diff --git a/examples/Advanced_Features/Hybrid_Switching_Tests.ipynb b/examples/Advanced_Features/Hybrid_Switching_Tests.ipynb
new file mode 100644
index 000000000..f963e0db8
--- /dev/null
+++ b/examples/Advanced_Features/Hybrid_Switching_Tests.ipynb
@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hybrid Switching\n",
+    "***\n",
+    "The Hybrid Switching solver is designed to allow a model's species to be represented dynamically as either continuous or stochastic throughout the simulation.  This solver will represent a reaction channel deterministically at high populations in order to favor performance, and will automatically switch to a stochastic representation at lower populations.  This allows for substantial run-time improvements over the SSA, minimizing the loss of stochastic accuracy.  \n",
+    "  \n",
+    "There is no special setup for the standard case of using this switching mechanism, however for cases where a particular species should always be represented as 'stochastic' or always represented as 'continuous,' the GillesPy2.species can be constructed with kwarg 'mode' locking that into place.  \n",
+    "\n",
+    "**mode='dynamic'(default) - allows for hybrid switching  \n",
+    "mode='continuous' - forces a species to be modeled continuously/deterministically  \n",
+    "mode='discrete' - forces a species to be modeled discretely/stochastically**  \n",
+    "\n",
+    "Ex:  \n",
+    "A = GillesPy2.Species(name='A', initial_value=400, mode='continuous')\n",
+    "***\n",
+    "## Setup the Environment\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.insert(1, os.path.abspath(os.path.join(os.getcwd(), '../..')))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MatPlotLib is used for creating custom visualizations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gillespy2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gillespy2.solvers.numpy.tau_hybrid_solver"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "## Create the Automatic Switching Model\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_automatic_switch_example(parameter_values=None, tol=None):\n",
+    "    # Initialize Model\n",
+    "    model = gillespy2.Model(name=\"Automatic Switch Example\")\n",
+    "\n",
+    "    # Define Variables (GillesPy2.Species)\n",
+    "    if tol is None:\n",
+    "        A = gillespy2.Species(name='A', initial_value=400  )\n",
+    "        B = gillespy2.Species(name='B', initial_value=10000)\n",
+    "        C = gillespy2.Species(name='C', initial_value=10000)\n",
+    "    else:\n",
+    "        A = gillespy2.Species(name='A', initial_value=400  , switch_tol=tol)\n",
+    "        B = gillespy2.Species(name='B', initial_value=10000, switch_tol=tol)\n",
+    "        C = gillespy2.Species(name='C', initial_value=10000, switch_tol=tol)\n",
+    "\n",
+    "    # Add Variables to Model\n",
+    "    model.add_species([A, B, C])\n",
+    "    \n",
+    "    # Define Parameters\n",
+    "    k1 = gillespy2.Parameter(name='k1', expression= 3e-4)\n",
+    "    k2 = gillespy2.Parameter(name='k2', expression= 0.5e-2)\n",
+    "    k3 = gillespy2.Parameter(name='k3', expression = 2e-1)\n",
+    "    \n",
+    "    # Add Parameters to Model\n",
+    "    model.add_parameter([k1, k2, k3])\n",
+    "\n",
+    "    # Define Reactions\n",
+    "    r1 = gillespy2.Reaction(name=\"r1\",reactants={'A': 1, 'B': 1}, products={'B': 1, 'C': 1}, rate='k1')\n",
+    "    r2 = gillespy2.Reaction(name=\"r2\",reactants={'B': 1}, products={}, rate='k2')\n",
+    "    r3 = gillespy2.Reaction(name=\"r3\",reactants={'C': 1}, products={'A': 1}, rate='k3')\n",
+    "\n",
+    "    # Add Reactions to Model\n",
+    "    model.add_reaction([r1, r2, r3])\n",
+    "    \n",
+    "    # Define Timespan\n",
+    "    tspan = gillespy2.TimeSpan.linspace(t=600, num_points=601)\n",
+    "    \n",
+    "    # Set Model Timespan\n",
+    "    model.timespan(tspan)\n",
+    "    for k,v in model.listOfSpecies.items():\n",
+    "        print(f\"\\t{k} tol={v.switch_tol}\")\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_automatic_switch_min_example(parameter_values=None, max_stoch_pop=100):\n",
+    "    # Initialize Model\n",
+    "    model = gillespy2.Model(name=\"Automatic Switch Example\")\n",
+    "\n",
+    "    # Define Variables (GillesPy2.Species)\n",
+    "    A = gillespy2.Species(name='A', initial_value=400, switch_min=max_stoch_pop)\n",
+    "    B = gillespy2.Species(name='B', initial_value=10000, switch_min=max_stoch_pop)\n",
+    "    C = gillespy2.Species(name='C', initial_value=10000, switch_min=max_stoch_pop)\n",
+    "    \n",
+    "    # Add Variables to Model\n",
+    "    model.add_species([A, B, C])\n",
+    "\n",
+    "    # Define Parameters\n",
+    "    k1 = gillespy2.Parameter(name='k1', expression= 3e-4)\n",
+    "    k2 = gillespy2.Parameter(name='k2', expression= 0.5e-2)\n",
+    "    k3 = gillespy2.Parameter(name='k3', expression = 2e-1)\n",
+    "    \n",
+    "    # Add Parameters to Model\n",
+    "    model.add_parameter([k1, k2, k3])\n",
+    "\n",
+    "    # Define Reactions\n",
+    "    r1 = gillespy2.Reaction(name=\"r1\",reactants={'A': 1, 'B': 1}, products={'B': 1, 'C': 1}, rate='k1')\n",
+    "    r2 = gillespy2.Reaction(name=\"r2\",reactants={'B': 1}, products={}, rate='k2')\n",
+    "    r3 = gillespy2.Reaction(name=\"r3\",reactants={'C': 1}, products={'A': 1}, rate='k3')\n",
+    "\n",
+    "    # Add Reactions to Model\n",
+    "    model.add_reaction([r1, r2, r3])\n",
+    "    \n",
+    "    # Define Timespan\n",
+    "    tspan = gillespy2.TimeSpan.linspace(t=600, num_points=601)\n",
+    "    \n",
+    "    # Set Model Timespan\n",
+    "    model.timespan(tspan)\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Instantiate the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tA tol=0.04\n",
+      "\tB tol=0.04\n",
+      "\tC tol=0.04\n",
+      "CPU times: user 23.3 s, sys: 164 ms, total: 23.4 s\n",
+      "Wall time: 23.4 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_cv = create_automatic_switch_example(tol=0.04)\n",
+    "%time resultsP_cv = model_cv.run(solver=gillespy2.TauHybridSolver)\n",
+    "resultsP_cv.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 10.7 ms, sys: 12 ms, total: 22.7 ms\n",
+      "Wall time: 13.7 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%time resultsC_cv = model_cv.run(solver=gillespy2.TauHybridCSolver)\n",
+    "#solver_cv=gillespy2.TauHybridCSolver(model=model_cv)\n",
+    "#%time resultsC_cv = solver_cv.run()\n",
+    "resultsC_cv.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_min = create_automatic_switch_min_example(max_stoch_pop=1500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 23.8 s, sys: 208 ms, total: 24 s\n",
+      "Wall time: 24 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%time resultsP_min = model_min.run(solver=gillespy2.TauHybridSolver)\n",
+    "resultsP_min.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 13.2 ms, sys: 12.1 ms, total: 25.3 ms\n",
+      "Wall time: 13.7 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%time resultsC_min = model_min.run(solver=gillespy2.TauHybridCSolver)\n",
+    "#solver=gillespy2.TauHybridCSolver(model=model_min)\n",
+    "#%time resultsC_min = solver.run()             \n",
+    "resultsC_min.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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/gillespy2/solvers/cpp/build/build_engine.py b/gillespy2/solvers/cpp/build/build_engine.py
index bdfa7d459..ae60efddc 100644
--- a/gillespy2/solvers/cpp/build/build_engine.py
+++ b/gillespy2/solvers/cpp/build/build_engine.py
@@ -115,8 +115,8 @@ def prepare(self, model: "Union[Model, template_gen.SanitizedModel]", variable=F
         # If a raw GillesPy2 model was provided, convert it to a sanitized model.
         if isinstance(model, gillespy2.Model):
             model = template_gen.SanitizedModel(model, variable=variable)
-        elif not isinstance(model, template_gen.SanitizedModel):
-            raise TypeError(f"Build engine expected gillespy2.Model or SanitizedModel type: received {type(model)}")
+        elif not isinstance(model, template_gen.SanitizedModel) and type(model).__name__ == "SanitizedModel":
+            raise TypeError(f"Build engine expected gillespy2.Model or SanitizedModel type: received {type(model)} , __name__={type(model).__name__}")
 
         # Build the template and write it to the temp directory and remove the sample template_definitions header.
         template_file = self.template_dir.joinpath(self.template_definitions_name)
diff --git a/gillespy2/solvers/cpp/build/template_gen.py b/gillespy2/solvers/cpp/build/template_gen.py
index a89dbcf3a..f9f9b9618 100644
--- a/gillespy2/solvers/cpp/build/template_gen.py
+++ b/gillespy2/solvers/cpp/build/template_gen.py
@@ -87,6 +87,8 @@ def __init__(self, model: Model, variable=False):
         # Reactions: maps reaction names to their stoichiometry matrix.
         # Stoichiometry matrix maps a sanitized species name to its stoichiometry.
         self.reactions: "OrderedDict[str, dict[str, int]]" = OrderedDict()
+        self.reaction_reactants: "OrderedDict[str, dict[str, int]]" = OrderedDict()
+        self.reaction_products: "OrderedDict[str, dict[str, int]]" = OrderedDict()
         # Rate Rules: maps sanitized species names to their corresponding rate rule expression.
         self.rate_rules: "OrderedDict[str, str]" = OrderedDict()
         # Options: custom definitions that can be supplied by the solver, maps macros to their definitions.
@@ -122,15 +124,17 @@ def use_reaction(self, reaction: "Reaction") -> "SanitizedModel":
         :type reaction: gillespy2.Reaction
         """
         self.reactions[reaction.name] = {spec: int(0) for spec in self.species_names.values()}
+        self.reaction_reactants[reaction.name] = {spec: int(0) for spec in self.species_names.values()}
+        self.reaction_products[reaction.name] = {spec: int(0) for spec in self.species_names.values()}
         for reactant, stoich_value in reaction.reactants.items():
-            if isinstance(reactant, Species):
-                reactant = self.species_names[reactant.name]
+            reactant = self.species_names[reactant.name]
             self.reactions[reaction.name][reactant] -= int(stoich_value)
+            self.reaction_reactants[reaction.name][reactant] = int(stoich_value)
 
         for product, stoich_value in reaction.products.items():
-            if isinstance(product, Species):
-                product = self.species_names[product.name]
+            product = self.species_names[product.name]
             self.reactions[reaction.name][product] += int(stoich_value)
+            self.reaction_products[reaction.name][product] = int(stoich_value)
 
         return self
 
@@ -401,18 +405,30 @@ def template_def_reactions(model: SanitizedModel, ode=False) -> "dict[str, str]"
     """
     num_reactions = str(len(model.reactions))
     reaction_set = OrderedDict()
+    reactants_set = OrderedDict()
+    products_set = OrderedDict()
 
     for rxn_name, reaction in model.reactions.items():
         stoich = [str(int(reaction[species])) for species in model.species_names.values()]
         reaction_set[rxn_name] = f"{{{','.join(stoich)}}}"
+    for rxn_name, reaction_reactants in model.reaction_reactants.items():
+        reactants_count = [str(int(reaction_reactants[species])) for species in model.species_names.values()]
+        reactants_set[rxn_name] = f"{{{','.join(reactants_count)}}}"
+    for rxn_name, reaction_products in model.reaction_products.items():
+        products_count = [str(int(reaction_products[species])) for species in model.species_names.values()]
+        products_set[rxn_name] = f"{{{','.join(products_count)}}}"
 
     reaction_names = " ".join([f"REACTION_NAME({rxn})" for rxn in reaction_set.keys()])
     reaction_set = f"{{{','.join(reaction_set.values())}}}"
+    reactants_set = f"{{{','.join(reactants_set.values())}}}"
+    products_set = f"{{{','.join(products_set.values())}}}"
 
     return {
         "GPY_NUM_REACTIONS": num_reactions,
-        "GPY_REACTIONS": reaction_set,
         "GPY_REACTION_NAMES": reaction_names,
+        "GPY_REACTIONS": reaction_set,
+        "GPY_REACTION_REACTANTS": reactants_set,
+        "GPY_REACTION_PRODUCTS": products_set,
     }
 
 
diff --git a/gillespy2/solvers/cpp/c_base/Tau/tau.cpp b/gillespy2/solvers/cpp/c_base/Tau/tau.cpp
index 45cd0d851..bcac5b44c 100644
--- a/gillespy2/solvers/cpp/c_base/Tau/tau.cpp
+++ b/gillespy2/solvers/cpp/c_base/Tau/tau.cpp
@@ -42,12 +42,12 @@ namespace Gillespy
 
             for (int spec = 0; spec < model.number_species; spec++)
             {
-                if (model.reactions[r].species_change[spec] > 0)
+                if (model.reactions[r].products_change[spec] > 0)
                 {
                     tau_args.products[r].push_back(spec);
                 }
 
-                else if (model.reactions[r].species_change[spec] < 0)
+                else if (model.reactions[r].reactants_change[spec] > 0)
                 {
                     rxn_order += 1;
                     tau_args.reactions_reactants[r].push_back(spec);
diff --git a/gillespy2/solvers/cpp/c_base/model.cpp b/gillespy2/solvers/cpp/c_base/model.cpp
index 28c572c19..675a5ed33 100644
--- a/gillespy2/solvers/cpp/c_base/model.cpp
+++ b/gillespy2/solvers/cpp/c_base/model.cpp
@@ -46,9 +46,13 @@ namespace Gillespy {
         for (unsigned int reaction = 0; reaction < number_reactions; reaction++) {
             reactions[reaction].name = reaction_names[reaction];
             reactions[reaction].species_change = std::make_unique<int[]>(number_species);
+            reactions[reaction].reactants_change = std::make_unique<int[]>(number_species);
+            reactions[reaction].products_change = std::make_unique<int[]>(number_species);
 
             for (unsigned int species = 0; species < number_species; species++) {
                 reactions[reaction].species_change[species] = 0;
+                reactions[reaction].reactants_change[species] = 0;
+                reactions[reaction].products_change[species] = 0;
             }
 
             reactions[reaction].affected_reactions = std::vector<unsigned int>();
@@ -66,7 +70,8 @@ namespace Gillespy {
         for (unsigned int r1 = 0; r1 < number_reactions; r1++) {
             for (unsigned int r2 = 0; r2 < number_reactions; r2++) {
                 for (unsigned int s = 0; s < number_species; s++) {
-                    if (reactions[r2].species_change[s] != 0) {
+                     if(reactions[r1].species_change[s]  != 0  &&
+                        reactions[r2].reactants_change[s] > 0 ){
                         reactions[r1].affected_reactions.push_back(r2);
                     }
                 }
diff --git a/gillespy2/solvers/cpp/c_base/model.h b/gillespy2/solvers/cpp/c_base/model.h
index cf0585048..3e65c8f51 100644
--- a/gillespy2/solvers/cpp/c_base/model.h
+++ b/gillespy2/solvers/cpp/c_base/model.h
@@ -72,6 +72,8 @@ namespace Gillespy
 
         // List of reactions who's propensities will change when this reaction fires.
         std::unique_ptr<int[]> species_change;
+        std::unique_ptr<int[]> reactants_change;
+        std::unique_ptr<int[]> products_change;
 
         inline static double propensity(
                 ReactionId reaction_id,
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.cpp b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.cpp
index 73b6f92d9..4eb3bd9fe 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.cpp
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.cpp
@@ -304,37 +304,35 @@ namespace Gillespy
             int num_species = species.size();
             std::set<int> det_rxns;
 
-            for (int rxn_i = 0; rxn_i < reactions.size(); ++rxn_i)
+            for (int rxn_i = 0; rxn_i < num_reactions; ++rxn_i)
             {
                 // start with the assumption that reaction is determinstic
                 HybridReaction &rxn = reactions[rxn_i];
                 rxn.mode = SimulationState::CONTINUOUS;
 
                 // iterate through the dependent species of this reaction
-                // Loop breaks if we've already determined that it is to be marked as discrete.
                 for (int spec_i = 0; spec_i < num_species && rxn.mode == SimulationState::CONTINUOUS; ++spec_i)
                 {
-                    // Reaction has a dependency on a species if its dx is positive or negative.
-                    // Any species with "dependency" change of 0 is by definition not a dependency.
-                    if (rxn.get_base_reaction()->species_change[spec_i] == 0)
-                    {
+                    if (rxn.get_base_reaction()->reactants_change[spec_i] == 0 && 
+                        rxn.get_base_reaction()->products_change[spec_i] == 0) {
                         continue;
                     }
 
                     // if any of the dependencies are set by the user as discrete OR
                     // have been set as dynamic and has not been flagged as deterministic,
                     // allow it to be modelled discretely
-                    if (species[spec_i].user_mode == SimulationState::DYNAMIC)
-                    {
+                    if (species[spec_i].user_mode == SimulationState::DYNAMIC) {
                         rxn.mode = species[spec_i].partition_mode;
-                    } else
-                    {
+                    } else {
                         rxn.mode = species[spec_i].user_mode;
                     }
+                    // Loop breaks if we've already determined that it is to be marked as discrete.
+                    if(rxn.mode == SimulationState::DISCRETE){
+                        break;
+                    }
                 }
 
-                if (rxn.mode == SimulationState::CONTINUOUS)
-                {
+                if (rxn.mode == SimulationState::CONTINUOUS) {
                     det_rxns.insert(rxn_i);
                 }
             }
@@ -343,6 +341,7 @@ namespace Gillespy
         }
 
         void partition_species(
+                double current_time,
                 std::vector<HybridReaction> &reactions,
                 std::vector<HybridSpecies> &species,
                 const std::vector<double> &propensity_values,
@@ -350,83 +349,112 @@ namespace Gillespy
                 double tau_step,
                 const TauArgs<double> &tauArgs)
         {
+            
+
             // coefficient of variance- key:species id, value: cv
-            std::map<int, double> cv;
+            std::map<int, double> CV;
             // means
             std::map<int, double> means;
             // standard deviation
             std::map<int, double> sd;
 
             // Initialize means and sd's
-            for (int spec_i = 0; spec_i < species.size(); ++spec_i)
-            {
+            for (int spec_i = 0; spec_i < species.size(); ++spec_i) {
                 HybridSpecies &spec = species[spec_i];
 
-                if (spec.user_mode == SimulationState::DYNAMIC)
-                {
+                if (spec.user_mode == SimulationState::DYNAMIC) {
                     means.insert({spec_i, curr_state[spec_i]});
                     sd.insert({spec_i, 0});
                 }
             }
 
             // calculate means and standard deviations for dynamic-mode species involved in reactions
-            for (int rxn_i = 0; rxn_i < reactions.size(); ++rxn_i)
-            {
+            for (int rxn_i = 0; rxn_i < reactions.size(); ++rxn_i) {
                 HybridReaction &rxn = reactions[rxn_i];
-
-                for (int spec_i = 0; spec_i < species.size(); ++spec_i)
-                {
-                    // Only dynamic species whose mean/SD is requested are to be considered.
-                    if (means.count(spec_i) <= 0)
-                    {
+                for (int spec_i = 0; spec_i < species.size(); ++spec_i) {
+                    HybridSpecies &spec = species[spec_i];
+                    // Only dynamic species are to be considered.
+                    if (spec.user_mode != SimulationState::DYNAMIC) {
                         continue;
                     }
-                    // Selected species is either a reactant or a product, depending on whether
-                    //   dx is positive or negative.
-                    // 0-dx species are not dependencies of this reaction, so dx == 0 is ignored.
-                    int spec_dx = rxn.get_base_reaction()->species_change[spec_i];
-                    if (spec_dx < 0)
-                    {
+                    // Selected species is either a reactant or a product, 
+                    if(rxn.get_base_reaction()->reactants_change[spec_i] > 0) {
                         // Selected species is a reactant.
-                        means[spec_i] -= (tau_step * propensity_values[rxn_i] * spec_dx);
-                        sd[spec_i] += (tau_step * propensity_values[rxn_i] * std::pow(spec_dx, 2));
-                    } else if (spec_dx > 0)
-                    {
+                        means[spec_i] -= (propensity_values[rxn_i] * rxn.get_base_reaction()->reactants_change[spec_i]);
+                        sd[spec_i] += (propensity_values[rxn_i] * std::pow(rxn.get_base_reaction()->reactants_change[spec_i], 2));
+                    } 
+                    if(rxn.get_base_reaction()->products_change[spec_i] > 0) {
                         // Selected species is a product.
                         HybridSpecies &product = species[spec_i];
-                        means[spec_i] += (tau_step * propensity_values[rxn_i] * spec_dx);
-                        sd[spec_i] += (tau_step * propensity_values[rxn_i] * std::pow(spec_dx, 2));
+                        means[spec_i] += (propensity_values[rxn_i] * rxn.get_base_reaction()->products_change[spec_i]);
+                        sd[spec_i] += (propensity_values[rxn_i] * std::pow(rxn.get_base_reaction()->products_change[spec_i], 2));
                     }
                 }
             }
-
             // calculate coefficient of variation using means and sd
+            for (int spec_i = 0; spec_i < species.size(); ++spec_i) {
+                HybridSpecies &spec = species[spec_i];
+                // Only dynamic species are to be considered.
+                if (spec.user_mode != SimulationState::DYNAMIC) {
+                    continue;
+                }
+                if (means[spec_i] > 0 && sd[spec_i] > 0) {
+                    CV[spec_i] = (sqrt(sd[spec_i]) / means[spec_i]);
+                } else {
+                    CV[spec_i] = 1;
+                }
+            }
+
+            // keep a history of the past CV values, and calculate a time-average values
+            std::map<int, double> CV_a;
+            static std::map<int, std::queue<double>> cv_history;
+            static std::map<int, double> cv_history_sum;
+            int history_length = 12;
+            // 
+            if( current_time == 0.0 ){  // reset cv_history at start of trajectory
+                cv_history.clear();
+                cv_history_sum.clear();
+            }
+
             for (int spec_i = 0; spec_i < species.size(); ++spec_i)
             {
-                if (means.count(spec_i) <= 0)
-                {
+                
+                HybridSpecies &spec = species[spec_i];
+                // Only dynamic species are to be considered.
+                if (spec.user_mode != SimulationState::DYNAMIC){
                     continue;
                 }
-                
+                if(cv_history.count(spec_i) == 0){
+                    cv_history[spec_i] = std::queue<double>();
+                }
+                if(cv_history_sum.count(spec_i) == 0){
+                    cv_history_sum[spec_i] = 0.0;
+                }
+                cv_history[spec_i].push(CV[spec_i]);
+                cv_history_sum[spec_i] += CV[spec_i];
+                if(cv_history[spec_i].size() > history_length){
+                    double removed = cv_history[spec_i].front();
+                    cv_history[spec_i].pop();
+                    cv_history_sum[spec_i] -= removed;
+                }
+                CV_a[spec_i] = cv_history_sum[spec_i]/cv_history[spec_i].size();
+            }
 
+            // Select DISCRETE or CONTINOUS mode for each species
+            for (int spec_i = 0; spec_i < species.size(); ++spec_i) {
                 HybridSpecies &spec = species[spec_i];
-                if (spec.switch_min == 0)
-                {
-                    // (default value means switch  min not set, use switch tol)
-                    if (means[spec_i] > 0)
-                    {
-                        cv[spec_i] = (sd[spec_i] / means[spec_i]);
-                    } else
-                    {
-                        cv[spec_i] = 1;
-                    }
-                    //std::cerr<<"\t\tspec"<<spec_i<<" cv="<<cv[spec_i]<<"=("<<sd[spec_i]<<" / "<<means[spec_i]<<") switch_tol="<<spec.switch_tol<<"\n";
 
-                    spec.partition_mode = cv[spec_i] < spec.switch_tol
+                // Only dynamic species are to be considered.
+                if (spec.user_mode != SimulationState::DYNAMIC) {
+                    continue;
+                }
+                unsigned int prev_partition = spec.partition_mode;
+                if (spec.switch_min == 0) {
+                    // (default value means switch  min not set, use switch tol)
+                    spec.partition_mode = CV_a[spec_i] < spec.switch_tol
                                           ? SimulationState::CONTINUOUS
                                           : SimulationState::DISCRETE;
                 } else {
-                    //std::cerr<<"\t\tspec"<<spec_i<<" mean="<<means[spec_i]<<" switch_min="<<spec.switch_min<<"\n";
                     spec.partition_mode = means[spec_i] > spec.switch_min
                                           ? SimulationState::CONTINUOUS
                                           : SimulationState::DISCRETE;
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.h b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.h
index 50053f770..c3de19763 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.h
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.h
@@ -308,6 +308,7 @@ namespace Gillespy
                 std::vector<HybridSpecies> &species);
 
         void partition_species(
+                double current_time,
                 std::vector<HybridReaction> &reactions,
                 std::vector<HybridSpecies> &species,
                 const std::vector<double> &propensity_values,
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.cpp b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.cpp
index 91e4247cd..a44c636bb 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.cpp
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.cpp
@@ -62,17 +62,10 @@ namespace Gillespy
                 population_changes[p_i] = 0;
             }
 
-            // Start with the species concentration as a baseline value.
-            // Stochastic reactions will update populations relative to their concentrations.
-            //for (int spec_i = 0; spec_i < num_species; ++spec_i) {
-            //    current_state[spec_i] = result.concentrations[spec_i]; 
-            //}
-
             if (!rxn_roots.empty()) {
                 // "Direct" roots found; these are executed manually
                 for (unsigned int rxn_i : rxn_roots)
                 {
-                    //std::cerr << "reaction "<< rxn_i<<" found via root\n";
                     // "Fire" a reaction by recording changes in dependent species.
                     // If a negative value is detected, break without saving changes.
                     for (int spec_i = 0; spec_i < num_species; ++spec_i) {
@@ -98,13 +91,11 @@ namespace Gillespy
                             if(only_reaction_to_fire == rxn_i){
                                     rxn_state = log(urn.next());
                                     rxn_count = 1;
-                                    //std::cerr << "  rxn"<<rxn_i<<" 1 single SSA\n";
                             }
                         }else if(rxn_state > 0){
                             std::poisson_distribution<int> poisson(rxn_state);
                             rxn_count = 1 + poisson(generator);
                             rxn_state = log(urn.next());
-                            //std::cerr << "  rxn"<<rxn_i<<" "<<rxn_count<<" poisson\n";
 
                         }
                         if(rxn_count > 0){
@@ -147,7 +138,6 @@ namespace Gillespy
             // Explicitly check for invalid population state, now that changes have been tallied.
             for (int spec_i = 0; spec_i < num_species; ++spec_i) {
                 if (current_state[spec_i] + population_changes[spec_i] < 0) {
-                    //std::cerr<<"\tNegative state detected\n";
                     return true;
                 }
             }
@@ -297,6 +287,7 @@ namespace Gillespy
                             current_state
                     );
                     partition_species(
+                            simulation->current_time,
                             simulation->reaction_state,
                             simulation->species_state,
                             sol.data.propensities,
@@ -326,23 +317,6 @@ namespace Gillespy
                     // This is a temporary fix. Ideally, invalid state should allow for integrator options change.
                     // For now, a "guard" is put in place to prevent potentially infinite loops from occurring.
 
-                    //***********************************************************
-                    //std::cerr<<simulation->current_time<<" tau="<<tau_step<<" [";
-                    //for(int spec_i = 0; spec_i < num_species; ++spec_i){
-                    //    std::cerr<<current_state[spec_i]<<" ";
-                    //}
-                    //std::cerr<<"] [";
-                    //for(int spec_i = 0; spec_i < num_species; ++spec_i){
-                    //    HybridSpecies *spec = &simulation->species_state[spec_i];
-                    //    if( spec->partition_mode == SimulationState::CONTINUOUS ){
-                    //        std::cerr<<"C ";
-                    //    }else if( spec->partition_mode == SimulationState::DISCRETE ){
-                    //        std::cerr<<"D ";
-                    //    }
-                    //}
-                    //std::cerr<<"]\n";
-                    //***********************************************************
-
                     IntegrationResults result;
 
                     if(!TauHybrid::TakeIntegrationStep(sol, result, next_time, population_changes, current_state, rxn_roots, event_roots, simulation, urn, -1)){
@@ -369,34 +343,22 @@ namespace Gillespy
                             HybridReaction &rxn = simulation->reaction_state[rxn_k];
                             double propensity_value = rxn.ssa_propensity(current_state.data());
                             double floored_propensity_value = rxn.ssa_propensity(floored_current_state);
-                            //*************************************************************
-                            //std::cerr<<"\t\trxn"<<rxn_k<<" a="<<propensity_value<<" floor(a)="<<floored_propensity_value;
-                            //*************************************************************
                             //estimate the zero crossing time
                             if(floored_propensity_value > 0.0){
-                                // Python code:
-                                //rxn_times[rname] = -1* curr_state[rname] / propensities[rname]
-                                // C++ code:
                                 double est_tau = -1*  rxn_state[rxn_k] / propensity_value;
 
                                 if(rxn_selected == -1 || est_tau < min_tau ){
                                     min_tau = est_tau;
                                     rxn_selected = rxn_k;
                                 }
-                                //std::cerr<<" est_tau="<<est_tau;
                             }
-                            //std::cerr<<"\n";
                         }
                         if(rxn_selected == -1){
-                            std::cerr << "Negative State detected in step, and no reaction found to fire.\n";
                             simulation->set_status(HybridSimulation::NEGATIVE_STATE_NO_SSA_REACTION);
                             return;
                         }
                         // if min_tau < 1e-10, we can't take an ODE step that small.
                         if( min_tau < 1e-10 ){
-                            //***********************************
-                            //std::cerr<<"\t firing rxn"<<rxn_selected<<" at current time\n";
-                            //***********************************
                             // instead we will fire the reaction
                             CalculateSpeciesChangeAfterStep(result, population_changes, current_state, rxn_roots,  event_roots, simulation, urn, rxn_selected);
                             // re-attempt the step at the same time 
@@ -407,7 +369,6 @@ namespace Gillespy
                             next_time = simulation->current_time + min_tau;
 
                             //***********************************
-                            //std::cerr<<"\t firing rxn"<<rxn_selected<<" at tau="<<min_tau<<"\n";
                             //***********************************
 
                             // Integreate the system forward
@@ -418,19 +379,6 @@ namespace Gillespy
                         // check for invalid state again
                         if (TauHybrid::IsStateNegativeCheck(num_species, population_changes, current_state)) {
                             //Got an invalid state after the SSA step
-                            //***********************************
-                            //std::cerr << "Invalid state after single SSA step\n";
-                            //std::cerr<<"\t current_state=[";
-                            //for(int spec_i = 0; spec_i < num_species; ++spec_i){
-                            //    std::cerr<<current_state[spec_i]<<" ";
-                            //}
-                            //std::cerr<<"]\n";
-                            //std::cerr<<"\t population_changes=[";
-                            //for(int spec_i = 0; spec_i < num_species; ++spec_i){
-                            //    std::cerr<<population_changes[spec_i]<<" ";
-                            //}
-                            //std::cerr<<"]\n";
-                            //***********************************
                             simulation->set_status(HybridSimulation::INVALID_AFTER_SSA);
                             return;
                         }
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.cpp b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.cpp
index 3f5b05775..b3fe78d9d 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.cpp
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.cpp
@@ -32,9 +32,10 @@ namespace Gillespy
     {
         void map_species_modes(std::vector<HybridSpecies> &species)
         {
-            #define SPECIES_MODE(spec_id, user_min, spec_mode, boundary_mode) \
+            #define SPECIES_MODE(spec_id, user_min, user_tol, spec_mode, boundary_mode) \
             species[spec_id].user_mode = spec_mode; \
             species[spec_id].switch_min = user_min; \
+            species[spec_id].switch_tol = user_tol; \
             species[spec_id].boundary_condition = boundary_mode;
             #define CONTINUOUS_MODE SimulationState::CONTINUOUS
             #define DISCRETE_MODE   SimulationState::DISCRETE
diff --git a/gillespy2/solvers/cpp/c_base/template/template.cpp b/gillespy2/solvers/cpp/c_base/template/template.cpp
index c83d7b0b3..d5727f395 100644
--- a/gillespy2/solvers/cpp/c_base/template/template.cpp
+++ b/gillespy2/solvers/cpp/c_base/template/template.cpp
@@ -49,6 +49,8 @@ namespace Gillespy
         s_names + sizeof(s_names) / sizeof(std::string));
 
     int reactions[GPY_NUM_REACTIONS][GPY_NUM_SPECIES] = GPY_REACTIONS;
+    int reaction_reactants[GPY_NUM_REACTIONS][GPY_NUM_SPECIES] = GPY_REACTION_REACTANTS;
+    int reaction_products[GPY_NUM_REACTIONS][GPY_NUM_SPECIES] = GPY_REACTION_PRODUCTS;
     std::string r_names[GPY_NUM_REACTIONS] = 
     {
         #define REACTION_NAME(name) #name,
@@ -241,6 +243,8 @@ namespace Gillespy
             for (spec_i = 0; spec_i < GPY_NUM_SPECIES; ++spec_i)
             {
                 model.reactions[rxn_i].species_change[spec_i] = reactions[rxn_i][spec_i];
+                model.reactions[rxn_i].reactants_change[spec_i] = reaction_reactants[rxn_i][spec_i];
+                model.reactions[rxn_i].products_change[spec_i] = reaction_products[rxn_i][spec_i];
             }
         }
 
diff --git a/gillespy2/solvers/cpp/tau_hybrid_c_solver.py b/gillespy2/solvers/cpp/tau_hybrid_c_solver.py
index 1ad2ac049..0bf931cf4 100644
--- a/gillespy2/solvers/cpp/tau_hybrid_c_solver.py
+++ b/gillespy2/solvers/cpp/tau_hybrid_c_solver.py
@@ -80,7 +80,7 @@ def __create_options(cls, sanitized_model: "SanitizedModel") -> "SanitizedModel"
             # Explicit cast to bool for safety, in case boundary_condition is given weird values
             boundary_keyword = boundary_condition_types[int(bool(species.boundary_condition))]
             # Example: SPECIES_MODE(2, 10, CONTINUOUS_MODE, BOUNDARY)
-            entry = f"SPECIES_MODE({spec_id},{species.switch_min},{mode_keyword},{boundary_keyword})"
+            entry = f"SPECIES_MODE({spec_id},{species.switch_min},{species.switch_tol},{mode_keyword},{boundary_keyword})"
             species_mode_list.append(entry)
 
         # EVENT(event_id, {targets}, trigger, delay, priority, use_trigger, use_persist)
diff --git a/gillespy2/solvers/numpy/tau_hybrid_solver.py b/gillespy2/solvers/numpy/tau_hybrid_solver.py
index fbad4cbc2..bd54f79ec 100644
--- a/gillespy2/solvers/numpy/tau_hybrid_solver.py
+++ b/gillespy2/solvers/numpy/tau_hybrid_solver.py
@@ -73,7 +73,7 @@ class TauHybridSolver(GillesPySolver):
     result = None
     stop_event = None
 
-    def __init__(self, model=None):
+    def __init__(self, model=None, profile_reactions=False):
         if model is None:
             raise SimulationError("A model is required to run the simulation.")
 
@@ -81,6 +81,12 @@ def __init__(self, model=None):
         rc = 0
         self.model = copy.deepcopy(model)
         self.is_instantiated = True
+        self.profile_reactions = profile_reactions
+        self.profile_data = {}
+        if self.profile_reactions:
+            self.profile_data['time'] = []
+            for k in list(self.model.listOfSpecies)+list(self.model.listOfReactions):
+                self.profile_data[k] = []
 
     def __toggle_reactions(self, all_compiled, deterministic_reactions, dependencies, 
                             curr_state, det_spec, rr_sets):
@@ -95,15 +101,6 @@ def __toggle_reactions(self, all_compiled, deterministic_reactions, dependencies
         rate_rules = all_compiled['rules']
         rxns = all_compiled['rxns']
 
-        #print(f"\t__toggle_reactions()")
-        #print(f"\t\tinactive_reactions={inactive_reactions}")
-        #print(f"\t\tdeterministic_reactions={deterministic_reactions}")
-        #print(f"\t\trate_rules=[",end='')
-        #for k,v in rate_rules.items():
-        #    print(f"{k}, ",end='')
-        #print("]")
-        #print(f"\t\trxns={list(rxns.keys())}")
-
         # If the set has changed, reactivate non-determinsitic reactions
         reactivate = []
         for r in inactive_reactions:
@@ -124,22 +121,11 @@ def __toggle_reactions(self, all_compiled, deterministic_reactions, dependencies
 
         # Check if this reaction set is already compiled and in use:
         if deterministic_reactions in rr_sets.keys():
-            #print(f"\treturn rr_sets[{deterministic_reactions}]=[")
-            #for k,v in rr_sets[deterministic_reactions].items():
-            #    print(f"{k}={v}, ",end='')
-            #print("]")
             return rr_sets[deterministic_reactions]
         else:
         # Otherwise, this is a new determinstic reaction set that must be compiled
             tmp =  self.__create_diff_eqs(deterministic_reactions,
                                             dependencies, rr_sets, rate_rules)
-            #print(f"\tdeterministic_reactions={deterministic_reactions}")
-            #print(f"\tdependencies={dependencies}")
-            #print(f"\trr_sets={rr_sets}")
-            #print(f"\treturn __create_diff_eqs()=", end='')
-            #for k,v in tmp.items():
-            #    print(f"{k}={v}, ",end='')
-            #print()
             return tmp
 
     def __create_diff_eqs(self, comb, dependencies, rr_sets, rate_rules):
@@ -217,17 +203,25 @@ def __flag_det_reactions(self, det_spec, det_rxn, dependencies):
             if det_rxn[rxn]:
                 deterministic_reactions.add(rxn)
         deterministic_reactions = frozenset(deterministic_reactions)
-        #print(f"\t__flag_det_reactions() deterministic_reactions={deterministic_reactions}")
         return deterministic_reactions
 
-    def __calculate_statistics(self, *switch_args):
+    def __calculate_statistics(self, curr_time, propensities, curr_state, tau_step, det_spec, cv_history={}):
         """
         Calculates Mean, Standard Deviation, and Coefficient of Variance for each
         dynamic species, then set if species can be represented determistically
+
+        NOTE: the argument cv_history should not be passed in, this is modified by the function
+        to keep a persistent data set.
         """
-        propensities, curr_state, tau_step, det_spec = switch_args
+        #TODO: move configuration to solver init
+        history_length = 12 #cite: "Statistical rules of thumb" G Van Belle
+
+        if curr_time==0.0: #re-set cv_history
+            for k in list(cv_history.keys()):
+                del cv_history[k]
 
         CV = OrderedDict()
+        # calculate CV by estimating the next step
         mn = {species: curr_state[species] for species, value in
               self.model.listOfSpecies.items() if value.mode == 'dynamic'}
         sd = {species: 0 for species, value in
@@ -236,26 +230,42 @@ def __calculate_statistics(self, *switch_args):
         for r, rxn in self.model.listOfReactions.items():
             for reactant in rxn.reactants:
                 if reactant.mode == 'dynamic':
-                    mn[reactant.name] -= (tau_step * propensities[r] * rxn.reactants[reactant])
-                    sd[reactant.name] += (tau_step * propensities[r] * rxn.reactants[reactant] ** 2)
+                    mn[reactant.name] -= (propensities[r] * rxn.reactants[reactant])
+                    sd[reactant.name] += (propensities[r] * (rxn.reactants[reactant] ** 2))
             for product in rxn.products:
                 if product.mode == 'dynamic':
-                    mn[product.name] += (tau_step * propensities[r] * rxn.products[product])
-                    sd[product.name] += (tau_step * propensities[r] * rxn.products[product] ** 2)
-
-        # Get coefficient of variance for each dynamic species
+                    mn[product.name] += (propensities[r] * rxn.products[product])
+                    sd[product.name] += (propensities[r] * (rxn.products[product] ** 2))
+        # Calcuate the derivative based CV
+        for species,value in self.model.listOfSpecies.items():
+            if value.mode == 'dynamic':
+                if mn[species] > 0 and sd[species] > 0:
+                        CV[species] = math.sqrt(sd[species]) / mn[species]
+                else:
+                    CV[species] = 1  # value chosen to guarantee species will be discrete
+
+        # Keep a history of the past CV values, calculate a time-averaged value
+        CV_a = OrderedDict() # time-averaged CV (forward derivative based)
+        for species,value in self.model.listOfSpecies.items():
+            if value.mode == 'dynamic':
+                if species not in cv_history:
+                    cv_history[species] = []
+                cv_history[species].append(CV[species])
+                if len(cv_history[species]) > history_length:
+                    cv_history[species].pop(0) #remove the first item
+                CV_a[species] = sum(cv_history[species])/len(cv_history[species])
+
+        # Select DISCRETE or CONTINOUS mode for each species
         for species in mn:
+            prev_det = det_spec[species]
             sref = self.model.listOfSpecies[species]
             if sref.switch_min == 0:
-                if mn[species] > 0:
-                    CV[species] = sd[species] / mn[species]
-                else:
-                    CV[species] = 1  # value chosen to guarantee discrete
                 # Set species to deterministic if CV is less than threshhold
-                det_spec[species] = CV[species] < sref.switch_tol
+                det_spec[species] = CV_a[species] < sref.switch_tol
             else:
                 det_spec[species] = mn[species] > sref.switch_min
-        return sd, CV
+
+        return mn, sd, CV
 
     @staticmethod
     def __f(t, y, curr_state, species, reactions, rate_rules, propensities,
@@ -267,32 +277,24 @@ def __f(t, y, curr_state, species, reactions, rate_rules, propensities,
         state_change = [0] * len(y_map)
         curr_state['t'] = t
         curr_state['time'] = t
-        #print(f"\t\ty_map = {y_map}")
         for item, index in y_map.items():
             if item in assignment_rules:
                 curr_state[assignment_rules[item].variable] = eval(assignment_rules[item].formula,
                                                                    {**eval_globals, **curr_state})
             else:
-                #print(f"\t\t\t __f() curr_state[{item}] = y[{index}] = {y[index]}")
                 curr_state[item] = y[index]
-        #print(f"\t\t___f() active_rr={active_rr}")
         for s, rr in active_rr.items():
             try:
                 state_change[y_map[s.name]] += eval(rr, {**eval_globals, **curr_state})
-                #print(f"\t\t\t'{s.name}': state_change[{y_map[s.name]}] += {eval(rr, {**eval_globals, **curr_state})}")
             except ValueError as e:
-                #print(f"\t\t\t__f() rr eval failed:{e}") 
                 pass
         for i, r in enumerate(compiled_reactions):
             propensities[r] = eval(compiled_reactions[r], {**eval_globals, **curr_state})
             state_change[y_map[r]] += propensities[r]
-            #print(f"\t\t\tpropensities[{r}]={propensities[r]}")
         for event in events:
             triggered = eval(event.trigger.expression, {**eval_globals, **curr_state})
             if triggered: 
                 state_change[y_map[event]] = 1
-        #print(f"\t\tcompiled_reactions={compiled_reactions}")
-        #print(f"\t\tstate_change={state_change}")
         return state_change
 
     def __find_event_time(self, sol, start, end, index, depth):
@@ -377,7 +379,6 @@ def __get_next_step(self, event_times, reaction_times, delayed_events,
         # Set time to next action
         curr_time = min(sim_end, next_tau, next_event_trigger,
                         next_delayed_event)
-        #print(f"__get_next_step() {next_step[curr_time]}, {curr_time}")
         return next_step[curr_time], curr_time
 
     def __process_queued_events(self, event_queue, trigger_states,
@@ -522,7 +523,6 @@ def __integrate(self, integrator_options, curr_state, y0, curr_time,
         while sol.t < next_tau:
             counter += 1
             sol.step()
-            #print(f"\tt={curr_time}  : sol.y={sol.y}")
 
 
         # Update states of all species based on changes made to species through
@@ -530,15 +530,8 @@ def __integrate(self, integrator_options, curr_state, y0, curr_time,
         # 'continuous', as well as 'dynamic' mode species which have been
         # flagged as deterministic.
         
-        #print(f"\tactive_rr=[",end='')
-        #for k,v in active_rr.items():
-        #    print(f"{k}, ",end='')
-        #print("]")
         for spec_name, species in self.model.listOfSpecies.items():
             if not species.constant:
-                #print(f"\tcurr_state[{spec_name}] = {sol.y[y_map[spec_name]]} mode={self.model.listOfSpecies[spec_name].mode}", end=' ')
-                #if self.model.listOfSpecies[spec_name] in active_rr: print("in active_rr", end='')
-                #print()
                 curr_state[spec_name] = sol.y[y_map[spec_name]]
 
         # Search for precise event times
@@ -606,12 +599,11 @@ def __simulate_invalid_state_check(self, species_modified, curr_state, compiled_
             # check each species to see if they are negative
             for s in species_modified.keys():
                 if curr_state[s] < 0:
-                    #print(f"========='{s}' has negative state '{curr_state[s]}'=====")
                     invalid_state = True
                     err_message += f"'{s}' has negative state '{curr_state[s]}'"
             return (invalid_state, err_message) 
 
-    def __simulate(self, integrator, integrator_options, curr_state, y0, curr_time,
+    def __simulate(self, integrator_options, curr_state, y0, curr_time,
                    propensities, species, parameters, compiled_reactions,
                    active_rr, y_map, trajectory, save_times, save_index,
                    delayed_events, trigger_states, event_sensitivity,
@@ -641,10 +633,6 @@ def __simulate(self, integrator, integrator_options, curr_state, y0, curr_time,
         prev_curr_time = curr_time
         loop_count = 0
         invalid_state = False
-        # check to see if we are starting in an invalid state (this could happen)
-        #(invalid_state, invalid_err_message) = self.__simulate_invalid_state_check(self.model.listOfSpecies, curr_state, compiled_reactions)
-        #if invalid_state:
-        #    raise Exception(f"Invalid state when starting a step. curr_state={curr_state} compiled_reactions={compiled_reactions}\nerror_message: {invalid_err_message} ")
 
         
         starting_curr_state = copy.deepcopy(curr_state)
@@ -653,13 +641,16 @@ def __simulate(self, integrator, integrator_options, curr_state, y0, curr_time,
         species_modified=None
         rxn_count=None
         loop_err_message=""
+        if self.profile_reactions:
+            self.profile_data['time'].append(curr_time)
+            for k in list(self.model.listOfSpecies)+list(self.model.listOfReactions):
+                self.profile_data[k].append(curr_state[k])
 
 
         # check each reaction to see if it is >=0. If we have taken a single SSA step, this could be >0 for the non-selected reactions, check if propensity is zero and reset if so
         for r in compiled_reactions.keys():
             if curr_state[r] >= 0 and propensities[r] == 0:
                 curr_state[r] = math.log(random.uniform(0, 1))
-        #print(f"\tpropensities={propensities}")
 
 
         sol, curr_time = self.__integrate(integrator_options, curr_state,
@@ -674,30 +665,11 @@ def __simulate(self, integrator, integrator_options, curr_state, y0, curr_time,
                                           pure_ode)
 
         species_modified,rxn_count = self.__update_stochastic_rxn_states(compiled_reactions, curr_state)
-        #print(f"\tafter __integrate()")
-        #print(f"\tX=",end='')
-        #differ=[]
-        #for k,v in curr_state.items():
-        #    print(f"{k}:{v} ",end='')
-        #    try:
-        #        if k =='t' or k=='time': continue
-        #        if curr_state[k] != starting_curr_state[k]:
-        #            differ.append(f"{k} ({curr_state[k]}v{starting_curr_state[k]}")
-        #    except:
-        #        pass
-        #print()
-        #print(f"\tdiffer={differ}")
-        #print(f"\tspecies_modified={species_modified}")
-        #print(f"\trxn_count={rxn_count}")
-
-        #raise Exception('stop')
 
         # Occasionally, a tau step can result in an overly-aggressive
         # forward step and cause a species population to fall below 0,
         # which would result in an erroneous simulation. 
-        #    (PREVIOUS METHOD:)If this occurs, back simulation up one step
-        #    and attempt forward simulation using a smaller tau step.
-        # (NEW METHOD:) Instead we estimate the time to the first 
+        # We estimate the time to the first 
         # stochatic reaction firing (assume constant propensities) and
         # simulate the ODE system until that time, fire that reaction 
         # and continue the simulation.
@@ -726,7 +698,6 @@ def __simulate(self, integrator, integrator_options, curr_state, y0, curr_time,
                 except Exception as e:
                     raise SimulationError('Error calculation propensity for {0}.\nReason: {1}\nfloored_propensities={2}\ncompiled_reactions={3}'.format(r, e, floored_propensities,compiled_reactions))
             curr_state = saved_curr_state
-            #print(f"\tfloored_propensities={floored_propensities}")
 
             rxn_times = OrderedDict()
             min_tau = None
@@ -742,7 +713,7 @@ def __simulate(self, integrator, integrator_options, curr_state, y0, curr_time,
 
             tau_step = min_tau #estimated time to the first stochatic reaction
 
-            sol, curr_time = self.__integrate(integrator, integrator_options, curr_state,
+            sol, curr_time = self.__integrate(integrator_options, curr_state,
                                           y0, curr_time, propensities, y_map,
                                           compiled_reactions,
                                           active_rr,
@@ -1268,10 +1239,7 @@ def __run(self, curr_state, curr_time, timeline, trajectory_base, initial_state,
 
                 # Process switching if used
                 if not pure_stochastic and not pure_ode:
-                    switch_args = [propensities, curr_state[0], tau_step, det_spec]
-                    sd, CV = self.__calculate_statistics(*switch_args)
-
-                #print(f"det_spec={det_spec}")
+                    mn, sd, CV = self.__calculate_statistics(curr_time[0], propensities, curr_state[0], tau_step, det_spec)
 
                 # Calculate sd and CV for hybrid switching and flag deterministic reactions
                 if pure_stochastic:
@@ -1280,7 +1248,7 @@ def __run(self, curr_state, curr_time, timeline, trajectory_base, initial_state,
                     deterministic_reactions = self.__flag_det_reactions(det_spec, det_rxn, dependencies)
 
                 if debug:
-                    print('mean: {0}'.format(mu_i))
+                    print('mean: {0}'.format(mn))
                     print('standard deviation: {0}'.format(sd))
                     print('CV: {0}'.format(CV))
                     print('det_spec: {0}'.format(det_spec))
@@ -1299,11 +1267,7 @@ def __run(self, curr_state, curr_time, timeline, trajectory_base, initial_state,
                                              compiled_reactions, self.model.listOfEvents, curr_state[0])
 
                 # Run simulation to next step
-                #print(f"\nt={curr_time[0]} curr_state=",end='')
-                #for k,v in curr_state[0].items():
-                #    print(f"{k}:{v} ",end='')
-                #print(f"\t\ttau={tau_step}")
-                sol, curr_state[0], curr_time[0], save_times, save_index = self.__simulate(integrator, integrator_options,
+                sol, curr_state[0], curr_time[0], save_times, save_index = self.__simulate(integrator_options,
                                                                                curr_state[0], y0, curr_time[0],
                                                                                propensities, species,
                                                                                parameters, compiled_reactions,