtuB4bnyadSc9UTE0+l1E3Az2SVBMzNrkSLDZxkwVtKRkoaShUJ7TZ12YEYa9TYB2Ji+rqE3dwKTACQdQ/aNqs+m+b2AD5DdXyIta5M0PL3fGzgb2GkEnZmZlaew0W4RsU3SbLIvoRsCzI+I1ZJmpfJrgbuBs4BOYDNwcXV9SYuA04HhkrqAKyPiemA+MD8Nwd4KXBQR1Ut1pwFdEbE215V9gCUpeIYAPwKuK2i3zcysAdrxe9vyKpVKdHR4ZLaZWX9IWh4Rlb7q+QkHZmZWOoePmZmVzuFjZmalc/iYmVnpHD5mZlY6h4+ZmZXO4WNmZqVz+JiZWekcPmZmVjqHj5mZlc7hY2ZmpXP4mJlZ6Rw+ZmZWOoePmZmVzuFjZmalc/iYmVnpHD5mZlY6h4+ZmZXO4WNmZqVz+JiZWekKDR9JkyWtkdQp6fI65ZI0J5U/JGlcrmy+pA2SHq6z3mWp3dWSrkrLxkh6SdLKNF2bq3+ypFVpO3Mkqah9NjOzvhUWPpKGANcAU4DjgAskHVdTbQowNk0zgbm5sgXA5DrtvhOYCpwYEccDX80VPx4RJ6VpVm753NR+dVs7tWtmZuUp8sznVKAzItZGxFZgMVlo5E0FFkbmfmCYpMMAImIp8Fyddi8FvhwRW1K9Db11IrV3cETcFxEBLATOHcB+mZnZABUZPiOBp3LzXWlZf+vUOgaYKOkBST+RdEqu7EhJK9LyibltdDWyDUkzJXVI6uju7u6jG2Zm1qy2Atuud18lmqhTqw04BJgAnALcKukoYD1weET8VtLJwJ2Sju/PNiJiHjAPoFKp9NUPMzNrUpFnPl3A6Nz8KGBdE3XqtXt7ulT3ILAdGB4RWyLitwARsRx4nOwsqSu1259tmJlZgYoMn2XAWElHShoKTAPaa+q0AzPSqLcJwMaIWN9Hu3cCkwAkHQMMBZ6VNCINciCdCY0F1qb2NkmakEa5zQDuGpxdNDOzZhR22S0itkmaDSwBhgDzI2K1pFmp/FrgbuAsoBPYDFxcXV/SIuB0YLikLuDKiLgemA/MT0OwtwIXRURIOg34gqRtwMvArIioDli4lGz03H7APWkyM7MWUTYAzGpVKpXo6OhodTfMzHYrkpZHRKWven7CgZmZlc7hY2ZmpXP4mJlZ6Rw+ZmZWOoePmZmVzuFjZmalc/iYmVnpHD5mZlY6h4+ZmZXO4WNmZqVz+JiZWekcPmZmVjqHj5mZlc7hY2ZmpXP4mJlZ6Rw+ZmZWOoePmZmVzuFjZmalc/iYmVnpHD5mZla6QsNH0mRJayR1Srq8TrkkzUnlD0kalyubL2mDpIfrrHdZane1pKvSsjMlLZe0Kr1OytW/N9VfmaZDi9pnMzPrW1tRDUsaAlwDnAl0AcsktUfEL3PVpgBj0zQemJteARYAVwMLa9p9JzAVODEituSC5FngnIhYJ+lNwBJgZG7V6RHRMYi7aGZmTSryzOdUoDMi1kbEVmAxWWjkTQUWRuZ+YJikwwAiYinwXJ12LwW+HBFbUr0N6XVFRKxLdVYD+0raZ9D3yszMBqzI8BkJPJWb7+LVZyKN1ql1DDBR0gOSfiLplDp13gesqAZUckO65HaFJNVrWNJMSR2SOrq7u/vohpmZNavI8Kn3Cz6aqFOrDTgEmAB8Crg1HyaSjge+AlySW2d6RJwATEzThfUajoh5EVGJiMqIESP66IaZmTWryPDpAkbn5kcB65qoU6/d29OlugeB7cBwAEmjgDuAGRHxeHWFiHg6vW4Cbia7JGhmZi1SZPgsA8ZKOlLSUGAa0F5Tpx2YkUa9TQA2RsT6Ptq9E5gEIOkYYCjwrKRhwPeBT0fEz6qVJbVJqobT3sDZwE4j6MzMrDyFhU9EbANmk406ewS4NSJWS5olaVaqdjewFugErgP+srq+pEXAfcCxkrokfTQVzQeOSkOwFwMXRUSkbR0NXFEzpHofYImkh4CVwNNpW2Zm1iLKfm9brUqlEh0dHpltZtYfkpZHRKWven7CgZmZlc7hY2ZmpXP4mJlZ6Rw+ZmZWOoePmZmVzuFjZmalc/iYmVnpHD5mZlY6h4+ZmZXO4WNmZqVz+JiZWekcPmZmVjqHj5mZlc7hY2ZmpXP4mJlZ6Rw+ZmZWOn+ZXA8kdQO/bnU/SjQceLbVndhF+Fjs4GOxg49Fprfj8CxAREzuqxGHjwEgqaORbx98LfCx2MHHYgcfi8xgHQdfdjMzs9I5fMzMrHQOH6ua1+oO7EJ8LHbwsdjBxyIzKMfB93zMzKx0PvMxM7PSOXzMzKx0Dp/XAEmTJa2R1Cnp8l7qnSLpZUnvr1k+RNIKSd8rvrfFGchxkPSEpFWSVkrqKKfHxRngsRgm6TZJj0p6RNJby+l1MZo9FpKOTZ+H6vQ7SZ8oreMFGODn4q8lrZb0sKRFkvbtdWMR4WkPnoAhwOPAUcBQ4N+B43qo92PgbuD9NWWfBG4Gvtfq/WnVcQCeAIa3ej92kWNxI/Cx9H4oMKzV+9SqY1FT/hvgiFbvUyuOBTAS+BWwX5q/FfhIb9vzmc+e71SgMyLWRsRWYDEwtU69y4DvABvyCyWNAt4DfKvojhZsQMdhD9P0sZB0MHAacD1ARGyNiBcK73FxButz8S7g8YjYnZ+KMtBj0QbsJ6kN2B9Y19vGHD57vpHAU7n5rrTsFZJGAn8BXFtn/W8AfwNsL6h/ZRnocQjgh5KWS5pZWC/LMZBjcRTQDdyQLsV+S9IBRXa2YAP9XFRNAxYNeu/K1fSxiIinga8CTwLrgY0R8cPeNubw2fOpzrLa8fXfAP42Il5+1YrS2cCGiFheUN/K1PRxSN4WEeOAKcDHJZ02yP0r00CORRswDpgbEW8Bfg/0eG9gNzDQzwWShgLvBf5lcLtWuoH8rjiE7CzpSOANwAGSPtzbxtqa76ftJrqA0bn5Uex8OlwBFkuC7KGBZ0naBowH3ivpLGBf4GBJ346IXj9Uu6imj0NE3BkR6wAiYoOkO8guUSwtvtuFGMhn4n6gKyIeSPVuY/cOnwF9LlL5FOAXEfFMwX0t2kA+F3sDv4qIbgBJtwP/Cfh2j1tr9U0uT4XfRGwD1pL9j6R6E/H4XuovoP4N1dPZvQccNH0cgAOAg3Lvfw5MbvU+teozAfwUODa9/zzwv1q9T606FmnZYuDiVu9LK48F2X9UV5Pd6xHZoJTLetuez3z2cBGxTdJsYAnZKJX5EbFa0qxU3tt17D3GAI/D64A70v/22oCbI+IHRfe5KIPwmbgMuCldbloLXFxohws00GMhaX/gTOCSwjtbsIEci4h4QNJtwC+AbcAK+ngMjx+vY2ZmpfOAAzMzK53Dx8zMSufwMTOz0jl8zMysdA4fMzMrncPHzMxK5/AxM7PS+Y9MzUok6QpgOtkDHJ8FlgMbgZlkf1XeCVwYEZslLQBeAt4IHEH2x5wXAW8FHoiIj6Q2XwSuAc4Angc+A1wFHA58IiLaJY0B/g/ZExoAZkfEzwveXbMe+czHrCSSKsD7gLcA55E9Jwvg9og4JSLeDDwCfDS32iHAJOCvge8CXweOB06QdFKqcwBwb0ScDGwCvkj2V/d/AXwh1dkAnBnZw1HPB+YUsY9mjfKZj1l53g7cFREvAUj6blr+JklfBIYBB5I93qTquxERklYBz0TEqrTuamAMsBLYClQf97MK2BIRf0zrjEnL9wauToH1MnBMAftn1jCHj1l56j2yHrIHNJ4bEf8u6SNkD3Gt2pJet+feV+er/37/GDuek/VKvYjYnr7YC7Izp2eAN5Nd8fhD03thNgh82c2sPP8GnCNpX0kHkn1DLMBBwHpJe5PdDyrCnwDrI2I7cCHZgyPNWsZnPmYliYhlktrJHlX/a6CDbLDBFcADadkqsjAabN8EviPpA8C/kn0JnFnL+KnWZiWSdGBEvJgexb8UmBkRv2h1v8zK5jMfs3LNk3Qc2TfD3ujgsdcqn/mYmVnpPODAzMxK5/AxM7PSOXzMzKx0Dh8zMyudw8fMzEr3/wHyz04Ba85vsQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon theta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon zeta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon eta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon mu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon nu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon lamb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon kappa\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ZWXYOGzMzy85hY2Zm2TlszMwsO4eNmZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOGzMzy85hY2Zm2TlszMwsu6xhI2mmpE2SWiRdWaNekham+vWSppTqFkvaLmlDVZ/lktal7RlJ60p1n0ljbZL0zlL5aZIeTXULJSnTks3MrIZsYSNpGHA9MAs4CbhI0klVzWYBk9I2H1hUqrsVmFk9bkRcGBGTI2Iy8E3g7nS8k4A5wMmp3z+nOZDGnV86Vodx+8Kre1/lhz//Ic1bm4mIHIcwM8tn0yb4wQ9g584+H7qhswpJKyLifZIeBcqvnAIiIk7tYuypQEtEbE7jLQNmA4+V2swGlkTxyrxK0ihJYyJiW0Q8JGnCAeYn4H3AWaWxlkXEbuBpSS3AVEnPAEdFxMOp3xLgAuD+Lubfbfc9eR/LNy5HiCvefAWnve60vj6EmVkeW7fCtdfC7t2wdi1ccUWfDt9p2ACfTI9zgN9U1b29jrHHAltK+63AtDrajAW21TH+DOC5iHiyNNaqGmO9mp5Xl3cgaT7FGRDHHntsHVPY365Xd3GIDmFf7ON3e37X7f5mZgNm927YuxeGD4ff/rbPh+80bCKi/QV/ObAE+N/AYcBXgArFZa4DqfW+SPW1pXradOYiYGkdY9V9jIi4EbgRoFKpdPs62Pknno8QIw8dydSxU7vb3cxs4EyYAB/6EDz9NMzs+3caDnRm024acC3wY+BI4A7gLXX0awXGl/bHAVt70KYDSQ3Au4HydarOxmpNz7t1jJ44YvgRzDllTo6hzczykuBtbyu2DOq5QeBV4GVgJMWZzdMRsa+OfquBSZImShpOcTmuqapNEzAv3ZU2HdhROqM6kLOBJyKifHmsCZgjaYSkiRQ3AjySxtspaXp6n2ce8K06jmFmZn2knrBZTRE2pwNvpbir7K6uOkXEHuByYCXwOLAiIjZKWiBpQWp2H7AZaAFuAj7W3l/SUuBh4ERJrZI+XBp+DvtfQiMiNgIrKG5A+Dfg4xGxN1VfBnwjHecpMtwcYGZmnVNXt+hKqkREc1XZxRHxr1lnNsAqlUo0Nzd33dDMzH5P0pqIqFSXd3lmUx00qeygDhozM+tb/rgaMzPLzmFjZmbZOWzMzCw7h42ZmWXnsDEzs+wcNmZmlp3DxszMsnPYmJlZdg4bMzPLzmFjZmbZOWzMzCw7h42ZmWXnsDEzs+wcNmZmlp3DxszMsnPYmJlZdg4bMzPLzmFjZmbZOWzMzCy7rGEjaaakTZJaJF1Zo16SFqb69ZKmlOoWS9ouaUONfp9I426U9JVUNlfSutK2T9LkVPdAat9ed0zGZZuZWZWGXANLGgZcD5wDtAKrJTVFxGOlZrOASWmbBixKjwC3AtcBS6rGfTswGzg1Ina3B0dE3AHckdqcAnwrItaVus6NiOa+XKOZmdUn55nNVKAlIjZHxCvAMoqQKJsNLInCKmCUpDEAEfEQ8EKNcS8DvhwRu1O77TXaXAQs7aN1mJlZL+UMm7HAltJ+ayrrbptqJwAzJP1E0oOSTq/R5kI6hs0t6RLaVZJUa2BJ8yU1S2pua2vrYhpmZlavnGFT6wU9etCmWgNwNDAd+DSwohwekqYBuyKi/F7P3Ig4BZiRtotrDRwRN0ZEJSIqjY2NXUzDzMzqlTNsWoHxpf1xwNYetKk17t3p0tsjwD5gdKl+DlVnNRHxbHrcCdxJcYnPzMz6Sc6wWQ1MkjRR0nCKEGiqatMEzEt3pU0HdkTEti7GvRc4C0DSCcBw4Pm0fwjwXor3h0hlDZJGp+eHAucBHe5wMzOzfLLdjRYReyRdDqwEhgGLI2KjpAWp/gbgPuBcoAXYBVza3l/SUuBMYLSkVuDqiLgZWAwsTrdEvwJcEhHtl97OAFojYnNpKiOAlSlohgHfA27KtGwzM6tBf3idtrJKpRLNzb5T2sysOyStiYhKdbk/QcDMzLJz2JiZWXYOGzMzy85hY2Zm2TlszMwsO4eNmZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOGzMzy85hY2Zm2TlszMwsO4eNmZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOGzMzy85hY2Zm2WUNG0kzJW2S1CLpyhr1krQw1a+XNKVUt1jSdkkbavT7RBp3o6SvpLIJkl6WtC5tN5Tanybp0XSchZKUa81mZtZRtrCRNAy4HpgFnARcJOmkqmazgElpmw8sKtXdCsysMe7bgdnAqRFxMvDVUvVTETE5bQtK5YvS+O3H6jCumZnlk/PMZirQEhGbI+IVYBlFSJTNBpZEYRUwStIYgIh4CHihxriXAV+OiN2p3fYDTSKNd1REPBwRASwBLujFuszMrJtyhs1YYEtpvzWVdbdNtROAGZJ+IulBSaeX6iZKWpvKZ5SO0VrPMSTNl9Qsqbmtra2LaZiZWb0aMo5d632R6EGbag3A0cB04HRghaTjgW3AsRHxK0mnAfdKOrk7x4iIG4EbASqVSlfzMDOzOuU8s2kFxpf2xwFbe9Cm1rh3p0tvjwD7gNERsTsifgUQEWuApyjOglrTuN05hpmZ9aGcYbMamCRpoqThwBygqapNEzAv3ZU2HdgREdu6GPde4CwASScAw4HnJTWmmxJIZzqTgM1pvJ2Spqe70OYB3+qbJZqZWT2yXUaLiD2SLgdWAsOAxRGxUdKCVH8DcB9wLtAC7AIube8vaSlwJjBaUitwdUTcDCwGFqdbol8BLomIkHQG8AVJe4C9wIKIaL/B4DKKu9tGAvenzczM+omKG7SsWqVSiebm5oGehpnZkCJpTURUqsv9CQJmZpadw8bMzLJz2JiZWXYOGzMzy85hY2Zm2TlszMwsO4eNmZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOGzMzy85hY2Zm2TlszMwsO4eNmZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOGzMzyy5r2EiaKWmTpBZJV9aol6SFqX69pCmlusWStkvaUKPfJ9K4GyV9JZWdI2mNpEfT41ml9g+k9uvSdkyuNZuZWUcNuQaWNAy4HjgHaAVWS2qKiMdKzWYBk9I2DViUHgFuBa4DllSN+3ZgNnBqROwuBcfzwPkRsVXSG4GVwNhS17kR0dyHSzQzszrlPLOZCrRExOaIeAVYRhESZbOBJVFYBYySNAYgIh4CXqgx7mXAlyNid2q3PT2ujYitqc1G4DBJI/p8VWZm1m05w2YssKW038r+Zxr1tql2AjBD0k8kPSjp9Bpt3gOsbQ+k5JZ0Ce0qSao1sKT5kpolNbe1tXUxDTMzq1fOsKn1gh49aFOtATgamA58GlhRDg9JJwPXAh8t9ZkbEacAM9J2ca2BI+LGiKhERKWxsbGLaZiZWb1yhk0rML60Pw7Y2oM2tca9O116ewTYB4wGkDQOuAeYFxFPtXeIiGfT407gTopLfGZm1k9yhs1qYJKkiZKGA3OApqo2TcC8dFfadGBHRGzrYtx7gbMAJJ0ADAeelzQK+A7wmYj4UXtjSQ2S2sPoUOA8oMMdbmZmlk+2sImIPcDlFHeFPQ6siIiNkhZIWpCa3QdsBlqAm4CPtfeXtBR4GDhRUqukD6eqxcDx6ZboZcAlERHpWK8Hrqq6xXkEsFLSemAd8Gw6lpmZ9RMVr9NWrVKpRHOz75Q2M+sOSWsiolJd7k8QMDOz7Bw2ZmaWncPGzMyyc9iYmVl2DhszM8vOYWNmZtk5bMzMLDuHjZmZZeewMTOz7Bw2ZmaWncPGzMyyc9iYmVl2DhszM8vOYWNmZtk5bMzMLDuHjZmZZec/ntYJSW3Azwd6HgcwGnh+oCfRBw6WdcDBsxavY/AZSms5LiIaqwsdNkOUpOZafw1vqDlY1gEHz1q8jsHnYFiLL6OZmVl2DhszM8vOYTN03TjQE+gjB8s64OBZi9cx+Az5tfg9GzMzy85nNmZmlp3DxszMsnPYDEKSZkraJKlF0pU16iVpYapfL2lKqW6UpLskPSHpcUlv7t/Z7zfP3qzjv0vaKGmDpKWSDuvf2e83z67W8Z8kPSxpt6S/6k7f/tTTdUgaL+kH6ftpo6RP9u/MO+rN1yTVD5O0VtK3+2fGtfXye2vQ/KzXJSK8DaINGAY8BRwPDAf+H3BSVZtzgfsBAdOBn5TqbgM+kp4PB0YNtXUAY4GngZFpfwXwwUG8jmOA04EvAX/Vnb5DZB1jgCnp+ZHAzwZqHb1dS6n+CuBO4NtDdR2D5We93s1nNoPPVKAlIjZHxCvAMmB2VZvZwJIorAJGSRoj6SjgDOBmgIh4JSJe6se5l/V4HamuARgpqQE4HNjaXxOv0uU6ImJ7RKwGXu1u337U43VExLaI+Gl6vhN4nOIXgoHSm68JksYB7wK+0R+TPYAer2OQ/azXxWEz+IwFtpT2W+n4g91Zm+OBNuCWdIngG5Jek3OyB9DjdUTEs8BXgV8A24AdEfHdjHM9kHrWkaNvX+uTuUiaALwJ+EnfTKtHeruWfwT+GtjXh3Pqid6sYzD9rNfFYTP4qEZZ9f3pnbVpAKYAiyLiTcBvgYF6n6DH65B0NMVveBOB1wGvkfSBPp5fvepZR46+fa3Xc5F0BPBN4FMR8es+mVXP9Hgtks4DtkfEmr6dUo/05msymH7W6+KwGXxagfGl/XF0vITUWZtWoDUi2n/rvIviG3Ig9GYdZwNPR0RbRLwK3A38l4xzPZB61pGjb1/r1VwkHUoRNHdExN19PLfu6s1a3gL8uaRnKC5bnSXp9r6dXt16+701WH7W6+KwGXxWA5MkTZQ0HJgDNFW1aQLmpbu5plNcZtoWEb8Etkg6MbV7B/BYv818fz1eB8Xls+mSDpckinU83p+TL6lnHTn69rUezyV9DW4GHo+If8g4x3r1eC0R8ZmIGBcRE1K/f4+IgTpr7s06BtPPen0G+g4Fbx03iru0fkZxp8rfprIFwIL0XMD1qf5RoFLqOxloBtYD9wJHD9F1/B3wBLAB+FdgxCBex2spftP8NfBSen5UZ32H2jqAt1Jc3lkPrEvbuUNxLVVjnMkA3o3WB99bg+ZnvZ7NH1djZmbZ+TKamZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOG7MhRtKft39CsKRran2qsdlg0zDQEzCz7omIJgbuP4ea9YjPbMz6maQPSHpE0jpJ/5L+tspvJH1N0k8lfV9SY2r7l5IeU/H3fpalsg9Kuq7GuJMlrUpt70mfMYekByRdm475M0kz+nfFZg4bs34l6Q3AhcBbImIysBeYC7wG+GlETAEeBK5OXa4E3hQRp1L8z/IDWQL8TWr7aGkMgIaImAp8qqrcrF/4MppZ/3oHcBqwuvjIMUYC2yk+7n55anM7xYePQvFRJHdIupfiI0lqkvQnFH8868FUdBvwf0pN2sdbA0zo5RrMus1nNmb9S8BtETE5bSdGxDU12rV/jtS7KD4/7jRgTfpjcj2xOz3uxb9k2gBw2Jj1r+8DfyHpGABJfyrpOIqfxb9Ibd4P/FDSIcD4iPgBxR/7GgUcUWvQiNgBvFh6P+ZiistxZoOCf8Mx60cR8ZikzwHfTWHyKvBxij9+dbKkNcAOivd1hgG3p0tkAr4eES+ly2+1XALcIOlwYDNwad7VmNXPn/psNghI+k1E1DxrMTsY+DKamZll5zMbMzPLzmc2ZmaWncPGzMyyc9iYmVl2DhszM8vOYWNmZtn9f5XySvs/SYrjAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta zeta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta eta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta mu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta nu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta lamb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta kappa\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeta eta\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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vq2oF3YcNStoBBz37KE5/1Tn82JIf4+1v/O9zXY7UZLqHql4wOtN/n/iLZ74c6anj+GVv4fhlb5nrMqRm29zjSPLuJJuAo5I83N82ARuAT8xKhZKkXco2g6Oqfquq9gYuAN4G/G4/fwzw8VmoT5K0i5nu5bj70L3p75R+fhNw4SAVSZJ2adM9x3FcVb0oyW0AVfVg/3WwkqSnmOnucTzWnxAvgCSLgMcHq0qStMuabnD8Pt05jQOTfAD4e+A3B6tKkrTLmu4bAC9PcgvwSiDAyVW1ZtDKJEm7pGl/rHr/+VR3DliLJGke8IucJElNDA5JUpNBgyPJsiR3JVmb5Jwp+o9IclOSR5KcPdK+JMmNSdYkWZ3krCnWPTtJJTlgyDFIkp5o2uc4WvWX714I/CSwDliZZEVV/ePIYg8AZwInj62+GXhnVd2aZG/gliTXb1k3yZJ+u18Zqn5J0tSG3OM4DlhbVfdU1aPAFcBJowtU1YaqWgk8Nta+vqpu7ac3AWuAxSOLfBj4Vfr3lUiSZs+QwbEYuG9kfh1PfPGfliRL6T4b6+Z+/rXAV6vq9u2sd1qSySSTGzf6LbeSNFOGDI5M0da0h5BkL+AqYHlVPZzkGcB7gPO2t25VXVxVE1U1sWjRopa7lSRtw5DBsQ5YMjJ/CA1fN5tkd7rQuLyqru6bfwQ4DLg9yb39Nm9NctCMVCxJ2q7BTo4DK4HDkxwGfJXuk3VfP50VkwS4BFhTVRdsaa+qO4ADR5a7F5ioqm/MYN2SpG0YLDiqanOSM4DrgAXApVW1Osnpff9F/Z7CJN3Htj+eZDlwJHAU8CbgjiSr+k2eW1XXDFWvJGl6htzjoH+hv2as7aKR6a/RHW4a9/dMfY5kfPtLd7JESVIj3zkuSWpicEiSmhgckqQmBockqYnBIUlqYnBIkpoYHJKkJgaHJKmJwSFJamJwSJKaGBySpCYGhySpicEhSWpicEiSmhgckqQmBockqYnBIUlqYnBIkpoYHJKkJgaHJKmJwSFJamJwSJKaDBocSZYluSvJ2iTnTNF/RJKbkjyS5OyR9iVJbkyyJsnqJGeN9P1OkjuTfD7Jx5PsO+QYJElPNFhwJFkAXAicCBwJvC7JkWOLPQCcCZw/1r4ZeGdVPR84HnjbyLrXAy+sqqOALwLvHmgIkqQpDLnHcRywtqruqapHgSuAk0YXqKoNVbUSeGysfX1V3dpPbwLWAIv7+U9W1eZ+0c8Chww4BknSmCGDYzFw38j8ur6tSZKlwDHAzVN0/wJw7Y4UJ0naMUMGR6Zoq6YNJHsBVwHLq+rhsb730B3Sunwr656WZDLJ5MaNG1vuVpK0DUMGxzpgycj8IcD90105ye50oXF5VV091ncq8BrgDVU1ZRhV1cVVNVFVE4sWLWouXpI0tSGDYyVweJLDkiwETgFWTGfFJAEuAdZU1QVjfcuAdwGvrapvzXDNkqTt2G2oDVfV5iRnANcBC4BLq2p1ktP7/ouSHARMAvsAjydZTncF1lHAm4A7kqzqN3luVV0D/AGwB3B9ly98tqpOH2ockqQnGiw4APoX+mvG2i4amf4aU18V9fdMfY6EqnrOTNYoSWrjO8clSU0MDklSE4NDktTE4JAkNTE4JElNDA5JUhODQ5LUxOCQJDUxOCRJTQwOSVITg0OS1MTgkCQ1MTgkSU0MDklSE4NDktTE4JAkNTE4JElNDA5JUhODQ5LUxOCQJDUxOCRJTQwOSVITg0OS1GTQ4EiyLMldSdYmOWeK/iOS3JTkkSRnj7QvSXJjkjVJVic5a6Rv/yTXJ7m7/7nfkGOQJD3RYMGRZAFwIXAicCTwuiRHji32AHAmcP5Y+2bgnVX1fOB44G0j654D3FBVhwM39POSpFky5B7HccDaqrqnqh4FrgBOGl2gqjZU1UrgsbH29VV1az+9CVgDLO67TwIu66cvA04ebASSpCcZMjgWA/eNzK/j+y/+05ZkKXAMcHPf9KyqWg9dwAAHbmW905JMJpncuHFj691KkrZiyODIFG3VtIFkL+AqYHlVPdyyblVdXFUTVTWxaNGillUlSdswZHCsA5aMzB8C3D/dlZPsThcal1fV1SNdX09ycL/MwcCGGahVkjRNQwbHSuDwJIclWQicAqyYzopJAlwCrKmqC8a6VwCn9tOnAp+YoXolSdOw21AbrqrNSc4ArgMWAJdW1eokp/f9FyU5CJgE9gEeT7Kc7gqso4A3AXckWdVv8tyqugb4IHBlkl8EvgL83FBjkCQ9WaqaTjvMSxMTEzU5OTnXZUjSvJLklqqaGG/3neOSpCYGhySpicEhSWpicEiSmhgckqQmBockqYnBIUlqYnBIkpoYHJKkJgaHJKmJwSFJamJwSJKaGBySpCYGhySpicEhSWpicEiSmhgckqQmBockqYnBIUlqYnBIkpoYHJKkJgaHJKmJwSFJamJwSJKapKrmuobBJdkIfHmu69gBBwDfmOsidsJ8rx8cw65gvtcP83cMh1bVovHGp0RwzFdJJqtqYq7r2FHzvX5wDLuC+V4//GCMYZSHqiRJTQwOSVITg2PXdvFcF7CT5nv94Bh2BfO9fvjBGMP3eI5DktTEPQ5JUhODQ5LUxOCYJUmWJbkrydok50zRf0SSm5I8kuTskfYlSW5MsibJ6iRnjfTtn+T6JHf3P/ebh2P4nSR3Jvl8ko8n2Xc+1T+yzNlJKskBQ9U/5BiSvL3f7uokH5pvY0hydJLPJlmVZDLJcbtg/Xsm+VyS2/v63z/SN6vP5Z1WVd4GvgELgC8BzwYWArcDR44tcyBwLPAB4OyR9oOBF/XTewNf3LIu8CHgnH76HOC35+EYfgrYrZ/+7aHGMFT9fdsS4Dq6N5keMA8fg1cAfwPssWUb83AMnwRO7KdfDXx6F6w/wF799O7AzcDx/fysPZdn4uYex+w4DlhbVfdU1aPAFcBJowtU1YaqWgk8Nta+vqpu7ac3AWuAxX33ScBl/fRlwMmDjWCgMVTVJ6tqc7/oZ4FD5lP9vQ8DvwoMfaXJUGN4K/DBqnpkyzbm4RgK2KeffiZw/y5Yf1XVN/vZ3fvblr+Z2Xwu7zSDY3YsBu4bmV/HE194piXJUuAYuv9UAJ5VVeuhe1LR/aczlKHGMOoXgGt3pLhpGKT+JK8FvlpVt89Ajdsz1GPwXOAnktyc5G+THLuzhW7DUGNYDvxOkvuA84F371SVW7dT9SdZkGQVsAG4vqrm4rm80wyO2ZEp2pr+O02yF3AVsLyqHp6RqtoMOoYk7wE2A5fvcIXbufsp2naq/iTPAN4DnDcD9U2rhCnaZuIx2A3YDzge+BXgyiRT3ddMGGoMbwXeUVVLgHcAl+xUldu4+ynapl1/VX23qo6m27M+LskLZ6qw2WRwzI51dMfBtziEhl3pJLvTPVEur6qrR7q+nuTgfpmD6f6LGcpQYyDJqcBrgDdUf5B3AEPU/yPAYcDtSe7tt3lrkoNmpOInG+oxWAdc3R9K+RzwON2H8g1hqDGcCmyZ/wu6Q0pD2Kn6t6iqh4BPA8v6ptl8Lu80g2N2rAQOT3JYkoXAKcCK6azY/+d3CbCmqi4Y615B94Sh//mJGap3KoOMIcky4F3Aa6vqWzNc86gZr7+q7qiqA6tqaVUtpXtReVFVfW3myweG+zv6S+CEfrnn0p30HeqTXIcaw/3Ay/rpE4C7Z6jecTtT/6L0Vw0meTrwKuDOvns2n8s7b67Pzj9VbnRXenyR7oqM9/RtpwOn99MH0b3wPAw81E/vA7yUblf488Cq/vbqfp0fAm6ge5LcAOw/D8ewlu6Y8Zb2i+ZT/WPbv5cBr6oa8DFYCHwU+AJwK3DCPBzDS4Fb6K5yuhl48S5Y/1HAbX39XwDOG9nmrD6Xd/bmR45Ikpp4qEqS1MTgkCQ1MTgkSU0MDklSE4NDktTE4JDmWJLl/bvQpXnBy3GlOda/63yiqoZ60500o9zjkAaQ5PT+uyFWJfmn/nskfqr/noZbk/xFkr2SnAn8MHBjkhv7df+w/06JJ3xng7SrcI9DGlD/2UqfAi4GfonuOyP+Ncm76L7/4tfH9ziS7F9VDyRZQPcu4jOr6vNzNATpSXab6wKkH3AfoQuOB4Ejgc/0Hzy7ELhpK+v85ySn0T0/D+7XMzi0yzA4pIEkeTNwKHAG8DN037/wuu2scxhwNnBsVT2Y5E+APQcuVWriOQ5pAEleTBcAb6yqx+m+3fDHkzyn739G/0m0AJvovgoVug/D+1fgX5I8CzhxdiuXts89DmkYZwD70530BpgE3gz8eZI9+mV+je5TVi8Grk2yvqpekeQ2YDVwD/CZ2S5c2h5PjkuSmnioSpLUxOCQJDUxOCRJTQwOSVITg0OS1MTgkCQ1MTgkSU3+P9kjBnrcf2fTAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeta mu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeta nu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeta lamb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeta rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeta kappa\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeta xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeta sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeta tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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fBXY8cM/Uoa0ZrANO65dPAz41sP3kJHskWUZ38v/zI4xTktTIKKHy/ar6BrBbkt2q6rPAU2Zr1J93OQO4lG7P5sKqWp9kTZI1fbWLgY3ABuBc4DVT7ZN8nO4k+1FJNiV5ZV/0HuDEJLcBJ/brVNV64EK6CwH+FnhtVT00wvwkSY2ku/BqhgrJZ4CTgN8BDqA73DRRVU8f++jGbGJioiYnJ+d6GJI0ryS5tqqmvSxxlKu/bgDuB34VeAnwI8De7YYnSfqPYpRQ+dmqehh4mO59ISS5cayjkiTNS48YKkleTXeO48eGQmQfRjtRL0l6lJlpT+XPgUvozqUMfm7XvVX1zbGOSpI0Lz1iqFTVPcA9wCk7bziSpPnML+mSJDVjqEiSmjFUJEnNGCqSpGYMFUlSM4aKJKkZQ0WS1IyhIklqxlCRJDVjqEiSmjFUJEnNGCqSpGYMFUlSM4aKJKkZQ0WS1IyhIklqxlCRJDVjqEiSmjFUJEnNGCqSpGYMFUlSM4aKJKkZQ0WS1IyhIklqxlCRJDUz1lBJsjLJrUk2JFk7TXmSvL8vvzHJMbO1TfKTSa5K8sUkf51k33777knO67ffnOTN45ybJGlrYwuVJAuAs4BVwArglCQrhqqtApb3t9OBs0do+0FgbVU9CbgIeGO//UXAHv32pwKvSrJ0PLOTJE1nnHsqxwIbqmpjVT0AXACsHqqzGji/OlcDi5McPEvbo4Ar+uXLgBf2ywU8NslC4DHAA8C3xzQ3SdI0xhkqhwC3D6xv6reNUmemtjcBL+iXXwQc1i9/AvgOsBn4GvD7VfXNHZuCJGlbjDNUMs22GrHOTG1fAbw2ybXAPnR7JNDt3TwEPB5YBvxakiO3GlRyepLJJJNbtmyZfRaSpJGNM1Q28YO9CIBDgTtGrPOIbavqlqp6blU9Ffg48OW+zouBv62q71fVXcCVwMTwoKrqnKqaqKqJJUuWbPfkJElbG2eoXAMsT7IsySLgZGDdUJ11wKn9VWDHA/dU1eaZ2iY5sL/fDXgr8IG+r68BJ/R9PRY4HrhljPOTJA0ZW6hU1YPAGcClwM3AhVW1PsmaJGv6ahcDG4ENwLnAa2Zq27c5JcmX6ALjDuDD/fazgL3pzrlcA3y4qm4c1/wkSVtL1fBpjkePiYmJmpycnOthSNK8kuTaqtrq9AL4jnpJUkOGiiSpGUNFktSMoSJJasZQkSQ1Y6hIkpoxVCRJzRgqkqRmDBVJUjOGiiSpGUNFktSMoSJJasZQkSQ1Y6hIkpoxVCRJzRgqkqRmDBVJUjOGiiSpGUNFktSMoSJJasZQkSQ1Y6hIkpoxVCRJzRgqkqRmDBVJUjOGiiSpGUNFktSMoSJJasZQkSQ1Y6hIkpoxVCRJzYw1VJKsTHJrkg1J1k5TniTv78tvTHLMbG2T/GSSq5J8MclfJ9l3oOzJfdn6vnzPcc5PkvTDxhYqSRYAZwGrgBXAKUlWDFVbBSzvb6cDZ4/Q9oPA2qp6EnAR8Ma+zULgo8CaqjoaeBbw/XHNT5K0tXHuqRwLbKiqjVX1AHABsHqozmrg/OpcDSxOcvAsbY8CruiXLwNe2C8/F7ixqm4AqKpvVNVD45qcJGlr4wyVQ4DbB9Y39dtGqTNT25uAF/TLLwIO65efCFSSS5N8IcmvTzeoJKcnmUwyuWXLlm2ckiRpJuMMlUyzrUasM1PbVwCvTXItsA/wQL99IfAM4CX9/X9J8uytOqk6p6omqmpiyZIls89CkjSyhWPsexM/2IsAOBS4Y8Q6ix6pbVXdQneoiyRPBH5+oK+/r6qv92UXA8cAlzeYiyRpBOPcU7kGWJ5kWZJFwMnAuqE664BT+6vAjgfuqarNM7VNcmB/vxvwVuADfV+XAk9Osld/0v6ZwD+NcX6SpCFj21OpqgeTnEH3x34B8KGqWp9kTV/+AeBi4HnABuB+4OUzte27PiXJa/vlTwIf7tvcneR9dIFUwMVV9Tfjmp8kaWupGj7N8egxMTFRk5OTcz0MSZpXklxbVRPTlfmOeklSM4aKJKkZQ0WS1IyhIklqxlCRJDVjqEiSmjFUJEnNGCqSpGYMFUlSM4aKJKkZQ0WS1IyhIklqxlCRJDVjqEiSmjFUJEnNGCqSpGYMFUlSM4aKJKkZQ0WS1IyhIklqxlCRJDVjqEiSmjFUJEnNGCqSpGZSVXM9hjmTZAvw1bkex3Y4APj6XA9iB833Ocz38YNz2BXM1/EfUVVLpit4VIfKfJVksqom5nocO2K+z2G+jx+cw65gvo9/Oh7+kiQ1Y6hIkpoxVOanc+Z6AA3M9znM9/GDc9gVzPfxb8VzKpKkZtxTkSQ1Y6hIkpoxVOZYkpVJbk2yIcnaacr/U5KrknwvyRsGth+W5LNJbk6yPsmvDJTtn+SyJLf19/vNwzn8XpJbktyY5KIki+fbHAbqvCFJJTlgvo0/yev6ftcn+d1xjX9cc0jylCRXJ7k+yWSSY3fROeyZ5PNJbujn8I6Bsp36et5hVeVtjm7AAuDLwJHAIuAGYMVQnQOBpwHvBt4wsP1g4Jh+eR/gS1Ntgd8F1vbLa4H3zsM5PBdY2C+/dz7Ood92GHAp3ZtsD5hP4wd+FvgMsMdUH/PtOQA+Dazql58HfG4XnUOAvfvl3YF/BI7v13fa67nFzT2VuXUssKGqNlbVA8AFwOrBClV1V1VdA3x/aPvmqvpCv3wvcDNwSF+8GjivXz4POGlsMxjTHKrq01X1YF/1auDQ+TaH3pnArwPjvCJmXON/NfCeqvreVB/zcA4F7Nsv/whwx/imsENzqKq6r1/dvb9N/c7szNfzDjNU5tYhwO0D65v44T9II0myFPgpuv9uAA6qqs3QveDo/jsal3HNYdArgEu2Z3AjGssckrwA+JequqHBGGcyrufgicBPJ/nHJH+f5Gk7OtAZjGsOrwd+L8ntwO8Db96hUc5sh+aQZEGS64G7gMuqai5ezzvMUJlbmWbbNv1Hm2Rv4C+B11fVt5uMatuMdQ5J3gI8CHxsu0c4whCm2bZDc0iyF/AW4G0Nxjfrw0+zrcVzsBDYDzgeeCNwYZLpHquFcc3h1cCvVtVhwK8Cf7ZDo5xlCNNsG3kOVfVQVT2Fbq/82CQ/0WpgO5OhMrc20R1zn3Io27B7nmR3uhfRx6rqkwNFdyY5uK9zMN1/PuMyrjmQ5DTg+cBLqj+gPCbjmMOPAcuAG5J8pe/zC0ke12TEP2xcz8Em4JP9oZnPAw/TfQDiOIxrDqcBU+t/QXeIalx2aA5TqupbwOeAlf2mnfl63mGGyty6BlieZFmSRcDJwLpRGvb/Mf4ZcHNVvW+oeB3di4n+/lONxjudscwhyUrgTcALqur+xmMe1nwOVfXFqjqwqpZW1VK6PzjHVNW/th/+2H6P/go4oa/3RLqTz+P6RN1xzeEO4Jn98gnAbY3GO50dmcOS9Fc4JnkM8Bzglr54Z76ed9xcXynwaL/RXZHyJbqrRt7Sb1sDrOmXH0f3B+nbwLf65X2BZ9DtWt8IXN/fnte3+VHgcroX0OXA/vNwDhvojk9Pbf/AfJvDUP9fYUxXf43xOVgEfBS4CfgCcMJ8ew76smvprsT6R+Cpu+gcngxc18/hJuBtA33u1Nfzjt78mBZJUjMe/pIkNWOoSJKaMVQkSc0YKpKkZgwVSVIzhoq0i0ry+v6d+dK84SXF0i6qfyf+RFWN6w2HUnPuqUg7UZI1/Xd7XJ/kn/vvAXlu/x0bX0jyF0n2TvLLwOOBzyb5bN/27P47QX7o+zakXYl7KtIc6D+r6u+Ac4BfovvOj+8keRPd95e8c3hPJcn+VfXNJAvo3ln9y1V14xxNQZrWwrkegPQo9Ud0oXI3sAK4sv8A4EXAVY/Q5r8nOZ3udXtw385Q0S7FUJF2siQvA44AzgB+nu67M06Zpc0y4A3A06rq7iQfAfYc81ClbeY5FWknSvJUunB4aVU9TPetlk9P8oS+fK/+E4EB7qX7elzoPnTwO8A9SQ4CVu3ckUujcU9F2rnOAPanOwEPMAm8DPh4kj36Om+l+6Tbc4BLkmyuqp9Nch2wHtgIXLmzBy6NwhP1kqRmPPwlSWrGUJEkNWOoSJKaMVQkSc0YKpKkZgwVSVIzhookqZn/Dwci49/pxE1BAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eta mu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eta nu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eta lamb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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xM7NsHCpmZpaNQ8XMzLJxqJiZWTYNf/S9pCMovnc+gFUR8cfKemVmZi2poSMVSR8CVgJ/B/w9sFzSB6vsmJmZtZ5Gj1Q+A5zU/3W9kl4O/Bq4pqqOmZlZ62n0mkov8GRp+Ulgc/7umJlZKxvwSEXSuWn2QWCFpJ9QXFOZS3E6zMzM7FmDnf46OP3cmKZ+P6mmO2Zm1soGDJWIuHh/dcTMzFpfo3d/dUi6QdIdktb1Tw2s1ylpg6QeSefVqZekK1L9OknTU/loSSslrZW0XtJu4Sbp05JC0mGlsvPTtjZIekcjYzMzs3wavfvrOoo7wO4CnmlkBUltwJXA2ygu9K+S1BURd5eazQampOlk4Kr0cwcwKyK2SRoJ/FLSzRGxPG17YtruH0r7mwrMA44HjgR+JunYiNjV4BjNhoUnH3mI7tuXMG7Cq3n1jDnN7o7ZkDQaKn0R0TXEbc8AeiJiE4CkJRQX+MuhMhe4NiKC4r0vYyWNi4gtwLbUZmSaorTe5cBnef61nbnAkojYAdwnqSf14TdD7LdZU333Ox+hu28tB2okl449nPHHdjS7S2YNazRUvijpu8DPKY4iAIiIHw2wznief9txL8VRyGBtxgNb0pHOauBVwJURsQJA0unAgxGxVlLttpbX2dbzSJoPzAc46qijBui+WXP85eltjNJIdvEMO57aNvgKZsNIo6FyDvBqiiOG/tNfAQwUKqpTFo22SaetpkkaC9wg6QRgE3AB8Pa93B8RcTVwNUBHR8du9WbN9qH3X85P//WbTJrwWia/9tRmd8dsSBoNlddFxGuHuO1eYGJpeQLw0FDbRMRWScuATuAWYDLQf5QyAbhD0owG92c27B1xzIl84GPfa3Y3zPZKo++oX54uhA/FKmCKpMmSRlFcRK+9LtMFnJXuApsJPB4RWyS1pyMUJI0BTgPujYi7IuLwiJgUEZMogmR6+nDLLmCepAMlTaa4+O83aJqZ7UeNHqm8CThb0n0U11QEREScuKcVImKnpIUURxdtwDURsV7SglS/CLgJmAP0ANspTrMBjAMWp+sqI4ClEXHjQB1M215KcSPATuCjvvPLzGz/UnHj1SCNpKPrlUfEA9l7tB91dHREd3d3s7thZtZSJK2OiLq3JTZ0pNIfHpIOB0Zn7JuZmb2ANPqO+tMl/R64D7gduB+4ucJ+mZlZC2r0Qv2lwEzgdxExGXgr8KvKemVmZi2p0VD5a/qCrhGSRkTEvwHTquuWmZm1okbv/toq6SDgF8B1kh6muMPKzMzsWY0eqcwFngI+BfyU4rtV3lVVp8zMrDU1evfXX0qLiyvqi5mZtbjBvk74Sep8fhbPvfnxkEp6ZWZmLWmwb348eKB6MzOzskavqZiZmQ3KoWJmZtk4VMzMLBuHipmZZeNQMTOzbBwqZmaWjUPFzMyycaiYmVk2DhUzM8vGoWJmZtk4VMzMLJtKQ0VSp6QNknoknVenXpKuSPXrJE1P5aMlrZS0VtJ6SReX1rk0tV0j6VZJR6bySZKeSuVrJC2qcmxmZra7ykJFUhtwJTAbmAq8V9LUmmazgSlpmg9clcp3ALMi4nUU3zDZKWlmqvtaRJwYEdOAG4ELS9vbGBHT0rSggmGZmdkAqjxSmQH0RMSmiHgaWELxZV9lc4Fro7AcGCtpXFreltqMTFMARMQTpfVfSv2P5jczsyaoMlTGA5tLy72prKE2ktokrQEeBm6LiBX9jSRdJmkzcCbPP1KZLOlOSbdLOqVepyTNl9Qtqbuvr28vh2ZmZvVUGSqqU1Z7VLHHNhGxK53imgDMkHTCsw0iLoiIicB1wMJUvAU4KiJOAs4Frpe025eIRcTVEdERER3t7e1DHZOZmQ2gylDpBSaWlicADw21TURsBZYBnXX2cT3wntRuR0Q8kuZXAxuBY/e692ZmNmRVhsoqYIqkyZJGAfOArpo2XcBZ6S6wmcDjEbFFUruksQCSxgCnAfem5Sml9U8vlbenmwOQdAzFxf9NlY3OzMx2M+DXCe+LiNgpaSFwC9AGXBMR6yUtSPWLgJuAOUAPsB04J60+DlicQmIEsDQibkx1X5Z0HPAM8ADQf5fXqcAlknYCu4AFEfFoVeMzM7PdKeLFe/NUR0dHdHd3N7sbZmYtRdLqiOioV+d31JuZWTYOFTMzy8ahYmZm2ThUzMwsG4eKmZll41AxM7NsHCpmZpaNQ8XMzLJxqJiZWTYOFTMzy8ahYmZm2ThUzMwsG4eKmZll41AxM7NsHCpmZpaNQ8XMzLJxqJiZWTYOFTMzy8ahYmZm2ThUzMwsm0pDRVKnpA2SeiSdV6dekq5I9eskTU/loyWtlLRW0npJF5fWuTS1XSPpVklHlurOT9vaIOkdVY7NzMx2V1moSGoDrgRmA1OB90qaWtNsNjAlTfOBq1L5DmBWRLwOmAZ0SpqZ6r4WESdGxDTgRuDCtL+pwDzgeKAT+Hbqg5mZ7SdVHqnMAHoiYlNEPA0sAebWtJkLXBuF5cBYSePS8rbUZmSaAiAiniit/9L+8rStJRGxIyLuA3pSH8zMbD+pMlTGA5tLy72prKE2ktokrQEeBm6LiBX9jSRdJmkzcCbpSKXB/SFpvqRuSd19fX17My4zM9uDKkNFdcqi0TYRsSud4poAzJB0wrMNIi6IiInAdcDCIeyPiLg6IjoioqO9vX3wUZiZWcOqDJVeYGJpeQLw0FDbRMRWYBnFdZJa1wPvGcL+zMysQlWGyipgiqTJkkZRXETvqmnTBZyV7gKbCTweEVsktUsaCyBpDHAacG9anlJa//T+8rSteZIOlDSZ4uL/yorGZmZmdRxQ1YYjYqekhcAtQBtwTUSsl7Qg1S8CbgLmUFxU3w6ck1YfByxOd2+NAJZGxI2p7suSjgOeAR4A+re3XtJS4G5gJ/DRiNhV1fjMzGx3itjtssOLRkdHR3R3dze7G2ZmLUXS6ojoqFfnd9SbmVk2DhUzM8vGoWJmZtk4VMzMLBuHipmZZeNQMTOzbBwqZmaWjUPFzMyycaiYmVk2DhUzM8vGoWJmZtk4VMzMLBuHipmZZeNQMTOzbBwqZmaWjUPFzMyycaiYmVk2DhUzM8vGoWJmZtk4VMzMLJtKQ0VSp6QNknoknVenXpKuSPXrJE1P5aMlrZS0VtJ6SReX1vmapHtT+xskjU3lkyQ9JWlNmhZVOTYzM9tdZaEiqQ24EpgNTAXeK2lqTbPZwJQ0zQeuSuU7gFkR8TpgGtApaWaquw04ISJOBH4HnF/a3saImJamBRUMy8zMBlDlkcoMoCciNkXE08ASYG5Nm7nAtVFYDoyVNC4tb0ttRqYpACLi1ojYmeqWAxMqHIOZmQ1BlaEyHthcWu5NZQ21kdQmaQ3wMHBbRKyos48PAjeXlidLulPS7ZJOqdcpSfMldUvq7uvrG9KAzMxsYFWGiuqURaNtImJXREyjOBKZIemE560oXQDsBK5LRVuAoyLiJOBc4HpJh+y28YirI6IjIjra29uHMh4zMxtElaHSC0wsLU8AHhpqm4jYCiwDOvvLJJ0NvBM4MyL6Q2hHRDyS5lcDG4FjM4zDzMwaVGWorAKmSJosaRQwD+iqadMFnJXuApsJPB4RWyS1l+7qGgOcBtybljuBzwGnR8T2/g2lddrS/DEUF/83VTg+MzOrcUBVG46InZIWArcAbcA1EbFe0oJUvwi4CZgD9ADbgXPS6uOAxSkkRgBLI+LGVPct4EDgNkkAy9OdXqcCl0jaCewCFkTEo1WNz8zMdqd09uhFqaOjI7q7u5vdDTOzliJpdUR01KvzO+rNzCwbh4qZmWXjUDEzs2wcKmZmlo1DxczMsnGomJlZNg4VMzPLxqFiZmbZOFTMzCwbh4qZmWXjUDEzs2wcKmZmlo1DxczMsnGomJlZNg4VMzPLxqFiZmbZvKi/pEtSH/BAs/uxFw4D/tzsTuyjVh9Dq/cfPIbhoFX7f3REtNereFGHSquS1L2nb11rFa0+hlbvP3gMw0Gr978en/4yM7NsHCpmZpaNQ6U1Xd3sDmTQ6mNo9f6DxzActHr/d+NrKmZmlo2PVMzMLBuHipmZZeNQaTJJnZI2SOqRdF6d+ldL+o2kHZI+XSqfKOnfJN0jab2kT5TqXibpNkm/Tz8PbcExfE3SvZLWSbpB0thWG0OpzaclhaTDWq3/kj6Wtrte0ler6n9VY5A0TdJySWskdUuaMUzHMFrSSklr0xguLtXt1+fzPosIT02agDZgI3AMMApYC0ytaXM48AbgMuDTpfJxwPQ0fzDwu/51ga8C56X584CvtOAY3g4ckOa/0opjSGUTgVso3mR7WCv1H/gPwM+AA/u30WqPAXArMDvNzwGWDdMxCDgozY8EVgAz0/J+ez7nmHyk0lwzgJ6I2BQRTwNLgLnlBhHxcESsAv5aU74lIu5I808C9wDjU/VcYHGaXwycUdkIKhpDRNwaETtT0+XAhFYbQ3I58Fmgyjtiqur/R4AvR8SO/m204BgCOCTN/w3wUHVD2KcxRERsS4sj09T/N7M/n8/7zKHSXOOBzaXlXp7/D6khkiYBJ1G8ugF4RURsgeIJR/HqqCpVjaHsg8DNe9O5BlUyBkmnAw9GxNoMfRxIVY/BscApklZIul3SG/a1owOoagyfBL4maTPwdeD8ferlwPZpDJLaJK0BHgZui4hmPJ/3mUOluVSnbEivaCUdBPwQ+GREPJGlV0NT6RgkXQDsBK7b6x420IU6Zfs0BkkvAS4ALszQv0F3X6csx2NwAHAoMBP4DLBUUr195VDVGD4CfCoiJgKfAr63T70cpAt1yhoeQ0TsiohpFEflMySdkKtj+5NDpbl6Kc6595vAEA7PJY2keBJdFxE/KlX9SdK41GYcxSufqlQ1BiSdDbwTODPSCeWKVDGGVwKTgbWS7k/bvEPSEVl6/HxVPQa9wI/SqZmVwDMUH4BYharGcDbQv/x/KE5RVWWfxtAvIrYCy4DOVLQ/n8/7zKHSXKuAKZImSxoFzAO6GlkxvWL8HnBPRPxTTXUXxZOJ9PMnmfpbTyVjkNQJfA44PSK2Z+5zrexjiIi7IuLwiJgUEZMo/uFMj4g/5u9+ZX9HPwZmpXbHUlx8ruoTdasaw0PAm9P8LOD3mfpbz76MoV3pDkdJY4DTgHtT9f58Pu+7Zt8p8GKfKO5I+R3FXSMXpLIFwII0fwTFP6QngK1p/hDgTRSH1uuANWmak9Z5OfBziifQz4GXteAYeijOT/eXL2q1MdRs/34quvurwsdgFPB94LfAHcCsVnsMUt1qijuxVgCvH6ZjOBG4M43ht8CFpW3u1+fzvk7+mBYzM8vGp7/MzCwbh4qZmWXjUDEzs2wcKmZmlo1DxczMsnGomA0zkj7f7D6Y7S3fUmw2zEjaFhEHNbsfZnvjgGZ3wOzFTNL7gY9TvNFwBcWb4sakDxZcHxFnSvoxxcd/jAa+GREvuO81txcOh4pZk0h6DfCfgTdGxF8lfRu4C3gqig8W7PfBiHg0fXzHKkk/jIhHmtBls0E5VMya563A6ymCAmAM9T8s8OOS3p3mJwJTAIeKDUsOFbPmEbA4Ip73HR81XzP7FooPF/zbiNguaRnFaTCzYcl3f5k1z8+Bv5d0ODz7XeRHA39NH+UOxbcVPpYC5dUU321iNmz5SMWsSSLibklfAG6VNILiK2Y/ClwNrJN0B8W3Xi6QtA7YQPHVymbDlm8pNjOzbHz6y8zMsnGomJlZNg4VMzPLxqFiZmbZOFTMzCwbh4qZmWXjUDEzs2z+P/nfNUcYUY0vAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eta rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eta kappa\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eta xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eta sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eta tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mu nu\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mu lamb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mu rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mu kappa\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mu xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mu sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mu tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nu lamb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nu rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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mZtk4VMzMLBuHipmZZeNQMTOzbBwqZmaWjUPFzMyycaiYmVk2DhUzM8vGoWJmZtk0FSqSXinpBkkdafmCpFdWPTgzMxtdmr1SWQ08A7wrLc8AX61qUGZmNjo1GyqvjohPRsTutFwLzBqok6R2STsldUm6okG9JN2Y6u+XNC+VT5K0RdIOSZ2Srm3Q908lhaSTSmVXpn3tlHRhk3MzM7NMmg2VA5Le2Lch6Q3AgaN1kNQCrAQWA3OASyTNqWu2GJidlmXATan8ILAwIs4B5gLtkhaU9j0deCvws1LZHOBi4EygHfhKGoPZqPLsk4/zj9/5Ig9tuWO4h2I2aOObbLccWJueowh4Cnj/AH3mA10RsRtA0jpgKfBAqc1SYG1EBLBJUqukKRHRA+xLbSakJUr9bgA+Dvxd3b7WRcRB4GFJXWkM/9LkHM1GhL++5YN09O7gOE3gU60nM/X02nAPyaxpTYVKROwAzpF0Qtp+poluU4E9pe1u4Lwm2kwFetJVxjbgNcDKiNgMIOkdwGMRsUNS/b42NdjXi0haRnFVxKmnntrENMyG1q+f28dETeAwz3PwwL6BO5iNIE2FiqTjgHcCM4Dxfd/MI+K6o3VrUBbNtomIw8BcSa3AdyWdBewGrgL+4BiPR0TcDNwMUKvV+tWbDbcPvPcG/uHvv8SMaa9l5msvGO7hmA1Ks7e//g54muLK4WCTfbqB6aXtacDjg20TEXslbaR4TnIXMBPou0qZBvxQ0vwmj2c24r1q1tm8/8N/M9zDMDsmzYbKtIhoH+S+twKzJc0EHqN4iP7uujYbgBXpect5wNMR0SOpDfi3FCiTgUXAX0bEj4CT+zpLegSoRcQvJW0AbpP0ReAUiof/WwY5ZjMz+y00Gyo/kPTa9E29KRFxSNIKiquLFmB1RHRKWp7qVwF3AEuALmA/cFnqPgVYk56rjAPWR8TtAxyvU9J6ihcBDgEfSrfQzMxsiKh48eoIldKPKJ5VTAZOpXimcTCVRUScPRSDrEqtVouOjo7hHoaZ2agiaVtENHwtcaArlbdRXCn0vYVlZmZ2REcNlYh4FEDSN4CTI2LrkIzKzMxGpWafqfw+cLmkR4FfM0Zuf5mZWV7NhsriSkdhZmZjQrO/Uf9o1QMxM7PRz3+ky8zMsnGomJlZNg4VMzPLxqFiZmbZOFTMzCwbh4qZmWXjUDEzs2wcKmZmlo1DxczMsnGomJlZNg4VMzPLxqFiZmbZOFTMzCwbh4qZmWXjUDEzs2wqDRVJ7ZJ2SuqSdEWDekm6MdXfL2leKp8kaYukHZI6JV1b6vOp1Ha7pLslnZLKZ0g6kMq3S1pV5dzMzKy/ykJFUguwkuKvRs4BLpE0p67ZYmB2WpYBN6Xyg8DCiDgHmAu0S1qQ6j4XEWdHxFzgduDq0v52RcTctCyvYFpmZnYUVV6pzAe6ImJ3RDwHrAOW1rVZCqyNwiagVdKUtL0vtZmQlgCIiGdK/V/eV25mZsOvylCZCuwpbXensqbaSGqRtB14ArgnIjb3NZJ0vaQ9wHt48ZXKTEn3SbpX0vmNBiVpmaQOSR29vb3HODUzM2ukylBRg7L6q4ojtomIw+kW1zRgvqSzftMg4qqImA7cCqxIxT3AqRFxLvAx4DZJJ/TbecTNEVGLiFpbW9tg52RmZkdRZah0A9NL29OAxwfbJiL2AhuB9gbHuA14Z2p3MCKeTOvbgF3A6cc8ejMzG7QqQ2UrMFvSTEkTgYuBDXVtNgCXprfAFgBPR0SPpDZJrQCSJgOLgIfS9uxS/3eUytvSywFImkXx8H93ZbMzM7N+xle144g4JGkFcBfQAqyOiE5Jy1P9KuAOYAnQBewHLkvdpwBrUkiMA9ZHxO2p7jOSzgCeBx4F+t7yugC4TtIh4DCwPCKeqmp+ZmbWnyJeui9P1Wq16OjoGO5hmJmNKpK2RUStUZ1/o97MzLJxqJiZWTYOFTMzy8ahYmZm2ThUzMwsG4eKmZll41AxM7NsHCpmZpaNQ8XMzLJxqJiZWTYOFTMzy8ahYmZm2ThUzMwsG4eKmZll41AxM7NsHCpmZpaNQ8XMzLJxqJiZWTYOFTMzy8ahYmZm2VQaKpLaJe2U1CXpigb1knRjqr9f0rxUPknSFkk7JHVKurbU51Op7XZJd0s6pVR3ZdrXTkkXVjk3MzPrr7JQkdQCrAQWA3OASyTNqWu2GJidlmXATan8ILAwIs4B5gLtkhakus9FxNkRMRe4Hbg6HW8OcDFwJtAOfCWNwczMhkiVVyrzga6I2B0RzwHrgKV1bZYCa6OwCWiVNCVt70ttJqQlACLimVL/l/eVp32ti4iDEfEw0JXGYGZmQ6TKUJkK7Cltd6eyptpIapG0HXgCuCciNvc1knS9pD3Ae0hXKk0eD0nLJHVI6ujt7T2WeZmZ2RFUGSpqUBbNtomIw+kW1zRgvqSzftMg4qqImA7cCqwYxPGIiJsjohYRtba2toFnYWZmTasyVLqB6aXtacDjg20TEXuBjRTPSerdBrxzEMczM7MKVRkqW4HZkmZKmkjxEH1DXZsNwKXpLbAFwNMR0SOpTVIrgKTJwCLgobQ9u9T/HX3laV8XSzpO0kyKh/9bKpqbmZk1ML6qHUfEIUkrgLuAFmB1RHRKWp7qVwF3AEsoHqrvBy5L3acAa9LbW+OA9RFxe6r7jKQzgOeBR4G+/XVKWg88ABwCPhQRh6uan5mZ9aeIfo8dXjJqtVp0dHQM9zDMzEYVSdsiotaozr9Rb2Zm2ThUzMwsG4eKmZll41AxM7NsHCpmZpaNQ8XMzLJxqJiZWTYOFTMzy8ahYmZm2ThUzMwsG4eKmZll41AxM7NsHCpmZpaNQ8XMzLJxqJiZWTYOFTMzy8ahYmZm2ThUzMwsG4eKmZll41AxM7NsKg0VSe2SdkrqknRFg3pJujHV3y9pXiqfJGmLpB2SOiVdW+rzOUkPpfbfldSaymdIOiBpe1pWVTk3MzPrr7JQkdQCrAQWA3OASyTNqWu2GJidlmXATan8ILAwIs4B5gLtkhakunuAsyLibOAnwJWl/e2KiLlpWV7BtMzM7CiqvFKZD3RFxO6IeA5YByyta7MUWBuFTUCrpClpe19qMyEtARARd0fEoVS3CZhW4RzMzGwQqgyVqcCe0nZ3KmuqjaQWSduBJ4B7ImJzg2P8CXBnaXumpPsk3Svp/EaDkrRMUoekjt7e3kFNyMzMjq7KUFGDsmi2TUQcjoi5FFci8yWd9aKO0lXAIeDWVNQDnBoR5wIfA26TdEK/nUfcHBG1iKi1tbUNZj5mZjaAKkOlG5he2p4GPD7YNhGxF9gItPeVSXof8DbgPRHRF0IHI+LJtL4N2AWcnmEeZmbWpCpDZSswW9JMSROBi4ENdW02AJemt8AWAE9HRI+kttJbXZOBRcBDabsd+DPgHRGxv29HqU9LWp9F8fB/d4XzMzOzOuOr2nFEHJK0ArgLaAFWR0SnpOWpfhVwB7AE6AL2A5el7lOANSkkxgHrI+L2VPdl4DjgHkkAm9KbXhcA10k6BBwGlkfEU1XNz8zM+lO6e/SSVKvVoqOjY7iHYWY2qkjaFhG1RnX+jXozM8vGoWJmZtk4VMzMLBuHipmZZeNQMTOzbBwqZmaWjUPFzMyycaiYmVk2DhUzM8vGoWJmZtk4VMzMLBuHipmZZeNQMTOzbBwqZmaWjUPFzMyycaiYmVk2L+k/0iWpF3h0uMdRkZOAXw73ICo01ucHY3+Ont/odVpEtDWqeEmHylgmqeNIf5ltLBjr84OxP0fPb2zy7S8zM8vGoWJmZtk4VMaum4d7ABUb6/ODsT9Hz28M8jMVMzPLxlcqZmaWjUPFzMyycaiMUJLaJe2U1CXpigb1knRjqr9f0rxUPl3SP0p6UFKnpI/U9ftw2m+npM+Wyq9M+9op6cKxND9JMyQdkLQ9Lauqnl9Vc5T0zdI8HpG0vVQ36s/hkeY3xs7hXEmb0jw6JM0v1Q3pOaxERHgZYQvQAuwCZgETgR3AnLo2S4A7AQELgM2pfAowL60fD/ykry/w+8D3gOPS9snp45x0jOOAmenYLWNofjOAH4+Fc1jX/wvA1WPpHB5lfmPmHAJ3A4tL/TcOxzmsavGVysg0H+iKiN0R8RywDlha12YpsDYKm4BWSVMioicifggQEc8CDwJTU58PAp+JiIOp/onSvtZFxMGIeBjoSmMYK/MbDlXNESh+QgbeBXyjtK+xcA6BhvMbDlXNMYAT0vorgcdL+xrKc1gJh8rINBXYU9rupu4fXTNtJM0AzgU2p6LTgfMlbZZ0r6TXD+J4OQ31/ABmSrovlZ+fYQ4DqWqOfc4HfhERPx3E8XIa6vnB2DmHHwU+J2kP8HngykEcb8QbP9wDsIbUoKz+3e+jtpH0CuDbwEcj4plUPB44keIy/fXAekmzmjxeTkM9vx7g1Ih4UtLrgL+VdGapXxWqmmOfS3jxT/Fj5Rz2qZ/fWDqHHwT+W0R8W9K7gL8BFjV5vBHPVyojUzcwvbQ9jRcukQdsI2kCxRfyrRHxnbo+30mX6luA5yn+07tmjpfTkM4v3U54EiAitlHcqz4943waqWqOSBoP/DHwzUEeL6chnd8YO4fvA/q2/zcv3OIa6nNYjeF+qOOl/0LxE/duiod1fQ8Iz6xr84e8+AHhllQuYC3wVw32uxy4Lq2fTnGpLeBMXvyAcDfVPuQd6vm19c2H4qHrY8DvjMZzmOrbgXvrysbEOTzK/MbMOaR4vvLmtP4WYNtwnMPKPm/DPQAvRzgxxVshP6H4ieyqVLYcWJ7WBaxM9T8Caqn8jRSXzPcD29OyJNVNBL4O/Bj4IbCwdLyr0r52kt5MGSvzA94JdKZ/sD8E3j5az2Gq/1rfPuqON+rP4ZHmN5bOYarbluayGXjdcJ3DKhb/Ny1mZpaNn6mYmVk2DhUzM8vGoWJmZtk4VMzMLBuHipmZZeNQMTOzbBwqZmaWjUPFbJilvxXyoKRb0t/euFvSZEkbJdVSm5MkPTLMQzUbkEPFbGSYDayMiDOBvRS/QW426jhUzEaGhyNie1rfRvFHqcxGHYeK2chwsLR+mOI/MzzEC/9GJw35iMyOgUPFbOR6BHhdWv8PwzgOs6Y5VMxGrs8DH5T0A4q/e2M24vl/KTYzs2x8pWJmZtk4VMzMLBuHipmZZeNQMTOzbBwqZmaWjUPFzMyycaiYmVk2/x/p6FSWBKYp/wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nu kappa\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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m2TlszMwsO4eNmZll57AxM7PsGv5YaEkfAKYBh1XLIuLqHIMyM7PhpaEzG0k3AhcAl1F84NmHgGMyjsvMzIaRRi+j/eeIWAy8FBF/AfwO+3/omZmZWZcaDZs96etuSe8A/gOYlGdIZmY23DR6z+Zb6ZMw/xr4ARD4A8jMzKxBDYVNRPxleniPpG8Bh0XEznzDMjOz4aShsJF0GMVHNr+H4qzm/0m6ISJ+mXNwZmY2PDR6GW0FsAv43+n5QuAfKValmZmZdavRsDk+In679PxfJP1bjgGZmdnw0+hqtHWSZlWfSJoJfK+nnSTNlbRJUruky+vUS9KyVL9e0vRS3XJJOyRtqNnnLkltadsiqa1Ud0Xqa5OkM0vlMyQ9keqWSVKD8zYzs37QaNjMBL6ffrhvAR4B3pd+gK+vt4OkEcD1wDxgKrBQ0tSaZvOAyWlbAtxQqrsNmFvbb0RcEBEnRcRJwD3Avel4U4EFFO9yMBf4uzQGUr9LSsc6oF+zg1289ho/+Oc7+NeW63hlz88HezhmvdLoZbS+/HA+BWiPiM0AklYB84EfltrMB1ZERACPShotaWxEbI+IhyVN7KrzdHZyPnBaqa9VEbEX+ImkduCUFI5HRsQjab8VwLnAA32Yk9mgaXv4Lq799qeJCDpf+CnnXfylwR6SWcMaOrOJiGeAt1P8QP994O0R8Ux162K3ccDW0vOOVNbbNl2ZDTwXEU/10Ne49LjHY0haIqlVUmtnZ2eDwzAbGHt/+QuC4BAdwi/2/Gywh2PWK40uff4Mxcqze1PRrZK+FhGf6263OmXRhzZdWQisbKCvho8RETcBNwFUKpVGx2E2IGbM+TALdvyEn+9+mXM+9OnBHo5ZrzR6GW0h8O7q39VIuobinQS6C5sO9n//tPHAtj60OYCkJuA8YEYDfXWkx706htnB5tDD3sLvL/78YA/DrE8aXSCwhdJHCwCjgKd72GcNMFnSJEkjKW7et9S0aQEWp1Vps4CdEbG9gfGcDvwoIsqXx1qABZJGSZpEsRDg8dTfLkmz0n2excA3GziGmZn1k0bPbPYCGyX9X4pLUGdQvIvAMoCI+FjtDhGxT9KlwGpgBLA8IjZKWprqbwTuB84C2oHdwMXV/SWtBOYAYyR1AFdFxC2pegH7X0Ij9X03xQKEfcCfRsSrqfoSitVth1MsDPDiADOzAaRiIVgPjaSLuquPiNv7bUQHiUqlEq2trYM9DDOzIUXS2oio1JY3+kacwy5MzMxs4DS6Gm0y8AWKP84sfyz0sZnGZWZmw0ijCwRupfgr/H3AqRRvzPmPuQZlZmbDS6Nhc3hE/BPFPZ5nIuKzvP6X+2ZmZt1qdDXaLyUdAjyVVpg9Cxydb1hmZjacNHpm8wngLcDHKP6Q8kLgq5nGZGZmw0yjYfP3wKSI6IiIi4GvAWfnG5aZmQ0njV5G+wPg65IWUXw09IXA72UblZmZDSuN/p3NZkkLgPso3ln5zIjYk3NgZmY2fHQbNpKeYP93SH4bxVvPPCaJiDgx5+DMzGx46OnMxvdlzMzsDes2bLr5YDQzM7OGNboazczMrM8cNmZmlp3DxszMsnPYmJlZdg4bMzPLzmFjZmbZOWzMzCw7h42ZmWWXNWwkzZW0SVK7pMvr1EvSslS/XtL0Ut1ySTskbaiz32Wp342SvpTKFklqK22vSTop1T2Y2lfr/Fk8ZmYDqNF3fe41SSOA64EzgA5gjaSWiPhhqdk8YHLaZlJ89PTMVHcbcB3FR1CX+z0VmA+cGBF7q8EREXcAd6Q2JwDfjIi20q6LIqK1P+doZmaNyXlmcwrQHhGbI+IVYBVFSJTNB1ZE4VFgtKSxABHxMPBinX4vAa6JiL2p3Y46bRYCK/tpHmZm9gblDJtxFB9HUNWRynrbptYUYLakxyQ9JOnkOm0u4MCwuTVdQrtSkup1LGmJpFZJrZ2dnT0Mw8zMGpUzbOr9QI8+tKnVBBwFzAI+CdxdDg9JM4HdEVG+17MoIk4AZqftwnodR8RNEVGJiEpzc3MPwzAzs0blDJsOYELp+XhgWx/a1Ov33nTp7XHgNWBMqX4BNWc1EfFs+roLuJPiEp+ZmQ2QnGGzBpgsaZKkkRQh0FLTpgVYnFalzQJ2RsT2Hvq9DzgNQNIUYCTwfHp+CPAhivtDpLImSWPS40MpPqPngBVuZmaWT7bVaBGxT9KlwGqKT/dcHhEbJS1N9TcC9wNnAe3AbuDi6v6SVgJzgDGSOoCrIuIWYDmwPC2JfgW4KCKql97eC3RExObSUEYBq1PQjAC+C9ycadpmZlaHXv85bWWVSiVaW71S2sysNyStjYhKbbnfQcDMzLJz2JiZWXYOGzMzy85hY2Zm2TlszMwsO4eNmZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOGzMzy85hY2Zm2TlszMwsO4eNmZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOGzMzy85hY2Zm2WUNG0lzJW2S1C7p8jr1krQs1a+XNL1Ut1zSDkkb6ux3Wep3o6QvpbKJkvZIakvbjaX2MyQ9kY6zTJJyzdnMzA6ULWwkjQCuB+YBU4GFkqbWNJsHTE7bEuCGUt1twNw6/Z4KzAdOjIhpwJdL1U9HxElpW1oqvyH1Xz3WAf2amVk+Oc9sTgHaI2JzRLwCrKIIibL5wIooPAqMljQWICIeBl6s0+8lwDURsTe129HdIFJ/R0bEIxERwArg3DcwLzMz66WcYTMO2Fp63pHKetum1hRgtqTHJD0k6eRS3SRJ61L57NIxOho5hqQlkloltXZ2dvYwDDMza1RTxr7r3ReJPrSp1QQcBcwCTgbulnQssB14Z0S8IGkGcJ+kab05RkTcBNwEUKlUehqHmZk1KOeZTQcwofR8PLCtD23q9XtvuvT2OPAaMCYi9kbECwARsRZ4muIsqCP125tjmJlZP8oZNmuAyZImSRoJLABaatq0AIvTqrRZwM6I2N5Dv/cBpwFImgKMBJ6X1JwWJZDOdCYDm1N/uyTNSqvQFgPf7J8pmplZI7JdRouIfZIuBVYDI4DlEbFR0tJUfyNwP3AW0A7sBi6u7i9pJTAHGCOpA7gqIm4BlgPL05LoV4CLIiIkvRe4WtI+4FVgaURUFxhcQrG67XDggbSZmdkAUbFAy2pVKpVobW0d7GGYmQ0pktZGRKW23O8gYGZm2TlszMwsO4eNmZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOGzMzy85hY2Zm2TlszMwsO4eNmZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOGzMzy85hY2Zm2TlszMwsO4eNmZll57AxM7PssoaNpLmSNklql3R5nXpJWpbq10uaXqpbLmmHpA119rss9btR0pdS2RmS1kp6In09rdT+wdS+LW1H55qzmZkdqClXx5JGANcDZwAdwBpJLRHxw1KzecDktM0EbkhfAW4DrgNW1PR7KjAfODEi9paC43ngnIjYJuldwGpgXGnXRRHR2o9TNDOzBuU8szkFaI+IzRHxCrCKIiTK5gMrovAoMFrSWICIeBh4sU6/lwDXRMTe1G5H+rouIralNhuBwySN6vdZmZlZr+UMm3HA1tLzDvY/02i0Ta0pwGxJj0l6SNLJddp8EFhXDaTk1nQJ7UpJqtexpCWSWiW1dnZ29jAMMzNrVM6wqfcDPfrQplYTcBQwC/gkcHc5PCRNA74IfLS0z6KIOAGYnbYL63UcETdFRCUiKs3NzT0Mw8zMGpUzbDqACaXn44FtfWhTr99706W3x4HXgDEAksYD3wAWR8TT1R0i4tn0dRdwJ8UlPjMzGyA5w2YNMFnSJEkjgQVAS02bFmBxWpU2C9gZEdt76Pc+4DQASVOAkcDzkkYD3wauiIjvVRtLapJUDaNDgbOBA1a4mZlZPtnCJiL2AZdSrAp7Erg7IjZKWippaWp2P7AZaAduBv6kur+klcAjwPGSOiR9JFUtB45NS6JXARdFRKRjHQdcWbPEeRSwWtJ6oA14Nh3LzMwGiIqf01arUqlEa6tXSpuZ9YaktRFRqS33OwiYmVl2DhszM8vOYWNmZtk5bMzMLDuHjZmZZeewMTOz7Bw2ZmaWncPGzMyyc9iYmVl2DhszM8vOYWNmZtk5bMzMLDuHjZmZZeewMTOz7Bw2ZmaWncPGzMyy84endUFSJ/DMYI8jgzHA84M9iMyG+xw9v6FvOM/xmIhori102LzJSGqt9yl6w8lwn6PnN/S9GeZYy5fRzMwsO4eNmZll57B587lpsAcwAIb7HD2/oe/NMMf9+J6NmZll5zMbMzPLzmFjZmbZOWyGGElzJW2S1C7p8jr1krQs1a+XND2VT5D0L5KelLRR0sdr9rss9btR0pdK5VekvjZJOjP/DAd2jpImStojqS1tNw7F+Um6qzSHLZLaSnXD4jXsao7D6DU8SdKjaQ6tkk4p1Q34a9jvIsLbENmAEcDTwLHASODfgKk1bc4CHgAEzAIeS+Vjgenp8RHAj6v7AqcC3wVGpedHp69T0zFGAZPSsUcMszlOBDYM9dewZv9rgc8Mt9ewmzkOi9cQ+A4wr7T/g4P1GubYfGYztJwCtEfE5oh4BVgFzK9pMx9YEYVHgdGSxkbE9oj4AUBE7AKeBMalfS4BromIval+R6mvVRGxNyJ+ArSnMeQ00HMcaLnmBxS/UQPnAytLfQ2X1xCoO8eBlmt+ARyZHv8asK3U10C/hv3OYTO0jAO2lp53UPMfsZE2kiYC7wYeS0VTgNmSHpP0kKSTe3G8/jbQcwSYJGldKp/dD3PoTq75Vc0GnouIp3pxvP420HOE4fEafgL4a0lbgS8DV/TieAe9psEegPWK6pTVrl3vto2ktwL3AJ+IiJ+l4ibgKIrT/ZOBuyUd2+Dx+ttAz3E78M6IeEHSDOA+SdNK+/W3XPOrWsj+v/EPp9ewqnaOw+U1vAT4bxFxj6TzgVuA0xs83kHPZzZDSwcwofR8PK+favfYRtKhFP/A74iIe2v2uTed8j8OvEbxRoGNHK+/Degc06WJFwAiYi3F9fAp/TifWrnmh6Qm4Dzgrl4er78N6ByH0Wt4EVB9/jVev1Q2GK9h/xvsm0beGt8ofjvfTHGTsHpjclpNmw+w/43Jx1O5gBXA39TpdylwdXo8heKUXcA09r8xuZn8N5cHeo7N1TlR3PB9FnjbUJtfqp8LPFRTNmxew27mOCxeQ4r7N3PS4/cDawfrNczyfRvsAXjr5QtWrFL5McVvb59OZUuBpemxgOtT/RNAJZW/h+LUez3QlrazUt1I4KvABuAHwGml43069bWJtFJmOM0R+CCwMf1n/gFwzlCcX6q/rdpHzfGGxWvY1RyHy2uY6tameTwGzBjM17C/N79djZmZZed7NmZmlp3DxszMsnPYmJlZdg4bMzPLzmFjZmbZOWzMzCw7h42ZmWXnsDE7iKXPanlS0s3p80++I+lwSQ9KqqQ2YyRtGeShmnXLYWN28JsMXB8R04CXKf5i3mxIcdiYHfx+EhFt6fFaig8LMxtSHDZmB7+9pcevUrwR5D5e//972ICPyKyXHDZmQ9MWYEZ6/AeDOA6zhjhszIamLwOXSPo+xWcPmR3U/K7PZmaWnc9szMwsO4eNmZll57AxM7PsHDZmZpadw8bMzLJz2JiZWXYOGzMzy+7/A7au0hF7v05NAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nu xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nu sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nu tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lamb rho\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lamb kappa\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lamb xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lamb sigma\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lamb tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rho kappa\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rho xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rho sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rho tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kappa xi\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kappa sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kappa tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xi sigma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LzsnGzMyyc7IxM7PsnGzMzCw7JxszM8vOycbMzLJr+Guh01ctnwocUS2LiDXdL2FmZlZoKNlIuh6YQ5Fs7gfmA/8PcLIxM7NeNXoZ7d8CHwB+FhFXAb9L8eh+MzOzXjWabF6LiAPAfknHALuBqfnCMjOzt5JG79m0pm/CvAXYCLwKPJErKDMze2tpKNlERPUbNFdI+jvgmIjYnC8sMzN7K+nLaLQzgMnVZSSdFBH3ZIrLzMzeQhodjbYKOAPYChxIxQE42ZiZWa8aPbOZFRGnZo3EzMzeshodjfaopD4nG0nzJG2T1C7p2jr1krQs1W+WNKNUt0rSbklbapa5U1Jbmp6V1Faq+0zqa5ukD5XKz5L0ZKpbJkl93RYzM+u/RpPNNykSzraUFJ6U1OMAAUkjgJsp/gH0VOCKOglrPjAtTUuA5aW61cC82n4j4vKIODMizgS+TbqUl/peCExPy/11ioHU75LSurr0a9bs4sAB/vHvb+cfWm7i9ddeHepwzPqk0ctoq4ArgSd5855Nb84B2iNiO4CkdcAC4IelNguANRERwGOSxkgaHxG7IuIRSZO76zydnVwGzC31tS4i9gE/ltQOnCPpWYrRc4+m5dYAFwMPNLgdZk2h7ZE7ufG7nyMi6Hzxp1xy1Q1DHZJZwxpNNj+NiJY+9j0BeK403wHMbKDNBGBXA/3PBp6PiKdLfT1Wp69fp/e15V1IWkJxBsQJJ5zQQAhmg2ffr35JEBymw/jla78Y6nDM+qTRZPOUpDuA/w3sqxb2MvS53n2R6Eeb7lwBrG2gr4bXERErgZUAlUql0TjMBsVZcz7Kwt0/5tW9L3PRRz431OGY9UmjyeZIiiTzwVJZb0OfO4BJpfmJwM5+tOlC0kjgEuCsBvrqSO/7tA6zZnP4Ee/g3yz+ylCHYdYvjT5B4Kp+9L0BmCZpCrCD4ub9R2vatADXpPs5M4FXIqKRS2jnAU9FRPnyWAtwh6S/Ao6nGAjwRES8IWmPpFnA48Bi4H/0Y3vMzKyfGv2nzmV1il8BWiPiO/WWiYj9kq4B1gMjgFURsVXS0lS/guLrCi4A2oG9wG+SmqS1FF9rMFZSB3B9RNyaqhdy8CU0Ut93UQxA2A/8SUS8kaqvphjddiTFwAAPDjAzG0QqBoL10khaCfw28L9S0aUUTxOYBGyPiD/LFeBQqVQq0draOtRhmJkNK5I2RkSltrzRezYnAXMjYn/qbDnwPeB8iuHQZmZm3Wr0nzonAEeV5o8Cjk+XqfbVX8TMzKzQ6JnNDUCbpIcohhKfC/y5pKOABzPFZmZmbxGNjka7VdL9FE8FEPDZiKgOH/50ruDMzOytocfLaJJ+O73OAMZT/Lf/T4F3lx+aaWZm1pPezmw+RfH4lhtLZeXha3MxMzPrRY9nNhGxJL1dDiyIiPcD/5fif2z+U+bYzMzsLaLR0Wifj4hfSHovxXDn1Rz8dQBmZmbdajTZVP8T/8PAivTUgFF5QjIzs7eaRpPNDkl/Q/H9MfdLGt2HZc3M7G2u0YRxGcUzzuZFxMvAu/CQZzMza1Cj/2ezl9LXCaQnMzfydGYzMzNfCjMzs/ycbMzMLDsnGzMzy87JxszMsnOyMTOz7JxszMwsOycbMzPLzsnGzMyyy5psJM2TtE1Su6Rr69RL0rJUv7n8HTmSVknaLWlLneU+mfrdKumGVLZIUltpOiDpzFT3UGpfrTsu42abmVmNRr8Wus8kjQBupnhKdAewQVJLRPyw1Gw+MC1NMymeJD0z1a0GbgLW1PT7fmABcEZE7Ksmjoi4Hbg9tTkd+E5EtJUWXRQRrQO5jWZm1picZzbnAO0RsT0iXgfWUSSJsgXAmig8BoyRNB4gIh4BXqrT79XAVyNiX2q3u06bK4C1A7QdZmZ2iHImmwkUXyNd1ZHK+tqm1snAbEmPS3pY0tl12lxO12RzW7qEdp0k1etY0hJJrZJaOzs7ewnDzMwalTPZ1PtAj360qTUSOBaYRfHk6bvKyUPSTGBvRJTv9SyKiNOB2Wm6sl7HEbEyIioRURk3blwvYZiZWaNyJpsOYFJpfiKwsx9t6vV7T7r09gRwABhbql9IzVlNROxIr3uAOygu8ZmZ2SDJmWw2ANMkTZE0iiIJtNS0aQEWp1Fps4BX0tcX9OQ+YC6ApJMpvjH0hTR/GPARivtDpLKRksam94cDFwJdRriZmVk+2UajRcR+SddQfOnaCGBVRGyVtDTVrwDuBy4A2oG9wFXV5SWtBeYAYyV1ANdHxK3AKmBVGhL9OvCxiKheejsX6IiI7aVQRgPrU6IZATwI3JJps83MrA69+TltZZVKJVpbPVLazKwvJG2MiEptuZ8gYGZm2TnZmJlZdk42ZmaWnZONmZll52RjZmbZOdmYmVl2TjZmZpadk42ZmWXnZGNmZtk52ZiZWXZONmZmlp2TjZmZZedkY2Zm2TnZmJlZdk42ZmaWnZONmZll52RjZmbZOdmYmVl2TjZmZpadk42ZmWWXNdlImidpm6R2SdfWqZekZal+s6QZpbpVknZL2lJnuU+mfrdKuiGVTZb0mqS2NK0otT9L0pNpPcskKdc2m5lZV9mSjaQRwM3AfOBU4ApJp9Y0mw9MS9MSYHmpbjUwr06/7wcWAGdExHTgL0vVz0TEmWlaWipfnvqvrqtLv2Zmlk/OM5tzgPaI2B4RrwPrKJJE2QJgTRQeA8ZIGg8QEY8AL9Xp92rgqxGxL7Xb3VMQqb9jIuLRiAhgDXDxIWyXmZn1Uc5kMwF4rjTfkcr62qbWycBsSY9LeljS2aW6KZI2pfLZpXV0NLIOSUsktUpq7ezs7CUMMzNr1MiMfde7LxL9aFNrJHAsMAs4G7hL0lRgF3BCRLwo6SzgPknT+7KOiFgJrASoVCq9xWFmZg3KeWbTAUwqzU8EdvajTb1+70mX3p4ADgBjI2JfRLwIEBEbgWcozoI6Ur99WYeZmQ2gnMlmAzBN0hRJo4CFQEtNmxZgcRqVNgt4JSJ29dLvfcBcAEknA6OAFySNS4MSSGc604Dtqb89kmalUWiLge8MzCaamVkjsl1Gi4j9kq4B1gMjgFURsVXS0lS/ArgfuABoB/YCV1WXl7QWmAOMldQBXB8RtwKrgFVpSPTrwMciIiSdC3xJ0n7gDWBpRFQHGFxNMbrtSOCBNJmZ2SBRMUDLalUqlWhtbR3qMMzMhhVJGyOiUlvuJwiYmVl2TjZmZpadk42ZmWXnZGNmZtk52ZiZWXZONmZmlp2TjZmZZedkY2Zm2TnZmJlZdk42ZmaWnZONmZll52RjZmbZOdmYmVl2TjZmZpadk42ZmWXnZGNmZtk52ZiZWXZONmZmlp2TjZmZZedkY2Zm2WVNNpLmSdomqV3StXXqJWlZqt8saUapbpWk3ZK21Fnuk6nfrZJuSGXnS9oo6cn0OrfU/qHUvi1Nx+XaZjMz62pkro4ljQBuBs4HOoANkloi4oelZvOBaWmaCSxPrwCrgZuANTX9vh9YAJwREftKieMF4KKI2CnpNGA9MKG06KKIaB3ATTQzswblPLM5B2iPiO0R8TqwjiJJlC0A1kThMWCMpPEAEfEI8FKdfq8GvhoR+1K73el1U0TsTG22AkdIGj3gW2VmZn2WM9lMAJ4rzXdw8JlGo21qnQzMlvS4pIclnV2nzaXApmpCSm5Ll9Cuk6R6HUtaIqlVUmtnZ2cvYZiZWaNyJpt6H+jRjza1RgLHArOATwN3lZOHpOnA14BPlJZZFBGnA7PTdGW9jiNiZURUIqIybty4XsIwM7NG5Uw2HcCk0vxEYGc/2tTr95506e0J4AAwFkDSROBeYHFEPFNdICJ2pNc9wB0Ul/jMzGyQ5Ew2G4BpkqZIGgUsBFpq2rQAi9OotFnAKxGxq5d+7wPmAkg6GRgFvCBpDPBd4DMR8f1qY0kjJVWT0eHAhUCXEW5mZpZPtmQTEfuBayhGhf0IuCsitkpaKmlpanY/sB1oB24B/ri6vKS1wKPAKZI6JH08Va0CpqYh0euAj0VEpHWdBFxXM8R5NLBe0magDdiR1mVmZoNExee01apUKtHa6pHSZmZ9IWljRFRqy/0EATMzy87JxszMsnOyMTOz7JxszMwsOycbMzPLzsnGzMyyc7IxM7PsnGzMzCw7JxszM8vOycbMzLJzsjEzs+ycbMzMLDsnGzMzy87JxszMsnOyMTOz7JxszMwsO395WjckdQI/GeIwxgIvDHEMfTXcYh5u8YJjHizDLeZmiPcFgIiYV1vhZNPEJLXW+8a7ZjbcYh5u8YJjHizDLeZmj9eX0czMLDsnGzMzy87JprmtHOoA+mG4xTzc4gXHPFiGW8xNHa/v2ZiZWXY+szEzs+ycbMzMLDsnmwEmaZ6kbZLaJV1bp16SlqX6zZJmlOpWSdotaUud5T6Z+t0q6YZUdr6kjZKeTK9zS+0fSu3b0nRcE8Q7WdJrpZhWlNqflbajPa1PTbKPF5XibZN0QNKZfdnHuWKWdGdp3c9KaivVfSb1tU3Sh5plP3cXc7Meyz3E27THcg8xD8ix3G8R4WmAJmAE8AwwFRgF/BNwak2bC4AHAAGzgMdLdecCM4AtNcu8H3gQGJ3mj0uv7wGOT+9PA3aUlnkIqDRZvJNr25aWeQL4vbSeB4D5zRBzTZvTge192cc5Y65Z/kbgC+n9qWkdo4Epad0jmmE/9xBzUx7LPcTbtMdydzEPxLF8KJPPbAbWOUB7RGyPiNeBdcCCmjYLgDVReAwYI2k8QEQ8ArxUp9+rga9GxL7Ubnd63RQRO1ObrcARkkY3a7zdSf0dExGPRnHkrwEubsKYrwDW9rQtgxwzUPz1C1xWim0BsC4i9kXEj4F24Jwm2c91Y27iY7luvD20a9p9XKO/x3K/OdkMrAnAc6X5jlTW1za1TgZmS3pc0sOSzq7T5lJgU/XDMrktnRJf182p/FDEO0XSplQ+u7SOjgbXMZT7+HK6/oL2to9zxlw1G3g+Ip7upa9m2M/dxVzWTMdyT/E267HcU8xV/T2W+23kQHf4NlfvB1Q7tryRNrVGAsdSnEafDdwlaWr6ywlJ04GvAR8sLbMoInZIOhr4NnAlxV9ZQxYvsAs4ISJelHQWcF+KvS/rGKp9PBPYGxHl6+ON7OOcMVfV/pXaXV/NsJ+r6v5l3YTHcnfxNvOxXNXdPj6UY7nffGYzsDqASaX5icDOfrSp1+896VT6CeAAxUP3kDQRuBdYHBHPVBeIiB3pdQ9wB8Up+5DGmy7rvJji2khxvfrk1H5ig+sY9H2cLKTmF7fBfZwzZiSNBC4B7mygr2bYz93F3KzHct14m/xY7nYfJ4dyLPdfZLwh9HabKP463k5xU7Z6w296TZsPc/ANvydq6ifT9eb1UuBL6f3JFKfVAsakdVxaJ46x6f3hwN3A0iaIdxxv3qieCuwA3pXmN6T+qzdVL2iGfZzmD6P4pZ/a132cM+ZUPg94uKZsOgcPENhe2u9Dup97iHkMTXgs9xBv0x7L3cU8EMfyoUyD9kH8dpkoRo/8M8VfOp9LZUurP7x00Nyc6p+kNAKE4q+NXcCv0wHx8VQ+CvgWsAX4R2BuKv888EugrTQdBxwFbAQ2U9xs/Ub1F2OI4700xfNPqfyiUl+V1P4Z4CbSB/1Qx5zq5gCP1cTQ8D7OFXOqW039D9/Ppb62URoNNdT7ubuYadJjuYd4m/ZY7uW4mMMhHsv9nfy4GjMzy873bMzMLDsnGzMzy87JxszMsnOyMTOz7JxszMwsOycbs2FI0vGS7h7qOMwa5aHPZmaWnc9szJqcpLPTd5kcIekoFd+3c1rt95iYNTM/iNOsyUXEBkktwJeBIymedPDq0EZl1je+jGY2DEgaRfHMrV8B/5ri4Yx/GxGnDWlgZg3ymY3Z8PAu4J0UD0o8YohjMesz37MxGx5WAtcBt1N834vZsOIzG7MmJ2kxsD8i7pA0AvgBMHeIwzLrE9+zMTOz7HwZzczMsnOyMTOz7JxszMwsOycbMzPLzsnGzMyyc7IxM7PsnGzMzCy7/w/AP8ur8+EkNgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xi tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sigma tau\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_choices = list(param_choices_true_false[0].keys())\n",
+    "parameter_list = ([elt for elt in param_choices if elt != 'assignment'])\n",
+    "for i in range(len(parameter_list)):\n",
+    "    for j in range(i+1, len(parameter_list)):\n",
+    "        param1 = parameter_list[i]\n",
+    "        param2 = parameter_list[j]\n",
+    "        print(param1, param2)\n",
+    "        xsample = [result[param1] for result in param_choices_true_false]\n",
+    "        ysample = [result[param2] for result in param_choices_true_false]\n",
+    "        colors = ['green' if result['assignment'] == '1' else 'red' for result in param_choices_true_false]\n",
+    "        plt.title(\"Parameter Value Pairings\")\n",
+    "        plt.xlabel(param1)\n",
+    "        plt.ylabel(param2)\n",
+    "\n",
+    "        area = 5  # 0 to 15 point radii\n",
+    "\n",
+    "        plt.scatter(xsample, ysample, s=area, c=colors, alpha=0.5)\n",
+    "        plt.show()\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
+}
diff --git a/scratch/notebooks/sidarthe-query-positional-2d.ipynb b/scratch/notebooks/sidarthe-query-positional-2d.ipynb
new file mode 100644
index 00000000..19ac9ecb
--- /dev/null
+++ b/scratch/notebooks/sidarthe-query-positional-2d.ipynb
@@ -0,0 +1,208 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize recording of parameter choices and true/false\n",
+    "\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.08,0.1]\n",
+    "gamma_search_bounds = [0.02,0.03]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# USER: list how many points for each parameter you'd like to synthesize\n",
+    "\n",
+    "beta_values_to_synthesize = 10\n",
+    "gamma_values_to_synthesize = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_points_beta = np.linspace(beta_search_bounds[0], beta_search_bounds[1], beta_values_to_synthesize)\n",
+    "search_points_gamma = np.linspace(gamma_search_bounds[0], gamma_search_bounds[1], gamma_values_to_synthesize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# set parameters\n",
+    "def ps(param_synth_method):\n",
+    "    param_choices_true_false = []\n",
+    "    for i in range(len(search_points_beta)):\n",
+    "        beta_val = search_points_beta[i]\n",
+    "        for j in range(len(search_points_gamma)):\n",
+    "            gamma_val = search_points_gamma[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 = 0.01, 0, 0, 0, 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",
+    "\n",
+    "            # plot results - uncomment next line to plot time series.  not recommended for large numbers of points\n",
+    "            # sir_model.plotSIR(tvect, S, I, R)\n",
+    "            # USER: write query condition.\n",
+    "            query_condition =  0.15 <= max(I) <= 0.3  #((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "            query = '1' if query_condition else '0'\n",
+    "            param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(param_choices_true_false)\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sidarthe_query_auto_2_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot \"true/false\" points.  Is there any way of characterizing these regions?\n",
+    "\n",
+    "sir_model.plot_two_params(\"gamma\", \"beta\", param_choices_true_false)"
+   ]
+  },
+  {
+   "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/scratch/notebooks/sidarthe-query-positional-all-dimensions.ipynb b/scratch/notebooks/sidarthe-query-positional-all-dimensions.ipynb
new file mode 100644
index 00000000..2487c696
--- /dev/null
+++ b/scratch/notebooks/sidarthe-query-positional-all-dimensions.ipynb
@@ -0,0 +1,445 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "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": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize recording of parameter choices and true/false\n",
+    "\n",
+    "\n",
+    "# USER: set bounds\n",
+    "search_bounds = [0,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# USER: list how many points for each parameter you'd like to synthesize\n",
+    "\n",
+    "values_to_synthesize = 5\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_points = np.linspace(search_bounds[0], search_bounds[1], values_to_synthesize)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0.0 0.0\n",
+      "0.0 0.0 0.25\n",
+      "0.0 0.0 0.5\n",
+      "0.0 0.0 0.75\n",
+      "0.0 0.0 1.0\n",
+      "0.0 0.25 0.0\n",
+      "0.0 0.25 0.25\n",
+      "0.0 0.25 0.5\n",
+      "0.0 0.25 0.75\n",
+      "0.0 0.25 1.0\n",
+      "0.0 0.5 0.0\n",
+      "0.0 0.5 0.25\n",
+      "0.0 0.5 0.5\n",
+      "0.0 0.5 0.75\n",
+      "0.0 0.5 1.0\n",
+      "0.0 0.75 0.0\n",
+      "0.0 0.75 0.25\n",
+      "0.0 0.75 0.5\n",
+      "0.0 0.75 0.75\n",
+      "0.0 0.75 1.0\n",
+      "0.0 1.0 0.0\n",
+      "0.0 1.0 0.25\n",
+      "0.0 1.0 0.5\n",
+      "0.0 1.0 0.75\n",
+      "0.0 1.0 1.0\n",
+      "0.25 0.0 0.0\n",
+      "0.25 0.0 0.25\n",
+      "0.25 0.0 0.5\n",
+      "0.25 0.0 0.75\n",
+      "0.25 0.0 1.0\n",
+      "0.25 0.25 0.0\n",
+      "0.25 0.25 0.25\n",
+      "0.25 0.25 0.5\n",
+      "0.25 0.25 0.75\n",
+      "0.25 0.25 1.0\n",
+      "0.25 0.5 0.0\n",
+      "0.25 0.5 0.25\n",
+      "0.25 0.5 0.5\n",
+      "0.25 0.5 0.75\n",
+      "0.25 0.5 1.0\n",
+      "0.25 0.75 0.0\n",
+      "0.25 0.75 0.25\n",
+      "0.25 0.75 0.5\n",
+      "0.25 0.75 0.75\n",
+      "0.25 0.75 1.0\n",
+      "0.25 1.0 0.0\n",
+      "0.25 1.0 0.25\n",
+      "0.25 1.0 0.5\n",
+      "0.25 1.0 0.75\n",
+      "0.25 1.0 1.0\n",
+      "0.5 0.0 0.0\n",
+      "0.5 0.0 0.25\n",
+      "0.5 0.0 0.5\n",
+      "0.5 0.0 0.75\n",
+      "0.5 0.0 1.0\n",
+      "0.5 0.25 0.0\n",
+      "0.5 0.25 0.25\n",
+      "0.5 0.25 0.5\n",
+      "0.5 0.25 0.75\n",
+      "0.5 0.25 1.0\n",
+      "0.5 0.5 0.0\n",
+      "0.5 0.5 0.25\n",
+      "0.5 0.5 0.5\n",
+      "0.5 0.5 0.75\n",
+      "0.5 0.5 1.0\n",
+      "0.5 0.75 0.0\n",
+      "0.5 0.75 0.25\n",
+      "0.5 0.75 0.5\n",
+      "0.5 0.75 0.75\n",
+      "0.5 0.75 1.0\n",
+      "0.5 1.0 0.0\n",
+      "0.5 1.0 0.25\n",
+      "0.5 1.0 0.5\n",
+      "0.5 1.0 0.75\n",
+      "0.5 1.0 1.0\n",
+      "0.75 0.0 0.0\n",
+      "0.75 0.0 0.25\n",
+      "0.75 0.0 0.5\n",
+      "0.75 0.0 0.75\n",
+      "0.75 0.0 1.0\n",
+      "0.75 0.25 0.0\n",
+      "0.75 0.25 0.25\n",
+      "0.75 0.25 0.5\n",
+      "0.75 0.25 0.75\n",
+      "0.75 0.25 1.0\n",
+      "0.75 0.5 0.0\n",
+      "0.75 0.5 0.25\n",
+      "0.75 0.5 0.5\n",
+      "0.75 0.5 0.75\n",
+      "0.75 0.5 1.0\n",
+      "0.75 0.75 0.0\n",
+      "0.75 0.75 0.25\n",
+      "0.75 0.75 0.5\n",
+      "0.75 0.75 0.75\n",
+      "0.75 0.75 1.0\n",
+      "0.75 1.0 0.0\n",
+      "0.75 1.0 0.25\n",
+      "0.75 1.0 0.5\n",
+      "0.75 1.0 0.75\n",
+      "0.75 1.0 1.0\n",
+      "1.0 0.0 0.0\n",
+      "1.0 0.0 0.25\n",
+      "1.0 0.0 0.5\n",
+      "1.0 0.0 0.75\n",
+      "1.0 0.0 1.0\n",
+      "1.0 0.25 0.0\n",
+      "1.0 0.25 0.25\n",
+      "1.0 0.25 0.5\n",
+      "1.0 0.25 0.75\n",
+      "1.0 0.25 1.0\n",
+      "1.0 0.5 0.0\n",
+      "1.0 0.5 0.25\n",
+      "1.0 0.5 0.5\n",
+      "1.0 0.5 0.75\n",
+      "1.0 0.5 1.0\n",
+      "1.0 0.75 0.0\n",
+      "1.0 0.75 0.25\n",
+      "1.0 0.75 0.5\n",
+      "1.0 0.75 0.75\n",
+      "1.0 0.75 1.0\n",
+      "1.0 1.0 0.0\n",
+      "1.0 1.0 0.25\n",
+      "1.0 1.0 0.5\n",
+      "1.0 1.0 0.75\n",
+      "1.0 1.0 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from itertools import product\n",
+    "\n",
+    "for i,j, k in product(search_points, search_points, search_points):\n",
+    "    print(i,j, k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set parameters\n",
+    "def ps(param_synth_method):\n",
+    "    param_choices_true_false = []\n",
+    "    for alpha_val, beta_val, gamma_val, delta_val, \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 = 0.01, 0, 0, 0, 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",
+    "\n",
+    "                        # plot results - uncomment next line to plot time series.  not recommended for large numbers of points\n",
+    "                        # sir_model.plotSIR(tvect, S, I, R)\n",
+    "                        # USER: write query condition.\n",
+    "                        query_condition =  0.15 <= max(I) <= 0.3  #((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "                        query = '1' if query_condition else '0'\n",
+    "                        param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'alpha': alpha_val, 'delta': delta_val, 'epsilon': epsilon_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\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": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'beta': 0.0, 'gamma': 0.0, 'alpha': 0.0, 'assignment': '0'}, {'beta': 0.0, 'gamma': 0.0, 'alpha': 0.1111111111111111, 'assignment': '0'}, {'beta': 0.0, 'gamma': 0.0, 'alpha': 0.2222222222222222, 'assignment': '0'}, {'beta': 0.0, 'gamma': 0.0, 'alpha': 0.3333333333333333, 'assignment': '0'}, {'beta': 0.0, 'gamma': 0.0, 'alpha': 0.4444444444444444, 'assignment': '1'}, {'beta': 0.0, 'gamma': 0.0, 'alpha': 0.5555555555555556, 'assignment': '1'}, {'beta': 0.0, 'gamma': 0.0, 'alpha': 0.6666666666666666, 'assignment': '0'}, {'beta': 0.0, 'gamma': 0.0, 'alpha': 0.7777777777777777, 'assignment': '0'}, {'beta': 0.0, 'gamma': 0.0, 'alpha': 0.8888888888888888, 'assignment': '0'}, {'beta': 0.0, 'gamma': 0.0, 'alpha': 1.0, 'assignment': '0'}, {'beta': 0.0, 'gamma': 0.1111111111111111, 'alpha': 0.0, 'assignment': '0'}, {'beta': 0.0, 'gamma': 0.1111111111111111, 'alpha': 0.1111111111111111, 'assignment': '0'}, {'beta': 0.0, 'gamma': 0.1111111111111111, 'alpha': 0.2222222222222222, 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0.4444444444444444, 'alpha': 0.4444444444444444, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.4444444444444444, 'alpha': 0.5555555555555556, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.4444444444444444, 'alpha': 0.6666666666666666, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.4444444444444444, 'alpha': 0.7777777777777777, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.4444444444444444, 'alpha': 0.8888888888888888, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.4444444444444444, 'alpha': 1.0, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.5555555555555556, 'alpha': 0.0, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.5555555555555556, 'alpha': 0.1111111111111111, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.5555555555555556, 'alpha': 0.2222222222222222, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.5555555555555556, 'alpha': 0.3333333333333333, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.5555555555555556, 'alpha': 0.4444444444444444, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.5555555555555556, 'alpha': 0.5555555555555556, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.5555555555555556, 'alpha': 0.6666666666666666, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.5555555555555556, 'alpha': 0.7777777777777777, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.5555555555555556, 'alpha': 0.8888888888888888, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.5555555555555556, 'alpha': 1.0, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.6666666666666666, 'alpha': 0.0, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.6666666666666666, 'alpha': 0.1111111111111111, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.6666666666666666, 'alpha': 0.2222222222222222, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.6666666666666666, 'alpha': 0.3333333333333333, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.6666666666666666, 'alpha': 0.4444444444444444, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.6666666666666666, 'alpha': 0.5555555555555556, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.6666666666666666, 'alpha': 0.6666666666666666, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.6666666666666666, 'alpha': 0.7777777777777777, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.6666666666666666, 'alpha': 0.8888888888888888, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.6666666666666666, 'alpha': 1.0, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.7777777777777777, 'alpha': 0.0, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.7777777777777777, 'alpha': 0.1111111111111111, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.7777777777777777, 'alpha': 0.2222222222222222, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.7777777777777777, 'alpha': 0.3333333333333333, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.7777777777777777, 'alpha': 0.4444444444444444, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.7777777777777777, 'alpha': 0.5555555555555556, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.7777777777777777, 'alpha': 0.6666666666666666, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.7777777777777777, 'alpha': 0.7777777777777777, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.7777777777777777, 'alpha': 0.8888888888888888, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.7777777777777777, 'alpha': 1.0, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.8888888888888888, 'alpha': 0.0, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.8888888888888888, 'alpha': 0.1111111111111111, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.8888888888888888, 'alpha': 0.2222222222222222, 'assignment': '1'}, {'beta': 1.0, 'gamma': 0.8888888888888888, 'alpha': 0.3333333333333333, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.8888888888888888, 'alpha': 0.4444444444444444, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.8888888888888888, 'alpha': 0.5555555555555556, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.8888888888888888, 'alpha': 0.6666666666666666, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.8888888888888888, 'alpha': 0.7777777777777777, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.8888888888888888, 'alpha': 0.8888888888888888, 'assignment': '0'}, {'beta': 1.0, 'gamma': 0.8888888888888888, 'alpha': 1.0, 'assignment': '0'}, {'beta': 1.0, 'gamma': 1.0, 'alpha': 0.0, 'assignment': '1'}, {'beta': 1.0, 'gamma': 1.0, 'alpha': 0.1111111111111111, 'assignment': '1'}, {'beta': 1.0, 'gamma': 1.0, 'alpha': 0.2222222222222222, 'assignment': '1'}, {'beta': 1.0, 'gamma': 1.0, 'alpha': 0.3333333333333333, 'assignment': '0'}, {'beta': 1.0, 'gamma': 1.0, 'alpha': 0.4444444444444444, 'assignment': '0'}, {'beta': 1.0, 'gamma': 1.0, 'alpha': 0.5555555555555556, 'assignment': '0'}, {'beta': 1.0, 'gamma': 1.0, 'alpha': 0.6666666666666666, 'assignment': '0'}, {'beta': 1.0, 'gamma': 1.0, 'alpha': 0.7777777777777777, 'assignment': '0'}, {'beta': 1.0, 'gamma': 1.0, 'alpha': 0.8888888888888888, 'assignment': '0'}, {'beta': 1.0, 'gamma': 1.0, 'alpha': 1.0, 'assignment': '0'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(param_choices_true_false)\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sidarthe_query_auto_all_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Plot \"true/false\" points.  Is there any way of characterizing these regions?\n",
+    "\n",
+    "# sir_model.plot_two_params(\"gamma\", \"beta\", param_choices_true_false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta gamma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta alpha\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma alpha\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_choices = list(param_choices_true_false[0].keys())\n",
+    "parameter_list = ([elt for elt in param_choices if elt != 'assignment'])\n",
+    "for i in range(len(parameter_list)):\n",
+    "    for j in range(i+1, len(parameter_list)):\n",
+    "        param1 = parameter_list[i]\n",
+    "        param2 = parameter_list[j]\n",
+    "        print(param1, param2)\n",
+    "        xsample = [result[param1] for result in param_choices_true_false]\n",
+    "        ysample = [result[param2] for result in param_choices_true_false]\n",
+    "        colors = ['green' if result['assignment'] == '1' else 'red' for result in param_choices_true_false]\n",
+    "        plt.title(\"Parameter Value Pairings\")\n",
+    "        plt.xlabel(param1)\n",
+    "        plt.ylabel(param2)\n",
+    "\n",
+    "        area = 5  # 0 to 15 point radii\n",
+    "\n",
+    "        plt.scatter(xsample, ysample, s=area, c=colors, alpha=0.5)\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "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
+}
diff --git a/scratch/notebooks/sidarthe_check_BIOMD0000000955_askenet_request.ipynb b/scratch/notebooks/sidarthe_check_BIOMD0000000955_askenet_request.ipynb
new file mode 100644
index 00000000..d413cab3
--- /dev/null
+++ b/scratch/notebooks/sidarthe_check_BIOMD0000000955_askenet_request.ipynb
@@ -0,0 +1,715 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "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": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize recording of parameter choices and true/false\n",
+    "\n",
+    "\n",
+    "# USER: set bounds\n",
+    "theta_search_bounds = [0.2968,0.4452]\n",
+    "epsilon_search_bounds = [0.1368,0.20520000000000002]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# USER: list how many points for each parameter you'd like to synthesize\n",
+    "\n",
+    "theta_values_to_synthesize = 5\n",
+    "epsilon_values_to_synthesize = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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": 22,
+   "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": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.2968, 'epsilon': 0.1368, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.2968, 'epsilon': 0.1539, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.2968, 'epsilon': 0.171, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.2968, 'epsilon': 0.18810000000000002, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.2968, 'epsilon': 0.20520000000000002, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.3339, 'epsilon': 0.1368, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.3339, 'epsilon': 0.1539, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.3339, 'epsilon': 0.171, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.3339, 'epsilon': 0.18810000000000002, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.3339, 'epsilon': 0.20520000000000002, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.371, 'epsilon': 0.1368, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.371, 'epsilon': 0.1539, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.371, 'epsilon': 0.171, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.371, 'epsilon': 0.18810000000000002, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.371, 'epsilon': 0.20520000000000002, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.4081, 'epsilon': 0.1368, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.4081, 'epsilon': 0.1539, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.4081, 'epsilon': 0.171, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.4081, 'epsilon': 0.18810000000000002, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.4081, 'epsilon': 0.20520000000000002, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.4452, 'epsilon': 0.1368, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.4452, 'epsilon': 0.1539, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.4452, 'epsilon': 0.171, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.4452, 'epsilon': 0.18810000000000002, 'assignment': '0'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'theta': 0.4452, 'epsilon': 0.20520000000000002, 'assignment': '0'}\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 = 3.33333333000000e-6, 3.33333333000000e-7, 1.66666666000000e-8, 3.33333333000000e-8, 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",
+    "\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 =  max(I+D+A+R+T) <= 0.02  #((0.15 <= I[10]) and I[10] <= 1.0)\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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot \"true/false\" points.  Is there any way of characterizing these regions?\n",
+    "\n",
+    "sir_model.plot_two_params(\"theta\", \"epsilon\", param_choices_true_false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'theta': 0.2968, 'epsilon': 0.1368, 'assignment': '0'}, {'theta': 0.2968, 'epsilon': 0.1539, 'assignment': '0'}, {'theta': 0.2968, 'epsilon': 0.171, 'assignment': '0'}, {'theta': 0.2968, 'epsilon': 0.18810000000000002, 'assignment': '0'}, {'theta': 0.2968, 'epsilon': 0.20520000000000002, 'assignment': '0'}, {'theta': 0.3339, 'epsilon': 0.1368, 'assignment': '0'}, {'theta': 0.3339, 'epsilon': 0.1539, 'assignment': '0'}, {'theta': 0.3339, 'epsilon': 0.171, 'assignment': '0'}, {'theta': 0.3339, 'epsilon': 0.18810000000000002, 'assignment': '0'}, {'theta': 0.3339, 'epsilon': 0.20520000000000002, 'assignment': '0'}, {'theta': 0.371, 'epsilon': 0.1368, 'assignment': '0'}, {'theta': 0.371, 'epsilon': 0.1539, 'assignment': '0'}, {'theta': 0.371, 'epsilon': 0.171, 'assignment': '0'}, {'theta': 0.371, 'epsilon': 0.18810000000000002, 'assignment': '0'}, {'theta': 0.371, 'epsilon': 0.20520000000000002, 'assignment': '0'}, {'theta': 0.4081, 'epsilon': 0.1368, 'assignment': '0'}, {'theta': 0.4081, 'epsilon': 0.1539, 'assignment': '0'}, {'theta': 0.4081, 'epsilon': 0.171, 'assignment': '0'}, {'theta': 0.4081, 'epsilon': 0.18810000000000002, 'assignment': '0'}, {'theta': 0.4081, 'epsilon': 0.20520000000000002, 'assignment': '0'}, {'theta': 0.4452, 'epsilon': 0.1368, 'assignment': '0'}, {'theta': 0.4452, 'epsilon': 0.1539, 'assignment': '0'}, {'theta': 0.4452, 'epsilon': 0.171, 'assignment': '0'}, {'theta': 0.4452, 'epsilon': 0.18810000000000002, 'assignment': '0'}, {'theta': 0.4452, 'epsilon': 0.20520000000000002, 'assignment': '0'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(param_choices_true_false)\n",
+    "\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sidarthe_july_demo_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)"
+   ]
+  },
+  {
+   "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
+}
diff --git a/scratch/notebooks/sir-auto-eval-constrained-trajectory.ipynb b/scratch/notebooks/sir-auto-eval-constrained-trajectory.ipynb
new file mode 100644
index 00000000..2cf050be
--- /dev/null
+++ b/scratch/notebooks/sir-auto-eval-constrained-trajectory.ipynb
@@ -0,0 +1,664 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize recording of parameter choices and true/false\n",
+    "\n",
+    "\n",
+    "beta_search_bounds = [1000*0.000001,1000*0.002]\n",
+    "gamma_search_bounds = [0.082,0.085]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Start here at each iteration by changing the number of values of beta_val and gamma_val to synthesize , then run the following cells to observe the simulation and tell whether the parameter settings are what you're looking for (1) or not (0)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta_values_to_synthesize = 100\n",
+    "gamma_values_to_synthesize = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_points_beta = np.linspace(beta_search_bounds[0], beta_search_bounds[1], beta_values_to_synthesize)\n",
+    "search_points_gamma = np.linspace(gamma_search_bounds[0], gamma_search_bounds[1], gamma_values_to_synthesize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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//EE7xh88CN27wzPPwJAhEB/vd+mMMcZEkZdtFluAk8P2G6M1WuFGAQ875xywTkR+AE4HvvKwXOVCt26alX7jRvjhh9BcxeY4s2ePpoNIToabbtKJqY0xxhyXvKzpWgS0FJHmgc7xI4CZBc7ZBJwLICInAq2ADR6WqdxISAilYLIO9ceZ9PTQdkoK3HknPP20BVzGGHOc8yzocs7lALcCHwDfAq8751aJyE0iclPgtD8DZ4jIN8DHwBjn3C6vylTeBJsYP/kEsrN9LYqJBud0uoFrrw0lOQXNt1W9un/lMsYYExOeDolyzs0CZhU4NilsexswwMsylGfNm0OzZtrEuGgRnHGG3yUypbZrF/zjH/D117q/ZAm0KZhBxRhjzPHMMtKXYZaz6zixaBHcdpsGXElJcO+9mnPLGGNMhWJBVxnXt68OYluyBNLS/C6NKZGcHJg8GR58UPtxdeoEEydCjx5+l8wYY4wPig26RKSXiHwoIt+LyAYR+UFErLN7jCQlQdeumsrJcnaVM/v2wccfa6bbkSPhgQf0DTXGGFMhRdKnazLwO2AJkOttcUxh+vWDL7/UJsaLL7Y5jsuN5GTNwVWlCpx+ut+lMcYY47NIgq69zrn3PC+JKVLnzlpBsnkzbNoETZv6XSJTKOfg1VehalWNjkFnMDfGGGOILOiaKyKPAG8BmcGDzrmlnpXK5JOQoMlSP/xQa7ws6CqD9u+Hxx/XyaoTEuDMM6FePb9LZYwxpgyJJOjqHlh3CTvmgHOiXxxTlJ49Q0HX8OF+l8bks2ULPPQQbN2q+bb+8AcLuIwxxhyh2KDLOXd2LApiji4lRbsGrVunKZ/q1vW7RAaAxYvh73/XeRObNtV0EA0a+F0qY4wxZVAkoxeTRORxEVkcWB4TERuCFWOVKmnGAdDaLlMGzJun6SAOHtTmxEcftYDLGGNMkSLJ0zUFSAeGBZZ9wAteFsoULpjeyYKuMqJ1a6hTB0aMCI1SNMYYY4oQSZ+uU5xzl4bt/0lElntUHnMUXbtqyqdvvoGMDKhRw+8SVUDZ2dpRXkT7bU2caG+EMcaYiERS03VQRM4M7ohIL+Cgd0UyRalRA9q310Spixb5XZoKaO9eGDsW3ngjdMwCLmOMMRGKJOi6GZgoIhtF5Efgn8BN3hbLFMWaGH2yc6c2Ia5ZAx98AIcO+V0iY4wx5UyxQZdzbrlzLgXoALR3znV0zq3wvmimMMGga+lSyMrytywVxpYtMGYMbNsGLVpoh3nrv2WMMaaEiuzTJSJXOudeEZE7CxwHwDn3uMdlM4WoWxdOPVVTR6xYof28jIfWr4f779d5FNu00e3q1f0ulTHGmHLoaDVdwU+WmoUs1pHFR8Hari++8Lccx73vvoM//lEDrs6dNT2EBVzGGGNKqciaLufcs4HNj5xzn4c/FuhMb3zSowe88orOOJOXpyMajQfq1tWO8p07w5136qhFY4wxppQi+bh+KsJjJkaaNNEcnHv3amWM8UjduvDII3DXXRZwGWOMOWZH69PVEzgDqFegX1ctIN7rgpmiiWht19tv6yjGNm38LtFxZMkS2LABLrtM95OT/S2PMcaY48bRaroqoX23Esjfn2sfMNT7opmjCU8d4Zy/ZTluLF0K48fDyy9r8GWMMcZE0dH6dH0KfCoiLzrnfoxhmUwETj8dkpJg+3bYvFmbHM0xWLYMHnpIM86ff35ooktjjDEmSiLp03VARB4RkVkiMie4eF4yc1RxcaG4YOlSf8tS7i1fnj/guukmbcM1xhhjoiiSoGsq8B3QHPgTsBGwSWjKgGDQZS1hx2DNGg24srJg0CALuIwxxngmkqDrBOfcZCDbOfepc+5aoIfH5TIR6NhR44NVqyAz0+/SlEPOwdNP64t37rkwerQFXMYYYzwTSdCVHVhvF5ELRKQj0NjDMpkIJSXBKadoq9jKlX6XphwSgfvug4sugt/+1gIuY4wxnook6HpIRJKA3wN3Ac8Dv/O0VCZiwSbGZcv8LUe5Ej5pZd26cP31EG9ZUIwxxngrkgmv33XO7XXOrXTOne2c6+ycmxmLwpnideyoa+tMH6H9++Huu2HqVMu1YYwxJqaOlhz1KaDITyXn3G2elMiUyOmnQ9Wqmjbi55+hXj2/S1SGZWdrHq4NG+DgQfjVr2wuRWOMMTFztLlNFsesFKbUEhIgJUWTpC5dCgMH+l2iMso5eOop+OYbzTL/0EMWcBljjImpoyVHfSmWBTGl16mTBV3Feu01mDsXqlSBceOgfn2/S2SMMaaCKXYWXxGZSyHNjM65czwpkSmxYGf6FSsgN9f6hB/hk0+0D5cI/OEP0KKF3yUyxhhTARUbdKEjFoOqAJcCOZFcXEQGAU+iE2Q/75x7uJBz+gJPAInALudcn0iubUJOPBEaNoRt2+D776F1a79LVIbk5GgtF8BvfgNdu/pbHmOM8Uhubi5paWlkZ2cXf7KJisTERJKTk4mPsLaj2KDLOVcw3/nnIvJpcc8TkXhgItAf2AIsEpGZzrnVYefUBp4GBjnnNomItfmUUqdOGnQtXWpBVz4JCfDXv8K8eXDhhX6XxhhjPJOWlkaVKlWoW7cuYnkHPeecIyMjg7S0NOpFOIqt2JQRIpIcttQVkYHASRFcuxuwzjm3wTmXBUwDLipwzuXAW865TYEfYGdEpTZHsHkYC8jNDW3Xrg1DhvhWFGOMiYXs7Gxq1KhhAVeMiAg1atQoUc1iJM2LS9A+XYI2K/4AXBfB8xoBm8P2twDdC5xzGpAoIp8ANYEnnXMvR3BtU0D79lqps3YtpKdDzZp+l8hHeXk6OvHkk+Gaa6yTmzGmwrCAK7ZK+npHkhy1uXOuRWDd0jk3wDn3WSRlKexyBfYTgM7ABcBA4D4ROS2Ca5sCqlSBNm00M8Ly5X6XxmdTp8LixfDRR/DLL36XxhhjKpTx48fTtm1bOnToQGpqKgsXLvSlHMuXL2fWrFmH92fOnMnDD2vX8pEjRzJ9+vQjnvPJJ58wePBgz8oUyejFKsBo4Ew0aPoMeMY5d6iYp24BTg7bbwxsK+ScXc65/cB+EZkHpADfR1Z8E65TJ/j6a21i7N3b79L4ZMECeP11Hak4ZoxO82OMMSYmvvjiC959912WLl1K5cqV2bVrF1nhU6/F0PLly1m8eDHnn38+AEOGDGGIz11NIpl78WWgLfAU8E+gNfDvCJ63CGgpIs1FpBIwAig4fdA7QG8RSRCRamjz47eRFt7kF96vq0LOcLN5M/zjH7o9apRmjTXGGBMz27dvp27dulSuXBmAunXr0rBhQ5o1a8auXbsAWLx4MX379gXg008/JTU1ldTUVDp27Eh6ejoAf//732nfvj0pKSncc889AKxfv55BgwbRuXNnevfuzXfffQdordVNN91E7969Oe2003j33XfJysri/vvv57XXXiM1NZXXXnuNF198kVtvvfVwWT/66KN8zylo//79XHvttXTt2pWOHTvyzjvvHPPrE0mfrlbOufBPr7kisqK4JznnckTkVuADNGXEFOfcKhG5KfD4JOfctyLyPvA1kIemlVhZ8h/DADRrBnXqQFoa/Pij7lcY+/drP65Dh6BPH53ixxhjKiivBmv/979Hf3zAgAE8+OCDnHbaafTr14/hw4fTp0/RmaAeffRRJk6cSK9evcjIyKBKlSq89957zJgxg4ULF1KtWjXS0tIAuOGGG5g0aRItW7Zk4cKFjB49mjlz5gCwceNGPv30U9avX8/ZZ5/NunXrePDBB1m8eDH//Oc/AXjxxRfz3buw54QbP34855xzDlOmTGHPnj1069aNfv36Uf0YZjOJJOhaJiI9nHNfAohId+DzSC7unJsFzCpwbFKB/UeARyIrrjkaEZ0Ae84c7ddVoYKuF1/UnBnNm8Nvf6svhjHGmJiqUaMGS5YsYf78+cydO5fhw4cf7kdVmF69enHnnXdyxRVXcMkll9C4cWM++ugjRo0aRbVq1QBITk4mIyODBQsWcNlllx1+bmZm5uHtYcOGERcXR8uWLWnRosXhWrCjKe45s2fPZubMmTz66KMAHDp0iE2bNtH6GPIyRRJ0dQeuFpFNgf0mwLci8g3gnHMdSn13E3WpqaGgq0JV9lx9tdZ2XXMNBKq1jTGmoiquRspL8fHx9O3bl759+9K+fXteeuklEhISyMvLAzR4Cbrnnnu44IILmDVrFj169OCjjz7COXfEqMC8vDxq167N8iJGihU8P5JRhcU9xznHm2++SatWrYq9VqQi6dM1CGgO9AkszYHzgcGAZZssY4LdmFauhAqVlLhmTZ3i58QT/S6JMcZUWGvWrGHt2rWH95cvX07Tpk1p1qwZS5ZorvU333zz8OPr16+nffv2jBkzhi5duvDdd98xYMAApkyZwoEDBwBN+lqrVi2aN2/OG2+8AWhAtGJFqKfTG2+8QV5eHuvXr2fDhg20atWKmjVrHu4jVpjCnhNu4MCBPPXUU7hAJ+lly5Yd46sTWcqIH4HaaIB1IVDbOfdjcDnmEpioSk6Gpk0hMxPWrPG7NB7buxdefVWn+jHGGOO7jIwMrrnmGtq0aUOHDh1YvXo1DzzwAOPGjeP222+nd+/e+abMeeKJJ2jXrh0pKSlUrVqV8847j0GDBjFkyBC6dOlCamrq4ea9qVOnMnnyZFJSUmjbtm2+ju2tWrWiT58+nHfeeUyaNIkqVapw9tlns3r16sMd6Qsq7Dnh7rvvPrKzs+nQoQPt2rXjvvvuO+bXR1wxw9xE5HbgN8BbgUMXA88555465rtHqEuXLm7x4sWxul259/zz8M47MGwYXHWV36XxiHPwwAM6VHPwYLjxRr9LZIwxvtq2bRsNGzb0uxgxN3LkSAYPHszQoUN9uX9hr7uILHHOdSl4biTNi9cB3Z1z9zvn7gd6oEGYKaNSU3W9otgxpuXYm29qwFWzJlx6qd+lMcYYY4oVSUd6AcImsiOXwrPNmzKiXTudEuj77yEjA2rU8LtEUbZ6Nfw7kCruzjstAaoxxlRgBVNBlGWRBF0vAAtF5G002LoImOxpqcwxqVIFTj9dO9N/8w307BnlGzgH69ZpRFenji61asUmTUN6OjzyiM6veMkl0OWI2ltjjDGmTCo26HLOPR6YkPrMwKFRzrlj78JvPJWSokHX8uVRDLoyMmDuXHjvPc3+Hi4+HmrXhhYtNFdF+/bRD8Kcg3/+E3bt0qjyuO2wZowx5ngUSU1XkKBZ461psRzo2FHnfY7K5Ndr18L//gfz50NwDq2kJB0m+csvumRkwO7duixaBKedBkOHQo8e0Qu+srJ0WGbVqnD33dqGaowxxpQTkUx4fT9wGfAmGnC9ICJvOOce8rpwpvROPRWqV9ck7Tt3Qv36pbzQhx/CU0+FJnNMTYVBg6B79/xBT3a2zj/0yScwc6Z2KPvLX+Dkk+Gyy6Bv32MPvipXhnHj9Icq9Q9kjDHG+COS0Yu/Bro65x5wzo1DRy9e4W2xzLGKj9cWPjiGUYwffggTJmjANXgwPPcc/PnP0KvXkbVMiYmamHT4cJg8GW64QTu4b94Mjz8O48drf6zSyMuD3MBYDhFo1KiUP5Axxhgv1Yhg5Nb8+fNp27YtqampHDx4sETXnzFjBqtXr/akXLEQSdC1EQjPGFYZWO9JaUxUBVNHlCqJbjDgArj2Ws2D1aBBZM+tUkVnW/3Xv+C227TKbeFC3V61quRlmT4d7rkHtm8v+XONMcaUKVOnTuWuu+5i+fLlVK1atUTPLW3QVVZEEnRlAqtE5EUReQFYCWSIyAQRmeBt8cyxCM/XVUwO3PwKBlwXX1y6AiQkQP/+eq1WrbQD/NixMG2a1l5FYu1a+M9/4Lvv4KefSlcOY4wxMfXJJ5/Qt29fhg4dyumnn84VV1yBc47nn3+e119/nQcffJArrtBGs0ceeYSuXbvSoUMHxo0bd/gaL7/8Mh06dCAlJYWrrrqKBQsWMHPmTO6++25SU1NZv34969evZ9CgQXTu3JnevXsfnrT6hx9+oGfPnnTt2jUqmeSjJZKeyG8HlqBPvCmKibaGDbWFb9cu+OEHHVhYrGgFXOHq14eHH9bgafp07eG/YoXWXiUlFf28zEx47DFtWrzwQh0dYIwxpngXejQ1cglm0l62bBmrVq2iYcOG9OrVi88//5zrr7+ezz777HAG+dmzZ7N27Vq++uornHMMGTKEefPmccIJJzB+/Hg+//xz6tatS1paGsnJyQwZMiRf9vlzzz2XSZMm0bJlSxYuXMjo0aOZM2cOt99+OzfffDNXX301EydO9Oa1KIVIUka8FIuCmOgT0Tjlww91FGOxQdeyZdEPuIISEuDqq6FDB+3jtXKlTlD9pz/BSScV/pyXXoKtW7Uz/siR0SuLMcYYz3Xr1o3GjRsDkJqaysaNGznzzDPznTN79mxmz55Nx8CX6oyMDNauXcuKFSsYOnQodQPJr5OTk4+4fkZGBgsWLOCyyy47fCwzMxOAzz///PDE2ldddRVjxoyJ/g9YCjbm/jiXmqpB17Jlmku0SFlZ8PTTuj1iRHQDroIFevJJnTdxwwZN/fCnPx0ZEa5eDe++C3Fx8PvfQ6VK3pTHGGOORyWokfJK5cqVD2/Hx8eTk5NzxDnOOcaOHcuNBebPnTBhAlLMiPe8vDxq167N8iJyIxX3fD9E0qfLlGMpKbpevTqUYqtQb7yhfaaaNtURiF6qUwf++lct3J492swY/keTlaWBmXOabuKUU7wtjzHGGF8MHDiQKVOmkJGRAcDWrVvZuXMn5557Lq+//jq7d+8GIC0tDYCaNWuSHhgJX6tWLZo3b84bb7wBaAC3IjBcv1evXkybNg3QjvtlRZFBl4j8O7C+PXbFMdGWlATNm2sc8+23RZy0dav2tQIYPTo2SUerVdParrPOgoMHtbZr3jx9LD4eBg6Eli29DwCNMcb4ZsCAAVx++eX07NmT9u3bM3ToUNLT02nbti333nsvffr0ISUlhTvvvBOAESNG8Mgjj9CxY0fWr1/P1KlTmTx5MikpKbRt25Z33nkHgCeffJKJEyfStWtX9u7d6+ePmI+4Ioa1ichq4DxgJtCXApnonXNpXhcuqEuXLm7x4sWxut1xZ8oUePttuPTSQrpGOQf33acd2/v1g9tjHGM7p3m9An8ojB4N552n23l52rxojDGmWNu2baNhw4Z+F6PCKex1F5ElzrkjJgc+2ifaJOB94HRgSYHFIqBypFMnXS9dWsiD8+drwFWzJowaFdNyAdrb//rr9d55efDEEzBrlj5mAZcxxpjjSJGfas65Cc651sAU51wL51zzsCWS5AOmjGjTRmfQ+eEHnannsP374fnndXvUKKhVy5fyAdrLv2VLHdX4l7/oXI/GGGPMcaTYqgTn3M0ikiIitwaWDrEomImeSpVCUwLly07/yis6WXXr1tq06Ke1a3U0Y+PGOqXQpEllYvSNMcYYEy3FBl0ichswFagfWKaKyG+9LpiJriOaGNev19qkuDjtR+Xn0NqcnNAcjzfeCHfdpcefe04nzzbGGGOOA5EMU7se6O6c2w8gIn8DvgCe8rJgJrqCQdeyZYH+6dOmaZAzZAg0a+Zr2XjnHdi4USfMvuIKbQsVgWee0fkbRbzLrmyMMcbESCQ9lQXIDdvPpcBIRlP2NWyoMU16Omz8fKtOQJ2QoEMa/bRjh04PBHDLLRpwAZx/vu6D1ni9/74/5TPGGGOiJJKg6wVgoYg8ICIPAF8Ckz0tlYk6EejcWbfTXpihtVznnKOJSv00aZImEevT58i5FQcN0uZG0Gz5c+bEvnzGGGMiFh8fT2pqKu3atePCCy9kz549fhepxB544AEeffRRT64dSUf6x4FRQBrwCzDKOfeEJ6UxnurUCapn76HyZx/rAa+m+imJESOgXTtNG1GYwYN1ZKVzmk5i/vyYFs8YY0zkqlatyvLly1m5ciXJycllZrJp5xx5eXl+FyOyaYCcc0sDKSSedM4tK/4Zpizq0AG6//wuB/dlk9mxu44U9FurVjolUO3aRZ9zySXa18s5ePRRbRo1xhhTpvXs2ZOtW7cCsH79egYNGkTnzp3p3bs33333HQA7duzg4osvJiUlhZSUFBYsWADA448/Trt27WjXrh1PPPEEAGPGjOHp4BzBaI3UY489BsAjjzxC165d6dChA+PGjQNg48aNtG7dmtGjR9OpUyc2b95c6HkA48ePp1WrVvTr1481a9Z49prYhNcVSFU5xNmHZpHlYFWrS+nkZ2E2b4aTT478/OHDITNTpyt6+GH4v/8LtZcaY4w50tEGIN1yi3bhAO0ze7QaqVKk78nNzeXjjz/muuuuA+CGG25g0qRJtGzZkoULFzJ69GjmzJnDbbfdRp8+fXj77bfJzc0lIyODJUuW8MILL7Bw4UKcc3Tv3p0+ffowYsQI7rjjDkaPHg3A66+/zvvvv8/s2bNZu3YtX331Fc45hgwZwrx582jSpAlr1qzhhRde4Omnny7yvOrVqzNt2jSWLVtGTk4OnTp1orNHny8WdFUkH35I/arpfFnjdLbsbu1f0LVqlU5yfe65Ou1QJOkqRODqq7X/18yZmkD1gQdCCciMMcb47uDBg6SmprJx40Y6d+5M//79ycjIYMGCBVx22WWHz8vMzARgzpw5vPzyy4D2B0tKSuKzzz7j4osvpnr16gBccsklzJ8/n9tuu42dO3eybds2fv75Z+rUqUOTJk2YMGECs2fPpmOgX3BGRgZr166lSZMmNG3alB49egAwe/bsQs9LT0/n4osvplq1agAMGTLEs9fH06BLRAYBTwLxwPPOuYeLOK8r2kF/uHNuupdlqrByc2HGDJKSYEGVS9i9TFvrYp6eKydHO8UD1KtXsgIEpwzKytJvZg8+CH/+M5x+ujdlNcaY8izSGqpBg0K1Xsco2Kdr7969DB48mIkTJzJy5Ehq167N8uXLI7pGUXNCAwwdOpTp06fz008/MWLEiMPnjx07lhuDA68CNm7ceDhwO9p5TzzxBBKjD8NIkqNeIiJrRWSviOwTkXQR2RfB8+KBieik2W2AX4tImyLO+xvwQcmLbyL2+eewcyfVTmnIzmbd2bVLW/hi7t13YdMmOOkkCPvWEzERTeZ69tlw6BCMGwfr1kW/nMYYY0otKSmJCRMm8Oijj1K1alWaN2/OG2+8AWjws2LFCgDOPfdcnnnmGUCbJPft28dZZ53FjBkzOHDgAPv37+ftt9+md+/eAIwYMYJp06Yxffp0hg4dCsDAgQOZMmUKGRkZAGzdupWdO3ceUaaizjvrrLN4++23OXjwIOnp6fzXw9lQIulI/3dgiHMuyTlXyzlX0zkXySR93YB1zrkNzrksYBpwUSHn/RZ4EzjyFTLR4Ry89RYAcsnFdOysb/uSJTEuR1oaTJ2q2zfcoPMTlYaINkueeSYcOAD33w8//hi9chpjjDlmHTt2JCUlhWnTpjF16lQmT55MSkoKbdu25Z133gHgySefZO7cubRv357OnTuzatUqOnXqxMiRI+nWrRvdu3fn+uuvP9wk2LZtW9LT02nUqBENGjQAYMCAAVx++eX07NmT9u3bM3ToUNLT048oT1HnderUieHDh5Oamsqll156OMDzghytGg9ARD53zvUq8YVFhgKDnHPXB/avQjPb3xp2TiPgP8A5aO6vdwtrXuzSpYtbvHhxSYtggr7+Gu69F5KSYMoUPv2iEo8+Cqmp2joXM488AvPmQffu2hH+WOXkaN+uRYt09OPDD0OjRsd+XWOMKYe2bdtGw4YN/S5GhVPY6y4iS5xzXQqeG0lN12IReU1Efh1oarxERC6J4HmFNZAWjPCeAMY453ILOddES7CqdPBgqFSJ1FStLFq1SgcExsTXX2vAVamS1nJFQ0KCdshPTYU9ezSw3L49Otc2xhhjoiySoKsWcAAYAFwYWAZH8LwtQHhOgMbAtgLndAGmichGYCjwtIj8KoJrm0ilpcFXX0F8PAwcCGiF16mnQnY2rFwZo3K0aAEXXKDJUOvXj951K1XSYKtdO9i9W7cLacs3xhhj/Fbs6EXn3KhSXnsR0FJEmgNbgRHA5QWu3Ty4LSIvos2LM0p5P1OYOXN0huuePfNN+dOpE6xdC4sXxyjdVY0acNNN3ly7ShXt1zVuHHz7Lfzxj9rUWLeuN/czxhhjSiGS0YuNReRtEdkpIjtE5E0RKTaVuXMuB7gVHZX4LfC6c26ViNwkIh59+pp8nIPZs3U7UMsV1K2brr/8Uk/zTHq6pnjwWtWqGnS1bKmTaN97r9byGWNMBVJcP20TXSV9vSOd8Hom0BBoBPw3cCySwsxyzp3mnDvFOTc+cGySc25SIeeOtBxdUfbNN9rHqW7dIyaTbtkSkpNh1y5Yv97DMjzzjGY+/v57D28SUL265u5q0QK2bdPAqxxOtmqMMaWRmJhIRkaGBV4x4pwjIyODxMTEiJ8TSXLUes658CDrRRG5o6SFMz74IJD6rH9/iMsfX4tAjx4wa5bWdp16qgf3X7lSJ6iuVOnocytGU40a8NBD2sS4caMGXn/5i3ZkM8aY41hycjJpaWmFpksw3khMTCQ5OTni8yMJunaJyJXAq4H9XwO7S1E2E0vp6bBggUZX/fsXekrPnhp0ffEFXHlllO+fmwvPPqvbl10W3c7zxalZMxR4bdoEY8dq4BWrwM8YY3wQHx9PvXr1/C6GOYpImhevBYYBPwHb0VGG13pZKBMFc+ZoHqtOnXS6nUK0a6ctcps2aWtcVH3wgdY01a8Pl0SSYSTKkpI00GraVFPvjx1rfbyMMcb4qtigyzm3yTk3xDlXzzlX3zn3K+ecpf8uy5wLNS0OGFDkaQkJ0CWQum3hwijePz0dXnlFt6+7rvSZ549VUhKMHw/NmsGWLVrzZYGXMcYYnxQZdInIHwLrp0RkQsEldkU0JbZmjdbu1K4dGqZYhJ49df3FF1G8/9SpGnh16BC6gV/CA6+tWzXw2m2t48YYY2LvaDVd3wbWi4ElhSymrArWcvXrp9VZR9GpEyQmwnffwS+/ROn+bdtqs+INN2ifMr/VqqVNjS1aaOA1dqwlUDXGGBNzRQZdzrngNNsHnHMvhS9ohnpTFh04oCMGocgO9OGqVtVZdJzTxPVR0bs3PPec9qcqK4Kd6085RdNojBnjQUc2Y4wxpmiRdKQfG+ExUxZ8+qlOqNi+PUQ48WnUmhhzckLb8fHHeDEP1KypTY2tW2uCsjFjtLO/McYYEwNH69N1nog8BTQq0J/rRSCnqOcZn334oa6P0oG+oG7dtBVwxQqtKCuVrCxNgvrii7HJQF9awQSqwUmyx46NTeJWY4wxFd7Rarq2of25DpG/L9dMYOBRnmf8snGjTqhYvTqccUbET0tK0sqfnBxYurSU937rLW2uW7Kk2H5kvqtSBe67D7p3h4wMTaAas5m/jTHGVFRH69O1ItB/qz3wSlh/rneAzFgV0JTARx/puk+fEqdpOKYmxl27YHpgBqcbbjgi+32ZVKkS3HOPvlaHDum8jVHNm2GMMcbkF8mn42ygath+VeAjb4pjSi0nBz75RLf79Svx07t31/Xixfm7ZkXkpZe0H9kZZ2hfsvIiIQHuvBPOO0+bRMePh/ff97tUxhhjjlORBF1VnHMZwZ3AdjXvimRKZdEi2LtXRwyWYiLFBg00ldWBA/D11yV44po1GuwlJsK15XCigrg4uPlmuOIKHcI5cSL85z+6bYwxxkRRJEHXfhHpFNwRkc7AQe+KZEol2IG+f/9S58bq0UPXETcxOqepIQAuvhhOPLFU9/WdCIwYAbfeqtuvvqrBV26u3yUzxhhzHIkk6LoDeENE5ovIfOA14FZPS2VKJi1NO7DHx0PfvqW+TK9eup4/P8IBiAcOaC/85GSd1Lq8GzhQO9VXqqQJZv/6V+3vZYwxxkRBJHMvLgJOB24GRgOtnXOWkb4smTsX8vI090NSUqkv06yZ5g7dvz/CRKnVq8P998OTT+qIwONB9+6aRLVGDe1YbxNlG2OMiZJIh5m1AtoAHYFfi8jV3hXJlIhzoVGLpehAX9C55+r6o5IMlahd+5jvW6a0bg1//zucdBKsW6ed7Tds8LtUxhhjyrligy4RGQc8FVjOBv4ODPG4XCZSa9bAli1Qpw507nzMl+vTRwf1LV16lAqenTs1z9X69cd8vzLr5JPh0UehTRudIHvMGEspYYwx5phEUtM1FDgX+Mk5NwpIASp7WioTuWCV1NlnR2XqnVq1tJXSOW21LNTkybB8OcyYccz3K9OSkrSp8eyztW/X+PHw9ts2stEYY0ypRBJ0HXTO5QE5IlIL2Am08LZYJiKHDsG8ebodhabFoGAT48cfFxJffP01LFgAlSvDNddE7Z5lVmIi/O53cOWV+mJMmQJPPFG2pzoyxhhTJkUSdC0WkdrAv9BpgJYCkXSzNl774gs4eBBatdLmsCjp1EkreTZv1lmFDsvNhX/9S7eHDYO6daN2zzJNBIYP1ybGSpVgzhz4wx+0mdUYY4yJ0FGDLhER4K/OuT3OuUlAf+CaQDOj8dvs2bru3z+ql01I0BY10Nquwz74QOd3rF8ffvWrqN6zXDjzTO3ndeKJ2p/td78rYSZZY4wxFdlRgy7nnANmhO1vdM7Zp0xZsHmzTtJcpQr07h31ywebGD/9NNCSlp4Or7yiB6+7rsRzOx43mjeHf/xDqwP37YP/+z/t22b9vIwxxhQjkubFL0Wkq+clMSXz3nu67tsXqkV/VqYjcnatXw/Z2dChQ2h27IqqZk2dIHvYMA22Jk+Ghx/WF8sYY4wpQiRB19lo4LVeRL4WkW9ExGq7/JSZqf2KQCdr9ki+nF2pqfDss6Gpciq6uDi46ipNnlq1qg4uuP32Ap3gjDHGmJAigy4RaRLYPA8drXgOcCEwOLA2fpk3T2tVWrWCFt4NJD0iZ1dyss6MbULOOEMz8p96KuzYoR3s33nHmhuNMcYc4Wg1XTMAnHM/Ao87534MX2JSOlO4YNOih7VcoDm7Lmq4iE4/zWLux3me3qtca9BAM9gPGQI5OfD88/DnP2s/OGOMMSbgaEFXeBuS5eUqK9au1aVGDU860Odz6BAXbHqG8398hh9emkeexV1FS0yE3/xGJ8yuXh0WLdKm2CU2Takxxhh1tKDLFbFt/BSs5erXz/sRhG+8QV33M/vqtWAeZ7Fggbe3Oy706AETJuj0QWlp8MAD8MwzmsjWGGNMhXa0oCtFRPaJSDrQIbC9T0TSRWRfrApowmRkaA4HgEGDvL3X1q3w1luIQOXbb8ZJHG+8YV2VIlK/Pvz1r5qxPyEBZs3STvZr1vhdMmOMMT4qMuhyzsU752o552o65xIC28H9WrEspAmYO1eTZqWkQKNG3t3HOXjuOe2f1K8f3a85ndq1YcMGWLbMu9seV+LiYOhQePxxzb+xbRvcfTf8+982hZAxxlRQkaSMKDURGSQia0RknYjcU8jjVwTSUHwtIgtEJMXL8pRrzoWaFs8/39t7ffmlDlmsXh1GjqRSJbjoIn1o+nRvb33cad5cA69LLtH911+H226D1av9LZcxxpiY8yzoEpF4YCKacqIN8GsRaVPgtB+APs65DsCfgee8Kk+5t3KlZqFPToZu3by9V3Dun6uu0kkY0TivenX45htrJSuxxEQYNQr+9jdo3FibbseM0b5eBw74XTpjjDEx4mVNVzdgnXNug3MuC5gGXBR+gnNugXPul8Dul0BjD8tTvs2apesBA7SfkJf++Ee48858KSmqVQtVsFltVym1bq2d7EeMgPh4fU9vuSWQ8t8YY8zxzsugqxGwOWx/S+BYUa4D3vOwPOXXzp2a8TwuDgYO9P5+cXE643Vc/l+PIUN0wOSXX2qlmymFxES44gp44glo2RJ27dKcXg8+CNu3+106Y4wxHvIy6CpsrphCx76JyNlo0DXGw/KUX2++CXl5miK+bl1v7uEcTJ0aSD1fuNq1NVNFsEjmGDRrBo8+qrm9qlXTvF633KLvQWam36UzxhjjAS+Dri3AyWH7jYFtBU8SkQ7A88BFzrndHpanfEpLgw8/1O3LLvPuPh98ANOmadPiUbKgXnKJVoB98gn8/LN3xakQ4uK0+nDSJK1ZzM7W92D0aPjiC8vPYYwxxxkvg65FQEsRaS4ilYARwMzwEwLzO74FXOWc+97DspRfM2boh/EZZ8DJJxd7eqns3g0vvKDbV155RLNiuBNP1ET4ubnw9tveFKfCqVNH+9D97W9aA7ZzJ/zlLxoA2wTaxhhz3PAs6HLO5QC3Ah8A3wKvO+dWichNInJT4LT7gROAp0VkuYgs9qo85VJ6eihNhFe1XM6FRtF16wa9ehX7lKFDdf3++9YNKaratNG+XjfeCDVr6ojVO++Exx7TQMwYY0y55mmeLufcLOfcac65U5xz4wPHJjnnJgW2r3fO1XHOpQaWLl6Wp9yZOVOnj+ncGU491Zt7LFgACxdC1apw880ghXXFy69ZMzjnHK2Ae/ZZawWLqvh4GDwY/vUvbctNSNC23JtughdftEm0jTGmHPM06DLH4MAB+O9/dXvYMG/ukZGh/YkARo4sUSf9UaM0b9eSJdr9yERZ9er6Ij/7LJx1lka4b74J118P//kP7N/vdwmNMcaUkAVdZdWsWfrB2q6dNjt5YcUK2LtXrx+WkysStWvD1Vfr9r/+ZfM5e6Z+fZ0+6PHHoWNHDcZffVWDr+nT7YU3xphyRFw5aBvq0qWLW7y4AnX3ysyE667TgOjBB/XD1itr12rKglLM5ZiXB3fdpZe45BKtmDEeW7UKXnlF+3uBzhhw0UWhKQOMMcb4TkSWFNZlymq6yqLZszXgatkSUlO9vVfLlqWePDsuLtQN7J134Mcfo1w2c6S2bXVk45//DK1a6e/Jyy/Dtdfqeu9ev0tojDGmCBZ0lTVZWfDWW7o9bFhEHdtLxDl46ilNxhkFLVtqy2Rurg6CLAcVp+WfiAbjjzyiwVeHDtrs+MYbGnw9+yz89JPfpTTGGFOABV1lzfTpOjVMs2bQvXv0r//BB1qT9thj2pE+CoLzYq9aBXPnRuWSJhLB4Gv8eA3AunXToP3dd+GGG/T4ypUWCRtjTBlhQVdZsn17aDbpm26Kfi3X5s3a6x0063mNGlG5bI0aof5ckydrzGhi7PTT4b77tBbznHM09cSXX8LYsfC732k0nJ3tdymNMaZCs6CrrHBOm4Wys/VDs23b6F4/O1trQ7Ky4NxzNQ1BFJ1zjla67NunXY6ysqJ6eROpZs00yJo8GUaMgFq1YP16Hf04cqTm+rKmR2OM8YUFXWXFl19q0qtgfqZoe+kl+OEHaNBAM55HmYhmNqhfX0czPv20tWr5KjkZrrhCp3e67TYNxvbt01xfv/kNjBunv3O5uX6X1BhjKowEvwtg0FxLwWa/q67SJFjRtGSJDi+Mj9ccD1WrRvf6AbVqwf/9n97i44/hlFPgwgs9uZWJVKVK0L8/9OsHa9botFLz58PSpbrUrq2TbffrB02a+F1aY4w5rllNV1nw+uvw88/QokWJk5RGJClJaz4uvxxOOy361w/TvDnccYduP/88fPONp7czkRLRfl+/+53Wel5/vaYK2bNHZy6/5Rad5/F//7OphowxxiOWHNVvW7bAb38LOTnw6KOae8kLe/dqVVS0O+cX4aWXdExArVrwj39os6MpY5yD77+Hjz7S2q/g1ELx8ZqQ96yzoEcPz2pGjTHmeFVUclQLuvzkHNx/PyxfDgMGaPAVLTk58PXX0KlT9K5ZAnl58Kc/aQtWixbaud4SppdhWVnax+vjj/X3MS9Pj1eqpKkozjxTJ16vUsXXYhpjTHlgQVdZNGOGjjKrWVMnnq5VKzrXdQ7++U/Nx3XttXDxxdG5bgllZGiL1fbt2uz44IPR765mPLB3L3z+Ocybp8nXgipV0iD+jDOga9eopRwxxpjjjQVdZc3y5VrL5ZzmUjrjjOhdOxjMVaoEDz+saeN9snOnpo/atk0HTj70kDU1liu7dmnT44IF8N13oePx8dC+vQZfXbvqm2uMMQawoKts+eknrQJKT4fhw+HKK6N37YULNRO5czBmjDYL+WzvXo0vN2yAE07QmWtOPtnvUpkSS0vTJsgFC3SERLAJErRTfrdu0KULtG4NiYn+ldMYY3xmQVdZceiQJrTauFFrCO67L3qd2xcu1JqtnBzN0TRiRHSuGwX792uwtWqVtqb+6U++VsCZY5WerqlIFi3SdbATPkDlyloL1rGjLo0bx2wAhzHGlAUWdJUFzsHf/w6ffaY1A489Fr3e5V99pb3Vc3NhyBBNCVDGPugyMzUmXLxY+2OPHg19+5a5YpqSys2Fb7/VAGzpUv1CES45WSflbt9el5NOsjfdGHNcs6CrLJg+XXMpVKum07I0ahS9a+/YAffcA336wDXXlNkPtZwcePJJ+OQT3T/zTA2+atb0tVgmmtLSYMUKWLZMlz178j9et64GX23aaFNkkyZl9vfVGGNKw4Iuv334oU5GDNqk2LVr9O+Rnq4jysr4B5hzmhrquee0tTU5WWeq6dzZ75KZqHMONm3SPmDBpWDy1erVNfhq3VoTuJ56qn4xMcaYcsqCLr84pxnnX3lF90eOhEsvjc5133sPDhyAoUOP/Xo++OknrfD79lvdP/98uPpqy+d1XHMOfvwRVq7UN371ah0hGU5E+4G1aqUzKJx6qs4daZ3zjTHlhAVdfsjLg2efhVmz9IPkpps0sjhWhw7pjNJz5+r+44+X217peXnw1lswdao2PdaooTHp4MGWh7PC+PnnUAD2/fc6MXtOTv5zEhK0GfKUUzQIa9ECmja1bPnGmDLJgq5Yy8rSaX2++EK/od91V3RycW3eDH/9q64rV9Y5884++9iv67MNG3TO75Urdb92bRg2DAYO1HRjpgLJytLA6/vvdZLu9eth61atJSuoQQOtBQsuTZrosfj4GBfaGGNCLOiKpT17NDBavVrbyu67D9q2Pfbrzp0LEyfqMMCTT9aO802aHPt1ywjntP/1yy/D2rV6rG5drfU691zLZl+hHTyogdj69bBunY6Q3LTpyBox0FqxRo30b6NJE91u3BgaNtQvKsYY4zELumLBOZ1654UXNG9R3bqakCoagdEnn2iKCdA8C7fccty2vzmnGTBeeSWUfSA+XnNvDhigM9HExflaRFMW5ORoDdjGjRqQbdqk/cV27iz6OfXqaRDWsKEuDRro+sQTrc+YMSZqLOjy2ubNOt/h6tW637kz3HqrBl7RkJGh1/v1rzXyKOMjFKPBOc3pNXu2BmHBBOgnnKCpJrp21QrEhAR/y2nKmEOH9O9x0ybYskW3t27VSUBzcwt/joj+Yp10kgZgJ56o2/Xq6bxVJ5xgTZbGmIhZ0OWVzEzNvzV9un7zTkqCG26A3r1LHxg5p1OtzJoF48aFOjUdOnTc1m4VJy0N5szRAGz79tDxqlW15qtrV0hN1c9GYwqVk6P57LZt02X79tB6x47C+4wFBYOy+vX1i1TB5YQTtP3bqmCNMVjQFX07d8L//qdRQEaGHhs4UFNC1KhRumtmZ2uw9d//agdigJtvjs6Ix+OEczrv8ldfaQL0H3/M/3i9eqF0T61ba99qqwkzxcrJ0dQVO3ZoLpMdO3TZuVOXX345elAGGpjVqaMBWHKybhdcatfWL2YV9MuTMRWFBV3R4JxOHvjf/+qoxOBr16oVjBpV+s7yO3bA++9rAtW9e/VYrVo6EfaAAdascRQ7d2rwtXixZh0InwIQNOBq3FiDr6ZNQwPc6ta1SglTAsGgbOdO2L1b01yEr3fvDv3tRqJyZQ2+gkFYrVpHrmvWDC01atj/AWPKEQu6SiszE77+Wj/VFy8OddJNSNCORRdeqAkcS8q5UPPj3Xdr9Q1A8+Zas9W3r30bLqFg8vNvvw0t4U2R4RIStKWoQQPtunPSSaFWorp1tVLCashMieTk6MjlYBC2Z4/WkIUve/bokp1d8utXr67BV3AJBmPVq4eWGjU0m3/16vnXVatWiH6gxpQVFnRFKj1dh6WvXx+atiQrK/R4nTrajHjeedqEUJLrrl2rEwIvXqyd4tu108fmzdNj55+vtWb2zzFqDh7UQGzjRm2K3LhR+1SnpR39eSKhiojgEtwPft4FP/OCn3tVq1rtmYmAc/qLuXevBmD79un2vn1HLunpumRkFN+8eTQi+iUuGIAF1+FLlSqhdXCpWlVr5YL7lSuHlipVrPbNmCL4EnSJyCDgSSAeeN4593CBxyXw+PnAAWCkc25pwet4GnRlZcGMGaH8P4UNN2/ZErp00aVly8iCIufgzTf1muvWaRNiuGHD4KqrovIjmJLLzNSuO9u36xJsNdq1S9eRdOEpTPDzLLiEf3YV/MwKXxITdbxEcElMzL8kJOTfjo+32LxCycvTtvOMjNASDMYOHAg9FlwfOBA6fuCADsLxQnx8/l/kSpVC6+AS/AUv+IuekBD6ZQ+uw3/RC/sDKGxt33RMGVRU0OVZA4qIxAMTgf7AFmCRiMx0zq0OO+08oGVg6Q48E1jHTmKiBkcHDuh+pUraxHfKKdps2KmT1m4FZWRoVUmw6SC4/ukn/Sf4+ON6nkj+oXbB67Zvr8Fbq1ax/ClNAZUrax+vpk0LfzzYUhSsjNi7N7Qd/pkX/Nzbv18rL4LL7t3e/wzB4KuodVFLXFxoXXC7qCUY5ImEjhW2HX5OwWOFLcHPy4LnBY9B4c8LP17wnIKPHW0/knPC9wtbH227qPOLek5hgbQeiwNqIlITEoE6gaWY6x2Wl0dc5kHkUGA5eCC0zjwUOn7oIJKdqevMQ8ihQ7rOOoRkZga2dU1WJrhc2H8A/c4cuPeRP8KxKe6CIhCfAAkJuIQE3Y6PxyUmQpz+Qbj4+NDxhAQkPl6PxcUf/qNx8fEgcYf/gFxc2B9LcD/sj+Xw88P+SFxcnF4jLg5JiMdJ3JF/aCL5j4cvIqH7FvgDO/ycAn9kEh865qSYP8bg61XB1azpX55kL3utdAPWOec2AIjINOAiIDzough42Wl125ciUltEGjjniuiJ4wER7bD+8cfaRlSlSmgakm+/hf/8B0aMgP799fwvvoAJE4q+3v79oRmbhw3T659yimaQt6r4ciMhIZQNIFLO5a9gyMwMBWGHDuk6MzO0ZGXp8exs3S64ZGfnX3JydMnNDW3n5Oi1jDm6OKB6YIkCASo54lwuiXmZ+ZaEvCwS8rJ022Uf3k/IyyLRhbbjXTYJedmH1wl5WSS4bOLzsg+v411OvvPiXQ4JednEudzDj4lzQHZgMZFwIjjicAhOBJBCtx0FH0OfF7Ydul4wmBPyJO7wdvB48Nr6vPzXLOy8/Psc8dyCP0/h1wk97/BzRTjt3mF0uqZ9yV+4KPAy6GoEbA7b38KRtViFndMIiF3QBdoZfurUI4e+Bf3yS2j7xBO1iTHY0Sc4DDzYGzt8At5+/TwstClrREL9mevV8/ZezuUPvoLbubkaoOXl6XbBJXg8L0/Pz8sregmel5en9ytqO7gfvi7qWGFLUY9D6LHgz1zw8aL2w1+n8MeOdqywdVHnF3Xu0R6LZDuSa5fkPpE8t7hjRT9f0I+QBILBnHMQSQh0LGUsSFwecbnZxOXlHA7G4vJyiHc5SF5oP/iY5OUS58KWwGPhi7i8w8fF5YWtc4hzeQWO5V8LecTlBbaD+4HHcRqoBo/h3OHtgueLywMccvic3MM/rx5zgWMuEHjq+vB55B0+Hnpu8AV1QBGJgkuj4PtUxruKV8oc6Nu9vQy6CqvDLPhWRHJObNx4o35qhncSrVJFP0GTkkLndegQakI0xiciWhtnIyyNiQMqBxZzVOFRemHfdo72Ledo34wKnl/Y8cLOKe03qII/S8F1cd+OiupXEgNe/sveApwctt8Y2FaKc2Lj7LN9ua0xxhgTE+GdB20Agi+8fNUXAS1FpLmIVAJGADMLnDMTuFpUD2BvTPtzGWOMMcbEiGc1Xc65HBG5FfgATRkxxTm3SkRuCjw+CZiFpotYhw5/GeVVeYwxxhhj/ORpjxDn3Cw0sAo/Nils2wG3eFkGY4wxxpiywBp1jTHGGGNiwIIuY4wxxpgYsKDLGGOMMSYGLOgyxhhjjIkBTye8jhYR+Rn40e9yGGOMMcZEoKlz7oi5ScpF0GWMMcYYU95Z86IxxhhjTAxY0GWMMcYYEwMWdAEiMkhE1ojIOhG5x+/yVFQicrKIzBWRb0VklYjcHjieLCIfisjawLqO32WtiEQkXkSWici7gX17X8oAEaktItNF5LvA305Pe2/8JyK/C/wfWykir4pIFXtf/CEiU0Rkp4isDDtW5HshImMD8cAaERkYzbJU+KBLROKBicB5QBvg1yLSxt9SVVg5wO+dc62BHsAtgffiHuBj51xL4OPAvom924Fvw/btfSkbngTed86dDqSg75G9Nz4SkUbAbUAX51w7dCq8Edj74pcXgUEFjhX6XgQ+c0YAbQPPeToQJ0RFhQ+6gG7AOufcBudcFjANuMjnMlVIzrntzrmlge109MOjEfp+vBQ47SXgV74UsAITkcbABcDzYYftffGZiNQCzgImAzjnspxze7D3pixIAKqKSAJQDdiGvS++cM7NA9IKHC7qvbgImOacy3TO/YDODd0tWmWxoEs/1DeH7W8JHDM+EpFmQEdgIXCic247aGAG1PexaBXVE8AfgLywY/a++K8F8DPwQqDp93kRqY69N75yzm0FHgU2AduBvc652dj7UpYU9V54GhNY0AVSyDHLo+EjEakBvAnc4Zzb53d5KjoRGQzsdM4t8bss5ggJQCfgGedcR2A/1mTlu0D/oIuA5kBDoLqIXOlvqUyEPI0JLOjSKPbksP3GaDWw8YGIJKIB11Tn3FuBwztEpEHg8QbATr/KV0H1AoaIyEa0+f0cEXkFe1/Kgi3AFufcwsD+dDQIs/fGX/2AH5xzPzvnsoG3gDOw96UsKeq98DQmsKALFgEtRaS5iFRCO9DN9LlMFZKICNo35Vvn3ONhD80ErglsXwO8E+uyVWTOubHOucbOuWbo38cc59yV2PviO+fcT8BmEWkVOHQusBp7b/y2CeghItUC/9fORfuo2vtSdhT1XswERohIZRFpDrQEvorWTS0jPSAi56N9VuKBKc658f6WqGISkTOB+cA3hPoO/RHt1/U60AT9Z3aZc65gp0gTAyLSF7jLOTdYRE7A3hffiUgqOsChErABGIV+obb3xkci8idgODoqexlwPVADe19iTkReBfoCdYEdwDhgBkW8FyJyL3At+t7d4Zx7L2plsaDLGGOMMcZ71rxojDHGGBMDFnQZY4wxxsSABV3GGGOMMTFgQZcxxhhjTAxY0GWMMcYYEwMWdBljfCciJ4jI8sDyk4hsDWxniMjTHt3zDhG5upDjzURkZRTvU0lE5gXm4DPGVGD2T8AY4zvn3G4gFUBEHgAynHOPenW/QAB0LZq93VPOuSwR+RjN2TTV6/sZY8ouq+kyxpRZItJXRN4NbD8gIi+JyGwR2Sgil4jI30XkGxF5PzCFFCLSWUQ+FZElIvJBcKqPAs4BljrncsKes0JEvgBuCbt/MxGZLyJLA8sZgeP/FpGLws6bKiJDRKStiHwVqKX7WkRaBk6ZAVzhxWtkjCk/LOgyxpQnpwAXoJMJvwLMdc61Bw4CFwQCr6eAoc65zsAUoLAZJnoB4RN4vwDc5pzrWeC8nUB/51wntKZqQuD482jmd0QkCZ1XbxZwE/Ckcy4V6ILO4wawEuhayp/ZGHOcsOZFY0x58p5zLltEvkGn7Xo/cPwboBnQCmgHfKhT3hEPbC/kOg3QufCCQVNt59yngcf+DZwX2E4E/hmYaicXOA3AOfepiEwUkfrAJcCbzrmcQE3ZvSLSGHjLObc2cH6uiGSJSE3nXHqUXgtjTDljQZcxpjzJBHDO5YlItgvNY5aH/j8TYFUhNVYFHQSqBLYFKGo+tN+hc7WloC0Dh8Ie+zfaZDgC7R+Gc+4/IrIQrY37QESud87NCZxfucDzjTEVjDUvGmOOJ2uAeiLSE0BEEkWkbSHnfQucCuCc2wPsDUy4Dvn7XiUB251zecBVaM1Z0IvAHYFrrArcrwWwwTk3AZgJdAgcPwH42TmXfew/ojGmvLKgyxhz3HDOZQFDgb+JyApgOdrfqqD3gLPC9kcBEwPNgwfDjj8NXCMiX6JNi/vD7rUDDd5eCDt/OLBSRJYDpwMvB46fjfb5MsZUYBKqnTfGmIpDRN4G/hDsd1WK51dD+5J1cs7tLebct4Cxzrk1pbmXMeb4YDVdxpiK6h60Q32JiUg/4DvgqQgCrkrADAu4jDFW02WMMcYYEwNW02WMMcYYEwMWdBljjDHGxIAFXcYYY4wxMWBBlzHGGGNMDFjQZYwxxhgTAxZ0GWOMMcbEwP8DcPGy5gelY/UAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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QNRPobmbdzKwBMAEfSoz3FXAMgJntC/QEliWxTVW210FXURFMmwZvv+1jk1I1W7fCbbfBzTf7+/naa6lukYiISLmSNrwYQigws18A/wUygUkhhPlmdlHk8YnADcDDZvYZPhx5eQhhfbLaVB32Ouh6/30vXxDVtSvk5PgyYIBXRpfELFwIt97qyXUNGsBPfuK5WyIiImksqd/0IYRpwLQSxybGba8Gjk9mG6rbXgddn37q6/3284Sw5ct9eeEFD7xuuEEJ3xUJwYucPvaYbx98MPz2t9CpU6pbJiIiUiF1r1RSdCqgSifSL4hUyvj1r6F7d/j8c58D8KWXYO5cH3YcMaI6m1r3rFsHTz7pAdf48XD22eohFBGRWqPCbywzGwZcCxwQOd+AEEI4MLlNS0/Rnq5KpWXl5cGXX3pl1e7dfd23ry/77Qd33gkPP+xzAariatn23deryzduDIMGpbo1IiIilZJIIv2DwG3AUcAQYHBkXS+1auXrDRs8Nz4hn3/u6x49POCKd8wxcNBBsH49PPNMdTWzbggBXn65eJL8UUcp4BIRkVopkaBrUwjhpRDCuhDCt9El6S1LU1lZPm1fCLA50Wpi8+f7ulevPR/LyPBEcIBnn4VvvqmWdtZ6BQVw991wzz1eYX59Wt9fISIiUqFEgq43zOwWMzvCzAZGl6S3LI1VOq8rGnT17l364717w/DhXuTzoYeq3L5ab/NmuOoqmD7d70785S9Vd0tERGq9RLKQo1P3DI47FvAq8vVS69aeopXQHYw7d8KSJX5n4iHl1H294AKfvuadd+DEE8sO0Oq6FSvg+uth7VqPbv/4R8+DExERqeUq7OkKIYwqZam3ARdUsmzE//7nQ2Vdu0LTpmWf1749nH66b//zn5VIGKtDPv3US0CsXet5brfdpoBLRETqjAqDLjNraWa3mdmsyPI3M2tZE41LV5UKuqKlIkrL5yrpjDOgbVtYtqx+Vlhv1cqT5YYNg7/+1d8LERGROiKRnK5JQB7wvciyGajXiUeVCroqyueK16iRDzMCPPqoD03WJ126eO/W5ZdDw4apbo2IiEi1SiToOiiEcE0IYVlkuQ6olzW6oqKJ9BUGXUVFPmUNJNbTBXD00T60tnEj/Pe/e9vE2qGoyO9OfOWV2LHOnVWZX0RE6qREgq5tZnZUdCdSLHVb8pqU/uJrdZVr+XLYts2LeiY6VGYGEyb49jPPwK5de9nKNLdzJ9x0k9fh+uc/YdOmVLdIREQkqRK5e/FnwCORPC4DcoHzk9modJdwyYjKDC3GGzrUE++XL4dXX4WxYyvZwjS3ZQv86U8wb57fXHDVVdCyXqcJiohIPZDI3YtzQgg5QD+gbwhhQAhhbvKblr4SzumqTBJ9PDM480zffuopv/uxrsjLgz/8wQOuNm28t6u+lscQEZF6pcyeLjP7QQjhMTO7rMRxAEIItyW5bWmrSROv2bl9uy+lTpcYQizo2pug4sgjYf/9vW7VG2/AccdVqc1pIS/P624tWwYdOnhv1z77pLpVIiIiNaK8nq5oUanmpSzNktyutGaWQF7X2rU+/tiiBXTqVPkXyciA733Pt598EgoL96ap6WXDBp/mqGNH+MtfFHCJiEi9UmZPVwjhH5HNV0MI78U/Fkmmr9dat4Z16zyu6tChlBPihxb39m684cPh8cdh9Wp4+20YNWqv25sWunSBP//ZA9FoYpyIiEg9kcjdi3cleKxeqbBsRHmTXCcqMzPW2/XEE7WzSv3GjfDuu7H9rl0VcImISL1UXk7XEcCRQPsSeV0tgMxkNyzdVZhMX5V8rngjRsC//w2rVnnwcvTRVbteTYomza9Y4cOlRx6Z6haJiIikTHk9XQ3w3K0siudzbQbGJ79p6a3coGvTJg+SGjaEA6tYRzYrq3hvVwhVu15N2bYNrr0WvvrKC57qDkUREannysvpegt4y8weDiF8WYNtqhWiQVeptbqivVw9e3rQVFXHHANTpngA8/77PjdhOtu50+9M/N//PFn+T39SHS4REan3Esnp2mpmt5jZNDN7PbokvWVpLpqWtHFjKQ+uWOHr7t2r58WysuC73/XtKVPSu7ersBBuuQU+/dQj0z/9STlcIiIiJBZ0TQY+B7oB1wHLgZlJbFOtEC0ZUWpPV3TMsTqDjeOOg3btvEr9Bx9U33Wr24MPwocfeqX5668v49ZOERGR+ieRoKttCOFBYFcI4a0Qwg+Bw5PcrrRX7t2L0UgsOgZZHbKzYXwklS6de7uOP94DrWuv9TsVRUREBEgs6IrOuLzGzE40swFA5yS2qVaIpiht2lRKJYdoJFadQRd4b1ebNvDFFzBjRvVeu7p07Qr33guHHJLqloiIiKSVRIKuP0Umu/4N8FvgAeDXSW1VLZCV5TU+Qyglryt6oLqDrgYNYr1djz+ePr1dM2fC63FpftVx84CIiEgdU+G3YwjhxcjmJqCWl0SvXm3awObNHmMVS99KVk8XwOjR8PTTPn/hRx/B0KHV/xqVsWwZ3HyzT0LZrh3065fa9oiIiKSp8oqj3gWU2ZUSQvhVUlpUi8Qn0+8uxxWdBbtBA2jcuPpftEEDOOMMuP9+7+067LC9n2aoqtavh+uu85931Cjo2zc17RAREakFyuvpmlVjrailSk2mj+/lSlYwNGYMPPUULF0Ks2bBkCHJeZ3ybNsGN9zgEWefPvDLX6Yu+BMREakFyiuO+khNNqQ2KrUqfTKHFqOivV0PPui9XYMH12zAU1TktbiWLYOOHeH//s/vrhQREZEyVZjTZWZvUMowYwjhO0lpUS2SsqALYOxYz+1avLjmc7umTPHk+ebN4ZprfC0iIiLlSuQ2s9/GbTcCzgAKErm4mY0B7sAnyH4ghHBTKeeMBG4HsoH1IYQRiVw7HZQ6FVAyanSVpmFDn5Px/vvhX/+q2dyuI4+Et9+Giy/2ni4REUm5wsJCcnNz2bVrV8UnS7XIzs6mTZs2ZGZmJnR+Incvzi5x6D0ze6ui55lZJnAPcBywEphpZlNDCAvizmkF3AuMCSF8ZWb7JNTqNFFqTleyykWUZuxYeP55+PJLeOstGDky+a8JXovrnnsgwX9kIiKSfLm5uTRq1Ih27dphyrFNuhAC+fn55Obm0r59+4SeU2GdLjNrE7e0M7PRwH4JXPswYEkIYVkIYScwBTilxDlnAc+GEL6K/ADrEmp1mkjp8CJ4HtVZZ/n25MlQkFAH5N7Ztg3efTe2r4BLRCSt7Nq1i2bNmingqiFmRrNmzSrVs5hIcdTZ+J2Ms4EP8CKpP0rgeZ2AFXH7KyPH4vUAWpvZm2Y228zOTeC6aSNaMmLDhrg6pTUZdIGXaujcGdauhenTk/MaIcAdd8Bf/+p3TYqISFpSwFWzKvt+Vxh0hRC6hRAOjKy7hxCODyG8W9HzgNJaUjIhPwsYBJwIjAauMrMeCVw7LTRp4jcS7tjhHUFAzQddmZnwgx/49hNPeGOq23PPwXvved2xI4+s/uuLiEidceONN9K7d2/69etH//79mZGiaevmzJnDtGnTdu9PnTqVm27y1PLzzz+fp59+eo/nvPnmm5x00klJa1Midy82Ai4GjsKDpneB+0II2yt46kpg/7j9zsDqUs5ZH0LYAmwxs7eBHOB/iTU/tcw8tvr6a4+1mjSh5oMu8EDo4INhyRL4z39iUwVVh3nz4OGHffuyy6BTyc5KERER98EHH/Diiy/y8ccf07BhQ9avX8/OnTtT0pY5c+Ywa9YsTjjhBADGjRvHuHHjUtKWqESGFx8FegN3AXcDhwL/SuB5M4HuZtbNzBoAE4CpJc55ARhuZllm1gQYCixMtPHpoFgyffxEjNEZsWuCGZwbGZl95hnYsqV6rpuXB3/7m/9c48fD4YdXz3VFRKROWrNmDe3ataNhw4YAtGvXjo4dO9K1a1fWr18PwKxZsxgZufHrrbfeon///vTv358BAwaQl5cHwM0330zfvn3JycnhiiuuAGDp0qWMGTOGQYMGMXz4cD7//HPAe60uuugihg8fTo8ePXjxxRfZuXMnV199NU888QT9+/fniSee4OGHH+YXv/jF7ra++uqrxZ5T0pYtW/jhD3/IkCFDGDBgAC+88EKV359ESkb0DCHkxO2/YWZzK3pSCKHAzH4B/BcvGTEphDDfzC6KPD4xhLDQzF4GPgWK8LIS8yr/Y6ROsWT6zZu9cGjz5jVfLLR/f5+G57PP4Nln4Zxzqna9EODuu32qn5494eyzq6WZIiKSfCefnJzr/uc/5T9+/PHHc/3119OjRw+OPfZYzjzzTEaMKLsS1K233so999zDsGHDyM/Pp1GjRrz00ks8//zzzJgxgyZNmpAbKcV04YUXMnHiRLp3786MGTO4+OKLef311wFYvnw5b731FkuXLmXUqFEsWbKE66+/nlmzZnH33XcD8HB01CaitOfEu/HGG/nOd77DpEmT2LhxI4cddhjHHnssTZs2reS7FpNI0PWJmR0eQvgQwMyGAu8lcvEQwjRgWoljE0vs3wLcklhz00+xoCs6tBjNsK9JZnDeefDb38ILL8BJJ1VtiHPzZli+3MdMf/c7yErkn4qIiNRnzZo1Y/bs2bzzzju88cYbnHnmmbvzqEozbNgwLrvsMs4++2xOP/10OnfuzKuvvsoFF1xAkyZNAGjTpg35+fm8//77fPe739393B1xOczf+973yMjIoHv37hx44IG7e8HKU9Fzpk+fztSpU7n11lsB2L59O1999RWHHnpopd6TeIl8kw4FzjWzryL7XYCFZvYZEEII/fb61euA6Cjipk2kJp8rXs+eXpl+xgwvIRHXjVppLVv6HYtffAH77lt9bRQRkaSrqEcqmTIzMxk5ciQjR46kb9++PPLII2RlZVFUVAR48BJ1xRVXcOKJJzJt2jQOP/xwXn31VUIIe9wVWFRURKtWrZgzZ06pr1ny/ETuKqzoOSEEnnnmGXr27FnhtRKVSE7XGKAbMCKydANOAE4CktSBWXtEg67Nm4kFXdFEr1S44AK/o3H6dA+YKivE3WDaqBFUIaIXEZH6ZdGiRSxevHj3/pw5czjggAPo2rUrs2d7rfVnnnlm9+NLly6lb9++XH755QwePJjPP/+c448/nkmTJrF161bAi762aNGCbt268VSkbFEIgblzY5lOTz31FEVFRSxdupRly5bRs2dPmjdvvjtHrDSlPSfe6NGjueuuuwiR78VPPvmkiu9OYiUjvgRa4QHWyUCrEMKX0aXKLajlWrTwdVr0dIHfXXjiiR48PfBA8SAqEf/4h+dyJaP0hIiI1Gn5+fmcd9559OrVi379+rFgwQKuvfZarrnmGi655BKGDx9ebMqc22+/nT59+pCTk0Pjxo0ZO3YsY8aMYdy4cQwePJj+/fvvHt6bPHkyDz74IDk5OfTu3btYYnvPnj0ZMWIEY8eOZeLEiTRq1IhRo0axYMGC3Yn0JZX2nHhXXXUVu3btol+/fvTp04errrqqyu+PhQq+lM3sEuAnwLORQ6cB/wwh3FXlV0/Q4MGDw6xZs2rq5Srl00/hD3+A3r3hpoMf8HyqH/4QTjstdY3Ky4MLL4T8fPjjHxOfDHvmTLj+es/f+tvf4MADk9tOERGpNqtXr6ZjPZwP9/zzz+ekk05ifHWWS6qE0t53M5sdQhhc8txEhhd/BAwNIVwdQrgaOBwPwoQycrpSkUgfr3nz2PRADz6Y2PRA+fnewwV+56MCLhERkWqVSNBlQGHcfiGlV5uvl4oNL9bkZNcVGTvWpwdaswZKqT+yhwcegNxcz+E69dSkN09ERKQ6PPzwwynr5aqsRIKuh4AZZnatmV0HfAg8mNxm1R7Nm/s6Px9CbhrkdEVlZcGPIlNkTpkSyfQvw8yZ8NprPqfRJZdARiL/LERERKQyEkmkvw24AMgFvgUuCCHcnuR21RpZWdCsmeer71rnBdzSIugCGDQIBgzwCvWTJ5d+TslhRU3zIyIikhSV6dIwfO5FDS2W0LIlZBXtpHDTFo/Cot1fqWYGP/6xr19+Gb76qvRzcnJ8WDHFc1KJiIjUZRUGXWZ2NfAI0BpoBzxkZn9MdsNqkxYtoOmujewqwJPoEyjKVmO6dPH8rqIiuO++PUtING3qE1nfcIOGFUVERJIokW/Z7wNDQgjXhhCuwe9e1ER8cVq0gGa7NvhNgukytBjvBz/w7rh58yAyTxXbtsGuXbFzIpOTioiI7K1mzZpVeM4777xD79696d+/P9u2bavU9Z9//nkWLFiQlHbVhESCruVAfMWwhsDSpLSmlmrZ0nu6CnaR+nIRpWne3GuHAUya5HW8HnjAk+aXL09p00REpH6ZPHkyv/3tb5kzZw6NGzeu1HP3NuhKF4kEXTuA+Wb2sJk9BMwD8s3sTjO7M7nNqx1atvSerl3p2tMFMGoU9OnjdzH+9a8+TdCaNT5lkIiISDV68803GTlyJOPHj+eQQw7h7LPPJoTAAw88wJNPPsn111/P2Wf7oNktt9zCkCFD6NevH9dcc83uazz66KP069ePnJwczjnnHN5//32mTp3K7373O/r378/SpUtZunQpY8aMYdCgQQwfPnz3pNVffPEFRxxxBEOGDKmWSvLVJZEJr5+LLFFvJqcptVfaDy+C55ldfLFPgj15MnTt6r1f+++f6paJiEh1OzlJUyNXYibtTz75hPnz59OxY0eGDRvGe++9x49//GPefffd3RXkp0+fzuLFi/noo48IITBu3Djefvtt2rZty4033sh7771Hu3btyM3NpU2bNowbN65Y9fljjjmGiRMn0r17d2bMmMHFF1/M66+/ziWXXMLPfvYzzj33XO65557kvBd7ocKgK4TwSE00pDbb3dNVSPoGXeABVteu8OGH8PXXcPrpqW6RiIjUUYcddhidO3cGoH///ixfvpyjjjqq2DnTp09n+vTpDBgwAPC5GxcvXszcuXMZP3487dq1A6BNmzZ7XD8/P5/333+f7373u7uP7YjMG/zee+/tnlj7nHPO4fLLL6/+H3AvJNLTJRXwnq5cCopI76Br/XpYudKT5tu29SHGVM4RKSIiyVGJHqlkaRh3g1ZmZiYFpUxJF0Lgyiuv5Kc//Wmx43feeSdWQSWAoqIiWrVqxZw5c0p9vKLnp4JqBFQD7+mKJNKnc9B1//2wc6d3O7dqBf/+twdiIiIiKTB69GgmTZpEfn4+AKtWrWLdunUcc8wxPPnkk3z77bcA5OZ68fHmzZuTl5cHQIsWLejWrRtPPfUU4AHc3LlzARg2bBhTpkwBPHE/XZQZdJnZvyLrS2quObWT1+lK80T6EGDwYNhnH7juOjjySNi+He65Z8/aXSIiIjXg+OOP56yzzuKII46gb9++jB8/nry8PHr37s0f/vAHRowYQU5ODpdddhkAEyZM4JZbbmHAgAEsXbqUyZMn8+CDD5KTk0Pv3r154YUXALjjjju45557GDJkCJs2bUrlj1iMhTK+cM1sATAWmAqMpEQl+hBCbrIbFzV48OAwa9asmnq5StuxPTC72xlkh10M/uIprHGjip+UKoWFfsdibq4n1m/ZApdeCscck+qWiYhIFaxevZqOHTumuhn1Tmnvu5nNDiEMLnluecOLE4GXgUOA2SWW9I2AUqBh4VYasIsdGY3ZFtIw4Nq5M7YdLRHRpg1ceKFv338/RLpwRUREJDnKDLpCCHeGEA4FJoUQDgwhdItbDqzBNqa/DRvIyob87NakUS+mW78eLrgApkzZcxhx1CgYMsR7u+66S8OMIiIiSVRhIn0I4WdmlmNmv4gs/WqiYbXKhg1kZ3nQtXlzqhtTwiOPeEHUL7/cc05IM6/b1bQpzJ4Nr72WmjaKiIjUA4lMeP0rYDKwT2SZbGa/THbDapW4nq60Crr+9z94803IzvbertK0aQPRW3Xvv193M4qIiCRJIiUjfgwMDSFcHUK4Gp/w+ifJbVYts2EDWVlpNrwYgs+vCHDKKX7XYllGjoTDDoOtW+HuuzXMKCIikgSJBF0GFMbtF1LiTsZ6L254MW2Crvfeg4ULvYhYXLXeUpnBz38OzZr5MOOrr9ZMG0VEROqRRIKuh4AZZnatmV0LfAg8mNRW1TZxPV1pMby4cyc89JBv/+AH0KRJxc+Jv5vxn/+EVauS1z4REamTMjMz6d+/P3369OHkk09m48aNqW5SpV177bXceuutSbl2Ion0twEXALnABuCCEMLtSWlNbbVhA9npdPdiXp4PJx5wABx3XOLPGzkSjj7ai6becguUMmWDiIhIWRo3bsycOXOYN28ebdq0SZvJpkMIFBUVpboZiU0DFEL4OFJC4o4QwifJblStk249XW3bwp//DDfeGKvLlQgzL5i6zz6wdCk8+mjy2igiInXaEUccwarIqMnSpUsZM2YMgwYNYvjw4Xz++ecAfP3115x22mnk5OSQk5PD+++/D8Btt91Gnz596NOnD7fffjsAl19+Offee+/u61977bX87W9/A+CWW25hyJAh9OvXj2uuuQaA5cuXc+ihh3LxxRczcOBAVqxYUep5ADfeeCM9e/bk2GOPZdGiRUl7TzThdXWI3L24JbtVevR0gQdQLVtW/nlNm8Lvf+/Lc89BTg4MGlT97RMRkeQ6+eSyH/v5z2HMGN9++WWfEq4sezF5dmFhIa+99ho/+tGPALjwwguZOHEi3bt3Z8aMGVx88cW8/vrr/OpXv2LEiBE899xzFBYWkp+fz+zZs3nooYeYMWMGIQSGDh3KiBEjmDBhApdeeikXX3wxAE8++SQvv/wy06dPZ/HixXz00UeEEBg3bhxvv/02Xbp0YdGiRTz00EPce++9ZZ7XtGlTpkyZwieffEJBQQEDBw5kUJK+9xR0VVVhIWzeTFa2sTW7ZWp7utau9Vyu738funbd++v07Om5YI8+Cn//uxdOTdc5JUVEJG1s27aN/v37s3z5cgYNGsRxxx1Hfn4+77//Pt+Nu6lrx44dALz++us8GhlVyczMpGXLlrz77rucdtppNG3aFIDTTz+dd955h1/96lesW7eO1atX880339C6dWu6dOnCnXfeyfTp0xkwYAAA+fn5LF68mC5dunDAAQdw+OGHAzB9+vRSz8vLy+O0006jSST/edy4cUl7fxR0VdWmTRACWW1bUZSfmdqern//G95/Hxo39vkUq+KMM2DOHPj0Uw+8rrtuz+KqIiKSvhLtoRozJtbrVUXRnK5NmzZx0kkncc8993D++efTqlUr5syZk9A1ypoTGmD8+PE8/fTTrF27lgkTJuw+/8orr+Sn0ZqTEcuXL98duJV33u23347V0PdbIsVRTzezxWa2ycw2m1memSXUn2NmY8xskZktMbMryjlviJkVmtn4yjQ+LWzYAEBWu9ZkZsK2bbBrVwrasWKFF0LNyvKerqrKyIDLLoPmzeGTT3yoUUREJAEtW7bkzjvv5NZbb6Vx48Z069aNp556CvDgZ+7cuQAcc8wx3HfffYAPSW7evJmjjz6a559/nq1bt7Jlyxaee+45hg8fDsCECROYMmUKTz/9NOPHe8gwevRoJk2aRH5+PgCrVq1i3bp1e7SprPOOPvponnvuObZt20ZeXh7/2Yvh1EQlkkh/MzAuhNAyhNAihNA8hNCioieZWSZwDzAW6AV838x6lXHeX4H/Vq7paSISdFmb1rSIvCspGWJ87DEvanr88bDvvtVzzbZtYz1mjzwC8+ZVz3VFRKTOGzBgADk5OUyZMoXJkyfz4IMPkpOTQ+/evXnhhRcAuOOOO3jjjTfo27cvgwYNYv78+QwcOJDzzz+fww47jKFDh/LjH/9495Bg7969ycvLo1OnTnTo0AGA448/nrPOOosjjjiCvn37Mn78ePLy8vZoT1nnDRw4kDPPPJP+/ftzxhln7A7wksHK68YDMLP3QgjDKn1hsyOAa0MIoyP7VwKEEP5S4rxLgV3AEODFEMLTJa81ePDgMGvWrMo2oWa88grceScccwy/WHIpX34Jd9wBB9bklOBLlsCvfw0NGvhUPm3aVO/1H34YnnnGE/Nvvx3atave64uISJWtXr2ajh07proZ9U5p77uZzQ4hDC55biI9XbPM7Akz+35kqPF0Mzs9ged1AlbE7a+MHItvVCfgNGBiAtdLT5GeLlq33n2zYI33dD32mK9PPLH6Ay6Ac86B/v09f+2mm1I0fioiIlK7JRJ0tQC2AscDJ0eWkxJ4XmlZaSW71W4HLg8hFJZybu0QDbpatdo9vFijyfRffw1z50KjRjA+SSlxmZnwu99B+/awaJFXrBcREZFKqfDuxRDCBXt57ZXA/nH7nYHVJc4ZDEyJ3DXQDjjBzApCCM/v5WvWvNxcX7dtm5qcrn33hX/8A774gt0NSIYWLeD//s/rd738MvToUblq9yIiIvVcIncvdjaz58xsnZl9bWbPmFnnBK49E+huZt3MrAEwAZgaf0IIoVsIoWsIoSvwNHBxrQq4AL791tdt2+4eXqzxshH77ANDhyb/dQ4+2AvqAdx3HyxenPzXFBGRhFWUpy3Vq7Lvd6ITXk8FOuI5Wf+JHKuoIQXAL/C7EhcCT4YQ5pvZRWZ2UaVamc6iQVebNjUbdIXgdbRq+j/YMcfACSd4Xtef/xzr6RMRkZTKzs4mPz9fgVcNCSGQn59PdnZ2ws9JpDhq+xBCfJD1cOSOw0QaNA2YVuJYqUnzIYTzE7lmWgkhltMVF3TVyPDi3Llw1VXQt68HPzXpJz+B5cthwQK4/npPrm/UqGbbICIixbRp04bc3NxSyyVIcmRnZ9OmEjewJRJ0rTezHwCPR/a/D3y7F22rezZt8mmAWrSA7Oyazel68klf9+9fAy9WQlYW/OEP8Nvf+sTYt97q+V4ZCc2fLiIiSZCZmUn79u1T3QwpRyLfkj8EvgesBdYA4yPHJG5oEai5uxcXLoTPPvPJqU88MckvVoYWLeCaa6BZM5gxAyZNSk07REREaokKg64QwlchhHEhhPYhhH1CCKeGEL6sicalvbg7F4Gay+mK9nKdeKIHXqnSqZP3eGVlwQsvwLRpFT9HRESknipzeNHMfh9CuNnM7mLP+lqEEH6V1JbVBiV6upo39928PCgqStJo27JlMGsWNGwIp5yShBeopD594Je/9EmxJ070EhaDBqW6VSIiImmnvLBgYWQ9C5hdyiJx5SLAO3yaNvX8+sh8mtUvMmEoY8Ykty5XZXznOzBhgv/gN90E//tfqlskIiKSdsoMukII0Wm2t4YQHolf8Ar1UiLogiQPMYYAPXt6Xa7TTkvCC1TBWWfBqFGwfTtcey2sWFHhU0REROqTRAbArkzwWP1TIqcLkhx0mcGpp8IDDxR7zbRgBr/6FQwZ4uOrf/yjT1EkIiIiQDlBl5mNjeRzdTKzO+OWh4GCGmthOiulp6tGykZYadNapoGsLLjiCs/zys31OmLROmYiIiL1XHk9XavxfK7tFM/lmgqMTn7TaoESifSQxKBr8mS/a3HLlmq+cDVr0MCDrYMOgjVr4Oqrk5jgJiIiUnuUl9M1N5K/1Rd4LC6f6wVgR001MG3t2uXDaJmZsTFFkjS8uGkTPPss/OtfsUAvnTVpAtddB507e+X6666DbdtS3SoREZGUSiSnazrQOG6/MfBqcppTi0TzuVq3Ljbcl5Sg66WXYOdOz5fq0qUaL5xELVvCDTdA+/bw+edeSHWr7r8QEZH6K5Ggq1EIYff4UGS7SfKaVEuUks8FVP/8i7t2wf/7f7596qnVdNEa0q4d3Hijrxcu9KHGdB8eFRERSZJEgq4tZjYwumNmgwCNFZVy5yIkYSqgt9+GjRuha1ef3Lq26dDBa3ftsw8sWuT5Xgq8RESkHkok6LoUeMrM3jGzd4AngF8ktVW1QRk9XdWaSB8CPP+8b596avretViRffeFv/zF14sXezmJvLxUt0pERKRGJTL34kzgEOBnwMXAoSEEVaQv5c5FqObhxfnzPRG9VSs4+uhquGAK7bOPB1777QdLlnjglfRJKkVERNJHorMD9gR6AQOA75vZuclrUi1RQU7Xpk3eUVUlvXt7AvpPfgLZ2VW8WBpo394Drw4dfA7Jyy+HdetS3SoREZEaUWHQZWbXAHdFllHAzcC4JLcr/ZWR09WwoZeq2rXLZ8SpEjMYPLj293LFa9fOc7y6dYNVq+B3v/PePBERkToukZ6u8cAxwNoQwgVADtAwqa2qDcoYXoRqKhtRl8srtGnjPV7RyvVXXOFDqSIiInVYIkHXthBCEVBgZi2AdcCByW1WmguhzOFFqIa8rrw8OP98uOUWKCzcy4ukuaZNvWjqkUf63YxXXw0zZqS6VSIiIkmTSNA1y8xaAffj0wB9DHyUzEalva1bvVhpo0bQuPEeD1e5p+ull7yC+5YtXvG+rmrQwPO6xozx9/PGG2HatFS3SkREJCmyynvQzAz4SwhhIzDRzF4GWoQQPq2JxqWtcnq5oIplIwoKam8x1L2RkQEXX+yV/R9/HO67D776ym8eqMsBp4iI1Dvl9nSFEALwfNz+8nofcEGZSfRRVSqQ+uGHfv3994ecnL1rX21jBmedBZddBllZHnRee60myhYRkTolkeHFD81sSNJbUptU0NNVpeHFaC/XSSfV3mKoe2vUKE+wb9kS5syB3/4WVq9OdatERESqRSJB1yg88FpqZp+a2WdmVr97uyoIutq39/XatZW87ooVMG+e54qNHLnXzavVDjkEbrvNpz1atQp+8xv45JNUt0pERKTKygy6zKxLZHMsfrfid4CTgZMi6/qrnHIRAJ06+XrVqkped/58790aNQqa1OM5xffZx+/cHDrUhxivuQamTKmGarMiIiKpU14i/fPAwBDCl2b2TAjhjBpqU/qroKcrGnStXu1xQsKjhGPGwKBB9W9YsTSNGsEf/uDB1uOPw+TJsHCh93xFk+ZERERqkfKGF+O/+et3Xa6Soon0ZfR0NWniD+3aBd98U8lrt2/vVdvFg8/vf9+T6ps3h48/hksugUWLUt0yERGRSisv6AplbEsFPV0AHTv6euXKBK4XAixYoOGzsgwcCHfeCT17wvr1XsH+hRf0fomISK1SXtCVY2abzSwP6BfZ3mxmeWa2t7XWa7+iItiwwbdbty7ztM6dfZ1QXtfnn3uR0CuvrHr76qronI0nn+y1zB54wHO9or2OIiIiaa7MoCuEkBlCaBFCaB5CyIpsR/frb1LNxo3ew9KypdeUKkOlkumjZSJ6965y8+q0rCy48ELP9Wre3O9q/MUv4IMPUt0yERGRCiVSMmKvmdkYM1tkZkvM7IpSHj87UobiUzN738zSvxpoAkOLUImga9MmeO89z18aPbrq7asPDj8c7r7bhx3z8uDPf/bhx+3bU90yERGRMiUt6DKzTOAevOREL+D7ZtarxGlfACNCCP2AG4B/Jqs91aaCavRR0aCrwpyu6dN9uGzIEC+VIIlp08YT7C+8ELKz4ZVXvNdr7txUt0xERKRUyezpOgxYEkJYFkLYCUwBTok/IYTwfgghkiDFh0DnJLaneiTY07Xvvj4atn59OR0wRUU+uTXACSdUXxvrCzPP8fr736FbN/j6a/jjH73Xa8uWVLdORESkmGQGXZ2AFXH7KyPHyvIj4KUktqd6VFAYNSozE/bbz7fXrCnjpNmzvabEfvv5UJnsnQMO8Cr255zjke4rr/gk2jNmpLplIiIiuyUz6Cqtwmep9/ib2Sg86Lo8ie2pHgn2dEECeV1bt/odkKNHqyBqVWVlwfe+571chxziw8B/+hP89a/e3SgiIpJi5VWkr6qVwP5x+52BPWYvNrN+wAPA2BDCt0lsT/WooDBqvArzukaMgGHDoLCwetomsP/+Hmj9v/8Hjz4K774Ls2bBmWfCqaeWe8epiIhIMiWzp2sm0N3MuplZA2ACMDX+hMj8js8C54QQ/pfEtlSfSvR0JVSrKysLGjaserskJiPDc73uvReOPNKT6h55xBPt58xJdetERKSeSlrQFUIoAH4B/BdYCDwZQphvZheZ2UWR064G2gL3mtkcM5uVrPZUmwTvXoRyhhdD8AT6vLzqbZsU1769F5y9/nr/MFatgquugr/8pZxEOxERkeSwUAumUhk8eHCYNSsN4rGdO+GMM7x36tlnK8zD2rQJfvADn4txypS40z/+2Kup778/3HOP8rlqwq5dPnXQlCmwY4d/hmPHwoQJmkBbRESqlZnNDiEMLnk8qcVR65z4OxcTCJRatICmTT1ffuPGuAf++19fjxypgKumZGfD+PEwcSIce6zn0f3nP/CTn8DTT3tALSIikkQKuiqjEvlc4PHUHnldGzZ4KYOMDP/yl5rVrh1cconf5ThwoEfEjzwCP/2pD/kWFKS6hSIiUkcp6KqMSuRzRe2R1/Xaa97LMnRoQndASpJ07QrXXQc33OCFVdev98T7n/40NkuAiIhINVLQVRkJFkaNVyzoCiE2tKh5FtND//5wxx1w+eWeY7duHdx1F1x0kRdZVfAlIiLVREFXZVRyeBFK1OqaOxfWrvW76gYMqP72yd4xg6OO8km0f/c7/9C+/tqHIH/yE0/A12TaIiJSRaoUWRlV6OlavRqvx9W/P/Tp4zldkl4yMuDooz0Ae/ttePJJWLECHnjA73o88USv/9WyZapbKiIitZCCrspYtMjXBxyQ8FM6dvSOlLVroaD7oWTdcIMPM0r6ysjwO0tHjICPPoJnnoGFC+GJJ+C55/yxk0/2vDAREZEEKehK1Lp1Pjl1s2aV+rJt0MBHE9et8xGrTp1QmYjawsxveBg6FBYs8NISM2d6ov306dC3L5x0kj+emZnq1oqISJpT0JWozz7zde/elQ6aOncoZMDMB1n/3gg6fbeHgq7aqFcvuPpqvyPixRfh1Vf938Rnn3lUPXq0lwCpRL6fiIjUL0osSlQ06Orbt9JPHVAwk8O+/g8tHvhbNTdKalynTl5W4pFH4MILoUMH7wF97DG44AIvQfHRR5rEXERE9qCerkRVIejqvfxFNgDzDziRburlqhuaNPG8rpNOgk8+8VIgM2Z4wPXRR36zxXe+A6NGQZcuqW6tiIikAQVdiVi3zpemTSufPL1iBfusmcu6jIbMaH4sJyWlgZIyZl7ZfuBAn2zztdc8AFu92nPAnn4aDjzQA7Cjj4bWrVPdYhERSREFXYmYN8/XvXtXvtTDtGk0agSfthvFF+uaVn/bJH20bAmnnw6nneaJ92+8Ae++C8uW+fLgg5CT4yUpjjhCE22LiNQzCroSEQ26+vSp3PO2boVXX6VBA5jb6UQ2bYItW7zDTOowMw/Qe/f2vK+ZMz0AmzUL5szx5d57oV8/GD4cDj9cAZiISD2goCsRe5vP9cYbsH071qcPDbZ0hS/85rcePaq9hZKuGjSAYcN8ycuDDz/03q+5c2MB2N13w6GHevB12GGxiroiIlKnKOiqyPr1Xtm0SRPPzamMQYPg1FOhTx86vQlfKOiq35o3h+OO8yUagL33ngdgCxb4MmkSdO4MQ4bA4MFeqiJL/01FROoC/TavSHx9rsrmc+23H/zoRwB0WuyHVq2qxrZJ7RUfgG3dCh9/7Hc9zpzpE3WuXOnV7xs18jywgQM9iN9331S3XERE9pKCrorsbT5XCdERoyVLqtgeqXuaNPHk+qOOgoIC7/GaPdsDseXLvRTFjBl+7r77ei5YTo6vdTekiEitoaCrInuTz/XNN/CnP3kdp2OPBbyjIjvbv0dXr/Y5GUX2kJXlwVS/fl5sdf16rwM2a5YPQ379Nbzyii8A++8fS9rv3dur44uISFpS0FWeb7+FNWugcePK5XM9/bSXCPjkk91BV8uWPk/yK6/A1Klw0UXJabLUMe3axYYhi4r839Wnn3oANn8+rFjhy8sv+/nt23vwdeih0LOnT86unDARkbSg38bliQ4t9uqV+ITGy5fDSy95/teZZxZ76JRTPOh69VU4+2xP6xFJWEYGHHywL6ef7kORixd78DV/Pixc6L2sb77pC/jdk927ewDWvbsv++yj+T9FRFJAQVd5KpvPFQI88ICvTzhhj+lfDjgABgyIzRozfnw1t1fql6ws79E69FD/x1RUBF9+6cHX55/DokU+lh0NyqKaN4eDDvIA7MADoVs3H+9WICYiklQKuspT2Xyujz7yYZ9mzbwrqxSnnOJB13/+49UkNPIj1SYjwwOobt086AfYvBn+9z8PwBYv9js5Nm2K1QiLatTI/yo48ECf6qpLF99Xd6yISLXRV35ZcnO9vkOjRt4rUJFdu3yaF4Czzirzy2rgQM99XrHCa2SOHFl9TRbZQ4sWXu9r8GDfD8FzFaMB2BdfeJ7Yt996YLZoUfHnt2njAViXLl4/bP/9fd2ypXrGREQqSUFXWeLzuRLpjlq3DgoL/Utp7NgyTzPzHq677oLnn4cRI/TdJTXIzJPz27Xz+R+jNm/2AOyLL3yI8ssv4auv/I+P3NzivWLgc1l17uzDktGlQwdfa54rEZFSKegqS2XzuTp1gvvu81v8KwjSRo6ERx+FpUs91aaKJcBEqq5FC6/9lZMTOxaC/zGxfLkXa12xIla4dcuW0nvGwHt599vPa4pF1/vu6wn87dt7cr+ISD2koKs0y5fDW2/5dr9+iT+vQYOECnA1aOCdYVOmeG+Xgi5JS2axgGno0NjxEGDjRh9+X7PGk/Xjl7w8XxYvLv26rVr5NaM9btGlfXsfzmzdWsmOIlIn6TdbSbm5cN11PjXLUUdVPFHi4sUeoE2Y4An0CTrxRC/n9dFH/r3VoUMV2y1SU8w8MGrdes+/GELwRP21a/0f9tdf+/Y33/j2+vUesG3cWHovWfT6LVt6ANa2bSwQi19atfKlUaPk/qwiItVIQVe87ds94Fq/3m/D//Wvy0+4mjMH/vpXyM/3cvPnnZfwS7VqBaNGed2uxx7zl9If91LrmcUCokMO2fPxoiL/w+abb3xZv96Xb77xZP5vv4UNG2KB2bJl5b9ew4YeoLVu7UOkLVvG1tHt5s19adHC880qO4eqiEg10dd8VGEh3Hyz/5Lv0AH++Meyc09CgGnT4J//9C+RoUP3KISaiFNPhddfh7ff9jSZyy7zu/RF6qyMjNhw4qGHln5OYaEHXLm5sSAsfsnN9d60jRthxw7PO1u3LrHXN/PAq3lz75kuuV1y3aSJL9Hthg1154uI7DULIaS6DRUaPHhwmDVrVvJeIASYONEDqebN4dZby87NKijwYOull3x//Hg499y9/kU8bx78/e/+nZGVBeec48GY/hgXqUAI3jsd7RXbvNmDseg6uh3NMdu82W8AqIqMDJ8WrEmT2LpJEx/mbNw4tkT3GzUqfWnYsPiiQE6kTjGz2SGEwXscT2bQZWZjgDuATOCBEMJNJR63yOMnAFuB80MIH5e8TtKDrueeg0mTfIjwxhvL/gt8+3a4/novmpqdDb/8pY8RVtG2bV7Ifvp03+/VCy680GtcKvgSqUaFhZ4OUNqyZUtsHd3eutWXLVt8vXNnctrVoEHxIKxBg9ix+O3s7Ni6QYM911lZsf3sbN+Pbsfvx6+zsnyaMwV+ItWmrKAracOLZpYJ3AMcB6wEZprZ1BDCgrjTxgLdI8tQ4L7IuuasWwePPOLbv/518YArL88nF+7d23NUGjb0pN5WrXz4sWfPamlC48Yevx1+uNfvWrAALr3Uj3fv7rn8PXp4fUqlpYhUQWZmLN9rbxQU+F9J27Z5EBa/3rbN/zCL3y5r2bEjtt65M7bk5VXvz1sZ0QAsfsnMLL4ueazkdsklK8t/WZU8npERO15yu6LH4xez4o9F90s+Xtp2aY/FH49ux+9Hf/EqQJW9lLSeLjM7Arg2hDA6sn8lQAjhL3Hn/AN4M4TweGR/ETAyhLAm/lpJ7+n66CMfMuza1f+6zcvz9YYNPoRxySVw7LF+bvQXY9u2SWlKXp7X8Jo923OLSxOfltKkSfE/WKPb8b9Lon/Exi/R68T/7ijv90hpj+n3jkg1CIHMgh1kFOwks2DH7mX3fuHO3dsZhbvIKNwVO1a4c/exjIK47aJdse3CgrjjsW0rKojsF2BFhal+F2ofM4IZwaKBWAbBDLDi693bfl70segv0NKO796OPx59zfjt8s4r67GS1ynl/OgxYI/zYtfbfbCS5xd/XqnXKut6JR4rqbzXiX9ex4vGccDx1dNpUpYa7+kCOgEr4vZXsmcvVmnndALWUJMOOwxuuw1mzCh+PCvLx/rip/Rp0CBpARf4S/385769YYNPmxddvv7ag7LoyEd+ftKaISI1xoBGkaUaZUaWRIRAZijYvWQUFZBJIZlFu3w/FJa5LnehkMyiAjIoIiMUYqFo9/GMEHeM6HOKIvvxjxXtfp5R5OdE1/HbhGL7EMgMhUDAQij2XN8veT5xj8WOlXx+3JsWWYqQvVfR3+7J+Nt+07FHQJKDrrIkM+gq7b0q2a2WyDk14+c/9yAreidT9Bbz7OyUNAf8LvihQ4vXpQS/YXLLFg/Atm71aR8LCnwd3Q7Bzysqim2HsOdSUqLHakotuM9DpA4wIDuy1B2hxLpa+vOiv5SCB23xv0yj+xYNziKPGZFfwpEAbo/HSl4z7nFgz3NK7Ed/Qivxy73YefHPLfFzFDs//rHSnhf/PoSS7zDF21HGe1f+tUp5vGR7y1PRdYB9h1dQfzOJkhl0rQT2j9vvDKzei3NqxvDhKXnZvZGRESs9JCIiNcni1kqulcpJ5r+YmUB3M+tmZg2ACcDUEudMBc41dziwqWQ+l4iIiEhdkLSerhBCgZn9AvgvnlkwKYQw38wuijw+EZiGl4tYgpeMuCBZ7RERERFJpaRWpA8hTMMDq/hjE+O2A/DzZLZBREREJB1oQFpERESkBijoEhEREakBCrpEREREaoCCLhEREZEakNQJr6uLmX0DfJnqdoiIiIgk4IAQQvuSB2tF0CUiIiJS22l4UURERKQGKOgSERERqQEKugAzG2Nmi8xsiZldker21Fdmtr+ZvWFmC81svpldEjnexsxeMbPFkXXrVLe1PjKzTDP7xMxejOzrc0kDZtbKzJ42s88j/3eO0GeTemb268jvsXlm9riZNdLnkhpmNsnM1pnZvLhjZX4WZnZlJB5YZGajq7Mt9T7oMrNM4B5gLNAL+L6Z9Uptq+qtAuA3IYRDgcOBn0c+iyuA10II3YHXIvtS8y4BFsbt63NJD3cAL4cQDgFy8M9In00KmVkn4FfA4BBCH3wqvAnoc0mVh4ExJY6V+llEvnMmAL0jz7k3EidUi3ofdAGHAUtCCMtCCDuBKcApKW5TvRRCWBNC+DiynYd/eXTCP49HIqc9ApyakgbWY2bWGTgReCDusD6XFDOzFsDRwIMAIYSdIYSN6LNJB1lAYzPLApoAq9HnkhIhhLeB3BKHy/osTgGmhBB2hBC+wOeGPqy62qKgy7/UV8Ttr4wckxQys67AAGAGsG8IYQ14YAbsk8Km1Ve3A78HiuKO6XNJvQOBb4CHIkO/D5hZU/TZpFQIYRVwK/AVsAbYFEKYjj6XdFLWZ5HUmEBBF1gpx1RHI4XMrBnwDHBpCGFzqttT35nZScC6EMLsVLdF9pAFDATuCyEMALagIauUi+QHnQJ0AzoCTc3sB6ltlSQoqTGBgi6PYveP2++MdwNLCphZNh5wTQ4hPBs5/LWZdYg83gFYl6r21VPDgHFmthwffv+OmT2GPpd0sBJYGUKYEdl/Gg/C9Nmk1rHAFyGEb0IIu4BngSPR55JOyvoskhoTKOiCmUB3M+tmZg3wBLqpKW5TvWRmhuemLAwh3Bb30FTgvMj2ecALNd22+iyEcGUIoXMIoSv+/+P1EMIP0OeSciGEtcAKM+sZOXQMsAB9Nqn2FXC4mTWJ/F47Bs9R1eeSPsr6LKYCE8ysoZl1A7oDH1XXi6oiPWBmJ+A5K5nApBDCjaltUf1kZkcB7wCfEcsd+j88r+tJoAv+y+y7IYSSSZFSA8xsJPDbEMJJZtYWfS4pZ2b98RscGgDLgAvwP6j12aSQmV0HnInflf0J8GOgGfpcapyZPQ6MBNoBXwPXAM9TxmdhZn8Afoh/dpeGEF6qtrYo6BIRERFJPg0vioiIiNQABV0iIiIiNUBBl4iIiEgNUNAlIiIiUgMUdImIiIjUAAVdIpJyZtbWzOZElrVmtiqynW9m9ybpNS81s3NLOd7VzOZV4+s0MLO3I3PwiUg9pl8CIpJyIYRvgf4AZnYtkB9CuDVZrxcJgH6IV29PqhDCTjN7Da/ZNDnZryci6Us9XSKStsxspJm9GNm+1sweMbPpZrbczE43s5vN7DMzezkyhRRmNsjM3jKz2Wb23+hUHyV8B/g4hFAQ95y5ZvYB8PO41+9qZu+Y2ceR5cjI8X+Z2Slx5002s3Fm1tvMPor00n1qZt0jpzwPnJ2M90hEag8FXSJSmxwEnIhPJvwY8EYIoS+wDTgxEnjdBYwPIQwCJgGlzTAxDIifwPsh4FchhCNKnLcOOC6EMBDvqbozcvwBvPI7ZtYSn1dvGnARcEcIoT8wGJ/HDWAeMGQvf2YRqSM0vCgitclLIYRdZvYZPm3Xy5HjnwFdgZ5AH+AVn/KOTGBNKdfpgM+FFw2aWoUQ3oo89i9gbGQ7G7g7MtVOIdADIITwlpndY2b7AKcDz4QQCiI9ZX8ws87AsyGExZHzC81sp5k1DyHkVdN7ISK1jIIuEalNdgCEEIrMbFeIzWNWhP8+M2B+KT1WJW0DGkW2DShrPrRf43O15eAjA9vjHvsXPmQ4Ac8PI4TwbzObgffG/dfMfhxCeD1yfsMSzxeRekbDiyJSlywC2pvZEQBmlm1mvUs5byFwMEAIYSOwKTLhOhTPvWoJrAkhFAHn4D1nUQ8Dl0auMT/yegcCy0IIdwJTgX6R422Bb0IIu6r+I4pIbaWgS0TqjBDCTmA88FczmwvMwfOtSnoJODpu/wLgnsjw4La44/cC55nZh/jQ4pa41/oaD94eijv/TGCemc0BDgEejRwfhed8iUg9ZrHeeRGR+sPMngN+H8272ovnN8FzyQaGEDZVcO6zwJUhhEV781oiUjeop0tE6qsr8IT6SjOzY4HPgbsSCLgaAM8r4BIR9XSJiIiI1AD1dImIiIjUAAVdIiIiIjVAQZeIiIhIDVDQJSIiIlIDFHSJiIiI1AAFXSIiIiI14P8DAWq3o1r87K8AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set parameters\n",
+    "\n",
+    "\n",
+    "param_choices_true_false = []\n",
+    "\n",
+    "def ps(param_synth_method):\n",
+    "    for i in range(len(search_points_beta)):\n",
+    "        beta_val = search_points_beta[i]\n",
+    "        for j in range(len(search_points_gamma)):\n",
+    "            gamma_val = search_points_gamma[j]\n",
+    "\n",
+    "            # parameters\n",
+    "\n",
+    "            def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n",
+    "            def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n",
+    "\n",
+    "\n",
+    "            # set initial conditions\n",
+    "            I0, R0 = 0.01, 0\n",
+    "            S0 = 1-I0-R0\n",
+    "            y0 = S0, I0, R0 # Initial conditions vector\n",
+    "            # 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.SIR_model, y0, tvect, args=(beta, gamma))\n",
+    "            S, I, R = sim.T\n",
+    "\n",
+    "            # plot results\n",
+    "            #sir_model.plotSIR(tvect, S, I, R)\n",
+    "            ## log parameters and sat/unsat\n",
+    "\n",
+    "            query_condition =  all(I[0:50] >= (0.9141058921813964/1000)) and all(I[0:50] <= (909.6296997070312/1000)) and all(I[50:] >= (0.0005660105962306/1000)) and all(I[50:] <= (154.95701599121094/1000))\n",
+    "           \n",
+    "            #print(query_condition)\n",
+    "            query = '1' if query_condition else '0'\n",
+    "            param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\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\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta gamma\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_choices = list(param_choices_true_false[0].keys())\n",
+    "parameter_list = ([elt for elt in param_choices if elt != 'assignment'])\n",
+    "for i in range(len(parameter_list)):\n",
+    "    for j in range(i+1, len(parameter_list)):\n",
+    "        param1 = parameter_list[i]\n",
+    "        param2 = parameter_list[j]\n",
+    "        print(param1, param2)\n",
+    "        xsample = [result[param1] for result in param_choices_true_false]\n",
+    "        ysample = [result[param2] for result in param_choices_true_false]\n",
+    "        colors = ['green' if result['assignment'] == '1' else 'red' for result in param_choices_true_false]\n",
+    "        plt.title(\"Parameter Value Pairings\")\n",
+    "        plt.xlabel(param1)\n",
+    "        plt.ylabel(param2)\n",
+    "\n",
+    "        area = 5  # 0 to 15 point radii\n",
+    "\n",
+    "        plt.scatter(xsample, ysample, s=area, c=colors, alpha=0.5)\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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
+}
diff --git a/scratch/notebooks/sir-demo-tkinter.ipynb b/scratch/notebooks/sir-demo-tkinter.ipynb
new file mode 100644
index 00000000..f1c8098c
--- /dev/null
+++ b/scratch/notebooks/sir-demo-tkinter.ipynb
@@ -0,0 +1,335 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "60c811e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tkinter as tk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "67d7cb90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n",
+      "Requirement already satisfied: scipy in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (1.13.1)\n",
+      "Requirement already satisfied: numpy<2.3,>=1.22.4 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from scipy) (1.26.4)\n",
+      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.0 is available.\n",
+      "You should consider upgrading via the '/Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
+      "\u001b[0mRequirement already satisfied: matplotlib in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (3.9.0)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from matplotlib) (1.2.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from matplotlib) (4.53.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from matplotlib) (24.0)\n",
+      "Requirement already satisfied: importlib-resources>=3.2.0 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from matplotlib) (6.4.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from matplotlib) (3.1.2)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from matplotlib) (0.12.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from matplotlib) (1.4.5)\n",
+      "Requirement already satisfied: pillow>=8 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from matplotlib) (10.3.0)\n",
+      "Requirement already satisfied: numpy>=1.23 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from matplotlib) (1.26.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from matplotlib) (2.9.0.post0)\n",
+      "Requirement already satisfied: zipp>=3.1.0 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.19.2)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.0 is available.\n",
+      "You should consider upgrading via the '/Users/dmosaphir/SIFT/Projects/ASKEM/code/ts-trajectory/.venv/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "!pip install scipy\n",
+    "!pip install matplotlib\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": 23,
+   "id": "42a8bb3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "line_id = None\n",
+    "line_points = []\n",
+    "line_options = {}\n",
+    "line_points_saved = []\n",
+    "clicked_points_saved = []\n",
+    "\n",
+    "def draw_line(event):\n",
+    "    global line_id\n",
+    "    line_points.extend((event.x, event.y))\n",
+    "    if line_id is not None:\n",
+    "        canvas.delete(line_id)\n",
+    "    line_id = canvas.create_line(line_points, **line_options)\n",
+    "\n",
+    "\n",
+    "def set_start(event):\n",
+    "    line_points.extend((event.x, event.y))\n",
+    "\n",
+    "\n",
+    "def end_line(event=None):\n",
+    "    global line_id\n",
+    "    line_points_saved.extend(line_points)\n",
+    "    line_points.clear()\n",
+    "    line_id = None\n",
+    "    \n",
+    "def draw_point(event):\n",
+    "    canvas.create_oval(event.x, event.y, event.x, event.y, width = 5, fill = 'black')\n",
+    "    clicked_points_saved.extend((event.x, event.y))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5b04085d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root = tk.Tk()\n",
+    "\n",
+    "canvas = tk.Canvas()\n",
+    "canvas.pack()\n",
+    "\n",
+    "#canvas.bind('<Button-1>', draw_point)\n",
+    "canvas.bind('<Button-1>', set_start)\n",
+    "canvas.bind('<B1-Motion>', draw_line)\n",
+    "canvas.bind('<ButtonRelease-1>', end_line)\n",
+    "\n",
+    "root.mainloop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "2817cbec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[17, 19, 21, 23, 26, 30, 33, 37, 40, 43, 45, 47, 49, 52, 55, 58, 62, 65, 68, 70, 73, 76, 79, 81, 83, 84, 86, 87, 89, 91, 94, 96, 98, 100, 101, 102, 102, 103, 104, 104, 105, 106, 106, 107, 107, 107, 108, 109, 109, 110, 111, 111, 112, 113, 114, 115, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 129, 132, 135, 138, 141, 144, 146, 148, 150, 153, 155, 157, 160, 162, 165, 167, 169, 172, 173, 175, 177, 179, 181, 183, 185, 186, 188, 189, 191, 193, 194, 196, 197, 198, 200, 202, 204, 205, 207, 208, 210, 212, 214, 216, 218, 220, 222, 223, 224, 226, 227, 229, 230, 232, 234, 236, 237, 239, 241, 243, 244, 246, 247, 248, 249, 250, 251, 252]\n",
+      "[183, 183, 183, 183, 183, 184, 184, 184, 184, 185, 185, 185, 185, 185, 184, 184, 183, 183, 182, 180, 178, 176, 174, 172, 171, 169, 167, 165, 163, 162, 160, 159, 158, 157, 156, 155, 154, 152, 150, 148, 145, 142, 139, 136, 133, 130, 126, 123, 121, 119, 117, 115, 112, 110, 108, 106, 105, 104, 102, 101, 99, 98, 97, 95, 93, 91, 90, 88, 87, 85, 83, 81, 80, 79, 78, 77, 77, 77, 76, 76, 75, 74, 73, 73, 72, 71, 70, 69, 69, 69, 69, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67]\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_vals = line_points_saved[::2]\n",
+    "y_vals = line_points_saved[1::2]\n",
+    "print(t_vals)\n",
+    "print(y_vals)\n",
+    "y_rescaled = y_vals/np.max(y_vals)\n",
+    "# print(y_rescaled)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "5224c14b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize recording of parameter choices and true/false\n",
+    "\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.1,0.25]\n",
+    "gamma_search_bounds = [0.01,0.04]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c826d8b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# USER: list how many points for each parameter you'd like to synthesize\n",
+    "\n",
+    "beta_values_to_synthesize = 10\n",
+    "gamma_values_to_synthesize = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "96747c5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_points_beta = np.linspace(beta_search_bounds[0], beta_search_bounds[1], beta_values_to_synthesize)\n",
+    "search_points_gamma = np.linspace(gamma_search_bounds[0], gamma_search_bounds[1], gamma_values_to_synthesize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "129918df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def eval_point(beta_val, gamma_val, plot=False, rtol=1e-4):\n",
+    "    # parameters\n",
+    "    def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n",
+    "    def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n",
+    "\n",
+    "\n",
+    "    # USER: set initial conditions\n",
+    "    I0, R0 = 0.01, 0\n",
+    "    S0 = 1-I0-R0\n",
+    "    y0 = S0, I0, R0 # Initial conditions vector\n",
+    "    # USER: set simulation parameters\n",
+    "    dt = 1\n",
+    "    tstart = 0\n",
+    "    tend = 260\n",
+    "    tvect = np.arange(tstart, tend, dt)\n",
+    "    # simulate/solve ODEs\n",
+    "    # sim = odeint(sir_model.SIR_model, y0, tvect, args=(beta, gamma), hmin=dt, mxstep=1) # similar resolution to FUNMAN\n",
+    "    sim = odeint(sir_model.SIR_model, y0, tvect, args=(beta, gamma), hmin=dt, mxstep=1) # default odeint settings\n",
+    "    S, I, R = sim.T\n",
+    "\n",
+    "    # print(list(zip(range(tstart, tend+1, dt), I)))\n",
+    "\n",
+    "    # plot results - uncomment next line to plot time series.  not recommended for large numbers of points\n",
+    "    if plot:\n",
+    "        sir_model.plotSIR(tvect, S, I, R)\n",
+    "    # USER: write query condition.\n",
+    "    # query_condition =  0.15 <= max(I) <= 0.3  #((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "    # query_condition = (0.45 <= max(I)) and (max(I) <= 0.55) and (45 <= np.argmax(I)*dt) and (np.argmax(I)*dt <= 55)\n",
+    "    # query_values = [0.45 <= I[int(t/dt)] <= 0.55 for t in range(tstart, tend+1, dt) if 45 <= t < 55]\n",
+    "    query_values = [y_rescaled[i] - 10 <= R[int(t_vals[i]/dt)] <= y_rescaled[i] + 10 for i in range(len(t_vals))]\n",
+    "    query_condition = all(query_values) \n",
+    "    # print(I[9:11])\n",
+    "    query = '1' if query_condition else '0'\n",
+    "    param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\n",
+    "    return param_assignments, sim\n",
+    "\n",
+    "# set parameters\n",
+    "def ps(param_synth_method, rtol=1e-3):\n",
+    "    param_choices_true_false = []\n",
+    "    for i in range(len(search_points_beta)):\n",
+    "        beta_val = search_points_beta[i]\n",
+    "        for j in range(len(search_points_gamma)):\n",
+    "            gamma_val = search_points_gamma[j]\n",
+    "\n",
+    "            param_assignments, _ = eval_point(beta_val, gamma_val, rtol=rtol)\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "c5df90ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fg/63k_244d5jg2d0hp2j_fb21w0000gp/T/ipykernel_84289/164580243.py:18: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n",
+      "  sim = odeint(sir_model.SIR_model, y0, tvect, args=(beta, gamma), hmin=dt, mxstep=1) # default odeint settings\n"
+     ]
+    }
+   ],
+   "source": [
+    "param_choices_true_false = ps(\"all\", rtol=1e-3)\n",
+    "#print(param_choices_true_false)\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sir_query_auto_2_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "12901feb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sir_model.plot_two_params(\"gamma\", \"beta\", param_choices_true_false)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95378ef1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d02089e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b732c635",
+   "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": 5
+}
diff --git a/scratch/notebooks/sir-eval-smt-encoding.ipynb b/scratch/notebooks/sir-eval-smt-encoding.ipynb
new file mode 100644
index 00000000..c761e693
--- /dev/null
+++ b/scratch/notebooks/sir-eval-smt-encoding.ipynb
@@ -0,0 +1,392 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1:54:49.48 to get a functioning encoding with just compartmental constraints.  Needs more time on probing the parameters\n",
+    "# 0 - 32:00.68: Setup basic encoding\n",
+    "# 32:00.68 - 48:00: Setup z3 solver and get results\n",
+    "# 48:00 - 1:27:15: Setup solution extraction\n",
+    "# 1:27:15 - 1:54:49.48: Setup solution plotting and explore 8 parameter bound settings to test\n",
+    "# 1:54:49.48 - 2:09:43.40: Cleanup code, comments, and improve plotting to interpolate\n",
+    "# 2:09:43.40 - 2:17:19.51: Add dreal and test\n",
+    "\n",
+    "from pysmt.shortcuts import And, Or, Plus, Minus, Times, Div, REAL, LE, LT, GE, GT, Equals, Symbol, Real, Solver\n",
+    "from pysmt.fnode import  FNode\n",
+    "from typing import Dict\n",
+    "import pandas as pd\n",
+    "from decimal import Decimal\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def dataframe(assignment: Dict[Symbol, float], state_variables, parameters, max_step, step_size) -> pd.DataFrame:\n",
+    "    timepoints = list(range(0, max_time+step_size, step_size))\n",
+    "    timeseries = {sv: [None]*len(timepoints) for sv in state_variables}\n",
+    "    for k, v in assignment.items():\n",
+    "        sym = k.symbol_name()\n",
+    "        if \"_\" in sym:\n",
+    "            sv = sym.split(\"_\")[0]\n",
+    "            t = sym.split(\"_\")[1]\n",
+    "            value =Decimal(v.numerator) / Decimal(v.denominator)\n",
+    "            timeseries[sv][timepoints.index(int(t))] = value\n",
+    "        else:\n",
+    "            timeseries[sym] = [v]*len(timepoints)\n",
+    "    df = pd.DataFrame(timeseries, index=timepoints).astype(float)\n",
+    "    return df\n",
+    "\n",
+    "def reindex_and_interpolate(df, new_index):\n",
+    "    df_reindexed = df.reindex(index = new_index)\n",
+    "    df_reindexed.interpolate(method = 'linear', inplace = True)\n",
+    "    return df_reindexed\n",
+    "\n",
+    "\n",
+    "def plot_results(values, max_step, step_size):\n",
+    "    timepoints = list(range(0, max_time+step_size, step_size))\n",
+    "    results: pd.DataFrame = dataframe(values, [\"S\", \"I\", \"R\"], [\"beta\", \"gamma\"], max_step, step_size)\n",
+    "    newindex = np.linspace(timepoints[0], timepoints[-1], timepoints[-1]+1)\n",
+    "    results = reindex_and_interpolate(results, newindex)\n",
+    "    print(f\"beta = {results['beta'][0]}, gamma = {results['gamma'][0]}\")\n",
+    "    print(results[[\"S\", \"I\", \"R\"]])\n",
+    "    ax = results[[\"S\", \"I\", \"R\"]].plot()\n",
+    "    ax.set_xlabel=\"Time\"\n",
+    "    return ax, results\n",
+    "\n",
+    "def run_solver(solver, formula):\n",
+    "    solver.add_assertion(formula)\n",
+    "    result = solver.solve()\n",
+    "    if result:\n",
+    "        model = solver.get_model()\n",
+    "        variables = formula.get_free_variables()\n",
+    "        values = {}\n",
+    "        for var in variables:\n",
+    "            try:\n",
+    "                values[var] = model.get_value(var).constant_value()\n",
+    "            except Exception as e:\n",
+    "                pass\n",
+    "    else:\n",
+    "        print(\"Unsat\")\n",
+    "        values = None\n",
+    "    return values\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate model diagram\n",
+    "\n",
+    "# from funman.api.run import Runner\n",
+    "# import json\n",
+    "\n",
+    "# amr_model = \"../../resources/amr/petrinet/amr-examples/sir.json\"\n",
+    "# m = Runner().get_model(amr_model)\n",
+    "# g = m[0].to_dot()\n",
+    "# g.render(\"sir.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure these bounds (lower, upper) to configure the parameter space\n",
+    "\n",
+    "beta_bounds = (0.25, 0.25)\n",
+    "gamma_bounds = (0.027755102040816326, 0.027755102040816326)\n",
+    "\n",
+    "# Set these values for the initial state\n",
+    "\n",
+    "S_0_value = 0.99\n",
+    "I_0_value = 0.01\n",
+    "R_0_value = 0\n",
+    "\n",
+    "# Infected constraint bounds\n",
+    "I_bounds = (0.45, 0.55)\n",
+    "I_bounds_t = (45, 55)\n",
+    "\n",
+    "# Timepoints\n",
+    "step_size = 1\n",
+    "max_time = 40\n",
+    "\n",
+    "noise = 1e-3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The main encoding code\n",
+    "def encode(step_size=1, max_time=10, noise=1e-3, strict_upper_bound_parameters = False, use_compartmental=True):\n",
+    "    time_format = lambda t: f\"{t:03d}\"\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Initial States ###############################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    S_0 = Symbol(f\"S_{time_format(0)}\", REAL)\n",
+    "    I_0 = Symbol(f\"I_{time_format(0)}\", REAL)\n",
+    "    R_0 = Symbol(f\"R_{time_format(0)}\", REAL)\n",
+    "\n",
+    "    population_size = S_0_value + I_0_value + R_0_value\n",
+    "\n",
+    "    # SIR Model Initial State\n",
+    "    initial_state = And([\n",
+    "        Equals(S_0, Real(S_0_value)),\n",
+    "        Equals(I_0, Real(I_0_value)),\n",
+    "        Equals(R_0, Real(R_0_value))\n",
+    "    ])\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Parameters     ###############################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "\n",
+    "    # Parameters\n",
+    "    beta = Symbol(\"beta\", REAL)\n",
+    "\n",
+    "    gamma = Symbol(\"gamma\", REAL)\n",
+    "\n",
+    "    upper_op = LT if strict_upper_bound_parameters else LE\n",
+    "\n",
+    "    parameters = And([\n",
+    "        And(LE(Real(beta_bounds[0]), beta), (upper_op(beta, Real(beta_bounds[1])) if beta_bounds[0] < beta_bounds[1] else LE(beta, Real(beta_bounds[1])))),\n",
+    "        And(LE(Real(gamma_bounds[0]), gamma), (upper_op(gamma, Real(gamma_bounds[1])) if gamma_bounds[0] < gamma_bounds[1] else LE(gamma, Real(gamma_bounds[1]))))\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "    timepoints = list(range(0, max_time+step_size, step_size))\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Transitions ##################################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    S_next = lambda t: Symbol(f\"S_{time_format(t+step_size)}\", REAL)\n",
+    "    S_now = lambda t: Symbol(f\"S_{time_format(t)}\", REAL)\n",
+    "    I_next = lambda t: Symbol(f\"I_{time_format(t+step_size)}\", REAL)\n",
+    "    I_now = lambda t: Symbol(f\"I_{time_format(t)}\", REAL)\n",
+    "    R_next = lambda t: Symbol(f\"R_{time_format(t+step_size)}\", REAL)\n",
+    "    R_now = lambda t: Symbol(f\"R_{time_format(t)}\", REAL)\n",
+    "    dt = Real(float(step_size))\n",
+    "\n",
+    "    S_Trans = lambda t: Equals(S_next(t), \n",
+    "                                    Minus(\n",
+    "                                        S_now(t), \n",
+    "                                        Times([beta, S_now(t), I_now(t), dt])))\n",
+    "\n",
+    "    I_Trans = lambda t: Equals(I_next(t), \n",
+    "                                    Plus(\n",
+    "                                        I_now(t), \n",
+    "                                        Times(\n",
+    "                                            Minus(\n",
+    "                                                Times([beta, S_now(t), I_now(t)]), \n",
+    "                                                Times(gamma, I_now(t))), dt)))\n",
+    "\n",
+    "    R_Trans = lambda t: Equals(R_next(t), \n",
+    "                                    Plus(\n",
+    "                                        R_now(t), \n",
+    "                                        Times(\n",
+    "                                            Times(gamma, I_now(t)), \n",
+    "                                            dt)))\n",
+    "\n",
+    "    Trans = lambda t: And(S_Trans(t), I_Trans(t), R_Trans(t))\n",
+    "\n",
+    "    All_Trans = And([Trans(t) for t in timepoints[:-1]])\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Constraints ##################################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    compartmental_constraint = And([\n",
+    "        And( LE(Real(0.0), Plus(S_now(t), Real(noise))),\n",
+    "        LE(Real(0.0), Plus(I_now(t), Real(noise))),\n",
+    "        LE(Real(0.0), Plus(R_now(t), Real(noise))),\n",
+    "        LT(Plus([S_now(t), I_now(t), R_now(t)]), Plus(Real(population_size), Real(noise))), \n",
+    "        GE(Plus([S_now(t), I_now(t), R_now(t)]), Minus(Real(population_size), Real(noise))))\n",
+    "    for t in timepoints]) if use_compartmental else TRUE()\n",
+    "\n",
+    "    # 10m to add and check\n",
+    "    I_peak = (0.45, 0.55) # .45, .55\n",
+    "    I_peak_t = (45.0, 55.0) # 45, 55\n",
+    "    peak_I = And([\n",
+    "        And(LE(Real(I_peak[0]), I_now(t)),\n",
+    "        LT(I_now(t), Real(I_peak_t[1])))\n",
+    "        for t in timepoints \n",
+    "        if I_peak_t[0] <= t and t <= I_peak_t[1]\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "    ################################################################################\n",
+    "    ################# Combine Assertions ###########################################\n",
+    "    ################################################################################\n",
+    "\n",
+    "    consistency = And([\n",
+    "        initial_state,\n",
+    "        parameters,\n",
+    "        All_Trans,\n",
+    "        compartmental_constraint,\n",
+    "        peak_I\n",
+    "        ])\n",
+    "    # consistency.serialize()\n",
+    "    return consistency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Solve encoding\n",
+    "# with Solver() as solver:\n",
+    "#     values = run_solver(solver)\n",
+    "    \n",
+    "# if values:\n",
+    "#     plot_results(values, timepoints)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "beta = 0.25, gamma = 0.027755102040816326\n",
+      "             S         I         R\n",
+      "0.0   0.990000  0.010000  0.000000\n",
+      "1.0   0.987525  0.012197  0.000278\n",
+      "2.0   0.985050  0.014395  0.000555\n",
+      "3.0   0.982575  0.016592  0.000833\n",
+      "4.0   0.980100  0.018790  0.001110\n",
+      "5.0   0.977625  0.020987  0.001388\n",
+      "6.0   0.972496  0.025534  0.001970\n",
+      "7.0   0.967366  0.030081  0.002553\n",
+      "8.0   0.962237  0.034628  0.003135\n",
+      "9.0   0.957107  0.039175  0.003718\n",
+      "10.0  0.951978  0.043722  0.004300\n",
+      "11.0  0.941572  0.052914  0.005514\n",
+      "12.0  0.931167  0.062106  0.006727\n",
+      "13.0  0.920761  0.071298  0.007941\n",
+      "14.0  0.910356  0.080490  0.009154\n",
+      "15.0  0.899950  0.089682  0.010368\n",
+      "16.0  0.879773  0.107370  0.012857\n",
+      "17.0  0.859596  0.125058  0.015346\n",
+      "18.0  0.839418  0.142747  0.017835\n",
+      "19.0  0.819241  0.160435  0.020324\n",
+      "20.0  0.799064  0.178123  0.022813\n",
+      "21.0  0.763481  0.208762  0.027757\n",
+      "22.0  0.727898  0.239401  0.032701\n",
+      "23.0  0.692315  0.270040  0.037645\n",
+      "24.0  0.656732  0.300679  0.042589\n",
+      "25.0  0.621149  0.331318  0.047533\n",
+      "26.0  0.569699  0.373572  0.056728\n",
+      "27.0  0.518250  0.415826  0.065924\n",
+      "28.0  0.466800  0.458080  0.075120\n",
+      "29.0  0.415351  0.500333  0.084316\n",
+      "30.0  0.363901  0.542587  0.093511\n",
+      "31.0  0.314539  0.576890  0.108571\n",
+      "32.0  0.265177  0.611192  0.123631\n",
+      "33.0  0.215815  0.645495  0.138690\n",
+      "34.0  0.166453  0.679797  0.153750\n",
+      "35.0  0.117091  0.714100  0.168809\n",
+      "36.0  0.096187  0.715183  0.188629\n",
+      "37.0  0.075284  0.716267  0.208449\n",
+      "38.0  0.054380  0.717351  0.228269\n",
+      "39.0  0.033476  0.718435  0.248089\n",
+      "40.0  0.012573  0.719518  0.267909\n",
+      "41.0  0.010311  0.701810  0.287879\n",
+      "42.0  0.008050  0.684101  0.307849\n",
+      "43.0  0.005788  0.666392  0.327820\n",
+      "44.0  0.003526  0.648684  0.347790\n",
+      "45.0  0.001265  0.630975  0.367760\n",
+      "46.0  0.001065  0.613662  0.385273\n",
+      "47.0  0.000866  0.596348  0.402786\n",
+      "48.0  0.000666  0.579035  0.420299\n",
+      "49.0  0.000467  0.561722  0.437811\n",
+      "50.0  0.000267  0.544409  0.455324\n",
+      "Population at end: 0.9999999999999999\n",
+      "[-0.002475000000000005, -0.002475000000000005, -0.002474999999999894, -0.002475000000000005, -0.002475000000000005, -0.005129413823341866, -0.005129413823341866, -0.005129413823341755, -0.005129413823341866, -0.005129413823341866, -0.010405546792922049, -0.010405546792922049, -0.010405546792921938, -0.010405546792922049, -0.010405546792922049, -0.020177337323093902, -0.020177337323093902, -0.020177337323093902, -0.020177337323093902, -0.020177337323093902, -0.03558290467585801, -0.03558290467585801, -0.03558290467585801, -0.03558290467585801, -0.03558290467585801, -0.051449529036565966, -0.051449529036565855, -0.051449529036565966, -0.05144952903656591, -0.05144952903656591, -0.04936205284144529, -0.04936205284144529, -0.049362052841445236, -0.04936205284144529, -0.049362052841445264, -0.020903674421399204, -0.020903674421399218, -0.020903674421399218, -0.020903674421399204, -0.020903674421399204, -0.002261573475552607, -0.002261573475552609, -0.002261573475552608, -0.002261573475552608, -0.002261573475552606, -0.00019952023486947967, -0.00019952023486947946, -0.00019952023486947967, -0.00019952023486947957, -0.00019952023486947957]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Use dreal instead of z3\n",
+    "from pysmt.logics import QF_NRA\n",
+    "\n",
+    "from funman_dreal.solver import ensure_dreal_in_pysmt\n",
+    "\n",
+    "ensure_dreal_in_pysmt()\n",
+    "\n",
+    "opts = {\n",
+    "        \"dreal_precision\": 1e-3,\n",
+    "        \"dreal_log_level\": \"none\",\n",
+    "        \"dreal_mcts\": True,\n",
+    "    }\n",
+    "\n",
+    "max_time = 50\n",
+    "step_size = 5\n",
+    "\n",
+    "values= None\n",
+    "with Solver(name=\"dreal\",\n",
+    "            logic=QF_NRA,\n",
+    "            solver_options=opts\n",
+    "            ) as solver:\n",
+    "    values = run_solver(solver, encode(max_time=max_time, step_size=step_size, noise=noise, use_compartmental=True))\n",
+    "    pass\n",
+    "    \n",
+    "if values:\n",
+    "    ax, results = plot_results(values,  max_time, step_size)\n",
+    "    f_pop = results.loc[float(max_time)][[\"S\", \"I\", \"R\"]].sum()\n",
+    "    print(f\"Population at end: {f_pop}\")\n",
+    "    s = list(results.S.values)\n",
+    "    print([s[i]-s[i-1] for i in range(1, len(s))])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "funman_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scratch/notebooks/sir-manual-true-false.ipynb b/scratch/notebooks/sir-manual-true-false.ipynb
new file mode 100644
index 00000000..eb3d98ab
--- /dev/null
+++ b/scratch/notebooks/sir-manual-true-false.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# initialize recording of parameter choices and true/false\n",
+    "\n",
+    "param_choices_true_false = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set these calibration parameters once:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# USER: set initial conditions\n",
+    "I0, R0 = 0.01, 0\n",
+    "S0 = 1-I0-R0\n",
+    "\n",
+    "# USER: set simulation parameters\n",
+    "dt = 1\n",
+    "tstart = 0\n",
+    "tend = 100\n",
+    "\n",
+    "# USER: set simulation parameters\n",
+    "dt = 1\n",
+    "tstart = 0\n",
+    "tend = 100\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### After setting your calibration parameters, run the following cell as many times as you'd like to experiment with different parameter values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def ps(beta_val, gamma_val):\n",
+    "    def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n",
+    "    def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n",
+    "\n",
+    "    y0 = S0, I0, R0 # Initial conditions vector\n",
+    "\n",
+    "    tvect = np.arange(tstart, tend, dt)\n",
+    "\n",
+    "    sim = odeint(sir_model.SIR_model, y0, tvect, args=(beta, gamma))\n",
+    "    S, I, R = sim.T\n",
+    "\n",
+    "    # plot results\n",
+    "    sir_model.plotSIR(tvect, S, I, R)\n",
+    "\n",
+    "    ## log parameters and sat/unsat\n",
+    "\n",
+    "    param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'assignment': input(\"Please enter 0 if the parameter choices were bad and 1 if they were good:\")}\n",
+    "\n",
+    "    param_choices_true_false.append(param_assignments)\n",
+    "\n",
+    "\n",
+    "beta_value = input(\"Please enter a value for beta:\")\n",
+    "gamma_value = input(\"Please enter a value for gamma:\")\n",
+    "ps(beta_value, gamma_value)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### At any point in the process, you can visualize the true/false points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot \"true/false\" points.  \n",
+    "\n",
+    "sir_model.plot_two_params(\"beta\", \"gamma\", param_choices_true_false)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Once you're done, save your true/false points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save results\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sir_manual_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)"
+   ]
+  },
+  {
+   "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/scratch/notebooks/sir-ps-auto.ipynb b/scratch/notebooks/sir-ps-auto.ipynb
new file mode 100644
index 00000000..deaf255d
--- /dev/null
+++ b/scratch/notebooks/sir-ps-auto.ipynb
@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize recording of parameter choices and true/false\n",
+    "\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0,1]\n",
+    "gamma_search_bounds = [0,1]\n",
+    "\n",
+    "# USER: list how many points for each parameter you'd like to synthesize\n",
+    "beta_values_to_synthesize = 10\n",
+    "gamma_values_to_synthesize = 10\n",
+    "\n",
+    "# USER: set values for all parameters that you are not synthesizing \n",
+    "\n",
+    "\n",
+    "# USER: set initial conditions\n",
+    "I0, R0 = 0.01, 0\n",
+    "S0 = 1-I0-R0\n",
+    "\n",
+    "# USER: set simulation parameters\n",
+    "dt = 1\n",
+    "tstart = 0\n",
+    "tend = 100\n",
+    "\n",
+    "# USER: set parameter synthesis method: \"all\" or \"any\"\n",
+    "method = \"all\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_points_beta = np.linspace(beta_search_bounds[0], beta_search_bounds[1], beta_values_to_synthesize)\n",
+    "search_points_gamma = np.linspace(gamma_search_bounds[0], gamma_search_bounds[1], gamma_values_to_synthesize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# set parameters\n",
+    "def ps(param_synth_method):\n",
+    "    param_choices_true_false = []\n",
+    "    for i in range(len(search_points_beta)):\n",
+    "        beta_val = search_points_beta[i]\n",
+    "        for j in range(len(search_points_gamma)):\n",
+    "            gamma_val = search_points_gamma[j]\n",
+    "\n",
+    "            # parameters\n",
+    "            # set parameter values\n",
+    "            def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n",
+    "            def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n",
+    "\n",
+    "            \n",
+    "\n",
+    "            y0 = S0, I0, R0 # Initial conditions vector\n",
+    "\n",
+    "            tvect = np.arange(tstart, tend, dt)\n",
+    "            # simulate/solve ODEs\n",
+    "            sim = odeint(sir_model.SIR_model, y0, tvect, args=(beta, gamma))\n",
+    "            S, I, R = sim.T\n",
+    "            # write query condition.\n",
+    "            query_condition = (0.999 <= (S+I+R).all() <= 1.001)  # compartmental constraint\n",
+    "            # query_condition =  (0.45 <= max(I)) and (max(I) <= 0.55) and (45 <= np.argmax(I)*dt) and (np.argmax(I)*dt <= 55) #max constraint\n",
+    "            query = '1' if query_condition else '0'\n",
+    "            # plot results - uncomment next line to plot time series.  not recommended for large numbers of points\n",
+    "            # if query == '1':\n",
+    "            #     print('beta:', beta_val, 'gamma:', gamma_val)\n",
+    "            #     sir_model.plotSIR(tvect, S, I, R)\n",
+    "            ## end plot\n",
+    "            param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\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(method)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save results\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sir_query_auto_2_param_{id_number}_{method}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot \"true/false\" points.  \n",
+    "\n",
+    "sir_model.plot_two_params(\"beta\", \"gamma\", param_choices_true_false)"
+   ]
+  },
+  {
+   "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/scratch/notebooks/sir-query-positional-1d.ipynb b/scratch/notebooks/sir-query-positional-1d.ipynb
new file mode 100644
index 00000000..6d9ef0bc
--- /dev/null
+++ b/scratch/notebooks/sir-query-positional-1d.ipynb
@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: this notebook is identical to sir-query-positional-2d.ipynb with the exception that gamma_search_bounds is set to have the same lower and upper bound, and gamma_values_to_synthesize is set to 1.\n",
+    "\n",
+    "### For consistency scenarios, both of the search upper and lower bounds should be set to the same number and values to synthesize of each parameter should be set to 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize recording of parameter choices and true/false\n",
+    "\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.08,0.10]\n",
+    "gamma_search_bounds = [0.02,0.02]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# USER: list how many points for each parameter you'd like to synthesize\n",
+    "\n",
+    "beta_values_to_synthesize = 1\n",
+    "gamma_values_to_synthesize = 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_points_beta = np.linspace(beta_search_bounds[0], beta_search_bounds[1], beta_values_to_synthesize)\n",
+    "search_points_gamma = np.linspace(gamma_search_bounds[0], gamma_search_bounds[1], gamma_values_to_synthesize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# set parameters\n",
+    "def ps(param_synth_method):\n",
+    "    param_choices_true_false = []\n",
+    "    for i in range(len(search_points_beta)):\n",
+    "        beta_val = search_points_beta[i]\n",
+    "        for j in range(len(search_points_gamma)):\n",
+    "            gamma_val = search_points_gamma[j]\n",
+    "\n",
+    "            # parameters\n",
+    "            def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n",
+    "            def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n",
+    "\n",
+    "\n",
+    "            # USER: set initial conditions\n",
+    "            I0, R0 = 0.01, 0\n",
+    "            S0 = 1-I0-R0\n",
+    "            y0 = S0, I0, R0 # Initial conditions vector\n",
+    "            # USER: set simulation parameters\n",
+    "            dt = 5\n",
+    "            tstart = 0\n",
+    "            tend = 100\n",
+    "            tvect = np.arange(tstart, tend, dt)\n",
+    "            # simulate/solve ODEs\n",
+    "            sim = odeint(sir_model.SIR_model, y0, tvect, args=(beta, gamma))\n",
+    "            S, I, R = sim.T\n",
+    "\n",
+    "            # plot results - uncomment next line to plot time series.  not recommended for large numbers of points\n",
+    "            # sir_model.plotSIR(tvect, S, I, R)\n",
+    "            # USER: write query condition.\n",
+    "            query_condition =  0.15 <= max(I) <= 0.16  #((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "            query = '1' if query_condition else '0'\n",
+    "            param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\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(\"any\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(param_choices_true_false)\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sir_query_auto_1_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sir_model.plot_two_params(\"beta\", \"gamma\", param_choices_true_false)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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/scratch/notebooks/sir-query-positional-2d.ipynb b/scratch/notebooks/sir-query-positional-2d.ipynb
new file mode 100644
index 00000000..b787df84
--- /dev/null
+++ b/scratch/notebooks/sir-query-positional-2d.ipynb
@@ -0,0 +1,221 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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\n",
+    "import itertools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def query_1(sim, tstart, tend, dt):\n",
+    "    S, I, R = sim.T\n",
+    "    # USER: write query condition.\n",
+    "    # query_condition =  0.15 <= max(I) <= 0.3  #((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "    # query_condition = (0.45 <= max(I)) and (max(I) <= 0.55) and (45 <= np.argmax(I)*dt) and (np.argmax(I)*dt <= 55)\n",
+    "    query_values = [0.45 <= I[int(t/dt)] <= 0.55 for t in range(tstart, tend+1, dt) if 45 <= t < 55]\n",
+    "    query_condition = all(query_values) \n",
+    "    # print(I[9:11])\n",
+    "    return query_condition\n",
+    "\n",
+    "def eval_point(beta_val, gamma_val, query_condition=query_1, plot=False, rtol=1, atol=1, mxstep=10, mxordn = 1, mxords=1, hmin=1):\n",
+    "    # parameters\n",
+    "    def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n",
+    "    def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n",
+    "\n",
+    "\n",
+    "    # USER: set initial conditions\n",
+    "    I0, R0 = 0.01, 0\n",
+    "    S0 = 1-I0-R0\n",
+    "    y0 = S0, I0, R0 # Initial conditions vector\n",
+    "    # USER: set simulation parameters\n",
+    "    dt = 1\n",
+    "    tstart = 0\n",
+    "    tend = 55\n",
+    "    tvect = np.arange(tstart, tend, dt)\n",
+    "    # simulate/solve ODEs\n",
+    "    sim = odeint(sir_model.SIR_model, y0, tvect, args=(beta, gamma), mxstep=mxstep, rtol=rtol, atol=atol)\n",
+    "    S, I, R = sim.T\n",
+    "\n",
+    "    # print(list(zip(range(tstart, tend+1, dt), I)))\n",
+    "\n",
+    "    # plot results - uncomment next line to plot time series.  not recommended for large numbers of points\n",
+    "    if plot:\n",
+    "        sir_model.plotSIR(tvect, S, I, R)\n",
+    "\n",
+    "    query = '1' if query_condition(sim, tstart, tend, dt) else '0'\n",
+    "    param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\n",
+    "    return param_assignments, sim\n",
+    "\n",
+    "\n",
+    "\n",
+    "# set parameters\n",
+    "def ps(param_synth_method, parameter_search_bounds, rtol=1e-3, num_dim_points=10):\n",
+    "    parameters = list(parameter_search_bounds.keys())\n",
+    "    parameter_points = {p : np.linspace(parameter_search_bounds[p][0], parameter_search_bounds[p][1], num_dim_points) for p in parameters}\n",
+    "    points = itertools.product(*[parameter_points[p] for p in parameters])\n",
+    "    param_choices_true_false = []\n",
+    "    for point in points:\n",
+    "        point_values = {p: point[i] for i, p in enumerate(parameters)}\n",
+    "\n",
+    "        param_assignments, _ = eval_point(point_values[\"beta\"], point_values[\"gamma\"], rtol=rtol)\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'beta': 0.12, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.017222222222222222, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.019444444444444445, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.021666666666666667, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.02388888888888889, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.026111111111111113, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.028333333333333335, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.030555555555555558, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.03277777777777778, 'assignment': '0'}, {'beta': 0.12, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.1311111111111111, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.1311111111111111, 'gamma': 0.017222222222222222, 'assignment': '0'}, {'beta': 0.1311111111111111, 'gamma': 0.019444444444444445, 'assignment': '0'}, {'beta': 0.1311111111111111, 'gamma': 0.021666666666666667, 'assignment': '0'}, {'beta': 0.1311111111111111, 'gamma': 0.02388888888888889, 'assignment': '0'}, {'beta': 0.1311111111111111, 'gamma': 0.026111111111111113, 'assignment': '0'}, {'beta': 0.1311111111111111, 'gamma': 0.028333333333333335, 'assignment': '0'}, {'beta': 0.1311111111111111, 'gamma': 0.030555555555555558, 'assignment': '0'}, {'beta': 0.1311111111111111, 'gamma': 0.03277777777777778, 'assignment': '0'}, {'beta': 0.1311111111111111, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.14222222222222222, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.14222222222222222, 'gamma': 0.017222222222222222, 'assignment': '0'}, {'beta': 0.14222222222222222, 'gamma': 0.019444444444444445, 'assignment': '0'}, {'beta': 0.14222222222222222, 'gamma': 0.021666666666666667, 'assignment': '0'}, {'beta': 0.14222222222222222, 'gamma': 0.02388888888888889, 'assignment': '1'}, {'beta': 0.14222222222222222, 'gamma': 0.026111111111111113, 'assignment': '1'}, {'beta': 0.14222222222222222, 'gamma': 0.028333333333333335, 'assignment': '0'}, {'beta': 0.14222222222222222, 'gamma': 0.030555555555555558, 'assignment': '0'}, {'beta': 0.14222222222222222, 'gamma': 0.03277777777777778, 'assignment': '0'}, {'beta': 0.14222222222222222, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.15333333333333332, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.15333333333333332, 'gamma': 0.017222222222222222, 'assignment': '0'}, {'beta': 0.15333333333333332, 'gamma': 0.019444444444444445, 'assignment': '0'}, {'beta': 0.15333333333333332, 'gamma': 0.021666666666666667, 'assignment': '0'}, {'beta': 0.15333333333333332, 'gamma': 0.02388888888888889, 'assignment': '0'}, {'beta': 0.15333333333333332, 'gamma': 0.026111111111111113, 'assignment': '1'}, {'beta': 0.15333333333333332, 'gamma': 0.028333333333333335, 'assignment': '1'}, {'beta': 0.15333333333333332, 'gamma': 0.030555555555555558, 'assignment': '1'}, {'beta': 0.15333333333333332, 'gamma': 0.03277777777777778, 'assignment': '0'}, {'beta': 0.15333333333333332, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.16444444444444445, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.16444444444444445, 'gamma': 0.017222222222222222, 'assignment': '0'}, {'beta': 0.16444444444444445, 'gamma': 0.019444444444444445, 'assignment': '0'}, {'beta': 0.16444444444444445, 'gamma': 0.021666666666666667, 'assignment': '0'}, {'beta': 0.16444444444444445, 'gamma': 0.02388888888888889, 'assignment': '0'}, {'beta': 0.16444444444444445, 'gamma': 0.026111111111111113, 'assignment': '0'}, {'beta': 0.16444444444444445, 'gamma': 0.028333333333333335, 'assignment': '1'}, {'beta': 0.16444444444444445, 'gamma': 0.030555555555555558, 'assignment': '1'}, {'beta': 0.16444444444444445, 'gamma': 0.03277777777777778, 'assignment': '1'}, {'beta': 0.16444444444444445, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.17555555555555555, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.17555555555555555, 'gamma': 0.017222222222222222, 'assignment': '0'}, {'beta': 0.17555555555555555, 'gamma': 0.019444444444444445, 'assignment': '0'}, {'beta': 0.17555555555555555, 'gamma': 0.021666666666666667, 'assignment': '0'}, {'beta': 0.17555555555555555, 'gamma': 0.02388888888888889, 'assignment': '0'}, {'beta': 0.17555555555555555, 'gamma': 0.026111111111111113, 'assignment': '0'}, {'beta': 0.17555555555555555, 'gamma': 0.028333333333333335, 'assignment': '1'}, {'beta': 0.17555555555555555, 'gamma': 0.030555555555555558, 'assignment': '1'}, {'beta': 0.17555555555555555, 'gamma': 0.03277777777777778, 'assignment': '0'}, {'beta': 0.17555555555555555, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.18666666666666665, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.18666666666666665, 'gamma': 0.017222222222222222, 'assignment': '0'}, {'beta': 0.18666666666666665, 'gamma': 0.019444444444444445, 'assignment': '0'}, {'beta': 0.18666666666666665, 'gamma': 0.021666666666666667, 'assignment': '0'}, {'beta': 0.18666666666666665, 'gamma': 0.02388888888888889, 'assignment': '0'}, {'beta': 0.18666666666666665, 'gamma': 0.026111111111111113, 'assignment': '0'}, {'beta': 0.18666666666666665, 'gamma': 0.028333333333333335, 'assignment': '0'}, {'beta': 0.18666666666666665, 'gamma': 0.030555555555555558, 'assignment': '1'}, {'beta': 0.18666666666666665, 'gamma': 0.03277777777777778, 'assignment': '0'}, {'beta': 0.18666666666666665, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.19777777777777777, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.19777777777777777, 'gamma': 0.017222222222222222, 'assignment': '0'}, {'beta': 0.19777777777777777, 'gamma': 0.019444444444444445, 'assignment': '0'}, {'beta': 0.19777777777777777, 'gamma': 0.021666666666666667, 'assignment': '0'}, {'beta': 0.19777777777777777, 'gamma': 0.02388888888888889, 'assignment': '0'}, {'beta': 0.19777777777777777, 'gamma': 0.026111111111111113, 'assignment': '0'}, {'beta': 0.19777777777777777, 'gamma': 0.028333333333333335, 'assignment': '0'}, {'beta': 0.19777777777777777, 'gamma': 0.030555555555555558, 'assignment': '0'}, {'beta': 0.19777777777777777, 'gamma': 0.03277777777777778, 'assignment': '0'}, {'beta': 0.19777777777777777, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.2088888888888889, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.2088888888888889, 'gamma': 0.017222222222222222, 'assignment': '0'}, {'beta': 0.2088888888888889, 'gamma': 0.019444444444444445, 'assignment': '0'}, {'beta': 0.2088888888888889, 'gamma': 0.021666666666666667, 'assignment': '0'}, {'beta': 0.2088888888888889, 'gamma': 0.02388888888888889, 'assignment': '0'}, {'beta': 0.2088888888888889, 'gamma': 0.026111111111111113, 'assignment': '0'}, {'beta': 0.2088888888888889, 'gamma': 0.028333333333333335, 'assignment': '0'}, {'beta': 0.2088888888888889, 'gamma': 0.030555555555555558, 'assignment': '0'}, {'beta': 0.2088888888888889, 'gamma': 0.03277777777777778, 'assignment': '0'}, {'beta': 0.2088888888888889, 'gamma': 0.035, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.015, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.017222222222222222, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.019444444444444445, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.021666666666666667, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.02388888888888889, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.026111111111111113, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.028333333333333335, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.030555555555555558, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.03277777777777778, 'assignment': '0'}, {'beta': 0.22, 'gamma': 0.035, 'assignment': '0'}]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# USER: set num_dim_points with how many points for each parameter you'd like to evaluate\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.12,0.22]\n",
+    "gamma_search_bounds = [0.015,0.035]\n",
+    "\n",
+    "param_choices_true_false = ps(\"all\", {\"beta\": beta_search_bounds, \"gamma\": gamma_search_bounds}, rtol=1e-3, num_dim_points=10)\n",
+    "print(param_choices_true_false)\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sir_query_auto_2_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)\n",
+    "\n",
+    "\n",
+    "# Plot \"true/false\" points.  Is there any way of characterizing these regions?\n",
+    "sir_model.plot_two_params(\"gamma\", \"beta\", param_choices_true_false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10, 0.936144057777959)\n",
+      "{'beta': 0.20408163265306123, 'gamma': 0.02836734693877551, 'assignment': '1'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "def query_2(sim, tstart, tend, dt):\n",
+    "    S, I, R = sim.T\n",
+    "    # USER: write query condition.\n",
+    "    # query_condition =  0.15 <= max(I) <= 0.3  #((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "    # query_condition = (0.45 <= max(I)) and (max(I) <= 0.55) and (45 <= np.argmax(I)*dt) and (np.argmax(I)*dt <= 55)\n",
+    "    query_values = [0.4 <= I[int(t/dt)] <= 0.6 for t in range(tstart, tend+1, dt) if 45 <= t < 55]\n",
+    "    query_condition = all(query_values) \n",
+    "    # print(I[9:11])\n",
+    "    return query_condition\n",
+    "\n",
+    "\n",
+    "my_params = [p for p in param_choices_true_false if p['assignment']==\"1\"][-1]\n",
+    "param_assignment, sim = eval_point(my_params[\"beta\"], my_params[\"gamma\"], plot=True, mxstep=20, query_condition=query_1)\n",
+    "# param_assignment, sim = eval_point(0.25, 0.027755102040816326, plot=True, rtol=1e-10)\n",
+    "S, I, R = sim.T\n",
+    "\n",
+    "print(list(enumerate(S))[10])\n",
+    "# print([S[i]-S[i-1] for i in range(1, len(S))])\n",
+    "print(my_params)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "funman_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scratch/notebooks/sir-query-positional.ipynb b/scratch/notebooks/sir-query-positional.ipynb
new file mode 100644
index 00000000..e51ec48b
--- /dev/null
+++ b/scratch/notebooks/sir-query-positional.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "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": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize recording of parameter choices and true/false\n",
+    "\n",
+    "param_choices_true_false = []\n",
+    "\n",
+    "# USER: set bounds\n",
+    "beta_search_bounds = [0.08,0.1]\n",
+    "gamma_search_bounds = [0.02,0.03]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# USER: list how many points for each parameter you'd like to synthesize\n",
+    "\n",
+    "beta_values_to_synthesize = 50\n",
+    "gamma_values_to_synthesize = 50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_points_beta = np.linspace(beta_search_bounds[0], beta_search_bounds[1], beta_values_to_synthesize)\n",
+    "search_points_gamma = np.linspace(gamma_search_bounds[0], gamma_search_bounds[1], gamma_values_to_synthesize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set parameters\n",
+    "\n",
+    "for i in range(len(search_points_beta)):\n",
+    "    beta_val = search_points_beta[i]\n",
+    "    for j in range(len(search_points_gamma)):\n",
+    "        gamma_val = search_points_gamma[j]\n",
+    "\n",
+    "        # parameters\n",
+    "        def beta(t): return np.piecewise(t, [t>=0], [beta_val])\n",
+    "        def gamma(t): return np.piecewise(t, [t>=0], [gamma_val])\n",
+    "\n",
+    "\n",
+    "        # USER: set initial conditions\n",
+    "        I0, R0 = 0.01, 0\n",
+    "        S0 = 1-I0-R0\n",
+    "        y0 = S0, I0, R0 # Initial conditions vector\n",
+    "        # USER: set simulation parameters\n",
+    "        dt = 5\n",
+    "        tstart = 0\n",
+    "        tend = 100\n",
+    "        tvect = np.arange(tstart, tend, dt)\n",
+    "        # simulate/solve ODEs\n",
+    "        sim = odeint(sir_model.SIR_model, y0, tvect, args=(beta, gamma))\n",
+    "        S, I, R = sim.T\n",
+    "\n",
+    "        # plot results - uncomment next line to plot time series.  not recommended for large numbers of points\n",
+    "        # sir_model.plotSIR(tvect, S, I, R)\n",
+    "        # USER: write query condition.\n",
+    "        query_condition = ((0.15 <= I[10]) and I[10] <= 1.0)\n",
+    "        query = '1' if query_condition else '0'\n",
+    "        \n",
+    "        # log parameters and sat (1)/unsat (0)\n",
+    "\n",
+    "        param_assignments = {'beta': beta_val, 'gamma': gamma_val, 'assignment': query} # for \"all\", go through every option. for \"any\", only need one good parameter choice.\n",
+    "        param_choices_true_false.append(param_assignments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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0.09959183673469388, 'gamma': 0.02959183673469388, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02979591836734694, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.03, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02020408163265306, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.020408163265306124, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.020612244897959184, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.020816326530612245, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.021020408163265305, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02122448979591837, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02142857142857143, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02163265306122449, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.021836734693877553, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.022040816326530613, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.022244897959183673, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.022448979591836733, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.022653061224489797, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.022857142857142857, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.023061224489795917, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02326530612244898, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02346938775510204, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.0236734693877551, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.023877551020408165, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.024081632653061225, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.024285714285714285, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.024489795918367346, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02469387755102041, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02489795918367347, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02510204081632653, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02530612244897959, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.025510204081632654, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.025714285714285714, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.025918367346938774, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.026122448979591838, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.026326530612244898, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.026530612244897958, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.026734693877551022, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.026938775510204082, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.027142857142857142, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.027346938775510206, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.027551020408163266, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.027755102040816326, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02795918367346939, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.028163265306122447, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02836734693877551, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02857142857142857, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02877551020408163, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.028979591836734694, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.029183673469387755, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.029387755102040815, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02959183673469388, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02979591836734694, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.03, 'assignment': '1'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(param_choices_true_false)\n",
+    "\n",
+    "id_number = randint(10**5, 10**6 - 1)\n",
+    "\n",
+    "with open(f'sir_query_auto_2_param_{id_number}.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(param_choices_true_false, f, ensure_ascii=False, indent=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot \"true/false\" points.  Is there any way of characterizing these regions?\n",
+    "\n",
+    "\n",
+    "xsample = [result['gamma'] for result in param_choices_true_false]\n",
+    "ysample = [result['beta'] for result in param_choices_true_false]\n",
+    "colors = ['green' if result['assignment'] == '1' else 'red' for result in param_choices_true_false]\n",
+    "plt.title(\"Parameter Value Pairings\")\n",
+    "plt.xlabel(\"gamma\")\n",
+    "plt.ylabel(\"beta\")\n",
+    "\n",
+    "area = 5  # size of marker\n",
+    "\n",
+    "plt.scatter(xsample, ysample, s=area, c=colors, alpha=0.5)\n",
+    "\n",
+    "# 1d - change color coding\n",
+    "# val = 0. # this is the value where you want the data to appear on the y-axis.\n",
+    "# plt.plot(xsample, np.zeros_like(xsample) + val, 'x', c='green')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'beta': 0.08, 'gamma': 0.02, 'assignment': '1'}, {'beta': 0.08, 'gamma': 0.02020408163265306, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.020408163265306124, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.020612244897959184, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.020816326530612245, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.021020408163265305, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02122448979591837, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02142857142857143, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02163265306122449, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.021836734693877553, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.022040816326530613, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.022244897959183673, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.022448979591836733, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.022653061224489797, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.022857142857142857, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.023061224489795917, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02326530612244898, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02346938775510204, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.0236734693877551, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.023877551020408165, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.024081632653061225, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.024285714285714285, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.024489795918367346, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02469387755102041, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02489795918367347, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02510204081632653, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02530612244897959, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.025510204081632654, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.025714285714285714, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.025918367346938774, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.026122448979591838, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.026326530612244898, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.026530612244897958, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.026734693877551022, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.026938775510204082, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.027142857142857142, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.027346938775510206, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.027551020408163266, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.027755102040816326, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02795918367346939, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.028163265306122447, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02836734693877551, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02857142857142857, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.02877551020408163, 'assignment': '0'}, {'beta': 0.08, 'gamma': 0.028979591836734694, 'assignment': '0'}, {'beta': 0.08, 'gamma': 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0.02959183673469388, 'assignment': '1'}, {'beta': 0.09918367346938776, 'gamma': 0.02979591836734694, 'assignment': '1'}, {'beta': 0.09918367346938776, 'gamma': 0.03, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02020408163265306, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.020408163265306124, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.020612244897959184, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.020816326530612245, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.021020408163265305, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02122448979591837, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02142857142857143, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02163265306122449, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.021836734693877553, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.022040816326530613, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.022244897959183673, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.022448979591836733, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.022653061224489797, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.022857142857142857, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.023061224489795917, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02326530612244898, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02346938775510204, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.0236734693877551, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.023877551020408165, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.024081632653061225, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.024285714285714285, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.024489795918367346, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02469387755102041, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02489795918367347, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02510204081632653, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02530612244897959, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.025510204081632654, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.025714285714285714, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.025918367346938774, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.026122448979591838, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.026326530612244898, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.026530612244897958, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.026734693877551022, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.026938775510204082, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.027142857142857142, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.027346938775510206, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.027551020408163266, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.027755102040816326, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02795918367346939, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.028163265306122447, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02836734693877551, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02857142857142857, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02877551020408163, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.028979591836734694, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.029183673469387755, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.029387755102040815, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02959183673469388, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.02979591836734694, 'assignment': '1'}, {'beta': 0.09959183673469388, 'gamma': 0.03, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02020408163265306, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.020408163265306124, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.020612244897959184, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.020816326530612245, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.021020408163265305, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02122448979591837, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02142857142857143, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02163265306122449, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.021836734693877553, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.022040816326530613, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.022244897959183673, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.022448979591836733, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.022653061224489797, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.022857142857142857, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.023061224489795917, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02326530612244898, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02346938775510204, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.0236734693877551, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.023877551020408165, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.024081632653061225, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.024285714285714285, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.024489795918367346, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02469387755102041, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02489795918367347, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02510204081632653, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02530612244897959, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.025510204081632654, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.025714285714285714, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.025918367346938774, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.026122448979591838, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.026326530612244898, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.026530612244897958, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.026734693877551022, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.026938775510204082, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.027142857142857142, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.027346938775510206, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.027551020408163266, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.027755102040816326, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02795918367346939, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.028163265306122447, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02836734693877551, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02857142857142857, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02877551020408163, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.028979591836734694, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.029183673469387755, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.029387755102040815, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02959183673469388, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.02979591836734694, 'assignment': '1'}, {'beta': 0.1, 'gamma': 0.03, 'assignment': '1'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(param_choices_true_false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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
+}
diff --git a/scratch/notebooks/sir_model.py b/scratch/notebooks/sir_model.py
new file mode 100644
index 00000000..11770710
--- /dev/null
+++ b/scratch/notebooks/sir_model.py
@@ -0,0 +1,170 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy
+from scipy.integrate import odeint
+
+
+# simulate SIR model - normalized (populations S,I,R add to 1)
+def SIR_model(y, t, beta, gamma):
+    S, I, R = y
+    dSdt = -S * (beta(t) * I)
+    dIdt = S * (beta(t) * I) - gamma(t) * I
+    dRdt = gamma(t) * I
+
+    return dSdt, dIdt, dRdt
+
+
+#  plot SIR model
+def plotSIR(t, S, I, R):
+    f, ax = plt.subplots(1, 1, figsize=(10, 4))
+    ax.plot(t, S, "b", alpha=0.7, linewidth=2, label="Susceptible")
+    ax.plot(t, I, "r", alpha=0.7, linewidth=2, label="Infected")
+    ax.plot(t, R, "r--", alpha=0.7, linewidth=2, label="Recovered")
+
+    ax.set_xlabel("Time (days)")
+    ax.set_ylabel("Fraction of population")
+
+    ax.yaxis.set_tick_params(length=0)
+    ax.xaxis.set_tick_params(length=0)
+    legend = ax.legend()
+    legend.get_frame().set_alpha(0.5)
+    plt.show()
+
+
+# simulate SIDARTHE model - normalized (populations S,I,R add to 1)
+def SIDARTHE_model(
+    y,
+    t,
+    alpha,
+    beta,
+    gamma,
+    delta,
+    epsilon,
+    mu,
+    zeta,
+    lamb,
+    eta,
+    rho,
+    theta,
+    kappa,
+    nu,
+    xi,
+    sigma,
+    tau,
+):
+    S, I, D, A, R, T, H, E = y
+    dSdt = -S * (alpha(t) * I + beta(t) * D + gamma(t) * A + delta(t) * R)
+    dIdt = (
+        S * (alpha(t) * I + beta(t) * D + gamma(t) * A + delta(t) * R)
+        - (epsilon(t) + zeta(t) + lamb(t)) * I
+    )
+    dDdt = epsilon(t) * I - (eta(t) + rho(t)) * D
+    dAdt = zeta(t) * I - (theta(t) + mu(t) + kappa(t)) * A
+    dRdt = eta(t) * D + theta(t) * A - (nu(t) + xi(t)) * R
+    dTdt = mu(t) * A + nu(t) * R - (sigma(t) + tau(t)) * T
+    dHdt = lamb(t) * I + rho(t) * D + kappa(t) * A + xi(t) * R + sigma(t) * T
+    dEdt = tau(t) * T
+
+    return dSdt, dIdt, dDdt, dAdt, dRdt, dTdt, dHdt, dEdt
+
+
+#  plot SIDARTHE model
+def plotSIDARTHE(t, S, I, D, A, R, T, H, E):
+    f, ax = plt.subplots(1, 1, figsize=(10, 4))
+    ax.plot(t, S, "b", alpha=0.7, linewidth=2, label="Susceptible")
+    ax.plot(
+        t,
+        I,
+        "r",
+        alpha=0.7,
+        linewidth=2,
+        label="Infected (Asymptomatic, Infected, Undetected)",
+    )
+    ax.plot(
+        t,
+        D,
+        "r.",
+        alpha=0.7,
+        linewidth=2,
+        label="Diagnosed (Asymptomatic, Infected, Detected)",
+    )
+    ax.plot(
+        t,
+        A,
+        "r:",
+        alpha=0.7,
+        linewidth=2,
+        label="Ailing (Symptomatic, Infected, Undetected)",
+    )
+    ax.plot(
+        t,
+        R,
+        "r--",
+        alpha=0.7,
+        linewidth=2,
+        label="Recognized (Symptomatic, Infected, Detected)",
+    )
+    ax.plot(
+        t,
+        T,
+        "r-.",
+        alpha=0.7,
+        linewidth=2,
+        label="Threatened (Acutely Symptomatic)",
+    )
+    ax.plot(t, H, "g", alpha=0.7, linewidth=2, label="Healed")
+    ax.plot(t, E, "k", alpha=0.7, linewidth=2, label="Extinct (Dead)")
+
+    ax.set_xlabel("Time (days)")
+    ax.set_ylabel("Fraction of population")
+
+    ax.yaxis.set_tick_params(length=0)
+    ax.xaxis.set_tick_params(length=0)
+    legend = ax.legend()
+    legend.get_frame().set_alpha(0.5)
+    plt.show()
+
+
+def SIHDR_model(y, t, beta, ae, bf, cg, dh, N):
+    S, I, H, D, R = y
+    dSdt = -(beta(t) * S * I / N(t))
+    dIdt = (beta(t) * S * I / N(t)) - ae(t) * I - bf(t) * I
+    dHdt = bf(t) * I - cg(t) * H - dh(t) * H
+    dDdt = dh(t) * H
+    dRdt = ae(t) * I + cg(t) * H
+
+    return dSdt, dIdt, dHdt, dDdt, dRdt
+
+
+#  plot SIHDR model
+def plotSIHDR(t, S, I, H, D, R):
+    f, ax = plt.subplots(1, 1, figsize=(10, 4))
+    ax.plot(t, S, "b", alpha=0.7, linewidth=2, label="Susceptible")
+    ax.plot(t, I, "r", alpha=0.7, linewidth=2, label="Infected")
+    ax.plot(t, H, "g", alpha=0.7, linewidth=2, label="Hospitalized")
+    ax.plot(t, D, "r.", alpha=0.7, linewidth=2, label="Dead")
+    ax.plot(t, R, "r--", alpha=0.7, linewidth=2, label="Recovered")
+
+    ax.set_xlabel("Time (days)")
+    ax.set_ylabel("Fraction of population")
+
+    ax.yaxis.set_tick_params(length=0)
+    ax.xaxis.set_tick_params(length=0)
+    legend = ax.legend()
+    legend.get_frame().set_alpha(0.5)
+    plt.show()
+
+
+def plot_two_params(param1, param2, param_choices_true_false):
+    xsample = [result[param1] for result in param_choices_true_false]
+    ysample = [result[param2] for result in param_choices_true_false]
+    colors = [
+        "green" if result["assignment"] == "1" else "red"
+        for result in param_choices_true_false
+    ]
+    plt.title("Parameter Value Pairings")
+    plt.xlabel(param1)
+    plt.ylabel(param2)
+    area = 5  # size of marker
+    plt.scatter(xsample, ysample, s=area, c=colors, alpha=0.5)
+    plt.show()
diff --git a/scratch/test/test_pde_as_petrinet.py b/scratch/test/test_pde_as_petrinet.py
index ce0fd6f0..bc810504 100644
--- a/scratch/test/test_pde_as_petrinet.py
+++ b/scratch/test/test_pde_as_petrinet.py
@@ -137,7 +137,7 @@ def test_advection(self):
                 "label_marker": {"true": ",", "false": ","},
                 "xlabel": "Time",
                 "ylabel": "Height",
-                "legend": variables
+                "legend": variables,
                 # ,"label_color":{"true": "g", "false":"r"}
             },
             num_points=None,
diff --git a/setup.py b/setup.py
index 9d8f3560..ffb7bb73 100644
--- a/setup.py
+++ b/setup.py
@@ -30,12 +30,13 @@
         "pysmt @ git+https://github.com/danbryce/pysmt@add-abs-value#egg=pysmt-0.9.6.dev1",
         "pandas",
         "matplotlib",
-        "pydantic>=2"
+        "pydantic>=2",
+        "sympy",
         # "automates @ https://github.com/danbryce/automates/archive/e5fb635757aa57007615a75371f55dd4a24851e0.zip#sha1=f9b3c8a7d7fa28864952ccdd3293d02894614e3f"
     ],
     # dependency_links=["git+https://github.com/danbryce/pysmt/pysmt@add-abs-value#egg=pysmt-0.9.6.dev1"],
     extras_require={"dreal": ["funman_dreal"]},
-    tests_require=["unittest"],
+    tests_require=["unittest", "pytest-cov", "scipy"],
     zip_safe=False,
     entry_points={
         "console_scripts": [
diff --git a/src/funman/__init__.py b/src/funman/__init__.py
index e7ed885c..42c8e5c1 100644
--- a/src/funman/__init__.py
+++ b/src/funman/__init__.py
@@ -7,6 +7,7 @@
 
 - Consistency: Check that a parameterized model is self-consistent.
 """
+
 from .utils import *
 
 setup_logging()
diff --git a/src/funman/_version.py b/src/funman/_version.py
index 1388158e..3bca77a1 100644
--- a/src/funman/_version.py
+++ b/src/funman/_version.py
@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "1.8.0"
+__version__ = "1.9.0"
diff --git a/src/funman/api/__init__.py b/src/funman/api/__init__.py
index 7397d24c..fe649ab9 100644
--- a/src/funman/api/__init__.py
+++ b/src/funman/api/__init__.py
@@ -1,5 +1,6 @@
 """
 This module contains class definitions used to implement the FUNMAN API
 """
+
 # from .api import *
 from .run import *
diff --git a/src/funman/api/api.py b/src/funman/api/api.py
index 00c18ef9..746cec27 100644
--- a/src/funman/api/api.py
+++ b/src/funman/api/api.py
@@ -6,13 +6,16 @@
 HTTPException
     HTTPException description
 """
-import sys
+
+import logging
 import traceback
 import uuid
 from contextlib import asynccontextmanager, contextmanager
 from typing import Optional, Union
 
 import uvicorn
+
+l = logging.getLogger(__name__)
 from fastapi import (
     APIRouter,
     Depends,
@@ -155,7 +158,7 @@ def internal_error_handler():
     except HTTPException:
         raise
     except Exception:
-        print(f"Internal Server Error ({eid}):", file=sys.stderr)
+        l.error(f"Internal Server Error ({eid}):")
         traceback.print_exc()
         raise HTTPException(
             status_code=500, detail=f"Internal Server Error: {eid}"
@@ -199,8 +202,8 @@ async def get_queries(
     with internal_error_handler():
         try:
             return worker.get_results(query_id)
-        except NotFoundFunmanException:
-            raise HTTPException(404)
+        except NotFoundFunmanException as e:
+            raise HTTPException(404, detail=str(e))
 
 
 @api_router.post(
diff --git a/src/funman/api/client.py b/src/funman/api/client.py
index 50dc7733..8e24aaa3 100644
--- a/src/funman/api/client.py
+++ b/src/funman/api/client.py
@@ -1,6 +1,7 @@
 """
 Client API Generation and usage functionality.
 """
+
 import json
 import os
 import re
diff --git a/src/funman/api/run.py b/src/funman/api/run.py
index 44344f0d..559de4f7 100644
--- a/src/funman/api/run.py
+++ b/src/funman/api/run.py
@@ -158,6 +158,8 @@ def run(
         parameters_to_plot: Optional[List[str]] = None,
         point_plot_config: Dict = {},
         num_points: Optional[int] = None,
+        dump_results: bool = True,
+        print_last_time: bool = False,
     ) -> FunmanResults:
         """
         Run a FUNMAN scenario.
@@ -180,6 +182,8 @@ def run(
             Matplotlib flags and special key "variables" to select variables to plot , by default {}
         num_points : Optional[int], optional
             The number of points to plot in the trace plot, by default None
+        print_last_time: bool
+            Only print parameter space for last time
 
         Returns
         -------
@@ -194,6 +198,8 @@ def run(
             parameters_to_plot=parameters_to_plot,
             point_plot_config=point_plot_config,
             num_points=num_points,
+            dump_results=dump_results,
+            print_last_time=print_last_time,
         )
         return results
         # ParameterSpacePlotter(
@@ -211,6 +217,8 @@ def run_test_case(
         parameters_to_plot=None,
         point_plot_config={},
         num_points=None,
+        dump_results=True,
+        print_last_time: bool = False,
     ):
         if not os.path.exists(case_out_dir):
             os.mkdir(case_out_dir)
@@ -229,6 +237,8 @@ def run_test_case(
             parameters_to_plot=parameters_to_plot,
             point_plot_config=point_plot_config,
             num_points=num_points,
+            dump_results=dump_results,
+            print_last_time=print_last_time,
         )
 
         self._worker.stop()
@@ -236,40 +246,84 @@ def run_test_case(
 
         return results
 
-    def get_model(self, model_file: str):
+    def get_model(
+        self, model_file: Union[str, Dict]
+    ) -> Tuple[FunmanModel, Optional[FunmanWorkRequest]]:
+        m = None
+        req = None
         for model in models:
             try:
-                with open(model_file, "r") as mf:
-                    j = json.load(mf)
-                    m = _wrap_with_internal_model(model(**j))
-                return m
+                if isinstance(model_file, str):
+                    with open(model_file, "r") as mf:
+                        j = json.load(mf)
+                else:
+                    j = model_file
+
+                if "model" in j and "request" in j:
+                    req = j["request"]
+                    if "petrinet" in j["model"]:
+                        mod = j["model"]["petrinet"]
+                    else:
+                        mod = j["model"]
+                else:
+                    mod = j
+                    req = None
+                m = _wrap_with_internal_model(model(**mod))
+                break
+
             except Exception as e:
                 pass
-        raise Exception(f"Could not determine the Model type of {model_file}")
+
+        if m is None:
+            raise Exception(
+                f"Could not determine the Model type of {model_file}"
+            )
+
+        r = (
+            (
+                FunmanWorkRequest.model_validate(req)
+                if req is not None
+                else None
+            )
+            if req is not None
+            else None
+        )
+
+        return m, r
 
     def run_instance(
         self,
-        case: Tuple[str, Union[str, Dict], str],
+        case: Tuple[Union[str, Dict], Union[str, Dict], str],
         out_dir=".",
         dump_plot=False,
         parameters_to_plot=None,
         point_plot_config={},
         num_points=None,
+        dump_results=False,
+        print_last_time: bool = False,
     ):
         killer = GracefulKiller()
         (model_file, request_file, description) = case
 
-        model = self.get_model(model_file)
+        model, request = self.get_model(model_file)
 
-        try:
-            with open(request_file, "r") as rf:
-                request = FunmanWorkRequest(**json.load(rf))
-        except TypeError as te:
-            # request_file may not be a path, could be a dict
+        assert request is None or (
+            request_file is None or request_file == ""
+        ), f"Ambiguous Requests specified, both in model file and as a requests file"
+
+        if request is None and request_file is not None and request_file != "":
             try:
-                request = FunmanWorkRequest.model_validate(request_file)
-            except Exception as e:
-                raise e
+                with open(request_file, "r") as rf:
+                    request = FunmanWorkRequest(**json.load(rf))
+            except TypeError as te:
+                # request_file may not be a path, could be a dict
+                try:
+                    request = FunmanWorkRequest.model_validate(request_file)
+                except Exception as e:
+                    raise e
+
+        if request is None:
+            request = {}
 
         work_unit: FunmanWorkUnit = self._worker.enqueue_work(
             model=model, request=request
@@ -279,7 +333,7 @@ def run_instance(
         outfile = f"{out_dir}/{work_unit.id}.json"
         plotted = False
         while not killer.kill_now:
-            if self._worker.is_processing_id(work_unit.id):
+            if dump_results and self._worker.is_processing_id(work_unit.id):
                 l.info(f"Dumping results to {outfile}")
                 results = self._worker.get_results(work_unit.id)
                 with open(outfile, "w") as f:
@@ -295,9 +349,11 @@ def run_instance(
                         num_points,
                         point_plot_config,
                         parameters_to_plot,
+                        print_last_time,
                     )
+
                 sleep(10)
-            else:
+            elif not self._worker.is_processing_id(work_unit.id):
                 results = self._worker.get_results(work_unit.id)
                 break
 
@@ -309,6 +365,7 @@ def run_instance(
                 num_points,
                 point_plot_config,
                 parameters_to_plot,
+                print_last_time,
             )
 
         if killer.kill_now:
@@ -334,6 +391,7 @@ def create_plots(
         num_points,
         point_plot_config,
         parameters_to_plot,
+        print_last_time,
     ):
         points = results.parameter_space.points()
         if len(points) > 0:
@@ -362,26 +420,34 @@ def create_plots(
             plt.savefig(point_plot_filename)
             plt.close()
 
-        boxes = results.parameter_space.boxes()
+        boxes = (
+            results.parameter_space.boxes()
+            if not print_last_time
+            else results.parameter_space.last_boxes()
+        )
         if parameters_to_plot is None:
-            parameters_to_plot = results.model._parameter_names() + [
-                "timestep"
-            ]
-        assert (
-            len(parameters_to_plot) > 1
-        ), "Cannot plot a parameter space for one parameter"
-        if len(boxes) > 0 and len(parameters_to_plot) > 1:
+            parameters_to_plot = results.model._parameter_names()
+            if not print_last_time:
+                parameters_to_plot += ["timestep"]
+
+        if len(boxes) > 0 and len(parameters_to_plot) > 0:
             space_plot_filename = (
                 f"{out_dir}/{work_unit.id}_parameter_space.png"
             )
             l.info(f"Creating plot of parameter space: {space_plot_filename}")
             ParameterSpacePlotter(
                 results.parameter_space,
+                boxes=boxes,
                 plot_points=False,
                 parameters=parameters_to_plot,
+                synthesized_parameters=parameters_to_plot,
             ).plot(show=True)
             plt.savefig(space_plot_filename)
-        plt.close()
+            plt.close()
+        else:
+            l.warn(
+                "Cannot plot a parameter space for zero boxes or zero parameters"
+            )
 
 
 def get_args():
@@ -392,9 +458,7 @@ def get_args():
         help=f"model json file",
     )
     parser.add_argument(
-        "request",
-        type=str,
-        help=f"request json file",
+        "request", type=str, help=f"request json file", default=None, nargs="?"
     )
     parser.add_argument(
         "-o",
@@ -406,10 +470,18 @@ def get_args():
     parser.add_argument(
         "-p",
         "--plot",
+        # action="store_true",
+        nargs="*",
+        help=f"Write plots in outdir. Optionally list parameters to plot",
+    )
+    parser.add_argument(
+        "-l",
+        "--last-time",
         action="store_true",
         default=False,
-        help=f"Write plots in outdir.",
+        help=f"Create parameter space plot with only the last timestep.",
     )
+
     parser.set_defaults(plot=False)
     return parser.parse_args()
 
@@ -419,8 +491,18 @@ def main() -> int:
     if not os.path.exists(args.outdir):
         os.mkdir(args.outdir)
 
+    to_plot = (
+        args.plot + (["timestep"] if not args.last_time else [])
+        if args.plot
+        else None
+    )
     results = Runner().run(
-        args.model, args.request, case_out_dir=args.outdir, dump_plot=args.plot
+        args.model,
+        args.request,
+        case_out_dir=args.outdir,
+        dump_plot=args.plot is not None,
+        parameters_to_plot=to_plot,
+        print_last_time=args.last_time,
     )
     print(results.model_dump_json(indent=4))
 
diff --git a/src/funman/api/server.py b/src/funman/api/server.py
index 7335acfc..a6009698 100644
--- a/src/funman/api/server.py
+++ b/src/funman/api/server.py
@@ -1,6 +1,7 @@
 """
 Server for API 
 """
+
 import contextlib
 import threading
 import time
diff --git a/src/funman/config.py b/src/funman/config.py
index 3189f5c6..3a30b3f9 100644
--- a/src/funman/config.py
+++ b/src/funman/config.py
@@ -2,9 +2,10 @@
 This module defines the Funman class, the primary entry point for FUNMAN
 analysis.
 """
+
 import logging
 import os
-from typing import Optional, Union
+from typing import List, Optional, Union
 
 from pydantic import BaseModel, ConfigDict, field_validator, model_validator
 
@@ -33,59 +34,99 @@ class FUNMANConfig(BaseModel):
 
     queue_timeout: int = 1
     """Multiprocessing queue timeout, used by BoxSearch"""
+
     number_of_processes: int = 1  # mp.cpu_count()
     """Number of BoxSearch processes"""
+
     _handler: Union[
         ResultCombinedHandler, NoopResultHandler, ResultHandler
     ] = NoopResultHandler()
     wait_timeout: Optional[int] = None
     """Timeout for BoxSearch procesess to wait for boxes to evaluate"""
+
     _wait_action: WaitAction = None
     wait_action_timeout: float = 0.05
     """Time to sleep proceses waiting for work"""
+
     _read_cache: ResultHandler = None
-    # episode_type: =None,
+
     _search: str = None
     """Name of search algorithm to use"""
+
     solver: str = "dreal"  # "z3"
     """Name of pysmt solver to use"""
+
     num_steps: int = 2
     """Number of timesteps to encode"""
+
     step_size: int = 1
     """Step size for encoding"""
+
     num_initial_boxes: int = 1
     """Number of initial boxes for BoxSearch"""
+
+    solver_timeout: Optional[int] = None
+    """Number of seconds to allow each call to SMT solver"""
+
     initial_state_tolerance: float = 0.0
     """Factor used to relax initial state values bounds"""
+
     save_smtlib: Optional[str] = None
     """Whether to save each smt invocation as an SMTLib file"""
+
     dreal_precision: float = 1e-1
     """Precision delta for dreal solver"""
+
     dreal_log_level: str = "off"
     """Constraint noise term to relax constraints"""
+
     constraint_noise: float = 0.0
     """Use MCTS in dreal"""
+
     dreal_mcts: bool = True
     """Substitute subformulas to simplify overall encoding"""
+
     substitute_subformulas: bool = False
     """Enforce compartmental variable constraints"""
+
     normalization_constant: Optional[float] = None
     """ Simplify query by propagating substutions """
+
     use_compartmental_constraints: bool = False
     """Normalize scenarios prior to solving"""
-    normalize: bool = False
+
+    compartmental_constraint_noise: float = 0.01
+    """Additional factor used to relax compartmental constraint (needed due to floating point imprecision)"""
+
+    normalize: bool = True
     """Normalization constant to use for normalization (attempt to compute if None)"""
+
     simplify_query: bool = False
-    """ Series approximation threshold for dropping series terms """
+    """ Convert query object to a single formula based only on the parameters (deprecated) """
+
     series_approximation_threshold: Optional[float] = None
-    """ Generate profiling output"""
+    """ Series approximation threshold for dropping series terms """
+
     profile: bool = False
-    """ Use Taylor series of given order to approximate transition function, if None, then do not compute series """
+    """ Generate profiling output"""
+
     taylor_series_order: Optional[int] = None
-    """ Compute Corner points of each box """
+    """ Use Taylor series of given order to approximate transition function, if None, then do not compute series """
+
     corner_points: bool = False
-    """ Verbosity (INFO, DEBUG, WARN, ERROR)"""
+    """ Compute Corner points of each box """
+
     verbosity: int = logging.INFO
+    """ Verbosity (INFO, DEBUG, WARN, ERROR)"""
+
+    use_transition_symbols: bool = False
+    """ Use transition symbols in encoding transition functions """
+
+    uniform_box_splits: bool = False
+    """ Uniformly split boxes in box search, instead of separating points in boxes """
+
+    dreal_prefer_parameters: List[str] = []
+    """ Prefer to split the listed parameters in dreal """
 
     @field_validator("solver")
     @classmethod
diff --git a/src/funman/constants.py b/src/funman/constants.py
index 8165198b..2c7ddcc7 100644
--- a/src/funman/constants.py
+++ b/src/funman/constants.py
@@ -1,6 +1,7 @@
 """
 This module contains defintions of constants used within FUNMAN
 """
+
 import sys
 from typing import Literal, Union
 
diff --git a/src/funman/funman.py b/src/funman/funman.py
index d8d409d4..2c87757a 100644
--- a/src/funman/funman.py
+++ b/src/funman/funman.py
@@ -2,6 +2,7 @@
 This module defines the Funman class, the primary entry point for FUNMAN
 analysis.
 """
+
 import logging
 import threading
 from typing import Callable, Optional
@@ -63,11 +64,15 @@ def solve(
                 import cProfile
 
                 with cProfile.Profile() as pr:
-                    result = problem.solve(
-                        config,
-                        haltEvent=haltEvent,
-                        resultsCallback=resultsCallback,
-                    )
+                    try:
+                        result = problem.solve(
+                            config,
+                            haltEvent=haltEvent,
+                            resultsCallback=resultsCallback,
+                        )
+                    except Exception as e:
+                        pr.dump_stats("profile.stats")
+                        return None
                     pr.dump_stats("profile.stats")
             else:
                 result = problem.solve(
@@ -77,4 +82,10 @@ def solve(
                 )
             return result
         except Exception as e:
-            l.exception(f"funman.solve() exiting due to exception: {e}")
+            try:
+                l.error(
+                    f"funman.solve() exiting due to exception: {e.backtrace()}"
+                )
+            except:
+                l.error(f"funman.solve() exiting due to exception: {e}")
+            raise e
diff --git a/src/funman/model/__init__.py b/src/funman/model/__init__.py
index dc4fab9d..591884de 100644
--- a/src/funman/model/__init__.py
+++ b/src/funman/model/__init__.py
@@ -14,11 +14,13 @@
 
 * simulator: classes to wrap simulators as models
 """
+
 from .model import *
 from .query import *
 from .bilayer import *
 from .encoded import *
-from .gromet import *
+
+# from .gromet import *
 from .simulator import *
 from .decapode import *
 from .petrinet import *
diff --git a/src/funman/model/bilayer.py b/src/funman/model/bilayer.py
index e86e8984..2ff76247 100644
--- a/src/funman/model/bilayer.py
+++ b/src/funman/model/bilayer.py
@@ -130,12 +130,12 @@ class BilayerGraph(ABC, BaseModel):
     model_config = ConfigDict()
 
     json_graph: Dict
-    _node_incoming_edges: Dict[
-        BilayerNode, Dict[BilayerNode, BilayerEdge]
-    ] = {}
-    _node_outgoing_edges: Dict[
-        BilayerNode, Dict[BilayerNode, BilayerEdge]
-    ] = {}
+    _node_incoming_edges: Dict[BilayerNode, Dict[BilayerNode, BilayerEdge]] = (
+        {}
+    )
+    _node_outgoing_edges: Dict[BilayerNode, Dict[BilayerNode, BilayerEdge]] = (
+        {}
+    )
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -190,15 +190,15 @@ class BilayerDynamics(BilayerGraph):
 
     model_config = ConfigDict()
 
-    _tangent: Dict[
-        int, BilayerStateNode
-    ] = {}  # Output layer variables, defined in Qout
-    _flux: Dict[
-        int, BilayerFluxNode
-    ] = {}  # Functions, defined in Box, one param per flux
-    _state: Dict[
-        int, BilayerStateNode
-    ] = {}  # Input layer variables, defined in Qin
+    _tangent: Dict[int, BilayerStateNode] = (
+        {}
+    )  # Output layer variables, defined in Qout
+    _flux: Dict[int, BilayerFluxNode] = (
+        {}
+    )  # Functions, defined in Box, one param per flux
+    _state: Dict[int, BilayerStateNode] = (
+        {}
+    )  # Input layer variables, defined in Qin
     _input_edges: BilayerEdge = []  # Input to flux, defined in Win
     _output_edges: BilayerEdge = []  # Flux to Output, defined in Wa,Wn
 
@@ -332,9 +332,9 @@ class BilayerMeasurement(BilayerGraph, BaseModel):
     """
 
     state: Dict[int, BilayerStateNode] = {}
-    flux: Dict[
-        int, BilayerFluxNode
-    ] = {}  # Functions, defined in observable, one param per flux
+    flux: Dict[int, BilayerFluxNode] = (
+        {}
+    )  # Functions, defined in observable, one param per flux
     observable: Dict[int, BilayerStateNode] = {}
     input_edges: BilayerEdge = []  # Input to observable, defined in Win
     output_edges: BilayerEdge = []  # Flux to Output, defined in Wa,Wn
diff --git a/src/funman/model/gromet.py b/src/funman/model/gromet.py
index dd754a86..e4ba37d9 100644
--- a/src/funman/model/gromet.py
+++ b/src/funman/model/gromet.py
@@ -1,6 +1,7 @@
 """
 This module represents GrometModel-related classes.
 """
+
 from automates.model_assembly.gromet.model import GrometFNModule
 from automates.program_analysis.JSON2GroMEt.json2gromet import json_to_gromet
 
diff --git a/src/funman/model/model.py b/src/funman/model/model.py
index 761c8e69..8a755d0a 100644
--- a/src/funman/model/model.py
+++ b/src/funman/model/model.py
@@ -1,6 +1,7 @@
 """
 This module represents the abstract base classes for models.
 """
+
 import copy
 import uuid
 from abc import ABC
@@ -103,6 +104,16 @@ def _get_init_value(
             value = Div(value, norm)
         return value
 
+    def _try_float(self, num):
+        """
+        Try to convert a str to a float.
+        """
+        try:
+            n = float(num)
+            return n
+        except Exception:
+            return num
+
     def variables(self, include_next_state=False):
         """
         Get all initial values and parameters.
@@ -129,10 +140,13 @@ def calculate_normalization_constant(
     #         self._normalization_term = Real(self._normalization_constant)
     #     return self._normalization_term
 
-    def compartmental_constraints(self, populuation: int):
+    def compartmental_constraints(self, populuation: int, noise: float):
         return None
 
     def _is_normalized(self, var: str):
+        if var == "N":  # FIXME hack
+            return True
+
         try:
             name, time = var.rsplit("_", 1)
             return name in self._state_var_names()
diff --git a/src/funman/model/petrinet.py b/src/funman/model/petrinet.py
index 15314e07..63ddcf49 100644
--- a/src/funman/model/petrinet.py
+++ b/src/funman/model/petrinet.py
@@ -5,9 +5,10 @@
 from pydantic import BaseModel, ConfigDict
 from pysmt.shortcuts import REAL, Div, Real, Symbol
 
-from funman.utils.sympy_utils import substitute, to_sympy
+from funman.utils.sympy_utils import substitute, sympy_to_pysmt, to_sympy
 
 from ..representation.interval import Interval
+from .generated_models.petrinet import Distribution
 from .generated_models.petrinet import Model as GeneratedPetrinet
 from .generated_models.petrinet import State, Transition
 from .model import FunmanModel
@@ -148,7 +149,7 @@ def calculate_normalization_constant(
             )
 
     def compartmental_constraints(
-        self, population: Union[float, int]
+        self, population: Union[float, int], noise: float
     ) -> List["Constraint"]:
         from funman.representation.constraint import LinearConstraint
 
@@ -157,8 +158,8 @@ def compartmental_constraints(
             LinearConstraint(
                 name="compartmental_constraint_population",
                 additive_bounds={
-                    "lb": population - 1e-5,
-                    "ub": population + 1e-5,
+                    "lb": population - noise,
+                    "ub": population + noise,
                     "closed_upper_bound": True,
                 },
                 variables=vars,
@@ -244,7 +245,11 @@ def _get_init_value(
         if isinstance(value, int):
             value = Real(float(value))
         elif isinstance(value, str):
-            value = Symbol(value, REAL)
+            expr = to_sympy(value, self._symbols())
+            if expr.is_symbol:
+                value = Symbol(value, REAL)
+            else:
+                value = sympy_to_pysmt(expr)
 
         if scenario.normalization_constant and config.normalize:
             value = Div(value, Real(scenario.normalization_constant))
@@ -254,7 +259,7 @@ def _get_init_value(
     def _parameter_lb(self, param_name: str):
         return next(
             (
-                p.distribution.parameters["minimum"]
+                self._try_float(p.distribution.parameters["minimum"])
                 if p.distribution
                 else p.value
             )
@@ -265,7 +270,7 @@ def _parameter_lb(self, param_name: str):
     def _parameter_ub(self, param_name: str):
         return next(
             (
-                p.distribution.parameters["maximum"]
+                self._try_float(p.distribution.parameters["maximum"])
                 if p.distribution
                 else p.value
             )
@@ -303,10 +308,10 @@ def _transition_rate(self, transition, sympify=False):
                 t_rates = [
                     (
                         to_sympy(r.expression, self._symbols())
-                        if not any(
-                            p == r.expression for p in self._parameter_names()
-                        )
-                        else r.expression
+                        # if not any(
+                        #     p == r.expression for p in self._parameter_names()
+                        # )
+                        # else r.expression
                     )
                     for r in self.petrinet.semantics.ode.rates
                     if r.target == transition.id
@@ -363,6 +368,31 @@ def _variable_values(self):
 
         return values
 
+    def contract_parameters(
+        self, parameter_bounds: Dict[str, Interval]
+    ) -> GeneratedPetrinet:
+        contracted_model = self.petrinet.copy(deep=True)
+
+        for param in contracted_model.semantics.ode.parameters:
+            new_bounds = parameter_bounds[param.id]
+            if param.distribution:
+                param.distribution.parameters["minimum"] = max(
+                    new_bounds.lb, param.distribution.parameters["minimum"]
+                )
+                param.distribution.parameters["maximum"] = min(
+                    new_bounds.ub, param.distribution.parameters["maximum"]
+                )
+            else:
+                param.distribution = Distribution(
+                    parameters={
+                        "minimum": new_bounds.lb,
+                        "maximum": new_bounds.ub,
+                    },
+                    type="StandardUniform1",
+                )
+
+        return contracted_model
+
 
 class PetrinetDynamics(BaseModel):
     json_graph: Dict[
diff --git a/src/funman/model/query.py b/src/funman/model/query.py
index 145c4f95..b1c9f7ba 100644
--- a/src/funman/model/query.py
+++ b/src/funman/model/query.py
@@ -1,6 +1,7 @@
 """
 This module defines all Query classes.  Queries are combined with Model objects in Scenarios to determine whether the model satisfies the query.
 """
+
 from typing import List, Optional, Union
 
 from pydantic import BaseModel, ConfigDict
diff --git a/src/funman/model/simulator.py b/src/funman/model/simulator.py
index 7554bd96..bbe1bc76 100644
--- a/src/funman/model/simulator.py
+++ b/src/funman/model/simulator.py
@@ -2,7 +2,6 @@
 This module defines classes that wrap a simulator to function as a Model.
 """
 
-
 from funman.model.model import FunmanModel
 
 
diff --git a/src/funman/representation/__init__.py b/src/funman/representation/__init__.py
index c863b6a2..e99e2bbf 100644
--- a/src/funman/representation/__init__.py
+++ b/src/funman/representation/__init__.py
@@ -1,6 +1,7 @@
 """
 Classes for representing analysis elements, such as parameter, intervals, boxes, and parmaeter spaces.
 """
+
 from typing import Union
 
 Timepoint = Union[int, float]
diff --git a/src/funman/representation/assumption.py b/src/funman/representation/assumption.py
index c28f1479..e587cae8 100644
--- a/src/funman/representation/assumption.py
+++ b/src/funman/representation/assumption.py
@@ -13,8 +13,11 @@ def relevant_at_time(self, timepoint: Timepoint) -> bool:
         return self.constraint.relevant_at_time(timepoint)
 
     def __str__(self) -> str:
-        if hasattr(self.constraint, "name"):
-            return f"assume_{self.constraint.name}"
+        assert (
+            self.constraint._escaped_name is not None
+        ), f"Assumption {self.name} does not have an '_escaped_name'."
+        if hasattr(self.constraint, "_escaped_name"):
+            return f"assume_{self.constraint._escaped_name}"
         else:
             return f"assume_{str(self.constraint)}"
 
diff --git a/src/funman/representation/box.py b/src/funman/representation/box.py
index a2579078..ce1f9f1c 100644
--- a/src/funman/representation/box.py
+++ b/src/funman/representation/box.py
@@ -1,6 +1,8 @@
 import copy
 import logging
 from decimal import ROUND_CEILING, Decimal
+from functools import reduce
+from math import log2
 from pickle import FALSE
 from statistics import mean as average
 from typing import Dict, List, Literal, Optional, Union
@@ -36,11 +38,27 @@ class Box(BaseModel):
     _points_at_step: Dict[Timestep, List[Point]] = {}
 
     @staticmethod
-    def from_point(point: Point) -> "Box":
+    def from_point(
+        point: Point, radius: float = None, radius_vars=None
+    ) -> "Box":
         box = Box()
-        box.bounds = {
-            p: Interval.from_value(v) for p, v in point.values.items()
-        }
+        if radius is not None and radius_vars is not None:
+            assert (
+                radius > 0
+            ), "Cannot create a bounding box around a point using a non-positive radius."
+            box.bounds = {
+                p: (
+                    Interval(lb=v - radius, ub=v + radius)
+                    if p in radius_vars
+                    else Interval.from_value(v)
+                )
+                for p, v in point.values.items()
+            }
+
+        else:
+            box.bounds = {
+                p: Interval.from_value(v) for p, v in point.values.items()
+            }
         box.points.append(point)
         box.schedule = point.schedule
         box.label = point.label
@@ -75,7 +93,11 @@ def explain(self) -> "BoxExplanation":
         return expl
 
     def timestep(self) -> Interval:
-        return self.bounds["timestep"]
+        return (
+            self.bounds["timestep"]
+            if "timestep" in self.bounds
+            else Interval(lb=0, ub=0, closed_upper_bound=True)
+        )
 
     def __hash__(self):
         return int(sum([i.__hash__() for _, i in self.bounds.items()]))
@@ -260,20 +282,74 @@ def _copy(self):
         )
         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))
+
+        bias: inverse weight given to positive points
+
+        Returns
+        -------
+        float
+            Entropy of the box
+        """
+        tp = len(self.true_points()) * bias
+        fp = len(self.false_points())
+        p = tp / (tp + fp) if (tp + fp) > 0 else 0.5
+
+        if p == 0.0:
+            H = -((1.0 - p) * log2(1.0 - p))
+        elif p == 1.0:
+            H = -(p * log2(p))
+        else:
+            H = -(p * log2(p)) - ((1.0 - p) * log2(1.0 - p))
+        return H
+
     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 = len(self.true_points())
-            o_t = len(other.true_points())
-            if s_t == o_t:
-                if self.timestep().lb == other.timestep().lb:
-                    return self.normalized_width() > other.normalized_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
+            )
+            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 self.timestep().lb > other.timestep().lb
+                    return s_t > o_t
             else:
-                return s_t > o_t
+                return self.timestep().lb > other.timestep().lb
         else:
             raise Exception(f"Cannot compare __lt__() Box to {type(other)}")
 
@@ -295,7 +371,7 @@ def __str__(self):
                 for k, v in self.bounds.items()
             ]
         )
-        box_str = f"Box(\n|+pts|: {len(self.true_points())}\n|-pts|: {len(self.false_points())}\nlabel: {self.label}\nwidth: {self.width()},\ntimepoints: {Interval(lb=self.schedule.time_at_step(int(self.timestep().lb)), ub=self.schedule.time_at_step(int(self.timestep().ub)), closed_upper_bound=True)},\n{bounds_str}\n)"
+        box_str = f"Box(\n|+pts|: {len(self.true_points())}\n|-pts|: {len(self.false_points())}\nlabel: {self.label}\nwidth: {self.width()}\nnorm-width: {self.width(normalize=True)}\nvolume: {self.volume()}\nnorm-volume: {self.volume(normalize=True)},\ntimepoints: {Interval(lb=self.schedule.time_at_step(int(self.timestep().lb)), ub=self.schedule.time_at_step(int(self.timestep().ub)), closed_upper_bound=True)},\n{bounds_str}\n)"
         return box_str
         # return f"Box(t_{self.timestep()}={Interval(lb=self.schedule.time_at_step(int(self.timestep().lb)), ub=self.schedule.time_at_step(int(self.timestep().ub)), closed_upper_bound=True)} {self.bounds}), width = {self.width()}"
 
@@ -331,7 +407,13 @@ def contains(self, other: "Box") -> bool:
             ]
         )
 
-    def contains_point(self, point: Point) -> bool:
+    def _denormalize(self):
+        for p, interval in self.bounds.items():
+            interval._denormalize()
+
+    def contains_point(
+        self, point: Point, denormalize_bounds: bool = False
+    ) -> bool:
         """
         Does the box contain a point?
 
@@ -339,6 +421,8 @@ def contains_point(self, point: Point) -> bool:
         ----------
         point : Point
             a point
+        denormalize_bounds : bool
+            if true, and self has unnormalized_lb and unormalized_ub, use these insead of lb and ub.
 
         Returns
         -------
@@ -347,7 +431,9 @@ def contains_point(self, point: Point) -> bool:
         """
         return all(
             [
-                interval.contains_value(point.values[p])
+                interval.contains_value(
+                    point.values[p], denormalize_bounds=denormalize_bounds
+                )
                 for p, interval in self.bounds.items()
             ]
         )
@@ -412,6 +498,40 @@ def intersects(self, other: "Box") -> bool:
             ]
         )
 
+    def intersection(self, other: "Box", param_list=None) -> Optional["Box"]:
+        """
+        Return the intersection of two boxes (which is also a box)
+
+        Parameters
+        ----------
+        other : Box
+            other box
+
+        Returns
+        -------
+        Box
+            self intersected with other
+        """
+        result = Box()
+        result_bounds = {}
+        if self.label == other.label:
+            for p, interval in self.bounds.items():
+                if param_list is None or p in param_list:
+                    result_bounds[p] = interval.intersection(other.bounds[p])
+            result.label = self.label
+            result.bounds = result_bounds
+        for (
+            p,
+            interval,
+        ) in (
+            result.bounds.items()
+        ):  # If any intervals are empty, there is no intersection
+            if interval is None:
+                return None
+        return (
+            result  # FIXME should this also include points and unknown boxes?
+        )
+
     def _get_max_width_point_Parameter(
         self, points: List[List[Point]], parameters: List[Parameter]
     ):
@@ -428,13 +548,19 @@ def _get_max_width_point_Parameter(
         Parameter
             parameter (dimension of box) where points are most distant from the center of the box.
         """
-        parameter_names = [p.name for p in parameters]
+        parameter_names = [p.name for p in parameters if p.is_synthesized()]
         group_centers = {
-            p: [average([pt.values[p] for pt in grp]) for grp in points]
+            p: [
+                average([pt.values[p] for pt in grp])
+                for grp in points
+                if len(grp) > 0
+            ]
             for p in self.bounds
             if p in parameter_names
         }
-        centers = {p: average(grp) for p, grp in group_centers.items()}
+        centers = {
+            p: average(grp) for p, grp in group_centers.items() if len(grp) > 0
+        }
         # print(points)
         # print(centers)
         point_distances = [
@@ -445,6 +571,7 @@ def _get_max_width_point_Parameter(
             }
             for grp in points
             for pt in grp
+            if len(grp) > 0
         ]
 
         parameter_widths = {
@@ -484,6 +611,7 @@ def _get_max_width_Parameter(
                     self.bounds[parameter.name].width(normalize=normalize)
                 )
                 for parameter in parameters
+                if parameter.is_synthesized()
             }
         else:
             widths = {
@@ -583,6 +711,18 @@ def volume(
         product *= num_timepoints
         return product
 
+    def normalized_volume(self, parameters: List[ModelParameter] = None):
+        params = self.bounds.keys()
+        if parameters:
+            params = [p.name for p in parameters]
+
+        norm_volume = reduce(
+            lambda a, b: a * b,
+            [Decimal(self.bounds[p].normalized_width()) for p in params],
+            Decimal(1.0),
+        )
+        return norm_volume
+
     def normalized_width(self, parameters: List[ModelParameter] = None):
         p = self._get_max_width_Parameter(
             normalize=True, parameters=parameters
@@ -643,7 +783,11 @@ def split(
             Boxes resulting from the split.
         """
         p = None
-        if points:
+        if (
+            points
+            and len(points) > 1
+            and all([len(grp) > 0 for grp in points])
+        ):
             p = self._get_max_width_point_Parameter(
                 points, parameters=parameters
             )
@@ -692,64 +836,14 @@ def split(
             for step, pts in b2._points_at_step.items()
         }
 
-        l.info(
-            f"Split({p}[{self.bounds[p].lb, mid}][{mid, self.bounds[p].ub}])"
+        l.debug(
+            f"Split[{self.timestep()}]({p}[{self.bounds[p].lb, mid}][{mid, self.bounds[p].ub}])"
         )
-        l.info(
-            f"widths: {self.width():.5f} -> {b1.width():.5f} {b2.width():.5f} (raw), {self.normalized_width():.5f} -> {b1.normalized_width():.5f} {b2.normalized_width():.5f} (norm)"
+        l.debug(
+            f"widths: {self.width(parameters=parameters):.5f} -> {b1.width(parameters=parameters):.5f} {b2.width():.5f} (raw), {self.normalized_width(parameters=parameters):.5f} -> {b1.normalized_width(parameters=parameters):.5f} {b2.normalized_width(parameters=parameters):.5f} (norm)"
         )
         return [b2, b1]
 
-    def intersect(self, b2: "Box", param_list: List[str] = None):
-        """
-        Intersect self with box, optionally over only a subset of the dimensions listed in param_list.
-
-        Parameters
-        ----------
-        b2 : Box
-            box to intersect with
-        param_list : List[str], optional
-            parameters to intersect, by default None
-
-        Returns
-        -------
-        Box
-            box representing intersection, optionally defined over parameters in param_list (when specified)
-        """
-        params_ans = []
-        common_params = (
-            param_list if param_list else [k.name for k in self.bounds]
-        )
-        for p1 in common_params:
-            # FIXME iterating over dict keys is not efficient
-            for b, i in self.bounds.items():
-                if b == p1:
-                    b1_bounds = Interval(lb=i.lb, ub=i.ub)
-            for b, i in b2.bounds.items():
-                if b == p1:
-                    b2_bounds = Interval(lb=i.lb, ub=i.ub)
-            intersection_ans = b1_bounds.intersection(
-                b2_bounds
-            )  ## will be a list with 2 elements (lower and upper bound) or empty list
-            if (
-                len(intersection_ans) < 1
-            ):  ## empty list: no intersection in 1 variable means no intersection overall.
-                return None
-            else:
-                new_param = ModelParameter(
-                    name=f"{p1}",
-                    interval=Interval(
-                        lb=intersection_ans[0], ub=intersection_ans[1]
-                    ),
-                )
-                params_ans.append(new_param)
-        return Box(
-            bounds={
-                p.name: Interval(lb=p.interval.lb, ub=p.interval.ub)
-                for p in params_ans
-            }
-        )
-
     def symm_diff(b1: "Box", b2: "Box"):
         result = []
         ## First check that the two boxes have the same variables
@@ -767,7 +861,7 @@ def symm_diff(b1: "Box", b2: "Box"):
             )
         ### Find intersection
         desired_vars_list = list(vars_b1)
-        intersection = b1.intersect(b2, param_list=desired_vars_list)
+        intersection = b1.intersection(b2, param_list=desired_vars_list)
         ### Calculate symmetric difference based on intersection
         if (
             intersection == None
@@ -789,20 +883,6 @@ def symm_diff(b1: "Box", b2: "Box"):
                     true_boxes.append(b)
             return true_boxes
 
-    def __intersect_two_boxes(b1, b2):
-        # FIXME subsumed by Box.intersect(), can be removed.
-        a = list(b1.bounds.values())
-        b = list(b2.bounds.values())
-        result = []
-        d = len(a)  ## dimension
-        for i in range(d):
-            subresult = a[i].intersection(b[i])
-            if subresult == []:
-                return None
-            else:
-                result.append(subresult)
-        return result
-
     @staticmethod
     def _subtract_two_1d_boxes(a, b):
         """Given 2 intervals a = [a0,a1] and b=[b0,b1], return the part of a that does not intersect with b."""
@@ -830,83 +910,3 @@ def _subtract_two_1d_boxes(a, b):
                 return []
 
         return [lhs, rhs]
-
-    @staticmethod
-    def __intersect_two_boxes(a: "Box", b: "Box"):
-        # FIXME not sure how this is different than Box.intersect()
-        a_params = list(a.bounds.keys())
-        b_params = list(b.bounds.keys())
-
-        beta_0_a = a.bounds[a_params[0]]
-        beta_1_a = a.bounds[a_params[1]]
-        beta_0_b = b.bounds[b_params[0]]
-        beta_1_b = b.bounds[b_params[1]]
-
-        beta_0 = beta_0_a.intersection(beta_0_b)
-        if len(beta_0) < 1:
-            return None
-        beta_1 = beta_1_a.intersection(beta_1_b)
-        if len(beta_1) < 1:
-            return None
-
-        return Box(
-            bounds={
-                ModelParameter(
-                    name=a_params[0], lb=beta_0[0], ub=beta_0[1]
-                ): Interval(lb=beta_0[0], ub=beta_0[1]),
-                ModelParameter(
-                    name=a_params[1], lb=beta_1[0], ub=beta_1[1]
-                ): Interval(lb=beta_1[0], ub=beta_1[1]),
-            }
-        )
-
-    ### Can remove and replace this with Box.symm_diff, which works for any number of dimensions.  TODO write a corresponding parameter space symmetric difference and use case.
-    def _symmetric_difference_two_boxes(self, b: "Box") -> List["Box"]:
-        result: List["Box"] = []
-        # if the same box then no symmetric difference
-        if self == b:
-            return result
-
-        ## no intersection so they are disjoint - return both original boxes
-        if Box.__intersect_two_boxes(self, b) == None:
-            return [self, b]
-
-        # There must be some symmetric difference below here
-        a_params = list(self.bounds.keys())
-        b_params = list(b.bounds.keys())
-
-        beta_0_a = self.bounds[a_params[0]]
-        beta_1_a = self.bounds[a_params[1]]
-        beta_0_b = b.bounds[b_params[0]]
-        beta_1_b = b.bounds[b_params[1]]
-
-        # TODO assumes 2 dimensions and aligned parameter names
-        def make_box_2d(p_bounds):
-            b0_bounds = p_bounds[0]
-            b1_bounds = p_bounds[1]
-            b = Box(
-                bounds={
-                    ModelParameter(
-                        name=a_params[0], lb=b0_bounds.lb, ub=b0_bounds.ub
-                    ): Interval(lb=b0_bounds.lb, ub=b0_bounds.ub),
-                    ModelParameter(
-                        name=a_params[1], lb=b1_bounds.lb, ub=b1_bounds.ub
-                    ): Interval(lb=b1_bounds.lb, ub=b1_bounds.ub),
-                }
-            )
-            return b
-
-        xbounds = Box._subtract_two_1d_intervals(beta_0_a, beta_0_b)
-        if xbounds != None:
-            result.append(make_box_2d([xbounds, beta_1_a]))
-        xbounds = Box._subtract_two_1d_intervals(beta_0_b, beta_0_a)
-        if xbounds != None:
-            result.append(make_box_2d([xbounds, beta_1_b]))
-        ybounds = Box._subtract_two_1d_intervals(beta_1_a, beta_1_b)
-        if ybounds != None:
-            result.append(make_box_2d([beta_0_a, ybounds]))
-        ybounds = Box._subtract_two_1d_intervals(beta_1_b, beta_1_a)
-        if ybounds != None:
-            result.append(make_box_2d([beta_0_b, ybounds]))
-
-        return result
diff --git a/src/funman/representation/constraint.py b/src/funman/representation/constraint.py
index df9a6b1c..f32c505c 100644
--- a/src/funman/representation/constraint.py
+++ b/src/funman/representation/constraint.py
@@ -6,6 +6,7 @@
     Field,
     ValidationInfo,
     field_validator,
+    model_validator,
 )
 from typing_extensions import Annotated
 
@@ -19,6 +20,7 @@
 class Constraint(BaseModel):
     soft: bool = True
     name: str
+    _escaped_name: str
 
     def time_dependent(self) -> bool:
         return False
@@ -32,13 +34,18 @@ def encodable(self) -> bool:
     def relevant_at_time(self, time: int) -> bool:
         return True
 
+    @model_validator(mode="after")
+    def check_name(self) -> "FUNMANConfig":
+        self._escaped_name = self.name.replace(" ", "_").replace("-", "_")
+        return self
+
 
 class TimedConstraint(Constraint):
     timepoints: Optional["Interval"] = None
 
     def contains_time(self, time: Union[float, int]) -> bool:
         return (
-            self.timepoints.contains_value(time)
+            self.timepoints is None or self.timepoints.contains_value(time)
             if self.time_dependent()
             else True
         )
@@ -73,7 +80,7 @@ def encodable(self) -> bool:
         return not isinstance(self.parameter, StructureParameter)
 
     def relevant_at_time(self, time: int) -> bool:
-        return True  # time == 0
+        return time == 0
 
 
 class QueryConstraint(TimedConstraint):
@@ -103,9 +110,54 @@ class LinearConstraint(TimedConstraint):
     weights: Annotated[
         Optional[List[Union[int, float]]], Field(validate_default=True)
     ] = None
+    derivative: bool = False
 
     model_config = ConfigDict(extra="forbid")
 
+    def next_timestep(
+        self, timestep: "Timestep", schedule: "EncodingSchedule"
+    ) -> "Timestep":
+        """
+        Get a Timestep in the timepoints other than that is in timepoints
+
+        Parameters
+        ----------
+        timestep : Timestep
+            the reference Timestep
+        schedule : EncodingSchedule
+            the timepoints used for the encoding
+        """
+        # if (
+        #     schedule.time_at_step(timestep) in schedule.timepoints
+        #     and timestep + 1 < len(schedule.timepoints)
+        #     and schedule.time_at_step(timestep + 1) in schedule.timepoints
+        # ) and (
+        #     (self.timepoints is None)
+        #     or self.timepoints.contains_value(schedule.time_at_step(timestep))
+        # ):
+        #     return timestep + 1
+        if (
+            schedule.time_at_step(timestep) in schedule.timepoints
+            and timestep - 1 >= 0
+            and schedule.time_at_step(timestep - 1) in schedule.timepoints
+        ) and (
+            (self.timepoints is None)
+            or self.timepoints.contains_value(schedule.time_at_step(timestep))
+        ):
+            return timestep - 1
+        else:
+            raise Exception(
+                f"Cannot determine a suitable timepoint relative to step = {timestep} where the encoding schedule = {schedule} is defined and constraint timepoints ={self.timepoints} are defined"
+            )
+
+    def time_dependent(self) -> bool:
+        return self.derivative or super().time_dependent()
+
+    def relevant_at_time(self, time: int) -> bool:
+        return (not self.derivative or time > 0) and super().relevant_at_time(
+            time
+        )
+
     @field_validator("weights")
     @classmethod
     def check_weights(
@@ -117,6 +169,12 @@ def check_weights(
 
         if weights is None:
             weights = [1.0] * len(info.data["variables"])
+        else:
+            vars = info.data["variables"]
+            assert len(weights) == len(
+                vars
+            ), f"Linear Constraint must have equal number of weights (found {len(weights)}) and variables (found {len(vars)})"
+
         return weights
 
     def __hash__(self) -> int:
diff --git a/src/funman/representation/explanation.py b/src/funman/representation/explanation.py
index 8e1a7fa8..0946f214 100644
--- a/src/funman/representation/explanation.py
+++ b/src/funman/representation/explanation.py
@@ -7,18 +7,34 @@
 
 
 class Explanation(BaseModel):
-    _expression: FNode
+    expression: Optional[str] = None
+    symbols: Optional[List[str]] = None
 
     def explain(self) -> Dict[str, Any]:
         return {
             "description": "The expression is implied by this scenario and is unsatisfiable",
-            "expression": self._expression.serialize(),
+            "expression": self.expression,
         }
 
+    def set_expression(self, e: FNode):
+        self.expression = e.serialize()
+        self.symbols = [str(s) for s in e.get_free_variables()]
+
+    def check_assumptions(
+        self,
+        episode: "BoxSearchEpisode",
+        my_solver: Callable,
+        options: "EncodingOptions",
+    ) -> List[Assumption]:
+        return []
+
+
+class TimeoutExplanation(Explanation):
+    pass
+
 
 class BoxExplanation(Explanation):
     relevant_assumptions: List[Assumption] = []
-    expression: Optional[str] = None
 
     def check_assumptions(
         self,
@@ -41,21 +57,16 @@ def check_assumptions(
         List[Assumption]
             _description_
         """
-        self.expression = self._expression.serialize()
+
         # FIXME use step size from options
         assumption_symbols: Dict[str, Assumption] = {
             str(a): a for a in episode.problem._assumptions
         }
-        # with my_solver() as solver:
-        #     solver.add_assertion(self._expression)
-        #     solver.push(1)
-        expression_symbols = [
-            str(v) for v in self._expression.get_free_variables()
-        ]
+
         self.relevant_assumptions = [
             a
             for symbol, a in assumption_symbols.items()
-            if any(symbol in es for es in expression_symbols)
+            if any(symbol in es for es in self.symbols)
         ]
         return self.relevant_assumptions
 
diff --git a/src/funman/representation/interval.py b/src/funman/representation/interval.py
index 711e73da..5ad5598e 100644
--- a/src/funman/representation/interval.py
+++ b/src/funman/representation/interval.py
@@ -28,11 +28,30 @@ class Interval(BaseModel):
     ub: Optional[Union[float, str]] = POS_INFINITY
     closed_upper_bound: bool = False
     original_width: Optional[Decimal] = None
+    normalized: bool = False
+    unnormalized_lb: Optional[Union[float, str]] = None
+    unnormalized_ub: Optional[Union[float, str]] = None
 
     @field_serializer("original_width")
     def ser_wrap(self, v: Decimal, _info) -> float:
         return float(v)
 
+    def normalize_bounds(self, normalization_constant):
+        assert (
+            not self.normalized
+        ), f"Cannot normalize an interval twice: {self}"
+
+        return Interval(
+            lb=math_utils.div(self.lb, normalization_constant),
+            ub=math_utils.div(self.ub, normalization_constant),
+            normalized=True,
+            unnormalized_lb=self.lb,
+            unnormalized_ub=self.ub,
+            original_width=self.original_width
+            / Decimal(normalization_constant),
+            closed_upper_bound=self.closed_upper_bound,
+        )
+
     @staticmethod
     def from_value(v: Union[float, str]):
         return Interval(lb=v, ub=v, closed_upper_bound=True)
@@ -91,7 +110,7 @@ def is_unbound(self) -> bool:
     def normalize(self, normalization_factor: Union[float, int]) -> "Interval":
         return Interval(
             lb=math_utils.div(self.lb, normalization_factor),
-            ub=math_utils.div(self.lb, normalization_factor),
+            ub=math_utils.div(self.ub, normalization_factor),
         )
 
     def __lt__(self, other):
@@ -193,7 +212,7 @@ def intersects(self, other: "Interval") -> bool:
             )
         )
 
-    def intersection(self, b: "Interval") -> "Interval":
+    def intersection(self, b: "Interval") -> Optional["Interval"]:
         """
         Given an interval b with self = [a0,a1] and b=[b0,b1], check whether they intersect.  If they do, return interval with their intersection.
 
@@ -226,9 +245,12 @@ def intersection(self, b: "Interval") -> "Interval":
             if math_utils.gt(
                 minArray.ub, maxArray.lb
             ):  ## has nonempty intersection. return intersection
-                return [float(maxArray.lb), float(minArray.ub)]
+                result = Interval()
+                result.lb = float(maxArray.lb)
+                result.ub = float(minArray.ub)
+                return result
             else:  ## no intersection.
-                return []
+                return None
 
     def subtract(self, b: "Interval") -> "Interval":
         """
@@ -325,7 +347,9 @@ def union(self, other: "Interval") -> List["Interval"]:
             # ]
             return ans
 
-    def contains_value(self, value: float) -> bool:
+    def contains_value(
+        self, value: float, denormalize_bounds: bool = False
+    ) -> bool:
         """
         Does the interval include a value?
 
@@ -333,22 +357,46 @@ def contains_value(self, value: float) -> bool:
         ----------
         value : float
             value to check for containment
+        denormalize_bounds : bool
+            if true, and self has unnormalized_lb and unormalized_ub, use these insead of lb and ub.
 
         Returns
         -------
         bool
             the value is in the interval
         """
-        lb_sat = math_utils.gte(value, self.lb)
+        lb = (
+            self.lb
+            if not self.unnormalized_lb or not denormalize_bounds
+            else self.unnormalized_lb
+        )
+        ub = (
+            self.ub
+            if not self.unnormalized_ub or not denormalize_bounds
+            else self.unnormalized_ub
+        )
+
+        lb_sat = math_utils.gte(value, lb)
         ub_sat = (
-            math_utils.lte(value, self.ub)
+            math_utils.lte(value, ub)
             if self.closed_upper_bound
-            else math_utils.lt(value, self.ub)
+            else math_utils.lt(value, ub)
         )
         return lb_sat and ub_sat
 
+    def _denormalize(self):
+        self.lb = self.lb if not self.unnormalized_lb else self.unnormalized_lb
+        self.unnormalized_lb = None
+        self.ub = self.ub if not self.unnormalized_ub else self.unnormalized_ub
+        self.unnormalized_ub = None
+
     @model_validator(mode="after")
     def check_interval(self) -> str:
+        if self.lb is None:
+            self.lb = NEG_INFINITY
+        if self.ub is None:
+            self.ub = POS_INFINITY
+
         # Assume that intervals where lb == ub imply closed_upper_bound
         if self.lb == self.ub and not self.closed_upper_bound:
             l.warning(
diff --git a/src/funman/representation/parameter.py b/src/funman/representation/parameter.py
index 8bd7f900..ddfdabb4 100644
--- a/src/funman/representation/parameter.py
+++ b/src/funman/representation/parameter.py
@@ -25,6 +25,7 @@
 class Parameter(BaseModel):
     name: Union[str, ModelSymbol]
     interval: Interval = Interval()
+    _escaped_name: str
 
     def width(self) -> Decimal:
         return self.interval.width()
@@ -35,11 +36,19 @@ def is_unbound(self) -> bool:
     def __hash__(self):
         return abs(hash(self.name))
 
+    @model_validator(mode="after")
+    def check_name(self) -> "FUNMANConfig":
+        self._escaped_name = self.name.replace(" ", "_").replace("-", "_")
+        return self
+
     @model_validator(mode="after")
     def set_interval_original_width(self) -> str:
         self.interval.original_width = self.interval.width()
         return self
 
+    def normalize_bounds(self, normalization_constant):
+        self.interval = self.interval.normalize_bounds(normalization_constant)
+
 
 class LabeledParameter(Parameter):
     label: Literal["any", "all"] = LABEL_ANY
@@ -54,19 +63,17 @@ def is_synthesized(self):
 
 
 class NumSteps(StructureParameter):
-    @field_validator("name")
-    @classmethod
-    def check_name(cls, name: str, info: ValidationInfo):
-        assert name == "num_steps", "NumSteps.name must be 'num_steps'"
-        return name
+    @model_validator(mode="after")
+    def check_name(self) -> "FUNMANConfig":
+        assert self.name == "num_steps", "NumSteps.name must be 'num_steps'"
+        return self
 
 
 class StepSize(StructureParameter):
-    @field_validator("name")
-    @classmethod
-    def check_name(cls, name: str, info: ValidationInfo):
-        assert name == "step_size", "StepSize.name must be 'step_size'"
-        return name
+    @model_validator(mode="after")
+    def check_name(self) -> "FUNMANConfig":
+        assert self.name == "step_size", "StepSize.name must be 'step_size'"
+        return self
 
 
 StepListValue = List[Union[float, int]]
@@ -75,11 +82,10 @@ def check_name(cls, name: str, info: ValidationInfo):
 class Schedules(StructureParameter):
     schedules: List[EncodingSchedule]
 
-    @field_validator("name")
-    @classmethod
-    def check_name(cls, name: str, info: ValidationInfo):
-        assert name == "schedules", "StepList.name must be 'step_list'"
-        return name
+    @model_validator(mode="after")
+    def check_name(self) -> "FUNMANConfig":
+        assert self.name == "schedules", "Schedules.name must be 'schedules'"
+        return self
 
     @model_validator(mode="before")
     def check_empty_name(self) -> str:
diff --git a/src/funman/representation/parameter_space.py b/src/funman/representation/parameter_space.py
index 6a622305..4b3b9400 100644
--- a/src/funman/representation/parameter_space.py
+++ b/src/funman/representation/parameter_space.py
@@ -5,7 +5,14 @@
 from matplotlib.lines import Line2D
 from pydantic import BaseModel
 
-from ..constants import LABEL_DROPPED, LABEL_FALSE, LABEL_TRUE, LABEL_UNKNOWN
+from ..constants import (
+    LABEL_DROPPED,
+    LABEL_FALSE,
+    LABEL_TRUE,
+    LABEL_UNKNOWN,
+    NEG_INFINITY,
+    POS_INFINITY,
+)
 from . import Interval, Point
 from .box import Box
 from .interval import Interval
@@ -69,6 +76,48 @@ def points(self) -> List[Point]:
     def boxes(self) -> List[Box]:
         return self.true_boxes + self.false_boxes
 
+    def last_boxes(self) -> List[Box]:
+        last_step = max(
+            max([b.timestep().ub for b in self.true_boxes] + [0]),
+            max([b.timestep().ub for b in self.false_boxes] + [0]),
+        )
+        return [b for b in self.true_boxes if b.timestep().ub == last_step] + [
+            b for b in self.false_boxes if b.timestep().ub == last_step
+        ]
+
+    def outer_interval(self, param_name: str) -> Interval:
+        """
+        Get the infimum and supremimum values of parameter param_name among all true boxes
+
+        Parameters
+        ----------
+        param_name : str
+            Parameter name
+
+        Returns
+        -------
+        Interval
+            Interval where the lb and ub are the minimum and maximum values taken by parameter among all true boxes.
+        """
+        if len(self.true_boxes) > 0:
+            lb = min(
+                map(
+                    lambda b: b.project([param_name]).bounds[param_name].lb,
+                    self.true_boxes,
+                )
+            )
+            ub = max(
+                map(
+                    lambda b: b.project([param_name]).bounds[param_name].ub,
+                    self.true_boxes,
+                )
+            )
+        else:
+            lb = NEG_INFINITY
+            ub = POS_INFINITY
+
+        return Interval(lb=lb, ub=ub, closed_upper_bound=(lb == ub))
+
     def explain(self) -> "ParameterSpaceExplanation":
         from .explanation import ParameterSpaceExplanation
 
@@ -120,24 +169,6 @@ def project() -> "ParameterSpace":
         raise NotImplementedError()
         return ParameterSpace()
 
-    @staticmethod
-    def _intersect_boxes(b1s, b2s):
-        results_list = []
-        for box1 in b1s:
-            for box2 in b2s:
-                subresult = Box.__intersect_two_boxes(box1, box2)
-                if subresult != None:
-                    results_list.append(subresult)
-        return results_list
-
-    # STUB intersect parameters spaces
-    @staticmethod
-    def intersect(ps1, ps2):
-        return ParameterSpace(
-            ParameterSpace._intersect_boxes(ps1.true_boxes, ps2.true_boxes),
-            ParameterSpace._intersect_boxes(ps1.false_boxes, ps2.false_boxes),
-        )
-
     @staticmethod
     def symmetric_difference(ps1: "ParameterSpace", ps2: "ParameterSpace"):
         return ParameterSpace(
@@ -269,7 +300,7 @@ def consistent(self) -> bool:
         for i1, b1 in enumerate(boxes):
             for i2, b2 in enumerate(boxes[i1 + 1 :]):
                 if b1.intersects(b2):
-                    l.exception(f"Parameter Space Boxes intersect: {b1} {b2}")
+                    l.error(f"Parameter Space Boxes intersect: {b1} {b2}")
                     return False
         for tp in self.true_points():
             if not any([b.contains_point(tp) for b in self.true_boxes]):
@@ -285,6 +316,29 @@ def consistent(self) -> bool:
             return False
         return True
 
+    def intersection(self, ps2: "ParameterSpace") -> "ParameterSpace":
+        """
+        Intersect two parameter spaces.
+        """
+        result = ParameterSpace(num_dimensions=self.num_dimensions)
+        true_result = []
+        false_result = []
+        ps1_boxes = self.true_boxes
+        ps2_boxes = ps2.true_boxes
+        for i1, b1 in enumerate(ps1_boxes):
+            for i2, b2 in enumerate(ps2_boxes):
+                if b1.intersects(b2):
+                    true_result.append(b1.intersection(b2))
+        result.true_boxes = true_result
+        ps1_boxes = self.false_boxes
+        ps2_boxes = ps2.false_boxes
+        for i1, b1 in enumerate(ps1_boxes):
+            for i2, b2 in enumerate(ps2_boxes):
+                if b1.intersects(b2):
+                    false_result.append(b1.intersection(b2))
+        result.false_boxes = false_result
+        return result
+
     def _reassign_point_labels(self) -> None:
         """
         For every point, update the label based on the box that contains it.
@@ -299,7 +353,7 @@ def _reassign_point_labels(self) -> None:
             for point in iter(
                 pt for pt in points if pt not in assigned_points
             ):
-                if box.contains_point(point):
+                if box.contains_point(point, denormalize_bounds=True):
                     point.label = box.label
                     box.points.append(point)
                     assigned_points.add(point)
@@ -308,6 +362,11 @@ def _reassign_point_labels(self) -> None:
             pt for pt in points if pt not in assigned_points
         ]
 
+    def _denormalize(self):
+        boxes: List[Box] = self.true_boxes + self.false_boxes
+        for box in boxes:
+            box._denormalize()
+
     def _compact(self):
         """
         Compact the boxes by joining boxes that can create a box
diff --git a/src/funman/representation/representation.py b/src/funman/representation/representation.py
index c5571c2a..6a26ac02 100644
--- a/src/funman/representation/representation.py
+++ b/src/funman/representation/representation.py
@@ -2,6 +2,7 @@
 This submodule contains definitions for the classes used
 during the configuration and execution of a search.
 """
+
 import logging
 import math
 from typing import Dict, Literal, Optional, Set
@@ -29,7 +30,7 @@ class Point(BaseModel):
     #     self.values = kw['values']
 
     def timestep(self) -> int:
-        return int(self.values["timestep"])
+        return int(self.values["timestep"]) if "timestep" in self.values else 0
 
     def __str__(self):
         return f"Point({self.model_dump()})"
@@ -71,6 +72,7 @@ def denormalize(self, scenario):
                 values=denormalized_values,
                 normalized_values=self.values,
                 type=self.type,
+                schedule=self.schedule,
             )
             return denormalized_point
         else:
diff --git a/src/funman/scenario/consistency.py b/src/funman/scenario/consistency.py
index b3ac78f7..ffc5235a 100644
--- a/src/funman/scenario/consistency.py
+++ b/src/funman/scenario/consistency.py
@@ -1,6 +1,7 @@
 """
 This submodule defines a consistency scenario.  Consistency scenarios specify an existentially quantified model.  If consistent, the solution assigns any unassigned variable, subject to their bounds and other constraints.  
 """
+
 import logging
 import threading
 from typing import Callable, Dict, Optional
diff --git a/src/funman/scenario/parameter_synthesis.py b/src/funman/scenario/parameter_synthesis.py
index 51ffcf12..8e13ba59 100644
--- a/src/funman/scenario/parameter_synthesis.py
+++ b/src/funman/scenario/parameter_synthesis.py
@@ -1,6 +1,7 @@
 """
 This module defines the Parameter Synthesis scenario.
 """
+
 import threading
 from typing import Callable, List, Optional, Union
 
@@ -102,6 +103,7 @@ class ParameterSynthesisScenarioResult(AnalysisScenarioResult, BaseModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.parameter_space._reassign_point_labels()
+        self.parameter_space._denormalize()
         self.parameter_space._compact()
 
     def plot(self, **kwargs):
diff --git a/src/funman/scenario/scenario.py b/src/funman/scenario/scenario.py
index c8b201c4..0ae4812f 100644
--- a/src/funman/scenario/scenario.py
+++ b/src/funman/scenario/scenario.py
@@ -117,10 +117,14 @@ def initialize(self, config: "FUNMANConfig") -> "Search":
         ]
 
         self._set_normalization(config)
+        if config.normalize:
+            self._normalize_parameters()
 
         if config.use_compartmental_constraints:
+            capacity = self.normalization_constant
+            # 1.0 if config.normalize else self.normalization_constant
             ccs = self.model.compartmental_constraints(
-                self.normalization_constant
+                capacity, config.compartmental_constraint_noise
             )
             if ccs is not None:
                 self.constraints += ccs
@@ -153,6 +157,9 @@ def _initialize_encodings(self, config: "FUNMANConfig"):
         for schedule in self._smt_encoder._timed_model_elements[
             "schedules"
         ].schedules:
+            assert (
+                0 in schedule.timepoints
+            ), "Schedule for encoding does not include a timepoint 0"
             encoding = self._smt_encoder.initialize_encodings(
                 self, len(schedule.timepoints)
             )
@@ -222,6 +229,9 @@ def structure_parameters(self):
     def model_parameters(self):
         return self.parameters_of_type(ModelParameter)
 
+    def synthesized_model_parameters(self):
+        return [p for p in self.model_parameters() if p.is_synthesized()]
+
     def synthesized_parameters(self):
         return [p for p in self.parameters if p.is_synthesized()]
 
@@ -311,6 +321,11 @@ def _filter_parameters(self):
             ]
             self.parameters = filtered_parameters
 
+    def _normalize_parameters(self):
+        for p in self.parameters:
+            if p.name == "N":
+                p.normalize_bounds(self.normalization_constant)
+
     def _set_normalization(self, config):
         if config.normalization_constant is not None:
             self.normalization_constant = config.normalization_constant
diff --git a/src/funman/scenario/simulation.py b/src/funman/scenario/simulation.py
index 7c4b7201..24863183 100644
--- a/src/funman/scenario/simulation.py
+++ b/src/funman/scenario/simulation.py
@@ -1,6 +1,7 @@
 """
 This module wraps a simulator invocation as a Scenario.
 """
+
 from importlib import import_module
 from typing import Any
 
diff --git a/src/funman/search/box_search.py b/src/funman/search/box_search.py
index 42a422d7..fc461bb4 100644
--- a/src/funman/search/box_search.py
+++ b/src/funman/search/box_search.py
@@ -2,6 +2,7 @@
 This module defines the BoxSearch class and supporting classes.
 
 """
+
 import glob
 import logging
 import multiprocessing as mp
@@ -45,7 +46,11 @@
 from funman.config import FUNMANConfig
 from funman.representation.assumption import Assumption
 from funman.representation.constraint import ParameterConstraint
-from funman.representation.explanation import Explanation
+from funman.representation.explanation import (
+    BoxExplanation,
+    Explanation,
+    TimeoutExplanation,
+)
 from funman.search import Box, ParameterSpace, Point, Search, SearchEpisode
 from funman.search.search import SearchStaticsMP, SearchStatistics
 from funman.translate.translate import EncodingOptions, EncodingSchedule
@@ -202,7 +207,7 @@ def get_candidate_boxes_for_point(self, point: Point) -> List[Box]:
     def _initialize_boxes(self, expander_count, schedule: EncodingSchedule):
         # initial_box = self._initial_box()
         # if not self.add_unknown(initial_box):
-        #     l.exception(
+        #     l.error(
         #         f"Did not add an initial box (of width {initial_box.width()}), try reducing config.tolerance, currently {self.config.tolerance}"
         #     )
         initial_boxes = QueueSP()
@@ -219,7 +224,7 @@ def _initialize_boxes(self, expander_count, schedule: EncodingSchedule):
         for i in range(num_boxes):
             b = initial_boxes.get()
             if not self._add_unknown(b):
-                l.exception(
+                l.error(
                     f"Did not find add an initial box (box had width {b.normalized_width()}), try reducing config.tolerance, currently {self.config.tolerance}"
                 )
             # l.debug(f"Initial box: {b}")
@@ -245,7 +250,8 @@ def _on_iteration(self):
     def _add_unknown_box(self, box: Box) -> bool:
         if (
             box.width(
-                parameters=self.problem.model_parameters(), normalize=True
+                parameters=self.problem.synthesized_model_parameters(),
+                normalize=True,
             )
             > self.config.tolerance
         ):
@@ -364,8 +370,11 @@ class BoxSearch(Search):
 
     def _split(self, box: Box, episode: BoxSearchEpisode, points=None):
         normalize = episode.problem._original_parameter_widths
+        split_points = (
+            points if not episode.config.uniform_box_splits else None
+        )
         b1, b2 = box.split(
-            points=points,
+            points=split_points,
             normalize=normalize,
             parameters=episode.problem.model_parameters(),
         )
@@ -519,24 +528,27 @@ def _initialize_model_encoding(
                     self._solve_at_step_symbol(t), formula_encoded_constraints
                 )
 
-                # symbols = formula_encoded_constraints.get_free_variables()
-                neg_formula = Implies(
-                    Not(self._solve_at_step_symbol(t)),
-                    And(
-                        [
-                            Equals(
-                                encoding._encoder._encode_state_var(
-                                    s, time=timepoint
-                                ),
-                                Real(0.0),
-                            )
-                            for s in episode.problem.model._state_var_names()
-                        ]  # + [Not(s) for s in symbols if s.symbol_type() == BOOL]
-                    ),
+                # neg_formula = Implies(
+                #     Not(self._solve_at_step_symbol(t)),
+                #     And(
+                #         [
+                #             Equals(
+                #                 encoding._encoder._encode_state_var(
+                #                     s, time=timepoint
+                #                 ),
+                #                 Real(0.0),
+                #             )
+                #             for s in episode.problem.model._state_var_names()
+                #         ]  # + [Not(s) for s in symbols if s.symbol_type() == BOOL]
+                #     ),
+                # )
+
+                layer_formulas.append(
+                    # And(
+                    formula
+                    # , neg_formula)
                 )
 
-                layer_formulas.append(And(formula, neg_formula))
-
             for layer, formula in enumerate(layer_formulas):
                 episode._formula_stack.push(1)
                 episode._formula_stack.add_assertion(formula)
@@ -788,7 +800,9 @@ def _get_points(
                         f"box_search_{episode._iteration}",
                     )
                 )
-            result = self.invoke_solver(solver)
+            result = self.invoke_solver(
+                solver, timeout=episode.config.solver_timeout
+            )
             if result is not None and isinstance(result, pysmtModel):
                 # If substituted formulas are on the stack, then add the original formulas to compute the values of all variables
                 if (
@@ -826,13 +840,15 @@ def _get_false_points(
             episode._add_false_point,
             my_solver,
             options,
-            _smtlib_save_fn=partial(
-                self.store_smtlib,
-                episode,
-                box,
-            )
-            if episode.config.save_smtlib
-            else None,
+            _smtlib_save_fn=(
+                partial(
+                    self.store_smtlib,
+                    episode,
+                    box,
+                )
+                if episode.config.save_smtlib
+                else None
+            ),
         )
 
         return box.false_points(step=box.timestep().lb), explanation
@@ -889,7 +905,9 @@ def _get_witness_assumptions(
         )
         episode._formula_stack.push()
         episode._formula_stack._add_assertion(negated_assumptions)
-        result = self.invoke_solver(solver)
+        result = self.invoke_solver(
+            solver, timeout=episode.config.solver_timeout
+        )
         if result is not None and isinstance(result, pysmtModel):
             # The assumptions are not minimal
             pass
@@ -922,7 +940,9 @@ def _find_witness_points(
                         f"wp_{episode._iteration}.smt2",
                     ),
                 )
-            result = self.invoke_solver(solver)
+            result = self.invoke_solver(
+                solver, timeout=episode.config.solver_timeout
+            )
             if result is not None and isinstance(result, pysmtModel):
                 # Record the false point
                 point = episode._extract_point(result, box)
@@ -967,11 +987,15 @@ def _get_true_points(
                 episode._add_true_point,
                 my_solver,
                 options,
-                _smtlib_save_fn=partial(self.store_smtlib, episode, box)
-                if episode.config.save_smtlib
-                else None,
+                _smtlib_save_fn=(
+                    partial(self.store_smtlib, episode, box)
+                    if episode.config.save_smtlib
+                    else None
+                ),
             )
-            if len(box.true_points(step=box.timestep().lb)) == 0:
+            if len(box.true_points(step=box.timestep().lb)) == 0 or isinstance(
+                explanation, TimeoutExplanation
+            ):
                 # Could not find a point at the current step, so there won't be any at subsequent steps
                 # fall out of loop, after setting the upper bound on the box timestep
                 # if couldn't find a point, then remove all points from box
@@ -989,6 +1013,17 @@ def _get_true_points(
                 # lb == ub and have a point, so break
                 break
 
+            # if (
+            #     len(box.false_points(step=box.timestep().lb)) > 0
+            #     and len(box.true_points(step=box.timestep().lb)) > 0
+            # ):
+            #     # Do not continue if there is a true and a false point.  It means we already know we need to split this box.
+            #     break
+
+            # if box.timestep().lb == 0:
+            #     # Don't check for later timepoints when looking at the initial time step.  This helps evaluate parameter constraints that would only apply to the initial time step.
+            #     break
+
         # reinstate the original lower bound on timestep so that we will check
         # whether no false points exist in the main loop of the box search
         box.timestep().lb = original_box_timestep_lb
@@ -1007,7 +1042,9 @@ def get_box_corners(
             ]._encoder.point_to_smt(point)
             episode._formula_stack.push()
             episode._formula_stack.add_assertion(f)
-            result = self.invoke_solver(solver)
+            result = self.invoke_solver(
+                solver, timeout=episode.config.solver_timeout
+            )
             if result is not None and isinstance(result, pysmtModel):
                 corner_point = episode._extract_point(result, box)
                 box.corner_points.append(corner_point)
@@ -1056,13 +1093,14 @@ def _expand(
         """
         process_name = f"Expander_{(idx if idx else 'S')}_p{os.getpid()}"
         # l = self._logger(episode.config, process_name=process_name)
-
+        last_progress = -1.0
         try:
             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 episode.problem.parameters] if episode.config.dreal_prefer_parameters else [],
                 }
             else:
                 opts = {}
@@ -1100,6 +1138,9 @@ def _expand(
                             continue
                     else:
                         l.debug(f"Expanding box: {box}")
+                        l.debug(
+                            f"Evaluating box: +: {len(box.true_points())}, -: {len(box.false_points())}, H: {box.point_entropy()}"
+                        )
                         # Setup the model constraints up to the box.timestep.lb and add box constraints
                         self._initialize_model_for_box(
                             solver, box, episode, options
@@ -1160,7 +1201,9 @@ def _expand(
                                     f"Split @ {box.timestep().lb}, (width: {box.width():.5f} (raw) {box.normalized_width():.5f} (norm))"
                                 )
                                 l.trace(f"XXX Split:\n{box}")
-                            else:
+                            elif isinstance(
+                                not_false_explanation, BoxExplanation
+                            ):
                                 # box does not intersect f, so it is in t (true region)
                                 curr_step_box = box.current_step()
                                 episode._add_true(
@@ -1172,22 +1215,42 @@ 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
                                 next_box = box.advance()
                                 if next_box:
                                     episode._add_unknown(next_box)
-                        else:
+                            else:  # Timeout FIXME copy of split code
+                                if self._split(
+                                    box,
+                                    episode,
+                                    points=[true_points, false_points],
+                                ):
+                                    l.trace(f"{process_name} produced work")
+                                else:
+                                    rval.put(box.model_dump())
+                                if episode.config.number_of_processes > 1:
+                                    # FIXME This would only be none when
+                                    # the number of processes is 1. This
+                                    # can be done more cleanly.
+                                    if more_work:
+                                        with more_work:
+                                            more_work.notify_all()
+                                l.debug(
+                                    f"Split @ {box.timestep().lb}, (width: {box.width():.5f} (raw) {box.normalized_width():.5f} (norm))"
+                                )
+                                l.trace(f"XXX Split:\n{box}")
+                        elif isinstance(not_true_explanation, BoxExplanation):
                             if len(box.points) == 0:
                                 # If we cannot find a true point, the box is false and we may have not computed any false points, so ensure we have at least one.
                                 self._get_false_points(
@@ -1206,22 +1269,49 @@ 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
+                            if self._split(
+                                box,
+                                episode,
+                                points=[true_points],
+                            ):
+                                l.trace(f"{process_name} produced work")
+                            else:
+                                rval.put(box.model_dump())
+                            if episode.config.number_of_processes > 1:
+                                # FIXME This would only be none when
+                                # the number of processes is 1. This
+                                # can be done more cleanly.
+                                if more_work:
+                                    with more_work:
+                                        more_work.notify_all()
+                            l.debug(
+                                f"Split @ {box.timestep().lb}, (width: {box.width():.5f} (raw) {box.normalized_width():.5f} (norm))"
+                            )
+                            l.trace(f"XXX Split:\n{box}")
                         episode._formula_stack.pop()  # Remove box constraints from solver
                         episode._on_iteration()
                         if handler:
                             handler(rval, episode.config, all_results)
-                            if "progress" in all_results:
+                            if (
+                                "progress" in all_results
+                                and all_results["progress"].progress
+                                > last_progress
+                            ):
+                                last_progress = all_results[
+                                    "progress"
+                                ].progress
                                 l.info(all_results["progress"])
                         l.trace(f"{process_name} finished work")
                 self._initialize_model_encoding(
@@ -1443,7 +1533,11 @@ def handler(rval, config: "FUNMANConfig", results) -> Dict[str, Any]:
                     config=config, problem=problem, schedule=schedule
                 )
                 episode._initialize_boxes(config.num_initial_boxes, schedule)
-                options = EncodingOptions(schedule=schedule)
+                options = EncodingOptions(
+                    schedule=schedule,
+                    normalize=config.normalize,
+                    normalization_constant=config.normalization_constant,
+                )
                 self._expand(
                     rval,
                     episode,
@@ -1471,6 +1565,8 @@ def handler(rval, config: "FUNMANConfig", results) -> Dict[str, Any]:
             options = EncodingOptions(
                 num_steps=structural_configuration["num_steps"],
                 step_size=structural_configuration["step_size"],
+                normalize=config.normalize,
+                normalization_constant=config.normalization_constant,
             )
             self._expand(
                 rval,
@@ -1504,6 +1600,8 @@ def _search_mp(
             options = EncodingOptions(
                 num_steps=structural_configuration["num_steps"],
                 step_size=structural_configuration["step_size"],
+                normalize=config.normalize,
+                normalization_constant=config.normalization_constant,
             )
             expand_count = processes - 1
             episode._initialize_boxes(expand_count)
diff --git a/src/funman/search/search.py b/src/funman/search/search.py
index 24502a16..110385d5 100644
--- a/src/funman/search/search.py
+++ b/src/funman/search/search.py
@@ -9,18 +9,64 @@
 
 import pysmt
 from pydantic import BaseModel, ConfigDict
-from pysmt.shortcuts import Solver
+from pysmt.shortcuts import TRUE, Solver
 from pysmt.solvers.solver import Model as pysmtModel
 
 from funman import Box, Interval, ModelParameter
 from funman.translate.translate import EncodingSchedule
 
 from ..config import FUNMANConfig
-from ..representation.explanation import BoxExplanation
+from ..representation.explanation import BoxExplanation, TimeoutExplanation
 from ..scenario.scenario import AnalysisScenario
 
 l = logging.getLogger(__name__)
 
+import queue
+from multiprocessing import JoinableQueue, Process
+
+
+def run_with_limited_time(func, args, kwargs, time):
+    p = Process(target=func, args=args, kwargs=kwargs)
+    l.debug(f"start: {datetime.datetime.now()}, with timout: {time}")
+    p.start()
+    # p.join(timeout=time)
+    try:
+        result = args[-1].get(timeout=time)
+
+    except queue.Empty:
+        l.debug("empty queue")
+        result = None
+        # l.debug("get timedout or done")
+        # try:
+        #     args[-1].task_done()
+        #     l.debug("signaled q task done")
+        # except ValueError:
+        #     pass
+
+    try:
+        args[-1].task_done()
+        l.debug("signaled q task done")
+    except ValueError:
+        pass
+
+    l.debug("Awaiting join ...")
+    p.join(timeout=time)
+    l.debug("joined process")
+
+    # print(f"exitcode: {p.exitcode}, return: {r}")
+    # print(args)
+    if p.is_alive():
+        l.debug(f"kill: {datetime.datetime.now()}")
+        p.terminate()
+        # sys.exit(1)
+        return None
+    # p.join()
+    l.debug(f"end: {datetime.datetime.now()}")
+    # print(args[-1].get())
+    # sys.exit(1)
+    return result
+    # return True
+
 
 class SearchStatistics(BaseModel):
     model_config = ConfigDict(
@@ -117,14 +163,49 @@ def search(
     ) -> SearchEpisode:
         pass
 
-    def invoke_solver(self, s: Solver) -> Union[pysmtModel, BoxExplanation]:
-        l.trace("Invoking solver ...")
+    def invoke_solver(
+        self, s: Solver, timeout: int = None
+    ) -> Union[pysmtModel, BoxExplanation]:
+        l.debug("Invoking solver ...")
+        q = JoinableQueue()
+
+        if timeout is not None:
+            result = run_with_limited_time(
+                Search._internal_invoke_solver, (self, s, q), {}, timeout
+            )
+            # print(f"get from q, empty?: {q.empty()} ")
+
+            if result is None:
+                result = TimeoutExplanation()
+                result.set_expression(TRUE())
+        else:
+            result = self._internal_invoke_solver(s, None)
+        # print(f"invoke_solver, result: [{result}]")
+        return result
+
+    # @timeout_decorator.timeout(1)
+    def _internal_invoke_solver(self, s: Solver, q):
+        l.debug("Solver started")
         result = s.solve()
         l.trace(f"Solver result = {result}")
-        if result:
-            result = s.get_model()
-        else:
-            result = BoxExplanation()
-            result._expression = s.get_unsat_core()
 
+        try:
+            if result:
+                # print(f"put: {s.get_model()}")
+                result = s.get_model()
+                if q:
+                    q.put(result)
+            else:
+
+                result = BoxExplanation()
+                result.set_expression(s.get_unsat_core())
+                # print(f"put: {result}")
+                if q:
+                    q.put(result)
+
+        except RecursionError:
+            l.error("Recursion error pickling solver result in queue.")
+            pass
+        l.debug("Solver completed")
+        # print(result)
         return result
diff --git a/src/funman/search/smt_check.py b/src/funman/search/smt_check.py
index 675e1cd2..c60618fb 100644
--- a/src/funman/search/smt_check.py
+++ b/src/funman/search/smt_check.py
@@ -50,7 +50,11 @@ def search(
             episode = SearchEpisode(
                 config=config, problem=problem, schedule=schedule
             )
-            options = EncodingOptions(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(
@@ -100,7 +104,7 @@ def search(
                 box = Box(
                     bounds={
                         p.name: p.interval.model_copy()
-                        for p in problem.parameters
+                        for p in problem.model_parameters()
                     },
                     label=LABEL_FALSE,  # lack of a point means this must be a false box
                     points=[],
@@ -198,7 +202,7 @@ def solve_formula(
                 filename=filename,
             )
         l.trace(f"Solving: {formula.serialize()}")
-        result = self.invoke_solver(s)
+        result = self.invoke_solver(s, timeout=episode.config.solver_timeout)
         s.pop(1)
         l.trace(f"Result: {type(result)}")
         return result
@@ -218,6 +222,7 @@ def expand(
                 "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 = {}
diff --git a/src/funman/server/query.py b/src/funman/server/query.py
index 422544ac..20d3d74b 100644
--- a/src/funman/server/query.py
+++ b/src/funman/server/query.py
@@ -1,5 +1,7 @@
+import logging
 import random
 from collections import Counter
+from datetime import datetime, timedelta
 from typing import Dict, List, Optional, Tuple, Union
 
 import pandas as pd
@@ -12,6 +14,7 @@
 from funman.model.decapode import DecapodeModel
 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.petrinet import GeneratedPetriNetModel, PetrinetModel
 from funman.model.query import QueryAnd, QueryFunction, QueryLE, QueryTrue
 from funman.model.regnet import GeneratedRegnetModel, RegnetModel
@@ -37,6 +40,8 @@
 
 from ..representation.parameter_space import ParameterSpace
 
+l = logging.getLogger(__name__)
+
 
 class FunmanWorkRequest(BaseModel):
     query: Optional[Union[QueryAnd, QueryLE, QueryFunction, QueryTrue]] = None
@@ -44,9 +49,12 @@ class FunmanWorkRequest(BaseModel):
     parameters: Optional[List[ModelParameter]] = None
     config: Optional[FUNMANConfig] = None
     structure_parameters: Optional[
-        List[Union[Union[NumSteps, StepSize], Schedules]]
+        List[Union[Schedules, Union[NumSteps, StepSize]]]
     ] = None
 
+    def parameter(self, name: str) -> ModelParameter:
+        return next(iter([x for x in self.parameters if x.name == name]))
+
     @field_validator("constraints")
     @classmethod
     def check_unique_names(
@@ -136,6 +144,7 @@ def to_scenario(
             not hasattr(self.request, "parameters")
             or self.request.parameters is None
             or all(p.label == LABEL_ANY for p in self.request.parameters)
+            or all(p.width() == 0.0 for p in self.request.parameters)
         ):
             return ConsistencyScenario(
                 model=self.model,
@@ -157,6 +166,38 @@ def to_scenario(
         )
 
 
+class FunmanResultsTiming(BaseModel):
+    start_time: datetime = None
+    end_time: Optional[datetime] = None
+    total_time: Optional[timedelta] = None
+    solver_time: Optional[timedelta] = None
+    encoding_time: Optional[timedelta] = None
+    progress_timeseries: List[Tuple[datetime, float]] = []
+
+    def update_progress(
+        self, progress, granularity=timedelta(seconds=1)
+    ) -> None:
+        last_update = (
+            self.progress_timeseries[-1][0]
+            if len(self.progress_timeseries) > 0
+            else None
+        )
+
+        now = datetime.now()
+
+        if last_update is not None:
+            time_delta = now - last_update
+        else:
+            time_delta = now - self.start_time
+
+        if time_delta > granularity:
+            self.progress_timeseries.append((now, progress))
+
+    def finalize(self):
+        """Calculate total time"""
+        self.total_time = self.end_time - self.start_time
+
+
 class FunmanResults(BaseModel):
     _finalized: bool = False
 
@@ -174,11 +215,41 @@ class FunmanResults(BaseModel):
     request: FunmanWorkRequest
     done: bool = False
     error: bool = False
+    error_message: Optional[str] = None
     parameter_space: Optional[ParameterSpace] = None
+    timing: FunmanResultsTiming = FunmanResultsTiming()
+    contracted_model: Optional[GeneratedPetriNet] = None
+
+    def start(self):
+        self.timing.start_time = datetime.now()
+
+    def stop(self):
+        self.timing.end_time = datetime.now()
+        self.timing.finalize()
 
     def is_final(self):
         return self._finalized
 
+    def contract_model(self):
+        """
+        Use the parameter_space to contract the model parameter bounds and set self.contracted_model
+
+        """
+        if not isinstance(self.model, GeneratedPetriNetModel):
+            raise NotImplemented(
+                f"Cannot contract model of type {type(self.model)}"
+            )
+
+        # Get new bounds for each parameter
+        amr_parameters = self.model._parameter_names()
+        parameter_bounds = {
+            param: self.parameter_space.outer_interval(param)
+            for param in amr_parameters
+        }
+        self.contracted_model = self.model.contract_parameters(
+            parameter_bounds
+        )
+
     def update_parameter_space(
         self, scenario: AnalysisScenario, results: ParameterSpace
     ) -> FunmanProgress:
@@ -207,6 +278,10 @@ 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)
+
+        self.contract_model()
+
         return self.progress
 
     def finalize_result(
@@ -227,18 +302,21 @@ def finalize_result(
 
         if ps is None:
             raise Exception("No ParameterSpace for result")
-
-        self.update_parameter_space(scenario, ps)
+        try:
+            self.update_parameter_space(scenario, ps)
+        except Exception as e:
+            l.error(
+                f"Could not update the parameter space while finalizing the result because: {e}"
+            )
         self.done = True
         self.progress.progress = 1.0
 
-    def finalize_result_as_error(
-        self,
-    ):
+    def finalize_result_as_error(self, message=None):
         if self._finalized:
             raise Exception("FunmanResults was already finalized")
         self._finalized = True
         self.error = True
+        self.error_message = message
         self.done = True
         self.progress.progress = 1.0
 
diff --git a/src/funman/server/storage.py b/src/funman/server/storage.py
index c6267b8c..b96ccfd8 100644
--- a/src/funman/server/storage.py
+++ b/src/funman/server/storage.py
@@ -59,8 +59,8 @@ def add_result(self, result: FunmanResults):
                 raise FunmanException(
                     f"Id {id} does not exist in results (is unclaimed)."
                 )
-            if self.results[id] is not None:
-                raise FunmanException(f"Id {id} was already set to a value.")
+            # if self.results[id] is not None:
+            #     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))
@@ -72,7 +72,9 @@ def get_result(self, id: str) -> FunmanResults:
                 return self.results[id]
             path = self.path / f"{id}.json"
             if not path.is_file():
-                raise NotFoundFunmanException("Result for id '{id}' not found")
+                raise NotFoundFunmanException(
+                    f"Result for id '{id}' not found"
+                )
             with open(path, "r") as f:
                 result = FunmanResults.model_validate(json.load(f))
             self.results[id] = result
diff --git a/src/funman/server/worker.py b/src/funman/server/worker.py
index 1fb3d648..28afbed9 100644
--- a/src/funman/server/worker.py
+++ b/src/funman/server/worker.py
@@ -1,7 +1,6 @@
 import copy
 import logging
 import queue
-import sys
 import threading
 import traceback
 from enum import Enum
@@ -75,12 +74,17 @@ def in_state(self, state: WorkerState) -> bool:
     def enqueue_work(
         self, model: FunmanModel, request: FunmanWorkRequest
     ) -> FunmanWorkUnit:
-        if not self.in_state(WorkerState.RUNNING):
-            raise FunmanWorkerException(
-                f"FunmanWorker must be starting or running to enqueue work: {self.get_state()}"
-            )
         id = self.storage.claim_id()
         work = FunmanWorkUnit(id=id, model=model, request=request)
+
+        self.storage.add_result(
+            FunmanResults(
+                id=id,
+                model=work.model,
+                request=work.request,
+                parameter_space=ParameterSpace(),
+            )
+        )
         self.queue.put(work)
         with self._set_lock:
             self.queued_ids.add(work.id)
@@ -142,14 +146,15 @@ def is_processing_id(self, id: str):
             return self.current_id == id
 
     def get_results(self, id: str):
-        if not self.in_state(WorkerState.RUNNING):
-            raise FunmanWorkerException(
-                f"FunmanWorker must be running to get results: {self.get_state()}"
-            )
-        with self._id_lock:
-            if self.current_id == id:
-                return copy.copy(self.current_results)
+        if not self.in_state(WorkerState.RUNNING) or self.current_id != id:
+            # raise FunmanWorkerException(
+            #     f"FunmanWorker must be running to get results: {self.get_state()}"
+            # )
             return self.storage.get_result(id)
+        with self._id_lock:
+            # if self.current_id == id:
+            return copy.copy(self.current_results)
+            # return self.storage.get_result(id)
 
     def halt(self, id: str):
         if not self.in_state(WorkerState.RUNNING):
@@ -215,15 +220,13 @@ def _run(self, stop_event: threading.Event):
 
                 with self._id_lock:
                     self.current_id = work.id
-                    self.current_results = FunmanResults(
-                        id=work.id,
-                        model=work.model,
-                        request=work.request,
-                        parameter_space=ParameterSpace(),
+                    self.current_results = self.storage.get_result(
+                        self.current_id
                     )
 
                 l.info(f"Starting work on: {work.id}")
                 try:
+                    self.current_results.start()
                     # convert to scenario
                     scenario = work.to_scenario()
 
@@ -246,25 +249,30 @@ def _run(self, stop_event: threading.Event):
                         self.current_results.finalize_result(scenario, result)
                     l.info(f"Completed work on: {work.id}")
                 except Exception as e:
-                    l.exception(
-                        f"Internal Server Error ({work.id}):", file=sys.stderr
-                    )
+                    l.error(f"Internal Server Error ({work.id}):")
                     traceback.print_exc()
                     with self._results_lock:
-                        self.current_results.finalize_result_as_error()
-                    l.exception(f"Aborting work on: {work.id}")
+                        self.current_results.finalize_result_as_error(
+                            message=str(e)
+                        )
+                    l.error(f"Aborting work on: {work.id}")
                 finally:
+                    self.current_results.stop()
                     self.storage.add_result(self.current_results)
                     self.queue.task_done()
                     with self._id_lock:
                         self.current_id = None
                         self.current_results = None
-        except Exception:
-            l.exception("Fatal error in worker!", file=sys.stderr)
+        except Exception as e:
+            l.error("Fatal error in worker!")
             traceback.print_exc()
             # Only mark the state as errored if the thread
             # has not yet been told to stop
             if not stop_event.is_set():
                 with self._state_lock:
                     self._state = WorkerState.ERRORED
+                    result = self.storage.get_result(work.id)
+                    result.error = True
+                    result.done = True
+                    # self.storage.add_result(result)
         l.info("FunmanWorker exiting...")
diff --git a/src/funman/translate/__init__.py b/src/funman/translate/__init__.py
index 6e44c3a5..778ab051 100644
--- a/src/funman/translate/__init__.py
+++ b/src/funman/translate/__init__.py
@@ -1,12 +1,14 @@
 """
 The funman.translate package defines how to translate funman.model models into SMTLib format for different analysis scenarios.
 """
+
 from .encoding import *
 from .simplifier import *
 from .translate import *
 from .bilayer import *
 from .encoded import *
-from .gromet import *
+
+# from .gromet import *
 from .petrinet import *
 from .ensemble import *
 from .simplifier import *
diff --git a/src/funman/translate/bilayer.py b/src/funman/translate/bilayer.py
index 3c654fb4..3b705644 100644
--- a/src/funman/translate/bilayer.py
+++ b/src/funman/translate/bilayer.py
@@ -2,6 +2,7 @@
 This module encodes bilayer models into a SMTLib formula.  
 
 """
+
 import logging
 from functools import reduce
 from typing import Dict, Set, Tuple
@@ -219,7 +220,7 @@ def encode_model(
                         }
                     )
                 parameter_constraints = self.box_to_smt(
-                    parameter_box, closed_upper_bound=True
+                    parameter_box  # , closed_upper_bound=True
                 )
             else:
                 parameter_constraints = TRUE()
diff --git a/src/funman/translate/encoded.py b/src/funman/translate/encoded.py
index 2b6ebcec..da497742 100644
--- a/src/funman/translate/encoded.py
+++ b/src/funman/translate/encoded.py
@@ -2,6 +2,7 @@
 This module defines enocoders for already encoded models.  (Technically, a
 pass-through that helps make the encoder abstraction uniform.)
 """
+
 from typing import List
 
 from funman.model import FunmanModel
diff --git a/src/funman/translate/encoding.py b/src/funman/translate/encoding.py
index f6f42c5a..38d39199 100644
--- a/src/funman/translate/encoding.py
+++ b/src/funman/translate/encoding.py
@@ -1,5 +1,5 @@
 import logging
-from typing import Dict, List, Union
+from typing import Dict, List, Optional, Union
 
 from pydantic import BaseModel, ConfigDict
 from pysmt.formula import FNode
@@ -17,7 +17,7 @@ class EncodingOptions(BaseModel):
 
     schedule: EncodingSchedule
     normalize: bool = False
-    normalization_constant: float = 1.0
+    normalization_constant: Optional[float] = 1.0
 
 
 class Encoding(BaseModel):
diff --git a/src/funman/translate/ensemble.py b/src/funman/translate/ensemble.py
index b14d7137..3a4b7b42 100644
--- a/src/funman/translate/ensemble.py
+++ b/src/funman/translate/ensemble.py
@@ -1,7 +1,9 @@
+from numbers import Number
 from typing import Dict, Set
 
 from pysmt.formula import FNode
 from pysmt.shortcuts import REAL, TRUE, And, Symbol, Times, substitute
+from pysmt.solvers.solver import Model as pysmtModel
 
 from funman.model.model import FunmanModel
 
@@ -100,3 +102,39 @@ def _get_timed_symbols(self, model: FunmanModel) -> Set[str]:
             state variable names
         """
         return set(model._state_var_names())
+
+    def symbol_values(
+        self, model_encoding: Encoding, pysmtModel: pysmtModel
+    ) -> Dict[str, Dict[str, float]]:
+        """
+         Get the value assigned to each symbol in the pysmtModel.
+
+        Parameters
+        ----------
+        model_encoding : Encoding
+            encoding using the symbols
+        pysmtModel : pysmt.solvers.solver.Model
+            assignment to symbols
+
+        Returns
+        -------
+        Dict[str, Dict[str, float]]
+            mapping from symbol and timepoint to value
+        """
+
+        vars = self._symbols(model_encoding.symbols())
+        vals = {}
+        for var, model_vars in vars.items():
+            for model_idx, model_var in model_vars.items():
+                vals[var] = {}
+                for t in vars[var]:
+                    try:
+                        symbol = vars[var][t]
+                        value = pysmtModel.get_py_value(symbol)
+                        # value = pysmtModel.completed_assignment[symbol]
+                        if isinstance(value, Number):
+                            value = 0.0
+                        vals[var][t] = float(value)
+                    except OverflowError as e:
+                        l.warning(e)
+        return vals
diff --git a/src/funman/translate/gromet.py b/src/funman/translate/gromet.py
index 974a7e39..21a25ea1 100644
--- a/src/funman/translate/gromet.py
+++ b/src/funman/translate/gromet.py
@@ -2,6 +2,7 @@
 This module encodes Gromet models as SMTLib formulas.
 
 """
+
 from automates.model_assembly.gromet.model.function_type import FunctionType
 from automates.model_assembly.gromet.model.gromet_box_function import (
     GrometBoxFunction,
diff --git a/src/funman/translate/petrinet.py b/src/funman/translate/petrinet.py
index 7142c361..f2058377 100644
--- a/src/funman/translate/petrinet.py
+++ b/src/funman/translate/petrinet.py
@@ -140,13 +140,17 @@ def _encode_next_step(
             }
 
         # for each var, next state is the net flow for the var: sum(inflow) - sum(outflow)
-        net_flows = []
+        # Transition id -> State -> net flow
+        net_flows = {}
+        # transition_values = {}
+        # State -> [Transition id]
+        state_var_flows = {}
         for var in state_vars:
-            state_var_flows = []
+            state_var_id = scenario.model._state_var_id(var)
+            state_var_flows[state_var_id] = []
             for transition in transitions:
-                state_var_id = scenario.model._state_var_id(var)
-
                 transition_id = scenario.model._transition_id(transition)
+
                 outflow = scenario.model._num_flow_from_transition_to_state(
                     state_var_id, transition_id
                 )
@@ -156,23 +160,96 @@ def _encode_next_step(
                 net_flow = outflow - inflow
 
                 if net_flow != 0:
-                    state_var_flows.append(
-                        [
-                            Times(Real(net_flow) * t)
-                            for t in transition_terms[transition_id]
-                        ]
+                    tnf = net_flows.get(transition_id, {})
+                    tnf[state_var_id] = net_flow
+                    net_flows[transition_id] = tnf
+                    # tv = .get(transition_id, {})
+                    # transition_values[transition_id] = [
+                    #     Times(Real(net_flow) * t)
+                    #     for t in transition_terms[transition_id]
+                    # ]
+                    # transition_values[transition_id] = tv
+                    # transition_values[transition_id] = [
+                    #     Times(Real(net_flow) * t)
+                    #     for t in transition_terms[transition_id]
+                    # ]
+                    state_var_flows[state_var_id].append(transition_id)
+
+        # Encode a timed symbol for the transition value
+        # tr_k = flow_k
+        # transition_values_encoded = {
+        #     transition_id: Or(
+        #         [
+        #             Equals(
+        #                 self._encode_state_var(transition_id, time=step),
+        #                 rule,
+        #             )
+        #             for rule in flow
+        #         ]
+        #     ).simplify()
+        #     if self.config.use_transition_symbols else
+        #     Or(flow).simplify()
+        #     for transition_id, flow in transition_values.items()
+        # }
+
+        # Simplify transition_values
+        if self.config.substitute_subformulas:
+            for (
+                transition_id,
+                possible_transitions,
+            ) in transition_terms.items():
+                possible_transitions = [
+                    FUNMANSimplifier.sympy_simplify(
+                        # flows.substitute(substitutions),
+                        to_sympy(
+                            transition.substitute(substitutions).simplify(),
+                            scenario.model._symbols(),
+                        ),
+                        parameters=scenario.model_parameters(),
+                        threshold=self.config.series_approximation_threshold,
+                        taylor_series_order=self.config.taylor_series_order,
                     )
-            if len(state_var_flows) > 0:
-                # FIXME: the below should involve computing update as the cross product of all transition_rate equations
+                    for transition in possible_transitions
+                ]
+
+                substitutions[
+                    self._encode_state_var(transition_id, time=step)
+                ] = transition_terms[transition_id][0]
+
+        # Compose transitions and previous state
+        var_updates = []
+        for var in state_vars:
+            state_var_id = scenario.model._state_var_id(var)
+            if len(state_var_flows[state_var_id]) > 0:
+                # FIXME: the below should involve computing update as the cross product of all transition_rate equations if there is more than one rate per transition
+                #  s + (tr_0 + tr_1 ...) * dt
                 flows = Plus(
+                    current_state[state_var_id],
                     Times(
-                        Real(step_size),
                         Plus(
-                            [s[0] for s in state_var_flows]
-                        ),  # FIXME see above
-                    ),  # .simplify()
-                    current_state[state_var_id],
-                )  # .simplify()
+                            [
+                                (
+                                    self._encode_state_var(
+                                        transition_id, time=step
+                                    )
+                                    if self.config.use_transition_symbols
+                                    else Times(
+                                        Real(
+                                            net_flows[transition_id][
+                                                state_var_id
+                                            ]
+                                        ),
+                                        transition_terms[transition_id][0],
+                                    )  # FIXME see above
+                                )
+                                for transition_id in state_var_flows[
+                                    state_var_id
+                                ]
+                            ]
+                        ),
+                        Real(step_size),
+                    ).simplify(),
+                )
                 if self.config.substitute_subformulas:
                     # flows = flows.substitute(substitutions)
                     flows = FUNMANSimplifier.sympy_simplify(
@@ -189,10 +266,23 @@ def _encode_next_step(
                 flows = current_state[state_var_id]
                 # .substitute(substitutions)
 
-            net_flows.append(Equals(next_state[state_var_id], flows))
+            var_updates.append(Equals(next_state[state_var_id], flows))
             if self.config.substitute_subformulas:
                 substitutions[next_state[state_var_id]] = flows
 
+        if self.config.use_transition_symbols:
+            var_updates.append(
+                And(
+                    [
+                        Equals(
+                            self._encode_state_var(transition_id, time=step),
+                            rate[0],
+                        )
+                        for transition_id, rate in transition_terms.items()
+                    ]
+                )
+            )
+
         # If any variables depend upon time, then time updates need to be encoded.
         if time_var is not None:
             # time_increment = (
@@ -204,12 +294,19 @@ def _encode_next_step(
             # time_update = Equals(next_time_var, time_increment)
             time_update = Equals(next_time_var, next_time)
             if self.config.substitute_subformulas:
-                substitutions[next_time_var] = time_increment
+                substitutions[next_time_var] = next_time
         else:
             time_update = TRUE()
 
+        normalization_constraint = TRUE()
+        # if self.config.normalize:
+        #     normalization_constraint = And([
+        #         And(LE(Real(0.0),  current_state[scenario.model._state_var_id(var)]), LE( current_state[scenario.model._state_var_id(var)], Real(1.0)))
+        #     for var in state_vars
+        #     ])
+
         return (
-            And(net_flows + [time_update]),
+            And(var_updates + [time_update, normalization_constraint]),
             substitutions,
         )
 
@@ -219,20 +316,6 @@ def _define_init(
         initial_state, substitutions = super()._define_init(
             scenario, init_time=init_time
         )
-        # state_var_names = scenario.model._state_var_names()
-        # initial_substitution = {}
-
-        if self.config.use_compartmental_constraints:
-            compartmental_bounds = self._encode_compartmental_bounds(
-                scenario, 0
-            )
-            if self.config.substitute_subformulas and substitutions:
-                compartmental_bounds = compartmental_bounds.substitute(
-                    substitutions
-                ).simplify()
-        else:
-            compartmental_bounds = TRUE()
-        initial_state = And(initial_state, compartmental_bounds).simplify()
 
         return initial_state, substitutions
 
@@ -276,7 +359,7 @@ def _encode_compartmental_bounds(
 
             bounds += [lb, ub]
         # noise_var = Symbol("noise", REAL)
-        noise_const = Real(1e-3)
+        noise_const = Real(self.config.compartmental_constraint_noise)
         sum_vars = Plus(
             [
                 self._encode_state_var(
@@ -286,8 +369,8 @@ def _encode_compartmental_bounds(
             ]
         )
         total = And(
-            LE(sum_vars, Plus(population, noise_const)),
-            LE(Minus(population, noise_const), sum_vars),
+            LE(sum_vars, Plus(population, noise_const).simplify()),
+            LE(Minus(population, noise_const).simplify(), sum_vars),
         )
 
         return And(bounds + [total])
diff --git a/src/funman/translate/translate.py b/src/funman/translate/translate.py
index b82eb1b2..ba5ee9bf 100644
--- a/src/funman/translate/translate.py
+++ b/src/funman/translate/translate.py
@@ -1,6 +1,7 @@
 """
 This module defines the abstract base classes for the model encoder classes in funman.translate package.
 """
+
 import logging
 from abc import ABC
 from typing import Callable, Dict, List, Optional, Set, Tuple, Union
@@ -20,6 +21,7 @@
     Equals,
     Iff,
     Implies,
+    Minus,
     Plus,
     Real,
     Symbol,
@@ -697,9 +699,9 @@ def encode_parameter(
         parameter = constraint.parameter
         if parameter in scenario.model_parameters():
             formula = self.interval_to_smt(
-                parameter.name,
+                parameter._escaped_name,
                 parameter.interval.model_copy(deep=True),
-                closed_upper_bound=False,
+                # closed_upper_bound=False,
                 infinity_constraints=False,
             )
             return (
@@ -723,20 +725,78 @@ def encode_linear_constraint(
         timestep = options.schedule.time_at_step(layer_idx)
         parameters = scenario.parameters
         encoded_vars = [
-            self._encode_state_var(v)
-            if len([p for p in parameters if p.name == v]) > 0
-            else self._encode_state_var(v, time=timestep)
+            (
+                self._encode_state_var(v)
+                if len([p for p in parameters if p.name == v]) > 0
+                else self._encode_state_var(v, time=timestep)
+            )
             for v in vars
         ]
-        expression = Plus(
-            [
-                Times(
-                    Real(weights[i]),
-                    encoded_vars[i],
+        if constraint.derivative:
+            next_timestep = constraint.next_timestep(
+                layer_idx, options.schedule
+            )
+            encoded_dt_vars = [
+                (
+                    self._encode_state_var(v)
+                    if len([p for p in parameters if p.name == v]) > 0
+                    else self._encode_state_var(
+                        v, time=options.schedule.time_at_step(next_timestep)
+                    )
                 )
-                for i in range(len(vars))
+                for v in vars
             ]
-        )
+            curr = (
+                encoded_dt_vars if next_timestep < layer_idx else encoded_vars
+            )
+            curr_t = (
+                options.schedule.time_at_step(next_timestep)
+                if next_timestep < layer_idx
+                else timestep
+            )
+            nxt = (
+                encoded_dt_vars if next_timestep > layer_idx else encoded_vars
+            )
+            nxt_t = (
+                options.schedule.time_at_step(next_timestep)
+                if next_timestep > layer_idx
+                else timestep
+            )
+            expression = Div(
+                Minus(
+                    Plus(
+                        [
+                            Times(
+                                Real(weights[i]),
+                                nxt[i],
+                            )
+                            for i in range(len(vars))
+                        ]
+                    ),
+                    Plus(
+                        [
+                            Times(
+                                Real(weights[i]),
+                                curr[i],
+                            )
+                            for i in range(len(vars))
+                        ]
+                    ),
+                ),
+                Minus(Real(nxt_t), Real(curr_t)),
+            ).simplify()
+            pass
+        else:
+            expression = Plus(
+                [
+                    Times(
+                        Real(weights[i]),
+                        encoded_vars[i],
+                    )
+                    for i in range(len(vars))
+                ]
+            )
+
         if bounds.lb != NEG_INFINITY:
             lb = Real(bounds.lb)
         else:
@@ -748,11 +808,11 @@ def encode_linear_constraint(
             ub = None
 
         if options.normalize:
-            expression = Div(expression, options.normalization_constant)
+            # expression = Div(expression, Real(options.normalization_constant))
             if lb is not None:
-                lb = Div(lb, options.normalization_constant)
+                lb = Div(lb, Real(options.normalization_constant)).simplify()
             if ub is not None:
-                ub = Div(ub, options.normalization_constant)
+                ub = Div(ub, Real(options.normalization_constant)).simplify()
 
         if lb is not None:
             lbe = GE(expression, lb)
@@ -1056,9 +1116,10 @@ def _encode_state_variable_constraint(
                 Interval(
                     lb=lb,
                     ub=ub,
+                    closed_upper_bound=query.interval.closed_upper_bound,
                 ),
                 time=time,
-                closed_upper_bound=False,
+                # closed_upper_bound=query.interval.closed_upper_bound,
                 infinity_constraints=False,
             )
         else:
@@ -1185,7 +1246,7 @@ def interval_to_smt(
         p: str,
         i: Interval,
         time: int = None,
-        closed_upper_bound: bool = False,
+        # closed_upper_bound: bool = False,
         infinity_constraints=False,
     ) -> FNode:
         """
@@ -1213,7 +1274,7 @@ def interval_to_smt(
                 if i.lb != NEG_INFINITY or infinity_constraints
                 else TRUE()
             )
-            upper_ineq = LE if closed_upper_bound else LT
+            upper_ineq = LE if i.closed_upper_bound else LT
             upper = (
                 upper_ineq(symbol, Real(i.ub))
                 if i.ub != POS_INFINITY or infinity_constraints
@@ -1256,7 +1317,7 @@ def box_to_smt(
                 self.interval_to_smt(
                     p,
                     interval,
-                    closed_upper_bound=closed_upper_bound,
+                    # closed_upper_bound=closed_upper_bound,
                     infinity_constraints=infinity_constraints,
                 )
                 for p, interval in box.bounds.items()
diff --git a/src/funman/utils/handlers.py b/src/funman/utils/handlers.py
index 12dcf507..87720bbd 100644
--- a/src/funman/utils/handlers.py
+++ b/src/funman/utils/handlers.py
@@ -1,6 +1,7 @@
 """
 The handlers module includes several search handlers that deal with search episode data, such as plotting and writing results to a file.
 """
+
 import logging
 import traceback
 from abc import ABC, abstractmethod
diff --git a/src/funman/utils/sympy_utils.py b/src/funman/utils/sympy_utils.py
index 9e16ea41..86f66cf4 100644
--- a/src/funman/utils/sympy_utils.py
+++ b/src/funman/utils/sympy_utils.py
@@ -7,7 +7,7 @@
 import pysmt.typing as types
 import sympy
 from pysmt.formula import FNode, FormulaManager
-from pysmt.shortcuts import GE, GT, LE, LT, REAL
+from pysmt.shortcuts import FALSE, GE, GT, LE, LT, REAL, TRUE
 from pysmt.shortcuts import Abs as pysmt_Abs
 from pysmt.shortcuts import (
     And,
@@ -23,6 +23,7 @@
 )
 from pysmt.walkers import IdentityDagWalker
 from sympy import Abs, Add, Expr, Rational, exp, series, symbols, sympify
+from sympy.logic.boolalg import BooleanTrue
 
 l = logging.getLogger(__name__)
 
@@ -151,7 +152,15 @@ def to_sympy(
 
 def substitute(str_expr: str, values: Dict[str, Union[float, str]]):
     # Set which substrings are symbols
-    symbols = {s: Symbol(s) for s in values}
+    symbols = {
+        s: Symbol(
+            s,
+            typename=(
+                types.BOOL if isinstance(values[s], bool) else types.REAL
+            ),
+        )
+        for s in values
+    }
 
     # Get expression
     expr = to_sympy(str_expr, list(values.values()))
@@ -185,6 +194,8 @@ def rate_expr_to_pysmt(expr: Union[str, Expr], state=None):
 
 def sympy_to_pysmt(expr):
     func = expr.func
+    if func.is_Boolean:
+        return TRUE() if isinstance(expr, BooleanTrue) else FALSE()
     if func.is_Mul:
         return sympy_to_pysmt_op(Times, expr)
     elif func.is_Add:
@@ -210,12 +221,26 @@ def sympy_to_pysmt(expr):
             return sympy_to_pysmt_op(GT, expr, explode=True)
         elif func.rel_op == "==":
             return sympy_to_pysmt_op(Equals, expr, explode=True)
+    elif expr.is_Piecewise:
+        return sympy_to_pysmt_piecewise(expr)
     elif expr.is_constant():
         return sympy_to_pysmt_real(expr)
+
     else:
         raise Exception(f"Could not convert expression: {expr}")
 
 
+def sympy_to_pysmt_piecewise(expr):
+    l = list(expr.args)
+    l.reverse()
+    pwf = reduce(
+        lambda x, y: Ite(sympy_to_pysmt(y[1]), sympy_to_pysmt(y[0]), x),
+        l[1:],
+        sympy_to_pysmt(l[0][0]),
+    )
+    return pwf
+
+
 def sympy_to_pysmt_op(op, expr, explode=False):
     terms = [sympy_to_pysmt(arg) for arg in expr.args]
     return op(*terms) if explode else op(terms)
@@ -234,17 +259,21 @@ def sympy_to_pysmt_pow(expr):
 
 def sympy_to_pysmt_real(expr, numerator_digits=6):
     # check if underflow or overflow
-    if not isinstance(expr, float) and (
-        (expr != 0.0 and float(expr) == 0.0)
-        or (not expr.is_infinite and abs(float(expr)) == math.inf)
-    ):
-        # going from sympy to python to pysmt will lose precision
-        # need to convert to a rational first
-        r_expr = Rational(expr)
-        return Div(Real(r_expr.numerator), Real(r_expr.denominator)).simplify()
-    else:
-        return Real(float(expr))
-
+    try:
+        if not isinstance(expr, float) and (
+            (expr != 0.0 and float(expr) == 0.0)
+            or (not expr.is_infinite and abs(float(expr)) == math.inf)
+        ):
+            # going from sympy to python to pysmt will lose precision
+            # need to convert to a rational first
+            r_expr = Rational(expr)
+            return Div(
+                Real(r_expr.numerator), Real(r_expr.denominator)
+            ).simplify()
+        else:
+            return Real(float(expr))
+    except Exception as e:
+        pass
     # rnd_expr = Float(expr, 5)
     # r_expr = Rational(rnd_expr)
     # f_expr = Fraction(int(r_expr.numerator), int(r_expr.denominator))
@@ -253,7 +282,7 @@ def sympy_to_pysmt_real(expr, numerator_digits=6):
     # try:
     #     trunc_f_expr = f_expr.limit_denominator(max_denominator=max_denominator)
     # except Exception as e:
-    #     l.exception(f"max_denominator = {max_denominator} is not large enough to limit the denominator of {expr} during conversion from sympy to pysmt")
+    #     l.error(f"max_denominator = {max_denominator} is not large enough to limit the denominator of {expr} during conversion from sympy to pysmt")
 
     # r_value = Div(Real(trunc_f_expr.numerator), Real(trunc_f_expr.denominator)).simplify()
 
diff --git a/terarium/scripts/get_status.py b/terarium/scripts/get_status.py
index 9de30625..9a24d682 100755
--- a/terarium/scripts/get_status.py
+++ b/terarium/scripts/get_status.py
@@ -1,11 +1,13 @@
 #!/usr/bin/env python3
 
 import json
-import sys
+import logging
 
 import requests
 from post_query import QUERIES_ENDPOINT
 
+l = logging.getLogger(__name__)
+
 
 def make_status_endpoint(work_id):
     return f"{QUERIES_ENDPOINT}/{work_id}"
@@ -30,9 +32,9 @@ def get_status(url: str, uuid: str, timeout: float = None):
     parser.add_argument("uuid", type=str, help="the uuid of the request")
     args = parser.parse_args()
 
-    print(f"Getting status for query: {args.uuid}", file=sys.stderr)
+    l.info(f"Getting status for query: {args.uuid}")
     results = get_status(args.url, args.uuid)
 
     results["model"] = "Removed for brevity"
     results["request"] = "Removed for brevity"
-    print(json.dumps(results, indent=2), file=sys.stdout)
+    l.info(json.dumps(results, indent=2))
diff --git a/terarium/scripts/post_query.py b/terarium/scripts/post_query.py
index 3e98c435..917e4c69 100755
--- a/terarium/scripts/post_query.py
+++ b/terarium/scripts/post_query.py
@@ -1,11 +1,13 @@
 #!/usr/bin/env python3
 
 import json
-import sys
+import logging
 from pathlib import Path
 
 import requests
 
+l = logging.getLogger(__name__)
+
 QUERIES_ENDPOINT = "/api/queries"
 
 
@@ -13,7 +15,7 @@ def post_query(
     url: str, model_path: str, request_path: str, timeout: float = None
 ):
     if request_path is None:
-        print("Falling back to default request of {}", file=sys.stderr)
+        l.info("Falling back to default request of {}")
         request = {}
         payload = [f'"model": <Contents of {model_path}>', '"request": {}']
     else:
@@ -22,10 +24,10 @@ def post_query(
             f'"model": <Contents of {model_path}>',
             f'"request": <Contents of {request_path}>',
         ]
-    print("{", file=sys.stderr)
+    l.error("{")
     for p in payload:
-        print(f"    {p}", file=sys.stderr)
-    print("}", file=sys.stderr)
+        l.error(f"    {p}")
+    l.error("}")
 
     model = read_to_dict(model_path)
     endpoint = f"{url.rstrip('/')}{QUERIES_ENDPOINT}"
@@ -58,5 +60,5 @@ def read_to_dict(path: str):
     args = parser.parse_args()
 
     results = post_query(args.url, args.model, args.request)
-    print(f"Query received work id: {results['id']}", file=sys.stderr)
-    print(results["id"], file=sys.stdout)
+    l.error(f"Query received work id: {results['id']}")
+    l.info(results["id"])
diff --git a/terarium/scripts/terarium_examples.py b/terarium/scripts/terarium_examples.py
index a7951aa5..04e12d2f 100755
--- a/terarium/scripts/terarium_examples.py
+++ b/terarium/scripts/terarium_examples.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import json
+import logging
 import sys
 from pathlib import Path
 from time import sleep
@@ -8,6 +9,8 @@
 from get_status import get_status, make_status_endpoint
 from post_query import QUERIES_ENDPOINT, post_query
 
+l = logging.getLogger(__name__)
+
 
 def read_to_dict(path: str):
     fpath = Path(path).resolve()
@@ -41,48 +44,48 @@ def read_to_dict(path: str):
     names = list(examples.keys())
 
     if args.run is None:
-        print("Available examples:")
-        print("-  " + "\n-  ".join(names))
+        l.info("Available examples:")
+        l.info("-  " + "\n-  ".join(names))
         sys.exit()
     if args.run not in examples:
-        print(f"'{args.run}' is not a valid example!")
-        print("Available examples:")
-        print("-  " + "\n-  ".join(names))
+        l.info(f"'{args.run}' is not a valid example!")
+        l.info("Available examples:")
+        l.info("-  " + "\n-  ".join(names))
         sys.exit()
 
     example = examples[args.run]
-    print(f"Running example '{args.run}':\n{json.dumps(example, indent=2)}")
+    l.info(f"Running example '{args.run}':\n{json.dumps(example, indent=2)}")
 
     model_path = prefix / example["model-path"]
     request_path = None
     if example["request-path"] is not None:
         request_path = prefix / example["request-path"]
-    print(delim)
+    l.info(delim)
 
-    print(
+    l.info(
         f"Making POST request to {args.url}{QUERIES_ENDPOINT} with contents:"
     )
     results = post_query(args.url, model_path, request_path)
-    print(delim)
+    l.info(delim)
 
-    print("Response for query:")
+    l.info("Response for query:")
     results["model"] = "Removed for brevity"
     results["request"] = "Removed for brevity"
-    print(json.dumps(results, indent=2), file=sys.stdout)
-    print(delim)
+    l.info(json.dumps(results, indent=2))
+    l.info(delim)
 
     work_id = results["id"]
-    print(f"Work Id is '{work_id}'")
-    print("Waiting for 5 seconds...")
+    l.info(f"Work Id is '{work_id}'")
+    l.info("Waiting for 5 seconds...")
     sleep(5)
-    print(delim)
+    l.info(delim)
 
-    print(f"Making GET request to {args.url}{make_status_endpoint(work_id)}:")
+    l.info(f"Making GET request to {args.url}{make_status_endpoint(work_id)}:")
     results = get_status(args.url, work_id)
     results["model"] = "Removed for brevity"
     results["request"] = "Removed for brevity"
-    print(json.dumps(results, indent=2), file=sys.stdout)
-    print(delim)
+    l.info(json.dumps(results, indent=2))
+    l.info(delim)
 
-    print("The resulting ParameterSpace is:")
-    print(json.dumps(results["parameter_space"], indent=2), file=sys.stdout)
+    l.info("The resulting ParameterSpace is:")
+    l.info(json.dumps(results["parameter_space"], indent=2))
diff --git a/test/test_intersect.py b/test/test_intersect.py
new file mode 100644
index 00000000..828442bd
--- /dev/null
+++ b/test/test_intersect.py
@@ -0,0 +1,148 @@
+import unittest
+
+from funman.representation import Box, Interval, ParameterSpace
+
+
+class TestCompilation(unittest.TestCase):
+    def test_interval_intersection(self):
+        interval_1 = Interval(lb=0, ub=3)
+        interval_2 = Interval(lb=2, ub=5)
+        assert interval_1.intersection(interval_2) == Interval(lb=2.0, ub=3.0)
+
+    def test_box_intersection_identical(self):
+        box1 = Box(
+            bounds={
+                "x": Interval(lb=0, ub=2),
+                "timestep": Interval(lb=0, ub=0),
+            },
+            label="true",
+        )
+        box2 = Box(
+            bounds={
+                "x": Interval(lb=0, ub=2),
+                "timestep": Interval(lb=0, ub=0),
+            },
+            label="true",
+        )
+        result = box1.intersection(box2)
+        print("box intersection result:", result.__dict__)
+        assert (result == box1) and (
+            result == box2
+        )  # Since these boxes are identical, their intersection will be equal to each original box
+
+    def test_box_intersection_identical_diff_labels(self):
+        box1 = Box(
+            bounds={
+                "x": Interval(lb=0, ub=2),
+                "timestep": Interval(lb=0, ub=0),
+            },
+            label="true",
+        )
+        box2 = Box(
+            bounds={
+                "x": Interval(lb=0, ub=2),
+                "timestep": Interval(lb=0, ub=0),
+            },
+            label="false",
+        )
+        result = box1.intersection(box2)
+        assert result.bounds == {}  # Empty intersection
+
+    def test_box_intersection_nontrivial(
+        self,
+    ):  # Not intersecting on all dimensions, so intersection will be empty
+        box1 = Box(
+            bounds={
+                "x": Interval(lb=0, ub=2),
+                "y": Interval(lb=0, ub=2),
+                "timestep": Interval(lb=0, ub=0),
+            },
+            label="true",
+        )
+        box2 = Box(
+            bounds={
+                "x": Interval(lb=11, ub=13),
+                "y": Interval(lb=0, ub=2),
+                "timestep": Interval(lb=0, ub=0),
+            },
+            label="true",
+        )
+        result = box1.intersection(box2)
+        assert result is None
+
+    def test_box_intersection_nonidentical(self):  # Non-empty, non-identical
+        box1 = Box(
+            bounds={
+                "x": Interval(lb=0, ub=2),
+                "y": Interval(lb=0, ub=2),
+                "timestep": Interval(lb=0, ub=0),
+            },
+            label="true",
+        )
+        box2 = Box(
+            bounds={
+                "x": Interval(lb=1, ub=3),
+                "y": Interval(lb=1, ub=3),
+                "timestep": Interval(lb=0, ub=0),
+            },
+            label="true",
+        )
+        result = box1.intersection(box2)
+        assert result.bounds["x"] == result.bounds["y"]
+
+    def test_ps_intersection(self):
+        # Set up two parameter spaces to intersect
+        ps1 = ParameterSpace(num_dimensions=2)
+        ps2 = ParameterSpace(num_dimensions=2)
+        ps1.append_result(
+            {
+                "type": "box",
+                "label": "true",
+                "bounds": {
+                    "inf_o_o": {"lb": 0.0, "ub": 0.3},
+                    "rec_o_o": {"lb": 0.0, "ub": 0.5},
+                    "timestep": {"lb": 1.0, "ub": 1.0},
+                },
+            }
+        )
+        ps1.append_result(
+            {
+                "type": "box",
+                "label": "true",
+                "bounds": {
+                    "inf_o_o": {"lb": 0.5, "ub": 1.0},
+                    "rec_o_o": {"lb": 0.5, "ub": 1.0},
+                    "timestep": {"lb": 1.0, "ub": 1.0},
+                },
+            }
+        )
+        ps2.append_result(
+            {
+                "type": "box",
+                "label": "true",
+                "bounds": {
+                    "inf_o_o": {"lb": 0.0, "ub": 0.3},
+                    "rec_o_o": {"lb": 0.0, "ub": 0.5},
+                    "timestep": {"lb": 1.0, "ub": 1.0},
+                },
+            }
+        )
+        ps2.append_result(
+            {
+                "type": "box",
+                "label": "true",
+                "bounds": {
+                    "inf_o_o": {"lb": 0.75, "ub": 1.25},
+                    "rec_o_o": {"lb": 0.75, "ub": 1.25},
+                    "timestep": {"lb": 1.0, "ub": 1.0},
+                },
+            }
+        )
+
+        result = ps1.intersection(ps2)  ## should consist of 2 boxes
+        print(result.__dict__)
+        assert result
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/test_param_marginalize.py b/test/test_param_marginalize.py
index 334d5e21..2c93bfe9 100644
--- a/test/test_param_marginalize.py
+++ b/test/test_param_marginalize.py
@@ -60,7 +60,7 @@ def marginalize(b1, b2, var):
     # plt.legend(custom_lines, ['PS 1', 'PS 2'])
     # plt.show()
     ## Find the intersection (if it exists)
-    intersection_marginal = b1.intersect(b2, param_list=desired_vars_list)
+    intersection_marginal = b1.intersection(b2, param_list=desired_vars_list)
     ## Form versions of boxes minus the part that we're marginalizing: named box1_x_y and box2_x_y
     box1_x_y = b1.project(desired_vars_list)
     box2_x_y = b2.project(desired_vars_list)
diff --git a/test/test_param_space.py b/test/test_param_space.py
index e795aa05..048bb6b5 100644
--- a/test/test_param_space.py
+++ b/test/test_param_space.py
@@ -207,7 +207,7 @@ def test_search_space_volume(self):
             model=model,
             query=QueryTrue(),
         )
-        scenario.initialize(config=FUNMANConfig())
+        scenario.initialize(config=FUNMANConfig(normalize=False))
         assert (
             scenario.search_space_volume()
             == scenario.representable_space_volume()
diff --git a/test/test_plot_parameter_space.py b/test/test_plot_parameter_space.py
index 20e36279..dbacab47 100644
--- a/test/test_plot_parameter_space.py
+++ b/test/test_plot_parameter_space.py
@@ -29,7 +29,9 @@ def test_plot(self):
                     json.load(f)
                 )
                 ParameterSpacePlotter(
-                    results.parameter_space, plot_points=True
+                    results.parameter_space,
+                    parameters=["gamma", "beta_c"],
+                    plot_points=True,
                 ).plot(show=False)
                 plt.savefig(f"{out_dir}/{results.id}.png")
                 plt.close()
diff --git a/test/test_terarium.py b/test/test_terarium.py
index a0bcadd8..38eea9c0 100644
--- a/test/test_terarium.py
+++ b/test/test_terarium.py
@@ -19,42 +19,82 @@
 
 
 class TestTerarium(unittest.TestCase):
-    def test_terarium(self):
+    def get_test_by_name(self, name):
+        try:
+            test_case = next(
+                iter(t for t in TEST_JSON["tests"] if t["name"] == name)
+            )
+        except StopIteration as e:
+            print(f"Could not find {name} model in tests")
+            raise e
+        return test_case
+
+    def test01_terarium(self):
         tests = TEST_JSON["tests"]
         for test in tests:
-            name = test["name"]
+            if not test["regression"]:
+                continue
 
-            # Read in the model dict
-            model = json.loads(
-                Path(f'{RESOURCES_PREFIX}/{test["model-path"]}').read_bytes()
-            )
+            (
+                name,
+                model,
+                request,
+                expected_outcome,
+                regression,
+            ) = self.get_model_and_request(test)
 
-            # Either read in the request json or default to an empty dict
-            if test["request-path"] is None:
-                request = {}
-            else:
-                request = json.loads(
-                    Path(
-                        f'{RESOURCES_PREFIX}/{test["request-path"]}'
-                    ).read_bytes()
-                )
+            if not regression:
+                continue
+
+            print(f"Testing: {name}")
 
             with self.subTest(name):
                 with TestClient(app) as client:
                     # run the defined test
-                    self.subtest_terarium(client, name, model, request)
+                    self.subtest_terarium(
+                        client, name, model, request, expected_outcome
+                    )
+
+    def get_model_and_request(self, test):
+        name = test["name"]
 
-    def subtest_terarium(self, client, name, model, request):
+        # Read in the model dict
+        model = json.loads(
+            Path(f'{RESOURCES_PREFIX}/{test["model-path"]}').read_bytes()
+        )
+
+        # model file has model and request pair inside
+        if "model" in model and "request" in model:
+            request = model["request"]
+            if "petrinet" in model["model"]:
+                model = model["model"]["petrinet"]
+            else:
+                model = model["model"]
+
+        # Either read in the request json or default to an empty dict
+        elif test["request-path"] is None:
+            request = {}
+        else:
+            request = json.loads(
+                Path(f'{RESOURCES_PREFIX}/{test["request-path"]}').read_bytes()
+            )
+
+        expected_outcome = test["expected-outcome"]
+        regression = test["regression"]
+        return name, model, request, expected_outcome, regression
+
+    def subtest_terarium(self, client, name, model, request, expected_outcome):
         results = self.post_query_and_wait_until_done(client, model, request)
         assert (
             "parameter_space" in results
         ), "Results does not contain a 'parameter_space' field"
         ps = results["parameter_space"]
         assert ps is not None, "ParameterSpace is None"
+
+        num_true = len(ps.get("true_boxes", []))
         assert (
-            len(ps.get("true_points", [])) > 0
-            or len(ps.get("true_boxes", [])) > 0
-        ), f"Terarium Test '{name}' has neither true points nor true boxes"
+            num_true == expected_outcome["true-boxes"]
+        ), f"Terarium Test '{name}' should have {expected_outcome['true-boxes']}, but has {num_true}  true boxes"
 
     def POST(self, client: TestClient, url: str, json: dict, *, expect=200):
         """
@@ -187,6 +227,94 @@ def post_query_and_wait_until_done(
         assert progress > 0.999999, "Progress was not at 100%"
         return results
 
+    def test02_stress_test(self):
+        # here is an example that starts to return 404s GET /api/queries/:id after successfully submitting through POST /api/queries
+        # Earlier the GET request was returning, albeit seemingly never finished. Roughly the steps:
+        # Submit this payload
+        # Query via the ID, couldn't get result, seems to be running forever
+        # Resubmit this payload
+        # Start to get 404s
+        with TestClient(app) as client:
+            test = TEST_JSON["tests"][1]
+            (
+                name,
+                model,
+                request,
+                expected_outcome,
+                regression,
+            ) = self.get_model_and_request(test)
+            uuid = self.post_query(client, model, request)
+            sleep(5)
+            response = client.get(f"/api/queries/{uuid}")
+
+            assert (
+                response.status_code == 200
+            ), f"Response code was not 200: {response.status_code}"
+
+            test1 = TEST_JSON["tests"][2]
+            (
+                name1,
+                model1,
+                request1,
+                expected_outcome,
+                regression,
+            ) = self.get_model_and_request(test1)
+
+            uuid1 = self.post_query(client, model1, request1)
+            sleep(5)
+            response1 = client.get(f"/api/queries/{uuid1}")
+
+            assert (
+                response1.status_code == 200
+            ), f"Response code was not 200: {response1.status_code}"
+
+            # # Get the results
+            # results = self.decode_response_to_dict(response)
+            # assert (
+            #     results.get("error", False) is False
+            # ), f"Request {uuid} errored during processing"
+            # # # Return if processing is done
+            # # if results.get("done", False):
+            # #     return results
+
+            # results = self.get_status(client, uuid)
+
+    def test03_stress_test(self):
+        # Send a second request before the first request can finish
+        # Motivated by bug where received 404 when work file wasn't created yet
+
+        with TestClient(app) as client:
+            test = self.get_test_by_name("sir")
+
+            (
+                name,
+                model,
+                request,
+                expected_outcome,
+                regression,
+            ) = self.get_model_and_request(test)
+            uuid = self.post_query(client, model, request)
+            # sleep(1)
+            response = client.get(f"/api/queries/{uuid}")
+
+            assert (
+                response.status_code == 200
+            ), f"Response code was not 200: {response.status_code}"
+
+            uuid1 = self.post_query(client, model, request)
+            # sleep(1)
+            response1 = client.get(f"/api/queries/{uuid1}")
+
+            assert (
+                response1.status_code == 200
+            ), f"Response code was not 200: {response1.status_code}"
+
+            response2 = client.get(f"/api/queries/{uuid}")
+
+            assert (
+                response2.status_code == 200
+            ), f"Response code was not 200: {response2.status_code}"
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/test/test_use_cases.py b/test/test_use_cases.py
index ec6e3602..0ae3b126 100644
--- a/test/test_use_cases.py
+++ b/test/test_use_cases.py
@@ -8,6 +8,7 @@
 from pysmt.typing import REAL
 
 from funman import (
+    LABEL_ALL,
     BilayerDynamics,
     BilayerModel,
     ConsistencyScenario,
@@ -164,7 +165,11 @@ def setup_use_case_bilayer_parameter_synthesis(self):
         [lb, ub] = model.parameter_bounds["beta"]
         scenario = ParameterSynthesisScenario(
             parameters=[
-                ModelParameter(name="beta", interval=Interval(lb=lb, ub=ub)),
+                ModelParameter(
+                    name="beta",
+                    interval=Interval(lb=lb, ub=ub),
+                    label=LABEL_ALL,
+                ),
                 NumSteps(name="num_steps", interval=Interval(lb=3, ub=3)),
                 StepSize(name="step_size", interval=Interval(lb=1, ub=1)),
             ],
@@ -182,14 +187,14 @@ def test_use_case_bilayer_parameter_synthesis(self):
             config=FUNMANConfig(
                 # solver="dreal",
                 # dreal_mcts=True,
-                # save_smtlib="./out",
-                dreal_log_level="info",
+                save_smtlib="./out",
+                # dreal_log_level="info",
                 tolerance=1e-3,
                 number_of_processes=1,
                 normalize=False,
                 simplify_query=False,
                 use_compartmental_constraints=False,
-                verbosity=5,
+                verbosity=10,
                 _handler=ResultCombinedHandler(
                     [
                         ResultCacheWriter(f"box_search.json"),
diff --git a/tools/post-profile.sh b/tools/post-profile.sh
new file mode 100755
index 00000000..a2e88db9
--- /dev/null
+++ b/tools/post-profile.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+# This script converts the gmon.out created by gprof into a png call graph
+
+rm prof.out prof.dot prof.dot.png || echo "No stale files to remove ... skipping"
+
+echo "Processing gmon.out ... "
+# -c: generate all children including those not compile with -pg
+# -L: print full path for functions
+gprof -c -L /usr/bin/dreal > prof.out
+
+echo "Generating call graph plot ... "
+gprof2dot prof.out -o prof.dot
+
+echo " Creating call graph figure ... "
+dot -Tpng -O prof.dot