diff --git a/docs/source/optimize_interface.ipynb b/docs/source/optimize_interface.ipynb
index 779b48b5c..b7d806818 100644
--- a/docs/source/optimize_interface.ipynb
+++ b/docs/source/optimize_interface.ipynb
@@ -1386,44 +1386,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 79%|███████▉  | 95/120 [25:32<05:57, 14.30s/it] C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:106: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n",
-      "  warnings.warn(\n",
-      "124it [28:55, 13.99s/it]                         "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Optimal policy: tensor([0.3500, 0.4602], dtype=torch.float64)\n",
-      "{'policy': tensor([0.3500, 0.4602], dtype=torch.float64), 'OptResults':                     message: ['requested number of basinhopping iterations completed successfully']\n",
-      "                    success: False\n",
-      "                        fun: 0.26016707274061507\n",
-      "                          x: [ 3.500e-01  4.602e-01]\n",
-      "                        nit: 3\n",
-      "      minimization_failures: 4\n",
-      "                       nfev: 120\n",
-      " lowest_optimization_result: message: Maximum number of function evaluations has been exceeded.\n",
-      "                             success: False\n",
-      "                              status: 2\n",
-      "                                 fun: 0.26016707274061507\n",
-      "                                   x: [ 3.500e-01  4.601e-01]\n",
-      "                                nfev: 30\n",
-      "                               maxcv: 0.0}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "  0%|          | 0/120 [00:00<?, ?it/s]"
      ]
     }
    ],
@@ -1479,7 +1449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1487,136 +1457,19 @@
      "output_type": "stream",
      "text": [
       "Fixed interventions:  {10.0: {'hosp': tensor(0.1000)}}\n",
-      "Optimal intervention (including fixed interventions):  {10.0: {'hosp': tensor(0.1000), 'beta_c': tensor(0.3500)}, 15.0: {'gamma': tensor(0.4602)}}\n"
+      "Optimal intervention (including fixed interventions):  {10.0: {'hosp': tensor(0.1000)}}\n"
      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR:root:\n",
-      "                ###############################\n",
-      "\n",
-      "                There was an exception in pyciemss\n",
-      "\n",
-      "                Error occured in function: sample\n",
-      "\n",
-      "                Function docs : \n",
-      "    Load a model from a file, compile it into a probabilistic program, and sample from it.\n",
-      "\n",
-      "    Args:\n",
-      "        model_path_or_json: Union[str, Dict]\n",
-      "            - A path to a AMR model file or JSON containing a model in AMR form.\n",
-      "        end_time: float\n",
-      "            - The end time of the sampled simulation.\n",
-      "        logging_step_size: float\n",
-      "            - The step size to use for logging the trajectory.\n",
-      "        num_samples: int\n",
-      "            - The number of samples to draw from the model.\n",
-      "        solver_method: str\n",
-      "            - The method to use for solving the ODE. See torchdiffeq's `odeint` method for more details.\n",
-      "            - If performance is incredibly slow, we suggest using `euler` to debug.\n",
-      "              If using `euler` results in faster simulation, the issue is likely that the model is stiff.\n",
-      "        solver_options: Dict[str, Any]\n",
-      "            - Options to pass to the solver. See torchdiffeq' `odeint` method for more details.\n",
-      "        start_time: float\n",
-      "            - The start time of the model. This is used to align the `start_state` from the\n",
-      "              AMR model with the simulation timepoints.\n",
-      "            - By default we set the `start_time` to be 0.\n",
-      "        inferred_parameters: Optional[pyro.nn.PyroModule]\n",
-      "            - A Pyro module that contains the inferred parameters of the model.\n",
-      "              This is typically the result of `calibrate`.\n",
-      "            - If not provided, we will use the default values from the AMR model.\n",
-      "        static_state_interventions: Dict[float, Dict[str, Intervention]]\n",
-      "            - A dictionary of static interventions to apply to the model.\n",
-      "            - Each key is the time at which the intervention is applied.\n",
-      "            - Each value is a dictionary of the form {state_variable_name: intervention_assignment}.\n",
-      "            - Note that the `intervention_assignment` can be any type supported by\n",
-      "              :func:`~chirho.interventional.ops.intervene`, including functions.\n",
-      "        static_parameter_interventions: Dict[float, Dict[str, Intervention]]\n",
-      "            - A dictionary of static interventions to apply to the model.\n",
-      "            - Each key is the time at which the intervention is applied.\n",
-      "            - Each value is a dictionary of the form {parameter_name: intervention_assignment}.\n",
-      "            - Note that the `intervention_assignment` can be any type supported by\n",
-      "              :func:`~chirho.interventional.ops.intervene`, including functions.\n",
-      "        dynamic_state_interventions: Dict[\n",
-      "                                        Callable[[torch.Tensor, Dict[str, torch.Tensor]], torch.Tensor],\n",
-      "                                        Dict[str, Intervention]\n",
-      "                                        ]\n",
-      "            - A dictionary of dynamic interventions to apply to the model.\n",
-      "            - Each key is a function that takes in the current state of the model and returns a tensor.\n",
-      "              When this function crosses 0, the dynamic intervention is applied.\n",
-      "            - Each value is a dictionary of the form {state_variable_name: intervention_assignment}.\n",
-      "            - Note that the `intervention_assignment` can be any type supported by\n",
-      "              :func:`~chirho.interventional.ops.intervene`, including functions.\n",
-      "        dynamic_parameter_interventions: Dict[\n",
-      "                                            Callable[[torch.Tensor, Dict[str, torch.Tensor]], torch.Tensor],\n",
-      "                                            Dict[str, Intervention]\n",
-      "                                            ]\n",
-      "            - A dictionary of dynamic interventions to apply to the model.\n",
-      "            - Each key is a function that takes in the current state of the model and returns a tensor.\n",
-      "              When this function crosses 0, the dynamic intervention is applied.\n",
-      "            - Each value is a dictionary of the form {parameter_name: intervention_assignment}.\n",
-      "            - Note that the `intervention_assignment` can be any type supported by\n",
-      "              :func:`~chirho.interventional.ops.intervene`, including functions.\n",
-      "        alpha: float\n",
-      "            - Risk level for alpha-superquantile outputs in the results dictionary.\n",
-      "\n",
-      "    Returns:\n",
-      "        result: Dict[str, torch.Tensor]\n",
-      "            - Dictionary of outputs with following attributes:\n",
-      "                - data: The samples from the model as a pandas DataFrame.\n",
-      "                - unprocessed_result: Dictionary of outputs from the model.\n",
-      "                    - Each key is the name of a parameter or state variable in the model.\n",
-      "                    - Each value is a tensor of shape (num_samples, num_timepoints) for state variables\n",
-      "                    and (num_samples,) for parameters.\n",
-      "                - quantiles: The quantiles for ensemble score calculation as a pandas DataFrames.\n",
-      "                - risk: Dictionary with each key as the name of a state with\n",
-      "                a dictionary of risk estimates for each state at the final timepoint.\n",
-      "                    - risk: alpha-superquantile risk estimate\n",
-      "                    Superquantiles can be intuitively thought of as a tail expectation, or an average\n",
-      "                    over a portion of worst-case outcomes. Given a distribution of a\n",
-      "                    quantity of interest (QoI), the superquantile at level \\alpha\\in[0, 1] is\n",
-      "                    the expected value of the largest 100(1 -\\alpha)% realizations of the QoI.\n",
-      "                    - qoi: Samples of quantity of interest (value of the state at the final timepoint)\n",
-      "                - schema: Visualization. (If visual_options is truthy)\n",
-      "    \n",
-      "\n",
-      "                ################################\n",
-      "            \n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\custom_decorators.py\", line 10, in wrapped\n",
-      "    result = function(*args, **kwargs)\n",
-      "  File \"C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py\", line 524, in sample\n",
-      "    **prepare_interchange_dictionary(\n",
-      "  File \"C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\result_processing.py\", line 19, in prepare_interchange_dictionary\n",
-      "    processed_samples = convert_to_output_format(\n",
-      "  File \"C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\result_processing.py\", line 118, in convert_to_output_format\n",
-      "    result = set_intervention_values(result, name, values, intervention_times)\n",
-      "  File \"C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\result_processing.py\", line 174, in set_intervention_values\n",
-      "    target_col = find_target_col(target_var, df.columns)\n",
-      "  File \"C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\result_processing.py\", line 153, in find_target_col\n",
-      "    raise ValueError(\n",
-      "ValueError: Could not uniquely determine target column for 'hosp'.  Found: ['persistent_hosp_param', 'persistent_death_hosp_param']\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "Could not uniquely determine target column for 'hosp'.  Found: ['persistent_hosp_param', 'persistent_death_hosp_param']",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[10], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOptimal intervention (including fixed interventions): \u001b[39m\u001b[38;5;124m\"\u001b[39m, opt_intervention)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mseed(rng_seed\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m----> 6\u001b[0m     result5 \u001b[38;5;241m=\u001b[39m \u001b[43mpyciemss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_opt2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m        \u001b[49m\u001b[43mend_time_SEIRHD2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlogging_step_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstart_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     12\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstatic_parameter_interventions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopt_intervention\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     13\u001b[0m \u001b[43m        \u001b[49m\u001b[43msolver_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrk4\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     14\u001b[0m \u001b[43m        \u001b[49m\u001b[43msolver_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstep_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1.\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m# Check risk estimate used in constraints\u001b[39;00m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRisk associated with QoI:\u001b[39m\u001b[38;5;124m\"\u001b[39m, result5[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrisk\u001b[39m\u001b[38;5;124m\"\u001b[39m][observed_params[\u001b[38;5;241m0\u001b[39m]][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrisk\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
-      "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\custom_decorators.py:29\u001b[0m, in \u001b[0;36mpyciemss_logging_wrapper.<locals>.wrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     17\u001b[0m log_message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;124m    ###############################\u001b[39m\n\u001b[0;32m     19\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[38;5;124m    ################################\u001b[39m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;124m\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m     28\u001b[0m logging\u001b[38;5;241m.\u001b[39mexception(log_message, function\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, function\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__doc__\u001b[39m)\n\u001b[1;32m---> 29\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n",
-      "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\custom_decorators.py:10\u001b[0m, in \u001b[0;36mpyciemss_logging_wrapper.<locals>.wrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m      9\u001b[0m     start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m---> 10\u001b[0m     result \u001b[38;5;241m=\u001b[39m function(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     11\u001b[0m     end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[0;32m     12\u001b[0m     logging\u001b[38;5;241m.\u001b[39minfo(\n\u001b[0;32m     13\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mElapsed time for \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%f\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, function\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, end_time \u001b[38;5;241m-\u001b[39m start_time\n\u001b[0;32m     14\u001b[0m     )\n",
-      "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py:524\u001b[0m, in \u001b[0;36msample\u001b[1;34m(model_path_or_json, end_time, logging_step_size, num_samples, noise_model, noise_model_kwargs, solver_method, solver_options, start_time, time_unit, inferred_parameters, static_state_interventions, static_parameter_interventions, dynamic_state_interventions, dynamic_parameter_interventions, alpha)\u001b[0m\n\u001b[0;32m    520\u001b[0m         sq_est \u001b[38;5;241m=\u001b[39m alpha_superquantile(qoi_sample, alpha\u001b[38;5;241m=\u001b[39malpha)\n\u001b[0;32m    521\u001b[0m         risk_results\u001b[38;5;241m.\u001b[39mupdate({k: {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrisk\u001b[39m\u001b[38;5;124m\"\u001b[39m: [sq_est], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mqoi\u001b[39m\u001b[38;5;124m\"\u001b[39m: qoi_sample}})\n\u001b[0;32m    523\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m--> 524\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[43mprepare_interchange_dictionary\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    525\u001b[0m \u001b[43m        \u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimepoints\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlogging_times\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_unit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtime_unit\u001b[49m\n\u001b[0;32m    526\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[0;32m    527\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrisk\u001b[39m\u001b[38;5;124m\"\u001b[39m: risk_results,\n\u001b[0;32m    528\u001b[0m }\n",
-      "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\result_processing.py:19\u001b[0m, in \u001b[0;36mprepare_interchange_dictionary\u001b[1;34m(samples, time_unit, timepoints, visual_options)\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprepare_interchange_dictionary\u001b[39m(\n\u001b[0;32m     12\u001b[0m     samples: Dict[\u001b[38;5;28mstr\u001b[39m, torch\u001b[38;5;241m.\u001b[39mTensor],\n\u001b[0;32m     13\u001b[0m     time_unit: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m     14\u001b[0m     timepoints: Optional[Iterable[\u001b[38;5;28mfloat\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m     15\u001b[0m     visual_options: Union[\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28mbool\u001b[39m, Dict[\u001b[38;5;28mstr\u001b[39m, Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m     16\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dict[\u001b[38;5;28mstr\u001b[39m, Any]:\n\u001b[0;32m     17\u001b[0m     samples \u001b[38;5;241m=\u001b[39m {k: (v\u001b[38;5;241m.\u001b[39msqueeze() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(v\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m v) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m samples\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[1;32m---> 19\u001b[0m     processed_samples \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_to_output_format\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     20\u001b[0m \u001b[43m        \u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_unit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtime_unit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimepoints\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimepoints\u001b[49m\n\u001b[0;32m     21\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     23\u001b[0m     result \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m: processed_samples, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munprocessed_result\u001b[39m\u001b[38;5;124m\"\u001b[39m: samples}\n\u001b[0;32m     25\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m visual_options:\n",
-      "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\result_processing.py:118\u001b[0m, in \u001b[0;36mconvert_to_output_format\u001b[1;34m(samples, time_unit, timepoints)\u001b[0m\n\u001b[0;32m    114\u001b[0m \u001b[38;5;66;03m# Set values to reflect interventions in time-order\u001b[39;00m\n\u001b[0;32m    115\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, values \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\n\u001b[0;32m    116\u001b[0m     intervention_data\u001b[38;5;241m.\u001b[39mitems(), key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m item: \u001b[38;5;28mint\u001b[39m(item[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m    117\u001b[0m ):\n\u001b[1;32m--> 118\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mset_intervention_values\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mintervention_times\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    120\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
-      "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\result_processing.py:174\u001b[0m, in \u001b[0;36mset_intervention_values\u001b[1;34m(df, intervention, intervention_values, intervention_times)\u001b[0m\n\u001b[0;32m    172\u001b[0m times \u001b[38;5;241m=\u001b[39m get_times_for(intervention, intervention_times)\n\u001b[0;32m    173\u001b[0m target_var \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(intervention\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m3\u001b[39m:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m--> 174\u001b[0m target_col \u001b[38;5;241m=\u001b[39m \u001b[43mfind_target_col\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_var\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    175\u001b[0m time_col \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    176\u001b[0m     c \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;28;01mif\u001b[39;00m c\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimepoint_\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m c \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimepoint_id\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    177\u001b[0m ][\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrework\u001b[39m(group):\n",
-      "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\integration_utils\\result_processing.py:153\u001b[0m, in \u001b[0;36mfind_target_col\u001b[1;34m(var, options)\u001b[0m\n\u001b[0;32m    151\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo target column match found for \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(options) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 153\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    154\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not uniquely determine target column for \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.  Found: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moptions\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    155\u001b[0m     )\n\u001b[0;32m    156\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m options[\u001b[38;5;241m0\u001b[39m]\n",
-      "\u001b[1;31mValueError\u001b[0m: Could not uniquely determine target column for 'hosp'.  Found: ['persistent_hosp_param', 'persistent_death_hosp_param']"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [